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PMC9577608 | Estrella Galicia-Campos,Ana García-Villaraco,M. B. Montero-Palmero,F. Javier Gutiérrez-Mañero,Beatriz Ramos-Solano | Bacillus H47 triggers Olea europaea metabolism activating DOXP and shikimate pathways simultaneously and modifying leaf extracts’ antihypertensive activity | 04-10-2022 | PGPB,secondary metabolism,oleuropein,flavonols,Olea europaea,plant adaptation,abiotic stress | Improvement of plant adaptation by beneficial bacteria (PGPB) may be achieved by triggering multiple pathways to overcome the environmental stress on plant’s growth cycle, activating plant’s metabolism. The present work reports the differential ability of three Bacillus strains to trigger olive tree metabolism, among which, only H47 was outstanding increasing iridoid and flavonol concentration. One-year old olive seedlings grown open air, under harsh conditions of water shortage in saline soils, were root-inoculated with three Bacillus PGPB strains throughout a 12-month period after which, photosynthesis was determined; photosynthetic pigments and bioactive secondary metabolites (iridoids and flavonols) were analyzed, and a study of gene expression of both pathways involved was undertaken to unravel molecular targets involved in the activation. All three strains increased plant fitness based on photosynthetic values, increasing energy dissipation capacity to lower oxidative stress; only H47 increased CO2 fixation and transpiration. Bacillus H47 was found to trigger synthases in the DOXP pathway (up to 5-fold in DOXP-synthase, 3.5-fold in Iridoid synthase, and 2-fold in secologanin synthase) associated to a concomitant increase in iridoids (up to 5-fold in oleuropein and 2-fold in its precursor secologanin). However, despite the 2-fold increases detected in the two predominant flavonols, gene expression was not enhanced, suggesting involvement of a pulse activation model proposed for innate immunity. Furthermore, the activity of leaf extracts to inhibit Angiotensin Converting Enzyme was evaluated, to explore further uses of plant debris with higher added value. Despite the increases in iridoids, leaf extracts from H47 did not increase ACE inhibition, and still, increased antihypertensive potential in oil obtained with this strain is to be explored, as leaves are the source for these metabolites which further translocate to fruits. In summary, Bacillus H47 is an effective strain to increase plant adaptation to dry and saline environments, activates photosynthesis and secondary metabolism in olive tree. | Bacillus H47 triggers Olea europaea metabolism activating DOXP and shikimate pathways simultaneously and modifying leaf extracts’ antihypertensive activity
Improvement of plant adaptation by beneficial bacteria (PGPB) may be achieved by triggering multiple pathways to overcome the environmental stress on plant’s growth cycle, activating plant’s metabolism. The present work reports the differential ability of three Bacillus strains to trigger olive tree metabolism, among which, only H47 was outstanding increasing iridoid and flavonol concentration. One-year old olive seedlings grown open air, under harsh conditions of water shortage in saline soils, were root-inoculated with three Bacillus PGPB strains throughout a 12-month period after which, photosynthesis was determined; photosynthetic pigments and bioactive secondary metabolites (iridoids and flavonols) were analyzed, and a study of gene expression of both pathways involved was undertaken to unravel molecular targets involved in the activation. All three strains increased plant fitness based on photosynthetic values, increasing energy dissipation capacity to lower oxidative stress; only H47 increased CO2 fixation and transpiration. Bacillus H47 was found to trigger synthases in the DOXP pathway (up to 5-fold in DOXP-synthase, 3.5-fold in Iridoid synthase, and 2-fold in secologanin synthase) associated to a concomitant increase in iridoids (up to 5-fold in oleuropein and 2-fold in its precursor secologanin). However, despite the 2-fold increases detected in the two predominant flavonols, gene expression was not enhanced, suggesting involvement of a pulse activation model proposed for innate immunity. Furthermore, the activity of leaf extracts to inhibit Angiotensin Converting Enzyme was evaluated, to explore further uses of plant debris with higher added value. Despite the increases in iridoids, leaf extracts from H47 did not increase ACE inhibition, and still, increased antihypertensive potential in oil obtained with this strain is to be explored, as leaves are the source for these metabolites which further translocate to fruits. In summary, Bacillus H47 is an effective strain to increase plant adaptation to dry and saline environments, activates photosynthesis and secondary metabolism in olive tree.
While growing within their natural habitat, plants are subjected to many different changes in their physical and biological surroundings. As sessile organisms, plants have developed an innate immune system and an active secondary metabolism to improve their adaptation to biotic and abiotic stress conditions (Oh et al., 2009). The plant immune system is activated in waves to cope with simultaneous changes in environmental parameters, usually combined with additional more persistent stress conditions, key for the overall fitness of the plant and its ability to survive rapid changes within its environment (Petridis et al., 2012; Mousavi et al., 2019); this has been termed as the pulse network response (Kollist et al., 2019). This adaptative metabolism demands energy and a strong carbon supply to build up carbon scaffoldings that are provided by photosynthesis, which is the key process for plant growth and survival. Furthermore, the fate of photosynthates has to be balanced between plant growth and adaptation so a good coordination for the best use of energetic resources is key for success (Mauch-Mani et al., 2017). However, photosynthesis arrest may happen due to many factors including high light intensity, temperature, and water resource availability (Skodra et al., 2021). On one hand, the usual oxidative stress of light reactions is further enhanced under stress and needs to be counteracted by an active ROS scavenging system, allowing ROS within healthy levels for systemic metabolic coordination (Baxter et al., 2014). On the other hand, with low water availability, stomatal closure occurs as an early response to abscisic acid (ABA) produced by dehydrated roots (Grace, 2005), decreasing CO2 entrance and compromising C assimilation. As water stress is a relevant problem, plants have many innate mechanisms that regulate adaptation to stress. In addition to plant’s genetic endowment to keep photosynthesis running, action has been taken to prevent the yield losses due to drought stress, and advances in agronomical practices, traditional breeding and modern biotechnological tools have been developed (Gill and Tuteja, 2010), aiming to improve adaptation of crops to drought stress (Sofo et al., 2004). Among these, beneficial bacteria have proved to be effective (Ennajeh et al., 2009; Oh et al., 2009), and appear as one of the most promising tools to achieve this goal, as they trigger multiple targets simultaneously (Ilangumaran and Smith, 2017) and allow fine-tunning of resources allocation (Mauch-Mani et al., 2017). The term plant growth-promoting rhizobacteria was coined by Kloepper et al. (1980) to refer to free-living beneficial bacteria that inhabit the rhizosphere enhancing plant growth. Although the term has evolved to the more inclusive Plant Growth Promoting Bacteria (PGPB) to integrate beneficial bacteria from other origins, the mechanisms by which they improve plant fitness remain the same (Sayyed et al., 2016; Rosier et al., 2018). PGPB are able to improve plant nutrition, to biocontrol soil microorganisms, to modify plant metabolism altering hormonal balance, photosynthesis or secondary metabolism by systemic induction, as well as triggering plant immune system. Thus, the role of beneficial rhizobacteria to improve photosynthetic performance and trigger secondary metabolism simultaneously appears as a good alternative to increase the levels of bioactive secondary metabolites (Algar et al., 2013; Lucas et al., 2014; García-Cristobal et al., 2015; Garcia-Seco et al., 2015), protect against biotic and abiotic stress (Barriuso et al., 2008), and other frequent situations in agriculture (Gutiérrez-Mañero et al., 2015). Considering the multitarget capacity of PGPB strains (Ilangumaran and Smith, 2017) and the pulse network model response of plants immune system (Kollist et al., 2019), a wave-like activation triggered by bacteria on different targets appears as a likely model to happen. Olea europaea is one of the most extended crops in the Mediterranean area, naturally endowed with mechanisms for high water use efficiency. The most characteristic secondary metabolites present in olive trees are iridoids, triterpenes, and phenolic compounds (flavonols), all conferring a high antioxidant potential (Khaliq et al., 2015; Skodra et al., 2021). Biosynthesis of these metabolites involves the DOXP-pathway for iridoids, the mevalonic acid pathway for triterpenes, and the shikimate-flavonol pathway for flavonols, which accumulate in fruits and also in leaves. Reported benefits of olive leaves include antihypertensive potential due to the coordinated effects of iridoids (oleuropein, oleacein, and ligustroside) and triterpenes (oleanolic acid; El Riachy et al., 2011; Alagna et al., 2012; Bulotta et al., 2013); antitumor potential has also been reported (Celano et al., 2019). As the use of beneficial rhizobacteria capable of modulating secondary metabolism pathways of plants and improve adaptation to abiotic stress has proved efficient in different species, including olive tree (Algar et al., 2013; Lucas et al., 2014; García-Cristobal et al., 2015; Garcia-Seco et al., 2015; Galicia-Campos et al., 2020; Gutierrez-Albanchez et al., 2020), the present study reports effects of three Bacillus strains to obtain polyphenol enriched extracts with enhanced antihypertensive potential, to improve value of byproducts in line with the green pact of the EU. To achieve this objective, olive plantlets grown in high saline conditions were inoculated along 12 months, after which photosynthesis, photosynthetic pigments, total phenols, bioactive secondary metabolites (flavonols, the iridoids secologanoside, and oleouropein) were analyzed; finally, expression of key genes involved in bioactive synthesis was studied by RT-qPCR in the most effective strain, and in vitro antihypertensive activity was assessed.
The three beneficial strains (G7, L44, and H47) assayed in this study were Gram-positive sporulated bacilli isolated from the rhizosphere of Pinus pinea (L44) and P. pinaster (G7 and H47; Barriuso et al., 2005). They were able to produce siderophores (G7, H47) and auxins (L44). They have been identified by 16s rDNA sequencing as Bacillus simplex G7 (OP324816), B. aryabhatai L44 (OP324815), and B. velezensis H47 (OP324817). Olea europaea (L) var. Arbequina plantlets were used for the study. Plantlets were bought from a local producer Lucena de Encinarejo S.L.(Córdoba).
Bacterial strains were maintained at −80°C in nutrient broth with 20% glycerol. Inocula were prepared by streaking strains from −80°C onto plate count agar (PCA) plates, incubating plates at 28°C for 24 h. Then, they were grown in Luria Broth liquid media (LB) under shaking (1,000 rpm) at 28°C for 24 h; inocula density was adjusted to 1 × 108 cfu/ml and 500 ml were delivered to roots of each plant every 15 days from October 2017 to October 2018.
Six-month olive plantlets were transplanted into 5 l pots with soil from the Guadalquivir Marshes. Plants were arranged in lines on an experimental plot within the marshes (37°06′34.5′′ N, 6°20′22.7′′ W); pot position was changed every 2 weeks to avoid side-effects. Plants were watered every 15 days. The electric conductivity of water and soil was 8.20 and of 6.07 dS/m, respectively. Bacteria were root-inoculated by soil drench every 15 days from October 2017 to October 2018, so plants received 500 ml of water every week, alternating inoculum and water. Six plants per treatment were inoculated, being one bacterial strain a treatment, with three replicates (two plants each). Samples were taken in October 2018 and photosynthesis was measured (fluorescence and CO2 fixation). Leaves from two plants in each treatment were pooled before powdering in liquid nitrogen and constituted a replicate; powder was stored at −80°C till analysis. Photosynthetic pigments were determined as well as total phenols, flavonols, and iridoids as metabolic markers of the induction. Expression of genes involved in the biosynthesis of flavonols and iridoids were analyzed by RT-qPCR in the most effective strain and controls; finally, the potential antihypertensive effect of leaf extracts was evaluated in vitro, calculating its ability to inhibit Angiotensin Converting Enzyme as indicated in 3.10.
Photosynthetic efficiency was determined through the chlorophyll fluorescence emitted by photosystem II. Chlorophyll fluorescence was measured with a pulse amplitude modulated (PAM) fluorometer (Hansatech FM2, Hansatech, Inc., United Kingdom). After dark-adaptation of leaves, the minimal fluorescence (Fo; dark-adapted minimum fluorescence) was measured with a weak modulated irradiation (1 μmol m−2 s−1). Maximum fluorescence (Fm) was determined for the dark-adapted state by applying a 700 ms saturating flash (9,000 μmol m−2 s−1). The variable fluorescence (Fv) was calculated as the difference between the maximum fluorescence (Fm) and the minimum fluorescence (Fo). The maximum photosynthetic efficiency of photosystem II (maximal PSII quantum yield) was calculated as Fv/Fm. Immediately, the leaf was continuously irradiated with red-blue actinic beams (80 μmol m−2 s−1) and equilibrated for 15 s to record Fs (steady-state fluorescence signal). Following this, another saturation flash (9,000 μmol m−2 s−1) was applied and then Fm′ (maximum fluorescence under light-adapted conditions) was determined. Other fluorescent parameters were calculated as follows: the effective PSII quantum yield ΦPSII = (Fm′ − Fs)/Fm′ (Genty et al., 1989); and the non-photochemical quenching coefficient NPQ = (Fm − Fm′)/Fm′. All measurements were carried out in the six plants of each treatment.
Leaf photosynthetic rate (Pn; mmol CO2/m2), transpiration rate, E (mmol/ m2s), and stomatal conductance, C (mmol/ m2s) were measured in fully expanded leaves (third leaf from apex) with a portable photosynthetic open-system (CI-340, CID, Camas, WA, United States; Schlosser et al., 2012). Water use efficiency (WUE) was calculated as net photosynthesis (Pn) divided by transpiration (E) as an indicator of stomatal efficiency to maximize photosynthesis, minimizing water loss due to transpiration.
Extraction was done according to Porra et al. (1989). One hundred milligram of leaves powdered in liquid nitrogen was dissolved in 1 ml of acetone 80% (v/v), incubated overnight at 4°C, and then centrifuged 5 min at 10,000 rpm in a Hermle Z233 M-2 centrifuge. One milliliter of acetone 80% was added to the supernantant and was mixed with a vortex. Immediately afterwards, absorbance at 647, 663, and 470 nm was measured on a Biomate 5 spectrophotometer to calculate chlorophyll a, chlorophyll b, and carotenoids (xanthophylls and carotenes) using the formulas indicated below (Lichtenthaler, 1987; Porra et al., 1989). Tubes were protected from light throughout the whole process.
Leaf extracts were prepared from 0.25 g of leaves (powdered in liquid nitrogen) in 2.25 ml methanol 80%, sonicated for 10 min, and centrifuged for 5 min at 5,000 rpm. Total phenols were quantitatively determined with Folin–Ciocalteu agent (Sigma. Aldrich, St Louis, MO, United States) by a colorimetric method described by Xu and Chang (2007), with some modifications. Twenty microliters of extract was mixed with 0.250 ml of Folin–Ciocalteu 2 N and 0.75 ml of Na2CO3 20% solution. After 30 min at room temperature, absorbance was measured at 760 nm. Gallic acid was used as standard (Sigma-Aldrich, St Louis, MO, United States); a calibration curve was made (r = 0.99). Results are expressed in mg of gallic acid equivalents per 100 g of fresh weight (FW).
Two hundred and fifty milligram of each powdered sample was weighed into a tube and 2.25 ml of methanol–water (80:20, v/v) were added. Then, the mixture was vortexed and sonicated for 30 min in an ultrasonic bath. The resulting extract was centrifuged for 5 min at 5,974 g, the supernatant was collected and the residue was re-extracted again following the same procedure as above. Both supernatants were pooled and evaporated to dryness under reduced pressure at 35°C in a rotavapor R-210 (Buchi Labortechnik AG, Flawil, Switzerland). Next, the residue was reconstituted with 5 ml methanol, filtered through a 0.22 μm NylafloTM nylon membrane filter from Pall Corporation (Ann Arbor, MI, United States), and subsequently analyzed (or stored in a freezer below −20°C prior to analysis). Each sample was prepared in triplicate. Every sample was extracted and analyzed by UHPLC–MS. According to Olmo-García et al. (2018), analyses were done at SIDI.
Prior to RNA extraction, samples were removed from the −80°C freezer and ground to a fine powder with liquid nitrogen using a sterilized mortar and pestle. Total RNA was isolated from each replicate with GeneJET Plant RNA Purification Mini Kit (Thermo Scientific; DNase treatment included) and after confirmation of RNA integrity using NanodropTM, a retrotranscription followed by a RT-qPCR was performed. This analysis was performed only in controls and H47, as this strain caused the highest increases in bioactives. The retrotranscription was performed using iScript tm cDNA Synthesis Kit (Bio-Rad). All retrotranscriptions were performed using a GeneAmp PCR System 2700 (Applied Bio-systems): 5 min 25°C, 30 min 42°C, and 5 min 85°C and hold at 4°C. Amplification was performed with a MiniOpticon Real-Time PCR System (Bio-Rad): 3 min at 95°C and then 39 cycles consisting of 15 s at 95°C, 30 s at 55°C, and 30 s at 72°C, followed by melting curve to check the results. To describe the expression obtained in the analysis, cycle threshold (Ct) was used. Standard curves were calculated for each gene, and the efficiency value ranged between 80 and 120%. The reference gene used was GADPH2. Key genes controlling the shikimate-flavonol pathway and DOXP pathway were studied and primers used for each appear in Supplementary Table 1 of Supplementary material. Primers for Flavonol-3-hydroxylase(OeF3H), Flavonol-3′-hydroxylase (OeF3’H), and Flavonol synthase (OeFLASYN) were obtained from Iaria et al. (2016). Primers for Chorismate mutase (OeCHOMU; XM_ 023023569.1), Chalcone synthase (OeCHASIN; XM_023018868.1), Chalcone isomerase (OeCHAISO; XM_023011594.1), Arogenate dehydrogenase (OeARODESHIDRO; XM_022989811.1), 1-deoxy-D-xylulose synthase (OeDOXP; XM_022992625.1), 8-hydroxigeraniol synthase (Oe8HYDROXY; XM_022988413.1), Iridoid synthase (OeIRISY2; KX944708.1), and Secologanin synthase (OeSECSIN; KX944713.1) were designed on PRIMER3 based on genomes from Olea europaea. Var sylvestris and Olea europaea. Var koronieki. Results for gene expression were expressed as a differential expression according to Pfaffl (2001). Control expression is set at 1; therefore, only changes above 1 are considered.
Extract preparation is described in the section “Bioactive quantification by UHPLC-MS.” The reaction measured was the transformation of the substrate hyppuryl-histidyl-leucine (HHL) into the product hippuric acid (HA), catalyzed by the ACE. The purpose behind this experiment was to determine the inhibitory capacities of the different extracts (same as the one described for the previous analysis) by measuring both the substrate and the product concentrations. The protocol used was based on the work of Wu et al. (2002) although modifications were performed for its optimisation. Agilent 1100 series equipment was used. Samples were prepared with 20 μl of ACE, 20 μl of HHL (enzyme’s substrate), 40 μl of sample (ACE inhibitor), and 40 μl of borate buffer. Oleuropein at 25 ppm concentration was used as the reference ACE inhibitor in the negative control, and 80% methanol was used instead for the positive control. A column C18 100A 150 × 4.6 mm 5 micron was used, with a 0.5 ml/min flow of the mobile phase (75% miliQ water, 0.1%TFA, and 25% acetonitrile), running for 21 min. A UV sensor was used, set at λ = 226 nm. The % of inhibition was calculated using the formula: Where A is the area under the curve (AUC) of the HA peak without ACE inhibitor (C+) and B is HA AUC when ACE inhibitors are added (samples).
To evaluate treatment effects, one way t-student (Statgraphics Centurion XVIII) were performed for each of the variables.
Photosynthesis (Figure 1) was improved by the three strains. All three decreased F0, the minimum fluorescence of adaptation to darkness, reaching similar values under all 3 (Figure 1A), and they also increased the maximum potential photosynthetic capacity of PSII (Fv/Fm) that reached optimal values (0.82–0.85) in inoculated plantlets, while controls were below optimal values (Figure 1B). Efficiency of PSII (ePSR; Figure 1C) was decreased by all three strains although only significantly with G7 and L44, being G7 the lowest; finally, energy dissipation (NPQ) was enhanced by all 3, being significant only by H47 (Figure 1D). Net carbon fixation (Figure 2A) in control plants was 2.6 μmol CO2 /m2 s. This parameter was significantly increased a rough 60% by two strains G7 and H47. As regard to transpiration, control plants averaged 0.6 μmol H2O /m2 s; only H47 increased transpiration significantly (+46%), while L44 decreased significantly (Figure 2B), dramatically increasing water use efficiency (Figure 2C). Photosynthetic pigments in controls showed similar values of chlorophyll a and carotenoids (65 and 60 μg/g FW, respectively) while chlorophyll b averaged 29 μg/g FW. Bacterial treatments did not cause significant changes, although G7 and H47 slightly increased chlorophylls and carotenes (Figure 3). Total phenols (Figure 4) in controls averaged 370 meq gallic acid/100 g fresh weight. Phenols were significantly enhanced by all three strains, ranging from 19% (H47) to 37% (G7, L44) increases. The concentration of the most abundant flavonoids, rutoside, and luteolin-7-glucoside in controls was 0.042 and 0.15 μg/g, while secologanoside and oleuropein 0.085 and 0.18 μg/g respectively, and hydroxytirosol was 0.045 μg/g (Figure 5). Flavonoid concentration was increased by 2-fold under the influence of H47, as well as secologanoside (Figure 5C) and oleuropein (Figure 5E) with increases of 2-fold and 8-fold, respectively. Hydroxytyrosol was also enhanced by H47 but not significantly (Figure 5D). G7 and L44 caused mild increases in oleuropein, G7 decreased hydroxytirosol and L44 increased luteolin-7-glucoside. The expression of key genes controlling the shikimate-flavonol pathway and DOXP pathway was analyzed by RT-qPCR in controls and in H47-treated plants, as this strain showed the highest increases in bioactive metabolites (Figure 6). Shikimate pathway control point, Chorismate mutase, was similarly expressed in both treatments. In the flavonol-pathway, Chalcone synthase, Chalcone isomerase, and Flavonol-3-hydroxylase were downregulated, and F-3’hydroxylase and Flavonol synthase were evenly expressed; arogenate dehydrogenase was slightly downregulated. As regards to enzymes in the DOXP pathway, all three synthases were upregulated, being DOXP synthase the most affected (8-fold), iridoid synthase by 3.5-fold and secoiridoid-synthase by 2-fold. The ability of extracts from the three strains and the non-inoculated controls to inhibit Angiotensin Converting enzyme was evaluated (Figure 7). Only H47 slightly decreased olive leaf extracts inhibitory potential.
The ability of certain PGPBs to trigger plant metabolism has been widely reported and still, specificity of plant-bacterium and specific targets triggered by each bacterial strain continue to be a challenge of interest. All three strains were able to trigger olive metabolism differently reinforcing the high specificity of the plant-microbe interactions on one hand, and the multitarget potential of each strain to improve adaptation. Furthermore, potential of PGPB to improve plant adaptation was potentiated by the harsh growth conditions assayed, sampling after summer period when extremely high temperatures called for bacterial induced benefits. Photosynthesis determined by fluorescence indicated that the three Bacillus strains were able to improve plant fitness, bringing (Fv/Fm) to optimal values and decreasing basal stress according to Fo values (Lucena et al., 2012). The decrease of PSII efficiency (ePSR; Figure 1C) together with the significant increase of energy dissipation support the ability of the three strains to alleviate oxidative stress induced by excess UV radiation and water stress, enabling a better use of resources (Ilangumaran and Smith, 2017; Sachdev et al., 2021; Gupta et al., 2022). Net carbon fixation (Figure 2) was differently modified by each Bacillus strain. L44 dramatically decreased transpiration suggesting a protective mechanism based on keeping water by closing stomata to prevent its loss, increasing WUE, which limits CO2 fixation (Jones and Sutherland, 1991; Aires et al., 2022). G7 increased CO2 fixation not altering transpiration, suggesting that metabolic changes that allow improved water potential happened. Only H47 increased transpiration significantly, improving plant adaptation by allowing the plant to keep stomata opened while keeping an active C fixation, hence improving C fixation (Hura et al., 2007); the improvement in CO2 assimilation together with the improvement in light energy absorption support the increase in secondary metabolites in this case, instead of the reported tomato yield increase (Aires et al., 2022). Effects on photosynthetic parameters reveal the different targets that each strain is able to trigger in order to improve plant adaptation (Ilangumaran and Smith, 2017). Interestingly, Fv/Fm is a common target to all strains, despite the different mechanism used by each of them; it confirms enhanced plant fitness under water and salt stress as it reaches standard values for healthy plants. Also, total phenols are increased by all three strains, revealing activation of antioxidant molecules to cope with oxidative stress due to environmental conditions (Alsayed et al., 2012). Enhancement of secondary metabolites such as polyphenols upon PGPB delivery, bacterial or chemical elicitors such as salicylic acid have been widely reported (Figure 8; Preciado-Rangel et al., 2019; Gutierrez-Albanchez et al., 2020; Martin-Rivilla et al., 2021). Despite the increase in total phenols, HPLC-MS analysis revealed different profiles on major secondary metabolites present in olive leaves induced by PGPBs, being H47 outstanding in terms of concentration. The striking 2-fold increases in flavonols and in secologanoside, and the 5-fold increase in oleuropein (Figure 5) were further studied by exploring changes in gene expression (Figure 6) in the shikimate-flavonol pathway leading to flavonols and to hydroxytirosol, and the DOXP pathway, leading to the iridoids secologanoside, ligstroside, and oleuropein (Figure 8). The biosynthetic pathway to iridoids in olive has been unveiled in the last years to describe oleuropein synthesis to follow an independent pathway from secologanoside. The early steps of iridoid synthesis result in 8-oxogeranial, substrate to iridoid synthase, that is transformed into 7-ketologanin in several steps and finally in oleoside 11-methyl ester by OeOMES, which will finally be transformed into ligstroside to be condensed with 3-hydroxytirosol to release OLE (Koudounas et al., 2021); Rodríguez-López et al. (2021) demonstrated that is independent of secoxyloganin in Olea europaea. The shikimate pathway has been widely studied as it is central to plant’s metabolism, leading to aromatic aminoacids trp, phe, and tyr (Oliva et al., 2021). After addition of side chain by enol-pyruvyl-shikimate synthase, the first control point is on Chorismate mutase and determines trp or phe/tyr synthesis; accumulation of either product inhibits the enzyme. Next control point is on L-arogenate processing enzymes, as arogenate dehydratase will lead to phe, head of flavonoids, while arogenate dehydrogenase will determine tyrosin biosynthesis, leading to 3-hydroxy-tyrosol, one moiety of oleuropein (Supplementary Table 2 of Supplementary material). In this metabolic pathway, accumulation of end-products inhibits branching point enzymes (Maeda and Dudareva, 2012; Oliva et al., 2021). Bacillus H47 kept the shikimate pathway active, as Chorismate mutase was similarly expressed in both groups of plants (Figure 6) despite differential accumulation of target metabolites (Figure 5) which did not play an inhibitory role, suggesting additional control of the pathway by H47, probably blocking expression of transcription factors in charge of this inhibition, such as MYB4 (Garcia-Seco et al., 2015; Gutierrez-Albanchez et al., 2020). Despite the higher accumulation of flavonoids in H47 treated plants (Figure 5), expression of enzymes involved in flavonol synthesis was not enhanced as compared to controls (Figure 6). This fact has been described before (Gutierrez-Albanchez et al., 2020) and is consistent with dynamics of plant innate immunity, for which the “network pulse model” has been proposed (Kollist et al., 2019). In this model, the response is quickly triggered upon stress to activate systemically plant’s immune system and integrated into the whole plant by pulses of gene expression, registering peaks and valleys on expression, with responses that run from seconds to minutes, to hours (Kollist et al., 2019). Interestingly, this pulse-powered response has been also described to control rapid stomatal responses, enabling a fine-tunning response of adaptation to water stress (Melotto et al., 2006) keeping high rates of C assimilation; the simultaneous enhancement of flavonol concentration with lower expression and higher transpiration (Figure 2), supports the regulation of the response through the pulse model network (Kollist et al., 2019). Furthermore, H47 seems to be controlling carbon allocation preferentially to secondary metabolites under extreme adverse conditions, further exacerbated by summer temperatures (Mauch-Mani et al., 2017). According to this hypothesis, G7 and L44 could have been sampled on valleys of signal transduction, but effects are not shown on the secondary metabolites evaluated, so bacterial activation targets fall out of focus set in this case. On the other hand, the DOXP pathway was also activated, and there is a consistent accumulation of secologanoside (Figure 5) with expression of the key synthases in the pathway. Interestingly, an 8-fold increase of the first enzyme in this pathway DOXP-synthase is detected, while intermediate synthases exclusive to iridoid synthesis, are only 3.5-and 2-fold. The enhanced activation of DOXP-synthase speaks of an activation devoted to feed synthesis of other relevant terpenes with physiological or biological activity, such as ABA, key for stomatal control, or oleacin synthesis (El and Karakaya, 2009; Li et al., 2020; Skodra et al., 2021; Xie et al., 2021). A striking 5-fold increase in the target metabolite oleuropein was found, suggesting either activation of the final step in the pathway, or a strong inhibition of oleuropein-β-glucosidase (OeGLU), the enzyme that would transform oleuropein into its aglycon, which would be further transformed into oleacin by demethylation and decarboxylation (Koudounas et al., 2021). However, as transitory silencing of OeGLU results in secoiridoid biosynthesis arrest (Koudounas et al., 2021), which is not the case in this study, the activation of the final step by H47 seems more likely to explain oleuropein increases. The increased expression of OeIRISY2 (Figure 6) may be masking expression of oleoside-methyl ester synthase (OMES), as OeSECSIN cannot differentiate the amplification of OMES2 (genbank nr MT909124.1) and OMES (oleoside methyl ester synthase; genbank nr MT909123.1). They hybridize 100% to both genes. Also they do not hybridize to secoxyloganin synthase (SXS genbank nr. MT909125). Oleuropein is an interesting polyphenol accumulated in leaves, where it plays a role as antioxidant and osmolyte, contributing to plant adaptation to water stress (Mechri et al., 2020) and involved in plant defense to pathogens (Benyelles et al., 2014) and to hervibores (Koudounas et al., 2021). Cytological accumulation occurs in vacuoles so activation of specific oleuropein carriers to vacuoles must be involved in the increase (Ghimire et al., 2021). Therefore, the reported increase in leaves by H47 contributes to improved plant adaptation modifying water potential, allowing more efficient water absorption, and probably contributing to the enhanced transpiration detected. Interestingly, oleuropein is also a beneficial molecule for humans, as it is one of the bioactive molecules contributing with antihypertensive effect (Romani et al., 2019), so the potential of leaf extracts enriched in oleuropein were expected to show increased antihypertensive effect. To test our hypothesis, the ability to inhibit the Angiotensin Converting enzyme as an in vitro marker of this activity was evaluated. Opposite to our expectations, the inhibition of ACE activity was even lower than controls (Figure 7), which could be explained on one hand, because the molecule directly inhibiting ACE is oleacin, another iridoid, which concentration may be lower due to preferential accumulation of oleuropein, or, on the other hand, because oleacin might be translocated to fruits, where it would preferentially accumulate, accounting for the benefits of olive oil, as previously reported for flavonols in blackberry and tomato (Gutierrez-Albanchez et al., 2021; Aires et al., 2022).
In conclusion, Bacillus H47 improved plant photosynthetic efficiency and C assimilation by keeping stomata open; simultaneous activation of secondary metabolites biosynthesis at DOXP and shikimate pathways resulted in increased flavonol and iridoid concentration in leaves, although the antihypertensive activity was not enhanced.
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author. Data is available upon request.
FG-M, AG-V, and BR-S: conceptualization. EG-C and MM-P: formal analysis. FG-M: resources. AG-V, MM-P, and BR-S: data curation. EG-C: writing—original draft preparation. BR-S and AG-V: writing—review and editing. FG-M: supervision. FG-M and BR-S: funding acquisition. All authors contributed to the article and approved the submitted version.
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.
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PMC9577732 | Kai Cai,Yang Yang,Zi-Jian Guo,Rui-Lin Cai,Hiroki Hashida,Hong-Xia Li | Amentoflavone inhibits colorectal cancer epithelial-mesenchymal transition via the miR-16-5p/HMGA2/β-catenin pathway | 01-09-2022 | Amentoflavone,colorectal cancer (CRC),miR-16,HMGA2,epithelial to mesenchymal transition (EMT) | Background Amentoflavone is a type of bioflavonoid that exists in many Chinese medicines and has anti-inflammatory, antioxidant, antiviral, and anticancer effects. However, the effect of amentoflavone on epithelial to mesenchymal transition (EMT) in human colorectal cancer (CRC) has not been studied. In this study, we aim to explore the effect of amentoflavone on EMT in CRC. Methods The effects of long noncoding RNA (lncRNA) miR-16-5p on proliferation, migration, and invasion were determined by in vitro and in vivo experiments. A luciferase reporter assay was carried out to reveal the interaction between miR-16-5p and targeted genes. Reverse transcription polymerase chain reaction (RT-PCR) was used to evaluate the expression of miR-16-5p. A western blot assay was used to detect the expression of targeted genes in CRC cells. Results The results showed that amentoflavone significantly inhibited CRC migration, invasion, and EMT by increasing miR-16-5p expression. Mechanistically, amentoflavone induced inactivation of the Wnt/β-catenin pathway via miR-16-5p, directly targeting 3'-UTR of HMGA2 to suppress HMGA2 expression in CRC. Clinically, combined miR-16-5p and HMGA2 levels may serve as a predictor for poor prognosis in patients with CRC. Furthermore, an in vivo PDX model suggested that amentoflavone exhibited antitumor effects in vivo via the miR-16-5p/HMGA2/β-catenin pathway. Conclusions This is the first study to show that amentoflavone inhibits CRC EMT via the miR-16/HMGA2/β-catenin pathway. Amentoflavone may be beneficial in treating CRC patients in the clinic. | Amentoflavone inhibits colorectal cancer epithelial-mesenchymal transition via the miR-16-5p/HMGA2/β-catenin pathway
Amentoflavone is a type of bioflavonoid that exists in many Chinese medicines and has anti-inflammatory, antioxidant, antiviral, and anticancer effects. However, the effect of amentoflavone on epithelial to mesenchymal transition (EMT) in human colorectal cancer (CRC) has not been studied. In this study, we aim to explore the effect of amentoflavone on EMT in CRC.
The effects of long noncoding RNA (lncRNA) miR-16-5p on proliferation, migration, and invasion were determined by in vitro and in vivo experiments. A luciferase reporter assay was carried out to reveal the interaction between miR-16-5p and targeted genes. Reverse transcription polymerase chain reaction (RT-PCR) was used to evaluate the expression of miR-16-5p. A western blot assay was used to detect the expression of targeted genes in CRC cells.
The results showed that amentoflavone significantly inhibited CRC migration, invasion, and EMT by increasing miR-16-5p expression. Mechanistically, amentoflavone induced inactivation of the Wnt/β-catenin pathway via miR-16-5p, directly targeting 3'-UTR of HMGA2 to suppress HMGA2 expression in CRC. Clinically, combined miR-16-5p and HMGA2 levels may serve as a predictor for poor prognosis in patients with CRC. Furthermore, an in vivo PDX model suggested that amentoflavone exhibited antitumor effects in vivo via the miR-16-5p/HMGA2/β-catenin pathway.
This is the first study to show that amentoflavone inhibits CRC EMT via the miR-16/HMGA2/β-catenin pathway. Amentoflavone may be beneficial in treating CRC patients in the clinic.
Colorectal cancer (CRC) is one of the most common malignant tumors in humans and is the main cause of cancer-related deaths worldwide (1). The Wnt/β-catenin signaling pathway plays a role in the invasion and metastasis of CRC (2-5). Epithelial to mesenchymal transition (EMT) is the key mechanism that drives most cancers, including CRC invasion and metastasis (6). Dysregulation of the Wnt/β-catenin signaling pathway has been shown to play an important role in the EMT required for colorectal tumor metastasis (7,8). Amentoflavone is a natural biflavone compound with many biological properties, including anti-inflammatory, anti-oxidative, and anti-apoptotic effects (9,10). Previous studies have also found that amentoflavone exerts antitumor effects in solid tumors (11-13). However, the underlying mechanisms and functions of amentoflavone on CRC EMT have not been fully explored. MicroRNAs (miRNAs) are a large class of small non-coding RNAs (14). The miR-16 family comprises six mature miRNAs (miR15a/b, miR-16-5p, miR-195, miR-424, and miR-497) widely explored in various cancers and considered tumor suppressors that inhibit tumor progression (15-20). MiR-16-5p has been reported to inhibit CRC progression (21). However, the potential role of miR-16-5p in amentoflavone-suppressed CRC tumor progression and its mediation of EMT in CRC remains unknown. In the present study, we demonstrated both in vitro and in vivo that amentoflavone increases miR-16-5p expression to inhibit CRC EMT and has an antitumor effect by blocking the HMGA2/Wnt/β-catenin pathway. We present the following article in accordance with the ARRIVE reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-22-3035/rc).
Ninety-six CRC tissues and paired adjacent normal tissues were obtained from patients diagnosed with CRC who received surgery at the First Affiliated Hospital of Guangxi University of Chinese Medicine. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Medical Research Ethics Committee of the First Affiliated Hospital of Guangxi University of Chinese Medicine (No. UEXP00001325). Informed consent was taken from all the patients.
Human CRC cell lines (HCT116 and SW480) from the Chinese Type Culture Collection (Shanghai, China) were cultured in an Roswell Park Memorial Institute (RPMI) 1640 medium (RPMI 1640; Gibco, USA) supplemented with 10% fetal bovine serum (FBS; Gibco, USA) and 2% penicillin/streptomycin (HyClone, Shanghai, China). All cell lines were authenticated by short tandem repeat (STR) profiling and all experiments were performed with mycoplasma-free cells. The cells were treated with amentoflavone (50 µM) followed by Lipopolysaccharide (1 µg/mL). The miRNA inhibitors and the miRNA negative control (NC) were synthesized by the Shanghai Gene Pharma Co., Ltd. (Shanghai, China) and transiently transfected as previously described (22). The concentration of amentoflavone in the study was reference as previous (11-13).
TRIzol reagent (Invitrogen, Carlsbad, CA, USA) was used to extract total RNA from the CRC tissues and CRC cells. qRT-PCR was performed as previously described (22). The primers involved in this study were designed and provided by Sangon Biotech (Shanghai) Co. Ltd. The specific miRNA and mRNA expression levels were quantified using the 2−∆∆CT method. U6 and ACTIN genes were used as normalization controls.
A 24-well chamber with an 8-µm pore size (cat. no. 3422; Corning, Inc.) was used to detect the cell migratory and invasive abilities according to the manufacturer’s protocol and performed as described previously (22). Use hole diameter of 8 µM Transwell chamber (Corning Inc., Corning, NY, USA) to evaluate cell migration and invasion. 48 hours after transfection, 5× the transfected cells were put into 300 µL medium without FBS and placed in the upper chamber, and 500 µL medium with 20% FBS was placed in the lower chamber. For matrix invasion test, Transwell chamber is coated with matrix (BD Biosciences, San Jose, CA, USA). Inoculate 5 with 300 µL culture medium without FBS × 104 transfected cells were transferred to Transwell upper chamber, and 500 µL culture medium with 20% FBS was placed in the lower chamber. The cells were incubated at 37 ℃ for 24 hours for migration and 48 hours for invasion. In the two tests, the cells were fixed with 100% methanol for 5 min (Baotai Biotechnology Research Institute, Haimen, China), and then stained with 0.5% crystal violet (Baotai Biotechnology Research Institute) for 5 min. Subsequently, the cells remaining on the upper surface of the membrane were carefully removed with a cotton swab. Then, in five randomly selected fields of view, an inverted microscope (magnification, ×200) count migrating and invading cells. Each experiment was repeated 3 times.
Total protein from the CRC cell lines and tissues was isolated, and the protein concentrations were measured using a bicinchoninic acid (BCA) protein assay kit (Pierce, IL, USA). The follow-up experiments were performed as described previously (22). E-Cadherin as the epithelial marker, and N-Cadherin and Vimentin as the mesenchymal marker were selected. The primary antibodies are listed in Table S1.
The pmirGLO-HMGA2 3'-UTR (WT) or pmirGLO-mutant HMGA2 3'-UTR (MUT) were co-transfected into cells with miR-16-5p or miR-NC by Lipofectamine 2000. The follow-up experiments were performed as described previously (22).
A TOP/FOP flash assay was performed as previously described (23). Luciferase activities were measured using the Dual-Glo Luciferase Assay System (Promega, Madison, Wisconsin, USA).
To evaluate the therapeutic effect and molecular mechanism of Amentoflavone against CRC, the murine CRC model was used in this study. The female C57BL/6 mice were purchased from Changsha Tianqin BioTech Ltd. (China). All the mice were housed and bred under SPF condition, a 12-hour light/12-hour dark cycle was set under the animal facility, temperature was set 18–23 ℃ with 40–60% humidity. For the PDX model, primary tumor samples were obtained for xenograft establishment as described previously (24). The mice were given oral doses of amentoflavone (50 mg/kg body weight) or vehicle control daily for 45 days (n=5 for each group). In this study, roughly 120 mice were used. The animals were euthanized when the diameter of the tumor exceeded 200 mm, to reduce any unnecessary pain, suffering and distress. No unexpected adverse events had been observed throughout this study. In this study, the endpoint refers the end stage of experimental period, and/or the tumor exceeded 200 mm. The mice with a CRC tumor were randomized into different groups. Animal experiments were performed under a project license (No. ANIEXP00003106) granted by the Ethical Committee of the First Affiliated Hospital of Guangxi University of Chinese Medicine, in compliance with national guidelines for the care and use of animals. A protocol was prepared before the study without registration.
To maintain the reliability of the conducted experiments, the performers were blinded, only the experiment designer(s) were aware of the group allocations. The data were analyzed using Prism 5 (GraphPad Software, San Diego, CA, USA) and SPSS 17.0 software (SPSS Inc., Cary, NC, USA). The values are presented as the means ± structural equation modeling (SEM). The details of the statistical methods are described in the Figure legends. All experiments were performed at least three times in duplicate.
The Transwell assay indicated that compared with the control group, the migrative and invasive ability of HCT116 and SW480 cells treated with amentoflavone (50 µM) was significantly decreased (Figure 1A,1B). Therefore, migration and invasion of HCT116 and SW480 cells may be inhibited in response to amentoflavone treatment. The qRT-PCR and WB analyses indicated that the relative mRNA and protein expression of E-cadherin in HCT116 and SW480 treated with amentoflavone (50 µM) were significantly increased, while N-cadherin and vimentin were significantly decreased (Figure 1C,1D). The aforementioned results suggest that amentoflavone may inhibit CRC cell migration and invasion by EMT.
The miR-16 family (miR-16-5p/miR-195-5p/miR-424-5p/miR-497-5p) exhibits a tumor suppressive potential, reducing EMT in many tumors. As shown in Figure 2A and Figure S1A, we found an increased level of miR-16-5p in HCT116 and SW480 cells treated with amentoflavone, while no changes were found in other family members. Additionally, we found that inhibited miR-16-5p enhanced EMT and reinstated the EMT ability in HCT116 and SW480 cells treated with amentoflavone (Figure 2B,2C and Figure S1B). Meanwhile, amentoflavone treatment inhibited the migration and invasion of HCT116 and SW480 cells, whereas inhibited miR-16-5p expression enhanced HCT116 and SW480 cell migration and invasion (Figure 2D,2E). Moreover, inhibited miR-16-5p reversed the migration and invasion ability in HCT116 and SW480 cells treated with amentoflavone (Figure 2D,2E). The above results indicated that amentoflavone inhibited EMT, migration, and invasion ability in CRC cells by increasing miR-16-5p levels.
To explore the EMT function of miR-16-5p in CRC, we used the TargetScan database (http://www.targetscan.org/vert_71/) to identify the target genes of miR-16. Among the predicted targets, we focused on eight genes that had cumulative weighted context++ scores >0.50 and an aggregate PCT (percentage) >0.80 (Table S2). As shown in Figure 3A, HMGA2, reported to be involved in inducing EMT in CRC (25), was increased in HCT116 and SW480 cells treated with miR-16-5p inhibitor, while there was no change in the other 21 genes. Therefore, we selected HMGA2 for further validation in CRC. According to the prediction by the seed complementarity, a potential miR-16-5p-binding site was found at nt1302–1309 of HMGA2 3'-UTR (Figure 3B). Additionally, the results showed that co-transfection of miR-16-5p inhibitor significantly increased the firefly luciferase activity of the reporter with WT 3'-UTR of HMGA2 but not that of the mutant reporter (Figure 3C), which indicates that miR-16-5p directly targets the 3'-UTR of HMGA2.
As shown in Figure 3D,3E, amentoflavone treatment suppressed the mRNA and protein of HMGA2 in HCT116 and SW480 cells, whereas inhibited miR-16-5p expression increased the mRNA and protein of HMGA2 in HCT116 and SW480 cells. Moreover, inhibited miR-16-5p replenished the mRNA and protein of HMGA2 in HCT116 and SW480 cells treated with amentoflavone (Figure 3D,3E). All the above results indicated that amentoflavone decreased HMGA2 via miR-16.
As HMGA2 is a key regulator activating the Wnt/β-catenin pathway and miR-16-5p targets HMGA2 in CRC, we wondered whether amentoflavone inactivates the Wnt/β-catenin pathway via miR-16. As shown in Figure 4A, amentoflavone treatment suppressed the protein level of β-catenin in HCT116 and SW480 cells, whereas inhibited miR-16-5p expression increased the protein level of β-catenin in HCT116 and SW480 cells. Moreover, inhibited miR-16-5p replenished the protein level of β-catenin in HCT116 and SW480 cells treated with amentoflavone (Figure 4B). Additionally, amentoflavone treatment suppressed the Wnt/TCF luciferase reporter activity in HCT116 and SW480 cells, whereas inhibited miR-16-5p expression increased the Wnt/TCF luciferase reporter activity in HCT116 and SW480 cells. Moreover, inhibited miR-16-5p replenished the Wnt/TCF luciferase reporter activity in HCT116 and SW480 cells treated with amentoflavone (Figure 4B). As shown in Figure 4C, amentoflavone treatment suppressed the mRNA expression of Wnt targets in HCT116 and SW480 cells, whereas inhibited miR-16-5p expression increased the mRNA expression of Wnt targets in HCT116 and SW480 cells. Moreover, inhibited miR-16-5p replenished the mRNA expression of Wnt targets in HCT116 and SW480 cells treated with amentoflavone (Figure 4C). Meanwhile, amentoflavone treatment suppressed the protein level of β-catenin in HCT116 and SW480 cells, whereas overexpression of HMGA2 expression increased the protein level of β-catenin in HCT116 and SW480 cells. Moreover, overexpression of HMGA2 expression replenished the protein level of β-catenin in HCT116 and SW480 cells treated with amentoflavone (Figure 4D). Additionally, amentoflavone treatment suppressed the Wnt/TCF luciferase reporter activity in HCT116 and SW480 cells, whereas overexpression of HMGB2 expression increased the Wnt/TCF luciferase reporter activity in HCT116 and SW480 cells. Moreover, overexpression of HMGA2 expression replenished the Wnt/TCF luciferase reporter activity in HCT116 and SW480 cells treated with amentoflavone (Figure 4E). As shown in Figure 4F, amentoflavone treatment suppressed the mRNA expression of Wnt targets in HCT116 and SW480 cells, whereas overexpression of HMGA2 expression increased the mRNA expression of Wnt targets in HCT116 and SW480 cells. Moreover, overexpression of HMGB2 expression replenished the mRNA expression of Wnt targets in HCT116 and SW480 cells treated with amentoflavone (Figure 4F). All the above results indicated that amentoflavone inactivated the Wnt/β-catenin pathway via the miR-16/HMGA2 axis.
To assess the clinical significance of the miR-16-5p/HMGA2 axis in CRC, we examined the expression of miR-16 and HMGA2 mRNA in 96 CRC patients. As shown in Figure 5A,5B, miR-16-5p was decreased in CRC tumors compared to adjacent normal tissues, while HMGA2 mRNA was increased in CRC tumors compared to adjacent normal tissues. The expression of miR-16-5p was negatively correlated with the expression of HMGA2 mRNA in the 96 patients with CRC (Figure 5C and Table S3). Kaplan-Meier analysis revealed the survival benefits in CRC patients with low miR-16-5p or high HMGA2 mRNA levels (Figure 5D,5E). Of note, the combination of low miR-16-5p and high HMGA2 mRNA predicted a better overall survival of CRC patients (Figure 5F). Collectively, these results suggest the prognostic value of the combination of miR-16-5p and HMGA2 in predicting the outcome of CRC patients.
Next, by virtue of PDXs from CRC tumors, we found that, compared with the vehicle control, amentoflavone partially blocked the growth of PDXs derived from CRC tumors (Figure 6A,6B). Additionally, as shown in Figure 6C,6D, amentoflavone treatment suppressed the mRNA of HMGA2, whereas it increased miR-16-5p expression in PDXs. Meanwhile, amentoflavone treatment suppressed HMGA2 and β-catenin protein in the PDXs (Figure 6E,6F), which demonstrates that amentoflavone inhibited CRC tumor growth in vivo via the miR-16-5p/HMGA2/β-catenin pathway.
In this study, we revealed that amentoflavone reduced the migration, invasion, and EMT in CRC cell lines by increasing miR-16-5p levels. Mechanistically, amentoflavone treatment inactivated the Wnt/β-catenin pathway via miR-16-5p, directly targeting 3'-UTR of HMGA2 to suppress HMGA2 expression in CRC. In addition, our clinical investigations revealed a close correlation between miR-16-5p and HMGA2 levels and CRC progression and patient prognosis. Moreover, the in vivo PDX model suggested that amentoflavone exhibited antitumor effects in vivo via the miR-16-5p/HMGA2/β-catenin pathway. Hence, these findings indicate that amentoflavone’s ability to inhibit migration, invasion, and EMT and exert antitumor effects might be attributable to the inhibition of the HMGA2/Wnt/β-catenin pathway activation by the increase in miR-16-5p expression (Figure 7). In our previous study, amentoflavone exhibited antitumor effects in several solid tumors (19-21). However, the anticancer effect and the mechanism of action of amentoflavone in CRC are unknown. In this study, we demonstrated the anti-CRC cell migration, invasion, and EMT effect of amentoflavone and the role of increased miR-16-5p expression on amentoflavone-induced inhibition of tumor progression in CRC. miR-16-5p is a member of the miR-15/-16/-195/-424/-497/-503 family. A remarkably inhibitory effect of miR-16-5p on the progression of several types of cancer has been reported previously (10-15). However, the function of miR-16-5p in the progression of CRC remains unknown. In this study, we found that amentoflavone reduced migration, invasion, and EMT in CRC cell lines dependent on an increased miR-16-5p expression, suggesting that miR-16-5p is a crucial tumor suppressor in CRC. In addition, our clinical investigations revealed a close correlation between miR-16-5p levels and CRC patient prognosis, thus making miR-16-5p an optimal target for CRC therapy. MicroRNAs can function as tumor suppressors or oncogenes by targeting their respectively associated genes (23). Using TargetScan bioinformatics, the HMGA2 gene was potentially identified as a direct target for miR-16-5p. HMGA2 is a nonhistone and architectural transcription factor, which exerts an oncogenic effect on numerous cancers. Recently, overexpression of HMGA2 in CRC patients has been shown to play an important role in metastasis development and is a potential indicator of poor survivability (26-29). In this study, we demonstrated a negative regulation of HMGA2 by miR-16-5p using a luciferase reporter assay. Following amentoflavone treatment, HMGA2 RNA and protein levels decreased and were recovered by inhibiting miR-16-5p expression. In addition, we observed that miR-95-3p expression was negatively correlated with HMGA2 transcription in CRC. This finding implies that the miR-16-5p/HMGA2 axis might serve as a novel therapeutic target in patients with CRC. There are multiple reports of Wnt/β-catenin pathway activation by HMGA2 to enhance migration, invasion, and EMT (30-32). In this study, we found that amentoflavone inhibited the Wnt/β-catenin pathway, and inhibiting miR-16-5p could reverse this inhibitory effect. Wnt/β-catenin signaling has been confirmed to promote tumor metastasis by inducing EMT (33). EMT is a process in which cancer cells lose their epithelial characteristics, obtain mesenchymal phenotypes, and develop motility and invasiveness (34). In the current study, we found that amentoflavone played an antitumor role in CRC by increasing miR-16-5p expression and suppressing HMGA2 and β-catenin levels, which suggests that the miR-16-5p/HMGA2/β-catenin axis determines the amentoflavone response. There are some limitations associated with the current study. Future studies need to establish a metastatic model, such as a tail vein injection model, to validate the effects of amentoflavone on CRC cell metastasis in vivo. In this study, only the TargetScan database was explored to identify miR-16 target genes. Therefore, future studies should use additional online databases to define the potential targets of miR-16 in CRC cells. Our PDX model showed amentoflavone partially blocked the growth of PDXs derived from CRC tumors, but the in vitro data failed to show that amentoflavone inhibited the proliferation of CRC cells. Further research should use more PDX models to define the effect of amentoflavone in inhibiting CRC cell growth; to explore the novel lncRNA regulating the miR-16/HMGA2/β-catenin pathway in CRC; to explore the effect of amentoflavone on T cell infiltration and tumor microenvironment during EMT in CRC; supplemented results of morphological experiments in vivo experiments. Till now, the achievements from this study are still in preclinical stage, though it’s still need to accomplish comprehensive studies to evaluate its therapeutic effect on patients, it shed lights on discovery of new biomarkers for clinical purposes. Taken together, our results demonstrated that amentoflavone has the potential to inhibit CRC progression through suppression of HMGA2/Wnt/β-catenin activation by increasing miR-16-5p levels.
The article’s supplementary files as 10.21037/atm-22-3035 10.21037/atm-22-3035 10.21037/atm-22-3035 10.21037/atm-22-3035 | true | true | true |
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PMC9577797 | Jianhua Wang,Min Wu,Le Chang,Zhankui Jin,Xiaoli Yang,Dongliang Li,Jiaojiao Wang,Jie Qu,Qiang Hou,Xiaoyan Huang,Cuixiang Xu | The lncRNA TERC promotes gastric cancer cell proliferation, migration, and invasion by sponging miR-423-5p to regulate SOX12 expression | 01-09-2022 | Long non-coding RNA telomerase RNA component (lncRNA TERC),microRNA-423-5p (miR-423-5p),sex determining region Y-box 12 (SOX12),gastric cancer (GC) | Background Long non-coding RNAs (lncRNAs) play critical roles in gastric cancer (GC) initiation progression. However, the biological function of the lncRNA telomerase RNA component (TERC) remains unknown in human GC. The present study sought to determine the biological function and underlying molecular mechanism of the lncRNA TERC in GC progression. Methods The expression levels of the lncRNA TERC in GC tissues and cell lines were analyzed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The effects of the lncRNA TERC on the proliferation, migration, and invasion of GC cells were determined using Cell Counting Kit-8 (CCK-8) and Transwell assays. Dual luciferase reporter and argonaute 2 (AGO2)-RNA immunoprecipitation (RIP) assays were used to detect the binding between the lncRNA TERC and microRNA-423-5p (miR-423-5p). Western blotting was performed to measure the expression levels of sex determining region Y-box 12 (SOX12), N-cadherin, E-cadherin, matrix metallopeptidase 9 (MMP9), and proliferating cell nuclear antigen (PCNA). Results The results demonstrated that the lncRNA TERC expression levels were upregulated in GC cells and tissues, while miR-423-5p expression levels were downregulated. The upregulation of the lncRNA TERC was associated with a shorter overall survival in patients with GC. The knockdown of the lncRNA TERC significantly reduced the proliferation, migration, and invasion of human GC cell lines HGC-27 and SNU-1 cells. Further, the lncRNA TERC knockdown in the HGC-27 and SNU-1 cells significantly downregulated the expression levels of SOX12, N-cadherin, MMP9, and PCNA, and upregulated the expression levels of miR-423-5p and E-cadherin. MiR-423-5p was also identified as a target of the lncRNA TERC and was found to directly bind to the lncRNA TERC. Additionally, miR-423-5p was found to directly target SOX12 to inhibit the proliferation, migration, and invasion of the HGC-27 and SNU-1 cells. Conclusions In conclusion, the findings of this study suggested that the lncRNA TERC may regulate the miR-423-5p/SOX12 signaling axis by directly sponging miR-423-5p and inhibiting SOX12 expression, thereby leading to the progression of GC. These findings may reveal novel targets for future GC therapy. | The lncRNA TERC promotes gastric cancer cell proliferation, migration, and invasion by sponging miR-423-5p to regulate SOX12 expression
Long non-coding RNAs (lncRNAs) play critical roles in gastric cancer (GC) initiation progression. However, the biological function of the lncRNA telomerase RNA component (TERC) remains unknown in human GC. The present study sought to determine the biological function and underlying molecular mechanism of the lncRNA TERC in GC progression.
The expression levels of the lncRNA TERC in GC tissues and cell lines were analyzed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The effects of the lncRNA TERC on the proliferation, migration, and invasion of GC cells were determined using Cell Counting Kit-8 (CCK-8) and Transwell assays. Dual luciferase reporter and argonaute 2 (AGO2)-RNA immunoprecipitation (RIP) assays were used to detect the binding between the lncRNA TERC and microRNA-423-5p (miR-423-5p). Western blotting was performed to measure the expression levels of sex determining region Y-box 12 (SOX12), N-cadherin, E-cadherin, matrix metallopeptidase 9 (MMP9), and proliferating cell nuclear antigen (PCNA).
The results demonstrated that the lncRNA TERC expression levels were upregulated in GC cells and tissues, while miR-423-5p expression levels were downregulated. The upregulation of the lncRNA TERC was associated with a shorter overall survival in patients with GC. The knockdown of the lncRNA TERC significantly reduced the proliferation, migration, and invasion of human GC cell lines HGC-27 and SNU-1 cells. Further, the lncRNA TERC knockdown in the HGC-27 and SNU-1 cells significantly downregulated the expression levels of SOX12, N-cadherin, MMP9, and PCNA, and upregulated the expression levels of miR-423-5p and E-cadherin. MiR-423-5p was also identified as a target of the lncRNA TERC and was found to directly bind to the lncRNA TERC. Additionally, miR-423-5p was found to directly target SOX12 to inhibit the proliferation, migration, and invasion of the HGC-27 and SNU-1 cells.
In conclusion, the findings of this study suggested that the lncRNA TERC may regulate the miR-423-5p/SOX12 signaling axis by directly sponging miR-423-5p and inhibiting SOX12 expression, thereby leading to the progression of GC. These findings may reveal novel targets for future GC therapy.
Gastric cancer (GC) is a common type of malignancy and the 3rd leading cause of cancer-related mortality worldwide (1). The disease is associated with a significant economic burden, especially in China (2-4). In recent decades, the incidence of GC has decreased due to the development of effective screening technologies and methods for controlling Helicobacter pylori infection (5,6). Surgical resection, radiotherapy, chemotherapy, and combined therapy are currently the primary treatment options available for GC, and all have been reported to significantly improve the survival of patients with GC (7-10). However, the prognosis of patients with advanced-stage GC remains unsatisfactory, and the 5-year survival rate for patients with metastatic GC is ~30% (11). At present, the underlying molecular mechanism involved in GC development and progression remains unclear. Thus, further research to extend the current understanding of the molecular mechanisms of GC progression and identify novel therapies targeting metastasis in GC urgently needs to be conducted. Long non-coding RNAs (lncRNAs) are non-coding RNA molecules >200 nucleotides in length that have limited protein-coding potential (12). Numerous previous studies have demonstrated that lncRNAs play regulatory roles in various biological processes, including the cell cycle, cell differentiation, apoptosis, migration, invasion, and cancer progression (13-17). There is accumulating evidence that lncRNAs may also act as competing endogenous RNAs (ceRNAs) that are able to adsorb microRNAs (miRNAs/miRs), and thus influence tumorigenesis (18,19). For example, lncRNA long intergenic non-protein coding RNA 2620 (BCRT1) was found to promote breast cancer progression by binding with miR-1303 (20). In bladder cancer, lncRNA cancer suspectibility 9 (CASC9) adsorbed miR-758-3p to induce cell proliferation and epithelial-mesenchymal transition (EMT) by regulating transforming growth factor-beta 2 (TGF-β2) expression (21). It has also been reported that the lncRNA Pvt1 oncogene (PVT1) promotes cell migration by sponging miR-30a and regulating snail family transcriptional repressor 1 expression in GC (22). Qu et al. (23) also demonstrated that lncRNA HOXA cluster antisense RNA 3 promoted GC progression by sponging miR-29a-3p, which subsequently regulated lymphotoxin β receptor expression and activated nuclear factor kappa B signaling. We identified that the expression level of lncRNA telomerase RNA component (TERC) was consistently significantly up-regulated in GCs in the Gene Expression Omnibus (GEO) database via through bioinformatics analysis. The TERC is an important RNA component of telomerase, and the lncRNA TERC, a non-coding RNA, provides a template sequence for telomere synthesis (24). The lncRNA TERC has also been reported to alleviate the progression of osteoporosis by sponging miR-217 and upregulating RUNX family transcription factor 2 (RUNX2) expression (25). However, to the best of our knowledge, the biological functions of the lncRNA TERC in the progression of cancer, especially GC, remain largely unknown. The current study sought to determine the expression levels of the lncRNA TERC in GC tissues and cell lines. In addition, the effects of the lncRNA TERC on GC cell proliferation, invasion, and migration were analyzed. We further predicted that the binding site between lncRNA TERC and miR-423-5p, and the binding site between miR-423-5p and sex determining region Y-box 12 (SOX12). Therefore, further mechanistic studies were performed to explore the role of the lncRNA TERC/miR-423-5p/SOX12 signaling axis in the progression of GC. Together, the findings of the present study may provide novel insights into the potential of the lncRNA TERC/miR-423-5p/SOX12 signaling axis as a treatment target for GC. We present the following article in accordance with the MDAR reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-22-3545/rc).
A total of 20 human GC and corresponding and adjacent normal tissues were obtained from patients admitted to Shaanxi Provincial People’s Hospital (Xi’an, China) between July 2021 and December 2021. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). All participants provided written informed consent, and the study was approved by the Ethics Committee of Shaanxi Provincial People’s Hospital (No. 2021-186).
Human GC gene expression data were obtained from the GEO data set, GSE63288. The data analysis was performed using the DEGseq package of R software (1.12.0; RStudio, Inc., Boston, MA, USA). Genes with a log2|fold change (FC)| >1 and a P<0.05 were considered differentially expressed genes. The binding sites between the lncRNA TERC and miR-423-5p were predicted using the starBase database (https://starbase.sysu.edu.cn/). The binding sites between miR-423-5p and SOX12 were predicted using the TargetScan 7.1 database (https://www.targetscan.org/vert_80/).
The GES-1 human gastric mucosal epithelial cells and the NCI-N87, KATO3, Hs-746T, HGC-27, and SNU-1 human GC cell lines were obtained from Procell Life Science and Technology Co., Ltd. (Wuhan, China). The SNU-1, KATO3, and HGC-27 cell lines were cultured in Roswell Park Memorial Institute (RPMI)-1640 medium (Gibco; Thermo Fisher Scientific, Inc., Waltham, MA, USA) supplemented with 10% or 20% fetal bovine serum (FBS) (Gibco; Thermo Fisher Scientific, Inc.), and 1% penicillin-streptomycin solution. The GES-1, NCI-N87, and Hs-746T cell lines were cultured in Dulbecco’s Modified Eagle Medium (Gibco; Thermo Fisher Scientific, Inc.) supplemented with 10% FBS (Gibco; Thermo Fisher Scientific, Inc.), and 1% penicillin-streptomycin solution. All the cells were maintained at 37 ℃ in a 5% carbon dioxide humidified incubator.
A total of 5×105 HGC-27 and SNU-1 cells/well were seeded into a 6-well plate overnight at 37 ℃. The cells were then cultured in serum-free RPMI-1640 medium for 2 h prior to transfection. The cells were subsequently transiently transfected with small interfering RNA. The sequences of small interfering RNA in TERC and miR-423-5p were listed in Table 1. They were transfected into cells by using Lipofectamine® 2000 reagent (Invitrogen; Thermo Fisher Scientific, Inc.) in accordance with the manufacturer’s protocol.
The proliferative ability of the HGC-27 and SNU-1 cells was measured using CCK-8 assays. Briefly, 5×103 HGC-27 and SNU-1 cells/well were seeded into a 96-well plate and incubated overnight. The cells were then transfected for a further 48 h. Following the transfection, 10 µL of CCK-8 solution was added to each well and incubated at 37 ℃ for an additional 4 h. The absorbance of each well was measured at a wavelength of 450 nm using a microplate reader (Flexstation® 3; Molecular Devices, LLC, San Jose, CA, USA). The experiments were performed in triplicate.
The migratory and invasive abilities of the HGC-27 and SNU-1 cells were determined using Transwell plates (24-well inserts; Corning, NY, USA). Briefly, for the migration assays, 5×105 HGC-27 and SNU-1 cells/well were seeded into a 6-well plate and incubated overnight. The cells were transfected for 48 h, trypsinized and resuspended in serum-free RPMI-1640 medium at a density of 3×105 cells/mL. A volume of 200 µL of cell suspension was added to the upper chambers of the Transwell plate, while 800 µL of growth medium (RPMI-1640 medium supplemented with 10% or 20% FBS) was added to the lower chambers. Following incubation for 24 h, the migratory cells were stained with crystal violet and counted using an inverted microscope (ECLIPSE Ts2; Nikon Corporation; magnification, ×200). The invasion assays were performed as described, with a minor alteration—the upper chamber of the Transwell plate was pre-coated with 100 µL of Matrigel (1 mg/mL; Corning, Inc.).
Total RNA was extracted from the GC tissues, adjacent normal tissues, HGC-27, and SNU-1 cells using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.). Total RNA was reverse transcribed into complementary DNA (cDNA) using random primers and Hiscript Reverse Transcriptase (GeneCopoeia Company, USA) for messenger RNA quantification. For miRNA quantification, the RT step was performed using an Oligo (dT) 18/miRNA loop and Hiscript Reverse Transcriptase. The primer sequences are listed in Table 1. The PCR reaction conditions were as follows: 10 minutes at 95 ℃, followed by 40 cycles of 15 seconds at 95 ℃, and 60 seconds at 60 ℃. The expression levels were quantified using the 2−ΔΔCq method (26).
The binding relationships between the lncRNA TERC, miR-423-5p, and SOX12 were verified using dual luciferase reporter assays. Briefly, the cDNA fragments of the TERC and SOX12 containing the predicted miR-423-5p binding sites were inserted into the pYr-MirTarget luciferase reporter vector (Yingrun Biotechnologies Inc., China) to generate pYr-MirTarget-Homo SOX12-wild-type (WT) and pYr-MirTarget-Homo TERC-WT vectors, which are henceforth denoted as SOX12-WT and TERC-WT, respectively. A mutant (MUT) site in the miR-423-5p binding site was also designed and cloned into the pYr-MirTarget luciferase reporter vector to generate pYr-MirTarget-Homo SOX12-MUT (SOX12-MUT) and pYr-MirTarget-Homo TERC-MUT (TERC-MUT) vectors. The TERC or SOX12 plasmids (WT or MUT) were co-transfected with the miR-423-5p mimic or mimic-negative control (NC) into 293T cells. Following 48 h of transfection, a dual luciferase reporter gene assay kit (Beyotime Institute of Biotechnology, Suzhou, China) was used to determine the relative luciferase activity.
A RNA-binding protein immunoprecipitation kit (MilliporeSigma, Burlington, MA, USA) was used in accordance with the manufacturer’s protocol to determine the relationship between the lncRNA TERC and miR-423-5p. Anti-argonaute 2 (AGO2) (SAB4200085, MilliporeSigma) and control immunoglobulin G (IgG) (R9255, MilliporeSigma) antibodies were used to perform the RIP assays, and the expression levels of the lncRNA TERC and miR-423-5p were subsequently evaluated using qPCR.
The relative protein expression levels were examined using western blotting as previously described (27). Briefly, total protein was extracted from tissue samples and HGC-27 and SNU-1 cells using ristocetin-induced platelet aggregation lysis buffer (Beyotime Institute of Biotechnology) supplemented with 1% protease inhibitor cocktail (Sigma-Aldrich; Merck KGaA). Proteins were separated by sodium dodecyl-sulfate polyacrylamide gel electrophoresis and then transferred to nitrocellulose membranes (MilliporeSigma). After blocking with 5% non‐fat milk for 2 h at room temperature, the membranes were incubated with the following primary antibodies at 4 ℃ overnight: anti-N‐cadherin (cat. no. 13116, 1:1,000 dilution), anti-E‐cadherin (cat. no. 3195, 1:1,000 dilution), anti-matrix metallopeptidase 9 (MMP9; cat. no. 13667, 1:1,000 dilution), anti-proliferating cell nuclear antigen (PCNA; cat. no. 13110, 1:1,000 dilution), and anti-β-actin (cat. no. 4970, 1:1,000 dilution), which were purchased from Cell Signaling Technology, Inc. (Beverly, MA, USA). Anti-SOX12 (cat. no. 13116, 1:1,000 dilution) was purchased from Proteintech (Chicago, IL, USA). Following the primary antibody incubation, the membranes were incubated with the appropriate secondary antibodies (Cell Signaling Technology, Inc., cat. no. 7074, 1:1,000 dilution) for 2 h at room temperature. The protein bands were visualized using an enhanced chemiluminescence (ECL) kit (Pierce; Thermo Fisher Scientific, Inc.).
All the data are presented as the mean ± standard deviation. All the experiment were replicated 3 times. The statistical analysis was performed using GraphPad Prism 6.0 software (GraphPad Software, Inc., San Diego, CA, USA). The statistical differences between the groups were determined using a Student’s t-test or one-way analysis of variance followed by a Dunnett’s post-hoc test. A P value <0.05 indicated a statistically significant difference.
The present study first identified differentially expressed lncRNAs in GC using the GSE63288 data set from the GEO database. Volcano plot (see Figure 1A) and heat map (see Figure 1B) showed differentially expressed lncRNAs identified from the GEO database. The identified upregulated lncRNAs that were thought to play important roles in GC progression were subsequently further analyzed. Among the lncRNAs, the expression levels of the lncRNA TERC were found to be consistently significantly upregulated in GC in the GEO database. Thus, GC and adjacent normal tissues (n=20) were collected and the expression levels of the lncRNA TERC were determined using RT-qPCR. As Figure 1C shows, lncRNA TERC expression was significantly more upregulated in GC tissues than adjacent normal tissues. Additionally, the expression levels of the lncRNA TERC in human gastric mucosal epithelial cells and 5 GC cell lines were analyzed. Compared to the GES-1 cells, the expression levels of the lncRNA TERC were significantly upregulated in the SNU-1, HGC-27, and KATO3 cells (see Figure 1D). Additionally, as Figure 1E shows, higher lncRNA TERC expression levels were found to be associated with shorter overall survival in patients with GC. Collectively, these results suggested that lncRNA TERC expression may be upregulated in GC tissues and poor survival outcomes may be associated with lncRNA TERC expression levels in GC.
To explore the biological function of the lncRNA TERC in GC, the effects of the lncRNA TERC on the proliferation, migration, and invasion of the HGC-27 and SNU-1 cells were investigated. The si-TERC was used to knockdown the expression levels of the lncRNA TERC in the HGC-27 and SNU-1 cells, and the interference efficiency of the lncRNA TERC was detected using RT-qPCR. Compared to the si-NC group, the expression levels of the lncRNA TERC were significantly downregulated in the HGC-27 and SNU-1 cells in the si-TERC group (see Figure 2A). The lncRNA TERC knockdown also significantly reduced the proliferation of the HGC-27 and SNU-1 cells (see Figure 2B). Moreover, the migratory and invasive abilities of the HGC-27 and SNU-1 cells were significantly more reduced in the si-TERC group than the si-NC group (see Figure 2C-2F). EMT plays an important role in cancer invasiveness and metastasis (28,29). Thus, the effect of the lncRNA TERC on EMT-related markers, such as N-cadherin and E-cadherin, was also analyzed using western blotting. As Figure 3A-3C show, the expression levels of N-cadherin were significantly more downregulated in the si-TERC group, while the expression levels of E-cadherin were more upregulated compared to those in the si-NC group. Additionally, the protein expression levels of MMP9 and PCNA, which are closely associated with tumor metastasis and cell proliferation, were investigated (30,31). The results revealed that the lncRNA TERC knockdown significantly downregulated the protein expression levels of N-cadherin, MMP9 and PCNA in the HGC-27 and SNU-1 cells (see Figure 3A,3D,3E), while significantly upregulated the levels of E-cadherin (see Figure 3B). These data indicated that the lncRNA TERC may regulate the proliferation, migration, and invasion of GC cells by regulating EMT and the protein expression levels of MMP9 and PCNA.
There is increasing evidence that the lncRNA TERC acts as a ceRNA to regulate the biological function of miRNAs (20-23). Using the starBase database (https://starbase.sysu.edu.cn/), miR-423-5p was identified as a potential target of the lncRNA TERC (see Figure 4A). To further examine the binding relationship between the lncRNA TERC and miR-423-5p, the expression levels of miR-423-5p in GC tissues and adjacent normal tissues (n=20) were analyzed using RT-qPCR. The results revealed that miR-423-5p was significantly more downregulated in the GC tissues than the normal tissues (see Figure 4B). To verify the binding relationship between the lncRNA TERC and miR-423-5p, a dual luciferase reporter assay was performed. The results showed that the overexpression of miR-423-5p significantly decreased the relative luciferase activity of the TERC-WT group, while the relative luciferase activity was unaltered in the TERC-MUT group (see Figure 4C). Moreover, the RIP analysis demonstrated that the anti-AGO2 antibody could pulldown the lncRNA TERC (see Figure 4D), and revealed the enrichment of lncRNA TERC, miR-423-5p, and SOX12 in IgG or AGO2 pulled-down RNA products in HGC-27 and SNU-1 cells (see Figure S1). The RIP analysis also revealed that the overexpression of miR-423-5p led to the substantial upregulation of lncRNA TERC expression in the RIP-AGO2 group compared to the RIP-IgG + miR-423-5p mimic or RIP-AGO2 + mimic-NC groups (see Figure 4D). Additionally, lncRNA TERC knockdown significantly upregulated the expression levels of miR-423-5p in the HGC-27 and SNU-1 cells (see Figure 4E,4F). These results suggested that miR-423-5p may be a direct target of the lncRNA TERC, and lncRNA TERC expression may be negatively associated with miR-423-5p expression.
To further confirm the biological function of miR-423-5p in GC, the effects of miR-423-5p on the proliferation, migration, and invasion of the HGC-27 and SNU-1 cells were determined. As Figure 5A,5B show, the cell viability in miR-423-5p mimic group was significantly reduced compared to mimic-NC group. The migratory and invasive abilities of the HGC-27 and SNU-1 cells were also examined using Transwell assays. The results revealed that the migration of the HGC-27 and SNU-1 cells was significantly impaired in the miR-423-5p mimic group compared to the mimic-NC group (see Figure 5C,5D). And the invasion was also significantly impaired in the miR-423-5p mimic group compared to the mimic-NC group in the HGC-27 and SNU-1 cells (see Figure 5E,5F). These results indicated that miR-423-5p may play crucial roles in the proliferation, migration, and invasion of GC cells.
Using the online software TargetScan Human 7.1, SOX12 was identified as a candidate target gene of miR-423-5p (see Figure 6A). To further validate the binding association between miR-423-5p and SOX12, the expression levels of SOX12 in GC tissues and adjacent normal tissues (n=20) were first analyzed using western blotting and RT-qPCR. As Figure 6B,6C show, SOX12 expression levels were significantly more upregulated in the GC tissues than the adjacent normal tissues. Subsequently, SOX12-WT or SOX12-MUT luciferase reporter vectors were constructed, and a dual luciferase reporter assay was performed. The results revealed that the overexpression of miR-423-5p significantly attenuated the relative luciferase activity in the SOX12-WT group, while the relative luciferase activity was unaltered in the SOX12-MUT group (see Figure 6D). Further, the effect of miR-423-5p on SOX12 expression was examined in the HGC-27 and SNU-1 cells using western blotting. The protein expression level in miR-423-5p mimic group was significantly reduced compared to mimic-NC group in the HGC-27 and SNU-1 cells (see Figure 6E,6F). These results indicated that miR-423-5p may negatively regulate SOX12 expression in GC cells.
The results of the present study revealed that lncRNA TERC expression was more upregulated in GC tissues and cell lines than adjacent normal tissues and gastric mucosal epithelial cells. High lncRNA TERC expression was also found to be associated with the poor prognosis of patients with GC. In addition, the lncRNA TERC knockdown significantly inhibited the proliferation, migration, and invasion of the HGC-27 and SNU-1 GC cell lines. The functional mechanistic studies further revealed that the lncRNA TERC regulated the expression of the SOX12 protein via miR-423-5p in GC. These data suggested that the lncRNA TERC may function as an oncogene in GC. An increasing number of studies have suggested that lncRNAs are closely associated with the occurrence and progression of various types of cancer (32,33). For example, it was previously reported that lncRNA PVT1 was involved in the pathogenesis of human colorectal cancer (34), and lncRNA small nucleolar RNA host gene 3 (SNHG3) induced proliferation, migration, invasion and EMT in bladder cancer cells (35). Huang et al. (36) reported that lncRNA AK023391 promoted cell proliferation and invasion by targeting the phosphatidylinositol-3-kinase and protein kinase B signaling pathway in GC. Wu et al. (37) demonstrated that lncRNA SNHG11 promoted cell proliferation, migration, invasion, and EMT in GC. Consistent with these findings, the results of the present study showed that lncRNA TERC expression was more upregulated in GC tissues and the HGC-27 and SNU-1 cell lines than the adjacent normal tissues and the GES-1 gastric mucosal epithelial cells. Upregulated lncRNA TERC expression was also found to be associated with the poor prognosis of patients with GC. Further, the results of the current study demonstrated that the knockdown of the lncRNA TERC significantly increased the proliferation, migration, and invasion of the HGC-27 and SNU-1 GC cell lines. Numerous previous studies have reported that PCNA, EMT, and MMP9 play important roles in the proliferation, migration, and invasion of cancer cells (38-43). Notably, in the present study, the results demonstrated that the lncRNA TERC knockdown significantly upregulated the expression levels of the EMT-related marker, E-cadherin, and downregulated the expression levels of PCNA, MMP9, and N-cadherin in the HGC-27 and SNU-1 GC cell lines. These results indicated that the lncRNA TERC may play a key role in GC progression. MiRNAs are a class of non-coding RNAs, 18–25 nucleotides in length, which have been found to be involved in the regulation of tumorigenesis and cancer progression (44). It was previously reported that miR-3622a increased the proliferation and invasion of bladder cancer cells by decreasing ceramine synthase 2 expression (45). Further, miR-325-3p was discovered to promote breast cancer cell proliferation, invasion, and EMT by targeting S100 calcium binding protein A2 (46). The miR-200a/205 has been reported to be involved in the EMT process in GC cells (47). MiR-216a inhibits the metastasis of GC cells by regulating the EMT process by targeting the JAK2/STAT3 signaling pathway (48). Tang et al. (49) also reported that the overexpression of miR-423-5p significantly inhibited the proliferation, colony formation, and invasion of the ovarian cancer A2780s (also known as A2780) and A2780cp (cisplatin resistant) cell lines. The findings of the present study revealed that the expression levels of miR-423-5p were significantly more downregulated in the GC tissues than the adjacent normal tissues. The overexpression of miR-423-5p also significantly reduced the proliferation, migration, and invasion of the HGC-27 and SNU-1 GC cell lines. These data suggested that miR-423-5p may play a significant role in the regulation of GC progression. There is increasing evidence that lncRNAs act as endogenous sponges to modulate miRNA expression and biological functions (20-23). For example, lncRNA BCRT1 promoted breast cancer progression by sponging miR-1303, thereby modulating the expression of polypyrimidine tract binding protein 3 (20). It was also reported that lncRNA CASC9 induced bladder cancer cell proliferation and EMT by sponging miR‑758‑3p, thereby upregulating TGF-β2 expression (21). Du et al. (50) found that lncRNA long intergenic non-protein coding RNA 319 acted as the sponge for miR-423-5p, which subsequently upregulated nucleus accumbens associated 1 expression and promoted the proliferation, migration, and invasion of ovarian cancer cells. Lin et al. (51) also demonstrated that lncRNA PVT1 acted as a ceRNA to sponge miR-423-5p and promoted thyroid cancer cell proliferation and invasion by upregulating p21 (RAC1) activated kinase 3 expression. In the present study, an online prediction tool analysis identified target binding sites between the lncRNA TERC and miR-423-5p. The binding relationship between the lncRNA TERC and miR-423-5p was further determined using dual luciferase reporter and RIP assays. The results revealed that miR-423-5p significantly reduced the relative luciferase activity in the TERC-WT group, while the relative luciferase activity was unaltered in the TERC-MUT group. The results of the RIP assays also demonstrated that the overexpression of miR-423-5p significantly upregulated lncRNA TERC expression in the RIP-AGO2 group compared to the RIP-IgG + miR-423-5p mimic or RIP-AGO2 + mimic-NC groups. Additionally, the lncRNA TERC knockdown significantly upregulated miR-423-5p expression in the HGC-27 and SNU-1 cells. These results suggested that the lncRNA TERC may promote GC progression by sponging miR-423-5p. The SOX transcription factor family comprises 20 members in vertebrates, which play an important role in cell differentiation, tumorigenesis, and embryonic development (52-54). SOX12 is a member of the SOXC family and has been reported to promote multiple malignant processes in various types of cancer (55-58). For example, it was previously demonstrated that SOX12 mediated cell proliferation and metastasis by regulating asparagine synthesis in colorectal cancer (55). Another study also found that miR-370 inhibited cell proliferation, migration, and invasion by downregulating SOX12 expression in bladder cancer (56). Ge et al. (59) reported that lncRNA long intergenic non-protein coding RNA 2908 promoted cell proliferation by regulating the miR-663a/SOX12 signaling axis in pancreatic cancer. Du et al. (60) also found that SOX12 promoted cell migration, invasion, and metastasis by upregulating MMP7 and insulin-like growth factor 1 expression in GC. The results of the present study revealed that SOX12 was more upregulated in GC tissues than matched adjacent normal tissues. An online prediction tool analysis also identified target sites between miR-423-5p and SOX12. To further determine the binding relationship between miR-423-5p and SOX12, dual luciferase reporter assays were performed in the 293T cells. The results illustrated that the overexpression of miR-423-5p significantly attenuated the relative luciferase activity in the SOX12-WT group, while the relative luciferase activity was unaltered in the SOX12-MUT group. Further, the overexpression of miR-423-5p significantly downregulated the expression levels of SOX12 in the HGC-27 and SNU-1 cells. These data indicated that miR-423-5p may inhibit GC progression by downregulating SOX12 expression. In our next experiment, we will further confirm the associations between EMT relative proteins (such as N-cadherin, E-cadherin, MMP9, and PCNA) and miR-423-5p and SOX12, to further verify whether overexpression of miR-423-5p and knockdown of SOX12 can significantly upregulate the expression level of E-cadherin and downregulate the expression levels of PCNA, MMP9 and N-cadherin in HGC-27 and SNU-1 GC cell lines. In conclusion, the findings of the current study indicated that lncRNA TERC expression may be significantly upregulated in GC tissues and cells and closely associated with a poor prognosis in patients with GC. The lncRNA TERC was discovered to promote the proliferation, invasion, and migration of GC cells. Further mechanistic studies revealed that the lncRNA TERC promoted cell proliferation, invasion, and migration by acting as a natural sponge of miR-335-5p and affecting SOX12 expression. Thus, these findings suggested that the lncRNA TERC may act as an oncogene in GC, and it may represent a promising prognostic biomarker and novel therapeutic target for the disease.
The article’s supplementary files as 10.21037/atm-22-3545 10.21037/atm-22-3545 10.21037/atm-22-3545 10.21037/atm-22-3545 | true | true | true |
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PMC9577936 | 36252997 | Qi Zhang,Bingqiu Xiu,Liyi Zhang,Ming Chen,Weiru Chi,Lun Li,Rong Guo,Jingyan Xue,Benlong Yang,Xiaoyan Huang,Zhi-Ming Shao,Shenglin Huang,Yayun Chi,Jiong Wu | Immunosuppressive lncRNA LINC00624 promotes tumor progression and therapy resistance through ADAR1 stabilization | 17-10-2022 | Breast Neoplasms,Immunity, Innate,Antigen Presentation | Background Despite the success of HER2-targeted therapy in achieving prolonged survival in approximately 50% of treated individuals, treatment resistance is still an important challenge for HER2+ breast cancer (BC) patients. The influence of both adaptive and innate immune responses on the therapeutic outcomes of HER2+BC patients has been extensively demonstrated. Methods Long non-coding RNAs expressed in non-pathological complete response (pCR) HER2 positive BC were screened and validated by RNA-seq. Survival analysis were made by Kaplan-Meier method. Cell death assay and proliferation assay were performed to confirm the phenotype of LINC00624. RT-qPCR and western blot were used to assay the IFN response. Xenograft mouse model were used for in vivo confirmation of anti-neu treatment resistance. RNA pull-down and immunoblot were used to confirm the interaction of ADAR1 and LINC00624. ADAR1 recombinant protein were purified from baculovirus expression system. B16-OVA cells were used to study antigen presentation both in vitro and in vivo. Flow cytometry was used to determine the tumor infiltrated immune cells of xenograft model. Antisense oligonucleotides (ASOs) were used for in vivo treatment. Results In this study, we found that LINC00624 blocked the antitumor effect of HER2- targeted therapy both in vitro and in vivo by inhibiting type I interferon (IFN) pathway activation. The double-stranded RNA-like structure of LINC00624 can bind and be edited by the adenosine (A) to inosine (I) RNA-editing enzyme adenosine deaminase RNA specific 1 (ADAR1), and this editing has been shown to release the growth inhibition and attenuate the innate immune response caused by the IFN response. Notably, LINC00624 promoted the stabilization of ADAR1 by inhibiting its ubiquitination-induced degradation triggered by β-TrCP. In contrast, LINC00624 inhibited major histocompatibility complex (MHC) class I antigen presentation and limited CD8+T cell infiltration in the cancer microenvironment, resulting in immune checkpoint blockade inhibition and anti-HER2 treatment resistance mediated through ADAR1. Conclusions In summary, these results suggest that LINC00624 is a cancer immunosuppressive lncRNA and targeting LINC00624 through ASOs in tumors expressing high levels of LINC00624 has great therapeutic potential in future clinical applications. | Immunosuppressive lncRNA LINC00624 promotes tumor progression and therapy resistance through ADAR1 stabilization
Despite the success of HER2-targeted therapy in achieving prolonged survival in approximately 50% of treated individuals, treatment resistance is still an important challenge for HER2+ breast cancer (BC) patients. The influence of both adaptive and innate immune responses on the therapeutic outcomes of HER2+BC patients has been extensively demonstrated.
Long non-coding RNAs expressed in non-pathological complete response (pCR) HER2 positive BC were screened and validated by RNA-seq. Survival analysis were made by Kaplan-Meier method. Cell death assay and proliferation assay were performed to confirm the phenotype of LINC00624. RT-qPCR and western blot were used to assay the IFN response. Xenograft mouse model were used for in vivo confirmation of anti-neu treatment resistance. RNA pull-down and immunoblot were used to confirm the interaction of ADAR1 and LINC00624. ADAR1 recombinant protein were purified from baculovirus expression system. B16-OVA cells were used to study antigen presentation both in vitro and in vivo. Flow cytometry was used to determine the tumor infiltrated immune cells of xenograft model. Antisense oligonucleotides (ASOs) were used for in vivo treatment.
In this study, we found that LINC00624 blocked the antitumor effect of HER2- targeted therapy both in vitro and in vivo by inhibiting type I interferon (IFN) pathway activation. The double-stranded RNA-like structure of LINC00624 can bind and be edited by the adenosine (A) to inosine (I) RNA-editing enzyme adenosine deaminase RNA specific 1 (ADAR1), and this editing has been shown to release the growth inhibition and attenuate the innate immune response caused by the IFN response. Notably, LINC00624 promoted the stabilization of ADAR1 by inhibiting its ubiquitination-induced degradation triggered by β-TrCP. In contrast, LINC00624 inhibited major histocompatibility complex (MHC) class I antigen presentation and limited CD8+T cell infiltration in the cancer microenvironment, resulting in immune checkpoint blockade inhibition and anti-HER2 treatment resistance mediated through ADAR1.
In summary, these results suggest that LINC00624 is a cancer immunosuppressive lncRNA and targeting LINC00624 through ASOs in tumors expressing high levels of LINC00624 has great therapeutic potential in future clinical applications.
The efficacy of antitumor treatment relies on IFN response. ADAR1 inhibits the overactivation of double-stranded RNA sensors such as RIG-I and MDA5, therefore mitigate the alert system and thus shape a ‘cold’ tumor microenvironment.
We found an immunosuppressive lncRNA LINC00624 restrains the activation of IFN pathway through stabilizing ADAR1. LINC00624 also relies on the A-to-I RNA editing ability of ADAR1 to inhibit MHC class I antigen presentation and limited CD8+T cell infiltration in the cancer microenvironment, resulting in immune checkpoint blockade inhibition and anti-HER2 treatment resistance.
LINC00624 could be a biomarker for anti-HER2 and immune therapy. Targeting LINC00624 through antisense oligonucleotides could show great therapeutic potential for future clinical use.
The human innate immune system has evolved a well-designed mechanism for providing the first line of defense against viral infection. When viral double-stranded RNAs (dsRNAs) are sensed by cytosolic pattern recognition receptors, such as RIG-I and MDA5,1 IFNs are secreted by most human cells, and they trigger the expression of IFN stimulated genes (ISGs). ISGs stimulate antigen presentation pathways, which lead to the recruitment of immune cells and facilitate antiviral responses.1 2 In addition, immune systems are well adapted to avoid eliciting damage in normal tissue3 4 when mistranscribed RNAs are expressed by cells or released after physiological cell death.5 Tumors can evade T cell-mediated antitumor immunity by decreasing IFN or MHC class I antigen induction triggered by the dsRNA or cytoplasmic DNA products of aberrant transcription or mitosis.6 7 Therefore, the efficacy of antitumor treatment also relies on autonomous autocrine of type I IFN in tumor cells.8 In addition to traditional cytotoxic drugs, tyrosine kinase inhibitors (TKIs) and humanized antibodies can enhance type I IFN and antigen presentation pathway activation.9–11 Therefore, the regulation of the innate immune response is critical for therapeutic efficacy in breast cancer (BC). Adenosine deaminase RNA specific 1 (ADAR1) is a regulator of the innate immune response. ADAR1 catalyzes the conversion of adenosine (A) to inosine (I) in a dsRNA substrate and destroys the dsRNA structure, and therefore, ADAR1 inhibits the overactivation of dsRNA sensors such as RIG-I and MDA5.7 12 13 The involvement of ADAR1 RNA-editing in cancer development and immune therapy failure has been established.7 12 13 Loss of ADAR1 in melanoma promotes antigen presentation and reverses cellular resistance to immune checkpoint blockade.14 Therefore, ADAR1 can mitigate the alert system and thus shape a ‘cold’ tumor microenvironment. Human epidermal growth factor receptor 2 (HER2, also known as ErbB-2 or Neu) is an oncogene overexpressed in 20%–30% of BCs.15 Humanized monoclonal antibodies such as trastuzumab and TKIs such as lapatinib can prolong the survival of BC patients. However, treatment resistance is still an important issue for at least 50% of patients.16 17 It has been reported that HER2 suppresses the innate immune response and antitumor immunity.9 11 BC cell lines from transgenic mice expressing HER2 express low levels of MHC class I antigens.18 Blocking IFN receptor 1 (IFNAR1) weakens the therapeutic efficacy of anti-HER2 monoclonal antibodies.19 Interestingly, HER2-positive (HER2+) BC patients with higher ISG scores or more tumor-infiltrating lymphocytes have a better outcome after anti-HER2 treatment.20 21 These findings suggest that the activation of the innate immune response, particularly with respect to IFNs and the antigen presentation pathway, can enhance HER2+BC treatment, and the underlying mechanisms should be further clarified. To discover new regulators that can affect HER2+BC treatment outcomes, we focused on long non-coding RNAs (lncRNAs), which might be involved in disease progression and therapeutic resistance. We compared lncRNA expression in tumors before neoadjuvant chemotherapy between HER2+BC patients in the pathological complete response (pCR) and non-pCR groups and found that LINC00624 was enriched in the non-pCR group. Overexpression of LINC00624 inhibited the IFN-related innate immune response and MHC class I antigen presentation, which subsequently induced cancer cell proliferation and blocked the antitumor effect of lapatinib and trastuzumab. We found that the ADAR1 Editing Region (AER) of LINC00624 could be edited in which adenosine was modified to inosine by ADAR. The edited LINC00624 stabilized ADAR1 and further suppressed the IFN induced expression of ISGs. Our data support the supposition that LINC00624 plays a critical role in ADAR regulation and may serve as an antitumor target in future BC combination treatments.
Detailed methods have been described in online supplemental files. 10.1136/jitc-2022-004666.supp1
To screen for lncRNAs involved in the modulation of the HER2-targeted treatment response, 20 core needle biopsy specimens taken from primary tumors in patients with HER2+BC before neoadjuvant therapy were retrospectively collected and subjected to RNA sequencing. The cohort was classified into the pCR and non-pCR groups based on the outcomes determined on pathological evaluation. We analyzed the expression profiles and found that LINC00624 was the most significantly increased long non-coding gene in the non-pCR group after the exclusion of pseudogenes (figure 1A, B). When the sample size was expanded to 100, LINC00624 was still significantly higher in non-pCR group (figure 1C). To further determine the potential function of LINC00624 in BC pathogenesis, we analyzed two independent clinical patient datasets. In early-stage BC patient samples in The Cancer Genome Atlas database and a cohort of 319 RNA samples in our center, high LINC00624 expression was significantly correlated with poor disease-free survival and overall survival (figure 1D, online supplemental figure 1B, and online supplemental tables 1 and 2). Among the patients characterized by molecular subtype, patients with HER2+or luminal A BC with high LINC00624 expression showed worse outcomes than those with low LINC00624 expression (figure 1D, online supplemental figure 1A, B), suggesting that LINC00624 may be involved in the progression of BC. Based on the expression difference in the HER2+BC pCR and non-pCR groups, we focused on the role of LINC00624 in HER2+BC. To further characterize LINC00624, rapid amplification of cDNA ends was performed to obtain the full-length sequence of LINC00624. We found that LINC00624 was transcribed from chromosome 1q21.1-1q21.2 as four isoforms, all of which carrying intergenic regions between BCL9 and CHD1L in BC cell lines (online supplemental file 1). In contrast to full-length isoform 1, isoform-specific internal deletions in exon 4 were found in isoforms 2, 3, and 4 (online supplemental figure 1C, D). However, we did not observe the 3088 nt transcript (RefSeq Accession: NR_038423) annotated in the National Center for Bioinformation database; the 3088 nt sequence overlaps with the CHD1L gene locus. Full-length isoform 1 (RefSeq Accession: NR_038423) was the most abundant among all isoforms, and the other three isoforms were expressed in low abundance in the examined BC cell lines (online supplemental figure 1E). According to phylogenetic codon substitution frequencies and Coding Potential Assessing Tool,22 23 LINC00624 showed no coding ability (online supplemental figure 1F, G). Fluorescence in situ hybridization assays showed that LINC00624 was mainly located in the cell nucleus of HER2+BC cell lines (figure 1E). Cytoplasmic and nuclear RNA purification also confirmed that the four isoforms were mainly located in the cell nucleus (online supplemental figure 2A). The BT-474 and SK-BR-3 ectopic overexpression cell lines were constructed with isoform 1 of LINC00624 by lentiviral infection, and knockout (KO) cells were generated by using CRISPR-cas9 to delete part of the promoter region and exons 1–2, which abolished the expression of all the isoforms (online supplemental figure 2B, C). To illustrate the biological function of LINC00624, we found overexpressed LINC00624 accelerated cell growth (figure 1F and online supplemental figure 2D). Furthermore, cells with LINC00624 overexpression were resistant to lapatinib and trastuzumab treatment (figure 1G, H), while KO cells were more sensitive (online supplemental figure 2E). We also employed a BT-474 xenograft tumor model to evaluate the oncogenic functions of LINC00624 in vivo. We found LINC00624 promoted tumor growth and the resistantance to anti-HER2/neu treatment (figure 1I). In summary, these data suggest that LINC00624 may promote the treatment resistance of HER2+BC.
To further investigate the mechanism involved in LINC00624 signaling in BC, we performed RNA-seq with pCDH/LINC00624-expressing cells and WT/LINC00624-KO cells and analyzed candidate genes and pathways regulated by LINC00624. Interestingly, we found that LINC00624 expression was negatively correlated with the interferon α response, TNFα via NF-κB, and the innate immune response (figure 2A), and hallmarks related to IFN pathways were enriched in LINC00624-KO cells (online supplemental figure 3A), implying a role for LINC00624 in regulating the type I IFN response and antigen presentation. Then, we analyzed the involvement of LINC00624 in immune reactions with ImmLnc, a public database used for investigating the immune-related function of lncRNAs.24 In this analysis, LINC00624 exhibited a strong negative correlation with antigen processing and presentation (figure 2B). Consistently, RT-qPCR detection also showed the induction of ISGs by IFNα was increased significantly in LINC00624-KO SK-BR-3 cells, as well as MHC class I pathway-related genes (figure 2C). Then, we used polyinosinic:polycytidylic acid (poly(I:C)), a synthetic analog of dsRNA that can activate cytosolic RNA sensors to stimulate inflammatory signaling pathways.25 We found that LINC00624 inhibited the induction of ISGs by dsRNAs (figure 2C, online supplemental figure 3B), and the phosphorylation of STAT1 was inhibited on LINC00624 overexpression (online supplemental figure 3C). Then, we evaluated the signaling pathway in the dsRNA-triggered IFN response. We found that the phosphorylation of TBK1/IRF3/STAT1 was increased in LINC00624-KO cells (figure 2D). A previous study has reported that the type I IFN-generated antiviral response causes cell growth arrest and apoptosis.3 We found that overexpression of LINC00624 attenuated the cell apoptosis caused by the stimulation of dsRNA sensors (figure 2E, online supplemental figure 3D, E). Thus, we believe LINC00624 is a potential immunosuppressive lncRNA. Furthermore, treatment with poly(I:C) or IFNα induced the expression of LINC00624 (online supplemental figure 3F), indicating that LINC00624 is an ISG and can serve as a negative feedback regulator in the IFN signaling pathway. Previous studies have reported that HER2 amplification leads to the impairment of IFN pathway activation and antitumor immune responses through inhibition of TBK1 phosphorylation.9 Moreover, HER-2/neu overexpression is associated with a reduction in MHC class I molecules at the cell surface, possibly induced through IFN response inhibition.18 26 In our study, treatment with the anti-HER2 antibody trastuzumab markedly induced ISG and antigen presentation-related gene expression in a HER2-driven BC cell line while has little effect in HER2 negative cell lines (online supplemental figure 3G, H). In addition, the LINC00624 level was increased (online supplemental figure 3H), suggesting that LINC00624 was possibly elevated by treatment-induced IFN activation. To determine whether LINC00624 could inhibit the induction of the type I IFN response after anti-HER2 treatment, we compared the expression of ISGs and antigen presentation-related genes after trastuzumab treatment of wild-type and LINC00624-KO cells. In the LINC00624-depleted cells, the number of ISG transcripts increased significantly in response to HER2 blockade compared with that in the wild-type cells (figure 2F, G). These results indicate that LINC00624 inhibits the anti-HER2-induced cell inflammatory response, which further contributes to treatment resistance.
To understand the underlying mechanism of LINC00624 in innate immune response blockade, we performed an RNA pull-down assay to explore its potential protein partners (figure 3A). We found that LINC00624 bound several RNA-binding proteins (online supplemental table 3). Among them, ADAR1, an A-to-I RNA-editing protein that can inhibit the innate immune response and is related to type I IFN response regulation, attracted our attention. ADAR1 has two isoforms: the longer ADAR1 p150 is expressed from an interferon (IFN)-inducible promoter and both nuclear and cytoplasmic, while the shorter p110 is constitutively expressed and mainly nuclear. The p110 could be translated from an alternative ATG start codon within the transcript of p150.12 27 We first confirmed the interaction between ADAR1 and LINC00624 (online supplemental figure 4A). As the expression of p150 isoform is relatively low under normal conditions, we found LINC00624 mainly binds p110 in BC cells. It has been reported that ADAR1 is a major RNA editor, catalyzing the deamination of A to generate I, which is prevalent across the whole transcriptome.13 By disrupting the secondary structure of self-generated or virus-produced dsRNAs through RNA editing, ADAR1 hinders the innate immune response, especially the type I IFN pathway-related response, which is activated by multiple RNA sensors in the presence of dsRNAs.13 28 Therefore, we speculated that the interaction between ADAR1 and LINC00624 might contribute to tumor progression and innate immune response repression. Next, we investigated the binding affinity between ADAR1 and different isoforms of LINC00624. Isoform 1 and isoform 2 showed a high affinity for ADAR1, while isoform 3 negligibly bound ADAR1, which suggested that isoform 3 missed a structure critical for LINC00624 and ADAR1 binding (figure 3B). To further elaborate the ADAR1-binding sequence on LINC00624, we employed the RNAfold tool to predict the secondary structure of LINC00624.29 We found a folded dsRNA-like structure (an AER) transcribed from inverted repeats on both sides of the S3 segment of LINC00624 (figure 3C); this fragment in full-length LINC00624 was absent in isoform 3. Then, we truncated LINC00624 according to its secondary structure and found that only S3 binds ADAR1 in human BC cells, and that the AER region-deletion mutation in S3 caused isoform 3 to lose its ADAR1-binding ability, suggesting that the AER region is the major domain contributing to ADAR1 binding (figure 3D). This hypothesis was confirmed through RNA electrophoretic mobility shift assay showing that the AER domain was shifted after incubation with recombinant ADAR1 protein (figure 3E and online supplemental figure 4B). As reported, ADAR1 contains three dsRNA-binding domains (dsRBDs) that are involved in RNA binding under different circumstances (figure 3F).30 31 The A-to-I editase is located in the C-terminus and is the functional group for RNA editing.13 30–32 Therefore, we truncated the three dsRBDs and editase separately to map the ADAR1 domains involved in LINC00624 binding. The RNA pull-down assay showed that dsRBD3 of ADAR1 was essential to ADAR1 interaction with LINC00624 (figure 3G). As dsRBD3 domain is shared between ADAR1 p110 and p150,13 LINC00624 could be bound by both of the isoforms. As previously reported, the KKxxK motif in the dsRBD region is critical for dsRNA binding.33 We mutated KKxxK to EExxA in dsRBD3. Consistently, LINC00624 failed to bind the ADAR1 mutant with EExxA in dsRBD3 (online supplemental figure 4C). As A-to-I RNA editing can be catalyzed by ADAR enzymes, which converted ‘A’s in dsRNA structures into ‘I’s via hydrolytic deamination, we asked whether LINC00624 could be edited by ADAR1. As expected, we found that the AER structure of LINC00624, which was bound by ADAR1, was edited in BC cell lines, and the portion that was edited was further increased after poly(I:C) or IFNα treatment (figure 3H). This finding was consistent with observations of BC tissues (online supplemental figure 4D). In addition, the frequency of AER region editing events was reduced in ADAR1-KO BT-474 cells, confirming that ADAR1 is the major editase involved in LINC00624 A-to-I substitution (figure 3I). This discovery also explained the multiple inconsistent A-to-G mutations found in the cDNA of LINC00624 when we cloned LINC00624 extracted from human cell lines. To confirm the A-to-I editing ability of ADAR1 on LINC00624, we incubated recombinant ADAR1 with transcribed LINC00624 in vitro. The AER region was also edited at the same sites as those in the regions examined in vivo (figure 3J). These data demonstrated that LINC00624 could be bound and edited by ADAR1.
As LINC00624 interacted with ADAR1, we hypothesized that LINC00624 might affect ADAR1 function. First, to determine whether the RNA-editing events of ADAR1 were affected by LINC00624 in BC cells, the Alu-Editing Index (AEI) score, a normalized measure based on hyperediting of Alu elements that allows comparison of editing activity across tissues and tumors, was used to evaluate the editase activity in cancer cells.34 35 The AEI score has been validated with experimental data obtained with both clinical samples and cell lines, and an increased AEI score is correlated with higher ADAR1 activity.34 35 We first validated the correlation between the AEI score and ADAR1 expression by assessing the AEI score in ADAR1-WT and ADAR1-KO cells. The AEI score was indeed higher in the ADAR1-WT cells than in the ADAR1-KO cells, as we expected (online supplemental figure 4E). Interestingly, the AEI score was decreased in LINC00624-depleted SK-BR-3 cells compared with that in wild-type cells (figure 4A), supporting the idea that LINC00624 can enhance the RNA-editing ability of ADAR1. We next sought to determine whether LINC00624 promoted ADAR1 RNA-editing ability by regulating ADAR1 expression. The mRNA level of ADAR1 was stable on exposure to different LINC00624 levels (online supplemental figure 4F). However, the protein expression of ADAR1 was correlated with the level of LINC00624 in BT474 and SK-BR-3 cells (figure 4B). In SK-BR-3 cells, the half-life of ADAR1 was prolonged significantly after LINC00624 overexpression, indicating that LINC00624 could stabilize the ADAR1 protein (figure 4C, D). KO of LINC00624 promoted the degradation of ADAR1 (figure 4C, D). As LINC00624 isoform 3 binds only weakly to ADAR1, we reconstituted either LINC00624 (isoform 1) or isoform 3 in LINC00624-KO cells. As expected, LINC00624 isoform 1, but not isoform 3, restored ADAR1 protein expression (online supplemental figure 4G). In addition, ectopic overexpression of LINC00624 in 293 T cells promoted the stability of coexpressed ADAR1, while the half-life of ADAR1 with EExxA (EAA) mutation in dsRBD3 remained unchanged with or without LINC00624 (figure 4E). These results confirmed the stability of ADAR1 depended on LINC00624 binding. A previous study has showed that ADAR1 is degraded through the ubiquitin-proteasome pathway in human cells.36 Similarly, we found that the proteasomal degradation of ADAR1 enhanced by on LINC00624-KO was blocked by MG132 (figure 4F). To evaluate the role of LINC00624 in ADAR1 ubiquitination, we transfected hemagglutinin (HA)-ubiquitin into WT and LINC00624-KO SK-BR-3 cells. We found that LINC00624-KO promoted the ubiquitination-related degradation of ADAR1 (figure 4G). To verify that the binding of ADAR1 by LINC00624 is critical for ADAR1 ubiquitination inhibition, we coexpressed LINC00624 (isoform 1 or 3), HA-ubiquitin, and FLAG-ADAR1 in 293 T cells. Overexpression of LINC00624, but not the AER region-deleted isoform 3, inhibited the ubiquitination of ADAR1 (figure 4H). The E3 ligase β-TrCP has been demonstrated to be involved in ADAR1 ubiquitination. We found that ADAR1 bound to β-TrCP in BC cells and that overexpression of LINC00624 inhibited the binding of β-TrCP with ADAR1 (figure 4I). These results suggest that LINC00624 stabilizes ADAR1 by inhibiting ADAR1 ubiquitination-related degradation by blocking the interaction of the ubiquitin ligase β-TrCP and ADAR1.
Although our data confirmed that LINC00624 was A-to-I edited both in vitro and in vivo, we found that overexpression or knockdown of ADAR1 did not affect the RNA expression of LINC00624 (online supplemental figure 5A, B). Next, we questioned whether immune response inhibition by LINC00624 was mediated through ADAR1. To answer this question, we overexpressed LINC00624 with ADAR1 knocked down in SK-BR-3 and BT-474 cells. Antigen presentation-related gene and ISG expression was recovered after ADAR1 knockdown (figure 5A, online supplemental figure 5C), suggesting that ADAR1 was involved in IFN response inhibition by LINC00624. In addition, we found that ADAR1-depleted cells were more sensitive to lapatinib (figure 5B). Overexpression of LINC00624 in ADAR1-KO cells failed to enhance the survival of SK-BR-3 cells treated with lapatinib (figure 5C), indicating that the molecular mechanism of LINC00624 in anti-HER2 treatment resistance depends on ADAR1. Next, we questioned whether the function of LINC00624 was dependent on editing by ADAR1. When ADAR1 was constitutively expressed, LINC00624 was spontaneously edited in BC cell lines and clinical samples (figure 3H and online supplemental figure 4D). To generate an unedited isoform of LINC00624, we artificially mutated ADAR1-sensitive bases to render them uneditable. RNAfold was used to simulate the secondary structure of LINC00624 with or without edits (figure 5D). When we substituted editable A bases with C bases, the structure and free energy of the mutant S3 region were found to be similar to those of the natural A-to-I edited isoform, while mutating the bases from A to G rendered the mutant S3 similar to that of the unedited wild type (figure 5D). RNA pull-down assays showed that ADAR1 could bind to all three isoforms (figure 5E). Interestingly, ADAR1 bound even more tightly to the spontaneously edited WT or artificially edited A-to-C isoform than to the A-to-G isoform. In addition, we found that in the simulated A-to-C isoform (representing edited LINC00624) and WT isoform, the half-maximal inhibitory concentration (IC50) of lapatinib was higher than that in the A-to-G isoform (representing unedited LINC00624) (figure 5F). These results indicate that LINC00624 relies on ADAR1 A-to-I RNA editing to function. Then, we overexpressed the A-to-C isoform in ADAR1-KO cells. We found that the A-to-C isoform failed to inhibit the lapatinib response (figure 5G), an outcome similar to that of WT LINC00624 in ADAR1-KO cells, indicating that edited LINC00624 cannot function without ADAR1.
To further investigate the immune inhibition phenotype of LINC00624 in vivo, mouse cell lines and immunocompetent xenograft mouse model were then used. First, through Pipeline for lncRNA annotation from RNA-seq data (PLAR),37 we did not find orthologs or ‘synteny with sequence conservation’ of LINC00624 in mouse. LINC00624 has orthologs in rhesus and dog only (online supplemental table 5). Therefore, we overexpressed human LINC00624 in mouse cell lines. We found that LINC00624 could inhibit the type I IFN response induced by poly(I:C) in B16-OVA and NF639 cells, which was consistent with the phenotype of human cell lines (figure 6A and online supplemental figure 6A–C). Furthermore, LINC00624 promoted cell proliferation and inhibited the lapatinib response in NF639 cells, a neu-positive cell line derived from MMTV-neu tumors (online supplemental figure 6D-E). Next, we validated that the function of LINC00624 relied on ADAR1 in mouse cells. RNA pull-down confirmed the interaction between ADAR1 and LINC00624 (online supplemental figure 7A). we reconfirmed that LINC00624 could decrease mouse ADAR1 degradation in NF639 cells through ubiquitination inhibition and the blockade of ADAR1-β-TrCP interaction (online supplemental figure 7B–E). Furthermore, KO of ADAR1 in NF639 cells inhibited their proliferation (online supplemental figure 7F). Similar to their human cell counterparts, ADAR1-depleted cells were more sensitive to lapatinib in mouse cells(online supplemental figure 7G). Moreover, overexpression of LINC00624 in WT cells, but not in ADAR1-KO cells, decreased cell sensitivity to lapatinib (online supplemental figure 7G), supporting the idea that the function of LINC00624 was dependent on ADAR1 in mouse cells. We also evaluated the role of LINC00624 in antigen presentation in mouse model. LINC00624 decreased the levels of major histocompatibility complex (MHC) class I-bound SIINFEKL, an eight-amino-acid peptide derived from OVA, in B16-OVA cells treated with IFNα and IFNγ (figure 6B, C). Coculture of tumor cells overexpressing LINC00624 with CD8+T cells from OT-I mice significantly inhibited IFNγ production (figure 6D), confirming the inhibitory effect of LINC00624 on antigen processing and presentation. To determine whether LINC00624 can render tumor cells immunotolerant in vivo, we inoculated B16-OVA cells with or without LINC00624 overexpressing vectors into the flanks of immunocompetent C57BL/6J mice. First, LINC00624 overexpression increased B16-OVA xenograft tumor growth, compared with the control group (figure 6E). The MHC class I-bound SIINFEKL level was also lower in LINC00624-overexpressing tumors (figure 6F). Transcription of antigen presentation-related genes and ISGs was inhibited by LINC00624 in vivo, confirming the in vitro results (figure 6G). These results indicate the antigen presentation process of tumor cells were inhibit by LINC00624.
To further analyze whether LINC00624 inhibits antitumor immunity, we first investigated the infiltrated immune cells in mouse tumors. Xenograft tumors from B16-OVA cells with or without LINC00624 were dissected and digested to single cells. Flow cytometry analyses indicated a decrease in CD8+T cells, CD45+immune cells, CD3+T cells, CD4+T cells, CD8+T cells, and CD49f+ monocytes in the immune microenvironment of tumors with high LINC00624 levels, while the population of myeloid-derived suppressor cells was increased significantly (figure 6H–J, online supplemental figure 8A, B). Through immunohistochemical (IHC) staining, we confirmed a significant decrease in the infiltration of CD8+T cells in the immune microenvironment of tumors with high LINC00624 levels (figure 6K). A previous study showed that loss of ADAR1 sensitized tumors to the innate immune response.14 Type I or type II IFNs led to growth arrest and death of ADAR1-KO B16-OVA cells, indicating that ADAR1 was involved in the modulation of the innate immune response.12 In addition, ADAR1 also promoted the blockade of immune checkpoint inhibitors. Loss of ADAR1 reversed cell resistance to immune therapy. As LINC00624 can inhibit the degradation of ADAR1, we hypothesized that LINC00624 caused resistance to immune checkpoint blockers. To test this, a B16-OVA murine model was used to investigate the role of LINC00624 in immune checkpoint blockade in vivo with a whole tumor cell vaccine loaded with poly(I:C) and anti-PD-1 (figure 6L). We found that LINC00624 significantly inhibited the tumor response to the anti-PD-1 treatment compared with the control group (figure 6M). To investigate whether the inhibition of immune therapy was dependent on ADAR1, ADAR1-KO B16 cells with different LINC0624 expression were used to address this issue. After ADAR1 knocked out, the growth of B16 tumors were significantly reduced (online supplemental figure 9A, B). Overexpression of LINC00624 could not promote tumor growth in ADAR1 null tumors. In addition, tumors were regressed after the treatment of PD-1 in ADAR1 null tumors without the vaccination process, consistent with previous study14 (online supplemental figure 9B). Overexpression of LINC00624 could not further cause treatment resistance of PD-1 (online supplemental figure 9B). Furthermore, we found tumor-infiltrating CD8+cells were significantly increased after ADAR1 KO, while LINC00624 could not inhibit CD8+cells infiltration in ADAR1 null tumors (online supplemental figure 9C), confirming the function of LINC00624 was dependent on ADAR1. All these in vivo data confirmed that LINC00624 inhibited antitumor immunity and promoted immune checkpoint inhibitor blockade.
To investigate the potential therapeutic target of LINC00624 in BC, we designed five independent antisense oligonucleotides (ASOs) complementary to the LINC00624 S3 region (figure 7A). An ASO with a scrambled sequence in BC was used as the negative control. Transfection with each of the 5 ASOs reduced LINC00624 RNA levels in BT-474 cells, while ASO-2 and ASO-3 showed the highest knockdown efficiency (figure 7B). Consistently, the expression of ADAR1 was reduced, similar to the in vitro results (figure 7C). Next, we synthesized cholesterol-conjugated locked nucleic acid-modified ASOs for in vivo use. Free uptake assay results showed the downregulation of LINC00624 (figure 7D). To determine the potential clinical application of ASOs in treating BC, we generated an orthotopic mammary tumor model with BT-474 WT cells in nude mice, and the mice were treated with either control (ASO-Ctrl) or ASO-2 and ASO-3 mixtures (ASO-2/3) 10 days after inoculation (figure 7E). Although this model did not present with an adaptive immune response, we found that targeting LINC00624 significantly inhibited the proliferation of BT474 tumor cells (figure 7F, G). Indeed, xenograft tumors treated with ASOs exhibited decreased ADAR expression compared with the control, as determined by IHC (figure 7H). Moreover, the expression levels of ISGs and innate immune response genes were significantly increased in the ASO-treated xenograft tumors (figure 7I). Altogether, these data strongly support the supposition that LINC00624 promotes therapy resistance and tumor progression by inhibiting the immune response in BC cells exposed to HER2-targeted treatment. Therefore, LINC00624 can serve as a future therapeutic target in HER2+BC.
Tumors can escape elimination by immune cells at the initiation stage. By decreasing the expression of mutated or fusion proteins, reducing antigen presentation of neoantigens, or secreting immune suppressive signals, tumor cells can evade recognition by the immune system.38 The underlying mechanisms that tumors shape the immunosuppressive microenvironment have attracted considerable attention in recent years. Among them, the suppression of the innate immune response that prevents tumors from turning from ‘cold’ to ‘hot’ has been demonstrated.38–40 Type I IFNs recently re-entered the focus of investigation in tumor biology.1 8 Induced by the activation of nucleic sensors through transductors such as TBK1 and IRF3, type I IFNs activate the phosphorylation of STAT1, leading to increased transcription of ISGs.1 41 42 Hundreds of genes have been identified as ISGs under transcriptional regulation, and they are elevated 3- to 100-fold after type I IFN stimulation.42 43 The protein products of ISGs play different roles in antitumor biology, including immune regulation, protein synthesis suppression and apoptosis induction.43–45 Even without the involvement of immune cells, the proliferation of cancer cells were found to be inhibited when the IFN pathway was stimulated,3 consistent with our results showing that proliferation was inhibited and apoptosis was induced in LINC00624-KO cells on IFN signaling activation. In addition, upregulation of the expression of ISGs such as MHC class I proteins enhances antigen presentation to infiltrated T and B cells, eliciting an adaptive immune response.1 As we shown in our work, LINC00624 inhibits antitumor responses both in tumor cells and in the tumor microenvironment. The tumor cell response to conventional treatments is modulated by the activation of the type I IFN pathway. In addition to cytotoxic drugs, the blockade of growth signaling pathways such as the EGFR and HER2 pathways relies on IFN signaling. Previous studies have shown that PI3K-AKT, a signaling axis downstream of the EGFR and HER2 pathways, can suppress the expression of MHC class I proteins.46 47 HER2 amplification also reduces TBK1/NAK phosphorylation, leading to the inhibition of STING pathway activation, reduction of MHC class I protein expression, and compromise of antitumor immune response.9 18 A previous study has showed that loss of ADAR1 in tumor cells enhances tumor inflammation, increases infiltrated immune cells, and sensitizes tumor cells to the blockade of immune checkpoints.7 13 14 Several researchers are exploring ADAR1 inhibitors.48 49 However, ADAR1 plays immunoregulatory roles in normal cells, inhibition of ADAR1 editase activity may raise concerns about autoimmune reactions. In our study, we found targeting LINC00624 through ASOs can significantly inhibit tumor cell proliferation, suppress ADAR1 activity and promote the type I IFN response. Through the regulation of LINC00624, we can possibly modulate ADAR1 function in tumor cells. As LINC00624 is evolutionarily new and expressed only in human cells, the experimental models are limited and sometimes artificial models are used, especially in immune research. To tackle this issue, we overexpressed LINC00624 in mammary mouse cell lines and its effectiveness has been proven to be the same as it is in human cells. In addition, B16-OVA contains a model antigen OVA. In our study, it was used to investigate antigen presentation process as many other studies did.14 50 Through this model, we illustrated how LINC00624 regulates antigen presentation, antitumor immunity and immunotherapy response in vivo, confirming the immune suppression role of LINC00624 in immunocompetent model. Bitransgenic mice with LINC00624 overexpression in MMTV/neu mice could be used in further study to confirm how LINC00624 inhibits tumor immunity and the immunotherapy response with anti-HER2 therapy. In conclusion, our findings demonstrate that LINC00624 plays an important role in inhibiting the IFN response and results in anti-HER2 treatment resistance. Targeting LINC00624 through ASOs shows great therapeutic potential for future clinical use. | true | true | true |
PMC9578049 | Jody Groenendyk,Konstantin Stoletov,Tautvydas Paskevicius,Wenjuan Li,Ning Dai,Myriam Pujol,Erin Busaan,Hoi Hei Ng,Aristeidis E. Boukouris,Bruno Saleme,Alois Haromy,Kaisa Cui,Miao Hu,Yanan Yan,Rui Zhang,Evangelos Michelakis,Xing-Zhen Chen,John D. Lewis,Jingfeng Tang,Luis B. Agellon,Marek Michalak | Loss of the fructose transporter SLC2A5 inhibits cancer cell migration 10.3389/fcell.2022.896297 | 30-09-2022 | metastasis,cancer,mitochondria,invadopodia,cell migration | Metastasis is the primary cause of cancer patient death and the elevation of SLC2A5 gene expression is often observed in metastatic cancer cells. Here we evaluated the importance of SLC2A5 in cancer cell motility by silencing its gene. We discovered that CRISPR/Cas9-mediated inactivation of the SLC2A5 gene inhibited cancer cell proliferation and migration in vitro as well as metastases in vivo in several animal models. Moreover, SLC2A5-attenuated cancer cells exhibited dramatic alterations in mitochondrial architecture and localization, uncovering the importance of SLC2A5 in directing mitochondrial function for cancer cell motility and migration. The direct association of increased abundance of SLC2A5 in cancer cells with metastatic risk in several types of cancers identifies SLC2A5 as an important therapeutic target to reduce or prevent cancer metastasis. | Loss of the fructose transporter SLC2A5 inhibits cancer cell migration 10.3389/fcell.2022.896297
Metastasis is the primary cause of cancer patient death and the elevation of SLC2A5 gene expression is often observed in metastatic cancer cells. Here we evaluated the importance of SLC2A5 in cancer cell motility by silencing its gene. We discovered that CRISPR/Cas9-mediated inactivation of the SLC2A5 gene inhibited cancer cell proliferation and migration in vitro as well as metastases in vivo in several animal models. Moreover, SLC2A5-attenuated cancer cells exhibited dramatic alterations in mitochondrial architecture and localization, uncovering the importance of SLC2A5 in directing mitochondrial function for cancer cell motility and migration. The direct association of increased abundance of SLC2A5 in cancer cells with metastatic risk in several types of cancers identifies SLC2A5 as an important therapeutic target to reduce or prevent cancer metastasis.
Metastasis, which is defined as the development of secondary tumors, remains the major cause of death for patients with cancer (Steeg, 2006). The process of metastasis depends on enhanced cancer cell motility, proliferation, and subsequent colonization of a new microenvironment. Melanomas and pancreatic cancers are particularly metastatic if not caught early during the course of the disease (Chaffer & Weinberg, 2011; Budczies et al., 2015). Fructose is transported across membranes by SLC2A5/GLUT5, a member of the facilitative glucose transporter family. SLC2A5 was first identified in the intestine (Douard & Ferraris, 2008), and exhibits high specificity for fructose, and does not transport glucose nor galactose (Uldry & Thorens, 2004). Notably, increased abundance of SLC2A5 mRNA and protein in cancer cells is associated with cancer progression, increased frequency of metastasis, and an unfavorable prognosis for many cancers (Zamora-Leon et al., 1996; Chen et al., 2016; Bu et al., 2018; Hamann et al., 2018; Weng et al., 2018; Jin et al., 2019; Lin et al., 2021). SLC2A5 expression is higher in metastatic liver lesions than in normal liver; and elevated in primary lung tumors; as well as in brain, colon, testis, and uterine cancers, including breast carcinoma cell lines (Uldry & Thorens, 2004). Moreover, recent studies have shown that fructose serves as an efficient energy source for lung cancer cells (Chen et al., 2020) and facilitates tumor cell proliferation (Liang et al., 2021). Many strategies that target metastasis have been studied and explored (Steeg, 2016), however, the role SLC2A5 plays in promoting metastasis and metastatic progression remains unknown. Here we show that silencing of the SLC2A5 fructose transporter by gene editing in cancer cells inhibited cell motility and proliferation in vitro as well as inhibited cancer cell invasion and metastasis in chicken embryo, mouse, and zebrafish models of human cancer progression. Moreover, attenuation of SLC2A5 function caused a reduction in the number of mitochondria, alteration of their morphology as well as their localization in cancer cells, underscoring the importance of mitochondrial function in cell motility.
Ethics. All methods were carried out in accordance with relevant guidelines and regulations and approved by Biosafety Officers in the Department of Environment, Health and Safety, at the University of Alberta. All animal experiments were carried out according to the University of Alberta Animal Policy and Welfare Committee and the Canadian Council on Animal Care Guidelines. The approval for use of mice in research was granted by the Animal Care and Use Committee for Health Sciences, a University of Alberta ethics review committee (Permit AUP297). CRISPR/Cas9 Gene Editing and Cell Culture. MIA-PaCa-2 (ATCC# CRM-CRL-1420), MDA-MB-231 (ATCC# CRM-HTB-26), HeLa (ATCC# CRM-CCL-2) were obtained from ATCC (Manassas, VA) and HT1080 (ATCC# CCL-121) through Thermo Fisher Scientific. MIA-PaCa-2 cancer cells were isolated from a male patient (Yunis et al., 1977). HT1080 fibrosarcoma cells, originally isolated from a male patient (Rasheed et al., 1974), expressing the red fluorescent protein tdTomato (cells referred to hereafter as HT1080tdT) (Leong et al., 2014) were used in this study. For CRISPR/Cas9 gene editing, the sequence of the guided RNA was identified using the WTSI Genome Editing website (www.sanger.ac.uk). The two following ssDNA guide oligonucleotides were targeted to Exon 3 of human SLC2A5: 5′-GATCCGATAAACCCTCCAAA-3′; 5′-GAAGTCTTCCATGAATTCAC-3′. The ssDNA guide oligonucleotides were annealed with their complement and cloned into the PX459 plasmid containing the gRNA scaffold and the Cas9 nickase. Cells for targeted gene editing were grown under standard culturing conditions (5% CO2; 37°C) and standard cell culture medium (DMEM; Gibco Cat# 11995073) plus 10% fetal bovine serum. Positive clones were selected with puromycin for 24 h and seeded onto a 96 well plate to generate single cell colonies. Clones were cultured and genomic DNA harvested using the DNeasy Blood and Tissue kit (Qiagen) with genomic DNA utilized for PCR, with the PCR product gel purified (Qiagen) and sequenced using SLC2A5 specific primers. Positive clones were identified by aligning with the SLC2A5 gene sequence. Clones carrying an edited SLC2A5 allele (identified with the suffix -ε2A5) were determined to have 62 base pairs removed, leading to an early stop codon 14 amino acids after the second oligonucleotide sequence. Off-target effects were monitored by sequencing ALDH1b1 and HADHB genes, which were identified as potential off-target genes. Full-length cDNA encoding human SLC2A5 was subcloned into the pCMV6 expression vector using restriction enzymes AsiSI (also called Sgf1) and MluI using following DNA primers: SLC2A5-Sgf1 forward primer: 5′-CGCGCGATCGCATGGAGCAACAGGATCAGAG-3′ and SLC2A5-Mlu1 reverse primer: 5′-CGCACGCGTCTGTTCCGAAGTGACAGGTG-3′. SLC2A5 E401A mutant was generated using Q5® High-Fidelity 2X Master Mix (New England Biolabs). Primers used were as follows: SLC2A5-E401A forward primer, 5′-GCTCATCACTGCTATCTTCCTGCAGTCCTC-3′, and SLC2A5-E401A reverse primer, 5′-AGCGCGGGTATGGGACTG-3′. Cells were transfected with the assembled expression vector encoding either human wild-type SLC2A5 or the non-functional SLC2A5-E401A mutant (Nomura et al., 2015). Scratch test, proliferation assay and mitochondria imaging were carried out after the times indicated in the Figure legend. Immunoblot Analysis and Flow Cytometry. Protein assay, SDS-PAGE, and immunoblotting analysis were carried out as described (Groenendyk et al., 2014). Anti-β-tubulin antibodies were from Thermo Fisher (Cat# MA5-16308) and used at a dilution of 1:2000; anti-GAPDH antibodies (Cat# Ab8245) and anti-mitofusin-1 antibodies (Cat# ab129154) were purchased from Abcam and used at the dilution of 1:1000. Anti-SLC2A5 antibodies were purchased from Boster (Cat# PA 2064) and used at 1:500 dilution. Anti-Flag tag antibodies were from Thermo Fisher (Cat# 740001) and used at 1:100 dilution. Secondary antibodies, IRDye® 680RD donkey anti-mouse IgG secondary (Cat# 925–68072) and IRDye® 800CW donkey anti-rabbit IgG secondary antibodies (Cat# 926–32212) were from LI-COR and were used at a dilution of 1:10000. Densitometry was performed using ImageJ software (NIH) and plotted in Excel. For Flow Cytometry analysis MIA-PaCa-ε2A5 or MIA-PaCa-ε2A5 transfected with expression vector encoding wild-type SLC2A5 or SLC2A5 E401A mutant were grown to 80% confluency and harvested with TrypLE (Thermo Fisher). Cells were pelleted and incubated in the Fixation Buffer (Invitrogen) for 10 min followed by pelleting and permeabilization with Permeabilization Buffer (Invitrogen) for 10 min. To block non-specific interactions, cells were pelleted and incubated for 30 min in the Permeabilization Buffer containing 1% BSA. Cells were incubated at 4°C for 16 h with mouse anti-Flag antibodies (1:100 dilution; Pierce, Cat# MA1-91878) in Permeabilization Buffer containing 1% BSA. Cells were washed 3 times with Permeabilization Buffer containing 1% BSA followed by addition of anti-mouse Alexa Fluor 488 secondary antibody (1:100 dilution; Thermo Fisher), washed 3 times with Permeabilization Buffer containing 1% BSA and resuspended in PBS for Flow Cytometry using an LSRFortessa™ X-20 Flow cytometer (BD Biosciences Pharmingen). Data analysis was performed using FlowJo™ vX for PC (TreeStar). qPCR Analysis. RNA was isolated from MIA-PaCa-2 and MIA-PaCa-ε2A5 cells, HT1080tdT cells and SLC2A5-deficient HT1080tdT-ε2A5 cells using the RNeasy Kit (Qiagen) according to the manufacturer’s protocol. Quantitative PCR (qPCR) analysis was used for determination of the mRNA abundance using a RotorGene Q rapid thermal cycler system (GE Life Sciences) according to the manufacturer’s instructions. Total RNA (200 ng) was used to synthesize cDNA (BioRad) according to manufacturer’s protocol then the resulting cDNA was diluted 5-fold, and 2 μL of the diluted sample was used in qPCR reactions with primers targeting the mRNA of interest (see below). qPCR reactions were conducted in duplicate on three separate occasions. The Ct values for selected targets were normalized to the Ct value of glyceraldehyde 3-phosphate dehydrogenase (GAPDH). The following primers were used for qPCR analyses: GAPDH 5′-AATGTGTCCGTCGTGGATCTGA-3′; 5′-AGTGTAGCCCAAGATGCCCTTC-3′ NRF1 5′- GCCACAGCCACACATAGTATAG-3′; 5′- CGTACCAACCTGGATAAGTGAG-3′ SLC2A1 5′-GTGCTCCTGGTTCTGTTCTT-3′; 5′-CTCGGGTGTCTTGTCACTTT-3′ SLC2A2 5′-GGGACTTGTGCTGCTGAATA-3′; 5′-CCTGGCCCAATTTCAAAGAAG-3′ SLC2A4 5′-CTGGACGAGCAACTTCATCA-33′; 5′-CAGGAGGACCGCAAATAGAA-3′ SLC2A5 (targeted to Exon 12) 5′- CCTCACCACCATCTACATCTTC-3′, 5′-GGGTACACTTCAGACACCTTATT-3′ ALU 5′-GGTGAAACCCCGTCTCTACT-3′; 5′-GGTTCAAGCGATTCTCCTGC-3′ MFN1 5′-GGGCCCTAGAAATGCTCAAA-3′; 5′-GCAGTGGGAGTAGAAGCTAAAG-3′ NCAD 5′-GACAGTTCCTGAGGGATCAAAG-3′; 5′-CGATTCTGTACCTCAACATCCC-3′ VIM 5′-GCTGTGGATGTGAGGTGAGC-3′; 5′-GCTAAAATCAAGGCAAACCCTAAGTC-3′. Scratch Test, Proliferation Assay, and Colony Formation Assay. Scratch tests were performed with MIA-PaCa-2 and SLC2A5-deficient MIA-PaCa-ε2A5 cells, HT1080tdT and HT1080tdT-ε2A5 cells, MDA-MB-231 and MDA-MB-231-ε2A5, HeLa and HeLa-ε2A5 cells. Cells were plated at equal cell numbers in a 12-well dish and grown to confluency. The cell layer was scratched with a P200 pipet tip straight down the middle of the well. The wells were washed with 2 ml of phosphate-buffered saline (PBS) and then fresh cell culture medium was added. Each scratch was photographed at times indicated in the Figure legend. The width of the scratch was measured at three locations along the length of the scratch using ImageJ then the results were compiled and analyzed in GraphPad Prism. Cell viability was assessed using the 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay. Cells (1 × 103 or 2 × 103) were seeded in triplicate into 96-well plates and grown in standard culture medium. The next day, the growth medium was replaced with fresh standard culture medium supplemented with MTS (1:10 dilution of a 10% MTS) (Promega; Cat# G3582), incubated for 2 h at 37°C, then samples of the culture medium was taken from each well for determination of absorbance at 490 nm was measured using a 96-well plate reader. MIA-PaCa-2 and MIA-PaCa-ε2A5 (clone B3 or F11) cells were plated at a density of 1.0 × 104 cells/well into 96-well plates and maintained in regular growth medium. HT1080tdT and HT1080tdT-ε2A5 cells were plated at a density of 5.0 × 103 cells/well into 96 well plates and maintained in regular growth medium. Fructose was added to the media at 1 and 10 mM concentrations and MIA-PaCa-2 and MIA-PaCa-ε2A5 cells (clones B3 or F11) were incubated for 48 h and HT1080tdT and HT1080tdT-ε2A5 for 24 h. Cell proliferation in the absence or presence of added fructose was determined using the MTS assay as described above. For soft agar colony assays, cells (3 × 103) were plated in triplicate in 0.35% low melting point agarose layered on top of 0.7% agarose in 6-well plates and covered with standard culture medium (Chen et al., 2016). Colonies were counted and photographed after 2 weeks. For the colony formation assay, cells (1 × 103) were seeded in triplicate into 6-well plates and grown in standard culture medium. Colonies were fixed with 70% ethanol and stained with 1% trypan blue (Sigma-Aldrich) (Figure 3B), rinsed with water and dried. Plates were photographed, colonies counted and analyzed by the OpenCFU software. For agarose colony assay (Supplementary Figure S3) MIA-PaCa-2 and MIA-PaCa-ε2A5 cell lines (1 × 104 cells/well) were mixed with 0.6% agarose in normal growth media and plated on top of the 1% agarose layer (Weng et al., 2018). Cells were grown for 3 weeks with media changed every 3 days. Plates were photographed and colonies counted and analyzed by OpenCFU software. Experiments were performed on at least three independent occasions. Transmigration Assay. Cell migration was assayed in 24-well Transwell plates (8.0-μm pore size; BD Pharmingen). Cells (1 × 104 cells/insert) were cultured for 24 h on the upper side of the filter with 100 µl of serum-free medium. The lower chambers were filled with 700 µl of DMEM supplemented with 10% FBS. After 24 h, non-migrating cancer cells on the upper surface of the membrane were removed using a Q-tip® and migrated cancer cells on the undersides of the Transwell membranes were fixed with cold methanol then stained for 60 min with a solution containing 1% Coomassie brilliant blue dye, 50% methanol, 10% glacial acetic acid. The cell chamber was washed 3 times with 45% methanol and 10% glacial acetic acid, dried, and photographed. The cell number was determined by counting cells using ImageJ. Cells that dropped from the membrane to the well below were stained with Alamar Blue according to the manufacturer’s protocol (Thermo Fisher Scientific). FITC gelatin degradation assay. FITC gelatin degradation assay was performed as previously described (https://bio-protocol.org/e997). Briefly, FITC gelatin coated 18 mm cover slides were prepared using glutaraldehyde fixation and dropped into 12 well tissue culture plates. HT1080tdT or HT1080tdT-ε2A5 cells (2 × 104) were seeded on the top of the FITC gelatin coated 18 mm cover slides and incubated for 10 h. Cells were fixed using formaldehyde and stained with Alexa Fluor 568 Phalloidin (to detect actin) and DAPI (to visualize nuclei). Images were acquired using Nikon A1 confocal microscope and analyzed using ImageJ. Data are displayed as degraded FITC gelatin area fraction (arbitrary units). Experiments were performed in triplicates. Electron Microscopy and Confocal Imaging. Transmission electron microscopy experiments were performed by plating MIA-PaCa-2 and MIA-PaCa-ε2A5 cells on special coverslips with a Cu-300 mesh (Maxtaform) for use in a Hitachi HT-7650 electron microscope with a bottom mount AMT camera (4864 × 3264 pixel fixed bottom). Cells were only allowed to grow to 30% confluency to prevent crowding. Coverslips were fixed, negative stained, and then imaged using a Hitachi H-7650 transmission electron microscope at 60 kV. Twenty images (3,000x magnification) were taken of two samples for each cell line. Mitochondria were hand traced and the mitochondria length and diameter were determined using ImageJ. Confocal imaging was performed on living cell cultures for each cell line as previously described (Prins et al., 2011). Staining and Quantitation of Mitochondria. MitoTracker® Green FM mitochondria staining was carried out as described by manufacturer (Invitrogen). Briefly, a working solution containing 10 nM MitoTracker® Green FM and 0.1 μg/ml in a standard complete medium prewarmed to 37°C was applied to live cells cultured in a 4-well Nunc Lab Tek chamber slide. Chamber slides were sealed, and cells were allowed to recover at 37°C for 30 min. Mitochondria were visualized using Nikon A1 microscope equipped with 10x (for localization) or 63× (for number and length) objectives. Mitochondria length was quantified manually using Nikon Elements length measurement module. Ex Vivo Chick Embryo Cancer Xenograft Model and Mouse Spontaneous Metastasis Model. Fertilized White Leghorn chicken eggs were purchased from the University of Alberta Poultry Research Centre and incubated in a humidified chamber at 38°C. At Day 4, embryos were removed from their shells using a cutting wheel and maintained in a covered dish at 38°C and 60% humidity. On Day 10 of development, the chicken embryos were injected intravenously with 2.5 × 104 of HT1080tdT or HT1080tdT-ε2A5 cells, and metastatic colonies were allowed to grow for 6 days. For time lapse imaging of metastatic colonies, sterilized coverslips were applied on top of the embryos that contained metastatic colonies by 24 h post tumor cell application. The tumor xenograft study in mice was carried out in 8-week-old female BALB/c-nude mice housed in a pathogen-free environment. HT1080tdT cells or HT1080tdT-ε2A5 cells (2 × 106) were injected subcutaneously in the bilateral rear flank. After 5 days of injection, the tumor volumes were measured every day. Mice were euthanized after 12 days of cell injection. The xenograft tumors were removed for weighing and photographing, and the lungs were taken to observe metastasis using 2-photon microscopy (Zeiss) as previously described (Stoletov et al., 2018). For the microscopic imaging of mouse lungs, freshly excised lungs were washed twice with PBS and placed under the cover glass and into the humidified chamber. Nikon A1 microscope equipped with 10× objective was used for imaging. To quantify metastatic HT1080-derived cancer cells, the murine lung tissue was removed, followed by extraction of genomic DNA (Stoletov et al., 2018). Metastasis was quantified using qPCR with primers directed at human Alu elements and normalized to the mouse GAPDH gene copy number (Stoletov et al., 2018). Mouse lung tissue was collected, homogenized in sterile sample tubes and genomic DNA was extracted using SYBR® Green Extract-N-Amp Tissue PCR Kit (Sigma-Aldrich). Human Alu element sequences were detected and quantified by qPCR (PCR profile: 95°C for 3 min followed by 40 cycles of 95°C for 30 s, 60°C for 30 s, 72°C for 30 s). Data are displayed as 2ΔΔCt. Injection of Cancer Cells into Zebrafish Embryos. Two days post fertilization transgenic zebrafish embryos Tg (fli1:EGFP) were anesthetized using 1.2 mM tricaine and transferred to a Petri dish coated with 1% agarose. Fibrosarcoma cells were trypsinized and collected as a single cell suspension in a 15 ml collection tube. After 5 min of centrifugation at 6,000 g, the supernatant was removed, and the pellet was resuspended in PBS. After a second centrifugation, the supernatant was removed. The pellet was resuspended in 20 μL 2% polyvinylpyrrolidone (PVP) in PBS and held at room temperature before implantation. Approximately 300 HT1080tdT cells or HT1080tdt-ε2A5 cells in a volume of 5 nL were injected into the pre-cardiac sinus of zebrafish embryos using the microinjector (PV820, World Precision Instruments). After the injection, the embryos were removed from the agarose plate, placed into the 10 cm Petri dish containing fresh water and incubated at 33°C for 3 and 5 days. Three days post injection embryos were randomly selected for fluorescent imaging. Fluorescent images were taken using a LEICA M165 FC stereo fluorescence microscope. Confocal imaging was performed at 5 dpi using a LEICA SP8 confocal microscope. Prior to confocal imaging, zebrafish embryos were immobilized using 1.2 mM tricaine and then embedded into a thin layer of low-melting point agarose to maintain the fish in a lateral position. Fluorescent and confocal images were analyzed using ImageJ software. Statistical analysis. Statistical analysis was performed using GraphPad Prism with a Student’s t-test used to compare the mean of two independent groups or one-way ANOVA used to compare the mean of three or more independent groups with the difference determined to be significant if the p < 0.05.
Attenuation of SLC2A5 function inhibits cancer cell migration. Increased expression of SLC2A5 has been associated with disease progression, increased metastasis, and an unfavorable prognosis of several types of cancers including cervical cancer, renal carcinoma, lung carcinoma, hepatocellular carcinoma, endometrial cancer cells, and pancreatic cancer (Zamora-Leon et al., 1996; Chen et al., 2016; Bu et al., 2018; Hamann et al., 2018; Weng et al., 2018; Jin et al., 2019; Lin et al., 2021) (Supplementary Figure S1A). Increased abundance of SLC2A5 is also associated with reduced survival in pancreatic adenocarcinoma and hepatocellular carcinoma (Supplementary Figure S1B). To test directly if deletion of the SLC2A5 gene impacts cancer progression and migration, we used CRISPR/Cas9 gene editing to inactivate the SLC2A5 gene in a highly metastatic pancreatic ductal adenocarcinoma MIA-PaCa-2 cell line, and fibrosarcoma HT1080tdT cells (Figure 1A). Editing of the SLC2A5 gene in MIA-PaCa-2 cells (hereafter referred to as MIA-PaCa-ε2A5 cells; Figure 1B) or HT1080tdT cells (hereafter referred to as HT1080tdT-ε2A5 cells; Figure 1C) had no effect on the abundance of mRNA encoding other members of the SLC2 family of solute transporters, namely SLC2A1, SLC2A2, SLC2A4 (Figures 1D,E). Editing of the SLC2A5 gene also did not alter the expression of N-cadherin and vimentin, markers of the mesenchymal phenotype (Supplementary Figure S2). Two independent clones of MIA-PaCa-2 cells with edited SLC2A5 genes (referred to as clones MIA-PaCa-ε2A5 B3 and MIA-PaCa-ε2A5 F11) were established (Figure 1B). First, MIA-PaCa-2 and MIA-PaCa-ε2A5 cells were grown to confluency and subjected to a scratch test assay. After 48 h, only a small portion of the scratch remained for MIA-PaCa-2 cells (Figures 2A,B). Strikingly, the majority of the scratch remained for MIA-PaCa-ε2A5 cells d since they were unable to migrate efficiently during the same time period (MIA-PaCa-ε2A5 clone B3, Figure 2A and MIA-PaCa-ε2A5 clone F11, Figure 2B). Furthermore, both clones of MIA-PaCa-ε2A5 cells exhibited reduced average colony count (Figure 2C) and reduced proliferation (Figure 2D) compared to MIA-PaCa-2 cells. Addition of fructose to the culture medium of MIA-PaCa-2 cells increased proliferation (Figure 2D) whereas neither clone of MIA-PaCa-ε2A5 responded (Figure 2D). As well, MIA-PaCa-ε2A5 cells showed inhibited colony formation in the soft agar transformation assay (Supplementary Figure S3) (Borowicz et al., 2014). We also used CRISPR/Cas9 gene editing to silence the SLC2A5 gene in HeLa cervical cancer cells (Supplementary Figure S4A) and in breast cancer MDA-MB-231 cells (Supplementary Figure S4B). Similar to MIA-PaCa-ε2A5 cells (Figure 2B), attenuated SLC2A5 gene expression in HeLa-ε2A5 or MDA-MB-231-ε2A5 cells rendered these cells unable to close the scratch by 48 h (Supplementary Figure S4). The HT1080tdT fibrosarcoma cells display higher metastatic potential than MIA-PaCa-2. Similar to MIA-PaCa-ε2A5 cells, HT1080tdT cells with attenuated SLC2A5 gene expression (HT1080tdT-ε2A5) were also delayed in closing the scratch (Figure 3A). In addition, these cells showed a 20% reduction in colony formation on plastic compared to the HT1080tdT cells (Figure 3B). Proliferation of HT1080tdT-ε2A5 cells was reduced, and as expected, in response to fructose supplementation (Figure 3C). Interestingly, although we observed reduced proliferation of HT1080tdT-ε2A5 compared to HT1080tdT in response to fructose supplementation, the difference was not statistically significant (Figure 3C). Next, we carried out a series of experiments to analyze SLC2A5 function in cell invasion and migration using a FITC-gelatin degradation assay. Compared to HT1080tdT, HT1080tdT-ε2A5 cells exhibited reduced invasiveness (Figure 4A). In transmigration experiments, HT1080tdT-ε2A5 cells were unable to migrate efficiently across an 8 µm pore membrane, and had a reduced ability to drop to the lower chamber (Figures 4B,C). These results demonstrate that limiting SLC2A5 gene expression in a variety of cancer cells has an inhibitory effect not only on proliferation but also on cell migration. To determine if the alterations in cellular proliferation and migration of SLC2A5 gene-edited cells were caused by the inhibition of the SLC2A5 gene, expression vectors encoding wild-type or mutant SLC2A5 were created and introduced into MIA-PaCa-ε2A5 and HT1080tdT-ε2A5 cells. Trans-expression of wild-type SLC2A5 restored cellular proliferation of MIA-PaCa-ε2A5 and HT1080tdT-ε2A5 and motility increased to a level similar to that seen for the unedited cell lines (Figures 5A–D, respectively). The glutamic acid residue at position 400 of the rat SLC2A5 protein (corresponds to E401 in the human SLC2A5 protein) forms a critical inter-bundle salt bridge with the rat E151 (E152 in human SLC2A5) to enable fructose binding and transport, and replacement of this glutamic acid residue with alanine reduces d-fructose binding to the transporter by 90% (Nomura et al., 2015). To test whether fructose binding/transport activity is involved in the enhanced motility of cancer cells, we introduced the E401A mutant of the human SLC2A5 into SLC2A5 gene-edited MIA-PaCa-2 or HT1080tdT cells. Expression of the E401A SLC2A5 mutant in MIA-PaCa-ε2A5 cells or HT1080tdT-ε2A5 had no effect on cell proliferation (solid vs. dashed orange lines, Figures 5A,C, respectively) nor motility of MIA-PaCa-ε2A5 cells in the scratch test (Figure 5B). FACS analysis of MIA-PaCa-ε2A5 cells transfected with transgenes encoding Flag-tagged wild-type or E401A mutant of SLC2A5 confirmed the production and cell membrane localization of the re-introduced SLC2A5 proteins (Supplementary Figure S5). Collectively, these trans complementation experiments demonstrated that enhanced proliferation and motility of MIA-PaCa-2 and HT1080tdT cells require the full activity of wild-type SLC2A5. Inhibition of SLC2A5 limits HT1080tdT fibrosarcoma cancer cell invasion and metastasis in vivo. We used three animal models to evaluate the importance of SLC2A5 in cancer metastasis in vivo: the chicken embryo chorioallantoic membrane (CAM) (Willetts et al., 2016), xenograft murine model and the zebrafish. For these experiments, we used HT1080-derived cells due to the highly metastatic nature of the parental cell line (Stoletov et al., 2007). First, in the chicken CAM model, intravenous injection of red fluorescent protein labelled HT1080tdT fibrosarcoma cells robustly formed colonies within 3–5 days, and these cells formed extended contacts with the CAM vasculature (Figure 6A and Supplementary Video S1). Strikingly, attenuation of the SLC2A5 gene expression led to decreased contacts with the CAM vasculature (Figure 6B) and ∼50% reduction in metastatic colony size (Figure 6C). Moreover, HT1080tdT-ε2A5 cells displayed rounded morphology with a significant decrease both in cancer cell-blood vessel wall contact length and number of blood vessel contacting cells (Figures 6D,E). Furthermore, intravital time-lapse analysis of HT1080tdT-ε2A5 cells showed markedly less cell directionality, and often changed their movement direction (Figures 6F,G). However, attenuation of the SLC2A5 gene expression had no apparent influence on cancer cell migration velocity (Figure 6H). Second, we employed a xenograft murine model of spontaneous metastasis to the lungs to test for HT1080 metastasis. Human HT1080tdT cells or HT1080tdT-ε2A5 cells were injected into the flank of nude mice, followed by monitoring of tumor formation and lung metastasis using quantitative PCR assay of human Alu elements and confocal microscopy. As expected, HT1080tdT cells robustly metastasized and formed multicellular metastatic lesions with visible protrusions extending into the mouse lung tissue as compared to HT1080tdT-ε2A5 (Figure 7A). In contrast, HT1080tdT-ε2A5 cells grew slower and the metastatic lesions formed by the HT1080tdT-ε2A5 cells were reduced in volume and comprised of fewer cells (Figures 7B,C). Third, we used the zebrafish to assess the ability of HT1080tdT cells to survive in the blood circulation and establish secondary metastatic colonies. HT1080tdT or HT1080tdT-ε2A5 cells were injected into the pericardium of transgenic zebrafish that express GFP throughout their vasculature and then analyzed 3–5 days after injection. Zebrafish that were injected with HT1080tdT cells developed large tumors as well as metastatic lesions in the tail segments (Figures 8A,B) whereas the zebrafish injected with HT1080tdT-ε2A5 cells developed fewer and smaller tumors (Figure 8C). Collectively, these results illustrate that decreased SLC2A5 function resulted in substantially impaired efficiency of HT1080 cells to form metastases in vivo. Reduced SLC2A5 function alters distribution and morphology of mitochondria in cancer cells. Mitochondrial dynamics are linked to cancer cell migration (Desai et al., 2013; Zhao et al., 2013; Landry et al., 2014; Schuler et al., 2017; Sun et al., 2018; Denisenko et al., 2019; Furnish & Caino, 2020). Thus, we analyzed the impact of SLC2A5 inhibition on mitochondrial localization and morphology in MIA-PaCa-2 and HT1080tdT cells. In MIA-PaCa-2 cells, electron microscopy analysis showed clusters of mitochondria localized adjacent to the nucleus in parental cells (Figure 9A, encircled in the left image). In MIA-PaCa-ε2A5 cells, however, the mitochondria were dispersed throughout the cell body (Figure 9A, right), and increased in both total surface area and length (elongated) (Figures 9B,C). We carried out qPCR and immunoblot analyses of the abundance of MFN-1 (mitofusin-1) mRNA and protein, a mediator of mitochondria fusion (Legros et al., 2002). Although the MFN-1 mRNA abundance in MIA-PaCa-ε2A5 cells only tended to be higher compared to MIA-PaCa-2 cells (Supplementary Figure S6A), the MFN-1 protein abundance was clearly increased as a consequence of SLC2A5 attenuation (Supplementary Figure S6B). Analysis of HT1080tdT-ε2A5 cells revealed a similar pattern of changes in mitochondrial distribution and morphology as seen in MIA-PaCa-ε2A5 cells (Figure 9D). Mitochondria in HT1080tdT-ε2A5 cells were also dispersed throughout the cell (Figure 9D), decreased in number (Figure 9E) and became elongated (Figure 9F). Importantly however, trans-expression of wild-type SLC2A5 in HT1080tdT-ε2A5 cells restored the perinuclear distribution as seen in HT1080tdT cells (Figure 9D). In the time-lapse video of HT1080tdT cells grown in cell culture, we observed mitochondrial trafficking from the central cell body to the leading edge of cells migrating toward each other (Supplementary Video S2, arrowhead). In contrast, HT1080tdT-ε2A5 cells showed loss of directional movement of mitochondria, and the mitochondria remained dispersed throughout the cell body (Supplementary Video S2). Cancer cell extravasation drives tumor cell protrusions across the endothelium from the vessel lumen into tissue and it is a key step in cancer metastasis (Strilic & Offermanns, 2017). Therefore, we injected HT1080 cells into the chicken CAM vasculature to observe extravasation. Extravasating HT1080tdT cells exhibited prominent concentration of mitochondria in the leading invadopodium (Figures 10A,B, upper panels). Time-lapse video of extravasating HT1080tdT cells revealed directional movement of mitochondria towards the leading edge of migrating cells (Supplementary Video S3). In contrast, HT1080tdT-ε2A5 cells caused both dispersed localization and loss of directional movement of mitochondria, and formation of multiple invadopodia pointing to random directions (Figures 10A,B, lower panels). Migration of HT1080tdT-ε2A5 cells also lacked defined directionality (Supplementary Video S3). Importantly, the loss of SLC2A5 resulted in attenuation of extravasation in vivo (Figures 10A,B and Supplementary Video S3). Taken together, our data showed that SLC2A5 function is necessary for polarization of mitochondrial distribution and directional cancer cell migration, which impact on cancer cell motility and extravasation.
Cell motility and migration is crucial for organism development, including organogenesis, normal growth, and repair such as wound healing. However, in inappropriate contexts, such as cancers, cell motility and migration can have devastating consequences. Metastasis constitutes the primary cause of death for >90% of patients with cancer (Steeg, 2006). Understanding the molecular players involved in this process should help identify targets for molecular therapies that can halt or even prevent cancer metastasis. The gene encoding SLC2A5, a fructose-specific transporter, is highly expressed in cancers whereas it is tightly regulated in healthy tissues (Douard & Ferraris, 2008). Specifically, increased abundance of both SLC2A5 mRNA and protein have been associated with cancer progression and increased frequency of metastasis of many cancers (Zamora-Leon et al., 1996; Chen et al., 2016; Bu et al., 2018; Hamann et al., 2018; Weng et al., 2018; Jin et al., 2019; Chen et al., 2020; Liang et al., 2021; Lin et al., 2021). In this study, we assessed the importance of the SLC2A5 gene on cancer cell proliferation, migration, extravasation, and colony formation. We found that CRISPR/Cas9-mediated inactivation of the SLC2A5 gene reduced cancer cell proliferation and inhibited motility in a variety of cancer cell lines. Specifically, the attenuation of the SLC2A5 gene expression inhibited cancer cell invasion and metastasis in vivo as we demonstrated in chick embryo, mouse, and zebrafish models. Furthermore, we discovered that suppression of the SLC2A5 gene in cancer cells resulted in notable changes in mitochondrial architecture and distribution, which substantially altered cell migration. Finally, our trans complementation experiments demonstrated that full activity of SLC2A5 is necessary for the enhanced proliferation and motility exhibited by cancer cells since the re-introduction of a mutant SLC2A5 defective for fructose binding/transport was unable to restore the phenotype of SLC2A5-attenuated cancer cells to that observed for cancer cells with wild-type SLC2A5. Fructose can enter several important metabolic pathways critical for cancer growth including the hexosamine biosynthetic pathway (Chiaradonna et al., 2018), the pentose phosphate pathway (Stincone et al., 2015), and de novo lipogenesis (Ameer et al., 2014). Fructose can be metabolized to fatty acids and triglycerides, providing components essential for the synthesis of membrane lipids to sustain cancer growth and proliferation (Ter Horst and Serlie, 2017). Recent studies have also shown that fructose supplementation stimulates lung cancer cell proliferation in vivo (Chen et al., 2020; Liang et al., 2021). Accordingly, we found that supplementation of the cell culture medium with fructose robustly stimulated MIA-PaCa-2 cell proliferation while inactivation of the gene encoding SLC2A5 abolished this response. Curiously however, we observed that HT1080tdT cells, a fluorescently-tagged derivative of the highly tumorigenic and metastatic HT1080 fibrosarcoma (Gupta et al., 2001; Zuber et al., 2008; Castoria et al., 2013), did not respond to fructose supplementation as expected (Figure 3C). Although there was a tendency towards higher growth rate in response to fructose supplementation, the increment was not statistically significant. It is possible that HT1080 cells harbor additional pathways that enable them to grow more aggressively than other cancer cell lines. Regardless, inactivation of SLC2A5 did significantly decrease the HT1080 growth rate, suggesting that these cells can and do utilize fructose as a fuel source. Given that SLC2A5 is responsible for the import of fructose, our finding suggests that at least some of the fructose endogenously produced by HT1080 cells leave the cell and must re-enter via SLC2A5 before it can be used. The attenuation of cell proliferation as a consequence of SLC2A5 gene editing further supports the idea that HT1080 cells depend on fructose as a fuel. One consequence of SLC2A5 inactivation common to both MIA-PaCa-2 and HT1080 cells relates to the remarkable alteration of mitochondrial distribution and morphology. Mitochondria are dynamic organelles that supply the energy required to drive the key cellular processes involved in metastasis, including proliferation and migration (Trotta & Chipuk, 2017). Critical to cancer cell proliferation and migration are changes in mitochondrial architecture, fusion, fission, and networking (Trotta & Chipuk, 2017). Mitochondria localize to the cell migratory front edge where they participate in podosome formation, and cell migration, invasion, and subsequently establish the metastatic site (Han et al., 2013; Landry et al., 2014; Leong et al., 2014; Stoletov & Lewis, 2015; Caswell & Zech, 2018; Denisenko et al., 2019; Furnish & Caino, 2020). During cancer cell migration, cells form directional invadopodia that ultimately penetrate the vascular wall (Stoletov et al., 2010; Stoletov et al., 2018). It is thought that mitochondria localized to these cellular protrusions provide the energy required for cellular movement and traversal of the vascular wall (Zhao et al., 2013; Caino et al., 2015; Caswell & Zech, 2018). The inhibition of the SLC2A5 gene resulted in changes in mitochondrial distribution and morphology that affected cellular migration. In SLC2A5-deficient cancer cells, mitochondria became elongated, increased in number, and dispersed throughout the cell, which prevented efficient cellular extravasation. This indicates that SLC2A5 function is required for mitochondrial polarization towards the cell protrusions and directional migration of cancer cells. In conclusion, we demonstrated that limiting the function of the SLC2A5 (GLUT5) fructose transporter inhibited cell proliferation, motility and cancer cell metastasis. We also unexpectedly discovered that the localization and structure of mitochondria in cancer cells with attenuated SLC2A5 function contribute a role in the metastatic potential of cancer cells. Based on our findings, inhibition of SLC2A5 is a useful strategy for reducing the risk of metastasis, a deadly aspect of human cancers. | true | true | true |
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PMC9578481 | Danyang Li,Tong Wang,Qianli Ma,Lu Zhou,Yanqing Le,Yafei Rao,Liang Jin,Yuqiang Pei,Yaning Cheng,Chen Huang,Xiaoyan Gai,Yongchang Sun | IL-17A Promotes Epithelial ADAM9 Expression in Cigarette Smoke-Related COPD Li et al | 14-10-2022 | chronic obstructive pulmonary disease,a disintegrin and metalloproteinase 9,interleukin-17A,airway epithelium | Background It has been reported that a disintegrin and metalloproteinase 9 (ADAM9) is involved in the pathogenesis of cigarette smoke (CS)-associated chronic obstructive pulmonary disease (COPD). But how CS exposure leads to upregulation of ADAM9 remains unknown. Methods Patients who underwent lobectomy for a solitary pulmonary nodule were enrolled and divided into three groups: non-smokers with normal lung function, smokers without COPD and smoker patients with COPD. Immunoreactivity of interleukin (IL)-17A and ADAM9 in small airways and alveolar walls was measured by immunohistochemistry. Wild-type and Il17a−/− C57BL/6 mice were exposed to CS for six months, and ADAM9 expression in the airway epithelia was measured by immunoreactivity. In addition, the protein and mRNA expression levels of IL-17A and ADAM9 were assessed in CS extract (CSE) and/or IL-17A-treated human bronchial epithelial (HBE) cells. Results The immunoreactivity of ADAM9 was increased in the airway epithelia and alveolar walls of patients with COPD compared to that of the controls. The expression of IL-17A was also upregulated in airway epithelial cells of patients with COPD and correlated positively with the level of ADAM9. The results from the animal model showed that Il17a−/− mice were protected from emphysema induced by CS exposure, together with a reduced level of ADAM9 expression in the airway epithelia, suggesting a possible link between ADAM9 and IL-17A. Consistently, our in vitro cell model showed that CSE stimulated the expression of ADAM9 and IL-17A in HBE cells in a dose- and time-dependent manner. Recombinant IL-17A induced ADAM9 upregulation in HBE cells and had a synergistic effect with CSE, whereas blocking IL-17A inhibited CSE-induced ADAM9 expression. Further analysis revealed that IL-17A induced c-Jun N-terminal kinase (JNK) phosphorylation, thereby increasing ADAM9 expression. Conclusion Our results revealed a novel role of IL-17A in CS-related COPD, where IL-17A contributes to ADAM9 expression by activating JNK signaling. | IL-17A Promotes Epithelial ADAM9 Expression in Cigarette Smoke-Related COPD Li et al
It has been reported that a disintegrin and metalloproteinase 9 (ADAM9) is involved in the pathogenesis of cigarette smoke (CS)-associated chronic obstructive pulmonary disease (COPD). But how CS exposure leads to upregulation of ADAM9 remains unknown.
Patients who underwent lobectomy for a solitary pulmonary nodule were enrolled and divided into three groups: non-smokers with normal lung function, smokers without COPD and smoker patients with COPD. Immunoreactivity of interleukin (IL)-17A and ADAM9 in small airways and alveolar walls was measured by immunohistochemistry. Wild-type and Il17a−/− C57BL/6 mice were exposed to CS for six months, and ADAM9 expression in the airway epithelia was measured by immunoreactivity. In addition, the protein and mRNA expression levels of IL-17A and ADAM9 were assessed in CS extract (CSE) and/or IL-17A-treated human bronchial epithelial (HBE) cells.
The immunoreactivity of ADAM9 was increased in the airway epithelia and alveolar walls of patients with COPD compared to that of the controls. The expression of IL-17A was also upregulated in airway epithelial cells of patients with COPD and correlated positively with the level of ADAM9. The results from the animal model showed that Il17a−/− mice were protected from emphysema induced by CS exposure, together with a reduced level of ADAM9 expression in the airway epithelia, suggesting a possible link between ADAM9 and IL-17A. Consistently, our in vitro cell model showed that CSE stimulated the expression of ADAM9 and IL-17A in HBE cells in a dose- and time-dependent manner. Recombinant IL-17A induced ADAM9 upregulation in HBE cells and had a synergistic effect with CSE, whereas blocking IL-17A inhibited CSE-induced ADAM9 expression. Further analysis revealed that IL-17A induced c-Jun N-terminal kinase (JNK) phosphorylation, thereby increasing ADAM9 expression.
Our results revealed a novel role of IL-17A in CS-related COPD, where IL-17A contributes to ADAM9 expression by activating JNK signaling.
Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease characterized by a progressive decline of lung function resulting from alveolar destruction and airway remodeling. It has become a leading cause of morbidity and mortality worldwide and results in a substantial economic and social burden.1 Cigarette smoking is a major risk factor for COPD.1 Inhalation of cigarette smoke (CS) activates the innate and adaptive immune systems, leading to the release of a range of inflammatory mediators, including cytokines, chemokines, and proteases.2 Uncontrolled protease secretion contributes to the degradation of the extracellular matrix (ECM) components, and hence airspace enlargement (emphysema), a hallmark of COPD.3 A variety of proteases, such as serine proteinases, cysteine proteinases, and matrix metalloproteases (MMPs), have been reported to participate in the development of emphysema,3 whereas a disintegrin and metalloproteinases (ADAMs), such as ADAM8,4 ADAM9,5,6 ADAM15,7 and ADAM33,8 as a less studied subgroup of metalloproteinases, were found to be involved in COPD pathogenesis in recent years. ADAMs are type I transmembrane proteins containing six important domains, among which the metalloproteinase and disintegrin domains endow them with proteolytic and adhesive functions, respectively.9 ADAM9, one of the ADAM proteins, is widely expressed in a variety of cells, such as neutrophils, monocytes, macrophages, T cells, epithelial cells, endothelial cells, fibroblasts, and many types of tumor cells, and is demonstrated to play crucial roles in many inflammatory and neoplastic diseases.10 Elevated ADAM9 expression could promote cancer progression by increasing cell proliferation, invasion and migration.10 For example, ADAM9 is involved in lung cancer metastasis to the brain through facilitating CUB-domain–containing protein 1 cleavage.11 Moreover, polymorphonuclear neutrophils-derived ADAM9 contributes to acute lung injury development by degrading ECM proteins and promoting alveolar-capillary barrier injury.12 ADAM9 was also shown to be associated with COPD pathogenesis. Patients with COPD had a higher ADAM9 expression in lung epithelial cells and alveolar macrophages than non-smokers and smokers without COPD.5,6 In addition, ADAM9 deficiency could reduce lung inflammation and emphysema in a CS-exposed mouse model.5 But how CS exposure leads to enhanced expression of ADAM9 in COPD remains unknown. Interleukin (IL)-17A is a multifunctional pro-inflammatory cytokine involved in COPD pathogenesis. Increased IL-17A+ T cells in the peripheral blood and lung tissues have been observed and associated with the severity of lung function in COPD.13 In addition, IL-17A signaling is implicated in the formation of tertiary lymphatic tissues in patients with end-stage COPD and long-term CS-exposed mice.14,15 Besides its role in chronic airway inflammation, IL-17A is required for emphysema development. Il17a−/− mice are protected against emphysema induced by elastase or CS exposure partly through regulating neutrophilic inflammation and macrophage recruitment.16–19 Furthermore, IL-17A can mediate protease-antiprotease imbalance by promoting MMP expression, leading to the development of emphysema and airway inflammation. For instance, IL-17A promoted MMP12 expression in CS-exposed models,19,20 and MMP-12 overexpression was the major cause of alveolar wall destruction.21 Moreover, intranasal stimulation with IL-17A increased the concentration of MMP-9 and its precursor proMMP-9 in mouse airways,22 whereas IL-17A neutralization reduced the expression of MMP-9 and other pro-inflammatory cytokines and attenuated Pseudomonas aeruginosa infection in a COPD mouse model.23 Considering the function of IL-17A in mediating MMP expression and that ADAM9 is also a metalloprotease, we speculate that IL-17A may be responsible for enhanced ADAM9 expression in CS-related COPD. In this study, we firstly evaluated the expression of ADAM9 and IL-17A in lung tissue samples from smokers with or without COPD and from non-smokers and found that the expression of ADAM9 and IL-17A was elevated in COPD patients. In a well-established model of COPD induced by long-term CS exposure, we demonstrated that ADAM9 expression was reduced in Il17a−/− mice compared with WT mice. In an in vitro model, CS exposure induced expression of ADAM9 and IL-17A in HBE cells, and IL-17A promoted ADAM9 expression by activating c-Jun N-terminal kinase (JNK) signaling. Taken together, our results revealed a novel role of IL-17A in the regulation of ADAM9 in CS-associated COPD.
Patients undergoing lobectomy for solitary pulmonary nodules (including benign and malignant nodules) at Peking University Third Hospital and China-Japan Friendship Hospital from 2018 to 2022 were recruited and divided into three groups: age-matched non-smokers with normal lung function (NS), smokers without COPD (S), and smoker patients with COPD (COPD). COPD was diagnosed by a post-bronchodilator forced expiratory volume in one second/forced vital capacity (FEV1/FVC) < 0.70 and the patients did not have exacerbations for at least three months before the study. Patients with a smoking history of ≥ 10 packs/year and a post-bronchodilator FEV1/FVC > 0.70 were defined as smokers without COPD. Only male patients were enrolled, because female smokers with COPD were less common in the Chinese population. Individuals with other chronic pulmonary diseases, such as asthma, bronchiectasis, and interstitial lung disease, autoimmune diseases, acute infection, a history of thoracic surgery, and long-term use of oral glucocorticoids or immunosuppressants were excluded. The study was approved by the Ethics Committee of Peking University Third Hospital [batch number: (2018)208-01], and conducted following the Declaration of Helsinki. Informed consent was obtained from all participants. Demographic data are presented in Table 1.
Lung sections were obtained from lobectomy specimens from patients with solitary pulmonary nodules. Lung tissues were taken > 2 cm from the nodule, and specimens were taken > 10 cm from the tumor margin in cases with malignant tumors.
Six-to-eight-week-old female WT C57BL/6 mice were purchased from Beijing Vital River Experimental Animal Company (Beijing, China). Il17a−/− mice with a C57BL/6 background were generously donated by Dr. Huanzhong Shi (Capital Medical University, Beijing, China). Animals were housed under specific pathogen-free conditions with a light-dark cycle of 12 h. All animal protocols were approved by the Ethics Committee of Peking University Third Hospital [batch number: (2018)208-01] and followed the “Laboratory animal—Guideline for ethical review of animal welfare (GB/T 35892-2018)” and the “Guide for the Care and Use of Laboratory Animals: Eighth Edition (2011)”. WT and Il17a−/− mice were exposed to CS for six months to develop a COPD mouse model, as previously described.24 Briefly, mice were exposed to cigarette smoke (Baisha cigarettes, Hunan, China; tar: 11 mg, nicotine: 0.9 mg, CO:12 mg) for 50 min, twice a day, five days per week using a nose-only smoke exposure system (SG-300; SIBATA, Tokyo, Japan). The smoke was suctioned at a speed of 20 mL per 8 s, mixed with room air at a ratio of 1:9, and injected into the inhalation tower. The control groups were exposed to room air for six months. Hematoxylin and eosin (H&E) staining was performed to assess the severity of emphysema in the CS-exposed mice. The mean linear intercept (MLI) and destructive index (DI) were used to quantify the degree of airspace enlargement and alveolar wall destruction, respectively.
Lung tissues were fixed with 4% paraformaldehyde and paraffin-embedded after lung resection. Paraffin-embedded tissues were cut into 4 μm-thick sections. After dewaxing, hydration, and antigen retrieval, the sections were blocked with goat serum (ZSGB-Bio, Beijing, China) at room temperature for 40 min and immunostained with rabbit anti-ADAM9 (ab186833, Abcam, Cambridge, MA, USA) or rabbit anti-IL-17A (ab79056, Abcam, Cambridge, MA, USA) antibodies overnight. Subsequently, sections were incubated with horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG (ZSGB-Bio, Beijing, China). Immunoreactivity was visualized using a DAB Detection System kit (ZSGB-Bio, Beijing, China). Images were captured using a NanoZommer-SQ Digital slide scanner (Hamamatsu, Japan) and analyzed using ImageJ software (National Institutes of Health, MD, USA). Two to three images per lung sections were randomly selected to measure the mean optical density of ADAM9 and IL-17A in epithelial cells. The average was taken for further statistical analysis.
CSE was prepared as previously described.25 Briefly, smoke from five cigarettes (Baisha, Hunan, China) was slowly bubbled into a tube containing 10 mL Dulbecco’s modified Eagle medium (DMEM) (Biological Industries Ltd, Kibbutz Beit Haemek, Israel). Solutions with an absorbance value of approximately 4.0 were considered acceptable and were defined as 100% CSE solution. The CSE solution was filtered through a 0.22 µm filter (Millipore, Sigma, Billerica, MA, USA) to remove bacteria. The cytotoxic effect of CSE at different concentrations (0%, 1%, 3%, 5%, 7%, 10%) on HBE cells was detected using a Cell Counting Kit-8 (CCK-8; Yeasen Biotech, Shanghai, China).
The HBE cell line was purchased from the American Type Culture Collection (ATCC, VA, USA). HBE cells were cultured in DMEM medium (Biological Industries Ltd, Kibbutz Beit Haemek, Israel) supplemented with 10% fetal bovine serum (Gibco, Thermo Fisher Scientific, Waltham, MA, USA), 100 U/mL penicillin and 100 μg/mL streptomycin (Hyclone, Global Life Sciences Solutions USA LLC, Marlborough, MA, USA) at 37 °C in 5% CO2-humidified atmosphere. Different concentrations of CSE and/or recombinant human IL-17A (200-17, PeproTech, Rocky Hill, NJ, USA) were added to the medium when cells grew to 70% confluence. The cells were collected after 0 to 48 h for further analysis. IL-17A neutralizing antibody (69021-1-Ig, Proteintech, Rosemont, IL, USA; 200ng/mL) was used to neutralize cell-intrinsic IL-17A. To investigate the effect of JNK signaling on IL-17A and/or CSE-induced ADAM9 expression in HBE cells, cells were pretreated for 1 h with 10 µM JNK inhibitor SP600125 (MedChemExpress, Monmouth Junction, NJ, USA).
Total RNA was extracted from HBE cells by using the TRIzol reagent (Life, Thermo Fisher Scientific, Waltham, MA, USA). Reverse transcription-polymerase chain reaction (RT-PCR) was performed using the QuantStudio 5 Real-Time PCR system (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA) and One Step TB Green® PrimeScript™ RT-PCR Kit II (Perfect Real Time) (Takara, Tokyo, Japan). Relative gene expression was analyzed using the ΔΔCt method. The PCR-specific primer sequences used for analysis were as follows: human ADAM9 forward primer, 5′-ATAGTGCTCCCTCCTGTGGT-3′; human ADAM9 reverse primer, 5′-ACAGGTACTTCCTTCGCAGC-3′; human IL-17A forward primer, 5′- CGGACTGTGATGGTCAACCT-3′; human IL-17A reverse primer, 5′- TCCTCATTGCGGTGGAGATT-3′; human GAPDH forward primer, 5′-AAATCAAGTGGGGCGATGCTG-3′; human GAPDH reverse primer, 5′-GCAGAGATGATGACCCTTTTG-3′.
Cells were lysed in RIPA lysis buffer (Applygen, Beijing, China) containing protease inhibitors (Applygen, Beijing, China) and phosphatase inhibitors (Applygen, Beijing, China). Protein concentrations were measured using a BCA kit (Applygen, Beijing, China). After sodium dodecyl sulfate-polyacrylamide gel electrophoresis for 45 min at a constant voltage of 80 V and 45 min at 120 V, the separated proteins were transferred to PVDF membranes (Millipore, Sigma, Billerica, MA, USA). The membranes were blocked with 5% skimmed milk powder at room temperature for 1.5 h and then incubated overnight at 4 °C with primary antibodies: rabbit anti-ADAM9 mAb (1:1000, 4151, Cell Signaling Technology, Danvers, MA, USA), mouse-anti-IL-17A mAb (1:1000, 66148-1-lg, Proteintech, Rosemont, IL, USA), rabbit anti-JNK mAb (ab208035, Abcam, Cambridge, MA, USA), and rabbit anti-phospho-JNK mAb (ab76572, Abcam, Cambridge, MA, USA). HRP-labeled goat anti-rabbit or goat anti-mouse lgG (ZSGB-Bio, Beijing, China) was used as secondary antibodies. Immunoreactive bands were detected using Immobilon Western Chemiluminescent HRP Substrate (Millipore, Sigma, Billerica, MA, USA) and analyzed using ImageJ software (National Institutes of Health, MD, USA).
Data are presented as mean ± SEM or mean (range). Comparisons between two groups were analyzed using the Student’s t-test after normality and lognormality tests. Statistical differences among multiple groups were determined using a one-way analysis of variance (ANOVA) test followed by Tukey’s multiple comparisons test or Kruskal–Wallis test with Dunn’s multiple comparisons test. Correlations were determined using Pearson’s correlation analysis. All statistical data were analyzed using GraphPad Prism 8 software (GraphPad Software, La Jolla, CA, USA). Statistical significance was set at p < 0.05.
To explore whether IL-17A is associated with ADAM9 expression in COPD, we performed immunostaining to assess the distribution and expression levels of ADAM9 and IL-17A in lung tissue samples from smokers with or without COPD and non-smokers. As shown in Figures 1A and 2A, both ADAM9+ and IL-17A+ cells were detected in the small airways, alveolar walls, and infiltrating immune cells. The immunoreactivity of ADAM9 was significantly increased in the bronchiolar (Figure 1B) and alveolar epithelial cells (Figure 2B) in COPD compared with non-smokers. Increased expression of IL-17A was also observed in the airway epithelia of COPD patients (Figure 1C), whereas no difference in IL-17A expression was detected in the alveolar epithelia among the three groups (Figure 2C). Furthermore, the expression of ADAM9 in airway epithelial cells was correlated positively with IL-17A expression in all three groups (Figure 1D). A similar pattern was observed in the alveolar epithelia (Figure 2D). In addition, the expression of IL-17A in the airway epithelia showed a negative correlation with FEV1/FVC (Figure 1E). However, we did not find a relationship between the expression level of ADAM9 and FEV1/FVC or FEV1% pred in the airway epithelia or the alveolar epithelia in smokers with or without COPD.
After six months of CS exposure, the mice were evaluated for emphysema and expression of ADAM9. As expected, WT mice receiving chronic CS exposure displayed a significant increase in MLI and DI in lung tissue slices compared to that in WT mice exposed to room air, indicating that a COPD-like phenotype was established in CS-exposed mice (Figure 3A, C and D). In contrast, alveolar enlargement was ameliorated in Il17a−/− CS-exposed mice (Figure 3A, C and D), which was consistent with previous findings that IL-17A contributed to the development of COPD.16,17 Notably, ADAM9 expression was reduced in Il17a−/− CS-exposed mice compared to that in WT mice receiving the same amount of CS exposure (Figure 3B and E). Considering the previous report that Adam9−/− mice were protected from emphysema development,5 we propose that the role of IL-17A in COPD pathology may be partly attributed to its induction of ADAM9 expression.
CCK-8 assay was performed to evaluate the cytotoxicity of CSE to HBE cells, and a concentration of 5% was found to start inhibiting cell activity (Figure 4A). Therefore, HBE cells were cultured with a supplement of different concentrations of CSE (from 0% to 5%) for 24 or 48 h. As the concentration of CSE increased, the expression level of Adam9 mRNA showed a tendency to increase (Figure 4B and C), as did the expression of Il17a mRNA (Figure 4E). Moreover, transcription of Adam9 was upregulated by CSE over time (Figure 4D). The CSE-induced upregulation of ADAM9 and IL-17A was further confirmed at the protein levels (Figure 4F–H). In addition, we observed an earlier upregulation of IL-17A, which was significantly increased at 6 h, whereas the expression of ADAM9 was increased at 12 h after CSE treatment (Figure 4I–K).
We next investigated the effect of IL-17A on CSE-induced ADAM9 upregulation in HBE cells. Treatment with recombinant human IL-17A alone led to a dose-dependent increase in Adam9 transcription, with no effect on cell vitality confirmed by CCK-8 assay (Figure 5A and B). A similar result was obtained by Western blot, although ADAM9 only exhibited a mild upregulation at the protein level (Figure 5D and E). However, co-treatment with IL-17A and CSE significantly increased ADAM9 production in vitro (Figure 5C, F and G), suggesting a synergistic effect of IL-17A and CS exposure in the induction of ADAM9 expression. In addition, IL-17A neutralizing antibody significantly reduced CSE-induced ADAM9 expression in HBE cells (Figures 5H, I and S1A). These results indicate that HBE cell-intrinsic IL-17A is involved in CSE-induced ADAM9 expression.
Several studies have demonstrated that JNK signaling is involved in COPD pathogenesis, including airway inflammation, mucus secretion, EMT, and airway remodeling.26–28 Therefore, we investigated whether the JNK signaling pathway also participated in the IL-17A-mediated regulation of ADAM9 expression in CSE-treated HBE cells. We found that both IL-17A and CSE induced JNK phosphorylation in HBE cells, whereas total JNK was not affected by IL-17A or CSE (Figure 6A–C). The combined use of IL-17A and CSE synergistically induced a higher level of JNK phosphorylation, which was blocked by the addition of the JNK inhibitor SP600125 (Figures 6A, B, D and S1B). Moreover, decreased ADAM9 expression was observed in SP600125-treated cells, which could be reversed by adding IL-17A and/or CSE (Figure 6E and F).
Excess ECM degradation is involved in COPD-associated emphysema, and abnormal metalloprotease activity participates in this process.29 A recent study demonstrated that ADAM9, which reportedly promotes acute lung injury by degrading the ECM and damaging the alveolar-capillary barrier,12 was also involved in the development of COPD.5 ADAM9 has been suggested to degrade lung elastin and induce alveolar cell death by shedding EGFR and VEGFR2, thus promoting emphysema development.5 However, the mechanism by which ADAM9 production is induced in COPD was unclear. In this study, we provided evidence that IL-17A, a crucial contributor to COPD pathogenesis, was an upstream modulator of ADAM9. Multiple studies have explored IL-17A expression in COPD. The number of IL-17A+ inflammatory cells was higher in the small airway subepithelia in patients with COPD than in healthy people with or without smoking history.30–33 In contrast, the results regarding epithelial IL-17A immunoreactivity were conflicting. Some studies reported higher levels of immunoreactivity of IL-17A in the airway epithelia and alveolar walls in COPD,31,34 while others observed no differences.30,33 In this study, we observed increased IL-17A expression in the small airways, but not in the alveolar walls. The inconsistencies in different studies could be due to sample differences, as IL-17A expression was significantly increased in severe to extremely severe COPD compared to that in controls,14 or due to different methods used to measure immunoreactivity. In line with a previous report,5 we observed enhanced ADAM9 expression in small airways and alveolar walls of COPD patients compared with controls. Notably, we found a positive correlation between ADAM9 and IL-17A expression in the airway epithelia as well as in the alveolar walls. To confirm the effect of IL-17A on ADAM9 expression in COPD, we performed an experiment using Il17a−/− mice exposed to long-term CS. After six months of CS exposure, Il17a−/− mice exhibited less ADAM9 expression in small airways than WT mice. Moreover, Il17a knockout attenuated the severity of emphysema, similar to the results obtained by Adam9 deletion in a previous study.5 These findings provide evidence that IL-17A induces upregulation of ADAM9 in COPD. To elucidate the effect of IL-17A on ADAM9 expression, we further demonstrated, for the first time to our knowledge, that ADAM9 was increased in HBE cells in response to CSE stimulation in a time- and concentration-dependent manner. Recombinant IL-17A also induced ADAM9 expression in HBE cells. It is well known that IL-17A expression is increased in COPD patients as well as in mouse models.13 CS could induce the expansion of Th17 and Tc17 cells in the lungs, which are the primary source of IL-17A.13 Moreover, epithelial cells could express IL-17A in an autocrine manner.35 Consistent with other studies, we observed that IL-17A was expressed at a relatively low level in unstimulated HBE cells but was upregulated by CS exposure. The increase of IL-17A was earlier than ADAM9 after CSE treatment. In addition, IL-17A neutralization inhibited CSE-induced ADAM9 expression. Hence, apart from IL-17A that originates from infiltrating immune cells, epithelial cell-derived IL-17A is likely to contribute to ADAM9 expression in lung epithelial cells of smokers with COPD. Furthermore, we explored IL-17A-mediated signaling in the regulation of ADAM9 expression. We revealed that both IL-17A and CSE induced JNK phosphorylation, whereas inhibiting JNK phosphorylation suppressed ADAM9 upregulation by IL-17A with or without CSE, indicating that IL-17A induced ADAM9 expression through JNK activation in CS-related COPD. Our study had several limitations. We did not perform experiments to explore the role of ADAM9 in COPD-associated emphysema, although it has been suggested that ADAM9 could induce alveolar epithelial cell death.5 Whether IL-17A neutralization could reduce CSE-induced epithelial cell death through inhibiting ADAM9 expression remains to be clarified. IL-17A and ADAM9 are reportedly implicated in EMT in some diseases.36,37 IL-17A has been demonstrated to synergize with CS to induce EMT in bronchial epithelial cells by activating nuclear factor-kappa B (NF-κB) and CCAAT/enhancer-binding protein beta (CEBPβ) signaling.38,39 Similarly, Adam9 deletion was shown to protect CS-exposed C57BL/6 mice from airway fibrosis.5 Adam9−/− mice had fewer α- spinal muscular atrophy+ (a-SMA+) myofibroblasts around the small airways after six months of CS exposure than WT mice.5 Further experiments should be performed to examine whether IL-17A induces EMT through ADAM9 signaling in COPD. In addition, Umeda et al reported that ADAM9 could be exclusively expressed in Th17 cells and enhance their differentiation, thus contributing to the development of systemic lupus erythematosus, indicating that a reciprocal connection may exist between ADAM9 and IL-17A.40 As increased Th17 cells and ADAM9 expression in epithelial cells can be simultaneously observed in COPD, there may be a positive feedback between ADAM9 and IL-17A in COPD, which also needs further investigation. | true | true | true |
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PMC9578485 | 36250718 | Yunan Mao,Jinze Shen,Yuchen Wu,Ruan Wenjing,Feng Zhu,Shiwei Duan | Aberrant expression of microRNA-4443 (miR-4443) in human diseases BIOENGINEERED | 17-10-2022 | miR-4443,expression,cancer,target gene,signaling pathway | ABSTRACT miRNA is a small endogenous RNA and an important regulator of gene expression. miR-4443 is abnormally expressed in 12 diseases including cancer. The expression of miR-4443 is regulated by 3 upstream factors. miR-4443 has 12 downstream target genes. miR-4443 inhibits the expression of its target genes, thereby affecting the migration, proliferation, and invasion of pathological cells. miR-4443 participates in 4 signaling pathways and plays a role in the occurrence and development of several diseases. In addition, miR-4443 can also promote resistance to multiple drugs. Here, this article summarizes the aberrant expression of miR-4443 and its pathogenic molecular mechanisms in human diseases, which provides clues and directions for the follow-up research of miR-4443. | Aberrant expression of microRNA-4443 (miR-4443) in human diseases BIOENGINEERED
miRNA is a small endogenous RNA and an important regulator of gene expression. miR-4443 is abnormally expressed in 12 diseases including cancer. The expression of miR-4443 is regulated by 3 upstream factors. miR-4443 has 12 downstream target genes. miR-4443 inhibits the expression of its target genes, thereby affecting the migration, proliferation, and invasion of pathological cells. miR-4443 participates in 4 signaling pathways and plays a role in the occurrence and development of several diseases. In addition, miR-4443 can also promote resistance to multiple drugs. Here, this article summarizes the aberrant expression of miR-4443 and its pathogenic molecular mechanisms in human diseases, which provides clues and directions for the follow-up research of miR-4443.
MicroRNA (miRNA) is a small non-coding RNA that can target and inhibit messenger RNA (mRNA) expression. miRNAs act similarly to other RNAs of the ribonucleoprotein (RNP) complex, providing sequence-specific binding components that enable RNPs to act on specific targets[1]. miRNAs typically bind to the 3’-UTR (untranslated region) of target mRNAs and inhibit protein production by degrading mRNAs and silencing translation [2]. In recent years, numerous studies have found that long non-coding RNAs (lncRNAs) may act as endogenous sponges to regulate the expression and function of miRNAs [3]. The competing endogenous RNA (ceRNA) axis is the main mode of action of lncRNAs [4]. Competing endogenous RNAs (ceRNAs) compete with common microRNAs (miRNAs) for binding, thereby inhibiting the targeting of downstream mRNAs by miRNAs [5]. There is increasing evidence that miR-4443, a recently discovered miRNA, is an important endogenous regulatory molecule. However, miR-4443 has not yet been comprehensively summarized. In this review, we outline the aberrant expression of miR-4443 in various diseases and its physiological significance. In addition, the relationship between abnormality of miR-4443 and activation of signaling pathway and drug sensitivity is also discussed. Our comprehensive overview of miR-4443 may provide potential clues for future studies of miR-4443.
Previous studies have shown that miR-4443 is abnormally expressed in 9 cancers (Table 1). Among them, the samples or cell lines involved in miR-4443 research can be seen in Table 2. The up-regulation of miR-4443 in breast tissue is significantly associated with the risk of breast cancer (BC) and the invasion of breast cancer cells [6]. Up-regulation of miR-4443 expression was also associated with the risk of non-small cell lung cancer (NSCLC) [7] and Glioma [8]. In contrast, the down-regulation of miR-4443 expression in papillary thyroid carcinoma (PTC) cell lines is significantly related to the energy metabolism and metastasis of PTC in vitro [9]. In addition, the down-regulated expression of miR-4443 is associated with the risk of colorectal cancer (CRC) [10], osteosarcoma (OS) [11], hepatocellular carcinoma (HCC) [12], glioblastoma (GBM) [13], and head and neck squamous cell carcinoma (HNSCC) [14]. There are highly expressed ceRNAs of miR-4443 in these 4 tumors. These ceRNAs can competitively inhibit the expression of miR-4443, comprising lncRNA FEZF1-AS1 in OS and HCC, lncRNA MNX1-AS1 in GBM, and lncRNA LINC00460 in head and neck squamous cell carcinoma (HNSCC). In addition, miR-4443 was abnormally expressed in 3 non-tumor diseases. Up-regulation of miR-4443 expression in plasma is associated with the risk of acute ischemic stroke (AIS) [15], while down-regulation of miR-4443 expression in plasma is a risk factor of atrial fibrillation (AF) [16]. In CD4+ T cells, the significantly up-regulated expression of miR-4443 is also associated with the risk of Graves’ disease (GD) [17]. Therefore, abnormal expression of miR-4443 affects the risk of various diseases. Competitive endogenous RNAs (ceRNAs) regulate genes at the post-transcriptional level by competing for miRNAs [18]. In 4 types of tumors, there are highly expressed ceRNAs that regulate cancer risk by inhibiting miR-4443 expression.
We downloaded the TCGA (Pan-Cancer) dataset from the UCSC Xena database (https://xenabrowser.net/). Further, we extracted and log2(x + 1) transformed the expression data of miR-4443 in each sample. We eliminated the samples with 0 expression and the cancer species without control samples and finally obtained the expression data of 19 cancer types. In addition, we calculated the median expression of all miRNAs in these 19 cancer types and calculated the rank percentage of miR-4443 among all miRNAs with non-zero expression. As shown in Figure 1a, miR-4443 was highly expressed in LUAD (0.5–0.75 quantile, Q3). miR-4443 was moderately expressed (0.25–0.5 quantile, Q2) in 16 tumors, including THCA, HNSC, CESC, LUSC, GBMLGG, COAD-READ, UCEC, BRCA, BLCA, LICH, STAD, PRAD, PAAD, KICH, ESCA, KIRP. In addition, miR-4443 expression was lower in 2 tumors (KIRC and CHOL) (0–0.25 quantile, Q1). We calculated differences in miR-4443 expression between normal and tumor samples for 19 cancer types using the unpaired Wilcoxon test of R software (version 4.1.3). As shown in Figure 1b, miR-4443 was significantly up-regulated in 6 tumors (BRCA, CESC, LUAD, LUSC, THCA, and UCEC); miR-4443 was significantly down-regulated in 3 tumors (CHOL, COAD-READ, and LIHC); miR-4443 was not significantly different among 10 tumors, including BLCA, ESCA, GBM-LGG, HNSC, KICH, KIRC, KIRP, PAAD, PRAD, and STAD. Previous studies found that miR-4443 was highly expressed in BC tumor tissues [6] and NSCLC tumor cells [7], which was consistent with the results in TCGA-BRCA and LUSC/LUAD. In addition, miR-4443 was lowly expressed in HCC tumor tissues [12] and CRC tumor cells [10], which was also validated in TCGA-LIHC and TCGA-COAD/READ. miR-4443 is lowly expressed in OS tumor cells [11]. Since there were no control samples in the TCGA-SARC database, we could not verify the correlation between miR-4443 and OS in TCGA-SARC. miR-4443 is lowly expressed in GBM tumor cells [13] and highly expressed in mast cells from Glioma patients [8]. However, in TCGA GBM-LGG, the difference of miR-4443 expression between carcinoma and adjacent carcinoma was not obvious. miR-4443 was highly expressed in HNSCC tumor cells (CAL-27 and WSU-HN4) [14], while in TCGA-HNSC miR-4443 was not significantly different between cancerous and paracancerous tissues. By qRT-PCR method, miR-4443 expression was significantly decreased [9] in PTC tumor tissues (n = 30) than in adjacent tissues (n = 30). However, in the TCGA-THCA dataset, the expression of miR-4443 in cancer tissues was higher than that in adjacent tissues. In conclusion, differences in miR-4443 expression may be related to sample types, tumor subtypes, and gene expression detection methods. Furthermore, the association of miR-4443 with cancer risk in different tumors may be related to the presence of tissue-specific upstream regulators such as ceRNAs.
As shown in Figure 2, miR-4443 not only targets and inhibits 12 target genes, but is also regulated by 3 ceRNAs. The abnormal expression of miR-4443 can lead to dysregulation of downstream gene expression, which in turn affects the abnormality of cellular behaviors, and ultimately leads to the occurrence and development of diseases. Cell migration is the basis for establishing and maintaining the normal tissues of multicellular organisms [19]. The target genes of miR-4443 are closely related to the migration of cancer cells (Figure 3). In breast cancer, miR-4443 inhibits the expression of TIMP metallopeptidase inhibitor 2 (TIMP2) [20] and phosphatidylethanolamine binding protein 1 (PEBP1) [6], thereby promoting the metastasis and invasion of breast cancer. In OS, lncRNA FEZF1-AS1 can sponge miR-4443, thereby promoting the expression of nuclear protein 1 (NUPR1), and leading to the migration, proliferation, and invasion of OS cells [11]. In HCC, lncRNA FEZF1-AS1 can compete to bind miR-4443, thereby up-regulating AKT serine/threonine kinase 1 (AKT1) expression, and ultimately promoting cancer cell metastasis and tumorigenesis [21]. Cell proliferation is an important part of cell growth and differentiation [22]. In different diseases, the effect of miR-4443 on cell proliferation is different. The expression of miR-4443 is upregulated in CD4 + T cells, thereby inhibiting the expression of TNF receptor associated factor 4 (TRAF4) and facilitating the proliferation of CD4 + T cells, and ultimately promoting the occurrence of GD [17]. In OS, the expression level of miR-4443 decreases, which in turn leads to the up-regulation of NUPR1, thereby stimulating the proliferation of OS cells [11]. In the serum of atrial fibrillation (AF) patients, the expression of miR-4443 was significantly reduced, thereby up-regulating the expression level of thrombospondin 1 (THBS1), promoting the proliferation of human cardiac fibroblasts (HCFB), and attenuating cell apoptosis [16]. Invasion of pathological cells to surrounding tissues is a difficult problem for disease treatment [23]. miR-4443 shows two opposite effects in cell invasion. Leptin and insulin treatment of colorectal cancer (CRC) cell line (HCT-116) can elevate the expression of miR-4443, which subsequently leads to down-regulation of nuclear receptor coactivator 1 (NCOA1) and TRAF4, and ultimately hinders the invasion and proliferation of HCT-116 cells [10]. In patients with atrial fibrillation, inhibition of miR-4443 can increase the level of THBS1 expression, thereby promoting the invasion of cardiac fibroblast [16]. In OS, FEZF1-AS1 can sponge miR-4443, thereby up-regulating NUPR1 and promoting OS cell invasion [11]. However, in the BC cell line (MDA-MB-231), the overexpression of miR-4443 suppresses the expression of PEBP1 and promotes the invasion and metastasis of breast cancer cells [6]. In summary, the abnormal expression of miR-4443 in some diseased cells can lead to the dysregulation of downstream gene expression, which in turn affects the abnormal behavior of cells (including migration, proliferation, and invasion), and ultimately leads to the occurrence and development of the disease. In BC and GD, overexpression of miR-4443 promoted cell migration, proliferation, and invasion. However, overexpression of miR-4443 in OS, HCC, AF, and CRC inhibited cell migration, proliferation, and invasion. Due to the small number of samples in miR-4443-related studies, further studies are required to obtain more convincing results.
There are at least 12 target genes for miR-4443. These include inositol polyphosphate-4-phosphatase type IA (INPP4A) in the Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) signaling pathway, THBS1 in the transforming growth factor beta 1 (TGF-β1) signaling pathway, TRAF4 in the nuclear factor kappa B (NF-κB) signaling pathway, and protein tyrosine phosphatase receptor type J (PTPRJ) in the Ras signaling pathway (Figure 5). The excessive activation of the JAK2/STAT3 signaling pathway is closely related to the occurrence and development of cancer [24]. In non-small cell lung cancer (NSCLC), miR-4443 is significantly related to Epirubicin (EPI) resistance. miR-4443 was highly expressed in the EPI-resistant H1299 cell line. Specifically, miR-4443 promotes the resistance of NSCLC cells to EPI by targeting INPP4A and regulating the activation of the JAK2/STAT3 signaling pathway [7]. TGF-β/Smad signaling promotes the proliferation of fibroblasts and the development of tissue fibrosis [25]. Fibrosis caused by human cardiac fibroblasts plays an important role in the occurrence and development of atrial fibrillation (AF) [26]. In AF patients, thrombospondin-1 (THBS1) can up-regulate the expression of TGF-β1 and smad2/3/4 genes, thereby promoting the proliferation, migration, and invasion of cardiac fibroblasts, and ultimately leading to the differentiation of cardiac fibroblasts. miR-4443 can inhibit THBS1, thereby alleviating fibrosis and AF symptoms [6]. The NF-κB signaling pathway is an important immune-related pathway that can regulate the inflammatory response in body [27]. In AIS patients, TRAF4 increases the phosphorylation level of IκBα in peripheral blood mononuclear cells (PBMCs) and activates the NF-κB signaling pathway. miR-4443 can inhibit the NF-κB signaling pathway by targeting TRAF4, thereby increasing the expression of anti-inflammatory cytokines, inducing immunosuppression, and ultimately increasing the risk of infection after stroke [15]. In addition, miR-4443 inhibits the NF-κB signaling pathway by targeting TRAF4, promotes the transcription of cytokines and chemokines, and up-regulates the proliferation of CD4 + T cells, thereby promoting the risk of GD [17]. The RAS signaling pathway can integrate extracellular signals to control the growth, survival, and differentiation of cell lines. Abnormal activation of the RAS signaling pathway is a major and highly common carcinogenic event [28]. In mast cells, elevated levels of miR-4443 can inhibit PTPRJ, thereby activating the Ras signaling pathway, increasing the release of IL-8 [8], and ultimately inducing angiogenesis and enhancing tumor invasiveness [29]. Accordingly, miR-4443 participates in the regulation of four signaling pathways by regulating downstream genes, thereby affecting the behavior of diseased cells, which is closely related to the expression of cytokines, cell growth and differentiation, and drug resistance.
As shown in Figure 4, miR-4443 is closely related to epirubicin (EPI) and cisplatin resistance of cancer cells. Non-small cell lung cancer (NSCLC) accounts for about 85% of lung cancers, and its incidence is increasing year by year worldwide [30]. Despite significant advances in available therapies for NSCLC, acquired resistance remains a major barrier to NSCLC treatment [31]. Epirubicin (EPI) is an anthracycline antibiotic. It can be used alone or in combination with other drugs to treat advanced non-small cell carcinoma [32]. In NSCLC, miR-4443 is highly expressed in patients insensitive to EPI chemotherapy and EPI-resistant H1299 cells. Overexpression of miR-4443 activates the JAK2/STAT3 pathway by inhibiting the INPP4A gene, and ultimately promotes EPI resistance [7]. In breast cancer, the up-regulation of miR-4443 is also closely related to EPI resistance [33]. Cisplatin is an anti-tumor compound, which can induce ferroptosis and apoptosis of non-small cell lung cancer cells [34]. A previous study has shown that upregulation of miR-4443 can induce A549 resistance to DDP [35]. miR-4443 is significantly up-regulated in exosomes released from cisplatin-resistant NSCLC tumors, resulting in a decrease in the expression of methyltransferase 3 (METTL3) in cisplatin-resistant A549 cells, thereby enhancing the expression of FSP1. Subsequently, the down-regulation of METTL3 also reduces FSP1 m6A modification and FSP1-mediated ferroptosis, and ultimately increases the cisplatin resistance of NSCLC cell lines [36]. Hence, miR-4443 in 2 types of NSCLC cells (A549 and H1299) can induce drug resistance in tumor cells by regulating targets or activating signaling pathways. At present, there is no data to support the link between miR-4443 and drug resistance through the regulation of target genes in other diseases, which requires further research. Furthermore, target genes regulated by miR-4443 are linked to drug resistance in other diseases. In OC, inhibiting the expression of TIMP2 promoted the proliferation, migration, and cisplatin resistance of A2780 cells [37]. The high expression of THBS1 in GC is not only related to tumor adhesion but also reduces the sensitivity to twelve anticancer drugs such as Oxaliplatin and Tamoxifen [38].
miR-4443 is differentially expressed in 12 diseases. Among them, the expression of miR-4443 is up-regulated in breast cancer(BC), non-small cell lung cancer(NSCLC), glioma, Graves’ disease(GD), and acute ischemic stroke(AIS). The expression of miR-4443 is down-regulated in hepatocellular carcinoma(HCC), osteosarcoma(OS), glioblastoma(GBM), papillary thyroid carcinoma(PTC), head and neck squamous cell carcinoma(HNSCC), colorectal cancer(CRC), and atrial fibrillation(AF). In addition, miR-4443 also plays an important role in human non-cancer diseases (Figure 2). The low expression of miR-4443 in AF upregulates the expression of its target gene THBS1, which promotes HCFB proliferation and leads to AF. Upregulation of miR-4443 in acute ischemic stroke (AIS) increases its risk. High expression of miR-4443 in Graves’ disease (GD) promotes the transcription of cytokines and increases the risk of GD. miR-4443 is highly expressed in AIS and increases the risk of post-stroke infection by inhibiting TRAF4. The expression of miR-4443 is regulated by three ceRNAs. Although long non-coding RNAs (lncRNAs) do not encode proteins, they can regulate the expression of downstream target protein-coding genes through a variety of mechanisms. Especially for cancer, dysregulation of lncRNAs is associated with various malignant phenotypes and leads to cancer progression and metastasis [39]. Therefore, the differential expression of miR-4443 in different diseases may be related to the regulation of ceRNA. The study found that compared with the normal control group, miR-4443 was highly expressed in glioma tissue and serum [40], showing its great potential in diagnosis. In addition, our work also shows that miR-4443 regulates cell migration, proliferation, and invasion by down-regulating the expression level of its target genes, and participates in the regulation of signaling pathways such as JAK2/STA T3, TGF-β1, NF-κB, and Ras. The aberrent expression of miR-4443 is also closely related to the resistance of EPI and cisplatin. miR-4443 is closely related to the prognosis of various tumors. In hepatocellular carcinoma (HCC), patients with low miR-4443 expression had significantly reduced overall survival [12]. In breast cancer (BC) [33] and lung adenocarcinoma (LUAD) [41], high expression of miR-4443 resulted in decreased overall survival. Upstream regulators of miR-4443 include lncRNA FEZF1-AS1 [12] and lncRNA ENST0000630242 [41]. The overall survival rate of HCC patients with high expression of lncRNA FEZF1-AS1 was significantly decreased [12], while the overall survival rate of LUAD patients with high expression of lncRNA ENST0000630242 was increased [41]. Therefore, the difference in the relationship between miR-4443 and the prognosis of patients with different cancers may be related to the existence of different upstream regulators of miR-4443.Due to the limitation of sample type and number of samples, the role of miR-4443 in diagnosis and prognosis needs to be further verified. Therefore, there are still many shortcomings in the related research of miR-4443. First of all, the sample sizes in the current studies are small, and their results need to be verified in larger samples and other populations. Secondly, the molecular mechanisms of miR-4443 in diseases are still not fully understood, and more in-depth research is needed in the future. Finally, the diagnostic and prognostic value of miR-4443 remains to be further evaluated.
As an important regulatory molecule, miR-4443 is closely related to the development of many diseases, especially cancer. This work provides an overview of the aberrant expression of miR-4443 in cancer and non-cancer diseases, revealing its molecular mechanisms in cellular behavior. In addition, this study elucidates the regulatory role of miR-4443 in signaling pathways and the drug resistance induced by miR-4443, which provides potential clues and directions for the follow-up research of miR-4443. | true | true | true |
PMC9578494 | 36239618 | Min Song,Xihe Cui,Jing Zhang,Yujie Li,Jingjing Li,Yuanlong Zang,Qi Li,Qing Yang,Ying Chen,Weiyan Cai,Xiaogang Weng,Yajie Wang,Xiaoxin Zhu | Shenlian extract attenuates myocardial ischaemia-reperfusion injury via inhibiting M1 macrophage polarization by silencing miR-155 | 14-10-2022 | MI/RI,apoptosis,Salvia miltiorrhiza,Andrographis paniculata | Abstract Context Shenlian extract (SL) is a combination of Salvia miltiorrhiza Bge. (Labiatae) and Andrographis paniculata (Burm. F.) Wall. Ex Nees (Acanthaceae) extracts, which promote blood circulation and clear endogenous heat toxins. Myocardial ischaemia-reperfusion injury (MI/RI) is aggravated myocardial tissue damage induced by reperfusion therapy after myocardial infarction. Objectives This study explores the effect of SL on MI/RI and the underlying mechanism. Materials and methods Primary peritoneal macrophages (pMACs) were treated with LPS and SL (5, 10 or 20 μg/mL) for 24 h. The myocardial ischaemia-reperfusion (MI/R) model was established after administration of different doses of SL (90, 180 or 360 mg/kg). Myocardial tissue injury was assessed by methylthiazolyl tetrazolium (TTC) staining and levels of creatine kinase (CK), lactate dehydrogenase (LDH) and superoxide dismutase (SOD) in mice. The double immunofluorescence staining of iNOS/F4/80 and CD86/F4/80 was used to detect macrophage M1 polarization. The levels of miR-155, inflammatory factors and chemokines were detected by qRT-PCR or ELISA. CD86, iNOS, SOCS3, JAK2, p-JAK2, STAT3 and p-STAT3 proteins expressions in macrophages were analyzed by western blotting. Conditioned medium transfer systems were designed to unite M1 macrophages with H/R cardiomyocytes, and cell apoptosis was detected by TUNEL staining, western blotting or immunohistochemistry. Results SL reduced apoptosis, diminished CK and LDH levels, raised SOD concentration and decreased infarct size in the MI/R model. Meanwhile, SL decreased miR-155 level, inhibited M1 macrophage polarization and inflammation. Furthermore, SL promoted SOCS3 expression and blocked JAK2/STAT3 pathway in vitro. Conclusions SL may be a promising TCM candidate for MI/RI. The underlying mechanisms could be associated with inhibition of M1 macrophage polarization via down-regulating miR-155. | Shenlian extract attenuates myocardial ischaemia-reperfusion injury via inhibiting M1 macrophage polarization by silencing miR-155
Shenlian extract (SL) is a combination of Salvia miltiorrhiza Bge. (Labiatae) and Andrographis paniculata (Burm. F.) Wall. Ex Nees (Acanthaceae) extracts, which promote blood circulation and clear endogenous heat toxins. Myocardial ischaemia-reperfusion injury (MI/RI) is aggravated myocardial tissue damage induced by reperfusion therapy after myocardial infarction.
This study explores the effect of SL on MI/RI and the underlying mechanism.
Primary peritoneal macrophages (pMACs) were treated with LPS and SL (5, 10 or 20 μg/mL) for 24 h. The myocardial ischaemia-reperfusion (MI/R) model was established after administration of different doses of SL (90, 180 or 360 mg/kg). Myocardial tissue injury was assessed by methylthiazolyl tetrazolium (TTC) staining and levels of creatine kinase (CK), lactate dehydrogenase (LDH) and superoxide dismutase (SOD) in mice. The double immunofluorescence staining of iNOS/F4/80 and CD86/F4/80 was used to detect macrophage M1 polarization. The levels of miR-155, inflammatory factors and chemokines were detected by qRT-PCR or ELISA. CD86, iNOS, SOCS3, JAK2, p-JAK2, STAT3 and p-STAT3 proteins expressions in macrophages were analyzed by western blotting. Conditioned medium transfer systems were designed to unite M1 macrophages with H/R cardiomyocytes, and cell apoptosis was detected by TUNEL staining, western blotting or immunohistochemistry.
SL reduced apoptosis, diminished CK and LDH levels, raised SOD concentration and decreased infarct size in the MI/R model. Meanwhile, SL decreased miR-155 level, inhibited M1 macrophage polarization and inflammation. Furthermore, SL promoted SOCS3 expression and blocked JAK2/STAT3 pathway in vitro.
SL may be a promising TCM candidate for MI/RI. The underlying mechanisms could be associated with inhibition of M1 macrophage polarization via down-regulating miR-155.
Myocardial ischaemia (MI) is associated with high morbidity and mortality. Timely reperfusion therapies, such as percutaneous coronary intervention and thrombolysis, are effective treatments to reduce ischaemic injury (Ibáñez et al. 2015). However, myocardial ischaemia reperfusion (MI/R) can also lead to further damage to myocardial tissue, myocardial ischaemia reperfusion injury (MI/RI), which increases initial infarct size (Toldo et al. 2018). Recent studies have suggested that the ischaemic cardiomyocytes can initiate activation of the innate immune system and induce acute inflammatory response featured with leukocyte infiltration in the infarcted heart at a background of MI/RI (Pluijmert et al. 2021). In addition, pharmacological inhibition of inflammation alleviated MI/RI in vivo and in vitro. Macrophages are the dominant innate immune cells that regulate the progression and resolution of inflammation. Functionally, macrophages can be divided into two typical subgroups: pro-inflammatory macrophages (M1) and anti-inflammatory macrophages (M2) (Hu et al. 2021). M1 macrophages express proinflammatory cytokines and proteolytic enzymes, which are used to clear cellular debris in the myocardial microenvironment. Nevertheless, the prolonged presence of M1 macrophages lengthens the pro-inflammatory state, degrades the extracellular matrix excessively and then induces cell death, which results in enlargement of infarct size and unexpected obstacles to heart repair (Ge et al. 2021). MicroRNAs (miRNAs/miRs) are endogenous, noncoding RNAs with 19-24 nucleotides that are involved in cell proliferation, differentiation, apoptosis and metastasis. miR-155, as a well-known immunomodulatory miRNA, is mostly expressed in macrophages, monocytes and neutrophils (Mashima 2015). It has been reported that miR-155 has a significant effect on cardiovascular diseases, such as myocardial ischaemia-reperfusion injury, acute myocardial infarction, atherosclerosis, heart failure and diabetic heart (Faraoni et al. 2009). the miR-155 level was significantly raised in rats with MI/RI and miR-155 knockout can ameliorate myocardial infarct and restrain cardiomyocyte apoptosis induced by ischaemia reperfusion (Guo et al. 2019). Furthermore, the increased expression of miR-155 leads to M1 macrophage polarization and inflammatory responses. Suppressor of cytokine signalling 3 (SOCS3), one of the targets of miR-155. miR-155 negatively regulates SOCS3 gene expression by binding to the 3- UTR of SOCS3 mRNAs and then suppresses the expression of the target protein SOCS3 (Henao Agudelo et al. 2017). Traditional Chinese Medicine (TCM) has been used in diagnosing and treating diseases or disorders for thousands of years and is widely used in cardiovascular disease based on clinical experience. Shenlian extract (SL) is a combination of Salvia miltiorrhiza Bge. (Labiatae) and Andrographis paniculata (Burm. F.) Wall. Ex Nees (Acanthaceae) extracts. According to TCM theory, S. miltiorrhiza promotes blood circulation and removes blood stasis, A. paniculata has an impact on clearing endogenous heat toxins (Zhang et al. 2018; Li et al. 2021). Our previous study showed that SL stabilized atherosclerotic plaques in the atherosclerosis model constructed by ApoE−/− mice and limited the crosstalk between macrophage and smooth muscle cells by down-regulate TGF-β expression (Liu et al. 2021). Meanwhile, SL improved myocardial ischaemic injury and regulated the immune system by reducing inflammatory cytokines and modulating the NF-kB pathway (Guo et al. 2020). Whether SL alleviates myocardial ischaemia-reperfusion injury has not been reported. In this study, we concentrate on investigating the effect of SL on MI/RI and the underlying mechanism. The research may be conducive to the potential application of SL in the clinical therapy of MI/RI.
Salvia miltiorrhiza and A. paniculate were purchased from Beijing Tongrentang and taxonomic authenticity was identified by Prof. Xi-rong He. Lipopolysaccharide (Cat. No: L2880-100MG), dimethyl sulfoxide (DMSO) (Cat. No: 276855) and methylthiazolyl tetrazolium (TTC) (Cat. No: CT01) were bought from Sigma. Foetal bovine serum (Cat. No: 10099141) and 0.25% Trypsin EDTA (Cat. No: 25300120) were obtained from Gibco. RIPA buffer (Cat. No: R0010) was purchased from Solarbio. Phosphatase inhibitor (Cat. No: 0490837001) and phenylmethylsulfonyl fluoride (Cat. No: P0100) were bought from Roche. p-STAT3 (Cat. No: 9415), STAT3 (Cat. No: 9139), cleaved caspase-9 (Cat. No: 9507S) and cleaved caspase-3 (Cat. No: 9661 T) antibodies were provided by CST. p-JAK2 (Cat. No: YP0785), JAK2 (Cat. No: YT2426) antibody was purchased from Immunoway. CD86 (Cat. No: A1199) antibody was bought from ABclonal. SOCS3 (Cat. No: A00274-2) antibody was purchased from BOSTER. iNOS (Cat. No: 18985-1-AP) antibody was obtained from Proteintech. Mouse TNF-α (Cat. No: 1217202), IL-1β (Cat. No:1210122), IL-6 (Cat. No: 1210602), IL-10 (Cat. No: 1211002) pre-coated ELISA kits were all purchased from Dakewe Biological Technology Ltd. RevertAid First Strand cDNA Synthesis Kit (Cat. No: 00984912) was bought from Thermoscientific. BCA (Cat. No: P0009) protein assay kit and SDS-PAGE (Cat. No: vP0009) were provided by Beyotime Biotechnology. SDS PAGE Running Buffer (Cat. No: vB1005) was bought from Applygen. Skim Milk (Cat. No: 3106120) was purchased from BD Biosciences. Bax (Cat. No: ZS74800), Bcl2 (Cat. No: ZS73820) and antibodies for immunohistochemistry were bought from ZSGB-BIO. SABC-AP (rabbit IgG) kit (Cat. No: SA1052) was obtained from BOSTER. A specific CY3-labelled miR-155 probe (Cat. No: RX040864) was purchased from Servicebio. Creatine kinase (CK) (Cat. No: A032-1-1), lactate dehydrogenase (LDH) (Cat. No: A020-1-2) and superoxide dismutase (SOD) (Cat. No: A001-3-1) kits were bought from Nanjing Jiangcheng. PerfectStart® Green qPCR SuperMix (Cat. No: AQ601) was purchased from Transgen.
The SL extract is composed of S. miltiorrhiza and A. paniculata extracts at a ratio of 5:3. Salvia miltiorrhiza extract consisted of two components, one was soaked in ethanol and extracted twice by refluxing for 2 h, and the filtrates were combined and concentrated under reduced pressure and below 60 °C, the centrifugal precipitate was the lipo-soluble extract. The second component was prepared by soaking in ethanol, after concentration and centrifugation, the supernatant was purified using macroporous resin (SP825). The A. paniculata extract was prepared by dilute ethanol soaking and purified by a macroporous resin (SP825). The extraction rate of the water-soluble partial extract of S. miltiorrhiza was 2.27% and that of the fat-soluble extract was 1.31%. The extraction rate of A. paniculata was 2.11%. In this study, Tanshinone IIA was detected from the lipo-soluble extract of S. miltiorrhiza, salvianolic acid B was analyzed from the water-soluble extract of S. miltiorrhiza, and andrographolide was tested from the extract of A. paniculata. The components from SL were tanshinone IIA (3%), salvianolic acid B (38%), and andrographolide (20%) and were detected by HPLC (Guo et al. 2020). During the in vitro experiment, the mixed extracts were dissolved in DMSO to prepare mother liquor, and then stored at −20 °C at 50 mg/mL.
The study protocol was approved by the Experimental Animal Ethical Committee of the Institute of Chinese Materia Medica within the China Academy of Chinese Medical Science (2021B136). Adult C57BL/6J mice 6–8 weeks old (weighted 20–25 g) were purchased from the Experimental Animal Centre of Military Medical Science Academy. The animals were housed in a quiet environment at 25 ± 2 °C and 55 ± 10% humidity, under a 12 h light/dark cycle.
After 1 week of adaptation, all mice were divided into 6 groups (n = 10). Different groups adopted different treatment plans after grouping the mice. The positive control of the chemotherapy group was given 2.3 mg/kg atorvastatin (ATO) dissolved in 1% CMC-Na. The mice in the sham and model groups were given 1% CMC-Na only. The SL groups were given respectively three dosages of 90, 180 and 360 mg/kg SL (equivalent to 1-, 2- and 4-times of the clinical dose) dissolved in 1% CMC-Na. All mice were orally administered from day 1 after grouping, then every day for 8 days. On day 7 after treatment, the mice underwent surgery to induce MI/R model, briefly, after anaesthesia with an injection of avertin (16.5 mL/kg) into the abdominal cavity, mice were orally intubated and fixed using a small animal respirator. An incision was made from the third to fourth ribs to expose the heart. Hearts were then exposed through the left lateral thoracotomy. The left anterior descending (LAD) coronary artery was visualized and ligated with a 7-0 suture line. The suture was loosened after occlusion for 2 h, which was followed by 24 h reperfusion of LAD.
After mice were sacrificed by anaesthesia, the hearts were taken and frozen for 20 min. Tissue sections under the ligation point were sliced, and then slices were placed in 2% TTC dye for 15 min at 37 °C. Next, the redundant staining solution was removed, and slices were washed three times with PBS and photographed for observation immediately.
CK, LDH and SOD in mice serum or heart tissue contents analysis was carried out according to the kit instructions.
Peritoneal macrophages were elicited by intraperitoneal injection of 2 mL 3% sterile starch solution, 3 days later, the animals were sacrificed, peritoneal lavage with 10 mL PBS and was rotated 225 g for 5 min to collect macrophages. The peritoneal macrophages were cultured in an RPMI-1640 cell culture medium containing 10% FBS, 1% penicillin and streptomycin. After incubation at 37 °C and 5% CO2 for 24 h, non-adherent cells were removed to obtain pure peritoneal macrophages.
Peritoneal macrophages were divided into 5 groups (n = 3). The pMACs in the control group were given 1% DMSO. The pMACs in the model group were stimulated with LPS (600 ng/mL) supplementing 1% DMSO. The pMACs in the SL groups (named SL-5, SL-10 and SL-20, respectively) were treated with 5, 10 or 20 μg/mL SL containing LPS (600 ng/mL) and 1% DMSO. Then pMACs were cultured for 24 h in the medium supplementing 10% FBS, 1% penicillin and streptomycin at 37 °C and 5% CO2.
H9C2 rat cardiomyoblast cell line was purchased from ATCC and cultured in DMEM supplemented with 10% FBS, 1% streptomycin and penicillin. Cells were maintained at 37 °C in a humidified incubator with 5% CO2.
When cell density reached 90%, cells were exposed to hypoxic conditions (oxygen deprivation, 94% N2, 5% CO2, and 1% O2) at 37 °C for 6 h in a culture medium with lower glucose and serum-free. After hypoxia, the medium was removed and the cells were placed in a reoxygenation environment (reoxygenation, 21% O2 and 5% CO2) at 37 °C for 5 h in a normal medium. The control group cells without hypoxia treatment were maintained in normoxic conditions.
To demonstrate that SL had an effect on H9C2 cells under H/R through M1 macrophage polarization, we established conditioned transfer model 1. As shown in Figure 5(A), peritoneal macrophages were seeded and then stimulated with LPS (600 ng/mL) and treated with SL (5, 10, 20 μg/mL) for 24 h, the medium (LPS and SL-containing) was collected as macrophage-conditioned media 1(CM1), and then CM1 was diluted to one third (CM1/3) of its original concentration and applied for H/R H9C2 cells. To eliminate the direct influence of SL on H9C2, conditioned medium transfer model 2 was established. In Figure 5(D), peritoneal macrophages were seeded and then stimulated with LPS (600 ng/mL) and treated with SL (5, 10 and 20 μg/mL) for 24 h. Then, the medium (LPS and SL-containing) was removed and fresh medium was added to pMACs for 24 h and the medium (LPS and SL-free) was collected as macrophage-conditioned media (CM2). Finally, CM2 was diluted to one-third (CM2/3) of its original concentration and applied to H/R H9C2 cells.
Western blotting analyses were done to measure the expression of the p-STAT3, STAT3, p-JAK2, JAK2, CD86, iNOS, SOCS3, cleaved caspase-3, cleaved caspase-3, caspase-3, Bcl2 and Bax. For the preparation of protein extracts, cell pellets were homogenized by RIPA buffer supplemented with aprotinin (10 mg/mL) and leupeptin (10 mg/mL) for 30 min on ice. The mixture was rotated at 12,000 g for 15 min to remove the insoluble fragments. the supernatant was collected to obtain the total protein. After being subjected to quantification using the BCA assay, the protein samples were separated by 10% SDS-PAGE and transferred to PVDF membranes. Membranes were blocked for 3 h with 5% skim milk or 5% BSA and then incubated with primary antibodies at 4 °C overnight. After washing, appropriate secondary antibodies were used to the membranes for 1 h at room temperature. The blots were developed by enhanced chemiluminescence detection reagents. Quantification of the gray intensity of the protein band performed by using ImageJ and normalized to the gray intensity of GAPDH.
miR-155 expression was detected by qRT-PCR. Briefly, total RNA from tissue or cell pellets was extracted with Trizol Reagent. cDNA was synthetized by a Transcriptor First Strand cDNA Synthesis Kit. Then cDNA templates were amplified using RNA primers and PerfectStart® Green qPCR SuperMix according to the manufacturer’s instructions. The following mouse primers (forward and reverse, respectively) were used: 5′-GGGCTTAATGCTAATTGTGAT-3′ and 5′-CAGTGCGTGTCGTGGAGT-3′ for miR-155; 5′-CTCGCTTCGGCAGCACA-3′ and 5′-AACGTTCACGAATTTGCGT-3′ for U6. Expression was calculated using the comparative-threshold cycle method and normalized to the expression of U6 mRNA. Genes expression of CCR2, CCL2, CXCL1, IL-6, TNF-α, CXCR1 were detected by RT-PCR. Similarly, the cDNA templates were amplified using RNA primers and 2 × GoldStar Best MasterMix according to the manufacturer’s protocol after total RNA had been extracted and cDNA synthetized. The following mouse primers (forward and reverse, respectively) were used: 5′-CTGAACGGGAAGCTCACTGG-3′ and 5′-TCCGATGCCTGCTTCACTAC-3′ for GAPDH; 5′-CCACTCACCTGCTGCTACTCATTC-3′ and 5′-CTGCTGCTGGTGATCCTCTTGTAG-3′ for CCL2; 5′-GCTCATCTTTGCCATCATGATT-3′ and 5′-TCATTCCAAGAGTCTCTGTCAC-3′ for CCR2; 5′-CCGGTACCGGAGGCCGCGCTCGCGGG-3′ and 5′-AACAGATCTCGCGCAGCACCAAACTGCC-3′ for SOCS3. RNA expression was measured through agarose-gel electrophoresis and results were visualized and photographed using an ultraviolet transilluminator. We quantified the gray intensity of the DNA band using ImageJ and normalized it to the gray intensity of GAPDH.
Primary macrophages were treated for 24 h and cells supernatant was collected and stored at −20 °C. The concentrations of IL-1β, IL-6, TNF-α and IL-10 in the supernatant were determined by the corresponding mouse ELISAs according to manufacturer instructions.
Mouse heart tissue was harvested and fixed with 4% paraformaldehyde, dehydrated, embedded in paraffin, and transversely sectioned into 5 µm pieces, which were baked at 60 °C for 12 h and dehydrated with graded ethanol series for histological assessments. In the immunofluorescence assay, the tissues were heated in citrate buffer (pH = 7.4) for 15 min to recover antigens. After being blocked with 5% BSA in Tris-buffered saline for 30 min at 37 °C and sections were incubated with antibody mixes at 4 °C overnight, after that, slides were incubated with the secondary antibodies (1:200 dilution each) for 1 h, and then DAPI was used as counterstain. Finally, Fluorescence images were acquired using a Nikon inverted fluorescence microscope. Primary antibodies used for immunofluorescence staining are followed: anti-F4/80 rat monoclonal (1:100 dilution), anti-CD86 rabbit polyclonal (1:100 dilution), anti-iNOS rabbit polyclonal (1:100 dilution). For immunohistochemical staining, first, the sections were covered with 3% H2O2 for 15 min at room temperature and antigen retrieval was carried out at 95 °C for 15 min in a citrate buffer (pH = 7.4). The sections were incubated with primary antibodies at 4 °C overnight after being blocked with 5% BSA. Then, the slides were incubated with biocatalytic secondary antibody (1:200 dilution) for 1 h at room temperature and streptavidin-horseradish peroxidase for another 30 min, positive signalling was magnified by adding a DAB-H2O2 solution. Finally, the slides were counterstained with haematoxylin for 3 min and fixed in neutral balsam after dehydration with ethanol. Primary antibodies used for immunohistochemistry staining are followed: mouse anti-Bax (1:100 dilution) and mouse anti-Bcl2 (1:100 dilution). Apoptosis of mouse heart tissue and H9C2 cells were detected by TUNEL assay using terminal deoxynucleotidyl transferase dUTP nick end labelling staining using the TUNEL cell death detection kit. H9C2 cells were seeded in 24 well plates, at 4 × 104 cells/well. 5 μm thick sections of paraffin-embedded tissue were deparaffinized and hydrated in graded alcohol solutions. After slides and cells were incubated with 0.1% Triton X-100 for 10 min at 37 °C, TdT Equilibrium solution was added for 30 min, next, labelling working dye composed of TDT Equilibration, labelling solution and TDT Enzyme was employed to banding the apoptotic cells for 1 h at 37 °C. DAPI was used to stain the nuclei. Finally, TUNEL-positive nuclei were detected at 570–620 nm by fluorescence microscopy.
Immunofluorescence in situ hybridization was performed as described previously (Wang et al. 2019). Slices were dewaxed in reduced concentration ethanol. Primary macrophages were seeded in 24 well plates and were incubated with 0.1% Triton X-100 for 10 min at 37 °C. And then added prehybridization buffer on the cells and slices at 37 °C for 1 h, next the tissue and cells were incubated using mixes containing CY3-labelled miR-155 probe (50 nM) and anti-F4/80 rat monoclonal antibody or mixes containing CY3-labelled miR-155 probe (50 nM) and anti-CD86 rabbit polyclonal antibody at 4 °C overnight, Finally, cells and slices were incubated with the secondary antibodies (1:200 dilution) for 1 h and DAPI was employed for nuclear staining.
Results are expressed as the mean ± SD of three or more independent experimental results. Statistical analysis was performed by Student’s t-test or one-way analysis of variance followed by SPSS 17.0 software using the one-way ANOVA. p < 0.05 was considered statistically significant.
We evaluated the effect of SL on myocardial ischaemia-reperfusion injury in vivo by TTC staining, results revealed that a remarkable increase in infarcted size was observed in the model group compared with the sham group, and infarcted size in SL (180 mg/kg), SL (360 mg/kg) and ATO groups were decreased sharply in comparison to the model group (Figure 1(A,B); p < 0.01 or p < 0.001). Additionally, data suggested that compared with those in the sham group, the levels of CK and LDH in serum or tissues were increased significantly and SOD activity declined dramatically in the model group (Figure 1(C–F); p < 0.05, p < 0.01 or p < 0.001). In SL groups, the levels of CK and LDH in serum or tissue were lower and activity of SOD was higher than those in the model group, and in SL (180 mg/kg) group, the levels of LDH were lower notably and SOD concentration was significantly higher than the model group (Figure 1(C–F); p < 0.05).
Furthermore, we examined the effects of SL on apoptosis in MI/R mice by TUNEL staining. More positive signals of TUNEL were found in the model group and less positive signals of TUNEL were found in SL (360 mg/kg) group (Figure 2(A,B); p < 0.05). Immunohistochemistry results also showed that compared with the sham group, the levels of Bax increased significantly and expression of Bcl2 diminished notably in the model group. However, compared with the model group, the levels of Bax decreased obviously and expression of Bcl2 raised remarkably in SL (360 mg/kg) groups (Figure 2(C–F); p < 0.05). Continuously, western blotting results also suggested that the levels of cleaved caspase-3 and cleaved caspase-9 increased significantly in the model group and decreased dramatically in SL groups (Figure 2(G–I); p < 0.05 or p < 0.01).
Subsequently, we tested the effect of SL on macrophage polarization by double immunofluorescence staining in vivo. F4/80 is a macrophage surface maker, and CD86 and iNOS are M1 macrophages signals, thus, double immunofluorescence staining of iNOS/F4/80 and CD86/F4/80 was used to bind M1 macrophages. Our results showed that iNOS/F4/80 and CD86/F4/80 positive signals increased significantly in the model group compared with those in the sham group, in SL (180 mg/kg) and SL (360 mg/kg) groups, F4/80/iNOS and F4/80/CD86 positive markers decreased notably compared with that in the model group (Figure 3(A–D); p < 0.05). Based on the effect of SL on M1 macrophage polarization, we detected levels of inflammatory cytokines and chemokines in mice. qRT-PCR and ELISA results suggested that in the model group, IL-6, TNF-α, CXCR1increased and anti-inflammatory factors IL-10 decreased significantly compared with that in the sham group. However, in SL (360 mg/kg) group, IL-6, TNF-α, CXCR1 levels were notably lower and IL-10 concentration was obviously higher than model group (Figure 3(E–I); p < 0.05).
A previous study reported that increasing the expression of miR-155 resulted in M1 macrophage polarization (Pasca et al. 2020). To explore the potential mechanism concerning the inhibition effect of SL on M1 macrophage polarization, we examined the expression of miR-155 in macrophages by qRT-PCR. As shown in Figure 3(A,B), gene expression of miR-155 increased significantly in the model group by comparison to the sham group (p < 0.05). However, compared with that in model mice, expression of miR-155 decreased notably in SL (180 mg/kg) and SL (360 mg/kg) groups (Figure 4(A,B); p < 0.05). miR-155/F4/80 double-staining results showed that compared to the sham group, miR-155/F4/80 positive makers in the model group raised evidently and miR-155/F4/80 positive expression diminished significantly in SL groups in comparison with the model group (Figure 4(C,D); p < 0.05). Simultaneously, miR-155/CD86 double-staining demonstrated that miR-155/CD86 positive expression in the model group was remarkably more than in the sham group, and miR-155/CD86 positive signals in SL (180 mg/kg) and SL (360 mg/kg) groups evidently were less than the model group (Figure 4(E,F); p < 0.05).
To investigate whether SL relieved M1 macrophage conditioned media-induced H/R cardiomyocyte damage, we designed the conditioned medium transfer models. In conditioned medium transfer model 1, TUNEL immunofluorescence results suggested that positive signals of H9C2 were increased notably in the H/R + CM1(model) group in comparison with the H/R + CM1(control) group, however, apoptotic cells were significantly decreased in H/R + CM1(SL-20) group compared with H/R + CM1(model) group (Figure 5(B,C), p < 0.05). Meanwhile. In the conditioned medium transfer model 2, apoptotic H9C2 was elevated in the H/R + CM2(model) group in relation to the H/R + CM2(control) group. Concurrently, the ratio of the positive cell declined notably in H/R + CM2(SL-10) and H/R + CM2(SL-20) groups compared with the H/R + CM2(model) group (Figure 5(D–F), p < 0.05). Besides, we tested proteins expression of Bcl2, Bax, caspase-3 and cleaved caspase-3 by western blotting in the conditioned medium transfer model 2. Our results showed that compared with the CM2(control) group, levels of cleaved caspase-3, Bax and ratio of cleaved caspase-3/caspase-3 increased and the ratio of Bcl2/Bax decreased in H/R + CM2(model) group. However, compared with the CM2 (model) group, levels of cleaved caspase-3, the ratio of cleaved caspase-3/Caspase-3 diminished, Bcl2 expression and the ratio of Bcl2/Bax raised in H/R + CM2 (SL-10) and H/R + CM2 (SL-20) groups (Figure 5(G–I), p < 0.05).
In vitro, we established the protocol to polarize pMACs into M1 macrophages via exposure to LPS (600 ng/mL). After 24 h of co-culture with LPS, the morphological changes of pMACs were observed, and LPS-treated pMACs showed obvious morphological abnormalities from the oval to the spindle. Besides, there were dendritic processes on the edge of cells in comparison with unstimulating pMACs. Conversely, when macrophages were incubated with LPS in the presence of SL (5, 10 or 20 μg/mL), the cells did not tally with the larger and more fusiform appearance characteristic of LPS-treated macrophages but more closely resemble macrophages cultured in medium alone which suggested that SL can restore M1 macrophages morphological changes induced by LPS (Figure 6(A)). Concurrently, we labelled M1 macrophages by double immunofluorescence staining of iNOS/F4/80 and CD86/F4/80. We discovered obviously more iNOS/F4/80 and CD86/F4/80 makers in the model group, whereas iNOS/F4/80 and CD86/F4/80 signals were reduced significantly in SL-10 and SL-20 groups (Figure 6(B–E); p < 0.05). Next, we detected the M1 macrophage marker proteins expression of CD86 and iNOS by western blotting. As shown in Figure 6(F–H), LPS at 600 ng/mL upregulated the expressions of CD86 and iNOS of macrophages significantly compared with the control group. And after treatment with SL, the expressions of CD86 and iNOS decreased remarkably (p < 0.05). Concurrently, we tested the levels of inflammatory cytokines by ELISA and chemokines by qRT-PCR. In the model group, levels of proinflammatory factors IL-1β, IL-6 and TNF-α notably increased and expression of anti-inflammatory factors IL-10 decreased significantly compared with the control group (Figure 6(I–L); p < 0.05). Contrarily, in SL groups, levels of IL-6 and TNF-α were less and in SL-10 and SL-20 groups, IL-10 concentration was remarkably more than in the model group (Figure 6(I–L); p < 0.05). Similarly, genes expression of chemokines CCL2 and CCR2 raised markedly in LPS-treated macrophages in comparison with control macrophages, while in SL-10 and SL-20 groups, genes expression of CCL2 and CCR2 reduced notably compared with the model group (Figure 6(M–O); p < 0.05 or p < 0.01).
Next, we examined the expression of miR-155 in vitro. In Figure 7(A,B), gene expression of miR-155 increased significantly in LPS-treated macrophages in comparison with control macrophages, and in co-treated LPS and SL macrophages gene, expression of miR-155 reduced in a dose-dependent compared with LPS-treated macrophages (Figure 7(A,B), p < 0.05). Meanwhile, fluorescence in situ hybridization results showed that miR-155/F4/80 positive makers in the model group were more than that in the control group, and in the SL groups miR-155/F4/80 expressions were less than that in the model group (Figure 7(C,D), p < 0.05). Furthermore, fluorescence in situ hybridization of miR-155/CD86 results illustrated that miR-155/CD86 positive signals in the model group raised significantly compared with the control group, but in the SL groups, miR-155/CD86 expressions decreased notably compared with the model group (Figure 7(E,F), p < 0.05).
SOCS3 is one of the targets of miR-155 while SOCS3 deficiency induces M1 macrophage polarization and inflammatory (Qin et al. 2012). In this study, we tested SOCS3, as shown in Figure 8(A–D), gene and protein expression of SOCS3 notably decreased in LPS-stimulated macrophages compared with that in untreated macrophages, and in macrophages treated with LPS in the presence of SL (10 and 20 μg/mL) macrophages, gene and protein expression of SOCS3 increased remarkably in comparison with that in LPS-treated macrophages (p < 0.05).
JAK2/STAT3 pathway activation induces the inflammatory reaction (Chen et al. 2019). In this study, we tested JAK2, p-JAK2, STAT3 and p-STAT3 expressions by western blotting. In Figure 9(A–G), the expression of JAK2, STAT3, p-JAK2, p-STAT3 expressions and ratios of p-JAK2/JAK2, p-STAT3/STAT3 in the model group were higher than the control group. In SL-10 and SL-20 groups, JAK2, STAT3, p-JAK2 and p-STAT3 were lower than model group (p < 0.05), but in SL groups, ratios of p-JAK2/JAK2, p-STAT3/STAT3 were no difference compared with model group, which indicated that SL blocked JAK2/STAT3 pathway owing to decreased the expression of total proteins of JAK2 and STAT3.
In the present study, we established a mouse MI/R model and conditioned medium transfer models of macrophages and cardiomyocytes. Results showed that SL treatment reduced infarct size and decreased myocardial tissue apoptosis. Meanwhile, SL reduced cardiomyocyte apoptosis in conditional medium transfer models of macrophages and cardiomyocytes. Furthermore, SL suppressed M1 macrophage polarization by down-regulating miR-155 expression, promoting SOCS3 expression and blocking JAK2/STAT3 signalling pathway. Taken together, these observations suggest that SL could be a potential therapy in MI/RI. Myocardial ischaemia-reperfusion injury refers to myocardial tissue injury induced by reperfusion therapy after myocardial infarction. Myocardial ischaemia reperfusion usually leads to failure of cardiac function, increased infarct size and disordered biochemical metabolism. The infarcted area is the most direct evaluation of cardiac function. CK level is viewed as a symbol of the effects and prognosis of MI/RI, lower activity of CK stands for an obvious protective effect after cardiac muscle injury (Apple 1989). LDH is also a biochemical index to diagnose ischaemic heart disease and was closely associated with heart disease development. During MI/R, timely blood perfusion results in reactive oxygen species (ROS) overproduction, SOD is the primary mediator of oxygen free radicals and can reduce the contents of ROS and alleviate oxidative stress in MI/R (Grill et al. 1992). In our experiment, the results proved that SL shrinks infarcted size, decreased CK, LDH levels and increases SOD activity, which illustrated SL attenuated MI/RI. Myocardial cell apoptosis is viewed as an important mechanism of MI/RI. In the process of MI/RI, excessive ROS stimulates the opening of mitochondrial permeability transition pore, expedites the release of pro-apoptotic protein, Cytochrome c (Cyt c), into the cytosol, and then Cyt c can bind and activate pro-caspase-9. Eventually, caspase-9 and downstream protein, caspase-3, were activated and apoptosis occurs (Li et al. 2019). It is well known that the Bcl-2 protein family regulates mitochondrial-initiated apoptosis by taking part in the release of Cyt c, some of the Bcl-2 members are proapoptotic such as Bax, Bak, Bid, and Bim, while some have an anti-apoptotic function such as Bcl-2, Bcl-xL, and Bcl-W (Siddiqui et al. 2015). In our experiment, SL reduced the percent apoptosis of cardiomyocytes, downregulated the expression of pro-apoptotic proteins (cleaved caspase-3, cleaved caspase-9, Bax), and upregulated the expression of an anti-apoptotic protein (Bcl-2) in MI/R mice, meanwhile, in the conditional medium transfer models, our results also showed that SL reduced H/R cardiomyocytes apoptosis and rate of cleaved-caspase-3/caspase-3 and raised Bcl2/Bax ratio, which suggested SL reduced cardiomyocyte apoptosis in MI/R mice and H/R cardiomyocytes induced by M1 macrophages. MI/R aggravates tissue injury by recruiting immune cells to the impaired tissues. Macrophages, as the predominant inflammatory cells in repairing myocardial tissue, can infiltrate the myocardium and settle for a long time (2 weeks) (Lafuse et al. 2020). Pro-inflammatory macrophages (M1 macrophages) mainly exist in the early stage of MI/RI and peak within 1–3 days. M1 macrophages increase the secretion of pro-inflammatory factors and chemokines, such as TNF-α, IL-1β, IL-6, CXCL1, CCL2 and CCR2, which leads to a strong pro-inflammatory reaction and contribute to MI/RI (Guicciardi et al. 2018). Thus, it is confirmed that inflammatory responses mediated by M1 macrophages are a critical reason for aggravating MI/R tissue damage. Importantly, inhibiting extravagant inflammatory by down-regulating M1 macrophages can reduce myocardial necrosis and decrease the infarct zone. Some monomer compounds of Traditional Chinese Medicine such as tanshinone II A and baicalin, have also been reported to reduce myocardial ischaemia-reperfusion injury by inhibiting M1 macrophage polarization (Hu et al. 2015; Xu et al. 2020). In the present study, our results also determined that SL obviously diminished iNOS/F4/80 and CD86/F4/80 positive cells ratio in MI/R mice and LPS-induced pMACs, which indicated that SL inhibited M1 macrophage polarization in MI/RI. Meanwhile, SL down-regulated the expression of pro-inflammatory factors (TNF-α, IL-1β, IL-6, CXCL1, CCL2, CCR2) and increased the expression of IL-10 in vivo and in vitro. Thus, we believed that SL inhibited the M1 macrophage polarization and reduced the inflammatory response in the process of MI/R. MiR-155 is a microRNA associated with inflammation and is highly expressed in activating macrophages. Particularly, miR-155 has also been found to relate to MI/RI, endogenous suppression of miR-155 reduces effectively myocardial infarct size, diminishes cardiomyocyte apoptosis, alleviates cardiac hypertrophy and remodelling, suppresses the development of heart failure and enhances the survival rate of rats, whereas miR-155 knock-out protects the cardiac fibroblasts (He et al. 2016; Hu et al. 2019). Meanwhile, miR-155, as an LPS-responsive miRNA, is upregulated in macrophages and promotes the production of proinflammatory cytokines and increased miRNA-155 expression results in the polarization of M1 macrophages (Jablonski et al. 2016; Pasca et al. 2020). SOCS3 is one of the target proteins of miR-155 and miR-155 suppresses SOCS3 expression (Wang et al. 2019). A previous study suggested that SOCS3 deficiency in macrophages promotes M1 macrophage polarization and inflammatory responses (Qin et al. 2012). In this study, we found that miR-155 expression was up-regulated in MI/R mice as well as in LPS-stimulated pMACs, but the miR-155 level was down-regulated after SL treatment. In addition, SL decreased miR-155/F4/80 and miR-155/CD86 positive makers in MI/R mice and M1 macrophages. Therefore, we speculated that SL blocked M1 macrophage-mediated pathological processes in MI/RI involving miR-155 silence. Furthermore, SL increased gene and protein expression of SOCS3 level in LPS-treated macrophages, which is consistent with the inhibition effect of SL to miR-155. In addition, SOCS3 is a known prevailing inhibitor of JAK2/STAT3 signalling and can block JAK2 tyrosine kinase activity through the kinase inhibitory region (Inagaki-Ohara et al. 2013). The JAK2/STAT3 signalling pathway is viewed as a critical inflammatory mediator, in macrophages, JAK2 can dimerize and autophosphorylate to form p-JAK2, which further activates STAT3 into p-STAT3, then the activated p-STAT3 enhances transcription of inflammatory factors (Chen et al. 2019). To explore the second possible reason for the inhibitive effect of SL on inflammation, which is related to the JAK2/STAT3 pathway regulated by miR-155-targeting SOCS3, we examined protein expression of JAK2, p-JAK2, STAT3, p-STAT3. In this study, our results showed that the inhibitive effect of SL on of JAK2/STAT3 pathway was not decreased the ratio of p-JAK2/JAK2 and p-STAT3/STAT3, but diminished the expression of total proteins of JAK2 and STAT3. Therefore, we inferred that SL repressed inflammation in connection with JAK2/STAT3 signalling pathway blocking. However, the primary cause of the inhibitive effect of SL on the JAK2/STAT3 pathway could not be associated with up-regulated SOCS3 expression. Collectively, we believed that SL inhibited M1 macrophage polarization though reducing the expression of miR-155 and increasing the level of SOCS3, thus reduce the inflammatory response during MI/R. Meanwhile, the inhibition of SL on inflammatory response may be related to blocking JAK2/STAT3 pathway and the underlying mechanism remains to be determined in future. Through the results in vivo and in vitro, we believe that SL can reduce cardiomyocyte apoptosis and tissue damage in MI/RI by suppressing M1 macrophage polarization. In conclusion, the present study demonstrated that SL mitigates tissue damage and cell apoptosis in MI/RI by restricting M1 macrophage polarization and inflammation. The potential mechanism may be related to its effect on miR-155 inhibition, SOCS3 up-regulation and JAK2/STAT3 signalling pathway blocking (Figure 10). These results also offer available evidence for potential therapy of SL in MI/RI.
Xiao Z, Ya W and Min S contributed to the conception of the study. Min S, Xi C, Jing Z, Jing Li, Yuan Z and Qing Y performed the experiment. Qi L, Yu L, Ying C, Xiao W and Wei C performed the data analyses. Min S, Ya W and Xiao Z wrote the manuscript. | true | true | true |
PMC9578556 | Guillermo Aquino-Jarquin | Early “reduction to practice” of the CRISPR–Cas9 invention in eukaryotic cells 10.3389/fgene.2022.1009688 | 04-10-2022 | CRISPR-Cas9 applications,inventive step,reduction to practice,substancial evidence,Broad Institute,CVC | Early “reduction to practice” of the CRISPR–Cas9 invention in eukaryotic cells 10.3389/fgene.2022.1009688
The CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)–Cas (CRISPR-associated proteins) are microbial adaptive immune mechanisms that have revolutionized many areas of Life Sciences research and innovation and potentially could transform the lives of patients with limited medical treatment options. Although CRISPR’s seminal contributions range from fundamental explorations to the first demonstrations of CRISPR–mediated genome editing in eukaryotic cells, current discoveries using CRISPR likely represent “the tip of the iceberg.” Several CRISPR–Cas systems require multiple proteins to function. Notably, the type II systems found in many bacteria require a single endonuclease known as Cas9, a CRISPR RNA (crRNA), and a trans-activating CRISPR RNA (tracrRNA), which form a functional DNA-targeting complex (Mojica et al., 2009). Charpentier & Doudna simplified the crRNA and the tracrRNA by combining these transcripts into a single molecule known as a single-guide RNA (the sgRNA). They were the first to show that sgRNA is sufficient for programming Cas9 to direct the nuclease activity to any target site (Jinek et al., 2012). Molecular Biologists have quickly adopted this bacterial immune system into eukaryotic systems to modify the genome of practically any organism with unprecedented ease. In principle, through the CRISPR–Cas9 systems, double-strand breaks (DSB) can be induced in any given chromosomal region-of-interest (Jinek et al., 2012; Kleinstiver et al., 2015), followed by repair of the target site via Non-Homologous End Joining (ligation of DNA-ends with potential incorporation of insertions and deletions into the sequence) or Homology-Directed Repair (the exchange of genetic information between DNA segments with similar sequences) mechanisms (Doudna and Charpentier, 2014). This allows a range of permanent modifications, including eliminating entire genes or pathogenic DNA variants and inserting therapeutic genes; thus, CRISPR could treat or even cure severe genetic disorders (Porteus, 2019).
There has been a 7-year long dispute between “Broad” (Feng Zhang) and “CVC” (Jennifer Doudna–Emmanuelle Charpentier) about who possesses exclusive patent rights for the foundational CRISPR–Cas9 genome-editing technology in eukaryotic cells. When Broad received its first CRISPR patent on 15 April 2014 (Figure 1), CVC claimed that this was patent interference with the same invention, in that Doudna-Charpentier filed the first patent application on 25 May 2012, which led to a legal dispute. An “interference proceeding” is a lawful procedure exclusive to U.S. patent law that attempts to ascertain whether two related patents claim the same invention. If so, such a process determines which party was the first to invent it (Sherkow, 2017). On 28 February 2022, after two interference proceedings and other appeals (Figure 1), the Patent Trial and Appeal Board (PTAB) and U.S. federal courts issued a judgment and decision (Decision on Priority), establishing the claims that Broad’s patents for genome editing methods in eukaryotic cells are patentably distinct. The latter implies that their results are not reasonably expected from CVC’s in vitro and in bacterial systems experiments (Sherkow, 2022). Concerning this, although Doudna and Charpentier first published evidence that the CRISPR system could be used as an RNA-programmable genome editing tool (28 June 2012), 7 months before the Broad team led by Feng Zhang did, Doudna and Charpentier did not show in the initial paper that the system worked in eukaryotic cells (Jinek et al., 2012). In this regard, the CVC’s invention claims the design of the RNA molecule that guides the critical step in CRISPR–Cas9 gene editing, directing the Cas9 nuclease to a specific site in the genome. Nevertheless, achieving the system’s functioning in eukaryotes was an additional inventive step that ultimately awarded the court ruling to the Broad Institute (Broad Communications, 2022; Sherkow, 2022). Is Broad’s work in mammalian cells “obvious” in light of Doudna’s work in bacterial systems, or did it possess an “inventive step” to render it separately patentable? According to Jacob Sherkow, professor of law at the University of Illinois, Urbana-Champaign, the review in the CRISPR patent dispute was the “substantial evidence” standard (Sherkow, 2018). This means that a fact-finder examiner based its decision on substantially sufficient evidence for this to be reasonable (Sherkow, 2018). The question then arises of why CVC could not provide this “substantial evidence” at the time. Interestingly, details of experiments conducted by Doudna and Charpentier (as specified on pages from dated lab notebooks) revealed difficulties in inducing their invention to work, suggesting that the eukaryotic experiment would not succeed (Sherkow, 2022). In this regard, based on the PTAB decision, a “person skilled in the art” (e.g., the average Molecular Biologist) would not have reasonably expected the CRISPR–Cas9 system to succeed in a eukaryotic environment without “substantial evidence” of its functionality (Sherkow, 2022). In analyzing the context of this “substantial evidence” that was crucial to the decision made by the attorneys, we would have to focus on critical aspects for the CRISPR–Cas9 system to work in a eukaryotic environment. In this respect, as Cas9 is a bacterial protein, suitable codon usage was required to create a “humanized” Cas9 version (altering its encoded sequence, but without changing the amino-acid composition and protein structure), therefore, enhancing Cas9 activity in eukaryotic cells, including human cells. Furthermore, redirecting the Cas9 protein from the cytoplasm into the nucleus of eukaryotic cells to carry out editing events also required incorporating a short functional peptide known as nuclear localization signal (NLS) without affecting the structure and function of Cas9. Feng Zhang’s group codon-optimized the Cas9 nuclease from Streptococcus pyogenes (SpCas9) and attached an NLS to ensure nuclear compartmentalization in mammalian cells (Cong et al., 2013). Thus, the Feng Zhang group reconstituted the non-coding RNA components of the S. pyogenes type II CRISPR–Cas system by the incorporation of expression vectors (known as plasmids) for producing an 89-nucleotide tracrRNA under the RNA polymerase III U6 promoter (a regulatory region commonly used to express small RNAs in eukaryotic cells) (Cong et al., 2013). Thus, this strategy carried out efficient RNA-guided genome modification in mammalian cells (Cong et al., 2013). Accordingly, these improvements in the CRISPR–Cas system employed by Zhang’s group were not “obvious” extensions of the work of Doudna & Charpentier, whose scope was limited to cutting purified DNA in cell-free environments (Jinek et al., 2012), based on the patent application filed. Therefore, such evidence led the examiners to conclude that Broad’s claims and those of the CVC’ do not interfere with each other and that the inventive step claimed by Broad is not derived from experiments conducted in test tubes, stated in the first and second interference proceedings (Sherkow, 2022) (Figure 1). Such technical hurdles (“experimental uncertainty”) that the CVC presented in getting their invention to work (the so-called “reduction to practice” of the invention) probably gave Broad the upper hand in showing that this CRISPR–Cas9 system worked in human and mouse cells (Cong et al., 2013), regardless of whether the CVC conceived the idea first. Finally, the evidence obtained by Jennifer Doudna’s group, confirming that the CRISPR–Cas9 system works to edit genes in eukaryotic cells, was published online in eLife on 29 January 2013 (Jinek et al., 2013). Nevertheless, Feng Zhang’s group had first published such a demonstration [manuscript submitted to Science on 5 October 2012 and published online on 3 January 2013 (Cong et al., 2013); (Figure 1)]; thus, Broad maintains priority in its demonstration of use. Interestingly, the other two papers describing the application of CRISPR–Cas9 gene-editing technology in eukaryotes had already been published in January 2013 (Hwang et al., 2013; Mali et al., 2013) prior to the filing of the patent application (December 2013) by Broad at the European Patent Office (EPO). However, according to the EPO, Broad’s patent claims were no longer entirely novel because the technology was already in the public domain (Harrison, 2018). Therefore, Broad’s patent application was discarded in the European Union. By contrast, CVC has the upper hand in Europe, which has issued patents concerning the CRISPR–Cas9 systems in over 30 countries, unaffected by any U.S. interference proceedings (Sanders, 2022). In case of a new appeal by CVC (possibly by a third interference), it is unlikely to persuade the attorneys and claim sole ownership of the CRISPR patent if CVC does not provide “substantial evidence” demonstrating the application of the first-time CRISPR system in eukaryotic cells (and if the decision of the court is based only on this). Based on Broad Institute statements (Broad Communications, 2017), Broad Institute could grant licenses for using CRISPR non-exclusively and through the open ‘inclusive innovation’ model for therapeutic development across many human diseases (where this technology can be applied) instead of obtaining commercial licenses (Broad Communications, 2017). This model offers one license for its exclusive use for 2 years. After this period, there is an open call for applications by Parties seeking to license the CRISPR–Cas9 technology for application through the Broad website (Broad Communications, 2017). Furthermore, Broad has encouraged establishing a worldwide CRISPR–Cas9 licensing pool or a coordinated licensing approach, such as the joint licensing framework (an agreement that has made CRISPR–Cas9 technology available non-exclusively) previously developed for the use of CRISPR in agriculture (Broad Communications, 2022). Based on the UC Berkeley website (Sanders, 2022), CVC has more than 40 issued U.S. patents that were not implicated in this Decision on Priority, which involve various applications of CRISPR–Cas9 genome editing systems in different environments, including eukaryotic cells (Sanders, 2022). However, biotech start-ups such as CRISPR Therapeutics (co-founded by Charpentier), Caribou Biosciences, and Intellia Therapeutics (co-founded by Doudna) might require obtaining licenses with Broad to apply this technology in therapeutic interventions for further research and development plans, probably through the open “inclusive innovation” model.
The discovery of new CRISPR–Cas systems (orthologs or Cas9 equivalents) or the engineering of new optimized systems for genome editing will lead to application for new patents. In those cases, the following critical questions arise: To what extent could the functions of new CRISPR–Cas systems be considered "non-obvious" regarding the original inventions of CRISPR–Cas9? Because experimental uncertainty is more in line with the realities of scientific research [based on Doudna’s hurdles to achieving “reduction to practice” of the CRISPR–Cas system in eukaryotic cells (Sherkow, 2017)], should patent-law legal and regulatory framework consider the “unpredictability of success” for future CRISPR patents and not only issue a pragmatic judgment (based on “substantial evidence”)? The panorama is not entirely clear. The first demonstration of genome editing ability by the CRISPR–Cas9 system in a cellular environment different from the bacterial one could be considered substantially more “unpredictable” (Sherkow, 2017). This implies that the sgRNA interaction with Cas9 nuclease and the consequent recognition of the target site to be edited in a eukaryotic system involves several variables that require assessment in terms of the performance and reproducibility of the experiments. In this regard, Molecular Biology tends to be substantially more “unpredictable” (even when there are excellent theoretical arguments that the system can work properly), which could lead to the outcome of any experiment conducted being uncertain (Sherkow, 2017). In this scenario, achieving the functioning of a bacterial system in a eukaryotic system could take time due to technical or methodological difficulties, even though Molecular Biologists have great expertise in performing the experiments. Due to the latter, it is difficult to establish whether a “person of ordinary skill in the art” would interpret the invention as “obvious” or as lacking an “inventive step” (Sherkow, 2017), which represents a relevant aspect that should be considered in issuing rulings. However, Molecular Biologists have learned from the first descriptions and characterizations of CRISPR–Cas systems. In the case of new nucleases for genome editing applications, it is feasible to implement different laboratory techniques and novel experimental strategies to investigate whether CRISPR–Cas systems function correctly in eukaryotic systems (despite their bacterial origin) and under other conditions in which these CRISPR–Cas systems are tested for the first time. After discovering new Cas effector proteins, their biochemical characterization is faster, and their functionality is rigorously evaluated in in-vitro and in-vivo models. Thus, the therapeutic breakthrough of these CRISPR tools might be rapidly incorporated into new gene-editing clinical studies, where perhaps the therapeutic success rate of this technology can be estimated. However, although many CRISPR-based treatments in advanced clinical phases would be available in the short term, beyond new litigation, CVC and Broad should resolve the exclusivity issue for human therapeutics and ensure that such CRISPR-based therapies maximize patient benefit, considering significant ethical, safety, and societal aspects of this technology. | true | true | true |
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PMC9578625 | 36201530 | Javier A. Romero,Paulina Putko,Mateusz Urbańczyk,Krzysztof Kazimierczuk,Anna Zawadzka-Kazimierczuk | Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins | 06-10-2022 | NMR spectroscopy is key in the study of intrinsically disordered proteins (IDPs). Yet, even the first step in such an analysis—the assignment of observed resonances to particular nuclei—is often problematic due to low peak dispersion in the spectra of IDPs. We show that the assignment process can be aided by finding “hidden” chemical shift patterns specific to the amino acid residue types. We find such patterns in the training data from the Biological Magnetic Resonance Bank using linear discriminant analysis, and then use them to classify spin systems in an α-synuclein sample prepared by us. We describe two situations in which the procedure can greatly facilitate the analysis of NMR spectra. The first involves the mapping of spin systems chains onto the protein sequence, which is part of the assignment procedure—a prerequisite for any NMR-based protein analysis. In the second, the method supports assignment transfer between similar samples. We conducted experiments to demonstrate these cases, and both times the majority of spin systems could be unambiguously assigned to the correct residue types. | Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins
NMR spectroscopy is key in the study of intrinsically disordered proteins (IDPs). Yet, even the first step in such an analysis—the assignment of observed resonances to particular nuclei—is often problematic due to low peak dispersion in the spectra of IDPs. We show that the assignment process can be aided by finding “hidden” chemical shift patterns specific to the amino acid residue types. We find such patterns in the training data from the Biological Magnetic Resonance Bank using linear discriminant analysis, and then use them to classify spin systems in an α-synuclein sample prepared by us. We describe two situations in which the procedure can greatly facilitate the analysis of NMR spectra. The first involves the mapping of spin systems chains onto the protein sequence, which is part of the assignment procedure—a prerequisite for any NMR-based protein analysis. In the second, the method supports assignment transfer between similar samples. We conducted experiments to demonstrate these cases, and both times the majority of spin systems could be unambiguously assigned to the correct residue types.
This is a PLOS Computational Biology Methods paper.
Intrinsically disordered proteins (IDPs) play an essential biological role in eukaryotes, being involved in differentiation, transcription regulation, spermatogenesis, mRNA processing and many other processes [1]. Research into IDPs is thus crucial, but it is also very challenging, as the high mobility of the polypeptide chain prevents crystallization, hampering the use of X-ray crystallography. This dynamic behavior by IDPs also prevents the use of cryogenic electron microscopy [2]. In light of this, nuclear magnetic resonance spectroscopy (NMR) remains the most appropriate method for atomic-level analysis, providing information on structure, dynamics and interactions with other molecules. The most important observables in NMR are the resonance frequencies of nuclear magnetic moments placed in an external magnetic field. These frequencies, typically expressed in a chemical shift scale, depend on the local moieties of the nuclei. In particular, they are characteristic of the different amino acid residue types in a protein. In the case of folded proteins, the chemical shifts are further influenced by the secondary structure, but for IDPs the effect is far weaker [3]. Although IDPs do not behave in a purely random way and often form “compact states” [4], these states are only adopted transiently. For this reason the structure-induced chemical shift effects are averaged, resulting in spectra that can be very crowded and difficult to analyze. The assignment of NMR signals to the nuclei of a protein is based on an analysis of a set of heteronuclear (1H, 13C, 15N) spectra that provide information about the sequential connectivities of chemical shifts. Such a set can include standard three-dimensional spectra such as HN(CA)CO or HNCA [5, 6], for example, but it can also use those of higher dimensionality (4D and more) to resolve signal overlap [7, 8]. The resonance assignment procedure comprises several steps (see Fig 1). The first step is peak picking, followed by the formation of spin systems. Each spin system contains information on the chemical shifts of nuclei interacting via scalar coupling (typically belonging to two adjacent residues, i and i-1, although some experiments can reach further [8]). To this end, peaks from different spectra that share certain resonance frequencies must be gathered. Next, by finding identical chemical shifts in different spin systems, sequential connectivities are established and spin-system chains are formed. Although the protein’s primary structure is a single, long linear chain of amino acid residues, the analysis of sequential connectivities in NMR spectra almost always leads to the formation of many shorter chains. The chains are interrupted when chemical shifts are found to be missing from the linking spectra, connectivities are ambiguous, or at proline positions (in the case of amide proton-detected experiments). In the particular case of IDPs, many very short chains appear due to poor peak separation. The final step is mapping the formed spin system chains onto the protein sequence. This mapping typically involves identifying characteristic amino acids by using the chemical shift statistics found in the Biological Magnetic Resonance Data Bank (BMRB), for example [9]. IDP-tailored statistics are also available [10], providing additional information about the influence of adjacent residues on the chemical shifts. The dependence of chemical shifts on neighbors further away (i-2, i+2), pH, ionic strength and temperature can also be exploited [11]. Typically, Cβ chemical shifts are used for mapping, with further assistance from Hβ, if available, or Cα. Certain amino acid residue types can be excluded based on the structure of the residue [12]. For example, the presence of Hβ or Cβ chemical shifts indicates a non-glycine residue. The presence of two different Hβ chemical shifts excludes alanine, isoleucine, threonine and valine, as these residues do not contain chemically inequivalent Hβ protons. In short, we can easily recognize alanine, glycine, serine and threonine, but more detailed analysis is needed for other amino acid residue types. Some of the above-mentioned recognition procedures are embedded in automatic assignment programs [13–16]. Clearly, the longer the chain in question, the easier the mapping step. In the case of shorter chains, which are very common in IDP analysis, it is essential to recognize the amino acids of as many residues as possible in order to map the chains accurately. Fig 2 shows the distributions of the chemical shifts in a set of 17 IDPs from the BMRB. Clearly, in most cases, using just two chemical shifts for amino-acid recognition is insufficient: On two-dimensional planes, regions corresponding to different types of residues often overlap partially or even completely. Although some residue types can be clearly recognized—alanine, glycine, serine and threonine, for instance—recognizing others can be problematic. The Cβ-Hβ plane turns out to be the best choice for grouping residue types into spectral regions. Nonetheless, even here the regions are not fully separated. Adding more chemical shifts would improve the separation of regions, but also complicate visualization and manual analysis. So, to fully exploit the rich statistical information available, the recognition should be assisted with an algorithm that operates easily in a multidimensional space. In this paper, we attempt to develop just such an algorithm. Below, we propose a statistical method based on Linear Discriminant Analysis (LDA) for the automatic recognition of amino acid residue types. It is worth mentioning that this method can be integrated into automatic assignment programs to facilitate the mapping step. Yet, it is not the only application for LDA, as shown in the results section. Of note, other classification methods were also tested (as shown in S1 Text and S1 Fig), but LDA obtained the highest performance scores among all of them. To the best of our knowledge, such a method has only been used once before in protein NMR, to detect beta-hairpin regions [17].
The mapping procedure described above can be regarded as a classification problem, as the aim is to assign amino acid residue types to different spin systems. This classification is based on the variables that define spin systems, namely the set of chemical shifts of nuclei belonging to the residue in question, measured in one or more experiments. LDA is a classification method well suited for this purpose. LDA is related to the more popular method known as principal component analysis (PCA). Both methods look for linear combinations of variables that best explain variance in the data. But while PCA finds new coordinates that maximize the variance of the data, LDA maximizes the variance between the different classes (residue types) and at the same time minimizes the variance within each class [18]. Another important distinction is that in PCA the directions of maximal variance do not depend on the classes, so residue types are not taken into account. By contrast, LDA uses an already classified dataset (the training set) to explicitly attempt to model the difference between the classes. Once adequately trained, the model can then be used to classify the spin systems of an unassigned protein. An LDA classification model comprises discriminant functions that appear based on the linear combination of predictive variables providing the best separability between classes. These functions are derived from the training set, in which the classifications of the spin systems are known. The training set can be thought of as an N × (M + 1) table, with NMR data from N spin systems defined by M chemical shift values, where the last column of the table gives the residue types. For the purpose of training the model, the LDA method assumes that the scattering of the chemical shift values of the spin systems belonging to each of the amino acid residue types can be accurately characterized by normal distributions [19]. In this way, the spin systems from the k-th residue type are described by a mean vector μk and a covariance matrix Σk, given by: where xi is a vector representing the i-th spin system, with dimensionality equal to the number of chemical shifts (M), and nk is the total number of spin systems of amino acid residue type k in the training set. To build the LDA classification model, we further assume that the covariance matrices for all classes are the same [18]. This common covariance matrix is usually called a pooled covariance matrix, and is given by: where the summation runs through all the residue types. If we have a total of 20 amino acid types in the training set, the classification model consists of 20 discriminant functions {f1, f2, …, f20}. These functions define the regions of maximal probability for each residue type in the multidimensional chemical shift space, which may in particular correspond to a single spectrum. Equal probabilities between classes are used as boundary conditions and define hyperplanes that separate each cluster within a class. For new, unassigned spin systems we get probabilities corresponding to each of the amino-acid residue types. The classification score needed for a new spin system x to belong to residue type k is given by [20]: where we define the a priori probability as πk = nk/N, and N is the total number of spin systems in the training set. Eq 3 is a linear equation on the variable x, and hence the class boundary is a hyperplane of linear shape. Within a single region of this type, a given discriminant function has a higher classification score than all other functions, and all resonances that fall inside this region are classed as belonging to the corresponding residue type. Importantly, this method assumes that the distribution of the chemical shifts in the mapped protein is similar to that of the training set. For resonance mapping in IDPs, this requirement can be met by choosing, for the training set, proteins described in the BMRB database as “unstructured”, “unfolded” or “disordered”. Our training set consisted of 1,613 spin systems from 17 such proteins (BMRB Entry IDs: 6436, 11526, 15176, 15179, 15180, 15201, 15225, 15430, 15883, 15884, 16296, 16445, 17290, 17483, 19258, 25118 and 30205). The training set must contain samples from all the amino acid residue types present in the IDP that is to be mapped. Of course, if certain residue types are missing from the protein under investigation, the spin systems corresponding to residues of this type should be removed from the training set, as should all spin systems that lack any of the chemical shifts that we choose to use for discrimination. Classification models lacking the assumption of Eq 2 are called Quadratic Discriminant Analysis (QDA) models [21]. Initially, one might assume that a QDA model would outperform its LDA sibling. However, in S1 Text and S1 Fig we show that LDA is actually the better choice for protein mapping. In SI we also compare the performance of LDA to two other well-known classification methods: “k-nearest neighbors” and “support vector machines”. LDA scores highest in mean accuracy, sensitivity and specificity (although not by a large margin), while at the same time it grades lowest in variance of accuracy across all 17 training proteins. To summarize our approach, although other methods can achieve a classification accuracy similar to that of LDA, we choose to use LDA because it demonstrates the lowest variance in classification accuracy after performing cross-validation with different proteins. LDA therefore allows for more consistent predictions across different IDPs.
The 13C,15N-uniformly labeled α-synuclein was expressed as described by [22]. The sample concentration was 1.35 mM in 20 mM sodium phosphate buffer at pH 6.5. The buffer contained 200 mM NaCl, 0.5 mM EDTA, 0.02% NaN3 and a Protease Inhibitor Cocktail (Roche). 10% D2O was added for lock.
All experiments were performed at a temperature of 288.5 K on an Agilent 700 MHz spectrometer equipped with a 5 mm HCN room-temperature probe and DD2 console. The experiments—3D HNCO [23], 4D HNCACO [24], 4D HabCab(CO)NH [25, 26] and 4D (H)N(CA)CONH [26]—were acquired using non-uniform sampling. 3D data was processed using a multidimensional Fourier transform [27] and 4D data was processed using a sparse multidimensional Fourier transform [26], with HNCO as a basis spectrum. Spin systems were formed by gathering data from the peaks appearing on cross-sections corresponding to individual basis spectrum peaks. In the case of overlap of the basis spectrum peaks, when on a given cross-section appeared peaks from more than one spin system, the discrimination of spin systems was performed according to recommendations described in [13]: peaks were regarded as belonging to a cross-section on which they had higher absolute intensity. Using 3D HNCO (instead of 15N-HSQC) as a basis spectrum made this approach more efficient; amide proton and nitrogen chemical shifts were determined more accurately. Thus, the overlapping cross-sections were more shifted than they would be if 2D basis spectrum was used. The experimental parameters are shown side-by-side in Table 1. All spectra were displayed and analyzed using the Sparky program [28]. The experimental data—raw signals and Sparky spectral files—are available at zenodo.org (10.5281/zenodo.7032142).
To use the proposed approach to solve the practical challenges of IDP resonance assignment, we first need to answer the following research questions: Is the training set of BMRB entries mentioned above consistent? Can it be used to classify unknown spin systems? What is the optimal set of chemical shifts that provides efficient discrimination? Can this method assist the chain-mapping step in the assignment procedure? And can it help in other resonance assignment problems, such as peak list transfer between the spectra of samples measured under slightly different conditions? We address these questions in this section. All the BMRB entries that we used for training contain seven chemical shifts: HN, N, CO, Cα, Cβ, Hα and Hβ. Based on these chemical shifts, we constructed three training sets, corresponding to typical experimental setups (see Section Optimal set of chemical shifts): subset (i) HN, N, Cα and CO; subset (ii) Cα, Cβ, Hα and Hβ; and subset (iii), which was actually the complete set, containing all seven chemical shifts. Some spin systems in the testing set may be incomplete, that is to say, lacking certain chemical shifts; this is usually the case for the residue preceding the formed chain of spin systems. To classify such spin systems we used a separate, restricted training set consisting of those chemical shifts that are present.
We evaluated the consistency of the training dataset (17 proteins from the BMRB) by performing a leave-one-out cross-validation using subset (iii): Train the classification model using the NMR data from 16 proteins and test it on the remaining protein, then repeat the process swapping the test protein until all proteins have been used for testing. Fig 3 shows the accuracy of amino acid type recognition by LDA, defined as the number of correct classifications over the total number of classifications. We found the weighted mean accuracy to be 89.43%, weighted by the number of residues in the proteins. It is worth noting that this level of accuracy is high, despite the fact that several of the proteins contained numerous residues lacking one or more chemical shifts values. This high level of accuracy leads us to the conclusion that the selected BMRB entries do indeed show similar chemical shift distributions for the same residue types. It is therefore not unreasonable to suggest that the chemical shifts of a new, as yet unassigned IDP will be correctly classified using this training data. It is worth mentioning that chemical shift values of training and test proteins are normalized simultaneously using Pareto scaling to correct for possible referencing errors.
Some chemical shifts are more important than others in the classification process, that is to say, they have greater predictive power. We measured the importance of the chemical shifts involved to evaluate their impact on the accuracy of the model (Fig 4). To do this, we first trained the model with the complete N × (M + 1) table, representing the entire training set. Next, we randomly shuffled a single M column (chemical shift value) from the training set. Finally, we used the model that we had previously trained to classify the set with one shuffled column. Randomly shuffling one column removes the correlation between the chemical shift values and the amino acid type for each spin system, while preserving the descriptive statistical information in the column. By measuring the error rate in the classification (defined as 1 minus the accuracy), we can evaluate which chemical shifts are more important for making accurate classifications. The conclusions in Fig 4 form the basis of the model that we present here. It is well known from statistics [29] that Cβ and Hβ, followed by Cα, are the most characteristic chemical shifts. Yet, the data in Fig 4 shows that the aggregated effect of the other chemical shifts is not negligible, resulting in a complex seven-dimensional pattern that is impossible to analyze manually. When choosing the optimal chemical shift set, we have to take into account the limitations of available NMR experiments. These experiments differ in their dimensionality, sensitivity and established correlations. What we want is a set of experiments providing information about the desired chemical shifts that can be obtained in a minimum amount of experimental time. Our goal is to obtain sets of chemical shifts that characterize different residues. Typically, however, the triple-resonance experiment providing intra-residual correlations also gives additional inter-residual peaks. For example, 4D HNCACO provides the desired correlation -Ni-Cαi-COi (subset (i)) but it also provides -Ni-Cαi−1-COi−1. For our purpose, the two types of correlations need to be distinguished by means of an extra experiment, namely 4D HNCOCA, that contains only the latter, inter-residual types of peaks. On the other hand, we can obtain the chemical shifts for subset (ii) with just a single experiment, namely 4D HabCab(CO)NH. In this case, the four chemical shifts (Cα, Cβ, Hα and Hβ) correspond to the residue preceding the correlated amide group. The experimental setups providing the chemical shifts of the aforementioned subsets (i), (ii) and (iii) are presented side-by-side in Table 2. We also performed the leave-one-out analysis described in Section Consistency of the training data to evaluate how well the method performed for subsets (i), (ii) and (iii). Fig 5 shows the confusion charts (average LDA probability matrix) for different amino acid types. Even for subset (i), the overwhelming majority of the classifications were correct. However, the results for subset (ii) were much better, that is to say, less ambiguous. The results for subset (iii) are slightly better than for subset (i), but not as good as those for subset (ii). We may therefore conclude that subset (ii) is the optimal choice for LDA analysis.
We tested the proposed approach on experimental data from α-synuclein. Fig 6 shows the peak positions in a Cβ/Hβ spectral projection, with the chemical shift distribution for the training data set from the 17 BMRB entries superimposed on top of it. Although Fig 4 shows that Cβ and Hβ are the most discriminating, they clearly provide insufficient resolution for most of the groups—only alanine, leucine and isoleucine can be clearly distinguished, while the remaining spin systems are impossible to identify based on Cβ and Hβ alone. This provides additional motivation for using the more advanced LDA approach. Fig 7 shows the results of using LDA on the α-synuclein data. To make the comparison clear, we used only residues for which all seven chemical shifts were known, allowing us to construct subset (iii). The only exceptions were glycine and proline residues, which naturally lack chemical shifts of missing nuclei (Cβ and Hβ for glycine, HN for proline). These residues were included if all other chemical shifts (5 or 6, respectively) were present. In the figure we did not take into account several residues with resonances missing for any other reason. However, as emphasized previously and shown later in Sections Application 1: Mapping spin-system chains and Application 2: Resonance list transfer, it is generally possible to perform classification of spin systems with missing chemical shifts. The trends observable in Fig 7 are in line with the predictions given in Fig 4. LDA of subset (i) enables unambiguous recognition of some amino acid types (alanine, serine, threonine and glycine), but it is ambiguous for others. In particular, some groups of amino acid residue types can be confused: a) aspragine and aspartic acid; b) glutamine, glutamic acid and lysine; c) phenylalanine and tyrosine; and d) isoleucine and valine. For these residue types, the probabilities are similar for each of the amino acid types within the group—although, typically, the highest probability corresponds to the correct type. Residues also exist for which the correct amino acid type was not recognized at all using the chemical shifts of data subset (i), namely one methionine, one leucine and one histidine. The ambiguity is reduced when using the chemical shifts of subsets (ii) and (iii), in which case for asparagine, aspartic acid, histidine, isoleucine, leucine and valine, the probability of the identifying the correct amino acid type is almost 100%. For lysine, methionine and tyrosine, the probability exceeds 70%. The only ambiguities that remain are glutamine and glutamic acid (although now the probability of identifying the correct type are higher) and phenylalanine (that can still be confused with tyrosine). We may conclude that subset (ii) provides results that are almost as good as those for subset (iii), and that both subsets allow much more effective LDA than subset (i). As subset (ii) can be obtained from a single experiment, we recommend using this subset as the approach of choice. The python code used for LDA analysis along with NMR data of the α-synuclein IDP and all input files needed to reproduce the results shown in Fig 7 are readily available at a public GitHub repository [30].
The assignment of backbone resonances in a protein is a two-step process: First, we form spin system chains, then we map them onto the known amino acid sequence. The latter step can be greatly enhanced by LDA. LDA identifies residues in a chain more efficiently than traditional “manual” recognition, which typically finds only glycines, alanines and serines/threonines. Optionally, the LDA analysis can be followed by filtering the results using an amino-acid sequence of the protein under consideration. Filtering “impossible” chains can be done automatically by using the output of the LDA analysis. First, for each chain a number of amino acid sequences are formed, which rise from all the combinations of amino acid types that LDA predicts as probable for each spin system in the chain. Then, combinations which are not present in the sequence of the test protein are discarded. This procedure is included in the code provided in the GitHub repository as an optional feature: if an input file containing the spin systems chains is given as input, then the code will give an extra spreadsheet as output detailing all possible amino acid sequences for each chain, their probabilities and the discarded combinations. In the examples below we did use this option. Fig 8 shows chain mapping cases of increasing difficulty. The easiest task is to map relatively long chains with several easily recognizable residues. The chain of seven residues shown in Fig 8A) contains two glycines and can be mapped manually without ambiguity. Nevertheless, LDA provides even more reliable mapping, as it recognizes all seven residue types with > 90% probability. Fig 8B) shows the more difficult case of a shorter chain. It is possible to manually identify one of the three residues as serine or threonine. However, this is not sufficient for unambiguous mapping. On the other hand, LDA followed by amino acid sequence filtering provides precise result. Sometimes, as in Fig 8C), the probability corresponding to the correct amino acid type is not the highest one. Fortunately, we can make the correct choice based on a protein sequence that lets us rule out “impossible” chains, even if the LDA implies that they are the most probable ones. Notably, unambiguous manual mapping of the chain in Fig 8C) is not possible, as only one characteristic residue (glycine) is present. Often, short chains do not contain even a single easily recognizable residue. Fig 8D) gives an example of such a chain, one that is practically impossible to map manually. LDA makes mapping possible, but we need to consider various combinations of residues. In the case in Fig 8D), only one combination—that with the second-highest LDA probability for two of the three residues—corresponds to the fragment of the protein sequence allowing unambiguous mapping. Finally, in rare cases, LDA may produce the wrong result for specific residues. An example is shown in Fig 8E). The “preceding” residue (6K) is wrongly identified by LDA as glutamic acid or glutamine; in fact, it is lysine. It could not be glutamic acid or glutamine, as this would lead to “impossible” chains (EGLS or QGLS) that do not exist in the protein sequence. In such cases correct mapping would require manual intervention or employment of more advanced assignment algorithm. Notably, in this example it is again impossible to unambiguously map the chain manually, as with one glycine, three mappings are possible.
Another example of where LDA can be used is the common task of transferring a resonance list from the repository (for example, the BMRB) to the experimental spectrum of a new sample. Usually, the experimental conditions (temperature, pH, ionic strength, concentration, and so on) are not exactly the same as those reported in the repository, and peaks may be shifted in a non-systematic manner. To illustrate this problem, we investigated one of the regions of the 15N HSQC spectrum of α-synuclein, containing a variety of amino acid residue types (see Fig 9A). We picked the 16 resonances in the region of interest (indicated by dots in Fig 9A). We also loaded the peak list from the BMRB entry 6968 (indicated by crosses). It appears that almost all peaks from the BMRB list deviated from the peaks generated in the experiment, resulting in ambiguity during transfer of assignment. For this reason, we decided to facilitate the transfer by means of LDA. Using 1H and 15N experimental peak positions, we peak picked the 4D HabCab(CO)NH spectrum, obtaining the Hα, Hβ, Cα and Cβ chemical shifts of the preceding residues. We then performed LDA on them (Fig 9C). In many cases, where only one resonance corresponding to the given amino acid type occurred in the proximity of the peak under consideration, LDA recognition allowed for unambiguous transfer of assignment (solid arrows in Fig 9B and 9C). However, where several peaks corresponding to the same type occurred close to the peak, ambiguity remained (assignment shown with dashed arrows). For instance, in the region considered in Fig 9A there were four alanine peaks. The patterns of the experimental and BMRB peaks corresponding to this amino acid type (indicated by blue dots and crosses in Fig 9B) were very similar, and indeed, in this case, choosing the nearest option was correct. However, care is called for in such situations, as some deviations may be significant; in the absence of additional information (for example, about the sequential connectivities) the mapping may be incorrect. An interesting case is the BMRB peak corresponding to Y39-V40. Two experimental peaks occur in its vicinity, both with significant probabilities of being a tyrosine residue at i − 1 position (43% in the case of the closest experimental peak, 89% in the case of the second-closest peak). However, another BMRB peak also occurs close by, corresponding to F94-V95. As the probabilities of phenylalanine for the experimental peaks in question were 56% and 11%, the F94-V95 was assigned to its closest peak and Y39-V40 to its second-closest peak. Thus, the probabilities represent valuable information in such cases. To sum up, using LDA can greatly facilitate assignment transfer, often preventing incorrect transfer to the closest neighboring peak. However, even when using LDA, care is called for and different possibilities should be considered.
Our experiments show that LDA is a versatile tool that can support spectral analysis in many ways. Users will no doubt develop their own habits and practices when it comes to employing the tool. Below, we summarize our own practical recommendations, based on our experience. As in all machine learning methods (MLMs), the best training set for LDA will have similar features to those of the test data. For the chemical shifts of proteins, that means that all the molecules in the training set and the test data should lack a secondary structure. This condition is not strict, however, and—as shown in this paper—quite impressive results are possible even with a very coarse selection of proteins for the training data. In fact, the training data contained small regions with residual structure: alpha-helical (up to 50%) for residues 60–78 in BMRB data set 11526, similarly long (19 residues) α-helical linker in set 15176 and several transient helices in data set 15179 (one of them with secondary shifts of Cα of up to 4 ppm, suggesting complete formation of the helix). As many training spin systems as possible should be used (again, this is true for all MLMs). Interestingly, for very large proteins it might be possible to use data from the same molecule for both training and testing. In other words, LDA can be used to complete the assignment after the majority (for example, 80%) of the residues have been assigned using traditional methods, and then used to form the training set. 4D HabCab(CO)NH is the experiment that provides the best data for LDA. For optimal resolution, non-uniform sampling (NUS) must be used for signal acquisition, with a variety of possibilities for processing. As we had a 3D HNCO at our disposal, we were able to process the data using a sparse multidimensional Fourier transform [26, 31], but numerous other options are possible, including compressed sensing [32, 33], maximum entropy [34], variants of the CLEAN algorithm [35, 36], projection spectroscopy [37], and many others [38–41]. The proper separation of NH resonances is particularly important, since it affects the proper determination of the most differentiating chemical shifts (Cβ, Hβ) and formation of spin systems. Besides resolution-enhancement by NUS, one may solve the problem by increasing the experiment dimensionality—acquisition of 5D HabCabCONH would allow separating the spin systems using triples of frequencies (, Ni and COi−1) which significantly reduces the overlap problem. [25, 26] Another approach can be the application of methods based on 13C detection. [42] On the other hand, the ambiguous cases that result from peak overlap can be easily detected and not taken into consideration. Thus, the situation is not as “dangerous” as e.g. for the sequential assignment. The results of the LDA should always be combined with other available information. For example, incorrect classifications can often be detected by comparing the results from LDA for the spin system chains with the protein’s primary structure. “Impossible” chains should not be considered, even if the amino acids that compose it have the highest probability according to LDA. Automatic filtering of impossible chains is incorporated as an option in our code.
In this paper, we show that linear discriminant analysis (LDA) is a reliable classification method supporting the assignment of resonances in NMR spectra. The method can help with many tasks, such as chain mapping and assignment transfer. In our experience, it is easy to obtain a training set from the BMRB with only coarse filtering—that is to say, using proteins marked as “unfolded”, “unstructured” or “disordered”. We believe that, with a growing number of IDP resonance assignments in the databases, this method will become even more powerful and reliable in the future.
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PMC9578789 | Yongsheng Gao,Bing Liu,Xin Liu,Yang Gao,Junqi Shan,Yanliang Li | lncRNA TRPM2-AS Promotes Colorectal Cancer Progression by Regulating miR-22-3p and FSTL1 | 11-10-2022 | Background In recent years, long noncoding RNAs (lncRNAs) relate to many biological processes, which affect the progression of tumors. Transient receptor potential melastatin 2 antisense RNA (TRPM2-AS) is reported to play an oncogene-like role in tumors. TRPM2-AS is highly expressed in colorectal cancer (CRC), but the mechanism of TRPM2-AS is still unclear. The regulatory mechanism of TRPM2-AS in the occurrence of CRC was explored, so as to find new markers and therapeutic targets for CRC. Methods TRPM2-AS and miR-22-3p expression in CRC cells were measured through reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Then, TRPM2-AS knockdown cell lines were constructed, and cell counting kit-8 (CCK-8), clone formation, wound healing, and invasion assays were used to detect cell malignant behavior. Follistatin-like 1 (FSTL1) protein was detected by western blotting. The interaction between miR-22-3p and TRPM2-AS or FSTL1 was verified by the luciferase reporter and RNA immunoprecipitation (RIP) assay. Subcutaneous xenografts were performed using animal experiments. Results TRPM2-AS expression in CRC cells was increased, and miR-22-3p expression was decreased in CRC cells. TRPM2-AS inhibition inhibited cell malignant behavior. miR-22-3p has a targeting relationship with TRPM2-AS and FSTL1. In cells, downregulation of TRPM2-AS expression promoted miR-22-3p and inhibited FSTL1 expression, while mimics inhibited FSTL1 expression. miR-22-3p inhibition or FSTL1 overexpression could offset the inhibition of TRPM2-AS downregulation on CRC cells. Conclusions The TRPM2-AS/miR-22-3p/FSTL1 regulation axis could regulate CRC cell malignant behavior, which may provide a new perspective for interpreting the mechanism of CRC development. | lncRNA TRPM2-AS Promotes Colorectal Cancer Progression by Regulating miR-22-3p and FSTL1
In recent years, long noncoding RNAs (lncRNAs) relate to many biological processes, which affect the progression of tumors. Transient receptor potential melastatin 2 antisense RNA (TRPM2-AS) is reported to play an oncogene-like role in tumors. TRPM2-AS is highly expressed in colorectal cancer (CRC), but the mechanism of TRPM2-AS is still unclear. The regulatory mechanism of TRPM2-AS in the occurrence of CRC was explored, so as to find new markers and therapeutic targets for CRC.
TRPM2-AS and miR-22-3p expression in CRC cells were measured through reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Then, TRPM2-AS knockdown cell lines were constructed, and cell counting kit-8 (CCK-8), clone formation, wound healing, and invasion assays were used to detect cell malignant behavior. Follistatin-like 1 (FSTL1) protein was detected by western blotting. The interaction between miR-22-3p and TRPM2-AS or FSTL1 was verified by the luciferase reporter and RNA immunoprecipitation (RIP) assay. Subcutaneous xenografts were performed using animal experiments.
TRPM2-AS expression in CRC cells was increased, and miR-22-3p expression was decreased in CRC cells. TRPM2-AS inhibition inhibited cell malignant behavior. miR-22-3p has a targeting relationship with TRPM2-AS and FSTL1. In cells, downregulation of TRPM2-AS expression promoted miR-22-3p and inhibited FSTL1 expression, while mimics inhibited FSTL1 expression. miR-22-3p inhibition or FSTL1 overexpression could offset the inhibition of TRPM2-AS downregulation on CRC cells.
The TRPM2-AS/miR-22-3p/FSTL1 regulation axis could regulate CRC cell malignant behavior, which may provide a new perspective for interpreting the mechanism of CRC development.
CRC is one most common digestive tract malignancy [1]. Its easy recurrence and metastasis often lead to a poor prognosis for patients [2]. Although treatments such as colonoscopy, colectomy, chemotherapy, and immunotherapy are improving, 5-year survival rates remain poor [3]. Diagnosis and treatment of CRC have made great progression, but its pathogenesis remains unclear. Thus, to further explore CRC progression molecular mechanisms is necessary, which can help improve CRC patient treatment, prognosis, and survival rate. lncRNA is noncoding functional RNA, which plays an important biological role [4]. lncRNA is dysregulated in various human diseases and relates to the progression, metastasis, and drug resistance of tumors [5]. lncRNA is abnormally expressed in CRC, which relates to the occurrence and development of a tumor and affects the prognosis of patients [6]. Therefore, lncRNAs are expected to be used as disease-specific biomarkers or therapeutic targets. TRPM2-AS has been reported to have potential diagnostic and prognostic value in many malignancies. Studies have shown that TRPM2-AS can promote the malignant phenotype of ovarian cancer (OC) [7], retinoblastoma (RB) [8], gastric cancer (GC) [9], esophageal cancer (EC) [10], and other tumor cells and then promote the development of tumors. TRPM2-AS downregulation inhibits progression and interferes with cisplatin resistance in OC [7]. TRPM2-AS is upregulated in RB, and TRPM2-AS downregulation obviously inhibits the malignant behavior and promotes apoptosis of RB cells [8]. In GC, TRPM2-AS promotes the progression by miR-612/IGF2BP1 and radioresistance by FOXM1 [9]. In EC, tumorigenesis and metastasis are promoted by TRPM2-AS upregulation [10]. In CRC, TRPM2-AS promotes the proliferation of cells by enhancing TAFL5-mediated TRPM2 mRNA stability [11]. However, the role of TRPM2-AS is poorly studied in CRC. Therefore, exploring the function and mechanism of TRPM2-AS in CRC will be of great significance for marker screening, molecular diagnosis, and targeted therapy of CRC. The aim of this study is to investigate the expression pattern and regulatory role of TRPM2-AS in CRC. MicroRNAs (miRNAs) are short endogenous noncoding molecules (with length approximately 19 to 23 nucleotides) that regulate target genes via binding to 3′-UTRs [12]. At present, it is widely believed that miRNAs may act as oncogenes or antioncogene in tumor progression [13]. The competitive endogenous RNA (ceRNA) hypothesis has proved that lncRNAs act as miRNA sponges to regulate target genes of miRNA and thus participating in cancer progression [14]. StarBase online target gene prediction software showed that TRPM2-AS may target miR-22-3p. Upregulation of miR-22-3p observably impedes cell proliferation and promotes apoptosis of CRC [15]. However, whether TRPM2-AS interacts with miR-22-3p to regulate the progression of CRC remains to be further elucidated. Therefore, TRPM2-AS expression in CRC progression and possible mechanisms were explored in this study.
NCM-460 and CRC cells (HCT116, DLD-1, SW480, SW620, and LoVo) were cultured in RPMI 1640 (Hyclone, USA), DMEM (Gibco, USA), or McCoy's 5A medium (Gibco) containing 10% fetal bovine serum (FBS) (Gibco), respectively. The culture medium contained 1% double antibody (100 μg/mL streptomycin + 100 U/mL penicillin) in an incubator at 37°C with 5% CO2. The cells at the logarithmic growth stage were collected for subsequent experiments.
Cells were inoculated into 6-well plates and cultured until cell fusion was about 80%. TRPM2-AS knockdown plasmid (si-TRPM2-AS), miR-22-3p mimic (mimic) and inhibitor (inhibitor), FSTL1 overexpression plasmid (pc-FSTL1), and their negative controls were purchased from GenePharma (Shanghai, China). Transfection was performed according to Lipofectamine 2000 instructions (Invitrogen, USA). After 6 h, cells were replaced with a complete medium and continued for 24 h.
CCK-8 was conducted as previously described [16]. Cells were inoculated into 96-well plates. The plates were placed in an incubator at 37°C with 5% CO2 for 24, 48, and 72 h. 10 μL CCK-8 solution was added to each well. The absorbance (A) of each well at 450 nm was measured by a microplate reader.
The colony formation assay was conducted as previously described [16]. Cells were seeded into 6-well plates with 1 × 103 cells/well. The cells were cultured at 37°C for 14 days, fixed with paraformaldehyde, and stained with crystal violet. The clone formation with more than 50 cells was calculated.
Based on the published reports [17], cells were inoculated in 6-well culture plates. When cells grew to the degree of 80%~90% integration, a fine line was drawn perpendicular to the cells with a sterile head as far as possible. Cells were cleaned with phosphate-buffered saline (PBS) to remove floating cells. The culture medium containing FBS was added at 37°C and cultured for 48 h in a 5% CO2 incubator. Scratch width which represented migration ability was measured under a microscope.
On the bases of the previously described [18], cells were added to the Transwell upper chamber pretreated with Matrigel (Invitrogen) with 2 × 104 cells/well. 600 μL medium containing 10% FBS was added into the Transwell chamber. After 24 h of culture at 37°C and 5%CO2, cells at the bottom of the chamber were fixed and stained. Cells were photographed and counted with the microscope and statistically analyzed.
The dual-luciferase reporter assay was performed as previously described [19]. Possible TRPM2-AS target miRNAs were predicted by the StarBase online software tool (http://starbase.sysu.edu.cn/), and miR-22-3p target genes were predicted by TargetScan 7.2 (http://www.targetscan.org/vert_72/). Wt (luciferase reporter vector containing TRPM2-AS or FSTL1 binding site) or mut (luciferase reporter vector containing TRPM2-AS or FSTL1 binding site after mutation) and mimics or miR-NC were cotransfected in CRC cells, respectively. Luciferase activity was detected in each group after 48 h culture.
Following previously described methods [20], the Magna RIP kit (Millipore, USA) was used in the RIP assay according to the instructions. Cells were collected and lysed with RIP lysis buffer. Ago2 immunoprecipitation was then performed using anti-coated anti-human Ago2 antibody magnetic beads, while IgG antibody was used as control. Then, immunoprecipitated RNA was isolated, and expression of TRPM2-AS and miR-22-3p was detected by qRT-PCR.
Total RNA was extracted with TRIzol (Invitrogen). After concentration determination, cells were reversed into cDNA using a reverse transcription kit (Takara, China). qRT-PCR was performed by PrimeScript RT Master Mix (Takara) on the Applied Biosystems 7500 real-time PCR system (ABI, USA). RNA relative expression was analyzed by the alternative 2-∆∆Ct method [21].
As described in other articles [22, 23], total protein was extracted by RIPA solution (Beyotime, China) containing protease inhibitor (Roche). Protein sample concentration was detected by a bicinchoninic acid (BCA) protein detection kit (Beyotime). Protein extracts were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to a polyvinylidene fluoride (PVDF) membrane. The membrane was isolated in 5% skim milk powder for 1 h. The membrane was incubated with the primary antibody (Abcam Company) at 4°C overnight and secondary antibody at room temperature. Electrochemiluminescence solution was added, and images were collected and analyzed in the Bio-rad imaging system.
To evaluate the in vivo tumor growth abilities, the method described before was performed [7]. Cells in each group were prepared into suspension with 1 × 106 cells/mL. Ten female nude mice were randomly divided into 2 groups (5 mice in each group). 0.5 mL cells transfected with sh-NC and sh-TRPM2-AS were injected subcutaneously into the right hind limb of each nude mouse, respectively. After 35 days, nude mice were sacrificed. The transplanted tumor was stripped off and weighed. Tumor volume was calculated as 1/2ab2 (long diameter (a), short diameter (b)).
GraphPad Prism 6 software performed the statistical analysis. Data were expressed as and the t-test was used to compare the differences between the two groups. P < 0.05 was considered statistically significant.
TRPM2-AS expression in CRC cells and NCM460 was detected by qRT-PCR. TRPM2-AS expression was found to be increased in CRC cells versus NCM460 (Figure 1). HCT116 and LoVo cells with high TRPM2-AS expression were selected for the next experiment.
TRPM2-AS expression was decreased in the si-TRPM2-AS group versus the si-NC group (Figure 2(a)). This suggested that TRPM2-AS was successfully inhibited in HCT116 and LoVo cells. After transfection, cell proliferation was measured through CCK-8 and clone formation. The proliferation activity of CRC cells was decreased in the si-TRPM2-AS group versus the si-NC group (Figures 2(b) and 2(c)). The clone formation assay showed that after TRPM2-AS inhibition, the number of clones of the two CRC cells decreased versus the control group (Figures 2(d) and 2(e)). This indicated that TRPM2-AS inhibition could reduce CRC cell proliferation.
In CRC cells, the scratch healing ability of the si-TRPM2-AS group was reduced versus the si-NC group at 48 h (Figures 3(a) and 3(b)). The Transwell assay showed that the invading cell number was notably reduced in the si-TRPM2-AS group compared with the si-NC group (Figures 3(c) and 3(d)). These results suggested that TRPM2-AS inhibition could inhibit CRC cell migration and invasion ability in vitro.
StarBase online prediction software showed that TRPM2-AS had targeted binding sites with miR-22-3p (Figure 4(a)). miR-22-3p expression in the si-TRPM2-AS group was increased versus the si-NC group (Figure 4(b)). Luciferase activity was decreased in cells cotransfected miR-22-3p mimic and TRPM2-AS-wt, while luciferase activity has no significant change in other groups (Figures 4(c) and 4(d)). Meanwhile, RIP experiment showed miR-22-3p was enriched in the Ago2 group (Figures 4(e) and 4(f)). These results suggest that TRPM2-AS can target miR-22-3p.
Bioinformatics software predicted that the miR-22-3p targets FSTL1 (Figure 5(a)). The relative luciferase activity of FSTL1-wt+mimic was declined versus FSTL1-wt and miR-NC cotransfection (Figures 5(b) and 5(c)). WB showed that the FSTL1 protein expression level was obviously decreased after miR-22-3p overexpression or TRPM2-AS inhibition (Figure 5(d)). These suggest that miR-22-3p targets FSTL1.
In order to confirm that TRPM2-AS affects CRC cell malignant behavior by miR-22-3p/FSTL1, a rescue assay of FSTL1 overexpression or miR-22-3p inhibition was performed. Compared with the si-TRPM2-AS+anti-NC group, cotransfection of si-TRPM2-AS and inhibitor obviously increased cell viability, colony forming ability, migration ability, and cell invasion number (Figure 6). Similarly, FSTL1 overexpression has the same effect as miR-22-3p inhibition on CRC cells (Figure 7).
The effect of TRPM2-AS on CRC cell proliferation in vivo was also explored. In our experiment, HCT116 cells transfected with short hairpin RNAs for TRPM2-AS (sh-TRPM2-AS) or its control (sh-NC) were selected for tumor formation by subcutaneous injection. Tumor tissue in the sh-TRPM2-AS group was smaller than that in the sh-NC group (Figure 8(a)). Tumor volume has the same trend as tumor tissue (Figure 8(b)). The tumor weight of the sh-TRPM2-AS group was markedly decreased versus the sh-NC group (Figure 8(c)). Thus, TRPM2-AS inhibition could significantly hamper the proliferation of CRC cells in vivo.
Most CRC patients are diagnosed as advanced and miss the chance of radical surgery [24]. Early diagnosis and early treatment can effectively reduce the mortality of CRC patients and improve the cure rate. With the development of precision medicine, new biomarkers should be developed to help patients' diagnosis, improve the treatment effect, improve the prognosis, and better guide clinical practice. In CRC, many studies reported that lncRNA is abnormally expressed, which has potential application value in the diagnosis, prognosis assessment, drug resistance assessment, targeted therapy, and other aspects of CRC [25]. lncRNA is a multifunctional noncoding regulatory transcript. Researches on the relationship between differentially expressed lncRNAs and the mechanism of tumorigenesis and development are also being gradually carried out and deepened. TRPM2-AS is a newly discovered noncoding RNA molecule that promotes cancer progression in RB, GC, EC, CRC, and other cancers [7–10]. We found that TRPM2-AS expression was increased in CRC cells and the inhibition of TRPM2-AS hampered CRC cell proliferation, which was consistent with a previous study [11]. Moreover, the scratch healing ability of cells and the number of invading cells were notably decreased by TRPM2-AS inhibition. This suggests that TRPM2-AS downregulation inhibits CRC cell proliferation, migration, and invasion. lncRNAs can act as miRNA precursors to regulate mRNA stability and have great potential in tumor diagnosis and treatment [26]. In order to further analyze the mechanism of TRPM2-AS in the occurrence and development of CRC, StarBase software was used to predict the microRNA molecules that TRPM2-AS might bind. It was found that miR-22-3p could bind TRPM2-AS. Tian et al. suggested that bladder cancer progression could be promoted by TRPM2-AS through miR-22-3p and GINS2 [19]. LINC00858 promoted progression by the miR-22-3p/YWHAZ axis in CRC [27]. miR-22-3p expression was decreased in CRC, and its overexpression hampered CRC cell malignant behavior [28]. RIP and double luciferase experiment in this study showed that TRPM2-AS could complement miR-22-3p. miR-22-3p expression was increased by TRPM2-AS inhibition, suggesting that TRPM2-AS may negatively regulate miR-22-3p in CRC. Furthermore, TargetScanHuman 7.2 website prediction showed that miR-22-3p may complement FSTL1 mRNA. FSTL1, an extracellular glycoprotein, is associated with cell survival, proliferation, differentiation and migration, and embryonic organ maturation [29]. FSTL1 not only can be used as a marker for disease diagnosis and development but also relates to the occurrence and development of related diseases [29]. FSTL1 is upregulated in many solid tumor cells including glioma, gastric cancer, and hepatocellular carcinoma, and inhibiting the expression of FSTL1 can effectively reduce various malignant biological behaviors of cancer cells [30–32]. In CRC, FSTL1 is upregulated, and FSTL1 stable overexpression can promote CRC cell migration and invasion and shorten the survival time of nude mice [33–35]. This study confirmed that miR-22-3p targets FSTL1. What is more, both TRPM2-AS inhibition and miR-22-3p overexpression markedly decreased FSTL1 expression. Moreover, rescue further experiments showed that the effect of TRPM2-AS inhibition of CRC cell malignant behaviors was partially relieved after miR-22-3p inhibition or FSTL1 overexpression. These results indicated that the TRPM2-AS/miR-22-3p/FSTL1 signaling axis may be involved in regulating CRC progression. This study not only has a certain reference value for the study of the mechanism of CRC metastasis but also has important significance for the study of its treatment mechanism. However, the current research content of this study is not in-depth and needs to be further verified in a later stage, such as TRPM2-AS expression in the tissues of patients with CRC and its relationship with clinicopathological characteristics and the effect of TRPM2-AS on CRC cell apoptosis.
This study demonstrated that TRPM2-AS was significantly increased in CRC. Mechanistically, TRPM2-AS could regulate CRC malignant behavior by inhibiting the miR-22-3p/FSTL1 axis. This proves the role of the TRPM2-AS/miR-22-3p/FSTL1 molecular axis in CRC and provides an experimental basis for the diagnosis and targeted therapy of CRC. | true | true | true |
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PMC9578796 | Shiji Wang,Guang Wang,Lihua Dong,Xingang Liu,Weiyun Pan,Jinfeng Han,Ying Lu | The Overexpression of miR-377 Aggravates Sepsis-Induced Myocardial Hypertrophy by Binding to Rcan2 and Mediating CaN Activity | 11-10-2022 | Sepsis remains a complicated and incompletely understood syndrome, and myocardial dysfunction is one of the main complications contributing to poor clinical outcomes. Accumulating evidence has revealed the critical involvement of the deregulated expression of specific microRNAs (miRNAs) in cardiac pathologies caused by sepsis. Intriguingly, miR-377 has been correlated with cardiomyocyte apoptosis, whereas its effect on myocardial hypertrophy remains to be illustrated. Thus, the current study sets out to explore the impact and underlying mechanism of miR-377 on myocardial hypertrophy induced by sepsis. The expression pattern of miR-377 was detected in myocardial tissues of septic mice induced by cecal ligation-perforation (CLP). We found that miR-377 was highly expressed in myocardial tissues of CLP-induced septic mice with cardiomyocyte hypertrophy. Besides, miR-377 inhibition could relieve cardiomyocyte hypertrophy and reduce inflammation in septic mice. Further, mechanistic studies found that miR-377 could target Rcan2 and then regulate calcineurin (CaN) activity via Ca2+/CaN signaling pathway. Collectively, our findings illuminate that miR-377 enhances myocardial hypertrophy caused by sepsis, by targeting Rcan2 and further regulating the Ca2+/CaN signaling pathway. This work highlights downregulation of miR-377 as a novel target for the management of sepsis-induced myocardial hypertrophy. | The Overexpression of miR-377 Aggravates Sepsis-Induced Myocardial Hypertrophy by Binding to Rcan2 and Mediating CaN Activity
Sepsis remains a complicated and incompletely understood syndrome, and myocardial dysfunction is one of the main complications contributing to poor clinical outcomes. Accumulating evidence has revealed the critical involvement of the deregulated expression of specific microRNAs (miRNAs) in cardiac pathologies caused by sepsis. Intriguingly, miR-377 has been correlated with cardiomyocyte apoptosis, whereas its effect on myocardial hypertrophy remains to be illustrated. Thus, the current study sets out to explore the impact and underlying mechanism of miR-377 on myocardial hypertrophy induced by sepsis. The expression pattern of miR-377 was detected in myocardial tissues of septic mice induced by cecal ligation-perforation (CLP). We found that miR-377 was highly expressed in myocardial tissues of CLP-induced septic mice with cardiomyocyte hypertrophy. Besides, miR-377 inhibition could relieve cardiomyocyte hypertrophy and reduce inflammation in septic mice. Further, mechanistic studies found that miR-377 could target Rcan2 and then regulate calcineurin (CaN) activity via Ca2+/CaN signaling pathway. Collectively, our findings illuminate that miR-377 enhances myocardial hypertrophy caused by sepsis, by targeting Rcan2 and further regulating the Ca2+/CaN signaling pathway. This work highlights downregulation of miR-377 as a novel target for the management of sepsis-induced myocardial hypertrophy.
Sepsis, a common disease accompanied by high morbidity and mortality rates, is an inflammatory response syndrome resulting from the invasion of pathogenic microorganisms [1]. The toxins and metabolites produced by bacterial, fungal, or viral infection invade into the blood and consequently activate cells and immune system to rapidly augment the production of cytokines and endogenous mediators [2]. Clinically, sepsis is often complicated with acute organ dysfunction, which remains one of the leading causes of death in sepsis patients [3]. It is notable that myocardial tissues are the main target organ in the progression of sepsis, and the damage of myocardial tissue is generally regarded as the commencement of various organ dysfunction syndromes [4]. Moreover, numerous studies have revealed that sepsis has a negative effect to confer on cardiomyocytes, which is an important contributor to hemodynamic instability and cardiac insufficiency [5–7]. In this sense, it would be prudent to investigate the unclear mechanism of sepsis on myocardial injury to further explore the potential targets and biomarkers for sepsis treatment. microRNAs (miRNAs), small RNAs with 20-24 nucleotides, possess the ability to restrict gene expression by affecting mRNA degradation or blocking translation [8]. Furthermore, miRNAs play critical roles in the development and maintenance of heart physiological functions, with various miRNAs being highlighted as mediators or therapeutic targets for heart failure [9]. In addition, dysregulation of miRs has been implicated in various biological processes related to cardiovascular diseases [10, 11]. One such miRNA, miR-377, targeting many genes, was previously indicated to be highly expressed in disorders associated with immune response [12, 13]. Further, miR-377 has been suggested to mediate cardiomyocyte apoptosis induced by cyclosporin A [14]; yet, its role in cardiomyocyte hypertrophy caused by sepsis remains to be investigated. Initial bioinformatic prediction indicated regulators of calcineurin 2 (Rcan2) as a target gene of miR-377. RCANs, also known as calcipressin, are regarded as pivotal regulators of different cellular processes, such as muscle fiber remodeling and immune response [15]. In addition, RCAN proteins were previously associated with the progression of several pathological conditions, including cardiac hypertrophy [16]. Mechanistically, RCANs play roles in physical binding and regulation of the Ca2+ and calmodulin-dependent serine-threonine phosphatase calcineurin (CaN) [16, 17]. Furthermore, Ca2+/CaN signaling pathway is crucial for regulating the function of cardiomyocytes. For instance, aspirin impeded cardiac hypertrophy through inhibition of the Ca2+/CaN signaling pathway in vitro and in vivo [18]. On the basis of the aforementioned lines of evidence, the current study is aimed at investigating the regulatory effect of miR-377 on the cardiomyocytes of CLP-induced septic mice and further explored its downstream active pathways.
Animal experimentation protocols were approved by the Animal Care and Use Committee of the First Bethune Hospital of Jilin University, and all procedures were in compliance with the Guide for the Care and Use of Laboratory Animals.
Kunming (KM) male mice (weighing 25-30 g, aged 6-8 weeks) were provided by the Cyagen Biosciences Inc. (Guangzhou, China), and all mice were raised under specific pathogen-free (SPF) conditions. After 3-5 days of acclimatization, the mice were subjected to cecal ligation-perforation (CLP) to establish a mouse model of sepsis. Briefly, all the mice were fasted for 12 h presurgery, with free access to water. Then, the mice were intraperitoneally anesthetized with 3% pentobarbital sodium (3 mL/kg) and fixed in the supine position. After routine skin preparation and disinfection, the cecum was separated by laparotomy at 1.5 cm along the midline of the abdomen. Subsequently, a 2-0 silk thread was used to ligate the end of cecum, and a No. 18-gauge needle was employed for ligation for 3 times. The intestinal feces were squeezed out, and then the incision was closed with layered suture. Afterwards, the mice were divided into 8 groups, 9 mice per group. Untreated mice were set as the control group, and sham-operated mice (only treated with cecum exposure and abdominal suture incision) were referred to as the sham group. Mice of the agomir negative control (NC), antagomir NC, micro-RNA- (miR-) 377 agomir, and miR-377 antagomir groups were injected with the plasmids (5 mg/mL) expressing corresponding molecules via caudal vein and further subjected to CLP 4 days later; mice of other two groups were injected in the same way with the combination of miR-377 agomir plasmids and Rcan2 overexpression plasmids (oe-Rcan2) or the combination of miR-377 antagomir plasmids and plasmids carrying small interfering RNA (siRNA) targeting Rcan2 (si-Rcan2), referred to as the miR-377 agomir + oe-Rcan2 group and the miR-377 antagomir + si-Rcan2 group. The miRNA agomir (miR40003123-4-5) and miRNA antagomir (miR30003123-4-5) used in the present study were purchased from the RiboBio company (Guangzhou, Guangdong, China). The lentiviral vector was constructed by Shanghai GenePharma Co., Ltd. (Shanghai, China) and packaged in HEK293T cells obtained from American Type Culture Collection (ATCC Rockville, Maryland). HEK293T cells were cultured in RPMI-1640 with 10% fetal calf serum (FBS) and passaged every other day. The lentiviral vectors were collected and diluted to a density of 1 × 109 pfu/100 μL, and then 10 μL of lentiviral vector was slowly injected into the vein of the anesthetized mice with a 27-gauge needle and processed for three consecutive days. Following that, 7 mice from each group were adopted for the experiment. After the successful development of mouse models of different groups, blood samples were drawn from the inferior vena cava and allowed to stand for 2 h. Afterward, the blood sample was centrifuged at 2000 r/min for 20 min, and the supernatant was obtained and stored at -80°C. In addition, the ventricular muscle tissues were harvested, quick-frozen in liquid nitrogen, and stored at -80°C for subsequent index detection.
Echocardiography was performed at 8 weeks after the operation to examine the thickness of left ventricle posterior wall (LVPW), the left ventricular end-systolic diameter (LVESD), ejection fraction (EF), and fractional shortening (FS). The heart of mouse was taken out, rinsed with phosphate-buffered saline (PBS) buffer, and wiped with filter paper. Subsequently, the heart weight (HW) and left ventricle weight (LVW) were measured by an electronic balance, and the ratio of LVW to body weight (LVW/BW) was calculated.
According to the instructions of the ELISA kit (R&D System, Minneapolis, MN), the levels of serum cardiac troponin I (CTn-I) and brain natriuretic peptide (BNP) were measured. Moreover, the concentration of interferon gamma (IFN-γ), tumor necrosis factor alpha (TNF-α), interleukin IL-6 and interleukin IL-8, and other cytokines were also quantified using corresponding ELISA kits.
HE staining was employed to observe the structure of cardiomyocytes. After fixing the myocardial tissue with 4% paraformaldehyde for 24 hours, the sections were deparaffinized and stained with HE for 10 min. Then, the sections were successively treated with 1% hydrochloric acid ethanol and 2% sodium bicarbonate and immersed in eosin staining solution. Following dehydration with gradient alcohol, the sections were sealed with neutral resin and photographed after drying for 72 h. Subsequently, about 1 mm3 of myocardial tissue was fixed with 2.5% glutaraldehyde solution and 1% osmium acid, then dehydrated with ethanol and embedded with epoxy resin Epon 812. Later, an ultramicrotome (Olympus, Tokyo, Japan) was adopted to slice the abovementioned tissue. The slices were stained by uranyl acetate and lead citrate, and then the ultrastructural changes of cardiomyocytes were observed under a transmission electron microscope (Hitachi, Tokyo, Japan).
Mice aged 1 to 3 days were fixed on the foam board under aseptic conditions, and the surfaces of mice and foam board were disinfected with alcohol. The chest cavity along the 3rd and 4th intercostal space of mice was opened, and the ventricles were extracted and placed in a petri dish with precooled PBS. Afterwards, the large blood vessels and other nonmyocardial tissues were removed, and the ventricles were sliced into 1 mm3 blocks on ice. After adding 5 times of the volume of pancreatin and collagenase mixture (1 : 1), the tissue blocks were digested at 37°C for 10 min, and the mixture was shaken every 2 min. Later, the tissue blocks were pipetted, and the digestion step was repeated. The cell suspension was collected in a 15 mL centrifuge tube and then added equal amounts of DMEM/F12 medium containing 20% FBS to terminate the digestion. Following 10 min centrifugation at 800 rpm, the supernatant was discarded, and a single cell suspension was obtained by pipetting. After passing a 200-mesh sieve, the cardiomyocytes were purified by differential adherence to remove noncardiomyocytes. The nonadherent cell suspension was transferred to another culture flask, and 5′-bromodeoxyuridine (0.1 mmol/L) was added to inhibit the growth of noncardiomyocytes. Cells were seeded into plates at a density of 5 × 105, cultured at 37°C, 5% CO2, and saturated humidity.
After 48 h of culture, the primary cardiomyocytes were exposed to Ang II at a final concentration of 106 mol/L for 24 h and then classified into the sham group (received no further treatment) and experimental groups, wherein the Ang II-treated cells were, respectively, transfected with plasmids expressing mimic NC, miR-377 mimic, inhibitor NC, miR-377 inhibitor, miR-377 inhibitor + si-Rcan2, and oe-miR-377 + oe-Rcan2. All the aforementioned plasmids were purchased from Dharmacon, Inc. (Lafayette, CO). Briefly, cells were seeded in a 6-well plate at a density of 3 × 105/well, and upon reaching 80% confluence, the cell transfection was conducted with Lipofectamin 2000 kits (Invitrogen) (Thermo Fisher Scientific, Waltham, MA). For the transfection, 4 μg of the target plasmids and 10 μL of Lipofectamine 2000 were separately diluted with 250 μL of serum-free Opti-MEM medium (Gibco, Grand Island, NY), mixed and placed in a 37°C, 5% CO2 incubator. Then, cells were cultured with complete medium after 6 h and collected after 48 h of continuous culture.
Total RNA content was extracted from myocardial tissues and cells using the TRIzol Reagent (Takara Bio, Shiga, Japan) for detection of miR and mRNA levels. The primer sequences of miR-377, Rcan2, CaN, U6, and β-actin were designed and synthesized by Invitrogen (Carlsbad, CA), as presented in Supplementary Table 1. U6 and β-actin were separately used as the internal reference for miR-377 and Rcan2. For miRNA quantification, cDNA was obtained with poly(A) tailing method using a miRNA reverse transcription kit (D1801, HaiGene, Harbin, China); for mRNA quantification, 2 μg RNA was reversely transcribed into cDNA using the ReverTra Ace qPCR RT Kit (TOYOBO, Osaka, Japan). Then, RT-qPCR was performed with the help of SYBR Green PCR Master Mix kit (Roche, Indianapolis, IN), and the expression of target genes was calculated according to the 2-ΔΔCt method.
Total proteins were extracted from tissues and cells by RIPA lysate (Beyotime Biotechnology, Shanghai, China) containing 1% phenylmethylsulfonyl fluoride (PMSF), followed by the determination of protein concentration using the bicinchoninic acid (BCA) kit (Beyotime Biotechnology, Shanghai, China). The extracted protein was separated by 10% polyacrylamide gel electrophoresis and then transferred onto PVDF membrane (Merck Millipore, Massachusetts). Subsequently, the membrane was blocked with 5% bovine serum albumin (BSA) for 1 h and then incubated with rabbit anti-mouse primary antibody (Rcan2, 1 : 1000, 254029, Abbiotec, San Diego, CA; CaN, 1 : 500, PAB8606, Abnova, Walnut, CA) overnight at 4°C. After washing three times with TBST buffer, goat anti-rabbit secondary antibody conjugated to peroxidase (1 : 1000, A0208, Beyotime Biotechnology, Shanghai, China) was added to incubate above membrane at room temperature for 1 h. Furthermore, the protein bands were visualized with enhanced chemiluminescence (ECL) reagent, and the gray value of the target protein band was analyzed using the ImageJ software, with β-actin serving as an internal reference.
The microarray GSE9667 related to sepsis-induced cardiomyocyte hypertrophy was retrieved from the Gene Expression Omnibus (GEO) database, and the affy package in R language was adopted to standardize the expression data. Additionally, the target genes of miR-377, the target relationship between miR-377 and Rcan2, and the binding sites of miR-377 and Rcan2 3′UTR were analyzed using a biological prediction website (http://www.microrna.org/). Next, the promoter region of Rcan2 was constructed into pGL3-Basic vector (Promega, Madison, WI), as the pGL3-Rcan2 recombinant vector. Then, HEK293T cells were seeded in 24-well plates at a density of 3 × 104/well. Based on site-directed mutagenesis method, the Rcan2 3′-UTR fragment with site mutation was constructed and inserted into the pGL3-Basic vector, and the inserted sequence was verified by sequencing. Using the liposome transfection method, pGL3-Rcan2 or pGL3-mut Rcan2 was, respectively, cotransfected with mimic NC, miR-377 mimic, inhibitor NC, and miR-377 inhibitor into HEK293T cells, and the Renilla plasmid was simultaneously transfected as reference. After 48 h of transfection, the luciferase reporter gene detection was carried out using a dual luciferase reporter gene analysis system (Promega, Madison, WI). Luminescence intensity was examined by the multifunctional microplate reader SpectraMaxM5 (Molecular Devices, Shanghai, China), with Renilla luciferase as the internal reference gene.
The cardiomyocytes loaded with fura-2 were placed under a fluorescence microscope, with the excitation wavelength of 340/380 nm and an emission wavelength of 510 nm. Following, the fluorescence signal was processed using the Felix software. The concentration of [Ca2+]i was calculated with the formula provided by Gynkiewicz: [Ca2+]i = [(R−−Rmin)/(Rmax−−R)] × (Sf/Sb) × Kd. Among them, Kd was 224 nmol/L, R represented the fluorescence value, Sf indicated free calcium fluorescence intensity, Sb was bound calcium fluorescence intensity, and [Ca2+]i concentration unit was nmol/L.
To detect the CaN activity, cardiomyocytes were washed twice with ice-cold Tris-HCl buffer and added with 1 mL of homogenization buffer for protein phosphorylation. Cells were collected and placed on the ice for 10 min, and meanwhile, 22 g needles were adopted to promote lysis. Following centrifugation at 20000 g for 10 min at 4°C, the supernatant was harvested, and a small amount of supernatant was used to measure the protein concentration. The activity of CaN was measured according to the instructions of the CaN assay kit.
Cell proliferation was examined utilizing the 5-ethynyl-2′-deoxyuridine (EdU) method. Cardiomyocytes at the logarithmic phase of growth were plated at a density of 4 × 103 to 1 × 105 and cultured to normal growth stage. According to the kit instructions, the EdU proliferation detection kit was used to measure cell proliferation of each group.
After 48 h of transfection, the cells were digested by trypsin without EDTA and collected. Subsequently, the AnnexinV-FITC Apoptosis Detection Kit (CA1020, Beijing Solarbio Science & Technology Co., Ltd., Beijing, China) was employed to detect cell apoptosis. After washing with binding buffer, the cells were resuspended in the mixture of Annexin-V-FITC and binding buffer (1 : 40), shaken for mixture, and then incubated at room temperature for 30 min. Then, the mixture of propidium iodide (PI) and binding buffer was added to above solution and incubated for 15 min after mixing. Later, the cell apoptosis was measured using a flow cytometer.
The statistical analyses were performed using the SPSS 21.0 (IBM Corp. Armonk, NY). Measurement data were expressed as mean ± standard deviation. Unpaired t-test was adopted to analyze the unpaired data in two groups. Data comparisons between multiple groups were performed by one-way analysis of variance (ANOVA) with Tukey's posthoc test. A value of p < 0.05 was considered statistically significant.
It has been reported that miR-377 was overexpressed in disorder associated with immune response [13], but its expression in septic cardiomyocytes is yet to be reported. Herein, we established a mouse model of sepsis to explore the expression of miR-377 in septic cardiomyocytes. As compared with sham-operated mice, CLP-treated presented with signs of lethargy, poor response, less activity, erect hair, chills, little will to drinking, low appetite, increased secretions at the corners of the eyes, and morbidity became more obvious over time. After 8 weeks of model establishment, the results of echocardiography displayed upregulated indices of LVPW, LVESD, HW, and LVW/BW as well as down-regulated indices of BW and FS in mouse models, relative to sham-operated mice (Figure 1(a)). Moreover, the myocardial tissues in sham-operated mice were in normal conditions, without edema, degeneration, and atrophy, as illustrated by HE staining. In contrast, CLP-induced mice presented with more inflammatory cell infiltration, mononuclear neutrophil changes, inflammatory cells, and myocardial degeneration of small blood vessel stasis (Figure 1(b)). Furthermore, results of transmission electron microscopy revealed that the in the sham-operated mice, the nucleus structure was complete; nucleus was uniformly stained; the myocardial T tube and sarcoplasmic reticulum were few but not dilated; the extrafascicular matrix exhibited no edema and penetration; the mitochondria were neatly arranged, with large and clear cristae; and vascular endothelial cells had moderate cell vesicles, whereas, oncolysis, edema, interstitial inflammation, enlargement of myocardial T-tube, and sarcoplasmic reticulum were observed in part of myocardial sarcoplasm in the CLP-induced mice, and myocardial mitochondria proliferated under the sarcoplasmic line were observed in the CLP-induced mice (Figure 1(c)). In addition, qRT-PCR results illustrated that in relative to that in sham-operated mice, miR-377 was overexpressed in the myocardial tissues of CLP-treated mice (Figure 1(d)). Results of ELISA assay showed that levels of TNF-α, IL-6, and IL-8 in myocardial tissues, as well as CTn-I and BNP in serum, were higher in mice of the CLP-induced mice versus the sham-operated mice (Figure 1(e)). These data collectively supported the successful establishment of the mouse model of sepsis and confirmed that miR-377 was highly expressed in myocardial tissues of CLP-induced septic mouse.
Further, to elucidate the role of miR-377 in cardiomyocyte hypertrophy of septic mice, we performed lentiviral transfection to overexpress or inhibit the miR-377 expression. According to results of echocardiography and LVW/BW measurement, CLP induction could lead to elevated LVPW, LVESD, HW, and LVW/BW as well as reduced BW and FS, as compared with sham-operated mice. These effects were further strengthened in response to miR-377 agomir, while the presence of miR-377 antagomir resulted in diminished LVPW, LVESD, HW, and LVW/BW as well as elevated BW and FS (Figure 2(a)). The results of HE staining and electron microscope observation demonstrated the increased damage of myocardial ultrastructure, the expansion of sarcoplasmic reticulum and plasma reticulum, and obvious infiltration of inflammatory cell in CLP-induced septic mice in response to the miR-377 overexpression. In addition, miR-377 inhibition significantly alleviated the symptoms of myocardial injury in mice and its myocardial tissue inflammation (Figures 2(b) and 2(c)). Subsequently, the focus shifted to validate whether miR-377 was involved in the inflammatory response induced by CLP. The results of ELISA assay showed that in relative to the sham-operated mice, the expression of cytokines (including IL-6, IFN-γ, IL-8 and TNF-α) was enhanced in the CLP-induced mice. Besides, the levels of these cytokines were observed to be elevated in response to the miR-377 overexpression, while being reduced in response to miR-377 silencing (Figure 2(d)). These data indicated that suppressing miR-377 could alleviate cardiomyocyte hypertrophy in septic mice.
Furthermore, the sequence of 3′-UTR region of Rcan2 gene targeted by miR-377 was predicted using a biological prediction website (http://www.microRNA.org) (Figure 3(a)). Subsequent luciferase activity assay demonstrated that the luminescence signal decreased after transfection of miR-377 mimic and Rcan2-Wt, while the transfection of miR-377 inhibitor and Rcan2-Wt augmented the luminescence signal. Meanwhile, the luciferase intensity of each group transfected with Rcan2-Mut exhibited no significant difference (Figure 3(b)). These results implied that miR-377 could specifically bind to Rcan2. Profiling of the GSE9667 microarray dataset further suggested that Rcan2 was poorly expressed in the myocardium of septic mice induced by CLP (Figure 3(c)). Thus, we focused our efforts to investigate the expression of Rcan2 in cardiomyocyte hypertrophy of septic mice. The miR-377 expression was enhanced, while Rcan2 level was downregulated in response to miR-377 mimic, as measured by qRT-PCR and Western blot. Meanwhile, the levels of miR-377 and Rcan2 showed opposing trends in response to miR-377 inhibitor (Figures 3(d) and 3(e)). Taken together, these findings revealed that miR-377 targeted and negatively regulated Rcan2.
Rcan2 is known to ameliorate the hypertrophy of cardiomyocytes by suppressing CaN activity [19]. The results of qRT-PCR and Western blot assays demonstrated that the Rcan2 expression was enhanced, and the CaN expression was reduced in response to oe-Rcan2. Meanwhile, combined treatment with miR-377 agomir and oe-Rcan2 was found to decrease the Rcan2 level and promote the CaN expression. Moreover, si-Rcan2 diminished the Rcan2 level and improved the CaN expression. Additionally, the Rcan2 expression was promoted, and CaN level was reduced in response to miR-377 antagomir and si-Rcan2 (Figure 4). Overall, these findings suggested that miR-377 mediated CaN activity through targeted inhibition of Rcan2.
Following the aforementioned findings, we then explored the role of Ca2+-CaN signaling pathway in sepsis-induced myocardial injury. We measured the calcium ion concentration ([Ca2+]i) in the cardiomyocytes of mice. The results illustrated that [Ca2+]i was upregulated as a result of the miR-377 overexpression, while the simultaneous overexpression of Rcan2 negated the upregulation of [Ca2+]. Meanwhile miR-377 inhibition led to reduced [Ca2+], and simultaneous treatment of miR-377 inhibitor and si-Rcan2 obviously promoted [Ca2+]i versus miR-377 silencing alone (Figure 5(a)). Moreover, miR-377 mimic alone elevated levels of miR-377 and CaN and diminished the Rcan2 expression, while the co-overexpression of miR-377 and Rcan2 reversed the effects of miR-377 mimic alone. miR-377 inhibitor alone diminished levels of miR-377 and CaN and enhanced the Rcan2 expression, while simultaneous depletion of miR-377 and Rcan2 negated the effects of miR-377 inhibitor alone (Figures 5(b) and 5(c)). Further, miR-377 mimic alone impeded cardiomyocyte proliferation and augmented cardiomyocyte apoptosis, accompanied by elevated levels of inflammatory factors TNF-α, IL-6, and IL-8, whereas the simultaneous overexpression of miR-377 and Rcan2 still reversed the effects of miR-377 mimic alone. Consistently, miR-377 depletion alone enhanced cardiomyocyte proliferation and repressed cardiomyocyte apoptosis and inflammatory responses, the results of which were reversed by simultaneous depletion of miR-377 and Rcan2 (Figures 5(d)–5(f)). Altogether, these results indicated that miR-377 inhibited Rcan2 and further restrict CaN via the Ca2+-CaN signaling pathway, thereby promoting cardiomyocyte hypertrophy in septic mice induced by CLP.
Sepsis is regarded as a severe organ dysfunction caused by uncontrolled host responses to infection, presenting with major symptoms such as myocardial injury [20]. Under the condition of pathological hemodynamic overload, the dysregulation of specific miRNAs may change the cellular responses in cardiomyocytes and noncardiomyocytes, causing cardiac hypertrophy and heart failure [21]. Notably, miR-377 is known to possess the ability to target multiple genes and exhibit involvement in cardiomyocyte apoptosis [14]; yet, only a handful of studies have investigated its effect on sepsis-induced cardiomyocyte hypertrophy. Thereafter, we investigated in the present study the impact of miR-377 on myocardial hypertrophy caused by CLP-induced sepsis and elucidated the downstream mechanism. An ever-increasing number of studies have indicated that miRNAs play critical roles in cardiac development and disease [22, 23]. Moreover, alterations in the miRNA expression in the hearts of hypertrophic mice are unraveled to be similar to those in the idiopathic end-stage failing human hearts, which suggests that miRNAs may exhibit molecular signatures of cardiac hypertrophy and have a role to confer in the pathological process of cardiac disease [24]. In addition, miRNA profiling studies have demonstrated that the level of specific miRNAs undergo progressive changes during the progression of cardiac hypertrophy caused by pressure overload [25]. Consistently, Care et al. illustrated in their study that inhibition of miR-133 in vivo cause sustained cardiac hypertrophy, indicating it is a key regulator of cardiac hypertrophy [26]; meanwhile, Huang et al. have revealed the correlation between the miR-221 expression and myocardial hypertrophy and fibrosis [27]. The topic of our focus, miR-377, was similarly associated with changes in cardiomyocyte apoptosis [14], while its effect on cardiomyocyte hypertrophy caused by sepsis remains to be explored. Expanding on current information, findings obtained in our study demonstrated that miR-377 could promote cardiomyocyte hypertrophy in CLP-induced sepsis in vivo, which highlights its negative-regulatory effect on cardiac hypertrophy. Furthermore, bioinformatic analyses and mechanistic experimentation in our study revealed that Rcan2 is a downstream gene of miR-377, which was poorly expressed in the cardiac hypertrophy tissues of septic mice. Originally, Rcan2 was originally recognized as a thyroid hormone-responsive gene in human skin fibroblasts [28] and then highlighted as a mediator of CaN [29, 30]. More interestingly, alteration in Rcan2 levels was previously found to be correlated with the development of tumor [31], whereas Rcan2 is also known to regulate the obesity progression via a mechanism-independent of leptin signaling [32, 33]. Interestingly, the RCAN protein family has vital roles to confer in regulating inflammation [34]; for example, Rcan1 serves a negative regulator of inflammation in response to respiratory tract infections [35]. Elaborating on the significance of Rcan2, our findings further illustrated that miR-377 could specifically bind to Rcan2 and negatively regulated the Rcan2 expression, and downregulation of Rcan2 could reduce the expression of inflammatory factors in myocardial tissues of septic mice via a CaN activity-dependent mechanism. RCAN2 family could interact physically with CaN and further regulate Ca2+/CaN signaling pathway [16]. Besides, Ca2+/CaN signaling is triggered by Ca2+ entering the cell from the extracellular space, which play a regulatory role in multiple disorders [36]. For example, the pathogenesis of cardiac dysfunction is attributed by sentrin/SUMO-specific protease 1- (SENP1-) mediated mitochondrial abnormities, and the upregulation of SENP1 in diseased heart is regulated by Ca2+/CaN pathway [37]; Ca2+/CaN signaling is important for the treatment of neurological insults [38]. Our experiment results demonstrated that the impact of miR-377 on myocardial tissue is linked to the Ca2+/CaN signaling pathway, which implies that miR-377 may regulate other diseases. In addition, Wu et al. has demonstrated that miR-30s could regulate Ca2+/CaN signaling in cardiomyocytes [36], which indicates that other miRNAs may be also potential targets for the treatment of myocardial hypertrophy and need further investigate in the future research. It should also been noted that apart from targeting Rcan2, miR-377 had previously been reported to modulate various genes such as VEGF, CD133, and SIRT1 [39–41]. Herein, whether other miR-377-mediated signaling pathways or mechanisms are involved in the promoting effects of miR-377 on cardiac hypertrophy still requires further investigations. On the other hand, the downstream effectors of the RCAN2-Ca2+/CaN signaling pathway in sepsis-induced cardiac hypertrophy remain to be established. An elevated expression level of Rcan2 has been highlighted to reduce CaN activity and thereby blocking the activation of the CaN-NFAT signaling in denervated gastrocnemius muscle [42]. Additionally in gastric cancer, the participation of Rcan2 in tumor progression has been associated with EGFR, nuclear β-catenin, MMP7, laminin-γ2, and VEGF [31]. These lines of evidence indicate the necessity of future exploration of possible downstream mechanisms of the RCAN2-Ca2+/CaN signaling pathway. Moreover, miRNA profiling could be done in future studies to explore more potential miRNA with key roles in sepsis-induced cardiac hypertrophy.
To sum up, we found that miR-377 was a significant mediator of Ca2+/CaN signaling pathway and could regulate the activity of CaN by targeting Rcan2. Thus, our study provides interesting targets and biomarkers for novel strategies of the management of cardiac hypertrophy induced by sepsis. | true | true | true |
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PMC9578797 | Junjun Luan,Congcong Jiao,Cong Ma,Yixiao Zhang,Xiangnan Hao,Guangyu Zhou,Jingqi Fu,Xingyu Qiu,Hongyu Li,Wei Yang,Gabor G. Illei,Jeffrey B. Kopp,Jingbo Pi,Hua Zhou | circMTND5 Participates in Renal Mitochondrial Injury and Fibrosis by Sponging MIR6812 in Lupus Nephritis | 11-10-2022 | Recent studies have focused on nuclear-encoded circular RNAs (circRNAs) in kidney diseases, but little is known about mitochondrial circRNAs. Differentially expressed circRNAs were analyzed by RNA deep sequencing from lupus nephritis (LN) biopsies and normal human kidneys. In LN renal biopsies, the most downregulated circRNA was circMTND5, which is encoded in the mitochondrial genome. We quantitated circMTND5 by qPCR and localized by fluorescence in situ hybridization (FISH). Mitochondrial abnormalities were identified by electron microscopy. The expression of mitochondrial genes was decreased, and the expression of profibrotic genes was increased on qPCR and immunostaining. RNA binding sites for MIR6812 and circMTND5 were predicted. MIR6812 expression was increased by FISH and qPCR. In HK-2 cells and its mitochondrial fraction, the role of circMTND5 sponging MIR6812 was assessed by their colocalization in mitochondria on FISH, RNA immunoprecipitation, and RNA pulldown coupled with luciferase reporter assay. circMTND5 knockdown upregulated MIR6812, decreased mitochondrial functional gene expression, and increased profibrotic gene expression. Overexpression of circMTND5 reversed these effects in hTGF-β stimulated HK-2 cells. Similar effects were observed in HK-2 cells with overexpression and with knockdown of MIR6812. We conclude that circMTND5 alleviates renal mitochondrial injury and kidney fibrosis by sponging MIR6812 in LN. | circMTND5 Participates in Renal Mitochondrial Injury and Fibrosis by Sponging MIR6812 in Lupus Nephritis
Recent studies have focused on nuclear-encoded circular RNAs (circRNAs) in kidney diseases, but little is known about mitochondrial circRNAs. Differentially expressed circRNAs were analyzed by RNA deep sequencing from lupus nephritis (LN) biopsies and normal human kidneys. In LN renal biopsies, the most downregulated circRNA was circMTND5, which is encoded in the mitochondrial genome. We quantitated circMTND5 by qPCR and localized by fluorescence in situ hybridization (FISH). Mitochondrial abnormalities were identified by electron microscopy. The expression of mitochondrial genes was decreased, and the expression of profibrotic genes was increased on qPCR and immunostaining. RNA binding sites for MIR6812 and circMTND5 were predicted. MIR6812 expression was increased by FISH and qPCR. In HK-2 cells and its mitochondrial fraction, the role of circMTND5 sponging MIR6812 was assessed by their colocalization in mitochondria on FISH, RNA immunoprecipitation, and RNA pulldown coupled with luciferase reporter assay. circMTND5 knockdown upregulated MIR6812, decreased mitochondrial functional gene expression, and increased profibrotic gene expression. Overexpression of circMTND5 reversed these effects in hTGF-β stimulated HK-2 cells. Similar effects were observed in HK-2 cells with overexpression and with knockdown of MIR6812. We conclude that circMTND5 alleviates renal mitochondrial injury and kidney fibrosis by sponging MIR6812 in LN.
Lupus nephritis (LN) is a major complication of systemic lupus erythematosus (SLE) and affects up to 82% of SLE patients; as many as 44% of LN patients progress to end-stage kidney disease over a period of 15 years [1, 2]. Developing a deeper understanding of the mechanisms of LN and exploring novel therapeutic targets may slow and possibly halt the progression of LN. Interest in circRNA is expanding rapidly in 2013; two studies on the biogenesis and functions of circRNAs brought these molecules to wider attention [3, 4]. The circBase database for circular RNAs currently lists hundreds of human circRNAs [5]. The genes encoding circRNAs are located in both nuclear and mitochondrial genomes. In the past years, circRNAs have been reported to contribute to the pathogenesis of diverse renal diseases, including renal cell carcinoma, acute kidney injury (AKI), hypertensive nephropathy, diabetic nephropathy, and LN [6, 7]. We previously described upregulation of circHLA-C in renal biopsies from LN patients and suggested that this molecule might contribute to pathogenesis [8]. Subsequently, Zhang et al. have reported that hsa_circ_0123190 acts as a competitive endogenous RNA to sponge miR-483-3p in renal tissues from three untreated LN patients [9]. Other groups reported that hsa_circ_0012919 sponges miR-125a-3p and circIBTK sponges miR-29b in peripheral blood mononuclear cells (PBMCs) from lupus patients [10, 11]. Studies of circRNA in LN have focused on discovering novel biomarkers. Homo sapiens (Hsa) circ_0049224, circ_0049220, and circPTPN22 in PBMCs are associated with SLE activity or severity [12, 13]. Plasma circRNA_002453 may serve as a diagnostic biomarker in LN [14]. These kidney disease-related circRNAs are all nuclear encoded. To date, there has been no report exploring the role of mitochondrial genome-encoded circRNAs in nontumor kidney diseases. Mitochondrial dysfunction recently has been recognized an important contributor to the pathogenesis of SLE [15, 16]. Mitochondrial uncoupling protein 2- (UCP2-) deficient mice show severe renal mitochondrial fragmentation after ischemia/reperfusion-induced AKI [17]. Overexpression of peroxisomal proliferator-activated receptor gamma-coactivator (PGC-1α) can maintain mitochondrial hemostasis of renal tubular cells [18]. Mitochondrial injury can increase the production of reactive oxygen species, which may accelerate cell injury, and promote accumulation of the extracellular matrix, including fibronectin (FN) and collagens (COL), eventuating in progressive fibrosis [19]. Renal fibrosis is a common pathological feature in advanced LN [20]. It remains unknown whether circRNAs influence mitochondrial function and contribute to renal fibrogenesis in LN. In this study, we aimed to determine whether mitochondrial circMTND5 contributes to the pathogenesis of LN in patients. We identified circMTND5 as the most downregulated circRNA by deep sequencing in LN renal biopsies. We verified that circMTND5 is located in mitochondria and participates in renal mitochondrial injury and kidney fibrosis by sponging MIR6812.
Fourteen LN patients with proliferative glomerulonephritis were prospectively enrolled in a clinical study between January 2016 and January 2019 at the department of nephrology of the Affiliated Hospital of China Medical University (Table S1). Twelve renal tumor patients, seen in the department of urology, served as the control group (Table S2). Fresh, unfixed cortical renal biopsy tissues were obtained from LN patients before initiation of steroid and immunosuppressive treatment. Similar normal control cortical kidney tissues were obtained from renal tumor patients; these tissues were located at least 5 cm from the renal tumor. Normal control tissues were stained with periodic acid-Schiff (PAS) and confirmed to be histologically normal by two nephropathologists. Kidney tissues were stored at −80°C until RNA extraction as described previously [8]. A human subject research protocol was approved in advance by the institutional review boards of Shengjing Hospital of China Medical University (15052111). All subjects provided written informed consent prior to research participation.
The HK-2 cell line, a well-characterized human renal tubular epithelial cell line, and HEK293T cells were purchased from ATCC (Manassas, VA, USA). Cells were cultured in growth medium, DMEM/F-12 or RPMI 1640 medium supplemented with 100 U/mL penicillin G, 100 μg/mL streptomycin, and 10% bovine calf serum at 37°C in a humid atmosphere of 95% air and 5% CO2.
RNA library preparation and circRNA sequencing were performed by CloudSeq Biotech (Shanghai, China). The rRNA and line RNA-depleted RNA were used to construct RNA libraries with the TruSeq Stranded Total RNA Library Prep Kit (Illumina, CA, USA) according to the manufacturer's instructions. RNA libraries were denatured as single-stranded DNA molecules. The cDNAs were captured on Illumina Flow Cells (Illumina, CA, USA), amplified in situ as clusters, and sequenced with 150 bp paired reads on the HiSeq 4000 sequencing system (Illumina, CA, USA). To generate the profiling of differentially expressed circRNAs between LN kidneys and normal control kidneys, the XY scatter plot was analyzed based on the expression levels of all identified circRNAs and the significant difference between LN and control kidneys by Cluster and TreeView software. The binding sites linking circMTND5 and MIR6812 were predicted by TargetScan and miRanda.
Total RNA from HK-2 cells was amplified by PCR, and then, PCR products were analyzed by Sanger sequencing. In additional experiment, total RNA from HK-2 cells was incubated without or with RNase R digestion (Epicentre, Madison, WI, USA) for 30 minutes at 37°C. The relative levels of circMTND5 and GAPDH were assayed by qPCR, normalizing to those measured in the Mock group.
Fluorescence in situ hybridization (FISH) was performed in human kidney tissues and HK-2 cells following the protocol from the manufacturer. Paraffin-embedded human kidney tissue sections were cut at 4 μm thickness. Sections were deparaffinized, rehydrated, and digested with trypsin at 37°C for 30 min. The slides of kidney sections or cultured HK-2 cells were hybridized with a digoxigenin-horseradish peroxidase- (DIG-HRP-) labeled oligonucleotide probe complementary to circMTND5 or MIR6812 (Table S4) at 37°C overnight followed by incubation with anti-DIG-HRP (Servicebio, Wuhan, China) for 50 min, fluorescein isothiocyanate-tyramide signal amplification for 5 min, and diamidino-phenyl indole (DAPI) to stain DNA for 5 min. Images were captured by immunofluorescence microscopy (Nikon, Tokyo, Japan). The extent of the hybridization signal was semiquantified as previously reported by Huang et al. [21].
Human kidney tissues were cut into pieces of ~1 mm3 size, transferred to 4% paraformaldehyde, post-fixed in 1% osmium tetroxide, dehydrated in graded alcohols, and embedded in Epon (Sigma-Aldrich, MO, USA). Semithin sections were cut in order to confirm that the tissue orientation was satisfactory under the light microscope. Ultrathin sections (30–60 nm) were obtained using a Leica Ultracut UC6 ultramicrotome (Leica Microsystems, Vienna, Austria), mounted on Formvar-coated copper grids, stained with uranyl acetate and lead citrate, and examined using a JEM-1400 digital electron microscopy (JEOL, Tokyo, Japan). Mitochondria injuries were captured at magnification of 15000x and 25000x.
Paraffin-embedded human kidney tissues were cut at 2 μm thickness and were deparaffinized and rehydrated. Antigens were retrieved and nonspecific binding was blocked as previously described [22]. Kidney sections and slides with HK-2 cells were incubated with primary antibodies at 4°C overnight, followed by incubation with Alexa-594/Alexa-488 donkey anti-rabbit/anti-mouse IgG (Table S5). After three washes with PBS, slides were mounted with DAPI for 10 min. Images were captured by immunofluorescence (IF) microscopy (Nikon, Tokyo, Japan). IF was quantified by Image-Pro Plus 6.0 (Media cybernetics, MD, USA).
HK-2 cells were cultured for 6 h on the slides that were placed in culture dishes with HK-2 medium until cells were attached. Next, 100 nM MitoTracker Red CMXRos (Invitrogen, Carlsbad, CA) was added in the medium and incubated for 30 min at 37°C (Solarbio, Beijing, China). The medium was removed and the slides were fixed for FISH.
HK-2 cells were homogenized by lysis buffer (Solarbio, Beijing, China) and ground 30 times on ice. The homogenate was transferred to a 1.5 mL of a microfuge tube. The tube was centrifuged at 1000 g for 5 min at 4°C. The supernatant was transferred to a fresh tube and centrifuged again. The supernatant from second centrifugation was collected and was centrifuged at 12000 g for 10 min at 4°C. The crude mitochondria-containing pellet was suspended in 500 μL wash buffer (Solarbio, Beijing, China) and centrifuged at 12000 g for 10 min to obtain final pellet. This pellet, containing a mitochondria-enriched fraction and cytosolic fraction from the final spin, were used for extraction of total RNA. The expression levels of circMTND5 and MIR6812 were measured in mitochondrial fractions by qPCR.
HK-2 cells or their mitochondria fraction was homogenized in RNA immunoprecipitation (RIP) lysis buffer (Sigma-Aldrich, MO, USA). Antibodies against immunoglobulin G (Anti-IgG) or Argonaute 2(AGO2) conjugated with magnetic beads (Sigma-Aldrich, MO, USA) were incubated with cell lysates overnight at 4°C. Enrichment of circMTND5 in the immunoprecipitation with anti-AGO2 or anti-IgG was measured by qPCR.
The biotinylated circMTND5 or its negative control (Table S4) was transfected into HK-2 cells. The cells were lysed using lysis buffer at 48 h following transfection. Streptavidin agarose beads (Invitrogen, CA, USA) were incubated with the cell lysates, and the RNA complex that was bound to the beads was eluted and purified using TRIzol. qPCR was performed to measure the MIR6812 copy number in the RNA complexes.
The wide-type binding sites of circMTND5/UCP2 were inserted into the pmirGLO Dual-Luciferase miRNA Target Expression Vector (Promega, CA, USA). The mutated binding site sequences of circMTND5/UCP2 were generated by CloudSeq Biotech (Shanghai, China). Wild-type or mutated circMTND5/UCP2 was cotransfected with MIR6812 mimic/negative control into HEK293T cells. After transfection for 48 h, cells were harvested and the luciferase activity was measured using the Dual-Luciferase Reporter Gene Assay Kit in a luciferase reporter system (Promega, CA, USA).
To knockdown expression of circMTND5, the cultured HK-2 cells were transfected with circMTND5 RNA interference (RNAi)/negative control or MIR6812 mimic/negative control (SyngenTech, Beijing, China) (Table S3) using Lipofectamine 3000 (Invitrogen, CA) for 24 h according to the manufacturer's instructions. On the other hand, the cultured HK-2 cells were stimulated with human transforming growth factor β (hTGF-β, 250 pg/mL) for 24 h to downregulate circMTND5 expression. Then, the cells were transfected with one of two pairs of vectors: (1) circMTND5 containing vector (pCDH-CMV-5′ Circular Frame-circMTND5-3′ Circular frame-EF1-copGFP-T2A-Puro) and its corresponding empty vector (pCDH-CMV-5′ Circular Frame-MCS-3′ Circular frame-EF1-copGFP-T2A-Puro) (SyngenTech, Beijing, China) and (2) MIR6812 inhibitor/its negative control (SyngenTech, Beijing, China). Transfections were accomplished using lipofectamine 3000 and/or P3000 (Invitrogen, CA, USA) for 24 h. Cells were collected for RNA and protein extraction 24 h after the transfection of specific genes for examination of qPCR and Western blotting.
Total RNA (250 ng per sample) from kidney tissues and from HK-2 cells was subjected to reverse transcription using the PrimeScript RT Reagent Kit or TransScript miRNA First-Strand cDNA Synthesis SuperMix (TransGen Biotech, Beijing, China) followed by PCR with SYBR Premix Ex Taq (TaKaRa, Dalian, China). Primers were designed using Primer Express (Applied Biosystems, CA, USA) and synthesized by Life Technologies (Shanghai, China) (Table S3). Real-time fluorescence was detected with QuantStudio 6 Flex quantitative PCR system (Applied Biosystems, CA, USA).
HK-2 cells were homogenized in RIPA buffer with protease inhibitor cocktail. An equal amount of individual protein was separated by SDS-PAGE, and the gels were transferred to PVDF membranes (Millipore-Sigma, MA, USA). After blocking with 5% milk, membranes were incubated with primary antibody (Table S5) overnight at 4°C. The blots were incubated with peroxidase-conjugated goat anti-rabbit/mouse IgG for 60 min at room temperature. The antibody-antigen reactions were detected by High-sig ECL Western Blotting Substrate and visualized by the Tanon 5500 imaging system (Tanon Science and Technology, Shanghai, China). Blot densities were analyzed using ImageJ software (NIH, MD, USA).
GraphPad Prism 9 (GraphPad, San Diego, CA, USA) was used for statistical analysis and graphing. Quantitative data were expressed as mean ± SD. Differences between groups were analyzed for statistical significance by one or two-way ANOVA or t-tests. A p value < 0.05 was accepted as statistically significant.
The profiling data of renal circRNAs in these biopsies and normal control kidneys can be found at the following two websites: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108340 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108735 [8]. In total, 168 differentially expressed circRNAs were identified between LN renal biopsies and normal kidney tissues. 136 circRNAs were significantly upregulated and 32 circRNAs were significantly downregulated in LN biopsies compared to normal control kidney tissues (fold change ≥ 2.0 and p < 0.05) on the XY scatter plot (Figure 1(a)). We focused on the most downregulated renal circRNA from the list of 32 decreased circRNAs (Table S6). This is a novel human circRNA that we have termed “circMTND5” (Figure 1(b)), with a decrease to 1.4% of the normal control value. The circRNA gene is located on the tin mitochondrial genome (chrM: 14068-14413+), and the host gene is MTND5, encoding the mitochondrially encoded NADH: ubiquinone oxidoreductase. Next, we validated circMTND5 in different sets of kidney tissue samples by qPCR. circMTND5 was also decreased on qPCR (Figure 1(c)) as that on microarray. To validate this novel mitochondrial circMTND5 as a real circRNA, Sanger sequencing and RNase R digestion were applied to HK-2 cells. The junction sequence of circMTND5 back splicing was ACCT validated by Sanger sequencing (Figure 1(d)). In addition, the expression of circMTND5 decreased about 10% of HK-2 cells treated with Mock after RNase R digestion (Figure 1(e)). To verify the distribution and expression changes of circMTND5 in renal biopsies from LN patients, FISH showed circMTND5 mainly located in cytoplasm of renal tubular epithelial cells. Further, circMTND5 was colocalized with mitochondrial cytochrome c oxidase subunit 4 (COX IV), a mitochondria-specific endogenous biomarker by double staining of FISH and IF staining. In addition, circMTND5 expression was decreased in the LN kidney compared to the normal kidney (Figure 1(f)).
We examined morphological changes of mitochondria in renal biopsies from LN patients using electron microscopy (EM). Various morphological features of mitochondrial damages including vacuolization, swollen, shrinking, and autophagy bodies were seen in renal tubular cells and glomerular podocytes in LN renal biopsies on EM under different magnification (Figures 2(a)–2(b)). In addition to morphologic evidence of mitochondria injury seen on EM, we found downregulation of mRNA encoding certain mitochondrial functional genes, including UCP2 and PGC1A in LN renal biopsies compared to normal control kidney tissues on qPCR (Figure 2(c)). Expression of the above two key proteins involved in mitochondria bioenergetics control was also decreased on the IF staining and their semiquantifications (Figure 2(e)). The expression of profibrotic genes, including COL3 and FN, was upregulated in LN kidneys compared to normal kidneys (Figure 2(d)). Similar changes were seen on the IF staining of these two profibrotic proteins and on their semiquantifications (Figure 2(f)).
Since the gene encoding circMTND5 is located in the mitochondrial genome, we verified the localization of circMTND5 by three approaches. First, we demonstrated colocalization of MitoTracker Red staining and circMTND5 by FISH in cytoplasm of HK-2 cells (Figure S1A). Second, we showed colocalization of circMTND5 RNA and the mitochondrion-specific protein COX IV by double staining of FISH and IF staining (Figure S1B). Third, using isolated mitochondrial fraction from HK-2 cells, we confirmed that the circMTND5 level was increased 86-fold in the mitochondrial fraction compared to the cytosolic fraction lacking mitochondria (Figure S1C).
Studies of the function of circRNAs have been focused on their roles in sponging miRNAs [23]. TargetScan and miRanda predicted the binding site sequences between circMTND5 and its top five microRNAs including MIR6812 (Figure S2). Further bioinformatics analysis found that circMTND5 and MIR6812 had two binding site sequences (Figure 3(a)). The renal expression of MIR6812 was increased in LN renal biopsies compared to normal kidney tissues on FISH staining (Figures 3(b) and 3(c)) and qPCR assay (Figure 3(d)). In addition, MIR6812 colocalized with COX IV in mitochondria of renal tubular cells of human kidney tissues (Figure 3(b)). After, the colocalization of circMTND5 and MIR6812 was found in the cytosol of HK-2 cells on double staining of FISH (Figure 4(a)). A standard set of RIP, RNA pulldown, and luciferase activity assay was performed to verify the interaction between circMTND5 and MIR6812. circMTND5 levels were significantly increased 86-fold in HK-2 cells exposed to AGO2 antibody compared to cells exposed to nonspecific IgG (Figure 4(b)). MIR6812 expression was increased 13.5-fold in HK-2 cells transfected with the bio-circMTND5 compared to the negative control (Figure 4(c)). With cotransfection of MIR6812 mimic and wild-type DNA sequence encoding circMTND5 (circMTND5 WT) or a mutated sequence encoding circMTND5 (circMTND5 MUT) into HEK293T cells, luciferase reporter gene assay was performed. Luciferase activity was decreased to 47% of the baseline with circMTND5 WT and MIR6812 mimic compared to the cells receiving circMTND5 MUT plus MIR6812 mimic (Figure 4(d)). The next question that we clarified is that how mitochondrial-derived circMTND5 sponge MIR6812 and affect mRNA. We first investigated whether AGO2 also localizes in mitochondria. The colocalization of MitoTracker Red and AGO2 was shown in cytosols of HK-2 cells on the costaining (Figure 4(e)). Further RIP experiment demonstrated that the circMTND5 level was increased much higher in the mitochondrial fraction than in whole cells exposed to AGO2 antibody compared to the cells exposed to normal IgG antibody (Figure 4(f)).
We next sought to determine the effect of circMTND5 on MIR6812, mitochondrial injury, and cellular fibrosis. We performed knockdown and overexpression experiments of circMTND5 in HK-2 cells and in HK-2 cells with hTGF-β-induced decrease of circMTND5. Knockdown of circMTND5 in HK-2 cells upregulated the expression of MIR6812 (Figure 5(a)). In addition, perfect binding sites were also predicted between MIR6812 and mitochondrial inner-membrane-localized gene UCP2, and direct interaction of MIR6812 and UCP2 was confirmed through luciferase reporter assays (Figure S3). The downregulation of mitochondrial UCP2 and PGC-1α and upregulation of profibrotic COL3 and FN genes were confirmed on both mRNA by qPCR analysis (Figure 5(b)) and protein levels by Western blot and IF staining (Figures 5(c) and 5(d)). In contrast, overexpression of circMTND5 in HK-2 cells with hTGF-β-induced decrease of circMTND5 reversed three sets of effects from hTGF-β stimulation including the upregulation of MIR6812, downregulation mRNA and protein levels of UCP2 and PGC-1α, and the increased expression of COL3 and FN on qPCR, Western blot, and IF staining (Figure 6).
After confirming the effect of the circMTND5 on MIR6812, mitochondrial injury, and cellular fibrosis, we further investigated the direct effect of a MIR6812 mimic and an inhibitor on mitochondrial function and fibrogenesis. Overexpression of MIR6812 was seen in HK-2 cells following MIR6812 mimic transfection (Figure 7(a)). The downregulation of UCP2 and PGC-1α genes and the upregulation of profibrotic COL3 and FN genes were found at both mRNA and protein levels (Figures 7(b)–7(d)). The direct effect of the MIR6812 mimic on gene expression was similar to the effect of circMTND5 knockdown. On the other hand, the MIR6812 inhibitor also reversed hTGF-β-induced downregulation of mitochondrial functional UCP2 and PGC-1α gene as well as the upregulation of profibrotic COL3 and FN compared to their respective negative control on qPCR, Western blot, and IF staining (Figure 8).
The main findings of this study are as follows: (1) circMTND5 localized to mitochondria and was the most downregulated circRNA in LN biopsies, (2) circMTND5 served as a sponge of MIR6812 in human kidney tissues and HK-2 renal tubular cells, and (3) circMTND5/MIR6812/UCP2 pathway participated in renal mitochondrial injury and renal fibrosis in LN (Figure 9). In the past few years, circRNAs have been reported to play important roles in the pathogenesis of certain renal diseases, including renal cell carcinoma, AKI, diabetic nephropathy, hypertensive nephropathy, and LN [6, 7]. Here, we have identified circHLA-C as the top upregulated circRNA in renal biopsies from LN patients. We also found that renal circHLA-C correlates with proteinuria, renal function, and clinical pathological indices [8]. After our report, two studies showed that hsa_circ_0012919 serves as a sponge of miR-125a-3p in CD4+ T cells and circIBTK can sponge miR-29b in PBMCs from SLE patients [10, 11]. hsa_circ_0049224, has_circ_0049220, and circPTPN22 in PBMCs [12, 13] and plasma circRNA_002453 may serve as potential diagnostic biomarkers for LN [14]. Tian et al. found that circRNA-34428 is the most overexpressed circRNA in lupus mice but this study did not elucidate its functions in renal disease progression [24]. Beyond lupus, circRNAs have been demonstrated to play an important role in other kidney diseases, in animals, and in human beings. Three groups explored the differential expression of circRNA profiles in animal kidney tissue from rats with hypertension or kidney stones and from mice with AKI induced by ischemia/reperfusion or cisplatin [25–28]. Deng et al. reported that silencing the circular ANRIL protected HK-2 cells from lipopolysaccharide-induced inflammatory injury through upregulating miR-9 in vitro [29]. We also reported that renal circZNF609 participated in the pathogenesis of focal segmental glomerulosclerosis [30]. circ_0000524 promotes podocyte apoptosis by sponging miR-500a in membranous nephropathy [31]. circRNA_010383 sponging miR-135a, circHIPK3 sponging miR-185, circEIF4G2 sponging miR-218, circ_0000491 sponging miR-455-3p, circLRP6 sponging miR-205, and circ_15698 sponging miR-185 contribute to cell death, extracellular matrix production, or renal fibrosis in diabetic nephropathy [32–37]. Our group reported that circHIPK3 aggravate renal tubulointerstitial fibrosis by regulating miR-30a [38]. In human kidney diseases, circRNAs have been profiled in exosomes isolated from serum and urine samples from patients with idiopathic membranous nephropathy [39]. In AKI patients, circRNA-126 can predict mortality [40]. To date, the circRNAs studied in renal diseases have all been encoded on autosomal chromosomes. The role of mitochondrial-encoded circRNAs in kidney diseases has rarely been reported. circHLA-C was identified as the top upregulated one in our previous study, but its molecule is too long to transfect into cells, which limits mechanistic studies. In the present study, we found that circMTND5 was the most downregulated circRNA in LN. In addition, circMTND5 is encoded by a mitochondrial gene, it was most expressed on renal tubular cells by FISH staining. Since circMTND5 was identified as a novel circRNA, we identified its back splicing junction sequence by Sanger sequencing and validated its stability by RNase R digestion (Figure 1). Liu et al. found that mitochondrial-encoded circular RNA (mecci) ND1 and mecciND5 facilitate mitochondrial protein importation by serving as molecular chaperones. This study demonstrates that mecciND5 is encoded in the mitochondrial genome, from position 13846 and to 13999, including 153 nucleic acids, and the function as molecular chaperones in HEK293T cells [41]. We investigated the roles of circMTND5 using human kidney tissue and HK-2 cells. The difference between the circMTND5 reported here and mecciND5 described by Liu et al. might be due to differences in human cell types. circRNAs typically manifest tissue-specific and cell type-specific features [23]. In addition, Mance et al. demonstrated that mitochondrial mRNA fragments can be circularized in HEK293T cells [42]. Mitochondrial genome-derived circRNA-COX2, one more mitochondrial circRNA so far, was demonstrated to serve as an oncogene in chronic lymphocytic leukemia [43]. Our data, together with the work of others, suggest that mitochondrial circRNAs may maintain mitochondrial functions via multiple mechanisms. The kidney is the organ with the second highest mitochondria abundance following the heart [44]. In the kidney, tubular cells contain the highest mitochondrial numbers [45]. We found that various mitochondrial injury features in lupus renal biopsies on EM (Figure 2) with similar findings in lupus mice and CKD rats resulted from AKI [45, 46]. Renal fibrosis is a common pathological feature in kidney biopsies from LN patients [22]. What is the role of circMTND5 in mitochondrial injury and renal fibrosis? UCP2-deficient mice can aggravate kidney mitochondrial injury from AKI [17]. PGC-1α is a well-known key regulator of mitochondria biogenesis [47]. PGC-1α transgenic mice manifest protection from Notch-induced kidney fibrosis [18]. Consistent with these findings, we found that the expression of UCP2 and PGC-1α was decreased in renal biopsies from LN patients compared to normal kidney tissue. Mitochondrial damages can cause renal fibrosis via increasing inflammation [19]. We also found that the expression of renal COL3 and FN was also increased in the kidneys of LN patients (Figure 2). These data suggest that renal downregulation of circMTND5 may contribute to renal mitochondrial injury and fibrosis in LN. What are the underlying mechanisms of circMTND5 in the pathogenesis of LN? Several studies have demonstrated that circRNAs serve as a sponge of miRNAs to prevent their action [23]. MIR6812 was predicted to have two perfect binding site sequences with circMTND5 by TargetScan and miRanda. Thus, we further examined the expression of MIR6812 in renal biopsies from LN patients by FISH, IF staining, and qPCR. We found that MIR6812 was increased while circMTND5 decreased. In addition, MIR6812 colocalized with COX IV, a mitochondrion-specific endogenous control protein (Figure 3). Since the circMTND5/MIR6812 axis has not been reported in any diseases, we verified the direct interaction between circMTND5 and MIR6812 by double FISH of circMTND5 and MIR6812, RIP assay, and RNA pulldown coupled with luciferase reporter assay. We further confirmed enriched circMTND5 in the mitochondria of renal tubular cells by RIP with AGO2 antibody compared to normal IgG antibody. We also showed the enriched level of circMTND5 in mitochondrial fraction compared to cytosolic fraction in HK-2 cells (Figure 4(f)). This data suggests that circMTND5 mainly sponged MIR6812 in mitochondria. Based on this direct evidence of circMTND5 sponging MIR6812 and the ideal length of circMTND5 including 346 nucleic acids for transfection studies, we further studied the effect of circMTND5 and MIR6812 in HK-2 cells by knockdown and overexpression of two genes. circMTND5 knockdown upregulated MIR6812, decreased expression of mitochondrial functional genes (UCP2 and PGC-1α), and increased expression of profibrotic genes (COL3 and FN). The overexpression of circMTND5 reversed these effects in hTGF-β-stimulated HK-2 cells on qPCR analysis, Western blot, and IF staining (Figures 5–6). Knockdown and overexpression of circMTND5 displayed the expected changes of MIR6812, mitochondrial genes UCP2, and PGC-1α as well as profibrotic genes COL3 and FN. These data suggested that circMTND5 is the trigger factor in the mitochondria injury and fibrosis formation in the development of LN. Based on the above findings, we further investigate the effect of knockdown and overexpression of MIR6812, one of the top five microRNAs of circMTND5. We found that that MIR6812 directly downregulated mitochondrial UCP2 by luciferase reporter assays though binding 3′UTR of UCP2 (Figure S3). Further, MIR6812 mimic transfection to HK-2 cells downregulated expression of mitochondrial UCP2 and PGC-1α gene and upregulated expression of profibrotic COL3 and FN (Figure 7). On the other hand, MIR6812 inhibitor reversed hTGF-β-induced downregulation of UCP2 and PGC-1α gene and the upregulation of COL3 and FN in HK-2 cells (Figure 8). Taken together, our data suggested that circMTND5 might contribute to mitochondrial injury and further promote renal fibrosis by sponging MIR6812; further, MIR6812 regulate UCP2 and PGC-1α to participate renal fibrosis in lupus nephritis.
circMTND5 may play an important role in improving mitochondrial injury and attenuating renal fibrosis in LN by sponging MIR6812. The interference of the circMTND5/MIR6812 axis may offer potential novel avenues for RNA therapeutics to treat renal mitochondrial injury and renal fibrosis in LN. | true | true | true |
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PMC9578799 | Yuan Cheng,Qing He,Na Li,Mengdi Luo | Activation of PTEN/P13K/AKT Signaling Pathway by miRNA-124-3p-Loaded Nanoparticles to Regulate Oxidative Stress Attenuates Cardiomyocyte Regulation and Myocardial Injury | 11-10-2022 | As a common cardiovascular disease, acute myocardial infarction seriously affects the health and life of patients. miRNAs play an important role in acute myocardial infarction. Based on miRNA obtained from the previous sequencing, this study investigated whether miRNA (miR)-124-3p-loaded nanoparticles (NPs) affect the phenotype of the acute myocardial infarction (AMI) rat. Nano-miR-124-3p decreased the myocardial infarction area, improved the myocardial tissue structure, and increased the degree of fibrosis. Nano-miR-124-3p decreased apoptosis and the expression of cleaved caspase 3, indicating its role in protecting and repairing the myocardium. To further verify the action mechanism of miRNA, a potential target gene of miR-124-3p, PTEN was identified by STARBASE and further confirmed using double luciferase assays. Following cotransfection of nano-miR-124-3p and PTEN, the areas of tissue structure damage, myocardial infarction, and fibrosis were substantially elevated. The expression of cleaved caspase 3 and the apoptosis rate in the nano-miR-124-3p and PTEN cotransfection group was also significantly increased. Bioinformatics analysis revealed that miRNA-124-3 may regulate oxidative stress injury by targeting PTEN. Taken together, miR-124-3p could protect and repair myocardial tissues through targeting PTEN. | Activation of PTEN/P13K/AKT Signaling Pathway by miRNA-124-3p-Loaded Nanoparticles to Regulate Oxidative Stress Attenuates Cardiomyocyte Regulation and Myocardial Injury
As a common cardiovascular disease, acute myocardial infarction seriously affects the health and life of patients. miRNAs play an important role in acute myocardial infarction. Based on miRNA obtained from the previous sequencing, this study investigated whether miRNA (miR)-124-3p-loaded nanoparticles (NPs) affect the phenotype of the acute myocardial infarction (AMI) rat. Nano-miR-124-3p decreased the myocardial infarction area, improved the myocardial tissue structure, and increased the degree of fibrosis. Nano-miR-124-3p decreased apoptosis and the expression of cleaved caspase 3, indicating its role in protecting and repairing the myocardium. To further verify the action mechanism of miRNA, a potential target gene of miR-124-3p, PTEN was identified by STARBASE and further confirmed using double luciferase assays. Following cotransfection of nano-miR-124-3p and PTEN, the areas of tissue structure damage, myocardial infarction, and fibrosis were substantially elevated. The expression of cleaved caspase 3 and the apoptosis rate in the nano-miR-124-3p and PTEN cotransfection group was also significantly increased. Bioinformatics analysis revealed that miRNA-124-3 may regulate oxidative stress injury by targeting PTEN. Taken together, miR-124-3p could protect and repair myocardial tissues through targeting PTEN.
AMI is a common acute ischemic heart disease in clinics [1]. The morbidity and mortality rate of acute myocardial infarction due to chronic diseases is also gradually increasing [2]. Without timely diagnosis and treatment, it is very easy to miss the best treatment time, which seriously affects the patient's health and prognosis [3–5]. In recent years, miRNA has been widely proved to function as an important part in the occurrence and development of cardiovascular disease [6, 7]. miR-124-3p was identified as a potential regulatory candidate for AMI [8]. Inhibition of miR-124-3p expression reduced apoptosis by targeting the SIRT1-activated FGF21/CREB/PGC1α pathway and attenuating both inflammation response and oxidative stress in AMI rats [9]. In conclusion, miR-124-3p might be a target site for disease management to slow down the development of myocardial remodeling after AMI. Biodegradable synthetic polymers are increasingly used in supportive therapy drug delivery devices [10]. As a synthetic polymer, poly (d,l-lactic-co-glycolic acid) (PLGA) could transport proteins [11], peptides [12], bacterial or viral DNA [13], and various anticancer drugs [14]. PLGA presents excellent biodegradability, biocompatibility, and sustained release [15]. PLGA particles overcome some of the limitations faced by current miRNA therapeutics. Although miR-124-3p has been identified functioning in AMI, there are currently no studies showing its effect in conjunction with PTEN. And nanoparticles have demonstrated in many studies that better therapeutic effects can be achieved through their delivery. Therefore, the aim of this study was to deliver miR-124-3p via PLGA nanoparticles to a rat model of myocardial infarction to explore how it regulates PTEN to act on myocardial tissue.
PLGA nanospheres were loaded with miR-124-3p and set a polymer solution at a concentration of 10%. Following the reaction between n-hydroxysuccinimide (NHS) and diethyl carbonate, methanol/Et2O (1 : 1) was added, centrifuged, and followed by abandoning the supernatant. PLGA-NHS ester was ultimately yielded after the pellet dried, which was then mixed with diisopropylethylamine and NH2-PEG-COOH (MW 3400) by dissolution. By the addition of MeOH/Et2O (1 : 1), the mixture was followed by two cycle washing, dried in vacuo, and gave PLGA-B-PEG-COOH. Cy5-conjugated miR-124-3p (Cy5/miR-124-3p) was supplied with DNase/RNase-free water and spermidine (15 : 1). The arginine-miR-124-3p complex was provided by dropwise to the obtained solution and sonicated at 40% amplitude with gentle stirring. The mixture was then treated using 5 ml of 1% PVA (w/v), gently stirred and followed by sonication under specified conditions to form a second emulsion. Finally, the enhanced nanoparticles were filtered, sterilized, and washed. miR-124-3p content was detected by a double-stranded DNA quantitative kit. Drug encapsulation efficiency (%) = measured amount of drug in nanoparticles/dosage (μg/mg) × 100%. Drug loading (%) = nanoparticle drug content/total drug loading nanoparticles (μg/mg) × 100% [16, 17].
A total of 60 SD rats (male, six-eight weeks) were employed for constructing the model of rats. All were purchased from Chongqing Enswell Biotechnology Co., Ltd. The animals were anesthetized first to prepare for model construction. Here are the brief procedures during modeling: the rats were intubated after the removal of hair in the neck. The next step was to remove the hair in the chest, incise the skin on the left sternum edge, and open both third and fourth intercostal muscles to ensure a full exposure of the heart. Subsequently, a forceps was used to remove left atrial appendage (LAA) which was carefully lift up using a cotton swab. The vein between LAA and the lung cone was also visualized. The ligation marker could be positioned using the secondary vein. Suture 6-0 went through 1-2 mm from the left atrial root of the inferior coronary muscle to a depth of 2 mm. A double chain wire 2 was placed below the connecting wire. The surgical procedure was considered successful when the myocardium altered its color into dark red swiftly and turned white gradually. When ischemia occurred after ligation 30 min, reperfusion was formed by removing the suture to exhaust the air in the chest. Finally, the rats were administered with 80000 units of penicillin sodium.
The rats were randomly administrated by injection with nano-NC agomir, nano-miR-124-3p agomir, nano-miR-124-3p agomir+NC-virus, and nano-miR-124-3p agomir+PTEN Lvx-virus in the tail vein 3 days after successful molding.
The animals were anesthetized with sodium pentobarbital and placed supine on a surgical bench. The heart was collected using a clean petri dish, and the heart was flushed with 0.9% saline into the aortic opening using a syringe to drain residual blood. The sections were placed in a 1% TTC solution, wrapped in tin foil to protect them from light and incubated at 37°C for 30 minutes. Following the removal of TTC staining solution, tissue samples were fixed and observed under a microscope, showing pale white in the infarcted area and purplish red in normal myocardial tissue. Infarct zone measurements: infarct zones (ISS) and ischemic zones (AARs) were calculated by IPP6.0 software.
Hematoxylin staining for 8-15minutes, rinse the excess dye with tap water. Differentiation with 1% hydrochloric acid and alcohol for the 30s and full washing with tap water for 10 minutes. Dye with 1% eosin solution for 10 min and rinse with tap water for 1 min. Then gradient alcohol dehydration, xylene transparent, and finally neutral gum seal, dry, and observe under the optical microscope.
Tissue sections were subsequently prepared for masson staining. Firstly, the sections were stained with lapis lazuli blue staining, followed by Mayer hematoxylin staining and then differentiated using acidic ethanol differentiation solution. Following treated using aniline blue solution, the slices were dehydrated, hyalinized, and sealed with neutral resin.
To determine apoptosis of sample cells, we subsequently perform TUNEL assays. The cells were fixed, washed, blocked, and incubated under appropriate conditions as per conventional operation procedures. Color development was performed for visualization.
For qPCR detection, RNA was extracted first, then reverse transcription and quantitative detection were performed using Goldenstar™ RT6 cDNA Synthesis Kit Ver.2 and 2 × T5 Fast qPCR Mix (SYBR Green I), respectively. The primer sequences are as follows: miR-124-3p-FTGGCTGGACAGAGTTGTCAT miR-124-3p-RCTGTACAGGTGAGCGGATGTT PTEN-FACCAGGACCAGAGGAAACCT PTEN-RCCTTGTCATTATCCGCACGC GAPDH-F GCAAGTTCAACGGCACAG. GAPDH-R GCCAGTAGACTCCACGACATA.
Firstly, we performed total protein extraction to collect the supernatant. Total protein was mixed with SDS sample buffer, denatured by boiling, followed by SDS-PAGE seperation, PVDF membrane transferrence, and membrane blocking using 5% skim milk. Subsequently, incubation was performed at appropriate concentrations of primary antibodies cleaved caspase 3 (China, ABclonal, A19654), PTEN (China, ABclonal, A19104), P13K (China, abcam, ab182651), AKT (China, abcam, ab38449), and β-actin (China, ABclonal, AC026) at 4°C overnight. Secondary antibodies were also supplied. Finally, the enhanced chemiluminescence (ECL) detection reagent was used to mix and evenly cover the entire film, and after 1 minute of reaction, it was placed in an exposure meter for exposure detection.
We performed miR-124-3p and PTEN lentiviruse packaging. The rat model was injected 108 unit virus in caudal vein, and samples were taken subsequently.
Briefly, PGL4.11-BASIC, the reporter gene vector was used for preparing PETN-WT-LUC2-RLUC and PETN-MUT-LUC2-RLUC, respectively. Working solution luciferase assay reagent II (Progema) was applied. The expression of the reporter genes was monitored, recorded accordingly, and used as internal value.
Data related to prostate cancer and oxidative stress were obtained from the database https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE180765; the online database STRING (http://string-db.org) was utilized for the analysis of protein-protein interaction. The obtained DEGs were subjected to gene ontology (GO) analysis with the help of the R software packages cluster Profiler, enrichplot, and ggplot2. Only pathways with both P and Q values less than 0.05 were considered significantly enriched.
Infarct areas were visualized by TTC staining in the model group (Figure 1(a)). Meanwhile, myocardial tissue structure damage and fibrosis were also marked increased in the model by HE and Masson staining (Figures 1(b) and 1(c)). Furthermore, apoptosis of the model cells was substantially elevated after TUNEL method (Figure 1(d)), and so did the expression of cleaved caspase 3 in the model group as per the results of western blot (Figure 1(e)).
The average size and zeta potential of NC miR-encapsulated PLGA nanoparticles were 137.2 ± 5.636 nm and -14.1 ± 1.300, respectively, and those of nano-miR-124-3p were 137.9 ± 6.007 nm and –13.8 ± 0.748 mV. A certain amount of nanoparticles was added to DMSO to destroy the nanoparticle structure and release miR-124-3p, and the content of miR-124-3p was detected using a double-stranded DNA quantitative kit. The encapsulation efficiency and drug loading efficiency of miR-124-3p in PLGA-PEG nanoparticles were 78.4% ± 1.497% and 4.9% ± 0.3%, respectively (Table 1).
Based on previous studies, we identified the miRNA with significant differential expression as miR-124-3p, whose expression was markedly decreased in model compared to Sham (Figure 2(a)). To clarify whether miR-124-3p-loaded nanoparticles exert a regulatory function in myocardial infarction rats, we transfected cultured rats with miR-124-3p and nano-miR-124-3p. TTC staining showed that the transfected latter nanoparticles significantly reduced infarct size of the animals (Figure 2(b)). Finding of HE staining revealed that myocardial cells in model presented an irregular arrangement, there were more collagen fibers, and the myocardial transverse striation was seriously damaged, accompanied by inflammatory cell infiltration, but nano-miR-124-3p improved the myocardial tissue structure (Figure 2(c)). Masson staining showed that a large number of blue collagen fibers were deposited in the myocardial tissue of model group, and degrees of fibrosis were serious. Nano-miR-124-3p reduced myocardial fibrosis and tissue damage (Figure 2(d)). The apoptosis results of TUNEL were similar to the above findings: the nano-miR-124-3p decreased apoptosis of the cells (Figure 2(e)). Overall, miR-124-3p has protective and repair functions in the myocardium.
STARBASE prediction of possible miR-124-3p target genes indicated that PTEN might be one of its target genes (Figure 3(a)). The double luciferase assay further confirmed the previous test result that PTEN acted as a target gene of miR-124-3p (Figure 3(b)). Both western blot (Figure 3(c)) and qPCR (Figure 3(d)) findings revealed that PTEN expression increased in model compared to Sham. After miR-124-3p overexpression, PTEN expression decreased substantially in samples with miR-124-3p agomir and antagomir. Inhibition of miR-124-3p expression markedly elevated PTEN levels. Meanwhile, both P13K and AKT expression was increased significantly following overexpression of miR-124-3p but declined greatly following inhibiting miR-124-3p (Figure 3(e)).
Through the myocardial infarction rat model cotransfected with nano-miR-124-3p agomir and PTEN group, it is proved that nano-miR-124-3p can protect and recover myocardial infarction by targeting PTEN. Cotransfection of nano-miR-124-3p and PTEN overexpression markedly elevated myocardial infarct size, structure injury, as well as fibrosis (Figures 4(a) and 4(c)). In addition, the apoptosis rate and the expression of cleaved caspase 3 in the nano-miR-124-3p and PTEN overexpression cotransfection group were also significantly higher than those in the nano-miR-124-3p group alone (Figure 4(d)). Collectively, miR-124-3p protected and repaired myocardial tissues by targeted inhibition of PTEN, whereas after overexpression of PTEN, it was proven that nano-miR-124-3p could not exert its protective function which might be hampered.
Myocardial cell injury occurs in myocardial tissue after ischemia-reperfusion injury, and oxidative stress also links to myocardial damage expansion during myocardial ischemia-reperfusion injury and has an association with acute myocardial infarction. To investigate whether miRNA-124-3p protects myocardial tissue by regulating oxidative stress injury, we screened 1223 differentially expressed genes for myocardial infarction and miR-124. GO enrichment analysis showed that these differentially expressed genes were involved in oxidative stress (Figure 5(a)). Strings screened 12 genes associated with PTEN (Figure 5(c)). Venn showed 3 genes at the intersection of the two mentioned above (Figure 5(b)). The chord diagram shows that TP53 genes in both intersecting sets link to the regulation of oxidative stress (Figure 5(d)).
MI results from the blockage of the heart's own blood supply channels due to various factors [18]. With the change in people's lifestyles and the improvement of living standards, the incidence of acute myocardial infarction is increasing year by year. Acute myocardial infarction has a rapid onset and many complications, which seriously endanger human health [19]. Therefore, for acute myocardial infarction, early diagnosis and treatment are particularly important, which can improve the prognosis and improve the treatment effect. Postinfarction remodeling is largely dependent on infarct size [20, 21]. In this experiment, HE and Masson staining indicated an apparent damage in myocardial tissue structure of model rats and much worse myocardial fibrosis; the proportion of apoptotic cells and the expression of cleaved caspase 3 were also increased.
As a nonexpressed RNA, miRNAs are involved in the early development of the heart, and some miRNAs are only detectable in the heart, further confirming the value of miRNAs as cardiac markers [22, 23]. PLGA polymer nanoparticles can capture bioactive molecules and escape from cellular lysosomes into the cytoplasm, thereby inducing sustained release of intracellular transporters and prolonging the therapeutic effect. This study demonstrated that nano-miR-124-3p delivered via PLGA significantly reduced infarct size, improved myocardial tissue structure, and attenuated myocardial fibrosis. In addition, nano-miR-124-3p could significantly decrease apoptosis.
As a member of the miRNA family, miRNA-124-3p correlates with the proliferation and apoptosis of cells [24]. Multiple researches have indicated the great importance of miR-124-3p in cardiovascular diseases [25]. The PTEN gene is the first novel tumor suppressor gene with specific phosphatase activity that was cloned in 1997 and can effectively inhibit the growth of tumor cells [26]. The current research elucidated that the target gene of miR-124-3p PTEN mediated the progression of myocardial infarction. Cotransfection of nano-miR-124-3p and PTEN overexpression greatly enlarged the size of myocardial infarction, structure damage, and fibrosis. Apoptosis rate and the expression of cleaved caspase 3 were also much higher than those in the nano-miR-124-3p group, implying the protection function of nano-miR-124-3p in myocardial tissues via targeting the inhibition of PTEN, which did not work under the context of PTEN overexpression.
During myocardial ischemia, hypoxia occurs simultaneously and the body's ability to scavenge oxygen radicals is insufficient, while when reperfusion restores blood supply, a large amount of oxygen-rich blood flow rapidly enters the ischemic region in a short period of time, generating more cytotoxic substances such as oxidized radicals, causing cell damage, necrosis, or apoptosis [27, 28]. Reducing the formation of oxygen free radicals is beneficial to reducing apoptosis, mitigating myocardial injury, and improving myocardial function [29]. During AMI, reduced myocardial antioxidant capacity, increased oxidative stress, and increased cardiomyocyte apoptosis were observed [30]. Studies have shown that miR-340-5p protected cardiomyocyte apoptosis and oxidative stress induced by hypoxia and reoxygenation by regulating the Act1/NF-κB signaling pathway [31]. miR-223-3p and miR-210 are also involved in oxidative stress injury and apoptosis in cardiomyocytes [32, 33]. In this study, bioinformatics analysis revealed that miRNA-124-3 may regulate oxidative stress injury by targeting PTEN.
This study demonstrates that PLGA nanoparticles can effectively deliver miR-124-3p to a rat model of myocardial infarction so that it can protect and repair myocardial tissue in myocardial infarction. Furthermore, it also reveals that nano-miR-124-3p protects myocardial tissue via regulating the PTEN/PI3K/AKT signaling pathway. This study confirms the role of miR-124-3p in myocardial injury, and it is expected to offer some directions for the management against this disease clinically. | true | true | true |
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PMC9578823 | Yulong Wang,Shuiting Zhai,Jianwu Xing,Jinchi Zhang,Yingkun He,Guoquan Wang,Tianxiao Li | Long Noncoding RNA DSCAM-AS1 Facilitates Proliferation and Migration of Hemangioma Endothelial Cells by Targeting miR-411-5p/TPD52 Axis | 11-10-2022 | Background Diagnosed as a kind of vascular neoplasm of infancy, hemangioma (HA) occurs mainly due to the aberrant proliferation of endothelial cells. Existing evidence has manifested the close relationship of long noncoding RNAs (lncRNAs) with the pathogenesis of HA. Although lncRNA DSCAM antisense RNA 1 (DSCAM-AS1) has been revealed to be implicated in the progression of human diseases, the underlying mechanism DSCAM-AS1 exerts in HA formation is unclear. Aims To figure out how DSCAM-AS1 may regulate the progression of human hemangioma endothelial cells (HemECs). Methods DSCAM-AS1 expression was verified through RT-qPCR detection. Functional assays including EdU assay, colony formation assay, flow cytometry analysis, TUNEL assay, and transwell assay were applied to evaluate cell proliferation, apoptosis, and migration upon DSCAM-AS1 knockdown. Moreover, RNA pull-down assay, luciferase reporter assay, RIP assay, and other mechanism experiments were utilized for evaluating the correlation of DSCAM-AS1 and RNAs in HemECs. Results DSCAM-AS1 knockdown inhibited proliferative capability and migratory capability of HemECs whereas expedited apoptosis. Molecular mechanism results testified DSCAM-AS1 could function as a ceRNA to bind miR-411-5p in HemECs. Besides, it was confirmed that tumor protein D52 (TPD52) served as a downstream target of miR-411-5p in HemECs. More importantly, related rescue assays uncovered that elevated expression of TPD52 or inhibited expression of miR-411-5p reversed the repressive progression of HemECs mediated by DSCAM-AS1 depletion. Conclusion DSCAM-AS1 expedited HA progression via miR-411-5p/TPD52 pathway, which provided a novel therapeutic option for HA treatment. | Long Noncoding RNA DSCAM-AS1 Facilitates Proliferation and Migration of Hemangioma Endothelial Cells by Targeting miR-411-5p/TPD52 Axis
Diagnosed as a kind of vascular neoplasm of infancy, hemangioma (HA) occurs mainly due to the aberrant proliferation of endothelial cells. Existing evidence has manifested the close relationship of long noncoding RNAs (lncRNAs) with the pathogenesis of HA. Although lncRNA DSCAM antisense RNA 1 (DSCAM-AS1) has been revealed to be implicated in the progression of human diseases, the underlying mechanism DSCAM-AS1 exerts in HA formation is unclear.
To figure out how DSCAM-AS1 may regulate the progression of human hemangioma endothelial cells (HemECs).
DSCAM-AS1 expression was verified through RT-qPCR detection. Functional assays including EdU assay, colony formation assay, flow cytometry analysis, TUNEL assay, and transwell assay were applied to evaluate cell proliferation, apoptosis, and migration upon DSCAM-AS1 knockdown. Moreover, RNA pull-down assay, luciferase reporter assay, RIP assay, and other mechanism experiments were utilized for evaluating the correlation of DSCAM-AS1 and RNAs in HemECs.
DSCAM-AS1 knockdown inhibited proliferative capability and migratory capability of HemECs whereas expedited apoptosis. Molecular mechanism results testified DSCAM-AS1 could function as a ceRNA to bind miR-411-5p in HemECs. Besides, it was confirmed that tumor protein D52 (TPD52) served as a downstream target of miR-411-5p in HemECs. More importantly, related rescue assays uncovered that elevated expression of TPD52 or inhibited expression of miR-411-5p reversed the repressive progression of HemECs mediated by DSCAM-AS1 depletion.
DSCAM-AS1 expedited HA progression via miR-411-5p/TPD52 pathway, which provided a novel therapeutic option for HA treatment.
Hemangioma (HA) is a benign tumor commonly diagnosed in childhood, specifically in infants with low birth weight as well as premature infants. The etiology and pathogenesis of HAs have been investigated in recent years, but not completely clarified because of its complexity [1, 2]. Females are the majority of HA patients, with a female: male rate of approximately 3 : 1, and HA generally occurs about 14 days after birth [3, 4]. Severe HA results in a serous burden to patients and their beloved ones both physically and mentally, particularly on account of the disfigurement of skin lesions that severely influence the function of skin [3, 5]. Unfortunately, it is of great difficulty in treating severe HA, even with today's advances made in medical domain. Multiple researchers have been dedicated to studying and developing therapeutic strategies for HA, and advocate early diagnosis along with combined treatments so as to efficaciously limit HA growth and cut down the incidence of its complications [6]. What is more, it has been uncovered that the aberrant proliferation of endothelial cells is implicated in HA progression [7, 8]. Nevertheless, there is not much discussion on the molecular mechanism contributing to HA formation. Long noncoding RNAs (lncRNAs) are identified as a group of transcripts with limited or no capacity of protein coding. Besides, lncRNAs are noncoding RNAs (ncRNAs) that possess longer than 200 nucleotides [9]. A previous study indicated that lncRNAs participate in a variety of biological courses, involving transcriptional and posttranscriptional regulation of genes, as well as epigenetics [10]. Abundant evidence has emphasized the importance of lncRNAs involvement in the occurrence and progression of diverse human tumors and diseases [11, 12]. More importantly, the critical regulatory effect of abnormally expressed lncRNA together with its molecular mechanism on the progression of HA has been manifested in many HA-related literatures. Among these molecular mechanisms, competing endogenous RNA (ceRNA) is the most documented. For example, lncRNA NEAT1 as a ceRNA promotes HA development via miR-361-5p/VEGFA axis [13]. LncRNA SNHG16 contributes to the proliferation, migration, and invasion of hemangioma endothelial cells (HemECs) via regulation of miR-520d-3p/STAT3 axis [14]. Recently, DSCAM antisense RNA 1 (DSCAM-AS1) has been recognized as an oncogenic lncRNA in breast cancer and elicits promoting effect on tumor growth [15]. It also contributes to the tumorigenesis of cervical cancer [16] and predicts a poor prognosis in ovarian cancer [17]. However, how DSCAM-AS1 may exert its functions in HA remains unknown, which arouses our interest. In summary, our purpose is to clarify how DSCAM-AS1 may mediate HA development, and through relevant functional and mechanism experiments, we are going to verify the expression pattern of DSCAM-AS1 in HemECs and further uncover its potential function in the modulation of cell behaviors in HA progression. A series of rescue assays were taken to verify the interaction among involved RNAs in HemECs. We hope that what we have revealed through our research may shed some new lights on the researches of HA treatment.
HemECs cell lines were commercially acquired from the Institute of Biochemistry and Cell Biology (Shanghai, China) and maintained under 5% CO2 and 37°C. Samples were cultured in Endothelial Basal Medium-2 (EBM-2) with 10% FBS and 1% Pen/Strep solution as supplements.
Total RNA extracted from human HA tissue and HemEC cell samples were achieved by TRIzol reagent (Invitrogen) as instructed by supplier. 1 mg of total RNA was used for cDNA synthesis applying PrimeScript™ RT Master Mix (TaKaRa, Shiga, Japan), and then SYBR® Premix Ex TaqTM II kit (TakaRa) was applied for qPCR on the 7500 Fast Real-Time PCR System (Applied Biosystems, Foster City, CA). Relative target gene expression was calculated by 2−∆∆Ct method with standardization to GAPDH or U6.
Specific shRNAs and the corresponding negative control (NC) were commercially acquired from Genepharma (Shanghai, China), which were applied for the depletion of DSCAM-AS1 and TPD52 in HemECs. The pcDNA3.1/TPD52 and pcDNA3.1, as well as the miR-411-5p mimics/inhibitor and NC mimics/inhibitor were all designed by RiboBio Co., Ltd. (Guangzhou, China). Transfection was performed for 48 h using Lipofectamine2000 (Invitrogen).
Proliferation of HemECs was detected using EdU incorporation assay with Click-iT® EdU Imaging Kits (Invitrogen) as guided by supplier. Cultured cell samples in 24-well plates were labeled with EdU for 2 h, then fixed and permeabilized. DAPI was added for 30 min for observing with fluorescence microscopy (Olympus, Tokyo, Japan).
500 clonogenic cell lines in the 6-well plates were processed with the 14 days of incubation, then fixed and stained with 0.5% crystal violet. Clones were counted manually.
Cells in 6-well plates were trypsinized for resuspending in the Binding buffer adding the FITC-conjugated Annexin V and PI dye for 15 min at room temperature. Flow cytometer was employed with FACS Calibur (BD Bioscience, San Jose, CA).
The caspase-3 activity kit was procured from Beyotime (Shanghai, China) for the determination of caspase-3 activity in cell lysates as per the user manual. The microplate reader (Tecan, Männedorf, Switzerland) was used.
TUNEL staining assay was conducted as per the instruction of One-Step TUNEL Apoptosis Assay Kit (Beyotime) for detecting the cell apoptosis. TUNEL-positive cells were determined by fluorescence microscopy.
The 24-well transwell chambers were procured from Corning Incorporated (Corning, NY) for cell migration. 5 × 103 cells in serum-free medium were added into the upper chamber; the complete medium was added into the lower chamber. After 24 hours, cells in the bottom were all stained with crystal violet in 4% paraformaldehyde for observing.
The lysed cell samples in cell fractionation buffer were collected to centrifuge for nucleus-cytoplasm separation. After removing supernatant, samples were processed with cell disruption buffer. DSCAM-AS1 expression level in nuclear and cytoplasmic fractions went through RT-qPCR analysis.
RNA pull-down analysis was achieved using Pierce™ Magnetic RNA-Protein Pull-Down Kit (Thermo Fisher Scientific, Waltham, MA) in light of the guidebook. Protein extracts were mixed with DSCAM-AS1 biotin probe and magnetic beads. The pull-downs were analyzed by RT-qPCR.
The constructed reporter plasmids pmirGLO-DSCAM-AS1-WT/Mut and pmirGLO-TPD52-WT/Mut were cotransfected into cultured HemEC cells for 48 h. Dual-Luciferase Reporter Assay System (Promega, Madison, WI) was employed for detection of fluorescence intensity changes as required by supplier.
Using Magna RIP™ RNA Binding Protein Immunoprecipitation Kit (Millipore), RIP assay was conducted with the human anti-Ago2 and anti-IgG (Millipore). Precipitated RNAs were all estimated by RT-qPCR.
Statistical analysis was achieved by SPSS 22.0 (IBM, Armonk, NY, USA) with significant level at P < 0.05, and data were all expressed as the mean ± SD. Prism version 5.0 (GraphPad Software, La Jolla, CA) was applied to generate images. Group comparison was performed with Student's t test and one-way ANOVA analysis. Bio-triple replications were required in each assay.
For purpose of researching the underlying function of DSCAM-AS1 on HA formation, we knocked down its expression in HemECs. After downregulating DSCAM-AS1 expression in HemECs utilizing sh-DSCAM-AS1#1/2 (Figure 1(a)), we performed a serious of functional assays. EdU assay illustrated that EdU positive cells were significantly decreased in the sh-DSCAM-AS1 transfection groups, suggesting cell proliferative capability was repressed by DSCAM-AS1 downregulation (Figure 1(b)). Further, it was illustrated from colony formation assay that cell colonies were reduced, which proved that cell proliferative capability could be repressed by DSCAM-AS1 knockdown (Figure 1(c)). Then flow cytometry analysis indicated that the cell apoptosis rate was significantly increased by the transfection of sh-DSCAM-AS1 (Figure 1(d)). Besides, caspase-3 activity was elevated in the transfection cell groups, which further confirmed the promotion of cell apoptosis ability (Figure 1(e)). It was further proved that the quantity of TUNEL positive cells was obviously increased by DSCAM-AS1 downregulation (Figure 1(f)). In the end, transwell assay was conducted to measure cell migration capability, and we discovered that after transfection, the number of migrated cells was decreased (Figure 1(g)). In short, silenced DSCAM-AS1 attenuates cell proliferation and migration whereas motivates cell apoptosis in HemECs.
Through subcellular fractionation analysis of DSCAM-AS1 distribution, we realized that DSCAM-AS1 was mainly scattered in the cytoplasm of HemECs, suggesting that DSCAM-AS1 may regulate gene expression via a posttranscriptional way (Figure 2(a)). Further, RIP assay proved that DSCAM-AS1 was bound to Ago2 protein (Figure 2(b)). As mostly documented, lncRNA mediates cancer progression by serving as microRNA (miRNA) sponge. Thus, we speculated that DSCAM-AS1 probably contributed to HA formation via binding to certain miRNA. Through starBase (http://starbase.sysu.edu.cn/) and DIANA (http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php) database, 13 miRNAs were predicted to possess binding capacity with DSCAM-AS1 (Figure 2(c)). Subsequently, we observed that only miR-411-5p was conspicuously enriched in DSCAM-AS1 biotin probe group, confirming that miR-411-5p combined with DSCAM-AS1 in HemECs (Figure 2D). Besides, through starBase, a binding site between miR-411-5p and DSCAM-AS1 was predicted (Figure 2(e)). After elevated miR-411-5p expression in HemECs (Figure 2(f)), the luciferase activity of DSCAM-AS1-WT was markedly reduced whereas the luciferase activity of DSCAM-AS1-Mut presented no evident changes (Figure 2(g)). These results demonstrated that DSCAM-AS1 can sponge miR-411-5p via acting as a ceRNA.
To study whether miR-411-5p elicit effect on HemEC progression, we employed gain-of-function assays. EdU staining assay and colony formation assay unveiled that miR-411-5p overexpression hampered cell proliferation (Figures 3(a) and 3(b)). Conversely, data from flow cytometry analysis, caspase-3, and TUNEL experiments verified the facilitating effect of miR-411-5p upregulation on cell apoptosis (Figures 3(c)–3(e)). Furthermore, transwell assay delineated that the migration ability of HemECs was suppressed by miR-411-5p overexpression (Figure 3(f)). Taken together, miR-411-5p represses cell proliferation and migration of HemECs.
To further probe into the mechanism of DSCAM-AS1 in HemECs, we planned to find the target gene of miR-411-5p. After searching starBase under particular condition (CLIP − Data > = 5; pan − Cancer > = 10), some mRNA candidates were screened out (Figure 4(a)). In order to screen out the most suitable mRNAs, we performed luciferase reporter assay and found that only the luciferase activity of miR-411-5p–overexpressed HemECs was obviously declined in TPD52 group (Figure 4(b)). Further, after upregulating miR-411-5p expression or downregulating DSCAM-AS1 expression, TPD52 expression was remarkably decreased (Figures 4(c) and 4(d)). Thus, we selected TPD52 to conduct the further assays. To make further exploration of the interaction between miR-411-5p and TPD52, online search of starBase predicted the binding sites between them (Figure 4(e)). The luciferase activity of TPD52-WT, but not that of TPD52-Mut, was lowered after miR-411-5p expression was elevated in HemECs (Figure 4(f)). Subsequent analysis of RIP assay depicted that DSCAM-AS1, miR-411-5p, and TPD52 were enriched in anti-Ago2 group, testifying the coexistence of them in RISC (Figure 4(g)). Besides, TPD52 was significantly pulled down by the biotinylated miR-411-5p probe (Figure 4(h)). These results demonstrated that miR-411-5p could bind to TPD52. To investigate the biological role of TPD52 in HemECs, we knocked down TPD52 prior for loss-of-function assays (Figure S1A). As displayed in Figure S1B-C, TPD52 downregulation impaired cell proliferation. Besides, cell apoptosis capability was enhanced by inhibiting TPD52 expression, as manifested by flow cytometry, TUNEL and caspase-3 expression detection (Figure S1D-F). What is more, the migration ability of HemECs was suppressed after TPD52 expression was reduced (Figure S1G). In brief, TPD52 is directly targeted by miR-411-5p in HemECs and it accelerates cell proliferation and migration.
On the basis of the above results, we realized that DSCAM-AS1 could indirectly regulate TPD52 expression through binding to miR-411-5p. Herein, we wondered whether and how DSCAM-AS1/miR-411-5p/TPD52 pathway modulated the progression of HemECs. Prior to rescue assay to testify the probable mechanism of DSCAM-AS1, the overexpression efficiency of TPD52 and the inhibition efficiency of miR-411-5p were tested in HemECs, and the results appeared to be satisfactory (Figure 5(a)). Then, data obtained from EdU and colony formation assays suggested that miR-411-5p inhibition or TPD52 upregulation could countervail the suppressive effect of DSCAM-AS1 deficiency on the proliferation of HemECs (Figures 5(b) and 5(c)). Likewise, inhibiting miR-411-5p expression or elevating TPD52 expression could reverse the promoting function on cell apoptosis mediated by DSCAM-AS1 knockdown (Figures 5(d)–5(f)). Besides, the suppressed capacity of HemECs to migrate induced by DSCAM-AS1 downregulation could be recovered by miR-411-5p repression or TPD52 overexpression (Figure 5(g)). All in all, DSCAM-AS1 downregulation impairs HemEC progression via targeting miR-411-5p/TPD52 axis.
Accumulating evidence has verified that lncRNAs exert significant function on different kinds of human diseases through ceRNA network, including HA. For instance, lncRNA H19 improves mesenchymal stem cells survival and their angiogenic capability though regulating miR-199a-5p/VEGFA axis [18]. LncRNA FTX targets miR-29b-1-5p and Bcl2l2 to modulate apoptosis of cardiomyocyte [19]. LncRNA CASC9 as a ceRNA mediates HA development via miR-125a-3p/Nrg1 pathway [20]. Although DSCAM-AS1 upregulation has been revealed to expedite breast cancer malignancy [15], the knowledge about whether and how DSCAM-AS1 regulate HA progression remains to be deciphered. In this study, we proved that DSCAM-AS1 downregulation could restrain the proliferation and migration of HemECs. These results confirmed that DSCAM-AS1 functioned as an oncogene in HA. As largely uncovered, lncRNA as a ceRNA is involved in the progression of human malignancies and diseases via sponging miRNA and targeting mRNA [21–23]. According to the findings that DSCAM-AS1 located in cytoplasm of HemECs, we speculated that DSCAM-AS1 mediated HA formation though sponging miRNA. Through bioinformatics tools, we sifted out several miRNA candidates that may combine with DSCAM-AS1, but the RNA pull-down data later manifested that only miR-411-5p was conspicuously pulled down by DSCAM-AS1 biotin in HemECs. Furthermore, we proved that miR-411-5p alleviate the malignant cell behaviors in HemECs. MiR-411-5p, a miRNA previously investigated in multiple cancers, has been manifested to suppress non-small-cell lung cancer cell migration and invasion by inhibiting PUM1 expression [24] and inhibit breast cancer cell proliferation and metastasis via targeting GRB2 [25]. Previous studies have revealed DSCAM-AS1's involvement in cancer progression via ceRNA pattern. For example, DSCAM-AS1 aggravates non-small-cell lung cancer progression via sponging miR-577 to further modulate HMGB1 expression [26]. DSCAM-AS1 is regarded as a molecular sponge of miR-384 to modulate AKT3 expression, thereby aggravating colorectal cancer malignancy [27]. This is the first time that we have revealed the ceRNA interaction of DSCAM-AS1 and miR-411-5p on the progression of HA. Further, upregulation of miR-411-5p was verified to impair HA progression. Tumor protein D52 (TPD52), an identified mRNA with protein coding potential, has been unveiled to function as an oncogene in human cancers. For example, TPD52 was highly expressed in colorectal cancer cells and correlated with poor prognosis [28]. TPD52 promoted cell growth in nasopharyngeal carcinoma [29]. Silencing of TPD52 inhibited the malignant cell behaviors in pancreatic cancer by deactivating Akt pathway [30]. Importantly, TPD52 was reported to take part in the cell growth and aggressiveness of HA [31]. In the current research, TPD52 expression in HemECs was validated to be negatively modulated by miR-411-5p but positively regulated by DSCAM-AS1. TPD52 was a direct target gene of miR-411-5p. Moreover, knockdown of TPD52 represses HemEC proliferation and migration. What is more, the inhibitive effect of DSCAM-AS1 downregulation on HemEC progression could be rescued by inhibiting miR-411-5p expression or elevating TPD52 expression.
DSCAM-AS1 elevates TPD52 expression to drive the progression of HA through sponging miR-411-5p. This finding provides evidence of DSCAM-AS1 promoting function on HA progression. Besides, the discovery of the DSCAM-AS1/miR-411-5p/TPD52 pathway suggests an innovative clue for HA treatment. | true | true | true |
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PMC9578906 | Xiaoxuan Zhang,Yan Zhang,Xin Qiu,Jing Cai,Zhenzhou Yang,Fangzhou Song | Extracellular Vesicles Derived from Lung Cancer Cells Induce Transformation of Normal Fibroblasts into Lung Cancer-Associated Fibroblasts and Promote Metastasis of Lung Cancer by Delivering lncRNA HOTAIR | 11-10-2022 | Human lung cancer (LC) cells A549/H358, normal lung epithelial cells BEAS-2B, and lung normal fibroblasts (NFs) were cultured, followed by transfection of H358 cells with HOTAIR shRNA. Extracellular vesicles (EVs) extracted from H358 cells were identified. The internalization of Dil-labeled-EVs by NFs was tested, and protein levels of cancer-associated fibroblast (CAF) surface markers, inflammatory cytokines, cell proliferation, invasion, and migration, and lncRNA HOTAIR levels were determined. A549 cells were cultured in an H358-EVs-treated conditioned medium of NFs (NFCM), followed by intravenous injection of A549 cells into nude mice. The lesions and Ki-67-positive cells in lung tissues were measured. The results showed that tumor cell-derived EVs (T-EVs) motivated the transformation of NFs into CAFs. Specifically, EVs can be internalized by NFs, and the protein levels of CAF surface markers and inflammation levels were elevated in H358-EVs-treated NFs. The proliferation, invasion, and migration of A549 cells cultured in T-EVs-treated NFCM were increased. H358-EVs carried HOTAIR into NFs and promoted the transformation of NFs into CAFs. Inhibition of HOTAIR partially reversed the promoting effect of H358-EVs on the transformation of NFs into CAFs and invasion and migration of LC cells. T-EVs promoted metastasis of LC in vivo by transforming NFs into CAFs. | Extracellular Vesicles Derived from Lung Cancer Cells Induce Transformation of Normal Fibroblasts into Lung Cancer-Associated Fibroblasts and Promote Metastasis of Lung Cancer by Delivering lncRNA HOTAIR
Human lung cancer (LC) cells A549/H358, normal lung epithelial cells BEAS-2B, and lung normal fibroblasts (NFs) were cultured, followed by transfection of H358 cells with HOTAIR shRNA. Extracellular vesicles (EVs) extracted from H358 cells were identified. The internalization of Dil-labeled-EVs by NFs was tested, and protein levels of cancer-associated fibroblast (CAF) surface markers, inflammatory cytokines, cell proliferation, invasion, and migration, and lncRNA HOTAIR levels were determined. A549 cells were cultured in an H358-EVs-treated conditioned medium of NFs (NFCM), followed by intravenous injection of A549 cells into nude mice. The lesions and Ki-67-positive cells in lung tissues were measured. The results showed that tumor cell-derived EVs (T-EVs) motivated the transformation of NFs into CAFs. Specifically, EVs can be internalized by NFs, and the protein levels of CAF surface markers and inflammation levels were elevated in H358-EVs-treated NFs. The proliferation, invasion, and migration of A549 cells cultured in T-EVs-treated NFCM were increased. H358-EVs carried HOTAIR into NFs and promoted the transformation of NFs into CAFs. Inhibition of HOTAIR partially reversed the promoting effect of H358-EVs on the transformation of NFs into CAFs and invasion and migration of LC cells. T-EVs promoted metastasis of LC in vivo by transforming NFs into CAFs.
Lung cancer (LC) remains one of the most deadly malignancies, accounting for one-fifth of all cancer deaths [1]. Meanwhile, LC is a highly heterogeneous disease that occurs in a variety of anatomical sites throughout the respiratory tract [2]. It has been reported that repeated damage in lung cells caused by various environmental factors leads to lung tissue damage, thereby inducing genetic and epigenetic changes as well as global transcriptome changes [3]. Moreover, long-term changes at the DNA level lead to abnormal activation of multiple oncogenic pathways and inactivation of tumor suppressor pathways, as well as irreversible changes in cell function, which ultimately induce precancerous lesions, including dysplasia and clonal plaques (early stage of LC) [4, 5]. In addition, the growth of tumor cells is provoked by additional alterations, such as co-occurrence of mutations, metabolic changes, and immune evasion, which eventually leads to the invasion and metastasis of tumor cells (advanced LC) [6, 7]. Since the clinical symptoms of early LC are not obvious and difficult to identify, most patients with LC are commonly diagnosed at an advanced stage [8, 9]. In recent years, despite progress in the treatment options, the prognosis of patients with advanced LC is still poor [10]. Moreover, due to delayed diagnosis, the 5-year survival rate of LC is less than 5%, which is much lower than other cancer patients [11, 12]. More importantly, LC has high incidence and high metastasis rates and has become the number one killer in all cancer [13, 14]. Currently, the tumor microenvironment (TME) associated with LC progression has been a hot topic in the study of effective prognosis and therapeutic targets of LC [15]. TME consists of cancer cells, immune cells, stromal cells, and cytokines, among which stromal cells are composed of cancer-associated fibroblasts (CAFs), immune cells, and vascular endothelial cells [16–19]. What is noteworthy is that normal fibroblasts (NFs) are essential in tissue homeostasis under normal conditions and mediate proper cellular communication and functions; additionally, NFs can be activated by cancer-secreted factors, and these activated NFs are considered CAFs [20]. CAFs are the most important stromal component of the TME, encouraging tumor growth and metastasis [21]. Specifically, CAFs secrete growth factors, chemokines, matrix metalloproteases, and extracellular matrix (ECM) to regulate tumor growth, angiogenesis, and recruitment of bone marrow-derived cells, thereby promoting metastasis [22–24]. Additionally, CAFs, characterized by expression of alpha-smooth muscle actin (α-SMA) and fibroblast activation protein- (FAP-) α, are the most representative stromal cells in the TME, providing nutrients for tumor development and metastasis by interacting with tumor cells [25, 26]. Meanwhile, CAFs can regulate the inflammatory microenvironment by expressing proinflammatory genes such as interleukin (IL)-1β, IL-6, IL-8, TGF-β, CXCL12, and collagen [27–29]. Importantly, CAFs produce a large amount of IL-6 in the TME, induce epithelial-mesenchymal transition through the IL-6/STAT3 pathway, and enhance the metastasis potential of LC [30]. Hence, elucidating the regulatory mechanism of CAF activity may lay a theoretical foundation for new therapies targeting TME. External vesicles (EVs) are a group of heterogeneous cell-derived vesicles composing of small exosomes and large microvesicles [31]. It has been verified that tumor cell-derived EVs (T-EVs) regulate the motivation of CAF phenotypes in the TME, which can be mediated by several EV cargoes such as miRNAs, proteins, mRNAs, and long noncoding RNA (lncRNAs) [32]. LncRNAs, a subgroup of ncRNAs with a length of more than 200 nt and without protein-coding function, have been recognized for their mediating effects on the development of a variety of tumors [33–35]. Transforming growth factor-β (TGF-β), fibroblast growth factor 2, epidermal growth factor, hypoxia, reactive oxygen species, and ncRNAs are the key regulators of fibroblast activation [36, 37]. With the development of sequencing technologies such as gene chips and next generation sequencing, lncRNAs are identified to play a role as a transmitter between tumor cells and CAFs and participate in the activation of NFs to CAFs [21]. In addition, tumor cells and CAFs can communicate more directly through Exos-carrying lncRNAs, and Exos released by cancer cells can also promote the transformation and activation of CAFs [32]. Meanwhile, Exos secreted by tumors may be optimal lncRNA carriers to provide a mechanism for lncRNA transport to the TME [38]. It has been reported that the tumor-derived exosomal lncRNA POU3F3 promotes the resistance of esophageal squamous cell carcinoma cells to cisplatin by inducing the transformation of fibroblasts into CAFs [39]. LncRNA HOTAIR has been reported to be highly expressed in LC and promotes cisplatin resistance in non small-cell LC cells [21, 40], thus being as a marker of abnormal regulation of the LC cell cycle [41]. Moreover, HOTAIR knockdown could partially reverse the promoting effect of Caveolin-1 on cell viability and invasiveness, and HOTAIR may be a new therapeutic target for LC [42]. Besides, inhibition of HOTAIR inhibits LC cell growth [43]. In light of the above literature, we speculate that T-EVs may facilitate the activation of CAFs by carrying HOTAIR, thus promoting the metastasis of LC. Nevertheless, it remains unclear whether LC cells activate CAFs by secreting EV-carrying HOTAIR. This study set out to explore the underlying mechanism of LC cell-derived EVs affecting the invasion and metastasis of LC by accelerating the activation of CAFs.
All animal experiments were conducted under the guidelines and ratified by the Animal Care and Use Committee of Chongqing Medical University. The animal experiments were conducted on the principle of minimized animal number and the least pain.
Human LC cell lines A549 and H358, normal lung epithelial cell line BEAS-2B (ATCC, Rockville, MD, USA), and lung NFs (Procell, Wuhan, China) were subcultured in DMEM (Gibco, NY, USA) supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin, and 100 μg/mL streptomycin in a humidified incubator at 37°C with 5% CO2. The medium was replaced every 2-3 days.
When H358/BEAS-2B cells reached about 80% confluence, they were cultured in an EV-free medium for 48 hours, followed by centrifugation at 2000 g for 10 minutes at 4°C to collect the supernatant, and centrifugation at 10000 g for 40 minutes at 4°C, and then the supernatants were collected. Afterwards, the samples were centrifuged at 110000 g at 4°C for 90 minutes followed by supernatant removal. Then, the precipitates were washed with phosphate-buffered solution (PBS) and centrifuged at 110000 g at 4°C for 90 minutes, and the supernatants were discarded to obtain EVs. Lastly, the EVs resuspended with PBS were preserved at −80°C. EVs were identified by the following methods: (A) the morphology of EVs was observed by a transmission electron microscope (TEM); (B) the particle size distribution of EVs was analyzed by nanoparticle tracking analysis (NTA); (C) Western blotting was employed to measure the level of positive markers, CD9 and CD81, and negative marker Calnexin on EV surface; (D) cell supernatant treated with EV inhibitor GW4869 (10 nm, Sigma-Aldrich, MO, USA) for 2 hours was served as the control (GW) [44]. The total protein content of EVs was determined with the bicinchoninic acid (BCA) kits (23225, Thermo Fisher, Shanghai, China) according to the instructions, and protein content was considered the standard when EVs were used. EVs were grouped as follows: the GW (H358-GW/BEAS-2B-GW) group, the EVs (H358-EVs/BEAS-2B-EVs) group, the H358-EVs+Rnase group (EVs treated with Rnase), the H358-EVs+Rnase+sodium-dodecyl-sulfate (SDS) group (treated with Rnase and SDS), the H358-EVs-si-negative control (NC) group (EVs were extracted after transfection of H358 cells with HOTAIR scramble), and the H358-EVs-si-HOTAIR group (EVs were extracted after transfection of H358 cells with HOTAIR shRNA). HOTAIR shRNA and HOTAIR scramble (GenePharma, Shanghai, China) were transfected into cells using Lipofectamine 2000 (Invitrogen, CA, USA).
NFs were cultured to the third generation. When the cell confluence reached about 90%, the cells were treated and grouped as follows: (1) the NFs group: without any treatment; (2) the EVsBEAS-2B group: treated with BEAS-2B-EVS (50 μg) for 6 hours [45]; (3) the GWBEAS-2B group: treated with an equal amount of BEAS-2B-GW for 6 hours; (4) the EVsH358 group: treated with H358-EVs (50 μg) for 6 hours; (5) the GWH358 group: treated with the same amount of H358-GW for 6 hours; (6) the EVsH358-si-HOTAIR group: treated with H358-EVS-si-HOTAIR (50 μg) for 6 hours; and (7) the EVsH358-si-NC group: treated with H358-EVs-si-NC (50 μg) for 6 hours.
When NFs reached about 50% confluence, the medium was supplemented and incubated for 48 hours. After centrifugation at 200 g for 10 minutes, the collected supernatants were NFs-conditioned culture medium. A549 cells were cultured in a mixture of a conditioned medium of NFs (NFCM) and A549 cell medium in a volume ratio of 1 : 2 for 24 hours [45]. A549 cells were allocated into the following 4 groups: the A549+GWH358 group (cultured in NFCM treated with H358-GW), the A549+EVsH358 group (cultured in NFCM treated with H358-EVs), the A549+EVsH358-si-NC group (cultured in NFCM treated with H358-EVs-si-NC), and the A549+EVsH358-si-HOTAIR group (cultured in NFCM treated with H358-EVs-si-HOTAIR).
EVs were labeled with Dil dye (Invitrogen) and coincubated with NFs for 24 hours. NFs were rinsed twice with PBS, fixed with 4% paraformaldehyde, counterstained with 4′,6-diamidino-2-phenylindole (Beyotime, Shanghai, China), and observed under the BX53 fluorescence microscope (×400) (Olympus, Japan).
The radio-immunoprecipitation assay lysis buffer (Beyotime) containing protease inhibitors (Sigma-Aldrich) was mixed with cells or EVs, dissolved on ice for 30 minutes, and followed by centrifugation and supernatant collection. The protein concentration was determined by BCA kits (Pierce, IL, USA). Subsequently, the proteins were separated by 10% SDS-PAGE and electrically transferred onto polyvinylidene fluoride membranes. Then the membranes were placed in 5% skim milk prepared by Tris-buffered saline-tween (TBST), shaken and sealed for 1 hour to block the nonspecific binding. Afterwards, the membranes were incubated with primary antibodies anti-CD9 (ab236630, 1 : 1000, Abcam, Cambridge, UK), anti-CD81 (ab109201, 1 : 1000, Abcam), Anti-Calnexin (ab133615, 1 : 1000, Abcam), anti-α-SMA (ab124964, 1 : 1000, Abcam), anti-FAP-α (ab207178, 1 : 1000, Abcam), and anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (ab181602, 1 : 10000, Abcam) at 4°C overnight. The membranes were rinsed thrice with TBST for 5 minutes each and then probed with horseradish peroxidase-labeled secondary antibody (ab6721, 1 : 2000, Abcam) for 1 hour. Finally, the bands were developed using an enhanced chemiluminescence kit (Pierce). ImageJ software (Version 1.48, NIH, Bethesda, MD, USA) was utilized to quantify the gray values of each band, with GAPDH as an internal reference.
The levels of inflammatory cytokines IL-1β (ab214025, Abcam), IL-6 (ab178013, Abcam), and IL-8 (ab214030, Abcam) were measured using the corresponding kits.
The cell viability was measured using the MTT kits (M1020, Solarbio, Beijing, China), and the optical density value was measured at 490 nm on a microplate reader (Bio-Tek Instruments, VT, USA).
Cells were seeded in 96-well plates at 2000/well, and cell proliferation was examined at 0, 6, 12, and 24 h using the CCK-8 kits. Experimental operations were carried out strictly under the kit instructions.
A 6.5 mm Transwell chamber (Costar, Jiangsu, China) with an aperture of 8 μm was applied for the transwell invasion assay following the manufacturer's instructions. Briefly, Matrigel (YB356234, Yu Bo Biotech, Shanghai, China) stored at -80°C was melted at 4°C overnight. Next, Matrigel (200 μL) was added to 200 μL serum-free medium at 4°C for complete dilution. Then, 50 μL of Matrigel was incubated in the apical chamber at 37°C for 2-3 hours. A549 cells (1 × 105) were cultured in a serum-free medium to prepare cell suspension. Cell suspension (200 μL) was paved on the apical chamber, and 800 μL of cell suspension containing 10% FBS was added to the basolateral chamber for 24 hours. After that, the transwell plate was removed, washed twice with PBS, soaked in formaldehyde for 10 minutes, and rinsed with water 3 times. The cells stained with 0.5% crystal violet (Sigma-Aldrich) were left at room temperature for 30 minutes, and the cells in the upper surface were discarded with cotton balls. Later, the cells were observed and photographed with an inverted microscope (IX53, Olympus) and counted using the ImageJ software. Matrigel was not used in the transwell migration experiment, and cells were incubated for 24 hours. At least 4 areas were randomly selected for cell counting under the microscope.
A549 cells were seeded into 6-well plates, and scratches were made in the center of the slide with a 200 μL sterile pipette tip when the cells grew to about 2 × 106/well. After 24 hours, the remaining cell-free area was measured and compared with the initial scratched area (in percentage). Images were acquired using a microscope (Zeiss, Germany), and the wound healing was analyzed using IncuCyte software (Essen BioScience, USA) to quantify cell migration.
A total of 48 BALB/c nude mice (weighing 18 ± 2 g/mouse, aged 6-8 weeks) were purchased from Cavens Biogle Model Animal Research (Suzhou, China). To ensure that the experimental mice purchased were healthy, we followed the principles: (1) general conditions: good development, bright eyes, flexible response, free movement, good appetite, no congestion in the conjunctiva, no secretion in the pupil, no nasal agitation, sneezing, and scratching the cheek. (2) Fur color: clean, soft, glossy, no hair loss, fluffy, and fungal infection. (3) Abdominal respiration: the abdominal breathing of mice was even, without abdominal swelling. (4) External genitals: no damage, no purulent pain, and no odorous sticky secretions. (5) Characteristics of claw toe: no bite, no ulcer, and no scab. All BALB/c mice were fed in a standard animal house with free drinking water and a diet at 24 ± 1°C with a humidity of 30%-40% for 1 week. They were kept under 12 hours of light/dark conditions with the light cycle from 8 : 00 to 20 : 00. A549 cells (2 × 106) were injected into nude mice via the tail vein, with untreated A549 cells (2 × 106) as the control group. Eight weeks later, mice were euthanized with pentobarbital sodium (100 mg/kg) followed by weighing lung tissue and counting metastatic nodules. Afterwards, lung tissues of 6 mice were embedded in paraffin, and lungs of 6 mice were homogenized into tissues and then stored at -80°C. The mice were grouped as follows: the control group (injected with A549 cells), the NFs group (injected with A549 cells cultured in NFCM), the GW group (injected with A549 cells cultured in H358-GW-treated NFCM), and the EVs group (injected with A549 cells cultured in H358-EVs-treated NFCM).
Total RNA was extracted from cells and tissues using the TRIzol reagent (Invitrogen) and reversely transcribed into cDNA using the PrimeScript RT reagent kits (Takara, Dalian, China). The TaqMan primers and probes used for detection were all from Takara. RT-qPCR was performed using ABI PRISM 7900 sequence detection system of SYBR Green II (Takara). PCR reaction conditions: predenaturation at 95°C for 5 minutes and 40 cycles of denaturation at 95°C for 15 seconds, annealing at 60°C for 20 seconds, and extension at 72°C for 35 seconds. With GAPDH as an internal reference, the data were analyzed with the 2−ΔΔCt method. Primer sequences (synthesized by Sangon Biotech, Shanghai, China) are shown in Table 1 [46].
After routine dewaxing and hydration, paraffined sections were stained with hematoxylin, rinsed with running water, and differentiated with 0.7% ethanol hydrochloride. Next, the sections turned blue for about 15 minutes, immersed in 95% ethanol for 30 seconds, stained with eosin-containing ethanol for 30 seconds, soaked in gradient ethanol, cleared with xylene carbonate, sealed with neutral gum, and scanned with TissueFAXS (Tissuegnosti). The metastatic load was calculated as a percentage of tumor area to lung areas.
After dewaxing and hydration, the anti-Ki-67 antibody (Cat# M7240, Dako, Glostrup, Denmark) was used for immunohistochemical detection on the slides using the immunohistochemical kit (YDDEF001, Yuduo Biotech, Shanghai, China). Ki-67 was positive with brown-yellow granules in the nucleus. The number of Ki-67-positive tumor cells in 5 fields was calculated under a light microscope and averaged.
GraphPad Prism 8.01 (GraphPad Software Inc., CA, USA) statistical software was used for statistical analysis and plotting of data. Graphics were typeset and drawn using Adobe Illustrator CC 2018 (Adobe Systems Incorporated, CA, USA). Data were expressed as mean ± standard deviation (SD). The t-test was used for data comparison between two groups, one-way analysis of variance (ANOVA) was used for data comparison among multiple groups, and Tukey's test was used for the post hoc test. P < 0.05 was considered statistically significant.
It has been reported that cancer cells stimulate mesenchymal fibroblasts through EVs in the TME and induce the activation of CAFs [47, 48]. We first cultured LC cells H358, normal lung epithelial cell line BEAS-2B, and lung NFs in vitro, and isolated EVs from H358 and BEAS-2B cell culture medium, respectively. EVs were “cup-shaped” under the TEM (Figure 1(a)). NTA showed that the particle size distribution of EVs ranged from 30 nm to 100 nm (Figure 1(b)). The protein levels of positive markers CD9 and CD81 and negative marker Calnexin on EV surface were examined by Western blotting, which manifested that compared to the GW (H358-GW/BeAS-2B-GW) group, CD9 and CD81 in EVs (H358-EVs/BeAS-2B-EVs) group were positive, while Calnexin was negative (Figure 1(c)). In addition, EVs labeled with red fluorescent dye Dil were cocultured with NFs. After 24 hours, immunofluorescence assay revealed that EVs (H358-EVs/BEAS-2B-EVs) were internalized by NFs (Figure 1(d)). EVs were successfully isolated and obtained, and NFs could internalize EVs (H358-EVs/BEAS-2B-EVs). Subsequently, the isolated EVs (H358-EVs/BEAS-2B-EVs) were cocultured with NFs. The protein levels of α-SMA and FAP-α on the surface of CAFs were measured by Western blotting, and levels of IL-1β, IL-6, and IL-8 were determined by ELISA. The results displayed that the protein levels of α-SMA and FAP-α as well as the levels of IL-1β, IL-6, and IL-8 in the EVsH358 group were significantly higher than those in the GWH358 group and theEVsBEAS-2B group (P < 0.01), while there was no statistical difference among the NFs group, the GWBEAS-2B group, the EVsBEAS-2B group, and the GWH358 group (P > 0.05) (Figures 1(e) and 1(f)). Taken together, H358 cell-derived EVs accelerated the activation of CAFs.
Studies have reported that CAFs regulate the TME by secreting a large number of proinflammatory factors (such as IL-6) and chemokines, thus enhancing the migration potential of LC cells [30, 48]. To further investigate whether H358-EVs can facilitate the invasion and migration of LC cells by inducing CAFs activation, the A549 cells were cultured in NFCM treated with EVs. MTT assay uncovered that relative to the GWBEAS-2B group, the EVsBEAS-2B group had high cell viability (P < 0.05), and the cell viability of the A549+EVsH358 group was visibly higher than that of the A549+GWH358 group and the EVsBEAS-2B group (all P < 0.01) (Figure 2(a)). CCK-8 assay illustrated that the cell proliferation of the EVsBEAS-2B group was greater than that of the GWBEAS-2B group (P < 0.05), and the cell proliferation ability of the A549+EVsH358 group was stronger than that of the A549+GWH358 group or the EVsBEAS-2B group (P < 0.01) (Figure 2(b)). Transwell assays and scratch tests illustrated that the EVsBEAS-2B group had increased invasion and migration relative to the GWBEAS-2B group (P < 0.05); compared to the A549+GWH358 group or the EVsBEAS-2B group, the invasion and migration in the A549+GWH358 group were enhanced (all P < 0.01) (Figures 2(c)–2(e)). These results suggested that T-EVs can accelerate the invasion and migration of LC cells through CAFs activation.
LncRNA HOTAIR is highly expressed in LC [21, 40], and inhibition of HOTAIR can inhibit the growth of LC cells [43]. To elucidate whether H358-EVs carried HOTAIR into NFs, we first detected HOTAIR levels in H358 and BeAS-2B cells and the derived EVs by RT-qPCR. The results illustrated that HOTAIR levels in H358 cells and H358-EVs were significantly higher than those in BEAS-2B cells and BEAS-2B-EVs (all P < 0.01) (Figures 3(a) and 3(b)). H358-EVs were subsequently treated with Rnase and SDS, and no significant changes were discovered in HOTAIR levels after Rnase treatment (P > 0.05); however, HOTAIR level was lowered after treatment with Rnase and SDS simultaneously (P < 0.01) (Figure 3(b)). The above results suggest that HOTAIR was encapsulated in H358-EVs. Finally, after H358-EVs (50 μg) were cocultured with NFs for 6 hours, HOTAIR level was visibly elevated (P < 0.01) (Figure 3(c)). In short, H358-EVs delivered HOTAIR into NFs.
To further investigate whether H358-EVs promoted the activation of CAFs through HOTAIR transmission, EVs were isolated and extracted from H358 cells transfected with HOTAIR shRNA and cocultured with NFs for 6 hours. RT-qPCR showed that HOTAIR levels in H358 cells, and H358-EVs were reduced after treatment with HOTAIR shRNA (P < 0.01) (Figures 4(a) and 4(b)). Further detection showed lower HOTAIR levels in the EVsH358-si-HOTAIR group than the EVsH358-si-NC group (P < 0.01) (Figure 4(c)). Subsequently, Western blotting exhibited decreases in α-SMA and FAP-α protein levels in the EVsH358-si-HOTAIR group relative to the EVsH358-si-NC group (P < 0.01) (Figure 4(d)). ELISA showed that the levels of IL-1β, IL-6, and IL-8 in the EVsH358-si-HOTAIR group were lower than those in the EVsH358-si-NC group (P < 0.01) (Figure 4(e)). In summary, inhibition of HOTAIR expression in EVs partially averted the promotion effect of H358-EVs on CAFs activation, and H358-EVs expedited CAFs activation through HOTAIR transmission.
The A549 cells were further cultured in NFCM treated with H358-EVs-si-HOTAIR followed by assessment of cell invasion and migration 24 hours later. After culture in H358-EVs-si-HOTAIR-treated NFCM, the cell viability was limited (P < 0.01) (Figure 5(a)), the cell proliferation was reduced (P < 0.05) (Figure 5(b)), and the invasion and migration were decreased (all P < 0.05) (Figures 5(c)–5(e)). To conclude, suppression of HOTAIR partially reversed the accelerating effect of H358-EVs-mediated CAFs activation on the invasion and migration of LC cells.
To validate the effect of H358-EVs-mediated CAFs activation on LC metastasis in vivo, A549 cells (2 × 106) were injected into nude mice. After 8 weeks, no prominent difference was observed in lung tissue weight and tumor nodules among the control group, NFs group, and GW group (P > 0.05), while these two indicators were higher in the EVs group than the GW group (P < 0.01) (Figures 6(a) and 6(b)). HE staining uncovered that a large number of tumor foci were formed in the lung of the EVs group, which was observably more than that of the GW group, accompanied by epithelial cell damage and disordered lamellar or fibrous arrangement of tumor cells. No statistical difference was found among the control group, NFs group, and GW group (Figure 6(c)). Immunohistochemistry exhibited more Ki-67-positive cells in lung tissues in the EVs group than that in the GW group (P < 0.01), whereas there was no prominent difference in Ki-67-positive cells in the control group, NFs group, and GW group (P > 0.05) (Figure 6(d)). Finally, RT-qPCR revealed a higher level of HOTAIR in lung tissues in the EVs group than that in the GW group (P < 0.01) and no evident difference in the control group, NFs group, and GW group (P > 0.05) (Figure 6(e)). Overall, T-EVs can promote LC metastasis in vivo by stimulating CAFs.
LC is a common cancer with poor prognoses [49]. A recent study indicated that EVs can achieve molecular exchange between cancer cells and stromal cells, reshape local TME, and provoke tumorigenesis, development, and distant metastasis [49]. EVs contain abundant lncRNAs, and their interactions are essential in cell-to-cell information exchange [50]. CAFs, as activated fibroblasts in the tumor stroma, are known to promote cancer progression by altering the primary TME [51]. Our findings demonstrated that T-EVs carried HOTAIR into NFs and encouraged the activation of CAFs, thus promoting the invasion and metastasis of LC. It has been acknowledged that communication networks mediated by EVs in the TME are critical in tumor development [52]. Specifically, T-EVs activate NFs to form a CAF-like phenotype and maintain a pro-TME [53]. Moreover, α-SMA and FAP are markedly expressed in CAFs and are regarded as surface markers of CAFs [54]. By expressing proinflammatory cytokines such as IL-1β, IL-6, and IL-8, CAFs regulate the inflammatory microenvironment [27, 29]. Firstly, LC cells (H358), normal lung epithelial cell line (BEAS-2B), and NFs were cultured in vitro, and EVs were extracted and identified. We found NFs could internalize EVs and the levels of CAFs surface markers (α-SMA/FAP-α) and cytokines (IL-1β/IL-6/IL-8) in T-EVs-treated NFs were elevated. These results strongly support the promoting effect of LC cell-derived EVs on CAFs activation. CAFs are actively recruited during cancer progression to support and promote tumor progression by secreting cytokines and growth factors [55]. Hence, LC cells A549 were cultured in H358-EVs-treated NFCM. Not surprisingly, A549 cells exhibited significant increases in cell viability, proliferation, invasion, and migration. In line with our findings, T-EVs motivate the activation of CAFs and accelerate the invasion of ovarian cancer [45]. Altogether, the aforementioned findings highlighted that T-EVs expedite the invasion and migration of LC cells by promoting CAFs activation. EVs have been reported to deliver functional proteins, mRNAs, lncRNAs, and miRs to recipient cells [56, 57]. LncRNAs are critical players in the invasion and metastasis of LC [58]. In particular, lncRNAs carried by tumors participate in the activation of CAFs [21, 32]. Importantly, lncRNA HOTAIR level is upregulated in LC [21, 40]. Similarly, our findings revealed overexpressed HOTAIR levels in LC cells and their derived EVs, and HOTAIR was encapsulated in T-EVs. Moreover, the level of HOTAIR in T-EVs-treated NFs was substantially increased. Briefly, T-EVs transferred HOTAIR into NFs. Abnormal expression of lncRNAs in T-EVs induces NFs differentiation into CAFs, which plays a vital role in tumorigenesis [21, 32]. Given the abnormal expression of exosomal HOTAIR, we explored its role in the biological processes of LC cells. Subsequent experimentation in our study revealed that the knockdown of HOTAIR in EVs partially averted the promotive effects of T-EVs on CAFs stimulation and LC cell growth. Consistently, the knockout of HOTAIR inhibits CAF-induced tumor growth and lung metastasis in vivo [59]. In short, HOTAIR silencing partially reversed the promotion of T-EVs-mediated-CAFs activation on LC cell invasion and migration. Finally, the metastasis of LC in vivo was analyzed by establishing nude mouse models. The high expression of Ki-67 is associated with the prognosis and clinicopathological features of LC patients [60]. We observed significant lung tissue damage in mice after injection of A549 cells cultured in H358-EVs-treated NFCM. Moreover, the number of Ki-67-positive cells in mouse lung tissue was markedly enhanced, suggesting that the tumor is prone to recurrence and metastasis. Besides, HOTAIR levels in mouse lung tissues were raised. Guo et al. demonstrated that CAFs-EVs promote the growth and metastasis of lung squamous cell carcinoma through in vivo tumor formation and metastasis experiments [61]. On the other hand, tumor-derived exosomal miR-1247-3p induces CAF activation to promote lung metastasis of hepatocellular carcinoma [48]. In light of the preceding literature, we concluded that T-EVs encouraged LC metastasis in vivo by activating CAFs. In conclusion, we cultured A549 cells in NFCM treated with LC cell-derived EVs and verified that T-EVs induced the activation of CAFs by carrying HOTAIR, thus promoting the invasion and metastasis of LC. However, other molecular mechanisms of EVs promoting CAFs activation have not been described. Moreover, in addition to lncRNAs, T-EVs carry many other substances, such as proteins and miRNAs, which have very complex effects on the activation of CAFs, and these will be the direction of our further research in the future. | true | true | true |
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PMC9578976 | Muyu Xu,Jiying Zhang | A siRNA screening of UBE2 family demonstrated that UBE2R1 had a high repressive effect on HIV Tat protein | 15-10-2022 | HIV,Tat,Transcription elongation,UBE2R1,RNF20,H2Bub1, H2B-monoubiquitylation,p-TEFb, positive transcription elongation factor-b,dCA, Didehydro-cortistatin A,UBE2, ubiquitin ligase E2,UPS, Ubiquitin Proteasome System | HIV Tat is an essential protein required for the transcription elongation of HIV genome. It has been shown that Tat can be degraded by either proteasome or autophagy pathways. In this study, it was shown that proteasome inhibitor MG132 could significantly prevent HIV Tat protein degradation in Tat over-expressing HeLa cells but it had a moderate effect in preventing Tat protein degradation in Jurkat T cells. A screening of the available UBE2 siRNA family identified that UBE2R1 had a high repressive effect on Tat protein but not on Tat mRNA level. This study further showed that RNF20 might not be the E3 ligase of Tat but was required to maintain a high level of H2B-monoubiquitylation (H2Bub1) on HIV-1 genome for efficient elongation. Overall, our study indicated that UBE2R1 might be the potential ubiquitin E2 ligase for HIV Tat protein turnover and RNF20 regulated HIV expression in the transcription elongation level. | A siRNA screening of UBE2 family demonstrated that UBE2R1 had a high repressive effect on HIV Tat protein
HIV Tat is an essential protein required for the transcription elongation of HIV genome. It has been shown that Tat can be degraded by either proteasome or autophagy pathways. In this study, it was shown that proteasome inhibitor MG132 could significantly prevent HIV Tat protein degradation in Tat over-expressing HeLa cells but it had a moderate effect in preventing Tat protein degradation in Jurkat T cells. A screening of the available UBE2 siRNA family identified that UBE2R1 had a high repressive effect on Tat protein but not on Tat mRNA level. This study further showed that RNF20 might not be the E3 ligase of Tat but was required to maintain a high level of H2B-monoubiquitylation (H2Bub1) on HIV-1 genome for efficient elongation. Overall, our study indicated that UBE2R1 might be the potential ubiquitin E2 ligase for HIV Tat protein turnover and RNF20 regulated HIV expression in the transcription elongation level.
HIV Tat recruits positive transcription elongation factor-b (P-TEFb) complex to stimulate RNA Polymerase II transcription elongation on HIV genome [[1], [2], [3]]. Thus, the proteins and compounds that can regulate Tat expression level are valuable target candidates in the pharmaceutical research of HIV/AIDS. Up-to-date, it has been shown that MG132, Didehydro-cortistatin A (dCA), Triptolide, Curcumin and JIB-04 can regulate Tat protein level [[4], [5], [6], [7], [8]]. MG132 was shown to prevent Tat protein from degradation, Triptolide and Curcumin were shown to promote Tat protein degradation [4,6,7], while dCA was reported to relocate Tat from nucleus to cytoplasm [5]. In our previous study, additionally, JIB-04 was shown to promote Tat degradation through the autophagy pathway [8]. To sum up, these publications suggest that Tat protein can be degraded through either proteasome or autophagy pathways in different cell lines. Although MG132 has long been shown to prevent Tat from degradation in Tat over-expressing cancer cells [4], the cellular ubiquitin ligase E2 (UBE2) and E3 for Tat protein turnover have not been well characterized. The major degradation pathway for Tat in T cells seems to be the autophagy pathway rather than the proteasome pathway [8]. RNF20 has also been shown to involve in HIV Tat transcriptional activation [9], with the molecular mechanisms to be uncovered. RNF20 is the E3 ligase for H2B monoubiquitylation [10] and H2Bub1 modification is required for efficient transcription elongation [11]. In this study, it was shown that proteasome inhibitor MG132 could significantly prevent HIV Tat protein degradation in Tat over-expressing HeLa cells but it had a moderate effect in preventing Tat protein degradation in Jurkat T cells. A screening of the available UBE2 siRNA family identified that UBE2R1 had a high repressive effect on Tat protein. Furthermore, it was shown that RNF20 knockdown reduced Tat protein level and RNF20 was required to maintain a high level of H2Bub1 on HIV-1 genome for efficient elongation. Overall, this study suggested that UBE2R1 might be the potential ubiquitin E2 ligase for HIV Tat protein turnover and RNF20 could regulate HIV transcription through H2B monoubiquitylation.
Specific antisera were obtained from the following sources: HIV-1 Tat (ab42359 & ab43014), Csn3 (sc-100693), Cyclin T1 (sc-10750), SHMT1 (ProteinTech, 14149-1-AP), UBE2R1 (ProteinTech, 10964-2-AP), UBE2Z (R&D, AF8154), RNF20 (ProteinTech, 21625-1-AP), H2B (Millipore, 07–371), H2Bub1 (Active Motif, 39624). Doxycycline was purchased from Sigma (D3447), while MG132 and JIB-04 were purchased from Selleckchem (S2619 & S7281). Western blotting was previously described [12].
The detailed protocols had been described [8]. 2D10 Jurkat T cells (A gift of Dr. Jonathan Karn Lab to Dr. Katherine A. Jones lab) were cultured using RPMI-1640 medium plus 10% FBS and 1% antibiotic-antimycotic solution. Stemfect RNA transfection kit (Stemgent, 00–0069) was used for RNAi in 2D10 T cells. Recombinant TNFα was used to induce HIV-1 expression.
The detailed protocols had been described [8]. Tet-on-Tat-off HeLa cells and HeLa P4 cells (Dr. Katherine A. Jones lab) were cultured by DMEM medium plus 10% FBS and 1% antibiotic solution. RNAi with final 100 nM siRNA was tranfected by Lipofectamine 2000. Forty-eight hours after transfection, cells were lysed and analyzed by Dual-Luc assay and Western blotting. The sequences of siRNAs for UBE2 family were as follows: Primers for qRT-PCR: Chromatin Immunoprecipitation (ChIP). The ChIP primers and protocols had been described [8,13]. Briefly, formaldehyde was added for cross-linking at 1% final concentration. About 0.5–2 μg of antibodies were used for ChIP. High salt at 5 M NaCl was added for reverse-crosslinking. At last step, DNA was purified with Qiagen quick-spin PCR Purification Kit and eluted in H2O.
As previously reported, MG132 can prevent HIV Tat protein degradation [4]. Doxycycline was washed off from the Tet-on-Tat-off HeLa cells integrated with HA-Tat 86 for 16 h, and then the cells were treated with JIB-04 or MG132 for 8 h as indicated (Fig. 1A). The results showed that 1 μM of JIB-04 could greatly reduce Tat protein level [8], while MG132 had the opposite effect to prevent Tat protein from degradation in this Tat over-expressing physiological-unrelevant HeLa cells (Fig. 1A). For 2D10 Jurkat T cells, however, titrating MG132 up-to 6 h only slightly increased Tat protein level. Thus, it was indicated that Tat might be mainly degraded through autophagy pathway in physiological-relevant T cells, compared to both autophagy and proteasome pathways in Tat over-expressing HeLa cells (Fig. 1B). Our data suggested that Tat protein could be degraded through either autophagy or proteasome pathways but it might be mainly degraded by autophagy pathway in T cells.
To degrade Tat through the autophagy pathway in T cells, K63-type ubiquitin needs to be added by the E2 and E3 ubiquitin ligases to the Tat protein. Luckily, there are only about 20 ubiquitin E2 ligases in the cells. Therefore, we used all the available UBE2 family siRNAs in stock, including siRNAs to UBE2A, UBE2B, UBE2C, UBE2D3, UBE2E1, UBE2F, UBE2G1, UBE2O, UBE2R1 and UBE2Z (See Materials and Methods for the sequences), and performed a siRNA knockdown screening of UBE2 protein family to see which UBE2 had the most repressive effect on HIV Tat protein. The knockdown efficiency of each siRNA of the UBE2 protein family was all above 80% in mRNA level (Supplemental Fig. 1A). Moreover, the efficient knockdown in the protein level was shown by antibody targeting UBE2Z (Fig. 2A). Among all the UBE2 siRNA tested, UBE2R1/CDC34 knockdown had increased Tat protein level most significantly (Fig. 2A), suggesting that UBE2R1 might be a direct ubiquitin E2 ligase for Tat. Importantly, UBE2R1 siRNA was singled out and its knockdown effect was tested on Tat protein again in both HeLa and 2D10 T cells. The results confirmed that UBE2R1 knockdown could also greatly increase Tat protein level (Fig. 2B and C), indicating that UBE2R1 might also play an important role in the Tat-autophagy degradation pathway in T cells. Intriguingly, the mRNA of Tat only slightly increased when UBE2R1 was knocked down (Supplemental Fig. 1B), manifesting that UBE2R1 might regulate either the stability or translation level of Tat protein. In summary, these data showed that UBE2R1 might be a direct ubiquitin E2 ligase for Tat protein and further experiments are needed to explore this possibility.
Considering the involvement of RNF20 in HIV Tat transcription [9], we tested the effect of RNF20 knockdown on Tat protein, and found that RNF20 knockdown reduced Tat protein level in 2D10 T cells (Fig. 3A), which was opposite to the effect of UBE2R1 on Tat. The result suggested that RNF20 might regulate HIV1 Tat through a distinct molecular mechanism. RNF20 has previously been shown to be the E3 ligase of H2B monoubiquitylation [10] and H2Bub1 is required for the efficient transcription elongation [11]. Consistently, our study showed that RNF20 knockdown reduced H2Bub1 protein level (Fig. 3A). Next, ChIP assay was performed to analyze the binding of RNF20 and H2Bub1 on HIV-1 genome. The efficient HIV-1 transcription elongation happened about 6 h after TNFα induction [8]. The result of ChIP showed that RNF20 and H2Bub1 were gradually enriched on HIV-1 genome during 0-6 h of TNFα induction, and the binding patterns of RNF20 and H2Bub1 were highly consistent with each other (Fig. 3B, top). The results indicated that RNF20 was needed to maintain a high level of H2Bub1 on HIV-1 genome for the efficient transcription elongation. Furthermore, RNF20 knockdown reduced the H2Bub1 level on HIV-1 genome (Fig. 3B, bottom), demonstrating that RNF20 was critical to maintain the efficient transcription elongation of HIV-1 genome. Overall, our results showed that although RNF20 might not be the E3 ligase of Tat, it was required to maintain a high H2Bub1 level on HIV-1 genome for efficient elongation.
HIV-1 Tat is the essential protein that is required to support the efficient viral transcription elongation through recruiting the P-TEFb complex [14]. In our previous study, it was shown that Tat could be K63-ubiquitylated and degraded by JIB-04 through the autophagy pathway in Jurkat T cells [8]. Besides, it have been shown that Tat can also be degraded by the Ubiquitin Proteasome System (UPS) degradation pathway [4,6,7]. However, the ubiquitin E2 and E3 ligases for Tat have not been uncovered. UBE2O has been shown to interact with HIV Tat protein to reorganize the 7SK snRNP for transcription activation [15]. Our siRNA screening results showed that the knockdown of UBE2R1 had a much greater effect in increasing HIV Tat protein level than those of other UBE2 ligases (Fig. 2A). It had been shown that UBE2R1/Cdc34 was a lysine48-specific E2 ligase and it could function to add ubiquitin without the downstream E3 ligase [16,17]. Thus, it would be very interesting and important to further investigate how over-expression of UBE2R1 affects Tat protein level and whether UBE2R1 is the direct ubiquitin E2 ligase that does not need a E3 ligase. Furthermore, UBE2R1 knockdown also increased Tat protein level in 2D10 Jurkat T cells (Fig. 2C), indicating that lysine48-specific proteasome pathway might also play an important function in T cells. The cellular ubiquitin E3 ligase protein CHIP was recently shown to be likely the E3 ligase for HIV-1 Tat protein [18], suggesting that there might be multiple E2 ligases for Tat protein in cells. Besides CHIP, PJA2 has been shown to ubiquitinate the HIV-1 Tat protein with atypical chain linkages to activate viral transcription, Cullin 3 E3 ligase can also regulate HIV-1 Transcription [19,20], and RNF20 is involved in HIV transcription [9]. Subsequently, RNF20 was shown to be the ubiquitin E3 ligase for H2Bub1, and H2Bub1 was required to maintain the efficient transcription elongation [10,11]. Therefore, we wanted to explore the effects of RNF20 on the transcription elongation of HIV-1 genome. It was shown that RNF20 knockdown reduced HIV Tat level, and additionally, ChIP binding patterns of RNF20 and H2Bub1 to HIV genome were highly overlapping with each other. Our observations support the notion that RNF20 is required to maintain high H2Bub1 on HIV-1 genome for efficient elongation. Since UBE2R1 and RNF20 are both ligases of ubiquitin with enzymatic activity, the development of inhibitors to such enzymes can potentially reactivate HIV from latency or inhibit HIV transcription. Either direction is of great interest to the field of HIV/AIDS drug discovery.
It was shown in this study that MG132 could prevent HIV-Tat protein from degradation more significantly in Tat over-expressing HeLa cells than in T cells. And it was likely that HIV Tat could be degraded through both proteasome and autophagy pathways. This study further showed that UBE2R1/CDC34 had a high repression effect on Tat protein, and RNF20 was required to support HIV transcription elongation by maintaining a high level of H2Bub1. The molecular mechanisms of how UBE2R1/CDC34 regulates HIV Tat protein level need to be further investigated.
Muyu Xu: Conceptualization, Methodology, Investigation, Data analysis, Writing, Co-Funding & Corresponding; Jiying Zhang: Writing, Editing, Reviewing & Proofing.
The authors have declared no competing interest. | true | true | true |
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PMC9579003 | Bon-Kwan Koo,Eun-Ji Choi,Eun-Hye Hur,Ju Hyun Moon,Ji Yun Kim,Han-Seung Park,Yunsuk Choi,Jung-Hee Lee,Kyoo-Hyung Lee,Eun Kyung Choi,Jinhwan Kim,Je-Hwan Lee | Antileukemic activity of YPN-005, a CDK7 inhibitor, inducing apoptosis through c-MYC and FLT3 suppression in acute myeloid leukemia | 12-10-2022 | Acute myeloid leukemia (AML),Cyclin dependent kinase 7 (CDK7) inhibitor,c-MYC,MCL1,FLT3 | Acute myeloid leukemia (AML) is an aggressive blood cancer with a high rate of relapse associated with adverse survival outcomes, especially in elderly patients. An aberrant expression of cyclin dependent kinase 7 (CDK7) is associated with poor outcomes and CDK7 inhibition has showed antitumor activities in various cancers. We investigated the efficacy of YPN-005, a CDK7 inhibitor in AML cell lines, xenograft mouse model, and primary AML cells. YPN-005 effectively inhibited the proliferation of AML cells by inducing apoptosis and reducing phosphorylation of RNA polymerase II. The c-MYC expression decreased with treatment of YPN-005, and the effect of YPN-005 was negatively correlated with c-MYC expression. YPN-005 also showed antileukemic activities in primary AML cells, especially those harboring FMS-like tyrosine kinase 3–internal tandem duplication (FLT3-ITD) mutation and in in vivo mouse model. Phosphorylated FLT3/Signal transducer and activator of transcription 5 (STAT5) was decreased and FLT3/STAT5 was downregulated with YPN-005 treatment. Our data suggest that YPN-005 has a role in treating AML by suppressing c-MYC and FLT3. | Antileukemic activity of YPN-005, a CDK7 inhibitor, inducing apoptosis through c-MYC and FLT3 suppression in acute myeloid leukemia
Acute myeloid leukemia (AML) is an aggressive blood cancer with a high rate of relapse associated with adverse survival outcomes, especially in elderly patients. An aberrant expression of cyclin dependent kinase 7 (CDK7) is associated with poor outcomes and CDK7 inhibition has showed antitumor activities in various cancers. We investigated the efficacy of YPN-005, a CDK7 inhibitor in AML cell lines, xenograft mouse model, and primary AML cells. YPN-005 effectively inhibited the proliferation of AML cells by inducing apoptosis and reducing phosphorylation of RNA polymerase II. The c-MYC expression decreased with treatment of YPN-005, and the effect of YPN-005 was negatively correlated with c-MYC expression. YPN-005 also showed antileukemic activities in primary AML cells, especially those harboring FMS-like tyrosine kinase 3–internal tandem duplication (FLT3-ITD) mutation and in in vivo mouse model. Phosphorylated FLT3/Signal transducer and activator of transcription 5 (STAT5) was decreased and FLT3/STAT5 was downregulated with YPN-005 treatment. Our data suggest that YPN-005 has a role in treating AML by suppressing c-MYC and FLT3.
Acute myeloid leukemia (AML) is an aggressive myeloid neoplasm with the highest incidence in the elderly with a median age of diagnosis at 68 years. Although high-intensity treatments including intensive induction chemotherapy followed by allogeneic stem cell transplantation offer long-term survival in some percentage of young and fit patients, substantial number of patient experience disease relapse. In addition, the treatment outcomes are even worse in older patients because of unfavorable genetic features, antecedent hematologic diseases, and poor performance status. Some targeted agents, IDH inhibitors, FLT3 inhibitors, and BCL-2 inhibitor showed improved response rates in patients who relapsed, are refractory or unfit old AML patients. However, the duration of response rate and long-term outcomes remained unsatisfactory (Almeida and Ramos, 2016; Medeiros et al., 2015). Cyclin dependent kinases (CDKs) play a crucial role in controlling cell cycle progression and transcription regulation. Certain CDKs are involved in cell cycle regulation, whereas several other CDKs in control gene transcription by phosphorylating RNA polymerase II (Malumbres, 2014). CDK7, a CDK family member, has an important function in transcription regulation and cell cycle transition (Diab et al., 2020). It controls gene transcription through phosphorylation at serine 5 and 7 of RNA polymerase II required for transcription initiation (Glover-Cutter et al., 2009; Mosley et al., 2009). Previously reported aberrant expression of CDK7 in various solid tumors has been correlated with poor clinical outcomes and aggressive pathological parameters (J. R. Huang et al., 2018; Wang et al., 2018; Zhou et al., 2019). Therefore, CDK7 has been considered as a potential target for cancer treatment, and its inhibition has been shown to have antitumor effects by suppressing cell proliferation and impeding cell cycle progression in various cancer cells (Diab et al., 2020). CDK7 blockade has been associated with transcriptional repression of proto-oncogenes such as c-MYC gene family and tumor regression in solid cancer (Chipumuro et al., 2014; Christensen et al., 2014). Several CDK7 inhibitors have showed antileukemic effects in preclinical AML models. CT7001 and SY-1365, inhibitors of CDK7, have shown to inhibit cell proliferation both in AML cell lines and tumor growth in a xenograft model. Their antileukemic effects were attributed to induction of apoptotic cell death and decreased expression of c-MYC and MCL1 (Clark et al., 2017; Hu et al., 2019). c-MYC has been shown to be frequently activated in AML and plays an important role in the induction of leukemogenesis and leukemic progression (Liman et al., 2020; Luo et al., 2005). Moreover, c-MYC overexpression contributes to leukemia stem cell self-renewal, cell growth, and drug resistance in AML cells (Pan et al., 2014). However, the efficacy and mechanism of CDK7 inhibitors in AML remain to be elucidated further. YPN-005, a highly selective CDK7 inhibitor, showed antiproliferative effects in primary sample and drug-resistant lung cancer (Choi et al., 2021). Therefore, in this study, we explored the potential efficacy of YPN-005 in AML cell lines, mouse models, and primary cells from AML patients. In addition, we also evaluated a possible mechanism of the antileukemic effect of YPN-005 in AML.
The information on cell lines has been described in Supplementary T 1. YPN-005 (C26H28N6O2S) and SY1365 were kindly provided from Yungjin Pharm. Co., Ltd (Gyeonggi-do, Korea) (Choi et al., 2021), venetoclax (cat. no. S8048) and gilteritinib (cat. no. S7754) were purchased from Selleck Chemicals (Houston, TX), and cytarabine (cat. no. SC1768), azacitidine (cat. no. A2385), and decitabine (cat. no. A3656) were purchased from Sigma-Aldrich (St. Louis, MO).
Primary mononuclear cells were collected from bone marrow (BM) samples of 26 AML patients enrolled in this study (Supplementary T 2). Informed consents were obtained from the patients in accordance with Declaration of Helsinki, and the protocol was approved by the institutional review board of Asan Medical Center (2016-1373). All samples were stored in Bio-Resource Center in Asan Medical Center (Seoul, Korea).
Cryopreserved BM cells were washed with ice-cold phosphate-buffered saline and then counted using acridine orange (AO)/propidium iodide (PI) (Nexcelom, Lawrence, MA) staining method. For cell viability assay, 2 × 104 cells/well were seeded in a 96-well opaque plate (SPL Life Science, Korea) in complete media and incubated with 20 nM and 100 nM of YPN-005 for 3 days in a humidified atmosphere with 5% CO2 at 37 °C. The complete media was composed of X-Vivo™ 15 media (Lonza, Switzerland), supplemented with 20% fetal bovine serum (Hyclone Inc., Logan, UT), 50 ng/mL IL-3 (R&D systems, Inc., Minneapolis, MN), 40 ng/mL IL-6 (R&D systems, Inc.), 200 ng/mL GM-CSF (R&D systems, Inc.), 200 ng/mL G-CSF (R&D systems, Inc.), 2 mM L-glutamine (Thermo Fisher Scientific, UK), 4 U/mL EPO (R&D systems, Inc.), and 5.510−5 β-mercaptoethanol (Sigma-Aldrich). Cell proliferation was evaluated using CellTiter-Glo® Luminescent Cell Viability Assay (Promega Corp., Madison, WI) following manufacturer's protocol. Luminescence was detected using Victor3 multilabel plate reader following manufacturer's protocol (PerkinElmer, Waltham, MA).
Isolated total RNA following manufacturer's protocol (RNeasy® Plus Mini Kit, Qiagen Gmbh, Germany) was converted to cDNA using RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific), according to the manufacturer's recommendation. Quantitative PCR was performed using LightCycler® 480 SYBR Green Master mix (Roche, Switzerland) for FLT3, STAT5, c-MYC, MCL1, β-actin, and 18s with specific primers listed in Supplementary T 3. Relative fold changes in mRNA expression were calculated using formula 2−ΔΔCT.
Here 1 × 106 cells were seeded in 6-well plates and incubated with different concentrations of YPN-005 for 24 h. Thereafter, the cells were harvested and stained with Annexin V-FITC apoptosis detection kit I (BD Biosciences, Franklin Lakes, NJ) following the manufacturer's protocol. Stained cells were measured via flow cytometry on FACSCanto II (BD Bioscience) and analyzed using BD FACSDiva v8.0.2 software (BD Bioscience).
Cell lysates were prepared using cell lysis buffer (Cell Signaling Technology, Inc., Danvers, MA). Then, incubated with Xpert protease inhibitor cocktail solution (GenDEPOT, Barker, TX) and phosphatase inhibitor cocktail set II (Calbiochem, EMD Biosciences, Inc. La Jolla, CA) on ice for 30 min and centrifuged at 14,000 × g for 10 min at 4 °C. The protein concentration was measured using Bradford (Coomassie) protein assay plus kit (GenDEPOT) and 595 nm absorption was measured using molecular devices SpectraMax M2 Multilabel Microplate Reader (Marshall Scientific, Hampton, NH). A total of 10 μg protein extract was loaded onto a SDS polyacrylamide gel. Following electrophoresis, proteins were transferred to polyvinylidene difluoride membranes (Bio-Rad Laboratories, Inc. Hercules, CA) and had blocked with 2% skim milk for 30 min and then the primary antibodies listed in Supplementary T 4 at a dilution of 1:1,000 at 4 °C overnight. The blots were incubated with a secondary horseradish peroxidase (HRP)-conjugated specific antibody (Enzo Life sciences, Inc. Farmingdale, NY) 1:3,000 at room temperature for 1 h. The signal was visualized using an enhanced BioFX chemiluminescent sensitive plus HRP microwell and/or membrane substrate (BioRX Laboratories, Owing Mills, MD), and chemiluminescence was measured using Ez-capture ST system (ATTO Corporation, Japan). Quantification of Immunoblot band intensity was performed using CS analyzer 4 software (ATTO Corporation).
Lentiviral products was harvested using pCDH-CMV-MCS-EF1a-GFP (Addgene, CD513B-1, Cambridge, MA) as expressing vector, pMDLg/pRRE and pRSV-REV as packaging plasmid, and pH27G as envelope plasmid in 293FT cell line. The lentiviral particles was infected into MOLM-13 cell line using 8 μg/mL polybrane (TR-1003, Sigma-Aldrich) and then selected using 5 μg/mL puromycin (Enzo Life sciences, Inc. Farmingdale, NY). Animal experiments were conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee of the Asan Medical Center (2020-12-080). Leukemic orthotopic mouse model was established by tail-vein injection of 5 × 106 pGFP-transduced MOLM-13 cells in 6 weeks female NSG mouse (Jackson Laboratory, Bar Harbor, ME). YPN-005 at a dose of 1 or 2 mg/kg was injected intravenously via tail vein on 2-day-on/5-day-off or a biweekly schedule. Tumor load was measured based on bioluminescence imaging using the IVIS spectrum in vivo imaging system (PerkinElmer) after epilation. Fluorescence signal intensity was quantified using the Living Imaging software (PerkinElmer).
Data analysis for cell viability, apoptosis, and mRNA expression was performed using one-way analysis of variance followed by post hoc Tukey's multiple comparisons or Bonferroni tests when appropriate. To evaluate the correlation between cell viability and FLT3-ITD mutation in patient samples, we used Student's t-test or Mann–Whitney U test as appropriate. A relationship between cell viability and protein expression was evaluated using Spearman's correlation. The Survival analyses were performed using the Kaplan–Meier method and the resulting survival curves were compared using the log-rank test. The graphs were plotted and analyzed using GraphPad Prism 5 (GraphPad Software, Inc. La Jolla) and p < 0.05 was considered to indicate a statistical significance. Data were accumulated from three independent experiments and presented as mean ± standard deviation (SD).
To evaluate the efficacy of YPN-005 in AML, we screened the cell growth inhibitory activity of YPN-005 in nine AML cell lines (Table 1 and Supplementary F 1). It was observed that upon comparison with SY1365 (a CDK7 inhibitor) and other anti-leukemic medications including daunorubicin, cytarabine, and venetoclax, YPN-005 showed similar or superior efficacy. Venetoclax was observed to be highly sensitive in cell lines MOLM-13, MOLM-14, MV4-11, and OCI-AML2. However, cytarabine and daunorubicin, a backbone of cytotoxic chemotherapy in AML, showed heterogeneous results. YPN-005, however, showed a similar antiproliferative activities at IC50 concentrations ranging from 1.95 to 29.04 nM in all cell lines, suggesting that it might have an antileukemic efficacy irrespective of specific biologic characteristics of each AML cell lines.
To identify the mechanism by which YPN-005 exhibits growth inhibitory effects in AML cells, we performed FACS analysis using Annexin V/PI staining and immunoblotting assay 24 h post treatment with YPN-005 in AML cell lines. Apoptosis was observed in all three AML cell lines (MOLM-13, OCI-AML2, and U937), and it increased with increasing dose of YPN-005 as shown in Figure 1A, B. In concordance with flow cytometric results, immunoblotting revealed an increase in cleaved poly (ADP-ribose) polymerase (PARP) and caspase-3, and a decrease in X-linked inhibitor of apoptosis protein (XIAP) in YPN-005-treated AML cell lines, which indicate induction of apoptosis (Figure 1C). Together, the above results suggest that YPN-005 induces apoptosis in AML cells in a dose-dependent manner.
It has been reported previously that CDK7 indirectly regulates transcription of MCL1 (Tibes and Bogenberger, 2019), and the expression of c-MYC and MCL1 is reduced by the CDK7 inhibitor (T. Huang et al., 2019; Li et al., 2017). In addition, c-MYC seems to directly control the transcription of MCL1 in gastric cancer cells (Labisso et al., 2012). Thus, to evaluate the mechanism of YPN-005 in AML cell lines, the protein expression of MCL1, c-MYC, and phosphorylated RNA polymerase (Rpb) was determined using immunoblot assay (Figure 2A). It was observed that the expression of MCL1, c-MYC, and phosphorylated Rpb decreased with YPN-005 treatment. Moreover, the mRNA expression of c-MYC was observed to be suppressed with increasing dose of YPN-005 in all three cell lines, whereas that of MCL1 was decreased in OCI-AML2 cell line but not in MOLM-13 and U937 showing inconsistent results between cell lines (Figure 2B). These results suggest that YPN-005 induced an antileukemic effect by controlling the transcriptional regulation of c-MYC. We further examined the endogenous c-MYC and MCL1 protein expression levels in AML cell lines and its correlation with drug sensitivities (Supplementary F 2). As shown in Figure 2C, positive correlation was observed between the IC50 value of YPN-005 and c-MYC expression with correlation coefficient of 0.762 (p = 0.017), whereas no such significant correlation could be observed with MCL1 expression (correlation coefficient = 0.650, p = 0.158). We also assessed the cell cycle status in MOLM-13 and MOLM-14 cell lines upon treatment with different concentrations of YPN-005, but no significant change in accumulation pattern was observed compared to baseline (Supplementary F 3). Taken together, these results indicate that the antileukemic activity of YPN-005 in AML cells is possibly derived from transcription regulation through RNA polymerase II with c-MYC regulation rather than cell cycle arrest to induce apoptosis.
Thereafter, we evaluated the efficacy of YPN-005 in an orthotopic mouse model generated using GFP-transduced MOLM-13 cell line. The tumor load was assessed using fluorescent imaging. YPN-005-treated groups showed significantly improved survival compared with control group (p < 0.01) (Figure 3A). The median survival of each group was as follows: 18 days in control group, 24 days in 1 mg/kg group, 35 days in 2 mg/kg of 2 days-on/5 days-off group, and 42 days in 2 mg/kg biweekly (BIW) group, respectively. Rapid and prominent weight loss was observed in the control group compared to the YPN-005 treated groups (Figure 3B). Fluorescent imaging showed that YPN-005 treatment induced suppression of luminescence (Figure 3C) indicating a cytostatic rather than cytotoxic activity of YPN-005 against AML cells. In one of the 2 mg/kg BIW-treated groups, a lump was observed on flank at 31 days, which not only increased in size, but also spread to the groin by 38 days despite the decrease in body volume (Supplementary F 4). Further to delineate the efficacy of YPN-005, a cell proliferation inhibition assay in primary leukemic cells obtained from 26 AML patients was performed (Supplementary F 5). YPN-005 showed significant growth inhibition in primary AML cells in a dose-dependent manner (Figure 3D). AML cells harboring FLT3-ITD mutation (n = 18) were found to be significantly more sensitive to YPN-005 compared with cells without this mutation (n = 8) (Figure 3E and F).
YPN-005 exhibited potential antileukemic properties in AML cells with FLT3-ITD mutation; therefore, we examined the changes in mRNA and protein expression levels of FLT3 and STAT5, a known downstream signaling molecules for FLT3. It was observed that both the RNA expression of FLT3 and STAT5 and phosphorylated protein expression of FLT3 and STAT5 decreased post treatment with YPN-005 in a dose-dependent manner in all three cell lines as shown in Figure 4a and b. Even though OCI-AML2 cells do not harbor FLT3 gene mutation, the overexpressed levels of endogenous phosphorylated form of FLT3 decreased after YPN-005 treatment. Moreover, in order to confirm the expression status of FLT3 in OCI-AML2 cells, they were treated with gilteritinib. The viability assay showed that antiproliferative effect of gilteritinib in OCI-AML2 cells was higher than that in U937 cell line (Supplementary F 6), suggesting that YPN-005 shows antileukemic effects by suppressing FLT3 and STAT5 irrespective of FLT3 mutation status.
In this study, YPN-005, a CDK7 inhibitor, exerted potent antileukemic activity in AML both in vitro and in vivo by inducing apoptotic cell death. These apoptosis-inducing antiproliferative effects of YPN-005 were related to inhibition of c-MYC and MCL1 expression consistent with previous reports. Several CDK7 inhibitors have showed anticancer effects by inducing cell apoptosis in AML, B-cell acute lymphoblastic leukemia, and solid tumors (Abudureheman et al., 2021; Clark et al., 2017; Hu et al., 2019; T. Huang et al., 2019; Li et al., 2017; Zhang et al., 2019; Zhong et al., 2019). In addition, inhibition of CDK7 has been associated with decreased expression of c-MYC and MCL1 (Clark et al., 2017; Li et al., 2017). Association between CDK7 inhibition and repression of MYC-driven transcriptional core set has been well described in MYC-driven medulloblastoma models (Veo et al., 2021). A previous study showed that THZ1 treatment induced downregulation of genes, which have a significant association with DNA repair function by diminishing RNA polymerase II and MYC association. We observed that both protein and mRNA levels of c-MYC were downregulated with YPN-005 treatment, but only protein expression was consistently suppressed for MCL1. Similar finding was also reported in a previous study of SY-1365, a CDK7 inhibitor where SY-1365 treatment was associated with decreased protein expression of MCL1 but not its mRNA expression (Hu et al., 2019). Although CDK inhibitors are known to target MCL1 indirectly, the exact mechanism of regulating MCL1 expression through blocking CDK7 is not well identified. As MCL1 is regulated by numerous modulators at multiple cellular levels including transcription, translation, ubiquitination, and degradation, further research is warranted to reveal the mechanism of MCL1 downregulation using CDK7 inhibitors (Senichkin et al., 2020). Another known function of CDK7 is regulating cell cycle by forming CDK-activating kinase, which leads to the phosphorylation of CDK4/6 (Diab et al., 2020). Previous studies have shown that when CDK7 was selectively inhibited in colon cancer cells, both CDK1 and CDK2 were activated and cell-cycle arrest was induced at G1/S and G2/M phase, respectively (Larochelle et al., 2007). We also observed that YPN-005 effectively inhibited proliferation of FLT3-ITD mutated primary AML cells and downregulated FLT3 and STAT5 expression in both FLT3-ITD mutated or FLT3 unmutated AML cell lines. To the best of our knowledge, this is the first finding to show the potential inhibitory property of CDK7 inhibition on FLT3 downstream signaling. A previous study demonstrated that a small molecule inhibitor that co-targets CKIɑ and CDK7/9 showed antileukemic activity in FLT3-ITD mutated mouse models where inhibition of both CKIɑ and CDK7/9 was related to stabilization of p53 leading to apoptosis of leukemia cells, but the effect on FLT3-STAT5 pathway was not fully discovered (Minzel et al., 2018). Our finding suggested that CDK7 blockade could be one of the therapeutic strategies for FLT3-mutated AML and further mechanistic evidence needs to be gathered. In clinical setting, there have been phase I or II clinical trials with several CDK7 inhibitors including SY1365 (clinical trial number NCT03134638), SY-5609 (clinical trial number NCT04247126), CT7001 (clinical trial number NCT03363893), and LY3405105 (clinical trial number NCT03770494) in patients with advanced solid tumors. Some trials were terminated because of lack of efficacy or presence of toxicities, which is expected because CDK7 is ubiquitously expressed in all cell types. Our in vivo study showed that YPN-005 treatment prolonged survival of orthotopic xenograft mouse models with no significant weight loss or phenotypical injuries. In this regard, YPN-005 might be of an effective and feasible therapeutic value in AML, and should be studied further. However, our study had certain limitations. First, while CDK7 is well known to be involved in the transcription cycle of RNA polymerase II and the inhibitory effects of YPN-005 were observed in this study, the genetic studies regarding the relevant targets of YPN-005 were not fully addressed. Second, the mechanistic studies regarding CDK7 inhibition and decreased expression of c-MYC and MCL1 are lacking. Despite these limitations, our study provided some evidences of efficacy and novel mechanism of a CDK7 inhibitor in AML. In conclusion, our results demonstrate that YPN-005, a CDK7 inhibitor showed a significant antiproliferative efficacy in AML both in vitro and in vivo by inducing apoptosis. Moreover, YPN-005 treatment suppressed the expression of c-MYC and FLT3-STAT5. Therefore, this study provides substantial preclinical evidence of YPN-005 being a potential candidate for future therapeutic strategies to treat AML patients.
Bon-Kwan Koo: Conceived and designed the experiments; Performed the experiments; Wrote the paper. Eun-ji Choi: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper. Eun-Hye Hur: Conceived and designed the experiments; Wrote the paper. Ju Hyun Moon; Ji Yun Kim: Performed the experiments. Han-Seung Park; Yunsuk Choi; Kyoo-Hyung Lee; Eun Kyung Choi; Jinhwan Kim; Je-Hwan Lee: Contributed reagents, materials, analysis tools or data. Jung-Hee Lee: Analyzed and interpreted the data.
Eun Kyung Choi was supported by the Ministry of Health & Welfare, Republic of Korea [HI20C1586].
Data included in article/supp. material/referenced in article.
The authors declare no conflict of interest.
Supplementary content related to this article has been published online at [URL]. | true | true | true |
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PMC9579004 | Ting-Ting Sun,Xiu-Miao Li,Jun-Ya Zhu,Wen Yao,Tian-Jing Yang,Xiang-Rui Meng,Jin Yao,Qin Jiang | Regulatory effect of long-stranded non-coding RNA-CRNDE on neurodegeneration during retinal ischemia-reperfusion | 10-10-2022 | Retinal ischemia-reperfusion injury,Long non-coding RNA,Retinal neurodegeneration | Ischemia/reperfusion (I/R) injury is a common pathological mechanism involved in many ocular diseases. I/R is characterized by microvascular dysfunction and neurodegeneration. However, the mechanisms of neurodegeneration induced by I/R remain largely unknown. This study showed that the expression of long non-coding RNA-CRNDE was significantly upregulated after retinal ischemia-reperfusion (RIR). LncRNA-CRNDE knockdown alleviated retinal neurodegeneration induced by RIR injury, as shown by decreased reactive gliosis and reduced retinal cells loss. Furthermore, lncRNA-CRNDE knockdown directly regulated Müller cell function and indirectly affected RGC function in vitro. In addition, lncRNA-CRNDE knockdown led to a significant reduction in the release of several cytokines after RIR. This study suggests that lncRNA-CRNDE is a promising therapeutic target for RIR. | Regulatory effect of long-stranded non-coding RNA-CRNDE on neurodegeneration during retinal ischemia-reperfusion
Ischemia/reperfusion (I/R) injury is a common pathological mechanism involved in many ocular diseases. I/R is characterized by microvascular dysfunction and neurodegeneration. However, the mechanisms of neurodegeneration induced by I/R remain largely unknown. This study showed that the expression of long non-coding RNA-CRNDE was significantly upregulated after retinal ischemia-reperfusion (RIR). LncRNA-CRNDE knockdown alleviated retinal neurodegeneration induced by RIR injury, as shown by decreased reactive gliosis and reduced retinal cells loss. Furthermore, lncRNA-CRNDE knockdown directly regulated Müller cell function and indirectly affected RGC function in vitro. In addition, lncRNA-CRNDE knockdown led to a significant reduction in the release of several cytokines after RIR. This study suggests that lncRNA-CRNDE is a promising therapeutic target for RIR.
Retinal ischemia-reperfusion (RIR) is a major pathological process contributing to permanent visual impairment and blindness associated with multiple ocular diseases, such as age-related macular degeneration, diabetic retinopathy, glaucoma, retinal vein occlusion, and retinopathy of prematurity [1]. Current treatments of RIR mainly focus on arresting disease progression using intraocular anti-angiogenic vascular endothelial growth factor (VEGF) injections, angiostatic steroids and anti-inflammatory eye drops, or surgery [2, 3, 4]. However, the retinal neuroprotective effects of these therapeutic approaches are limited by the fact that these treatments only target late-stage pathology. It is reported that RIR injury is characterized by sequential events of reactive oxygen species (ROS), leukocyte aggregation, inflammatory response, intracellular calcium overload, glutamate-induced excitotoxic damage, and retinal neurodegeneration [5, 6, 7]. Nevertheless, various underlying physiological and pathological mechanisms contributing to RIR injury need further elucidation. Consequently, these new insights may provide us with new clinically effective therapeutic methods for many retinal diseases. Long non-coding RNAs (lncRNAs) have been identified as non-coding transcripts of longer than 200 nucleotides that do not harbor protein-coding signatures [8]. Growing evidence has revealed that lncRNAs may function as the novel gene expression moderators, playing a pivotal role in the various biological processes, such as cell cycle, cell differentiation, epigenetic regulation, and tissue homeostasis [9]. Recent studies have shown that aberrant lncRNA expression is implicated in multiple human diseases ranging from neurodegenerative diseases to cancers. Furthermore, the homeostasis and plasticity of neuronal signaling are required for sophisticated gene regulatory mechanisms [10]. Given the important roles of lncRNAs in gene regulation and tissue homeostasis, the findings suggest that lncRNAs play critical roles in neurodegeneration in RIR injury. Colorectal neoplasia differentially expressed (CRNDE) is located on the long arm of chromosome 16 of the human genome. Although first identified as a novel lncRNA gene in human colorectal cancer [11], it was upregulated in multiple neoplastic tissues, such as colorectal cancer, renal cell carcinoma, breast cancer, and glioma. Indeed, subsequent analysis revealed that CRNDE is the most upregulated lncRNAs in glioblastoma multiforme. It was found that lncRNA-CRNDE could regulate the tumor cells’ proliferation, invasion, metastasis, and cellular pluripotency via specific pathways [12]. Besides cancer progression, lncRNA-CRNDE is involved in fundamental processes of hypoxic-ischemic brain damage, and its expression level is increased in a time-dependent manner. This study also suggested lncRNA-CRNDE silencing alleviated ischemic brain injury [13]. The retina and optic nerve develop as a direct extension of the diencephalon in the course of embryonic development. As a result, the brain and eye share several characteristics, including similar microvasculature and many underlying gene regulatory networks. Thus, lncRNA-CRNDE may play a functional role in the pathogenesis of RIR injury. In the present study, we constructed a mouse RIR model to investigate the expression pattern of lncRNA-CRNDE, and evaluated the effect of CRNDE silencing in vivo and in vitro. Our study demonstrated that lncRNA-CRNDE expression was significantly upregulated after RIR. LncRNA-CRNDE silencing alleviated retinal neurodegeneration induced by RIR injury, as shown by decreased reactive gliosis and reduced retinal cells loss in vivo. LncRNA-CRNDE silencing directly regulated Müller cell function and indirectly affected RGC function in vitro. LncRNA-CRNDE knockdown led to a significant reduction in the release of several cytokines after RIR. This study provides novel insights into the molecular mechanisms of RIR injury and suggests that lncRNA-CRNDE could serve as a therapeutic target for RIR.
All animal experiments performed in this study followed the guidelines of the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and approved by the Animal Ethics and Experimentation Committee of Nanjing Medical University (NJMUEC-2018-28). Four-week-old C57BL/6J male mice (supplied by the Experimental Animal Center of Nanjing Medical University, China) were fed a standard diet, provided water randomly, and housed in a 12-h light/12-h dark cycle.
The RIR model was induced as previously described [14]. Briefly, mice were anesthetized with an intraperitoneal injection of a mixture of ketamine (80 mg/kg) and xylazine (4 mg/kg). Pupils were dilated with 0.5% tropicamide and 2.5% phenylephrine (Mydrin-P; Santen, Osaka, Japan) followed by a few drops of topical anesthetic 0.4% oxybuprocaine hydrochloride (Benoxil; Santen, Osaka, Japan) on the ocular surface. For retinal ischemia, the anterior chamber was cannulated with a 30-gague infusion needle connected by silicone tubing to a reservoir of sterile 0.9% saline, and intraocular pressure (IOP) increased to 90 mmHg. The needle was pulled out carefully 45 min later, and the retinal blood supply was restored after reperfusion. The sham operation, which served as the control, was performed without elevating the IOP. The animals were excluded if the cornea, iris and lens were injured during puncture, if the puncture needle slipped out during anterior chamber pressor perfusion, or if the retinal ischemia did not last for 45 min. After surgery, tobramycin ointment (Alcon, USA) was applied to prevent bacterial infections. Eyes were processed after 6h, 1,3, or 7 days postinjury.
Total mice were randomly divided into 4 groups (4 animals/group), (1) normal control group; (2) RIR group; (3) RIR + scrambled (Scr) small hairpin (shRNA) group; (4) RIR + CRNDE shRNA. Random numbers were generated using a online random order generator (https://www.graphpad.com/quickcalcs/randomize1/). All mice were anesthetized before intravitreal injection by pupil dilation with 0.5% tropicamide and 2.5% phenylephrine. The adeno-associated virus (AAV) solution (2 μL, 1012 v. g/ml) loaded with CRNDE shRNA or Scr shRNA was delivered into the vitreous using a 33-gauge needle four weeks before the onset of reperfusion to maximize the transfection efficiency. CRNDE shRNA targeting sequence was 5’- TCCCTTCACCTCACCTGGATCTCTT -3’ and Scr shRNA targeting sequences was 5’- TTCTCCGAACGTGTCACGT -3’.
On the seventh day after RIR injury, retinal function was monitored using full-field flash electroretinography. C57BL/6J male mice were maintained in the complete darkness overnight before the ERG recording session. Mice were anesthetized with ketamine (80 mg/kg) and xylazine (4 mg/kg), and the pupils were dilated with topical 0.5% tropicamide and 2.5% phenylephrine before recording. Then, 0.4% oxybuprocaine hydrochloride was applied to topically anesthetize the corneas before a Gold-plated wire loop contact lens electrode was placed on the tested eye. The full-field flash ERG was recorded using custom DTL fiber electrodes with an Espion testing system and ColorDome LED/Xenon-full field stimulator (Diagnosys LLC, Lowell, MA). Stainless steel needle electrodes were inserted into the skin between the two ears and into the base of the tail, serving as reference and ground leads, respectively. After completing a scotopic intensity series, photopic flash responses were recorded in the presence of an adapting white light. Flash ERG records were bandpass filtered at 0.3 and 500 Hz. The amplitude of the a-wave and b-wave was measured by the Roland Consult Color Ganzfeld Q450C recording machine.
Seven days after RIR, eyes were enucleated and processed for immunohistology. For immunohistology, the eyeballs were fixed in 4% paraformaldehyde (PFA) overnight, two days of incubated in 30% sucrose, embedded in OCT compound (Thermo Scientific, 6502), and stored at −80 °C. The tissue was then sectioned into 10-μm-thick slices by a cryostat (Thermo Fisher Scientific, Walldorf, Germany). Immunofluorescent analysis was performed as previously described [15]. The retinal cross-sections were first dried and rehydrated in PBS. Retinal sections were permeabilized with 0.5% Triton X-100 for 30 min and then blocked with 10% bovine serum albumin (BSA) for 30 min. Six retinal sections per eye were used for each staining. Tissues were incubated with the appropriate primary antibodies at 4 °C overnight for each staining. Subsequently washed with PBS, retinal sections were incubated with fluorophore-conjugated secondary antibodies for 3 h at room temperature (Table 1). RGCs, amacrine cells, horizontal cells, bipolar cells, microglia, Müller cells, and photoreceptor cells were labeled using specific antibodies. Finally, cell nuclei were stained with 4'6-diamidin-2-phenylindol (DAPI, Beyotime, c1002). The retinal sections were observed using an Olympus IX-73 microscopy, and the fluorescent signals were analyzed by ImageJ.
For qRT-PCR analyses, the retina was dissected out, snap-frozen in a lysis buffer with TRIzol reagent (Life Technologies, 15596026) in liquid nitrogen, and stored at −80 °C until RNA extraction. The quality and purity of RNA were detected spectrophotometrically. cDNA was converted from total RNA using Prime Script RT Master Mix (Takara, RR036A) according to the manufacturer’s instructions. Quantitative real-time PCR was performed with SYBR Green (Thermo Fisher Scientific, 100029284), and the data collection was performed on the PikoReal Real-Time PCR System (Thermo Scientific). The primers were synthesized by Gene Pharma (Shanghai, China). The relative expression level of indicated genes (lnc-CRNDE, ICAM-1, IL-6, TNF-α, and Rpl13a) was compared with that of Rpl13a, and expression fold changes were calculated using 2-△△Ct methods [16]. The primer sequences of different genes are tabulated in Table 2.
For histological analysis, the eyeballs were fixed in 4% paraformaldehyde (PFA, Biosharp Biotechnology, BL539A) overnight at 4 °C, paraffin embedded, and sectioned (6 μm thick). Three sections per eye were stained with HE to obtain a structural overview of the retinal layers. After the HE staining, all slides were dehydrated in ethanol and then incubated in xylene before being mounted with neutral balsam (Biosharp, 69070060) and observed under an Olympus IX-73 microscope. For each analysis, values were measured in triplicates and averaged.
Primary Müller cells were isolated from mice at postnatal day 7. Briefly, eyeballs were incubated in serum-free media overnight. Retinas were enzymatically incubated with trypsin (0.25%)/collagenase (65U/mL) at 37 °C for 20 min, and then rinsed in Dulbecco’s modified Eagle medium (DMEM, Gibco, C11995500BT) containing 10% fetal bovine serum (FBS, Gibco, 10099141) to terminate digestion. Retinas were dissected into small aggregates from the remaining tissue and plated in primary Müller glia cell culture media: DMEM-high glucose, without L-glutamine, with sodium pyruvate +1% GlutaMax, 1% Penicillin/Streptomycin, and 10% FBS. The isolated cells became confluent after the cultures had been maintained for 7–10 days. The third passaged cells were used for the subsequent experiments, and cultured cells were identified using the antibodies against the glial fibrillary acidic protein (GFAP) and glutamine synthetase (GS). Primary RGCs were isolated and cultured according to the two-step immunopanning protocol. Retinas were collected from C57BL/6J mouse pups at postnatal day 0–3 and dissociated in papain solution (15 U/mL) and collagenase (70 U/mL) for 15 min. Subsequently, retina cell suspensions were incubated with antimacrophage antiserum antibody-coated flasks to remove adherent macrophages and microglial cells. Non-adherent cells were incubated with mouse Thy1.2 monoclonal antibody to purify RGCs. The purity of the primary RGCs in cultures were determined by staining with the antibody Tuj1 (Abcam, ab18207), specific RGCs markers. Primary RGCs were cultured in serum-free Neurobasal-A medium (Gibco) supplemented with penicillin, streptomycin, 25 ng/mL CNTF, 25 ng/mL BDNF, 10% FBS, 10 mM forskolin, and B27. All cultures were incubated at 37 °C, in 5% CO2, and with 95% relative humidity. Müller cells were transfected using Lipofectamine 2000 (Life Technologies, 13778150) with three small interfering RNAs (siRNAs; Gene Pharm) targeting lncRNA-CRNDE according to the manufacturer’s protocol, and the transfection efficiencies of three CRNDE siRNAs were 77.2%, 34.1% and 37.1%, respectively (Figure 5B). The siRNA target sequence was shown as follows (Table 3):
Cell proliferation was determined using 5-ethynyl-2’-deoxyuridine (EdU) DNA Cell Proliferation kit (Beyotime, C0071S) following the manufacturer’s protocol. After the required treatment, cells were incubated with 1×EdU working liquid for 2 h and then fixed in 4% PFA for 15 min. After washing, cells are blocked with 0.3%Triton X–PBS for 15 min and then incubated with click reaction liquid for 30 min at room temperature in the dark. The cells were finally mounted using the anti-fade medium containing 1× Hoechst 33342 (Beyotime, C0071S) and observed under an Olympus IX-73 microscope.
The MTT assay was used to measure the viability of the cells. Briefly, Müller Cells (1×104) were plated in each well of a 96-well plate and allowed to adhere for 24 h. After specific treatment, 30 μL of 0.5 mg/mL MTT (BioFroxx, 1334GR001) (in PBS) was added to each well and incubated for an additional 3 h at 37 °C. After discharging the medium, 200 μL DMSO/well was added to the culture cells to dissolve formazan crystals [17]. The absorbance value in each well was measured at the wavelength of 595 nm using a microplate reader (Molecular Devices).
Rhodamine123 assays were performed to determine the DNA condensation and nuclear fragmentation. After the required treatment, cells were incubated with the 10 μmol/L rhodamine123 (Solarbio, R8030) at 37 °C for 1.5 h in the dark. The fluorescence intensity of harvested cells was detected using an Olympus IX-73 microscope.
To mimic the murine model of RIR, primary Müller cells and RGCs were subjected to oxygen-glucose deprivation for 6 h and subsequently returned to normal environment for 12 h. Specifically, cells were cultured in glucose-free medium containing 0.2% FBS and 1% penicillin/streptomycin in a hypoxia chamber (95% N2 and 5% CO2) for 6 h after transfection. Cells were then shifted to DMEM-high glucose (4.5 g/L), supplemented with 10% FBS and reoxygenated in normoxic conditions (95% air, 5% CO2) for 12 h. Normoxia control cells were maintained in complete media under normoxic conditions.
Calcein-AM staining and PI double staining were used to discriminate between viable and dead cells. Cells were stained with a mixture of 10 μmol/L Calcein-AM solutions (AAT Bioquest, 22002), 10 μmol/L PI (Biofroxx, 1246MG100), and 3 μM Hoechst 33342 (Biofroxx, 2289MG025) for 15 min at 37 °C in the dark. After washing with PBS three times, images of live cells (green) and dead cells (red) were captured using a fluorescent microscope. The average percentage of PI-positive cells was counted using the Image J software.
Hoechst 33342 fluorescent dye was used to detect DNA condensation and nuclear fragmentation. After the required treatment, these cells were washed with PBS three times and fixed with 4% PFA for 15 min at room temperature. Subsequently, cells were washed with PBS and then stained with Hoechst 33342 (Biofroxx, 2289MG025) for 15 min. Finally, these stained cells were observed using an Olympus IX-73 microscope.
Mice were anesthetized with intraperitoneal injection of a mixture of ketamine (80 mg/kg) and xylazine (4 mg/kg), and then euthanized by intraperitoneal injection of sodium barbiturate (100 mg/kg). Then retinal tissue was collected and the samples of all groups were kept at 4 °C for biochemical assay. Biochemical indexes were determined using commercial reagent kits (Abcam, UK) according to the manufacturer's instructions. The indirect ELISA assay determined the concentrations of TNF-α, IL-6, and ICAM-1 in the medium of retinal tissue. A microplate reader measured the optical density (OD) values were measured at 450 nm by a microplate reader.
Data analysis was performed using GraphPad Prism 8 (GraphPad Software, San Diego, CA). All results are expressed as means ± SEM. For the normally distributed data with equal variance, the significant difference was determined by one-way ANOVA to test the effect of group followed by post-hoc Bonferroni’s test (when >2 groups were compared). P < 0.05 was considered statistically significant.
A previous study revealed that lncRNA-CRNDE expression levels are significantly upregulated in ischemia-reperfusion injury [18]. Here, we further determined whether RIR injury influenced lncRNA-CRNDE expression in vivo and in vitro. The rodent RIR model is a well-established animal model which resembles human acute retinal artery occlusion such as CRAO or acute ocular hypertension such as glaucoma. To test whether retinal lncRNA-CRNDE expression level altered in response to RIR injury, eyes were collected at 6 h, 24 h, 3 d, 7 d, and qPCR analysis demonstrated increased lncRNA-CRNDE expression in RIR retinas (Figure 1A). In the RIR model, the circulatory disorder of the retina is induced by elevating the IOP for a definite period. Induced high IOP causes compressed retinal blood vessels and impaired blood flow. The following natural reperfusion induces excessive oxidative stress. Our preliminary results showed that CRNDE was mainly expressed in retinal endothelial cells and Müller cells (data not shown). It is reported that OGD/R is a classical in vitro model of RIR [5, 19]. Müller cells were subjected to glucose free media in hypoxia condition to mimic the ischemic phase of I/R, and then returned to normoxia and normal media to mimic the reperfusion phase of I/R. qRT-PCR revealed that lncRNA-CRNDE expression was significantly upregulated in Müller cells subjected to OGD/R (Figure 1B). Collectively, these results indicate that lncRNA-CRNDE is potentially involved in RIR injury.
To evaluate the effect of lncRNA-CRNDE in RIR injury, lncRNA-CRNDE was knocked down in retinas before the onset of retinal ischemia. The experiments were divided into four groups, lncRNA-CRNDE silencing group (lncRNA-CRNDE shRNA), Sc shRNA group, RIR group, and untreated group. qRT-PCR showed that CRNDE shRNA injection significantly reduced the levels of CRNDE in the retinas (Figure 2A). After I/R injury induction, histomorphology of the HE-stained retina was observed (Figure 2B). We found that retinal damage occurred rapidly in mice with retinal edema, vacuolar degeneration, condensation of nuclear chromatin by HE staining as early as 6 h after RIR (Figure 2B). Furthermore, the inner plexiform layer (IPL) thickness, total retina thickness, and cell density in the ganglion cell layer (GCL) were measured after the RIR injury. Regarding these histological features, a distinct degradation of the whole retina started at the 6th hour after RIR, and these changes persisted for seven days (Figure 2B). Furthermore, a significantly reduced thickness of the whole retina and IPL were detected at days 3 and 7 in ischemic eyes compared to the control group. LncRNA-CRNDE shRNA treatment dramatically relieved the damage (Figures 2C, 2D). Consistently, cell counts of GCL decreased after 6 h of RIR, while lncRNA-CRNDE shRNA mice presented no significant difference at this point. Whereas, surviving cells decreased in the GCL of retinas on days 3 and 7 after RIR, which was relieved by lncRNA-CRNDE shRNA (Figure 2E). These results indicated that lncRNA-CRNDE knockdown attenuated progressive retinal thinning and loss of RGCs induced by RIR injury, especially on day 7 after RIR. Considering these results, each group was observed after 7 days of RIR for all studies below. To further assess retinal function, we employed retinal ERG to detect the effect of lncRNA-CRNDE on visual function. The representative ERG waveforms for each group were shown. The ERG measurements 7 days after RIR showed a significant decrease in the a-wave and b-wave amplitude compared to the control group. Compared with RIR and Scr shRNA mice, the amplitudes of b-wave were increased in the lncRNA-CRNDE shRNA group, but the amplitudes of a-wave were not improved in the lncRNA-CRNDE shRNA group (Figure 2F), suggesting that lncRNA-CRNDE shRNA silencing partially prevents ERG abnormality induced by RIR injury and ameliorate retinal function. These results revealed that lncRNA-CRNDE knockdown could partially alleviate retinal function under RIR injury. Encouraged by these findings, we further investigated the potential neuroprotective effects of lncRNA-CRNDE in the RIR mice model. RGC degeneration and reactive gliosis are two important features of retinal neurodegeneration under RIR injury. To this end, we performed protein immunolabeling experiments to explore if lncRNA-CRNDE is capable of regulating neuroinflammation, as well as its underlying mechanisms. Müller glia is the major glial component of the retina, and its excessive activation is a general response to injury and inflammation in the damaged the retina [20]. As expected, RIR notably increased reactive gliosis as shown by increased GFAP staining and GS staining in the retina. However, aberrant gliosis was inhibited by lncRNA-CRNDE shRNA with lower expression of GFAP and GS, thus exerting protection against RIR injury (Figures 3A, 3B). Meanwhile, current study shows that microglia are the major performer in neuroinflammation, and their activation could aggravate retinal disease by releasing several toxic and pro-inflammatory mediators [21]. Immunofluorescence staining of microglial marker Iba-1 showed that RIR injury increased the number of Ibal-1+ cells, but lncRNA-CRNDE silencing reduced this trend (Figure 3C). Moreover, RIR injury caused a marked decrease in RGC number as shown by decreased NeuN (the specific marker of RGCs), which was mostly prevented in lncRNA-CRNDE shRNA retinas as shown by the increased number of NeuN-positive RGCs. The positive signals located in the GCL suggested the presence of live RGCs. Thus, lncRNA-CRNDE silencing may have a beneficial effect on facilitating RGC survival, as shown by increased NeuN staining (Figure 3D). The ERG measurements showed a functional disorder of the cells in the inner nuclear layer (INL) 7 days after ischemia induction. Accordingly, we examined those cells in the next step. In the normal retina, bipolar cells extend throughout the IPL, INL, and OPL [22]. Anti-PKCα was used to visualize bipolar cells [23]. The immunolabeling showed a progressive diminution of PKCα+ cells in the RIR retina, whereas lncRNA-CRNDE shRNA prevented the reduction in the PKCα positive bipolar cells (Figure 3E). In the normal retina, starburst amacrine cells distribute in the GCL and the innermost side of the INL, and their dendrites stratify into two distinct layers in the IPL [24]. Compared with RIR retinas, lncRNA-CRNDE shRNA protected RGCs as shown by the increased number of calretinin-labeled cells in the GCL, while lncRNA-CRNDE shRNA allowed no significant retention of amacrine cells (calretinin-labeled cells in the INL) (Figure 4A). Moreover, RIR caused observable changes in horizontal cells. However, lncRNA-CRNDE shRNA did not further affect the number of calbindin labeled horizontal cells compared with RIR retinas (Figure 4B). Similarly, rhodopsin immunolabeling revealed that lncRNA-CRNDE shRNA did not affect photoreceptors (rod cells) compared with noticeable changes in RIR injury (Figure 4C). Collectively, these results indicate that lncRNA-CRNDE affects reactive retinal gliosis and the survival of RGCs and bipolar cells.
The above mentioned results showed that lncRNA-CRNDE is mainly expressed in Müller cells and RGCs. Primary Müller cells were cultured and identified with GFAP and GS antibodies, the result showed that the percentage of GFAP+/GS + cells was more than 90% (Figure 5A). Next, OGD/R was conducted to determine the role of lncRNA-CRNDE on Müller cells and RGCs function in vitro. We designed three different CRNDE siRNAs, and qPCR analysis revealed that all CRNDE siRNAs could significantly reduce CRNDE expression levels in Müller cells (Figure 5B). We selected CRNDE siRNA1 for the subsequent experiments due to its higher silencing efficiency. We observed that lncRNA-CRNDE knockdown further reduced Müller cells viability treated with OGD/R (Figure 5C). EdU immunofluorescence staining showed that lncRNA-CRNDE knockdown significantly decreased the proliferation of Müller cells compared with the OGD/R group (Figure 5D). We then determined whether lncRNA-CRNDE regulates the development of OGD/R-induced apoptosis using Hoechst 33342, PI/Calcein-AM staining. The combination of lncRNA-CRNDE knockdown and OGD/R resulted in a higher apoptotic percentage than OGD/R alone, as shown by more PI-positive cells (dying or dead cells) (Figure 5E) and increased apoptotic nuclei (condensed or fragmented) (Figure 5F). These results suggest that lncRNA-CRNDE silencing decreases the viability and proliferation，and promotes the apoptosis of Müller cells after OGD/R in vitro. To evaluate the effect of Müller cells on RGCs survival when RIR injury occurred, we cultured primary RGCs and performed co-cultures of Müller cells after OGD/R treatment in an approach in vitro. Immunofluorescent staining of Tuj1 showed that the percentage of RGCs was about 90% (Figure 6A). MTT assay revealed that co-culture with Müller cells reduced cells viability, while CRNDE knockdown in Müller cells significantly increased the viability of RGCs (Figure 6B). Immunofluorescent staining results showed that OGD/R shortened the synaptic length of RGCs, co-culture with Müller cell further shortened the synaptic length of RGCs, while CRNDE knockdown in Müller cells obviously alleviated this trend (Figure 6C). Propidium iodide (PI) staining revealed that OGD/R increased apoptotic RGCs, Müller cell co-culture evidently increased the number of apoptotic RGCs, while CRNDE knockdown in Müller cells alleviated this harmful effect (Figure 6D). The above results revealed that the Müller cell co-culture significantly increased the number of apoptotic RGCs induced by OGD/R, and lncRNA-CRNDE knockdown in Müller cells significantly reduced the damaging effect on RGCs. Taken together, these results indicate that lncRNA-CRNDE may be a crucial regulator of Müller cell function and has an indirect effect on RGC function in vitro.
As is well known to us all, the balance of reactive glial cells is important for neuron survival as reactive glial cells can produce growth factors and immunomodulatory cytokines [25]. To determine whether lncRNA-CRNDE regulates glial cell reactivity and affects cytokine release, we detected the cytokine profile of the lysates from RIR-injured retinas and lncRNA-CRNDE shRNA-injected RIR-injured retinas via qRT-PCR and ELISA assays. We observed clearly from qRT-PCR array that lncRNA-CRNDE knockdown resulted in a significant reduction in the amount of three mRNAs, namely, ICAM-1, TNF-α, and IL-6 (Figure 7A). ELISA assays also showed that lncRNA-CRNDE silencing reduced retinal inflammation (Figure 7B). These data suggest that the increased secretion of pro-inflammatory factors in the retinas might be one of the mechanisms of RIR injury.
LncRNAs are involved in regulating important biological processes, including genomic imprinting, dosage compensation (e.g., X chromosome inactivation), regulation of the cell cycle, and the differentiation and development programs of somatic cells [26]. Multiple research studies have highlighted the importance of lncRNAs dysregulation in human disorders, including cancer, and cardiac and neurodegenerative disorders [27]. As an oncogene, lncRNA-CRNDE is highly expressed in hypoxic-ischemic brain damage, lncRNA-CRNDE silencing alleviates hypoxic-ischemic brain damage, at least partially, through promoting autophagy [13]. This study shows that lncRNA-CRNDE expression levels are significantly up-regulated in the retinas following ischemia-reperfusion (IR) and in Müller cells upon hyperoxia stress. LncRNA-CRNDE knockdown decreases Müller cell over-activation and increases RGC survival in vivo and in vitro. This study may provide a novel insight for investigating the role of lncRNA-CRNDE in neurodegeneration induced by RIR injury and provides a promising target for preventing retinal neurodegeneration in RIR. RIR injury is a common pathological process in various ocular diseases, including diabetic retinopathy, retinal vascular occlusion, anterior optic neuropathy, and glaucoma [28]. Therapies that delay or halt the loss of RGCs have been proven effective in preserving the vision of patients with glaucoma [14]. In this study, the ocular hypertension-induced RIR injury model has been used to investigate the pathogenesis of neurodegenerative diseases and explore new neuroprotective therapies. We demonstrate that lncRNA-CRNDE knockdown could significantly reduce the percentage of apoptotic retinal cells and alleviate RIR injury. Moreover, a retinal electrophysiology assay showed that lncRNA-CRNDE knockdown improved visual function after RIR injury. Thus, these results suggest that lncRNA-CRNDE is involved in regulating RIR-induced retinal neurodegeneration in vivo. Glial cells provide structural and metabolic support to retinal neurons and become reactive in response to external stresses [29]. Müller cells are the most abundant glial cells in the retina that span the entire thickness of the retina [30]. They constitute an anatomical and functional link with retinal neurons and contribute to intraretinal homeostasis. Under the pathological condition, including photic damage, retinal trauma, ischemia, glaucoma and diabetic retinopathy, Müller cells exhibit nonspecific gliotic responses. Reactive gliosis includes cell proliferation, changes in cell shape, and upregulation of the intermediate filament system [31]. However, repetitive pathological stimulation exacerbates the proliferation of Müller cells, causing cell dysfunction, damaging photoreceptor cells, and neurons, leading to glial scars, inhibiting retinal remodeling, and limiting the ability to regenerate damaged retinal tissue, eventually leading to blindness [32]. As a result, we performed the MTT assay, EdU staining, and PI/Calcein-AM staining to examine the role of lncRNA-CRNDE in Müller cell function. Results showed that lncRNA-CRNDE knockdown significantly reduces Müller glial cell viability, decreases the proliferation of Müller glial cells, and promotes the apoptosis of Müller glial cells induced by OGD/R. In addition, lncRNA-CRNDE knockdown decreases GFAP and GS expression in the retina induced by RIP injury in vivo. Thus, it is not surprising that lncRNA-CRNDE is involved in the pathological process of RIR injury. A previous study showed a close relationship between Müller cells and RGCs in RIR injury [33]. In addition, our previous study showed the Müller cells are activated after STZ-indued diabetes, while suppressing the active Müller cells by AQP4-AS1 knockdown could improve the survival of RGCs [34]. RGC death in glaucoma and neural degeneration models is associated with accumulation of activated glia, and inhibition of Müller glia and astrocytes result in an increase in RGCs [35, 36, 37, 38]. Müller cells are among the first responders following intraocular pressure increase. Studies of the DBA/2J mouse model of glaucoma and episcleral vein cauterization-induced glaucoma rat model suggest that Müller cell hypertrophy and activation were detected at the early stages of glaucoma (as early as 2–3 days), preceding detectable RGC disease [39, 40]. Reactive Müller glia could increase the susceptibility of RGCs to stress signals by releasing neurotoxic factors and contribute to disease progression [41]. Thus, modulation of Müller cell responses may alter disease progression. It is known that the presence and distribution of retinal astrocytes are correlated with the density of the nerve fibers that they ensheath and the presence of retina blood vessels [42]. As the main producers of VEGF, astrocytes are implicated in retinal vascularization and vascular mediation. However, the transformation of surveying astrocytes into alerted or reactive states, in response to a perceived threat, could have a detrimental effect on RGCs axons via secretion of proinflammatory cytokines such as IL-6, TNF-α, and nitric oxide [43, 44, 45]. In our results, the number of apoptotic RGCs was significantly increased after OGD/R treatment, as in those co-cultured with Müller cells, compared with those that were not co-culture with Müller cells. Furthermore, lncRNA-CRNDE knockdown in Müller cells significantly reduced the damaging effect on the co-cultured RGCs in response to OGD/R. These studies show that lncRNA-CRNDE knockdown could indirectly protect the RGCs after OGD/R in vitro. Consistently, in vivo studies reveal that the lncRNA-CRNDE knockdown group has more RGC survival than the Scr shRNA-injected group after RIR. When tissues are exposed to ischemia followed by reperfusion, ROS are extensively generated in the early reperfusion stage. ROS could cause heavy damage to the retina [46]. Moreover, increasing evidence demonstrates that oxidative stress induced by ROS plays a key role in the pathophysiological mechanisms in RIR injury [47]. Interestingly, RIR injury could induce the production of a variety of inflammatory mediators in the retina, including IL-1β, ICAM-1, and IL-6 [48]. Oxidative stress was found to be involved in the production of these inflammatory mediators and is now considered as one of the most important factors that mediate the process of apoptosis [49]. LncRNA-CRNDE knockdown inhibited the expression of IL-1β, ICAM-1, and IL-6 in the retina induced by RIR injury. These results suggest that lncRNA-CRNDE plays an important role in the pathological process of RIR injury by regulating the release of immunomodulatory cytokines. In conclusion, this study reveals the role of lncRNA-CRNDE in RIR injury. LncRNA-CRNDE directly regulates the biological functions of Müller cells and indirectly regulates the functions of RGCs. These findings indicate that lncRNA-CRNDE is involved in retinal neurodegeneration dysfunction. Furthermore, mechanistically, lncRNA-CRNDE silencing inhibits the release of immunomodulatory cytokines, alleviating RIR-induced neurodegeneration dysfunction. Collectively, this study suggests that lncRNA-CRNDE is a promising target for the prevention of retinal neurodegeneration complications.
Ting-Ting Sun: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper. Xiu-Miao Li, Jun-Ya Zhu, Wen Yao and Tian-Jing Yang: Performed the experiments; Analyzed and interpreted the data; Wrote the paper. Xiang-Rui Meng: Performed the experiments; Analyzed and interpreted the data. Jin Yao and Qin Jiang: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Qin Jiang, Jin Yao, Mr Qin Jiang, Xiu-Miao Li were supported by National Natural Science Foundation of China (81870679, 81970823, 82070983, 81800858) respectively.
Data will be made available on request.
The authors declare no conflict of interest.
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PMC9579039 | Shuhei Ishikura,Kazumasa Yoshida,Toshiyuki Tsunoda,Senji Shirasawa | Death domain–associated protein DAXX regulates noncoding RNA transcription at the centromere through the transcription regulator ZFAT | 23-09-2022 | centromere,transcription regulation,protein–protein interaction,zinc finger,DNA-binding protein,DAXX,noncoding RNA,ZFAT,AD, acidic domain,ChIP, chromatin immunoprecipitation,co-IP, coimmunoprecipitation,DAXX, death domain–associated protein,FLAG-CENP-B, FLAG-tagged CENP-B,FLAG-DAXX, FLAG-tagged DAXX,HA, hemagglutinin,HEK293, human embryonic kidney 293 cell line,ncRNA, noncoding RNA,qPCR, quantitative PCR,RIPA, radioimmunoprecipitation assay,SIM, SUMO-interacting motif,ZF, zinc finger,ZFAT, zinc-finger protein with AT hook,ZFAT-HA, hemagglutinin-tagged ZFAT | The centromere is an essential chromosomal structure for faithful chromosome segregation during cell division. No protein-coding genes exist at the centromeres, but centromeric DNA is actively transcribed into noncoding RNA (ncRNA). This centromeric transcription and its ncRNA products play important roles in centromere functions. We previously reported that the transcriptional regulator ZFAT (zinc-finger protein with AT hook) plays a pivotal role in ncRNA transcription at the centromere; however, it was unclear how ZFAT involvement was regulated. Here, we show that the death domain–associated protein (DAXX) promotes centromeric localization of ZFAT to regulate ncRNA transcription at the centromere. Coimmunoprecipitation analysis of endogenous proteins clearly reveals that DAXX interacts with ZFAT. In addition, we show that ectopic coexpression of ZFAT with DAXX increases the centromeric levels of both ZFAT and ncRNA, compared with ectopic expression of ZFAT alone. On the other hand, we found that siRNA-mediated depletion of DAXX decreases the centromeric levels of both ZFAT and ncRNA in cells ectopically expressing ZFAT. These results suggest that DAXX promotes the centromeric localization of ZFAT and ZFAT-regulated centromeric ncRNA transcription. Furthermore, we demonstrate that depletion of endogenous DAXX protein is sufficient to cause a decrease in the ncRNA levels at the centromeres of chromosomes 17 and X in which ZFAT regulates the transcription, indicating a physiological significance of DAXX in ZFAT-regulated centromeric ncRNA transcription. Taken together, these results demonstrate that DAXX regulates centromeric ncRNA transcription through ZFAT. | Death domain–associated protein DAXX regulates noncoding RNA transcription at the centromere through the transcription regulator ZFAT
The centromere is an essential chromosomal structure for faithful chromosome segregation during cell division. No protein-coding genes exist at the centromeres, but centromeric DNA is actively transcribed into noncoding RNA (ncRNA). This centromeric transcription and its ncRNA products play important roles in centromere functions. We previously reported that the transcriptional regulator ZFAT (zinc-finger protein with AT hook) plays a pivotal role in ncRNA transcription at the centromere; however, it was unclear how ZFAT involvement was regulated. Here, we show that the death domain–associated protein (DAXX) promotes centromeric localization of ZFAT to regulate ncRNA transcription at the centromere. Coimmunoprecipitation analysis of endogenous proteins clearly reveals that DAXX interacts with ZFAT. In addition, we show that ectopic coexpression of ZFAT with DAXX increases the centromeric levels of both ZFAT and ncRNA, compared with ectopic expression of ZFAT alone. On the other hand, we found that siRNA-mediated depletion of DAXX decreases the centromeric levels of both ZFAT and ncRNA in cells ectopically expressing ZFAT. These results suggest that DAXX promotes the centromeric localization of ZFAT and ZFAT-regulated centromeric ncRNA transcription. Furthermore, we demonstrate that depletion of endogenous DAXX protein is sufficient to cause a decrease in the ncRNA levels at the centromeres of chromosomes 17 and X in which ZFAT regulates the transcription, indicating a physiological significance of DAXX in ZFAT-regulated centromeric ncRNA transcription. Taken together, these results demonstrate that DAXX regulates centromeric ncRNA transcription through ZFAT.
The kinetochore attaches chromosomes to spindle microtubules in mitosis to segregate each sister chromatid into daughter cells. The centromere is an essential chromosomal structure, in which kinetochore protein complex is assembled, to ensure accurate chromosome segregation (1). The eukaryotic centromeres that lack protein-coding genes had long been thought to be transcriptionally silent regions. However, recent many studies have demonstrated that transcription into noncoding RNA (ncRNA) occurs at the centromeres although the centromeric transcription levels are low (2, 3, 4, 5, 6). Centromeric transcription and its ncRNA products have been shown to play crucial roles in centromere functions and kinetochore assembly (7, 8, 9, 10). However, there is limited understanding regarding molecular properties of centromeric ncRNA, including nucleotide sequences and transcription regulation. We have previously reported that the nuclear protein ZFAT (zinc-finger protein with AT hook) plays a pivotal role in ncRNA transcription at the centromere (11, 12, 13). ZFAT specifically induces acetylation of lysine 8 in histone H4 at the centromere by recruiting the histone acetyltransferase KAT2B (12). The KAT2B-catalyzed acetylation of lysine 8 in histone H4 at the centromere functions as a binding site for the bromodomain-containing protein BRD4, which stimulates RNA polymerase II–dependent ncRNA transcription (12). ZFAT specifically binds to 8-bp DNA sequences at the centromere, named the ZFAT box (12). Furthermore, we recently reported that the centromeric protein CENP-B interacted with ZFAT to promote the centromeric localization of ZFAT (11). We have previously demonstrated that ZFAT plays important roles in cell proliferation and survival mainly using mouse (14, 15, 16, 17, 18, 19). On the other hand, various studies in human have reported that genetic variants of the ZFAT gene are associated with particular human diseases, including autoimmune thyroid diseases (20, 21), aneurysms (22), hypertension (23), and type 2 diabetes mellitus (24). Furthermore, mutations and altered expression of the ZFAT gene are also observed in several human cancers (25, 26, 27, 28, 29, 30). For example, a genome-wide association study of diffuse large B-cell lymphoma patients showed that specific mutations in the ZFAT genes were strongly associated with poorer survival of diffuse large B-cell lymphoma patients (31). Thus, dysregulations of ZFAT function and expression are thought to be related to various human diseases although roles of ZFAT-regulated centromeric transcription in pathogenesis of these diseases are still unknown. DAXX (death domain–associated protein) is a multifunctional protein involved in many cellular processes, including apoptosis, protein stability, and transcription regulation (32). DAXX itself does not contain any enzymatic catalytic domains, and, thus, functions in these processes through interaction with various molecules. DAXX regulates gene transcription through interaction with various transcription factors. For example, p53 has been identified as a DAXX-interacting protein, and the p53 transcriptional activity is negatively regulated by DAXX (33). On the other hand, Pax5, an essential transcription factor for B-cell development, interacts with DAXX, and their interaction leads to transcription activation in B cells (34). Furthermore, it has been reported that DAXX regulates gene transcription through interaction with histone-modifying enzymes, including the CREB-binding protein (35) and the histone deacetylase 2 (36). Interestingly, through interaction with the chromatin-remodeling protein ATRX (α-thalassemia and mental retardation X-linked), DAXX functions as a histone chaperone for the deposition of histone H3 variant H3.3, which is also related to transcription activation (37). Thus, the dynamic interaction between DAXX and its associated proteins is tightly controlled. Intriguingly, it was previously reported that DAXX was involved in transcription at the centromere through deposition of H3.3 (38). However, the precise mechanisms by which DAXX regulates centromeric transcription remain elusive. In this study, we show that DAXX interacts with ZFAT to promote centromeric localization of ZFAT, leading to stimulation of ncRNA transcription. Coimmunoprecipitation (co-IP) analyses of endogenous and ectopically expressed proteins clearly reveal that DAXX interacts with ZFAT. Ectopic expression of DAXX increases the centromeric ZFAT levels and stimulates ZFAT-regulated ncRNA transcription at the centromeres. On the other hand, knockdown of DAXX decreases the centromeric levels of ZFAT as well as those of ncRNA. These results indicate that DAXX regulates ncRNA transcription at the centromeres through ZFAT.
We previously showed that the centromeric protein CENP-B interacted with ZFAT through its acidic domain (AD) (11). Similar to CENP-B, DAXX is also known as a protein that localizes at the centromeres and contains the AD (32, 39). Therefore, we examined the interaction between DAXX and ZFAT in human embryonic kidney 293 (HEK293) human cells through co-IP analysis using anti-ZFAT and anti-DAXX antibodies. The endogenous ZFAT protein was coimmunoprecipitated with the endogenous DAXX protein using anti-DAXX antibody and vice versa (Fig. 1A). These results indicate that DAXX interacts with ZFAT. To compare an interaction affinity with ZFAT between DAXX and CENP-B, we ectopically expressed hemagglutinin (HA)-tagged ZFAT (ZFAT-HA) and FLAG-tagged DAXX (FLAG-DAXX) or FLAG-tagged CENP-B (FLAG-CENP-B) in HEK293 cells and examined their interaction through co-IP analysis using anti-HA and anti-FLAG antibodies. Similar to endogenous proteins, the FLAG-DAXX protein was coimmunoprecipitated with the ZFAT-HA protein using an anti-HA antibody and vice versa (Fig. 1B). The protein levels of ZFAT-HA, which was coimmunoprecipitated with FLAG-DAXX, were relatively lower than those of ZFAT-HA coimmunoprecipitated with FLAG-CENP-B (Fig. 1B). Similarly, the protein levels of FLAG-DAXX, which was coimmunoprecipitated with ZFAT-HA, were relatively lower than those of FLAG-CENP-B coimmunoprecipitated with ZFAT-HA (Fig. 1B). Furthermore, we examined the interaction between ZFAT and DAXX after DNase treatment through co-IP analysis (Fig. 1C). Disappearance of the genome DNA by the DNase treatment was confirmed in our previous study (11). The DNase treatment did not affect the interaction between ZFAT and DAXX. Together, these results suggest that DAXX interacts with ZFAT independently of DNA, and that the interaction affinity between ZFAT and DAXX is slightly weaker than that between ZFAT and CENP-B.
The DAXX protein is composed of several domains, including two SUMO-interacting motifs (SIMs) at the N and C terminus (SIM1 and SIM2, respectively), four helix bundle domains, histone-binding domain, and AD (Fig. 2A, (32)). To elucidate DAXX domains involved in the interaction with ZFAT, we examined the interaction between ZFAT-HA and the deletion mutants of FLAG-DAXX in HEK293 cells using co-IP analysis. Deletion of SIM2 hardly affected the interaction with ZFAT, whereas loss of SIM1 slightly decreased it (Fig. 2B). Furthermore, deletion of either four helix bundle domain, histone-binding domain, or AD markedly inhibited the interaction of DAXX with ZFAT (Fig. 2B). These results suggest that multiple domains of DAXX are involved in the interaction with ZFAT. The ZFAT protein is composed of 18 zinc-finger (ZF) domains (Fig. 3A, (13)). To determine which ZFAT regions were involved in the interaction with DAXX, we evaluated the interaction between FLAG-DAXX and the deletion mutants of ZFAT-HA in HEK293 cells. While the ZF-ΔN-2 deletion mutant, which was composed of ZF9-18, retained the ability to interact with DAXX, the ZF-ΔC-2 deletion mutant, which was composed of ZF1-8, did not interact with DAXX, suggesting involvement of the C-terminal region of ZFAT in the interaction with DAXX (Fig. 3B). Furthermore, the interaction with DAXX was clearly observed in the ZF-ΔN-3 deletion mutant, which had ZF13–18 (Fig. 3B). These results suggest that the C-terminal region containing ZF13–18 of ZFAT is involved in the interaction with DAXX.
To elucidate roles of interaction between DAXX and ZFAT in their centromeric localization, we examined the protein levels of ZFAT and DAXX at the centromeres in HEK293 cells, which transiently expressed ZFAT-HA, FLAG-DAXX, or both, using chromatin immunoprecipitation (ChIP)–quantitative PCR (qPCR) analysis that we established previously (11, 12) (Fig. 4, A and B). We previously showed that ZFAT was bound to the centromeres of every chromosome but regulated the centromeric ncRNA transcription only at particular chromosomes (12). Activation of the centromeric ncRNA transcription by ectopic expression of ZFAT was observed in qRT–PCR analysis using primers for Chr17, and ChrX-a and ChrX-b, but not primers for Chr13/21-a, whereas ZFAT binding at the centromeres was detected in ChIP–qPCR analysis using all these primer sets (12). In the ChIP–qPCR analysis in this study, we used primers for Chr17 and Chr13/21-a to examine whether DAXX was involved in the centromeric localization of ZFAT only at chromosomes where ZFAT activated the ncRNA transcription. We clearly observed that ZFAT-HA and FLAG-DAXX were bound to the centromeres of both Chr17 and Chr13/21, but not to regions for the 5S ribosomal RNA gene, which were used as a negative control (Fig. 4B). Interestingly, ectopic coexpression of ZFAT-HA with FLAG-DAXX further increased the protein levels of ZFAT at the centromeres of both Chr17 and Chr13/21, compared with those in cells expressing ZFAT-HA alone. On the other hand, their coexpression did not affect the protein levels of DAXX at the centromeres, compared with those in cells expressing FLAG-DAXX alone. These results suggest that DAXX plays positive roles in the centromeric localization of ZFAT at every chromosome independently of transcription activation by ZFAT, whereas ZFAT is not involved in the centromeric localization of DAXX. Furthermore, ectopic coexpression of FLAG-DAXX with ZFAT-HA did not affect the ZFAT protein levels at the promoter regions of the BRPF1, PDE12, and TELO2 genes to which we previously reported that ZFAT was bound (Fig. 4, B and C, (15)), suggesting that DAXX is involved in the localization of ZFAT specifically at the centromeres but not at the gene promoter regions. To further examine the involvement of DAXX in the centromeric localization of ZFAT, we next evaluated the effect of loss of the endogenous DAXX protein on the centromeric ZFAT levels in HEK293 cells transiently expressing ZFAT-HA (Figs. 5, A and B, and S1A and B). We used two siRNAs (siDAXX-1 and siDAXX-2) targeting distinct sequences of DAXX to deplete the DAXX protein. Transfection of cells with siRNAs for DAXX significantly decreased the expression levels of endogenous DAXX protein but not those of ectopically expressed ZFAT-HA (Figs. 5A and S1A). Loss of DAXX resulted in the significant decrease in the protein levels of ZFAT at the centromeres, compared with a control siRNA (Figs. 5B and S1B). On the other hand, the DAXX siRNAs did not affect the protein levels of ZFAT at the promoter regions of the BRPF1, PDE12, and TELO2 genes (Figs. 5, B and C, and S1B). Together, these results suggest that DAXX promotes the localization of ZFAT specifically at the centromeres.
To investigate the involvement of DAXX in ZFAT-regulated centromeric ncRNA transcription, we examined the effects of ectopic expression of ZFAT-HA, FLAG-DAXX, or both on the centromeric ncRNA levels in HEK293 cells (Fig. 6, A and B). The expression levels of centromeric ncRNA were determined by qRT–PCR analysis, which we established previously (11, 12). As previously shown (12), ectopic expression of ZFAT alone caused a marked increase in the centromeric ncRNA levels but not in the RNA expression levels of the Alu element that was noncentromeric repetitive DNA sequence (Fig. 6B). Interestingly, ectopic coexpression of ZFAT with DAXX resulted in a further increase in the centromeric ncRNA levels, compared with those in cells expressing ZFAT alone (Fig. 6B). Furthermore, DAXX, which was ectopically expressed alone, slightly increased the centromeric ncRNA levels although it was not statistically significant (Fig. 6B). These results suggest that DAXX plays positive roles in ZFAT-regulated centromeric ncRNA transcription. We next evaluated the effects of loss of endogenous DAXX protein on the centromeric ncRNA levels in HEK293 cells transiently expressing ZFAT-HA (Figs. 7, A and B, and S1C). siRNA-mediated knockdown of DAXX significantly decreased the centromeric ncRNA levels in cells expressing ZFAT-HA, compared with a control siRNA (Figs. 7B and S1C). Together, these results suggest that DAXX is involved in ZFAT-regulated ncRNA transcription at the centromeres.
To further elucidate the role of DAXX in the ZFAT-regulated ncRNA transcription at the centromeres, we evaluated the effects of DAXX depletion on the centromeric ncRNA levels in HEK293 cells without ectopic expression of ZFAT-HA (Figs. 8, A and B, and S2A and B). An siRNA-mediated knockdown of DAXX had no effects on the expression levels of endogenous ZFAT protein (Figs. 8B and S2B). A depletion of DAXX caused a significant decrease in the ncRNA levels at the centromeres of chromosomes 17 and X in which ZFAT regulated the transcription (Figs. 8A and S2A). These results suggest that DAXX is involved in the ZFAT-regulated centromeric ncRNA transcription at a physiological state. Transcription at the centromeres and its ncRNA products are important for centromere functions in accurate chromosomal segregation. We next examined the effects of DAXX depletion on formation of mitotic spindle using immunofluorescence analysis of α-tubulin (Figs. 8, C and D and S2C and D). In these experiments, we used HT1080 cells, which were near diploid cells, instead of HEK293 cells, because abnormal spindle morphology was frequently observed in HEK293 cells without any treatments. Depletion of DAXX significantly increased the proportion of mitotic cells that had abnormal spindle morphology, compared with cells transfected with a control siRNA (Fig. 8, C and D and S2C and D). Together, these results suggest that DAXX is involved in the accurate chromosome segregation probably through the ZFAT-regulated centromeric ncRNA transcription.
DAXX was previously reported to localize at the centromeres in human cells (39). Furthermore, Morozov et al. (38) reported that depletion of DAXX resulted in decreases in the centromeric levels of both ncRNA and the histone H3 variant H3.3. It was also shown that DAXX-mediated deposition of H3.3 was related to the activation of ncRNA transcription in pericentromeric regions in mouse cells (37). These previous studies have together suggested that DAXX is involved in ncRNA transcription at the centromeres by facilitating the deposition of H3.3. However, heat shock treatment of human cells caused marked increases in the centromeric levels of DAXX, but not ncRNA, indicating that the DAXX levels at the centromeres are not completely correlated to the centromeric ncRNA levels (38). Thus, mechanisms by which DAXX regulates ncRNA transcription at the centromeres remain elusive. In this study, we identified the transcription regulator ZFAT as a novel interacting protein of DAXX (Figure 1, Figure 2, Figure 3). Furthermore, we showed that the protein levels of DAXX were obviously correlated to the centromeric levels of both ZFAT and ncRNA (Figure 4, Figure 5, Figure 6, Figure 7). These results demonstrate that DAXX regulates ncRNA transcription at the centromeres, at least in part, through interaction with ZFAT. Taken together, we propose that DAXX plays crucial roles in centromeric ncRNA transcription through both deposition of H3.3 and localization of ZFAT (Fig. 8E). We have recently reported that the centromeric protein CENP-B interacts with ZFAT to promote the centromeric localization of ZFAT (11). Interestingly, it was also previously reported that CENP-B interacted with DAXX (40). Depletion of CENP-B decreases the centromeric levels of DAXX, indicating that CENP-B is required for the centromeric localization of DAXX (40). In this study, we showed that the C-terminal region of ZFAT was involved in the interaction with DAXX (Fig. 3). On the other hand, we previously showed that the middle domain of ZFAT was required for the interaction with CENP-B (11). Thus, ZFAT separately interacts with DAXX and CENP-B through distinct regions. A ternary complex of CENP-B, DAXX, and ZFAT would play important roles in ncRNA transcription at the centromeres (Fig. 8E). In this study, we showed that the ZFAT protein levels at the centromeres relied on the presence of the DAXX protein (Figs. 4 and 5). Furthermore, evident interaction between ZFAT and DAXX was observed in human cells (Figure 1, Figure 2, Figure 3). On the other hand, we have previously shown that ZFAT binds directly to the centromeric DNA with specific DNA sequences (12), indicating that ZFAT would not bind to the centromeric DNA through the DAXX protein. Thus, DAXX may contribute to the centromeric localization of ZFAT through stabilization of the binding of ZFAT to the centromeric DNA, cooperatively with CENP-B (Fig. 8E). Here, we showed that loss of DAXX resulted in the decreased centromeric ncRNA levels as well as abnormal spindle morphology (Fig. 8). Centromeric ncRNA transcription plays important roles in centromere functions, and its dysregulation causes chromosome segregation error, leading to aneuploidy, which is one of the representative characteristics of tumor cells. Indeed, aberrant expression of centromeric ncRNA is observed in several human cancer tissues (41, 42). Interestingly, mutations and altered expression of DAXX are observed in diverse human cancers. Given that ectopic expression of DAXX alone resulted in a slight increase in the centromeric ncRNA levels (Fig. 6B), the activation of the ZFAT-regulated centromeric transcription may be related to the tumorigenicity by an increased expression of DAXX in cancer cells. On the other hand, roles of DAXX in tumorigenesis are different in diverse cancers. For example, DAXX is overexpressed in ovarian cancer tissues and promotes the development of ovarian tumors (43). On the other hand, it was recently reported that DAXX functions as a suppressor for epithelial–mesenchymal transition and invasion of lung cancer cells through interaction with the ZF transcription factor Slug (44). Thus, roles of DAXX in cancer cells are dependent on the cellular contexts and gene expression profiles of cells, suggesting that the expression levels and/or mutations of DAXX-interacting partners may determine roles of DAXX in cancer cells. Interestingly, mutations in the ZFAT gene have also been found in several human cancers (25, 26, 27, 28, 30, 31). Furthermore, overexpression of ZFAT is observed in ovarian cancer (29). Therefore, impaired centromeric transcription caused by simultaneous dysregulation of ZFAT and DAXX may be cooperatively involved in development and progression of cancer cells, including ovarian cancer. We have previously shown that percentages of mitotic cells with abnormal spindle morphology in ZFAT-depleted cells are 84% and 74% using two distinct siRNAs (12). In contrast, those in DAXX-depleted cells were 67% and 74%, as shown in Figs. 8D and S2D in this study. These results are consistent with our conclusion that mitotic spindle defects observed in DAXX-depleted cells mainly result from the impaired centromeric localization of ZFAT. On the other hand, DAXX has been known to interact with various molecules that are involved in mitosis (45, 46). Thus, we cannot rule out the possibility that the abnormal spindle morphology observed in DAXX-depleted cells is unrelated with the ZFAT-regulated centromeric ncRNA transcription. Elucidating roles of DAXX through the centromeric ncRNA transcription in accurate chromosome segregation will be addressed in future studies. In summary, our study elucidated the molecular mechanisms of centromeric ncRNA transcription regulated by DAXX. We identified ZFAT as a novel interacting protein of DAXX. The centromeric levels of ZFAT were correlated with the presence of DAXX. Furthermore, depletion of DAXX resulted in the decreased centromeric ncRNA levels and abnormal spindle formation. Thus, we propose that DAXX controls ncRNA transcription at the centromeres through interaction with ZFAT. DAXX plays crucial roles in centromeric ncRNA transcription through both deposition of H3.3 and localization of ZFAT. These findings would lead to a better understanding of functional significance of DAXX and ZFAT in cell survival and death as well as tumorigenesis.
The cell lines HEK293 and HT1080 were cultured at 37 °C with 5% CO2 in Dulbecco’s modified Eagle's medium (Wako Pure Chemical Industries; catalog no.: 041-30081) and supplemented with 10% fetal bovine serum and penicillin/streptomycin (Gibco; catalog no.: 15140122).
The constructs and primers used in this study are detailed in Tables S1 and S2, respectively. The expression vectors for human ZFAT and CENP-B have been previously described (11, 12). Complementary DNA for human DAXX was obtained from Addgene (#119021) and cloned into pcDNA3 plasmid DNA for expression in cultured mammalian cells. The expression vectors were verified by DNA sequencing.
The immunoblotting procedure was performed as previously described using antibodies detailed in Table S3 (11, 15, 47). co-IP was performed in HEK293 cells as previously described (12, 48). Briefly, HEK293 cells were lysed in co-IP buffer (50 mM Tris–HCl, pH 7.5; 150 mM NaCl; 5 mM MgCl2; 10% glycerol; and 1% NP-40) supplemented with cOmplete EDTA-Free Protease Inhibitor (Sigma–Aldrich) by incubation for 30 min at 4 °C. Cell pellets were removed by centrifugation, and the supernatants were precleaned by incubation with rat, mouse, or rabbit immunoglobulin G and Protein G Sepharose (GE Healthcare Life Sciences) for 30 min at 4 °C under gentle rotation. After centrifugation, the supernatants were coimmunoprecipitated by incubation with primary antibodies (Table S3) conjugated with Protein G Sepharose at 4 °C under gentle rotation overnight. The beads were washed with co-IP buffer three times and then boiled in Laemmli sample buffer. The eluates were subjected to immunoblotting. To treat lysates with DNase before co-IP, cells were lysed in co-IP buffer for DNase (50 mM Tris–HCl, pH 8; 150 mM NaCl; 60 mM MgCl2; 10 mM CaCl2; 10% glycerol; and 1% NP-40) by incubation for 30 min at 4 °C. Cell pellets were removed by centrifugation, and supernatants were treated with 100 units/ml of DNase (Roche; catalog no.: 04716728001) for 15 min at 37 °C, and then co-IP was performed.
The siRNA against DAXX was purchased from Thermo Fisher Scientific (siDAXX-1, DAXXHSS175936, 5′-GAUCAUCGUGCUCUCAGACUCUGAU-3′; siDAXX-2, DAXXHSS175937, 5′-AGCAGUAGUUCGGGCGGCAAGAAAU). HEK293 cells were transfected with siRNA using Lipofectamine RNAiMAX (Invitrogen; catalog no.: 13778150) according to the manufacturer’s reverse transfection protocol. Briefly, cells were seeded with siRNA (20 pmol)–Lipofectamine RNAiMAX (5 μl) complexes in 6-well plates at a density of 4 × 105 cells per well. After 24 h, the cells were transfected with plasmid DNA using Lipofectamine 3000 (Invitrogen; catalog no.: L3000015) and incubated for a further 24 h. Following incubation, the cells were utilized for downstream analyses.
ChIP–qPCR analysis was performed as previously described (12, 15). The antibodies and primers used in ChIP and qPCR are detailed in Tables S2 and S3. For ChIP–qPCR analysis of ZFAT-HA and FLAG-DAXX using anti-HA and anti-FLAG antibodies, HEK293 cells were transfected with the expression vectors using Lipofectamine 3000. After 24 h, the cells were crosslinked with 1% formaldehyde, lysed in radioimmunoprecipitation assay (RIPA) buffer (50 mM Tris–HCl, pH 8.0; 150 mM NaCl; 1% Triton X-100; 0.5% sodium deoxycholate; 0.1% SDS), and sonicated using a Bioruptor (Cosmo Bio) for 15 cycles at 1 min with 30 s on/off. After the ChIP procedure, the beads were serially washed with either RIPA, RIPA containing 500 mM NaCl, and RIPA containing 250 mM LiCl and TE buffers (10 mM Tris–HCl, pH 8.0, 1 mM EDTA). qPCR was performed using TB Green Premix Ex Taq GC (Perfect Real Time) (Takara Bio; catalog no.: RR071B) with 7500 Fast Real-Time PCR system (Applied Biosystems). Results of ChIP–qPCR analysis are represented as the percentage (%) of input, which represents the amount of DNA pulled down by using an antibody for HA or FLAG tag in the ChIP reaction, relative to the amount of genome DNA used. The amount of pulled down DNA is normalized to the number of their binding sites in the whole genome.
qRT–PCR analysis was performed as previously described (11, 12, 48). For those HEK293 cells that were transfected with siRNA against DAXX using RNAiMAX for 72 h, the total RNA was extracted using TRIZol reagent (Invitrogen; catalog no.: 15596018). Complementary DNA was synthesized using the ReverTra Ace qPCR RT Master Mix with gDNA Remover (Toyobo; catalog no.: FSQ-301). Control samples without reverse transcriptase were performed to identify DNA contamination. qPCR was performed using the Thunderbird SYBR qPCR Mix (Toyobo; catalog no.: QPX-201) with 7500 Fast Real-Time PCR system according to the manufacturer’s instructions. The primers used for qPCR are detailed in Table S2.
Immunofluorescence analysis was performed as previously described (11, 12). HT1080 cells were seeded with siRNA (5 pmol)–Lipofectamine RNAiMAX (1.25 μl) complexes onto a 12 mm diameter glass coverslip and placed into 24-well plates. After 72 h, the transfected cells were fixed with 100% methanol for 20 min at −20 °C, subsequently washed thrice with PBS, permeabilized, and blocked with 5% fetal bovine serum in PBS containing 0.3% Triton X-100 for 30 min at room temperature, and subsequently incubated with an anti-α-tubulin antibody at 4 °C overnight. Following incubation, the cells were washed thrice with PBS and subsequently incubated with secondary antibodies conjugated with fluorescent dyes for 1 h at room temperature. Cells were then washed thrice with PBS, stained with 4′,6-diamidino-2-phenylindole, mounted using Fluorescence Mounting Medium (Dako; catalog no.: S3023), and viewed using a TCS SP5 laser-scanning confocal microscope (Leica Microsystems).
The data were expressed as the mean ± standard deviation. The statistical analyses were performed using an unpaired two-tailed Student’s t test. A p < 0.05 denoted a statistically significant difference.
All data are contained in the article or available on request by contacting the corresponding author: [email protected].
This article contains Supporting information (12).
The authors declare that they have no conflicts of interest with the contents of this article. | true | true | true |
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PMC9579143 | Xinxin Lu,Xinyue Huang,Haiqi Xu,Saien Lu,Shilong You,Jiaqi Xu,Qianru Zhan,Chao Dong,Ning Zhang,Ying Zhang,Liu Cao,Xingang Zhang,Naijin Zhang,Lijun Zhang | The role of E3 ubiquitin ligase WWP2 and the regulation of PARP1 by ubiquitinated degradation in acute lymphoblastic leukemia | 18-10-2022 | Acute lymphocytic leukaemia,Ubiquitylation,Biomarkers | Acute lymphoblastic leukemia (ALL) has been a huge threat for people's health and finding effective target therapy is urgent and important. WWP2, as one of E3 ubiquitin ligase, is involved in many biological processes by specifically binding to substrates. PARP1 plays a role in cell apoptosis and is considered as a therapeutic target of certain cancers. In this study, we firstly found that WWP2 expressed higher in newly diagnosed ALL patients comparing with complete remission (CR) ALL patients and normal control people, and WWP2 in relapse ALL patients expressed higher than normal control people. WWP2 expression was related with the FAB subtype of ALL and the proportion of blast cells in bone marrow blood tested by flow cytometry. We demonstrated knockout WWP2 inhibited the ALL growth and enhanced apoptosis induced by Dox in vitro and vivo for the first time. WWP2 negatively regulated and interacted with PARP1 and WWP2 mechanically degraded PARP1 through polyubiquitin-proteasome pathway in ALL. These findings suggested WWP2 played a role in ALL development as well as growth and apoptosis, and also displayed a regulatory pathway of PARP1, which provided a new potential therapeutic target for the treatment of ALL. | The role of E3 ubiquitin ligase WWP2 and the regulation of PARP1 by ubiquitinated degradation in acute lymphoblastic leukemia
Acute lymphoblastic leukemia (ALL) has been a huge threat for people's health and finding effective target therapy is urgent and important. WWP2, as one of E3 ubiquitin ligase, is involved in many biological processes by specifically binding to substrates. PARP1 plays a role in cell apoptosis and is considered as a therapeutic target of certain cancers. In this study, we firstly found that WWP2 expressed higher in newly diagnosed ALL patients comparing with complete remission (CR) ALL patients and normal control people, and WWP2 in relapse ALL patients expressed higher than normal control people. WWP2 expression was related with the FAB subtype of ALL and the proportion of blast cells in bone marrow blood tested by flow cytometry. We demonstrated knockout WWP2 inhibited the ALL growth and enhanced apoptosis induced by Dox in vitro and vivo for the first time. WWP2 negatively regulated and interacted with PARP1 and WWP2 mechanically degraded PARP1 through polyubiquitin-proteasome pathway in ALL. These findings suggested WWP2 played a role in ALL development as well as growth and apoptosis, and also displayed a regulatory pathway of PARP1, which provided a new potential therapeutic target for the treatment of ALL.
Acute lymphoblastic leukemia (ALL) is malignant transformation and uncontrolled proliferation of lymphoid hematopoietic progenitor cells, finally invading bone marrow and blood [1, 2]. Although high cure rate can be achieved due to exist therapeutic regimens, subsequent chemotherapy resistance, disease relapse and poor prognosis remain a significant challenge [3]. Therefore, it is urgent and important to study the molecular mechanism of ALL and explore effective target therapy of ALL. Protein ubiquitination is one of the most important post-translational modifications, leading to protein degradation by the proteasome or lysosome [4, 5]. The ubiquitin-proteasome system is composed of a ubiquitin-activating enzyme (E1), ubiquitin conjugation enzyme (E2), and ubiquitin ligase (E3). E1 activates and transfers ubiquitin to E2, and E3 recruits ubiquitin protein substrates specifically [6]. WWP2 is a HECT-type E3 ubiquitin ligase, one of the major members of the NEDD4 family [7], involved in many biological processes including cell cycle, immune response, apoptosis, and cell signal transduction [8–11]. Studies show WWP2 also participates in the regulation of the proliferation of malignant tumors such as liver cancer, lung cancer and gastric cancer [12–15]. Poly (ADP-ribose) polymerase 1 (PARP1) is the founding member of PARP family [16, 17], which participates in DNA repair and gene integrity [18–20]. And excessive activation of PARP1 induced by oxidative stress leads to the depletion of ATP, which causes cell apoptosis [21]. PARP1 is also involved in transcriptional and posttranscriptional regulation of gene expression. It is reported as a substrate of ubiquitin ligase and plays a role in oxidative-related cardiovascular disease [22]. Due to the crucial roles in many cellular procedures, PARP1 has been considered as a therapeutic target for the potential treatment of cancers [23, 24]. In this study, we firstly tested WWP2 expression in different period ALL patients and normal control people, and analyzed the relationship with clinicopathological factors. We determined that knockout WWP2 significantly inhibit the growth and enhance the apoptosis in ALL xenograft tumors induced by doxorubicin (Dox), as well as at cellular level. We also described WWP2 interacted with PARP1 and mechanically degraded PARP1 through polyubiquitin-proteasome pathway in ALL. The results above showed that WWP2 played a role in multiple effects of ALL, which provided a new potential therapeutic target for ALL.
WWP2 relative expression in patients and normal control samples were evaluated by relative quantification using real-time PCR. The results showed WWP2 expression in newly diagnosed ALL patients (0.1405 ± 0.1609) was higher than that in CR ALL patients (0.0588 ± 0.1029) and normal control people (0.0099 ± 0.0092). And the expression of WWP2 in relapse ALL patients (0.0424 ± 0.0346) and CR ALL patients (0.0588 ± 0.1029) was higher than that in normal control people (0.0099 ± 0.0092) (Fig. 1A). It indicated that WWP2 expression differences existed in different period of ALL patients and normal control people, and WWP2 was related with ALL development.
Next, we analyzed the clinicopathologic factors of ALL patients with WWP2 expression. Patients were divided into groups according to categorical variables (gender, FAB subtype, T/B subtype, BCR/ABL appearance, karyotype) and the median of continuous variables (age, blast cell proportion, WBC level, Hb level, PLT level). It was found that WWP2 expression was related with FAB subtype and WWP2 expressed higher in a larger proportion of blast cells in bone marrow blood tested by flow cytometry. While, there was no relationship with WWP2 expression and other clinicopathologic factors such as gender, age, T/B subtype, the proportion of blast cells (bone marrow smear), WBC level, Hb level, PLT level, BCR/ABL appearance and karyotype (Table 1).
We established xenograft tumor model in order to investigate the WWP2 function in tumor growth. Nude mice were divided into two groups randomly and given subcutaneous injection of NC or shWWP2 Jurkat cells. Then we gave intraperitoneal injection of normal saline (NS) or Dox randomly in each group, aiming to evaluate WWP2 function to tumor growth under the stimulation of Dox. The results showed under the subcutaneous injection of the same Jurkat cells, tumors with Dox intraperitoneal injection had less volume and weight than tumors with NS intraperitoneal injection. And the volume and weight of tumor of NC + NS group is greater than that of shWWP2 + NS group. The volume and weight of tumor of NC + Dox group is greater than that of shWWP2 + Dox group (Fig. 1B–E). It indicated that Dox intraperitoneal injection inhibited tumor growing obviously and knockout WWP2 inhibited the growth of ALL xenograft tumor under both normal conditions and Dox stimulation.
We tested the expression of WWP2 through immunofluorescence assays and western blot. The results showed WWP2 expression in shWWP2+NS group is much lower than that in NC + NS group, which confirmed the WWP2 knockout efficiency in vivo. And the intraperitoneal injection of Dox reduced the expression of WWP2 (Fig. 2A–D). The apoptosis level in xenograft tumors was assessed by two apoptosis proteins (Bax and Cleaved-Caspase3(Cleaved-C3)) through western blot. The expression of Bax and Cleaved-C3 in both Dox groups is much higher than that in both NS groups, which proved the apoptosis-inducing efficiency of Dox in vivo. And the expression of Bax and Cleaved-C3 in shWWP2+Dox group is significantly higher than that in NC + Dox group (Fig. 2E, F). It indicated that knockout WWP2 enhanced apoptosis of ALL tumor cells induced by Dox in vivo.
PARP1 was reported to be involved in apoptosis induced by oxidative stress and considered as a therapeutic target for cancers. In order to investigate the potential relationship between WWP2 and PARP1, we tested the expression of PARP1 by western blot meanwhile. It showed WWP2 influenced PARP1 stability and negatively regulate PARP1 expression in ALL xenograft tumors (Fig. 2G, H).
As verified above, WWP2 was involved in apoptosis of ALL xenograft tumors and the downregulation of PARP1 may play a role in this process. Therefore, we further explored it in cellular level in order to confirm the results and elucidate this mechanism clearly. Given that no study has evaluate the effect of Dox-induced Jurkat cells apoptosis on WWP2 expression, we set a concentration gradient of Dox (0, 0.05, 0.1,0.2, 0.4 μM) for western blot and cell viability assay and concentration gradient of Dox (0, 0.025, 0.05, 0.075, 0.1 μM) for flow cytometry analysis. Results showed that the cell viability was decreasing while the apoptosis rate was increasing with growing Dox concentration (Fig. 3A, C). As Dox concentration increasing, the expression of WWP2 increased slightly at the beginning and following decreased gradually, while apoptosis proteins (Bax and Cleaved-C3) expressed higher and higher (Fig. 3B). The results above indicated that WWP2 was involved in apoptosis of ALL cells induced by dox.
In order to explore the role of WWP2 in ALL cell apoptosis, we made WWP2 overexpression and WWP2 knockout in Jurkat cells under stimulation of Dox (0.075 μM for flow cytometry; 0.02 μM for western blot and cell viability assay), respectively. It was found that the apoptosis rate and apoptosis protein expression were at a lower level both in NC and WWP2 overexpression Jurkat cells, and Dox stimulation increased apoptosis level significantly. As a result, WWP2 overexpression alleviated the apoptosis rate and decreased apoptosis protein (Bax and Cleaved-C3) expression under Dox stimulation compared with NC + Dox group (Fig. 3D, E). And WWP2 overexpression increased cell viability in different concentration of Dox (Fig. 3F). Similarly, Apoptosis level of both NC and WWP2 knockout Jurkat cells increased under Dox stimulation. However, WWP2 knockout increased the apoptosis rate and the expression of Bax and Cleaved-C3 more under Dox stimulation compared with NC + Dox group (Fig. 3G, H). And WWP2 knockout decreased cell viability under Dox stimulation in different concentration (Fig. 3I). The series of tests suggested WWP2 overexpression alleviated apoptosis of Jurkat cells, but knockout WWP2 enhanced this effect contrarily.
In previous tests, we have proved WWP2 negatively regulate PARP1 expression in vivo. Subsequently, coimmunoprecipitation tests indicated WWP2 interacted with PARP1 in ALL cells. This interaction was bidirectional in Jurkat cells and this interaction was bidirectionally weakened under Dox stimulation (Fig. 4A–D). It indicated that WWP2 and PARP1 interacted with each other by some mechanism in ALL and the interaction was affected by Dox stimulation. The results above also suggested that the interaction between WWP2 and PARP1 was the basis of WWP2 regulating PARP1.
We successively transfected 0, 10, 20, 40 μg WWP2 plasmid with HA tag into Jurkat cells. It was found PARP1 expression decreased gradually as increasing expression of WWP2 (Fig. 4E). We tested PARP1 expression in three targeted shWWP2 Jurkat cells and higher expressions of PARP1 were observed in all of shWWP2 Jurkat cell lines (Fig. 4F). It suggested that WWP2 negatively regulated PARP1 in Jurkat cells. It was verified that WWP2 had interaction with PARP1 and the negative regulation on PARP1 so far. Therefore, we further investigated WWP2 downregulated PARP1 whether by inhibiting PARP1 transcription or promoting proteasome degradation of PARP1. The protein synthesis inhibitor CHX and proteasome inhibitor MG132 were used to determine the effect of WWP2 on PARP1. In Jurkat cells with transfection of HA-WWP2 plasmid, PARP1 expression was lower and attenuated faster than that in cells with transfection of empty vector under the treatment of CHX (Fig. 4G). And PARP1 expressed lower but accumulated more obviously than that in controls under the treatment of MG132 (Fig. 4H). In Jurkat cells with WWP2 knockout, PARP1 expression was higher and attenuated more slowly than that in normal controls under the treatment of CHX (Fig. 4I), while, PARP1 expressed higher and accumulated more slowly compared with that in normal Jurkat cells under the treatment of MG132 (Fig. 4J). The results above showed WWP2 overexpression contributed to a reduction of PARP1 half-life while WWP2 knockout could prolong it. A decreasing trend of PARP1 was tested in CHX assays, but accumulation of PARP1 existed in both MG132 assays whether WWP2 was overexpressed or knocked out. It indicated that MG132 did affect the degradation of PARP1 and WWP2 regulated PARP1 through proteasome degradation.
As mentioned above, WWP2 regulated PARP1 through proteasome degradation. Considering WWP2 was functioned as E3 ubiquitin ligase, it strongly suggested that PARP1 was one of the substrates of WWP2. We further studied whether WWP2 mediated polyubiquitination of PARP1. Firstly, we confirmed a stronger interaction in Jurkat cells between WWP2 and PARP1 when proteosome degradation activity was blocked by MG132 endogenously and half-exogenously (Fig. 5A, B). It proved inhibiting proteosome degradation activity enhanced the interaction between WWP2 and PARP1. Next, we tested polyubiquitination extent of PARP1 by transfection of HA-ubiquitin and using MG132 in WWP2-overexpression assay and WWP2 knockout assay respectively. The polyubiquitination level of PARP1 was increased after WWP2 overexpressed, while decreased after WWP2 knocked out (Fig. 5C, D). These results suggested WWP2 mediate PARP1 proteosome degradation by polyubiquitination of PARP1 in Jurkat cells.
ALL has always been huge threat to human health and finding effective therapeutic target for ALL make great significance. In this study, we firstly analyzed WWP2 expression in different period ALL patients and the relationship with clinicopathological factors. We explored the role of WWP2 in ALL growth and apoptosis in vitro and vivo, and elaborated that WWP2 downregulated PARP1 through ubiquitin-proteasome degradation in ALL cells for the first time in order to provide a new idea for target treatment for ALL. WWP2, as a member of NEDD4 family ubiquitin ligases, was discovered for binding to atrophin-1 by yeast two-hybrid and vitro binding analysis in 1997 (ref. [9]). WWP2 widely expressed [25] and played a crucial part in pathogenesis in different types of tumors such as lung cancer, gastric cancer and liver cancer by regulating ubiquitin-dependent degradation of substrate proteins [14, 15, 26] In our study, we firstly analyzed ubiquitin ligase WWP2 expression level in ALL patients and the relationship with clinicopathological factors. The WWP2 expression difference in different period of ALL patients and normal control people and the relationship with FAB subtype and proportion of blast cells in bone marrow blood tested by flow cytometry indicated that WWP2 played a role in ALL development. And subsequent experiment proved that knockout WWP2 inhibit growth and aggravated apoptosis in ALL xenograft tumor. The experiment in cellular level also confirmed overexpression of WWP2 improved cells viability and alleviated apoptosis in ALL, while, knockout WWP2 inhibited cells viability and enhanced apoptosis in ALL, which was consistent with our results in vivo. It was obviously that WWP2 played a role in ALL growth and apoptosis. Protein ubiquitination is involved in a wide range of cellular biological process [27]. E3 ubiquitin ligase plays an important part in ubiquitin-protein system for binding substrate specifically. Proteins such as membrane proteins, cell cycle regulators, transcription factors, tumor suppressors and oncogenes, are ubiquitinated and play roles in different cellular activities [4, 5, 7]. Ubiquitin-proteasome pathway is reported to be essential in leukemia [28–30] and ubiquitin ligases participated in the development and resistance of different leukemia [31–34]. And it was reported that E3 ubiquitin ligases also play other roles in leukemia. HERC1, as one member of HECT-type ubiquitin ligases, was recently discovered to expressed aberrantly in myeloid-related disorders and act as potential player in leukemic cell differentiation [35, 36]. In our study, we have proved that knockout WWP2 enhanced ALL apoptosis induced by dox in vitro and vivo. Therefore, we had a thought that WWP2 play roles in ALL by regulating substrate protein as ubiquitin ligase through ubiquitin-proteasome pathway. PARP1 is known to play crucial roles in DNA damage response and promotes DNA repair serving as early sensor of DNA damage [37]. But PARP1 induces cell death for genome integrity in the case of extensive damage [17]. With the research goes further, PARP1 is reported be involved in many cellular processes such as cell apoptosis [18, 23], chromatin remodeling [38] and gene expression [20, 39]. PARP1 is also involved in malignant tumors development and resistance of cancers [40–44], and it is regarded as a potential therapeutic target in certain leukemia [24, 45]. Particularly, previous studies showed PARP1 regulated in cancers by inducing cell apoptosis [23, 46]. These years, PARP1 is reported to regulate in cardiac disease serving as substrate protein of many ubiquitin ligases and participating in ubiquitin-proteasome pathway [7, 47], which broaden the molecular mechanism study of PARP1. In this study, we firstly discovered WWP2 negatively regulated and interacted with PARP1 in ALL, and this interaction reduced under the apoptosis induced by Dox. Considering ubiquitin ligase function of WWP2 and the role of PARP1 in ubiquitin-proteasome as reported, it is suggested PARP1 may be a substrate of WWP2 in ALL and contribute to the regulation of apoptosis of ALL. CHX and MG132 assays further proved that WWP2 down-regulated PARP1 by proteasome degradation. And the polyubiquitination level of PARP1 in overexpression WWP2 and knockdown WWP2 assays showed WWP2 mediate PARP1 expression by polyubiquitination proteasome pathway. Doxorubicin is widely applied in clinical therapy of many malignant tumors such as leukemia, lymphomas and several solid tumors [48]. It works through intercalation into double-strand DNA, inhibition of topoisomerase II and formation of ROS leading to cell apoptosis [49]. The apoptosis models in vitro and vivo in this study were induced by Dox, which could be regarded as a microcosm of ALL in treatment of Dox. The findings in this study suggested WWP2 was involved in ALL treatment process. The reduced interaction between WWP2 and PARP1, the decreased expression of WWP2 and increased expression of PARP1 participated in ALL apoptosis, which was consistent with PARP1 function mentioned above. Therefore, the regulation of PARP1 by WWP2 was probably potential target for ALL therapy. According to results above, we firstly reported a new function of WWP2 and provide insight into a related mechanism in ALL. We determined the different expression of WWP2 in different period ALL patients and normal control people and analyzed relationship with clinicopathological factors for the first time. We proved knockout WWP2 inhibited ALL growth and enhanced ALL apoptosis in vitro and vivo, while overexpressed WWP2 showed opposite effect. We also illustrated WWP2 mechanically down-regulated PARP1 by polyubiquitinated-proteosome degradation in ALL. These findings suggested WWP2 played a role in ALL development as well as growth and apoptosis of ALL, and displayed a regulatory pathway of PARP1, which provide a new potential therapeutic target for the treatment of ALL.
Bone marrow blood samples were collected from 30 newly diagnosed ALL patients, 30 CR ALL patients, 7 relapse ALL patients and 10 normal control people from December 2018 to October 2020 in the Department of Hematology of the First Hospital of China Medical University. Diagnosis of patients were based on morphology, immunology, cytogenetics and molecular biology (MICM) according to WHO classification criteria [50] and normal control samples were selected with no malignancy and infectious disease. The mononuclear cells of bone marrow blood were collected after the centrifugation of samples at 800 × g for 20 min. All research was approved by the Ethics Committee of the First Hospital, China Medical University (No. [2021]110).
RNA from bone marrow blood samples was extracted by TRIzol (Takara Bio, Japan), and reverse transcription was performed according to the manufacturer’s protocol of PrimeScript™ RT reagent Kit (Takara Bio, Japan). The PCR amplification of cDNA fractions was conducted by TB Green® Premix Ex Taq™ II (Takara Bio, Japan). The primer sequences were as follows: WWP2: forward primer: 5′-GGTGCGATACTTTGTGGACCAC-3′, reverse primer: 5′-GATACTTCCACCGAAAACTGCGG-3′, GAPDH: forward primer: 5′-CACCCACTCCTCCACCTTTG-3′, reverse primer: 5′-CCACCACCCTGTTGCTGTAG-3′. Relative expression levels were calculated using 2−ΔCt method.
The study was approved by the Animal Subjects Committee of China Medical University (No. CMU2021473) and all animal experiment follow the NIH Guide for the Care and Use of Laboratory Animals. Four-week-old BALB/c nude mice were purchased from Vital River Laboratories (Beijing, China). Mice were randomly divided into two groups for subcutaneous injection of normal control (NC) or shWWP2 Jurkat cells (1 × 107/200 μl) with cell organoid culture hydrogel (Biozellen, USA). When the tumor size reached 80 mm3, the tumor-bearing mice were randomly given normal saline (NS) or Dox (2 mg/kg/d) intraperitoneal injection for 7 days, which were divided into four groups (NC + Nacl group, NC + Dox group, shWWP2+Nacl group and shWWP2+Dox group) (n = 5/group). Tumors were observed and diameters (x, y) were measured every 3 days, and tumor volumes (V) were calculated as V = 1/2 xy2(mm3). On the 20th day, the mice were killed and photographed, and then the tumors were excised, weighed, and photographed. There was no blinding in this experiment.
Polyclonal rabbit anti-WWP2 (ab103527, Abcam, USA, WB: 1:1000; 12197-1-AP, Proteintech, China, WB: 1:1000, IF: 1:200), monoclonal rabbit anti-PARP1 (9532S, Cell Signaling Technology, USA, WB: 1:1000), monoclonal rabbit anti-Cleaved-caspase3 (9664 S, Cell Signaling Technology, USA, WB: 1:1000), monoclonal mouse anti-Bax (60267-1-Ig, Proteintech, China, WB: 1:1000), monoclonal rabbit anti-HA (3724S, Cell Signaling, USA, WB: 1:1000), polyclonal rabbit anti-β-tubulin (10094-1-AP, Proteintech, China, WB: 1:1000), Goat Anti-Rabbit IgG (A21020, Abbkine, China, WB: 1:10,000), Goat Anti-Mouse IgG (A21010, Abbkine, China, WB: 1:10,000) and Donkey anti-Rabbit IgG Secondary Antibody and Alexa Fluor 594 (A21207, Invitrogen, USA, IF: 1:500)were obtained commercially. MG132 (A2585; 20 μM) and cycloheximide (CHX) (A8244; 100 μM) were obtained from ApexBio (USA) and reconstituted in DMSO. Doxorubicin (Dox) (D8740; 25 mg) was obtained from Solarbio (China) and reconstituted in DMSO.
Human acute lymphoblastic leukemia Jurkat cell line was procured from iCell Bioscience Inc (China), and recently authenticated by STR profiling and tested for mycoplasma contamination. Cells were cultured in RPMI-1640 medium (HyClone, USA) with 10% fetal bovine serum (HyClone, USA) in a humidified atmosphere of 5% CO2 at 37 °C.
Plasmids encoding full-length human WWP2 (Genechem, China) and ubiquitin (Genechem, China) were cloned into HA-tagged destination vectors for immunoprecipitation or immunoblotting. INVI DNA RNA Transfection Reagent (Invigentech, USA) was used in plasmid transfection following the manufacturer’s instructions.
Control and WWP2 shRNAs were obtained from GeneChem (China). WWP2 silencing was performed with lentivirus and puromycin was used for selecting shWWP2 Jurkat cells. To prevent off-target effects, three sequences were employed: WWP2 shRNA-1: GGAGAACAAAGGCAGCGTTGT WWP2 shRNA-2: GCCAACTGTTGATCTGGGAAA WWP2 shRNA-3: GTCAAGAACTCAGGCCACAGT The efficiency of WWP2 knockdown by shRNA was confirmed by Western blot analysis.
Cell Counting Kit-8 assay (Bimake, USA) was used for evaluating Jurkat cell viability. WWP2 overexpressed, WWP2 knockdown and control Jurkat cells stimulated with Dox in different concentration or not were seeded into 96-well plates at 5 × 103 cells/well in 100 μl RPMI-1640 complete medium. CCK-8 reagents were added into wells at 10 μl/well and cells were further incubated for 2 h. Cell viability (optical density) was measured at 450 nm by a Bio-Rad microplate reader (Model 680; Bio-Rad Laboratories, Inc., Hercules, CA, USA).
Annexin V, FITC Apoptosis Detection Kit (Dojindo, Japan) was used for detecting apoptosis rate of Jurkat cells in different treatment through flow cytometry. Cells were incubated in 800 μl binding buffer with 5 μl annexin V for 30 min and 2 μl PI solution for 5 min at room temperature (RT) in the dark. Finally, apoptotic cells were identified and quantified with flow cytometer (BD LSRFortessa, USA).
Cells were washed with PBS three times and lysed with cell lysis buffer (50 mmol/L Tris, 137 mmol/L NaCl, 1 mmol/L EDTA, 10 mmol/L NaF, 0.1 mmol/L Na3VO4, 1% NP-40, 1 mmol/L DTT, 10% glycerol, pH 7.8 and 100× protease inhibitor (Roche, Switzerland). After centrifugation (4 °C, 13,300 rpm, 15 min), the cell lysates were incubated with specific antibodies and 30 μl of magnetic beads (Bimake, USA) at 4 °C for 12 h. Then, the bound complexes were washed with cell lysis buffer and subjected to SDS-PAGE. Protein samples were separated by 8% or 12% SDS-polyacrylamide gels and transferred to PVDF membranes (Millipore USA). After blocking with 5% bovine serum albumin (BSA) in Tris-buffered saline containing Tween (TBST) at RT for 1 h, the membranes were incubated with corresponding primary antibody diluted in 1% BSA at 4 °C overnight. Membranes were washed in TBST before and after incubation in secondary antibodies for 1 h at RT the next day. Image J 1.52v (National Institutes of Health, USA) was used to quantify the immunoreactive bands.
Paraffin‐sectioned slides from xenograft tumors excised from nude mice were deparaffinized and rehydrated by dimethylbenzene and ethanol. Antigen retrieval was performed by incubating slides in 0.01 mol/L citrate buffer (pH 6.0) at 95 °C for 20 min. The samples were then blocked with 1% BSA in PBS with 0.3% Triton X-100 for 1 h and incubated with primary antibody at 4 °C overnight. The next day, slides were rinsed with PBS before and after the incubation in fluorescent secondary antibody for 1 h at RT in the dark, which were used for fluorescently labeling in tumor tissues. Cell nuclei were counter‐stained with DAPI. Digital images were observed and captured with a fluorescence microscope (BX61, Olympus, Japan).
Data are presented as the mean ± standard deviation (SD). F-test was performed to evaluate the homogeneity of variance and Shapiro-Wilk test was used for evaluating data normality. Unpaired Student’s t test, one-way ANOVA and two-way ANOVA followed by Bonferroni’s post-hoc test were performed to assess differences in multiple groups, which involved one and two factors, respectively. Experiments in this study were replicated three times for statistical analysis. P-values were adjusted for multiple comparisons when applicable. All data were analyzed by SPSS 21.0 software (IBM SPSS, USA), P < 0.05 was considered statistically significant.
Supplemental material-Original western blot | true | true | true |
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PMC9579175 | Piotr Klukowski,Roland Riek,Peter Güntert | Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA | 18-10-2022 | Solution-state NMR,Machine learning | Nuclear Magnetic Resonance (NMR) spectroscopy is a major technique in structural biology with over 11,800 protein structures deposited in the Protein Data Bank. NMR can elucidate structures and dynamics of small and medium size proteins in solution, living cells, and solids, but has been limited by the tedious data analysis process. It typically requires weeks or months of manual work of a trained expert to turn NMR measurements into a protein structure. Automation of this process is an open problem, formulated in the field over 30 years ago. We present a solution to this challenge that enables the completely automated analysis of protein NMR data within hours after completing the measurements. Using only NMR spectra and the protein sequence as input, our machine learning-based method, ARTINA, delivers signal positions, resonance assignments, and structures strictly without human intervention. Tested on a 100-protein benchmark comprising 1329 multidimensional NMR spectra, ARTINA demonstrated its ability to solve structures with 1.44 Å median RMSD to the PDB reference and to identify 91.36% correct NMR resonance assignments. ARTINA can be used by non-experts, reducing the effort for a protein assignment or structure determination by NMR essentially to the preparation of the sample and the spectra measurements. | Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA
Nuclear Magnetic Resonance (NMR) spectroscopy is a major technique in structural biology with over 11,800 protein structures deposited in the Protein Data Bank. NMR can elucidate structures and dynamics of small and medium size proteins in solution, living cells, and solids, but has been limited by the tedious data analysis process. It typically requires weeks or months of manual work of a trained expert to turn NMR measurements into a protein structure. Automation of this process is an open problem, formulated in the field over 30 years ago. We present a solution to this challenge that enables the completely automated analysis of protein NMR data within hours after completing the measurements. Using only NMR spectra and the protein sequence as input, our machine learning-based method, ARTINA, delivers signal positions, resonance assignments, and structures strictly without human intervention. Tested on a 100-protein benchmark comprising 1329 multidimensional NMR spectra, ARTINA demonstrated its ability to solve structures with 1.44 Å median RMSD to the PDB reference and to identify 91.36% correct NMR resonance assignments. ARTINA can be used by non-experts, reducing the effort for a protein assignment or structure determination by NMR essentially to the preparation of the sample and the spectra measurements.
Studying structures of proteins and ligand-protein complexes is one of the most influential endeavors in molecular biology and rational drug design. All key structure determination techniques, X-ray crystallography, electron microscopy, and NMR spectroscopy, have led to remarkable discoveries, but suffer from their respective experimental limitations. NMR can elucidate structures and dynamics of small and medium size proteins in solution and even in living cells. However, the analysis of NMR spectra and the resonance assignment, which are indispensable for NMR studies, remain time-consuming even for a skilled and experienced spectroscopist. Attributed to this, the percentage of NMR protein structures in the Protein Data Bank (PDB) has decreased from a maximum of 14.6% in 2007 to 7.3% in 2021 (https://www.rcsb.org/stats). The problem has sparked research towards automating different tasks in NMR structure determination, including peak picking, resonance assignment, and the identification of distance restraints. Several of these methods are available as webservers. This enabled semi-automatic but not yet unsupervised automation of the entire NMR structure determination process, except for a very small number of favorable proteins. The advance of machine learning techniques now offers unprecedented possibilities for reliably replacing decisions of human experts by efficient computational tools. Here, we present a method that achieves this goal for NMR assignment and structure determination. We show for a diverse set of 100 proteins that NMR resonance assignments and protein structures can be determined within hours after completing the NMR measurements. Our method, Artificial Intelligence for NMR Applications, ARTINA (Fig. 1), combines machine learning for tasks that are difficult to model otherwise with existing algorithms—evolutionary optimization for resonance assignment with FLYA, chemical shift database searches for torsion angle restraint generation with TALOS-N, ambiguous distance restraints, network-anchoring and constraint combination for NOESY assignment and simulated annealing by torsion angle dynamics for structure calculation with CYANA. Machine learning is used in multiple flavors—deep residual neural networks for visual spectrum analysis to identify peak positions (pp-ResNet) and to deconvolve overlapping signals (deconv-ResNet) in 25 different types of spectra (Supplementary Table 1), kernel density estimation (KDE) to reconstruct original peak positions in folded spectra, a deep graph neural network (GNN) for chemical shift estimation within the refinement of chemical shift assignments, and a gradient boosted trees (GBT) model for the selection of structure proposals. A major challenge in developing ARTINA was the collection and preparation of a large training data set that is required for machine learning, because, in contrast to assignments and structures, NMR spectra are generally not archived in public data repositories. Instead, we were obliged to collect from different sources and standardize complete sets of multidimensional NMR spectra for the assignment and structure determination of 100 proteins. In the following work, we describe the algorithm, training and test data, and results of ARTINA automated structure determination, which are on par with those achieved in weeks or months of human experts’ labor.
One of the major obstacles for developing deep learning solutions for protein NMR spectroscopy is the lack of a large-scale standardized benchmark dataset of protein NMR spectra. To date, published manuscripts presenting the most notable methods for computational NMR, typically refer to less than 50 2D/3D/4D NMR spectra in their experimental sections. Even the well-recognized CASD-NMR competition cannot serve as a major source of training data for deep learning, since only the NOESY spectra of 10 proteins were used in the last round of the event. To make our study possible, we established a standardized benchmark of 1329 2D/3D/4D NMR spectra, which allows 100 proteins to be recalculated using their original spectral data (Fig. 2 and Supplementary Table 2). Each protein record in our dataset contains 5–20 spectra together with manually identified chemical shifts (usually depositions at the Biological Magnetic Resonance Data Bank, BMRB) and the previously determined (“ground truth”) protein structure (PDB record; Supplementary Table 3). The benchmark covers protein sizes typically studied by NMR spectroscopy with sequence lengths between 35 and 175 residues (molecular mass 4–20 kDa).
The accuracy of protein structure determination with ARTINA was evaluated in a 5-fold cross-validation experiment with the aforementioned benchmark dataset. Five instances of pp-ResNet and GBT were trained, each one using data from about 80% of the proteins for training and the remaining ones for testing. Since each protein was present exactly once in the test set, reported quality metrics were obtained directly in the cross-validation experiment, and no averaging between data splits was required. To deploy pp-ResNet and GBT models in our online system, we constructed an ensemble by averaging predictions of all 5 cross-validation models. The other models were trained only once using either generated data (deconv-ResNet, Supplementary Fig. 1) or BMRB depositions excluding all benchmark proteins (GNN, KDE). In this experiment, we reproduced 100 structures in fully automated manner using only NMR spectra and the protein sequences as input. Since ARTINA has no tunable parameters and does not require any manual curation of data, each structure was calculated by a single execution of the ARTINA workflow. All benchmark datasets were analyzed by ARTINA in parallel with execution times of 4–20 h per protein. All automatically determined structures, overlaid with the corresponding reference structures from the PDB, are visualized in Fig. 3, Supplementary Fig. 2, and Supplementary Movie 1. ARTINA was able to reproduce the reference structures with a median backbone root-mean-square deviation (RMSD) of 1.44 Å between the mean coordinates of the ARTINA structure bundle and the mean coordinates of the corresponding reference PDB structure bundle for the backbone atoms N, Cα, C’ in the residue ranges determined by CYRANGE (Fig. 4a and Supplementary Table 4). ARTINA automatically identified between 459 and 4678 distance restraints (2198 on average over 100 proteins), which corresponds to 4.25–33.20 restraints per residue (Fig. 4b). This number is mainly influenced by the extent of unstructured regions and the quality of the NOESY spectra. In agreement with earlier findings, it correlates only weakly with the backbone RMSD to reference (linear correlation coefficient −0.38). As a more expressive validation measure for the structures from ARTINA, we computed a predicted RMSD to the PDB reference structure on the basis of the RMSDs between the 10 candidate structure bundles calculated in ARTINA (see “Methods”, Fig. 5, and Supplementary Table 5). The average deviation between actual and predicted RMSDs for the 100 proteins in this study is 0.35 Å, and their linear correlation coefficient is 0.77 (Fig. 5). In no case, the true RMSD exceeds the predicted one by more than 1 Å. Additional structure validation scores obtained from ANSSUR (Supplementary Table 6), RPF (Supplementary Table 7), and consensus structure bundles (Supplementary Table 8) confirm that overall the ARTINA structures and the corresponding reference PDB structures are of equivalent quality. Energy refinement of the ARTINA structures in explicit water using OPALp (not part of the standard ARTINA workflow) does not significantly alter the agreement with the PDB reference structures (Supplementary Table 9). The benchmark data set comprises 78 protein structures determined by the Northeast Structural Genomics Consortium (NESG). On average, ARTINA yielded structures of the same accuracy for NESG targets (median RMSD to reference 1.44 Å) as for proteins from other sources (1.42 Å). On average, ARTINA correctly assigned 90.39% of the chemical shifts (Fig. 4c), as compared to the manually prepared assignments, including both “strong” (high-reliability) and “weak” (tentative) FLYA assignments. Backbone chemical shifts were assigned more accurately (96.03%) than side-chain ones (86.50%), which is mainly due to difficulties in assigning lysine/arginine (79.97%) and aromatic (76.87%) side-chains. Further details on the assignment accuracy for individual amino acid types in the protein cores (residues with less than 20% solvent accessibility) are given in Supplementary Table 10. Assignments for core residues, which are important for the protein structure, are generally more accurate than for the entire protein, in particular for core Ala, Cys, and Asp residues, which show a median assignment accuracy of 100% over the 100 proteins. The lowest accuracies are observed for core His (83.3%), Phe (83.3%), and Arg (87.5%) residues. The three proteins with highest RMSD to reference, 2KCD, 2L82, and 2M47 (see below), show 68.2, 83.8, and 75.7% correct aromatic assignments, respectively, well below the corresponding median of 85.5%. On the other hand, the assignment accuracies for the methyl-containing residues Ala, Ile, Val are above average and reach a median of 100, 97.6, and 98.6%, respectively. The quality of automated structure determination and chemical shift assignment reflects the performance of deep learning-based visual spectrum analysis, presented qualitatively in Figs. 6–7, Supplementary Fig. 3, and Supplementary Movies 2–4. In this experiment, our models (pp-ResNet, deconv-ResNet) automatically identified 1,168,739 cross-peaks with high confidence (≥0.50) in the benchmark spectra. All 1329 peak lists, together with automatically determined protein structures and chemical shift lists, are available for download.
The largest deviations from the PDB reference structure were observed for the proteins 2KCD, 2L82, and 2M47, for which the pRMSD consistently indicated low accuracy (Fig. 5). Significant deviations are mainly due to displacements of terminal secondary structure elements (e.g., a tilted α-helix near a chain terminus), or inaccurate loop conformations (e.g., more flexible than in the PDB deposition). We investigated the origin of these discrepancies. 2KCD is a 120-residue (14.4 kDa) protein from Staphylococcus saprophyticus with an α-β roll architecture. Its dataset comprises 19 spectra (8 backbone, 6 side-chain, and 5 NOESY). The ARTINA structure has a backbone RMSD to PDB reference of 3.13 Å, which is caused by the displacement of the C-terminal α-helix (residues 105–109; Supplementary Fig. 4a). Excluding this 5-residue fragment decreases the RMSD to 2.40 Å (Supplementary Table 11). The positioning of this helix appears to be uncertain, since an ARTINA calculation without the 4D CC-NOESY spectrum yields a significantly lower RMSD of 1.77 Å (Supplementary Table 12). 2L82 is a de novo designed protein of 162 residues (19.7 kDa) with an αβ 3-layer (αβα) sandwich architecture. Although only 9 spectra (4 backbone, 2 side-chain and 3 NOESY) are available, ARTINA correctly assigned 97.87% backbone and 81.05% side-chain chemical shifts. The primary reason for the high RMSD value of 3.55 Å is again a displacement of the C-terminal α-helix (residues 138–153). The remainder of the protein matches closely the PDB deposition (1.04 Å RMSD, Supplementary Fig. 4b). The protein with highest RMSD to reference (4.72 Å) in our benchmark dataset is 2M47, a 163-residue (18.8 kDa) protein from Corynebacterium glutamicum with an α-β 2-layer sandwich architecture, for which 17 spectra (7 backbone, 7 side chain and 3 NOESY) are available. The main source of discrepancy are two α-helices spanning residues 111–157 near the C-terminus. Nevertheless, the residues contributing to the high RMSD value are distributed more extensively than in 2L82 and 2KCD just discussed. Interestingly, 2 of the 10 structure proposals calculated by ARTINA have an RMSD to reference below 2 Å (1.66 Å and 1.97 Å). In the final structure selection step, our GBT model selected the 4.72 Å RMSD structure as the first choice and 1.66 Å as the second one (Supplementary Fig. 4c). Such results imply that the automated structure determination of this protein is unstable. Since ARTINA returns the two structures selected by GBT with the highest confidence, the user can, in principle, choose the better structure based on contextual information. In addition to these three case studies, we performed a quantitative analysis of all regular secondary structure elements and flexible loops present in our 100-protein benchmark in order to assess their impact on the backbone RMSD to reference (Supplementary Table 11). All residues in the structurally well-defined regions determined by CYRANGE were assigned to 6 partially overlapping sets: (a) first secondary structure element, (b) last secondary structure element, (c) α-helices, (d) β-sheets, (e) α-helices and β-sheets, and (f) loops. Then, the RMSD to reference was calculated 6 times, each time with one set excluded. In total, for 66 of the 100 proteins the lowest RMSD was obtained if set (f) was excluded from RMSD calculation, and 13% benefited most from removal of the first or last secondary structure element (a or b). Moreover, for 18 out of the 19 proteins with more than 0.5 Å RMSD decrease compared to the RMSD for all well-defined residues, (a), (b), or (f) was the primary source of discrepancy. These results are consistent with our earlier statement that deviations in automatically determined protein structures are mainly caused by terminal secondary structure elements or inaccurate loop conformations.
During the experiment, we captured the state of each structure determination at 9 time-points, 3 per structure determination cycle: (a) after the initial FLYA shift assignment, (b) after GNN shift refinement, and (c) after structure calculation (Fig. 1). Comparative analysis of these states allowed us to quantify the contribution of different ARTINA components to the structure determination process (Table 1). The results show a strong benefit of the refinement cycles, as quantities reported in Table 1 consistently improve from cycle 1 to 3. The majority of benchmark proteins converge to the correct fold after the first cycle (1.56 Å median backbone RMSD to reference), which is further refined to 1.52 Å in cycle 2 and 1.44 Å in cycle 3. Additionally, within each chemical shift refinement cycle, improvements in assignment accuracy resulting from the GNN predictions are observed. This quantity also increases consistently across all refinement cycles, in particular for side-chains. Refinement cycles are particularly advantageous for large and challenging systems, such as 2LF2, 2M7U, or 2B3W, which benefit substantially in cycles 2 and 3 from the presence of the approximate protein fold in the chemical shift assignment step.
As presented in Fig. 2, 26 out of 100 benchmark datasets contain 4D CC-NOESY spectra, which require long measurement times and were used in the manual structure determination. To quantify their impact, we performed automated structure determinations of these 26 proteins with and without the 4D CC-NOESY spectra (Supplementary Table 12). On average, the presence of 4D CC-NOESY improves the backbone RMSD to reference by 0.15 Å (decrease from 1.88 to 1.73 Å) and has less than 1% impact on chemical shift assignment accuracy. However, the impact is non-uniform. For three proteins, 2KIW, 2L8V, and 2LF2, use of the 4D CC-NOESY decreased the RMSD by more than 1 Å. On the other hand, there is also one protein, 2KCD, for which the RMSD decreased by more than 1 Å by excluding the 4D CC-NOESY. These results suggest that overall the amount of information stored in 2D/3D experiments is sufficient for ARTINA to reach close to optimal performance, and only modest improvement can be achieved by introducing additional information redundancy from 4D CC-NOESY spectra.
Apart from structure determination, our data analysis pipeline for protein NMR spectroscopy can address an array of problems that are nowadays approached manually or semi-manually. For instance, ARTINA can be stopped after visual spectrum analysis, returning positions and intensities of cross-peaks that can be utilized for any downstream task, not necessarily related to protein structure determination. Alternatively, a single chemical shift refinement cycle can be performed to get automatically assigned cross-peaks from spectra and sequence. We evaluated this approach with three sets of spectra: (i) Exclusively backbone assignment spectra were used to assign N, Cα, Cβ, C’, and HN shifts. With this input, ARTINA assigned 92.40% (median value) of the backbone shifts correctly. (ii) All through-bond but no NOESY spectra were used to assign the backbone and side-chain shifts. This raised the percentage of correct backbone assignments to 94.20%. (iii) The full data set including NOESY yielded 96.60% correct assignments of the backbone shifts. These three experiments were performed for the 45 benchmark proteins, for which CBCANH and CBCAcoNH, as well as either HNCA and HNcoCA or HNCO and HNcaCO experiments were available. The availability of NOESY spectra had a large impact on the side-chain assignments: 86.00% were correct for the full spectra set iii, compared to 73.70% in the absence of NOESY spectra (spectra set ii). The presence of NOESY spectra consistently improved the chemical shift assignment accuracy of all amino acid types (Supplementary Tables 13 and 14). The improvement is particularly strong for aromatic residues (Phe, 61.6 to 76.5%, Trp 52.5 to 80%, and Tyr 71.4 to 89.7%), but not limited to this group.
The results obtained with ARTINA differ in several aspects substantially from previous approaches towards automating protein NMR analysis. First, ARTINA comprehends the entire workflow from spectra to structures rather than individual steps in it, and there are strictly no manual interventions or protein-specific parameters to be adapted. Second, the quality of the results regarding peak identification, resonance assignments, and structures have been assessed on a large and diverse set of 100 proteins; for the vast majority of which they are on par with what can be achieved by human experts. Third, the method provides a two-orders-of-magnitude leap in efficiency by providing assignments and a structure within hours of computation time rather than weeks or months of human work. This reduces the effort for a protein structure determination by NMR essentially to the preparation of the sample and the measurement of the spectra. Its implementation in the https://nmrtist.org webserver (Supplementary Movie 5) encapsulates its complexity, eliminates any intermediate data and format conversions by the user, and enables the use of different types of high-performance hardware as appropriate for each of the subtasks. ARTINA is not limited to structure determination but can be used equally well for peak picking and resonance assignment in NMR studies that do not aim at a structure, such as investigations of ligand binding or dynamics. Although ARTINA has no parameters to be optimized by the user, care should be given to the preparation of the input data, i.e., the choice, measurement, processing, and specification of the spectra. Spectrum type, axes, and isotope labeling declarations must be correct, and chemical shift referencing consistent over the entire set of spectra. Slight variations of corresponding chemical shifts within the tolerances of 0.03 ppm for 1H and 0.4 ppm for 13C/15N can be accommodated, but larger deviations, resulting, for instance, from the use of multiple samples, pH changes, protein degradation, or inaccurate referencing, can be detrimental. Where appropriate, ARTINA proposes corrections of chemical shift referencing. Furthermore, based on the large training data set, which comprises a large variety of spectral artifacts, ARTINA largely avoids misinterpreting artifacts as signals. However, with decreasing spectral quality, ARTINA, like a human expert, will progressively miss real signals. Regarding protein size and spectrum quality, limitations of ARTINA are similar to those encountered by a trained spectroscopist. Machine-learning-based visual analysis of spectra requires signals to be present and distinguishable in the spectra. ARTINA does not suffer from accidental oversight that may affect human spectra analysis. On the other hand, human experts may exploit contextual information to which the automated system currently has no access because it identifies individual signals by looking at relatively small, local excerpts of spectra. In this paper, we used all spectra that are available from the earlier manual structure determination. For most of the 100 proteins, the spectra data set has significant redundancy regarding information for the resonance assignment. Our results indicate that one can expect to obtain good assignments and structures also from smaller sets of spectra, with concomitant savings of NMR measurement time. We plan to investigate this in a future study. The present version of ARTINA can be enhanced in several directions. Besides improving individual models and algorithms, it is conceivable to integrate the so far independently trained collection of machine learning models, plus additional models that replace conventional algorithms, into a coherent system that is trained as a whole. Furthermore, the reliability of machine learning approaches depends strongly on the quantity and quality of training data available. While the collection of the present training data set for ARTINA was cumbersome, from now on it can be expected to expand continuously through the use of the https://nmrtist.org website, both quantitatively and qualitatively with regard to greater variability in terms of protein types. spectral quality, source laboratory, data processing (including non-linear sampling), etc., which can be exploited in retraining the models. ARTINA can also be extended to use additional experimental input data, e.g., known partial assignments, stereospecific assignments, 3J couplings, residual dipolar couplings, paramagnetic data, and H-bonds. Structural information, e.g., from AlphaFold, can be used in combination with reduced sets of NMR spectra for rapid structure-based assignment. Finally, the range of application of ARTINA can be generalized to small molecule-protein complexes relevant for structure-activity relationship studies in drug research, protein-protein complexes, RNA, solid state, and in-cell NMR. Overall, ARTINA stands for a paradigm change in biomolecular NMR from a time-consuming technique for specialists to a fast method open to researchers in molecular biology and medicinal chemistry. At the same time, in a larger perspective, the appearance of generally highly accurate structure predictions by AlphaFold is revolutionizing structural biology. Nevertheless, there remains space for the experimental methods, for instance, to elucidate various states of proteins under different conditions or in dynamic exchange, or for studying protein-ligand interaction. Regarding ARTINA, one should keep in mind that its applications extend far beyond structure determination. It will accelerate virtually any biological NMR studies that require the analysis of multidimensional NMR spectra and chemical shift assignments. Protein structure determination is just one possible ARTINA application, which is both demanding in terms of the amount and quality of required experimental data and amenable to quantitative evaluation.
To collect the benchmark of NMR spectra (Fig. 2 and Supplementary Table 2), we implemented a crawler software, which systematically scanned the FTP server of the BMRB data bank, identifying data files relevant to our study. Additional datasets were obtained by setting up a website for the deposition of published data (https://nmrdb.ethz.ch), from our collaboration network, or had been acquired internally in our laboratory. NMR data was collected from these channels either in the form of processed spectra (Sparky, NMRpipe, XEASY, Bruker formats), or in the form of time-domain data accompanied by depositor-supplied NMRpipe processing scripts. No additional spectra processing (e.g., baseline correction) was performed as part of this study. The most challenging aspects of the benchmark collection process were: scarcity of data—only a small fraction of all BMRB depositions are accompanied by uploaded spectra (or time-domain data), lack of standards for NMR data depositions—each protein data set had to be prepared manually, as the original data was stored in different formats (spectra name conventions, axis label standards, spectra data format), and difficulties in correlating data files deposited in the BMRB FTP site with contextual information about the spectrum and the sample (e.g., sample characteristics, measurement conditions, instrument used). Manually prepared (mostly NOESY) peak lists, which are available from the BMRB for some of the proteins in the benchmark, were not used for this study. Different approaches to 3D 13C-NOESY spectra measurement had to be taken into account: (i) Two separate 13C NOESY for aliphatic and aromatic signals. These were analyzed by ARTINA without any special treatment. We used ALI, ARO tags (Supplementary Movie S5) to provide the information that only either aliphatic or aromatics shifts are expected in a given spectrum. (ii) Simultaneous NC-NOESY. These spectra were processed twice to have proper scaling of the 13C and 15N axes in ppm units, and cropped to extract 15N-NOESY and 13C-NOESY spectra. If nitrogen and carbon cross-peak amplitudes have different signs, we used POS, NEG tags to provide the information that only either positive or negative signals should be analyzed. (iii) Aliphatic and aromatic signals in a single 13C-NOESY spectrum. These measurements do not require any special treatment, but proper cross-peak unfolding plays a vital role in aromatic signals analysis.
ARTINA uses as input only the protein sequence and a set of NMR spectra, which may contain any combination of 25 experiments currently supported by the method (Supplementary Table 1). Within 4–20 h of computation time (depending on protein size, number of spectra, and computing hardware load), ARTINA determines: (a) cross-peak positions for each spectrum, (b) chemical shift assignments, (c) distance restraints from NOESY spectra, and (d) the protein structure. The whole process does not require any human involvement, allowing rapid protein NMR assignment and structure determination by non-experts. The ARTINA workflow starts with visual spectrum analysis (Fig. 1), wherein cross-peak positions are identified in frequency-domain NMR spectra using deep residual neural networks (ResNet). Coordinates of signals in the spectra are passed as input to the FLYA automated assignment algorithm, yielding initial chemical shift assignments. In the subsequent chemical shift refinement step, we bring to the workflow contextual information about thousands of protein structures solved by NMR in the past using a deep GNN that was trained on BMRB/PDB depositions. Its goal is to predict expected values of yet missing chemical shifts, given the shifts that have already been confidently and unambiguously assigned by FLYA. With these GNN predictions as additional input, the cross-peak positions are reassessed in a second FLYA call, which completes the chemical shift refinement cycle (Fig. 1). In the structure refinement cycle, 10 variants of NOESY peak lists are generated, which differ in the number of cross-peaks selected from the output of the visual spectrum analysis by varying the confidence threshold of a signal selected by ResNet between 0.05 and 0.5. Each set of NOESY peak lists is used in an independent CYANA structure calculation, yielding 10 intermediate structure proposals (Fig. 1). The structure proposals are ranked in the intermediate structure selection step based on 96 features with a dedicated GBT model. The selected best structure proposal is used as contextual information in a consecutive FLYA run, which closes the structure refinement cycle. After the two initial steps of visual spectrum analysis and initial chemical shift assignment, ARTINA interchangeably executes refinement cycles. The chemical shift refinement cycle provides FLYA with tighter restraints on expected chemical shifts, which helps to assign ambiguous cross-peaks. The structure refinement cycle provides information about possible through-space contacts, allowing identified cross-peaks (especially in NOESY) to be reassigned. The high-level concept behind the interchangeable execution of refinement cycles is to iteratively update the protein structure given fixed chemical shifts, and update chemical shifts given the fixed protein structure. Both refinement cycles are executed three times.
We established two machine learning models for the visual analysis of multidimensional NMR spectra (see downloads in the Code availability section). In their design, we made no assumptions about the downstream task and the 2D/3D/4D experiment type. Therefore, the proposed models can be used as the starting point of our automated structure determination procedure, as well as for any other task that requires cross-peak coordinates. The automated visual analysis starts by selecting all extrema , in the NMR spectrum, which is represented as a D-dimensional regular grid storing signal intensities at discrete frequencies. We formulated the peak picking task as an object detection problem, where possible object positions are confined to . This task was addressed by training a deep residual neural network, in the following denoted as peak picking ResNet (pp-ResNet), which learns a mapping that assigns to each signal extremum a real-valued score, which resembles its probability of being a true signal rather than an artefact. Our network architecture is strongly linked to ResNet-18. It contains 8 residual blocks, followed by a single fully connected layer with sigmoidal activation. After weight initialization with Glorot Uniform, the architecture was trained by optimizing a binary cross-entropy loss using Adam with learning rate 10–4 and gradient clipping of 0.5. To establish an experimental training dataset for pp-ResNet, we normalized the 1329 spectra in our benchmark with respect to resolution (adjusting the number of data grid points per unit chemical shift (ppm) using linear interpolation) and signal amplitude (scaling the spectrum by a constant). Subsequently, 675,423 diverse 2D fragments of size 256 × 32 × 1 were extracted from the normalized spectra and manually annotated, yielding 98,730 positive and 576,693 negative class training examples. During the training process, we additionally augmented this dataset by flipping spectrum fragments along the second dimension (32 pixels), stretching them by 0–30% in the first and second dimensions, and perturbing signal intensities with Gaussian noise addition. The role of the pp-ResNet is to quickly iterate over signal extrema in the spectrum, filtering out artefacts and selecting approximate cross-peak positions for the downstream task. The relatively small network architecture (8 residual blocks) and input size of 2D 256 × 32 image patches make it possible to analyze large 3D 13C-resolved NOESY spectra in less than 5 min on a high-end desktop computer. Simultaneously, the first dimension of the image patch (256 pixels) provides long-range contextual information on the possible presence of signals aligned with the current extremum (e.g., Cα, Cβ cross-peaks in an HNCACB spectrum). Extrema classified with high confidence as true signals by pp-ResNet undergo subsequent analysis with a second deep residual neural network (deconv-ResNet). Its objective is to perform signal deconvolution, based on a 3D spectrum fragment (64 × 32 × 5 voxels) that is cropped around a signal extremum selected by pp-ResNet. This task is defined as a regression problem, where deconv-ResNet outputs a 3 × 3 matrix storing 3D coordinates of up to 3 deconvolved peak components, relative to the center of the input image. To ensure permutation invariance with respect to the ordering of components in the output coordinate matrix, and to allow for a variable number of 1–3 peak components, the architecture was trained with a Chamfer distance loss. Since deconv-ResNet deals only with true signals and their local neighborhood, its training dataset can be conveniently generated. We established a spectrum fragment generator, based on rules reflecting the physics of NMR, which produced 110,000 synthetic training examples (Supplementary Fig. 1) having variable (a) numbers of components to deconvolve (1–3), (b) signal-to-noise ratio, (c) component shapes (Gaussian, Lorentzian, and mixed), (d) component amplitude ratios, (e) component separation, and (f) component neighborhood type (i.e., NOESY-like signal strips or HSQC-like 2D signal clusters). The deconv-ResNet model was thus trained on fully synthetic data.
To use ResNet predictions in automated chemical shift assignment and structure calculation, detected cross-peak coordinates must be transformed from the spectrum coordinate system to their true resonance frequencies. We addressed the problem of automated signal unfolding with the classical machine learning approach to density estimation. At first, we generated 105 cross-peaks associated with each experiment type supported by ARTINA (Supplementary Table 1). In this process, we used randomly selected chemical shift lists deposited in the BMRB database, excluding depositions associated with our benchmark proteins. Subsequently, we trained a Kernel Density Estimator (KDE):which captures the distribution of true peaks being present at position in spectrum type , based on Ne = 105 cross-peaks coordinates generated with BMRB data, and being the Gaussian kernel. Unfolding a k-dimensional spectrum is defined as a discrete optimization problem, solved independently for each cross-peak observed in a spectrum of type :where is a vector storing the spectral widths in each dimension (ppm units), is element-wise multiplication, is a vector indicating how many times the cross-peak is unfolded in each dimension, and is the optimal cross-peak unfolding. As long as regular and folded signals do not overlap or have different signs in the spectrum, KDE can unfold the peak list regardless of spectrum dimensionality. The spectrum must not be cropped in the folded dimension, i.e., the folding sweep width must equal the width of the spectrum in the corresponding dimension. All 2D/3D spectra in our benchmark were folded in at most one dimension and satisfy the aforementioned requirements. However, the 4D CC-NOESY spectra satisfy neither, as regular and folded peaks both overlap and have the same signal amplitude sign. This introduces ambiguity in the spectrum unfolding that prevents direct use of the KDE technique. To retrieve original signal positions, 4D CC-NOESY cross-peaks were unfolded to overlap with signals detected in 3D 13C-NOESY. In consequence, 4D CC-NOESY unfolding depended on other experiments, and individual 4D cross-peaks were retained only if they were confirmed in a 3D experiment.
Chemical shift assignment is performed with the existing FLYA algorithm that uses a genetic algorithm combined with local optimization to find an optimal matching between expected and observed peaks. FLYA uses as input the protein sequence, lists of peak positions from the available spectra, chemical shift statistics, either from the BMRB or the GNN described in the next section, and, if available, the structure from the previous refinement cycle. The tolerance for the matching of peak positions and chemical shifts was set to 0.03 ppm for 1H, and 0.4 ppm for 13C/15N shifts. Each FLYA execution comprises 20 independent runs with identical input data that differ in the random numbers used in the optimization algorithm. Nuclei for which at least 80% of the 20 runs yield, within tolerance, the same chemical shift value are classified as reliably assigned and used as input for the following chemical shift refinement step.
We used a graph data structure to combine FLYA-assigned shifts with information from previously assigned proteins (BMRB records) and possible spatial interactions. Each node corresponds to an atom in the protein sequence, and is represented by a feature vector composed of (a) a one-hot encoded atom type code (e.g., Cα, Hβ), (b) a one-hot encoded amino acid type, (c) the value of the chemical shift assigned by FLYA (only if a confident assignment is available, zero otherwise), (d) atom-specific BMRB shift statistics (mean and standard deviation), and (e) 30 chemical shift values obtained from BMRB database fragments. The latter feature is obtained by searching BMRB records for assigned 2–3-residue fragments that match the local protein sequence and have minimal mean-squared-error (MSE) to shifts confidently assigned by FLYA (non-zero values of feature (c) in the local neighborhood of the atom). The edges of the graph correspond to chemical bonds or skip connections. The latter connect the Cβ atom of a given residue with Cβ atoms 2, 3, and 5 residues apart in the amino acid sequence, and have the purpose to capture possible through-space influence on the chemical shift that is typically observed in secondary structure elements. The chemical shift refinement task is defined as a node regression problem, where an expected value of the chemical shift is predicted for each atom that lacks a confident FLYA assignment. This task is addressed with a DeepGCN model that was trained on 28,400 graphs extracted from 2840 referenced BMRB records. Each training example was created by building a fully assigned graph out of a single BMRB record, and dropping chemical shift values (feature (c) above) for randomly chosen atoms that FLYA typically assigns either with low confidence or inaccurately. Our DeepGCN model is designed specifically for de novo structure determination, as it uses only the protein sequence and partial shift assignments to estimate values of missing chemical shifts. Its predictions are used to guide the FLYA genetic algorithm optimization by reducing its search range for assignments. The precise final chemical shift value is always determined by the position of a signal in the spectrum, rather than the model prediction alone.
Before each structure calculation step, torsion angle restraints for the ϕ and ψ angles of the polypeptide backbone were obtained from the current backbone chemical shifts using the program TALOS-N. Restraints were only generated if TALOS-N classified the prediction as ‘Good’, ‘Strong’, or ‘Generous’. Given a TALOS-N torsion angle prediction of ϕ ± Δϕ, the allowed range of the torsion angle was set to ϕ ± max(Δϕ, 10°) for ‘Good’ and ‘Strong’ predictions, and ϕ ± 1.5 max(Δϕ, 10°) for ‘Generous’ predictions, and likewise for ψ.
Given the chemical shift assignments and NOESY cross-peak positions and intensities, the structure is calculated with CYANA using the established method that comprises 7 cycles of NOESY cross-peak assignment and structure calculation, followed by a final structure calculation. In total, 8 × 100 conformers are calculated for a given input data set using 30,000 torsion angle dynamics steps per conformer. The 20 conformers with the lowest final target function value are chosen to represent the solution structure proposal. The entire combined NOESY assignment and structure calculation procedure is executed independently 10 times based on 10 variants of NOESY peak lists, which differ in the number of cross-peaks selected from the output of the visual spectrum analysis. The first set generously includes all signals selected by ResNet with confidence ≥0.05. The other variants of NOESY peak lists follow the same principle with increasingly restrictive confidence thresholds of 0.1, 0.15, …, 0.5. The CYANA structures calculations are followed by a structure selection step, wherein the 10 intermediate structure proposals are compared pairwise by a Gradient Boosted Tree (GBT) model that uses 96 features from each structure proposal (including the CYANA target function value, number of long-range distance restraints, etc.; for details, see downloads in the Code availability section) to rank the structures by their expected accuracy. The best structure from the ranking is subsequently used as contextual information for the chemical shift refinement cycle (Fig. 1), or returned as the final outcome of ARTINA. The second-best final structure is also returned for comparison. To train GBT, we collected a set of successful and unsuccessful structure calculations with CYANA. Each training example was a tuple (si, ri), where si is the vector of features extracted from the CYANA structure calculation output, and ri is the RMSD of the output structure to the PDB reference. The GBT was trained to take the features si and sj of two structure calculations with CYANA as input, and to predict a binary order variable oij, such that oij = 1 if ri < rj, and 0 otherwise. Importantly, the deposited PDB reference structures were not used directly in the GBT model training (they are used only to calculate the RMSDs). Consequently, the GBT model is unaffected by methodology and technicalities related to PDB deposition (e.g., the structure calculation software used to calculate the deposited reference structure).
As an accuracy estimate for the final ARTINA structure, a predicted RMSD to reference (pRMSD) is calculated from the ARTINA results (without knowledge of the reference PDB structure). It aims at reproducing the actual RMSD to reference, which is the RMSD between the mean coordinates of the ARTINA structure bundle and the mean coordinates of the corresponding reference PDB structure bundle for the backbone atoms N, Cα, C’ in the residue ranges as given in Supplementary Table 4. The predicted RMSD is given by pRMSD = (1 – t) × 4 Å, where, in analogy to the GDT_HA value, t is the average fraction of the RMSDs ≤ 0.5, 1, 2, 4 Å between the mean coordinates of the best ARTINA candidate structure bundle and the mean coordinates of the structure bundles of the 9 other structure proposals. Since t ∈ [0, 1], the pRMSD is always in the range of 0–4 Å, grouping all “bad” structures with expected RMSD to reference ≥ 4 Å at pRMSD = 4 Å.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Supplementary Info File #1 Description of Additional Supplementary files Supplementary Movie 1 Supplementary Movie 2 Supplementary Movie 3 Supplementary Movie 4 Supplementary Movie 5 Reporting Summary Peer Review File | true | true | true |
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PMC9579715 | Rui Bi,Yu Li,Min Xu,Quanzhen Zheng,Deng-Feng Zhang,Xiao Li,Guolan Ma,Bolin Xiang,Xiaojia Zhu,Hui Zhao,Xingxu Huang,Ping Zheng,Yong-Gang Yao | Direct evidence of CRISPR-Cas9-mediated mitochondrial genome editing | 27-09-2022 | mitochondrial disease,mtDNA editing, mito-Cas9,third-generation sequencing | Pathogenic mitochondrial DNA (mtDNA) mutations can cause a variety of human diseases. The recent development of genome-editing technologies to manipulate mtDNA, such as mitochondria-targeted DNA nucleases and base editors, offer a promising way for curing mitochondrial diseases caused by mtDNA mutations. The CRISPR-Cas9 system is a widely used tool for genome editing; however, its application in mtDNA editing is still under debate. In this study, we developed a mito-Cas9 system by adding the mitochondria-targeted sequences and 3′ untranslated region of nuclear-encoded mitochondrial genes upstream and downstream of the Cas9 gene, respectively. We confirmed that the mito-Cas9 system was transported into mitochondria and enabled knockin of exogenous single-stranded DNA oligonucleotides (ssODNs) into mtDNA based on proteinase and DNase protection assays. Successful knockin of exogenous ssODNs into mtDNA was further validated using polymerase chain reaction-free third-generation sequencing technology. We also demonstrated that RS-1, an agonist of RAD51, significantly increased knockin efficiency of the mito-Cas9 system. Collectively, we provide direct evidence that mtDNA can be edited using the CRISPR-Cas9 system. The mito-Cas9 system could be optimized as a promising approach for the treatment of mitochondrial diseases caused by pathogenic mtDNA mutations, especially those with homoplasmic mtDNA mutations. | Direct evidence of CRISPR-Cas9-mediated mitochondrial genome editing
Pathogenic mitochondrial DNA (mtDNA) mutations can cause a variety of human diseases. The recent development of genome-editing technologies to manipulate mtDNA, such as mitochondria-targeted DNA nucleases and base editors, offer a promising way for curing mitochondrial diseases caused by mtDNA mutations. The CRISPR-Cas9 system is a widely used tool for genome editing; however, its application in mtDNA editing is still under debate. In this study, we developed a mito-Cas9 system by adding the mitochondria-targeted sequences and 3′ untranslated region of nuclear-encoded mitochondrial genes upstream and downstream of the Cas9 gene, respectively. We confirmed that the mito-Cas9 system was transported into mitochondria and enabled knockin of exogenous single-stranded DNA oligonucleotides (ssODNs) into mtDNA based on proteinase and DNase protection assays. Successful knockin of exogenous ssODNs into mtDNA was further validated using polymerase chain reaction-free third-generation sequencing technology. We also demonstrated that RS-1, an agonist of RAD51, significantly increased knockin efficiency of the mito-Cas9 system. Collectively, we provide direct evidence that mtDNA can be edited using the CRISPR-Cas9 system. The mito-Cas9 system could be optimized as a promising approach for the treatment of mitochondrial diseases caused by pathogenic mtDNA mutations, especially those with homoplasmic mtDNA mutations.
Mitochondria play essential roles in cell metabolism, energy production, apoptosis, calcium homeostasis, and immunity., Mammalian mitochondria are double-membrane organelles with their own genome (mitochondrial DNA [mtDNA]). Human mtDNA is a double-stranded circular molecule composed of 16,569 base pairs (bp), containing 37 genes encoding 13 respiratory chain subunits, 22 transfer RNAs, and 2 ribosomal RNAs (rRNAs). Mitochondrial dysfunction resulting from mtDNA mutations can cause a variety of human diseases., There are 100–100,000 copies of the mtDNA genome in a cell, depending on the cell type., Mutant and wild-type mtDNA can co-exist in one cell, known as heteroplasmy. The level of heteroplasmy in pathogenic mtDNA mutations can affect disease onset and clinical phenotype, with a general threshold of 60%–95% of mutant mtDNA causing biochemical and clinical defects.6, 7, 8 Currently, curing mitochondrial diseases remains a daunting task. Gene therapy through allotopic expression of mitochondrial genes shows promise for the treatment of Leber hereditary optic neuropathy caused by mtDNA mutations. Another approach is mitochondrial replacement therapy, which transfers the patient’s spindle, pronuclear, or polar body genome into healthy enucleated donor oocytes or embryos to circumvent mother-to-child mtDNA disease transmission.,, With the rapid development of genome-editing technology, several approaches for manipulating mtDNA in vitro and in vivo have been established in recent years,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 which exploit the rapid degradation of damaged mtDNA with double-strand breaks (DSBs) via mtDNA replisome components., For instance, mitochondria-targeted DNA nucleases specific to mutant mtDNA, including mitochondrial endonucleases,,, mitochondrial transcription activator-like effector nucleases (mito-TALENs),18, 19, 20, mitochondrial zinc-finger nucleases (mito-ZFNs),,,, and mitochondrial meganucleases, can specifically induce DSBs and promote the degradation of mutant mtDNA. These approaches can effectively shift the heteroplasmic level of mutant mtDNA13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 but are limited by the unavailability of homoplasmic pathogenic mtDNA mutations. A bacterial cytidine deaminase fused with mito-TALEN (DdCBE) was recently established to induce base editing for C > T transition in mtDNA, with successful application in mice, rats, zebrafish, plants, and human embryos.27, 28, 29, 30, 31 More recently, small-sized zinc-finger deaminases (ZFDs) were engineered for precise C > T base editing of nuclear and mitochondrial genes, offering several advantages in therapeutic application. The same research team also developed an A > G base editor for mtDNA (TALED, transcription activator-like effector-linked deaminases), providing a broader scope for mtDNA editing. However, although base editors are promising tools for mtDNA editing, their substantial off-target effects on nuclear genes remain poorly resolved., Moreover, no current tools are able to induce or edit other types of mutations such as insertions, deletions, and transversions in mtDNA molecules. The CRISPR-Cas9 system is a widely used tool for genome editing. The core principle is to induce DSBs in the targeted genomic region, then complete genome editing via DSB repair pathways, including the non-homologous end joining (NHEJ), microhomology-mediated end joining (MMEJ), and homologous recombination (HR) pathways. The CRISPR-Cas9 system is user friendly and flexible, and can circumvent the limitations of TALENs and ZFNs. However, the suitability and efficiency of mtDNA editing by CRISPR-Cas9 remains controversial. First, DSB repair is inefficient in mammalian mitochondria, but is essential for successful editing by CRISPR-Cas9. Second, delivery of small-guide RNA (sgRNA) and Cas9 protein complexes into mitochondria is challenging. Several recent studies have attempted to manipulate mtDNA using the CRISPR-Cas9 system,40, 41, 42, 43, 44, 45 which confirmed that the Cas9 protein can be transported into mitochondria, and that sgRNAs show mitochondrial localization., Most of these studies assessed successful manipulation of mtDNA meditated by CRISPR-Cas9 based on a decrease in mtDNA copy number40, 41, 42, 43 and positive signals of allele-specific polymerase chain reaction (PCR) and/or PCR-based sequencing., Although these studies suggest the possibility of using CRISPR-Cas9 to manipulate mtDNA, PCR-based methods may be limited in their detection of edited mtDNA due to “template switching” or other technical artifacts.46, 47, 48 Furthermore, evidence showing successful mtDNA editing by CRISPR-Cas9 remains inconclusive, which is why Gammage et al. have argued that the mitochondrial genome may not be “CRISPR-Ized.” In this study, we constructed a mito-Cas9 system, and confirmed that the system enabled successful knockin of exogenous single-stranded DNA oligonucleotides (ssODNs) into mtDNA using PCR-free third-generation sequencing technology. Furthermore, overexpression or activation of RAD51 significantly increased the knockin efficiency of the mito-Cas9 system. These results provide direct evidence of successful CRISPR-Cas9-mediated mitochondrial genome editing.
We established the mito-Cas9 system by replacing the nuclear localization sequence (NLS) at the N terminus of Cas9 in the px330-mCherry vector with the MTS of COX8A, and the NLS at the C terminus of Cas9 with the 3′ untranslated region (UTR) of SOD2 (Figure S1). The MTSs and 3′ UTR of the respective mitochondrial genes efficiently mediated the mitochondrial localization of mRNA., Small-guide RNA, sgRNA1ND4 (Table S1), was designed to target m.11 697-11 716 in the MT-ND4 gene, with a G added to the 5′-position of the 20-bp guide sequence to obtain efficient U6 transcription of sgRNA and overall efficiency of the CRISPR-Cas9 system,,, and was cloned to create the mito-Cas9 construct sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 (Figure S1). Previous studies have shown that unmodified sgRNAs can co-fractionate with mitochondria,,, therefore we did not further modify the sgRNAs. We constructed other mito-Cas9 constructs with sgRNA2ND4 and MTSs of mitochondrial genes COX10 and SOD2 using a similar strategy (Figure S1). As expected, HEK293T cells transfected with the Cas9 construct containing the MTS of COX8A (sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2) contained more Cas9 protein in the cytoplasmic components than cells transfected with the same amount of px330-mCherry vector or sgRNAAPP-NLS-Cas9 vector (Figures 1A and 1B). We performed protease protection assays and confirmed the successful translocation of the Cas9 protein into the mitochondria of cells transfected with the mito-Cas9 system, albeit with low efficiency (Figure 1C). As shown in Figure 1C, the Cas9 proteins showed multiple bands (around 80–160 kDa) in samples with or without proteinase K treatment. A small fraction of the Cas9 protein with MTSs, together with COXIV (positive control for mitochondrial inner membrane proteins), was protected from proteinase K digestion by the mitochondrial membrane. In contrast, the cytoplasmic protein β-actin and mitochondrial outer membrane protein MFN2 (used as negative controls) were completely digested by proteinase K (Figure 1C). Unlike COXIV, which was completely localized in the mitochondria, Cas9 (with or without MTSs) was partially localized in the mitochondria and its protein level decreased significantly upon protease K treatment. In the immunofluorescence assays, the Cas9 protein with MTSs showed preferred co-localization with mitochondrial green fluorescent protein (Figure 1D). Notably, despite the lack of NLS or MTS, overexpression of the Cas9 protein was still observed in the nucleus and crude mitochondria (Figures 1A–1D). However, the reason for this Cas9 distribution pattern remains unknown. We extracted mitochondria from HEK293T cells co-transfected with 6-carboxyfluorescein (FAM)-labeled sgRNA1ND4 (100 bp) and pDsRed2-mito vector (expressing mitochondria-targeted red fluorescent protein [mito-RFP]). We observed significant co-localization of FAM-labeled sgRNA1ND4 and mito-RFP (Figure 1E). We further used flow cytometry to quantify the proportion of labeled mitochondria in the total mitochondrial extract. FAM-labeled sgRNA1ND4 signals were detected in the mitochondria labeled with mito-RFP, albeit at a relatively low frequency (Figure S2A). We observed similar results using MitoTracker to label mitochondria (Figure S2B). These results suggest that Cas9 with MTSs and sgRNA can be (partially) transported into mitochondria. Within cells, mtDNA with DSBs is rapidly degraded, therefore, a reduction in mtDNA copy number can be used as a marker for mtDNA manipulation.40, 41, 42 Consistent with previous studies,40, 41, 42, 43 we observed a significant reduction in mtDNA copy number in HEK293T cells overexpressing the mito-Cas9 construct sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 compared with cells overexpressing nuclear-targeted Cas9 (sgRNAAPP-NLS-Cas9) or non-targeted controls (MTSCOX8A-Cas9-UTRSOD2, mito-Cas9 without sgRNA) (Figure 1F). We further assessed the effect of the mito-Cas9 system on mitochondrial function. Cells transfected with mitochondria-targeted Cas9 showed significantly increased levels of reactive oxygen species (ROS) and significantly decreased levels of adenosine triphosphate compared with cells transfected with nuclear-targeted Cas9 or Cas9 without any targeting sequence (Figure S3A), indicating decreased mitochondrial function upon mito-Cas9 expression. To investigate whether the reduction in mtDNA copy number in mito-Cas9-transfected cells was caused by increased ROS, we determined the mtDNA copy number in cells treated with vitamin K3 (vitK3, reported to increase cellular ROS level) and melatonin (reported to decrease cellular ROS). Compared with the other groups, cells transfected with mito-Cas9 showed a significant decrease in mtDNA copy number, regardless of treatments (Figure S3C). To investigate whether the mito-Cas9 construct with an alternative sgRNA targeting a different mtDNA region could still be applied for mtDNA editing, we used sgRNA2ND4 (Table S1) targeting the m.11 851–11 868 (20 nt) region in the MT-ND4 gene. Consistently, upon overexpression of the sgRNA2ND4-MTSCOX8A-Cas9-UTRSOD2 construct, the mtDNA copy number decreased significantly (Figure S4A). Thus, the mito-Cas9 system established here can successfully target mitochondria and induce a decrease in mtDNA copy number.
Increasing evidence has demonstrated the existence of MMEJ-mediated and HR-mediated DSB repair in mammalian mitochondria.55, 56, 57, 58 Here, we investigated whether the mito-Cas9 system can mediate homology-directed repair in mtDNA. Based on previous observations that homologous arms longer than 40 bp can achieve high knockin efficiency,, we designed a 96-bp ssODN1 donor template (Table S1) that contained 45-bp homologous arms (left arm: homologous to m.11 669–11 713; right arm: homologous to m.11 714–11 758) flanking a 6-bp insertion of the EcoRI site (GAATTC) (Figure 2A; Table S1). The ssODN1 donor template and Cas9 constructs were co-transfected into HEK293T cells for 48 h, after which homology-directed repair of mtDNA was analyzed using PCR and quantitative real-time PCR (qRT-PCR) with the EcoRI site-specific primer pair L11338/EcoRI-R (Figures 2A and 2B; Table S1). EcoRI site-specific PCR products were not detected in cells transfected with MTSCOX8A-Cas9-UTRSOD2 alone, but were detected in cells transfected with ssODN1 alone (Figures 2B and S5), which may be, in part, due to potential “template switching artifacts” between the ssODN1 template and mtDNA molecule.46, 47, 48 In contrast, cells transfected with a combination of sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 and ssODN1 showed significantly increased EcoRI site-specific PCR products compared with different controls (Figure 2B). We also constructed mito-dCas9 (dead Cas9) with a targeting sgRNA1ND4 as a control, in which the catalytic activity of the SpCas9 protein was abolished by introducing two mutations, p.D10A and p.H840A., The relative level of EcoRI site-specific PCR products of the mito-dCas9 system showed no significant differences compared with the controls transfected with ssODN1 alone or with ssODN1 + MTSCOX8A-Cas9-UTRSOD2 (no sgRNA) (Figure 2C). These findings indicated that Cas9 activity is essential for the mito-Cas9 system. To investigate whether the increase in EcoRI site-specific PCR products was caused by the potentially higher transfection efficiency/stability of ssODN1 with the mito-Cas9 system, we transfected HEK293T cells with FAM-labeled ssODN1 (which can be introduced into mitochondria after transfection, Figure S6A) and mCherry-tagged Cas9 vectors (with or without different target sequences). The proportion of fluorescently labeled cells was quantified by flow cytometry. We observed similar levels of labeled cells among the transfected cells (Figure S6B), indicating that ssODN1 exhibits a similar level of transfection efficiency in different ssODN1 and Cas9 vector combinations. We next verified insertion of the “GAATTC” sequence in the sgRNA1ND4-target site of mtDNA by direct sequencing of the EcoRI site-specific PCR product (Figure 2D). Furthermore, we found that the mito-Cas9 constructs with different MTSs of mitochondrial genes (COX8A, COX10, or SOD2) exhibited similar knockin efficiencies of exogenous ssODNs (Figure 2E). To exclude the potential effects of “template switching artifacts” during PCR, we quantified the level of ssODN1 within the transfected cells at 48, 96, and 144 h after transfection. We found that the relative amount of the EcoRI site-specific PCR product in cells transfected with ssODN1 significantly diminished with time and was barely detected at 144 h (Figure S7). In contrast, cells transfected with ssODN1 and the mito-Cas9 system contained a significantly higher level of the EcoRI site-specific PCR product relative to cells transfected with ssODN1 alone at each time point (Figure S7). Of note, a reduction in the EcoRI site-specific PCR product was observed with time (Figure S7), which might be attributed to the higher proliferation rate of cells with wild-type mtDNA compared with cells with edited mtDNA. To further exclude potential contamination from other sources of mtDNA (e.g., leakage of mtDNA fragments in cytosol from damaged mitochondria) or mtDNA-like fragments (e.g., nuclear mitochondrial pseudogenes in the nuclear genome) outside the mitochondria, which could potentially be recognized as a target by the mito-Cas9 system, we performed a DNase protection assay to validate the successful knockin of exogenous ssODNs into mtDNAs located within the mitochondria (Figure 2F). Upon DNase I treatment, no PCR product could be amplified for the nuclear APP gene (Figure 2F), whereas the mtDNA PCR product (amplified by primer pair L11338/H11944) and EcoRI site-specific PCR product were visible, indicating that exogenous ssODNs were inserted into mtDNA and were protected from DNase I digestion by the mitochondrial membrane (Figure 2F). Quantification of the EcoRI site-specific PCR products showed that the mito-Cas9 construct sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 significantly increased the knockin efficiency of ssODN1 in both crude mtDNA (DNase−) and purified mtDNA (DNase+) (Figure 2G). The EcoRI site-specific PCR product was also detected when the target and knockin sites were changed (Figures S4B–S4D). Compared with cells transfected with ssODN2 alone or with a combination of ssODN2 and MTSCOX8A-Cas9-UTRSOD2 (no sgRNA), cells transfected with a combination of sgRNA2ND4-MTSCOX8A-Cas9-UTRSOD2 and ssODN2 showed a significant increase in EcoRI site-specific PCR product (Figure S4D), indicating that mito-Cas9 system-mediated knockin can be applied to any site suitable for CRISPR-Cas9 editing. These results demonstrate that the mito-Cas9 system can mediate knockin of exogenous ssODNs into mtDNA through the HR repair pathway.
To exclude the possibility that the EcoRI site-specific PCR products were caused by PCR artifacts, such as template-switching artifacts46, 47, 48 between exogenous ssODNs and mtDNA, we performed PCR-free third-generation sequencing to verify the knockin of exogenous ssODNs into mtDNA (Figure 3A). The ssODN1 donor template and mito-Cas9 construct sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 were co-transfected into HEK293T cells for 48 h. Cells transfected with ssODN1 only or with a combination of ssODN1 and MTSCOX8A-Cas9-UTRSOD2 (no sgRNA) were considered as controls. BamHI digestion was used to linearize purified mtDNA isolated from DNase I-treated mitochondria (Figure 3A). Total genomic DNA was completely digested by BamHI and showed no clear bands on the gel, whereas successfully linearized mtDNA was evidenced by a single band of mtDNA after BamHI digestion (Figure S8A). We also measured the efficiency of linearization by quantifying the mtDNA fragment flanking the BamHI site using qRT-PCR with the primer pair L14054/H14573 (Table S1; linearized mtDNA could not be amplified by L14054/H14573) and observed a significant decrease in the PCR product for purified mtDNA subjected to BamHI compared with undigested samples (Figure S8B), indicating that mtDNA was thoroughly linearized. Direct sequencing of linearized mtDNA using PCR-free PacBio sequencing technology yielded a distinct sequence peak at ∼16 kb (Figure 3B, left), indicating full-length capture and sequencing of the mtDNA genome. Reads Mapping using the mtDNA reference sequence and nuclear mitochondrial (NUMTs) reference sequences showed that most reads mapped to NUMTs were <16 kb (Figure 3C, left). Therefore, we only used reads >16 kb in length, and with higher mapping percentage and concordance to mtDNA than to NUMTs, for the following analyses, thus reliably excluding the potential effects of NUMTs and other artifacts. We identified precise insertion of GAATTC at the sgRNA1ND4 target site in mtDNA reads from the entire mitochondrial genome (Figure 3D). Of note, a higher frequency of GAATTC insertion at the sgRNA1ND4 targeting site was observed for cells transfected with sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 + ssODN1(3/11,484, 0.026%) than for control cells transfected with MTSCOX8A-Cas9-UTRSOD2 (no sgRNA) + ssODN1 (3/22,995, 0.0087%) (Figure 3E, top). In both samples, we observed randomly distributed GAATTC insertions across the mtDNA genome at extremely low frequencies (Figure 3E, top), which may be caused by potential errors in the third-generation sequencing technology. To exclude the possibility that these targeted insertions were introduced by sequencing errors in PacBio Sequel II, we used another PCR-free sequencing method, Nanopore sequencing, to sequence mtDNAs extracted from cells transfected with sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 + ssODN1. In the captured reads across the entire mtDNA genome (peak near 16,569 bp; Figures 3B and 3C, right), we confirmed a higher rate of targeted GAATTC insertion in cells transfected with sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 + ssODN1 (2/411,780) compared with cells transfected with ssODN1 alone (1/314,840) (Figure 3E, bottom). We observed several untargeted GAATTC insertions across the mtDNA genome in the PacBio data (Figure 3E, top), but these insertions were not replicated in the Nanopore sequencing data (Figure 3E, bottom), suggesting that most untargeted insertions were introduced by PacBio sequencing errors. We further analyzed the number of reads with targeted insertions in the mtDNA sequencing data of different lengths. We identified a higher frequency of precise GAATTC insertions in mtDNA sequences larger than 10 kb at the sgRNA1ND4 target region in cells transfected with sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 + ssODN1 (n = 6) than in cells transfected with ssODN1 alone (n = 2) (Figure 3F). Overall, the total numbers of targeted insertions in the mtDNA sequences with different length cutoffs (from 10 to 16 kb) were consistently higher in cells transfected with the mito-Cas9 system than cells transfected with ssODN1 alone (Figure 3F). Therefore, the higher rate of targeted insertions in cells transfected with the mito-Cas9 system is unlikely to be due to random sequencing errors, but may be indicative of the editing capability of the mito-Cas9 system. The occurrence of targeted insertions in cells transfected with ssODN1 alone, even at a very low frequency, deserves further attention and focused study in the future, which may suggest an unexpected role of ssODN1 in the initiation of mtDNA replication in the absence of the mito-Cas9 system (Bi et al., unpublished data).
We further tested whether modulating those factors involved in genome stability maintenance and repair pathways could improve the knockin efficiency of the mito-Cas9 system. HR is essential for maintaining mitochondrial genome integrity.,, Key factors in the HR pathway, such as RAD51 and XRCC3, can be recruited to mitochondria and participate in mtDNA maintenance under DNA damage stress.,, We confirmed the presence of RAD51 in mitochondria using a proteinase protection assay (Figure 4A). As our mito-Cas9 system was based on HR-mediated effects, we hypothesized that enhancing RAD51 function would increase the knockin efficiency of ssODNs into mtDNA, as shown by the Cas9-mediated knockin efficiency for nuclear DNA.68, 69, 70 We treated HEK293T cells transfected with the mito-Cas9 construct sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 and/or ssODN1 with RAD51 agonist RS-1 (10 μM) for 42 h, then quantified the level of edited mtDNA (Figure 4B). Stimulation of RAD51 with RS-1 significantly increased the knockin efficiency of the mito-Cas9 system (Figure 4B), whereas RS-1 had no significant effect on cells transfected with ssODN1 alone Figures 4B and S9A) or on the other three controls (mito-Cas9 without ssODN1, mito-Cas9 without sgRNA, and mito-Cas9 with sgRNA2ND4 targeting another region) (Figures 4B and S9A). Overexpression of the RAD51 protein resulted in a significant increase in the knockin efficiency of the mito-Cas9 system, similar to the treatment of the RAD51 agonist (Figures 4C and S9B). However, knockdown of RAD51 or inhibition of RAD51 by RI-1 had no or a weak effect on the knockin efficiency of the mito-Cas9 system (Figures 4C, 4D, S9C, and S9D), suggesting a potential compensatory effect in cells with RAD51 knockdown or inhibition during the maintenance of genome stability. Interestingly, an increase in the EcoRI site-specific PCR product signal was observed in cells transfected with sgRNA1ND4-NLS-Cas9 and ssODN1 compared with cells transfected with ssODN1 alone (Figure 4B). We speculated that this may be caused by a minimum level of sgRNA1ND4 guiding Cas9 and ssODN1 or penetrance of Cas9 and ssODN1 into the mitochondria, similar to that observed for nuclear-imported Cas9 in the absence of NLS (Figure 1A). We further quantified the knockin efficiency of the mito-Cas9 system using second-generation sequencing technology. Using a primer pair outside of ssODN1, we amplified a fragment flanking the knockin site (m.11 600–11 820) in crude and purified mtDNAs (Table 1; Figure S9E), respectively, then subjected the PCR products to second-generation sequencing. Knockin of ssODNs was identified in the sequencing reads (Table 1; Figure S9E). Consistent with the qRT-PCR results, in HEK293T cells transfected with the mito-Cas9 construct sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 and ssODN1, RS-1 treatment resulted in a nearly 5-fold increase in knockin efficiency in the crude mtDNA (0.14%) relative to that in cells without RS-1 treatment (0.03%; p < 2.2 × 10−16, Fisher’s exact test) (Table 1). Knockin frequency further increased in the purified mtDNA (0.23%; p < 2.2 × 10−16, Fisher’s exact test) (Table 1). Of note, the relative number of sequence reads with knockin was significantly lower (p < 2.2 × 10−16, Fisher’s exact test) in cells transfected with ssODN1 alone than in cells transfected with mito-Cas9 constructs and ssODN1 (Table 1). Furthermore, no significant changes in knockin efficiency were observed in the ssODN1-transfected cells with or without RS-1 treatment, suggesting that sequence reads with GAATTC knockin in cells transfected with ssODNs alone were not HR mediated (Table 1). Collectively, these results suggest that RAD51 is a key factor for the knockin of exogenous ssODNs into mtDNA. Small-molecule RS-1 increased knockin efficiency of the mito-Cas9 system through RAD51 activation, further supporting HR-mediated knockin of exogenous ssODNs into mtDNA. Although template switching artifacts or other potential factors may introduce some noise in regard to the interpretation of knockin frequency, this noise is unlikely to have contributed to the significant differences observed between groups.
We performed whole-genome sequencing (WGS) for genomic DNA isolated from HEK293T cells transfected with sgRNA1ND4-MTSCOX8A-Cas9-UTRSOD2 + ssODN1 and sgRNA2ND4-MTSCOX8A-Cas9-UTRSOD2 + ssODN2, respectively. Mean sequence depth for the two samples was about 34×. The nuclear genome was screened for potential off-target sites with “NGG” or “NAG” protospacer adjacent motif and with up to nine mismatches relative to sgRNA. No insertions or deletions were identified in the potential off-target sites of sgRNA1ND4 and sgRNA2ND4 (Table S2). We further screened the whole genome for GAATTC insertions using the WGS data. No nuclear DNA reads with the GAATTC insertion were detected, indicating that GAATTC knockin in the mtDNA reads of third-generation sequencing data was not caused by potential off-target and/or unspecific knockin in nuclear DNA or from NUMTs.
Intragenic inversion mutation m.3902_3908inv (m.3902_3908 ACCTTGC>GCAAGGT) in the MT-ND1 gene is reported to cause fatal infantile lactic acidosis and mitochondrial myopathy., At present, however, the underlying mechanism remains unclear. Here, using the same strategy, we designed the mito-Cas9 system to introduce the m.3902_3908inv mutation into HEK293T cells (Figure 5A). Successful knockin of ssODN3902 in mtDNA within the mitochondria was demonstrated using a DNase protection assay (Figure 5B). Consistent with previous observations from the ssODN1ND4 and ssODN2ND4 knockin experiments, cells co-transfected with the mito-Cas9 constructs and ssODN3902 showed a significant increase in m.3902_3908inv-specific PCR products compared with cells transfected with ssODN3902 alone or with a combination of ssODN3902 and MTSCOX8A-Cas9-UTRSOD2 (no sgRNA) (Figure 5C). In addition, mutation m.3902_3908inv was identified in the second-generation sequencing reads (Figure 5D). The knockin efficiency of mutation m.3902_3908inv was about 0.05% (2,432 reads successfully edited among 5,284,619 raw reads). These results suggest that the mito-Cas9 system may serve as a promising approach for targeted knockin in mtDNA and could be optimized as a workable way to establish cellular models for studying mtDNA pathogenic mutations.
Currently, whether mtDNA can be edited by CRISPR-Cas9 remains controversial.,, Due to the lack of NHEJ repair, mtDNA with DSBs degrades rapidly, resulting in a reduction in mtDNA copy number, and thus mtDNA-edited products have not been directly detected in previous research.40, 41, 42 In this study, in addition to adding MTSs to Cas9,40, 41, 42, 43, 44, 45 we optimized the mitochondria-targeted Cas9 system by adding the 3′ UTR of the SOD2 gene to the downstream region of the Cas9 gene to facilitate efficient mitochondrial localization of Cas9 mRNA. We confirmed that the developed mito-Cas9 system transported Cas9 into the mitochondria (Figure 1C) and enabled mtDNA manipulation. Importantly, we developed a PCR-free third-generation sequencing technology that effectively avoids artifacts caused by PCR amplification (e.g., template switching artifacts) and verified the accurate knockin of exogenous ssODNs into mtDNA using the mito-Cas9 system (Figure 3). Another straightforward approach to confirm the effectiveness and efficiency of mitochondria-targeted CRISPR-Cas9 system can be engineered by introducing mtDNA-based drug resistance. Currently, we are attempting to establish a drug-resistant cell line with the mtDNA mutation m.2991T>C in the 16S rRNA of mtDNA, which could facilitate selection by chloramphenicol. Previous studies have shown that homologous arms longer than 40 bp exhibit higher HR efficiency than shorter arms., Based on this observation, we designed the homologous arms of three ssODNs (ssODN1, ssODN2, and ssODN3902), which were all 45 bp in length. We also investigated the knockin efficiency of ssODN with shorter arms (22 bp). Results showed that the knockin efficiency of ssODN was significantly lower than that of ssODN1 (Figure S10). Thus, the knockin efficiency of ssODN may be affected by its length, and optimizing homologous arm length to balance the stability and mitochondrial transport efficiency of ssODN could be helpful for increasing the knockin efficiency of the mito-Cas9 system. The mechanism that maintain mitochondrial genome stability remain elusive. Notably, for the two main DNA repair pathways, NHEJ cannot be detected in mitochondria and the existence of HR in mitochondria is controversial.,,, Consistent with previous study, we found that RAD51, a key nuclear factor of the HR repair pathway, was translocated into the mitochondria (Figure 4A) and its activation with agonist RS-1 enabled a 2- to 5-fold increase in knockin efficiency of ssODNs into mtDNA (Figure 4B). In addition, the effects of RAD51 activation appeared to be specific to cells transfected with the mito-Cas9 system (Figure 4, Table 1). These findings and third-generation sequencing results suggest that mtDNA can be affected by the HR pathway, and activation of the HR pathway by RAD51 stimulation may enhance CRISPR-Cas9-mediated knockin in mtDNA. One unresolved question is how ssODN is imported into mitochondria, which requires further research. Several studies have shown that DNA and RNA can be transported into mitochondria in animal cells and in plants,,77, 78, 79, 80 and our study provides further evidence that FAM-labeled sgRNA and ssODN1 can be transported into mitochondria (Figures 1E, S2, and S6A). Enthusiasm for mtDNA editing stems from the clinical need for the treatment of mitochondrial diseases caused by pathogenic mtDNA mutations, most of which are in a heteroplasmic state. Both mito-ZFN,,, and mito-TALEN18, 19, 20, are effective in altering the heteroplasmic level of mutant mtDNA.17, 18, 19, For homoplasmic mtDNA mutations, base editing,, and CRISPR-Cas9-mediated HR knockin can be used to edit mutant mtDNA. However, one of the key problems with mtDNA editing is that each cell may have hundreds to thousands of mtDNA copies and the editing of each copy is unlikely. Thus, employing mtDNA-editing technology to cure mitochondrial diseases caused by mtDNA mutations remains a considerable challenge. In our study, knockin efficiency of the mito-Cas9 system was rather low (0.03%–0.23%) compared with the recently developed DdCBE method (5%–50%). We speculate that the low efficiency may be due to inefficient mitochondrial transport of the editing system and limited editing efficiency of the Cas9 protein to mtDNA. Therefore, mito-Cas9 system optimization, either by improving the mitochondrial transport efficiency such as mitochondrial RNA transport (although we did not achieve better mtDNA editing efficiency in cells overexpressing PNPASE, which can regulate RNA-import to mitochondria, compared with cells overexpressing RAD51 [data not shown]) or by using engineered Cas proteins with higher editing efficiency, is essential for the application of this mtDNA editing system. Furthermore, the knockin efficiency varied for different types of mutations, which may be due to different sequence features of the targeted region (i.e., GC% content) or different targeting efficiencies of the sgRNAs. Despite its limited editing efficiency, the mito-Cas9 system has the potential for wider scope of variant replacement and can introduce accurate knockin of target variants without changing other loci in the same editing window, thereby serving as an alternative strategy for manipulating mtDNA, especially considering recent findings that mitochondrial base editors may induce extensive off-target editing in the nuclear genome., In addition, inducing a small fraction of wild-type mtDNA using the mito-Cas9 system, then altering the heteroplasmic level of the wild-type mtDNA using mito-ZFN or mito-TALEN technology, offers a promising strategy for editing homoplasmic pathogenic mtDNA mutations. In conclusion, we established an mtDNA editing system based on CRISPR-Cas9-mediated knockin via the HR pathway and found that RAD51 agonist RS-1 significantly enhanced mtDNA knockin efficiency. Using PCR-free third-generation sequencing, we provide direct evidence for mtDNA editing mediated by the CRISPR-Cas9 system. Future studies devoted to increasing editing efficiency are essential for expanding the application and safety of the mito-Cas9 system in the treatment of mitochondrial diseases caused by pathogenic mtDNA mutations.
Detailed materials and methods are included in the supplemental information.
The data that support the findings of this work are available from the corresponding author upon reasonable request. The sequencing data were deposited at GSA (https://ngdc.cncb.ac.cn/gsa/) under accession number HRA001435. | true | true | true |
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PMC9579860 | Zhi Xie,Chen Wang,Li Li,Xianfeng Chen,Guanjing Wei,Yan Chi,Yanping Liang,Lizhen Lan,Jiqiong Hong,Lili Li | lncRNA-AC130710/miR-129-5p/mGluR1 axis promote migration and invasion by activating PKCα-MAPK signal pathway in melanoma | 18-10-2022 | melanoma,lncRNA,mGluR1,migration,invasion | Abstract Invasion and metastasis of melanoma are a series of complicated biological events regulated by multiple factors. The coregulation of many molecules involved in the development and progression of melanoma contributes to invasion and migration. mGluR1 is a metabotropic glutamate receptor that is overexpressed in melanocytes and is sufficient to induce melanoma. In our study, we found that mGluR1 was obviously increased in melanoma. Furthermore, we found that miR-129-5p could directly target and regulate mGluR1 mRNA, which was significantly reduced in A375 cells. Overexpression of miR-129-5p inhibited cell migration, invasion and clonal formation. lncRNA-AC130710 directly targeted and suppressed miR-129-5p in A375 cells. Downregulation of lncRNA-AC130710 suppressed the levels of mGluR1 mRNA by promoting miR-129-5p expression and further inhibiting migration, invasion and colony formation in A375 cells, which was associated with the activation of the PKCα-MAPK signaling pathway. Taken together, our study showed that the lncRNA-AC130710/miR-129-5p/mGluR1 axis plays an important role in the invasion and metastasis of melanoma. | lncRNA-AC130710/miR-129-5p/mGluR1 axis promote migration and invasion by activating PKCα-MAPK signal pathway in melanoma
Invasion and metastasis of melanoma are a series of complicated biological events regulated by multiple factors. The coregulation of many molecules involved in the development and progression of melanoma contributes to invasion and migration. mGluR1 is a metabotropic glutamate receptor that is overexpressed in melanocytes and is sufficient to induce melanoma. In our study, we found that mGluR1 was obviously increased in melanoma. Furthermore, we found that miR-129-5p could directly target and regulate mGluR1 mRNA, which was significantly reduced in A375 cells. Overexpression of miR-129-5p inhibited cell migration, invasion and clonal formation. lncRNA-AC130710 directly targeted and suppressed miR-129-5p in A375 cells. Downregulation of lncRNA-AC130710 suppressed the levels of mGluR1 mRNA by promoting miR-129-5p expression and further inhibiting migration, invasion and colony formation in A375 cells, which was associated with the activation of the PKCα-MAPK signaling pathway. Taken together, our study showed that the lncRNA-AC130710/miR-129-5p/mGluR1 axis plays an important role in the invasion and metastasis of melanoma.
Malignant melanoma is a type of malignant tumor that originates from neural crest melanoma cells [1]. Although it accounts for only approximately 4% of all dermatological cancers, it contributes to more than 80% of deaths in skin cancer patients [2]. The invasion and metastasis of melanoma is a complex process with multiple stages and is affected by many factors that directly affect the melanoma prognosis for patients [3,4]. The coregulation of many molecules contributes to invasion and migration, which thereby participates in the development and progression of melanoma. Metabotropic glutamate receptors (mGluRs) can mediate neuronal excitability and neurotransmitter release and have been extensively studied in the central nervous system. Certain cancers, such as melanoma, express various mGluR subtypes that might play a role in disease progression. Namkoong et al. and Lee et al. found abnormal expression of mGluR1 in human melanoma cell lines and tissue sections [5,6]. Suppression of mGluR1 and glutamate signaling can inhibit the progression of melanoma [5]. In our previous study, we also found that mGluR1 expression was elevated in melanoma. Therefore, we wanted to inhibit the invasion and metastasis of melanoma by regulating mGluR1. lncRNAs are noncoding RNAs of the more than 200 nt in length and regulate various biological events [7–9]. Recently, studies have shown that there are multiple interactions between noncoding RNAs and coding RNAs, such as the lncRNA‒miRNA–mRNA regulatory network [10]. As a representative mechanism, lncRNAs can function as miRNA sponges [11]. For example, Wei et al. demonstrated that the lncRNA UCA1-miR-507-FOXM1 axis participates in melanoma cell proliferation and invasion by regulating the G0/G1 cell cycle [12]. Sun et al. showed that lncRNA MALAT1, as the competitive endogenous RNA sponge of miR-183, promoted the occurrence and development of melanoma by regulating the miR-183-ITGB1 axis [13]. Therefore, elucidating the regulatory network of lncRNA–miRNA–mRNA is critical to determine the specific role of each molecule in the metastasis and invasion of malignant melanoma [14,15]. Xu et al. determined that lncRNA-AC130710 plays an important role in regulating the invasion of gastric cancer cells by targeting miR-129-5p [16]. Previously, we predicted the interaction between miR-129-5p and mGluR1 by miRanda (www.microrna.org) and RNAhybrid (https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid/). Therefore, we wanted to explore whether lncRNA-AC130710 promoted melanoma invasion and metastasis by interacting with miR-129-5p to upregulate the expression of mGluR1.
A375 cells, HEK-293T cells and a human primary melanocyte cell line were purchased from iCell Bioscience Inc. (Shanghai, China). A375 cells were cultured in DMEM with 10% fetal bovine serum (FBS, HyClone) and penicillin‒streptomycin (100 U/mL) at 37°C with 5% CO2. The human primary melanocyte cell line was cultured in a special medium for primary melanocytes (iCell Bioscience Inc.) with 0.5% FBS (HyClone), special culture additives for primary melanocyte cell lines (iCell Bioscience Inc.) and penicillin‒streptomycin (100 U/mL) at 37°C with 5% CO2. HEK-293T cells were cultured in DMEM with 10% FBS, 1% Glutamax, 1% NEAA and penicillin‒streptomycin (100 U/mL) at 37°C with 5% CO2.
The miR-129-5p mimic, negative control (NC) mimic, three lncRNA-AC130710 siRNAs and one siRNA NC were synthesized by Sangon Biotech (Shanghai, China). The sequences were as follows: hsa-miR-129-5p-S, CUUUUUGCGGUCUGGGCUUGC; hsa-miR-129-5p-A, AAGCCCAGACCGCAAAAAGUU; mimic NC-S, UUGUACUACACAAAAGUACUG and mimic NC-A, GUACUUUUGUGUAGUACAAUU. The vectors used in our study included pcDNA3.1-lncRNA-AC130710 and pcDNA3.1 NC. A375 cells were transfected using Lipofectamine 2000 (Invitrogen, USA) for 24 h according to the manufacturer’s instructions.
Total RNA was extracted from cells using the TaKaRa MiniBEST Universal RNA Extraction Kit according to the manufacturer’s procedure. Then, cDNA was synthesized by using a miRNA 1st Strand cDNA Synthesis Kit (Vazyme) and Hifair®Ⅱ 1st Strand cDNA Synthesis SuperMix (YEASEN, Shanghai). After that, Hieff® qPCR SYBR Green Master Mix (YEASEN, Shanghai) was used to perform the RT-qPCR on an ABI QuantStudio™ 12K Flex according to the manufacturer’s procedure. The qPCR conditions were as follows: 95°C for 5 min followed by 40 cycles of 95°C for 10 s, 55–60°C for 20 s and 72°C for 20 s. The primers used were as follows: mGluR1, F: 5′–CCAACTTCAACGAGGCCAAA–3′, R: 5′–CATGCGGACAACATCAGAGG–3′; miR-129-5p, F: 5′–CGCTTTTTGCGGTCTGG–3′, R: 5′–CAGTGCGTGTCGTGGAGT–3′; lncRNA-AC130710: F: 5′–AGGACAGTCTCAAGGGGGTTA–3′, R: 5′–CTGCCTTCTCACATGGAACTC–3′; GAPDH: F, 5′–TCAAGAAGGTGGTGAAGCAGG–3′, R: 5′–TCAAAGGTGGAGGAGTGGGT–3′ and U6, F: 5′–GCTTCGGCAGCACATATACTAAAAT–3′, R: 5′–CGCTTCACGAATTTGCGTGTCAT–3′. The primers were obtained from Sangon Biotech (Shanghai, China). The levels of mGluR1 and lncRNA-AC130710 were normalized to that of GAPDH. The levels of miR-129-5p were normalized to that of U6. The relative expression levels of genes were calculated using the 2−ΔΔCt method.
mGluR1-WT-pmirGLO, mGluR1-Mut-pmirGLO, lncRNA-AC130710-WT-pmirGLO and lncRNA-AC130710-Mut-pmirGLO plasmids were constructed by GenScript (Wuhan, China). Cells were seeded in 24-well plates and grown for 24 h before transfection. Lipofectamine 2000 (Invitrogen, USA) was used to cotransfect miR-129-5p mimics or the miR-NC and wild-type or mutant plasmids into cells. Transfected cells were harvested after 48 h and then analyzed using a Dual-Luciferase Reporter Assay System (Promega, USA).
A375 cells were transfected with miR-129-5p mimic, mimic NC, pcDNA3.1-lncRNA-AC130710, pcDNA3.1 NC, lncRNA-AC130710 siRNA or siRNA NC for 48 h, respectively. Then, these cells were seeded into six-well plates at a density of 3.5 × 105 cells per well at 37°C. A 10 μL pipette tip was used to make a straight scratch. The culture medium was discarded, and the cells were washed three times in PBS. Then, serum-free medium was added. An Olympus IX71 microscope was used to take images at 0, 24 and 48 h after scratching, and then the migration distances of the cells were calculated. Scratch width was subtracted from time 0 scratch widths at the same location to determine cell migration distance. Migration distances were averaged to determine overall migration.
Cell invasive capacity was assessed by using a transwell chamber. Matrigel was diluted ten times with serum-free medium, and 50 µL was added to each transwell chamber, followed by incubation at 37°C for 30 min. Transfected cells were harvested and suspended in serum-free DMEM. Then, 300 μL of cell suspension (2 × 104/mL cells) was placed into the upper chamber, and 600 μL medium containing 10% FBS was added to the lower chamber. Cells were cultured at 37°C for 24 h. After 24 h, cells on the upper surface were removed with a cotton swab, while the invasive cells in the lower chamber were fixed with 4% paraformaldehyde and stained with crystal violet. The stained cells were counted in three randomly selected visual fields per well under an Olympus IX71 microscope.
Transfected cells were seeded into six-well plates at a density of 200 cells/well and cultured at 5% CO2 and 37°C for 2 weeks. Cells were washed with PBS, fixed with 4% paraformaldehyde for 10 min and stained with crystal violet. The colony number was then determined with an Olympus IX71 microscope. The assays were independently repeated three times.
Total cellular protein was quantified using a BCA protein assay kit (Thermo Fisher Scientific). Equal amounts of protein were separated by 12% SDS‒PAGE and transferred to a polyvinylidene fluoride membrane (Hybond, CA). The membranes were blocked and then incubated overnight at 4°C with antibodies against mGluR1, PKCα, MAPK42/44, p-MAPK42/44 and GAPDH (1:1,000, ABclonal). The membranes were incubated with secondary antibodies conjugated to horseradish peroxidase (1:5,000; Beyotime Biotechnology, China) at room temperature for 2 h. Protein bands were detected using an efficient enhanced chemiluminescence (ECL) kit (GE Healthcare, UK) and quantitated using Quantity One software (Bio-Rad Laboratories, UK). Band intensities were normalized to that of GAPDH.
All data are expressed as the mean ± standard deviation. Statistical analysis was carried out using SPSS 13.0. Comparisons among multiple groups were conducted using one-way analysis of variance; p < 0.05 was considered to indicate a statistically significant difference.
mGluR1 is a metabotropic glutamate receptor, and mGluR1 expression in mouse melanocytes was determined to be sufficient to induce melanoma. Consistent with a previous study, our results showed that the expression of mGluR1 was significantly increased in A375 cells compared with the human primary melanocyte cell line (Figure 1a). Our previous study found that the level of miR-129-5p was obviously decreased in A375 cells compared with the human primary melanocyte cell line (Figure 1b), and we also found a targeting relationship between miR-129-5p and mGluR1. We used miRanda (www.microrna.org) and RNAhybrid (https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid/) to predict the putative complementary binding sites of miR-129-5p and mGluR1 (Figure 1c). Furthermore, we wanted to determine whether miR-129-5p directly targets mGluR1 using a luciferase reporter assay. As shown in Figure 1d, overexpression of miR-129-5p decreased the luciferase activity of the wild-type mGluR1 3′-UTR reporter in 293T cells. However, miR-129-5p mimics did not influence the luciferase activity of the reporter carrying the mutated mGluR1 3′-UTR. Moreover, the addition of miR-129-5p resulted in significantly downregulated expression of mGluR1 in both the qPCR and Western blot results (Figure 1e and f).
We examined the effect of miR-129-5p on cell migration, cell invasion and colony formation in A375 cells (Figure 2). The results of the scratch assay showed that the relative migration distance was 5.32% at 24 h and 11.82% at 48 h in cells transfected with the miR-129-5p mimic, and the relative migration distance was 13.9% at 24 h and 30.11% at 48 h in control cells (Figure 2a). Overexpression of miR-129-5p significantly inhibited A375 cell migration by 61.72% at 24 h and 60.74% at 48 h compared with the control. We also examined the effect of miR-129-5p expression on the invasion of A375 cells (Figure 2b). The results of the invasion assay showed that overexpressed miR-129-5p inhibited A375 cell invasion by 70.29%, which showed obvious inhibition of melanoma cell invasion. The effect of miR-129-5p on the proliferation capacity of tumor cells was also examined with the colony formation assay. When plated at a density of 200 cells/well, miR-129-5p mimic-transfected cells generated a lower rate of colony formation (15.83%) than control cells (45.67%) (Figure 2c). Overexpression of miR-129-5p significantly inhibited cell migration, cell invasion and colony formation in melanoma.
Previous research has determined that lncRNA-AC130710 directly targets and suppresses miR-129-5p in gastric cancer. Here, we wanted to verify whether there is a relationship between lncRNA-AC130710 and miR-129-5p in melanoma. First, we determined the level of lncRNA-AC130710 in A375 cells and the human primary melanocyte cell line. We found that the level of lncRNA-AC130710 was significantly increased in A375 cells compared with the human primary melanocyte cell line (Figure 3a). We used miRanda and RNAhybrid to predict the putative complementary binding sites of lncRNA-AC130710 and miR-129-5p (Figure 3b). We further determined whether lncRNA-AC130710 targets miR-129-5p using a luciferase reporter assay. As shown in Figure 3c, overexpression of miR-129-5p decreased the luciferase activity of the wild-type lncRNA-AC130710 3′-UTR reporter in 293T cells. However, miR-129-5p mimics did not influence the luciferase activity of the reporter carrying the mutated lncRNA-AC130710 3′-UTR. We also constructed lncRNA-AC130710 overexpression plasmids (pcDNA3.1-lncRNA-AC130710) and interference plasmids (lncRNA-AC130710 siRNA) (Figure 3d). The results showed that transfection of pcDNA3.1-lncRNA-AC130710 significantly reduced the level of miR-129-5p; in contrast, transfection of lncRNA-AC130710 siRNA obviously increased the level of miR-129-5p in A375 cells (Figure 3e). We also investigated whether miR-129-5p affected the levels of lncRNA-AC130710. We found that transfection of the miR-129-5p mimic significantly decreased the level of lncRNA-AC130710, indicating that miR-129-5p affected the levels of lncRNA-AC130710 (Figure 3f). Moreover, modulation of lncRNA-AC130710 expression affected the level of mGluR1 mRNA. As shown in Figure 3g, transfection of pcDNA3.1-lncRNA-AC130710 significantly increased the expression of mGluR1, and lncRNA-AC130710 siRNA obviously decreased the expression of mGluR1 in A375 cells. Subsequently, we investigated the effect of the interaction between lncRNA-AC130710 and miR-129-5p on mGluR1 expression. When cells were transfected with miR-129-5p RNAi, the expression of mGluR1 was increased. Conversely, when cells were transfected with lncRNA-AC130710 RNAi, the expression of mGluR1 was decreased. However, when the levels of lncRNA-AC130710 and miR-129-5p were decreased or both increased, the effect on the expression level of mGluR1 was reversed, i.e., the expression level was almost the same as that in the control group. This indicated that the regulation between lncRNA-AC130710 and mGluR1 might be dependent on miR-129-5p (Figure 3h).
We examined the effect of lncRNA-AC130710 on cell migration, cell invasion and colony formation in A375 cells (Figure 4). The results of the scratch assay showed that the relative migration distance was 26.29% at 24 h and 48.78% at 48 h in cells transfected with pcDNA3.1-lncRNA-AC130710; however, the relative migration distance was 4.75% at 24 h and 12.66% at 48 h in cells transfected with lncRNA-AC130710 siRNA. The relative migration distance was 8.89% at 24 h and 28.86% at 48 h in control cells (Figure 4a). Overexpression of lncRNA-AC130710 significantly promoted A375 cell migration by 66.15% at 24 h and 44.93% at 48 h compared with the control. Inhibition of lncRNA-AC130710 significantly inhibited A375 cell invasion by 46.64% at 24 h and 52.88% at 48 h compared with the control. We also examined the effect of lncRNA-AC130710 expression on the invasion of A375 cells (Figure 4b). The results of the invasion assay show that overexpressed lncRNA-AC130710 promotes A375 cell invasion by 80.0%; however, suppressed lncRNA-AC130710 expression inhibits cell invasion by 59.61%. The effect of lncRNA-AC130710 on the proliferation capacity of tumor cells was also examined with the colony formation assay. pcDNA3.1-lncRNA-AC130710-transfected cells generated a higher rate of colony formation (77.17%) than control cells (50.17%); however, lncRNA-AC130710 siRNA-transfected cells generated a lower rate of colony formation (29.17%) than control cells (Figure 4c). Overexpression of lncRNA-AC130710 significantly promoted cell invasion, cell migration and colony formation in melanoma, while decreasing lncRNA-AC130710 expression obviously suppressed cell invasion, cell migration and colony formation. Furthermore, we wanted to investigate whether the effects of lncRNA-AC130710 expression on cell invasion, cell migration and colony formation depend on mGluR1 levels. We constructed mGluR1 overexpression plasmids (pcDNA3.1-mGluR1) and transfected pcDNA3.1-mGluR1 and lncRNA-AC130710 RNAi into A375 cells. We found that even when mGluR1 was overexpressed, cell invasion, cell migration and colony formation in melanoma were also repressed when lncRNA-AC130710 expression was suppressed in cells (Figure 5a–c).
We tested the activation of the PKCα-MAPK pathway to explore the potential underlying mechanisms of the lncRNA-AC130710/miR-129-5p/mGluR1 axis in A375 cells. The results showed that the expression of PKCα and p-MAPK was decreased in miR-129-5p mimic-transfected cells compared with NC mimic-transfected cells (Figure 6a). Moreover, transfection with pcDNA3.1-lncRNA-AC130710 increased the expression of PKCα and p-MAPK in A375 cells, and transfection with lncRNA-AC130710 siRNA decreased the levels of PKCα and p-MAPK (Figure 6b). The results indicated that overexpression of miR-129-5p inactivated the PKCα-MAPK pathway. Expression of lncRNA-AC130710 suppressed the regulatory role of miR-129-5p, thereby inactivating the PKCα-MAPK pathway and further promoting cell invasion, cell migration and colony formation in melanoma.
In our study, we found that miR-129-5p suppressed invasion and migration of melanoma cancer cells through targeting mGluR1 transcripts. By suppressing miR-129-5p, lncRNA-AC130710 was capable of promoting metastasis, which was associated with the activation of PKCa-MAPK signal pathway. In recent years, the glutamate signaling pathway has been reported to be related to tumorigenesis [17]. Glutamate receptors constitute two main groups. One group is comprised of ionotropic receptors, which form ion channels, and the opening and closing of these channels are regulated by glutamate. Methyl-d-aspartate receptor (NMDAR) is a subtype with strong voltage dependence and high Ca2+ permeability. The other type is mGluRs, which belongs to the superfamily of G-protein coupled receptors [18]. mGluR1 is a metabotropic glutamate receptor. Lee et al. demonstrated that the expression of mGluR1 in mouse melanocytes was sufficient to induce melanoma [19]. Blocking the activity of GluR1 with an antagonist has been shown to significantly inhibit the invasion and motility of melanoma cells [20]. Previous studies and our results have shown that the expression of mGluR1 is obviously upregulated in melanoma cells [20]. Inhibition of mGluR1 expression can suppress the development of melanoma, including proliferation, cell invasion and cell migration. Furthermore, we found that miR-129-5p directly targeted and regulated mGluR1. Our results show that miR-129-5p was significantly reduced in melanoma. In addition, the dual luciferase assay verified that miR-129-5p targets mGluR1 and inhibits the expression of mGluR1. Overexpression of miR-129-5p inhibited tumor cell characteristics such as cell migration, cell invasion and clonal formation. Our results demonstrated that inhibiting melanoma can be achieved by regulating miR-129-5p. LncRNAs function as miRNA sponges to suppress miRNA targeting of mRNAs and the degradation mediated by miRNAs [21,22]. For example, Chen et al. determined that lncRNA FOXD3-AS1 promotes proliferation, invasion and migration of cutaneous malignant melanoma by regulating the miR-325/MAP3K2 axis [23]. Wu et al. indicated that lncRNA MEG3 might inhibit the tumor growth, tumor metastasis and formation of melanoma by modulating the miR-21/E-cadherin axis [24]. Although many studies have described multiple lncRNAs related to melanoma, the involvement of lncRNAs in melanoma tumorigenesis and progression has not been fully studied [25]. A previous study showed that lncRNA-AC130710 directly targets and suppresses miR-129-5p in gastric cancer [16]. LncRNAs target miRNAs through their own miRNA reaction elements at binding sites, further suppressing miRNA targeting of mRNAs and the degradation mediated by miRNAs [26]. In our study, we found that lncRNA-AC130710 has the complementary binding sites miR129-5p, and the dual luciferase experiment also confirmed their relationship. Furthermore, the expression of lnRNA-AC130710 was determined to be significantly increased in melanoma cells. Overexpression of lncRNA-AC130710 reduced the level of miR-129-5p, while downregulation of lncRNA-AC130710 increased the level of miR-129-5p. This result indicated that there is indeed an interactive relationship between them. We also found that overexpression of miR-129-5p significantly decreased the level of lncRNA-AC130710, indicating that miR-129-5p also affected the levels of lncRNA-AC130710. Furthermore, overexpression of lncRNA-AC130710 upregulated mGluR1, while downregulation of lncRNA-AC130710 induced a reduction in mGluR1 expression, demonstrating that the expression of lncRNA-AC130710 suppressed miR-129-5p, a tentative negative regulator of mGluR1 transcripts, and promoted mGluR1 expression increases in melanoma. The regulation between lncRNA-AC130710 and mGluR1 might be dependent on miR-129-5p. We also demonstrated the effects of modulation of lncRNA-AC130710 expression on cell invasion, cell migration and colony formation in melanoma. We found that overexpression of lncRNA-AC130710 significantly promotes cell invasion, cell migration and colony formation in melanoma, while decreasing lncRNA-AC130710 expression obviously suppresses cell invasion, cell migration and colony formation. Expression of lncRNA-AC130710 suppressed miR-129-5p, a tentative negative regulator of mGluR1 transcripts, which was sufficient to inhibit cell invasion, migration and colony formation in melanoma. Even after overexpression of mGluR1, cell invasion, cell migration and colony formation in melanoma were also repressed when lncRNA-AC130710 expression was suppressed in cells. These results further demonstrate that miR-129-5p suppressed the migration and invasion of A375 cells by decreasing the expression of mGluR1. However, lncRNA-AC130710 promoted the level of mGluR1 mRNA and the migration and invasion of cells by negatively regulating miR-129-5p. Activation of NMDAR and mGluR leads to an increase in the activities of PKCα [27]. Inhibiting the expression of mGluR induced the decreased expression or activity of PKCα. The MAPK cascade is considered to be one of the main signaling pathways activated by PKCα [28]. Activation of the MAPK cascade plays a crucial role in many signaling pathways related to cell proliferation. Inhibition of the MAPK cascade can suppress the cell migration and invasion of tumor cells. Our results showed that the expression of lncRNA-AC130710 promoted PKCα activity and MAPK phosphorylation by suppressing miR-129-5p in melanoma, while the downregulation of lncRNA-AC130710 increased miR-129-5p expression and reduced PKCα expression and MAPK phosphorylation. Our study demonstrated that lncRNA-AC130710 promotes cell migration and invasion by suppressing miR-129-5p, which is associated with the activation of the PKCα-MAPK signaling pathway.
Taken together, our study showed that the lncRNA-AC130710/miR-129-5p/mGluR1 axis plays an important role in the invasion and metastasis of melanoma cell lines. | true | true | true |
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PMC9580090 | 36254536 | Fatima Akram,Ikram ul Haq,Sania Sahreen,Narmeen Nasir,Waqas Naseem,Memoona Imitaz,Amna Aqeel | CRISPR/Cas9: A revolutionary genome editing tool for human cancers treatment | 18-10-2022 | CRISPR/Cas9,cancer,diagnosis,gene editing,immunotherapy | Cancer is a genetic disease stemming from genetic and epigenetic mutations and is the second most common cause of death across the globe. Clustered regularly interspaced short palindromic repeats (CRISPR) is an emerging gene-editing tool, acting as a defense system in bacteria and archaea. CRISPR/Cas9 technology holds immense potential in cancer diagnosis and treatment and has been utilized to develop cancer disease models such as medulloblastoma and glioblastoma mice models. In diagnostics, CRISPR can be used to quickly and efficiently detect genes involved in various cancer development, proliferation, metastasis, and drug resistance. CRISPR/Cas9 mediated cancer immunotherapy is a well-known treatment option after surgery, chemotherapy, and radiation therapy. It has marked a turning point in cancer treatment. However, despite its advantages and tremendous potential, there are many challenges such as off-target effects, editing efficiency of CRISPR/Cas9, efficient delivery of CRISPR/Cas9 components into the target cells and tissues, and low efficiency of HDR, which are some of the main issues and need further research and development for completely clinical application of this novel gene editing tool. Here, we present a CRISPR/Cas9 mediated cancer treatment method, its role and applications in various cancer treatments, its challenges, and possible solution to counter these challenges. | CRISPR/Cas9: A revolutionary genome editing tool for human cancers treatment
Cancer is a genetic disease stemming from genetic and epigenetic mutations and is the second most common cause of death across the globe. Clustered regularly interspaced short palindromic repeats (CRISPR) is an emerging gene-editing tool, acting as a defense system in bacteria and archaea. CRISPR/Cas9 technology holds immense potential in cancer diagnosis and treatment and has been utilized to develop cancer disease models such as medulloblastoma and glioblastoma mice models. In diagnostics, CRISPR can be used to quickly and efficiently detect genes involved in various cancer development, proliferation, metastasis, and drug resistance. CRISPR/Cas9 mediated cancer immunotherapy is a well-known treatment option after surgery, chemotherapy, and radiation therapy. It has marked a turning point in cancer treatment. However, despite its advantages and tremendous potential, there are many challenges such as off-target effects, editing efficiency of CRISPR/Cas9, efficient delivery of CRISPR/Cas9 components into the target cells and tissues, and low efficiency of HDR, which are some of the main issues and need further research and development for completely clinical application of this novel gene editing tool. Here, we present a CRISPR/Cas9 mediated cancer treatment method, its role and applications in various cancer treatments, its challenges, and possible solution to counter these challenges.
Cancer, a complex disease, is the second largest cause of death across the globe. Primarily, cancer is a genome ailment, feeding off mutations in DNA that consequently result in activating oncogenes and inactivating tumor suppressors, along with dysregulating the epigenome, which regulates the normal expression of genes. Additionally, cancer can also be defined as the disease of a cell resulting from the changes in the structure of the cell, metabolism, and motility to permit growth in bleak and unreceptive conditions. It eventually becomes a sickness for the organism, absorbing normal cell types, tissue functions and outmaneuvering the host’s defense mechanisms. With the development of high throughput sequencing technologies, a myriad of genes coupled with the initiation and progression of cancer have been identified during the past 2 decades. Despite the thrilling accomplishments in cancer therapeutics which include surgery, targeted biotherapy, chemotherapy, and radiotherapy, increased rates of postoperative relapse, resistance to chemotherapy/radiation along with detrimental off-target effects continue to be a hurdle in life span and standard life quality of a cancer patient. For the development of effective and efficient treatment options aiming to enhance the denouement for millions of people diagnosed with cancer annually, it is critical to understand how genetic alterations, cellular adaptations, and modifications in the microenvironment of tumor influence the initiation, development, and treatment response of certain malignancies. In recent years, noteworthy advances in biotechnology have been made, and the field of genetic engineering is now progressing at an exponential rate, offering countless benefits. Genome editing tools have transformed biological and genetic research and exploration through their groundbreaking ability to squarely amend, alter and reconfigure the genomes of living organisms. In current times, diverse genome manipulating techniques have been utilized to study simple, intricate, and raveled genomes. Since the introduction of genome editing technologies in the 1990s, various strategies for targeted genome editing have been devised. One important use of gene-editing technology is genome modification, which comprises gene inactivation, insertion of a new sequence, and/or rectification of mutant regions with proper nucleotide sequences. To modify genes, many approaches are used, including zinc-finger endonucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and the clustered regularly interspaced short palindromic repeats (CRISPR) associated nuclease system. CRISPR, a dominant and powerful gene-editing technique, emerged in 1986 and quickly rose to prominence as the century's most important genetic tool outweighing the previous methods owing to its outstanding advantages such as simplicity, cost-effectiveness, speed, and high efficiency. The CRISPR/Cas system was initially found in Escherichia coli, but it is now known to be prevalent across a vast population of prokaryotic species. In bacteria and archaea, it is a sequence-specific adaptive immune system that provides resistance to viruses, phages, and other genetic materials. Apart from its role in antiviral immunity, CRISPR serves as a barricade against horizontal gene transfer mechanisms and therefore plays an imperative role in sustaining the integrity of the genome. The CRISPR/Cas9 framework is a hereditary defense system in bacteria and archaea that utilizes RNA-directed nucleases to cleave targeted DNA. CRISPR locus is an assembly of short direct repeats spaced by spacer sequences. The repeats within a particular locus are identical in sequence and length. The spacers are likewise uniform in length, although their sequence composition varies greatly. Repeats in various species range from 21 to 47 bp, with an average of 32 bp, while spacers range from 20 to 72 bp. Although related species may share identical repeat sequences, the overall sequence diversity of both spacers and repeats in archaea and bacteria is quite high. This system is consisting of DNA endonuclease (Cas9) and single guide RNA (sgRNA). The Cas9/sgRNA complex binds specifically to target dsDNA which has a complementary sequence match to 5′ end of sgRNA (match of first 17 to 20 nt of sgRNA) and also has a PAM area (protospacer adjacent motif). The sgRNA guides the Cas9 endonuclease to break the DNA strands at the desired target site. Cas9, DNA nuclease with two active domains, cleave the target DNA strands at 3 nt upstream of PAM and produce a double-stranded break (DSB). DSBs are repaired through a host natural repair mechanism, i.e., NHEJ method (nonhomologous end joining) or HDR method (homologous direct repair pathway). CRISPR/Cas9 mediated cleavage is not always specific and sometimes may result in unwanted binding of Cas9 to target DNA producing nonspecific cleavage, and giving-off target effects. CRISPR/Cas9 target efficiency and subsequent cleavage specificity depend on gRNA and PAM location to the target loci. Off targets effects and strategies to avoid them have been discussed in CRISPR/Cas9 challenges section. To minimize the off-target effects, tools to design desirable sgRNA have been provided in Table 1. CRISPR/Cas9 technology offers a unique and striking future in laboratory cancer research, with the possibility to generate superior disease models for cancer, therapeutic targets, and the identification and exploration of treatment resistance. It is an affordable technology offering a high level of flexibility, potency, and ease to use in laboratory research. CRISPR/Cas9 has effectively been exploited to develop cancer disease models, by inactivating multiple tumor suppressor genes such as Nf1, P53, Ptch1, and Pten in mouse brain which has resulted in the creation of medulloblastoma and glioblastoma disease models. General overview of cancer modeling with CRISPR/Cas9 technology is given in Figure 1.
CAR-T cells are T lymphocytes (human immune cells) that have been genetically modified to express a chimeric antigen receptor (CAR). Chimeric antigen receptor, commonly known as CAR, is a synthetic receptor that can recognize specific antigens. It consists of an antibody-driven target-binding extracellular domain, a hinge region, a trans-membrane domain, and an intracellular signaling moiety that can activate T-cells. A CAR-targeted antigen can be recognized by the CAR-bearing altered T-cells, this consequently provokes the proliferation of T-cells, manufacturing of cytokine, and critical and targeted cytotoxicity versus tumor cells. The performance of CARs is determined by several factors. A few of these include the position of the target cell surface antigen as well as careful adjustment of intracellular and extracellular domains, including the nonfunctional linking transmembrane domain and hinges. CAR-T-cell-based clinical trials for cancer treatment have shown promise in eliciting long-term full remissions in patients with a range of hematologic and solid malignancies, including relapsed/refractory acute lymphoblastic leukemia (ALL) and multiple myeloma, with response rates ranging from 80% to 100%. CAR-T cell therapy for hematological malignancies, particularly B-cell malignancies, has a high success rate. However, their application in treating solid tumors has not been successful yet. One of the barriers to its extensive use is CAR-T cell dysfunction, counting both the senescence and exhaustion of the cells limiting their effectiveness. Other barriers are the higher prevalence of toxicities, such as cytokine release syndrome, the possibility of graft versus host disease. Currently, only five CAR-T products have been approved, for hematologic malignancies, by the United States Food and Drug Administration (FDA) which are (i) Abecma (idecabtagene vicleucel) for patients with relapsed or refractory (R/R) myeloma, (ii) Breyanzi (lisocabtagene maraleucel) for R/R large B-cell lymphoma patients, (iii) Tecartus (brexucabtagene autoleucel) for R/R mantle-cell lymphoma patients, (iv) Kymriah (tisagenlecleucel) for patients especially peds and young adults suffering with CD19 + R/R B-cell, (v) Yescarta (axicabtagene ciloleucel) specific for refractory large B-cell lymphoma patients. A big restrictive factor in CAR-T cell therapy for cancer treatment is that the patients must wait for long periods before the treatment can be applied since every step of harvesting, engineering, expanding, and transfusing cells is tailored to each patient and takes time. Moreover, this procedure requires logistical and scientific support. CRISPR/Cas9-mediated genetic manipulation has the potential to increase the efficacy of immunotherapy by generating a universal “off-the-shelf” cellular product or modifying immune cells for overcoming resistance to hematological or solid malignancies. The idea of universal allogeneic CAR-T products eliminates the requirement of harvesting, modifying, expanding, and transfusing T-cells from each patient, conserving resources and time. By altering allogeneic (healthy donor) T-cells to express the CAR by knocking-in and then knocking-out the genes involved in immune recognition of these nonself cells, the cells can be effectively transferred into patients without the danger of immunological rejection. It may also be used to precisely alter some cytokine genes for boosting the production of cytokine and immune cell reaction, preventing T-cell depletion and autoimmune responses correlated with other techniques of cytokine increase like viral transduction. To ensure long-lasting engagement of T-cells, the CRISPR/Cas9 system can be employed for knocking out checkpoint inhibitor genes, for instance, the genes that code for CTLA-4 and PD-1. The CRISPR-manipulated cells have the ability to be mass-produced, lyophilized, and kept at medical centers across the country, alleviating the need of transporting living cells for every patient from the manufacturing sites. The precise insertion of CAR into the exact position in the genome of the T-cells to ensure its sufficient expression can be facilitated by CRISPR.
In this therapy, the T-cells of patients are modified to express specific TCRs that can recognize antigens released by tumor cells and trigger T-cells to kill the tumor cells. Tumor-infiltrating lymphocyte (TIL) treatment and chimeric antigen receptor T-cell (CAR-T-cell) therapy are the two forms of adoptive cell therapy. ACT for cancer is a prime focus for the gene-editing trials. During adoptive T-cell therapy, a biopsy is performed, and tumor cell are subjected to whole-exome sequencing and RNA sequencing. Sequencing results along with bioinformatic prediction algorithms help in identifying immunogenic neoantigens. T-cells specific for these immunogenic neoantigens are isolated from the patient (either from peripheral blood mononuclear cells [PBMCs] or tumor-infiltrating lymphocytes (TILs) of a patient). Later, T-cell receptors (TCRs) are sequenced and electroporated into the same patient's PBMCs along with Cas9 and gRNA. This results in the production of neoantigen-specific T-cells which can be expanded and introduced back into the same patient for cancer therapy. In 2016, researchers from the Chinese PLA General Hospital started the first CRISPR phase I clinical research in humans by infusing PD-1-KO essential T-cells into patients with stage IV metastatic nonsmall cell cellular breakdown in the lungs (NCT02793856). The clinical productivity of the treatment was not resolved thus the viability of this study is questionable. Three further stage I clinical investigations are being led to assess PD-1-KO essential T-cells in stage IV bladder disease (NCT02863913), hormone-resistant prostate malignant growth (NCT02867345), and metastatic renal cell carcinoma (NCT02867332), starting around the same time. Be that as it may, later every one of these was removed due to lack of funding, which resulted in the studies being withdrawn. Although TILs have shown viability in metastatic diseases, especially melanoma, and have shown similarly more particularity and effectiveness than essential altered T-cells, hardly any preliminaries are following hereditarily adjusted TILs. Two clinical preliminaries are, as of now, in progress utilizing CRISPR/Cas9 to disturb CISH in TILs taken from gastrointestinal growth areas (NCT04089891 and NCT04426669). The silencer of cytokine signaling (SOCS) protein, cytokine-induced SH2 (CISH), is a part of the SOCS family, CISH protein is expanded in CD8 + T-cells in light of TCR initiation, subsequently hindering T-cells hostile to growth action. In-vivo examinations have shown how CISH cancellation causes TIL development, capacity, and cytokine discharge, as well as growth backslide. Currently, mispairing of α and β chains of the therapeutic TCR complex is a main problem with ACT. This results in lessened effectuality for engaging TCR with the target antigen. Moreover, PD-1 expression on T-cells has a negative regulatory effect, which lowers the antigen reaction and, thus, the effectiveness of T-cell-mediated tumor killing.
Cancer may be detected and treated earlier, which reduces the risk of mortality and improves patients’ quality of life following the treatment. Many cancer detection techniques are extensively employed, however, they ought to be improved in terms of sensitivity, specificity, and speed. As a result, for cancer prevention identifying vulnerable genes by genetic diagnosis is critical. CRISPR has remarkable potential to be used in diagnostics where it can detect genes involved in various cancer developments, their proliferation, and metastasis or in drug resistance. Detection of cancer-specific sequence changes can be done with the help of CRISPR-mediated enzymatic digestion which works as a diagnostic tool. CRISPR can detect microsatellites (a diagnostic marker in various cancers), which are made of short tandem repeats (STRs) through detecting these STRs. There are four Cas systems, i.e., Cas9, Cas12, Cas13, and Cas14, which are currently being used in CRISPR-based molecular diagnostics. These four CAS systems use different techniques for diagnosis such as Cas9 uses CRISPR-Chip and CRISDA, Cas12 uses DETECTR, HOLMES, and SHERLOCKv2, Cas13 uses SHERLOCK, and SHERLOCKv2 while Cas14 uses Cas14 DETECTR. In the processes of DNA recognition, shearing, and degradation, Cas12a works as a corresponding protein of Cas13a. Based on Cas12a, RPA was introduced as a form of new nucleic acid detection technique named DETECTR which compared with SHERLOCK can eliminate transcription of amplified DNA products into RNA step. Researchers have also used Cas13a system as a basis and introduced RPA (temperature amplification of DNA technology) to create SHERLOCK (specific high sensitivity enzymatic reporter UnLOCKing). It has a site-specific amplification principle such as in the case of DNA; it will be followed by amplification through RPA and in the case of RNA detection; it will be amplified by RT-RPA. Target RNA transcribed from amplified DNA products is detected by Cas13a-crRNA and reporter RNA. This technique has its advantage as it is highly specific, sensitive in nature, and has many uses in CRISPR-based diagnostics. Its high sensitivity allows it to do single-base resolution based on its crRNA design. It has its applications in the detection of specific nucleic acid molecules, pathogen identification, detection of drug-resistant genes, liquid biopsy, virus and its subtype detection and differentiation, and cancer mutation analysis. Similar to this, Cas12-DETECTR was designed due to four characteristics of Cas14: (1) small size of Cas14 makes its large-scale production easy; (2) target DNA is ssDNA; (3) it has no PAM sequence and thus any site sequence in the desired DNA can be targeted; (4) can detect DNA through a single base resolution. It has its applications in fast and efficient cancer and another disease diagnosis. SNP genotype analysis method was also made possible to establish due to the properties of Cas14. Other CRISPR-based diagnostic techniques for cancer detection include improved SHERLOCK, which is used in fast liquid biopsies of cancer patient. It can detect very small amounts of viral RNA ≈2 aM. HOLMES is another CRISPR-based diagnostic tool based on the Cas12a system and PCR. It is fast, highly specific, simple, low-cost, sensitive, and needs no professional equipment for its working. It has its applications in the detection of cancer mutations, SNPs, and pathogens. CRISPR-Chip is another diagnostic technique that has a big advantage over SHERLOCK and DETECTR and that is it need no amplification of tested DNA. Cas12 and Cas13 detect nucleic acids through SHERLOCK (specific high-sensitivity enzymatic reporter unlocking) and DETECTR (DNA endonuclease-targeted CRISPR trans reporter) for an easy, affordable diagnosis of SARS-CoV-2 infection. Currently, CRISPR-DS is being clinically evaluated for p53 mutation detection in ovarian tumors. Thus, it can be freely said that CRISPR has its advantages in diagnosis as a personalized, sensitive, and safe monitoring system for cancer patients. MicroRNAs (miRNAs) have a promising role in cancer pathologies by repression of protein-coding oncogenes or by impeding the expression of tumor suppressors. It can aid in the detection of and therapy for cancer. miRNAs are composed of a set of short, noncoding proteins, and small RNA molecules (20–24 nt in length). These RNA molecules are made from an endogenous transcript which contains a hairpin local structure, with the help of RNase-III-type enzyme Dicer. CRISPR/Cas9 technique targets the terminal loop or 50 regions of pre-RNA and thus suppressed the expression of miRNA. CRISPR/Cas9 targets desired DNA/ hsa-miR-17 gene with the help of sgRNA which binds with the target DNA at upstream of PAM (5′-NGG-3′). Cas9 nuclease introduces DSB in targeted DNA at Drosha or Dicer processing sites which are 3 bp upstream of PAM. This DSB is repaired by host own repair mechanisms. CRISPR/Cas9 with the help of sgRNA can also alter miRNA biogenesis process through intentional targeting of sequences either within or adjacent to Dicer and Drosha repairing sites in the secondary stem-loop structure of primary miRNAs. Mechanism of CRISPR/Cas9 mediated miRNA has been given in Figure 2. Functional genome screening techniques based on CRISPR system might show variations in the expression of a gene after medication and pinpoint genes linked to drug resistance thus adding novel biomarkers for precision therapy and offering an additional understanding of cancer development. One promising example includes the screening of a gene related to cancer metastasis. Where after mice were infected with lung cancer metastasis, genome-scale CRISPR knockout (mGeCKO) sgRNA library was created, and transduced cells were implanted into immuno-compromised mice. Six weeks later, mice with lung cancer metastasis were chosen for sequencing of the enhanced sgRNA. Finally, several candidate genes interrelated to lung metastasis were identified and verified, these included Pten, miR-152, and miR-345, along with many novel genes such as Nf2, Trim72, and Fga.
There are many types of cancer treatment methods, all have their own pros and cons. The type of treatment method one received mostly depends on cancer type, and its cancer progress stage. More often than not, combinations of treatment methods are suggested. Some of the most common cancer treatment methods are (1) surgery (to remove cancerous body tissue/part); (2) chemotherapy (drugs are prescribed to kill cancer cells); (3) hormone therapy (effective on hormones released during a certain type of cancer such as breast and prostate cancer); (4) Hypothermia (heat up to 113°F is provided to kill or stop cancer cell with little to no damage to normal cells); (5) Radiation therapy where radiations are used to treat cancer, etc. Among all these methods, CRISPR/Cas-mediated genome editing offers vast promise in cancer therapy, to make quick progress in fundamental oncology research. Tumorigenesis is a multistep process in which cancer cells communicate with the host immune system. Cancer immunotherapy is a well-known treatment option after surgery, chemotherapy, and radiation therapy. Adoptive T-cell immunotherapy, specifically chimeric antigen receptor (CAR) T-cell therapy, has marked a turning point in cancer treatment. In the United States in 2019, the first clinical trial for the treatment of cancer using CRISPR was studied at The University of Pennsylvania, funded by the National Cancer Institute. In this type of immunotherapy, the immune cells of patients are genetically engineered to detect and destroy tumors in a better way. The treatment involves the introduction of four genetic changes into the T-cells. These are immune cells capable of battling and killing cancerous cell. First, a claw-like protein is provided to T-cells by the addition of a synthetic gene. This claw-like protein commonly known as a receptor recognizes NY-ESO-1, a chemical found on certain cancer cells. Subsequently, three genes are deleted with the help of the CRISPR system. Among these three, two have the ability to interact with the NY-ESO-1 receptor and one gene is capable to hinder the ability of cells to fight malignancy. It results in the abundant production of NYCE T-cells which are then injected into patients. In the long run, it is one of the precise and safe strategies to kill cancer cells. It also helps in improving the efficacy of T-cells. A diagrammatic representation of the study performed at The University of Pennsylvania for the treatment of cancer using CRISPR Cas9 in the human body is illustrated in Figure 3. According to studies conducted by Edward Stadtmauer, an improved response was shown by NY-ESO-1–directed T-cells, and that too with minimal toxicity. Edward and his colleagues also tried to observe whether the function of T-cells is improved with the deletion of three genes using CRISPR. The trials proved that the treatment so done employing CRISPR is harmless. The treatment was provided to three patients out of which two had severe multiple myeloma and one had metastatic sarcoma. All three patients had malignancies that contained the T-cell therapy's target (NY-ESO-1). This therapy was proven to be safe on the basis of the preliminary data. According to the researchers, some side effects occurred, but they were most likely caused by chemotherapy in patients before injecting them with NYCE cells. Furthermore, there was no evidence of an immune response to the CRISPR-edited cells. List of ongoing CRISPR/Cas9 mediated cancer treatment clinical trials have been provided in Table 2.
Cancer is the second most common cause of death across the world, representing around 8.8 million passing in 2015. Throughout the following 20 years, the quantity of new cases is anticipated to ascend by almost 70% internationally. Current cancer-fighting methods are insufficient, and scientists are continually on the lookout for new technology that can help. CRISPR/Cas9 system offers new promise in this area. CRISPR technology allows humans to rewrite their genetic code. CRISPR induction is easier and faster than previous technologies, and it will likely speed up gene-editing processes all around the world. It’s been utilized in a variety of in-vivo and in-vitro malignant tumor models up to this point. Oncologists are especially excited about CRISPR's viability, precision, and potential in malignant tumor research. CRISPR/Cas9 innovation has been utilized by researchers from one side of the planet to the other to address malignant tumor treatment from many examination perspectives.
Colorectal malignant growth (CRC) is a type of cancer that occurs in colon or rectum. To investigate the function of potential colorectal cancer-associated genes, in-vivo studies using genetically modified mouse models (GEMMs) are performed. In GEMMs, the ApC and Trp53 tumor suppressor genes in colon epithelial cells were base edited by CRISPR/Cas9 and through orthotopic transplantation of ApC-edited colon organoids to produce tumor in mice. The study was successful in activation of an oncogene within the model mice. The study helped in cancer-associated genes characterization and to understand tumor progression and metastasis. This gene editing technique can be utilized in numerous model-based cancer studies to investigate genes responsible for a certain cancer type and the associated changes due to its manifestation in the human body.
Bosom disease (BC) is another tumor that exterminates women all around the globe. TNBC has the most exceedingly terrible forecast of all the BC subtypes on the grounds that it needs articulation of the estrogen receptor, progesterone receptor85, and HER2/neu tyrosine kinase receptor. The CRISPR/Cas9 technology was utilized to restrict cancer advancement and lung metastasis by reducing Cripto-1, an early-stage undeveloped cell marker of cancer stem-like cells. In this review, Cripto-1 was found to be a potential therapeutic target for TNBC. The Brahma (BRM) and Brahma-related gene 1 (BRG1) are both overexpressed in essential BCs. BRM and BRG1 work as ATPases inside mammalian SWI/SNF complexes and are important for their functioning. The CRISPR/Cas9 gene editing machinery was used to take out the BRG1 or BRM genes, revealing that these two genes play an important role in BC cell multiplication. Therefore, both BRG1 and BRM could be utilized to treat BC. One more type of human BC is intrusive lobular carcinoma (ILC). The deficiency of cell grip protein and methylation of the CDH1 gene advertiser are normal elements of this disease. This recently made innovation can be utilized to examine potential cancer silencer genes embroiled in ILC in-vivo rapidly. Comparable frameworks could be utilized to make new in-vivo models for the ID and treatment of different BC subtypes. Table 3 shows targeted genes and their functions in various cancer types.
Epithelial-to-mesenchymal translation (EMT) is an event during cancer metastasis. During this cycle, epithelial cells lose their affiliations, and gene expression is changed. This shift is accomplished by different master regulators, including record factors like Snail1, TWIST, and zinc-finger E-box confining (ZEB). The Snail1 factor was taken out using CRISPR/Cas9 advancement, revealing that the actin cytoskeleton is changed when Snail1 is deleted. This approach was also used to analyze the components of Snail1 and to decrease it in human ovarian illness RMG-1 cells. The CRISPR/Cas9 innovation can be utilized to study the genetic reason for chemoresistance in epithelial ovarian disease. Chemoresistance was reversed when the ovarian malignant growth biomarker HE4 was altered. In-vitro, taking out LY75 diminished cancer cell rearrangement and obtrusiveness, and in-vivo, taking out LY75 brought down the metastatic capability of EOC cell lines. The ovarian carcinoma immuno-reactive antigen area containing 1 (OCIAD1) gene was additionally taken out using CRISPR/Cas9 in a BJNhem20-OCIAD1-CRISPR-39 line. This ex-vivo study, as well as other CRISPR/Cas9-based research, prepares for future ovarian disease treatment. More generally, CRISPR/Cas9 has been broadly utilized in disease research, for certain promising discoveries.
Cellular breakdown in the lungs is the most terrible cancer among different tumors occurring in both industrialized and underdeveloped countries, including China, United States, and countries in Europe. Cellular breakdown in the lungs is brought about by various genes and signaling pathways. It has been a subject of broad clinical exploration and dynamic gene modifications therapy is considered as disease quality treatment. It has been a subject of broad clinical exploration. Compared with other gene editing technologies like ZFNs and transcription activator-like effector nucleases (TALENs), CRISPR/Cas9 framework is a much more desirable method with its high target specificity, ease of use, and thus is being used more recently to study lung cancer, its prognosis, and treatment. CRISPR-based approach can be used to investigate lung cancer in individual patients, obstacles in anti-cancer medication efficacy, and/or to treat genetic causes of cancer before it progresses any further. The applications of CRISPR/Cas9 in studies on the treatment of lung cancer is given in Figure 4.
In children and adolescents, osteosarcoma (OS) is a highly vascular and particularly destructive tumor. Distant metastases (25%–30%) and pathologic fracture due to bone loss are the most common consequences. The most common metastatic location of OS is the lung. Surgery in combination with chemotherapy is the standard treatment for OS.117–121, Patients with OS who have metastasis, on the other hand, have a much worse prognosis As a result, developing novel therapeutic techniques for these people is critical. The angiogenic agent VEGFA (vascular endothelial growth factor A) is an inducer of angiogenesis and lymphangiogenesis. Studies reveal that VEGF's effect on cancer development is not restricted to angiogenesis. In light of hypoxia, VEGFA is delivered to the cancer microenvironment. From one perspective, malignant growth cells’ paracrine VEGFA advances angiogenesis through endothelial cells, which convey oxygen and supplements. VEGFA, then again, goes about as an autocrine growth cell endurance factor. VEGFA is presently known to be profoundly transformed in OS cells and to be firmly related to an unfortunate disease in patients. VEGFA suppression repressed OS development, metastasis, and 72 angiogenesis, inferring that VEGFA is a possible therapeutic option for OS.
Prostate cancer (PCa) is treated primarily by inhibiting androgen receptor (AR) signaling, although the disease eventually advances to castration-resistant prostate cancer (CRPC). Patients with CRPC have seen a significant improvement in survival thanks to next-generation AR signaling inhibitors, but resistance is still an issue. Regardless of the way, genomic studies have recognized hereditary causes (ETS combinations, CDKN2A misfortune, PTEN, RB1, and SPOP changes, among others) and subatomic subtypes of PCa for designated treatment, some of the inherited anomalies found in patients’ of prostate cancer have shown promise as suitable drug targets. Thus, getting a more profound understanding of PCa's crucial conditions might prompt more sensible restorative methodologies. In contrast to shRNA-or siRNA-based hereditary reliance testing, CRISPR/Cas9 innovation limits adverse consequences, is efficient, and recognizes more positive benefits. CRISPR/Cas9 framework has been effective in recognizing genes important for malignant tumor cell endurance as forthcoming targets, yet benefits of combining it with existing therapeutics have gotten less consideration. A summary of the genes targeted for the treatment of different cells or/and tissues in various human cancers is provided in Table 4.
CRISPR/Cas9 has in no time evolved as a productive genome editing technology in only a couple of years. It has enormous potential in cancer therapy; however, there are still many challenges in its full clinical applications.
The first challenge in CRISPR-based cancer therapy is off-target effects. In therapeutics, every small and low off-target editing is detrimental thus they should be controlled and identified accurately. Off-target DSB can cause small- and large-scale indels, inversions, and translocations at the nontarget sites. Through whole genome sequencing, large-scale off the target mutations can be detected. However, it is very difficult both technically and economically to do the same for small-scale off target mutations as whole genome sequencing is unable to distinguish between SNP and small genetic alterations. A better, practical and cost-effective option for detection of small scale indels is through whole exome sequencing which focuses on off-target mutations occurring in coding region of genes. Various strategies have been developed to avoid or minimize off-target mutations such as (1) identification of a unique target sequence with very few homologous sequences in then whole genome; (2) high GC content up to 75% in target sequence; (3) use of more potent Cas9 variants to avoid off-target effects; (4) designing of more suitable, and powerful sgRNA, i.e., truncated, 17 to 18 nt long, stem loop 2 of sgRNA, etc. A combination of these approaches can be used to completely avoid off-target effects.103,106,
Editing efficiency is another challenge in CRISPR Cas9 therapy in cancer patients. Unmodified cells tend to proliferate more quickly than modified cells and can lead to relapse, thus CRISPR-based therapies need very high editing efficiencies. Editing efficiency is based on (1) careful selection of target sites; (2) efficient and reliable delivery vectors; (3) potent Cas9 selection; (iv) careful selection and designing of sgRNA. Further research insights are needed to get a higher genome editing efficiency.
Other than off-target effects and editing efficiency, another challenge in CRISPR-based cancer treatment is the efficient delivery of CRISPR/Cas components at target sites. The most widely used vehicles for delivery are viral vectors and in particular adeno-associated virus vectors (AAVs). AAV is the most preferable choice because of its transient expression, impressive efficiency, and low cytotoxicity. AAV has a packaging obstacle that has just been solved with the help of Staphylococcus aureus (SaCas9) based split-Cas9 system or Streptococcus thermophiles (St1Cas9) based smaller Cas9 orthologs. There are some other nonviral delivery methods aimed to enhance cell membrane permeability and target specificity of CRISPR/Cas9, i.e., cationic lipids, electroporation-mediated gene transfer, nanoparticles, and cell-penetrating peptides (CPPs). In a study on adult mice, a combination of AAV encoding sgRNA, homologous template, and lipid nanoparticle-mediated delivery of Cas9 mRNA was successfully applied to activate gene repair in adult mice. This suggests that a careful combination of viral and nonviral delivery methods may be an alternative approach for cancer treatment in patients.
In CRISPR-based cancer therapies, the low efficiency of HDR is another issue on which further work is needed. Both NHEJ and HDR repair DSB however, NHEJ is more preferable than HDR. In some cases, HDR-based DSB repair is needed where low efficiency of HDR becomes a problem. One way to increase HDR efficiency is by inhibition of NHEJ pathway. Recently, in a study paired Cas9 nickase has been found to increase HDR efficiency by producing single-strand nicks.
CRISPR/Cas9 is a novel and effective way of genome editing that has lately outperformed other approaches due to its significant characteristics. This multifaceted tool, which is often described as an umbrella terminology, has transformed life sciences by enabling advancements in basic research for a wide range of applications. The CRISPR/Cas9 genome altering apparatus has colossal research and clinical possibilities in disease treatment. This straightforward and scalable approach has the potential to aid in the comprehension of cancer predisposition and metastatic pathways, along with the prediction of therapy response and drug tolerance. CRISPR is expected to be implemented eventually in clinics, permitting an extensive range of treatment opportunities for human diseases, particularly cancer. The ongoing effort in developing and revolutionizing new ways to deliver genome manipulating tools into cells, as well as refining their modification capabilities, will enable these technologies to be utilized in a number of therapeutic applications. The production of chimeric antigen receptor (CAR) T-cells that can identify specific antigens on cancer cells is a prominent application of the CRISPR technique. CRISPR/Cas9 can play a critical role in CAR-T cell research, which has surged in recent years. However, certain concerns including off-target effects, in-vivo delivery, immunogenicity, and ethical considerations must be handled in order for it to be implemented successfully. Nevertheless, many clinical trials are being conducted to overcome these issues, and we anticipate gene-editing technology to play an important role in the future. | true | true | true |
PMC9580158 | Yizhi Zhan,Zhanqiao Zhang,Yuechen Liu,Yuan Fang,Yuwen Xie,Yilin Zheng,Guoxin Li,Li Liang,Yi Ding | NUPR1 contributes to radiation resistance by maintaining ROS homeostasis via AhR/CYP signal axis in hepatocellular carcinoma | 19-10-2022 | NUPR1,Reactive oxygen species,Hepatocellular carcinoma,Radioresistance,Oxidative stress,Aryl hydrocarbon receptor | Background Radiotherapy (RT) is one of the major therapeutic approaches to hepatocellular carcinoma (HCC). Ionizing radiation (IR) inducing the generation of reactive oxygen species (ROS) leads to a promising antitumor effect. However, the dysregulation of the redox system often causes radioresistance and impairs the efficacy of RT. Increasing evidence indicates that nuclear protein 1 (NUPR1) plays a critical role in redox reactions. In this study, we aim to explore the role of NUPR1 in maintaining ROS homeostasis and radioresistance in HCC. Methods The radioresistant role of NUPR1 was determined by colony formation assay, comet assay in vitro, and xenograft tumor models in vivo. Probes for ROS, apoptosis assay, and lipid peroxidation assay were used to investigate the functional effect of NUPR1 on ROS homeostasis and oxidative stress. RNA sequencing and co-immunoprecipitation assay were performed to clarify the mechanism of NUPR1 inhibiting the AhR/CYP signal axis. Finally, we analyzed clinical specimens to assess the predictive value of NUPR1 and AhR in the radiotherapeutic efficacy of HCC. Results We demonstrated that NUPR1 was upregulated in HCC tissues and verified that NUPR1 increased the radioresistance of HCC in vitro and in vivo. NUPR1 alleviated the generation of ROS and suppressed oxidative stress, including apoptosis and lipid peroxidation by downregulating cytochrome P450 (CYP) upon IR. ROS scavenger N-acetyl-L-cysteine (NAC) and CYP inhibitor alizarin restored the viability of NUPR1-knockdown cells during IR. Mechanistically, the interaction between NUPR1 and aryl hydrocarbon receptor (AhR) promoted the degradation and decreased nuclear translation of AhR via the autophagy-lysosome pathway, followed by being incapable of CYP’s transcription. Furthermore, genetically and pharmacologically activating AhR abrogated the radioresistant role of NUPR1. Clinical data suggested that NUPR1 and AhR could serve as novel biomarkers for predicting the radiation response of HCC. Conclusions Our findings revealed the role of NUPR1 in regulating ROS homeostasis and oxidative stress via the AhR/CYP signal axis upon IR. Strategies targeting the NUPR1/AhR/CYP pathway may have important clinical applications for improving the radiotherapeutic efficacy of HCC. Supplementary Information The online version contains supplementary material available at 10.1186/s12916-022-02554-3. | NUPR1 contributes to radiation resistance by maintaining ROS homeostasis via AhR/CYP signal axis in hepatocellular carcinoma
Radiotherapy (RT) is one of the major therapeutic approaches to hepatocellular carcinoma (HCC). Ionizing radiation (IR) inducing the generation of reactive oxygen species (ROS) leads to a promising antitumor effect. However, the dysregulation of the redox system often causes radioresistance and impairs the efficacy of RT. Increasing evidence indicates that nuclear protein 1 (NUPR1) plays a critical role in redox reactions. In this study, we aim to explore the role of NUPR1 in maintaining ROS homeostasis and radioresistance in HCC.
The radioresistant role of NUPR1 was determined by colony formation assay, comet assay in vitro, and xenograft tumor models in vivo. Probes for ROS, apoptosis assay, and lipid peroxidation assay were used to investigate the functional effect of NUPR1 on ROS homeostasis and oxidative stress. RNA sequencing and co-immunoprecipitation assay were performed to clarify the mechanism of NUPR1 inhibiting the AhR/CYP signal axis. Finally, we analyzed clinical specimens to assess the predictive value of NUPR1 and AhR in the radiotherapeutic efficacy of HCC.
We demonstrated that NUPR1 was upregulated in HCC tissues and verified that NUPR1 increased the radioresistance of HCC in vitro and in vivo. NUPR1 alleviated the generation of ROS and suppressed oxidative stress, including apoptosis and lipid peroxidation by downregulating cytochrome P450 (CYP) upon IR. ROS scavenger N-acetyl-L-cysteine (NAC) and CYP inhibitor alizarin restored the viability of NUPR1-knockdown cells during IR. Mechanistically, the interaction between NUPR1 and aryl hydrocarbon receptor (AhR) promoted the degradation and decreased nuclear translation of AhR via the autophagy-lysosome pathway, followed by being incapable of CYP’s transcription. Furthermore, genetically and pharmacologically activating AhR abrogated the radioresistant role of NUPR1. Clinical data suggested that NUPR1 and AhR could serve as novel biomarkers for predicting the radiation response of HCC.
Our findings revealed the role of NUPR1 in regulating ROS homeostasis and oxidative stress via the AhR/CYP signal axis upon IR. Strategies targeting the NUPR1/AhR/CYP pathway may have important clinical applications for improving the radiotherapeutic efficacy of HCC.
The online version contains supplementary material available at 10.1186/s12916-022-02554-3.
Hepatocellular carcinoma (HCC) represents 85% to 90% of all primary liver cancer with poor prognosis [1]. Unfortunately, most HCC patients are diagnosed at an advanced stage and are ineligible for surgery. Radiotherapy (RT), as a local non-invasive treatment, becomes an important alternative approach for advanced HCC [2, 3]. Nevertheless, the anti-HCC efficacy of RT is often limited in intrinsic radioresistant cells or blunted over time by therapy-induced radioresistance [4–6]. Therefore, the molecular mechanisms that govern the radioresistance of HCC urgently need to be explored. Ionizing radiation (IR) potently induces massive cell death by triggering various biological signals. Among them, reactive oxygen species (ROS) are the key regulator for IR-induced cytotoxicity [7]. Excessive ROS levels caused by IR can disrupt the electron transport chain complexes in mitochondria and induce oxidative stress by reacting with biological molecules, such as lipids, proteins, and DNA [8]. Previous studies showed that ROS homeostasis mediated by the redox system was associated with radioresistance in several malignancies [9, 10]. Tumors resist IR-induced damage by restricting ROS generation or activating antioxidant systems to scavenge free radicals. In breast cancer, activation of STAT3 and Bcl-2 resulted in a persistent reduction of ROS and remarkable radioresistance [11]. In glioblastoma, 6-phosphogluconate dehydrogenase (6PGD) enhanced the pentose phosphate pathway (PPP) to NADPH to detoxify ROS, thereby promoting the radioresistance of cancer [12]. Based on the crucial role of ROS in IR-induced damage, the key molecules that regulate ROS homeostasis and lead to radioresistance in HCC need to be investigated. NUPR1 (nuclear protein 1) is primarily identified as a transcriptional cofactor strongly induced by several cellular stress [13, 14]. NUPR1 is widely reported to be upregulated in multiple cancers and involved in many cancer-associated processes, including tumor growth [15], invasiveness [16], apoptosis [17], and autophagy [18]. Increasing evidence indicated that NUPR1 could be activated by intracellular ROS and empower tumor cells to survive upon oxidative stress. The inactivation of NUPR1 triggers ROS overproduction due to mitochondrial failure in pancreatic cancer [19]. In addition, NUPR1 is implicated as a modifier on the expression of a series of antioxidant genes, including heme oxygenase-1 (HO-1) [20] and aurora kinase A (AURKA) [21]. NUPR1 protected cancer cells from ferroptosis, one of oxidative cell death, by participating in iron metabolism [22]. All these studies shed light on the possibility that NUPR1 might regulate ROS in HCC. Herein, we aimed to explore the functional role and potential mechanism of NUPR1 in the radioresistance of HCC.
HCC cell lines MHCC-97H, MHCC-97L, QGY-7701, Hep3B, Hep1, and Huh7 were purchased from Shanghai Institutes for Biological Sciences (China) and cultured in high glucose Dulbecco’s modified Eagle’s medium (DMEM, Gibco, USA) supplemented with 10% fetal bovine serum (Gibco, USA) at 37°C, 5% CO2. Cells were passed every 2–3 days to maintain logarithmic growth and cultured within 35 generations. The short tandem repeat (STR) analysis was used to verify the identity of cell lines. The human HCC tissue samples and the benign counterparts used in IHC staining of NUPR1 were obtained from the Department of Pathology at Nanfang Hospital (Guangzhou, China) in 2018. A total of 13 specimens from HCC patients who underwent hepatectomy or ultrasonically guided liver biopsy before RT from 2011 to 2019 were also collected from the Department of Pathology at Nanfang Hospital. The therapeutic response of the tumor was evaluated according to the Modified Response Evaluation Criteria in Solid Tumor (mRECIST) as previously described [6]. The collection of human specimens was approved by the Institute Research Medical Ethics Committee of Nanfang Hospital.
Lentivirus containing pLent-NUPR1-RFP-Puro (LV-NUPR1) or empty vector (LV-NC) pLent-RFP-Puro were synthesized by Vigene (Vigene Biology, Shandong, China) and used to infect MHCC-97H and MHCC-97L cells with enhanced infection solution (EIS) (Vigene Biology). Similarly, pLent-GFP-sh-NUPR1-Puro (sh-NUPR1) or its negative control (sh-NC) pLent-GFP-Puro (Vigene Biology) was used to infect QGY-7701 and Hep3B cells. Seventy-two hours after the cells were infected with lentivirus, 5 μg/ml puromycin was added to kill the cells that had not been transfected. The pcDNA3.1 vector (Vigene Biology) containing the full-length cDNA sequence of AhR and empty pcDNA3.1 vector as negative control were used for transient transfection by Lipofectamine 3000 reagents (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. Small interfering RNA (siRNA) against NUPR1 (si-NUPR1) and its negative control (si-NC) were obtained from Genechem (Genechem, Shanghai, China) and transfected into HCC cells using Lipofectamine 3000 reagents. The RNA sequences used for transfection in this study are shown in Additional file 1: Table 1. ROS inhibitor NAC, NUPR1 inhibitor ZZW-115, an agonist of AhR (6-formylindolo[3,2-b]carbazole, FICZ), and specific antagonist of AhR (CH223191) were obtained from MedChemExpress, LLC (Princeton, NJ). Chloroquine (CQ), bafilomycin A1 (BafA1), MG132, and alizarin were purchased from Selleck Chemicals (Houston, USA).
MHCC-97H/MHCC-97L/Hep3B cells (1000–6000/well) and QGY-7701 cells (250–1000/well) were seeded in 6-well plates and treated with different doses of IR (0, 2, 4, 6 Gy). Three thousand MHCC-97H/MHCC-97L/Hep3B cells and five hundred QGY-7701 cells were pretreated with different drugs following exposure to IR (6 Gy). After being cultured for approximately 2 weeks, cells were fixed in methanol and stained with 0.1% crystal violet. Plating efficiency was calculated as the eligible colonies (with > 50 cells)/the number of seeded cells. The survival fraction of cells was the ratio of the plating efficiency of treated cells to that of control cells. All related data were analyzed in GraphPad Prism 8 software, and survival curves of clone formation assays were plotted by using a single-hit multi-targeted model (y=1−(1−exp(−k*x))^N).
Protein was separated by 8–12% SDS-PAGE gels, transferred to PVDF membranes (Millipore), and blocked in 5% BSA for 1 h at room temperature. The membranes were incubated overnight at 4°C with primary antibodies. The sources of antibodies against the following proteins were as follows: NUPR1 (15056-1-AP), CYP1B1 (18505-1-AP), CYP3A4 (18227-1-AP), ARNT (14105-1-AP), HSP90 (60318-1-Ig), β-actin (20536-1-AP), LaminB1 (12987-1-AP), LC3 (14600-1-AP) and GAPDH (10494-1-AP) from Proteintech Group; AhR (A4000), CYP1A1 (A2159), caspase 3 (A19654), PARP (A19596), cleaved PARP (A19612) from ABclonal Technology. γH2AX (Ser139; #80312) and p62 (#8025S) was purchased from Cell Signaling Technology. The membranes were washed in PBST and incubated with HRP-conjugated secondary antibodies at room temperature for 1 h. Protein-antibody complexed were visualized using the enhanced chemiluminescence kit (Thermo Fisher Scientific).
Co-immunoprecipitation (Co-IP) assays were performed using MHCC-97H/MHCC-97L cells with NUPR1 overexpression and QGY-7701/Hep3B cells. The cells were harvested in RIPA lysis buffer (P0013D, Beyotime) with a protease inhibitor cocktail for 30 min on ice. The supernatant was collected by centrifugation at 12,000 × g for 15 min. The protein A/G agarose beads were incubated with antibodies overnight at 4°C while rotating. After washing, the complexes were subjected to western blotting analysis. Antibodies used in the study were anti-Flag (F1804, Sigma-Aldrich), NUPR1 (15056-1-AP, Proteintech), AhR (GTX22770, GeneTex), and AhR (A4000, ABclonal). Mouse IgG (B900620) and secondary antibody HRP-goat anti-rabbit IgG (SA00001-2) were from Proteintech.
Cells were seeded on culture dishes and allowed to grow to 70–80% confluency. Cells were washed with PBS and fixed in 4% paraformaldehyde for 15 min. Cells were permeabilized with 0.5% Triton X-100 in PBS for 10 min and washed again with PBS before being blocked with goat serum for 30 min. The fixed cells were incubated overnight at 4°C with primary antibodies against NUPR1 (ab234696, 1:100, Abcam), AhR (GTX22770, 1:100, GeneTex), ARNT (14105-1-AP, 1:200, Proteintech), and LAMP1 (ab208943, 1:100, Abcam). After that, cells were incubated with Alexa Fluor 488-conjugated or Alexa Fluor 555-conjugated secondary antibodies (1:100, Bioss, Beijing) and then mounted with DAPI.
Total RNA was extracted with TRIzol reagent (Invitrogen) and reverse-transcribed into cDNA using the PrimeScript RT Reagent Kit (RR037A, TaKaRa Bio). Quantitative real-time PCR assays were performed by using TB Green Premix Ex Taq II (RR820A, TaKaRa Bio) through an Applied Biosystems 7500 Fast Real-Time PCR System (Thermo Fisher Scientific). Relative expressions were normalized to the geometric mean of housekeeping gene β-actin and were analyzed by using the 2-ΔΔCt method. The primer sequences were listed in Additional file 1: Table 2.
MHCC-97H cells transfected with NUPR1 overexpression vector or control vector were seeded in 6-well plates and exposed to 8 Gy irradiation. After 24 h, total RNA was isolated and subjected to the construction of RNA-seq libraries. The quality of the RNA libraries was evaluated using the Agilent 2200 TapeStation (Agilent Technologies, USA). Library sequencing was performed on a HiSeq 3000 sequencing platform (Illumina Company, USA) by Guangzhou RiboBio Corp., China.
Cells were seeded in triplicate in 6-well plates and allowed to grow to 70-80% confluency. The cells were pretreated with or without drugs for 24 h and then irradiated. After irradiation for 24 h, MHCC-97H/MHCC-97L cells transfected with RFP protein were replaced with fresh medium containing 5 μM CM-H2DCFDA (C6827, Thermo Fisher) for ROS measurements. QGY-7701/Hep3B cell lines carrying GFP protein were treated with 5 μM CellROX Deep Red Reagent (C10422, Thermo Fisher) to determine ROS levels. A fresh medium with 5 μM BODIPY 581/591 C11 dye (D3861, Invitrogen) was added to each well for lipid peroxidation measurements. After incubation for 30 min in a humidified incubator (37°C, 5% CO2), the cells were washed with PBS, digested with trypsin, and measured by flow cytometry using FACS Canto II cytometer (BD Biosciences). The results were analyzed by Flow Jo 7.6.1 software (Treestar).
Cells were seeded in triplicate in 6-well plates and exposed to 8 Gy of irradiation. After IR, cells were replaced with a fresh medium and cultured for three days. Next, cells were washed with PBS, digested by trypsin solution without EDTA, and resuspended in 500 μL assay buffer containing 5 μM of 7-aminoactinomycin D (7-AAD) (C1053S, Beyotime). After incubation for 15 min at room temperature, cell samples were detected and analyzed by flow cytometry (BD Biosciences).
For apoptosis analysis, cells were pretreated with or without drugs for 24 h and exposed to a single dose of radiation (8 Gy) for 48 h. The cells were washed by PBS, trypsinized, and resuspended in 500 μL of FITC-Annexin V or APC-Annexin V solution (KeyGen BioTech, Nanjing). After incubation on ice for 15 min, DAPI was added to a final concentration of 10 μg/mL. The samples were then analyzed by flow cytometry (BD Biosciences).
For the establishment of HCC xenografts, 2 × 106 MHCC-97H cells transfected with LV-NC/LV-NUPR1 were suspended in 150 μL of serum-free DMEM containing 50 μL Matrigel and subcutaneously injected into nude mice (male, 4–6 weeks). Tumor volumes were measured using digital vernier calipers and calculated by a standard formula: length × width2/2. When tumor volume reached 100 mm3, irradiation (8 Gy/day × 2 days) was administered to each xenograft. Mice were divided into 4 groups (n = 5/group): control, NUPR1 overexpressing, control plus IR, or NUPR1 overexpression plus IR. 1 × 107 Hepa1-6 cells were subcutaneously injected into C57/BL6 mice (male, 4–6 weeks). When xenografts reached about 200 mm3, mice were randomly divided into 4 groups (n = 5/group): control, ZZW115, IR, or ZZW115 plus IR. A single dose of IR (10 Gy) was given on the first day, and ZZW115 (1 mg/kg) was concurrently given by intraperitoneal injection for 7 consecutive days. The tumors were measured every 4 days and collected when the biggest reached about 1000 mm3.
In brief, paraffin-embedded tissues were cut into 3 μm sections. Sections were deparaffinized, rehydrated, subjected to antigen retrieval, and treated with 3% hydrogen peroxide to block endogenous peroxidase activity. Antibodies against NUPR1 (15056-1-AP, 1:500, Proteintech), AhR (GTX22770, 1:200, GeneTex), CYP1A1 (13241-1-AP, 1: 200, Proteintech), Ki67 (#9449, 1:800, Cell Signaling), PCNA (A12427, 1:200, ABclonal), γH2AX (#9718, 1:400, Cell Signaling), MDA (ab24066, 1:100, Abcam) were incubated with the sections overnight at 4°C, respectively. After incubation with a secondary antibody, the visualization signal was stained with 3, 3′-diaminobenzidine (DAB) and then counterstained with hematoxylin. We regarded the multiplication of staining intensity and the extent of staining as the final score (0–12). Staining intensity was scored as 0 (negative), 1 (weak), 2 (medium) and 3 (strong). The extent of staining scored was as 0 (0%), 1 (1–25%), 2 (26–50%), 3 (51–75%), and 4 (>75%). The stained tissue sections were reviewed and scored separately by two pathologists blinded to the clinical parameters.
Comet assay was analyzed using DNA Damage Detection Kit (KeyGen BioTech, Nanjing) according to the manufacturer’s instructions. Briefly, transfected cells were irradiated at a dose of 8 Gy. The following day, cells were collected and suspended in PBS containing 1% low-melting agarose and layered onto adhesive microscope slides previously covered with 0.5% normal-melting agarose. The cells were dipped in a specific lysed buffer at 4°C for 2 h. Then, the DNA was uncoiled and unwound in an alkalescent electrophoresis buffer for 30 min. Electrophoresis was carried out and the cells were stained with DAPI solution for 10 min in a dark room. The slides were examined with an Olympus BX63 fluorescence microscope. Tail moment was calculated by using CASP software.
Cells were seeded at 6-well plates, allowed to attach, and exposed to 8 Gy irradiation. After 24 h, cells were washed with cold PBS and extracted with NADP+/NADPH extraction buffer, followed by centrifugation at 10,000 × g for 10min to remove insoluble material. Samples were deproteinized by filtering through a 10-kDa cut-off spin filter. To detect NADPH, NADP+ was decomposed by centrifuging tubes and heating to 60°C for 30 min in a water bath followed by cooling on ice. Samples were quickly spun to remove any precipitates, leaving only NADPH. NADP+ and NADPH samples were incubated with a Master Reaction mixture for an appropriate time before the absorbance was measured at 450 nm according to the manufacturer’s protocol (MAK038, Sigma-Aldrich).
Statistical analysis was performed using SPSS 20.0 software, GraphPad Prizm 8, ImageJ. Two-tailed and unpaired Student’s t-tests and two-way ANOVA tests were performed to compare differences. The differences in NUPR1 expression levels between the paired HCC tissues and adjacent nontumorous liver tissues in the TCGA database were compared by paired t-tests. Pearson correlation analysis was performed to analyze the correlation between two molecules. Survival curves were estimated using the Kaplan-Meier method and compared using the log-rank test. Data were presented as the means ± standard deviation (SD). Statistical significance was defined as a P-value less than 0.05. *P < 0.05; **P < 0.01; ***P < 0.001; ns, no significance.
To clarify the radioresistant role of NUPR1 on HCC cells, we firstly examined NUPR1 expression in a series of HCC cell lines by western blot and qRT-PCR analyses (Fig. 1a and Additional file 1: Fig.S1a). A previous study demonstrated that γ-irradiation could increase NUPR1 expression and influence DNA damage responses [23]. We evaluated the transcriptional level of NUPR1 in MHCC-97H cells treated with different doses of IR and collected at different timing. However, the level of NUPR1 was not altered after treating X-ray radiation (Additional file 1: Fig. S1b). Next, we established NUPR1-overexpressing cell lines in MHCC-97H, MHCC-97L (LV-NC/LV-NUPR1), and NUPR1-knockdown cell lines in QGY-7701, Hep3B cells (sh-NC/sh-NUPR1) with lentiviral transfection. The protein and mRNA levels of NUPR1 in NUPR1-overexpressing or knockdown HCC cells were verified by western blot and qRT-PCR analyses (Fig. 1b). CCK8 assays indicated that ectopic expression of NUPR1 increased the proliferation of HCC cells. In contrast, knockdown of NUPR1 led to a significant reduction in proliferation (Additional file 1: Fig. S1c). Colony formation assays demonstrated that the clonogenicity of NUPR1-overexpressing cell lines was significantly elevated, whereas NUPR1 knockdown diminished the clonogenicity after IR (Fig. 1c and Additional file 1: Fig. S1d). Moreover, the cell death rates in different NUPR1 expression cell lines were quantified after IR. NUPR1 overexpression significantly repressed cell death, while NUPR1 knockdown increased cell death in response to IR (Fig. 1d). Since IR can trigger DNA double-strand breaks (DSBs) directly by ionization or indirectly by ROS generation [7]. We treated HCC cells with IR and then measured DSBs by comet assays. As shown in Fig. 1e and Additional file 1: Fig. S1e, compared to control cell lines, LV-NUPR1 cells had shorter tails length, whereas sh-NUPR1 cells extended tails length after IR exposure. Cellular DNA damage was also monitored by γH2AX, a well-known marker of DSBs. We found that NUPR1 overexpression led to a decreased γH2AX expression and a better recovery back to the basal level. In contrast, knockdown of NUPR1 showed the opposite changes in HCC cells after IR treatment (Fig. 1f).
To extend the in vitro results, we explored the radioresistant effect of NUPR1 by using a xenograft model. As shown in Fig. 2a, b, NUPR1-overexpressing MHCC-97H cells enhanced tumor growth in nude mice. After being treated with IR, the NUPR1-overexpressing tumors exhibited minor regression and larger volumes than control tumors. NUPR1 inhibitor ZZW-115 was proved to sensitize pancreatic cancer cells to genotoxic agents, including γ-irradiation [24]. Therefore, combined treatment with ZZW-115 and IR was utilized in C57/BL6 mice after implanting murine Hepa1-6 HCC cells. We observed a synergistic tumor regression in xenografts following the combined treatment of ZZW-115 and IR (Fig. 2c, d). IHC staining showed that Ki67 was relatively unregulated in the NUPR1-overexpressing tumors compared with control tumors after IR exposure, whereas γH2AX was significantly downregulated (Fig. 2e). In addition, another marker of proliferation, PCNA was repressed considerably in tumors with combined ZZW-115 and IR treatment, while γH2AX showed opposite changes (Fig. 2f). Collectively, all of these results indicated that NUPR1 played a crucial role in the radiation resistance of HCC.
In order to uncover the radioresistant mechanism of NUPR1 on HCC, RNA sequencing was performed and screened several upregulated (n = 263, FC > 1.5, P < 0.05) and downregulated (n = 269, FC < 0.67, P < 0.05) genes in LV-NUPR1 MHCC-97H cells compared with LV-NC cells. We utilized KEGG pathway enrichment analysis and identified that the cytochrome P450 (CYP)-mediated metabolism pathway was notably downregulated in LV-NUPR1 cells (Fig. 3a). Western blot and qRT-PCR analyses showed that the expressions of several cytochrome P450 enzymes (CYPs) such as CYP1A1, CYP1B1, and CYP3A4 were significantly reduced in cells with NUPR1 overexpression (Fig. 3b, c). In contrast, knockdown of NUPR1 resulted in a notable increase in their expressions (Fig. 3c and Additional file 1: Fig. S2a). Since CYP enzymes are responsible for detoxifying toxic metabolites and lead to the formation of ROS, such as superoxide anion (O2−) and hydrogen peroxide (H2O2) [25]. Therefore, we focused on the potential regulation of NUPR1 in ROS generation. Our results showed that NUPR1 overexpression prevented the formation of ROS compared with control cells, whereas NUPR1 knockdown induced higher ROS levels after IR (Fig. 3d). NADPH, a scavenger of ROS, was reported to be consumed by CYP [25]. We detected the ratio of NADPH/NADP+ in cells with a different NUPR1 expression status. The results showed that LV-NUPR1 cells exhibited a remarkable increase in NADPH/NADP+ ratio, while an opposite change was seen in sh-NUPR1 cells upon IR (Fig. 3e). High amount of ROS production can result in a series of oxidative stress such as apoptosis, lipid peroxidation, and DNA oxidative damage [8]. As expected, ectopic NUPR1 expression in MHCC-97H and MHCC-97L cells significantly inhibited cell apoptosis and lipid peroxidation, whereas silencing of NUPR1 exhibited opposite results after IR treatment (Fig. 3f and Additional file 1: Fig. S2b-d). Molecular markers of apoptosis, such as cleaved PARP and cleaved caspase-3, were decreased by NUPR1 overexpression but elevated in NUPR1-knockdown cells in response to IR (Fig. 3g). Altogether, these results demonstrated that NUPR1 could attenuate CYPs-mediated ROS generation and the downregulation of ROS may enhance the radioresistance of HCC.
Next, we sought to validate the potential effect of ROS in NUPR1-mediated radioresistance in HCC. We utilized ROS scavenger, N-acetyl-L-cysteine (NAC), to examine its impact on the colony formation of cells with a different NUPR1 expression status under IR. As shown in Fig. 4a, b, relative to LV-NUPR1 cells, treatment with NAC significantly restored clonogenicity in LV-NC cells, while a reduced clonogenicity in NUPR1-knockdown cells was also abrogated by adding NAC. The cell lines with NUPR1 silencing were more vulnerable to cell death upon exposure to IR. When adding NAC, the cell death rates of these cell lines had a significant restoration (Fig. 4c). Furthermore, IR-induced oxidative stress, including apoptosis and lipid peroxidation, also had a remarkable restoration following a combined treatment of IR and NAC in LV-NC cells and sh-NUPR1 cells (Additional file 1: Fig. S3a-c). Consistent with flow cytometry data, the expression of cleaved PARP and cleaved caspase-3 in indicated cells were alleviated by NAC treatment following IR (Fig. 4d). A previous study demonstrated that alizarin strongly inhibited the activities of CYP1A1 and CYP1B1 [26]. To specifically evaluate the functional role of ROS derived from CYPs’ catalysis. We applied a concentration gradient of alizarin to culture MHCC-97H and MHCC-97L cells followed by IR exposure. As shown in Additional file 1: Fig. S4a, alizarin significantly antagonized ROS generation in tumor cells in a dose-dependent manner. Additionally, relative to LV-NUPR1 and sh-NC cells, alizarin significantly reduced the ROS levels in LV-NC and sh-NUPR1 cells (Fig. 4e). In line with ROS analyses, increased ROS-mediated apoptosis in cell lines with NUPR1 inactivation was abrogated by alizarin upon IR treatment (Additional file 1: Fig. S4b, c). Colony formation assays revealed that alizarin could modestly increase the clonogenicity of LV-NC MHCC-97H and sh-NUPR1 QGY-7701 cells in response to IR (Fig. 4f and Additional file 1: Fig. S4d). The results supported the notion that excessive ROS generated by CYPs contributed to oxidative stress and radiation sensitivity in tumor cells with NUPR1 inactivation. NAC and alizarin enabled tumor cells to survive upon IR treatment by eliminating ROS.
AhR/ARNT complex is well known for transcriptional regulation of CYPs, such as CYP1A1 and CYP1B1 [27]. Upon activation by various cellular stress, the aryl hydrocarbon receptor (AhR) dissociates from HSP90 and heterodimerizes with the aryl hydrocarbon receptor nuclear translocator (ARNT) in the nucleus to regulate the transcriptional expression of target genes [28]. Hence, we hypothesized that NUPR1 might regulate AhR/ARNT complex to mediate CYPs expression and subsequent ROS formation upon IR. To test this hypothesis, we examined the protein expression of the primary molecules in the AhR/CYPs pathway and found that AhR and ARNT were significantly reduced in NUPR1-overexpressing cells, whereas they increased in cells with NUPR1 knockdown (Fig. 5a). Interestingly, the mRNA levels of AhR and ARNT did not display consistent changes (Additional file 1: Fig. S5a). Moreover, NUPR1 overexpression decreased the nuclear expression of AhR and ARNT, while NUPR1 knockdown caused an opposite change (Additional file 1: Fig. S5b). Immunofluorescent (IF) staining of LV-NC cells showed that AhR was mainly located in the nucleus. Ectopic NUPR1 expression increased the cytoplasmic distribution of AhR and weakened the colocalization between AhR and DAPI. Conversely, NUPR1 knockdown increased the nuclear translocation of AhR and overlapped with DAPI (Fig. 5b). However, the distribution of ARNT was primarily located in the nucleus and was not affected by different NUPR1 expressions (Additional file 1: Fig. S5c). In light of the reverse regulation of AhR by NUPR1, we firstly treated cells with cycloheximide (CHX) to determine the stability of AhR. Our results showed that the half-life periods of AhR were much shorter in cells with NUPR1 overexpression than were in control cells, whereas AhR protein degraded slower in NUPR1 knockdown cells (Fig. 5c and Additional file 1: Fig. S5d). As we know, lysosome acts as a recycling center for regulating the degradative endpoint of the endosomal pathway. Therefore, we treated cells with selective lysosomal inhibitor chloroquine (CQ) or proteasome inhibitor MG132. Results revealed that CQ treatment could significantly increase AhR levels in LV-NUPR1 and sh-NC cells, while a slight upregulation of AhR was observed in LV-NC and sh-NUPR1 cells (Fig. 5d). Treatment with CQ also restored the nuclear expression of AhR in indicated cells (Additional file 1: Fig. S6a). Nevertheless, MG132 treatment did not alter the AhR protein levels (Additional file 1: Fig. S6b). Furthermore, overexpression of NUPR1 in tumor cells significantly increased the distribution of AhR to lysosome marker LAMP1 (Fig. 5e and Additional file 1: Fig. S6c). Previous works revealed that NUPR1 regulated autophagic flux and autolysosomal efflux in multiple cancer cells [17, 18, 29]. Autophagy is a conserved self-eating process that cells perform to allow degradation by formatting a double-membrane containing the sequestered cytoplasmic material and ultimately fuses with lysosome [30]. To verify the involvement of autophagy in AhR’s degradation, we analyzed the expression of two autophagy-related proteins, LC3-II and p62. Results showed that NUPR1 knockdown caused a significant increase of LC3-II and p62 expression levels, suggesting the autophagic flux was impeded, whereas the opposite change was seen in NUPR1-overexpressing cells (Fig. 5f and Additional file 1: Fig. S6d). Treatment with autophagy inhibitors bafilomycin A1 (BafA1) can also restore AhR expression in LV-NUPR1 MHCC-97H and sh-NC Hep3B cells (Additional file 1: Fig. S6e). These results suggested that NUPR1 modulated the protein stability of AhR via the autophagy-lysosome pathway. To gain insight into the potential interaction between NUPR1 and AhR, we performed co-immunoprecipitation (Co-IP) combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) using Flag-tagged NUPR1-overexpressing cells lysates and anti-Flag antibody. AhR was identified as one of the NUPR1-interacting proteins (Additional file 1: Fig. S6f). Co-IP and western blot were applied to confirm the interaction between NUPR1 and AhR (Fig. 5g). Reciprocal Co-IP assays were conducted with antibodies against AhR to co-precipitate NUPR1 and further confirmed the interaction of these two proteins (Fig. 5h). Using an anti-AhR antibody to incubate the lysate from QGY-7701 and Hep3B cells, the consistent results validated the endogenous interaction between NUPR1 and AhR in HCC cells (Additional file 1: Fig. S6g). Additionally, we analyzed the subcellular location of NUPR1 and AhR by IF staining. NUPR1 signal primarily overlapped with AhR in the cytoplasm but not in the nucleus (Fig. 5i). These results demonstrated that NUPR1 bound to AhR and modulated its cellular distribution, which may downregulate IR-induced oxidative stress by inhibiting AhR/CYP signaling axis.
To further explore the impact of AhR on the NUPR1-mediated radioresistance of HCC cells. We generated AhR-overexpressed plasmids and transfected them into cells with a different NUPR1 expression status, followed by exposure to IR. In our study, AhR overexpression led to an upregulation of CYP1A1 and CYP1B1 in HCC cells. In contrast, a larger increase of CYPs expression was seen in LV-NUPR1 and sh-NC cells (Fig. 6a). Next, our results showed that ectopic expression of AhR significantly improved ROS generation and cell death rates in LV-NUPR1 cells and sh-NC cells, whereas a slight upregulation of ROS and cell death was seen in LV-NC cells and sh-NUPR1 cells upon IR (Fig. 6b, c). Cleaved PARP and cleaved caspase-3 were increased in LV-NUPR1 cells and sh-NC cells by AhR-overexpressing after IR treatment (Additional file 1: Fig. S7a). Since the pharmacological intervention of AhR is available, we supposed that AhR might serve as a potential target for reversing the radioresistant role mediated by NUPP1 in HCC. Our results showed that pharmacological activation with AhR agonist FICZ significantly elevated ROS levels in cells with NUPR1-overexpression, while specific inhibiting AhR by CH223191 resulted in a suppression of ROS levels in NUPR1-knockdown cells in response to IR (Fig. 6d). The plate colony formation assays revealed that FICZ treatment significantly inhibited the clonogenic survival in LV-NUPR1 cells, whereas CH223191 treatment could restore the clonogenicity in sh-NUPR1 cells after IR (Fig. 6e, f). IHC staining of xenograft tumors derived from MHCC-97H cells showed that the expression levels of AhR and CYP1A1 were lower in LV-NUPR1 tumors than in LV-NC tumors. Upon IR treatment, the expression of malondialdehyde (MDA), a lipid peroxidation product, was significantly increased in LV-NC tumors, while a slight upregulation was seen in LV-NUPR1 tumors (Additional file 1: Fig. S7b). These results implicated that NUPR1 may diminish ROS generation and oxidative stress via AhR/CYP signal axis in HCC cells under IR treatment.
To further assess the potential correlation of NUPR1 and clinical data of HCC patients, we analyzed NUPR1 mRNA expression in GEO and TCGA databases. As shown in Fig. 7a, NUPR1 mRNA expression was significantly higher in HCC tissues than in benign counterparts. We then verified the protein levels of NUPR1 by IHC and found that NUPR1 was upregulated in HCC tissues relative to the matched adjacent liver tissues (Fig. 7b and Additional file 1: Fig. S8a). Furthermore, we performed gene-set enrichment (GSEA) analysis with RNA sequencing data from TCGA LIHC and GSE14520 datasets. Results showed that the ROS pathway and glutathione metabolism pathway were enriched in the HCC specimens with relatively high expression of NUPR1 (Fig. 7c and Additional file 1: Fig. S8b). Pearson correlation analysis showed that NUPR1 mRNA level was negatively correlated with CYP1B1 and CYP3A4 mRNA levels in HCC tissues from the GSE15654 dataset (Fig. 7d). Kaplan–Meier survival analysis indicated that the patients with high expression of NUPR1 and low-expressed AhR or CYP1B1 showed significantly worse overall survival in TCGA LIHC and GSE15654 datasets. The mRNA expression of NUPR1 was correlated with worse overall survival in HCC patients but did not have a significant statistic value (Fig. 7e). Given that NUPR1 conferred a radioresistant effect to HCC via the downregulation of AhR, we detected the expression of NUPR1 and AhR in 13 HCC samples from patients who underwent hepatectomy or liver biopsy guided by ultrasound before RT. HCC samples were collected from the Department of Pathology at Nanfang Hospital, and the detailed clinical information was published in our previous research [6]. Among these patients, six were defined as “non-response” who had tumor recurrence or metastasis after RT treatment within half a year, while seven were classified as “response” without tumor progression after RT. Our IHC results showed that the NUPR1 level of HCC was higher in “non-response” patients relative to “response” patients, while the levels of AhR exhibited an opposite change and had a lower significant statistic value (Fig. 7f). Taken altogether, our finding supported a role of the NUPR1/AhR/CYP signal axis in promoting radioresistance of HCC and suggested that NUPR1 and AhR might serve as potential targets for the development of radiation sensitization in HCC.
During RT, ROS is generated from various sources, for example, the radiolysis of water, electron transport chain in mitochondria, and ROS-induced enzymes, including NADPH oxidase, lipoxygenases, and CYP [31, 32]. Excessive ROS levels induce oxidative stress by reacting with lipids, proteins, and DNA to cause lipid peroxidation, protein misfolding, and DNA strand breaks [8]. ROS maintained at a moderate level is crucial for cancer cells to prevent oxidative damage [10]. Studies reported that many complementary approaches that enhanced ROS production were applied to improve radiosensitivity in cancers [33, 34]. Recently, NUPR1 has been demonstrated to impact ROS production and redox homeostasis in multiple types of cancers [19, 20]. In this study, our results revealed that NUPR1 potently reduced ROS generation by attenuating CYP catalytic activity, therefore enhancing cell viability during IR. When treated with NAC, NUPR1-silencing cells significantly repressed ROS levels and oxidative stress upon IR exposure. The results strongly supported the role of NUPR1 in alleviating ROS formation and oxidative stress after IR treatment in HCC. NUPR1 is widely reported to act as an oncogene in several types of cancers and regulates a series of downstream genes via interacting with transcription factors [15–18]. Consistent with previous findings, we found that NUPR1 was upregulated in HCC tissues compared with adjacent liver tissues, and NUPR1 overexpression significantly enhanced the proliferation of HCC cells. Notably, we found that CYP-mediated metabolism was the downstream signal upon ectopic expression of NUPR1 in HCC. CYP enzymes are not only known for regulating substrate oxidation, particularly in phase I metabolism of xenobiotics, but also involved in the biosynthesis of cholesterol, fatty acids, and steroid hormones [35]. Early works in CYP biology revealed that CYP could produce ROS due to the inefficiency of electron transfer from NADPH to CYP for monooxygenation of substrate, which was known as “reaction uncoupling”. Besides, continued production of ROS is inevitable for NADPH consumption both in presence and in absence of substrates [36, 37]. A previous study illustrated that cytochrome P450 oxidoreductase (POR), an enzyme required for electron transfer from NADPH to CYPs, was indispensable for lipid peroxidation in ferroptotic cell death of cancer cells [38]. Based on previous findings and our results, it was reasonable to propose that the downregulation of CYPs and ROS levels mediated by NUPR1 could be a novel mechanism for the radioresistance of HCC. Simultaneously, we observed that CYPs expression was elevated by IR treatment in HCC cells, which was similar to the results found in the previous studies [39]. But how IR regulates the activation of CYPs is unclear and warrants further investigations. Mechanistically, we found that NUPR1 bound to and regulated the degradation process of AhR. As a ligand-activated transcription factor, AhR enables cells to adapt to changing environments and exerts a critical role in the development of cancer [40, 41]. Upon ligand binding, AhR translocates into the nucleus and forms with ARNT as a heterodimer to induce the transcription of target genes [27]. Next to xenobiotics, natural ligands derived from endogenous metabolisms, such as tryptophan catabolite 6-formylindolo[3,2-b]carbazole (FICZ) and kynurenine (Kyn), are potent AhR agonist [42, 43]. Initially, several studies proved that AhR mediated the toxic effect of organic pollutants via the transcriptional induction of CYP and sustained generation of ROS [44]. In this study, ectopic expression of NUPR1 in HCC cells resulted in a downregulation of AhR and impaired its nuclear translocation. Genetic upregulation and pharmacological activation of AhR significantly improved intracellular ROS levels and radiosensitivity in NUPR1-overexpressing cell lines. Treatment with AhR inhibitor CH223191 strikingly restored the radioresistant effect in HCC cells. These results implicated that AhR was indispensable for NUPR1 restraining ROS generation and oxidative stress during IR. Accumulating evidence highlights the role of AhR in cancer development encompasses both pro- and anti-tumorigenic activities. AhR was proposed to display tumor suppressor function in multiple cancers associated with the brain, liver, digestive system, and skin (melanoma) [45]. Targeting AhR must be dependent on tumor-specific AhR expression. Our study revealed that AhR was relatively low expressed in radiotherapy non-response HCC patients, which may be indicated to enhance the radiosensitivity of HCC by pharmacological activation of AhR. A study demonstrated that knockdown of p23 could drive the autophagy-mediated degradation of AhR [46], although it was well known that AhR was degraded by ubiquitin-proteasome after translocating into nucleus [47]. Moreover, NUPR1 was proven as a potent regulator of autolysosome processing in the late stages. Our data suggested that NUPR1 caused the induction of autophagy flux and enhanced the protein instability of AhR via the autophagy-lysosome pathway, but ANRT might not be directly regulated by NUPR1. The detailed biological mechanism of the interaction between NUPR1 and AhR still needs more effort to dissect.
In summary, our study attempted to validate the radioresistant role of NUPR1 in HCC. Our findings provide new insights for understanding the underlying mechanism of NUPR1, that is to attenuate CYPs-mediated ROS formation and oxidative stress by complexing with and downregulating AhR, therefore promoting radioresistance of HCC. Clinical data suggested that NUPR1 and AhR could be predictive biomarkers for the RT response of HCC patients. Specific inhibiting NUPR1 by ZZW-115 significantly improved the vulnerability of HCC to IR in the xenograft mice model. All the results implicated that NUPR1/AhR/CYP signaling axis might serve as the potential target for improving radiotherapeutic efficacy in HCC (Fig. 8).
Additional file 1: Figure S1. NUPR1 promotes tumor growth and radiation resistance of HCC. Figure S2. NUPR1 suppresses IR-induced apoptosis and lipid peroxidation. Figure S3. NAC attenuates oxidative stress induced by NUPR1 silencing upon IR. Figure S4. CYP inhibitor alizarin impedes ROS generation and oxidative stress upon IR exposure. Figure S5. NUPR1 modulates the protein levels and nuclear translocation of AhR. Figure S6. NUPR1 interacts with AhR and promotes degradation via the autophagy-lysosome pathway. Figure S7. NUPR1 inhibits oxidative stress via AhR/CYP signaling. Figure S8. NUPR1 is upregulated in HCC tissues and correlates with glutathione metabolism. Table S1. List of NUPR1 shRNA and siRNA coding sequences. Table S2. List of primers used in this study.Additional file 2. The images of the original, uncropped gels/blots. | true | true | true |
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PMC9580243 | Mario E. Lloret-Torres,Roxsana N. Ayala-Pagán,Freddyson J. Martínez-Rivera,Pedro Bonilla-Rullán,Jennifer L. Barreto-Estrada | Hippocampal and amygdalar increased BDNF expression in the extinction of opioid-induced place preference | 19-10-2022 | BDNF,Morphine,Conditioned Place Preference,Extinction,Hippocampus,Amygdala,Nucleus Accumbens | The opioid crisis was exacerbated during the COVID-19 pandemic in the United States with alarming statistics about overdose-related deaths. Current treatment options, such as medication assisted treatments, have been unable to prevent relapse in many patients, whereas cue-based exposure therapy have had mixed results in human trials. To improve patient outcomes, it is imperative to develop animal models of addiction to understand molecular mechanisms and identify potential therapeutic targets. We previously found increased brain derived neurotrophic factor (bdnf) transcript in the ventral striatum/nucleus accumbens (VS/NAc) of rats that extinguished morphine-induced place preference. Here, we expand our study to determine whether BDNF protein expression was modulated in mesolimbic brain regions of the reward system in animals exposed to extinction training. Drug conditioning and extinction sessions were followed by Western blots for BDNF in the hippocampus (HPC), amygdala (AMY) and VS/NAc. Rears, as a measure of withdrawal-induced anxiety were also measured to determine their impact on extinction. Results showed that animals who received extinction training and successfully extinguished morphine CPP significantly increased BDNF in the HPC when compared to animals deprived of extinction training (sham-extinction). This increase was not significant in animals who failed to extinguish (extinction-resistant). In AMY, all extinction-trained animals showed increased BDNF, regardless of behavior phenotype. No BDNF modulation was observed in the VS/NAc. Finally, extinction-trained animals showed no difference in rears regardless of extinction outcome, suggesting that anxiety elicited by drug withdrawal did not significantly impact extinction of morphine CPP. Our results suggest that BDNF expression in brain regions of the mesolimbic reward system could play a key role in extinction of opioid-induced maladaptive behaviors and represents a potential therapeutic target for future combined pharmacological and extinction-based therapies. | Hippocampal and amygdalar increased BDNF expression in the extinction of opioid-induced place preference
The opioid crisis was exacerbated during the COVID-19 pandemic in the United States with alarming statistics about overdose-related deaths. Current treatment options, such as medication assisted treatments, have been unable to prevent relapse in many patients, whereas cue-based exposure therapy have had mixed results in human trials. To improve patient outcomes, it is imperative to develop animal models of addiction to understand molecular mechanisms and identify potential therapeutic targets. We previously found increased brain derived neurotrophic factor (bdnf) transcript in the ventral striatum/nucleus accumbens (VS/NAc) of rats that extinguished morphine-induced place preference. Here, we expand our study to determine whether BDNF protein expression was modulated in mesolimbic brain regions of the reward system in animals exposed to extinction training. Drug conditioning and extinction sessions were followed by Western blots for BDNF in the hippocampus (HPC), amygdala (AMY) and VS/NAc. Rears, as a measure of withdrawal-induced anxiety were also measured to determine their impact on extinction. Results showed that animals who received extinction training and successfully extinguished morphine CPP significantly increased BDNF in the HPC when compared to animals deprived of extinction training (sham-extinction). This increase was not significant in animals who failed to extinguish (extinction-resistant). In AMY, all extinction-trained animals showed increased BDNF, regardless of behavior phenotype. No BDNF modulation was observed in the VS/NAc. Finally, extinction-trained animals showed no difference in rears regardless of extinction outcome, suggesting that anxiety elicited by drug withdrawal did not significantly impact extinction of morphine CPP. Our results suggest that BDNF expression in brain regions of the mesolimbic reward system could play a key role in extinction of opioid-induced maladaptive behaviors and represents a potential therapeutic target for future combined pharmacological and extinction-based therapies.
Opioid addiction is one of the leading causes of death in the United States (Hedegaard et al., 2020). Since the Covid-19 pandemic began in 2020, a spike of more than 25,000 annual overdose related deaths from 2018 to 2020 was reported. Over 60% of these overdoses were opioid related, now accounting for over 1% of all deaths in the United States (Ahmad et al., 2021). Currently, opioid addiction and its withdrawal symptoms are treated with a combination of behavioral therapies and medication assisted treatment (MAT). While MAT modalities have been conventionally used to prevent opioid relapse and overdose, the use of behavioral approaches are needed to ensure long-term abstinence (Maglione et al., 2018, SAMHSA, 2021). For example, in cue-based exposure therapy, the subjects are exposed to previous drug-associated contexts/cues to weaken the link between these cues and the drug, thereby extinguishing the drug-associated memories and preventing relapse (Mellentin et al., 2017, Everitt, 2014, Torregrossa and Taylor, 2013). However, the current extinction-based therapies have shown limited clinical success, perhaps due to lack of a deeper understanding of the molecular and biological mechanisms underlying extinction (Millan et al., 2011). For this purpose, we have previously reported that the extinction of morphine-induced conditioned place preference (CPP) in rats, through receiving extinction training, displayed a different expression profile for synaptic plasticity genes of the ventral striatum/nucleus accumbens (VS/NAc), as compared with rats receiving withdrawal (i.e., forced abstinence in their home-cages). Particularly, our results showed a significant increase in brain derived neurotrophic factor (bdnf) mRNA in rats who successfully extinguished morphine CPP (Martínez-Rivera et al., 2019), suggesting this neurotrophin as a potential molecular mediator for extinction of drug-associated conditioned stimuli. BDNF is a neurotrophic growth factor that has been linked to extinction processes in fear (Rosas-Vidal et al., 2018) and drugs of abuse (Bobadilla et al., 2019, Castino et al., 2018, Règue-Guyon et al., 2018). Multiple studies in opioid addiction have shown extensive associations between BDNF and extinction of morphine (Koo et al., 2012). In fact, rats that were chronically exposed to heroin, decrease BDNF expression (Li et al., 2017). These effects seem to involve BDNF’s activity through its receptor Trk-B, as rats infused with its antagonist (ANA-12) impairs extinction of morphine CPP (Jorjani et al., 2021). Similarly, inhibition of accumbal BDNF signaling impairs extinction of cocaine seeking, while exogenous BDNF microinjections facilitate this process (Bobadilla et al., 2019). Also, BDNF has been associated with extinction of nicotine and alcohol-related behaviors (Barker et al., 2015). Gene expression can be a transient process that do not necessarily translate to long-term differences in protein expression within the targeted area and its circuits (Vélez-Bermúdez and Schmidt, 2014). Thus, while we previously showed an increase of bdnf mRNA in the VS/NAc, it remains to be determined if the bdnf transcript is translated to its mature protein, and whether the abundance in the VS/NAc and other interconnected regions of the mesolimbic reward circuit are modulated. Given that VS/NAc has low BDNF expression (Conner et al., 1997, Li et al., 2013), it is possible that this neurotrophin can be transported through VS/NAc afferents (Altar et al., 1997) expressing high levels of BDNF such as the hippocampus (HPC) and amygdala (AMY) (Conner et al., 1997). Therefore, in this study we combined morphine-CPP followed by extinction training to interrogate BDNF protein expression in the mesolimbic reward system. We further compared the level of BDNF expression in rats displaying different extinction phenotypes (i.e., success or failure). To test for anxiety-related behaviors associated with drug withdrawal we measured the frequency of rears according to extinction phenotype. Understanding the molecular milieu of the extinction of drug-related behaviors in preclinical models of addiction represents a step closer to develop combined pharmacological and behavioral treatments that facilitate abstinence in patients suffering from opioid addiction.
Adult male Sprague Dawley rats (∼350 g; Envigo Laboratories., Indianapolis, IN) were individually housed with food and water available ad libitum (12:12 h light/dark cycle; 64°F, 30% humidity). Behavioral experiments were performed during the light phase of the cycle, and procedures were in accordance with the IACUC of the University of Puerto Rico, Medical Sciences Campus. Morphine sulfate (Sigma Aldrich, St. Louis MO; 5 mg/kg) was dissolved in saline (0.09%; 0.2 ml/100 g of body weight) and administered subcutaneously to all experimental groups. This dose of 5 mg/kg was chosen as previous studies found that it is sufficient to induce morphine-place preference without impacting mobility (Martínez-Rivera et al., 2019, Heinrichs et al., 2010, Mueller et al., 2002), and able to elicit withdrawal-induced anxiety (Zhang and Schulteis, 2008), as demonstrated by them when a repetitive 5 mg/kg dose was administered by 4 days, resembling early stages of drug-dependence.
Protocols for morphine conditioning and extinction were performed as previously described (Martínez-Rivera et al., 2019); Fig. 1 A, adapted from Martínez-Rivera et al., 2019). Animals were habituated in the behavioral chamber for 20 min (Day 1). A 20-min test was performed to determine side preference (baseline; Day 2). They were then morphine-conditioned for 45 min over an 8-day period (Days 3–10) with alternating morphine and saline injections. Next day (Day 11; conditioning test), animals were allowed to move freely between compartments for 1 h, and their side preference was determined as measured by the percentage of time spent in the drug-paired side. Increased percentage of time as compared to baseline was considered as the conditioning index. Forced extinction (Heinrichs et al., 2010, Leite-Morris et al., 2014), in which animals were restricted to the drug-associated side for 1 h in the absence of drug, was performed on Days 12–15. Next day (Day 16; extinction test), animals were allowed to move freely between compartments for 20 min, and their side preference was determined. Decreased percentage of time in the drug-paired side as compared to the conditioning test was considered as an index of extinction. Animals that exhibited high CPP (below 20% reduction in preference for the drug paired side) after extinction training were categorized as extinction resistant. In sham-extinction, animals did not receive extinction training and were kept in their home cages (Fig. 1A). Behavioral data was acquired using the Any-Maze tracking system (Stoelting Co., Wood Dale, IL).
Drug-conditioned rats are likely to experience withdrawal. In animal studies, anxiety-related behaviors are used to assess drug-withdrawal as they are easily observable (Sarnyai et al., 1995, Lu et al., 2005). Rearing, defined as an animal standing on its hind legs has been commonly used as a measure of anxiety in the absence of fear (McNaughton and Gray, 2000, Myers and Carlezon, 2010), although it is highly influenced by novelty (Lever et al., 2006). In this study, we measured the frequency of rears as indicative of withdrawal-induced anxiety in animals that received either extinction training (extinction and extinction-resistant groups) or sham-extinction. Scores of the behaviors were performed from video recordings during a period of 20-min and compared at three time points: baseline, conditioning test day, and extinction test day. Hand scores were observer-blind regarding animal group or treatment.
Western blots and tissue dissection were performed as previously described (Martínez-Rivera et al., 2015, Sharma and Fulton, 2013). Reagents were purchased to Bio-Rad Laboratories, CA. In brief, animals were sacrificed within 1 h of behavioral testing (Day 16), and the VS/NAc, HPC and AMY were dissected, homogenized and total protein concentration determined. Samples were denatured and loaded in a 4–20% SDS polyacrylamide gel. After electrophoresed, proteins were transferred into nitrocellulose membranes and verified by Ponceau’s staining. Blotto blocking buffer (5% milk protein) was applied to the membranes and incubated overnight with primary anti-BDNF rabbit monoclonal antibody (# ab108319, Abcam, MA) using the following dilutions: HPC, 1:2000; AMY and VS/NAc; 1:1000). Secondary mouse anti-rabbit IgG-HRP antibody was used at 1: 2000 (Santa Cruz Biotechnology, TX). Primary anti-ß-actin rabbit polyclonal antibody (# ab8227; Abcam, MA) was used for normalization (1:5000). Blots were visualized using an enhanced chemiluminescence kit (SuperSignal Femto, Pierce IL) and images obtained using a VersaDoc 1000 system (Bio-Rad, CA). Western blots were performed in triplicate technical replicates from three (AMY) or four (HPC, VS/NAc) independent experiments. Densitometric analysis was performed using NIH ImageJ software (v1.47d).
Data is presented as mean + SEM and statistical significance established at p < 0.05. CPP behaviors were analyzed using Two-way repeated measure ANOVA, followed by Tukey post-hoc tests. Rears and Western blots were analyzed using one-way ANOVA, followed by Tukey post-hoc tests (GraphPad Prism 9.0).
To model extinction learning, rats were first conditioned to express drug place preference by morphine administration in an alternate pattern (morphine/saline), followed by 4 days of either extinction or sham-extinction sessions (Fig. 1A). Learning index after conditioning sessions was measured by increased time spent on the morphine-paired side in the test day as compared to baseline. Conversely, extinction learning was determined by a reduction of time in the drug-paired side comparable to conditioning-test levels. Two-way repeated measures ANOVA showed significant effects in time (CPP Phases: baseline, conditioning test, extinction test) and groups (sham-extinction, extinction, and extinction-resistant); main effect of time: F (1.5, 14) = 37; p < 0.0001; main effect of group: F (2, 9) = 9.5; p < 0.01; interaction: F (4, 18) = 3.6; p < 0.05. Like our previous findings (Martínez-Rivera et al., 2019), results showed increased preference (CPP) for the morphine-paired side in all groups (Fig. 1B; Tukey post hoc; p’s < 0.01). For extinction learning, we showed that animals in the extinction group significantly reduced their preference for the morphine-paired side (below 50% of time) when compared to the conditioning test (Fig. 1B; Tukey post hoc; p’s < 0.01). This result contrast with animals that retained their preference for morphine in the extinction-resistant and sham-extinction groups (both behaved similarly [p = 1.0]), suggesting different rates of extinction learning. Indeed, heatmaps of representative animals in each experimental group showed that animals extinguishing morphine-place preference spent more time in the saline-paired side (Fig. 1C; green signal), as compared to animals in the sham-extinction and extinction-resistant groups. No significant differences were observed between individuals in each group at baseline or conditioning test (Fig. 1B; Tukey post hoc; p’s > 0.05). Overall, we were able to capture individual phenotypes to facilitate the identification of molecular substrates signaling specific extinction-related behaviors.
Withdrawal induced anxiety has the potential to interfere with the effectiveness of the extinction process (Lu et al., 2005, Myers and Carlezon, 2010). To determine whether withdrawal-related behaviors influenced the behavioral outcomes of extinction trained animals, we measured the frequency of rears which has been associated with opiate withdrawal (Azorlosa and Simmons, 1999, McKendrick et al., 2020). A one-way ANOVA showed a significant interaction between groups: F (2, 41) = 26, p < 0.0001) corresponding to a significant reduction in rears during conditioning (55 + 2.7) and extinction tests (55 + 3.8) as compared to baseline (79 + 2.2) (Fig. 2A, Tukey post hoc: sham-extinction vs. extinction, p < 0.0001; sham-extinction vs. extinction-resistant p < 0.0001). To detect potential differences in extinction outcome between extinction-trained animals and those in their home cage (sham-extinction), we performed a separate analysis according to groups for the extinction test day. A one-way ANOVA showed a significant interaction between groups: F (2, 9) = 8.5, p = 0.008) which corresponds to a significant increase in the frequency of rears of the sham-extinction group (72 + 2.3) compared to both extinction (52 + 3.0) and extinction-resistant (46 + 6.0) (Fig. 2B, Tukey post hoc: sham-extinction vs. extinction, p = 0.029; sham-extinction vs. extinction-resistant, p = 0.007). This increase in rears is similar to values seen at baseline (p = 0.9769) suggesting an environmental novelty effect. As the frequency of rears did not differ in animals in the extinction or extinction-resistant groups, our data suggest that withdrawal-induced anxiety is not a key factor for the extinction of opiate-related behaviors in early stages of drug dependence.
To determine whether extinction training led to changes in BDNF expression, we performed Western blots on tissue lysates from HPC, AMY, and VS/NAc. In the HPC, One-way ANOVA showed a significant effect of group: F (2, 9) = 5.250 p = 0.0308), and a significant interaction: F (2, 9) = 5.250; p < 0.05). The extinction group increased BDNF expression (230%) as compared to a normalized sham-extinction group (Fig. 3 A, Tukey post hoc; p < 0.05). A non-significant increase in BDNF was observed in the extinction-resistant group (173%; Tukey post hoc; p = 0.2154), although no significant difference was observed between extinction and extinction-resistant groups (Tukey post hoc; p = 0.3791). In AMY, One-way ANOVA showed a significant effect in group: F (2, 6) = 8.99 p = 0.0156) and a significant interaction: F (2, 6) = 8.996; p < 0.05). An increase in BDNF in both extinction (157%) and extinction-resistant (138%) groups was observed as compared to sham-extinction (Fig. 3B, Tukey post hoc; sham-extinction vs. extinction p = 0.0191; sham-extinction vs. extinction-resistant p = 0.0323). Analysis of the VS/NAc did not reveal any significant differences (Fig. 3 C). Increased expression of BDNF in HYP and AMY were specific to the mature protein, as Pro-BDNF showed no significant changes. These findings suggest that hippocampal BDNF may be an important molecular mediator for the extinction of conditioned opioid-related behaviors, whereas increased amygdalar BDNF may be encoding environmental cues when exposed to extinction training.
Improving clinical outcomes in exposure-based therapies requires further study into the molecular mechanisms of extinction learning. Several brain regions, including the VS/NAc, HPC, and AMY, among others, are involved in the extinction of behaviors related to addiction (Goode and Maren, 2019). Our study provides continuity to previous molecular findings that implicates BDNF as a key player for extinction learning (Andero and Ressler, 2012, Cowansage et al., 2010, Martínez-Rivera et al., 2019). In the present study we assessed relative BDNF expression in these brain regions, in animals that extinguished morphine CPP, as compared to animals receiving sham-extinction, or those that were extinction-resistant. The most significant finding was a BDNF increase in HPC of rats that received extinction training and subsequently extinguished morphine CPP. This increase was the highest among all the studied regions and its value doubled the sham-extinction group.
In the VS/NAc, we found no significant differences in BDNF expression after extinction training for morphine in either the extinction or resistant groups. This result was surprising as the NAc has been extensively associated with extinction (Gibson et al., 2019, Dutta et al., 2021, Fatahi et al., 2020a). In fact, animals who extinguished morphine CPP significantly increased bdnf transcript in this region (Martínez-Rivera et al., 2019). Evidence of BDNF as a pro-extinction molecule in the VS/Nac comes from pharmacological studies in which the Trk-B antagonist, ANA-12, increased withdrawal symptoms (Rezamohammadi et al., 2020) and facilitated acquisition and expression of morphine CPP (Jorjani et al., 2021). Similarly, Trk-B knockdown in medium spiny neurons enhanced morphine CPP (Koo et al., 2014). Therefore, it is possible that the major changes of BDNF protein expression during extinction training are occurring in other sources which potentially target the VS/NAc. Inputs coming from prefrontal and hippocampal regions have been shown to transport BDNF to the VS/NAc (Conner et al., 1997, Giannotti et al., 2018). Moreover, the pro-extinction effect appears to be subregion specific (Chiara, 2002). For example, BDNF infusions to the NAc core decrease cocaine seeking (Bobadilla et al., 2019), while infusions to the NAc shell only increased reinstatement (Graham et al., 2007). As we examined the entire NAc region in our study, we do not rule out the possibility of missing subregion specific BDNF modulation, in particular the shell, which is more commonly associated with extinction (Gibson et al., 2019, Dutta et al., 2021). Post-translational modifications or afferent BDNF activity in the HPC and AMY are also plausible mechanisms that warrant further research. Studies in fear learning were at the forefront in our understanding of extinction processes (Milad and Quirk, 2012) as they demonstrated that fear and addiction shared common extinction pathways (Myers et al., 2011, Peters et al., 2009). Fear studies also provided evidence for the importance of BDNF in extinction. In the HPC, reduced BDNF expression was observed in rats that failed extinction (Peters et al., 2010), while in the ventral hippocampus (vHPC), increased BDNF immunoreactivity was shown in rats that underwent extinction training (Rosas-Vidal et al., 2018). When infused in the vHPC of fear conditioned rats, BDNF also increased the firing rate of structures signaling extinction learning (i.e., infralimbic cortex) (Rosas-Vidal et al., 2014). The dorsal HPC also may be involved in addiction processes given its dense connections to the VS/NAc and its associations with behavioral sensitization (Degoulet et al., 2008). In addiction studies, extinction of cocaine self-administration increased expression of the BDNF receptor, Trk-B (Hastings et al., 2020), and morphine-trained rats increased hippocampal BDNF expression when naloxone-induced withdrawal was applied in a delay-based decision-making task (Fatahi et al., 2020b). In our study, both extinction and extinction-resistant animals showed increases in BDNF, although only the extinction group attained significance. This increase is similar to previous findings from Martinez-Rivera et., al (2019), Rosas-Vidal et al. (2018) were Bdnf mRNA transcripts were only significantly increased in animals in the extinction group but not in the extinction-resistant. Nonetheless, it is possible that extinction-resistant animals may require additional training sessions to attain extinction. Whether BDNF is transported from the HPC to the VS/NAc, or to other brain regions, remains to be determined. In AMY, our study showed that both extinction and extinction-resistant animals significantly increased BDNF expression. Like the HPC, the role of BDNF in AMY are scarce in addiction. However, studies in fear showed that AMY plays a critical role in context-based learning were connections from the vHPC to the basal amygdala contribute to encoding conditioned fear (Kim and Cho, 2020). In addition, AMY also regulates fear extinction mediated by signaling from the central nucleus and basolateral amygdala (Pare and Duvarci, 2012). It is suggested that regulation of fear extinction relies on BDNF signaling as TrK-B knockdown blocks consolidation of extinction (Chhatwal et al., 2006), Trk-B agonists facilitate extinction (Andero et al., 2011), and cortical BDNF/Trk-B signaling to the AMY is required for fear extinction learning (Meis et al., 2020). The role of amygdalar BDNF in extinction of addictive behaviors is understudied, though it may be regulating aversive responses to withdrawal (Heldt et al., 2014). Yet, opposing effects were reported for alcohol and opioids; having decreased BDNF in AMY during alcohol withdrawal (You et al., 2014), but an increase during morphine-induced withdrawal (Martínez-Laorden et al., 2020). However, decreased BDNF expression in alcohol withdrawal might be due to the absence of an extinction phase. As the AMY is associated with context-based learning, the increased amygdalar BDNF expression observed in our study is likely due to the extinction training, as it was present in both extinction and extinction-resistant groups. Given that these two groups did not differ in BDNF expression in the AMY after extinction training, it seems that successful extinction may be dictated by the HPC through a BDNFergic mechanism.
To determine whether differences in extinction outcomes were due to the severity of withdrawal-induced anxiety, we assessed rears at various time points of the protocol. Increased rearing activity has been previously associated with morphine conditioning (Lu et al., 2005), as well as other factors such as exposure to novel environments (Lever et al., 2006). In our study, we observed increased rears in baseline as compared to conditioning or extinction-tests, which may be due to an environmental novelty effect, as animals receiving extinction training had been exposed to the CPP chamber for 8 additional sessions. Similarly, during the extinction-test day, we observed and increase in rears in sham-extinction animals. This could also be a novelty effect, as this group showed no increase in rears during the conditioning test day (no drug was administered) suggesting the increase was not due to lack of the expected morphine. Previous studies have shown that reintroducing animals to the context restores the effects of novelty (Lever et al., 2006). In our study, extinction trained animals showed no differences in rears despite extinction phenotype, suggesting that differences in extinction between groups are not based on severity of drug-withdrawal, at least during early stages of drug dependence.
A growing number of studies suggest that interfering with maladaptive emotional memories may lead to better treatment outcomes for diseases (Liu et al., 2020). As BDNF has been suggested as a potential treatment for addiction (Barker et al., 2015), our findings show that extinction training elicits changes in BDNF expression in the HPC and AMY, although only the HPC seems to dictate successful extinction learning. Differences in successful extinction vs extinction-resistant are not likely due to withdrawal-induced anxiety. Future studies will interrogate downstream effector molecules of the BDNF signal transduction cascade, such as Trk-B and pERK. Therefore, our preclinical model of addiction suggests that BDNF could play a key role in the extinction of opioid-related behaviors that combined with behavioral therapies could be a potential pharmacological target for addiction.
Mario E. Lloret-Torres: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing. Jennifer L. Barreto-Estrada: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing. Roxsana N. Ayala-Pagan: Data curation, Investigation, Visualization, Project administration. Freddyson J. Martinez-Rivera: Conceptualization, Investigation, Validation, Writing – review & editing. Bonilla Pedro: Data curation, Formal analysis, Investigation, Writing – original draft. | true | true | true |
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PMC9580248 | Shaolong Hao,Wei Han,Yu Ji,Hao Sun,Haowei Shi,Jihong Ma,James Yip,Yuchuan Ding | BANCR positively regulates the HIF-1α/VEGF-C/VEGFR-3 pathway in a hypoxic microenvironment to promote lymphangiogenesis in pancreatic cancer cells | 11-10-2022 | pancreatic cancer,long non-coding RNA,hypoxic microenvironment,lymphangiogenesis | The aim of the present study was to explore the effects of BRAF-activated non-protein coding RNA (BANCR) on pancreatic microlymphangiogenesis in pancreatic cancer (PC) and its molecular mechanism under hypoxic conditions. Reverse transcription-quantitative PCR (RT-qPCR) was used to detect the expression of BANCR in SW1990 and PANC-1 PC cell lines under normoxic and hypoxic conditions. Subsequently, the expression of BANCR in the PC cells was knocked down using small interfering RNAs (siRNAs). Western blotting and RT-qPCR analyses were performed to detect the expression of hypoxia-inducible factor (HIF-1α), VEGF-C and VEGFR-3 in the transfected cells. In addition, the transfected PC cells were co-cultured with human lymphatic endothelial cells and the lymphatic microvessel density (MLVD) was detected under normal and hypoxic conditions. Furthermore, HIF-1α expression in the PC cells was knocked down using siRNAs, and VEGF-C and VEGFR-3 mRNA expression in the HIF-1α knockdown cells was detected using RT-qPCR. The results showed that the expression of BANCR in the SW1990 and PANC-1 PC cell lines was significantly higher than that in human pancreatic duct endothelial cells. Additionally, the expression of BANCR was significantly increased in PC cells under hypoxic conditions compared with normoxic conditions. The MLVD of PC cells under hypoxic conditions was significantly higher compared with that under normoxic conditions, and the MLVD in the si-BANCR group was lower than that in the si-NC group, indicating that si-BANCR downregulated MLVD. These results indicate that BANCR positively regulated the expression of HIF-1α in PC cells at the transcriptional and translational levels. Finally, the expression levels of VEGF-C and VEGFR-3 in PC cells were significantly reduced when BANCR or HIF-1α expression was knocked down. In conclusion, the results demonstrate that the expression of BANCR in PC cells was significantly increased under hypoxic conditions and suggest that BANCR promoted tumor cell lymphangiogenesis by upregulating the HIF-1α/VEGF-C/VEGFR-3 pathway, which plays an important role in the process of PC lymph node metastasis. | BANCR positively regulates the HIF-1α/VEGF-C/VEGFR-3 pathway in a hypoxic microenvironment to promote lymphangiogenesis in pancreatic cancer cells
The aim of the present study was to explore the effects of BRAF-activated non-protein coding RNA (BANCR) on pancreatic microlymphangiogenesis in pancreatic cancer (PC) and its molecular mechanism under hypoxic conditions. Reverse transcription-quantitative PCR (RT-qPCR) was used to detect the expression of BANCR in SW1990 and PANC-1 PC cell lines under normoxic and hypoxic conditions. Subsequently, the expression of BANCR in the PC cells was knocked down using small interfering RNAs (siRNAs). Western blotting and RT-qPCR analyses were performed to detect the expression of hypoxia-inducible factor (HIF-1α), VEGF-C and VEGFR-3 in the transfected cells. In addition, the transfected PC cells were co-cultured with human lymphatic endothelial cells and the lymphatic microvessel density (MLVD) was detected under normal and hypoxic conditions. Furthermore, HIF-1α expression in the PC cells was knocked down using siRNAs, and VEGF-C and VEGFR-3 mRNA expression in the HIF-1α knockdown cells was detected using RT-qPCR. The results showed that the expression of BANCR in the SW1990 and PANC-1 PC cell lines was significantly higher than that in human pancreatic duct endothelial cells. Additionally, the expression of BANCR was significantly increased in PC cells under hypoxic conditions compared with normoxic conditions. The MLVD of PC cells under hypoxic conditions was significantly higher compared with that under normoxic conditions, and the MLVD in the si-BANCR group was lower than that in the si-NC group, indicating that si-BANCR downregulated MLVD. These results indicate that BANCR positively regulated the expression of HIF-1α in PC cells at the transcriptional and translational levels. Finally, the expression levels of VEGF-C and VEGFR-3 in PC cells were significantly reduced when BANCR or HIF-1α expression was knocked down. In conclusion, the results demonstrate that the expression of BANCR in PC cells was significantly increased under hypoxic conditions and suggest that BANCR promoted tumor cell lymphangiogenesis by upregulating the HIF-1α/VEGF-C/VEGFR-3 pathway, which plays an important role in the process of PC lymph node metastasis.
Pancreatic cancer (PC) is a malignant tumor of the digestive system that is challenging to diagnose and treat, exhibits a high degree of malignancy and is associated with poor prognosis (1). According to the literature, the 5-year survival rate of PC is <8% (2), and >90% of PC cases are pancreatic ductal adenocarcinoma (3). As the onset of PC is often missed, patients are frequently diagnosed in the first instance with metastatic or advanced cancer, which limits the clinical treatment options. Lymph node metastasis, as the primary pathway and an early event in PC, is an important factor that influences the clinical stage, treatment and prognosis of patients with PC (4). Clinical studies have shown that the incidence of lymphatic invasion by cancer cells is 3–5-fold higher than that of vascular invasion (5). Although the lymph node metastasis of PC is important clinically, the molecular mechanism that induces PC cells to separate from the primary focus, invade lymphatic vessels and metastasize to regional lymph nodes is unclear. Therefore, it is of great value to explore the molecular mechanism of lymph node metastasis of PC to improve the clinical diagnosis, treatment and prognosis of patients with PC. Lymphatic system vessels are the primary channels through which cancer cells spread from a local tumor to lymph nodes and then the lymphatic circulation to distant sites (6). Lymphangiogenesis is considered to be the most critical and rate-limiting step in the development of lymph node metastasis by malignant tumor cells (7). Due to the lack of specific molecular markers for lymphatic endothelial cells, the study of lymph node metastasis has been overshadowed by the study of vascular system invasion. However, since specific markers for lymphatic endothelial cells have been discovered, these now provide a basis for further study of the mechanism of lymph node metastasis. VEGF-C has been shown to be an important growth factor in the lymphangiogenesis of solid malignant tumors, which promotes lymphangiogenesis and microlymphangiogenesis by binding to VEGFR-3. The VEGF-C and VEGFR-3 axis has been shown serve a central role in the initiation of lymphangiogenesis (8). A study by Ochi et al (9) showed that the expression levels of VEGF-C and VEGFR-3 were significantly correlated with each other in patients with pancreatic carcinoma. The study also demonstrated that VEGFR-3 combined with VEGF-C to stimulate the formation of lymphatic vessels in pancreatic carcinoma, which induced the formation of new lymphatic capillaries and increased the risk of lymph node metastasis. The aforementioned studies indicate that VEGF-C/VEGFR-3 in PC tissues is an important pathway that induces the formation of lymphatic vessels and lymph node metastases. However, the regulatory mechanism regulating the VEGF-C/VEGFR-3 pathway in PC has not been determined previously, to the best of our knowledge. Therefore, the present study investigated the potential regulatory role of BRAF-activated non-protein coding RNA (BANCR) and hypoxia-inducible factor (HIF)-1α in the VEGF-C/VEGFR-3 pathway in PC by knocking down their expression. Studies have shown that lncRNAs are abnormally expressed in several types of human malignant tumors and participate in the proliferation, invasion and metastasis of tumor cells (10). Previous studies by Jiang et al (11) and Shen et al (12) have demonstrated the important role of the long non-coding RNA (lncRNA) BANCR in the tumor lymph node metastasis of breast and colorectal cancer, respectively. BANCR was first reported by Flockhart et al (13), who identified it in melanoma cells in 2012. It is a ~693-bp lncRNA that is present on chromosome 9 and specifically activated by a mutation in the BRAFV600E gene; its expression is upregulated in melanoma cells and it plays an important role in the promotion of lymph node metastasis. With increased interest in BANCR, subsequent studies found that in addition to melanoma, BANCR also serves a key role in other types of tumors (14). Studies have detected the upregulated expression of BANCR in gastric cancer (15), colorectal cancer (12), hepatocellular carcinoma (16), esophageal squamous cell carcinoma (17), osteosarcoma (18) and thyroid cancer (19,20), and shown BANCR to be significantly associated with a poor prognosis, tumor cell proliferation, invasion and local lymph node metastasis. Notably, a study by Wu et al (4) demonstrated that the expression of BANCR was upregulated in PC tissues and cell lines, namely PANC-1 and SW1990, and found that BANCR upregulation was closely associated with lymph node metastasis in patients with PC. In addition, the study demonstrated that interfering with the expression of BANCR effectively inhibited the proliferation and invasion of PC cells. The present study aimed to verify the important role of BANCR in PC proliferation, invasion and lymph node metastasis. The expression levels of BANCR in human PC cells were compared with those in normal human pancreatic cells, and the role and molecular mechanisms of BANCR in the lymphangiogenesis of PC were determined for the first time, to the best of our knowledge. A hypoxic microenvironment is common for the occurrence and development of malignant tumors. In particular, a hypoxic microenvironment is important in the induction of highly invasive PC, and is established due to a poor blood supply to the rapidly proliferating cells (21); it is also a key factor underlying the high degree of malignancy and poor curative outcomes (22). A previous study on hypoxia focused on tumor angiogenesis and drug resistance (23); however, little is known regarding the mechanism of tumor lymphangiogenesis and lymph node metastasis. The activation of HIF is the key molecular characteristic of tumor cell changes occurring in a hypoxic microenvironment, which is closely associated with the occurrence, development, invasion and metastasis of tumors (24). HIF has three subtypes: HIF-1, −2 and −3, of which HIF-1 is the most important subtype in hypoxic tumor cells and is widely expressed in a variety of human tumors (25). HIF-1 is composed of HIF-1α and HIF-1β, and the former is the key transcription factor in the hypoxic response, due to its important role in the regulation of hypoxic gene expression and in the signal transduction network (26). Previous studies have shown that the upregulated expression of HIF-1α in PC is associated with tumorigenesis and progression (27,28). Liu et al (29) confirmed the high expression of HIF-1α in PC and its association with lymph node metastasis and TNM staging. In the present study, the expression levels of HIF-1α in PANC-1 and SW1990 PC cell lines were assessed, and the effect of knocking down the expression HIF-1α was evaluated. Furthermore, the ability of BANCR to regulate the expression of HIF-1α was investigated, and the effect of HIF-1α on the transcription and translation of VEGF-C in PC cells was explored. The role of BANCR in the regulation of HIF-1α was thereby revealed, and the potential value of the BANCR/HIF-1α/VEGF-C/VEGFR-3 pathway in the lymphangiogenesis and lymph node metastasis in PC was determined.
The PC cell lines PANC-1 and SW1990, immortalized human pancreatic ductal epithelial cells (HPDCs) and human lymphatic tube endothelial cells (HDLECs) were purchased from Guangzhou Genio Biotech Co., Ltd. The SW1990 and HPDC cells were cultured in DMEM (MilliporeSigma), the PANC-1 cells were cultured in RPMI-1640 (HyClone; Cytiva) and the HDLECs were cultured in Endothelial Cell Medium (ScienCell Research Laboratories, Inc.), each supplemented with 10% FBS (Thermo Fisher Scientific, Inc.) and 1% antibiotics (penicillin-streptomycin; Thermo Fisher Scientific, Inc.). Culture was performed under normoxic or hypoxic conditions in a humidified incubator at 37°C. The normoxic conditions were 20% O2, 5% CO2 and 75% N2, and the hypoxic conditions were 1% O2, 5% CO2 and 94% N2. Cells in the logarithmic growth stage were selected for subsequent experiments.
PC cell lines in the logarithmic growth stage were divided into two groups after digestion. Lipofectamine™ 3000 (Thermo Fisher Scientific, Inc.) was used to transfect the PC cell lines with a small interfering (si)RNA BANCR knockdown plasmid (TIANpure Mini Plasmid Kit II; Tiangen Biotech Co., Ltd.) and negative control plasmid (si-NC) to establish the si-BANCR and si-NC groups, respectively. In brief, PC cells at 80% confluence were plated in a 12-well plate. A total of 100 µl Opti-MEM was used to dilute the Lipofectamine™ 3000 (4 µl) and vector/pcDNA (6 µl; 100 ng/µl siRNA/plasmid), incubated at room temperature for 5 min, mixed gently and then incubated for a further 20 min at 37°C. Next, the cell medium was replaced with Opti-MEM (700 µl/well; Thermo Fisher Scientific, Inc.), and the aforementioned transfection mixture was added to each well. After 8 h at 37°C, the medium was replaced with standard supplemented medium, and after transfection for 48 h, the transfected cells were used for subsequent experiments. The si-BANCR sequence was 5′-GGUGTGGCGUCTUGCUUTT-3′. The si-NC sequence was 5′-GGCCGGUTTCCUUTTCUGCG-3′. In a subsequent experiment, the PC cell lines were transfected with the si-HIF-1α sequence 5′-CTGATGACCACACAACTTGA-3′ to establish a HIF-1α knockdown group using the aforementioned transfection protocol. Transfection success was evaluated by the detection of green fluorescent protein and reverse transcription-quantitative PCR (RT-qPCR) as shown in Figs. S1 and S2.
PC cells stably transfected with si-BANCR or si-NC were digested and HDLECs were added to establish a mixed cell suspension. The mixed cell suspension (PC cells:HDLEC cells, 1:1; 7.5×103 cells/well) was seeded on a Matrigel basement membrane (BD Biosciences) coating in 96-well plates at 100 µl/well, with 3 wells per condition, and the cells were cultured under the aforementioned normoxic or hypoxic conditions. The formation of microlymphatic vessels was observed after culturing for 12 h.
The lymphatic vessel distribution was observed using a low magnification inverted fluorescence microscope (×100) and assessed in a double-blinded manner by two pathologists. Subsequently, 3 hot-spot areas (areas that appear to have a high MLVD density) were selected and the positive structure of each area was observed using a high-power field of view (×400). Finally, 5 regions were randomly selected for counting the number of vessels, and the maximum value was selected as the MLVD of each hot-spot area for statistical analysis.
A TRIzol® RNA extraction kit (Invitrogen; Thermo Fisher Scientific, Inc.) was used to extract total RNAs from the cells according to the manufacturer's instruction. The RNA was reverse transcribed into cDNA using PrimeScript RT Master Mix (Takara Biotechnology Co., Ltd.) at 37°C for 15 min. The relative expression levels of BANCR, HIF-1α, VEGF-C and VEGFR-3 in each group were detected. RT-qPCR was performed in strict accordance with the instructions of the SYBR® Primx Ex Taq™ (TIi RNaseH Plus) (cat. no. RR420A; Takara Bio, Inc.), with GAPDH as the internal reference gene in a reaction system of 20 µl. The thermocycling conditions were as follows: Pre-denaturation at 95°C for 5 min; followed by 38 cycles of denaturation at 95°C for 30 sec, annealing at 65°C for 30 sec and extension at 72°C for 30 sec; and a final extension step of 72°C for 8 min. Primers were designed based on the following human gene sequences in NCBI GeneBank: BANCR (NC_000009.12), HIF-1α (NC_000014.9), VEGF-C (NC_000004.12), VEGFR-3 (NC_000005.10) and GAPDH (NC_000012.12). The primers were synthesized by Bio-Engineering Co., Ltd., and their sequences are provided in Table I. Quantitative analysis of relative gene expression data used the 2−ΔΔCq method, and GAPDH was used as the internal reference control (30).
The transfected cells were examined by western blotting. Total protein was extracted under different treatment conditions using RIPA buffer (cat. no. R0010; Beijing Solarbio Science & Technology Co., Ltd.) and protein concentration was quantified using the BCA method (Thermo Fisher Scientific, Inc.). A total of 20 µg protein/lane was loaded for electrophoresis. SDS-PAGE on a 10% gel was performed at a constant voltage of 100 V for 40 min. After electrophoresis, the electroporation apparatus was used to transfer the resolved proteins to a PVDF membrane using a constant current of 250 mA for 2 h. The membranes were subsequently blocked at room temperature for 1 h with 5% skimmed milk/TBS-0.1% and Tween-20 (TBST) solution. Primary antibodies against HIF-1α (cat. no. ab2185; 1:500; Abcam), VEGF-C (cat. no. 22601-1-AP; 1:1,000; ProteinTech Group, Inc.) and VEGFR-3 (cat. no. Ab27278; 1:1,000; Abcam) were used. A GAPDH antibody (cat. no. ab9485; 1:3,000; Abcam) was also used to detect GAPDH as an internal reference. The membrane was incubated overnight with the primary antibodies at 4°C. After washing the film with TBS-0.1% and Tween-20 (TBST) three times, the film was incubated with the secondary antibody (Alexa Fluor® 568; cat. no. ab175473; 1:5,000; Abcam) at room temperature for 1 h. TBST was used to wash the films again three times, after which the signals were developed and visualized using an ECL reagent (Thermo Fisher Scientific, Inc.). A CanoScan Lide 120 scanner (Canon, Inc.) was used to scan the film for densitometric analysis. Densitometric analysis was performed using ImageJ 1.48 (National Institutes of Health).
SPSS version 19.0 (IBM Corp) was used for statistical analysis. The relative expression levels of BANCR, HIF-1α, VEGF-C and VEGFR-3 in PC cells and MLVD values are expressed as the mean ± standard deviation. The relative expression levels of BANCR in HPDCs and the PANC-1 and SW1900 cell lines were analyzed by one-way ANOVA followed by Tukey's multiple comparison test. Differences between two groups were compared using an independent samples Student's t-test. P<0.05 was considered to indicate a statistically significant difference.
RT-qPCR was used to detect the relative expression of BANCR in the HPDCs and the PANC-1 and SW1990 PC cell lines. The results showed that the expression of BANCR in the PANC-1 and SW1990 cells was significantly upregulated compared with that in the HPDCs (P<0.05). No significant difference in the expression of BANCR was detected between the PANC-1 and SW1990 cells (Fig. 1).
RT-qPCR was used to detect the expression of BANCR in PANC-1 and SW1990 cells under normoxic and hypoxic conditions. The results showed that the expression of BANCR was significantly higher under hypoxic conditions compared with normoxic conditions (P<0.05; Fig. 2).
BANCR expression was knocked down in PANC-1 and SW1990 cells, after which the cells were co-cultured with HDLECs under normoxic and hypoxic conditions. In the PANC-1 cell line, the MLVD values in the si-NC group and the si-BANCR group increased significantly under hypoxic conditions compared with those in cells grown under normoxic conditions (Fig. 3A and B). In addition, the degree of MLVD was reduced significantly following the knockdown of BANCR expression in PANC-1 cells (si-BANCR: hypoxia vs. normoxia: 9.133±3.925 vs. 5.8±3.189, respectively, P<0.05; si-NC: hypoxia vs. normoxia: 61.6±12.53 vs. 18.13±6.128, respectively, P<0.05). Similar results were observed in the SW1990 cells (si-BANCR: hypoxia vs. normoxia: 9.2±4.411 vs. 4.933±2.604, respectively, P<0.05; si-NC: hypoxia vs. normoxia: 59.0±14.89 vs. 18.2±6.45, respectively, P<0.05; Fig. 3C and D).
SW1990 and PANC-1 cells transfected with si-BANCR or si-NC were cultured under hypoxic conditions. Western blotting was used to detect the relative protein expression levels of HIF-1α in each group, and the results are shown in Fig. 4A and B. The relative protein expression of HIF-1α in the si-BANCR group was significantly lower compared with that in the si-NC group in each cell line (SW1990: 0.646±0.200 vs. 1.630±0.453, respectively, P<0.05; PANC-1: 0.815±0.195 vs. 1.838±0.228, respectively, P<0.05). RT-qPCR was used to detect the relative mRNA expression levels of HIF-1α in the si-BANCR and si-NC groups and the results were consistent with the protein results (SW1990: 1.076±0.430 vs. 2.253±0.950, P<0.05; PANC-1: 1.248±0.228 vs. 2.309±0.426, P<0.05; Fig. 4C).
RT-qPCR and western blotting were used to detect the relative protein and mRNA expression levels of VEGF-C and VEGFR-3 in SW1990 cells transfected with si-BANCR, si-HIF-1α or si-NC under hypoxic conditions. The relative protein and mRNA expression levels of VEGF-C and VEGFR-3 the si-BANCR group were significantly lower compared with those in the si-NC group (P<0.05; Fig. 5A-C). The relative protein and mRNA expression levels of VEGF-C and VEGFR-3 in the si-HIF-1α group were also decreased significantly compared with those in the si-NC group (P<0.05; Fig. 5D-F). These results indicate that BANCR may upregulate VEGF-C and VEGFR-3 by regulating HIF-1α expression.
PC exhibits a high degree of malignancy and is associated with a poor prognosis. The majority of patients with PC are first diagnosed with middle- to late-stage cancer, and this limits the clinical curative effects of treatments. Despite advancements in surgery, radiotherapy and chemotherapy, the prognosis of patients with PC has not substantially improved in the past 20 years (1). Lymph node metastasis is the primary pathway and an early event in PC metastasis, which affects the clinical stage, treatment and prognosis of patients with PC (4). Previous studies on the lymph node metastasis of PC have focused on clinical high-risk factors and their correlations; however, less research has been performed to determine the molecular mechanisms underlying lymph node metastasis. In the present study, the molecular mechanism underlying lymph node metastasis in PC was preliminarily explored at the cellular level. PANC-1 and SW1990 cell lines were chosen for use in the study as they are the most widely used representative PC cell lines, which are easy to cultivate and use in lymphangiogenesis experiments and also exhibit high BANCR expression levels. The present study demonstrated that under hypoxic conditions, BANCR promoted the lymphangiogenesis of PC cells by a mechanism that may be associated with increased activity of an HIF-1α/VEGF-C/VEGFR-3 axis. Lymphangiogenesis is the formation of new lymphatic vessels in tissues. Liu et al (31) detected the presence of what they termed microlymphatics, new lymphatic vessels, or a lymphoid labyrinth in the tissues of patients with gastric or colorectal cancer, and this was significantly positively associated with lymph node metastasis in nude mice xenografts. In PC, Sipos et al (32) detected microlymphatics in PC tissues and an increased number of lymphatic vessels around malignant tumor tissues. Additionally, a significant association between the number of lymphatic vessels and lymph node metastasis was observed. The aforementioned studies together indicated that lymphangiogenesis is a key step in tumor lymph node metastasis. In the present study, it was shown that lymphangiogenesis in PC cells increased significantly under hypoxic conditions, and this was decreased by knocking down the expression of BANCR. These results reveal that a hypoxic microenvironment and upregulation of BANCR expression are key factors in lymphangiogenesis of PC. A study by Keklikoglou et al (33) revealed that the expression of VEGF-C was upregulated in PC and positively associated with MLVD, the Dukes stage and lymph node metastasis. VEGFR-3 was the first marker of the tyrosine-protein kinase family to be discovered and is the specific receptor for VEGF-C. Yang et al (34) demonstrated that downregulation of VEGFR-3 inhibits lymphangiogenesis, which further inhibits the lymphatic metastasis of bladder cancer. In addition, a review conducted by Winder and Lenz (35) described data indicating that VEGFR-3 combined with VEGF-C induces the formation of new lymphatic capillaries and increases the risk of lymph node metastasis in colon cancer. Furthermore, Ochi et al (9) detected a correlation between VEGF-C and VEGFR-3 expression levels in PC by analyzing the clinical and pathological data from patients; the authors concluded that VEGFR-3 combined with VEGF-C induced the formation of new lymphatic capillaries and increased the risk of lymph node metastasis in PC. These findings indicate that the VEGF-C/VEGFR-3 pathway is important in the formation of microlymphatic vessels and lymph node metastasis in PC. However, the molecular mechanisms regulated by the VEGF-C/VEGFR-3 pathway in PC remain to be determined. A meta-analysis showed that BANCR is upregulated in a variety of solid malignancies and is closely associated with a poor overall survival rate, lymph node metastasis and distant metastasis (36). In the present study, the expression of BANCR in SW1990 and PANC-1 cells was detected. The results showed that BANCR was upregulated in PC cells, and the upregulated expression of BANCR was significantly associated with lymphangiogenesis. Furthermore, knocking down the expression of BANCR significantly downregulated the expression of HIF-1α, VEGF-C and VEGFR-3 at the transcriptional and translational levels. Based on the aforementioned results, it may be assumed that BANCR is upregulated in PC and can promote tumor lymphangiogenesis via the HIF-1 α/VEGF-C/VEGFR-3 pathway, which may lead to tumor lymph node metastasis. In the present study, the effects of hypoxia on the expression of BANCR in PC cells were also investigated. The results showed that the expression of BANCR in PC cells was significantly increased under hypoxic conditions. Therefore, it is suggested that hypoxia and BANCR are closely associated with the occurrence and development of PC. Microlymphangiogenesis was also assessed, and the results showed that the MLVD of PC cells increased significantly under hypoxic conditions, and the MLVD in the negative control cells was higher than that in the cells in which BANCR was knocked down. The aforementioned study by Sipos et al (32) detected microlymphatics, new lymphatics or a lymphoid labyrinth in the tissues of patients with PC. Another study of tissue samples from patients with PC, conducted by Cheng et al (37), obtained similar results, with the observation of microlymphatics, new lymphatic vessels and/or a lymphoid labyrinth in PC tissues. The aforementioned results indicate that BANCR may promote the formation of PC microlymphatics under hypoxic conditions. Nakajima et al (38) reported a significant association between the elevated expression of HIF-1α and VEGF-C mRNA and lymph node metastasis in patients non-small cell lung cancer. In addition, Schoppmann et al (39) provided evidence that HIF-1α is involved in the regulation of VEGF-C expression and lymphangiogenesis in breast cancer, and a study by Katsuta et al (40) in esophageal cancer presented a similar result. However, the role of HIF-1α in PC is not fully understood. The activation of HIF-1α is the most notable molecular tumor cell alteration that occurs under hypoxic conditions, and its abnormal expression is associated with a poor prognosis in numerous types of tumors (41). A recent study by Liu et al (29) reported that the increased expression of HIF-1α in the tumor tissues of patients with PC is associated with tumor lymph node metastasis, late-stage tumors and a poorer predicted prognosis at first diagnosis, and revealed the high expression of HIF-1α in PC tissues and its relationship with TNM stage and lymph node metastasis. The results of the present study showed an association between the expression levels of BANCR and HIF-1α in PC cells. Knocking down the expression of BANCR induced a significant reduction in the expression of HIF-1α in PC cells at the transcriptional and translational levels, demonstrating the positive regulation effect of BANCR on HIF-1α in PC cells. An increase in lymphangiogenesis and the infiltration of lymphatic vessels into solid malignant tumor tissues are necessary conditions for local lymph node metastasis, and MLVD is an important quantitative index of these processes. VEGF-C has been shown to be an important growth factor in the lymphangiogenesis of solid malignant tumors, which can promote lymphangiogenesis and microlymphangiogenesis via its combination with VEGFR-3. In the present study, the expression levels of VEGF-C and VEGFR-3 were significantly reduced by knocking down the expression of BANCR. Furthermore, VEGF-C and VEGFR-3 expression levels were also significantly decreased by knocking down the expression of HIF-1α. The present study revealed that the expression of BANCR was significantly increased in PC cells under hypoxic conditions, and higher levels of BANCR were associated with higher expression of components of the HIF-1α/VEGF-C/VEGFR-3 axis at the transcriptional and translational levels. In conclusion, the expression of BANCR in PC cells was significantly increased under hypoxic conditions and the upregulation of BANCR promoted lymphangiogenesis and upregulated the expression of all components of the HIF-1α/VEGF-C/VEGFR-3 pathway, which plays an important role in the process of PC lymph node metastasis. These findings suggest that BANCR may be a useful biomarker and potential novel target for the diagnosis, treatment and prognostic prediction of PC. | true | true | true |
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PMC9580931 | Yu-Peng Hsieh,Yuan-Mao Hung,Mong-Hsun Tsai,Liang-Chuan Lai,Eric Y. Chuang | 16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences 16S-ITGDB | 03-08-2022 | taxonomy assignment,16S full length,ITGDB,sequence classification,16S rRNA (16S rDNA),metagenomics 16S,third-generation sequencing | Analyzing 16S ribosomal RNA (rRNA) sequences allows researchers to elucidate the prokaryotic composition of an environment. In recent years, third-generation sequencing technology has provided opportunities for researchers to perform full-length sequence analysis of bacterial 16S rRNA. RDP, SILVA, and Greengenes are the most widely used 16S rRNA databases. Many 16S rRNA classifiers have used these databases as a reference for taxonomic assignment tasks. However, some of the prokaryotic taxonomies only exist in one of the three databases. Furthermore, Greengenes and SILVA include a considerable number of taxonomies that do not have the resolution to the species level, which has limited the classifiers’ performance. In order to improve the accuracy of taxonomic assignment at the species level for full-length 16S rRNA sequences, we manually curated the three databases and removed the sequences that did not have a species name. We then established a taxonomy-based integrated database by considering both taxonomies and sequences from all three 16S rRNA databases and validated it by a mock community. Results showed that our taxonomy-based integrated database had improved taxonomic resolution to the species level. The integrated database and the related datasets are available at https://github.com/yphsieh/ItgDB. | 16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences 16S-ITGDB
Analyzing 16S ribosomal RNA (rRNA) sequences allows researchers to elucidate the prokaryotic composition of an environment. In recent years, third-generation sequencing technology has provided opportunities for researchers to perform full-length sequence analysis of bacterial 16S rRNA. RDP, SILVA, and Greengenes are the most widely used 16S rRNA databases. Many 16S rRNA classifiers have used these databases as a reference for taxonomic assignment tasks. However, some of the prokaryotic taxonomies only exist in one of the three databases. Furthermore, Greengenes and SILVA include a considerable number of taxonomies that do not have the resolution to the species level, which has limited the classifiers’ performance. In order to improve the accuracy of taxonomic assignment at the species level for full-length 16S rRNA sequences, we manually curated the three databases and removed the sequences that did not have a species name. We then established a taxonomy-based integrated database by considering both taxonomies and sequences from all three 16S rRNA databases and validated it by a mock community. Results showed that our taxonomy-based integrated database had improved taxonomic resolution to the species level. The integrated database and the related datasets are available at https://github.com/yphsieh/ItgDB.
Since the advent of next-generation sequencing (NGS) technology, analyzing 16S ribosomal RNA (rRNA) has allowed biologists to assess the bacterial or archaeal composition of an environment. The 16S rRNA gene consists of nine hypervariable regions (V1–V9) and includes approximately 1,500 ∼1,600 nucleotides (Bukin et al., 2019; Johnson et al., 2019). These regions have varying conservation and are rich in taxonomic information. Different hypervariable regions were investigated to improve the taxonomic assignment performance (Wang and Qian, 2009; Allard et al., 2015; Yang et al., 2016; Bukin et al., 2019; Johnson et al., 2019; Abellan-Schneyder et al., 2021). In the past decade, the 16S rRNA V4 or V3–V4 regions were targeted for microbial composition analysis (Richards et al., 2017; Jha et al., 2018; Moustafa et al., 2018; Peters et al., 2018). However, NGS technology generated short reads that covered only a few 16S rRNA regions (Yang et al., 2016). Using only one or two hypervariable regions makes it difficult to classify the bacterial 16S rRNA sequences down to the species level in taxonomic assignment tasks (Johnson et al., 2019). For a prokaryotic 16S sequence classifier, it requires at least 400 nucleotides to assign a 16S sequence down to the genus level (Okubo et al., 2009). However, after quality control, the read length of the trimmed 16S sequences was about 250 ∼500 base-pairs (bp), which limits the taxonomic resolution only to the genus levels. Thus, full-length 16S rRNA sequence analysis could be the resolution to improve the taxonomic depth down to the species level. In recent years, third-generation sequencing (TGS) technology, such as Pacific BioScience (PacBio) (Rhoads and Au, 2015; Schloss et al., 2016) and Nanopore (Lu et al., 2016; Lin et al., 2021), has provided long-read sequencing methods, making it possible for researchers to analyze the full-length of 16S rRNA (Cuscó et al., 2018; Klemetsen et al., 2019). The full-length sequence analysis could enhance taxonomic resolution to the species level because the long reads that include the V1–V9 regions provide more comprehensive taxonomic information (Johnson et al., 2019). The single-molecule real-time (SMRT) and circular consensus sequencing (CCS) technologies developed by PacBio could provide high quality 16S full-length sequencing (Korlach, 2013). During the past 5 years, a growing number of studies took the advantage of long read sequencing technology to attain more comprehensive microbial composition of the environments (Hur and Park, 2019; Tremblay and Yergeau, 2019; Lam et al., 2020; Wade and Prosdocimi, 2020; Mahmud et al., 2021; Pootakham et al., 2021). However, although there were several widely used 16S analytical pipelines for NGS data analysis, such as QIIME2 (Bolyen et al., 2019), Mothur (Schloss, 2020), and UPARSE (Edgar, 2013), there still lacks comprehensive and convenient 16S tools for TGS data analysis. Researchers may need to build their own 16S full-length analytical pipeline. Yet, the advantages of 16S full-length sequence analysis could only be demonstrated when the taxonomic assignment tools, including 16S rRNA classifiers and sequence databases, are well prepared. Several classification algorithms have been proposed to classify bacterial 16S rRNA sequences (Wang et al., 2007; Allard et al., 2015; Edgar, 2016; Bokulich et al., 2018; Schloss, 2020). These classification algorithms used prokaryotic 16S databases, such as the ribosomal database project (RDP) (Maidak et al., 1997), SILVA (Quast et al., 2012), or Greengenes (DeSantis et al., 2006), as references. The RDP and SILVA databases are still being updated regularly, whereas Greengenes was not updated after August of 2013. Therefore, Greengenes includes fewer bacterial species than RDP and SILVA. Next, regarding these 16S rRNA databases, some taxonomies have annotated to the species level, while others may only include information to the genus, family, order, class, or even just phylum level. Even among the sequences with taxonomic information at the species level, the species information does not always have exact species name (sometimes the species names are listed as metagenome, candidate_division, bacterium, etc.). Sequences with anomalous nucleotide composition or labeled with low-resolution taxonomy dramatically limits the performance of classifiers. Furthermore, RDP, SILVA, and Greengenes have their own unique taxonomies (Abellan-Schneyder et al., 2021; Balvočiūtė and Huson, 2017), and it is impossible for a classifier to identify the bacterial taxonomy from these three databases other than the reference database used to establish the classifier. Therefore, in order to improve the classification performance, the 16S rRNA integrated database (ITGDB) was developed in this study by two ways: sequence-based and taxonomy-based integration. Both of the integrated databases were compared with RDP, SILVA, Greengenes, and other curated 16S reference databases, including 16S-UDb (Agnihotry et al., 2020), Genomic-based 16S rRNA database (Abellan-Schneyder et al., 2021), and Genome taxonomy database (Parks et al., 2021). The integrated database (ITGDB) can be used for any classifier that was developed in a specific reference database and largely improved the assignment resolution to the species level. The proposed 16S rRNA integrated databases can be downloaded from https://github.com/yphsieh/ItgDB.
RDP (version NO.18 trainset) (Maidak et al., 1997), SILVA (version 138, 99% clustering similarity) (Quast et al., 2012), and Greengenes (version 13_8, 99% clustering similarity) (DeSantis et al., 2006) databases were used for integration. Redundant sequences were removed by clustering all the sequences in these databases with 99% similarity. The sequence numbers of RDP, SILVA, and Greengenes were 21,295, 436,681, and 203,452, respectively. The percentages of the sequences that had exact species names in RDP, SILVA, and Greengenes were 94.86, 16.10, and 10.19%, respectively. Among these databases, RDP had the smallest quantity of sequences but possessed the highest percentage of sequences with exact species names. SILVA had the largest quantity of sequences, but most of the sequences did not have taxonomic resolution to the species level. The sequences without exact species names were manually removed from the databases. In our integration workflow, since RDP and SILVA included the newest information on bacteria and archaea, these two databases were firstly integrated. This integration produced an intermediate database—RDP and SILVA integrated database (RS-ITGDB). Next, the intermediate RS-ITGDB was further integrated with the Greengenes database. There were two types of integration—sequence-based integration and taxonomy-based integration (Figure 1). Both integrations were developed by using Python scripts. The algorithms were described as follows.
The concept of sequence-based integration was to collect all the sequences from RDP, SILVA, and Greengenes, regardless of the quality of taxonomic annotation. The workflow of sequence-based integration of any two databases (called the ‘basis’ database and the ‘candidate’ database) is shown in Figure 1 (A). The algorithm first took RDP as the basis database and integrated RDP with SILVA to produce the intermediate RDP-SILVA integrated database (RS-ITGDB). Next, the algorithm took RS-ITGDB as the basis database and integrated RS-ITGDB with Greengenes to produce the final sequence-based integrated database (ITGDB). During the sequence-based integration, the algorithm checked whether each sequence S i in the candidate database already existed in the basis database by comparing the nucleotide composition between the sequences. If the nucleotide composition of sequence S i contained the nucleotide composition of a sequence S j from the basis database, i.e., S i was longer than S j , then sequence S j would be replaced with sequence S i in the integrated database. If sequence S i could not be found in the basis database, then sequence S i would be directly added to the integrated database. Sequences S i and S j were regarded as different sequences (not contain each other) even if they only had one nucleotide difference. The algorithm terminated after comparing all the sequences between the basis database and candidate database.
For taxonomy-based integration, all sequences without exact species names were manually removed from RDP, SILVA, and Greengenes. For example, Acidocella_sp. only indicates the genus name with the abbreviation “sp.” in the species name. Some taxonomies only showed ambiguous description at the species level, such as “bacterium,” “metagenome,” “candidate_division,” “human_gut,” and “unidentified.” All sequences with such ambiguous species names were manually removed from the 16S databases to ensure each sequence had taxonomic resolution to the species level. The concept of taxonomy-based integration was first to collect the unique taxonomy from RDP, SILVA, and Greengenes and then integrate the different sequences for each taxonomy. The workflow of taxonomy-based integration of any two databases is shown in Figure 1B. It is similar to the sequence-based integration. The algorithm first took RDP as the basis database and integrated RDP with SILVA to produce the intermediate RDP-SILVA integrated database (RS-ITGDB). Next, the algorithm took RS-ITGDB as the basis database and integrated RS-ITGDB with Greengenes to produce the final taxonomy-based integrating database. During the taxonomy-based integration procedure, if a sequence S i from the candidate database had taxonomy that could not be found in the basis database, then sequence S i was added to the integrated database. The algorithm checked whether the taxonomy of sequence S i already existed in the basis database by comparing the string of taxonomic label of sequence S i with all taxonomies in the basis database. If the taxonomy of sequence S i already existed in the basis database, then the algorithm further compared the nucleotide composition between sequence S i and all the sequences of the basis database that possess the same taxonomy as S i . If the nucleotide composition of S i had at least one nucleotide difference with the sequences of the basis database under the same taxonomy, then sequence S i was added to the integrated database. Inversely, if sequence S i had already been collected in the basis database, no integration occurred.
Two experiments were carried out to validate the performance of the developed ITGDBs. One was database comparison, and the other was the ITGDBs’ performance with different classifiers. The purpose of the database comparison analysis was to compare the performance of our developed ITGDBs with other 16S reference databases. Another experiment was to measure the performance of several widely used 16S sequence classifiers using the ITGDB as the reference database.
Our proposed sequence-based ITGDB and taxonomy-based ITGDB were compared with RPD, SILVA, Greengenes, and other manually curated 16S sequence datasets, such as 16S-UDb (Agnihotry et al., 2020), Genomic-based 16S rRNA database (GRD) (Abellan-Schneyder et al., 2021) (https://metasystems.riken.jp/grd/), and Genome taxonomy database (GTDB) (Parks et al., 2021). Part of the 16S-UDb content was curated from early versions of SILVA (version 123), Greengenes (version 13_5), and RDP (version 11.4) based on 97% similarity in OTU clustering threshold. The 16S sequences in the GRD dataset were curated from the complete genome sequences and had sequence length from 65 to 2,900 nucleotides (Desai et al., 2020). Each sequence in 16S-UDb and GRD had taxonomic information down to the species level. The sequence numbers of 16S-UDb and GRD were 13,078 and 13,202, respectively. GTDB is a comprehensive metagenomic database that curated prokaryotic genome and taxonomies from the NCBI Assembly database (Parks et al., 2021). GTDB also supported 16S rRNA sequences that were extracted from the genomic database (Alishum, 2021). The sequence number of GTDB 16S dataset was 32,884.
The validation dataset for sequence-by-sequence validation was created by integrating the public mock communities, including Mockrobiota (Bokulich et al., 2016), PacBio HMP (Callahan et al., 2019), and PacBio Zymo (Callahan et al., 2019). First, unique sequences in 15 mock communities with comprehensive taxonomy information in Mockrobiota (Bokulich et al., 2016), such as mock 3, 4, 5, and 12 to 23, were used for the experiments. Next, PacBio HMP (Callahan et al., 2019) and PacBio Zymo (Callahan et al., 2019) mock communities were used, too. Since sequences in the PacBio HMP and Zymo mock community lacked taxonomy information, BLAST accompanied with the NCBI microbial 16S rRNA database was performed to annotate all sequences with species information (Bokulich et al., 2016). Finally, the validation dataset was created by combining Mockrobiota with the PacBio HMP and Zymo dataset. In total, the combined mock validation dataset contained 98,284 reads with taxonomy names to the species level in 94 species. The average sequence length was 1,548 bp. To test whether ITGDB had better performance in identifying the unique taxonomies than other three databases, another three validation datasets were prepared—Union, Exclusion, and Intersection. Among these datasets, Union and Exclusion were designed to collect the unique taxonomies from different databases, while the Intersection dataset was used to validate the performance of different reference databases without unique taxonomies. The concepts of producing Union, Exclusion, and Intersection datasets are shown in Figure 2. All the sequences in the validation datasets had exact species names. The Union dataset contained all the available sequences with exact species names in any of the three source databases. The Exclusion dataset contained the sequences whose species names were only available in one of the databases. The Intersection dataset contained the sequences whose species names were present in all three databases.
To assess the ITGDBs’ performance with compatible classifier experiments, we chose several widely used 16S classifiers: QIIME2 (RDP Bayesian classifier, version 2020.8) (Bokulich et al., 2018), SINTAX (usesarch version 11.0.667) (Edgar, 2016), SPINGO (version 1.3) (Allard et al., 2015), and Mothur (RDP Bayesian classifier, version 1.45.2) (Schloss, 2020). For the database comparison analysis, SINTAX was used as the standard for taxonomic assignment because SINTAX provided more comprehensive assignment results. Just like other 16S RDP-like classifiers, SINTAX also calculated a confidence score for each taxonomic level and used confidence thresholds to filter out the taxonomic levels that had scores lower than the threshold. SINTAX provided both “cut-off” and “no cut-off” results for its users. The setting of /the SINTAX classifier for the “cut-off” results was 0.8 (default setting). The “no cut-off” results included the assignment information from the kingdom to the species level, and these results were used for validation to ensure that each sequence included species information. Given the 16S full-length reads provided by the third-generation sequencing technology include approximately 1,200 ∼1,500 nucleotides, the “no cut-off” assignment was applied in this study to assign the sequences to the species level.
The validation metrics included accuracy, precision, recall, and F1-score, as shown in the following equations: where TP is true positive, FP is false positive, TN is true negative, and FN is false negative. We measured all four metrics for each taxonomic level. For a classified sequence, if the assigned taxonomic name in a taxonomic level matched the name in the validation dataset’s corresponding level, it was regarded as a correct assignment for the taxonomic level. However, the scientific names in some databases were used to describe the microbial taxonomy, while others might apply different naming conventions (Federhen, 2012). This situation formed an obstacle to comparing the taxonomic names from phylum to the species levels. Therefore, NCBI taxonomy dump files (https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/), which included scientific names and all possible synonyms of each taxonomic level for one microbial species, were applied to address this issue.
SINTAX was used for taxonomy assignment in the database comparison experiment because SINTAX showed good performance in sequence classification and provided comprehensive assignment results (Hung et al., 2022). Each reference database, including RDP, SILVA, and Greengenes, was used as the SINTAX’s reference for taxonomic assignment tasks. The assignment results were compared with the correct taxonomies in the validation data to calculate the accuracy, precision, recall, and F1-score for comparison. Then, the performance of using different reference databases for taxonomic assignment was compared. As mentioned before, SINTAX provided both “cut-off” and “no cut-off” assignment results. “No cut-off” taxonomies were applied to ensure the assignment results including species information. For the “cut-off” results, the cut-off value was set at 0.8 (default setting).
The performance of the widely used 16S sequence classifiers, such as SINTAX, SPINGO, Mothur, and QIIME2, was compared with our proposed integrated database. All the classifiers were set at default values and in “no cut-off” mode to ensure the assignment results to the species names. The settings of the SINTAX classifier were the same as described previously in Section 2.3.5. For the SPINGO classifier, the k-mer size and bootstrap value were set as 8 and 10 (default values). The Mothur classifier was set as “wang,” which was an RDP-like classification method. The k-mer size was 8 (default), and the cut-off value was set as 0. For the QIIME2 Bayesian classifier, the k-mer size parameter was set as 7 (default) and the confidence threshold value was set as “disable.” Accuracy, precision, recall, and F1-score were measured for each classifier.
To enhance taxonomic assignment resolution, we manually curated RDP, SILVA, and Greengenes datasets and removed the sequences that did not have exact species names. In total, the numbers of sequences that were manually removed were 1,095 from RDP, 366,392 from SILVA, and 182,728 from Greengenes, respectively. The final numbers of sequences in the sequence-based and taxonomy-based ITGDBs were 486,640 and 110,780, respectively. For ITGDBs and the source databases, the sequence counts of the hypervariable regions for 16S metabarcoding studies are listed in Table 1. RDP and sequence-based ITGDB have the minimum (4,644) and maximum (113,460) V1-V9 sequences, respectively. Taxonomy-based ITGDB (34,639) has fewer number of V1-V9 sequences than SILVA (101,649), Greengenes (49,286), and sequence-based ITGDB (113,460) due to the removal of the sequences with blurred species information. The accuracy results of all databases using the mock community, Union, Exclusion, and Intersection validation datasets are shown in Figure 3A, Figure 3C, Figure 3E, and Figure 3G. In Figure 3, the taxonomy-based ITGDB had the highest accuracy at the family, genus, and species levels in all the validation datasets, while the sequence-based ITGDB had the second highest accuracy in the Union and Exclusion test cases. When compared with RDP, SILVA, Greengenes, GRD, 16S-UDb, and GTDB, the taxonomy-based ITGDB had at least 16, 21, and 1% higher accuracy than the above databases at the species level in Union, Exclusion, and Intersection datasets, respectively. The results of accuracy, precision, recall, and F1-score of the different databases are shown in Table 2. The scatter plots in Figure 3B, Figure 3D, Figure 3F, and Figure 3H illustrate precision and recall for each reference database. The taxonomy-based ITGDB also showed the best performance in all the validation datasets. For the mock community, SILVA’s performance was in the second place in most of the validation metrics. For Union and Exclusion datasets, sequence-based ITGDB demonstrated the second-best performance in all the validation metrics. The accuracy difference between the ITGDBs and SILVA became larger in the Exclusion dataset than Union because ITGDBs contained more complete taxonomies than SILVA. For the Intersection dataset, Greengenes and sequence-based ITGDB were in the second place in most of the validation metrics. Greengenes did not show good performance in the mock community, Union, and Exclusion datasets, but inversely demonstrated accuracy similar to the taxonomy-based ITGDB in the Intersection dataset. As in Table 2 and Figure 3, 16S-UDb and GRD showed good performance on mock community classification. GRD had higher accuracy, precision, recall, and F1-score than 16S-UDb. However, for Union, Exclusion, and Intersection datasets, the trend was shown inversely that 16S-UDb had better performance than GRD. GRD did not demonstrate good accuracy at the family, genus, and species levels in Union and Exclusion datasets. GTDB did not have good accuracy at the species level in all the test cases. Since the taxonomy-based ITGDB showed the best performance in the database comparison analysis, we further used the taxonomy-based ITGDB to compare the accuracy with different 16S rRNA classifiers, as shown in Figure 4 and Table 3. SINTAX and Mothur showed similar accuracy at the family and genus levels (Figure 4). For species level assignment, SINTAX and SPINGO had an accuracy of more than 80% in all the validation datasets. QIIME2 had lower accuracy in all the validation datasets. For the mock community dataset, SINTAX demonstrated the best performance in most of the validation metrics (Figures 4A,B; Table 3). For the Union dataset, SINTAX showed the best performance at species level assignment, while Mothur was in the second place in most of the metrics (Figure 4C, Figure 4D, and Table 3). For the Exclusion dataset, SINTAX had the highest scores in all the validation metrics. The Mothur classifier was in the second place in most of the metrics in the Exclusion dataset (Figure 4E, Figure 4F, and Table 3). For the Intersection dataset, SINTAX, SPINGO, and Mothur had accuracy more than 90%. Both SINTAX and Mothur possessed the best or the second best in most of the metrics (Figure 4G, Figure 4H, and Table 3). Setting a confidence threshold for full-length sequence assignment can limit a classifier’s performance. The comparison results of using “Confidence threshold” and “No confidence threshold” settings in SINTAX are shown in Table 4. When setting the confidence threshold (default = 0.8) to limit the assignment depth, less than 50% of the sequences in Union, Exclusion, and Intersection datasets could be assigned at the species level. Conversely, when classifying the sequences without limitation, more than 99% of the sequences of all the validation datasets could be assigned to the species level, and most of the sequences were correctly assigned (Figure 3 and Table 2).
In this study, we proposed two types of 16S rRNA integrated databases for prokaryotic sequence classification—taxonomy-based integration and sequence-based integration databases. The taxonomy-based integration database, assembled by collecting the sequences with exact species names and then integrating all the unique sequences from RDP, SILVA, and Greengenes, showed the best performance in most of the validation metrics. Reasons of the taxonomy-based integration database with the best performance are discussed below. In this study, sequence-based integration collected all the sequences from RDP, SILVA, and Greengenes without taking taxonomic annotation quality into consideration, which was used to show that only collecting all the sequences could not give promised performance. Sequence-based integration included more sequences than taxonomy-based integration. Intuitively, a database with more reference sequences might provide better classification performance. However, if the collected sequences were annotated with ambiguous taxonomy names or only had low taxonomic depth information (e.g., only included taxonomic information down to the phylum, class, or order level), the blurred sequences limit a classifier’s performance (Lan et al., 2012). This situation could be observed from Figure 3 and Table 2 when comparing the performance between taxonomy-based ITGDB and sequence-based ITGDB. Only integrating all 16S sequences could not guarantee the classification performance. Therefore, taxonomy-based integration is suggested for application. In the past, NGS platforms sequenced part of the 16S rRNA hypervariable regions to identify the species to which a sample belonged. These sequenced regions included approximately 200 ∼500 nucleotides. The 16S rRNA classifiers set their confidence thresholds to prevent the over-classification issue based on these short reads. Previous studies reported that in order to assign a sequence to the genus level accurately, the sequence length needs to be at least 400 nucleotides (Okubo et al., 2009), and a full-length sequence could provide taxonomic resolution to the species level (Jeong et al., 2021). Notice that the 16S rRNA full-length sequences include approximately 1,500 ∼1,600 nucleotides (Nossa et al., 2010; Wagner et al., 2016). Since our classification target was the prokaryotic 16S full-length sequences, we found that using confidence thresholds to limit the taxonomic assignment depth made the prediction too conservative to reach the species level (Table 4). Therefore, the “no cut-off” assignment results were applied in our analyses. The database comparison analyses indicated that the taxonomy-based ITGDB had the best performance. In the Union dataset, the taxonomy-based ITGDB showed better accuracy than other databases, especially at the species level. There were two factors that explain why the taxonomy-based ITGDB could identify most of the species. One was that the taxonomy-based ITGDB covered all of the available species of RDP, SILVA, and Greengenes. The other was that the taxonomy-based ITGDB removed a considerable number of anomalous sequences by only integrating the sequences with exact species names. The Venn diagram in Figure 5 investigates the unique species names collected in RDP, SILVA, and Greengenes. The unique species taxonomies in RDP, SILVA, and Greengenes were 1,113, 31,509, and 411, respectively. Greengenes included the smallest number of species labels because this database had not been updated for many years, which was also the reason why Greengenes had the lowest performance among all the databases. However, Greengenes showed good performance with the Intersection dataset (the second highest scores in most of the metrics) because this dataset did not have unique taxonomy (the taxonomies only exist in one of RDP, SILVA, and Greengenes). The sequence-based ITGDB ranked second in accuracy when using the Union and Exclusion datasets for validation (Table 2). However, the accuracy performance of the sequence-based ITGDB became worse than RDP and Greengenes with the Intersection dataset. This situation indicated that simply collecting more sequences could not enhance the classification performance. The reason why the sequence-based ITGDB performed well with the Union and Exclusion datasets was that the sequence-based ITGDB included all the available taxonomies from RDP, SILVA, and Greengenes to overcome the unique taxonomy issue. However, collecting all the available sequences also meant having more sequences with low resolution taxonomies. Namely, the information at the species level did not have an exact species name, which could interfere with the taxonomic assignment procedure (Xue et al., 2022). This shortcoming was exposed when the validation dataset did not have unique taxonomy issues (e.g., the Intersection dataset). The sequence-based ITGDB showed better performance than SILVA with the Intersection dataset because the sequence-based ITGDB collected longer sequences under the same taxonomies. This might be the reason why the sequence-based ITGDB could identify the sequences more accurately than the SILVA database (Karagöz and Nalbantoglu, 2021). The reason why SILVA had better performance than Greengenes and RDP with the Union and Exclusion datasets, but lower performance with the Intersection dataset, was similar to the reasons outlined above for the sequence-based ITGDB. RDP had the smallest number of sequences, but it contained better curated sequences and taxonomies than SILVA (Edgar R., 2018), with 94.86% of sequences in RDP having taxonomic resolution at the species level. This could be the reason why RDP showed better performance than SILVA with the Intersection dataset. However, RDP included much less unique taxonomy than SILVA, and this prevented RDP from having better performance than SILVA with the Union and Exclusion datasets. For mock community validation, the reason why SILVA had better performance than RDP might be that SILVA included much more sequences than RDP. More reference reads allow SILVA to identify the type strain sequences more efficiently. Greengenes did not perform well in most of the analyses. For the mock community, Union, and Exclusion datasets, Greengenes showed low accuracy at the species level because most of Greengene’s sequences did not have taxonomic resolution to the species level, and the fact that its content had not been updated for many years. It is impossible for a classifier to identify the newly discovered bacteria using Greengenes as a reference database. The 16S-UDb had mediocre performance among the test cases. Two reasons may explain that 16S-UDb had lower performance than taxonomy-based ITGDB, especially for the species level assignment. One was that 16S-UDb collected the 97% OTU clustering sequences from RDP, SILVA, and Greengenes, which may put the sequences of different species into the same cluster and lost considerable taxonomies and reference sequences (Edgar RC., 2018; Chiarello et al., 2022). Inversely, taxonomy-based ITGDB applied 99% OTU clustering sequences from the reference databases to retain the taxonomies and sequences, ensuring taxonomy-based ITGDB could have better classification ability. Another reason was that 16S-UDb was built based on the older version of SILVA, Greengenes, and RDP, which meant it lacked the newly updated taxonomies. In Figure 3 and Table 2, 16S-UDb had better performance with the mock community and Intersection datasets than with the Union and Exclusion datasets because the mock community and Intersection datasets did not include unique taxonomies. Each sequence in 16S-UDb was full-length and with an exact species name, which could provide good performance of identifying the type-strain sequences in mock community and non-unique taxonomies in the Intersection dataset. Inversely, the Exclusion and Union datasets included a large number of unique taxonomies, which exposed the shortcoming that 16S-UDb did not collect enough reference sequences and taxonomies. GRD also identified the sequences of the mock communities quite well, but had worse performance than 16S-UDb, when classifying the sequences of the Intersection dataset. The collected species number of GRD and 16S-UDb was 2,603 and 7,399, respectively. The difference of the collected species number might be the reason why 16S-UDb could have better ability to overcome the unique taxonomy issues than GRD when classifying the sequences of the Union, Exclusion, and Intersection datasets. GTDB did not have good performance at the species level. Reasons for this phenomenon were that many sequences in the GTDB dataset did not have exact species names (only showed “sp [number]” at the species level) because some metagenomics assembled genomes did not include 16S gene fragments (Alishum, 2021), which interfered the performance of the classification algorithm. By observing the number of full-length sequences (V1-V9) in Table 1, the database performance comparison in Table 2, and the species Venn diagram in Figure 5, we found that taxonomy-based ITGDB did not possess the largest number of full-length sequences (Table 1) but had the best performance in all the validation datasets (Table 2). Inversely, sequence-based ITGDB and SILVA had the largest and the second largest number of full-length sequences (Table 1) but did not have the highest scores in all the test cases. This situation indicates that large quantity of full-length sequences alone could not ensure good assignment results. The completeness of taxonomy information also needs to be considered. A large proportion of sequences without exact species names limited the classification performance of sequence-based ITGDB and SILVA. Since taxonomy-based ITGDB included all the taxonomies of RDP, SILVA, and Greengenes and each sequence was assigned with an exact species name, this is the reason why taxonomy-based ITGDB could have the best performance in all the validation datasets. In summary, taking reference sequence count, taxonomy completeness, and taxonomy count into consideration could enhance a sequence classifier’s taxonomic resolution. Analyses of the ITGDBs’ performance with different classifiers demonstrated that the taxonomy-based ITGDB could work well with several widely used classifiers. For the mock community dataset, SINTAX showed the best performance at the family, genus, and species levels (Figure 4). For the Union, Exclusion, and Intersection datasets, SINTAX, SPINGO, and Mothur showed good performance at all the taxonomic levels. QIIME2 had lower accuracy in all the test cases. We found that the QIIME2 classifier worked normally when classifying the sequences of HMP and Zymo mocks but did not work well with Mockrobiota sequences (97% Mockrobiota sequences were classified as “Spiroplasma mirum” species). However, other classifiers, SINTAX, SPINGO, and Mothur, did not have such a problem. Therefore, for species-level assignment, SINTAX, SPINGO, and Mothur are suggested to be used with taxonomy-based ITGDB.
This work proposed two types of 16S rRNA integrated databases—sequence-based integration and taxonomy-based integration. The experimental results showed that taxonomy-based integration provided better performance and could work well with the widely used 16S rRNA classifiers. The proposed databases can support full-length 16S rRNA classification and enhance the taxonomic resolution to the species level. | true | true | true |
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PMC9581637 | Wenbo Zhang,Yanchun Wang,Pu Xu,Yongxiu Du,Weiwei Guan | lncRNA DLEU2 Accelerates Oral Cancer Progression via miR-30a-5p/RAP1B Axis to Regulate p38 MAPK Signaling Pathway | 12-10-2022 | Background Oral cancer (OC) is common cancer in the world. Long noncoding RNAs (lncRNAs) have been shown to be involved in cancer regulation, including oral cancer (OC). The aim of this study was to investigate the role of lncRNA deleted in lymphocytic leukemia 2 (DLEU2) in oral cancer. Method The Gene Expression Omnibus database was used to analyze differentially expressed lncRNA/microRNA (miRNA, miR)/mRNA. The expression levels of DLEU2, miR-30a-5p, and RAP1B in OC cells were detected by RT-qPCR. Dual-luciferase was used to analyze the binding of lncRNA/miRNA/mRNA. Cell Counting Kit-8 was used to measure cell proliferation. Transwell assay was used to inspect cell migration and invasion abilities. Western blot was used to detect MAPK pathway-related protein levels. Result Our research shows that, in contrast to miR-30a-5p, DLEU2 or RAP1B was upregulated in OC cells, and high expression of DLEU2 or RAP1B was associated with poorer overall survival. Inhibiting the expression of DLEU2 slowed the proliferation and reduced the ability of migration and invasion of Tca8113 and CAL-27 cells. miR-30a-5p was predicted to interact with DLEU2 or RAP1B by bioinformatics, and dual-luciferase analysis confirmed this interaction. Notably, si-DLEU2 suppressed RAP1B expression and protein level, and after overexpression of RAP1B in OC cells, reversal of suppressed DLEU2 expression was observed. Furthermore, the inhibitory effect of si-DLEU2 on MAPK signaling was reversed by overexpression of RAP1B. Therefore, si-DLEU2 regulates MAPK signaling through the miR-30a-5p/RAP1B axis and inhibits OC development. Conclusion DLEU2 contributed to proliferation, migration and invasion via miR-30a-5p/RAP1B axis to regulate MAPK signaling pathway in OC cells. | lncRNA DLEU2 Accelerates Oral Cancer Progression via miR-30a-5p/RAP1B Axis to Regulate p38 MAPK Signaling Pathway
Oral cancer (OC) is common cancer in the world. Long noncoding RNAs (lncRNAs) have been shown to be involved in cancer regulation, including oral cancer (OC). The aim of this study was to investigate the role of lncRNA deleted in lymphocytic leukemia 2 (DLEU2) in oral cancer.
The Gene Expression Omnibus database was used to analyze differentially expressed lncRNA/microRNA (miRNA, miR)/mRNA. The expression levels of DLEU2, miR-30a-5p, and RAP1B in OC cells were detected by RT-qPCR. Dual-luciferase was used to analyze the binding of lncRNA/miRNA/mRNA. Cell Counting Kit-8 was used to measure cell proliferation. Transwell assay was used to inspect cell migration and invasion abilities. Western blot was used to detect MAPK pathway-related protein levels.
Our research shows that, in contrast to miR-30a-5p, DLEU2 or RAP1B was upregulated in OC cells, and high expression of DLEU2 or RAP1B was associated with poorer overall survival. Inhibiting the expression of DLEU2 slowed the proliferation and reduced the ability of migration and invasion of Tca8113 and CAL-27 cells. miR-30a-5p was predicted to interact with DLEU2 or RAP1B by bioinformatics, and dual-luciferase analysis confirmed this interaction. Notably, si-DLEU2 suppressed RAP1B expression and protein level, and after overexpression of RAP1B in OC cells, reversal of suppressed DLEU2 expression was observed. Furthermore, the inhibitory effect of si-DLEU2 on MAPK signaling was reversed by overexpression of RAP1B. Therefore, si-DLEU2 regulates MAPK signaling through the miR-30a-5p/RAP1B axis and inhibits OC development.
DLEU2 contributed to proliferation, migration and invasion via miR-30a-5p/RAP1B axis to regulate MAPK signaling pathway in OC cells.
Oral cancer (OC) is common cancer in the world [1, 2], and the current treatment of OC is usually surgery combined with radiotherapy and chemotherapy [3], but the mortality rate of OC is still high [4], which is due to the degree of tumor invasion and progression affecting the survival of patients with OC. Moreover, once lymph node metastasis occurs, the risk to patients' lives will rise dramatically [5]. Therefore, early detection of OC is very important. Therefore, OC tumor markers have important clinical value for identifying the mechanism of occurrence and metastasis of OC and finding new therapeutic methods. Study have found that long noncoding RNAs (lncRNAs, >200 nt) do not encode proteins [6] but can directly participate in the regulation of gene expression [7] and can also interact with miRNAs as competing endogenous RNAs (ceRNAs) [8]. miRNAs play key roles in the posttranscriptional regulation of mRNAs by targeting their 3′ untranslated regions (UTRs), leading to mRNA degradation or translational repression [9]. In recent years, many studies have found that lncRNAs are involved in the occurrence and development of OC as ceRNAs [10–12]. As a known oncogene, lncRNA deleted in lymphocytic leukemia 2 (DLEU2) plays a role in promoting cancer development in most cancers [13, 14], but the molecular mechanism in OC has not yet been reported. In recent years, alterations in signaling pathways involved in OC have been shown to be important factors affecting OC development [15, 16]. Among them, the canonical mitogen-activated protein kinase (MAPK) signaling pathway is considered to be involved in the growth and development of most cells [17, 18]. As an important member of MAPK, phosphorylation of p38 MAPK mediates the migration and growth of OC cells [19]. In this article, we sought to examine the expression and molecular mechanism of lncRNA DLEU2 in OC and to evaluate its impact on the biological behavior of 2 OC cell lines. Furthermore, we revealed a novel mechanism by which lncRNA DLEU2 regulates OC cell proliferation and distal migration through miR-30a-5p/member of RAS oncogene family (RAP1B)/MAPK signaling, which may provide new ideas for the discovery of OC therapeutic strategies.
Raw data are from National Center for Biotechnology Information Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). The expression profiles of lncRNA/mRNA in OC samples (GSE25099) were analyzed using GEO, which were divided into two groups: from 10 normal oral tissues (GSM616588-GSM616597) and 10 OC patient tissues (GSM616647-GSM616656). The expression profiles of miRNAs in samples (GSE98463) were divided into two groups: from 4 normal oral tissues (GSM2596879-GSM2596882) and 4 OC patient tissues (GSM2596874-GSM2596877). Differentially expressed lncRNAs/mRNAs/miRNAs were identified according to the following criteria: P < 0.05 and |fold change| ≥ 2. A heat map or volcano plot was constructed using differentially expressed lncRNA/mRNA/miRNA analysis results. KEGG pathway enrichment analyses were performed using the functional annotation tool of DAVID Bioinformatics Resources 6.8 (https://david.ncifcrf.gov/summary.jsp). The miRNA-lncRNA interactions between lncRNA and miRNA were predicted using starBase (https://starbase.sysu.edu.cn/index.php). Putative targets of miRNA-mRNA were predicted using TargetScan 7.2 (https://www.targetscan.org/vert_72/).
Human oral cancer cell lines (CAL-27, Tca8113, and C4-2, 22RV1), normal oral keratinocytes (NOKs), and human embryonic kidney cells 293 (HEK 293T) were purchased from American Type Culture Collection (ATCC; VA, USA). All cells were maintained in DMEM medium (HyClone, UT, USA) with 10% fetal bovine serum (FBS; HyClone). RNAs was transfected into cells using Lipofectamine 3000 (Invitrogen; Thermo Fisher Scientific, Inc.).
si-DLEU2-1, si-DLEU2-2, si-DLEU2-3, and scrambled siRNA (si-NC) were designed and synthesized by Sigma-Aldrich. The siRNA sequences were as follows: si-DLEU2-1, 5′-GGTACTTCACTATAGTTTAdTdT-3′; si-DLEU2-2, 5′-GAATAACATCAATATGCAAdTdT-3′; si-DLEU2-3, 5′-GTATGAGAATATTATACTAdTdT′; and si-NC, 5′-TTCTCCGAACGTGTCACGdTdT-3′. After the above siRNAs were transfected into Tca8113 and CAL-27 cells, the cells were harvested 24 hours later. The inhibitory efficiency of the three si-DLEU2 was evaluated by RT-qPCR, and the siRNA with the best inhibitory efficiency will be used as a DLEU2 antagonist for subsequent research and analysis.
Total RNA was extracted from cells using TRIzol (Invitrogen, MA, USA) according to the manufacturer's instructions. Real-time mRNA quantification for DLEU2, miR-30a-5p, RAP1B, U6, and 18sRNA was performed using SYBR Green qPCR SuperMix (Invitrogen) on a 7500 RT-qPCR System (Applied Biosystems, MA, USA). PCR experiments were carried out under the following conditions: 95°C for 10 min, 55°C for 2 min, and 72°C for 2 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. The primers for DLEU2, RAP1B, miR-30a-5p, 18sRNA (as internal normalization control for DLEU2 and RAP1B), and U6 (as internal normalization control for miR-30a-5p) were as follows: DLEU2 forward, 5′-TCCTTCCCTGGAAGAGCACA-3′, and DLEU2 reverse, 5′-TTGGAGCTGCTATGCTTGTCA-3′; RAP1B forward, 5′-ACAGCGTGAGAGGTACTAGGT-3′, and RAP1B reverse, 5′-GTAAATTGCTCCGTTCCTGC-3′; miR-30a-5p forward, 5′-ACACTCCAGCTGGGTGTAAACATCCTCGAC-3′, and miR-30a-5p reverse, 5′-CTCAACTGGTGTCGTGGA-3′; 18sRNA forward, 5′-CCTGGATACCGCAGCTAGGA-3′, and 18sRNA reverse, 5′-GCGGCGCAATACGAATGCCCC-3′; and U6 forward, 5′-CTCGCTTCGGCAGCACA-3′, and U6 reverse, 5′-AACGCTTCACGAATTTGCGT-3′. Relative DLEU2, RAP1B, and miR-30a-5p expression levels were calculated using the 2−ΔΔCt method [20]. All RT-PCR experiments were repeated three times.
Cell Counting Kit-8 (CCK8) reagent (Solarbio; 10 μL) was added to 96-well plates (containing Tca8113 and CAL-27 4 × 103 cells) at 0, 24, 48, and 72 h, respectively. The absorbance was measured at 450 nm using an enzyme-labeled instrument (Thermo Fisher Scientific) after 60 min of incubation in the dark at 37°C.
The migration and invasion of Tca8113 and CAL-27 cells were determined using 24-well Transwell inserts (BD Biosciences). For the migration assay, the cells (0.5 × 105) in serum-free medium were placed in the top chamber, whereas culture medium containing 10% FBS was added to the lower chamber. After incubating for 24 h, invasive cells were fixed using anhydrous ethanol for 30 min and stained with 0.1% crystal violet (Solarbio; 25°C) for 25 min. The cells were counted using a light microscope (Olympus Corporation) at ×200 magnification. For the invasion assay, inserts were first coated with Matrigel® (BD Biosciences) for 6 h at 37°C, then fixed, stained, and counted.
The whole sequence of DLEU2 or RAP1B 3′UTR was inserted into the psi-CHECK2 basic construct. We transfected HEK 293 T cells with a 0.5 μg reporter construct and 50 nM miRNA mimic per well using Lipofectamine 3000. Following 4 h of transfection, the transfection medium was replaced with complete culture medium. Following 48 h of culture, the cells were lysed with passive lysis buffer (Promega), and luciferase activity was measured at 490 nm using the dual-luciferase reporter assay system (Promega). The ratio of firefly to Renilla luciferase activity was used to normalize firefly luciferase values.
Tca8113 and CAL-27 cells were lysed using RIPA lysis buffer (Solarbio) and estimated using a BCA protein assay kit (Solarbio). Denatured proteins were resolved using 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE, Solarbio), and protein bands were transferred to a polyvinylidene fluoride membrane, blocked with 5% bovine serum albumin (Solarbio), and incubated overnight (4°C) with RAP1B (1 : 500; ab154756, Abcam, Cambridge, UK), p38 (1 : 1000; ab170099, Abcam), and p-p38 (1 : 1000; ab178867, Abcam) primary antibodies. Thereafter, the sections were rinsed with TBST buffer (Solarbio; containing 0.05% Tween 20) twice for 15 min and incubated with goat anti-rabbit antibody (1 : 20,000, ab205718; Abcam) for 2 h at 25°C. Anti-GAPDH antibody (1 : 5,000, ab181602; Abcam) was used as a loading control. Proteins were visualized using Immobilon Western Chemiluminescent HRP Substrate (Millipore). Chemiluminescence signal acquisition was performed using an X-ray film (Kodak).
Statistical analysis was performed using GraphPad Prism software (v8.3.0), and data were expressed as mean ± SD. To determine whether there was an overall statistically significant difference, a one-way ANOVA was performed with a Bonferroni post hoc test before Student's t-test was performed to analyze the difference between any two groups. It was considered statistically significant if P > 0.05.
Analysis of the results of OC patients and normal subjects based on lncRNA microarray data identified 20 differentially expressed transcripts of lncRNAs, including 15 upregulated and 5 downregulated (Figure 1(a)). Among them, DLEU2, as a known oncogene, expression was upregulated in the dataset and has not yet been studied on the molecular mechanism in OC. Furthermore, based on RT-qPCR assays, DLEU2 expression was significantly upregulated in 4 OC cells (CAL-27, FaDu, HSC-2, and Tca8113) (Figure 1(b)). Therefore, we have reason to believe that DLEU2 may be closely related to the development of OC. Notably, DLEU2 was most expressed in Tca8113 and CAL-27 cells, so these two cell lines were selected to explore the functional role of DLEU2 in OC. RT-qPCR results showed that after transfecting the synthesized three kinds of siRNA-DLEU2 into Tca8113 and CAL-27 cells, siRNA-DLEU2-1 was found to have the best inhibitory effect. We used it as an antagonist of DLEU2 to study the molecular mechanism (Figure 1(c)). The results of CCK8 and Transwell showed that inhibiting the expression of DLEU2 slowed the proliferation of Tca8113 and CAL-27 cells and reduced the ability of migration and invasion (Figures 1(d)–1(g)). Furthermore, by RT-qPCR analysis, we observed that DLEU2 was mainly distributed in the cytoplasm of cells (Figure 1(h)), so the effect of DLEU2 on OC might be achieved through a ceRNA mechanism.
According to the miRNA microarray data analysis results, using 4 OCs and 4 normal oral tissues, 209 differentially expressed hsa-miRNAs were identified, of which 88 were upregulated and 121 miRNAs including miR-30a-5p were downregulated (Figure 2(a)). Combined with the analysis of starBase database and miRNA microarray (Figure 2(b)), we observed that 3 miRNAs intersected, and miR-30a-5p was confirmed to be downregulated in OC [21], so we screened it as a potential target of DLEU2. The results showed that miR-30a-5p was downregulated in OC cells (Figure 2(c)), and inhibiting the expression of DLEU2 could increase the expression of miR-30a-5p (Figure 2(d)). The starBase database predicts that DLEU2 has a binding site for miR-30a-5p (Figure 2(e)); subsequent dual-luciferase experiments confirmed that DLEU2 directly targets miR-30a-5p (Figure 2(f)). Here, we can confirm that DLEU2 can sponge miR-30a-5p to regulate OC development.
According to the mRNA microarray data analysis results, a total of 1496 upregulated mRNAs including RAP1B were identified (Figure 3(a)). KEGG can analyze and predict that different mRNAs affect different signaling pathways. We found that the classical MAPK involved in cell growth and development is one of the key signaling pathways that affect the progression of OC (Figure 3(b)), and 44 mRNAs are predicted to be involved. Combined with the 4117 potential target genes of miR-30a-5p predicted by the starBase database, we observed that 8 mRNAs intersected (Figure 3(c)). The study has confirmed that RAP1B is upregulated in OC [22]; therefore, we screened the RAP1B gene as a potential target for validation. The QRT-PCR results showed that RAP1B was highly expressed in OC cells (Figure 3(d)). The TargetScan database predicts that RAP1B has a binding site for miR-30a-5p (Figure 3(e)); subsequent dual-luciferase assays confirmed that RAP1B directly targets miR-30a-5p (Figure 3(f)). After transfection of si-DLEU2, RAP1B expression was downregulated in Tca8113 and CAL-27 cells (Figure 3(g)).
To confirm the association between DLEU2 and RAP1B, we constructed a RAP1B overexpression plasmid (ov-RAP1B). The QRT-PCR and western blot results indicated that the gene or protein levels of RAP1B was upregulated in ov-RAP1B-transfected Tca8113 and CAL-27 cells, confirming the effectiveness of the RAP1B overexpression plasmid (Figures 3(h) and 3(i)). After cotransfection with the addition of si-DLEU2, the inhibition of DLEU2 expression was reversed by ov-RAP1B (Figure 3(j)). Thus, miR-30a-5p directly targets DLEU2 and RAP1B, and DLEU2 positively correlates with RAP1B.
The above results suggest that MAPK is one of the key signaling pathways affecting OC development, and p38 MAPK is the core protein of MAPK. Based on western blot results, we found that knockdown of DLEU2 significantly reduced p-p38 MAPK protein levels in Tca8113 and CAL-27 cells (Figure 4(a)). However, the presence of ov-RAP1B reversed the effect of si-DLEU2 (Figure 4(b)). In addition, the results of cell function experiments showed that si-DLEU2 inhibited the proliferation of Tca8113 and CAL-27 cells (Figures 4(c) and 4(d)), and the effects of reducing migration and invasion ability (Figures 4(e) and 4(f)) were also reversed by ov-RAP1B. In conclusion, the DLEU2 sponge adsorbs miR-30a-5p and regulates the transcription and translation of RAP1B, thereby affecting downstream MAPK signaling and intervening in OC development.
Our goal was to identify a novel lncRNA as a potential therapeutic target for OC therapy; therefore, a comprehensive understanding of the regulatory mechanisms of lncRNAs may help to develop new and promising therapeutic strategies for OC. Previous reports indicated that lncRNAs are important players in the development of OC [23]. For example, Zhang et al. [24] found that the lncRNA LINC01296 promoted the development of oral squamous cell carcinoma by binding to SRSF1. Lu et al. [25] demonstrated that lncRNA HOTAIR inhibited cancer stemness and metastasis in oral cancer stem cells. In our study, the oncogenic roles of DLEU2 and RAP1B in OC and the tumor suppressor role of miR-30a-5p were first determined. Mechanistically, we found that DLEU2 sponges adsorb miR-30a-5p, RAP1B is a target of miR-30a-5p, and DLEU2 is upregulated synergistically with RAP1B in OC progression. In addition, si-DLEU2 also inhibited the p38 MAPK pathway. MAPK family members mediate a variety of cellular behaviors in response to extracellular stimuli [26]. As one of the major members of MAPKs, p38 MAPKs function in a cell environment-specific and cell type-specific manner to integrate signals affecting proliferation and migration [18]. Taken together, our results suggest that the DLEU2/miR-30a-5p/RAP1B axis can regulate the progression of OC through the p38 MAPK pathway, providing a new therapeutic strategy for OC. In various cancers, DLEU2 plays a promoting regulatory role in a variety of cancers. For example, He et al. found that DLEU2 promotes cervical cancer progression [27] and promotes the proliferation and invasive capacity of colorectal cancer cells [28]. Likewise, our results showed that DLEU2 was upregulated in OC cells, its high expression significantly shortened the disease-free survival of OC patients, and si-DLEU2 inhibited OC cell proliferation, migration, and invasion by reducing the phosphorylation of p38 MAPK. It is suggested that DLEU2, as an oncogene, will block the development of OC when its expression is suppressed. In addition, DLEU2, as a ceRNA, plays a role in phagocytosing miRNAs in cancer. For example, Wu et al. found that DLEU2 accelerated tumorigenesis and invasion of non-small-cell lung cancer by sponging miR-30a-5p [29]. Li et al. [30] found that DLEU2 promotes gastric cancer progression through sponge adsorption of miR-23b-3p. In this study, inhibiting the expression of DLEU2 promoted the expression of miR-30a-5p in OC cells, so that the transcription and translation of RAP1B directly bound to miR-30a-5p were also simultaneously degraded. This result indicates that miR-30a-5p as a tumor suppressor gene was confirmed in this study, which is consistent with the previous studies on the role of miR-30a-5p in suppressing cancer such as breast cancer [31] and lung adenocarcinoma [32]. The bioinformatics database predicted that miR-30a-5p targets the 3′UTR of RAP1B, and we confirmed its targeting mechanism by dual-luciferase reporter gene experiments. Furthermore, in OC cells, RAP1B expression was negatively correlated with miR-30a-5p expression. Notably, our study shows that RAP1B promotes OC progression. Furthermore, in the ceRNA axis, DLEU2 was coordinated upregulated with RAP1B, a target gene of miR-30a-5p, in OC cells, and DLEU2 decreased the expression of RAP1B, whereas overexpression of RAP1B reversed si-DLEU2-mediated degradation of p38 MAPK phosphorylation. These results suggest that si-DLEU2 exerts a tumor suppressor effect through the interaction with miR-30a-5p and RAP1B. Furthermore, the tumor suppressor effect of RAP1B on OC is consistent with its role in other cancers such as thyroid cancer [33] and colorectal cancer [34]. The MAPK pathway directly regulates OC development [35, 36]. In the present study, when the phosphorylation level of p38 MAPK was decreased, the proliferation inhibition, migration, and invasion abilities of OC cells were decreased. si-DLEU2 inhibited OC cell growth and decreased p38 MAPK phosphorylation levels. Excessive RAP1B promotes cancer cell development, elevates p38 MAPK phosphorylation levels, and reverses the effects of si-DLEU2 on OC cells. Therefore, the DLEU2-miR-30a-5p-RAP1B axis controls the p38 MAPK signaling pathway, thereby regulating OC progression. In conclusion, this study confirms that DLEU2 is a potential therapeutic target and provides more directions and theoretical basis for the treatment of OC. | true | true | true |
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PMC9581677 | Qi Feng,Donglai Wang,Peng Guo,Zibo Zhang,Jiangang Feng | Apatinib Functioned as Tumor Suppressor of Synovial Sarcoma through Regulating miR-34a-5p/HOXA13 Axis | 12-10-2022 | Objective Synovial sarcoma is a rare malignant tumor. The role of apatinib in synovial sarcoma remains unclear. In this study, we aimed to determine the biological functions and the potential molecular mechanism of action of apatinib in synovial sarcoma. Methods SW982 cells were stimulated with apatinib. The relative expression of the genes was determined by performing qPCR. Protein levels were evaluated by western blot and immunohistochemistry assays. Proliferation, apoptosis, migration, and invasion of SW982 cells were determined by the CCK-8 assay, clone formation assay, flow cytometry, wound healing, and the transwell assay, respectively. Additionally, SW982 cells were injected into mice to induce synovial sarcoma. Results Apatinib decreased the proliferation, migration, and invasion but increased the apoptosis of SW982 cells. Apatinib repressed tumor growth in vivo and elevated miR-34a-5p in SW982 cells. The inhibition of miR-34a-5p repressed the reduction of proliferation, migration, and invasion and also the elevation of apoptosis in apatinib-treated SW982 cells. The luciferase activity decreased after cotransfection of the miR-34a-5p mimic and the wild-type HOXA13 vector. Additionally, an increase in miR-34a-5p repressed the levels of HOXA13 mRNA and protein. Moreover, HOXA13 reversed these patterns caused by the inhibition of miR-34a-5p in apatinib-treated SW982 cells. Conclusion Apatinib elevated miR-34a-5p and reduced HOXA13, leading to a significant decrease in proliferation, migration, and invasion, along with an enhancement of apoptosis in SW982 cells. Apatinib suppressed tumorigenesis and tumor growth in SW982 cells in vivo. | Apatinib Functioned as Tumor Suppressor of Synovial Sarcoma through Regulating miR-34a-5p/HOXA13 Axis
Synovial sarcoma is a rare malignant tumor. The role of apatinib in synovial sarcoma remains unclear. In this study, we aimed to determine the biological functions and the potential molecular mechanism of action of apatinib in synovial sarcoma.
SW982 cells were stimulated with apatinib. The relative expression of the genes was determined by performing qPCR. Protein levels were evaluated by western blot and immunohistochemistry assays. Proliferation, apoptosis, migration, and invasion of SW982 cells were determined by the CCK-8 assay, clone formation assay, flow cytometry, wound healing, and the transwell assay, respectively. Additionally, SW982 cells were injected into mice to induce synovial sarcoma.
Apatinib decreased the proliferation, migration, and invasion but increased the apoptosis of SW982 cells. Apatinib repressed tumor growth in vivo and elevated miR-34a-5p in SW982 cells. The inhibition of miR-34a-5p repressed the reduction of proliferation, migration, and invasion and also the elevation of apoptosis in apatinib-treated SW982 cells. The luciferase activity decreased after cotransfection of the miR-34a-5p mimic and the wild-type HOXA13 vector. Additionally, an increase in miR-34a-5p repressed the levels of HOXA13 mRNA and protein. Moreover, HOXA13 reversed these patterns caused by the inhibition of miR-34a-5p in apatinib-treated SW982 cells.
Apatinib elevated miR-34a-5p and reduced HOXA13, leading to a significant decrease in proliferation, migration, and invasion, along with an enhancement of apoptosis in SW982 cells. Apatinib suppressed tumorigenesis and tumor growth in SW982 cells in vivo.
Soft tissue tumors are a group of more than 60 tumors formed by the overproliferation of mesodermal cells and range from benign lipomas to aggressive metastatic angiosarcomas [1]. Among these, sarcoma is a type of malignant tumor that is very aggressive and has a high infiltration ability. There are two broad categories of sarcomas, comprising the synovial sarcoma and the bone sarcoma [2]. There are approximately 4–5 cases of sarcoma in every 100,000 individuals [3]. Unfortunately, there is a marginal effect of cancer treatment on the recurrence and survival of patients with sarcoma due to the lack of effective treatment methods to inhibit tumorigenesis in synovial sarcoma [4]. The advancement in molecular genetics has contributed to the management of synovial sarcoma [5, 6]. The mechanism of tumorigenesis plays a key role in the development of malignant behaviors of sarcomas [7]. A study suggested that treatment based on molecular genetics might contribute to the clinical outcome of advanced sarcoma and indicated that molecular genetics is needed for clinical treatment to elucidate the molecular pathogenesis of synovial sarcoma [8]. Mesylate apatinib, a novel tyrosine kinase inhibitor of vascular endothelial growth factor receptor 2 (VEGFR2), has been used for the treatment of various types of cancers due to its antiangiogenic role via competition for the ATP-binding site of VEGFR2 [9]. Yang et al. [10] showed that mesylate apatinib significantly reduced the hyperproliferation and malignant metastasis in epithelioid malignant peritoneal mesothelioma. By performing a randomized trial, a study showed that apatinib significantly improved tumor symptoms and progression-free survival of patients with advanced progressed lung adenocarcinoma [11]. A study suggested that mesylate apatinib combined with recombinant human endostatin can improve non-small-cell lung cancer in the short term and enhance the results in the long term [12]. Apatinib has a potential role in the clinical treatment of sarcoma. Tian et al. [13] found that the treatment of apatinib improved the progression-free survival and the overall survival substantially and also significantly inhibited the progression to advanced sarcoma. Apatinib can increase autophagy and apoptosis in osteosarcoma by targeting the VEGFR2-mediated signal transducer and activator of transcription 3 (STAT3)/B-cell lymphoma-2 (Bcl-2) pathway [14]. Apatinib reduces the doxorubicin resistance of osteosarcoma via the STAT3-mediated SRY-box transcription factor 2 (Sox2) pathway and represses the programmed cell death 1 ligand 2 (PD-L2) mediated immune escape in osteosarcoma by mediating the VEGFR2 and STAT3/ras homolog family member A (RhoA)/rho-associated coiled-coil containing protein kinase 1 (ROCK1)/LIM domain kinase 2 (LIMK2) pathways [15, 16]. However, the mechanism of action of apatinib in synovial sarcoma is unknown. MicroRNAs (miRNAs) play an important role in tumorigenesis and tumor growth associated with synovial sarcoma by deregulating cellular proteins or related pathways [17]. Dysregulation of miRNAs acts as the potential biomarkers of synovial sarcoma based on their association with malignant tumors [18]. The miR-34 family, including miR-34a/b/c, a critical tumor regulator and potential therapeutic target in malignant tumor, is downregulated in ovarian cancer, gastric cancer, colon cancer, and sarcoma [19–23]. The miR-34a-5p, a mature form of miR-34a, is associated with the tumorigenesis of sarcoma. Li et al. [24] found that miR-34a-5p functions by reducing the proliferation and elevation of apoptosis in Ewing sarcoma. Sciandra et al. [25] suggested that the expression of miR-34a might contribute to the prognosis of synovial sarcoma in the clinic. An increase in the expression of miR-34a improved the outcome of patients with Ewing sarcoma [26]. However, the mechanism of action of miR-34a-5p in synovial sarcoma is unclear. We found a complementary fragment of miR-34a-5p in the Homeobox A13 (HOXA13) mRNA based on starBase 2.0, which suggested a potential interaction between miR-34a-5p and HOXA13. HOXA13 is located on chromosome 7 and encodes a protein (transcription factor) with DNA-binding activity that modulates gene expression. The deregulated expression of HOXA13 occurs in esophageal squamous cell carcinoma, hepatocellular carcinomas, and gastric cancer [27–29], thus resulting in the development and progression of malignant tumors. The miR-34a-5p can target HOXA13 through miRNA-mRNA interaction in bone sarcoma [30]. However, it is not known whether miR-34a-5p binds to the HOXA13 mRNA to modulate its expression that, in turn, can contribute to tumorigenesis and progression in synovial sarcoma. Also, whether the miR-34a-5p/HOXA13 axis mediates the mechanism of action of apatinib during the progression of synovial sarcoma is unclear. Based on this information, we hypothesized that mesylate apatinib might upregulate miR-34a-5p and decrease HOXA13 levels, leading to the inhibition of tumorigenesis and the progression of synovial sarcoma. We aimed to elucidate a novel mechanism of action of mesylate apatinib in synovial sarcoma for improving the marginal effect of drug treatment in the clinic.
Synovial sarcoma cells (SW982) purchased from the American Type Culture Collection (ATCC, USA) were cultured with Dulbecco's modified Eagle medium (DMEM, Hyclone, USA) supplemented with 10% fetal bovine serum (FBS, Gibco, USA) at 37°C with 5% CO2 and 95% air. Then, the miR-34a-5p mimic, negative control of miR-34a-5p mimic (NC mimic), miR-34a-5p inhibitor, negative control of miR-34a-5p inhibitor (NC inhibitor), and HOXA13 shRNA were purchased from Sangon Biotech Pvt., Ltd. (China) and transfected into SW982 cells via Lipofectamine 2000 (Invitrogen, USA). The sequences of shRNAs, miRNA mimic, and miRNA inhibitor were listed as follows: sh-HOXA13, 5′-GTT CCA GAA CAG GAG GGT TAA-3′; miR-34a-5p mimic, 5′-UGG CAG UGU CUU AGC UGG UUG U-3′; and miR-34a-5p inhibitor, 5′-ACC GUC ACA GAA UCG ACC AAC A-3′.
The SW982 cells were incubated in 96-well plates supplemented with DMEM and 10% FBS for 48 h. To determine the optimum concentration of apatinib in the SW982 cells, the cells were incubated with 0, 5, 10, 20, and 50 μM apatinib for 48 h. Then, the cell viability was measured by performing the CCK-8 assay (Elabscience, China). Briefly, the SW982 cells were cultured in the CCK-8 solution for 3 h at 37°C with 5% CO2 and 95% air. Then, they were measured using an enzyme-linked immunometric meter at 450 nm. To demonstrate the role of apatinib in cell proliferation, the SW982 cells were incubated with 10 μM apatinib for 48 h, followed by analysis via the CCK-8 assay.
Initially, 10 μM apatinib-treated SW982 cells with different transfection treatments were seeded in six-well plates incubated with DMEM containing 10% FBS for 14 d, followed by immobilization with 4% paraformaldehyde for 30 min. Then, the cells were stained using crystal violet dye for 20 min. Finally, clone formation was evaluated from images taken with a microscope (Nikon, Japan).
After different transfection treatments, 10 μM apatinib-treated SW982 cells at a density of 100,000 cells were incubated with ice-cold 70% ethanol solution, followed by incubation with Annexin V-FITC (Procell, China) and propidium iodide buffer (Procell, China) for 20 min in the dark. The visualization of apoptosis was performed by using a flow cytometry system (BD Biosciences, USA).
Briefly, 10 μM apatinib-treated SW982 cells with different transfection treatments were incubated in six-well plates for 24 h at 37°C with 5% CO2 and 95% air. Then, the cells were scraped using a sterile pipette tip. The photographs of the SW982 cells were taken using a microscope (Nikon, Japan) at 0 h and 24 h, respectively.
Initially, 10 μM apatinib-treated SW982 cells with different transfection treatments were seeded in the upper chamber, which was precoated with 8% Matrigel (BD, USA) and supplemented with FBS-free DMEM. The bottom chamber was supplemented with DMEM with 20% FBS to induce invasion of the SW982 cells. After incubation for 24 h, the cells in the bottom chamber were counted in five randomly selected fields using a microscope (Nikon, Japan).
Total RNA was extracted from 10 μM apatinib-treated SW982 cells with different transfection treatments using TRIzol reagent and measured using a spectrophotometer at 260 nm and 280 nm. The reverse transcription and quantification of the RNA sample were performed using the TaqMan One-Step RT-qPCR Kit (Solarbio, China) and the ABI7000 Sequence Detection System (Applied Biosystems, USA). The primers of miR-34a-5p and HOXA13 were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China). GAPDH and U6 were used as internal controls. The relative expression was calculated by the 2–ΔΔCq method. The miR-34a-5p-forward primer was 5′-AAC GTG CAG CAC TTC TAG GG-3′; the miR-34a-5p-reverse primer was 5′-GGC CAG CTG TGA GTG TTT CT-3′; the HOXA13-forward primer was 5′-TTG GGG GTT GAC GTT TGA CA-3′; the HOXA13-reverse primer was 5′-ACA GGA TTG TAC AGC GGG TG-3′; the U6-forward primer was 5′- CTC GCT TCG GCA GCA CA-3′; the U6-reverse primer was 5′-AAC GCT TCA CGA ATT TGC GT-3′; the GAPDH-forward primer was: 5′-CCA GGT GGT CTC CTC TGA-3′; and the GAPDH-reverse primer was 5′-GCT GTA GCC AAA TCG TTG T-3′.
Based on starBase 2.0 (https://starbase.sysu.edu.cn/starbase2/index.php), a complementary fragment of miR-34a-5p was found in the HOXA13 mRNA. The 3′-UTR sequences of HOXA13 with prediction site were amplified, which were loaded in the pmirGLO (pmirGLO-HOXA13 wt). The pmirGLO loaded with HOXA13 mutant without prediction site (pmirGLO-HOXA13 mut) was used for the control of pmirGLO-HOXA13 wt. Then, the HEK293 cells purchased from ATCC were seeded and cultured in cell plates, which were transfected pmirGLO-HOXA13 wt, pmirGLO-HOXA13 mut and the miR-34a-5p mimic or the NC mimic using Lipofectamine 2000 (Invitrogen, USA). The luciferase activity was detected through the dual-luciferase reporter assay system (Promega, USA).
This study was approved by the hospital ethics committee following the Health Guide of National Institutes for the Care and Use of Laboratory Animals (approval no. MDKN-2021-027). Six-week-old female BALB/c nude mice purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (China) were subcutaneously injected with SW982 cells at a density of 2,000,000 cells. The mice were randomly divided into the dimethyl sulfoxide (DMSO) group or the apatinib group, with 10 mice in each group. Briefly, the mice in the DMSO group were treated with DMSO orally daily for 30 d, while the mice in the apatinib group were orally administered 50 mg/kg apatinib daily for 30 d. The tumor volume was measured every five days. Tumor volume was calculated as tumor volume = width2 × length/2. On day 30, the tumors were dissected, photographed, and weighed.
The tumor tissues from the mice were fixed using a 10% neutral formalin-buffered solution and repaired with Tris-EDTA solution, followed by incubation with Tris-buffered saline containing 1% BSA and 10% normal serum at room temperature for 2 h. The tissues were incubated with the VEGFR2 antibody (ab115805, 1 : 100, Abcam, Cambridge, UK), Ki67 antibody (ab15580, 1 : 1000, Abcam, Cambridge, UK), cleaved caspase-3 (9661S, 1 : 400, Cell Signaling, Danvers, Massachusetts, USA), or cleaved caspase-9 (10380–1-AP, 1 : 50, ProteinTech, USA) for 12 h at 4°C, followed by incubation with the secondary antibody (ab150077, 1 : 500) for 1 h at room temperature.
Total protein was extracted from the SW982 cells or tumor tissues using RIPA lysis buffer (Beyotime, China), followed by separation using an SDS-PAGE electrophoresis system (Bio-Rad, USA) after the total protein was measured by performing bicinchoninic acid (BCA) assay (Beyotime, China). Then, the protein samples were transferred from the gel onto a polyvinylidene difluoride (PVDF) membrane (Millipore, Germany) and blocked with 5% skimmed milk for 12 h at 4°C, followed by incubation with the HOXA13 antibody (ab172570, 1 : 1000, Abcam, Cambridge, UK), VEGFR2 antibody (ab221679, 1 : 1000, Abcam, Cambridge, UK), Ki67 (ab16667, 1 : 1000, Abcam, Cambridge, UK), cleaved caspase-3 antibody (ab214430, 1 : 5000, Abcam, Cambridge, UK), and cleaved caspase-9 antibody (ab2324, 1 : 1000, Abcam, Cambridge, UK) for 12 h at 4°C. The protein blots were visualized using the ECL kit (Thermo Scientific, China) and the Bio-Rad XR gel imaging analysis system (Bio-Rad, USA) after incubation with Goat Anti-Rabbit IgG H&L antibody (1 : 10000). All antibodies were purchased from Abcam (UK).
The data were presented as the mean ± standard deviation (SD) and processed using the GraphPad 8.0 software. Data were collected from at least three independent experiments. The differences between any two groups were determined by performing independent sample t-tests. The differences among three or more groups were determined by performing one-way analysis of variance (ANOVA), followed by multiple comparisons by performing the LSD test. The differences between and among groups were considered to be statistically significant at P < 0.05 and 95% confidence interval, based on the two-sided test.
To investigate the effect of apatinib on malignant behaviors, the SW982 cells were stimulated with various concentrations of apatinib for 48 h. The cell viability decreased with an increase in the concentration of apatinib (Figure 1(a)), and 10 μM apatinib was selected as the optimal concentration and used in the following study. Similarly, apatinib significantly reduced the proliferation of SW982 cells, determined by the colony formation assay (Figure 1(b)). The results of the flow cytometry assay showed that there was also an increase in the apoptosis of apatinib-treated SW982 cells compared to the level of apoptosis in cells not administered apatinib treatment (Figure 1(c)). Apatinib significantly reduced the migration and invasion of the SW982 cells compared to those activities of the cells in the control group (Figures 1(d) and 1(e)). At the protein level, apatinib decreased the expression of VEGFR2 and Ki67 in SW982 cells and increased the expression of cleaved caspase-3 and cleaved caspase-9 (Figure 1(f)). Moreover, to determine the effect of apatinib on miR-34a-5p, qPCR analysis was performed, and the results showed that the level of miR-34a-5p increased substantially after apatinib treatment compared to the level of miR-34a-5p in the control (Figure 1(g)). This indicated that miR-34a-5p might be the mediator of apatinib in SW982 cells. Overall, apatinib decreased the migration, invasion, proliferation, and elevation of apoptosis in SW982 cells.
To determine whether apatinib can inhibit SW982 cells via miR-34a-5p, miR-34a-5p was repressed by the miR-34a-5p inhibitor in SW982 cells (Figure 2(a)). The downregulation of miR-34a-5p increased the proliferation of SW982 cells following apatinib treatment (Figures 2(b) and 2(c)). Subsequently, we examined the effect of miR-34a-5p on apatinib-stimulated apoptosis. The results suggested that after treatment with apatinib along with the miR-34a-5p inhibitor, the apoptosis of SW982 cells was considerably reduced (Figure 2(d)). The decrease in miR-34a-5p suppressed the apatinib-induced reduction of migration and invasion of the SW982 cells (Figures 2(e) and 2(f)). The results of the western blot assay showed that the miR-34a-5p inhibitor increased the levels of the VEGFR2 and Ki67 proteins that were reduced by apatinib and suppressed the levels of cleaved caspase-3 and cleaved caspase-9 that were increased by apatinib (Figure 2(g)). These results suggested that apatinib inhibits multiple malignant behaviors of SW982 cells by regulating miR-34a-5p.
To determine the function of miR-34a-5p in apatinib-inhibited malignant behaviors, we examined the downstream target genes. The complementary of miR-34a-5p in the 3′-UTR of HOXA13 was obtained by using bioinformatics (Figure 3(a)). The miR-34a-5p mimic significantly reduced the luciferase activity of pmirGLO-HOXA13 wt in the SW982 cells but had nonsignificant effects on the pmirGLO-HOXA13 mut-transfected cells (Figure 3(b)). Additionally, the miR-34a-5p mimic significantly suppressed the mRNA and protein levels of HOXA13 in the SW982 cells (Figures 3(c) and 3(d)). Our study demonstrated that HOXA13 was the target of miR-34a-5p.
To determine the relationship between apatinib and the miR-34a-5p/HOXA13 axis in synovial sarcoma, both miR-34a-5p and HOXA13 were downregulated in SW982 cells. A decrease in the HOXA13 levels repressed the elevated proliferation in apatinib-treated SW982 cells in response to the miR-34a-5p inhibitor (Figure 4(a)). Silencing HOXA13 reduced clone formation in apatinib-treated SW982 cells in the presence of the miR-34a-5p inhibitor (Figure 4(b)). After apatinib increased cell apoptosis, treatment with the miR-34a-5p inhibitor remarkably reduced cell apoptosis, which was reversed after the combined knockdown of miR-34a-5p and HOXA13 (Figure 4(c)). The miR-34a-5p inhibitor significantly promoted the migration and invasion of SW982 cells that was inhibited by apatinib, and this effect almost disappeared after a decrease in HOXA13 (Figures 4(d) and 4(e)). We determined the role of miR-34a-5p in VEGFR2, Ki67, cleaved caspase-3, and cleaved caspase-9. We found that HOXA13 knockdown in apatinib-treated SW982 cells significantly reversed the effect of the miR-34a-5p inhibitor on VEGFR2, Ki67, cleaved caspase-3, and cleaved caspase-9 (Figure 4(f)). Therefore, apatinib can regulate the miR-34a-5p/HOXA13 axis to inhibit malignant behaviors of SW982 cells.
To determine the effect of apatinib on synovial sarcoma in vivo, SW982 cells were subcutaneously injected into BALB/c female mice, which were treated with 50 mg/kg apatinib for 30 d. Then, the tumor volume was measured every 5 d. The tumor volume decreased significantly after apatinib treatment compared to the tumor volume in the control group treated with DMSO (Figures 5(a) and 5(b)). VEGFR2 and Ki67 were used as the markers of angiogenesis and proliferation, respectively. From the results of immunohistochemistry, we found that apatinib reduced VEGFR2 and Ki67 and enhanced cleaved caspase-3 and cleaved caspase-9 in synovial sarcoma mice of the treatment group compared to their levels in the mice of the DMSO group (Figure 5(c)). According to the results of the western blot assay, cleaved caspase-3 and cleaved caspase-9 levels increased, and VEGFR2 and Ki67 levels decreased in apatinib-treated synovial sarcoma mice compared to their levels in the mice of the DMSO group (Figure 5(d)). We further examined the tumor tissues by performing qPCR and western blot analysis. The expression of miR-34a-5p was upregulated (Figure 5(e)) in the apatinib-treated synovial sarcoma mice, accompanied by a downregulation in the expression of HOXA13 (Figure 5(f)). Collectively, the results suggested that apatinib inhibited tumor growth in synovial sarcoma.
We investigated the application of apatinib in synovial sarcoma treatment and determined its molecular regulation in tumorigenesis. In this study, a novel mechanism was elucidated, where apatinib played antiproliferative and proapoptotic roles in synovial sarcoma in vivo and in vitro via the miR-34a-5p/HOXA13 axis. Apatinib can inhibit the tyrosine kinase activity of VEGFR2 in cells [31]. Previous studies showed that apatinib stimulated the inactivation of VEGFR2 to play antiproliferative and proapoptotic roles in ovarian cancer and colorectal cancer [32–34]. The mechanism of action of apatinib in synovial sarcoma remains unclear. A clinical report suggested that apatinib improved the prognosis of sarcoma patients in whom chemotherapy was ineffective [35]. In our study, the reduction of malignant behaviors and the enhancement of apoptosis in synovial sarcoma cancer cells in response to apatinib showed that apatinib can potentially inhibit the tumorigenesis of synovial sarcoma. We also found that the administration of apatinib significantly enhanced miR-34a-5p in synovial sarcoma cells. miR-34a-5p plays an important role in cellular processes by regulating pathways associated with protein synthesis or tumorigenesis. Several studies have found that miR-34a-5p can regulate cellular processes in bone sarcoma. Two reports by Pu et al. [36, 37] suggested that mi-34a-5p developed osteosarcoma chemoresistance by targeting DLL1 and ATGR1, respectively. Silencing of miR-34a-5p by promoter methylation is associated with the detection of synovial sarcoma [23]. However, the function of miR-34a-5p in the tumorigenesis of synovial sarcoma remains unknown. In our study, an increase in the miR-34a-5p levels stimulated by apatinib attenuated proliferation, migration, and invasion of synovial sarcoma cells; also, it significantly increased apoptosis in these cells. These results indicated a novel drug-miRNA relationship where apatinib increases apoptosis and reduces the proliferation, invasion, and migration in synovial sarcoma cells by increasing the levels of miR-34a-5p. We also found that HOXA13 might be the target of miR-34a-5p in cells. HOXA13 was repressed by miR-34a-5p in SW982 cells. Thus, we hypothesized that apatinib increases miR-34a-5p levels and, in turn, decreases HOXA13, leading to the inhibition of tumorigenesis and the progression of SW982 cells. The regulation of mRNA by miRNA significantly contributes to maintaining cellular processes. Empirical evidence suggests that HOXA13 expression can be regulated by miRNA at the posttranscriptional level. Liu et al. [38] showed that HOXA13 was the target of miR-381 and mediated the malignant behaviors of cervical cancer cells. Sun et al. [39] found that miR-185–5p targeted HOXA13 to increase cell survival in laryngeal squamous cell carcinoma. In our study, HOXA13 silencing reversed the antiapatinib effect due to miR-34a-5p inhibition in synovial sarcoma cells, indicating that apatinib inhibited HOXA13 to repress tumorigenesis in synovial sarcoma cells via miR-34a-5p. By elucidating the novel mechanism of action of apatinib, we showed its potential role in the diagnosis and prognosis of synovial sarcoma. The expression of miR-34a-5p and HOXA13 may be used as the biomarkers for the identification of synovial sarcoma and evaluation of cancer progression. Also, regulating the miR-34a-5p/HOXA13 axis might enhance the antitumorigenic effect of apatinib during synovial sarcoma treatment. VEGFR2-mediated angiogenesis effectively regulates nutrient supply and oxygen transport during tumorigenesis, leading to the enhancement of tumor metastasis and immune escape [40–42]. Our study showed that apatinib repressed the level of VEGFR2 in vivo, indicating that it had antiangiogenic effects on tumorigenesis in synovial sarcoma. However, we failed to understand its role in the angiogenesis of synovial sarcoma, which should be investigated in follow-up studies in vivo and in vitro. In conclusion, we found that apatinib increased the levels of miR-34a-5p and repressed the expression of HOXA13, leading to a significant reduction in malignant behaviors and the enhancement of apoptosis in synovial sarcoma cells. Additionally, apatinib induced the inhibition of tumorigenesis in synovial sarcoma via the VEGFR2 pathway in vivo. Our study provided a mechanism of action of apatinib for the treatment of synovial sarcoma, suggesting a novel strategy for the management and diagnosis of the progression of synovial sarcoma. | true | true | true |
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PMC9581691 | Hanbi Wang,Simiao Liu,Wanyu Zhang,Meizhi Liu,Chengyan Deng | Human Umbilical Cord Mesenchymal Stem Cell-Derived Exosome Repairs Endometrial Epithelial Cells Injury Induced by Hypoxia via Regulating miR-663a/CDKN2A Axis | 12-10-2022 | Aim Thin endometrium remains a severe clinical challenge with no effective therapy to date. We aimed at exploring the role and molecular mechanism of human umbilical cord mesenchymal stem cell- (hucMSC-) derived exosomes (hucMSC-Ex) in repairing hypoxic injury of endometrial epithelial cells (EECs). Methods Exosomes were harvested from the conditioned medium of hucMSC and characterized using western blot, transmission electron microscopy (TEM), flow cytometry, and nanoparticle tracking analysis (NTA). EECs were subjected to hypoxic conditions before cocultured with hucMSC-Ex. Cell viability, apoptosis, and migration were determined with CCK-8, flow cytometry, and wound healing assay, respectively. Apoptosis/EMT-related proteins were detected by western blot. The miRNA profiling was determined by RNA sequencing. The expression of miR-663a and CDKN2A was measured by qRT-PCR. MiR-663a in EECs was overexpressed by transfecting with miR-663a mimics. Results Mesenchymal stem cells (MSCs) markers CD73, CD90, and CD106 were positively expressed in hucMSCs. Exosome isolated from hucMSC expressed CD63 and TSG101, and were 100–150 nm in diameter. HucMSC-Ex promoted cell proliferation inhibited by hypoxia. And hucMSC-Ex also inhibited hypoxia-induced apoptosis, migration, and EMT of EECs by upregulating the expression of Bcl-2 and E-cadherin and downregulating Bax and N-cadherin levels. Further, bioinformatics research found that hucMSC-Ex coculture can significantly upregulate the expression of miR-663a and decrease the expression of CDKN2A in hypoxia-induced EECs. Furthermore, miR-663a overexpression inhibited CDKN2A expression and increased the expression of Bcl-2 and E-cadherin in hypoxia-induced EECs. Conclusions HucMSC-Ex promoted cell proliferation, inhibited cell apoptosis, migration, and EMT in hypoxia-induced EECs, thereby alleviating hypoxia-induced EECs injury, which may be related to its regulation of miR-663a/CDKN2A expression. Our study indicated that hucMSC-Ex might benefit for repairing thin endometrium. | Human Umbilical Cord Mesenchymal Stem Cell-Derived Exosome Repairs Endometrial Epithelial Cells Injury Induced by Hypoxia via Regulating miR-663a/CDKN2A Axis
Thin endometrium remains a severe clinical challenge with no effective therapy to date. We aimed at exploring the role and molecular mechanism of human umbilical cord mesenchymal stem cell- (hucMSC-) derived exosomes (hucMSC-Ex) in repairing hypoxic injury of endometrial epithelial cells (EECs).
Exosomes were harvested from the conditioned medium of hucMSC and characterized using western blot, transmission electron microscopy (TEM), flow cytometry, and nanoparticle tracking analysis (NTA). EECs were subjected to hypoxic conditions before cocultured with hucMSC-Ex. Cell viability, apoptosis, and migration were determined with CCK-8, flow cytometry, and wound healing assay, respectively. Apoptosis/EMT-related proteins were detected by western blot. The miRNA profiling was determined by RNA sequencing. The expression of miR-663a and CDKN2A was measured by qRT-PCR. MiR-663a in EECs was overexpressed by transfecting with miR-663a mimics.
Mesenchymal stem cells (MSCs) markers CD73, CD90, and CD106 were positively expressed in hucMSCs. Exosome isolated from hucMSC expressed CD63 and TSG101, and were 100–150 nm in diameter. HucMSC-Ex promoted cell proliferation inhibited by hypoxia. And hucMSC-Ex also inhibited hypoxia-induced apoptosis, migration, and EMT of EECs by upregulating the expression of Bcl-2 and E-cadherin and downregulating Bax and N-cadherin levels. Further, bioinformatics research found that hucMSC-Ex coculture can significantly upregulate the expression of miR-663a and decrease the expression of CDKN2A in hypoxia-induced EECs. Furthermore, miR-663a overexpression inhibited CDKN2A expression and increased the expression of Bcl-2 and E-cadherin in hypoxia-induced EECs.
HucMSC-Ex promoted cell proliferation, inhibited cell apoptosis, migration, and EMT in hypoxia-induced EECs, thereby alleviating hypoxia-induced EECs injury, which may be related to its regulation of miR-663a/CDKN2A expression. Our study indicated that hucMSC-Ex might benefit for repairing thin endometrium.
Human endometrium is a highly dynamic renewable tissue which receives embryos by implantation during a woman's reproductive period [1]. Endometrial impairments initiated by infection, caesarean section, recurrent curettage, or myomectomy can result in thin endometrium [2]. It is reported that a thin endometrial lining is related to declined rates of implantation and pregnancy [3]. Hormone-therapy, vasoactive measures, intrauterine infusion of growth factor, and regenerative medicine are modalities offered for the treatment of thin endometrium [4], whereas clinical efficacy of these means is negligible. Hence, innovative strategies to rebuild the endometrium to its normal morphology and function are essential for treatment. Mesenchymal stem cells (MSCs) are a particular stromal cell type which presents structural and functional benefits in diseases, such as thin endometrium [5]. The transplantation of MSCs has been studied as a scheme to regenerate the endometrium because of their capability to differentiate into endometrial stromal cells (ESCs) and endometrial epithelial cells (EECs) [6]. Accumulating evidence has demonstrated the therapeutic impacts of MSCs on endometrium, such as the increased endometrium thickness, better formed tissue construction, protected implantation, and ameliorated pregnancy [7]. Exosomes are potent secretory products of MSCs that play a crucial role in biological effects mediated by MSCs [8]. MSCs-derived exosomes reverse epithelial-mesenchymal transition (EMT) and facilitate repair of damaged endometrium [9]. Moreover, the local transplantation of umbilical cord mesenchymal stem cells- (UCMSCs-) derived exosomes (hucMSCs-Ex) loaded in collagen scaffold facilitates endometrium regeneration and promotes fertility restoration [10]. Exosome-shuttled miR-7162-3p from hucMSCs repairs ESCs injury [11]. In comparison with their source cells, exosomes display the advantages of easier storage, easier perfusion into tissues, immune-privileged status, and higher biological stability [12]. Therefore, exosome-based therapeutic strategies hold promise as a prospective approach to promoting endometrial regeneration. MicroRNAs (miRNAs), posttranscriptionally controlling the translation and stability of mRNAs, are a crucial component of the exosomal cargo [13]. It has been proved that exosome-encapsulated miRNA can be stored stably to avoid nuclease degradation [14]. miR-663a is reported to be able to suppress early apoptosis and stimulate proliferation of human spermatogonial stem cells [15]. MiR-663a is found to be associated with the development of renal cell carcinoma, which promotes cellular proliferation and migration while inhibiting apoptosis [16]. Cyclin-dependent kinase inhibitor 2A (CDKN2A, also known as p16 gene, which is located on chromosome 9P21) is the first antioncogene directly involved in cell cycle regulation [17]. CDKN2A protein competes with cyclin-dependent kinases 4/6 (CDK4/6) to inhibit its activity, inducing cells to stop in G1 phase, thereby inhibiting cell proliferation [18]. CDKN2A is reported to mediate the development of the disease by regulating apoptosis [19, 20]. Online software predicts the binding relationship between miR-663a and CDKN2A. However, a relationship between the anti-injury effect of MSCs-derived exosomes on EECs in hypoxic conditions and miR-663a regulation of CDKN2A has not been established. Therefore, we hypothesized that MSCs-derived exosome are involved in regulating apoptosis and migration in EECs under hypoxic injury environment through miR-663a. We further investigated the potential target of exosomal derived from hucMSCs in attenuating EECs apoptosis and migration.
Primary human EECs were bought from iCell Bioscience Inc. (HUM-iCELL-f004, China) and cultured in Dulbecco's modified Eagle's medium (Gibco, 11965092, America) with 10% fetal bovine serum (Gibco, 10100147, America). hucMSCs at passage 3 were obtained from iCell Bioscience Inc. (HUM-iCell-e011, China), and they were maintained in DMEM/F12 (Gibco, A4192001, America) containing 10% exosome-depleted FBS (Gibco, A2720801, America). As previously described, hucMSCs at passage 4–6 were applied for the subsequent experiments. 100 ng/ml penicillin and 100 U/ml streptomycin were added in the culture medium, and cells were then maintained in normoxic (21% O2, 75% N2, and 5% CO2) condition at 37°C. Cells were stained by immunofluorescence to determine their purity after 24 h of culture.
The phenotype profile of hucMSCs was evaluated through flow cytometry analysis by using antibodies against positive markers (CD73, CD90, and CD105) and negative markers (CD14, CD34, and CD45). All details of antibodies are shown as follow: fluorescein isothiocyanate- (FITC-) conjugated antibodies against CD73 (BioLegend, 344016, America), CD105 (BD, 562351, America), CD90 (BioLegend, 328108, America), phycoerythrin- (PE-) conjugated antibodies against CD34 (BD, 348057, America), CD45 (BD, 560975, America), and CD14 (BD, 567731, America).
Exosomes were separated from hucMSCs as previously described [21]. The separation procedure included an additional centrifugation step to remove small cell debris prior to ultracentrifugation for 1 h at 100,000 g to generate an exosome pellet. Next, PBS was used to resuspend the pelleted exosomes. Exosomal markers including TSG101 (Abcam, ab125011, UK) and CD63 (Abcam, ab134045, UK) were used for western blot. The morphology of exosomes was observed under transmission electron microscopy (TEM). The size distribution of exosomes was examined by nanoparticle tracking analysis (NTA) [10]. The concentration of exosomes was determined using bicinchoninic acid (BCA) protein kit (Thermo, 23227, USA).
EECs were cultured in normoxic condition and grown to approximately 60–70% confluence. EECs incubated in normoxic condition were used as control, named as the normal group. For hypoxia treatment, cells were cultured under humidified hypoxic air (1% O2, 94% N2, and 5% CO2) in a modular incubator chamber (Thermo, America) at 37°C for indicated periods (1 h, 4 h, 8 h, 16 h, and 24 h), named as the hypoxia group. Hypoxia injury of EECs was evaluated by western blot. Hypoxia-induced EECs (1 × 105/per well) was cocultured with 2 μg of hucMSC-Ex on the basis of protein measurement, named as the hypoxia+hucMSC-Ex group.
Exosomes from hucMSCs were labeled with PKH67 (Sigma-Aldrich, MINI67-1KT, Germany) in accordance with the protocol. After incubation with PKH67-labeled exosomes for 24 h, EECs were fixed with paraformaldehyde (4%), permeabilized with Triton X-100 (0.5%), and stained with DAPI. Afterwards, a laser scanning confocal microscope (Olympus FLUOVIEW FV3000) was used to detect the uptake of labeled exosomes by EECs.
The cell counting kit 8 (Beyotime Biotechnology, C0038, China) was used to measure cell viability. EECs were seeded in 96-well plate and cocultured with or without hucMSC-Ex for 24 h. Next, cells in each well were added with CCK-8 reagent (Solarbio, China) and incubated for 2 h at 37°C. OD value was measured with a microplate reader (molecular devices-spetramax paradigm) at 450 nm.
EECs were planted in 6-well plate (5 × 105 cells/well) and cocultured with or without hucMSC-Ex for 24 h. The annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) apoptosis detection kit (Beyotime Biotechnology, C1062, China) was used to determine apoptosis according to the manufacturer's guidelines. Cells were sorted within 1 h by a FACScan flow cytometer (BD FACS Calibur).
EECs were planted in 6-well plate in culture medium and grown to confluence. A sterile 200 μl pipette tip was utilized to scratch the monolayer of exposed EECs. Cells were rinsed by PBS prior to incubating in fresh medium. At 0 h and 24 h, wounds were imaged under phase-contrast microscopy. Subsequently, wound closure was evaluated and showed as the percentage of closure regarding initial wound width.
Total RNA was extracted from EECs using RNAzol® RT kit (Sigma-Aldrich, R4533, Germany), cDNA of mRNAs was synthesized by Prime Script™ RT reagent (TIANGEN BIOTECH, KR118, China), and cDNA of miRNA was synthesized by TransScript® miRNA First-Strand cDNA Synthesis SuperMix (TransGen Biotech, AT351, China). SYBR® Premix Ex Taq™ (Tli RNaseH Plus, FP205, China) were applied to determine the relative levels of target genes. StepOnePlus Real-Time PCR System (ABI 7500 Fast) was used to perform real-time PCR procedure. U6 and GAPDH were used as the internal reference. The RNA expressions were quantified and calculated with the 2−ΔΔCt method. Primers are shown in Table 1.
Total proteins of cells were extracted with RIPA lysis buffer (25 mM Tris-HCl (PH7.4), 150 mM NaCl, 1% NP40, and 0.25% sodium deoxycholate) and separated with 10% SDS-polyacrylamide gel electrophoresis (SDS-PAGE), followed by transferred onto polyvinylidene fluoride (PVDF) membranes (Sigma-Aldrich, IPFL00010, Germany). Membranes were incubated in primary antibodies at 4°C overnight including CD63 antibody (Abcam, ab134045, UK), TSG101 antibody (Abcam ab125011, UK), VEGF antibody (Abcam, ab214424, UK), avβ3 antibody (Abcam, ab179473, UK), CDKN2A/p16INK4a antibody (Abcam, ab108349, UK), Bax antibody (Abcam, ab182734, UK), cleaved-caspase3 antibody (Abcam, ab32042, UK), P53 antibody (Abcam, ab26, UK), Bcl-2 antibody (Abcam, ab182858, UK), E-cadherin antibody (Abcam, ab40772, UK), N-cadherin antibody (Abcam, ab76011, UK), β-actin antibody (Cwbio, CW0096, China), and β-tubulin antibody (Cwbio, CW0098, China). After incubation with secondary antibodies (ZSGB-BIO, China), protein signals were measured and visualized with an ECL chemiluminescence kit (Cytiva, RPN2232, China) under a luminescent imaging system (CIiNX ChemiScoe 6100).
After being extracted from cells, the quality of the total RNA was examined with an Agilent Technologies 2100 Bioanalyzer. TruSeq small RNA library prep kit (RiboBio, China) was applied to prepare the small RNA library. After multiplexed in equimolar amounts, indexed small RNA libraries were denatured and loaded for cluster generation on GAIIx flow cell lanes with cBot station and Illumina cluster generation kits. Differentially expressed miRNAs presenting raw reads ≥5 in samples and P value <0.05 were selected. The target genes of identified miRNAs were predicted using miRNA target prediction algorithms TargetScan (https://www.targetscan.org/vert_80/) [22].
miR-663a mimics (miR-663a mimics) and mimics NC (NC) were synthesized by Sangon Biotech (Shanghai) Co., Ltd., before transfection, EECs (2 × 104/well) were seeded in 6-well plates and grown to 80% confluence. miR-663a mimics and mimics NC (final concentration: 30 nM/well) were transfected into EECs using transfection jetPRIME® (Polyplus, 114-15, France). Cells were incubated in normoxic or hypoxic condition for 4 h prior to collected for the subsequent detections.
Data were analyzed and visualized using GraphPad Prism 6.0 and presented as mean ± standard deviation (SD). Significant differences between the two groups were determined by student's t-test. While one-way ANOVA analysis followed by Tukey's post hoc was carried out to compare the differences among more than two groups. P < 0.05 was considered statistically significant.
First, we identified the surface antigens of hucMSCs. The surface antigens of hucMSCs were detected by flow cytometry, which presented that the negative markers (CD14, CD45, and CD34) were almost no expressed, while the positive markers (CD73, CD90, and CD105) for MSCs were highly expressed (Figures 1(a)–1(f)). Next, we extracted exosomes from hucMSCs culture medium. Western blot showed that typical positive markers (TSG101 and CD63) were expressed in the isolated exosomes (Figure 2(a)). TEM imaging exhibited that the vesicle-like exosomes were spherical (Figure 2(b)). Moreover, NTA results revealed a narrow size distribution of exosomes, and the main peak was at 100-150 nm (Figure 2(c)). These data indicated that the purified exosomes were successfully extracted from the culture medium of hucMSCs.
To investigate the potential of hucMSC-Ex on thin endometrium, EECs were exposed to a hypoxic environment to establish a cellular model. With increasing incubation time under hypoxia condition, the protein expression of hypoxia-related proteins VEGF and avβ3 were increased in EECs, while the level of epithelial marker E-cadherin was decreased (Figures 3(a)–3(d)). To verify the uptake efficiency of hucMSC-Ex by EECs, we cocultured hypoxia-induced EECs with PKH67-labeled hucMSC-Ex for 24 h. Compared with the normal group, the uptake of hucMSC-Ex by EECs cells in the hypoxia group was significantly increased (Figure 3(e)). To further explore the function of hucMSC-Ex in cell proliferation in hypoxic-induced EECs, hypoxia-induced EECs were cocultured with hucMSC-Ex. Cell proliferation of EECs was reduced in hypoxic-induced EECs, which can be elevated after coculture with hucMSC-Ex (Figure 3(f)). The findings suggested that hucMSC-Ex promoted the cell survival of hypoxic-induced EECs.
Then, we further explored the role of hucMSC-Ex in cell apoptosis, migration, and EMT of hypoxia-induced EECs. The results showed that cell apoptosis in EECs with hypoxia treatment for 4 h was significantly increased compared with that in normoxic condition-incubated EECs, while apoptosis in hypoxia-induced EECs was significantly inhibited after treatment with hucMSC-Ex (Figures 4(a) and 4(b)). Moreover, compared with the normal group, hypoxia treatment promoted EECs migration, while hucMSC-Ex treatment significantly inhibited cell migration in hypoxia-induced EECs (Figures 4(c) and 4(d)). The promoted apoptosis of EECs under hypoxic condition was also evidenced by the elevated proapoptotic proteins (Bax, cleaved-caspase3, and P53) and downregulated Bcl-2. Besides, the enhanced migration of EECs induced by hypoxia was supported by the elevated mesenchymal biomarker N-cadherin and decreased epithelial marker E-cadherin, suggesting that hypoxia promoted EMT of EECs. However, hucMSC-Ex treatment reversed the protein expressions in EECs induced by hypoxia (Figures 4(e)–4(j)). Collectively, the data revealed that hucMSC-Ex inhibited cell apoptosis, migration, and EMT in hypoxia-induced EECs.
To explore differential expression miRNAs (DEmiRNAs) in EECs after treatment with hypoxia or hucMSC-Ex, miRNA sequencing analysis was performed. The results showed that 45 (36 upregulated and 9 downregulated) DEmiRNAs were identified in hypoxia, normal, and hypoxia+hucMSC-Ex groups (Figure 5(a)). Among that, miR-663a was significantly downregulated in hypoxia treated EECs, while its expression recovered after coculturing with hucMSC-Ex (Figure 5(b)). The mRNA expression of miR-663a was also validated with qRT-PCR (Figure 5(c)). We speculated that miR-663a in hucMSC-Ex exerted a protective effect on hypoxia-induced EECs. The online website TargetScan predicted that miR-663a could bind to CDKN2A 3′-UTR, and the targeting sequence was shown in Figure 5(d). We found that the expression of CDKN2A was upregulated in hypoxia-induced EECs, which was decreased after hucMSC-Ex coculture (Figure 5(e)). Then, we upregulated the miR-663a expression in hypoxia-induced EECs by transfecting with miR-663 mimics (Figure 5(f)). After transfection with miR-663a mimics, CDKN2A, N-cadherin, and Bax were downregulated, while antiapoptosis protein Bcl-2 and epithelial marker E-cadherin were both upregulated in hypoxia-induced EECs (Figures 5(g)–5(l)). The data indicated that CDKN2A could be negatively modulated by miR-663a. In addition, the effects of miR-663a overexpression was consistent with that of hucMSC-Ex coculture.
MSCs have acquired much interest in the therapy of many disorders, which also displayed better protection in the treatment of thin endometrium [23]. Currently, MSCs-derived exosomes have been noticed to contain many kinds of mediators including proteins and miRNAs, mediating the function of MSCs [9, 24]. Moreover, the altered levels of miRNAs reported in thin endometrium indicate that miRNAs may be involved in the pathogenesis of this disorder [25]. Therefore, we investigated the function of hucMSC-Ex in EECs within hypoxic conditions. Collectively, this study demonstrated that hucMSC-Ex could indeed suppress EECs apoptosis and migration through regulating the miR-663a/CDKN2A axis. MSCs are widely applied in the repair of damaged tissues [26]. MSCs can play its biological roles in repairing damaged tissues through secreting exosomes [27]. For instance, exosomes derived from bone marrow mesenchymal stem cells (BMSCs) can reverse EMT in EECs and are implicated in repairing the induced endometrium [9]. Exosomal transfer of BMSCs-derived miR-340 attenuates endometrial fibrosis [28]. Exosomes derived from hucMSCs promote proliferation of allogeneic ESCs [29]. hucMSCs-derived exosomal miR-7162-3p reduces ESCs apoptosis induced by mifepristone [11]. Exosomes derived from hucMSCs can improve cell viability and exhibit anti-inflammatory properties in EECs induced by oxygen and glucose deprivation/reoxygenation (OGD/R) [30]. HucMSC-Ex enhances the migratory ability of endometrial glandular epithelial cells isolated from endometriosis patients via promotion of EMT [31]. Collagen scaffold/UCMSCs facilitates endometrial regeneration and fertility restoration, in addition, better collagen remodeling, obvious luminal structures, and thicker endometrium are noticed [10, 32]. Importantly, MSCs can play an antiapoptosis role in disorders including cerebrovascular disease [33], cardiovascular disease [34], and reproductive disorder [13]. Transplantation of MSCs-derived exosomes can inhibit apoptosis and promote proliferation and migration in endometrial cells, thus improving endometrial repair [11, 29, 35]. On the basis of these discoveries, we proposed that MSCs-derived exosomes could be employed in thin endometrium therapy. In our cellular model of thin endometrium, we found that the number of viable EECs in hypoxic condition decreased and apoptosis and migration increased, whereas apoptosis and migration inhibited by coculture with hucMSC-Ex. In EECs under hypoxic condition, the elevated proapoptotic proteins and downregulated antiapoptotic protein were observed. Besides, migration related proteins N-cadherin and E-cadherin were increased and decreased, respectively. After treatment with hucMSC-Ex, the number of viable EECs within hypoxia condition was increased, while apoptosis and migration were inhibited. Thus, hucMSC-Ex exerted a protective effect on hypoxia-induced EECs injury. Further, we screened differentially expressed miRNAs in EECs following hypoxia or hucMSC-Ex treatment by RNA-seq. It was found that miR-663a was significantly downregulated in hypoxia-induced EECs, while its expression was restored after coculture with hucMSC-Ex. This result was also validated by qRT-PCR. A previous study shows altered expression of miR-663a associated with hypoxia in bronchoalveolar lavage fluid [36]. miR-663a can suppress early apoptosis and stimulate proliferation of human spermatogonial stem cells [15]. miR-663a stimulates cell proliferation and migration in osteosarcoma [37]. In addition, bioinformatics analysis predicted that miR-663a could target CDKN2A 3′-UTR. Studies showed that apoptosis and proliferation are intimately coupled. Many proteins and signal pathways have been proved to play key role in cell proliferation and apoptosis, such as c-Myc, p53, pRb, Ras, PKA, PKC, Bcl-2, NF-κB, CDK, cyclins, CKI, and MAPK/ERK, but several variables, including cell type, cellular microenvironment, and genetic background, could affect the outcome [38, 39]. CDKN2A (p16) is generally understood to be an apoptosis regulatory gene, which may be involved in the pathogenesis and development of endometriosis [40]. In women with endometriosis, the altered p16 expressions has been evidenced in eutopic endometrium [41]. Stromal p16 level is a representative discovery in endometrial polyps [42]. In benign lesions, overexpression of p16 suppresses cell proliferation, keeping cells from malignant transformation [43]. We found that hucMSC-Ex downregulated the increased CDKN2A induced by hypoxia in EECs. CDKN2A expression could be negatively modulated by miR-663a. Moreover, overexpression of miR-663a in hypoxia-induced EECs increased antiapoptotic protein Bcl-2, as well as increased epithelial marker E-cadherin. Therefore, hucMSC-Ex exerted a protective function in hypoxia-induced EECs injury, which may be related to the regulation of the miR-663a/CDKN2A axis. Studies have observed that exosomes can carry functional RNAs, miRNAs, and proteins among cells [44, 45]. Exosomes derived from MSCs inhibit apoptosis through miRNA regulating signaling pathway in ESCs, which could effectively improve endometrial repair [11]. Although we found that hucMCS-Ex could improve hypoxia-induced cell viability and inhibit apoptosis in EECs, it may play a role in regulating the miR-663a/CDKN2A axis. But whether miR-663a also acts on EECs cells through hucMCS-Ex as a vector requires further experimental investigation. In addition, more experiments are needed to further explore the role and mechanism of miR-663a and CDKN2A in hypoxia-induced proliferation, migration, and apoptosis of EECs.
In conclusion, hucMSC-Ex treatment upregulated the expression of miR-663 in EECs that were downregulated by hypoxia, and miR-663 targeted and regulated the expression of CDKN2A. These results indicate that the protective function of hucMSC-Ex in hypoxia-induced EECs injury may be related to regulation of the miR-663a/CDKN2A axis. This research may provide new insights into thin endometrium treatment. | true | true | true |
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PMC9581776 | 36224386 | Seiichi Hirano,Kalli Kappel,Han Altae-Tran,Guilhem Faure,Max E. Wilkinson,Soumya Kannan,F. Esra Demircioglu,Rui Yan,Momoko Shiozaki,Zhiheng Yu,Kira S. Makarova,Eugene V. Koonin,Rhiannon K. Macrae,Feng Zhang | Structure of the OMEGA nickase IsrB in complex with ωRNA and target DNA | 12-10-2022 | Enzyme mechanisms,Cryoelectron microscopy,DNA metabolism,RNA metabolism | RNA-guided systems, such as CRISPR–Cas, combine programmable substrate recognition with enzymatic function, a combination that has been used advantageously to develop powerful molecular technologies. Structural studies of these systems have illuminated how the RNA and protein jointly recognize and cleave their substrates, guiding rational engineering for further technology development. Recent work identified a new class of RNA-guided systems, termed OMEGA, which include IscB, the likely ancestor of Cas9, and the nickase IsrB, a homologue of IscB lacking the HNH nuclease domain. IsrB consists of only around 350 amino acids, but its small size is counterbalanced by a relatively large RNA guide (roughly 300-nt ωRNA). Here, we report the cryogenic-electron microscopy structure of Desulfovirgula thermocuniculi IsrB (DtIsrB) in complex with its cognate ωRNA and a target DNA. We find the overall structure of the IsrB protein shares a common scaffold with Cas9. In contrast to Cas9, however, which uses a recognition (REC) lobe to facilitate target selection, IsrB relies on its ωRNA, part of which forms an intricate ternary structure positioned analogously to REC. Structural analyses of IsrB and its ωRNA as well as comparisons to other RNA-guided systems highlight the functional interplay between protein and RNA, advancing our understanding of the biology and evolution of these diverse systems. | Structure of the OMEGA nickase IsrB in complex with ωRNA and target DNA
RNA-guided systems, such as CRISPR–Cas, combine programmable substrate recognition with enzymatic function, a combination that has been used advantageously to develop powerful molecular technologies. Structural studies of these systems have illuminated how the RNA and protein jointly recognize and cleave their substrates, guiding rational engineering for further technology development. Recent work identified a new class of RNA-guided systems, termed OMEGA, which include IscB, the likely ancestor of Cas9, and the nickase IsrB, a homologue of IscB lacking the HNH nuclease domain. IsrB consists of only around 350 amino acids, but its small size is counterbalanced by a relatively large RNA guide (roughly 300-nt ωRNA). Here, we report the cryogenic-electron microscopy structure of Desulfovirgula thermocuniculi IsrB (DtIsrB) in complex with its cognate ωRNA and a target DNA. We find the overall structure of the IsrB protein shares a common scaffold with Cas9. In contrast to Cas9, however, which uses a recognition (REC) lobe to facilitate target selection, IsrB relies on its ωRNA, part of which forms an intricate ternary structure positioned analogously to REC. Structural analyses of IsrB and its ωRNA as well as comparisons to other RNA-guided systems highlight the functional interplay between protein and RNA, advancing our understanding of the biology and evolution of these diverse systems.
The RNA-guided IsrB protein is an OMEGA family member encoded in the IS200/IS605 superfamily of transposons. IsrB is the likely antecedent of IscB, another OMEGA family member that is the apparent ancestor of Cas9, as indicated both by phylogenetic analysis and by the shared unique domain architecture. Like IscB and Cas9, IsrB contains a RuvC-like nuclease domain that is interrupted by the insertion of a bridge helix (BH) (Fig. 1a). However, in contrast to IscB and Cas9, IsrB lacks the HNH nuclease domain, the REC lobe and large portions of the protospacer adjacent motif- (PAM-)interacting domain and, accordingly, is much smaller (at roughly 350 amino acids) than Cas9. IsrB additionally contains an N-terminal PLMP domain (named after its conserved amino acid motif) and an uncharacterized C-terminal domain (Fig. 1b). Previous work has shown that IsrB associates with a roughly 300-nt ωRNA, which guides IsrB to nick the non-target strand of double-stranded (ds) DNA containing a 5′-NTGA-3′ target-adjacent motif (TAM).
To characterize the molecular mechanism of ωRNA-guided DNA targeting by IsrB, we analysed a ternary complex comprising Desulfovirgula thermocuniculi IsrB (DtIsrB), a 284-nt ωRNA containing a 20-nt guide segment, a 31-nt target DNA strand and a 10-nt non-target DNA strand using single-particle cryo-EM (Fig. 1c). We obtained a three-dimensional (3D) reconstruction of the ternary complex with an overall resolution of 3.1 Å (Fig. 1d, Extended Data Fig. 1a–c and Extended Data Table 1). Some regions of the map corresponding to the ωRNA, however, were resolved at a lower resolution. To refine the modelling of the RNA coordinates, we used an RNA-specific modelling tool, auto-DRRAFTER, together with a covariance-based secondary structure model to build an initial ωRNA model. On the basis of this ωRNA model and an initial IsrB model generated by protein structure prediction, we determined the IsrB–ωRNA–DNA structure (Fig. 1e and Extended Data Figs. 1d,e and 2). The structure revealed that IsrB extensively binds to target DNA through a 20-nt duplex between the ωRNA and target DNA (Fig. 1e). The RuvC domain (residues 60–253) encompasses the three catalytic motifs (RuvC I–III) and three insertions (BH (residues 92–112), A (residues 113–129) and B (residues 161–179)) (Fig. 1b). Insertion A is a ‘shortcut’ linker between BH and RuvC II; this linker is replaced with the REC lobe in Cas9. Thus, we denote this insertion the REC linker (RECL). Insertion B, between RuvC II and III, is a simple linker consisting of a loop and an α helix that in the IsrB structure occupies a position corresponding to that of the HNH domain in Cas9. Thus, we denote it the HNH linker (HNHL). The C-terminal domain (residues 287–351) adopts a core fold comprising two distorted β sheets (β1/2/6 and β3/4/5) and binds to the TAM-containing DNA duplex (Fig. 1e and Extended Data Fig. 3a). We denote this domain as the TAM-interacting (TI) domain because of structural and functional similarities to the PAM-interacting domain of Cas9 (Extended Data Fig. 3b). The extra β strand (β7) extensively interacts with the core fold of the TI domain and shares a common β sheet with the RuvC core that adopts the RNaseH fold (Extended Data Fig. 3a). This arrangement suggests that the TI and RuvC domains cooperate to define the distance between the RuvC active site and the TAM-binding site (Fig. 1e). The intermediate regions A (residues 254–267) and B (268–286) between the RuvC and TI domains seem to be functionally analogous to the phosphate-lock loop and WED domain of Cas9, respectively, and we therefore adopted those terms for IsrB (Fig. 1e). The PLMP domain (residues 1–59) features a four-stranded, antiparallel β sheet (β1–4) and an α helix, and is structurally similar to the N-terminal domain of translation initiation factor 3 (Fig. 1e and Extended Data Figs. 3a and 4). In this domain, the PLMP motif-containing strand (β2) is bulged due to two prolines (Pro17 and Pro20) disrupting one of the hydrogen bonds, but seems to keep the integrity of a coherent strand (β1). The PLMP domain extensively interacts with the RuvC and TI domains, suggesting a role in supporting their functions.
The ωRNA consists of the 20-nt guide segment, which base pairs with the target DNA, and the 262-nt ωRNA scaffold. This scaffold consists of 12 helices (four stems (S1–4) and eight stem loops (SL1–8)), which are located on three layers (layer 1, S1/3 and SL1/2/5/6; layer 2, S2/4 and SL3/4; layer3, SL7/8) (Fig. 2a,b). All the RNA helices are packed together by various RNA interactions. The S1-SL1, S2-SL3 and S3-SL6 combinations are directly stacked in each combination. S4 and SL4 are co-axially stacked due to the direct stack between A152 and U154 and the base-triple formation among A152, U179 and U183. SL2 and SL5 form a pseudoknot (which we denote as the adaptor pseudoknot), which is capped by a base-triple formed by G81, A192 and U197 (Fig. 2c). Some RNA helices connect layers within the globular ωRNA structure. S2, C107, A108, G245 and A246 form the nexus region, which is widely conserved in the tracrRNA of Cas9s (ref. ) (Fig. 2a). This nexus region and S4 are directly connected to S1 and SL5, respectively, between layers 1 and 2. SL4 forms a pseudoknot (which we denote as the nexus pseudoknot) with the region between S2 and SL7, enabling interactions between layers 2 and 3 (Fig. 2a,b). Mutations disrupting base pairs in the pseudoknots abolished the DNA nicking activity, and subsequent mutations restoring base pairs in the adaptor pseudoknot partially restored this activity, highlighting the importance of the pseudoknots for ωRNA function (Fig. 2d). These structural and biochemical data show that the ωRNA forms a compact, globular structure achieved by various RNA interactions. Such a structure may be beneficial for OMEGA systems: if the ωRNA autonomously forms its globular structure and functions as a scaffold (in contrast to tracrRNA), the effector protein does not need auxiliary motifs/domains to support RNA folding and function. Furthermore, if the globular shape provides some resistance to endogenous RNA degradation, it could facilitate ωRNA functioning in trans with an effector protein. This latter possibility is supported by the finding of standalone ωRNAs that can function with the related OMEGA effector IscB. The 5′-stem region of ωRNA (S1, SL1 and SL2) is designated the guide adaptor region. It seems that during the evolutionary transition from OMEGA system to CRISPR–Cas, SL2 and the descending strands of S1/SL1 of the ωRNA were adapted to form the CRISPR array to enable the formation of the functional Cas9–CRISPR RNA (crRNA)–tracrRNA complex (Fig. 1a). The genomic sequence encoding the guide adaptor region is important for IS200/IS605 transposon activity in bacterial genomes (Fig. 2a). We truncated part of this region, SL1 (ΔSL1 ωRNA), and found that the resulting RNA still supported robust DNA nicking activity by IsrB (Fig. 2d). Furthermore, we reconstituted ΔSL1 ωRNA with the IsrB protein and target DNA and performed a single-particle analysis, generating a 6.9-Å resolution map (Extended Data Fig. 6a–e). Comparing this map with that of the full-length RNA validated the SL1 position determined from our RNA model and revealed conformational similarity between the full-length and ΔSL1 RNAs (Extended Data Fig. 6a,b). These results indicate that SL1 in the guide adaptor region is not required for target DNA nicking by IsrB and instead may contribute to other functions involved in the mobility of IsrB-encoding transposons. The ωRNA scaffold extensively interacts with all parts of IsrB except for the HNHL region (Fig. 1e). In particular, the PLMP domain interacts with the tandem hairpins (SL7 and SL8) near the 3′ end of the ωRNA. The truncation of SL7/8, but not SL8 reduced the nicking activity of IsrB (Fig. 2d). Given that the terminal hairpin (SL7) of the ωRNA contains the Shine–Dalgarno sequence located immediately upstream of the IsrB-coding region, these results indicate that the IsrB–ωRNA interaction is important for IsrB function and could contribute to the regulation of IsrB expression in its native context.
We next sought to leverage structural information to decipher the DNA-targeting mechanism of IsrB. The gRNA–target DNA heteroduplex is surrounded by S2/S3/S4/SL2/SL4/SL5 of the ωRNA as well as the RuvC domain and the BH/RECL/HNHL regions of IsrB (Figs. 1e and 2b). SL2, SL4 and SL5 directly contact the heteroduplex backbone through hydrogen bonds and van der Waals interactions (Fig. 3a,b). S2, S3 and S4 indirectly recognize the heteroduplex backbone, using a short peptide linker, RECL, in which residues 113–124 are induced to fit into the grooves of S2/S3/S4 and the heteroduplex (Fig. 3b). Mutating F119 and R124 to alanine reduced the DNA nicking activity of IsrB, highlighting the functional importance of these residues in the RECL (Fig. 3c). In addition to the ωRNA, the IsrB protein binds extensively to the heteroduplex (Fig. 1e). The HNHL recognizes the minor groove of the heteroduplex through interactions with the backbone ribose moieties (Fig. 3d). We confirmed the importance of this interaction by deleting residues V161–F174 in the HNHL, which abolished the DNA nicking activity (Fig. 3c and Extended Data Fig. 5b). Several arginine residues in the BH contact the phosphate backbone of the ωRNA guide segment in a similar manner to that in the Cas9–guide RNA complex, in which the guide RNA–BH interactions preorder the guide region for DNA recognition and unwinding (Fig. 3f). Mutating R104, but not R100, to alanine reduced the DNA nicking activity of IsrB, highlighting the functional importance of R104 in the BH (Fig. 3c). Downstream of the target region (dG1–dC20), the ωRNA-complementary DNA strand (that is, the target strand) flipped and base-paired with the non-target DNA strand to form a TAM-containing duplex (dA[−1]-dA[−10]–dT1*-dT10*) (Fig. 1c,e). The backbone phosphate group between dC20 and dA(−1) in the target strand is recognized by Asn265 in the phosphate-lock loop, thereby facilitating heteroduplex formation (Fig. 3e). Mutating N265 to alanine reduced the nicking activity, suggesting the importance of this residue for DNA unwinding (Fig. 3c). The PLMP domain and the β7 motif in the TI domain are the pivotal units in the RuvC–TI–PLMP scaffold (Extended Data Fig. 3a). Truncating these domains/motifs abolished the DNA nicking activity of IsrB, indicating the importance of the rigid scaffold of RuvC–TI–PLMP (Fig. 3c and Extended Data Fig. 5b). These findings show that both IsrB and the ωRNA scaffold substantially contribute to the recognition of the guide–target heteroduplex for DNA targeting. We previously found that DtIsrB shows a NTGA TAM preference, but given that DtIsrB is a thermophilic enzyme, we repeated the TAM identification assay at 60 °C. At this temperature, we observed a TTGA TAM preference (Fig. 4b). We then sought to characterize this preference structurally. The TAM-containing duplex is bound in the cleft between the WED and TI domains, in which the TAM-nucleobases in the non-target strand are read out by the residues in the TI domain (Figs. 1e and 3g). Although the dT1* nucleobase does not directly contact the protein, the C5 of the dT2* nucleobase forms van der Waals interactions with that of dT1* and the aliphatic portion of the Arg323 side chain, consistent with the preference for the first and second Ts in the TAM. The O6 and N7 of dG3* interact with R323, in line with the preference for the third G of the TAM. The R323A mutant lacked cleavage activity, supporting a role for R323 in TAM recognition (Fig. 3c). The N6 and N7 of dA4* interact with Gln326, consistent with the preference for the fourth A in the TAM. To test whether Q326 recognizes the fourth TAM nucleotide, we mutated this residue to alanine and found that this mutation abolished target cleavage (Fig. 3c). The wild-type IsrB showed cleavage activity on targets with TTGA/ATGA TAMs, but not with TTGG/ATGG TAMs (Fig. 3h). However, the Q326R mutant was active with all four of these TAMs. These results indicate that Q326 recognizes the fourth nucleotide in the TAM. In SpCas9, the PAM preference can be modified through alteration of thehydrogen-bonding interactions between the amino acid at position 1,335 (Arg in wild-type SpCas9 or Gln in SpCas9 VQR-variant) and the third nucleotide of the PAM (G or A, respectively). Analogously, in IsrB, the TAM preference can be modified through alteration of the hydrogen-bonding interactions between the amino acid at position 326 and the fourth nucleotide of the TAM. Together, these results indicate that DtIsrB recognizes the TTGA TAM in the non-target strand by a combination of hydrogen bonds and van der Waals interactions, and indicate that altering these interactions could expand the TAM preference. To investigate the DNA nicking mechanism of IsrB, we identified the nicked site in the DNA by Sanger sequencing. IsrB nicked the non-target strand 8–11 nt upstream of the TAM (Extended Data Fig. 6a), in contrast to Cas9s, which cleave the non-target strand 2–5 nt upstream of the PAM. To mimic the nicked product, we added 10 nt to the 5′ end of the non-target strand in the SL1-truncated IsrB complex structure (Extended Data Fig. 6b). We observed EM density of the extended part of the non-target strand, which is docked into the RuvC domain (Extended Data Fig. 6e). In the IsrB structures, the TAM and TAM-proximal parts of the non-target strand are removed from the RuvC domain (Extended Data Fig. 6e,f), whereas in the SpCas9 structure, the PAM-proximal part of the non-target strand interacts with the RuvC and HNH domains (Extended Data Fig. 6g). The conformational difference between the non-target strands loaded onto the RuvC domains explains the distinct location of the DNA cut made by IsrB compared to that made by SpCas9.
To assess the conservation of the ωRNA ternary structure across IsrBs, we identified five orthologues (CwIsrB, IsrB from Crocosphaera watsonii; DsIsrB, IsrB from Dolichospermum sp.; CsIsrB, IsrB from Calditerricola satsumensis; BbIsrB, IsrB from Burkholderiales bacterium; K2IsrB, IsrB discovered from contig k249_576930 of viral metagenome assembly) and their cognate ωRNAs (Fig. 4a). A TAM discovery assay showed that CwIsrB/K2IsrB/CsIsrB/DsIsrB recognize an NTG TAM, whereas BbIsrB recognizes an NTGG TAM (Fig. 4b). We confirmed the functionality of these ωRNAs and validated the TAM preferences using a DNA cleavage assay with the target DNA containing the single TAM (Fig. 4c). We generated 3D structure models of these IsrB orthologues and the covariance folded two-dimensional (2D) structure models of their cognate ωRNAs (Extended Data Fig. 7). The protein 3D-model and the RNA 2D model were compatible with the experimentally determined structures of DtIsrB and its cognate ωRNA, demonstrating the general reliability of structural prediction (Fig. 2a and Extended Data Fig. 7a,b). In the secondary structure prediction, the ωRNAs of DtIsrB and the other five orthologues maintain the core domain composition consisting of four stems (S1–4) and five stem loops (SL1/2/4/5/7) (Fig. 4d and Extended Data Fig. 7a). In the cryo-EM structure of the DtIsrB ωRNA (DtRNA), SL3, SL6 and SL8 are located at the periphery of the scaffold and do not contribute to the formation of the core (Fig. 2b). Truncation of SL8 did not appreciably affect DtIsrB cleavage activity, indicating that the ωRNAs lacking this motif support at least the minimal functionality of IsrB (Fig. 2d). In the ωRNAs of CwIsrB and DsIsrB, SL2 and SL5 as well as SL4 and the SL7-adjacent single-stranded region are predicted to form two pseudoknot structures, consistent with the structure of the DtRNA (Fig. 4d and Extended Data Fig. 7a). By contrast, in the ωRNAs of CsIsrB, K2IsrB and BbIsrB, two pseudoknot structures are predicted to be formed by SL2 and SL4 as well as SL5 and the SL7-adjacent single-stranded region (Fig. 4d and Extended Data Fig. 7a). This SL4–SL5 shuffling involved in the pseudoknot formation has been reported previously and highlights the structural robustness of ωRNAs, which maintain overall similar structures despite structural rearrangements. Taken together, the demonstrated functionality of IsrB orthologues and the predicted structural similarities of IsrBs and their ωRNAs indicate the generality of the ωRNA-guided DNA-targeting mechanism suggested by the present cryo-EM structure.
To trace the protein domain evolution from IsrB to Cas9, we compared the structure of IsrB with the structure of one of the largest known IscBs (OgeuIscB), a distant relative of IsrB containing the HNH nuclease domain, and the predicted structure of YnpsCas9-1 (an early branching Cas9 of subtype II-D from Ga0315277_10040887 that is among the Cas9s most closely similar to IscB) (Extended Data Fig. 8). Apart from the gain of the HNH domain in IscB, we also observe big differences in other regions. For example, the RECL in some, but not all clades of IscB, is larger than the corresponding linker region in IsrB and folds into a minimal secondary structure, whereas in YnpsCas9-1, a large globular domain was acquired in the REC region. In other Cas9, such as SpCas9, this domain is even larger and more complex. The RuvC domain in OgeuIscB contains a few larger loops, whereas in YnpsCas9-1, it contains long insertions that seem to have further evolved into highly structured domains in other Cas9s including SpCas9. This enlargement of the RuvC domain in Cas9 is accompanied by the loss of the PLMP domain. Similarly, the WED and TI domains have minimal size in other IsrBs and IscBs except specifically in OgeuIscB and other large IscBs in which these domains are expanded. The WED and TI domains probably continued expanding into the large, globular versions found in YnpsCas9-1 and SpCas9. SpCas9 harbours a larger PAM-interacting domain that contains an extra globular region located downstream of the common core PAM-interacting domain. The size reduction and split of the ωRNA into dual RNA guides in Cas9 (for example, tracrRNA–crRNA) probably accompanied the acquisition of the REC domain and the overall enlargement of all domains of Cas9. To characterize in greater detail the minimization of the ωRNA as it evolved into cr/tracrRNAs, we compared the structure of DtIsrB ωRNA (DtRNA) with those of OgeuIscB ωRNA (OgRNA), CjCas9 single-guide RNA (CjRNA) and SpCas9 sgRNA in their protein/target DNA-bound states (Extended Data Fig. 9). On the basis of topology, location and secondary structure, we mapped DtRNA structural features (S1–4 and SL1–8) on other RNA species and named unidentified structural motifs as motifs 1–5 (M1–5). The structures of the 5′-stem region (S1 and SL1 in DtRNA) and the nexus region (S2 in DtRNA) are conserved in all four RNA species. The ascending strand of the 5′-stem region is replaced with crRNA in the evolutionary transition from OMEGA-IsrB/IscB to CRISPR–Cas9. Moreover, as ωRNAs evolved into tracrRNAs, the inserted helices (S3/S4/SL4/SL5/SL6 in DtRNA) within the nexus region degenerated, contributing to the compaction and simplification of the RNA structure. The SL4 motifs of DtRNA and OgRNA form nexus pseudoknots that are conserved in ωRNAs, whereas some base pairings in CjRNA M3 are well superposed with those nexus pseudoknots. An embedded stem loop in DtRNA 5′-stem region (SL2) base pairs with one of the embedded stem loops in the nexus region (SL5), forming a functional pseudoknot (adaptor pseudoknot) that recognizes the target DNA. One base adjacent to the adaptor pseudoknot (C198), forms several contacts between 3 and 5 Å with the phosphate and deoxyribose moieties of the DNA at position 6 (G6) and 7 (T7) (Fig. 3a), conferring a unique adaptation in which the ωRNA scaffold can recognize the RNA–DNA duplex. The adaptor pseudoknot is conserved in IsrB ωRNAs but is degenerated in the transition to IscB ωRNAs and Cas9 tracrRNAs, a change that correlates with and is probably compensated by the REC-region expansion. We also sought to better understand the mechanistic changes associated with the domain acquisitions in IsrB and Cas9 during their evolution from the compact RuvC-like ancestor. To this end, we compared the target-bound structures of Thermus thermophilus RuvC (TtRuvC), IsrB, CjCas9 and SpCas9 (Fig. 5). As RuvC domain-containing proteins evolved to interact with ωRNAs, they acquired TI/PI, PLMP and BH domains. In the structures of both IsrB and Cas9, the RuvC, WED, TI/PI and BH domains as well as the phosphate-lock loop form a functional core with similar configurations; the guide–target heteroduplex and the TAM/PAM duplex are bound to this core in a similar position and orientation. The TI/PI domain recognizes the TAM/PAM nucleobases, probably functioning as a primer for target DNA unwinding and heteroduplex formation, with the assistance of the phosphate-lock loop, BH and ωRNA/gRNA. Although IsrB and Cas9 share homologous RuvC and BH domains, IsrB (as well as IscB) uniquely contains the PLMP domain, which directly interacts with RuvC I. Examination of the IsrB structure further reveals a role of the PLMP domain in stabilizing the base of the terminal hairpin of the ωRNA and contacting the Shine–Dalgarno sequence. Furthermore, IsrB contains only minimal RECL and HNHL regions (17 and 19 amino acids, respectively, in DtIsrB), and they probably play different roles in DNA targeting from those performed by the larger REC lobe and HNH domain in Cas9 (for example, 625 and 135 amino acids, respectively, in SpCas9). In SpCas9, the REC lobe probes the target DNA through interactions with the heteroduplex, activates the DNA-bound RuvC nuclease through the communication with the HNH domain and facilitates R-loop formation. However, in IsrB, this interdomain communication is probably aided by the ωRNA both through backbone-backbone and base-backbone interactions because RECL and HNHL are comparatively small. The comparatively large ωRNA (roughly 300-nt compared to 100-nt sgRNA used by Cas9) seems to contribute to the connection between DNA targeting and nicking activities, compensating for the small RECL and HNHL regions (Extended Data Fig. 10). In the multi-layered ωRNA architecture, the upper layer RNA helices (S2/S3/S4/SL2/SL4/SL5), which form an interaction network for ωRNA-driven heteroduplex recognition, are associated with the lower layer RNA helices (SL7/SL8) and extensively interact with the nicking module (PLMP/RuvC/TI domains) by the nexus pseudoknot interactions between S2, SL4 and SL7. Given that mutations in the adaptor pseudoknot in the ωRNA abolished the nicking activity of IsrB (Fig. 2d), even though the pseudoknot is distant from the target DNA, the ωRNA structural motifs could be important for allosteric regulation of DNA sensing by the ωRNA/RECL and DNA nicking by the RuvC nuclease domain, providing an avenue for integrating further forms of regulation. This ωRNA-driven allosteric regulation mechanism is supported by the overall high surface charge and area through which IsrB contacts ωRNA. Other large (roughly 400–900-nt) functional non-coding RNAs, such as group I intron, group II intron and Ribonuclease P, have complex ternary structures and their peripheral regions can control their central catalytic cores by allosteric mechanisms. Future structural studies of IsrB in other conformations, such as the catalytically active IsrB R-loop complex, will address this hypothesis and deepen our mechanistic understanding of OMEGA systems.
The gene encoding full-length DtIsrB (residues 1–353) was codon optimized, synthesized (Twist Bioscience) and cloned into a modified pC013 vector (Addgene Plasmid no. 90097). The DtIsrB-coding region consists of His6-Twinstrep-tag, SUMO-tag, DtIsrB and GFP-tag. Wild-type DtIsrB was expressed at 18 °C in Escherichia coli Rosetta(DE3)pLysS cells (Novagen). E. coli was cultured at 37 °C in Luria-Bertani medium (containing 100 mg l−1 ampicillin) until the OD600 reached 0.5, and then protein expression was induced by the addition of 0.1 mM isopropyl-β-d-thiogalactopyranoside and incubation at 18 °C for 20 h. The E. coli cells were resuspended in buffer A (50 mM Tris-HCl, pH 8.0, 20 mM imidazole and 1 M NaCl), lysed by sonication and then centrifuged. The supernatant was mixed with Ni-NTA Agarose (Qiagen). The protein-bound column was washed with buffer A, buffer B (50 mM Tris-HCl, pH 8.0, 20 mM imidazole and 0.3 M NaCl) and buffer C (50 mM Tris-HCl, pH 8.0, 0.3 M imidazole and 0.3 M NaCl). The protein was eluted with buffer D (50 mM Tris-HCl, pH 8.0, 0.3 M imidazole and 1 M NaCl). The cognate ωRNA of DtIsrB was transcribed in vitro with T7 RNA polymerase, using a PCR-amplified DNA template and HiScribe T7 Quick High Yield RNA Synthesis kit (NEB). The template consists of the T7 promoter (TAATACGACTCACTATAGG),guide (GCCTTATTAAATGACTTCTC) (residues 1–20) and ωRNA scaffold (residues 21–282). The transcribed RNA was purified using an RNeasy kit (Qiagen) according to the manufacturer’s instructions. The target and non-target DNA strands (GATCAGCTCAAGAGAAGTCATTTAATAAGGC and TTGAGCTGAT, respectively) were purchased from GENEWIZ. For the reconstitution of complex A, the purified DtIsrB protein was mixed with the ωRNA, the target DNA strand and the non-target DNA strand (the TTGA TAM) (molar ratio, 2.3:1:7:7) in buffer E (10 mM Tris-HCl, pH 8.0 and 50 mM NaCl, 5 mM MgCl2) and incubated at 37 °C for 15 min. Complex A was purified by gel filtration chromatography on a Superose 6 Increase 10/300 column (Cytiva) equilibrated with buffer F (20 mM HEPES-NaOH, pH 7.0 and 50 mM NaCl, 5 mM MgCl2). Complex A (final concentration: 0.1 mg ml−1) was incubated with BS3 (final concentration: 0.5 mM) at 4 °C for 2 h. For the reconstitution of complex B, the lambda N protein (MDAQTRRRERRAEKQAQWKAAN) was inserted between DtIsrB and GFP-tag. Residues 34–67 of ωRNA scaffold (residues 21–282) were replaced by a GAAA linker. The GAAA linker-fused boxB RNA (GAAAGCCCUGAAGAAGGGC) (residues 283–302) was appended to the 3′ end of the ωRNA scaffold. The same target DNA strand was used for this reconstitution. The 5′ extended non-target DNA strand (TACTGAAGAGTTGAGCTGAT) was purchased from GENEWIZ. The purified DtIsrB protein was mixed with the ωRNA, the target DNA strand, and the non-target DNA strand (the TTGA TAM) (molar ratio, 2.3:1:1.5:1.5) in buffer G (10 mM Tris-HCl, pH 8.0 and 50 mM NaCl) and incubated at 37 °C for 15 min. Complex B was purified by the same size-exclusion column equilibrated with buffer G. For the grid preparation, purified complex A and B solutions (0.1 mg ml−1, 3 µl) were applied to freshly glow-discharged UltrAuFoil 300 mesh R1.2/1.3 grids (Quantifoil) in a Vitrobot Mark IV (FEI) at 4 °C with a waiting time of 0 and 10 s and a blotting time of 2 and 4 s under 95% humidity, respectively.
Cryo-EM data for complex A were collected at HHMI Janelia Research Campus using a Titan Krios G2 microscope (Thermo), operated at 300 kV and equipped with a Gatan Bioquantum energy filer (Gatan) and a postfilter K3 direct electron detector (Gatan) in the electron counting mode. Each video was recorded at a nominal magnification of ×105,000, corresponding to a 0.839 Å per physical pixel (0.4195 Å per super-resolution pixel) at the electron exposure of 12.075 electrons per Å2 per second and total exposure time was 5.0 s, resulting in an accumulated exposure of 60 e−/Å2. Then 50 frames per video were collected at 1.2 e−/Å2 dose per frame for a total of 60 e−/Å2 dose per video. The nominal defocus range was set at −0.8 to −2.2 µm. Automated data collection was carried out using scripts in SerialEM. For each stage position, image shift was used to collect data from nine holes with two video acquisitions per hole. Image shift induced beam tilt was calibrated and beam-tilt correction was applied during the data collection. Cryo-EM data for complex B were collected at MIT.nano using a Talos Arctica G2 microscope (FEI), operated at 200 kV and equipped with a Falcon 3EC direct electron detector (Thermo) in the linear mode. Each video was recorded at a nominal magnification of ×120,000, corresponding to a calibrated pixel size of 1.2550 Å at the electron exposure of 24.54 e−/pix s−1 for 3.99 s, resulting in an accumulated exposure of 62.53 e−/Å2. Next, 20 frames per video were collected at 3.1265 e−/Å2 dose per frame for a total of 62.53 e−/Å2 dose per video. The nominal defocus range wasset at −2.6 to −1.0 µm. Automated data collection was carried out using the EPU software (Thermo). For each stage position, image shift was used to collect data from nine holes. To obtain the 3D reconstruction of complex A, data were processed using RELION-4.0 (ref. ). The video frames were aligned in 5 × 5 patches and dose weighted in MotionCor2 (ref. ). Defocus parameters were estimated by CTFFIND-4.1 (ref. ). From the 4,142 preprocessed micrographs, 1,626,574 particles were picked up by TOPAZ based auto-picking and extracted in 3.146 Å pixel−1. The selected 107,066 particles were then re-extracted in 1.144 Å pixel−1 and subjected to one round of 3D refinement and 3D classification without alignment. The selected 58,188 particles were subjected to per-particle defocus estimation and Bayesian polishing. For beam-tilt refinement, the optics group of each micrograph is set on the basis of their hole position from stage. The polished particles were subjected to 3D refinement, and yielded a map with a global resolution of 3.10 Å according to the Fourier shell correlation 0.143 criterion. To obtain the 3D reconstruction of complex B, data were processed using the same programs. From the 2,542 motion-corrected and dose-weighted micrographs, 1,595,800 particles were picked up by TOPAZ based auto-picking and extracted in 3.138 Å pixel−1. These particles were subjected to several rounds of 2D and 3D classifications. The selected 50,661 particles were then re-extracted in 1.255 Å pixel−1 and subjected to homogeneous refinement using cryoSPARC, yielding a map with a global resolution of 6.85 Å according to the Fourier shell correlation 0.143 criterion.
The initial protein model was generated using AlphaFold2 (ref. ) under the ColabFold framework using default parameters and MMseqs2 to search for homologues into the ColabFold database, and manually modified using COOT and ISOLDE against the density map of complex A. The initial nucleic acid model was built with auto-DRRAFTER using the density map of complex A and the covariance-based secondary structure model of ωRNA. The ωRNA (query) secondary structures were predicted using cmsearch with the –max option to identify the highest scoring IscB/IsrB ωRNA covariance model from a previous study. For the best model, query regions aligning to the model were assigned secondary structures from the model’s predictions. Stem loop secondary structures that were found to be erroneously assigned to base pairs with one of the base identities equalling a gap character were reassigned to having no secondary structure. Secondary structures for query regions without coverage (≥8 bp of no match to the best covariance model), barring the low conservation region at the 3′ end beyond the nexus, were then predicted using mfold. Pseudoknots were assigned manually by identifying matching base pairs at the pseudoknot locations expected for the given ωRNA type. ωRNA coordinates were modelled with auto-DRRAFTER starting from a slightly modified version of the covariance-based secondary structure model in which all non-canonical base pairs and most helices consisting of just a single base pair were removed. The dot-bracket notation for this secondary structure is provided below: .((((((((((((((((((((((((.((.(((((((((...((((((((....))))))))...))))))))).((((((({..{)))))))........)).))))..(((.((((((....))))))....((((((((((((.....(((..(((((((...[[[[[.))))))).....)))((((...}..}....))))..(((.((......)).)))..))))))))))))...)))..]]]]]......((((....)))).(((.....)))..<<<<<<<<<<))))))))))))))))))))>>>>>>>>>> All DNA nucleotides were modelled as RNA because auto-DRRAFTER cannot model DNA nucleotides. The guide/ωRNA scaffold/target DNA/non-target DNA were assigned to residues 1–20/21–282/283–313/314–323, respectively. The full RNA sequence used for modelling is provided below: ggccuuauuaaaugacuucucgucaaccaccccugacugaagucagaggcuugcuucuggccugaguugggggcccgguuuggcggggccgggggcaacuggcugaccaggcggcccgguucgccgggcagggguccgcggggcuaccaaggacuuccggguguuucgccagcccggacuaucuccggcagaaccgcucaaugccgcggccggccaagaccggccuaagcccugcggacagcgccgaggcgacaaucacuccgaaaggaggccguguaucggcgaucagcucaagagaagucauuuaauaaggcuugagcugau Auto-DRRAFTER modelling was performed in the absence of protein coordinates using the density map with regions corresponding to protein density removed. All initial rounds of modelling were performed in a preliminary 4.3-Å resolution density map. The modelling was set up manually by fitting helices corresponding to residues W:1–14 W:41–48 W:53–60 W:258–269 W:271–281 X:2–11 X:18-31 Y:1-10 into the density map. In the second round of auto-DRRAFTER modelling, the helix corresponding to residues W:41–48 and W:53–60 was allowed to move from its initial placement. Five rounds of modelling were performed, followed by one final round of modelling. For each round, between 2,000 and 6,000 models were built. One of the top ten scoring models was selected for further refinement by ISOLDE and Phenix, together with the protein model, to optimize the geometry and improve the fit to the cryo-EM density. After inspecting the optimized model and covariance-based secondary structure, two more rounds of auto-DRRAFTER modelling, including one final round, were performed in which the base pairing for the adaptor pseudoknot was modified slightly so that residues 81–84 and 194–197 were paired rather than residues 81–84 and 193–196. For this extra modelling, only residues W:73–99 and W:186–206 were rebuilt; all other residues remained fixed. One more final round of modelling was performed using the 3.1 Å resolution density map low-pass filtered to 4 Å. The final convergence of these models (pairwise root mean square deviation between models) is 4.1 Å. Auto-DRRAFTER convergence values have previously been shown to be predictive of model accuracy. Using a previously determined linear relationship between convergence and model accuracy (accuracy of 0.61 × convergence + 2.4 Å), the estimated accuracy of these initial computationally generated models is 4.9 Å. To further improve the accuracy, one of these models was refined with COOT, ISOLDE and Phenix together with the protein to produce the final IsrB–ωRNA-target DNA complex model. The final model (lacking protein residues 1–5/211–224/348–353, RNA residues 1–2/37–64/119–122/212–219/263–265 and target DNA residues 1/30–31, which were poorly resolved and omitted from the final model) was evaluated by MolProbity and Q-score. Molecular graphics and EM density figures were prepared with CueMol (http://www.cuemol.org), PyMOL (https://pymol.org/2/), UCSF Chimera (https://www.cgl.ucsf.edu/chimera/) or Chimera X (https://www.cgl.ucsf.edu/chimerax/).
The IsrB protein and ωRNA templates were prepared for an in vitro transcription/translation expression system. The IsrB protein template consists of the T7 promoter and translation initiationsequences (GCGAATTAATACGACTCACTATAGGGCTTAAGTATAAGGAGGAAAAAATATG), IsrB ORF sequence and T7 terminator sequence (CTAGCATAACCCCTTGGGGCCTCTAAACGGGTCTTGAGGGGTTTTTTG). The ωRNA template consists of the T7 promoter sequence (GGAAATTAATACGACTCACTATAGG) and ωRNA sequence. The IsrB protein and ωRNA templates were embedded in the modified pC013 vector (Addgene Plasmid no. 90097) and the pCOLADuet-1 vector. Mutations in the IsrB protein and ωRNA were introduced by a PCR-based method and the sequences were confirmed by DNA sequencing. The 320-bp PCR-amplicon (30 ng), which contains the 20-nt target sequence and the TAM and was fluorescently labelled by 5′ IRDye 700 (IDT), was incubated with the IsrB protein template (50 ng) and the ωRNA template (125 ng) in 12.5 μl of reaction buffer, containing 5 µl Solution A and 3.75 µl Solution B of PURExpress In vitro Protein Synthesis Kit (NEB). The reaction conditions were optimized as follows. Fig. 2d, 3 h: 2 h at 37 °C, 1 h at 60 °C; Fig. 3c, 2.1 h: 2 h at 37 °C, 5 min at 60 °C; Fig. 3h, 3 h: 2 h at 37 °C, 1 h at 60 °C; Fig. 4c (CwIsrB, DsIsrB and BbIsrB), 6 h at 37 °C; Fig. 4c (CsIsrB), 6 h: 2 h at 37 °C, 4 h at 60 °C; Fig. 4c (K2IsrB) and 2 h at 37 °C. DtIsrB is derived from a thermophilic organism, D. thermocuniculi, which grows at 60–80 °C (ref. ). The reaction was stopped by the addition of 3 µg of RNase A (Qiagen) and 0.24 units of Proteinase K (NEB). The reaction products were purified using a Wizard SV Gel and PCR Clean-Up System (Promega), resolved on a Novex 10% TBE-Urea Gel (Invitrogen) and then visualized using a ChemiDoc Imaging System (Bio-Rad). To examine the protein stability of deletion mutants, IsrB proteins were produced in the bacterial expression system used in the cryo-EM sample preparation. The E. coli cells were resuspended in buffer A (50 mM Tris-HCl, pH 8.0, 20 mM imidazole and 1 M NaCl), lysed by sonication and then centrifuged. The supernatant was mixed with MagneHis beads (Promega). The protein-bound column was washed with buffer A. The protein was eluted with buffer B (50 mM Tris-HCl, pH 8.0, 0.3 M imidazole and 1 M NaCl) and analysed by SDS–PAGE (Extended Data Fig. 5b). To determine the IsrB DNA cleavage sites, the 816-bp PCR-amplicon (400 ng) containing the 20-nt target sequence (GCCTTATTAACCTCAGCCTC) and the TAM was incubated with the IsrB protein template (100 ng) and the ωRNA template (125 ng) in 25 μl of reaction buffer, containing 10 µl Solution A and 7.5 µl Solution B of PURExpress In vitro Protein Synthesis Kit. After purifying the reaction product, the nicked product was cleaved using Nb.BbvCI (NEB). The cleaved products were gel-extracted, purified and analysed by DNA sequencing (GENEWIZ).
Representative IsrBs with intact RuvC active catalytic site residues and no signs of truncations were selected from among the three major clades of IsrBs as identified in a previous study, corresponding to IsrBs with ωRNAs of type G1b, G1c and G1h. ωRNAs corresponding to each IsrB were taken from the predictions in a previous study and modified such that the end of the ωRNA occurred at the start of the IsrB. IsrBs were then discarded if the corresponding ωRNA’s secondary structure, as determined by mfold, did not contain the conserved stem loops and pseudoknots (as manually identified) found in the covariance-based ωRNA secondary structure for the given ωRNA type. The analysis nominated the CwIsrB, CsIsrB, DsIsrB, BbIsrB, K2IsrB sequences and corresponding ωRNAs. Covariance-based secondary structure and pseudoknot predictions were determined for the corresponding ωRNAs as described for the DtRNA. All ωRNAs were then visualized using forna. For analysis of the PLMP domain, the DtIsrB PLMP domain was searched in HHPred for remote homologues, identifying IF-3 as a putative remote homologue. Representative sequences containing IF-3-N-terminal regions and PLMP domains from the IscB/IsrB family were obtained from UniProt and the National Center for Biotechnology Information, and aligned using MAFFT-einsi. Structural representatives were aligned and superimposed using the pymol super function.
The TAM identification assay was performed using a TAM library, prepared as previously described. Single-stranded DNA oligonucleotides (IDT), containing eight randomized nucleotides downstream of a 20-nt target sequence (GCCTTATTAACCTCAGCCTC), were converted to dsDNA by fill-in with PrimeSTAR Max DNA Polymerase (Takara) and cloned into pUC19 by Gibson cloning (NEB) to generate a TAM library. The library (25 ng) was digested using an in vitro transcription/translation expression system containing the IsrB protein (50 ng) and ωRNA (125 ng) templates, as described in the in vitro cleavage experiment section. The reactions of CwIsrB, DsIsrB, CsIsrB, BbIsrB and K2IsrB were incubated for 4 h: 2 h at 37 °C, 1 h at 50 °C and 1 h at 60 °C. The reaction of DtIsrB was incubated for 3 h: 2 h at 37 °C and 1 h at 60 °C. It was then stopped by the addition of 3 µg of RNase A (Qiagen) and 0.24 units of Proteinase K (NEB). The reaction products were purified using a Wizard SV Gel and PCR Clean-Up System (Promega), and digested using Nb.BbvCI (NEB). The purified reaction products were subjected to end labelling and adaptor ligation using an NEBNext Ultra II End Repair/dA-Tailing Module (NEB), an NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB) and an NEBNext Adaptor for Illumina (NEB). The USER Enzyme (NEB)-digested and purified DNA was amplified with a 12-cycle PCR using one primer specific to the TAM library backbone and one primer specific to the NEBNext adaptor, and with a subsequent 18-cycle PCR to add the Illumina i5 adaptor. To normalize the distribution of the 8N degenerate flanking sequences, the library plasmid was amplified with a 12-cycle PCR using primers specific to the library backbone and with a subsequent 18-cycle PCR to add the Illumina i5 adaptor. The amplified libraries were isolated on 2% agarose E-gels (Invitrogen) and sequenced on a MiSeq sequencer (Illumina). The resulting sequence data were analysed by extracting the six nucleotide TAM regions, counting the individual TAMs and normalizing the TAM to the total reads for each sample. Sequence motifs were generated using the selected TAMs in the top scoring fraction with the custom Python script used in our previous report.
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/s41586-022-05324-6.
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PMC9581865 | 35802304 | Dake Tong,Yanyin Zhao,Yang Tang,Jie Ma,Miao Wang,Bo Li,Zhiwei Wang,Cheng Li | MiR-487b suppressed inflammation and neuronal apoptosis in spinal cord injury by targeted Ifitm3 | 08-07-2022 | Spinal cord injury (SCI),Inflammation,Bioinformatics,ceRNA network,MiR-487b,Ifitm3 | Spinal cord injury (SCI) was a serious nerve injury, which involves complex genetic changes. This paper was intended to investigate the function and mechanism of differentially expressed genes in SCI. The three datasets GSE92657, GSE93561 and GSE189070 of SCI from GEO database were used to identify differentially expressed genes (DEGs). We identified the common DEGs in the three datasets GSE92657, GSE93561 and GSE189070 of SCI from GEO database. Next, a protein-protein interaction (PPI) network of DEGs was constructed. Subsequently, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that DEGs were significantly enriched in immune response, inflammatory response. The expression level of immune-related genes (Arg1, Ccl12, Ccl2, Ifitm2, Ifitm3, and et al.) at different time points of SCI were analyzed in GSE189070 dataset. Next, differentially expressed miRNAs (DE-miRNAs) were identified in SCI compared with normal based on GSE158194 database. DE-miRNA and targeted immune-related genes were predicted by miRwalk, including miR-487b-5p targeted Ifitm3, miR-3072-5p targeted Ccl3, and et al. What’s more, the miR-487b was identified and verified to be down-regulated in Lipopolysaccharide (LPS)-induced BV-2 cell model. Further, the miR-487b inhibited cell inflammation and apoptosis in LPS-induced BV2 cell by targeted Ifitm3. For the first time, our results revealed that miR-487b may play an important regulatory role in SCI by targeted Ifitm3 and provide further evidence for SCI research. | MiR-487b suppressed inflammation and neuronal apoptosis in spinal cord injury by targeted Ifitm3
Spinal cord injury (SCI) was a serious nerve injury, which involves complex genetic changes. This paper was intended to investigate the function and mechanism of differentially expressed genes in SCI. The three datasets GSE92657, GSE93561 and GSE189070 of SCI from GEO database were used to identify differentially expressed genes (DEGs). We identified the common DEGs in the three datasets GSE92657, GSE93561 and GSE189070 of SCI from GEO database. Next, a protein-protein interaction (PPI) network of DEGs was constructed. Subsequently, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that DEGs were significantly enriched in immune response, inflammatory response. The expression level of immune-related genes (Arg1, Ccl12, Ccl2, Ifitm2, Ifitm3, and et al.) at different time points of SCI were analyzed in GSE189070 dataset. Next, differentially expressed miRNAs (DE-miRNAs) were identified in SCI compared with normal based on GSE158194 database. DE-miRNA and targeted immune-related genes were predicted by miRwalk, including miR-487b-5p targeted Ifitm3, miR-3072-5p targeted Ccl3, and et al. What’s more, the miR-487b was identified and verified to be down-regulated in Lipopolysaccharide (LPS)-induced BV-2 cell model. Further, the miR-487b inhibited cell inflammation and apoptosis in LPS-induced BV2 cell by targeted Ifitm3. For the first time, our results revealed that miR-487b may play an important regulatory role in SCI by targeted Ifitm3 and provide further evidence for SCI research.
Spinal cord injury (SCI), continues to be a severe health problem worldwide, usually leads to permanent motor and sensory disturbances, which seriously affects the quality life of the people. The primary reasons of leading to SCI were mechanical injury (Ahuja et al. 2017), iatrogenic surgery (Hewson et al. 2018), tumor(Ge et al. 2019) or infection (Krause et al. 2016). There was a series of complications after SCI such as neuropathic pain (Zhang and Yang 2017), cardiovascular dysfunction (West et al. 2013), gastrointestinal dysfunction (Holmes and Blanke 2019), and cancer (Nahm et al. 2015). Because of the molecular mechanisms and pathophysiological events of SCI are complicated, which also makes it difficult to cure SCI (Fakhoury 2015). According to previous work and the progress of SCI research, inflammatory reaction play a pivotal role in the progression of SCI, which contributes to secondary tissue damage that leads to further functional loss (David et al. 2012). Studies have reported that chemokines, immune cytokines, and apoptosis factors are differentially expressed after SCI. Moreover, immune response plays an important role in many diseases. For example, immune cell infiltration of M1 and M2 macrophages, natural killer, NKT cells, effector and memory T cells, and granulocytes was a prominent feature in dysfunctional adipose tissue (Guzik et al. 2017), A sustained active immune response usually leaded to chronic inflammation, which was characterized by prolonged acuteness over time and simultaneous tissue destruction and repair. The infiltration of immune cells in the myocardium adversely affected the heart and led to the pathogenesis of heart failure (Carrillo-Salinas et al. 2019). Therefore, to uncover the change of pivotal molecules that participate in the immune/inflammation response of SCI is important. Non-coding RNAs (ncRNAs) have been found play a very important role in the pathogenesis of SCI (Guo et al. 2019; Viereck and Thum 2017;Wang et al. 2019), which includes microRNA (miRNA). Increasingly studies have shown that miRNA is involved in the secondary injury and repair process after SCI. After SCI, dysregulated miRNAs can participate in inflammatory responses, as well as the inhibition of apoptosis and axon regeneration through multiple pathways(Liu et al. 2020). However, the functions of miRNAs in SCI progression need further specific elucidation.In this study, we aim to investigate the role and functions of differentially expressed genes (DEGs) in SCI by bioinformatics analysis. We next explore the function of miR-487b and its targeted gene interferon-induced transmembrane protein 3 (Ifitm3) in LPS induced BV2 microglial, it have a strong correlation with immune/inflammation response. These findings provide further evidence for SCI research.
The microarrays data of mRNAs expression profile of SCI and normal tissues obtained from Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo), which contained three datasets GSE92657 (Lou et al. 2017), GSE93561 (Takano et al. 2017) and GSE189070. All datasets were based on the SCI models of C57BL/6J mice to analysis the microarrays data of mRNA expression profile. GSE92657 included 3 SCI and 3 normal samples, GSE93561 included 6 SCI and 6 normal samples. GSE189070 included transcriptomic profile of astrocytes from uninjured spinal cord tissue and nearby the lesion epicenter at 3, 7, 14 days after mouse hemisection spinal cord tissue.
We carried out GEO2R which is an interactive web tool that could be used to identify DEGs. After the RNA-seq data of SCI and the sham operation groups normalized, the differential expression of mRNA (DE-mRNAs) or miRNAs (DE-miRNAs) were analyzed using the R package DEseq2, which with the threshold of adjusted P-value < 0.05 and fold change ≥ 2. We used the miRwalk website (http://mirwalk.umm.uni-heidelberg.de/) to predict miRNA-mRNA interaction information.
The overlap DE-mRNAs of the GSE92657 and GSE93561 datasets obtained by Venn 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/) analysis. To evaluate the interacting relationship of overlapped genes, we analyzed these genes using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) online database (version 11.0, http://string-db.org/). The minimum required interaction score is default as 0.4. Next, a visualized PPI network of these DE-mRNAs was constructed using Cytoscape software (version v3.7.2, https://cytoscape.org/) (Doncheva et al. 2019; Saha et al. 2020; Shannon et al. 2003). The plug-in MCODE of Cytoscape was used to identify the most significant module of the PPI network.
The Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to investigate the roles of all DE-mRNAs. GO annotations including biological process (BP) was performed using DAVID database (https://david.ncifcrf.gov) (Dennis et al. 2003) KEGG network was constructed by Cytoscape ClueGo. The pathways were significant enrichment with P-value < 0.05.
Microglial BV2 cells were obtained from the Institute of Basic Medical Sciences of the Chinese Academy of Medical Sciences (Beijing, China). BV2 microglial cells were cultured in DMEM containing 10% fetal bovine serum, penicillin 100 U/mL, and streptomycin 100 µg/mL at 37 °C in a humidified atmosphere of 5% CO2. Cells were cultured in serum-free DMEM for at least 4 h before treatments. The LPS-induced BV2 cells were treated with 1 µg/mL LPS, and the control group treated with the same volume of culture medium. After 20 h, the culture medium supernatant and cells were collected.
Plasmids were purchased from Sangon Biotech (Shanghai). BV2 microglia were transfected with the miRNA-487b mimics plasmid using Lipofectamine 2000 (Invitrogen, Rockville, MD, USA) according to the manufacturer’s instructions, while the control group was transfected with the empty plasmid. After 6 h, the cells were washed and maintained in culture for 48 h for further analysis. The transfection efficiency was determined by detecting fluorescence.
Mouse factors tumor necrosis factor (TNF)-α and interleukin (IL)-6 in ELISA kits (Proteintech, Wuhan, China) were used to detect cytokine concentrations in supernatants of microglial cultures. Briefly, 100 µL of cultured media from different groups were added to each well of 96-well plates coated with anti-mouse cytokine antibodies. The plates were incubated at 37 °C for 90 min and then washed 5 times. Next, 100 µL of biotinylated cytokine-specific antibody were added to each well and incubated at 37 °C for 60 min. Then, the plates were washed, treated with 100 µL of diluted streptavidin-HRP, and incubated at 37 °C for 30 min. The color was produced by the addition of 100 µL of substrate solution and an incubation for 10–15 min after washing. Finally, 100 µL of stop solution were added to terminate the reaction. Finally, the optical density at 450 nm was measured within 10 min.
Total RNA was extracted from BV2 cells using a commercial TRIzol kit (Invitrogen, USA), and then RNA was reverse-transcribed into cDNAs with a PrimeScript RT reagent Kit (Takara, Dalian, China). The quantitative experiment was completed using an ABI 7500 PCR instrument (Applied Biosystems, USA) and a SYBR green Kit (Applied Biosystems, USA), with the relative gene expression levels normalized to GAPDH. Primers are shown in Table 1.
For the assessment of cell death levels, cultured BV2 cells were collected after transfection and rinsed with chilled PBS, followed by an incubation with Annexin V-fluorescein isothiocyanate (FITC)/propidium iodide(PI) (No.C1062S, Beyotime, Nanjing, China) staining in the dark for 15 min. Then, the percentages of apoptotic cells were detected using flow cytometry (Beckman Coulter, Brea, CA, USA).
The miR-487b sequence in the BV2 cells was subcloned into the luciferase reporter psiCHECK2 (Promega, Madison, WI, USA) and designated as psiCHECK2- circ-Usp10-WT. The circ-Usp10 sequence with mutation of miR-152 binding site was synthesized using overlap extension PCR and cloned into psiCHECK2 vector designated as psiCHECK2- circ-Usp10-Mut. The mutant vector for the miR-152 binding site was constructed and termed as psiCHECK2-miR-152-3′UTR-Mut. A total of 3 × 104 BV2 cells were seeded in 24-well plates in triplicate. At 48 h following transfection with miR-152 mimics, luciferase reporter assays were conducted using the dual-luciferase reporter assay system (Promega) according to the manufacturer’s instructions. Relative luciferase activity was normalized to the Renilla luciferase internal control.
We performed correlation analysis using Student’s t test by GraphPad Prism 8, p-value < 0.05 was considered statistically significant. Data processing and analysis using Microsoft Excel and R software (R software, version 3.5.1).
To explore the difference in molecular expression between SCI tissue and normal spinal cord tissue, DE-mRNAs were screened out by microarray data analysis. P-value < 0.05 and fold change ≥ 2 were used as the threshold of screening differentially expressed genes. As shown in Fig. 1A and C of the volcano plot, a total of 233 and 6105 DE-mRNAs were obtained from the datasets GSE92657 and GSE93561, respectively. Otherwise, to have a clearer understanding of the expression distribution of differential genes in the SCI group and the normal group, we perform heatmap cluster analysis on DE-mRNAs (Fig. 1B and D). Subsequently, we performed Venn analysis to obtain more credible DE-mRNAs. As shown in Fig. 2A, there were 144 common DE-mRNAs in the datasets GSE92657 and GSE93561, of which 116 were up-regulated genes and 28 were down-regulated genes. The heatmap showing the relative expression of common DE-mRNAs in datasets GSE92657 and in GSE93561 (Fig. 2B). For intuitively understand the interaction of these DEGs, we constructed a PPI network showing the high expression gene network and low expression network (Fig. 2C).
To further explore the function of common DE-mRNAs shared by the GSE92657 and GSE93561 in the biological processes (BP) afterSCI, we performed GO using DAVID. The top 10 significantly enriched terms of BP were shown in Fig. 3A. The terms of immune response, positive regulation of inflammatory response, and inflammatory response were significantly enriched, indicating that the immune response plays an important role in the progress of SCI, which is consistent with a previously reported study (Alizadeh et al. 2018). Besides, the representative BP of signaling pathways for the DEGs was constructed based on STRING database and Cytoscape (ClueGO) (Fig. 3B). It is found that positive regulation of leukocyte migration, neutrophil chemotaxis, neutrophil chemotaxis and so on signaling pathways showed consistent higher correlation with DEGs. Genes like Cxcl2, II1b, and Ccl2 contribute to the regulation signaling pathways. The above results indicated that SCI is associated with inflammation and immune response. What’s more, to further verify the timely change of immune-related genes, we also verify their expression level in the testing dataset GSE189070 (Fig. 3C). As demonstrated in Fig. 3D, most of the immune-related genes like Ch25h, Thbs1, Cxcl2, Gbp2 and Ccl3, etc. were up-regulated expressed in 3th day, indicating that these genes were activated at this time point.
In the next step, we screened out 24 DE-miRNAs based on dataset GSE158194 as shown in Fig. 4A. Otherwise, to have a clearer understanding of the expression distribution of differential genes in the SCI group and the normal group, heatmap cluster analysis on DE-miRNAs (Fig. 4B) were carried out. Using the immune-related DE-mRNAs identified in Fig. 3, the targeted genes of the DE-miRNAs were predicted by miRWalk, which included 6 up-regulated DE-mRNAs, 6 up-regulated DE-miRNAs and 2 down-regulated miRNAs (Fig. 4C). To further verify the expression level of these DE-miRNA in the SCI, we carried out the model of SCI in vitro (BV2 cell line) induced by LPS. The relative expression level of miRNAs was shown in Fig. 4D, which miR-709, miR-149-5p and miR-487b-5p were significantly down-regulated and miR-21a-3p was significantly up -regulated. Because the expression level of miR-487b-5p was down-regulated most significantly among the three miRNAs, we choose miR-487b-5p for the following further investigation.
SCI result in microglial and astrocyte activation, neuroinflammation and neuronal cell death (Hausmann 2003). Microglia is the reactive resident of neuroinflammation at the injury site (Plemel et al. 2014). After SCI, microglia can play important role (Bowes and Yip 2014). We next explore the function of miR-487b and its targeted gene interferon-induced transmembrane protein 3 (Ifitm3) in LPS induced BV2 microglial. As shown in Fig. 5A, when the BV2 cell treated with LPS and miR-487b mimics, the relative expression of miR-487b was significantly enhanced compared to control group and decreased the expression level of Ifitm3. Thereafter, we found that miR-487b mimics significantly inhibited the expression of the proinflammatory factors TNF-α, IL-6 in microglia treated with LPS (Fig. 5B). The flow cytometry showing that the miR-487b mimics remarkably inhibitscell apoptosis in LPS induced BV-2 (Fig. 5C). Luciferase reporter gene experiments verified the tight binding of miR-487b and Ifitm3 (Fig. 5D and E). The results above revealed that miR-487b reduced LPS-induced BV2 cell inflammatory response and apoptosis by targeted Ifitm3.
According to previous reports, most spinal cord injuries were caused by mechanical injuries, and there were also some non-mechanical factors, including degenerative CNS disorders, genetic and metabolic, infectious, inflammatory, ischemia, and other pathogenic factors (El Masri and Kumar 2011; Lynch et al. 2016; McDonald and Sadowsky 2002; van Middendorp et al. 2011). Although the pathogenic factors were well known, there are still great challenges in the rehabilitation after SCI, mainly due to the nerve injury and complexity of molecular mechanism after SCI (Tran et al. 2018; van Niekerk et al. 2016). In addition, the immune/ inflammation related genes of mice tissues of SCI were worth more attention. Here, our research was mainly devoted to explore the key genes in SCI, and to identify the immune-related differentially expressed genes. First, we selected two datasets GSE92657 and GSE93561 from public database GEO, which contained SCI and normal spinal cord tissue RNA-seq data. In total, 144 common DE-mRNAs in datasets GSE92657 and GSE93561 were obtained by Venn analysis. Subsequently, we performed GO functional enrichment analysis on the differential genes shared by the two datasets. We found that many DEGs were enriched in the biological pathways of immune response and inflammatory response. Hence, we speculate that these DEGs may affect the level of immunity and inflammation of SCI tissues to some extent. We also found that many chemokines were enriched in the PPI network, and the genes enriched were related to the occurrence of cell metastasis and inflammation. For instance, FN1 is a member of the fibronectins (FN) family, and once it is overexpressed, the TGF-β/PI3K/Akt signaling pathway can be activated to promote fracture healing (Zhang et al. 2021). Besides, another important molecule of tissue inhibitor of metalloproteinase 1 (TIMP1) play an important role in the SCI, which was consistent with the results of a previous study (Liu et al. 2015). Therefore, we speculated that these changes at the molecular level of DEGs greatly regulate the immunity and inflammation level of SCI. MicroRNAs play a significant role in the regulation of SCI. After SCI, dysregulated miRNAs can participate in inflammatory responses, as well as the inhibition of apoptosis and axon regeneration through multiple pathways(Liu et al. 2020). Sun et al. indicated that miRNA-411 attenuates inflammatory damage and apoptosis following SCI(Sun et al. 2020). Chen et al. found that miRNA-194-5p inhibits inflammatory response after SCI via regulating TRAF6(Chen et al. 2020). Zhang et al. suggest that miR-223 targets NLRP3 to relieve inflammation and alleviate SCI. In the present study, we identified the miR-487b and found that the expression level of miR-487b was down-regulated significantly, which is consistent with the previous study that miR-487b was observed to target cholesterol metabolism-associated DGEs in rats with SCI (Chen et al. 2015). Besides, overexpression of miR-487b in BV-2 cell was observed to alleviative the proinflammatory factors, indicating its regulation role in inflammatory responses in SCI. What’s more, the results of luciferase reporter gene experiments verified the tight binding of miR-487b and Ifitm3. Ifitm3 was identified as an innate immunity protein that predominantly associated with Alzheimer’s disease(Hur et al. 2020) and cancers(Rajapaksa et al. 2020). For the first time, we suspect that Ifitm3 is an important molecule that has changed after SCI and miR-487b reduced LPS-induced BV2 neuronal apoptosis by targeted Ifitm3. These results suggest that the aberrant miR-487b is possibly regulated immune/inflammation signaling pathway and continuously affects the physiological and biochemical status of cells, thus participating in the regulation of SCI.
In conclusion, we identified common DEGs based on the public datasets and found that these DEGs were predominantly associated with immune and inflammation response by functional enrichment analysis. Further, the miR-487b was identified and verified to down-regulated in SCI. The miR-487b suppressed inflammation and reduced LPS-induced BV2 neuronal apoptosis by targeted Ifitm3. For the first time, our results reveal that miR-487b may play an important regulatory role in SCI by targeted Ifitm3 and provide further evidence for SCI research. The specific molecular regulation mechanism needs to be embodied in the further experiments. | true | true | true |
PMC9582134 | Xiang Dong,Yuxin Chen,Jun Pan,Wenliang Ma,Peng Zhou,Ming Chen,Hongqian Guo,Weidong Gan | Clinicopathological features and prognosis of TFE3-positive renal cell carcinoma | 06-10-2022 | TFE3,Xp11.2 translocation,RCC (Renal Cell Carcinoma),Clinico pathologic characteristics,prognosis | Background This study aimed to investigate the expression profile of TFE3 in renal cell carcinoma (RCC) and the clinicopathological features as well as prognosis of TFE3-positive RCC. Methods Tissue sections from 796 patients with RCC were collected for immunohistochemical staining of TFE3. Molecular TFE3 rearrangement tests were also carried out on the TFE3-positive RCCs using fluorescence in situ hybridization and RNA-sequencing assays. Both clinicopathological features and follow-up information were collected for further analysis. Results The present study showed that 91 patients with RCC (91/796, 11.4%) were TFE3 positive expression but only 31 (31/91, 34.1%) of the patients were diagnosed with Xp11.2 translocation RCC. Further, it was found that the patients with TFE3-positive RCCs were more likely to develop lymph node and distant metastasis at diagnosis as well as presented a significantly higher WHO/ISUP nuclear grade and AJCC stage as compared with patients with TFE3-negative RCCs (p<0.01). Results of univariate and multivariate analyses showed that TFE3 positive expression was an independent prognostic factor associated with poor progression-free survival. Further, the findings of survival analysis showed that patients with positive TFE3 expression showed a shorter progression-free survival as compared with the patients with negative expression of TFE3 (p<0.001). In addition, results of the survival analysis found that there was no significant difference in progression-free survival between the Xp11.2 translocation RCC and TFE3-positive non-Xp11.2 translocation RCC groups (p=0.9607). Conclusion This study found that nuclear TFE3 expression is not specific to the Xp11.2 translocation RCC. Moreover, the positive TFE3 expression is associated with tumor progression and poor prognosis in patients with RCC irrespective of the presence of TFE3 translocation. | Clinicopathological features and prognosis of TFE3-positive renal cell carcinoma
This study aimed to investigate the expression profile of TFE3 in renal cell carcinoma (RCC) and the clinicopathological features as well as prognosis of TFE3-positive RCC.
Tissue sections from 796 patients with RCC were collected for immunohistochemical staining of TFE3. Molecular TFE3 rearrangement tests were also carried out on the TFE3-positive RCCs using fluorescence in situ hybridization and RNA-sequencing assays. Both clinicopathological features and follow-up information were collected for further analysis.
The present study showed that 91 patients with RCC (91/796, 11.4%) were TFE3 positive expression but only 31 (31/91, 34.1%) of the patients were diagnosed with Xp11.2 translocation RCC. Further, it was found that the patients with TFE3-positive RCCs were more likely to develop lymph node and distant metastasis at diagnosis as well as presented a significantly higher WHO/ISUP nuclear grade and AJCC stage as compared with patients with TFE3-negative RCCs (p<0.01). Results of univariate and multivariate analyses showed that TFE3 positive expression was an independent prognostic factor associated with poor progression-free survival. Further, the findings of survival analysis showed that patients with positive TFE3 expression showed a shorter progression-free survival as compared with the patients with negative expression of TFE3 (p<0.001). In addition, results of the survival analysis found that there was no significant difference in progression-free survival between the Xp11.2 translocation RCC and TFE3-positive non-Xp11.2 translocation RCC groups (p=0.9607).
This study found that nuclear TFE3 expression is not specific to the Xp11.2 translocation RCC. Moreover, the positive TFE3 expression is associated with tumor progression and poor prognosis in patients with RCC irrespective of the presence of TFE3 translocation.
The TFE3 gene which is located on the chromosome band Xp11.2 belongs to a member of the microphthalmia transcription family (MiTF) (1). The MiTF family of genes plays crucial role on autophagy, lysosome generation, and have been involved in the progression of various tumors (2, 3). When cells encounter hypoxia or starvation, it has been found that the TFE3 proteins translocate from cytoplasm into the nucleus (4, 5). It has been shown that the translocation of TFE3 genes may result in fusion of the TFE3 gene with other partner genes (6–8). This leads to expression of TFE3 proteins. Translocation of TFE3 and expression of TFE3 proteins can be identified in different types of tumors, such as Xp11.2 translocation renal cell carcinoma (RCC), epithelioid haemangioendothelioma, alveolar soft part sarcoma, perivascular epithelioid cell tumor, ossifying fibromyxoid tumor, and malignant chondroid syringoma (2). Xp11.2 translocation RCC has been shown to be the most representative tumor among the mentioned tumors. The Xp11.2 translocation RCC was first recognized as a separate entity by the World Health Organization (WHO) in 2004. It was then reclassified as a member of the MiTF translocation RCC in 2016. The tumor accounts for approximately 30% of pediatric RCCs and between 1 and 5% of adult RCCs (9–11). In adults, the tumor (Xp11.2 translocation RCC) remains a rare disease with an invasive course and poor prognosis (7, 11–13). With the advancement of research, several studies have shown that even tumors lacking TFE3 translocation still show nuclear TFE3 immunoreactivity. This include solid pseudopapillary neoplasm of the pancreas, granular cell tumor, and ovarian sclerosing stromal tumor (2, 14). The RCCs with positive expression of TFE3 but without translocation have also been reported. This is an indication that not all TFE3-positive RCCs that belong to Xp11.2 translocation RCCs (7, 11, 12, 15, 16). However, available information regarding the characteristics of such RCCs remain rare. Meanwhile, the current studies showed that positive expression of TFE3 was associated with invasive course and poor prognosis in patients with RCC which was similar to that of patients with Xp11.2 translocation RCC (12, 17). This is the first study to report on the expression of TFE3 in such a relatively large cohort of RCCs. Furthermore, the present study also investigated clinicopathological features and prognosis of patients with TFE3-positive RCCs.
A total of 796 adult patients with RCCs who had undergone radical or partial nephrectomy in Nanjing Drum Tower Hospital were reviewed between January 2018 and September 2021. The inclusion criteria were (1): pathologically confirmed RCCs (2); complete clinicopathological and follow-up information (3); enough tumor specimens for further analysis. Immunohistochemical (IHC) examination for TFE3 was conducted in all selected patients. Fluorescence in situ hybridization (FISH) assay was performed on all the TFE3-positive patients to confirm the diagnosis of Xp11.2 translocation RCC. For cases with NONO-TFE3, GRIPAP1-TFE3, RBX-TFE3, and RBM10-TFE3 rearrangement which was caused by the X-chromosome inversion, there were previous studies that showed the cases have strong positive TFE3 IHC staining but equivocal split signals (18–20). The cases in this study with moderate or strong nuclear TFE3 immunoreactivity were included for further RNA-sequencing. This was in consideration of potential false negative cases with equivocal split signals. Clinicopathological and survival data were also collected for every patient, including age, gender, maximum tumor diameter, tumor location, WHO/ISUP (World Health Organization/International Society of Urologic Pathology) nuclear grade, AJCC (American Joint Committee on Cancer) stage, and follow-up information. Remarkably, the WHO/ISUP grading system was found not applicable for the Xp11.2 translocation RCC and chromophobe RCC (21). The approval of the present retrospective study was provided by the institutional review board of Nanjing Drum Tower Hospital and the informed consents was waived from the selected patients in the current study.
Immunohistochemistry of TFE3 were performed on the four-μm-thick formalin-fixed paraffin-embedded (FFPE) sections of all the RCC cases. IHC staining of the sections was carried out using anti-TFE3 antibody (SC-5958,1:300; Santa Cruz, CA). Further, IHC assay of the TFE3 was conducted though labeled streptavidin-biotin method followed by overnight incubation (22). The Xp11.2 translocation RCC and IgG were used as positive and negative controls for the IHC assay, respectively. Only nuclear TFE3 staining was considered as the positive result. The final result was analyzed by two independent observers and the inconsistent result was resolved by an experienced pathologist. To perform a semi-quantitative assessment, the results of this study were analyzed in reference to the previously reported intensity and degree of nuclear staining (22, 23). The IHC score was calculated by multiplying the percentage of positive cells (0–100) by the staining intensity (0=no staining, 1 = weak staining, 2 = moderate staining, and 3 = strong staining). For the result of immunostaining, a score of between 0 and 25 was considered negative (–), a score of between 26 and 100 was considered weak (+), a score of between 101 and 200 was considered moderate (++), and a score of between 201 and 300 was considered strong (+++).
In the present study four-μm-thick FFPE tissue sections were prepared for FISH assay using dual-color break-apart TFE3 Probes (LBP, Guangzhou, China). Fluorescence signals were analyzed using an Olympus BX51TRF fluorescence microscope (Olympus, Tokyo, Japan) with a triple emission filter (DAPI/FITC/Texas Red) and the FISH assay analysis software (Imstar, Paris, France). Further, at least 100 non-overlapping nuclei were counted in every sample. A minimum of 100 tumor nuclei were analyzed through fluorescence microscopy, and the split signal was defined as the distance >2 signal diameter. In addition, positive result of the FISH assay was defined as more than 10% of tumor nuclei with the split signal. In male patients, the positive results consisted of a single pair of separated red and green signals. In female patients, the positive results consisted of a fused signal pair (yellow) and an additional pair of split signals.
Total RNA was first extracted from the sections of FFPE tissue using the RNeasy kit (Qiagen, Hilden, Germany) according to instruction provided by the manufacturer. RNase H enzyme was used to deplete ribosomal RNA. KAPA Stranded RNA-seq Kit containing RiboErase enzyme (HMR) (KAPA Biosystems) was then used for preparation of the library. In addition, the quality of library was assessed using an Agilent Bioanalyzer 2100 system (Agilent, USA). Final libraries were subjected to a high-throughput Illumina HiSeqTM 2000 platform (California, USA), which was conducted by the GloriousMed Technology (Beijing, China).
Statistical analyses of the data obtained in the current study was conducted using SPSS 23.0 software (Chicago, IL, USA). The Kaplan-Meier survival curves were drawn using GraphPad Prism 8.0 (GraphPad Software, USA). Evaluated characteristics were compared among the three groups using the Mann-Whitney U test or χ2 test. Survival analysis was performed using the Kaplan-Meier survival curves and log-rank tests were used to compare the drawn Kaplan-Meier survival curves. Cox proportional hazards regression model was conducted for both univariate and multivariate analysis. Statistical significance was set at P value less than 0.05.
The RCCs in the present study were divided into two groups according to the positive and negative expression of TFE3. The clinicopathological characteristics were then compared between the two groups ( Table 1 ). It was evident that there was a significant difference in the median age of patients between the TFE3-positive and negative RCC groups (p<0.001). Results of the present study showed that the patients with TFE3-positive RCCs were significantly more likely to develop lymph node and distant metastasis at diagnosis as compared with the patients having TFE3-negative RCCs (p<0.001). Furthermore, it was noted that the TFE3-positive RCC group was significantly associated with a higher WHO/ISUP nuclear grade and AJCC stage as compared with the TFE3-negative RCC group (p<0.01). A total of 91 TFE3-positive RCCs were identified in the current study. Results of a comparative analysis of the clinicopathological characteristics between Xp11.2 translocation RCCs and TFE3-positive non-Xp11.2 translocation RCCs were also as shown in Table 1 . It was evident that the patients with Xp11.2 translocation RCCs were statistically younger (median age, 43) as compared with patients with TFE3-positive non-Xp11.2 translocation RCCs (median age, 59) (p<0.001). In addition, it was found that the patients with Xp11.2 translocation RCCs were more frequently female predominated (55%) as compared with the patients with TFE3-positive non-Xp11.2 translocation RCCs (33%) (p=0.048). Further, the results of the present study showed that there were no significant differences in the median tumor size, tumor location, lymph node metastasis, distant metastasis, and AJCC stage between the two studied groups. According to the WHO/ISUP grading system, 33 out of 57 patients with TFE3-positive non-Xp11.2 translocation RCCs were at grade III or IV. A total of 91 RCCs showed positive expression of TFE3, including 56, 18, and 17 cases with weak, moderate, and strong expression, respectively. All of the 17 strong expression cases belonged to patients with Xp11.2 translocation RCCs. Among the 56 weak expression cases, only 6 cases were confirmed to be from Xp11.2 translocation RCC group ( Table 2 ). Typical images of the IHC staining for both TFE3 and FISH were as shown in Figures 1 , 2 , respectively. For further analysis, RNA-sequencing was performed on the cases with moderate or strong TFE3 expression. Ultimately, two TFE3-positive RCC cases with equivocal split signals were detected using FISH assay and were further diagnosed with NONO-TFE3 RCC by RNA-sequencing in the cases. The obtained results of the typical FISH assay and RNA-sequencing were as shown in Figure 3 . Univariate and multivariate Cox proportional hazard model were performed in the present study to clarify the risk factors of progression-free survival. Results of the univariate analysis study showed that age, AJCC stage, and expression of TFE3 were significantly correlated with progression-free survival (p<0.05). On the other hand, the obtained results of multivariate analysis showed that higher AJCC stage (Hazard Ratio, HR=11.4; 95% Confidence interval, CI 7.463-17.414; p<0.001) and positive expression of TFE3 (HR=2.32; 95% CI 1.344-4.000; p=0.002) were the independent prognostic factors associated with poor progression-free survival ( Table 3 ). To conduct the survival analysis, the patients selected for the current study were divided into two groups (TFE3-positive RCC group and TFE3-negative RCC group). The obtained results of survival analysis revealed that positive expression of TFE3 was correlated with a shorter progression-free survival in patients with RCC (p<0.0001, Figure 4A ). In addition, the patients involved in the present study were divided into three groups (1): TFE3-negative RCC group (2), Xp11.2 translocation RCC group, and (3) TFE3-positive non-Xp11.2 translocation RCC group. After survival analysis study, it was found that the patients in TFE3-negative RCC group were associated with significantly longer progression-free survival time as compared with patients in the other groups in the survival analysis (p<0.05). Meanwhile, results of the survival analysis showed that there was no significant difference in progression-free survival between patients in Xp11.2 translocation RCC group and those in the TFE3-positive non-Xp11.2 translocation RCC group (p=0.9607, Figure 4B ).
The current study found that the patients with TFE3-positive RCCs accounted for 11.4% (91 out of 796) of RCCs. However, it was evident that only one-third (31 out of 91) of the TFE3-positive RCCs belonged to the Xp11.2 translocation RCC in the present study. The current study further focused on the clinicopathological characteristics and prognosis of the patients with TFE3-positive RCCs. It was study found that TFE3-positive RCC groups (Xp11.2 translocation RCC and TFE3-positive non-Xp11.2 translocation RCC) showed significantly aggressive clinicopathological characteristics and poor prognosis as compared with the TFE3-negtive RCC group. Previous studies have regarded TFE3 IHC staining as an important method for diagnosing Xp11.2 translocation RCCs. An early study conducted by Argani et al. (24) showed that the TFE3 IHC staining was highly sensitive and specific for diagnosing Xp11.2 translocation RCCs (97.5 and 99.6%, respectively). With the continuous progress of research on Xp11.2 translocation RCCs, different research studies have also found that not all patients with TFE3-positive RCCs that belongs to the group of Xp11.2 translocation RCCs (12, 23, 25–27). According to the study conducted by Lee et al. (12), 10.2% (31 out of 303) of RCCs expressed positive nuclear TFE3 immunoreactivity and 19.4% (6 out of 31) of TFE3-positive RCCs did not belong to the Xp11.2 translocation RCC group. In the present study, the proportion was increased to 66% (60 out of 91). The difference in the results obtained in the current study as compared with the finding of the previous studies could be attributed to the different definition of TFE3 positive expression. Findings of some previous studies have shown that only moderately or strongly positive expression of TFE3 was considered significant, whereas the weak expression of TFE3 was ignored (7, 12, 15). Notably, RCCs with weak TFE3 staining may also be diagnosed as Xp11.2 translocation RCC using FISH assay and especially in the RCCs with classic histological morphology of Xp11.2 translocation RCC (11, 16). Based on the previously reported descriptions, the classic morphological presentation feature was defined as tumor cells with abundant eosinophilic or clear cytoplasm with papillary or micropapillary structure, with or without psammoma bodies (16). However, it was found that the classic morphology only presented in half of the TFE3-positive RCCs. Therefore, it was not considered as a significant predictor of rearrangement in TFE3 gene in the previous study (16). The weak expression of nuclear TFE3 was detected in six out of 31 RCCs (19%) Xp11.2 translocation RCCs in the current study and not all RCCs were presented with typical morphology. Currently, TFE3 break-apart FISH assay is currently regarded as the golden standard for the diagnosis of Xp11.2 translocation RCC in clinical practice (8, 16, 28). However, for translocation RCCs with inverted X-chromosome, the RCCs may be presented with equivocal split signals (18–20). Therefore, the results of TFE3 IHC staining are particularly important in such a situation. Previously, we and other found that Xp11.2 translocation RCCs with equivocal FISH results showed varying degrees of positive staining for TFE3 in both the present and the previous studies (19, 20). According to our previous study, a novel NONO-TFE3 dual-fusion FISH assay was developed and the accuracy of this probe validated for diagnosis of NONO-TFE3 RCC (19). In the present study, the NONO-TFE3 fusion was also identified by RNA-sequencing in TFE3-positive RCCs with equivocal split signals. However, the high cost and laborious procedure of RNA-sequencing restricts the wide use of the procedure in the ordinary clinical practice. Therefore, combination of TFE3 IHC with FISH assay is still currently the first choice for diagnosis of the Xp11.2 translocation RCC. The proportion of TFE3-positive RCCs was 11.4% (91 out of 796) of RCCs in the present study and this was higher than that in the previous studies (9-10.2%) (12, 27). Among the TFE3-positive RCCs in the current study, it was found that approximately one-third (31 out of 91) of the RCCs belong to the Xp11.2 translocation RCC. The incidence of Xp11.2 translocation RCC in the present study was 3.9% (31 out of 796 RCCs), whereas that in previous studies ranged from 1 to 5% among all the RCCs (9–11). It was evident that patients with Xp11.2 translocation RCC in the present study were significantly younger and predominantly female as compared with patients with other RCCs where the patient were significantly older and predominantly male. Meanwhile, the observations were in consonance with those reported in previous studies (8, 10, 29). Xp11.2 translocation RCC often develop lymph node and distant metastasis because of its aggressive characteristics (7, 8, 11, 16). In a separate study, Classe et al. found that the probability of developing lymph node and distant metastasis in Xp11.2 translocation RCCs were 25% (5 out of 20) and 15% (3 out of 20), respectively (11). However, the present study found that six (19.4%) and three (9.7%) patients with Xp11.2 translocation RCC had lymph node metastasis and distant metastasis, respectively. Furthermore, similar probabilities were found in TFE3-positive RCCs, although such probabilities are not as high as that for Xp11.2 translocation RCCs. In the present study, it was found that the probability of lymph node and distant metastasis in the TFE3-positive RCC were 10.9% (10 out of 91 patients) and 6.6% (6 out of 91), respectively, whereas the patient in the TFE3-negative RCC were 1.3% (9 out of 705 patients) and 0.9% (6 out of 105 patients), respectively. Although some previous studies have suggested that Xp11.2 translocation RCCs tended are of a higher nuclear grade (12, 27, 30), our previous study showed that both the WHO/ISUP and Fuhrman grading system are not suitable for Xp11.2 translocation RCCs (21). Results of the present study showed that the proportion of high nuclear grade was 55% (33 out of 60 patients) and 22% (153 out of 705 patients) in TFE3-positive non-Xp11.2 translocation RCC group and TFE3-negative RCC group, respectively. In addition, the present study evidently found that the proportion of TFE3-positive RCCs with high WHO/ISUP grade or AJCC stage was significantly higher than that of TFE3-negative RCC group. In conclusion, RCCs with positive expression of TFE3 were associated with higher rates of metastasis and higher WHO/ISUP grade as well as AJCC stage. The findings of the multivariate analysis showed that positive expression of TFE3 was an independent prognostic factor affecting the progression-free survival. According to the expression of TFE3 proteins, the RCCs were divided into two subgroups for survival analysis. Results of the analysis found that the progression-free survival of TFE3-positive RCC group was significantly shorter as compared with that of the TFE3-negative RCC group. When RCCs was classified as TFE3-negative RCC, Xp11.2 translocation RCC, and TFE3-positive non-Xp11.2 translocation RCC groups, it was evident that the TFE3-negative RCC group had a significantly longer progression-free survival as compared with the other groups. Meanwhile, it was noted that there was no significant difference in progression-free survival between Xp11.2 translocation RCC and TFE3-positive non-Xp11.2 translocation RCC groups. Therefore, the present study proposes that expression of TFE3 may be an independent prognostic factor irrespective of the TFE3 translocation states. Furthermore, as long as the RCC has positive expression of TFE3, it may show a worse prognosis compared with the RCC having negative expression of TFE3. Currently, the reason for TFE3 expression in RCCs could be explained: First, translocation leads to the fusion of the TFE3 gene with several partner genes which results in the overexpression of the fusion proteins (24). Overexpression of the fused of the TFE3 gene facilitates tumor progression and this is also a typical feature of the Xp11.2 translocation RCCs. Second, the presence of the gene amplification may also relate to the expression of TFE3 (27, 31). Previous studies have shown that amplified TFEB tumors express aggressive characteristics, although the gene rearrangement was not observed (16, 32, 33). Third, although it is unknown whether the nuclear localization promotes the expression of TFE3, inactivation of the tumor suppressor gene, FLCN, contributes to the increased TFE3 transcriptional activity and nuclear localization (34). Apart from the described reasons, there are further unknown molecular mechanisms leading to the overexpression of TFE3 in RCC waiting for us to explore. Meanwhile, TFE3, as a member of MiTF family, is involved in the occurrence and development of RCC. Multiple autophagy-associated signaling pathways are regulated by the TFE3 gene, thereby influencing tumor growth (35). The TFE3 protein could inhibit the p21-mediated pRB pathway and activate the P13K/AKT/mTOR pathway, thereby leading to excessive proliferation tumor cells and ultimately causing progression of the tumors (36–38). There is still need for further studies to verify the prognostic value of TFE3 in patients with RCC. The present study had some limitations. First, the relatively small population of patient and the short patient of follow-up period may affect the accuracy of the obtained results. Second, further molecular detection was not performed on all TFE3-positive RCCs beyond FISH assay, and this could affect the accuracy of the diagnosis conducted in the current study. RNA-sequencing in the present study was only conducted on the RCCs with moderate or strong nuclear TFE3 immunoreactivity because performing RNA-sequencing on every TFE3-positive RCCs would be expensive. Third, the definition of the TFE3-positive RCC remains controversial. Whereby, weak expression of TFE3 is considered insignificant in some studies. However, it was found that the RCC with weak positive expression can be diagnosed as Xp11.2 translocation RCC. Furthermore, even when there is no gene rearrangement, such RCCs have a poor prognosis as compared with the TFE3-negative RCCs. The FISH assay should be performed in every TFE3-positive RCC. This is because the nuclear expression of TFE3 is not exclusive to the Xp11.2 translocation RCC, but also appears in other types of RCCs. In addition, RNA-sequencing is necessary as a diagnostic test for detection of TFE3 rearrangement. This is for the cases with equivocal split signals, especially with moderate or strong nuclear TFE3 immunoreactivity. Results of the present study demonstrated that the expression of TFE3 in RCCs was significantly associated with higher nuclear grade, tumor stage, and metastasis. Furthermore, it was found that the expression of TFE3 protein in the RCC correlates with the tumor progression and poor prognosis irrespective of the presence of TFE3 translocation.
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.
The studies involving human participants were reviewed and approved by Institutional review board of Nanjing Drum Tower Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
XD and WG conceived and designed the study. YC, JP, and WM contributed to the acquisition of data. PZ, MC, and HG supervised the study and review the manuscript. All authors read and approved the final manuscript. All authors contributed to the article and approved the submitted version.
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. | true | true | true |
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PMC9582150 | Bin Liu,Tingting Lu,Yongfeng Wang,Guangming Zhang,Liangyin Fu,Miao Yu,Kehu Yang,Hui Cai | Overexpression of LncRNA SNHG14 as a biomarker of clinicopathological and prognosis value in human cancers: A meta-analysis and bioinformatics analysis 10.3389/fgene.2022.945919 | 06-10-2022 | lncRNA,SNGH14,cancer,meta-analysis,prognostic lncRNA: long noncoding RNA,ceRNA: competing endogenous RNAs,SNHG14: small nucleolar | Background: SNGH14 is a newly discovered long non-coding RNA (lncRNA) highly associated with tumorigenesis. However, whether the level of SNHG14 is related to the prognosis of patients with different cancer types is unclear. Methods: PubMed, Web of Science, Cochrane Library, and Embase were searched to identify eligible studies from inception to November 2021. The odds ratio (OR) and 95% confidence interval (CI) were utilized to analyze dichotomous variables, while the hazard ratio (HR) and 95% CI were used for survival outcomes. We also included trial sequential analysis (TSA) to assess whether the current evidence was sufficiently conclusive. Stata 15.0 and TSA 0.9 software were used for data analyses. Results: A total of 21 studies involving 1,080 patients, mainly from China, were included. Our results revealed that high SNHG14 expression was associated significantly with poor overall survival (OS) [HR = 1.39; 95% CI: (1.06–1.83); p = 0.017]. In addition, elevated SNHG14 expression was related to tumor size (> 3.5 cm) [OR = 1.60; 95% CI: (1.20–2.14); p = 0.001], TNM staging [OR = 0.54; 95% CI: (0.40–0.71); p < 0.001], lymph node metastasis [OR = 1.86; 95% CI: (1.35–2.55); p < 0.001], differentiation grade [OR = 1.95; 95% CI: (1.36–2.80); p < 0.001], and distant metastasis [OR = 2.44; 95% CI: (1.30–4.58); p = 0.005]. However, no significant difference was observed between age [OR = 0.98; 95% CI: (0.72–1.35); p = 0.915] and gender [OR = 0.98; 95% CI: (0.72–1.35); p = 0.915] from the enhanced expression of SNHG14. Conclusion: The current study revealed that overexpression of SNGH14 is associated with low OS rate and clinicopathological characteristics. SNGH14 can be a novel tumor marker that aids in tumor diagnosis, thereby improving patient prognosis. | Overexpression of LncRNA SNHG14 as a biomarker of clinicopathological and prognosis value in human cancers: A meta-analysis and bioinformatics analysis 10.3389/fgene.2022.945919
Background: SNGH14 is a newly discovered long non-coding RNA (lncRNA) highly associated with tumorigenesis. However, whether the level of SNHG14 is related to the prognosis of patients with different cancer types is unclear. Methods: PubMed, Web of Science, Cochrane Library, and Embase were searched to identify eligible studies from inception to November 2021. The odds ratio (OR) and 95% confidence interval (CI) were utilized to analyze dichotomous variables, while the hazard ratio (HR) and 95% CI were used for survival outcomes. We also included trial sequential analysis (TSA) to assess whether the current evidence was sufficiently conclusive. Stata 15.0 and TSA 0.9 software were used for data analyses. Results: A total of 21 studies involving 1,080 patients, mainly from China, were included. Our results revealed that high SNHG14 expression was associated significantly with poor overall survival (OS) [HR = 1.39; 95% CI: (1.06–1.83); p = 0.017]. In addition, elevated SNHG14 expression was related to tumor size (> 3.5 cm) [OR = 1.60; 95% CI: (1.20–2.14); p = 0.001], TNM staging [OR = 0.54; 95% CI: (0.40–0.71); p < 0.001], lymph node metastasis [OR = 1.86; 95% CI: (1.35–2.55); p < 0.001], differentiation grade [OR = 1.95; 95% CI: (1.36–2.80); p < 0.001], and distant metastasis [OR = 2.44; 95% CI: (1.30–4.58); p = 0.005]. However, no significant difference was observed between age [OR = 0.98; 95% CI: (0.72–1.35); p = 0.915] and gender [OR = 0.98; 95% CI: (0.72–1.35); p = 0.915] from the enhanced expression of SNHG14. Conclusion: The current study revealed that overexpression of SNGH14 is associated with low OS rate and clinicopathological characteristics. SNGH14 can be a novel tumor marker that aids in tumor diagnosis, thereby improving patient prognosis.
Cancer is one of the leading causes of global human death and a major public health problem (Lee and Sanoff, 2020). Cancer research has progressed significantly in recent years, but clinical outcomes are still not optimistic. The main reason is that cancers have not been effectively diagnosed and treated early, causing unsatisfactory clinical curative effects, which significantly impact the prognosis of patients (Hiatt and Beyeler, 2020). Therefore, there is an urgent need to discover novel biomarkers that can facilitate early diagnosis and prognostic evaluation among tumor patients (Wang et al., 2020). Due to the development of whole-genome and transcriptome sequencing technology and the ENCODE project (Harrow et al., 2012), researchers have observed that most genome DNA is present in processed translation scripts. However, these translation scripts may not be translated into functional proteins, i.e., non-coding ribonucleic acid (ncRNA) (2012; Xie et al., 2019). Long non-coding ribonucleic acid (lncRNA) is a non-coding ribonucleic acid series with more than 200 nucleotides (Tang and Yang, 2020). Although lncRNA has almost no protein-coding ability, they have a vital role in regulating gene expression at various points during the transcription/translation process (Dong et al., 2018; Wanowska et al., 2018; Cheng et al., 2020). They have been considered promising markers for cancer prognosis, diagnosis, and development. The association between lncRNA and the carcinogenesis of many cancer types has been well established (Yu et al., 2019; Liu et al., 2020). Small nucleolar RNA host gene 14 (SNHG14) is located on the human chromosome 15q11.2 and has been reported to accelerate tumor development in many malignant tumors (Hou and Mao, 2020). It is a proven proto-oncogene in various cancers, including lung and cervical cancer. It is essential in activating inflammatory microglia, sepsis-induced acute kidney injury, and LPS-induced acute kidney injury (Zhong et al., 2019; Jiang et al., 2021; Shi et al., 2021; Yang et al., 2021). Xie et al. (2020) identified that SNHG14 regulates E-cadherin expression by interacting with EZH2, enhancing the progression of pancreatic ductal adenocarcinoma. Recently, Liu et al. (2017) described that SNHG14 could be used as ceRNA to promote the initiation and clearance progress. It can overlap with the entire UBE3A gene and promoter, inhibiting UBE3A expression and causing neurogenetic diseases like Angelman syndrome (Sadikovic et al., 2014). However, the regulatory mechanism of SNHG14 remains unclear. There is no prominent article to confirm the relationship between SNHG14 and cancer prognosis. In addition, several studies on SNHG14 have only obtained independent results due to limitations, including short follow-up durations. To provide better clinical guidance to clinicians, we have performed a meta-analysis of the existing literature to investigate the relationship between SNHG14 and clinicopathological features and patient prognosis in the present study.
The present meta-analysis was conducted and reported based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidance (Liberati et al., 2009). This study has also been registered in PROSPERO (No. CRD42021287397).
PubMed, Web of Science, Cochrane Library, and Embase were searched from inception to November 2021. The main combinations of search terms incorporated were as follows: (“small nucleolar RNA host gene 14” OR “SNHG14” OR “115HG” OR “IC-SNURF-SNRPN” OR “LNCAT” OR “NCRNA00214” OR “U-UBE3A-ATS” OR “UBE3A-AS” OR “UBE3A-AS1” OR “UBE3A-ATS” OR “UBE3AATS”) AND (“cancer” OR “carcinoma” OR “neoplasm” OR “tumor”) without any restrictions on population, and the reference list of included studies was also checked. The detailed search strategy is presented in Supplementary Material S1.
The inclusion criteria were as follows: (a) assessment of the expression level of SNHG14 among cancer patients; (b) patients were divided into high and low expression groups; (c) outcomes describing overall survival (OS) and related clinicopathological parameters (including age, gender, tumor size, TNM staging, lymph node metastasis, differentiation grade, and distant metastasis) should be considered; (d) case-control studies (CCSs) or cohort studies (CS). The exclusion criteria were: (a) reviews, case reports, conference abstracts, animal studies, fundamental experimental research, etc.; (b) duplicate publications; (c) studies lacking survival or clinicopathological data; (d) non-English language literature.
Two reviewers independently screened the literature and extracted data. Any disagreements were resolved by consulting a third investigator. The following information was extracted: first author, year of publication, country/region, cancer type, sample type, number of samples, detection method, cut-off values, outcomes, and follow-up periods. The expression of LncRNA is detected by qRT-PCR, first using Trizol reagent to extract total RNA from tissues and cells. RNA integrity was evaluated by standard agarose/ethidium bromide gel electrophoresis. Then, total RNA was reverse transcribed into cDNA through a reverse transcription kit. The expression level of lncRNA was detected using fluorescence quantification. Glyeraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an endogenous control. The results of expression level of lncRNA were analyzed using the comparative 2–ΔΔCT method. The cut-off value generally chooses the mean or median (Huang et al., 2021).
Newcastle-Ottawa Scale (NOS) was utilized to assess the quality of included studies (Stang, 2010). The scale consisted of the following domains: selection of study groups, comparability, exposure (case-control study), or outcome (cohort study). The total NOS score ranged from 0 to 9, and the score of high-quality studies was ≥ 7. Any disagreements were resolved through consultation with a third investigator. We used trial sequential analysis (TSA) to assess whether the current evidence was sufficient and sufficiently conclusive to prevent the risk of false-positive (type I) errors. In this review, the required information size was estimated through α = 0.05 (two sides), β = 0.20 (power 80%). The O'Brien-Fleming function was implemented. If the cumulative Z-curve crossed the monitoring boundary, a sufficient level of evidence for the effect of the intervention could have been reached. More research is required if the cumulative Z-curve crosses neither the traditional boundary nor the null region.
The dichotomous outcomes were represented as odds ratio (OR) and 95% confidence interval (CI). Engauge Digitizer V.4.1 software extracted HR and 95% CI from Kaplan-Meier (KM) curves. We determined logHR and SE logHR= [log(Upper Limit)-log (Lower Limit)]/3.92 and performed a meta-analysis through the inverse variance method to summarize OS (Parmar et al., 1998). Heterogeneity was assessed through Chi-square (χ2) test and I-square (I 2). A random-effects model was incorporated for data with significant heterogeneity (P Q < 0.1 and I 2 > 50%). Otherwise, a fixed-effects model was utilized. Subgroup analysis was performed according to follow-up period, sample size, analysis method, and cancer type. Publication bias was assessed through Egger’s test and Begg’s funnel plot, and sensitivity analysis was conducted to identify the source of heterogeneity. Stata version 15.0 software (Stata Corporation, College Station, TX, United States) and TSA 0.9 (http://www.ctu.dk/tsa) software were used for data analyses, and p < 0.05 was considered statistically significant.
The related genes for SNHG14 were retrieved from the MEM-Multi Experiment Matrix database (https://biit.cs.ut.ee/mem/index.cgi). Then, Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted depending on SNHG14-related genes using R software (p < 0.05). Moreover, a signaling pathway network was constructed through the Cytoscape software.
Using Spearman’s correlation analysis, we analyzed the association of tumor mutational burden (TMB) and microsatellite instability (MSI) with SNHG14 expression. The data were obtained from the TCGA database through the Genomic Data Commons (GDC) data portal website (https://portal.gdc.cancer.gov/) and statistically analyzed through R software v4.0.3.
Newman and colleagues created CIBERSORT, which quantifies immune cell infiltration in all malignancies by estimating the number of specific cell types in mixed cell populations through gene expression data. Thus, we used CIBERSORT to evaluate the relationship between SNHG14 expression and immune cell infiltration with R packages “ggplot2,” “ggpubr,” and “ggExtra.” (p < 0.001 was the cut-off value).
A total of 208 studies were identified after removing duplication. One hundred thirteen studies required further screening based on their title and abstract. Subsequently, 47 studies were eligible to read the full text, and eventually, 21 cohort studies (Zhang Y. Y. et al., 2019; Zhang Z. et al., 2019; Deng et al., 2019; Ji et al., 2019; Li et al., 2019; Pei et al., 2019; Zhao et al., 2019; Zhao and Huang, 2019; Zhang H. et al., 2020; Zhang K. et al., 2020; Chen et al., 2020; Zhang W. et al., 2020; Luo et al., 2020; Sun et al., 2020; Wang and Wen, 2020; Xu et al., 2020; Zhou et al., 2020; Wang et al., 2021a; Wang et al., 2021b; Feng et al., 2021; Liao et al., 2021) were included in the meta-analysis (Figure 1).
Twenty-one studies involving 1,080 patients were from China. The sample sizes ranged from 24 to 99. The recruited studies had 11 cancer types, including colorectal cancer, hepatocellular carcinoma, bladder cancer, non-small cell lung carcinoma, endometrial carcinoma, acute myeloid leukemia, retinoblastoma, prostate cancer, cervical cancer, pancreatic cancer, and ovarian cancer. The expression of SNHG14 was detected using qPCR within the included studies. In addition, 16 studies (Zhang Y. Y. et al., 2019; Zhang Z. et al., 2019; Ji et al., 2019; Li et al., 2019; Pei et al., 2019; Zhao et al., 2019; Zhao and Huang, 2019; Zhang H. et al., 2020; Zhang K. et al., 2020; Chen et al., 2020; Zhang W. et al., 2020; Luo et al., 2020; Sun et al., 2020; Wang and Wen, 2020; Feng et al., 2021; Liao et al., 2021) reported on OS, and 17 (Zhang Y. Y. et al., 2019; Zhang Z. et al., 2019; Deng et al., 2019; Ji et al., 2019; Zhang H. et al., 2020; Zhang K. et al., 2020; Chen et al., 2020; Zhang W. et al., 2020; Luo et al., 2020; Sun et al., 2020; Wang and Wen, 2020; Xu et al., 2020; Zhou et al., 2020; Wang et al., 2021a; Wang et al., 2021b; Feng et al., 2021; Liao et al., 2021) on clinical outcomes. The NOS scores of included studies were as follows: two studies (Zhang Z. et al., 2019; Zhang W. et al., 2020) with a NOS score of 8, five studies (Ji et al., 2019; Zhang K. et al., 2020; Wang et al., 2021a; Wang et al., 2021b; Feng et al., 2021) with a NOS score of 7, eight studies (Zhang Y. Y. et al., 2019; Deng et al., 2019; Pei et al., 2019; Chen et al., 2020; Luo et al., 2020; Wang and Wen, 2020; Xu et al., 2020; Liao et al., 2021) with a NOS score of 6 and six studies (Li et al., 2019; Zhao et al., 2019; Zhao and Huang, 2019; Zhang W. et al., 2020; Sun et al., 2020; Zhou et al., 2020) with a NOS score of 5. The details are provided in Table 1.
Sixteen studies involving 1,578 patients reported OS among cancer patients. Our meta-analysis depicted a statistically significant difference (HR = 1.39; 95%CI: (1.06–1.83); p = 0.017) (Figure 2A) and observed low heterogeneity (I 2 = 9.0%, p = 0.35), fixed-effects model was used. In the high SNHG14 expression group, patients with low survival rates significantly increased, indicating that SNHG14 is an independent factor in the survival of patients having malignant tumors. Subgroup analysis demonstrated that the high expression SNHG14 during multivariate analysis [HR = 1.60; 95% CI: (1.07–2.40); p = 0.022] (Figure 2B), the female reproductive system cancer [HR = 1.97; 95% CI: (1.18–3.30); p = 0.009] (Figure 2C), the follow-up time of > =60 months [HR = 1.53; 95% CI: (1.12–2.07); p = 0.007] (Figure 2D) and the sample size of < 60 tissues [HR = 1.57; 95% CI: (1.07–2.29); p = 0.002] (Figure 2E) were statistically significant and related to low OS. These analyses are depicted in Table 2.
Seventeen reported a link between clinicopathological characteristics and SNHG14. High SNHG14 expression was observed to be significantly correlated with TNM Staging (II-III) (OR = 0.54; 95% CI: (0.40–0.71); p < 0.001) having high heterogeneity I 2 = 61.1%, p = 0.001) (Figure 3C), tumor size > 3.5 cm (OR = 1.60; 95% CI: (1.20–2.14); p = 0.001) showing high heterogeneity (I 2 = 53.9%, p = 0.011) (Figure 3D), lymph node metastasis [OR = 1.86; 95% CI: (1.35–2.55); p < 0.001] having high heterogeneity (I 2 = 63.7.9%, p = 0.002) (Figure 3E), low differentiation grade [OR = 1.95; 95% CI(1.36–2.80); p < 0.001] with significant heterogeneity (I 2 = 60.3%, p = 0.010) (Figure 3F), and distant metastasis [OR = 2.44; 95% CI: (1.30–4.58); p = 0.005] without any heterogeneity (I 2 = 0.0%, p = 0.624) (Figure 3G). However, the meta-analysis revealed that there was no significant correlation between SNHG14 expression and age [OR = 0.98; 95% CI: (0.72–1.35); p = 0.915] without any heterogeneity (I 2 = 0.0%, p = 0.455) (Figure 3A) or gender [OR = 0.98; 95% CI: (0.72–1.35); p = 0.915] having low heterogeneity (I 2 = 27.8%, p = 0.172) (Figure 3B). These analyses are represented in Figure 3; Table 3.
Begg’s and Egger’s regression tests were utilized to explore the publication bias of the studies in our meta-analysis, and a funnel plot was created to determine the publication bias. No publication bias was observed [Begg funnel plot (Pr>|z| = 0.528) (Figure 4A) and Egger funnel plot (p>|t| = 0.480) (Figure 4B)], suggesting that our pooled results were credible. In addition, a sensitivity analysis explored their potential source and assessed the robustness of these outcomes. After omitting each included study in turn for each outcome, the results of OS remained stable. Therefore, the predicted aggregated results of the OS based on SNHG14 expression were reliable (Figure 5).
The TSA of OS, tumor size, TNM staging, Lymph node metastasis, and Differentiation grade showed that the Z-curve crossed the conventional boundary and RIS, indicating robust evidence. The TSA of age and gender grade showed that the Z-curve crossed the RIS but not the conventional boundary, which stated that false positive conclusions might be obtained. However, The TSA for the distant metastasis revealed that the Z-curve did not cross the conventional or trial sequential monitoring boundary and the RIS (= 355). Therefore, the evidence on the effect of distant metastasis was insufficient. (Figure 6).
The MEM database was utilized to screen the top 150 co-expressed genes of SNHG14. SNORD108, AC124312.1, and PWAR6 were the top three target genes ranked by p-value, significantly associated with SNHG14 gene expression (Figure 7). GO and KEGG pathway analyses were performed to explore the underlying molecular mechanisms. The results of GO analysis depicted those co-expressed genes primarily involved in biological processes (BP), such as the ribonucleoprotein complex assembly, ribonucleoprotein complex subunit organization, and RNA splicing; cellular components (CC), including nuclear speck, ATPase complex, and SWI/SNF superfamily-type complex; molecular function (MF), involving helicase activity, nucleosome binding, and single−stranded RNA binding (Figure 7). In addition, the KEGG pathway analysis revealed that co-expressed genes were implicated in Morphine addiction, Spliceosome, and Wnt signaling pathway (Figure 8). Moreover, a signal pathway network was developed using the Cytoscape software (Figure 9; Table 4).
TMB and MSI are essential determinants in tumor incidence and progression. Thus, we evaluated the association between SNHG14 expression and TMB or MSI to assess its immunogenicity (Chalmers et al., 2017). Our findings described that SNHG14 expression was positively related to TMB in colon adenocarcinoma (COAD), thymoma (THYM), and acute myeloid leukemia (LAML), However, the SNHG14 expression was negatively associated with TMB in 11 cancers, including esophageal carcinoma (ESCA), lung adenocarcinoma (LUAD), PAAD, stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), bladder urothelial carcinoma (BLCA), head and neck squamous cell carcinoma (HNSC), LGG, LIHC, and rectum adenocarcinoma (READ) (Figure 10A). Moreover, we investigated the association of SNHG14 expression with MSI in specific cancers. Our findings showed that SNHG14 expression was strongly related to MSI in nine cancer types, with four (LUAD, cholangiocarcinoma (CHOL), LGG, and lung squamous cell carcinoma (LUSC)) positively associated with MSI. However, in ESCA, STAD, COAD, lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), and PAAD, the SNHG14 expression was negatively associated with MSI (Figure 10B).
Our findings revealed that SNHG14 expression was positively related to infiltrated active mast cells and monocytes in LGG. However, it was negatively correlated with infiltrating M0 macrophages, M1 macrophages, and CD8 T cells within PAAD. Moreover, SNHG14 expression was positively related to naïve B cells and CD8 T cells infiltrated but negatively correlated with memory B cells, M0 macrophages, and activated NK cells infiltrated inside PAAD. The expression of SNHG14 in LIHC was positively associated with infiltrating M0 macrophages and negatively correlated with infiltrating CD8 T cells in LIHC. In SKCM, the SNHG14 expression was positively related to resting memory CD4 T cells and regulatory T cells (Tregs) but negatively associated with CD8 T cells (Figure 11). Thus, SNHG14 may be involved in the immune infiltration of M0 macrophages and could cause cancer by affecting the tumor microenvironment.
Cancer poses a severe threat to human health. Despite significant cancer detection and treatment advancements, cancer incidence has gradually increased in recent years (Jiang and Le, 2020). Many studies have indicated that lncRNA is dysregulated in cancer and vital to tumor development and progression. For instance, a meta-analysis by Zhou et al. (2018) showed that overexpression of lncRNA-XIST is correlated with poor prognosis and clinicopathological characteristics. LncRNA-XIST could be a promising non-invasive biomarker for determining prognosis and clinical pathology. SNHG14 is highly expressed in various cancers and is reported to accelerate tumor development (Xu et al., 2019). SNHG14 is upregulated within tumor tissues, including colorectal cancer, hepatocellular carcinoma, bladder cancer, non-small cell lung carcinoma, endometrial carcinoma, acute myeloid leukemia, retinoblastoma, prostate cancer, cervical cancer, pancreatic cancer, and ovarian cancer. SNHG14 participates in reprogramming glucose metabolism and tumorigenesis within gliomas by interacting using the RNA-binding protein Lin28A. Silencing SNHG14 inhibits glycolysis and proliferation of glioma cells while enhancing cell apoptosis (Lu et al., 2020). In contrast, another study demonstrated the role of SNHG14 in inhibiting glioma cell proliferation, invasion and promoting apoptosis. The role of SNHG14 in different cancers is still controversial (Wang et al., 2018). Therefore, this meta-analysis was undertaken to investigate the relationship between the expression of SNHG14 and clinicopathological features and patient prognosis. According to the results of the meta-analysis, high expression of the SNHG14 gene is associated with a poor prognosis. The overall results revealed that high SNHG14 expression is associated with poor tumor prognosis. Increased SNHG14 expression was observed to associate with tumor size (> 3.5 cm), TNM staging (II-III), lymph node metastasis, low differentiation grade, and distant metastasis but not with age and gender. Although data have revealed SNHG14 as an important prognostic factor for different types of tumors, the molecular mechanism behind how it affects cancer is unknown. SNHG14 enhances the progression of DLBCL by isolating miR-152-3p, preventing it from inhibiting the PD-1/PD-L1 checkpoint (Tian et al., 2021). The LncRNA SNHG14 is upregulated and activated with SP1 regulators in ccRCC cells. SNHG14 can promote renal cancer cell migration and invasion through sponge miR-203 and release N-WASP as ceRNA (Liu et al., 2017). Furthermore, the transfer of lncRNA SNHG14 mediated using exosomes induces breast cancer cell resistance to trastuzumab. Moreover, exosomal lncRNA SNHG14 within human serum is a potential breast cancer diagnostic biomarker enhancing the clinical benefit of trastuzumab therapy. To further explore the relationship between SNHG14 and other cancers, SNHG14 and its functions and related genes have been summarized in Table 5. The target genes of SNHG14 were predicted and functionally annotated. SNORD108, AC124312.1, and PWAR6 were significantly co-expressed with SNHG14. GO and KEGG analysis revealed that co-expressed genes were involved in essential cell signaling pathways and physiological processes. SNHG14 functioned as a competing endogenous RNA for microRNAs-382-5p (miR-382-5p) to regulate the SPIN1 expression in non-small cell lung cancer (Chen et al., 2020). Another finding by bioinformatics analysis is that SNHG14 expression could be associated with TMB in 14 cancer types and MSI in nine cancer types. Robert’s research showed that TMB is associated with improved survival in patients receiving immune checkpoint inhibitors (ICI) across various cancer types (Samstein et al., 2019). Another finding by bioinformatics analysis is that SNHG14 expression may be related to TMB in 14 cancer types and MSI in 9 cancer types. Dynamic features of the TME, tumor-infiltrating cells, and immune biomarkers are critical for immunotherapy response (Kaderbhaï et al., 2019). Our study suggests that SNHG14 may play a vital role in the recruitment and regulation of immune-infiltrating cells in cancer, ultimately affecting the prognosis of patients. This is the first study analyzing the relationship between the expression level of SNHG14 and the prognosis and clinical characteristics among cancer patients. Shen et al. (2021) previously described the expression profile, biological function, and molecular mechanism of SNHG14 in cancer, facilitating a molecular basis for future clinical application of SNHG14. We conducted a meta-analysis to evaluate the association between SNHG14 expression, OS, and the clinicopathological significance of different cancer types. This study included more original studies and detailed subgroup and sensitivity analyses compared to previous studies. There are several limitations to our research. First, the HR in the survival analysis was determined based on the KM curve from the literature, resulting in errors. Second, the patients included in the study were all China. Thus interpretation and application of the results require caution. Third, the individual differences of various cancer patients and their different lifestyles could also increase heterogeneity. In conclusion, this meta-analysis demonstrated that high expression of the SNHG14 gene is associated with poor prognosis of East Asian cancer patients, predominantly female reproductive system cancer. Furthermore, differentially expressed SNHG14 could be used as an oncogene or cancer suppressor to improve cancer prognosis and identify potential therapeutic targets. In addition, well-designed studies with a larger sample size in different countries worldwide are expected to confirm our findings.
“Clinicopathological significance and prognosis of long noncoding RNA SNHG14 expression in human cancers: A Meta-Analysis and bioinformatics analysis” (https://www.researchsquare.com/article/rs-1209386/v1) has been previously submitted in BMC Cancer. When we submitted it, the option to agree to publish a preprint was checked. However, the paper was rejected on 31 December 2021. According to the suggestions of reviewers, we have revised and removed several sections through discussion, which has been presented as the current version. | true | true | true |
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PMC9582274 | Lin Qiu,Rui Zhou,Ling Zhou,Shiping Yang,Jiangxue Wu | CAPRIN2 upregulation by LINC00941 promotes nasopharyngeal carcinoma ferroptosis resistance and metastatic colonization through HMGCR | 06-10-2022 | CAPRIN2,ferroptosis,metastasis,nasopharyngeal carcinoma,survival | Distant metastasis is the main cause of death in nasopharyngeal carcinoma (NPC) patients. There is an urgent need to reveal the underlying mechanism of NPC metastasis and identify novel therapeutic targets. The ferroptosis resistance and survival ability of extracellular matrix (ECM)-detached tumor cells are important factors in determining the success of distant metastasis. In this study, we found that CAPRIN2 contributes to the ferroptosis resistance and survival of ECM-detached NPC cells. Moreover, CAPRIN2 serves as a positive regulator of NPC cell migration and invasion. HMGCR, the key metabolic enzyme of the mevalonate pathway, was identified as the key downstream molecule of CAPRIN2, which mediates its regulation of ferroptosis, survival, migration and invasion of NPC cells. Lung colonization experiments showed that downregulation of the CAPRIN2/HMGCR axis resulted in reduced lung metastasis of NPC cells. Erastin treatment inhibited the ability of NPC cells to colonize the lungs, which was further enhanced by CAPRIN2/HMGCR axis downregulation. Regulated by upstream LINC00941, CAPRIN2 is abnormally activated in NPC, and its high expression is associated with a poor prognosis. In conclusion, CAPRIN2 is a molecular marker of a poor prognosis in NPC, and the LINC00941/CAPRIN2/HMGCR axis provides a new target for the treatment of NPC metastasis and ferroptosis resistance. | CAPRIN2 upregulation by LINC00941 promotes nasopharyngeal carcinoma ferroptosis resistance and metastatic colonization through HMGCR
Distant metastasis is the main cause of death in nasopharyngeal carcinoma (NPC) patients. There is an urgent need to reveal the underlying mechanism of NPC metastasis and identify novel therapeutic targets. The ferroptosis resistance and survival ability of extracellular matrix (ECM)-detached tumor cells are important factors in determining the success of distant metastasis. In this study, we found that CAPRIN2 contributes to the ferroptosis resistance and survival of ECM-detached NPC cells. Moreover, CAPRIN2 serves as a positive regulator of NPC cell migration and invasion. HMGCR, the key metabolic enzyme of the mevalonate pathway, was identified as the key downstream molecule of CAPRIN2, which mediates its regulation of ferroptosis, survival, migration and invasion of NPC cells. Lung colonization experiments showed that downregulation of the CAPRIN2/HMGCR axis resulted in reduced lung metastasis of NPC cells. Erastin treatment inhibited the ability of NPC cells to colonize the lungs, which was further enhanced by CAPRIN2/HMGCR axis downregulation. Regulated by upstream LINC00941, CAPRIN2 is abnormally activated in NPC, and its high expression is associated with a poor prognosis. In conclusion, CAPRIN2 is a molecular marker of a poor prognosis in NPC, and the LINC00941/CAPRIN2/HMGCR axis provides a new target for the treatment of NPC metastasis and ferroptosis resistance.
Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus (EBV)-associated tumor, and the main pathological type is undifferentiated carcinoma. It is characterized by high aggressiveness and metastatic potential (1–6). At the time of onset, 5% to 11% of patients have distal metastases (1). During the course of treatment, 50% to 60% of patients develop distal metastasis (1). Due to advances in radiotherapy techniques and increases in the accuracy of disease staging, the overall prognosis of NPC has improved significantly over the past three decades (1–5). However, distal metastasis is still the main cause of death in NPC patients (1–5). Therefore, there is an urgent need to explore the potential mechanism of NPC metastasis and identify specific biomarkers. Ferroptosis is a type of iron-dependent cell death characterized by lipid peroxidation mediated by reactive oxygen species (ROS) (7–18). Until now, the role of ferroptosis in the complex process of tumor cell metastasis has remained poorly understood. To successfully metastasize from the primary site to the distal organ, tumor cells must overcome several obstacles, including long-term survival after extracellular matrix (ECM) detachment and distal organ colonization (19–28). Only tumor cells that are resistant to ECM detachment-induced cell death and can adapt to the distal organ microenvironment are likely to survive and successfully colonize to form metastases (23–27). It is well known that ECM-detached tumor cells undergo anoikis, a type of caspase-dependent programmed cell death (19–21). However, as the understanding of the complex changes in cells induced by ECM detachment has deepened, studies have shown that resistance to anoikis alone is not sufficient to maintain long-term cell survival after ECM detachment, suggesting that other modes of death may be involved (19–22). In 2017, Brown et al. reported that ECM detachment is an important trigger factor for ferroptosis (29). ECM detachment results in a dramatic increase in ROS and leads to the ferroptosis of breast cancer cells (29). In addition, metastatic tumor cells in the lung are exposed to a high oxygen microenvironment. Only the cells that can successfully resist the oxidative damage and ferroptosis induced by high oxygen levels can colonize and form clones in the new microenvironment (30). To date, the role of ferroptosis in NPC metastasis has not been studied. The mechanism by which ECM-detached NPC cells resist ferroptosis to maintain survival remains unknown. Caprin family member 2 (CAPRIN2) is an RNA-binding protein (RBP) that functions in the central osmotic defense response and eye development (31–33). The function of CAPRIN2 in tumors is still poorly understood. Jia et al. identified gain-of-function CAPRIN2 mutations (R968H/S969C) in hepatoblastoma that promote the growth of hepatoblastoma cells (34). In addition, upregulation of CAPRIN2 was found to promote oral squamous cell carcinoma (OSCC) by activating the canonical WNT/β-catenin signaling pathway (35). Thus far, the role of CAPRIN2 in NPC remains unknown. Moreover, the functions of CAPRIN2 in tumor ferroptosis have not been reported. Here, we investigated the potential role of CAPRIN2 in NPC ferroptosis and metastasis. Our results indicated that CAPRIN2 acts as a protector against NPC cell ferroptosis. Moreover, the upregulation of CAPRIN2 promotes the survival, migration and invasion of NPC cells. The 3-hydroxy-3-methylglutaryl-CoA reductase (HMG-CoA reductase, HMGCR) functions as the key downstream molecule of CAPRIN2. CAPRIN2/HMGCR might be novel therapeutic targets for the development of treatments for NPC.
The NPC cell lines involved in the study included the EBV-negative cell lines 5-8F (poorly differentiated), 6-10B (poorly differentiated) and HK-1 (well differentiated); the EBV-positive cell line C666-1 (undifferentiated); and the immortalized normal nasopharyngeal epithelial cell line NP69. C666-1, HK-1 and NP69 cells were kindly provided by Dr. Saiwah Tsao (University of Hong Kong, Hong Kong, P.R. China), and the 5-8F and 6-10B cell lines were maintained by our laboratory. Cells were maintained in DMEM or RPMI-1640 medium supplemented with 10% fetal bovine serum, 100 units/mL penicillin, and 100 μg/mL streptomycin at 37°C in a 5% humidified CO2 atmosphere. The indicated cell lines were routinely detected and ensured to be mycoplasma-free using a PCR-based method.
The ferroptosis inducer and cell death inhibitor were all obtained from Selleck (Shanghai, China). The ferroptosis activator used was erastin. In order to facilitate understanding the effects of CAPRIN2 on ferroptosis, we chose the erastin doses with a growth inhibition rate of 30-40% of the control group. Otherwise, if the erastin dose is too low, the growth inhibition effect will be too weak to study the effect of CAPRIN2 on ferroptosis resistance. Similarly, it is not suitable to study the effect of knockdown of CAPRIN2 on ferroptosis if the erastin dose is too high. The ferroptosis inhibitor used was ferrostatin-1. Ferrostatin-1 is a lipophilic antioxidant that acts through a free radical trapping mechanism that can prevent the accumulation of lipid peroxidation induced by erastin, thereby inhibiting ferroptosis (9). MVA was obtained from Sigma (Shanghai, China). Primary antibodies against CAPRIN2 (NBP1-88318, Novus), HMGCR (sc-271595, Santa Cruz) and β-actin (66009-1-Ig, Proteintech, Wuhan, China) were commercially obtained.
The siRNAs applied in the study were all products of Santa Cruz (Shanghai, China) and are listed as follows: CAPRIN2 siRNA, HMGCR siRNA and negative control siRNA. The pcDNA3.1 vector carrying the cDNA sequence of CAPRIN2 or HMGCR was constructed by Generay Biotech (Shanghai, China). The cDNA sequence of CAPRIN2 or HMGCR was also subcloned into the lentivirus vector pHBLV-CMV-MCS-EF1-NEO. The obtained plasmids were named pHBLV-CAPRIN2 or pHBLV-HMGCR. Lipofectamine 2000 (Thermo Fisher, Shanghai, China) was used for transient transfection of the indicated siRNA or plasmid.
To construct stable cell lines with knockdown of CAPRIN2 or HMGCR, lentiviruses carrying CAPRIN2 shRNA, HMGCR shRNA or scramble shRNA were purchased from Santa Cruz (CA, USA) and used to infect the indicated NPC cell lines for 48 h. The sequence of human LINC00941 shRNA was 5′- GAGACAGTTGATAGCCAAA -3′ (36), and the constructs were cloned into pHBLV-U6-MCS-PGK-PURO, named pHBLV-shLINC00941. pHBLV-shLINC00941 was transfected into 293T cells along with the corresponding packaging vector PMD2.G and pSPAX2. Cell supernatants were harvested at 48 h after transfection and used to infect the indicated NPC cells. The stably infected cells above were selected with puromycin (2 µg/mL) for two weeks. To stably overexpress CAPRIN2 or HMGCR, the pHBLV-CAPRIN2 or pHBLV-HMGCR plasmid was transfected into 293T cells along with the corresponding packaging vector PMD2.G and pSPAX2, respectively. Cell supernatants were harvested at 48 h after transfection and were subsequently used to infect the indicated NPC cells. Stably infected cells were selected with G418 (0.5 mg/mL) for two weeks. For the above stable cell lines, the overexpression or knockdown efficiency of the indicated genes or lncRNA was validated by qRT-PCR and/or Western blot analysis.
Total RNA was extracted from NPC cell lines or tissues using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. A reverse transcriptase system (Promega, Madison, WI, USA) was applied to synthesize cDNA, and real-time PCR was performed using SYBR green master mix (Invitrogen, CA, USA). The relative expression of target genes was normalized to that of β-actin, and quantified by the 2-ΔΔCt method. All reactions were performed in triplicate in three independent experiments. The primers used for the amplification of the indicated genes or lncRNA are listed in Supplementary Table S1 .
Cells were collected and total protein was extracted in a lysis buffer containing protease inhibitors (Thermo Fisher, Shanghai, China). Western blot analysis was performed as previously described (37).
Cell viability was evaluated using the AlamarBlue Cell Viability Assay Kit (Thermo Fisher, IL, USA) according to the manufacturer’s instructions. The growth inhibition rate presented reflects the growth inhibitory effect of erastin treatment on cells compared to the cells without erastin treatment. The growth inhibitory rate is obtained as follows: growth inhibition rate (%)=100%-(viability of the indicated group with erastin treatment)/(viability of the control group without erastin treatment) × 100%. For the control group without erastin treatment, the inhibition rate is 0.
The Lipid Peroxidation (MDA) Assay Kit was purchased from Abcam (MA, USA). The MDA content was tested according to the manufacturer’s instructions.
The Reduced Glutathione Assay Reagent Kit purchased from Solarbio (Beijing, China) was used to measure the cellular GSH concentration.
The cells were inoculated on a 24-well ultralow attachment plate with the optimal cell density (500 cells/well for 5-8F; 1000 cells/well for C666-1), and serum-free DMEM/F-12 culture medium (20 ng/ml bFGF, 20 ng/ml EGF, and 20 ng/ml insulin) was used to study the survival of ECM-detached NPC cells. Fresh serum-free DMEM/F12 medium containing growth factors was supplemented every other day. The culture medium and reagents were products of Cell Signaling (Shanghai, China) and Thermo Fisher (IL, USA). After 72 h of culture, the viability of NPC cells was assessed by Alamar Blue assay.
Transwell assays were performed using 24-well Transwell chambers (8-μm pore; BD Falcon) to evaluate the migration or invasion properties of the indicated cells. For the 5-8F cells, 1.5×104 cells in serum-free 1640 medium were added into the upper sides of the insert membrane with or without Matrigel, while the bottom chamber was supplemented with 1640 medium containing 10% FBS. After 16 h of incubation, the NPC cells that did not migrate or invade the membrane were scraped off, and cells on the bottom of the membrane were fixed with crystal violet. For the C666-1 cell line, 5×104 cells suspended in 1640 medium containing 1% FBS were added to the upper sides of the inserts coated with 20 μg/mL fibronectin (Cell Signaling, Shanghai, China). Fibronectin was applied as a chemoattractant at a final concentration of 50 μg/mL. After 24 h, cells on the bottom of the membrane were stained. The cells in five independent fields were counted under a microscope at a magnification of 10×. The experiments above were performed in triplicate.
The animal experiments in the study were conducted in accordance with the NIH animal use guidelines and were approved by the Sun Yat-sen University Institutional Animal Care and Use Committee. Nude female BALB/c mice (4 weeks) were purchased from the SLACCAS Experimental Animals Co., Ltd. (Shanghai, China), and were maintained under specific pathogen-free conditions. For the lung metastasis model, mice were randomly assigned to four groups (n=6), and a total of 1×106 of the indicated cells in 100 μL PBS were injected into the tail vein of the mice. Three weeks after the injection, the mice were euthanized, and the lungs were harvested and stained with hematoxylin-eosin for pathologic analysis. Metastatic nodules were observed using a microscope. To further explore the role of the CAPRIN2/HMGCR axis in the lung colonization capacity of NPC cells treated with erastin, the mice were divided into four groups (n=6) and injected with 1×106 of the indicated stable cell lines into the tail veins. From day 0 of cell injection, the mice were intraperitoneally administered erastin (40 mg/kg) or vehicle as control twice every other day. After 21 days of cell injections, the mice were sacrificed and the lungs were harvested for histological analysis. The number of metastatic nodules was calculated under a microscope.
NPC tumor tissues were collected from 104 patients histologically diagnosed with NPC at Sun Yat-sen University Cancer Center (SYSUCC) between 2007 and 2012. The inflammatory nasopharynx tissues were collected via outpatient biopsy. The TMA was generated from formalin-fixed, paraffin-embedded NPC tissues. Informed consent forms were obtained, and the study was approved by the Institutional Research Ethics Committee of SYSUCC. No patient received treatment before biopsy. The CAPRIN2 protein level was assessed according to immunohistochemical staining intensity. Immunohistochemistry was performed as previously described with the appropriate modifications (38). A polyclonal anti-CAPRIN2 antibody was obtained from Novus (1:500, NBP1-88318). The degree of staining in the sections was observed and scored independently by two observers who were not informed of the clinical data of the patients evaluated the staining intensity. The expression intensity was classified as negative = 0, weak = 1, moderate = 2, or strong = 3. The final H score was calculated based on multiplying the intensity score by the percentage of the staining area. A receiver operating characteristic (ROC) curve analysis was applied to determine a cutoff value for CAPRIN2 low expression and high expression. The sensitivity and specificity for the H score was plotted, thus generating a ROC curve. The score that was closest to the point with both the maximum sensitivity and specificity was selected as the cut-off value.
All statistical analyses were completed with SPSS statistical software (version 17.0, SPSS Inc., Chicago, IL, USA). Student’s t-test was applied to determine the difference between two groups. Survival curves were plotted using the Kaplan-Meier method and compared using the log-rank test. A two-tailed chi-square test was used to analyze the correlation between CAPRIN2 expression and the clinicopathological characteristics. Univariate and multivariate survival analyses were conducted using Cox’s proportional hazard model. The relationship between two independent variables was evaluated by the Pearson correlation coefficients method. p < 0.05 was considered to indicate a statistically significant difference.
The ability to survive the stress of ECM detachment is one of the important factors to determine the successful metastasis of tumor cells. To evaluate the biological effects of CAPRIN2 on the ferroptosis resistance and survival of ECM-detached NPC cells both in vitro and in vivo, we constructed NPC cell lines with stable CAPRIN2 knockdown or overexpression. First, we determined the endogenous expression levels of CAPRIN2 in NPC cells by qRT-PCR and Western blotting. The results showed that CAPRIN2 was consistently highly expressed in all NPC cell lines assessed compared to the nasopharyngeal epithelial cell line NP69 ( Supplementary Figures S1A, B ). Then, we selected 5-8F (a poorly differentiated NPC cell line with high metastatic capacity) and C666-1 (an EBV-positive undifferentiated NPC cell line) cells to construct NPC cell lines with stable CAPRIN2 knockdown and overexpression, respectively ( Supplementary Figure S2 ). Next, stable NPC cell lines were cultured using ultralow attachment plates and treated with erastin to investigate the ferroptosis of ECM-detached NPC cells. Twenty-four hours later, the viability of cells was evaluated by Alamar Blue Assay. The results showed that erastin inhibited the growth of NPC cells, and this effect was attenuated by ferrostatin-1 (ferroptosis inhibitor) ( Figure 1A ). The stable knockdown of CAPRIN2 in NPC cells enhanced erastin induced ferroptosis, while the stable overexpression of CAPRIN2 promoted ferroptosis resistance in NPC cells ( Figure 1A ). The survival of cells under ECM-detached culture conditions was also evaluated by Alamar Blue Assay. As shown in Figure 1B , in 5-8F or C666-1 cells, knockdown CAPRIN2 inhibited the survival of ECM-detached cells, whereas overexpression of CAPRIN2 promoted cell survival.
Migration and invasion are also important factors affecting NPC cell metastasis. We examined the migration and invasion ability of NPC cells after knockdown or overexpression of CAPRIN2. The results of the Transwell assay indicated that downregulation of CAPRIN2 reduced the migration and invasion capability of 5-8F cells, while overexpression of CAPRIN2 significantly promoted cell migration and invasion ( Figure 1C ). Consistent results were also obtained in C666-1 cells ( Figure 1D ).
The main ferroptosis suppression systems include the cyst(e)ine/GSH/GPX4 axis, the NAD(P)H/FSP1/CoQ10 system and the GCH1/BH4/DHFR system (9). In the 5-8F cell line with stable CAPRIN2 knockdown, we evaluated key molecules involved in the above inhibition systems. CAPRIN2 significantly regulated HMGCR, and consistent regulation was also detected in C666-1 cells ( Supplementary Figure S3A ). However, no significant change in the expression level of the remaining ferroptosis regulatory molecules was detected ( Supplementary Figure S3A ). In addition, we examined key molecules related to iron metabolism, but no significant regulatory effect was found ( Supplementary Figure S3A ). HMGCR is a key rate-limiting enzyme in the mevalonate (MVA) pathway that catalyzes the conversion of HMG-CoA to MVA (39–41). Next, MVA can be further transformed into IPP and CoQ10, and these metabolites directly or indirectly promote cell ferroptosis resistance through the GSH/GPX4 axis and FSP1/CoQ10 axis (39–41). As shown, erastin inhibited the growth of NPC cells, and its inhibitory effect was attenuated by ferrostatin-1 ( Supplementary Figure S4A ). Knockdown of HMGCR alleviated ferroptosis resistance in ECM-detached NPC cells, while ectopic expression of HMGCR promoted ferroptosis resistance in cells ( Supplementary Figure S4A ). Moreover, the addition of MVA, whose production is catalyzed by HMGCR, reversed the regulatory effect of CAPRIN2 on NPC cell ferroptosis ( Supplementary Figure S3B ).
To investigate whether HMGCR was involved in the regulation of CAPRIN2 on NPC cell ferroptosis, we constructed CAPRIN2/HMGCR double stable NPC cell lines ( Supplementary Figures S5A, B ). The results indicated that stable overexpression of HMGCR partially reversed the regulatory effects of CAPRIN2 knockdown on ferroptosis resistance ( Figure 2A ). In the indicated erastin-treated cells, we evaluated the level of MDA, a lipid peroxidation product used as a ferroptosis marker. The results showed that HMGCR overexpression partially reversed the increase in MDA levels resulting from CAPRIN2 knockdown ( Figure 2B ). Erastin treatment inhibits cysteine uptake, resulting in decreased GSH synthesis in cells. We measured GSH levels in the indicated NPC stable cell lines after erastin administration. We found that knockdown of CAPRIN2 further enhanced erastin-induced GSH reduction in 5-8F cells, while overexpression of HMGCR partially reversed this effect ( Figure 2C ). Consistent results were also obtained in C666-1 cells ( Figure 2C ). In addition, we investigated the regulatory effect of the CAPRIN2/HMGCR axis on the survival of ECM-detached NPC cells. Knockdown of HMGCR decreased the survival of ECM-detached NPC cells, while ectopic expression of HMGCR promoted survival ( Supplementary Figure S4B ). As shown in Figure 2D , overexpression of HMGCR partially reversed the inhibitory effects of CAPRIN2 knockdown on the survival of ECM-detached cells.
Knockdown of HMGCR inhibited the migration and invasion of NPC cells, while overexpression of HMGCR promoted NPC cell migration and invasion ( Supplementary Figures S6A, B ). Moreover, the inhibition of migration or invasion caused by CAPRIN2 knockdown was partially reversed by HMGCR overexpression in 5-8F stable cell lines ( Figure 2E ). Similar results were obtained in C666-1 stable cell lines ( Figure 2F ).
We first examined the effect of erastin, a ferroptosis inducer, on the lung colonization of NPC cells. The results showed that erastin significantly inhibited the lung metastasis of NPC cells ( Figures 3A, B ). Knockdown of CAPRIN2 promoted the antimetastatic effect of erastin, which was partially reversed by overexpression of HMGCR ( Figures 3A, B ). ECM detachment alone is enough to be an important trigger for ferroptosis. Tumor cells detached from primary foci must survive ECM detachment stress in blood vessels to reach distal organs and eventually form metastases. Our results showed that knockdown of CAPRIN2 significantly reduced the lung metastasis ability of NPC cells injected through the tail vein, while overexpression of HMGCR partially reversed this effect ( Figures 3C, D ).
It has been reported that CAPRIN2 is activated by LINC00941 through DNA looping in OSCC, which is involved in promoting cell proliferation and tumor formation (35). At present, it is not clear whether CAPRIN2 is also regulated by LINC00941 in NPC and whether the LINC00941/CAPRIN2 axis is involved in regulating ferroptosis and metastasis of tumor cells. To investigate whether LINC00941 is the upstream regulator of CAPRIN2 in NPC, LINC00941 was stably knocked down in 5-8F or C666-1 cells ( Supplementary Figures S5C, D ). As shown, downregulation of LINC00941 led to a decrease in CAPRIN2 and HMGCR expression levels in NPC cells ( Supplementary Figures S5C, D ). Moreover, knockdown of LINC00941 weakened the ferroptosis resistance and survival of ECM-detached NPC cells, while overexpression of CAPRIN2 partially rescued the effects of LINC00941 ( Figures 4A, B ). Downregulation of LINC00941 led to a decrease in the migration and invasion capability of NPC cells, which could be partially reversed by CAPRIN2 overexpression ( Figures 4C, D ).
The expression level of CAPRIN2 was detected in NPC tissues and nasopharynx tissues, and the results showed that CAPRIN2 was highly expressed in NPC tissues ( Figure 5A ). We also examined the pairwise correlations among the expression levels of LINC00941, CAPRIN2, and HMGCR in the above NPC tissues by qRT-PCR. The results indicated that positive correlations between LINC00941 and CAPRIN2, CAPRIN2 and HMGCR, LINC00941 and HMGCR were detected in the above NPC tissues ( Figure 5B ). To further assess the clinical significance of CAPRIN2 expression in NPC patients, we performed immunohistochemistry and Kaplan-Meier analysis. The results showed that CAPRIN2 was overexpressed in NPC tissues, and high expression of CAPRIN2 indicated a shorter progression-free survival (PFS) and overall survival (OS) time than low expression of CAPRIN2 ( Figures 5C, D ). In addition, we also analyzed the association between CAPRIN2 expression and clinical characteristics. The results revealed that there were significant correlations between CAPRIN2 expression and clinicopathologic characteristics, including tumor-node-metastasis (TNM) stage, tumor invasion depth, node metastasis, and distant metastasis ( Table 1 ). As shown in Table 2 , multivariate Cox proportional hazards regression analysis indicated that CAPRIN2 expression acted as an independent prognostic factor for OS in NPC patients.
Distant metastasis requires the adaptation of tumor cells to the new microenvironment. To successfully form a lung metastatic lesion, ECM-detached tumor cells need to survive in the harsh oxidizing condition of the blood and then adapt to the high oxygen tension in the pulmonary microenvironment (30, 42). To date, little is known about the mechanisms that protect ECM-detached tumor cells from ferroptosis and thus survive. Brown et al. reported that α6β4 integrin promotes resistance to ECM detachment induced ferroptosis (29). Our study showed that CAPRIN2 can inhibit ferroptosis of ECM detached tumor cells and promote cell survival. This is the first time that CAPRIN2 has been reported to play a role in promoting tumor metastasis at the stage of ECM detachment. The high-oxygen lung environment also induces ferroptosis in tumor cells, which is a hindering factor for the formation of lung metastases (30, 42). Alvarez et al. reported that high expression of cysteine desulfurase NFS1 in lung adenocarcinoma protects against oxidative damage in high-oxygen environments (30). Knocking down NFS1 sensitizes cells to glutathione biosynthesis inhibition, which increases ROS and induces tumor cell ferroptosis (30). In our study, the results revealed CAPRIN2 contributes to ferroptosis resistance and lung metastasis loci establishment in NPC cells. In addition, we also found that CAPRIN2 promotes NPC cell migration and invasion. This result is consistent with Zheng et al’s report in 2021 that CAPRIN2 can promote the migration and invasion of colorectal cancer cells (43). In conclusion, we believe that CAPRIN2 can be used as a ferroptosis resistance marker and therapeutic target in NPC. Selective inhibition of CAPRIN2 may sensitize NPC cells to oxidative stress and inhibit lung metastasis. In our research on the effects of ECM stiffness on ROS levels, metastasis and ferroptosis of NPC cells (unpublished data), we found that the expression level of CAPRIN2 was upregulated along with increasing ECM stiffness. Whether CAPRIN2 is involved in mediating the effects of ECM stiffness on NPC cell metastasis and ferroptosis remains unknown. In this study, our report on the biological function of CAPRIN2 may provide clues to understand the mechanism by which ECM stiffness affects NPC cell function. Cells maintained ECM-detached when cultured with ultralow attachment plates, and ECM-attached when cultured with standard cell culture plates. In our preliminary experiments, we also evaluated the effects of CAPRIN2 on proliferation and ferroptosis of NPC cells under ECM-attached conditions. The results showed that knockdown of CAPRIN2 inhibited the proliferation of 5-8F cells at 72h in viability assays and promoted erastin-induced ferroptosis (data not shown), which suggested that CAPRIN2 might also be involved in the malignant phenotype of NPC cells under ECM-attached conditions. In the process of searching for the mechanism how CAPRIN2 regulates the antioxidant defense molecules involved in cellular ferroptosis resistance, we focused on three antioxidant axes, which mainly involved in regulating cell ROS level and mediating ferroptosis resistance (9). Related core molecules that associated with these regulatory axes were selected for evaluation. For the GSH/GPX4 axis, SLC7A11 (one subunit of the anionic amino acid transport system that is highly specific for cysteine and glutamate), GPX4 (antioxidant selenium enzyme), HMGCR (the key rate-limiting enzyme of MVA pathway) and GCLC (the first rate-limiting enzyme of glutathione synthesis) were evaluated (9). For the FSP1/CoQ10 axis, we examined the level of FSP1, which acts as an independent parallel system to protect cells from ferroptosis (9). For the GCH1/DHFR axis, GCH1 (the rate-limiting enzyme for BH4 synthesis) and dihydrofolate reductase DHFR were detected (9). Addtionally, we also evaluated several key molecules involving in various stages of iron metabolism, including Fe transport (TFRC), Fe storage (FTH1、FTL) and ferritinophagy (NCOA4) (9). The results showed that HMGCR, which mediates ferroptosis resistance through MVA pathway, was activated by CAPRIN2. Therefore, our study uncovered one of the mechanisms by which CAPRIN2 activates the cellular antioxidant defense system in NPC cells. In addition to being an RNA-binding protein, Caprin2 can also bind to Wnt receptor LRP5/6. The Wnt pathway is one of the carcinogenic pathways that are abnormally activated in NPC. Aberrant activation of this pathway is associated with the promoter methylation of Wnt inhibitors (DKK1, WIF1, SFRP1, SFRP2, SFRP4, and SFRP5) (4). As an LRP5/6-binding protein, Caprin2 is reported to activate the canonical Wnt pathway by regulating LRP5/6 phosphorylation (44). Therefore, the high level of CAPRIN2 may also be involved in the activation of the Wnt pathway in NPC. It has been reported that the Wnt pathway can act as an activator of the MVA pathway (39). Therefore, the positive regulation of HMGCR by CAPRIN2 found in this study might be mediated by the Wnt pathway. It has been reported that products of the MVA pathway can also act as activators to activate the Wnt pathway (41). Therefore, there may be positive feedback regulation between MVA pathway molecules and Wnt pathway molecules. In summary, we found that CAPRIN2 is a novel regulator of ferroptosis and metastasis in NPC and plays a role through HMGCR, a key enzyme in the MVA pathway. Our study is expected to provide a new marker of ferroptosis resistance and a new therapeutic target for NPC.
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
The studies involving human participants were reviewed and approved by SYSUCC Institutional Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by Sun Yat-sen University Institutional Animal Care and Use Committee.
JW contributed to the concept and design of the study. LQ, RZ, LZ, and SY performed the experiments. LQ, SY, and JW contributed to data analysis and interpretation. JW wrote the manuscript. All authors contributed to the article and approved the submitted version.
This work was supported by the National Natural Science Foundation of China (No. 81772885).
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. | true | true | true |
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PMC9582353 | Xiaochen Bai,Jinghui Wang,Xiaoshuang Zhang,Yilin Tang,Yongtao He,Jiayin Zhao,Linlin Han,Rong Fang,Zhaolin Liu,Hongtian Dong,Qing Li,Jingyu Ge,Yuanyuan Ma,Mei Yu,Ruilin Sun,Jian Wang,Jian Fei,Fang Huang | Deficiency of miR-29a/b1 leads to premature aging and dopaminergic neuroprotection in mice | 06-10-2022 | Parkinson’s disease,miR-29a/b1,glial cells,neuroinflammation,AMPK | Parkinson’s disease (PD) is a neurodegenerative disorder characterized by progressive degeneration of midbrain dopaminergic neurons. The miR-29s family, including miR-29a and miR-29b1 as well as miR-29b2 and miR-29c, are implicated in aging, metabolism, neuronal survival, and neurological disorders. In this study, the roles of miR-29a/b1 in aging and PD were investigated. miR-29a/b1 knockout mice (named as 29a KO hereafter) and their wild-type (WT) controls were used to analyze aging-related phenotypes. After challenged with the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), dopaminergic injuries, glial activation, and mouse behaviors were evaluated. Primary glial cells were further cultured to explore the underlying mechanisms. Additionally, the levels of miR-29s in the cerebrospinal fluid (CSF) of PD patients (n = 18) and healthy subjects (n = 17) were quantified. 29a KO mice showed dramatic weight loss, kyphosis, and along with increased and deepened wrinkles in skins, when compared with WT mice. Moreover, both abdominal and brown adipose tissues reduced in 29a KO mice, compared to their WT counterpart. However, in MPTP-induced PD mouse model, the deficiency of miR-29a/b1 led to less severe damages of dopaminergic system and mitigated glial activation in the nigrostriatal pathway, and subsequently alleviated the motor impairments in 3-month-old mice. Eight-month-old mutant mice maintained such a resistance to MPTP intoxication. Mechanistically, the deficiency of miR-29a/b-1 promoted the expression of neurotrophic factors in 1-Methyl-4-phenylpyridinium (MPP+)-treated primary mixed glia and primary astrocytes. In lipopolysaccharide (LPS)-treated primary microglia, knockout of miR-29a/b-1 inhibited the expression of inflammatory factors, and promoted the expression of anti-inflammatory factors and neurotrophic factors. Knockout of miR-29a/b1 increased the activity of AMP-activated protein kinase (AMPK) and repressed NF-κB/p65 signaling in glial cells. Moreover, we found miR-29a level was increased in the CSF of patients with PD. Our results suggest that 29a KO mice display the peripheral premature senility. The combined effects of less activated glial cells might contribute to the mitigated inflammatory responses and elicit resistance to MPTP intoxication in miR-29a/b1 KO mice. | Deficiency of miR-29a/b1 leads to premature aging and dopaminergic neuroprotection in mice
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by progressive degeneration of midbrain dopaminergic neurons. The miR-29s family, including miR-29a and miR-29b1 as well as miR-29b2 and miR-29c, are implicated in aging, metabolism, neuronal survival, and neurological disorders. In this study, the roles of miR-29a/b1 in aging and PD were investigated. miR-29a/b1 knockout mice (named as 29a KO hereafter) and their wild-type (WT) controls were used to analyze aging-related phenotypes. After challenged with the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), dopaminergic injuries, glial activation, and mouse behaviors were evaluated. Primary glial cells were further cultured to explore the underlying mechanisms. Additionally, the levels of miR-29s in the cerebrospinal fluid (CSF) of PD patients (n = 18) and healthy subjects (n = 17) were quantified. 29a KO mice showed dramatic weight loss, kyphosis, and along with increased and deepened wrinkles in skins, when compared with WT mice. Moreover, both abdominal and brown adipose tissues reduced in 29a KO mice, compared to their WT counterpart. However, in MPTP-induced PD mouse model, the deficiency of miR-29a/b1 led to less severe damages of dopaminergic system and mitigated glial activation in the nigrostriatal pathway, and subsequently alleviated the motor impairments in 3-month-old mice. Eight-month-old mutant mice maintained such a resistance to MPTP intoxication. Mechanistically, the deficiency of miR-29a/b-1 promoted the expression of neurotrophic factors in 1-Methyl-4-phenylpyridinium (MPP+)-treated primary mixed glia and primary astrocytes. In lipopolysaccharide (LPS)-treated primary microglia, knockout of miR-29a/b-1 inhibited the expression of inflammatory factors, and promoted the expression of anti-inflammatory factors and neurotrophic factors. Knockout of miR-29a/b1 increased the activity of AMP-activated protein kinase (AMPK) and repressed NF-κB/p65 signaling in glial cells. Moreover, we found miR-29a level was increased in the CSF of patients with PD. Our results suggest that 29a KO mice display the peripheral premature senility. The combined effects of less activated glial cells might contribute to the mitigated inflammatory responses and elicit resistance to MPTP intoxication in miR-29a/b1 KO mice.
Parkinson’s disease (PD), characterized by progressive degeneration of midbrain dopaminergic neurons (Kikuchi et al., 2017), ranks second among the most common neurodegenerative disorders (Kam et al., 2018; Surguchov, 2022). Clinical motor symptoms are triggered by progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and consequently malnourished projection in the striatum (Kam et al., 2018). Also, increasing evidence indicates the role of gliosis and inflammatory response mechanisms followed by dopamine neuronal loss in the pathogenesis of PD (Martinez and Peplow, 2017). miRNAs, small non-coding RNA molecules with only about 21 nucleotides in length, emerged as ideal powerful candidates for genetic programing (Kosik, 2006). They exert complex effects on target gene expression post-transcriptionally by degrading mRNA or repressing translation through targeting 3′ untranslated regions (UTR) of mRNA (Selbach et al., 2008). A diversity of miRNAs is exclusively abundant in the nervous system, where they could contribute to neuronal apoptosis, axonal path finding, neural plasticity, and particularly the development of neurological diseases (Kosik, 2006; Hebert et al., 2008; Roshan et al., 2014). Accumulated studies have pointed miRNAs dysfunction, including miR-29 family, exists in the pathology of neurodegenerative diseases (Kosik, 2006; Hebert et al., 2008; Roshan et al., 2014; Papadopoulou et al., 2015). miR-29 family (miR-29s) consists of four members (miR-29a, miR-29b1, miR-29b2, and miR-29c), of which miR-29b1 and miR-29b2 share identical mature sequence (Roshan et al., 2014; Papadopoulou et al., 2015). All the members have highly conserved mature sequences and identical seed sequences, and miR-29a/b1 and miR-29b2/c are encoded by two genomic clusters on different chromosomes (Roshan et al., 2014; Papadopoulou et al., 2015). miR-29s, highly expressed in the brain, are implicated in aging, metabolism, neuronal survival, and neurological disorders (Ugalde et al., 2011; Papadopoulou et al., 2015; Caravia et al., 2018). Down-regulation of miR-29a/b1 was reported in neurodegenerative disorders, like Alzheimer’s disease (Hebert et al., 2008) and Huntington’s disease (Roshan et al., 2014). Simultaneously, our previous study revealed that miR-29s in the blood serum of patients with PD were significantly downregulated (Bai et al., 2017), but the mechanisms of miR-29s perturbations on PD progression are not clear. In this study, by using miR-29a/b1 knockout (miR-29a KO) mice, changes in periphery were investigated. Further a subacute regimen of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) was applied to generate PD model. Damages of the nigrostriatal pathway, mouse behaviors and the underlying mechanisms were comprehensively assessed. Additionally, miR-29a levels were evaluated in the cerebrospinal fluid (CSF) of PD patients. miR-29a KO mice showed obvious premature aging, implied by weight loss, fat decreasing, kyphosis, muscle weakness, gait disorder, and wrinkle increasing and deepening. However, deficiency of miR-29a/b1 brought about mitigation of dopaminergic injury and glial activation, and consequently the alleviated behavioral impairments.
The sporadic PD patients refrained from taking any anti-Parkinsonian medications and fasted for at least 12 h before CSF samples were taken. Control subjects fasted for 12 h before CSF samples were taken. The CSF was collected by standardized lumbar puncture procedures. Shipment and storage were performed according to the protocols from Parkinson Progression Marker Initiative (PPMI). The CSF were aliquoted (200 μl/tube), flash frozen, and stored at –80°C. All participants provided written informed consent in accordance with the Declaration of Helsinki. This study was approved by the Human Studies Institutional Review Board, Huashan Hospital, Fudan University. All methods were performed in accordance with the relevant guidelines and regulations.
miR-29a/b1 knockout mice (Liao et al., 2019) and their wild-type (WT) littermates (Shanghai Research Center for Model Organisms, China) were housed in a room with constant temperature (20–22°C) and light/dark cycle (12 h), and had free access to food and water. All experimental procedures were performed with the permission of the Institutional Animal Care and Use Committee of Fudan University, Shanghai Medical College. All surgeries were conducted under general anesthesia, and all efforts were made to minimize adverse effects and the number of animals used. Using a subacute dosing regimen of MPTP (20 mg/kg in normal saline, Sigma, USA), mice were intraperitoneally treated with MPTP-HCl (Sigma, USA) in 0.9% NaCl or normal saline (NS) as control for five consecutive days at 24 h intervals as described (Selvaraj et al., 2012).
Total RNA from the tissue was extracted using TRIzol reagent (TIANGEN, China) following the manufacturer’s protocol. Reverse transcription was performed using random primers and the primers used in the qPCR are listed in Table 1. Relative expression levels were calculated using the comparative ΔΔCt method with β-actin as the normalizing control.
Total RNA including miRNA from the CSF was extracted using miRNeasy Serum/Plasma Kit (Qiagen, Germany) following the manufacturer’s protocol. Then, 5 μl of total RNA was reverse transcribed using a miRcute miRNA First-Strand cDNA Synthesis Kit (Tiangen, China). Subsequently, 2 μl of the product was used to detect miR29s expression by quantitative real-time PCR (qPCR) using a miRcute miRNA qPCR Detection kit (Tiangen, China). The PCR primer sequences were as follows: miR-29a (5′-TAGCACCATCTGAAATCGG-3′); miR-29b (5′-TAGCACCATTTGAAATCAGT-3′); miR-29c (5′-TAGCACCATTTGAAATCGG-3′). Relative expression levels were calculated using the comparative ΔΔCt method with cel-miR-39 as the normalizing control.
Mouse brain tissues or cell pellets were lysed in protein extraction reagents supplemented with a protease inhibitor cocktail. 30 μg of protein samples were separated by sodium dodecyl sulfate-polyacrylamide gels and then transferred onto polyvinylidene difluoride membranes (Millipore, USA) as described previously (Wang et al., 2020). The primary and secondary antibodies used were listed in Supplementary Table 1. The protein bands were detected with an Odyssey infrared imaging system (Li-Cor, USA). The relative expression levels of protein were quantified by densitometry analysis using Quantity One 4.5.2 software (Bio-Rad, Hercules, CA, USA). All the original images of Western blot were shown in the end of Supplementary Information section.
After anesthesia, mice were transcardially perfused with cold NS solution. Brains were harvested and post-fixed with 4% paraformaldehyde at 4°C overnight, and subsequently immersed in 20 and 30% sucrose solution at 4°C overnight. Embedded in the OCT compound, brains were cut into 30 μm thick coronal sections using a freezing microtome (Leica, Germany) and then stored in a cryoprotectant solution at 20°C. The primary and secondary antibodies used were listed in Supplementary Table 1. For immunohistochemical staining, mouse brain sections were permeabilized, quenched the endogenous peroxidases with 0.3% H2O2 and blocked in phosphate-buffered saline with 0.2% Triton X-100 (PBS-T) containing 10% goat serum and then incubated at 37°C for 1 h. Then the sections were incubated with primary antibodies in PBS with 1% goat serum at 4°C overnight. After washing, the sections were incubated with biotinylated secondary antibodies at 37°C for 45 min and then with AB peroxidase (1:200; Vector Laboratories, USA) at 37°C for 45 min. The peroxidase reaction was detected with DAB Peroxidase (HRP) Substrate Kit (Vector Laboratories, USA). For immunofluorescence staining, brain sections were blocked in PBS-T containing 10% goat serum and then incubated at 4°C overnight with the primary antibodies. After washing, the sections were incubated for 2 h at room temperature with secondary antibodies. All sections were counterstained with 4’,6-diamidino-2-phenylindole (DAPI) (400 ng/ml, Beyotime, China) for 5 min. Images were collected using an Olympus FV1000 confocal microscope (Japan).
The total numbers of tyrosine hydroxylase-positive (TH+) neurons in the SNpc were counted using the optical fractionator method on a Stereo Investigator system (Micro Brightfield, USA) attached to an (Olympus, Japan) as previously described (Liberatore et al., 1999). Briefly, one out of four 30 μm-thick sections and a total of six sections from bregma –2.80 to –3.65 mm were collected. The SN region was delineated using a 5× objective, and the actual counting was performed under a 40× objective. Stereological counting was performed in a double-blind fashion by two operators.
To measure the number of ionized calcium binding adapter molecule 1-positive (Iba1+) cells, and glial fibrillary acidic protein-positive (GFAP+) cells, we performed cell counting according to the published method and analyzed with Image-Pro Plus 6.0 (Media Cybernetics, USA) (Baiguera et al., 2012). Briefly, in both the SN and the dorsal striatum, two square 300 μm × 300 μm frames were placed and cell somata within the confines of the frames were counted. The positive cell numbers in the frames in each brain section divided by the volume of the region produced the cell density. Measurements from six sections were averaged to obtain one value per mouse. The observer blinded to experimental groups performed the analysis.
Densitometric analysis of TH-positive fibers in the striatum was performed as previously described (Huang et al., 2018). An average of six sections from bregma +1.60 to 0.00 mm were examined at 5× magnification with a light microscope (Leica, Germany). To determine the density of TH-immunoreactive staining in the striatum, a 700 μm × 700 μm frame was placed in the dorsal part of the striatum. Another 200 μm × 200 μm frame was placed in the corpus callosum to measure background values. The average of the background density readings from the corpus callosum was subtracted from the average of the density readings in the striatum for each section. Then, the average of all sections from each animal was calculated.
The striatum was dissected from the brain tissue, then weighed and sonicated in 0.4 M HClO4 on ice. The homogenate was centrifuged at 15,294 g at 4°C for 15 min. The supernatant was removed for determining the concentration of monoamines and their metabolite using the chromatograph (ESA, Chelmsford, MA, USA) with a 5014B electrochemical detector.
One day before the test, mice were given a training session the same as the test mode (4–40 rpm constant accelerating mode for 5 min) on the rotarod (MED Associates, USA) three times separated by 1 h intervals. All animals learned to perform. On the testing day, the time on the rod, with a maximum recording time of 300 s, was recorded according to the references (Hinkle et al., 2012; Liu et al., 2015). Data were collected from three trials separated by 1 h intervals. Then, the average time on the rod of all trials from each animal was calculated.
A 50 cm wide 2-mm thick metallic wire is secured to two vertical stands and the wire is maintained 35 cm above a layer of bedding material. One day before the test, mice were pre-trained three times separated by 30 min intervals. On the testing day, mice suspended by their forelimbs from the wire and subjected to a 180 s lasting hanging test. The suspended mice tended to support themselves with their hind paws to avoid falling and to walk along the wire to reach the platform. The number of falls (up to a maximum of 10) and reaches (up to a maximum of 10) during a period of 180 s were recorded. The test was carried out three times with 30 min intervals. An aggregate score from the number of falls and reaches was derived using the formula: (10-falls + reaches) (van Putten et al., 2011).
Mice were placed on a grid where it stood using all four limbs. Subsequently, the grid was turned upside down 35 cm above the home cage filled with bedding. One day before the test, mice were given a training session three times at 10 min intervals. All mice learned to perform. On the testing day, the trial ended after a hanging time of 3 min was achieved. The latency to when the animal falls is recorded. The test was carried out three times with 10 min intervals, the final results were an average of the three trials as previously described (van Putten et al., 2012).
One day before the test, mice received a training session consisted of three trials to habituate to the CatWalk XT gait analysis system (Noldus, Netherlands). Rest between trials was approximately 1 h. Each animal was allowed an uninterrupted crossing of the recording field of the runway (length of approximately 40 cm) in both directions with three independent attempts at a 60% variation threshold. A high-speed camera carried out data acquisition and the software automatically classified the paw prints. Overall, runs for analysis were selected based on a minimum of five step cycles in the crossing field (Chen and Wu, 2022). After classification of the footprints in the CatWalk software, data were exported for external analysis using the Prism 7 software (GraphPad Software Inc., San Diego, CA, USA).
To assess the motor behavior, mice were placed individually in 400 ml glass beaker and the number of rearing events were recorded for 3 min. The beaker was cleaned with 75% ethanol between each animal.
Primary glial cells were prepared from miR-29s deficient mice and their WT littermates at P1–P3, as described previously (Shao et al., 2013). The brains were dissected and meninges were removed in D-Hanks’ solution rapidly. Then brains were thoroughly snipped and trypsinized (0.25% trypsin) at 37°C for 5 min followed by termination with dulbecco’s modified eagle medium (DMEM) medium containing 10% fetal bovine serum (FBS). The cells were plated in 75 cm2 flask in DMEM medium with 10% FBS. Culture media were changed 48 h later to complete medium and subsequently twice a week. After 2 weeks, astrocytes were separated from microglia by shaking at 200 rpm for 12 h. Before experimental treatments, astrocytic cultures were plated in a six-well-plate at a density of 1 × 106/well. The preliminary culture of microglia cells was the same as that of primary astrocytes, but the purification of microglia cells were prepared as described previously (Saura et al., 2003). Briefly, at day 21 in vitro, cultures were mildly trypsinized (0.0625% trypsin in D-Hanks’ solution) at 37°C for 30–40 min. Floating cells (mainly astrocytes) were removed. Microglia cells were cultured with supernatant of mixed glia cells. Before experimental treatments, microglia were plated in a twenty-four well-plate at a density of 3 × 105/well.
ProcartaPlex kit (Thermo Fisher, USA) was used to measure the concentrations of cytokines/chemokines in the culture supernatants of primary astrocytes and microglia and homogenates of mouse striatum according to the manufacturer’s instructions. Briefly, 50 μl cell culture supernatants or 25 μl tissue homogenates were added into each filter plate well containing conjugated antibody beads, then the plate was incubated at room temperature for 30 min at 500 rpm and 4°C overnight. After washing, the beads were incubated with detection antibody and subsequently SA-PE at 500 rpm for 30 min, respectively, at room temperature. The beads were re-suspended in reading buffer and then the signals were detected by Luminex 200 (Luminex, USA).
A whole-body micro-computed tomography (microCT) scan was performed to visualize adipose tissue and skeleton of mice. The detailed three-dimensional images of the structure of mice were obtained by high resolution X-ray microCT scanning (Quantum FX; PerkinElmer, USA) (Liu et al., 2018). Mice were anesthetized with 0.8% pentobarbital sodium (the volume was 10 times of their body weight), and put on the platform. The current and voltage were set to 74 μA and 70 kVp. It took about 4 min per mouse. Image segmentation was conducted using a volume-editing tool, and volumes were quantified using the region of interest module within the software package (AnalyzeDirect, USA).
Data are presented as the means ± SEM. Statistical analyses were performed using Prism 7 software (GraphPad Software Inc., USA). Statistical significance was determined using two-tailed unpaired Student’s T-test for comparisons between two groups or Two-way analysis of variance (ANOVA) followed by least significant difference (LSD) for comparisons among more groups. P < 0.05 was considered statistically significant.
miR-29a/b1 knockout mice (29a KO) were constructed by the method of CRISPR-Cas9 (Liao et al., 2019). The strategy and the results of genotyping of mutant mice were shown in Supplementary Figure 1. At 3 and 6 months old, the body weights of 29a KO mice were reduced significantly compared to their wild type counterpart (Figure 1A). Six-month-old 29a KO mice developed apparent dermis thickening, along with increased and deepened wrinkles shown by hematoxylin and eosin (H&E) staining (Figure 1B). Mouse bone and fat tissues were analyzed by X-Ray micro-computed tomography (microCT) scan. At 3 months old, 29a KO mice displayed obvious kyphosis (Figure 1C). Abdominal fat (subcutaneous fat and visceral fat together) and brown fat decreased in 3-month-old 29a KO mice compared to their WT littermate (Figures 1D,E). In brain, the transcripts of aging marker p21, but not p53, increased in the hippocampus of 29a KO mice at 6 months old. The expression levels of p21 and p53 did not alter in the cortex of 29a KO and WT mice. In addition, p53 and p16 proteins in the hippocampus of 29a KO mice showed no difference compared to their WT controls (Supplementary Figure 2).
Next, we evaluated whether deficiency of miR-29a/b1 led to behavioral changes. Wire hanging test and Grid hanging test were performed to measure the muscle strength. 29a KO mice gained lower scores in the Wire hanging test, indicated reduced forelimb strength (Figure 2A). In Grid hanging test, mutant mice showed shorter latency before falling compared to their WT counterparts (Figure 2B). However, there was no difference between WT and 29a KO mice in the Rotarod test (Figure 2C). Mouse gait was assessed by Catwalk XT gait analysis system. The speed and stride length of 29a KO and WT mice were close, however, the step cycle, stand and swing time were shorter and the duty cycle was significantly decreased, in mutant mice (Figure 2D).
A close association of miR-29s and PD has been revealed in our previous studies (Bai et al., 2017, 2021; Han et al., 2020). To address whether deficiency of miR-29a/b1 affected the progression of PD in vivo. Three-month-old miR-29a/b1 knockout mice and their WT littermates received five consecutive intraperitoneal injections of MPTP or NS at 24 h intervals as shown in the experimental schedule diagram (Figure 3A). Deficiency of miR-29a/b1 had no effect on the metabolic rate of MPTP indicated by the concentration of 1-Methyl-4-phenylpyridinium (MPP+) in the striatum 90 min after MPTP exposure (Supplementary Figure 3). 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine did not alter the striatal expression of aging marker genes p21, p53, and Pai 1 in the two genotypes of mice (Supplementary Figure 4). In MPTP-challenged mouse nigrostriatal pathway, TH+ dopaminergic neurons in the SNpc, TH+ nerve fiber density and TH protein levels in the striatum all decreased dramatically (Figures 3B–D), and consequently, striatal dopamine (DA) and its metabolite 3,4-dihydroxyphenylacetic acid (DOPAC) and homovanillic acid (HVA) were reduced (Figure 3E). However, MPTP-induced damages of the nigrostriatal pathways in 29a KO were markedly mitigated indicated by less severe loss of dopaminergic neurons in the SNpc and dopaminergic nerve terminals in the striatum, higher striatal TH protein levels and DA concentrations, and reduced changes in the ratios of DOPAC to DA and HVA to DA (Figure 3E). Notably, under physiological conditions, DOPAC itself and HVA to DA ratio were lower, NE level was higher in 29a KO mice compared to their WT counterpart. Moreover, 5-HT and its metabolite 5-hydroxyindoleacetic acid (5-HIAA) did not differ between the two genotypes of mice (Figure 3E). To know if the expression of miR-29s in mouse brain responses to the challenge of a subacute regimen of MPTP, miR-29s levels in the striatum, ventral midbrain and hippocampus were detected by qPCR. We found none of the levels of miR-29a, miR-29b, or miR-29c changed (Supplementary Figure 5).
The effects of miR-29a/b1 deficiency on MPTP-induced behavioral impairment were further investigated. Rearing behavior test, a measurement of spontaneous vertical activity (Willard et al., 2015; Chinta et al., 2018), was performed for 3 min at 48 h after the last MPTP injection. In the last 2 min, a relatively stable period, rearing frequency of WT mice but not 29a KO mice was reduced after MPTP exposure (Figure 3F). Likewise, in the Pole test, a classical locomotor activity detection method in PD model (Kam et al., 2018), total time was noticeably elevated in WT mice after MPTP exposure, while it did not alter in 29a KO mice compared to their NS controls (Figure 3G).
1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine induces glial cell activation in the nigrostriatal axis, and glial cells-mediated neuroinflammation exerts an important impact on PD pathology (Huang et al., 2017). At 3 days after MPTP administration, we assessed whether deficiency of miR-29a/b1 influenced the activation of astrocytes and microglial cells in the SNpc and the striatum. Astrocytes increased dramatically in the SNpc and the striatum of both WT and 29a KO mice as revealed by immunofluorescence staining of GFAP and cell counting, however, astrocytic densities were significantly reduced in MPTP-treated 29a KO mice (Figures 4A,B). Likewise, Iba 1+ microglial cells increased in the SNpc and the striatum of WT mice, and in the striatum of 29a KO mice. Microglial densities were significantly decreased in the nigrostriatal axis of MPTP-treated 29a KO mice (Figures 4C,D). Moreover, pro-inflammatory cytokines interleukin-1β (IL-1β), IL-6, and interferon-γ (IFN-γ) were measured by multiplex immunoassay, they did not differ at baseline and 3 days after MPTP administration between the two genotypes of mice (Supplementary Figure 6).
Deficiency of miR-29a/b1 led to pre-mature aging and dopaminergic protection. It was interesting to test the vulnerability of older mutant mice to MPTP-induced injury. Structurally, brains of 8-months-old 29a KO mice and their WT littermate were similar (Supplementary Figure 7). Three days after MPTP administration, the striatal TH protein levels in 29a KO mice were markedly higher compared to WT mice, whereas, GFAP proteins did not alter between the two genotypes of mice (Supplementary Figure 8).
Primary cultured microglial cells, astrocytes and midbrain neurons were challenged with 100 ng/ml LPS (for microglia) or 1 mM and 15 μM MPP+ (for astrocytes and neurons, respectively), miR-29s expression were then evaluated. We found the expression levels of miR-29s did not change in MPP+-treated midbrain neurons (Supplementary Figure 9A). However, all three members of miR-29s were upregulated in MPP+-treated primary astrocytes (Figure 5A). In LPS-treated primary microglia, their expression levels were downregulated, only miR-29a expression decreased significantly (Supplementary Figure 9B).
1-Methyl-4-phenylpyridinium exposure induced the expression of neurotrophic factors and inflammation-related genes in astrocytes. At 6, 12, and 24 h after the exposure, brain-derived neurotrophic factor (BDNF) transcripts increased in WT and 29a KO astrocytes. Transforming growth factor-β1 (TGF-β1) transcript levels were dramatically elevated in 29a KO astrocytes after 6 h and 12 h treatment, and in WT astrocytes after 12 h treatment, and insulin-like growth factor-1 (IGF-1) transcript only increased in 29a KO astrocytes after 24 h treatment (Figures 5B,C and Supplementary Figure 10A). Expression levels of IL-1β increased in WT astrocytes after 6 and 24 h treatment, and in 29a KO astrocytes after 6, 12, and 24 h treatment. IL-6 transcripts were upregulated and did not differ in the astrocytes of two genotypes. Inducible nitric oxide synthase (iNOS) transcripts increased after 24 h treatment and did not vary between WT and 29a KO astrocytes (Figure 5D and Supplementary Figure 10B). Tumor necrosis factor-α (TNF-α) and complement component 3 (C3) transcripts did not change after MPP+ treatment for 24 h (Supplementary Figure 10B). Activated astrocytes can be further divided into two subgroups: neurotoxic A1 type and neuroprotective A2 type. Here, we found A1 marker genes H2-T23, H2-D1, Gbp2 and Ggta1, and A2 marker CD14, but not cyclooxygenase-2 (COX-2), Clcf1 and S100α10, were significantly lower in non-treated 29a KO astrocytes compared to WT control. At 6 h after the treatment, H2-T23 and CD14 decreased, COX-2 and Clcf1 increased, whereas H2-D1, Gbp2, Ggta1, and S100α10 did not change in WT astrocytes; H2-T23, COX-2, Clcf1, and S100α10 increased, whereas H2-D1, Gbp2, Ggta1, and CD14 did not change in 29a KO astrocytes. At 12 h after the treatment, CD14 decreased, H2-D1, Gbp2, Ggta1, COX-2, Clcf1, and S100α10 increased, whereas H2-T23 did not change in WT astrocytes; H2-T23, H2-D1, Ggta1, COX-2, Clcf1, and S100α10 increased, whereas Gbp2 and CD14 had no alteration in 29a KO astrocytes. In addition, H2-T23 transcripts were higher, whereas H2-D1 and Gbp2 transcripts were lower in 29a KO astrocytes compared to WT controls (Figures 5E,F). Pro-inflammatory cytokines including IL-1β, IL-6, TNF-α, IFN-γ, and monocyte chemoattractant protein-1 (MCP-1), anti-inflammatory cytokines IL-4 and IL-10 in the supernatants of primary astrocytes were measured at 24 h after MPP+ intoxication. Tumor necrosis factor-α and IFN-γ increased significantly in WT astrocytes, but not in 29a KO astrocytes after MPP+ treatment. Notably, TNF-α level in MPP+-treated 29a KO astrocytes was markedly lower compared to MPP+-treated WT astrocytes. Monocyte chemoattractant protein-1 levels in 29a KO astrocytes were downregulated compared to WT astrocytes at baseline and after MPP+ treatment. IL-6 levels were upregulated, whereas IL-1β, IL-4, and IL-10 did not change, in both WT and 29a KO astrocytes after the treatment of MPP+ (Figure 5G). By western blot assay, phosphorylated-AMPK protein level was increased in 29a KO astrocytes at 6 h after MPP+-treatment, while phosphorylated-AMPK protein level did not change in WT astrocytes after the treatment, and sirtuin 1 (Sirt1) protein levels did not alter between WT and 29a KO astrocytes (Figure 5H). Aging markers were further evaluated. p19, p21, p16, and Pai1 transcript levels were increased in WT astrocytes at 24 h after MPP+ treatment, whereas only p21 transcript, but not the other three increased in 29a KO astrocytes, and p19 and Pai1 transcript levels were even markedly lower in 29a KO astrocytes compared to WT controls (Supplementary Figure 11A). Moreover, anti-apoptotic Bcl-2 proteins did not alter in the two genotypes of primary astrocytes with or without MPP+ exposure (Supplementary Figure 11B).
Inflammation-provoking molecule LPS is widely used as a stimulator for microglia. In non-treated microglia, BDNF, glial cell line-derived neurotrophic factor (GDNF) and IGF-1 transcripts were markedly increased in 29a KO microglia compared to WT control (Figures 6A,B). At 6 h after LPS treatment, transcripts of pro-inflammation genes IL-1β, IL-6, TNF-α, COX-2, and iNOS, and anti-inflammation gene IL-10 were increased, those of BDNF and IGF-1 were decreased in both WT and 29a KO microglia, whereas, expression levels of anti-inflammation genes YM1 and TGF-β1 decreased, GDNF transcript did not change in WT microglia. Likewise, GDNF transcript increased, and YM1 and TGF-β1 did not alter in 29a KO microglia after LPS challenge. Moreover, the transcripts of BDNF, GDNF, IL-10, TGF-β1, iNOS were significantly higher, and IL-1β, IL-6, TNF-α, and COX-2 was lower in LPS-treated 29a KO microglia compared to WT control (Figures 6A–C). Pro-inflammatory cytokines IL-1β, IL-6, IFN-γ and MCP-1, anti-inflammatory cytokine IL-4 and IL-10, were significantly upregulated in the supernatants of LPS-treated WT and 29a KO primary microglia, TNF-α level was elevated only in WT microglia after LPS treatment; however, levels IL-1β, TNF-α and IFN-γ were dramatically reduced in 29a KO microglia compared to WT controls at 24 h after the treatment of LPS (Figures 6D,E). In addition, nitrite product was elevated in WT microglia, but not in 29a KO microglia at 24 h after LPS treatment (Figure 6F). By western blot, phosphorylated-AMPK (p-AMPK) protein levels were markedly upregulated in 29a KO microglia compared to WT microglia at baseline and 24 h after LPS administration. COX-2 proteins were increased in two genotypes of microglia, however, COX-2 protein level in 29a KO microglia was obviously reduced compared to WT microglia, at 24 h after LPS intoxication (Figure 6G). At 60 min after LPS treatment, phosphorylated-p65 (p-p65) and the ratio of p-p65 to p65, but not p65, were elevated in both WT and 29a KO microglia, however, p-p65 and the ratio were significantly reduced in 29a KO microglia compared to WT controls (Figure 6H).
1-Methyl-4-phenylpyridinium treatment increased the expression of neurotrophic factor BDNF, GDNF, anti-inflammatory factor TGF-β1, and pro-inflammatory IL-1β, IL-6, and COX-2 as well, in both WT and 29a KO primary mixed glia. At 12, 24, and 36 h after the treatment, the increases of BDNF transcripts were more dramatic in 29a KO mixed glia, also was the increase of GDNF at 24 h, compared to WT mixed glia. The transcripts of TGF-β1, IL-1β, IL-6, and COX-2 did not differ between the primary mixed glia of the two genotypes (Figures 7A–C). By Western blot assay, we found phosphorylated-AMPK protein level in 29a KO mixed glia was upregulated after a 12 h-treatment of MPP+ compared to PBS control and MPP+-treated WT mixed glia (Figure 7D).
Our previous study has revealed decreasing miR-29s levels in blood serum of PD patients (Bai et al., 2017). Here through quantitative PCR, we measured miR-29s levels in the CSF of PD patients and healthy subjects. Demographic and clinical profiles of PD patients and control groups were in Table 2. We found that miR-29a, but not miR-29b and miR-29c, was upregulated in the CSF of PD patients (Figure 8). Moreover, there were no differences in the CSF levels of miR-29s between drug-naive PD patients and PD patients with medication (Data not shown).
miR-29a/b1 gene locus is on chromosome six in mouse genome, nearby there is no protein-coding gene. In this study, roles of miR-29a/b1 in aging and PD were investigated. We found that miR-29 a/b1 null mutation leads to premature aging, however, mutant mice manifest dopaminergic neuroprotection after MPTP administration. Such characteristics are similar, but more pronounced compared to miR-29 b2/c knockout mice (Bai et al., 2021). miR-29 family members show increased expression in multiple tissues including brain, muscle, and liver during aging (Ugalde et al., 2011; Fenn et al., 2013; Hu et al., 2014). miR-29s upregulate p53 expression and induce cell cycle arrest (Varela et al., 2005; Park et al., 2009), and participate in p16/Rb-driven cellular senescence as well (Martinez et al., 2011). However, both pro-aging and anti-aging roles of miR-29s have been reported. miR-29s are induced during aging in short-lived turquoise killifish brain, where they elicit neuroprotection through inhibiting oxidative stress (Ripa et al., 2017). On the other hand, significant up-regulation of miR-29s contributes to aging-induced sarcopenia in rodents (Hu et al., 2014). Functions of miR29s in aging are very complex. Even within one single system, different functions of miR-29s have been explored (Boon et al., 2011; Heid et al., 2017). Dooley et al. (2016) found 10 weeks old miR-29a/b1–/– mice exhibited reduced body weights and lengths, and had less white fat compared to the WT mice. Similarly, we observed that 8-week-old miR-29a/b-1–/– mice were shorter compared to their WT littermate (data not shown). 29a KO mice at 3 months old showed dramatic weight loss. Moreover, their abdominal fat (subcutaneous fat and visceral fat together) and brown fat all decreased. Aging-associated kyphosis was apparent in mutant mice. Changes in skin and muscle are markers of aging (Fong et al., 2004; Liu et al., 2019; Ahmed et al., 2020). Six-month-old 29a KO mice developed apparent thickening of dermis, along with increased and deepened wrinkles. Metalloproteinase Zmpste24-deficient mice at 16 weeks old completely loss the subcutaneous fat layer and develop muscle weakness (Pendas et al., 2002; Fong et al., 2004). Such lipodystrophy and muscle weakness were observed in miR-29a/b1 knockout mice at 3 months old. However, in the brains of 29a KO mice, p53 and p16 protein levels did not alter compared to their WT littermate, and brains of WT and KO mice were structurally similar. Therefore, cellular senescence was not obvious in the brain of miR-29a/b1 KO mice; Premature aging phenotypes of mutant mice mainly displayed in the periphery. Aging is defined as a multifactorial process that affects most of the biological functions of the organism and increases susceptibility to diseases and death (Ugalde et al., 2011). However, aging processes in different tissue/organs can be non-synchronized. Parkinson’s disease is thought as an age-related neurological disease. We found the premature phenotype and dopaminergic protection are not tied together in miR-29a/b1 deficient mice. Both beneficial and detrimental roles of miR-29 family have been reported in diseases of the central nervous system (CNS). The miR-29 family was significantly decreased in neuroblastoma and neuronal cells following oxygen and glucose deprivation/reperfusion (OGD/R) treatment (Cao et al., 2018; Wei et al., 2018), and miR-29a significantly increased in the resistant dentate gyrus, but decreased in the vulnerable CA1 region of the hippocampus after transient forebrain ischemia and short periods of reperfusion (Ouyang et al., 2013). Thus, miR-29s play a protective role in ischemic injury. Papadopoulou et al. (2015) have reported that miR-29a/b1 knockout mice develop a progressive disorder characterized by locomotor impairment and ataxia. In this study, we found mutant mice exhibited posture instability. In the striatum of miR-29a/b1 KO mice, the concentrations of HVA and DOPAC, but not DA, were reduced, and the ratio of HVA to DA also decreased, indicating a reduction of DA metabolism. Enzymes monoamine oxidase-B (MAO-B), MAO-A and catechol-O-methyltransferase (COMT) are responsible for the metabolism of DA. By Western blot, striatal MAO-B and MAO-A proteins did not differ between WT and 29a KO mice (data not shown). The reduction of DA metabolism might be due to the insufficiency of COMT proteins in mutant mice, and warrants further studies. After challenged with MPTP, 29a KO mice showed lower vulnerability of the dopaminergic system, and behavioral resistance to some extent. Survival of dopaminergic neurons is regulated by glial cells. Astrocytes and microglia produce multiple neuron-supporting neurotrophic factors. Meanwhile, they are the major innate immune cells in the CNS, and involve in the progression of neuroinflammation and PD. miR-29s expression did not alter in MPP+-exposed midbrain neurons, however, MPP+ induced the expression of all three members of miR-29s in primary cultured astrocytes, and miR-29s expression, especially miR-29a, was downregulated in LPS-treated primary microglial cells. The results suggest that in different types of cells, the response of miR-29s to stimuli varies. In MPP+-challenged mixed glia culture, deficiency of miR-29a/b1 increased the expression of BDNF and GDNF. Moreover, MPP+ enhanced the expression of IGF-1 in miR-29a/b1 mutant astrocytes, while Pai 1 transcript, a molecule involved in aging and pro-inflammation, was inhibited in MPP+-treated 29a KO astrocytes. The secretion of pro-inflammation cytokines TNF-α and MCP-1 was also repressed in 29a KO astrocytes after the treatment of MPP+. Additionally, MPP+ stimulation resulted in complex changes in A1 and A2 markers depended on the duration and particular markers per se. That reactive astrocytes are grouped into types of A1 and A2 is worthy of further studies. In primary microglial culture, deficiency of miR-29a/b1 mitigated LPS-induced inflammatory response, and simultaneously promoted the transcriptional expression of anti-inflammation cytokines and neurotrophic factors. The secretion of pro-inflammation cytokines IL-1β, TNF-α, and IFN-γ reduced in LPS-treated 29a KO microglia compared to LPS-treated WT microglia. There are more than one thousand predicted target genes of miR-29s. miR-29s and many of the predicted target genes are involved in the cell survival and metabolism processes (Dooley et al., 2017; Massart et al., 2017; Caravia et al., 2018; Kwon et al., 2019). PI3K p85 and AKT3, targets of miR-29s are critical for cell survival. The protein levels of these molecules were not changed in the striatum of 29a KO mice (data not shown). As an essential metabolic regulator, adenosine monophosphate-activated protein kinase (AMPK) pathway was further investigated. In all three types of primary glial cultures, phosphorylated AMPK protein levels were upregulated in mutant cells. Under LPS treatment, phosphorylated NF-κB p65 subunit and the ratio of p-p65 to p65 were reduced in miR-29a/b1 mutant microglial cells. Activation of AMPK can further stimulate Sirtuin 1, and indirectly inhibit NF-κB pathway and the expression of downstream inflammation-related target genes (Salminen et al., 2011). Adenosine monophosphate-activated protein kinase activation has been reported to be neuroprotective in MPTP- induced PD mice (Lu et al., 2016). Our experimental results suggested that knockout of miR-29a/b1 gene increased the activity of AMPK, which might subsequently inhibit the activation of NF-κB pathway and the inflammatory responses in microglia, and might consequently inhibit the astrocyte activation. All in all, enhanced AMPK and/or reduced NF-κB p65 signaling might contribute to the milder inflammation response in miR-29a/b1 mutant glial cells and the neuroprotection of nigrostriatal axis in miR-29a/b1 deficient mice (Supplementary Figure 12). miR-29s family is highly expressed in brain (Ugalde et al., 2011; Papadopoulou et al., 2015). We found that the expression of miR-29s in the ventral midbrain and striatum did not alter in MPTP-injected mice. In the CSF of patients with PD, miR-29a level was significantly elevated compared to healthy subjects, whereas in our previous studies, the serum levels of miR-29s were markedly downregulated in PD patients (Bai et al., 2017), and miR-29s were associated with cognitive impairment in PD as well (Han et al., 2020). Changes of miRNAs are not necessarily parallel among the brain tissues, the CSF and the serum of patients with neurodegenerative diseases. For examples, miR-29a is downregulated in the frontal cortex of AD patients (Shioya et al., 2010), it does not change in the serum of patients with AD in our previous study (Bai et al., 2017) and studies from other labs (Kiko et al., 2014). miRNAs are differentially expressed in CSF and serum of patients with PD (Burgos et al., 2014). miR-29a was upregulated in gyri cinguli of PD patients (Schulz et al., 2019), while its expression did not change in the frontal cortex of PD patients compared to healthy subjects (Shioya et al., 2010). The upregulation of miR-29a in the CSF is not specific to PD, miR-29a levels in the CSF of AD patients were significantly higher than in control subjects (Kiko et al., 2014; Müller et al., 2016). The differences of serum and CSF miR-29s levels might be attributed to the different origination, dysfunction of the blood-CSF barrier, dysregulation of miRNA transportation, and worthy of further studies. There are some limitations of the study. The underlying mechanisms of miR-29a/b1 deficiency causing resistance to MPTP intoxication in mice are broad and general. The biological significance of the elevated level of miR-29a in the CSF of PD patients is not much clear. Collectively, the present study shows that deficiency of miR-29a/b1 leads to pre-mature aging in the periphery, however, such mutation maintains mouse brain in a low-inflammatory microenvironment, and elicits certain resistance to the dopaminergic neurotoxin in adult and older mice. The family of miR-29s undertakes quite close functions in mice.
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author/s.
The studies involving human participants were reviewed and approved by Human Studies Institutional Review Board, Huashan Hospital, Fudan University. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by Institutional Animal Care and Use Committee of Fudan University, Shanghai Medical College.
FH, JF, JW, and RS proposed and supervised the study. FH, JF, JW, RS, XB, JHW, and XZ designed the research studies, acquired the data, analyzed the data, and wrote the manuscript. YT and LH contributed to the sample collection and clinical characterization of the patients and analyzed data. XB, JHW, XZ, YH, JZ, RF, ZL, HD, QL, JG, MY, and YM conducted the experiments. All authors contributed to the interpretation of data, revision of the manuscript, and approved the submitted version. | true | true | true |
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PMC9582642 | 35594181 | Lauren J. Sundby,William M. Southern,Katelin M. Hawbaker,Jesús M. Trujillo,Benjamin J. Perrin,James M. Ervasti | Nucleotide- and Protein-Dependent Functions of Actg1 | 21-07-2022 | Cytoplasmic β- and γ-actin proteins are 99% identical but support unique organismal functions. The cytoplasmic actin nucleotide sequences Actb and Actg1, respectively, are more divergent but still 89% similar. Actb–/– mice are embryonic lethal and Actb–/– cells fail to proliferate, but editing the Actb gene to express γ-actin (Actbc–g) resulted in none of the overt phenotypes of the knockout revealing protein-independent functions for Actb. To determine if Actg1 has a protein-independent function, we crossed Actbc–g and Actg1–/– mice to generate the bG/0 line, where the only cytoplasmic actin expressed is γ-actin from Actbc–g. The bG/0 mice were viable but showed a survival defect despite expressing γ-actin protein at levels no different from bG/gG with normal survival. A unique myopathy phenotype was also observed in bG/0 mice. We conclude that impaired survival and myopathy in bG/0 mice are due to loss of Actg1 nucleotide-dependent function(s). On the other hand, the bG/0 genotype rescued functions impaired by Actg1–/–, including cell proliferation and auditory function, suggesting a role for γ-actin protein in both fibroblasts and hearing. Together, these results identify nucleotide-dependent functions for Actg1 while implicating γ-actin protein in more cell-/tissue-specific functions. | Nucleotide- and Protein-Dependent Functions of Actg1
Cytoplasmic β- and γ-actin proteins are 99% identical but support unique organismal functions. The cytoplasmic actin nucleotide sequences Actb and Actg1, respectively, are more divergent but still 89% similar. Actb–/– mice are embryonic lethal and Actb–/– cells fail to proliferate, but editing the Actb gene to express γ-actin (Actbc–g) resulted in none of the overt phenotypes of the knockout revealing protein-independent functions for Actb. To determine if Actg1 has a protein-independent function, we crossed Actbc–g and Actg1–/– mice to generate the bG/0 line, where the only cytoplasmic actin expressed is γ-actin from Actbc–g. The bG/0 mice were viable but showed a survival defect despite expressing γ-actin protein at levels no different from bG/gG with normal survival. A unique myopathy phenotype was also observed in bG/0 mice. We conclude that impaired survival and myopathy in bG/0 mice are due to loss of Actg1 nucleotide-dependent function(s). On the other hand, the bG/0 genotype rescued functions impaired by Actg1–/–, including cell proliferation and auditory function, suggesting a role for γ-actin protein in both fibroblasts and hearing. Together, these results identify nucleotide-dependent functions for Actg1 while implicating γ-actin protein in more cell-/tissue-specific functions.
Actin is an essential cellular protein involved in many important functions including cell motility, cytokinesis, muscle contraction, structural support, and regulation of gene expression (Pollard and Cooper, 2009). In mammals, these functions are carried out by the actin family of proteins, which is composed of the four muscle-specific actins, α-skeletal, α-smooth, α-cardiac, and γ-smooth, and the two ubiquitously expressed cytoplasmic actins, β- and γ-actin, each expressed from a unique gene. As a key component of many essential cellular processes, it is unsurprising that mutations in either cytoplasmic actin gene are associated with severe developmental defects in humans, and as β- and γ-actin are also important constituents of the stereocilia in the inner ear, mutations also often lead to different kinds of syndromic and nonsyndromic deafness (Zhu et al., 2003; Rivière et al., 2012; Rubenstein & Wen, 2014; Latham et al., 2018; Miyajima et al., 2020). While all six mammalian actins are highly similar, the cytoplasmic actins share 99% sequence identity, differing by only 4 of 375 amino acids. The cytoplasmic actin gene sequences, Actb and Actg1, are highly conserved and also very similar, sharing 89% of their coding sequences in mice (Perrin and Ervasti, 2010). However, despite the high sequence similarity, the cytoplasmic actins seem to occupy unique biological niches, as demonstrated by the distinct differences in their respective gene knockout models. Actb–/– mice are embryonic lethal, with all embryos dying by E8.5 (Shawlot et al., 1998; Shmerling et al., 2005; Bunnell et al., 2011) while Actg1–/– mice are viable with increased perinatal lethality (Belyantseva et al., 2009). Actb–/– mouse embryonic fibroblasts (MEFs) also failed to proliferate and had significantly impaired cell migration (Bunnell et al., 2011), while Actg1–/– MEFs presented with a small, but significant, decrease in proliferation and normal motility (Bunnell and Ervasti, 2010). The specific mechanisms by which nearly identical β- and γ-actin function differently remain an outstanding question. Current data suggest that functional differences between them may be due to isoform specific interactions with actin-associated proteins, distinct patterns of posttranslational modifications, or variations in mRNA abundance and localization (reviewed in Kashina, 2020). At the protein level, a number of studies have reported isoform-specific interactions with different actin-binding proteins. β-Actin has shown preferential binding to myosin 2B, tropomyosin (Pathan-Chhatbar et al., 2018), myosin 2C1 (Müller et al., 2013), betacap73 (Shuster et al., 1996; Welch et al., 2005), and DIAPH3 (Chen et al., 2017), while γ-actin shows preference for myosin 7a (Müller et al., 2013). Between the two cytoplasmic actins, studies have shown that only β-actin undergoes N-terminal arginylation (Karakozova et al., 2006; Kashina, 2006; Saha et al., 2010; Pavlyk et al., 2018); however, a new study suggests that N-terminal arginylation of β-actin is nominal in the presence of very high rates of N-terminal acetylation (Drazic et al., 2021). β- and γ-actin have also shown unique cellular distribution in different tissues and during different cell stages, with β-actin localizing to the cleavage furrow and γ-actin to the cell cortex during cell division (Otey et al., 1986, 1988; Dugina et al., 2009; Chen et al., 2017). The Actb mRNA contains a unique “zipcode” sequence in the 3′ UTR that interacts with RNA localization proteins, such as Zipcode binding protein 1, to promote local translation of the β-actin protein (Kislauskis et al., 1994; Ross et al., 1997; Hüttelmaier et al., 2005; Pan et al., 2007). While much has been revealed about the different roles of the two cytoplasmic actins, the essential differences conferring unique impacts on organismal survival have recently been linked to the nucleotide sequence of Actb, rather than the β-actin amino acid sequence. We and others (Vedula et al., 2017; Patrinostro et al., 2018) used different gene editing technologies to generate mice that expressed γ-actin protein from Actb, establishing a β-actin protein specific knockout that maintains an intact Actb nucleotide sequence, named Actbc–g. In direct contrast to the embryonic lethality of the Actb–/– (Bunnell et al., 2011), the Actbc–g mice were largely phenotypically normal with no defect in survival (Patrinostro et al., 2018; Vedula et al., 2017). Actbc–g MEFs also had normal proliferation rates and migration patterns (Patrinostro et al., 2018; Vedula et al., 2017). These results demonstrated that it is not the loss of β-actin that causes embryonic lethality in Actb–/– mice, but rather the loss of the intact Actb nucleotide sequence, suggesting that Actb must have protein-independent functions. However, the Actbc–g mice also developed progressive hearing loss due to degradation of inner ear hair cell stereocilia, supporting a tissue-specific function for β-actin protein (Patrinostro et al., 2018). In this study, we assessed whether the Actg1 nucleotide sequence also supports essential protein-independent functions by generating a novel mouse model where the only cytoplasmic actin expressed is γ-actin from Actbc–g, named bG/0. We found that these mice are viable and express γ-actin protein levels no different from those in control littermates. However, we observed unique phenotypes that suggest that Actg1 and γ-actin have nucleotide- and protein-dependent functions that differ from those of Actb or β-actin. Together, these results reveal novel insights into the differential functions of the highly similar cytoplasmic actins, and further support previous studies implicating the importance of nucleotide specific differences between Actb and Actg1.
To determine if reducing the cytoplasmic actin pool in mice to γ-actin expressed exclusively from the edited Actb gene supports viability, we crossed Actbc–g mice (Patrinostro et al., 2018) with Actg1+/–mice (Belyantseva et al., 2009) to generate Actbc–g/c–g Actg1–/– mice. Hereafter, Actbc–g/c–gActg1–/– mice will be referred to as bG/0, where the lowercase letter indicates the gene, the uppercase letter indicates the protein expressed, 0 indicates Actg1–/–, and +/– indicates Actg1+/– (Supplemental Table S1). The bG/0 mice were viable but were observed in sub-Mendelian ratios at weaning: 35.02% bG/gG, 58.23% bG/gG+/–, and only 6.75% bG/0 rather than the expected ratios of 25:50:25. bG/0 mice presented with a median survival of only 163 d, which is significantly less than bG/gG or bG/gG+/– littermates (Figure 1A). bG/0 mice were also significantly smaller than their bG/gG or bG/gG+/– littermates, and male bG/0 mice were significantly smaller than WT controls (Figure 1, B and C). Decreased survival and size of the bG/0 mice is consistent with results previously reported for Actg1–/– mice (Belyantseva et al., 2009). Because hypomorphic expression of cytoplasmic actins would explain the decreased viability in bG/0 mice most simply, we utilized quantitative real-time PCR (qRT-PCR) to measure isoactin transcript levels in WT, bG/gG, bG/gG+/–, and bG/0 brain, lung, and MEF tissue. bG/0 lung and MEF total transcript levels were not significantly different from those in WT controls, while brain tissue showed a significantly increased level of total actin transcript in all bG/gG, bG/gG+/–, and bG/0 samples (Supplemental Figure S1). Upon loss of intact Actg1 in bG/0 mice, the tissues showed loss of expression of the Actg1 transcript and a corresponding increase in expression of the Actbc–g transcript. bG/0 lungs also showed a nonsignificant increase in Acta2 expression while MEFs showed a significant increase in Acta2 (Figure 2, A–F). These data suggest that the bG/0 tissues compensate for loss of Actg1 by up-regulating expression of other actin isoforms, including the edited Actbc–g allele. We also measured relative protein levels of cytoplasmic, αsm-, and total actin using quantitative Western blotting of WT, bG/gG, bG/gG+/–, and bG/0 brain, lung, and MEF tissue. Similar to the measured transcript levels, cytoplasmic actin protein levels appeared to undergo compensatory up-regulation to maintain a WT level of total actin protein. Despite the loss of Actg1, we measured γ-actin protein levels that remain constant across bG/gG, bG/gG+/–, and bG/0 mice (Figure 3, A–F), demonstrating that the Actbc–g allele is able to compensate for loss of endogenous γ-actin expressed from Actg1. Because we measured constant levels of γ-actin between bG/gG and bG/0 and only bG/0 presents with decreased survival, we conclude that the survival defect observed in the bG/0 mice is due to loss of the intact Actg1 nucleotide sequence and not to the loss of γ-actin protein expression.
Actb–/– MEFs show significant defects in proliferation and motility not observed in gene-edited Actbc–g MEFs (Bunnell and Ervasti, 2010; Bunnell et al., 2011; Patrinostro et al., 2017, 2018; Vedula et al., 2017). Previously generated Actg1–/– MEFs also have significantly impaired proliferation (Bunnell & Ervasti, 2010), so to determine if this phenotype is caused by a loss of Actg1, we assessed bG/0 MEF morphology and function. Immunostained bG/0 MEFs showed γ-actin localization throughout the entirety of the cell, from the cell body to the periphery, similar to WT and bG/gG cells and present with no significant morphological differences (Figure 4A; Supplemental Figure S2, A–C). Phalloidin staining showed no overt changes in actin filaments in Figure 4B. The measured proliferation rate of bG/0 MEFs was not different from WT and bG/gG controls (Figure 4C). MEFs have the capacity to differentiate into myofibroblasts in response to a number of environmental stimuli. During this transition, cytoplasmic actin polymerizes into stress fibers and cells up-regulate expression of αsm-actin (Tomasek et al., 2002; Hinz, 2007; Davis and Molkentin, 2014). We have previously observed myofibroblast-like phenotypes in Actb–/–, Actg1–/–, and Actb–/–/Actg1–/– MEFs (Patrinostro et al., 2017). Despite unchanged levels of αsm-actin (Figure 3C), we quantified stress fiber numbers and thickness in phalloidin-stained cells to determine if there might be other myofibroblast-like phenotypes in bG/0 MEFs. In bG/0 MEFs, no change in fiber thickness or number was observed from those in WT and bG/gG controls (Figure 4, D–F). Together, these results suggest that it is the loss of γ-actin and not the Actg1 nucleotide sequence that causes MEFs to undergo a fibroblast-to-myofibroblast transition The polymerization state of actin in cells is an important regulator of cell function and elicits changes through shifting the ratio of polymerized F-actin to monomeric G-actin (reviewed in Kashina, 2020). Previous studies have shown that loss of Actb, but not β-actin or Actg1, causes a significant decrease in G-actin (Bunnell and Ervasti, 2010; Bunnell et al., 2011; Patrinostro et al., 2018). In bG/0 MEFs, we observed an insignificant decrease in the G- to F-actin ratio of γ- and αsm-actin and a significant decrease in the amount of G-actin for total actin from those in WT and bG/gG controls (Figure 4G). The significance of the G-actin decrease for total actin is likely due to the combined insignificant decrease observed for both γ- and αsm-actin. One of the key signaling pathways that responds to changes in the polymerization state of actin is the serum response factor (SRF)/myocardin-related transcription factor (MRTF) signaling pathway (Vartiainen et al., 2007; Olson and Nordheim, 2010; Baarlink et al., 2013; Esnault et al., 2014). Quantitative Western blotting revealed no change in SRF or MRTF-A expression in bG/0 MEFs from WT, bG/gG, and bG/gG+/– (Figure 4H). Changes in cellular F-actin also impacts the Hippo signaling pathway through central effectors Yes-associated protein 1 (YAP) and transcriptional coactivator with PDZ binding motif (TAZ; reviewed in Seo and Kim, 2018). To gauge the impact of the bG/0 genotype on the Hippo pathway, we quantified YAP expression using quantitative Western blotting; no significant differences were observed (Figure 4H). Finally, we quantified bG/0 MEF migration habits using a random cell migration assay (Figure 5, A–C). We observed that bG/0 MEFs migrated at rates similar to those in WT and bG/gG controls. No significant differences were observed for bG/0 directionality, direction autocorrelation, mean squared displacement (MSD), or speed from WT and bG/gG controls (Figure 5, D–G). These results lead us to conclude that the intact Actg1 nucleotide sequence is not required for cell migration.
While β- and γ-actin are expressed in miniscule amounts in adult skeletal muscle compared with α-skeletal actin (Goldberg et al., 1980; Hanft et al., 2006), muscle-specific knockout of either Actb or Actg1 results in a mild, but progressive age-dependent myopathy (Sonnemann et al., 2006; Prins et al., 2011). However, conversion of β-actin to γ-actin protein via gene editing had no effect on muscle function (Patrinostro et al., 2018). bG/0 muscles were not different from WT or bG/gG controls in the percentage of centrally nucleated fibers (CNF), susceptibility to eccentric contraction induced force loss, muscle mass, fiber size, or fiber number (Figure 6, A and B; Supplemental Figure S3), but bG/0 muscles did present with significantly decreased specific isometric force (Figure 6C). bG/0 mice also displayed a hyperactivity phenotype in an open field assay (Supplemental Figure S4). Additionally, γ-actin protein expression in bG/0 muscle was not different from that in bG/gG (Figure 6D), suggesting that the myopathy is not due to hypomorphic γ-actin expression. Because these data suggest that bG/0 myopathy is not due to altered γ-actin levels, and because the myopathy differs from both Actb and Actg1 conditional muscle-specific knockout models, we conclude that the novel skeletal muscle weakness of bG/0 is due to the loss of Actg1 from a nonmuscle cell or tissue that supports skeletal muscle function, or loss of Actg1 in the earliest stages of development.
Based on studies in gene knockout and Actbc–g mice, both cytoplasmic actins have been shown to be important for maintenance of stereocilia in the inner ear with loss of either causing progressive hearing loss (Perrin et al., 2010; Patrinostro et al., 2018). To determine if the bG/0 genotype further compromises the structure or function of the inner ear, we employed scanning electron microscopy (SEM) and auditory brainstem response (ABR) testing in 6 wk-old and 16 wk-old WT, bG/gG, and bG/0 mice. Outer hair cell (OHC) stereocilia in 6 wk-old bG/0 mice had normal morphology. In contrast, at 16 wk of age we observed variable lengths in OHC stereocilia rows 2 and 3 from both bG/gG, and bG/0 mice, which resembles the Actb–/–, rather than the Actg1–/– phenotype. Additionally, some OHCs were lost in the base of cochlea (Figure 7, A and B). The ABR thresholds of bG/0 were not significantly different from those of bG/gG at either 6 or 16 wk of age, and both lines showed significant hearing loss at high frequencies compared with WT mice at 16 wk of age (Figure 7, C–D). This pattern of progressive high-frequency hearing loss is again consistent with that previously seen in hair cell-specific Actb–/– mice, but is different than that in Actg1–/– mice, which had progressive hearing loss at all sound frequencies. These data suggest that the Actg1 nucleotide sequence is not necessary for auditory function because OHC structure and ABR thresholds were similar in bG/gG and bG/0 mice.
Previous data using various Actb–/– models suggested that loss of β-actin is lethal in mice, leading to the conclusion that β-actin is an essential cellular protein (Shawlot et al., 1998; Shmerling et al., 2005; Bunnell et al., 2011). However, more recent studies utilizing CRISPR/Cas9 or TALENs generated a β-actin protein-specific knockout by editing the Actb nucleotide sequence to express γ-actin. These Actbc–g mice were overtly normal, demonstrating that it is not the β-actin protein that is essential for mouse development, but rather the intact Actb nucleotide sequence, suggesting that Actb has protein-independent function (Vedula et al., 2017; Patrinostro et al., 2018). An attempt was made to generate a mouse that expressed β-actin from Actg1, Actg1c–b, but only three of the four amino acids were successfully edited. The partially edited Actg1 mouse presented with no abnormal phenotypes, suggesting that the survival defect observed in Actg1–/– mice may also be due to loss of intact Actg1 nucleotide sequence (Vedula et al., 2017). Here we addressed whether expression of γ-actin exclusively from the edited Actbc–g allele could support mouse and cell viability. Most interestingly, our data revealed that the bG/0 mice present with significantly impaired survival, while expressing the same relative amount of γ-actin protein as the Actbc–g line with normal survival. Our data support an important protein-independent role for the Actg1 nucleotide sequence. Data collected here corroborate years of studies that emphasize that despite high sequence identity between β- and γ-actin, and their respective nucleotide sequences Actb and Actg1, the cytoplasmic actin genes and proteins have unique functions. Many of these studies have centered on genetically modified mice, and the results of these various mouse models reveal that we cannot predict if an organismal function requires the cytoplasmic actin nucleotide sequence or protein, despite the high similarity of the two. While mouse survival is dependent on Actb (Bunnell et al., 2011), we confirm here that it is also negatively impacted by loss of Actg1. Previous studies attributed the importance of γ-actin or Actg1 for mouse lifespan and body mass to the protein (Belyantseva et al., 2009; Bunnell & Ervasti, 2010), but here we demonstrate that the Actg1 nucleotide sequence is required for normal mouse survival and growth. On the other hand, we have also identified protein-dependent functions for γ-actin that are not the same for β-actin. The Actb nucleotide sequence is required for cell proliferation (Bunnell & Ervasti, 2010; Patrinostro et al., 2017), but here we showed that it is the loss of γ-actin protein that impairs proliferation in Actg1–/– MEFs rescued by reintroduction of γ-actin in the bG/0 mice (summarized in Table 1). The phenotypic consequences of perturbing either a cytoplasmic actin gene or a protein were also observed through a novel skeletal muscle weakness phenotype in bG/0 mice. bG/0 muscle weakness was characterized by a significant decrease in specific isometric force measured without increases in the percentage of CNF, indicative of muscle necrosis and regeneration or increased susceptibility of force generation to eccentric contraction associated with muscle-specific ablation of Actb or Actg1 (Sonnemann et al., 2006; Prins et al., 2011). Because γ-actin protein expression in bG/0 skeletal muscle was not different from that in bG/gG, which has normal muscle function, our data suggest that the skeletal muscle weakness novel to bG/0 may be due to the loss of Actg1 and/or γ-actin from a nonmuscle cell or tissue that more indirectly impacts muscle strength. Alternatively, this discrepancy between models may arise from small differences in the knockout mechanism. The Actg1–/– knockout in bG/0 is constitutive and present in the earliest stages of embryogenesis (Belyantseva et al., 2009), while muscle-specific Actg1–/– is a conditional knockout that is triggered by expression of Cre from the human α-skeletal actin (HSA) promotor, which only begins to express around 9 d post coitum (dpc; Miniou et al., 1999). Therefore, if intact Actg1 is required in the earliest stages of muscle development, the muscle-specific Actg1–/– mice may escape the myopathy observed in the bG/0 mice. Exactly how the nucleotide sequences of the cytoplasmic actin genes confer their important functions remains elusive. Multiple lines of evidence identify functional noncoding regions of either gene. The importance of Actb may be due to local translation of the transcript via the Actb zipcode (Kislauskis et al., 1994; Ross et al., 1997; Artman et al., 2014). Other regulatory elements have been identified in the Actb 3′ UTR and intron 3 of Actg1 (DePonti-Zilli, Seiler-Tuyns, & Paterson, 1988; Lloyd & Gunning, 1993; Drummond & Friderici, 2013), suggesting these or other unidentified functional regions within noncoding sequences of either gene may serve Actb or Actg1 nucleotide-dependent functions. While the aforementioned regions are largely involved in regulating expression of β- and γ-actin, other sequence elements could be involved in various cell functions through regulating expression of other genes. Microarray analysis of Actb–/– MEFs revealed dysregulation of genes involved in the cell cycle, actin dynamics, and myosin activity (Bunnell et al., 2011); however, further characterization studies are needed to identify if Actg1–/– cells have similar expression changes and to clarify how either gene may be causing changes in gene expression. Another theory posits that differential translation rates resulting from the higher percentage of noncoding nucleotide differences between Actb and Actg1 contribute to the importance of the cytoplasmic actin genes. It has been observed that γ-actin from Actg1 is translated more slowly than β-actin from Actb, and these differential translation rates confer differences in focal adhesion turnover and cell migration (Zhang et al., 2010; Vedula et al., 2021). Recent work by Vedula et al. (2021) found that exogenous expression of the γ-actin coding sequence flanked by the Actb promoter and 3′ untranslated sequence resulted in increased directional migration rates in immortalized MEFs, but had no effect on random cell migration rates, while expression of the β-actin coding sequence in the same context decreased migration rates. In this study, we found that exclusively expressing endogenous Actb-coded γ-actin in the bG/0 mice had no significant impact on random cell migration rates. We did not assess directional migration in bG/0 MEFs because previous work on Actg1–/– MEFs found that directional migration was unaffected by loss of the Actg1 coding sequence and γ-actin protein (Bunnell and Ervasti, 2010). Discrepancies between these studies suggest that differences in the biological system may alter cytoplasmic actin function. Migration studies in Actg1–/– and bG/0 cells were conducted in primary cells with germline gene edits, while the study by Vedula et al. (2021) was conducted in immortalized cells expressing both exogenous and endogenous cytoplasmic actins. In addition to demonstrating nucleotide-dependent functions for Actg1 in mouse survival and skeletal muscle strength, we have also confirmed protein-dependent functions of cytoplasmic actins in hearing function and stereocilia structure in the inner ear. γ-Actin-specific functions might be revealed if the fully edited Actg1c–b mouse were generated. Vedula et al., (2017) attempted to generate this γ-actin-specific knockout, but was only partially successful, so another attempt to complete this model would be beneficial in fully defining the differential roles of the cytoplasmic actin proteins. However, from a completely different perspective, we examined this same question with the bG/0 mice to uncover novel data identifying protein-independent functions of Actg1. Further investigation into the protein-independent functions of Actb and Actg1 will be essential to determining how these nucleotide sequences are conferring important functions within an organism and likely reveal novel roles for noncoding DNA that may be relevant to other genes. Cytoplasmic β- and γ-actin have been highly evolutionarily conserved from birds to mammals, despite being 99% similar at the amino acid level and 89% identical at the nucleotide level (Perrin and Ervasti, 2010). Further clarification on the functional differences between the two will provide novel insights into the evolutionary significance of highly similar molecules that might be applied to other proteins and/or genes.
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Animals were housed and treated in accordance with the standards set by the University of Minnesota Institutional Animal Care and Use Committee. All animal experiments were approved by the University of Minnesota Institutional Animal Care and Use Committee under protocol numbers 1806A36018 and 2106A39169. Mice were housed in a specific pathogen-free facility on a 12 h light/dark cycle with ad libitum access to food and water. All mice used in this study were on the C57BL/6J background. Actbc–g/c–g (Patrinostro et al., 2018) and Actg1+/– (Belyantseva et al., 2009) mice were crossed and genotypes were determined as described previously (Patrinostro et al., 2018; Sonnemann et al., 2006). Mice were killed by cervical dislocation after anesthesia with Avertin at 3 mo of age for phenotypic analysis. Tissue was dissected and snap-frozen in liquid nitrogen.
Primary WT, bG/gG, bG/gG+/–, and bG/0 mouse embryonic fibroblasts (MEFs) were isolated from E13.5 embryos as described previously (Bunnell and Ervasti, 2010). MEFs were grown to approximately 80% confluency in MEF media (DMEM supplemented with 10% fetal bovine serum, 1% Pen/Strep, and 0.5 ug/mL Fungizone) and 1 × 106 cells were frozen at passage one in MEF freezing media (95% fetal bovine serum + 5% DMSO). MEFs were then thawed and cultured in MEF media.
MEFs were seeded at a density of 5 × 104/well of a six-well plate, in duplicate, in MEF media and a single well of each plate was counted every day for 6 d using a hemocytometer.
Coverslips were coated in 5 µg/ml fibronectin, seeded with 2 × 104 MEFs, and incubated overnight in MEF media. The following day, cells were fixed with 4% paraformaldehyde in PBS for 30 min at room temperature (RT), washed 3 × 5 min with phosphate-buffered saline (PBS), permeablized for 10 min at RT with 0.1% Triton in PBS, and blocked for 30 min at RT with 3% bovine serum albumin (BSA) + 0.1% Triton in PBS. Coverslips were washed once with 0.1% Triton in PBS. For isoform-specific staining, coverslips were stained with the following primary antibodies in 3% BSA + 0.1% Triton in PBS overnight at 4°C: β-actin (1:400; Sigma-Aldrich, AC15) and γ-actin (affinity-purified γ-cyto actin rabbit 7577). For F-actin staining, coverslips were stained with Acti-Stain 555 phalloidin (cytoskeleton PHDH1-A) according to manufacturer’s instructions. All coverslips were then washed 3 × 5min with PBS, rinsed with ddH2O, and mounted with ProLong Gold Antifade with 4′,6-diamidino-2-phenylindole (DAPI) (Cell Signaling Technology 8961S). Images were collected using a 20×/NA0.75 or 60×/NA1.42 objective on a DeltaVision personalDV microscope with softWorx 7.2.1 (GE Technologies) using the same laser intensities and exposure times.
Images were analyzed using ImageJ (National Institutes of Health, Bethesda, MD, version 1.52p). Actin stress fiber thickness was quantified using fluorescence intensity of linescans across the widest portion of the cell body perpendicular to the fibers. The peaks and corresponding valleys were then determined, excluding the first and last peaks, which correspond to the edges of the cell. Fiber number totals were measured as the total number of peaks normalized to the width of the linescan. To quantify the proportion of peripheral γ-actin, cells were masked and all background fluorescence was cleared. Cell masks were then outlined and dilated to define the cell periphery. From these measurements, the ratio of raw fluorescence intensity in the cell periphery to the raw fluorescence intensity of the internal area was then calculated. The cell masks were also used to measure circularity and aspect ratio.
A sample of 2 × 104 MEFs were cultured overnight in MEF media in a Nunc glass-bottomed dish (Thermo Scientific 150680). The following day, the MEF media was replaced with MEF media without phenol red + HEPES to stabilize the pH and the dishes were sealed with vacuum grease and a glass coverslip. Images were collected every 10 min for 4 h using a 10×/NA0.25 objective with phase contrast illumination on a DeltaVision personalDV in an environmental chamber maintained at 37°C. Cells were tracked using the Manual Tracking plugin for ImageJ (National Institutes of Health, Bethesda, MD, version 1.52 h) and the xy track data was analyzed using the DiPer plugin for Excel (Gorelik and Gautreau, 2014). Cells that divided or contacted other cells were excluded from analysis
WT and Actbc–g mouse isoactin controls were generated and respective primer sets were verified previously to be isoform-specific (Patrinostro et al., 2017, 2018). For whole tissue, samples were pulverized using liquid nitrogen and a mortar and pestle and homogenized in Trizol using a 27G needle and syringe. Total RNA was extracted from homogenized tissue and MEF samples using the Bio-Rad Aurum Total RNA Mini-Kit (7326820) according to manufacturer’s instructions. RNA concentration was measured using a NanoDrop 1000 spectrophotometer (ThermoFisher Scientific). A Bio-Rad iScript Advanced cDNA synthesis kit (1725037) was used to synthesize first-strand cDNA from a standard amount of RNA. Isoactin and Actbc–g control samples were used in a series of 10-fold dilutions to generate standard curves, and MEF or tissue cDNA samples were amplified in parallel using Bio-Rad SsoAdvanced Universal SYBR Green Supermix (1725270) and isoform-specific primers using the Bio-Rad CFX96 Real Time System C1000 touch thermal cycler. Transcript quantities were calculated in picomoles using the standard curves.
Brain, lungs, and gastrocnemius muscles were pulverized in liquid nitrogen using a mortar and pestle. MEF, brain, and lung protein was extracted in 1% SDS buffer in 1X PBS with a cocktail of protease inhibitors (100 μM aprotinin, 0.79 mg/ml benzamide, 1 μM calpain, 1 μM calpeptin, 10 μM E-64, 10 μM leupeptin, 0.1 mg/ml pepstatin, 1 mM phenylmethylsulfonyl fluoride) and MEF lysates were sonicated (Model 150V/T ultrasonic homogenizer; BioLogics). All samples were then boiled and centrifuged to remove the insoluble fraction. Pulverized gastrocnemius muscle was subjected to low-salt extraction and DNase enrichment as described previously (Hanft et al., 2006). Equal amounts of cleared lysates (25 µg brain or lung, 20 μg gastrocnemius, and 15 µg MEF) were separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) before being transferred to a PVDF membrane and blocked for 30 min in 5% nonfat milk in PBS. The following antibodies were used: β-actin (1:5,000; Sigma-Aldrich, AC15), γ-actin (1:5,000 mAB 2-4), αsm-actin (1:5,000; Sigma-Aldrich, 1A4), Pan-actin (1:5,000; Seven Hills Bioreagents, C4), SRF (1:1,000; Santa Cruz Biotechnology, G-20), MRTF-A (1:1,000; Cell Signaling Technology, E2V2I), or YAP (1:500, Abnova) with glyceraldehyde 3-phosphate dehydrogenase (GAPDH; 1:5,000; Sigma-Aldrich, G9545) as a loading control and secondary antibodies DyLight 800 anti-mouse IgG (1:10,000; Cell Signaling Technology, 5257S) and DyLight 680 anti-rabbit IgG (1:10,000; Cell Signaling Technology, 5366S). Blots were imaged using the Odyssey CLx infrared scanner (LI-COR Biosciences) and protein bands were quantified using LI-COR Image Studio Software.
Equal numbers of WT, bG/gG, and bG/0 MEFs were pelleted before the experiment. G- and F-actin fractions were isolated from MEFs using the G-actin/F-actin in vivo assay kit (Cytoskeleton, #BK037) according to manufacturer’s instructions and Western blotted for β-actin, γ-actin, αsm-actin, and total actin (see Western Blotting for a complete list of antibodies). Blots were imaged using the Odyssey CLx Infrared scanner (LI-COR Biosciences) and fluorescence intensities of G- and F-actin protein bands were quantified using LI-COR Image Studio Software to determine the ratio of G- to F-actin for each genotype.
Mice were placed in an open-field apparatus for 15 min and total horizontal distance and vertical movement counts were measured based on infrared beam breaks. Activity was measured using the AccuScan system (Columbus Instruments).
Quadriceps muscles from each mouse line were cryopreserved in melting isopentane for 30 s and 7-µm transverse cryosections were obtained (Leica CM3050 S). For immunofluorescence, sections were fixed in acetone at -20°C for 15 min and subsequently washed three times in PBS before being blocked in 5% goat serum for 30 mins at RT. Sections were incubated for >1 h in primary antibody (rat monoclonal anti-Laminin 1:500; Sigma, L06631) at RT. Slides were then washed three times in PBS before incubation with Alexa Fluor 488 goat anti-rat IgG (1:1000; ThermoFisher, A-11006) secondary for 30 mins at RT. Sections were finally washed three times in PBS and mounted in ProLong Gold Antifade with 4′,6- diamidino-2-phenylindole (DAPI) to visualize nuclei (ThermoFisher Scientific). Images were acquired on a Leica DM5500 B microscope equipped with a Leica HC PLAN APO 10× objective and stitched together with LASX software (Leica) to allow visualization of the entire quadriceps. SMASH—semi-automatic muscle analysis using segmentation of histology software—was used to analyze and quantify centrally located nuclei, fiber number, and fiber size (Smith and Barton, 2014).
Contractile function of EDL muscles was assessed according to methods described previously (Moran et al., 2005). Mice were anesthetized with sodium pentobarbital (75-100 mg/kg body mass). EDL muscles were dissected and mounted on a 300B-LR dual-mode muscle lever system (Aurora Scientific) with 5-0 suture in a 1.2-ml bath assembly with oxygenated (95:5% O2/CO2) Krebs Ringer bicarbonate (Krebs) buffer maintained at 25°C. The stimulator and muscle lever system were controlled by computer using a KPCI-3108 interface board (Keithley Instruments) and TestPoint software (SuperLogics). Muscles were adjusted to their anatomical optimal length (Lo) based on resting tension, with length being measured from the distal myotendonous junction to the proximal myotendonous junction using digital calipers. Before eccentric contractions were performed, maximal isometric tetanic force (Po) was measured every 2 min by stimulating the muscle to contract for 200 ms at 175 Hz until force plateaued. A series of 10 eccentric contractions (ECC) were performed and the peak force of each contraction was recorded. For each ECC force measurement, the muscle was passively shortened to 95% Lo and then stimulated for 200 ms while the muscle was simultaneously lengthened to 105% Lo at a velocity of 0.5 Lo/s. Each eccentric contraction was separated from the next eccentric contraction by 3 min of rest to prevent fatigue (Lowe et al., 1994). The force measured at each eccentric contraction was expressed as a percentage of the force produced during the first contraction.
Auditory brainstem response (ABR) waveforms for mice were collected using a Tucker Davis Technologies System 3 at frequencies of 4, 8, 11, 16, 22, and 32 kHz as described previously (Patrinostro et al., 2018). Scalp potentials were recorded using subdermal electrodes following anesthetization with Avertin. Waveforms for each frequency were collected starting at 90 dB, decreasing in 5-dB steps to a subthreshold level. The collected waveforms were stacked and the lowest level of stimulation that resulted in a definite waveform was considered to be the threshold.
Dissected cochlea were fixed in 2.5% glutaraldehyde, 0.1 M sodium cacodylate, 2 mM CaCl2 overnight at 4°C and then decalcified in 170 mM EDTA in PBS for 16 h at 4°C. Dissected organ of Corti was incubated in 2% each of arginine, glutamine, glycine, and sucrose in water overnight at RT, followed by incubation in 2% tannic acid and guanidine hydrochloride for 2 h at RT and 1% OsO4 in water for 1 h at RT, with extensive water washes between steps. The samples were transitioned to 100% ethanol, critical point dried from CO2 and sputter coating with gold. Samples were imaged using a JEOL JSM-7800F field emission scanning electron microscope.
All statistics were calculated using GraphPad Prism Software (version 9.0.2). One- or two-way ANOVAs with Bonferroni posttest were performed based on the specific data set and significance was determined with *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.
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PMC9582767 | Xiao-Xiao Quan,Yuan-Yuan Huang,Lu Chen,Jing-Quan Yuan | Traditional uses, phytochemical, pharmacology, quality control and modern applications of two important Chinese medicines from Rosa laevigata Michx.: A review 10.3389/fphar.2022.1012265 | 06-10-2022 | Rosa laevigata Michx.,Fructus R. laevigata,Radix R. laevigata,traditional medicine,phytochemistry,pharmacology,quality control | Rosa laevigata Michx. is an ethnic medicine that have strong biological activities used in the traditional medicine system for the treatment of diabetes, nephropathy, myocardial damage, oxidative damage, liver damage and so on. Currently, The Chinese herb R. laevigata Michx. can be divided into two important medicines: Fructus R. laevigata and Radix R. laevigata, from which approximately 148 chemical components have been isolated, including flavonoids, lignans, polyphenols, steroids, triterpenoids, tannins as well as other components. Pharmacological studies have already confirmed that both of these herbs have antioxidant, anti-inflammatory, antiviral and anti-tumor activities, as well as renal protective, immunomodulatory, lipid-lowering, cardiovascular protective, bacteriostatic, and other pharmacological effects. Toxicological tests and quality control studies revealed the safety and nontoxicity of R. laevigata Michx. Therefore, this paper systematically summarizes the traditional uses, botanical, phytochemical, and pharmacology as well as the quality control and toxicology of Fructus and Radix, which in order to provide a comprehensive reference for its continued research. | Traditional uses, phytochemical, pharmacology, quality control and modern applications of two important Chinese medicines from Rosa laevigata Michx.: A review 10.3389/fphar.2022.1012265
Rosa laevigata Michx. is an ethnic medicine that have strong biological activities used in the traditional medicine system for the treatment of diabetes, nephropathy, myocardial damage, oxidative damage, liver damage and so on. Currently, The Chinese herb R. laevigata Michx. can be divided into two important medicines: Fructus R. laevigata and Radix R. laevigata, from which approximately 148 chemical components have been isolated, including flavonoids, lignans, polyphenols, steroids, triterpenoids, tannins as well as other components. Pharmacological studies have already confirmed that both of these herbs have antioxidant, anti-inflammatory, antiviral and anti-tumor activities, as well as renal protective, immunomodulatory, lipid-lowering, cardiovascular protective, bacteriostatic, and other pharmacological effects. Toxicological tests and quality control studies revealed the safety and nontoxicity of R. laevigata Michx. Therefore, this paper systematically summarizes the traditional uses, botanical, phytochemical, and pharmacology as well as the quality control and toxicology of Fructus and Radix, which in order to provide a comprehensive reference for its continued research.
Rosa laevigata Michx. belongs to the Rosaceae family and is a widely used plant in China. Its different parts are used as herbs in Chinese medicine, its flowers, leaves and stems have different applications, but the main parts used are its fruits and roots, which are two herbs with so much important applications in Chinese medicine. R. laevigata is divided into many types of herbs according to its different medicinal parts, and among these the two most used are: Fructus R. laevigata and Radix R. laevigata. One of these herbs is Fructus R. laevigata (JinYingZi) which is the dried ripe fruit of the Rosaceae plant that is often called Prickly Elm. It was first recorded in the Shu Ben Cao (during 935-960 AD) written by Han Baosheng and now included in the Chinese Pharmacopoeia. It belongs to the kidney, bladder and large intestine meridian. It has the effects of consolidating the essence and shrinking of urine, consolidating the collapse and stopping belt as well as astringent intestines and stopping diarrhea. It is mainly used for spermatorrhea, frequent enuresis, metrorrhagia and diarrhea (National Pharmacopoeia Committee, 2020). Another one of these herbs is Radix R. laevigata (JinYingGen) which is also known as Tuogudan. It was first recorded in the Ri Hua Zi Ben Cao and it is flat and non-toxic. It is used to strengthen the essence and astringe the intestines as well as the treatment of spermatorrhea, enuresis, dysentery, diarrhea, metrorrhagia, uterine prolapse, hemorrhoids, scalding and so on (Tan et al., 2010). Studies have shown that the different parts of the herbs lead to small differences in their efficacy. The extracts of Radix R. laevigata can treat age-related urinary incontinence by causing changes in bladder forcing muscles and bladder capacity during the filling phase (Tianjin No.3 Hospital, 2004). Fructus R. laevigata can treat the symptoms of kidney deficiency, its administration can improve kidney function as well as improve any difficulties in urination and edema in men and women. It also can reduce the frequent night urination. In addition, fructus R. laevigata can improve the function of the gastrointestinal tract, promoting the peristalsis of the intestines, increasing food digestion and reducing the accumulation of harmful substances in the intestinal tract. It also plays a role in elimination of the stool and combats diarrhea and it can reduce the occurrence of gastrointestinal diseases. In terms of chemical composition, both Fructus and Radix are similar. Triterpenoids are almost the same, while the flavonoids, tannins and other components are somewhat different. The corresponding pharmacological activities are also slightly different, with anti-inflammatory, antioxidant, anti-tumor and immunological activities allotted to both medicinal herbs, while other pharmacological activities are different. Fructus R. laevigata and Radix R. laevigata are also different in their applications and, each has its own focus. The Ministry of Health of China has rated Fructus R. laevigata as a new food resource, which has been developed into a third generation of wild fruit food. Therefore, Fructus R. laevigata is widely used in food ingredients, such as its addition when developing fruit juices, fruit wines and yogurt (Li et al., 2022). In addition, a brown pigment for use as a food additive can also be extracted from Fructus. It is also used as raw material for Chinese medicine to treat pelvic inflammation and diabetic cataract, while Radix R. laevigata is used as raw material in Sanjin tablets and gynecological Qianjin tablets to treat gynecological infections. In addition, and it is also used as the main raw material of Guangdong herbal tea. At present, there are an ever-growing list of reports on Fructus R. laevigata and Radix R. laevigata. Therefore, this review respectively summarizes the chemical composition, pharmacological effects, and quality control as well as the extraction, separation and processing of Fructus R. laevigata and Radix R. laevigata in order to provide a basis for subsequent research on these two important Chinese medicines.
The available information on Rosa laevigata Michx. was collected from scientific databases published from 1989 up to 2021. Information on R. laevigata was obtained from published sources, including monographs on medicinal plants, ancient and modern recorded classics, the Chinese Pharmacopoeia and electronic databases, such as Web of Science, PubMed, CNKI, Wanfang DATA, Google Scholar, Baidu Scholar and Flora of China (FOR). The search terms used for this review included “Rosa laevigata Michx.”, “Fructus Rosa laevigata”, “The root of Rosa laevigata”, “Radix Rosae laevigate” and “Rosaceae” all of which are accepted names and synonyms, “botanical characterization”, “compounds”, “traditional uses”, “pharmacology”, “toxicology”, “quality standard”, “extraction and purification” and “applications”. No language restrictions were applied during the search.
Fructus Rosa laevigata Michx. is the dried ripe fruit of Rosaceae plant family. Flora of China describes it as an evergreen climbing shrub that is approximately 5 m tall and the branchlets are stout with a glabrous stem and having thin stripes. The leaflets are leathery, usually three but more rarely can be five and are 5–10 cm long with petioles. The leaves are elliptical and ovate, obovate or lanceolate-ovate, measuring approximately 2–6 cm long and 1.2–3.5 cm wide. The apex of each leaf is acute or rounded with a sparsely caudate-acuminate and the margin is sharply serrated. The coloration is bright green when viewed from above and yellow-green from below with glabrous. The petiolules and leaf rachis have prickles and glandular hairs. The stipules are free or the base are united with petiole presenting in a lance shape and the margins are finely toothed with glandular tips and are caducous. The flowers are solitary in the leaf axils and are 5–7 cm in diameter, whereas the pedicel are 1.8–2.5 cm long and can occasionally be up to 3 cm. Additionally, the pedicels and calyx tubes are densely glandular and hairy, and these become acicular as the fruit grows. The spals are ovate-lanceolate with a leaf-like apex and the margin are pinnately lobed or entire which often with spiny and glandular hairs. The inner surface is densely pilose and it is slightly shorter than the petals which are white and are broadly obovate. The apex is retuse. There are numerous stamens and carpels, and styles are free and hairy and much shorter than stamens. The purple-brown fruit is pear-shaped, obovate or sparsely sub-globose. The outer layer is densely covered with prickly hairs and the pedicel of fruit is approximately 3 cm long with persistent, sepals. The flowers can be collected in early summer (April to June) and the seeds are collected in July to November (Editorial Board of Flora of China, Chinese Academy of Sciences, 2004).
Roseceae are widely distributed in the temperate and subtropical regions of the northern hemisphere, and their centres are localized in Central and South-West Asia. Asia has the largest number of wild rose species and the longest history of their existence. China is the modern distribution centre of Rosaceae plants, and R. laevigata is mainly found in south-western, southern, central and eastern China. They grow in area such as Shaanxi, Anhui, Jiangxi, Jiangsu, Zhejiang, Hubei, Hunan, Guangdong, Guangxi, Taiwan, Fujian, Sichuan, Yunnan and Guizhou. This plant is usually found in sunny mountain fields, field margins and streamside, and it is generally found in the mountains at altitudes of 200–1600 m (Liu X. G. et al., 2013). R. laevigata is also found in Tibet, mainly in the sunny mountains of southern Tibet at altitudes of 1500–3500 m. R. laevigata produced in this region is influenced by the special climatic conditions and is of better medicinal quality than the mainland varieties (Liu et al., 2010a).
Rosa laevigata Michx. was first published in the Shu Ben Cao(Han, Five Dynasties) written by Han Baosheng during 935-960 AD (Later Shu of the Five Dynasties) (Han et al., 935-960 AD). Its description in this auspicious work was: “R. laevigata is found everywhere. The flowers are white. The seeds resemble quince but are small and yellow with spines. It is often used in Chinese traditional medicine.” Dream Pool Essays (Shen et al., the end of the 11th century), which was written at the end of the 11th century, mentioned the following: “R. laevigata, stop the ejaculation, to take its warm and astringent, the world with gold poppy, to be its red ripe, take the juice and boil the paste to use, a big mistake, red is the taste of sweet, boil the paste is all broken astringent taste, all lose the nature. Nowadays we should pick the half yellow leaves, and these should be dried and pounded before use.” It is also written in the Compendium (Li et al., the middle of the Ming Dynasty) (written in the middle of the Ming Dynasty, during the Jiajing period) that “If one takes R. laevigata for no reason, in order to obtain a quick desire, one should not; if one takes them if one’s essence is not solid, there is no blame.” Written by Huangfu Zhong between 1,368 and 1644 AD, the Guide to Famous Doctors (Huangfu et al., 1368-1644 AD)states that “R. laevigata is useful for treating dream loss and spermatorrhea”. There are also many local stories that include the effects of R. laevigata. For example, the Min Dong Materia Medica (The Editorial Committee of Eastern Fujian Materia Medica, 1962) mentions that R. laevigata can be used to treat hypospermia and spermatorrhea in men and leucorrhoea in women, as well as to treat pubic erections. The Quanzhou Materia Medica (Quanzhou Municipal Science and Technology Commission et al., 1961)also records that R. laevigata can cure frequent urination, polyuria, incontinence of urine, and also the chronic deficiencies diarrhea and dysentery. Radix R. laevigata has also been included in many local chronicles: the Lingnan Herb Journal (Guangdong Institute of Traditional Chinese Medicine et al., 1961) records that it can cure spermatorrhea and treat stomach pains. The Hunan Medicinal Journal (Hunan Institute of Traditional Chinese Medicine, 2017) mentions that the roots of R. laevigata is a remedy for children’s enuresis, for diarrhoea, and as a treatment for bruises. It is also described in the Jiangxi Folk Herbal Recipes (Gong et al., 1963) as a remedy for women’s leaks and for fire and soup injuries, as well as for recurrent fires in the lower limbs. In the Min Dong Materia Medica (Editorial Committee of Eastern Fujian Materia Medica., 1962), the roots of R. laevigata are used to treat lumbar spinal pain and rheumatic joint pain. The Chinese Medicine in Guangdong (Guangdong Provincial Department of Health, 1959) documents its effectiveness in treating a prolapsed uterus. A summary of the prescription names, sources of prescriptions and formulas for R. laevigata and its roots is given in Supplementary Table S1. This shows that the Chinese patent medicines which have R. laevigata as one of the main ingredients include Shuiluerwei pill, Zhuangyaojianshen pills, Zhuangyaojianshen tablets, ancient Chinese health essences, ancient Chinese health tablets and so on. Chinese patent medicines with the roots of R. laevigata as the main ingredient include Gynecological Qian Jin Tablets, San Jin Tablets, Gynecological Qian Jin Capsules, Jin Ji Capsules and Guangdong herbal tea. The sales of these products support the Chinese economy to approximately US $5 billion.
The four representative prescription formulations that existed in traditional applications have been optimized with modern technology to produce the widely used proprietary Chinese mediciens (pCms) of today, namely Guangdong Herbal Tea Granules, Gynecology Qianjin Tablets, Sanjin Tablets and Jin Ji Capsules. Guangdong Herbal Tea Granules (State Drug Certification Z44020615) are granules, which are manufactured by the Guangzhou Wanglaoji Pharmaceutical Co. They are used for clearing away damp-heat, relieving summer heat and producing body fluids containing waste products harmful to the person. They are also used for treating colds of the four seasons, fever and sore throat as well as damp-heat stagnation, dry mouth and redness of the urine. Gynecology Qianjin Tablets (State Drug Certification Z43020027) are manufactured by the Zhuzhou Qianjin Pharmaceutical Co. These are used for clearing away heat, tonifying Qi and resolving blood stasis. For heat and stagnation caused by these symptoms that originate in the abdomen, manifesting as profuse, yellowish and thick, foul-smelling discharge, abdominal pain, lumbosacral pain and fatigue. These are also useful for treating chronic pelvic inflammatory disease, endometritis and chronic cervicitis with the above symptoms. As a Qianjin Pharmaceutical’s flagship product,“Gynecological Qianjin Tablets” still accounts for a disproportionately large share of the main business sales revenue, accounting for more than 80% of its sales revenues, and it is the “only product” of note from this company. Sanjin Tablets (State Drug Certification Z45021645) are manufactured by the Guilin Sanjin Pharmaceutical Co. They are used for clearing away heat and detoxifying harmful toxins in the body, clearing away damp-heat and promoting drenching, and benefiting the kidneys. They are also used for treating pyorrhea and redness in the urine, dripping and astringent pain, frequent urination due to humidity and heat in the lower jiao as well as acute and chronic pyelonephritis, cystitis and urinary tract infections associated with the above symptoms. By the end of 2015, “Sanjin Tablets” had established long-term stable customer relationships with more than 500 distributors and more than 2,300 hospitals of Grade IIA or above as well as more than 21,000 pharmacies. They occupied the herbal markets in more than 30 provinces and cities across the country, with leading levels of quality, technology, efficacy and other indicators always being assured. This product has proven to have outstanding qualities and it has remained on the market for more than 30 years. Jin Ji Capsules (State Drug Certification Z45020293) are manufactured by the Guangxi Lingfeng Pharmaceutical Co. Its functions include clearing away heat and detoxifying harmful toxins, invigorating the spleen and clearing away damp-heat, promoting circulation and activating circulation of the blood. In addition, for adnexitis, endometritis and pelvic inflammatory disease caused by damp-heat infiltration. These proprietary Chinese medicines are mainly made of Radix R. laevigata, are household names in China and have a pharmaceutical output value of several billion RMB in the country.
So far 148 components have been isolated from Rosa laevigata Michx. of which (82 and 68 compounds are from Fructus R. laevigata and Radix R. laevigata and 15 and six are new, respectively. In addition, 12 identical chemical components are present in both herbs) and these include triterpenes, flavonoids, tannins, lignans, phenolics and other compounds. The chemical constituents that have been identified are listed in Table 1 and their corresponding structures are diagrammatically shown in Figures 1–7. Among these, the triterpenes are its main active ingredients and are characteristic components of this herb.
The triterpenoids and their derivatives isolated from this species are divided into three types: the ursane, the oleanolane and lupine-types, of which the uresane-type accounts for the majority. To date, 76 triterpenoids have been identified from Fructus R. laevigata and Radix R.laevigata, with 57 (1-57) of the ursane-type, 12 (58-69) of the oleanane-type and 6 (70-75) of the lupine-type. In addition, there is one other conformation of triterpene (76), of which there are a total of 10 novel compounds. There are: 3β-[(R-l-arabinopyranosyl)oxy]-20β-hydroxyursan-28-oic acidδ-lactone (4), 2ɑ,3β,23-trihydroxy-12,17-dien-28-norursane (6), 2ɑ,3β,23-trihydroxy-19-oxo-18,19-seco-12,17-dien-28-norursane (7), 2ɑ,3ɑ,23-trihydroxy-19-oxo-18,19-seco-urs-11.13 (18)-dien-28-oic acid (8), 18,19-split ring-2α,3β,23α-trihydroxy-19-carbonyl-ursane-11.13 (18)-diene-28-carboxylic acid (9), 2α,3α,24-trihydroxy-urs-12,18-dien-28-oic acid-β-d-glucopyranosyl ester (14), 2α,3α,23-trihydroxy-urs-12.19 (29)-dien-28-oic acid-β-d-glucopyranosyl ester (16), 19α-OH-3β-E-feruloyl corosolic acid (42), 2α,3α,19α,23-tetrahydroxyolean-12-en-28-oic acid 59) and 2α,3β-dihydrox-yolean-13 (18)-en-28-oic acid (69).
The flavonoids are widely found in natural plants and can be isolated from Fructus R. laevigata and Radix R. laevigata, and about 25 compounds have so far been isolated. These flavonoids are subdivided into flavonols (77-84), flavan-3-ols (85-91), flavan-3,4-diols (92-96), flavanones (97-99), flavonoids (100) and one other conformation of flavonoids (101). Of these 25 compounds, two are new and these are (+)-catechin-8-acetic acid (87) and guibourtacacidine 4-methyl ether (94). The extraction and isolation of the total flavonoids from Fructus R. laevigata and Radix R. laevigata have been a particular focus of attention and most of these components have already been identified.
To date a variety of tannins have been isolated from Fructus R. laevigata and Radix R. laevigata, including dimeric ellagitannins (102-104), hydrolysable tannins (105-108), condensed tannins (117-121) and a few other tannins (109-116). Among these seven newly discovered tannins have been identified and these are: tannin E-G (102-104), laevigatin A-D (105-108).
In addition to the three groups mentioned above, Fructus R. laevigata and Radix R. laevigata also contain lignans (122,131), phenols (123-130,132-137,140-142), catechins (138), benzoic acid derivatives (139), sterols (143-144), stilbene compounds (146-147), polysaccharide 148) and other compounds glucose 145).
Pharmacological studies have shown that Fructus Rosa laevigata and Radix Rosa laevigata have a variety of pharmacological activities, including antioxidant activity and renal protection. Moreover, they play a role in immune regulation and have hypolipidemic, anti-inflammatory, cardiovascular protective and antibacterial activities. In addition, Rosa laevigata Michx. also has an important role in combating diabetes and it has antiviral and anti-tumour activities. The pharmacological effects of R. laevigata and its monomers are summarized in Table 2.
The antioxidant activity exhibited by R. laevigata depends on its ability to scavenge free radicals and cause the reduction of metal ions. Studies have shown that Fructus R. laevigata is rich in total flavonoids (TFs), a component that is closely associated with antioxidant activity. This suggests that TFs in R. laevigata may be a rich source of natural antioxidants for the prevention and treatment of diseases caused by oxidative stress such as cardiovascular disease (Li et al., 2021). Liu et al. (2010b) found that TFs of Fructus R. laevigata have a scavenging effect on 2,2-diphenyl-1-picrylhydrazyl (DPPH), hydroxyl and superoxide anion radicals and they have a strong reducing ability. Intragastric administration of TFs (25, 50 mg/kg/day) to hyperlipidemic mice for 4 weeks, showed that TFs significantly increased the levels of several antioxidant substances including catalase (CAT), superoxide dismutase (SOD), GSH and GPX in the liver. It was also seen that TFs significantly reduce hepatic malondialdehyde (MDA) concentrations in a dose-dependent manner. Liu et al. (2014, 2019) also found a protective effect of TFs of Fructus R. laevigata against hydrogen peroxide-induced cell damage. The data showed that TFs down-regulated the expression of CYP2E1, iNOS, nuclear factor-kappab (NF-κB), BaK and caspase-3 and significantly reduced the mRNA levels of TNF-α and Fas/Fasl. In addition, Jia et al. (2012) concluded that TFs of Fructus R. laevigata can reduce the expression of fragmented DNA, Bax, Bid and p53 as well as activity the activities of caspases-3 and -9. They also increased the protein expression of procaspase-3 and Bcl-2. It is clear that these three mechanisms of antioxidant activity are similar. In summary, TFs can exert antioxidant activity by reducing oxidative stress, cell inflammation and apoptosis, and there is a good quantitative relationship between the TF content and antioxidant activity at a range of concentrations. There are also several flavonoid quercetin and proanthocyanidins that have antioxidant activity and their scavenging effects of free radicals are also strong. In addition, quercetin has some inhibitory effects on linoleic acid peroxidation, and proanthocyanidins have inhibitory effect on lipid oxidation (Xiao et al., 2010; Chen et al., 2013). Of course, the flavonoids are not the only compounds to have antioxidant activities in R. laevigata, and the others are the total phenols. Meng et al. (2012) found that total phenols have strong scavenging ability for 2.2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radicals. They also have strong copper ion reduction and metal chelating abilities, Moreno et al. (2007), Apak et al. (2004) and, Du et al. (2009) investigated the ABTS scavenging, copper ion reducing, and metal chelating abilities, respectively. Their results showed a correlation between total phenolic content and antioxidant activity. From a review of the relevant literature, it san be seen that polysaccharides in Fructus R. laevigata also have antioxidant activity. When the protection rate of vegetable and animal oils was approximately 50%, the mass concentration of polysaccharides was 2.8 and 5.9ng/ml, respectively. The scavenging of hydroxyl radicals by polysaccharides was 4.7 ng/ml when the mass concentration of R. laevigata was at 50%. The scavenging rate of superoxide anion radicals by the same concentration of polysaccharides of Fructus R. laevigata was only 23%. In addition the reduction ability of polysaccharides of R. laevigata to Fe3+ was also found to be very strong. The results showed that the antioxidant activity of Fructus R. laevigata had a good quantitative relationship with the polysaccharide content. However, a single approach cannot fully demonstrate the extent of the antioxidant capacity of a substance. Therefore, it is not always straightforward to correlate the antioxidant capacity of different substances (Wang Y. M. et al., 2016).
Fructus R. laevigata is believed to be able to improve kidney health. There are many studies that have verified the kidney-protective effects of this herb. Zhou et al. (2012) investigated the effects of R. laevigata on renal oxidative stress in streptozotocin-induced diabetic rats. The results showed that R. laevigata significantly improved the renal dysfunction of these diabetic rats. The mechanism involves an increase in SOD and total antioxidant activities and a reduction in the levels of MDA and reactive oxygen species (ROS). The expression of nuclear factor-κB p65 and monocyte chemotactic protein-1 was inhibited at the protein and mRNA levels, respectively, while the expression of IκBα protein was increased. Zhao et al. (2016) found that TFs in R. laevigata have a significant nephron-protective effect on ischemia-reperfusion injury (IRI) by affecting the Sirt1/Nrf2/NF-κB signaling pathway. After treatment with R. laevigata, SOD activity and total antioxidant power increased and the levels of MDA and ROS decreased in rats with kidney injury, achieving an overall reduction in kidney injury. Thus, the effect of adminishtration of R. laevigata extracts on kidney damage is mainly dependent on Sirt 1.
In recent years, a number of studies have shown that the ethanol extracts of Fructus R. laevigata can have some immunological activity. Zhan et al. (2020) identified a novel acidic polysaccharide, PPRLMF-2, which recognizes pattern recognition receptors (PRRs) of macrophages and enhances their immunomodulatory activity by activating MAPKs and NF-κB signaling pathways. The mechanism of action is that PPRLMF-2 can significantly increase the phagocytosis and the secretion of cytokines in murine RAW264.7 cells lines. In addition, SR, GR, Toll-like receptor-2 (TLR-2) and Toll-like receptor-4 (TLR-4) are the major PRRs that upregulate the expression of p-Extracellular signal-regulated kinase (p-ERK), p-c-Jun N-terminal kinase (p-JNK), phospho-NF-κB p38 (p-p38) and phospho-NF-κB p65 (p-p65). In addition, Gao et al. (2018) also found the presence of polyhydroxy triterpenoids which have anti-acetylcholinesterase activity in extracts from R. laevigata. Experiments showed that two pure polyhydroxytriterpenes, 1 and 2, were isolated by the authors and these showed potent anti-acetylcholinesterase activities. Diao et al. (2014) studied the effects of fermented R. laevigata on the immune performance of weaned piglets. They showed that fermented R. laevigata could significantly increase the levels of immunoglobulin IgA, IgM and IgG in these animals (the most suitable concentration was 0.2%) and this improved the immunity of the weaned piglets. Peng et al. (2014) performed a study on the immunomodulatory activity of Radix and Rhizome R. laevigata of different origins. Their results showed that the hydroextracts of Radix R. laevigata had some specific immunosuppressive activities. It was concluded that different medicinal parts of R. laevigata different origins had similar immunological activities without any significant differences.
TFs components of Fructus R. laevigata have received more attention as lipid-lowering. Zhang et al. (2013a) investigated the effects of TFs in R. laevigata on non-alcoholic fatty liver disease (NAFLD) induced by high-fat diets in animal studies. The results suggest that TFs inhibit hepatic fat accumulation by suppressing the expression of some key molecules in the fatty acid synthesis pathway and promoting the β-oxidation fatty acids. They also showed that TFs did not achieve lipid-lowering activity by inhibiting cholesterol synthesis. In addition, Liu Y. et al. (2010) also found that TFs had strong hypolipidemic activity and the levels were governed partly by enhancing the antioxidant system. A review of other related literature revealed that the polysaccharides in R. laevigata also have hypo-lipidemic activity. Zhang et al. (2020) found that low molecular polysaccharides extracted from the fruits of Rosa laevigata (RLPs) reduced serum lipid levels and increased those of HDL cholesterol. After further study, the results showed that RLPs (with molecular weights of 9,004, 8,761 and 7,571 Da) exhibited significant hypolipidemic effects in high-fat diet-induced muscle, and they did so by integrating with the PPPAR signaling pathway to improve lipid metabolism dysfunction. The study also illustrated that high doses of RLPs havd stronger hypo-lipidemic effects, and these were more positive when compared to that of polymeric polysaccharides. Another paper also investigated the hypo-lipidemic activity of the polysaccharide, RLP-2 (21.5 kDa) (Yu et al., 2013). They found that the mechanism of action in both studies were similar, and further studies determined that the strength of the activities of these polysaccharides verified the effects of molecular weight on the hypolipidemic activity. In addition, the total polyphenols and saponins in R. laevigata also have been shown to have some hypo-lipidemic activity (Dong et al., 2019; Li J. et al., 2019). However, because the mechanism of lipid synthesis is complex and it has been difficult to identify a specific major factor or mechanism to explain the hypo-lipidemic activity of R. laevigata, so further studies still need to be performed.
Among the studies on the anti-inflammatory activity of Fructus R. laevigata, triterpenoids have received the most attention. Yan et al. (2013) investigated the anti-inflammatory activity of triterpenoids by using the LPS macrophage-induced luciferase assay. Binding of NF-κB to the κB site can control cytokine expression, and the mechanism by which triterpenoids inhibit NK-κB activation may be determined by performing NF-κB luciferase assays in NF-κB-luc293 cells. The triterpenoids 4, 9, 11, and 12 in this study were found to have inhibitory effects on cytokine release in LPS-stimulated mouse macrophages RAW264.7. It was also found that compound 12 had a much stronger anti-inflammatory activity than compound 11. This was probably, due to the presence of a hydroxyl group at C-23, which may have greatly reduced the release of inflammatory factors. Compound 12 showed moderate inhibition of the transcriptional activity of NF-κB with an IC50 of 23.21 μM. Its inhibitory activities against TNFα-release, IL-1β-release, IL-6-release and IL-10-release were 14.32, 8.53, 8.04 and 10.38μM, respectively. The presence of glucoside in this compound may have caused an increase in the anti-inflammatory activity. Several scholars have also found anti-inflammatory effects of R. laevigata in animal disease models. Ko et al. (2020) found that R. laevigata inhibited the expression levels of MAPK/NF-κB pathways and its downstream signal COX-2 in PM10-induced A549 cells in an induced lung inflammatory disease model. In this dhtudy, R. laevigata was shown to reduce the pro-inflammatory factors activated by PM10. The results in the literature suggest that R. laevigata alleviates PM10-induced lung inflammation, and the mechanism may be that pretreatment with this herb medicine inhibits PM10-induced activation of MAPK phosphorylation and suppresses nuclear translocation of NF-κB p65. In contrast, in a mouse model of chronic inflammation, Lee et al. (2020) investigated whether R. laevigata exhibited anti-inflammatory effects associated with acute asthma in both in vitro and in vivo experiments. Their results showed that after RLM pretreatment, which significantly inhibited EGF-induced NF-κB activity and COX-2 expression levels in A549 cells, inflammatory cells, IgE secretion and related substances were reduced in the model in a dose-dependent manner to reduce allergic airway inflammation. The anti-inflammatory mechanism seen may act through the inhibition of IgE and related cytokines. It was not only Fructus R. laevigata which had anti-inflammatory activity, but also Radix R. laevigata. Its chemical composition is high in tannins, which play an important role as anti-inflammatory agents (Zou and Qiu, 2011). Zou and Qiu (2011) established a model and found Radix had a better anti-inflammatory effect by measuring its effect on NO release from lipopolysaccharide-induced peritoneal macrophages in mice, and this effect was related to the inhibition of NO release.
The protective effects of Fructus R. laevigata on cardiovascular and cerebrovascular are mainly due to the antioxidant and anti-inflammatory activities of its active ingredients. For example, Luo et al. (2014) found that R. laevigata could increase the mRNA expression levels of CuZn-SOD protein as well as glutathion peroxidase (GSH-PX), catalase (CAT) and superoxide dismutase (SOD) activities in myocardial tissue. High doses of R. laevigata reduced myocardial apoptosis induced by adriamycin and enhanced Bcl-2 gene expression and decreased Bax levels. This confirmed the protective effects of R. laevigata on adriamycin-induced myocardial injury. Liu et al. (2010a) found that high doses of oral TFs reduced total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C). Jia et al. (2012) demonstrated the protective effects of TFs in R. laevigata against hydrogen peroxide-induced injury in human umbilical vein endothelial cells. Qu et al. (2015) established a myocardial infarction (MI) rat model to study the ability of the active ingredients of R. laevigata in the treatment of this condition. The results showed oral administration of the active ingredients to MI-induced animals gradually restored the decline in cardiac function caused by MI and induced the myocardial regeneration and replacement of necrotic heart tissues. The authors hypothesized that the mechanism of myocardial regeneration induced by the active ingredients of R. laevigata may be the multiple properties of the active ingredients in anti-inflammatory and anti-oxidative stress. This promoted cell survival and prevented IRI. However, the action of the specific ingredients and the specific mechanisms involved are still unclear and need to be further investigated. Chen et al. (2014) found that medium and high concentrations of TFs significantly reduced whole blood viscosity in rats and had some inhibitory effects on platelet aggregation, thus exerting antithrombotic effects. Additionally, Luo et al. (2014) found that R. laevigata could improve doxorubicin (DOX)-induced myocardial toxicity and that co-administration with cloxacin (LOS) provided better myocardial protection. This mechanism may also be related to the anti-inflammatory and antioxidant effects of this plant.
One of the many pharmacological effects of Fructus R. laevigata, such as its antibacterial effect, also plays an important role in daily life. Staphylococcus aureus, Bacillus subtilis and Escherichia coli are considered to be serious modern day hazards and these can affect food hygiene and safety. Liu et al. (2009) preliminarily studied the inhibitory effect of extracts of polysaccharides and flavonoids from the fruits of R. laevigata on these three species of bacteria. The results showed that both extracts had some antibacterial effects, with the polysaccharide having the best antibacterial effect on E. coli (MICs of polysaccharides of R. laevigata on these three bacteria were 25, 50 and 3.13 mg/ml, respectively). This was followed by S. aureus, while the inhibitory effect of flavonoids was exactly the opposite (MICs of flavonoids for these three bacteria were 3.13, 12.5 and 6.25 mg/ml, respectively). Both the polysaccharides and flavonoids showed the weakest inhibition with B. subtilis. However, the relationship between its inhibitory mechanism and its specific structural composition needs to be further investigated. A review of the relevant literature revealed that the roots and stems of R. laevigata are also known to possess antibacterial activities. The acetone extract of R. laevigata had an inhibitory effect on Streptococcus mutans which usually exist in the oral cavity, and the inhibitory effect of this extract was mainly derived from the tannin component of R. laevigata (Li, 2006). Lai et al. (2009) determined the bacterial inhibitory effects of the roots and stems polysaccharides of R. laevigata using the punch-hole method and an agar diffusion technique. The results showed that the polysaccharides in the roots and stems of R. laevigata inhibited Staphylococcus albicans, Staphylococcus citricola, S. aureus, Klebsiella pneumoniae and Bacillus dysenteriae in a dose-dependent manner. There were also no significant differences in the inhibitory effects of extracts from the two sites. In addition, Lai et al. (2012) also investigated the differences in the antibacterial effects between extracts obtained from using different polar solvents of the roots. The results showed that the aqueous and alcoholic extracts of Radix R. laevigata had better antibacterial effects, mainly due to the higher content of flavonoids, tannins and triterpenoids in these two extracts.
Huang and Liu. (2017) studied the effects of two flavonoid active components of Fructus R. laevigata, rutin and quercetin, on the proliferation of human hepatocellular carcinoma cells cultured in vitro. Both flavonoids were found to exert some inhibitory effect on the proliferative effects of human hepatoma cells BEL-7402 cultured in vitro, with rutin and quercetin having IC50s of 29.91 ± 3.05 and 7.625 ± 2.02 μmol/L, respectively. There were also less toxic to normal human hepatocytes cultured in vitro HL-7702, with IC50s of 29.91 ± 3.05 and 35.54 ± 4.37 μmol/L, respectively. Others also investigated the in vitro antitumor activity of polysaccharides from R. laevigata and found that these compounds had some inhibitory effects on human hepatocellular carcinoma cells cultured in vitro (Huang and Liu, 2015). The inhibitory effect on the proliferation of human hepatocellular carcinoma cells, BEL-7402, cultured in vitro, had an IC50 of (12.43 ± 1.95) μmol/L. There was a lower toxicity to normal human hepatocytes, HL- 7,702, in vitro, with an IC50 of (31.04 ± 3.68) μmol/L. Therefore, it was also concluded that the polysaccharide compounds in R. laevigata also had some anti-tumor activity. However, polysaccharides in Radix R. laevigata had no significant therapeutic effect when used directly as an anti-tumour drug. However, when combined with 5-Fu, it had a significant potentiation and toxicity reduction effect (Feng and Tian, 2011), suggesting that polysaccharides in the root of R. laevigata may act as a potential anti-tumour adjuvant.
Liu et al. (2017a) studied the direct inactivation, prophylaxis and post-viral penetration inhibition of respiratory syncytial virus (RSV), herpes simplex virus-1 (HSV-1), coxsackie B5 (COX-B5) and enterovirus 71 (EV71) by different extracts of Fructus R. laevigata. Their results showed that Fructus R. laevigata had no preventive effect and no post-penetration inhibition against these viruses. The direct inactivation of RSV by ethyl acetate extract was good with a therapeutic index (TI) of 19.333, which was comparable to that of the positive control, ribavirin (TI of 19.760). The n-butanol extract was able to directly inactivate COX-B5 with a TI of 16.622 and the positive control, ribavirin, with a TI of 17.562. The alcohol extract was most effective against HSV-1 with a TI of 18.922 and the positive control, acyclovir, with a TI of 23.742, while there was no direct inactivation of EV71. However, when using the sonicated of R. laevigata in acetone, methanol and ethanol it was found that the 60% sonicated ethanol extract was relatively more effective against RSV and EV71 than the other two (Li N. et al., 2019). Liu et al. (2017b) also investigated the in vitro antiviral activity of polysaccharide in R. laevigata and studied their inhibitory effects on RSV, COX-B5 and EV71. They showed that the polysaccharides had in vitro anti-RSV, COX-B5 and EV71 activities, and the anti-RSV and COX-B effects were better than those of the positive control drug ribavirin, with TI of 80.895 and 41.541, respectively. Also some studies have shown that a combination of polysaccharides of R. laevigata and adriamycin has a significant potentiation and toxicity reduction effect, which could significantly increase the tumour inhibition rate. In addition, some studies have shown that the active ingredients of R. laevigata against HSV is a polyhydroxyl pigment (Luo et al., 1989). However, the active ingredients against other viruses have not yet been identified and further research is still needed in this area.
Diabetes is now a “very common” disease and is increasingly being researched and the active use of herbal medicines to treat diabetes and its related diseases are increasing. Liu et al. (2015) investigated the effects of Fructus R. laevigata on the production of ROS and the mitochondrial membrane potential (MMP) in lens epithelial cells under high glucose conditions by establishing a cell model of SRA01/04 in diabetic cataract. The results showed that under high glucose conditions, R. laevigata inhibited ROS production and increased MMP by inducing HO-1 expression. This effect was mediated by the PI3K/AKt and Nrf2/ARE pathways, which are lost if one of these pathways when they are inhibited. Thus it was concluded that R. laevigata can play an important role in the treatment of diabetic cataract. In a study by Zhou et al. (2014), Fructus R. laevigata was found to inhibit the apoptosis of rat diabetic cataract lens epithelial cells by increasing the Bcl-2/Bax expression ratio, and thus delaying the onset and development of diabetic cataract. In addition, Kumar et al. (2021) used in vitro DNS glucose uptake and western blotting analysis to study the hypoglycaemic effects of ethanolic extracts of R. laevigata and its derivative subfractions (in water, n-butanol, ethyl acetate and n-hexane). They studied the effects of its main bioactive compound, glutarone, on normal and high glucose-induced insulin-resistant hepatic HepG2 cells. The results showed that the ethanolic extract of R. laevigata and its derivative sub-fractions significantly increased glucose uptake in hepatocytes, and the bioactive glutamatergic ingredients significantly increased glucose uptake in insulin resistant cells and promoted insulin sensitivity. This led them to conclude that R. laevigata had an important role in hypoglycaemia and that it can also be effective in improving the hyperglycaemic and hyperlipidemic states of patients with type Ⅱ diabetes (Deng and Zhang, 2018).
The liver is the largest metabolic organ in the body and is associated with a large number of diseases related to its damage and hepatotoxicity. Many studies have revealed that the mechanism of action of Fructus R. laevigata in protecting liver tissues is most closely linked to their anti-inflammatory activity, antioxidant capacity and resistance to oxidative stress. Generally, the main components that exert hepatoprotective effects are the TFs. For example, Zhang et al. (2013b) studied the protective effect of TFs on CCl4-induced hepatotoxicity in mice and found that pretreatment with TFs reduced the expression of CYP2E1, iNOS, NF-κB, Bak, Caspase-3, TNF-ɑ and Fas/FasL, and upregulated the levels of Bcl-2. This reduced the incidence of liver lesions and improved the abnormalities of hepatocytes, thus achieving a protective effect on the liver. In addition, TFs significantly reduced the CCl4-induced increase in aspartate aminotransferase (AST) and alanine transferase (ALT) activities. The TFs of R. laevigata also exerted protective effects against lipopolysaccharide (LPS)-induced liver injury in mice by modulating FXR signaling (Dong et al., 2018). They also significantly reduced serum ALT, AST, total triglycerides (TG), total cholesterol (TC) levels and the relative liver weight, resulting in improvement of pathological changes in the liver. TFs could also significantly reduce tissue MDA levels and increased SOD and GSH-Px levels, and a mechanistic study showed that these compounds significantly increased nuclear erythroid-like factor 2-related factor 2 (Nrf2), heme oxygenase 1 (HO-1), NAD(P)H dehydrogenase (quinone 1) (NQO1), glutamate-cysteine ligase catalytic (GCLC) subunit and glutamate-cysteine ligase regulatory (GCLM) subunit expression levels. They also decreased the expression levels of ECH-like associated protein 1 (Keap1). Additionally, TFs significantly inhibited the nuclear translocation of NF-κB and subsequently reduced the expression levels of interleukin (IL)-1β, IL-6, high mobility group box 1(HMGB-1) and cyclooxygenase-2 (COX-2) by activating FXR and forkhead box O3 (FOXO3a) against inflammation. They could notably reduce the expression levels of sterol regulatory element binding protein-1c (SREBP-1c), acetyl coenzyme a carboxylase-1 (ACC1), fatty acid synthase (FASN) and stearoyl coenzyme A desaturase 1 (SCD1). These compounds increased the expression levels of carnitine palmitoyltransferase 1 (CPT1) by activating FXR to regulate lipid metabolism. In addition to TFs, the total saponins (TSs) in Fructus R. laevigata also had a protective effect against CCl4-induced acute liver injury in mice. A study showed that pretreatment with these compounds significantly reduced the protein expression levels of CYP2E1, ATF6, GRP78, EIF2, COX-2, NF-κB, p53, Caspase-9 and cytokeratin 18 and the phosphorylation levels of MAPKs. They also significantly reduced the gene expression of TNF-ɑ, IL-6, Fas/FasL and Bax. Like TFs, TSs also upregulated Bcl-2 expression and also reduced serum AST and ALT activities, therefore exerting a hepatoprotective effect by these mechanisms (Dong et al., 2013). Dong et al. (2015) found a protective effect of TSs against CCl4-induced hepatic fibrosis. The mechanism may be that TSs act as an anti-fibrotic agent by down-regulating matrix metalloproteinases for matrix degradation and also by modulating the signaling pathways related to TGFβ/Smad, focal adhesion kinase 1 (FAK), phosphatidylinositol three kinase (PI3K) protein kinase B (Akt)-p70S6 kinase and MAPKs. Liu et al. (2010b) found that TFs of Fructus R. laevigata also had a protective effect against paracetamol-induced liver injury. Dong et al. (2014) investigated the effects of TSs in R. laevigata on liver injury together with acetaminophen and showed that TSs exerted a protective effect against acetaminophen-induced liver injury by inducing autophagy and anti-inflammatory effects as well as apoptotic cell death. Xie et al. (2001) found strong scavenging effects of Fructus R. laevigata extracts on NO2- under pH acidic conditions that simulated gastric juice production. It has also been found that TFs and the polysaccharide, TNBS, from Fructus R. laevigata exerted mucosal protective effects in mice with Crohn’s disease by reducing the weight/length ratios of their colons. The TNF-ɑ and IFN-γ levels in the colonic tissues were also reduced (Bian et al., 2018). An aqueous extract of Fructus R. laevigata was also found to play an important role in the urinary system, which may be of benefit in reflex incontinence (Lu et al., 1995). In addition, it was shown that a compounded extract of Fructus R. laevigata could effectively treat white diarrhoea in piglets in a dose-dependent manner (Xiang et al., 2015). One study also found that the alcoholic extract of Radix R. laevigata exhibited significant tolerance to hypoxia and prolonged the survival periods of mice after its administration (Huang and Liu, 2015).
It is well known that many herbs have therapeutic effects as well as adverse reactions. Relatively few toxicity studies have been reported for Rosa laevigata Michx., but as it becomes more widely used, such issues should receive more attention and raise serious questions about its safety in clinical settings. Zhang et al. (2012) studied the subchronic toxicity of TFs obtained from R. laevigata in a 90-day subchronic toxicity study in rats given oral doses of 500, 1,000 and 2000 mg/kg/day of TFs. Toxicity assessment of TFs based on ophthalmic examination, body weight, feed and water consumption, urinalysis, haematology, clinical biochemistry and pathology were performed in rats. No signs of toxicity of TFs were observed at doses of 500 and 1,000 mg/kg/day, while a decrease in platelet count and an increase in cardiomyocyte voids were found in rats at a dose of 2000 mg/kg/day compared to the control group. In the 2000 mg/kg/day dose group, the relative weight of cardiomyocytes increased significantly in male rats, while the absolute and relative weight of the adrenal glands decreased significantly in female rats at doses of 1,000 and 2000 mg/kg/day. The analysis concluded that at a dose of 1,000 mg/kg/day, the TFs caused only mild side-effects in both male and female rats. This led to the selection of a dose of 500 mg/kg/day for rats as the no visible adverse effect level (NOAEL) for R. laevigata. Li and Feng. (1990) conducted the first toxicological evaluation test on the brown pigment found in R. laevigata. The Ames test is one used for assessment of acute toxicity and the mouse testicular chromosome test that is a mouse bone marrow micronucleus test for harmful elements such as lead and arsenic were performed. The test results showed that the brown pigment was non-toxic, and all other tests were negative, thus concluding that brown pigment in R. laevigata is a safe natural pigment. Pang et al. (2006) investigated the mutagenic effect of R. laevigata on mice by performing the bone marrow micronucleus sperm deformation and sperm non-programmed DNA (UDS) tests. The results showed that within the dose range used in the experiments, R. laevigata did not cause an increase in the frequency of bone marrow micronuclei and hourly sperm malformations in mice. In addition, no induction effect on male mouse germ cells UDS were observed, indicating that R. laevigata caused no genetic damage on mice and that is a safer herbal medicine.
The factors affecting the variation in the constituent composition of Rosa laevigata Michx. are mainly related to geographical distribution. The polysaccharides and TFs in R. laevigata varies greatly from different locations and this can range from 30.5 to 42.7% and from 3.1 to 5.3%, respectively (Shi, 2012). In Fructus R. laevigata, the polysaccharides and TFs are in negative correspondence, and in general, the herb containing high polysaccharide contents will have a relatively low TF content (Zhang et al., 2014). In addition, Lin and Zhou. (2009) determined the tannin content in the fruits of R. laevigata according to the method of the Chinese Pharmacopoeia (I) 2005 edition and concluded that the concentration ranged from 10.20 to 29.76%. Their experiments also showed that the tannin content in R. laevigata also varies according to the geographical environment. Xu et al. (2002) determined the content of triterpenoid acids in R. laevigata from different origins by HPLC, and they showed that the levels of triterpenoid organic acids in the herbs from different origins differed greatly. National Pharmacopoeia Committee, 2020 edition stipulates that the polysaccharides obtained from R. laevigata should not be less than 25.0% with respect to the anhydrous glucose (C6H12O6) content.
National Pharmacopoeia Committee, 2020 edition stipulates that the moisture content of R. laevigata should not exceed 18.0% and the total ash content should not exceed 5.0%.
By conducting thermos-gravimetric analysis (TGA) studies on four different parts of R. laevigata including the roots, stems, pulp and seeds, Zhu et al. (2009) analyzed the characteristic parameters of TGA pyrolysis of different parts of the plant. They also derived the pattern of influence of the rate of temperature rise and species characteristics on the pyrolysis of R. laevigata. In addition, their study also showed the ease of decomposition (from easy to difficult) of four parts: seeds, pulp, stem and roots.
Chen et al. (2011) investigated the variation of odour fingerprints of R. laevigata at different time-periods, and then conducted principal component analysis (PCA) and discriminant factor analysis (DFA) on the odour response values measured by using electronic nose (EN) sensors. They also performed statistical quality control (SQC) analysis. The experimental results showed that the combined use of EN, PCA and DFA for odour analysis can be used for quality control of R. laevigata.
In the case of Radix R. laevigata, this herb is relatively scarce and is not included in the pharmacopoeia. There is a lack of complete quality standards for it, and therefore there are only a few studies regarding its content determination. Nevertheless, a review of the relevant literature revealed that the amounts of gallic acid and catechin in radix of R. laevigata and concoction products from different origins was determined by HPLC. The results showed that the content of gallic acid and catechin in the root of R. laevigata from different origins varied significantly, but all the samples contained more catechin than gallic acid (Wei et al., 2021a). In addition, Tan (2012) determined the content of polysaccharides in the roots, stems and fruits of R. laevigata from different origins by taking UV spectrophotometry measurement. The results showed that the polysaccharide content of the roots and stems of R. laevigata from different origins were lower than the fruits. From this they concluded that there were also differences in the polysaccharide content of different parts of R. laevigata. Wei et al. (2021b) adopted ICP-MS analysis in order to compare the content of metal elements in roots of R. laevigata and its associated products from different origins. The results showed that the most abundant macronutrients were Al and Fe, with the concentration of Al reaching 33.9–270 mg/kg in its concoction. Other trace elements found were B, Ba, Mn, Zn and Sr Theis method provides a necessary reference for the development of better quality standards for Radix R. laevigata.
Since Radix R. laevigata is not included in the pharmacopoeia, Su et al. (2012) stipulated through experimental studies, that the moisture content should not exceed 14.0% and the total ash content should not exceed 6.0%, of which the acid insoluble ash content should not exceed 2.0%. The results of this experiment provide a scientific basis for better quality control of Radix R. laevigata.
Flavonoids and polysaccharides are the main components and active compounds in Rosa laevigata Michx., and the optimization studies with respect to their extraction are essential to ensure the efficacy and quality control of this herbal medicine.
Li et al. (2009) studied the extraction process of flavonoids in R. laevigata by using a warm water extraction method. They also used ethanol and acetone for extractions, and then compared the three methods. They found acetone was better than ethanol which was better than warm water for extraction the constituents. The best extraction conditions were obtained by an orthogonal experiment using 60% ethanol, in a 70°C water bath, at a solid-liquid ratio of 1:150. The best extraction conditions were obtained by using the acetone method: acetone concentration 60%, water bath temperature 50°C and a solid-liquid ratio 1:50. Jiang et al. (2015) used the Box-Behnken response surface method to optimize the extraction process of R. laevigata, and screened out the best extraction process to be: 60% ethanol concentration, 13:1 material-to-liquid ratio, 2 h extraction time and 2 times extraction.
Shang et al. (2018) used the water extraction method to optimize the extraction process of R. laevigata formula granules. Their preferred extraction process was eight times the amount of water and two reflux extractions for 2 hours each time. The average extraction rate of polysaccharides from R. laevigata was calculated to be 22.17%.
Yi et al. (2011) studied the extraction of TFs from the roots of R. laevigata by using ethanol. Then they optimized the process conditions for obtaining TFs by using the orthogonal test method. The results showed that the extraction conditions for TFs from the roots of R. laevigata were best with 80% ethanol, 50°C extraction, reflux for 2.5 h and a material-liquid ratio of 1:50, and the dissolution rate of TFs was measured as 33.90%. Wu et al. (2011) conducted a comparative study of four different resins to obtain the best macroporous adsorbent resin for the purification of TFs from the roots of R. laevigata and optimized this process. The results showed that AB-8 was the best macroporous adsorbent resin for the purification of TFs from the root of R. laevigata. The best process for the separation and purification was 20.0 ml of 0.200 mg/ml sample solution with the AB-8 resin as the adsorbent and 50% ethanol as the eluent. The purification rate of TFs from the roots of R. laevigata could reach 205.40% under these optimal conditions.
1) In terms of chemical composition: most of the current research is focused on the study of flavonoids and triterpenes, and there is relatively little work on other active ingredients such as phenols, tannins and polysaccharides. Therefore, there is a need to strengthen the research on the other ingredients in order to improve the progress of the research on the chemical composition of Fructus R. laevigata and Radix R. laevigata. 2) In terms of the pharmacology: as mentioned above, most of the research today is on flavonoids and triterpenes, so there is a lot of pharmacological studies corresponding to these, while the pharmacological activities of other components are less developed. Moreover, the current pharmacological studies on R. laevigata and its roots are mainly focused on its antioxidant and anti-inflammatory activities, and the many other pharmacological effects that are derived from these two activities, such as renal and cardiovascular protection. Therefore, the mechanism of action has to be studied in depth. In addition, the anti-tumour activity, which is widely studied today, is rarely addressed, which leads to the conclusion that research in this area should be strengthened to provide a scientific basis for the development of new drugs. 3) In terms of quality control: While Fructus R. laevigata has been included in the Chinese Pharmacopoeia, Radix R. laevigata is not yet included. Although there are studies on its quality standards, there is still a need to continue to explore its quality standards in depth and to include Radix R. laevigata in the pharmacopoeia at an the earliest possible date. 4) In terms of toxicity studies: the toxicity studies on R. laevigata are too limited at present and the only study that has produced data is a sub-chronic toxicity study on rats with the TFs of R. laevigata. There are no data to support the few other studies and no toxicity studies on other components have so far emerged. Therefore, extensive toxicity studies should be conducted on this aspect to study the toxic effects of the various components of Fructus R. laevigata and Radix R. laevigata to determine their side-effects, acute toxicity and chronic toxicity to provide a scientific basis for the clinical use of these herbs. 5) In terms of concoction processing: at present, the edible development of R. laevigata is limited to pure fruit juice drinks, compound drinks and fruit wine brewing. Further exploration is needed in terms of its food promotion and value innovation.
As one of the herbs of medicinal and food origin, Rosa laevigata Michx. has strong biological activities of its active chemical components, which has led to its wide use in clinical and daily life. The Chinese National Ministry of Health has rated it as a new food resource and it has now developed into a third generation wild fruit food. Therefore, it is widely used in food ingredients, such as the development of fruit juices, fruit wines and yoghurt. In addition, a brown pigment that can be used as a food additive can also be extracted from R. laevigata. Pharmacological studies have shown that R. laevigata has the effect of improving the gastrointestinal tract, promoting intestinal peristalsis, increasing the digestion of food, reducing the accumulation of harmful substances in the intestinal tract, playing a role in eliminating persistent stools and also reducing the occurrence of gastrointestinal diseases. It is also used as a raw material in Chinese medicines for the clinical treatment of pelvic inflammatory disease, diabetic cataract, etc. The roots of R. laevigata are used as raw materials in San Jin tablets and gynaecological Qian Jin tablets for the treatment of diseases related to gynaecological infections. R. laevigata and its roots also have antioxidant, renal protective, immunomodulatory, hypo-lipidemic, anti-inflammatory, antiviral, anti-tumour, cardiovascular protective and antibacterial activities. In addition, it also plays an important role in diabetes mellitus. Chemical composition studies have shown that R. laevigata and its roots are rich in triterpenoids, flavonoids and polysaccharides, which are inextricably linked to its diverse pharmacological activities. As more and more scholars continue to study Fructus and Radix Rosae laevigatae in depth, these two herbs have a very promising future in the pharmaceutical, health-care and food industry markets. | true | true | true |
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PMC9582936 | Mingze Zhang,Nan Liu,Jaime A. Teixeira da Silva,Xuncheng Liu,Rufang Deng,Yuxian Yao,Jun Duan,Chunmei He | Physiological and transcriptomic analysis uncovers salinity stress mechanisms in a facultative crassulacean acid metabolism plant Dendrobium officinale | 06-10-2022 | Dendrobium officinale,differentially expressed genes,metabolic adjustment,pigment biosynthesis,plant hormones signaling | Dendrobium officinale is a precious medicinal Chinese herb that employs facultative crassulacean acid metabolism (CAM) and has a high degree of abiotic stress tolerance, but the molecular mechanism underlying the response of this orchid to abiotic stresses is poorly understood. In this study, we analyzed the root microstructure of D. officinale plantlets and verified the presence of chloroplasts by transmission electron microscopy. To obtain a more comprehensive overview of the molecular mechanism underlying their tolerance to abiotic stress, we performed whole‐transcriptome sequencing of the roots of 10-month-old plantlets exposed to salt (NaCl) treatment in a time‐course experiment (0, 4 and 12 h). The total of 7376 differentially expressed genes that were identified were grouped into three clusters (P < 0.05). Metabolic pathway analysis revealed that the expression of genes related to hormone (such as auxins, cytokinins, abscisic acid, ethylene and jasmonic acid) biosynthesis and response, as well as the expression of genes related to photosynthesis, amino acid and flavonoid metabolism, and the SOS pathway, were either up- or down-regulated after salt treatment. Additionally, we identified an up-regulated WRKY transcription factor, DoWRKY69, whose ectopic expression in Arabidopsis promoted seed germination under salt tress. Collectively, our findings provide a greater understanding of the salt stress response mechanisms in the roots of a facultative CAM plant. A number of candidate genes that were discovered may help plants to cope with salt stress when introduced via genetic engineering. | Physiological and transcriptomic analysis uncovers salinity stress mechanisms in a facultative crassulacean acid metabolism plant Dendrobium officinale
Dendrobium officinale is a precious medicinal Chinese herb that employs facultative crassulacean acid metabolism (CAM) and has a high degree of abiotic stress tolerance, but the molecular mechanism underlying the response of this orchid to abiotic stresses is poorly understood. In this study, we analyzed the root microstructure of D. officinale plantlets and verified the presence of chloroplasts by transmission electron microscopy. To obtain a more comprehensive overview of the molecular mechanism underlying their tolerance to abiotic stress, we performed whole‐transcriptome sequencing of the roots of 10-month-old plantlets exposed to salt (NaCl) treatment in a time‐course experiment (0, 4 and 12 h). The total of 7376 differentially expressed genes that were identified were grouped into three clusters (P < 0.05). Metabolic pathway analysis revealed that the expression of genes related to hormone (such as auxins, cytokinins, abscisic acid, ethylene and jasmonic acid) biosynthesis and response, as well as the expression of genes related to photosynthesis, amino acid and flavonoid metabolism, and the SOS pathway, were either up- or down-regulated after salt treatment. Additionally, we identified an up-regulated WRKY transcription factor, DoWRKY69, whose ectopic expression in Arabidopsis promoted seed germination under salt tress. Collectively, our findings provide a greater understanding of the salt stress response mechanisms in the roots of a facultative CAM plant. A number of candidate genes that were discovered may help plants to cope with salt stress when introduced via genetic engineering.
Plants may face adverse environmental conditions, such as extreme temperatures, drought, and salinity are major abiotic stressors, that hamper their metabolism, delay growth and development, reduce productivity, or even cause plant death (Krasensky and Jonak, 2012; Raza et al., 2022a). High salinity strongly and negatively influences the productivity of agricultural crops by impacting seed germination and plant vegetative growth, and stimulates osmotic and oxidative stress, as well as ion toxicity (Deinlein et al., 2014). However, under salt stress, plants can decrease ion toxicity and scavenge reactive oxygen species (ROS) by regulating ion homeostasis, induce antioxidant defense systems, and synthesize a variety of plant hormones and osmoprotectants (Raza et al., 2022a). Salinity can impact plants rapidly by inducing osmotic stress, or slowly by inducing ionic stress (Munns and Tester, 2008). In addition, salinity can disrupt the integrity of the chloroplast envelope, leading to the disorganization of grana, resulting in a reduced photosynthetic rate that can be partly attributed to the reduction of photosynthetic pigments (Kwon et al., 2019). To allow plants to cope with salt stress, signal transduction pathways such as phytohormone-mediated and Ca2+ signaling pathways are activated (Knight et al., 1997; Zhu, 2002; Kaleem et al., 2018). Under high salinity stress, plants modify their levels of endogenous hormones such as gibberellins (GAs), abscisic acid (ABA) and jasmonic acid (JA), thereby activating the genes involved in the metabolism of those hormones to cope with this stress (Kaleem et al., 2018). GA is involved in salt stress response pathways by negatively regulating a class of DELLA proteins (Verma et al., 2016). ABA promotes stomatal closure to reduce water loss by rapidly altering ion fluxes in guard cells under osmotic stress (Munemasa et al., 2015). Most studies have illustrated the role of JA in response to biotic stresses, while evidence of the involvement of JA in salinity stress has only emerged over the last decade (Kazan, 2015; Wasternack, 2015; Abouelsaad and Renault, 2018). In addition, auxins and cytokinins mediate stress-adaptation responses in plants (Bielach et al., 2017). Some studies have demonstrated that hormone-mediated signal transduction pathways trigger transcription factors (TFs) such as members of the bZIP, NAC, ERF, WRKY, MYB, ZF-HD, and bHLH families to amplify signals by inducing or repressing functional genes, thereby initiating protective mechanisms that allow plants to cope with salinity stress (Deinlein et al., 2014; Kaleem et al., 2018; Shalmani et al., 2019; Mansour and Hassan, 2022). For example, the dehydration-responsive element binding/C-repeat-binding factor (DREB/CBF), which is a member of the AP2/ERF TF subfamily, can modulate drought-, high temperature-, salt-, and cold-responsive gene expression (Dubouzet et al., 2003). WRKY TFs are involved in the response of plants to salinity stress. In Arabidopsis thaliana, AtWRKY46 plays a role in regulating stomatal opening, with mutant wrky46 plants showing sensitivity to drought and salt stress (Ding et al., 2015). The interaction between GmWRKY27 and GmMYB174 proteins from Glycine max regulates GmNAC29 expression, improving salt and drought tolerance (Wang et al., 2015b). CgWRKY57, which was identified in an orchid (Cymbidium goeringii), sensed ABA signals and might play a role in stress response (Liu et al., 2021). Overexpression of WRKY genes from Dendrobium nobile enhanced salt and stress tolerance in transgenic tobacco (Xu et al., 2014). Compatible solutes, such as proline, glycine betaine, sugars, mannitol and polyols, may accumulate in the cytoplasm, allowing water potential to be adjusted and cell turgor to be maintained under salinity stress (Aziz and Larher, 1995; Krasensky and Jonak, 2012; Moharramnejad et al., 2015). In Medicago sativa, most MsZF-HDs responded to salt stress (He et al., 2022). In A. thaliana, MYC2, which is a bHLH TF, negatively regulated proline biosynthesis by repressing P5CS1 expression in response to salt stress (Verma et al., 2020). Moreover, an R2R3-MYB TF-encoding gene IbMYB308 from sweet potato (Ipomoea batatas) improve the salt tolerance of transgenic tobacco (Wang et al., 2022). The Orchidaceae is the most diverse flowering plant family and includes approximately 880 genera and 28,000 species around the world (Givnish et al., 2015). Dendrobium, which is the second largest genus in the Orchidaceae, has adapted to diverse natural habitats, ranging from high altitudes to lowland tropical forests, and even to the dry climate of the Australian desert, implying its strong adaptability to adverse environmental conditions. Dendrobium plants are known to survive under high salinity (15 dS m−1) (Abdullakasim et al., 2018), indicating that they display high endurance to salinity stress. Dendrobium officinale is a precious Chinese medicinal herbal orchid with various bioactivities (Teixeira da Silva and Ng, 2017). A previous study by our group showed that D. officinale could adapt to high concentrations of salt stress, and in doing so, accumulated bioactive metabolites (Zhang et al., 2021). Although a tremendous amount of research has been conducted on salt stress in model plants and crops, known strategies for coping with salt stress in orchids is not commensurate with their recalcitrant nature. Understanding the mechanisms by which orchid plants cope with the challenge of salt stress will pave a way for planning future initiatives for abiotic (salt) stress engineering in orchids to protect orchid plants in the wild. In this study, we investigated changes to the transcriptome under short-term (within 12 h) salinity stress in the roots of D. officinale plantlets. We identified differentially expressed genes (DEGs) and focused on phytohormone signaling, the SOS pathway, photosynthesis and metabolic adjustments such as changes in amino acid content and flavonoid metabolism. We also identified differentially expressed TFs and characterized the role of a single WRKY gene in seed germination under salinity stress. Our results provide new insight into the biochemical and molecular regulation of salt stress tolerance mechanisms of orchid plants that enhance their stress tolerance.
D. officinale plantlets that were geminated from seeds within the same capsule, were grown in vitro on half-strength Murashige and Skoog (MS) (Murashige and Skoog, 1962) medium containing 2% sucrose and 0.6% agar (pH 5.4) in a growth chamber (25 ± 1°C, 40 µmol m-2 s-1, 12-h photoperiod). Uniform plantlets 8-9 cm in height were transferred to half-strength MS containing 2% sucrose and 0.6% agar, and supplemented with 250 mM NaCl (pH 5.4) and kept at 25 ± 1°C, under 40 µmol m-2 s-1, in a 12-h photoperiod. Root samples were collected at three time points [0 (control), 4 and 12 h], frozen in liquid nitrogen and used to isolate RNA. Three independent biological replicates were used for each time point and six plantlets were used for each replicate. To analyze chlorophyll (Chl) and carotenoids, plantlets were subjected to salinity treatment or not (the control) for two weeks, and roots were harvested and used to detect these pigments. To analyze total flavonoids and free amino acids, roots were collected after two weeks from control and salt-treated plants and freeze dried. Root samples were ground to a fine powder by a RETSCH MM400 Mixer Mill (Retsch Technology, Haan, Germany). Three independent biological replicates were utilized for each sample.
The roots of plantlets 8-9 cm in height were collected and fixed in fixation buffer [2.5% glutaraldehyde and 2% paraformaldehyde in 0.1 M sodium phosphate buffer (pH 7.2)]. To facilitate penetration of the fixative, samples were vacuum infiltrated for at least 30 min. Root samples were rinsed in wash buffer (1% sodium phosphate) six times (30 min each time) after fixation, then fixed in a 0.1 M sodium cacodylate buffer (pH 7.2) containing 1% osmium tetroxide (OsO4) for 4 h. An ethanol concentration gradient (30, 50, 75, 85, 95, 100%, v/v) was used to dehydrate samples. After dehydration, samples were treated in different ratios of acetone and Epon812 (3:1, 1:1 and 1:3, v/v) for 30 min. Finally, samples were embedded in absolute Epon812 overnight and placed at 60°C for two days. Transverse sections of root samples were cut into slices of 1 μm thickness with an LKB-11800 ultramicrotome and stained by periodic acid Schiff (PAS) (Tütüncü Konyar et al., 2013). Root samples were cut into 50-70 nm thick slices with a LeicaUC6 ultramicrotome for observations using a JEOL JEM 1010 (JEOL Ltd., Tokyo, Japan) transmission electron microscope.
Total RNA from root samples at 0 (control), 4 and 12 h after salinity stress was isolated using Column Plant RNAout2.0 (Tiandz Inc., Beijing, China). Biological triplicates for each time point were used for RNA sequence (RNA-seq) analysis in this study. The mRNA of each sample was purified from total RNA using oligo d(T)25 magnetic beads (New England BioLabs Inc., Ipswich, MA, USA). A library of the isolated mRNA was prepared with the NEBNext® Ultra™ RNA Library Prep Kit (New England Biolabs Inc.) then subjected to paired-end sequencing with the Illumina Novaseq 6000 Sequencing System at Biomarker Technologies Inc. (Beijing, China). The raw reads produced from sequencing were processed through in-house perl scripts (Biomarker Technologies Inc.) to remove reads containing an adapter, ploy-N or poor-quality reads (the quality Q ≤ 30 accounted for more than 50% of all reads). The remaining reads were clean reads, which were mapped with the D. officinale version 2 genome generated by Zhang et al. (2017) using TopHat version 2.0.8 (Kim et al., 2013a). About 90% of clean reads were mapped to the D. officinale genome. We performed differential expression analysis of RNA-seq data by comparing the expression of genes in treatments (4 h and 12 h) and the control (0 h) by using DESeq2 (Love et al., 2014). Genes with a fold change (treatment/control) ≥ 2.0 and a false discovery rate (FDR) ≤ 0.01 were regarded as up-regulated genes while genes with a fold change (treatment/control) ≤ 0.5 and an FDR ≤ 0.01 were regarded as down-regulated genes. All the up- and down-regulated genes were defined as DEGs. The clean reads generated in this study were submitted to the Sequence Read Archive (SRA) database of the National Center for Biotechnology (NCBI) under the following accession numbers: SRS8480001, SRS8480000 and SRS8480010 for 0 h; SRS8480011, SRS8480012 and SRS8480013 for 4 h; SRS8480016, SRS8480004 and SRS8480002 for 12 h.
Seven public gene functional annotation databases, NCBI non-redundant protein sequences database (Nr, http://www.ncbi.nlm.nih.gov), Protein family (Pfam, http://pfam.xfam.org/) (Finn et al., 2013), Clusters of Orthologous Groups of proteins (COG, http://www.ncbi.nlm.nih.gov/COG) (Galperin et al., 2021), a manually annotated and reviewed protein sequence database (Swiss-Prot, https://www.expasy.org/) (The UniProt Consortium, 2017), Gene Ontology (GO, http://geneontology.org/) (Ashburner et al., 2000), Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/) (Kanehisa et al., 2004) and evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG, http://eggnog.embl.de/) (Huerta-Cepas et al., 2019), were used to annotate gene functions. We identified genes based on functional gene annotation. Information about the metabolic pathway of genes was obtained from KEGG annotation. Based on GO annotation, the number of DEGs assigned to each GO term was calculated and visualized in a diagram. To cluster the DEGs, the Short Time-series Expression Miner (STEM) method in STEM software was used (Ernst and Bar-Joseph, 2006). The maximum number of model profiles was 20, and the maximum unit change in model profiles between time points was 1. KOBAS software (Xie et al., 2011) was used to test the statistical enrichment of DEGs in pathways.
Eight-day-old seedlings of wild-type Arabidopsis (Col-0) and transgenic lines were grown in half-strength MS medium supplemented with 1.5% sucrose, 0.8% agar (pH 5.7) and placed under a 16-h photoperiod (100 µmol m-2 s-1) at 22°C. Total RNA was isolated using Column Plant RNAout2.0 (Tiandz Inc.). RNA samples were used for first-strand cDNA synthesis with the GoScript Reverse Transcription System (Promega, Madison, WI, USA). The cDNA of each sample (400 ng μL-1) was used as a template for PCR amplification. The genes were amplified by 30 cycles of 98°C for 10 sec, 55°C for 30 sec and 72°C for 30 sec. PCR products (5 mL) of each sample were surveyed by 1% agarose gel electrophoresis and photographed with a gel imaging system (GenoSens1880, Shanghai Qinxiang Scientific Instrument Co. Ltd., Shanghai, China). Quantitative real-time PCR (qRT-PCR) was performed with the Unique Aptamer™ qPCR SYBR® Green Master Mix (Beijing Novogene Technology Co. Ltd.) in a LightCycler 480 system (Roche, Basel, Switzerland). Primer sets are listed in Supplementary Table 1 .
Fresh root samples were homogenized in a mortar with a pestle using silica sand and 80% acetone as the extracting solvent. The homogenized mixture was placed at 4°C for 1 h in the dark before centrifuging at 10,000 rpm for 15 min at 4°C. The supernatant was collected and used immediately to detect absorption at 663.2, 646.8 and 470 nm with a UV-6000 spectrophotometer (Shanghai Metash, Shanghai, China). Chl a was calculated as 12.25A663.2 – 2.79A646.8, Chl b was calculated as 21.5A646.8 – 5.1A663.2, and total carotenoids was calculated as (1000 A470 – 1.82 Chl a – 85.02 Chl b)/198 (Sumanta et al., 2014).
Root powder (100 mg) was solubilized in 5 mL of 0.01 M HCl. After incubating for 30 min at room temperature, samples were centrifuged at 13,000 rpm for 10 min. The supernatant was mixed with absolute ethyl alcohol (20:80, v/v). The mixture was incubated for 15 min at room temperature, then centrifuged at 13,000 g for 10 min. The extract was dried by evaporation under vacuum by a rotary evaporator (Eyela N-1300V-W, Tokyo Rikakikai Co. Ltd., Tokyo, Japan). HCl (0.01 M) was added to dissolve the isolated free amino acids. After filtering through a 0.22 μm filter membrane (Corning Inc., Corning, NY, USA), amino acid profiles were quantified using an Automatic Amino Acid Analyzer (S 433-D, Sykam GmbH, Eresing, Germany). Ninhydrin-derivatized amino acids were measured at 570 nm and at 440 nm. Amino acid concentrations were reported as mg of amino acid per 100 mg of dry weight. A standard stock solution type pH (for physiological amino acid analysis) (Sykam catalog No. S000031, Sykam GmbH) was used as the standard solution.
A colorimetric method described by Ren et al. (2020) was used to analyze total flavonoid content with rutin solutions (4, 8, 12, 16 and 20 μg/mL) serving as standards. Briefly, powdered samples were extracted with 50% (v/v) methanol in an ultrasonication bath (VCX600, Sonics and Materials Inc., Newtown, CT, USA) for 90 min at room temperature, then centrifuged at 12,000 rpm for 20 min. The supernatant was collected and used to measure absorbance at 360 nm with a UV-6000 spectrophotometer. The calibration standard was 50% (v/v) methanol.
The DoWRKY69 gene was isolated and inserted into the NcoI site of the pCAMBIA1302 vector. The validated recombinant vector was transformed into Agrobacterium tumefaciens EHA105 (Shanghai Weidi Biotechnology Co. Ltd, Shanghai, China) by the freeze-thaw method (Weigel and Glazebrook, 2006) and then transformed into A. thaliana by the floral dip method (Clough and Bent, 1998). Seeds of both wild type (WT) and transgenic lines were surface-disinfected then seeded on half-strength MS medium containing 1.5% sucrose, 0.8% agar (pH 5.7), and different concentrations of NaCl (150, 200, and 250 mM). Medium without NaCl served as the control. Seeds were kept at 4°C in the dark for 2 d, then transferred to a 16-h photoperiod (100 µmol m-2 s-1) at 22°C. When the radicle emerged from the testa, a seed was considered to have germinated. About 60 seeds of each genotype were used and the entire experiment was repeated in triplicate.
Statistical analyses were performed with SigmaPlot12.5 software (Systat Software Inc., San Jose, CA, USA). The Dunnett test (P < 0.05) was used to indicate statistically significant differences.
The roots of D. officinale plantlets were green and possessed meristematic and elongation zones ( Figure 1A ). The differentiation zone could not clearly or easily be distinguished from the meristematic and elongation zones because root hairs were absent ( Figure 1A ). To better understand the anatomical traits of D. officinale plantlet roots, we performed histological analysis to investigate the microstructure in transverse sections. The root consists of a velamen, cortex and stele, the largest proportion consisting of the cortex and a smaller stele ( Figure 1B ). The cortex is composed of an epidermis, an exodermis and a mass of cortical parenchyma tissue ( Figure 1B ). Starch granules (purple spots) were observed in the cortical parenchyma tissue but were absent in the stele and velamen ( Figure 1B ). Transmission electron microscopic observations revealed irregularly shaped starch granules that were easy to distinguish ( Figures 1C, D ). The stele cell contained a large (about 10 µm) nucleus and nucleolus, as well as small chloroplasts ( Figure 1E ). The thylakoids were stacked, similar to grana in leaves ( Figure 1F ). These observations demonstrate that D. officinale plantlet roots contain chloroplasts in their stele.
In order to reveal global transcriptional dynamics in the roots of D. officinale in vitro plantlets exposed to salinity stress, we performed a time course transcriptomic analysis at 0, 4 and 12 h after exposure to 250 mM NaCl. In this study, total clean bases of each library generated from sequencing amounted to more than 6 Gb ( Supplementary Table 2 ). The percentage of bases of each library having a quality score of 30 or higher exceeded 93% ( Supplementary Table 2 ). We verified the expression patterns of 12 selected genes by qRT-PCR ( Supplementary Figure 1 ). These were in agreement with the changes in fragments per kilobase per million (FPKM) values, supporting the reliability of our RNA-seq data. A total of 7376 DEGs were identified in the 0 h vs 4 h and 0 h vs 12 h comparisons. DEGs from the 0 h vs 4 h comparison consisted of 2282 down-regulated genes and 2844 up-regulated genes, while the number of DEGs from the 0 h vs 12 h comparison consisted of 2877 down-regulated genes and 3236 up-regulated genes ( Figure 2A ). Among all DEGs, 3863 genes showed a differential expression pattern at both time points: 2155 genes were up-regulated and 1702 genes were down-regulated ( Figure 2B ). We first performed a GO classification analysis of all the DEGs: 2538 and 3098 DEGs were assigned to three major GO categories (biological processes, cellular components, and molecular functions) in the 0 h vs 4 h and 0 h vs 12 h comparisons, respectively ( Figure 2C ). In the biological processes category, ‘metabolic process’ contained the greatest number of DEGs in both comparisons ( Figure 2C ). In the molecular function category, most DEGs were assigned to ‘catalytic activity’ in both comparisons ( Figure 2C ). To decipher the general trends of gene expression profiles, all DEGs were subjected to STEM clustering analysis ( Figure 2D ). These results suggest that salinity stress triggers a series of biological processes and molecular functions that mainly affect metabolism and defense in D. officinale roots.
To analyze the relationship between plant hormones and salinity response in D. officinale roots, we investigated the genes related to the biosynthesis of plant hormones under salinity stress using RNA-seq data. The indole-3-acetic acid (IAA) biosynthetic pathway genes TAA1 and YUC2 showed a down-regulated expression pattern after salinity stress ( Figure 3A ). The genes involved in zeatin biosynthesis showed a similar expression pattern as the IAA biosynthetic pathway genes. For example, the expression of three IPT genes (encoding ADP/ATP‐dependent enzymes, isopentenyltransferases) and one cytokinin trans-hydroxylase gene CYP735A was repressed after salinity stress ( Figure 3B ). As expected, most of the genes involved in ABA, ethylene and JA biosynthesis were induced by salinity stress ( Figures 3C-E ). The NCED genes, which encode a key enzyme (9-cis-epoxycarotenoid dioxygenase) for ABA biosynthesis, were induced by salinity stress. The expression of one NCED gene increased at 4 h but dropped to its initial level at 24 h; another NCED gene was detected strongly at both 4 h and 12 h (more than 85-fold increase in expression) ( Figure 3C ). The DEGs related to ethylene biosynthesis increased at both time points after salinity treatment ( Figure 3D ). In total, we found that among the 14 DEGs related to JA biosynthesis, 13 were up-regulated at least at one time point while only one gene AOC was down-regulated at both time points ( Figure 3E ). We then analyzed the auxin, cytokinin, ABA, ethylene and JA signal transduction pathway genes. AUX1 is a transmembrane amino acid transporter family protein that is involved in an early step of auxin signaling. Two AUX1 homologs found in D. officinale were down-regulated after salinity treatment ( Figure 4A ). Our data showed the down-regulation of auxin responsive factors (ARFs) in response to salt stress ( Figure 4A ). In addition, auxin-responsive genes coding for proteins such as auxin/indole acetic acid (AUX/IAA), GH3, and small auxin-up RNA (SAUR) were differentially expressed after salinity stress, suggesting the importance of auxin in the salt stress response. For the cytokinin signal transduction pathway, signal transduction occurs via phosphotransfer between the sensor kinase and the receiver domain of the response regulator (Kieber and Schaller, 2018). In the cytokinin signal transduction pathway, HK2/3, which encodes a histidine kinase and AHP, which encodes a histidine-containing phosphotransfer factor, were down-regulated after salinity stress at both time points ( Figure 4B ). There are two types of response regulators (B-ARRs and A-ARRs) involved in cytokinin signaling (Kieber and Schaller, 2018). Type-B ARRs directly activate type-A ARRs in response to cytokinin (Kieber and Schaller, 2018). Four B-ARR genes were up-regulated and one was down-regulated after salt stress ( Figure 4B ). One A-ARR gene was repressed by salinity stress ( Figure 4B ). In addition, our transcriptomic analysis showed that more that 50% of ABA-, ethylene- and JA-responsive genes were up-regulated after salt stress ( Figures 4C-E ). These results indicate that the biosynthesis and signal transduction of auxin and cytokinin were repressed while the biosynthesis and signal transduction of ABA, ethylene and JA were activated in response to salinity stress, suggesting that auxin, cytokinin, ABA, ethylene and JA may be required for the response of D. officinale roots to salinity.
In addition to the plant hormone signaling transduction pathway, another plant signaling pathway – the calcium signaling pathway – is also involved in the salt stress response. The SOS signaling network is activated by Ca2+ signaling (Kaleem et al., 2018). Three genes Salt Overly Sensitive 1-3 (SOS1-3) are require for salt tolerance in plants (Ji et al., 2013). SOS3, which is a calcineurin B‐like protein that serves as a Ca2+‐binding protein, transduces the signal downstream by sensing changes in Ca2+ concentration in the cytoplasm (Tuteja, 2007). As expected, the SOS3 gene was rapidly up-regulated at 4 h, but decreased at 12 h relative to 4 h ( Figure 5 ). SOS2 (also known as AtCIPK24) encodes a CBL-interacting serine/threonine-protein kinase (CIPK), which interacts with the SOS3 protein to form the SOS3–SOS2 protein kinase complex (Tuteja, 2007). Only one CIPK DEG, annotated as CIPK24, was found in this study, and its expression was repressed after salinity stress treatment ( Figure 5 ). The SOS1 gene encodes a Na+/H+ antiporter that results in a low concentration of cytoplasmic Na+ ions by enabling an efflux of excess Na+ ions across the plasma membrane. The vacuolar Na+/H+ exchanger 1 (NHX1) can reduce cytoplasmic Na+ by transferring cytoplasmic Na+ into vacuoles and maintaining osmotic balance in vacuoles (Apse et al., 1999; Bhaskaran and Savithramma, 2011). The SOS3–SOS2 protein kinase complex activates both SOS1 and NHX to trigger an Na+ exclusion response (Tuteja, 2007). No differential expression of the SOS1 gene was observed in this study, while the expression of one gene encoding a vacuolar Na+/H+ exchanger was up-regulated after salt stress, with 2.4-fold and 2.2-fold greater expression than the control at 4 h and 12 h, respectively ( Figure 5 ).
When salt concentration increases, this results in osmotic stress and the absorption of more Na+ and Cl− by roots, which may negatively affect plant growth by decreasing photosynthetic efficiency (Deinlein et al., 2014). To reveal the impact of photosynthesis in D. officinale roots in response to salinity stress, we first analyzed photosynthesis-related biosynthetic genes and pigments. After examining the transcription profiles of Chl biosynthesis and catabolic genes, we found that all the Chl biosynthesis genes, except for one chlorophyll(ide) b reductase gene NYC1 and one chlorophyllase gene CLH2, the expression of all other genes was down-regulated ( Figure 6A ). For example, the protochlorophyllide reductase gene POR was strongly expressed at 0 h with an average FPKM value > 750, but its expression dropped rapidly after salt stress treatment, with average FPKM values of 137 and 46 at 4 and 12 h, respectively ( Figure 6A ). In addition, the pheophorbide a oxygenase gene PAO, which encodes a key enzyme in the catabolism of Chl, was considerably up-regulated (with an average FPKM of about 90 at 0 h in contrast to an average FPKM value of > 500 at both 4 and 12 h) ( Figure 6A ). These results suggest that the decrease in Chl biosynthesis and reduced Chl degradation led to a decrease in Chl content in roots under salinity stress. In the carotenoid biosynthetic pathway, all of the identified carotenoid biosynthetic genes, except for CRTISO2, showed lowest expression levels at 12 h, but some of these genes were up-regulated at 4 h in response to salinity stress ( Figure 6B ). For example, the β-carotene 3-hydroxylase gene CRTZ had a mean FPKM value of 11 at 0 h, 57 at 4 h and 7 at 12 h ( Figure 6B ). However, Chl a, Chl b and total carotenoid content were not different between the control and salinity stress at 24 h ( Supplementary Figure 2 ), but pigment content, especially Chl a, decreased significantly after two weeks’ exposure to salt stress ( Figure 6C ). These results indicate that photosynthetic pigments were reduced after salt stress, even a long time after exposure. We also isolated the DEGs that encode photosynthetic proteins, including those associated with the photosystem I complex, light harvesting complex I, photosystem II complex, light harvesting complex II, and cytochrome (Cyt) b6f complex, as well as DEGs related to carbon fixation. A small number of photosynthetic genes were up-regulated, such as PsaC, PsbA, PsbK and petD, while all of the remaining genes were down-regulated in response to salt stress ( Figure 7A ). In addition, the expression of genes in the Calvin cycle decreased ( Figure 7B ). For example, three RBCS genes, which encode ribulose bisphosphate carboxylases, were strongly expressed before salinity treatment, but were down-regulated more than 2.5-fold after salt stress ( Figure 7B ). D. officinale is regarded as a facultative crassulacean acid metabolism (CAM) species (Zhang et al., 2014). Hence, the genes encoding key enzymes for CAM photosynthesis were identified and analyzed. Two CA genes that encode carbonic anhydrase were down-regulated ( Figure 7C ). Other genes involved in CAM photosynthesis, such as the malate dehydrogenase gene MDH and pyruvate phosphate dikinase gene PPDK, were highly expressed in the control, but were suppressed rapidly after salinity treatment ( Figure 7C ). These results indicate that photosynthesis was considerably repressed by salinity stress and that the CAM pathway was not active at an early stage of salt stress in D. officinale roots.
In plants, amino acids serve as precursors for the synthesis of a wide range of biologically important compounds, but they also play a role in stress response. A total of 218 DEGs related to the amino acid metabolic pathway were found. They were clustered into two main groups: down-regulated DEGs after salt stress, and up-regulated DEGs at least one time point after treatment ( Supplementary Figure 3 ). In the ‘Arginine biosynthesis’ pathway, the genes involved in glutamate and ornithine biosynthesis were up-regulated after salinity stress ( Figure 8A ). For example, ALT, glnA and gdnA, which are related to glutamate biosynthesis, increased at least 2-fold at 4 h and 3-fold at 12 h after salinity stress ( Figure 8A ). The arg gene, which encodes an arginase that converts arginine to urea and ornithine, was up-regulated about 3-fold after salinity stress ( Figure 8A ). The Asparagine synthetase gene ASNS and the L-aspartate oxidase gene nadB were up-regulated in response to salt stress ( Figure 8B ). In addition, two lysC genes that encode aspartate kinases catalyzing the synthesis of asparagine from aspartate were up-regulated more than 3-fold at 12 h after salinity stress ( Figure 8C ). In lysine metabolism, the gene AASS, which encodes α-aminoadipic semialdehyde synthase involved in lysine catabolism, was up-regulated ( Figure 8C ). In proline biosynthesis, one PRODH gene was up-regulated while another was down-regulated ( Figure 8D ). Moreover, two P4HA genes involved in proline catabolism were down-regulated ( Figure 8D ). The dry weight content of free amino acids in roots decreased about 2-fold between control and salinity stress, even two weeks after salinity treatment ( Table 1 ). The content of all of the main free amino acids (asparaginate, O-phosphoethanolamine, arginine, lysine, and aspartate), except for O-phosphoethanolamine, declined after salinity stress ( Table 1 ). The contents of proline and ornithine, which are involved in osmotic stress responses such as salt and drought stresses in plants (Hussein et al., 2019), were significantly up-regulated after salinity stress ( Table 1 ). These results suggest that the biosynthesis of main free amino acids such as aspartate and arginine is blocked, while the biosynthesis of stress-related amino acids like proline and ornithine is promoted in response to salinity stress.
In the flavonoid metabolic pathway, the expression level of two genes coding for flavonoid 3′-hydroxylase (CYP75A and CYP75B1) increased after salinity stress, suggesting that the conversion of dihydrokaempferol to dihydroquercetin or dihydromyricetin was enhanced ( Figure 9A ). Flavonoid biosynthetic genes like HTC and CCOAMT were also affected after salinity stress and their expression was up-regulated ( Figure 9A ). However, expression of the genes coding for chalcone isomerase (CHI) and chalcone synthase (CHS), which are key enzyme genes in the synthetic pathway of flavonoids, declined after salinity stress ( Figure 9A ). Total flavonoid content decreased in roots after salinity stress ( Figure 9B ). These results suggest that the biosynthesis of flavonoids is suppressed by salinity stress in D. officinale roots.
TFs are widely involved in the regulation of metabolism and physiological processes, including stress response, in plants. A total of 226 and 243 TFs were up-regulated while 174 and 210 TFs were down-regulated at 4 and 12 h, respectively after salinity stress ( Figure 10A ). Among the down- or up-regulated genes at both time points, the largest number of down-regulated TFs was from the MYB family (22 genes) and the largest number of up-regulated TFs was from the APETALA2/ETHYLENE RESPONSE FACTOR (AP2/ERF) family (30 genes) ( Supplementary Figure 4 ). Of note, all of the identified heat stress transcription factors were up-regulated at 4 h and 12 h. Only two WRKY genes were down-regulated while 17 WRKY genes were up-regulated at the detected time points after salt stress ( Supplementary Figure 4 ). To investigate the role of TFs in response to salinity stress, DoWRKY69 with significantly up-regulated expression in WRKY family was selected and analyzed. DoWRKY69 belongs to Group IIb ( Supplementary Figure 5 ) and was up-regulated by about 4-fold at 4 h and by about 10-fold 12 h after salinity stress in roots ( Supplementary Figure 6 ). The coding sequence (CDS) of DoWRKY69 without a stop codon was cloned into pCAMBIA1302 at the NcoI site and is driven by the 35S promoter ( Figure 10B ). Ten independent transgenic lines for DoWRKY69 were generated. Lines 7, 9 and 10 were used to analyze expression level and seed germination in response to three NaCl concentrations (150, 200, and 250 mM). The DoWRKY69 gene was detected in all three transgenic lines but was not detected in WT plants ( Figures 10C, D ). In WT and transgenic lines, seeds germinated well and their germination percentage was nearly 100% in control medium 24 h after incubation ( Figure 10E ). However, the difference between WT and transgenic lines was obvious when the growth medium was supplemented with salt ( Figures 10F-H ). The germination rate of all transgenic lines was significantly higher than WT seeds within 24 h in response to 150 mM NaCl ( Figure 10F ). No WT seeds germinated within 24 h, while more than 3% of transgenic seeds of each transgenic line germinated with 24 h after exposure to 200 mM NaCl ( Figure 10G ). In addition, the germination of transgenic lines was significantly higher than WT seeds at 36 h after exposure to 200 mM NaCl ( Figure 10H ). Although no seeds germinated within 48 h in response to 250 mM NaCl, the germination of transgenic lines was significantly higher than WT seeds within 72 h after exposure to 250 mM NaCl ( Figure 10H ). These results suggest that DoWRKY69 plays a positive role in salt stress tolerance in A. thaliana.
Salinity is one of the most severe environmental factors limiting the productivity of agricultural crops. It is necessary to explore salinity tolerance mechanisms to help improve salt tolerance of crops via genetic engineering. In this study, we investigated transcriptomic reprogramming in D. officinale plantlets at an early phase of salinity stress, and characterized the ability of an up-regulated gene DoWRKY69 to improve A. thaliana seed germination under salt stress. The roots of D. officinale plantlets are photosynthetic. The biosynthesis and signal transduction of hormones, such as IAA and cytokinin, which are essential for plant growth and development, were repressed, while stress-responsive hormones such as ABA, JA and SOS pathway genes were up-regulated at an early phase in response to salt stress. In order to survive and adapt to salinity stress, physiological and metabolic adjustments were made through extensive transcriptomic reprogramming. Our results suggest that physiological and metabolic processes and molecular functions are reprogrammed in the photosynthetic roots of this orchid when plants are exposed to salinity stress. Plant hormones are crucial signaling molecules and their signaling depends on their spatio-temporal distribution (Waadt, 2020). Phytohormones coordinate all aspects of plant growth and development, as well as stress responses (Shan et al., 2012). Plant hormones interact with five key plant neurotransmitters, including serotonin, melatonin, dopamine, acetylcholine and γ-aminobutyric acid, and participate in many physiological processes such as photosynthesis, oxidative stress and osmotic regulation (Raza et al., 2022b). Two groups of phytohormones, auxins and cytokinins, have been clearly demonstrated as the main regulators of plant development (Benjamins and Scheres, 2008; Werner and Schmülling, 2009; Schaller et al., 2015) and stress responses (Hare et al., 1997; Blakeslee et al., 2019). For example, the YUC gene, which encodes indole-3-pyruvate monooxygenase, plays an important role in auxin (IAA) biosynthesis (Zhao, 2012). Overexpression of the YUC gene exhibited a drought-resistant phenotype in A. thaliana (Lee et al., 2012), and conferred water-deficit tolerance in potato (Kim et al., 2013b). However, improvement of drought tolerance by overexpression of the YUC6 gene was not due to an increase in auxin synthesis, but rather due to YUC6-processed thiol-reductase, which inhibited the generation of ROS (Cha et al., 2015). Only one YUC gene was identified in this study and its expression was down-regulated after salt stress, suggesting that the synthesis of IAA might decrease in D. officinale roots in response to salt stress ( Figure 3A ). Moreover, the IAA biosynthesis pathway gene IAA1 and auxin signal-mediated genes (AUX1 and ARF) were down-regulated after salt stress, suggesting a reduction in auxin signals in response to salinity stress. Cytokinin biosynthesis pathway genes (three IPT genes and CYP735A) and the expression of cytokinin signal transduction pathway genes (HK, AHP and A-ARR, as well as one B-ARR gene) declined in D. officinale roots after salt stress, indicating the inhibition of cytokinin signaling in roots after salt stress. Biosynthetic genes such as CYP735A2 from A. thaliana and IPT (a key enzyme for cytokinin biosynthesis) from tomato were predominantly expressed in roots, suggesting that roots are a major site for cytokinin synthesis (Takei et al., 2004; Ghanem et al., 2011). The down-regulated expression of IPT genes and CYP735A in D. officinale roots indicates a decrease in cytokinin. Some studies have shown that cytokinin signaling plays a negative role in stress responses. For example, two histidine kinase genes (AHK2 and AHK3) negatively controlled osmotic stress responses in A. thaliana, and their single or double mutants displayed strong tolerance to drought and salt stress (Tran et al., 2007). These results indicate that cytokinin was a negative regulator of stress and that repression of the cytokinin signal in D. officinale roots might help plants to cope with salt stress. Most of the genes related to the biosynthesis of ABA, ethylene and JA and their signal transduction pathways were up-regulated after salt stress in D. officinale roots ( Figures 3 , 4 ). ABA, ethylene and JA signal transduction pathways after exposure to salt stress were described in greater detail in a fairly recent review (Kaleem et al., 2018). This indicates the conserved role of ABA, ethylene and JA in the response of plants to salt stress. Photosynthesis is an essential process, converting solar energy into chemical energy. Salt stress plays a negative role in the photosynthesis of leaves (Sudhir and Murthy, 2004; Silva et al., 2011). Genes that encode the components of both the light and dark reactions of photosynthesis in the leaves of salt-treated soybean seedlings were slightly repressed or maintained at the early phase within 4 h, but were inhibited at a later phase (after 24 h) (Liu et al., 2019). Chloroplasts are important organelles central to plant photosynthesis and are affected by salt-induced toxicity, although different plant species and development stages display different degrees of resistance to salt stress (Suo et al., 2017; Hameed et al., 2021). In this study, an anatomical analysis demonstrated that D. officinale plantlet roots contained chloroplasts ( Figure 1 ), suggesting that the roots were capable of photosynthesis. The genes involved in the biosynthesis of photosynthetic pigments, as well as the genes encoding photosynthetic components, were strongly repressed in roots of salt-treated D. officinale plantlets within 12 h. These results indicate that photosynthesis is inhibited in both roots and leaves in response to salt stress, while the influence of time in both organs is different. Photosynthetic pigments were not discernibly altered under short-term salt stress (within 24 h) in the roots of D. officinale plantlets, although the expression of biosynthetic genes dropped drastically within 12 h ( Figure 3 ). It is possible that the photosynthetic pigments were not damaged by salt stress in the first 12 h of exposure. A decrease in photosynthetic rates under stress (dehydration, salt, extended darkness) may lead to an insufficient supply of carbohydrates. Free amino acids, which are tightly linked to carbohydrate metabolism, can be used as alternative substrates for mitochondrial respiration or act as precursors for the biosynthesis of secondary metabolites or immune signaling metabolites (Hildebrandt et al., 2015; Chen et al., 2018; Hildebrandt, 2018). In this study, the biosynthesis of arginine was repressed and its breakdown was induced, leading to a decrease of arginine in D. officinale roots after exposure to salt stress, suggesting the arginine might be used as a precursor for the biosynthesis of other compounds. For example, polyamines, which are synthesized from arginine, are responsive to plant stresses (Alcázar et al., 2006; Groppa and Benavides, 2008). Auxin can be synthesized from tryptophan (Wang et al., 2015a) and ethylene is synthesized from methionine (Sauter et al., 2013). There were no differences in tryptophan content while the expression of auxin biosynthetic genes was repressed by salt stress ( Table 1 and Figure 8A ). The increased expression of methionine and the deregulation of ethylene biosynthetic genes after salt stress ( Table 1 and Figure 8D ) suggest that ethylene synthesis is related to the methionine cycle. In addition, the higher level of stress-related proline and organic acids has been observed in salt cress (Eutrema salsugineum) exposed to extreme salt stress (Li et al., 2022). In the roots of D. officinale, proline biosynthetic genes were induced by salt and their expression was correlated with the increase in proline content. Flavonoids, a diverse group of bioactive polyphenolic compounds, are catalyzed by a series of enzymes. CHS is first rate-limiting enzyme in flavonoid biosynthesis and catalyzes the production of naringenin chalcone using p-coumaroyl-CoA as the starting substrate (Martens et al., 2010; Dao et al., 2011). Under salinity stress, CHS and CHI were down-regulated, and this might have led to a decrease of total flavonoid production in D. officinale roots. Flavonoids are effective antioxidants that are involved in the response mechanisms of plants under adverse environments, including biotic and abiotic stresses (Agati et al., 2012; Petrussa et al., 2013). Studies have shown that salinity stress can enhance total flavonoid concentration in plant leaves. For example, flavonoid biosynthesis pathway genes such as the phenylalanine ammonia lyase gene PAL, the chalcone synthase gene CHS and the flavonol synthase gene FLS, as well as total flavonoids, were up-regulated in the leaves of Solanum nigrum under salt stress (Ben Abdallah et al., 2016). In our previous study, flavonoid biosynthesis genes (such as CHS, CHI and F3H) were up-regulated and total flavonoid content was enhanced in D. officinale leaves under salt stress (Zhang et al., 2021). Flavonoids act as antioxidants by scavenging ROS in stressed plant leaves (Agati et al., 2012). However, the content of flavonoids decreased in D. officinale roots under salt stress, suggesting that the path or mechanism by which ROS is scavenged in the roots of D. officinale is different from that in leaves. Transcriptional activation of functional genes involved in stress responses by TFs leads to the adjustment of specific metabolism of metabolites, which is one strategy to cope with stress in plants. Different studies have identified many TFs involved in regulating the response to salt stress (Kaleem et al., 2018). Most up-regulated TFs belong to AP2/ERF, MYB, NAC and WRKY families (Supplementary Figure 3 ). Accumulating evidence has shown that WRKY genes play positive or negative roles in the regulation of the salt stress response. Maize (Zea mays) WRKY17 and WRKY114 negatively regulate salt stress tolerance (Cai et al., 2017; Bo et al., 2020). GhWRKY17 from cotton (Gossypium hirsutum) and WRKY46 from A. thaliana contribute to salt stress tolerance (Yan et al., 2014; Ding et al., 2015). A WRKY gene DoWRKY69 was up-regulated after salt stress treatment. Over-expression of DoWRKY69 demonstrated its role in improving A. thaliana seed germination under salt stress. These results indicate that stress-related TFs can be induced by salt stress treatment and might be responsible for the stress response.
During the salt stress response mechanism of D. officinale in vitro plantlets, the expression of genes involved in photosynthesis and flavonoid biosynthesis, as well as in the biosynthesis of auxin and cytokinin, declined, whereas the expression of genes coding for stress-related plant hormones (ABA, ethylene and JA), the signal transduction pathway, the SOS pathway, and the biosynthesis of amino acids related to osmotic adjustment, were activated ( Figure 11 ). This indicates that D. officinale adapted to salt stress by reducing growth, the accumulation of compatible solutes, and increasing the exclusion of excess Na+ in roots ( Figure 11 ). Our findings illustrate a response mechanism of the roots of a facultative CAM plant to salinity stress. Our study also provides a large number of candidate stress-responsive genes that could be used to develop and strengthen economically important crops using biotechnological approaches, allowing them to cope with salt stress.
The original contributions presented in the study are publicly available. This data can be found here: PRJNA715099
CH and MZ supervised the project. MZ and CH conceived the research and designed the experiments. RD performed histological analysis. MZ, CH, NL, XL, YY, JT and JD collectively interpreted the results and wrote all drafts of the manuscript. All authors contributed to the article and approved the submitted version.
This research was funded by the National Natural Science Foundation of China (32071819 and 31800204), the Youth Science and Technology Talent Growth Project of Education Department of Guizhou Province of China (No. [2022]099), the Science and Technology Department Foundation of Guizhou Province of China (No. [2020]1Y120), and the Project of High-Level Talents Introduction in Qiannan Normal University for Nationalities (2021qnsyrc06).
We thank Dr. Minglei Zhao (South China Agricultural University) for helpful suggestions and Jian Liu (South China Botanical Garden, Chinese Academy of Sciences) for analyzing free amino acid content.
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. | true | true | true |
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PMC9583512 | Yuepeng Zhang,Yueli Tian | Comprehensive analysis of lncRNA-mediated ceRNA regulatory networks and key genes associated with papillary thyroid cancer coexistent with Hashimoto’s thyroiditis | 20-10-2022 | Papillary thyroid cancer,Hashimoto’s thyroiditis,lncRNA,microRNA,ceRNA | Objective The incidence of papillary thyroid cancer (PTC) concomitant with Hashimoto’s thyroiditis (HT) is gradually increasing over the past decades. This study aims to identify differentially expressed lncRNAs between tumor tissues of PTC with or without HT and further to confer a better understanding of lncRNA-based competing endogenous RNA (ceRNA) network in PTC with HT. Methods GSE138198 containing tissue mRNA data and GSE192560 containing lncRNA data were utilized to perform differentially expression analysis. The ceRNA network was constructed based on miRNA-mRNA interactions merging with lncRNA-microRNA interactions. Functional enrichment analysis and protein–protein interaction (PPI) analysis were performed. The mRNA levels of core genes in the PPI analysis in tumor tissues collected from 112 PTC patients including 35 cases coexistent with HT were determined by quantitative real-time polymerase chain reaction (qRT-PCR). Results A total of 57 genes and 40 lncRNAs, with value of |log2 fold change (FC)|≥ 1 and the adjusted P-value < 0.05, were deemed as differentially expressed genes and lncRNAs between PTC with and without HT. The pathways most significantly enriched by differentially expressed genes between PTC with and without HT were viral protein interaction with cytokine and cytokine receptor and cytokine-cytokine receptor interaction. CXCL10, CXCL9, CCL5, FCGR3A, and CCR2 owned degree values not less than 10 were deemed as core genes differentially expressed between PTC with and without HT. A total of 76 pairs of lncRNA-miRNA-mRNA ceRNA were obtained. Results of qRT-PCR partially demonstrated the bioinformatics results that the mRNA levels of CXCL10, CXCL9, CCL5, and CCR2 were remarkably elevated in tumor tissues collected from PTC patients coexistent with HT than those without HT (P < 0.001). Conclusion Our study offers a better understanding of the lncRNA-related ceRNA network involved in PTC with HT, providing novel key genes associated with PTC coexistent with HT. | Comprehensive analysis of lncRNA-mediated ceRNA regulatory networks and key genes associated with papillary thyroid cancer coexistent with Hashimoto’s thyroiditis
The incidence of papillary thyroid cancer (PTC) concomitant with Hashimoto’s thyroiditis (HT) is gradually increasing over the past decades. This study aims to identify differentially expressed lncRNAs between tumor tissues of PTC with or without HT and further to confer a better understanding of lncRNA-based competing endogenous RNA (ceRNA) network in PTC with HT.
GSE138198 containing tissue mRNA data and GSE192560 containing lncRNA data were utilized to perform differentially expression analysis. The ceRNA network was constructed based on miRNA-mRNA interactions merging with lncRNA-microRNA interactions. Functional enrichment analysis and protein–protein interaction (PPI) analysis were performed. The mRNA levels of core genes in the PPI analysis in tumor tissues collected from 112 PTC patients including 35 cases coexistent with HT were determined by quantitative real-time polymerase chain reaction (qRT-PCR).
A total of 57 genes and 40 lncRNAs, with value of |log2 fold change (FC)|≥ 1 and the adjusted P-value < 0.05, were deemed as differentially expressed genes and lncRNAs between PTC with and without HT. The pathways most significantly enriched by differentially expressed genes between PTC with and without HT were viral protein interaction with cytokine and cytokine receptor and cytokine-cytokine receptor interaction. CXCL10, CXCL9, CCL5, FCGR3A, and CCR2 owned degree values not less than 10 were deemed as core genes differentially expressed between PTC with and without HT. A total of 76 pairs of lncRNA-miRNA-mRNA ceRNA were obtained. Results of qRT-PCR partially demonstrated the bioinformatics results that the mRNA levels of CXCL10, CXCL9, CCL5, and CCR2 were remarkably elevated in tumor tissues collected from PTC patients coexistent with HT than those without HT (P < 0.001).
Our study offers a better understanding of the lncRNA-related ceRNA network involved in PTC with HT, providing novel key genes associated with PTC coexistent with HT.
A significantly increased detection rate of thyroid nodule has been noted over the past three decades, mainly due to the increasing use of diagnostic imaging techniques. Thyroid nodule usually appears non-palpable and asymptomatic, and it is frequently incidentally found in radiological examination of unrelated disease. Although thyroid nodule poses little threat to the health of affected patients, the risk of thyroid malignancy in thyroid nodules remains 10–15% [1]. In 2015, 62,000 populations were estimated to be newly diagnosed with thyroid cancer, ranking the fifth common cancer in women [2]. Thyroid cancer describes a heterogeneous pool of tumors including the predominant papillary thyroid cancer (PTC) subtype owning good survival rates, as well as the poorly differentiated thyroid cancer (PDTC) and anaplastic thyroid cancer (ATC) forms being responsible for most of the disease-related morbidity and mortality [3, 4]. According to the origin of cancer cells, thyroid cancer includes follicular-derived thyroid cancers and neuroendocrine C-cell derived thyroid cancer represented by medullary thyroid cancer, accounting for 1–2% of all thyroid cancers. More than 95% of thyroid cancers are finally diagnosed as differentiated thyroid cancer which is a follicular-derived thyroid cancer [5]. The main cause of thyroid cancer has not been determined, but family history, especially radiation exposure in children and adolescents, including natural radiation, iodine intake, and radiation in treatment and diagnosis are potential risk factors for thyroid cancer [6, 7]. Hashimoto’s thyroiditis, also known as chronic lymphocytic thyroiditis, is a common autoimmune thyroid disease, and its inflammation has been reported to be associated with the occurrence of thyroid cancer. Increasing evidence shows that patients with PTC concomitantly experience Hashimoto’s thyroiditis. Furthermore, a strong correlation has been found between the rising incidence of thyroid cancer and the increase in autoimmune thyroid disease [8–10]. Long noncoding RNAs (lncRNAs) represent a group of transcripts with > 200 nucleotides and have been characterized as crucial regulators of biological processes and tumorigenesis by control of various key cellular genes. Recent studies have established that lncRNAs, such as m6A RNA methylation-related lncRNAs and differentiation-related lncRNAs are deregulated in PTC [11, 12]. An increasing number of microRNAs (miRNAs) with less than 200 nucleotides were found to be abnormally expressed in human thyroid cancer and involved in the development of tumor biology [13, 14]. The hypothesis of competing endogenous RNA (ceRNA) network represents one of attractive paradigms of lncRNA regulation. LncRNAs share microRNA (miRNA) binding sites and modulates posttranscriptional messenger RNA (mRNA) by depletion of miRNA when the ceRNA network is interpreted [15, 16]. A surge of attention has been paid to involvement of lncRNA-based ceRNA network that expedites the development of human diseases including PTC. For example, lncRNA XIST, as ceRNA for miR-34a, regulates cell proliferation and tumor growth of thyroid cancer through the phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) signaling [17]. In a study of papillary thyroid carcinoma, silencing of lncRNA MIAT, acting as ceRNA for miR-150-5p, promoted cell proliferation and migration, and these results were achieved by inhibiting EZH2 (direct downstream target of miR-150-5p) expression [18]. Several lncRNAs such as IFNG-AS1, NR_038461, and T204821 have been shown to play a role in the pathogenesis of autoimmune thyroid disease through modulation of cellular immune response pathways [19]. An increased expression of IFNG-AS1 was observed in patients with Hashimoto's thyroiditis, and there was a positive correlation between the IFNG-AS1 level and the proportion of circulating Th1 cells [20]. However, the value of lncRNAs in the differences in the presence or absence of HT has not yet been investigated. In this study, we attempt to identify differentially expressed lncRNAs between tumor tissues of PTC with or without HT and further to confer a better understanding of lncRNA-based ceRNA action in PTC with HT.
We downloaded raw data derived from PTC patients with or without HT from the Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo), and these data must be sourced from human PTC tissue samples, profiled by same technology, and supplemented with clear series matrix files and gene symbols. Accordingly, GSE138198 containing tissue mRNA data and GSE192560 containing lncRNA data were eligible for this study and utilized to perform differentially expression analysis. The GSE138198 dataset, processed on the GPL6244 platform and public on Jun 16, 2020, encompasses 6 pieces of PTC without HT and 8 pieces of PTC with HT. The GSE192560 dataset, processed on the GPL16956 platform and public on Mar 09, 2022, encompasses 5 pieces of PTC with or without HT for each. The mRNAs and lncRNAs deemed differentially expressed should fulfill log2-fold change |log2FC|≥ 1 and the adjusted p-value < 0.05.
The “clusterProfiler,” “enrichplot,” and “ggplot2” packages in the R environment were employed to retrieve the functional enrichment analysis for genes differentially expressed between PTC background with or without HT, focusing on of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The GO analysis applied biologic process (BP), cellular component (CC), and molecular function (MF), to deem gene annotation and gene products attributes. The KEGG analysis offers annotation information with regard to gene signal transduction and disease pathways. The p-value less than 0.5 denoted the GO term and pathways significantly enriched by genes, and the top 10 GO terms for each domain and the top 20 KEGG-defined pathways were visualized as bubble plots using the “pathview” package in R software.
The PPI analysis was carried for genes differentially expressed between PTC background with or without HT using the Search Tool for the Retrieval of the Interacting Genes (STRING) (v11.0) (https://string-db.org/). The PPI network was presented using Cytoscape software (v3.9.0), where the confidence level of node was 0.4, otherwise the sparse genes were removed.
At first, we mapped the core genes ranking the top 5 in the PPI network into TargetScan (http://www.targetscan.org/), miRDB (http://mirdb.org/), and mirDIP (http://ophid.utoronto.ca/mirDIP/index.jsp) databases to predict miRNA-mRNA interactions. We continued to import the miRNAs in the miRNA-mRNA interactions obtained above into the RNA22 database to identify lncRNA-miRNA interactions. The lncRNAs in the lncRNA-miRNA interactions obtained above were overlapped with lncRNAs differentially expressed between PTC background with or without HT by Venn intersection tool which must follow the principle of ceRNA hypothesis that lncRNAs shared same expression patterns as gene differentially expressed between PTC background with or without HT.
This cohort study analyzed retrospective data from 112 consecutive patients undergoing partial or entire thyroidectomies for PTC at Zhongnan Hospital of Wuhan University between January 1, 2021 and December 31, 2021. Fresh-frozen thyroid specimens were obtained from 112 patients. The preoperative diagnosis of TNM stage adopts the 8th edition of American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) tumor-node-metastasis (TNM) staging system for PTC [21]. The inclusion criteria were: i) histopathological examination confirmed with primary PTC with or without coexistence of HT, ii) no earlier history of any treatment for thyroid conditions, iii) no levothyroxine or anti-thyroid drugs were administrated 30 days prior to surgery, and iv) no evidence of immunodeficiency. The exclusion criteria were: i) the presence of other autoimmune diseases in addition to HT, ii) having other pathological types of thyroid malignancies, iii) secondary PTC to other malignancies, or iv) family history of malignant thyroid tumors. The coexistence of HT in PTC was confirmed by two experienced pathologists according to the histological examination of surgical specimens of tumorous thyroid tissues and detections of antithyroglobulin (anti-TGAb) and (TPO-Ab), including the following characteristics no exceptions: i) the histological pathology showed cancer tissues infiltrated and densely packed by a large number of lymphocytes, plasma cells and oxyphilic cells, focal infiltration in the cancer parenchyma, lymphoid follicle formation, and the presence of reactive germinal centers [22]; the infiltrate occurred in a normal region of the thyroid gland not just a peritumoral inflammatory response; ii) the serum concentration of antithyroglobulin (anti-TGAb) ≥ 115 IU/mL; and iii) the serum concentration of antithyroid peroxidase (TPO-Ab) ≥ 34 IU/mL. The concentrations of anti-TGAb and TPO-Ab were determined within a month prior to thyroidectomy using the immune-electrochemiluminescence method. We reviewed the demographic and clinicopathological data of included patients including age, gender, tumor size (the largest diameter for multifocal carcinoma), multifocality, the concentrations of thyroid stimulating hormone (TSH), anti-TGAb and TPO-Ab before surgery, extrathyroidal extension, the presence of central compartment lymph node metastasis (CLNM), and the TNM stage.
The total RNA of tissue samples was extracted using TRIzol Reagent (Invitrogen, USA) and then utilized as template to generate cDNA by using a PrimeScript RT Reagent Kit (Takara, Dalian, China). The qRT-PCR run on a thermal cycler ABI Prism 7500 using SYBR Green dye based on the manufacturer's recommended protocol (Takara, Dalian, China) to determine expressions of CXCL10, CXCL9, CCL5 and CCR2 relative to GAPDH by adopting comparative cycle threshold values (2−ΔΔCt). Primer sequences are listed in Table 1.
A manner of mean and standard deviation (sd) is used to present continuous variables. Independent t-tests were used for continuous variables. Rate (%) or composition ratio is used to present categorical variables. Statistical tests including unpaired t test and chi-square test were conducted, graphs and figures were produced in the GraphPad Prism 8.0 software (GraphPad Software, La Jolla, CA, USA). P < 0.05 denotes a significant difference.
The mRNA and lncRNA data deposited in the GSE138198 and GSE192560 datasets were differentially analyzed, and those with value of |log2FC|≥ 1 and the adjusted p-value < 0.05 were deemed as differentially expressed genes and lncRNAs between PTC with and without HT. Accordingly, we acquired 57 genes differentially expressed between PTC with and without HT, including 43 upregulated genes and 14 downregulated ones, and 40 lncRNAs differentially expressed between PTC with and without HT, including 13 upregulated lncRNAs and 27 downregulated ones (Fig. 1A, B).
We then conducted GO annotation and KEGG pathway analyses to evaluate the main functional pathways associated with 57 genes differentially expressed between PTC with and without HT. After GO analysis, it was found there were 489 GO terms significantly enriched by these 57 genes (P < 0.05), including 426 terms belonging to the BP domain, 18 terms belonging to the CC domain, and 45 terms belonging to the MF domain. The most enriched GO terms at the BP domain were “chemokine-mediated signaling pathway”, followed by “response to chemokine”, and then “cellular response to chemokine”, while the most enriched GO terms in the CC and MF domains were “external side of plasma membrane” and “chemokine receptor binding”, respectively. The top 10 GO terms for each domain are presented in Fig. 2A. After KEGG pathway analysis, it was found there were 16 KEGG pathways significantly enriched by these 57 genes (P < 0.05) (Fig. 2B). The most enriched KEGG pathways were “viral protein interaction with cytokine and cytokine receptor” and “cytokine-cytokine receptor interaction”.
We subsequently conduct PPI analysis by importing genes differentially expressed between PTC with and without HT into the STRING database. Figure 3 presents the PPI network in which 23 nodes with 70 interactions were characterized. Chemokines CXC chemokine ligand (CXCL) 10, CXCL9, CC chemokine ligand 5 gene (CCL5), Fcgamma receptor 3A (FCGR3A), and CC chemokine receptor 2 (CCR2) owned degree values not less than 10 were deemed as core genes differentially expressed between PTC with and without HT, all which were upregulated in tumor tissue samples of PTC with HT compared with those without HT.
We searched the TargetScan, miRDB, and mirDIP databases for putative miRNAs based on CXCL10, CXCL9, CCL5, FCGR3A, and CCR2. There were two miRNAs targeting CXCL10, three miRNAs targeting CXCL9, six miRNAs targeting CCL5, a miRNA targeting FCGR3A, and four miRNAs targeting CCR2. After removing the overlapping these miRNAs, miR-617, miR-619-5p, miR-645, miR-4725-3p, miR-5194, miR-6778-3p, miR-8070, miR-4786-3p, miR-4679, and miR-3940-5p were imported into the RNA22 database to obtain lncRNAs interaction with these miRNAs. These putative lncRNAs were overlapped with 40 lncRNAs differentially expressed between PTC with and without HT. On the basis of the principle of ceRNA hypothesis, the overlapped lncRNAs shared same expression patterns as CXCL10, CXCL9, CCL5, FCGR3A, and CCR2 were selected to construct the ceRNA network associated with PTC coexistent with HT. A total of 76 pairs of lncRNA-miRNA-mRNA ceRNA were obtained, including 15 pairs based on C22orf34, 12 pairs based on RFPL1S, and 10 pairs based on LINC00996 (Fig. 4).
We collected tumor tissue specimens from 112 consecutive patients undergoing partial or entire thyroidectomies for PTC. According to the results of postoperative pathological examinations, detection of anti-TGAb and TPO-Ab concentrations, there were 35 PTC patients coexistent with HT. As shown by Table 2, PTC patients with or without HT did not exhibit statistically considerable difference in light of age, gender, tumor size, multifocality, extrathyroidal extension, the presence of CLNM, and the TNM stage (P > 0.05). PTC patients with HT had higher concentrations of TSH, anti-TGAb, and TPO-Ab than those without HT before surgery (P < 0.05). Results of qRT-PCR partially demonstrated the bioinformatics results that the mRNA levels of CXCL10, CXCL9, CCL5, and CCR2 were remarkably elevated in tumor tissues collected from PTC patients coexistent with HT than those without HT (Fig. 5, P < 0.001), while the mRNA level of FCGR3A did not differ (P > 0.05).
The prevalence of PTC coexistent with HT has increased continuously and the relationship between two of them has received much attention [24]. Previous evidence has shown the incidence of PTC in HT patients is 1.3–2 times higher than those with thyroid benign diseases in the absence of HT [25]. However, another evidence has emerged that PTC patients coexistent with HT are more likely to have favorable prognosis as less invasive disease at presentation and a lower recurrence rate were observed [26]. This observation is rational concerning the mechanism that lymphocyte infiltration resulting from HT may cause antitumor immunity [27, 28]. However, relevant evidence is lacking in most multivariate analyses, which makes a definite association between PTC and HT is still a matter of debate [29]. Molecular, hormonal and histopathalogical basis of this association requires better interpretation. The aim of this study was to identify differentially expressed lncRNAs between tumor tissues of PTC with or without HT and further to confer a better understanding of lncRNA-based ceRNA action in PTC with HT. An exploration of key genes between tumor tissues of PTC with or without HT may provide a more precise strategy for the diagnosis and treatment of PTC of different types and characteristics, particularly PTC with HT. Our results obtained by bioinformatics methods characterized a total of 76 pairs of lncRNA-miRNA-mRNA ceRNA associated with PTC coexistent with HT, including 15 pairs based on C22orf34, 12 pairs based on RFPL1S, and 10 pairs based on LINC00996. On the basis of the principle of ceRNA hypothesis, C22orf34, RFPL1S, and LINC00996 shared same expression patterns as CXCL10, CXCL9, CCL5, FCGR3A, and CCR2, all which were upregulated in tumor tissues of PTC with HT compared with those without HT. A possible association between the single nucleotide variant rs35198919 in intron 1 of the C22orf34 and interstitial lung disease that may be associated with autoimmune disorders [30, 31]. C22orf34 was reported to be a lncRNA signature to predict prognosis and immune response in diffuse large B-cell lymphoma [32]. RFPL1S was identified as 6-kb noncoding antisense mRNAs of RFPL1S antisense gene in 199 by Seroussi et al. [33], whose role in human disease requires further better investigation. LINC00996 was associated with immune cell infiltration status and immunotherapy responses in head and neck squamous cell carcinoma [34]. Multiple cytokines exert a dangerous influence on the body by amplifying inflammatory reactions, causing a series of autoimmune diseases including HT [35, 36]. In addition to autoimmune thyroid disease, cytokines are concerned about their contributions to tumor growth and metastasis in PTC [37]. lncRNAs are believed to function as important regulators of cytokine regulation by the ceRNA phenomenon in PTC complicated with HT [38]. As evidenced by our study, CXCL10, CXCL9, CCL5, and CCR2 as key cytokines were associated with the presence of HT in PTC, all which involved in the C22orf34-, RFPL1S-, and LINC00996-based ceRNA network. Results of qRT-PCR partially demonstrated the bioinformatics results that the mRNA levels of CXCL10, CXCL9, CCL5, and CCR2 were remarkably elevated in tumor tissues collected from PTC patients coexistent with HT than those without HT. The initiation and perpetuation of chronic autoimmune inflammation depends on the recruitment, trafficking, and in situ maintenance of specific subsets of activated lymphocytes [39]. IFN-γ stimulation leading to the release of CXCR3-binding chemokines from thyrocytes in turn recruits Th1 lymphocytes expressing CXCR3 and producing IFN-γ, which suggests that the interferon-γ inducible chemokines (CXCL9, CXCL10, and CXCL11) and their receptor CXCR3 play an important role in the initiation of autoimmune thyroid diseases [40]. CXCL10 and CXCL9 as two interferon(IFN)γ-dependent chemokines of C-X-C chemokine receptor (CXCR)3 are implicated in the immune-pathogenesis of autoimmune thyroiditis [41]. A previous study found that serum CXCL10 levels have been shown to be elevated in autoimmune thyroid diseases [42]. The TSH-lowering effect of selenium supplementation is unlikely to be related to changes in humoral markers of autoimmunity and/or circulating CXCL9 [43]. In vitro experiments showed that the expressions of CCL5 and migration of peripheral blood mononuclear cells were markedly increased, while the level of PPARγ was significantly decreased after the lentivirus-mediated knockdown of Cav-1 in Nthy-ori 3–1 cells [44]. Extensive attention has been paid to PTC and coexistent HT to elucidate their association. In this study, we collected 112 PTC patients including 35 cases with HT. PTC patients with or without HT did not exhibit statistically considerable difference in light of age, gender, tumor size, multifocality, extrathyroidal extension, the presence of CLNM, and the TNM stage. Given relatively small sample size, our data were insufficient to support the concept of HT as a protective or risk factor for PTC. However, we found that PTC patients with HT had higher concentrations of TSH, anti-TGAb, and TPO-Ab than those without HT before surgery. A gradual increase of TSH is common in chronic lymphocytic thyroid autoimmunity. TSH is not only an endogenous stimulator of thyroid hormone production but also a growth factor for thyrocytes, serum TSH elevations may be associated with increased risk of PTC [45]. Accordingly, HT brings a chronic inflammatory condition that awakens an immune response leading to a continuous damage of surrounding stromal cells, leading to an inappropriate cell proliferation and thus increasing the risk of neoplastic transformation [46]. On the opposite, it was reported that PTC patients with HT may have favorable clinicopathologic characteristics compared to PTCs without HT [47]. This inconsistency may result from low mortality rate associated with PTC and the protective effect of HT not being independent of tumor characteristics. There are two limitations needed to inform when interpreting our results. On the one hand, the sample size of clinical human tissues for RT-qPCR validation of core genes was relatively small, which may weaken the reliability of clinical data. In the future, we will collect more samples to perform RNA-sequencing or arraying for better clinical validation. On the other hand, given the preliminary nature of our study, further functional studies elaborating the specific signal mechanism of CXCL10, CXCL9, CCL5, and CCR2 in cellular and animal models of PTC when coexistent with HT were required to elucidate the pathogenesis of PTC coexistent with HT focusing on lncRNA-based ceRNA action.
In conclusion, our results indicate that the lncRNA-based ceRNA network might provide new insights from the perspective of RNA for obtaining a further understanding of the clinical features related to PTC with HT. The CXCL10, CXCL9, CCL5, and CCR2 were associated with the presence of HT in PTC. These chemokines may facilitate immunotherapy in PTC when coexistent with HT. | true | true | true |
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PMC9583857 | Na Zhang,Shiguang Cao,Ruiying Sun,Yibei Wang,Luna Liu,Wei Wang,Xia Meng | Signal peptidase 21 suppresses cell proliferation, migration, and invasion via the PTEN-PI3K/Akt signaling pathway in lung adenocarcinoma | 17-10-2022 | SPC21,Lung adenocarcinoma,Akt,PTEN | Background In a previous study, a total of 568 differentially expressed proteins including the signal peptidase SPC21 were identified from lung adenocarcinoma (LUAD) and paired normal lung tissues. In this study, the role of SPC21 in LUAD progression was investigated. Methods The relationships and protein-protein interaction network of proteins differentially expressed between paired LUAD samples and adjacent normal tissues samples were identified via the String and Pajek software, respectively. The expression levels of the hub protein SPC21 were analyzed in 84 LUAD-normal paired tissues via immunohistochemistry. The prognostic value of SPC21 mRNA was investigated in 478 LUAD patients from TCGA and GTEx datasets. siRNAs were used in A549 and NCI-H1299 cells to knockdown SPC21. The SPC21 biological function was evaluated using the CCK-8, EdU, plate colony formation, transwell, wound healing, and adhesion assays. Results Patients with lower SPC21 mRNA levels tended to have worse prognosis (overall survival) than those with higher mRNA levels. SPC21 expression was significantly downregulated in LUAD tumor tissues compared with that in paired normal tissues (P < 0.001). Functionally, SPC21 knockdown promoted cell growth, migration, and invasion. Further analyses showed that SPC21 inactivated Akt signaling, and the Akt inhibitor MK-2206 blocked the tumor-promoting effects of SPC21 knockdown. Conclusions SPC21 plays a tumor suppressor role in LUAD cells by targeting the PTEN-PI3K/Akt axis and might be used as a prognostic indicator and therapeutic target in LUAD patients. | Signal peptidase 21 suppresses cell proliferation, migration, and invasion via the PTEN-PI3K/Akt signaling pathway in lung adenocarcinoma
In a previous study, a total of 568 differentially expressed proteins including the signal peptidase SPC21 were identified from lung adenocarcinoma (LUAD) and paired normal lung tissues. In this study, the role of SPC21 in LUAD progression was investigated.
The relationships and protein-protein interaction network of proteins differentially expressed between paired LUAD samples and adjacent normal tissues samples were identified via the String and Pajek software, respectively. The expression levels of the hub protein SPC21 were analyzed in 84 LUAD-normal paired tissues via immunohistochemistry. The prognostic value of SPC21 mRNA was investigated in 478 LUAD patients from TCGA and GTEx datasets. siRNAs were used in A549 and NCI-H1299 cells to knockdown SPC21. The SPC21 biological function was evaluated using the CCK-8, EdU, plate colony formation, transwell, wound healing, and adhesion assays.
Patients with lower SPC21 mRNA levels tended to have worse prognosis (overall survival) than those with higher mRNA levels. SPC21 expression was significantly downregulated in LUAD tumor tissues compared with that in paired normal tissues (P < 0.001). Functionally, SPC21 knockdown promoted cell growth, migration, and invasion. Further analyses showed that SPC21 inactivated Akt signaling, and the Akt inhibitor MK-2206 blocked the tumor-promoting effects of SPC21 knockdown.
SPC21 plays a tumor suppressor role in LUAD cells by targeting the PTEN-PI3K/Akt axis and might be used as a prognostic indicator and therapeutic target in LUAD patients.
Lung cancer is still the leading cause of cancer-related deaths worldwide, with the highest morbidity and mortality rates, resulting in serious universal health matters and considerable societal burdens (Sung et al., 2021). Lung adenocarcinoma (LUAD) is the most prevalently diagnosed pathological subtype of lung cancer. The insensitivity and development of resistance to existing therapies render the discovery of new biomarkers urgent. In a previous study, we used manual microdissection to isolate the cancer target cells from LUAD tissues and matched normal tissues. A total of 568 differentially expressed proteins in the membrane structures were identified using isobaric tags for relative and absolute quantification combined with liquid chromatography-tandem mass spectrometry (Zhang et al., 2015). The possible interactions of these proteins were analyzed using bioinformatics, and several hub proteins were identified. Among these, we have already verified the expression and function of DHX9 (Cao et al., 2017; Yan et al., 2019), ADFP (Meng et al., 2021; Zhang et al., 2014), and GALNT2 (Wang et al., 2021) in LUAD in vitro and in vivo models, patient databases and tissue samples. In the present study, we analyzed the role of SPC21 in LUAD progression and evaluated its value as a prognostic indicator and therapeutic target. The signal peptides, anchored on the N-terminus of membrane or secreted proteins guide the newly synthesized proteins to the endoplasmic reticulum. Subsequently, the signal peptides are degraded by signal peptidases (SPs), and the proteins continue to transfer to their final functional cellular allocations. If the SP is missing, the protein carrying the signal peptide cannot be correctly positioned in its functional area (Paetzel et al., 2002). SPs form a signal peptidase complex (SPC), which hydrolyze signal peptides in the endoplasmic reticulum of the eukaryotic cells. The SPC contains five subunits: SPC12, SPC18, SPC21, SPC22/23, and SPC25, and among these, SPC21 is the main catalytic site (Dalbey et al., 1997; Tjalsma et al., 2004). Currently, the role of SPC21 in tumors have not been extensively investigated. SPC21 has been identified as a metastasis-specific gene using cDNA microarray and RT-PCR in an intrapancreatic transplantation Syrian golden hamster model, where it was downregulated and shown to be involved in cancer invasion and metastasis (Tan et al., 2010). Chai et al. (2019) compared the RNA expression of 10 parathyroid adenoma tissues with five normal parathyroid gland tissues using transcriptome analysis, and identified a densely connected submodule that included SPC21 by network analysis. These results suggest that SPC21 may play a role in the development of tumors, but its biological functions for tumor progression have not been explored yet. In the present study, we evaluated the expression of SPC21 in LUAD tissues and paired normal tissues. In addition, we suppressed SPC21 expression in A549 and NCI-H1299 cell lines using siRNAs and evaluated its biological functions and possible mechanisms for tumor progression.
The data for the comparative membrane proteomic analysis between LUAD and normal tissues were obtained from our previous study (Zhang et al., 2015). The String software (http://string.embl.de/) was used to predict the relationship among differentially expressed proteins. The constructed protein-protein-interaction networks were visualized using the Pajek software (OmicX, Rouen, France) for network data integration and visualization, and the degree of interaction of each protein was calculated. Datasets from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) (Tang et al., 2017) were analyzed for prognosis by the website tool GEPIA (http://gepia.cancer-pku.cn/).
A total of 84 tumor tissues and 84 paired adjacent non-tumor tissues from 84 LUAD patients, operated between 2018 and 2021, were used in this study. All samples were fixed in formalin and embedded in paraffin. The Institutional Review Board of the Second Affiliated Hospital of Xi’an Jiaotong University approved this study (2021084) and patients or legal guardians of patients wrote informed consents.
Sections were deparaffinized and rehydrated using xylene and a gradient of decreasing concentrations of ethanol, placed in citrate buffer and boiled for antigen retrieval for 10 min. The samples were then incubated with 3% H2O2 for 10 min, goat serum for 30 min, and a rabbit anti SPC21 antibody (1:500; Novusbio, Hubei, China) at 4 °C overnight. After rinsing with TBS buffer three times for 5 min, the sections were incubated with a goat anti rabbit biotinylated secondary antibody at room temperature for 1 h. After being washed with TBST three times for 15 min, the sections were incubated by streptomycin-HRP for 30 min. Antibody binding was visualized using DAB. Next, the sections were counterstained with hematoxylin, and washed with running water. After that, sections were dehydrated by a gradient of increased concentrations of ethanol and xylene. Finally, sections were sealed with neutral balsam and coverslipped. Two pathologists scored the samples separately and provided immunoreactivity scores ranging from 0 to 12. The immunoreactivity score was calculated as the product of the positive cell proportion score (0 means no positive cells, 1 means 1%–25%, 2 means 26%–50%, 3 means 51%–75% and 4 means 76%–100%) and the staining intensity score (0 means no color reaction, 1 means mild, 2 means moderate, and 3 means intense). Samples were divided into low (<8) or high (≥8) grades according to the final scores.
Two human LUAD cell lines, A549 and NCI-H1299, were obtained from the National Collection of Authenticated Cell Cultures (Shanghai, China). Cells were cultured in RPMI-1640 medium (Gibco, Waltham, MA, USA) with 10% FBS (BI, Tel Aviv, Israel) at 37 °C and 5% CO2. MK-2206 (Selleck, Shanghai, China), inhibiting the phosphorylation process against Akt1, Akt2 and Akt3, was diluted with DMSO (Sigma, Waltham, MA, USA) to 20 mM. Next, the solution was diluted to 1 mM with RPMI-1640 and stored at −20 °C. Cells were treated with MK-2206 at 5 µM after culturing for 24 h, which was marked as the 0 h in MK-2206 group.
A549 and NCI-H1299 cells were plated in 24-well plates at a density of 1 ×104 cells per well in one mL of complete medium (RPMI-1640 medium with 10% fetal bovine serum). The siRNAs (GenePharma, Suzhou, China) for SPC21 (siR-1 sense: 5′-UCUUCUGCACUCAUGAUAUTT-3′, siR-1 antisense: 5′-AUAUCAUGAGUGCAGAAGATT-3′; siR-2 sense: 5′-GGGUGCAUAUGUGUUACUATT-3′, siR-2 antisense: 5′-UAGUAACACAUAUGCACCCTT-3′; siR-3 sense: 5′-CCAAUAGUUCACAGAGUAATT-3′, siR-3 antisense: 5′-UUACUCUGUGAACUAUUGGTT-3′), and control siRNA labeled with fluorescein were transfected using the X-tremeGENE siRNA Transfection Reagent (Roche, Basel, Switzerland) into cells following overnight incubation. The efficiency of transfection was evaluated by measuring fluorescence (≥ 90% of total cells).
An RNA Extraction Kit (Takara, Kuratsu, Japan) was used to extract whole cellular RNAs, which were reverse transcribed into cDNAs by PrimeScript™ RT Master Mix Kit (Takara, Japan). The corresponding primers were designed and synthesized (Sangon Biotech, China). Quantification of mRNA was performed using TB Green® Premix Ex Taq™ II Kit (Takara, Japan). The relative mRNA levels of SPC21 were normalized by GAPDH with 2−ΔΔCt method. The primers sequences were as follows: SPC21 (forward 5′- AGGCCAGAACTGGCTGGAA-3′, reverse 5′- TCTGGTCCCAGGAACTGCTT-3′); GAPDH (forward 5′-GTCTCCTCTGACTTCAACAGCG-3′, reverse 5′-ACCACCCTGTTGCTGTAGCCAA-3′).
All cellular proteins were extracted using lysis buffer (Beyotime, China) with phosphatase inhibitor (Roche, Basel, Switzerland). The proteins were loaded in SDS-PAGE to be separated and transferred to PVDF membranes (Millipore, Burlington, MA, USA). The PVDF membranes were blocked with 10% skimmed milk in TBST and incubated with the primary antibodies specific for a rabbit anti SPC21 antibody at 1:500 (Novusbio Biologicals, Hubei, China), a rabbit anti PTEN antibody at 1:800 (Abcam, Waltham, MA, USA), a rabbit anti p-Akt antibody at 1:1000 (CST, USA), a rabbit anti Akt antibody at 1:1000 (CST, USA), and a rabbit anti β-actin antibody at 1:2000 (Abcam). After being washed with TBST three times for 15 min, the membranes were incubated with secondary antibodies anti-rabbit or anti-mouse IgG biotinylated at 1:10000 (Solarbio, Beijing, China) for 2 h at room temperature. Protein bands were visualized by the ECL Chemiluminescent Kit (Millipore) and a ChemiDoc Touch imaging system (Bio-Rad, Hercules, CA, USA).
In brief, both cell lines were plated in 96-well plates at a density of 6 ×103 cells in 100 µL of complete medium per well. At 24, 48, 72, or 96 h, cells were then incubated with 10 µL of CCK-8 reagent (7 Sea Biotech, Shanghai, China) per well and cultured at 37 °C for 1 h and the absorbance was measured at a wavelength of 450 nm by an absorbance microplate reader (Thermo, USA).
Both cell lines were plated in 96-well plates at a density of 3 × 103 cells per well. After starvation in RPMI-1640 medium for 24 h, cells were back to complete medium and cultured with EdU according to the instructions of EdU kit (Ribobio, Guangzhao, China) for 2 h. Cells were then stained with Apollo and DNA staining solution, according to the manufacturer’s instructions. Finally, the cells were photographed by fluorescence microscope (Leica, Japan). Cell nucleuses were stained to blue light by hoechst 33342, representing cell DNAs. Replicated DNAs were stained to red light by Apollo solution, representing proliferating cells.
Both cell lines were seeded in six-well plates at a density of 200 cells per well and cultured for 7–10 days. Cells were then fixed in 4% paraformaldehyde (Honeywell, USA), and then stained with 0.1% crystal violet (Yantuo, China). After being washed with PBS three times, colony numbers that contained at least 50 cells was counted.
Briefly, both cell lines were plated in 24-well transwell inserts (Millipore, USA) coated with or without Matrigel (Corning, NY, USA) at a density of 5 × 105 cells or 1.5 × 105 cells for invasion assay or migration assay. 200 µL of RPMI-1640 medium was added to each insert. Complete medium (800 µL) was placed in each lower well for attraction. Then cells were incubated for 24 h (NCI-H1299 cells) or 48 h (A549 cells). Subsequently, cells migrating or invading through membranes were counted using a microscope (Nikon, Tokyo, Japan).
Both cell lines were plated in six-well plates at a density of 1 ×105 cells and cultured until reaching 100% confluence. Next, a scratch was made in each well using a 100 µL tip, and the scratched area was drawn on the plate surface with a black marker. Cells were then cultured in RPMI-1640 medium for 24 h. Finally, the scratched area was visualized under a light microscope (Nikon, Tokyo, Japan) at 0 h and 24 h following the black labeling drew on the plate.
The difference between two groups or among multiple groups were performed to access by the Student’s t-test, one-way analysis of variance (ANOVA), repeated-measures ANOVA, chi-square test, Fisher’s exact test, McNemar’s test, or logistic regression, as appropriate. All cellular experiments were conducted at least in triplicate. Data were presented as a mean ± SD. All statistical tests were two-sided, and values of P < 0.05 were considered significantly different.
Based on our previous comparative membrane proteomics data (Zhang et al., 2015), we analyzed the possible interactions among the differentially expressed proteins using String software (Fig. S1A), which showed the protein network and the connectivity degree of each protein (Fig. S1B). In line with the results of the proteomic analysis, we screened out the hub protein SPC21 and investigated the mRNA levels of SPC21 in TCGA and GTEx datasets. Kaplan–Meier plots showed that LUAD patients with low SPC21 expression had shorter overall survival (OS) than those with high SPC21 expression (Fig. 1A, P = 0.0037). Tumor tissues from 84 LUAD patients and paired adjacent non-tumor tissues were stained by IHC. The results showed lower SPC21 protein expression in LUAD than that in control tissues (Figs. 1B–1C). The analysis of the relationship between SPC21 expression in tumor tissues and the clinical paraments indicated that SPC21 was negatively associated with age and American Joint Committee on Cancer (AJCC) stage (Table 1). Furthermore, univariate logistic analysis of several variables revealed significantly low odds of high SPC21 expression in tumor tissues from elderly patients (≥60) and high AJCC stage (III/IV) (Table 2). However, after significant features were adjusted in the multivariate model, only high AJCC stage (III/IV) (OR 0.114, 95% CI [0.021–0.601], P = 0.010) showed significantly low odds of high SPC21 expression (Table 2).
SPC21 was knockdown using several siRNAs (siR-1, siR-2, and siR-3) in the LUAD cell lines A549 and NCI-H1299. Both siR-1 and siR-2 suppressed SPC21 expression at not only the mRNA but also protein levels in LUAD cells, whereas siR-3 did not (Figs. 2A–2B). Therefore, only siR-1 and siR-2 were used for further experiments. The CCK-8, EdU, and colony formation assays indicated that knockdown of SPC21 substantially promoted cell proliferation in A549 and NCI-H1299 (Figs. 2C–2E). In addition, the results of the wound healing and transwell assays showed that SPC21 knockdown enhanced the migration and invasion abilities of LUAD cells (Figs. 3A–3B).
In order to explore the potential mechanism, the protein levels of PTEN, and the phosphorylation level of Akt in A549 and NCI-H1299 cells with or without SPC21 were examined using western blotting. PTEN protein expression decreased in SPC21 knockdown LUAD cells, whereas p-Akt/Akt ratio increased (there is the same amount of protein but a higher phosphorylation) (Fig. 4A). As PTEN inhibited PI3K to regulate the phosphorylation of Akt, we speculated that SPC21 might regulate PTEN-PI3K/Akt pathway. To further investigate this hypothesis, NCI-H1299 cells were treated with MK-2206, an inhibitor of Akt phosphorylation. Western blotting analysis showed that MK-2206 reduced p-Akt levels but exerted no significant effect on SPC21 and PTEN protein levels. Interestingly, MK-2206 treatment rescued the increase phosphorylation in Akt induced by SPC21 knockdown (Fig. 4B) in NCI-H1299 cells and diminished the proliferation, migration, and invasion abilities back to the levels in the cells of control group (Figs. 4C–4F).
Here, we first found that SPC21 significantly decreases in LUAD, and play a role as a tumor-inhibiting gene via the PTEN-PI3K/Akt pathway. Low level of SPC21 mRNA indicates poor prognosis. Recent studies on SPC21 in tumors have focused on gene and transcriptional levels. Chai et al. (2019) performed a differential transcriptome analysis on parathyroid adenomas and normal parathyroid gland tissues. In that study, the protein-protein interaction network analysis of differentially expressed genes revealed SPC21 as one of eight hub candidates, suggesting that it may play a crucial role in the development of parathyroid adenoma. However, parathyroid adenoma, as a benign tumor disease, provides limited evidence for the role of SPC21 in malignant tumors. Tan et al. (2010) established two pancreatic cancer cell lines with low and high potential for invasion metastasis after intrapancreatic transplantation in Syrian golden hamsters. SPC21 expression was downregulated in pancreatic cancer cells with high potential for invasion metastasis. However, the researchers did not continue to verify the cellular and molecular mechanisms of the effect of SPC21 on the invasion and metastasis in pancreatic cancer. In this study, we analyzed TCGA and GTEx databases using the GEPIA website and found that high levels of SPC21 mRNA were favorable for OS. In addition, SPC21 mRNA level was higher in LUAD than that in adjacent normal paired lung tissues. SPC21 is the main active subunit of the SPC (Dalbey et al., 1997; Tjalsma et al., 2004), and its mRNA increased in LUAD tissue, in agreement with previous observations indicating that signal peptides, the guides for the processing and transportation of secretory proteins, and the related SPs are in high demand in tumors, which might be associated with the active processes of tumor cell with an increase in the synthesis, modification, and secretion of many proteins (Gottfried, Kreutz & Mackensen, 2012). In the present study, SPC21 was higher at mRNA level in LUAD but lower at protein level, validated by mRNA database and LUAD tissue microarray, respectively. We speculate that SPC21, as an important molecule for degrading signal peptides, is transcriptionally increased in metabolically active tumor cells. This hypothesis is also supported by the fact that LUAD patients with higher mRNA levels have a better prognosis. However, SPC21 is significantly inhibited during translation, and the SPC21 protein cannot function in tumor cells due to reduced protein levels. As a result, the hydrolysis of a series of signal peptides is inhibited, and the newly synthesized proteins anchored by the signal peptides cannot be properly localized to their functional regions in cells, which may further exacerbate cellular metabolic disorders. The mechanism of this post-transcriptional regulation is unclear and requires further experimental exploration. In addition, our omics and clinical data analyses suggested that SPC21 may inhibit tumor development. Therefore, we explored the tumor suppressor effects of SPC21 and observed that SPC21 knockdown potentiated proliferation, invasion, and migration in LUAD cells as well as the activity of the PTEN-PI3K/Akt axis. Furthermore, inhibition of Akt phosphorylation with the small-molecule inhibitor MK-2206 led to a decrease in p-Akt/Akt levels, whereas SPC21 and PTEN expression levels were not significantly altered. Interestingly, MK-2206 treatment reversed the cancer promoting effects of SPC21 knockdown. Altogether, these results suggest that SPC21 exerts a tumor suppressor effect through the PTEN-PI3K/Akt axis. However, the potential mechanism through which SPC21 regulates PTEN still needs to be further explored.
In summary, SPC21 protein expression is downregulated in LUAD with a higher mRNA level. SPC21 suppresses the proliferation and metastasis of LUAD cells via the PTEN-PI3K/Akt signaling pathway. Our findings throw new mechanistic insights into the basic theory of LUAD progression.
10.7717/peerj.14206/supp-1 Click here for additional data file. 10.7717/peerj.14206/supp-2 Click here for additional data file. 10.7717/peerj.14206/supp-3 Click here for additional data file. 10.7717/peerj.14206/supp-4 Click here for additional data file. | true | true | true |
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PMC9583869 | Shasha Zhu,Xiangbing Kong,Mengru Song,Mingyang Chi,Yitong Liu,Peng Zhang,Qiao Zhang,Pingping Shang,Feifei Feng | MiR-223-3p attenuates the migration and invasion of NSCLC cells by regulating NLRP3 | 06-10-2022 | miR-223-3p,NLRP3,NSCLC,invasion,migration | Lung cancer is the malignant tumor with high invasion and metastasis, which seriously threatens public health. Previous study showed that NLRP3 could promote the occurrence of lung tumors in B(a)P-induced mice. MicroRNAs are closely related to the progression and metastasis of lung cancer by regulating target genes. However, which miRNAs affect the migration and invasion of lung cancer cells through regulating NLRP3 remains poorly defined. In this study, the miRNAs targeting NLRP3 were selected from TargetScan and miRDB database and finally miR-223-3p was chosen due to the consistent expression in both A549 and H520 cells. Then, the migration and invasion of lung cancer cells were detected with miR-223-3p mimic and inhibitor using Transwell assay, at the same time the expression of NLRP3, cleaved caspase-1, IL-1β and IL-18 was determined using Western Blot and immunohistochemistry assay. Our data demonstrated that miR-223-3p was upregulated in both A549 and H520 cells. Furthermore, the migration and invasion of A549 and H520 cells were promoted after inhibiting miR-223-3p. Besides, the levels of NLRP3, cleaved caspase-1, IL-1β and IL-18 were increased in the two lung cancer cells. And the corresponding results were contrary in miR-223-3p mimic group. Taken together, miR-223-3p attenuates the migration and invasion of NSCLC cells by regulating NLRP3, which provides evidence for the prevention and targeted treatment of NSCLC. | MiR-223-3p attenuates the migration and invasion of NSCLC cells by regulating NLRP3
Lung cancer is the malignant tumor with high invasion and metastasis, which seriously threatens public health. Previous study showed that NLRP3 could promote the occurrence of lung tumors in B(a)P-induced mice. MicroRNAs are closely related to the progression and metastasis of lung cancer by regulating target genes. However, which miRNAs affect the migration and invasion of lung cancer cells through regulating NLRP3 remains poorly defined. In this study, the miRNAs targeting NLRP3 were selected from TargetScan and miRDB database and finally miR-223-3p was chosen due to the consistent expression in both A549 and H520 cells. Then, the migration and invasion of lung cancer cells were detected with miR-223-3p mimic and inhibitor using Transwell assay, at the same time the expression of NLRP3, cleaved caspase-1, IL-1β and IL-18 was determined using Western Blot and immunohistochemistry assay. Our data demonstrated that miR-223-3p was upregulated in both A549 and H520 cells. Furthermore, the migration and invasion of A549 and H520 cells were promoted after inhibiting miR-223-3p. Besides, the levels of NLRP3, cleaved caspase-1, IL-1β and IL-18 were increased in the two lung cancer cells. And the corresponding results were contrary in miR-223-3p mimic group. Taken together, miR-223-3p attenuates the migration and invasion of NSCLC cells by regulating NLRP3, which provides evidence for the prevention and targeted treatment of NSCLC.
Lung cancer is the leading cause of cancer-related death worldwide. According to Global Cancer Statistics 2020, lung cancer ranked first on the mortality and ranked second on the incidence among all the top 10 most common cancers, which is severely threatening to human health (1). Among various kinds of lung cancer, there are approximately 80-85% are non-small cell lung cancer (NSCLC), including adenocarcinoma and squamous cell carcinoma (2). Recently, though a large number of studies about lung cancer have been carried out all over the world, the prognosis of lung cancer is still not optimistic and the case fatality rate is still high. One of the main reasons is that most lung tumors have invaded and migrated before they were first diagnosed by available medical methods (3). Therefore, it is vital to explore the mechanism of the development of NSCLC for effective therapy. Chronic inflammation is closely related to the occurrence and progression of lung cancer (4). The pattern recognition receptors of the leucine-rich nucleotide binding domain receptor family (NLR) play an important role in inflammation caused by innate immune system (5). NLRP3 is an important member of NLRs family. It forms the NLRP3 inflammasome with pro-caspase-1 and ASC (apoptosis-associated speck-like protein containing a carboxy-terminal CARD) (6). After NLRP3 inflammasome being activated, pro-caspase-1 was cleaved to cleaved caspase-1, and subsequently, the cleaved caspase-1 matured pro-IL-1β and pro-IL-18 into IL-1β and IL-18. NLRP3 inflammasome has anti-cancer effects on colon cancer and pro-cancer effects on gastric cancer and lung cancer (7–9). Its downstream product, IL-18, can increase the cytotoxicity of NK cells, suggesting that NLRP3 inflammatory bodies have anti-cancer effects, and IL-1β and IL-18 have also been proven to inhibit anti-metastasis and immune surveillance mediated by NK cells and T cells to promote the occurrence and development of cancer (10). Our previous study showed that NLRP3 inflammasome participated in the tumorigenesis of lung cancer and deletion of NLRP3 gene could inhibit the occurrence of lung tumors in mice induced by B(a)P or B(a)P combined with LPS (11). Moreover, NLRP3 could be regulated by microRNAs. microRNA (miRNA) is a class of small noncoding RNA which negatively regulate the gene translation or degrade mRNAs by binding to the 3′-untranslated region (3′-UTR) of target genes (12). Studies have shown that miRNAs are overexpressed in malignant tumors such as breast cancer, lung cancer, gastric cancer and prostate cancer which indicates that there is an intimate relationship between tumorigenesis and abnormal expression of miRNAs (13–16). In particular, the abnormal expression of miRNA is closely related to the diagnosis, progression, metastasis, treatment and prognosis of lung cancer (17). A previous study demonstrated that miRNA inhibited the proliferation and migration of malignant glioma cells, human gastric cancer cells and oral squamous carcinoma cells by regulating NLRP3 (18–20). In addition, it has been reported that the activation of NLRP3 inflammasome can promote the proliferation, migration and invasion of A549 cells (21). However, which miRNAs affect the migration and invasion of lung cancer cells through regulating NLRP3 still remain unknown. In this study, we aimed to select the miRNAs targeting NLRP3 from TargetScan and miRDB Online Database and finally choose the most suitable miRNA for the further experiments due to the expression in lung cancer cells. Then the selected miRNA was overexpressed or inhibited in lung adenocarcinoma cells (A549) and lung squamous carcinoma cells (H520), respectively. After that, the migration and invasion ability of the two cells were measured, and the protein expression of NLRP3, cleaved caspase-1, IL-1β and IL-18 were detected to reflect the expression and activation of NLRP3 inflammasome. This study will provide clues for the regulatory mechanism of the migration and invasion of NSCLC, and finally promote the prevention and targeted treatment of NSCLC.
The aimed miRNAs were selected from the TargetScan and miRDB Online Database which directly target at NLRP3 by the following two standards: 1) TargetScan database conservative target probability (PCT)≧0.3 or miRDB database prediction target score>85; 2) The selected miRNAs meet the homologous condition of human and mouse. The selected miRNAs targeting NLRP3 were miR-223-3p, miR-22-3p and miR-1305.
Human lung adenocarcinoma cell line (A549), human lung squamous carcinoma cell line (H520) and normal human bronchial epithelial cell (BEAS-2B) were purchased from Shanghai Institute of Biochemistry and Cell Biology, CAS. All of the cell lines were cultured in RPMI 1640 medium (Solarbio, Beijing, China) containing 10% fetal bovine serum (FBS; Solarbio, Beijing, China), under a 5% CO2 atmosphere at 37°C.
The total RNA from A549 and H520 cells were extracted with Trizol Reagent (Invitrogen, USA) according to the operation instructions. cDNA was synthesized with a reverse transcription kit (TIANGEN BIOTECH, China), and then the quantitative real-time polymerase chain reaction was conducted in a 7500 Fast Real-time PCR System. The primer sequences in this study were as follows: miR-22–3p, 5′-AACAGTGAAGCTGCCAGTTGAA3′ (reverse); miR-223–3p, 5′-CGCTGTCAGTTTGT-CAAATACCCCA-3′ (reverse); miR-1305, 5′-GCCGCGCGTTTTCAACTCTAATGGGAG-3′ (reverse); U6, 5′-CTCGCTTCGGCAGCACA-3′ (forward) and 5′-AACGCTTCACGAATTTGCGT-3′ (reverse). The data obtained was analyzed as 2-△△Ct. Each experiment was carried out in triplicate.
miR-223-3p mimic and miR-223-3p inhibitor (Guangzhou Ruibo Biotechnology Co, Ltd) were all given to A549 and H520 cells respectively to overexpress miR-223-3p or suppress the function of miR-223-3p. miR-223-3p inhibitor control was inhibitor NC and miR-223-3p mimic control was mimic NC. The sequences were as follows: miR-223-3p inhibitor: 5′-UGGGGUAUUUGACAAACUGACA-3′; miR-223-3p mimic: 5′-UGUCAGUUUGUCAAAUACCCCA-3′ (forward) and 5′- UGGGGUAUUUGACAAACUGACA-3′ (reverse); inhibitor NC: 5′-CAGUACUUUUGUGUAGUACAAA-3′; mimic NC: 5′-UUUGUACUACACAAAAGUACUG-3′ (forward) and 5′- CAGUACUUUUGUGUAGUACAAA -3′ (reverse). miR-223-3p inhibitor and miR-223-3p mimic were used with transfection kits (Ruibo Biotechnology Co, Ltd, China) according to the manufacturer’s instruction when the cells in the six-well plate were fused to 40–50%. The concentrations of miR-223-3p inhibitor and miR-223-3p mimic are both 100nM per well. After 6h transfection, cells are replaced with new RPMI 1640 medium. Follow-up experiments were performed when cells fused to 80%-90%.
Matrigel matrix glue diluted in precooled serum-free medium was spread on the culture chamber with 8µm small-hole polycarbonate filter membrane, and 100 µL serum-free medium diluted A549 cells or H520 cells were inoculated; in the lower chamber, RPMI 1640 culture medium containing 10% serum of 600pL was added, each group had 6 multiple holes. After being incubated at 37°C and 5% CO2 for 24 hours, the culture chamber was taken out, then it was fixed with 2.5% glutaraldehyde for 15min, treated with 0.5% TritonX-100 for 3min and stained with crystal violet for 15min. Invert the culture chamber, observe and photograph it under an optical microscope (Leica, Germany) to count the average number of cells at the bottom of the filter membrane per high vision field. The cell migration experiment was carried out in the Transwell chamber without Matrigel matrix glue in the same method.
Cells were collected, trypsinized and lysed in RIPA lysis buffer. Transfer the electrophoresed proteins to a poly (vinylidene fluoride) membrane and incubate for 2 hours at room temperature in blocking solution. Then they were incubated in the membrane overnight at 4°C in antibody solution containing primary antibody, including anti-NLRP3 antibody and caspase-1 antibody (Cell Signaling Technology, USA). After overnight incubation, second antibody goat-anti-rabbit (1:5000) were added at 37 °C for 1.5h. The membrane was washed at room temperature for 30min and then detected with Amersham Imager 600 automatic chemiluminescence gel imaging analyzer.
After the cells were incubated with hydrogen peroxide for 20 min and blocked with TBST containing goat serum for 25 min at room temperature, they were incubated overnight at 4°C in primary antibody, including anti-IL-1β antibody and anti-IL-18 antibody (1:100 dilution), respectively. On the second day, the second antibody, goat anti-rabbit (1:100 dilution), were added at 37°C for 40 min. In 200 high vision field of microscope, the cell was stained in blue while the positive one was brown. We used the semi-quantitatively analysis to analyze the collected images and the results were expressed by the average optical density of the brown area.
The data was analyzed using SPSS 23.0 software. Data was expressed as mean ± standard deviation (SD). The independent sample t test was used to compare the data between two groups. The difference was statistically significant when α=0.05.
We selected the miRNAs from the TargetScan and miRDB Online Database which directly target at NLRP3 gene, and they were miR-223-3p, miR-22-3p, and miR-1305 ( Figure 1A ). To figure out whether the miRNA above have the same influence on the progress of A549 andF H520 cells, qRT-PCR was first carried out to detect the expression in the cells. As presented in Figure 1 , comparing to BEAS-2B, the three miRNAs above were all expressed irregularly in A549 and H520 cells. The results showed that the expression of miR-223-3p was remarkably increased in both A549 and H520 cells ( Figure 1B ); the expression of miR-22-3p and miR-1305 was increased in A549 cells while was decreased in H520 cells ( Figures 1C, D ). These results suggested that the selected miRNAs were differently dysregulated in different lung cancer cells but miR-223-3p was upregulated in both A549 and H520 cells.
According to the results of qRT-PCR, the irregular expression of miR-223-3p was consistent in both A549 and H520 cells, so we focused on miR-223-3p for the further experiments. The A549 and H520 cells were transfected with miR-223-3p inhibitor which could bind to miR-223-3p in the form of base complementary pairing to suppress the function of miR-223-3p so that the inhibitor groups were established successfully as Figure 2A . Next, the effect of miR-223-3p suppression on the migration and invasion of A549 and H520 cells was assessed by Transwell assay. When A549 and H520 cells were transfected with miR-223-3p inhibitor, the average number of cells at the bottom of the filter membrane without or with Matrigel matrix glue was significantly increased which meant that the migration ( Figures 2B, C ) and invasion ( Figures 2D, E ) of A549 and H520 cells were promoted remarkably. These results indicated that it would promote the migration and invasion of A549 and H520 cells with the inhibition of miR-223-3p.
To assess whether miR-223-3p regulated the migration and invasion of A549 and H520 cells through the NLRP3 inflammasome regulation pathway, NLRP3 protein was firstly detected by Western Blot assay to reflect the expression of NLRP3 inflammasome. As presented in Figures 3A, B , NLRP3 protein was increased in A549 and H520 cells transfected with miR-223-3p inhibitor (P<0.05). Similar to NLRP3 protein, cleaved caspase-1 was upregulated following suppression with miR-223-3p ( Figures 3C, D ). The concentration of IL-1β and IL-18 in the inhibitor group was higher than that in the control group ( Figures 3E–H ). The results indicated that suppression of miR-223-3p promoted the expression and activation of NLRP3 inflammasome.
When A549 and H520 cells were transfected with miR-223-3p mimic to overexpress miR-223-3p ( Figure 4A ), the migration and invasion were also detected by Transwell assay. From Figures 4B, C , we found that the average number of cells at the bottom of the filter membrane without Matrigel matrix glue was reduced in miR-223-3p mimic group comparing to the mimic NC group. Moreover, in invasion experiment, the average number of cells at the bottom of the filter membrane with Matrigel matrix glue was also reduced in miR-223-3p mimic group ( Figures 4D, E ). These results indicated that overexpressing miR-223-3p could inhibit the migration and invasion of A549 and H520 cells.
To measure the expression and activation of NLRP3 inflammasome in miR-223-3p mimic group. NLRP3, cleaved caspase-1, IL-1β and IL-18 were also detected respectively. After miR-223-3p was overexpressed in A549 and H520 cells, NLRP3 and cleaved caspase-1 protein were both got a decline in western blot analysis ( Figures 5A–D ). Besides, Immunohistochemistry analysis showed that IL-1β and IL-18 were also remarkably reduced after miR-223-3p overexpression ( Figures 5E–H ). These results showed that miR-223-3p suppressed the expression and activation of NLRP3 inflammasome, and the effect could be enhanced under overexpression of miR-223-3p.
Lung cancer is the leading cause of cancer-related death worldwide. Most lung tumors have developed distant metastasis at the time of initial diagnosis, but the regulatory mechanism of the migration and invasion has not been figured out entirely yet. Therefore, it is vital to explore the mechanism of the development of lung cancer, which will provide an important clue for the effective treatment for lung cancer. NLRP3 is an important member of NLRs family. The role of NLRP3 in cancer cells remains controversial. The studies of Salcedo R. et al. and Takagi H. et al. showed that the NLRP3 had a protective role in colitis-associated colorectal cancer because of its ability to mediate secretion of IL-18, a cytokine which contributed to epithelial barrier repair against damage (22, 23). However, as to other cancers, such as fibrosarcoma, melanoma, gastric carcinoma, and lung cancer, NLRP3 functioned as a deleterious protein owing to its ability to suppress activation of NK cells that secrete IFN-γ and kill tumor cells (24). Besides, it was found that NLRP3 inflammasome activation could promote nicotine-induced lung cancer cell proliferation and migration (25). NLRP3 is regulated by many miRNAs. However, which miRNAs influence the migration and invasion of NSCLC through regulating NLRP3 were ill-defined. In this study, we selected the miRNAs targeting NLRP3 from TargetScan and miRDB Online Database, and they were miR-223-3p, miR-22-3p and miR-1305. Reports showed that miR-223-3p regulated the proliferation and migration of lung cancer cells by targeting the human transforming growth factor β receptor 3 (TGFBR3) (26). MiR-22-3p suppressed cell growth via MET/STAT3 signaling in lung cancer (27). MiR-1305 was down-regulated in NSCLC tissues and cell lines and it inhibited the progression of NSCLC cells by regulating MDM2 (28). According to the qRT-PCR results, we found the expression of miR-223-3p was upregulated in both A549 and H520 cells ( Figure 1B ). Therefore, miR-223-3p was verified further. In the present study, the results demonstrated that the migration and invasion of A549 and H520 cells were promoted after inhibiting miR-223-3p, and the corresponding results were contrary in miR-223-3p mimic group, which suggested miR-223-3p could attenuate the migration and invasion of NSCLC cells. In addition to NSCLC, miR-223-3p influences the progression of various solid tumor types. In human osteosarcoma, miR-223-3p functioned as a tumor suppressor to inhibit the metastasis and progression of osteosarcoma through regulating Cadherin-6 (CDH6) (29). This tumor-inhibitory role of miR-223-3p was also reported in oral squamous cell carcinoma (OSCC) that miR-223-3p inhibited the proliferation and metastasis of OSCC cells by targeting SHOX2 (30). However, it was found that miR-223-3p promoted the proliferation, invasion and migration of colon cancer by negative regulating PRDM1 (31). Moreover, miR-223 was revealed to promote the invasion and metastasis of gastric cancer by regulating erythrocyte membrane protein band 4.1-like 3 (EPB41L3) (32). These studies comprehensively indicated bidirectional roles of miR-223-3p during tumorigenesis and progression. In addition, the levels of NLRP3, cleaved caspase-1, IL-1β and IL-18 were increased in the two lung cancer cells with miR-223-3p inhibition, and the corresponding results were contrary in miR-223-3p mimic group. These results implied that miR-223-3p suppressed the migration and invasion of the two cell lines by directly regulating NLRP3. The regulation of NLRP3 by miR-223-3p was also reported in other diseases. Previous studies observed that miR-223-3p could regulate NLRP3 to promote apoptosis and inhibit proliferation of hep3B cells (33). In addition, miR-223-3p was found to influence the proliferation and migration of bladder cancer through regulating NLRP3 (34). Furthermore, dual-luciferase reporter observed that co-transfection with miR-223 reduced the luciferase activity of the plasmid containing the wild-type of the respective fragment of NLRP3 3’-UTR, while the luciferase activity of the plasmid containing the mutant NLRP3 3’-UTR fragment was not affected by co-transfection with miR-223 mimics or negative control, which indicated that miR-223 directly interacted with the 3’-UTR of NLRP3 mRNA (33). Moreover, we infer that the reason of miR-223-3p suppressing the migration and invasion of NSCLC cells by regulating NLRP3 is the reduced change of maturation and expression of IL-1β and IL-18. Both of the two cytokines were proven to have tumor-promoting effects on cancers. The studies of Saijo, Y. et al. showed that IL-1β enhanced the metastasis of lung cancer cells because of its ability to enhance angiogenesis (35). And in both murine and human breast cancer models, tumor progression was associated with elevated levels of IL-1β at primary and metastatic sites (36). IL-18 has also been proven to promote the occurrence and development of cancer by inhibiting anti-metastasis and immune surveillance mediated by NK cells and T cells (10). In gastric cancer, IL-18 induced the expression of the pro-angiogenic factor, vascular endothelial growth factor (VEGF), which finally promoted tumor growth and metastasis (37). These studies further suggested that miR-223-3p may inhibit the migration and invasion of NSCLC cells through regulating NLRP3. In this study, we observed that miR-223-3p suppressed the pro-cancer effects of NLRP3 on the invasion and migration of NSCLC cells. And hopefully, this discover might contribute to the targeted therapy of NSCLC. To our knowledge, this is the first study to determine the role of miR-233-3p/NLRP3 axis in the migration and invasion of NSCLC. Of note, there were certain limitations in our study. Firstly, only two NSCLC cell lines, A549 and H520 cells, were used in all the experiments. Considering various kinds of cell lines of NSCLC, more experiments about other NSCLC cell lines are required in later studies to fully reveal the effects of miR-223-3p/NLRP3 axis on the progression of NSCLC. Furthermore, scientific in vivo experiments are also needed in further research to confirm the results in present study. In conclusion, this research demonstrated the role of miR-223-3p in NSCLC cells and the relationship between miR-223-3p and NLRP3, revealing a novel mechanism in regulating the progression of NSCLC. This study may provide a new insight in the therapy of NSCLC in future.
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 in the article/ Supplementary Material .
FF, PS conceived and designed the study. XK performed the experiments. SZ wrote the paper. MS, MC, YL, PZ, QZ reviewed and edited the manuscript. All authors read and approved the manuscript. All authors contributed to the article and approved the submitted version.
This work was supported by the National Natural Science Foundation of China (No. 81402712); Natural Science Foundation of Henan Province (No. 202300410457); National innovation and entrepreneurship training program for College Students (202110459056); the training grant for young teachers of Henan Province (2020GGJS011) and Zhengzhou University (JC21838046); the grant of Medical Science Research Foundation of Henan Province (No. YXKC2021031); the grant from the Department of Education of Henan Province, China (No. 20B330004 and 20B320042); the Project from China National Tobacco Corporation (No. 110202102015); and the scientific research program of innovation platform in State Tobacco Monopoly Administration (No. 312021AW0420).The authors declare that this study received funding from China National Tobacco Corporation and State Tobacco Monopoly Administration. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
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. | true | true | true |
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PMC9584272 | 36264297 | Hongyu Zhou,Shengjun Bu,Yao Xu,Lulu Xue,Zhongyi Li,Zhuo Hao,Jiayu Wan,Feng Tang | CRISPR/Cas13a combined with hybridization chain reaction for visual detection of influenza A (H1N1) virus | 20-10-2022 | Influenza H1N1 virus detection,CRISPR/Cas13a,Hybridization chain reaction,DNAzyme | This study provides proof of concept of a colorimetric biosensor for influenza H1N1 virus assay based on the CRISPR/Cas13a system and hybridization chain reaction (HCR). Target RNA of influenza H1N1 virus activated the trans-cleavage activity of Cas13a, which cleaved the special RNA sequence (-UUU-) of the probe, further initiating HCR to copiously generate G-rich DNA. Abundant G-quadruplex/hemin was formed in the presence of hemin, thus catalyzing a colorimetric reaction. The colorimetric biosensor exhibited a linear relationship from 10 pM to 100 nM. The detection limit was 0.152 pM. The biosensor specificity was excellent. This new and sensitive detection method for influenza virus is a promising rapid influenza diagnostic test. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s00216-022-04380-1. | CRISPR/Cas13a combined with hybridization chain reaction for visual detection of influenza A (H1N1) virus
This study provides proof of concept of a colorimetric biosensor for influenza H1N1 virus assay based on the CRISPR/Cas13a system and hybridization chain reaction (HCR). Target RNA of influenza H1N1 virus activated the trans-cleavage activity of Cas13a, which cleaved the special RNA sequence (-UUU-) of the probe, further initiating HCR to copiously generate G-rich DNA. Abundant G-quadruplex/hemin was formed in the presence of hemin, thus catalyzing a colorimetric reaction. The colorimetric biosensor exhibited a linear relationship from 10 pM to 100 nM. The detection limit was 0.152 pM. The biosensor specificity was excellent. This new and sensitive detection method for influenza virus is a promising rapid influenza diagnostic test.
The online version contains supplementary material available at 10.1007/s00216-022-04380-1.
Influenza virus is an enveloped, segmented, negative-stranded, single-stranded RNA virus [1]. Most warm-blooded animals can become the host of influenza virus. Influenza virus is a serious threat to human health and has historically caused many pandemics, with considerable morbidity and mortality worldwide [2]. Currently, influenza viruses comprise types A, B, C, and D [3, 4]. Influenza A virus is the most severe and deadly. Among them, influenza A (H1N1) virus (hereafter influenza H1N1), which has three different RNA fragment sequences, can be isolated from swine, avian, and humans [5]. In one year, this virus spread to 214 countries, causing more than 18,000 deaths worldwide, and has become a major international public health problem [6]. Developing a sensitive, specific, rapid, and reliable sensor of H1N1 is essential for controlling outbreaks. Virus isolation and culture was the gold standard for diagnosing influenza virus. This has been supplanted by reverse transcriptase polymerase chain reaction (RT-PCR), with its superior analytical and clinical sensitivity [7]. Concurrently, several traditional detection methods are widely used in the detection of influenza virus. These include ELISA [8] and protein identification [9]. The traditional detection of influenza viruses has improved with time. However, the method has some inherent disadvantages. It is complicated to perform and require professionally trained personnel or sophisticated laboratory instruments. Therefore, this technique is not suitable for routine healthcare and resource-poor areas [10]. Many alternative methods have been assessed for the detection of influenza viruses. These include an electrochemical immunosensor [11], a surface-enhanced Raman scattering–based imaging aptasensor platform [12], fluorescence [13], colorimetric [14], immunochromatographic [15], and microfluidic chip [16] approaches. Among them, colorimetry is a fast and convenient option that does not require advanced instruments. Despite promising progress in emerging diagnostic tests to detect influenza virus, a perfect detection method has yet to be developed. Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas systems have attracted a lot of attention due to their simplicity. Only one single effector is required. The CRISPR-Cas module present in most archaea and many bacteria belongs to the adaptive immune system and provides sequence-specific protection to RNA in the presence of foreign invading DNA [17]. The CRISPR family is used for gene editing and transcriptional regulation [18], and is also widely used in biosensing. Examples of the latter include detection of DNA [19] and microRNA [20]. Cas13a was first proposed in 2016 [21]. Compared with the other Cas proteins (Cas9, Cas12a, Cas14), Cas13 can directly detect RNA without reverse transcription because Cas13a is a single-stranded (ss) RNA-targeted RNase that exhibits both cis- and trans-cleavage activities [22]. In particular, Cas13a can directly and specifically recognize target RNA and subsequently activate its trans-cleavage activity. The resulting nonspecific cleavage of nearby RNAs makes Cas13a more suitable for detection of RNA than other Cas proteins. Cas13a is used in a variety of RNA detection methods, such as microRNA [23], SARS-CoV-2 [24], and RNA N6-methyl-adenosine [25]. To further improve analysis performance, the Cas13a system has been combined with various signal amplification techniques, such as rolling circle amplification (RCA) [26], recombinase polymerase amplification (RPA) [27], and loop-mediated isothermal amplification (LAMP) [28]. Some of these isothermal nucleic acid amplification techniques employ expensive enzymes, some are tedious to perform since they require reverse transcription, and the correct design of species-specific primers is challenging for some. Toehold-mediated strand displacement reactions have received extensive attention due to their superior cascade properties in terms of cost, programmability, universality, and modularity [29]. Hybridization chain reaction (HCR) is a toehold-mediated strand displacement reaction first developed by Dirks and Pierce [30]. HCR is an enzyme-free, entropy-driven, isothermal spontaneous DNA assembly process [31]. An ordinary HCR needs three components including one DNA initiator and two DNA hairpins, In particular, two stable DNA hairpins coexist in solution until the first hairpin is opened after a promoter strand is introduced. The loss of secondary structure leads to the opening of a second and similar hairpin. This process is a series of DNA assembly events. Ultimately, the HCR forms a nicked double helix with many repeating units. Compared with PCR, HCR is used for isothermal nucleic acid amplification without cumbersome temperature changes. Compared with RCA and LAMP, HCR does not require enzymes and a complex primer design, respectively. Here, we introduce horseradish peroxidase–mimicking DNAzyme (HRP-DNAzyme) to combine Cas13a and HCR. HRP-DNAzyme is one of the most commonly used catalytic DNAzymes in biosensors and biorecognition. The single-stranded guanine-rich nucleic acid and hemin complex components [32] interact to catalyze a redox reaction between hydrogen peroxide (H2O2) and 2,2′-azino-bis(3-ethyl-benzothiozoline)-6-sulfonic acid (ABTS2−) with an accompanying color change [33]. This method does not require cumbersome instruments and expensive modified probes (such as fluorophores, quenchers, methylene blue, etc.) to achieve the transduction of analyte recognition events into an optical detection mode. The colorimetric biosensor amplifies the signal and reduces the experimental conditions to visually detect influenza H1N1. We observed an excellent linear relationship of the colorimetric biosensor from 10 pM to 100 nM, with a detection limit of 0.152 pM. Because of the outstanding selectivity of Cas13a, unexpected influenza A virus subtypes can be excluded in the amplification reaction. The data presented here demonstrate that this Cas13a-based colorimetric biosensor is a promising analytical method for biological analyses and clinical diagnoses.
All DNA sequences used in this study were synthesized by Sangon Biotechnology Co., Ltd. (Shanghai, China). All the oligonucleotides are given in Table S1 (see Supplementary information Table S1). The HiScribe T7 High Yield RNA Synthesis Kit and Monarch RNA Purification Columns were obtained from New England BioLabs (Ipswich, MA, USA). Trizol Reagent and Dynabeads M-270 Streptavidin (magnetic beads [MBs], 2.8 µm in diameter, 10 mg/mL) were provided by Thermo Fisher Scientific (Waltham, MA, USA). Purified Leptotrichia wade (Lwa)Cas13a protein and LwaCas13a buffer were obtained from Tolo Biotech (Shanghai, China). RNase inhibitor was obtained from Promega Corporation (Madison, WI, USA). Hemin and AzBTS-(NH4)2 were purchased from Sigma-Aldrich, Inc. (Saint Louis, MO, USA). RNase-free water and all chemicals were bought from Sangon Biotechnology (Shanghai, China) and used without further purification. The allantoic fluid of chick embryos infected with influenza H1N1 was stored and provided by Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences. Absorbance measurements were measured using a Nano Drop ND-1000 spectrophotometer (Thermo Fisher Scientific).
The target RNA sequence was designed according to the specific sequence fragment of influenza H1N1. Mature LwaCas13a crRNA was designed. It contained a 36-nucleotide (nt) direct repeat sequence and a 25-nt spacer that was completely complementary to the target RNA. The crRNA and target RNA were synthesized by in vitro transcription using T7 RNA polymerase and the HiScribe T7 High Yield RNA Synthesis Kit (New England Biolabs) to transcribe and Monarch RNA Cleanup Kit to purify RNA. Two microliters of 100 μM T7 promoter, 2 μL of 100 μM ssDNA templates containing T7 promoter sequence, and 14 μL RNase-free water were incubated at 95 °C for 5 min and then gradient cooled to 25 °C. The transcription reaction was established with the above solution, 2 μL T7 RNA polymerase (50 U/μL), and 10 μL NTP Buffer mix (20 mM) at 37 °C for 16 h. Then, 2 μL DNaseI (2 U/μL) was added and mixed to degrade the DNA in the solution. The concentrations of crRNA and target RNA were determined using a Nanodrop 1000 UV–vis spectrophotometer (Thermo Fisher Scientific) and stored at – 80 °C.
Ten microliters of a CRISPR/Cas13a cleavage reaction system contained 1 μL of various concentrations of target ssRNA, 1 μL of 0.5 μM crRNA, 1 μL of 5 U RNase inhibitor, 1 μL of 0.8 μM Cas13a protein, 2 μL of 1 × Cas13a buffer, and 4 μL of 1.25 μM probe I. The mixture was incubated at 37 °C for 30 min, followed by deactivation of Cas13a enzyme activity at 80 °C for 10 min. Streptavidin-coated MBs (SA-MBs) were prepared according to standard methods. Five microliters of a suspension of SA-MBs (10 mg/mL) was rinsed three times with 15 µL of PBS buffer (0.08 mM NaH2PO4 and 0.02 mM Na2HPO4, pH 7.4). The SA-MBs were resuspended in 5 μL Tris–HCl buffer (20 mM Tris, 2 mM MgCl2, 200 mM NaCl, 20 mM KCl, pH 7.4). The obtained Cas13a cleavage product was added to the suspension of SA-MBs, and the mixture was incubated at 37 °C for 10 min. Subsequently, the beads were separated using a magnet and the supernatant was transferred to a new centrifuge tube. Ten microliters of 2 μM H1 and 10 μL of 1.5 μM H2 were added to the solution followed by heating at 37 °C for 40 min. Ten microliters of 10 μM hemin was added and reacted at 37 °C for 40 min. Finally, 10 μL of 50 mM ABTS was added, followed by 10 μL of 40 mM H2O2. The solution was incubated at room temperature for 5 min. The absorbance of the resulting samples at a wavelength of 414 nm was measured by a Nano Drop ND-1000 spectrophotometer.
The products of Cas13a/crRNA trans-cleavage and HCR were analyzed using 15% native PAGE. Ten microliters of product and 2 μL of 6 × loading buffer were mixed evenly. Ten microliters of mixed liquid was loaded in the gel lane. The gel was run in 1 × TBE (89 mM Tris, 89 mM boric acid, 2.0 mM EDTA, pH 8.2) at 180 V for 40 min. The gel was dyed by SYBR Green I for 15 min and imaged using a C600 ultimate western blot imaging system (Azure Biosystems, Dublin, CA, USA).
The allantoic fluid of chick embryos infected with influenza H1N1 was collected. The total viral RNA was extracted by Trizol method. Two hundred fifty microliters of sample was thoroughly mixed with 750 μL of Trizol reagent and allowed to stand for 10 min at room temperature. Two hundred microliters of chloroform was added and mixed by violent oscillation for 30 s and allowed to stand for 10 min at room temperature. The solution was centrifuged at 12,000 rpm for 15 min at 4 °C. At this time, the solution appeared layered. Five hundred microliters of the supernatant was transferred to a new enzyme-free tube. Five hundred microliters of isopropanol was added; the contents were gently inverted several times to mix and allowed to stand for 10 min at room temperature. After centrifugation at 12,000 rpm for 10 min at 4 °C, the supernatant was discarded, and the pellet was resuspended in 750 μL of cold 75% ethanol. The mixture was centrifuged at 12,000 rpm for 5 min at 4 °C. The supernatant was also discarded. Finally, the total viral RNA was resuspended in 50 μL RNase-free water and stored at – 80 °C.
Here, we developed a dual signal amplification strategy based on Cas13a for a visual assay of influenza H1N1. In the principle depicted in Scheme 1, the first step is the recognition of H1N1 by the colorimetric biosensor and ensured specificity of the reaction by the CRISPR/Cas13a protein system. Target RNA extracted from influenza H1N1 is bound to crRNA through base complementation pairing after entering the interior of the CRISPR/Cas13a protein. This alters the protein structure and activates the trans-cleavage ability of the CRISPR/Cas13a protein. A DNA oligonucleotide strand containing three uridine monophosphate was created (probe I). The probe was designed to serve as the substrate for trans-cleavage of Cas13a/crRNA. DNA probe I consists of an I1 strand at the 5′ end, a special RNA sequence (-UUU-) in the middle, and an I2 strand at the 3′ end. Modification of the I1 strand with biotin allows the strand to initiate the HCR. The special RNA sequence (-UUU-) is recognized and cut by the CRISPR/Cas13a system. Cas13a is activated and cleaves the RNA sequence of probe I, which separates the I1 and I2 strands. The SA-MBs are added, and the biotin interacts with SA. The uncleaved probe I and I1 strands containing biotin are adsorbed by the SA-MBs, which are then removed from the solution by magnetic separation. After adding hairpin DNA H1 and H2, the I2 chain conjugates with H1 and opens the H1 hairpin via toehold-mediated strand displacement. Hybridization of the I2-H1 complex opens the hairpin structure of H2. The opened H2 chain is complementary to the hairpin H1 chain, which results in the opening of the hairpin structure of the H1 strand, thereby initiating the HCR. Because the H1 and H2 hairpin structures are continuously opened and assembled, the G-quadruplex fragment of H1 is exposed. Subsequently, in the presence of hemin, the G-quadruplex structure folds and hemin intercalates into the structure to form a DNAzyme. The G-quadruplex DNAzyme can catalyze the redox reaction between ABTS2− and H2O2. The color of the ABTS solution changes from light green to dark green. The resulting colorimetric reaction produces a color change of the reaction solution that is visible to the naked eye, permitting the ultra-sensitive visual detection of influenza H1N1.
To verify the feasibility of the CRISPR/Cas13a cleavage effect, the CRISPR/Cas13a cleaved product was examined by PAGE. Lanes 1–3 of Fig. 1A respectively contain bands without Cas13a, without crRNA, and without target RNA in the complete Cas13a/crRNA trans-cleavage system. Lane 4 contains the complete system. In contrast to lane 4, lanes 1–3 displayed a pronounced band at the same location. Conversely, the band of lane 4 at this location was weaker, with the appearance of new bands of cleaved probe I further down in the gel. Only in the complete system was the target RNA of influenza H1N1 able to activate the Cas13a/crRNA trans-cleavage system. In addition, the trans-cleavage ability of Cas13a was confirmed by PAGE again. As shown in Supplementary information Figure S1, a low concentration of H1N1 could activate the CRISPR/Cas13a system to cleave a high concentration of probe I, and in this range, probe I could be completely cleaved. Subsequently, the activated Cas13a/crRNA trans-cleavage system cleaved the DNA probe I to produce the I1 and I2 chains. The feasibility of the detection system was verified by PAGE. The I2 strand commenced the subsequent HCR to generate H1–H2 double helices (Fig. 1B). Lanes 1 and 2 show that H1 and H2 were both in monomeric form. Lane 3 displays the situation when H1 and H2 coexist. Lane 4 shows a nicked double-helix structure formed when DNA probes I, H1, and H2 coexist. Lane 5 and 6 display the co-mixture of H1 and H2 after adding the Cas13a/crRNA system and MBs in the absence (lane 5) and presence (lane 6) of H1N1 target RNA. As anticipated, high molecular weight bands with extremely low electrophoretic mobility were generated in lanes 4 and 6. In contrast, these bands were absent in lanes 3 and 5. Compared to lanes 1 and 2, the H1 and H2 bands were less prominent in lanes 4 and 6, and were absent in lanes 3 and 5. The findings demonstrate the successful initiation of HCR and verifies the separation capability of the MBs for further development of the colorimetric biosensor. G-quadruplex formation, catalysis, and the overall feasibility of the protocol were validated using colorimetry. The products in each of the above lanes were incubated with hemin, followed by the addition of hemin and ABTS2−. As shown in Fig. 1C, in positive samples and positive control samples, the colorless mixture turned green. Only these samples displayed a significant increase in absorption intensity. The absorbance of the positive sample was almost indistinguishable from that of the positive control sample.
To maximize the detection of influenza H1N1 viruses, pivotal assay factors that needed to be optimized were the concentration of Cas13a, CRISPR/Cas13a cleavage temperature, the concentration ratio of Na+ to K+, the concentration of probe I, the concentration ratio of hairpin H1 and H2, the concentration of hemin solution, CRISPR/Cas13a cleavage time, and HCR time. The ratio of I/I0, in which I is the absorbance in the presence of the target RNA and I0 is the absorbance in the absence of target RNA, was used to evaluate the performance of the assay parameters. The concentration of Cas13a/crRNA affected the degree of the trans-cleavage of probe I, which influenced the amplification efficiency of subsequent HCRs. As shown in Fig. 2A, the I/I0 value varied with the concentration of Cas13a/crRNA and tended to be stable when the concentration of Cas13a/crRNA was ≥ 0.8 µM. This concentration was determined to be the optimum concentration of Cas13a/crRNA. The I/I0 value changed with the difference Cas13a/crRNA trans-cleavage temperature (Fig. 2B). When the temperature was 37 ℃, the value of I/I0 was maximum. When the temperature value exceeded 37 ℃, there was a subsequent decrease in the value of I/I0. The findings indicated that a temperature that was too high would affect the activity of the Cas13a protein. Insufficient probe I was not conducive to HCRs, while excess probe I increased the background signal. The I/I0 value gradually increased from 0.25 to 1.75 µM with increasing probe I concentration (Fig. 2C). When the concentration of probe I exceeded 1.25 μM, there was only a small and negligible increase in absorbance. Considering the cost, 1.25 μM was selected as the optimum amount of primer probe I added. The effect of the concentration between hemin and G-quadruplex was also monitored. The I/I0 value initially increased and then decreased (Fig. 2D). The I/I0 value reached a maximum when the concentration of hemin was 10 µM. The concentration was selected as the optimum concentration of hemin. The I/I0 value increased as the Cas13a/crRNA trans-cleavage time increased from 10 to 40 min (Fig. 2E). When the trans-cleavage time reached 30 min, further increases in time led to a smooth increase in I/I0 followed by a slight decrease. Thirty minutes was identified as the best digestion time. The I/I0 value increased with increasing HCR time and reached a peak at 40 min (Fig. 2F). Forty minutes was sufficient for HCR to fully react and was chosen. The concentration ratio of Na+ and K+, and of hairpin H1 and H2, was also optimized. Formation and stabilization of G-quadruplexes depended on cations. In the presence of monovalent cations such as Na+, K+, NH4+, Rb+, and others, guanylate-rich DNA or RNA could form a stable G-quadruplex structure. Of these cations, K+ and Na+ are ubiquitous in cells, so the concentration ratio of Na+ and K+ in the buffer was optimized. When the concentration ratio of Na+ to K+ was 1:10, the absorbance value of the sample was the highest (see Supplementary information Figure S2A). The amount of G-quadruplex that formed was closely related to hairpin H1 and H2. Therefore, the concentration ratio of these hairpins was optimized. The absorbance value increased with increasing concentration ratio. When the concentration ratio of hairpin H1 and H2 reached 2:1.5, the absorbance value was highest. Further increases of the hairpin H2 concentration produced significantly decreased absorbance (see Supplementary information Figure S2B). Therefore, the 2:1.5 concentration ratio of hairpin H1 to H2 was chosen as the optimal condition for the experiment.
The linear range and sensitivity of the developed sensing system for influenza H1N1 detection were measured using the optimized experimental conditions. The susceptive response relationships between the concentration of the influenza H1N1 target RNA probe and the variance of absorption intensity indicated variable absorption intensity with increasing concentration of target RNA extracted from influenza H1N1 (101, 102, 103, 104, and 105 pM) (Fig. 3A). Over the range of 101 ~ 105 pM, good linear correlation from signal change and lg (target RNA concentration) was observed (Fig. 3B). The regression equation was I/I0 = 1.057 log10C − 0.1560 (R2 = 0.912). The limit of detection was calculated as 0.152 pM based on 3σ/slope, where σ is the standard deviation and k represents the slope of the line. Compared with several CRISPR/Cas detection systems, in terms of time and sensitivity, our detection method was comparable or superior (see Supplementary information Table S2).
An excellent virus method has the ability to distinguish influenza viruses based on their subtype and high similarity sequence. The specificity of the colorimetric biosensor was evaluated by four influenza viruses (H3N2, H5N1, H9N2, and H7N9) and other three types of probes for base mutation (M1, M2, and M3 represent one base mutation, two base mutations, and three base mutations, respectively). As shown in Fig. 4, at the same concentration (100 nM), the altered signal intensity about influenza viruses in the same system and similar sequences did not obviously differ from that of the background (no addition of target RNA); only H1N1 RNA produced a prominent altered signal. These results indicated that the detection method has good specificity, with good resolution of even a single base mutation.
Influenza H1N1 virus can be detected in serum following infection. To assess the practical feasibility of the developed method, different concentrations of H1N1 target RNA probe (1, 10, and 100 nM) were added to 10 × diluted serum. The H1N1 assay was then performed. The recovery for the 1, 10, and 100 nM concentrations of H1N1 target RNA probe was 83.90%, 104.50%, and 104.77%, respectively. The relative standard deviation ranged from 0.27 to 6.95% (Table 1). These findings suggested the potential value and practicability of this colorimetric biosensor to monitor influenza H1N1 in spiked biological samples.
We further examined the applicability and specificity of the colorimetric biosensor in detecting target RNA in complicated RNA extracts. To verify the accuracy of this sensing method, the colorimetric biosensor was used to detect influenza H1N1 in allantoic fluid of chicken embryo samples. The total RNA of the virus at this concentration was extracted from 1.0 × 108 copies/mL virus titer, according to this experimental method. As shown in Fig. 5, the results of the experimental group (infected group) and the control group (uninfected group) were significantly different. This result demonstrates that this experimental approach could accurately capture target RNAs in complex RNA samples. Therefore, the assay presented in this experiment is a promising tool for the detection of H1N1 influenza virus in complex matrices.
We describe the establishment and verification of a CRISPR/Cas13a-based visual influenza H1N1 viruses label-free and isothermal detection method. An oligonucleotide probe I strand was designed as a trans-cleavage substrate for Cas13a/crRNA and was used as a primer for HCR after cleavage. Our method adopts a dual signal amplification strategy, which can greatly increase sensitivity. The novel detection method has a detection limit of 0.152 pM for influenza H1N1 with excellent linearity between 10 pM and 100 nM. The assay also has excellent specificity. Other influenza A viruses and highly homologous RNA probes with only a single base difference can be clearly distinguished. The method does not require a variable-temperature environment and special expensive equipment, and is fast and efficient. The novel method could be valuable for biological research and clinical detection of virus.
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 286 KB) | true | true | true |
PMC9584656 | Gang Du,Chongren Ren,Ju Wang,Jun Ma | The Clinical Value of Blood miR-654-5p, miR-126, miR-10b, and miR-144 in the Diagnosis of Colorectal Cancer | 13-10-2022 | Colorectal cancer (CRC) is the third cause of cancer-related death and the fourth most frequently diagnosed cancer across the globe. The objective of this study is to obtain novel and effective diagnostic markers to enrich CRC diagnosis methods. Herein, exosomal miRNA expression data of CRC and normal blood were subjected to XGBoost algorithm, and 5 miRNAs related to CRC diagnosis were primarily confirmed. Then multilayer perceptron (MLP) classifiers were constructed based on different subsets. Via integrated feature selection (IFS), we noticed that the MLP classifier constructed by the first four miRNAs (miR-654-5p, miR-126, miR-10b, and miR-144) had the highest Matthews correlation coefficient (MCC). Subsequently, principal component analysis (PCA) for dimensionality reduction was performed on samples based on the miR-654-5p, miR-126, miR-10b, and miR-144 expression data. The signature based on these four feature miRNAs, as the analysis indicated, could effectively distinguish CRC samples from normal samples. Further, we extracted the exosomes from clinical blood samples and applied qRT-PCR analysis, which revealed that the expression of these four feature miRNAs was in the trend of that in the test set. Collectively, these four feature miRNAs might be tumor biomarkers in the serum, and our study offers innovative thinking on early-stage CRC diagnosis. | The Clinical Value of Blood miR-654-5p, miR-126, miR-10b, and miR-144 in the Diagnosis of Colorectal Cancer
Colorectal cancer (CRC) is the third cause of cancer-related death and the fourth most frequently diagnosed cancer across the globe. The objective of this study is to obtain novel and effective diagnostic markers to enrich CRC diagnosis methods. Herein, exosomal miRNA expression data of CRC and normal blood were subjected to XGBoost algorithm, and 5 miRNAs related to CRC diagnosis were primarily confirmed. Then multilayer perceptron (MLP) classifiers were constructed based on different subsets. Via integrated feature selection (IFS), we noticed that the MLP classifier constructed by the first four miRNAs (miR-654-5p, miR-126, miR-10b, and miR-144) had the highest Matthews correlation coefficient (MCC). Subsequently, principal component analysis (PCA) for dimensionality reduction was performed on samples based on the miR-654-5p, miR-126, miR-10b, and miR-144 expression data. The signature based on these four feature miRNAs, as the analysis indicated, could effectively distinguish CRC samples from normal samples. Further, we extracted the exosomes from clinical blood samples and applied qRT-PCR analysis, which revealed that the expression of these four feature miRNAs was in the trend of that in the test set. Collectively, these four feature miRNAs might be tumor biomarkers in the serum, and our study offers innovative thinking on early-stage CRC diagnosis.
As the third cause of cancer-related death and the fourth most frequently diagnosed cancer [1], colorectal cancer (CRC) presents a growing morbidity and death rate, making it a public health burden [2]. According to population and disease statistics, nearly 2.2 million new cases would be developed by 2030 [3]. CRC is a genotypically and phenotypically heterogeneous disease characterized by different molecular characteristics [4]. Accurate early diagnosis enables CRC patients to receive timely and precise treatment, thereby reducing CRC mortality. Although colonoscopy screening is the gold standard for CRC screening, its participation rate in population screening programs is still poor due to the invasive nature of the test and the need for adequate bowel preparation [5–8]. In addition, some studies have implied that carcinoembryonic antigen and calprotectin can be used as diagnostic markers for CRC, but their specificity and sensitivity are low, and they cannot be effectively applied to the early diagnosis of clinical CRC at present [9, 10]. Hence, it is necessary to develop effective biomarkers for CRC to improve the early diagnosis rate for CRC and offer effective biomarkers for CRC treatment. Recently, exosome biomarkers containing multiple RNA and proteins have become the focus of research in cancer diagnosis and treatment [11]. Exosomes are tiny goblet vesicles with 30-140 nm in diameter that are secreted by cells including immune cells, neural cells, stem cells, and tumor cells [12–14]. Increasing research manifested that exosomes relate to tumorigenesis. Tumor-derived exosomes are involved in the exchange of genetic information between tumor cells and basal cells, thereby regulating angiogenesis and promoting tumor growth and invasion [15]. Currently, useful biomarkers have been identified from exosomes for the application in CRC diagnosis. It has been demonstrated that in blood exosomes, miR-125a-3p and miR-638 are helpful for early diagnosis of CRC in clinical practice [16, 17]. These all demonstrated the importance of exosomal miRNAs in screening early-stage CRC. Therefore, we further identified potentially effective exosomal miRNAs that may work for CRC diagnosis, so as their regulatory networks, which are beneficial for comprehensively understanding the molecular mechanisms underlying CRC development. The rapid development of biotechnology in the age of big data stimulated the application of bioinformatics in medical research; bioinformatics technology based on high-throughput sequencing data is an effective and promising analytical tool for analyzing and identifying biomarkers for cancer diagnosis [18, 19]. Machine learning is a new artificial intelligence technique that has been gradually applied to medical research in recent years. Lian et al. [20] trained medulloblastoma stemness index based on a machine learning method of one-class logistic regression to obtain gene expression-based stemness index and methylation-based stemness index and further identified their corresponding potential drugs, which provides new ideas for the survival of medulloblastoma patients or targeting stem cells. Koppad et al. [21] screened diagnostic candidate genes for CRC based on six methods of machine learning classification including Adaboost, ExtraTrees, logistic regression, Naive Bayes classifier, random forest, and XGBoost. Thus, there is potential for wider application of novel bioinformatics methods to identify novel diagnostic biomarkers based on public databases. In this study, by analyzing the miRNA expression data of CRC patients and normal people in the Gene Expression Omnibus (GEO) database, we preliminarily screened miRNAs with potential diagnostic value based on XGBoost and established a multilayer perceptron (MLP) classifier to determine the optimal miRNA combination by taking integrated feature selection (IFS). Thereafter, the clinical value of diagnostic markers in CRC was dissected by testing their levels in the blood exosomes of clinical patients with CRC. To conclude, our study provided potential biomarkers which are supposed to be effective to CRC clinical diagnosis.
Exosomal miRNA data of CRC patients and normal people were downloaded as GSE39833 (tumor: 88 and normal: 11) from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/), annotated by the platform of Agilent-021827 Human miRNA Microarray G4470C GPL14767. Differential analysis was performed by R package “limma” [22] on the standardized miRNA expression data (|logFC| > 1.5, adjPvalue < 0.05).
XGBoost is a tree boosting scalable machine learning system, which generates a single strong learner by combining multiple weak learners. XGBoost estimates the value of the loss function through a second-order Taylor series and further reduces the likelihood of overfitting by applying regularization [23]. The objective function of XGBoost is a gradient advancing decision tree approach defined as Loss means training loss, Ω(f) represents the complexity of trees, and k stands for the amount of trees. The model can be optimized by minimizing the objective function. Hence, we adopted the addition training method to calculate the training loss and rapidly optimized the prediction of the nth round of addition training by taking the Taylor expansion method. The optimal complexity of the tree was determined via the greedy algorithm. In order to find miRNAs that could distinguish CRC from normal samples in GSE39833, we utilized XGBoost to rank the importance of feature miRNAs. Five characteristic miRNAs associated with CRC diagnosis were filtered for subsequent analysis. Then, based on SMOTE method, we applied python package “imblearn” and Bayesian optimization to resample the training set to reduce the effect caused by data disequilibrium.
To construct a diagnostic classifier that was more precise, we constructed MLP classifiers of different subsets based on these five characteristic miRNAs by python package “sklearn” [24] after XGBoost feature selection. For the MLP classifier, hidden layers were set as 2, and all possible combinations were scanned in the first layer (the number of nodes from 1 to 5) and in the second layer (the number of nodes from 1 to 5) by sklearn.neural_network. Other parameters included (1) solver = “adam”, (2) alpha = 0.001, (3) random_state = 1, and (4) max_iter = 1000.
The MCC of the above classifiers was obtained using IFS. MCC is the correlation coefficient of binary classification between the observation and prediction, with its value between -1 and +1. +1 stands for a perfect prediction, while -1 for a total inconsistency between observation and prediction. The MCC value is a single score that is the most informative for the prediction quality of binary classifiers built in a confusion matrix environment [25]. The IFS curves were plotted, with abscissa for MLP classifiers based on different subsets and ordinate for MCC of subsets. The classifier with the highest MCC was selected as the optimal classifier for CRC diagnosis.
PCA is a dimensionality reduction algorithm that is most widely adopted. Its main idea is to map the n-dimensional data in space onto the k-dimension, a novel orthogonal feature that is the principal component [26]. We performed PCA by the R package “FactoMineR” [27] based on the characteristic miRNA expression data in the optimal MLP classifier to explore the sample discriminatory capability of this classifier (https://www.rdocumentation.org/packages/FactoMineR/versions/2.4).
Between 10-2018 and 10-2021, 100 patients with CRC and 120 healthy participants were recruited from Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University in Taiyuan city, Shanxi province, with their clinical information and serum samples collected (Supplementary Table 1). None of the CRC patients received any treatment, while their cancer stages were judged on the basis of the American Joint Committee on Cancer (AJCC) Cancer Staging Manual (7th Edition) [28]. Peripheral blood (5 ml) from all participants was collected in 5 ml clotting tubes (Greiner Bio-One, Austria). Serum was separated by centrifugation and stored at -80°C for subsequent miRNA extraction. This research is approved by the Ethics Committee of Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University. Besides, all participants were well-informed about the necessary information of this study and signed the written informed consent.
The exosome separation followed the steps described by Han et al. [29]. And the exosomes acquired were resuspended in phosphate-buffered saline (PBS). The suspension was placed on a chloroform-coated copper grid with 0.125% Formvar and negatively stained with uranyl acetate. Morphological identification of the exosomes was by a transmission electron microscopy (TEM).
Total RNA from the obtained exosomes was extracted following the miRNeasy Micro Kit (QIAGEN, Germany), and RNA quantity and quality were tested via Agilent Bioanalyzer 2100 (Agilent, USA). cDNA was synthesized by reverse transcription from total RNA using SuperScript III Reverse Transcriptase kit (Invitrogen, USA), and qPCR was performed using SYBR Premix Ex Taq II (Takara, Japan). qRT-PCR was performed using ABI7500 (7500, ABI, USA), and the relative expression of all miRNAs was calculated using the 2-ΔΔCT method. U6 was the internal reference. Table 1 shows primer sequences for feature miRNAs.
Based on analysis performed by GraphPad 8.0, box plots were drawn. Differences in the relative expression of miRNAs between tumor and normal samples were analyzed using the t-test, and p < 0.05 indicated a difference that was statistically significant.
56 differentially expressed miRNAs (DEmiRNAs) were obtained by normalization and differentially analyzing miRNAs data derived from CRC and normal exosomes. Subsequent XGBoost feature selection indicated the top five miRNAs with the best ability to distinguish sample types. To determine the optimal diagnostic classifier for CRC, we constructed different MLP classifiers and plotted IFS curves to visually select miRNA combinations. Through the IFS curve, it was found that the classification effect of the MLP classifier composed of the first four miRNAs (miR-654-5p, miR-126, miR-10b, and miR-144) was good, and the 10-fold cross-validation results showed that its MCC value was high (Figure 1), and the sensitivity of this model was 0.977, the specificity was 1.000, the accuracy was 0.980, and the MCC was 0.909.
The expression data of four miRNAs in MLP classifiers in CRC and normal samples were subjected to PCA dimensionality reduction. Shown in Figure 2 were that PCA could significantly distinguish CRC and normal samples. Dim1 contributed 41.6% and Dim2 contributed 32.2%. From the violin plots, we could see that levels of blood exosomal miR-654-5p, miR-126, and miR-10b from CRC patients were markedly higher, but miR-144 was markedly lower than normal participants (Figures 3(a)–3(d)). The above results exhibited that the MLP formed by the former four miRNAs showed the value to assist CRC diagnosis.
To validate the performance of this model in clinical CRC diagnosis, we recruited 100 CRC and 120 healthy participants (Table 2), collected their blood samples, and extracted exosomes for qRT-PCR. Exosomes were first extracted from the blood of CRC patients as well as healthy participants, and the isolated exosomes were subsequently validated for size and morphology. Under a TEM, we could observe that the extracted exosomes were oval membrane-bound vesicles, which were about 50 nm-150 nm in diameter (Figure 4(a)). Thereafter, the qRT-PCR revealed that levels of blood exosomal miR-654-5p, miR-126, and miR-10b from CRC patients were markedly higher (Figures 4(b)–4(d)), but miR-144 was markedly lower than normal participants (Figure 4(e)). Data from qRT-PCR were collected for validation of the performance of the diagnostic model in CRC diagnosis. As results suggested, the ROC of the 4-miRNA diagnostic model was 0.913 (Figure 4(f)), and the recall of the model was 0.91, specificity was 0.34, accuracy was 0.6, and f1 was 0.67. Collectively, qRT-PCR on clinical samples validated that this 4-miRNA model could distinguish CRC and normal samples precisely, enabling these miRNAs to be biomarkers for CRC diagnosis.
As key regulators in a variety of biological and physiological processes, miRNA dysregulation may be tightly linked to changes in the pathological environment of disease [30–32]. Colonoscopy is the gold standard for the pathological diagnosis of CRC, but it causes a large physical as well as psychological burden to patients due to its high invasiveness [5–8]. Owing to patients' avoidance of colonoscopy, CRC cannot be diagnosed promptly at the early stage and is only diagnosed at advanced stages when tumor metastasizes to other tissue [33]. The advantage of miRNA detection relative to invasive colonoscopy is that samples are more accessible in clinical practice both in body fluids and blood. At the same time, this noninvasive examination greatly alleviates the physical burden on patients [32, 34, 35]. Given its noninvasive and easily accessible properties, miRNAs are promising biomarkers in CRC diagnosis. We here utilized XGBoost to determine the key features by ranking feature importance and recursive elimination. We determined the top 5 miRNAs that could accurately distinguish CRC cancer patients from healthy individuals and subsequently found via IFS method that the MLP classifier composed of the top four miRNAs was the best for CRC diagnosis. MLP is a dynamic classifier based on neural network, which could directly determine the separating hyperplanes between the two types of events, with high accuracy of classification and strong ability of parallel distribution processing. At present, there are also some studies on constructing CRC diagnostic classifiers based on machine learning algorithms. Koppad et al. [21] screened CRC diagnosis-related genes by the random forest algorithm, which has the advantage of avoiding data overfitting and reducing the computational load of the model. We aimed to filter biomarkers that could diagnose cancer. While MLP is to classify two types of events, therefore, it was our tool for identifying miRNAs that could assist CRC diagnosis. The top four miRNAs selected by IFS (miR-654-5p, miR-126, miR-10b, and miR-144) could accurately diagnose CRC. These four miRNAs have all been reported in CRC. Reported by Li et al. [36], the decreased level of miR-654-5p is markedly correlated with the clinical stage of colon cancer by analyzing miR-654-5p level in tissue from CRC patients and normal participants, indicating that its level might be closely related to the CRC progression. As stated by Ebrahimi et al. [37], low miR-126 level in CRC is linked to CRC histological subtype, perineural tumor invasion, microsatellite instability pathological analysis, and lymph node distal metastasis. One study indicated that upregulated miR-10b is discovered in CRC patients with liver metastases, positively linked to advanced TNM stage, and able to predict advanced clinicopathological features and liver metastasis in CRC [38]. Research by Choi et al. [39] indicated that stool from CRC patients is a novel screening biomarker, and the miR-144 level in the stool has good sensitivity and specificity for CRC detection. Finally, we collected blood samples from CRC patients and normal participants for qRT-PCR, and the expression trends of miRNAs were consistent with those reported in the literature, which also validated the accuracy of our study. Further, PCA revealed that the MLP diagnostic classifier composed of miR-654-5p, miR-126, miR-10b, and miR-144 could well distinguish samples from CRC patients and normal individuals. Hence, these four miRNAs could be unique biomarkers for noninvasive examination of CRC. However, limitations still exist. Our study utilized the limited numbers of public datasets and did not take into account factors like age, gender, ethnicity, and tumor TNM stages, which may affect miRNA expression. Hence, the construction of a more precise diagnosis model can be achieved by carrying a more detailed analysis on these factors, providing science-based evidence for the clinical noninvasive diagnosis of CRC. Overall, we performed XGBoost and constructed an MLP classifier to identify four miRNAs with the highest diagnostic value. PCA and ROC curves suggested favorable performance of the 4-miRNA classifier to distinguish CRC patients from normal individuals. This study sheds light on science-based theory for the noninvasive diagnosis of CRC. | true | true | true |
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PMC9584676 | Jiang Shen,Hong Lou,Qihua Yu,Hongyan Yao,Jianshu Yuan | The Connection between High Myopia Patients and MiR-708a or MiR-148 Expression Levels in Aqueous Studies of Visual Acuity | 13-10-2022 | Myopia goes far beyond the inconvenience it brings. It is a prevailing and vision-threatening eye disease, especially in Asia. Aberrantly expressed miR-708a and miR-148 are critical for accurate diagnosis, good prognosis, and precise response prediction of myopia. In this paper, we aim to examine the potential contributions of miR-708a, miR-148a, and PAX6 to high myopia (HM). First, aqueous samples were taken from 25 exclusively HM eyes and 25 exclusively cataract eyes. For next-generation sequencing and bioinformatics analysis, RNA from sample 30one was used. Twenty more samples were used for RT-qPCR. 341 miRNAs in total were found in HM eyes; 249 mature miRNAs and 17 new miRNAs showed differential expression. The expression of hsa-miR-127-3p, hsa-let-7i-5p, and hsa-miR-98-5p was identified using RT-qPCR. MiR-708a and miR-148, which may be linked to the development of myopia and serve as possible biomarkers, are notably highly expressed in atrial tissues of HM patients. Our findings may help deepen the understanding of the mechanisms behind the high expression of miR-708a and miR-148 in atrial tissues of patients with HM. | The Connection between High Myopia Patients and MiR-708a or MiR-148 Expression Levels in Aqueous Studies of Visual Acuity
Myopia goes far beyond the inconvenience it brings. It is a prevailing and vision-threatening eye disease, especially in Asia. Aberrantly expressed miR-708a and miR-148 are critical for accurate diagnosis, good prognosis, and precise response prediction of myopia. In this paper, we aim to examine the potential contributions of miR-708a, miR-148a, and PAX6 to high myopia (HM). First, aqueous samples were taken from 25 exclusively HM eyes and 25 exclusively cataract eyes. For next-generation sequencing and bioinformatics analysis, RNA from sample 30one was used. Twenty more samples were used for RT-qPCR. 341 miRNAs in total were found in HM eyes; 249 mature miRNAs and 17 new miRNAs showed differential expression. The expression of hsa-miR-127-3p, hsa-let-7i-5p, and hsa-miR-98-5p was identified using RT-qPCR. MiR-708a and miR-148, which may be linked to the development of myopia and serve as possible biomarkers, are notably highly expressed in atrial tissues of HM patients. Our findings may help deepen the understanding of the mechanisms behind the high expression of miR-708a and miR-148 in atrial tissues of patients with HM.
Myopia is the most prevailing causative factor for refractive error (RE) globally. High myopia (HM) is severe, usually accompanied by fundus lesions [1]. Myopia is due to the mismatch between the eyes' axial length (AL) and the energy of its refractive components, resulting in the image focusing ahead the retina and blurring of vision in the distance [2]. As the leading contributor to RE, myopia results in impaired vision and even blindness [3, 4], with over 80% prevalence in young population in China and Singapore [5]. HM is regarded as a RE ≤ -6.00 diopters (D), usually along with excessive AL (≥26 mm) and other complicating diseases, like retinal detachment, cataract, macular degeneration, and glaucoma, which are also known as pathological myopia [6]. Patients undergoing cataract surgery have many choices of intraocular lens (IOL), which depends on their requirements for spectacle independence and tolerance for latent visual disturbance. Bifocal and trifocal IOLs are superior to extended depth of focus (EDOF) or monofocal IOLs, since they offer better near vision [7, 8]. Some researchers demonstrated that multifocal IOLs were more prone to visual disturbance than EDOF lenses, but others reported no difference [9, 10]. Previously studied EDOF and multifocal IOLs are related to higher rates of visual disturbance compared to monofocal IOLs [7], however which may be addressed by EDOF lens with monovision correction since IOL effect and monovision offset will produce cumulative effects [11]. Besides, relative to traditional implantable collamer lens (ICL) without central hole, it is more effective in diminishing major postoperative complication, cataract [12, 13], possibly due to the improved aqueous humor (AqH) circulation to the crystalline lens anterior surface [14]. However, as our findings elicited, ICL implantation will cause corneal astigmatism of about 0.5 diopter (D) with-the-rule shift [15]. The upper corneal incision may help to reduce astigmatism clinically, and subsequent vertical ICL fixation is easier to operate than horizontal fixation since ICL rotation is no more needed. However, the efficacy of this novel technique remains unclear [16]. MicroRNAs (miRNAs) are noncoding RNAs consisting of 19 to 22 nucleotides (nt) [17]. It is of importance to understand aberrantly expressed miRNAs in myopia. A previous study assessing the peripheral blood of myopia patients revealed the association between highly expressed miR-328 and the miR-29a rs157907 A/G polymorphism with HM incidence [18]. Understanding the possible intraocular profiling and regulation of miRNAs is imperative since they are tissue/cell-specific [19]. miRNA profiling was only studied in ocular tissues of myopia murine models, and the results were contradictory [20, 21]. In human AqH, compared with circulating blood, miRNA expression was eye-specific [22]. The miRNAs in AqH were thought to involve in eye development and diseases [23]. Paired box protein 6 (PAX6) is crucial for eye and retinal development [24]. It modulates the levels of transcription factors, hormones, cell adhesion molecules, and structural proteins [25] and thus involved in major biological processes, like adhesion, signal transduction, and cell proliferation in physiological and pathological progresses [26, 27]. Therefore, investigating this procedure may offer promising insights into the improvement of HM. We choose 25 exclusively HM eyes and 25 exclusively cataract eyes as aqueous samples and selected miR-708a and miR-148 as the two miRNAs. We used SPSS 21.0 to analyze data and applied the t-test for pairwise comparison and one-way ANOVA and Tukey's test for multi-group comparisons. We believe that our research could develop innovative options to improve the management of myopia.
Patients looking for correction of HM at our institution were recruited, while those with mild to moderate cataracts undergoing ultrasound emulsion surgery served as controls. Each participant provided written informed consent. The study was ratified by ethics committee and followed the Declaration of Helsinki. Inclusion criteria for HM group included (1) ≥18 years; (2) AL ≥26 mm; (3) RE (spherical equivalent) >6.00 D prior to the operation; (4) without other ocular diseases (except myopia). Inclusion criteria for the cataract group included (1) ≥18 years; (2) AL between 22 and 24 mm; (3) age-related mild/moderate cataract (e.g., nuclear, cortical, and posterior subcapsular cataracts) diagnosed by dilated pupil examination with slit lamp; without premature, complex and congenital cataract; (4) without any ocular diseases. Participants with serious systemic disease or a history of endophthalmic surgery or ocular trauma were excluded from both groups. Only one eye was included from each participant. The AL of all participants was determined by an experienced clinician with an IOL Master (Carl Zeiss, Jena, Germany). Next, all subjects were allocated to the group A (HM) and group B (controls) as per the above criteria.
The Aqueous (100-150 μL) was first harvested from each included eye under sterile conditions through an anterior chamber puncture and centrifuged (3,000 × g, 5 min, 4°C; 12,000 × g, 20 min, 4°C) immediately to discard cells and cellular debris. To avoid blood contamination, collect Aqueous before performing any conjunctival or intraocular procedures. The Aqueous harvested from 3 eyes was mixed (300 μL Aqueous per sample). Thereby, we got 5 samples from each group. Meanwhile, total RNA was extracted by means of TRIzol reagent (Life Technologies, USA) and then preserved at -80°C.
The miRNA sequencing library was built by each RNA sample with an initial RNA amount (100 ng) and TruSeq RNA Library Preparation Kit (RS-122-2301; Illumina). Following cDNA synthesis, PCR amplification and PAGE were conducted to retrieve PCR products of 0-150 bp (0-22 nt miRNA) and sequencing library quality was determined using the Bioanalyzer. The RNA yield of the sequencing libraries was tested through the ABI StepOnePlus RT-PCR System (Life Technologies, Inc.). Subsequently, sequencing libraries were denatured to single-stranded DNAs and captured into Illumina flow cells, followed by amplification in situ. Sequencing50 cycles were conducted using the Hieq4000 sequencing platform (Illumina, USA) with Q30 as a quality control. Based on the principal algorithm of miRDeep, differential miRNAs are distinguished from small RNA fragments based on miRNAs location and frequency, loop fragments on miRNA ∗ and precursor sequences, minimal free energy and stability, 2008 and similarity. 5'ends of recognized mature miRNAs (Friedlander et al.) The original sequencing data were subsequently eliminated and filtered and normalized to the number of tags per million paired miRNAs. miRNA profiles were verified by arranging the miRNA expression in a descending order and miRDeep2 software (https://www.mdc-berlin.de/content/mirdeep2-documentation) was utilized to forecast differential miRNAs, ploidy changes, p-values (probability values), and FDRs (p-values corrected by the Benjamini-Hochberg method). Differentially expressed miRNAs were distinguished as ploidy change ≥ 2.0 and p ≤0.05. After RNA extraction, protein concentration was testified using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, USA) and RNA quality was assessed by a Bioanalyzer (2100, Agilent Technologies), and cDNA was synthesized by the PrimeScript synthesis cDNA kit (Takara, Japan). SYBR Green PCR Master Mix (Takara) was adopted for qPCR to normalize miR-708a/miR-148 expression to U6.
Online software TargetScan predicted binding sites of miR-708a/miR-148 and PAX6. PCR amplified the complementary binding sequences of miR-708a, miR-148, and PAX6, respectively, and cloned them into the pmiR-GLO (Promega, USA) to construct PAX6-WT and PAX6-MUT, which were mixed with mimics NC and miR-708a and miR-148mimics, respectively, and transfected into HEK-293T cells after mixing with LipofectamineTM 2000 liposomes for 48, followed by detecting luciferase activity. SPSS 21.0 (IBM Corp. Armonk, NY, USA) was employed for data analyses. Data were normally distributed as the Kolmogorov-Smirnov test confirmed and depicted as mean ± standard deviation. The t-test was applied for pairwise comparison and one-way ANOVA and Tukey's test were applied for multi-group comparisons. Fisher's exact test was adopted for counting data; correlation analysis was undertaken by Pearson's test; ROC curves were then plotted to evaluate the effect of serum miR-708a/miR-148. p-value was attained from two-sided tests and p < 0.05 indicated statistically significant.
We discovered 341 miRNAs in the Aqueous of HM eyes by sequencing (Figure 1(a)). Based on it, we selected miR-708a and miR-148 as the two miRNAs with the most significant expression differences in sequencing. The miR-708a and miR-148 levels in the Aqueous detected by qPCR were markedly higher in HM patients than cataract patients (Figure 1(b)). Besides, we analyzed the correlation between the myopia of HM patients and miR-708a/miR-148 expression, and we noticed that as miR-708a/miR-148 expression increased, the patients also had notably higher reading (Figure 1(c)).
To further clarify the mechanism of action of miR-708a/miR-148, we used TargetScan and RNA Hybrid website to predict and screen miR-708a/miR-148 downstream targets, and we screened to PAX6 (Figure 2(a)). Subsequently, we first verified the target binding relationship between miR-708a and miR-148 and PAX6 using dual luciferase, respectively, and we found reduced luciferase activity in 293T cells delivered with miR-708a and miR-148mimic, and no apparent change in cells with mimic NC or PAX6-MT (Figures 2(b)–2(c)), indicating that miR-708a and miR-148 can have a target binding relationship with the 3′-UTR sequence of PAX6.
To verify that PAX6 is modulated by miR-708a/miR-148, we first compared the PAX6 levels in HM patients and cataract patients through RT-qPCR and ELISA, which showed lower PAX6 levels in the Aqueous of HM patients (Figures 3(a)–3(b)). Besides, PAX6 was negatively correlated with miR-708a and mIR-148 (Figures 3(c)–3(d)), and positively correlated with the visual acuity level of the patients (Figure 3(e)).
Without enough intervention, the current prevalence of myopia is supposed to 50% of the world's population by 2050 and become the main reason of irreversible blindness. Though the main symptom blurred vision can be improved through contact lenses, glasses, or refractive surgery, correcting myopia, especially HM, is still in risk of secondary blinding complications, like myopic maculopathy, glaucoma, and retinal detachment, so it needs to be prevented [28]. This is particularly of concern for HM patients [> −5 Dioptres (D)] who have the risk of pathologic myopia and other related eye diseases such as retinal detachment, choroidal neovascularization, glaucoma, and myopic macular degeneration [29, 30]. Pathological myopia is the chief contributor to visual disturbance and blindness in Asian [31]. Tears, vitreous humor, and AqH are main sources of fluids containing extracellular miRNAs in eyes [32, 33]. Several reports have revealed highly expressed miR-29a in AqH of myopia patients and it prevented collagen I synthesis in SF cells, indicating its importance in myopia development [34]. The AqH analysis is very useful to study the molecular mechanism of axial elongation essential for myopia and to understand the role in HM, which will help to develop new therapeutic approaches [35, 36]. As an intraocular fluid, AqH provides nutrition and eliminates metabolic wastes from avascular tissues, which is utilized to determine the link of changed protein levels and prognoses of several eye diseases [37, 38]. Nevertheless, no proteomic study has reported the mechanism behind HM-induced eye injury. Proteomics could display high-throughput quantitative protein levels, providing theoretical foundation and methods to verify the mechanism [39]. Recent research showed miR-328 was higher in peripheral blood of myopia patients than controls [18]. PAX6 is a key player in eye development and shows low expression in myopia patients [40, 41]. Some studies have provided a comprehensive miRNA profiling of AqH in HM through next-generation sequencing. Regarding aberrant expression of miRNAs in myopia, combined with informatics analyses, it is suggested to confirm these results [42]. Our experiment was completed in three steps. First, we selected miR-708a and miR-148 as the two miRNAs with the most significant expression differences in sequencing. The expression levels of miR-708a and miR-148 detected using qPCR were notably higher in atrial fluid of HM patients than cataract patients, and we found that miR-708a/miR-148 were markedly higher in atrial fluid of HM patients. Then, to clarify the mechanism of miR-708a/miR-148, we used TargetScan and RNA Hybrid website to predict and screen the downstream targets of miR-708a/miR-148. We screened to PAX6, and we confirmed that miR-708a and miR-148 target PAX6 mRNA 3′-UTR sequence. Finally, RT-qPCR and Western blotting examined the level of lncRNA, miRNA, mRNAs, and proteins. Functional experiments measured cell proliferation, apoptosis, and migration. Additionally, the luciferase assay validated the relation of ZFPM2-AS1, miR-511-3p, and PAX6 [43]. With the attention to confirm that PAX6 is modulated by miR-708a/miR-148, we firstly compared PAX6 levels in atrial water from HM patients and cataract patients by RT-qPCR and ELISA, which revealed elevated PAX6 in serum of neonatal retinopathy patients relative to healthy participants. However, this study may have a positive bias, since the older the age, the greater the choroidal atrophy [44]. Although some therapeutic interventions have improved the HM, its pathogenesis is still unclear.
In summary, miR-708a and miR-148 are significantly highly expressed in atrial tissues of patients with HM, which may be related to the pathogenesis of myopia and are potential biomarkers. The current study provides a holistic view of miRNA profile in AqH of HM eyes. Those features are possibly related to the pathogenesis of myopia and are underlying biomarkers. Our study will yield good results in safety, predictability, efficacy, and stability. However, there are some limitations. As a single-site and single-arm study with relatively small sample size, it cannot be directly compared with other treatment designs. In addition, the lack of multiple clinical trials is one of our limitations. Long-term, careful follow-up of more patients is warranted to confirm our preliminary findings, and it is a target to become our treatment strategy. | true | true | true |
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PMC9584677 | Chunlin Ke,Minmin Shen,Peirong Wang,Zhihua Chen,Suyong Lin,Feng Dong | ALDH1A3–Linc00284 Axis Mediates the Invasion of Colorectal Cancer by Targeting TGFβ Signaling via Sponging miR-361-5p | 13-10-2022 | ALDH1A3 and Linc00284 involve in colorectal cancer (CRC) development; however, the regulatory mechanism is still unclear. In this study, we collected clinicopathological characteristics and tissue samples from 73 CRC patients to analyze the expression of ALDH1A3, Linc00284, TGFβ signaling and miR-361-5p using qPCR, Western blotting, and ELISA. Multiple CRC cell lines were evaluated in this study, and the highest level of ALDH1A3 was observed in SW480 cells. To investigate the regulatory mechanism, RIP and luciferase assays were used to validate the interaction between Linc00284, miR-361-5p, and TGFβ. Proliferation, viability, migration, and invasion assays were performed to profile the effects of the ALDH1A3–Linc00284 axis in CRC cell functions, which was upregulated in CRC tissues. Knockdown ALDH1A3 or Linc00284 significantly reduced TGFβ expression and suppressed the EMT process, while overexpression had opposite effects. miR-361-5p targeted TGFβ directly, which negatively correlated with ALDH1A3–Linc00284 expression and CRC progression. Mechanistically, upregulation of ALDH1A3–Linc00284 promotes colorectal cancer invasion and migration by regulating miR-361-5p/TGFβ signaling pathway. Dysregulation of the ALDH1A3–Linc00284-miR-361-5p-TGFβ axis causes CRC invasion, which might provide a new insight into the treatment of CRC. | ALDH1A3–Linc00284 Axis Mediates the Invasion of Colorectal Cancer by Targeting TGFβ Signaling via Sponging miR-361-5p
ALDH1A3 and Linc00284 involve in colorectal cancer (CRC) development; however, the regulatory mechanism is still unclear. In this study, we collected clinicopathological characteristics and tissue samples from 73 CRC patients to analyze the expression of ALDH1A3, Linc00284, TGFβ signaling and miR-361-5p using qPCR, Western blotting, and ELISA. Multiple CRC cell lines were evaluated in this study, and the highest level of ALDH1A3 was observed in SW480 cells. To investigate the regulatory mechanism, RIP and luciferase assays were used to validate the interaction between Linc00284, miR-361-5p, and TGFβ. Proliferation, viability, migration, and invasion assays were performed to profile the effects of the ALDH1A3–Linc00284 axis in CRC cell functions, which was upregulated in CRC tissues. Knockdown ALDH1A3 or Linc00284 significantly reduced TGFβ expression and suppressed the EMT process, while overexpression had opposite effects. miR-361-5p targeted TGFβ directly, which negatively correlated with ALDH1A3–Linc00284 expression and CRC progression. Mechanistically, upregulation of ALDH1A3–Linc00284 promotes colorectal cancer invasion and migration by regulating miR-361-5p/TGFβ signaling pathway. Dysregulation of the ALDH1A3–Linc00284-miR-361-5p-TGFβ axis causes CRC invasion, which might provide a new insight into the treatment of CRC.
Colorectal cancer is the second highest mortality tumor worldwide [1, 2]. By 2030, the number of global cases of CRC is expected to increase by 60%, with more than 2.25 million new cases and 1.15 million deaths from CRC cancer each year [3, 4]. In the past few decades, CRC has become one of the most common cancers, the incidence has increased from 1.0% annually to 2.4% since 1974 in the USA [5]. In most Europe, the incidence of CRC is increasing every year, ranging from 0.4% to 3.6% [6]. The existing therapies for CRC include surgery, radiotherapy, chemotherapy, targeted drug therapy, and so on. Immunotherapy is an encouraging weapon to treat CRC, but so far it has only succeeded in a small proportion of CRC [7]. The prognosis of metastatic CRC is worse, with an overall survival time of only about 30 months [8]. Therefore, it is very urgent to develop new effective strategies to control CRC, especially metastatic CRC. Cumulative evidence indicates that ALDH1A3 and Linc00284 play an important role in the progression of CRC [9–12]. ALDH1A3 is a known marker of cancer stem cells which has been shown to be important for the proliferation, migration, and maintenance of the mesenchymal cancer stem cell phenotype [13]. Durinikova et al. demonstrated that ALDH1A3 was increased in CRC tissues, which promotes spontaneous metastasis formation and associates with acquired chemoresistance of colorectal cancer [9]. Long noncoding RNAs (lncRNAs) Linc00284 has been reported to be involved in the initiation and progression of many cancers, including oral squamous cell carcinoma, ovarian carcinoma, papillary thyroid cancer, lung cancer, CRC, and so on [12, 14–17]. Our recent study demonstrated that Linc00284 exhibits oncogenic function and promotes the progression of CRC through upregulating the expression of c-Met [12]. However, the underlying molecular mechanisms of ALDH1A3 and Linc00284 on the growth and metastasis of CRC are still unclear. Here, we investigated the role of the ALDH1A3–Linc00284 signal in CRC progression and in the tumor microenvironment through in-depth analysis of clinical data and in vitro experiments. This study proves that ALDH1A3–Linc00284 upregulates TGFβ signaling through miR-361-5p, and then promotes the epithelial–mesenchymal transition (EMT) process and CRC tumor metastasis. This research will provide a key theoretical basis for the CRC treatment and drug development.
CRC tissues and the paired adjacent nontumor samples were derived from 73 CRC patients, which are consistent with the patients and samples in our previous study [12], including the patient's information, inclusion and exclusion criteria, and metastatic and relapse records. The TNM stages of CRC in the above patients were classified based on the American Joint Committee on Cancer (AJCC) tumor, lymph node, metastasis (TNM) system. The study was obtained the written informed consent from all participants and approved by the ethics committee of the First Affiliated Hospital of Fujian Medical University.
HEK293T cell lines and CRC cell lines, SW480, HCT116, LS174T, DLD-1, HCT15, and SW620, were ordered from ATCC (Rockville, MD) and used in this study. HEK293T, SW480, HCT116, SW620, and LS174T cells were cultured in DMEM supplemented with 100 U/ml penicillin/streptomycin (Gibco, Grand Island, NY) and 10% fetal bovine serum (Gibco). DLD-1 and HCT15 cells were cultured in RPMI-1640 supplemented with100 U/ml penicillin/streptomycin (Gibco) and 10% fetal bovine serum (Gibco). Cell cultures were placed in cell culture incubator with 95% humidity and 5% CO2 at 37°C.
The small hairpin RNA (shRNA) of ALDH1A3 was purchased from Sigma-Aldrich (TRCN0000027144, TRCN0000027160, TRCN0000027183, St. Louis). The primers for shRNA chains sequences were: TRCN0000027144: forward, 5'-CCGGGCTG TATTAGAACCCTCAGATCTCGAGAAATTGTGTCTGAAGAGAATGTTTTTG-3', reverse, 5'-AATTCAAAAACATTCTCTTCAGACACAATTTCTCGAGATCTGAGGGT TCTAATACAGC-3'; TRCN0000027160: forward, 5'-CCGGGAGCAGGTCTACTCTGA GTTTCTCGAGAAATTGTGTCTGAAGAGAATGTTTTTG-3', reverse, 5'-AATTCAA AAACATTCTCTTCAGACACAATTTCTCGAGAAACTCAGAGTAGACCTGCTC-3'; TRCN0000027183: forward, 5'-CCGGGCCGAATACACAGAAGTGAAACTCGAGAA ATTGTGTCTGAAGAGAATGTTTTTG-3', reverse, 5'-AATTCAAAAACATTCTCTTC AGACACAATTTCTCGAGTTTCACTTCTGTGTATTCGGC-3'. Linc00284 shRNA, mimic and inhibitor of miR-361-5p, full-length of ALDH1A3 and Linc00284, and negative controls were ordered from Genechem (Shanghai, China). The stable ALDH1A3 knockdown and Linc00284 knockdown SW480 cells were established by using the corresponding lentivirus generated from HEK293T cells as described previously [12]. Lentivirus expression and package vectors, pLVX pVSVG, pRSV-REV, and pMDLg/pRRE were purchased from Genechem. The pcDNA3.1 vector was used to construct the overexpression plasmid of ALDH1A3 and Linc00284. Lipofectamine™ 2000 Transfection Reagent (Thermo Fisher Scientific Inc, Waltham, MA) for cell transfection following the manufacturer's instructions.
Total RNA from homogenize tissue samples and lysed cells was extracted using Trizol reagent (Invitrogen, Waltham, MA) according to the protocol provided by the manufacturer. TaqMan® Reverse Transcription Reagents and random primers were used to synthesize the cDNA. StepOne™ PCR System (Thermo Fisher Scientific Inc) was used to perform qPCR reaction. The expression of target genes was normalized by GAPDH or U6. All primers used in this study were listed in Table 1.
Proteins were extracted from homogenized tissue samples by using RIPA buffer containing PMSF and protease inhibitor cocktails (KeyGEN BioTECH, Nanjing, China). Protein levels of ALDH1A3 and β-Actin were determined using Western blotting as described previously [12]. The primary antibodies and information were listed below: β-Actin (Proteintech, 60008-1-Ig, 1 : 2000), ALDH1A3 (Novus, NBP2-46510, 1 : 1000), E-cadherin (Cell Signaling Technology, 3195, 1 : 2000), N-cadherin (Cell Signaling Technology, 13116, 1 : 2000), Vimentin (Cell Signaling Technology, 5741, 1 : 2000).
The protein level of TGFβ in Linc00284- and ALDH1A3-overexpressed SW480 cells medium was determined by ELISA (DB100B, R&D Systems, Minneapolis, MN) according to the manufacturer's instructions.
RNA Immunoprecipitation (RIP) assay was performed to detect the interaction between Linc00284 and miR-361-5p by using the Magnetic RNA-Protein Pull-Down Kit (Pierce, Waltham, MA). In brief, SW480 cells were lysed in RIP buffer firstly, and then incubated with Argonaute2 (Ago2) antibody beads (Sigma-Aldrich) overnight at 4°C, anti-mouse IgG magnetic beads were used as negative control. The RNA that bound to beads was extracted and detected by RT-qPCR.
The TGFβ 3'-UTR WT, Linc00284 WT, and the corresponding mutated fragments were cloned into an overexpression vector (pcDNA3.1). The above constructs or control vectors were co-transfected with the luciferase reporter into HEK293T cells using Lipofectamine™ 2000 (Invitrogen). Two days after transfection, HEK293T cells were lysed and luciferase activity was determined by the Dual-Luciferase® Reporter (DLR™) Assay System (Promega) following the manufacturer's instruction.
Cell viability of ALDH1A3- or Linc00284-silenced SW480 cells (placed in 96-well plates) at indicated days was evaluated by CCK-8 assay (ab228554, Abcam) following the manufacturer's instructions. Cell proliferation of transfected SW480 cells was determined by Attune Flow Cytometers (Thermo Fisher Scientific Inc)gating with the Ki67 antibody (ab92742, Abcam, 1 : 100 dilution) as described previously [18].
To analyze cell migration, 5 × 106 SW480 cells were seeded in 6-well culture plate. The cell monolayer was scratched by a sterile tip when they reached 90% confluence. The wound healing rate was imaged and calculated every 24 hours. CRC cell invasion was performed by trans-well assay as described previously [12].
SPSS 22.0 statistical software (SPSS Inc.) was used to analyze the data in this study. A two-sided unpaired t test was used to compare differences between the two groups. One-way analysis of variance (ANOVA) and post-hoc least significant difference (LSD) tests were used to analyze the differences among multiple groups. The significant difference or correlation among groups was analyzed by two-way ANOVA and/or two-sided paired t-test, and Pearson's correlation analysis. Kaplan–Meier curves with the log rank test was used to calculate the overall survival (OS) of CRC patients. All data are presented as mean ± SD.
The expression level of ALDH1A3 in CRC tissues (n = 73) was measured by qRT-PCR, in comparison with paired adjacent normal tissues, ALDH1A3 mRNA was significantly increased in tumor samples (Figure 1(a)). Western blot results showed markedly elevated protein level of ALDH1A3 in CRC tissues compared to that in controls (Figure 1(b)). Moreover, ALDH1A3 expression was higher in tumors of patients with metastatic CRC (n = 43) than those without metastasis (n = 30) (Figure 1(c)). In addition, patients with relapse CRC (n = 49) had higher ALDH1A3 expression compared with patients without relapse (n = 24) (Figure 1(d)). Besides, higher ALDH1A3 expression positively correlated with the TNM stages of CRC and poor overall survival. ALDH1A3 expression was higher in tumors with higher CRC TNM grade (Figure 1(e)). Patients with higher ALDH1A3 expression in had a shorter survival time compared to patients with lower expression of ALDH1A3 (Figure 1(f)).
To investigate the role of ALDH1A3 in CRC, we examined the mRNA level of ALDH1A3 in six CRC cell lines (SW480, HCT116, LS174T, DLD-1, HCT15, and SW620), and found that ALDH1A3 was highest expressed in SW480 cells, which was used in further studies (Figure 2(a)). ALDH1A3 knockdown SW480 cell line was established through shRNA method. Among three shRNAs, shRNA TRCN0000027144 showed the highest efficacy and was used for silencing ALDH1A3 in following experiments (Figure 2(b)). CCK-8 assay indicated that ALDH1A3 knockdown reduced cell viability of CRC cells significantly (Figure 2(c)). We performed flow cytometry analysis using Ki 67 staining to examine the cancer cell proliferation with and without ALDH1A3. Upon ALDH1A3 knockdown, Ki67 positive SW480 cell was decreased markedly relative to that in control group (Figure 2(d)). In addition, migration and trans-well assays showed that ALDH1A3 silencing decreased the ability of wound healing and invasion significantly in SW480 cells (Figures 2(e) and 2(f)).
Our previous study indicated that Linc00284 plays an important role in CRC progression [12], qPCR analysis showed that knockdown of ALDH1A3 significantly reduced the expression level of Linc00284 (Figure 3(a)), while overexpression of ALDH1A3 upregulated Linc00284 expression markedly (Figure 3(b)). Interestingly, ALDH1A3 expression was correlated with Linc00284 expression positively (Figure 3(c)). The above data indicated that ALDH1A3 might promote invasion of CRC by regulating the expression level of Linc00284.
To explore the mechanism of ALDH1A3–Linc00284 signal regulating CRC metastasis, EMT-related genes were examined by qPCR and Western blots. Knockdown ALDH1A3 or Linc00284 significantly upregulated E-cadherin expression, while downregulated Vimentin and N-cadherin levels in SW480 cells (Figures 4(a)–4(g)). On contrast, overexpression ALDH1A3 or Linc00284 exhibited the opposite effect on the expression of EMT-related genes, as indicated by deceased mRNA and protein expression of E-cadherin, and increased mRNA and protein expression of Vimentin and N-cadherin (Figures 4(j)–4(p)), which suggests the inhibition of EMT process by overexpression of ALDH1A3 or Linc00284. TGFβ signaling is a key protein factor that mediates the process of EMT [19]. Both qPCR and ELISA data showed that knockdown ALDH1A3 or Linc00284 significantly reduced the expression level of TGFβ, while overexpression had the opposite effect (Figures 4(h)–4(i) and 4(q)–4(r)). Moreover, overexpression of Linc00284 could reverse the downregulation of ALDH1A3 knockdown on TGFβ mRNA and protein (Figures 4(s)–4(t)). These results suggested that ALDH1A3–Linc00284 might promote EMT process by regulating TGFβ signal, and then promote CRC tumor metastasis.
We searched the TargetScan database and identified the microRNA candidate that can bind to Linc00284 and TGFβ. Sequence analysis showed that miR-361-5p has the complementary sequences that match Linc00284 and TGFβ, respectively (Figures 5(a) and 5(g)). To confirm the interaction between miR-361-5p and Linc00284 or TGFβ, we constructed the wild type and mutant overexpression plasmids of Linc00284 and TGFβ (Figures 5(a) and 5(g)), and then performed luciferase assay. The results indicated that miR-361-5p bound to Linc00284 and TGFβ (3'-UTR) directly (Figures 5(b) and 5(h)), which was further confirmed by RIP assay (Figures 5(c) and 5(d)). Accordingly, miR-361-5p was increased in ALDH1A3- or Linc00284-silenced CRC cells, while decreased in ALDH1A3- or Linc00284-overexpressed CRC cells significantly (Figures 5(e) and 5(f)). As shown in Figures 5(i) and 5(j), miR-361-5p mimics (overexpression) decreased the TGFβ level, while miR-361-5p inhibitors (knockdown) increased TGFβ expression significantly. Identical results were observed on the expression of EMT-related genes, E-cadherin (Figure 5(k)), N-cadherin (Figure 5(l)), and Vimentin (Figure 5(m)).
In human CRC tissues, the expression of miR-361-5p was decreased significantly relative to that in adjacent normal tissues (Figure 6(a)). Interestingly, miR-361-5p was reduced in distant metastasis (Figure 6(b)), relapse (Figure 6(c)), and high TNM staging CRC (Figure 6(d)). Furthermore, miR-361-5p expression negatively correlated with the expression of both ALDH1A3 and Linc00284 in CRC tissues (Figures 6(e) and 6(f)). Next, we silenced miR-361-5p in ALDH1A3- or Linc00284-knockdown SW480 cells and examined the proliferation and metastasis of CRC cells. As expected, inhibition of miR-361-5p reversed the effects of ALDH1A3- or Linc00284-knockdown on cell viability (Figure 7(a)). Identical effects of miR-361-5p inhibition on CRC cell migration and invasion were observed by wound healing assay and trans-well assay (Figures 7(b) and 7(c)). The above data further indicated that ALDH1A3-Linc00284 mediates CRC invasion through regulating the expression miR-361-5p.
Long noncoding RNAs play an important role in the tumor initiation and progression [11, 20–24]. Multiple studies showed higher Linc00284 expression in breast cancer and liver cancer, which can promote the tumor progression and associated with poor overall survival [11, 25, 26]. Our recent study demonstrated that the upregulation of Linc00284 in tumor samples of CRC patients; in addition, Linc00284 expression positively correlated with metastasis, recurrence, and poor survival [12]. However, the underlying mechanism of Linc00284-mediated CRC progression is still unclear. In this study, we revealed that Linc00284 was upregulated by ALDH1A3 in CRC tissues and cells, and the ALDH1A3–Linc00284 axis promoted the invasion of CRC through activation of TGFβ signaling and downstream EMT process. Mechanistically, upregulation of ALDH1A3–Linc00284 promotes colorectal cancer invasion and migration by regulating the miR-361-5p/TGFβ signaling pathway, which might provide a new insight into the treatment of CRC. EMT not only plays a critical role in tumor stemness, proliferation, migration, and invasion but also associates with the tumor microenvironment to induce immunosuppression and cause resistance to therapy [27, 28]. TGFβ signaling can trigger EMT when cells are in the certain disease microenvironment, such as fibrosis and cancer [28, 29]. In CRC, TGFβ and downstream factors are mobilized by integrin, which promotes the EMT processed in both cell and animal models [30]. On the contrary, decreasing TGFβ expression and activity in the tumor microenvironment leads to potent immune responses of CRC in rodent models [31, 32]. In line with the above findings, knockdown ALDH1A3–Linc00284 axis significantly affects the expression level of EMT-related genes E-cadherin, N-cadherin, and Vimentin, and inhibits the EMT process, while overexpression of each of them has opposite effects. Moreover, changing the expression levels of the ALDH1A3–Linc00284 axis also affects the viability and invasion of CRC cells. Importantly, we found that higher expression of ALDH1A3–Linc00284 positively correlates with the TNM staging and poor survival of CRC patients. These findings suggested that ALDH1A3–Linc00284 promotes the EMT process by regulating the TGFβ signal, and then promotes CRC tumor metastasis. microRNAs are small noncoding RNAs that function as either tumor suppressors or oncogenes under certain conditions [33]. More and more studies have identified miRNAs as potential biomarkers for human cancer diagnosis, prognosis and treatment targets. The miR-361-5p has been reported widely expressed in many tissues. Higher expression of miR-361-5p indicates better prognosis of breast cancer patients [34]. In gastric cancer, miR-361-5p can suppress chemoresistance of SGC-7901 and MKN-28 cells through inhibition of the expression of FOXM1 [35]. Here, we identified that TGFβ is a direct target of miR-361-5p in CRC cells, silencing miR-361-5p can induce the expression of TGFβ and promote the EMT process. Interestingly, sequence analysis revealed that miR-361-5p can be bound by Linc00284 in SW480 cells. In tumor samples of CRC patients, miR-361-5p expression is negatively correlated with the expression of both ALDH1A3 and Linc00284. More importantly, inhibition of miR-361-5p can rescue the effect ALDH1A3 or Linc00284 knockdown in CRC cells. Our findings indicate that the ALDH1A3–Linc00284 axis mediates the progression of CRC by targeting TGFβ signaling via sponging miR-361-5p. Animal models need to be established to further investigate and confirm the regulatory effect of ALDH1A3–Linc00284-miR-361-5p in the CRC tumor microenvironment in the future studies.
Our findings indicate that the ALDH1A3–Linc00284 axis mediates the progression of CRC by targeting TGFβ signaling via sponging miR-361-5p in CRC cells, providing new insight into the pathogenesis and treatment of colorectal cancer. | true | true | true |
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PMC9584683 | Ying Huang,Chengyong Wu,Chunmei Wei,Yekun Chen,Fei Xing | The Low Endometrial Expression of Long Non-Coding RNA NORAD Is Associated with Recurrent Pregnancy Losses and Unexplained Infertility | 13-10-2022 | Objective Unexplained infertility (UIF) or recurrent pregnancy loss (RPL) affects 10%–15% of couples in their reproductive years and is multifactorial and not completely elucidated. In this study, we attempt to determine the endometrial expression pattern of non-coding RNA activated by DNA damage (NORAD) in women with UIF and RPL, as well as its clinical significance. Methods The microarray dataset GSE165004 was used to identify differentially expressed RNAs in the endometrial samples between women with RPL and fertile women and between women with UIF and fertile women. A total of 142 women were included in this retrospective analysis, including 32 women with UIF, 48 women with RPL, and 62 fertile women. The relative expression level of NORAD in the endometrial tissues was quantified by qRT-PCR. Results NORAD stood out as an only overlapped lncRNA among differentially expressed RNAs in the endometrial samples between RPL and fertile women and between UIF and fertile women. It was showed that the endometrial tissues of UIF and RPL both were demonstrated with lower relative expression levels of NORAD (UIF: 2.09 ± 0.68; RPL: 1.98 ± 0.65) than the endometrial tissues of normal fertility (4.32 ± 1.04) (P < 0.001). Pearson correlation analysis demonstrated that the serum level of E2 was negatively correlated with the relative expression level of NORAD in the endometrial tissues of UIF (r = −0.630) and RPL (r = −0.696). Results of ROC curves showed that the endometrial expression of NORAD could be used to differentiate RPL and UIF with an AUC of 0.977 (95% CI: 0.956–0.999) and 0.970 (95% CI: 0.941–0.998), sensitivity of 0.873 and 0.955, and specificity of 0.845 and 0.948, respectively. Conclusion The findings obtained from the study showed that the low endometrial expression of NORAD was linked to fertility-related problems, such as UIF and RPL. | The Low Endometrial Expression of Long Non-Coding RNA NORAD Is Associated with Recurrent Pregnancy Losses and Unexplained Infertility
Unexplained infertility (UIF) or recurrent pregnancy loss (RPL) affects 10%–15% of couples in their reproductive years and is multifactorial and not completely elucidated. In this study, we attempt to determine the endometrial expression pattern of non-coding RNA activated by DNA damage (NORAD) in women with UIF and RPL, as well as its clinical significance.
The microarray dataset GSE165004 was used to identify differentially expressed RNAs in the endometrial samples between women with RPL and fertile women and between women with UIF and fertile women. A total of 142 women were included in this retrospective analysis, including 32 women with UIF, 48 women with RPL, and 62 fertile women. The relative expression level of NORAD in the endometrial tissues was quantified by qRT-PCR.
NORAD stood out as an only overlapped lncRNA among differentially expressed RNAs in the endometrial samples between RPL and fertile women and between UIF and fertile women. It was showed that the endometrial tissues of UIF and RPL both were demonstrated with lower relative expression levels of NORAD (UIF: 2.09 ± 0.68; RPL: 1.98 ± 0.65) than the endometrial tissues of normal fertility (4.32 ± 1.04) (P < 0.001). Pearson correlation analysis demonstrated that the serum level of E2 was negatively correlated with the relative expression level of NORAD in the endometrial tissues of UIF (r = −0.630) and RPL (r = −0.696). Results of ROC curves showed that the endometrial expression of NORAD could be used to differentiate RPL and UIF with an AUC of 0.977 (95% CI: 0.956–0.999) and 0.970 (95% CI: 0.941–0.998), sensitivity of 0.873 and 0.955, and specificity of 0.845 and 0.948, respectively.
The findings obtained from the study showed that the low endometrial expression of NORAD was linked to fertility-related problems, such as UIF and RPL.
Infertility refers to the inability to establish clinical pregnancy after 1 year of regular and unprotected sexual intercourse, affecting 10–15% of reproductive-aged couples worldwide [1]. Approximately 72.4 million populations are estimated to suffer from infertility and 40.5 million people are currently seeking medical care [2]. Identifiable causes, such as ovulatory dysfunction, male factor infertility, and tubal disease, have been confirmed in 85% of those who experience infertility. Unexplained infertility (UIF) exists in the remaining 15% of infertile couples [3]. No direct explanations are identified in the couples with UIF presenting normal spermatogenesis and ovulation. Despite extensive research on unexplained infertility has been explored for decades, UIF still remains to a great extent unexplained [4]. Recurrent pregnancy loss (RPL) is a painful pregnancy disorder. It is defined as a failure of spontaneous pregnancy clinically recognized twice or more before 20–24 weeks of gestation including embryo and fetal loss but excludes ectopic pregnancies and molar pregnancies [5]. The progress in predicting and preventing RPL has been advanced. However, the diagnosis of RPL remains difficult due to its highly variable clinical manifestations and the uncertainty of pathogenesis [6]. Infertility in female is extremely heterogeneous in etiology, which may be due to complex interaction female development, hormone, and environment and genetic factors [7]. The non-coding RNA does not have the ability to encode proteins but contains information and function. These RNAs regulate gene expression in physiology and development including chromatin structure or epigenetic memory and transcription through activating or inactivating internal signals [8]. Non-coding RNA activated by DNA damage (NORAD) also known as LINC00657 is a highly conserved long non-coding RNA (lncRNA) compared to other lncRNAs, which is profusely expressed in a great quantity of cells due to DNA damage. Considerable studies revealed that NORAD has been involved in numerous processes concerning cancer promotion, such as cell proliferation, apoptosis, invasion, and metastasis. It may be a potential biomarker in pancreatic cancer, lung cancer, and colorectal cancer by regulating the downstream mechanisms [8, 9]. Another study demonstrated that lower expression of NORAD was detected in the breast milk exosomes of mothers of preterm infants compared with mothers who gave birth at term. It suggested that NORAD participated in the early human development [10]. The functional role of NORAD in female infertility is unclear at present. Therefore, this study recruited females with RPL or UIF and fertile females to explore the impacts of NORAD on occurrence of RPL or UIF and provide some evidences for clinical treatment.
A microarray dataset deposited in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/gds, submission date: Jan 2021) and accessioned as GSE165004 was used to identify differentially expressed RNAs. This dataset was generated on the GPL16699 platform and contained endometrial samples from 24 women with RPL, 24 women with UIF, and 24 fertile women at days 19–21 of the menstrual cycle. Differentially expressed RNAs in the endometrial samples between women with RPL and fertile women and between women with UIF and fertile women were, respectively, sorted using the online tool GEO2R [11] based on the R software limma package. Sorted differentially expressed RNAs must fulfill log2|fold change (FC)| > 1 and adjusted P < 0.05.
This retrospective study included women diagnosed with either UIF or RPL with no offspring from spontaneous pregnancies in our infertility center between January 2020 and December 2021. Women were diagnosed with UIF after routine fertility tests showing (i) infertility of more than 12 months, (ii) normospermic male partner according to the World Health Organization (2010) criteria [12], (iii) regular menstrual cycle of 25–35 days, positive ovulation tests, and/or progesterone levels ≥ 25 mmol/l, (iv) normal uterine cavity and bilateral tubal patency on the hysterosalpingogram or laparoscopy, and (v) normal hormonal tests (follicle stimulating hormone (FSH) ≤ 13 UI/l and anti-Müllerian hormone (AMH) ≥ 0.4 ng/ml) [13]. Women were diagnosed with RPL if they failed to conceive after ≥2 fresh IVF-ET/ICSI (in vitro fertilization-embryo transfer cycles/intracytoplasmic sperm injection) or had ≥3 consecutive miscarriages occurring before 20 weeks of gestation, documented by ultrasonography or histopathological examination. Eligible women with either UIF or RPL must have age between 18 and 40 years and detailed reports of laparoscopy and hysteroscopy (done within 1 month) and sign a written consent to participate in the study. Those with an identifiable cause of reproductive failure such as chromosomal abnormalities or anatomic defects identified on initial screen, with known endometriosis, adenomyosis, endocrine disorders (polycystic ovary syndrome), autoimmune diseases, or thrombophilia (inherited or acquired), with previous use of hormone therapy, with severe obesity (body mass index (BMI) > 35), or using antibiotics within at least two weeks before sample collection were excluded from this retrospective analysis. Women undergoing dilatation and curettage in our hospital at the same period, with regularly cycling women, at least one live birth, no history of infertility/treatment, no previous miscarriages and no associated gynecologic (endometriosis, fibroids, active or history of pelvic inflammatory disease) or other medical comorbidities such as hyperprolactinemia and thyroid disease were served as fertile controls. The study was approved by the Ethics Committee of our hospital.
Endometrial tissue was obtained from included women in 7–9 days after the luteinizing hormone surge detected using urine luteinizing hormone tests at the time of their medically indicated hysteroscopic endometrial biopsy or endometrial curettage.
Total RNA was extracted from obtained endometrial tissues using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer's manual. The generation of complementary DNA (cDNA) template was carried out using the PrimeScript RT Reagent kit (Takara, Dalian, China) following the manufacturer's manual. The qPCR was carried out using the SYBR® Premix Ex Taq™ II kit (Takara) and a ABI PRISM®7500 System (Applied Biosystems, Foster City, CA, USA) under the thermocycling conditions (95°C for 5 min, followed by 40 cycles at 95°C for 15 s, 60°C for 30 s, and 72°C for 1 min). The primer sequence information of NORAD was 5′-AAGCTGCTCTCAACTCCACC-3' (forward) and 5′-GGACGTATCGCTTCCAGAGG-3' (reverse), and that of GAPDH was 5′-GGAGCGAGATCCCTCCAAAAT-3' (forward) and 5′-GGCTGTTGTCATACTTCTCATGG-3' (reverse). The cycle threshold (Ct) values were normalized to the level of GAPDH, and results were then converted into fold change using the 2−ΔΔCt formula.
All included women were subjected to venous blood collection in the morning for detection of serum levels of hormones including FSH, estradiol (E2), progesterone (P), luteinizing hormone (LH), testosterone (T), and prolactin (PRL) by ELISA methods. All ELISA procedures were performed in accordance with the protocol supplied by the kits' manufactures (R&D Systems, USA).
Statistical analysis and figure creation were performed using GraphPad Prism 8 (GraphPad Software, CA, USA) and SPSS version 22.0 statistical package (IBM Corp, Armonk, NY, USA). Categorical data were shown by number with percentage and analyzed by chi-square test or Fisher's exact test (P < 0.05 regarded as significant difference). Measurement variables normally distributed are shown as mean ± standard deviation and analyzed by independent Student's t-test (P < 0.05 regarded as significant difference). The Pearson correlation test was used to assess the association between NORAD and serum levels of E2 and P. The receiver operating characteristic (ROC) and logistic regression analysis were performed to estimate the diagnostic values of NORAD in UIF and RPL.
After GEO2R bioinformatics analysis, we identified six downregulated RNAs (log2FC > 1 and P < 0.05) in the endometrial samples between women with RPL and fertile women and three differentially expressed RNAs (two downregulated RNAs and an upregulated RNA, log2|FC| > 1 and P < 0.05) in the endometrial samples between women with UIF and fertile women (Table 1). NORAD stood out as an only overlapped lncRNA among differentially expressed RNAs in the endometrial samples between RPL and fertile women and between UIF and fertile women.
A total of 142 women were included in this retrospective analysis, including 32 women with UIF, 48 women with RPL, and 62 fertile women. Demographics and clinical characteristics of three groups of women are listed in Table 2, showing no significant difference on age, body mass index (BMI), the proportions of history of taking oral contraceptives, algomenorrhea, and family history among them. Of note, the serum levels of FSH, LH, T, PRL, and E2 were higher but the serum level of P was lower in women with either UIF or RPL than those in fertile women (P < 0.05). No significant difference was noted in these serum levels of hormones between the women with UIF and those with RPL (P > 0.05).
The relative expression level of NORAD in the endometrial tissues obtained from 32 women with UIF, 48 women with RPL, and 62 fertile women was quantified by qRT-PCR. It was showed that the endometrial tissues of UIF and RPL both were demonstrated with lower relative expression levels of NORAD (UIF: 2.09 ± 0.68; RPL: 1.98 ± 0.65) than the endometrial tissues of normal fertility (4.32 ± 1.04) (P < 0.001, Figure 1(a)). No evident significance was noted in the endometrial expression of NORAD between UIF and RPL (P > 0.05). Pearson correlation analysis demonstrated that the serum level of E2 was negatively correlated with the relative expression level of NORAD in the endometrial tissues of UIF (Figure 1(b), r = −0.630) and RPL (Figure 1(c), r = −0.696), but the serum levels of FSH, LH, T, PRL, and P were not correlated (P > 0.05).
Results of ROC curves showed that the endometrial expression of NORAD could be used to differentiate RPL and UIF with an AUC of 0.977 (95% CI: 0.956–0.999) and 0.970 (95% CI: 0.941–0.998), sensitivity of 0.873 and 0.955, and specificity of 0.845 and 0.948 (Figure 2), respectively.
Placenta is the active interface between mother and fetus, which is related to the rapid development and exposure of molecular markers in the uterus. Genomic imprinting is involved in the development of placenta. For instance, increased mRNA expression of IGF2 and decreased expression of H19 were detected in endometrial tissues of females with UIF [14]. A variety of factors such as chromosomal abnormalities, maternal immunological rejection, and hormonal imbalance are associated with RPL. Normal cellular regulation of these factors is essential for maintaining normal pregnancy, and differential gene expression affects the biological processes of RPL [15]. lncRNAs have attracted extensive attention in disease development because of their enormous diversity in evolutionary conservation, expression level, molecular function, and cellular localization [16]. Previous studies have indicated that lncRNAs including lnc32058, lnc09522, and lnc98497 were differentially expressed in male infertility. Regulation role of lncRNAs on gene expression has been identified in female reproductive disorders [17]. The patients with polycystic ovary syndrome showed elevated expression of lnc-MAP3K13-7:1 in inhibited granulosa cell [18]. HZ07 lncRNA was upregulated induced by benzo(a)pyrene in RPL and overexpression of HZ07 inhibited trophoblast cell migration [19]. Human LINC00657 RNA or alternatively named as NORAD is abundantly expressed in human tissues and cell lines after DNA damage. NORAD was reported as an oncogene of most cancer-related diseases. The role of NORAD in increasing cell viability, proliferation, migration, and invasion while inhibiting apoptosis has been explored in colorectal cancer study presented by Wang et al. [20] and Zhang et al. [21]. Besides, Yang et al. revealed that overexpression of NORAD enhanced migration and invasion of hepatocellular carcinoma cells by suppressing miR-202-5p [22]. However, upregulation of NORAD contributed to suppress tumor growth and enhance apoptosis of endometrial cancer cells. The effects have been exerted through interaction between NORAD and far upstream element binding protein 1 (FUBP1), resulting in attenuation of nuclear localization of this anti-apoptotic protein and releasing pro-apoptotic gene promoters [23]. In this study, we compared the relative expression of NORAD in endometrial tissues from females with UIF, females with RPL, and fertile females. The qRT-PCR manifested that lower expression of NORAD was detected from females with UIF and females with RPL compared with females with normal fertility. UIF and RPL females showed no significant difference concerning NORAD expression. Abnormal female hormone levels lead to negative impact on the reproductive system. FSH is the most commonly used indicator in determining ovarian reserve and represents an indispensable part of fertility treatment [24]. LH plays a vital role in role in promoting follicular growth and maturation in ovarian function. It can be used as an effective predictor of ovarian function when it is combined with FSH [25]. P is essential for the establishment and maintenance of pregnancy through its role in endocrine and immunity [26]. This study found that the fertile females had lower level of FSH, LH, T, PRL, and E2 in serum but showed higher P level than the females with either UIF or RPL. A slight difference in these hormones was noted in females with UIF and females with RPL. Furthermore, Pearson correlation analysis in our study also confirmed that no correlations were identified between NORAD expression and these hormones levels except for E2, whereas E2 level was negatively correlated with NORAD expression. Lastly, we detected the diagnostic value of NORAD in RPL and UIF and found that NORAD yielded AUC of 0.977 and 0.970, respectively, for differentiating RPL and UIF. However, some limitations should be noted in this study. First, the study consisted of bioinformatics analysis, and qPCR experiment, RNA-sequencing, or arraying in included endometrial tissues will be required in further study. Second, small sample size used for NORAD expression relation to RPL and UIF may reduce reliability of results. Third, there is no evidence presenting the discriminatory nature of this lncRNA in RPL or UIF, and the functions of this lncRNA are yet unclear. Finally, two different datasets were used in the study, but how their endometrial tissues were collected remains unclear. In conclusion, the present study identified that NORAD expression in endometrial tissues was associated with the occurrence of UIF and RPL in females, and negative correlation was observed between E2 level in serum and NORAD expression. The result may trigger a series of special diagnostics and treatment options for females with UIF and RPL. | true | true | true |
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PMC9584697 | Qiaohui Gao,Zhenghua Ren,Shengyuan Jiao,Juan Guo,Xia Miao,Jin Wang,Junye Liu | HIF-3α-Induced miR-630 Expression Promotes Cancer Hallmarks in Cervical Cancer Cells by Forming a Positive Feedback Loop | 13-10-2022 | Purpose Hypoxia has crucial functions in the development and metastasis of cervical cancer by inducing the expression of numerous genes, including microRNA genes. But we know little about how the hypoxia factors and microRNAs orchestrate to regulate hallmarks of cervical cancer cells. Methods We conducted RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) experiments to investigate the targets of HIF-3α or miR-630. ChIP-qPCR and RT-qPCR were carried out to validate the results of ChIP-seq and RNA-seq. Cellular, molecular, and radiation experiments were conducted to explore the functions of miR-630. Results In this study, we showed that hypoxia-induced overexpression of HIF-3α increased the expression of dozens of miRNAs, including miR-630. Hypoxia could also directly induce miR-630 expression. ChIP-seq data showed that HIF-3α activates miR-630 expression by directly binding to the promoter of its host gene. Meanwhile, stable overexpression of miR-630 increased the expression of HIF-3α, but repressed the expression of HIF-1α, indicating a positive feedback loop between HIF-3α and miR-630. Consequently, stable overexpression of miR-630 in HeLa cells promotes cancer hallmarks, including radioresistance, inhibition of apoptosis, increased migration and invasion, and EMT-mediated metastasis. Meanwhile, inhibition of miR-630 showed opposite features. Conclusion Taken together, our findings indicate a novel hypoxia-induced HIF-3α and miR-630 regulatory feedback loop contributing to metastasis and progression of cervical cancer cells and suggest that HIF-3α and miR-630 might act as potential biomarkers and therapeutic targets for cervical cancer in the future. | HIF-3α-Induced miR-630 Expression Promotes Cancer Hallmarks in Cervical Cancer Cells by Forming a Positive Feedback Loop
Hypoxia has crucial functions in the development and metastasis of cervical cancer by inducing the expression of numerous genes, including microRNA genes. But we know little about how the hypoxia factors and microRNAs orchestrate to regulate hallmarks of cervical cancer cells.
We conducted RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) experiments to investigate the targets of HIF-3α or miR-630. ChIP-qPCR and RT-qPCR were carried out to validate the results of ChIP-seq and RNA-seq. Cellular, molecular, and radiation experiments were conducted to explore the functions of miR-630.
In this study, we showed that hypoxia-induced overexpression of HIF-3α increased the expression of dozens of miRNAs, including miR-630. Hypoxia could also directly induce miR-630 expression. ChIP-seq data showed that HIF-3α activates miR-630 expression by directly binding to the promoter of its host gene. Meanwhile, stable overexpression of miR-630 increased the expression of HIF-3α, but repressed the expression of HIF-1α, indicating a positive feedback loop between HIF-3α and miR-630. Consequently, stable overexpression of miR-630 in HeLa cells promotes cancer hallmarks, including radioresistance, inhibition of apoptosis, increased migration and invasion, and EMT-mediated metastasis. Meanwhile, inhibition of miR-630 showed opposite features.
Taken together, our findings indicate a novel hypoxia-induced HIF-3α and miR-630 regulatory feedback loop contributing to metastasis and progression of cervical cancer cells and suggest that HIF-3α and miR-630 might act as potential biomarkers and therapeutic targets for cervical cancer in the future.
Cervical cancer (CC) is a common gynecological cancer that seriously threatens women's health worldwide. An estimated half a million newly diagnosed cases and over 300 000 deaths occur by cervical cancer each year [1, 2]. CC is mainly caused by the infection of high-risk subtypes of the human papilloma virus (HPV). Advances in radiotherapy technology have resulted in less treatment-related toxicity for women with cervical cancer [3]. Meanwhile, tumor recurrence and metastasis may occur to approximately one-third of cervical cancer patients within the first 2 years after radiotherapy [4]. Extrinsic abnormalities of tumor microenvironment, particularly tumor hypoxia, reduce the efficacy of radiotherapy, shorten survival time, and increase recurrences [5]. A recent review article summarized hypoxia-targeted strategies and identified further research and new treatment paradigms needed to improve patient outcomes [6]. Hypoxia-inducible factor (HIF) is a transcriptional activator of various genes related to cellular adaptive responses to hypoxia. To date, three HIF family members have been identified in mammals (HIF-1α, HIF-2α, and HIF-3α). HIF-1α-targeted genes are most significantly associated with metabolism of carbohydrates, diabetes pathways, pathways in cancer, and integration of energy metabolism [7]. HIF-2α overexpression in CC mouse model promoted tumor growth and reduced cisplatin sensitivity by inducing excessive autophagy [8]. Upregulation of HIF-3α can accelerate the progression of ovarian cancer and promote metastatic phenotypes in pancreatic cancer [9]. However, it remains largely unknown how HIF-3α functions in cervical cancer. Recent studies demonstrate that miRNAs play an important role in modulating the process of epithelial mesenchymal transformation (EMT) [10]. For example, miR-27b could induce EMT and promote cervical cancer metastasis [11]. Our previous data indicated that a specific miRNA signature, including miR-630, miR-1246, miR-1290, and miR-3138, could promote radioresistance of human cervical cancer cells [12]. miR-630, identified from the miRNA cluster at chromosome 15q24.1, has been found to be deregulated and involved in several human malignancies [13]. Further study is needed to clarify the mechanisms of miR-630 in cancer development and progression. In the current study, we investigated the regulatory loop among HIFs (HIF-1α and HIF-3α) and miR-630 to identify their functions in cervical cancer progression. Firstly, we confirmed that miR-630 is upregulated by HIF-3α instead of by HIF-1α. Then, we explored the relationship between hypoxia and radiation through the overexpression and repression of miR-630. At last, we found that HIF-1α can be regulated by HIF-3α and that miR-630 could increase HIF-3α expression but repress HIF-1α expression. Cellular phenotype experiments also demonstrated the important functions of miR-630 in the cancer hallmarks of HeLa cells. Our results identified a positive feedback loop between hypoxia factor HIF-3α and miRNA miR-630 and illustrated the underlying mechanisms. We also identified the functional role of miR-630 in regulating radioresistance and cancer hallmarks in cervical cancer cells. The discovery highlights the regulatory functions of HIF-3α and miR-630 and identifies new therapeutic candidates for hypoxia and radiotherapy strategies to treat cervical cancer.
HeLa cells were purchased from the Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, in 2018. HeLa cells were maintained as a monolayer in Dulbecco's modified Eagle's medium (DMEM, Invitrogen, Carlsbad, CA) supplemented with 10% fetal bovine serum (FBS, Sijiqing Biological Engineering Materials Co., Hangzhou, China) at 37°C in the presence of 5% CO2-balanced air. The other three stable transformants were maintained as monolayers in DMEM with 10% FBS and 2 μg/ml puromycin (Sigma, German).
To induce hypoxia, HeLa cell was rendered in a chamber with a gas mixture of 1% O2- and 5% CO2-balanced N2 at 37°C. The level of oxygen in the chamber was verified using a gas monitor (SKC, Inc., Eighty Four, PA). To mimic hypoxia using chemicals, cells were cultured under 20% oxygen in the presence or absence of 100 μM CoCl2 for a specified time period.
Full-length human HIF3A cDNA was cloned into a Pcmv-ORF-flag-his expression vector and HIF1A was cloned into a pBabe-puro-HA expression vector (TranSheepBio, Shanghai, China). Transfection was performed with Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) 24 h after cell seeding. Lentiviral constructs containing upregulating miR-630 (LV-hsa-mir-630), inhibiting miR-630 (LV-hsa-mir-630-inhibitor), and the negative control lenti-vector (LV-negative control) were designed and provided by Genechem Inc. (Shanghai, China). HeLa cell at 60–70% confluence was infected with three lentiviruses at a multiplicity of infection (MOI) of 10 with enhanced infection solution (ENI.S) according to the manufacturer's protocol. At 10 hours postinfection, viruses were replaced by complete DMEM, and at 48 hours postinfection, three stably infected cells (LV-hsa-mir-630, LV-hsa-mir-630-inhibitor, and LV-negative control) were selected by DMEM with 2 μg/ml puromycin (Sigma, Germany).
Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA), and reverse transcription was performed according to the manufacturer's instructions (638315, Clontech Laboratories, Inc. A Takara Biocompany, USA). U6 was used as an internal control, and each primer contains one cDNA sample as a correct sample in each plate. Primers are shown in Table S1.
The invasive ability of cells was assessed in 24-well transwell chambers (Corning, NY). The polycarbonate filters containing 8 μm pores were coated on ice with 80 μl of Matrigel (Sigma-Aldrich) at 5 mg/l. After blocking with 1% BSA for 1 h at 37°C, the cells (5 × 105/ml) were suspended in serum-free culture medium, and then, 100 μl of medium was added to the upper compartments of chamber. In each lower chamber, 500 μl of medium (10% FBS) was added. After 24 h incubation, the cells from the upper compartment were removed, and the cells on the lower surface were fixed in ethanol and stained with hematoxylin-eosin. Cells on the lower surface were quantified in 10 random microscopic fields per filter at a magnification of 200x (DMI4000B, LEICA, Germany).
When 80% confluence in 25 cm2 flasks (Nunc, Roskilde, Denmark) was reached, the cells were lysed with RIPA lysis buffer (Beyotime, Shanghai, China) on ice and then centrifuged at 12000 rpm for 20 min. Supernatants were collected and protein was determined using bicinchoninic acid (BCA) kit (Boster, Wuhan, China). The extracts (20 μg per lane) were fractionated by 10% SDS-PAGE and then transferred onto PVDF membranes (0.45 μm; Millipore, Bedford, MA). After blocking with TBST buffer (20 mM Tris-buffered saline and 0.1% Tween-20) containing 5% nonfat milk for 1 h at 25°C, the membranes were incubated with primary antibodies against E-cadherin, N-cadherin (1 : 5000, Epitomics, USA), cytokeratin (1 : 400, Boster, China), EP300 (1 : 1000, Abnova, USA), and β-actin (1 : 5000, CMCTAG, USA) overnight at 4°C. Membranes were washed three times for 5 min with TBST before incubation with horseradish peroxidase-conjugated secondary antibody (1 : 3000, CST, USA) for 60 min at 25°C. The membranes were exposed by chemiluminescence (Millipore, Billerica, MA), and images were acquired by ChemiDoc XRS (Bio-Rad, Hercules, CA). Semiquantification of scanned films was performed using Quantity One-4.6.2 (Bio-Rad).
Cells were grown on 24-well m-Slides (NEST Biotechnology Co. Ltd. Wuxi, China) and fixed with 4% paraformaldehyde for 30 min at 4°C, followed by treatment with 0.1% Triton for 10 min. The samples were blocked with PBST buffer (0.1% Tween-20) containing 10% goat serum at room temperature for 1 h and incubated with primary antibodies E-cadherin, N-cadherin (1 : 500, Epitomics, USA), and cytokeratin (1 : 200, Boster, China), overnight at 4°C. The cells then were washed in PBST and incubated with DyLight 488- and DyLight 594-conjugated secondary antibodies (ZS-Bio) at room temperature in the dark for 1 h. Nuclei were counterstained with DAPI for 5 min. After being washed three times, the cells were maintained with 50% glycerin in PBS and observed by laser confocal microscopy (Fluoview FV1000; Olympus, Tokyo, Japan). Photographs were taken with a digital camera (Olympus Fluoview FV1000) attached to a microscope. Ten images (approximately 30 cells per field) were acquired in each group, and the quantification of gray value was analyzed with Olympus Fluoview software FV10-ASW 1.7.
Total RNA (5 μg) was used for RNA-seq library preparation. Polyadenylated mRNAs were purified and concentrated with oligo (dT)-conjugated magnetic beads (Invitrogen) before directional RNA-seq library preparation. Purified mRNAs were iron-fragmented at 95°C followed by end repair and 5′ adaptor ligation. Then, reverse transcription was performed with RT primer harboring 3′ adaptor sequence and randomized hexamer. PCR products corresponding to 200-500 bps were purified, quantified, and stored at -80°C for sequencing. The libraries were prepared and applied to Illumina NextSeq 500 system with 150 × 2 paired-end type (ABLife Inc., Wuhan, China).
Total RNA (3 μg) was used for small RNA cDNA library preparation with Balancer NGS Library Preparation Kit for small/microRNA (Genome Gen). Briefly, RNAs were ligated to 3′ and 5′ adaptor sequentially and reversely transcribed to cDNA and then amplified by PCR. Whole library was applied to 10% native PAGE gel electrophoresis, and bands corresponding to microRNA insertion were cut and eluted. The purified small RNA libraries were quantified and stored at -80°C. The libraries were prepared following the manufacturer's instructions and applied to Illumina NextSeq 500 system with 150 × 2 paired-end type (ABLife Inc., Wuhan, China).
Total cell extracts were prepared from 2 × 107 formaldehyde-fixed cells resuspended in 1 ml lysis buffer containing 50 mM Tris 7.4, 150 mM NaCl, 2 mM EDTA, 0.1% SDS, 0.5% NP-40, and 0.5% deoxycholate. The suspension was sonicated to generate DNA fragments of 200-500 bp and centrifuged for 10 min at 12000 g. Then, 1000 μl cell extracts were incubated with HIF-3α antibody (orb101652, Biorbyt, China) overnight at 4°C. The detailed steps of ChIP experiment were according to the published method [14]. Purified DNA fragments were end-repaired, adenylated, ligated to adaptors, and amplified by PCR for 12 cycles. The PCR products corresponding to 300-500 bps were gel purified, quantified, and stored at -80°C. The libraries were prepared and applied to Illumina X-Ten system with 150 × 2 paired-end type by Novogene.
Raw reads containing more than 2-N bases were first discarded. Then, adaptors and low-quality bases were trimmed from raw sequencing reads using FASTX-Toolkit (Version 0.0.13). The short reads less than 16 nt were also dropped. Subsequently, clean reads were aligned to the GRCh38 genome by TopHat2 [15], allowing 4 mismatches. Uniquely mapped reads were used to calculate reads number and FPKM values (fragments per kilobase of transcript per million fragments mapped) [16] for each gene.
The R Bioconductor package edgeR [17] was utilized to screen the differentially expressed genes (DEGs). A false discovery rate < 0.05 and fold change > 2 or <0.5 were set as the cut-off criteria. To predict the gene function and calculate the functional category distribution frequency, Gene Ontology (GO) analysis and enriched KEGG pathways were conducted using KOBAS 2.0 server [18]. Hypergeometric test and Benjamini-Hochberg FDR controlling procedure were used to define the enrichment of each pathway (corrected p value < 0.05).
All the statistics were expressed as mean ± standard deviation (SD) and processed using SPSS 16.0 statistical software (Chicago, IL). All experiments were performed in duplicate, and p < 0.05 was considered significant.
Previous study has demonstrated that the expression pattern of miRNAs was regulated by hypoxia [19]. According to the established work, the hypoxia-inducible factors, including HIF-1α, HIF-2α, and HIF-3α, are regulators of oxygen homeostasis [20]. We selected the well-studied HIF-1α and poorly studied HIF-3α and confirmed their elevated expression level in HeLa cells cultured under hypoxia (Figures 1(a) and 1(b)). Consistent with previous study [9], the expression of HIF-3α was stimulated to a higher level than that of HIF-1α (Figures 1(a) and 1(b)). Reanalysis of TCGA cervical cancer revealed their converse expression pattern between normal and tumor samples (Figure S1A). Higher expression of HIF1A and HIF3A could shorten the survival time of cervical cancer patients (Figure S1B), indicating their oncogenic functions. We then investigated if the expression of miRNAs was also regulated by HIF-3α, as was the case with HIF-1α. We utilized the HIF-1α or HIF-3α overexpression (OE) and vacant HeLa cells to generate miRNA expression profile by miRNA-seq. Differentially expressed miRNA (DEmiR) analysis revealed that the upregulated miRNAs were dominant in both HIF-1α and HIF-3α overexpression samples (Figure 1(c)). Meanwhile, we analyzed the expression of miRNAs that are involved in radioresistance in cervical cancer cells [12]. The expression of miR-630 was increased in both HIF-1α and HIF-3α OE samples, especially in the HIF-3α OE samples (Figure 1(d)). In addition to miR-630, miR-137 was significantly upregulated in HIF-3α overexpression HeLa cells. miR-1246 and miR-137 were significantly upregulated in HIF-1α overexpression cells, whereas miR-15b-3p was significantly downregulated in HIF-1α overexpression cells (Figure 1(d)). RT-qPCR also showed that HIF-3α increased the expression level of miR-630, which was consistent with RNA-seq results (Figure 1(e)). We then measured miR-630 expression level in HeLa cells which were cultured under hypoxia for 24 h and 48 h, respectively. We found that miR-630 was upregulated in response to hypoxia. After 24 h exposure to hypoxia, HeLa cells were incubated in normoxia for another 24 h. We found that the miR-630 level was still higher than that of the control group (Figure 1(f)). These results show that the activation of miR-630 induced by hypoxia is fast and long-lasting in HeLa cells. Together with the induced expression of HIF-3α under hypoxia, these results demonstrate that hypoxia promotes miR-630 expression by activating HIF-3α in HeLa cells.
To further explore the functions of targets regulated by HIF-3α, we performed HIF-3α overexpression and silencing experiments and utilized transcriptome profiling of HIF-3α OE and negative control samples by RNA-seq in HeLa cells. Sample correlation analysis revealed the global alteration of transcriptome by HIF-3α overexpression (Figure 2(a)). We finally obtained a total of 201 DEGs, including 172 up- and 29 downregulated genes (Figures 2(b) and 2(c)). GO and KEGG pathway analysis for the upregulated genes by HIF-3α OE was conducted. The upregulated target genes are enriched in terms including responses to virus, type I interferon-mediated signaling pathway, and negative regulation of apoptosis. Enriched KEGG pathways included hematopoietic cell lineage, TNF signaling pathway, cytokine-cytokine receptor interaction, and transcriptional misregulation in cancer (Figure 2(d)). These data indicated that HIF-3α can upregulate the transcriptional levels of cancer-related genes, including ETV1, ETV5, ETV4, CXCL8, HMGA2, IL6, DUSP6, ARNT2, PTGS2, SOCS3, CCL5, FOS, JUN, IL6, and VEGFC (Figure 2(e)). Immune- and inflammatory-related terms were also significantly enriched (Figure 2(d)). HIF-3α overexpression significantly increased the RNA level of HIF1A (Figure 2(f)), indicating that HIF-3α positively regulated HIF1A expression.
To decipher how HIF-3α regulated the expression of their target genes, we performed ChIP-seq experiment. After aligning the quality-filtered reads to the human genome, we detected the binding peaks using MACS2 software [21]. We then assessed the read distribution around the transcription start sites (TSS) of all genes. We observed that ChIP-seq tags were obviously enriched in the TSS region of HIF3A (Figure 3(a)), suggesting that HIF-3α binds to the promoter region to regulate gene expression. By classifying the HIF3A-bound peaks according to their genomic distribution, approximately 21~30% peaks were located at the TSS region of genes (within 2 kb to TSS, Figure 3(b)). We also found that HIF3A globally bound to the promoters of miRNAs, mRNAs, and lncRNAs (Figure 3(c)). The bound miRNAs of the first and second replicates of HIF-3α accounted for a proportion of 9% and 12%, respectively. We then assessed how HIF-3α regulated gene expression by directly binding to their genomic locus. A total of 41.8% (84) HIF-3α-regulated DEGs were bound by HIF-3α (Figure 3(d)), among which, miR-630 and miR-137 were bound by HIF-3α at the promoter region of their host genes, with miR-137 shown in Figure 3(e) as an example. To verify the ChIP-seq results, ChIP-qPCR experiment was conducted for selected miRNAs (Figure 3(f)), which confirmed that miR-155, miR-158, miR-95, miR-1290, and miR-137 were directly bound by HIF-3α.
miR-630 is an intronic miRNA that shares the same promoter with its host gene ARIH1. Sequencing reads with the HRE binding motif were enriched in the promoter of the ARIH1 gene in HIF3A IP ChIP-seq samples (Figure 4(a)), which was validated by ChIP-qPCR (Figure 4(b)). Taken together, these results demonstrated that expression of miR-630 was activated by HIF-3α binding to its host gene promoter. Since radiation and hypoxia both induced the expression of miR-630 in cervical cancer cells, we investigated the potential relationships between miR-630 and HIFs. By checking the protein levels of HIF-1α and HIF-3α, we found that miR-630 could significantly reduce the expression of HIF-1α and increase the expression of HIF-3α (Figures 4(c) and 4(d)). It is clear that miR-630 can promote cell proliferation and invasion, whereas the associated regulatory mechanism is unclear. We performed RNA-seq for miR-630 knockdown and vacant HeLa cells. A total of 8413 DEGs were detected, of which 5107 were upregulated and 3306 were downregulated (Table S2). Functional enrichment analysis of the DEGs (Figure 4(e)) showed that the upregulated genes were enriched in DNA-dependent transcription, regulation of DNA-dependent transcription, response to DNA damage stimulus, mitotic cell cycle, and homologous recombination-mediated double-strand break repair. The downregulated genes are enriched in mRNA metabolic process pathways, translation, and extracellular matrix organization (Figure 4(e)). We also found that the expressions of ATM and ATR genes, the master regulators of cell cycle checkpoint signaling pathways [22], were upregulated by silencing miR-630 in HeLa cells (Figure 4(f)). RNA-seq data also revealed miR-630 knockdown-induced downregulation of HIF1A (Figure 4(g)) and upregulation of HIF-3α in HeLa cells (Figure 4(g)).
To explore the cellular influence of miR-630 on cervical cancer cells, we established cervical cancer HeLa cells with stably overexpressed and inhibited miR-630 level by lentivirus transfection (Figure 5(a)). Our previous study showed that radiation induced the expression of miR-630 in a time- and dose-dependent manner in cervical cancer cells [12]. Colony formation assay was used to analyze the survival rate of HeLa cells in single-dose radiotherapy (0 Gy, 4 Gy, 8 Gy, and 10 Gy). We found that overexpression of miR-630 attenuated radiotherapy-induced apoptosis of HeLa cells, whereas silencing of miR-630 accelerated radiotherapy-induced apoptosis (Figure 5(b)), indicating the radioresistant function of miR-630. To further explore the functions of miR-630, we measured cell proliferation after 6 Gy and 8 Gy radiotherapy. Overexpression of miR-630 had a positive effect on the proliferation capacity of HeLa cells. By contrast, silencing of miR-630 resulted in decreased proliferation (Figures 5(c) and 5(d)). Taken together, these results demonstrated that miR-630 enhanced the radioresistance and increased proliferation of HeLa cells.
We then conducted flow cytometry analysis to investigate the effect of miR-630 on apoptosis level in HeLa cells in cervical cancer. We found that overexpressing miR-630 inhibited spontaneous apoptosis of HeLa cells (p < 0.05) and that silencing miR-630 had no influence on spontaneous apoptosis of HeLa cells (p > 0.05) (Figure 5(e)). Furthermore, when HeLa cells were treated with 6 Gy and 8 Gy doses of radiotherapy, we found that overexpressing miR-630 suppressed the apoptosis of HeLa cells in vitro (p < 0.05) (Figures 5(f) and 5(g)) and that silencing miR-630 promoted the apoptosis of HeLa cells in vitro (p < 0.05) (Figures 5(f) and 5(g)). Subsequently, we detected the apoptosis proteins such as BCL2, BAX, Caspase 3, Caspase 7, and Caspase 9 in 6 Gy HeLa cells. We found that the expression of BCL-2 was increased, whereas the expression of BAX was decreased (Figure 5(h)). Caspase 3, Caspase 7, and Caspase 9 were all significantly decreased upon miR-630 overexpression (Figure 5(i)). Taken together, these results demonstrated that miR-630 inhibited spontaneous and radiation-induced apoptosis of HeLa cells in vitro.
To determine the role of miR-630 in regulating migration and invasion of HeLa cells, we carried out wound-healing assay and transwell chamber assay (Figures 6(a) and 6(c), Figure S2A). Wound-healing assay indicated that overexpression of miR-630 enhanced HeLa cell migration (p < 0.05), while the relative migration distance of miR-630-inhibited cells was significantly decreased (p < 0.05) (Figure 6(a) and Figure S2A). Furthermore, the transwell assay showed that the invasion rate was significantly increased (p < 0.05) (Figures 6(b) and 6(c)). In contrast, silencing miR-630 significantly inhibited the invasion (p < 0.05) (Figure 6(c)). Real-time cellular analysis (RTCA) revealed the significantly higher motility of HeLa cells upon miR-630 overexpression (Figure S2B, p = 0, K-S test). In conclusion, overexpression of miR-630 significantly enhanced the migration and invasion capacity of cervical cancer in vitro.
Based on the above-mentioned RNA-seq results, we noted that the EMT-related transcription factors SNAI1, ZEB1, and ZEB2 were upregulated, whereas TWIST1 and SMAI2 were downregulated (Figure 6(d)). We also found that EP300, a metastasis suppressor gene and a direct target of miR-630 [23], was one of the upregulated DEGs by miR-630 inhibition (Figure 6(d)). EP300 was upregulated upon miR-630 silencing and downregulated upon miR-630 overexpression (Figure 6(e)). These results suggested that miR-630 may promote EMT in HeLa cells. To further confirm the functions of miR-630 in EMT, we performed cellular morphological change experiment. We found that miR-630-induced morphological changes in HeLa cells were consistent with EMT treatment (Figure 6(f)). We checked the expression changes of canonical EMT markers, including E-cadherin and N-cadherin. We found that overexpression of miR-630 increased the expression of N-cadherin and decreased the expression of E-cadherin compared with the control group (p < 0.05) (Figures 6(g) and 6(h)). These results suggest that miR-630 can promote HeLa metastasis by EMT in cervical cancer cells. The induction of HIF-3α under hypoxia increases the transcription of miR-630, and miR-630 overexpression also has positive impact on HIF-3α expression. In conclusion, we described a positive regulatory loop between miR-630 and HIF-3α. Our results also showed that overexpression of miR-630 could significantly reduce the apoptosis and increase the proliferation, invasion, metastasis, and EMT in HeLa cells (Figure 6(i)).
In our study, we described a regulatory loop between HIF-3α and miR-630 (Figure 6(i)). Overexpression of miR-630 increases the expression of HIF-3α, but decreases the expression of HIF-1α. Overexpression of HIF-3α increases the expression of miR-630. The cellular functions of miR-630 were extensively investigated to support its carcinogenic function. We also found that HIF-3α cooperates in the expression of cancer cell transcriptome. These results demonstrated that the novel HIF-3α-miR-630 axis promotes the development of HeLa cell at multiple aspects. Several miRNAs have been found to be related to radioresistance in cancers, such as miRNA-668 in breast cancer [24] and miR-125 in cervical cancer [25]. Our previous study demonstrated that overexpression of miR-630 in HeLa cells resulted in radioresistance [12]. In this study, we further showed that overexpression of miR-630 significantly promoted the migration, invasion, and EMT-mediated metastasis of HeLa cells and inhibited the apoptosis. Several studies have demonstrated the bidirectional functions of miR-630. On the bright side, miR-630, as a tumor suppressor, inhibited tumor progression [26]. Simultaneously, miR-630, as an oncogene, promoted tumor progression, consequently resulting in poor prognosis [27]. Yuan-Yuan Lyu reported that miR-630 acts as a tumor suppressor and inhibits EMT in cervical cancer [28]. We conjectured that one miRNA may target a set of mRNAs and affect radiosensitivity [29]. The mechanisms of HIF-1α have been extensively studied in multiple cancer types [30]. However, the functional studies focusing on HIF-3α only emerged in recent years. HIF-3α was considered to play a negative role in gene expression by competing with HIF-1α and HIF-2α via binding to transcriptional elements in target genes during hypoxia [31]. HIF-3α acts as a transcription activator in zebrafish, the expression of which is oxygen-dependent [32]. According to our study, HIF-3α, induced by hypoxia, increased the expression of multiple miRNAs, consistent with the master regulator of hypoxia in microRNA biogenesis and activity [19]. We found that HIF-3α activates the expression of miRNA by directly binding to their promoter region. Transcriptome sequencing suggested the transcriptional activation role of HIF-3α. Upregulating HIF-3α can greatly activate the cancer development-related genes. Bioinformatics analysis of genes indicated that HIF-3α and HIF-1α regulate the common biological processes. We focused on miR-630, a HIF-3α-activated miRNA, to explore its cellular functions. miR-630, upregulated under hypoxia, increased tumor growth and metastasis by being delivered into a model of ovarian cancer in mouse [33]. Overexpression of miR-630 could also enhance HIF-3α expression, forming a positive feedback loop. Several reports have indicated how hypoxia-induced miRNAs regulated the switch between HIF-1α and HIF-3α in human endothelial cells [34] and how their regulatory feedback circuit enhanced tumor metastasis in hepatocellular carcinoma [35]. We found that HIF-3α binds to miR-630 promoter region and activates its expression. Meanwhile, miR-630 overexpression enhances the expression level of HIF-3α, but represses HIF-1α level. Given that HIF-3α and miR-630 were both consistently upregulated by hypoxia, we proposed that they may cofunctionally regulate the cellular processes of HeLa cells under hypoxia. Our results also highlighted the prognosis effects of HIF-1α and HIF-3α on CC patients. Studies are needed to further explore the contribution of miR-630 to prognosis by regulating the expression of HIF-1α and HIF-3α and by modulating cancer hallmarks of HeLa cells. Taken together, our results for the first time revealed the mechanisms of HIF-3α. We extensively investigated the cellular functions of HIF-3α-induced microRNA miR-630 and detected the HIF-3α/miR-630-positive loop to regulate multiple cellular processes. The potential miR-630-mediated radioresistance can provide useful information on how to treat miR-630-mediated resistance to radiotherapy and hypoxia in the near future. The newly identified functions of HIF-3α and its regulatory loop with miR-630 in HeLa cells also provide theoretical basis for future clinical prognosis and treatment of cervical cancer. | true | true | true |
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PMC9584731 | Yong Yu,Xianghong Lu,Yang Yan,Yonggang Wang,Jiangyun Meng,Shufeng Tian,Jinsong Mu | The lncRNA KIF9-AS1 Accelerates Hepatocellular Carcinoma Growth by Recruiting DNMT1 to Promote RAI2 DNA Methylation | 13-10-2022 | Background Hepatocellular carcinoma (HCC) is a very common malignant tumor. Long noncoding RNAs (lncRNAs) enable discoveries of new therapeutic tumor targets. We aimed to study the role and potential regulatory mechanisms of the lncRNA KIF9-AS1 in HCC. Methods CCK-8, scratch assay, and flow cytometry were used to detect cell proliferation, migration, and apoptosis, respectively. Bax, Bcl-2, ERK, and pERK expression were measured by western blotting. StarBase predicted KIF9-AS1 expression in HCC and paracancerous tissues. RPISeq predicted the interaction score of KIF9-AS1 and DNMT1, and MethyPrimer revealed the CpG island distribution in the RAI2 promoter. MSP was performed to measure RAI2 methylation. RIP and ChIP were performed to examine lncRNA KIF9-AS1, DNMT1, and RAI2 interactions. Finally, the effect of KIF9-AS1 knockdown on HCC was verified with nude mice. Results We found that KIF9-AS1 expression was increased in HCC tissues. KIF9-AS1 knockdown inhibited the proliferation and migration, and facilitated the apoptosis of HCC cells. lncRNA KIF9-AS1-mediated RAI2 expression led to DNMT1 recruitment and regulated RAI2 DNA methylation. RAI2 overexpression inhibited the proliferation and migration and promoted the apoptosis of HCC cells. KIF9-AS1 knockdown inhibited subcutaneous tumor formation in vivo. Conclusion This study shows that KIF9-AS1 accelerates HCC growth by inducing DNMT1 promotion of RAI2 DNA methylation. | The lncRNA KIF9-AS1 Accelerates Hepatocellular Carcinoma Growth by Recruiting DNMT1 to Promote RAI2 DNA Methylation
Hepatocellular carcinoma (HCC) is a very common malignant tumor. Long noncoding RNAs (lncRNAs) enable discoveries of new therapeutic tumor targets. We aimed to study the role and potential regulatory mechanisms of the lncRNA KIF9-AS1 in HCC.
CCK-8, scratch assay, and flow cytometry were used to detect cell proliferation, migration, and apoptosis, respectively. Bax, Bcl-2, ERK, and pERK expression were measured by western blotting. StarBase predicted KIF9-AS1 expression in HCC and paracancerous tissues. RPISeq predicted the interaction score of KIF9-AS1 and DNMT1, and MethyPrimer revealed the CpG island distribution in the RAI2 promoter. MSP was performed to measure RAI2 methylation. RIP and ChIP were performed to examine lncRNA KIF9-AS1, DNMT1, and RAI2 interactions. Finally, the effect of KIF9-AS1 knockdown on HCC was verified with nude mice.
We found that KIF9-AS1 expression was increased in HCC tissues. KIF9-AS1 knockdown inhibited the proliferation and migration, and facilitated the apoptosis of HCC cells. lncRNA KIF9-AS1-mediated RAI2 expression led to DNMT1 recruitment and regulated RAI2 DNA methylation. RAI2 overexpression inhibited the proliferation and migration and promoted the apoptosis of HCC cells. KIF9-AS1 knockdown inhibited subcutaneous tumor formation in vivo.
This study shows that KIF9-AS1 accelerates HCC growth by inducing DNMT1 promotion of RAI2 DNA methylation.
Hepatocellular carcinoma (HCC), the most common primary liver cancer, is an invasive disease that develops in patients with chronic liver disease [1, 2]. HCC is characterized by a high degree of molecular phenotypic heterogeneity and is a major cause of cancer-related death [3, 4]. In recent years, the incidence and mortality of HCC have continued to rise, especially due to the obesity pandemic leading to non-alcoholic fatty liver disease, and the global HCC mortality is expected to increase by another 41% by 2040 [5, 6]. The main causes of HCC development are cirrhosis, alcoholic liver disease, diabetes, and obesity [7]. However, effective treatments are lacking. Drugs targeting specific epigenetic mechanisms are suitable tools for effective clinical treatment of various diseases [8]. Wilms' tumor 1-associated protein (WTAP) has been reported to promote HCC progression through m6A-HuR-dependent epigenetic silencing of ETS1 [9]. Circulating tumor DNA methylation markers also play vital roles in HCC diagnosis and prognosis [10]. However, the epigenetic changes in HCC and possible use of DNA methylation markers as prognostic biomarkers remain unclear. Long noncoding RNAs (lncRNAs) play essential roles in epigenetic and gene expression regulation and have been reported to be critically involved in HCC progression [11]. lncRNA KIF9-AS1 has been reported and studied in cancers not related to HCC. An analysis with the lncATLAS website revealed that lncRNA KIF9-AS1 is located mainly in the nucleus, indicating that KIF9-AS1 may play epigenetic roles. In different diseases, KIF9-AS1 expression levels and functions differ, suggesting that KIF9-AS1 may regulate microRNA (miRNA) expression. For example, lncRNA KIF9-AS1 might promote nasopharyngeal carcinoma progression by targeting miR-16 [12]. lncRNA KIF9-AS1 has also been shown to enhance chemotherapy resistance in renal cell carcinoma mediated by microRNA-497-5p [13]. In addition, we previously found that lncRNA KIF9-AS1 expression was upregulated in liver hepatocellular carcinoma (LIHC) patients through StarBase database (https://starbase.sysu.edu.cn/index.php). It can be inferred that lncRNA KIF9-AS1 may promote HCC development. Moreover, the analysis of lncATLAS database revealed that the lncRNA KIF9-AS1 is located mainly in the nucleus, suggesting that it may play a regulatory role through the epigenetic modification of target genes. However, the function and potential molecular mechanisms of lncRNA KIF9-AS1 action in HCC remain unclear. DNA methylation is an epigenetic process, and DNA methyltransferase 1 (DNMT1) can maintain the methylation of newly copied DNA [14]. A previous report revealed oncogenic roles played by DNMT1 in various malignancies [15]. lncRNAs might influence DNMT1 action, and the dysregulation of lncRNAs has been shown to lead to abnormal DNA methylation patterns [16]. For example, DNMT1-mediated MEG3 methylation facilitated the endothelial-mesenchymal transformation (EMT) mediated through the PI3K/Akt/mTOR pathway in diabetic retinopathy [17]. Moreover, the lncRNA MIR210HG promoted the proliferation and invasion of non-small-cell lung cancer cells by binding DNMT1 and thus upregulating CACNA2D2 promoter methylation [18]. Li et al. found that the lncRNA DDX11-AS1 epigenetically suppressed LATS2 action by interacting with EZH2 and DNMT1 in HCC [19]. Xu et al. showed that the lncRNA Linc-GALH promoted tumorigenesis via the AKT pathway by regulating gankyrin promoter methylation in HCC [20]. Retinoic acid-induced 2 (RAI2) is a newly discovered tumor suppressor, and RAI2 promoter region methylation has been reported to indicate poor prognosis in colorectal cancer, RAI2 downregulated expression or hypermethylation has been identified in colorectal cancer [21]. In the present study, we predicted that there was CpG island distribution in the RAI2 promoter by MethyPrimer database. Meanwhile, we also predicted that lncRNA KIF9-AS1 could interact with DNMT1. Taking prediction results into account, we speculated that lncRNA KIF9-AS1 may be involved in HCC development by recruiting DNMT1 to promote DNA methylation of RAI2. However, regulatory mechanism was not reported in the other literatures, which needs to be explored in our study. In addition, the upregulation of the circular RNA (circRNA), circRBPMS inhibited bladder cancer cell proliferation and metastasis by targeting the miR-330-3p/RAI2 axis and inhibiting ERK pathway activation [22]. It has been reported that anlotinib induced HCC cell apoptosis and repressed HCC cell proliferation via the ERK and Akt pathways [23]. Chen et al. found that loss of RAD52 motif 1 accelerated the progression of HCC mediated through the p53 and Ras/Raf/ERK pathways [24]. However, the relationship of RAI2 and the ERK pathway, and the role they play in HCC need further study. Considering the aforementioned studies, we mainly wanted to explore the role played by the lncRNA KIF9-AS1 in HCC and identify the potential regulatory mechanism. We found that the lncRNA KIF9-AS1 promoted HCC cell proliferation and migration and inhibited HCC cell apoptosis. Furthermore, the lncRNA KIF9-AS1 promoted RAI2 DNA methylation by recruiting DNMT1 and repressing its expression, thus activating the ERK pathway. This finding might lead to new diagnostic and therapeutic targets for HCC treatment.
Normal human hepatic cells (HHL-5 cells) and HCC cell lines (Huh-7, BEL-7405, SNU-398, SNU-387, and Li-7 cell lines) were provided by American Type Culture Collection (ATCC, Virginia, USA). The HHL-5, Huh-7, and BEL-7405 cells were cultured in Dulbecco's modified Eagle's medium (DMEM, Gibco, New York, USA) containing 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin (Beyotime Biotechnology, Shanghai, China). The SNU-398, SNU-387, and Li-7 cells were cultured in the Roswell Park Memorial Institute (RPMI)-1640 with 10% FBS and 1% penicillin/streptomycin (Beyotime). All cells were cultured in a 5% CO2 incubator at 37°C.
Short hairpin (sh)-KIF9-AS1, overexpressing (oe)-DNMT1, sh-DNMT, oe-RAI2, sh-RAI2, sh − KIF9 − AS1 + sh − RAI2, and their corresponding negative control were transfected into the cells using Lipofectamine 3000 reagent (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's instructions. After transfection for 48 h, the cells were used for subsequent experiments. All sequences were synthesized by Sangon Biotech (Shanghai, China).
Cells (1 × 104 cells per well) were inoculated into 96-well plates and incubated at 37°C and 5% CO2 for 24, 48, and 72 h. Then, 10 μL of Cell Counting Kit-8 (CCK-8) reagent (MedChemExpress, New Jersey, USA) was added to each well and incubated for 2 h. A microplate reader (Infinite M200, Tecan, Austria) was used to detect the absorbance at 450 nm, and the cell proliferation capacity of each group was evaluated.
Cells (5 × 105 cells/well) were inoculated into 6-well culture plates. The cells were incubated at 37°C and 5% CO2 for approximately 24 h, at which time, the cells covered the 6-well plate. Then, a scratch was made with a 200-μL pipette tip in a diagonal line on each 6-well plate. PBS was used to wash the cells three times to remove the cells detached by scratching. Then, the cells were incubated for 24 h. The scratched monolayers were photographed with an inverted biological microscope (DSZ2000X, Cnmicro, Beijing, China) 0 and 24 h after wounding. The distances that the cells migrated into the wound were measured with ImageJ software (National Institutes of Health, Bethesda, MD, USA).
Cells treated in each group were digested with trypsin without EDTA and collected. Then, PBS was used to wash the cells before centrifugation at 2000 rpm for 5 min. Approximately 5 × 105 cells were collected. Then, 500 μL of binding buffer was added to suspend the cells. Next, 5 μL of Annexin V-FITC (KGA108, KeyGen, China) and 5 μL of propidium iodide were thoroughly mixed. Finally, the cells were incubated at room temperature with the Annexin V-FITC/propidium iodide mixture in the dark for 15 min. Flow cytometry (A00-1-1102, Beckman, USA) was performed within 1 h of this treatment.
MSP was performed to measure RAI2 promoter methylation. Briefly, genomic DNA was extracted and modified with bisulfite. Then, we amplified the target fragments with MSP primers by following the procedure in which predenaturation was performed at 94°C for 10 min, denaturation was performed at 94°C for 30 s, and annealing was performed at 56°C for 30 s with an extension at 72°C for 1 min in 35 total cycles. A final extension was performed at 72°C for 5 min. Finally, the target fragment was detected by agarose electrophoresis. The following primers were designed using MethyPrimer: M-forward: TTAGTATTTGGTAAATATTAGGCGT; M-reverse: AAAAAAATAAAAAAAACTCAACGAT. U-forward: TTAGTATTTGGTAAATATTAGGTGT; U-reverse: AAAAAAATAAAAAAAACTCAACAAT.
The interaction of KIF9-AS1 with DNMT1, DNMT3a, and DNMT3b was verified using an EZ-Magna RIP kit (Millipore, Massachusetts, USA). Briefly, Huh-7 cells were dissolved in RIP lysis buffer at 4°C for 30 min and then incubated with RIP buffer. Then, an antibody against Ago2 (CST, Boston, USA) or anti-rabbit IgG (the negative control, CST, Boston, USA) were added to magnetic beads. The samples and inputs were processed with proteinase K to extract RNA. Then, qRT–PCR was performed to identify precipitated KIF9-AS1. Total RNA was the input control.
A ChIP Kit (Sigma, USA) was used to detect the interaction between DNMT1 and the RAI2 promoter. Cells were immobilized with 1% formalin for 10 min, and then DNA was randomly segmented by ultrasound into 200-800-bp fragments. This DNA was immunoprecipitated with an antibody against the target protein RAI2 (ab247100, 0.2 μg/mL, Abcam, UK). Finally, ChIP DNA was purified and eluted with 100 μL of H2O, and 2.5 μL of ChIP-DNA was analyzed by qPCR.
StarBase (http://starbase.sysu.edu.cn/index.php) was used to predict KIF9-AS1 expression in HCC and paracancerous tissues. LncATLAS (https://lncatlas.crg.eu/) was used to predict lncRNA KIF9-AS1 subcellular localization. The online RNA protein interaction prediction site RPISeq (http://pridb.gdcb.iastate.edu/RPISeq/) was used to predict the KIF9-AS1 and DNMT1 interaction scores, and the online MethyPrimer tool was used to predict CpG island distribution in the RAI2 promoter.
Twenty-four specific-pathogen-free (SPF)-grade and four-week-old mice were randomly assigned to a control group, an sh-NC group and an sh-KIF9-AS1 group, with eight mice in each group. The animal studies were approved by the Animal Ethics Committee of The Fifth Medical Center, Chinese PLA General Hospital. The subcutaneous tumor-forming nude mouse model was established by injecting Huh-7 cells transfected with sh-NC or sh-KIF9-AS1. The tumor volume in each group was measured every seven days. The nude mice were sacrificed 28 days after the injection. The tumor tissues were removed and weighed. Then, the tumor tissues of six mice were embedded into paraffin and cut into sections. The tissue from the other two mice in each group were prepared as tissue homogenates.
The paraffin sections were dewaxed and dehydrated following standard procedures. A Ki67 primary antibody (ab15580, Abcam, UK) was incubated with the sections overnight at 4°C, and then, the sections were rinsed 3 times with PBS for 5 min each time. A secondary antibody was incubated with the sections at 37°C for 30 min. DAB was used for color development. Hematoxylin stain was also incubated with the sections for 5-10 min. Then, the sections were washed with distilled water. PBS turned blue. An alcohol series (60-100%) was used for dehydration with the sections incubated for 5 min at each gradient level. The sections were removed from the gradient and placed twice in xylene for 10 min each time, sealed with neutral gum, and observed under a microscope.
A PARIS™ kit (Invitrogen) was used to isolate nuclear and cytoplasmic RNA from cells according to the manufacturer's instructions. The RNA levels of GAPDH, U6, and KIF9-AS1 were detected by qRT–PCR.
Total RNA was extracted by the TRIzol method (Invitrogen, USA), and a cDNA reverse transcription kit (Invitrogen) was used to reverse transcribe RNA into cDNA. SYBR Green qPCR Mix (Invitrogen) was used to test the relative gene expression with an ABI 7900 system (PRISM® 7900HT, ABI, Massachusetts, USA). GAPDH was the reference gene, and the 2−ΔΔCt method was used to calculate the relative gene expression level. The following primer sequences were used in this study: the lncRNA KIF9-AS1 forward: 5′- ACCCTCAGCCCTTCCACTAA -3′ and the lncRNA KIF9-AS1 reverse: 5′- TGGTTTACTTCCACATAGCTGACT-3′. DNMT1 forward: 5′- ATGCTTACAACCGGGAAGTG -3′ and DNMT1 reverse: 5′- TGAACGCTTAGCCTCTCCAT -3′. RAI2 forward: 5′- TGGAAATCAGGTCTCTGCAAAT -3′ and RAI2 reverse: 5′- TCACTGCTGAAGAAATGGCTC-3′. GAPDH forward: 5′- CCAGGTGGTCTCCTCTGA -3′ and GAPDH reverse: 5′- GCTGTAGCCAAATCGTTGT -3′.
Total protein in tumor tissues and cells was extracted with RIPA lysis buffer (Beyotime) according to the manufacturer's instructions. Protein quantification was performed with bicinchoninic acid (BCA) protein assay kit (Thermo Fisher Scientific). Then, the proteins were mixed with SDS–PAGE loading buffer (Meilunbio, Dalian, China), and the mixture was heated for 5 min at 100°C. The proteins were transferred to a PVDF membrane by gel electrophoresis and then blocked with a 5% skim milk solution at room temperature for 2 h. The proteins were incubated with primary antibodies against DNMT1 (ab188453, 1 : 1000, Abcam, UK), RAI2 (ab247100, 0.2 μg/mL, Abcam, UK), Bax (50599-2-Ig, 1 : 3000, Proteintech, USA), Bcl-2 (12789-1-AP, 1 : 2000, Proteintech, USA), ERK (ab109282, 1 : 1000, Abcam, UK), pERK (ab229912, 1 : 1000, Abcam, UK), and β-actin (66009-1-Ig, 1 : 1000, Proteintech, USA) overnight at 4°C. The membranes were then rinsed three times with TBST, and the membrane was incubated with horseradish peroxidase- (HRP-) conjugated goat anti-mouse IgG (SA00001-1, 1 : 5000, Proteintech, USA) or HRP-conjugated goat anti-rabbit IgG (SA00001-2, 1 : 5000, Proteintech, USA) secondary antibodies. The protein bands were detected with an Odyssey Infrared Imaging System (Li-Cor Biosciences, Lincoln, NE, USA), and β-actin (ab8227, 1 : 1000, Abcam) was used as the internal reference.
Statistical analyses were performed with SPSS 20.0 software (IBM, NY, USA), and the experimental data are expressed as the means ± standard deviation (SD). Each group included three replicates in cell experiments. Student's t-test was performed to compare two groups. One-way analysis of variance (ANOVA) followed by Tukey's post-hoc test was performed to compare multiple groups. P < 0.05 was considered to be statistically significant.
lncRNA KIF9-AS1 had been previously speculated to promote nasopharyngeal carcinoma progression by targeting miR-16 [12]. In this study, we found that KIF9-AS1 expression was upregulated in LIHC patients, as determined through an analysis of the StarBase database (Figure 1(a)). However, no relevant literature on KIF9-AS1 expression in HCC had been reported. Therefore, we detected KIF9-AS1 expression in HCC cells and normal hepatic cells by performing qRT–PCR. Compared with that in normal hepatic cells (HHL-5 cells), KIF9-AS1 expression was upregulated in HCC cells (Huh-7, BEL-7405, SNU-398, SNU-387, and Li-7 cells), and the expression of KIF9-AS1 was most significantly upregulated in the Huh-7 cells (Figure 1(b)). Therefore, Huh-7 cells were selected for further study. To study the effects of the lncRNA KIF9-AS1 on Huh-7 cells, we knocked down KIF9-AS1 expression in the Huh-7 cells (Figure 1(b)). The results showed that after knocking down lncRNA KIF9-AS expression, cell proliferative and migratory capacities were significantly reduced, while the cell apoptosis rate was significantly increased (Figures 1(c)–1(e)). Moreover, the expression of the proapoptotic protein Bax was elevated, and the expression of the antiapoptotic protein Bcl-2 was reduced after KIF9-AS knockdown (Figure 1(f)). Moreover, after KIF9-AS knockdown, ERK pathway activation was inhibited, and the phosphorylation level of ERK was decreased (Figure 1(f)). These results indicated that lncRNA KIF9-AS1 knockdown repressed HCC cell proliferation and migration and promoted HCC cell apoptosis.
The subcellular localization website lncATLAS was used to predict that the lncRNA KIF9-AS1 is mainly located in the nucleus (Figure 2(a)). As shown in Figure 2(b), the qRT–PCR data verified that the lncRNA KIF9-AS1 is mainly expressed in the nucleus. These results suggested that KIF9-AS1 might play a regulatory role through the epigenetic modification of target genes. An analysis of the website RPISeq (http://pridb.gdcb.iastate.edu/RPISeq/) revealed that lncRNA KIF9-AS1 bound DNMT1 (the random forest [RF] classifier was 0.6, and the support vector machine [SVM] classifier was 0.99). RIP assays confirmed interactions between the lncRNA KIF9-AS1 and DNMT1, DNMT3a, and DNMT3b. The results further revealed that only DNMT1 interacted with KIF9-AS1 (Figure 2(c)). DNMT1 expression was significantly decreased after KIF9-AS knockdown (Figures 2(d) and 2(e)). These results indicated that the lncRNA KIF9-AS1 interacted with DNMT1. The online MethyPrimer tool was used to predict CpG island distribution in the RAI2 promoter, and we found that the CpG islands in the RAI2 promoter region might be methylated. The results of promoter methylation of RAI2 assay showed that RAI2 was hypermethylated in Huh-7 cells that after the addition of the demethylation reagent 5-aza-dc or DNMT1 knockdown, RAI2 was unmethylated, and that after DNMT1 was overexpressed, RAI2 was hypermethylated (Figure 3(a)). Furthermore, after lncRNA KIF9-AS1 knockdown, the RAI2 promoter was unmethylated, but it was hypermethylated after lncRNA KIF9-AS1 overexpression (Figure 3(b)). After DNMT1 was overexpressed, the degree of DNMT1 binding to RAI2 was decreased, and ChIP revealed an interaction between DNMT1 and the RAI2 promoter (Figure 3(c)). The expression of RAI2 was significantly downregulated after DNMT1 overexpression (Figures 3(d) and 3(e)). These findings suggested that DNMT1 regulated RAI2 expression by regulating RAI2 DNA methylation. They also revealed that the lncRNA KIF9-AS1 regulated RAI2 expression by recruiting DNMT1, which regulated RAI2 DNA methylation.
A previous study showed that the expression of RAI2 was downregulated and that it was hypermethylated in colorectal cancer [21]. To explore the role played by RAI2 in HCC, we measured the expression of RAI2 by qRT–PCR. Compared with normal hepatic cells (HHL-5 cells), RAI2 expression was downregulated in HCC cells (Huh-7, BEL-7405, SNU-398, SNU-387, and Li-7 cells), and the expression of RAI2 was found to be the most significantly upregulated in the Huh-7 cells. Subsequently, to further investigate the role played by RAI2 in HCC, RAI2 was overexpressed in Huh-7 cells. The qRT–PCR and western blot results indicated that the RAI2 was successfully overexpressed (Figures 4(a) and 4(b)). After RAI2 overexpression, cell proliferation and migration were significantly inhibited, and cell apoptosis was significantly increased (Figures 4(c)–4(e)). Moreover, after RAI2 was overexpressed, the expression of the proapoptotic protein Bax was elevated, and the expression of the antiapoptotic protein Bcl-2 was reduced. In addition, the ERK pathway was inhibited when RAI2 was overexpressed (Figure 4(f)). These results indicated that RAI2 overexpression repressed HCC cell proliferation and migration and promoted HCC cell apoptosis.
To investigate whether the lncRNA KIF9-AS1 plays a role in HCC by regulating RAI2 expression, we knocked down only RAI2 expression in Huh-7 cells and set up a functional rescue experiment with sh − KIF9 − AS1 + sh − NC and sh − KIF9 − AS1 + sh − RAI2 groups. The results showed that RAI2 expression was downregulated when only RAI2 expression was knocked down, but lncRNA KIF9-AS1 expression remained unchanged. After simultaneously knocking down lncRNA KIF9-AS1 and RAI2, RAI2 expression was decreased, and lncRNA KIF9-AS1 expression remained unchanged (Figures 5(a) and 5(b)). After only RAI2 expression was knocked down, the HCC cell proliferation and migration rates were significantly increased, and HCC cell apoptosis was suppressed. In addition, after lncRNA KIF9-AS1 and RAI2 expression was knocked down, the proliferative and migratory abilities of the HCC cells were significantly enhanced, and the apoptosis rate of these cells was significantly decreased, which weakened the inhibitory effect of KIF9-AS1-only expression knockdown (Figures 5(c)–5(e)). Moreover, Bax expression was downregulated and Bcl-2 expression was upregulated after only RAI2 expression was knocked down. Moreover, the ERK pathway was activated after only RAI2 expressed was knocked down. However, the effect of KIF9-AS1 knockdown on apoptosis and the ERK pathway was weakened after KIF9-AS1 and RAI2 expressions were knocked down (Figure 5(f)). These results suggested that knocking down RAI2 expression reversed lncRNA KIF9-AS1-knockdown effects on the proliferation, migration, and apoptosis of HCC cells.
To further explore the effect of the lncRNA KIF9-AS1 on HCC in vivo, we constructed a subcutaneous tumor-forming model with nude mice. Tumor volume and weight were decreased after lncRNA KIF9-AS1 knockdown (Figures 6(a)–6(c)). IHC results showed that after lncRNA KIF9-AS1 knockdown, Ki67 expression was reduced in tumor tissues (Figure 6(d)). Furthermore, lncRNA KIF9-AS1 and DNMT1 expression was decreased, and RAI2 expression was increased after lncRNA KIF9-AS1 knockdown (Figures 6(e) and 6(f)). These results revealed that lncRNA KIF9-AS1 knockdown inhibited subcutaneous tumor formation in nude mice with HCC.
Most cases of HCC are diagnosed in the late stage of the disease, and HCC is among the most aggressive human tumors, making it the second leading cause of tumor death worldwide and a continuing major global health care problem [25, 26]. Targeted epigenetics has become a promising anticancer strategy [27]. In this study, we revealed that lncRNA KIF9-AS1 accelerated HCC growth by recruiting DNMT1 to promote RAI2 DNA methylation. Increasing evidence has shown that lncRNAs are key regulators of various physiological processes, including HCC development [28]. As biomarkers or targets, lncRNAs may provide new insights into the diagnosis and treatment of diseases [29, 30]. The lncRNA SNHG7 has been reported to promote the proliferation, migration, and invasion of HCC cells by regulating miR-122-5p and RPL4 [31]. The lncRNA MYCNOS accelerated the proliferation and invasion of liver cancer by regulating miR-340 [32]. lncRNA KIF9-AS1 is one of the most widely studied lncRNAs. It has been reported that lncRNA KIF9-AS1 regulated TGF-β and autophagy signaling through miR-497-5p to enhance chemoresistance in renal cell carcinoma [33]. However, the role that lncRNA KIF9-AS1 plays in HCC has been unclear. In this study, we found that KIF9-AS1 expression was increased in HCC tissues and cells. Moreover, knocking down lncRNA KIF9-AS1 expression repressed HCC cell proliferation and migration, promoted HCC cell apoptosis in vitro and inhibited subcutaneous HCC tumor formation in nude mice. This was the first report of lncRNA KIF9-AS1 expression in HCC and its effect on HCC cell proliferation, migration, and apoptosis. Our study suggested that lncRNA KIF9-AS1 was involved in HCC and played a vital role in HCC. DNA methylation induces epigenetic changes and the potential reversibility of this modification suggests opportunities for the development of new HCC biomarkers and treatments [34]. The DNA methyltransferase DNMT1 is related to lncRNAs [35]. For example, DNMT1 has been reported to regulate the lncRNA H19 or ERK signaling pathway during hepatic stellate cell activation and fibrosis [36]. The lncRNA HOTAIRM1 promoted the osteogenesis of HDFSCs in vitro by regulating HOXA2 expression mediated through DNMT1 epigenetic modification [37]. lncRNA PHACTR2-AS1 inhibited the proliferation, invasion, and migration of breast cancer cells by suppressing PH20 expression, which is mediated by DNMT1 methylation [38]. In our study, RIP confirmed the interaction between lncRNA KIF9-AS1 and DNMT1. Other proteins involved in DNA methylation, such as TET1/2, MBD1/2/4, and MeCP2 were not analyzed in our study reported here, but we will conduct further studies on these proteins as conditions permit in the future. RAI2 is methylated on CpG islands, and the common DNA methylation-related enzymes DNMT1, DNMT3a, and DNMT3b are involved. In addition, 5-Aza-CdR and SAHA have been reported to induce cell growth inhibition and apoptosis induction by downregulating DNMT1, DNMT3a, and DNMT3b expression in HCC [39]. HCC incidence has been related to increased DNMT1, DNMT3a, and DNMT3b expression [40]. Previous study revealed that silencing of miR-152 contributed to DNMT1-mediated CpG methylation of the PTEN promoter in bladder cancer [41]. Furthermore, DNMT1 maintained the methylation of miR-152-3p to regulate TMSB10 expression, thereby affecting the biological characteristics of colorectal cancer cells [42]. In this study, we found that KIF9-AS1 could recruit DNMT1 to promote RAI2 DNA methylation, which revealed that KIF9-AS1 functions by mediating epigenetic modifications in HCC. Due to the complex and diverse mechanisms of disease regulation, it is unclear whether epigenetic modification is the main function of KIF9-AS1. Moreover, RAI2 has been reported to play a role in the metastasis of breast cancer, and the expression of RAI2 may be a promising candidate biomarker for breast cancer patient prognosis [43]. However, no studies of RAI2 in HCC had been reported to date. We found that RAI2 overexpression repressed the proliferation and migration and promoted the apoptosis of HCC cells. Meanwhile, knocking down RAI2 expression reversed the effects of lncRNA KIF9-AS1 knockdown on the proliferation, migration and apoptosis of HCC cells.
In conclusion, our findings suggested that lncRNA KIF9-AS1 accelerated HCC growth by recruiting DNMT1 to promote RAI2 DNA methylation. Our study provides a theoretical basis for HCC pathogenesis and provides new molecular targets for HCC epigenetic approaches to the diagnosis, prevention, and treatment of HCC. | true | true | true |
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PMC9584894 | Daniel Roca-Lema,Macarena Quiroga,Vineeta Khare,Andrea Díaz-Díaz,Aida Barreiro-Alonso,Andrea Rodríguez-Alonso,Ángel Concha,Gabriela Romay,M. Esperanza Cerdán,Christoph Gasche,Angélica Figueroa | Role of the E3 ubiquitin-ligase Hakai in intestinal inflammation and cancer bowel disease | 20-10-2022 | Cancer,Cell biology,Molecular biology,Diseases | The E3 ubiquitin-ligases are important for cellular protein homeostasis and their deregulation is implicated in cancer. The E3 ubiquitin-ligase Hakai is involved in tumour progression and metastasis, through the regulation of the tumour suppressor E-cadherin. Hakai is overexpressed in colon cancer, however, the implication in colitis-associated cancer is unknown. Here, we investigated the potential role of Hakai in intestinal inflammation and cancer bowel disease. Several mouse models of colitis and associated cancer were used to analyse Hakai expression by immunohistochemistry. We also analysed Hakai expression in patients with inflamed colon biopsies from ulcerative colitis and Crohn's disease. By Hakai interactome analysis, it was identified Fatty Acid Synthase (FASN) as a novel Hakai-interacting protein. Moreover, we show that Hakai induces FASN ubiquitination and degradation via lysosome, thus regulating FASN-mediated lipid accumulation. An inverse expression of FASN and Hakai was detected in inflammatory AOM/DSS mouse model. In conclusion, Hakai regulates FASN ubiquitination and degradation, resulting in the regulation of FASN-mediated lipid accumulation, which is associated to the development of inflammatory bowel disease. The interaction between Hakai and FASN may be an important mechanism for the homeostasis of intestinal barrier function and in the pathogenesis of this disease. | Role of the E3 ubiquitin-ligase Hakai in intestinal inflammation and cancer bowel disease
The E3 ubiquitin-ligases are important for cellular protein homeostasis and their deregulation is implicated in cancer. The E3 ubiquitin-ligase Hakai is involved in tumour progression and metastasis, through the regulation of the tumour suppressor E-cadherin. Hakai is overexpressed in colon cancer, however, the implication in colitis-associated cancer is unknown. Here, we investigated the potential role of Hakai in intestinal inflammation and cancer bowel disease. Several mouse models of colitis and associated cancer were used to analyse Hakai expression by immunohistochemistry. We also analysed Hakai expression in patients with inflamed colon biopsies from ulcerative colitis and Crohn's disease. By Hakai interactome analysis, it was identified Fatty Acid Synthase (FASN) as a novel Hakai-interacting protein. Moreover, we show that Hakai induces FASN ubiquitination and degradation via lysosome, thus regulating FASN-mediated lipid accumulation. An inverse expression of FASN and Hakai was detected in inflammatory AOM/DSS mouse model. In conclusion, Hakai regulates FASN ubiquitination and degradation, resulting in the regulation of FASN-mediated lipid accumulation, which is associated to the development of inflammatory bowel disease. The interaction between Hakai and FASN may be an important mechanism for the homeostasis of intestinal barrier function and in the pathogenesis of this disease.
Inflammation is an important risk factor for the development of human cancers. Colitis-associated colorectal cancer (CAC) is a specific type of colorectal cancer developed from colitis in inflammatory bowel disease (IBD) patients. IBD, including ulcerative colitis (UC) and Crohn’s disease (CD), is characterized by chronic inflammation in the gastrointestinal tract and can induce pre-neoplastic lesions, being an important risk factor for the onset of colorectal cancer. Ubiquitination is one of the most important translational modifications in eukaryotes that induces substrate degradation, which in consequence controls the “quantity” and “quality” of specific proteins, ensuring cell homeostasis. Ubiquitination process consists in the ubiquitin moiety linkage to a specific substrate that signals for degradation. Three-enzymes are involved in the ubiquitination cascade: the E1 ubiquitin-activating enzyme that activates the ubiquitin molecule, the E2 ubiquitin-conjugating enzyme that carries the activated ubiquitin molecule as a thioester to the E3 ubiquitin-ligase enzyme that transfers the activated ubiquitin from the E2 to the lysine residue of substrate protein. Among all RING-type E3 ligases, there are a number that require substrate phosphorylation at a tyrosine residue (pTyr) for their recognition, including Cbl family. Excessive ubiquitin-mediated proteolysis has been observed in different types of cancer as well as in colitis, and its inhibition was shown to improve the colitis in the mouse model. Hakai protein is an E3 ubiquitin-ligase, identified as the first posttranslational regulator of the E-cadherin stability. Hakai induces E-cadherin ubiquitination and degradation in a Src-dependent manner; which in turn causes the alteration of cell–cell adhesions. E-cadherin is the best-characterized member of adherens junctions, a type of cell–cell contacts that participate in embryogenesis and tissue homeostasis. The loss of E-cadherin at cell–cell contacts induces the epithelial-to-mesenchymal transition (EMT), a process by which cells lose cell polarity and acquire a migratory phenotype, a fundamental program during colon tumor progression and metastasis. In human biopsies from IBD patients, E-cadherin expression is downregulated. Indeed, E-cadherin is the strongest candidate for UC susceptibility and plays a key role in epithelial restitution and repair following mucosal damage. In fact, expression of E-cadherin is significantly reduced in areas of active UC. Of note, E-cadherin has recently been associated with susceptibility to colorectal cancer, which is an established complication of longstanding UC. The new associations suggest that changes in the integrity of the intestinal epithelial barrier may contribute to the pathogenesis of UC. Moreover, In an in vivo mouse model of experimentally induced colitis, it is shown that E-cadherin deficiency was associated with exacerbated acute and chronic inflammation highlighting its role in the pathogenesis of UC. We have previously demonstrated that Hakai is an important regulator of cell proliferation, epithelial-to-mesenchymal transition, cell invasion, and that it induces tumour progression and metastasis in vivo. We have also shown that Hakai expression is increased in colon and gastric cancer compared to adjacent human colon healthy tissues, with important clinical implications for designing new therapeutic strategies. Several E3 ubiquitin-ligases were reported to be implicated in the pathogenesis and development of human IBD. However, whether the E3 ubiquitin-ligase Hakai participates in IBD is still unknown. The main purpose is to explore the implication of Hakai in IBD by analyzing Hakai expression in murine models for CAC. In the present study, we have shown that Hakai is downregulated under pro-inflammatory environment in the mice intestinal tissue regardless of origin of that inflammation. Moreover, by an interactome analysis we have identified Fatty Acid Synthase (FASN) as a novel Hakai-interacting protein. Hakai induces FASN ubiquitination and degradation via lysosome, regulating FASN-mediated lipid accumulation, which is associated to the development of IBD. Importantly, an inverse association of FASN expression with Hakai expression was detected in inflammatory AOM/DSS compared to tumour tissue of CAC and healthy tissues.
Hakai overexpression in cytoplasm and nucleus was reported in human colon cancer compared to adjacent normal healthy colon tissues. In order to compare Hakai expression in the context of intestinal inflammation, three different mouse models that mimic different origins and stages of the IBD disease were used. Firstly, an AOM-DSS model of CAC was used. This model replicates the tumour development process of the human CAC by using the combination of a pro-inflammatory agent (DSS) and a carcinogenic compound (AOM), as previously described. As shown in Fig. 1a, the immunohistochemistry analysis of the whole intestine samples using Hakai antibody revealed a significant decreased expression of Hakai in inflammatory conditions (AOM/DSS) of the gut epithelium compared to tumour tissue from the CAC (AOM DSS/Tumour) or healthy tissues. The same pattern of expression was observed when analysing an animal model of acute colitis (acute DSS), which resemble the acute phase of the disease or flare-ups by using higher concentrations of pro-inflammatory agent DSS at short times (Fig. 1b). Finally, a third stablished mouse model was used based in a genetically modified mice deficient for the IL-10 gene (IL-10 KO). These mice spontaneously develop a chronic inflammatory bowel disease (IBD) due to the important control of IL-10 on the gut microbiota. The absence of IL-10 modulates cellular immune response and reduces mice tolerance towards bacterial antigens of enteric bacteria which causes the inflammatory process. In this model, Hakai expression was also statistically reduced in inflamed conditions compared to gut epithelium of healthy mice (Fig. 1c). All these results point out to a regulation of Hakai in different inflammatory conditions, with a great reduction of Hakai expression in the inflamed epithelium. Remarkably, high expression of Hakai in the epithelium of healthy untreated mouse was detected while this observation was not shown in human tumour-adjacent healthy tissue. Then we further analysed the Hakai mRNA expression by using microarray data from similar experiments in NCBI’s Gene Expression Omnibus (GEO). Three different mouse models were analysed: the colitis-associated colorectal cancer (AOM/DSS), the acute colitis (acute-DSS), and the IL-10 KO mouse models. As shown, the levels of Hakai mRNA were reduced in the inflamed tissue in AOM/DSS model (Fig. 2a), the DSS mouse model (Fig. 2b) and the IL-10 KO mouse model (Fig. 2c). Moreover, Hakai mRNA expression was recovered during tumour progression (low grade dysplasia, high grade dysplasia and colorectal adenocarcinoma) in the AOM/DSS model (Fig. 2a). Taking together these results suggest that, mRNA and protein expression of Hakai are clearly regulated under a pro-inflammatory environment in the intestinal tissue regardless of origin of that inflammation.
In order to understand the potential molecular mechanism by which Hakai could be involved in inflammation, HCT116 colon cell line was used to perform a large-scale immunoprecipitation of Hakai followed by a mass spectrometric analysis in order to determine novel Hakai-interacting proteins that could be related to inflammatory process. For Hakai interactome, immunoprecipitation of endogenous Hakai protein levels was firstly confirmed (Fig. 3a). A total of 26 proteins with a peptide match ≥ 2 were identified by proteomic analysis in Hakai immunoprecipitated samples compared to non-specific immunoprecipitation using IgG control (Table 1). It should be noted that, Hakai is among the identified proteins and also HSP90 protein, another previously Hakai-interacting protein reported, ensuring the specificity of the developed technique. A map of Hakai molecular interactions was performed by using the biological database STRING that provide information about both physical and functional properties of proteins (Fig. 3b). Interestingly, the Fatty Acid Synthase (FASN) protein was found as a novel Hakai-interacting protein. FASN is a multifunctional enzyme that catalyses the novo synthesis of long chain saturated fatty acids, from acetyl-CoA and malonyl-CoA, in the presence of NADPH. Long-chain fatty acids considerably contribute to the pathology of intestinal epithelial cells, however, under normal conditions, the synthesis of fatty acids takes place in order to store energy. Abnormalities of lipid metabolism through overexpression of FASN, are associated with the development of inflammatory bowel disease (IBD). On the other hand, FASN expression has been detected in some benign and preneoplastic lesions of several organs such as colon, prostate, breast, lung, stomach, and skin nevi. In order to determine the possible link between Hakai and FASN, HCT116 cells were transfected with Hakai, in presence of Src, and the effect on FASN expression levels was assessed. Hakai overexpression strongly reduced FASN protein expression in HCT-116 cells, while no effect was detected at mRNA levels (Fig. 3c,d), further suggesting that Hakai may regulate FASN at a post-transcriptional level. Then, we tested the effect of Hakai-silencing on FASN expression. Transient transfections of two different oligos of siRNA Hakai in HCT116 cells and viral transduction of shRNA-Hakai silencing in an inducible system in HT29 were performed. Hakai silencing strongly increased FASN protein expression in both cell lines (Fig. 3e,f), while mRNA expression of FASN was not affected by Hakai silencing (Fig. 3g), further supporting the effect of Hakai on FASN at post-transcriptional level. Then it was analysed the effect of Hakai-silencing in the localization of FASN by immunofluorescence. The strong reduction of Hakai in the cells after inducing the knockdown with doxycycline confirmed the increased of FASN, without affecting FASN cellular localization (Fig. 3h). At the phenotypic level, silencing of Hakai induced a more epithelial phenotype, with cells closer attached between each other accompanied to a more three-dimensional cell sheet, compared to control conditions (Fig. 3i).
Given that our results suggested that Hakai may control FASN at post-transcriptional level, inhibition of protein synthesis with cycloheximide (CHX) was performed in order to determine the half-life of FASN protein in presence or absence of Hakai (Fig. 4a, left and middle panel). FASN is a rather stable protein in HT29 cell, with an approximate half-life of 9.5 h (Fig. 4a, right panel). FASN half-life was increased to 11.5 h in Hakai-silencing conditions, suggesting Hakai effect on FASN protein stability. We then analysed whether Hakai may act as an E3 ubiquitin-ligase for FASN inducing its ubiquitination and degradation. After Hakai overexpression, an interaction between Hakai and FASN was detected (Fig. 4b), further confirming the results obtained by the interactome analysis (Fig. 3b). Moreover, Hakai induced FASN ubiquitination when transfecting cells in presence of pcDNA-Flag-Hakai, pBSSR-HA-ubiquitin and pSG-v-Src (Fig. 4c). This effect was also detected when Src was not overexpressed (Fig. S1). Taken together these results indicate that Hakai binds to FASN and induces its ubiquitination and degradation in vitro. There are three described pathways by which ubiquitinated substrates can be degraded: proteasome degradation, lysosome degradation and autophagy system. Treatment with the lysosome inhibitor chloroquine did increase FASN protein levels in HCT116 cells, while the autophagy inhibitor 3-methyladenine (3-MA) did not (Fig. 4d). Given that both inhibitors exert their inhibitory function in the autophagy pathway, 3-MA at early stages of autophagosome formation and chloroquine at late-autophagy stage targeting the lysosome, their eeffectiveness were confirmed by using LC3 I/II levels as positive control. Furthermore, the 26S proteasome inhibitor MG132 did not increase FASN protein (Fig. 4d), suggesting that FASN degradation does occur via lysosome degradation. Besides, FASN levels were recovered after Hakai overexpression when lysosome inhibitor chloroquine was present (Fig. 4e), indicating that Hakai induced FASN degradation is, at least in part, via lysosome. Finally, we used a specific reported Hakai inhibitor, Hakin-1, to inhibit Hakai-mediated ubiquitination without affecting Hakai protein levels. As shown, Hakin-1 was able to increase FASN protein levels without affecting Hakai protein levels, further supporting Hakai-mediated action on FASN (Fig. 4f). We also observed a reduction of Hakai-dependent ubiquitination of FASN when cells were treated with Hakin-1 (Fig. 4g). Collectively, our data support that Hakai control FASN ubiquitination and degradation via lysosome.
Given the demonstrated role of Hakai controlling FASN protein at posttranslational level, we assessed whether Hakai may have an impact in lipid accumulation. For this purpose, HCT116 cells were first transfected with Flag-Hakai to analyze the effect of its overexpression in lipid accumulation. When Hakai is overexpressed (Fig. 5a), a decrease in lipid accumulation was confirmed by oil red staining (Fig. 5b). Moreover, lower FASN levels decreased lipid accumulation, while the dual knockdown of siRNA-FASN and siRNA-Hakai, did not allow to recover lipid accumulation supporting that Hakai may control lipid accumulation, at least in part, through FASN (Fig. 5c,d). Given the previous results showing that Hakai expression in AOM-DSS model of CAC was decreased in inflammatory conditions compared to tumour tissue of colitis-associated colorectal cancer (AOM DSS/Tumour) and healthy tissues, we extended our results to analyse FASN expression in this mouse model (Fig. 5e). An inverse expression of FASN expression with Hakai expression was detected in inflammatory AOM/DSS compared to tumour tissue of colitis-associated colorectal cancer and healthy tissues (Fig. 5e) further suggesting that Hakai may regulate FASN expression in mouse model of inflammatory bowel disease.
Given that in the mouse model of inflammation-driven colorectal cancer, an inverse expression of Hakai and FASN expression was detected, we decided to analyse the expression of Hakai and FASN in human inflammatory colon disorders, including UC and CD. These two processes are chronic inflammatory conditions of the gastrointestinal tract that increase the risk of developing pre-neoplastic and neoplastic lesions. To compare the expression pattern of Hakai and FASN in the context of intestinal inflammation, human tissue samples from CAC, including UC and CD were analysed by immunohistochemistry. As shown in Fig. 6a, Hakai expression was significantly upregulated in UC and CD compared to normal tissues, and higher expression was detected compared to colorectal adenocarcinoma TNM-stage IV tissues. On the other hand, FASN expression was increased in CD samples compared to healthy tissues, but no significant differences were detected while comparing UC and healthy samples (Fig. 6b). Collectively, these results indicate that Hakai and FASN expression in the IBD mouse models do not mimic in the human IBD.
IBD is a chronic inflammatory disorder in gastrointestinal tract that increases the risk of developing colon cancer. In the last years, important number of publications highlight the importance of protein ubiquitination in IBD. The E3 ubiquitin-ligase Hakai, highly expressed in colon cancer tissues compared to adjacent normal tissues, plays a critical role during tumor progression, however, the potential implication in IBD is still unknown. In the current study, we have shown that Hakai expression is downregulated in inflamed intestinal epithelium in different mouse models for IBD (Fig. 1), while higher expression was detected in tumor tissues from AOM-DSS CAC model (Fig. 1a). Furthermore, the Fatty Acid Synthase (FASN) is a novel Hakai-interacting protein identified in the present study. We show that Hakai induces FASN ubiquitination and degradation, thereby resulting in the downregulation of FASN-mediated fatty acid accumulation (Figs. 4, 5). The abnormalities in fatty acid metabolism have been reported in IBD, and are considered as one of the etiologies for the development of this disease. By immunohistochemistry, we have shown that FASN expression increases in the mucosa in the mice with colitis. This observation agrees with our results and previous reported studies showing high FASN expression in patients with IBD and colorectal neoplasia. In our in vitro model system, we have demonstrated that Hakai overexpression induces FASN degradation, while Hakai-silencing increases FASN expression and, in consequence, fatty-acid accumulation, which is associated to the development of IBD. In the mouse model, Hakai expression is downregulated in inflamed intestinal epithelium accompanied to an increase expression of FASN. Although, several studies have reported the role of Hakai in different cancers, to our knowledge, this is the first study that links Hakai with IBD. Other E3 ubiquitin-ligases were reported to be involved in IBD. For instance, RNF186 controls protein homeostasis in colonic epithelial and regulates intestinal inflammation. Indeed, RNF186 is expressed in colonic epithelial of the mice, and Rnf186−/− mice are reported to lead to an increased risk of intestinal inflammation. Accordingly, our results open the possibility to elucidate whether, in a similar manner, a decreased expression of Hakai in inflamed intestinal epithelia in mice may increase the risk of IBD. Importantly, in the tumors of the CAC mouse model, a higher expression of FASN and Hakai was detected, further supporting the implication of both proteins in colon cancer. However, in other studies, by gene expression prolife analysis, a decrease of FASN expression was described in patients with UC. FASN was also described to play a crucial role in maintaining the homeostasis of intestinal barrier function, showing that loss of FASN in mouse intestine can initiate intestinal inflammation. Intestinal homeostasis depends on multiple factor including the interaction between intestinal epithelium and the host immune system and the microbiota. Several regulatory mechanisms are reported to maintain intestinal homeostasis, and how the alteration of these pathways may precipitate the IBD. Therefore, further studies are required in order to elucidate in which specific situation Hakai/FASN axis may contribute to intestinal homeostasis or IBD, as well as to reveal additional molecules that may help to orchestrate this regulatory process. Previous studies have shown an increased ubiquitination mediated proteolysis in colonic biopsy samples from patients with IBD. Indeed, other E3 ubiquitin ligases, such as RNF183, are reported to be up-regulated in the inflamed IBD mucosa, including UC and CD. RNF183 induces ubiquitination-mediated degradation of IκBα leading to the activation of NFκB-p65 in intestinal epithelial cells, further suggesting RNF183 role in NF-κB pathway in intestinal inflammation. Hakai was recently reported to be associated to immune microenvironment regulation of periodontitis, a chronic inflammatory disease occurring in periodontal that involves complex interactions between pathogens and immune reactions. This work demonstrates that Hakai is an important regulator of TNF and cytokine in immune reaction in periodontitis. Given that TNF-α is a well-known inflammatory mediator that is highly expressed in the inflamed intestines of CD and UC patients, it opens the possibility to explore whether Hakai may be linked to TNF-α signaling pathway in IBD. In IBD human biopsies, Hakai expression was significantly upregulated in UC and CD compared to normal tissues, and higher expression was even detected in TNM-stage IV colorectal adenocarcinoma tissues. On the other hand, FASN expression was only increased in CD samples compared to healthy tissues, but no significant differences were detected between IBD and TNM-stage IV colorectal cancer. These results are not according to the ones observed in IBD mice, suggesting that Hakai regulation in mouse models does not accurately mimic human IBD, as observed in UC and CD. Although mouse models have been extensively used to study basic pathophysiological mechanisms, important controversies are reported regarding to how well murine models reflect human inflammatory diseases. Indeed, Seok et at show that the genomic responses to different acute inflammatory stresses are highly similar in humans, but are not reproduced in the mouse models. Several factors can contribute to the differences seen in the molecular response of inflammatory mice and human disease including the evolutional and distance differences in cellular composition between mice and humans, as well as the complexity of the human disease. Taken together, we found that the E3 ubiquitin-ligase Hakai increases ubiquitination and degradation of FASN, thereby resulting in the regulation of FASN-mediated lipid accumulation, which is associated to the development of IBD. Further studies are needed to deepen into the role of Hakai in IBD in mouse models in order to better understand the regulatory mechanism and pathways that may influence intestinal homeostasis and its breakdown in IBD. Moreover, the role of Hakai/FASN axis in other diseases such as fatty liver disease awaits to be elucidated.
Human biopsies from patients with UC and CD and colorectal cancer were obtained from the Pathological Anatomy Department from the “Complejo Hospitalario Universitario A Coruña” (CHUAC), under informed consent signed from all patients. Research investigation was approved by the Research Ethics Committee from A Coruña-Ferrol (Ethical Protocol Codes: 2018/257 and 2017/570) following standard ethical procedures of the Spanish regulation (Ley Orgánica de Investigación Biomédica, 14 July 2007) and according to the ethical standards in the 1964 Declaration of Helsinki. Paraffin samples were obtained from CHUAC Biobank integrated in the Spanish Hospital Platform Biobanks Network. Serial 4 µm sections from archived, formalin-fixed and paraffin-embedded intestinal tissue samples from patients (CD, n = 10; UC, n = 8 and colorectal cancer, n = 6) were analysed for Hakai protein expression.
Three different mouse models of CAC were used to analyze Hakai protein expression. First, a chronic inflammation-associated carcinogenesis mouse model from which biopsies were taken from C57BL/6J mice, in which the development of colorectal cancer associated with inflammation, was promoted through intraperitoneal administration a chemical carcinogen Azoxymethane (AOM) and the inflammatory agent, dextran sodium sulfate (DSS), was given with the drinking water as reported. Second, a DSS model that mimics the acute colitis, on which only DSS is used in a single cycle of 8 days and concentration (3%). Animals were sacrificed 4 days after the end of the DSS cycle. Finally, an IL10−/− model, a genetically modified mice resulting in the enterocolitis appearance in presence of intestinal bacteria and imbalance in the function of the intestinal mucosa. All mouse intestinal tissue samples from C57BL/6J and C57BL/6 IL10 KO mice were kindly provided by Prof. Dr. Christoph Gasche (Medical University of Vienna, Austria).
Data from Hakai expression obtained from a gene-centric and an experiment-centric perspective was obtained from the GEO Database (https://www.ncbi.nlm.nih.gov/sites/GDSbrowser/). All the data sets available were filtered looking for microarrays of models of colitis associated cancer. Exclusion criteria was common for the 3 searches and included mice models that could interfere in the evaluation of the physiological process, principally studies that evaluated the effect of any drug to prevent the development of the inflammatory process. For the AOM/DSS model subgroups were formed according to the exposure time to the AOM/DSS, which was 2 weeks for the inflamed tissue, 4 weeks for those with low dysplasia, between 6 and 8 weeks for high dysplasia and 20 weeks for CRC with the control group only treated with saline. For the other two models, acute colitis and IL-10 KO, criteria for subgroup formation were only case/control. The final studies selected (GDS4367, GSE31106 and GSE107810) were analysed using GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/), in order to identify genes differentially expressed across the experimental conditions. The results of the search for Hakai expression in the created subgroups are presented. Parameters for the GEO2R tool were not customized and were used as default.
Hakai antibody (Invitrogen, Carlsbad, CA, USA) was used for western-blotting, and Hakai-2498 antibody used for immunohistochemistry was kindly provided by Dr. Fujita and Hakai antibody (Bethyl, Montgomery, TX, USA) was used for immunoprecipitation. The rest of antibodies used for western-blotting are FASN antibody (Santa Cruz, Dallas, TX, USA), LC3 A/B antibody (Cell Signaling, Leiden, The Netherlands), E-cadherin antibody (BD Trans Lab, Franklin Lakes, NJ, USA), β-catenin antibody (Cell Signaling, Leiden, The Netherlands), GAPDH antibody (Invitrogene) and mouse and rabbit secondary antibodies (GE Healthcare, Chicago, IL, USA). Proteasome inhibitor MG132 (Sigma-Aldrich, St. Louis, MO, USA) was added for 6 h using 10 µM and 30 µM. Lysosome degradation inhibitor Chloroquine (Sigma-Aldrich, St. Louis, MO, USA), was added for 24 h at 50 µM and for 6 h at 100 µM. Autophagy inhibitor 3-Methyladenine (Sigma-Aldrich, St. Louis, MO, USA) was added for 24 h at 5 mM and 10 mM. Protein synthesis inhibitor cycloheximide (Sigma-Aldrich) was used at 10 μg/mL for the indicated times.
For immunohistochemistry, slides containing sections of mice and human intestinal tissues (4 µm) were deparaffinised, rehydrated and processed as previously described. Incubation with primary antibody was carried out in a wet chamber overnight at 4 °C. Hakai dilution was 1:700 and FASN dilution was 1:500. Commercial kit for immunohistochemistry was Dako EnVision + System, Peroxidase (EnVision + System, HRP) purchased to Agilent Technologies, Inc. Nuclei were counterstained with Gill´s haematoxylin and mounted with DePeX. Pictures were taken with an Olympus BX50 microscope. Quantification of HRP signals was performed with ImageJ software by analysing 5 photographs of each sample, and the represented results are shown as mean ± SEM.
HCT 116 cell line was cultured in Dulbecco’s Modified Eagle Medium (DMEM) and HT29 cell line was cultured in McCoy's 5A medium. All media were supplemented with 1% penicillin/streptomycin and 10% of heat-inactivated fetal bovine serum (FBS). All cell lines were grown at 37 °C in a humidified incubator with 5% of CO2. Cells were regularly tested for mycoplasma. Plasmids used for transfection were pcDNA-Flag-Hakai, pBSSR-HA-Ubiquitin and pSG-v-Src which were kindly provided by Dr. Fujita (University College London, UK). Transient transfection for Hakai silencing was performed by employing two different siRNA oligonucleotides for Hakai: Hakai-1 (5′-CTCGATCGGTCAGTCAGGAAA-3′) and Hakai-2 (5′-CACCGCGAACTCAAAGAACTA-3′) or siRNA FASN oligonucleotide (5′-GCUACAUGGCCCAAGGGAA-3′). Transfection experiments were performed by using Lipofectamine 2000 Transfection Reagent and Opti-MEM (Thermo Fisher Scientific) media following manufacturer’s protocol. Universal Non-coding siRNA (Sigma-Aldrich, St. Louis, MO, USA) was used as a negative control of transfection. Lentiviral vector system for Hakai expression (SMARTvector Inducible Lentiviral shCBLL1) by doxycycline inducible sh-RNAs particles were acquired in Dharmacon (Horizon Perkin Elmer Group). Lentiviruses were propagated using previously described methods. Doxycycline (Sigma) was added to the medium at final concentration of 1 µg/ml, and cells were incubated for 72 h. Two different clones of HT29 colon cancer cells transduced with viral supernatant, containing sh-Hakai (sh-Hak1 and sh-Hak2) or sh-control (sh-), were selected and Hakai knockdown efficiency was monitored by western blot.
HCT116 cells were used for interactome analysis. Cell pellets were incubated with lysis buffer (20 mM Tris–HCl pH 7,5, 150 mM NaCl and 1% Triton X-100, 125 mg/mL N-ethylmaleimide) in rotation at 4 °C for 30 min. Pre-clearing was performed to eliminate unspecific interactions. For large scale immunoprecipitation, cells were used per point of the experiment (IgG and IP). 40 μL of Dynabeads protein A (Thermo Fisher Scientific) were resuspended in filtered PBS-T 0,1% and incubated with 5 μg of Hakai Bethyl antibody or control IgG 2 h at 4 °C on rotation for beads-antibody coupling. Then, beads and protein sample were incubated overnight at 4 °C on rotation. Samples were loaded into SDS-PAGE gel and band was stained with Sypro-Ruby (Lonza) fluorescent and processed to in gel digestion as previously described. Samples were reduced in 10 mM dithiothreitol and dissolved in 50 mM ammonium bicarbonate (AMBIC) (Sigma-Aldrich). Then samples were alkylated with iodoacetamide 55 mM dissolved in AMBIC 50 mM (Sigma-Aldrich). The gel pieces were rinsed with AMBIC 50 mM in 50% methanol (HPLC grade, Scharlau) and acetonitrile (HPLC grade, Scharlau) was added for dehydration. Finally, they were dried in a SpeedVac (Thermo Fisher Scientific) and digested with porcine trypsin (Promega) to a final concentration of 20 ng/µL in AMBIC 20 mM for a final overnight incubation at 37 °C. For mass spectrometric analysis, samples were processed by the Proteomics Platform of Biomedical Research Institute of Santiago de Compostela (IDIS). The separation of peptides was done by Reverse phase chromatography. The 400 micro nanoLC liquid chromatography system (Eksigent Technologies, ABSciex) combined with a Triple Time-of-flight (TOF) 6600 high speed mass spectrometer (ABSciex) were used for creating a gradient. Analysis of peptides was performed on the C18CL reverse phase column (150 × 0.30 mm, 3 µm, 120 Å) (Eksigent, ABSciex). The peptides were separated using a 90-min gradient ranging from 2 to 90% of mobile phase B (mobile phase A: 0.1% formic acid, 2% acetonitrile; mobile phase B: 0.1% formic acid, 100% acetonitrile). Acquisition of data was performed on a Triple TOF 6600 system (ABSciex). The TF 1.7.1 analyst software was used for instrument operation (ABSciex). A switching criterion was used for ions greater than the mass/charge ratio (m/z) 350 and less than m/z 1400, with a mass tolerance of 250 ppm, a charge state of 2–5 and a threshold of abundance of more than 200 accounts (cps). The computer analysis of the raw data obtained was carried out using ProteinPilotTM 5.0.1 software (ABSciex). Analysis was performed with the Significance Analysis of INTeractome (SAINT) score SAINTexpress. Preys with SAINT probability score cut-off of 1 detected by at least two exclusive spectral counts were deemed high confidence Hakai-interacting proteins.
For western blot analysis, whole cell extracts were obtained as described previously. For RT-qPCR, total RNA was extracted using TriPure isolation reagent (Roche,Basel, Switzerland). mRNA levels were analysed in technical triplicates by quantitative RT-PCR, following specifications of reverse retrotranscriptase kit (NZYTech, Lisbon, Portugal). Amplification was performed in a Light Cycler 480 (Roche, Basel, Switzerland) and data was analysed by qBase + analysis software (Biogazelle, Zwijnaarde, Belgium). Primers used for FASN were F′ TTCTACGGCTCCACGCTCTTCC and R′ GAAGAGTCTTCGTCAGCCAGGA. As housekeeping control HPRT primers F′-TGACCTTGATTTATTTTGCATACC and R′-CGAGCAAGACGTTCAGTCCT or RPL13A F′-CTCAAGGTGTTTGACGGCATCC and R′-TACTTCCAGCCAACCTCGTGAG were used. Immunofluorescence was performed as described. Briefly, cells were fixed with PFA 4% for 15 min and permeabilized with 0.5% Triton X-100/PBS for 15 min. Primary antibodies were incubated for 2 h, at RT and secondary antibody was incubated at RT for 1 h. Nuclear staining was performed using 1:10,000 Hoechst dilution (Life Technologies, Carlsbad, CA, USA). ProLong Gold Antifade Mountant (Life Technologies, Carlsbad, CA, USA) was employed for coverslips mounting. Images were obtained by using fluorescence microscope “Monitorized reflected Fluorescence System” (Olympus).
For immunoprecipitation experiments, cells were lysed for 20 min in 1 ml of 1% Triton X-100 lysis buffer (20 mM Tris–HCl pH 7.5, 150 mM NaCl and 1% Triton X-100) containing 10 μg/ml leupeptin, 10 μg/ml aprotinin and 1 mM phenylmethanesulphonyl fluoride (PMSF). Supernatants were immunoprecipitated for 2 h with 2 μg of anti-Hakai antibody or anti-HA bound to protein G PLUS-Agarose beads (Santa Cruz Biotechnology, USA), followed by SDS–polyacrylamide gel electrophoresis (PAGE) and western blotting with the indicated antibodies as previously reported. For ubiquitination assays, HCT116 cells were transfected with 0.25 µg Src, 0.75 µg Flag-Hakai, and 0.5 µg HA-ubiquitin using Lipofectamin 2000 (Invitrogen, UK). Cell extracts were obtained in the above lysis buffer used for immunoprecipitation, supplemented with 10 mM N-ethylmaleimide.
HCT116 and HT29 cells (105) were seeded in 8-well chambers (Millicell EZ SLIDE 8-well glass, Millipore) were washed with 1 × PBS and stained with a freshly prepared Oil Red O (ORO) working solution as described previously. Staining was observed using light microscope (Olympus BX61). Positively-stained areas were used to quantify lipid accumulation using ImageJ software by analysing 5 photographs. The represented results are shown as mean ± SEM.
Shapiro–Wilk test was used to check a normal distribution and Levene test to assess the equality of variances. Statistical significance of data was determined by applying a two-tailed Student t-test or ANOVA depending on the data. Results obtained are expressed as mean ± SD or mean ± SEM. Quantification of human IHQ did not follow a normal distribution therefore we used Kruskal–Wallis with Tukey correction test. Significant differences among the experimental groups indicated in the figures is shown as *P < 0.05, **P < 0.01 ***P < 0.001 and ****P < 0.0001.
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PMC9584950 | Hanqing Guo,Kun Zhuang,Ning Ding,Rui Hua,Hailing Tang,Yue Wu,Zuyi Yuan,Ting Li,Shuixiang He | High-fat diet induced cyclophilin B enhances STAT3/lncRNA-PVT1 feedforward loop and promotes growth and metastasis in colorectal cancer | 20-10-2022 | Long non-coding RNAs,Colorectal cancer | High-fat diet (HFD) has been implicated to promote colorectal cancer (CRC). Recently, oncogene Cyclophilin B (CypB) is reported to be induced by cholesterol. However, the role of CypB in CRC carcinogenesis and metastasis associated with HFD remains unknown. In the present study, we showed that HFD-induced CypB enhances proliferation and metastasis through an inflammation-driven circuit, including Signal Transducer and Activator of Transcription 3 (STAT3)-triggered transcription of lncRNA-PVT1, and its binding with CypB that promotes activation of STAT3. CypB was found to be upregulated in CRC, which was correlated with elevated body mass index and poor prognosis. HFD induced CypB expression and proinflammatory cytokines in colon of mice. Besides, CypB restoration facilitated growth, invasion and metastasis in CRC cells both in vitro and in vivo. Moreover, RIP sequencing data identified lncRNA-PVT1 as a functional binding partner of CypB. Mechanistically, PVT1 increased the phosphorylation and nuclear translocation of STAT3 in response to IL-6, through directly interaction with CypB, which impedes the binding of Suppressors Of Cytokine Signalling 3 (SOCS3) to STAT3. Furthermore, STAT3 in turn activated PVT1 transcription through binding to its promoter, forming a regulatory loop. Finally, this CypB/STAT3/PVT1 axis was verified in TCGA datasets and CRC tissue arrays. Our data revealed that CypB linked HFD and CRC malignancy by enhancing the CypB/STAT3/PVT1 feedforward axis and activation of STAT3. | High-fat diet induced cyclophilin B enhances STAT3/lncRNA-PVT1 feedforward loop and promotes growth and metastasis in colorectal cancer
High-fat diet (HFD) has been implicated to promote colorectal cancer (CRC). Recently, oncogene Cyclophilin B (CypB) is reported to be induced by cholesterol. However, the role of CypB in CRC carcinogenesis and metastasis associated with HFD remains unknown. In the present study, we showed that HFD-induced CypB enhances proliferation and metastasis through an inflammation-driven circuit, including Signal Transducer and Activator of Transcription 3 (STAT3)-triggered transcription of lncRNA-PVT1, and its binding with CypB that promotes activation of STAT3. CypB was found to be upregulated in CRC, which was correlated with elevated body mass index and poor prognosis. HFD induced CypB expression and proinflammatory cytokines in colon of mice. Besides, CypB restoration facilitated growth, invasion and metastasis in CRC cells both in vitro and in vivo. Moreover, RIP sequencing data identified lncRNA-PVT1 as a functional binding partner of CypB. Mechanistically, PVT1 increased the phosphorylation and nuclear translocation of STAT3 in response to IL-6, through directly interaction with CypB, which impedes the binding of Suppressors Of Cytokine Signalling 3 (SOCS3) to STAT3. Furthermore, STAT3 in turn activated PVT1 transcription through binding to its promoter, forming a regulatory loop. Finally, this CypB/STAT3/PVT1 axis was verified in TCGA datasets and CRC tissue arrays. Our data revealed that CypB linked HFD and CRC malignancy by enhancing the CypB/STAT3/PVT1 feedforward axis and activation of STAT3.
Colorectal cancer (CRC), a multistep disease in which accumulated genetic and epigenetic alterations were driven by inflammation, is the third leading cause of new cancer cases and the second leading cause of cancer-related deaths worldwide [1, 2]. Strong associations between a sedentary lifestyle and obesity with CRC were conducted in both clinical and epidemiological studies [3, 4]. Recent studies suggest that CRC tumorigenesis is initiated by high-fat diet (HFD)-induced proinflammatory cytokines such as interleukin-6 (IL-6) and exacerbated by subsequent inflammatory burden [5, 6]. As the classic intracellular IL-6 signaling transductor, Signal Transducer and Activator of Transcription 3 (STAT3) regulates the expression of a variety of genes in response to inflammatory stimuli [3]. As a point of convergence for numerous oncogenic signaling pathways, excessive STAT3 activation within cancer cells can be viewed as a neoplastic mimic of an inflammation-driven repair response that collectively promotes tumor progression [7]. Extracellular binding of cytokines such as IL-6 to their cognate receptors induce activation of the intracellular Janus kinase (JAK) that phosphorylates a specific tyrosine residue in the STAT3 protein [8]. Once phosphorylated, STAT3 form homo-or hetero-dimers through interactions of phosphorylated tyrosine of one STAT and SH2 domain of another, then translocate into the nucleus [7, 9]. Unrestrained STAT3 activation is one of the hallmarks of CRC, contributing to its progression. It’s also considered that activation of STAT3 maintained obesity-related metastatic growth of CRC cells [10, 11]. However, the regulatory mechanism supporting the cascade amplification of IL-6/STAT3 pathway related to obesity in CRC is still not fully elucidated. Recent studies have suggested that Cyclophilin B (CypB) overexpression is crucial in supporting the activation of STAT3 in tumors [12–16]. CypB is an endoplasmic reticulum (ER)-resident protein with peptidyl-prolyl cis/trans-isomerase activity (PPIase) [17], which belongs to a conserved protein family Cyclophilin, expressed ubiquitously in prokaryotic and eukaryotic organisms [18]. CypB is associated with the malignant progression and regulation of a variety of tumors [12–14, 19, 20]. We have reported functions of CypB in proliferation and survival of stomach cancer [14], demonstrating a supporting role of CypB in constitutive activation of IL-6/STAT3 pathway. Interestingly, several recent studies suggest that cholesterol induced CypB participated in initiation of metabolic syndrome and lung cancer [21, 22], suggesting a possible role of CypB connecting obesity with cancer. To date, few studies have elucidated CypB’s function on HFD-associated CRC proliferation and metastasis. Besides, although CRC is among several solid tumors that are characterized by constitutively STAT3 activation, which is closely connected with oncogenic CypB, the relationship of CypB and STAT3 activation in CRC remains largely unclear. Long noncoding RNAs (lncRNAs) are a class of highly multifunctional noncoding RNAs (ncRNAs) larger than 200 nucleotides in size that lack coding potential [23], which were recently found to contribute to CRC tumorigenesis [24]. As several studies found that translocation of CypB from cytoplasm to nuclei where lots of functional lncRNA were synthesized, it’s reasonable to elucidate if CypB’s function is linked to any lncRNAs in CRC. However, few study have focused on the connection between CypB and lncRNAs. Here we present evidence that CypB/STAT3/lncRNA-PVT1 feedback loop triggered by HFD-associated IL-6 regulates progression of CRC. Our study found that HFD induced overexpression of CypB is required for STAT3 activation in the proliferation, survival and metastasis of CRC. We found that lncRNA-PVT1, binding directly to CypB, is transcriptionally promoted by STAT3, thus forming a feedforward circuit which potentially explain HFD-associated inflammation and subsequent IL-6-stimulation-triggered constitutive activation of STAT3 in CRC tumorigenesis.
To examine the significance of CypB in CRC development, we first measured CypB expression in a cohort of 240 CRC samples (TMA cohort) using immunohistochemistry (IHC). CypB was significantly upregulated in CRC tissues and metastatic lymph nodes compared with adjacent non-cancerous colorectal tissues (Fig. 1A, Table S1). Correlation analysis revealed that higher level of CypB expression in CRC tissues was significantly associated with a more aggressive tumor phenotype (Table S2). Kaplan–Meier analysis further revealed that high-level CypB expression was associated with shorter disease-free survival time for CRC patients (Fig. 1B). Cox regression analysis also indicated that high CypB expression was an independent prognostic factor for poor survival in CRC patients (Table S3). Besides, we analyzed the expression of CypB in tissues of colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) based on the TCGA cohort and CPTAC cohort and found similar results (Fig. 1C), suggesting that both mRNA and protein levels are upregulated in CRC tissues. Similarly, pan-cancer analysis of CypB using TCGA data was conducted and CypB transcripts were found to be increased in 20 out of 31 kinds of cancer types, particularly gastrointestinal cancers such as ESCA (Esophageal carcinoma), STAD (Stomach adenocarcinoma), COAD and READ (Supplementary Fig. S1A). Further Kaplan–Meier analysis based on GenomicScape database [25] revealed that high-level CypB expression were associated with shorter disease-free survival time for CRC patients (Fig. 1D). TCGA data also showed that methylation levels of CypB promoter were decreased compared with that in normal tissues (Fig. 1E), possibly accounting for the overexpression of CypB in CRC. Further integrative analysis of Gene Sets Enrichment Analysis (GSEA) showed that CypB expression is positively associated with genes enriched in pathways of cell cycle, ER stress and regulation of Interleukin regulation and β-catenin (Fig. 1F, supplementary Fig S1B–D), which play important roles in the regulation of tumor proliferation and metastasis. Moreover, as CypB can be secreted into serum, we measured CypB expression in sera from 60 CRC patients and 20 healthy volunteers. ELISA analysis indicated that the CypB concentrations in serum samples from CRC patients were significantly increased compared with those from volunteers (Fig. 1G). Interestingly, increased serum CypB concentrations were associated with tumor stage and BMI (Fig. 1G), indicating that overexpression of CypB may mediate the effects of high fat diet on the malignancy of CRC. Further, CypB expression was assayed in several CRC cell lines. Western blotting revealed that CypB expression was greater in several CRC cell lines compared with HIEC cells, an immortalized intestinal epithelial cell line (Fig. 1H). Compared with its expression in SW480 cells, CypB in SW620 cells is higher, suggesting its potential role in metastasis as SW620 cells are derived from the same patient’s metastatic lymph nodes as SW480. Together, these results suggest that CypB upregulation, occurring during CRC development, was associated with obesity and may have a vital role in colorectal carcinogenesis.
To investigate whether CypB is involved in high-fat diet induced CRC growth regulation, C57BL/6 mice were treated with normal diet (ND) and high fat diet (HFD) (Fig. 2A). Overexpression of CypB were detected in colon tissues in HFD group (Fig. 2B). Similarly, proinflammatory cytokines such as IL-6 and TNF-α were also found to be induced by HFD (Fig. 2C). CypB expression were positively correlated with expression of IL-6 and TNF-α (Fig. 2D), suggesting a possible role of CypB in HFD-induced colon inflammation. To further determine function of CypB, HCT116 and SW620 cells were then infected with lentiviral vectors expressing shRNA against CypB or a control, while Caco-2 and SW480 cells were infected with CypB vectors and control vectors. The expression of CypB were evaluated by immunoblotting after transfection (Fig. 2E). XTT assays revealed that cell growth was significantly reduced by CypB downregulation compared with the control, while restoration of CypB increased cell growth (Fig. 2F). Moreover, cell cycle assays showed that silencing CypB increased the G2/M population and reduced the S and G0/G1 population compared with control cells, while CypB overexpression increased S phase population (Fig. 2G). Apoptosis assays further revealed that CypB inhibition led to an increased percentage of apoptotic CRC cells, whereas CypB overexpression decreased percentages of apoptosis (Fig. 2H). Furthermore, transwell assays showed that silencing CypB in HCT116 and SW620 cells induced decreased invasion and metastasis (Fig. 2I), while upregulation of CypB in Caco-2 and SW480 cells increased invasive and metastatic potential (Fig. 2J). These data showed that HFD-induced CypB increased CRC cell growth, invasion and metastasis in vitro.
To extend the above findings to an in vivo setting, SW620 cells infected with CypB shRNA as Fig. 2A were subcutaneously injected into the flanks of SCID mice treated with ND or HFD. Analysis showed that HFD indeed promoted the growth of tumor, while silencing CypB in SW620 cells caused dramatic reductions in tumor weight and volume in mice (Fig. 3A). Ki-67 staining in xenografts also showed that CypB-downregulated cells exhibited decreased proliferation (Fig. 3B). To further determine the role of CypB in CRC metastasis, we established lung and liver metastasis models by injecting CypB-silenced HCT116 and SW620 cells into the tail veins and spleens of SCID mice, respectively. Compared to the control condition, silencing CypB decreased the incidence of lung metastasis (Fig. 3C up) and the number of metastatic nodules (Fig. 3D), and improved survival in mice (Fig. 3D). Similarly, CypB downregulation reduced liver metastasis following spleen injection (Fig. 3C down, Fig. 3E). These results indicate that silencing CypB suppress tumor progression and reinforce responsiveness to chemotherapy of CRC cells in vivo.
As emerging evidence has implicated that lncRNAs play vital roles in colon carcinogenesis [26], we set out to determine the functional connection between oncogene CypB and lncRNAs in CRC. RNA sequencing following RNA Binding Protein Immunoprecipitation (RIP) was performed using CypB antibody and total cell RNA of HCT116 cells treated with IL-6, which showed that multiple lncRNAs were captured by CypB (Supplementary Fig. S2A). GO analysis showed that CypB-binding RNAs were enriched in pathways of endoplasmic reticulum stress, apoptotic signaling and actin filament organization (Fig. 4A, B, Supplementary Fig. S2B–D), which are essential in tumor growth, survival and metastasis [27], indicating that CypB-binding lncRNAs may be involved in CypB’s oncogenic roles in CRC. The 20 most abundant lncRNAs binding to CypB were shown in the heatmap (Fig. 4C) and further validated by RIP-qPCR analysis (Fig. S3A). Compared to IgG, several candidate lncRNAs were screened and confirmed to bind CypB appreciably with p value < 0.05 (Supplementary Fig. S3A, B). Among them, lncRNA PVT1 was chosen for further investigation not only because it was reported to be an oncogene in various cancer types[28], but also because it was found to be an important regulator in STAT3 function in cancer cells [29] and we previously showed that CypB is also essential for STAT3 activation [14]. Further evidence demonstrated that PVT1 binds to CypB. First, the coverage tracks from the RIP-seq showed that the CypB-bound RNAs cover genomic position of PVT1 (chromosome 8q24.21, nucleotides 127,794,533-128,101,253) (Fig. 4D). Besides, the assays combining RNA FISH with protein immunofluorescence showed that CypB, which translocate from cytoplasm to nucleus, is strongly co-localized with PVT1 in HCT116 cells upon IL-6 treatment (Fig. 4E). Moreover, CypB was immunoblotted in RNA pull-down products using biotin-labeled oligo probe sets targeting PVT1 (Fig. 4F). Meanwhile, this was validated by RIP assays which showed that PVT1 was significantly enriched in pull-downs using antibodies against CypB compared to control IgG (Fig. 4G). To determine the specific region of PVT1 that binds to CypB, a series of PVT1 mRNA fragments were generated based on its secondary structure predicted by ViennaRNA Database (http://rna.tbi.univie.ac.at/) (Fig. 4H) and then these constructs were used in biotin-labeled RNA pull-down assays. The results showed that the PF00160 domain of CypB mainly binds to a PVT1 fragment that is transcribed from either nucleotide 1 to 170 or 1760 to 1957 (Fig. 4I, Supplementary Fig. S3C). Together, these results demonstrated that LncRNA PVT1 directly interacts with CypB in CRC cells.
To investigate the function of PVT1 with regards to CRC cell growth, HCT116 and SW620 cells were infected with PVT1 shRNAs, while Caco-2 and SW480 cells were infected with overexpressing vectors, and PVT1 expression was confirmed by qRT-PCR (Fig. 5A). Cell proliferation assays indicated that PVT1 shRNA significantly inhibited CRC cell growth, while restoration of PVT1 increased CRC cell proliferation (Fig. 5B). Cell cycle analysis showed that PVT1 shRNA induced G2/M arrest, whereas PVT1 overexpression reduced the proportion of cells in G2/M (Supplementary Fig. S4A). Furthermore, PVT1 shRNA increased the proportion of cells undergoing apoptosis, while PVT1 restoration reduced the number of apoptotic cells (Supplementary Fig. S4B). Besides, PVT1 shRNA also decreased tumor cell invasion and metastasis in HCT116 cells and SW620 cells (Fig. 5C), while PVT1 upregulation increased these phenotypes in Caco-2 and SW480 cells (Fig. 5D). The effects of PVT1 on CRC progression were also studied in vivo: SW620 cells infected with lentiviral vectors expressing PVT1 shRNA were subcutaneously injected into the right flanks of SCID mice. At 30 days post-injection, the mean xenograft tumor volume and weight was significantly lower for shPVT1-SW620 xenografts than for control xenografts (Fig. 5E). Lung and liver metastasis models using SW620 cells further showed that silencing PVT1 decreased the incidence of lung and liver metastasis, and improved survival in mice (Fig. 5F, G). These results provided evidence showing LncRNA PVT1 promotes CRC cell growth and metastasis in vitro and in vivo.
We previously reported that STAT3 activation upon IL-6 treatment was exaggerated by CypB and their interaction in nuclei activate transcription of downstream genes [14]. Combining these results and the findings suggesting direct interaction of PVT1 and CypB, we were interested in whether the connection between PVT1 and CypB play roles in STAT3 function in cancer cells. We used STAT3 shRNA and CypB shRNA in Caco-2 cells transfected with PVT1 overexpressing vectors. Expression of CypB and phosphorylation of STAT3 were validated by immunoblotting, showing that phosphorylation of STAT3 at Tyr705 were inhibited by knockdown of either CypB or STAT3(Supplementary Fig. S5A). Proliferation assays showed that silencing either CypB or STAT3 rescued the promotion of growth induced by PVT1 restoration (Fig. 6A). Besides, CypB shRNA or STAT3 shRNA also abrogated the increase of S-phase cell distribution induced by PVT1 upregulation (Fig.6B), as well as the PVT1-caused decrease of apoptotic population (Fig. 6C). Moreover, transwell assays showed similar results, suggesting that knockdown of either CypB or STAT3 decreased PVT1-induced migration and invasion (Supplementary Fig. S5B). These data suggest CypB-STAT3 axis may mediate the PVT1 function on tumor growth and metastasis. Caco-2 cells were then treated with IL-6 for 0–15 min followed by transfection of PVT1 vectors. Immunoblotting results showed that the IL-6-induced time-dependent increase of STAT3 phosphorylation (Tyr705) was also promoted by the restoration of PVT1 (Fig. 6D). Meanwhile, silencing PVT1 induced decrease of transcription of STAT3 downstream targets including c-Myc, CCND1, Bcl-2, BCL-xL, Survivin and Twist (Fig. 6E), which were considered to play vital roles in the regulation of cell cycle, apoptosis, and metastasis. More interestingly, confocal immunofluorescence showed that CypB co-localized with STAT3 in the nucleus following IL-6 treatment as expected, while upon PVT1 knockdown using shRNA, the number of cells with the nuclear distribution of STAT3 was significantly decreased (Fig. 6F). Meanwhile, immunoblotting assays showed that upon IL-6 stimulation for 30 min, the nuclear protein levels of STAT3 and CypB were both increased, while knockdown of lncPVT1 partially abrogated the elevation of nuclear protein levels of CypB and STAT3 (Supplementary Fig. S6A–C). We then evaluated whether CypB directly associates with STAT3. Indeed, using co-immunoprecipitation (co-IP) assays, CypB was found to interact with STAT3, which was enhanced by IL-6 stimulation (Fig. 6G). Interestingly, the CypB-STAT3 interaction upon IL-6 treatment was suppressed by PVT1 knockdown, while SOCS3-STAT3 interaction were increased (Fig. 6H). Finally, Flag-tagged STAT3 vectors and truncated vectors were constructed and transfected into Caco2 cells. Co-IP results showed that CypB bind the SH2-TA domain of STAT3, containing the phosphorylation and activation site Tyr705 (Fig. 6I), possibly explaining the reason why CypB exaggerated IL-6-triggered STAT3 activation. Taken together, these results suggest that PVT1 promoted CRC cell growth and metastasis through activating interaction and nuclear translocation of CypB-STAT3 complexes.
To further explore the mechanism by which PVT1 is upregulated in CRC cells, we analyzed potential TF binding motifs in the promoter region of PVT1 in JASPAR database. Interestingly, 13 STAT3 binding sites were identified in PVT1 promoter (Supplementary Table S4), suggesting that PVT1 may be a potential downstream transcript of STAT3. HCT116 cells and Caco2 cells were then treated with IL-6. Interestingly, PVT1 expression was induced in a time-dependent manner (Fig. 7A), which can be blocked by STAT3 knockdown (Fig. 7B), strongly indicating that STAT3 positively regulates PVT1 expression. Thus, we tested if STAT3 directly targets PVT1. We generated a series of PVT1 promoter truncation mutants and determined whether STAT3 transcriptionally promotes PVT1. A luciferase assay after IL-6 treatment showed that the regulatory region might be between −1158 and −414 bp (Fig. 7C). Site-directed mutagenesis of the PVT1 promoter were generated (Supplementary Fig. S6A), which further showed that both STAT3-binding sites (−646 to −636bp and −179 to −169bp) are the predominant sites for STAT3-mediated transcriptional activation (Fig. 7D, Supplementary Fig. S6B). Chromatin immunoprecipitation assay (ChIP) assays further confirmed that STAT3 binds to the two sites of PVT1 promoter in HCT116 cells (Fig. 7E). Consistently, qRT-PCR of ChIP products showed that IL-6 treatment significantly increased the association of STAT3 with the PVT promoter (Fig. 7F). Together, these results indicate that the IL-6/STAT3 pathway directly promotes PVT1 transcription in CRC cells.
Finally, to test whether the regulation described above in CRC cell lines is clinically relevant, TCGA cohorts focusing on PVT1 and CypB were studied. Correlation analysis showed that both PVT1 expression and CypB expression were both positively correlated with STAT3 targets in cell cycle including CCND1 (Fig. 8A left: R = 0.26, P = 3e−8; Right: R = 0.32, P = 2.7e−11) and CDK1 (Fig. 8B left: R = 0.24, P = 5e−7; Right: R = 0.32, P = 1.8e−11). Besides, apoptosis-related regulators regulated by STAT3 such as Bax (Fig. 8C left: R = 0.13, P = 0.0058; Right: R = 0.27, P = 3.4e−8) and BIRC5 (Fig. 8D left: R = 0.25, P = 2.8e−7; Right: R = 0.43, P = 0) were also found to be positively related with PVT1and CypB. Moreover, similar results were also found in correlation among PVT1, CypB and STAT3 downstream SNAIL1, CTNNB1, which play pivotal roles in metastasis (Fig. 8E left: R = 0.29, P = 2.1e−9; Right: R = 0.22, P = 8.5e−6; Fig. 8F left: R = 0.22, P = 0.3.5e−6; Right: R = 0.137, P = 0.0095). These results were consistent with GSEA assays on CypB in TCGA data (Fig. 1F) and GO analysis of RIPseq results (Fig. 4A, B). Similar results were also found in STAT3-associated interleukins such as IL-1B, IL-6, IL-11, CCL3, CXCL8, CXCL10, LIF (leukemia inhibitory factor) and OSM (oncostatin M) using COAD and READ data in TCGA cohorts (Supplementary Fig. S7A–H). These clinical data further indicate cooperative effects of CypB and PVT1 in promoting tumorigenesis during inflammation, which is triggered by HFD and maintained by constitutive activation of STAT3 pathway. We also measured PVT1 expression by in situ hybridization (ISH) in the TMA cohort. Compared with normal tissues, PVT1 levels were increased in CRC tissues (Fig. 8G). In addition, we found that patients with higher BMI tended to have increased PVT1 levels, nuclear expression of CypB and pSTAT3 expression (Fig. 8G). The 80 CRC patient cases were then divided into groups with relatively high or low levels of BMI, PVT1, CypB, and pSTAT3. From this analysis, we observed a positive relation of BMI with PVT1, nuclear CypB and pSTAT3 expression (Fig. 8H). We also found that patients with high PVT1 expression had significantly poorer prognoses than those with low PVT1 expression (Fig. 8I). Further expression analysis on TCGA data showed that PVT1 transcripts were extremely higher in cancer tissues of COAD and READ, compared with that in normal tissues (Fig. 8J). Kaplan–Meier analysis further revealed that high-level PVT1 expression was associated with shorter disease-free survival time for COAD patients, but not for READ patients (Fig. 8K). In Summary, these results showed that the inflammatory PVT1/CypB/STAT3 axis is active in primary human colorectal carcinogenesis.
Previous studies suggest that elevated CypB expression significantly contributes to the survival and proliferation of cancer cells in several kinds of solid tumor [13, 20, 30]. But few study have elucidated the function of CypB in terms of inner links between HFD-associated inflammation and CRC. In this study, we investigated roles of HFD-induced CypB in regulation of proliferation and metastasis of CRC, and identified an IL-6-triggered circuit involving STAT3-mediated transcription of PVT1 and its binding-partner CypB which facilitates STAT3 activation, connecting chronic inflammation with colorectal carcinogenesis and metastasis. CypB is mainly located in ER and attenuates ER stress-induced cell injury by interacting with the ER-related chaperones [19], which is associated with the malignant progression and regulation of a variety of tumors [12–14, 19, 20]. It is previously reported to promote gastric carcinogenesis and inflammation-cancer progression through the activation of STAT3 in our recent study [14]. But its research in CRC is rarely reported. Herein, our data based on TCGA cohorts and independent cohort of tissue microarrays showed that levels of CypB transcripts and protein are both increased in CRC tissues, indicating its potential role in promoting the progression of CRC. This is consistent with our previous findings in stomach cancer [14] and other kinds of solid tumors [12, 13, 19]. The mechanism of upstream regulation of CypB were explored in several recent studies including our previous work, showing that hypoxia-associated Activating transcription factor 4 (ATF4) transcription and IL-6 induced post-transcriptional regulators such as microRNA were both responsible for upregulation of CypB in cancer [20, 31]. Another results that should be noted is that serum CypB levels of CRC patients were tested as CypB can be secreted into blood and results showed that serum CypB detection may be a novel, non-invasive approach for early CRC screening. One recent study reported that 27-hydroxycholesterol induced inflammatory pathways including the phosphorylation of NF-κB p65 accounted for the overexpression of CypB [22]. Similarly in this study, we found that CypB in sera of CRC patients is positively associated with BMI of patients. Combining these clinical data with in vivo results showing CypB expression in colon tissues was promoted by HFD, which is associated with proinflammatory cytokines, we suggest that HFD-induced CypB may be involved in increased proliferation and metastasis in CRC. Recent studies indicated that CypB promotes proliferation and survival of cancer cells [12, 13, 16, 19, 20], while few studies have focused on its effects on metastasis. Thus, we further tested if CypB promotes tumor cell growth and metastasis by establishing gain-of- and loss-of-function models using lentiviral vectors. Indeed, knockdown of CypB suppressed HFD-supported tumor cell growth by inducing G0/G1 cell cycle arrest and apoptosis. Meanwhile, CypB downregulation also decreased cell migration and invasion in vitro and inhibited lung and liver metastasis in immunodeficiency mice. It is proved in many studies that CypB protected tumor cells from stress-induced apoptosis mostly through activation of STAT3 signaling pathway [14, 15, 30, 32]. Similarly in this study, co-localization of CypB with STAT3 in nuclei of cell were detected in CRC cell lines in response to IL-6 treatment, which is typical stimulator for activation of STAT3. We further found that silencing STAT3 rescued CypB- induced proliferation, migration and metastasis. Direct interaction of STAT3 and CypB was found in CRC cell lines, suggesting a possible mechanism of protein modification on STAT3 protein by CypB. This is consistent with our previous findings and other studies [12, 14, 30] showing CypB’s important effects in STAT3 activation and transcription of its downstream targets which regulates cell proliferation and survival. Tumorigenesis of CRC is involved with the transformation of normal colorectal epithelium to an invasive and metastatic tumor in response to chronic stimulation of inflammation, which is considered to be triggered by STAT3 [9]. Thus, our study may provide new appealing anti-cancer strategy to inhibit the oncogenic functions of STAT3 by targeting CypB in CRC. lncRNAs often function through binding to chaperone proteins [33, 34]. As a typical chaperone protein coordinates with ER-stress and STAT3 activation, CypB were found to be translocated into cell nuclei together with STAT3 following IL-6 treatment [12, 14]. We explored if CypB interact with functional RNAs in nuclei. Indeed, our RIPseq results here showed that CypB directly bound to lots of lncRNAs, which were enriched in signaling pathways including regulation of ER-stress, peptidyl-prolyl cis/trans isomerase (PPIase) and apoptosis, suggesting these ncRNAs may be functional as CypB itself were proved to participate in these processes [16, 18, 19]. After screening by integrating the sequencing results and online database, LncRNA PVT1 were chosen for further study [28, 35, 36], not only because it has been shown to have important oncogenic features in several types of cancer, but also because it was reported to interact directly with STAT3 in cancer cells [29]. LncRNA PVT1 transcription site is located in a cancer susceptibility locus downstream of Myc, and ablation of PVT1 from MYC-driven colon cancer line HCT116 diminished its tumorigenic potency [37]. Herein, we found that PVT1 physically binds to CypB domain (PF00160), which was detected from RIPseq results and validated by RIP and RNA pull-down technology. We further showed that knockdown of PVT1 suppressed tumor cell proliferation and metastasis in vitro and in vivo, while ectopic expression of PVT1 increased tumor growth and migration, which were rescued by silencing of either CypB or STAT3. Besides, our results showing nuclear translocation of CypB and following co-localization with lncPVT1 in response to IL-6 treatment indicate potential roles of lncPVT1 and CypB in mediating activation of STAT3 in colon cancer cells. However, future studies will be needed to elucidate the specific effects of PVT1 on CypB/STAT3 complex assembling and nuclear translocation. More interestingly, silencing PVT1 impaired IL-6 triggered STAT3 activation and decreased transcription of downstream targets of STAT3 by inhibiting its interaction with CypB and following phosphorylation and nuclear translocation. This may be due to its dual effects of sponging CypB and STAT3, which were proved in this study and a recent study [29], respectively. To date, most studies focusing on PVT1 reported its oncogenic function were exerted mainly through sponging tumor suppressive microRNAs [38]. For example, PVT1 were found to be upregulated in pancreatic cancer and overcome gemcitabine resistance through sponging miR-619- 5p, leading to upregulation of Pygo2, which is a downstream target of miR-619-5p [39]. However, our study demonstrated that PVT1 regulates CRC carcinogenesis and metastasis through directly binding to the two sequential signaling factors of CypB/STAT3 pathway, which were also proved binding to each other. This is particularly interesting because it is one of the few studies indicating PVT1 exert its action through binding to oncogenic proteins, instead of by sponging oncosuppressor miRNAs. The binding of PVT1 with CypB did not seem to affect the protein levels, while stabilizing the interaction between CypB and STAT3 and promoting the nuclear translocation of the CypB/STAT3 complex in CRC cells treated with IL-6. Besides, binding of CypB with SH2-TA domain of STAT3 were verified by Co-IP, containing the phosphor-site of Tyr705. This binding ameliorated the binding of STAT3 with SOCS3, a natural inhibitor of activated STAT3 dimer, indicating a possible mechanism of STAT3 activation caused by CypB. Whether protein modification of STAT3 and CypB is involved in the underlying mechanism will be explored in future study, since PVT1 were previously reported to stabilize target proteins through ubiquitination or acetylation [29, 40]. ncRNAs such as PVT1 and its targeted transcription factors (TF) often form feedforward loop in cancer progression [28], as ncRNAs themselves may be downstream transcripts of TF. Here we found that PVT1, supporting STAT3 activation by binding to CypB, was a transcriptional target of STAT3, forming a feedforward circuit in CRC cells. Recent studies have identified several ncRNAs that can be transcriptionally promoted or repressed by STAT3 [41–44], indicating a potential role for STAT3 in regulating a ncRNA network. STAT3 are important regulators of c-Myc, through binding to its promoter that is overlapping with E2F [45–47]. Given the fact that the PVT1 gene is located on 8q24.21 region that is close to Myc, it’s extremely interesting to determine if STAT3 regulate both Myc and PVT1 transcription by binding to their mutual promoter. Indeed, our results demonstrated that PVT1 expression was induced in a time-dependant manner following IL-6 treatment, which was abrogated by knockdown of STAT3. We predicted multiple potential binding sites within the PVT1 promoter. Further luciferase reporter assay and ChIP assay both showed that STAT3 directly binds to the two sites in PVT1 promoter including −646 to −636 bp and −179 to −169bp to CDS region. This demonstrates a new proof in inflammation-cancer transition of CRC involving CypB-supported STAT3 activation and their interaction with downstream ncRNAs induced by IL-6 cascades. The integrative analysis of TCGA data and TMA cohort also support the results. In CRC tissues, we verified the positive correlation between PVT1/CypB and STAT3 phosphorylation. We showed that both PVT1 and CypB were significantly correlated with multiple STAT3 downstream targets, which are important regulators of cell cycle, apoptosis and metastasis. Meanwhile, expression of interleukins that are involved in STAT3 pathway, including IL-1, IL-6, IL-11, CCL3, CXCL8 etc, were also positively correlated with transcripts of CypB and PVT1 in TCGA cohorts of COAD and READ. These results suggest that the CypB/STAT3/PVT1 feedback loop is also present in clinical samples. STAT3 inhibition using Jak inhibitors such as Ruxolitinib and Momelotinib, has been practised in clinical treatment against metastatic CRC [48]. Recent studies also support that targeting specific oncogenic lncRNAs by ASOs have significant therapeutic value [49]. Thus, our study may be beneficial in STAT3-inhibitory therapy against metastatic CRC by developing ASO targeting PVT1.
In summary, we elucidated the schematic model of CRC development shown in Fig. 8L. This figure depicts that HFD and following inflammatory IL-6 activates the STAT3 pathway and transcriptionally increase PVT1 expression, which binds to CypB and then aids STAT3 phosphorylation and nuclear translocation, resulting in STAT3 activation and increased CRC growth and metastasis. In this view, PVT1 and CypB may be important mediator that connects obesity and inflammation with constitutively activation of STAT3 in CRC cells and this new CypB/STAT3/PVT1 feedback loop may contribute to an improved understanding of inflammatory signaling in colorectal carcinogenesis during obesity.
CRC tissue microarrays containing 80 cases of matched primary CRC tissues, metastatic lymph nodes, and adjacent non-cancerous tissue was purchased from Shanghai Outdo Biotech. Blood samples from 60 CRC patients (without overlap with the cases of tissue array) and 20 healthy volunteers were collected from Xi’an Central Hospital, Xi’an Jiaotong University, Xi’an, China. This study was approved by the Hospital’s Protection of Human Subjects Committee. 8 CRC cell lines (DLD-1, RKO, LoVo, HT-29, HCT-116, SW480, SW620, and Caco-2 cells) and immortalized human normal intestinal epithelial cells (HIEC cells) were used in this study. All cell lines were purchased from American Type Culture Collection (ATCC, Virginia, USA) and cultured in DMEM (Gibco, USA) with 10% fetal bovine serum (Gibco, USA). All cell lines were confirmed to be free of mycoplasma contamination.
TCGA data were collected from the database of The Cancer Genome Atlas Program following the procedure as described [50] and CRC patients with a follow-up time exceeding 2000 days were excluded. A cohort [25] on GenomicScape database (http://www.genomicscape.com) were recruited to analyze the survival of CRC patients and results were generated online (Smith, Colon cancer 1 (Moffitt)). Gene set enrichment analysis (GSEA) was performed using GSEA software v2.07. Respective gene expression data and clinical information were further analyzed on GEPIA database (http://gepia.cancer-pku.cn/), UALCAN database (http://ualcan.path.uab. edu/), and LinkedOmics (http://www.linkedomics.org) [51].
Plasma levels of CypB were measured with an ELISA kit (R&D Systems) and CypB antibodies (Abcam) according to the manufacturer’s protocol as described before [14].
Total proteins were prepared from cultured cell samples by complete cell lysis (Roche, Mannheim, Germany) with protease and phosphatase inhibitors. Nuclear proteins were prepared by Nuclear Protein Extraction Kit (Solarbio, Beijing, China) following the instruction from the manufacturer. Denatured proteins were separated on SDS-PAGE and transferred to membranes, followed by immunoblotting using antibodies of CypB (Abcam #ab16045), STAT3 (Cell Signaling Technology, #9139, #12640), pSTAT3 Tyr 705 (Cell Signaling Technology, #9145), SOCS3 (Cell Signaling Technology, #52113), β-actin (Sigma-Aldrich) and Histone H3 (Cell Signaling Technology, #4499). The bands were scanned and quantified as described [52].
Expression vectors encoding CypB and shRNA sequences of CypB and STAT3 were constructed and the lentivirus packaging was conducted as previously described [14]. The shRNA sequences were subcloned into the LV-12 (pGLVH6-CMV-LUC-2A-Puro-U6-shRNA) vector to generate a PVT1-shRNA lentivirus (shPVT1) (GenePharma) and the PVT1 cDNA was PCR amplified and subcloned into the LV-13 (pLenti-EF1a-LUC-F2A-Puro-CMV) vector (GenePharma) as described [53]. The infected cells were cultured in selection medium (culture medium with 1.5 μg/ml of puromycin) and collected for the downstream analysis at 2 weeks post-infection. All the sequences for targets are described in Supplementary Table S5.
For IHC, the target molecules were performed on tissue microarray chips or mice tissue using CypB antibody (Abcam), phospho-STAT3antibody (Cell Signaling Technology) and Ki67 (Santa Cruz). For ISH, a 5′- and 3′-digoxigenin (DIG)-labeled locked nucleic acid-based probe specific for PVT1 (Exiqon) was incubated with the same tissue microarray chip. The results of IHC and ISH were independently scored by two independent observers. Expression levels were visualized and classified based on the percentage of positive cells and the intensity of staining as previously described [14, 54, 55]. The percentage of positive cells was divided into four grades (percentage cores): <1% (0), 1–25% (1), 26–50% (2), 51–75% (3) and >75% (4). The intensity of staining was divided into four grades (intensity scores): negative (0), weak (1), moderate (2), and strong (3). The histological score was determined by the following formula: overall scores = percentage score × intensity score. An overall score of 0–12 was calculated and graded as low (score: 0–4), high (score: 5–12).
Target cells were seeded in 96-well plates (1 × 103/well) at 48 h post-transfection. XTT assays were conducted to determine the cell growth according to the manufacturer’s instructions (Cell Proliferation Kit II (XTT), Roche) for 5 days. XTT labeling reagents were mixed and added to the wells following manufacture’s instruction. Varioskan Flash Multimode Reader (Thermo-Fisher) was used to read the absorbance at 466 nm with a reference wavelength at 650 nm.
Cells were seeded in 6-well plates at 2 × 105 per well and harvested using trypsin. For cell cycle analysis, target cells were fixed in 75% ethanol and stained with propidium iodide (Sigma Aldrich) supplemented with RNase A (Roche) for 30 min at 22 °C 72 h post-transfection. The Annexin V-FITC Apoptosis Detection Kit (BD Biosciences) was used for apoptosis assays. Cells (1 × 104) were starved in serum-free medium for 24 h, stained according to the manufacturer’s protocol and sorted using a fluorescence-activated cell sorting sorter (BD), and the data were analyzed using the Modfit software (BD).
For invasion assays, chamber inserts with an 8-μm pore size were first coated with 200 mg/mL Matrigel (Corning), and the uppermost chamber was plated with 1 × 105 cells. For cell migration assays, the upper chamber with a noncoated membrane was plated with 5 × 104 cells. Each assay was repeated three times, and three different inserts were used to obtain the mean number of cells in five fields per membrane as described [50].
For mice assays in Fig. 2A–D, 12 weeks-old female C57BL/6 mice were treated with normal diet or high fat diet (#D12108C, RDI, US). For the tumor xenograft mice model, 5 × 106 cancer cells infected with lentiviral vectors or control were implanted into the flanks of 6-week-old female NOD/SCID (Server Combined Immune-deficiency) mice. The mice were sacrificed 20 days after injection, and tumors were collected and weighed. The tumor volume was calculated with the following formula: Tumor maximum diameter (L)×diameter along the perpendicular axis (W)2/2. For the in vivo metastasis assays, infected cells (5 × 106 cells/100 μL of PBS) were injected into the tail vein or the spleen of NOD/SCID mice. The mice were sacrificed 4 weeks later, and the lungs and tumor tissues derived from various organs were dissected and examined as described [56]. All animals were housed and maintained in pathogen-free conditions. All animal studies complied with the Xi’an Jiaotong University animal use guidelines, and the protocol was approved by the University Animal Care Committee.
For luciferase assays, cells were transfected with appropriate plasmids in 24-well plates. Cells were harvested and lysed for luciferase assays 48 h after transfection. Luciferase assays were performed using a Dual-Luciferase Reporter Assay System (Promega, WI, USA) according to the manufacturer’s protocol. Firefly luciferase activity normalized to Renilla luciferase was used as an internal control. A site-directed mutagenesis kit (Agilent Technologies) was used to mutate the STAT3 binding sites of these vectors. The transfection experiments were performed in triplicate for each plasmid construct.
Cells were plated onto glass coverslips and fixed with 4% paraformaldehyde for 20 min and permeabilized with 0.1% Triton X-100 in PBS for 15 min. Blocking solution was applied for 1 h at room temperature. Primary antibodies for CypB (Abcam #ab16045), STAT3 (Cell Signaling Technology, #12640) were applied at 4 °C overnight. FITC-conjugated and Cy3-conjugated secondary antibodies were loaded and incubated for 2 h at room temperature. Immunostaining signals and DAPI-stained nuclei were visualized and quantitation of CypB and STAT3 distribution was evaluated, as previously described [14]. The images were adjusted using the levels and brightness/contrast tools in Photoshop according to the guidelines for the presentation of digital data. FISH assays were performed with a FISH Kit (GenePharma, Shanghai, China) according to the manufacturer’s protocol. PVT1 probe labeled with CY3 was designed and synthesized by GenePharma Company. The cell nucleus was stained with DAPI. Signals were detected by confocal laser scanning microscopy.
Total RNA was extracted using a RNeasy Plus Mini Kit (Qiagen) according to the manufacturer’s instructions. cDNA was synthesized using a PrimeScript RT reagent kit (TaKaRa). SYBR Premix Ex Taq II (TaKaRa) was used to amplify the double-stranded cDNA of interest. RT-qPCR primers for the genes of interest were synthesized by TaKaRa. The levels of GAPDH were used as internal controls. A standard curve was established by amplifying diluted cDNA samples for calculation of relative target concentrations using Express SYBR GreenER qPCR SuperMix with Premixed ROX (Life Technologies). The 2–ΔΔCt method was used to determine the relative expression level of RNA between groups. The primer sequences used in this study are listed in Supplementary Table 5.
RIP was carried out with a Magna RIP™ RNA-Binding Protein Immunoprecipitation Kit (Millipore) with reference to the manufacturer’s instructions. Briefly, cells treated with IL-6 (50 ng/mL) or control for 24 h were harvested and then lysed in lysis buffer (50 mM Tris-HCl, pH = 7.4, 150 mM NaCl, 1% Triton-100, 0.1% SDS, 1.5 mM EDTA). Thereafter, cell lysates were incubated with RIP buffer containing magnetic beads. The beads were conjugated with the indicated antibody CypB (Abcam) or anti-IgG (Abcam) as a negative control. Then, the samples were digested by applying DNase I and proteinase K, and the immunoprecipitated RNA was isolated. Eventually, the enrichment of the purified RNAs was detected by RT-qPCR. The products were accurately quantified for sequencing applications using a quantitative real-time PCR (qRT-PCR)-based KAPA Biosystems Library Quantification Kit. Single-end sequencing (50 bp) was performed on a Angilent 4200 TapeStation.
Full-length, serial truncations and antisense of PVT1 mRNA (#1, #2, #3, and #4 respectively) were transcribed with a HiScribe™ T7 Quick High Yield RNA Synthesis Kit (NEB) and purified with an RNeasy MinElute Cleanup Kit (QIAGEN), followed by labeling using an RNA 3′ End Desthiobiotinylation Kit (Thermo Fisher Scientific). Purified biotin-labeled RNA was heated and annealed to form a secondary structure, mixed with whole cell extract in RIP buffer for 1 h, and incubated with streptavidin agarose beads (Invitrogen) for 1 h. Finally, the RNA-binding proteins were analyzed by Western blot targeting CypB (Abcam).
ChIP assays were performed using the Magna ChIP G Assay kit (EMD Millipore). Cells were cross-linked with 1% formaldehyde for 10 min at room temperature and quenched in glycine. DNA was immunoprecipitated from the sonicated cell lysates using STAT3 antibody (Cell Signaling Technology) and subjected to PCR to amplify the STAT3 binding sites. The amplified fragments were then analyzed using agarose gel. A total of 10% of chromatin before immunoprecipitation was used as the input control, and a nonspecific antibody against IgG (BD) served as the negative control. PCR primers for Ch-IP are shown in Supplementary Table S5.
SPSS software (version 19.0, SPSS Inc., Chicago, IL, USA) was used for statistical analyses. Continuous data were presented as the mean ± SD. and were compared between two groups by Student’s unpaired t-test. Frequencies of categorical variables were compared using the χ2 test. Spearman’s rank correlation coefficients were computed for assessing mutual association among clinical results. P < 0.05 was considered to be statistically significant (*P < 0.05, **P < 0.01).
Supplementary Figure S1-S8 Supplementary Figure S9 Supplementary Table S1-S5 Reproducibility checklist | true | true | true |
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PMC9585019 | 36266274 | Carlos Anerillas,Allison B. Herman,Rachel Munk,Amanda Garrido,Kwan-Wood Gabriel Lam,Matthew J. Payea,Martina Rossi,Dimitrios Tsitsipatis,Jennifer L. Martindale,Yulan Piao,Krystyna Mazan-Mamczarz,Jinshui Fan,Chang-Yi Cui,Supriyo De,Kotb Abdelmohsen,Rafael de Cabo,Myriam Gorospe | A BDNF-TrkB autocrine loop enhances senescent cell viability | 20-10-2022 | DNA damage response,Senescence,Ageing | Cellular senescence is characterized by cell cycle arrest, resistance to apoptosis, and a senescence-associated secretory phenotype (SASP) whereby cells secrete pro-inflammatory and tissue-remodeling factors. Given that the SASP exacerbates age-associated pathologies, some aging interventions aim at selectively eliminating senescent cells. In this study, a drug library screen uncovered TrkB (NTRK2) inhibitors capable of triggering apoptosis of several senescent, but not proliferating, human cells. Senescent cells expressed high levels of TrkB, which supported senescent cell viability, and secreted the TrkB ligand BDNF. The reduced viability of senescent cells after ablating BDNF signaling suggested an autocrine function for TrkB and BDNF, which activated ERK5 and elevated BCL2L2 levels, favoring senescent cell survival. Treatment with TrkB inhibitors reduced the accumulation of senescent cells in aged mouse organs. We propose that the activation of TrkB by SASP factor BDNF promotes cell survival and could be exploited therapeutically to reduce the senescent-cell burden. | A BDNF-TrkB autocrine loop enhances senescent cell viability
Cellular senescence is characterized by cell cycle arrest, resistance to apoptosis, and a senescence-associated secretory phenotype (SASP) whereby cells secrete pro-inflammatory and tissue-remodeling factors. Given that the SASP exacerbates age-associated pathologies, some aging interventions aim at selectively eliminating senescent cells. In this study, a drug library screen uncovered TrkB (NTRK2) inhibitors capable of triggering apoptosis of several senescent, but not proliferating, human cells. Senescent cells expressed high levels of TrkB, which supported senescent cell viability, and secreted the TrkB ligand BDNF. The reduced viability of senescent cells after ablating BDNF signaling suggested an autocrine function for TrkB and BDNF, which activated ERK5 and elevated BCL2L2 levels, favoring senescent cell survival. Treatment with TrkB inhibitors reduced the accumulation of senescent cells in aged mouse organs. We propose that the activation of TrkB by SASP factor BDNF promotes cell survival and could be exploited therapeutically to reduce the senescent-cell burden.
The cell senescence program is implemented in response to different forms of non-lethal cell damage. Senescent cells are characterized by several traits, including morphological changes, indefinite growth arrest, and metabolic reprogramming that includes altered lysosomal function with increased presence of senescence-associated β-Galactosidase (SA-β-Gal) activity. However, one of their most prominent features is the senescence-associated secretory phenotype (SASP), a trait defined as the active secretion of pro-inflammatory factors and matrix-remodeling enzymes. Through these secreted proteins, senescent cells modify their environment and influence physiologic and disease processes. On the one hand, senescence has been identified as critically beneficial for tissue repair, embryonic development, and tumor suppression; on the other, the uncontrolled accumulation of senescent cells within organs can lead to tissue dysfunction and disease. With advancing age, senescent cells increase in tissues and organs, exacerbating a range of aging-related physiologic declines and diseases. The pharmacologic removal of senescent cells using senolytics in mouse models was recently found to improve several age-associated disorders. Many senolytic strategies exploit the fact that senescent cells exist in a state of potent suppression of apoptosis, for instance, by inhibiting the pro-survival effect of anti-apoptotic proteins in the BCL2 family. A range of roles have been proposed for mitogen-activated protein kinase (MAPK) signaling pathways in senolysis. To address the influence of MAPKs experimentally and systematically, we employed a chemical library including inhibitors of every step in the MAPK cascade, from the receptors down to the final effector proteins. This survey identified two drugs, both inhibitors of tropomyosin receptor kinase (Trk) activity, that effectively and specifically reduced the viability of senescent WI-38 and BJ human diploid fibroblasts. Trk receptors are best known for transducing signals from their known ligands, the neurotrophins, to modulate neuronal development and communication. Among the Trk family, which includes TrkA, TrkB, and TrkC, we identified TrkB as being primarily responsible for maintaining senescent cell viability in this unexpected context. Further interrogation revealed that cells rendered senescent in different ways secrete the neurotrophin BDNF (brain-derived neurotrophic factor), and that in a range of senescence models, autocrine/paracrine activation of TrkB by BDNF sustained ERK5 activation and BCL2L2-dependent viability of senescent cells. The discovery that the early commitment to senescence required a rise in BDNF mRNA levels suggested that BDNF production helps to adjust the cellular outcome to the extent of damage received. Finally, in light of evidence that inhibiting this regulatory paradigm reduces the senescent cell burden in mouse tissues, we propose that suppressing the signaling through TrkB and BDNF can be exploited to eliminate senescent cells for therapeutic benefit.
As discussed recently, the role of MAPK pathways in senescence-associated apoptosis is poorly understood. To better understand how targeting MAPKs in senescence might offer therapeutic advantage, we screened a custom library (Tocris) comprising 43 compounds that inhibit different mediators of MAPK signaling cascades (Fig. 1a, Source Data). The screen included a known senolytic, ABT-737, as a positive control, and was carried out in parallel on proliferating cells to ensure that the identified drugs selectively affected senescent cells. To strengthen our screen, we performed it on two human diploid fibroblast lines (WI-38 and BJ) that are well-established models for senescence studies. The experimental conditions were previously optimized to trigger cellular senescence in both cell types with minimal cell death. Briefly, WI-38 and BJ fibroblasts were treated with 50 or 25 µM etoposide, respectively, refreshing the drug every three days. After ten days, when cells had reached senescence, etoposide was removed, and the different compounds from the library were tested for an additional 48 h (Fig. 1b). The induction of senescence in WI-38 and BJ cells was confirmed by measuring senescence-associated β-Galactosidase (SA-β-Gal) activity (Supplementary Fig. 1a–d); growth arrest in the senescent cultures was confirmed by measuring BrdU incorporation (Supplementary Fig. 1e, f). Next, we assessed the effect of treatment with the drugs in the library (each at 10 µM) for 48 h on both proliferating and senescent WI-38 or BJ cells. The impact of each drug was evaluated by direct cell counts at the end of the treatment relative to the initial cell numbers. As shown in Fig. 1c (further information in Source Data), treatment with several inhibitors caused specific decreases in the number of live senescent cells; the heat map (Fig. 1d) represents the percentage of senolysis caused by a certain drug (proportion of dead cells 48 h later). The drugs were grouped by the target proteins within the MAPK pathways upon which they act. We excluded from the heat map those drugs that decreased the viability of proliferating cells, such as CMPD-1 (an inhibitor of MK2 and microtubular assembly), BRD 7389 (a p90 S6K inhibitor), HI TOPK 032 (a TOPK inhibitor), and AC 710 (a PDGFR inhibitor). Strikingly, two of the drugs with the most pronounced senolytic effect were the TrkB receptor inhibitors GNF 5837 and ANA 12 (blue arrows in Fig. 1c, d). TrkB belongs to a family of Receptor Tyrosine Kinases (RTKs) whose ligands are neurotrophins, a group of secreted proteins crucial for engaging neuronal survival. Although GNF 5837 also inhibits TrkA and TrkC, other inhibitors included in the library selectively targeting other members of this family, such as TrkA (GW 441756) and NGFR (PD 90780), did not show any senolytic properties. Thus, in this screen, TrkB appeared to be the main Trk receptor responsible for maintaining the viability of senescent cells. Together, these data indicate that TrkB inhibitors have potential senolytic effects.
We sought to explore further the ability of Trk inhibitors to selectively induce apoptosis in senescent cells. In addition to GNF 5837 and ANA 12, our analysis included PF 06273340, an additional Trk inhibitor that does not cross the blood-brain barrier (BBB). Given that Trk receptor activity is essential for cognitive function, drugs incapable of crossing the BBB, such as GNF 5837 and PF 06273340, would have less cognitive side effects and thus might be more attractive for therapy; by contrast, ANA 12 does enter the central nervous system. Therefore, we evaluated a range of doses of each drug (1.25 to 50 µM) in proliferating and senescent cells and established their EC50 (Fig. 2a). Analysis of viability and caspase 3/7 activity (an indicator of apoptosis) 48 h later revealed that the optimal senolytic doses were 10 µM GNF 5837, 20 µM ANA 12, and 30 µM PF 06273340 (Fig. 2b, Supplementary Fig. 2a), since they caused maximal death of senescent cells with minimal consequences on proliferating cells. Importantly, similar doses of TrkB inhibitors reduced the viability of several other primary cells that were subjected to etoposide-induced senescence (ETIS), including human BJ and IMR-90 fibroblasts, HUVECs (human umbilical vein endothelial cells), HSAECs (human small airway epithelial cells), and HRECs (human renal mixed epithelial cells). Similar reductions were seen in the viability of WI-38 cells that were rendered senescent by other means, specifically by exposure to ionizing radiation (IR) and by long-term culture until they reached replicative senescence (IRIS and RS, respectively) (Fig. 2c; Supplementary Fig. 2b–d). We confirmed the induction of senescence in these models by measuring SA-β-Gal activity and BrdU incorporation (Supplementary Fig. 2b–d). In this panel of 9 senescent populations, similar doses of TrkB inhibitors also significantly increased caspase 3/7 activity relative to untreated senescent cells (Fig. 2d). Importantly, these doses did not reduce the viability of proliferating cells (Supplementary Fig. 2e), supporting the notion that senescent cells selectively experienced increased apoptotic death in response to TrkB inhibition. Finally, to confirm that TrkB inhibitors caused apoptotic cell death, we blocked caspase activity by incubating cells with the pan-caspase inhibitor Z-VAD-FMK along with GNF 5837, ANA 12, or PF 06273340. As shown, the increased cell death and caspase 3/7 activity seen by inhibiting TrkB were reversed in cells simultaneously treated with Z-VAD-FMK (Fig. 2e, f). These data support the notion that inhibiting Trk proteins enhanced pro-apoptotic signaling in senescent cells, leading to cell death.
We then set out to further characterize the role of Trk receptors in cellular senescence. In neuronal cells, activation of Trk receptors strongly promotes cell viability through specific gene expression programs. Although our data pointed to a prominent pro-survival role for TrkB in senescent cells, we also analyzed the structurally similar TrkA and TrkC. Western blot analysis revealed that the levels of TrkA, TrkB [mostly full-length TrkB (FL) and not truncated TrkB (T)], as well as senescence marker p21 were low in proliferating (P) WI-38 fibroblasts, but increased robustly in WI-38 fibroblasts rendered senescent (S), as described in Fig. 2c (ETIS, OSIS, IRIS, RS) (Fig. 3a). As anticipated, we also found increased levels of p16/CDKN2A and IL6 mRNAs, two markers of senescence, as quantified by reverse transcription (RT) followed by real-time quantitative (q)PCR analysis (Fig. 3b). We similarly assessed the levels of NTRK1, NTRK2, and NTRK3 mRNAs (encoding TrkA, TrkB, and TrkC, respectively) by RT-qPCR analysis, but were unable to detect NTRK3 mRNA in WI-38 fibroblasts, and only NTRK2 mRNA was consistently detected in all WI-38 S populations, and was generally higher than in P populations (Supplementary Fig. 3a). Similarly, the levels of TrkB protein, and p16, IL6, and NTRK2 mRNAs were preferentially elevated in senescent BJ, IMR-90, HSAEC, HREC, and HUVEC cultures (Fig. 3c, d; Supplementary Fig. 3a). To determine the role of each Trk receptor in senescent cell viability, we reduced the levels of TrkA or TrkB by transfecting specific small interfering (si)RNAs. After silencing was achieved, we triggered senescence with etoposide and monitored cell viability. As observed in Fig. 3e, neither cells in which TrkA was silenced (siNTRK1) nor control cells showed reduced viability, while silencing TrkB (siNTRK2) significantly decreased the number of live cells. The efficiency of the silencing interventions was confirmed by measuring mRNA levels (Fig. 3f). In sum, despite the rise in TrkA levels in some senescence models, the enhanced viability of senescent cells appeared to be mediated by TrkB. Next, since TrkB protein levels increased proportionally more than NTRK2 mRNA levels in senescent cells, we evaluated the stability of TrkB protein by treating proliferating and senescent cells with cycloheximide (CHX), an inhibitor of translation. This analysis included positive control p53 (TP53), a protein showing increased stability in senescence. Western blot analysis (Fig. 3g) revealed that TrkB was more stable in senescent fibroblasts, as was p53, suggesting that the increased TrkB protein stability contributes to its accumulation in senescent cells. A time-course analysis after triggering senescence with etoposide revealed that TrkB protein levels increased by day 2 and continued to rise until day 10 (Fig. 3h). Early markers of senescence, p21 protein and TGFB1 mRNA (Fig. 3h, i), as well as late markers of senescence, p16 and IL6 mRNAs (Fig. 3i), were included in the analysis. Although with different kinetics, the levels of TrkB as well as TGFB1, p16, and IL6 mRNAs also increased progressively with senescence in BJ and IMR-90 cells (Supplementary Fig. 3b–e). Finally, to study if TrkB was found on the plasma membrane of senescent cells, we enriched the proteins present on the plasma membrane using a biotinylation strategy (Methods) and found TrkB in the enriched ‘Pulldown’ sample; we included as a positive control DPP4, a protein reported to increase on the plasma membrane of senescent cells (Fig. 3j). Detection of TrkB by immunofluorescence using non-permeabilizing conditions confirmed that TrkB was strongly abundant on the membrane of senescent cells, as was DPP4 (Fig. 3k). Together, these data indicate that TrkB protein increases during senescence in several cell systems, and contributes to ensuring senescent cell viability.
Trk receptors are activated by a family of secreted ligands known as neurotrophins, comprising NGF, NTF3, NTF4, and BDNF, which are essential for neuronal function. Even though BDNF is the preferred ligand of TrkB, we explored possible roles for all neurotrophins in this paradigm of cell senescence. First, by RT-qPCR analysis, we measured the levels of NGF, NTF3, NTF4, and BDNF mRNAs in all nine senescence models; interestingly, only BDNF mRNA was consistently elevated in all of them (Fig. 4a). Second, we found increased BDNF mRNA levels in published transcriptomic datasets obtained from different models of senescence: in WI-38 fibroblasts rendered senescent by treatment with doxorubicin and in HUVECs and human aortic endothelial cells (HAECs) rendered senescent by treatment with IR (Supplementary Fig. 4a). Third, to test if BDNF is secreted by senescent cells, we collected media from the senescence models used in Fig. 4a; as shown, the levels of secreted BDNF, as analyzed by ELISA, were significantly higher in media collected from all senescent cells compared to the proliferating counterparts (Fig. 4b); in proliferating or senescent WI-38 cells, NGF, NTF3, and NTF4 were undetectable by ELISA (Supplementary Fig. 4b). The levels of BDNF and other SASP cytokines [CXCL1 (GRO-alpha), IL6, HGF, or CXCL10] were also elevated in media from senescent cells as detected by using a cytokine array although NTF3 and NTF4 were not (Fig. 4c). Together, these data indicate that a broad range of senescent cells secrete BDNF. By immunofluorescence microscopy, we found that senescent WI-38 cells were strongly positive for BDNF (Fig. 4d), as more of the cells were positive (Fig. 4e) and the signals were more intense (Fig. 4f) than those observed in proliferating controls. RT-qPCR analysis likewise revealed an early rise in BDNF mRNA levels two days after triggering senescence by treatment of WI-38, BJ, and IMR-90 fibroblasts with etoposide (Fig. 4g). Since many SASP members are regulated by NF-κB and/or p53, we investigated if these two transcription factors induced BDNF production in senescent cells. Following p53 and RELA silencing by transfection of specific siRNAs (siTP53, siRELA), we observed that the rise in BDNF mRNA levels in senescent cells was dramatically suppressed by silencing p53 and modestly reduced by silencing RELA, compared with control (siCtrl) transfections (Fig. 4h and Supplementary Fig. 4c). The senescence-associated rise in p21 mRNA levels, which is dependent on the transcriptional activity of p53, was similarly reduced by silencing p53, while induction of the senescence-associated IL6 mRNA, an NF-κB-regulated mRNA, was mostly abrogated by silencing RELA. The rise in p16 mRNA was not reversed by either silencing intervention (Fig. 4h). We then asked if BDNF might directly affect senescent cell survival through TrkB activation, as it promotes neuronal viability. We silenced BDNF by using a specific siRNA (siBDNF), and then we triggered senescence in WI-38 fibroblasts with either etoposide, H2O2, or IR, as well as in HUVECs with etoposide. As shown in Fig. 4i, silencing BDNF significantly reduced cell viability relative to control (siCtrl) cells throughout the process of reaching senescence; RT-qPCR analysis confirmed the persistence of silencing by 10 days (Supplementary Fig. 4d). Importantly, the loss of viability after BDNF silencing was partially rescued by supplementation of exogenous BDNF (200 pg/ml) (Fig. 4j). To further assess if the effect of BDNF was autocrine/paracrine, we employed anti-BDNF blocking antibodies (or non-specific IgG antibodies in control incubations); 48 h after adding the antibodies (at 4 µg/ml) to the media, proliferating WI-38 cells showed no change in replication or viability, while senescent WI-38 cells showed markedly decreased viability (Fig. 4k, l) and increased caspase 3/7 activity (Fig. 4m) in the presence of anti-BDNF antibodies. Finally, BDNF cleavage by proteases such as Furin or matrix metalloproteinases (MMPs), which increase with senescence, is key to its function as a ligand of TrkB. Western blot analysis of BDNF revealed overall higher levels of total BDNF and processed BDNF in conditioned media from senescent than proliferating WI-38 cells (Fig. 4n). We tested if senescence-associated increases in Furin or MMPs might cause BDNF cleavage and activation, by first analyzing the levels of FURIN, MMP1, MMP3, MMP7, and MMP9 mRNAs after etoposide treatment in WI-38, BJ, and IMR-90 cells; as shown, the levels of FURIN and MMP3 mRNAs increased significantly during senescence (Supplementary Fig. S4e), while MMP1 mRNA levels did not change, and MMP7 and MMP9 mRNAs were not detected in these cell types. We then tested if FURIN and MMP3 participated in BDNF processing during senescence by using Furin Inhibitor II (FURINi) and MMP Inhibitor II (MMPi). As shown, only treatment with MMPi reduced WI-38 cell viability and BDNF processing (Fig. 4o, p) during senescence, suggesting that MMP3 contributes to BDNF maturation during senescence. In sum, our results identify BDNF as a SASP factor whose abundance and function are regulated at several levels, and is implicated in promoting senescent cell survival in an autocrine and/or paracrine fashion.
We then sought to investigate if the activation of TrkB by BDNF was linked to the TrkB-mediated survival of senescent WI-38 cells. First, to visualize protein phosphorylation we employed phos-tag polyacrylamide gels, which capture phosphorylated proteins and markedly reduce their migration, while non-phosphorylated proteins migrate at the expected size. As shown, slow-migrating TrkB bands appeared by 2 to 3 days of etoposide treatment, increasing in intensity in subsequent days (Fig. 5a, left). As the rise in phosphorylation mirrored the rise in BDNF mRNA abundance, we sought evidence that BDNF expression was linked to TrkB phosphorylation. As shown, silencing BDNF in WI-38 cells before triggering senescence completely prevented the appearance of the phosphorylated TrkB band (Fig. 5a, right), suggesting that BDNF production is critical for inducing TrkB phosphorylation in senescent cells. Second, we tested whether both TrkB and BDNF depletion affected senescent cell viability in a similar time frame. Given that TrkB and BDNF levels rose markedly by 2 days into senescence, and that silencing either TrkB or BDNF increased cell death, we evaluated whether their depletion affected senescent cell survival at early and/or late senescence, two stages defined previously. We found that silencing TrkB or BDNF decreased cell survival at both early and late senescence, although this effect was more pronounced in late senescence (Fig. 5b). We then sequenced the bulk RNA present in late senescent cells (8 days into senescence) in which either TrkB or BDNF were silenced, and compared it to siCtrl-transfected senescent counterparts (Fig. 5c). The RNA-seq data are deposited in GSE202951. GSEA analysis of mRNAs differentially abundant after silencing TrkB or BDNF revealed enriched pathways of apoptosis and caspase activity (Fig. 5d, left). The heat map in Fig. 5d, comprising differentially abundant mRNAs encoding apoptosis-related proteins, revealed jointly modulated transcripts including BCL2L2 mRNA, which encodes a protein essential for the survival of senescent cells. We used RT-qPCR analysis to validate these results by measuring the levels of BCL2L2 mRNA, along with the levels of other mRNAs encoding senescence-associated proteins implicated in apoptosis, such as BCL2L1 and PUMA (Supplementary Fig. 5a). Notably, while the levels of p53-induced pro-apoptotic PUMA mRNA were not affected, the levels of BCL2L2 mRNA, encoding BCL2L2, decreased after TrkB or BDNF silencing at both early and late senescence (Fig. 5e). This effect appeared specific for BCL2L2 mRNA, as BCL2L1 mRNA levels were unchanged after silencing TrkB or BDNF (Fig. 5e), as were the levels of the p53-regulated p21 mRNA, while IL6 mRNA levels unexpectedly declined after silencing TrkB or BDNF (Supplementary Fig. 5a). Furthermore, BDNF-blocking antibodies specifically blunted the rise in BCL2L2 mRNA in senescent cells, while PUMA and BCL2L1 mRNAs were unaffected (Fig. 5f). As seen after silencing TrkB or BDNF in senescent cells (Supplementary Fig. 5a), treatment with BDNF-blocking antibodies did not change p21 mRNA levels but reduced IL6 mRNA levels (Supplementary Fig. 5b). Western blot analysis of BCL2L2 (BCLW) expression levels confirmed the changes in BCL2L2 mRNA levels and further showed that p53 levels were unchanged (Fig. 5g); in agreement with these findings, all three TrkB inhibitors studied specifically reduced BCLW protein levels (Supplementary Fig. 5c). Next, we analyzed the major pathways downstream of BDNF and TrkB signaling. Phosphorylation of ERK5 was dramatically reduced by silencing TrkB or BDNF, in both early and late senescence (days 2 and 8, respectively; Fig. 5h), a notable finding, given that ERK5 elevates BCL2L2 levels downstream of BDNF-TrkB for the survival of neuronal cells. We then utilized a highly selective ERK5 inhibitor (ERK5-IN-1, 1 µM) to reduce ERK5 activity in senescent cells and found that it lowered both BCL2L2 levels and senescent-cell viability, but it did not affect proliferating cells (Fig. 5i, j and Supplementary Fig. 5d, e). In keeping with the results after silencing TrkB or BDNF (Supplementary Fig. 5a), inhibiting ERK5 specifically lowered the levels of BCL2L2 and IL6 mRNAs, but not the levels of PUMA, BCL2L1, or p21 mRNAs (Fig. 5i and Supplementary Fig. 5f), while TrkB inhibitors reduced ERK5 phosphorylation levels (Supplementary Fig. 5g). In sum, our data suggest that activation of TrkB by BDNF specifically activates ERK5, which in turn increases BCL2L2 levels.
In light of our earlier findings that p53 increases BDNF production, we investigated if BDNF induction contributed to the actions of p53 in implementing senescence or apoptosis programs. First, we treated WI-38 cells with a range of doses of etoposide that had different effects on cell proliferation and viability. Untreated cells proliferated as expected, but increasing doses of etoposide reduced proliferation to different degrees (5 to 50 µM etoposide) or caused cell death (100 to 200 µM etoposide) (Fig. 6a). BDNF mRNA levels increased at etoposide concentrations that reduced proliferation (≤50 μM) but not at concentrations that triggered cell death (100–200 µM) (Fig. 6b); by contrast, p21 and IL6 mRNAs accumulated with increasing DNA damage (Fig. 6b). Increasing H2O2 doses similarly elevated BDNF mRNA levels only at sublethal levels (Fig. 6c, d), suggesting that other senescence-causing stressors function similarly in controlling BDNF mRNA production. Second, we modulated p53 function to study if p53 influenced BDNF mRNA levels. Silencing p53 prevented the rise in BDNF mRNA elicited by 50 μM etoposide treatment for 48 h (Fig. 6e) while it promoted apoptosis over senescence (Supplementary Fig. 2d); as anticipated, the same intervention reduced the levels of p21 mRNA but not IL6 mRNA (Supplementary Fig. 6a, b). These experiments underscore a requirement for functional p53 to induce BDNF and promote survival at early stages of senescence, despite the absence of p53 sites on the BDNF promoter. We then tested if increasing p53 function affected the production of BDNF after treatment with etoposide (50 µM for 48 h) by employing Nutlin-3a (Nut3a), a p53-stabilizing compound. Notably, increasing p53 levels in cells treated with 50 µM etoposide significantly decreased BDNF levels (Fig. 6e), resembling the reduction observed at higher levels of DNA damage; in contrast, p21 mRNA levels increased in the presence of Nut3a (Supplementary Fig. 6c) and Nut3a reduced cell viability (Supplementary Fig. 6d). These data suggest that, while p53 is required for BDNF induction after sublethal damage, only moderate p53 levels induce BDNF production and higher levels of p53 prevent the rise in BDNF that promotes survival. Several mechanisms can explain how p53 could both induce and repress BDNF production depending on the extent of DNA damage. STAT3 was recently reported to promote BDNF expression in the lung upon injury and we found that STAT3 phosphorylation at Tyrosine 705 (Tyr705) decreased in IMR-90 fibroblasts committed to apoptosis but remained phosphorylated in cells committed to senescence. Therefore, we silenced STAT3 by transfection with siRNA (Supplementary Fig. 6e) and evaluated BDNF levels after treatment with etoposide (50 µM, 48 h). As shown in Fig. 6f, the rise in BDNF mRNA triggered by senescence-inducing etoposide (50 µM) was prevented by STAT3 silencing; by contrast, p21 mRNA levels were unchanged and the slight rise in IL6 mRNA levels was suppressed (Supplementary Fig. 6e). Even though silencing STAT3 lowered BDNF levels, silencing STAT3 did not reduce viability, suggesting that STAT3 might be one of several factors governing the survival-apoptosis balance in early senescence (Supplementary Fig. 6f). Interestingly, both silencing and activating p53 decreased STAT3 phosphorylation (Fig. 6g), mirroring the effects seen for BDNF. Moreover, p53 levels increased linearly along with DNA damage (Fig. 6h), but STAT3 phosphorylation at Tyr705 peaked at 25 μM, when p53 levels were moderately elevated. Similar responses were seen when comparing H2O2 doses causing senescence or apoptosis (Fig. 6i), indicating that this mechanism of cell fate determination is not limited to etoposide. We then performed single-cell RNA-seq analysis to evaluate if different subpopulations of senescent WI-38 cells expressed different BDNF mRNA levels, and if cells expressing higher BDNF levels displayed a transcriptomic profile associated with STAT3 activation. After clustering cells into different subgroups (Supplementary Fig. 6g), BDNF mRNA levels were highest in clusters 4 and 5 and lowest in clusters 0 and 1 (Fig. 6j, Supplementary Fig. 6h). BDNF mRNA levels correlated significantly with mRNAs transcriptionally upregulated by STAT3, such as THBS1 or FN1 mRNAs, as determined by Gene Set Enrichment Analysis (GSEA; Fig. 6j, k, and Supplementary Fig. 6i) and by ChIP-seq-identified targets (Supplementary Fig. 6h). Clusters 4 and 5 were linked to Epithelial-Mesenchymal Transition (EMT) programs and low DNA damage levels (Supplementary Fig. 6j), in agreement with cell programs associated with senescence and survival. In validation experiments, immunofluorescence analysis revealed that most cells expressing the highest levels of BDNF were also strongly positive for THBS1 or FN1 (Supplementary Fig. 6k), two proteins encoded by STAT3-regulated mRNAs (cluster 5, Fig. 6j). Finally, we studied if both p-STAT3 and BDNF were simultaneously present at a single-cell level during senescence in WI-38 fibroblasts and in an in vivo mouse model of doxorubicin-induced senescence (Supplementary Fig. 6l). By double immunofluorescence staining of senescent WI-38 cells with antibodies recognizing p-STAT3(Tyr705) and BDNF, we found that most BDNF-positive cells were p-STAT3(Tyr705)-positive, but virtually every p-STAT3(Tyr705)-positive cell was BDNF-positive (Fig. 6l, m). Similarly, in the mouse model, lung cells positive for the DNA damage marker γH2AX were also positive for p-STAT3 (Tyr705) and BDNF (Supplementary Fig. 6m). These results indicate that p53 influence on STAT3 activation can both induce and repress BDNF production depending on the level of DNA damage. In turn, BDNF secretion by senescent cells can trigger signaling through TrkB → ERK5 → BCL2L2 to ensure survival in an autocrine manner (Fig. 6n).
Finally, we tested the relevance of the BDNF-TrkB paradigm on senescent cell viability in vivo using C57BL/6 J mice. We chose naturally aged mice as a well-established and relevant in vivo cellular senescence model. The appearance of senescent cells in older tissues such as lung and liver has been documented by different methods, such as assessment of SA-β-Gal activity and detection of senescence markers like p16. Initially, we studied the extent to which p16- and BDNF-positive cells colocalized in tissues in 24-month-old (m.o.) mice; we employed an antibody recently shown to recognize p16 and verified the detection of p16-positive cells by seeing colocalization of tdTomato from a knock-in mouse strain that expressed tdTomato from the endogenous p16Ink4 gene (Supplementary Fig. 7a, b). Approximately one-half of the p16-positive cells in lung and liver were also BDNF-positive (Fig. 7a), suggesting that the two proteins were co-expressed in aging. We then tested the ability of TrkB inhibitors to reduce cell senescence markers in old mice. We administered the Trk inhibitors characterized in Fig. 2 by monthly injections into 21 m.o. mice until they were 24 m.o. (Fig. 7b and Supplementary Methods). We assayed the same inhibitors we had tested in culture (GNF 5837, ANA 12, and PF 06273340), but halted treatments with ANA 12 due to toxicity; in fact, ANA 12 was a poor candidate for therapy due to its ability to penetrate the BBB. Notably, compared with control mice [vehicle-treated 24 m.o. and untreated young (3 m.o.) mice], GNF 5837 and PF 06273340 showed promising effects on senescence markers and traits. The increases in levels of p16 and p21 mRNAs observed in kidney, lung, and liver of old mice were significantly mitigated by each drug (Fig. 7c); the same trends were seen for Il6 and Il1b mRNAs, but they did not reach significance in all organs (Fig. 7c). Similarly, the levels of Bdnf mRNA and the senescence marker Gdf15 mRNA were not significantly elevated in kidney, lung, and liver of old mice (Supplementary Fig. 7c), possibly because the percentages of senescent cells in organs are typically low, and/or senescent cells may not express Bdnf mRNA at all stages of senescence. These observations indicate that these drugs, which do not cross the BBB, were able to decrease the levels of prominent senescence markers such as p16 or p21 mRNAs in several tissues in aged mice. We then studied senescence-related serum markers that were recently found to increase with age. One such marker, GDF15, increased markedly in old mice; this increase was significantly mitigated by treatment with PF 06273340, while GNF 5837 showed the same trend without reaching significance (Fig. 7d). Other serum markers (TIMP-1 and PAI-1) were not elevated with age (Supplementary Fig. 7d) and thus they could not be used to test the effects of these drugs; importantly, these drugs did not affect body weight (Supplementary Fig. 7e). Immunofluorescence analysis further revealed that both GNF 5837 and PF 06273340 treatments reduced the percentage of p16-positive cells in all three organs tested (Fig. 7e and Supplementary Fig. 7f). As was the case for GDF15 serum levels, PF 06273340 appeared to be more effective than GNF 5837 in reducing p16 levels. Assessment of the canonical SA-β-Gal marker revealed that both drug treatments reduced the age-associated increases observed both in the intensity and the number of SA-β-Gal-positive cells in kidney, lung, and liver (Fig. 7g, h). Finally, we asked if the observed reductions in senescent cells ameliorated physiological declines linked to cell senescence during aging. First, we assessed tissue fibrosis by monitoring extracellular matrix deposition using Sirius (Picro Sirius) red staining. As shown, old tissues displayed increased Sirius red staining that was significantly reduced with both TrkB inhibitors (Supplementary Fig. 7g). Furthermore, we quantified the area of fibrosis in the kidney in each group, and measured different serum markers that were increased with an age-associated loss of renal fitness, such as serum urea and creatinine levels; all three markers of renal disfunction increased with age but the increase was ameliorated by treatment with either GNF 5837 or PF 06273340 (Fig. 7i, j). Finally, given earlier reports that these drugs do not cross the blood-brain barrier, we asked if these drugs affected structure or cell viability in the brain cortex. As shown, there was no sign of toxicity as determined by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) analysis to measure apoptosis, staining for neuronal density using the neuronal marker NeuN, or measuring the total number of cells by staining nuclei using DAPI (Supplementary Fig. 7h, i). Together, we propose that the reduction in senescence markers is evidence that treatments with GNF 5837 and PF 06273340 represent promising strategies to reduce the accumulation of senescent cells with aging.
Strategies to remove detrimental senescent cells from tissues and organs are gaining interest in the clinic, particularly for the treatment of chronic age-related diseases. Given that senescent cells bring about a proinflammatory state through the SASP, they have been found to exacerbate many aging-associated pathologies. Therefore, extensive efforts are underway to develop pharmacological approaches to eliminate them with minimum side effects. Here, the discovery that Trk inhibitors selectively reduced senescent cell viability (Fig. 1) led us to propose a previously unrecognized role for TrkB in supporting the survival of senescent cells. Although a role for TrkB in senescence was not previously described, TrkB is a well-established pro-survival receptor in neurons and thus we set out to characterize the function of TrkB in senescent cell survival. Interestingly, the levels of TrkB ligand BDNF, a neurotrophin, were found specifically elevated in several senescence models (Fig. 4a, b), and interfering with BDNF function by silencing or blocking antibodies caused a specific reduction in the viability of senescent cells (Figs. 4, 5). The active secretion of molecules by senescent cells through the SASP is a key feature of the response to damage, and SASP factors help to establish a repair program that includes the promotion of fibrosis, inflammation, recruitment of immune cells, and remodeling of the tissue architecture. The discovery that the SASP factor BDNF promotes senescent cell survival by activating TrkB uncovers an important dimension of the SASP in strengthening the anti-apoptotic attributes of senescent cells. The finding that a neuronal survival paradigm is utilized by senescent cells to support viability was interesting, but it was not entirely unexpected, as activation of TrkB by BDNF (and more generally Trk receptors by neurotrophins) has been described outside of the nervous system, particularly in developmental and tissue repair processes in several organs. On the other hand, aberrant activation of the BDNF-TrkB axis has been described in age-related pathologies such as lung fibrosis, sarcopenia, cancer, and kidney disease; interestingly, all these processes causing age-associated declines and diseases have been linked to cellular senescence. Thus, it will be important to determine if BDNF secreted by senescent cells contributes to these paradigms by influencing surrounding cells, and possibly by promoting an EMT-like program. In cells facing damage that might result in either senescence or death, production of BDNF appears to be a better predictor of a path to senescence than other, more established, senescence markers such as p21 or IL6 (Fig. 6), which are also strongly induced by acute DNA damage but are not always good indicators of senescence. Furthermore, given that depleting BDNF levels in cells undergoing senescence drastically reduced viability (Figs. 4, 5), BDNF appears to function as a key effector of the anti-apoptotic program of senescence. In sum, the activation of BDNF-TrkB in senescent cells supports the notion that senescence is a developmental program set in motion by cell damage that shares previously unrecognized molecular similarities with other cell responses such as neuronal survival. In fact, the activation of TrkB by BDNF enhanced ERK5 signaling to increase the levels of BCL2L2, a trait shared with neurons. Together with the fact that BCL2L2 was identified as a core senescence-associated protein elevated in multiple cell senescence models, and the fact that the senolytic drug ABT-737, an inhibitor of BCL2L2, potently sensitizes senescent cells to apoptotic death, a prominent role for BCL2L2 in senescent cell survival is becoming increasingly apparent. In further support for this notion, we recently found that BCL2L2, but not BCL2L1, contributed to sustaining cell viability in early senescence in a model of senescence that relied on the activation of the oncoprotein SRC. Moreover, while direct inhibition of pro-survival BCL2 proteins can cause notable side effects, reductions in BCL2L2 by suppressing BDNF-TrkB signaling might offer advantages over other therapies. A major caveat that must be overcome before the BDNF-TrkB paradigm can be exploited therapeutically is the fact that BDNF-TrkB activity is required to maintain a fully functional nervous system. Since Trk inhibition is used to treat chronic pain, various compounds have been developed that prevent adverse side effects within the central nervous system. Two out of the three drugs tested in this study, GNF 5837 and PF 06273340, are peripherally restricted Trk inhibitors, i.e., they cannot cross the blood-brain barrier, and thus avoid impacting neuronal function. Nonetheless, these compounds reduced senescence-associated markers in tissues such as kidney, lung, and liver in a mouse model of senescence triggered by old age (Fig. 7 and Supplementary Fig. 7). Eventually, it will be essential to study if these or other TrkB inhibitors are viable strategies to reduce the burden of senescent cells in human conditions of dysfunction and disease. In sum, we have identified a role for BDNF in promoting cell survival through the activation of TrkB, and propose that this signaling paradigm might be exploited therapeutically to lower senescent cells in older organs.
Human IMR-90 (ATCC), WI-38 (Coriell Institute), and BJ fibroblasts (ATCC) were cultured in Dulbecco’s modified Eagle’s medium (DMEM, Gibco) supplemented with 10% heat-inactivated fetal bovine serum (FBS, Gibco), 0.5% Penn/Strep (Gibco), Sodium Pyruvate (Gibco), and non-essential amino acids (Gibco) in a 5% CO2 incubator. Human umbilical vein endothelial cells (HUVECs), small airway epithelial cells (HSAECs), and renal mixed epithelial cells (HRECs), all from ATCC, were cultured in their respective media [vascular cell basal medium plus endothelial cell growth kit-BBE (bovine brain extract), airway epithelial cell basal medium plus bronchial epithelial cell growth kit, and renal epithelial cell basal medium plus renal epithelial cell growth kit, respectively], supplemented with 0.5% Penn/Strep (Gibco), Sodium Pyruvate (Gibco) and non-essential amino acids (Gibco), and cultured under the same conditions (5% CO2 incubator). Cells were maintained at low population doubling levels (PDL) for the different experiments included in this article. Cellular senescence was triggered by different means. Etoposide-induced senescent was achieved by culturing WI-38, BJ, and IMR-90 fibroblasts for 10 days in the presence of etoposide (Selleckchem) at 50, 25, and 50 µM, respectively. To achieve senescence by exposure to ionizing (γ) radiation (IR), WI-38 cells were exposed to 15 Gray (Gy) and cells were cultured for up to 10 days. Replicative senescence was achieved after serial passaging of WI-38 cells until replicative exhaustion (typically at ~PDL55). Oxidative stress-induced senescence was achieved by adding 0.75 mM H2O2 directly to WI-38 fibroblasts in complete medium and replacing fresh medium 2 h later; cells were then assayed at the indicated times. Etoposide-induced senescence was achieved by treating HUVECs (at 10 μM), HSAECs (at 20 μM), and HRECs (at 20 μM) for 3 days, whereupon media was refreshed without etoposide until senescence was reached at day 8. All drugs and compounds used were refreshed every 48 h. The concentration of exogenous BDNF (R&D Systems) was calculated based on the observed levels of BDNF production by ELISA (200 pg/ml, refreshed every 48 h). The doses of MMP and FURIN inhibitors (MMP inhibitor II and FURIN inhibitor II, Sigma-Aldrich), Nutlin3a (Selleckchem), Z-VAD-FMK (Selleckchem), and the library of MAPK inhibitors (Tocris) are indicated throughout the manuscript. The drugs used are listed (Source Data). Cells were transfected with RNAiMAX (Invitrogen) following the manufacturer’s instructions. Briefly, cells at 50% confluency were transfected with ON-TARGETplus SMARTPool (Dharmacon) non-targeting siCtrl (Catalog ID: D-001810-10-05), siNTrk1 (Catalog ID: L-003159-00-0005), siNTrk2 (Catalog ID: L-003160-00-0005), siTP53 (Catalog ID: L-003329-00-0005), siRELA (Catalog ID: L-003533-00-0005), siBDNF (Catalog ID: L-017626-00-0005), or siSTAT3 (Catalog ID: L-003544-00-0005) siRNAs at a final concentration of 25 nM; 24 h later, treatments were initiated as indicated. Cell viability was assessed by direct cell counting; all cell counts were performed manually by using ImageJ and performed in at least 3 independent replicates. From each replicate, 3 fields were randomly selected and counted. Cell viability was represented as the percentage of remaining cells compared to the number of cells present at the beginning of the experiment.
Tissues or cells were lysed in either Tri-Reagent (Invitrogen) or RLT buffer (Qiagen), and the lysate was processed with the QIAcube (Qiagen) to purify total RNA, which was then reverse-transcribed (RT) to create cDNA using Maxima reverse transcriptase (Thermo Fisher Scientific) and random hexamers. Real-time, quantitative (q)PCR analysis was then performed using SYBR Green mix (Kapa Biosystems), and the relative expression was determined by the 2−ΔΔCt method. The levels of mRNAs were normalized to human ACTB mRNA or mouse Actb mRNA levels. The primers used for human transcripts, each forward (F) and reverse (R) were: GTTACGGTCGGAGGCCG and GTGAGAGTGGCGGGGTC for p16/CDKN2A mRNA; AGTCAGTTCCTTGTGGAGCC and CATGGGTTCTGACGGACAT for p21/CDKN1A mRNA; AGTGAGGAACAAGCCAGAGC and GTCAGGGGTGGTTATTGCAT for IL6 mRNA; CTTCCAGCCGAGGTCCTT and CCCTGGACACCAACTATTGC for TGFB1 mRNA; GATGGTGGCCTACCTGGAGA and AGAGCTGTGAACTCCGCCCA for BCL2L2 mRNA; GGCTTGACATCATTGGCTGAC and CATTGGGCCGAACTTTCTGGT for BDNF mRNA; GCAAGGCTGATAACGCTGAGGA and CCTGGGCATCAGCGGTCAATG for NTF4 mRNA; CAAGCAGATGGTGGACGTTAAGG and TCGCAGCAGTTCGGTGTCCATT for NTF3 mRNA; ACCCGCAACATTACTGTGGACC and GACCTCGAAGTCCAGATCCTGA for NGF mRNA; CCGACACTGTGGTCATTGGCAT and CAGTTCTCGCTTCAGCACGATG for NTRK3 mRNA; TCGTGGCATTTCCGAGATTGG and TCGTCAGTTTGTTTCGGGTAAA for NTRK2 mRNA; CACTAACAGCACATCTGGAGACC and TGAGCACAAGGAGCAGCGTAGA for NTRK1 mRNA; CATGTACGTTGCTATCCAGGC and CTCCTTAATGTCACGCACGAT for ACTB mRNA; GACCTCAACGCACAGTACGAG and AGGAGTCCCATGATGAGATTGT for PUMA mRNA; GAGCTGGTGGTTGACTTTCTC and TCCATCTCCGATTCAGTCCCT for BCL2L1 mRNA; CCTCAGCATCTTATCCGAGTGG and TGGATGGTGGTACAGTCAGAGC for TP53 mRNA; ATGTGGAGATCATTGAGCAGC and CCTGGTCCTGTGTAGCCATT for RELA mRNA; TCGGGGACTATTACCACTTCTG and CCAGCCACTGTACTTGAGGC for FURIN mRNA; CGGTTCCGCCTGTCTCAAG and CGCCAAAAGTGCCTGTCTT for MMP3 mRNA; and CAGCAGCTTGACACACGGTA and AAACACCAAAGTGGCATGTGA for STAT3 mRNA. The primers used for mouse transcripts, each forward (F) and reverse (R) were: CTGCAAGAGACTTCCATCCAG and AGTGGTATAGACAGGTCTGTTGG for Il6 mRNA; GAAATGCCACCTTTTGACAGTG and TGGATGCTCTCATCAGGACAG for Il1b mRNA; CCCAACGCCCCGAACT and GCAGAAGAGCTGCTACGTGAA for p16/Cdkn2a mRNA; TTCTTTGCAGCTCCTTCGTT and ATGGAGGGGAATACAGCCC for Actb mRNA; and TTGCCAGCAGAATAAAAGGTG and TTTGCTCCTGTGCGGAAC for p21/Cdkn1a mRNA.
Apoptosis-related caspase 3/7 activity was assayed by using the caspase-Glo® 3/7 Assay System (Promega). Briefly, caspase-Glo® 3/7 solution was added directly to each well. The plate was then shaken vigorously for 30 s and incubated at 25 °C in the dark for 30 to 180 min. Luminescence was measured using a GloMax plate reader (Promega) and normalized to cell numbers.
Cells were fixed in 4% PFA (paraformaldehyde) for 10 min and then permeabilized by incubating cells with 0.2% TX-100 for 5 min at 25 °C, unless stated otherwise. After blocking with 10% goat serum for 1 h, primary antibodies were added in the same 10% goat serum buffer and incubated overnight at 4 °C. Antibodies recognized phosphorylated STAT3 (Tyr705, 1:50, Cell Signaling Technology 9145), BDNF (1:40, Santa Cruz Biotechnology sc-65514), TrkB (1:40, Santa Cruz Biotechnology sc-377218), DPP4 (1:100, Cell Signaling Technology 67138), FN1 (1:50, Abcam ab2413), and THBS1 (1:50, Abcam ab85762). Fluorescent signals were detected by adding fluorescent secondary antibodies [Invitrogen, Goat anti-Mouse IgG (H + L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 568; and Goat anti-Rabbit IgG (H + L), Superclonal Recombinant Secondary Antibody, Alexa Fluor 488] in 10% goat serum buffer (1:1000) for 1 h at 25 °C. Finally, DAPI was used to counterstain the nuclei; images were taken using a fluorescence microscope (BZ-X Analyzer, Keyence) and analyzed with ImageJ. The intensity of BDNF immunofluorescence was calculated using ImageJ by analyzing the fluorescent signal present within an area of 10 µm radius from the center of the DAPI-stained nuclei. A total of 60 cells were analyzed per experimental group (20 from each replicate).
Protein extracts were obtained by lysing cells with a denaturing buffer containing 2% sodium dodecyl sulfate (SDS) (Sigma-Aldrich) in 50 mM HEPES. After boiling and sonication, whole-cell protein extracts were size-separated through polyacrylamide gels and transferred to nitrocellulose membranes (Bio-Rad). Membranes were blocked with 5% non-fat dry milk and immunoblotted. Specific primary antibodies were used that recognized phosphorylated p38 MAPK (T180/Y182, Biolegend, Ref. 903501), phosphorylated SAPK/JNK (T183/Y185, 81E11, Cell Signaling Technology, Ref. 4668S), phosphorylated ERK1/2 (T202/Y204, Biolegend, Ref. 675502), phosphorylated AKT (Ser473, Cell Signaling Technology, Ref. 4060S), phosphorylated STAT3 (Y705, D3A7, XP®, Cell Signaling Technology, Ref. 9145S), p53 (DO-1, Cell Signaling Technology, Ref. 18032S), ACTB (β-Actin C4, Santa Cruz Biotechnology, sc-47778), and BCL2L2/BCL-w (Cell Signaling Technology, Ref. 2724S), phosphorylated PKCα/β II (Thr638/641, Cell Signaling Technology, Ref. 9375 S), phosphorylated ERK5 (Thr218/Tyr220, Cell Signaling Technology, Ref. 3375), BCLxL (54H6, Cell Signaling Technology, Ref. 2764S), BCL2 (D55G8, Cell Signaling Technology, Ref. 4223S), phosphorylated Histone H2A.X (Ser139, 20E3, Cell Signaling Technology, Ref. 9718S), p21 (Santa Cruz Biotechnology, sc-53870), TrkA (Cell Signaling Technology, Ref. 2510S), and TrkB (Santa Cruz Biotechnology, sc-136990). After incubation with the required secondary antibodies conjugated with horseradish peroxidase (HRP, Jackson Immunoresearch), the chemiluminescent signals were detected by using the Chemidoc system (Bio-Rad). For western blot analysis of conditioned media, the protein present in 1 ml of medium used for 48 h was precipitated by adding 250 µl of 6.1 N trichloroacetic acid (TCA, Sigma-Aldrich). The mixture was left on ice for 30 min, centrifuged at 14,000 × g for 30 min, washed 3 times in acetone, dried out, and resuspended in 100 µl of 2% SDS and 50 mM HEPES. The processing of BDNF was analyzed by western blot analysis using an anti-BDNF antibody (EPR1292, Abcam, ab108319). To assess TrkB phosphorylation, SuperSep™ Phos-tag™ Precast Gels (Fujifilm) were employed, as they markedly slow down the migration of phosphorylated proteins while unphosphorylated proteins migrate at the expected size. The densitometry analysis of western blots was carried out with ImageJ.
Cell surface proteins were isolated by using the Pierce™ Cell Surface Biotinylation and Isolation Kit (ThermoFisher Scientific, Catalog No. A44390). Briefly, cells were washed twice with PBS and cell surface proteins were labeled for 10 min at 25 °C with a solution of EZ-Link Sulfo-NHS-SS-Biotin, a membrane-impermeable biotinylation reagent, followed by several washes in TBS. Cells were then lysed with the detergent provided in the kit and the same amount of protein extract between different samples was used for the subsequent pulldown. The biotinylated proteins were captured by biotin-affinity purification with a NeutrAvidin™ Agarose Resin (Product No. 29200, ThermoFisher Scientific), followed by several washes with the wash buffer. Dithiothreitol (DTT) (10 mM) was used in the elution buffer to reduce the disulfide bonds in the biotin label and release the bound proteins.
All mouse work, including the import, housing, experimental procedures, and euthanasia, were approved by the Animal Care and Use Committee (ACUC) of National Institute on Aging (NIA). C57BL/6 J Mice (50% male, 50% female) were provided standard chow ad libitum and maintained under a 12:12 h light/dark cycle. For the delivery of Trk inhibitors, 21-month-old mice were injected intraperitoneally for three months and euthanized at 24 months of age. The treatments were performed at the beginning of each month for 5 consecutive days, at the indicated doses (GNF 5837 at 15 mg/kg, PF 06273340 at 20 mg/kg).
Mouse plasma was collected, allowed to clot for 2 h at 25 °C, and centrifuged for 20 min at 2000 × g. Serum was removed and frozen at −80 °C. For the multiplex assay, serum was thawed and centrifuged at 16,000 × g for 4 min. A custom murine Luminex Assay kit was designed by R&D Biosystems to include the following analytes: GDF-15, PAI-1, and TIMP-1. Serum was diluted 1:2 using Calibrator Diluent RD6-52 provided in the kit. Standards (provided with the kit), blanks and serum were incubated with the microparticle cocktail for 2 h at 25 °C, followed by incubation with a biotin-antibody cocktail for 1 h. A final incubation of 30 min with Streptavidin-PE was performed with shaking at 25 °C before running the plate on the Biorad Bioplex-200 instrument. Each incubation was followed by three sets of washes with wash buffer (provided in the kit). Instrument settings were adjusted to the following: 50 µl sample volume, Bio-Plex MagPlex Beads (Magnetic), Double Discriminator Gates set at 8000 and 23,000, low RP1 target value for the CAL2 setting, 50 count/region. The results were analyzed with the Bio-Plex Manager software.
Cells were incubated with (4 µg/ml) 5-Bromo-2′-deoxyuridine (BrdU) diluted in DMEM media with 10% FBS for 24 h. BrdU incorporation was measured following the manufacturer’s protocol (Cell Signaling Technology). Briefly, cells were fixed and denatured prior to the addition of BrdU mouse mAb, and BrdU was detected using the GloMax plate reader (Promega).
Senescence-associated β-Galactosidase (SA-β-Gal) activity was assessed following the manufacturer’s instructions (Cell Signaling Technology). For the in vivo samples, dried slides were pre-washed with PBS plus 1 mM Magnesium Chloride before the addition of staining solution. All the slides were stained at once for 16 h at 37 °C. The resulting images were captured by a fluorescence microscope (BZ-X Analyzer, Keyence) and analyzed with ImageJ.
To perform RT-qPCR analysis of tissue samples, organ pieces were flash-frozen in liquid nitrogen and preserved at −80 °C until required. To extract RNA, tissue samples were introduced in Tri-Reagent (Invitrogen) and disrupted using a tissue homogenizer (Bertin Instruments). RNA extraction was then carried out as indicated by the manufacturer’s instructions. For histological analysis, tissue biopsies were immediately fixed in 4% PFA in PBS at 4 °C overnight. The following day, tissues were cryoprotected in 30% sucrose solution in PBS at 4 °C overnight, and subsequently included in OCT embedding compound, and stored at −80 °C until needed. For tissue cutting, OCT blocks were processed using a cryostat at −20 °C to obtain 10-µm sections that were mounted onto Superfrost™ Plus Microscope Slides (Fisher Scientific) and dried out overnight at 25 °C. Before incubating with antibodies, antigen retrieval was performed with a sodium citrate-based buffer (Abcam) following the manufacturer’s instructions. Tissue slides were then permeabilized with 0.2% TX-100 in PBS for 10 min, blocked with goat serum blocking solution (Biolegend) for 1 h, and incubated overnight with the antibodies. The antibodies used for immunofluorescence recognized BDNF (EPR1292, Abcam, ab108319; 1:100 dilution in blocking solution) and CDKN2A/p16INK4a (2D9A12, Abcam, ab54210; 1:250 dilution in blocking solution). Fluorescent secondary antibodies (Invitrogen) were added in 10% goat serum for 1 h at 25 °C and slides were mounted with ProLong Diamond Antifade Mountant with DAPI (Life Technologies) and coverslips. Immunofluorescence analysis to detect multiple proteins was performed using antibodies that recognized γH2AX (ab140498, Abcam), p-STAT3 (Tyr705, 4113, Cell Signaling Technology), and BDNF in lung, as well as with antibodies that detect p16 (2D9A12, Abcam, ab54210) and tdTomato (TA180009, Thermo Fisher) in liver. Immunofluorescent detection of neuronal marker NeuN (24307, Cell Signaling Technology) together with TUNEL staining in brain cortex were performed by iHisto (iHisto.io). Staining with Sirius red (Picro Sirius Red Stain Kit, ab150681, Abcam) was performed following the manufacturer’s instructions.
Media were collected from cultured cells and centrifuged for 10 min at 1000 × g to remove any precipitates. The arrays (Cytokine Array C5 from RayBiotech) containing the pre-adsorbed antibodies, were blocked for 1 h at room temperature and incubated overnight with the media samples. The arrays were then incubated with a detection antibody and with detection buffers, and developed and analyzed on a Bio-Rad ChemiDoc Imaging System. Arrays included reference points as negative and positive controls for relative quantification.
The concentration of neurotrophins (BDNF, NGF, NT-3, and NT-4) in conditioned media was quantified by performing individual ELISA assays (Raybiotech) following the manufacturer’s instructions. Conditioned media were collected after 48 h of culture in different cell conditions (proliferation or senescence). Subsequent analyses were performed without media dilution. Values were normalized to cell counts at the time of media collection.
Urea and creatinine assay kits (MAK006 and MAK080, respectively, Sigma-Aldrich) were used with mouse serum following the manufacturer’s protocol.
Bulk RNA was extracted with RLT buffer (Qiagen) and purified using the QIAcube system (Qiagen) using RNeasy plus. The quality and quantity of RNA were assessed using the Agilent RNA 6000 nano kit on the Agilent Bioanalyzer. High-quality RNA (125 ng) was used to prepare a sequencing library using Illumina TruSeq Stranded mRNA Library prep kit following the manufacturer’s protocol (Illumina, San Diego, CA). The quality and quantity of the libraries were checked using the Agilent DNA 1000 Screen Tape on the Agilent Tapestation. Paired-end sequencing was performed for 103 cycles with an Illumina NovaSeq sequencer. BCL files were de-multiplexed and converted to FASTQ files using bcl2fastq program (v2.20.0.422). FASTQ files were trimmed for adapter sequences using Cutadapt version v1.18 and aligned to human genome hg19 Ensembl v82 using STAR software v2.4.0j. featureCounts (v1.6.4) software was used to generate gene counts. The Bioconductor package DESeq2 version 1.30.0 in R (version 4.0.3) was used to compare counts levels; statistical testing was carried out with Wald test. Transcripts were considered differentially regulated if absolute log2 fold change was >1 and the Benjamini–Hochberg adjusted p-value was <0.05. Functional analysis of the differentially expressed genes was performed by using normalized counts in the GSEA platform. Single-cell RNA-seq libraries were prepared according to the user guide of the Chromium Next GEM Single Cell 3′ Reagent Kits v3.1 (10x Genomics). Briefly, etoposide-induced senescent WI-38 cells were trypsinized, washed with PBS, and resuspended in 10% FBS and 0.1 mM EDTA in PBS at a concentration of 900–1,000 cells/μl. The single-cell suspension was loaded into a Chromium Next GEM Chip G (10x Genomics), and GEMs were generated using the Chromium Single Cell Controller (10x Genomics). After 11 and 13 cycles of cDNA amplification and Sample Index PCR (Single Index Kit T Set A, 10x Genomics), paired-end sequencing was performed on a SP100 flow cell on an Illumina NovaSeq platform. The raw single-cell RNA-seq data were processed using Cell Ranger software 6.0.0 (10x Genomics) and sequencing reads were mapped to the pre-built human reference (GRCh38) (version 2020-A, 10x Genomics). Filtered matrix files generated by Cell Ranger were imported and analyzed using R package Seurat 4.1.0. Cells expressing <200 or >6,000 genes and with >10% expression of mitochondrial genes were excluded from downstream analysis. The data were normalized and linearly transformed, and were subjected to variable features identification, principal component analysis, cluster analysis, and dimensional reduction analysis (UMAP), following the standard pre-processing workflow for single-cell RNA-seq data in Seurat. Gene Set Enrichment Analysis (GSEA) was performed using R packages including Seurat 4.1.0, msigdbr 7.4.1, fgsea 1.18.0, and presto 1.0.0, with identification of ‘HALLMARK_IL6_JAK_STAT3_SIGNALING’ from hallmark gene sets, and ‘DAUER_STAT3_TARGETS_UP’ and ‘AZARE_STAT3_TARGETS’ from curated gene sets are enriched in BDNF-expressing single-cell clusters. RNA-seq data (bulk and single-cell) are deposited in GSE202951 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE202951).
The schematics in Figs. 1 and 5–7 were created using BioRender, with a license to the NIA IRP. Data are presented as the means ± standard deviation (S.D.) of at least n = 3 independent experiments. Individual data points are displayed in all the bar plots. Significance was established using two-tailed Student’s t-test (*p < 0.05, **p < 0.01, ***p < 0.001). All analyses were performed with Prism 9.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
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PMC9585075 | Jicong Du,Lan Fang,Jianpeng Zhao,Yike Yu,Zhenlan Feng,Yuedong Wang,Ying Cheng,Bailong Li,Fu Gao,Cong Liu | Zymosan-A promotes the regeneration of intestinal stem cells by upregulating ASCL2 | 20-10-2022 | Experimental models of disease,Drug development,Gastroenteritis | Intestinal stem cells (ISCs) are responsible for intestinal tissue homeostasis and are important for the regeneration of the damaged intestinal epithelia. Through the establishment of ionizing radiation (IR) induced intestinal injury model, we found that a TLR2 agonist, Zymosan-A, promoted the regeneration of ISCs in vivo and in vitro. Zymosan-A improved the survival of abdominal irradiated mice (81.82% of mice in the treated group vs. 30% of mice in the PBS group), inhibited the radiation damage of intestinal tissue, increased the survival rate of intestinal crypts and the number of ISCs after lethal IR in vivo. Through organoid experiments, we found that Zymosan-A promoted the proliferation and differentiation of ISCs after IR. Remarkably, the results of RNA sequencing and Western Blot (WB) showed that Zymosan-A reduced IR-induced intestinal injury via TLR2 signaling pathway and Wnt signaling pathway and Zymosan-A had no radioprotection on TLR2 KO mice, suggesting that Zymosan-A may play a radioprotective role by targeting TLR2. Moreover, our results revealed that Zymosan-A increased ASCL2, a transcription factor of ISCs, playing a core role in the process of Zymosan-A against IR-induced intestinal injury and likely contributing to the survival of intestinal organoids post-radiation. In conclusion, we demonstrated that Zymosan-A promotes the regeneration of ISCs by upregulating ASCL2. | Zymosan-A promotes the regeneration of intestinal stem cells by upregulating ASCL2
Intestinal stem cells (ISCs) are responsible for intestinal tissue homeostasis and are important for the regeneration of the damaged intestinal epithelia. Through the establishment of ionizing radiation (IR) induced intestinal injury model, we found that a TLR2 agonist, Zymosan-A, promoted the regeneration of ISCs in vivo and in vitro. Zymosan-A improved the survival of abdominal irradiated mice (81.82% of mice in the treated group vs. 30% of mice in the PBS group), inhibited the radiation damage of intestinal tissue, increased the survival rate of intestinal crypts and the number of ISCs after lethal IR in vivo. Through organoid experiments, we found that Zymosan-A promoted the proliferation and differentiation of ISCs after IR. Remarkably, the results of RNA sequencing and Western Blot (WB) showed that Zymosan-A reduced IR-induced intestinal injury via TLR2 signaling pathway and Wnt signaling pathway and Zymosan-A had no radioprotection on TLR2 KO mice, suggesting that Zymosan-A may play a radioprotective role by targeting TLR2. Moreover, our results revealed that Zymosan-A increased ASCL2, a transcription factor of ISCs, playing a core role in the process of Zymosan-A against IR-induced intestinal injury and likely contributing to the survival of intestinal organoids post-radiation. In conclusion, we demonstrated that Zymosan-A promotes the regeneration of ISCs by upregulating ASCL2.
The intestinal epithelium ensures the intestinal to achieve important functions such as absorptive, secretory, and barrier functions [1]. The intestinal epithelium is renewed approximately every 5 days and this renewal is accomplished by intestinal stem cells (ISCs) located at the base of the crypts [2]. ISCs are intermingled with Paneth cells and marked by the expression of several markers such as Lgr5. By using Lgr5-EGFP-IRES-creERT2 mice, researchers have found that Lgr5+ ISCs are responsible for intestinal tissue homeostasis and are important for the regeneration of the damaged intestinal epithelia [3]. ISCs are activated rapidly in response to stressful insults, allowing rapid and efficient restoration of the damaged epithelium [4]. This response can be observed upon infection with a pathogen, as well as damage induced by IR [5, 6]. Intestinal tissue is extremely sensitive to IR and exposure to high doses of IR can cause severe intestinal injury and the death of ISCs. We established mice and intestinal organoids models to explore the mechanism of ISCs regeneration after IR. Toll-like receptors (TLRs), as one of the major families of patterns, recognize receptors (PRRs) and play significant roles in the immune system process and inflammatory response [7, 8]. Recent Studies showed that the functions of TLR2/4/5/8 are involved in the pathogenesis of various intestinal diseases such as inflammatory bowel disease and colorectal cancer in vivo [9, 10]. The study of TLRs and intestinal homeostasis has brought a new perspective to solve the problem of IR-induced intestinal damage. Matthew A Ciorba et al. reported that Lactobacillus probiotic protects intestinal epithelium from radiation injury in a TLR2/COX2-dependent manner [11]. Our team also demonstrated that the activation of TLR4 by monophosphoryl lipid A (MPLA) played a role in intestinal radioprotection [12]. These studies remind us that targeting TLRs can prevent and treat IR-induced intestinal injury. Zymosan-A, which is extracted from the cell wall of yeast (Saccharomyces cerevisiae), has been demonstrated as a potent ligand of TLR2 [13]. In this study, we found that Zymosan-A can upregulate the expression of ASCL2 and promote the regeneration of ISCs and activate TLR2 signaling pathway and WNT signaling pathway, resulting in mitigated IR-induced intestinal injury and improved mouse survival. In addition, Zymosan-A might be a potentially highly effective and selective intestinal radioprotective agent.
Zymosan-A was purchased from Sigma Aldrich Corp (St. Louis, MO, USA), WR2721 and normal saline (NS) was obtained from ChangHai Hospital (Shanghai, China). The PCR kit (RR036A and RR420A) was purchased from TAKARA (Japan). PBS, RPMI 1640, DMEM, and fetal bovine serum (FBS) were supplied by Gibco (New York, USA). IntestiCult™ Organoid Growth Medium was obtained from STEM CELL (Canada). The antibodies for Western blot (GAPDH, YAP1, WNT5A, WNT3A, MYD88, TLR2, OLFM4, ASCL2, CYCLIND1, AXIN2) were purchased from Cell Signaling Technology (Massachusetts, USA). The antibody for western blot (ASCL2) was purchased from Biorbyt (Cambridge, United Kingdom). In Situ Cell Death Detection Kit was obtained from Roche (Basel, Switzerland). Small molecule Foscenvivint (ICG-001) was purchased from Selleck. The primes were obtained from Shenggong Biotech (Shanghai, China).
Male and female C57BL/6 mice, aged 6–8 weeks old, were obtained from China Academy of Science (Shanghai, China). TLR2 KO mice and Lgr5-EGFP-IRES-creERT2 mice (JAX stock #008875, RRID: IMSR_JAX:008875), aged 6–8 weeks old, were purchased from Cyagen (Jiangsu, China). All mice were housed in a laboratory animal room under standard conditions. The experiments were approved by the Laboratory Animal Center of the Naval Medical University, China in conformance with the National Institute of Health Guide for the Care and Use of Laboratory Animals. We used WR2721 as the positive agent and PBS as the negative agent. The mice were treated with Zymosan-A (25.0 mg/Kg, dissolved in NS) or PBS (200 μl/mice) via peritoneal injection 24 and 2 h before IR, WR2721 (360.0 mg/kg) was used as a positive agent.
60Co (Naval Medical University, China) was used to irradiate the mice and the intestinal organoids at room temperature. The mice were irradiated at 8.0, 8.5, 9.5, or 12.0 Gy to establish the Total Body Irradiation (TBI) model. In order to establish the Abdominal Irradiation (ABI) model, mice hind limbs were shielded with the lead plate to avoid hematopoietic cell death before 20.0 Gy IR exposure.
Small intestinal tissues were collected from mice and then fixed in 4% paraformaldehyde after IR. Hematoxylin and Eosin (HE), TUNEL (Terminal deoxynucleotidyl transferase dUTP nick-end labeling) staining, Ki-67 staining, and BrdU staining, and Phloxine staining were performed according to the manufacturer’s instructions. The TUNEL+ cells were counted in 10 crypts per section. The Ki-67-positive area per section was measured using ImageJ software (National Institutes of Health, Bethesda, MD, USA).
Lgr5 FISH was used to detect the level of Lgr5 transcripts in mice intestines. The small intestine was removed from mice and then fixed in 4% paraformaldehyde after IR. The Lgr5 FISH probe (Sequence: 5‘-CY3-GACGACAGGCGGTTGGACGATAGGT-CY3-3’) is designed to hybridize to the LGR5 gene. Lgr5+ FISH was conducted according to the manufacturer’s instructions. Fluorescence microscopy was used to observe FISH results.
Immunofluorescence analysis was used to detect the expression of OLFM4. The intestinal tissues or intestinal organoid was fixed in 4% paraformaldehyde for 25 min and permeabilized in 0.5% Triton X-100 for 10 min. After blocking in BSA, the intestinal tissues or intestinal organoid was stained with antibodies, followed by the secondary antibody (1:1000). The images were obtained with a fluorescent microscope.
The small intestine was removed from the mice and rinsed with cold PBS after a longitudinal incision. The villi were gently scraped and the remaining tissue was washed ~10 times with cold PBS. The tissue was cut into 2–3 mm fragments, transferred to 15 mM EDTA/PBS, and incubated at 4 °C for 1 h. After incubation, the tissue fragments were shaken vigorously and pelleted approximately three times with cold PBS at 290 rpm for 5 min. Then isolated crypts were embedded in Matrigel (Corning, New York, USA) and cultured in a crypt culture medium (IntestiCult™ Organoid Growth Medium, STEM CELL, Canada). The intestinal organoid culture medium was changed every 3 days. For radiation experiments, mouse intestinal isolated crypts suspended in Matrigel (250 crypts/50 μl per well) were placed at the center of each well of 24-well plates. Next, 500 μl organoid growth medium was dispensed to each well. Organoids were irradiated 24 h after inoculation. Next, Zymosan-A was added to the cells 12 and 2 h before IR. The mature organoids were observed under a microscope on day 5 after IR. For radiation response assays, the surface area and budding situation of the organoids were measured using Image J software (National Institutes of Health, Bethesda, MD, USA). Lgr5+ FISH, OLFM4 Immunofluorescence, and Ki-67 staining were used to observe the organoids.
Total RNA was isolated from the intestine of mice using Trizol (Invitrogen, USA) at 24 h after radiation. NanoVue (GE, USA) was used to assess RNA purity. Each RNA sample had an A260:A280 ratio greater than 1.8 and an A260:A230 ratio greater than 2.0. Sequencing was performed at Oebiotech (Shanghai, China) with the Illumina HiSeq 2500 system. Prior to sequencing, the raw data were filtered to produce high-quality clean data. All the subsequent analyses were performed using clean data. All the differentially expressed genes were used for heatmap analysis, Gene Ontology Analysis, and KEGG ontology enrichment analyses.
Total protein from the intestine was extracted using a mammalian protein extraction reagent according to the manufacturer’s protocol and then analyzed by western blotting to detect GAPDH, TLR2, MYD88, YAP1, WNT5A, WNT3A, OLFM4, ASCL2, CYCLIND1, and AXIN2. The secondary antibody (1:1000) was also purchased from Cell Signaling Technology.
Data were expressed as means ± the standard errors of means. Two-tailed Student’s t-test was used to analyze the differences between two groups. One-way ANOVA was employed to analyze the differences among three groups. Kaplan–Meier analysis was applied to estimate the difference in overall survival between two groups. The data were analyzed using SPSS ver. 19 software (IBM Corp, Armonk, New York, USA). P < 0.05 was considered statistically significant.
In our previous work, we showed that the radioprotective effect of Zymosan-A is even better when Zymosan-A was given 24 and 2 h before IR. To find out the best dose of Zymosan-A, we did the dose-response analysis of Zymosan-A, and WR2721 (360 mg/kg) was used as the positive agent. The dose-response curve of Zymosan-A revealed that the optimal dose of the Zymosan-A is 25.0 mg/kg at 24 and 2 h before IR (Fig. S1A). To explore the roles of Zymosan-A in the process of ISCs regeneration in vivo, C57BL/6 mice were treated with Zymosan-A (25.0 mg/kg) intraperitoneally at 24 h and 2 h before IR. The negative control mice were treated with the same amount of PBS. Then mice were exposed to 9.5 Gy and 12.0 Gy total body irradiation (TBI). The results showed that the survival rates of Zymosan-A group were increased after 9.5 and 12.0 Gy TBI (Fig. 1A, B). The TBI results proved the radioprotection of Zymosan-A. Then we established the 20.0 Gy abdominal irradiation (ABI) model, and we found that Zymosan-A protected 81.82% of the mice from IR-induced death, while only 30% of control mice survived beyond 30 days (Fig. 1C). These survival results showed that Zymosan-A could significantly improve the 30-day survival rate and prolong the average survival time of irradiated mice under TBI and ABI. Regretfully, we found that Zymosan-A had a weak therapeutic effect on mice after irradiation by the established mice TBI model (Fig. S1B). In addition, Zymosan-A also had a significant protective effect on the body weight change of mice after 20.0 Gy ABI (Fig. 1D). In order to observe the effect of Zymosan-A on the intestinal function after IR exposure, we recorded the feces of the mice at different time points after 20.0 Gy ABI and found that the fecal quality of the mice in the Zymosan-A treated group was significantly higher than that in PBS group (Fig. 1E, F). Moreover, the gross changes in mice intestines were observed 84 h after IR, which showed that the Zymosan-A could significantly reduce the degree of bleeding and edema in the intestinal tissue (Fig. 1G). Taken together, these results revealed that Zymosan-A has an obvious radioprotective effect on irradiated mice and Zymosan-A mitigated IR-induced intestinal injury in mice.
Subsequently, the small intestine of mice was collected after IR to evaluate the degree of intestinal injury. C57BL/6 mice were treated with Zymosan-A (25.0 mg/kg) intraperitoneally at 24 and 2 h before IR. The control mice were treated with the same amount of PBS. Then mice were exposed to 9.5 Gy TBI. At 4, 24, 48, and 84 h after 9.5 Gy TBI. The small intestine of mice was taken for HE staining. HE staining results showed that Zymosan-A could improve the intestinal integrity of mice after IR (Fig. 2A). The number of crypts per circumference (Fig. 2B), the villi length (Fig. 2C), and the depth of crypts (Fig. 2D) in Zymosan-A treated group was better than that in the control group. Microcolony formation assay serves as a surrogate for ISCs survival. Ki-67 immunohistochemistry was applied to help visualize the microcolony formation assay and the results of Ki-67 showed that Zymosan-A group had more Ki-67+ cells in crypts at 24 and 48 h after IR, which means that Zymosan-A could promote the regeneration of ISCs (Fig. 2E, F). Moreover, BUDR staining was also preformed and BUDR staining results showed that Zymosan-A could promote the regeneration of ISCs (Fig. S1D). In addition, TUNEL staining was used to assay the apoptosis of crypts showing that Zymosan-A could significantly inhibit the apoptosis of intestinal crypts after IR (Fig. 2G, H). These results proved that Zymosan-A had great radioprotective effects on IR-induced intestinal injury.
ISCs are responsible for intestinal tissue homeostasis and are important for the regeneration of the damaged intestinal epithelia. The Ki-67 result suggested Zymosan-A could promote the regeneration of ISCs. Next, we focused on the regeneration of ISCs after being treated with Zymosan-A or PBS. Lgr5 (leucine-rich-repeat-containing G-protein-coupled receptor 5) is both a marker of adult ISCs and a critical modulator of their activity via its role as an effector of Wnt signaling [14]. As a marker of ISCs, the Lgr5+ crypt base columnar cells generate all epithelial lineages, suggesting that it represents the genuine stem cell of the small intestine [1]. Lgr5+ ISCs has great major advances in the process of stem cell biology during homeostasis, regeneration, and disease [15]. To determine whether Zymosan-A acted directly on ISCs, Lgr5+ fluorescence in situ hybridization experiments (FISH) were conducted. The surviving ISCs in the intestinal crypts at 48 and 84 h after IR with or without Zymosan-A treatment were evaluated and the Lgr5+ FISH result showed that IR could significantly reduce the number of Lgr5+ ISCs, while Zymosan-A significantly increased the number of Lgr5+ ISCs (Fig. 3A, C). These data proved that Zymosan-A exerted a protective effect on IR-induced ISCs injury. Furthermore, we applied another ISCs marker, OLFM4, to investigate the mitigation effect of Zymosan-A on IR-induced ISCs injury. OLFM4 is expressed by intestinal epithelial cells residing at the base of the crypt, including the crypt base columnar cells. The numbers and functions of ISCs were assessed by analyzing OLFM4+ ISCs. The results of OLFM4 immunofluorescences showed that Zymosan-A could significantly increase the number of OLFM4+ ISCs in the intestine of irradiated mice (Fig. 3B, D). In addition, Phloxine staining was preformed to evaluate the number of Paneth cells and we found that there were no changes in the number of Paneth cells between Zymosan-A treated group and the control group (Fig. S2). Taken together, Zymosan-A could significantly increase the number of Lgr5+ ISCs and OLFM4+ ISCs in the intestinal tract of irradiated mice, suggesting that Zymosan-A may play a radioprotective effect by promoting the regeneration of ISCs after IR.
To better analyze the regeneration of ISCs after IR, we used Lgr5-EGFP-IRES-creERT2 mice as a way to quantitate Lgr5+ ISCs. Lgr5-EGFP-IRES-creERT2 mice were treated with Zymosan-A or PBS before IR. Then the intestine fluorescence analysis was used to detect the expression of Lgr5. This result showed that IR could significantly reduce the number of Lgr5+ ISCs, while Zymosan-A significantly increased the number of Lgr5+ ISCs (Fig. 4A). Intestinal organoids are three-dimensional spheroids with an intact gut epithelial structure and contain all types of enteric epithelial functional cells, including intestinal epithelial cells, enteroendocrine cells, goblet cells, Paneth cells and Lgr5+ ISCs [16]. The intestinal organoid is a great technology to study the proliferation, differentiation, and regeneration of ISCs in vitro. Intestinal organoid culturing has also been applied to study the pathophysiology of intestinal diseases [17]. In this work, Intestinal organoids were also used to explore the regeneration of ISCs after Zymosan-A treatment. Intestinal crypts of C57BL/6 mice were extracted for organoid culture, and then it was stimulated with Zymosan-A (25.0 μg /ml) or PBS 12 and 2 h before 6.0 Gy IR. The changes in organoids were observed after 7 days. Crypts were extracted from Lgr5-EGFP-IRES-creERT2 to culture intestinal organoids. As shown in Fig. 4B. Lgr5 fluorescence was also conducted in intestinal organoids and results showed that Zymosan-A also increased the number of Lgr5+ ISCs (Fig. 4B). Moreover, the results of intestinal organoids from wild mice showed that Zymosan-A could improve the ability of organoid formation (Fig. 4C). Compared with the PBS group, the relative volume of single intestinal organoid in the Zymosan-A group was increased (Fig. 4D). And the percent of budding intestinal organoids was increased in the Zymosan-A group (Fig. 4E). Ki-67 and TUNEL staining were also performed on the intestinal organoids after 7 days. We found that the MFI of Ki-67 was increased in Zymosan-A (Fig. 4F, G), and the MFI of TUNEL was decreased in Zymosan-A group. These data suggested that Zymosan-A could significantly promote the proliferation and inhibit apoptosis of the intestinal organoids after IR (Fig. 4H, I). These results suggested that Zymosan-A could significantly improve the proliferation and differentiation of irradiated ISCs.
RNA-sequence (RNA-seq) technology was performed to elucidate the mechanism of Zymosan-A mediated ISCs regeneration. The intestinal tissues of mice were taken for RNA-seq at 24 h after 9.5 Gy TBI. We screened out differentially expressed genes (DEGs) and performed secondary analysis, and a total of 169 DEGs were screened out by the secondary analysis (Supplementary Table S1), including 102 upregulated DEGs and 67 downregulated DEGs, and cluster analysis was conducted on the DEGs (Fig. 5A). Then we performed KEGG and GO analyses to elucidate the biological functions of DEGs. KEGG pathway enrichment analysis showed that DEGs were significantly enriched in MMU04620: Toll-like receptor signaling Pathway, MMU04310: Wnt Signaling Pathway, and MMU04150: MTOR Signaling Pathway, MMU04390: Hippo Signaling Pathway, etc. (Fig. 5B) (Supplementary Table S2). GO functional enrichment analysis also found that GO0016055: Wnt signaling Pathway and GO0002755: MyD88-Dependent Toll-like receptor signaling Pathway were significantly enriched (Fig. 5C) (Supplementary Table S3). RNA-Seq results indicated that Zymosan-A may promote the regeneration of ISCs by activating the Toll-like receptor signaling pathway and Wnt signaling pathway. Zymosan-A has been demonstrated as a potent ligand of TLR2. In our previous study, we found that TLR2 had a critical role in radioresistance in vivo [18]. Compared with WT mice, TLR2 KO mice were more susceptible to radiation-induced death. Furthermore, TLR2 KO mice were used to verify the function of TLR2 in the process of Zymosan-A mediated intestinal radioprotection. The survival date showed that Zymosan-A had no radioprotective effects on TLR2 KO mice (Fig. 5D), suggesting that Zymosan-A played a radioprotective effect by targeting the activation of the TLR2 signaling pathway. In addition, we detected the level of IL-6 and GM-CSF in the serum of mice pretreated with Zymosan-A or PBS, and we found that the serum levels of IL-6 and GM-CSF were upregulated in vivo at 24 h after IR (Fig. 5E). The KEGG pathway enrichment analysis showed that DEGs were significantly enriched in Wnt Signaling Pathway and Toll-like receptor signaling Pathway. Subsequently, we verified the changes of key proteins in Toll-like receptor signaling Pathway in intestinal tissues. The WB result showed that Zymosan-A significantly upregulated the expression levels of key molecules such as TLR2, MyD88 with or without IR exposure (Fig. 5F), which was consistent with the previous RNA-Seq (Fig. 5B, C). Moreover, the expression levels of key molecules in Wnt signaling Pathway and Toll-like receptor signaling Pathway were also evaluated in intestinal organoids. By using WB, we found that TLR2, Wnt 3a, β-catenin, OLFM4, and ASCL2 were increased (Fig. 5G). We used Foscenvivint (ICG-001), a selective small molecule inhibitor of CBP/β-catenin complex formation, to inhibit the Wnt Signaling Pathway. Compared with the Zymosan-A treated organoids, the Zymosan-A + ICG-001 treated organoids were more susceptible to radiation-induced deaths (Fig. 5H). Zymosan-A protected PBS-treated organoids from radiation-induced death but had no radioprotective effects on the ICG-001 treated organoids (Fig. 5H). These findings consistently indicated that Zymosan-A induced radioprotective effects via Wnt Signaling Pathway. Taken together, the RNA-Seq and TLR2 KO mice results suggested that Zymosan-A may promote the regeneration of ISCs by activating TLR2 signaling pathway and Wnt signaling pathway.
The RNA-Seq result suggested that Zymosan-A could significantly regulate the expression of several genes related to ISCs and intestinal injury response. Among the genes regulating intestinal stem cells, we found that ASCL2 was significantly upregulated by Zymosan-A (Fig. 6A). ASCL2 (Achaette-scute Homologue 2) is a member of the basic helical ring helical (bHLH) family [19]. As a transcription factor, ASCL2, itself encoded by a crypt-specific Wnt target gene, is a master regulator of intestinal stem cell identity. Ectopic expression of ASCL2 using the intestinal epithelium-specific Villin promoter induces hyper-proliferation of crypts, expansion of the expression domain of the ISCs markers Lgr5 and Sox9, and the formation of hyperproliferative pockets on the villus. This has led to the conclusion that ASCL2 is a master regulator of crypt stemness [20]. As a downstream target of the Wnt pathway, ASCL2 protein upregulates LGR5 and OLFM4. According to the result of RNA-Seq and published research, we try to figure out the function of ASCL2 in the process of Zymosan-A mediated intestinal radioprotection. Then we found that the expression of ASCL2 could be significantly upregulated by Zymosan-A by using QT-PCR (Fig. 6B) and WB (Fig. 5G). The expression of ASCL2 in the irradiated intestine was also assayed by immunofluorescence, and we found that the expression of ASCL2 could be significantly upregulated by Zymosan-A (Fig. 6C, E). Moreover, the expression of ASCL2 in intestinal organoids was evaluated. As shown in Fig. 6D, F, Zymosan-A could significantly upregulate the expression of ASCL2 in intestinal organoids after IR. To further explore the role of ASCL2 in the process of Zymosan-A against IR-induced intestinal injury, shRNA was used to knock down (KD) the level of ASCL2 in intestinal organoids. Verified by quantitative PCR, Sequence 146 was used to establish ASCL2 KD intestinal organoids (Fig. 6G). As shown in Fig. 6H, ASCL2 KD Organoids have altered morphology and are more sensitive to IR, and the intestinal radioprotection of Zymosan-A was significantly inhibited after ASCL2 KD. The survival rate, size, and budding rate of intestinal organoids significantly decreased in ASCL2 KD group (Fig. 6I–K). The results of organoids showed that ASCL2 plays a central role in the process of Zymosan-A mediated ISCs regeneration, and ASCL2 may be a potential new therapeutic target. Taken together, these results suggest that Zymosan-A promotes the regeneration of ISCs by upregulating ASCL2.
The continuous regeneration of intestinal epithelium emphasizes the vital role of ISCs in the intestinal tissue, which requires a tightly regulated balance between ISCs proliferation and differentiation [21]. ISCs located at the crypt base migrate upward and differentiate into all the different types of mature epithelial cells to maintain the homeostasis of the intestinal epithelium under steady conditions [22, 23]. Since intestinal epithelial cells directly contact pathogenic environmental factors, ISCs are susceptible to epithelial damage induced by chemicals, pathogens, or irradiation [24–26]. The intestine must respond positively when subjected to stressful insults like IR and pathogens to maintain epithelial integrity. ISCs are much radiosensitive. The common outcome of IR-induced intestinal damage is the loss of Lgr5+ ISCs [4, 27]. However, there is more than one type of intestinal stem cell that plays an important role in epithelial damage. When Lgr5+ ISCs are depleted, Bmi1+ cells localized around the +4 position possess ISCs properties and rapidly revert to Lgr5+ ISCs to sustain intestinal homeostasis [28]. These re-emerged Lgr5+ stem cells have been proven to be essential for post-radiation epithelial repair because Lgr5+ ISCs are required for post-radiation epithelial recovery [29]. As sensors of microbial infection, TLRs are critical for the initiation of immune defense responses and inflammatory [30, 31]. In recent years, research has been reported about TLRs playing a critical role in radiation protection [11, 32, 33]. Zymosan-A, an extract of the yeast cell wall, has been proven to protect against radiation-induced hematopoietic damage by targeting TLR2 in our previous study [18]. In this study, we found that Zymosan-A exhibited significant radioprotective effects on IR-induced intestinal injury in vivo and in vitro. Through animal experiments, we found that Zymosan-A could significantly improve the 30-day survival rate and prolong the average survival time of irradiated mice under TBI and ABI. The gross changes in mice intestines also showed that the Zymosan-A could significantly reduce the degree of bleeding and edema in the intestinal tissue. The results of HE staining, Ki-67 immunohistochemistry, and TUNEL staining revealed that Zymosan-A could significantly promote the regeneration of ISCs and inhibit the apoptosis of intestinal crypts after IR. ISCs are responsible for intestinal tissue homeostasis and are important for the regeneration of the damaged intestinal epithelia. Over the years, many studies have been carried out on the mechanism and prevention of IR-induced intestinal injury, proving that ISCs play a central role in the process of IR-induced intestinal injury [34]. IR can directly damage ISCs, and impair the proliferation and differentiation of ISCs [35]. Understanding the regulatory mechanisms of ISCs is key to preventing and treating IR-induced intestinal injury. So we focused on the regeneration of ISCs after IR and found that Zymosan-A could significantly increase the number of Lgr5+ ISCs and OLFM4+ ISCs in the intestinal tract of irradiated mice. To better analyze the regeneration of ISCs after IR, Lgr5-EGFP-IRES-creERT2 mice were used to quantitate Lgr5+ ISCs, and the intestine fluorescence analysis showed that IR could significantly reduce the number of Lgr5+ ISCs, while Zymosan-A significantly increased the number of Lgr5+ ISCs. These results suggested that Zymosan-A may play a radioprotective effect by promoting the regeneration of ISCs after IR. Intestinal organoids have unlimited proliferation ability and can simulate many characteristics of the intestine [16]. It can accurately simulate the physiological state of the intestinal epithelium and provided a new research approach for ISCs [36]. Through organoid experiments, we found that Zymosan-A improved the ability of crypt organoid formation. Compared with the IR group, the Zymosan-A group had more extensive crypt organoid formation, and the number and volume of single organoid buds were increased. Ki-67 staining and TUNEL staining of intestinal organoids showed that Zymosan-A could promote the proliferation of intestinal organoids and inhibit the apoptosis of intestinal organoids after IR. These results confirmed that Zymosan-A significantly prompted the regeneration of ISCs in intestinal organoids after IR. To determine the possible mechanism underlying radioprotection by Zymosan-A, we performed RNA-Seq technology to explore the mechanism of Zymosan-A mediated intestinal radioprotection. We screened out 169 DEGs, including 102 upregulated DEGs and 67 downregulated DEGs. The results of KEGG and GO analyses indicated that Zymosan-A may play a radioprotective role in IR-intestinal injury by activating Toll-like receptor signaling pathway and Wnt signaling pathway. Moreover, the survival data showed that Zymosan-A had no radioprotective effects on TLR2 KO mice, and the expression levels of key molecules in Wnt signaling Pathway and TLR2 signaling Pathway were also evaluated in intestinal organoids by using WB. By using ICG-001, a selective small molecule inhibitor of CBP/β-catenin complex formation, we found that Zymosan-A protected PBS-treated organoids from radiation-induced death, but had no radioprotective effects on the ICG-001 treated organoids, which indicated that Zymosan-A induced radioprotective effects via Wnt Signaling Pathway. Taken together, the RNA-Seq and TLR2 KO mice results suggested that Zymosan-A may play a radioprotective role in IR-induced intestinal injury by activating TLR2 signaling pathway and Wnt signaling pathway. The RNA-Seq result suggested that Zymosan-A could significantly regulate the expression of several genes related to ISCs and intestinal injury response. Among the genes regulating ISCs, we found that ASCL2 was significantly upregulated by Zymosan-A. As a transcription factor, ASCL2 also has been identified as a WNT/β-catenin target gene, and which was shown to be expressed in the mouse intestinal crypt as well as human and mouse intestinal cancers [37]. ASCL2 is a highly restricted fashion in ISCs. Ectopic of ASCL2 in the mouse intestine led to crypt hyperplasia, an expansion in the size of the crypt domain and the formation of ectopic crypts, expansion of the expression domain of the ISCs markers Lgr5 and Sox9, and the formation of hyperproliferative pockets on the villus, whereas genetic deletion of ASCL2 led to a loss of the Lgr5+ ISCs. According to the result of RNA-Seq and published research, we try to further explore the role of ASCL2 in the process of Zymosan-A against IR-induced intestinal injury, firstly we used immunofluorescence and found that the expression of ASCL2 in the irradiated intestine and intestinal organoids were evaluated. Moreover, the intestinal radioprotection of Zymosan-A was significantly inhibited after ASCL2 KD. These results suggest that the intestinal radioprotection of Zymosan-A was ASCL2-dependent.
In conclusion, Zymosan-A can upregulate the expression ASCL2 and promote the regeneration of ISCs by activating TLR2 signaling pathway and WNT signaling pathway, resulting in mitigated IR-induced intestinal injury and improved mouse survival (Fig. S3). Our work demonstrated that Zymosan-A promotes the regeneration of ISCs by up-regulating ASCL2. Zymosan-A may be an effective radioprotective drug for the prevention and treatment of IR-induced intestinal injury.
supplementary figure and table legends Figure S1. Figure S2. Figure S3. Figure S4. Supplementary Table 1. Supplementary Table 2. Supplementary Table 3. Reproducibility checklist | true | true | true |
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PMC9585189 | Shijia Yu,Jing An,Ran Sun,Juan Feng,Mingjun Yu | LncRNA KCNQ1OT1 predicts further cerebral events in patients with transient ischemic attack 10.3389/fphar.2022.961190 | 07-10-2022 | lncRNAs,transient ischemic attack,KCNQ1OT1,hs-CRP,inflammation,cerebral ischemic stroke | Transient ischemic attack (TIA) poses a great threat of cerebrovascular diseases to a large number of patients, despite its reversible neurological dysfunction. Long non-coding RNAs (lncRNAs) have been proven to play critical roles in the pathophysiological development of cerebrovascular events. Exploring the function of lncRNAs in modulating TIA prognosis would help to develop individualized therapeutics. A total of 231 participants with the first onset of TIA were recruited in the study, including 65 subsequent stroke patients. The expression of lncRNA potassium voltage-gated channel subfamily Q member 1 opposite strand 1 (KCNQ1OT1) was upregulated in patients with recurrent ischemic events after TIA. Additionally, KCNQ1OT1 could be regarded as an independent predictor for subsequent ischemic stroke. The optimal diagnostic value was determined at 1.29 with a sensitivity of 63% and a specificity of 72%. Fewer patients would survive from further ischemic stroke with their KCNQ1OT1 level over 1.29. Furthermore, the expression of KCNQ1OT1 was elevated with a growing serum high-sensitivity C-reactive protein (hs-CRP) level. KCNQ1OT1 might be involved in the regulation of early inflammatory response during recurrence of TIA. | LncRNA KCNQ1OT1 predicts further cerebral events in patients with transient ischemic attack 10.3389/fphar.2022.961190
Transient ischemic attack (TIA) poses a great threat of cerebrovascular diseases to a large number of patients, despite its reversible neurological dysfunction. Long non-coding RNAs (lncRNAs) have been proven to play critical roles in the pathophysiological development of cerebrovascular events. Exploring the function of lncRNAs in modulating TIA prognosis would help to develop individualized therapeutics. A total of 231 participants with the first onset of TIA were recruited in the study, including 65 subsequent stroke patients. The expression of lncRNA potassium voltage-gated channel subfamily Q member 1 opposite strand 1 (KCNQ1OT1) was upregulated in patients with recurrent ischemic events after TIA. Additionally, KCNQ1OT1 could be regarded as an independent predictor for subsequent ischemic stroke. The optimal diagnostic value was determined at 1.29 with a sensitivity of 63% and a specificity of 72%. Fewer patients would survive from further ischemic stroke with their KCNQ1OT1 level over 1.29. Furthermore, the expression of KCNQ1OT1 was elevated with a growing serum high-sensitivity C-reactive protein (hs-CRP) level. KCNQ1OT1 might be involved in the regulation of early inflammatory response during recurrence of TIA.
Transient ischemic attack (TIA) is one of the most common alerting events for ischemic cerebral vascular attacks, with the sudden onset of neurological disorders such as paralysis, paresthesia, and speech, visual, or hearing impairments. These symptoms were temporary and alleviated in 24 h without any sequelae. However, these patients are at a high risk of further ischemic events after TIA. According to a systemic review of several follow-up studies, the approximate proportion of further stroke in patients with a first episode of TIA varied from approximately 8% to 12% in 7 days; however, it varied from 10% to 22% in 90 days (Giles and Rothwell, 2007). Risk levels of TIA which were measured by the ABCD2 score scale exerted pivotal roles in predicting subsequent ischemic stroke of TIA (Giles and Rothwell, 2007; Perry et al., 2021), whereas patients were usually scored depending on their medical histories and characteristic clinical manifestations. In addition to this, we wondered whether any molecular biomarkers could help to evaluate the latent possibility for recurrent cerebral ischemia after TIA. According to previous studies, inflammation could exacerbate cerebral ischemic stroke by inducing atherosclerosis, facilitating instability of plaque, as well as overactivating platelets and coagulation (Esenwa and Elkind, 2016; Patel et al., 2018). The expression of C-reactive protein (CRP) was upregulated within 24 h after acute stroke and will sustain at a high level for nearly 6 months (Arenillas et al., 2003). Both clinical evidence and laboratory evidence suggested that anti-inflammatory therapies effectively decreased vascular damages and suppressed cerebral ischemic stroke (Kelly et al., 2018). Moreover, a high level of residual inflammatory risk (RIR) was proven to imply patients’ poor prognosis of acute cerebral ischemic stroke (Li et al., 2021a), while high-sensitivity C-reactive protein (hs-CRP), which represented early inflammatory response, was addressed as a decisive factor of RIR (Ridker et al., 2017). Low-level inflammation reflected by hs-CRP indicated recurrent ischemic events independently (Mengozzi et al., 2020; Coveney et al., 2021). Potassium voltage-gated channel subfamily Q member 1 opposite strand 1 (KCNQ1OT1) is defined as a long non-coding RNA with more than 200 nucleotides. It was the overlapping transcript 1 located on the opposite strand of KCNQ1. KCNQ1OT1 influenced multiple target gene expression and function through epigenetic modifications (Pandey et al., 2008). Previously, we verified that KCNQ1OT1 could accelerate autophagy of neurons, thus exacerbating ischemia and reperfusion (I/R) injury in acute cerebrovascular diseases (Yu et al., 2019). Evidence demonstrated that KCNQ1OT1 activated inflammatory response and promoted apoptosis of microvascular endothelial cells in acute myocardial infarction (Wang et al., 2019; Liao et al., 2020). Moreover, KCNQ1OT1 could aggravate atherosclerosis via regulation of lipid metabolism (Yu et al., 2020). Nevertheless, little has been known about the effects of KCNQ1OT1 on subsequent ischemic stroke after TIA. In our study, the mounting expression of KCNQ1OT1 was detected in plasma from TIA patients. Moreover, early inflammation illustrated by the serum hs-CRP level was positively associated with KCNQ1OT1 expression. Our findings will provide not only a novel biomarker for the risk of recurrence but also the latent therapeutic target in patients who suffered TIA.
This study was a clinical observational cohort study. The Institutional Review Board of Shengjing Hospital of China Medical University had approved our study (IRB number, 2017PS161K). Written informed consent was provided by all the participants or their legal representatives.
A total of 231 individuals hospitalized at Shengjing Hospital from January 2017 to May 2018 were recruited into our study. All of them had experienced their first sudden onset of TIA events within 24 h and were diagnosed by two specialists in stroke treatment according to the diagnostic criteria (Simmons et al., 2012; Easton and Johnston, 2022). These patients complained transient neurological dysfunction, including weakness of limbs, paresthesia, and aphasia. These symptoms were relieved in 1 day without any treatment. Computed tomography (CT) scans were conducted to exclude other brain diseases such as cerebral hemorrhage or tumor. Patients with poor health condition and with chronic, infectious, or systemic diseases, including cardiovascular disease, diabetes, tumor, liver, kidney, or other cerebral diseases, were excluded from our study. In addition, individuals with atrial fibrillation or mural thrombus detected by echocardiogram were also excluded. Venous blood was collected for laboratory tests when patients were hospitalized. Basic information was collected and analyzed. Carotid atherosclerosis was detected by ultrasonography.
To observe recurrent ischemic events in 3 months, our research associates followed up all the individuals in-person or online at 7, 14, 30, and 90 days after the first onset of TIA. All the participants followed doctors’ directions for secondary prevention strictly. During the investigation, the primary outcome was set as the onset of recurrence of ischemic stroke, while the secondary outcome was all-cause death. Among all the participants, there were 65 patients suffering subsequent stroke.
Blood samples were centrifuged to obtain the supernatant plasma at the first onset of TIA. The TRIzol reagent (Life Technologies Corporation, Carlsbad, CA, United States) was utilized to dissociate the total RNAs from patients’ plasma. Then, the plasma KCNQ1OT1 level was detected via real-time quantitative polymerase chain reaction (qPCR) using the One-Step SYBR Primer Script RT-PCR Kit (Takara Bio, Inc., Japan). The total volume of the reaction system is 20 μl. Melt curves were analyzed to determine the specificity of the amplified products. Primer sequences for KCNQ1OT1 detection were designed as follows: forward 5′-TGCAGAAGACAGGACACTGG-3′ and reverse 5′-CTTTGGTGGGAAAGGACAGA-3’. GAPDH was regarded as the endogenous reference with the following sequences of primers: forward 5′- TGCACCACCAACTGCTTAGC-3′ and reverse 5′-GGCATGCACTGTGGTCATGAG-3’.
The level of hs-CRP was tested to evaluate the early inflammatory response in all cases. Immunoturbidimetric analysis was conducted according to the manufacturer’s instructions (Beckman Coulter Inc., Brea, CA, United States). The IMMAGE 800 Immunochemistry System (Beckman Coulter Inc., Brea, CA, United States) was utilized for the analysis.
The risk for further stroke after TIA was determined by two experienced neurologists using the ABCD2 score scale based on demographic characteristics and clinical manifestations (Wardlaw et al., 2015). Patients who scored 0–3 were considered to be at the low-risk level, while those who scored 4–5 and 6–7 were considered to be at the moderate-risk level and at the high-risk level, respectively.
Measurement data were presented as mean ± SD. Student’s t-test or two-way ANOVA was applied for parametric comparison between data with normal distribution. Non-normal distributed data were analyzed by the Mann–Whitney U-test. The percentage of measurement data was calculated and compared using the chi-squared test. The Kruskal–Wallis test was used for the nonparametric comparison between multiple groups. Logistic regression analysis was conducted to determine latent independent risk factors for subsequent ischemic stroke with odd ratios (ORs) and 95% confidence intervals (CIs). The relationship between the expression of KCNQ1OT1 and ABCD2 score or hs-CRP was evaluated through Spearman analysis. The receiver operating characteristic (ROC) curve was applied to calculate the predictive value of KCNQ1OT1 in further events after TIA. Kaplan–Meier analysis was applied to draw the survival curve in the follow-up study. All the statistics were performed using SPSS 23.0 (IBM Corp., Armonk, NY, United States). p < 0.05 was considered statistically significant.
To determine the role of KCNQ1OT1 after TIA, we studied peripheral plasma samples from 231 individuals, including 166 patients without further neurological dysfunction after TIA and 65 patients who suffered subsequent ischemic stroke. Demographic characteristics are listed in Table 1. Clinical assessments of BMI and alcohol assumption, etc., were performed as previously described (Yu et al., 2020). There were differences with risk factors such as hypertension, diabetes, and carotid stenosis between these two groups (p < 0.05). The relative expression of KCNQ1OT1 was detected by qPCR. It showed that the KCNQ1OT1 level was prominently upregulated in the subsequent stroke- positive group (p < 0.05; Figure 1). Moreover, logistic regression analysis was performed to elucidate that KCNQ1OT1 was an independent risk factor for further ischemic events in TIA patients (p < 0.001, OR 21.591, 95% CI 5.884–79.220; Table 2).
Risk stratification of stroke in TIA was decided on the basis of the ABCD2 scoring system (low risk: 0–3, moderate risk: 4–5, and high risk: 6–7). As shown in Table 1, there are differences in the proportion of the three risk levels from two groups. Therefore, we wondered whether the expression of KCNQ1OT1 was changed in patients at different risk levels. Three subgroups were framed out according to their stroke risks from low to high level. In the moderate/high-risk subgroups, KCNQ1OT1 expression was significantly upregulated in patients who suffered further ischemic events. For those who had subsequent stroke after TIA, the plasma KCNQ1OT1 level was increased strikingly in moderate/high-risk subgroups (Figure 2A). In addition, the ABCD2 score elevated along with the increased expression of KCNQ1OT1 in TIA patients who suffered recurrent stroke (R 2 = 0.2577, p < 0.05; Figure 2B).
To depict more clinical significance of KCNQ1OT1 in TIA, a ROC curve was drawn according to the relative expression of KCNQ1OT1 in plasma, ABCD2-based risk levels, and combination of them (Figure 3). The area under the ROC curve (AUC) was 0.714, 0.672, and 0.778, correspondingly. KCNQ1OT1 was considered the predictive factor for further events after TIA, with a sensitivity of 63% and specificity of 72%. An optimal diagnostic point for the relative expression of KCNQ1OT1 was determined at 1.29 via the Youden Index analysis. Combined with ABCD2 score scale-based risk levels, sensitivity and specificity of predictive factors were increased up to 67.7% and 76.5%, correspondingly. These results suggested that KCNQ1OT1 could predict further ischemic stroke events and improve the efficacy of evaluating risk levels of TIA patients by ABCD2 criteria.
According to the optimal diagnostic point, the expression level of KCNQ1OT1 over 1.29 was determined as an independent predictor for subsequent stroke events (p < 0.05, OR 6.142, and 95% CI 2.723–13.857; Table 3). Furthermore, the survival curve was established using the Kaplan–Meier analysis during a 90-day follow-up. It was clearly demonstrated that fewer patients could survive from the subsequent ischemic events when their plasma KCNQ1OT1 level was over 1.29 (p < 0.001; Figure 4).
Since early inflammatory response reflected by hs-CRP was proven to play pivotal roles in ischemic stroke following TIA (Mengozzi et al., 2020), we wondered whether there was any association between the expression of KCNQ1OT1 and hs-CRP in plasma. Enzyme-linked immunosorbent assays (ELISA) implicated that the hs-CRP level was higher in the subsequent stroke-positive group (p < 0.05; Figure 5A). Further study revealed that hs-CRP was increased with the increase in the expression of KCNQ1OT1 in patients who suffered further ischemic events after TIA (R 2 = 0.3733, p < 0.05; Figure 5B). This result implied that KCNQ1OT1 might participate in the early inflammatory response in further stroke after TIA.
In this study, we proposed that the expression of KCNQ1OT1 was upregulated in further ischemic events and positively associated with the stroke risk levels of TIA for the first time. Moreover, our study indicated that KCNQ1OT1 could be regarded as a predictive factor for subsequent stroke. With the expression level of KCNQ1OT1 over 1.29, the survival rate in 90 days after the first onset of TIA was decreased apparently. In addition, the early inflammatory response detected by hs-CRP was enhanced with an increasing KCNQ1OT1 level. TIA has been considered the predominant risk factor for further stroke of unreversible neurological impairments. Studies reported that patients possessed 5.4%–14.2% possibility to develop into permanent neurological deficits after their first onset of TIA (Khanevski et al., 2019). Although neurologists strived to prohibit recurring TIA, there were still 25–30% of patients who suffered secondary stroke within 5 years after TIA (Valls et al., 2017). Currently, patients’ age, blood pressure, diabetes history, clinical manifestations, and duration of clinical symptoms are the most acknowledged factors that would influence further stroke after TIA (Wardlaw et al., 2015). Recent research revealed that patients whose systolic blood pressure was over 140 mmHg exposed a higher risk of recurring neurological dysfunction (de Havenon et al., 2021). Moreover, studies also discovered that further stroke was more likely to happen in TIA patients with intracranial atherosclerosis and aberrant inflammatory response (Luijendijk et al., 2011; Colas-Campas et al., 2020; Lindenholz et al., 2020). In the previous study, we have proven that overactivated autophagy and apoptosis of neurons could exacerbate cerebral ischemia–reperfusion injury and aggravated neurological impairments (Yu et al., 2020; Yu et al., 2021). In spite of no neurological deficits left, TIA was also caused by cerebral ischemia. Thus, we further studied the latent factors reflecting the development and prognosis of TIA. Mounting evidence manifested that lncRNA KCNQ1OT1 participated in regulating cell progression and differentiation among various diseases such as cancer, fracture, and myocardial infarction (Feng et al., 2020; Wang et al., 2020; Li et al., 2021b; Hong et al., 2021). According to the previous study, KCNQ1OT1 was upregulated in the ischemic brain tissue, and its expression was growing with the patients’ severity of neurological impairments. Knockdown of KCNQ1OT1 could attenuate neural apoptosis induced by autophagy (Yu et al., 2019). In cerebral ischemia–reperfusion injury, silencing KCNQ1OT1 would attenuate endoplasmic reticulum stress via regulating the downstream miR-30b/GRP78 signaling cascade (Li et al., 2020). Moreover, KCNQ1OT1 aggravated fibrosis and pyroptosis in diabetic cardiomyopathy by targeting the miR-214-3p/caspase-1/TGF-β1/smad pathway (Yang et al., 2018). KCNQ1OT1 could competitively bind with miR-466 to mediate downstream Tead1 expression, promoting apoptosis of cardiomyocytes during acute myocardial infarction (Liao et al., 2020). Evidence also showed that the rising level of KCNQ1OT1 repressed cholesterol excretion and exacerbated lipid deposition in macrophages, which could facilitate the formation of atherosclerosis (Yu et al., 2020). However, there were few research studies about the roles of KCNQ1OT1 in TIA. In this study, we detected the upregulated level of KCNQ1OT1 and proposed KCNQ1OT1 as an independent risk factor in patients with further ischemic events after TIA. In addition, an optimal diagnostic value of KCNQ1OT1 expression as 1.29 was determined using the ROC curve analysis. Furthermore, we explored the underlying pathophysiological process associated with KCNQ1OT1 in TIA. Inflammatory response was proven to be activated in acute cerebral cardiovascular diseases. In cerebral ischemia–reperfusion (I/R) injury, NLRP3 inflammasomes were accumulated in neurons and induced immune cell infiltration to destroy the integrity of the blood–brain barrier (Franke et al., 2021). In addition, microglial HDAC3 stimulated neuroinflammation and aggravated neurological impairments in I/R injury via the cGAS-STING pathway (Liao et al., 2020). Studies have reported that activated inflammation, which was reflected by the CRP level over 2 mg/L, would predict a high probability of recurrent cardiovascular diseases (McMurray et al., 2009; Blanco-Colio et al., 2021). In addition, inflammation could increase vulnerability of atherosclerotic plaques, which is the most common cause of recurrent ischemia (Badimon and Vilahur, 2014; Brinjikji et al., 2016). Inflammatory cytokines including IL-1β, IL-8, and hs-CRP were considered to predict recurrent ischemic stroke independently (Coveney et al., 2021). However, other researchers found that IL-6 and S-100B were decreased in the further events of TIA (Colas-Campas et al., 2020). Anti-inflammatory therapy with salvianolic acid D could impede the activation of the NF-κB pathway mediated by HMGB1 translocation in cerebral I/R injury (Zhang et al., 2020). Arginine acted as a neuroprotector to alleviate neurological impairments via inhibiting HIF-1α/LDHA-induced inflammation in microglia (Chen et al., 2020). Moreover, exosomes with conjugated curcumin could target the suppression of cerebral inflammatory response, so as to reduce neural apoptosis (Tian et al., 2018). As previously reported, KCNQ1OT1 could aggravate inflammatory response in myocardial infarction via the Notch pathway (Wang et al., 2019). Oxidative stress following myocardial infarction could be accelerated via the KCNQ1OT1/miR-130a-3p/ZNF791 axis (Xin et al., 2022). In addition, knockdown of KCNQ1OT1 would reduce the activation of NLRP3 inflammasome in acute kidney injury (Wang et al., 2021). Recent studies put forward that a high level of hs-CRP reflecting the early inflammation was an independent predictor for ischemic stroke after TIA or minor stroke (Mengozzi et al., 2020). Thus, we wondered whether the expression of KCNQ1OT1 was related to hs-CRP in further ischemic events after TIA. In our study, hs-CRP expression was found to be elevated up to 2.25 folds in patients with subsequent stroke, which was in agreement with the previous findings. Furthermore, we explored the latent association between upregulated KCNQ1OT1 expression and activated early inflammation reflected by hs-CRP in patients who had suffered further ischemic events of TIA. It was confirmed that the level of KCNQ1OT1 increased with the elevated expression of hs-CRP. This result implied that KCNQ1OT1 might be related to the activated early inflammatory response in the recurrence of TIA. Nevertheless, there were still limitations to the research. Multiple-center research studies should be conducted to obtain more subjective data. Neither carotid stenosis nor low-density lipoprotein cholesterol (LDL-C) was identified with significant difference in patients who suffered subsequent stroke. It may be due to the relatively small sample size. More participants are needed in further studies. In conclusion, patients with upregulated KCNQ1OT1 expression were at a high risk of further ischemic stroke after TIA. Our results indicated that KCNQ1OT1 had great predictive value for further ischemic events, especially when taken together with the ABCD2 score. Patients with the expression level of KCNQ1OT1 over 1.29 had a robust increased risk for further ischemic events. It would be due to the early inflammatory response after TIA, which was reflected by the hs-CRP level. These findings revealed the crucial roles of KCNQ1OT1 in ischemic events in patients with TIA. The underlying relationship between KCNQ1OT1 and early inflammation was discussed for the first time. However, there were still some gaps left in our study. The number of participants was relatively limited. More patients could be recruited to support our conclusion. Molecular biology experiments are needed to explore more specific regulatory mechanisms in our further study. | true | true | true |
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PMC9585215 | Lanlan Lin,Guofu Lin,Hai Lin,Luyang Chen,Xiaohui Chen,Qinhui Lin,Yuan Xu,Yiming Zeng | Integrated profiling of endoplasmic reticulum stress-related DERL3 in the prognostic and immune features of lung adenocarcinoma | 07-10-2022 | DERL3,lung adenocarcinoma,endoplasmic reticulum stress,immune infiltration,B cell | Background DERL3 has been implicated as an essential element in the degradation of misfolded lumenal glycoproteins induced by endoplasmic reticulum (ER) stress. However, the correlation of DERL3 expression with the malignant phenotype of lung adenocarcinoma (LUAD) cells is unclear and remains to be elucidated. Herein, we investigated the interaction between the DERL3 and LUAD pathological process. Methods The Cancer Genome Atlas (TCGA) database was utilized to determine the genetic alteration of DERL3 in stage I LUAD. Clinical LUAD samples including carcinoma and adjacent tissues were obtained and were further extracted to detect DERL3 mRNA expression via RT-qPCR. Immunohistochemistry was performed to evaluate the protein expression of DERL3 in LUAD tissues. The GEPIA and TIMER website were used to evaluate the correlation between DERL3 and immune cell infiltration. We further used the t-SNE map to visualize the distribution of DERL3 in various clusters at the single-cell level via TISCH database. The potential mechanisms of the biological process mediated by DERL3 in LUAD were conducted via KEGG and GSEA. Results It was indicated that DERL3 was predominantly elevated in carcinoma compared with adjacent tissues in multiple kinds of tumors from the TCGA database, especially in LUAD. Immunohistochemistry validated that DERL3 was also upregulated in LUAD tissues compared with adjacent tissues from individuals. DERL3 was preliminarily found to be associated with immune infiltration via the TIMER database. Further, the t-SNE map revealed that DERL3 was predominantly enriched in plasma cells of the B cell population. It was demonstrated that DERL3 high-expressed patients presented significantly worse response to chemotherapy and immunotherapy. GSEA and KEGG results indicated that DERL3 was positively correlated with B cell activation and unfolded protein response (UPR). Conclusion Our findings indicated that DERL3 might play an essential role in the endoplasmic reticulum-associated degradation (ERAD) process in LUAD. Moreover, DERL3 may act as a promising immune biomarker, which could predict the efficacy of immunotherapy in LUAD. | Integrated profiling of endoplasmic reticulum stress-related DERL3 in the prognostic and immune features of lung adenocarcinoma
DERL3 has been implicated as an essential element in the degradation of misfolded lumenal glycoproteins induced by endoplasmic reticulum (ER) stress. However, the correlation of DERL3 expression with the malignant phenotype of lung adenocarcinoma (LUAD) cells is unclear and remains to be elucidated. Herein, we investigated the interaction between the DERL3 and LUAD pathological process.
The Cancer Genome Atlas (TCGA) database was utilized to determine the genetic alteration of DERL3 in stage I LUAD. Clinical LUAD samples including carcinoma and adjacent tissues were obtained and were further extracted to detect DERL3 mRNA expression via RT-qPCR. Immunohistochemistry was performed to evaluate the protein expression of DERL3 in LUAD tissues. The GEPIA and TIMER website were used to evaluate the correlation between DERL3 and immune cell infiltration. We further used the t-SNE map to visualize the distribution of DERL3 in various clusters at the single-cell level via TISCH database. The potential mechanisms of the biological process mediated by DERL3 in LUAD were conducted via KEGG and GSEA.
It was indicated that DERL3 was predominantly elevated in carcinoma compared with adjacent tissues in multiple kinds of tumors from the TCGA database, especially in LUAD. Immunohistochemistry validated that DERL3 was also upregulated in LUAD tissues compared with adjacent tissues from individuals. DERL3 was preliminarily found to be associated with immune infiltration via the TIMER database. Further, the t-SNE map revealed that DERL3 was predominantly enriched in plasma cells of the B cell population. It was demonstrated that DERL3 high-expressed patients presented significantly worse response to chemotherapy and immunotherapy. GSEA and KEGG results indicated that DERL3 was positively correlated with B cell activation and unfolded protein response (UPR).
Our findings indicated that DERL3 might play an essential role in the endoplasmic reticulum-associated degradation (ERAD) process in LUAD. Moreover, DERL3 may act as a promising immune biomarker, which could predict the efficacy of immunotherapy in LUAD.
Lung cancer remains the most prevalent cause of cancer-related death worldwide (1, 2). Histologically, lung cancer is categorized into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) (3). Lung adenocarcinoma (LUAD) is the major subtype of NSCLC with increasing morbidity annually (4). Despite advances in the intensive investigation of molecular mechanisms underlying lung cancer and new breakthroughs in immunotherapy in NSCLC (5), the 5-year overall survival rate of lung cancer is only 11% (6). Therefore, it is necessary to elucidate the profound mechanisms and explore the potential pathogenic genetic variants of lung cancer. DERL3 (also known as Derlin-3) is a member of the Derlin family which contains additional members, including DERL1 and DERL2 (7). DERL3 gene lies on chromosome 22q11.23 and its mature protein localizes primarily on the endoplasmic reticulum (ER) membrane (8). Previous studies indicated that DERL3 was involved in the metastasis and apoptosis of colorectal cancer (9), gastric cancer (10), and breast cancer (8). It has been reported that DERL3 was mainly involved in the endoplasmic reticulum associated degradation (ERAD) process via targeting SLC2A1 in the Warburg effect (11). ERAD is one of the most important mechanisms for adaptation to ER stress and intracellular quality control for eliminating unfolded and misfolded proteins (12). Collectively, the above researches indicated that aberrant expression of DERL3 might be applied as a promising biomarker in malignant tumors. Although emerging evidence has been revealed that DERL3 promoted the proliferation and migration of LUAD (13), the physiological significance and profound mechanism of DERL3 in LUAD ER stress and immune infiltration had not been elucidated. In the present study, bioinformatics techniques were applied to comprehensively elaborate the pathway enrichment and immune infiltration of DERL3 in a tumor microenvironment (TME). Our results identified that DERL3 might be involved in the pathogenesis of lung cancer and could play an essential role in ERAD process to mediate ER stress.
Clinical samples including malignant and non-neoplastic lung tissues were obtained from The Second Affiliated Hospital of Fujian Medical University. The application of archived cancer samples was approved by the relevant Ethics Commission (approval No. 2022-89). All patients had been diagnosed as stage I LUAD based on their histological and pathological characteristics. None of the patients received any pre-operative chemotherapy or radiotherapy prior to tissue sampling. All patients were given written informed consent. Excised tissues were stored at -80°C for long-term conservation.
Total RNA was extracted from 10 paired stage I LUAD tissues and corresponding adjacent tissues using the RNeasy Mini Kit (Qiagen, Cat No. 74104, Germany) according to the manufacturer’s protocol. Then, rRNA was removed from the total RNA to obtain the maximum residual ncRNA. After fragmenting rRNA-depleted RNA, the cDNA library was performed using the TruSeq RNA sample Prep Kit (Illumina, RS-122-2001, USA). The mRNA sequencing libraries were prepared using the VAHTS total RNA-seq Library Prep kit for Illumina (Vazyme, NR603, China) following the manufacturer’s protocol. After sequencing, the reads files (fastq) were mapped to the Hg19 reference using STAR, and gene expression was determined using RSEM. Differential expression analysis for mRNA was performed using the DESeq2 R package (https://bioconductor.org/packages/release/bioc/html/DESeq2.html). Differentially expressed genes (DEGs) were obtained by comparing gene expressions between two groups using DESeq2 (v1.10.1). A corrected p value of <0.05 and |Log2 (fold change)| (|log2 FC|) ≥1 were considered statistically significant. Heat maps were generated using the R package with a hierarchical clustering algorithm.
Immunohistochemical staining containing 16 LUAD patients was carried out. All tissue specimens were fixed and embedded in paraffin. Antigen retrieval was performed in a pH 6.0 citrate buffer for 10 min after deparaffinization. Peroxide blocking was performed for 30 min with 3% H2O2 to block endogenous peroxidases. The slides were incubated with a primary antibody of anti-DERL3 (ab78233, abcam) at 4°C overnight, and an HRP-labeled secondary antibody for 30 min at room temperature. After peroxidase substrate DAB staining, slices were counterstained with hematoxylin for 3 min and final images were captured using a fluorescence microscope (OLYMPUS CKX41, Japan).
Bronchial epithelial cell line HBE and LUAD cell lines containing A549, H1299, H1975, SPCA-1, and H460 were purchased from Cell Bank of Chinese Academy of Science (Shanghai, China). Cells were cultured in RPMI-1640 or DMEM medium (Gibco, USA) containing 10% fetal bovine serum (FBS) (Gibco, USA) and 1% penicillin–streptomycin (Beyotime, Tianjin, China) at a 37°C humidified incubator with 5% CO2 (Thermo Scientific, Waltham, MA, USA). The medium was replaced during incubation based on the cellular demand.
Total RNA of 33 paired LUAD and adjacent tissues were extracted using a TRIzol® reagent (Invitrogen, Thermo Fisher Scientific, Inc.) according to the manufacturer’s protocol. After the quantification and purity were confirmed, the extracted RNA was reverse-transcribed into cDNA using the Reverse Transcription Kit (Fermentas, Vilnius, Lithuania). Then, RT-qPCR was conducted on ABI 7300 Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) with a SYBR Green PCR Kit (Thermo Fisher Scientific, Inc.). The primer sequences are listed in Table S1 . The relative gene expression level was measured using the 2-ΔΔCt method.
DERL3 small interfering RNAs (siRNAs) and DERL3-flag plasmid were synthesized by Hanheng (Shanghai, China). A549 and H1975 cells were cultured in six-well plates at 3.0 × 105 cells/well overnight and transfected with siRNA using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. SPCA-1 cells were seeded into six-well plates at 4.0 × 105 cells/well overnight and transfected with 1 µg of plasmid. After transfection for 48 h, cells were harvested to identify the efficiency using RT-qPCR. The sequences for DERL3 siRNA are listed in Table S1 .
Transwell migration assay was conducted to assess cellular migrative ability. The density of cells was adjusted to 5.0 × 104 cells/well and resuspended in a serum-free medium; the RPMI-1640 medium containing 10% FBS was applied to the lower chambers. After incubation at 37°C for 24 h, non-invaded cells on the upper chambers were scraped out. Cells on the lower chambers were fixed in 4% paraformaldehyde and stained with 1% crystal violet (Solarbio Life Sciences, China) for 30 min. The number of cells was counted under microscope from at least three random fields. Scratch assay was performed to assess cell migration. Cells were plated into the six-well plate with complete medium. When cells reached full confluence, scratch wounds were scraped in a straight line using a 200 μl pipette tip. Cell debris was removed using phosphate-buffered saline (PBS) and the serum-free medium was replaced. Photographs were captured at 0 h and 24 h, respectively. The scratch healing rate was calculated as follows: (scratch area of 0 h − scratch area of 24 h)/scratch area of 0 h × 100%.
Cells at the density of 4.0 × 103 cells/well were seeded into a 96-well plate and cultured at a 37°C incubator overnight. After incubation for 0, 24, 48, and 72 h, a 100 µl mixture of CCK-8 and serum-free medium at a volume ratio of 1:10 was added and incubated for an additional 1 h at 37°C. The absorbance value (OD) was measured at 450 nm.
The TIMER 2.0 database (http://timer.cistrome.org/) consists of three major components, including immune association, cancer exploration, and immune estimation. We used the “cancer exploration” module to explore DERL3 differential expression. The expression of DERL3 was further performed by Sangerbox (http://sangerbox.com/tool) using the pan-cancer monogenic fast comprehensive analysis. Furthermore, the RNA-seq dataset was obtained from The Cancer Genome Atlas (TCGA) database to analyze DERL3 expression in stage I LUAD and normal tissues. The Human Protein Atlas (HPA) is a comprehensive resource for mapping human proteins in cells, tissues, and organs through multiple omics technologies (14). We used the “Tissue Atlas” module to present the distribution of DERL3 in pulmonary tissues.
The associations of DERL3 expression with overall survival (OS) and first progression (FP) analysis were performed using the Kaplan–Meier plotter (https://kmplot.com/analysis/). Raw counts of RNA-sequencing data and corresponding prognostic information were obtained from the TCGA dataset (https://portal.gdc.cancer.gov/) to further assess the expression of DERL3 in the different stages of lung cancer.
UALCAN (http://ualcan.path.uab.edu) is a convenient database to provide an analysis of transcriptional expressions based on the TCGA database. We evaluated the promoter methylation level of DERL3 in normal tissues and primary pulmonary tumors based on different tumor stages.
UCSC Xena (http://xena.ucsc.edu) empowers biologists to explore data across multiple Xena Hubs with a variety of visualizations and analyses (15). RNA-seq, phenotype profiles, and DNA methylation (Methylation 450) were accessed to validate DERL3 methylation signatures and clinicopathological features in lung cancer. The methylation level was assessed by β value. β value ≥0.6 was considered as methylated, while β value ≤0.2 was considered to be unmethylated (16). Intermediate β value was considered to be partially methylated.
Protein–protein interaction (PPI) networks were constructed using the GeneMANIA (http://www.genemania.org/). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) functional enrichment analyses were conducted using DAVID Functional Annotation Tools (https://david.ncifcrf.gov/).
The TIMER 2.0 database was applied to investigate the relationship between immune cell infiltration and DERL3 expression. Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/) is a visualization website based on TCGA datasets. The GEPIA 2021 website was further used for the correlation between DERL3 and immune cell infiltration. Immune scores and stromal scores were calculated using the ESTIMATE algorithm. The EPIC algorithm was utilized to quantify the proportions of immune cells. The infiltration abundance of immune cells in heterogeneous LUAD tissues was assessed via using the Microenvironment Cell Population-counter (MCP-counter) algorithm.
The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was a computational method to model two primary mechanisms of tumor immune escape including the dysfunction of tumor-infiltrating cytotoxic T lymphocytes (CTL) and the rejection of CTL by immunosuppressive factors (17). We applied the algorithm to predict the potential response to immune checkpoint blockade (ICB) therapy (18). Higher TIDE scores indicate worse clinical efficacy of immune checkpoint inhibitors. Chemotherapeutic response was predicted based on the largest publicly available pharmacogenomics database, the Genomics of Drug Sensitivity in Cancer (GDSC) (https://www.cancerrxgene.org/). We assessed the correlation between DERL3 expression and half-maximal inhibitory concentration (IC50) values of gemcitabine, etoposide and docetaxel, cisplatin, and paclitaxel. The IC50 was estimated by ridge regression. All parameters were set as the default values.
The Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org) database is a comprehensive web resource enabling interactive single-cell transcriptome visualization of the tumor microenvironment (19). Gene expression data was originated from the GEO database (GSE131907). All downloaded data were uniformly processed with a standardized cell-type annotation and differential expression analysis.
GSEA was performed using the Java GSEA software v4.1.0 (http://www.broadinstitute.org/gsea). Enrichment map analysis was further applied for GSEA results. A nominal p-value <0.05 and a false discovery rate (FDR) q value <0.25 were considered as significant.
The Kaplan–Meier analysis and the log-rank test were applied to identify the associations between DERL3 expression and the prognosis of LUAD patients. The correlation between DERL3 expression and clinicopathological characteristics were statistically analyzed by the χ2 test. All data are presented as mean ± standard deviation. All experiments were performed in triplicate. Statistical analysis was conducted using the GraphPad Prism 7.0 software (La Jolla, CA, USA). p < 0.05 was considered as statistically different.
DERL3 expression profiles were identified in tumor and normal tissues according to TCGA transcriptomic data. As presented in Figures 1A, B , DERL3 expression was elevated in the majority of malignant tissues including LUAD and lung squamous cell carcinoma (LUSC). To further elucidate the expression of DERL3 in different stages of LUAD, RNA-seq dataset of DERL3 was obtained from TCGA. The result revealed that DERL3 was significantly upregulated in stage I LUAD (n = 276) when compared with normal pulmonary tissues (n = 59) ( Figure 1C ). We further identified the protein expression of DERL3 using immunohistochemistry data from the HPA database. The results indicated that DERL3 was medium-stained in normal pulmonary tissues ( Figure 1E ) but strong-stained in LUAD tissues ( Figure 1F ). The Kaplan–Meier plot was applied to predict the prognostic value of DERL3 in lung cancer patients. It was revealed that the higher level of DERL3 was significantly associated with poorer OS by TCGA LUAD RNA-seq datasets (upper row of Figure 1D ). Consistently, Kaplan–Meier plotter data also supported that DERL3 was a potential unfavorable prognostic biomarker for OS (HR = 1.34, log-rank p = 0.017) and first progression survival (FPS) (HR = 1.66, log-rank p = 0.0022) in LUAD (bottom row of Figure 1D ).
DERL3-related RNA-seq data and clinical phenotypes of LUAD patients were downloaded from the UCSC Xena database. We removed the samples with missing clinical information and integrated the final data. According to the relevant DERL3 expression in tumor tissues, 294 LUAD patients were classified into the DERL3 low expression group (n = 144) and high expression group (n = 150). The correlation between DERL3 expression and clinicopathological characteristics in LUAD patients are shown in Table 1 . The expression of DERL3 was identified to be correlated with tumor stage (p = 0.032) and T stage (p = 0.024) but not with other clinical characterizations. These results highlighted the clinical significance of DERL3 in LUAD.
DNA methylation is an essential epigenetic regulation mechanism of gene expression. We assessed the promoter methylation level of DERL3 in 32 normal pulmonary tissues and 473 primary LUAD tissues. The results indicated that methylation levels of the DERL3 promoter in LUAD tumors significantly downregulated compared with normal tissues ( Figure 2A ). Moreover, promoter methylation of DERL3 was statistically decreased in clinical stage I LUAD ( Figure 2B ). We further performed the Sankey diagram to identify the association between DERL3 methylation, expression profiles, clinical characteristics, and prognostic signature. As shown in Figure 2C , hypermethylation of DERL3 was potentially associated with early stage of lung cancer and expressed an inclination to indicate better survival outcomes. To further identify the potential protein targets and functional prediction of DERL3 in LUAD, we applied GeneMANIA to generate the protein interaction network. The result indicated that DERL3 was mainly co-expressed with DERL2, DERL1, and EDEM1. Functional prediction revealed that these proteins were all involved in the ERAD process including ER to cytosol transport, protein exit from ER, ERAD pathway, regulation of retrograde protein transport, and cellular response of unfolded protein ( Figure 2D ). We then performed GO and KEGG pathway analysis via using the DAVID database. Consistently, GO revealed that DERL3 was correlated with the ER-associated ubiquitin-dependent protein catabolic process, unfolded protein response (UPR), regulation of retrograde protein transport, and IRE1-mediated UPR ( Figure 2E ). Similar to the results obtained from GO annotation, the KEGG pathway indicated that DERL3 was related to the ERAD pathway, ER quality control (ERQC) compartment, and protein export ( Figure 2F ). Moreover, RT-qPCR results presented that the downregulation of DERL3 may significantly alter essential biomarkers of ER stress in LUAD ( Figure S1 ).
The TIMER database was applied to preliminarily explore whether DERL3 was involved in immune infiltration. The result suggested that DERL3 was positively correlated with immune cells including B cells, CD4+ T cells, CD8+ T cells, macrophages, and dendritic cells, especially in B cells (Spearman’s ρ = 0.442, p = 1.38e-24) ( Figure 3A ). We then evaluated the immune state of DERL3 with the immune score and stromal score, calculated using the ESTIMATE algorithm to estimate infiltrating immune cells and stromal cells in a tumor microenvironment (20). The results demonstrated that the stromal score and immune score significantly differ between the two groups ( Figure 3B ), indicating that DERL3 activation may be linked to immunosuppressive TME. We further explored the correlations between DERL3 and immune cells markers in lung cancer via the TIMER ( Table S2 ) and GEPIA databases ( Figure 3C ). Table S2 demonstrated that DERL3 presented significant correlations with B cells, CD8+ T cells, Treg cells (Regulatory T cells), exhausted T cells, TAM (Tumor-associated macrophages), etc. Figure 3C illustrated that DERL3 was enriched in B cells. The above-described results showed that DERL3 may be an immune-related molecule and may appear to be correlated with B cells. We then further investigated the cell surface markers altered with DERL3 activation. In Figure 3D , DERL3 was positively correlated with the B cell marker, particularly in CD19 (Spearman’s ρ = 0.623, p < 0.0001), CD79a (Spearman’s ρ = 0.791, p < 0.0001), CD79b (Spearman’s ρ = 0.636, p < 0.0001). This implied that DERL3 was involved in B cell proliferation. We further utilized the MCP counter to analyze the degree of infiltration of immune cells with DERL3 expression. MCP counter is a transcriptome-based computational method that quantifies the abundance of eight major immune and two stromal cell populations from RNAseq tumor bulk (21). As presented in Figure 3E , DERL3 was observed to be differentially expressed in B cells. Based on the strong correlation between DERL3 and B cells, T-SNE map was applied to visualize the distribution of DERL3 gene signature expressed in clusters. Results revealed that DERL3 was predominantly enriched in plasma cells of the B cell population ( Figure 3F ).
For medication sensitivity analysis, we assessed the correlation between DERL3 expression and IC50 values of chemotherapeutic agents from GDSC database. The results indicated that the DERL3 low-expression group significantly responded sensitively to gemcitabine, etoposide, and docetaxel, while the tendency was not observed in cisplatin and paclitaxel groups ( Figure 4A , Figure S2 ). In addition, we evaluated the associations between immune checkpoint molecules and DERL3 expression profiles. It has been demonstrated that the induction of immune checkpoint molecules are inhibitory receptors expressed on immune cells and may trigger immunosuppressive signaling pathways (22, 23). As presented in Figure 4B , the expression of DERL3 was elevated in the majority of immune checkpoint molecules, including PD-1, PD-L1, PD-L2, and CTLA-4. It indicated that DERL3 might help the tumor evade from immune surveillance by upregulating immune checkpoint molecules of TME in LUAD. We further applied the TIDE algorithm to predict the response to immunotherapy. Significant differences in response to immunotherapy were observed between the DERL3 high- and DERL3 low-expression groups ( Figure 4C ). Moreover, TIDE results showed higher response rates in the DERL3 low expression group ( Figure 4D , Figure S3 , 46.48% vs. 30.35%, p = 8.9e-07). The above-described results revealed that DERL3 might be an indicator of poor therapeutic outcomes.
GSEA was conducted to explore the potential mechanisms of DERL3-mediated biological process in LUAD. The results indicated that DERL3 was positively correlated with B cell activation (NES = 4.47, p = 0.0), B cell proliferation (NES = 3.74, p = 0.0), and B cell differentiation (NES = 3.74, p = 0.0). Moreover, GSEA also presented that gene signatures of UPR (NES = 1.53, p = 0.0), inflammatory response (NES = 1.39, p = 0.0), and IL-2 STAT5 signaling pathways were enriched when DERL3 was upregulated ( Figures 5A, B ).
We first performed high-throughput sequencing in 10 pairs of stage I LUAD and adjacent non-cancerous lung tissues. The heat map clearly showed a Derlin-related cluster ( Figure S4 ), which exhibited that DERL3 was significantly elevated compared with the corresponding adjacent tissue. We then conducted RT-qPCR in 33 paired LUAD and adjacent tissues to preliminarily investigate DERL3 expression. As shown in Figure 6A , DERL3 was upregulated in LUAD tissues when compared with adjacent tissues. We further identified DERL3 expression in LUAD cell lines. The results demonstrated DERL3 was upregulated in LUAD cell lines, especially in A549 and H1975 ( Figure 6B ). Additionally, IHC staining also revealed that DERL3 was statistically elevated in LUAD compared with adjacent pulmonary tissues ( Figure 6C ).
We noticed that DERL3 was significantly upregulated in lung cancer cell lines, especially in the A549 and H1975 cell lines. Therefore, A549 and H1975 cells with DERL3 siRNA were constructed using Lipofectamine 3000 transfection. As illustrated in Figures 7A, B , the mRNA expression of DERL3 was markedly decreased after si-DERL3-3 transfection in LUAD cells, indicating that the silencing efficacy of si-DERL3-3 was more effective than that of si-DERL3-1 and si-DERL3-2. Migrative ability was evaluated using scratch assays ( Figures 7C, D ) and transwell assays ( Figure 7E ). Indeed, the consequences indicated that migratory potentials were reduced by DERL3 knockdown in both A549 and H1975 cells ( Figures 7F, G ). In addition, the proliferation of A549 and H1975 cells were also decreased after DERL3 silencing ( Figure 7H ). On the other hand, we constructed DERL3 overexpression in SPCA-1 cells. The efficacy was confirmed by the elevated level of DERL3 mRNA expression ( Figure S5A ). The CCK-8 assay demonstrated that overexpression of DERL3 enhanced the proliferation of SPCA-1 cells ( Figure S5B ). Additionally, scratch and transwell assays presented that the migrative ability of SPCA-1 cells was significantly enhanced when compared with the control cells ( Figures S5C-F ). Collectively, the above consequence supported that DERL3 exerted a critical role in LUAD occurrence and progression.
Chronic and persistent ER stress has emerged as an essential pathophysiological paradigm underlying lung cancer (24, 25). DERL3, as an important element in ER homeostasis, was rarely investigated in previous molecular studies (26). Herein, we comprehensively explored gene expression, molecular function, and immune infiltration of DERL3 in LUAD. Our results identified that DERL3 was upregulated in lung cancer and high expression levels of DERL3 predicted an adverse prognosis of LUAD patients. Functional analysis revealed that DERL3 was associated with the ERAD process and involved in immune infiltration. Therefore, we speculated that DERL3 might serve as an oncogenic molecule in immune suppressive TME by inducing the ERAD process. Researches have demonstrated that the Derlin protein superfamily was involved in the ERAD process of cancer cells (27, 28). Tumor cells exposed to nutrient-deprived and inflammatory TME are more prone to activating ER stress (29, 30). Unchecked accumulation of aberrant proteins generates constant ER stress and underlies the most pressing maladies, including aging, cancer, and neurodegenerative diseases (31–33). To offset the catastrophic effect of unwanted proteins, ER is equipped with protein quality control systems for surveillance, including ERAD (34). DERL3 has emerged as a potential candidate for retro-translocating ERAD substrates tagged with ubiquitin out of the ER (35). Our results indicated that DERL3 was elevated in LUAD and associated with inflammatory TME. Therefore, we supposed that DERL3 differential expression may be attributed to the chronic inflammation of TME, inducing the activation of ER tress. Co-expressed molecules of DERL3 were primarily involved in the ERAD process; GO and KEGG annotation further implied that DERL3 was mainly enriched in ER-associated protein ubiquitination and ERAD. GSEA presented consistent consequences that DERL3 was related with UPR, an essential preventative system of the ER-induced quality control pathway. The above consequences support the hypothesis that DERL3 located in ER membrane can aid in the proteasomal degradation of proteins. Infiltrating immune cells are integral components of TME (36). A previous study has reported that cancer-derived factors in TME might trigger ER stress in innate immune cells to blunt anti-tumor immunity (37), which suggested an interplay between ER stress and immune cells. Interestingly, our results indicated that DERL3 was related to immune cell infiltration including B cells, CD4+ T cells, dendritic cells, and macrophages, particularly enriched in B cells. In addition, the efficacy of immune checkpoint blockade and chemotherapy was negatively related to the expression of DERL3. Consistently, Francesca et al. ascertained that DERL3 was elevated in plasma cells via RNA-seq identification (38). Moreover. Kriss et al. reported Derlin family members, DERL1 and DERL2, were transcriptionally upregulated in Eμ-TCL1-expressed murine B cells of mouse chronic lymphocytic leukemia (39), while Dougan et al. demonstrated that DERL2 absent in B lymphocytes may not alter cell development and antibody secretion (40). The potential association between DERL3 and B cell has not ever been reported; the exact mechanism of why DERL3 enriched in B cells remains unknown. Herein, we speculated that plasma cells need to expand the ER and Golgi networks to sustain antibody secretory capacity, whereas excessive production of immunoglobulins and ER remodeling may cause ER stress and trigger the UPR (41, 42), which may contribute to the elevated expression of the ER stress central regulatory molecule, including DERL3. However, this assumption still needs to be validated in vivo and in vitro. The present study remains several potential limitations listed as follows: Firstly, our results and conclusions lack of experimental validation and prospectively clinical cohort. Secondly, the heterogeneity of analytical variations, database application and sample heterogeneity may unintentionally impact the results. Our research was mainly summarized based on the public datasets, further studies based on experimental samples are required.
In conclusion, we demonstrated that high expression of DERL3 predicted an adverse outcome of LUAD patients. Moreover, functional enrichment analysis indicated that differentially expressed DERL3 was involved in ER stress, particularly in the ERAD process. Besides, DERL3 was proven to be associated with immune infiltration in suppressive TME, and overexpression of DERL3 may be an indicative predictor of immunotherapy resistance. These findings indicate a promising beginning for the discovery of potential prognostic predictors and immunotherapeutic strategies for LUAD.
The datasets presented in this study can be found in online repositories. The name of the repository and accession number can be found below: National Center for Biotechnology Information (NCBI) BioSample, https://www.ncbi.nlm.nih.gov/biosample/, SAMN19324451.
YZ and YX conceived and supervised the project. LL and GL performed the experiments, analyzed the data, and wrote the manuscript. HL assisted with experiments. LC and XC assisted with the computational analysis. All authors read and approved the final manuscript.
This work was supported by the Fujian Provincial Health Fund for Young and Middle-aged People (2019-ZQNB-7) and Startup Fund for scientific research, Fujian Medical University (Grant number: 2021QH2044).
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. | true | true | true |
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PMC9585222 | Zenan Chang,Yinan Zhang,Ming Lin,Shihong Wen,Hanjin Lai,Yaqing Zhan,Xiufen Zhu,Zhikun Huang,Xuyu Zhang,Zimeng Liu | Improvement of gut-vascular barrier by terlipressin reduces bacterial translocation and remote organ injuries in gut-derived sepsis 10.3389/fphar.2022.1019109 | 07-10-2022 | sepsis,gut-vascular barrier,bacterial translocation,terlipressin,PI3K | Gut-vascular barrier (GVB) serves as the last barrier to limit the migration of intestinal toxins into the blood circulation. The efficacy of terlipressin (a vasopressin V1 receptor agonist) in reducing GVB and multiple organ damage in gut-derived sepsis is unknown. In this study, we hypothesized that, besides other intestinal barriers, GVB play a key role in gut-derived sepsis and terlipressin improve GVB damage and then reduce bacterial translocation and organ injuries. In vivo, a cecal ligation and puncture mouse model was established. The mice were subjected to examine the damage of GVB determined by intestinal plasmalemma vesicle-associated protein-1(PV-1) and vascular endothelial-cadherin. And the intestinal permeability was assessed by translocation of intestinal bacteria and macromolecules. In vitro, transendothelial electrical resistance (TER) during interleukin (IL)-1β stimulation was measured on endothelial cells with or without small interfering RNA targeting β-catenin (si β-catenin). Terlipressin significantly improved GVB damage and reduced translocation of intestinal macromolecules and bacteria by activating PI3K signaling. Of note, intestinal PV-1 expression was significantly correlated with translocation of macromolecules, and dramatic increase of macromolecules was observed in intestinal tissues whereas fewer macromolecules and bacteria were observed in blood, liver and lung following terlipressin treatment. In vitro, terlipressin restored TER during IL-1β stimulation and si β-catenin transfection blocked the changes delivered by terlipressin. Collectively, terlipressin alleviated GVB damage and subsequent bacterial translocation via blood vessels after sepsis challenge, resulting in reduced distant organ injuries and the responsible mechanisms may involve the activation of PI3K/β-catenin pathway. | Improvement of gut-vascular barrier by terlipressin reduces bacterial translocation and remote organ injuries in gut-derived sepsis 10.3389/fphar.2022.1019109
Gut-vascular barrier (GVB) serves as the last barrier to limit the migration of intestinal toxins into the blood circulation. The efficacy of terlipressin (a vasopressin V1 receptor agonist) in reducing GVB and multiple organ damage in gut-derived sepsis is unknown. In this study, we hypothesized that, besides other intestinal barriers, GVB play a key role in gut-derived sepsis and terlipressin improve GVB damage and then reduce bacterial translocation and organ injuries. In vivo, a cecal ligation and puncture mouse model was established. The mice were subjected to examine the damage of GVB determined by intestinal plasmalemma vesicle-associated protein-1(PV-1) and vascular endothelial-cadherin. And the intestinal permeability was assessed by translocation of intestinal bacteria and macromolecules. In vitro, transendothelial electrical resistance (TER) during interleukin (IL)-1β stimulation was measured on endothelial cells with or without small interfering RNA targeting β-catenin (si β-catenin). Terlipressin significantly improved GVB damage and reduced translocation of intestinal macromolecules and bacteria by activating PI3K signaling. Of note, intestinal PV-1 expression was significantly correlated with translocation of macromolecules, and dramatic increase of macromolecules was observed in intestinal tissues whereas fewer macromolecules and bacteria were observed in blood, liver and lung following terlipressin treatment. In vitro, terlipressin restored TER during IL-1β stimulation and si β-catenin transfection blocked the changes delivered by terlipressin. Collectively, terlipressin alleviated GVB damage and subsequent bacterial translocation via blood vessels after sepsis challenge, resulting in reduced distant organ injuries and the responsible mechanisms may involve the activation of PI3K/β-catenin pathway.
Sepsis is a grave multi-organ dysfunction syndrome induced by the host’s maladjusted response to infection, with a mortality rate of up to 40% (Gotts and Matthay 2016; Shankar-Hari, Phillips et al., 2016). Gut-derived infection is regarded as a leading cause of sepsis in critical ill patients (Dickson 2016; Wang, Li et al., 2019). Intestinal barrier dysfunction caused by gut-derived sepsis induces the development of the bacterial translocation and multiple organ dysfunction syndrome (MODS) (Assimakopoulos, Triantos et al., 2018). The gut-vascular barrier (GVB) is the inner layer of defense in the multiple intestinal barriers (e.g., epithelium, mucus barrier, gut microbiota) that crucially regulates the translocation of substances from the intestinal lumen to the systemic circulation (Spadoni, Zagato et al., 2015; Spadoni, Pietrelli et al., 2016; Liu et al., 2020a; Chopyk and Grakoui 2020; Paone and Cani 2020; Brescia and Rescigno 2021). Several authors indicated that, in septic rats, the intestinal vascular permeability increased (He, Yuan et al., 2018; Li et al., 2020a). To date, however, among various components of intestinal barriers, the key role of GVB in limiting bacterial translocation and distant organ injuries caused by gut-derived sepsis remains unclear. Terlipressin, a highly selective vasopressin V1 receptor agonist, has become one of the commonly used vasoconstrictor drugs in the operating room and intensive care unit (ICU), and it successfully used in cases of septic shock (O’Brien, Clapp et al., 2002), hepatorenal syndrome (Uriz, Ginès et al., 2000), and gastrointestinal bleeding (Favalli, De Franceschi et al., 2004). Moreover, several clinical experiments are now investigating the impact of terlipressin on variceal hemorrhage (Poudel, Dhibar et al., 2022), post-hepatectomy (Li et al., 2020b), cirrhosis and ascites (Israelsen, Dahl et al., 2020). In our multicenter clinical trial, terlipressin effectively maintained the stability of circulation in the patients with septic shock (Liu, Chen et al., 2018). Importantly, we previously demonstrated that, in vivo and in vitro, terlipressin could protect against organ injury through phosphatidylinositol 3-kinase (PI3K) pathway following intestinal ischemia/reperfusion (I/R) attack (Liu, Zhang et al., 2017; Liu et al., 2020a). Although the previous studies have demonstrated that terlipressin improved intestinal microcirculation and organ functions in animals with endotoxemia (Lange, Ertmer et al., 2011; Qiu, Huang et al., 2014), the efficacy of terlipressin in reducing GVB and multiple organ damage in gut-derived sepsis is unknown. Therefore, we hypothesized that, besides other intestinal barriers, GVB play a key role in gut-derived sepsis and terlipressin improve GVB damage and then reduce bacterial translocation and organ injuries. To test this hypothesis, we examined the change of GVB, the translocation of intestinal bacteria and macromolecules, and the impairments of multiple organs after sepsis attack in vivo and vitro, and explored the potential signaling related to the protective effect of terlipressin.
The current animal protocol has been reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of Sun Yat-sen University (Guangzhou, China; Approval No. SYSU-IACUC-2021-000570). C57BL/6J male mice (8–12 weeks) were provided by Experimental Animal Center of Sun Yat-sen University. The mice were allowed free access to water and food. Sepsis model was established by cecal ligation and puncture (CLP) as previous described (Rittirsch, Huber-Lang et al., 2009). Briefly, the mice were anaesthetized with pentobarbital sodium (50 mg kg−1, intraperitoneally). The abdominal cavity was opened by a midline laparotomy. The cecum was exposed and ligated at 50% of the whole length. A needle (20 G) was used to penetrate the cecum once at the vascular-less part of the ligated segment and a drop of intestinal content was extruded. Then, the cecum was carefully replaced and the abdominal wall was closed. Immediately after the operation, 1 ml normal saline (NS) preheated at 36°C was injected subcutaneously (Rittirsch, Huber-Lang et al., 2009). In the sham procedure, the cecum was exposed without penetration.
Human umbilical vein endothelial cells (HUVECs; Sciencell) were cultured with endothelial culture medium (ECM; 1001, Sciencell) containing 5% fetal bovine serum, 1% streptomycin/penicillin solution in T75. Adherent endothelial cells were cultured in six-well plates and incubated with 10 ng/ml recombinant human interleukin-1β (IL-1β) for 24 h to mimic sepsis in vitro (Zhong, Wu et al., 2020).
Experiment 1To determine the change of GVB after CLP, mice were randomly allocated into five groups. In the CLP (CLP-6 h, CLP-24 h, CLP-48 h and CLP-72 h) groups, the mice were sacrificed 6 h, 24, 48, and 72 h after CLP operation respectively (n = 6 each). In the Baseline group, mice underwent the sham procedure and were sacrificed immediately after the procedure. Then, an indicated time point was selected for Experiment 2 according to the worst outcome of GVB damage after CLP (Supplementary Figure S1A). Experiment 2To investigate the effects of terlipressin on GVB and organ injuries after CLP, the mice were randomly divided into 4 groups (Control, CLP, TP, TP + LY, n = 8 each for “leakage test of macromolecules” test and n = 6 each for other tests). Control group: The mice underwent the sham procedure, and were injected intraperitoneally with 1 ml NS, and 5% dimethylsulfoxide (DMSO) and 95% Corn oil (8001-30-7, MedChemExpress, New Jersey, United States) in a total volume of 0.4 ml at 5 min after procedure. CLP group: The mice underwent the CLP procedure, and were injected intraperitoneally with 1 ml NS and 0.4 ml 5% DMSO and 95% Corn oil mixture at 5 min after CLP. TP group: The mice underwent the CLP procedure, and were injected intraperitoneally with 0.15 mg kg−1 terlipressin (Hybio Pharmaceutical Co., Shenzhen, China) dissolved in 1 ml NS, and 0.4 ml 5% DMSO and 95% Corn oil mixture at 5 min after CLP. TP + LY group: The mice underwent the CLP procedure, and were injected intraperitoneally with 0.15 mg kg−1 terlipressin dissolved in 1 ml NS at 5 min after CLP. Meanwhile, 40 mg kg−1 LY294002 (the specific inhibitor of PI3K, S1105, Selleck Chemicals, Houston, Texas, United States) dissolved in 0.4 ml 5% DMSO and 95% Corn oil mixture was also injected (Liu, Li et al., 2019). The mice were killed at the indicated time point and the biological samples were collected (Supplementary Figure S1B). In vitro, to determine the effect of terlipressin on the cultured HUVECs, the cells were divided into six groups. Control group: HUVECs were treated without any treatment. IL-1β group: HUVECs were subjected to 10 ng/ml recombinant human IL-1β for 24 h. IL-1β+TP (25 nM) and IL-1β+TP (100 nM) groups: HUVECs were treated with 10 ng/ml recombinant human IL-1β and terlipressin at concentration (25 nM or 100 nM) for 24 h. IL-1β+TP + si β-catenin and IL-1β+TP + si Negative control (NC) groups: Small interfering RNA (si β-catenin or NC) was transfected before HUVECs treating with IL-1β and terlipressin.
To investigate the permeability of GVB, 0.5 g kg−1 70kd-Fluorescin Isothiocyanate (FITC)-dextran (FD70, 60842-46-8, Sigma-Aldrich, St. Louis, United States), which cannot pass through the normal vascular wall (Spadoni, Zagato et al., 2015), was diluted in 0.5 ml PBS before anesthesia and then administered intragastrically to the independent mice because the usage of FD70 disturbed the detections of other variables. The optical density (OD) of FD70 in serum was read by a multi-label analyzer (INFINITE F500, Tecan, Austria) (Obermüller, Frisina et al., 2020). The ileum, colon, liver and lung tissue were sectioned and the nuclei were counterstained with 4′,6-diamidino-2-phenylindole (DAPI) and the images were obtained with an automatic inverted fluorescence microscope (Leica DMI8, Germany).
Hematoxylin-eosin (HE) staining of ileum, liver and lung was performed to evaluate the histopathological injury. Images were obtained with Olympus BX63 (Japan) microscope. Chiu’s score and Eckhoff’s score were used to evaluate the histopathological injury of intestine and liver respectively (Chiu, McArdle et al., 1970; Wen, Li et al., 2020). Lung injury score was carried out based on the previous literature (Li et al., 2020c). All the scoreing were evaluated by two experienced pathologists who were blinded to group allocation.
Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were detected by automatic biochemical analyser (Chemray 800, Shenzhen, China). Serum interleukin (IL)-6 and lipopolysaccharides (LPS) were detected by enzyme-linked immunosorbent assay (ELISA) kits (CSB-E04639m and CSB-E13066m, CUSABIO, Wuhan, China).
Immunofluorescence was used to detect the co-localization of endothelial marker (CD31) and other proteins. Primary antibodies included CD31 (1:400, ab24590, Abcam, Cambridge, United Kingdom), PV-1 (1:50, ab27853), β-catenin (1:400, ab16051) and vascular endothelial (VE)-cadherin (1:250, 555289, BD Pharmingen, New Jersey, United States), incubating overnight in a wet chamber at 4°C. Secondary antibodies included goat anti-mouse (1:200, Alexa Fluor 647, ab150115), goat anti-rat (1:200, Alexa Fluor 488, ab150157), and goat anti-rabbit (1:400, BS-0295G-FITC, Bioss, Beijing, China), incubating at room temperature in dark for 1 h. Then the nuclei were counterstained with DAPI and images were obtained with an automatic inverted fluorescence microscope (Leica DMI8, Germany).
Specimens were incubated with hybridization buffer at 37°C for 1 h. The pre-hybridization solution was removed, and the EUB338: 5′- GCT GCC TCC CGT AGG AGT -3′ bacterial probe labeled by Cy3 (red) was added to detect bacterial translocation. Sections were incubated with DAPI for 8 min in the dark, and then mounting with anti-fluorescence quenching sealing tablets.
The mRNA levels of IL-6, IL-1β, TNF-α, PV-1, β-catenin, VE-cadherin, occludin and zonula occludens 1 (Z O -1) were determined by q-PCR. After homogenate or scraping cells, HP Total RNA Kit (R6812-02, Omega, United States) was used to extract total RNA from tissue samples. RNA was reversely transcribed into cDNA with HiScript II Q RT Supermix for qPCR (R222-01, Vazyme, Nanjing, China). Q-PCR was performed with SYBR qPCR Master Mix (Q711-02, Vazyme, Nanjing, China) on Light Cycler 480 (Roche, Switzerland). The levels of target genes relative to β-actin were calculated by ∆∆CT method. Primer sequences were listed in Supplementary Table S1.
After homogenate or scraping and centrifugation at 12,000 rpm⋅min−1 for 20 min, the concentration of supernatant was determined by bicinchoninic acid method (BCA, C0020, SolarBio, Beijing, China). 50 ug total protein was electrophoresed on poly acrylamide gels and transferred to PVDF membrane. The membranes were blocked with 5% BSA at room temperature for 1 h. The primary antibodies included PV-1 (1:1000), VE-cadherin (1:500), β-actin (1:5000, 66009-1-Ig, Proteintech, Chicago, United States), phospho-Akt (Ser473, 1:1000, 9271, CST, Danvers, United States), Akt (1:1000, CST: 9272), phospho-β-catenin (Ser33/37/Thr41, 1:1000, CST: 9561), β-catenin (1:1000), phospho-GSK-3β (Ser9, 1:1000, CST: 9336), GSK-3β (1:1000, CST: 9315) and GAPDH (1:5000, FD0063, Fude Bio, Hangzhou, China). The membranes were incubated overnight with the primary antibodies in a shaking table at 4°C and then incubated with secondary antibodies for 1 h at room temperature, including HRP-labeled goat anti-mouse IgG (1:5000, GB23301, ServeBio, Wuhan, China), HRP-labeled goat anti-rat IgG (1:5000, GB23302, ServeBio) or HRP-labeled goat anti-rabbit IgG (1:5000, FD0128, Fude Bio). After washing with tris buffered saline tween, the images were obtained on chemiluminescence instrument (Amersham Imager 600, United States) and analyzed with ImageJ software. The bands were normalized with the housekeeping proteins β-actin or GAPDH and then presented as the relative value to Baseline or Control group.
Independent mice underwent the same procedure of Experiment 2 (Supplementary Figure S1B) and were used to evaluate survival time. After Sham or CLP operation, the mice were immediately transferred to their individual cages and allowed free access to water and food for 72 h.
The sense strand sequences of siRNA targeting β-catenin were: 5′-CAGTTGTGGTTAAGCTCTT-3′ (si β-catenin). The siRNA duplexes and scrambled siRNA (si Negative Control, si NC) were synthesized and purified by Tsingke (Beijng, China). siRNA transfection was performed using Lipofectamine 2000 (Invitrogen) for 24 h according to the manufacturer’s instructions.
The integrity of endothelial cell monolayer was quantified by transendothelial electrical resistance (TER) assay in hanging six-well plates (SPLInsert™ Hanging, 6 Inserts, Korea) by Volt/Ohm Meter for Epithelium (RE1600, jingong hongtai, Beijing, China). 5 × 104 HUVECs were grown on a transwell insert until confluency. After sterilizing, drying, and rinsing, the long ends of the electrode bridges was carefully placed into the basal chamber and the short ends was placed into the apical chamber. The longer electrode was touched the bottom of the dish, while keeping the shorter electrodes below the surface of the media but above the tissue culture inserts. The computational formula was Unit Area Resistance (Ω·cm2) = Resistance (Ω) × effective membrane area (cm2).
5 × 104 HUVECs were grown on a transwell insert (0.4 um pore size; SPLInsert™ Hanging, 6 Inserts) until confluency. At 24 h after treatment in each group, the supernatant was removed and FITC-dextran (1 mg/ml; 70 kDa; 60842-46-8, Sigma-Aldrich, St. Louis, United States) was added to the transwells. After 2 h, the FITC-dextran translocated to the lower compartment of the transwell was measured in a microplate reader (Thermo Scientific ™ Varioskan ™ LUX, United States) at excitation/emission wavelength of 490/520 nm. As a positive control a transwell without cells was used. By normalizing the fluorescence signals of the treatment group to the Control group a measure of endothelial layer leakiness was obtained.
The sample size analysis was performed based on our literature (Zhang, Chang et al., 2022) and the Power and Sample Size online software (http://powerandsamplesize.com/Calculators/). A minimum of six mice per group was required for 90% power to detect a mean difference between groups of 40% in the relative expression of PV-1, assuming type I error = 0.05 for a 2-sided hypothesis test. Survival time from the beginning of CLP was expressed as median (range) and compared by Kaplan-Meier curve with Log-rank test. The mortality was analyzed by Fisher exact test. The other data were analyzed by GraphPad Prism 9.3 software (La Jolla, CA, United States) and were distributed normally. Then the values were expressed as mean ± standard deviation (SD). One-way ANOVA (Tukey post hoc) was used for comparisons among groups. Pearson correlation analysis was performed to calculate correlation coefficient and p value. p < 0.05 was considered statistically significant.
As shown in Supplementary Figures S2A,B, LPS and IL-6 concentration in serum increased after CLP. The mRNA expression of IL-6, IL-1β and TNF-α were higher in the ileum, liver and lung after CLP insult (Supplementary Figure S2C). Furthermore, obvious intestinal damages were detected in CLP groups as evidenced by higher Chiu’s scores and depressed villus height of ileum (Supplementary Figure S2D). The injury score of liver and lung were also higher in CLP groups than those in Baseline group (Supplementary Figure S2E). These results showed that CLP procedure successfully induced gut-derived sepsis in the present study.
The data of immunofluorescence and western blot analysis showed that, in mice’s ileum, PV-1 expression (the specific biomarker of GVB damage) were significantly increased at 6 h after CLP (both p < 0.001; Figures 1A,B) and then gradually decreased. Moreover, VE-cadherin expression (another specific biomarker of GVB) significantly decreased after CLP (all p < 0.01; Figures 1B,C). Based on the changes of PV-1 and VE-cadherin, the critical time point (6 h after CLP) was selected for the subsequent experiments.
At 6 h after CLP, the colocalization analysis of VE-cadherin or PV-1 with CD31 revealed that terlipressin restored VE-cadherin and PV-1 expression in the intestinal vascular endothelium in the TP group (both p < 0.001 vs. CLP group; Figure 2A). Similarly, western blot analysis indicated that the expressions of VE-cadherin and PV-1 protein were significantly improved in the TP group (TP vs. CLP group: 1.663 ± 0.5141 vs. 4.054 ± 2.202, p = 0.0147 for PV-1 and 1.139 ± 0.2755 vs. 0.2932 ± 0.1854, p = 0.0001 for VE-cadherin; Figure 2B).
The analysis of migrating macromolecules was performed in the independent mice. At 6 h after CLP, the fluorescence intensity of FD70 in intestines in the Control group were higher than those in the CLP group, whereas the value of FD70 in liver, lung and serum were lower (all p < 0.001, Figures 3A–C). Interestingly, terlipressin dramatically increased the FD70 intensity in ileum and colon but decreased FD70 value in liver, lung and serum (all p < 0.001 vs. CLP group). In the CLP group, the data of correlation analysis indicated that the mRNA level of PV-1 in the ileum and colon were positively correlated with the OD value of FD70 in serum respectively (Figure 3D). Moreover, in the CLP group, the mRNA level of PV-1 in the ileum and colon were positively correlated with the FD70 intensity in the liver and lung whereas were negatively correlated with the FD70 in the ileum or colon, respectively (Figure 3E). The data of FISH detection showed that, in the CLP group, increased bacterial colonization was presented around blood vessels in liver and lung. Terlipressin significantly alleviated bacterial colonization in liver and lung (both p < 0.01 vs. CLP group, Figure 3F).
At 6 h after CLP, terlipressin decreased the mRNA expressions of inflammatory cytokines in liver and lung (all p < 0.05 vs. CLP group, Figure 4A). Meanwhile, terlipressin treatment reduced histological injury in liver and lung and improved liver function (all p < 0.01 vs. CLP group, Figures 4B–F). As shown in Figure 4G, the survival time of mice and mortality rate at 72 h after CLP in the CLP group was 64 h (10–72 h) and 50%, respectively (both p < 0.05 vs. Control group). Terlipressin significantly prolonged survival time which was 72 h (50–72 h; p = 0.0159 vs. CLP group) and decreased mortality (11.76%, p = 0.0439).
To investigate the role of PI3K signaling in the GVB protection conferred by terlipressin after CLP, several molecules related to PI3K pathway were examined and LY294002, a classic inhibitor of PI3K, was used following terlipressin treatment. The results showed that, at 6 h after CLP, phosphorylation of Akt (Ser473, p-Akt) decreased after CLP (p < 0.0001 vs. Control group), and terlipressin significantly restored the expression p-Akt (p = 0.0039 vs. CLP group, Figures 5A,B). GSK-3β and β-catenin are the downstream molecule of Akt. The expression of p-GSK-3β (Ser 9) and β-catenin in ileum significantly decreased whereas p-β-catenin (Ser33/37/Thr41) increased in the CLP group (all p < 0.05 vs. Control group, Figures 5A,C–G). The use of terlipressin obviously increased the expression of p-GSK-3β and β-catenin in ileum (all p < 0.05 vs. Control group). As shown in Figure 5, LY294002 abolished the impacts of terlipressin on the above-mentioned molecules (all p < 0.05 vs. TP group). Moreover, LY294002 totally diminished the protective effects of terlipressin on the GVB damage (Figure 2), the translocation of macromolecules and bacteria (Figure 3), organ injuries and survival (Figure 4) in mice after CLP (all p < 0.05, TP + LY group vs. TP group).
To further mimic the sepsis model in vivo, we stimulated HUEVCs with recombinant human IL-1β in vitro. Recombinant human IL-1β upregulated mRNA levels of PV-1, while downregulated β-catenin, VE-cadherin, occludin and zonula occludens 1 (ZO-1). Terlipressin (100 nM) significantly restored the tight and adhesin junction proteins (Figure 6A). siRNA targeting β-catenin successfully made β-catenin knockdown (Figure 6B). Compared with control, interleukin-1β stimulation resulted in increased endothelial permeability, and terlipressin treatment restored TER of endothelial cells, which β-catenin knockdown sharply demolished at the indicated time points (Figures 6C,D). The permeability was determined by measuring the passage of 70 kDa FITC-Dextran across HUVEC monolayers. The analysis showed that terlipressin decreased the endothelial permeability in HUVEC. β-catenin knockdown induced a 2-fold increase in the permeability of the monolayer compared to the IL-1β+TP group (Figure 6E). Moreover, β-catenin knockdown significantly decreased the mRNA levels of junction and adhesin proteins and increased PV-1 (Figure 6F).
The current study demonstrated that severe disruption of GVB occurred after sepsis insult, and a protected GVB conferred by terlipressin could independently block bacterial translocation via blood vessels and subsequently reduce distant organ injuries, although bacteria crossed other intestinal barriers. The PI3K/β-catenin signaling might be involved in the protective effects induced by terlipressin (Figure 7). The intestinal tract is regarded as the “motor” of critical diseases (Mittal and Coopersmith 2014). We have confirmed that the migrating protein and bacteria through bloodstream resulted in distant hepatic and renal injuries after intestinal I/R (Wen, Li et al., 2020; Lai, Zhan et al., 2021). GVB serves as the last firewall to limit the dissemination of intestinal toxins via the blood circulation (Spadoni, Zagato et al., 2015; Spadoni, Pietrelli et al., 2016). Increased intestinal vascular permeability in septic model was reported in previous literatures (He, Yuan et al., 2018; Li et al., 2020b). In the present study, we furtherly explored the change of GVB damage according to the data of two specific biomarkers (PV-1 and VE-cadherin) at different time point after CLP. The results showed that the destruction of GVB reached its peak at 6 h after CLP and then gradually recovered (Figure 1), indicating that a timely intestinal vascular protection is of important for relieving distant organ damages in gut-derived sepsis. Therefore, in this study, terlipressin 0.15 mg kg−1 was administered at 5 min after CLP challenge. Based on a classical conversion of drug dose from human to animal, this dosage of terlipressin in mice is approximately equal to 0.017 mg kg−1 in human, which has been commonly applied in the clinical settings (Treschan and Peters 2006; Reagan-Shaw, Nihal et al., 2008). The use of terlipressin significantly improved GVB damage (Figure 2) and reduced the translocation of macromolecules and bacteria (Figure 3) in mice after CLP, and raised TER in adherent endothelial cells (Figure 6). Consistent with our finding, He et al. demonstrated that early treatment with a selective V1a receptor agonist inhibited vascular leakage in ovine with septic shock (He, Su et al., 2016). Based on our findings in this study, terlipressin may be a preferable vasoconstrictor in gut-derived sepsis in the clinical settings. Moreover, based on the definite protective effect of terlipressin on GVB, the therapeutic possibilities of terlipressin for GVB damage induced by bacterial or viral intestinal infections, inflammatory bowel disease and other pathogenic challenges deserve to be explored seriously. Actually, several authors had investigated the therapeutic effects of terlipressin in spontaneous bacterial peritonitis (Lee, Han et al., 2009), gastrointestinal bleeding (Seo, Park et al., 2014) and hepatorenal syndrome (Uriz, Ginès et al., 2000). It has been demonstrated that gut microbiome, mucus and mucosal barrier limit intestinal bacterial translocation and subsequently alleviate liver injuries (Albillos, de Gottardi et al., 2020). In this study, to furtherly clarify the limiting effect of GVB in various gut barriers, we examined the fluorescence of macromolecules in intestinal tissues and distant organs and analyzed the correlations between intestinal PV-1 expression and fluorescence intensity. The results showed that the level of GVB damage was closely associated with the expression of macromolecules in different organs, and surprisingly, numerous macromolecules were detected in serum and distant organs but not in ileum and colon due to the impaired GVB caused by CLP, whereas the protected GVB conferred by terlipressin efficiently prevented macromolecules in the gut lumen from transferring to distant organs via blood circulation even though the macromolecules had crossed other barriers and entered the intestinal tissues (Figure 3). Moreover, in line with previous findings (Spadoni, Zagato et al., 2015), the protected GVB prohibited entry of the microbiota into liver and lung (Figure 3), and then improved multiple organ injuries and survival after CLP (Figure 4). To the best of our knowledge, our notable findings for the first time demonstrate that GVB plays a critical role in blocking the development of gut-derived sepsis, and suggest that a healthy GVB can independently inhibit intestinal bacterial translocation via circulation and terlipressin may be a promising vasoactive agent for the critical patients with intestinal impairments because of its protective property of GVB. It has been demonstrated that activation of the PI3K-Akt-Rac1 signaling can maintain the integrity of the blood-brain barrier (Wu, Chen et al., 2017) and our previous study showed that intravenous infusion of terlipressin markedly increased the expression of PI3K and p-Akt in ileal mucosa in rat’s intestinal ischemia model (Liu et al., 2020b). Hence LY294002, a specific inhibitor of PI3K, was used following terlrpressin treatment and p-Akt expression in ileum was examined in this study. Our results clearly showed that terlrpressin protected against gut-derived sepsis mainly through PI3K/Akt signaling (Figures 2–5). The PIK3/Akt/GSK3 is a classic signaling pathway and involved in many biochemical processes (Duda, Akula et al., 2020). GSK-3β is a key regulator for β-catenin expression and phosphorylation of GSK-3β can inhibit the degradation of β-catenin (p-β-catenin) and then increase β-catenin expression (MacDonald, Tamai et al., 2009; Nong, Kang et al., 2021). The current application of terlipressin increased p-GSK-3β and β-catenin expression, but decreased p-β-catenin in intestinal tissues (Figure 5). β-catenin is essential for reducing the vascular permeability and bacterial penetration (Spadoni, Zagato et al., 2015; Mouries, Brescia et al., 2019; Zhang, Chang et al., 2022). Several studies demonstrated that upregualtion of β-catenin could improve GVB in pathologic conditions (Birdsey, Shah et al., 2015; Mouries, Brescia et al., 2019; Zhang, Chang et al., 2022). When we knocked down β-catenin in endothelial monolayer, the increased TER, tight and adhesin junction proteins and decreased permeability across HUVEC monolayers conferred by terlipressin sharply changed (Figure 6). Whereas p-β-catenin caused adherence junction disruptions and cytoskeleton rearrangement, and then increased vascular endothelial hyperpermeability (Weng, Yu et al., 2019). Therefore, we suggested that activation of the PI3K/β-catenin pathway might be involved in the protective effect of terlipressin on GVB. There were some possible limitations in this study. First, because knock-out/knock down models are missing, we have not figured out the mechanism of GVB damage induced by gut-derived sepsis. Further studies are needed to explore the causative mechanism and to develop the corresponding treatment. Second, the impact of β-catenin on GVB maintenance is still controversial (Grander, Grabherr et al., 2020). The definite signaling pathway of terlipressin for GVB protection is needed to be investigated. Third, the effects of terlipressin on GVB and bacterial translocation in sepsis need to be confirmed in translational researches and clinical trials. Final, hemodynamic improvement caused by terlipressin might be partly responsible for the reduction of organ injuries and animal mortality in our experiments, hence another vasoconstrictor, such as norepinephrine, should be set as the positive control. Taken together, the present study reveals that sepsis leads to severe GVB disruption, and a vasoconstrictor, terlipressin, reduces GVB damage via PI3K/β-catenin signaling. The protected GVB conferred by terlipressin effectively prevents macromolecules and bacteria in the gut lumen from transferring to distant liver and lung via blood circulation and subsequently improves organ injuries and mortality after sepsis insult. Based on the key role of GVB in gut-derived sepsis, terlipressin may be a promising direction for the critical ill patients with gut-derived sepsis. | true | true | true |
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PMC9585262 | Qiaoling Xiao,Can Lin,Meixi Peng,Jun Ren,Yipei Jing,Li Lei,Yonghong Tao,Junpeng Huang,Jing Yang,Minghui Sun,Jing Wu,Zailin Yang,Zesong Yang,Ling Zhang | Circulating plasma exosomal long non-coding RNAs LINC00265, LINC00467, UCA1, and SNHG1 as biomarkers for diagnosis and treatment monitoring of acute myeloid leukemia | 07-10-2022 | acute myeloid leukemia,biomarker,exosome,long non-coding RNA,LINC00265,LINC00467,UCA1,SNHG1 | Exosomal long non-coding RNAs (lncRNAs) have emerged as a cell-free biomarker for clinical evaluation of cancers. However, the potential clinical applications of exosomal lncRNAs in acute myeloid leukemia (AML) remain unclear. Herein, we attempted to identify plasma exosomal lncRNAs as prospective biomarkers for AML. In this study, plasma exosomes were first successfully extracted from AML patients and healthy donors (HD). Subsequently, the downregulated plasma exosomal lncRNAs (LINC00265, LINC00467, and UCA1) and the upregulated plasma exosomal lncRNA (SNHG1) were identified in AML patients (n=65) compared to HD (n=20). Notably, individual exosomal LINC00265, LINC00467, UCA1, or SNHG1 had a capability for discriminating AML patients from HD, and their combination displayed better efficiency. Furthermore, exosomal LINC00265 and LINC00467 were increased expressed in patients achieving complete remission after chemotherapy. Importantly, there was upregulation of exosomal LINC00265 and downregulation of exosomal SNHG1 upon allogeneic hematopoietic stem cell transplantation. Additionally, these lncRNAs were high stability in plasma exosomes. Exosomal LINC00265, LINC00467, UCA1, and SNHG1 may act as promising cell-free biomarkers for AML diagnosis and treatment monitoring and provide a new frontier of liquid biopsy for this type of cancer. | Circulating plasma exosomal long non-coding RNAs LINC00265, LINC00467, UCA1, and SNHG1 as biomarkers for diagnosis and treatment monitoring of acute myeloid leukemia
Exosomal long non-coding RNAs (lncRNAs) have emerged as a cell-free biomarker for clinical evaluation of cancers. However, the potential clinical applications of exosomal lncRNAs in acute myeloid leukemia (AML) remain unclear. Herein, we attempted to identify plasma exosomal lncRNAs as prospective biomarkers for AML. In this study, plasma exosomes were first successfully extracted from AML patients and healthy donors (HD). Subsequently, the downregulated plasma exosomal lncRNAs (LINC00265, LINC00467, and UCA1) and the upregulated plasma exosomal lncRNA (SNHG1) were identified in AML patients (n=65) compared to HD (n=20). Notably, individual exosomal LINC00265, LINC00467, UCA1, or SNHG1 had a capability for discriminating AML patients from HD, and their combination displayed better efficiency. Furthermore, exosomal LINC00265 and LINC00467 were increased expressed in patients achieving complete remission after chemotherapy. Importantly, there was upregulation of exosomal LINC00265 and downregulation of exosomal SNHG1 upon allogeneic hematopoietic stem cell transplantation. Additionally, these lncRNAs were high stability in plasma exosomes. Exosomal LINC00265, LINC00467, UCA1, and SNHG1 may act as promising cell-free biomarkers for AML diagnosis and treatment monitoring and provide a new frontier of liquid biopsy for this type of cancer.
Acute myeloid leukemia (AML) is an aggressive and molecularly heterogeneous hematologic malignancy characterized by clonal differentiation arrest and uncontrolled proliferation of myeloid blasts in the bone marrow (1). As the most common form of acute leukemia in adults, AML has a worldwide incidence of approximately 5.4 per 100,000 per year (2). Nowadays, the diagnosis of AML is based on the conjoint assays of morphology, immunophenotype, cytogenetics, and molecular genetics, which requires the presence of circulating blasts in either a blood or a bone marrow sample (3). However, early detection of this disease remains a major challenge, due to the late dissemination of leukemic blasts to the peripheral blood and symptoms appearing late. Until recently, treatment options for AML in the clinic mainly involve cytotoxic chemotherapy, targeted therapy, and allogeneic hematopoietic stem cell transplantation (allo-HSCT) (4). Although the majority of AML patients respond to the initial standard induction chemotherapy, primary refractory and relapse lead to lower long-term survival rates (5). Therefore, it is particularly important to monitor treatment efficacy in real-time and make appropriate therapeutic judgments. It has been well acknowledged that the treatment monitoring of AML mainly depends on comprehensive cellular analyses, which is complicated by the prompt clearance of circulating leukemic blasts during therapy. Hence, the discovery of reliable cell-free biomarkers for early detection and treatment monitoring of AML has been urgently needed. Our previous study revealed that circulating DNA in plasma might be a prospective diagnostic and disease tracking predictor for acute leukemia (6), whereas plasma DNA reveals information from mostly dying cells and failed to detect early lesions (7). Herein, we attempt to identify novel cell-free biomarkers for AML timely diagnosis and surveillance. Exosomes are a subset of extracellular vesicles with lipid bilayer membranes, ranging from 30 to 150 nm in diameter (8). It is known that exosomes are released continuously by a variety of living cells and are present in human biofluids such as saliva, blood, and urine (9–11). In fact, exosomes can stably carry abundant parental cell-derived bioactive molecules (nucleic acids, proteins, and lipids) due to the bilayer membrane structure and provide real-time signals from living cells (12). Thus, circulating exosomes and especially their contained cargoes have gained increased attention in cancer liquid biopsy, highlighting the potential as biomarkers for cancer diagnosis, progression monitoring, and prognosis prediction (12). Notably, the extracellular vesicle concentration was found to elevate in the plasma of the AML patient at diagnosis and remained increased even at complete remission after chemotherapy (13). More importantly, in AML cell line Molm-14 and patient-derived xenografted murine models, circulating AML-derived extracellular vesicles were demonstrated to spread into the peripheral blood ahead of the leukemic blasts and associate with disease burden (14). These reports suggest that extracellular vesicles have a great promise to act as cell-free indicators for AML early diagnosis and disease tracking during therapy. Recently, the ongoing development of sequencing technologies has permitted an increase in the number of newly discovered long non-coding RNAs (lncRNAs). LncRNAs represent a kind of non-coding RNAs with limited protein-coding capacity longer than 200 nucleotides (15). Emerging evidence has corroborated that lncRNAs play important roles in leukemogenesis. Long intergenic non-protein coding RNA 265 (LINC00265) was highly expressed in the bone marrow and serum of AML patients and related to AML diagnosis (16). Furthermore, LINC00265 inhibited leukemic cell apoptosis by inducing autophagy (17). Silencing of long intergenic non-protein coding RNA 467 (LINC00467) suppressed the malignant phenotypes of AML cells (18). Liang et al. (19) reported lncRNA urothelial cancer associated 1 (UCA1) knockdown attenuated proliferation and accelerated apoptosis in AML cells. In addition, the upregulation of lncRNA small nucleolar RNA host gene 1 (SNHG1) was observed and SNHG1 served as an oncogene in AML (20). Besides, lncRNA prostate cancer associated transcript 18 (PCAT18) showed higher expression in AML samples and promoted leukemic cell proliferation (21). LncRNAs enclosed in exosomes are prevented from ribonuclease-mediated degradation and stably exist in body fluids (22). Accumulating studies have demonstrated that exosomal lncRNAs exert important functions in carcinogenesis and development. Zang et al. (23) reported that exosomal lncRNA UFC1 could facilitate tumor cell growth, migration, and invasion in non-small cell lung cancer (NSCLC). In another study, exosome-transmitted lncRNA Sox2ot promoted the epithelial-mesenchymal transition process and induced stem cell-like properties of pancreatic ductal adenocarcinoma cells (24). Additionally, LINC00461 was upregulated in mesenchymal stromal cell (MSC)-derived exosomes and further enhanced multiple myeloma cell proliferation (25). Based on essential roles in cancers, exosomal lncRNAs have become of tremendous interest in biomarker research. From recent studies, tumor-derived exosomal lncRNA GAS5 was reported to be highly sensitive to early-stage NSCLC (26). Sedlarikova et al. (27) identified serum exosomal lncRNA PRINS as a novel biomarker for multiple myeloma diagnosis. Besides, serum exosomal lncRNA aHIF might be an unfavorable prognostic factor of epithelial ovarian cancer (28). Our previous study also suggested that exosomal lncRNAs TBILA and AGAP2-AS1 exhibited powerful diagnostic efficiencies in NSCLC patients with different tumor pathologic subtypes and early stages (29). Nevertheless, the potential clinical utility of exosomal lncRNAs in AML has not been reported yet. In the present study, plasma exosomes were first successfully isolated from the AML patient and the healthy donor (HD). Subsequently, the decreased plasma exosomal lncRNAs (LINC00265, LINC00467, and UCA1) and the increased plasma exosomal lncRNA (SNHG1) were identified in AML patients (n=65) compared to HD (n=20). Of note, individual exosomal LINC00265, LINC00467, UCA1, or SNHG1 had a capability for distinguishing AML patients from HD, and the combination of these four exosomal lncRNAs exhibited the most powerful diagnostic accuracy. Furthermore, when the patients achieved complete remission but not non-remission or partial remission after the standard induction chemotherapy, the level of exosomal LINC00265 and LINC00467 elevated. More importantly, there was upregulation of the exosomal LINC00265 level and downregulation of the exosomal SNHG1 level undergoing the allo-HSCT treatment. Additionally, these lncRNAs were high stability in plasma exosomes. Our observations prove that exosomal LINC00265, LINC00467, UCA1, and SNHG1 may act as novel cell-free indicators for AML diagnosis and treatment monitoring and provide a new frontier of liquid biopsy for this type of cancer.
A total of 65 patients with newly diagnosed acute myeloid leukemia (AML) and 20 healthy donors (HD) were enrolled in this study from December 2020 to August 2021 at the First Affiliated Hospital of Chongqing Medical University. Under the 2019 National Comprehensive Cancer Network (NCCN) guideline of AML (30), all the patients were diagnosed with AML based on the conjoint analyses of morphology, immunophenotype, cytogenetics, and molecular genetics. On the basis of the French-American-British (FAB) classification of AML (31, 32), the patients were divided into six subtypes including M0 (minimally differentiated acute myeloid leukemia), M1 (acute myeloblastic leukemia without maturation), M2 (acute myeloblastic leukemia with maturation), M3 (acute promyelocytic leukemia), M4 (acute myelomonocytic leukemia), and M5 (acute monoblastic or monocytic leukemia). Meanwhile, the cases were separated into three risk classifications including favorable, intermediate, and adverse according to the 2017 European LeukemiaNet (ELN) risk stratification guideline based on cytogenetic abnormalities and genetic mutations (33). Consistent with the 2019 NCCN guideline, all the AML participants received the recommended treatment regimens, and remission responses to chemotherapy including non-remission (NR), partial remission (PR), complete remission (CR), and disease recurrence of patients were assessed. The AML patients co-existing with other types of tumors or subjected to chemotherapy or radiotherapy before blood collection were excluded. Additionally, 12 plasma samples from enrolled AML patients at the first CR stage after the standard induction chemotherapy were collected. Before and after allogeneic hematopoietic stem cell transplantation (allo-HSCT), 12 paired plasma samples from enrolled AML patients were collected. Besides, the healthy donors who underwent routine physical examinations and showed no signs of disease were recruited as controls. The healthy individuals were sex- and age-matched to AML patients. This research was approved by the Ethics Committee of Chongqing Medical University. The experiments were conducted in accordance with the Helsinki Declaration. Written informed consent was obtained from all the subjects for study purposes. Details of the clinical characteristics of all the enrolled study subjects are presented in Table 1 .
Human AML cell line NB4 was purchased from American Type Culture Collection (ATCC, Manassas, VA, USA) and cultured in RPMI-1640 medium (Thermo Fisher Scientific, Waltham, MA, USA, #11875093) containing 10% fetal bovine serum (Thermo Fisher Scientific, #10091155). The mediums were supplemented with 1% Penicillin-Streptomycin solution (Beyotime, Shanghai, China, #C0222) to protect cells from potential contamination, and cells were incubated at 37°C in the presence of 5% CO2.
Peripheral blood specimens from all the participants were collected in vacuum blood tubes with ethylenediaminetetraacetic acid (EDTA) anticoagulant. Followed by a two-step centrifugation protocol (2,000 g at 4°C for 10 min; 10,000 g at 4°C for 30 min), plasma samples were acquired. To eliminate contaminating cell debris and large diameter extracellular vesicles, the supernatants were filtered through a 0.22 µm filter (Biosharp, Beijing, China, #BS-PES-22) and stored at -80°C until use. Exosomes were isolated from pretreated plasma samples using the exoRNeasy Midi Kit (QIAGEN, Dusseldorf, Germany, #77144) according to the manufacturer’s guidelines. In brief, 1 volume of plasma was mixed with 1 volume of binding buffer (XBP) and the sample/XBP mix was added onto the exoEasy spin column to bind exosomes to the spin column membrane. After centrifugation at 3, 000 g at room temperature (RT) for 1 min, the flow-through was discarded and 3.5 mL wash buffer (XWP) was added to the spin column to remove residual buffer by centrifugation at 3, 000 g at RT for 5 min. The flow-through and the collection tube were discarded, and then the spin column was transferred to a fresh collection tube. Finally, 400 μL elution buffer (XE) (QIAGEN, #76214) was added to the spin column to elute exosomes, followed by incubation at RT for 5 min and centrifugation at 500 g at RT for 5 min. The exosome suspension was collected and stored at -80°C for further study.
The morphologies of isolated exosomes were visualized by transmission electron microscopy (TEM). Briefly, exosomes were loaded onto a carbon-coated 300 mesh copper grid (ProSciTech, Kirwan, Queensland, Australia). After drying at RT for 5 min, the grid was fixed with 2% glutaraldehyde in 0.1 M phosphate buffer (pH=7.4), and then stained with a drop of 2% uranyl acetate (Sigma-Aldrich, Burlington, MA, USA) at RT for 10 min. The morphologies of exosomes were observed by the JEM-1011 TEM (Hitachi, Tokyo, Japan). The size distribution and concentration of exosomes were determined by nanoparticle tracking analysis (NTA). Isolated exosomes were resuspended in phosphate-buffered saline, and then injected into the ZetaView PMX 110 (Particle Metrix, Meerbusch, Germany). Particles were tracked and the size of particles was measured based on Brownian motion and the diffusion coefficient. Data were analyzed using the manufacturer’s software, ZetaView (Version 8.02.28). The level of exosome marker proteins (CD63 and Alix) and non-exosomal protein (Calnexin) was detected by western blot analysis, as previously reported (34). In brief, Exosome suspensions were lysed in RIPA buffer (Beyotime, #P0013C) containing the protease inhibitor (Bimake, Houston, TX, USA, #B14001) on ice for 30 min. After centrifugation at 13,300 rpm at 4°C for 30 min, the supernatant was collected and quantified by the Enhanced BCA Protein Assay Kit (Beyotime, #P0010S), followed by boiling in 5×sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) loading buffer (Beyotime, #P0015) for 10 min. Next, the isolated exosomal protein was separated by 12% SDS-PAGE and electro-transferred onto polyvinylidene fluoride membranes (Bio-Rad, Hercules, CA, USA, #1620177). The membranes were incubated with primary antibodies against CD63 (1:1000, Bimake, #A5177), Calnexin (1:1000, Cell Signaling Technology, Danvers, MA, USA, #2679), and Alix (1:500, Wanleibio, Shenyang, China, #WL03338) at 4°C overnight. Then, the membranes were covered with the secondary antibody (1:5000, Biosharp, #BL003A) at RT for 1 h, and the signals of labeled proteins were visualized using an enhanced chemiluminescence solution (Bio-Rad, #1705062).
RNA was extracted from plasma exosomes by the aforementioned exoRNeasy Midi Kit according to the manufacturer’s guidelines. Briefly, 700 μL QIAzol was added to the spin column membrane capturing exosomes or exosome suspensions to lyse the exosomes, and the lysate was incubated at RT for 5 min. Then, 90 μL chloroform was added to the lysate, and the mix was shaken vigorously for 15 s, followed by incubation at RT for 2 min. After centrifugation at 12,000 g at 4°C for 15 min, the upper aqueous phase was collected and mixed with 2 volumes of 100% ethanol. The mix was transferred to the RNeasy MinElute spin column and centrifuged at 8,000 g at RT for 15 s. The spin column was washed once with 700 μL Buffer RWT, and then twice with 500 μL Buffer RPE. After centrifugation at a full speed at RT for 5 min with the open lid, the spin column was added with 14 μL RNase-free water and stood for 1 min, followed by centrifugation at a full speed at RT for 1 min to elute the RNA for further analysis.
Plasma exosomal RNA was reversely transcribed into cDNA in 20 μL reaction volume by using PrimeScript RT Master Mix (Perfect Real Time) (Takara, Kyoto, Japan, #RR036A). The qRT-PCR analysis was conducted on a CFX Connect real-time system (Bio-Rad) by using TB Green Premix Ex Taq II (Tli RNaseH Plus) (Takara, #RR820A). The thermal cycling conditions were as follows: 30 s at 95°C for initial denaturation, 49 cycles of 5 s at 95°C, 30 s at 58°C, and 20 s at 72°C for amplification, and finally 10 min at 72°C for extension. GAPDH was applied for an endogenous standard control. Each reaction was run in triplicates. The relative level of plasma exosomal lncRNAs was calculated using the 2-ΔCt method. The detailed sequences of all the primers (Sangon, Shanghai, China) used in this study are presented in Table 2 .
Three experiments were carried out to determine the stability of lncRNAs in plasma exosomes. Firstly, the exosome suspension derived from the plasma of the AML patient was aliquoted into four parts and stood at RT for 0, 12, 24, and 48 h, respectively. Additionally, the exosome suspension was aliquoted into three parts and subjected to freeze-thaw for 0, 4, and 6 cycles at -80°C, respectively. Furthermore, the exosome suspension was aliquoted into two parts and treated without or with RNase A solution (Solarbio, Beijing, China, #R1030) at a final concentration of 2 μg/mL at 37°C for 20 min. After processions of exosome samples following the above protocols, plasma exosomal RNA was isolated by the aforementioned exoRNeasy Midi Kit, and qRT-PCR analyses were used for plasma exosomal lncRNA quantification.
Statistical analysis was conducted using GraphPad Prism (Version 7.00) or SPSS (Version 25.00). For clinical parameter comparisons between groups, the chi-square test was used to compare the difference in categorical data. The distribution characteristics of the data were determined using the Shapiro-Wilk normality test. The homogeneous variance assumptions were performed using the Brown-Forsythe test. The date with normal distribution and homogeneous variances were presented as the mean ± standard deviation (SD). Comparisons between two groups were conducted using unpaired Student’s t-test. The data with non-normal distribution or heterogeneous variances were presented as the median (interquartile range) (IQR). Comparisons between two groups were conducted using the Wilcoxon matched-pairs signed-rank test or Mann-Whitney U test, and comparisons among three or more groups were conducted using the Kruskal-Wallis test. Multiple comparisons included two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli. For the diagnostic power analyses of the plasma exosomal lncRNAs, the area under the curve (AUC), sensitivity, and specificity were calculated using receiver operating characteristic (ROC) analysis. The optimal cut-off points of exosomal lncRNAs were determined with the largest Youden index (sensitivity+specificity-1). Binary logistic regression analysis was used to establish the combination of exosomal lncRNAs for discriminating AML from HD. The two-tailed p-value<0.05 was considered statistically significant.
We first verified whether exosomes were successfully extracted from the plasma specimens of the acute myeloid leukemia (AML) patient and the healthy donor (HD) by a membrane-based affinity binding kit. The purified exosomes were vesicles with a double-layer membrane structure under transmission electron microscopy (TEM) ( Figure 1A ). Nanoparticle tracking analysis (NTA) revealed that the size distribution of exosomes was approximately 60-100 nm in diameter ( Figure 1B ). Besides, the presence of exosome markers (CD63 and Alix) in the exosomes, but not in the lysates of AML cell line NB4, was confirmed by western blotting. Meanwhile, Calnexin as a non-exosome marker only exists in the cell lysates ( Figure 1C ). These data indicated that exosomes are successfully separated from plasma.
Next, to define the promising plasma exosomal long non-coding RNAs (lncRNAs) as cell-free biomarkers for AML, a total of five lncRNAs, which were differentially expressed in bone marrow or peripheral blood samples from AML patients and played important roles in leukemogenesis and development (17–21), were selected as candidates. The level of the selected lncRNAs in the plasma exosomes isolated from newly diagnosed AML patients (n=65) and HD (n=20) was analyzed by qRT-PCR. The results showed significant downregulation of the exosomal LINC00265 level in AML patient specimens compared with HD specimens (P < 0.05, Figure 2A ). Particularly, the exosomal LINC00265 downregulation appeared to occur preferentially in the patients with M1, M2, M3, and M4 French-American-British (FAB) subtypes. The exosomal LINC00467 level was decreased in AML cases, especially in the M2, M4, and M5 FAB subtypes (all, P < 0.05, Figure 2B ). Meanwhile, the reduction of the exosomal UCA1 level was also observed in the AML group, especially in the M5 subgroup (all, P < 0.05, Figure 2C ). Of note, the relative expression of exosomal UCA1 was lower in the patients with the M5 subtype than those with the M2 subtype. Additionally, exosomal SNHG1 was highly expressed in AML cases, including M1 and M2 subtypes (all, P < 0.05, Figure 2D ). Interestingly, the M2-subtype patients had a relatively higher exosomal SNHG1 expression than the M5-subtype patients. However, no significant difference in the exosomal PCAT18 level was measured between AML patients and HD (all, P > 0.05, Figure 2E ). These data identified the downregulated plasma exosomal lncRNAs (LINC00265, LINC00467, and UCA1) and the upregulated plasma exosomal lncRNA (SNHG1) in AML patients in comparison with HD, and showed that the dysregulations of these exosomal lncRNAs also exist in the patients with specific FAB subtypes.
Based on the aforementioned dysregulations, the diagnostic efficiency of plasma exosomal LINC00265, LINC00467, UCA1, and SNHG1 in differentiating AML patients (n=65) from HD (n=20) was evaluated by receiver operating characteristic (ROC) analysis. As a result, exosomal LINC00265 displayed a relatively high area under the curve (AUC) value of 0.7400 and the cut-off value was at 0.0090 (sensitivity, 85.0%; specificity, 64.6%) (P < 0.01, Figure 3A ). The AUC value of exosomal LINC00467 was 0.7246 and the cut-off value was at 0.0104 (sensitivity, 100.0%; specificity, 50.8%) (P < 0.01, Figure 3B ). Meanwhile, exosomal UCA1 exhibited an AUC value of 0.6623 and the cut-off value was at 0.0122 (sensitivity, 90.0%; specificity, 50.8%) (P < 0.05, Figure 3C ) and exosomal SNHG1 possessed an AUC value of 0.6631 and the cut-off value was at 0.0227 (sensitivity, 95.0%; specificity, 49.2%) (P < 0.05, Figure 3D ). Furthermore, the combination of these four exosomal lncRNAs provided the most powerful performance with an AUC of 0.8685 (P < 0.001, Figure 3E ). These data provided evidence that individual exosomal LINC00265, LINC00467, UCA1, or SNHG1 has a capability for discriminating AML patients from HD, and the combination of these four exosomal lncRNAs displayed the most powerful diagnostic accuracy.
The correlation between the expression of plasma exosomal LINC00265, LINC00467, UCA1, and SNHG1 at diagnosis and clinical characteristics of AML patients including gender, age, white blood cell (WBC) count, platelet (PLT) count, hemoglobin (Hb) level, lactate dehydrogenase (LDH) level, percentage of bone marrow blasts, FAB subtype, risk classification, remission response to chemotherapy, and disease recurrence was summarized in Table 3 . The low expression of exosomal LINC00265 and LINC00467 was associated with young age, while the low expression of exosomal LINC00467 and UCA1 was related to high WBC count (all, P < 0.05). However, the expression of these three exosomal lncRNAs was not associated with gender, PLT count, Hb level, LDH level, percentage of bone marrow blasts, FAB subtype, risk classification, remission response to chemotherapy, and disease recurrence (all, P > 0.05). Moreover, there was no statistical correlation between the exosomal SNHG1 level and clinical parameters (all, P > 0.05). These observations suggested that the low expression of exosomal LINC00265, LINC00467, and UCA1, but not SNHG1, is associated with young age or high WBC count of AML patients.
We further assessed the treatment monitoring power of plasma exosomal LINC00265, LINC00467, UCA1, and SNHG1 for AML. The relevance of the expression of these exosomal lncRNAs to the remission status of AML patients after the standard induction chemotherapy was first explored. The level of both exosomal LINC00265 and exosomal LINC00467 was elevated in the samples at first complete remission (CR) (all, P < 0.05, Figures 4A, C ), but not at non-remission (NR) or partial remission (PR) (all, P > 0.05, Figures 4B, D ), compared with the paired samples at newly diagnosis. However, the exosomal UCA1 or SNHG1 expression remained unchanged in the samples at the CR, NR, and PR stages (all, P > 0.05, Figures 4E-H ). Subsequently, the relationship between the expression of these four exosomal lncRNAs and the allogeneic hematopoietic stem cell transplantation (allo-HSCT) treatment was analyzed. The data showed upregulation of the exosomal LINC00265 level and downregulation of the exosomal SNHG1 level upon the allo-HSCT treatment (all, P < 0.05, Figures 4I, J ). Nevertheless, there was no significant change in the exosomal LINC00467 or UCA1 expression between the samples before and after allo-HSCT (all, P > 0.05, Figures 4K, L ). These results indicated that the increased expression of exosomal LINC00265 and LINC00467 is associated with the CR but not NR or PR status of AML patients, while the increased expression of exosomal LINC00265 and the decreased expression of SNHG1 is associated with the allo-HSCT treatment.
Given that better stability is an essential prerequisite for tumor biomarkers, we sought to assess the stability of LINC00265, LINC00467, UCA1, and SNHG1 in the isolated plasma exosomes. Firstly, the exosome suspensions were subjected to room temperature incubation for 0, 12, 24, and 48 h. As anticipated, the expression level of exosomal LINC00265, LINC00467, UCA1, and SNHG1 remained unchanged following prolonged exposure at room temperature (all, P > 0.05, Figure 5A ). In addition, multiple freeze-thaw cycles of the exosome suspensions made no significant difference in the level of these four exosomal lncRNAs (all, P > 0.05, Figure 5B ). Finally, the exosome suspensions were treated with RNase A and this treatment had little effect on the stability of exosomal lncRNAs (all, P > 0.05, Figure 5C ). These results demonstrated that the candidate lncRNAs are high stability in plasma exosomes.
Exosomes are a class of extracellular vesicles that deliver specific combinations of nucleic acids, proteins, and lipids, facilitating tumorigenesis and development (35). Recently, compelling evidence has corroborated that long non-coding RNAs (lncRNAs) can stably exist in exosomes, and be examined for diagnosis, prediction, and surveillance in various cancers (8). However, there is limited knowledge on the potential clinical utility of exosomal lncRNAs in acute myeloid leukemia (AML). Herein, our data proved that plasma exosomal LINC00265, LINC00467, UCA1, and SNHG1 might act as promising cell-free biomarkers for diagnosis and treatment monitoring of AML. In the present study, plasma exosomes, which were bilayer vesicles with a diameter of approximately 60-100 nm containing the exosome markers (CD63 and Alix), were first efficiently extracted from the AML patient and the healthy donor (HD) by a membrane-based affinity binding kit. Nowadays, multiple strategies have been developed for exosome isolation including ultracentrifugation, size-based separation, capture-based separation, acoustic-based separation, polymer precipitation methods, and so on (12, 36). Although considered as the gold standard, the process of ultracentrifugation is time-consuming and highly instrument-dependent (36). Given the advantages of a sample, high efficiency, and reproducibility, the membrane-based affinity binding technique was extensively used for exosome purification and easily adapted to clinical laboratory workflows (37, 38). Next, we sought to identify the aberrantly expressed lncRNAs in the plasma exosomes of AML patients. There were five lncRNAs included in our initial study due to their abnormal expression in bone marrow or peripheral blood samples of AML patients and crucial roles in leukemogenesis and development (17–21). Additionally, these five lncRNAs have not been reported relating to AML exosomes. The results showed that the plasma exosomal lncRNAs LINC00265, LINC00467, and UCA1 were downregulated while SNHG1 was upregulated in AML patients in comparison with those in HD. Meanwhile, the dysregulations were observed in the patients with specific French-American-British (FAB) subtypes. It has been well known that the FAB classification of AML is mainly based on the direction of differentiation along one or more cell lines and the maturation degree of the cells in the bone marrow and peripheral blood, which can reflect the progression of AML (31). Therefore, our findings suggest that the level of these four plasma exosomal lncRNAs may be associated with the progression of AML, making them particularly attractive as biomarkers for AML. Up to now, sequencing or microarray analysis has been broadly applied for the exploration of cancer biomarkers. Lin et al. (39) performed RNA sequencing to screen early gastric cancer (EGC)-specific exosomal lncRNAs. In addition, a study reported (40) that microarray analysis was used to explore the differential exosomal lncRNAs in colorectal cancer (CRC). Although four dysregulated exosomal lncRNAs were identified in our study, it is necessary for us to explore more exosomal lncRNAs by high-throughput technologies as potential biomarkers for AML in the future. In view of the aforementioned dysregulations, we further investigated the clinical significance of these four plasma exosomal lncRNAs (LINC00265, LINC00467, UCA1, and SNHG1) for AML. Firstly, the individual diagnostic efficiency of these exosomal lncRNAs for newly diagnosed AML by receiver operating characteristic (ROC) analysis was assessed. Our results showed that exosomal LINC00265, LINC00467, UCA1, or SNHG1 had the capability for discriminating AML patients from HD. Interestingly, a present study reported (27) that there was no significant difference in the serum exosomal UCA1 expression between healthy controls and newly diagnosed multiple myeloma patients, perhaps exosomal UCA1 exhibits relatively high specificity for AML diagnosis. Recently, Zhang et al. (41) identified a promising diagnostic panel based on three exosomal lncRNAs (PCAT-1, UBC1, and SNHG16) to differentiate bladder cancer cases from healthy controls with excellent accuracy. Our previous study showed that the combination of two exosomal lncRNAs (TBILA and AGAP2-AS1) failed to provide better results than individual exosomal lncRNAs in the detection of non-small-cell lung cancer (NSCLC) with pathologic subtypes and early-stage (29). Based on the above studies, we also analyzed the combined diagnostic efficacy of exosomal LINC00265, LINC00467, UCA1, and SNHG1. Intriguingly, this combination exhibited the highest discriminatory capacity for AML patients from HD. These observations suggest that these four plasma exosomal lncRNAs, alone or in combination, hold promise as diagnostic biomarkers for AML. In fact, exosomes are enriched in other kinds of non-coding RNA (ncRNA) molecules such as circular RNA (circRNA), tRNA-derived small RNA (tsRNA), ribosomal RNA, and transfer RNA (7, 42, 43). Pan et al. (44) identified serum exosomal circ-0004771 as a novel diagnostic biomarker of CRC. Besides, four tsRNAs were reported to be increased expressed in plasma exosomes from liver cancer patients (42). Thus, further study deserves to be performed to investigate the diagnostic performance and even other clinical significance of other exosomal ncRNAs for AML. Subsequently, the association between the expression of exosomal LINC00265, LINC00467, UCA1, and SNHG1 at diagnosis and clinical characteristics of AML patients was analyzed. The low expression of exosomal LINC00265 and LINC00467 was associated with young age, while the low expression of exosomal LINC00467 and UCA1 was related to high WBC count. These results showed the expression of exosomal LINC00265, LINC00467, and UCA1 was closely correlated with the progression of AML. Of note, there was no statistical correlation of the expression of these four exosomal lncRNAs at diagnosis with remission response to chemotherapy, disease recurrence, and risk classification, suggesting their limited value in AML therapeutic effect and prognostic prediction. Since plasma-derived exosomes are widely known to be abundantly generated by stressed cells (45), it seems conceivable that the level of exosomal lncRNAs is associated with disease severity and treatment response in AML. Indeed, our results showed that the level of exosomal LINC00265 and LINC00467 was elevated in the samples at first complete remission (CR), but not at the non-remission (NR) or partial remission (PR), indicating that the exosomal expression of LINC00265 and LINC00467 may be useful in remission assessment in AML patients receiving chemotherapy. Likewise, Hong et al. found significant alterations in the exosomal protein level after AML patients underwent chemotherapy. Additionally, in some patients under consolidation therapy who subsequently relapsed, the expression of exosomal proteins was upregulated (46). Thus, it is imperative for us to exploit the roles of exosomal lncRNAs in monitoring AML residual disease and recurrence. Meanwhile, our results revealed that there was upregulation of the exosomal LINC00265 level and downregulation of the exosomal SNHG1 level upon the allogeneic hematopoietic stem cell transplantation (allo-HSCT) treatment, suggesting that the expression of exosomal LINC00265 and SNHG1 may reflect the tumor burden of AML patients and can be applied for estimating the therapeutic efficacy of allo-HSCT. These findings indicated that the expression of exosomal LINC00265, LINC00467, and SNHG1, but not UCA1, was sensitive to the standard induction chemotherapy or allo-HSCT treatment, showing the potential of these three exosomal lncRNAs as indicators of disease activity during therapy. It is generally accepted that the assessment of AML treatment responses relies on comprehensive cellular analyses of morphology, immunophenotype, cytogenetics, and molecular genetics, which is made difficult by the rapid clearance of circulating leukemic blasts during therapy. In contrast, exosomal lncRNAs serve as cell-free biomarkers, representing an important advance in cancer liquid biopsy as the detection of their expression is less invasive, less expensive, and provides real-time insights into tumor status. Notably, several patients displayed unchanged or even decreased level of LINC00265 and LINC00467 at CR or after allo-HSCT, which might be due to the high heterogeneity of AML. Accordingly, treatment monitoring of these exceptional patients should depend on a combination of exosomal lncRNA detection and cellular analyses. In future studies, we will explore more effective cell-free biomarkers for estimating the therapeutic efficacy of AML patients. Given that better stability is an essential requirement for tumor biomarkers, the stability testing of LINC00265, LINC00467, UCA1, and SNHG1 in the isolated plasma exosomes was performed. The level of these four exosomal lncRNAs was not significantly influenced following prolonged exposure to 48 h at room temperature, multiple freeze-thaw cycles, or RNase A treatment. Recently, Li et al. (40) observed no significant change in the exosomal lncRNA expression when the exosome suspensions were incubated with prolonged exposure to room temperature or treated with RNase A. In another study, exosomes could protect contained lncRNA HOTTIP from degradation by prolonged exposure or multiple freeze-thaw cycles (47). Results from our experiments are consistent with the above reports, which reveal that the candidate lncRNAs in our study have good stability in plasma exosomes and are worthy of further research. Besides, the existing study also documented that serum exosomal lncRNA CRNDE-h remained stable after the exosome samples were subjected to acid-base incubation or stored at -80°C for a long time (48). Therefore, a deeper exploration of the stability of LINC00265, LINC00467, UCA1, and SNHG1 in plasma exosomes under other harsh conditions is required to improve the clinical potential of these molecules. Taken together, these preliminary data suggest that continuous monitoring of exosomal lncRNAs may be important in the search for new tests for AML diagnosis, disease progression, and response to treatment. Indeed, we are also aware of the potential limitations of this study. Firstly, the sample size needs to enlarge, and the clinical significance of these four exosomal lncRNAs in AML needs to be validated in prospective and multicenter studies. Secondly, the specificity of these exosomal lncRNAs for AML diagnosis is required to confirm by examining those in patients with other cancers. Additionally, the prognostic value of exosomal lncRNAs deserved to be investigated. Furthermore, more researches are worthy to illustrate the function and mechanisms of exosomal lncRNAs in AML.
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.
The studies involving human participants were reviewed and approved by the ethics committee of Chongqing Medical University. The patients/participants provided their written informed consent to participate in this study.
LZ, QX, and CL initiated the work and designed the experiments. QX and CL performed the experiments and wrote the manuscript. MP, JR, and YJ contributed techniques and commented on the manuscript. YT analyzed the data. LL and JH contributed analytic tools. ZSY, ZLY, and JW provided clinical assistance. JY and MS assisted with revising the manuscript. All authors contributed to the article and approved the submitted version.
This work was supported by the National Natural Science Foundation of China (NSFC81873973 and NSFC82072353), the Natural Science Foundation of CQ CSTC (cstc2021jcyj-msxmX0363), and Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau) (2020MSXM074).
The authors would like to acknowledge the Key Laboratory of Laboratory Medical Diagnostics Designated by the Ministry of Education, School of Laboratory Medicine (Chongqing Medical University, Chongqing, China) for providing the space and equipment for conducting the experiments.
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. | true | true | true |
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PMC9585280 | Huaying Xie,Tingting Yan,Xinxin Lu,Yueyao Du,Shuguang Xu,Yu Kong,Liangjie Yu,Jian Sun,Liheng Zhou,Jun Ma | GLDC mitigated by miR-30e regulates cell proliferation and tumor immune infiltration in TNBC | 07-10-2022 | TNBC,GLDC,proliferation,miR-30e,tumor immune | Background TNBC, whose clinical prognosis is poorer than other subgroups of breast cancer, is a malignant tumor characterized by lack of estrogen receptors, progesterone hormone receptors, and HER2 overexpression. Due to the lack of specific targeted drugs, it is crucial to identify critical factors involved in regulating the progression of TNBC. Methods We analyzed the expression profiles of TNBC in TCGA and the prognoses values of GLDC. Correlations of GLDC and tumor immune infiltration were also identified. CCK8 and BrdU incorporation assays were utilized to determine cell proliferation. The mRNA and protein levels were examined by using Real-time PCR and Western blot analysis. Results In the present study, we analyzed the mRNA expression profiles of TNBC in TCGA and found that GLDC, a key enzyme in glycine cleavage system, was significantly up-regulated in TNBC tissues and higher expression of GLDC was correlated with a worse prognosis in TNBC. Moreover, the expression of GLDC was negatively correlated with macrophage and monocyte and positively correlated with activated CD4 T cell and type 2 T helper cell in TNBC. Overexpression of GLDC facilitated the proliferation of TNBC cells, whereas GLDC knockdown had the opposite effects. Additionally, miR-30e acts as a functional upstream regulator of GLDC and the inhibitory effects of miR-30e on cell proliferation were mitigated by the reintroduction of GLDC. Conclusions These results imply that miR-30e-depressed GLDC acts as a tumor suppressive pathway in TNBC and provides potential targets for the treatment of TNBC. | GLDC mitigated by miR-30e regulates cell proliferation and tumor immune infiltration in TNBC
TNBC, whose clinical prognosis is poorer than other subgroups of breast cancer, is a malignant tumor characterized by lack of estrogen receptors, progesterone hormone receptors, and HER2 overexpression. Due to the lack of specific targeted drugs, it is crucial to identify critical factors involved in regulating the progression of TNBC.
We analyzed the expression profiles of TNBC in TCGA and the prognoses values of GLDC. Correlations of GLDC and tumor immune infiltration were also identified. CCK8 and BrdU incorporation assays were utilized to determine cell proliferation. The mRNA and protein levels were examined by using Real-time PCR and Western blot analysis.
In the present study, we analyzed the mRNA expression profiles of TNBC in TCGA and found that GLDC, a key enzyme in glycine cleavage system, was significantly up-regulated in TNBC tissues and higher expression of GLDC was correlated with a worse prognosis in TNBC. Moreover, the expression of GLDC was negatively correlated with macrophage and monocyte and positively correlated with activated CD4 T cell and type 2 T helper cell in TNBC. Overexpression of GLDC facilitated the proliferation of TNBC cells, whereas GLDC knockdown had the opposite effects. Additionally, miR-30e acts as a functional upstream regulator of GLDC and the inhibitory effects of miR-30e on cell proliferation were mitigated by the reintroduction of GLDC.
These results imply that miR-30e-depressed GLDC acts as a tumor suppressive pathway in TNBC and provides potential targets for the treatment of TNBC.
Breast cancer is the most common cancer in women and remains the second leading cause of cancer death among women worldwide (1). Triple-negative breast cancer (TNBC), defined by a lack of expression of both estrogen (ER) and progesterone receptor (PR) as well as human epidermal growth factor receptor 2 (Her2), is the most aggressive subgroup of breast cancer and accounts for 12–18% of all invasive breast cancers (2). Due to the lack of therapeutic targets, the clinical prognosis of TNBC is also worse than other subgroups of breast cancer. Therefore, it is of enormous therapeutic interest to explore the key molecules involved in affecting the development and diagnosis of TNBC and elucidate the regulatory mechanisms. Glycine Decarboxylase (GLDC) is a key enzyme in glycine cleavage system, which can convert glycine into a carbon unit. Abnormal regulation of glycine decarboxylase is related to the occurrence of various human tumors, but roles of GLDC in different cancers are not always consistent (3). Liu et al. demonstrated that rapamycin complex 1 (mTORC1) signal inhibits GLDC acetylation by inducing the transcription of deacetylase SIRT3 (SIRT3) and GLDC acetylation inhibits glycine catabolism, pyrimidine synthesis and glioma (4). A report has shown that the expression of GLDC is significantly increased in MYCN amplified neuroblastoma tumors and cell lines, and GLDC plays a key role in maintaining the proliferation of neuroblastoma cells (5). In lung cancer, it has been reported that GLDC induces significant changes in glycolysis and glycine/serine metabolism, leading to changes in pyrimidine metabolism, thereby regulating the proliferation of non-small cell lung cancer cells. Clinically, abnormal activation of GLDC is associated with poor survival in patients with lung cancer (6). However, other studies have shown that GLDC inhibits the metastasis and is positively correlated with the overall survival by acting as a tumor suppressor in HCC (7, 8). Until now, roles of GLDC in TNBC are not clear and need to be determined. In this research, we examined the regulatory roles and clinicopathologic significance of GLDC in TNBC and determined the underlying mechanism. Our results showed that GLDC was significantly increased in TNBC tissues and higher expression of GLDC was correlated with a worse prognosis. The expression of GLDC was closely correlated with several types of immune cells and GLDC facilitated the proliferation of TNBC cells. Moreover, miR-30e negatively regulated the expression of GLDC by acting as a functional upstream regulator. The findings elucidate an important regulatory mechanism and might provide potential therapy targets for TNBC.
MDA-MB-231 was purchased from the American Type Culture Collection (ATCC). MiR-30e mimics and the miR-30e inhibitor (anti-miR-30e) were synthesized in Ribobio (Guangzhou, China). The primary antibody for Rabbit anti-GLDC was bought from Abcam (ab97625, 1:500). Goat anti-Rabbit IgG was got from Cell Signaling Technology (7074, 1:5000). Dulbecco’s modified Eagle’s medium (DMEM) and fetal bovine serum (FBS) were purchased from hyclone and Gibco (Thermo Fisher Scientific), respectively. Bromodeoxyuridine (BrdU) incorporation assay kit was got from Roche Diagnostics (IN, USA).
Cell viability was examined by using the cell counting kit-8 (CCK-8). Briefly, 3000 cells/well were cultured in 96-well plate at 37°C for 24 hours. Then 10 μl of CCK-8 was added to each well in the plate. After 2 hours, we utilized a microplate reader (Thermo Scientific, Rockford, IL, USA) to determine the absorbance at 490nm.
Cell proliferation was determined by using a 5-bromo-2’-deoxyuridine (BrdU) kit to detect the BrdU incorporation. We first cultured the cells into a 96-well plate at a density of 5000 cells/well. After 12 hours, 10 μl BrdU labeling solution was added into each well and we incubated the plates at 37°C for 24 hours. Then 200 μl anti-BrdU peroxidase solutions were added to label cells for 1.5 hours at room temperature. Finally, we washed the sample with washing solution and then added 100 μl tetramethylbenzidine substrate solutions to each well at room temperature for 30 minutes. A microplate reader (Thermo Scientific) was utilized to determine the absorbance at 450nm.
Real-time PCR and Luciferase reporter assay were performed as our previous descriptions (9). Primers for GLDC and β-actin were designed and listed as follows: GLDC forward, 5’−CTGCTGTGCTACTGACCTTTT−3’ and reverse, 5’−CCAGGCATCATTCTCACCAAG−3’; β-actin forward, 5’−CATGTACGTTGCTATCCAGGC−3’ and reverse, 5’−CTCCTTAATGTCACGCACGAT−3’. Specific primers and Taqman probes for microRNA analysis were purchased from Applied Biosystems. The mRNA levels of beta-Actin and snRNA U6 were used as the internal normalization control, respectively. The kit for Dual-Luciferase Reporter Assay System was obtained from Promega Corporation and the experiment was performed as the protocol provided by the supplier.
We performed western blot to determine the protein levels of target proteins. Briefly, the cell lysates were quantified with the BCA methods. The protein extracts (50 μg) were separated by SDS PAGE electrophoresis and then transferred to PVDF membranes. The membranes were blocked by 5% non-fat milk and then incubated with primary antibodies at 4°C for 12 hours. After washing five times with PBS-T for 30 min, the membranes were incubated with secondary antibodies for 1 hours at room temperature. PBS-T was used to wash the membranes again for five times. Then the membranes were incubated with the ECL luminescence solution (Thermo Scientific) and the immunoreactive bands were acquired. Image J software was utilized to determine the optical density of the bands.
Statistical analysis was performed by using GraphPad Prism v.9.0. The data for statistical analyses was obtained from at least three independent experiments and presented by mean ± standard error of the mean (SEM). Student’s t-test or one-way ANOVA followed by Dunnett’s test was appropriately applied for identifying the statistical significance. The Kaplan-Meier method was used to analyze the cumulative survival rate. The P-value was 0.05 or less was regarded as the statistically significant difference.
We first analyzed the mRNA expression profiles of TNBC in TCGA to determine the critical factors involved in affecting the progression of TNBC. As shown in Figure 1A , volcano plots were used to assess the gene expression variation and the overall distribution of differentially expressed genes (1148 upregulated genes and 1686 downregulated genes) between the TNBC tissues and normal breast tissue. 100 differential genes (containing 50 upregulated genes and 50 downregulated genes) are selected to draw the heat map ( Figure 1B ). Moreover, GO analysis and KEGG pathway analysis were performed by using all differentially expressed mRNAs. We found the biological processes (BP) enriched by GO analysis were regulation of ion transmembrane transport, hormone levels, mitotic relevant events. Molecular function (MF) of GO terms was enriched in signaling receptor regulator and activator activities, receptor ligand activity, G protein−coupled receptor activity, and growth factor activity. Cellular component (CC) of GO terms was enriched in collagen−containing extracellular matrix, cell−cell junction, and transmembrane transporter complex ( Figure 1C ). As shown in Figure 1D , the enriched KEGG pathways were shown. The results showed that some pathways directly related to affect the progression of cancer, such as cell cycle, PI3K-Akt signaling pathway, cytokine-cytokine receptor interaction and PPAR signaling pathway, were enriched. Moreover, we found that Glycine, serine, and threonine metabolism was enriched by KEGG pathway analysis. GLDC, which acts as a key enzyme in glycine cleavage system, was obviously up-regulated in TNBC tissues ( Figure 1E ). The consistent result that the expression of GLDC was increased in breast cancer, especially in TNBC tissues, was obtained by utilizing the UALCAN database ( Figure 1F ). These results indicate that GLDC might participate in regulating the progression and development of TNBC.
To confirm changes of GLDC in TNBC tissue, we also analyzed its expression from a GEO dataset (GSE76250). The results showed that the expression of GLDC was significantly higher in the TNBC tissues compared with their paired adjacent normal tissues ( Figure 2A ). We further examined the expression levels of GLDC in the TNBC tissues based on the clinicopathological variables. We found that the expression of GLDC is higher in the patients younger than 55 years (age ≤ 55) than the patients older than 55 years (age>55) ( Figure 2B ). GLDC expression in patients with ki67 ≤ 30% was lower than that with ki67>30% ( Figure 2C ). However, there are no significant differences on the expression of GLDC between positive lymph nodes and no positive lymph nodes ( Figure 2D ). Furthermore, the prognoses values of GLDC in TNBC were also examined by utilizing the Kaplan-Meier Plotter (10). We found that patients with the high levels of GLDC were associated with the shorter recurrence free survival (RFS) (median RFS time (months), 22.37 (high levels of GLDC) and 43 (low levels of GLDC) months, respectively; P < 0.001) and the worse distant metastasis free survival (DMFS) (median DMFS time (months), 26.63 (high levels of GLDC) and 38.6 (low levels of GLDC) months, respectively; P < 0.05) than the patients with low GLDC expression in TNBC ( Figures 2E, F ). In addition, we also examined the prognoses values of GLDC in other types of breast cancer. We found that there were no significant differences on the RFS and DMFS between the low levels of GLDC and the high levels of GLDC in Luminal A, Luminal B and HER2 positive types of breast cancer ( Figures 2G–L ). The results imply that GLDC might be considered as a potential predictive molecule of prognosis for TNBC.
Previous studies have shown that tumor immune microenvironment plays an important role in affecting tumor growth. The immune cells and immune-related signaling pathways are involved in regulating the progression of cancer and the response to cancer therapy (11, 12). Therefore, we future examined whether GLDC was correlated with the tumor immune infiltration in TNBC. As shown in Figures 3A, B , we analyzed the infiltration abundance of immune cells in TNBC patients based on the levels of GLDC. The results showed that activated CD4 T cell, central memory CD4 T cell, and type 2 T helper cell were significantly enriched in the group with high expression of GLDC in TNBC, whereas high levels of macrophage, neutrophil, CD56 bright natural killer cell and plasmacytoid dendritic cell were acquired in the TNBC patients with low GLDC expression. Moreover, we further examined the relationship between GLDC expression and immune cell types. We found that the expression of GLDC was negatively correlated with macrophage, plasmacytoid dendritic cell, and monocyte, while GLDC expression was positively correlated with activated CD4 T cell and type 2 T helper cell ( Figure 3C ). By analyzing the relationship between GLDC and four immune checkpoint molecules (CTLA4, PD-1, PD-L1, and PD-L2), no significant correlations were acquired between GLDC and the four immune checkpoint molecules in TNBC ( Figure 3D ). These results imply that GLDC likely has the regulatory effects on tumor immune microenvironment in TNBC.
We then examined the effects of GLDC on the growth of TNBC cells. Overexpression of GLDC were established in MDA-MB-231 (a cell line of TNBC). As shown in Figure 4A , the overexpression efficiency was verified by real-time PCR. We found that cell viability was significantly increased by GLDC overexpression ( Figure 4B ). Overexpression of GLDC facilitated BrdU incorporation into newly synthesized DNA ( Figure 4C ). Consistently, the expression of GLDC was positively correlated with the expression of PCNA and MKI67 (two markers of cell proliferation) in TNBC tissues ( Figures 4D, E ). Furthermore, we also knocked down the expression of GLDC in TNBC cells to confirm its physiological function ( Figure 4F ). The results showed that the cell viability was mitigated by the knockdown of GLDC ( Figure 4G ). GLDC knockdown significantly repressed BrdU incorporation ( Figure 4H ). The results indicate that GLDC positively regulated the proliferation of TNBC cells.
By using the UALCAN database, we found that there was no significant difference on the promoter methylation level of GLDC between TNBC and normal breast tissues ( Figure 5A ). It is widely accepted that microRNAs play important roles in affecting the expression of target genes. To determine the mechanism of GLDC up-regulation in TNBC, we examined whether microRNAs might participate in regulating the expression of GLDC in TNBC. The result showed that miR-30e, a potential binding microRNA of GLDC, was negatively correlated with the expression of GLDC in the same TNBC tissues (GSE59595) (r=-0.5581, P=0.0014) ( Figures 5B, C ). As shown in Figure 5D , the mutated UTR, which was utilized in the luciferase reporter assay, was constructed based on the potential binding site of miR-30e conserved in the 3′UTR of GLDC. Results of luciferase reporter assay showed that the luciferase activity of the WT (wild type) group was significantly reduced by the treatment with miR-30e, whereas there was no detectable change on the luciferase activity of the MT (mutant type) group ( Figure 5E ). Moreover, miR-30e obviously mitigated the mRNA and protein expression of GLDC in TNBC cells ( Figures 5F, G ). Treatment with anti-miR-30e led to the increased expression of GLDC at both mRNA and protein levels ( Figures 5H, I ). These results indicate that miR-30e acts as a functional upstream regulator of GLDC in TNBC.
We then examined the roles of miR-30e in cell proliferation in TNBC. Our results showed that the expression of miR-30e was negatively correlated with PCNA expression in the same TNBC tissues ( Figure 6A ) and high levels of miR-30e were associated with better overall survival (OS) (median OS time (months), 115.73 (high levels of miR-30e) and 95.13 (low levels of miR-30e) months, respectively; P = 0.02 ( Figure 6B ). Treatment with anti-miR-30e increased cell viability and promoted BrdU incorporation, which was attenuated by the knockdown of GLDC ( Figures 6C, D ). Moreover, the inhibitory effects of miR-30e on cell proliferation were attenuated by the restoration of GLDC in TNBC cells ( Figures 6E, F ). These results imply that miR-30e inhibits the proliferation of TNBC cells, at least in part, by targeting GLDC.
TNBC, as the most aggressive subgroup of breast cancer, is lack of specific targeted drugs and the prognosis is far less than expected. Until now, pathological processes of TNBC remain largely unknown and still need to be determined. In the present study, our results showed that GLDC, which was significantly up-regulated in cancer tissues, facilitated cell proliferation and was negatively correlated with the RFS and DMFS in TNBC. The expression of GLDC was negatively correlated with macrophage and monocyte and was positively correlated with activated CD4 T cell and Type 2 T helper cell. Moreover, GLDC serves as a downstream target of miR-30e in TNBC and attenuated the inhibitory effects of miR-30e on cell proliferation. The results imply an important underlying mechanism of GLDC-regulated cell proliferation and tumor immune infiltration in TNBC. An important finding of this research is that GLDC promotes cell proliferation and could be considered as a potential predicting factor for prognosis in TNBC. Although GLDC has been demonstrated to participate in regulating the development of tumors in some types of cancers, its roles in different cancers are controversial and not always consistent. Previous studies have shown that GLDC overexpression or its gene alternative splicing enhances cellular transformation and tumorigenesis and is correlated with poorer survival in non-small cell lung cancer (NSCLC) (6, 13). Inhibition of GLDC transcript represses cell proliferation and colony formation in NSCLC and prostate cancer cells (14, 15). GLDC knockdown mitigates cell proliferation and tumorigenicity via causing G1 arrest in MYCN-amplified neuroblastoma cells (5). Knockdown of glycine decarboxylase represses the growth of the tumor by regulating mitochondrial protein lipoylation in hepatocellular carcinoma (HCC) (16). On the contrary, it is reported that GLDC negatively regulates the migration and invasion of HCC cells in vivo and in vitro (7). The overall survival is better in the group with high expression of GLDC in HCC and overexpression of GLDC obviously facilitates cell autophagy and depresses intrahepatic metastasis (8). However, roles of GLDC in TNBC are still unknown. In the present study, our results showed that GLDC, significantly upregulated in cancer tissues, was correlated with a worse prognosis related to RFS and DMFS in TNBC. Overexpression of GLDC promoted cell proliferation, whereas GLDC knockdown had the opposite effects. Furthermore, it is widely accepted that tumor immune microenvironment is involved in affecting the development of cancers and the response to cancer therapy (12). Our results showed that several immune cells were significantly enriched or decreased in the TNBC patients with high levels of GLDC. The expression of GLDC was negatively correlated with macrophage and monocyte, while GLDC expression was positively correlated with activated CD4 T cell and type 2 T helper cell in TNBC. These results imply that GLDC likely serves as an oncogenetic factor in the progression of TNBC by regulating cell proliferation and tumor immune microenvironment. Another important finding of this research is that miR-30e acts as a functional upstream regulator of GLDC in TNBC. It is known to all that dysregulation of microRNAs is a critical cause in the initiation and progression of various diseases. MicroRNAs are involved in regulating several cellular physiological functions of cancer cells, such as cell proliferation, survival and metastasis, by affecting the expression of target genes. MiR-30e, a multifunctional microRNA, has been reported to be involved in regulating the development of tumors. Previous studies have shown that miR-30e acts as a tumor suppressor and inhibits cell proliferation and metastasis in some cancers, including hepatocellular carcinoma (17), squamous cell carcinoma of the head and neck (18), colorectal cancer (19). However, other studies have reported that miR-30e promotes the progression and malignant processes of cancers, for instance, esophageal cancer (20), lung adenocarcinoma (21). Roles of miR-30e in breast cancer are also controversial. MiR-30e represses tumor growth, bone metastasis and chemosensitivity to paclitaxel in breast cancer (22, 23). Conversely, Overexpression of miR-30e-decreased expression of Tumor Suppressor Candidate 3 (TUSC3) leads to increased proliferation and migration of breast cancer cells (24). To date, the precise effects of miR-30e on TNBC are still inconclusive. Our results showed that miR-30e was positively associated with overall survival and negatively regulated cell proliferation in TNBC. The inhibitory effects of miR-30e on cell proliferation were attenuated by the restoration of GLDC. The results indicate that miR-30e-repressed GLDC defines a potentially suppressive pathway in TNBC. Although we have demonstrated that GLDC mitigated by miR-30e regulates cell proliferation and tumor immune infiltration in TNBC, the regulatory mechanisms remain unknown and will be determined in future studies. Additionally, we will further validate the regulatory effects of GLDC in TNBC and explore the possibility of GLDC as a potential therapeutic target for TNBC by utilizing more clinical samples and in vivo studies.
In summary, this research implies that GLDC, increased in the TNBC tissues, facilitates cell proliferation and is correlated with the poor prognosis in TNBC as an oncogenetic factor. Moreover, miR-30e acts as a functional upstream regulator of GLDC in TNBC. The findings demonstrate the important regulatory effects of GLDC in TNBC, which might provide potential targets for improving the molecular therapy of TNBC.
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.
JM, LZ, and JS designed this study. HX, XL, YK and LY analyzed the data. HX, TY and JM wrote the manuscript and performed the experiments. All authors contributed to the article and approved the submitted version.
This study was supported by the National Natural Science Foundation of China (82072923 and 82002777), Multidisciplinary Cross Research Foundation of Shanghai Jiao Tong University (YG2019QNA26).
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. The reviewer GL declared a shared parent affiliation with the authors HX, TY, YD, SX, YK, LY, LZ to the handling editor at the time of the review.
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. | true | true | true |
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PMC9585286 | Muluken Enyew,Tileye Feyissa,Anders S. Carlsson,Kassahun Tesfaye,Cecilia Hammenhag,Amare Seyoum,Mulatu Geleta | Genome-wide analyses using multi-locus models revealed marker-trait associations for major agronomic traits in Sorghum bicolor | 07-10-2022 | candidate gene,genome wide association study,linkage disequilibrium,population structure,quantitative trait locus,sorghum | Globally, sorghum is the fifth most important cereal crop, and it is a major crop in Ethiopia, where it has a high genetic diversity. The country’s sorghum gene pool contributes significantly to sorghum improvement worldwide. This study aimed to identify genomic regions and candidate genes associated with major agronomic traits in sorghum by using its genetic resources in Ethiopia for a genome-wide association study (GWAS). Phenotypic data of days to flowering (DTF), plant height (PH), panicle length (PALH), panicle width (PAWD), panicle weight (PAWT), and grain yield (GY) were collected from a GWAS panel comprising 324 sorghum accessions grown in three environments. SeqSNP, a targeted genotyping method, was used to genotype the panel using 5,000 gene-based single nucleotide polymorphism (SNP) markers. For marker-trait association (MTA) analyses, fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) models were used. In all traits, high phenotypic variation was observed, with broad-sense heritability ranging from 0.32 (for GY) to 0.90 (for PALH). A population structure, principal component analysis, and kinship analysis revealed that the accessions could be divided into two groups. In total, 54 MTAs were identified, 11 of which were detected by both BLINK and farmCPU. MTAs identified for each trait ranged from five (PAWT and GY) to fourteen (PH) representing both novel and previously identified quantitative trait loci (QTLs). Three SNPs were associated with more than one trait, including a SNP within the Sobic.004G189200 gene that was associated with PH and PAWT. Major effect SNP loci, Sbi2393610 (PVE = 23.3%), Sbi10438246 (PVE = 35.2%), Sbi17789352 (PVE = 11.9%) and Sbi30169733 (PVE = 18.9%) on chromosomes 1, 3, 5 and 9 that showed strong association signals for PAWD, DTF, GY and PALH, respectively, were major findings of this study. The SNP markers and candidate genes identified in this study provide insights into the genetic control of grain yield and related agronomic traits, and once validated, the markers could be used in genomics-led breeding. | Genome-wide analyses using multi-locus models revealed marker-trait associations for major agronomic traits in Sorghum bicolor
Globally, sorghum is the fifth most important cereal crop, and it is a major crop in Ethiopia, where it has a high genetic diversity. The country’s sorghum gene pool contributes significantly to sorghum improvement worldwide. This study aimed to identify genomic regions and candidate genes associated with major agronomic traits in sorghum by using its genetic resources in Ethiopia for a genome-wide association study (GWAS). Phenotypic data of days to flowering (DTF), plant height (PH), panicle length (PALH), panicle width (PAWD), panicle weight (PAWT), and grain yield (GY) were collected from a GWAS panel comprising 324 sorghum accessions grown in three environments. SeqSNP, a targeted genotyping method, was used to genotype the panel using 5,000 gene-based single nucleotide polymorphism (SNP) markers. For marker-trait association (MTA) analyses, fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) models were used. In all traits, high phenotypic variation was observed, with broad-sense heritability ranging from 0.32 (for GY) to 0.90 (for PALH). A population structure, principal component analysis, and kinship analysis revealed that the accessions could be divided into two groups. In total, 54 MTAs were identified, 11 of which were detected by both BLINK and farmCPU. MTAs identified for each trait ranged from five (PAWT and GY) to fourteen (PH) representing both novel and previously identified quantitative trait loci (QTLs). Three SNPs were associated with more than one trait, including a SNP within the Sobic.004G189200 gene that was associated with PH and PAWT. Major effect SNP loci, Sbi2393610 (PVE = 23.3%), Sbi10438246 (PVE = 35.2%), Sbi17789352 (PVE = 11.9%) and Sbi30169733 (PVE = 18.9%) on chromosomes 1, 3, 5 and 9 that showed strong association signals for PAWD, DTF, GY and PALH, respectively, were major findings of this study. The SNP markers and candidate genes identified in this study provide insights into the genetic control of grain yield and related agronomic traits, and once validated, the markers could be used in genomics-led breeding.
Globally, sorghum [Sorghum bicolor (L.) Moench] is the fifth most important cereal crop in terms of both production and acreage (FAOSTAT, 2020). Due to its high adaptability to diverse environments, drought tolerance, low input requirements, and high nutritional value, sorghum is considered a vital food security crop (Awika and Rooney, 2004; Dykes, 2019; Jankowski et al., 2020; Abreha et al., 2022). It has various interesting characteristics, including its C4 photosynthesis pathway, drought tolerance, and a relatively small genome that makes it a model crop for cereal genomics (Mullet et al., 2014). The first whole-genome sequence of sorghum was first released in 2009 (Paterson et al., 2009) and the latest version (version 3.1.1) has a genome size of 732.2 Mega base pairs (Mb) (McCormick et al., 2018). Publicly available sorghum genomic sequences provide opportunities for the development of informative molecular markers for different applications, including genome-wide association analyses for the identification of genomic regions associated with complex traits (Girma et al., 2019). Ethiopia is a center of origin and diversity of sorghum (De Wet and Harlan, 1971), and the Ethiopian sorghum gene pool has been widely used for enhancing desirable traits, such as drought tolerance, resistance to ergot disease and green bugs, and high lysine content (Singh and Axtell, 1973; Borrell et al., 2000; Wu et al., 2006). The gene pool comprises a genetically diverse germplasm that exhibits wide variation in grain yield and other major phenotypic traits such as days to flowering, plant height, biomass, and inflorescence architecture (Amare et al., 2015; Girma et al., 2019; Enyew et al., 2021). The use of such diverse germplasm widely adapted to both biotic and abiotic stresses is essential to understanding the genetics of the target trait variations. Genome-wide association study (GWAS) is an efficient method to discover genomic regions associated with traits of interest and has been successfully implemented in various crops, including maize (Cui et al., 2016), rice (Zhong et al., 2021), wheat (Alemu et al., 2021) and sorghum (Girma et al., 2019). In GWAS, differentiating true associations from false-positive marker-trait association (MTA) caused by population structure and kinship has been a major challenge (Kaler et al., 2020). Consequently, a number of statistical models have been developed in order to control these spurious marker-trait associations, including a single-locus mixed linear model (MLM) that incorporates these two confounding factors into the analysis as covariates (Price et al., 2006). Nonetheless, this model may lead to false-negative MTAs as result of overfitting, possibly resulting in missed opportunities to uncover loci associated with desirable traits (Liu et al., 2016). To overcome such a false-negative MTA, multi-locus models such as fixed and random model circulating probability unification (FarmCPU) (Liu et al., 2016) and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) (Huang et al., 2019) have been developed. The FarmCPU model is a multi-locus linear mixed model (MLMM) designed to eliminate false-positive MTAs without compromising true associations (Liu et al., 2016), by including multiple markers at the same time as covariates to partially eliminate the confounding effect of markers and kinship. It employs both the fixed-effect model (FEM) and random effect model (REM) iteratively in order to completely remove cofounding. Compared to other GWAS models, it provides higher statistical power and is more computationally efficient (Liu et al., 2016). However, FarmCPU is time inefficient when large numbers of markers and individuals are involved. Consequently, BLINK, which has a higher statistical power and is more time-efficient, was recently developed. BLINK reduces computing time by replacing random effect with fixed effect model through approximating maximum likelihood using Bayesian information criterion (BIC) (Huang et al., 2019). Unlike FarmCPU, BLINK uses linkage disequilibrium (LD), thereby removing the assumption that causal genes are evenly distributed across the genome. Through GWAS, multiple genomic regions associated with different agronomic traits, including grain yield, have previously been identified in sorghum (Morris et al., 2013; Boyles et al., 2016; Girma et al., 2019). However, most of these studies were based on germplasm that have gone through sorghum improvement programs (Morris et al., 2013; Boyles et al., 2016), which reduces the genetic diversity of the crop, as only genotypes bearing desirable traits are selected. For example, breeding to develop early maturing and photoperiod insensitive genotypes excludes late maturing and photoperiod sensitive genotypes (Klein et al., 2008). This limits the ability to detect genomic regions associated with traits of interest through GWAS. Properly planned and executed GWAS studies often reveal novel genomic regions associated with target traits, thereby facilitating the identification of genes that control those traits. However, because genetic variation exists both within and among different accessions, and genotype-by-environment interactions (G×E) have significant effects on complex traits, multi-environment field trials are crucial to detect stable QTLs through GWAS and validate their robustness in diverse germplasm (Russell et al., 2020). Therefore, this study aimed to conduct GWAS for grain yield and other agronomic traits of 309 diverse Ethiopian sorghum landrace accessions grown across three environments in Ethiopia. Thus, this study aimed at conducting genome-wide association studies (GWAS) for grain yield and other agronomic traits in sorghum, using 309 diverse Ethiopian sorghum landrace accessions, in order to identify novel genomic regions (QTLs) associated with these traits, in addition to confirming those already detected. Furthermore, the study aimed to determine their genetic diversity and population structure, as well as to locate candidate genes within the identified genomic regions.
A total of 320 landrace accessions and four accessions of improved varieties were used in this study ( Supplementary Table 1 ). Among the 320 landrace accessions, 261 were obtained from Melkassa Agricultural Research Center (MARC) but were originally collected by Ethiopian Biodiversity Institute (EBI) from different geographic regions across different agro-ecological zones. The 59 remaining landrace accessions were collected specifically for this study from farmers’ fields in areas prone to drought in the country. The four accessions of improved varieties (Argiti, Melkam, B35, and ESH4) are drought tolerant and high-yielding and were provided by MARC ( Supplementary Table 1 ).
Field trials were conducted using the 324 accessions during the main crop-growing season in 2019 at three locations in Ethiopia, Melkassa (MK; 8°24’N, 39°21’E), Mieso (MS; 12°9’N, 7°31’E), and Mehoni (MH; 8°41’N, 39°37’E), which represent different agro-ecological zones ( Supplementary Table 2 ). The field experimental design was alpha lattice, comprising 12 blocks with 27 plots in each block. The experiments were conducted in two replications at each of the three sites. The plot size was 2.25 m2 (3 m by 0.75 m), and the seeds were planted in a single row of 3 m along the center of each plot. The plants were then thinned out, at a seedling stage, to a spacing of 0.2 m between plants in each plot. The recommended amount of Di-Ammonium Phosphate (DAP) fertilizer (100 kgha-1) was applied during planting and Urea (50 kgha-1) was applied as a side dressing 40 days after planting. Phenotypic data was collected from five randomly selected and tagged plants in each plot for days to flowering (DTF), plant height (PH), panicle length (PALH), panicle width (PAWD), panicle weight (PAWT), and grain yield (GY) ( Supplementary Table 3 ).
For DNA extraction and subsequent genotyping of the 324 accessions, seeds harvested from the five phenotyped plants in the first replicate at the Melkasa site were used. The seeds were planted in a greenhouse at the Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), Sweden. Soon after germination, extra seedlings were removed and only one seedling per mother plant was maintained. The leaf tissue of the five seedlings of each genotype was collected together two weeks after planting, using the BioArk leaf collection kit (LGC Biosearch Technologies). Using a punch with a diameter of 6 mm that was provided with the kit, ten leaf discs (2 leaf discs per plant) were sampled from each genotype and placed in a single well of 96-well plates. Hence, a pool of leaf tissue from five plants were used to represent each of the 324 accessions. The samples were then sent to LGC Genomics (Berlin, Germany) where genomic DNA extraction was conducted. The Sbeadex plant kits were used to extract high-quality genomic DNA (https://biosearch-cdn.azureedge.net/assetsv6/sbeadex-plant-data-sheet.pdf).
The 324 sorghum accessions used in the present study were genotyped using SeqSNP, which is an advanced targeted genotyping by sequencing method. Genotyping was conducted using 5,000 SNP markers used in a recently published study on genetic diversity analysis of sorghum accessions (Enyew et al., 2022). The source of the vast majority of the SNPs (93.7%) used was the sorghum SNP database SorGSD (http://sorgsd.big.ac.cn), a web portal that provides genome-wide SNP markers for diverse sorghum genetic resources (Luo et al., 2016). The remaining 6.3% of the SNP markers were identified by aligning functionally annotated sorghum genes with the latest sorghum reference genome using BLAST (Basic Local Alignment Search Tool). The markers are somewhat evenly distributed across the ten sorghum chromosomes and their descriptions can be found in the supplementary material ( Supplementary Tables 2 and 3 ) of Enyew et al. (2022); https://www.frontiersin.org/articles/10.3389/fpls.2021.799482/full#supplementary-material). All the 5,000 markers were designed in a high-specificity assay that does not allow off-target hits against the sorghum reference genome, and are fully covered (two oligo probes were used per target), as described in Enyew et al. (2022). Following the production of the SeqSNP kit, containing the 10K high-specificity oligo probes for the 5,000 SNPs, and the preparation of the sequencing library, the target SNPs were sequenced on an Illumina Nextseq 500/550 v2 system in 75 bp single read sequencing mode. On average, the effective target SNP coverage per sample was 365x. Following sequencing, the quality trimmed reads were aligned to the reference genome using Bowtie2 v2.2.3 (Langmead and Salzberg, 2012), and the SNP genotyping pipeline was set to diploid. The variant identification and genotype calling were done using Freebayes v1.0.2-16 (Garrison and Marth, 2012). In accordance with the standard genotype calling pipeline, allele counts below eight were set to zero before genotype calling to remove SNPs resulting from sequencing errors.
The genotype calling for the 324 sorghum accessions across the 5,000 SNP loci resulted in 84.3% homozygous calls, 14.2% heterozygous calls, and 1.6% missing data. Among the 5,000 SNP loci, 4,695 (86%) were polymorphic whereas 305 loci (14%) were monomorphic across the 324 accessions. Among the 4,695 polymorphic SNP loci, 4,639 were bi-allelic whereas 56 were multi-allelic. Further filtering of the bi-allelic loci was carried out by eliminating 16 accessions that showed heterozygosity in more than 50% of the loci, leaving 309 accessions for further analysis. Among the 4,639 bi-allelic loci, those with missing data above 2%, or with minimum allele frequency (MAF) below 5%, or heterozygosity above 17% were excluded. This resulted in 3,143 high-quality SNP loci, which were used for the analyses of the 309 sorghum accessions. Data analyses were conducted using diploid genotypes (instead of allele frequencies) since the software packages used were developed for genotypic data analyses, regardless of the fact that each accession was represented by a pool of five genotypes. This approach was considered appropriate because the vast majority of the loci (at least 83%) were homozygous among the 309 accessions, indicating that the five individuals in each accession had the same genotype at those loci. Nevertheless, since both alleles were found at up to 17% of the loci, a genotype in each pool could be homozygous or heterozygous at these loci. The recent individual genotype-based study using these SNP markers revealed only 6.7% (on average) heterozygotes in each accession (Enyew et al., 2022). Thus, the individuals in each pool are mainly homozygotes at these loci. When there is a marker-trait association (MTA), a heterozygote may represent the average phenotypic value of the five plants better than a homozygote. Therefore, all loci containing both alleles were treated as “heterozygous” for the purpose of the data analyses, including the GWAS.
Phenotypic data analysis was carried out using the Multi-Environment Trial Analysis with R (META-R) software package (Alvarado et al., 2020). The combined analysis of variance (ANOVA) was conducted by incorporating genotypes, environments, genotype-by-environment interactions (G×E), replications, and blocks as variance components. The META-R was used to calculate the best linear unbiased prediction (BLUP) and to estimate the variance components using the restricted maximum likelihood (REML) method by implementing linear models in lmer function of lme4 package for R. The broad-sense heritability (H2) of all traits was also calculated using META-R.
Principal coordinate analysis (PCoA) was used to determine the population structure of the sorghum accessions using GenAlEx 6.5 software (Peakall and Smouse, 2012). A Bayesian clustering algorithm implemented in the STRUCTURE software v.2.3.4 (Pritchard et al., 2000) was used to determine the degree and pattern of population admixture. The STRUCTURE program was run using the admixture model with burn-in periods of 10,000 and a Markov chain Monte Carlo (MCMC) replications of 200,000. The analysis was performed for K ranging from two to ten, with 10 iterations at each K, to determine the optimum number of genetic populations. The optimum K value was predicted following the simulation method of Evanno et al. (2005) using STRUCTURE HARVESTER version 0.6.92 (Earl, 2012). Genome-wide LD analysis was carried out using Trait Analysis by Association, Evolution, and Linkage (TASSEL) through determining the pairwise squared allele-frequency correlations (r2) between SNP markers with sliding window of 50 SNPs. The r2 values were then plotted against physical distance to estimate the extent of LD between pairs of loci. The genome-wide LD decay curve line was fitted into the scatterplot using the smoothing spline regression line to estimate the LD decay rate as described by Hill and Weir (1988) in R environment.
GWAS was performed using the statistical genetics package Genome Association and Prediction Integrated Tool (GAPIT) (Tang et al., 2016) within the R environment (Team R.C, 2020). The GWAS was based on genotypic data for 3143 SNP markers alongside phenotypic data comprising six phenological and agro-morphological traits (DTF, PH, PALH, PAWD, PAWT and GY) for 309 sorghum accessions. Principal component analysis (PCA) and pairwise genetic relationship (kinship matrix) according to VanRaden (2008) were calculated through the pipeline implemented in GAPIT. The principal components and the kinship matrix were used to control the population and family structure for GWAS. Two multi-locus GWAS models, BLINK (Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway) (Huang et al., 2019) and FarmCPU (Fixed and random model Circulating Probability Unification) (Liu et al., 2016) were used to identify significant SNPs for the six traits. The P-value threshold of 0.05 with Bonferroni correction (0.05/number of markers) was used to determine the significant associations for each trait as implemented in GAPIT. Manhattan and QQ plots were generated using the R package qqman (Turner, 2014). The physical map positions of all significant SNPs were determined by aligning their reference sequences to a sorghum reference genome v3.1.1 (McCormick et al., 2018) using JBrowse (Skinner et al., 2009) in Phytozome v12.1 (Goodstein et al., 2012) in order to explore annotated genes within each QTL region.
Large phenotypic variations were observed among the sorghum accessions used in the field trials across three locations for the six phenotypic traits ( Figure 1 and Table 1 ). DTF varied from 76 to 138 days with a mean of 108 days whereas PH varied from 118 to 366 cm with a mean of 272 cm. PALH varied from 11 to 38 cm with a mean of 21 cm, PAWD varied from 6 to 19 cm with an average value of 10 cm. The PAWT for the accessions ranged from 82 to 137 g with a mean of 105 g. The average GY was 78 g, while individual accessions produced 62 to 101 g grain ( Figure 1 ). The broad-sense heritability (H2) was high for DTF (0.88), PH (0.85), PALH (0.90), and PAWD (0.81), whereas it was moderate for PAWT (0.37) and GY (0.32) ( Figure 1 ). The analysis of variance (ANOVA) revealed that genotypes, environments, and G×E had significant effects on phenotypic variation, except that environments had no effect on PH and PWAD ( Table 1 ). Phenotypic variation due to genotypes was higher for DTF, PH, PALH and PAWD compared to that of G×E and environments. In the case of PAWT and GY, environments had higher effects than genotypes and G×E on the phenotypic variation ( Table 1 ). For combined environments, a normal frequency distribution was observed for the traits, including grain yield ( Supplementary Figure 1 ).
The admixture model-based population genetic structure of the 309 sorghum accessions was inferred using STRUCTURE software. The analysis of the STRUCTURE output using STRUCTURE HARVESTER program (Earl, 2012), which implements ΔK method of Evanno et al. (2005) showed that the highest ΔK value was attained at K = 2, indicating that two genetic populations represent the sorghum accessions used in this study ( Supplementary Figure 2 ). In this analysis, 121 accessions (39%) were assigned to cluster-I (population one) while 188 accessions (61%) were assigned to cluster-II (population two). As shown in the STRUCTURE plot, some individual accessions possessed alleles inherited from both genetic populations ( Figure 2A ). Furthermore, the principal coordinate analysis (PCoA) revealed a population stratification among the accessions used in agreement with the results obtained with STRUCTURE analyses ( Figure 2B ). The kinship analysis clustered the sorghum accessions in to two distinct groups in agreement with the results of STRUCTURE ( Figure 3 ). The mean linkage disequilibrium for the ten chromosomes is quite similar, with an r2 value ranging from 0.09 to 0.12 with an overall average of 0.11 ( Table 2 ). LD was significant, on average, for 50% (71,611) of the marker pairs (mean r2 = 0.2; p ≤ 0.01) ( Table 2 ). On average, 35% (24,798) of the significant pairs were physically linked (r2 > 0.2) having an r2 value 0.42. The highest and lowest number of marker pairs that showed significant LD (p ≤ 0.01) were recorded on Chromosome 3 (8,938; r2 = 0.21) and chromosome 7 (5,309; r2 = 0.19) ( Table 2 ). Among the significant marker pairs, those on chromosome 8 had the strongest LD (mean r2 = 0.22), while those on chromosome 5 had the weakest LD (mean r2 = 0.18) ( Table 2 ). Detailed information regarding the relationship between r2 values and physical distances can be found in Supplementary Table 4 . At the genome level, the r2 value was 0.11, and the decay curve of the LD began at r2 value of 0.48 and reached half-decay at 0.23. The decay curve of the LD intersected the half-decay line at a distance of 449 kb ( Figure 4 ).
The marker-trait association analysis using genome-wide SNP markers was performed using two different multi-locus models, BLINK and farmCPU, which identified 39 and 26 SNP loci with significant association with one or two of the six traits studied, respectively. Among the significantly associated SNP loci, 11 were identified by both models ( Table 3 ). Furthermore, three of the SNP loci were associated with two different traits, which means that a total of 51 SNP loci ( Table 3 ) were associated with one or more traits. The Manhattan plots, which graphically display the GWAS outputs are provided in Figure 5 and Supplementary Figure 3 . The corresponding QQ plots showed that the observed and expected P-values for the vast majority of SNPs are matching, with a clear deviation of the observed values from the expected close to the right end of the plot, suggesting a realistic positive association between the SNPs and the traits ( Figure 5 and Supplementary Figure 3 ). Hence, the GWAS results are reliable and false negative results are less likely. The map positions of the SNPs associated with the traits, in Sorghum bicolor reference genome v3.1.1, enabled the identification of genes harboring those SNPs. The characteristics of those SNPs and the effects they have on their genes were presented in Supplementary Table 5 .
In the genome-wide association study, a total of 12 SNPs with significant associations with days to flowering were identified, through either the BLINK model or farmCPU model, or both ( Table 3 ). These markers are distributed across all chromosomes, except chromosomes 4 and 9. Two of the markers were identified through both BLINK and farmCPU models on chromosome 1 (sbi318688; position 8066218 bp) and chromosome 3 (sbi10438246; position 60811994) ( Table 3 ; Figure 5 , and Supplementary Figure 3 ). The phenotypic variance of the trait explained by the significant SNPs ranged from 0.9 to 35% ( Table 3 ). The marker sbi10438246 on chromosome 3, identified by both models, accounted for the highest percentage of the variance in DTF (35%; the strongest MTA signal) ( Table 3 ). Box plots depicting the effects of the alleles at this locus for this trait are shown in Figure 6A . The SNP, a missense variant that alters serine versus tyrosine, is located within the coding sequence (CDS) of the gene Sobic.003G271700, which codes for a protein of unknown function. The second major effect marker (a missense mutation on chromosome 5), accounting for 9.9% of the DTF variance, is located within gene Sobic.005G147700, which codes for extensin-2-like protein ( Table 3 and Supplementary Table 5 ). The SNP sbi24678469 and sbi982537 are DTF-associated markers present within the coding sequences of the genes Sobic.008G052000 and Sobic.001G230700, respectively. These genes encode fatty acid amide hydrolase, and RING finger and E3 ubiquitin-protein ligase MIEL1. For Sobic.001G230700 the most significant hit (homologue) in rice is RING finger and CHY zinc finger domain-containing protein 1, which has been shown to be involved in the regulation of seedling development and flowering time. Sobic.007G109800 (containing snp_sb042060813825) encodes late embryogenesis abundant protein D-34 (a seed maturation protein), which is involved in seed development and maturation, as well as response to biotic stress. Other genes containing the DTF-associated SNP markers include white-brown complex homolog protein 11, ATP binding microtubule motor family protein, phototropic-responsive NPH3 family protein and tetratricopeptide repeat (TPR)-like superfamily protein, and phototropin 1 (phot1) ( Supplementary Table 5 ).
In this study, the highest number of significant MTA was identified for plant height. In total, 14 SNPs with significant association with plant height were identified by BLINK and farmCPU models ( Table 3 , Figure 5 and Supplementary Figure 3 ). Both BLINK and farmCPU identified four significant markers on chromosomes 3, 4, 6, and 8. The percentage of phenotypic variance explained by these markers ranged from 1.6% (on chromosome 6) to 6.3% (on chromosome 3) ( Table 3 ). The effects of alleles on PH at sbi7769289 locus that accounted for the highest phenotypic variance (6.5%) and highest MTA signal are shown with box plots ( Figure 6B ). Additionally, four markers were identified only by BLINK on chromosomes 1, 5, and 7, explaining phenotypic variance ranging from 0.7 (chromosome 1) to 4.6% (chromosome 7) ( Table 3 and Figure 5 ). Similarly, six significant markers, on chromosomes 1, 3, 6, 9, and 10 were identified only by the farmCPU model, explaining phenotypic variance ranging from 0.8 (on chromosome 9) to 6.8% (on chromosome 10) ( Table 3 and Supplementary Figure 3 ). All markers with significant association with PH were located within genes ( Supplementary Table 5 ). The two genic SNP markers that had the largest effects on PH, on chromosome 3 at 11 Mb (PVE = 6.3%) and 10 at 7 Mb (PVE = 6.8%) ( Table 3 ) were located within the genes, Sobic.003G119600 and Sobic.010G085400, respectively. Sobic.003G119600 encodes zinc finger and C3HC4 type domain- containing protein while Sobic.010G085400 encodes K-box region and MADS-box transcription factor family protein. These genes play a significant role in various physiological and cellular processes including transcription, signal transduction, recombination, plant growth, and vegetative and reproductive developments ( Supplementary Table 5 ). Sobic.001G017500 (encoding hydroxysteroid dehydrogenase), and Sobic.009G223500 (encoding F-box family protein), which are known for their role in plant vegetative and reproductive growth and development, contain significant SNPs associated with PH. Similarly, Sobic.006G111800 (encoding ARM repeat superfamily protein), Sobic.006G235400 (encoding protein kinase superfamily protein), and Sobic.010G066100 (encoding phototropic-responsive NPH3 family protein) also had significant markers associated with PH. These genes are involved in the regulation of growth and development, stress signaling, and phototropin 1 signaling ( Supplementary Table 5 ).
The GWAS identified seven SNPs with significant association to PALH across five different chromosomes (1, 3, 7, 8 and 9) ( Table 3 ; Figure 5 , and Supplementary Figure 3 ), and all of them are located within genes ( Supplementary Table 5 ). Two of these SNP markers were identified by both farmCPU and BLINK models, and explained 9.6% (sbi3208134; on chromosome 1) and 18.9% (sbi30169733; on chromosome 1) of the phenotypic variance of PALH ( Table 3 ). The sbi3208134 is located within Sobic.001G516100, which encodes BR-signaling kinase 1 while sbi30169733 is located within Sobic.009G218450, which encodes P-glycoprotein 11, MDR-like ABC transporter ( Table 3 and Supplementary Table 5 ). The other five markers (located on chromosomes 3, 7, 8 and 9) were detected only by BLINK model, and accounted for up to 11.3% of the total phenotypic variance of the trait. The genes harboring these SNPs encode for proteins of different functions ( Supplementary Table 5 ). Among these genes, Sobic.009G199900 (on chromosome 9) contains the sbi30118348, which encodes phosphatidylethanolamine binding protein (PEBP), which is an important factor in regulating flowering in response to photoperiod.
The GWAS analyses detected eleven SNPs with significant association with PAWD ( Table 3 ; Figure 5 and Supplementary Figure 3 ). Three of these SNPs: sbi2029574 and sbi2393610 (on chromosome 1), and sbi8085609 (on chromosome 3) were detected by both BLINK and farmCPU. Among them, sbi2393610, which accounted for 23.3% of the variation in PAWD, is located within the Sobic.001G310300 gene that encodes glutathione S-transferase F11. Whereas, sbi8085609, which accounted for 10.9% of the variation in PAWD, is located in Sobic.003G154800 that encodes protein of unknown function (DUF594) ( Table 3 and Supplementary Table 5 ). Among the remaining eight markers, seven (on chromosomes 1, 2, 3, 5, 6 and 8) were detected only by BLINK and one (on chromosome 3) was detected only by farmCPU. The phenotypic variance of the trait explained by these SNPs ranged from 0.9 to 6.4% ( Table 3 ). These include SNPs within genes encoding appr-1-p processing enzyme family protein (Sobic.003G215900), RNA-binding family protein (Sobic.005G145400), S-locus lectin protein kinase family protein (Sobic.006G229100), protein kinase family protein with leucine-rich repeat domain (Sobic.001G273500), and magnesium transporter 4 (Sobic.003G395600) ( Supplementary Table 5 ).
The GWAS analyses detected five SNPs that were significantly associated with PAWT, explaining 1.7 to 8.0% of its phenotypic variance ( Table 3 , Figure 5 and Supplementary Figure 3 ). Four of these markers (on chromosomes 4, 5, 7 and 10) were identified by BLINK model whereas one marker (on chromosome 7) was detected by farmCPU model. The SNP marker sbi21265823 (on chromosome 7), which accounted for 8.0% of the PAWT phenotypic variance, is located within the Sobic.007G033500 gene, which encodes a protein of unknown function ( Supplementary Table 5 ). Among the remaining four markers, sbi17687423 (on chromosome 5), which accounted for 7% of the phenotypic variance of PAWT, is located within the Sobic.005G194000 gene, which encodes for a protein of unknown function. Whereas, sbi13732034 (on chromosome 4), which explained 5.9% of the phenotypic variance of PAWT, is located within the Sobic.004G189200 gene, which encodes for F-box domain and kelch repeat containing protein ( Table 3 and Supplementary Table 5 ).
GWAS identified five SNPs significantly associated with GY ( Table 3 , Figure 5 and Supplementary Figure 3 ). BLINK model identified four of these markers (on chromosomes 1, 5 and 10) whereas farmCPU model identified one marker (on chromosome 5). The percentage of phenotypic variance explained by these markers ranged from 3.1% (sbi3833 and sbi17653919; on chromosomes 1 and 5, respectively) to 11.5% (sbi17789352 on chromosome 5). The SNP marker sbi17789352 is located within the Sobic.005G209900 gene, which codes for protein of unknown function. The two SNP markers on chromosome 10, explained 6.7% (snp_sb001001045636) and 7.2% (sbi30583046) of the variation in grain yield. The SNP sbi30583046 is located within the coding sequence of the Sobic.010G074100 gene. This gene codes for a pentatricopeptide repeat (PPR) superfamily protein that plays a role in physiological processes contributing to plant growth and development ( Table 3 and Supplementary Table 5 ).
Genome-wide association mapping is a powerful method that facilitates the eventual identification of genes regulating traits of interest, whose efficiency are dependent on the genetic variation within the germplasm used as association panels. In this study, high phenotypic variation was observed within the Ethiopian sorghum accessions used as GWAS panel (as revealed by descriptive statistics) for each of the six target traits. Similarly high phenotypic variation was reported in previous studies on Ethiopian sorghum landrace accessions (Girma et al., 2019; Enyew et al., 2021). Hence, the sorghum landraces used for this study had a sufficiently high phenotypic variation that makes them suitable for use as GWAS panel as well as for selecting genotypes with desirable traits for use in sorghum breeding programs. The influence of environments and G×E were lower on DTF, PH, PALH, and PAWD, indicating a predominant contribution of genotypic variation to the high phenotypic variation in these traits. On the other hand, the contribution of genotypic variation to the phenotypic variation of PAWT and GY was low. For these traits, the vast majority of the phenotypic variation was due to the variances of environment and G×E, of which environment was a dominant factor. This was shown by a significantly lower broad-sense heritability of PAWT and GY as compared to that of DTF, PH, PALH, and PAWD. The high heritability for DTF, PH, and PALH and moderate heritability for GY were also reported in previous studies on sorghum (Amare et al., 2015; Zhao et al., 2016; Habyarimana et al., 2020; Luo et al., 2020). Overall, the large variation of the evaluated sorghum accessions and moderate to high broad-sense heritability of the traits suggest the significance of these genetic resources both for crop improvement through breeding as well as for the identification of genes regulating these traits.
Identifying the pattern of LD is crucial to design association studies and molecular breeding strategies (Thornsberry and Buckler, 2003). To characterize the LD decay in this study, LD was calculated at chromosome and genome levels using the SNP data from the 309 sorghum accessions. In this study, the average r2 value was 0.11 at the genome level. The LD started decaying at r2 value of 0.48 and reached its half-decay value (r2 = 0.23) by 448 kb. The LD decay curve intersected with the half-decay at a distance of 448 kb. These LD decay estimates are similar to the previously published value within 500 kb in sorghum (Marla et al., 2019). However, the estimates are higher than previously published values of r2 < 0.1 within 150 kb (Morris et al., 2013) and 100 kb (Bouchet et al., 2012). The average r2 in each sorghum chromosomes have similar rate of LD decay, between 0.09 and 0.12 which is in agreement with previous studies on sorghum (Wang et al., 2013; Hu et al., 2019).
In this study, two multi-locus GWAS models were used for GWAS to overcome the limitations arising from using single-locus models (Liu et al., 2016; Li et al., 2018a). The multi-locus models avoided the confounding effects of population structure by including kinship and principal components in the GWAS models. The QQ plots also confirmed that the power of the models to detect true marker-trait associations was high. In total, 51 MTAs were identified in this study with the number of MTAs for each trait ranging from five (for PAWT and GY) to 14 SNPs (for PH). Among the significant SNPs, 11% explained over 10% of the phenotypic variation of the corresponding traits. Whereas, 31% of the SNPs explained over 5% of the phenotypic variation of the corresponding traits. The significant markers together accounted for 25.3% (for PAWT) to 67.7% (for DTF). Therefore, sorghum could be improved significantly by pyramiding favorable alleles using the marker-assisted selection (MAS) approach using the MTAs identified for each trait.
The Maturity (Ma) loci (Ma1-Ma6) have been shown to regulate sorghum flowering time (Quinby and Karper, 1953; Rooney and Aydin, 1999). The Ma1 locus represent the Sobic.006G057866 gene located on chromosome 6, which encodes pseudo-response regulator protein 37 (PRR37). This gene has a significant effect on flowering time by controlling floral inhibitors and activators (Murphy et al., 2011). Ma2 represents the Sobic.002G302700 gene located on chromosome 2, which encodes a SET and MYND (SYMD) domain-containing lysine methyl transferase (Casto et al., 2019). Both Ma3 and Ma5 are located on chromosome 1 and represent the Sobic.001G394400 and Sobic.001G087100 genes, respectively. These genes encode phytochrome B (Childs et al., 1997) and phytochrome C (Yang et al., 2014), respectively. Ma6 represents Sobic.006G004400 gene located on chromosome 6, which encodes Ghd7, a CONSTANS, CO-like, and TOC1 (CCT)-domain protein (Murphy et al., 2014). The gene for the Ma4 locus has not yet been identified. Among 12 SNP loci identified as being associated with DTF in this study, seven are located in close proximity with previously identified marker loci for the same trait ( Supplementary Table 6 ). For example, the sbi982537 marker on chromosome 1 (at position 22.3 Mb) is in close proximity with the previously reported marker loci located at 21.5 Mb (Ritter et al., 2008) and 23.1 Mb (Kong et al., 2018), suggesting that they might refer to the same QTL. As sbi982537 is located within the CDS (causing missense mutation) of Sobic.001G230700, which codes for RING finger and CHY zinc finger domain-containing protein 1, it could be a potential candidate gene behind the QTL these markers share. The map position of sbi982537, however, is 15.5 Mb and 45.7 Mb away from Ma3 and Ma5 loci, respectively, making it unlikely that they are associated. In contrast, sbi318688 is located only 1.3 Mb from a well characterized maturity loci Ma5 (Sobic.001G087100), at position 6.7 Mb on chromosome-1 (Yang et al., 2014), which suggests that they may be associated. There is also a possibility that the Sobic.001G105400 gene containing sbi318688 is responsible for the phenotypic variation since its orthologue plays an important role in pollen development and seed set in rice (Zhang et al., 2020). In this study, three SNP loci located close to one another on chromosome 3 (56.3 Mb to 60.8 Mb) were found to be associated with DTF, and it is possible that they refer to the earlier identified QTL (Kong et al., 2018). This QTL region contains the sbi10438246 (located at position 60.8 Mb) that explained over a third (35.2%) of the phenotypic variation in DTF. This SNP is a missense mutation within the Sobic.003G271700 gene that encodes a protein of unknown function. It is therefore imperative to conduct further research to determine whether this gene or another gene nearby is responsible for the phenotypic variation the SNP explained. The SNP snp_sb001000704585 locus (a missense mutation) on chromosome 6 explaining 1% of the variation in DTF is located within the Sobic.006G120000 gene that encodes the phototropic-responsive NPH3 family protein, which is known to play important roles in photo-signaling in addition to phototropism (Christie et al., 2018), suggesting that it may be causal. Similarly, the sbi24678469 locus located at 52.4 Mb on chromosome 8 was co-localized with the previously identified QTLs (Zhao et al., 2016; Kong et al., 2018). The snp_sb042060813825 locus on chromosome 7 at position 39.7 Mb represents a novel QTL for DTF, as no MTA for this trait has been identified in this genomic region in previous studies. This SNP locus is located within the Sobic.007G109800 gene, which encodes for a protein referred to as seed maturation protein or late embryogenesis abundant protein D-34. The gene is involved in the regulation of seed maturation (Hundertmark and Hincha, 2008), and is probably behind the variation in DTF explained by this SNP locus. Another SNP locus, sbi17364528, at 61.6 Mb on chromosome 5 was located about 10 Mb away from the previously identified locus for DTF (Zhao et al., 2016). This SNP locus (a missense mutation) is located within the Sobic.005G147700 gene, which encodes a protein of unknown function. As the SNP explained 9.9% of the variation in DTF, it represents a major QTL, while the gene is likely a novel locus that regulates flowering time in sorghum. Similarly, an SNP locus at 13.3 Mb on chromosome 2 was located about 54 Mb away from the previously identified well-known maturity locus (Ma2) (Zhao et al., 2016).
Sorghum possesses four major genomic loci that control plant height, known as dwarfing loci (Dw1-Dw4) (Quinby and Karper, 1953), of which Dw1, Dw2, and Dw3 have been characterized. Dw1 encodes a putative membrane protein (Sobic.009G230800), which has a role in controlling cell proliferation of internodes (Hilley et al., 2016; Yamaguchi et al., 2016). Dw2 encodes a protein kinase (Sobic.006G067600), which regulates the length of stem internodes (Hilley et al., 2017). Dw3, which encodes a phosphoglycoprotein of the adenosine triphosphate-binding cassette (ABC) transporter superfamily (Sobic.007G163800), plays an important role in auxin transport (Multani et al., 2003). Dw4 has been mapped to chromosome 4 (Li et al., 2015), but the gene behind this locus has not been identified yet. Additionally, a fifth dwarfing locus (Dw5) has recently been reported (Chen et al., 2019). In this study, an SNP locus (sbi30188088) at 56.6 Mb on chromosome 9 showed a significant association with plant height. This locus is located only ca 3 Mb from the major dwarfing locus, Dw1, which lies at 59.6 Mb (Hilley et al., 2016; Yamaguchi et al., 2016). Nevertheless, it is unlikely that this SNP refers to Dw1, considering its minor effect on the variation in PH. Two recent GWAS on sorghum identified SNPs associated with plant height at 56.6 and 56.5 Mb (Habyarimana et al., 2020; Luo et al., 2020), which are co-localized with sbi30188088. Moreover, sbi30188088 is located within the Sobic.009G223500 gene, which encodes an F-box family protein that has known role in ethylene and gibberellic acid signaling (Dill et al., 2004) as well as acting as auxin receptors in Arabidopsis to regulate the stability of indole 3-acetic acid (IAA) proteins (Kepinski and Leyser, 2005). Therefore, it is likely that Sobic.009G223500 is a gene behind the PH variation explained by sbi30188088. Several linkage mapping and GWAS identified loci associated with plant height on chromosome 6 between 42.2 to 61.5 Mb (Wang et al., 2012; Morris et al., 2013; Burks et al., 2015; Zhang et al., 2015; Kong et al., 2018; Habyarimana et al., 2020). In the present study, two SNP loci located at positions 48.0 Mb and 57.7 Mb were identified in this genomic region although their effect on the phenotype was low (below 2%). Considering their low effect and large distance (6-15 Mb) from the Dw2 locus located at 42.2 Mb on this chromosome, which encodes a protein kinase that regulates stem internode length (Hilley et al., 2017), it is unlikely that the SNPs refer to Dw2. Nevertheless, these SNPs are the result of missense mutations in the Sobic.006G111800 and Sobic.006G235400 genes, which encode ARM repeat and protein kinase, respectively. It is possible that these mutations are causal for the variation in PH associated with these SNPs. The sb042060843522 locus associated with PH is within Sobic.007G165200 gene on chromosome 7 (at 60.0 Mb), which is only about 1.6 Mb away from the Dw3 gene. It is also co-localized with the previously identified QTL on chromosome 7 at 59.6 Mb in the Ethiopian sorghum landrace (Girma et al., 2019). The SNP locus for PH, sbi13732034, which is located at 54.1 Mb on chromosome 4, is in close proximity with a PH associated locus (at 52.6 Mb) reported by (Zhang et al., 2015). Although the exact location and the gene behind it are yet to be confirmed, the Dw4 locus has been mapped close to the end of chromosome 4 (Li et al., 2015). It is unlikely that sbi13732034 refers to Dw4, as its impact on PH variation is quite small (3.2%), and their map positions are quite different. However, sbi13732034 is located within the Sobic.004G189200 gene, which encodes F-box domain and kelch repeat containing protein. As discussed above, this protein has a known role in ethylene and gibberellic acid signaling (Dill et al., 2004) and auxin receptors in Arabidopsis (Kepinski and Leyser, 2005), and it is possible that it is an underlying gene for the variation explained by sbi13732034. Two SNP loci (sbi7769289 and sbi7901589) located at positions 10.8 and 13.5 Mb on chromosome 3 were found to be associated with PH in this study. None of previously identified loci for PH was mapped to this genomic region. Considering the fact that the map distance between the two SNPs is 2.7 Mb, they may refer to the same gene, as both explained 6.2% of the observed phenotypic variation. The SNPs are located within the genes Sobic.003G119600 and Sobic.003G139200, respectively. Sobic.003G119600 encodes RING/U-box superfamily protein, zinc finger, C3HC4 type domain containing protein which is involved in plant growth and development (Wu et al., 2014), and it is possible that it is the gene behind the variation observed. Another novel major effect SNP locus (sbi30645260) located within the Sobic.010G085400 gene (at 7.3 Mb) on chromosome 10 was also identified for PH. The gene ontology (GO) analysis of this gene revealed that it is associated with four GO terms. In the molecular function (MF) GO class, it is annotated as RNA polymerase II cis-regulatory region sequence-specific DNA binding (GO:0000978), and DNA-binding transcription factor activity, RNA polymerase II-specific (GO:0000981_MF). Under biological process (BP) GO class, it is annotated as regulation of transcription by RNA polymerase II (GO:0006357), and transcription by RNA polymerase II (GO:0006366). This candidate gene encodes MADS-box transcription factor family protein, which is known to be involved in the regulation of flowering time in Arabidopsis (Michaels and Amasino, 1999), vegetative and root growth (Zhang et al., 2018), as well as other functions, such as floral organ development (Dreni and Zhang, 2016). Further study may shed light as to whether it is involved in the regulation of plant height in sorghum.
In this study, significant SNP loci for panicle length were identified on chromosomes 1, 3, 7, 8 and 9. Previous linkage mapping and GWAS in sorghum detected MTA for panicle length on all chromosomes ( Supplementary Table 6 ). This study identified three major effect SNP loci (sbi3208134, sbi21359653, and sbi30169733) on chromosome 1 at 78.2 Mb (PVE = 9.7%), chromosome 7 at 8.6 Mb (PVE = 11.2%), and chromosome 9 at 56.2 Mb (PVE = 18.9%) that showed strong association signals for panicle length. These SNPs are located within the genes Sobic.001G516100, Sobic.007G075200, and Sobic.009G218450, respectively. They encode brassinosteroid (BR) signaling kinase 1, bifunctional purine biosynthesis protein, and P-glycoprotein 11, MDR-like ABC transporter, respectively. The BR signaling pathway is known to play a role in plant cell elongation and division, tissue differentiation, organogenesis (Sakamoto et al., 2006), and inflorescence architecture (Li et al., 2018b). In order to determine if any of these genes are directly involved in the regulation of panicle length in sorghum, further research is required. Two SNP loci at 1.9 and 9.0 Mb on chromosome 3 were located in close proximity with previously reported loci at 1.8 Mb (Habyarimana et al., 2020) and 9.9 Mb (Zhao et al., 2016), respectively. On chromosome 9, two SNP loci at 55.0 and 56.2 Mb were found in close proximity with previously reported SNP locus at 55.3 Mb (Habyarimana et al., 2020). Six of the eight SNP loci with significant association with panicle width (PAWD) were located in close proximity with the previously reported loci associated with this trait in sorghum ( Supplementary Table 5 ). For instance, two major effect SNPs on chromosome 1 at 59.7 and 73.7 Mb were co-localized with previously reported SNP loci for PWAD in sorghum (Zhang et al., 2015). The sbi2393610 at 59.7 Mb on chromosome 1 was located about 1.4 Mb away from the previously identified locus for PAWD (Zhang et al., 2015). This SNP is located within the gene Sobic.001G310300, which encodes glutathione S-transferase F11. Three GO terms were associated with this gene under the molecular function (MF) gene ontology class. These are glutathione transferase activity (GO:0004364), ion binding (GO:0043168), and amide binding (GO:0042277). In the cellular component (CC) GO class, the gene is associated with intracellular anatomical structure (GO:0005737), and it is likely to regulate PAWD. As the SNP explained 23.3% of the variation in PAWD, it represents a major QTL that regulate panicle width in sorghum. Similarly, sbi3026667 at 73.7 Mb (PVE = 4.5) on chromosome 1 was located about 1.3 Mb away from the previously identified locus for PAWD (Zhang et al., 2015). The sbi2029574 on chromosome 1 at 52.9 Mb (PVE = 4.8) which showed a strong association signal for PAWD represents a novel genomic region associated with this trait. The gene containing this SNP, Sobic.001G273500, encodes a kinase family protein with a leucine-rich repeat domain. Further research will shed light if this gene is involved in regulating PAWD. On chromosome 3, three SNP loci at positions 16.9, 55.1 and 70.6 Mb (PVE = 10.9, 1.0, and 6.4, respectively) were in close proximity with previously reported QTLs for PAWD in sorghum (Zhang et al., 2015). The sbi8085609, at 16.9 Mb on chromosome 3 was located about 4 Mb away from the previously identified locus for PAWD (Zhang et al., 2015). This SNP locus (a missense mutation) is located within the Sobic.003G154800 gene, which encodes a protein of unknown function. As the SNP explained 10.9% of the variation in PAWD, it represents a major QTL, while the gene is likely regulates PAWD in sorghum, which needs further investigation.
Previous linkage mapping and GWAS detected QTLs for panicle weight (PAWT) on chromosomes 1, 4, 6, 7, and 9 ( Supplementary Table 5 ). None of them, however, is located close to the SNPs on chromosomes 4, 5, 7, and 10 that were found to be associated with PAWT in the present study. The sbi13732034 locus on chromosome 4, which explained 5.9% of the variation in PAWT, is located within the Sobic.004G189200 gene. This gene encodes a protein containing F-box domains and kelch repeats, which modulates ethylene and gibberellic acid signaling (Dill et al., 2004), regulates leaf senescence, seed size and number, and panicle architecture (Chen et al., 2013). It serves as an auxin receptor (Kepinski and Leyser, 2005) in different plant species. Therefore, Sobic.004G189200 is likely to be one of the major genes regulating panicle weight in sorghum. On chromosome 7, the sbi21265823 at 6.7 Mb was associated with PAWT, which might be considered as novel region controlling the trait. This SNP explained 8.0% of the variation in PAWT and is located within Sobic.007G063500 that encodes a protein of unknown function. It is therefore important to carry out further research to know whether this gene or another gene nearby is responsible for the phenotypic variation the SNP explained. Previous association mapping studies detected several QTLs for grain yield on all chromosomes except chromosomes 1 and 4 ( Supplementary Table 5 ). The five SNPs on chromosomes 1, 5, and 10 that were associated with grain yield (GY) in the present study are distant from the previously reported QTLs for GY in sorghum. Therefore, these SNPs probably represent novel QTLs. The sbi17789352 locus on chromosome 5 at position 69.7 Mb represents a novel QTL for GY since no MTA for this trait has previously been identified in this genomic region. This SNP explained 11.5% of the phenotypic variation in GY and located within the Sobic.003G271700 gene that encodes a protein of unknown function. Nevertheless, further research is required to confirm the association between this genomic region and grain yield, and to determine whether Sobic.003G271700 is the gene responsible for the observed variation associated with the SNP. The sbi30583046 locus at position 60.7 Mb on chromosome 10 explained 2.3% and 7.2% variations in PAWT and GY, respectively, which probably refers to a single gene that regulates both PAWT and GY. The SNP locus (missense mutation) is located within the gene Sobic.010G074100, which encodes pentatricopeptide repeat (PPR) superfamily protein. The protein has been reported to play a role in pollen development and seed setting in rice (Zhang et al., 2020), and pollen and organ development in Arabidopsis (Prasad et al., 2005). Thus, it is likely that this SNP locus represents a novel QTL that regulates PAWT and GY in sorghum, which is an interesting finding that deserves further research.
This study used large number of Ethiopian sorghum accessions and gene-based SNP markers to identify genomic regions and candidate genes associated with grain yield and agronomic traits in sorghum. The population structure analysis revealed two genetic populations representing the sorghum accessions studied indicating the presence of stronger genetic relationships among individuals within each genetic population than the overall average. A number of novel and previously known genomic regions that are associated with the studied traits were identified in this study. It is expected that the identified MTAs will contribute significantly to the existing knowledge base of sorghum genomic architecture, which will increase the efficiency of sorghum breeding programs. Major effect SNP loci, Sbi2393610 (PVE = 23.3%), Sbi10438246 (PVE = 35.2%), Sbi17789352 (PVE = 11.9%) and Sbi30169733 (PVE = 18.9%) on chromosomes 1, 3, 5 and 9 that showed strong association signals for PAWD, DTF, GY and PALH, respectively, are major findings of this study, which need to be further investigated. These findings provide insight into the genetic control of grain yield and agronomic traits, and after validation, the identified novel candidate genes may be used in genomics-led breeding for sorghum genetic improvement.
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 in the article/ Supplementary Material. All raw sequences are available at Sequence Reads Archive (SRA) database, BioProject PRJNA780262.
All authors conceived and designed the experiment. ME conducted the field experiment. MG and ME mined and selected the SNP marker set for genotyping. ME analyzed the data with the guidance of MG. ME wrote the draft manuscript. MG, TF, AC, CH, KT, and AS revised the manuscript. All authors read the final version and approved the submission of the manuscript for publication.
This research work was financially supported by the Swedish International Development Cooperation Agency (Sida) and Research and Training Grant awarded to Addis Ababa University and Swedish University of Agricultural Sciences (AAU-SLU Biotech; https://sida.aau.edu.et/index.php/biotechnology-phd-program/; accessed on July 10, 2022).
The authors would like to thank the Swedish International Development Cooperation Agency (Sida) for financing this research, and Melkassa Agricultural Research Center and Ethiopia Biodiversity Institute for providing sorghum germplasm used in this study. Additionally, the authors would like to express their gratitude to Melkassa, agricultural research center and its Mehoni and Mieso sub-centers for allowing us to use their experimental stations, and for supplying tools and assisting with field activities. The authors thank Girma Gashu and Wubshet Mamo for their unreserved support in collecting phenotypic data.
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. | true | true | true |
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PMC9585709 | Haiyan Qiu,Di Yang,Xiaolin Li,Fabo Feng | LncRNA CASC9 promotes cell proliferation and invasion in osteosarcoma through targeting miR-874-3p/SOX12 axis | 20-10-2022 | Osteosarcoma,lncRNA,CASC9,miR-874-3p,SOX12 | Background Osteosarcoma (OS) is a common primary malignant bone tumor. This study aimed to explore the biological role of long on-coding RNA (lncRNA) CASC9 and its regulatory mechanism in OC. Methods The CASC9 expressions in OS cells and tissues were measured using qRT-PCR. The functional role of CASC9 in OC was studied using MTT assay, colony formation assay, transwell invasion assay, and xenograft tumor assay. In addition, the mechanism of CASC9 function was determined using luciferase reporter assay. Western blot was used to analyze protein expressions in our paper. Results LncRNA CASC9 was found to be up-regulated in OS. Knockdown of CASC9 inhibited the proliferation and invasion of OS cells. Besides, miR-874-3p was identified as the target of CASC9, and SOX12 acted as a potential target of miR-874-3p. The down-regulation of miR-874-3p recovered the reduction in cell invasion and proliferation in vitro which were induced by CASC9 knockdown and delayed the tumor progression in vivo. Conclusion LncRNA CASC9 promotes cell proliferation and invasion in OS via miR-874-3p/SOX12 axis. Our study might provide novel biomarkers and potential therapeutic targets for OS treatment. | LncRNA CASC9 promotes cell proliferation and invasion in osteosarcoma through targeting miR-874-3p/SOX12 axis
Osteosarcoma (OS) is a common primary malignant bone tumor. This study aimed to explore the biological role of long on-coding RNA (lncRNA) CASC9 and its regulatory mechanism in OC.
The CASC9 expressions in OS cells and tissues were measured using qRT-PCR. The functional role of CASC9 in OC was studied using MTT assay, colony formation assay, transwell invasion assay, and xenograft tumor assay. In addition, the mechanism of CASC9 function was determined using luciferase reporter assay. Western blot was used to analyze protein expressions in our paper.
LncRNA CASC9 was found to be up-regulated in OS. Knockdown of CASC9 inhibited the proliferation and invasion of OS cells. Besides, miR-874-3p was identified as the target of CASC9, and SOX12 acted as a potential target of miR-874-3p. The down-regulation of miR-874-3p recovered the reduction in cell invasion and proliferation in vitro which were induced by CASC9 knockdown and delayed the tumor progression in vivo.
LncRNA CASC9 promotes cell proliferation and invasion in OS via miR-874-3p/SOX12 axis. Our study might provide novel biomarkers and potential therapeutic targets for OS treatment.
Osteosarcoma (OS) is characterized by the proliferation of malignant tumor cells that can directly differentiate into bone or bone-like tissue [1]. OS has a high degree of malignancy and rapid progression, and it often metastases to the lung through blood in the early stage. OS accounts for 0.2% of the incidence of malignant tumors, and the overall incidence is about 3/1, 000, 000 [2]. Chemotherapy can significantly improve the prognosis of patients with non-metastatic OS, but chemotherapy efficacy is significantly lower in patients with combined metastasis, especially lung metastasis [3]. In recent years, radical tumor resection combined with neoadjuvant chemotherapy has been extensively used in OS clinical therapy [4]. Despite the advancement in diagnosis, chemotherapy, and surgical techniques, the prognosis of OS remains poor [5]. To develop effective targeted therapies for OS, extensive studies on the molecular mechanisms regulating the progression of OS are quite necessary. With the continuous development of studies, the potential roles of non-coding RNAs (ncRNAs) have been reported in musculoskeletal conditions [6–9]. LncRNAs have been reported to serve as tumor-suppressive or oncogenic roles in the tumorigenesis and progression of a variety of malignancies, including OS [10]. LncRNAs, contains about 200 nucleotides, are distributed in both cytoplasm and nucleus. It is believed that lncRNAs in cytoplasm influence mRNA transcription and degradation [11] and can also help protein transport to the nucleus and activate gene expression [12]. Other lncRNAs are localized in the nucleus, which are involved in crucial molecular processes, including RNA processing, chromatin organization and transcription, and play an important role in genome structure [13, 14]. Cancer susceptibility candidate 9 (CASC9) is a conserved lncRNA with 1500 nucleotides (nt) in length. CASC9 was firstly reported in esophageal cancer as an oncogene [15] and was subsequently confirmed to be associated with the occurrence and development of several cancers, such as breast cancer [16], gastric cancer [17] and colorectal cancer [18]. Our preliminary experiment screened several lncRNAs from OS tissues and paired adjacent tissues by performing qRT-PCR, and identified lncRNA CASC9 was highly expressed in GC tissues. Nevertheless, there are few studies on the function and mechanism of CASC9 in OS progression. In the current study, we aimed to explore the functional role of CASC9 in OS. Given its high expression in OC tissues, we hypothesized that CASC9 may play a tumor-promoting role in OC. Then, we investigated the expression of CASC9 in OC cell lines and detected the influences of CASC9 silencing on cell proliferation, and invasion in OS cells and in mouse model. In addition, the oncogenic mechanism of CASC9 in OS was also demonstrated.
A total of 30 paired of OS tumor tissues and adjacent non-tumor bone tissues were collected in our research. All OS patients involved in our study were enrolled from Department of Orthopedics from August 2015 to July 2020 at Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College hospital. The patients underwent surgical resection and the specimens were collected. None of the patients received chemotherapy or radiotherapy prior to surgery. Liquid nitrogen was immediately used to snap-freeze these tissues. And then the tissues were placed at − 80 °C. This study was conducted according to the guidelines of the Declaration of Helsinki of 1975, as revised in 2013 and approved by the Ethics Committee of Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College hospital (no.2020QT359). All patients or their advisers enrolled in our study signed the informed consent.
Human OS cell lines, containing MG63, 143B, SW1353, U-2OS and HOS, and normal osteoblastic cell line (hFOB1.19), were all bought from Cell Bank of Shanghai Biology Institute (Chinese Academy of Science, Shanghai, China). DMEM, purchased from Invitrogen (Carlsbad, CA, U.S.A.), was added with penicillin/streptomycin (1%, Invitrogen), l-glutamine (2 mM) and fetal bovine serum (FBS) (10%, Invitrogen) and then, used to culture these OS cell lines at 37 °C in a 5% CO2 atmosphere. The hFOB1.19 cells were grown at a permissive temperature of 33.5 °C.
Small interfering RNA against lncRNA CASC9 (si-CASC9), negative control (si-NC), short hairpin CASC9 (shCASC9)/shNC, miR-NC/miR-874-3p mimics, and miR-874-3p inhibitor/inhibitor NC were all designed and synthesized by GenePharma (Shanghai, China). The sequences were as follows: si-CASC9, 5′-AUGAACAUCCACAAACACCAA-3′; si-NC, 5′-GAAUCCUACUUUCACAGCCAU-3′; shCASC9, 5ʹ-CCGGAAACUCUGGAAAUUCUUCCCUCGAGGGAAGAAUUUCCAGAGUUUUUUUUG-3ʹ; shNC, 5ʹ-CCGGACGUGACACGUUCGGAGAUUCUCGAGUUCUCCGAACGUGUCACGUUUUUUG-3ʹ; miR-874-3p mimics, 5′-CUGCCCUGGCCCGAGGGACCGA-3′; miR-NC, 5′-ACUACUGAGUGACAGUAGA-3′; miR-874-3p inhibitor, 5′-UCGGUCCCUCGGGCCAGGGCAG-3′; inhibitor NC, 5′-CAGUACUUUUGUGUAGUACAA-3′. SOX12 over-expression plasmid was constructed using the pcDNA3.1 vector (Invitrogen). Cell transfection was conducted by using Lipofectamine™ RNAiMAX (Invitrogen) according to the manufacturer's protocol. For transfection, U-2OS and 143B cells were seeded into a 6‑well plate at a density of 5 × 105 cells/well and transfected with above plasmids or vectors (50 nM) until they reached 70% confluence. Subsequent experiments were performed 48 h post-transfection.
A TRIzol reagent that was bought from Invitrogen was applied to isolate total RNA with the accordance of the manufacturer’s instructions. A PrimeScript RT Master Mix (TaKaRa, Dalian, China) was used to reversely transcribe the total RNA. PrimeScript RT reagent Kit and SYBR Premix Ex Taq (TaKaRa) and PrimeScript RT reagent Kit (TaKaRa) were employed to carry out the qRT-PCR according to the manufacturer’s instruction. GAPDH was acted as an internal control. The PCR primers used in our paper were as follows: GAPDH sense, 5′-GTCAACGGATTTGGTCTGTATT-3′ and reverse, 5′-AGTCTTCTGGGTGGCAGTGAT-3′; lncRNA CASC9 sense, 5′-GTTTAGAATGGAAGCAGCAAA-3′ and reverse, 5′-CAAAGCAATGGAAGCATGTA-3′; SOX12 sense, 5′-GTGAGCCAGGACGCAAC-3′ and reverse 5′-AACTGGGGAGCGAGGAG-3′; miR-874-3p sense 5′-CTGCCCTGGCCCGAGG-3′ and reverse 5′-GTTGTGGTTGGTTGGTTTGT-3′. After that, qPCR was carried out with ABI 7500 Real-Time PCR system (Applied Biosystems, Carlsbad, CA, USA). The expression fold changes were computed using 2−ΔΔCT method.
Following transfection, U-2OS and 143B cells (2 × 103 cells per well) were seeded into 96-well plates. At 0, 24, 48, and 72 h after cell culture, 10 μL MTT solution (Solarbio, Beijing, China) was added to each well. Following incubation for 2 h, the medium was dumped. Then, a Microplate Reader (Bio-Rad, Hercules, CA, USA) was used to validate the optical density at 490 nm.
Matrigel-coated transwell chamber (Corning Incorporated, NY, USA) was used to examine the cell invasion following the user guide. The upper chamber with serum-free medium was added with transfected cell lines (5 × 103). The lower chamber was added with complete culture medium. After 24 h of cell incubation, PFA (4%) was applied to fix the cell lines in the lower chambers, and the crystal violet solution was used to stain the invaded cell lines. Five fields were randomly selected for counting penetrating cells under microscope (Olympus, Tokyo, Japan).
Cells were seeded in 6-well plates at a density of 500 cells/well, and culture medium was replaced every 3 days. After 14 days of culture in DMEM containing FBS, cell colonies were fixed with 4% paraformaldehyde for 10 min, stained with 0.1% crystal violet for 20 min, and photographed. The number of cell colonies with more than 50 cells was counted in each dish. A light microscope, provided by Olympus (Tokyo, Japan), was proceeded to counter the numbers of colony cells.
The target miRNA and mRNA of lncRNA CASC9 were predicted using miRDB and TargetScan, respectively. Mutant (Mut) and wild-type (Wt) and luciferase reporter vectors of lncRNA CASC9 containing miR-874-3p binding sites were harvested from GenePharma (Shanghai, China). SOX12-WT and SOX12-Mut luciferase reporter vectors were also purchased from GenePharma. Luciferase reporter vectors were transfected into HEK293T cells and then cotransfected with miR-NC/miR-874-3p mimics using Lipofectamine 2000. A dual-luciferase reporter assay system (Promega, Madison, WI, USA), was performed to validate the relative luciferase activity after transfection for 48 h.
A Radioimmunoprecipitation assay (RIPA) buffer (500 μL) was used to prepare the protein lysates. SDS–polyacrylamide gels (10%) were then used to resolve the proteins (60 μg per sample), and PVDF membranes were used to electrotransfer them. Nonfat dry milk (5%) was subsequently applied to block the membranes in Tris-buffered saline with Tween 20 (TBST) for 1 h at room temperature. Next, primary antibody was employed to incubate them overnight at 4 °C. After that, the HRP-conjugated secondary antibody (Santa Cruz, USA) was used to culture the membranes for 1 h at room temperature. Target proteins (Abcam, MA, USA) were probed using anti-SOX12 (ab54371, 1:2500) and anti-GAPDH (ab8245, 1:1000). Proteins were visualized using a gel imaging system (Bio-Rad) with enhanced chemiluminescence reagent (Pierce Technology, IL, USA). Protein bands were semi-quantified using ImageJ software (version 1.8.0; National Institutes of Health).
BALB/c nude mice, 6-week-old and 18–20 g weighed, were bought from Vital River (Beijing, China). The mice were kept in specific pathogen-free conditions. This research was approved by the Animal Ethics Committee of Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College and complied with The Guide for the Care and Use of Laboratory Animals (no.20180006). Thereafter, the mice were divided into three groups ad libitum, including the shNC group, shCASC9 group or shCASC9 + miR-874 inh group (n = 6 per group). 143B cells (1 × 107 cells/1 mL PBS) transfected with shNC, shCASC9 or shCASC9 + miR-874-3p inhibitor were subcutaneously injected into the right back of mice, respectively. Then, tumor size was recorded on the 7th, 14th, 21th and 28th day, and the tumor volume (V) was defined weekly using the following formula: V = [length (L) × width (W)2]/2. On the 28th day after injection, mice were anesthetized by an intraperitoneal injection of 50 mg/kg pentobarbital sodium and then, killed by cervical dislocation. The tumors were collected and weighed.
After collecting xenograft tumor tissues, 4% paraformaldehyde was used to fix the tissues for 2 days, and then, paraffin was used to embed them. The sections were blocked in 1% BSA for 20 min and subsequently cultured with primary antibody against Ki67 (1:20, ab21700, Abcam) at 4 °C overnight. Next, A HSP-labeled Goat Anti-Rabbit IgG H&L (1:500, ab205718, Abcam) was applied to incubate the sections for 30 min at room temperature. Later, hematoxylin was used to counterstain the slides. The images were taken under microscopy (Nikon Microsystems, Tokyo, Japan).
All experiments were performed in triplicates and were repeated at least three times. SPSS 20.0 software (IL, USA) was used for statistical analyses. Data were presented as mean ± standard deviation (SD). Correlation among lncRNA CASC9, miR-874-3p and SOX12 expressions in OS tumor tissues was assessed via Pearson’s correlation analysis. Student’s t-test and one-way analysis of variance (ANOVA) followed by Dunnett’s multiple comparison were performed to analyze the differences among different groups. The relationship between CASC9 and clinicopathological features of the 30 cases of OS patients was determined via χ2 test. Kaplan–Meier method was performed to carry out the survival analysis. P < 0.05 was considered statistically significant.
First of all, OS tumor tissues (n = 30) and matched adjacent non-tumor tissues (n = 30) were collected in our research, and then, qRT-PCR was proceeded to detect the expressions of CASC9 in these tissues. Data from qRT-PCR showed that the expression of CASC9 in OS tumor groups was memorably higher than that in the normal groups (Fig. 1A, P = 0.0156). And, we found that the higher expression of CASC9 indicated the less percent survival of OS patients via Kaplan–Meier method (P = 0.0133, Fig. 1B). Besides, the expression of CASC9 in OS tumor tissues was associated with tumor size and TMN stage of OS patients (Table 1). In addition, the CASC9 expressions in human OS cell lines (MG63, 143B, SW1353, U-2OS and HOS), and human normal osteoblastic cell line hFOB1.19 were also assessed by qRT-PCR. Data from qRT-PCR showed that CASC9 was significantly over-expressed in OS cell lines compared to that in hFOB1.19 cell lines (P < 0.01, Fig. 3C). Among these five OS cell lines used in our research, CASC9 showed a highest expression in U-2OS cell line as well as a lowest expression in 143B cell line. Therefore, 143B and U-2OS cell lines were selected to proceed following analyses. These results confirmed that lncRNA CASC9 was highly expressed in OS and foreboded poor prognosis of OS patients.
In order to understand the effect of CASC9 on OS cell lines, the expression of CASC9 in 143B and U-2OS cell lines was disrupted by si-CASC9 construction and transfection. Data from qRT-PCR exhibited that CASC9 was significantly knocked down in si-CASC9 groups (Fig. 2A, P < 0.01). Then, MTT assay was operated to examine the cell proliferation of 143B and U-2OS that transfected with si-NC/si-CASC9 and the result of MTT assay is showed as Fig. 2B. The data showed that si-CASC9 transfection decreased CASC9 expressions both in 143B and U-2OS cells when compared to si-NC transfection group (Fig. 2B, P < 0.01). Colony formation assay was subsequently carried out to analyze the colony ability of U-2OS and 143B cell line. Similarly, the colony cell number of si-CASC9 groups was markedly lower than that of si-NC groups (Fig. 2C, P < 0.01). In addition, the cell invasion of 143B and U-2OS cell line that treated with si-CASC9 or si-NC transfection was assessed using transwell assay. It was confirmed that si-CASC9 transfection remarkably reduced invasion cell number compared to si-NC transfection both in 143B and U-2OS cell line (Fig. 2D, P < 0.01). The above data revealed that the silence of lncRNA CASC9 inhibited cell proliferation, colony formation and invasion in OS.
We predicted that miR-874-3p served as the target miRNA of lncRNA CASC9 by miRDB. To verify the prediction, CASC9-Mut and CASC9-WT luciferase reporter plasmids were established (Fig. 3A), and luciferase reporter assay was proceeded. As shown as Fig. 3B, miR-874-3p mimics transfection significantly cut down the relative luciferase activity after being transfected with CASC9-WT (P < 0.01). Then, the miR-874-3p expressions in OS tumor tissues and adjacent normal tissues were detected using qRT-PCR. Data from qRT-PCR displayed that the expression of miR-874-3p was observably decreased in OS tumor tissues compared with that in normal tissues (Fig. 3C, P = 0.0002). And, Fig. 3D showed that CASC9 expression level was negatively correlated with miR-874-3p expression level in OS tissues (P < 0.0001). Similarly, we confirmed that miR-874-3p was notably up-regulated with si-CASC9 transfection in U-2OS and 143B cell lines compared to si-NC transfection by qRT-PCR (Fig. 4E, P < 0.01). Data from qRT-PCR also showed that when the U-2OS and 143B cell lines was transfected miR-874-3p mimics plasmids, the expression level of miR-874-3p was dramatically enhanced (Fig. 4F, P < 0.01). Then, the cell proliferation ability of U-2OS and 143B cell lines that were treated with miR-874-3p mimics or miR-NC was detected through MTT assay, and the results of MTT assay are showed as Fig. 3G. When the expression of miR-874-3p was increased in the U-2OS and 143B cell lines, the cell proliferation ability was decreased (Fig. 3G, P < 0.01). Besides, colony formation assay was performed to examine the colony viability of U-2OS and 143B cell lines with miR-874-3p mimics/miR-NC transfection. Figure 3H revealed that miR-874-3p mimics transfection memorably repressed cell colony formation in OS cell lines compared to that miR-NC groups (P < 0.01). In addition, the cell invasion was tested using transwell assay and the results from transwell assay defined that the up-regulation of miR-874-3p in OS cell lines remarkably suppressed cell invasion ability (Fig. 3I, P < 0.01). These results proved that lncRNA CASC9 acted as a sponge of miR-874-3p in OS, and the expression of CASC9 in OS tissues and cell lines was negatively related to the expression of miR-874-3p. And, the over-expression of miR-874-3p could inhibited cell proliferation, colony formation and invasion in OS cell lines.
To further explore the function of lncRNA CASC9 in OS, we predicted that SOX12 acted as the downstream mRNA of miR-874-3p by Targetscan and the binding sites are showed as Fig. 4A. SOX12-Mut and SOX12-WT were established to verify the prediction. As shown as Fig. 4B, the relative luciferase activity of miR-874-3p mimics group was significantly inhibited after being SOX12-WT transfection (P < 0.01). Then, the SOX12 expressions in 30 paired of OS tumor tissues and matched normal tissues were assessed by qRT-PCR, and Fig. 4C showed that SOX12 was markedly over-expressed in OS tumor tissues compared to that in normal tissues (P = 0.0135). Data from Pearson’s correlation analysis revealed that SOX12 expression was positively correlated with CASC9 expression and negatively correlated with miR-874-3p expression in OS tissues (Fig. 4D, E). In addition, the results of Western blot displayed that in U-2OS and 143B cell lines, the down-regulation of CASC9 significantly inhibited SOX12 expression (Fig. 4F, P < 0.01) while the up-regulation of miR-874-3p dramatically promoted SOX12 expression (Fig. 4G, P < 0.01). These data demonstrated that lncRNA CASC9 modulated the miR-874-3p expression, thereby influencing the SOX12 expression in OS cells.
To further identify our conclusion, the U-2OS cell lines were divided into 6 groups (si-NC, si-CASC9, inhibitor NC, miR-874-3p inhibitor, si-CASC9 + inhibitor NC and si-CASC9 + miR-874-3p inhibitor) according to different treatments. Then, the cell proliferation and invasion were, respectively, detected using MTT assay and transwell assay. As shown as Fig. 5A, the inhibition of miR-874-3p significantly increased the reduction in cell proliferation of U-2OS cell lines caused by si-CASC9 transfection (P < 0.01). And, Fig. 5B showed that miR-874-3p inhibitor transfection effectively recovered the decrease in U-2OS invasion cell number that induced by CASC9 down-regulating (P < 0.01). Then, U-2OS cell lines were, respectively, transfected with miR-NC, miR-874-3p mimics, Vector, SOX12, miR-874-3p mimics + vector or miR-874-3p + SOX12 plasmids. Data from MTT assay and transwell assay, respectively, exhibited that SOX12 over-expression enhanced the reduction in U-2OS cell proliferation and invasion which were caused by miR-874-3p up-regulation (Fig. 5B, P < 0.01). In summary, above data demonstrated that lncRNA CASC9 promoted cell proliferation and invasion through regulating miR-874-3p/SOX12 in OS.
To evaluate the influences of CASC9 on OS in vivo, OS mice model was established and divided into three groups (shNC, shCASC9, shCASC9 + miR-874 inh) based on different treatments. The knockdown of CASC9 inhibited OS tumor volume, size and weight in vivo (P < 0.01, Fig. 6A–C). However, these effects were recovered when miR-874 was silenced at the same time. Then, IHC was performed to measure the expression of Ki-67. Data from IHC exhibited that the expression of Ki-67 was notably inhibited with shCASC9 transfection and enhanced with the co-transfection of shCASC9 and miR-874 inh (Fig. 6D). The above results elucidated that the silence of CASC9 could suppress OS tumor growth and proliferation through regulating miR-874 in vivo.
Given that the prognosis of OS is unsatisfactory, the development of novel strategies in its therapy is still of great significance. Increasing evidences are improving the understanding that lncRNAs play crucial roles in the pathogenesis and development of many cancers [19, 20]. Therefore, it is important to elucidate the molecular mechanism of prognosis-related lncRNAs and explore potential therapeutic targets. This study focused on the mechanistic involvement of CASC9 in OS. First, it was found that CASC9 silencing inhibited the proliferation and invasion of U-2OS and 143B cells. Moreover, the inhibition of miR-874-3p remarkably recovered cell proliferation and invasion in CASC9-silenced cells. Additionally, this study found that SOX12 is the target of miR-874-3p. SOX12 expression was increased in OS tissues; however, it was inhibited in OS cells by silencing CASC9 or over-expressing miR-874-3p. The results revealed an abundance of CASC9 through sponging miR-874-3p and positive regulation of SOX12 protein level, consequently contributing to the proliferation and invasion of OS cells. To our knowledge, this is the first study that reported the essential regulatory role of CASC9 and its underlying mechanism in OS progression. The role of a small number of tumor-suppressive and oncogenic lncRNAs in OS has been elucidated. For example, Wang et al. [21] proved that lncRNA SNHG16 was up-regulated in OS, and the down-regulation of SNHG16 declined cell proliferation, invasion and migration ability via inversely regulating miR-1301 expression level in OS cell lines. Guo et al. [22] indicated that lncRNA steroid receptor RNA activator 1 (SRA1) acted as an antitumor role in OS, it inhibited cell activities and enhanced cell apoptosis by targeting miR-208a. Yu et al. [23] reported that lncRNA Taurine up-regulated gene 1 (TUG1) was abnormally expressed in OS and it accelerated cell metastasis through modulating HIF-1α via sponging miR-143-5p. However, the functional role of lncRNA CASC9 played in OS was still unclear. LncRNA CASC9 was defined as an oncogene in various cancers, such as breast cancer [16], colorectal cancer (CRC) [18], non-small cell lung cancer (NSCLC) [24], and esophageal squamous cell carcinoma [25] but never in OS. In the present study, we firstly proved that CASC9 was over-expressed in OS. Specifically, the overall survival of OS patients with high CASC9 levels was shorter than that of those with low CASC9 levels. CASC9 knockdown led to restricted proliferation and invasiveness in OS cells. Additionally, CASC9 down-regulation impeded tumor growth in vivo. Our results may provide a new biomarker for OS diagnosis and treatment. Next, we explored the potential mechanism of lncRNA CASC9 in OS. One of the main functions of lncRNAs was acting as ceRNAs to regulate miRNA expressions [26, 27]. In our current study, miR-874-3p was predicted by miRDB as a potential target of CASC9. MiR-874-3p plays a tumor-suppressive role in different types of cancers, including ovarian cancer [28], hepatocellular carcinoma [29] and OS [30]. In OS, miR-874-3p is down-regulated and targets RGS4 to suppress cell proliferation and migration [30]. Consistently, our study also showed the down-regulation of miR-874-3p in OS and its inhibitory effects on OS cell proliferation and invasion. Herein, we established the ceRNA network that linked CASC9 with miR-874-3p. Notably, miR-874-3p absence could reverse the effect of silencing CASC9 on the proliferation and invasion of OS cells. Moreover, in vivo experiments showed that the suppressive effect of CASC9 silencing on tumor growth was partly blocked by inhibiting miR-874-3p, suggesting that the effect of CASC9 on OS was partly exerted by regulating the expression of miR-874-3p. However, the molecular mechanisms that underlie the tumor suppressive role of miR-874-3p remain unknown. In the present study, the putative binding sites of the miR-874-3p and SOX12 were predicted via bioinformatic analysis and verified by luciferase reporter assay. SOX12 was identified to be abnormal expressed in various cancers, such as hepatocellular carcinoma [31], CRC [32] and lung cancer [33]. SOX12 was also reported to be over-expressed and facilitated cell proliferation, migration and invasion in OS [34]. Consistently, our study revealed that SOX12 was over-expressed in OS tumor tissues. In addition, it was found that SOX12 exhibited a negative correlation with miR-874-3p in OS tissues and could be targeted by miR-874-3p in OS cells. Using a rescue experiment, we found that SOX12 restoration could neutralize the effects of miR-874-3p over-expression in OS. These results suggested that lncRNA CASC9 promoted cell invasion and proliferation by modulating SOX12 via miR-874-3p. In conclusion, we demonstrated that CASC9 exacerbates the oncogenicity of OS cells by targeting the miR-874-3p/SOX12 axis. Our findings offered a novel insight of the therapeutic strategy in OS. | true | true | true |
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PMC9586079 | Wen Li,Zhifeng Wang,Hanlin Wang,Jian Zhang,Xiaobin Wang,Shaojun Xing,Si Chen | IQGAP3 in clear cell renal cell carcinoma contributes to drug resistance and genome stability | 18-10-2022 | Clear cell renal clear cell carcinoma,IQGAP3,Drug resistance,Genomic stability,DNA damage repair | Background Clear cell renal clear cell carcinoma (ccRCC) is resistant to most chemotherapeutic drugs and the molecular mechanisms have not been fully revealed. Genomic instability and the abnormal activation of bypass DNA repair pathway is the potential cause of tumor resistance to radiotherapy and chemotherapy. IQ-motif GTPase activating protein 3 (IQGAP3) regulates cell migration and intercellular adhesion. This study aims to analysis the effects of IQGAP3 expression on cell survival, genome stability and clinical prognosis in ccRCC. Methods Multiple bioinformatics analysis based on TCGA database and IHC analysis on clinical specimens were included. Quantitative real-time polymerase chain reaction (qRT-PCR) and western blot (WB) were used to determine protein expression level. MTT assay and 3D spheroid cell growth assay were used to assess cell proliferation and drug resistance in RNAi transfected ccRCC cells. Cell invasion capacity was evaluated by transwell assay. The influence of IQGAP3 on genome instability was revealed by micronuclei number and γ H2AX recruitment test. Results The highly expressed IQGAP3 in multiple subtypes of renal cell carcinoma has a clear prognostic value. Deletion of IQGAP3 inhibits cell growth in 3D Matrigel. IQGAP3 depletion lso increases accumulated DNA damage, and improves cell sensitivity to ionizing radiation and chemotherapeutic drugs. Therefore, targeting DNA damage repair function of IQGAP3 in tumorigenesis can provide ideas for the development of new targets for early diagnosis. | IQGAP3 in clear cell renal cell carcinoma contributes to drug resistance and genome stability
Clear cell renal clear cell carcinoma (ccRCC) is resistant to most chemotherapeutic drugs and the molecular mechanisms have not been fully revealed. Genomic instability and the abnormal activation of bypass DNA repair pathway is the potential cause of tumor resistance to radiotherapy and chemotherapy. IQ-motif GTPase activating protein 3 (IQGAP3) regulates cell migration and intercellular adhesion. This study aims to analysis the effects of IQGAP3 expression on cell survival, genome stability and clinical prognosis in ccRCC.
Multiple bioinformatics analysis based on TCGA database and IHC analysis on clinical specimens were included. Quantitative real-time polymerase chain reaction (qRT-PCR) and western blot (WB) were used to determine protein expression level. MTT assay and 3D spheroid cell growth assay were used to assess cell proliferation and drug resistance in RNAi transfected ccRCC cells. Cell invasion capacity was evaluated by transwell assay. The influence of IQGAP3 on genome instability was revealed by micronuclei number and γ H2AX recruitment test.
The highly expressed IQGAP3 in multiple subtypes of renal cell carcinoma has a clear prognostic value. Deletion of IQGAP3 inhibits cell growth in 3D Matrigel. IQGAP3 depletion lso increases accumulated DNA damage, and improves cell sensitivity to ionizing radiation and chemotherapeutic drugs. Therefore, targeting DNA damage repair function of IQGAP3 in tumorigenesis can provide ideas for the development of new targets for early diagnosis.
According to the pathological features, renal carcinoma can be divided into clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (PRCC), chromophobe renal cell carcinoma (CRCC) and a few tumors found in the kidney (collecting duct carcinoma, medullary renal cell carcinoma and urothelial carcinoma) (Jonasch, Gao & Rathmell, 2014). The transparent or eosinophilic cytoplasm is a typical feature in ccRCC, which accounts for about 70% of all renal cell carcinomas (Jonasch, Walker & Rathmell, 2021). Although early local ccRCC can be treated by partial or radical nephrectomy, ablation or regular radiation (Ljungberg et al., 2015; Pierorazio et al., 2015), up to one-third of patients develop into metastatic renal cell carcinoma which is difficult to conventional chemotherapy (Ljungberg et al., 2015; Hsieh et al., 2017). Abnormal DNA damage response (DDR) causes genomic instability to promote tumorigenesis. DNA mismatch repair (MMR) is suppressed in ccRCC by several ways: (1) The regulation of histone deacetylase HDAC6 by VHL gene deletion and ubiquitin-proteasome dependent MSH2 degradation (Dere et al., 2015; Zhang et al., 2014); (2) the haploid dose deficiency of MLH1 caused by deletion of chromosome 3p fragment (Wang et al., 2012); (3) the weakened MSH6 recruitment and transcriptional coupled repair by H3K36me3 depletion, which acts as a recognition target (Jonasch, Walker & Rathmell, 2021; Li et al., 2013). Homologous recombination (HR) repair is also suppressed in ccRCC due to the loss of VHL protein or ubiquitination modification (Metcalf et al., 2014). Inhibition of hyperactive DDR enhances the sensitivity of tumors to chemotherapy drugs causing DNA double-strand break (DSB, such as ionizing radiation, bleomycin and cisplatin), and minimize non targeted toxicity to normal tissues (Ferguson et al., 2015). The therapeutic strategy is to target key repair signals and promote cell death by increasing the number of DSBs (Pilie et al., 2019). The IQ-motif GTPase activating protein (IQGAP) includes IQGAP1, IQGAP2 and IQGAP3 in mammalian cells, which are closely related to intercellular adhesion, cell division, cell movement and migration, endocytosis and exocytosis (Shannon, 2012; Noritake et al., 2005; Brown & Sacks, 2006; White, Erdemir & Sacks, 2012). These isoforms share similar domains and bind the Rho family member CDC42 in a GTP-dependent manner to regulate the actin cytoskeleton (Mosaddeghzadeh et al., 2021; Briggs & Sacks, 2003). IQGAP2 and IQGAP3 have unique functions compared with IQGAP1. IQGAP2 contains all the domains of IQGAP1 with diverse interaction partners. Different from IQGAP1, IQGAP2 binds CDC42 but not RhoA or RAS (Brill et al., 1996). The tissue distribution and subcellular localization between the three isotypes showed significant difference. IQGAP1 was expressed in almost all tissues and mainly distributed at the cell contact sites at the cell edge, while IQGAP2 was significantly expressed in liver, stomach, platelets, prostate, kidney, thyroid, stomach, testis and salivary gland, and showed strong intranuclear localization (Briggs & Sacks, 2003; Yamashiro, Noguchi & Mabuchi, 2003; White, Brown & Sacks, 2009). IQGAP3 is mainly expressed in brain, testis, small intestine, lung and colon (White, Brown & Sacks, 2009). The function of IQGAP3 involved in DNA damage repair has been gradually revealed. In lung cancer, IQGAP3 directly bind repair protein Rad17 to regulate its expression and localization at the DNA damage site, so as to promote DNA repair (Zeng et al., 2020). In cervical cancer, IQGAP3 regulates cell cycle and promotes genome stability through MMS19/XPD/CAK axis (Leone et al., 2021). Compared with the other two widely studied isoforms, only IQGAP3 showed increased expression in different subtypes of renal cancer than normal tissue, indicating its general function in renal cancer. More studies are needed to elucidate the interaction partners and biological roles of IQGAP3. The expression of IQGAP3 in renal cell carcinoma, its correlation with prognosis or chemoradiotherapy sensitivity, the molecular mechanism involved in tumor malignant progression will help further biomarkers identification and combination therapy exploration.
Seven pairs of tumor and adjacent normal tissues were collected from the department of urology, Henan Provincial People’s Hospital. The study was approved by the medical ethics committee of Henan Provincial People’s Hospital (No. 2019074) following the Declaration of Helsinki. The experiments were undertaken with the understanding and written consent of each subject. The participants allowed the researchers to use their tissue during the tumor resection and conduct the study accordingly. The patient information was listed in Table 1.
Human clear cell adenocarcinoma cell lines 786-O and ACHN were seeded at 37 °C and 5% CO2 in RPMI-1640 and DMEM medium, respectively. All mediums were supplemented with 10% FBS and 1% Penicillin/Streptomycin. The cell lines were obtained from Shanghai Zhong Qiao Xin Zhou Biotechnology and were went through mycoplasma testing every month. The Lipofectamine RNAiMAX reagent (Invitrogen) was used to transfect siRNAs (50 nM) for 72 to 96 h. The siRNA sequences targeting IQGAP3 were as follows: siIQGAP3-1#: 5′-CGUCCGAACUGGCCAAAUA-3′; siIQGAP3-2#: 5′-GGGUGUGGCUGUCAUGAAA-3′.
The human IQGAP3 antibody was obtained from Proteintech (25930-1-AP, 1:1000). Human GAPDH antibody was obtained from Proteintech (10494-1-AP, 1:1000). Human Integrin Alpha 6 antibody was obtained from Proteintech (27189-1-AP, 1:1000). Human Twist antibody was obtained from Proteintech (11752-1-AP, 1:1000). Human Slug antibody was obtained from Proteintech (12129-1-AP, 1:1000). Human Vimentin antibody was obtained from Proteintech (10366-1-AP, 1:1000). Human Phospho-H2AX-S139 antibody was obtained from Abclonal (AP0687, 1:1000).
The collected cells were centrifuged and lysed in RIPA buffer (150 mM NaCl, 50 mM Tris–HCl (pH 7.4), 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS) with 1% PMSF for 30 min. The supernatant was separated by 13,000 g centrifugation at 4 °C for 20 min. The protein samples were denatured at 100 °C for 10 min and loaded in SDS-PAGE gel. After being transferred to a PVDF membrane, the blocking was performed with 5% skimmed milk for at room temperature 1 h. The membrane was incubated overnight at 4 °C with the primary antibodies and incubated at room temperature with the secondary antibody for 1 h. Signal detection was performed by enhanced chemiluminescence (PerkinElmer).
Cell proliferation was detected by MTT assay. Briefly, 2 × 103 cells were seeded in 96 well plate for 1 to 6 days. MTT reagent (Sigma-Aldrich) was added at the concentration of 5 mg/ml, followed by 4 h incubation at 37 °C. The culture supernatant was discarded and 100 µl DMSO (Sigma-Aldrich) was added. After 10 min of oscillation, 490 nm wavelength was selected on the enzyme-linked immunosorbent monitor to measure the light absorption value. For the ionizing radiation sensitivity test, 200 to 5000 cells were seeded in six well plate, followed by several doses of X-ray irradiation. The number of clones was counted after 14 days of cell culture. For drug sensitivity test, 2000 cells were seeded in 96 well plate with several dosed of cisplatin, camptothecin and doxorubicin (Selleck). The surviving cells were measured by MTT method after 48 h of culture.
For the 3D spheroid cell growth assay, 1 ×103 cells were seeded in 24 well plates with ultra-low protein adsorption. The cell images were taken and recorded by light microscope after 6 days, 12 days and 14 days.
IHC staining was performed on renal cell carcinoma tissue. Tissue sections were dewaxed in xylene and rehydrated in graded ethanol (100%, 95%, 80% and 70% ethanol for 10 min). The antigen was recovered by heat induced epitope recovery method. The slices were treated by 10 mmol/l EDTA (pH 8.0) at 98 °C for 15 min. 3% hydrogen peroxide were used in methanol at 37 °C for 15 min to quench the endogenous peroxidase activity, followed by blocking with 5% bovine serum albumin. The incubation condition of primary antibody was 4 °C overnight.
The growing cells were inoculated on the sterilized coverslips and cultured at 37 °C and 5% CO2 for 24 h. The medium was removed when the cell grew to about 70% and washed with PBS. The cells were fixed at room temperature with 4% paraformaldehyde for 15 min. After washing with PBS for three times, Triton was added at the final concentration of 0.3% to treat the cell at room temperature for 20 min. After blocking in 5% BSA for 30 min, the cells were stained with primary antibodies (diluted in 1% BSA) at room temperature for 2 h. Cells were washed with PBST (PBS with 0.1% Tween-20) three times and incubated with fluorescence-conjugated secondary antibodies at room temperature for 1 h. After being washed three times with PBST, cells were mounted with antifade mounting medium with 2-(4-Amidinophenyl)-6-indolecarbamidine dihydrochloride (DAPI). The slides were observed by confocal microscope, and the fluorescence intensity were calculated by ImageJ software.
The sequencing results (HTSeq FPKM data) were obtained and analyzed from TCGA database (https://portal.gdc.cancer.gov/). IQGAP3 expression between normal and tumor tissues was analyzed by UALCAN database (http://ualcan.path.uab.edu). Survival analysis between patients with low and high IQGAP3 expression was performed by Kaplan Meier database (https://kmplot.com/analysis/). Timer database (http://timer.cistrome.org/) was used to explore the relationship between IQGAP3 expression and immune infiltration. Student t-test and log rank test were used for data statistics.
Total RNA isolation by Trizol, mRNA enrichment with oligo DT magnetic beads, mRNA fragmentation and cDNA synthesis were all processed according to the manufacturer’s protocol. The cDNA was complemented and repaired. After amplification, the RNA library was sequenced by Illumina pe150 in Shenzhen Haplox company. The reference genome used was GRCh37 (hg19). The FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) of each gene was calculated according to gene length. Differential expression analysis was performed using the DESeq R package (Yu et al., 2012).
Each experiment was validated by three independent replicates. Unpaired two tailed Student t-test was used to analyze the statistical significance. The experimental values are expressed as the mean ± standard deviation (SD). Statistical significance was analyzed by GraphPad Prism 6.0 software (ns, P > 0.05; *, P < 0.05; **, P < 0.01; ***, P < 0.001).
The expression analysis of TCGA database showed that there was no significant difference in the expression of IQGAP1 in the cancer and adjacent tissues of the three renal cancer subtypes (data were not shown). IQGAP2 was downregulated in tumor tissues of kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP), and upregulated in tumor tissues of kidney chromophobe (KICH) (data were not shown). The mRNA sequencing data of IQGAP3 from 730 adjacent normal tissues and 10,363 tumor tissues in TCGA pan cancer database were extracted. Comparing unpaired samples and paired samples, IQGAP3 was highly expressed in most tumor types (Figs. 1A, 1B). All the three subtypes of renal cell carcinoma showed significantly enhanced IQGAP3 expression in tumor tissues than that in normal tissues (Fig. 1C). Due to the small number of samples contained in KICH, the followed bioinformatics analysis mainly focused on the common subtypes KIRC and KIRP. Gene expression profiles were obtained by high-throughput gene array analysis in GEO database (https://www.ncbi.nlm.nih.gov/geoprofiles/). The expression of IQGAP3 in 27 pair (Series: GSE66272) and 72 pair (Series: GSE53757) of ccRCC tumor tissues and matched normal tissues at different disease stages were extracted and analyzed. IQGAP3 increased significantly in tumor tissues at different stages (Figs. 1D, 1E). Interestingly, IQGAP3 was highly expressed in both cancer and adjacent paired samples with (13 pairs of samples, Series: GSE66271, Fig. 1F) or without metastasis (14 pairs of samples, Series: GSE66270, Fig. 1G). In patients with metastatic ccRCC, the expression is higher in tumor tissue than normal tissue. Immunohistochemistry also showed that the expression of IQGAP3 was high in tumor tissues, which is consistent with the analysis of the database (Fig. 1H).
According to clinical features in TCGA database, the higher the TNM (tumor-node-metastasis) stage, histological grade and pathological stage, the higher IQGAP3 expression in KIRC and KIRP (Figs. 2A, 2B). 265 ccRCC tumor samples from GEO database were compared (Series: GSE73731). The expression of IQGAP3 was higher in high-grade samples (Fig. 2C). The diagnostic value of IQGAP3 mRNA level was evaluated by ROC (receiver operating characteristic) curve and the area under the ROC curve (AUC). The AUC value of IQGAP3 were 0.934 and 0.939 in KIRC (Fig. 2D) and KIRP (Fig. 2E) respectively, which showed high diagnostic value. The mRNA expression of IQGAP3 have similar diagnostic value in different stages and grades.
Kaplan–Meier analysis showed that the high expression of IQGAP3 was correlated with low OS (Overall Survival), DSS (Disease Specific Survival) and PFS (Progression Free Survival) both in KIRC and KIRP (Figs. 3A, 3B). The “immune gene” module of Timer database was used to explore the relationship between IQGAP3 expression and immune infiltration. In KIRC, IQGAP3 expression was positively correlated with infiltrated Th2 cells, Treg cells, NK (CD56+) cells, Th1 cells, aDC cells, T cells, macrophages and B cells, but negatively correlated with iDC cells, NK cells, Tgd cells, pDC cells, Th17 cells and mast cells (Fig. 3C). In KIRP, IQGAP3 expression was positively correlated with infiltrated Th2 cells, aDC cells, pDC cells and T helper cells, as well as a negative correction with DC cells, cytotoxic cells, neutrophils, Tem cells, CD8 T cells, mast cells, iDC cells, eosinophils and macrophages (Fig. 3D).
IQGAP3 depletion was performed by siRNA transfection in ccRCC cell lines 786-O and ACHN (Fig. 4A). Cell proliferation was reduced after IQGAP3 depletion in both cell lines (Fig. 4B). Cell clone formation and cell metastasis were not affected (data were not shown). The cells present cell agglomerates in the 3D cell culture dish (Fig. 4C). At the early stage of culture (day 6), IQGAP3 knockdown had no significant effect on the growth of cell spheres. At the late stage of culture (day 12), deletion of IQGAP3 significantly inhibited the growth of 3D cell spheres (Fig. 4D). Cell growth in 3D culture was significantly suppressed after IQGAP3 depletion after 14 days both in 786-O and ACHN cells (Fig. 4E).
In the process of cell mitosis, some chromosome breaks will produce centromere-free chromosome fragments, which are wrapped in the nuclear membrane to form a micronucleus (MN) structure with a diameter less than 1/3 of the normal nucleus. In order to determine the effect of IQGAP3 on genomic stability in ccRCC, the formation of MNs was counted after 4 Gy ionizing radiation (IR) and recovery for 4 h (Fig. 5A). After IQGAP3 knockdown, the number of MNs in cells increased regardless of IR treatment (Fig. 5B). Phosphorylation of serine (Ser) at position 139 of histone H2AX (γ H2AX) is considered to be a marker of DNA breakage (Fig. 5C). As the initial signal molecule of damage induction, γ H2AX recruits a series of DNA damage repair proteins at the damage site to start the DNA repair cascade. After IR irradiation, the intensity and quantity of γH2AX were increased in 786-O and ACHN cells (Figs. 5D, 5E). With the increase of recovery time after radiation, DNA damage in IQGAP3 knockdown cells accumulated continuously (Fig. 5F). These results show that IQGAP3 can promote genome stability.
The cells were treated with different doses of IR, and the cell survival was counted. After IQGAP3 knockdown, the cell survival rate decreased significantly and the cell radiosensitivity increased (Fig. 6A). The three common chemotherapeutic drugs, cisplatin, camptothecin and doxorubicin were selected to test IC50 value. The sensitivity of cells to these drugs increased after IQGAP3 knockdown (Fig. 6B). The results show that IQGAP3 promotes genome stability, which may be the reason for chemoradiotherapy resistance of ccRCC.
In order to study the signal pathways of IQGAP3 stabilizing genome and then participating in the inhibition of 3D growth in ccRCC, the IQGAP3 knockdown cells were sequenced to compare the global gene expression profile. In total of 462 differentially expressed genes under the condition of fold change ≥ 2 and p < 0.05 were found, including 278 upregulated genes and 184 downregulated genes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for gene function research of these differentially expressed genes. The bubble chart displayed the affected genes were enriched in extracellular matrix (ECM)-receptor interaction, drug metabolism, regulation of actin cytoskeleton and cell adhesin molecules after IQGAP3 depletion (Fig. 6C). These results provide potential mechanistic insights into the promotion role of IQGAP3 in cell 3D growth and drug resistance in ccRCC. However, the interacting proteins and specific regulatory mechanisms need to be further studied.
IQGAP scaffold protein is evolutionarily conservative in eukaryotes and contributes to the regulation of cytoskeleton, intracellular signal transduction and intercellular interaction. IQGAPs are usually used as scaffold proteins, which is related to different cytoskeleton content. Single molecule imaging has confirmed that the combination of IQGAP1 and its binding proteins with actin can promote cell migration and adhesion (Hoeprich et al., 2022). Although the three proteins of IQGAP family have similar structure and high sequence homology, the difference of their binding proteins lead to the regulation difference of downstream signal in normal or disease state. The interaction of RHO-GTPase with IQGAPs is selective. IQGAP1 and IQGAP2 bind CDC42 and RAC1, but not RIF, RHOD or RHO-like proteins (Mosaddeghzadeh et al., 2021). Through the binding to E-cadherin and β-catenin, IQGAP1 reduces the interaction between cadherin system and cytoskeleton to weaken the cell–cell attachment (Kuroda et al., 1998). Ca2+ enhances the affinity of calmodulin and IQGAP1, reducing the transcriptional activity of β-catenin and E-cadherin dependent adhesion (Briggs, Li & Sacks, 2002; Li et al., 1999). Based on the above targets, IQGAP1 was found to promote invasion in tumors by attenuating E-cadherin-dependent cell adhesion (Li et al., 1999). The core factors Raf, MEK and ERK1/2 in the mitogen activated protein kinase (MAPK) pathway promote phosphorylation dependent signaling cascades by directly binding IQGAP1 (Ren, Li & Sacks, 2007). Disruption the interaction of IQGAP1 with ERK1/2 inhibits Ra s and RAF driven tumorigenesis (Jameson et al., 2013). IQGAP1 enhances the nuclear localization of β-catenin through its interaction with pathway proteins, thereby mediating the activation of cytoplasmic Wnt signaling (Goto et al., 2013). IQGAP1 is involved in the construction of the whole PI (3) k-Akt pathway, and the blocking of its interaction with PI (3) K inhibit tumor cell survival (Choi et al., 2016). Therefore, IQGAP1 plays an important role in cancer development, and anti-tumor therapy targeting IQGAP1 interacting proteins or related pathways may be beneficial for tumor therapy. IQGAPs anchored on the lipid membrane stabilize a single actin filament in a curved shape, helping to form a highly curved complete actin ring (Palani et al., 2021). These fine structural and biophysical calculations seem to indicate that the regulation of the actin cytoskeleton by IQGAP protein family takes place in the cytoplasm, and its molecular mechanism in the nucleus remains to be explored. The high expression of IQGAP3 promotes malignant processes such as tumor growth and invasion with different downstream signal pathways in many types of tumors, and recent studies have found that it is related to the treatment outcome. Unlike the oncogene IQGAP1, IQGAP2 is considered to be a tumor suppressor (Smith, Hedman & Sacks, 2015). The disruption of IQGAP2 in mice promoted the occurrence of hepatocellular carcinoma and was reversed by the deletion of both IQGAP1 and IQGAP2, indicating the opposite biological effects of the two isoforms (Schmidt et al., 2008). Decreased expression of IQGAP2 in prostate cancer promotes cell proliferation by activating Akt (Xie et al., 2012). Loss of IQGAP2 expression in gastric cancer promotes invasion and is associated with promoter methylation (Jin et al., 2008). The possible role of IQGAP3 in tumors is related to tumor types, and the mechanism research is still in the initial stage. IQGAP3 has been shown to be upregulated in breast cancer, pancreatic cancer, gastric cancer, hepatocellular carcinoma, colorectal cancer and bladder cancer, and is closely related to clinicopathological features, suggesting that it may be involved in tumor development (Hua et al., 2020; Xu et al., 2016; Shi et al., 2017; Huang et al., 2021; Cao et al., 2019; Xu et al., 2019). Several studies have found that IQGAP3 promotes cell growth and proliferation, cytoskeleton remodeling, cell migration and adhesion (Huang et al., 2021; Jinawath et al., 2020; Liu et al., 2020; Lin et al., 2019; Nojima et al., 2008). The expression level of IQGAP3 in radiation resistant breast cancer was higher than that in radiosensitivity group, which may be related to DNA repair and PI3K-Akt-mTOR signal pathway (Hua et al., 2020). In lung cancer, the interaction of IQGAP3 with DNA repair protein Rad17 was essential for Rad17 expression and foci formation, the Mre11-Nbs1-Rad50 complex formation, and ATM/Chk2 and ATR/Chk1 pathways activation (Zeng et al., 2020). IQGAP3 was also found to modulate cell cycle progression and genome stability through the interaction with MMS19 and regulation of MMS19/XPD/CAK axis (Leone et al., 2021).As the latest studied protein in family members, the unique function of IQGAP3 in different tumors remains to be verified. In our study, the deletion of IQGAP3 can significantly increase the genomic instability and improve the sensitivity of cells to radiation and chemical drugs. Therefore, looking for hyperactive DNA damage repair pathways and participating proteins is a new idea to further elaborate the special metabolic reprogramming of renal cell carcinoma cells and their resistance to traditional radiotherapy and chemotherapy.
In this work, IQGAP3 was overexpressed not only in many tumor types, but also in the three common subtypes of renal cell carcinoma. The higher the expression of IQGAP3 in patients with TNM or later clinical stage, and the higher the protein expression has a strong positive correlation with the poor survival rate. This suggests that IQGAP3 has good prognostic value in renal cell carcinoma and inhibitors of IQGAP3 function may prevent tumor invasion, proliferation and migration. It is a potential new biomarker and therapeutic target. IQGAP3 can not only regulate tumor 3D growth, but also cause drug resistance by stabilizing the genome and reducing the accumulation of DNA damage during radiotherapy and chemotherapy. The further excavation of the function of IQGAP3 in DNA damage repair is the embodiment of the application of the concept of synthetic lethality in tumor treatment, which will help to guide the clinical practice of precise individual treatment.
10.7717/peerj.14201/supp-1 Click here for additional data file. | true | true | true |
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PMC9586211 | Betul Copur,Serkan Surme,Ugurcan Sayili,Gulsah Tuncer,Melike Nur Ozcelik,Hulya Yilmaz-Ak,Muge Topal,Sumeyye Ustun-Al,Filiz Pehlivanoglu,Gonul Sengoz | Blood types (ABO/Rhesus) and SARS-CoV-2 infection: a retrospective, cross-sectional study in 2828 healthcare workers | 17-10-2022 | ABO blood groups,healthcare workers,Rhesus status,SARS-CoV-2 | Background: The authors aimed to investigate the relationship between ABO/Rhesus blood types and the risk of SARS-CoV-2 infection and hospitalization in healthcare workers (HCWs). Materials & methods: This study compared HCWs with (n = 510) and without (n = 2318) SARS-CoV-2 infection. Risk factors for SARS-CoV-2 infection and hospitalization in HCWs were shown as odds ratios with 95% CI. Results: Blood group O was found to be protective by 20% from the risk of developing SARS-CoV-2 infection in HCWs (29.2 vs 33.8%; odds ratio: 0.808; 95% CI: 0.655–0.996; p = 0.045). The prevalence of group O was lower in hospitalized patients than in outpatients (25 vs 29.5%; p = 0.614). Conclusion: These findings suggest that blood groups are associated with the development of SARS-CoV-2 infection. | Blood types (ABO/Rhesus) and SARS-CoV-2 infection: a retrospective, cross-sectional study in 2828 healthcare workers
Background: The authors aimed to investigate the relationship between ABO/Rhesus blood types and the risk of SARS-CoV-2 infection and hospitalization in healthcare workers (HCWs). Materials & methods: This study compared HCWs with (n = 510) and without (n = 2318) SARS-CoV-2 infection. Risk factors for SARS-CoV-2 infection and hospitalization in HCWs were shown as odds ratios with 95% CI. Results: Blood group O was found to be protective by 20% from the risk of developing SARS-CoV-2 infection in HCWs (29.2 vs 33.8%; odds ratio: 0.808; 95% CI: 0.655–0.996; p = 0.045). The prevalence of group O was lower in hospitalized patients than in outpatients (25 vs 29.5%; p = 0.614). Conclusion: These findings suggest that blood groups are associated with the development of SARS-CoV-2 infection.
Identifying risk factors for COVID-19 caused by SARS-CoV-2 and severe disease is vital for the prevention of mortality and morbidity. Although age, comorbid diseases and smoking history are associated with the severity of COVID-19, it is thought that genetic factors may also be effective in the host thromboinflammatory response in patients with COVID-19 [1,2]. The relationship between viral infections and ABO blood groups has been known before [3,4]. For example, in the Middle East respiratory syndrome coronavirus epidemic, it was shown that those with the blood group O were less infected with the SARS coronavirus [5]. There are studies reporting a lower prevalence of COVID-19 in blood group O and a higher prevalence in blood group A. In addition, some studies suggest that severe/critical disease frequency is lower in blood type O as well [6–10]. Moreover, some studies found a relationship between the presence of anti-A and mild COVID-19 [11,12]. Another study reported that the differences between cytokine levels in some blood types may be critical for poor outcomes such as the need for the intensive care unit (ICU) and mechanical ventilation support or death due to COVID-19 [13]. Although the above-mentioned studies have reported that ABO/Rhesus (Rh) blood groups are associated with the risk of SARS-CoV-2 infection and disease severity, a limited number of studies show the relationship between SARS-CoV-2 infection and blood groups in healthcare workers (HCWs). In this study, the authors aimed to investigate the relationship between ABO/Rh blood types and the risk of SARS-CoV-2 infection and hospitalization in HCWs in a tertiary hospital.
This single-center, retrospective study was carried out between 11 March 2020 and 15 January 2021 at Haseki Training and Research Hospital (Istanbul, Turkey) when there was no vaccination program for COVID-19 in the country yet. The authors' 800-bed-capacity tertiary care hospital was designed as a pandemic epicenter during the COVID-19 crisis. A total of 3082 HCWs who were employed at Haseki Training and Research Hospital were evaluated. Two-hundred fifty-two HCWs were excluded due to missing ABO/Rh blood group data. In addition, two HCWs who participated in the vaccine trial were excluded. Finally, 2828 HCWs met the inclusion criteria (Figure 1). The blood group distributions of the study group comprising HCWs in the hospital were comparable with those of the individuals in the province and country (Figure 2) [14,15]. Demographic characteristics, blood groups, smoking status, chronic diseases and SARS-CoV-2 PCR results of the HCWs participating in the study were obtained retrospectively from the hospital system. HCWs with SARS-CoV-2 infection (n = 510) were compared with those who were not infected (n = 2318). To determine the risk factors for hospitalization, HCWs with SARS-CoV-2 infection were divided into groups as hospitalized (n = 28) and nonhospitalized (n = 482), and the groups were compared using statistical methods. All patients with pneumonia were hospitalized and followed up in the hospital.
Patients with symptoms in whom a SARS-CoV-2 PCR was positive in respiratory samples and/or patients with COVID-19-specific radiological findings were classified as having SARS-CoV-2 infection. Negative samples were repeated after 48–72 h. According to the guidelines issued by the General Directorate of Public Health of the Turkish Ministry of Health for COVID-19, patients with at least one of the signs and symptoms of fever or acute cough and respiratory distress, a history of traveling abroad in the past 14 days or of close contact with a confirmed COVID-19 case or the presence of hospitalization for severe acute respiratory infections and the absence of an explanation for the clinical manifestation with another cause/disease were defined as probable cases. Patients diagnosed with SARS-CoV-2 infection with respiratory distress (respiratory rate per minute <24, saturation >93%) and no pulmonary involvement were considered to have uncomplicated SARS-CoV-2 infection. Patients with fever, muscle/joint pain, cough, sore throat and nasal congestion, tachypnea (≥30/min), SpO2 less than 90% on room air, poor prognostic values in blood work on admission (lymphocyte count <800/μl, C-reactive protein >40 mg/l, ferritin >500 ng/ml, d-dimer >1000 ng/ml etc.) and bilateral diffuse pneumonia on chest radiography or CT scan were defined as having severe pneumonia. Lung involvement without evidence of severe pneumonia was classified as mild-to-moderate pneumonia [16].
Samples were obtained with synthetic fiber swabs and placed in a sterile transfer tube containing a viral nucleic acid transport medium (Biospeedy, Bioexen, Istanbul, Turkey). Samples were stored at 2–8°C until they were transferred to the microbiology laboratory and processed. Since the maximum time for SARS-CoV-2 RNA extraction in transport tubes is 5 min, the PCR step was performed without intermediate processing. The Biospeedy SARS-CoV-2 Double Gene RT-qPCR, version 4 (Biospeedy, Bioexen, Istanbul, Turkey) kit was used with a step reverse transcription-PCR targeting the SARS-CoV-2-specific N gene and the ORF1AB gene region. The oligo mix contained an extraction control and an internal control targeting the human RNase P gene. Negative and positive controls were used for DNA isolation. A cycle threshold value of less than 40 was considered to be positive.
IBM SPSS Statistics for Windows was used for statistics. Categorical variables were expressed as frequencies (n) and percentages (%), while numerical variables were expressed as medians (interquartile range). The Mann–Whitney U test, since normal distribution conditions were not met in the analysis of continuous variables; χ2 test; or Fisher's exact test was used in the analysis of categorical variables. The relative risk was shown as the odds ratio (OR). p < 0.05 was considered statistically significant.
A total of 2828 HCWs were included in the study. Of those, 59.3% (n = 1678) were female, 40.7% (n = 1150) were male and the median age was 30 years (26–40). A total of 1187 (42%) patients had blood group A, 493 (17.4%) had blood group B, 215 (7.6%) had blood group AB, 933 (33%) had blood group O and 2464 (87.1%) were Rh-positive (Table 1). The distribution of HCWs was as follows: 650 (23%) doctors, 1130 (40%) nurses/medical technicians, 368 (13%) security/medical secretaries, 622 (22%) cleaning personnel and 58 (2%) others (Table 2). Among HCWs, 510 (18%) people were infected with SARS-CoV-2, while 28 (1%) were hospitalized. Age (p = 0.627) and sex (p = 0.098) characteristics were similar in the groups with and without SARS-CoV-2 infection. Of 1416 HCWs, 877 (61.9%) had never smoked. The incidence of SARS-CoV-2 infection was similar among nonsmokers, active smokers and ex-smokers (p = 0.561). The frequency of chronic disease in HCWs who had SARS-CoV-2 infection was significantly lower than that in HCWs who were not infected with SARS-CoV-2 (11.4% vs 27.4%; p < 0.001). The prevalence of chronic diseases such as hypertension (7.1% vs 15.8%; p < 0.001), coronary artery disease (0.8% vs 4.9%; p < 0.001) and diabetes mellitus (2.7% vs 10.0%; p < 0.001) was lower in those with SARS-CoV-2. The proportions of HCWs carrying Rh (p = 0.731), A (p = 0.278), B (p = 0.690) and AB (p = 0.335) groups were similar in groups with and without SARS-CoV-2 infection. Although the risk of SARS-CoV-2 infection was lower in HCWs who had anti-A compared with those who did not, this difference was not statistically significant (48.6% vs 51.1%; p = 0.308; Table 3). When blood groups were examined, the rate of those with O blood type was found to be significantly lower in those who had SARS-CoV-2 infection than in those who did not have O blood type (15.6% vs 23.5%; OR: 0.808; 95% CI: 0.655–0.996; p = 0.045; Tables 3 & 4). The prevalence of blood group A (50% vs 43.8%; p = 0.519) was higher in hospitalized patients than in outpatients. The prevalence of group O was lower in hospitalized patients than in outpatients (25% vs 29.5%; p = 0.614). However, these differences did not reach statistical significance (Table 5). Of 28 hospitalized patients, eight had severe pneumonia and 20 had mild-to-moderate pneumonia. Severe pneumonia developed in three patients from blood group A (0.25%), two patients from group B (0.45%), two patients from group AB (0.93) and one patient from group O (0.10). Due to SARS-CoV-2 infection, three patients were followed up in the ICU with invasive mechanical ventilation support. Two of the three HCWs admitted to the ICU died. Two of the three patients who needed mechanical ventilation were blood group A. Two deceased patients had group A as well (Table 6).
In this study, the relationship between blood groups (ABO/Rh) and SARS-CoV-2 infection was investigated among HCWs working in a tertiary hospital, and it was observed that SARS-CoV-2 infection developed at an ~20% lower rate in blood group O (15.6% vs 23.5%; OR: 0.808; 95% CI: 0.655–0.996; p = 0.045). In addition, the prevalence of group O was found to be lower in patients hospitalized due to SARS-CoV-2 infection than in nonhospitalized patients (25% vs 29.5%). Most likely, due to the low number of hospitalized patients, no significant difference was found (p = 0.614). The distributions of ABO blood groups in HCWs were similar to those of individuals in the province and country [14,15]. Of the blood donors of the Turkish Red Crescent Blood Center in 2005–2012, 38% had blood group A, 34% had blood group O, 16% had blood group B and 8% had blood group AB [14]. In another study conducted in Istanbul, the blood groups of 123,900 people were examined and it was reported that 33.8% of the people had blood group O, 42.8% had blood group A, 15.3% had blood group B and 7.1% had blood group AB [15]. The majority of the studies on ABO/Rh blood groups have included data from transfusion records. However, the present study enrolled HCWs who were closely followed-up for COVID-19. When examining the literature, despite different results, it was generally found that the risk of SARS-CoV-2 infection was high in blood group A, while blood group O was protective against SARS-CoV-2 infection [8,9]. While Zhao et al. found the risk of SARS-CoV infection to be 1.3-fold higher in blood group A, they found blood group O to be 32% protective against SARS-CoV-2 infection [8]. In a similar study conducted in Turkey, the prevalence of blood group A was higher in patients with SARS-CoV-2 infection than in the control group, whereas the prevalence of blood group O was lower. In the same study, blood groups were not associated with the prognosis of COVID-19 [9]. In a Canadian study, O blood and Rh- were associated with the reduced development of SARS-CoV-2 infection and severe COVID-19 [10]. In another study, blood type A was associated with increased SARS-CoV-2 infection, while blood type O was not associated with SARS-CoV-2 infection [17]. Like the results of the present study, Latz et al. did not find blood type A to be associated with infection risk (OR: 1.00; 95% CI: 0.88–1.13) but showed a reduced risk of COVID-19 in blood group O (OR: 0.84; 95% CI: 0.75–0.95). The same study reported that Rh status, B and AB groups were associated with increased COVID-19 [18]. According to the results of the present study, the risk of SARS-CoV-2 infection was lower in blood group O (15.6% and 23.5%; OR: 0.808; 95% CI: 0.655–0.996; p = 0.045); Rh status and other blood groups (A, B, AB) were not found to be associated with the risk of SARS-CoV-2 infection. Anti-A and anti-B antibodies have been shown to be one of the causes of SARS-CoV-2 infection susceptibility, which varies according to blood groups. One of the hypotheses on the subject is that anti-A antibodies inhibit the binding of SARS-CoV-2 spike protein and ACE-2 receptor, preventing the adhesion of the virus to the cell and thus causing viral neutralization [11]. Gérard et al. examined the Zhao et al. study, which has the largest patient population in the literature, from a different perspective and investigated the relationship between the presence of anti-A and COVID-19. They compared 1888 COVID-19 patients with 3694 controls. The researchers concluded that anti-A presence was lower in COVID-19 patients than in patients without COVID-19, while there was no difference in anti-B [12]. In a study comparing 430 patients with COVID-19 and 2212 healthy blood donors, male sex and advanced age were found to be risk factors for SARS-CoV-2 infection, while the anti-A group was found to be protective against SARS-CoV-2 infection (OR: 0.62; 95% CI: 0.50–0.78; p < 0.001) [19]. In the present study, the anti-A (B/O) group (48% vs 51%; p = 0.308) and anti-B (O/B) group (73% vs 75%; p = 0.450) did not increase the risk for the development of SARS-CoV-2 infection. Similarly, Almadhi et al. showed that there was no relationship between A and B group antibodies and infection [20]. Although a high ACE-2 receptor level increases SARS-2 susceptibility [21], there are reports showing that it may be protective against cardiovascular disease and severe SARS-CoV-2 [22]. The protective effect of blood group O on severe SARS-CoV-2 infection may be due to the presence of lower ACE and higher ACE-2 levels in blood group O due to the absence of some ABO polymorphism genes. It has been reported that more IL-6 is released in blood group O and that this cytokine causes an increase in ACE-2 levels by inhibiting ACE [23]. The A antigen in the A blood group creates endothelial damage as a result of increased ACE system activation and adhesion molecule release (P-selectin, ICAM-1), causing a susceptibility to thromboinflammatory events and an increased risk of serious SARS-CoV-2 infection [24,25]. There are different reports on the relationship between the prognosis of COVID-19 and blood groups (ABO/Rh). Wu et al. stated that there is no relationship between blood types and severe COVID-19 [26]. Muñiz-Diaz and colleagues reported that mortality was higher in the A blood group than in the O blood group and that the O and A blood groups were two important blood groups in determining the prognosis of the patients with COVID-19 [27]. Serum inflammatory markers C-reactive protein, procalcitonin, d-dimer, lymphocyte count, ferritin and albumin are closely related to the severity of SARS-CoV-2 infection [28–33]. Another hypothesis states that the relationship between blood groups and serum levels of inflammatory cytokines may determine disease severity [13,28]. In one study, the risk of mechanical ventilation or death was found to be more than twofold lower in patients with blood type O, and it was shown that only patients with blood type O were able to generate a good immune response with effective cytokine release. In this study, the levels of 45 different cytokines were measured in the serum of 108 patients with COVID-19 at the first admission and on the sixth day thereafter. It was found that all cytokine levels, except for HGF, were significantly higher in patients with blood group O than in the others. The authors concluded that higher cytokine levels were more clinically associated with blood group O than others [13]. In another study conducted with COVID-19 patients monitored in the ICU, it was pointed out that the need was greater for more mechanical ventilation (p = 0.02) and continuous renal replacement therapy (p = 0.04) and the ICU length of stay was longer (p = 0.03) in groups B and AB. However, when the researchers compared groups O and B with groups A and AB, they showed that there was no difference in the serum levels of inflammatory cytokines [28]. According to the present study's results, the hospitalization rate was higher in patients with COVID-19 with blood groups A and AB, whereas this rate was lower in blood groups O and B. However, the authors could not statistically prove the relationship between blood types and hospitalization, which may be due to the small number of hospitalized patients with COVID-19. Statistical analysis of mortality could not be performed because only three subjects were admitted to the ICU during the study of HCWs in the hospital. Two of the three subjects who required mechanical ventilation and were admitted to the ICU had blood type A (67%). SARS-CoV-2 infection risk factors have been investigated since the early stages of the pandemic, and sex, age, and comorbid conditions have been reported as host factors in SARS-CoV-2 infection and prognosis [2,34–36]. Advanced age may contribute to the severity of SARS-CoV-2 infection by creating negative effects, particularly on the target organ, the lung [33]. In a meta-analysis, age greater than 70 years and male sex were found to be associated with a higher risk of SARS-CoV-2 infection [34]. Among the reasons for the lower risk of disease in women is that they are more likely to maintain social distance and hygiene. In addition, it can be hypothesized that the estradiol hormone in women increases the amount of ADAM17 and thus ACE-2, which prevents SARS-CoV-2 from entering the host cell [36]. According to the results of the present study, sex and age did not cause a difference in the development of SARS-CoV-2 infection and hospitalization rates. This may be because the HCWs participating in the study were not advanced in age (median age was 30 years [26–40]). It is known that active smoking increases susceptibility to lung infections. However, the relationship of smoking to the risk of COVID-19 pneumonia and serious illness is not clear [37–40]. In a study conducted in Turkey country with outpatients who had mild-to-moderate COVID-19, it was reported that pneumonia occurred more frequently in nonsmokers [38]. In one meta-analysis, smoking was associated with a 1.9-fold increased risk for the progression of COVID-19 [39]. Another meta-analysis showed that active smoking was not associated with the prognosis of COVID-19 disease. Four of the five studies included in the analysis found no association between severe COVID-19 and active smoking [40]. Being a nonsmoker in the hospital in the present study was not found to be protective against the development of SARS-CoV-2 infection (62.9% vs 61.7%; p = 0.561) or related hospitalization (77.8% vs 61.8%; p = 0.875). The prevalence of comorbidities, especially hypertension and diabetes mellitus, is high in those with SARS-CoV-2 infection [41–45]. In a study examining 5700 patients hospitalized for SARS-CoV-2 infection, 89.3% had at least one chronic condition, with hypertension, obesity and diabetes mellitus ranking in the top three [44]. The frequency of coronary artery disease has been shown to be lower than that of diabetes mellitus and hypertension, but its contribution to mortality is higher [44,45]. In the present study, contrary to the literature data, the frequency of hypertension, coronary artery disease and diabetes mellitus, which are comorbid diseases, was found to be lower in those who had SARS-CoV-2 infection, while the frequency of chronic diseases was found to be similar between those with COVID-19 requiring hospitalization and outpatients (Tables 3 & 5). This can be explained by the fact that HCWs who had a chronic disease in the first months of the pandemic better complied with social distancing and hygiene rules. In addition, HCWs with comorbidities were kept away from risky practices and were less in contact with social life. This study had several strengths. First, the HCWs included in the study had a blood group distribution similar to that of the population of the province and country. Second, the authors strictly followed the recommendations of the Turkish Ministry of Health COVID-19 Guidelines for HCWs with close contact with confirmed COVID-19 [16] in the hospital, and there were no restrictions on SARS-CoV-2 PCR testing. Third, age, sex, and chronic diseases were homogeneously distributed among blood groups, and none of the participants had been vaccinated against COVID-19. This may have helped the authors rationally evaluate the relationship among blood groups, COVID-19 and disease severity. However, this study had some limitations. First, the results of the study, which was conducted at a single center and was retrospective in design, may not be generalizable. Second, the SARS-CoV-2 antigen was not tested in HCWs, so patients with asymptomatic COVID-19 were not excluded from the study. Finally, the relationship between hospitalization, the need for ICU admission and deaths due to COVID-19 and ABO/Rh blood groups could not be demonstrated because of the small number of hospitalized patients.
Our findings suggest that blood groups are associated with the development of SARS-CoV-2 infection in HCWs. Although the existence of a relationship between blood groups and the development of SARS-CoV-2 infection has been determined, we believe that more comprehensive studies are needed to understand the causative pathophysiological mechanisms.
SARS-CoV-2 infection developed at a rate approximately 20% lower rate in blood group O (15.6 vs 23.5%; odds ratio: 0.808; 95% CI: 0.655–0.996; p = 0.045). The rate of patients hospitalized due to SARS-CoV-2 infection in blood group O was found to be lower than that of the nonhospitalized group (25 vs 29.5%). The prevalence of blood group A was higher in hospitalized patients than in outpatients, but it was not statistically significant (50 vs 43.8%; p = 0.519). Although the risk of SARS-CoV-2 infection was lower in those who had anti-A compared with those who did not, no statistically significant difference was found (48.6 vs 51.1%; p = 0.308). | true | true | true |
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PMC9586477 | 36269826 | Xiancai Zhong,Hongmin Wu,Wencan Zhang,Yousang Gwack,Weirong Shang,Kyle O. Lee,Noah Isakov,Zhiheng He,Zuoming Sun | Decoupling the role of RORγt in the differentiation and effector function of TH17 cells | 01-10-2022 | RORγt is known to instruct the differentiation of T helper 17 (TH17) cells that mediate the pathogenesis of autoimmune diseases. However, it remains unknown whether RORγt plays a distinct role in the differentiation and effector function of TH17 cells. Here, we show that mutation of RORγt lysine-256, a ubiquitination site, to arginine (K256R) separates the RORγt role in these two functions. Preventing ubiquitination at K256 via arginine substitution does not affect RORγt-dependent thymocyte development, and TH17 differentiation in vitro and in vivo, however, greatly impaired the pathogenesis of TH17 cell–mediated experimental autoimmune encephalomyelitis (EAE). Mechanistically, K256R mutation impairs RORγt to bind to and activate Runx1 expression critical for TH17-mediated EAE. Thus, RORγt regulates the effector function of TH17 cells in addition to TH17 differentiation. This work informs the development of RORγt-based therapies that specifically target the effector function of TH17 cells responsible for autoimmunity. | Decoupling the role of RORγt in the differentiation and effector function of TH17 cells
RORγt is known to instruct the differentiation of T helper 17 (TH17) cells that mediate the pathogenesis of autoimmune diseases. However, it remains unknown whether RORγt plays a distinct role in the differentiation and effector function of TH17 cells. Here, we show that mutation of RORγt lysine-256, a ubiquitination site, to arginine (K256R) separates the RORγt role in these two functions. Preventing ubiquitination at K256 via arginine substitution does not affect RORγt-dependent thymocyte development, and TH17 differentiation in vitro and in vivo, however, greatly impaired the pathogenesis of TH17 cell–mediated experimental autoimmune encephalomyelitis (EAE). Mechanistically, K256R mutation impairs RORγt to bind to and activate Runx1 expression critical for TH17-mediated EAE. Thus, RORγt regulates the effector function of TH17 cells in addition to TH17 differentiation. This work informs the development of RORγt-based therapies that specifically target the effector function of TH17 cells responsible for autoimmunity.
Interleukin-17 (IL-17)–producing CD4+ T helper, or T helper 17, (TH17) cells participate in immune responses against pathogens and the pathogenesis of diverse immunological diseases such as autoimmune diseases and even autism (1–6). The transcription factor retinoid-related orphan receptor γt (RORγt), encoded by the gene Rorc, instructs the differentiation of TH17 cells (7–9). Mutations in Rorc affect IL-17 production and lead to severe immune deficiency in both mouse (7) and human (10). Thus, therapies that control the pathogenic TH17 responses are needed clinically (11, 12). Numerous pharmacological RORγt inhibitors have been developed for clinical application (5, 11–13). Those inhibitors are believed to prevent TH17-dependent autoimmunity by inhibiting the generation of TH17 cells due to the essential function of RORγt in TH17 differentiation. The strength of the TH17 immune responses is determined by the overall number of TH17 cells and their effector function. TH17 cells are derived from naïve CD4+ T cells upon activation in the presence of an appropriate cytokine milieu including IL-6, transforming growth factor–β (TGF-β), and/or IL-23 (7, 14). Although the function of RORγt in TH17 differentiation has long been demonstrated, it remains unknown whether RORγt plays a role in the effector function of TH17 cells and whether RORγt has a distinct role in the differentiation versus effector function of TH17 cells. In addition to regulating TH17 cells, RORγt enhances thymocyte survival (15–18) and is required for lymph node development (8, 18–20). Previously, we have generated a mutation in RORγt that disrupts TH17 differentiation but not thymocyte development (8), indicating that RORγt uses different mechanisms to regulate the function of TH17 cells and thymocytes. Ubiquitination is a posttranslational modification that regulates many aspects of cellular function (21). Ubiquitin is conjugated to the lysine residues of the proteins to modulate protein function by regulating protein stability and/or protein-protein interactions. Thus, cellular context-dependent ubiquitination of RORγt may be a mechanism to modulate the diverse RORγt functions. In vitro studies indicate the regulation of RORγt stability by ubiquitination (22–25). However, in vivo function of RORγt ubiquitination is difficult to prove, as it requires the generation of the mice expressing mutant RORγt incapable of being ubiquitinated. Previous studies used mice deficient in ubiquitin ligases or deubiquitinases to understand the role of ubiquitination in immune responses (26). Such an approach prevents the ubiquitination of all the substrates; thus, it is impossible to determine the function of a specific substrate and even less about the function of a specific ubiquitination site on the substrate in immunological function. In this study, three in vitro assays were developed to dissect RORγt function in thymocyte development, TH17 differentiation, and effector function in experimental autoimmune encephalomyelitis (EAE) induction. A RORγt mutation at a ubiquitination site, lysine (K)–256 to arginine (RORγtK256R), was found to specifically impair the effector function of TH17 cells in inducing EAE without interfering with RORγt function in TH17 differentiation and thymocyte development. A strain of mice was established to express RORγtK256R (RORγtK256R/K256R), which cannot be ubiquitinated at this site. This strain of mice allows us to determine the in vivo function of ubiquitination of RORγt at a specific site. RORγtK256R/K256R mice have normal thymocyte development, lymph node development, and TH17 differentiation. However, RORγtK256R/K256R mice display greatly impaired TH17 immune responses leading to EAE. Further, RORγtK256R/K256R TH17 cells showed decreased RORγtK256R binding to the promoter region of the Runx1 gene and reduced expression of Runx1. Forced expression of Runx1 in RORγtK256R/K256R TH17 cells restored the ability to induce EAE. Therefore, RORγt regulates the effector function of TH17 cells in addition to TH17 differentiation.
RORγt−/− mice display defects in thymic T cell development, TH17 differentiation, and development of TH17-dependent EAE (7, 18). To dissect the function of RORγt, we developed three assays to separate these three functions. To determine the function of RORγt in thymocytes, we used an in vitro thymocyte differentiation system (27). CD4−CD8− thymocytes from wild-type (WT) but not RORγt−/− mice could differentiate into CD4+CD8+ and CD4+ cells (Fig. 1A). Consistently, RORγt−/− CD4−CD8− thymocytes transduced with a retrovirus expressing a RORγt (RORγt) but not green fluorescent protein (GFP) alone [empty virus (EV)] rescued the development of CD4+CD8+ and CD4+ from RORγt−/− thymocyte (Fig. 1B and fig. S1A for gating strategy). We next used an in vitro assay to determine the function of RORγt in TH17 differentiation. Under TH17 polarization conditions, RORγt−/− naïve CD4+ T cells could not differentiate into TH17 cells unless exogenous RORγt was provided via retrovirus transduction (Fig. 1C and fig. S1B for gating strategy). Last, adoptive transfer EAE model enabled the testing of RORγt TH17 effector function by using 2D2 T cell receptor (TCR) transgenic mice (TgTcr2D2) that recognize myelin oligodendrocyte glycoprotein (MOG35–55) (28, 29). RORγt−/−/TgTcr2D2 CD4+ T cells were transduced with virus expressing RORγt, polarized under TH17 conditions and adoptively transferred into Rag1−/− recipients for inducing EAE (30). RORγt−/−/TgTcr2D2 CD4+ T cells expressing RORγt induced very severe EAE (RORγt−/−/TgTcr2D2 + RORγt); these mice had the highest disease score of 5 (the Institutional Animal Care and Use Committee protocol does not permit disease development beyond this point) equivalent to Rag1−/− recipients with WT TgTcr2D2 CD4+ T cells transduced with GFP alone (WT TgTcr2D2 + EV) (Fig. 1D). In contrast, RORγt−/−/TgTcr2D2 CD4+ T cells expressing GFP alone (RORγt−/−/TgTcr2D2 + EV) resulted in greatly delayed and impaired EAE with the highest disease score of 3. RORγt deficiency did not affect the proliferation and survival of TgTcr2D2 CD4+ T cells in vitro (fig. S1, C and D) and in vivo (fig. S1E), which is also indicated by an equivalent number CD4+ T cells recovered from the spleen after adoptive transfer (fig. S1F). The successful establishment of above assays allowed us to dissect three RORγt-regulated functions.
RORγt has been shown to regulate TH17 differentiation; it, however, remains unknown whether and how RORγt regulates the effector function of TH17 cells. To address this question, we aimed to identify RORγt mutations that specifically disrupt RORγt function in effector TH17 cell–mediated EAE but not in TH17 differentiation and thymocyte development. Previously, we mutated lysine residues (K) on RORγt to arginine (R) to study the function of posttranslational modification of RORγt (8). Thus, we first compared the ability of WT and RORγt mutants to rescue RORγt−/− thymocyte development. Only RORγt-K31R mutation moderately affected thymocyte development (Fig. 2A). In terms of TH17 differentiation, K31R, K69R, and K313R impaired TH17 differentiation (Fig. 2, B to D), consistent with our published results (8, 9, 31). Further, most RORγt mutations either impaired or potentiated TH17 differentiation (Fig. 2B), suggesting that RORγt uses very different mechanisms to regulate the function of thymocytes and TH17 cells. To separate RORγt function in TH17 differentiation versus effector TH17 cells, we focused on mutants that did not perturb TH17 differentiation and thymocyte development, such as K99R, K256R, and K288R (Fig. 2, A to D). K256 was identified as a prominent ubiquitination site by mass spectrometry analysis of immunoprecipitated RORγt (fig. S2A). To validate the K256 ubiquitination site, we generated a RORγt mutant with all K mutated to R except K256 (RORγtK256(K1)) so that only K256 can be ubiquitinated. In the presence of WT ubiquitin, RORγtK256(K1) was ubiquitinated, whereas the ubiquitination signals were absent in the presence of a mutant ubiquitin that had all K mutated to R (K0) so that it cannot be attached to the substrates (Fig. 2E). We next used a RORγt mutant carrying a single K256 to R mutation (RORγtK256R) that cannot be ubiquitinated only at the K256 site. WT RORγt or RORγtK256R was then expressed in RORγt−/− T cells under TH17-polarizing conditions. Ubiquitinated RORγt was readily detected in TH17 cells (Fig. 2F). Compared to the WT RORγt, RORγtK256R had obviously reduced ubiquitination signals. RORγt-K256 is thus ubiquitinated in TH17 cells. Next, we tested the effector function of TH17 cells in the induction of EAE. As described above (Fig. 1D), in vitro–differentiated RORγt−/−/TgTcr2D2 TH17 cells expressing exogenous RORγt, RORγtK99R, and RORγtK288R induced severe EAE after adoptive transfer into Rag1−/− recipients (Fig. 2G), suggesting that K99R and K288R do not affect the effector function of TH17 cells in the induction of EAE, whereas RORγtK256R-expressing RORγt−/−/TgTcr2D2 TH17 cells induced greatly impaired EAE. RORγtK69R also induced impaired EAE, as we previously observed because of reduced TH17 differentiation (8). There was no difference in the proliferation of the RORγt−/−/TgTcr2D2 cells expressing various RORγt (fig. S2B). The central nervous system (CNS) immune cell infiltrate was also analyzed (Fig. 2H and fig. S2C for gating strategy). Consistent with the impaired EAE, the total number of CD4+ T cells and monocytes/macrophages in the CNS was significantly reduced in recipients with RORγtK256R or RORγtK69R mutants, indicating reduced inflammation. In addition, recipients with RORγtK256R and RORγtK69R cells also showed reduced infiltrate IL-17A+ cells in the CNS (Fig. 2I and fig. S2D for gating strategy), whereas not obvious changes in interferon-γ–positive (IFN-γ+) TH1 cells from recipients with RORγtK256R compared to that with WT RORγt were observed (fig. S2, E and F). Therefore, the RORγt-K256 ubiquitination site, although dispensable for TH17 differentiation, is required for the effector function of TH17 cells in the pathogenesis of EAE.
To investigate the function of RORγt-K256 ubiquitination in vivo, we generated homozygous mice for RORγtK256R (RORγtK256R/K256R) under the control of endogenous RORγt locus (fig. S3A for genetic engineering strategy and fig. S3B for confirming the K256R mutation by sequencing). We first examined RORγt-dependent thymocyte development (18–20, 32). WT RORγt and RORγtK256R had the same expression patterns in thymocytes (Fig. 3A); our gene-targeting strategy thus did not affect RORγtK256R expression. Furthermore, RORγtK256R was as stable as WT RORγt (fig. S3, C to D), suggesting that K256 ubiquitination site does not affect RORγt stability. Thymic cellularity (Fig. 3B) and distribution of different developmental stages of thymocytes, CD4−CD8− double-negative (DN), CD4+CD8+ double-positive (DP), and CD4+ or CD8+ single-positive thymocytes (Fig. 3C) were comparable between RORγtK256R/K256R and WT mice, which were different from RORγt−/− mice, suggesting normal thymocyte development in RORγtK256R/K256R mice. Furthermore, the percentage of natural killer T (NKT) and γδ T cells in the thymus (Fig. 3, D and E) and spleens (fig. S3, E and F) was equivalent between WT and RORγtK256R/K256R mice. The accelerated CD4+CD8+ thymocyte apoptosis (Fig. 3F and fig. S3G for analysis of apoptotic cells) accounted for the reduced percentage of CD4+CD8+ thymocytes (Fig. 3C) and decreased thymic cellularity (Fig. 3B) in RORγt−/− mice (18), whereas the apoptosis of CD4+CD8+ thymocytes from RORγtK256R/K256R mice was the same as that of the WT mice (Fig. 3F). In addition, thymocytes from RORγt−/− mice had a higher percentage of cells with >2N of DNA (Fig. 3G), indicating more cells in the DNA synthesis phase of the cell cycle (18), while thymocytes from RORγtK256R/K256R mice did not show an increased percentage of the cells in the DNA synthesis phase compared to the WT mice. Therefore, RORγt-K256R mutation does not affect RORγt function in thymocyte development. Furthermore, unlike RORγt −/− mice that lack all the peripheral lymph nodes (18), RORγtK256R/K256R mice developed lymph nodes including inguinal lymph nodes (Fig. 3H) and Peyer’s patches (Fig. 3I), same as the WT mice. Together, prevention of RORγt-K256 ubiquitination does not affect the development of thymocytes and lymph nodes.
Upon maturation in the thymus, T cells migrate to the periphery to mediate immune responses. RORγtK256R/K256R and WT mice had comparable splenocytes (fig. S4A), CD4+, CD8+ (fig. S4B), CD62LhiCD44lo naïve, and CD62LloCD44hi memory-like T cell counts (fig. S4, C to D). We next examined TH17 differentiation using a GFP reporter mouse line of IL-17 (Il17aGFP). We confirmed that in vitro–differentiated RORγtK256R/K256R TH17 cells showed comparable RORγt (Fig. 4A) and IL-17 (Fig. 4B) expression compared to WT cells, consistent with the notion that RORγt-K256R mutation does affect TH17 differentiation (Fig. 2, B and D). In addition, RORγtK256R/K256R mice showed a normal percentage of splenic regulatory T cells (Tregs) (fig. S4E) and normal differentiation of Tregs from naïve CD4+ T cells (fig. S4F). To test whether RORγt-K256R affects the effector function of TH17 cells responsible for the induction of EAE, we induced EAE by immunization with MOG35–55 peptide. Before immunization, there were almost no TH17 cells detected in the spleens from WT and RORγtK256R/K256R mice (Fig. 4C, top). Six days after immunization, equivalent TH17 (fig. S4, G and H) as well as TH1 (fig. S4, I to J) cells were induced in spleens (fig. S4, G and I) and lymph nodes (fig. S4, H and J) of WT and RORγtK256R/K256R mice. Again, on day 12 after immunization, when EAE symptoms started to develop, the percentage of TH17 cells still showed no obvious difference in the spleens of WT and RORγtK256R/K256R mice (Fig. 4C, bottom), confirming that RORγt-K256R does not affect the generation of TH17 cells in vivo. However, compared to WT mice, RORγtK256R/K256R mice developed greatly impaired EAE (Fig. 4D), supporting impaired RORγtK256R/K256R TH17 effector function. Consistently, adoptive transfer of in vitro–differentiated RORγtK256R/K256R TH17 cells also induced less severe EAE in Rag1−/− recipients compared to that induced by WT TH17 cells (fig. S4K). In addition, impaired EAE induction was associated with reduced immune cell infiltrate including CD4+, CD8+, B cells, and monocytes in the CNS of RORγtK256R/K256R mice, although no obvious changes were observed in neutrophil numbers (Fig. 4E and fig. S4L for gating strategy). Further analysis of CNS lymphocyte infiltrate in the RORγtK256R/K256R mice showed a decreased percentage of IL-17A+CD4+ cells (Fig. 4F), particularly pathogenic IL-17A+ granulocyte-macrophage colony-stimulating factor–positive (GM-CSF+) cells that play an important role in EAE development (Fig. 4, G and H, and fig. S4M for gating strategy) (2, 3). These results suggest that RORγt-K256R mutation, which prevents the ubiquitination, impairs the effector function but not the differentiation of TH17 cells responsible for the pathogenesis of EAE.
To understand the mechanisms responsible for the RORγt-K256R mutation-disrupted effector function of TH17 cells responsible for EAE, GFP+ TH17 cells derived from WT and RORγtK256R/K256R/IL-17GFP CD4+ T cells were sorted to high purity (>98%) (fig. S5A for gating strategy) and subjected to RNA sequencing (RNA-seq) analysis. On the basis of the computational principal components analysis, the six RNA-seq samples were divided into two groups: WT and RORγtK256R/K256R TH17 cells (fig. S5B), indicating reproducible gene expression patterns within each group and thus the high quality of RNA-seq results. We identified 1375 differentially expressed genes (DEGs) [P < 0.05 and fold change (FC) > 1.5], with 943 up-regulated and 432 down-regulated genes, between WT and RORγtK256R/K256R TH17 cells (Fig. 5A, fig. S5C, and tables S2 and S3). Subjection of DEGs to pathway analysis did not find significant changes in the TH17 differentiation pathway (Fig. 5, B and C) between WT and RORγtK256R/K256R cells, confirming that RORγt-K256R mutation does not affect TH17 differentiation. Down-regulated pathways in RORγtK256R/K256R cells include IL-23 signaling (Fig. 5, B and D) and glycolysis pathways (Fig. 5, B and E); both play important roles in the pathogenesis of EAE (33, 34). In addition, inflammatory cytokines such as IL-9, IL-17, and IL-22 signaling pathways were also down-regulated, likely reflecting a reduced ability to induce inflammation responsible for the tissue damages. We next subjected DEGs to gene set enrichment analysis (GSEA) using the gene set specifically expressed in TH17 cells responsible for the development of pathogenic EAE (fig. S5D) (35) and found that RORγtK256R/K256R TH17 cells had significantly reduced enrichment of the genes important for the pathogenesis of EAE when compared to the WT TH17 cells (Fig. 5F). This result is consistent with the impaired EAE observed in RORγtK256R/K256R mice. To determine the DEGs that are directly regulated by RORγt, we performed chromatin immunoprecipitation sequencing (ChIP-seq) analysis to detect genome-wide RORγt occupancy. ChIP-seq analysis in TH17 cells revealed obvious RORγt-binding peaks at Il17a and Il17f loci (Fig. 5G), consistent with published results (8, 36). RORγtK256R-binding peaks at the Il17a and Il17f loci were comparable to that of WT RORγt, supporting that K256R did not affect RORγt binding to Il17 gene and thus its expression. Using our RNA-seq and ChIP-seq data, we cross-examined genes that were down-regulated in RORγtK256R/K256R TH17 cells with the genes that had reduced RORγtK256R-binding signals (see table S4 for the full list), identifying 31 genes (Fig. 5H). These 31 genes are considered directly regulated by RORγt, and their reduced expression in RORγtK256R/K256R TH17 cells is likely due to reduced RORγtK256R binding and activating their expression. The 31 genes were then cross-examined with the gene set specifically expressed in TH17 cells and responsible for the pathogenesis of EAE (fig. S5D) (35) and identified Runx1 (Fig. 5H). Furthermore, Runx1 was found to be a core regulator for the IL-23 signaling pathway that was down-regulated in RORγtK256R/K256R TH17 cells by a protein-protein interaction network analysis (Fig. 5I), indicating that down-regulated IL-23 signaling pathway is likely due to down-regulated Runx1 expression. Therefore, computational analysis of the transcriptome is consistent with the phenotypes observed in RORγtK256R/K256R mice that RORγt-K256R mutation does not affect TH17 differentiation but impairs the effector function of TH17 cells responsible for the development of EAE. Runx1 is thus a possible RORγt-regulated gene that is down-regulated in RORγtK256R/K256R TH17 cells and responsible for the observed defective effector function of RORγtK256R/K256R TH17 cells in the induction of EAE.
Our computational analysis identified Runx1 as a potential gene responsible for impaired effector function of RORγtK256R/K256R TH17 cells, because (i) Runx1 was down-regulated in RORγtK256R/K256R/TgTcr2D2 TH17 cells, which was also confirmed by individual quantitative polymerase chain reaction (qPCR) analysis (Fig. 6A); (ii) three prominent RORγt-binding peaks close to the transcription start site of Runx1 gene were identified by ChIP-seq analysis (Fig. 6B), and RORγtK256R binding signals at those peaks were substantially decreased (Fig. 6, B and C). RORγt-binding sites were identified within the peak region by sequencing analysis (fig. S6A). Furthermore, individual ChIP assays also confirmed RORγt binding to the peak region, whereas RORγtK256R-binding signals to this region were greatly decreased (Fig. 6D), which correlates to the decreased levels of Runx1 mRNA in RORγtK256R/K256R TH17 cells (Fig. 6A); (iii) Runx1 was a gene expressed in TH17 cells and responsible for the pathogenesis of EAE, as deletion of Runx1 impairs the EAE development (35, 37). To determine the effects of decreased RORγtK256R-binding peak signals on Runx1 gene transcription, we cloned the DNA fragment covering the region with two potential RORγt-binding sites (fig. S6A) to a luciferase reporter gene (pGL3) driven by a basic thymidine kinase (TK) promoter. The reporter activity was greatly stimulated by WT RORγt but not as much by RORγtK256R (Fig. 6E). Therefore, reduced RORγtK256R binding to the Runx1 gene correlates well with the reduced ability of RORγtK256R to stimulate Runx1 gene expression. In contrast, WT RORγt and RORγtK256R equivalently stimulated IL-17 promoter-luciferase report activity, which correlates with the equivalent binding of RORγt and RORγtK256R to and activation of the Il17 gene (Figs. 4B and 5G). To further determine whether identified RORγt-binding peaks are important for the expression of endogenous Runx1 gene, the region containing the RORγt-binding peaks was deleted using CRISPR-Cas9 with two guiding RNAs (ΔRgn1) in CD4+ T from mice expressing Cas9 (fig. S6A). At the same time, we used a nontargeting control (NTC) and a deleted adjacent region (ΔRgn2) as negative controls, whereas deleted Runx1 gene itself as a positive control (ΔRunx1) (fig. S6B for gating strategy for detecting Runx1 expression after deletion). Deletion of the Runx1 gene prevented Runx1 expression [Fig. 6F and fig. S6C for mean fluorescence intensity (MFI) of Runx1], demonstrating a successful deletion strategy with CRISPR-Cas9. Furthermore, deletion of the RORγt-binding region, but not the adjacent region or NTC, greatly reduced expression of Runx1, strongly indicating the critical function of the RORγt-binding region in the stimulation of Runx1 expression. Together with the results that RORγtK256R had decreased binding signals to Runx1 (Fig. 6, B to D), these results suggest that reduced Runx1 expression in RORγtK256R/K256R TH17 cells is due to impaired RORγtK256R-binding and activating Runx1 gene expression. To determine whether the reduced level of Runx1 in RORγtK256R/K256R cells is responsible for the impaired development of EAE, we force-expressed Runx1 in RORγtK256R/K256R/TgTcr2D2 CD4+ T cells (Fig. 6G) and adoptively transferred them to Rag1−/− mice to induce EAE (Fig. 6H). Forced expression of Runx1 significantly enhanced RORγtK256R/K256R/TgTcr2D2 CD4+ T cell function in the induction of EAE comparable to that of WT TgTcr2D2 T cells, which was also confirmed by increased infiltration of CD4+ T cells to the CNS (Fig. 6I). Furthermore, the percentage of pathogenic IL-17+GM-CSF+ and IL-17+IFN-γ+ cells in CNS was reduced in Rag1−/− mice transferred with RORγtK256R/K256R/TgTcr2D2 CD4+ T cells compared to that transferred with WT TgTcr2D2 T cells but restored to WT levels in RORγtK256R/K256R/TgTcr2D2 CD4+ T cells expressing Runx1 (Fig. 6J), consistent with rescuing the effector function of RORγtK256R/K256R T cells by expressing Runx1. Collectively, our results demonstrated that the ubiquitination site of RORγt-K256, which is not essential for TH17 differentiation, regulates the effector function of TH17 cells required for inducing EAE via up-regulating Runx1. By decoupling the function of RORγt in the differentiation and effector function of TH17 cells, we demonstrated that RORγt also regulates the effector function of TH17 cells.
The transcription factor RORγt, which is encoded by gene Rorc, is well known for instructing the differentiation of TH17 cells. Activation of naïve T cells in the presence of TGF-β, IL-6, and/or IL-23 is sufficient to up-regulate RORγt, which instructs the differentiation into TH17 cells (14, 38–41). The hallmark of TH17 differentiation is the activation and expression of IL-17. RORγt directly binds to Il17a and Il17f gene loci to stimulate their expression (8, 36), which explains the essential function of RORγt in the differentiation of IL-17–producing TH17 cells. Differentiated TH17 cells are able to induce tissue inflammation involved in the pathogenesis of autoimmune diseases, including psoriasis, inflammatory bowel disease, and multiple sclerosis (2, 3, 14). Previous studies using RORγt−/− mice demonstrated the essential function of RORγt in TH17 cell–mediated autoimmunity such as EAE (7, 9). RORγt−/− mice are resistant to EAE and other TH17-mediated autoimmunity, which is due to the lack of RORγt-dependent generation of TH17 cells. The question remains whether RORγt plays a role in the effector function of TH17 cells involved in autoimmunity. The mutation RORγtK256R we identified at the ubiquitination site does not affect the generation of IL-17–producing TH17 cells but impairs the effector function of TH17 cells in the induction of EAE. Further, RORγtK256R/K256R mice have normal TH17 differentiation both in vitro and in vivo but have greatly impaired TH17 immune responses that led to EAE. In addition, RORγtK256R/K256R mice have normal RORγt-dependent thymocyte development and lymph node genesis including Peyer’s patches. This study thus reveals a previously unidentified and essential RORγt function in effector TH17 cells in addition to TH17 differentiation. This study informs the development of RORγt-based therapies that specifically target the effector function of TH17 cells responsible for the pathogenesis of autoimmunity. This will have a significant impact on the clinical treatment of TH17-mediated autoimmunity, as usually medical treatment is performed after a diagnosis of autoimmune diseases resulting from the effector function of already developed TH17 cells. Runx1 is a transcription factor known to regulate hematopoiesis (42–44) and oncogenesis (45). Runx1 was reported to play a role in TH17 cells. Particularly relevant to TH17 cells involved in the pathogenesis of EAE, Runx1 has been shown to be up-regulated in TH17 cells and work together with T-bet to stimulate IFN-γ expression that is believed to be responsible for the induction of EAE (37). However, it is not clear how Runx1 is up-regulated in TH17 cells. Our results demonstrate that Runx1 expression is stimulated by RORγt, as RORγt-binding peaks were detected by ChIP-seq on the promoter region of the Runx1 gene. The conserved RORγt-binding sequence was identified within the detected peaks, and the deletion of the DNA fragment containing the RORγt-binding site greatly reduced Runx1 expression. Moreover, RORγtK256R has reduced binding signals at the RORγt-binding region, which correlates with the impaired ability of RORγtK256R in stimulating a luciferase reporter gene driven by the RORγt-binding region identified in the Runx1 locus. These results demonstrate the mechanisms for how RORγt regulates the effector function of TH17 cells via up-regulating Runx1 expression and why RORγt-K256R mutation impairs the effector function of TH17 cells. An in vitro study showed that Runx1 can stimulate RORγt expression, which is, however, inhibited by T-bet (46). Because T-bet is required for IFN-γ expression, this seems to suggest that T-bet and RORγt inhibit each other. In addition, Runx1 has been shown to be required for forkhead box P3 (Foxp3) expression (47), and Runx1 is able to stimulate itself expression via an autoregulation mechanism (48). The function of Runx1 is thus complicated and dependent on the microenvironment. In our study, both in vitro and in vivo, we did not find obvious changes in the levels of RORγt expression in RORγtK256R/K256R TH17 cells that have lower levels of Runx1, thus not supporting the role of K256 ubiquitination in the regulation of RORγt and TH17 differentiation. Furthermore, even forced expression of Runx1 in RORγtK256R/K256R CD4+ T cells does not affect TH17 differentiation. Therefore, RORγt-regulated Runx1 expression does not affect TH17 differentiation but is required for the effector function of TH17 cells that mediate pathogenic EAE. Dysregulated TH17 cells are often associated with autoimmune diseases such as EAE and psoriasis resulting from a reaction to self-antigens (49). In addition to IL-17, IL-23 also plays an important role in TH17 cell–dependent autoimmune diseases (1, 50) such as EAE and psoriasis (51, 52). Neutralizing antibodies for IL-23 and IL-17 or their receptors are used for the treatment of these autoimmune conditions (53–55). Therefore, inhibiting the TH17 pathway is effective for treating autoimmune conditions (52, 56). Our results show that the IL-23 signaling pathway is down-regulated in RORγtK256R/K256R TH17 cells. Further, network analysis supports that Runx1 is a core regulator for the IL-23 pathway and down-regulated IL-23 pathway thus likely resulting from down-regulated Runx1. Therefore, RORγt-regulated Runx1 seems to control the effector function of TH17 cells at least partially through regulating the IL-23 pathway known to be critical for TH17-mediated autoimmunity. Considering the essential function of RORγt in TH17 cells, RORγt inhibitors are being developed to treat TH17-dependent autoimmunity (5, 11–13, 57). However, these RORγt inhibitors mostly target TH17 differentiation and RORγt-dependent thymocyte development. Inhibition of RORγt-dependent thymocyte development leads to a high frequency of thymic lymphoma (8, 58, 59). Our results demonstrate that a posttranslational ubiquitination event can dictate RORγt function in TH17-dependent responses involved in autoimmunity. However, this ubiquitination event is dispensable for thymocyte development and TH17 differentiation. Therefore, targeting this RORγt ubiquitination event is a potential treatment for TH17-dependent autoimmune disease without induction of thymocyte lymphoma. Currently, it remains unknown about the RORγt ubiquitination pathway including the enzymes involved in the ubiquitination of RORγt at K256. Illustrating the detailed mechanisms responsible for ubiquitination of RORγt will facilitate the development of novel treatments that target the RORγt-dependent effector function of TH17 cells responsible for autoimmunity while minimizing the other toxic effects such as lymphoma.
The objective of this study was to determine whether RORγt plays a role in effector function of TH17 cells in addition to its known function in TH17 differentiation. To achieve this goal, we dissected the function of RORγt with K-R mutations in thymus development, TH17 differentiation, and induction of EAE. RORγt-K256R mutation did not affect TH17 differentiation but impaired the effector function of TH17 cells responsible for inducing EAE, which was also confirmed by the in vivo studies using RORγtK256R/K256R mice. RNA-seq and ChIP-seq assays identified Runx1 as a direct target of RORγt in the regulation of effector function of TH17 cells.
All male and female mice used for experiments were between 6 and 12 weeks old; age-matched littermates were used. The RORγt−/− (Rorctm1Litt, stock no. 007571) mouse strain was described previously (18). The RORγtK256R/K256R point-mutated mice were designed and generated by Biocytogen LLC. Rag1−/− (Rag1tm1Mom, stock no. 002216), TgTcr2D2 (Tcra2D2 and Tcrb2D2, stock no. 006912), IL-17A–GFP (Il17atm1Bcgen, stock no. 018472), CRISPR-Cas9– enhanced GFP (EGFP) [Gt(ROSA)26Sorem1.1(CAG-cas9*,-EGFP)Rsky, stock no. 028555], and C57BL/6J (stock no. 000664) mice were purchased from the Jackson Laboratory. For some assays, the mice were crossed to generate RORγt−/−/TgTcr2D2, RORγtK256R/K256R/TgTcr2D2, and RORγtK256R/K256R/IL-17A-IRES-GFP-KI mice. All mice were bred at the C57BL/6J background and maintained in a pathogen-free animal facility at City of Hope. All animal experiments were conducted per the protocols approved by the Institutional Animal Care and Use Committee at City of Hope. Statistical tests were not used to predetermine sample sizes. The sample sizes were chosen on the basis of previous studies of our own and by others in the field (8). The sample sizes are indicated in the figure legends or figures. Allocation of mice to experimental groups was random.
Active EAE was induced and assessed as previously described (8). Briefly, mice were immunized with 200 mg of MOG35–55 (Hooke Laboratories) in complete Freund’s adjuvant by subcutaneous injection at two dorsal sites at day 0, followed by two intraperitoneal injections of 80 ng of pertussis toxin at days 0 and 1. For passive EAE, Rag1−/− mice were adoptively transferred with 1 × 105 TCRMOG-expressing (TgTcr2D2) TH17 cells that were differentiated under TH17 polarization condition, followed by an immunization with MOG at 7 days after injection. In certain experiments, WT RORγt cells were virally transduced with retrovirus expressing GFP alone, while RORγtK256R/K256R cells were transduced with retrovirus expressing Runx1 and GFP before in vitro TH17 differentiation. In other experiments, RORγt−/− cells were transduced with an empty vector or vectors encoding WT RORγt, RORγtK87R, RORγtK99R, RORγtK256R, or RORγtK288R. All transduced cells were sorted for CD4 and GFP expression before adoptive transfer into mice. Severity of EAE was monitored, and a clinical score of 0 to 5 was assigned (30): 0 = no disease, 0.5 = partially limp tail, 1 = paralyzed tail, 2 = hindlimb weakness, 3 = hindlimb paralysis, 4 = hindlimb and forelimb paralysis, and 5 = moribundity and death.
Murine CD4+ T cells were isolated from spleens by negative selection using the Naive CD4+ T Cell Isolation Kit (Miltenyi Biotec). Suspensions of 4 × 105 cells/ml in RPMI 1640 medium (Corning Inc.) containing 2 mM l-glutamine, 50 μM β-mercaptoethanol, penicillin (100 U/ml), streptomycin (100 mg/ml), and 10% fetal bovine serum (FBS) were activated with hamster anti-CD3 (0.25 μg/ml; 145-2C11, BioLegend) and hamster anti-CD28 (1 μg/ml; 37.51, BioLegend) antibodies overnight in 24-well plates precoated with rabbit anti-hamster immunoglobulin G fraction (0.1 mg/ml; catalog no. 55398, MP Biomedicals). The following TH17 differentiation was carried out by supplementing to the culture medium mentioned above with TGF-β (2 ng/ml; Miltenyi Biotec), IL-6 (20 ng/ml; Miltenyi Biotec), anti–IL-4 (2 μg/ml; 11B11, BioLegend), and anti–IFN-γ (2 μg/ml; XMG 1.2, BioLegend), and additional IL-23 (20 ng/ml; Miltenyi Biotec) was also added for the induction of pathogenic TH17 cells.
The retroviral vector murine stem cell virus (MSCV)–internal ribosomal entry site (IRES)–GFP (MIGR1, a gift from W.S. Pear, University of Pennsylvania) was used to clone WT or mutated RORγt. MSCV vector for expressing Runx1 was a gift from I. Taniuchi (RIKEN Center for Integrative Medical Sciences, Japan). Vectors were first transfected to Platinum-E (Cell Biolabs) retroviral packaging cells using BioT transfection reagent (Bioland Scientific), followed by a change of fresh medium at 24 hours. The virus-containing supernatant was collected at 48 and 72 hours, filtered with a 0.45-μm polyvinylidene difluoride (PVDF) syringe filter (Millipore), and used to transduce T cells or stored for future use at −80°C. Transduction of activated CD4+ T was performed by spin infection with viral supernatants (two, 500g, 30°C for 2 hours) in the presence of polybrene (8 μg/ml; Sigma-Aldrich). After spinning, the plates were incubated at 37°C for 3 hours. The viral supernatant was replaced with fresh culture medium with polarizing cytokines for in vitro differentiation.
Single-guide RNA (sgRNA) of Runx1, targeting the exon region (Addgene, library 67988), was cloned to pMSCV-U6sgRNA(Bbs I)-PGKpuro2ABFP (Addgene, 102796) with modification of Bbs I sites (table S1) for using universal primer design through this study. To generate plasmids for the deletion of large fragments of genomic DNA, PCR products of two U6 promoter–sgRNA cassettes and a phosphoglycerate kinase (PGK) promoter–TagBFP cassette were assembled using the Golden Gate assembly method and inserted into the MIGR1 vector with disrupted Bsp MI site. Bbs I sites and newly introduced Bsp MI sites were used for the insertion of gRNAs into each cassette. sgRNAs were delivered to the cells by retroviral transduction. The U6 promoter–driven transcription of sgRNAs in each cassette was confirmed by examining sgRORγt expression in TH17 cells together with a simultaneously expressed NTC sgRNA in another cassette. Three sgRNAs (sgRNA1, sgRNA2, and sgRNA3; fig. S6A) targeting the sequence of the Runx1 gene were designed by using an online tool (CRISPOR, http://crispor.tefor.net/). A simultaneous expression of sgRNA1 with sgRNA2 or sgRNA2 with sgRNA3 in Cas9-expressing cells was designed for deletion of the RORγt binding region (Rgn1) and the adjacent region (Rgn2) without RORγt biding site. sgRNA primers are listed in table S1.
Murine thymocytes from RORγt−/− mice were subjected to fluorescence-activated cell sorting for isolating DN (Thy1.2+CD4−CD8−) cells. Sorted cells at 5 × 105 cells/ml were cultured overnight on an 80% confluent bone marrow-derived stromal cell line (OP9) expressing the delta like canonical Notch ligand 4 (Dll4/DL4) (OP9-DL4) monolayer (a gift from E.V. Rothenberg) in 24-well culture plates with α-modified minimum essential medium (Invitrogen Life Technologies) supplemented with 20% FBS, penicillin-streptomycin (100 U/ml), 2 mM l-glutamine (Invitrogen Life Technologies), and recombinant mouse IL-7 (5 ng/ml; PeproTech). The cells were then transduced with RORγt carrying K/R mutations as described above. Cocultures were maintained for an additional 3 days in the fresh medium containing murine IL-7 (5 ng/ml). Cells were harvested for flow cytometry analysis.
Murine thymocytes were collected by smashing the thymus in a 40-μm cell strainer. Cells were suspended in RPMI 1640 medium supplemented with 10% FBS, 1% penicillin-streptomycin, and 2 mM l-glutamine at 1 × 106 cells/ml and cultured for 0, 3, 6, 12, 18, or 24 hours. Thymocytes were then incubated with anti-Thy1.2 antibody and a fixable LIVE/DEAD near-infrared dye (Thermo Fisher Scientific). After two washes, the cells were stained with 5 μl of phycoerythrin–annexin V in 100 μl of binding buffer containing 0.01 M Hepes (pH 7.4), 0.14 M NaCl, and 2.5 mM CaCl2 for 15 min. An additional 400 μl of binding buffer was added to the suspension before analysis.
CD4+ T cells isolated from RORγtWT/IL-17A-GFP+/− and RORγtK256R/K256R/IL-17A-GFP+/− mice were differentiated into pathogenic TH17 cells as described above. RNA was extracted from sorted ~1 × 106 GFP-expressing TH17 cells (CD4+GFP+) using an RNAeasy mini kit (QIAGEN). Each group contained three replicates from different mice. Quality control, library preparation, and sequencing were performed at Novogene. The analysis was performed through Partek Flow. Briefly, the sequence reads were aligned to the mouse whole genome (GRCm38) with validation of quality through prealignment and postalignment quality assurance/quality control (QA/QC). Aligned reads were further subjected to quantification using the Partek expectation/maximization (E/M) algorithm and normalization to counts per million with 0.001 added to each. The identification of differentially expressed features was performed through the Partek gene specific analysis (GSA) algorithm that applies multiple statistical models to each gene. Genes with total counts over 30 were considered to be statistically expressed in the cells. The expression values of pathogenic genes were extracted and subjected to ingenuity pathway analysis (IPA), gene set enrichment assay (GSEA), and network analysis.
In vitro–activated (see above) RORγt−/− CD4+ T cells that were transduced with retroviruses carrying GFP, RORγt-3xFlag/GFP, or RORγtK256R-3xFlag/GFP were used. After TH17 polarization, 2 × 107 cells were fixed in 1% formaldehyde at room temperature for 10 min to cross-link proteins with chromatin. The reaction was stopped with incubation in glycine for 5 min. Genomic DNA was fragmented with enzyme cocktail (ChIP-IT Express Enzymatic kit, Active Motif) for 10 min as directed. Cell lysates were centrifuged at 15,000 rpm for 10 min to remove debris, and the supernatant was used for immunoprecipitation. An equal amount of DNA was incubated with anti-FLAG (M2, Sigma-Aldrich) overnight, followed by precipitation with protein G agarose beads. Beads complexed with DNA fragments were extensively washed five times, and DNA was eluted, followed by reverse cross-linking. Recovered DNA was subjected to NovaSeq with 51–base pair (bp) paired-end sequencing length. Primers used in reverse-transcription quantitative PCR (RT-qPCR) are listed in table S1. Reads were analyzed using Partek Flow through alignment to the mm10 mouse genome using the Burrow-Wheeler aligner (BWA). Peaks were identified with the model-based analysis of ChIP-seq 2 (MACS2) tool (version 2.1.1) and quantified with a minimum region size of 50 bp.
For surface staining, cells isolated from mice or in vitro culture were directly stained with antibodies in phosphate-buffered saline (PBS) with 2% FBS and 1 mM EDTA at 4°C for 15 min. A blocking of Fc receptors with anti-CD16/32 antibody was carried out in case monocytes/macrophages were present. For staining transcription factors, cells were prestained for surface markers, fixed, and permeabilized in TF Fix/Perm buffer (BD Biosciences) at 4°C for 20 min and washed once with TF Perm/Wash buffer. Cells were stained for target proteins (see antibody list below) in the TF Perm/Wash buffer at 4°C for 15 min. For cytokine staining, cells were prestimulated with phorbol 12-myristate 13-acetate (50 ng/ml; Sigma-Aldrich) and ionomycin (750 ng/ml; Sigma-Aldrich) for 3 hours ahead of staining. Meanwhile, GolgiStop (BD Biosciences) was cotreated to block protein transport. In certain experiments, cells were stained with surface markers and/or fixable live/dead dye (Thermo Fisher Scientific). Cells were fixed and permeabilized with CytoFix/CytoPerm buffer (BD Biosciences), followed by staining for cytokines in the Perm/Wash buffer (BD Biosciences) after washing. To measure cell proliferation, either naïve CD4+ T cells or in vitro–differentiated TH17 cells for adoptive transfer were stained with CellTrace Violet dye (Thermo Fisher Scientific) in PBS (1:5000) at room temperature for 20 min. After washing, naïve CD4+ T cells were subjected to anti-CD3/anti-CD28 stimulation and TH17 differentiation for measuring in vitro proliferation, and TH17 cells were sorted out and injected to Rag1−/− mice for measuring hemostatic proliferation at day 3. Subsequent analysis was performed in the BD LSRFortessa flow cytometer. The following antibodies were used for flow cytometric assay: anti-CD45 (BioLegend, clone 30-F11), anti-CD3 (BioLegend, 145-2C11), CD4 (BioLegend, RM4-5), anti-CD8 (BioLegend, 53-6.7), anti-CD19 (BioLegend, 1D3), anti-lymphocyte antigen 6 complex locus G6D (Ly6G) (BioLegend, 1A8), anti-CD62L (BioLegend, MEL-14), anti-CD44 (BioLegend, IM-7), anti–IFN-γ (BioLegend, XMG-1.2), anti–GM-CSF (BioLegend, MP1-22F9), killer cell lectin-like receptor subfamily B member 1C (Klrb1c/NK1.1) (BioLegend, PK136), anti-CD11b (eBioscience, M1/70), anti-Ly6C (eBioscience, HK1.4), anti-Thy1.2 (eBioscience, 53-1.2), anti–IL-17A (eBioscience, eBio17B7), anti-Runx1 (eBioscience, RXDMC), anti–IL-22 (eBioscience, 1H8PWSR), anti-Foxp3 (eBioscience, FJK-16 s), anti-RORγt (BD Biosicences, Q31-378), and CD1d-tetramer [National Institutes of Health (NIH), PBS-57].
A total 1.5 × 107 TH17 cells were lysed in radioimmunoprecipitation assay buffer containing 20 mM tris-HCl (pH 7.4), 150 mM NaCl, 1 mM Na2EDTA, 1 mM EGTA, 1% NP-40, 1% sodium deoxycholate, 2.5 mM sodium pyrophosphate, 1 mM β-glycerophosphate, 1 mM Na3VO4, and leupeptin (1 μg/ml). Ubiquitinated proteins were precipitated and enriched with 20 μl of equilibrated agarose-Tandem Ubiquitin Binding Entities 2 (TUBE2) (Lifesensors) at 4°C for 4 hours. Agarose-TUBE2-protein complex were washed with Tris buffered saline containing 0.1% Tween-20 (TBST) and subjected to heating in 2× Laemmli sample buffer (Bio-Rad) with β-mercaptoethanol at 90°C for 5 min. The supernatant containing precipitated proteins was subjected to SDS–polyacrylamide gel electrophoresis, and the protein was transferred to the PVDF membrane. Target proteins were sequentially immunoblotted with relevant primary antibodies and fluorescent secondary antibodies (LI-COR Biosciences), followed by measuring fluorescent intensity with a LI-COR Odyssey blot imager (LI-COR Biosciences). The eventual samples for Western blotting were pooled from three different experiments. Quantification of ubiquitination signals of blots showing in Fig. 2 (E and F) was performed to the area above 50 kDa.
Total RNA was extracted using the RNeasy mini kit (QIAGEN) as directed. A Tetro complementary DNA synthesis kit (Bioline) was used for reverse transcription. Subsequent qPCR was performed using PowerUp SYBR Green Master Mix (Applied Biosystems) in the QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific). The primers used for qPCR are listed in table S1. The amplification efficiency of all primers was tested and optimized. Gene expression was calculated with the delta-delta Ct (∆∆Ct) method normalized to the control gene encoding β-actin and glyceraldehyde-3-phosphate dehydrogenase, and all measurements were performed in triplicate.
Human embryonic kidney 293T cells were cultured in Dulbecco’s modified Eagle’s medium supplemented with 10% FBS, 2 mM glutamine, penicillin (100 U/ml), and streptomycin (100 mg/ml). A total of 8 × 105 cells were seeded to each well of a six-well plate and transfected with the reporter vectors (400 ng), pSV40-Renilla luciferase vector (200 ng), and expression vectors (2 μg) using BioT transfection reagent (Bioland Scientific, Paramount, CA). The same amount of plasmid DNA was used by adjusting with an empty vector. Luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega, Madison, WI) per the manufacturer’s instruction and normalized against Renilla luciferase activities. “Relative luciferase activities” were plotted with further normalization of luciferase activities of each group to the pGL3-basic reporter vector plus the empty vector group. The generation of reporter plasmids was done by PCR amplification of Runx1 genomic DNA containing the RORγt-binding region and a subsequent insertion upstream of a mini TK promoter that was cloned to pGL3-basic vector (Promega) for a minimal expression of luciferase. Cloning primers and mini TK promoter sequence are listed in table S1.
The ratio of rescue for thymocyte development and TH17 differentiation in Fig. 2 (A and B) was calculated as relative ratio of the extent of RORγt−/− cells transduced with RORγt mutants to that of cells transduced with WT RORγt. The statistical parameters are indicated in the figure legends. The results were analyzed for statistical significance with unpaired Student’s t test. Bodyweights are presented as means ± SD, and other data are shown as means ± SEM. P values are calculated using GraphPad Prism and presented where the statistical significance (P < 0.05) was found. | true | true | true |
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PMC9586727 | Wen Shen,Shukun Wang,Ruili Wang,Yang Zhang,Hong Tian,Xi Wang,Xin Wu,Xiaolei Yang,Wei Wei | Transcription Factor p300 Regulated miR-451b Weakens the Cigarette Smoke Extract-Induced Cellular Stress by Targeting RhoA/ROCK2 Signaling | 14-10-2022 | Background A previous study identified miR-451b as a potential biomarker in smoker with or without chronic obstructive pulmonary disease (COPD). However, the function and molecular mechanisms of miR-451b in the pathogenesis of COPD remain elusive. Methods Macrophages and lung fibroblasts were exposed to 10% cigarette smoke extract (CSE) solution for 24 h. Expression miR-451b and its potential transcription factor p300 were detected. The association between p300 and miR-451b, miR-451b and RhoA was validated by luciferase reporter assay. The release of IL-12 and TNF-αby macrophages was measured by ELISA assay, and Transwell assay was performed to analyze its migration and invasion. Collagen protein of fibroblasts was detected by Western blotting. Results Results showed that p300 and miR-451b was downregulated, while RhoA was upregulated in CSE-induced macrophages and lung fibroblasts. The stimulation of CSE promoted the degradation of p300 by ubiquitination, and RhoA was confirmed as the target gene of miR-451b. MiR-451b overexpression significantly decreased the release of IL-12 and TNF-α, downregulated the expression of RhoA, ROCK2, and p65, and suppressed cell migration and invasion in CES-induced macrophages. In addition, miR-451b overexpression decreased the expression of RhoA, ROCK2, COL1A1, and COL2A1 in lung fibroblasts. Conclusions Our data suggest that p300/miR-451b protects against CSE-induced cell stress possibly through downregulating RhoA/ROCK2 pathway. | Transcription Factor p300 Regulated miR-451b Weakens the Cigarette Smoke Extract-Induced Cellular Stress by Targeting RhoA/ROCK2 Signaling
A previous study identified miR-451b as a potential biomarker in smoker with or without chronic obstructive pulmonary disease (COPD). However, the function and molecular mechanisms of miR-451b in the pathogenesis of COPD remain elusive.
Macrophages and lung fibroblasts were exposed to 10% cigarette smoke extract (CSE) solution for 24 h. Expression miR-451b and its potential transcription factor p300 were detected. The association between p300 and miR-451b, miR-451b and RhoA was validated by luciferase reporter assay. The release of IL-12 and TNF-αby macrophages was measured by ELISA assay, and Transwell assay was performed to analyze its migration and invasion. Collagen protein of fibroblasts was detected by Western blotting.
Results showed that p300 and miR-451b was downregulated, while RhoA was upregulated in CSE-induced macrophages and lung fibroblasts. The stimulation of CSE promoted the degradation of p300 by ubiquitination, and RhoA was confirmed as the target gene of miR-451b. MiR-451b overexpression significantly decreased the release of IL-12 and TNF-α, downregulated the expression of RhoA, ROCK2, and p65, and suppressed cell migration and invasion in CES-induced macrophages. In addition, miR-451b overexpression decreased the expression of RhoA, ROCK2, COL1A1, and COL2A1 in lung fibroblasts.
Our data suggest that p300/miR-451b protects against CSE-induced cell stress possibly through downregulating RhoA/ROCK2 pathway.
Chronic obstructive pulmonary disease (COPD) is widely considered an incurable but preventable respiratory disease with a rise in prevalence and mortality, which has been the third most frequent cause of death worldwide [1, 2]. The main symptoms of COPD include chronic cough, expectoration, emphysema, chronic airway obstruction, and airway remodeling [3]. Cigarette smoke extract (CSE) induced inflammatory disorder [4, 5] and impaired functional properties of lung fibroblasts [6, 7], are the important mechanisms underlying COPD. Although the association between CSE and COPD development has been reported [8–10], the cellular and molecular mechanism underlying COPD remains largely unclear. MicroRNAs (miRNAs/miRs) are small non-coding RNA molecules that could regulate the transcriptional or translational gene expression via binding the 3′-untranslated region of multiple target mRNAs [11]. In recent years, some studies have shown that alterations in miRNA expression are closely associated with progression of smoking-induced patients with COPD [12, 13]. For example, miR-34a plays a key role in CSE-induced endothelial cell apoptosis by directly regulating its target gene Notch-1 [14]. Tang et al. [15] reported that miR-29b may participate in the airway inflammation in COPD by regulating inflammatory cytokine expression through targeting bromodomain protein 4 (BRD4). In addition, miR-146a is significantly downregulated in lung fibroblasts of COPD patients [16] and miR-26a acts as a regulator of the nuclear factor-κB (NF-κB) pathway in alveolar macrophages [17]. It is worth noting that our previous work identified several potential biomarkers, including miR-3202, miR-451b, and miR-149-3p in smokers with or without COPD and all their expression levels were downregulated in COPD group compared with control group [18]. In functional experiments, we demonstrated that reducing miR-149-3p may increase the inflammatory response in COPD patients through the regulation of the TLR-4/NF-κB signaling pathway [18]. Similarly, our data provided support for the protective role of miR-3202 in CSE-stimulated T lymphocytes and human bronchial epithelial cells through targeting Fas apoptotic inhibitory molecule 2 (FAIM2) [19]. However, whether miR-451 plays an important role in suppressing CSE-induced lung injury has not been reported yet. The small G-protein RhoA, as the master regulator of actin dynamics, is necessary for cell morphology, adhesion, proliferation, and migration [20]. Rho-kinase (ROCK-I or ROCKβ and ROCK-II or ROCKα) is the downstream effector of RhoA and constitutes the RhoA/ROCK signal pathway involved in pulmonary endothelial dysfunction in healthy smokers [21] and patients with COPD [22]. In diabetic nephropathy, the RhoA/ROCK pathway might regulate NF-κB activity to upregulate inflammatory genes [23]. In rheumatoid arthritis, blockade of ROCK inhibits the activation of NF-κB and the production of pro-inflammatory cytokines [24]. Interestingly, CSE-induced p120-catenin- (p120-) mediated NF-κB activation in human epithelial cells is dependent on the RhoA/ROCK pathway [25]. The online bioinformatics analysis suggests that RhoA was the target of miR-451b, which makes us hypothesize that the function of miR-451b in CSE-induced lung injury might through targeting RhoA/ROCK pathway. To validate our hypothesis, CSE was first used to treat the macrophages and lung fibroblasts as useful in vitro models to evaluate smoking-related COPD pathogenesis. Then, we examined transcriptional regulation of miR-451b and its effects on inflammatory mediators, RhoA/ROCK pathway, extracellular matrix (ECM) components, migration, and invasion.
Blood samples from non-smoking healthy volunteers (NS-H, n =10), smoking healthy volunteers (S-H, n =10), and smoking COPD patients (S-COPD, n =10) were collected from September 2019 to January 2020 at the Second Affiliated Hospital of Kunming Medical University. Total of 4 mL of peripheral blood was collected at fasting and half of it was used to isolate peripheral blood mononuclear cells (PBMCs) through gradient centrifugation with Ficoll-Hypaque (Ficoll-Paque PLUS; GE Healthcare Bio-Sciences AB, Uppsala, Sweden). The rest is used to separate serum.
Macrophages (RAW264.7), rat pulmonary microvascular endothelial cells (rPMECs), rat alveolar epithelial cells (rAECs), rat bronchial epithelial cells (rBECs), and rat lung fibroblasts (rLFs) were purchased from Procell Life Science & Technology Co., Ltd. (Wuhan, China), which were both cultured in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% fetal bovine serum, 2 mM/L glutamine, 100 units/mL penicillin, and 0.1 mg/mL streptomycin at a humidified incubator containing 5% CO2 at 37°C.
Preparation of CSE was performed using commercial cigarettes (Marlboro; Philip Morris USA, Richmond, VA, USA) by a modified method as previously described [26]. In brief, one cigarette was bubbled through 25 mL of DMEM at a constant rate, which was considered 100% CSE solution. Then, CSE solution was sterilized and diluted to a final working concentration (10%) before use.
MiR-451b mimics and inhibitor, specifically targeting RhoA small interference sequence, were synthesized by RiboBio (Guangzhou, China) (Table S1). Coding sequence was cloned and inserted into pcDNA 3.1 plasmid vector. For cell transfection, cells were seeded into six-well plates at a density of 3.0 × 107 cells per well and cultured overnight at 37°C. Next day, cells were transfected with 25 nM above material for 48 h with Lipofectamine 2000 (Invitrogen, Carlsbad, CA), followed by 10% CSE exposure for an additional 48 h.
Total RNA extraction was performed by Trizol reagent (Invitrogen) and cDNA was synthesized using a Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's instructions. Quantitative reverse transcription PCR was conducted with TaqMan Gene Expression Assays and the ABI Prism 7500 (Applied Biosystems) according to the thermal cycling conditions: 37°C for 10 min, 95°C for 5 min, followed by 50 cycles of 95°C for 15 s, 60°C for 20 s, and 68°C for 20 s. The primer sequences used in this study are showed in Table S1. Relative expression levels of miR-451b or RhoA were calculated by the 2–ΔΔCt method with U6 or GAPDH as endogenous controls.
Total protein samples were extracted from cell samples with RIPA buffer (CWBio, Beijing, China) and protein concentration was measured using BCA protein assay kit (Beyotime, Shanghai, China). Equal amount of protein sample was separated on 12% SDS-PAGE gels followed by transferred to nitrocellulose membranes. Then, the membranes were blocked in 5% non-fat milk dissolved in TBST solution and incubated with primary antibodies against p300 (Abcam, Cambridge, MA, USA; ab275378, 1:1000 diluted), RhoA (ab187027, 1:2000 diluted), ROCK (ab134181, 1:1000 diluted), NF-κB p65(ab207297, 1:1000 diluted), HDAC1 (ab109411, 1:4000 diluted), COL1A1 (ab270993, 1:3000 diluted), COL2A1 (ab34712, 1:3000 diluted), and GAPDH (ab8245, 1:5000 diluted) overnight at 4°C. After washing with TBST, membranes were incubated with HRP-conjugated secondary antibodies for 2 h. The protein bands were visualized using an enhanced chemiluminescence reagent (Pierce Biotech, Inc., Rockford, IL, USA).
Cells from different groups were fixed with 4% paraformaldehyde, washed with PBS, and permeabilized with 0.5% Triton X-100 dissolved in PBS. After blocked with 3% bovine serum albumin (BSA) for 1 h, the cells were incubated with primary antibodies against RhoA (ab187027, 1:500 diluted) or p300 (ab275378, 1:500 diluted) overnight at 4°C. Subsequently, cells were washed with PBS twice and incubated with appropriate secondary antibodies. The nucleus was labeled with 4′,6-diamidino-2-phenylindole for 5 min. All staining images were viewed under a fluorescence microscope (Thermo Fisher Scientific, Waltham, MA, USA).
The release concentration of IL-12 and TNF-α was measured in cell culture media by commercial ELISA according to the manufacturer's instructions (R&D Systems) according to the manufacturer's protocol. All samples were assayed in duplicate. Results are expressed as picograms of cytokine per milligram (pg/mL) of total protein in the homogenate.
The migrated and invasive ability of macrophages was assessed using Transwell chamber (Corning Inc., Corning, NY, USA). In brief, approximately macrophages prepared in serum-free medium were added into the upper chamber (normal chamber for migration assay and matrigel-coated chamber for invasion assay). Meanwhile, complete medium (500 μL) containing 10% FBS was added into the lower chamber. After 24 h incubation at 37°C, the macrophages that migrated into the lower chamber were fixed with methanol and stained with crystal violet, which were further counted with a microscope.
Briefly, the oligonucleotides containing wild-type or mutated RhoA-3′UTR of the predicted binding site were synthesized and subcloned into psiCHECK-2 vector (Promega, Madison, Wisconsin) to construct WT or MUT RhoA plasmids, respectively. Then, co-transfection of WT or MUT RhoA plasmid and miR-451b mimics or NC was performed in RAW264.7 cells using Lipofectamine 2000. After 48 h, luciferase activity was determined using the dual-luciferase reporter assay system (Promega). Relative luciferase activity was reported as luciferase activity/Renilla luciferase activity. In addition, the 5′-upstream sequence of pre-miR-451b was segmented, cloned, and inserted into pGL3-Basic plasmid, followed by transfecting to RAW264.7 cells and fluorescence detection. To test and verify p300 regulating transcription of pre-miR-451b, the binding site sequence (5′-TTAGGGACTGAGTCT-3′) was mutated (5′-TTAATGCGGGAGTCT-3) and used to repeat fluorescence detection.
Chromatin immunoprecipitation was performed according to the instruction of a ChIP Assay Kit (Beyotime). In brief, cells were lysed with ice-treated SDS lysis buffer and ultrasonication, and deoxyribonucleic acid (DNA) was extracted. Then, the sample was treated with ChIP dilution buffer and incubated with primary antibody targeted to p300. Subsequently, protein A agarose/salmon sperm DNA was added to precipitate the immune complex. Finally, wash the sediment, de crosslink, and recover DNA fragments. The 5′-upstream sequence of pre-miR-451b was detected by real-time quantitative PCR.
Promoter of pre-miR-451b-p300 banding was detected by probes that has been Biotin-labeled in the presence or absence of anti-p300 antibody. Prokaryotic expressed p300 protein was purified by gel filtration chromatography, followed by incubation with DNA probes in binding buffer. Anti-p300 antibody or specific mutant competitors were pre-incubated with p300. Finally, 4% PAGE gel was used to finish the electrophoretic separation to products.
Each experiment was performed in triplicates and data were expressed as mean ± SD. Statistical analyses were carried out with GraphPad Prism version 6.0 (GraphPad Software, San Diego, CA, USA). Different comparisons were performed by Student's t test between two groups and one-way analysis of variances (ANOVA) followed by Tukey's test for three groups. Statistical significance was accepted when the P-value less than 0.05.
To verify whether miR-451b is related to smoking, blood samples from non-smoking healthy volunteers (NS-H), smoking healthy volunteers (S-H), and smoking COPD patients (S-COPD) were collected and the expressions of miR-451b in which were detected. Results showed that the expression of miR-451b was significantly downregulated in serum and peripheral mononuclear cells (PBMCs) from both S-H group and S-COPD group, when compared with that from NS-H group (Figure 1(a)). Meanwhile, in vitro 10% CSE inhibited the expression of miR-451b in macrophages (RAW264.7), rat pulmonary microvascular endothelial cells (rPMECs), rat alveolar epithelial cells (rAECs), rat bronchial epithelial cells (rBECs), and rat lung fibroblasts (rLFs) (Figure 1(b)). Further, fluorescence report experiment in RAW264.7 showed that the core promoter sequence of miR-451b responding to CSE was region from -318 site ~ -207 site (Figure 1(c)). Through ALGGEN-PROMO database (http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3), the transcription factor, p300, could bind to the core promoter sequence of miR-451b (Figure 1(d)), and fluorescence report showed that there was no significant difference in fluorescence ratio between with or without CSE stimulation, when we mutated banding site of p300 in the core promoter sequence of miR-451b (Figure 1(e)). In addition, we overexpressed p300 in RAW264.7 cells, and fluorescence report showed that the fluorescence ratio was significantly increased (Figure 1(f)). Furthermore, the combined relationship between p300 and the core promoter sequence of miR-451b was confirmed by Chromatin Immunoprecipitation assay (Figure 1(g)) and Electrophoretic Mobility Shift assay (Figure 1(h)). Above results revealed that CSE may inhibited the expression of miR-451b in respiratory system related cells by regulating p300.
To explore the mechanism of miR-451b, we further detected the levels of p300 and miR-451b's potential downstream proteins, RhoA and ROCK2 in above PBMCs. Results showed that p300 in S-H and S-COPD group was decreased when compared with NS-H group, while RhoA and ROCK2 were increased in PBMCs derived from smokers (Figure 2(a)). In CSE-stimulated RAW264.7, rPMECs, rAECs, rBECs, and rLFs, we found that the protein levels of p300 were inhibited, and levels of RhoA were increased (Figure 2(b)). Further, we stimulated RAW264.7 and rLFs with CSE and cycloheximide (CHX), and cells were collected 0 h, 1 h, 2 h, 4 h, 8 h, 16 h, and 24 h after stimulation, followed by Western blotting to p300. Results showed that CSE stimulation significantly reduced the half-life of protein p300 (Figures 2(c) and 2(d)).
In p300-overexpressed RAW264.7 and rLFs, we found that the decreased expressions of miR-451b induced by CSE stimulation were reversed (Figure 3(a)). Then, the online bioinformatics software was used to predict downstream target gene of miR-451b. As shown in Figure 3(b), miR-451b could bind to 3′-UTR regions of RhoA gene, which has been associated with pulmonary endothelial dysfunction in patients with COPD [22]. Subsequently, we performed luciferase report assay to confirm the association between miR-451b and RhoA. The results (Figure 3(c)) showed that miR-451b mimics transfection significantly suppressed the relative luciferase activity of the WT RhoA 3′-UTR compared with NC transfection in RAW264.7cells. In contrast, co-transfection of miR-451b mimics did not affect the luciferase activity of the binding site mutant RhoA 3′-UTR reporter. Further, we found that the expression of RhoA mRNA and protein was markedly increased by CSE stimulation, while it was reverted to low level by overexpression of p300 in RAW264.7 and rLFs (Figures 3(d)–3(f), 3(j), and 3(l)). Meanwhile, the level of ROCK2 and nuclear NF-κB p65 was consistent with that of RhoA in RAW264.7 (Figures 3(e) and 3(g)), as well as the level of interleukin 12 (IL-12) and tumor necrosis factor-α (TNF-α) in culture supernatant (Figure 3(h)). Furthermore, CSE stimulation induced increasing migration ability of RAW264.7, and accumulation of proteins COL1A1 and COL2A1 in rLFs was both suppressed by overexpression of p300 (Figure 3(i)). These data suggest that RhoA might be a downstream target of miR-451b.
Since miR-451b was downregulated in RAW264.7 after CSE exposure, miR-451b mimics was transfected into RAW264.7 to investigate its impact on CSE-induced injury. At first, the expression of RhoA mRNA was demonstrated to be significantly upregulated in CSE exposure, which was notably decreased after miR-451b mimics transfection using quantitative reverse transcription PCR (Figure 4(a)). Results of Western blot analysis showed that miR-451b overexpression obviously downregulated the expression of RhoA, ROCK2, and nuclear NF-κBp65 in CSE-treated macrophages (Figure 4(b)). The immunofluorescence staining of RhoA (Figure 4(c)) also revealed similar result. Subsequently, the IL-12 and TNF-α levels (Figure 4(d)) were significantly decreased after miR-451b mimics transfection in CSE-treated RAW264.7, and the increased migratory cells in CSE group were remarkedly reduced after miR-451b overexpression (Figure 4(e)). In addition, we knockdown RhoA in CSE-stimulated RAW264.7 cells and results showed that RhoA mRNA and protein were both decreased (Figures 4(f)–4(h)). The protein level of ROCK2, concentration of IL-12 and TNF-α in culture supernatant, and cell migration ability were also suppressed by knockdown of RhoA (Figures 4(g), 4(i), and 4(j)).
Similarly, rLFs were transfected with miR-451b mimics and siRNA-specific targeting RhoA, followed by 10% CSE exposure. As shown in Figures 5(a) and 5(e), CSE-induced upregulation of RhoA mRNA in rLFs was significantly reduced after miR-451b mimics or siRhoA transfection. Consistently, immunofluorescence staining of RhoA further confirmed that upregulation of RhoA in CSE treatment was obviously impaired in rLFs after miR-451b mimics r siRhoA transfection (Figures 5(b) and 5(f)). What's more, we observed the obviously elevated protein expression of RhoA and ROCK2 induced by CSE was abolished by miR-451b mimics or siRhoA transfection (Figures 5(c) and 5(g)). We further found that accumulation of extracellular matrix (ECM) components (COL1A1 and COL2A1) in CSE group was significantly attenuated after miR-451b overexpression and knockdown of RhoA in rLFs (Figures 5(d) and 5(g)).
To further verify the role of miR-451b in cells and its targeted regulation to RhoA, we directly inhibited the expression of miR-451b using its inhibitor, and knock of RhoA down at the same time. Results found that both in RAW264.7 and rLFs, the expression of miR-451b was significantly downregulated by its inhibitor, while the RhoA mRNA and protein level was increased (Figures 6(a)–6(c) and 6(g)–6(i)). The protein level of ROCK2 and nuclear NF-κB p65 was also increased by miR-451b inhibitor (Figures 6(c) and 6(d)). The IL-12 and TNF-α levels in RAW264.7 culture supernatant were significantly increased in the miR-451b inhibitor group (Figure 6(d)), as well as the cell migration ability (Figure 6), which was both reversed by knockdown of RhoA. Furthermore, the accumulation of COL1A1 and COL2A1 in rLFs was induced by miR-451b inhibitor, and which was also reversed by knockdown of RhoA, too (Figure 6(j)). These results confirm miR-451b functions through regulating RhoA.
In the present study, we reported that miR-451b was downregulated in macrophages after CSE exposure. Overexpression of miR-451b significantly decreased the pro-inflammatory mediators (IL-12 and TNF-α), downregulated the expression of RohA, ROCK2, and NF-κB p65, and suppressed migration and invasion ability. Macrophages are mononuclear leukocyte-derived inflammatory cells, which are correlated with the inflammatory response and alveolar wall destruction in COPD [27]. Macrophages respond to cigarette smoke by producing pro-inflammatory mediators, including IL-8, IL-12, IL-1β, and TNF-α [28]. Previous work has reported the important role of IL-12 and TNF-α in inflammatory airway diseases [29, 30]. In addition, macrophages are also an important source of MMP production that contributes to alveolar wall destruction [31]. Moreover, activation of RhoA/ROCK signaling could mediate macrophage differentiation induced by PMA [32]. Here, we thus selected RAW264.7 to stimulate with CSE to study the role of miR-451b on CSE-induced inflammation, migration, and invasion. Much of the research has examined whether smoking influenced the expression of miRNAs in airway, and it is known that there is an association between differentially expressed miRNAs and COPD [33, 34]. The interactions between miRNA-mRNA-lncRNA expanded our understanding of the disease mechanism in smoking COPD [35]. As for the miR-451b in COPD pathogenesis, our previous work demonstrated that miR-451b expression was downregulated in the smoker without COPD, smoker with stable COPE, and smoker with acute exacerbation COPD groups compared with non-smoker non-COPD group [18]. Here, stimulation of CSE results in a decrease in miR-451b expression and in increase in the expression of its target gene, RhoA. Furthermore, transfection of miR-451b mimics induced the downregulation of RhoA. MiR-451b overexpression reversed the effects of CSE on macrophages. Similarly, a previous study indicated that miR-451b was associated with both childhood asthma and adult COPD exacerbations [36]. Our data further showed that increased miR-451b expression caused the alteration of RhoA/ROCK signaling and p65 protein levels. Accumulating evidence has indicated RhoA plays a crucial role on the development of COPD. For instance, CSE may impair efferocytosis through oxidant-dependent activation of RhoA [37]. CSE-induced p120-catenin- (p120-) mediated NF-κB activation in human epithelial cells is dependent on the RhoA/ROCK pathway [25]. Activity of RhoA/Rho-kinase was increased in pulmonary arteries of COPD patients as compared with control subjects [22]. Furthermore, miR-133a/RhoA axis has been reported to participate in the elevation of carbon dioxide in tissues in patients with severe lung diseases, including COPD [38]. Lung fibroblasts have been previously reported to play a significant role in orchestrating inflammatory responses and responding to cigarette smoke by increasing pro-inflammatory prostaglandins and other pro-inflammatory mediators [39]. Here, we showed that CSE exposure increased the expression of RhoA, ROCK2, COL1A1, and COL2A1 in lung fibroblasts. Importantly, miR-451b mimics transfection abolished these effects of CSE on lung fibroblasts. COL1A1 and COL2A1, as the ECM components, have been demonstrated to be the downstream of transforming protein RhoA and Rho-associated protein kinase 1 for the regulation of osteogenesis [40]. Wang et al. [41] further manifested that ECM proteins promoted proliferation, migration, and adhesion of ASMCs from rat models of COPD through activation of the PI3K/AKT signaling pathway. Based on these facts, we thus speculated that overexpression of miR-451b could attenuate the impaired functional properties of lung fibroblasts, as key players in maintaining tissue homeostasis, are believed to be an important mechanism underlying COPD (Figure 7). However, there is a lack of verification by animal experiment and which is one of the limitations in the presented study.
In summary, our results suggest that transcription factor p300 regulated the expression of miR-451b and the latter suppressed CSE-induced inflammation and impaired functional properties in macrophages and lung fibroblasts. These effects may be associated with the regulation of its target gene RhoA-mediated RhoA/ROCK2 signaling pathway. This study therefore identifies p300/miR-451b/RhoA axis as a potential therapeutic target for CSE-induced injury in COPD. | true | true | true |
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PMC9586760 | Lin Luo,Xin Zhang,Yiliyaer Rousuli,Alibiyati Aini | Exosome-Mediated Transfer of miR-3613-5p Enhances Doxorubicin Resistance by Suppression of PTEN Expression in Breast Cancer Cells | 14-10-2022 | Breast cancer is the most common malignancy among women worldwide, and patients easily develop resistance to the first-line drug doxorubicin. To elucidate the molecular mechanism of drug resistance in breast cancer is imperative. Exosomes mediate the crosstalk between neighboring cells and intercellular communication. Incorporation of miRNAs into exosomes prevents the degradation and facilitates the intercellular communication, which has been indicated in regulation of drug resistance. qRT-PCR revealed that miR-3613-5p is upregulated in drug-resistant breast cancer, and miR-3613-5p exists in exosomes. It is predicted that miR-3613-5p can bind to the tumor suppressor gene PTEN. In this study, our results showed that miR-3613-5p was upregulated in drug-resistant tissue and in exosomes of breast cancer cells resistant to doxorubicin. CCK8, crystal violet staining, and flow cytometry analysis demonstrated that exosome mediated miR-3613-5p transfer and enhanced the resistance to doxorubicin of breast cancer cells. Western blotting showed that miR-3613-5p could target PTEN and regulate the expression of PTEN. Exosome-mediated transfer of miR-3613-5p enhanced the resistance to doxorubicin by inhibition of PTEN in breast cancer cells. | Exosome-Mediated Transfer of miR-3613-5p Enhances Doxorubicin Resistance by Suppression of PTEN Expression in Breast Cancer Cells
Breast cancer is the most common malignancy among women worldwide, and patients easily develop resistance to the first-line drug doxorubicin. To elucidate the molecular mechanism of drug resistance in breast cancer is imperative. Exosomes mediate the crosstalk between neighboring cells and intercellular communication. Incorporation of miRNAs into exosomes prevents the degradation and facilitates the intercellular communication, which has been indicated in regulation of drug resistance. qRT-PCR revealed that miR-3613-5p is upregulated in drug-resistant breast cancer, and miR-3613-5p exists in exosomes. It is predicted that miR-3613-5p can bind to the tumor suppressor gene PTEN. In this study, our results showed that miR-3613-5p was upregulated in drug-resistant tissue and in exosomes of breast cancer cells resistant to doxorubicin. CCK8, crystal violet staining, and flow cytometry analysis demonstrated that exosome mediated miR-3613-5p transfer and enhanced the resistance to doxorubicin of breast cancer cells. Western blotting showed that miR-3613-5p could target PTEN and regulate the expression of PTEN. Exosome-mediated transfer of miR-3613-5p enhanced the resistance to doxorubicin by inhibition of PTEN in breast cancer cells.
Breast cancer is the most common malignancy in women worldwide, and the incidence continues to rise [1]. Despite substantial progress and improvements have been achieved over past few decades, it is still a major cause of mortality [1]. Metastasis remains a leading cause of mortality in breast cancer patients, accounting for more than 90% of mortality [1, 2]. Doxorubicin is the most extensively used first-line drug for breast cancer treatment. However, the rapid development of drug resistance has fundamentally weakened its anticancer efficacy [3]. Therefore, it is imperative to explore the potential molecular mechanisms of doxorubicin resistance and find new therapeutic targets for breast cancer. Emerging studies have demonstrated that exosomes secreted by cells can alleviate drug resistance and improve prognosis of malignancies [4–6]. Exosomes are nanoscale membrane vesicles with a diameter of 30-150 nm, and they participate in intercellular communication by transporting of lipids and nucleic acids to recipient cells [7]. Cell-secreted exosomes mediate the crosstalk between neighboring cells and transport to distal tissues, where signals and messages were sent to specific recipient cells [7]. MicroRNAs (miRNAs) are small noncoding RNAs with a length of about 22 nucleotides, which can posttranscriptionally regulate gene expression [8, 9]. Dysregulated miRNAs have been implicated in many different pathophysiological processes [10]. Multiple evidence indicate that miRNAs are involved in the regulation of drug resistance. miRNAs are protected by bilateral membrane structures upon its incorporation into exosomes, thereby reducing miRNA degradation and promoting intercellular communication [11]. Overexpressed miR-567 can be packaged into exosomes and incorporated into recipient cells, which then inhibits autophagy and reverses chemoresistance by targeting ATG5 [12]. miR-155 is induced in exosomes isolated from cancer stem cells and resistant breast cancer cells, and exosome-mediated transfer of miR-155 into breast cancer cells enhances resistance to chemotherapeutic drugs [13]. Based on the GEO database, miR-3613-5p is found to be upregulated in chemoresistant breast cancer, indicating that miR-3613-5p may be involved in the drug resistance of breast cancer. However, exosome-mediated miR-3613-5p transfer in drug resistance of breast cancer has not been studied yet. This present work demonstrated that exosome-mediated transfer of miR-3613-5p enhanced the resistance of breast cancer cells to doxorubicin by inhibition of PTEN.
MDA-MB-231 and MCF-7 cells were purchased from ATCC and maintained in rich DMEM. Fetal bovine serum was purchased from Thermo Fisher Scientific. CCK8 kit (96992) was purchase from Sigma-Aldrich.
The culture medium was supplemented with 10 mg/ml RNase A and incubated at 37°C for 1 h to remove RNA contamination.
Gene Expression Omnibus (GEO) series dataset (GSE73736) was downloaded from GEO. Differential expression analysis in drug-resistant and sensitive tissue of breast cancer was conducted. Estimation of the relative subsets of RNA transcript was performed.
Exosomes solution was dropped onto the formvar grid. Filter paper was used to remove excess water. The exosomes were fixed with 2% phosphotungstic acid for 10 min and then rinsed with deionized water. Then, exosomes were stained with 1% uranyl acetate for 15 min. Philips EM208S TEM (Netherlands) at 100 kV was used to photograph the exosome's morphology.
TRIzol (Invitrogen) was used for total RNA extraction. The reaction mixture was prepared according to the instruction of SYBR Green (Takara, Japan). The reaction was initiated and detected with ABI Prism 7500 RT PCR instrument. The relative level of mRNA was quantified with the 2-△△Ct method. The primers were as follows: U6-forward: 5′-GCTTCGGCAGCACATATACTAAAAT-3′ and U6-reverse: 5′-CGCTTCACGAATTTGCGTGTCAT-3′; miR-3613-5p-forward: 5′-CTTGTTTTTTTTTTCATGTTGT-3′ and miR-3613-5p-reverse: 5′-AGTCTCAGGGTCCGAGGTATTC-3′; PTEN-forward: 5′-TGGATTCGACTTAGACTTGACCT-3′ and PTEN-reverse: 5′-GGTGGGTTATGGTCTTCAAAAGG-3′; and GAPDH-forward: 5′-GTCTCCTCTGACTTCAACAGCG-3′ and GAPDH-reverse: ACCACCCTGTTGCTGTAGCCAA. For generation of PTEN knockdown cell line, the primers used to generate into pLKO.1-puro vector were as follows: sh-NC-sense strand: 5′-ACTGCCCTGATGCTAGCTAGCACCGGT-3′ and sh-NC-antisense strand: 5′-GCUCGATCCTGCTAGATCUUCGCUAC-3′; sh-PTEN-sense strand: 5′-GACAAAGCCAACCGATACTTT-3′; and sh-PTEN-antisense strand: 5′-AAAGTATCGGTTGGCTTTGTC-3′.
Exosomes were isolated and purified with an ExoQuick precipitation kit (System Biosciences, LLC, Palo Alto, CA). Briefly, cell culture medium was collected and centrifuged at 3000 × g for 15 min. Supernatant was collected and mixed with ExoQuick precipitation solution. The mixture was incubated at 4°C for 30 min and centrifuged at 1500 × g for 30 min. The supernatant was carefully removed and resuspended in 100 μl PBS.
Cells were collected and washed with prechilled PBS. Cells were incubated with Annexin V-PE/7-AAD and propidium iodide (PI) for 10 min at room temperature in accordance with the manufacturer's instruction. Cell apoptosis was detected with a flow cytometer.
The base layer was prepared with 5 ml rich medium supplemented with 0.75% agar. The top layer was prepared with 3 ml rich medium supplemented with 0.36% agar at a concentration of 3 × 104 cells/ml, incubated at 37°C for 3 weeks, and stained with 0.04% crystal violet in PBS and photographed with a scanner.
Cells were harvested and washed with PBS by centrifugation at 600 × g for 5 min. Cells were resuspended in reporter lysis buffer and kept on ice for 20 min. After a centrifugation at maximum speed for 10 min, the supernatant was collected. 20 μL supernatant and 100 μL luciferase assay reagent were mixed together. A luminometer was used to detect the fluorescence.
NC mimic, miR-3613-5p-mimic, and miR-3613-5p inhibitor were synthesized by GenePharma. Cells were transfected with a polyethylenimine- (PEI-) mediated method. Briefly, DNA was mixed with PEI at a ratio of 1 : 3 and diluted with free DMEM medium, followed by incubation at room temperature for 15 min. The mixture was added to the cell culture rich medium.
Cells were harvested and washed with PBS for three times by centrifugation at 600 × g for 5 min. Cells were lysed in RIPA lysis buffer supplemented with protease and phosphatase inhibitors. Proteins were subjected to SDS-PAGE electrophoresis and transferred to PVDF membranes. The membranes were blocked with 5% (w/v) dry milk and then incubated with corresponding primary antibodies at 4°C overnight. The membranes were washed with 1× TBST for three times and then incubated with an HRP conjugated secondary antibody at room temperature for 1 h. After the membranes were washed with 1× TBST for three times, an enhanced chemiluminescence was used to visualize the blots. The primary antibodies were supplied by Abcam (Cambridge, UK). The information of antibodies was as follows: TSG101 (ab30871), CD63 (ab134045), PTEN (ab32199), and GAPDH (ab8245). All the antibodies were diluted in TBST at 1 : 1000.
Data shown are as mean ± SD. Statistical significance was evaluated by GraphPad Prism software. Student's t-test or two-way ANOVA was used for statistical analysis. p < 0.05 was considered as statistically significant.
In drug-resistant tissue, the expression of miR-3613-5p was upregulated compared with drug-sensitive tissue (Figure 1(a)). Exosome-mediated transfer of long noncoding RNA H19 was used to generate resistant breast cancer cells to doxorubicin [14, 15]. Breast cancer cells became significantly resistant to the cytotoxicity of doxorubicin (Figure 1(b)). In breast cancer cells resistant to doxorubicin, the expression of miR-3613-5p was significantly increased (Figure 1(c)). These data demonstrated miR-3613-5p was upregulated in drug-resistant tissue and in breast cancer cells resistant to doxorubicin.
The addition of RNase A to the culture medium had no effect on the miR-3613-5p level, but the combined addition of Triton X-100 led to dramatical decrease in miR-3613-5p level (Figure 2(a)). This observation indicated that miR-3613-5p was surrounded by membranes but not directly released into the medium. Exosomes were isolated, the structure was observed by TEM, and the images showed that the particles were typical goblet-shaped vesicles with a double-membrane structure, approximately 100 nm in diameter (Figure 2(b)). Immunoblotting analysis of exosome markers TSG101 and CD63 confirmed the presence of exosome (Figure 2(c)). In the exosomes from doxorubicin-resistant breast cancer cells, the relative level of miR-3613-5p was significantly enhanced (Figure 2(d)). These observations demonstrated that miR-3613-5p level was upregulated in exosomes from doxorubicin-resistant breast cancer cells.
Incubation with exosomes from doxorubicin-resistant breast cancer cells promoted the relative level of miR-3613-5p, and miR-3613-5p inhibitor led to a significantly decrease in miR-3613-5p level in exosomes from breast cancer cells (Figure 3(a)). Cell viability (Figure 3(b)), colony formation (Figures 3(c) and 3(d)), and flow cytometry (Figures 3(e) and 3(f)) analysis revealed that incubation with exosomes from doxorubicin-resistant breast cancer cells increased the cell resistance to doxorubicin, and miR-3613-5p inhibitor treatment sensitized cell death to doxorubicin (Figures 3(b)–3(f)). These results indicated that exosome mediated miR-3613-5p transfer and enhanced doxorubicin resistance in breast cancer cells.
The molecular mechanism through which miR-3613-5p enhanced the resistance of breast cancer cells to doxorubicin was further explored. The relative mRNA and protein levels of PTEN were dramatically declined in doxorubicin-resistant breast cancer cells (Figures 4(a) and 4(b)). The website TargetScan predicted that miR-3613-5p could bind to PTEN (Figure 4(c)). The overexpression of miR-3613-5p induced the suppression of luciferase activity in wild-type, which was abolished in PTEN mutant, indicating that miR-3613-5p could interact with PTEN (Figure 4(d)). In MDA-MB-231 cells resistant to doxorubicin, the relative level of miR-3613-5p was much lower, while the relative level of PTEN was much higher than that in doxorubicin-resistant MCF-7 cells (Figure 4(e)). miR-3613-5p inhibitor strikingly enhanced the expression level of PTEN in doxorubicin-resistant breast cancer cells (Figure 4(f)). These data suggested that miR-3613-5p could target PTEN and regulate the expression of PTEN, which was involved in doxorubicin resistance of breast cancer cells.
Incubation with exosomes from doxorubicin-resistant breast cancer cells or knockdown of PTEN led to the significant decrease in the PTEN expression, which was rescued by the treatment of miR-3613-5p inhibitor (Figures 5(a) and 5(b)). Incubation with exosomes from doxorubicin-resistant breast cancer cells or knockdown of PTEN enhanced the resistance to doxorubicin, which was prevented by the treatment of miR-3613-5p inhibitor (Figures 5(c)–5(e)). These data indicated that exosome-mediated transfer of miR-3613-5p enhanced the resistance of breast cancer cells to doxorubicin by inhibition of PTEN.
Breast cancer is one of the most common malignancies with increasing incidence in women worldwide [1]. Doxorubicin is a well-accepted compound for breast cancer therapy, but patients easily develop doxorubicin resistance [3]. Therefore, it is urgent to further explore the molecular mechanisms of drug resistance and novel therapeutic strategy for breast cancer. Exosomes participate in intercellular communication and mediate crosstalk between neighboring cells [7]. miRNAs are involved in many diseases and have been shown in the regulation of drug resistance [10, 11]. miRNAs are protected by bilateral membrane structures after incorporation into exosomes, which prevents the degradation of miRNAs and facilitates the intercellular communication [11]. miR-3613-5p is abnormally expressed and carcinogenic in a variety of tumors, including pancreatic cancer [16] and non-small-cell lung cancer [17]. miR-3613-5p can be present in exosomes [18]. However, whether exosome-mediated miR-3613-5p transfer can regulate the drug resistance and the molecular mechanism remains to be investigated. According to GEO database analysis, miR-3613-5p is upregulated in drug-resistant breast cancer. It is predicted that miR-3613-5p can bind to PTEN, which is a well-known tumor suppressor gene that participates in tumor cell proliferation, cell apoptosis, invasion, migration, drug resistance, and many signaling pathways [19, 20]. It has been shown that inhibition of PTEN promotes cell proliferation of doxorubicin-resistant breast cancer cells and inhibits apoptosis, thus promoting drug resistance of breast cancer [21]. In this study, exosome-mediated transfer of long noncoding RNA H19 was used to generate doxorubicin-resistant breast cancer cells, and the expression of miR-3613-5p was significantly increased in these cells. It has been validated that miRNAs including miR-3613-5p was expressed in exosomes [18, 22], but the expression levels of miRNAs were significant differential [22]. In the exosomes from doxorubicin-resistant breast cancer cells, the relative level of miR-3613-5p was significantly enhanced. Incubation with exosomes from doxorubicin-resistant breast cancer cells promoted the relative level of miR-3613-5p and increased the resistance of breast cancer cells to doxorubicin. These results indicated that exosome mediated miR-3613-5p transfer and enhanced the resistance of breast cancer cells to doxorubicin. The molecular mechanism through which miR-3613-5p promoted drug resistance was then investigated. PTEN was known to be a key regulator of doxorubicin resistance in breast cancer [23]. miR-3613-5p could target PTEN and regulate the expression of PTEN, which was involved in doxorubicin resistance of breast cancer cells. Incubation with exosomes from doxorubicin-resistant breast cancer cells or knockdown of PTEN enhanced the resistance of breast cancer cells to doxorubicin, which was prevented by the treatment of miR-3613-5p inhibitor. These observations suggested that exosome-mediated transfer of miR-3613-5p enhanced the resistance of breast cancer cells to doxorubicin by inhibition of PTEN. This finding will provide a therapeutic target and strategy for breast cancer treatment. | true | true | true |
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PMC9586771 | Lu-lu Wang,Xue Tang,Guichi Zhou,Shilin Liu,Ying Wang,Fen Chen,Tonghui Li,Feiqiu Wen,Sixi Liu,Huirong Mai | PROM1 and CTGF Expression in Childhood MLL-Rearrangement Acute Lymphoblastic Leukemia | 14-10-2022 | The prognosis of over 90% of infant acute lymphoblastic leukemia (ALL) remains poor because of harboring the mixed-lineage leukemia gene (MLL) fusion. To give insight into the critical coexpressed genes related to the MLL-rearrangement (MLL-R) gene in childhood acute lymphoblastic leukemia, we integrated different bioinformatic methods. First, the gene expression data of MLL-R ALL and normal samples from GSE13159 and GSE13164 were analyzed using “compare” function in the Oncomine database. The top 150 overexpressed and 150 underexpressed genes were identified by the Oncomine website. Then, we employed the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) to define functional genes for the 300 DEGs. The Cytoscape identified two important networks for overexpressed genes, including 35 functional genes, among which PROM1, FLT3, CTGF, LGALS1, IGFBP7, ZNRF1, and RUNX2 were considered as the key genes because of their high expression in MLL-R ALL compared to the expression in other subclassification of leukemia in the MILE dataset. Further analysis of GSE68720, GSE19475, and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) ALL (phase I) database confirmed the robust expression of 7 key genes in MLL-R compared to MLL-germline (MLL-G) childhood ALL. Kaplan-Meier analysis indicated that childhood ALL patients with high PROM1 and CTGF expression had significantly poor overall survival. These findings suggest that PROM1 and CTGF represent two potential therapeutic targets for childhood MLL-R ALL. | PROM1 and CTGF Expression in Childhood MLL-Rearrangement Acute Lymphoblastic Leukemia
The prognosis of over 90% of infant acute lymphoblastic leukemia (ALL) remains poor because of harboring the mixed-lineage leukemia gene (MLL) fusion. To give insight into the critical coexpressed genes related to the MLL-rearrangement (MLL-R) gene in childhood acute lymphoblastic leukemia, we integrated different bioinformatic methods. First, the gene expression data of MLL-R ALL and normal samples from GSE13159 and GSE13164 were analyzed using “compare” function in the Oncomine database. The top 150 overexpressed and 150 underexpressed genes were identified by the Oncomine website. Then, we employed the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) to define functional genes for the 300 DEGs. The Cytoscape identified two important networks for overexpressed genes, including 35 functional genes, among which PROM1, FLT3, CTGF, LGALS1, IGFBP7, ZNRF1, and RUNX2 were considered as the key genes because of their high expression in MLL-R ALL compared to the expression in other subclassification of leukemia in the MILE dataset. Further analysis of GSE68720, GSE19475, and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) ALL (phase I) database confirmed the robust expression of 7 key genes in MLL-R compared to MLL-germline (MLL-G) childhood ALL. Kaplan-Meier analysis indicated that childhood ALL patients with high PROM1 and CTGF expression had significantly poor overall survival. These findings suggest that PROM1 and CTGF represent two potential therapeutic targets for childhood MLL-R ALL.
Acute lymphoblastic leukemia (ALL) is the most common form of childhood malignancies. It is a heterogeneous hematologic disease characterized by clonal proliferation of immature lymphoid progenitor cells both in bone marrow and extramedullary sites [1]. Thanks to the development of risk-directed chemotherapy and targeted therapy against the gene mutations/fusion, the 5-year survival rate of ALL exceeds 90% [2, 3]. However, the prognosis of over 90% of infant ALL and 35–50% of childhood acute myeloid leukemia remains poor because of harboring the mixed-lineage leukemia gene (MLL) fusion [4–8]. For infant MLL-rearrangement (MLL-R) ALL, the 5-year event-free survival is extremely low, ranging from 20 to 40% [6]. MLL-R ALL has unique clinical and biologic features, including the pro-B phenotype, prenatal origin, rapid onset, early relapse, and hyperleukocytosis. The MLL gene located in chromosome 11q23 fuses to generate chimeric genes with over 80 partners at the C-terminus and forms 135 different MLL rearrangements, of which the most common ones are AF4, AF9, AF17, ELL, and ENL [9]. These fusions are responsible for the gene expression alternation on histone methylation and transcriptional elongation. MLL-R activates target genes via H3K79 methylation by DOT1L, stimulation of elongation through P-TEFb, and suppression of the polycomb function [10]. However, as the breakthrough of genome-wide sequencing, a group of MLL target genes was distinguished. It has been reported that MLL fusion genes act as a global regulator by targeting more than 5000 genomic elements [11]. By far, the association between coexpressed genes and the MLL fusion gene has not been comprehensively investigated. To better understand the whole-genome alteration of leukemia, a retrospective study named Microarray Innovations in LEukemia (MILE) was carried out in 11 laboratories across three continents and included 3334 patients with leukemia [12, 13]. Blood or bone marrow samples of acute and chronic leukemia patients were hybridized to the microarray analysis. On the Gene Expression Omnibus (GEO) website, the MILE study fell into two stages, GSE13159 and GSE13164. In this study, we explored the GSE13159 and GSE13164 datasets on the Oncomine website and defined the top 300 differentiated expressed genes (DEGs) of MLL-R pro-B ALL vs. normal samples. Then, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for selected DEGs. Moreover, we investigated their protein-protein interaction (PPI) network based on the STRING website and selected functional genes by using Cytoscape software. The 7 key gene expression pattern and their relationship with clinical traits were searched on BloodSpot, and the UCSC Xena website was also constructed. Finally, two GEO datasets, including GSE68720 and GSE19475, studying the infant MLL-R and MLL-germline (MLL-G) ALL were employed to confirm the key genes. Exploring new genes and pathways associated with MLL-R ALL may help to identify potential molecular mechanisms, diagnostic markers, and therapeutic targets for MLL-R ALL.
Oncomine is an integrated data-mining platform that analyzes previously published or open-access cancer microarray data. Using the keywords “acute lymphoblastic leukemia” and “Cancer vs. Normal Analysis,” two studies were identified in the Oncomine database (https://www.oncomine.org) with the ID GSE13159 and GSE13164. Gene expression in pro-B ALL vs. normal was analyzed by the “compare” function in the Oncomine database. According to the description of MILE, all of the pro-B ALL patients harbored MLL fusion in GSE13159 and GSE13164. The result orders genes by median rank across the two analyses and displays the corresponding p values. The overexpressed and underexpressed genes with rank orders above 150 and p < 0.05 were selected for further analysis.
The top 150 over- and underexpressed genes were taken into DAVID website separately, analyzed by GO and KEGG enrichment (p < 0.05).
The 300 DEGs were taken into Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) with the maximum number of interactors = 0 and a confidence score ≥ 0.4 as the cutoff criteria. Then, to understand the function of the overexpressed gene, the biofunctional modules in the top 150 overexpressed genes were explored using a plug-in MCODE in Cytoscape with a node score cutoff of 0.2, degree cutoff of 2, and k-Core of 2. The top two gene modules with the highest MCODE scores were selected from the network. Then, the genes were taken into DAVID, as demonstrated above. KEGG enrichment analyses were carried out with the significance threshold p < 0.05.
BloodSpot is a database of mRNA expression in healthy and malignant hematopoiesis and includes data from both humans and mice [14]. The functional gene names were input into the search bar as a query. Gene expression data of the MILE study were identified on the BloodSpot website.
The gene expression, MLL status, and minimal residual disease (MRD) monitor were verified and analyzed in TARGET ALL (phase I) using the UCSC Xena browser.
Microarray expression data of GSE68720 and GSE19475 were downloaded from the GEO database. To explore the relationship between infant MLL-R ALL and MLL-G ALL, cel files of 17 MLL-G ALL samples and 80 MLL-R ALL samples from GSE68720 and 14 MLL-G ALL and 58 MLL-R ALL samples from GSE19475 were selected. The robust multiarray average in R was applied to explore the gene expression data in the cel files, including background correction, normalization, and summarization. All of the above operations were run with scripts in the R 3.6.3 version. The ggplot2 package in R was used to show the heat map of key genes.
Gene expression was obtained from UCSC Xena website, and the clinical survival information of TARGET ALL (phase I) was downloaded from the official TARGET database website. The ggplot2 of R software was used to plot the Kaplan-Meier survival curve. The TARGET ALL (phase I) project is obtained from patients enrolled on biology studies and clinical trials managed through the COG, POG 9906 (clinical trial for patients with newly diagnosed ALL between March 2000 and April 2003 that were defined as high risk for relapse). Patient samples for full characterization were chosen based on the following criteria: the disease onset at >9 years of age, did not have white blood cell count > 50000/μL, did not express the BCR/ABL fusion gene, were not known to be hypodiploid (DNA index > 0.95), and achieved remission (fewer than 5% blasts) following the standard two rounds of induction therapy. The primary patient samples were collected at diagnosis, and gene expression was analyzed following the protocol of Human Genome U133 Plus 2.0 Array (Affymetrix).
Student t-test of variance was used for comparing the statistical differences of gene expression of samples in GSE19475 and GSE68720. All the analyzes were two sided and p < 0.05 was considered to be significant.
The gene expression data of MLL-R ALL and normal samples from GSE13159 and GSE13164 were analyzed using the “compare” function in the Oncomine database. The median rank of the overexpressed and underexpressed genes with rank orders above 150 was identified as the genes and selected for further analyses (Figures 1(a) and 1(c)). Based on the result from the DAVID online analysis tool, the KEGG pathway and GO analysis were carried out to better understand the biological function of the key DEGs in MLL-R ALL. The GO enrichment analysis result showed that the overexpressed genes were mainly enriched in biological processes, including the B cell receptor signaling pathway, B cell activation, and negative regulation of transcription from the RNA polymerase II promoter, while KEGG pathway analysis showed that the result was significantly enriched in the B cell receptor signaling pathway, transcriptional misregulation in cancer, and primary immunodeficiency (Figure 1(b)). As for underexpressed genes, GO enrichement analysis demonstrated that they were mainly enriched in platelet degranulation pathway. KEGG pathway analysis showed that the underexpressed genes were mainly enriched in the hematopoietic cell lineage pathway (Figure 1(d)). A functional gene usually refers to what is significant in regulation and biological processes and closely interacts with other genes in a network. A total of 300 DEGs, including 150 overexpressed and 150 underexpressed genes, were shown in the overlap of the Venn diagram (Figure 2(a)). To further investigate the function of the DEGs in the GSE13159 and GSE13164 at the protein level, the STRING was employed to screen for functional genes. The PPI network consisted of 295 nodes and 1378 edges (Figure 2(b)). Afterwards, the interactive relationship of overexpressed genes was analyzed separately in Cytoscape. The MCODE, a plug-in in Cytoscape, was employed to calculate the k-Core of each gene. The top two significant modules in MCODE with high scores were selected from the PPI network, including module A (MCODE score = 7.556 with 10 nodes) and module B (MCODE score = 4.75 with 25 nodes) (Figure 2(c)). These genes were involved in 4 important KEGG pathways, including the hematopoietic cell lineage, transcriptional misregulation in cancer, ubiquitin-mediated proteolysis, and phagosome (Figure 2(d)).
To demonstrate the role of 35 functional genes in ALL subclassifications, we used the BloodSpot website to check their expression in different subclassifications of leukemia. As shown in Figure 3, PROM1, FLT3, CTGF, LGALS1, IGFBP7, ZNRF1, and RUNX2 were found highly expressed in the MLL-R pro-B ALL compared to the other subclassification of leukemia. To further verify the identified 7 key genes in MLL-R ALL, we detected the expression of PROM1, FLT3, CTGF, LGALS1, IGFBP7, ZNRF1, and RUNX2 between MLL-R ALL and MLL-G ALL in GSE68720 and GSE19475 datasets by using the R software. In both GSE68720 and GSE19475 datasets, the 7 key genes were significantly overexpressed in MLL-R compared to the MLL-G ALL samples, especially for PROM1. The heat map of the 7 key genes were shown in Figures 4(a) and 4(b). Further analysis in UCSC Xena demonstrated that high expression of these genes was significantly associated with the MLL status in the TARGET ALL (phase I) database, presenting a high correlation with the status of MLL fusion (Figure 5(a)). These results demonstrated that 7 key genes have extremely high expression in MLL-R ALL and maybe the critical targets for MLL fusion.
To delineate the prognostic value of potential key genes, the overall survival analyses of 7 key gene expression were detected in the TARGET ALL (phase I). The result showed that a high expression level of PROM1 and CTGF was associated with inferior overall survival of ALL (Figure 5(b)).
Although studies have demonstrated numerous fusion partner proteins, the target genes of MLL-fusion and the molecular mechanism involved in target genes were poorly understood. In the past decade, genomic analyses have revolutionized our understanding of the coexpression network in MLL-R ALL. HOX cluster genes and its cofactor MEIS1 were the most well-known target genes for the MLL fusion gene [15]. Both HOXA genes and MEIS1 are highly expressed in the stem cells and early progenitor cells. MLL drives the proliferation and self-renewal of immature hematopoietic cells by upregulating posterior HOX genes and their cofactor MEIS1 [16, 17]. Coincidentally, in this study, we examined the Oncomine website and investigated DEGs related to MLL-R ALL in the MILE study. Using PPI analysis, the critical pathway of functional genes was found involved in the hematopoietic cell lineage and transcriptional misregulation in cancer, including HOXA10, MEIS1, FLT3, CD14, PROM1, RUNX2, and RUNX1 (data not shown), indicating the dominant roles of HOXA and MEIS1 in MLL-R ALL. Posttranslational modifications of PROM1 play a critical role in MLL-R ALL [18, 19]. It was reported that AF4 recruited and activated DOT1L at the H3K79me2/3 locus of the PROM1 promoter, which is required for the growth of MLL-AF4 B-cell ALL cells [20–22]. CD133 is a kind of transmembrane glycoprotein encoded by the PROM1 gene. It is associated with cancer stem cells in diverse human tumors, including brain, liver, stomach, endometrium, ovary, and colorectum and gliomas and medulloblastoma [23]. Recent studies demonstrated that CD19/CD133 tandem CAR T induces robust cytotoxicity against CD19+ CD133+ and CD19− CD133+ B-cell lines, suggesting CD133 a promising target MLL-R ALL immunotherapy [24]. However, this study was challenged by “on-target off-tumor” myeloablative and life-threatening toxicity, because the CD133 was expressed in the hematopoietic stem and progenitor cells [25]. CTGF, CCN2 as the official name, is an extracellular matrix- (ECM) associated protein of 36–38 kDa and a member of the CCN family of proteins. It plays a great role in cell adhesion, proliferation, migration, and differentiation and improves the development of numerous tumor metastases [26–29]. Interestingly, elevated CTGF expression is also a feature of precursor B-cell ALL [30–33]. By analyzing COG trial P9906, high expression of BMPR1B, CTGF, TTYH2, IGJ, NT5E (CD73), CDC42EP3, and TSPAN7 was found to be associated with poor outcomes in precursor-B ALL patients [34]. Ruling out the possibility of structure alternation, amplification, or base mutation, Welch et al. demonstrated that the CTGF locus is hypomethylated in pediatric pre-B ALL [35]. Anti-CTGF monoclonal antibody attenuated tumor growth of precursor-B ALL from pediatric patients propagated in mice [36]. Here in this study, PROM1 and CTGF were overexpressed in MLL-R compared to MLL-G patients and those with high PROM1 and CTGF expression had significantly poor OS (Figure 5(b)). Further in vitro, in vivo, and clinical studies are warranted to delineate the role of PROM1 and CTGF in MLL-R ALL. In conclusion, we first demonstrated the top DEGs of GSE13159 and GSE13164 by using the Oncomine website. After integrated analyses, we identified from the 300 DEG genes that PROM1, FLT3, CTGF, LGALS1, IGFBP7, ZNRF1, and RUNX2 were the key genes, as they were highly expressed in MLL-R ALL compared to MLL-G ALL. Further investigation demonstrated that PROM1 and CTGF were the poor prognostic markers for childhood MLL-R ALL. Thus, we provide an insight into ALL that PROM1 and CTGF may be the novel potential target genes for the MLL fusion gene in childhood MLL-R ALL. | true | true | true |
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PMC9586783 | Xiaojia Zuo,Chaojun Lu,Yanjun Zheng,Donglin Lai,Dingsheng Liu,Guoqing Wan,Changlian Lu,Xuefeng Gu | Effects of the Targeted Regulation of CCRK by miR-335-5p on the Proliferation and Tumorigenicity of Human Renal Carcinoma Cells | 14-10-2022 | Cell cycle-related kinase (CCRK) is most closely related to cyclin-dependent protein kinase, which may activate cyclin-dependent kinase 2 and is associated with the growth of human cancer cells. However, the expression and function of CCRK in the pathogenesis of clear cell renal cell cancer (ccRCC) are unclear. Herein, this research aimed to explore the potential mechanism of the targeted regulation of CCRK by miR-335-5p on the proliferation and tumorigenicity of human ccRCC cells. The results showed that CCRK was significantly overexpressed in ccRCC tissues and cells, and knockdown of the CCRK expression by shRNA inhibited cell proliferation in vitro and in vivo and enhanced cell apoptosis in vitro, which indicated that CCRK could be a potential target for antitumour drugs in the treatment of ccRCC. Moreover, miR-335-5p was found to bind directly to the 3′ untranslated region of CCRK, was expressed at markedly low levels in ccRCC cells, and was closely associated with the tumour stage. The overexpression of CCRK partially reversed the inhibitory effects of miR-335-5p on the cell growth of ccRCC, which implied that miR-335-5p could serve as a promising tumour inhibitor for ccRCC. In summary, CCRK could serve as an alternative antitumour drug target, and miR-335-5p could be a promising therapeutic tumour inhibitor for ccRCC treatment. | Effects of the Targeted Regulation of CCRK by miR-335-5p on the Proliferation and Tumorigenicity of Human Renal Carcinoma Cells
Cell cycle-related kinase (CCRK) is most closely related to cyclin-dependent protein kinase, which may activate cyclin-dependent kinase 2 and is associated with the growth of human cancer cells. However, the expression and function of CCRK in the pathogenesis of clear cell renal cell cancer (ccRCC) are unclear. Herein, this research aimed to explore the potential mechanism of the targeted regulation of CCRK by miR-335-5p on the proliferation and tumorigenicity of human ccRCC cells. The results showed that CCRK was significantly overexpressed in ccRCC tissues and cells, and knockdown of the CCRK expression by shRNA inhibited cell proliferation in vitro and in vivo and enhanced cell apoptosis in vitro, which indicated that CCRK could be a potential target for antitumour drugs in the treatment of ccRCC. Moreover, miR-335-5p was found to bind directly to the 3′ untranslated region of CCRK, was expressed at markedly low levels in ccRCC cells, and was closely associated with the tumour stage. The overexpression of CCRK partially reversed the inhibitory effects of miR-335-5p on the cell growth of ccRCC, which implied that miR-335-5p could serve as a promising tumour inhibitor for ccRCC. In summary, CCRK could serve as an alternative antitumour drug target, and miR-335-5p could be a promising therapeutic tumour inhibitor for ccRCC treatment.
Renal carcinoma is one of the most common malignant tumours of the urinary tract, with over 400,000 new cases diagnosed and over 170,000 renal carcinoma-related deaths worldwide each year [1–3]. Renal cell carcinoma (RCC), the most prevalent form of renal carcinoma, originates from renal tubular epithelial cells and occupies over 90% of renal carcinoma cases [3, 4]. RCC encompasses more than 10 histological and molecular subtypes, of which clear cell RCC (ccRCC) is one of the most common subtypes, accounting for 65–70% of RCC cases [3]; ccRCC is characterized by high mortality, invasion, and metastasis [3]. Considering the poor survival rate and prognosis of ccRCC, it is essential to be diagnosed and treated in the early stage of patients with ccRCC. Thus, it is necessary to reveal the underlying molecular mechanisms involved in the pathogenesis and progression of ccRCC and to seek new therapies to improve the prognosis of patients with advanced-stage disease. Cell cycle-related kinase (CCRK), also known as cyclin-dependent kinase 20 (CDK20) or p42, a member of the CDK family, was first identified in HeLa cells in 2000 [5, 6]. Increasing studies have indicated that CCRK is closely associated with human cancers [7, 8]. CCRK is ubiquitously expressed in cells originating from various tumour tissues, but its expression is also significantly upregulated in lung, brain, colorectum, liver, and ovary cancers [9–12]. Such aberrant expression of CCRK is usually positively correlated with histopathological grade, advanced tumour stage, shorter patient survival, and poor prognosis, suggesting a vital role of CCRK in the pathogenesis and prognosis of human tumours [13]. CCRK is involved in various kinds of cell signalling pathways associated with the genesis and development of cancer, such as cell cycle and apoptosis pathways [14]. These findings suggest that CCRK is a promising target in the antitumour therapy. However, the expression and function of CCRK in the pathogenesis of ccRCC remain unknown. MicroRNAs (miRNAs) are highly conserved, small noncoding RNA molecules that are 17–25 nt in length, and they were first described in 1993 [15, 16]. miRNAs play a pivotal role in regulating gene expression at the posttranscriptional level by selectively and specifically binding to a target mRNA, resulting in mRNA translational inhibition or degradation [3, 17]. It has been shown that miRNAs regulate multiple cellular processes, including cell differentiation, proliferation, apoptosis, metastasis, and cell cycle progression [3, 18–20]. Many miRNAs, such as miR-191, miR-139-5p, and miR-29a, are involved in the development of cancer and have been shown to act as biomarkers, oncogenes, or tumour inhibitors [16, 21, 22]. More importantly, the target genes of these miRNAs and their underlying mechanisms in various human cancers have been revealed [21, 22]. It has been reported that miR-335-5p is expressed at low levels in various human tumours, including colorectal cancer, pancreatic cancer, uterine leiomyoma, gallbladder cancer, breast cancer, gastric cancer, and epithelial ovarian cancer [23–29], and it may play a role as a tumour inhibitor. Recent studies reported miR-335-5p is associated with RCC [3, 30]. However, the role of miR-335-5p and CCRK in the pathogenesis of ccRCC has not yet been determined. Here, this study focused on exploring the potential mechanism of the targeted regulation of CCRK by miR-335-5p on the proliferation and tumorigenicity of human ccRCC cells. The results revealed that CCRK could serve as an alternative antitumour drug target, and miR-335-5p could be a promising therapeutic tumour inhibitor for ccRCC treatment.
Clinical data, including 110 ccRCC tissues and 84 normal tissues, were downloaded from the Clinical Proteomic Tumour Analysis Consortium (CPTAC, https://proteomics.cancer.gov/programs/cptac). miRNA data, including 239 ccRCC tissues and 69 normal tissues, were analysed online with UALCAN (https://ualcan.path.uab.edu/index.html). This work was approved by the Ethics Committee of Shanghai University of Medicine and Health Sciences Affiliated Zhoupu hospital [31].
Human ccRCC cell lines (A498, 786-O, Caki-1, and ACHN cells) were purchased from ATCC (https://www.atcc.org/). The A498, Caki-1, and ACHN cell lines were cultured in EMEM. The 786-O cell line was cultured in RPMI-1640 medium. All the media were supplemented with 10% fetal bovine serum (Gibco, Waltham, MA) and 1 : 1 penicillin/streptomycin (final concentration of 100 U/mL) and incubated at 37°C in 5% CO2. CCRK (NM_178432.1) short hairpin RNA (shRNA), control scrambled shRNA (scr shRNA), and overexpression plasmids were designed and synthesized by RioScience (Shanghai, China). The target sequence of CCRK was 5′-GAAGGTGGCCCTAAGGCGGTTGGAAGACG-3′. Human miR-335-5p mimic, miR-335-5p inhibitor, and the corresponding controls were synthesized by GenePharma (Shanghai, China). To explore the function of CCRK, a rescue experiment was performed in A498 cells with CCRK knockdown. The CCRK plasmid (pcDNA3.1) was transfected into A498 cells after CCRK knockdown using Lipofectamine 2000 reagent (Invitrogen, CA, USA) in six-well plates.
The immunohistochemistry with the antibody HPA027379 against human CCRK tissue sections were obtained from the protein atlas (https://www.proteinatlas.org/ENSG00000156345-CDK20/pathology/renal+cancer). The expression of Ki-67 protein in mice tumour tissue was detected by immunohistochemistry to evaluate cell proliferation of the transplanted tumour in vivo. The transplanted tumour tissues of mice were routinely embedded in paraffin, and the sections were stained according to the protocols of immunohistochemical detection kit. The sections were added a drop of Ki-67 primary antibody (1 : 500) and incubated overnight at 4°C, then biotinylated secondary antibody was incubated at 37°C for 30 minutes. Then, the sections were incubated with streptavidin peroxidase from Streptomyces avidinii (Sigma-Aldrich#S5512) at 37°C for 30 minutes and colour rendered using the DAB chromogenic reagent, counterstained by hematoxylin, differentiated by hydrochloric acid ethanol, dehydrated, made transparent and sealed, and observed under optical microscope. The percentage of positive cells represented the proliferation index.
Total RNA was extracted from the cells with TRIzol™ (Cat#15596018, Invitrogen, USA). Reverse transcription PCR was performed using a one-step RNA PCR kit (Cat#RR064B, TaKaRa, China). SYBR Green Supermix kit (C11733046, Invitrogen, USA) was used to perform real-time quantitative PCR (RT-qPCR) on an ABI PRISM® 7500 sequence detection system. The primer sequences used for RT-qPCR were synthetized by Saiyin Biotechnology (Shanghai) Co., Ltd., and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the internal reference. The sequences of the primers used for RT-qPCR were as follows: CCRK-Forward: 5′-CCTCCATCAGTACTTCTTCACA-3′; CCRK-Reverse: 5′-GAATCAGCTCTGGGTTCAAC-3′; miR-335-5p Forward: 5′-ACACTCCAGCTGGGTCAAGAGCAATAACGAAA-3′; miR-335-5p Reverse: 5′-CTCAACTGGTGTCGTGGA-3′; and miR-335-5p RT primer: 5′-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGACATTTTTC-3′. Every experiment was repeated thrice.
Total protein was extracted from cells with radioimmunoprecipitation assay (RIPA) buffer (Thermo Fisher Scientific, Waltham, MA), and the concentration of the total protein was measured with a bicinchoninic acid (BCA) kit (Yeasen, Shanghai, China). Next, equal amounts of proteins were separated by 10% SDS-PAGE and transferred onto the PVDF membranes. Subsequently, the PVDF membranes were blocked with 5% nonfat milk for 2 h and incubated overnight at 4°C with the following diluted primary antibodies: anti-caspase-3 (Cat#9665S), anti-cleaved caspase-3 (Cat#9579S), anti-cyclin D1 (Cat#2922S), anti-Bax (Cat#2772S), and anti-β-actin (Cat#4967). Then, the PVDF membranes were further incubated with HRP-labelled goat anti-rabbit immunoglobulin G antibodies (Abcam, ab6721) for 2 h. Next, ECL luminescent (Cat#36208ES60, Yeasen, Shanghai, China) was used to visualize the colour of the PVDF membranes. Images of the PVDF membranes were obtained by a Bio-Rad image analysis system (Bio-Rad, Richmond, CA, USA), and the quantification of the target proteins was performed with ImageJ software [32].
Cell proliferation was assessed using a CCK-8 assay. In brief, 2 × 103 cells were seeded in 96-well plates. Then, after 1, 2, 3, 4, and 5 days, the media were replaced with fresh media containing 10% CCK-8 solution, and the cells were incubated for 2 h. The cell concentrations in the 96-well plates were evaluated based on the absorbance measured at 450 nm. For the colony-forming assay, cells were seeded in 6-well plates at 2 × 102 cells/well and incubated for 2 weeks. Then, the colonies were washed with PBS, fixed with absolute ethyl alcohol, and stained with 0.5% crystal violet. The colonies that turned blue were considered positive, and the cells of blue colonies were counted under an inverted microscope (Olympus, Japan).
Cells were cultured, collected, fixed at 4°C overnight, and then washed with PBS. A total of 0.1 ml cell suspension (1.0 × 106 cell/ml) was stained with propidium iodide (Cat#P34813, ABCONE, Shanghai, China) in the dark for 30 min at 4°C, and the stained cells were filtered through a 50 μm nylon mesh and routinely washed. Cell cycle progression and apoptosis were assessed by flow cytometry (FACSCalibur, Becton Dickinson), and the data obtained by flow cytometry were further analysed by FlowJo software (Tree Star, USA) to calculate the cell proliferation index.
The animal protocols [33] were approved by the Animal Experiments Ethics Committee of Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital. In brief, A498 cells (transfected with CCRK-shRNA or scr-shRNA) were transplanted into the subcutaneous tissue of 6-week-old male BALB/c nude mice (Shanghai Sippr BK Laboratory Animals Ltd., Shanghai, China) at a concentration of 2 × 106 cells/mL (n = 4 mice per group). Then, the tumour growth in mice was monitored every 2 days, and the tumour volume was calculated with the following formula: Tumour Volume = (length × width2) × 0.5. The mice were sacrificed, and the tumours were harvested on day 27.
Fragments of the 3′-untranslated region (UTR) of CCRK containing putative binding sequences of miR-335-5p were cloned into the pmirGLO reporter vector, and a mutated plasmid was used as a control. Cells were cultured in 96-well plates and cotransfected with a miR-335-5p mimic and negative control. After 24 h, the luciferase activity was measured using Envision HTS (PE, USA) according to the manufacturer's protocols.
The statistical analysis was performed with SPSS 22.0 software (Chicago, IL, USA), and the data are presented as the mean ± SEM. Statistical significance was determined using Student's t-test for comparisons between the two groups and one-way ANOVA for comparisons of more than two groups. Wilcoxon signed rank tests were applied to analyse the expression of CCRK in tissue samples. A P value < 0.05 was considered to indicate a significant difference.
CCRK expression in ccRCC cell lines (A498, 786-O, Caki-1, and ACHN cells) was assessed by RT-qPCR. The expression levels of CCRK were significantly upregulated in the ccRCC cell lines (Figure 1(a)), which was further confirmed at the protein level by western blotting (Figure 1(b)). According to the data from the CPTAC database, including 110 ccRCC patients and 84 normal subjects, the relative expression levels of CCRK were analysed by the Wilcoxon signed rank test. Compared with the normal group, CCRK expression was significantly increased in the ccRCC group (Figure 1(c)), and CCRK expression was significantly ascended in different stages (Figure 1(d)), similar results in different grades (Figure 1(e)). Moreover, CCRK expression in human ccRCC tissues were different based on the IHC results, and the majority of ccRCC patients expressed high level of CCRK (Figure 1(f)). These results indicated that CCRK might play an essential role in the pathogenesis of ccRCC.
To further explore the potential effects of CCRK on ccRCC cell proliferation, scr-shRNA and CCRK-shRNA were transfected into A498 cells and ACHN cells, respectively. The protein expression levels of CCRK were significantly downregulated in the A498 cells and ACHN cells transfected with CCRK-shRNA compared with the cells transfected with scr-shRNA. The cell proliferation rates of CCRK-shRNA-transfected A498 cells and ACHN cells were significantly decreased compared with those of the scr-shRNA-transfected cells according to the CCK-8 assay (Figures 2(a) and 2(b)). As shown in Figure 2(c), a rescue experiment was performed in A498 cells after CCRK knockdown and CCRK expression was downregulated. However, after transfecting with CCRK plasmid, the CCRK expression and cell viability was partly recovered (Figure 2(c)). Colony formation assays also showed that CCRK-shRNA significantly reduced the colony formation of A498 cells and ACHN cells compared with scr-shRNA (Figure 2(d)). The proliferation index of A498 and ACHN cells treated with CCRK-shRNA was also decreased compared with that of the control cells, as determined by flow cytometry (Figures 3(a) and 3(b)). In addition, the expression of cell cycle and apoptosis markers (cyclin D1 and caspase 3) was drastically reduced in the CCRK-shRNA group compared with the scr-shRNA group in vitro (Figure 4(a)). In short, the above results indicated that CCRK plays a tumour-promoting role in ccRCC in vitro.
The apoptosis rates of A498 and ACHN cells were detected by flow cytometry to investigate whether CCRK-shRNA could affect the apoptosis of ccRCC cells. The results showed that the apoptosis rate of the cells transfected with CCRK-shRNA was significantly increased compared with that of the cells transfected with scr-shRNA (Figures 3(c) and 3(d)). Then, the expression of apoptosis markers (Bax and cleaved caspase-3) in A498 cells and ACHN cells was significantly increased in the CCRK-shRNA groups compared with the scr-shRNA groups (Figure 4(a)). Collectively, these results revealed that CCRK inhibits apoptosis.
A498 cells transfected with CCRK-shRNA and scr-shRNA were subcutaneously inoculated into nude mice to assess the function of CCRK in vivo. Tumour volume formed in the CCRK-shRNA group was larger than that formed in scr-shRNA group (Figure 4(b)). In Figure 4(c), the percentage of Ki-67 positive cells in the CCRK-shRNA group were significantly lower than that in scr-shRNA group. The optical density (OD) of Ki-67 proliferation index in the CCRK-KD group was significantly lower than that in scr-shRNA group (Figure 4(d), P < 0.05). These results revealed that CCRK plays a positive and vital role in the tumorigenicity of ccRCC.
Considering that CCRK can affect ccRCC cell proliferation and apoptosis, it has become very important identifying a potential drug or small chemical molecule that can regulate its expression in cancer cells. Increasing studies have reported that miRNAs can play key roles by modulating gene expression to influence cancer progression, which attracted researchers' attention. Here, miRNA sequence data were downloaded from the cancer genome atlas database (TCGA, https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga), differentially expressed miRNAs (DEmiRNAs) were analysed with the relevant thresholds P < 0.05 and |log2FC| > 1. A total of 149 DEmiRNAs, shown in the volcano plot (Figure 5(a)), were identified between the ccRCC group and the normal group. In addition, the miRNAs that potentially target CCRK were identified using TargetScan (version 3.1). The results displayed that 5 overlapping miRNAs, including miR-335-5p (Table S1), were identified between 422 upregulated miRNAs and 20 downregulated miRNAs in ccRCC (Figure 5(b)). The relative expression of miR-335-5p was significantly downregulated in the ccRCC group compared with the normal group (Figure 5(c)), and miR-335-5p expression was negatively correlated with the CCRK expression, which indicated that miR-335-5p could be a negative predictive factor in ccRCC. Subsequently, the relative expression of miR-335-5p was investigated in A498, 786-O, Caki-1, and ACHN cells, and compared with 786-O cells, miR-335-5p expression was downregulated in A498, Caki-1, and ACHN cells (Figure 5(d)). The further results exhibited that tumour stage (grade I, II, and III) was associated with the miR-335-5p expression in ccRCC patients (Figure 5(e)). In short, these results revealed that miR-335-5p was downregulated in ccRCC and associated with the tumour stage.
To investigate the relationship between miR-335-5p and CCRK expression, we identified a highly conserved site in the CCRK 3′UTR that is targeted by miR-335-5p using the TargetScan database (Figure 5(f)). Subsequently, the western blotting results confirmed that the protein expression levels of CCRK were both downregulated in both A498 and ACHN cells treated with miR-335-5p mimics (Figure 5(g)). To further explore whether miR-335-5p directly binds to the 3′-UTR of CCRK, dual-luciferase reporter assays were performed, which involved wild-type and mutant CCRK 3′UTRs. The relative luciferase activity in A498 and ACHN cells transfected with the wild-type CCRK 3′-UTR was significantly reduced by miR-335-5p transfection, but there was no significant difference between the cells transfected with the CCRK 3′-UTR with mutated miR-335-5p binding sites compared with the cells transfected with miR-NC (Figure 5(h)). Overall, these results indicated that miR-335-5p directly targets and negatively regulates the expression of CCRK.
Given that miR-335-5p directly targets and negatively regulates the expression of CCRK in ccRCC, CCRK should reverse the effects of miR-335-5p. To test this hypothesis, miR-335-5p mimics or miR-335-5p mimics + LV-CCRK vectors were transfected into A498 and ACHN cells. The results displayed that the cell proliferation indexes were significantly decreased in the miR-335-5p mimic group compared with the miR-NC group, importantly, CCRK can reverse this inhibitory effect of miR-335-5p on ccRCC cell proliferation (Figures 6(a), 6(b), 6(e), and 6(f)). The apoptosis rate of ccRCC cells transfected with miR-335-5p and lentivirus carrying CCRK was similar to the cell proliferation index (Figures 6(c) and 6(d)). In addition, the results confirmed that CCRK expression was decreased in the miR-335-5p mimic group compared with the miR-NC group by western blotting; in contrast, the CCRK expression was notably recovered in the miR-335-5p mimic + LV-CCRK group after ccRCC cells retransfected with CCRK vector (Figures 6(g) and 6(i)). The expression of apoptosis markers (caspase-3, cleaved caspase-3, and Bax) and the cell cycle marker cyclin D1 in ccRCC cells was investigated by western blotting, the results also displayed that CCRK can recover the inhibitory effect of miR-335-5p on ccRCC cell proliferation (Figures 6(g) and 6(i)). Overall, these results confirmed that there are close relationships between miR-335-5p and CCRK in ccRCC.
CCRK is a known nuclear-cytoplasmic shuttling protein containing 11 conserved serine/threonine protein kinase subdomains [6]. CCRK is ubiquitously expressed in the brain and kidney and performs both cell cycle-dependent and cell cycle-independent functions in a wide range of human tissues [6]. In different types of human cancer, including colorectal cancer, hepatocellular carcinoma, lung cancer, medulloblastoma, and ovarian carcinoma, CCRK expression is aberrantly upregulated and plays an oncogenic role [14]. Aberrant expression of CCRK is closely associated with tumour staging, short survival, and poor prognosis [14]. Mechanistically, CCRK is involved in a wide array of cell signalling pathways associated with cell proliferation, which is essential for the genesis and evolution of cancer. For example, downregulation of CCRK-inhibited cell proliferation, caused G1 phase cell cycle arrest, decreased pCdk2 levels, and inhibited Cdk2 kinase activity in HeLa cervical adenocarcinoma cells and human glioblastoma [6, 11, 34]. In addition, CCRK is also involved in the AR and Wnt/β-catenin/TCF signalling pathway cascades in human liver malignant neoplasms [35, 36]. In this study, we confirmed that CCRK expression was significantly increased in the ccRCC tissues and several ccRCC cell lines. CCRK promoted cell proliferation and colony formation efficiency and decreased apoptosis in vitro. CCRK performs a function in renal cancer that is similar to its function in other cancers. At the protein level, cyclin D1 expression was downregulated when CCRK expression was knocked down. These results suggest that CCRK affects the cell cycle. The decreased proliferation index of CCRK-knockdown ccRCC cells, as observed by flow cytometry, verified this finding. In addition, analysis of apoptosis markers, including caspase-3, cleaved caspase-3, and Bax, also proved the effect of CCRK on cell growth. With xenograft mice models in vivo, we also found that CCRK knockdown decreased tumorigenicity. These results show that CCRK could be an oncogene in ccRCC and can be a potential target for cancer therapy. Furthermore, the expression of CCRK was inhibited by miR-335-5p in ccRCC, and the overexpression of CCRK could partly reverse the antitumour effect of miR-335-5p, which revealed a mechanism of negative regulation between CCRK and miR-335-5p. As prior studies and publications have shown, many miRNAs act as tumour inhibitors or oncogenes in human cancers by regulating the target gene expression [16], and targeting miRNA with mimics or inhibitors could be a possible treatment approach for the clinical therapy. For instance, lncRNA RP11-436H11.5 can regulate the cell proliferation and invasion of RCC by sponging miR-335-5p [30]. Here, our study shows that the expression of miR-335-5p is negatively correlated with the stage of ccRCC in patients. Our results are consistent with prior findings [30], namely, that miR-335-5p expression is downregulated in renal cancer and that the downregulation of miR-335-5p is associated with the disease state (lymph node metastasis, tumour size, and poor T stage) of patients. In brief, our study showed that miR-335-5p was expressed at notably low levels in ccRCC cells and closely associated with the tumour stage, which indicated that miR-335-5p could serve as a promising tumour inhibitor in ccRCC.
In summary, this work revealed that CCRK was significantly upregulated in ccRCC patients and that knockdown of CCRK-inhibited cancer cell proliferation and enhanced cell apoptosis in vitro, which indicated that CCRK could be an oncogene in ccRCC and may be a potential target for cancer therapy in patients with ccRCC. Furthermore, miR-335-5p was negatively related to the CCRK expression. miR-335-5p is downregulated in ccRCC patients and is closely associated with the cancer stage, which reveals that miR-335-5p could serve as a promising tumour inhibitor for the ccRCC therapy. | true | true | true |
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PMC9586796 | Lihua Wang,Lei Yang,Tingting Zhuang,Xiuqing Shi | Tumor-Derived Exosomal miR-29b Reduces Angiogenesis in Pancreatic Cancer by Silencing ROBO1 and SRGAP2 | 14-10-2022 | Background Exosomal miR-29b reportedly plays a role during cancer metastasis. However, its exact function and underlying mechanism during pancreatic cancer (PC) have not been investigated. Methods Exosomes from PC cells were prepared and identified. Transmission electron microscopy (TEM) and confocal microscopy were used to examine structural characteristics of the exosomes and verify their internalization by human umbilical vein endothelial cells (HUVECs). The tube formation and migration abilities of HUVECs were detected. VEGF content was assessed by ELISA. GW4869 was used to suppress exosome release. Luciferase reporter assays were performed to verify the predicted interaction of miR-29b with ROBO1 and SRGAP2 mRNA. Results Exosomal miRNA-29b was differentially expressed in the conditioned medium of PC cells. Exosomes from PC cells were verified by TEM and western blotting. Treatment with the exosomal inhibitor (GW4869) prevented an increase in miR-29b expression and recused the reduced VEGF expression and tube formation and migration abilities of HUVECs cocultured with BxPC3 and AsPC-1 cells that overexpressed miR-29b. Furthermore, the downregulation of ROBO1 and SRGAP2 in cocultured HUVECs was also reduced after additional treatment with GW4869. After incubation with miR-29b exosomes, HUVECs had lower VEGF concentrations and reduced migration and tube formation rates; however, those effects were eliminated by subsequent transfection with the miR-29b inhibitor. Luciferase reporter assays verified the interaction of miR-29b with ROBO1 and SRGAP2. That interaction was also supported by rescue assays showing that overexpression of ROBO1 and SRGAP2 also reduced the antiangiogenic effect of exosomal miR-29b in HUVECs. Conclusion Exosomal miR-29b originating from PC cells protected HUVECs from PC cell-induced angiogenesis by attenuating ROBO1 and SRGAP2 expression. Our findings suggest a strategy for treating PC. | Tumor-Derived Exosomal miR-29b Reduces Angiogenesis in Pancreatic Cancer by Silencing ROBO1 and SRGAP2
Exosomal miR-29b reportedly plays a role during cancer metastasis. However, its exact function and underlying mechanism during pancreatic cancer (PC) have not been investigated.
Exosomes from PC cells were prepared and identified. Transmission electron microscopy (TEM) and confocal microscopy were used to examine structural characteristics of the exosomes and verify their internalization by human umbilical vein endothelial cells (HUVECs). The tube formation and migration abilities of HUVECs were detected. VEGF content was assessed by ELISA. GW4869 was used to suppress exosome release. Luciferase reporter assays were performed to verify the predicted interaction of miR-29b with ROBO1 and SRGAP2 mRNA.
Exosomal miRNA-29b was differentially expressed in the conditioned medium of PC cells. Exosomes from PC cells were verified by TEM and western blotting. Treatment with the exosomal inhibitor (GW4869) prevented an increase in miR-29b expression and recused the reduced VEGF expression and tube formation and migration abilities of HUVECs cocultured with BxPC3 and AsPC-1 cells that overexpressed miR-29b. Furthermore, the downregulation of ROBO1 and SRGAP2 in cocultured HUVECs was also reduced after additional treatment with GW4869. After incubation with miR-29b exosomes, HUVECs had lower VEGF concentrations and reduced migration and tube formation rates; however, those effects were eliminated by subsequent transfection with the miR-29b inhibitor. Luciferase reporter assays verified the interaction of miR-29b with ROBO1 and SRGAP2. That interaction was also supported by rescue assays showing that overexpression of ROBO1 and SRGAP2 also reduced the antiangiogenic effect of exosomal miR-29b in HUVECs.
Exosomal miR-29b originating from PC cells protected HUVECs from PC cell-induced angiogenesis by attenuating ROBO1 and SRGAP2 expression. Our findings suggest a strategy for treating PC.
Pancreatic cancer (PC) is one of the most frequently diagnosed and life-threatening neoplasms occurring in alimentary canals. Global cancer statistics for 2020 estimated there were 500,000 new PC cases and 460,000 deaths from PC, making PC the seventh leading cause of cancer death [1]. In 2025, PC is projected to overtake breast cancer as the third major reason for tumor-related death [2]. Due to its insidious symptoms, most PC patients are diagnosed with late-stage disease and have a poor prognosis [3]. The poor survival rate of PC patients with advanced stage disease can be attributed to tumor metastasis. Angiogenesis is responsible for advanced pancreatic carcinogenesis and enables tumor neovascularization to occur, which favors distant metastasis [4]. While preclinical studies of angiogenesis inhibitors have been conducted, the results have been unsatisfactory [5]. Therefore, fully deciphering the mechanism of PC angiogenesis remains an urgent priority. MicroRNAs (miRNAs) are noncoding transcripts consisting of 18–23 nucleotides [6]. They are reported to regulate gene protein expression by binding to the 3′UTRs of mRNA molecules. In this way, miRNAs pleiotropically modulate gene functionality and are thereby implicated in various cellular functions, such as cellular growth and metabolic switching [7]. Exosomes are a family of extracellular vesicles with nanoscale sizes and are derived from various cells, including cancer cells [8]. They actively engage in molecular cross-talk and are involved in various physiopathological conditions, including sustained angiogenesis in cancer tissues [9]. For example, exosomes secreted by PC cells foster the recruitment of pancreatic stellate cells and stimulate distal metastases [10]. Exosomal miR-27a induces the angiogenesis of human microvascular endothelial cells [11]. Previous studies revealed that exosomal miR-29b attenuates oncogene behavior in lung cancer [12], colorectal cancer [13], and cervical cancer [14]. Exosomes derived from cancer-associated fibroblasts internalize miR-29b into hepatocellular carcinoma cells, where they negatively regulate cancer cell behavior [15]. miR-29b has been shown to be closely associated with angiogenesis-related factors [16], including pancreatic cancer. However, the effect of PC-derived exosomes and miR-29b on PC tumors remains unknown. Convincing evidence has shown that miRNAs mediate PC cell malignant behavior by silencing the expression of target genes [17]. In this study, an online prediction by StarBase revealed that miR-29b sequences could bind to the 3′UTRs of ROBO1 (roundabout guidance receptor 1) and SRGAP2 (SLIT-ROBO Rho GTPase activating protein 2). The ROBO1 gene is located on chromosome 3p12.3 and consists of 35 exons. It encodes an integral membrane protein that is a member of the immunoglobulin gene superfamily. ROBO1 is reported to have an oncogenic function in certain malignancies. For example, the amplification of ROBO1 causes chordoma cells to become invasive and metastasize [18]. In breast cancer, treatment with the anti-ROBO1 antibody reduces breast cancer-triggered angiogenesis and thereby retards cancer progression [19]. While Li et al. [19] reported the antitumorigenic function of ROBO1, ROBO1-driven tumor promotion has also been described [20, 21]. SRGAP2 is required to activate the GTPase activity of Rac. In hepatocellular carcinoma, an elevated level of SRGAP2 is an indicator of a poor prognosis, while SRGAP2 silencing drastically mitigates cancer metastasis. In contrast, SRGAP2 expression is downregulated in osteosarcoma and linked to an aggressive phenotype of that disease [22]. Therefore, the dual function of SRGAP2 in cancer is supported by experimental evidence. Researchers discovered that there is interaction between SRGAP2 and ROBO1. Considered that ROBO1 plays a key role in angiogenesis, and we hypothesized that SRGAP2 and ROBO1 coregulate angiogenesis in pancreatic cancer. Here, we assumed that exosomal miR-29b from PC cells targeting ROBO1 and SRGAP2 might affect PC angiogenesis. Our findings reveal how miR-29b functions in PC angiogenesis and provide information useful for developing a novel drug for treatment of PC.
PC cells (BxPC3, PANC1, CFPAC-1, Capan-2, and AsPC-1), human umbilical vein endothelial cells (HUVECs), and a human normal pancreatic ductal epithelial cell line (HPDE6-C7) were purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA). The BxPC3 and AsPC-1 cells were cultured in RPMI-1640 Medium (Thermo Fisher, Waltham, MA, USA), the PANC1 cells, HPDE6-C7 cells, and HUVECs were cultured in Dulbecco's Modified Eagle's Medium (Thermo Fisher, USA), the CFPAC-1 cells were cultured in Iscove's Modified Dulbecco's Medium (Thermo Fisher, USA), and the Capan-2 cells were cultured in McCoy's 5A Medium (Thermo Fisher, USA). All cells were cultured at 37°C in a 5% CO2 atmosphere. miR-29b mimics, an miR-29b inhibitor, and mimic/inhibitor NC recombinant constructs that overexpressed ROBO1 or SRGAP2 (pCDNA-ROBO1 and pCDNA-SRGAP2) were purchased from Genepharm (Sunnyvale, CA, USA). Lipofectamine 2000 (Invitrogen, Waltham, MA, USA) was used to facilitate the introduction of the miRNA oligonucleotides and overexpressing vectors into BxPC3 and AsPC-1 cells. Cells were was pretreated with GW4869 at concentration 10 μM (dissolved in DMSO) for 2 h prior to other treatments. RT-qPCR was performed to verify whether the transfections were successful.
An Exosome Isolation Kit (Denmark) was used to isolate exosomes (Exos/BxPC3 and Exos/AsPC-1) from BxPC3 and AsPC-1 cells per the manufacturer's instructions. Briefly, BxPC3 and AsPC-1 cells were cultured to 85% confluence in 6-well plates; after which, the cell supernatants were collected and exposed to Exosome Concentration Solution at 4°C. The mixture was then allowed to rest for 2 h at 4°C prior to centrifugation. The collected exosome pellets were purified using an Exosome Purification Filter and collected for subsequent use. For particle size measurement, the collected exosomes were resuspended in prechilled PBS and stained with 2% phosphotungstic acid (pH 6.8), and their morphology features were observed under a transmission electron microscope. The exosomes were also verified by western blotting with anti-TGS101 antibodies and anti-CD63 antibodies. The particle size of exosomes was also characterized by size distribution using particle size analyzer (N30E, NanoFCM).
To verify the internalization of Exos/BxPC3 and Exos/AsPC-1 by HUVECs, we first labeled the exosomes by using a PKH26 Red Fluorescent Cell Linker Mini Kit (Merck, Rahway, NJ, USA) as instructed by the manufacturer. HUVECs (1 × 10 [5]) were seeded onto round coverslips of 18 mm diameter. Twenty-four hours later, the labeled exosomes were added for an additional 12 h of incubation. Next, the HUVECs were washed with PBS, fixed with 4% paraformaldehyde for 10 min, and then stained with DAPI for 30 min. Exosome uptake by the recipient HUVECs was visualized under a Nikon A1-R confocal microscope (Nikon Instruments, Tokyo, Japan).
Total cellular RNA was extracted using TRIzol Reagent (Invitrogen, USA) and then reverse transcribed into cDNA by using a iScript™ cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA) or miRNA 1st Strand cDNA Synthesis Kit (Vazyme, China). The resultant cDNA was quantified by SYBR Green Quantitative PCR (Roche, South San Francisco, USA) performed on a PCRmax Eco 48 thermal cycler (Thermo Fisher, USA). Fold-changes in target gene expression were analyzed by the Delta-Delta CT method. The following primers were used. miR-29b, forward primer: 5′-UAGCACCAUUUGAAAUCAGUGUU-3′, reverse primer: 5′-CACUGAUUUCAAAUGGUGCUAUU-3′; ROBO1, forward primer: 5′-CCCGACTTCACTCTCTCCCT-3′, reverse primer: 5′-AAATGGTGGGCTCAGGATGG-3′; SRGAP2, forward primer: 5′-TGAGATGGACTACTCCCGCA-3′, reverse primer 5′TGGTAGCCTAAGTCACAACACT3′; U6, forward primer: 5′-CTCGCTTCGGCAGCACA-3′, reverse primer: 5′-AACGCTTCACGAATTTGCGT-3′; and GAPDH, forward primer: 5′-TGTTCGTCATGGGTGTGAAC-3′, reverse primer: 5′-ATGGCATGGACTGTGGTCAT-3′.
Cells were lysed with RIPA lysis buffer, and the total protein concentration in each supernatant was determined using a BCA Protein Assay Kit (Pierce Biotechnology, Waltham, MA, USA). Next, a 20 μg sample of total protein from each supernatant was loaded onto a 12% SDS-PAGE gel and separated at 80 V for 40 minutes. The protein bands were then transferred onto PVDF membranes, which were subsequently blocked with 10% nonfat milk. Next, the membranes were incubated with anti-TSG101 antibodies (Cat#: BM4821, 1 : 1000, Boster, China), anti-CD63 antibodies (Cat#: PB9250,1 : 1000, Boster, China), anti-GRP94 antibodies (Cat#: PROTP14625, 1 : 1000, Boster, China), anti-ROBO1 antibodies (Cat#: A01530-2, 1 : 1000, Boster, China), anti-SRGAP2 antibodies (Cat#PA5-55792, 1 : 1000, Invitrogen, USA), and anti-GAPDH antibodies (Cat#A00227-1, 1 : 1000, Boster, China) at 4°C overnight. Next, the membranes were incubated with secondary antibodies at room temperature for an additional 1 h. The immunostained protein bands were visualized with Pierce ECL Western Blot Substrate (Merck, USA).
A Human VEGF ELISA Kit (Solarbio, China) was used to detect the VEGF concentrations in HUVECs according to the manufacturer's instructions. Briefly, the supernatant fractions of HUVECs were collected and spread across standard samples that had been precoated with goat anti-hamster IgG for 2.5 h at room temperature. Next, biotinylated VEGF detection antibodies were added for 1 h, HRP-Streptavidin solution was added for additional 45 min, and TMB One-Step Substrate Reagent was added for another 30 min. When the reaction was stopped, cell absorption was analyzed at 450 nm.
HUVECs were plated into 12-well plates (2 × 103 cells per well) that had been precoated with BD Matrigel Basement Membrane Matrix (Bioscience, USA, San Francisco, CA, USA). BxPC3 and AsPC-1 cells were placed into the upper Transwell inserts, which allowed culture medium to flow into the Matrigel. Forty-eight hours later, a light microscope was used to view the capillary network. To assess the impact of Exos/BxPC3 and Exos/AsPC-1 on HUVEC tube formation, the extracted exosomes were directly incubated with HUVECs. After 48 h of incubation, the amount of tube formation was recorded.
Culture medium containing HUVECs was placed into the upper chambers of Transwell plates (2 × 103 cells/chamber), and the lower Transwell chambers were filled with 500 μL of culture medium containing 10% FBS and Exos/BxPC3 plus Exos/AsPC-1 or the conditioned medium. Twenty-four hours later, the upper inserts were removed and the migrated cells were fixed with 5% glutaraldehyde for 10 min. Next, 1% crystal violet in 2% ethanol was added to stain the migrated cells. Finally, the cells were visualized under a microscope.
The StarBase website was used to predict the targets of miR-29b. The ROBO1 3′UTR and SRGAP2 3′UTR wild-type sequences predicted to interact with miR-29b, and also, and the corresponding mutant (MUT) sequences were amplified and fused into pGL3 luciferase reporter vectors to produce the following recombinant luciferase vectors: pGL3-ROBO1 3′UTR WT, pGL3-ROBO1 3′UTR MUT, pGL3-SRGAP2 3′UTR WT, and pGL3-SRGAP2 3′UTR MUT. The newly established vectors were introduced into HUVECs along with the miR-29b mimic or mimic NC. Quantification measurements of luciferase activity were obtained by using a luciferase reporter system (Promega, Madison, WI, USA).
All statistical data were shown in mean ± SD and analyzed using the GraphPad Prism 8 software (GraphPad Software, Inc., La Jolla, CA, USA). Differences among multiple groups were analyzed by one-way ANOVA, followed by the Dunnett's post hoc test. A P value < 0.05 was considered to be statistically significant.
After considering the antimetastatic and antiantigenic potentials of miR-29b in different cancers [23, 24], we sought to investigate the mechanism by which exosomes might participate in pancreatic carcinogenesis. To do this, we first assessed the universal expression of exosomal miR-29b in a panel of PC cells. We found that when compared to normal human pancreatic HPDE6-C7 cells, the PC cells (BxPC3, PANC1, Capan-2, and AsPC-1) all showed a differential expression of exosomal miR-29b (Figure 1(a)). It was noted that BxPC3 and AsPC-1 cells exhibited a relatively low or high metastatic ability [25] when miR-29b was expressed at a relatively low or high level, respectively. To avoid biased results, we used both types of cells for subsequent assays. After cocultivation with HUVECs, the exosomes extracted from BxPC3 and AsPC-1 cells were successfully transferred into the HUVEC cells, as evidenced by an aggregated red fluorescence surrounding the HUVEC nucleus (Figure 1(b)). As shown in Figure 1(c), a TEM imaging analysis was performed to visualize the typical cup-shaped appearance of exosomes from both types of PC cells. A western blot analysis revealed that CD63, TSG101, and GRP94 were highly expressed in PC cell-derived exosomes, but not in the cells (Figure 1(d)). As shown in Figure 1(e), the particle size of exosomes was ranging from 50 to 100 nm (Figure 1(e)). In contrast to the differential expression of miR-29b in PC-derived exosomes, miR-29b expression was significantly downregulated in BxPC3 and AsPC-1 cells (Figure 1(f)), suggesting a role for exosomal miR-29b during PC malignancy.
Because miR-29b has been found to confer a defect in tumor cell-induced angiogenesis in several cancers [26, 27], we further investigated whether exosomes derived from PC cells might participate in miR-29b-mediated PC tumor suppression. To address that question, we transfected miR-29b mimics into BxPC3 and AsPC-1 cells and then cocultured the cells with HUVECs prior exposure to an exosome inhibitor (GW4869). The identification of exosomes has been shown in Figure S1. As shown in Figure 2(a), an accumulation of miR-29b in the HUVECs was verified; however, that significant increase in miR-29b expression was reduced by subsequent exposure to GW4869, indicating that miR-29b-containing exosomes had been received by the HUVECs. Furthermore, miR-29b enforced expression obviously reduced the levels of VEGF, a potent modifier of angiogenesis, while additional exposure to GW4869 partially rescued those reduced VEGF expression levels (Figure 2(c)). After cocultivation, HUVEC tube formation was inhibited by the miR-29b mimics, but that reduced tube formation ability was offset by subsequent treatment with GW4869 (Figure 2(d)). Likewise, miR-29b overexpression caused a reduction in HUVEC migration; however, that` reduction was eliminated by subsequent treatment with GW4869 (Figure 2(e)). Taken together, these findings indicate that miR-29b reduces angiogenesis by PC cells in vitro via exosome secretion.
To further investigate the effects of exosomal miR-29 on tumor cell-induced angiogenesis, exosomes derived from BxPC3 and AsPC-1 cells transfected with miR-29 mimics (Exos/BxPC3miR-29 and Exos/AsPC-1 miR-29) were incubated with HUVECs prior to treatment with the miR-29 inhibitor or inhibitor NC. As shown in Figure 3(a), a strong upregulation of miR-29 expression was detected in HUVECs transfected with Exos/BxPC3miR-29 or Exos/AsPC-1miR-29; however, that increase in miR-29 expression was eliminated after treatment with the miR-29 inhibitor, but not by treatment with the inhibitor NC. Furthermore, tube formation assays showed that HUVECs in the exosomal miR-29 group displayed reduced tube formation, which was rescued by subsequent treatment with the miR-29 inhibitor (Figure 3(b)). Moreover, ELISA results showed that the reduced VEGF levels in the culture medium of receipt HUVECs treated with Exos/BxPC3miR-29 or Exos/AsPC-1miR-29 could be rescued by treatment with the miR-29b inhibitor (Figure 3(c)), supporting the antiangiogenic effect of exosomal miR-29b on HUVECs. Similarly, the reduced HUVEC migration in the exosomal miR-29b group was also rescued along with miR-29 depletion (Figure 3(d)). In summary, exosomal miR-29 was found to be responsible for inhibition of angiogenesis during PC malignancy.
To decipher the mechanism behind the antiangiogenic effect of exosomal miR-29b in HUVECs, we used StarBase to search for possible miR-29b targets based on complimentary mRNA 3′UTR sequences. As shown in Figure 4(a), the ROBO1 3′UTR and SRGAP2 3′UTR matched 8 nucleotides of miR-29b. After fusing the wild-type (WT) and mutated (MUT) sequences of the ROBO1 3′UTR and SRGAP2 3′UTR into pGL3-luciferase reporter constructs, we cotransfected the resultant WT or MUT constructs into HUVECs treated with the miR-29b mimic or mimic NC. Considerably less luciferase activity resulting from the ROBO1 3′UTR WT and SRGAP2 3′UTR WT was observed in HUVECs transfected with the miR-29b mimics, while no significant change was detected in HUVECs cotransfected with the mimic NC (Figure 4(b)), suggesting that miR-29b targeted the ROBO1 3′UTR and SRGAP2 3′UTR. To verify this finding, we detected the expression of ROBO1 and SRGAP2 in recipient HUVECs that were coincubated with BxPC3 and AsPC-1 cells with or without GW4869 treatment. As shown in Figures 4(c) and 4(d), GW4869 treatment rescued the reduced expression of ROBO1 and SRGAP2 in recipient HUVECs that had been cocultured with BxPC3 and AsPC-1 cells transfected with miR-29 mimics, suggesting that exosomes containing miR-29b mimics simultaneously reduced ROBO1 and SRGAP2 expression in the HUVECs.
Having demonstrated that exosomal miR-29b from PC cells downregulated ROBO1 and SRGAP2 expression in HUVECs, we investigated whether ROBO1 and SRGAP2 were necessary for suppression of tumor cell-induced angiogenesis by exosomal miR-29b. Recombinant constructs that overexpressed ROBO1or SRGAP2 were delivered into HUVECs incubated with Exo/BxPC3miR-29b and Exo/AsPC-1miR-29b. A quantitative increase in miR-29b expression in the recipient HUVECs indicated that miR-29b had been internalized (Figure 5(a)). As anticipated, the internalization of miR-29b greatly reduced the expression of ROBO1 and SRGAP2, but both reductions in expression were recovered after transfection with the recombinant constructs overexpressing ROBO1 or SRGAP2 (Figure 5(b)). Western blot studies conducted to detect ROBO1 or SRGAP2 protein expression (Figure 5(c)) further confirmed that miR-29b was not needed for downregulation of ROBO1 and SRGAP2 in HUVECs. Functionally, the reduced expression of VEGF in HUVECs incubated with Exo/BxPC3miR-29b and Exo/AsPC-1miR-29b was rescued along with ROBO1 and SRGAP2 overexpression (Figure 5(d)). Moreover, the decreased migration and tube formation abilities of HUVECs with Exo/BxPC3miR-29b and Exo/AsPC-1miR-29b were mitigated by overexpression of ROBO1 or SRGAP2 (Figures 5(e) and 5(f)). Taken together, these results indicated that exosomal miR-29b from BxPC3 and AsPC-1 cells reduced angiogenesis in vitro by downregulating ROBO1 or SRGAP2.
Although angiogenesis is involved in PC malignancy [28], drugs that target angiogenesis have produced limited benefits in patients with PC [4]. Therefore, an in-depth exploration of the underlying mechanism of PC angiogenesis is required. In this study, we found that exosomal miR-29b considerably reduced HUVEC migration and angiogenesis by targeting ROBO1 and SRGAP2. There is compelling evidence for the importance of cell-to-cell cross-talk facilitated by tumor-derived exosomes during tumor-induced angiogenesis [29]. For example, exosomes derived from PC cells exposed to hypoxic conditions promote angiogenesis by transferring lncRNA UCA1 into HUVECs [3]. miR-29b has been reported to be downregulated in PC and serves as an antitumorigenic miRNA by inhibiting tumor growth and metastatic dissemination [30–32]. Zeng et al. [33] reported the association between a high level of miR-29b expression and a better prognosis for PC patients [33]. Consistent with previous investigations, we found that miR-29b expression was decreased in PC cells, while exosomal miR-29b displayed differential expression in the conditioned medium of PC cells. Furthermore, the exosomes had been internalized by HUVECs, supporting subsequent efforts to understand their role in PC-induced angiogenesis. We found that the unregulated levels of miR-29b expression in HUVECs coincubated with BxPC3 and AsPC-1 cells overexpressing miR-29b could be reduced by GW4869. In addition to the decreased accumulation of miR-29b in HUVECs treated with GW4869, our studies of HUVEC tube formation and migration abilities, coupled with the effects of a potent angiogenesis stimulator (VEGF), showed that the inhibition of HUVEC angiogenesis by exosomes from PC cells transfected with miR-29b mimics could be reversed by GW4869. This suggested that an exosome complex containing miR-29b contributed to tumor suppression. Consistent with the above results, we also found that exosomal miR-29b inhibited PC-induced angiogenesis in HUVECs, and those reductions could be rescued by the miR-29b inhibitor. Our findings further support an antitumorigenic role for miR-29b during PC malignancy. Canonically, miRNAs exert the effect by binding to sequences in the 3′UTRs of mRNA molecules [34]. Our data showed that miR-29b targeted ROBO1 mRNA and SRGAP2 mRNA. ROBO1 and SRGAP2 expression were both reduced in HUVECs incubated with BxPC3 and AsPC-1 cells that overexpressed miR-29b; however, those reductions were rescued by exposure to GW4869, suggesting that an exosome complex carrying miR-29b repressed ROBO1 and SRGAP2 expression. Consistent with those findings, the impaired expression of ROBO1 and SRGAP2 caused by exosomal miR-29b was also recovered by the miR-29b inhibitor, highlighting the interaction of miR-29b with ROBO1 and SRGAP2. Previous studies showed that ROBO1 increases PC cell proliferation, migration, and invasion and thereby promotes tumor growth [18–21]. Furthermore, a microarray study revealed that a high level of ROBO1 expression was associated with PC lymphatic metastasis [35]. While an unregulated level of ROBO1 expression in PC tumor stroma was found to support tumor invasiveness and metastasis [36], ROBO1 overexpression in PC cells (PANC-1 and MiaPaca-2) was found to reduce cell proliferation, suggesting a tumor suppressive effect [37]. Our data for HUVECs containing exosomal miR-29b showed that ROBO1 overexpression could rescue a decrease in VEGF expression, as well as decreases in cell migration and tube formation after miR-29b mimic transfection. Therefore, our data further support the oncogenic role of ROBO1 during PC progression. The discrepancy regarding the role played by ROBO1 in PC might be associated with cell-context. Furthermore, the role played by SRGAP2 in PC has not been described. Our data showed that ROBO1 overexpression reversed the antiangiogenic effect on HUVECs caused by exosomal miR-29b. While SRGAP2 is described as an oncogenic gene in hepatocellular carcinoma [38], it functions a metastasis suppressor in osteosarcoma [22]. Therefore, our findings provide further evidence of a context-dependent role for SRGAP2 during cancer progression. In conclusion, our study revealed for the first time that exosomal miR-29b secreted by PC cells inhibits angiogenesis by HUVECs by targeting SRGAP2 and ROBO1. Our data provide a theoretical basis for the use of exosomes in PC intervention. However, in vivo studies are also required to further address the in vivo role of exosomal miR-29b during PC progression. At the same time, there are limitations in this study. For example, angiogenesis-related factors were not examined in clinical samples, and we will explore in depth in subsequent studies. Besides, an animal experiment should be included in further exploration. | true | true | true |
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PMC9586885 | S. Galiani,K. Reglinski,P. Carravilla,A. Barbotin,I. Urbančič,J. Ott,J. Sehr,E. Sezgin,F. Schneider,D. Waithe,P. Hublitz,W. Schliebs,R. Erdmann,C. Eggeling | Diffusion and interaction dynamics of the cytosolic peroxisomal import receptor PEX5 | 28-03-2022 | Cellular functions rely on proper actions of organelles such as peroxisomes. These organelles rely on the import of proteins from the cytosol. The peroxisomal import receptor PEX5 takes up target proteins in the cytosol and transports them to the peroxisomal matrix. However, its cytosolic molecular interactions have so far not directly been disclosed. Here, we combined advanced optical microscopy and spectroscopy techniques such as fluorescence correlation spectroscopy and stimulated emission depletion microscopy with biochemical tools to present a detailed characterization of the cytosolic diffusion and interaction dynamics of PEX5. Among other features, we highlight a slow diffusion of PEX5, independent of aggregation or target binding, but associated with cytosolic interaction partners via its N-terminal domain. This sheds new light on the functionality of the receptor in the cytosol as well as highlighting the potential of using complementary microscopy tools to decipher molecular interactions in the cytosol by studying their diffusion dynamics. | Diffusion and interaction dynamics of the cytosolic peroxisomal import receptor PEX5
Cellular functions rely on proper actions of organelles such as peroxisomes. These organelles rely on the import of proteins from the cytosol. The peroxisomal import receptor PEX5 takes up target proteins in the cytosol and transports them to the peroxisomal matrix. However, its cytosolic molecular interactions have so far not directly been disclosed. Here, we combined advanced optical microscopy and spectroscopy techniques such as fluorescence correlation spectroscopy and stimulated emission depletion microscopy with biochemical tools to present a detailed characterization of the cytosolic diffusion and interaction dynamics of PEX5. Among other features, we highlight a slow diffusion of PEX5, independent of aggregation or target binding, but associated with cytosolic interaction partners via its N-terminal domain. This sheds new light on the functionality of the receptor in the cytosol as well as highlighting the potential of using complementary microscopy tools to decipher molecular interactions in the cytosol by studying their diffusion dynamics.
The peroxisomal import receptor PEX5 transports newly synthesized proteins from the cytosol to the peroxisomal matrix. Here, the cytosolic diffusion and interaction dynamics of PEX5 are characterized by advanced microscopic spectroscopy methods, revealing a so far unknown interaction partner.
Cellular signaling critically depends on accurate interaction between molecules, and alterations may lead to severe cellular dysfunctions. For example, organelle functions naturally rely on molecular interactions in the cellular cytosol, such as the import of proteins into peroxisomes. Peroxisomes are ubiquitous organelles in eukaryotic cells fulfilling many metabolic functions that are cell-type specific and variable as a response to environmental changes. Consequently, the pool of peroxisomal matrix proteins needs to be continuously adapted, entailing the necessity of a highly dynamic import system. Peroxisomal matrix proteins are synthesized on free ribosomes in the cytosol and transported into the organelle post-translationally. The peroxisomal cargo receptor PEX5 is one of the key proteins in the peroxisomal import process (Fig. 1). Most peroxisomal matrix proteins imported by PEX5 contain a peroxisomal targeting signal type 1 (PTS1) at their C-terminus, while cargo proteins with the less abundant PTS2 targeting sequence are recognized and transported by the PTS2 receptor PEX7. PEX5 appears as two splice variants, a shorter one (PEX5S) that can only recognize PTS1 cargo proteins and a longer variant (PEX5L) that contains an additional PEX7 binding site (1). Therefore, the import pathways of PTS1 and PTS2 proteins merge with the long splice form. After binding, PEX5 directs the cargo receptor complexes to the peroxisomal membrane and initiates the cargo translocation by interacting with the peroxisomal membrane protein PEX14. At the peroxisomal membrane, PEX5 is integrated into the membrane, forming a transient translocation pore to import the cargo protein into the peroxisome (2). Consequently, PEX5 is a shuttling receptor with a much larger fraction in the cytosol (searching to bind newly synthesized cargo proteins), and only a small fraction at a time binds to the peroxisomal membrane (mainly involved in cargo translocation) (3,4). While it has been shown that the import of cargo proteins depends on the affinity of the PTS1 signal sequence to PEX5 (5,6), no further details are known about the interaction time scales and, thus, dynamics, i.e., the diffusion dynamics of the cargo receptor complex in the cytosol are basically completely unknown, which yet can highlight important details of the involved interaction dynamics. Therefore, it is essential to employ observation techniques that disclose details of interaction dynamics in the living cells with high accuracy. One remedy is to study molecular diffusion dynamics, since diffusion will be hampered upon interactions (7). Various fluorescence microscopy approaches have been employed and optimized to detail molecular diffusion dynamics especially in the cellular plasma membrane, such as fluorescence recovery after photobleaching (FRAP) (8), fluorescence correlation spectroscopy (FCS) (9, 10, 11, 12), or single-particle tracking (SPT) (13,14), even in combination with super-resolution microscopy approaches (15,16). As a result of adding one spatial dimension from two- (2D) to three-dimensional (3D) diffusion, the application of the above techniques to cytosolic studies is more elaborate than on membranes and usually requires more data mining (10, 11, 12,17, 18, 19, 20). Therefore, it is necessary to further adapt techniques for studying cytosolic interaction dynamics and also to combine them with dedicated complementary tools and controls. Here, we present a detailed characterization of the diffusion and, thus, interaction dynamics of human cytosolic PEX5 in vitro and in living cells by combining state-of-the-art microscopy and spectroscopy techniques such as FCS in combination with multi-color detection and (super-resolution) stimulated emission depletion (STED) microscopy together with biological manipulation such as CRISPR/Cas9 and model systems. As a result, we demonstrated free diffusion of PEX5 in the cytosol, which was found to be unexpectedly slow and independent of cargo binding. Among many controls, we investigated PEX5 oligomerization, interactions with other proteins of the PTS2 import pathway, binding to constituents of the peroxisomal membrane, or association with the cytoskeleton, whereby we could show that none of these influence PEX5 diffusion. Interestingly, the slow diffusion of PEX5, which depended on its N-terminal half, was not linked to the intrinsically disordered structure of this region. By using the cell-derived giant plasma membrane vesicle (GPMV) model system and recombinant proteins in solution, we showed that the slow diffusion of PEX5 only occurred in the presence of cytosolic components, indicating that the characteristic diffusion of PEX5 is strictly linked to cytosolic factors yet to be identified.
Sequences of the primers used are shown in Table S1. For the simultaneous expression of different PEX5 variants together with eGFP-SKL (eGFP-PTS1), we used the dual-expression plasmid pIRES2/eGFP-SKL as described previously (21). First, a SNAP-tag was integrated by amplifying the SNAP sequence with plasmids RE4692/RE4693 and restriction sites SalI/BamHI. Thereafter, the full-length PEX5L was amplified from pIRES2 PEX5L/eGFP-SKL (22) with primers RE4640/RE4641 and cloned into the pIRES2 SNAP/eGFP-SKL using BglII/SalI restriction sites. This pIRES2 PEX5L-SNAP/eGFP-SKL plasmid was used to construct all variations of PEX5L for the FCS measurements. Most of these variations were created using FastCloning (23). In brief, the vector backbone and the insert were amplified by PCR with overlapping ends. Thereafter, PCR products were mixed, the template DNA was digested with DpnI, and overlapping sticky ends were annealed. Here the vector backbone was amplified from the pIRES2 PEX5L-SNAP/eGFP-SKL and the insert from different sources: For PEX5-C-Term (pIRES2 PEX5L 1-335-SNAP/eGFP-SKL): vector amplification with primers RE4816/ RE4817, and insert amplification from pIRES2 PEX5L/eGFP-SKL with primers RE6194/RE6195. For PEX5-N-Term (pIRES2 PEX5L 314-639-SNAP/eGFP-SKL), the PEX5L fragment was amplified with primers RE6196/RE6197 and subcloned into BglII/SalI digested pIRES2 PES5L-SNAP/eGFP-SKL. For PEX5 S600W-SNAPeGFP-SKL, the PEX5 S600W sequence was amplified with primers KR001/KR002 from PEX5 S600W (24) in pcDNA3.1 and ligated into the BglII/SalI digested pIRES2 PEX5-SNAP/eGFP-SKL. The PEX5S-HALO/eGFP-SKL plasmid was created from the pIRES2/eGFP-SKL as well. First, the HaloTag was integrated by amplification with primers RE4694/RE4695 and subcloning of the PCR product into the pIRES2/eGFP-SKL using restriction sites SalI/BamHI. Thereafter, the full-length PEX5S was amplified with primers RE4640/RE4641 and cloned into the pIRES2 HALO/eGFP-SKL using BglII/SalI restriction sites. For pIRES2 TbPex5 1-340 SNAP-eGFP-SKL: vector amplification with primers RE6496/RE6486; insert amplification from TbPEX5 (25) with primers RE6490/RE6491. The SNAP-eGFP fusion construct was created by amplifying the SNAP fragment from pIRES-PEX5-SNAP/eGFP-SKL with the primers KR011/KR012 and subcloning it into HindIII/BamHI digested peGFP-N1 (Clontech, Mountain View, CA). The PEX5L-SNAP fusion construct was created by amplifying the SNAP fragment from pIRES-PEX5-SNAP/eGFP-SKL with the primers KR022/KR026 and subcloning it into SalI/NotI digested peGFP-N1 (Clontech). For the heterologous expression of eGFP fusion proteins in Escherichia coli, the pET-9d (Merck, Darmstadt, Germany) vector was used. Here the N-terminal sequences of human PEX5L (amino acids (aa) 1–335) and PEX5 from Trypanosoma brucei (aa 1–340) were fused to eGFP using the FastCloning approach. The pET-9d His HsPEX5(1–335) was amplified using the primers RE7008/RE7009, and eGFP was amplified with primers RE7010/ RE7011. This vector was then used to construct the pET-9d His TbPEX5(1–340) eGFP by amplifying the backbone with the primers RE7012/RE7013 and the TbPEX5(1–340) using the primers RE7016/RE7017.
For measurement of diffusion coefficients of the N-terminal halves of HsPEX5L (aa 1–335) and TbPEX5 (aa 1–340), both fused to eGFP, these were heterologously expressed in E. coli and purified using Ni-NTA columns as described elsewhere (26). In brief, the cells were homogenized by sonication, sedimented, and the supernatant incubated with the Ni-NTA matrix for 1 h. Thereafter, the proteins were eluted with a 10–500 mM imidazole gradient. For confocal FCS measurements, concentrations of 5 nM for HsPEX5L N-Term and 20 nM for TbPEX5 N-Term were used. eGFP was obtained from Novus Biologicals (Littleton, CO) and used at a final concentration of 10 nM.
Human HEK-293 cells were harvested from two T75 flasks for each experiment. The cells were incubated in 1 mL of digitonin buffer (150 mM NaCl, 50 mM HEPES, 25 μg/mL digitonin) for 10 min, as described in (27). After sedimentation of the remaining cell fragments (2000 × g, 4°C, 5 min), the cytosol was in the supernatant. For measurement of diffusion speed, the recombinant proteins were diluted with purified cytosol to 5 nM for His-HsPEX5L(1–335) and 20 nM for His-TbPEX5(1–340) and analyzed by point FCS.
Deletion of PEX5 and PEX14 in HEK-293 cells was accomplished using a dual single-guide RNA (sgRNA) approach. In particular, the sgRNA pairs targeted defined critical exons of PEX5 (exon 2) and PEX14 (exon 3), resulting in nonsense-mediated mRNA decay, the likely event after homozygous deletion in both cases. sgRNAs were selected using the CRISPOR algorithm (28). sgRNAs targeting the intron upstream of the respective splice branch point were cloned into pX458 (Addgene 48138, Dr. Feng Zhang), and sgRNAs targeting the intron downstream of the target exon were cloned into pX458-Ruby (Addgene 110164, Dr. Philip Hublitz). sgRNA ON-target efficiency was evaluated by Surveyor assay (Surveyor Mutation Detection Kit; IDT, Coralville, IA), and the following guides were chosen: PEX14 5′, GGatcagctcgaatggagatc; PEX14 3′, GGaccccccagtggggcatgc; PEX5 5′, ggggtcgcagcaaaagcact; PEX5 3′, Ggtttataaacgctcagtaag (capitalized nucleotides within the sgRNA sequences correspond to added G residues to allow for proper polymerase III transcription). Cells were transfected with both sgRNA expressing plasmids. Seventy-two hours post transfection, double mRuby2/eGFP-positive cells were sorted using fluorescence-activated cell sorting into 96-well plates. After expansion, cells were analyzed by genomic PCR using primers spanning the deleted region (PEX14 FW, cagcatacagggcacaagggcgg; PEX14 RV, tgctactgaatgctgcctttgcc; PEX5 FW, ggtccaggcccctttgtggaggc; PEX5 RV, aacaagcaggcattctcattcgg). Mutations were verified by Sanger sequencing of the subcloned amplicons, and absence of the respective protein was confirmed by immunoblotting. Of note, we obtained several clones in which the excised exon was found inverted and reinserted, nevertheless giving a full knockout (KO) phenotype as described (29). All clones selected for this study were confirmed homozygous deletions.
Human fibroblasts (GM5756-T (RRID:CVCL_VQ75)) (30), HEK 293 (ATCC, Manassas, VA), HEK KO PEX5, and HEK KO PEX14 cells were maintained in a culture medium consisting of Dulbecco’s modified Eagle’s medium with 4500 mg glucose/L, 110 mg sodium pyruvate/L supplemented with 10% fetal bovine serum, glutamine (2 mM), and penicillin-streptomycin (1%). The cells were cultured at 37°C/5% CO2. Cells were grown on a #1.5 μ-Dish 35 mm (World Precision Instruments, Sarasota, FL) and transfected with 2.5 μg of DNA per dish using Lipofectamine 3000 transfection reagent (Invitrogen, Carlsbad, CA). Twenty-four hours after transfection, the cells were incubated at 37°C for 40 min with silicon rhodamine (SiRo) SNAP-tag (1.2 μM), SNAP-Cell 647-SiR, or SNAP-Cell 505-Star (both from New England Biolabs, Ipswich, MA) to label the SNAP-tagged PEX5L. HaLo-tagged PEX5S was labeled with Halo-Cell 647-SiR (New England Biolabs) in a final concentration of 1 μM. The samples were washed twice for 20 min with 1 mL of culture medium in an incubator at 37°C. For the measurement the culture medium was substituted with L-15 (Leibovitz’s) medium (Thermo Fisher Scientific, Waltham, MA) and placed on the microscope for data acquisition for no longer than 1 h. Each sample was kept in culture medium in the incubator until the measurement started.
Confocal FCS measurements on living cells were performed at a confocal Zeiss (Oberkochen, Germany) LSM 880 microscope equipped for FCS. The microscope provides 488 nm and 633 nm excitation lines focused into the sample via a 40×/1.2 (Zeiss C-APOCHROMAT) water immersion objective lens. Excitation powers were set respectively for calibration solution and cytosolic measurements at 7 μW and 0.5 μW for the 488 nm excitation laser and at 6 μW and 1 μW for the 633 nm excitation laser (power measured at the objective lens). The fluorescence emission was split into two detectors depending on the characteristic emission wavelengths and the counts per molecule maximized acting on the correction collar of the objective lens. A time trace was recorded for each spectral emission channel and both fluorescence auto- and cross-correlation curves calculated using Zen software (Zeiss). To calibrate the illumination volume for both excitation lines, an 18 nM solution containing a 1:1 mix of Alexa Fluor 647 (D = 330 μm2/s) (16) and Alexa Fluor 488 (D = 430 μm2/s) (31) was used in a μ-slide 8-well dish (ibidi, Martinsried, Germany). The excitation beams were focused a few micrometers away from the glass of the microscope slides into the solution, and an FCS measurement was repeated three times with a duration of 5 s per time-trace recording. The characteristic confocal full width at half maximum, d, was calculated, knowing the diffusion coefficient, D, and deducing an average transit time, τD, from the three acquired FCS curves, related by Eq. 1: The calibrated d per excitation line was then utilized to calculate D for each protein of interest labeled with a dye with similar spectral characteristics, implying the same excitation volume (eGFP and SiRo in our experiments). Each illumination volume was calibrated every day to monitor microscope performance and take small volume variations into consideration. Once the focal volume was calibrated, the actual measurements on live cells could be performed. Healthy cells expressing eGFP and/or SNAP/Halo-tagged SiRo signal were selected by visual inspection of the samples. In each sample, three data-collection locations in the cytosol of each cell were selected and the time-trace acquisition repeated three times per each set position for a 5 s recording. At least 10 cells for each sample were measured for the acquisition of one data set. Each data set was collected on at least three biological replicates. No appreciable photobleaching occurred during the acquisition. The Zeiss Zen software provides already auto-correlated or cross-correlated curves that were analyzed via FoCuS-point software (32). The data were fit using a 3D diffusion model that includes a triplet component. The overall generic model for analyzing the correlation curves waswhere τ represents the correlation lag time, Of represents the offset (fixed to 1), G0 is the amplitude of the correlation function at τ =0, GD(τ) is describing all correlation components relating to diffusion processes, and GT(τ) is an optional term accounting for a triplet state (dark state kinetics). To analyze our data, we used the following triplet equation:where T is the average triplet amplitude and is the triplet correlation time that we fixed at 0.005 ms for SiRo and 0.04 ms for eGFP (33, 34, 35). To analyze our cytosolic proteins’ mobility (GD(τ)), we considered a 3D diffusion model with multiple component fitting possibility:where τxyk is the lateral diffusion rate coefficient and represents the time taken for the diffusing species Kth to move laterally through the illumination area with its fraction Ak. α is the anomalous factor, important for compensating for when the diffusion kinetics are non-ideal. The aspect ratio that describes the illumination volume AR was here fixed to 6 and α, the anomalous factor, fixed to 1. The diffusion analysis was optimized by comparing the diffusion of PEX5L and PEX5 S600W, both expressed in combination with eGFP-PTS1 in human cells. Here, the dual plasmids PEX5L-SNAP/eGFP-PTS1 and PEX5L S600W-SNAP/eGFP-PTS1 were respectively expressed. When PEX5L S600W-SiRo was expressed in human fibroblasts, the collected correlation curves for this protein were fitted considering one diffusing component, as the S600W substitution inhibits PTS1 binding and therefore the PEX5 S600W cannot bind/co-diffuse with the eGFP-PTS1. Only a free component of its characteristic transit time and the corresponding diffusion coefficient have been considered and calculated. To study the dynamics of PEX5L in cells expressing PEX5L-SNAP-SiRo, we initially introduced a two-component fitting model (DS = 2 in the above model), trying to isolate a free diffusion contribution (that we could fix according to the previous analysis on PEX5L S600W) from a bound diffusion contribution (which we expected to diffuse together with PTS1 proteins). Clearly only one particular diffusion coefficient could be extracted from these data, comparable for PEX5L and PEX5 S600W, so the number of components that contribute to the fitting model related to PEX5L has been reduced to one. Since both variants of PEX5 (PTS1 binding competent PEX5L and the binding incompetent PEX5L S600W) are characterized by the same diffusion coefficient, this also indicated that the binding of eGFP-PTS1 to PEX5L-SNAP-SiRo did not influence the diffusion speed of the receptor. When PEX5 S600W was expressed in combination with eGFP-PTS1, the collected curves for the cargo protein eGFP-PTS1 were fitted considering two distinct populations (eGFP-PTS1 can still bind to the endogenous PEX5, fully functional but not labeled). The bound component was fixed in our analysis in accordance with the PEX5L characteristic diffusion coefficient (recalculated accordingly for 488 nm illumination volume) and the PTS1 free diffusion component extracted from the acquired FCS curves as well as the proportion between bound and unbound fraction. The majority (more than 87% on average) of the PTS1 in these samples was found to be unbound. In the case of eGFP-PTS1 expressed in combination with PEX5L (dual plasmid PEX5L-SNAP/eGFP-SKL), the fluorescence cross-correlation spectroscopy (FCCS) curves showed a clear co-diffusion of PEX5L and eGFP-PTS1 at a diffusion coefficient comparable with that of PEX5L. Therefore, we considered the calculated PEX5L diffusion coefficient as the bound contribution. We set this diffusion coefficient as the characteristic diffusion coefficient of eGFP-PTS1 when bound to PEX5L (recalculated accordingly for 488 nm illumination volume). Finally, we combined the extracted information obtained by eGFP-PTS1 bound diffusion and free diffusion coefficients (from PEX5 S600W/eGFP-SKL expression) to calculate the fraction of eGFP-PTS1 diffusing free and bound in the case of the simultaneous expression of PEX5L-SNAP/eGFP-SKL. Each calculated correlation curve was inspected by eye and eventually discarded when showing features clearly outside of the expected fitting model. In most cases discarded measurements showed a bright spike in the time trace that biased the correlation curve, possibly caused by a fluorescent cluster or a whole peroxisome moving into the focal volume. We did not explore the PEX5L diffusion at the peroxisomal membrane, as there the protein mobility is slower than the photobleaching rate at the set conditions, which made it impossible to measure characteristic diffusion coefficients in this location.
FCS measurements were performed with a confocal PicoQuant (Berlin, Germany) MicroTime 200 machine and a Zeiss 880 laser scanning microscope. The microscopes provided 488 nm excitation lines focused into the sample via a 60×/1.2 (Olympus (Tokyo, Japan) UPlanSApo) and a 40×/1.2 (Zeiss C-APOCHROMAT) water immersion objective lenses, respectively. Excitation power was set to 5 μW to minimize eGFP photobleaching. Counts per molecule were maximized acting on the correction collar of the objective lenses. Time traces were recorded and FCS curves calculated for each measurement. To measure the diffusion of eGFP and N-terminal part of PEX5L (human and from T. brucei, fused to eGFP) in solution, the proteins were diluted to ca. 10 nM. For each condition, recordings at three different spots were performed by measuring for 15–20 s five times at about 10 μm depth from the coverslip. Time traces acquired at the MicroTime were correlated using FoCuS-point software. Zeiss data were correlated using Zen software. The data were fitted using a 3D diffusion equation that includes a triplet component as described in the previous section.
We used a custom STED microscope built around a RESOLFT microscope from Abberior Instruments (Göttingen, Germany) as described previously (4,36). The microscope was equipped with a 640 nm pulsed excitation laser focused into the sample via a 100×/1.4 (Olympus UPLSAPO) oil immersion objective lens. Excitation power was set to 6 μW (measured at the back aperture of the objective lens) in cells. A 755 nm depletion beam pulsed at 80 MHz (Spectra-Physics (Milpitas, CA) MaiTai, pulse-stretched by a 40 cm glass rod and a 100 m single-mode fiber) was modulated in phase using a spatial light modulator (SLM) (Hamamatsu (Hamamatsu, Japan) LCOSX10468-02). STED power was set to 16 mW for the aberration correction procedure and varied between 6 and 33 mW for STED-FCS measurements. The fluorescence emitted by the sample was collected back by the objective lens, filtered via emission filters and a pinhole, and detected using an avalanche photodiode (Hamamatsu). The system was equipped with a correlation card (Flex02-08D) to acquire both time traces and FCS curves. The microscope was controlled by Imspector software (Abberior Instruments). The SLM was employed for both phase-mask generation and aberration correction and controlled by a bespoke Python software as described in (36). z-STED illumination volume calibration was carried out on a bead sample, FluoSpheres (Thermo Fisher), crimson (625/645), diameter = 0.04 μm. On each studied sample, an aberration correction procedure was run at the beginning of any data acquisition on one selected cell at approximately 3 μm from the slide into the cytosol. Data set collection consisted of a series of time traces acquired at increasing STED power distributed over a z-STED beam. For each sample at least five cells were selected and a STED power series (0, 7, 16, and 33 mW) was run over a selected point in the cytosol. Each measurement was acquired for 10 s and repeated three times. Each data set was collected on at least three biological replicates. The data were fitted using a standard 3D diffusion equation reported above (Eq. 4), with an α parameter set to 0.75 to account for a non-Gaussian point spread function in STED modality. (36,37). To account for the simultaneous decrease in lateral and axial size of the observation volume, we fitted STED-FCS curves using the model we previously developed (36). In short, confocal FCS curves were fitted first with an aspect ratio set to 4 (different from above as a higher-NA objective was used here). For z-STED recordings, the relative decreases in aspect ratios and lateral transit times with respect to the confocal values were fitted together using a single parameter, from which the axial transit times (tz) were calculated. The relationship between lateral and axial dimension of the observation volume was calibrated using images of fluorescent beads, as described in (36). No appreciable photobleaching occurred during the acquisition.
To investigate the mobility of human PTS1 receptors and peroxisomal proteins in the cytosol, we expressed them in fusion with fluorescent proteins or with a SNAP-tag, which allows covalent binding of a fluorescent dye, in human fibroblasts (GM5756-T), and measured their diffusion and interaction characteristics by FCS and two-color FCCS under a confocal microscope. FCS provides information on the molecular diffusion by recording the fluorescence signal over time and analyzing the fluctuations caused by stochastic movement of fluorescent molecules in and out of the confocal observation volume. From the auto-correlation function (ACF) of these fluctuations, it is possible to extract the average transit time of a protein of interest through the observation volume and to determine its characteristic diffusion coefficient D and, thus, mobility as well as changes due to potential molecular interaction dynamics (10, 11, 12). In FCCS, the cross-correlation function (CCF) of the characteristic fluctuations of two differently labeled proteins is determined, highlighting their co-diffusion in addition to the mobility (Fig. 2 a) (38). Specifically, we fluorescently labeled PEX5 with a SNAP-tag (denoted PEX5-SNAP) and the SNAP-tag binding dye SiRo (altogether denoted PEX5-SiRo). An artificial cargo protein was created by the conjugation of the tripeptide SKL, a peroxisomal targeting signal type 1, to the C-terminus of the enhanced green fluorescent protein (denoted eGFP-PTS1). We employed a dual-expression plasmid to ensure co-expression of both in the same cell (Fig. 2 b). We started with the longer variant of PEX5, PEX5L-SNAP, and in addition used the point mutant PEX5L S600W-SNAP (or PEX5L S600W-SiRo) as a control, which is not able to interact efficiently with PTS1 cargo proteins such as eGFP-PTS1 (24,39). Using confocal microscopy imaging of eGFP-PTS1, we first compared the import efficiency after expressing PEX5L and PEX5L S600W in human fibroblasts, respectively (Fig. 2 b). Clearly, the wild-type (WT) protein showed strong import of eGFP-PTS1 into peroxisomes, as indicated by the higher abundance of fluorescence in the peroxisomes and reduced cytosolic fluorescence background, while cells transfected with PEX5L S600W were characterized by mainly cytosolic fluorescence background. Thus, in the latter case the import was less efficient, which was to be expected since only the pool of endogenous unlabeled PEX5 could transport and thus import eGFP-PTS1. Fig. 2b also highlights representative FCS (or ACF) and FCCS (or CCF) curves of cytosolic PEX5L-SiRo, PEX5L S600W-SiRo, and the co-expressed eGFP-PTS1. While the presence of non-zero and decaying ACFs of all proteins highlights their mobility, the CCFs differ between PEX5L-SiRo and PEX5L S600W-SiRo: the non-zero and decaying CCF of PEX5L-SiRo and eGFP-PTS1 disclose their co-diffusion and, thus, efficient binding, and the non-existing CCF in the case of PEX5L S600W-SiRo and eGFP-PTS1 reveals their expected missing interaction. It is important to mention that we were only able to characterize mobility of the PEX5 proteins in the cytosol but not at the peroxisomal membrane, as the diffusion of PEX at peroxisomal membranes was so slow that photobleaching became a limiting factor (see materials and methods). However, we checked all cytosolic FCS and FCCS data for absence of bias due to photobleaching, as highlighted by constant (and not decreasing) average transit times through the observation volume within the employed excitation laser intensity range (40). Furthermore, we used FCCS to only qualitatively indicate the existence of co-diffusion between different potential binding partners but not to determine exact values of binding degrees. The latter would on one hand require detailed control experiments, such as quantification of green-red confocal overlap (11,38), and on the other hand would in our case be biased by the presence of non-labeled endogenous proteins.
We next analyzed the FCS data in more detail by fitting Eq. 1 to the ACF curves and extracting values of the diffusion coefficient D. The ACF curves of PEX5L-SiRo were well described by a one-component fit, revealing a rather slow mobility with a diffusion coefficient of D = 11 ± 3.5 μm2/s. In contrast, we needed to include two populations with different mobility to accurately describe the ACF curves of the eGFP-PTS1 cargo. Instead of letting all parameters freely float (which led to rather inaccurate results), we rather extracted the diffusion coefficients of the two populations by assigning the slower component to eGFP-PTS1 bound to PEX5L-SiRo with the value of the transit time fixed to the corresponding value of D = 11 μm2/s of PEX5L-SiRo, and the faster component to unbound eGFP-PTS1 with free-floating values of the transit time, resulting in D = 49 ± 19 μm2/s (see materials and methods for details) (Fig. 2 c). The distribution of values of D as highlighted in Fig. 2 c entail a Lorenz-like frequency distribution, as expected for free diffusion (41), and is not due to photobleaching (which we excluded from control experiments at different excitation intensities, see comment above) or other binding events. On average, around 75% of the eGFP-PTS1 was diffusion bound to the receptor (Fig. 2 c). However, the fractions of unbound and bound cargo protein varied between independent experiments, probably due to differences in the expression levels. We must also note that this fraction includes binding to expressed labeled as well as endogenous unlabeled PEX5. In general, all these factors impede the determination of exact absolute values of binding degrees (whether from FCS or FCCS data) or diffusion coefficients. However, we were rather interested in a qualitative assessment of binding by comparing relative values between different conditions, for which our approach seems appropriate. Within the error bars, the diffusion coefficient of unbound eGFP-PTS1 (D = 49 ± 19 μm2/s) was similar to that of cytosolic eGFP (D = 41 ± 11.5 μm2/s), which is also of similar molecular weight (MW) (26.95 kDa (eGFP) against 27.4 kDa (eGFP-PTS1), Fig. S2) and non-interacting, highlighting free diffusion of unbound eGFP-PTS1 (Fig. 2 c). Still, these results proved that cargo proteins could be found both bound and unbound to its receptor. As a control, we also studied cells co-expressing the PEX5L S600W mutant and eGFP-PTS1. For PEX5L S600W-SiRo, we found D = 12 ± 3.5 μm2/s, which is, within the errors, in the same range as PEX5L-SiRo (D = 11 ± 3.5 μm2/s) and indicates that the mobility of PEX5L was independent of whether PTS1 is bound (PEX5L-SiRo) or not (PEX5L S600W-SiRo). Furthermore, we again could fit the FCS data of eGFP-PTS1 with two components, the unbound free form (with a transit time corresponding to D = 49 μm2/s, as before) and a form bound to PEX5L with the transit time fixed to a value corresponding to D = 11 μm2/s of the mobility of PEX5L. However, the latter fraction was much lower (on average 12%) than for the previous WT PEX5L-SiRo expressing cells (on average 75%), since now only the small number of endogenous fully functional PEX5 was available to bind eGFP-PTS1 (Fig. 2 c). In both WT PEX5L-SiRo and PEX5L S600W-SiRo expressing cells, the respective FCS data were both well described by a single diffusing population (D = 11 ± 3.5 μm2/s for PEX5L-SiRo and D = 12 ± 3.5 μm2/s for PEX5L S600W-SiRo). This finding was somewhat surprising, as two populations might have been expected, one fast component without and one slower one carrying PTS1 cargo proteins. However, as highlighted already in the previous paragraph, the mobility of PEX5L seemed to be independent of whether PTS1 was bound or not. To further detail this, we compared the mobility of PEX5L-SiRo and PEX5L S600W-SiRo against the similar weighed aggregate of three eGFP molecules (80.85 kDa for 3xeGFP compared with 91.39 kDa for PEX5L-SNAP, Fig. S2). 3xeGFP showed a diffusion coefficient about twice as high (22 ± 5 μm2/s) as that for PEX5L, i.e., double the mobility (Fig. 2 c). Also, we confirmed the general slow and PTS1-independent diffusion of PEX5L for other cargoes such as eGFP-catalase and eGFP-SCP2. In all cases, diffusion of PEX5L-SiRo was equally slow (Fig. 3 a). Consequently, the slowdown in diffusion of PEX5L is not due to its MW and is independent of PTS1 cargo binding.
As already highlighted, the PEX5 protein exists in two different isoforms, a long PEX5L and a short PEX5S form. While both isoforms interact with PTS1 cargo proteins, the long isoform PEX5L also contains a binding site for the PTS2 receptor PEX7. Thus, cargo-loaded PEX7 binds to PEX5L and bridges the binding of PTS2 cargo proteins to the PTS1 import receptor PEX5. To investigate any influence of the binding of PEX7/PTS2 complexes on the mobility of PEX5, we compared the diffusion of the PEX5L and PEX5S isoforms. Both variants were again jointly expressed in human fibroblasts (GM5756-T) from dual PEX5L-SNAP/eGFP-PTS1 and PEX5S-Halo/eGFP-PTS1 plasmids, respectively, and fluorescent labeling realized with SiRo-SNAP and SiRo-Halo, respectively. Again, both variants showed undistinguishable mobility (Fig. 3 b), and we concluded that binding of PEX7 and its cargo was not rate limiting for the cytosolic mobility of PEX5.
As PEX5L showed an unexpectedly slow diffusion in the cytosol, we wanted to explore any heterogeneity in mobility (e.g., due to transient interactions) by comparing its diffusion mode with an inert and non-interacting and, thus, freely diffusing molecule (GFP-SNAP). For this, we employed spot-variation FCS (svFCS) (42). In svFCS, FCS data are taken for different sizes (or diameters) of the observation volume, and the dependency of the average transit time through the observation volume on the volume size is determined to highlight possible non-Brownian diffusion modes such as due to transient molecular interactions (16,42). Specifically, the plot of transit time against observation spot size (e.g., equatorial area) should be linear, with a y axis intercept of zero for Brownian and non-interacting diffusion and deviating otherwise. Here, we realized svFCS by taking FCS data on a super-resolution STED microscope (STED-FCS) (16,43). STED-FCS allows measurements of the mobility of a fluorescent molecule for different sizes of the observation volume from around 200–250 nm and 700 nm in lateral and axial diameter to below 50–80 nm and 300 nm, respectively, by varying the intensity of the STED laser, which is added for the spatial confinement of the fluorescence emission (43). Using STED-FCS in the context of svFCS, free and hindered diffusion modes such as due to transient, interaction-evoked slowdowns could indeed be distinguishing and characterized (16,43). While STED-FCS is an established technique for the study of diffusion dynamics in two dimensions such as on membranes (44), our application at a 3D cytosolic level with required refined technical implementation (43,45, 46, 47). To tune the size of the effective fluorescence observation spot along the axial z direction, a top-hat intensity-shaped STED laser beam was overlapped with the standard excitation beam, whereby the performance was optimized by reducing possible optical aberrations using adaptive optics (AO) (36). In these AO-z-STED-FCS measurements, the STED laser power was increased stepwise to record FCS data and thus determine cytosolic PEX5L mobility at varying observation volumes (Fig. 4 a). We first characterized the axial confinement of our observation volume from a standard confocal volume to the maximum compressed volume by acquiring a series of images of 40 nm sized fluorescent beads at different STED laser powers, highlighting a reduction of the axial diameter from 671 nm down to 256 nm (Fig. 4 b). We then used AO z-STED-FCS to measure the diffusion of eGFP-SNAP (labeled with SiRo dye) in the cytosol of living cells. eGFP-SNAP is an artificial protein that does not (to our knowledge) interacting with any cellular component and is therefore a suitable control to represent free diffusing modality. Its transit times through the observation volume decreased in coincidence with increasing STED power and, thus, confinement with a y axis intercept of zero (Fig. 4 c), indicating the absence of hindered diffusion (45). Using AO z-STED-FCS, we also highlighted free diffusion for cytosolic PEX5L and PEX5L S600W (labeled with SiRo). For both PEX5L and PEX5L S600W, the decrease in transit time with confinement of observation volume followed the same pattern as for freely diffusing eGFP-SNAP fusion protein (Fig. 4 c). Consequently, the slowed diffusion of PEX5L in the cytosol was not due to transient interactions with other more immobilized binding partners but rather a stable interaction with a permanent binding partner. Notably, this interaction was independent of the ability of PEX5 to bind to cargo proteins (as highlighted for non-PTS1-binding PEX5L S600W).
As highlighted, PEX5L integrates into the peroxisomal membrane to guide cargo proteins into the peroxisomal matrix. Owing to its accumulation at the membrane, it was previously hypothesized that PEX5L might oligomerize at the membrane or even already in the cytosol (48). To qualitatively investigate whether the observed slow cytosolic diffusion of PEX5L was linked to a possible homo-oligomerization, we conducted an FCCS study between differently labeled PEX5L to highlight their possible co-diffusion and thus potential homo-oligomerization. In Fig. 2 we show that our FC(C)S setup can indeed qualitatively pick up co-diffusion (i.e., oligomerization) events and distinguish them from non-interacting species through a non-zero versus zero amplitude of the cross-correlation curves. Here, cells expressing PEX5L-SNAP were incubated with a solution containing an approximately 1:1 (mol/mol) mixture of the red-fluorescing SNAP-SiRo and green-fluorescing SNAP-Cell505 dyes. While the auto-correlation curves for each signal (SiRo and Cell505) were quite similar (Fig. 5 b) and confirmed the slow diffusion (D = 10.5 ± 4.5 μm2/s and 13 ± 4 μm2/s for SiRo and Cell505 labeled PEX5L, respectively), as expected for the similarly tagged PEX5L proteins, there was no evidence of any cross-correlation signal. While we cannot exclude a weak or very transient interaction, we did not find any evidence of a strong PEX5L homo-oligomerization, i.e., a non-zero cross-correlation amplitude. In addition, the cytosolic mobility of PEX5L also did not change in a CRISPR/Cas9-derived PEX5 KO cell line (Fig. S1), precluding oligomerization of PEX5L with endogenous unlabeled PEX5. As for the experiments of Fig. 2, we used FCCS here only to qualitatively test for co-diffusion and oligomerization. A more quantitative assessment would have required accurate control experiments and more reliable equal labeling degrees by red SiRo and green Cell505 dyes, which we, however, did not achieve for our cell systems.
PEX5L interacts with peroxisomal membrane proteins and integrates into the membrane during cargo translocation. Such interaction with the peroxisomal membrane or membrane proteins could be a cause of the observed slowdown. Also, there are indications that PEX5L interacts with other organelles such as lipid droplets (49). Here, two different approaches were taken to test whether an interaction with peroxisomal membranes or other organelles could explain the slow PEX5L diffusion. First, a cell-free model system in the form of GPMVs was used. Here, upon treatment of the cells with N-ethylmaleimide (NEM), the plasma membrane becomes detached from the cytoskeleton and forms free-standing vesicles that contain cytosolic proteins and are devoid of organelles and cytoskeleton (including microtubules and actin filaments) (Fig. 6 a) (50,51). Therefore, proteins in these vesicles cannot interact with the cytoskeleton or intracellular membranes. To compare the diffusion of PEX5L in cells and in GPMVs, we expressed PEX5L-eGFP (MW 99.3 kDa) and 3xeGFP (MW 80.85 kDa) in cells, generated GPMVs by treatment with NEM, and determined the diffusion coefficient of PEX5L-eGFP and 3xeGFP in both systems using FCS as before: D = 10.5 ± 2.5 μm2/s (cells) and 20 ± 11 μm2/s (GPMVs) for PEX5L-eGFP, and D = 25 ± 4 μm2/s (cells) and 39 ± 13.5 μm2/s (GPMVs) for 3xeGFP (Fig. 6 b). For both PEX5L-eGFP and 3xeGFP there was a general increase in mobility in GPMVs compared with cells (factor 1.9 for PEX5L and 1.6 for 3xeGFP), owing to the decreased cytosolic crowding in GPMVs (50,51). Most importantly, the difference in mobility, or ratio between diffusion coefficients D, was similar in cells and GPMVs (D(3xeGFP)/D(PEX5L-eGFP) = 2.4 in cells compared with 2.0 in GPMVs), highlighting a slowdown of cytosolic PEX5L independent of potential interactions with organellar membranes or the cytoskeleton. In a second approach, we investigated a potential interaction of PEX5L with peroxisomal membranes by determination of PEX5L mobility in a PEX14-deficient KO cell line. As PEX5L binds PEX14 at the peroxisomal membrane (52), the interaction of PEX5L with peroxisomal membranes should be inhibited in the absence of PEX14 or at least significantly decreased. Here, the dual plasmid expressing PEX5L and eGFP-PTS1 was expressed in a HEK 293 PEX14 KO cell line created by CRISPR/Cas9. In these cells, the diffusion coefficient of PEX5L (D = 14 ± 5 μm2/s) was, however, comparable with that in WT fibroblasts (16 ± 6 μm2/s) or WT HEK cells (14.5 ± 4.5 μm2/s), indicating that the interaction of PEX5L with PEX14 did not cause its cytosolic slowdown (Fig. 6 c).
PEX5L can be divided into two different functional parts, the structurally disordered N-terminal half (aa 1–335) (53,54), containing several WxxxxF/Y motifs that play a role in docking to the peroxisomal membrane (21,55), and the globular C-terminal half (aa 314–639) containing tetratricopeptide repeat (TPR) motifs, which interact with the PTS1 signal sequence (56) (schematics in Figs. 1 and 7 a, top). We created truncations of PEX5 that comprise either the N-terminal or the C-terminal half of PEX5L (referred to as PEX5L N-Term and PEX5L C-Term), expressed them together with eGFP-PTS1 (in dual-expression plasmids) in human fibroblasts, and determined their diffusion coefficients as well as co-diffusion with eGFP-PTS1 using FCS and FCCS as already described. From theory, we would expect binding and, thus, co-diffusion and a non-zero cross-correlation curve for PEX5L C-Term and no binding and zero cross-correlation for PEX5L N-Term. As expected, FCCS highlighted co-diffusion and, thus, binding between PEX5L C-Term and eGFP-PTS1, while PEX5L N-Term did not (Fig. 7 b). On the other hand, mobility of PEX5L N-Term was similarly slow (D = 13 ± 5.5 μm2/s) as full-length PEX5L (D = 11.5 ± 4.5 μm2/s) and PEX5L S600W (D = 12 ± 5 μm2/s), while diffusion of PEX5L C-Term was 2-fold faster (D = 21.5 ± 10 μm2/s) and about the same as 3xeGFP (D = 22 ± 5 μm2/s) (Fig. 7 a). Interestingly, PEX5L C-Term and PEX5L N-Term have similar MW (58 kDa and 56.5 kDa, respectively). Therefore, we concluded that the factors responsible for the slow diffusion of the cargo receptor PEX5 are molecular parts located in the protein’s N-terminal half (e.g., their structurally large disorder) and/or other molecules interacting with this part.
The finding that the N-terminal half of PEX5L is responsible for the slow diffusion of the protein raised the question as to whether this slow diffusion was caused by cytosolic N-terminal binding partners or by the structurally disordered nature of the N-terminal half compared with the more ordered C-terminal PTS1 binding domain. Therefore, we compared the mobility of PEX5L N-Term with that of a similarly unstructured protein, the N-terminal half of PEX5 from T. brucei. While the structural architecture of PEX5L (and therefore also of its N-terminus) is similar between trypanosomes and the human PEX5L protein (Figs. 8 a and S3), we expected cytosolic interactions to differ between both variants due to their evolutionary distance. Therefore, we expressed the N-terminal half of human PEX5L (HsPEX5L N-Term, aa 1–335) and the N-terminal half of PEX5 from Trypanosoma (TbPEX5 N-Term, aa 1–340) in human fibroblast cells (labeled via SiRo-SNAP as before). Here, TbPEX5 N-Term diffused almost twice as fast as its human counterpart (D = 11 ±4 μm2/s vs. 17 ± 6 μm2/s) (Fig. 8 c). Consequently, the bulky unstructured character of the N-terminal part of PEX5 was not the main reason for the slowdown in diffusion. To test whether this effect was thus rather caused by a cytosolic interaction partner of the human PEX5L, we created recombinant versions of the two proteins, both fused to eGFP. This allowed us to measure the diffusion of the proteins in solution, i.e., without the presence of any potential binding partner. Here, both HsPEX5L N-Term and TbPEX5 N-Term diffused equally fast (D = 53 ± 6 μm2/s vs. 53 ± 6 μm2/s) (Fig. 8 d). We then isolated cytosolic components from HEK 293 cells and incubated them with the recombinant proteins HsPEX5L N-Term and TbPEX5 N-Term (Fig. 8 b). Interestingly, the human-based cytosolic components specifically slowed human PEX5L N-Term (D = 7 ± 5 μm2/s) but not its trypanosomal counterpart (D = 68 ± 6 μm2/s); TbPEX5 N-Term even diffused faster, which could for example be induced by cleavage actions from other factors such as human proteases (Fig. 8 d). Nevertheless, our data indicated that an interaction with a human-specific cytosolic binding partner was the most probable cause of slowdown in diffusion.
In this study, we applied advanced microscopy and spectroscopy techniques combined with biochemical methods to investigate the diffusion behavior of the peroxisomal import receptor PEX5 and its peroxisomal cargo proteins in the cellular cytosol. This combinatory approach established a toolbox necessary to reveal a broad range of biophysical information on cargo recognition and the migration behavior of the free and cargo-loaded receptor in the cytosol. Using FCS, we were able to fully characterize the diffusion properties of PEX5 and reveal and characterize features expected from its function in peroxisomal protein import: 1) co-diffusion in a complex with cargo proteins, proved by the cross-correlation of PEX5 and PTS1 (Fig. 2 b); 2) diffusion of the major fraction of PTS1 in a complex with PEX5 (≈75%), and only a minor part with free diffusion characteristics, i.e., not bound to PEX5 (≈25%), but this distribution might be influenced by the overexpression of the proteins in this experimental setup; 3) by cross-correlation analysis employing C- and N-terminal truncations of PEX5, we demonstrated in living cells that the C-terminal part but not the N-terminal part of PEX5 binds PTS1 cargo proteins (Fig. 7 b), which formerly has only been seen in vitro (57); 4) using super-resolution AO z-STED-FCS, we proved cytosolic free diffusion of PEX5 (Fig. 4 c). Strikingly, the analysis of the diffusion characteristics of PEX5 also revealed a very slow cytosolic diffusion of PEX5, which was much slower than expected from its MW. Furthermore, this slow diffusion was independent of 1) binding of PTS1-cargo (Fig. 2) and cargo type (Fig. 3 a), 2) interaction with PTS2 cargo and its cargo receptor PEX7 (Fig. 3 b), 3) possible (transient) interactions with peroxisomal membranes and other organelles (using PEX14 KO cells and GPMVs) (Fig. 6 c), 4) cytoskeleton meshwork (Fig. 6 b), 5) the structurally disordered N-terminal half of PEX5 (Figs. 7 and 8 c), and 6) possible and non-confirmed cytosolic oligomerization (Fig. 5). Related to the last issue, PEX5 binding of oligomerized cargo proteins has been reported before, as it is a precondition of piggy-back transport of proteins into peroxisomes (58,59). Along this line, a dimeric alanine-glyoxylate aminotransferase could also bind two PEX5 receptors (22). However, it is still a matter of debate whether PEX5 binds oligomerized cargoes, which then can form large complexes with several receptor proteins involved. This “preimplex” hypothesis was originally described by Gould and Collins (48) and is supported by findings that peroxisomal enzymes enter large protein complexes before they are translocated into the peroxisomal matrix (60). Although we cannot exclude possible very short and transient interactions, our data indicated no presence of PEX5 oligomerization in the cytosol, which is in line with other previous studies (53). Finally, we compared cytosolic diffusion of human PEX5L with that of PEX5 from T. brucei (61), disclosing distinct mobility differences. From the fact that TbPEX5 diffused faster than its human counterpart in the cytosol of mammalian cells, we discovered 1) that the unstructured N-terminus of PEX5 is not the cause for the non-typical slow diffusion of the receptor but that it only has a minimal influence on its mobility (as both variants are characterized by such an unstructured part), and 2) that the slow diffusion of PEX5 is not caused by the structure of the protein but by binding to another cytosolic protein. This was confirmed by in vitro measurements on recombinant human PEX5 and TbPEX5. While both recombinant variants of PEX5 showed no difference in their diffusion behavior in solution, only the human PEX5 was slowed down in the presence of cytosolic proteins. In addition, diffusion of PEX5L was very distinctly slow rather than heterogeneous over a larger range of mobilities, i.e., the interaction was rather stable and non-transient. These findings point to a so far unknown cytosolic interaction partner that binds to the N-terminal part of human PEX5 and determines its peculiar diffusion behavior. The identity of this interaction partner still remains to be shown. Possible are interactions with a larger chaperone assembly, which would accompany PEX5 in its recognition of cargo proteins or protect the intrinsically disordered N-terminal region of PEX5 from aggregation. Also, an interaction of PEX5 with ribosomes could be envisioned, which would be in line with the observation that the mRNAs for the synthesis of peroxisomal proteins are found near peroxisomes (62). In any event, as the next step we plan to identify this binding partner using, for example, chromatographic isolation approaches in conjunction with mass spectroscopy. Besides novel insights into diffusion and interaction dynamics of peroxisomal proteins and especially the essential cargo carrier and import protein PEX5 in the cytosol of living cells, our study highlights the potential of using complementary experimental tools from advanced fluorescence microscopy and spectroscopy over model systems as biochemical and molecular biology approaches to decipher molecular interactions in the cytosol by studying their diffusion dynamics. Here, the combinatory approach revealed characteristics of the cytosolic migration behavior of peroxisomal proteins and their receptor interaction before peroxisomal targeting and import, and disclosed the cytosolic interaction of the peroxisomal import receptor PEX5 with a novel interaction partner.
S.G., K.R., A.B., P.C., I.U., J.O., J.S., and P.H. performed the experiments. E.S., F.S., and D.W. helped in analyzing the FCS data. W.S., R.E., and C.E. provided input on the experimental design.
The authors declare no competing interests. | true | true | true |
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PMC9586913 | Na Ri Park,Jung Hoon Cha,Pil Soo Sung,Jeong Won Jang,Jong Young Choi,Seung Kew Yoon,Si Hyun Bae | MiR-23b-3p suppresses epithelial-mesenchymal transition, migration, and invasion of hepatocellular carcinoma cells by targeting c-MET | 17-10-2022 | Hepatocellular carcinoma (HCC),miR-23b-3p,c-MET,Transforming growth factor beta1 (TGF-β1),Epithelial-mesenchymal transition (EMT) | Background Aberrant expression of c-MET is known to be associated with tumor recurrence and metastasis by promoting cell proliferation, epithelial-mesenchymal transition (EMT), and migration in hepatocellular carcinoma (HCC). Recently, miR-23b-3p has been identified as a tumor suppressor, but detailed role of miR-23b-3p in HCC is still unclear. Our study aimed to investigate how miR-23b-3p is associated with the malignant potential of HCC cells. Methods HCC tissues and their adjacent non-tumor tissues were acquired from 30 patients with HCC. Expression of EMT- or stemness-related genes were examined in the two HCC cell lines. Migration of HCC cells was analyzed using transwell and wound healing assays. Results c-MET was overexpressed in HCC tissues compared to the adjacent non-tumor tissues. c-MET knockdown inhibited EMT and reduced migration and invasion of HCC cells. Furthermore, c-MET was a target of miR-23b-3p, and miR-23b-3p expression was decreased in HCC tissues compared to non-tumor tissues. Treatment of miR-23b-3p inhibitor in HCC cells promoted EMT, cell migration, and invasion. In contrast, miR-23b-3p overexpression suppressed EMT, cell migration, and invasion, concomitantly reducing c-MET expression. Transfection of miR-23b-3p inhibitor with concomitant c-MET knockdown mitigated the effects of miR-23b-3p inhibitor on EMT in HCC cells. In addition, transforming growth factor beta1 (TGF-β1) stimulation after miR-23b-3p overexpression induced neither the mesenchymal phenotype nor migratory property of HCC cells. Conclusion In this study, we confirmed that miR-23b-3p downregulation significantly increased EMT, migration, and invasion of HCC cells. In addition, c-MET was confirmed to be a target of miR-23b-3p in HCC cells and regulated the functional effects of miR-23b-3p. These results suggest that miR-23b-3p can be used as a prognostic biomarker and candidate target for HCC treatment. | MiR-23b-3p suppresses epithelial-mesenchymal transition, migration, and invasion of hepatocellular carcinoma cells by targeting c-MET
Aberrant expression of c-MET is known to be associated with tumor recurrence and metastasis by promoting cell proliferation, epithelial-mesenchymal transition (EMT), and migration in hepatocellular carcinoma (HCC). Recently, miR-23b-3p has been identified as a tumor suppressor, but detailed role of miR-23b-3p in HCC is still unclear. Our study aimed to investigate how miR-23b-3p is associated with the malignant potential of HCC cells.
HCC tissues and their adjacent non-tumor tissues were acquired from 30 patients with HCC. Expression of EMT- or stemness-related genes were examined in the two HCC cell lines. Migration of HCC cells was analyzed using transwell and wound healing assays.
c-MET was overexpressed in HCC tissues compared to the adjacent non-tumor tissues. c-MET knockdown inhibited EMT and reduced migration and invasion of HCC cells. Furthermore, c-MET was a target of miR-23b-3p, and miR-23b-3p expression was decreased in HCC tissues compared to non-tumor tissues. Treatment of miR-23b-3p inhibitor in HCC cells promoted EMT, cell migration, and invasion. In contrast, miR-23b-3p overexpression suppressed EMT, cell migration, and invasion, concomitantly reducing c-MET expression. Transfection of miR-23b-3p inhibitor with concomitant c-MET knockdown mitigated the effects of miR-23b-3p inhibitor on EMT in HCC cells. In addition, transforming growth factor beta1 (TGF-β1) stimulation after miR-23b-3p overexpression induced neither the mesenchymal phenotype nor migratory property of HCC cells.
In this study, we confirmed that miR-23b-3p downregulation significantly increased EMT, migration, and invasion of HCC cells. In addition, c-MET was confirmed to be a target of miR-23b-3p in HCC cells and regulated the functional effects of miR-23b-3p. These results suggest that miR-23b-3p can be used as a prognostic biomarker and candidate target for HCC treatment.
Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer, and its incidence is increasing worldwide [1, 2]. Despite the availability of various modalities for treatment, including surgery, liver transplantation, and target therapies, the survival rate of HCC patients is still low due to high rate of recurrence and metastasis [3, 4]. Recurrence and metastasis are closely related to epithelial-mesenchymal transition (EMT) in HCC [5, 6]. EMT is characterized by the loss of epithelial markers, such as E-cadherin, followed by upregulation of mesenchymal markers, such as N-cadherin, and strongly promotes cell migration and invasion in most cancers as the initial step of metastasis [7, 8, 9]. It is well established that transforming growth factor-β1 (TGF-β1) is a major inducer of EMT and plays a key role in tumor progression and metastasis in HCC [10]. There is a need for the development of more accurate prognostic predictions and novel targets for the treatment of HCC metastasis. MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding 3ʹUTR of target mRNAs and function as oncogenes or tumor suppressors in the development and progression of various human cancers [11]. They are involved in various biological processes in carcinogenesis, such as tumor initiation, development, and metastasis [12, 13]. Among various miRNAs, miR-23 is known to be tightly associated with liver diseases [14]. miR-23 comprises miR-23a and miR-23b, and these two miRNAs differ by only one nucleotide; miR-23a is located on chromosome 19, while miR-23b is located on chromosome 9 [15]. Among them, miR-23b has been shown to be downregulated in several cancers including HCC [16, 17], suggesting that miR-23b-3p may function as a tumor suppressor. Previously, we reported that dual expression of CD44 and TGF-β1 enhances EMT and migration than single expression of CD44 or TGF-β1 in HCC cells [18]. SNU-368 (CD44+/TGF-β1+) cells were more tightly associated with the metastatic potential of HCCs from its synergy with CD44 and TGF-β1 than SNU-354 (CD44+/TGF-β1-) cells. Based on these results, two cell lines (SNU-354 and SNU-368) were selected to screen potent miRNAs regulating EMT and migration. We selected miR-23b-3p as a biomarker for the regulation of EMT in two HCC cell lines. Furthermore, we identified mesenchymal-epithelial transition factor (c-MET) as a candidate target for miR-23b-3p using target prediction tools. In general, miRTarBase and miRDB are frequently used to predict the interactions between miRNAs and target genes; however, a probability of false predictions exists. To overcome such false predictions, clinical samples may be utilized for the validation of miRNA-mRNA relationships [19]. Considering this, in this study, we used 30 HCC tumor samples to validate the relationship between c-MET and miR-23b-3p to reduce the possibility of false predictions while using the TargetScan program. The purpose of this study is to show that miR-23b-3p or c-MET may play a role in suppressing EMT, migration, and invasion of HCC, as well as to analyze the interactions between miR-23b-3p and c-MET in the context of EMT and migration. Therefore, in this study, we investigated the role of miR-23b-3p in suppressing HCC migration and invasion by attenuating EMT through the control of c-MET.
To screen differential miRNA expression between control cells and TGF-β1-treated cells, Illumina small-RNA next-generation sequencing (NGS) was performed (Macrogen, Seoul, Korea). Briefly, total RNA was extracted from cells using TRIzol reagents (Invitrogen, Carlsbad, CA, USA), and the quality of RNA samples was confirmed using TruSeq small RNA library prep kit (Illumina San Diego, CA, USA). Then, the miRNA sequencing was loaded out at Illumina HiSeq 2500.
HCC tissues and their adjacent non-tumor tissues were acquired from 30 patients with HCC at Seoul St. Mary's Hospital, Catholic University of Korea (Seoul, South Korea) between June 2018 and April 2021. This study was approved by the Institutional Review Board (IRB) of Seoul St. Mary's Hospital, Catholic University of Korea, and written informed consent was obtained from all the patients (IRB approval number KC17TNSI0484). Clinical characteristics of the enrolled patients are described in Supplementary Table S1.
The human HCC cell lines SNU-354, SNU-368 and Huh7 were purchased from Korean Cell Line Bank (KCLB; Seoul, Korea), and HepG2, Hep3B and SK-HEP-1 cells were purchased from ATCC. SNU-354, SNU-368 and Huh7 cells were cultured in RPMI-1640 medium (Welgene, Gyongsan, Korea) containing 10% fetal bovine serum (FBS; Gibco, USA) and 1% antibiotic (Gibco). SK-HEP-1 cells was cultured in Dulbecco's modified Eagle's medium (DMEM; Invitrogen, Carlsbad, CA, USA) containing 10% FBS and 1% antibiotic. HepG2 and Hep3B cells were cultured in Minimum Essential Medium (MEM; Gibco) containing 10% FBS and 1% antibiotic. All cells were maintained at 37 °C in 5% CO2. SNU-354 cells were treated with 5 ng/mL TGF-β1 (R&D Systems, Minneapolis, MN, USA) for 48 h, and SNU-368 cells were treated with 1 μM TGF-β inhibitor (SB431542; Selleckchem, Houston, TX, USA) for 24 h.
The miR-23b-3p mimic, inhibitor, and corresponding controls were purchased from GenePharma (Shanghai, China). c-MET siRNA and negative control (NC) were purchased from Dharmacon (Lafayette, CO, USA). SNU-354 and SNU-368 cells were transiently transfected with Lipofectamine RNAiMAX (Invitrogen, Carlsbad, CA, USA) according to the instructions provided by the manufacturer. After transfection for 48 h, the cells were harvested, and the transfection efficiency was analyzed by qRT-PCR.
RNA was extracted from the cell lines using TRIzol reagent (Invitrogen) according to the manufacturer's instructions. The TaqMan® MicroRNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA) was used to synthesize cDNA to evaluate the expression level of miRNAs. We set up the miRNA cDNA synthesis program on the thermal cycler with the following setting conditions: 16 °C for 30 min, 42 °C for 30 min, 85 °C for 5 min, and hold at 4 °C. PCR was run on the Roche Light Cycler L480 (Roche Applied Science, Indianapolis, IN, USA) for 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 60 s. QuantiTect Reverse Transcription Kit (Qiagen, Hilden, Germany) was used to synthesize cDNA to assess the mRNA levels. We set up the mRNA cDNA synthesis program on the thermal cycler with the following setting conditions: 42 °C for 30 min, 95 °C for 3 min, and hold at 4 °C. PCR was run on the Roche LightCycler 480 (Roche Applied Science, Indianapolis, IN, USA) for 95 °C for 10 min, followed by 55 cycles of 95 °C for 10 s, 60 °C for 30 s, 72 °C for 1 s. Relative expression levels were normalized to those of U6 snRNA (001973) or GAPDH (Hs02786624) as endogenous controls. The following TaqMan probes were used: hsa-miR-23b-3p (000400), c-MET (Hs01565584), Snail (Hs00195591), Slug (Hs00161904), twist (Hs01675818), Nanog (Hs02387400) and KLF4 (Hs00358836). The relative expression levels were determined using the 2−ΔΔCt method. Total RNA from human normal liver tissue was purchased from Thermo Fisher (AM7960).
Proteins from the cells were lysed using the PRO-PREP protein extraction solution (iNtRON Biotechnology, Seoul, Korea). Equal concentrations of proteins were separated by SDS-PAGE and transferred to PVDF membranes. Then, 5% skim milk or BSA (bovine serum albumin) was used to block the membranes for 60 min at room temperature and the membranes were incubated overnight at 4 °C with the primary antibodies. The next day, membranes were washed thrice and incubated with specific secondary antibodies for 1 h at room temperature. Protein bands were detected using EZ-Western Lumi La (Dogen, Seoul, Korea) and analyzed using the LAS-4000 (Fuji-Film, Tokyo, Japan) imaging system. The following primary antibodies were used: c-MET (1:1000; Cell Signaling, 8198), Claudin-1 (1:1000; Invitrogen, 71-7800), E-cadherin (1:1000; Cell Signaling, 3195), CD44 (1:500; Cell Signaling, 3570), N-cadherin (1:1000; BD Biosciences, 610921), β-catenin (1:1000; Enzo Life Sciences, ALX-804-260), Fibronectin (1:1000; Abcam, ab2413), GAPDH (1:1000; cell signaling, 2118S) and β-actin (1:10000; Sigma-Aldrich, A5441).
Transwell chambers uncoated (for migration assay) or coated (for invasion assay) with Matrigel (Corning Incorporated, Corning, NY, USA) were used according to the manufacturer's protocol. To measure the migration and invasive ability of cells, transfected cells were seeded into each upper chamber in serum-free media, and the lower chamber was filled with a complete medium supplemented with 5% or 10% FBS-containing media. The inserts of the invasion chamber were used after rehydration in a serum-free medium for 2 h before the cells were seeded. After 24 or 48 h of incubation, non-migrating or non-invading cells that remained on the top of the transwell insert were removed using a cotton swab. The migratory and invasive cells were then stained with Diff-Quick solution (Sysmex, Japan) and counted.
Transfected cells were seeded onto 6-well plates. The next day, the cells were scratched using a sterile 200 μL pipette tip. The culture medium was replaced with a fresh 0.5% FBS-containing medium. The wound closure was photographed at 0 and 48 h under a microscope.
Statistical analysis was performed using GraphPad Prism version 7 (GraphPad software, San Diego, CA, USA) and SPSS 20.0 software (IBM, Armonk, NY, USA). All data were presented as mean ± standard deviation (SD) or median. Comparisons between two groups were evaluated using two-tailed Student's t-test or Mann-Whitney U-test. The experiments were performed at least in triplicate. Statistical significance was set at ∗p < 0.05, ∗∗p < 0.005, ∗∗∗p < 0.0005.
To identify miRNAs related to EMT, small-RNA next-generation sequencing (NGS) was performed using control cells and TGF-β1-treated cells. As a result, upregulation of two miRNAs and downregulation of seven miRNAs were noted in TGF-β1-treated cells. Of these, miR-23b-3p was selected because the other miRNAs were not reliably detected in HCC cells by qRT-PCR. Considering that miR-23b-3p acts as a tumor suppressor miRNA in HCC, all further analyses in this study focused on miR-23b-3p (Supplementary Figure 1A). Moreover, according to the prediction by using bioinformatics tools, c-MET was considered a candidate target for miR-23b-3p. Thus, we confirmed the expression pattern of c-MET and miR-23b-3p in HCC tissue and cell lines. c-MET was analyzed in 30 paired HCC and adjacent non-tumor specimens by western blotting (Figure 1A). The protein levels of c-MET were significantly higher in HCC tissues than in the adjacent non-tumor tissues by Mann-Whitney U-test (Figure 1B). On the other hand, the expression of miR-23b-3p in HCC tissues was lower than that in adjacent non-tumor tissues (Figure 1C). Moreover, c-MET was upregulated in HCC cell lines (Figure 1D), while miR-23b-3p expression was decreased in HCC cell lines compared to human normal liver (Figure 1E).
Our previous study confirmed that SNU-368 (CD44+/TGF-β1+) cells readily undergo EMT and have migratory property compared to SNU-354 (CD44+/TGF-β1-) cells via the synergy between CD44 and TGF-β1 [18]. c-MET siRNA was transfected into SNU-368 cells, and knockdown efficiency was determined by qRT-PCR (Figure 2A) and Western blot analysis (Figure 2C). c-MET knockdown decreased the mRNA expression levels of EMT transcription factors Snail, Slug, and Twist (Figure 2B). In addition, the protein expression levels of the mesenchymal marker N-cadherin were markedly reduced, and the protein expression levels of the epithelial marker Claudin-1 were increased (Figure 2C). Moreover, the mRNA expression levels of stemness-associated genes, Nanog and KLF4, were significantly downregulated in c-MET knockdown cells (Figure 2B). CD44 protein also showed similar results in these cells (Figure 2C). Finally, transwell assay showed that c-MET knockdown suppressed the migration and invasion of SNU-368 cells (Figure 2D). Collectively, these data confirmed that c-MET knockdown blocks EMT, migration, and invasion of HCC cells.
To validation the results of NGS, we induced EMT by TGF-β1 treatment or inhibited EMT by TGF-β1 inhibitors. TGF-β1-stimulated SNU-354 cells exhibited EMT induction through the loss of E-cadherin expression and gain of CD44, N-cadherin, β-catenin, Snail, and Slug expression (Figure 3A and B). In comparison, TGF-β1 inhibition in SNU-368 cells blocked EMT through increased E-cadherin and decreased CD44, N-cadherin, β-catenin, Snail, and Slug expression (Figure 3D and E). The expression of miR-23b-3p was downregulated by TGF-β1 stimulation (Figure 3C) and upregulated by TGF-β1 inhibitor (Figure 3F). These results suggest that miR-23b-3p may act as a tumor suppressor in HCC and that downregulation of miR-23b-3p is involved in TGF-β1-induced EMT. To explore the molecular mechanisms of HCC progression by miR-23b-3p, we used two different bioinformatics tools (miRTarBase and miRDB) to predict the putative target genes of miR-23b-3p and identified c-MET as a potential target. Binding sites of miR-23b-3p and c-MET were predicted with miRTarBase (Figure 3G). In addition, miR-23b has been reported to target c-MET [20, 21, 22]. We investigated whether miR-23b-3p regulates c-MET expression in two HCC cell lines by western blotting after miR-23b-3p inhibitor and mimic transfection. Inhibition of miR-23b-3p significantly increased c-MET protein expression in SNU-354 (CD44+/TGF-β1-) cells, whereas overexpression of miR-23b-3p significantly decreased c-MET protein expression in SNU-368 (CD44+/TGF-β1+) cells (Figure 3H and I). Collectively, these results confirmed that c-MET is a potential target of miR-23b-3p and that miR-23b-3p regulated c-MET expression. HGF is known to activate c-MET, and we investigated the effects of miR-23b-3p on c-MET activation by HGF. In the presence of 25 or 50 ng/mL HGF, HGF activated c-MET and decreased the expression of miR-23b-3p in a dose-dependent manner (Supplementary Figure 2A and B). Moreover, transfection of miR-23b-3p mimic inhibited c-MET activation in HGF-treated SNU-354 cells (Supplementary Figure 2C).
To investigate the functional impact of miR-23b-3p on EMT, SNU-354 cells were transiently transfected with an miR-23b-3p inhibitor, and SNU-368 cells were transfected with miR-23b-3p mimics. We confirmed the transfection efficiency of miR-23b-3p in the mimic and inhibitor groups by qRT-PCR. miR-23b-3p levels were decreased in SNU-354 cells transfected with miR-23b-3p inhibitors and increased in SNU-368 cells transfected with miR-23b-3p mimics compared to those in the negative control (Figure 4A and B). After 48 h of transfection, downregulation of miR-23b-3p in SNU-354 cells decreased the expression of the epithelial marker Claudin-1 and increased the expression of the stem-cell marker CD44 and the mesenchymal markers N-cadherin, β-catenin, and Fibronectin (Figure 4C). In comparison, upregulation of miR-23b-3p in SNU-368 cells significantly decreased the expression of the CD44 and N-cadherin, β-catenin, and Fibronectin (Figure 4D). These results revealed that inhibition of miR-23b-3p promoted EMT and overexpression of miR-23b-3p inhibited EMT in HCC cell lines. However, expression of the epithelial marker E-cadherin, which is predicted to be directly regulated by miR-23b-3p, showed a trend different from that of Claudin-1 (Figure 4C and D). We evaluated the effect of miR-23b-3p on the invasive capacity of SNU-354 and SNU-368 cells using migration, invasion, and wound healing assays. As expected, inhibition of miR-23b-3p strongly enhanced transwell migration/invasion and cell motility in SNU-354 cells compared to the negative control (Figure 4E and F). In contrast, the overexpression of miR-23b-3p strongly reduced transwell migration/invasion and cell motility in SNU-368 cells compared to the negative control (Figure 4G and H). Taken together, these results indicate that miR-23b-3p regulates cell migration and invasion capabilities of HCC cells.
To confirm the relationship between c-MET and miR-23b-3p expression, SNU-354 cells were co-transfected with an miR-23b-3p inhibitor in the presence or absence of c-MET knockdown. Transfection with miR-23b-3p inhibitor induced EMT; however, c-MET knockdown mitigated this EMT-promoting effect (Figure 5A). Moreover, c-MET knockdown dramatically reversed the effects of migration and invasion caused by miR-23b-3p inhibition (Figure 5B). These results demonstrate that c-MET is involved in the process of EMT by miR-23b-3p in HCC cells.
Next, we investigated whether miR-23b-3p could inhibit TGF-β1-induced EMT, migration, and invasion. To confirm that miR-23b-3p suppresses TGF-β1-induced EMT, an miR-23b-3p mimic was transfected into SNU-354 cells treated with TGF-β1 (Figure 6A). As shown in Figures 3 and 6, SNU-354 cells downregulated the expression of epithelial markers and upregulated the expression of mesenchymal marker by TGF-β1 treatment (Figure 6B and C). In addition, the expression of the stemness marker CD44 also increased (Figure 6C). Overexpression of miR-23b-3p in TGF-β1-treated HCC cells led to the reverse EMT with loss of mesenchymal and stemness markers and upregulation of epithelial markers except E-cadherin (Figure 6C). Consistent with these findings, miR-23b-3p overexpression strongly suppressed TGF-β1-induced cell migration and invasion (Figure 6D). Taken together, our results indicate that miR-23b-3p overexpression counteracted the effects of TGF-β1-induced EMT and migration in HCC cells.
HCC is a common malignant tumor with high recurrence and metastasis rates and is complicated by multiple lesions. miRNAs regulate proliferation, apoptosis, invasion, EMT, and drug resistance during the progression of HCC. The identification of cancer-related miRNAs and their target genes is necessary for the development of diagnostics and therapeutics for HCC. Several studies have shown that EMT can lead to epithelial cell trans-differentiation into mesenchymal cells, leading to cancer cell metastasis [6]. Our current study identified miR-23b-3p as an anticancer factor in TGF-β1-induced EMT models. Downregulated miR-23b-3p promotes HCC progression, and its reduction is associated with poor prognosis in patients with HCC [16]. Although some studies have reported that miR-23b may decrease the migration of HCC cells by targeting c-MET, the definite mechanism of action of the interactions between miR-23b-3p and c-MET during EMT has not been fully elucidated [20]. Interestingly, this study was developed based on the findings of previous studies, to screen for potent miRNAs that regulate EMT. SNU-354 (CD44+/TGF-β1−) cells were treated with TGF-β1, while SNU-368 (CD44+/TGF-β1+) cells were treated with SB431542 (TGF-β1 inhibitor). Among miRNAs, miR-23b-3p expression was distinctly downregulated during the process of TGF-β1-induced EMT and was notably upregulated in reverse EMT by SB431542 (Figure 3). In addition, miR-23b-3p overexpression reversed the effects of TGF-β1-induced EMT and migration (Figure 6). Further, we found that miR-23b-3p regulates both c-MET and CD44, which is associated with EMT. It is important to gain an in-depth understanding of the mechanism involved in the regulation of EMT by tumor-suppressive miR-23b-3p that regulate metastasis-promoting genes to obtain a successful therapeutic outcome in HCC. Hereby we elucidated the anti-metastatic role of miR-23b-3p as an important regulator that modulates EMT and migration by targeting c-MET in HCC cells. miR-23b-3p plays dual roles as a tumor suppressor and tumor inducer by regulating several genes in various human cancers [23]. For example, miR-23b is downregulated in colorectal cancer (CRC) cells and primary CRC tissues compared to non-malignant colorectal tissues, and is associated with poor prognosis in patients with CRC [24]. CB1R-induced tumor progression in gastric cancer may be suppressed by miR-23b-3p [25]. miR-23b-3p modulates the progression of cervical cancer and acts as a tumor suppressor by targeting c-MET [26]. Consistent with these findings, our study showed that miR-23b-3p is downregulated in HCC tissues and miR-23b-3p overexpression blocks EMT and invasive activity in HCC cells. These data revealed that miR-23b-3p might exhibit anti-cancer activity and act as a key regulator of metastasis in HCC cells. Furthermore, the bioinformatics analysis identified several potential downstream targets of miR-23b-3p, including c-MET, GSK3β, CDH1, CD44, and ELK3. Among the targets, the results of our study showed that miR-23b-3p regulated the expression of c-MET, thereby affecting EMT and migration in HCC cells. c-MET is a receptor tyrosine kinase activated by binding to hepatic growth factor (HGF) [27]. In general, c-MET is essential for embryonic development and regeneration [28, 29, 30]. However, aberrant c-MET activation can promote the development and progression of tumors [31]. Correspondingly, the inhibition of c-MET has a significant impact on the reduction of cell proliferation, EMT, migration, and metastasis. Accumulating evidence has shown that overexpression of c-MET promotes the proliferation, survival, and metastasis of tumor cells and leads to poor prognosis in HCC patients [32, 33, 34]. Therefore, activation of HGF/c-MET affects multiple events from tumorigenesis to metastasis and may be an important therapeutic target in HCC [29]. In this study, c-MET was a direct target of miR-23b-3p, as identified by bioinformatic analysis, and transfected miR-23b-3p negatively regulated the expression of c-MET. Furthermore, we found that c-MET silencing may not only restrain EMT, but also downregulates stemness genes, including CD44, Nanog, and KLF. We showed that c-MET silencing inhibited the migration and invasion of HCC cells (Figure 2). More importantly, EMT induction in HCC cells, which was caused by miR-23b-3p inhibition, was also rescued by c-MET silencing (Figure 5). Collectively, we confirmed that miR-23b-3p regulates malignant potential of HCC cells by targeting c-MET. CD44 has been widely studied as a surface biomarker of cancer stem cells and a critical regulatory factor in EMT [35]. CD44 is involved in several biological processes such as tumor initiation, development, and metastasis. Previous studies have shown that CD44 promotes EMT and migration in HCC cell lines and that the synergy of CD44 and TGF-β1 makes an increase in metastatic potential. Based on these findings, this study demonstrated that blocking the function of miR-23b-3p regulates CD44 expression by inducing EMT through the activation of c-MET. As CD44 is also a direct target of miR-23b-3p, our results indicate a potent anti-metastatic function of miR-23b-3p by targeting two major genes involved in EMT, the c-MET and CD44. The mutual regulation between miR-23b-3p and its target gene in this loop could more clearly tune gene expression in EMT-related cancer metastasis. Therefore, we speculated that miR-23b-3p could potently prevent HCC metastasis by repressing both c-MET and CD44. Furthermore, HCC is an inflammation-associated tumor, and recurrence after treatment often amplify its immunosuppressive state. Several studies have shown that miRNAs contribute to immune system development, response, and program activation, and can act as oncogenes or tumor suppressors by modulating immunological responses involved in cancer-associated pathways [36, 37, 38]. Therefore, the concept of immuno-miRNAs in HCC has attracted the attention of most researchers and will be an innovative therapeutic approach for HCC through its crucial role in immune response as well as oncogenic and antitumor pathways. Unfortunately, studies on miRNAs based on the activation or suppression of immune responses in HCC are in early stages. Further investigations pertaining to the role of miRNAs, including miR-23b-3p, in the immunotherapeutic response of liver cancer, their ability to regulate the immune response, and the related mechanisms are warranted. In addition, cabozantinib, a small-molecule inhibitor of c-MET and VEGFR2, has proven its efficacy in a phase III trial for patients with advanced HCC treated with sorafenib. The researchers observed a lower risk of death in the treatment group compared to the placebo group when considering the overall survival and progression-free survival of patients with HCC [39]. According to our analyses, the combination therapy of miR-23b-3p and the c-MET inhibitor cabozantinib can increase the treatment efficacy for patients with advanced or recurrent HCC; however, detailed future studies are required. Our study has the following limitations. First, in vivo validation of our observations should be performed. Second, more human HCC samples with different treatment modalities are needed to validate the suitability of miR-23b-3p as a potential biomarker for HCC treatment. Lastly, detailed mechanisms how c-MET regulates the EMT in HCC cells should be investigated further. Nevertheless, our results demonstrate that miR-23b-3p is an attractive therapeutic target for suppressing HCC metastasis by potently regulating EMT through c-MET inhibition.
In conclusion, we confirmed that miR-23b-3p downregulation significantly increased EMT, migration, and invasion of HCC cells, whereas overexpression of miR-23b-3p exerted the opposite effect. miR-23b-3p overexpression strongly suppressed TGF-β1-induced EMT and invasive activity. In addition, c-MET was confirmed to be a target of miR-23b-3p in HCC cells and regulated the functional effects of miR-23b-3p, including EMT, migration, and invasion. We have demonstrated that miR-23b-3p possesses antimetastatic properties by inhibiting EMT and migration by regulating c-MET in HCC cells. Thus, miR-23b-3p may act as a potentially reliable therapeutic target for EMT-related cancer metastasis. Future research will demonstrate the roles of specific miRNAs in regulating the responses to immunotherapy for HCC.
Na Ri Park: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper. Jung Hoon Cha: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper. Pil Soo Sung: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Jeong Won Jang: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data. Jong Young Choi; Seung Kew Yoon: Analyzed and interpreted the data. Si Hyun Bae: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.
Dr. Si Hyun Bae was supported by 10.13039/501100003621Ministry of Science, ICT and Future Planning [2017R1A2B4010197], Korean government (MSIT) [2020R1A2C3011569].
No data was used for the research described in the article.
The authors declare no conflict of interest.
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PMC9586935 | Fang Fang,Lei Xu,Liqing Liang,Mingyi Qu,Hailei Yao,Wen Yue,Lin Chen,Dongli Chen,Zeng Fan,Lijuan He,Xue Nan,Huanhuan Zhang,Xiaoyan Xie,Xuetao Pei | The accumulation of miR-125b-5p is indispensable for efficient erythroblast enucleation | 21-10-2022 | Haematopoietic stem cells,miRNAs | Erythroblast enucleation is a precisely regulated but not clearly understood process. Polycythemia shows pathological erythroblast enucleation, and we discovered a low miR-125b-5p level in terminal erythroblasts of patients with polycythemia vera (PV) compared to those of healthy controls. Exogenous upregulation of miR-125b-5p levels restored the enucleation rate to normal levels. Direct downregulation of miR-125b-5p in mouse erythroblasts simulated the enucleation issue found in patients with PV, and miR-125b-5p accumulation was found in enucleating erythroblasts, collectively suggesting the importance of miR-125b-5p accumulation for erythroblast enucleation. To elucidate the role of miR-125b-5p in enucleation, gain- and loss-of-function studies were performed. Overexpression of miR-125b-5p improved the enucleation of erythroleukemia cells and primary erythroblasts. Infused erythroblasts with higher levels of miR-125b-5p also exhibited accelerated enucleation. In contrast, miR-125b-5p inhibitors significantly suppressed erythrocyte enucleation. Intracellular imaging revealed that in addition to cytoskeletal assembly and nuclear condensation, miR-125b-5p overexpression resulted in mitochondrial reduction and depolarization. Real-time PCR, western blot analysis, luciferase reporter assays, small molecule inhibitor supplementation and gene rescue assays revealed that Bcl-2, as a direct target of miR-125b-5p, was one of the key mediators of miR-125b-5p during enucleation. Following suppression of Bcl-2, the activation of caspase-3 and subsequent activation of ROCK-1 resulted in cytoskeletal rearrangement and enucleation. In conclusion, this study is the first to reveal the pivotal role of miR-125b-5p in erythroblast enucleation. | The accumulation of miR-125b-5p is indispensable for efficient erythroblast enucleation
Erythroblast enucleation is a precisely regulated but not clearly understood process. Polycythemia shows pathological erythroblast enucleation, and we discovered a low miR-125b-5p level in terminal erythroblasts of patients with polycythemia vera (PV) compared to those of healthy controls. Exogenous upregulation of miR-125b-5p levels restored the enucleation rate to normal levels. Direct downregulation of miR-125b-5p in mouse erythroblasts simulated the enucleation issue found in patients with PV, and miR-125b-5p accumulation was found in enucleating erythroblasts, collectively suggesting the importance of miR-125b-5p accumulation for erythroblast enucleation. To elucidate the role of miR-125b-5p in enucleation, gain- and loss-of-function studies were performed. Overexpression of miR-125b-5p improved the enucleation of erythroleukemia cells and primary erythroblasts. Infused erythroblasts with higher levels of miR-125b-5p also exhibited accelerated enucleation. In contrast, miR-125b-5p inhibitors significantly suppressed erythrocyte enucleation. Intracellular imaging revealed that in addition to cytoskeletal assembly and nuclear condensation, miR-125b-5p overexpression resulted in mitochondrial reduction and depolarization. Real-time PCR, western blot analysis, luciferase reporter assays, small molecule inhibitor supplementation and gene rescue assays revealed that Bcl-2, as a direct target of miR-125b-5p, was one of the key mediators of miR-125b-5p during enucleation. Following suppression of Bcl-2, the activation of caspase-3 and subsequent activation of ROCK-1 resulted in cytoskeletal rearrangement and enucleation. In conclusion, this study is the first to reveal the pivotal role of miR-125b-5p in erythroblast enucleation.
Erythroblast enucleation, one of the steps of erythroid terminal maturation, is a critical step of erythropoiesis, and this process is intricately regulated. As a consequence, nucleated RBCs (NRBCs) are rarely found in human circulation. However, little is known regarding how mammalian erythrocytes extrude their nucleus [1]. Several chromatin remodeling enzymes, cytoskeletal regulators and miRNAs have been discovered to play roles in erythroblast enucleation [2–6]. Among these regulators, miRNAs are involved in multiple physiological and pathological processes, and the same miRNA may function differently according to the intracellular context. miR-125b-5p, a regulator of hematopoiesis, is dysregulated in blood malignancies and affects the self-renewal, proliferation and differentiation of various types of blood cells, including hematopoietic stem cells (HSCs), common myeloid progenitors, megakaryocytic/erythroid progenitors and megakaryocytic progenitors [7–9]. In Down’s syndrome (DS), trisomy 21 results in overexpression of miR-125b-5p, which is one of the key causes of DS-related acute megakaryoblastic leukemia, and miR-125b-5p is a positive regulator of megakaryopoiesis [10]. Based on the hematopoiesis differentiation tree, megakaryocytes have the closest relationship with erythrocytes among different types of blood cells [11]. Therefore, it is unsurprising that infants with DS also have a high risk of developing polycythemia [12, 13]. Interestingly, true adult polycythemia vera (PV) is also commonly associated with the upregulation of miR-125b-5p [14]. PV is a condition that occurs when bone marrow (BM) abnormally and excessively produces RBCs. The abnormal increase in RBCs may cause blood to thicken and clot. Moreover, 95% of patients with PV have the JAK2V617F mutation, and NRBCs are often observed in peripheral blood (PB) smears from these patients [15, 16]. This finding suggests a causal relationship between miR-125b-5p dysregulation and abnormal erythroblast maturation. To elucidate the role of miR-125b-5p in erythroblast enucleation, we examined the endogenous expression of miR-125b-5p during erythropoiesis of hematopoietic stem/progenitor cells from both healthy controls and patients with PV. The effect of miR-125b-5p gene modulation on erythroblast enucleation was further examined, and the downstream signal of miR-125b-5p during enucleation was also investigated.
To elucidate the potential role of miR-125b-5p in erythroid terminal maturation in patients with PV, we first compared the expression levels of miR-125b-5p in mononuclear cells (MNCs) and RBCs isolated from patients with PV or healthy controls. The expression of miR-125b-5p in PV-MNCs was higher than that in normal MNCs (Fig. 1A) as previously reported [10]. In addition, up to 3.2% of NRBCs in PB from patients with PV were detected by flow cytometry, whereas NRBCs in healthy controls were barely detected (Fig. 1B). Interestingly, the expression of miR-125b-5p was markedly suppressed in mature RBCs from patients with PV (Fig. 1C). These observations suggested a correlation between low miR-125b-5p levels and erythroblast enucleation issues. The level of miR-125b-5p was also examined in erythrocytes one step before enucleation (late erythroblasts) in CD71− (early stage erythroid marker), CD235a+ (late-stage marker) and LDS751+ cells. CD235+CD71−LDS751+ cells (NRBCs) were sorted from the BM biopsies of patients with PV because these cells are not mature enough to be released into PB. Similar to PB, the percentage of NRBCs in the BM of patients with PV was higher than that in healthy controls (Fig. 1D). Interestingly, a phased comparison of miR-125b-5p expression revealed that although PV miR-125b-5p remained higher than healthy miR-125b-5p in CD235+CD71+LDS751+ erythroblasts sorted from BM, the expression pattern was markedly inverted by the NRBC stage (Fig. 1E). The same trend was present in mature RBCs as shown in Fig. 1C. Accordingly, we hypothesized that a certain level of miR-125b-5p upregulation is required for terminal erythroblast enucleation. To confirm that lower miR-125b-5p expression impairs erythroblast enucleation in patients with PV, we examined hematopoietic stem/progenitor cell erythropoiesis in PV patients in vitro. A stepwise cell culture protocol was developed permitting human cord blood (hCB)-isolated CD34+ cells or MNCs to differentiate and enucleate toward erythrocytes in vitro (Fig. S1A). By Day 14 of culture, the percentage of CD235a+CD71+ cells reached 85% ± 10% (Fig. S1B and C), which suggested that the cells were primary basophilic normoblasts. The cells were then transferred to enucleation medium for an additional 4–6 days, and the output SYTO16-CD235a+ cells were considered as enucleated erythrocytes (Fig. S1B) [17, 18]. Under in vitro induction conditions, the erythroid differentiation pattern of PV patient BM-MNCs showed no difference compared to healthy controls as indicated by the percentages of CD71+ and CD235a+ cells (Fig. 1G). But erythroblasts derived from PV MNCs performed better in CD71high/CD235ahigh population development, which matched the consequences of the JAK2 V617F mutation (Fig. S2) and higher erythroid progenitor expansion in these patients [19, 20]. However, similar to the examination with primary counterparts, the PV group exhibited a low miR-125b-5p level after 7 days of second stage induction compared to the healthy control group (Fig. 1H). As expected, the enucleation efficiency (percentage of CD235a+SYTO16- and CD71+SYTO16- cells) of BM-MNC-derived erythroblasts in the PV group was significantly lower than that in the control group (Fig. 1I). In addition, exogenous miR-125b-5p overexpression with miRNA mimics (Fig. 1J) functionally restored the enucleation efficiency to a normal level (Fig. 1K). These findings indicated a potential correlation between decreased miR-125b-5p levels and elevated NRBCs.
Because miR-125b-5p functions inconsistently in different developmental stages, two miR-125b-5p knockdown (KD) mouse models were generated to further address the relationship between miR-125b-5p suppression and enucleation disorder. In a hematopoietic common KD model, miR-125b-5p inhibitors (miR-125b In) were injected into the BM cavity of Institute of Cancer Research (ICR) model mice. The impact of miR-125b-5p KD on NRBC occurrence in BM and PB was estimated by flow cytometry in a short period postinjection. Considering that terminal erythroblast maturation presents as full induction of Ter119 and sequential downregulation of CD71, [21] we separated Ter119+ erythroblasts by CD71 expression and examined nucleated cells in Ter119+CD71high, Ter119+CD71low, and Ter119+CD71− populations, which represent sequentially matured erythroblasts. Four days after inhibitor injections, miR-125b-5p expression was successfully downregulated in the examined tissues (Fig. 2A). Similar to the discovery in patients with PV, the downregulation of miR-125b-5p significantly increased the percentage of NRBCs in BM (Figs. 2B, D and S3) and PB (Fig. 2C) without altering the proportion of each erythroblast populations (Fig. 2B). Erythroblast-specific miR-125b-5p KD was established using adenovirus-associated virus (AAV) tail vein injection. Briefly, beta globin enhancer- and promoter-carrying eGFP-miR-125b sponges (8-repeat antisense-miR-125b-5p) were inserted into the AAV vector, and 8-repeat scramble nucleotides were set as a control (Fig. 2E). As confirmed with Day 7 PB samples, the sponge significantly reduced miR-125b-5p expression (Fig. 2F). By Days 3 and 7 post- AAV injection, more NRBCs (SYTO62 positive) were found in the sponge group PB, especially in the Ter119+CD71− populations (Fig. 2G). BM samples were examined 7 days post- AAV injection, and more NRBCs were detected in both the FCM and cell smears of the sponge group together with reduced Ter119+CD71− final stage erythroblast populations (Figs. 2H and I). Similar to the finding in PB, a significant difference was only found in the Ter119+CD71− populations. (Representative flow cytometry plots are shown in Fig. S4). Considering that active globin expression occurs in the late stage of erythropoiesis, [22] the miR-125b-5p sponge controlled by globin regulators showed no effect on BM HSC colony-forming ability (Fig. S5). Consistently, most of the hemogram indices remained intact except for higher MCHC and MCH after miR-125b-5p KD (Fig. S6). Impaired enucleation by the miR-125b-5p sponge might contribute to these changes.
We used an in vitro erythrocyte induction and maturation system (Fig. S1 and Fig. 1F) to monitor the endogenous expression of miR-125b-5p. miR-125b-5p was gradually upregulated during primary cell erythropoiesis (Fig. 3A). For the erythroleukemia cell lines, [23, 24] significant upregulation of miR-125b-5p was also detected in K562 and TF-1 erythroid enucleating cells (Fig. 3B). The expression of miR-125b-5p was further examined by the developmental stage of erythroblasts. Around induction Days 14–16, there are commonly five distinct populations of erythroblasts as indicated by the CD235a/CD71 or Band3/CD49d double-staining patterns, [25, 26] and these populations are classified as P1-P5 according to the elevated maturation level. In the present study, the P1 populations that were CD71lowCD235a− or CD49dlowBand3− are not shown. Cytospins of the P2-P5 populations sorted by CD235a/CD71 demonstrated morphological changes during erythropoiesis. Most of the cells at this stage were nucleated, as measured by CD71/Syto16 and CD235a/Syto16 flow cytometry analysis (Fig. S7). The endogenous expression of miR-125b-5p steadily increased from P2 to P5 as shown by qRT-PCR (Fig. 3C, D), suggesting that elevated miR-125b-5p is required for successful maturation and enucleation. We next conducted miR-125b-5p gain- and loss-of-function studies to further define the correlation between miR-125b-5p expression and enucleation. To avoid the delayed effect from early erythropoiesis with miR-125b-5p modification, basophilic erythroblasts at induction Day 14 were selected for miRNA transfection and the enucleation study. We transfected these hCB-MNC-derived basophilic erythroblasts with a miR-125b-5p mimic (miR-125b), miR-125b-5p inhibitor (miR-125b In) and corresponding controls (NC and NI, respectively). The transfection efficiency was measured by qRT-PCR (Fig. 3E left and 3 G left) and FACS with the aid of a FAM-stained miRNA mimic (Fig. S8). Five days after transfection, the enucleation efficiency was increased with miR-125b-5p overexpression (Fig. 3E). The impact of miR-125b-5p overexpression on enucleation was further confirmed by cytospin staining as more enucleated erythrocytes were observed in the images with higher miR-125b-5p levels (Fig. 3F). Correspondingly, miR-125b-5p suppression resulted in impaired enucleation (Fig. 3G). Using erythroleukemia cells, we generated a stable miR-125b-5p-overexpressing K562 cell line (K562-pc125b) for the miRNA function study. Overexpression of miR-125b-5p in K562 erythroid cells also significantly enhanced erythroblast enucleation as shown by the CD235+LDS751− population percentage (Fig. 3H).
More than 90% of cells from mouse embryonic Day 13.5 (E13.5) fetal livers were CD71+Ter119+, and most of these cells were nucleated erythroid cells as indicated by SYTO16/Ter119 staining (Fig. S9A and S9B). Similar to the findings in human erythroblasts, overexpression of miR-125b-5p increased the enucleation of these mFL-derived erythroblasts in vitro (Fig. S9C). To examine the in vivo function of miR-125b-5p, we transfected these cells with a miR-125b-5p mimic, labeled them with CFSE and transfused them into allogenic ICR mice (Fig. 4A). PB CFSE+ cells were detectable for 2 days in both the miR-125b-5p-overexpressing and control groups (Fig. 4B). At 24 h after transfusion, ~90% of CFSE+ cells were CD71−Ter119+ (Fig. 4C), indicating maturation of erythroblasts from both groups. The upregulation of miR-125b-5p facilitated enucleation, especially at 2 h and 16 h posttransfusion (Fig. 4D). The in vivo maturation of hCB-MNC-derived erythroblasts in irradiated NOD/SCID mice showed similar results. On approximately induction Day 14, induced erythroblasts were predominantly CD235a+CD71+ and nucleated (Fig. 4E). After overexpression of miR-125b-5p (Fig. 4F) and cell transfusion, hCB-MNC-derived erythroblasts became fully differentiated and matured as indicated by an increased PB CD235a+CD71− /CD235a+ ratio in both groups (Fig. 4G). Overexpression of miR-125b-5p accelerated enucleation at the early stage of infusion by 16 and 24 h (Fig. 4H). On posttransfusion Day 3, we sorted infused human cells by the expression of human CD235a. All isolated cells appeared similar to biconcave disks, which is the morphology of fully matured RBCs (Fig. 4I) [18].
Throughout terminal erythropoiesis, erythroid progenitors undergo morphological changes, including decreases in cell size, nuclear condensation and cytoskeletal remodeling [3]. The alteration of actin arrangement is a key link in this process. In addition, loss of mitochondrial membrane potential following the clearance of mitochondria is also required for terminal erythroid maturation [27–29]. To examine actin filaments, we stained fixed hCB-MNC-derived erythroblasts (Fig. 5A) and K562 cells (Fig. 5B) at the indicated induction times with rhodamine-phalloidin, CFSE, anti-tubulin antibody and 4',6-diamidino-2-phenylindole (DAPI) for actin, cytoplasm, tubulin and nuclei imaging, respectively. After erythroid induction, the upregulation of miR-125b-5p facilitated actin aggregation in both primary erythrocytes and K562 cells, which was indicated by a contracted fluorescence area and enhanced fluorescence intensity (Fig. 5C–F). Overexpression of miR-125b-5p also promoted chromatin condensation (Fig. 5G and I) and showed a certain effect on cell mass reduction (Fig. 5H and J; only Fig. 5J shows a statistically significant difference). Furthermore, electron microscopy imaging indicated that without alteration of mitochondrial perinuclear localization, miR-125b-5p overexpression resulted in a reduction in the number and volume of mitochondria as well as the destruction of the mitochondrial ultrastructure, suggesting the involvement of mitochondria in miR-125b-5p-mediated erythroid terminal maturation (Fig. 5K). Erythroblasts from mouse bone marrow carrying AAV-miR-125b-sponge were also examined for the status of nuclear, cytoplasm and actin filaments. Bone marrow smear indicated that miR-125b KD increased erythroblast cell size (Fig. 5L) and nuclear size (Fig. 5M), the nuclear/cytoplasmic ration also showed a trend of enhancement although non-statistically significant (Fig. 5N and Fig. S10A). Enlarged phalloidin staining area (Fig. 5O) and reduced phalloidin fluorescence intensity (Fig. 5P and Fig. S10B) from bone marrow sections suggested reduced actin aggregation under miR-125b-5p KD.
Terminal stage miR-125b-5p modification showed no impact on cell cycle (Fig. S11). Excepted for GATA-1, key erythroid regulators were not consistently altered with miR-125b-5p upregulation or downregulation. (Fig. S12) One of the most significant effects of miR-125b-5p on terminal erythropoiesis is enucleation. To further understand how miR-125b-5p regulates erythroblast enucleation, we selected the potential targets of miR-125b-5p using the following criteria: predicted as miR-125b-5p targets by TargetScan; implicated in cytokinesis or mitochondrial signaling, which were indicated to be involved in enucleation based on erythroblast microstructure imaging; developmentally downregulated in erythroid terminal differentiation; and downregulated upon miR-125b-5p overexpression in the K562 cell erythroid differentiation system (Fig. 6A). Among the genes tested, Bcl-2 met all five criteria [6]. Bcl-2 is a well-known antiapoptosis gene that functions via the mitochondrial pathway. To determine whether Bcl-2 expression is regulated by miR-125b-5p, we performed a luciferase reporter assay. Compared to the transfection of empty vector (pc3), the transfection of primary miR-125b-5p coding sequences (miR-125b2) significantly lowered Bcl-2 3'UTR-merged luciferase activity (Fig. 6B). In contrast to miR-125b-5p, Bcl-2 was downregulated during erythroblast maturation (Fig. 6C). In addition, the trends in Bcl-2 expression at both the mRNA (Fig. 6D) and protein levels (Fig. 6E) was opposite that of miR-125b-5p under miR-125b-5p expression modulation with a miRNA mimic [6]. Interestingly, abnormal Bcl-2 expression was also observed in the pathological state. The relative expression of Bcl-2 was higher in MNC-derived erythroblasts from PV patients than in those from healthy controls (Fig. 6F). The direct downregulation of miR-125b-5p by intra-BM injection of its inhibitor in ICR mice resulted in higher Bcl-2 expression upon miR-125b-5p suppression (Fig. 6G). These findings imply a direct regulatory role of miR-125b-5p on Bcl-2. The effect of Bcl-2 on erythroblast enucleation was further verified by direct Bcl-2 suppression with siRNA and small molecules. In hCB-MNC-derived erythroblasts, Bcl-2 knockdown with siRNA (Fig. 6H) doubled the erythroblast enucleation rate (Fig. 6I). Treatment with venetoclax, a Bcl-2-specific inhibitor and FDA-approved leukemia therapy drug, [30–32] also markedly promoted the enucleation of hCB-MNC-derived erythroblasts (Fig. 6J). Strikingly, Bcl-2 downregulation with shRNA (Figs. 6K, L) or venetoclax (Fig. 6M) also benefited the enucleation of PV patient-derived erythroblasts, which was consistent with the outcome of miR-125b-5p overexpression. For K562 cell erythropoiesis, the enucleation rate was also enhanced by Bcl-2 siRNA transfection (Fig. 6N). In contrast, the rescue of Bcl-2 expression in miR-125b-5p-overexpressing K562 cells with transfection of the Bcl-2 expression vector (Fig. 6O) decreased the enucleation efficiency (Fig. 6P). Therefore, miR-125b-5p may function in erythroblast enucleation by directly suppressing Bcl-2 expression.
To support a connection between miR-125b-5p-Bcl-2 and erythroblast enucleation, we investigated the downstream signaling of Bcl-2. Mitochondrial depolarization was first examined by JC-1 staining in K562 and hCB-MNC erythroid induction systems. Before induction, miR-125b-5p overexpression showed no effect on mitochondrial membrane potential in K562 cells (Fig. S13). Regarding the erythroid system, however, overexpression of miR-125b-5p enhanced the depolarization of mitochondria in both primary erythroblasts (Fig. 7A) and K562 cells (Fig. 7B) followed by the activation of caspase-8, caspase-9 (Fig. S14) and caspase-3 (Fig. 7C). To determine the involvement of caspase-3-induced apoptosis, we examined hCB-MNC-derived erythroblasts and K562 erythroid cells with Annexin-V/PI staining. miR-125b-5p overexpression resulted in no differences in apoptosis in either hCB-MNC-derived erythroblasts or K562 cells compared to controls (Fig. 7D and 7E) suggesting that apoptosis is not a designated consequence of miR-125b-5p signaling during erythroblast maturation. Similar to a previous report, [33] cleaved caspase-3 activated the Rho-associated kinase ROCK-1 (Fig. 7F) in K562 erythroid cells, which contributed to the phosphorylation of myosin light chain 2 (MLC2) (Fig. 7G). Phosphorylation of MLC2 may induce its interaction with actin and activate myosin ATPase, resulting in enhanced cell contractility. The latter events are responsible for asymmetric cytokinesis and enucleation [34]. Moreover, the addition of Y27632, a small molecule inhibitor of ROCK-1, severely suppressed the enucleation of hCB-MNC-derived erythroblasts (Fig. 7H), thereby indicating ROCK-1 signaling during enucleation. In conclusion, we demonstrated that caspase-3 activation followed by ROCK-1 activation is the downstream effect of Bcl-2 suppression, which is a consequence of miR-125b-5p overexpression. In addition, ROCK-1 activation followed by MLC2 phosphorylation may contribute to actin polymerization and terminal erythrocyte maturation/enucleation (Fig. 7I).
In the present study, the unique role of “ubiquitous” miR-125b-5p in erythroblast enucleation was discovered. Through endogenous miR-125b-5p expression analysis and gene gain- and loss-of-function studies, we confirmed the positive role of miR-125b-5p in erythroblast enucleation. We hypothesized that during physiological erythroblast maturation, miR-125b-5p accumulates to provide a final push for enucleation and that Bcl-2 is a mediator of gene function with the final outcome being cytoskeletal rearrangement and enucleation. Because in vitro-induced RBCs, which are separated from their common microenvironment, exhibit poor enucleation, [3] exogenous miR-125b-5p addition may promote enucleation and mature RBC production. miR-125b-5p has shown functions in various stem/progenitor cells, cancer cells, and mature cells. Verified targets of miR-125b-5p include regulators involved in cell proliferation, apoptosis, differentiation, migration, epithelial-mesenchymal transition, metastasis and immune response [35–38]. Sun et al. suggested that the intracellular context determines miR-125b-5p behavior [39]. Our previous study also demonstrated that although miR-125b-5p plays a positive role during megakaryogenesis, it only exerts its defined functions when lineage determination has been fulfilled [40]. In the present study, by comparing PV and normal erythropoiesis, we demonstrated that although miR-125b-5p expression was high in PV cells before the basophilic erythroblast stage, it was not sufficiently upregulated afterward. The overexpression of miR-125b-5p did not alter erythroid lineage determination, which was consistent with a previous report, [10] but insufficient miR-125b-5p accumulation hampered erythroblast enucleation. Thus, late-stage miR-125b-5p accumulation may be important for erythroid terminal maturation. Direct erythroblast miR-125b-5p inhibition also verified the positive role of miR-125b-5p in enucleation. Accordingly, we deduced that miR-125b-5p is required for terminal erythroblast maturation, prompting us to modulate the miR-125b-5p gene to confirm its function during the stage when most erythroid cells are CD235a and CD71 double positive, i.e., basophilic erythroblasts [25]. At the terminal stage of erythropoiesis, erythrocytes undergo chromatin condensation before nuclear extrusion, and most of the gene transcription is gradually shut down by this stage [3]. To determine the key target of miR-125b-5p during enucleation, we examined the commonly accepted regulators of enucleation. Apoptosis, asymmetric cytokinesis, epigenomic regulation and vesicle trafficking are generally accepted enucleation theories [1, 2]. The overlap of predicted miR-125b-5p targets and putative enucleation regulators was examined, and Bcl-2, an apoptosis-related gene, was confirmed to play an important role in mediating miR-125b-5p functions. Increased miR-125b-5p expression in terminal erythroblasts resulted in Bcl-2 downregulation and was correlated with mitochondrial membrane potential reduction followed by caspase-3 activation. As previously reported, during late-stage erythropoiesis, the consequence of these apoptotic signals is not apoptosis but instead the activation of cytoskeletal regulators followed by the formation of the cytoplasm shrinkage ring [17, 33]. In the present study, the intracellular response included ROCK-1 (a cytoskeletal regulator) activation and MLC2 phosphorylation, both of which induce erythroblast enucleation. This response is similar to the consequence of another proapoptotic signal, Fas, which provides a positive stimulus for erythroid maturation without altering cell proliferation and apoptosis [33]. Due to the complexity of miR-125b-5p targets, there may be other regulators that mediate miR-125b-5p function during enucleation. Changes in mitochondrial morphology and function suggested that miR-125b-5p might function in enucleation through mitochondrial autophagy or metabolic regulation [41–43]. The upregulation of RND2, a member of the Rho family of GTPases, and the downregulation of p19Ink4D, a cell cycle regulator (Fig. 6A), also suggested the involvement of other regulatory methods or signaling pathways during miR-125b-5p function in enucleation. To investigate the correlation between miR-125b-5p and erythroblast enucleation, we first examined the pathology of patients with PV. In addition to the emergence of NRBCs in PB, which may have resulted from extramedullary hematopoiesis or premature release, the enucleation rate of patients with PV was low even in BM CD235+CD71− erythrocytes (Fig. 1D). Higher miR-125b-5p levels in PB leukocytes may be responsible for the morbidity of PV [14]. Similarly, miR-125b-5p was overexpressed in MNCs isolated from PV PB samples. However, distinct from the significant upregulation of miR-125b-5p near the normoblast stage in healthy erythropoiesis, the level of miR-125b-5p in patients with PV was insufficiently augmented during the late stage of erythropoiesis. Overexpression of miR-125b-5p in PV patient-derived EPCs enhanced the enucleation efficiency to normal levels. Thus, insufficient miR-125b-5p accumulation may be one of the leading causes of redundant NRBCs detected in the PB of patients with PV. Although the upstream signal of miR-125b-5p remains unknown, the synchronous dysfunction of JAK2 in both DS and PV may influence the role of miR-125b-5p in enucleation pathology [14, 44]. Further studies are required to determine the regulators of miR-125b-5p. Taken together, these findings revealed the novel role of miR-125b-5p in the regulation of enucleation, the final step of erythropoiesis. The miR-125b-5p-Bcl-2-ROCK axis balances apoptosis and cytokinesis signaling, and it is implicated in erythroblast enucleation. Our study provides new insights into NRBC pathogenesis and in vitro RBC synthesis. Although targeted therapy with miR-125b-5p may not be applicable using current technology, gaining an understanding of the miR-125b-5p signaling pathway during erythrocyte maturation may help identify a solution for erythroid diseases. Regarding RBCs manufactured from stem cells, the small molecules involved in the enucleation signaling pathway as well as miR-125b-5p mimics may overcome the procedural bottleneck to allow the production of high-quality RBCs to meet the clinical demand for “man-made” blood.
After informed consent was obtained, human CB, PB and BM were collected from healthy volunteers and PV patients. The Research Ethics Committee of Beijing Institute of Transfusion Medicine approved all the studies of MNCs, HSCs and differentiated cells. hCB-, PB- and BM-derived MNCs and HSCs were isolated as previously described [7]. Erythroid induction was performed using a step-wise protocol modified from Ulirsch et al. [45] Briefly, 5 × 106 mL−1 cells were cultured in differentiation medium containing StemSpan Serum-Free Expansion Medium (SFEM, STEMCELL Technologies, Vancouver, Canada) supplemented with 100 ng mL−1 stem cell factor (SCF, PeproTech, Rocky Hill, NJ, USA), 40 ng mL−1 insulin-like growth factor 1 (IGF-1, R&D Systems, Minneapolis, MN, USA), 100 μg mL−1 holo-human transferrin (Sigma-Aldrich, St. Louis, MO, USA), 5 IU mL−1 erythropoietin (Epo, Kyowa Hakko Kirin, Tokyo, Japan), 1 μM dexamethasone (Sigma-Aldrich), 40 ng mL−1 lipid (Sigma-Aldrich) and 2 mM glutamine (Gibco, Big island, NY, USA) for 14–16 days. Next, in the terminal enucleation step, 5 × 105 mL−1 cells were cultured in the presence of 300 μg mL−1 holo-human transferrin (Sigma-Aldrich), 10 μg mL−1 recombinant human insulin (Sigma), 3 IU mL−1 heparin (STEMCELL Technologies) and 5% AB plasma (Atlanta Biologicals, Lawrenceville, GA, USA) in Iscove’s Modified Dulbecco’s Medium (IMDM, Gibco).
Human erythroleukemia K562 cells were grown in basic medium containing RPMI-1640 supplemented with 10% fetal bovine serum. The 2-step differentiation procedure was: first, 1 × 105 mL−1 cells were cultured in basic medium supplemented with 40 μM Hemin (Sigma-Aldrich) and 100 ng mL−1 ara-C (Sigma-Aldrich) for 6 days; second, 1 × 105 mL−1 cells were suspended in basic medium with 40 μM Hemin (Sigma-Aldrich) and 14 µl mL−1 dimethyl sulfoxide (DMSO, Sigma-Aldrich) for 4 days.
Erythroblasts were purified from E13.5 mFL cells and cultured and differentiated as previously described [6].
May-Grunwald (MG500, Sigma-Aldrich) and Giemsa solution (GS500, Sigma-Aldrich) were used as previously described [46].
Animal experiments were approved by the Beijing Medical Experimental Animal Care Commission (IACUC of AMMS-2014-036), and the procedures were in strict accordance with the Academy of Military Medical Sciences for the Care and Use of Laboratory Animals.
Administration of 50 μg miRNA inhibitor mimics or negative control mimics (Table S2) per mouse was achieved by intra-BM injection together with EntransterTM-in vivo (Engreen, Beijing, China) and glucose in vivo. After 40 h, the operation was repeated once. Twenty-four hours after the second injection, cells from PB, BM and spleen were harvested, washed and co-labeled with antibodies.
To specifically knockdown miR-125b-5p in erythroblasts, BALB/c mice were transduced with adeno-associated virus (AAV) serotype 6 which carried beta globin enhancer (GenBank: S73747.1) and promoter (GenBank: GU057255.1) [47] driving enhanced green fluorescent protein (eGFP) together with either miR-125b sponge (8-repeat antisense-miR-125b-5p) or control (8-repeat scramble nucleotides). The designed enhancer-promoter-eGFP with 8 tandem repeat miR-125b-5p sponges (5′-TCACAAGTTAGGGTCTCAGGGA-3′) or a control (5′-AAGTTTTCAGAAAGCTAACA-3′) [48] was cloned into AAV by Genechem Co. Ltd. (Shanghai, China). AAVs (1 × 1011 v.g) were tail vein injected into each mouse, and the effect of viral transfusion was followed for 7 days.
ICR mice (5–6 weeks of age) were initially irradiated with 6 Gy by a cobalt-60 source (1.096 Gy/min). Six hours later, mFL-derived erythroblasts (7.5 × 106 cells/mouse), which were washed and labeled with CFSE (Invitrogen, Carlsbad, CA, USA), were injected through the caudal vein. The erythroblasts were transfected with miR-125b-5p or control mimics (Table S2) and cultured for 3 days before injection. At defined time points, PB cells were harvested, washed and co-labeled with LDS-751, anti-CD71 and anti-Ter119 antibodies.
For MNC-derived erythroblast transplantation, cells were prepared and transfected with miR-125b-5p or NC mimics and cultured for 3 days. The recipient mice (5–6 weeks of age) were initially conditioned by sublethal irradiation with 3.5 Gy from a cobalt-60 source (1.091 Gy/min), followed by intravenous injection of 1 × 107 cells/mouse. At designated time points, PB cells were harvested, washed and co-labeled with LDS-751, anti-CD71 and anti-CD235a antibodies. On day 3, CD235a+ cells were sorted and examined by confocal laser scanning microscopy.
Pri-miR-125b2 coding sequence was transfected into K562 cells through a pcDNA3.1-neomycin vector. Stable transfections were selected with 500 μg mL−1 G418. Bcl-2 coding sequence (pCEP4 Bcl-2, #16461, Addgene, Cambridge, MA, USA) was transfected and selected with 50 μg mL−1 hygromycin B.
According to the manufacturer’s instructions, cells were seeded in 6-well plates and transfected with 3 μg per well miRNA mimics (Genepharma, Shanghai, China) or non-target NC mimics (Table S2) with the aid of EntransterTM-R4000 (Engreen).
K562-pc125b cells or K562-pc3 cells (6 × 104) were co-transfected with 8 ng pRL Renilla luciferase vector (Promega, Madison, WI, USA) as internal control and 800 ng pGL3-Bcl-2-3'UTR or pGL3 vector control. The cells were harvested 48 h post transfection and evaluated their luciferase activity using the dual-luciferase assay kit according to the manufacturer’s instruction (Promega). The luciferase activity in miR-125b-5p stably overexpressed cells (K562-pc125b) were normalized to vector transfection control (K562-pc3 cells).
qRT-PCR was carried out as previously reported [49]. Gene expression was normalized to GAPDH or HPRT. miRNA expression relative to U6 snRNA and cell numbers was assayed using All-in-One qPCR Mix (GeneCopoeia, Rockville, MD, USA). Error bars represent the standard deviation (SD), and the results are expressed as the mean ± SD. The primers are listed in supplementary material Table S1.
Western blots were performed by using antibodies against human Bcl-2 (Santa Cruz, Santa Cruz, CA, USA), GAPDH (Earth Ox, Millbrae, CA, USA), Caspase-3 (Cell Signaling Technology, CST, Danvers, MA, USA), ROCK-1 (CST), MLC2 (CST) and p-MLC2 (CST). The blots were visualized using an ECL kit (Santa Cruz).
Cells were fixed in 4% paraformaldehyde for 15 min and treated with 0.125% Triton X-100 for 10 min. Fixed cells were then blocked with 10% serum for 30 min. Next, the cells were incubated with tubulin antibodies overnight at 4 °C. After three washes with phosphate-buffered saline (PBS), the cells were incubated with phalloidin together with the corresponding secondary antibody. Then, the cells were stained with DAPI for 2 min. Finally, stained cells were visualized by confocal microscope (PE Ultra VIEW VoX).
The cells were firstly fixed with 4% paraformaldehyde and 1% glutaraldehyde for 48 h, then incubated with 1% osmium tetroxide for 1 h, and dehydrated using series of ethanol solutions. The dehydrated cells were embedded in Polybed 812 epoxy resin (Polysciences, Warrington, PA, USA), ultrathin sectioned and collected on 200 mesh copper grids. The cell sections were stained with 4% aqueous uranyl acetate for 15 min, and with Reynolds’ lead citrate for 7 min. Then stained sections were examined using a H7650 transmission electron microscope (HITACHI, Tokyo, Japan).
Cells were harvested and washed three times with PBS. Then, 5 μL per 1 × 106 cells were stained with either anti-human CD71-APC (BD Biosciences, Franklin, NJ, USA) or anti-human CD71-FITC (BD Biosciences), anti-human CD235a-PE (BD Biosciences), anti-human α4 integrin (CD49d-PE, eBioscience, San Diego, CA, USA), anti-human Band 3-APC (graciously provided by Professor Xiuli An) or anti-mouse CD71-PE (eBioscience) and anti-mouse Ter119-APC at 4 °C for 40 min. Subsequently, the cells were washed with PBS or normal saline (NS), followed by analysis using a FACS Calibur machine (BD Biosciences).
Cell nuclei were detected by staining with LDS-751 (Invitrogen), SYTO16 Green Fluorescent Nucleic Acid Stain (Life technologies, Inc., Grand Island, NY, USA) or SYTO62 Red Fluorescent Nucleic Acid Stain (Life technologies). LDS751 was supplemented together with antibodies used in FACS at 2 μg mL−1 for K562 cells, BM and PB derived cells. Samples were washed with PBS and analysis with FACS Calibur machine (BD Biosciences). When SYTO16 or SYTO62 was used for human and mouse erythroblast enucleation detection, cell suspensions in NS were adjusted to 250 μL and analyzed with a FACS Calibur machine by adding 25 nM SYTO16/SYTO62 for at least 10 min.
Mitochondrial membrane potential was estimated with fluorescent dye JC-1 (Molecular Probes, Grand Island, NY, USA). According to the manufacturer’s instructions, briefly, 5 × 105 cells were re-suspended in 1 mL fresh complete medium and incubated with JC-1 (2.5 mM) for 30 min at 37 °C in the dark. Then the cells were washed with PBS and analyzed using a flow cytometer with 488 nm excitation laser (BD Biosciences). The mitochondrial membrane potential was judged by the ratio of red (595 nm) to green (525 nm) fluorescence intensity. For apoptotic testing, cells were quantified by Annexin-V/PI staining kit (Dojindo Laboratories, Kumamoto, Japan) and flow cytometry (BD Biosciences) analysis.
Data are presented from at least three separate experiments and show as the mean ± SD. Two-tailed student’s t-test was used for significant differences evaluation.
supplementary imformation Original Data File Reproducibility checklist | true | true | true |
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PMC9586939 | Xianglong Pan,Qi Wang,Yue Yu,Weibing Wu,Liang Chen,Wei Wang,Zhihua Li | Antisense lncRNA NNT-AS1 promoted esophageal squamous cell carcinoma progression by regulating its sense gene NNT expression | 21-10-2022 | Long non-coding RNAs,Oncogenes | Antisense lncRNAs were endogenous productions from the antisense strand of coding genes and were transcribed in the reverse direction of the sense gene. The purpose of this study was to evaluate the roles and functions of antisense lncRNAs in esophageal squamous cell carcinoma (ESCC). Differentially expressed antisense lncRNAs were initially screened based on transcriptome data of 119 paired ESCC samples in GSE53624 and were further validated in 6 paired ESCC samples from our institution. Log-rank test was adopted to identify ESCC prognosis-associated lncRNAs. Finally, functional assays were performed to reveal the functions of our identified antisense lncRNAs. In total, 174 antisense lncRNAs were differentially expressed in both GSE53624 and JSPH databases. Five of them were significantly associated with ESCC prognosis (NNT-AS1, NKILA, CCDC18-AS1, SLCO4A1-AS1, and AC110619.1). Of note, NNT-AS1 showed the most significant association with ESCC prognosis. The upregulation of NNT-AS1 was further confirmed in ESCC cells. Knockdown of NNT-AS1 inhibited ESCC cell proliferation, migration, promoted ESCC cells apoptosis, and induced cell cycle arrest in the G2/M stage. NNT-AS1 expression significantly correlated with its sense gene NNT. As expected, NNT-AS1 knockdown suppressed NNT expression. Inhibition of NNT repressed ESCC cell proliferation and migration, and accelerated ESCC cell apoptosis. Overexpression of NNT could rescue the suppressed proliferation and migration of ESCC cells induced by the silencing of NNT-AS1. In terms of mechanism, NNT-AS1 served as a competing endogenous RNA to sponge the miR-382-5p, which could inhibit NNT expression. Pathway enrichment analysis and western blot assay indicated that NNT-AS1 and NNT could regulate the cell cycle pathway. In conclusion, antisense lncRNA NNT-AS1 facilitated ECSS progression by targeting its sense gene NNT through sponging miR-382-5p. This study provided us with a deeper insight into the roles of antisense lncRNAs in ESCC and identified novel potential therapeutic targets. | Antisense lncRNA NNT-AS1 promoted esophageal squamous cell carcinoma progression by regulating its sense gene NNT expression
Antisense lncRNAs were endogenous productions from the antisense strand of coding genes and were transcribed in the reverse direction of the sense gene. The purpose of this study was to evaluate the roles and functions of antisense lncRNAs in esophageal squamous cell carcinoma (ESCC). Differentially expressed antisense lncRNAs were initially screened based on transcriptome data of 119 paired ESCC samples in GSE53624 and were further validated in 6 paired ESCC samples from our institution. Log-rank test was adopted to identify ESCC prognosis-associated lncRNAs. Finally, functional assays were performed to reveal the functions of our identified antisense lncRNAs. In total, 174 antisense lncRNAs were differentially expressed in both GSE53624 and JSPH databases. Five of them were significantly associated with ESCC prognosis (NNT-AS1, NKILA, CCDC18-AS1, SLCO4A1-AS1, and AC110619.1). Of note, NNT-AS1 showed the most significant association with ESCC prognosis. The upregulation of NNT-AS1 was further confirmed in ESCC cells. Knockdown of NNT-AS1 inhibited ESCC cell proliferation, migration, promoted ESCC cells apoptosis, and induced cell cycle arrest in the G2/M stage. NNT-AS1 expression significantly correlated with its sense gene NNT. As expected, NNT-AS1 knockdown suppressed NNT expression. Inhibition of NNT repressed ESCC cell proliferation and migration, and accelerated ESCC cell apoptosis. Overexpression of NNT could rescue the suppressed proliferation and migration of ESCC cells induced by the silencing of NNT-AS1. In terms of mechanism, NNT-AS1 served as a competing endogenous RNA to sponge the miR-382-5p, which could inhibit NNT expression. Pathway enrichment analysis and western blot assay indicated that NNT-AS1 and NNT could regulate the cell cycle pathway. In conclusion, antisense lncRNA NNT-AS1 facilitated ECSS progression by targeting its sense gene NNT through sponging miR-382-5p. This study provided us with a deeper insight into the roles of antisense lncRNAs in ESCC and identified novel potential therapeutic targets.
Esophageal cancer is one of the most diagnosed and deaths-caused malignant tumors around the world. As estimated, there were about 604 thousand new esophageal cancer cases (ranked seventh) and 544 thousand new deaths caused by esophageal cancer (ranked sixth) in 2020 [1, 2]. In contrast to the epidemic of esophageal adenocarcinoma in European countries, Esophageal squamous cell carcinoma (ESCC) is the most prevalent subtype in eastern Asia, eastern and southern Africa [3]. The prognosis of ESCC is poor with a five-year overall survival rate less than 30% in most countries [4, 5]. Therefore, doctors and researchers have to find novel diagnostic and therapeutic targets for ESCC. Long non-coding RNAs (lncRNAs) could contribute to the development and progression of malignant tumors through multiple mechanisms [6, 7]. LncRNAs can be classified into several categories based on their positions relative to protein-coding regions: long intergenic noncoding RNAs, natural antisense transcripts, overlapping transcripts, bidirectional lncRNAs, and sense intronic lncRNAs [8]. Antisense lncRNAs are endogenous productions in nature that formed from the antisense strand of coding genes and transcribed in the opposite direction. They overlapped with sense genes or regulatory regions and could function both in cis or trans [9, 10]. Antisense lncRNAs could regulate protein-coding sense genes through the following mechanisms: transcriptional collision, gene recombination, promotor inactivation, alternative splicing, miRNA binding sites blocking, and endogenous siRNA formation [11–13]. For example, FOXP4-AS1 sponged miR-3184-5p to upregulate its host gene FOXP4 in prostate cancer [14]. SATB2-AS1 could cis-activate SATB2 transcription by decreasing the methylation level of SATB2 promoter through binding to WDR5 and GADD45A [15]. In ESCC, a few antisense lncRNAs that promoted or suppressed the tumorigenesis and progression of ESCC have been identified in previous studies. For example, silencing of ZEB1‐AS1 inhibited the expression of ZEB1 and suppressed ESCC progression [16]. EZR-AS1 could facilitate ESCC cell growth by positively regulating the expression of EZR via interacting with methyltransferase SMYD3 [17]. ZNF667-AS1 repressed ESCC cell proliferation and invasion by upregulating ZNF667 expression via interacting with TET1 and UTX, which could decrease histone H3K27 trimethylation to activate ZNF667 [18]. KLF3-AS1 functioned as a tumor suppressor in ESCC by sponging miR-185-5p and decreased its suppression on the expression of KLF3 [19]. All these studies suggested the vital roles of antisense lncRNAs and their sense genes in ESCC. However, no study has systematically evaluated the roles of antisense lncRNAs in ESCC progression. Therefore, we performed a systematic evaluation on the roles and functions of antisense lncRNAs in the progression of ESCC. Firstly, differentially expressed antisense lncRNAs (Annotated using Gencode V29) were screened using transcriptome profiles from GSE53624 and were validated in 6 paired ESCC samples from our Jiangsu Province Hospital (JSPH, Jiangsu, China) database. Then, the log-rank test was adopted to assess the associations between promising antisense lncRNAs and ESCC prognosis. Finally, functional assays were carried out to reveal the functions of our identified antisense lncRNA.
Antisense lncRNAs annotation was performed based on Gencode V29. In total, 1386 promising antisense lncRNAs were initially screened in GSE53624. Among them, 174 lncRNAs were validated in our JSPH samples (Fig. S1). Five of them were significantly associated with ESCC prognosis: NNT-AS1, NKILA, CCDC18-AS1, SLCO4A1-AS1, AC11069.1 (Fig. 1a, S2, S3). Notably, NNT-AS1 showed the most robust correlation with the overall survival of ESCC patients. NNT-AS1 was abnormally expressed in ESCC tumor tissues in both GSE53624 (Fig. 1b) and JSPH databases (Fig. 1c). A higher expression of NNT-AS1 in ESCC patients indicated a worse prognosis (Fig. 1d). Likewise, NNT-AS1 expression was upregulated in ESCC cell lines compared with HEEC (Fig. 1e).
To reveal the functions of NNT-AS1, two siRNAs were used to knock down NNT-AS1. The knockdown efficiency was shown in Fig. 2a. NNT-AS1 knockdown induced decreased colony formation (Fig. 2b) and suppressed ESCC cell viability (Fig. 2c). Likely, suppression of NNT-AS1 inhibited ESCC cell proliferation rate according to the EdU assays (Fig. 2d). Besides, NNT-AS1 inhibition induced the G2/M arrest in both Eca-109 and Kyse-30 cell lines (Fig. 2e). In addition, cell apoptosis assays demonstrated that the apoptosis rate of ESCC cells was increased after silencing NNT-AS1 (Fig. 2f). Furthermore, NNT-AS1 suppression inhibited the migration of ESCC cells (Fig. 2g). Taken together, NNT-AS1 knockdown restrained ESCC cell proliferation, migration, caused ESCC cell arrest, and facilitated cell apoptosis.
Considering that NNT-AS1 was the antisense lncRNA of NNT, we wondered whether NNT-AS1 expression was correlated with that of NNT. As shown in Fig. 3a, NNT-AS1 expression significantly positively correlated with its sense gene NNT (r = 0.915, P < 0.001). NNT expression was also aberrantly upregulated in ESCC tumor tissues (Fig. 3b, c, GSE53624 and JSPH). Similarly, the high expression of NNT contributed to an inferior ESCC prognosis in combined datasets of GSE53624 and GSE53622 (Fig. 3d, HR = 1.44 (1.01-2.11), P = 0.031). Consistent with tissue samples, NNT was notably upregulated in ESCC cells (Fig. 3e). To demonstrate the influence of NNT-AS1 on NNT expression, we knocked down NNT-AS1 and detected the NNT expression. As expected, NNT-AS1 silencing significantly suppressed NNT expression in mRNA level (Fig. 3f), as well as in the protein level (Fig. 3g), suggesting that NNT-AS1 could regulate its sense gene NNT expression. To reveal the functions of NNT in ESCC, three siRNAs were synthesized to knock down the expression of NNT (Fig. 4a). MTT, colony formation, and EdU assays indicated that NNT knockdown significantly inhibited the proliferation of ESCC cells (Fig. 4b–d). Transwell assay showed that inhibition of NNT suppressed ESCC cell migration (Fig. 4e). Furthermore, inhibition of NNT increased the apoptosis rate of ESCC cells (Fig. 4f). All the findings revealed that NNT functioned as a carcinogene in the tumorigenesis and development of ESCC.
LncRNA-miRNA-mRNA axis was a crucial mechanism through which lncRNAs influenced the tumorigenesis and progression of malignant tumors [20, 21]. We predicted the NNT-mediated miRNA using the ENCORI database, and 49 miRNAs were screened. Subsequently, we analyzed the differentially expressed miRNAs based on the GSE114110 database. As a result, six of them were markedly downregulated, including miR-382-5p, miR-26a-5, miR-26b-5p, miR-186-5p, miR-130a-3p, and miR-582-5p. Among them, the foldchange of miR-382-5p ranks first. Additionally, a previous investigation manifested that miR-382 could suppress tumor progression against ESCC [22]. Thus, we hypothesized that NNT-AS1 might regulate NNT expression through sponging miR-382-5p. Figure 5a illustrated the predicted binding site of NNT-AS1 and miR-382-5p by Starbase. As anticipated, miR-382-5p expression was apparently decreased in ESCC (Fig. 5b, c). Figure 5d showed the transfection efficacies of miR-382-5p mimics and inhibitors. The miR-382-5p mimics significantly inhibited NNT-AS1 expression (Fig. 5e). Knockdown of NNT-AS1 led to increased miR-382-5p expression (Fig. 5f). Further, dual-luciferase reporter assays indicated that miR-382-5p overexpression weakened the luciferase activity in the NNT-AS1-WT group (Fig. 5g). Furthermore, the rescue assays demonstrated that miR-382-5p inhibition partially rescued the declined proliferation and migration abilities of ESCC cells mediated by NNT-AS1 knockdown (Fig. 5h–j).
The predicted binding locus of miR-382-5p and NNT was shown in Fig. 6a. Co-transfection with NNT-WT and miR-382-5p mimics in Eca-109 weakened the luciferase activity compared to NNT-WT and miR-NC, while NNT-Mut failed to induce such a reduction. In contrast, NNT-WT and miR-382-5p inhibitor co-transfection led to increased luciferase activity in comparison with the miR-NC (Fig. 6b). Further, inhibition of miR-382-5p partly retrieved the decreased NNT expression in both mRNA and protein levels induced by NNT-AS1 knockdown (Fig. 6c, d). NNT overexpression facilitated ESCC cell proliferation and migration. As expected, overexpression of NNT could countervail the suppressed ESCC cell proliferation induced by NNT-AS1 inhibition (Fig. 6e–g). Furthermore, NNT rescued the increased apoptosis rate generated by NNT-AS1 suppression (Fig. 6h). NNT upregulation also recovered cell migration that declined by NNT-AS1 knockdown (Fig. 6i). Taken together, NNT-AS1 facilitated ESCC tumorigenesis and process by modulating its sense gene NNT expression.
The role that NNT-AS1 played in the tumor growth of ESCC was further evaluated by the xenograft model. As shown in Fig. 7a, b, mice with Eca-109 cells stably transfected with shNNT-AS1 had a smaller tumor volume than the shCtrl group. In line, compared to mice in the shCtrl arm, those in the shNNT-AS1 group exhibited a lower tumor weight (Fig. 7c). Moreover, the Ki-67 positivity was markedly decreased in tumors formed from Eca-109 cells transfected with shNNT-AS1 (Fig. 7d). All the findings suggested that the silence of NNT-AS1 could suppress ESCC tumor growth in vivo. To suggest the potential down-stream pathway through which NNT contributed to ESCC tumorigenesis and progression, pathway enrichment analysis was performed. As shown in Fig. 7e, NNT might participate in multiple signaling pathways, including the cell cycle pathway, one of the central pathways in cancers. This result was consistent with the above flow cytometry findings. Western blot assays indicated that NNT-AS1 and NNT silencing could suppress the levels of CCNB1, CCNB2, CDK1, and CDK2 (Fig. 7f). Taken together, NNT-AS1 contributed to ESCC initiation and development through regulating the cell cycle pathway.
In this study, we evaluated the roles of antisense lncRNAs in ESCC through a systematic analysis. As a result, NNT-AS1 was found aberrantly upregulated in ESCC and was significantly associated with a poorer prognosis. Functionally, we found that NNT-AS1 might contribute to ESCC progression through positively regulating its sense gene NNT expression by sponging miR-382-5p. NNT-AS1, located in 5p12, has been verified to play a carcinogenic role in multiple malignant tumors, including lung squamous cell carcinoma, cervical cancer, gastric cancer, and so on. Ma et al discovered that NNT-AS1 accelerated the progression of lung squamous cell carcinoma by positively modulating FOXM1, a member of the FOX transcription factor family [23]. Li et al revealed that NNT-AS1 facilitated breast cancer progression by regulating ZEB1 expression through sponging miR-142-3p [24]. Chen and colleagues demonstrated that NNT-AS1 could contribute to the tumorigenesis of gastric cancer by targeting E2F1 which served as a transcription factor and was an important regulator in cell cycle [25]. Nevertheless, the functions and roles of NNT-AS1 in ESCC remained unclear. NNT-AS1 was found aberrantly expressed in ESCC and showed a significant association with a poorer survival of ESCC for the first time. Functionally, silencing of NNT-AS1 restrained ESCC cell proliferation and migration. Besides, NNT-AS1 inhibition could induce the arrest of G2/M phase in cell cycle progression and promot ESCC cell apoptosis. Tumor xenograft experiment in nude mice indicated that silencing of NNT-AS1 slackened the tumor growth in vivo. All these results manifested that NNT-AS1 could promote the tumorigenesis and progression of ESCC. However, which was the target gene of NNT-AS1, and what was the molecular mechanism underlying NNT-AS1’s functions in ESCC? Considering that NNT was the sense gene of NNT-AS1 and regulating the sense gene expression was a vital mechanism through which antisense lncRNAs modulated tumorigenesis and progression, we analyzed the correlation ship between NNT-AS1 and NNT. Strikingly, the expression of NNT was significantly correlated with NNT-AS1 expression, suggesting that NNT-AS1 might regulate NNT expression. As expected, knockdown of NNT-AS1 significantly suppressed NNT expression. Functionally, NNT encoded an integral protein of the inner mitochondrial membrane. Previous studies showed that NNT served as an oncogene in multiple cancers. For example, knockdown of NNT significantly suppressed gastric cancer growth and metastasis through the oxidative stress pathway [26]. NNT could regulate mitochondrial metabolism in lung cancer by maintaining the function of the Fe-S protein [27]. Nevertheless, the roles of NNT in ESCC progression have not been revealed. In this study, we observed that the silence of NNT exerted inhibitory effects on the growth and migration of ESCC cells, while NNT overexpression partially retrieved the decreased proliferation and migration abilities induced by NNT-AS1 inhibition. Highlighting these findings, we speculated that NNT-AS1 could modulate ESCC tumorigenesis and progression by regulating its sense gene NNT. Given that ceRNA was a common mechanism through which lncRNAs regulated the target genes [28, 29], we predicted the potential miRNAs that could interact with NNT-AS1. Consequently, miR-382-5p was determined as the candidate miRNA due to its potential of binding to NNT-AS1 and aberrant expression in ESCC. Through dual-luciferase assays, we verified that miR-382-5p could combine with NNT-AS1 and NNT. MiR-382-5p inhibition retrieved the decreased NNT expression that NNT-AS1 knockdown mediated in ESCC cell lines. Functionally, rescue experiments further validated that miR-382-5p suppression reversed the inhibitory impacts of NNT-AS1 knockdown on ESCC progression. Additionally, miR-382-5p was reported to suppress ESCC progression and was associated with a favorable prognosis in previous studies [22, 30]. All the findings above suggested that NNT-AS1 acted as a ceRNA by sponging miR-382-5p to regulate its sense gene NNT. The cell cycle signaling pathway was crucial in the development of malignant tumors [31, 32]. This study found that NNT-AS1 knockdown induced the G2/M arrest in ESCC cell lines. Besides, pathway enrichment analysis indicated that NNT might participate in the modulation of the cell cycle signaling pathway in ESCC. Western blot assays further confirmed that inhibition of NNT and NNT-AS1 suppressed the expression levels of CDK1, CDK2, CCNB1, and CCNB2, all of which were vital proteins in the cell cycle signaling pathway. Taken together, these findings suggested that NNT-AS1 and NNT could promote ESCC progression by regulating the cell cycle signaling pathway.
Antisense lncRNA NNT-AS1 promoted ESCC progression by targeting its sense gene NNT through competitively sponging miR-382-5p. This study provided us with a deeper insight into the roles of antisense lncRNAs in ESCC and identified novel potential therapeutic targets.
Six paired ESCC tumor-normal tissues were obtained from JSPH. Total RNA was extracted from the samples. Then ribosomal RNAs were removed by TruSeq StrandedTotal RNA with Ribo-Zero Gold Kit (Illumina, San Diego, California, USA), and the residual RNAs were broken into short fragments. After purification, the end of cDNA was repaired and poly-A tail was added as well as the sequencing connector was connected to the cDNA. Finally, the RNA library was established by PCR amplification and assessed by Agilent2100Bioanalyzer. The sequencing was performed using the Illumina sequenator (Illumina, San Diego, California, USA). The DESeq2 software was used for the normalization and differential expression analysis. The ethics committee of the First Affiliated Hospital of Nanjing Medical University approved this study. All the patients signed the informed consent.
The GENCODE project provided comprehensive and detailed annotations for the human and mouse genomes [33]. In this study, we obtained the lncRNA annotation from GENCODE (Gencode V29, https://www.gencodegenes.org/). There were 5587 lncRNAs annotated as antisense lncRNAs according to the Gencode V29. The GSE53624 dataset that contained lncRNA and mRNA expression profiles of 119 paired ESCC tumor-normal samples was used for differential antisense lncRNAs screening. After annotation, 2003 antisense lncRNAs were identified in GSE53624. Among them, 1386 lncRNAs were significantly differentially expressed in ESCC (Paired Student’s t-test, P < 0.05, false discovery rate (FDR) correction).
Human ESCC cell lines (Kyse-30, Eca-109, and TE-1) and human normal esophageal epithelial cell line (HEEC) were purchased from iCell Bioscience, Shanghai, China. RPMI-1640 medium (Gibco, Rockville, Maryland, USA) with 10% fetal bovine serum (FBS; Biological Industries, Israel), penicillin-G (100 U/ml), and streptomycin (100 g/ml) (Gibco, Rockville, Maryland, USA) was used for Kyse-30 and TE-1 incubation. DMEN (Gibco, Rockville, Maryland USA) contains the same ingredients were used for Eca-109 and HEEC. The cultivation environment was man-made at 37 °C with 5% CO2. The sequence of transfected RNAs together with the name of suppliers were listed in Table S1. For overexpression of NNT, the cDNA encoding NNT was amplified and cloned into the pcDNA3.1 vector (Invitrogen, Carlsbad, New Mexico, USA) to form NNT overexpression plasmid. Transfections were carried out using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) following instructions.
The cell lines at the period of logarithmic growth were transfected. Then the total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) 48 h later. The extracted RNAs were reversely transcribed to cDNAs using PrimeScriptTM II Reverse Transcriptase (Takara, Tokyo, Japan). Gene expression was calculated as previously reported [34]. Primer sequences were illustrated in Table S1.
2 × 103 transfected cells were planted in a 96-well plate per well. At different checking points (0, 24, 48, 72, and 96 h), the cells were incubated in a complete medium with 20 μL MTT (concentration: 0.5 mg/mL) for 4 h. Then DMSO (150 μL per well) was used to dilute the formazan that formed from MTT. After 10 min concussion, the cell viability was measured at the absorbance of 490 nm.
2 × 103 transfected cells were planted in a 6-well plate per well and incubated in a complete medium until the colonies could be detected by naked eyes. Then the colonies were cleaned with normal saline and fixed with 4% paraformaldehyde (room temperature, 15 min) followed by staining using 0.5% crystal violet staining (Beyotime, Shanghai, China). ImageJ software was used to detect the number of colonies.
A total of 1 × 105 transfected cells were added evenly in 96-well plate per well and incubated for two days. EdU assays were performed using YF®555 Click-iT EdU Imaging Kits (US EVERBRIGHT, China) under the instruction of the manufacturer. The treated cells were detected under an inverted fluorescence Microscope and the graphs were captured simultaneously from three random fields. The proliferation rate in EdU staining was defined as the percentage of EdU staining cells relative to cells stained by DAPI.
Transwell assays were carried out using Transwell apical chamber (Corning Life Sciences, Corning, NY, USA). In total, 5 × 104 transfected cells were added into the upper compartment, while 700 µl complete medium was injected into the lower part. After 24 h, cells were fixed using 4% paraformaldehyde followed by staining with 0.5% crystal violet. Then, cells that failed to migrate to the outer surface of the upper compartment were scrubbed by cotton swabs. Five microscopic fields per chamber were observed for counting the migratory cells (at ×100 magnification). The count of migratory cells was performed using ImageJ software.
A total of 5 × 105 cells were cultured and transfected. 48 h after transfection, the collected adherent cells were fixed in 75% ethanol followed by staining with PI. Cell apoptosis assay was carried out according to the protocol of YF®488-Annexin V and PI Apoptosis Kit (US EVERBRIGHT, China). The results were assessed by FACSCalibur (BD, Franklin Lakes, NJ, USA). FlowJo software 10.8.0 (BD, Franklin Lakes, NJ, USA) was used to perform the analysis.
The potential binding sites of miR-382-5p in NNT-AS1 and NNT were predicted by Starbase. The binding sites were cloned in the pmirGLO vector (Promega, Madison, WI, USA) to generate luciferase reporter vectors of wild type (NNT-AS1 WT, NNT WT) and corresponding mutant type vectors (NNT-AS1 MUT, NNT MUT). Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) was used for co-transfection. The Dual-Luciferase Reporter Assay Kit (Vazyme, Nanjing, Jiangsu, China) was used for luciferase assays according to the protocols.
Proteins were extracted and quantified using RIPA lysis buffer (Beyotime, Shanghai, China) and BCA Protein Assay kit (Thermo Fisher Scientific, Waltham, MA, USA), respectively. The proteins with loading buffer (volume ratio=1:4) were added in lanes of 4–20% SurePAGE (GeneSript, Nanjing, Jiangsu, China). After electrophoresis, the protein gel was transferred to the PVDF membrane (Millipore, Massachusetts, USA) and blocked using 5% skimmed milk in TBST. Then, the membranes were incubated in specific antibodies as follows: GAPDH (AC001, 1:2000) NNT (A4561, 1:500), CDK1 (A0220, 1:1000), CDK2 (A18000, 1:1000), CCNB1 (A19037, 1:1000), and CCNB2 (A3351, 1:1000). All the antibodies mentioned above were purchased from Abcolonal, Wuhan, China. GelDoc XR + (Bio-Rad, Hercules, CA, USA) was adopted to develop the protein bands.
Stably transfected Eca-109 cells with NNT-AS1 silence (shNNT-AS1) and negative control (shCtrl) were constructed for animal experiments. The sequence of shNNT-AS1 was illustrated in Table S2. In brief, Eca-109 cells were infected with lentivirus-coated shNNT-AS1 and shCtrl, which contained the green fluorescent protein. The transfection efficiency was assessed through observing the fluorescence intensity under a fluorescence microscope and qRT-PCR was utilized for accurate evaluation. Six 6 weeks old nude male mice were purchased from Weitonglihua, Beijing, China. The stably transfected Eca-109 cells were inoculated into the back of the mice, right for shNNT-AS1 and left for shCtrl respectively. The diameter of the tumor was measured and recorded every 3 days. The volume of the tumor was determined as: V = LD × (SD)2/2 (LD: the largest diameter; SD: the shortest diameter). The nude mice were euthanized on the 15th day through cervical dislocation and the tumors were dissected and weighed. All animal experiments were performed under the protocols approved by the Experimental Animals Ethics Committee of Nanjing Medical University.
In this study, paired student’s t-test was adopted for the comparison of lncRNA expression in ESCC tumor and adjacent normal tissues. The false discovery rate (FDR) was used to correct the P values. Associations between lncRNA expression and ESCC prognosis were assessed using the Log-rank test. The unpaired student’s t-test was applied to test the differences between experimental groups. The expression relationship between NNT-AS1 and NNT was assessed based on Pearson correlation. Statistical tests were conducted based on R 3.6.0 and Graphpad Prism 8.0. The statistical significance level was set at P < 0.05.
Supplementary Tables Supplementary Figure Original Data File | true | true | true |
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PMC9587004 | Lu-Lu Yang,Wen-Chang Xiao,Huan Li,Zheng-Yang Hao,Gui-Zhi Liu,Dian-Hong Zhang,Lei-Ming Wu,Zheng Wang,Yan-Qing Zhang,Zhen Huang,Yan-Zhou Zhang | E3 ubiquitin ligase RNF5 attenuates pathological cardiac hypertrophy through STING | 21-10-2022 | Heart failure,Ubiquitylation | Ring-finger protein 5 (RNF5) is an E3 ubiquitin ligase which is expressed in a variety of human tissues. RNF5 is involved in the regulation of endoplasmic reticulum stress, inflammation, and innate immunity and plays an important role in the occurrence and development of various tumors. However, the role of RNF5 in cardiac hypertrophy has not been reported. In this study, we found the expression of RNF5 was increased in the hearts of mice with pathological cardiac hypertrophy. The loss-of-function research demonstrated that RNF5 deficiency exacerbated cardiac hypertrophy, whereas gain-of-function studies revealed that overexpression of RNF5 had opposite effects. The stimulator of interferon genes (STING) is a signaling molecule that can activate type I interferon immunity, which can meditate inflammation and immune response in many diseases. The protein–protein interaction experiments confirmed that STING interacted with RNF5. Further studies showed that RNF5 inhibited cardiac hypertrophy by promoting STING degradation through K48-linked polyubiquitination. Therefore, we defined RNF5 as importantly regulated signaling for cardiac hypertrophy. | E3 ubiquitin ligase RNF5 attenuates pathological cardiac hypertrophy through STING
Ring-finger protein 5 (RNF5) is an E3 ubiquitin ligase which is expressed in a variety of human tissues. RNF5 is involved in the regulation of endoplasmic reticulum stress, inflammation, and innate immunity and plays an important role in the occurrence and development of various tumors. However, the role of RNF5 in cardiac hypertrophy has not been reported. In this study, we found the expression of RNF5 was increased in the hearts of mice with pathological cardiac hypertrophy. The loss-of-function research demonstrated that RNF5 deficiency exacerbated cardiac hypertrophy, whereas gain-of-function studies revealed that overexpression of RNF5 had opposite effects. The stimulator of interferon genes (STING) is a signaling molecule that can activate type I interferon immunity, which can meditate inflammation and immune response in many diseases. The protein–protein interaction experiments confirmed that STING interacted with RNF5. Further studies showed that RNF5 inhibited cardiac hypertrophy by promoting STING degradation through K48-linked polyubiquitination. Therefore, we defined RNF5 as importantly regulated signaling for cardiac hypertrophy.
Pathological cardiac hypertrophy is caused by hypertension, valvular heart disease, coronary artery disease, and hereditary cardiomyopathy [1]. Pathological cardiac hypertrophy usually progresses to heart failure and is a major risk factor for arrhythmias [2]. In high-income North America, eastern Sub-Saharan Africa, East Asia, and Southeast Asia, heart failure caused the most significant reduction in healthy life year (HeaLY) for males [3]. Despite the increased use of techniques such as open heart transplantation and mechanical circulatory support device placement, morbidity and mortality in patients with heart failure remained high [3]. Therefore, exploring the underlying mechanisms of pathological cardiac hypertrophy is crucial to delay or even reverse the progression of heart failure. Ring-finger protein 5 (RNF5, also known as RMA1) is an E3 ubiquitin ligase, mainly located at the endoplasmic reticulum and mitochondria [4, 5]. RNF5 is anchored to the endoplasmic reticulum by its C-terminal and contains a classical RING domain (giving ligase activity) [6]. RNF5 plays an important role in endoplasmic reticulum stress response and unfolded protein response [7]. Cystic fibrosis is associated with the misfolding and premature degradation of the cystic fibrosis transmembrane conductance regulator (CFTR) mutant CFTRΔF508, it has been demonstrated that RNF5 targeted CFTRΔF508 for degradation through the endoplasmic reticulum-associated degradation (ERAD) [8, 9]. Virus-induced signaling adapter (VISA, also known as MAVS) is a critical adapter protein to RNA virus and plays a significant role in the innate immune responses of the host [10]. RNF5 modulates the cellular antiviral responses by K48-linked polyubiquitination and degradation of VISA [5, 11]. A recent study has shown that RNF5 ameliorated nonalcoholic steatohepatitis (NASH) through ubiquitin-mediated degradation of 3-hydroxy-3-methylglutaryl CoA reductase degradation protein 1 [ref. 12]. RNF5 is closely associated with proliferation, apoptosis, and autophagy. However, current studies on RNF5 have mainly focused on tumor and innate immune responses, and the role of RNF5 in pathological cardiac hypertrophy remains largely unknown. In this study, we find that RNF5 expression is upregulated in animal and cellular models of cardiac hypertrophy. After pathological stimulation, RNF5 deficiency significantly aggravates pathological cardiac hypertrophy, inflammatory response, and fibrosis, while overexpression of RNF5 has the opposite effects. However, RNF5 has no such effects under physiological conditions. We found a direct interaction between RNF5 and STING. STING aggravated pathological cardiac hypertrophy after PE treatment, while RNF5 could promote STING degradation through K48-linked polyubiquitination, thus alleviating cardiac hypertrophy. In conclusion, our results suggest that RNF5 may act as a promising therapeutic target in pathological cardiac hypertrophy.
All animal use protocols were approved by Zhengzhou University. The procedures were in accordance with the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. In order to obtain RNF5 knockout mice, we used the CRISPR online design tool (http://chopchop.cbu.uib.no/) to predict target DNA regions boot sequence-guideRNA target site: GCCCCGCTCGCGATTTGGCCCTTCGGG, RNF5-sgRNA expression vector was constructed using pUC57-sgRNA (Addgene,51132) as skeleton vector. The in vitro transcripts of Cas9 expression vector pST1374-Cas9 (Addgene 44758) and sgRNA expression vector were purified, recovered, and configured into a mixed system (Cas9 mRNA: 10 ng/ul;sgRNA: 10 ng/ul), the mixture was injected into single-cell fertilized eggs of C57BL/6 mice by FemtoJet 5247 microinjection system, and the injected fertilized eggs were transplanted into surrogate female mice, and F0 generation mice were obtained after about 19–21 days of gestation. The ear tissues of mice 2 weeks after birth were collected, genomic DNA was extracted, and the following primers were used to identify the genotypes of mice: RNF5-Check F1: 5′ -CTGGGGGTACTGAGGGCTAC-3′, RNF5-check R1:5′-GCCCTCTGGTCATCTGAAAA-3′. The selected Founder was then multiplied and constructed until RNF5-/- mice were obtained for subsequent experiments.
Male mice aged between 9 and 11 weeks with a body weight of 25.5–27 g were randomly divided into the TAC group and sham group. Mice in the TAC group were anesthetized by intraperitoneal injection with tribromoethanol (400 mg/Kg). After no obvious toe reaction and stable and even breathing rules were observed, the mice were placed in a supine position and fixed on a self-adjusting heating pad at 37 °C. Take the junction of the clavicle and thoracic vertebra as the center, cut it and use forceps to penetrate into the incision to tear the muscles on both sides, separate the thymus on both sides and expose the aortic arch. The 7-0 silk thread was passed through the aortic arch, and the 26-G cushion needle was placed parallel to the top of the aortic arch. After ligation of the blood vessels and the cushion needle, the cushion needle was pulled out to form a narrowing of the aortic arch. Animals in the sham group underwent all steps except aortic ligation. After surgery, the skin at the opening was sutured, and the mice were placed in a 37 °C temperature box to wake up.
Mice were anesthetized by inhalation of isoflurane (1.5–2%) and then fixed in a supine position on a thermostatic plate. Ultrasound detection was performed using Small Animal Ultrasound Imaging System (VEVO2100, FUJIFILM VISUALSONICS, Canada) with a 30-MHz(MS400) probe. The left ventricular volume and the thickness of the left ventricular wall were measured at the papillary muscle for three consecutive cycles in M-mode echocardiography mode. The left ventricular end-systolic diameter (LVESd), left ventricular end-diastolic diameter (LVEDd), left ventricular ejection fraction (EF%), and short axis shortening rate (FS%) were measured. Echocardiography was conducted by investigators blinded to the study.
Four weeks after TAC surgery, the mice were weighed and recorded. After the heart was removed, it was quickly placed in 10% KCl solution to stop the heart in the diastolic phase, weighed, and fixed in liquid nitrogen or 10% formalin. Lung weight and tibial length were also measured. The mice heart was fixed for 48 h, and the cross-cut wax block was sequentially sectioned. The sections were 5 μm thick and stained with hematoxylin (G1004, Servicebio) & eosin (BA-4024, Baso) (H&E) and picrosirius red (26357-02, Hedebiotechnology) (PSR) to measure the cross-sectional area and collagen fiber content of cardiomyocytes. Image-pro Plus 6.0 software was used for measurement.
Left ventricular tissue or cell samples were collected and lysed by RIPA buffer (720 μL of RIPA buffer, 20 μL of phenylmethylsulfonyl fluoride, 100 μL of complete protease inhibitor cocktail, 100 μL of Phos-stop, 50 μL of NaF, and 10 μL of Na3 VO4 in a final volume of 1 mL). After lysis and centrifugation, the supernatant was taken as total protein and quantified by a BCA protein kit (Pierce). After separation by using SDS polyacrylamide gel electrophoresis, the proteins were transferred to a 0.45 μm PVDF (IPVH00010, Millipore) membrane. The membrane was then blocked with 5% nonfat milk at room temperature for 1 h. The PVDF membrane was cleaned three times with TBST, 5 min each time. A primary antibody was added and incubated at 4 °C overnight. The secondary antibodies were added the next day. ECL luminescent substrate (1705062, Bio-Rad) was used for imaging and the Bole gel imaging system (ChemiDoc XRS+) was used for signal collection. Image Lab (Version5.1) software was used to analyze the results, and the corresponding antibody information is Supplementary Table 1.
TRIzol reagent (15596-026, Invitrogen) was used to extract total RNA from tissue or cell samples. The RNA was reversed into cDNA using the Transcriptor First Strand cDNA Synthesis Kit (04896866001, Roche). SYBR Green PCR Master Mix (04887352001, Roche) was added and the expression of selected genes was detected by RT-PCR (Roche). GAPDH (glyceraldehyde-3-phosphate dehydrogenase) was used as the reference gene, and the primer sequences used were Supplementary Table 2.
STING genes were subcloned into replication-deficient adenovirus vectors controlled by cytomegalovirus (CMV) promoter and used for overexpression of STING, with GFP expression as control. Replication-deficient adenovirus vectors carrying short hairpin RNA targeting RNF5 were used to knock down the expression of RNF5, while AdshRNA adenovirus was used as a control, and the adenovirus overexpressing RNF5 was purchased from Han Heng Biotechnology Co., LTD. Adenoviruses infected cardiomyocytes with a 50-particle/cell multiplicity of infection (MOI) for 24 h and were subsequently identified. Virus primer information was Supplementary Table 3.
The hearts of 1- to 2-day-old SD rats were taken and the blood vessels were removed. The tissue blocks were cut into 1–2 cubic millimeters and digested with 0.125% trypsin to obtain neonatal rat cardiomyocytes (NRCMs). Added DMEM/F12 (C11330, Gibco) medium (10% fetal bovine serum (FBS, 10099141C, GIBCO), 1% penicillin/streptomycin (15140-122, GIBCO), and 5-bromodeoxyuridine (0.1 mM, to inhibit fibroblast proliferation, B5002-250MG, sigma)) for 24 h. NRCMs infected with adenovirus were treated with serum-free medium for 12 h, and then stimulated with 50 μM PE (PHR1017, Sigma) for 24 h. The control group was added with the same amount of PBS. The whole cell culture process was carried out at 37.0 °C and 5% CO2.
NRCMs were cultured for 24 h and fixed with 4% formaldehyde (G1101-500ML, Servicebio) for 30 min, then permeated with 0.2%Triton X-100 in PBS and blocked with 10% BSA (BAH66-0100, Equitech Bio) at 37.0 °C. The cells on slides were incubated with α-actinin antibody (05-384, Merck Millipore, 1:100 dilution), followed by staining with a fluorescent secondary antibody (donkey anti-mouse IgG [H + L] secondary antibody, A21202, Invitrogen, 1:200) and then the slides were mounted with an antifade mounting medium containing DAPI. Cell surface area was measured using image-Pro Plus 6.0 software.
For RNA-SEQ sequencing, the total RNA of the sample was first extracted and the cDNA library was constructed. MGISEQ-2000 RS was used for RNA-sequencing of the single-end library, and the reading length was 50 bp. HISAT2 software (Version 2.1.0) was used to compare the sequence fragments to the mouse reference genome (mm10/GRCm38). The resulting files were then transformed by SAMtools (Version 1.4) into a binary BAM format that can store the comparison information. Next, the exon model value per kilobase fragment per million gene location fragment (FPKM) for each identified gene is calculated using the default parameter of StringTie (Version 1.3.3b). Then, DESeq2 (version 1.2.10) identified differentially expressed genes (DEG) based on the following two criteria:(1) multiple changes greater than 1.5; (2) The corresponding corrected p < 0.05.
In hierarchical clustering analysis, the similarity between different samples was calculated, and the Unweighted Pair Group Method With Arithmetic Mean (UPGMA) algorithm was used to establish the hierarchical nested clustering tree, and then the hclust function of the R package was used for visualization.
Gene set enrichment analysis (GSEA) uses the gene set in the KEGG pathway to sequence genes according to the degree of differential expression. Then check whether the gene sets are concentrated at the top or bottom of the sequencing list to obtain the overall expression changes of these gene sets. The analysis was performed on the Java GSEA (Version 3.0) platform using the “Signal2Noise” metric, and gene sets with p < 0.05 and FDR <0.25 were considered statistically significant.
First, the required plasmids were co-transfected in 293 T or indicated adenovirus were infected with NRCMs, and the primer information of plasmids was Supplementary Table 4. Twenty-four hours after plasmid transfection, IP lysis buffer (20 mM Tris-HCl, pH 7.4;150 mM NaCl; 1 mM EDTA; and 1% NP-40) were used to lysate cells. After high-speed centrifugation at 4 °C, the supernatant containing protein was incubated overnight with Protein G-agarose beads and anti-label antibodies at 4 °C. Centrifugation at 3000 rpm at 4 °C, wash beads with 300 mM and 150 mM NaCl buffers for about three times respectively, then re-suspend beads with 2x SDS loading buffer and boil them at 95 °C for 5–10 min. Then the analysis results were detected by WB.
293 T cells co-transfected with indicated plasmids were lysed in 80 μl 150 mM IP lysis buffer and 10 μl 10% SDS lysis buffer and then denatured by heating at 95 °C for 10 min. After heating, 900 μl 150 mM IP lysis buffer were added to the lysates. And then, after sonication and centrifugation (12,000 rpm for 15 min), collected the supernatant and incubated with indicated antibody and protein G-agarose beads for 3 h at 4 °C. Washing the beads with 500 mM IP lysis buffer (20 mM Tris-HCl, pH 7.4; 500 mM NaCl; 1 mM EDTA; and 1% NP-40) for three times, after centrifugation (3000 rpm for 2 min), the beads were boiled at 95 °C with 2x SDS loading buffer for 10 min and separated on the SDS-PAGE for western blotting as previously described before.
Flag-RNF5, GST-HA-STING, Flag-STING, and GST-HA-RNF5 were overexpressed in eukaryotic cells. The lysis solution (50 mM Na2HPO4, pH 8.0;300 mM NaCl;1% Triton X-100; Cocktail) were used to lyse cells. GST Beads were used to purify protein samples. GST-HA-STING and Flag-RNF5, GST-HA-RNF5 and Flag-STING were mixed and incubated overnight at 4 °C.Buffer solutions (20 mM; 150 mM NaCl; 0.2% Triton X-100) were used to wash beads three times, then the beads were re-suspend with 2x SDS loading buffer and boiled them at 95 °C for 5–10 min. Then the analysis results were detected by WB.
All data in this study were statistically analyzed in the form of mean ± SD. For data that showed a normal distribution, differences between two groups were compared with a two-tailed Student’s t-test, a One-way analysis of variance (ANOVA) was performed for data comparison between multiple groups, and the Bonferroni test (equal variances assumed) or Tamhane’s T2 test (equal variances not assumed) was used for correction. SPSS (Statistical Package for the Social Sciences) 25.0 software was used to analyze data, and p < 0.05 was considered to be statistically significant.
To explore the role of RNF5 in the development of cardiac hypertrophy and heart failure, we treated neonatal rat cardiomyocytes (NRCMs) with phenylephrine (PE) to induce cardiomyocyte hypertrophy in vitro. Compared with phosphate buffer saline (PBS), PE stimulation significantly increased the surface area of cardiomyocytes (Fig. 1A). RT-PCR and WB showed the mRNA and protein expression of atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP) and myosin heavy chain 7 (MYH7) were upregulated and the mRNA levels of myosin heavy chain 6 (MYH6) were downregulated in PE treated samples compared with those treated with PBS (Fig. 1B, C). With regard to RNF5, although the expression level of mRNA was not significantly different, the expression of the protein was significantly increased (Fig. 1B, C). On the other hand, cardiac tissue testing of these indicators yielded similar results after transverse aortic coarctation (TAC) surgery in wild-type (WT) mice (Fig. 1D, E). Further, immunofluorescence tests showed that the expression of RNF5 was significantly upregulated in heart sections of WT mice treated with TAC surgery (Fig. 1F). Altogether, the increased expression of RNF5 in cardiac hypertrophy samples suggests that RNF5 may be involved in the pathogenesis of cardiac hypertrophy.
To investigate whether RNF5 is involved in the regulation of cardiac hypertrophy, we constructed RNF5 gene knockout (KO) mice for the loss-of-function experiments (Fig. 2A). Under basal conditions, RNF5 deficiency had no significant effect on heart weight (HW), HW/body weight (BW), lung weight (LW) /BW and HW/tibia length (TL) compared with WT mice (Fig. 2B). However, after treated with TAC surgery for 4 weeks, the results showed that HW, HW/BW, LW/BW and HW/TL were significantly increased in RNF5 KO mice (Fig. 2B). Further echocardiographic examination revealed that RNF5 deficiency aggravated cardiac dysfunction in mice (Fig. 2C). Compared with WT mice, cardiac dysfunction was deteriorated in RNF5 KO mice, which was manifested by the significant increase of left ventricular end-diastolic dimension (LVEDd), left ventricular end-systolic dimension (LVESd), as well as the decrease of ejection fractions (EF) and fraction shortening (FS) (Fig. 2D). In conclusion, echocardiography and hemodynamic measurements showed that RNF5 deficiency significantly exacerbated TAC-induced cardiac dilation and dysfunction. We next assessed cardiac pathological sections after TAC surgery. Histological examination revealed that gross hearts and the cross-sectional area of cardiomyocytes of RNF5 KO mice were increased compared with WT mice after TAC surgery (Fig. 2E). Correspondingly, compared with WT mice, mRNA and protein levels of hypertrophic marker genes (ANP, BNP, and MYH7) in the heart tissues of RNF5 KO mice were significantly increased after TAC surgery, and the mRNA level of MYH6 was decreased (Fig. 2F, G). These data provide further evidence that the deletion of RNF5 promotes cardiac remodeling upon pressure overload.
Perivascular and interstitial fibrosis are important characteristics of cardiac hypertrophy caused by pressure overload [13]. We, therefore, evaluated TAC-induced cardiac fibrosis by staining with picrosirius red (PSR) to determine the degree of fibrosis. Both interstitial and perivascular fibrosis were markedly increased in TAC-treated WT mice hearts but to a more prominent extent in RNF5 KO mice hearts after TAC surgery (Fig. 3A). Correspondingly, fibrosis was further quantified by measuring the mRNA levels of fibrosis markers (Collagen Iα1, Collagen IIIα1, connective tissue growth factor (CTGF) and Collagen VIIIα1) and the expression of protein levels of fibrosis markers (Collagen Iα1, Collagen IIIα1, and CTGF), and it was found that TAC-induced fibrosis was significantly aggravated in RNF5 KO mice than WT mice (Fig. 3B, C). In the development of cardiac hypertrophy, the increased expression of IL-6, TNF-α, and IL-1β is closely related to Collagen I and Collagen III deposition [14]. As shown in Fig. 3D, the mRNA levels of inflammatory cytokines IL-6, IL-1β, and TNF-α were significantly higher in RNF5 KO mice than that in WT mice after TAC surgery. Activation of inflammatory signaling pathways promotes myocardial hypertrophy and fibrosis [15]. We further investigated the effects of RNF5 deficiency on NF‐κB signaling pathways. WB results showed that the phosphorylation of IKKβ, IkBα, and p65 proteins were significantly increased in RNF5 KO mice and WT mice after TAC surgery compared with the sham surgery group, and the expression of these proteins was higher in the RNF5 KO mice compared with the WT mice (Fig. 3E). These results indicate that RNF5 deficiency activates NF‐κB pathway dependent inflammatory responses under pressure overload in vivo.
Because cardiomyocyte enlargement is the defining characteristic of cardiac remodeling, we further evaluated the specific role of RNF5 in cardiomyocytes by infecting NRCMs with adenovirus harboring RNF5 short hairpin RNA (AdshRNF5) (Fig. 4A). After 24 h of PE or PBS treatment, the surface area of cardiomyocytes was determined by α-actinin immunofluorescence staining. Compared with the control group, AdshRNF5 significantly increased PE-induced cardiomyocyte enlargement (Fig. 4B), accompanied by significantly increased expression of ANP, BNP, and MYH7 and decreased expression of MYH6 (Fig. 4C, D). Furthermore, we infected NRCMs with adenovirus overexpression with RNF5 (AdFlag-RNF5) (Fig. 4E). In contrast, overexpression of RNF5 significantly reduced hypertrophy of cardiomyocytes (Fig. 4F). Compared with the control group, overexpression of RNF5 downregulated the protein and mRNA expression of ANP, BNP, and MYH7, and upregulated the mRNA expression of MYH6 (Fig. 4G, H). These results suggest that RNF5 could relieve the hypertrophic growth of PE-induced primary cardiomyocytes.
To explore the role of RNF5 in the pathogenesis of pathological cardiac hypertrophy at the transcriptomic level, RNA was extracted from the heart tissues of RNF5 KO mice and WT mice after TAC surgery for RNA-sequencing (RNA-Seq) analysis (Fig. 5A). The distribution profiles of RNA-Seq was analyzed by hierarchical clustering dendrogram analysis, which showed that the samples were divided into two clusters (Fig. 5B). Gene Set Enrichment Analysis (GSEA) analysis showed that inflammation, fibrosis, myocardial function, and protein process related pathways were all activated by RNF5 deficiency (Fig. 5C). Heat maps of transcriptome analysis showed increased effective activation of genes related to myocardial function, protein process, fibrosis, and inflammation in the RNF5 KO group (Fig. 5D–G). Taken together, these results suggest that RNF5 deficiency could activate signaling pathways and genes associated with cardiac hypertrophy.
To explore the potential mechanism of RNF5 regulating cardiac hypertrophy, we used IP-MS to screen out candidate proteins that might bind to RNF5 (Fig. 6A). According to IP-MS, we found that stimulator of interferon genes (STING) might be associated with RNF5. During the development of pathological cardiac hypertrophy, some inflammatory signaling pathways are activated, such as NF-κB [13]. STING, also known as Transmembrane protein 173 (TMEM173), has been identified as a key molecule in antiviral responses. RNF5 has been reported to inhibit virus-triggered IRF3, NF-κB activation, and cellular antiviral responses by regulating STING [16]. Therefore, we further explored the interaction between STING and RNF5 in cardiomyocytes. Immunofluorescence staining of NRCMs showed that RNF5 and STING were primarily co-located in the cytoplasm (Fig. 6B). HEK-293T cells were transfected with Flag-RNF5 and Myc-STING, then immunoprecipitation (IP) and WB (IB) analysis showed that exogenous expression of RNF5 interacted with exogenous expression of STING (Fig. 6C). Meanwhile, endogenous expression of STING and exogenous expression of RNF5 could also interact with each other in NRCMs (Fig. 6D). This interaction was further confirmed with a GST pull‐down assay (Fig. 6E). Previous studies have shown that STING could regulate pathological cardiac hypertrophy via endoplasmic reticulum stress [17]. We further explored the roles of RNF5 and STING in heart and NRCMs, and the results verified that STING expression was increased in pathological cardiac hypertrophy (Fig. 6F–H). RNF5 deficiency led to up-regulation of STING expression, while RNF5 overexpression had the opposite effect (Fig. 6F–H). Subsequent immunohistochemistry experiment confirmed the results of the RNF5 loss-of-function assay (Fig. 6I). These results suggest that there is a direct interaction between RNF5 and STING, and RNF5 could regulate the expression of STING in pathological cardiac hypertrophy.
E3 ubiquitin ligase can transfer ubiquitin from E2 ubiquitin-conjugating to host protein, thereby labeling substrates for proteasome digestion [18]. RNF5 is one of the E3 ubiquitin ligases, and we, therefore, postulated that RNF5 regulated the stabilization of STING. After we infected NRCMs with AdFlag-RNF5, as shown in Fig. 7A, the expression of STING protein was significantly downregulated with the increasing amount of AdFlag-RNF5 infection. Then, we found that the addition of a 26 S proteasome inhibitor (MG132), but not a lysosome inhibitor (chloroquine [CQ]), could abolish RNF5-induced degradation of STING (Fig. 7B), which indicated that RNF5 facilitated the proteasomal degradation of STING. Further experiments showed that RNF5 regulated the stabilization of STING by ubiquitination of STING (Fig. 7C). K48-linked polyubiquitin chains are sufficient to target the degradation of a substrate protein in ubiquitin-mediated proteolysis [19]. The ubiquitination assay results revealed that RNF5 promoted the addition of K48-linked polyubiquitin chains to degrade STING, and when K48 ubiquitination sites were mutated, RNF5-mediated ubiquitination of STING was abolished (Fig. 7D). Previous studies indicated that the ubiquitin sites of STING might be located at 1-160 [ref. 16]. By mutating lysine residues of K20, K137, and K150 in this region of STING to arginine, we found that RNF5-mediated STING ubiquitination was canceled only when K150 was mutated to arginine (Fig. 7E). RNF5C42S, in which the Cys42 in the ring-finger domain is mutated to serine, is well accepted as an inactive catalytic mutant of RNF5 [ref. 9], which loses the ability to ubiquitinate and degrade STING (Fig. 7F, G). To explore whether the function of RNF5 regulated cardiac hypertrophy depending on STING, we overexpressed RNF5 and STING, respectively, or simultaneously in NRCMs. RT-PCR, WB, and immunofluorescence confirmed that overexpression of STING abolished the regulation of RNF5 in cardiac hypertrophy (Fig. 7H–J). Collectively, these data suggest that RNF5 alleviates pathological cardiac hypertrophy through K48-linked ubiquitination and proteasomal degradation of STING.
In this study, we revealed the function of RNF5 in pathological cardiac hypertrophy. We constructed cellular and animal models of cardiac hypertrophy and found that the expression of RNF5 was increased in these models. The loss-of-function research showed that RNF5 deficiency exacerbated cardiac remodeling and dysfunction, reactivation of fetal gene expression, fibrosis, and significant activation of the NF-κB signaling. Whereas the gain-of-function studies demonstrated that overexpression of RNF5 attenuated cardiomyocyte hypertrophy. We further investigated the molecular mechanism of RNF5 in the development of pathological cardiac hypertrophy. The protein–protein interaction experiment confirmed the interaction between STING and RNF5. Rescue experiments showed that overexpression of STING could attenuate the effect of RNF5 on alleviating cardiac hypertrophy. Therefore, our study suggests that targeting RNF5 may be a promising strategy for the treatment of pathological cardiac hypertrophy. The essence of cardiac hypertrophy is the increase of protein content in cardiomyocytes, and the ubiquitin-proteasome system (UPS) plays an important role in regulating the dynamic balance of protein metabolism in cardiomyocytes [20]. RNF5 locates in the proximal region of the major histocompatibility complex (MHC) on chromosome 6 [ref. 21]. The ring-finger domain of RNF5 shows its activity of E3 ubiquitin ligase. Retention of misfolded Pendrin mutants in the endoplasmic reticulum is considered the main pathological mechanism of Pendred syndrome, and RNF5 has significant effects on Pendrin protein degradation [22]. Young et al. found that in the endoplasmic reticulum stress (ER stress) response of breast cancer cells induced by paclitaxel, RNF5 inhibited the mTOR signaling pathway through binding and ubiquitination of SLC1A5/38A2, thus regulating the response of breast cancer to ER stress-induced chemotherapy [23]. Kuang et al. found that under normal physiological conditions, RNF5 could combine AGT4B to promote the degradation of AGT4B and thus regulate autophagy [24]. However, the potential association between RNF5 and pathological cardiac hypertrophy has not been explored. Proinflammatory cytokines such as tumor necrosis factor (TNF-α), interleukin 1β (IL-1β), and interleukin 6 (IL-6) can inhibit myocardial contraction, promote myocardial fibrosis, and increase collagen synthesis in pathological cardiac hypertrophy [14, 25, 26]. Previous studies have demonstrated that RNF5 inhibits the activation of IRF3 and NF-κB signaling pathways by mediating the ubiquitination and degradation of STING triggered by viral infection to avoid excessive antiviral responses [16]. By infecting AdFlag-RNF5 in NRCMs and detecting the protein expression level of STING, we found that RNF5 was associated with regulating STING in cardiac myocytes. The correlation between RNF5 and STING was further verified by protein–protein interaction experiments. There are two main protein degradation systems in eukaryotic cells, the autophagy-lysosome system and the ubiquitin-proteasome system [27]. The application of a 26 S proteasome inhibitor (MG132) in NRCMs infected with AdFlag-RNF5 prevented RNF5 from degrading STING. The main ubiquitin chain type for proteasome pathway degradation of proteins is the Lys48 linkage [28]. RNF5-mediated STING ubiquitination was eliminated when the K48 ubiquitin site was mutated. By mutating the ubiquitination site of STING, we found that RNF5 targeted STING at K150 for ubiquitination. The C42 residue in the RING domain is required for RNF5 to exert ubiquitin ligase activity [9]. When the Cys42 was mutated to serine, RNF5 lost the ability to ubiquitinate and degrade STING. Based on these observations, we speculate that RNF5 is a negative regulator of STING-mediated signaling. There are some limitations to the study. Our results confirmed the role of RNF5 in regulating cardiac hypertrophy by using RNF5 knockout mice, but we did not perform RNF5 overexpression in vivo, which would better verify our results. In our study, we focused on the regulation of RNF5 on cardiomyocytes. We found that overexpression and inhibition of RNF5 could significantly inhibit and promote myocardial hypertrophy induced by PE stimulation, respectively. We also observed that RNF5 is involved in fibrosis and inflammation in cardiac hypertrophy, but whether RNF5 is involved by directly modulating the function of other cell types in the heart or by regulating cardiomyocyte paracrine function requires further study. In conclusion, our study suggests that RNF5 is one of the negative regulators of pathological cardiac hypertrophy. RNF5 inhibits the development of cardiac hypertrophy by targeting STING and promoting its K48- linked ubiquitination-mediated degradation. These results provide a new perspective for studying the role of ER-associated protein in the pathogenesis of pathological cardiac hypertrophy.
Reproducibility Checklist Supplementary tables Original western blots | true | true | true |
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PMC9587034 | Kaushik Bhattacharya,Samarpan Maiti,Szabolcs Zahoran,Lorenz Weidenauer,Dina Hany,Diana Wider,Lilia Bernasconi,Manfredo Quadroni,Martine Collart,Didier Picard | Translational reprogramming in response to accumulating stressors ensures critical threshold levels of Hsp90 for mammalian life | 21-10-2022 | Chaperones,Ribosome,Ageing | The cytosolic molecular chaperone Hsp90 is essential for eukaryotic life. Although reduced Hsp90 levels correlate with aging, it was unknown whether eukaryotic cells and organisms can tune the basal Hsp90 levels to alleviate physiologically accumulated stress. We have investigated whether and how mice adapt to the deletion of three out of four alleles of the two genes encoding cytosolic Hsp90, with one Hsp90β allele being the only remaining one. While the vast majority of such mouse embryos die during gestation, survivors apparently manage to increase their Hsp90β protein to at least wild-type levels. Our studies reveal an internal ribosome entry site in the 5’ untranslated region of the Hsp90β mRNA allowing translational reprogramming to compensate for the genetic loss of Hsp90 alleles and in response to stress. We find that the minimum amount of total Hsp90 required to support viability of mammalian cells and organisms is 50–70% of what is normally there. Those that fail to maintain a threshold level are subject to accelerated senescence, proteostatic collapse, and ultimately death. Therefore, considering that Hsp90 levels can be reduced ≥100-fold in the unicellular budding yeast, critical threshold levels of Hsp90 have markedly increased during eukaryotic evolution. | Translational reprogramming in response to accumulating stressors ensures critical threshold levels of Hsp90 for mammalian life
The cytosolic molecular chaperone Hsp90 is essential for eukaryotic life. Although reduced Hsp90 levels correlate with aging, it was unknown whether eukaryotic cells and organisms can tune the basal Hsp90 levels to alleviate physiologically accumulated stress. We have investigated whether and how mice adapt to the deletion of three out of four alleles of the two genes encoding cytosolic Hsp90, with one Hsp90β allele being the only remaining one. While the vast majority of such mouse embryos die during gestation, survivors apparently manage to increase their Hsp90β protein to at least wild-type levels. Our studies reveal an internal ribosome entry site in the 5’ untranslated region of the Hsp90β mRNA allowing translational reprogramming to compensate for the genetic loss of Hsp90 alleles and in response to stress. We find that the minimum amount of total Hsp90 required to support viability of mammalian cells and organisms is 50–70% of what is normally there. Those that fail to maintain a threshold level are subject to accelerated senescence, proteostatic collapse, and ultimately death. Therefore, considering that Hsp90 levels can be reduced ≥100-fold in the unicellular budding yeast, critical threshold levels of Hsp90 have markedly increased during eukaryotic evolution.
Stress is inevitable. Every organism is repeatedly exposed to stress, either intrinsic or extrinsic. Stress stimuli (stressors) induce cell-autonomous (cellular) or non-autonomous (organismal) stress responses, which may intersect and functionally interact. In an organism, exposure to stress has two different outcomes, adaptation or failure to adapt; the latter would be indicative of stress susceptibility. Whereas “good adaptation” is advantageous for natural selection and developmental robustness, “bad adaptation” drives diseases like cancer. Alternatively, hypersensitivity of diseased cells to therapeutic drugs can constitute a “good susceptibility” for the benefit of patients; “bad susceptibility” causes age-related degenerative processes, including neurodegeneration and aging itself. How a living organism manages its cellular and organismal stresses determines its fate. To confront cellular stress, organisms express stress sensors and managers, such as molecular chaperones, which can act as both. Molecular chaperones, including Hsp70 and Hsp90, are evolutionarily conserved proteins responsible for the assisted protein-folding processes of native, misfolded, or structurally labile proteins. Intriguingly, during evolution from prokaryotes to eukaryotes, while overall proteome complexity dramatically increased without any accompanying gain of genes for new types of core molecular chaperones, a plethora of co-chaperones appeared. Molecular chaperone functions in eukaryotic organisms may be critically regulated and controlled by co-chaperones in a context-specific manner, and these molecular chaperone machines might be associated with several fundamental biological processes beyond protein folding/refolding. These include transcription, translation, protein translocation, and protein degradation via the proteasome and chaperone-mediated autophagy. In contrast to the situation in prokaryotes, Hsp90 is essential for the viability and growth of eukaryotic cells and organisms even under normal permissive conditions. Budding yeast, a lower eukaryote, can grow normally with as little as 5% of its total Hsp90 protein levels at a slightly reduced temperature; with even more severely reduced levels, it can still grow albeit with a significant growth retardation. Hsp90, together with its co-chaperones and other molecular chaperones, is an integral part of the system maintaining cellular protein homeostasis (proteostasis). Not surprisingly, failure to maintain proteostasis is associated with developmental failure, neurodegeneration, and premature aging. Similar to yeast, mammals also have two different cytosolic Hsp90 isoforms, Hsp90α (encoded by the gene HSP90AA1 in humans; HSP82 in yeast) and Hsp90β (encoded by HSP90AB1 in humans; HSC82 in yeast), a stress-inducible and a constitutively expressed isoform, respectively. Individually, either of them is dispensable in yeast and human cancer cell lines. Hsp90α and Hsp90β share extensive sequence identity, and largely but not completely overlapping molecular and cellular functions. In the mouse, the absence of Hsp90α primarily causes male sterility and retinal degeneration, whereas the absence of Hsp90β causes early embryonic lethality. Although the two cytosolic Hsp90 isoforms are potentially differentially required in mammals, how and to which extent low levels of total Hsp90 can be tolerated by mammals was unknown. Furthermore, it was unclear whether and how mammals can adapt to the genetic or pharmacological loss of Hsp90 by activating compensatory mechanisms, and what physiological states would actuate these mechanisms. Here, we report our investigation of these questions at the cellular and organismal levels using different Hsp90 mutant mouse and cellular models under normal physiological and stressed conditions. We find that mammalian life requires much higher threshold levels of the molecular chaperone Hsp90 than budding yeast, that mammalian cells can fine-tune the expression levels of Hsp90 in response to physiologically accumulating cellular stress, and that an internal ribosome entry site (IRES) in the 5′-UTR of the Hsp90ab1 mRNA reprograms translation in stressed conditions contributing to maintaining threshold levels of Hsp90. These mechanisms serve to promote stress adaptation and survival of the organism.
Wild-type (WT) mice have two Hsp90α-encoding alleles (Hsp90aa1) and two Hsp90β-encoding alleles (Hsp90ab1). To investigate the impact of reducing the number of Hsp90 alleles, we set up crosses to generate mice with a compound genotype of homozygous Hsp90aa1 and heterozygous Hsp90ab1 knockouts (herein referred to as 90αKO 90βHET), along with 90αKO, 90βHET, 90αHET 90βHET, and WT mice (Supplementary Fig. 1a, b). We observed a striking reduction in the frequency of viable 90αKO 90βHET mice at birth relative to the expected Mendelian inheritance for the cross between 90αHET 90βHET and 90αKO mice (Fig. 1a, Supplementary Fig. 1c). To evaluate whether the reduced frequency of viable 90αKO 90βHET mice is due to embryonic lethality, the frequency of 90αKO 90βHET embryos was determined at embryonic stages E13.5 and E8.5 using the same breeding strategy (Supplementary Fig. 1c). We found a gradual loss of 90αKO 90βHET embryos during gestation (Fig. 1a). The heterozygous deletion of Hsp90ab1 has the most severe effect on the viability at birth in combination with a homozygous Hsp90aa1 knockout (Fig. 1b, Supplementary Fig. 1c–g). Very rarely, developmentally retarded and morphologically deformed dead 90αKO 90βHET pups were born from the breeding of 90αHET 90βHET with 90αKO mice (Fig. 1c). These findings lead us to speculate that the loss of 90αKO 90βHET embryos may start as early as at implantation and continue until birth. We further confirmed the reduced frequency of 90αKO 90βHET mice at birth by backcrossing 90αHET 90βHET male and 90αKO 90βHET female littermates; the results of this experiment further demonstrated that the ability of embryos to survive is not genetically or epigenetically transmissible to the offspring by adult survivors with the 90αKO 90βHET (Fig. 1d, Supplementary Fig. 1g). Remarkably, the few 90αKO 90βHET pups that are born alive seem to thrive normally with a lifespan (811.4 ± 152 days; n = 5) similar to that of other Hsp90 mutants and corresponding to that of WT mice reported in the literature.
To investigate the molecular basis of why a small proportion of 90αKO 90βHET pups manage to survive, we performed quantitative label-free proteomic analyses of brain, liver, and muscle. The choice of these three tissues was based on the differential ratios between Hsp90α and Hsp90β protein levels (Supplementary Fig. 2a). We included one set each of male and female mice in the proteomic analysis to avoid any sex-specific differences between the genotypes. As a quality control, we performed a correlation analysis with the data of the two independent replicates of each tissue. The calculated Pearson correlation coefficients (r) for all comparable datasets were close to 1, indicative of highly correlated replicates (Supplementary Fig. 2b). We considered changes significant when the comparison of the averages of these sets for a given protein indicated a Log2 fold change of >0.4 or <−0.4 with a p-value of <0.1. The proteomic analyses indicated that only a minor proportion of the identified proteins of brain, liver, and muscle tissues are differentially expressed between the genotypes (Supplementary Fig. 3a–c, Supplementary Data 1). Overall proteostasis appears to be maintained across all genotypes, including in the 90αKO 90βHET survivors. Next, we focused on the Hsp70-Hsp90-related chaperones, co-chaperones, and other stress-responsive proteins from the proteomic dataset of brain. Remarkably, compared to 90βHET mice, the loss of one Hsp90ab1 allele in the absence of both Hsp90aa1 alleles (90αKO 90βHET mice) is significantly compensated by the overexpression of Hsp90β from the remaining allele (Fig. 2a, Supplementary Fig. 4a, Supplementary Data 1). This phenomenon is also apparent across multiple other organs in 90αKO 90βHET mice, notably lungs, eyes, kidney, spleen, and heart (Supplementary Fig. 4a). Based on the relative levels of the two isoforms in wild-type tissues, and assuming that both alleles of a given gene contribute equally, one can calculate how much Hsp90 protein should still be made by the remaining allele(s) without any rectification. In tissues with the 90αKO 90βHET genotype, the expected Hsp90 levels range from about 25% in brain to about 40% in the muscle. What is experimentally observed is a rectification of Hsp90β levels that is correlated with the expected loss of total Hsp90 across the analyzed tissues, rather than with the loss of alleles for this or that isoform (Fig. 2b, Supplementary Fig. 4b, Supplementary Data 1). Mouse adult fibroblasts (MAFs) established from ear biopsies of WT and Hsp90 mutant mice displayed a similar pattern with regards to Hsp90β expression (Fig. 2c). Although obvious in MAFs, an increase of Hsp90α levels in 90βHET mouse tissues is not consistently seen across the two sexes and the analyzed tissues (Fig. 2b, c, Supplementary Fig. 4a, Supplementary Data 1). Further analysis of the tissue extracts of 90αHET 90βHET mice revealed that Hsp90β levels are more consistently increased to WT levels compared to those of Hsp90α upon the loss of one allele in each of the two Hsp90 isoforms in the same individual (Supplementary Fig. 4c). Moreover, though lacking one allele of Hsp90ab1, Hsp90β protein levels in cells and tissues with the 90αKO 90βHET genotype are indistinguishable from those associated with the 90αKO genotype (Fig. 2c, d, Supplementary Figs. 4a and 5a). Remarkably, the rectification of the Hsp90β protein levels associated with the 90αKO 90βHET genotype pushes them at least up to the WT levels and sometimes even surpasses that, notably in the brain, eye, heart, and lung tissues (Fig. 2d and Supplementary Figs. 4a and 5a). Considering that Hsp90β is the constitutively expressed and only poorly stress-inducible Hsp90 isoform, this increase of the Hsp90β level is unexpected. To understand the molecular basis of the increase of the Hsp90β levels, we quantitated the Hsp90 mRNA levels. Comparisons between the transcript and protein levels across the relevant genotypes revealed that raised mRNA levels are not a major contributor to the increase of the Hsp90β protein levels in 90αKO 90βHET mice (Fig. 2c, d and Supplementary Figs. 4a, and 5a, b). While this is clearly the case for a whole panel of tissues, we cannot rule out that transcriptional reprogramming of the Hsp90β gene Hsp90ab1 is involved in augmenting the Hsp90β protein levels in tissues and cell types that we did not check. We tentatively concluded from these experiments that a translational or post-translational mechanism may increase Hsp90β protein levels to ensure that total Hsp90 protein levels are maintained above a critical threshold level, as is most evident for the 90αKO 90βHET mice that survive to adulthood.
Since we found that the increase of Hsp90β levels in 90αKO 90βHET cells and tissues may be translational, we checked the translation rate of Hsp90β in WT and mutant MAFs. Analysis of the puromycin incorporation into nascent polypeptides revealed a reduced global translation in 90αKO 90βHET MAFs (Fig. 3a). Whether the increase in global translation in 90αKO cells can be confirmed with other MAFs and cell types remain to be seen, but we note that it is not the case in brain (see below). What is noteworthy is that the relative translation rate of Hsp90β in 90αKO 90βHET compared to WT and Hsp90 mutants MAFs, including 90αKO MAFs, is increased, ensuring equivalent steady-state levels of Hsp90β protein (Fig. 3a, b). Consistent with this finding, the abundance of ribosome-associated Hsp90ab1 mRNA is increased in 90αKO 90βHET compared to 90βHET MAFs (Fig. 3c), although there is no difference in the total Hsp90ab1 mRNA level (Supplementary Fig. 6a). Despite the fact that 90αKO 90βHET MAFs lack one Hsp90ab1 allele and display a reduced global translation, the abundance of ribosome-associated Hsp90ab1 mRNA over inputs in 90αKO 90βHET MAFs is indistinguishable compared to WT or 90αKO MAFs (Fig. 3c). This further supports the notion of an Hsp90β-specific translational activation in 90αKO 90βHET cells (Fig. 3c, Supplementary Fig. 6a). Polysome profiling of brain samples of 90αKO 90βHET mice confirmed the reduced global translation and a remarkably enhanced abundance of the polysome-associated Hsp90ab1 mRNA, which conceivably maintains the total Hsp90 protein at WT levels in the brain (Fig. 3d, Supplementary Fig. 6b, also see Fig. 2b). To evaluate the formal possibility that the translational upregulation of Hsp90β associated with the 90αKO 90βHET genotype may be due to a global stress response, ribosome association of mRNA for other molecular chaperones and co-chaperones was checked. Except for Hsp90ab1 mRNA, no other tested mRNA is noticeably enriched in the ribosome-bound fraction from 90αKO 90βHET MAFs compared to 90βHET MAFs (Supplementary Fig. 6c). This finding is paralleled by the insignificant upregulation of the protein levels of other molecular chaperones and co-chaperones in 90αKO 90βHET compared to 90βHET MAFs and mouse tissues (Fig. 2a (right), Supplementary Fig. 6d–f, Supplementary Data 1). Therefore, the translational upregulation of Hsp90β in 90αKO 90βHET cells and tissues is a specific response that occurs as a result of the loss of three quarters of the alleles and the specific contributions of each isoform to the total cytosolic Hsp90 pool rather than the manifestation of a global stress response. So far, our results support the conclusion that increased translation of the Hsp90β mRNA accounts for the increased Hsp90β protein levels. To exclude a contribution of reduced turnover of the Hsp90β protein, we performed a cycloheximide-chase assay comparing the turnover of Hsp90β and several Hsp90 client and non-client proteins in 90αKO 90βHET versus 90βHET MAFs. If anything, the results show that the turnover of Hsp90β and the two relatively short-lived Hsp90 clients AKT and c-Raf is higher in 90αKO 90βHET MAFs (Supplementary Fig. 6g). We conclude that an Hsp90β-specific translational activation underlies the increase in Hsp90β protein levels in cells and tissue of the 90αKO 90βHET genotype.
The reduced global translation rate in 90αKO 90βHET MAFs and brain correlates with the remarkably reduced activity of mTORC1 and hyperphosphorylation of eIF2α (Supplementary Fig. 7a). Since these major mediators of cap-dependent translation are inhibited in 90αKO 90βHET cells, we examined the possibility of cap-independent translation of the Hsp90β mRNA through an IRES. To this end, we used bicistronic expression plasmids where Renilla luciferase is translated in a cap-dependent manner, and firefly luciferase via an IRES from a single mRNA transcript. The 5′-UTR of mouse Hsp90ab1 was tested for its IRES function in parallel with a well-studied poliovirus IRES as a positive control (Supplementary Fig. 7b). We found not only that the Hsp90ab1 5′-UTR does have IRES function, but that this function is increased in 90αKO 90βHET MAFs indicating that the IRES-mediated translational reprogramming may account for the rectification of Hsp90β protein levels (Fig. 3e). We used quantitative RT-PCR to confirm that the IRES-driven enhanced translation of the reporter mRNA in 90αKO 90βHET MAFs is not due to increased levels of the bicistronic reporter mRNA (Supplementary Fig. 7c, d). To evaluate further the impact of the UTRs of the Hsp90ab1 mRNA on translation, reporter plasmids were generated where the firefly luciferase coding sequence was flanked either by the 5′- or 3′- or both UTRs (Supplementary Fig. 7e). Remarkably, in 90αKO 90βHET MAFs, the 5′-UTR of the Hsp90ab1 mRNA by itself enhances firefly luciferase expression (Fig. 3f), again independently of any impact on the steady-state abundance of the corresponding mRNA (Supplementary Fig. 7f). For all other genotypes, both UTRs are required for maximal stimulation of luciferase expression (Fig. 3f). Even though the mechanism of this genotype-dependent interplay remains to be elucidated, this complementary experiment supports the conclusion that the IRES of the 5´-UTR of the Hsp90ab1 mRNA potentiates translation more robustly in 90αKO 90βHET cells. So far, we had investigated the IRES-driven translational reprogramming in cells of the 90αKO 90βHET genotype under normal physiological conditions. We now turned to explore the effects of heat shock, a physiologically relevant stress that inhibits cap-dependent translation. Short-term mild heat stress (30 h at 40 °C) significantly increases the IRES function of the 5′-UTR of the Hsp90ab1 mRNA only in 90αKO 90βHET and 90βHET MAFs (Supplementary Fig. 7g). A long-term mild heat stress (6 days at 40 °C) remarkably boosts the IRES function even more in 90αKO 90βHET MAFs. During adaptation to long-term heat stress, the 5′-UTR also gained some IRES function in WT MAFs (Supplementary Fig. 7g). To reveal the potential impact of the IRES function of the 5′-UTR of the Hsp90ab1 mRNA on its endogenous protein production, WT and Hsp90 mutant MAFs were exposed to mild heat stress. Although Hsp90α and Hsp90β mRNA are induced, short-term mild heat stress reduces the steady-state Hsp90 protein levels of both isoforms except in 90αKO 90βHET MAFs (Supplementary Fig. 7h, i). Remarkably, in 90αKO 90βHET MAFs, the fold-induction of Hsp90β protein under short- and long-term heat stress overrides the fold-change of its mRNA (Fig. 3g, h and Supplementary Fig. 7h, i). During the long-term heat adaptation, whereas the protein levels of the Hsp90 isoforms are induced in the survivors with other genotypes, they cannot surpass the fold-change of the levels of the respective mRNA (Fig. 3g, h). Therefore, it is conceivable that the IRES function of the Hsp90ab1 5′-UTR not only rectifies the Hsp90β protein levels associated with the 90αKO 90βHET genotype under physiological conditions, but it may also help to fine-tune Hsp90β protein levels in response to stress. We next sought to determine whether IRES-mediated induction of Hsp90β synthesis in response to stress is conserved in human cells. We tested this with human RPE1 cells, which are a non-cancerous epithelial cell line, upon short-term (30 h) and long-term (4 days) adaptation to mild heat stress. While the stress-induced modest increase in HSP90AB1 mRNA is remarkably proportional to the corresponding increase in Hsp90β protein, there is a much stronger contribution of the mRNA induction for Hsp90α (Supplementary Fig. 7j). If one considers the fact that general cap-dependent translation is reduced in response to heat stress, it can be hypothesized that the increase in Hsp90β mRNA cannot fully account for the induction of Hsp90β protein, and that there must therefore be a contribution from IRES-dependent translation. The existence of an IRES in human Hsp90β mRNA is supported by the result of a genome-wide screen for IRES, and our own results obtained with the bicistronic reporter gene in RPE1 cells, which show a robust IRES activity upon prolonged (5 days) mild heat stress (Supplementary Fig. 7k). Taken together, our data suggest that the increase in both Hsp90α and Hsp90β proteins during short-term adaptation to mild heat stress is primarily transcriptional in normal mammalian cells, whereas the IRES of Hsp90β mRNA becomes important for the adaptation to prolonged heat stress (for MAFs, see also Supplementary Fig. 7g).
To investigate whether and how the rectification of Hsp90 levels is physiologically relevant, we analyzed the Hsp90β protein levels in postnatal day 1 (P1) pups. Despite the elevated embryonic lethality of the 90αKO 90βHET genotype, we managed to identify two, albeit developmentally retarded, pups to compare with live P1 90αKO pups (Fig. 4a; see also Fig. 1c). We found that the abundance of Hsp90β is remarkably reduced in the stillborn 90αKO 90βHET pups (Fig. 4b). However, the comparison of Hsp90β levels in tissues of adult survivors with the identical genotypes revealed a striking rectification of Hsp90β protein levels in these 90αKO 90βHET mice (Figs. 2a–d and 4b, and Supplementary Figs. 4a and 5a). Therefore, we speculated that below a certain threshold level, Hsp90 may not support the viability of a mammalian organism and that the rectified Hsp90 levels define a criterion for mammalian life. To further explore this hypothesis in a more controllable cellular model, we knocked down the remaining isoforms of Hsp90 in Hsp90α (encoded by the gene HSP90AA1) and Hsp90β (encoded by HSP90AB1) knockout (KO) HEK293T cells (Fig. 4c, Supplementary Data 2). Further reduction of Hsp90 levels in Hsp90α/β KO HEK293T cells led to a remarkable lethality during the “crisis” period, that is the initial phase of the knockdown (Fig. 4d, and Supplementary Fig. 8a, b). Consistent with our genetic models, functional inhibition of Hsp90 by geldanamycin (GA) revealed markedly enhanced cytotoxicity for all Hsp90 mutant cell lines from both mouse and human origins (Supplementary Fig. 8c–f). After the initial knockdown of the remaining Hsp90 isoform and the associated “crisis” period, a small number of Hsp90α/β KO HEK293T cells were clonally selected and started growing normally (Fig. 4d, and Supplementary Fig. 8a, b). Investigating the molecular basis of the long-term adaptation of Hsp90α/β KO HEK293T cells, we found that the initially reduced levels of the targeted Hsp90 isoform were largely restored both at the protein and the mRNA levels, whereas the knockdown remained stable in WT cells (Fig. 4e, and Supplementary Fig. 8g, h). These data, obtained with a totally orthogonal experimental system, support our conclusion that the viability of a mammalian cell or organism can only be supported by above-threshold total Hsp90 levels, irrespective of the isoform. We performed a proteomic analysis to quantitate the Hsp90 threshold levels in these Hsp90 KO lines more accurately. This revealed that in adapted cells, compared to the total Hsp90 levels of WT cells, the remaining Hsp90 isoform reaches levels of about 75% and 50% in Hsp90α and Hsp90β KO HEK293T cells, respectively (Supplementary Fig. 8i, Supplementary Data 2). Reducing these levels further causes severe lethality in Hsp90α/β KO HEK293T cells, as described above (Supplementary Fig. 8a, b, j and Fig. 4d). Therefore, at the cellular level of this mammalian model system, 50–75% of the total Hsp90 is required for viability. Quantitative proteomic analysis of tissues from WT and Hsp90 mutant mice showed that the minimum levels of total Hsp90 might vary depending on tissue-specific requirements (Fig. 2b). However, none of the analyzed tissues, notably of the 90αKO 90βHET genotype, showed <50% of total Hsp90 (Fig. 2b), far above the 1–5% required to support yeast viability. The incompressible part of the steady-state levels of Hsp90 may have increased to accommodate the ever-growing complexity of the proteome on the path towards mammals.
Hsp90 inhibitors induce the stress response and as a consequence the overexpression of other molecular chaperones and co-chaperones. This is a critical mechanism for adaptation and acquisition of drug resistance of cancer cells. Therefore, we wondered whether other stress-related proteins contribute to the adaptation of Hsp90α/β KO HEK293T cells after the initial knockdown of the remaining Hsp90 isoform. During the long-term adaptation, the levels of stress-related proteins, which are initially increased in Hsp90α/β KO HEK293T cells, revert back to their respective basal levels, in parallel with the rectification of total Hsp90 levels (Fig. 4e, and Supplementary Fig. 8g). To determine whether the adapted state is stable, we subjected the same cells to a second round of Hsp90β knockdown. Again, while this caused a severe lethality within the first week, during adaptation, a few survivors managed to increase the initially reduced Hsp90β levels (Supplementary Fig. 9a–c). While analyzing the levels of Hsp90 in the aforementioned experiments with immunoblots, we noticed that the basal levels of several stress-related proteins, including Hsp70, Hsp40, Hsp27, and Aha1, are elevated in Hsp90 KO cells (Fig. 4e, Supplementary Fig. 8g). This could be confirmed for an additional cell line (A549), in which we reduced Hsp90 levels with the CRISPR-Cas9 system (Supplementary Figs. 8e and 9d), and by quantitative proteomic analyses of the HEK293T KOs (Fig. 4f, Supplementary Data 2) and of liver and muscle of 90αKO 90βHET mice (Supplementary Fig. 9e, f, Supplementary Data 1). In contrast, clients and other interactors of Hsp90 do not significantly change in any of the Hsp90 mutant cells and tissues compared to WT under normal physiological conditions (Supplementary Fig. 9g, h, Supplementary Data 1 and 2). To evaluate further whether the above-mentioned increased basal level of some stress-related proteins can compensate for the reduced Hsp90 levels, WT and Hsp90 KO/mutant HEK293T and A549 cells were exposed to a long-term mild heat stress. All cell lines with reduced Hsp90 levels proved to be much more sensitive than the WT parent cell line (Fig. 4g, and Supplementary Fig. 10a–d). Taken together, all of these results demonstrate that the increased expression of other molecular chaperones cannot compensate for the lack of Hsp90 under stress conditions and that they cannot substantially contribute to the adaptation of Hsp90-deficient cells. While the results of the different types of loss-of-function experiments were consistent across the board, we performed a rescue experiment to provide further support for our conclusions. Transient overexpression of either one of the Hsp90 isoforms in Hsp90α/β KO HEK293T cells significantly improved cell viability under mild heat stress (Supplementary Fig. 10e–g). These results indicate that environmental conditions determine the Hsp90 requirements for mammalian life, and that the total abundance of cytosolic Hsp90 is the important parameter.
We next investigated the molecular mechanism behind the premature death of 90αKO 90βHET embryos during gestation. Although the basal levels of Hsp90β are similar in survivors with the 90αKO and 90αKO 90βHET genotypes (Fig. 2c, d and Supplementary Figs. 4a and 5a), short-term pharmacological inhibition of the remaining Hsp90 leads to an increased accumulation of total and polyubiquitinated insoluble proteins in 90αKO 90βHET MAFs (Fig. 5a). Reminiscent of these findings, stillborn 90αKO 90βHET pups also accumulate more total and polyubiquitinated insoluble proteins than live 90αKO P1 pups (Fig. 5b, and Supplementary Fig. 11a). As mentioned before, stillborn 90αKO 90βHET pups have low Hsp90β levels compared to both 90αKO P1 pups and 90αKO 90βHET adult survivors (Fig. 4a, b). The latter did not show any notable differential accumulation of total and polyubiquitinated insoluble proteins in the liver by comparison to 90αKO mice (Fig. 5b and Supplementary Fig. 11a). The accumulation of insoluble proteins can also be observed in Hsp90α/β KO HEK293T cells subjected to a long-term mild heat stress. When these cells are returned to normal temperature, they recover their ability to clear these protein aggregates (Fig. 5c). Moreover, increased levels of total Hsp90 by transient overexpression of either one of the Hsp90 isoforms in these Hsp90 KO cells significantly reduced proteotoxicity as evidenced by reduced heat stress-induced accumulation of total and polyubiquitinated insoluble proteins (Supplementary Fig. 11b). This finding parallels the improved cell viability of Hsp90α/β KO HEK293T cells under similar experimental condition (see Supplementary Fig. 10g). Hsp90 has been proposed to be a capacitor of morphological evolution by buffering preexisting genetic polymorphisms. The ability of Hsp90 to restrain the expression of transposable elements, including cellular retroviral genes, is thought to contribute to both short- and long-term buffering against phenotypic changes. Since we did not find any upregulation of two such cellular retroviral genes (MERVL and IAPEz) in the stillborn 90αKO 90βHET pups, it appears that the capacitor function of Hsp90 remained intact in these mutant mouse models (Supplementary Fig. 11c). Therefore, we conclude that below threshold levels of total cytosolic Hsp90 lead to the death of mouse embryos and mammalian cells via proteotoxicity.
Mammalian aging correlates with proteostatic collapse, including compromised molecular chaperone levels and functions. Since cellular senescence is a crucial biological process underlying aging, we investigated whether below threshold levels of Hsp90 trigger accelerated senescence. Knocking down the remaining Hsp90 isoform in Hsp90 α/β KO HEK293T cells (see Fig. 4c for strategy) increased several senescence markers, including p21, p16, and p27 (encoded by the CDKN1A, CDKN2A, and CDKN1B genes, respectively) during the crisis period (Fig. 5d, and Supplementary Fig. 11d). However, during long-term adaptation these senescence markers were reduced to the basal levels in parallel with the re-expression of the initially knocked-down Hsp90 isoform (Fig. 5d and Supplementary Fig. 11e). Hence, below threshold levels of Hsp90 appear to accelerate senescence, and they may therefore accelerate mammalian organismal aging, too. Using mild heat stress as a stimulus to accelerate senescence, we found that Hsp90α/β KO HEK293T cells are more prone to becoming senescent than WT cells (Fig. 5e). These results further support our earlier findings of reduced growth and enhanced death of Hsp90α/β KO HEK293T cells under similar stress conditions (Fig. 4g and Supplementary Fig. 10b). Therefore, maintaining Hsp90 above threshold levels attenuates senescence and may delay accelerated aging. Next, we investigated whether mammalian cells can actively tune Hsp90 levels, thereby delaying aging. We found that senescence induced by a short-term mild heat stress is remarkably low in 90αKO 90βHET MAFs (Fig. 5f and Supplementary Fig. 12a), which overexpress the remaining isoform Hsp90β from the beginning (see Supplementary Fig. 7h, i). When subjected to a long-term mild heat stress, these MAFs grew best and displayed the most normal morphology (Supplementary Fig. 12b, c). Prior pharmacological inhibition of Hsp90 with GA suppresses the superior Hsp90-dependent cellular fitness of 90αKO 90βHET MAFs under mild heat stress, which leads to increased cell death when combined with continued exposure to GA (Fig. 5g and Supplementary Fig. 12d). We conclude from this series of experiments that mammalian organisms and cells that cannot fine-tune Hsp90 to the physiologically required levels might experience accelerated aging and premature death, such as those 90αKO 90βHET embryos, which fail to increase Hsp90β levels. Several molecular chaperones, including Hsp90, are known to be reduced during aging in mammals, including in humans. We found that the mTOR inhibitor and anti-aging drug rapamycin reduces the heat stress-induced senescence marker p21 in Hsp90α/β KO HEK293T cells (Fig. 5h), consistent with the idea that it may be therapeutically beneficial to partially inhibit the growth stimulatory mTOR signal to overcome premature aging.
Survival is the fundamental criterion of living organisms. To do so, organisms must be able to adapt to intrinsic and environmentally imposed stress by regulating several biological processes, collectively referred to as the proteostasis system. Molecular chaperones, including the Hsp90 chaperone system, are both the sensors and effectors of stress responses. Organisms that fail to respond to stress are eliminated, and therefore, stress-response mechanisms affect natural selection and evolution. Although the reduced levels of molecular chaperones, such as Hsp90, likely have an impact on the evolution of species, whether organisms can actively act on the basal levels of Hsp90 to ensure survival and reproduction was unclear. Here we report, using different Hsp90 mutant mouse and cellular models, that eukaryotic organisms can actively manipulate Hsp90 levels to support development and survival (Fig. 5i). Organisms that are unable to maintain Hsp90 above cell- and tissue-specific threshold levels may not be naturally selected. Our data support the conclusion that declining proteostasis and accelerated senescence are the primary causes of death in organisms with reduced levels of Hsp90 (Fig. 5i). The fact that the expression of two mammalian retroviral genes (MERVL and IAPEz) was not unleashed in 90αKO 90βHET embryos suggests that genetic variations generated de novo by transposable elements may not account for the embryonic lethality associated with this genotype. However, more in-depth analyses of the dynamics of transposable elements and of other genomic alterations might help to clarify whether they need to be considered or whether the progressive deterioration of proteostasis is sufficient by itself to explain embryonic lethality. Hsp90 has been said to be the most abundant soluble protein of eukaryotic cells, contributing 1–2% of the total cellular proteome. We speculate that there are two different pools in this enormous amount of Hsp90 protein, a functionally “active” and a “latent” pool. The absolute and relative amounts of these pools may be specifically set for each tissue and cell type. We assume that the “active” pools determine the threshold levels for survival. Since these basal levels can be assumed to be “constitutively active,” any chemical or genetic perturbation of these levels would be highly detrimental. Our data clearly demonstrate that these threshold levels are significantly higher in mammalian cells than in lower eukaryotes such as in the budding yeast. This supports our initial hypothesis that the evolution to more complex proteomes imposed a need for considerably higher minimal levels of Hsp90 and possibly other molecular chaperones. The transcriptional upregulation of the mRNA transcripts for molecular chaperones is a well-known phenomenon amongst stress response mechanisms, including for the cytosolic heat shock response and the endoplasmic reticulum-specific unfolded protein response. Diverse extrinsic or intrinsic stress stimuli elicit a eukaryote-specific adaptive response, which has been termed the integrated stress response (ISR). In the ISR, global cap-dependent protein translation is specifically reduced by the hyperphosphorylation of eIF2α. However, a subset of cellular proteins necessary for the ISR escape this inhibition through cap-independent mechanisms, including by IRES-driven translation. IRES, which were first discovered in viral mRNAs, were also found in several cellular genes and are responsible for selective protein expression when cap-dependent translation is inhibited, including during mitosis, apoptosis, cell differentiation, and angiogenesis. We found that the 5´-UTR of the mouse Hsp90ab1 mRNA possesses IRES function and that this function is specifically required for the rectification of Hsp90β protein level in cells and tissues with the 90αKO 90βHET genotype where the global cap-dependent translation is markedly reduced. It is conceivable that genetic, metabolic or pharmacological perturbations of Hsp90 levels and functions may trigger aspects of the ISR. Cellular IRES functions are strongly influenced by IRES-transacting factors (ITAFs). We speculate that some ITAFs may differentially affect the IRES function of the 5′-UTR of the mouse Hsp90ab1 mRNA, notably in the context of the 90αKO 90βHET genotype and under stressed conditions. In addition, post-transcriptional modifications of the 5′-UTR, for example, by N6-methyladenosine (m6A) mRNA methylation, may also impact the cap-independent translation. This mechanism has recently been revealed for heat-stressed conditions using the molecular chaperone Hsp70 and Dnajb4 as models. It is conceivable that specific m6A “writers”, “readers”, or “erasers” may positively influence the IRES-driven expression of Hsp90β in the 90αKO 90βHET genotype. The identification of these additional molecular players, some of which could themselves be Hsp90 clients, will be necessary to explain why only very few 90αKO 90βHET mouse embryos manage to translationally rectify their Hsp90β protein levels sufficiently. As mentioned above, because the 5′-UTR of the human HSP90AB1 mRNA also carries an IRES, it can be expected that the inhibition of global cap-dependent translation may not have a strong negative impact on the expression of human Hsp90β. If this could be confirmed, temporary inhibition of global cap-dependent translation by therapeutic drugs such as rapamycin or other rapalogs might be beneficial to treat aging and other protein-misfolding diseases in humans. Rapamycin acts by increasing autophagy and decreasing cellular senescence and senescence-associated secretory phenotypes. It is noteworthy that all axes of proteostasis, including molecular chaperones, are overwhelmed by the accumulation of “difficult-to-fold” or “difficult-to-degrade” proteins and aggregates in all degenerative physiological states, including during aging. We speculate that rapamycin treatment may elevate the ratio of Hsp90β to substrate, and possibly that of Hsp90α to substrate if its mRNA can be shown to display some IRES activity. This would help overcome the proteostatic decline associated with aging and other protein misfolding disorders. Aging is the gradual degeneration of physiological states responsible for age-related morbidity and mortality. Aging is collectively the consequence of proteostatic collapse, cellular senescence, genomic instability, epigenetic reprogramming, inflammatory responses, and many more. Here we have connected aging to reduced levels of Hsp90, which cause proteotoxicity and accelerated cellular senescence. In apparent contradiction, Hsp90 inhibitors have been proposed as senolytic drugs, which can selectively kill senescent cells, thereby rejuvenating the organism. Although below-threshold levels of Hsp90 drive accelerated cellular senescence, we speculate that this residual and inhibitable Hsp90 still supports the senescence-associated phenotypes. It would be fascinating to find out in the future whether this “alternate” Hsp90 function is directed by post-translational modifications of Hsp90 or differential co-chaperone influence or the formation of an alternative Hsp90 interactome, an Hsp90 “epichaperome” as suggested for cancer and neurodegenerative diseases.
Animal breeding and all animal experiments were carried out according to Swiss laws and with formal authorizations (animal experimentation licences GE/55/15, GE/179/18, and GE/180/19) from the State (Direction générale de la santé, République et Canton de Genève) and Federal (Office fédéral de la sécurité alimentaire et des affaires vétérinaires) authorities.
Our Hsp90β mutant mice derive from embryonic stem (ES) cells of the C57BL/6 N strain background (subclone JM8.N4) with a targeted mutation of one allele of the mouse Hsp90ab1 gene (mutant allele Hsp90ab1tm1a(EUCOMM)Wtsi; for details, see “Mouse Genome Informatics” entry http://www.informatics.jax.org/allele/MGI:4433179, and Supplementary Fig. 1a). We obtained several of these mutated ES cell clones of project 31882 of the European Conditional Mouse Mutagenesis Program (EUCOMM) located at the Helmholtz-Zentrum München. Chimeras were obtained from two ES cell clones by blastocyst injection performed by Polygene (https://www.polygene.ch). Chimeric males were bred to WT C57BL/6N females to obtain germline transmission. Heterozygous mice were crossed with a ROSA26::FLPe deleter strain (a gift from Dmitri Firsov’s group at the University of Lausanne; originally from Cyagen and in the C57BL/6N strain background), using both male and female mice of 3–8 months of age, to remove the FRT-flanked gene trap construct with a βGEO cassette from the targeted Hsp90ab1 allele (Supplementary Fig. 1a). Subsequently, the floxed exons 2–6 of the Hsp90ab1 gene were deleted by crossing to mice with a transgene for Cre recombinase expression under the control of the CMV enhancer/promoter (a gift from the group of Ivan Rodriguez at the University of Geneva), thereby generating heterozygous Hsp90ab1 mutant mice (90βHET) (Supplementary Fig. 1a). Hsp90aa1 knockout (90αKO) mice had previously been established and characterized in our laboratory. In order to get different KO/HET combinations of Hsp90 alleles, we performed different breeding experiments as illustrated in Supplementary Fig. 1c–g. Since 90αKO male mice are sterile, we could only use 90αKO female mice for the relevant breeding experiments. PCR analyses confirmed mouse genotypes with DNA isolated from ear biopsies using the KAPA Mouse Genotyping Kits (Kapa Biosystems). All PCR primers are listed in Supplementary Table 1. Mice were housed in a licensed animal house at 21–23 °C, 45–55% humidity, and with a 12 h/12 h dark/light cycle.
HEK293T human embryonic kidney cells (ATCC, CRL-3216), A549 human lung epithelial carcinoma cells (ATCC, CCL-185) (as well as the corresponding Hsp90α/β KO or mutant cell lines), and RPE1 human retinal epithelial cells (ATCC, CRL-4000) were maintained in Dulbecco’s Modified Eagle Media (DMEM) supplemented with GlutaMAX, 10% fetal bovine serum (FBS), and penicillin/streptomycin (100 u/ml) with 5% CO2 in a 37 °C humidified incubator. To establish the culture of MAFs, ear biopsies of WT and Hsp90 mutant adult mice (both male and female animals of 7–9 months of age) were cut into small pieces using scalpels and incubated overnight in a digestion buffer (Roswell Park Memorial Institute (RPMI) medium, 1 mg/ml collagenase, 30% FBS, 1% L-glutamine, penicillin/streptomycin (100 u/ml)) in a 37 °C humidified incubator. Single-cell suspensions were seeded and cultured in RPMI medium supplemented with 20% FBS, 1% L-glutamine, and penicillin/streptomycin (100 u/ml). After several passages, when cells started growing normally and were not dying anymore, cells were considered to be immortalized MAFs and switched to DMEM supplemented with GlutaMAX, 20% FBS, and penicillin/streptomycin (100 u/ml). Experiments related to MAFs were performed with the cells at 12–24 passages.
Bicistronic reporter plasmid pcDNA3 RLUC POLIRES FLUC (a gift from Nahum Sonenberg) was obtained from Addgene (#45642). To evaluate IRES functions of the 5´-UTR of mouse Hsp90ab1, 105 nucleotides immediately upstream of the translation start codon (transcript ID ENSMUST00000024739.14) was PCR-amplified using cDNA from 90αKO 90βHET mouse brain as a template and cloned between HindIII and BamHI sites of plasmid pcDNA3 RLUC POLIRES FLUC such that the Polio IRES sequence was excised. The 5′-UTR of human HSP90AB1 (214 nucleotides immediately upstream of the start codon of transcript ENST00000371554.2) was PCR-amplified using cDNA from HEK293T cells and similarly cloned into the recipient plasmid. The resultant plasmid is referred to as pcDNA3 RLUC 5′-UTR FLUC. Note that nucleotides 182–184 are TCG instead of AGA and correspond to the sequence present in some polymorphic variants deposited in GenBank. With these reporter plasmids, Renilla luciferase, which serves as an internal control, is expressed by cap-dependent translation and firefly luciferase in a cap-independent manner via IRES from a single mRNA transcript. To evaluate the impacts of the UTRs of mouse Hsp90ab1 on the translation of a reporter gene, the firefly luciferase coding sequence was flanked either by the 5′- or 3′- or both UTRs. Briefly, 105 nucleotides immediately upstream of the translation start codon and 240 nucleotides immediately downstream of the stop codon (transcript ID ENSMUST00000024739.14) were PCR-amplified using cDNA from 90αKO 90βHET mouse brain as a template. 5′- and 3′-UTRs nucleotide sequences were inserted into NcoI and XbaI sites, respectively, of plasmid pGL3-CMV.Luc. All inserted UTR sequences were verified by DNA sequencing.
Human Hsp90α (HSP90AA1) and Hsp90β (HSP90AB1) KO/mutant A549 cells were generated by the CRISPR/Cas9 gene-editing technology as reported earlier for Hsp90α/β KO HEK293T cells. Individual targeted cell foci were picked, expanded, and analyzed by immunoblotting using primary antibodies specific to Hsp90α and Hsp90β. Clones that did not express or expressed significantly reduced levels of Hsp90α or Hsp90β compared to corresponding WT cells were considered KO/mutant cells. Hsp90α/β KO clones from HEK293T cells were also validated by MS analysis. Hsp90 mutant A549 clones were designated as “sg90α/β(clone number)” since the complete absence of full-length Hsp90 protein was only validated by immunoblotting but not by mass spectrometric analysis.
To determine the impact of heat shock on cell proliferation and viability, WT and Hsp90α/β KO/mutant HEK293T and A549 cells were seeded at a density of 3–5 × 106 cells per 20 ml and subjected to mild heat shock at 39 °C and 40 °C for HEK293T and A549 cell clones, respectively, with 5% CO2 in a humidified incubator. A parallel set was maintained at 37 °C as control. Every 7 days, cells were harvested by trypsinization, counted, and reseeded at the same density of initial seeding. This cycle continued for 3–4 weeks from the beginning of the heat shock induction. To evaluate the effect of heat shock on cell death WT and Hsp90α/β KO/mutant HEK293T and A549 cells were seeded at a density of 2.5 × 105 cells per 3 ml and cultured as described above for 4–7 days. To evaluate heat shock-induced senescence, WT and Hsp90α/β KO HEK293T cells were seeded at a density of 2 × 105 cells per 3 ml and cultured at 39 °C for 2 days. In some experiments, rapamycin (0–20 nM) was added during the heat shock (for 2 days) to check its impact on heat shock-induced senescence. A parallel set was maintained at 37 °C as control. MAFs were seeded at a density of 5 × 105 cells per 10 ml and cultured at 40 °C or 37 °C either for 30 h or 7 days. Subsequently, MAFs were harvested for immunoblot or quantitative RT-PCR analyses or cell counting experiments. Heat shock-induced changes were always compared to the time-matched 37 °C control sets.
Assays were performed with Hsp90 WT or KO/mutant human cells and MAFs. For immunoblot analyses of soluble and insoluble fractions, MAFs were seeded at a density of 1.5 × 106 cells per 10 ml and treated with GA (0–500 nM) for 20 h. To determine the impact of Hsp90 inhibition on cell death, human cells or MAFs were seeded at a density of 4 × 105 per 2 ml or 2 × 105 per 3 ml, respectively, and treated with GA (0–750 nM) for 48 h. In another experiment, MAFs were seeded at a density of 5 × 105 per 10 ml and treated with GA (0–200 nM) for 6 h at 37 °C. Subsequently, one set was kept at 40 °C and another set at 37 °C for another 20 h. Cell death and cellular morphology were further evaluated.
To generate lentiviral particles, HEK293T cells were co-transfected with plasmids pLKO.1shRNA (5 μg), PMDG.2 (1.25 μg), and psPAX.2 (3.75 μg) with PEI (1:3 DNA to PEI ratio). A non-targeting shRNA (not known to target any human gene) expressed from plasmid pLKO.1 was similarly used to generate lentiviral control particles. Suspensions of lentiviral particles were collected and added to the medium of WT and Hsp90α/β KO HEK293T cells to knock down the expression of specific genes. Transduced cells were selected by puromycin (3–4 μg/ml) and used for further experiments. Gene knockdowns were validated by immunoblot and quantitative RT-PCR analyses. shRNA sequences are listed in Supplementary Table 1.
For all flow cytometric analyses, a minimum of 10,000 cells were analyzed for each sample. We used a FACS Gallios flow cytometer (Beckman Coulter), and data were analyzed with the FlowJo software package. The FACS gating and analysis strategies are shown in Supplementary Fig. 13. Additional details are given in the following paragraphs. Cell death assays: Following a specific treatment, mouse and human cells were harvested by trypsinization, washed in phosphate-buffered saline (PBS), and resuspended in 100–200 μl PBS containing propidium iodide (PI; 2.5 μg/ml) for 15–20 min at room temperature (RT) before flow cytometric analysis. Cell cycle analyses: Cells were harvested as detailed above. Next, cells were fixed with 70% ice-cold ethanol, washed in PBS, treated with 100 μg/ml RNase A at RT for 5 min, then incubated with 50 μg/ml PI for 15–20 min at RT before flow cytometric analysis. Apoptotic cells were identified by the quantitation of the SubG0 (<2n DNA) cell population.
Cell viability assays were performed with the CellTiter-Glo (CTG) luminescent assay (Promega) according to the manufacturer’s instructions. Briefly, cells were seeded at a density of 5000 cells per 200 µl complete medium in 96-well plates. After seeding cells were allowed to grow for the next 72–96 h. 30 µl of CellTiter-Glo reagent was added per well, and the luminescence was measured with a Cytation 3 microplate reader (BioTek). The luminescence from the control cells was set to 100% viability.
WT and mutant MAFs were seeded at a density of 1.2–1.5 × 106 per 10 ml and were treated with puromycin (1 μM) for 0–2 h. After treatment cells were harvested and lysed in a lysis buffer (20 mM Tris-HCl pH 7.4, 2 mM EDTA, 150 mM NaCl, 1.2% sodium deoxycholate, 1.2% Triton-X100, 200 mM iodoacetamide, protease inhibitor cocktail [PIC]). 75 μg of clarified cell lysates were separated by SDS-PAGE, and immunoblotted for newly synthesized proteins or polypeptides with anti-puromycin antibodies. The translation rate of Hsp90β was analyzed by quantitating the incorporation of puromycin into newly synthesized Hsp90β over time. For this, nascent Hsp90β polypeptide chains were immunoprecipitated and subsequently revealed by immunoblotting with anti-puromycin antibodies. A relatively small proportion of newly synthesized proteins compared to the steady-state levels, and puromycin-induced premature termination of polypeptide elongation explain the apparently weaker bands of puromycin-labeled full-length Hsp90β. Only full-length (or nearly full-length) puromycin-labeled Hsp90β was considered for further analysis. 0 h puromycin treatment served as a negative control of puromycin labeling. The relative rate of Hsp90β translation to global translation was calculated from the densitometric scores. The rate of puromycin incorporation into newly synthesized proteins is directly proportional to the global rate of translation.
MAFs were seeded at a density of 4 × 105 cells per 4 ml and treated with cycloheximide (100 μg/ml) for 0–24 h to evaluate protein turnover rate. Cells were further processed for immunoblot analyses.
For ribosome fractionation, MAFs were lysed in a lysis buffer (10 mM HEPES pH 7.4, 100 mM KCl, 5 mM MgCl2, 100 μg/ml cycloheximide, 1 mM DTT, 2% Triton X-100, PIC). 1.5–2 mg of total protein were loaded on a 60% sucrose cushion (prepared in the same lysis buffer) and fractionated by ultracentrifugation at 70,000 × g for 3 h at 4 °C. Ribosome precipitates were washed with lysis buffer and processed for further experiments. Polysome profiling of murine brain tissues (from both male and female animals of 4–5 months of age) was performed as described before with some modifications. In brief, snap-frozen 100–150 mg brain tissue were pulverized under liquid nitrogen. Tissue powders were resuspended in a lysis buffer (50 mM Tris-HCl pH 7.4, 100 mM KCl, 1.5 mM MgCl2, 1 mM DTT, 1 mg/ml heparin, 1% Triton X-100, 0.5% sodium deoxycholate, 100 μg/ml cycloheximide, PIC, 100 U/ml SuperaseIn RNase inhibitor) for 15 min on ice. RNA amounts were quantified in the clarified tissue extracts, and 180 μg total RNA equivalents were loaded onto 20–60% linear sucrose density gradients (prepared in 40 mM HEPES pH 7.5, 40 mM KCl, 20 mM MgCl2). Fractionation was performed by ultracentrifugation (Optima XPN-100, Beckman; SW-41Ti swinging bucket rotor) at 210,100 × g for 3.5 h at 4 °C. The fractions (12 × 1 ml for each sample) were collected with a Foxy R1 fraction collector (ISCO), coupled with a UA-6 absorbance detector equipped with chart recorder (ISCO), and the profiles were recorded with the TracerDAQ Pro data acquisition software (MCC). The polysome profiles derived from the liver samples served as a standard to correctly trace the fraction positions of the “less prominent” brain polysome profiles. To extract polysome-associated RNA, 500 μl of each polysomal fraction (numbers 7–12) were precipitated with 3 volumes of ice-cold 100% ethanol overnight at −80 °C. After centrifugation the pellets from identical samples were pooled and processed for RNA extraction using the acid guanidinium thiocyanate-phenol-chloroform method.
Tissues were collected from euthanized mice (both male and female animals of 3–5 months of age), cut into multiple pieces, snap-frozen in liquid nitrogen, and stored at −80 °C. To prepare whole tissue extracts, frozen tissues were thawed on ice and resuspended in a lysis buffer (20 mM Tris-HCl pH 7.4, 2 mM EDTA, 150 mM NaCl, 1.2% sodium deoxycholate, 1.2% Triton-X100, PIC), and homogenized with a MEDIC⬢TOOL apparatus (Axonlab). Tissue homogenates were sonicated (high power, 20 cycles of 30 s pulses) and centrifuged at 16,100 × g for 20 min at 4 °C. Clarified tissue extracts were used for immunoblotting analyses.
After completion of the specific treatments, cells were lysed in a lysis buffer with mild detergents (20 mM Tris-HCl pH 7.4, 2 mM EDTA, 150 mM NaCl, 1.2% sodium deoxycholate, 1.2% Triton-X100, 200 mM iodoacetamide, PIC), sonicated (low power, 6–10 cycles of 20 s pulses), and centrifuged at 16,100 × g for 30 min. The supernatant was collected as the soluble fraction. The precipitate (insoluble fraction) was washed 5-6 times with PBS and solubilized in 2% SDS containing lysis buffer by sonicating (high power, 10–15 cycles of 30 s pulses). For the tissues (from both male and female animals of 3–5 months of age; for P1 stillborn pups, the sex was not determined), 60–70 mg of total wet mass was resuspended in the same lysis buffer, homogenized, sonicated (high power, 40 cycles of 30 s pulses), and centrifuged at 16,100 × g for 30 min at 4 °C. The supernatant was collected as the soluble fraction. The precipitate (insoluble fraction) was processed as described above. Both biochemical fractions were analyzed by SDS-PAGE, and in some experiments by subsequent immunoblotting with anti-ubiquitin antibodies. Amounts of the insoluble materials were normalized and adjusted to the corresponding amounts of soluble proteins before SDS-PAGE.
Lysates of cells or mouse tissues (20–100 μg) were subjected to SDS-PAGE and transferred onto a nitrocellulose membrane (GVS Life Science) with a wet blot transfer system (VWR). Membranes were blocked with 2–5% non-fat milk or BSA in TBS-Tween 20 (0.2%) and incubated with primary antibodies with the following dilutions: anti-Hsp90α (1:2000; polyclonal antiserum from Synaptic Systems and monoclonal antibodies from Enzo Lifesciences), anti-Hsp90β (1:2000), anti-Hop (1:1000), anti-GAPDH (1:7500), anti-β-actin (1:5000), anti-Hsp70 (1:2000), anti-Hsc70 (1:2000), anti-c-Raf (1:1000), anti-Ub (1:5000), anti-p23 (1:1000), anti-Cdc37 (1:1000), anti-Akt (1:1000), anti-Hsf1 (1:1000), anti-Hsp40/Hdj1 (1:1000), anti-Hsp110 (1:1000), anti-Aha1 (1:2000), anti-Hsp25/27 (1:2000), anti-Puromycin (1:22,000), anti-p-mTOR (1:1000), anti-mTOR (1:2000), anti-p-S6 (1:1000), anti-p-eIF2α (1:1000), anti-eIF2α (1:1000). Membranes were washed with TBS-Tween 20 (0.2%) and incubated with the corresponding secondary antibodies: anti-mouse IgG-HRP (1:10,000), anti-rabbit IgG-HRP (1:10,000), and anti-rat IgG-HRP (1:10,000). Immunoblots were developed using the WesternBrightTM chemiluminescent substrate (Advansta). Images were recorded by using a LI-COR Odyssey or Amersham ImageQuant 800 image recorder.
MAFs or RPE1 cells were seeded at a density of 3 × 104 or 6 × 104 per 0.5 ml, respectively, in 24-well cell culture plates. Cells were transfected either with plasmid pcDNA3 RLUC 5′-UTR FLUC or plasmid pcDNA3 RLUC POLIRES FLUC using PEI (1:3 DNA to PEI ratio). Plasmid pcDNA3 RLUC POLIRES FLUC was used as a positive control for IRES activity. The medium was changed 12–14 h after transfection to avoid toxicity. 48 h after transfection, cells were lysed with Passive Lysis Buffer (Promega), and firefly and Renilla luciferase activities were measured using the Dual-Luciferase detection kit (Promega) with a bioluminescence plate reader (Citation, BioTek). Firefly luciferase activities were normalized to those of Renilla luciferase, and IRES activities of the 5′-UTR were calculated by dividing the normalized firefly luciferase activities derived from the 5′-UTR reporter by those obtained with the Polio IRES for each given genotype. Similar experiments were performed under short and long-term heat shock. After transfection, MAFs or RPE1 cells were kept at 40 °C for 30 h for short-term heat shock and subsequently processed. For long-term heat stress adaptation, MAFs or RPE1 cells were maintained for 4 or 3 days at 40 °C, respectively, and subsequently transfected and processed as described above. For translation reporter assays, MAFs were co-transfected with plasmid pGL3-CMV.Luc plasmid or its derivatives (where the firefly luciferase coding sequence is flanked by 5′- or 3′- or both UTRs of mouse Hsp90ab1) and a constitutive Renilla luciferase expression plasmid (pRL-CMV). Bioluminescence was detected at 48 h post-transfection, as described above. Firefly luciferase activity from pGL3-CMV.Luc transfection was used as a normalization control to determine the impact of UTRs on firefly luciferase translation. Renilla luciferase activity was used as transfection control. In all these experiments, luciferase activities were considered to be directly proportional to luciferase translation and abundance.
RNA was isolated by the acid guanidinium thiocyanate-phenol-chloroform extraction method. Briefly, mouse tissues (from both male and female animals of 3–5 months of age) were homogenized, or cells were lysed, or ribosomal/polysomal precipitates were dissolved in the TRI reagent (4 M guanidium thiocyanate, 25 mM sodium citrate, 0.5% N-lauroylsarcosine, 0.1 M 2-mercaptoethanol, pH 7). Then consecutively 2 M sodium acetate pH 4, aquaphenol, and chloroform: isoamyl alcohol (49:1) were added to the lysates at a ratio of 0.1:1.0:0.2. RNA was precipitated by adding isopropanol to the aqueous phases. cDNA was prepared from RNA (400 ng) by using random primers (Promega), GoScript buffer (Promega), and reverse transcriptase (Promega) according to the manufacturer’s instructions. cDNAs were mixed with the GoTaq master mix (Promega), and specific primer pairs for relevant genes (Supplementary Table 1) for quantitative PCR with Biorad CFX96 or CFX Connect thermocyclers. mRNA expression of the gene of interest was normalized to GAPDH or ACTB mRNA as internal standards.
Cellular morphology was analyzed using an inverted light microscope (Olympus CK2) using a 5x magnification objective. Phase-contrast images were captured with a Dino-lite camera using the DinoXcope software.
Using a hemocytometer under the light microscope, we monitored cell growth and viability during heat-shock induction experiments by counting viable cells with the trypan blue exclusion assay.
Mouse whole tissue MS sample preparation: snap-frozen tissues (130–420 mg; from both male and female animals of 3–5 months of age) were resuspended in excess of chilled (−20 °C) 80% methanol and homogenized by shaking in the presence of ceramic beads on a FastPrep system for 3 cycles of 20 s pulses. After a short centrifugation, the solvent supernatants were removed, and the beads with homogenized tissues were dried. The pellets were resuspended in a lysis buffer (30 mM Tris-HCl pH 8.6, 1% Sodium deoxycholate, 10 mM DTT) in a ratio of 100 μl per 10 mg of initial tissue weight and were shaken again in the FastPrep system as described above. Then 300 μl solutions were mixed 1:1 (v/v) with lysis buffer, heated at 95 °C for 10 min, and used for all subsequent steps. Samples were digested following a modified version of the iST method. Briefly, 100 μg of proteins (based on tryptophan fluorescence quantitation) were diluted 1:1 (v/v) in water, chloroacetamide (at the final concentration of 32 mM) was added, and samples were incubated at 25 °C for 45 min in the dark to alkylate cysteine residues. EDTA was added to the samples at the final concentration of 3 mM, and samples were digested with 1 μg of Trypsin/LysC (Promega) for 1 h at 37 °C, followed by the identical second digestion. To remove sodium deoxycholate, two sample volumes of isopropanol containing 1% TFA were added to the digests, and the samples were desalted on a strong cation exchange (SCX) plate (Oasis MCX microelution plate; Waters Corp.) by centrifugation. Peptides were eluted in 250 μl of 80% acetonitrile, 19% water, 1% NH3. All eluates after SCX desalting were dried, dissolved in 200 μl of 2% acetonitrile, 0.1% TFA, and 2 μl solutions were analyzed by LC-MS/MS. In the case of brain samples, peptides were separated into three fractions during SCX desalting process. Peptides were eluted with 125 mM and 500 mM ammonium acetate in 20% acetonitrile, followed by the final elution in 80% acetonitrile, 19% water, 1% NH3. All eluates were dried and dissolved as described above, and 4 μl of samples were analyzed by LC-MS/MS. Whole-cell proteome MS sample preparation: Three biological replicates of WT and Hsp90α/β KO HEK293T cells were processed as reported earlier for the WT and Hop KO HEK293T cells. General LC-MS/MS analysis: LC-MS/MS analyses of processed samples were carried out on a Fusion Tribrid Orbitrap mass spectrometer (Thermo Fisher Scientific) interfaced through a nano-electrospray ion source to an Ultimate 3000 RSLCnano HPLC system (Dionex). Peptides were separated on a reversed-phase custom-packed 40 cm C18 column (75 μm ID, 100 Å, Reprosil Pur 1.9 μm particles; Dr. Maisch, Germany) with a 4–76% acetonitrile gradient in 0.1% formic acid (total time 140 min). Full MS survey scans were performed at 120'000 resolution. A data-dependent acquisition method controlled by Xcalibur 4.2 software (Thermo Fisher Scientific) was used that optimized the number of precursors selected (“top speed”) of charge 2+ to 5+ while maintaining scan cycle time between 0.6–1.5 s. HCD fragmentation mode was used at a normalized collision energy of 32%, with a precursor isolation window of 1.6 m/z, and MS/MS spectra were acquired in the ion trap. Peptides selected for MS/MS were excluded from further fragmentation during 60 s.
Data processing and analyses were performed using GraphPad Prism (version 8).
Tandem MS data of mouse tissue samples and human cell lines were processed by the MaxQuant software (version 1.6.14.0 and 1.6.3.4, respectively) incorporating the Andromeda search engine. The UniProt mouse reference proteome (RefProt) database of November 2019 (55,431 sequences) and human RefProt database of October 2017 (71,803 sequences) were used, supplemented with sequences of common contaminants. Trypsin (cleavage at K, R amino acid residues) was used as the enzyme definition, allowing two missed cleavages. Carbamidomethylation of cysteine was specified as a fixed modification. N-terminal acetylation of protein and oxidation of methionine were specified as variable modifications. All identifications were filtered at 1% FDR at both the peptide and protein levels with default MaxQuant parameters. iBAQ values were used for quality control assessments. Protein groups labeled as reverse hits, only identified by site, and potential contaminants were removed. LFQ values were further used for quantitation after log2 transformation. For the whole tissue or cell proteome experiments, from the relevant MS datasets, only protein groups were kept for the subsequent analyses that met the criteria of at least two unique peptides. For the brain dataset, we considered three unique peptides as the cut-off. In all these subsequent analyses, a log2 fold change of >0.4 or <−0.4 for a protein was considered to be a biologically significant difference, and a p-value < 0.1 was considered a statistically significant difference. Averages of biological replicates and their corresponding p-values were plotted as volcano plots using GraphPad Prism (version 8). To compare the Hsp90 interactome profiles of WT and Hsp90 mutant mouse tissues, Hsp90 interactors were filtered out from the whole tissue proteomic datasets of brain (267 interactors), liver (163 interactors), and muscle (84 interactors) using data from our own web server (https://www.picard.ch/Hsp90Int/index.php, initially reported in ref. 91.). A heatmap of the calculated average fold change of the LFQ values of each genotype as compared to WT was constructed using the “expression heatmaps” tool available from: http://www.heatmapper.ca/. Fold change values were ranked with respect to the values obtained with tissues of the 90αKO 90βHET genotype. Amounts of Hsp90α/β in proteomics samples used to compare genotypes were calculated using normalized intensities (iBAQ) as obtained from MaxQuant. Due to extensive sequence identity between the two isoforms, we verified the quantitative relationships between Hsp90α and Hsp90β using only unique (isoform-specific) peptides or “razor” and unique peptides. The presence of shared peptides did not significantly impact the overall ratios between the two isoforms (Supplementary Data 1 and 2). Peptides indicating the presence of truncated or full-length proteins of Hsp90α/β could not be unambiguously detected in KO mouse tissues, and human cells; this is consistent with the absence of such proteins in our immunoblot analyses. We therefore set the adjusted background intensities of such Hsp90α/β peptides in KO samples to 0.
Mouse Hsp90α and Hsp90β mRNAs were quantified by quantitative RT-PCR, and heat shock-induced expression of these mRNAs was determined by calculating the ratio of the values obtained for 40 °C and 37 °C. Therefore, the fold change at 37 °C was set to 1. Gapdh was used as the reference mRNA in these experiments. Mouse Hsp90α and Hsp90β protein levels were quantified by densitometric score analyses by ImageJ-Fiji on specific immunoblot images using β actin as a loading control. Heat shock-induced changes of Hsp90α and Hsp90β protein levels were calculated similarly as done for mRNAs. Since the Hsp90aa1 gene is mutated in mice with the 90αKO and 90αKO 90βHET genotypes, any changes of Hsp90α mRNA and protein in response to heat shock were considered insignificant and set to 0.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Supplementary Information Peer Review File Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Reporting Summary | true | true | true |
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PMC9587093 | 35715546 | Hend Mohamed Anwar,Sherin Ramadan Hamad,Gad Elsayed Mohamed Salem,Rania Hassan Mohamed Soliman,Eman Maher Elbaz | Inflammatory Modulation of miR-155 Inhibits Doxorubicin-Induced Testicular Dysfunction via SIRT1/FOXO1 Pathway: Insight into the Role of Acacetin and Bacillus cereus Protease | 18-06-2022 | Doxorubicin,miR-155,SIRT1,FOXO1,Acacetin,Bacillus cereus protease | Doxorubicin (DOX) is a chemotherapeutic agent that can disrupt testicular function leading to male infertility. This study examined the protective role of natural flavone, acacetin (ACA), and a protease of Bacillus cereus bacteria (B. cereus) as well as the potential role of miR-155/SIRT1/FOXO1 network in DOX-induced testicular injury. Twenty-four male Wistar rats were randomly allocated into four groups and treated as follows: Control, DOX (1 mg/kg, i.p) every other day for 21 days with a total dose equal to 10 mg/kg throughout the experiment, and pre-treated groups that received ACA (5 mg/kg/day, p.o) or B. cereus protease (36 mg/kg/day, p.o) for a week prior to DOX administration. DOX challenge reduced the testis weight coefficient, serum testosterone, and testicular 17β-hydroxysteroid dehydrogenase (17β-HSD). DOX caused a significant increase in testicular oxidative stress, inflammatory, and apoptotic markers. Aberrant testicular miR-34c, a germ-specific miRNA, and miR-155 expressions were observed, along with decreased protein expression of sirtuin1 (SIRT1) dependent forkhead box 1 (FOXO1) acetylation which induces apoptosis. Besides, abnormal histopathological architecture and a marked reduction in the testicular expression of proliferating cell nuclear antigen (PCNA) were observed. ACA or protease administration significantly improved the histopathological and immunohistochemical pictures compared with DOX alone and renovated testicular functions. Interestingly, treatment with protease was more significant than treatment with ACA in ameliorating DOX-induced testicular injury. Taken together, this study reveals the prophylactic role of these two regimens on male fertility by exhibiting antioxidant, anti-inflammatory, and anti-apoptotic effects against DOX-elicited testicular damage, possibly via modulating miR-155/SIRT1/FOXO1 network. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s12010-022-03992-8. | Inflammatory Modulation of miR-155 Inhibits Doxorubicin-Induced Testicular Dysfunction via SIRT1/FOXO1 Pathway: Insight into the Role of Acacetin and Bacillus cereus Protease
Doxorubicin (DOX) is a chemotherapeutic agent that can disrupt testicular function leading to male infertility. This study examined the protective role of natural flavone, acacetin (ACA), and a protease of Bacillus cereus bacteria (B. cereus) as well as the potential role of miR-155/SIRT1/FOXO1 network in DOX-induced testicular injury. Twenty-four male Wistar rats were randomly allocated into four groups and treated as follows: Control, DOX (1 mg/kg, i.p) every other day for 21 days with a total dose equal to 10 mg/kg throughout the experiment, and pre-treated groups that received ACA (5 mg/kg/day, p.o) or B. cereus protease (36 mg/kg/day, p.o) for a week prior to DOX administration. DOX challenge reduced the testis weight coefficient, serum testosterone, and testicular 17β-hydroxysteroid dehydrogenase (17β-HSD). DOX caused a significant increase in testicular oxidative stress, inflammatory, and apoptotic markers. Aberrant testicular miR-34c, a germ-specific miRNA, and miR-155 expressions were observed, along with decreased protein expression of sirtuin1 (SIRT1) dependent forkhead box 1 (FOXO1) acetylation which induces apoptosis. Besides, abnormal histopathological architecture and a marked reduction in the testicular expression of proliferating cell nuclear antigen (PCNA) were observed. ACA or protease administration significantly improved the histopathological and immunohistochemical pictures compared with DOX alone and renovated testicular functions. Interestingly, treatment with protease was more significant than treatment with ACA in ameliorating DOX-induced testicular injury. Taken together, this study reveals the prophylactic role of these two regimens on male fertility by exhibiting antioxidant, anti-inflammatory, and anti-apoptotic effects against DOX-elicited testicular damage, possibly via modulating miR-155/SIRT1/FOXO1 network.
The online version contains supplementary material available at 10.1007/s12010-022-03992-8.
Doxorubicin (DOX) is an anthracycline-derived antibiotic that is frequently used to treat a variety of tumor types [1]. Its effects are not only limited to cancer cells; but DOX can also harm healthy cells, particularly those that exhibit rapid and continuous proliferative activity such as male sperm cells [2]. DOX can impair spermatogenesis, trigger testicular damage, and eventually cause male infertility [3]. Noteworthy, the majority of patients treated with DOX were azoospermic, whereas one-third of them were still azoospermic 5 years afterward [4]. Oxidative stress, lipid peroxidation, and apoptosis are considered the main mechanisms responsible for DOX-induced testicular injury [5, 6]. DOX affects testicular integrity throughout both the prepubertal and postpubertal phases of development [7]. MicroRNAs (miRNAs) are non-coding RNAs of 19–25 nucleotides that play important roles in a variety of biological processes [8]. They have the potential as diagnostic markers and therapeutic targets in a variety of diseases [9]. Some miRNAs are elevated during the oxidative stress and inflammatory responses and contribute to degenerative diseases [10]. A well-studied miRNA, miR-155, is directly regulated by pro-inflammatory reactions [11], and miR-155 overexpression has been linked to nuclear factor-kappa B (NF-κB) transcriptional regulation, which is triggered by the inflammatory cytokine tumor necrosis factor-alpha (TNF-α) [9]. In addition, miR-155 upregulation was reported to inhibit the SIRT1 (sirutin1) pathway [12]. Moreover, miR-34c, a member of miR-34 family: miR23a-miR34b-and miR34c, is abundantly expressed in the testis [13, 14]. It has a role in spermatogenesis, and its downregulation causes infertility in male mice [15]. miR-34c is markedly decreased in both testicular tissues of patients with cryptorchidism and in a murine model of cryptorchidism [16]. SIRT1 is a NAD-dependent deacetylase that can deacetylate multiple transcriptional factors, including forkhead box O (FOXO), NF-κB, and the mitochondrial biogenesis coactivator PGC-1alpha [17–19]. It plays a crucial role in cell proliferation and differentiation [20]. In vitro studies indicate that SIRT1 suppresses the activity of Bcl-2-associated X protein (Bax), FOXO, and Rb (retinoblastoma) [21, 22]. SIRT1 was reported to protect b-cells from oxidative stress via a mechanism that involves the deacetylation of FOXO proteins [23]. FOXO transcription factors regulate several aspects of development, metabolism, and reproduction [24]. There are four members of the FOXO family: including FOXO1, FOXO2, FOXO3, and FOXO4 [25]. The FOXO protein family is broadly responsible for signal transduction, growth and development, apoptosis, and oxidative stress, of which FOXO1 and FOXO3 are the most prevalent [26]. Only three members of this family: FOXO1, FOXO3, and FOXO4 have been identified in humans [27]. FOXO1 plays an important role in the male germline [28], where it is expressed particularly in undifferentiated spermatogonia cells, which act as a stem cell population that drives spermatogenesis [28]. SIRT1 and FOXO interact in a complex manner to protect against oxidative damage [29]. SIRT1 binds to FOXO1, inhibits its acetylation, and reduces its transcriptional activity [30]. Cigarette smoke has been demonstrated to produce oxidative stress injury in lung cells by acting on the SIRT1/FOXO pathway [31]. Upon activation of the SIRT1/FOXO pathway, the degree of FOXO deacetylation not only regulates oxidative stress but also controls cell apoptosis and the cell cycle, in a complex and interactive process [30]. Therefore, studies on the role of this pathway in the injury of toxins warrant further study. Acacetin (ACA): 5,7-dihydroxy-4′-methoxyflavone is a natural flavone that occurs in a variety of plant pigments. It exhibits anti-oxidative, anti-inflammatory, and anti-apoptotic effects [32]. ACA is effective for treating doxorubicin cardiomyopathy by enhancing AMPK/Nrf2 antioxidative signaling molecules [1]. It protects the myocardium from ischemia/reperfusion and inhibits apoptosis of H9c2 cardiomyocytes through the PI3K/Akt pathway [33]. Although ACA has medicinal benefits, its effects on DOX-induced testicular damage are unknown. Proteases are a new class of therapeutics with significant potential. In the human genome, more than 2% of the genes encode for proteases [34]. For example, proteases modulate growth factors, cytokines, chemokines, and cell receptors, to affect gene regulation and downstream intracellular signaling [35]. The US FDA has approved a variety of proteases for therapeutic applications. For example: Tissue-type plasminogen activator (t-PA) and factor IX (FIX) are proteases used in the management of cardiovascular diseases such as stroke, acute myocardial infarction, and bleeding in patients with hemophilia, respectively [35]. Single orally administered proteolytic enzymes of plant and animal origin are widely used as a treatment for a variety of digestive, absorptive, and pancreatic disorders. Porcine and bovine pancreatic enzymes are the preferred form of supplementation for exocrine pancreatic insufficiency [35]. Plant-based enzymes, such as bromelain from pineapple, are also effective as digestive aids for the breakdown of proteins [36]. However, orally administered proteolytic enzyme combinations often supplemented with rutosid are widely used as an alternative or a supplementary treatment for various syndromes, such as acute and post-surgical trauma, phlebitis, rheumatoid arthritis, osteoarthritis, and as adjunctive therapy for cancer [37, 38]. Although proteases are synthesized by different plants, animals, and microorganisms, the latter are the most common in nature. Proteases have potential as anti-inflammatory agents. They have been shown to work in harmony with non-steroidal anti-inflammatory (NSAIDs) drugs, either independently or synergistically. However, the use of NSAIDs has negative side effects such as hepatorenal damage [39, 40], sperm cell toxicity, and testicular dysfunction in rats [41]. Therefore, using bioactives and enzymes with anti-inflammatory action may assist in reducing the use of NSAIDs [42]. Bacillus cereus is a type of gram-positive bacteria, which produces an alkaline protease commonly found in soil and exhibited high proteolytic activity. B. cereus produces an enzyme that possesses fibrinolytic potential in vitro [43]. Therefore, in the current study, we evaluated the protective effects of ACA or B. cereus protease against DOX-induced male infertility in rats and investigated the role of miR-155/SIRT1/FOXO1 signaling pathway.
Adult male Wistar albino rats, weighing 150–170 g, were obtained from the National Organization for Drug Control and Research (NODCAR), Giza, Egypt. Rats were housed in stainless steel cages under controlled environmental conditions: temperature (23 °C ± 2 °C), humidity (60% ± 10%), ventilation 10–20 changes/h, and a 12 h/12 h light/dark cycle at the animal house facility of NODCAR, Giza, Egypt. The rats were fed a standard chow diet and allowed water ad libitum. The investigation complied with the Guide for the Care and Use of Laboratory Animals and was approved by the Ethics Committee for Animal Experimentation at Faculty of Pharmacy, Cairo University (Permit Number: BC 3114).
DOX was obtained from Novartis Pharmaceutical Co. (El Amireya, Cairo, Egypt). ACA was purchased from Sigma-Aldrich (St. Louis, MO, USA) and diluted in dimethyl sulfoxide (DMSO) (Sigma-Aldrich, St. Louis, MO, USA). Protease B. cereus strain was isolated and purified as described below. All chemicals were of the highest purity and analytical grade.
The bacterial culture, B. cereus S6-3 was isolated from soil samples collected in Egypt’s Sharkia governorate. The molecular identification and optimization of fermentation parameters for optimum enzyme production were previously reported [44]. The medium used for the production of enzyme by parent and mutant strains consisted of (g/l): skimmed milk (6), fructose (10), K2HPO4 (0.5), yeast extract (1), MgSO4·7H2O (1), KCl (5), CaCl2·2H2O (0.2), and NaCl (5), at a final pH of 6.0 [44]. The selected mutant strain B. cereus S6-3/UM90, used for protease production, was described previously [45].
After centrifugation, the clear supernatant from the culture media was precipitated with acetone [46]. Briefly, the cell-free supernatant and cooled acetone (− 20 °C) were combined at a 1:2 ratio and centrifuged for 10 min at 10,000 rpm. The precipitate was collected and air-dried at room temperature. The dried pellet was re-suspended in a minimum volume of 10 mM phosphate buffer, pH 7.5. Partial purification of protease produced from mutant B. cereus-S6-3/UM90 is shown in Table 1. The enzyme solution was first precipitated with saturated acetone, which increased the protease activity by 1.72 fold with a 62% recovery, exhibiting a specific activity of 186 U/mg [47, 48].
The proteolytic activity was measured according to Kembhavi et al. [49] method with some minor modifications. Briefly, 5.0 ml of (1.0%, w/v) casein (substrate) was prepared in 10 mM carbonate-bicarbonate buffer-pH-10.5. A total of 1.0 ml of the supernatant solution was added to the substrate and incubated at 40 °C for 10 min. A blank test tube was incubated without the addition of enzyme solution. The enzymatic reaction was stopped by adding 5.0 ml of 0.4 M trichloroacetic acid solution. The reaction mixtures were allowed to stand for 25 min at room temperature. Then, the solutions were centrifuged at 5000 rpm for 10 min to remove the precipitate. The absorbance of the clear supernatant was measured at 660 nm. Tyrosine (0–50 mg/ml) calibration curve was used as a standard calibration curve. One protease activity unit was defined as the amount of enzyme required to liberate 1 mg of tyrosine/ml/min under the standard experimental conditions.
In the current study, 24 rats were randomly allocated into four groups (6 rats each), as follows: The first group received 1% dimethyl sulfoxide (DMSO) (1 ml/kg/day) orally for 28 days plus phosphate-buffered saline (PBS) (1 ml/kg) every other day for 21 days (starting from day 8) and served as a control group. The second group received DOX (1 mg/kg/i.p) in PBS every other day for 21 days, resulting in a total of 10 mg/kg throughout the experimental period [50] and served as a paradigm for reproductive damage. DOX was prepared in PBS as a stock solution at 1 mg/ mL (i.p volume 1 ml/kg). The third group received ACA (5 mg/kg/day p.o.) diluted in 1% DMSO for 1 week [51] then received DOX as in the second group concurrently with ACA (5 mg/kg/day p.o.) for 21 days. ACA was prepared in 1% DMSO as a stock solution at 5 mg/ mL (p.o. volume 1 ml/kg). The fourth group received bacterial protease (36 mg/kg/day, p.o.) in PBS for 1 week [52] then received DOX as in the second group concurrently with bacterial protease (36 mg/kg/day, p.o.) for 21 days. Protease was prepared in PBS as a stock solution at 36 mg/ mL (i.p volume 1 ml/kg). Animal body weight was recorded weekly throughout the study. 24 h after the end of the experiment, 2 ml of blood was withdrawn from the retro-orbital plexus vein under light anesthesia (thiopental sodium 5 mg/kg, i.p) [53]. Sera were separated for the measurement of serum testosterone levels. After that, the animals were euthanized. Both testes were immediately dissected out, washed with ice-cold saline, dried, and weighed. For each rat, the organ coefficient (testes weight/body weight) was calculated. A tissue portion was fixed in 10% formalin for histopathological examination. Another part was homogenized in ice-cold-buffered saline (1:9 w/v) for measuring 17β-hydroxysteroid dehydrogenase (17β-HSD), SIRT1, FOXO1, nitric oxide (NO), and oxidative stress markers. The remaining testicular tissue was kept at − 80 °C for gene expression analysis.
Serum testosterone levels were measured using an enzyme-linked immunosorbent assay kit (ELISA) supplied by Diametra (Perugia, Italy, Ref DK0015). 17β-HSD protein expression levels were determined in tissue homogenate with an ELISA kit obtained from MyBioSource, Inc. (San Diego, USA, Cat.No. MBS2104946) according to the manufacturer’s instructions. The results are expressed as pg/ml for serum testosterone and ng/g tissue for 17β-HSD.
Tissue-reduced glutathione (GSH) and malondialdehyde (MDA) levels were determined as previously described [54, 55]. The results are expressed as μmol/g tissue for GSH and nmol/g tissue for MDA. NO levels, superoxide dismutase (SOD) activity, and total antioxidant capacity (TAC) were measured using NO, SOD, and TAC kits, from Biodiagnostics, Egypt, (Cat. No. NO 25 33, SD 25 21, and TA 25 13, respectively). The results are expressed as μmol/L for NO, U/g tissue for SOD, and mM/L for TAC.
Samples were stored in RNA lysis solution at − 80 °C. The expression of Nrf2, TLR4, NF-κB, Bax, and Bcl2 mRNA was assessed by real-time quantitative reverse transcription PCR (RT-PCR) using standard protocols. The total RNA was converted into complementary DNA (cDNA) using ExcelRTTM Reverse Transcription Kit (SAMOBIO, Small Bio Smart tool, Cat. No. RP1300). Real-time PCR was conducted using a DTlite real-time PCR System (DTlite, DNA technology, LLC, Moscow, Russia) and BioEasy SYBR Green Master Mix (Bioer Technology, Cat. No. BSB25L1) in a final volume of 25 µl. Thermal cycling conditions included 95 °C for 15 s, followed by 40 cycles at 95 °C for 15 s, 60 °C for 15 s, and 72 °C for 45 s. The data were analyzed using ABI Prism software and quantified using PE Biosystems v1_7 Sequence Detection Software (Foster City, CA, USA). Using the comparative threshold cycle method, we calculated the relative expression of the genes. All values were normalized to the expression of an endogenous control gene (GAPDH) as an invariant control. Primer sequences for Nrf2, TLR4, NF-κB, Bax, and Bcl2 are listed in Table 2.
TRIzol® reagent (Invitrogen, Sigma-Aldrich, St. Louis, MO, USA) was used to extract the total RNA from frozen samples. For the evaluation of miR-34c and miR-155, the miRNeasy extraction kit (Qiagen, Cat. No. / ID: 217,084) was used. By standard protocols, the total RNA was converted into complementary DNA (cDNA) using ExcelRTTM Reverse Transcription Kit (SAMOBIO, Small Bio Smart tool, Cat. No. RP1300). The real-time PCR was conducted using a DTlite real-time PCR System (DTlite, DNA technology, LLC, Moscow, Russia) and BioEasy SYBR Green Master Mix (Bioer Technology, Cat. No. BSB25L1) in a final volume of 25 µl. Thermal cycling conditions included 95 °C for 15 s, followed by 40 cycles at 95 °C for 15 s, 60 °C for 15 s, and 72 °C for 45 s. Changes in the expression of each miRNA were normalized to the endogenous control gene RNU6B. Relative expression was calculated by 2 − ΔCt in each group. Primer sequences for miR-34c and miR-155 are listed in Table 2.
SIRT1 and FOXO1 levels were estimated using ELISA kits provided by MyBioSource, Inc. (San Diego, USA) (Cat.No. MBS060720, and MBS749342, respectively) according to the manufacturer’s instructions, and the results are expressed as ng/g tissue.
Testes were fixed in 10% formalin for 24 h. Pieces of testes were dehydrated in increasing concentrations of alcohol and cleaned in. After that, the samples were embedded in paraffin wax. Five-micrometer-thick sections were deparaffinized with xylene and stained with hematoxylin and eosin (H&E). The slides were analyzed by light microscopy, and photomicrographs were captured at a power of × 200.
Four-micrometer-thick sections of testis samples were placed into a pressure cooker containing Tris–EDTA buffer with 0.05% tween 20 (pH 9.0), for 3 min for antigen retrieval, followed by blocking of endogenous peroxidase with 3% hydrogen peroxide in phosphate-buffered saline for 5 min, then washing with distilled water and Tris-buffered saline containing 0.05% tween 20 (TBST, pH 8.4). Thereafter, sections were incubated with polyclonal primary antibodies for TNF-α (1:120) Cat. No. A356015, caspase-3 (1:150) Cat. No. PK-CA577-K16, and PCNA (1:50) Cat. No. OKCD02760 (Cloud-Clone Corp, USA) for 24 h at 4 °C. After washing with TBST, sections were incubated with Dako EnVision™ + System/HRP-labeled polymer containing goat anti-rabbit secondary antibody (Agilent Technologies, Inc. USA) for 30 min at room temperature. Visualization was performed using Dako 3,3′-diaminobenzidine substrate (Agilent Technologies, Inc. USA) for 5 min at room temperature. Sections were counter-stained in hematoxylin for 5 s, dehydrated, and viewed using a light microscope (Olympus BX41, UK). Quantitative measurement of the percentage area of TNF-, as well as caspase-3 and PCNA immunostaining color intensity, was done by analyzing the intensity of the brown stain in the image using ImageJ software. (ImageJ, NIH-Bethesda, MD, USA).
The data are presented as the mean ± standard deviation (SD), with a one‐way analysis of variance (ANOVA) followed by Tukey’s post-hoc test. Moreover, associations between different parameters were assessed using Pearson correlation analysis. GraphPad Prism software (version 8; GraphPad Software, Inc., San Diego, CA, USA) was used for statistical analyses and presenting the data. The level of significance P < 0.05 was fixed for all statistical tests.
As depicted in Fig. 1A, DOX administration significantly reduced the testis organ coefficient by 22% compared with the control (P < 0.0001). On the other hand, treatment with ACA or B. cereus normalized the testis organ coefficient. Therefore, ACA or B. cereus may preserve testicular growth and development. Moreover, DOX-induced a significant decrease in serum testosterone levels (Fig. 1B), and tissue 17β-HSD (Fig. 1C) by 93% and 68%, respectively, compared with the control group (P < 0.0001). However, treatment with ACA or B. cereus induced a significant increase in the aforementioned parameters compared with the rats treated with DOX alone.
DOX significantly increased MDA level, and NO content in testis by threefold and fourfold, respectively, when compared with the control (P = 0.009, and P < 0.0001, respectively), whereas pre-treatment with ACA markedly reduced MDA level and NO content (P = 0.03). Pretreatment with B. cereus protease significantly decreased MDA level (P = 0.0004) and NO content (P < 0.0001) (Fig. 2B). Similarly, Nrf2 gene expression, GSH, SOD, and TAC levels were significantly lowered in the testicular tissues of the DOX-only-treated group by 70%, 50%, 73%, and 64%, respectively, compared with that of the control (Fig. 2A, 2B, 2C, and 2D). However, pre-treatment with ACA or B. cereus protease significantly improved the antioxidant parameters. In addition to normalization of Nrf2 gene expression level in B. cereus-treated group (P = 0.97), B. cereus protease group exhibited a greater ameliorative effect in the abovementioned parameters (P = 0.0014, < 0.0001, 0.001, 0.0008, respectively) compared with ACA treatment (P = 0.023, 0.0052, 0.001, 0.028, respectively).
DOX significantly upregulated the testicular gene expression of TLR4, and NF-κB by threefold and 4.5-fold, respectively, compared with the control group (P = 0.0004 and P < 0.0001, respectively) (Fig. 3A and 3B). Moreover, DOX showed positive immunostaining for TNF-α (P < 0.0001) (Fig. 3C) compared with the control group. Nevertheless, pre-treatment with ACA or B. cereus protease significantly halted the upregulation of TLR4, and TNF-α (P = 0.0002 and P < 0.0001, respectively). However, the protease-treated group showed a more pronounced inhibitory effect on NF-κB expression (P < 0.0001) than ACA-treated group (P = 0.01) against DOX-induced testicular damage.
DOX-treated rats induced a significant downregulation of miR-34c expression level by 80% and a significant upregulation of miR-155 expression by 2.5-fold when compared with the control group. In contrast, pretreatment with either ACA or B. cereus protease significantly reduced the changes observed in miR-34c (P < 0.0001), and miR-155 (P = 0.0053 and P = 0.0003, respectively) expression levels (Fig. 4A).
DOX-treated group depicted a significant reduction in SIRT1 protein expression by 66% (Fig. 4B) and a subsequent significant increase in FOXO1 protein expression by fourfold (Fig. 4C) when compared with the control group (P < 0.0001). These impairments were mitigated in ACA and the bacterial protease–treated groups (P < 0.0001).
As demonstrated in Fig. 5C, a significant increase was observed in the Bax/Bcl2 ratio of the DOX-intoxicated group as compared with the control group (P < 0.0001). However, ACA-treated group showed a significant reduction in the Bax/Bcl2 ratio as compared with the DOX-treated group (P < 0.0001). Whereas the bacterial protease–treated group restored this ratio to the normal level (P = 0.46). DOX group showed significant positive staining for caspase-3 as compared with the control group (Fig. 5E). While ACA and bacterial protease–treated groups showed a significant reduction in caspase-3 staining intensity as compared with the DOX-treated rats (P < 0.0001). Interestingly, immunohistochemical staining of testicular tissues from DOX-treated rats revealed that the most seminiferous tubules with negative PCNA expression, and a few seminiferous tubules with few PCNA immunoreactive spots in the nuclei of the spermatogenic cells with negative spermatocytes. In contrast, adminstration of ACA or B. cereus protease exhibited positive brown nuclei of spermatogonia and PCNA immunoreactive spermatocytes (Fig. 6A). This was evidenced by the ability of ACA to improve the reduction in the calculated area percentage of PCNA (P < 0.0001), whereas B. cereus restored PCNA to the normal (P = 0.75) (Fig. 6B).
Microscopic examination of testis sections stained with H&E from the control group revealed normal testicular architecture with complete normal spermatogenic layers and well-developed sperm (Fig. 7A). In contrast, DOX provoked marked pathological alterations compared with control. Most areas revealed scattered seminiferous tubules with loosely normal architecture, and hyaline degenerative changes accompanied by exfoliated spermatogenic cells in the lumen. In addition, many scattered pyknotic nuclei were observed in the basal cell layers of another seminiferous tubule. Marked widened interstitial areas with hemorrhage; thickened, hyalinized walls of the blood vessels; and a marked reduction in interstitial Leydig cells were apparent (Fig. 7B1). Other areas showed scattered seminiferous tubules with severe degenerative changes, and a prominent reduction in spermatogenic layers and sperm (Fig. 7B2) compared with the control group. However, pretreatment with ACA showed a moderate improvement in the form of a normal appearance of spermatogenic layers and sperm in most of the seminiferous tubules. However, a mild reduction in spermatogenic layers and a complete absence of sperm were occasionally observed in some seminiferous tubules. Mild widened interstitial spaces and moderated reductions in Leydig cells were also apparent compared with the DOX group (Fig. 7C). B. cereus protease pre-treatment group showed marked improvement as evidenced by no histological alterations in seminiferous tubules, complete spermatogenic layers, and a normal appearance of sperm and interstitial Leydig cells compared with the DOX-treated group (Fig. 7D). It is worthy to be mentioned that B. cereus protease showed a stronger protective effect than ACA against DOX-induced testicular injury, as indicated by the protease’s capacity to restore the expression of Nrf2, TLR4, TNF-α, FOXO1, miR-155, caspase-3 levels, the Bax/Bcl2 ratio, and PCNA to the normal.
We found several pronounced correlations between parameters, as illustrated in. Table 3. miR-34c showed a positive correlation with SIRT1, serum testosterone, and 17β-HSD levels, whereas it was negatively correlated with miR-155 and FOXO1. Alternatively, miR-155 exhibited a negative correlation with the aforementioned parameters and a positive correlation with FOXO1. SIRT1, serum testosterone levels, and 17β-HSD levels were positively correlated with one another and negatively correlated with FOXO1.
Testicular damage is one of the most serious side effects of DOX exposure that eventually leads to male infertility [7]. In the current study, we demonstrated a protective effect of ACA and B. cereus protease against DOX-induced male infertility in rats. Our findings also support the involvement of miR-155/SIRT1/FOXO1 signaling pathway in DOX-induced male sterility, indicating that modulation of this network is implicated in the protective effects of ACA and B. cereus protease. In the study, DOX revealed variable pathological changes compared with the control. Histopathological results showed scattered seminiferous tubules with severe degenerative changes and a prominent reduction in spermatogenic layers and sperm count compared with the control group. In parallel, DOX also caused a marked decline in serum testosterone levels. These results are consistent with that of Rizk et al. [56] who reported that DOX evoked a significant decrease in serum testosterone levels which have an impact on spermatogenesis as well as on the structural morphology of seminiferous tubules. Consistent with our findings, they also reported that DOX significantly dampened the activity of 17β-HSD which is the principal enzyme in the synthesis of male sex hormone. Nevertheless, pre-administration with ACA or B. cereus protease rescued the histological features and toxic effects of DOX on androgenic hormone synthesis. This was evident by the improved H&E staining picture along with a marked increase in serum testosterone levels and 17β-HSD activity. Intriguingly, the amelioration with the protease isolated from B. cereus was superior to that with ACA. miR-34c is specifically expressed in germ cells. Bouhallier et al. [14] observed the highest expression of miR-34c in the testis, lower in the lungs, and virtually no expression in other organs. Moreover, they suggested that miR-34c expression is directly associated with germ cell numbers. Others showed that miR-34c is downregulated in the cryptorchidism model in mice [16]. Moreover, miR-34c downregulation in prostate cancer suppresses tumor migration and invasion [57]. Similar to previous studies, our findings revealed a significant downregulation in miR-34c expression in DOX-intoxicated rats compared with the normal which was reversed by pre-administration of ACA or B. cereus protease demonstrated its potential to protect against DOX-triggered male reproductive degeneration. Surprisingly, this protease was more effective than ACA. Nrf2 is a transcriptional factor that plays a fundamental role in antioxidant defense mechanisms in various body tissues. It mitigates the damage of DOX, possibly through stimulation of antioxidant defense systems along with suppression of DOX-induced fibrotic and inflammatory responses [6]. The role of Nrf2 in protection against DOX-induced testicular damage was confirmed by our data which showed that the addition of ACA or the protease boosted Nrf2 content and was associated with a significant increase in antioxidant enzyme activity. Wu et al. [1] demonstrated that Nrf2 is important in mediating the protective effects of ACA against DOX cardiotoxicity, which in turn boosts antioxidant mechanisms, possibly through AMPK activation. In the same context, Cavello et al. [58] reported the antioxidant potential of a protease from bacteria Bacillus cytotoxicus. GSH and SOD are nonenzymatic and enzymatic antioxidants that play a crucial role in reactive oxygen species (ROS) scavenging. Herein, as a result of DOX administration, the levels of these endogenous antioxidants decreased significantly, which may result from increased production of toxic DOX metabolites or reduced production of antioxidant defense systems [59], which results in oxidative stress. ACA and B. cereus protease caused a significant elevation in GSH level and SOD activity, which is consistent with a study by Wu et al. [1] who reported the antioxidant potential of ACA against DOX cardiotoxicity in cultured rat cardiomyoblasts and by others who examined the antioxidant defense of proteases of Bacillus spp. [58, 60], indicating the antioxidant activity of ACA and B. cereus protease protects against DOX-induced testicular damage. According to Uygur et al. [61], DOX-induced DNA damage enhances the formation of ROS, causing a marked deterioration of testicular function. In the present study, we showed that the administration of DOX triggers oxidative stress and testicular lipid peroxidation. These findings are consistent with those of previous studies that demonstrated severe pathologic alterations in testicular tissue are linked to a high degree of lipid peroxidation [62, 63]. Increased MDA levels in the DOX group may be related to the deteriorating changes in the testes, which may be linked to the male germ cell membrane that contains an abundance of polyunsaturated fatty acids (PUFA) thus rendering the testes susceptible to lipid peroxidation [64]. These effects were ameliorated upon treatment with ACA, which concurred with the result of Wu et al. [65] who demonstrated a significant reduction in MDA levels with different concentrations of ACA, and that of Shiravi et al. [66] who reported that ACA inhibited renal MDA levels and elevated TAC in an ischemic reperfusion rat kidney model. In addition, B. cereus protease counteracted the increased production of ROS and lipid peroxidation, which is consistent with the results of Manivasagan et al. [67] who demonstrated the antioxidant effects of protease from Streptomyces spp. NO is a reactive nitrogen species that contributes significantly to nitrosative stress. In the present study, we found that a significant increase in testicular NO levels in the DOX-treated group contributed to increased nitrosative stress. This may result from reduced SOD activity, which increased the availability of superoxide anion radicals, which then reacted with available NO to produce peroxynitrite, a cytotoxic agent and powerful radical [68]. On the other hand, pre-administration of ACA or the protease resulted in reduced NO overproduction and hence reduced nitrosative strain. Oxidative stress is frequently associated with inflammation, as ROS can trigger pro-inflammatory transcription factors [69]. Our results indicated upregulation of the pro-inflammatory transcription factors TLR4, NF-κB, and TNF-α which coincides with previous studies demonstrating that DOX upregulated NF-κB and enhances pro-inflammatory markers in the heart [69] and testis [68]. Additionally, our findings are in line with others [35, 70] who demonstrated the potential of proteases and ACA to reduce and alleviate inflammation, suggesting the anti-inflammatory potential of both regimens. SIRT1, is a deacetylase for many transcription factors, including FOXO1, and regulates several cellular processes, such as proliferation, differentiation, and apoptosis. miR-155 is a direct target of SIRT1 as miR-155 downregulated SIRT1 through SIRT1 3′ UTR binding. The current results indicate that increased TNF-α causes significant elevation of miR-155 expression, resulting in SIRT1 protein suppression in the DOX-treated group. These findings are in harmony with that of Guo et al. [71] who demonstrated that TNF-α significantly upregulated miR-155 expression which subsequently reduced SIRT1 expression. However, administration of ACA or B. cereus protease dampened the expression of miR-155 which may contribute to a reduced TNF-α induced SIRT1 suppression, suggesting an anti-inflammatory effect of ACA and B. cereus protease against DOX-induced testicular insults in rats via suppression of miR-155 and promotion of SIRT1 expression. The FOXO protein family is primarily controlled by post-translational modification, including phosphorylation, and acetylation [72]. FOXO1 is expressed in many cell types and tissues throughout development, including endothelial, smooth muscle, neural crest, and male germ cells [72]. Tothova and Gilliland [73] identified FOXO1 as a requirement for spermatogenesis. Changes in FOXO1 expression result in spermatogenetic failure [28, 74]; however, there have not been sufficient studies showing a functional role for FOXO1 in DOX-induced testicular dysfunction. FOXO1 is a potential target of SIRT1 because SIRT1 directly inhibits the expression of FOXO1via deacetylation [75, 76]. Our findings revealed that DOX-induced SIRT1 downregulation by miR-155 upregulation results in acetylation and activation of FOXO1 triggered apoptosis, suggesting a regulatory role for FOXO1 in DOX-induced testicular apoptosis. However, the data showed downregulation of FOXO1 expression following treatment with ACA or B. cereus, which may result from SIRT1 suppressing FOXO1-induced cell apoptosis through deacetylation. The current data provide experimental evidence that miR-155 promotes testicular apoptosis by modulating SIRT1-dependant FOXO1 acetylation during DOX-induced testicular injury. In addition, we provide a new therapeutic approach using ACA and B. cereus protease to prevent DOX-induced testicular degeneration by modulating miR-155/SIRT1/FOXO1 network. The expression of PCNA in spermatogonia and early phase primary spermatocytes at all stages in the seminiferous tubules occurs in testicular tissues. Because spermatogonia differentiation is a vulnerable step in the spermatogenic process, various chemicals can reduce the number of these cells [77]. In the present study, PCNA-positive cells were strongly detected in the spermatogonia of control rats. However, the number of PCNA-positive cells was considerably lower in the DOX-treated group. This observation is in harmony with other studies that reported DOX treatment is known to induce a reduction in PCNA in testicular germ cells, indicating a reduction in proliferating activity and spermatogenesis [78, 79]. DOX treatment is known to induce cell cycle arrest and death in replicating somatic cells [79]. In contrast, there was an increase in testicular PCNA expression in the ACA or B. cereus group compared with the DOX-treated group. The Bcl-2 family regulates the apoptotic pathway and includes the pro-apoptotic Bax and BH3 subfamily (also known as BH3-only protein) and anti-apoptotic Bcl-2 subfamily [80]. Bim (Bcl-2 interacting mediator of cell death), one of the BH3-only proteins, is a FOXOs downstream target gene that interacts with Bax/Bcl-2, thus activating the Bax-induced mitochondrial pathway [81, 82]. Yao et al. [83] showed that Bim expression was significantly increased in H2O2-treated cells and reduced in cells with SIRT1 overexpression, indicating that SIRT1 inhibits Bim expression by regulating FOXO proteins. Together with these results, we found that an elevation of the testis apoptotic Bax/Bcl2 ratio in DOX-intoxicated rats was ameliorated upon pretreatment with ACA or the protease. This suggests a role for both prophylactic regimens on modulating FOXO-induced apoptosis. In the same context, high caspase-3 expression was detected in DOX-treated testicular cells, which agrees with the study of Tacar and Dass [84]. However, ACA or the protease-pretreated group exhibited lower expression levels of testicular caspase-3, indicating that both pretreatment regimens can alleviate the apoptotic signaling cascade pathway induced by DOX. These results suggest that DOX-impaired rat testicular architecture and spermatogenesis induce cell apoptosis, whereas ACA or B. cereus pre-treatment effectively protects against testicular apoptosis. This indicates a role for these compounds as novel therapeutics for the management of reproductive injury associated with DOX exposure.
In the current study, we demonstrate for the first time that ACA or B. cereus protease are potential therapeutic agents that offer protection against the detrimental effects of DOX on the male reproductive system through modulation of miR-155/SIRT1/FOXO1 signaling. This treatment regimen may improve the quality of life and self-image of men.
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 17 KB) | true | true | true |
PMC9587254 | Eliot Y. Zhu,Jesse D. Riordan,Marion Vanneste,Michael D. Henry,Christopher S. Stipp,Adam J. Dupuy | SRC-RAC1 signaling drives drug resistance to BRAF inhibition in de-differentiated cutaneous melanomas | 21-10-2022 | Predictive markers,Melanoma | Rare gain-of-function mutations in RAC1 drive drug resistance to targeted BRAF inhibition in cutaneous melanoma. Here, we show that wildtype RAC1 is a critical driver of growth and drug resistance, but only in a subset of melanomas with elevated markers of de-differentiation. Similarly, SRC inhibition also selectively sensitized de-differentiated melanomas to BRAF inhibition. One possible mechanism may be the suppression of the de-differentiated state, as SRC and RAC1 maintained markers of de-differentiation in human melanoma cells. The functional differences between melanoma subtypes suggest that the clinical management of cutaneous melanoma can be enhanced by the knowledge of differentiation status. To simplify the task of classification, we developed a binary classification strategy based on a small set of ten genes. Using this gene set, we reliably determined the differentiation status previously defined by hundreds of genes. Overall, our study informs strategies that enhance the precision of BRAFi by discovering unique vulnerabilities of the de-differentiated cutaneous melanoma subtype and creating a practical method to resolve differentiation status. | SRC-RAC1 signaling drives drug resistance to BRAF inhibition in de-differentiated cutaneous melanomas
Rare gain-of-function mutations in RAC1 drive drug resistance to targeted BRAF inhibition in cutaneous melanoma. Here, we show that wildtype RAC1 is a critical driver of growth and drug resistance, but only in a subset of melanomas with elevated markers of de-differentiation. Similarly, SRC inhibition also selectively sensitized de-differentiated melanomas to BRAF inhibition. One possible mechanism may be the suppression of the de-differentiated state, as SRC and RAC1 maintained markers of de-differentiation in human melanoma cells. The functional differences between melanoma subtypes suggest that the clinical management of cutaneous melanoma can be enhanced by the knowledge of differentiation status. To simplify the task of classification, we developed a binary classification strategy based on a small set of ten genes. Using this gene set, we reliably determined the differentiation status previously defined by hundreds of genes. Overall, our study informs strategies that enhance the precision of BRAFi by discovering unique vulnerabilities of the de-differentiated cutaneous melanoma subtype and creating a practical method to resolve differentiation status.
Cutaneous melanoma largely depends on MAPK-signaling, with roughly half of patients harboring the V600E/K activating mutation in BRAF protein. Targeted inhibition of oncogenic BRAFV600 along with MEK, which is directly downstream of BRAF, is a mainstay of treatment for cutaneous melanoma with mutated BRAF. However, clinical response is not uniform, and most patients progress within two years of treatment. A potential mechanism of drug resistance is through RAC1, a member of the Rho family of small signaling GTPases. RAC1 is a signaling hub and contributes to many biological processes. Hyperactive RAC1, for example, RAC1P29S, is a previously-described driver of drug resistance to BRAF inhibition (BRAFi). However, this mutation is rare—present in around 5% of samples in the 448 TCGA skin cutaneous melanoma samples. Nonetheless, heterogeneity in the degree of RAC1 signaling may explain the variation in response to BRAFi. For instance, we have previously demonstrated that amplifying RAC1 signaling through overexpression of its GEF, VAV1, drives drug resistance to BRAFi in cutaneous melanoma. We have also shown that RAC1 expression can be used to predict response to BRAFi. The mechanism by which RAC1 drives resistance to BRAFi is not fully understood. PAK1, RAC1’s canonical downstream target, has been described to drive drug resistance. Alternatively, RAC1 drives the formation of dendritic actin, which leads to decreased dependence on MAPK in cutaneous melanoma. RAC1’s ability to regulate actin is consistent with RAC1’s ability to elicit a mesenchymal switch in cutaneous melanoma through the recruitment of the SRF/MRTF transcription factor, whose activity is regulated by actin dynamics. SRF/MRTF cooperate with other master regulators of the mesenchymal program and have been implicated in drug resistance to BRAFi. While melanoma cells are neither epithelial nor mesenchymal, they can be classified based on the expression programs that typify these states. Mesenchymal melanomas, driven by various mesenchymal-related transcriptional factors, such as Zeb1, TGF-β, AP-1, and Yap/Taz, have been shown to be more resistant to BRAFi. These observations led us to speculate that wildtype RAC1 signaling can drive innate resistance to BRAFi. We found that RAC1 is a driver of growth and innate drug resistance to BRAFi in some melanoma cell lines and that the reliance on RAC1 was associated with the de-differentiated phenotypic state. De-differentiation was also predictive of response to the co-inhibition of SRC and BRAF. We shed light on this connection by showing that RAC1 and SRC critically maintain melanoma de-differentiation. While our study is focused on drug resistance to BRAFi, our findings could also inform strategies that overcome drug resistance to immune checkpoint inhibition (ICI). The association between de-differentiation and ICI-resistance has been emphasized in several studies. Given that SRC and RAC1 maintain de-differentiation, it remains to be seen whether targeting these pathways could also influence sensitivity to ICI. The unique vulnerabilities of the de-differentiated subtype may inform strategies that sensitize these melanomas to BRAFi or ICI. Unfortunately, prior reports identify de-differentiated melanomas using large gene sets derived from transcriptome analysis. This approach is not readily translated to a clinical setting where pathological subclassification of tumors typically relies on a small number of markers. To address this limitation, we construct a binary classification using a small set of genes based on melanocyte differentiation. We evaluate the accuracy and clinical relevancy of our classification strategy using cell line and patient datasets.
To test the importance of RAC1 for drug resistance, we knocked down RAC1 in a panel of nine BRAFV600E cutaneous melanoma cell lines and assessed their growth in the presence or absence of a targeted inhibitor of BRAFV600E, vemurafenib (VEM). Most of the cell lines we studied belonged to the NCI-60 panel. Three cell lines, PDX10, vRPP1, and vRPP3 were generated in-house (see “Methods”). Importantly vRPP1 and vRPP3 are VEM-resistant derivatives of A375. vRPP3 served as a positive control as it harbors a heterozygous N92I mutation in RAC1, confirmed via Sanger sequencing (Supplementary Fig. 1a). N92I is a known gain-of-function mutation for RAC1. We confirmed that RAC1N92I drives drug resistance to VEM as does RAC1P29S (Supplementary Fig. 1b). vRPP1 has wildtype RAC1. Another important consideration is that neither vRPP1 nor vRPP3 harbor NRAS mutations or BRAF genomic alterations, which drive drug resistance through MAPK-reactivation. Lastly, the panel of cell lines we profiled differed in their sensitivity to BRAFi. RPMI7951, A2058, vRPP1, vRPP3 were among the most drug-resistant and proliferated even in >1 μM of VEM (Supplementary Fig. 2). Knockdown of RAC1 was confirmed via western blot. We achieved variable knockdown in the panel of cell lines (Fig. 1a, b). However, the growth of the intrinsically drug-resistant melanomas (A2058, RPMI7951, vRPP1, and vRPP3) was reduced by RAC1-knockdown either in standard conditions (RPMI7951, vRPP1, vRPP3) and/or during BRAFi (A2058, vRPP3). Among the BRAFi-sensitive cell lines, only two out of the five cell lines (A375 and 451Lu) were further sensitized to VEM by RAC1-knockdown (Fig. 1c and Supplementary Figs. 2 and 3). To determine if this result was reproducible, we repeated the knockdown of RAC1 with the same shRNA and another RAC1-targeting shRNA and determined the impact of RAC1-knockdown using a dose-response assay. We performed this experiment on six cell lines used previously (A2058, 451Lu, A375, SKMEL28, UACC257, and PDX10). Again, we achieved variable RAC1-knockdown across the panel of cell lines (Fig. 1d). Nonetheless, the outcome of the dose-response experiments was consistent with our prior observation that RAC1-knockdown sensitized a subset of cutaneous melanomas to BRAFi (Fig. 1c, e). Finally, we expressed an shRNA-resistant form of RAC1 in A375 expressing either RAC1-KD1 or RAC1-KD2 shRNAs. The shRNA-resistant cDNA rescued RAC1 protein levels and increased drug resistance to BRAFi (Fig. 1f). We were curious why some of the melanoma cell lines we studied were not sensitized to BRAFi upon RAC1-knockdown. Hyperactive RAC1-signaling is a well-established driver of drug resistance, but we wondered if this mechanism works in the cell lines where RAC1-knockdown had minimal impact on cell proliferation. To test this, we enforced expression of hyperactive RAC1, RAC1P29S, in the three melanoma cell lines (SKMEL28, UACC257, PDX10) where RAC1-knockdown had little impact. As positive controls, we also expressed RAC1P29S in two BRAFi-sensitive melanomas (A375 and 451Lu) in which RAC1-knockdown did impact sensitivity to BRAFi. We confirmed enforced expression via western blot (Fig. 2a, b). To determine whether the modified cell lines have increased RAC1-signaling, we measured levels of p-MEK_S298 in parental empty vector and modified cell lines. The S298 site on MEK is a well-established target of PAK1, which is a direct target of RAC1. Indeed, this marker of PAK1 activity was elevated in cell lines with enforced expression of RAC1P29S relative to that of empty vector (Fig. 2a, b). We found that RAC1P29S could drive BRAFi resistance in all five melanoma cell lines (Fig. 2c). However, RAC1P29S unexpectedly slowed the growth of three melanomas that were not impacted by RAC1-depletion (Fig. 2c). RAC1P29S also greatly altered the morphology of these three melanoma lines (Supplementary Fig. 4). Overall, these data highlight the importance of RAC1 in driving growth or innate drug resistance to BRAFi in cutaneous melanoma cells. Moreover, our findings suggests that melanomas differ with respect to utilization of RAC1-signaling.
Several studies have identified gene expression signatures correlated with clinical outcomes in cutaneous melanoma patients. Notably, the de-differentiated melanoma subtype typified by a MITFlo/AXLhi or MITFlo/NGFRhi transcriptional state is well-described to be more resistant to BRAFi. To determine whether these subtypes relate to RAC1-dependence, we queried CCLE gene expression data for markers of melanocyte differentiation and de-differentiation in the cell lines we studied (Fig. 3a). AXL, EGFR, and WNT5A are markers and/or drivers that are elevated in inherently drug-resistant cutaneous melanomas. Indeed, the cell lines affected by RAC1-knockdown showed elevated expression of de-differentiated genes and decreased expression of melanocyte differentiation genes. To find other markers of the de-differentiated state, we mined RPPA protein array data and identified proteins that separated de-differentiated from differentiated melanomas. We found that Cav1 and E-cadherin (CDH1) were upregulated in de-differentiated or differentiated melanomas, respectively (Supplementary Fig. 5a, b). E-cadherin is under the direct control of MITF, while CAV1 is a downstream target of the TEAD family of transcription factors. Next, we estimated the melanoma differentiation states of the cell lines we studied by western blot. We found that the cell lines that depended on RAC1 in the presence and/or absence of BRAFi had elevated Cav1 and AXL and no E-cadherin (Fig. 3b). Lastly, we wondered whether intrinsic or enforced RAC1 signaling regulates melanoma differentiation. We found that knockdown of RAC1 decreased AXL and/or Cav1 (Fig. 3c). Conversely, overexpression of RAC1P29S increased these genes in both differentiated and de-differentiated melanomas (Fig. 3d). The latter observation has been previously demonstrated in mouse melanocytes. To further interrogate the role of RAC1 in maintaining de-differentiation, we performed RNAseq on A375 and 451Lu with shRNA-depleted RAC1 and respective controls (Supplementary Data). Interestingly, depleting RAC1 resulted in the downregulation of several previously reported markers of de-differentiation (Fig. 3e). Globally, genes differentially expressed upon RAC1-knockdown negatively enriched for the Undifferentiated melanoma gene set defined by Tsoi et al. (Fig. 3f). RAC1-knockdown also influenced other pathways as demonstrated by enrichment of GSEA Hallmark gene sets shown in Fig. 3g. Overall, these findings suggest that even without gain-of-function mutations. RAC1 helps de-differentiated melanomas grow and/or withstand BRAFi and helps maintain the de-differentiated state.
Our results imply that de-differentiated melanomas would be vulnerable to compounds that inhibit RAC1. Unfortunately, RAC1 is a small signaling GTPase and cannot be targeted directly due to the lack of specificity and/or potency of proposed strategies. Instead, we sought to indirectly inhibit RAC1 signaling by blocking upstream or downstream components of RAC1 signaling. We narrowed our focus on drugs that have been previously reported to sensitize melanomas to BRAFi. These included saracatinib, JNK-IN-8, and MK-2206, which are selective inhibitors of SRC kinases, Akt, and JNK, respectively. We also included Fasudil, a ROCK inhibitor, since Rho signaling has also been linked to BRAFi resistance. We treated four de-differentiated and three differentiated melanomas with VEM alone or in combination with the inhibitors mentioned above. We found that only de-differentiated cell lines were sensitized to BRAFi by SRC inhibition (SRCi), while the differentiated melanomas only showed a modest decrease in the AUC compared to that of VEM alone (Fig. 4a, b and Supplementary Fig. 6). To inform the mechanism by which SRCi sensitizes melanoma cells to BRAFi, we performed RNAseq on A375 treated with 1 uM saracatinib (Supplementary Data). The genes differentially expressed upon SRCi negatively enriched for the Tsoi undifferentiated gene set. These genes were also negatively enriched for a set of genes downregulated by RAC1-knockdown in both A375 and 451Lu. We call this collection of genes the RAC1-responsive gene set (Fig. 4c and Supplementary Data). Only genes with absolute log2FC of < −2 were used to generate this gene set. Moreover, around 60% of genes that are significantly differentially expressed with SRCi are shared by RAC1-knockdown (Fig. 4d). These results suggest that SRCi can partially suppress the output of RAC1, reduce de-differentiation, and increase sensitivity to BRAFi in cutaneous melanoma.
Given the selective sensitivity to SRCi, divergence in RAC1 utilization, and association with drug resistance in de-differentiated melanomas, we aimed to create a practical strategy to determine melanoma differentiation status. Melanoma differentiation subtypes proposed by Hoek, Veraillie, or Tsoi use hundreds of genes, which is not feasible for a clinical test. We sought to find a small set of genes, with comparable performance to the larger gene sets, that is suitable for a cheap and practical clinical test. We intentionally excluded MITF from our gene set because MITF is a transcription factor and its transcriptional competency and stability are regulated by multiple mechanisms. Instead, we reasoned that MITF target genes may serve as more specific indicators of melanocyte differentiation. The basis for our small gene set is a study performed by Veraillie et al., which revealed that TEADs and AP-1 transcription factors maintains melanoma de-differentiation and the MITF and SOX10 transcription factors maintains melanoma differentiation. Interestingly, melanomas dependent on TEAD1 tended to also depend on RAC1 according to Depmap CRISPR dependency scores (Supplementary Fig. 7). Existing gene signatures used to define melanoma differentiation rely on the expression of hundreds of genes. Instead, we sought to use genes that are confirmed TEAD and MITF/SOX10 targets to reduce the risk of overfitting (Fig. 5a). This strategy differs from that of melanoma clinical diagnostic scoring systems, such as DecisionDx, which utilize genes that best explain relevant metrics, such as progression-free survival. Using TCGA SKCM RNAseq data, we separated tumors into two classes based on a reduced set of de-differentiation/differentiation genes that cluster tightly among themselves. We took this approach to achieve the cleanest separation of the two subtypes. A t-SNE visualization using this set of ~400 de-differentiation/differentiation genes showed the separation of these two states (Fig. 5b). Next, we sought to reduce the number of genes used in the classifier by selecting TEADs- or MITF- regulated genes that, on their own, could separate the two classes using a random-forest based analysis. We also selected genes with expression values comparable to that of cutaneous melanoma cell lines in the CCLE dataset to prevent selecting genes that are mostly expressed by stromal cells (Fig. 5c). To simplify the classification, we converted the expression of each gene to a binary value where samples within the top tertile of expression for a given gene was set to one, and zero otherwise. We settled on five genes representative of the de-differentiated and differentiated class that showed high specificity (Fig. 5d). A visualization of the binary version of the TCGA SKCM gene expression dataset using just the ten genes we have identified is shown in Fig. 5e. We then fed these genes into the Naïve Bayes machine learning algorithm to build a model that could classify a melanoma based on the binary expression of our ten genes. Intuitively, the algorithm generates a statistical model that computes the probability of being in each class given the expression for a set of genes based on the associations between each gene and class (Fig. 6a). Our training data was the TCGA SKCM gene expression dataset, and the test data was the CCLE cutaneous melanoma gene expression dataset. Our model achieved a balanced accuracy of 87% (Fig. 6b). Finally, to highlight the clinical value a binary differentiation-based classification system, we profiled drug response to BRAFi in de-differentiated vs. differentiated melanomas. We found that de-differentiated melanomas tended to be the most innately drug-resistant (Fig. 6c). Furthermore, in 5/6 patients of a previously published dataset, de-differentiated genes increased in cancers that progressed on BRAFi compared to pre-treatment (Fig. 6d). With respect to ICI, one study derived a cancer cell ICI drug resistance program using large-scale scRNAseq data and computed the enrichment of this signature in CCLE melanoma cell lines. When we compared these scores across the two subtypes, we again found that the de-differentiated subtype tended to be more drug-resistant (Fig. 6e). Previous gene expression profiling of patient tumors that responded to ICI vs. those that progressed found that AXL and E-cadherin correlated or anti-correlated with ICI-resistance, respectively. However, our curated gene set could not separate responsive from progressive disease using the same dataset (Fig. 6f).
The de-differentiated subtype of cutaneous melanoma is a recurrent transcriptional state linked to drug resistance to BRAFi. Several studies have shown that melanomas belonging to the de-differentiated state have increased expression of many markers that either drive or associate with resistance to BRAFi. Notable examples include, AXL, NGFR, EGFR, PDGFRB, WNT5A, ZEB1, SOX9. This subtype was originally described by Hoek et al. as the invasive subtype within their invasive/proliferative classification system. Veraillie et al. elucidated that the AP-1/TEAD served as master regulators of the de-differentiated state. Here, we show that RAC1 tends to be more important in de-differentiated melanomas for growth in standard conditions and/or during BRAFi. This pattern of dependence may owe to wildtype RAC1’s ability to maintain the de-differentiated state. This knowledge is important as targeting the RAC1-pathway may sensitize an intrinsically therapy-resistant subtype of melanoma to BRAFi. We also observed that RAC1 signaling opposes the proliferative effect of MITF because RAC1P29S suppressed the growth of differentiated melanomas. This result is consistent with a past study that used a marine-organism-derived compound, Plitidepsin, to hyperactivate RAC1 signaling in the differentiated cell lines, SKMEL28 and UACC257. Perhaps a negative feedback loop exists between RAC1 and MITF, as the growth of differentiated melanomas critically depends on MITF and that deletion of MITF results in the rampant activation of Rho family GTPases. Our evaluation of different BRAFi-based drug combinations suggests that inhibiting SRC kinases with saracatinib can sensitize, de-differentiated melanomas to BRAFi. Although saracatinib is not FDA approved, other inhibitors of SRC kinases such as dasatinib may have similar clinical impact. Indeed, we found that dasatinib increased the effect of BRAFi (Supplementary Fig. 8). However, dasatinib inhibits many kinases beyond the SRC family. Thus, it is unclear how dasatinib inhibits proliferation. Previous studies have demonstrated the promise of co-inhibiting SRC kinases and BRAF both in vitro and in vivo. SRCi may work through inhibiting the transmission of extracellular matrix (ECM) stiffness, activation of Hippo kinases, or suppression of RAC1 signaling through regulation of RAC1-specific GEFs, RhoGDIs, or CUL3. Here, we show that SRCi suppresses de-differentiation (Fig. 4c, d). Our findings are clinically meaningful because ECM remodeling and YAP transcriptional signatures are elevated in patient tumors that have progressed on VEM. Upregulation of these processes has been described as the most recurrent features of MAPK-redundant drug resistance. In a greater context, TEADs’ ability to promote resistance to MAPK-targeting therapies seems to conserved across cancer types. Strategies to target TEADs are currently limited, but inhibiting SRC kinases appears to be the most promising, as suggested by a recent study that performed pharmacogenomic analysis on Yap-On vs. Yap-Off tumors. Nonetheless, there are inhibitors of TEADs under development. An alternative strategy is to target the epigenetic regulators of melanoma differentiation. A limitation of our experiments is that we only examined the short-term benefit of SRCi in combination with the BRAFi. It is known that differentiated melanomas undergo drug-induced de-differentiation, and TEADs mediate drug resistance in melanomas that have undergone de-differentiation. Thus, it is unclear what impact SRCi will have on phenotypic plasticity during the emergence of drug resistance. Since melanoma differentiation influences drug resistance, knowing how to classify a patient’s cancer would have high clinical value. To simplify this task, we have identified a small set of genes based on the master regulators of cutaneous melanoma transcriptional states, i.e., TEADs and MITF. Certainly, a binary classification system is a simplification of multiple subtypes. Moreover, at the single-cell level, melanoma tumors are composed of a mixture of subtypes, while we are proposing to define subtypes based on the population average. However, our data would suggest that a bulk estimate can still have clinical value as melanomas classified, as de-differentiated are resistant to multiple therapies and have distinct signaling vulnerabilities (Fig. 6c–f). Several studies have underscored the connection between ICI and de-differentiation: Indirectly, exposure to inflammatory cytokines or cytotoxic T-cells induced de-differentiation in melanoma cells. Directly, a NGFR transcriptomic signature is elevated in persister cells that survive ICI treatment and that among four mouse melanomas that mimic human transcriptomic profiles, de-differentiated cancers were resistant to anti-PD1 therapy. Clinical samples also support the connection between de-differentiated cancers and ICI-resistance, as a recent study on 94 patient tumors at baseline and on ICI treatment revealed that de-differentiation was the only transcriptomic signature that was associated with MHC class I downregulation, which they define as a hallmark of resistance to anti-PD1 therapy. Finally, at the fundamental level, hyperactive RAC1 has been shown to increase PD-L1 protein levels. In summary, our work highlights the SRC-RAC1 axis as a vulnerability in de-differentiated melanomas. Additionally, we offer a practical solution to resolve melanoma differentiation status. Despite extensive data on the behavior and molecular features of cutaneous melanoma subtypes, this knowledge is still not utilized in the clinic. Our work seeks to bridge the gap through biomarker discovery and the characterization of the unique vulnerabilities of the de-differentiated subtype.
A375, 451Lu, and SKMEL28 were obtained from ATCC. UACC257, RPMI7951, and A2058 were obtained from NCI cell line repository. PDX10 was obtained from a patient-derived xenograft from a patient with BRAFV600E, NRAS WT, cutaneous melanoma. PDX10 was confirmed to be a human cell line via STR analysis. vRPP1 and vRPP3 are drug-resistant sub-lines of A375. We generated vRPP1 and vRPP3 by isolating colonies that formed while parental A375 was treated with a cytostatic dose of VEM. We have confirmed via sanger sequencing that vRPP1 and vRPP3 are BRAFV600E and NRAS WT. vRPP3 harbors RAC1N92I and vRPP1 is RAC1 WT. A375, 451Lu, A2058, and RPMI7951 were cultured in Gibco DMEM, supplemented with penicillin/streptomycin, and 10% FBS. SKMEL28 was cultured in Gibco DMEM, supplemented with penicillin/streptomycin, 10% FBS, Sodium pyruvate, and non-essential amino acids. PDX10 and UACC257 were cultured in Gibco RPMI, supplemented with penicillin/streptomycin, and 10% FBS. Written informed consent was obtained from the patient to create PDX10 cell line for research use. We complied with all relevant ethical regulations in creating this cell line. PDX10 was obtained through the University of Iowa Holden Comprehensive Cancer Center’s Melanoma: Skin and Ocular Tissue Repository (MAST), an Institutional Review Board-approved biospecimen repository and data registry (IRB protocols 201708847 and 200804792).
Knockdown of RAC1 was performed with lentivirus containing RAC1-shRNA (KD1) or non-targeting shRNA (NT) in a 6-well format. 48h post-transduction, cells were seeded in a 96-well plate. 24h after seeding, cells were treated with either DMSO or indicated dose of VEM and monitored for 72h after treatment. Viability was assessed with RealTime-Glo, a luminescence-based reagent. Different doses of VEM were used for each cell line based on their intrinsic drug sensitivities. Different doses were used to better assess the impact of RAC1-knockdown. Using too high a dose of VEM would mask the impact of RAC1-knockdown on VEM response. The luminescence signal for different cell lines became saturated at different times, leading to different end time points for graphs shown in Supplementary Fig. 2. No antibiotic selection was performed as some cell lines could not survive RAC1-depletion and selection process. Cell lysates were collected 72h post-transduction. Transduction efficiency was confirmed using flow cytometry. The sequence for RAC1-KD1 was GATCCGAAGGAGATTGGTGCTGTAAAATTCAAGAGATTTTACAGCACCAATCTCCTTTTTTTTCTAGACAATT. The sequence for RAC1-KD2 was GATCCGCAAGAAGATTATGACAGATTATTCAAGAGATAATCTGTCATAATCTTCTTGTTTTTTCTAGACAATT.
A375 was first transduced with lentivirus containing KD1 or NT shRNA. 48h later, the cells underwent puromycin selection (1 ug/ml) for 72h. These cells were then transfected with empty vector or shRNA-resistant RAC1 plasmid using the Qiagen Effectene transfection reagent. Afterwards, cells underwent one week of neomycin selection (500 ug/ml), 48h after transfection. The antibiotic media was changed every 48h. The seed sequences for RAC1- KD1 and KD2 shRNAs were AAGGAGATTGGTGCTGTAAAA and CAAGAAGATTATGACAGATTA, respectively. In the shRNA-resistant RAC1 construct, these sequences were changed to AAAGAAATCGGAGCGGTCAAG and CAGGAGGACTACGATAGGTTA.
Enforced expression RAC1P29S was performed using a piggyBac transposon-transposase system. Namely, cells were seeded in a 6-well format. 24h later, empty vector or RAC1P29S plasmid were mixed at 1:5 ratio with the PiggyBac transposase plasmid and delivered into cells with either the Qiagen Effectene or Jetoptimus DNA transfection reagent using the standard workflow. Media was changed 6–24h later. Cells were selected with puromycin for six days with media changes every two days.
Drug dose-response curves were generated using the Resazurin reagent. Viability at each dose was measured in triplicate. Cells were seeded in a 96-well plate. Cells were treated with drug 24h later. Data shown represents fluorescent signal detected at day 3–5 normalized to the vehicle-treated wells. For drug combination experiments, all the drug combinations were tested at the same time as BRAFi alone. The assay was performed by putting 100 ul of media and 20 ul of 6x stock (0.15 mg/ml) of resazurin onto cells, followed by a 2h incubation at 37 degrees in a tissue culture incubator.
Whole-cell lysates were separated on Tris-Glycine 4–20% gradient gels (Thermo Fisher) and transferred to nitrocellulose membranes overnight. Blots were blocked in Odyssey Blocking Buffer PBS (Licor) for 1h and incubated with primary antibodies overnight at 4 degrees, followed by 1h incubation at room temperature with secondary antibodies. Blots were imaged using the Odyssey 9210 (Licor). The control antibody was alpha-tubulin (12G10 UIOWA hybridoma bank) for all the blots except the vRPP3 blot in Fig. 1a, which we used beta-actin as the control (Sigma A1978). The other antibodies used were RAC1 (BD 610651), Cav1 (CST 3267S), AXL (CST C89E7), and E-cadherin (RD MAB1838), MEK (CST 9122), and p-MEK_S298 (CST 9128). All antibodies were used at 1:1000 dilution, except for E-cadherin (RD MAB1838), which was used at 1:250 dilution. Western blots were quantified using ImageStudioLite software. All western blots were done in triplicates, derived from same experiments, and processed in parallel. Unprocessed and uncropped blot scans can be found in the Supplementary Information (Supplementary Fig. 9).
For RAC1-knockdown, parental cell lines were transduced with shRAC1 (KD1) or shNT containing lentivirus on day zero. The media was changed on day two. Puromycin antibiotic selection (1 ug/ml) was then performed for three days. Cells were then grown in standard media for three days. RNA was extracted from the cells on day eight. For the SRCi RNAseq experiment, cells were treated with vehicle or 1uM of saracatinib for three days. The Monarch Total RNA Miniprep kit was used to extract the RNA. Samples were sequenced on the Illumina Novaseq 6000. FastQC was used to determine the quality of the fastq files. Transcript alignment/quantification was performed with Kallisto using default settings. Ensembl annotation v86 was used as the reference transcriptome. Differential expression analysis was performed using Deseq2 with default settings. Enrichment analysis was performed using the fgsea R package. fgsea is based on the original GSEA method. Genes were ranked using the log2 fold change (log2FC). Only genes with an absolute log2FC of >0.5 and adjusted p-value of <0.01 were used. Benjamini-Hochberg was used to compute the adjusted p-values. The Tsoi 2018 undifferentiated gene set included 224 genes belonging to “undifferentiated” and “undifferentiated-neural crest like” listed in Table S3 of that study. The Venn diagram in Fig. 4c was made using the ggvenn R package.
Depmap webtool (https://depmap.org/portal/) was used to generate Supplementary Fig. 7.
We selected TEADs and MITF target genes with confirmed binding of these transcription factors in melanomas, which decreased upon knockdown of these transcription factors. We derived these genes from two studies on the regulatory landscape of cutaneous melanoma. The gene expression values were converted into binary by setting those samples with the top tertile of expression to one and the rest to zero.
We used the e1071 R package to implement the Naive Bayes classifier with Laplace smoothing. The training data consisted of 472 SKCM TCGA samples that were labeled as de-differentiated or differentiated based on k-means clustering of ~400 invasive and proliferative genes previously described. The test data consisted of 49 cutaneous melanoma cell line samples from CCLE. Again, the correct labels were determined by k-means clustering of ~400 invasive and proliferative genes previously described.
Figure 5a was created using BioRender.com.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
REPORTING SUMMARY Supplementary figures Supplementary data | true | true | true |
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PMC9587275 | Maurizio Chioccioli,Subhadeep Roy,Rachel Newell,Linda Pestano,Brent Dickinson,Kevin Rigby,Jose Herazo-Maya,Gisli Jenkins,Steward Ian,Gauri Saini,Simon R. Johnson,Rebecca Braybrooke,Guying Yu,Maor Sauler,Farida Ahangari,Shuizi Ding,Joseph DeIuliis,Nachelle Aurelien,Rusty L. Montgomery,Naftali Kaminski | A lung targeted miR-29 mimic as a therapy for pulmonary fibrosis | 17-10-2022 | Idiopathic pulmonary fibrosis,MicroRNA,miR-29,RNA therapies | Summary Background MicroRNAs are non-coding RNAs that negatively regulate gene networks. Previously, we reported that systemically delivered miR-29 mimic MRG-201 reduced fibrosis in animal models, supporting the consideration of miR-29-based therapies for idiopathic pulmonary fibrosis (IPF). Methods We generated MRG-229, a next-generation miR-29 mimic based on MRG-201 with improved chemical stability due to additional sugar modifications and conjugation with the internalization moiety BiPPB (PDGFbetaR-specific bicyclic peptide). We investigated the anti-fibrotic efficacy of MRG-229 on TGF-β1 treated human lung fibroblasts (NHLFs), human precision cut lung slices (hPCLS), and in vivo bleomycin studies; toxicology was assessed in two animal models, rats, and non-human primates. Finally, we examined miR-29b levels in a cohort of 46 and 213 patients with IPF diagnosis recruited from Yale and Nottingham Universities (Profile Cohort), respectively. Findings The peptide-conjugated MRG-229 mimic decreased expression of pro-fibrotic genes and reduced collagen production in each model. In bleomycin-treated mice, the peptide-conjugated MRG-229 mimic downregulated profibrotic gene programs at doses more than ten-fold lower than the original compound. In rats and non-human primates, the peptide-conjugated MRG-229 mimic was well tolerated at clinically relevant doses with no adverse findings observed. In human peripheral blood from IPF patients decreased miR-29 concentrations were associated with increased mortality in two cohorts potentially identified as a target population for treatment. Interpretation Collectively, our results provide support for the development of the peptide-conjugated MRG-229 mimic as a potential therapy in humans with IPF. Funding This work was supported by NIH NHLBI grants UH3HL123886, R01HL127349, R01HL141852, U01HL145567. | A lung targeted miR-29 mimic as a therapy for pulmonary fibrosis
MicroRNAs are non-coding RNAs that negatively regulate gene networks. Previously, we reported that systemically delivered miR-29 mimic MRG-201 reduced fibrosis in animal models, supporting the consideration of miR-29-based therapies for idiopathic pulmonary fibrosis (IPF).
We generated MRG-229, a next-generation miR-29 mimic based on MRG-201 with improved chemical stability due to additional sugar modifications and conjugation with the internalization moiety BiPPB (PDGFbetaR-specific bicyclic peptide). We investigated the anti-fibrotic efficacy of MRG-229 on TGF-β1 treated human lung fibroblasts (NHLFs), human precision cut lung slices (hPCLS), and in vivo bleomycin studies; toxicology was assessed in two animal models, rats, and non-human primates. Finally, we examined miR-29b levels in a cohort of 46 and 213 patients with IPF diagnosis recruited from Yale and Nottingham Universities (Profile Cohort), respectively.
The peptide-conjugated MRG-229 mimic decreased expression of pro-fibrotic genes and reduced collagen production in each model. In bleomycin-treated mice, the peptide-conjugated MRG-229 mimic downregulated profibrotic gene programs at doses more than ten-fold lower than the original compound. In rats and non-human primates, the peptide-conjugated MRG-229 mimic was well tolerated at clinically relevant doses with no adverse findings observed. In human peripheral blood from IPF patients decreased miR-29 concentrations were associated with increased mortality in two cohorts potentially identified as a target population for treatment.
Collectively, our results provide support for the development of the peptide-conjugated MRG-229 mimic as a potential therapy in humans with IPF.
This work was supported by NIH NHLBI grants UH3HL123886, R01HL127349, R01HL141852, U01HL145567.
Research in contextEvidence before this studyIn the search for modifiers of complex disease phenotypes, oligonucleotide (ON) technologies, with their rich array of modalities and capabilities for gene silencing, gene activation and splice modulation, are of particular interest. Progress towards their clinical translation into approved, effective and safe therapies continues to be made.2, 3, 4 Within ON therapies, microRNAs have been shown to regulate normal lung development, maintenance of different lung cell populations, and participate in the lung's response to injury and repair and their levels are changed in advanced lung disease, including Idiopathic Pulmonary Fibrosis.5, 6, 7, 8, 9, 10 Among microRNAs, the miR-29 family has been extensively studied as a potential anti-fibrotic regulator based on its regulation of direct and downstream targets Collagen I and III, IGF1, and CTGF., Although constitutively highly expressed, its expression levels are decreased in kidney, lung,, liver, and myocardial fibrosis, suggesting that supplementing miR-29 could be a therapeutic strategy for reversing or mitigating organ fibrosis. In earlier work, we demonstrated that a first-generation synthetic ON mimic of miR-29b; Remlarsen/MRG-201, blunted fibrosis in the bleomycin-induced pulmonary fibrosis mouse model. In addition, in a randomized phase 1 clinical trial, intradermally administered Remlarsen/MRG-201 reduced collagen expression and delayed onset of fibroplasia in healthy volunteers, consistent with a broad anti-fibrotic therapeutic mechanism. However, microRNA mimics are unstable compounds and present numerous challenges for clinical translation. Due to their size and charge, oligonucleotide-based compounds cannot passively enter cells, and are vulnerable to nuclease degradation, sequestration in non-target tissues, and endo-lysosomal misrouting and degradation. These vulnerabilities can be mitigated with backbone modifications (replacing one of the non-bridging oxygens of inter-nucleotide phosphate groups with sulphur atoms to create phosphorothioate (PS) linkages) that retard nuclease degradation as well as increases circulation duration as clearance via the kidneys is reduced. In addition, 2’-O-methy or 2’-Fluoro- ribose sugar modifications further limit nuclease degradation and increase plasma stability. Finally, therapeutic ON delivery can be increased and directed through covalent conjugation with bioactive molecules such as lipids, cholesterol or tissue-targeting agents such as peptides.Added value of this studyWe generated MRG-229, a next-gen miR-29 mimic with improved stability and potential for targeted delivery. In cultured human lung fibroblasts and lung slice cultures, we demonstrate that MRG-229 can reduce TGF-β induced fibrosis, as evidenced by downregulation of direct and downstream miR-29 targets COL1A1 and ACTA2, respectively, at a 10-fold lower concentration than MRG-201. Similarly, in the bleomycin-induced mouse fibrosis model, we find that MRG-229 treatment counteracted upregulation of fibrosis-associated gene programs. At therapeutic dosing levels, MRG-229 therapy is associated with a favourable safety profile in mice, rats, and non-human primates (NHPs). Finally, decreased concentrations of circulating miR-29 in the peripheral blood of patients with IPF are associated with substantially reduced survival.Implications of all the available evidenceThese findings suggest that MRG-229 is an attractive preclinical candidate for therapeutic development in fibrosis-associated lung indications and potentially other fibrotic conditions.Alt-text: Unlabelled box
MicroRNAs have been shown to regulate normal lung development, maintain different lung cell populations, and participate in the lung's response to injury and repair; and their levels are changed in advanced lung disease, including Idiopathic Pulmonary Fibrosis,5, 6, 7, 8, 9, 10 a progressive, invariably lethal lung disease with two FDA approved therapies, which only slow down disease progression., Among microRNAs, the miR-29 family has been extensively studied as a potential anti-fibrotic regulator based on its regulation of direct and downstream targets Collagen I and III, IGF1, and CTGF., Although constitutively highly expressed, its expression levels are decreased in kidney, liver, myocardial fibrosis and pulmonary fibrosis,, suggesting that supplementing miR-29 could be a therapeutic strategy for reversing or mitigating organ fibrosis. In earlier work, we demonstrated that a first-generation synthetic oligonucleotide mimic of miR-29b; Remlarsen/MRG-201 (hereafter referred to as MRG-201), blunted fibrosis in the bleomycin-induced pulmonary fibrosis mouse model. In addition, in a randomized phase 1 clinical trial, intradermally administered MRG-201 reduced collagen expression and delayed onset of fibroplasia in healthy volunteers, consistent with a broad anti-fibrotic therapeutic mechanism. These observations supported the consideration of miR-29 based therapies for IPF. Here we describe MRG-229, a miR-29 mimic with improved chemical stability and conjugated to an internalization moiety BiPPB (bicyclic platelet-derived growth factor beta receptor PDGFβR-binding peptide), that demonstrated substantial antifibrotic activity in-vitro, ex-vivo, and in-vivo. In cultured NHLFs and hPCLS, MRG-229 reduces TGF-β1-induced increases in COL1A1 and ACTA2. Similarly, in the bleomycin-induced mouse fibrosis model, intravenous or subcutaneous MRG-229 blunted fibrosis equally regardless if used in preventative or therapeutic experimental designs. Assessment of safety demonstrated that MRG-229 was not associated with adverse effects in mice, rats, or non-human primates (NHPs). Finally, decreased concentrations of circulating miR-29 in the peripheral blood of patients with IPF are associated with substantially reduced survival. Taken together, these findings suggest that MRG-229 is an attractive preclinical candidate for therapeutic development in fibrosis-associated lung indications and potentially other fibrotic conditions.
All animal studies were conducted in accordance with the NIH guidelines for humane treatment of animals and were approved by the Animal Care and Use Committee (IACUC Animal Protocol # 2017-11592) at MiRagen Therapeutics, Inc. and at Yale University.
Normal Human Lung Fibroblasts (NHLFs) (LONZA Cat. # CC-2512), were cultured in Fibroblast Growth Medium (FGM) (LONZA Cat. # CC-3132) supplemented with Fibroblast Growth Factor (FGF, LONZA Cat. # CC-4068) and maintained at 37°C and 5% CO2. LL 29 (ATCC CCL-134™) cells were maintained in Ham's F12K medium (ThermoFisher Cat. # 11765047) with 15% FBS (ThermoFisher Cat. # 10082147) and maintained at 37°C and 5% CO2. Proliferation and viability were assessed using the WST-1 assay (Sigma Cat. # 5015944001).
In vitro and in vivo studies utilized oligonucleotides that were produced via solid phase support synthesis at miRagen and formulated in PBS. All oligonucleotides were sterile filtered during formulation. Vehicle control for animal studies was sterile PBS.
To quantify in vitro regulation of pro-fibrotic genes COL1A1 and ACTA2, NHLFs were treated with 5ng/mL TGF-b (Cat. # 240-B-002/CF, R&D Systems) to induce the fibrotic response. Cells were then treated with MRG-229 through passive administration at concentrations ranging from 0.3 to 10 mM or through transfection at concentrations ranging from 5nM to 50nM at 0.2 mL/well Dharmafect I (Horizon Cat. # T-2001-02) as per the manufacturer's instructions. After 72h, samples were harvested, and mRNA expression levels analysed with RT-qPCR (Life Technologies) using human Col1a1 and Acta2 primers from ABI (Applied Biosystems) for the specified gene and species. For in vivo real-time PCR analysis, 50-100 mg of tissue was homogenized in 1 mL Trizol (ThermoFisher Cat. # 15596026) in Lysing Matrix D tubes (MP Biomedicals Cat. # 116913050-CF) with shaking for 4 × 20 seconds using an Omni BeadRuptor 24 (Omni Intl.) at a speed of 5.65. Total RNA was extracted as per the manufacturer's standard protocol, after which 5 μL of 125 ng/μL RNA from each tissue sample was used to generate cDNA using Applied Biosystems High-Capacity cDNA Reverse Transcription Kit (ThermoFisher Cat. #18090010) per manufacturer's specifications. The expression of a subset of genes was analysed by quantitative real time PCR using Taqman probes (Applied Biosystems).
Samples of untreated and TGF-b treated LL 29 supernatant, and samples that had also received increasing concentrations of MRG-229, were dried until constant weight and hydrolysed in 12 N HCl (Sigma Cat. # 1090601003) overnight at 120°C and then dried by evaporation for 3 h at 120°C. Hydroxyproline was then detected using an in-house LC MS/MS assay developed at MiRagen. Data are expressed as percent difference relative to untreated LL 29 cells.
PIP secretion in NHLFs treated with MRG-201 or MRG-229 was assessed by testing the supernatant of treated cells in a procollagen type I PIP ELISA (Takara Cat. # MK101).
Bleomycin-treated mice were dosed through intravenous injection with saline, MRG-201 100 mpk (mg/Kg) on days 3, 7, 10 and 13. On day 14, animals were sacrificed, and lung tissue analysed for % total lung collagen quantified by Orbit machine learning image analysis software, qPCR analysis of downregulated gene expression levels of a panel of fibrosis-associated genes.
Bleomycin-treated mice were dosed through intravenous injection with saline, MRG-201 at 100 mpk, or MRG-229 at 10 mpk, on days 10, 13, 17 and 20. On day 21, animals were sacrificed, and lung tissue IGF-1 levels in bronchoalveolar lavage fluid and TIMP-1 levels in serum were analysed by ELISA. Mean % total lung collagen quantified by Orbit machine learning image analysis software.
Human lung segments were obtained through the National Disease Research Interchange (NDRI Protocol ID: RKAN1 01 002C)) and hPCLS generated as previously described. Briefly, low melting-grade agarose (3 wt-%) (Sigma Cat. # A9045) was slowly injected via a visible bronchus to artificially inflate the lung segments. Segments were cooled at 4°C for 30 minutes to allow gelling of the agarose and then cut to a thickness of 300µm using a Compresstome (VF-300-0Z by Precisionary) at cutting speed of 6 μm s−1 and oscillation frequency of 5Hz. The hPCLS were cultured in 24 multiwell plates in 500μL DMEM-F12 no-phenol red (Gibco Cat. # 21041025) containing 0.1% FBS (ThermoFisher Cat. # 10082147) and 1% Streptomycin, Amphotericin B, Penicillin 100X (Gibco Cat. # 15240096).
hPCLS, 3 slices for each experimental condition, were exposed to a fibrosis-inducing media (transforming growth factor-β (TGF-β) (Cat. #240-B-002/CF, R&D Systems), 5 μM platelet-derived growth factor-AB (PDGF-AB) (Cat. # PHG0134, GIBCO), 10 ng/ml tumor necrosis factor-α (TNF-α) (Cat. # P06804, R&D Systems), and 5 μM lysophosphatidic acid (LPA) (Cat. # 62215, Cayman Chemical) or a control cocktail (including vehicle control) for 120h with media replaced every 24h as previously described. In this blinded experiment, fibrosis-induced slices were treated with MRG-229 (sample #2), a cholesterol-conjugated miR-29 mimic (sample #4), each at 200µM final concentration, or control (samples #1, #3). After 5 days after treatment, lung slices were processed for analysis; divided for histology and RT-qPCR analysis, respectively.
hPCLS were fixed with 4% paraformaldehyde (ThermoFisher Cat. # FB002) overnight, and paraffin-embedded at 0h and 120h. 3µm sections were cut using a microtome, mounted on glass slides, and subjected to antigen retrieval. After deparaffinization and rehydration, staining was performed according to standard protocols for Masson's Trichrome, and samples mounted using mounting medium (VectorLabs Cat. # H-5700-60) and covered with a cover slip. For collagen quantification, bright field scanning with a Nikon inverted microscope at 20X magnification was used to acquire two representative images for each sample and at least 20 different random field of views as previously described. Collagen staining was quantified and determined by percentage of stained areas. Images were analysed with ImageJ software (ImageJ NIH).
Samples were sent to HistoTox Labs (Boulder CO USA) for paraffin embedding, sectioning, and staining with Hematoxylin and Eosin and Masson's Trichrome. Masson's Trichrome sections were evaluated for collagen deposition and fibrotic mass using Orbit image analysis (Orbit Idorsia Pharmaceuticals Ltd). Full sections were evaluated for the % collagen in the whole tissue, for the % collagen in the lung tissue and fibrotic mass (excluding structural collagen), for the % collagen in the lung tissue (excluding structural collagen and fibrotic mass), and for the % tissue area that was fibrotic mass (excluding structural collagen and normal lung tissue).
A sandwich hybridization assay was used for the quantification of promiR-29 in tissue samples. Probes for the hybridization assay were synthesized using 2’Ome, and LNA modified nucleotides (TriLink Biotech). Detection was accomplished using anti-fluorescence-POD, Fab fragments (Sigma Cat. # 11426346910) and TMB Peroxidase Substrate (KPL) (Seracare Cat. # 5120-0047 50-76-00). Standard curves were generated using non-linear logistic regression analysis with 4 parameters (4-PL). The working concentration range of the assay was 2-2000 ng/mL. Tissue samples were prepared at 100 mg/mL by homogenizing in 3M GITC buffer (3 M guanidine isothiocyanate, 0.5 M NaCl, 0.1 M Tris pH 7.5, 10 mM EDTA) for 2 × 45 seconds using an Omni BeadRuptor 24 (Omni Intl.) at a speed of 5.65. Tissue homogenates were diluted a minimum of 50-fold in 1 M GITC Buffer (1 M guanidine isothiocyanate, 0.5 M NaCl, 0.1 M Tris pH 7.5, 10 mM EDTA) for testing.
hPCLS samples were snap frozen in liquid nitrogen and homogenized using a hand-held homogenizer (Bio-Gen Cat. # 01-01200). QIAGEN miRNA was used for total RNA isolation (QIAGEN Cat. # 217684). The RNA concentration and quality were assessed using NanoDrop spectrophotometer. Relative expression of MiR-29 targets genes COL1A1 and COL3A1 mRNA levels from all ex-vivo experiments were determined by RT-qPCR using TaqMan gene expression assays (Applied Biosystems). Reverse transcription with random primers and subsequent PCR were performed with TaqMan RNA-to-CT 1-Step Kit (ThermoFisher Cat. # 4392653). All experimental groups were assessed as 6 technical replicates and repeated at least three times. Raw data for cycle threshold (Ct) values were calculated using the ViiA7 v.1 software (Applied Biosystems) with automatically set baseline. The results were analysed by the ΔΔCt method and normalized to GAPDH. Fold change was calculated by taking the average over all the control samples as the baseline. All the probes used in this study were purchased from Thermo Fisher Scientific.
Male C57Bl/6 mice, 9-10 weeks of age, were purchased from Taconic Biosciences, Hudson, NY and allowed to acclimate for at least one week prior to experiments. Mice were anesthetized with dexmedetomidine (Sigma Cat. # SML0956), 1 mg/kg IP, intubated, and given one, intra-tracheal dose of bleomycin (McKesson Corporation Cat. # 1129996) at 1.25 mg/kg in 50uL saline or an equivalent volume of saline. A reversal agent was given subcutaneously once the mice had been dosed. Animals were then treated with MRG-201, MRG-229, or an equivalent volume of 0.9% saline by intravenous injection and euthanized on days 8, 14, and 21 after bleomycin administration. Serum and BALF samples were collected as well as tissue samples. Serum was collected and used for measurement of ALT (Sigma Cat. # MAK052), AST (Sigma Cat. # MAK055), BUN (Sigma Cat. # MAS008), and creatinine activity (Sigma Cat. # MAK080). Bronchoalveolar lavage fluid (BALF) was collected, spun at 1200 x g for 15 min after which supernatant and pellet were separated and frozen. BALF supernatant will be decanted/collected and placed in a separate 1.5 mL Eppendorf tube. BAL cell pellets and supernatant will then be frozen and stored. The left lobe of the lung was dissected and used for histology and molecular assessment, whereas the right caudal lobe was flash frozen in liquid nitrogen and used for hydroxyproline/collagen assays and biodistribution analysis. Liver, kidney, spleen, and heart tissue was also collected and flash frozen.
46 and 213 patients with IPF diagnosis were recruited from Yale and Nottingham Universities, respectively. IPF diagnosis was based on guidelines of the American Thoracic Society and European Respiratory Society. Patients were followed until death or loss of follow up. Follow up time was limited to three years. Sample collection, Yale University cohort: Blood was collected in heparin tubes using a routine procedure and was immediately (within 10 minutes after blood collection) centrifuged at 4°C, 1200 × g, for 10 min). Plasma was aliquoted and frozen at −80°C until analysis. All patients provided written informed consent and a protocol incorporating biomarker-studies was approved by the Institutional Review Board (IRB), Yale School of Medicine (HIC#0706002766). Sample collection, Profile cohort. Exosomal miRNA isolation: Briefly, 400µl plasma or serum were used as starting material for exosomal miRNA's extraction using Plasma/Serum Circulating and Exosomal RNA Purification Mini Kit (Norgen Biotek Cat. # Dx42800). To detect circulating and exosomal miR-29b levels, we used multiplexed, color-coded probe pairs (Nanostring nCounter analysis system) using 20 ng of total RNA, following the manufacturer's protocol. All patients provided written informed consent and a protocol was approved by Central England Ethics Approval Number (IRB) REC ref. 10/H0402/2)
miRNA data was normalized using top 100 normalization and log2 transformed miR-29b levels were used for statistical analysis. Receiver Operating characteristics (ROC) curves were used to determine the optimal threshold for mortality prediction using exosomal miR-29b in IPF patients from both Yale and Profile cohorts. Cox proportional hazard's models and Kaplan-Meier curves were used to determine the association between exosomal miR-29b levels, adjusted to GAP index, and IPF mortality. We compared the resulting metabolic activities of the treatment groups and controls using one-way analysis of variance (ANOVA) and Tukey's multiple-comparison post-test. Differences between groups were significant at a P value of <0.05. Statistical analyses were performed with GraphPad Prism 5.0 (GraphPad Software, Inc., San Diego, CA).
Funders had no role in study design, data collection, data analysis, interpretation, or writing of report.
MiRNA mimics are chemically synthesized double-stranded RNA molecules designed to elicit biologic activity by imitating mature miRNA duplexes. To achieve therapeutic efficacy, mimics must be stabilized to ensure a long half-live after administration. To identify next-generation miR-29 mimics with improved in vivo stability, we performed an iterative discovery chemistry screen starting with MRG-201, our first-generation double strand miR-29 mimic. Our aim was to minimize nuclease-mediated degradation while maintaining RNA-induced silencing complex (RISC) loading and activity. In the resulting miR-29 mimic, all unmodified RNA residues were replaced with either a 2’F or 2’O-Me modified ribose sugars. Next, we assessed whether conjugation of BiPPB, known to target cargo to pro-fibrotic cells for internalization, could improve targeted delivery. After having optimized the stabilization modifications, we conjugated BiPPB to our modified miR-29 mimic, which we named MRG-229 (Figure 1). We also conjugated the miR-29-mimic to a cholesterol moiety to compare it to MRG-229 in downstream experiments.
To confirm that the added modifications did not interfere with miRNA mimicking activity, we assessed MRG-229 in NHLFs treated with TGFβ to induce pro-fibrotic genes. In control TGFβ-treated NHLFs, we found robustly increased expression of COL1A1 (a direct miR-29 target) and ACTA2 (a downstream effector of fibrotic signalling). In contrast, in the presence of MRG-229, COL1A1 and ACTA2 gene expression levels were reduced in a dose-dependent manner (Figure 2a, b). These data demonstrate that MRG-229 retains the anti-fibrotic properties of MRG-201 through regulating the expression of miR-29 direct and downstream targets (Figure 2a-b). We next compared the MRG-229-induced anti-fibrotic gene effects to MRG-201 by assessing Procollagen I C-peptide (PIP), a marker of newly synthesized, secreted collagen, without impacting cell viability. To this end, we administered MRG-229 or MRG-201 to TGFβ-treated NHLFs at concentrations ranging from 0.3-10 µM, assessed for cell viability using the WST-1 mitochondrial dehydrogenase assay, and PIP levels in cell culture supernatant by ELISA. Cell viability in MRG-229-treated NHLFs was significantly improved relative to MRG-201-treated NHLFs, indicative of a higher tolerance of MRG-229. Although even low doses of MRG-201 reduced cell viability, with the highest 10 µM dose being toxic (25% viability), only the highest dose of MRG-229 (10 µM) reduced viability (75% viability) with the second highest dose (5 µM) being equivalent to control (Figure 2c). We also observed MRG-229 robustly inhibited PIP, and at a superior potency relative to MRG-201 (Figure 2d). To assess whether similar effects could be achieved in already diseased cells, we treated LL29 fibroblasts (a cell line derived from a young female patient with pulmonary fibrosis) with TGFβ followed by increasing concentrations of MRG-229. After 72 hours, we assessed collagen synthesis and secretion by assessing hydroxyproline levels by liquid chromatography-mass spectrometry. Relative to TGFβ treatment alone, MRG-229 significantly reduced cumulative hydroxyproline levels by 0.9 at 0.1µM, 1.1 at 0.5µM, 1.2 at 1 and 3µM (Figure 2e). Together, these data demonstrate that MRG-229, with a greater potency and tolerability than MRG-201, mitigates TGFβ-induced upregulation of fibrosis-associated genes and blunts collagen synthesis and secretion in normal and IPF cells in vitro. Next, we assessed how MRG-229 regulates collagen production in hPCLS as previously described.,, Briefly, 300µm thick hPCLS derived from donors without a history of lung disease, were treated with control medium or medium containing a profibrotic cocktail (FC) (5 µg TGFβ, 50 µg PDGF-AB, 10 ng TNFα, and 10 mg LPA) for 5 days, after which we assessed expression of COL1A1, COL3A1 (Figure 3a, b), and collagen levels (Figure 3d, e). MRG-229-treated hPCLS had reduced expression of COL1A1 and COL3A1 (Figure 3a, b), and collagen levels (Figure 3d, e). In both experiments, MRG-229 activity was comparable to the activity of the miR-29-mimic with a conjugated cholesterol moiety. A BiPPB-conjugate or cholesterol conjugate to the non-targeting control oligonucleotide had no effect (Figure 3a, b). These data confirm that MRG-229 reduces experimentally induced fibrotic activity in both in vitro and ex vivo human disease models. Last, we assessed miR-29 levels in hPCLS exposed to FC (Figure 3c). PCR confirmed reduction of miR-29 levels after 120h of exposure to FC comparing to non-treated.
To assess in vivo activity of MRG-229, we used two dosing paradigms in the bleomycin-induced pulmonary fibrosis mouse model: first, a prophylactic paradigm, in which we administered compound at day 3 following bleomycin administration and collected tissue at day 14 (Figure 4a). We compared MRG-229 to MRG-201 in the prophylactic setting. Given that MRG-201 requires a 100 mg/kg dosing to achieve efficacy, we assessed whether we could lower MRG-229 to a commercially viable dosage. Accordingly, three days after bleomycin administration, we intravenously injected MRG-201 (100 mg/kg) and MRG-229 (10 mg/kg) twice weekly. At day 14 we found a comparable down-regulation of miR-29 direct targets as well as non-direct targets (i.e., CTGF) in MRG-201 and MRG-229 bleomycin-injured lungs (Figure 4c). In contrast, an unconjugated version of MRG-229 showed no in vivo activity compared to bleomycin/saline controls (Figure 4d). Similarly, bleomycin-treated animals injected with 100 mg/kg MRG-201 or 10 mg/kg MRG-229 showed reductions in total collagen content compared to controls as assessed by trichome staining (Figure 4b). Overall, these dosing comparison experiments suggested that MRG-229 could be dosed at 10 mg/kg in mice to achieve a similar efficacy response as MRG-201 at 100 mg/kg. We next asked if the anti-fibrotic effects observed with MRG-229 in mice extended to a therapeutic dosing paradigm (Figure 5a). Hence, we initiated twice-weekly dosing of 10 mg/kg MRG-229 10 days after bleomycin injury and collected tissue at day 21. In analysis of bronchoalveolar lavage fluid (BALF), we detected a ∼20% reduction in IGF-1 levels, a known miR-29 target, and a ∼40% reduction of TIMP1, a potential IPF biomarker in bleomycin-injured mice treated with MRG-229 relative to saline (Figure 5b). In quantitative histopathological analyses, we found that MRG-229 significantly reduced collagen deposition relative to saline, and preserved regions of normal alveoli architecture (Figure 5c, d). Next, we asked whether subcutaneous administration of MRG-229 would achieve comparable in vivo efficacy to intravenous administration in the bleomycin-induced lung fibrosis model. Using regulation of pro-fibrotic genes as our readout for varying doses of MRG-229 at 2, 5, 10, or 20 mg/kg administered either subcutaneously or intravenously in the prophylactic paradigm, we found that subcutaneous MRG-229 dosing achieves therapeutic efficacy between 2 and 5 mg/kg (Figure 5e). Next, we performed sandwich-based ELISA assay from lung tissue homogenate to compare distribution in the lung upon intravenous or subcutaneous administrations, we found that MRG-229 distribution was comparable and dose-proportionate between subcutaneously and intravenously routes (Figure 5f). These data support the potential for MRG-229 to be administered subcutaneously, while achieving therapeutic efficacy at a significantly lower and more commercially viable dosing regimen than MRG-201.
Next, we assessed MRG-229 safety and toxicity profiles. From the dose-response and route of administration studies in mice (Figures 4 and 5), our initial assessment of liver enzyme function and kidney damage markers showed that MRG-229 administration was not associated with any detrimental effect on either organ (even in the presence of bleomycin) at up to 10 mg/kg biweekly dosing (Figure 6). Furthermore, we performed a 2-week repeat dose-range study of intravenous MRG-229 in Sprague Dawley Rats (Non-GLP) (Table 1). Rats received formulation buffer (10 mM phosphate buffer diluted with isotonic buffered saline) or MRG-229 at 3, 10 or 30 mg/kg on days 1, 4, 7, 11 and 14, after which we collected blood for hematology, coagulation, and serum chemistry analyses from the vena cava of fasted animals at necropsy on Day 15. Using metabolic cages, we also collected and analysed urine samples from fasted animals on Day 15. In addition, we performed gross pathology examinations and organ weight measurements on all animals at the terminal necropsy, and histopathology examination on all tissues from Groups 1 (vehicle treated) and 4 (30 mg/kg MRG-229 treated), including gross lesions, liver, kidneys, spleen, lungs, and heart, from Groups 2 (3 mg/kg MRG-229) and 3 (10 mg/kg MRG-229) animals. Overall, we observed no measurable differences in body weight, food consumption or clinical observations of vehicle- or MRG-229-treated rats. Similarly, we did not observe any differences in hematology, clinical chemistry, coagulation, or urinalysis parameters. In histopathology analyses, we found that MRG-229 treatment was associated with minimal basophilic granularity in the tubular epithelium of the kidney with minimal tubular vacuolation found in one animal. In summary, MRG-229 administered intravenously at 3, 10, and 30 mg/kg twice weekly for two weeks in rats was well tolerated in both males and females, with no observable adverse effects at any dose tested. To assess potential toxicokinetic characteristics of MRG-229 by intravenous administration, we added three additional rats to Group 3 (10 mg/kg MRG-229) (Table 1). Samples for toxicokinetic analysis were collected on study day 1 and 15 before dosing and at 5 min, 30 min, 1 hr, 2 hrs, 4 hrs, 8 hrs and 24 hrs after injection for Group 3. Groups 2 and 4 had toxicokinetic samples collected on study Day 1 and 14 before dosing and at 5 min after injection. Plasma concentrations of MRG-229 decreased rapidly following intravenous administration, with 5 out of 6 24 hr plasma samples testing below the limit of quantification (BLOQ) (Supp Table 1). Selected pharmacokinetic parameters for the TK animals were as expected (Supp Table 2). Last dose Cmax values across the three dose levels were approximately dose proportional, with a trend towards sub proportional increases in Cmax with increases in dose with no apparent gender differences observed (Suppl Table 3). Finally, to assess MRG-229 distribution to organ tissues, we collected samples from heart, liver, lung, kidney, and spleen tissues from animals in terminal Groups 2-4 (Supp Table 1). High concentrations of drug were detected in kidney, moderate in liver, small in spleen, heart, and lung (Supp Table 4).
Toxicological studies of potential therapeutic compounds in non-human primates are critical to assess their potential for translation to the clinic. To this end, we assessed MRG-229 in a dose range finding study in non-human primates (NHPs) (Table 2). We administered MRG-229 by intravenous injection to naïve cynomolgus monkeys (1 animal/sex/group) at concentrations of 0, 5, 15, and 45 mg/kg on days 1, 4, 7, 11, 15, and performed necropsy and sample collection on day 16 (Supp Table 5). We did not observe any notable parameters related to clinical observations, food consumption, or bodyweights (data not showed). We found no evidence of MRG-229 related findings in the hematology, clinical chemistry, coagulation, or urinalysis parameters assessment. In all dose groups, a slight decrease in hematocrit and an increase in reticulocytes on day 2 and day 16 relative from pre-treatment values, likely due to the extensive blood sampling protocol requirements. We did not identify any MRG-229-related histologic findings in tissues from Group 4 (45 mg/kg MRG-229), suggesting that the dose tested was well tolerated. MRG-229 plasma concentrations in treated monkeys decreased rapidly (by 24 h MRG-229 concentration decreased to > 0.5% of initial values, with several samples testing below the quantifiable level (BQL) (Suppl Table 6). Mean and individual pharmacokinetic parameters are reported in Suppl Table 7. Male and female showed similar PK results in all groups. As expected for intravenous bolus, Tmax was at the time of the first sample collected after dosing (5 min) for almost all animals. Cmax concentrations were expectedly high following the intravenous bolus dose, reaching day 1 mean values of 312 µg/mL for the MRG-229 45 mg/kg group. There was no accumulation observed for either Cmax or AUC last values from Day 1 to 15 weeks to cynomolgus monkeys (Supp Table 7), however Cmax and AUClast values across the MRG-229 dose groups were nearly perfectly proportional, with dose normalized values being very similar across dose group for both day 1 and day 15 PK curves (Suppl Table 7), and nearly perfectly linear increases in Cmax and AUClast values across the dose range were observed (Suppl Table 8). Finally, 24 hours after the final dose, MRG-229 was detected in the lung tissue, consistent with our earlier mouse data (Supp Table 3).
miR-29 is a known regulator of fibrosis and a decrease in its expression is associated with lung fibrosis.,, We reasoned that miR-29 levels in humans could be used to develop a precision medicine-based approach to identify individuals at risk of death. To potentially identify such patients, we examined circulating and exosomal miR-29b levels in a cohort of 46 and 213 patients with IPF diagnosis recruited from Yale and Nottingham Universities Profile Cohort), respectively. Table 3 shows the clinical characteristics of IPF patients in both cohorts. For each patient, we performed miRNA extraction from plasma or serum samples isolated from blood, followed by multiplexed, color-coded probe pairs to assess levels of miR-29b specifically. miRNA data was normalized using top 100 normalization and log2 transformed miR-29b levels were used for statistical analysis. Receiving Operating characteristics (ROC) curves were used to determine the optimal threshold for mortality association using exosomal miR-29b in IPF patients from both Yale and Profile cohorts. Cox proportional hazard's models and Kaplan-Meir curves were used to determine the association between exosomal miR-29b levels, adjusted to GAP index, and IPF mortality. We found that ROC identified similar miR-29b exosomal RNA thresholds for mortality association in the Yale (plasma level threshold of 4.84) and Profile (serum level threshold of 4.32) cohorts. After adjusting for the GAP severity index, exosomal miR-29b levels in plasma (≤ 4.86) and serum (≤ 4.32) were significantly associated with mortality in the Yale (HR:0.156, 95%CI: 0.0404-0.6066, Statistical analyses Ordinary one-way ANOVA P=0.0073) and Profile (HR:0.5066, 95%CI: 0.2984-0.8599, Statistical analyses Ordinary one-way ANOVA P=0.011) cohorts, respectively (Figures 7A, B).
In this study, we report the development of MRG-229, a next-generation miR-29 mimic capable of reversing fibrosis-associated molecular transcription and secretion phenotypes in human cellular lung fibrosis models. Relative to our first-generation compound MRG-201, MRG-229 achieved comparable levels of fibrosis reversal in vitro at a ten-fold lower systemic dose level, an improvement which enabled us to explore both efficacy and safety parameters using commercially viable doses in preclinical animal models. In bleomycin-induced mice, we found that MRG-229 effectively achieved downregulation of direct and indirect miR-29 profibrotic target genes concomitant with reduced collagen secretion and preserved lung alveolar architecture. We also found that MRG-229 administration was associated with a favourable safety profile at 10 mg/kg dosing in mice, at 30 mg/kg in rats and at 45 mg/kg in NHP. Intravenous administration is inconvenient to patients and associated with a higher risk for adverse events relative to oral and subcutaneous delivery routes. In bleomycin-induced mice, we assessed both intravenous and subcutaneous MRG-229 administration and found that both delivery approaches reduced pro-fibrotic gene expression programs at therapeutically relevant doses. We further demonstrated that low levels of miR-29 in serum or plasma may be associated with increased mortality in IPF patients and could potentially be used to identify patients that could have a survival benefit from MRG-229 administration. Taken together, these data suggest that administration of MRG-229 is safe and effective at commercially viable dosing levels and may be an attractive candidate for treatment of IPF. A recent study reported the results of a Phase 1 clinical trial in which healthy volunteers (n=47) were treated with intradermal miR29b mimetic MRG-201 or placebo injections after receiving skin incisions. In this trial, MRG-201 had no impact on normal wound healing but significantly decreased fibroplasia relative to placebo. This study serves as a proof of concept for local MRG-201 administration in human skin as an approach to prevent formation of a fibrotic scar. In the context of our paper, this is an important result as it demonstrates that the miR-29b mimetic has a potential antifibrotic effect in vivo in humans. Indeed, in our study we show that MRG-229 can partially reverse fibrosis in human PCLS treated with a profibrotic cocktail. Our PCLS results establish a strong case that miR-29 mimicry will be a potential antifibrotic in humans, and that MRG-229, given its safety profile, stability and superior pharmacodynamic properties, may be a suitable agent to confer this effect. Of note, levels of MRG-229 were detected not only in the lung but also in several other organs. Such systemic delivery is an important limitation of oligo-based therapeutics in general; however previous studies have demonstrated that there is minimal on-target activity or pharmacodynamics in tissues where the miRNA is not dysregulated. Longer toxicology studies will need to be performed prior to more chronic dosing in humans, and while PK studies in rats and NHP provide important guidance on dosing and frequency of administration, dose escalation and PK studies in humans will be required to make final determinations before clinical efficacy trials. Finally, although in this manuscript we focus on systemic administration, inhaled delivery may still be considered as an alternative mode of administration to increase targeting of MRG-229 specifically to the lung. A common challenge with IPF therapeutics is that there is little evidence that the target mechanism is in fact implicated in the patients we treat. In the case miR-29, the therapeutic premise is straightforward: expression of miR-29 is decreased in lung fibrosis,, (as it is in nearly every fibrotic condition), leading to aberrant expression of profibrotic genes controlled by miR-29b, which is restored by delivery of the miR-29b mimetic MRG-229. McDonough et al assessed gene expression changes in the differentially affected regions in the IPF lung, and demonstrated that miR-29 was decreased in the IPF lung even in relatively conserved areas, whereas genes known to be regulated by miR-29 were increased as fibrosis progressed. Unfortunately, it is impossible to assess miR-29 in the lung in most patients as biopsies are limited. In this manuscript, we provide an observation that may be very important in this context. We demonstrate that in two independent cohorts, decreased peripheral blood exosomal miR-29 is associated with worse prognosis in patients with IPF. This finding suggests a connection between reduced miR-29 levels and IPF progression and could potentially be used in the future as a companion diagnostic. A limitation from this analysis is the fact that the cohorts we studied had differences in starting material (plasma – Yale cohort and serum – Profile cohort), sample size and disease severity. These differences may have affected our ability to identify similar miR-29 cut offs for mortality association. Future studies should be performed to validate our findings in multiple, large, and similar cohorts of patients using the same starting material (plasma or serum) to develop a prediction model that can be easily replicated in other cohorts. Nonetheless, the fact that low blood plasma and serum levels of miR29-b are associated with mortality in IPF patients supports the use of miR-229 as a potential therapy. Taken together, the findings reported here represent a solid range of IND-enabling work for further clinical development of MRG-229 in pulmonary fibrosis indications.
NK, RLM, MC, GY, SR, KR, and LP conceived, designed, and analysed experiments and results. BD, GJ, SI, GS, SJ, RB collect and analysed samples for the Profile Cohort. MC, KR, RN, GY, NA, DS, FA, MS, GD, OD and JHM performed and analysed results. MC, RLM and NK wrote the manuscript. All authors read and approved the final version of the manuscript. RLM and NK have verified the underlying data.
All data are available in the main text or the supplementary materials.
All miRagen employees were employed by miRagen Therapeutics, Inc at the time of studies and may have held stock in the company at the time. NK served as a consultant to Boehringer Ingelheim, Third Rock, Pliant, Samumed, NuMedii, Theravance, LifeMax, Three Lake Partners, Optikira, Astra Zeneca, RohBar, Veracyte, Augmanity, CSL Behring, Galapagos, Arrowhead, Spinnova, and Thyron over the last 3 years, reports Equity in Pliant and Thyron, and a grant from Veracyte, Boehringer Ingelheim, BMS and non-financial support from MiRagen and Astra Zeneca. NK has IP on novel biomarkers and therapeutics in IPF licensed to Biotech. GJ has institutional support for PROFILE study through an MRC Industrial Collaboration Agreement (MICA) (GSK). GJ has grants or contracts from Astra Zeneca, Biogen, Galecto, GSK, Nordic Biosciences, RedX, Pliant, with all payments going to his institutions. GJ served as consultant to Bristol Myers Squibb, Chiesi, Daewoong,Veracyte, Resolution Therapeutics, Pliant. GJ had payment or honoraria for lectures, presentations, speaker bureaus, manuscript writing or educational events to Boehringer Ingelheim, Chiesi, Roche, PatientMPower, AstraZeneca. GJ has participation on a data safety monitoring board or advisory board to Boehringer Ingelheim, Galapagos, Vicore. GJ has leadership or fiduciary role in other board, society, committee, or advocacy group, paid or unpaid to NuMedii. GJ is also a trustee to Action for Pulmonary Fibroisis. SR was provided funds for travelling to conferences by miRagen Therapeutics. SR has a patents planned, issued or pending: US Patent Application 20200318113 (miRagen Therapeutics). SR owns Miragen stock at the time this work was performed. | true | true | true |
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PMC9587674 | Peng Xi,Rui Mao,Shiyan Wu,Lei Liu,Ceng Cai,Lei Lu,Cailin Zhang,Yimei Li | Using Network Pharmacology and Animal Experiment to Investigate the Therapeutic Mechanisms of Polydatin against Vincristine-Induced Neuropathic Pain | 14-10-2022 | Background Polydatin (PD) is the primary active compound in Polygonum cuspidatum Sieb and has been demonstrated to exert anti-inflammatory and neuroprotective activities. In the present study, we aimed to explore the therapeutic mechanisms of PD against chemotherapy-induced neuropathic pain. Methods The putative targets of PD were obtained from the CTD and SwissTargetPrediction databases. Neuropathic pain- and VIN-related targets were collected from the CTD and GeneCards databases. Subsequently, the intersection targets were obtained using the Venn tool, and the protein-protein interaction (PPI) was constructed by the STRING database. GO and KEGG enrichment analyses were performed to investigate the biological functions of the intersection targets. Further, a rat model of VIN-induced neuropathic pain was established to confirm the reliability of the network pharmacology findings. Results A total of 46 intersection targets were identified as potential therapeutic targets, mainly related to neuroinflammation. KEGG pathway analysis indicated that the IL-17 signaling pathway was involved in the mechanism of the antinociceptive effect of PD. PPI network analysis indicated that RELA, IL-6, TP53, MAPK3, and MAPK1 were located at crucial nodes in the network. Additionally, PD exerted an antinociceptive effect by increasing the nociceptive threshold. The results of qRT-PCR, western blot, and immunohisochemistry indicated that PD inhibited the IL-6, TP53, and MAPK1 levels in VIN-induced neuropathic pain rats. Conclusions Overall, this research provided evidence that suppressing inflammatory signaling pathways might be a potential mechanism action of PD's antinociceptive effect against VIN-induced neuropathic pain. | Using Network Pharmacology and Animal Experiment to Investigate the Therapeutic Mechanisms of Polydatin against Vincristine-Induced Neuropathic Pain
Polydatin (PD) is the primary active compound in Polygonum cuspidatum Sieb and has been demonstrated to exert anti-inflammatory and neuroprotective activities. In the present study, we aimed to explore the therapeutic mechanisms of PD against chemotherapy-induced neuropathic pain.
The putative targets of PD were obtained from the CTD and SwissTargetPrediction databases. Neuropathic pain- and VIN-related targets were collected from the CTD and GeneCards databases. Subsequently, the intersection targets were obtained using the Venn tool, and the protein-protein interaction (PPI) was constructed by the STRING database. GO and KEGG enrichment analyses were performed to investigate the biological functions of the intersection targets. Further, a rat model of VIN-induced neuropathic pain was established to confirm the reliability of the network pharmacology findings.
A total of 46 intersection targets were identified as potential therapeutic targets, mainly related to neuroinflammation. KEGG pathway analysis indicated that the IL-17 signaling pathway was involved in the mechanism of the antinociceptive effect of PD. PPI network analysis indicated that RELA, IL-6, TP53, MAPK3, and MAPK1 were located at crucial nodes in the network. Additionally, PD exerted an antinociceptive effect by increasing the nociceptive threshold. The results of qRT-PCR, western blot, and immunohisochemistry indicated that PD inhibited the IL-6, TP53, and MAPK1 levels in VIN-induced neuropathic pain rats.
Overall, this research provided evidence that suppressing inflammatory signaling pathways might be a potential mechanism action of PD's antinociceptive effect against VIN-induced neuropathic pain.
Neuropathic pain is a chronic secondary pain, which is characterized by shooting pain or spontaneous persistent and induced magnified pain responses following harmful or non-harmful stimuli [1]. Vincristine (VIN) is a common antineoplastic drug, often used in the treatment of acute lymphoblastic leukemia and Hodgkin's lymphoma. However, a low dose of VIN could cause pain hypersensitivities such as hyperalgesia and allodynia [2]. VIN-induced neuropathic pain is one of the most painful side effects and decreases the life quality of patients [3]. It has been reported that the mechanical actions of neuropathic pain after VIN administration were complex [4]. Recent studies have shown that multiple mechanisms were implicated in the pathologic process of chemotherapy-induced neuropathic pain [5, 6]. Among the pathological factors causing neuropathic pain, neuroinflammation was considered to be one of the major driving factors causing chemotherapy-induced neuropathic pain [5, 7]. In addition, VIN has been revealed to initiate the damage of the blood-nerve barrier and lead to an inflammatory response involving activation of the NF-кB signaling pathway [8]. These proinflammatory cytokines also induced nerve and pain sensitivity and play a key role in maintaining persistent inflammatory pain [9]. Therefore, the efficient control of inflammation is a potential therapy for preventing and treating neuropathic pain. Polydatin (PD), also known as polygonin, is a stilbenoid originally extracted from the root of Polygonum cuspidatum Sieb, a traditional Chinese herbal in China. Previous reports have demonstrated multiple pharmacological effects of PD such as anti-inflammatory, neuroprotective, antioxidative, immune-regulating, cardioprotective, and antiplatelet aggregation activities [10, 11]. PD has been revealed to inhibit dopaminergic neurodegeneration via the inactivation of the NF-кB signaling pathway [12]. PD exhibited its anti-inflammatory effects in BV2 microglia via disrupting lipid rafts [13]. PD also exerted neuroprotective effects in spinal cord injury rats possibly via inhibiting NLRP3 inflammasome activation in the microglia [14]. Moreover, PD was also indicated to promote sciatic nerve repair in diabetic rats via inhibition of RAGE and Keap1 and activation of GLO1 and Nfr2 [15]. PD exerted the anxiolytic effects via suppressing proinflammatory cytokines in a chronic pain mouse model [16]. All these reports suggested that PD may exert therapeutic effects on neuropathic pain. However, there were no studies that investigated the therapeutic effect of PD against chemotherapy-induced neuropathic pain. Based on these studies, we performed network pharmacology analysis to screen the hub genes and potential therapeutic mechanisms of PD against VIN-induced neuropathic pain. Besides, the analgesic effect of PD associated with the suppression of inflammatory genes level was verified by animal experiments.
The 2D chemical structure of PD was downloaded from the PubChem Database (https://pubchem.ncbi.nlm.nih.gov/). The comparative toxicogenomics (CTD) database is a premier public resource that advances understanding about human health and chemical exposures [17]. SwissTargetPrediction is a web server for accurately prediction of the potential targets of bioactive compounds [18]. We used CTD (https://ctdbase.org/) and SwissTargetPrediction databases (http://www.swisstargetprediction.ch/) to obtain the PD-related targets.
The VIN-related and neuropathic pain-related targets were collected from the CTD database and GeneCards database (https://www.genecards.org/). Then, the intersection targets of PD, VIN, and neuropathic pain were visualized by using the Venn online tool (https://bioinfogp.cnb.csic.es/tools/venny/index.html).
The intersection genes were uploaded to the String 11.5 (https://cn.string-db.org/), with species limited to “Homo sapiens” and the highest confidence (0.9). Then, the TSV format file was downloaded and imported into Cytoscape software (3.8.0) to visualize the PPI network. The key genes were screened according to stress, betweenness, closeness, degree, DMNC, EPC, MNC, and radiality, and the intersection genes were identified as the hub genes. Besides, a compound-targets-pathways network was constructed via the Cytoscape software (3.8.0) [19].
Metascape could provide a comprehensive gene list annotation and analysis resource for experimental biologists [20]. The intersection targets were imported into the Metascape (https://metascape.org/gp/index.html) to obtain KEGG data and GO biological process data. Then, the clusterProfiler package of R software was used to visualize the results of the KEGG pathway analysis and GO enrichment analysis.
Male Sprague-Dawley rats (190-230 g) were obtained from the Animal Laboratory Center of Xinjiang province and housed under a specific pathogen-free environment (50-60% humidity, 18-22°C temperature, and a 12 h dark/light cycle) with food and water ad libitum. The animal experimental protocol was approved by the Animal Experimental Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University, and the experimental procedure was performed by the WHO guidelines for animal care. Animals were acclimated for one week before induction of chemotherapy pain. VIN was dissolved in sterile saline (0.9% NaCl). The rats from the model group were injected intraperitoneally with VIN (0.1 mg/kg) for ten days (in a two-five days cycle with two days pause) [21], while rats from the control group were injected intraperitoneally with an equal volume of sterile saline (0.9% NaCl). After that, rats from the model group were randomly divided into three groups: the VIN group, rats without drug intervention; low-dose group (VIN+LPD), rats received low dose of PD (15 mg/kg); and high-dose group (VIN + HPD), rats received a high dose of PD (30 mg/kg). PD was administered intraperitoneally daily for 21 days, and the dose was based on a previous study [22]. PD and VIN (purity > 95%) were purchased from Sigma-Aldrich (St. Louis, USA).
Mechanical allodynia was performed on days 0, 7, 14, and 21 according to a previous report [23]. Rats were placed individually in clear plexiglass boxes with a wire mesh floor. Automatic thin steel von Frey filaments were placed below the surface of the rear paw. We gradually increased the force until retracement of the claw was observed and measured the maximum force of the response induced by the mechanical stimulus. Thermal hyperalgesia was performed on days 0, 7, 14, and 21 according to a previous report [24]. Rats were placed individually in clear plexiglass boxes with a wire mesh floor. A radiative heat source was placed under the surface of the hind paw, and the paw latency reaction times were defined as thermal hyperalgesia. The cut-off point for avoiding tissue injury was set at 20 seconds. The experiment was repeated 3 times in each rat, and the mean value was measured.
After the last nociceptive behavioral tests, the animals were anesthetized with 50 mg/kg of pentobarbital sodium. L4-L6 dorsal root ganglion sections were harvested and homogenized on ice using a homogenizer. Afterward, the homogenate was centrifuged (10,000 g for 10 min, 4°C), and the supernatant was collected. The levels of inflammatory factors (TNF-α, IL-6, IL-1β, and IL-17) and macrophage marker (CD163) in the supernatant were measured by ELISA kits (Invitrogen, USA) based on the manufacturer's protocols. The catalog numbers of inflammatory cytokines and macrophage marker were as follows: IL-6 (BMS231-2), TNF-α (BMS2034), IL-17 (BMS6001TEN), CD163 (88-50361-22), and IL-1β (BMS224-2).
First, total RNA from the dorsal root ganglion was prepared using the TRIzol reagent (Invitrogen, USA) based on the manufacturer's protocols. Two microgram of RNA was used to synthesize cDNA by cDNA synthesis kits (Invitrogen, USA). qRT-PCR was carried out by using a CFX384 Real-Time System C100 Thermal Cycler (Bio-Rad) based on the manufacturer's protocols. Primer sequences used in the present experiment were presented in Table 1. Relative expression was normalized to the GAPDH using the 2-ΔΔCT method.
We carried out the western blot analysis based on the standard procedure in previous reports. The dorsal root ganglion tissues were homogenized in cold RIPA lysis buffer with protease inhibitors. After centrifugation at 10,000 g for 20 min, the supernatant was collected for protein quantification. 10 μg of protein was separated by 10% SDS-PAGE and transferred onto the PVDF membrane (Roche). Then, the blot was blocked with defatting milk powder (5%) at room temperature for one hour. After that, the blot was incubated overnight with the following primary antibodies: anti-IL-6 (1 : 1000, Proteintech, USA), anti-MAPK1 (1 : 200, Proteintech, USA), anti-TP53 (1 : 200, Proteintech, USA), and anti-GAPDH (1 : 1000, Proteintech, USA). After incubation, the blots were incubated with horseradish peroxidase-labeled secondary antibody for 60 min at room temperature. Following incubation, the blots were measured by a chemiluminescence reagent (PerkinElmer, USA), and the band intensity quantification was performed using ImageJ software (NIH). GAPDH was used as an endogenous control.
The dorsal root ganglion tissues were soaked in 4% formalin overnight. 5 μm paraffin sections of dorsal root ganglion was deparaffinized using xylene and rehydrated using a gradient of ethanol. Endogenous peroxidase was suppressed using H2O2 (3%) for 0.5 h. Then, the slices were incubated with normal goat serum (10%) and anti-IL-6 (1 : 200, Cell Signaling, USA) or anti-TP53 (1 : 200, Cell Signaling, USA) or anti-MAPK1 (1 : 200, Cell Signaling, USA) primary antibodies at 4°C overnight. The slices were washed twice in PBS and incubated with a goat anti-rabbit antibody (1 : 200) at room temperature for 60 min. Subsequently, slices were visualized using a DAB reagent. Finally, we used light microscopy to obtain immunohistochemistry images.
Data were statistically analyzed using GraphPad Prism 5 software and presented as mean ± SD. The results from behavioral tests were analyzed with repeated measures analysis of variance (ANOVA). Comparisons of results between groups were performed using one-way ANOVA followed by Bonferroni's test. Significance was set at P < 0.05.
The PD's 2D structure was shown in Figure 1(a). We used the CTD and SwissTargetPrediction databases and collected 79 PD-related targets (Figure 1(b)). A total of 2043 VIN-related targets and 22538 neurotoxicity-related targets were collected from the CTD and GeneCards databases. Finally, 46 intersection targets were identified as potential therapeutic genes via using a Venn tool (Figure 1(c) and supplementary file 1). The PPI network of intersection genes of PD acting on VIN-induced neurotoxicity was generated by the STRING database (Figure 1(d)).
The top 10 GO terms of biological process (BP), cellular component (CC), and molecular function (MF) were presented in Figure 2 and Supplementary file 2, our results revealed that these potential targets were significantly related to reactive oxygen species metabolic process, response to oxidative stress, response to lipopolysaccharide, focal adhesion, cytokine activity, and cytokine receptor binding, etc. The top 15 most significantly enriched KEGG pathways were presented in Figure 3 and Supplementary file 3, our findings also showed that these potential targets were significantly related to the IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, cellular senescence, FoxO signaling pathway, NOD-like receptor signaling pathway, Th17 cell differentiation, and HIF-1 signaling pathway, etc.
The top 10 key genes were selected based on the 8 classification methods (stress, betweenness, closeness, degree, DMNC, EPC, MNC, and radiality) in cytoHubba (Table 2) to screen hub genes. Then, five intersection genes were further screened as hub genes (Figure 4), including RELA, IL-6, TP53, MAPK3, and MAPK1.
We constructed a compound-targets-pathways network using Cytoscape software (3.8.0) to obtain a visual analysis result. As shown in Figure 5, the network contains 62 nodes and 203 edges. A green node represents PD, red nodes represent 46 intersection targets, and blue nodes represent the top 15 KEGG signaling pathways. Besides, it was also preliminarily speculated that PD could be used for the treatment of VIN-induced neuropathic pain via the IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, cellular senescence, FoxO signaling pathway, NOD-like receptor signaling pathway, Th17 cell differentiation, and HIF-1 signaling pathway due to the high representation of RELA, IL-6, TP53, MAPK3, and MAPK1 targets.
As shown in Figure 6, VIN injection-induced mechanical allodynia (20.06 ± 4.61 g) and thermal hyperalgesia (7.51 ± 1.30 s) in the VIN group compared to the control group (P < 0.05). A high dose of PD treatment effectively inhibited VIN-induced neuropathic pain via increasing paw withdrawal threshold (28.03 ± 1.87 g) and paw withdrawal latency (10.8 ± 1.43 s) (P < 0.05).
As shown in Figure 7, VIN injection dramatically increased the levels of TNF-α (235.6 ± 23.08 pg/mg prot), IL-6 (166.9 ± 15.36 pg/mg prot), IL-1β (175.3 ± 15.41 pg/mg prot), IL-17 (101.1 ± 9.32 pg/mg prot), and CD163 (125.3 ± 11.25 pg/mg prot) in the VIN group compared to those in the control group (P < 0.05). A high dose of PD treatment significantly inhibited TNF-α (155.9 ± 27.87 pg/mg prot), IL-6 (111.4 ± 11.75 pg/mg prot), IL-1β (125.2 ± 21.13 pg/mg prot), IL-17 (74.98 ± 12.51 pg/mg prot), and CD163 (92.10 ± 7.92 pg/mg prot) in VIN+HPD compared to those in the VIN group (P < 0.05).
We verified the expression of the three hub genes using qRT-PCR analysis and western blot analysis to further clarify the potential mechanism of PD against VIN-induced neurotoxicity. As shown in Figure 8, VIN injection upregulated the mRNA expression of IL-6 (2.9 ± 0.37), TP53 (3.36 ± 0.58), and MAPK1 (2.76 ± 0.41) in the VIN group compared to those in the control group (P < 0.05). A high dose of PD treatment downregulated the expression of IL-6 (2.03 ± 0.21), TP53 (2.08 ± 0.54), and MAPK1 (1.66 ± 0.34) in the VIN+HPD group (P < 0.05). Besides, VIN injection upregulated the protein expression of IL-6 (3.23 ± 0.51), TP53 (3.16 ± 0.70), and MAPK1 (4.16 ± 0.45) in the VIN group compared to those in the control group (P < 0.05) (Figure 9). A high dose of PD reversed those changes induced by VIN (P < 0.05), which was consistent with the above findings. We also performed the immunohistochemical experiment to further validate the above results. As shown in Figure 10, the expression levels of IL-6 (61.33 ± 5.71%), TP53 (71.70 ± 4.34%), and MAPK1 (72.73 ± 4.18%) in the VIN group were obviously upregulated (P < 0.05). However, the expression levels of IL-6 (31.30 ± 7.59%), TP53 (32.97 ± 5.35%), and MAPK1 (31.73 ± 2.95%) in the VIN+HPD group were lower than those of the VIN group (P < 0.05).
Chemotherapy-induced neuropathic pain is a complex chronic disease, which is caused by damage to the nervous system [25]. Chemotherapy-induced neuropathic pain also can lead to loss of functional capacity and negatively affect the quality of life, resulting in lower doses of chemotherapy drugs, and ultimately, impacting the overall survival rates of patients [26]. Therefore, it is necessary to explore novel therapeutic strategies for the treatment of chemotherapy-induced neuropathic pain. Recently, some studies have demonstrated that natural compounds exerted analgesic effects with little adverse effects, suggesting that plant-derived natural products have a therapeutic potential for developing new drugs in the treatment of neuropathic pain [27, 28]. PD is a stilbenoid and has been reported to inhibit apoptosis, inflammation, and oxidative stress as the main pathway for neurodegenerative diseases [11]. PD has been revealed to exert neuroprotective effects in spinal cord injury rats via inhibiting microglial inflammation [14]. Furthermore, PD had the anxiolytic effects via inhibiting inflammatory cytokines in a chronic pain mouse model [16]. These studies implied that PD may have pharmacological effects against pain. However, the therapeutic effect of PD against neuropathic pain remains unclear. In recent years, some researchers used network pharmacology analysis to screen and confirm the active ingredients and potential therapeutic targets. This method provides a powerful tool for elucidating the mechanisms of disease and facilitating the discovery of potential active ingredients [29]. In the present study, the network pharmacology and animal experimental approach were performed to reveal the potential therapeutic mechanisms of PD in the treatment of VIN-induced neurotoxicity. First, we identified 46 potential targets using the public databases. The results of KEGG enrichment analysis showed that these target genes were mainly involved in the IL-17 signaling pathway and cytokine-cytokine receptor interaction, which were closely associated with the pathologic process of chemotherapy-induced neuropathic pain. Based on the results of GO-BP enrichment analysis, these target genes mainly focused on the cellular response to lipopolysaccharide, cytokine-mediated signaling pathway, positive regulation of leukocyte cell-cell adhesion, humoral immune response, neuroinflammatory response, etc. In addition, we also identified five hub genes (RELA, IL-6, TP53, MAPK3, and MAPK1) as the most promising candidate targets of PD acting on the progression of chemotherapy-induced neuropathic pain. Neuroinflammation is a potential codriver of chemotherapy-induced neuropathic pain. It has been reported that the chemotherapy-induced increase in inflammatory factors and a close relationship with the occurrence and development of neuropathic pain [8, 30]. Macrophage infiltration causes a subsequent secretion and generation of various chemokines (CXC family) and inflammatory cytokines (IL-6, IL-1β, and TNF-α) [31]. These molecules were thought to be potential drivers for the initiation and development of neuropathic pain [32]. In the peripheral nervous system, proinflammatory cytokines not only modulate the sensitivity and activity of spontaneous nociceptors but also promote axonal injury via activation of inflammation [33]. Besides, a previous report revealed that IL-8 and IL-6 mRNA expressions were upregulated in suffering from neuropathic pain [34]. IL-17 is an important mediator of inflammatory responses and was implicated in evoking proinflammatory reactions. It has been demonstrated that IL-17 could promote neuroinflammation and pain hypersensitivity following peripheral nerve damage [35]. Moreover, IL-17 could mediate neuronal hyperexcitability and neuron-glial interactions in chemotherapy-induced neuropathic pain [36]. To support these findings, spironolactone, an aldosterone receptor antagonist with anti-inflammatory effects has been shown to have beneficial effects in ameliorating VIN-induced neuropathic pain [37]. In our study, KEGG enrichment analysis indicated that most of the targets were enriched in the inflammation-related signaling pathway, such as the IL-17 signaling pathway. Besides, IL-6 was identified as the hub gene in the PPI network. Our in vivo experiment further indicated that PD decreased the gene expression of IL-6 in VIN-induced neuropathic pain, implying that IL-6 was the potential therapeutic target of PD against VIN-induced neuropathic pain. TP53, a major neuronal proapoptotic gene, was implicated in synaptic terminal injury and apoptosis, and its activation was associated with the etiopathogenesis of Parkinson's disease [38, 39]. It has been demonstrated that the TP53 gene regulated dopaminergic neuronal injury in different neurotoxicant models [40]. In addition, suppression of the TP53 gene via using a dominant-negative form of TP53 or pharmacological inhibitors exerted protection to endogenous dopamine neurons [41]. Moreover, previous reports have revealed that TP53 and MAP2K2 might be involved in the pathological process of neuropathic pain via bioinformatics analysis [42, 43]. MAPK1, an extracellular signal-regulated kinase, was involved in various cellular processes. MAPK activation plays an important role in the pathophysiology of spinal cord injury, and inhibition of the MAPK3/MAPK1 signaling pathway might be effective in the treatment of inflammation, trauma, and spinal cord injury [44]. The previous report has indicated that the knockdown of NEAT1 inhibited the inflammation of spinal cord injury via the miR-211-5p/MAPK1 axis [45]. In the present study, TP53 and MAPK1 were identified as hub genes in the PPI network. Our in vivo experiment further indicated that PD regulated the gene expressions of TP53 and MAPK1 in VIN-induced neuropathic pain, suggesting that TP53 and MAPK1 were the potential therapeutic targets of PD against VIN-induced neurotoxicity. One of the limitations of this study is the species difference between rodent pain models and clinical chemotherapy-induced neuropathic pain. To overcome this hurdle, the use of species that are closer to humans than rodents, such as nonhuman primates, could hence understand the neuropathic pain and improve the passage of new therapies through clinical testing.
In conclusion, our study firstly demonstrated the therapeutic effects of PD on VIN-induced neurotoxicity via network pharmacology and experimental verification. These findings indicated that PD alleviated VIN-induced neurotoxicity via downregulation of IL-6, TP53, and MAPK1 expressions. This study provided a novel approach to exploring the potential therapeutic mechanism of PD in the treatment of neuropathic pain. | true | true | true |
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PMC9587682 | 35914269 | Fei Yao,Xiaoying Huang,Zhufu Xie,Jie Chen,Ling Zhang,Qiang Wang,Hui Long,Jue Jiang,Qingming Wu | LINC02418 upregulates EPHA2 by competitively sponging miR-372-3p to promote 5-Fu/DDP chemoresistance in colorectal cancer | 01-08-2022 | Abstract Chemoresistance is a huge clinical challenge in the treatment of advanced colorectal cancer (CRC). Non-coding RNAs (ncRNAs) and messenger RNA (mRNA) are involved in CRC chemoresistance. However, the profiles of long ncRNAs (lncRNAs), microRNAs (miRNAs), mRNAs and competing endogenous RNA (ceRNA) networks in CRC chemoresistance are still largely unknown. Here, we compared the gene expression profiles in chemosensitive (HCT8) and chemoresistant [HCT8/5-fluorouracil (5-Fu) and HCT8/cisplatin (DDP)] cell lines by whole-transcriptome sequencing. The common differentially expressed RNAs in two drug-resistant cells were selected to construct lncRNA–miRNA–mRNA networks. The ceRNA network closely related to chemoresistance was further established based on the widely accepted drug resistance-associated genes enriched in three signaling pathways involved in chemoresistance. In total 52 lncRNA–miRNA–mRNA pathways were screened out, among which EPHA2 and LINC02418 were identified as hub genes; thus, LINC02418/miR-372-3p/EPHA2 were further selected and proved to affect the 5-Fu and DDP resistance of CRC. Mechanistically, LINC02418 upregulated EPHA2 by functioning as a ‘sponge’ of miR-372-3p to modulate the chemoresistance of CRC. Collectively, our study uncovered the underlying mechanism of LINC02418/miR-372-3p/EPHA2 in 5-Fu and DDP resistance of CRC, which may provide potential therapeutic targets for improving the chemosensitivity of CRC. | LINC02418 upregulates EPHA2 by competitively sponging miR-372-3p to promote 5-Fu/DDP chemoresistance in colorectal cancer
Chemoresistance is a huge clinical challenge in the treatment of advanced colorectal cancer (CRC). Non-coding RNAs (ncRNAs) and messenger RNA (mRNA) are involved in CRC chemoresistance. However, the profiles of long ncRNAs (lncRNAs), microRNAs (miRNAs), mRNAs and competing endogenous RNA (ceRNA) networks in CRC chemoresistance are still largely unknown. Here, we compared the gene expression profiles in chemosensitive (HCT8) and chemoresistant [HCT8/5-fluorouracil (5-Fu) and HCT8/cisplatin (DDP)] cell lines by whole-transcriptome sequencing. The common differentially expressed RNAs in two drug-resistant cells were selected to construct lncRNA–miRNA–mRNA networks. The ceRNA network closely related to chemoresistance was further established based on the widely accepted drug resistance-associated genes enriched in three signaling pathways involved in chemoresistance. In total 52 lncRNA–miRNA–mRNA pathways were screened out, among which EPHA2 and LINC02418 were identified as hub genes; thus, LINC02418/miR-372-3p/EPHA2 were further selected and proved to affect the 5-Fu and DDP resistance of CRC. Mechanistically, LINC02418 upregulated EPHA2 by functioning as a ‘sponge’ of miR-372-3p to modulate the chemoresistance of CRC. Collectively, our study uncovered the underlying mechanism of LINC02418/miR-372-3p/EPHA2 in 5-Fu and DDP resistance of CRC, which may provide potential therapeutic targets for improving the chemosensitivity of CRC.
Colorectal cancer (CRC), a common gastrointestinal malignant tumor that occurs in the colon, ranks third in terms of incidence and is the second leading cause of cancer-related mortality around the world (1). Chemotherapy is an optimal method for treating advanced or inoperable CRC patients, and the basic and classic drugs commonly used to treatment of CRC include 5-fluorouracil (5-Fu) and cisplatin (DDP). However, some patients are primarily resistant to 5-Fu- or DDP-based chemotherapy, while some will acquire resistance after a period of treatment, resulting in a limited therapeutic effect and low 5-year survival rate (2–4). Thus, it is urgent to reveal the molecular mechanisms underlying the chemoresistance in CRC to identify more effective therapeutic targets and improve the efficacy of chemotherapy. Long non-coding RNA (lncRNA) is a type of RNA molecule that measures >200 nucleotides in length with a low protein-coding potential (5,6). With the development of high-throughput sequencing and bioinformatics technologies, a variety of lncRNAs have been explored and revealed (7–9). Recently, accumulating evidence has shown that lncRNAs are usually differentially expressed (DE) in various cancers and function as oncogenes or tumor suppressor genes to affect biological processes of cancers, such as cell proliferation, migration, metastasis and apoptosis (10–12). Moreover, lncRNAs have been proved to play an important role in tumor chemoresistance (13–15). For example, Chen et al. demonstrated that Forkhead box D1 could bind with the promoter of lncRNA CYTOR and then activate its transcription to induce the epithelial–mesenchymal transition and chemoresistance of oral squamous cell cancer (16). Yang et al. reported that the lncRNA SLC7A11-AS1 was overexpressed in gemcitabine-resistant pancreatic ductal adenocarcinoma cell lines, which can block the ubiquitination of NRF2, a key regulator of SCFβ-TRCP-mediated antioxidant defense, to eliminate reactive oxygen species and promote cancer cell stemness and chemoresistance (17). In addition, mesenchymal stem cells were found to be associated with the development of drug resistance through fatty acid oxidation. Mechanically, mesenchymal stem cells could secrete TGF-β1 to upregulate the lncRNA MACC1-AS1, resulting in fatty acid oxidation-dependent stemness and chemoresistance (18). Recently, mounting evidence has demonstrated that lncRNAs can serve as sponges of microRNAs (miRNAs) via competitive endogenous RNA (ceRNAs) to regulate the expression of target genes, then modulate the chemoresistance of cancers (19–21). Although a minor fraction of lncRNAs have been reported to be involved in the chemoresistance of CRC, the expressions and roles of most lncRNAs in CRC harboring both 5-Fu and DDP chemoresistance remain unclear, and lncRNA-associated ceRNA networks related to CRC chemoresistance have not been constructed, thus requiring further investigation. Therefore, identifying the key regulator of lncRNA and its ceRNA network related to chemoresistance is particularly important for improving the prognosis of CRC. In the current study, we identified DE lncRNAs, miRNAs and messenger RNAs (mRNAs) in CRC chemosensitive cells (HCT8) and two different chemoresistant cell lines (HCT8/5-Fu and HCT8/DDP) via whole-transcriptome sequencing. Then, lncRNA–miRNA–mRNA networks were predicted and constructed based on the common DE RNAs in two drug-resistant cells, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for mRNAs regulated by ceRNA networks. We screened and then found that LINC02418/miR-372-3p/EPHA2 may act as a key regulator network of 5-Fu and DDP resistance of CRC. It has been reported that LINC02418 participates in CRC tumorigenesis (22) and EPHA2 contributes to the chemoresistance of cancers (23,24). However, the effects of LINC02418/miR-372-3p/EPHA2 on 5-Fu and DDP resistance in CRC are unknown. In the study, LINC02418 and EPHA2 were significantly upregulated, while miR-372-3p was downregulated in chemoresistant cell lines, and all of these trends were related to CRC chemoresistance. Mechanically, LINC02418 functions as a sponge of miR-372-3p to upregulate EPHA2 expression. Therefore, this study reveals that LINC02418 exerts oncogenic potential and may be a new therapeutic target and a promising marker to predict chemoresistance in CRC.
A normal colon epithelial cell line (CCD-18Co) was purchased from the American Type Culture Collection (Manassas, VA), HCT8, HCT116 and SW480 CRC cell lines and 293T cells were purchased from the China Center for Type Culture Collection (Wuhan, China) with short tandem repeat DNA profiling analysis, and cultured for fewer than 6 months after resuscitation. Chemoresistant CRC cell lines (HCT8/5-Fu and HCT8/DDP) were derived from the parental cell line HCT8 by continuous exposure to drugs (25). CCD-18Co and 293T cells were cultured in Dulbecco’s modified Eagle’s medium (Gibco, MD) containing 10% fetal bovine serum (Gibco, MD), HCT8, HCT116, SW480, HCT8/5-Fu and HCT8/DDP cells were maintained in RPMI-1640 (Gibco, MD) supplemented with 10% fetal bovine serum (Gibco, MD) and 1% penicillin/streptomycin (Gibco, MD). Additionally, HCT8/5-Fu and HCT8/DDP cells were cultured with 5 µg/ml 5-Fu (Sigma–Aldrich, MO) and 1 µg/ml DDP (Sigma–Aldrich, MO to maintain drug resistance, respectively.
Total RNAs were extracted using TRIzol reagent (Life Technologies, CA) according to the manufacturer’s protocol. The quantity of total RNA samples was assessed according to the description in our previous study (25).
Total RNA (5 µg) of the per sample was used as input material for the preparation of strand-specific complementary DNA (cDNA). Strand-specific cDNA libraries were constructed as follows: ribosomal RNA depletion was first performed using the Ribo-Zero Magnetic kit (Epicentre, CA) and then was fragmented into 300 bp fragments with fragmentation buffer. Secondly, random hexamer primers were used to synthesize first-strand cDNA, and dUTPs were used to instead dTTPs when synthesizing second-strand cDNA. Double-stranded cDNA (ds cDNA) were separated from the second-strand reaction mix using AMPure XP beads, and a single ‘A’ nucleotide was added to the 3ʹ ends of blunt fragments. Lastly, the ends of the ds cDNA were ligated with multiple indexing adapters. Libraries were selected for cDNA target fragments of 300–400 bp, then were enriched by using Phusion DNA polymerase (NEB) for 15 cycles of PCR. After being quantified with a TBS-380 Fluorometer (Turner Biosystem, CA), the libraries were sequenced on Hiseq 2000 platform (2 × 150 bp paired-end reads). For small RNA, the sequencing libraries were constructed using the Truseq™ Small RNA sample prep kit (Illumina, CA) following the manufacturer’s introduction. Briefly, small RNA was ligated with sequencing adapters and cDNA was synthesized and amplified with 12 PCR cycles to produce libraries, and the products were then purified with 6% Novex TBE PAGE gel (Thermo Fisher Scientific, MA). The quality of the library was assessed with the 2100 bioanalyzer (Agilent Technologies, CA) and single-end sequencing was performed on the Hiseq 2000 platform. Each group had three replicates, and the whole-transcriptome sequencing was performed by Shanghai Majorbio Bio-Pharm Biotechnology Co., Ltd. (Shanghai, China).
The sequencing raw data were filtered some reads with adapters and low quality to obtain clean reads. The HISAT2 software (http://ccb.jhu.edu/software/hisat2/index.shtml) was used to compare the filtered reads to the reference genome. The expression levels of mRNAs and lncRNAs were normalized using the fragments per kilobase of exon per million mapped reads (FRKM) method, and the expression levels of miRNAs were determined by transcript per million. In addition, the DESeq software was used to identify DE lncRNAs, miRNAs and mRNAs between CRC chemosensitive and chemoresistant cell lines with |log2FoldChange| >1 and P < 0.05. Moreover, the common DE lncRNAs, miRNAs and mRNAs were selected between the two drug resistance groups to find the lncRNAs, miRNAs and mRNAs simultaneously associated with 5-Fu and DDP resistance in CRC.
A lncRNA–miRNA–mRNA regulatory network was constructed based on common DE lncRNA, miRNA and mRNA according to the ceRNA hypothesis. First, the miRNA–mRNA and miRNA–lncRNA pairs were predicted using miRanda and TargetScan software, mRNA and lncRNA in the ceRNA network generally had a positive correlation, while the expression levels of miRNAs were negatively correlated with those of mRNAs and lncRNAs. The pairs with Pearson’s correlation coefficients >0.7 were selected for further analysis. Second, two scoring methods were used to evaluate the regulatory function of miRNA on a competitive lncRNA–mRNA pairs, as follows: (i) regulate the similarity score, comparing the similarity of the expression correlation between miRNA–lncRNA and miRNA–mRNA pairs; and (ii) sensitivity correlation score, where the average value of lncRNA–miRNA–mRNA correlation was used as the sensitivity correlation between lncRNAs and mRNAs. Finally, lncRNA–miRNA–mRNA ceRNA networks (sensitivity correlation score >0.3) were visualized using the Cytoscape version 3.6 software (Supplementary Figure 1, available at Carcinogenesis Online).
GO analysis was used to investigate the possible functions of the mRNAs in the lncRNA–miRNA–mRNA ceRNA network. The results of GO enrichment analysis were classified into three categories, which were biological process, molecular function and cell component, and the top 10 significantly enriched GO terms in each category were displayed. KEGG pathway analysis was used to determine the biological pathway of the mRNAs in the ceRNA network, and P < 0.05 was considered to be statistically significant.
First, TRIzol reagent (Thermo Fisher Scientific, MA) was used to extract total RNAs from HCT8, HCT8/5-Fu and HCT8/DDP cell lines. Then, the ReverTra Ace qPCR RT kit (TOYOBO, Osaka, Japan) was used to synthesize cDNA following the manufacturer’s instructions. Subsequently, quantitative PCR was performed using iTaq™ Universal SYBR Green Supermix (Bio-Rad, CA). The primer sequences of DE lncRNA, miRNA and mRNA in the study were synthesized by Sangon Biotech (Shanghai, China), and are shown in Supplementary Table 1, available at Carcinogenesis Online. GAPDH (for lncRNA and mRNA) and U6 (for miRNA) were regarded as the internal controls, and the relative expression levels of lncRNAs, miRNAs and mRNAs were calculated with the 2−ΔΔCt method.
Proteins from cells were lysed using RIPA lysis buffer supplemented with phosphatase inhibitor (Beyotime Biotechnology, Shanghai, China), and its concentrations were quantified with a bicinchoninic acid protein assay kit (Biosharp, Shanghai, China). Protein samples (20 µg) were separated on 8.75% sodium dodecyl sulfate–polyacrylamide gel electrophoresis gels, then transferred to a polyvinylidene difluoride membrane (Bio-Rad, CA) and blocked using 5% skim milk at room temperature for 1 h, then incubated overnight with primary antibodies for EPHA2 (1:1000, Cell Signaling Technology, 6997T), phosphor-EPHA2-Y549 (1:1000, ABclonal, AP0818) and β-actin (1:10 000, ABclonal, AC026) at 4°C. Membranes were incubated with horseradish peroxidase-conjugated secondary antibodies at room temperature for 1 h. Finally, the expression of EPHA2 protein was visualized by enhanced chemiluminescence reagents (Bio-Rad, CA) and its relative level was determined by densitometric analysis using the ImageJ software.
Chemoresistant CRC cells were transiently transfected with (i) small interfering RNAs (siRNAs) for LINC02418 (si-LINC02418) and the corresponding negative control (si-NC) (GenePharma, Shanghai, China) or (ii) miR-372-3p mimics/inhibitors and corresponding negative control (NC mimics/inhibitors) (GenePharma, Shanghai, China). Cell transfection was performed using Lipofectamine 2000 (Invitrogen, CA) following the manufacturer’s protocol. In addition, cells were treated with serially diluted EPHA2 inhibitor (ALW-II-41-27) and its negative control dimethyl sulfoxide to explore the biological functions of EPHA2. The related siRNAs and miRNA mimic/inhibitor sequences are listed in Supplementary Table 2, available at Carcinogenesis Online.
To evaluate the effects of LINC02418, miR-372-3p and EPHA2 on chemosensitivity, the transfected HCT8/5-Fu and HCT8/DDP cells for 48 h were trypsinized and reseeded in 96-well plates (5000 cells/well), then combined with different concentrations of 5-Fu or DDP for 48 h to assess cell viability. Additionally, 5-Fu or DDP with the same concentration was supplemented into wells and cultured for 12, 24 or 36 h to assess the cell proliferation ability. The OD490 value was detected after adding the MTT reagent (Sigma–Aldrich, MO) for 4 h at 37°C.
Flow cytometry was used to measure cell apoptosis and cell cycle. For the apoptosis assay, the transfected HCT8/5-Fu and HCT8/DDP cells were harvested and washed with pre-cold phosphate-buffered saline, and about 1–5 × 105 cells were resuspended in 500 µl of binding buffer with 5 µl of Annexin V-FITC and 10 µl of PI staining solution and then incubated for 15 min at room temperature. For cell cycle analysis, the transfected cells were collected and fixed with pre-cold 70% ethanol at 4°C overnight. Then the cells were washed with pre-cold phosphate-buffered saline, PI staining solution and RNase solution were added to each sample, and the cells were resuspended and incubated for 30 min at 37°C water bath and 4°C for 30 min, protected from light. The cell apoptosis rate and cycle distribution were detected by the Accuri C6 flow cytometer (BD Biosciences, NJ) flow cytometer, and the data were analyzed with the FlowJo software (Tree Star, Ashland, OR).
The sequence of wild-type (WT) LINC02418 and EPHA2 and the sequence of mutant-type (MUT) LINC02418 and EPHA2 were inserted into the pmirGLO reporter vector (GenePharma, Shanghai, China). Then, the WT (MUT) 3ʹ-UTR of LINC02418 vector or WT (MUT) EPHA2 vector and control mimics or miR-372-3p mimics were cotransfected into 293T cells with Lipofectamine 2000 (Invitrogen, CA). Luciferase activity was measured with the Dual-Luciferase Reporter Assay System (Promega, WI) according to the manufacturer’s protocol, and Renilla luciferase was regarded as the control reporter for normalization.
SPSS 25.0 statistical software (SPSS, Chicago) and GraphPad Prism 5 Software (GraphPad, SanDiego, CA) were used for data analysis and graph representations, respectively. The experimental data were expressed as mean ± standard deviation (mean ± SD). The independent-sample t-test was used for the comparison of means between the two groups, P < 0.05 was considered statistically significant. The DE lncRNA, miRNA and mRNA were considered to be statistically significant with |log2FoldChange| >1 and P < 0.05.
In our previous study, we confirmed that CRC chemoresistant cell lines (HCT8/5-Fu, HCT8/DDP) were more resistant to drugs than parental cells HCT8 (25), which laid the foundation for whole-transcriptome sequencing to obtain the profiles of lncRNAs, miRNAs and mRNAs in the parental cell lines and two drug-resistant cell lines. Compared with parental cells, DE lncRNAs, miRNAs and mRNAs were screened out in drug-resistant cells with the criteria of |log2FoldChange| >1 and P < 0.05. A volcano plot and heatmap were used to display the expressions of mRNAs and ncRNAs. The results showed that there were 597 lncRNAs (255 upregulation and 342 downregulation), 98 miRNAs (52 upregulation and 46 downregulation) and 2464 mRNAs (1055 upregulation and 1409 downregulation) DE in HCT8/5-Fu cells; as well as 601 lncRNAs (274 upregulation and 327 downregulation), 79 miRNAs (50 upregulation and 29 downregulation) and 2378 mRNAs (1250 upregulation and 1128 downregulation) DE in HCT8/DDP cells compared with the chemosensitive group, respectively (Figure 1A and B). In addition, distinguishable lncRNA, miRNA and mRNA expression profiles among samples were revealed by hierarchical clustering analysis (Figure 1C). In order to find the same regulating RNAs involved in both 5-Fu and DDP resistance, Venn diagram analysis was used to screen out common DE lncRNAs, miRNAs and mRNAs in the two chemoresistant cell lines. This analysis showed that 295 lncRNAs, 64 miRNAs and 1779 mRNAs were common DE in two comparisons (Figure 1D and Supplementary Table 3, available at Carcinogenesis Online).
To further explore the same mechanisms in different chemoresistance of CRC, we selected the common DE lncRNAs, miRNAs and mRNAs in HCT8/5-Fu and HCT8/DDP cells to construct lncRNA–miRNA–mRNA ceRNA regulatory networks based on the filter conditions described previously. Finally, there were 1844 lncRNA–miRNA–mRNA relationship pairs (Supplementary Figure 2A, available at Carcinogenesis Online). Due to the numerous relationship pairs, GO enrichment and KEGG pathway analyses were performed on mRNAs in the lncRNA–miRNA–mRNA network to analyze functions. As shown in Supplementary Figure 2B, available at Carcinogenesis Online, GO analysis showed that these DE-mRNAs are mainly related to anatomical structure morphogenesis (biological process), plasma membrane (cell component) and transmembrane receptor protein tyrosine kinase activity (molecular function). KEGG pathway analysis revealed that there were 20 pathways enriched in these DE-mRNAs involved in the lncRNA–miRNA–mRNA networks. Of them, the mitogen-activated protein kinase (MAPK) signaling pathway was the most enriched (Supplementary Figure 2C, available at Carcinogenesis Online).
In the above ceRNA network, the relational pairs of upregulated lncRNAs attached our attention (Supplementary Figure 3A, available at Carcinogenesis Online). To fully explore the core regulators related to chemoresistance, we screened out six pathways related to drug resistance from 20 pathways enriched in the network, including the MAPK signaling pathway, TNF signaling pathway, focal adhesion, HIF-1 signaling pathway, PI3K–Akt signaling pathway and Ras signaling pathway, among which the MAPK, PI3K–Akt and Ras signaling pathways were the most enriched. Then, we further filtered the key DE-mRNAs closely related to drug resistance from the genes enriched in the above three pathways according to the literature. Based on the screening results, 52 lncRNA–miRNA–mRNA networks were selected from above 626 relational pairs, including 14 lncRNAs, 19 miRNAs and 12 mRNAs (Figure 2A). These networks were regarded to be related to chemoresistance, among which EPHA2 and LINC02418 were identified as the hub genes (Supplementary Figure 3B, available at Carcinogenesis Online). Moreover, the LINC02418 and EPHA2 expression data downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) showed that EPHA2 and LINC02418 were upregulated in human CRC tissues compared with normal tissues (Figure 2B and C). Next, the expressions of LINC02418 and EPHA2 were examined on CRC cell lines compared with normal colon epithelial cells and increased EPHA2 and LINC02418 expression levels were observed (Figure 2D and E). In addition, it was observed from TCGA data that the overall survival rate in the LINC02418 high expression group was shorter than that in the low expression group, but the difference was not statistically significant (P = 0.062, Figure 2F). Furthermore, Kaplan–Meier survival analysis showed that patients with high EPHA2 expression had a significantly poorer overall survival than those with low EPHA2 expression (P < 0.01, Figure 2G). Collectively, these results suggested that high EPHA2 and LINC02418 expression may indicate a poor prognosis among CRC patients. In this study, there were two relationship pairs found between EPHA2 and LINC02418, including LINC02418/miR-372-3p/EPHA2 and LINC02418/miR-33a-3p/EPHA2 (Supplementary Figure 3C, available at Carcinogenesis Online). The results of RNA-sequencing showed that miR-33a-3p and miR-372-3p were significantly downregulated in HCT8/5-Fu and HCT8/DDP cells, but the expression of miR-372-3p was particularly lower (Supplementary Table 4, available at Carcinogenesis Online). Additionally, as there is less research on miR-372-3p, miR-372-3p caught our attention. Therefore, we focused on the LINC02418/miR-372-3p/EPHA2 relationship pair to explore its roles in mediating CRC chemoresistance.
The results of RNA-sequencing showed that LINC02418 was significantly upregulated in HCT8/5-Fu and HCT8/DDP cells. In addition, quantitative real-time PCR (qRT-PCR) also showed that the expression level of LINC02418 in chemoresistant CRC cells was significantly increased compared with that in parental cells (Figure 3A). To explore the function of LINC02418 in CRC chemoresistance in vitro, three siRNAs were constructed and transfected into HCT8/5-Fu and HCT8/DDP cells, and the knockdown efficiency was evaluated by qRT-PCR, indicating si-LINC02418-1 with the highest inhibition effect to be used for further assays (Figure 3B). MTT assays were performed to measure the sensitivity of HCT8/5-Fu and HCT8/DDP cells to chemotherapeutic drugs, and the results indicated that the survival rate of cells transfected with si-LINC02418 was significantly lower than that of si-NC cells when combined with the same concentration. In addition, the IC50 values were remarkably decreased in cells with LINC02418 silencing (Figure 3C and D). Furthermore, depletion of LINC02418 in the two chemoresistant cell lines induced significant apoptosis and led to a decrease in the S phase of the cell cycle according to flow cytometry (Figure 3E–G). The same results were also detected in chemoresistant cells transfected with si-LINC02418-2 and si-LINC02418-3 (Supplementary Figure 4, available at Carcinogenesis Online). Collaboratively, these results indicated that silencing of LINC02418 significantly enhances the chemosensitivity of HCT8/5-Fu and HCT8/DDP cells.
The expression level of miR-372 in the CRC samples derived from TCGA database and CRC cell lines was analyzed. The expression of miR-372 was significantly upregulated in CRC tissues and cells (Supplementary Figure 5A and B, available at Carcinogenesis Online), and a positive correlation was found between high miR-372 expression and reduced overall survival rates using Kaplan–Meier analysis (P = 0.043, Supplementary Figure 5C, available at Carcinogenesis Online). In the study, the results of RNA-sequencing showed that miR-372-3p was significantly downregulated in HCT8/5-Fu and HCT8/DDP cells, and we subsequently confirmed that miR-372-3p was lowly expressed in chemoresistant cells compared with HCT8 cells by qRT-PCR (Figure 4A). Among the ceRNAs we selected above, miR-372-3p was predicted to have potential interaction sites with LINC02418, and highly expressed LINC02418 could act as a ‘sponge’ to adsorb miR-372-3p, which may be the reason for the low expression of miR-372-3p in chemoresistant cell lines. To further explore the functions of miR-372-3p, we overexpressed miR-372-3p in two chemoresistant cell lines using transfection of miR-372-3p mimics, then validated our findings with qRT-PCR (Figure 4B). Compared with cells transfected with NC mimic, it was obvious that overexpression of miR-372-3p enhanced the sensitivity of HCT8/5-Fu cells to 5-Fu and HCT8/DDP cells to DDP with reduced IC50 values (Figure 4C and D). In addition, cell proliferation, apoptosis and cell cycle were examined after transfection with NC mimics and miR-372-3p mimics, and the results showed that miR-372-3p upregulation could inhibit the proliferation of chemoresistant CRC cells compared with the NC mimics group (Figure 4E). The cell apoptosis ratios were increased following transfection with miR-372-3p mimics, in a manner more obvious than that seen when 5-Fu or DDP was added (Figure 4F). Additionally, compared with the NC mimics transfection group, the numbers of HCT8/5-Fu and HCT8/DDP cells transfected with miR-372-3p mimics were decreased in the S phase, and arrested in the G2/M phase (Figure 4G). Thus, these results indicated that the functions of overexpressing miR-372-3p are similar to that of silencing LINC02418, which strongly supports the idea that miR-372-3p plays important roles in the regulation of CRC chemoresistance.
In this study, EPHA2 was identified as a hub gene in the constructed ceRNA network. RNA-sequencing results showed that EPHA2 was upregulated in HCT8/5-Fu and HCT8/DDP cells, and we analyzed the expression and activity of EPHA2 and found EPHA2 and its phosphorylation levels were highly expressed in chemoresistant cells compared with HCT8 cells (Figure 5A). To assess whether EPHA2 is related to CRC chemoresistance, ALW-II-41-27, a novel EPHA2 receptor tyrosine kinase inhibitor, was used to block the EPHA2 function of chemoresistant cells, and a strong reduction in phosphorylation of EPHA2 was found after treatment with different concentrations of EPHA2 inhibitor (Figure 5B). Then, MTT assay results showed that the sensitivity of chemoresistant cells to chemotherapeutic drugs was enhanced and the value of IC50 was decreased following treatment with 1 µM of ALW-II-41-27 (Figure 5C and D). Additionally, the proliferation of drug-resistant cells was significantly inhibited, and apoptosis was promoted, results which were made even more obvious with the combination of ALW-II-41-27 and 5-Fu or DDP (Figure 5E and F). The cell cycle distributions were also altered, the drug-resistant cells were reduced in the S phase, and blocked in the G2/M phase when inhibited the activity of EPHA2 (Figure 5G). Collectively, inhibition of EPHA2 activity could increase the sensitivity of drug-resistant cells, indicating that EPHA2, like LINC02418 and miR-372-3p, is involved in regulating the chemoresistance of CRC.
It is well known that lncRNAs in the cytoplasm can act as ceRNAs to indirectly regulate downstream gene expression by competing for shared miRNAs (26). To investigate whether LINC02418 regulates CRC chemoresistance through the ceRNA mechanism, we first used lncLocator to predict the location of LINC02418 and found that it was mostly located in the cytoplasm of cells (Supplementary Table 5, available at Carcinogenesis Online). Then, we measured the expression changes of miR-372-3p when LINC02418 was downregulated, finding that the expression of miR-372-3p was significantly enhanced in LINC02418 knockdown cells (Figure 6A), indicating LINC02418 may influence the deregulation of miR-372-3p. Furthermore, luciferase reporter plasmids containing WT LINC02418 and MUT LINC02418 were constructed and transfected into 293T cells along with miR-372-3p mimics or NC mimics. The luciferase activity of LINC02418-WT was reduced by cotransfection with miR-372-3p mimics, while overexpression of miR-372-3p did not influence the luciferase activity of vectors containing LINC02418-MUT (Figure 6B), indicating that LINC02418 acts as a molecular sponge for miR-372-3p. In addition, EPHA2 was predicted to be a potential target of miR-372-3p, and we also found that overexpression of miR-372-3p reduced the expression of EPHA2 in two chemoresistant cell lines (Figure 6C and D), suggesting that EPHA2 is regulated by miR-372-3p. Then luciferase reporter assays confirmed the direct binding of miR-372-3p and EPHA2. We found that the overexpression of miR-372-3p decreased the luciferase activity driven by the WT 3ʹ-UTRs of EPHA2 rather than the mutant 3ʹ-UTRs of EPHA2 (Figure 6E). The expression of EPHA2 after transfection with LINC02418 or cotransfection of LINC02418 and miR-372-3p were detected to determine whether LINC02418 regulated EPHA2 in CRC chemoresistant cells, and whether or not this regulation depends on miR-372-3p. We found that EPHA2 mRNA and protein levels decreased with LINC02418 knockdown (Figure 6F and G). Meanwhile, we also observed that LINC02418 knockdown inhibited the expression of EPHA2, but this inhibition was abolished by simultaneous miR-372-3p knockdown (Figure 6H) in both HCT8/5-Fu and HCT8/DDP cells. Taken together, these results indicate that LINC02418 may function as a ceRNA to promote EPHA2 expression by sponging miR-372-3p in CRC chemoresistance.
As one of the most common malignant tumors of the digestive tract in humans, CRC is well known for its high morbidity and mortality rates (27). Although fluorouracil and platinum chemotherapy drugs are widely used to treat CRC, many patients develop drug resistance and carry a poor prognosis. Understanding the specific molecular mechanism involved in CRC chemoresistance is urgently needed to improve survival (28,29). In this study, we constructed a chemoresistance-related lncRNA-associated ceRNA network of CRC and confirmed the critical role of this regulatory network in 5-Fu and DDP resistance in CRC cells. We found that LINC02418 promoted CRC chemoresistance by upregulating EPHA2 through competitively sponging miR-372-3p. In addition, TCGA data indicated that LINC02418 and EPHA2 as well as miR-372 expression were significantly upregulated in CRC patients, and their high expression correlated with a worse prognosis, consistent with findings of previous studies on the prognostic significance of LINC02418 and EPHA2 and miR-372 in CRC patients (30–32). Previously, some studies have demonstrated that lncRNAs are involved in 5-Fu or DDP chemoresistance of CRC. For example, Zhu et al. indicated that the lncRNA NEAT1 could affect cancer cell stem to regulate 5-Fu resistance of CRC. Mechanistically, NEAT1 increased histone acetylation levels via affecting chromatin remodeling, resulting in increased acetylation levels of ALDH1 and c-Myc and enhanced the stemness of CRC cells (33). In addition, Han et al. found that knockdown of lncRNA SNHG14 or higher expressed miR-186 suppressed cell autophagy, thereby inhibiting the cell proliferation and promoting cell apoptosis of DDP-resistant CRC cell line, while overexpression of the autophagy-related gene ATG14 could significantly recover their effects, demonstrating that SNHG14/miR-186/ATG14 could affect the DDP resistance of CRC (34). However, most studies have only focused on 5-Fu or DDP resistance and specific lncRNAs. To the best of our knowledge, we are the first to identify novel lncRNAs, miRNAs and mRNAs that were both dysregulated in chemoresistant HCT8/5-Fu and HCT8/DDP cells using whole-transcriptome sequencing. Recent studies have uncovered a new mechanism of lncRNAs as ceRNAs, that is, lncRNAs functioned as ceRNAs to sponge miRNAs, then further affected the expression of mRNAs, thereby regulating carcinogenesis (10–12). At present, many studies have shown that lncRNAs play important roles in the chemoresistance of cancers (13–15). However, the mechanism research of lncRNA and CRC chemoresistance requires further exploration. Therefore, it is significant to identify the roles and mechanisms of lncRNAs as ceRNAs in the drug resistance of CRC. Based on the ceRNA hypothesis and bioinformatics approaches, we constructed chemoresistance-related ceRNA networks and identified LINC02418 and EPHA2 as the hub genes in the regulatory work. LINC02418, an oncogenic lncRNA, has been reported to be upregulated in CRC, mechanistically, LINC02418 can physically bind to miR-1273g-3p and then upregulate MELK expression to affect tumorigenesis (22). In addition, LINC02418 was also found to be upregulated in both tissues and cells of non-small cell lung cancer (NSCLC), and it could regulate SEC61G expression via interacting with miR-4677-3p to accelerate NSCLC progression (35). There is little knowledge regarding the role and regulatory mechanism of LINC02418 in the chemoresistance of CRC. In this study, we found that it was highly expressed in HCT8/5-Fu and HCT8/DDP cells, and we subsequently focused our research on the regulation of CRC progression and chemoresistance by LINC02418. Loss-of-function assays indicated that LINC02418 knockdown increased the chemosensitivity, affected cell cycle progression and induced apoptosis of CRC chemoresistant cells, indicating that LINC02418 not only affects chemoresistance, but is also important for CRC progression. The other hub gene, EPHA2, a key member of the erythropoietin-producing hepatocellular (Eph) receptor family, is abundantly expressed in several cancers, including CRC (36), gastric cancer (37) and lung cancer (38). It was reported that highly expressed EPHA2 closely relates to poor progression-free survival and an increased progression rate, which could be regarded as a predictive biomarker of resistance (39). However, whether EPHA2 is related to acquired resistance to 5-Fu and DDP in CRC remains unknown. In this investigation, EPHA2 was upregulated, and our functional studies indicated that inhibiting the activity of EPHA2 could increase chemosensitivity, promote cell apoptosis, inhibit cell proliferation and alter the cell cycle distribution of drug-resistant cells. To our surprise, the results showed that LINC02418 positively regulates EPHA2. Bioinformatics prediction was performed to clarify the underlying mechanism between LINC02418 and EPHA2. Here, miR-372-3p was identified as a target of LINC02418 and a novel regulator of EPHA2 through bioinformatics prediction. At present, only one study has indicated that miR-372-3p is dramatically increased in CRC tissues and may be regarded as an independent prognostic factor for recurrence-free survival (RFS) and disease-specific survival (DSS) in CRC patients, and its overexpression could promote tumor progression (32). In addition, miR-372-3p was recently reported to be downregulated in osteosarcoma, regulated by the lncRNA HULC to modulate HMGB1 expression, and ultimately promoted osteosarcoma progression (40). In this study, we observed that miR-372 was overexpressed in CRC cell lines, but lowly expressed in CRC chemoresistant cells, and overexpression of miR-372-3p could also enhance chemosensitivity and induce apoptosis, and inhibit cell proliferation and cell cycle progression of CRC chemoresistant cells. Then, we found that silencing of LINC02418 upregulated miR-372-3p expression in two chemoresistant cell lines, while luciferase reporter assay demonstrated that LINC02418 acted as a molecular sponge for miR-372-3p by directly binding to complementary sequences, which explained the reason for the low expression of miR-372 in CRC chemoresistant cells. Thus, we speculate that LINC02418 affects CRC chemoresistance via downregulating miR-372-3p expression. It is known that miRNAs can regulate gene expression by directly binding to the 3ʹ-UTR of target mRNAs and leading to mRNA degradation or translation inhibition (41). EPHA2 was identified as a direct target of miR-372-3p, and further luciferase reporter assay revealed that miR-372-3p could directly bind to EPHA2 and inhibit its expression. Furthermore, we observed that the reduced expression of EPHA2 by LINC02418 knockdown in HCT8/5-Fu and HCT8/DDP cells was restored by simultaneous transfection of miR-372-3p inhibitors. Together, these results indicate that LINC02418 may affect the chemosensitivity of CRC by regulating the expression of EPHA2 as a ceRNA for miR-372-3p. In summary, this study screened lncRNA, miRNA and mRNA profiles in CRC chemosensitive and chemoresistant cell lines through the whole-transcriptome sequencing. To find RNAs that regulate 5-Fu and DDP resistance, the common DE lncRNAs, miRNAs and mRNAs in HCT8/5-Fu and HCT8/DDP cells were screened out to construct the ceRNA regulatory network. GO and KEGG pathway analyses were used to find the potential functions of DE-mRNAs regulated by lncRNA–miRNA pairs. A total of 52 lncRNA–miRNA–mRNA pathways closely related to chemoresistance were further constructed, including 14 lncRNAs, 19 miRNAs and 12 mRNAs, among which LINC02418/miR-372-3p/EPHA2 was further selected. Collectively, this study demonstrated that LINC02418 and EPHA2 are upregulated and miR-372-3p is downregulated in HCT8/5-Fu and HCT8/DDP cells, and these trends are responsible for the 5-Fu and DDP resistance of CRC. Mechanistically, LINC02418 functioned as an oncogenic lncRNA by acting as a ceRNA to sponge miR-372-3p and subsequently enhanced EPHA2 expression. Our results indicate that targeting the LINC02418/miR-372-3p/EPHA2 axis might be a new therapeutic strategy to overcome chemoresistance in CRC.
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PMC9587810 | 35763638 | Syarifah Dewi,Muhammad Fakhri Ramadhan,Murdani Abdullah,Mohamad Sadikin | The Effect of Avidin on Viability and Proliferation of Colorectal Cancer Cells HT-29 | 01-06-2022 | Avidin,HT-29 Cells,viability,proliferation,Cyclin D1 | Objective: The aim of this study was to analyze the effect of avidin treatment on cell viability, proliferation and cyclin D1 expression in colorectal cancer cells HT-29. Methods: Colorectal cancer cell line HT-29 incubated with 50, 100, 150, and 200 μg/mL of avidin concentration during 24, 48, and 72 hours, then the cell viability and proliferation were analyzed. Each avidin concentration was conducted together with HT-29 cell line without avidin treatment as a control group. The cell viability was measured by MTS assay and the proliferation was measured by BrdU (5-bromo-2′-deoxyuridine) cell proliferation assay. According to cell viability and proliferation result, we determined the 100 μg/mL avidin concentration for analyzing mRNA and protein of cyclin D1. Results: We demonstrated that the viability and proliferation of HT-29 cells were significantly decreased in all concentration of avidin treatment compared to control. The cell proliferation showed larger reduction in avidin treatment rather than cell viability. This proves avidin could inhibit proliferation of colorectal cancer cell HT-29 quite well. The expression of cyclin D1, both mRNA and protein, was also significantly decreased after the avidin treatment group compared to control group, it supports the suppression of proliferation result. Conclusion: We concluded that avidin treatment could decrease cell viability and proliferation, accompanied by suppression of cyclin D1 expression in colorectal cells HT-29. | The Effect of Avidin on Viability and Proliferation of Colorectal Cancer Cells HT-29
The aim of this study was to analyze the effect of avidin treatment on cell viability, proliferation and cyclin D1 expression in colorectal cancer cells HT-29.
Colorectal cancer cell line HT-29 incubated with 50, 100, 150, and 200 μg/mL of avidin concentration during 24, 48, and 72 hours, then the cell viability and proliferation were analyzed. Each avidin concentration was conducted together with HT-29 cell line without avidin treatment as a control group. The cell viability was measured by MTS assay and the proliferation was measured by BrdU (5-bromo-2′-deoxyuridine) cell proliferation assay. According to cell viability and proliferation result, we determined the 100 μg/mL avidin concentration for analyzing mRNA and protein of cyclin D1.
We demonstrated that the viability and proliferation of HT-29 cells were significantly decreased in all concentration of avidin treatment compared to control. The cell proliferation showed larger reduction in avidin treatment rather than cell viability. This proves avidin could inhibit proliferation of colorectal cancer cell HT-29 quite well. The expression of cyclin D1, both mRNA and protein, was also significantly decreased after the avidin treatment group compared to control group, it supports the suppression of proliferation result.
We concluded that avidin treatment could decrease cell viability and proliferation, accompanied by suppression of cyclin D1 expression in colorectal cells HT-29.
Avidin, a protein found in egg whites, can bind biotin (vitamin B7/H) with high affinity (Kd = 10-15 M). Avidin-biotin interaction known to be the strongest non-covalent interaction in world (Holmberg et al., 2005). Because the strength and specificity of avidin-biotin interaction, so this pair widely used in several methods in molecular, immunological, and cellular assays (Bratthauer, 2010). Based on previous study, avidin can detract the availability of extracellular biotin, therefore impairing biotin-requiring enzymes and reducing cells viability and inhibiting its proliferation (Firakania et al., 2016; Zerega et al., 2001). Several enzymes are required biotin as co-enzymes for their activities. Most of them are carboxylases such as acetyl-CoA carboxylase (ACC) that involved in fatty acid synthesis (Bhattacharjee et al., 2020; Mozolewska et al., 2020), pyruvate carboxylase (PC) involved in gluconeogenesis pathway (Kiesel et al., 2021; Ngamkham et al., 2020) and methylcrotonyl-CoA carboxylase (MCC) involved in branched-chain amino acids catabolism (Chen et al., 2021; He et al., 2020). All these enzymes play an important role in survivability and proliferation of cancer cells, so inhibition of this enzyme activity will suppress cancer cells progression. Beside those enzymes, bifunctional enzyme phosporibosylaminoimidazole carboxylase and also act as phosphoribosylaminoimidazole succino-carboxamide synthetase (PAICS) in de novo purine nucleotide synthesis, has carboxylase part which also requires biotin (Yin et al., 2018). De novo purine nucleotide synthesis also known as one of pathway actively used by cancer cells to produce purine in high amount to do cells division and PAICS itself reported to have important roles in cancer cells (Agarwal et al., 2020; Meng et al., 2018). Cyclin D1 is an important protein in cell cycle regulation, it will form active complexes with cyclin dependent kinase (CDK) 4 and 6 that promote G1- to S-phase progression. Many studies prove that cyclin D1 act as key regulator in cell cycle progression, so it has been considered to be an oncogene in many cancers including colorectal cancer (Alao, 2007). Several studies reported that Cyclin D1 overexpressed in many cancers type and one-third or more in colorectal cancers. It also reported cyclin D1 expression is associated with poor prognostic factor in colorectal cancer patients (Y. Li et al., 2014). Cell line HT-29 is human colorectal adenocarcinoma cells with high expression of cyclin D1 which has important roles on cell proliferation (Mermelshtein et al., 2005). Proliferation and metabolism are a critical way to inhibit tumor progression and metastasis. Suppressing of them will halt cancer cells growth and lowering its survivability. In the last decade, there were developed various less invasive treatments to control cancer cells progression. Previous study reported that avidin treatment reduced biotin availability and could halted PHA-induced human peripheral blood mononuclear cells (PBMC) proliferation and viability (Firakania et al., 2016). According to those study, we want to analyze the potential of avidin treatment in reducing of cancer cell viability and proliferation. One example of cancer cell type appropriately used in exploring of avidin effect is colorectal cancer cells, because avidin could be found in daily food source i.e. egg whites. So, this study revealed the avidin effect in colorectal cancer cells HT-29 viability and proliferation, also cyclin D1 expression in those cells.
This is an in vitro experimental study using human colorectal cancer cell line HT-29 treated by avidin. Cell line HT-29 was obtained from American Type Culture Collection (ATCC). It was conducted in Biochemistry and Biology Molecular Department Laboratory, Faculty of Medicine Universitas Indonesia, and Molecular Biology and Proteomics Core Facilities (MBPCF)-Indonesia Medical Education and Research Institute (IMERI), Faculty of Medicine Universitas Indonesia. Colorectal cancer cells culture HT-29 Human colorectal cancer cells HT-29 were cultured in Dulbecco’s Modified Eagle Medium (DMEM)-high glucose (PAN Biotech, Germany) containing 10% fetal bovine serum (FBS) (Biowest, France), 1% penicillin-streptomycin (Sigma-Aldrich, USA), and 1% amphotericin B (PAN Biotech, Germany). The cells were incubated at 37°C in a humidified atmosphere containing 95% air and 5% CO2. The cells were harvested after 80% confluence and could be used for further experiments. Avidin Solution Preparation Avidin (Sigma-Aldrich, USA) 1000 µg/mL stock solution was made by dissolving 10 mg avidin in 10 mL NaCl 0,9%. The solution then filtered aseptically by 0,22 µm millipore sterile filter. The stock solution can be diluted by culture medium to make the desirable concentration for experiments (50, 100, 150, and 200 µg/mL). Cell Viability Assay HT-29 cells cultured in 96-well plates with a density of 5 x 104 cells/well and incubated overnight. Afterwards, the cells were treated with 50, 100, 150, and 200 µg/mL of avidin concentration. All avidin treatments were conducted 3 replications and HT-29 cells without avidin treatments were used as control group. We measured the cell viability by MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assay using CellTiter 96® Aqueous Solution Cell Proliferation Assay Kit (Promega, USA). This assay was performed according to manufacturer’s protocol. After the treatment and addition of reagents to each well, it was incubated at 37°C for 2 hours, then the absorbance was measured at 450 nm using ELISA reader. Cells viability was analyzed at 24, 48 and 72 hours of avidin incubation. Cell Proliferation Assay HT-29 cells cultured in 96-well plates with a density of 5 x 104 cells/well and incubated overnight. Afterwards, the cells were treated with 50, 100, 150, dan 200 µg/mL of avidin concentration. All avidin treatments were conducted 3 replications and HT-29 cells without avidin treatments were used as control group. BrdU Cell Proliferation ELISA kit (colorimetric) (Abcam, UK) was used for cells proliferation assay. This assay was performed according to manufacturer’s protocol. The results were obtained after absorbance reading at 450 nm using ELISA reader. Cells proliferation was measured at 24, 48 and 72 hours of avidin incubation. Relative mRNA expression of CCND1 HT-29 cells cultured in 12-well plates with a density of 5 x 105 cells/well and incubated overnight. Afterwards, the cells were treated with 100 µg/mL of avidin concentration then incubated in 24, 48 and 72 hours. Each avidin incubation was conducted 3 replications and HT-29 cells without avidin treatments were used as control group. The mRNAs were extracted from HT-29 cells by Quick-RNA™ Miniprep Plus Kit (Zymo Research, US). Quantitative RT-PCR was conducted by using SensiFAST™ SYBR® No-ROX One-Step Kit (Meridian Bioscience, USA) with human CCND1 (cyclin D1) Primer. Human 18sRNA was used as a housekeeping gene for calculating the relative expression of CCND1. Sequences of CCND1 primers are 5’-GAA GGA GAC CAT CCC CCT GA-3’ as forward and 5’-GAA ATC GTG CGG GGT CAT TG-3’ as reverse, it results 142 PCR product. Sequences of 18sRNA primers are 5’-AAA CGG CTA CCA CAT CCA AG-3’ as forward and 5’-CCT CCA ATG GAT CCT CGT TA-3’ as reverse, it results 155bp PCR product. Relative mRNA expression of CCND1 was calculated with the Livak method (2-ΔΔCt) (Livak and Schmittgen, 2001). Cyclin D1 Protein Level Assay Cyclin D1 protein levels were measured by Human Cyclin D1 ELISA Kit (Abcam, UK). HT-29 cells cultured in 12-well plates with a density of 5 x 105 cells/well and incubated overnight. Afterwards, the cells were treated with 100 µg/mL of avidin concentration then incubated in 24, 48 and 72 hours. Each avidin incubation was conducted 3 replications and HT-29 cells without avidin treatments were used as control group. Cells were harvested and washed by phosphate buffer saline (PBS). After that, cells were extracted by Cell Extraction Buffer from Human Cyclin D1 Elisa Kit (Abcam, UK) and incubated on ice for 20 minutes. Cells then centrifuged and the supernatant transferred to a clean tube and it can be used for measuring of cyclin D1 protein and total protein concentration. The supernatant then inserted to 96-wells plate from ELISA Kit to measure cyclin D1 protein levels. This assay was performed according to manufacturer’s protocol. Total protein concentration was measured by read the absorbance at 280 nm (Christian-Warburg method) from the samples and bovine serum albumin (BSA) as the standard. Cyclin D1 levels were presented per mg protein total (ng/mg protein). Statistical Analysis This study obtained numerical data for cell viability, proliferation, relative mRNA expression of CCND1 and protein level of Cyclin D1. We analyzed the significant difference of cell viability and proliferation between control and various avidin concentration treatment group using One-Way ANOVA test. We also used independent t-test to analyze the significant difference of CCND1 mRNA expression and Cyclin D1 level between avidin treatment and control group. All statistical test was performed using SPSS software version 20.
Viability of HT-29 cells after avidin treatment Cell viability and proliferation were measured after avidin treatment to HT-29 cells. Cell viability number was calculated by divide live cells in avidin treatment group per live cells in control group, so we reported this data in percentage value. We found decreasing of HT-29 cell viability after avidin treatment in all concentration (50, 100, 150 and 200 ug/mL) and all incubation time (24, 48 and 72 hours). As we see in Figure 1, the number of cell viabilities in 24 and 48 hours of incubation were quite similar, around 70-80% for all avidin concentration. Cell viability in 72 hours incubation was around 80-90%, slight increase than 24 and 48 hours. Although the cell viability of HT-29 after avidin treatment was not greatly decreased, it was statistical significantly both in 24 hours (p<0.001), 48 (p<0.05) and 72 hours (p<0.05) incubation compare to control group. Proliferation of HT-29 cells after avidin treatment Cell proliferation number was also reported in percentage value that calculated by divide the absorbance of avidin treatment group by the absorbance of control group. We demonstrated that avidin treatment lead suppression of HT-29 cells proliferation in each concentration and all incubation time. The decreasing number of cell proliferation seem avidin dose dependent. In 24 hours of incubation, we found the smallest reduction of cell proliferation was 77.78% for 50 µg/mL avidin and the greatest was 25.40% for 200 µg/mL avidin. Similar pattern also shown in 48 and 72 hours of incubation, however the greatest reduction of cell proliferation was found in 48 hours. In 48 hours of incubation, the decreasing of cell proliferation was greatly significant (p<0.001) for all avidin treatment group and incubation time, although cell proliferation tends to increase in 72 hours of incubation (see Figure 2). We show the appearances of HT-29 cells culture growth both normal and avidin treatment group under inverted microscope 40x (see figure 3). We found that in general the number of cells is less in the avidin treatment than control group. Relative mRNA expression of CCND1 and protein Cyclin D1 levels in HT-29 cells after avidin treatment We also measured the expression of cyclin D1 in HT-29 cells after avidin treatment both in mRNA and protein level expression. We decided to use avidin concentration 100 ug/mL according to cell viability results. The previous results have shown that the cell viability treated by avidin with concentrations of 100, 150 and 200 showed a very significant decrease, but among three of them there was no significant differences (One-Way ANOVA test). So, we decided to use the lowest concentration of avidin (100 g/mL) to save reagents. The relative mRNA expression of CCND1 showed significantly decreasing after 100 ug/mL avidin treatment in HT-29 cells both in 48 hours (p<0.05) and 72 hours (p<0.001) of incubation compared to control group. There is no difference of relative mRNA expression of CCND1 between avidin treatment in 24 hours of incubation and control (see Figure 4). This result support by the cyclin D1 protein levels finding, there are significant decreasing of cyclin D1 levels in HT-29 after 100 ug/mL avidin treatment both in 48 and 72 hours of incubation (p<0.001) compared to control group. While there is no difference of cyclin D1 levels between avidin treatment and control group in 24 hours of incubation (see Figure 5).
Our findings in this study uncover new insight in searching of alternative cancer treatment. Significant decreasing of cell viability and proliferation after avidin treatment, making this compound is a promising agent in the development of cancer therapy. We demonstrated that cell viability and proliferation of HT-29 cells was significantly decrease after avidin treatment in all avidin concentration and all incubation time. If we see the alteration decreasing in cell proliferation is higher than cell viability. This showed that avidin treatment more suppress cell proliferation than cell viability. This is in accordance with the mechanism of avidin binds to biotin, causes vital enzymes that play a role in cell proliferation cannot work. We also found the maximum suppression of cell viability and proliferation occur in 48 hours incubation time. It might be caused the doubling time of HT-29 cells is 48 hours. So, if incubation longer than 48 hours the effectivity of avidin will decrease because the total HT-29 cells more abundant. In this study we also demonstrated that cyclin D1 expression, both at mRNA and protein level, decreased in HT-29 cells after avidin treatment. Relative mRNA expression of CCND1 and cyclin D1 protein level were significantly lower compared to control, especially in 48 and 72 hours. This supports the result of declining proliferation of HT-29 cells after treated by avidin. Mechanism of avidin in decreasing cyclin D1 expression is still unclear, but this phenomenon might occur due to cell proliferation suppression. Other studies demonstrated that biotin required for biotinylation in histones (H2A, H3 and H4) by holocarboxylase synthetase and it influences some genes expression (Hassan and Zempleni, 2008; Narang et al., 2004). However, whether the biotinylation inhibition due to avidin treatment could affect the gene expression of cyclin D1, further study is required. Cyclin D1 plays a role in cell proliferation, especially regulate G1 to S phase of cell cycle (Yang et al., 2006). Many studies showed that cyclin D1 correlated with colorectal cancer cells proliferation and malignant cells transformation (Albasri et al., 2019; Marcolino et al., 2020). This study revealed the role of strong binding avidin to biotin will directly reduce cancer cells viability, proliferation, and cyclin D1 expression, because it reduces the availability of biotin for some important enzymes. Biotin takes an important role as coenzyme for a lot of notable enzymes in cell, particularly CO2-using carboxylases like acetyl-CoA carboxylase (ACC) in fatty acid synthesis, pyruvate carboxylase (PC) in gluconeogenesis, methylcrotonyl-CoA carboxylase (MCC) in leucine metabolism, propionyl-CoA carboxylase in odd-chain fatty acid and amino acid metabolism, also carboxylase part of bifunctional enzyme, phosporibosylaminoimidazole carboxylase, phosphoribosylaminoimidazole succinocarboxamide synthetase (PAICS) in purine nucleotide synthesis (Tong, 2013; Waldrop et al., 2012). ACC is a rate limiting enzyme in fatty acid (FA) synthesis. FA, on the other hand, is a building block for more complex lipids, as the vital source of structural membrane components, fuel source for growing and dividing cells, and second messengers in signal transduction (Bhattacharjee et al., 2020; Mozolewska et al., 2020). Cancer cells reportedly more rely on de novo FA synthesis pathway, while normal cells getting FA mostly from dietary sources (M. Chen and Huang, 2019). PC is an important enzyme for cellular energy metabolism by converting pyruvate to oxaloacetate in TCA cycle, key enzyme in gluconeogenesis, fatty acid synthesis, amino acid synthesis, and enhance protection from oxidative stress which are resulted from increasing metabolism. These functions enable metabolic plasticity to utilize any energy substrates depending on their availability, also support survival and growth cells, and metastases for some cases, the notable characteristic in cancer cells (Kiesel et al., 2021; Ngamkham et al., 2020). MCC is a mithocondrial enzyme that reported to be an oncogene and connected with tumor formation and progression, especially methylcrotonyl-CoA carboxylase 2 (MCC2), a subunit of MCC. MCC2 overexpression reported in breast cancer and correlated with tumor formation and progression, also supporting leucine oncogenic function to promote hepatocellular carcinoma development and prostate cancer cells (Y. Y. Chen et al., 2021; He et al., 2020; Liu et al., 2019). Phosporibosylaminoimidazole carboxylase, phosphoribosylaminoimidazole succinocarboxamide synthetase (PAICS), is the less known enzyme that requires biotin as its coenzyme. PAICS is a key enzyme which catalyzes two essential steps in de novo purine nucleotide synthesis, which catalyzes 5-aminoimidazole ribonucleotide (AIR) to make carboxyaminoimidazole ribonucleotide (CAIR) in vertebrates (Li et al., 2007). Purine nucleotide synthesis known to be high in cancer cells than normal cells, caused by high requirement for DNA replication due to uncontrolled cell proliferation. PAICS enzyme has a carboxylase part and using CO2 to catalyze the carboxylation, which is a similar trait to other biotin carboxylases (Tong, 2013). PAICS also reported to be involved in tumorigenesis, especially breast cancer and gastric cancer cells proliferation (Huang et al., 2020; Meng et al., 2018). These biotin-requiring enzymes showed similar notable traits. First, they were high expressed and associated with some poor prognosis and tumor progression on cancer cells. They are also key enzymes and catalyze reactions which produce important components for energy sources, survival, oxidative stress protection, and cells division like fatty acid, oxaloacetate, and purine nucleotide (Tong, 2013). Reduced biotin availability as the result of avidin treatment to the medium cell caused the impairment of those enzymes that lead to reducing the cells viability and proliferation. A study reported that avidin bound extracellular biotin and affected acetyl-CoA carboxylase, thus regulated chick chondrocytes proliferation by interfering with fatty acid biosynthesis (Zerega et al., 2001). Another study also reported that avidin reduced biotin availability and could halted PHA-induced human PBMC proliferation and viability (Firakania et al., 2016). These results revealed avidin as alternative agent, in reducing cells viability and inhibit cells proliferation, also decrease cyclin D1 expression in colorectal cancer cell lines HT-29. Moreover, avidin is easily to found in our daily food, it is contained in egg whites (Krkavcová et al., 2018). So, this can be another insight to use avidin as an anticancer candidate, especially colorectal cancer in the future. Although the exact mechanism has not known yet and need further research especially by in vivo experiment method. The study also can be expanded to avidin in egg whites as an ingestion therapy for cancer in digestive tract like colorectal cancer. According to these results, we concluded that avidin could decrease colorectal cancer cell HT-29 viability and proliferation with a greater reduction in proliferation than viability. In addition, the expression of cyclin D1, both mRNA and protein, was also decreased in cells treated with avidin, this supports that there was an inhibition of cell proliferation after avidin treatment.
The authors confirm contribution to the paper as follows: study conception and design: Mohamad Sadikin; data collection: Muhammad Fakhri Ramadhan; analysis and interpretation of results: Syarifah Dewi and Murdani Abdullah; draft manuscript preparation: Syarifah Dewi and Muhammad Fakhri Ramadhan. All authors reviewed the results and approved the final version of the manuscript. | true | true | true |
PMC9587885 | 35633562 | Hussein Samia,Ahmed El Shabrawy Lasheen,Amr A Abdelrahman,Amira S Al-Karamany,Reham Sameh,Ahmed Algazeery | Association between miR-196a-2 Gene Polymorphism and Ovarian Cancer Prognosis in Egyptian Females | 01-05-2022 | miR-196a-2,polymorphism,ovarian cancer,P53 | Background: Ovarian cancer is the fifth leading cause of cancer-related deaths among women worldwide. Unfortunately, early detection tests are relatively lacking. Diagnosis in the late stages of the disease carries a poor prognosis. Objective: To evaluate the relationship between miR-196a-2 rs11614913 polymorphism and ovarian cancer risk and prognosis in Egyptian females. Methods: In this case-control study, the participants were classified into 2 groups. Group A is the control group which included 50 healthy females. Group B included 50 patients newly diagnosed with ovarian carcinoma confirmed by histopathological analysis. Immunohistochemistry for P53 and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) for miR-196a-2 genotypes detection were performed. Results: There was a statistically significant difference among ovarian cancer cases and controls regarding genotypes (P = 0.003). However, the distribution of the T and C alleles in both studied groups showed no significant difference (P = 0.17). There was a statistically significant increase of CA 125 levels among CT and CC genotypes carriers of ovarian cancer cases (p = 0.04). Besides, there was a statistically significant correlation between miR-196a-2 polymorphism and each of tumor grade (P <0.001), p53 immunohistochemical expression (P= 0.002), and Figo classification (P <0.001). Conclusion: There was a statistically significant increase of CA 125 levels among C allele carriers of ovarian cancer cases. Besides, there was a statistically significant association between the miR-196a-2 polymorphism and each of tumor grade, p53 immunohistochemical expression, and Figo classification. So, miR-196a-2 polymorphism can be a possible prognostic factor in ovarian cancer. | Association between miR-196a-2 Gene Polymorphism and Ovarian Cancer Prognosis in Egyptian Females
Ovarian cancer is the fifth leading cause of cancer-related deaths among women worldwide. Unfortunately, early detection tests are relatively lacking. Diagnosis in the late stages of the disease carries a poor prognosis.
To evaluate the relationship between miR-196a-2 rs11614913 polymorphism and ovarian cancer risk and prognosis in Egyptian females. Methods: In this case-control study, the participants were classified into 2 groups. Group A is the control group which included 50 healthy females. Group B included 50 patients newly diagnosed with ovarian carcinoma confirmed by histopathological analysis. Immunohistochemistry for P53 and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) for miR-196a-2 genotypes detection were performed.
There was a statistically significant difference among ovarian cancer cases and controls regarding genotypes (P = 0.003). However, the distribution of the T and C alleles in both studied groups showed no significant difference (P = 0.17). There was a statistically significant increase of CA 125 levels among CT and CC genotypes carriers of ovarian cancer cases (p = 0.04). Besides, there was a statistically significant correlation between miR-196a-2 polymorphism and each of tumor grade (P <0.001), p53 immunohistochemical expression (P= 0.002), and Figo classification (P <0.001).
There was a statistically significant increase of CA 125 levels among C allele carriers of ovarian cancer cases. Besides, there was a statistically significant association between the miR-196a-2 polymorphism and each of tumor grade, p53 immunohistochemical expression, and Figo classification. So, miR-196a-2 polymorphism can be a possible prognostic factor in ovarian cancer.
MicroRNAs (miRNAs) are 18–25 nucleotide-long, single-stranded noncoding RNA that play an important role in the regulation of mammalian gene expression via post-transcriptional repression by directly binding to the 3′ untranslated region (UTR) of messenger RNAs (mRNAs), resulting in downregulation of their expression (Karabegović et al., 2017; Lu and Rothenberg, 2018). They play important roles in regulating different biological processes, including cell differentiation, proliferation, and apoptosis (Vidigal and Ventura, 2015). miRNA variants act as an oncogene or tumor suppressor gene indirectly (Ni et al., 2020). Single nucleotide polymorphisms (SNPs) of miRNAs may influence their functions through altering miRNA expression, maturation, and/or efficiency of targeting and, thereby, contribute to the risk of cancer (Zheng et al., 2017). There is a controversy regarding the role of miR-196a-2 in cancer. Some studies claimed that it has an oncogenic function. Others suggested that it acts as a tumor-suppressor. When it acts as an inhibitory factor of oncogenic molecules, it acts as a tumor suppressor and when it targets tumor suppressors, it acts as an oncogene (Chen et al., 2011). miR-196a-2 polymorphism has significant associations with various types of cancer, including breast, lung, esophageal, gastric, and hepatocellular cancer (Alshatwi et al., 2012; Hu et al., 2008; Tutar, 2014; Peng et al., 2010 and Gawish et al., 2020). Carriers of the homozygote variant CC are more likely to develop gastric cancer compared with wild-type homozygote TT and heterozygote CT carriers and the C allele was significantly associated with lymph node metastasis of gastric cancer (Peng et al., 2010). Hu et al., (2008) reported significantly higher expression of miR 196a in non small cell lung tumor samples with CC genotypes compared with that of CT and TT individuals. Ovarian cancer is the fifth leading cause of cancer-related deaths among women worldwide (Siegel et al., 2019). Unfortunately, early detection tests are relatively lacking. Furthermore, most women with ovarian cancer are diagnosed in the late stages of the disease, which carries a poor prognosis (Xu et al., 2017 and Buchanan et al., 2017). Risk factors of ovarian cancer include early menarche, late menopause, low parity, lack of physical activity, higher body mass index, and long-term use of estrogen replacement therapy (Romero and Bast, 2012). Family history is an important risk factor which suggests that genetic factors contribute to the susceptibility to ovarian cancer (Norquist et al., 2015). Early detection of ovarian cancer is difficult because its symptoms do not appear except in the late stages. Besides, screening modalities such as transvaginal ultrasound or serum cancer antigen 125 (CA125), are ineffective in early detection (Sun et al., 2017 and Lee et al., 2017). Despite the advancement of diagnostic techniques such as computed tomography/positron emission tomography scan and the use of targeted therapeutics, the 5-year survival rate ranges between ~30-50% (Suh et al., 2015). So, seeking for new biomarkers for ovarian cancer detection and progress indication is important for the patients. We conducted the present study to evaluate the relationship between miR-196a-2 gene polymorphism and ovarian cancer risk and prognosis in Egyptian females.
This study was conducted in the Departments of Obstetrics and Gynecology, Pathology and Medical Biochemistry & Molecular Biology - Faculty of Medicine, Zagazig University from December 2018 to December 2021. The study protocol was approved by the Institute Review Board of the Faculty of Medicine, Zagazig University. This is a case-control study. The participants were classified into 2 groups. Group A is the control group. It included 50 healthy females. Group B included 50 patients newly diagnosed with ovarian carcinoma confirmed by histopathological analysis. Informed consent was obtained from all participants. All patients were subjected to the following: full history taking and complete physical examination. Routine laboratory investigations: complete blood count (CBC), liver and kidney function tests, and tumor marker CA 125 measurement were performed. Histopathological analysis for confirming ovarian carcinoma and immunohistochemistry for P53 were analyzed. Specimens of healthy ovaries were taken from cases with a total abdominal hysterectomy and bilateral salpingo-oophorectomy (TAH+BSO) received at the Pathology Department. 2 ml venous blood was taken on EDTA K2 containing tubes for DNA extraction. It was analyzed for the miR-196a-2 polymorphism rs11614913. It was genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) by restriction MspI. PCR was performed with a total volume of 25 μl with 100 ng DNA template, 2.5 μl of 10X PCR buffer, 1 U of Taq DNA polymerase, 0.2 mM dNTPs (Invitrogen, Carlsbad, CA, USA), and 0.5 μmol/l of each primer (miR 196a 2 F 5’ CCC CTT CCC TTC TCC TCC AGA TA 3’ and R 5’ CGA AAA CCG ACT GAT GTA ACT CCG 3’). The PCR conditions were 94˚C for 5 min followed by 35 cycles of 30 sec at 94˚C, 30 sec at 63˚C, and 1 min at 72˚C, and the final elongation step at 72˚C for 10 min. A total of 10 μl PCR product was then digested using 2 μl (10 U/μl) MspI restriction enzyme (Thermo Fisher Scientific, Inc., Pittsburgh, PA, USA) for 16 h at 37˚C. The resulting fragments were separated by electrophoresis on a 3% agarose gel (Bio-Rad Laboratories, Inc., Hercules, CA, USA) and visualized in three distinct patterns of restriction fragments. The CC genotype produced two fragments (125 and 24 bp), the TT homozygote produced one 149 bp fragment and the TC heterozygote produced three fragments (125, 149, and 24 bp). The experiment was performed in tripli¬cate (Gawish et al., 2020). Immunohistochemical staining was carried out using the polymer Envision detection system; the Dako EnVision ™ kit (Dako, Copenhagen, Denmark). Tissue sections (3–5 μm) were deparaffinized in xylene and rehydrated in graded alcohol. To block endogenous peroxidase, slides were incubated for 10 min in hydrogen peroxide 3%. Dako target antigen retrieval solution (pH 6.0) was used. Then slides were incubated with Dako Mouse Primary Monoclonal (DO-7), The reaction was visualized by incubating the sections with diaminobenzidine (DAB) for 15 min then Mayer’s hematoxylin was used. P53 nuclear stain in more than 5% of malignant cells was considered a positive immunoreactivity and its expression was evaluated as follows: p53-negative (≤ 5%), low p53 (5% to 50%), and high p53 (> 50%) (Lotfi et al., 2011). Statistical analysis Continuous variables were expressed as the mean ± SD and median (range), and the categorical variables were expressed as a number (percentage). Percentage of categorical variables were compared using Pearson’s Chi-square test or Fisher’s exact test when was appropriate. A P-value <0.05 was considered significant. All statistics were performed using SPSS version 22.0 for Windows (IBM Corp., Armonk, NY, USA) and MedCalc Statistical Software version 18.9.1 (MedCalc Software bvba, Ostend, Belgium; http://www.medcalc.org; 2018).
Clinicopathological features of patients with ovarian cancer Low-grade serous ovarian carcinoma (LGSOC) represented 36% of cases while high-grade serous ovarian carcinoma (HGSOC) represented 64% of cases (Table 1). Regarding P53 expression, two patients (4%) showed focal expression, twenty patients (40%) showed negative expression, and twenty-eight (56%) patients showed diffuse expression (Table 1, Figures 1-3). The mean CA 125 level among ovarian cancer cases was 231.8 ± 251.8. There was a highly statistically significant increase of CA 125 levels among ovarian cancer cases than their controls (Table 1, Figure 4). miR 196 a 2 gene polymorphism in the two study groups There was a statistically significant difference between ovarian cancer cases and controls regarding genotypes (P = 0.003). However, the distribution of the T and C alleles in both studied groups showed no significant difference (P = 0.17) (Table 2). Relation between miR 196 a 2 gene polymorphism and clinicopathological features and There was a statistically significant increase in CA 125 levels among CT and CC genotypes carriers of ovarian cancer cases (p = 0.04) (Table 3). Besides, there was a statistically significant difference between miR-196 a-2 polymorphism and each of tumor grade, p53 immunohistochemical expression, and Figo classification (P < 0.001, 0.002, and < 0.001 respectively) (Table 4). No statistically significant difference was found between miR-196 a-2 variants and any of the studied basic or laboratory characters of the ovarian cancer cases (Table 5).
According to the cell origin, ovarian cancer is classified into epithelial, germ cell and stromal ovarian cancer. Other extremely rare cancers include small cell carcinoma and sarcomas (Boussios et al., 2017). Epithelial ovarian cancer (EOC) represents more than 85% of ovarian cancer cases and is the deadliest gynecological cancer, its major cause of death is mainly attributed to metastasis. EOC is further classified into five histological subtypes, including high-grade serous carcinomas (HGSC), low-grade serous carcinomas (LGSC), endometrioid carcinomas (EC), clear cell carcinomas (CCC), and mucinous carcinomas (MC). In our study we focused on epithelial serous carcinomas. In our study, there was a statistically significant difference between ovarian cancer cases and controls regarding genotypes. However, the distribution of T and C alleles in both studied groups showed no significant difference. Previous studies found no significant association between the miR-196a-2 polymorphism and cancer risk. Lukács et al., (2019) found no significant difference between high-grade serous papillary ovarian cancer and controls regarding miR-196a-2 genotypes or allele distribution. This result was similar to that found by Ni and Huang (2016) in ovarian cancer. Similar results were found by Chen et al., (2012) in colorectal cancer, Deng et al., (2015) in bladder cancer, and Pu et al., (2014) in gastric cancer. On the other hand, Song et al., (2016) observed that the CC genotype increased ovarian cancer risk compared with those carrying the wild-type TT and heterozygous CT genotypes. Moreover, they found increased production of mature miR 196a-2 in the C allele carriers compared to the T allele carriers and they considered that responsible for the abnormal cell viability and migration/invasion capacity in the human ovarian cell line. They explained that rs11614913 polymorphism may affect the processing of the pre miRNA to its mature form. Also, Liu et al., (2015) found that miR-196a2 polymorphism can influence the susceptibility to ovarian cancer in a Chinese population. In our study, there was a statistically significant difference between miR-196 a-2 polymorphism and each of tumor grade, p53 immunohistochemical expression, and Figo classification which indicated the association of miR-196 a-2 polymorphism with poor prognosis in ovarian cancer. Fan et al., (2015) reported the association between high levels of miR-196a expression and worse overall survival in ovarian cancer patients, especially in advanced-stage tumors. miR-196a expression was positively correlated with tumor stage and lymph node metastasis. miR-196a-2 role in carcinogenesis is by targeting many genes, such as lamin B receptor (LBR), rab4 interacting protein (RUFY2), autophagy-related 9a (ATG9A), methyl CpG binding domain 4 (MBD4), HOX gene, HMGA2, and annexin A1 (Rapado-González et al., 2019 and Lukács et al., 2019). Also, Ni et al (2020) found that miRNA-196a promotes cell proliferation and inhibits apoptosis in human ovarian cancer by directly targeting DDX3 and regulating the PTEN/ PI3K/AKT signaling pathway. In conclusion, There was a statistically significant increase of CA 125 levels among C allele carriers of ovarian cancer cases. Besides, there was a statistically significant association between the miR-196a-2 polymorphism and each of tumor grade, p53 immunohistochemical expression, and Figo classification. So, miR-196a-2 polymorphism can be a possible prognostic factor in ovarian cancer.
Conception: Ahmed Algazeery and Samia Hussein; Interpretation or analysis of data: Ahmed Algazeery, Reham Sameh, Amira S. Al-Karamany, and Samia Hussein; Preparation of the manuscript: Samia Hussein; Revision for important intellectual content: All authors; Supervision: Ahmed Algazeery and Samia Hussein.
The experimental protocol was approved by the Faculty of Science, Zagazig University, Zagazig, Egypt.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
the authors declare no conflict of interest. | true | true | true |
PMC9588054 | Riki Ishibashi,Ritsuko Maki,Satsuki Kitano,Hitoshi Miyachi,Fumiko Toyoshima | Development of an in vivo cleavable donor plasmid for targeted transgene integration by CRISPR-Cas9 and CRISPR-Cas12a | 22-10-2022 | Biological techniques,Biotechnology,Genetics | The CRISPR-Cas system is widely used for genome editing of cultured cells and organisms. The discovery of a new single RNA-guided endonuclease, CRISPR-Cas12a, in addition to the conventional CRISPR-Cas9 has broadened the number of editable target sites on the genome. Here, we developed an in vivo cleavable donor plasmid for precise targeted knock-in of external DNA by both Cas9 and Cas12a. This plasmid, named pCriMGET_9-12a ( p lasmid of synthetic CRI SPR-coded RNA target sequence-equipped donor plasmid- m ediated ge ne t argeting via Cas 9 and Cas 12a ), comprises the protospacer-adjacent motif sequences of Cas9 and Cas12a at the side of an off-target free synthetic CRISPR-coded RNA target sequence and a multiple cloning site for donor cassette insertion. pCriMGET_9-12a generates a linearized donor cassette in vivo by both CRISPR-Cas9 and CRISPR-Cas12a, which resulted in increased knock-in efficiency in culture cells. This method also achieved > 25% targeted knock-in of long external DNA (> 4 kb) in mice by both CRISPR-Cas9 and CRISPR-Cas12a. The pCriMGET_9-12a system expands the genomic target space for transgene knock-in and provides a versatile, low-cost, and high-performance CRISPR genome editing tool. | Development of an in vivo cleavable donor plasmid for targeted transgene integration by CRISPR-Cas9 and CRISPR-Cas12a
The CRISPR-Cas system is widely used for genome editing of cultured cells and organisms. The discovery of a new single RNA-guided endonuclease, CRISPR-Cas12a, in addition to the conventional CRISPR-Cas9 has broadened the number of editable target sites on the genome. Here, we developed an in vivo cleavable donor plasmid for precise targeted knock-in of external DNA by both Cas9 and Cas12a. This plasmid, named pCriMGET_9-12a (plasmid of synthetic CRISPR-coded RNA target sequence-equipped donor plasmid-mediated gene targeting via Cas9 and Cas12a), comprises the protospacer-adjacent motif sequences of Cas9 and Cas12a at the side of an off-target free synthetic CRISPR-coded RNA target sequence and a multiple cloning site for donor cassette insertion. pCriMGET_9-12a generates a linearized donor cassette in vivo by both CRISPR-Cas9 and CRISPR-Cas12a, which resulted in increased knock-in efficiency in culture cells. This method also achieved > 25% targeted knock-in of long external DNA (> 4 kb) in mice by both CRISPR-Cas9 and CRISPR-Cas12a. The pCriMGET_9-12a system expands the genomic target space for transgene knock-in and provides a versatile, low-cost, and high-performance CRISPR genome editing tool.
Clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated (Cas) systems are adaptive immunity systems of bacteria and archaea that prevent infection by viruses and plasmids. CRISPR-Cas systems are classified as Class 1 (Types I, III, and IV) and Class 2 (Types II, V, and VI). CRISPR-Cas9 is a Type II CRISPR-Cas system that has been widely used in genome editing technologies. The most commonly used CRISPR-Cas9-mediated genome editing system has two main components: a Cas9 nuclease from Streptococcus pyogenes (SpCas9) and a guide RNA (gRNA). When recruited to a target DNA locus by gRNA, SpCas9 induces a blunted-end DNA double-strand break in a manner that is dependent on a protospacer adjacent motif (PAM) sequence (NGG; N = A/T/G/C). CRISPR-Cas12a (also known as Cpf1) is a Type V CRISPR-Cas system that has been used as an alternative genome editing tool to CRISPR-Cas9. The gRNA of the CRISPR-Cas12a ribonucleoprotein complex is a 41–44-nucleotide-long crRNA that codes a complementary sequence of 20–24 nucleotides against the target DNA sequence and does not need tracrRNA to recruit Cas12a to the target DNA locus. The commonly used Acidaminococcus sp. Cas12a (AsCas12a) recognizes the T-rich PAM sequence (TTTV; V = A/C/G) for induction of a DNA double-strand break. These distinct features of CRISPR-Cas12a expand the target region for genome editing that cannot be edited by CRISPR-Cas9. Various technologies have been developed for the CRISPR-Cas9 system to increase the targeting efficiency. Microhomology-mediated end-joining (MMEJ) and homology-independent targeted integration (HITI) methods achieved targeted transgene integration with a minimum length of homology arms. The Easi-CRISPR targeting method, which uses long single-stranded DNA (ssDNA) as a donor template, has successfully generated knock-in mice. Another group reported the Tild-CRISPR method, in which PCR-amplified or in vitro-digested linearized double-stranded DNA is used as a donor template. A homology-mediated end-joining (HMEJ)-based targeting strategy, which uses in vivo cleavable plasmid as a donor template, also exhibited high knock-in efficiency. In addition, a donor DNA-Cas9 conjugation strategy, such as use of the Cas9-Avidin–Biotin ssDNA (CAB) system and SNAP-tag system, has been reported to improve knock-in efficiency. The two-cell homologous recombination (2C-HR)-CRISPR method and adeno-associated virus (AAV)-mediated large fragment delivery strategy have also achieved the integration of large transgenes. Although the CRISPR-Cas9 system has achieved targeted knock-in of long external DNA, the CRISPR-Cas12a-mediated large transgene integration has not been succeeded in mice. Previously, we developed an in vivo cleavable donor plasmid, pCriMGET (plasmid of synthetic CRISPR-coded RNA target sequence-equipped donor plasmid-mediated gene targeting), for CRISPR-Cas9 genome editing. The pCriMGET system linearized a donor cassette intracellularly and enhanced the integration of long (3 kb) external DNA by CRISPR-Cas9. Here, we developed the next generation of pCriMGET that enables precise knock-in of long (4.0–5.4 kb) external DNA by both CRISPR-Cas9 and CRISPR-Cas12a in culture cells and in mice.
The first-generation pCriMGET has an off-target-free synthetic crRNA target sequence (syn-crRNA-TS) with a CRISPR-Cas9 PAM sequence at the 3′-end. We modified this sequence by adding a CRISPR-Cas12a PAM sequence (TTTC) at the 5′-end (Fig. 1). The resultant syn-crRNA-TS_9-12a sequence has no potential off-target regions for CRISPR-Cas12a in the mouse or human genome that matched more than 20 out of the 23 bases of the spacer sequence (5′-GCTGTCCCCAGTGCATATTCAGG-3′) (Supplementary Table S1). Notably, the spacer sequence for CRISPR-Cas9 within the syn-crRNA-TS_9-12a (5′-GCTGTCCCCAGTGCATATTC-3′) has five potential off-target regions in the mouse genome that matched 17 out of the 20 bases, as we reported previously in syn-crRNA-TS (Supplementary Table S1). However, we have detected no indel mutations in these regions in all knock-in embryos, which indicates that syn-crRNA-TS_9-12a is largely or completely free of off-target effects for both CRISPR-Cas12a and CRISPR-Cas9. We also eliminated an extra synthetic poly(A) site sequence that was outside the syn-crRNA-TS of pCriMGET, and provided a more versatile multiple cloning site (Fig. 1). This second-generation pCriMGET, which we named pCriMGET_9-12a, is expected to function as a donor plasmid that can be cleaved in vivo by both CRISPR-Cas9 and CRISPR-Cas12a.
We examined whether pCriMGET_9-12a can be applied for the in-frame knock-in of exogenous DNA by both CRISPR-Cas9 and CRISPR-Cas12a in cultured cells. To assess this, we developed the Split-mCherry (SpmCherry) reconstitution system, whereby an in-frame knock-in is monitored by mCherry expression (Fig. 2A). First, we generated a reporter HEK293T clonal cell line, in which 5′-split mCherry (SpmCherry_n-Intron-P2A-Puro) was genomically integrated (Supplementary Fig. S1). Then, we constructed a donor plasmid pCriMGET_9-12a-SpmCherry_c that had a donor cassette 3′-split mCherry (SpmCherry_c) flanked by 800-bp homology arms (Fig. 2A). We confirmed that both Cas9 and Cas12a introduce double cuts in the pCriMGET_9-12a-SpmCherry_c plasmid in vitro (Supplementary Fig. S2). The reporter HEK293T cells were transfected with pCriMGET_9-12a-SpmCherry_c with or without a pX330.1-syn-crRNA-TS-sgRNA and pX330.1-SpmCherry-sgRNA set or a pY094.1-syn-crRNA-TS-crRNA and pY094.1-SpmCherry-crRNA set for CRISPR-Cas9- and CRISPR-Cas12a-mediated genome editing, respectively. The SpCas9-2A-EGFP and AsCas12a-2A-EGFP encoded on pX330.1 and pY094.1, respectively, are expected to induce double-strand breaks at the syn-crRNA-TS of pCriMGET_9-12a-SpmCherry_c and at the genomically integrated SpmCherry site in the transfected EGFP+ cells (Fig. 2A). The proportion of mCherry+ cells in the GFP+ cell population was quantified by fluorescence-activated cell sorting analyses (Supplementary Fig. S3A). The mCherry+ cells emerged when the reporter cells were transfected with both pCriMGET_9-12a-SpmCherry_c and pX330.1-SpmCherry-sgRNA or pY094.1-SpmCherry-crRNA, but not with pCriMGET_9-12a-SpmCherry_c alone, indicating that in-frame knock-in was induced by both CRISPR-Cas9 and CRISPR-Cas12a in the cells (Fig. 2B). Moreover, co-transfection of pX330.1-syn-crRNA-TS-sgRNA or pY094.1-syn-crRNA-TS-crRNA further increased the proportion of mCherry+ cells, indicating that linearization of the donor cassette enhanced knock-in efficiency (Fig. 2B). Genotyping PCR of single-cell clones isolated from the mCherry+ cell population confirmed the targeted knock-in at the genomic level in all of the tested clones (Fig. 2C). Notably, the knock-in efficiency also depended on the length of the homology arms, with a plateau at 400–600 bp (Supplementary Fig. S3B). These findings demonstrate that the pCriMGET_9-12a/CRISPR-Cas9 and pCriMGET_9-12a/CRISPR-Cas12a systems are capable of inducing in-frame knock-in of exogenous DNA in cultured cells.
We examined whether pCriMGET_9-12a can be applied to generate knock-in mice. We compared the knock-in frequency of the pCriMGET_9-12a/CRISPR-Cas9 system with that of the first-generation pCriMGET/CRISPR-Cas9 system. To this end, we designed a strategy for targeting knock-in of the tdTomato reporter gene into the Hipp11 safe harbor locus on the mouse genome (Fig. 3A). We constructed the donor plasmid pCriMGET_9-12a-CAG-tdTomato-woodchuck hepatitis virus post-transcriptional regulatory element (WPRE)-poly(A), which incorporates the tdTomato gene downstream of the CAG promoter flanked by 500-bp homology arms. The donor plasmid was microinjected into the pronuclei of pronuclear-stage mouse embryo together with syn-crRNA-TS-crRNA, Hipp11-crRNA, tracrRNA, and Cas9 protein (Fig. 3B). After microinjection, the blastocysts were collected and PCR genotyped at 5′ and 3′ junction loci (Supplementary Fig. S4). The results show that 17 of the 47 injected blastocysts (36.2%) were knocked in by pCriMGET/CRISPR-Cas9 (Fig. 3C, Supplementary Fig. S4A), and 24 of the 53 injected blastocysts (45.3%) were knocked in by pCriMGET_9-12a/CRISPR-Cas9 (Fig. 3C, Supplementary Fig. S4B). We confirmed the tdTomato reporter gene expression in multiple tissues of the F0 pups generated by pCriMGET_9-12a/CRISPR-Cas9 (Fig. 3D, Supplementary Fig. S5). No indels or frame-shifts were detected in the 5′ and 3′ junction regions of the knock-in pups (Fig. 3E). The knock-in frequency was higher with syn-crRNA-TS-crRNA (50%) than it was without syn-crRNA-TS-crRNA (6.5%), indicating that linearization of the donor cassette enhanced the knock-in efficiency of pCriMGET_9-12a/CRISPR-Cas9 (Supplementary Fig. S6). These findings demonstrate that the pCriMGET_9-12a/CRISPR-Cas9 system induced precise knock-in of exogenous DNA at an efficiency that was comparable to that of the pCriMGET/CRISPR-Cas9 system.
We assessed whether pCriMGET_9-12a/CRISPR-Cas12a can be used to generate knock-in mice. We designed a strategy for targeting knock-in of the loxP-3 × Stop-loxP-nuclear and membrane-mCherry (LSL-NuM-mCherry) reporter gene into the Rosa26 safe harbor locus on the mouse genome (Fig. 4A). We constructed the donor plasmid pCriMGET_9-12a-CAG-LSL-NuM-mCherry-WPRE-poly(A), which incorporates the LSL-NuM-mCherry gene downstream of the CAG promoter flanked by 500-bp homology arms (Fig. 4A). The donor plasmid was microinjected into the pronuclei of pronuclear-stage mouse embryo together with syn-crRNA-TS-crRNA, Rosa26-crRNA, and Cas12a protein (Fig. 4B). After transplantation into pseudopregnant mice, 3-week-old male and female mice were collected and PCR genotyped at 5′ and 3′ junction loci (Fig. 4C). The results showed that 4 of the 15 pups (26.7%) had been knocked in (Fig. 4C,D). No indels or frame-shifts were detected in the 5′ and 3′ junction regions of the knock-in pups (Fig. 4E). The knock-in frequency was higher in the presence of syn-crRNA-TS-crRNA (17.7%) than it was in its absence (7.7%), indicating that linearization of the donor cassette enhanced the knock-in efficiency of pCriMGET_9-12a/CRISPR-Cas12a (Supplementary Fig. S7A,B). Finally, we evaluated the functionality of the reporter gene in the knock-in mice. To this end, a Rosa26CAG-LSL-NuM/+ F0 pup (#9, Fig. 4C) was crossed with Tbx3creERT2 to obtain Rosa26CAG-LSL-NuM/+;Tbx3creERT2/+ F1 pups in which nuclear and membrane-mCherry signals were expected to be detected in Tbx3-expressing cells upon tamoxifen treatment (Fig. 5A, Supplementary Fig. S8). We administered tamoxifen to the F1 pups and dissected the liver at day 10 (Fig. 5A). Endogenous Tbx3 was detected in the hepatocytes (HNF4a+ cells), as reported previously (Fig. 5B, Tbx3). We found that > 20% of the Tbx3+/HNF4a+ hepatocytes had positive mCherry signals at the nucleus and cell cortex in the tamoxifen-administered mice, but the mCherry signal was barely detected in the control vehicle-injected mice (Fig. 5B,C). Similarly, nuclear and membrane mCherry signals were detected in plantar skin epidermis where Tbx3 is expressed (Supplementary Fig. S9). These results confirm the functionality of the transgene products in the knock-in mice, and demonstrate precise targeted integration of long (> 5 kb) external DNA by the pCriMGET_9-12a/CRISPR-Cas12a system in mice.
CRISPR-Cas12a has been shown to achieve gene knockout in mice and gene targeting in plants, bacteria, and mammalian cells. Although CRISPR-Cas12a also accomplished single-base editing in mice using short (180 bases) single-stranded oligoDNA as a donor, thus far, no study has reported the generation of transgene knock-in mice by CRISPR-Cas12a. We have shown that pCriMGET_9-12a overcomes the donor size limitation for transgene knock-in by CRISPR-Cas12a, and successfully integrated 5.4-kb-long exogenous DNA into a targeted genomic locus. Furthermore, pCriMGET_9-12a surpassed the donor size (4.0 kb) of the first-generation pCriMGET (3.0–3.3 kb) for CRISPR-Cas9-mediated transgene knock-in. This donor size is comparable to that of previously developed technologies for the integration of large transgenes, including the Cas9-Avidin/Biotin-donor DNA system, SNAP tag system, (2C-HR)-CRISPR method, and AAV-mediated donor gene infection. Although the transgene knock-in efficiency of pCriMGET_9-12a/CRISPR-Cas12a (17–26.7%) was lower than that of pCriMGET_9-12a/CRISPR-Cas9 (45.3–50%), the development of a modified Cas12a protein with enhanced function in future studies may improve the knock-in efficiency of this system, as was the case for Cas9. The HMEJ-based targeting strategy uses a single guide RNA targeting endogenous genomic locus and donor plasmid at the same time to simplify the system. The pCriMGET_9-12a system requires two guide RNAs, namely, for the endogenous genomic target site and for the donor plasmid at syn-crRNA-TS-crRNA. Although two guide RNAs, rather than the single guide RNA, might increase the off-target risk, syn-crRNA-TS-crRNA has been confirmed to be largely or completely free of off-target effects. Because pCriMGET_9-12a is equipped with a multiple cloning site, a transgene donor cassette can be simply incorporated into the plasmid with no need to add guide RNA sequences at both ends of the donor cassette. The first-generation pCriMGET system exhibited mosaicism, which is a common problem when generating mutant animals using CRISPR-Cas systems. Mosaicism presumably also occurred in the pCriMGET_9-12a systems. However, we confirmed that the donor gene was inherited by the next generation in accordance with Mendel’s law (Supplementary Fig. S8), indicating that germinal transmission of the donor gene was achieved. Successful germline transmission can also overcome random integration in mice; for example, backcrossing the mutant mice to an inbred control strain will eliminate the randomly integrated transgene cassette from the genome. A recent study showed that fusion of the Cas9 protein with a transcription factor reduced the frequency of random integration. Application of the modified Cas9 or modified Cas12a to the pCriMGET_9-12a system will help to improve this transgene knock-in methodology. Random integration is a detrimental issue in the genome editing of culture cells. Development of technology that circumvents undesirable nontargeted transgene integration in culture cells is a remaining challenge for cell-based regenerative medicine and gene therapy.
Mismatches of the syn-crRNA-TS_9-12a spacer sequence for CRISPR-Cas12a (5′-GCTGTCCCCAGTGCATATTCAGG-3′) and for CRISPR-Cas9 (5′-GCTGTCCCCAGTGCATATTC-3′) in the mouse (GRCm38/m10) and human (GRCh38/hg38) genomes were checked using Cas-OFFinder and CRISPOR. Synthesized oligoDNAs (Thermo Fisher Scientific) were annealed and inserted into KpnI and ScaI sites in the pBluescript II SK(+) plasmid. The insertions were confirmed by Sanger sequencing.
The pcDNA3-SpmCherry_n plasmid was constructed as follows: the 5′-split mCherry (1–396) sequence was amplified using pcDNA3-EF1 α-mCherry as a template. The mouse β-actin intron III sequence was amplified by PCR using genomic DNA from C57/B6J mouse. The mCherry (367–426)-P2A peptide-coding sequence (5′-GGAAGCGGAGCTACTAACTTCAGCCTGCTGAAGCAGGCTGGAGACGTGGAGGAGAACCCTGGACCT-3′) was fused to the 5′-end of the puromycin resistance gene by PCR amplification. The C402T silent mutation for introducing the CRISPR-Cas12a PAM was added in the PCR primer. All fragments were fused and inserted into HindIII–EcoRI sites of pcDNA3 using NEBuilder HiFi DNA Assembly Master Mix (New England Biolabs), in accordance with the manufacturer’s protocol. For construction of the pcDNA3-SpmCherry_full plasmid, the 3′-split mCherry (427–708) sequence was amplified using pcDNA3-EF1α-mCherry as a template, and inserted into the HindIII site of pcDNA3-SpmCherry_n using NEBuilder HiFi DNA Assembly Master Mix (New England Biolabs), in accordance with the manufacturer’s protocol.
To construct the pCriMGET_9-12a_SpmCherry_c plasmid, the SpmCherry knock-in donor (402–708) that included 800-bp homology arms’ sequences was amplified by PCR using pcDNA3-SpmCherry_full as a template. Five silent mutations (T406A, C407G, C408T, C411T, C414T) were added to the PCR primer to introduce resistance against CRISPR-Cas9 and CRISPR-Cas12a cleavage. These fragments were fused and inserted into the SalI and BamHI sites of the pCriMGET_9-12a multiple cloning site using NEBuilder HiFi DNA Assembly Master Mix, in accordance with the manufacturer’s protocol. Other donors, including those with homology arms of different lengths, were amplified by PCR using pCriMGET_9-12a_SpmCherry_c as a template. The fragments were fused and inserted into the SalI and BamHI sites of the pCriMGET_9-12a multiple cloning site using NEBuilder HiFi DNA Assembly Master Mix.
WPRE was amplified by PCR using the pCSII-EF-mRFP1-RfA plasmid (RIKEN BioResource Center) as a template, and inserted and fused into EcoRV and SacI sites in pCAG-LSL-ZsGreen (Addgene #51269) using NEBuilder HiFi DNA Assembly Master Mix to exchange the ZsGreen and WPRE sequences. Then, oligoDNA (5′-TCGACCCGCCACCAATCATTTAAATAGGTCCCTCGACCTGCA-3′) was ligated into PstI and SalI sites in pCAG-LSL-WPRE-pA to eliminate the LSL sequence. The tdTomato coding sequence was amplified by PCR using tdTomato-C1 (Addgene #54653) as a template, and inserted and fused into EcoRI and KpnI sites in pCAG-WPRE-pA using NEBuilder HiFi DNA Assembly Master Mix. The mouse genomic 500-bp upstream and downstream DNA sequences of the crRNA target site of the Hipp11 locus were amplified by PCR using the C57BL/6JJcl mouse genomic DNA as a template, and were used as left and right homology arms, respectively. The CAG-tdTomato-WPRE-pA fragment was purified from the SpeI-digested pCAG-tdTomato-WPRE-pA. Then, the three fragments were fused and inserted into KpnI and BamHI sites of pCriMGET_9-12a using NEBuilder HiFi DNA Assembly Master Mix.
The NuM-mCherry (nuclear and membrane-mCherry: H2B-mCherry-P2A-mCherry-CAAX) reporter expression plasmid was constructed as follows: H2B and mCherry-P2A-mCherry-CAAX sequences were amplified by PCR using cDNA from HeLa cells and pT2A-DW-NuCyM (kindly gifted by Dr. Matsuda and Dr. Terai) as templates, respectively. The NuM sequence was purified from the EcoRV-digested pBluescript II SK(+)-NuM and ligated into the EcoRV site of pcDNA3. Then, the NuM sequence was amplified by PCR using pcDNA3-NuM as a template and inserted and fused into the EcoRV site of pCAG-LSL-WPRE-pA. The mouse genomic 500-bp upstream and downstream DNA sequences of the crRNA target site of the Rosa26 locus were amplified by PCR using C57BL/6JJcl mouse genomic DNA as a template, and were used as left and right homology arms, respectively. The two fragments were inserted and fused into XhoI and SpeI sites of pCriMGET_9-12a. Finally, the CAG-LSL-NuM-WPRE-pA sequence was purified from the SpeI-digested pCAG-LSL-NuM-WPRE-pA, and inserted and fused into BamHI and EcoRI sites of pCriMGET_9-12a using NEBuilder HiFi DNA Assembly Master Mix.
The pX330.1 plasmid was constructed as follows: The P2A-EGFP sequence was amplified by PCR from pCriMGET-3 × Flag-P2A-EGFP. Then, the fragment was inserted into the EcoRI site of pX330 (Addgene #42230) to fuse the Cas9 coding sequence using NEBuilder HiFi DNA Assembly Master Mix (New England Biolabs). The oligonucleotides syn-crRNA-TS-sgRNA (5′-GCTGTCCCCAGTGCATATTC-3′) and SpmCherry-sgRNA (5′-gTGCATTACGGGGCCGTCGGA-3′) were annealed and ligated into the BbsI site of pX330.1. The pY094.1 plasmid was constructed as follows: the CBh promoter sequence was amplified by PCR from pX330. Then, the fragment was ligated into the MluI and KpnI sites of pY094 (Addgene #84743) to exchange the cytomegalovirus (CMV) promoter. The oligonucleotides syn-crRNA-TS-sgRNA (5′-GCTGTCCCCAGTGCATATTCAGG-3′) and SpmCherry-sgRNA (5′-CCTCCGACGGCCCCGTAATGCAG-3′) were annealed and ligated into the BsmBI site of pY094.1.
HEK293T cells (RIKEN BRC, RCB2202) were cultured in Dulbecco’s Modified Eagle’s Medium (Nissui) containing 10% fetal bovine serum (GIBCO), 4 mM L-glutamine (Nacalai Tesque), and 0.2% sodium bicarbonate. The HEK293T cells were transfected with plasmids using PEI MAX (Polysciences), in accordance with the manufacturer’s protocol.
HEK293T cells were transfected with ScaI-digested pcDNA3-SpmCherry_n and stably transduced cells were selected using puromycin (2 µg/mL). After 14 days of antibiotic selection, single colonies were isolated and PCR genotyped to confirm the insertion of the SpmCherry-Reporter sequence. Genomic DNA was extracted and purified using phenol–chloroform. SpmCherry-Reporter and AAVS1 locus genomic DNA sequences were amplified by PCR using the following primers: SpmCherry_F: 5′-tacgggccagatatacgcgttgac-3′ and SpmCherry_R: 5′-aggacagtgggagtggcaccttc-3′, and AAVS1_F: 5′-ttcttgtaggcctgcatcatcacc-3′ and AAVS1_R: 5′-atcctctctggctccatcgtaagc-3′. We established 22 reporter cell lines and used one cell line for the analysis.
SpmCherry-Reporter HEK293T cells were transfected with pCriMGET_9-12a_SpmCherry_c, pX330.1-syn-crRNA-TS-sgRNA, and pX330.1-SpmCherry-sgRNA or pY094.1-syn-crRNA-TS-sgRNA and pY094.1-SpmCherry-sgRNA. At 72 h post-transfection, the cells were dissociated with PBS containing 0.25% trypsin and 0.53 mM EDTA (Nacalai Tesque) and mCherry expression was checked on a BD LSRFortessa X-20 flow cytometer. The data were analyzed using FlowJo Software (BD). For determination of SpmCherry reconstitution at the genomic level, single-cell clones of the mCherry-expressing cells were obtained by sorting mCherry-positive cells with FACS AriaIIIu cell sorter (BD), followed by colony picking. The genomic DNA was purified by phenol/chloroform extraction from each clone, and subjected to genotyping PCR with KOD One Blue PCR Master Mix (TOYOBO) using 10 ng of DNA as a template. The following primer pairs were used: SpmCherry_GT001: 5′-gcagagctctctggctaactagagaacc-3′, SpmCherry_GT002: 5′-ccttccagggtcaaggaaggcac-3′.
For the CRISPR-Cas9 system, we used syn-crRNA-TS-crRNA (5′-GCUGUCCCCAGUGCAUAUUCguuuuagagcuaugcu-3′), Hipp11-crRNA (5′-GAACACUAGUGCACUUAUCCguuuuagagcuaugcu-3′), Rosa26-crRNA (5′-ACUCCAGUCUUUCUAGAAGAguuuuagagcuaugcu-3′), Alt-R CRISPR-Cas9 tracrRNA (Integrated DNA Technologies, IDT#1072532), and Sp HiFi Cas9 nuclease V3 (Integrated DNA Technologies, IDT#1081060) in this study. The crRNA and tracrRNA were annealed (gRNAs) in a thermocycler (95 °C for 5 min, ramped down to 77 °C at 2 °C/s, then ramped down to 25 °C at 0.1 °C/s) and then stocked as 50 µM gRNAs. Upper- and lower-case letters indicate sequences of the target-specific protospacer region and the tracrRNA fusion domain, respectively. For the CRISPR-Cas12a system, we prepared syn-crRNA-TS-crRNA (5′-uaauuucuacucuuguagauGCUGUCCCCAGUGCAUAUUCAGG-3′), Rosa26-crRNA (5′-uaauuucuacucuuguagauUAGAAGAUGGGCGGGAGUCUUCU-3′), and Alt-R SpCas12a nuclease V3 (Integrated DNA Technologies, IDT#1076158). Here, upper- and lower-case letters indicate sequences of the target-specific protospacer region and the loop domain, respectively. The Cas9-crRNA, tracrRNA, Cas12a-crRNA, and Cas nucleases were obtained from Integrated DNA Technologies.
Pronuclear injection was performed as described previously with modifications. Briefly, gRNA and Cas nuclease were mixed and incubated for 15 min at room temperature to form a ribonucleoprotein complex. Then, pCriMGET_9-12a_donor was added and the mixture was centrifuged at 20,400g for 30 min at 4 °C. The supernatant was microinjected into the pronuclei of zygotes. To generate Hipp11CAG-tdTomato knock-in mice, a cocktail containing pCriMGET_9-12a-Hipp11-CAG-tdTomato-WPRE-pA (25 ng/µL), Hipp11-gRNA (2 µM), syn-crRNA-TS-gRNA (1 µM), and Cas9 protein (50 ng/µL) was microinjected into the pronuclei of zygotes. To generate Rosa26CAG-LSL-NuM knock-in mice, a cocktail for CRISPR-Cas12a containing pCriMGET_9-12a-Rosa26-CAG-LSL-NuM-WPRE-pA (25 ng/µL), Rosa26-gRNA (2 µM), syn-crRNA-TS-gRNA (1 µM), and Cas12a protein (50 ng/µL) or a cocktail for CRISPR-Cas9 containing pCriMGET_9-12a-Rosa26-CAG-LSL-NuM-WPRE-pA (25 ng/µL), Rosa26-gRNA (2 µM), syn-crRNA-TS-gRNA (1 µM), and Cas9 protein (50 ng/µL) was microinjected into the pronuclei of zygotes.
C57BL/6JJmsSLC and Jcl:ICR mice were obtained from Japan SLC Inc. (Shizuoka, Japan) and CLEA Japan Inc. (Tokyo, Japan), respectively. The Tbx3CreERT2 strain was generated previously. All of the experiments were performed in accordance with ARRIVE guidelines (https://arriveguidelines.org) and the guidelines of the Kyoto University Regulation on Animal Experimentation, and were approved by the Committee for Animal Experiments of the Institute for Life and Medical Sciences, Kyoto University (A21-2-2).
Blastocysts and mouse tail samples were prepared for genotyping PCR as described previously. The PCR was carried out with 1–2 µL lysis samples using KOD One Blue PCR Master Mix (TOYOBO). To genotype the Hipp11CAG-tdTomato knock-in and Rosa26CAG-LSL-NuM knock-in blastocysts and mouse tail samples, the following primer pairs were used: 3′ junction of Hipp11CAG-tdTomato (Hipp11-CAG-tdTomato KI_GT001: 5′-gccatcatgctctcactgcctc-3′, Hipp11-CAG-tdTomato KI_GT002: 5′-gaatctctggctggccttgctc-3′, and Hipp11-CAG-tdTomato KI_GT003: 5′-ccctcagacgagtcggatctcc-3′), 5′ junction of Hipp11CAG-tdTomato (Hipp11-CAG-tdTomato KI_GT004: 5′-tgaaagtagcttgtggcaagtatcaagg-3′, Hipp11-CAG-tdTomato KI_GT005: 5′-cctgttccatcagcttcagcctg-3′, and CAG_Rev: 5′-tcatgtactgggcataatgccagg-3′), 3′ junction of Rosa26CAG-LSL-NuM (Rosa26-CAG-LSL-NuM_GT001: 5′-ctgcctcctggcttctgaggac-3′, Rosa26-CAG-LSL-NuM_GT002: 5′-ttgaggccccagctacagcctc-3′, and Rosa26-CAG-LSL-NuM_GT003: 5′-ccctcagacgagtcggatctcc-3′), and 5′ junction of Rosa26CAG-LSL-NuM (Rosa26-CAG-LSL-NuM_GT004: 5′-ctcgtcgtctgattggctctcg-3′, Rosa26-CAG-LSL-NuM_GT005: 5′-ttcaattcccctgcaggacaacg-3′, and CAG_Rev). To genotype the Tbx3creERT2 knock-in mice, the following primer pairs were used: Tbx3creERT2_GT001: 5′-cctctggctcagtgtccttgtcac-3′, Tbx3creERT2_GT002: 5′-tggcagggcctgtggtatctagc-3′, and Tbx3creERT2_GT003: 5′-acgtatatcctggcagcgatcgc-3′.
The expression of tdTomato in F0 4-week-old mouse was examined by the naked eye using Handy Green Pro Plus (540) LED light and red goggles (RelyOn Ltd.). After anesthetization, photos were taken of the whole body and tissues under 540-nm excitation light from a RelyOnTwin-LED Light using a Canon EOS Kiss X10 digital single-lens reflex camera (Canon Co. Ltd.) with a RelyOn 600-nm LP filter.
For determination of the tdTomato gene expression in F0 4-week-old Hipp11CAG-tdTomato knock-in mouse, real-time PCR was performed on RNA from the brain, heart, lung, liver, kidney, spleen, intestine, and skeletal muscle tissue. RNA from each tissue was extracted using ISOGEN (NIPPON GENE), in accordance with the manufacturer’s protocol. Samples were treated with DNase I (Takara) at 37 °C for 20 min, after which RNAs were purified with RNeasy Mini kit (Qiagen) for clean-up, as per the manufacturer’s protocol. cDNAs were synthesized by using ReverTra Ace® qPCR RT Master Mix (TOYOBO) and applied to a qPCR reaction mixture (20 µL) of the THUNDERBIRD SYBR qPCR Mix (TOYOBO). The reactions were performed in duplicate for each sample using the Applied Biosystems 7500 Real-Time system (Applied Biosystems). The following primer pairs were used: tdTomato_qPCR_Fw: 5′-atcgtggaacagtacgagcg-3′, tdTomato_qPCR_Rev: 5′-tgaactctttgatgacggcca-3′, and G3pdh_qPCR_Fw: 5′-aggtcggtgtgaacggatttg-3′, and G3pdh_qPCR_Rev: 5′-tgtagaccatgtagttgaggtca-3′.
Livers and plantar skin tissues from 6-week-old Rosa26CAG-LSL-NuM-mCherry;Tbx3CreERT2 mice that had been administered tamoxifen or corn oil were collected and cryoprotected in PBS containing 20% sucrose and frozen in optimal cutting temperature compound. The samples were sectioned, immunostained, fixed with 4% paraformaldehyde, and permeabilized with 0.5% Triton X-100 in Tris-buffered saline for 15 min at room temperature. Then, the sections were blocked with Blocking-One Histo (Nacalai Tesque) at room temperature for 1 h, incubated with primary antibodies at 4 °C overnight, washed, and incubated for 1 h with secondary antibodies. The samples were mounted using VECTASHIELD PLUS Antifade Mounting Medium with DAPI (Vector Laboratories). The primary antibodies were anti-Tbx3 (rabbit, 1:200, ab99302; Abcam), anti-mCherry (chicken, 1:1000, ab205402; Abcam), anti-HNF4a (goat, 1:500, sc-6556; Santa Cruz Biotechnology), and anti-CD49f (rat, 1:500, 313602; Biolegend). The secondary antibodies were Alexa Fluor 488-conjugated anti-rabbit, Cy3-conjugated anti-chicken, Alexa647-conjugated anti-mouse, and Alexa647-conjugated anti-rat (Jackson ImmunoResearch, West Grove, PA, USA). All images were acquired using an Olympus FV3000 confocal microscope.
The DNA cleavage activity was assayed using pCriMGET_9-12a_SpmCherry_c plasmid as a substrate. Mixtures of ribonucleoprotein complex (syn-crRNA-TS-crRNA:tracrRNA (Integrated DNA Technologies, IDT#1072532) and Sp HiFi Cas9 nuclease V3 (Integrated DNA Technologies, IDT#1081060), or syn-crRNA-TS-crRNA and Alt-R SpCas12a nuclease V3 (Integrated DNA Technologies, IDT#1076158) were added into reaction tubes together with 10 × NEB buffer 3.1 and pCriMGET_9-12a_SpmCherry_c plasmid. The mixtures were incubated at 37 °C for 1 h, followed by treatment with 20 mg/mL Proteinase K (Nacalai Tesque) for 10 min at 56 °C to release the DNA substrate from the Cas nuclease. The products of each reaction were electrophoresed on 1.0% agarose gel. As a single digestion control, EcoRV-HF (NEB) digested plasmid DNA was used.
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PMC9588225 | Huiping Ma,Shuyun Qu,Yao Zhai,Xiaofeng Yang | circ_0025033 promotes ovarian cancer development via regulating the hsa_miR-370-3p/SLC1A5 axis | 22-10-2022 | Ovarian cancer,circ_0025033,hsa_miR-370-3p,SLC1A5 | Background Circular RNAs (circRNAs) appear to be important modulators in ovarian cancer. We aimed to explore the role and mechanism of circ_0025033 in ovarian cancer. Methods qRT-PCR was conducted to determine circ_0025033, hsa_miR-370-3p, and SLC1A5 mRNA expression. Functional experiments were conducted, including Cell Counting Kit-8 (CCK-8), 5-ethynyl-2′-deoxyuridine (EdU), flow cytometry, transwell, tube formation, xenograft tumor model assay, western blot analysis of protein levels, and analysis of glutamine metabolism using commercial kits. Their predicted interaction was confirmed using dual-luciferase reporter and RNA pull-down. Results circ_0025033 was upregulated in ovarian cancer; its knockdown induced proliferation, invasion, angiogenesis, glutamine metabolism, and apoptosis in vitro, and blocked tumor growth in vivo. circ_0025033 regulated ovarian cancer cellular behaviors via sponging hsa_miR-370-3p. In parallel, SLC1A5 might abolish the anti-ovarian cancer role of hsa_miR-370-3p. Furthermore, circ_0025033 affected SLC1A5 via regulating hsa_miR-370-3p. Conclusion circ_0025033 might promote ovarian cancer progression via hsa_miR-370-3p/SLC1A5, providing an interesting insight into ovarian cancer tumorigenesis. Supplementary Information The online version contains supplementary material available at 10.1186/s11658-022-00364-2. | circ_0025033 promotes ovarian cancer development via regulating the hsa_miR-370-3p/SLC1A5 axis
Circular RNAs (circRNAs) appear to be important modulators in ovarian cancer. We aimed to explore the role and mechanism of circ_0025033 in ovarian cancer.
qRT-PCR was conducted to determine circ_0025033, hsa_miR-370-3p, and SLC1A5 mRNA expression. Functional experiments were conducted, including Cell Counting Kit-8 (CCK-8), 5-ethynyl-2′-deoxyuridine (EdU), flow cytometry, transwell, tube formation, xenograft tumor model assay, western blot analysis of protein levels, and analysis of glutamine metabolism using commercial kits. Their predicted interaction was confirmed using dual-luciferase reporter and RNA pull-down.
circ_0025033 was upregulated in ovarian cancer; its knockdown induced proliferation, invasion, angiogenesis, glutamine metabolism, and apoptosis in vitro, and blocked tumor growth in vivo. circ_0025033 regulated ovarian cancer cellular behaviors via sponging hsa_miR-370-3p. In parallel, SLC1A5 might abolish the anti-ovarian cancer role of hsa_miR-370-3p. Furthermore, circ_0025033 affected SLC1A5 via regulating hsa_miR-370-3p.
circ_0025033 might promote ovarian cancer progression via hsa_miR-370-3p/SLC1A5, providing an interesting insight into ovarian cancer tumorigenesis.
The online version contains supplementary material available at 10.1186/s11658-022-00364-2.
Ovarian cancer, a common gynecological malignancy, is considered to be a global health issue correlated with increased morbidity and mortality [1]. Many patients with ovarian cancer are not diagnosed until they reach an advanced stage because early lesions are not easy to detect [2]. Although tremendous efforts have been made in ovarian cancer treatment, the 5-year overall survival rate of patients with ovarian cancer is between 35% and 40% [3]. Hence, elucidating the molecular mechanism involved in ovarian cancer is crucial for discovering effective therapeutic targets. Unlike linear RNAs, circular RNAs (circRNAs) have special covalently closed loop structures [4]. Widely expressed in the cytoplasm of eukaryotic cells, they often exert a role in specific patterns of tissue and developmental stages [5]. circRNAs are becoming attractive biomarkers of human diseases owing to their abundance and stability [6, 7]. Emerging evidence has revealed that dysregulated circRNAs are implicated in cancer initiation and development in a wide range of tumors [8–10]. Apart from that, some circRNAs participate in ovarian cancer processes by acting as tumor suppressors or promoters [11–13]. circ_0025033 is produced by the back-splicing of its parental forkhead box M1 (FOXM1) gene (located at chr12: 2966846–2983691), whose spliced mature sequence length is 3410 bp. FOXM1 is an essential transcription regulator that might modulate multiple aspects of tumor progression [14, 15]. It has been confirmed that the downregulation of FOXM1 could effectively hinder the proliferation, migration, and invasion of ovarian cancer cells in vitro [16, 17]. A previous report indicated that circ_0025033 upregulation might boost ovarian cancer development [18]. Yet, its function and mechanism remain largely unknown in ovarian cancer. Research in the past decades has shown that circRNAs exert their functions via competitive endogenous RNAs (ceRNAs) through binding with miRNA response elements (MREs), thereby de-repressing target mRNA expression [19, 20]. As another type of ncRNA, miRNAs might achieve the regulation of target gene via binding to their 3′ untranslated region (UTR) [21]. miRNAs as anti-oncogenes or oncogenes regulate cellular biological activities in cancer progression [22–24]. A previous report showed that has_hsa_miR-370-3p could inhibit metastatic ability in ovarian cancer cells [25]. Moreover, a recent study indicated that SLC1A5 (also called ASCT2) plays a promoter role in ovarian cancer [26]. Here, by applying bioinformatics tools, we revealed that hsa_miR-370-3p possesses binding sites with circ_0025033 and SLC1A5. Hence, we further explored whether the regulatory impact of circ_0025033 ovarian cancer development is mediated via hsa_miR-370-3p–SLC1A5.
After obtaining informed consent, ovarian cancer tissue samples (n = 29) along with matched adjacent normal samples were harvested from sufferers of ovarian cancer at First Affiliated Hospital of Xi'an Jiaotong University. This research had acquired approval from the ethics committee of First Affiliated Hospital of Xi'an Jiaotong University.
Stored under standard conditions (37 ℃; 5% CO2) in RPMI-1640 medium, human ovarian surface epithelial cells (HOSEPiC) cells (cat. no. #7310) were purchased from ScienCell Research Laboratories (Carlsbad, CA, USA). Two ovarian cancer cell lines (HEY; cat. no. CL-0671, OVCAR3; cat. no. CL-0178) were supplied by Procell (Wuhan, China), and two other cell lines (SKOV3; cat. no. BNCC338639, A2780; cat. no. BNCC351906) were obtained from BeNa Culture Collection (Beijing, China). Human umbilical vein endothelial cells (HUVECs; Procell) were grown in HUVEC-specific complete medium (Procell). RiboBio (Guangzhou, China) provided circ_0025033 small interfering RNA (si-circ_0025033), circ_0025033-overexpressing RNA (circ_0025033), hsa_miR-370-3p mimic/inhibitor (hsa_miR-370-3p/anti-hsa_miR-370-3p), siRNA against SLC1A5 (si-SLC1A5), SLC1A5-overexpressing RNA (SLC1A5), and controls (si-NC, pCD5-ciR, miR-NC, anti-miR-NC, si-con, and pcDNA), followed by Lipofectamine 3000 reagent treatment.
After being fixed and embedded, tumor samples were cut into slices of 5 μm thickness. Then, Ki67 (ab15580; 1:200), SLC1A5 (ab237704; 1:500), c-Myc (ab32072; 1:200), or MMP9 (ab283575; 1:1000) at 4 ℃ were reacted with these sections overnight, which were further incubated with secondary antibody (ab205718; 1:2000). Finally, immunostaining images were obtained by microscope (Leica, Wetzlar, Germany). All antibodies were provided by Abcam (Cambridge, UK).
Using TRIzol reagent (Invitrogen), the generated total RNA was reverse transcribed according to PrimeScript RT Reagent Kit. An miRNA reverse-transcription PCR kit was used to reverse transcribe has_mR-370-3p. Subsequently, cDNA amplification was implemented according to SYBR Green Master Mix (Roche, Shanghai, China) on CFX96 PCR equipment. After GAPDH or U6 normalization, the gene levels were evaluated via the 2−ΔΔCt method. The primer sequences are listed in Table 1. In addition, to validate the circular structure of this circRNA, the RNAs generated at 37 ℃ were reacted with RNase R (Seebio, Shanghai, China). Finally, RNA expression levels were assessed with qRT-PCR. Meanwhile, to check the distribution of circ_0025033 in ovarian cancer cells, the RNA from the nuclear and cytoplasmic fractions was distinguished using PARIS Kit (Invitrogen), followed by qRT-PCR analysis.
After 48 h of transfection, we seeded SKOV3 and A2780 cells (5 × 103 cells per well) into 96-well plates. After incubation for 24 h, Cell Counting Kit-8 (CCK-8) solution (10 μL; Beyotime, Jiangsu, China) was added to each well, followed by analysis via microplate reader. After 48 h of transfection, 5-ethynyl-2′-deoxyuridine (EdU) assay was conducted, where tumor cells were cultured at 2 × 104 cells per well. At 24 h post-incubation, EdU solution and paraformaldehyde (4%) were mixed with the cells into each well, which were next incubated with DAPI and analyzed using a microscope.
Annexin V-FITC and PI apoptosis detection kit purchased from Yeasen (Shanghai, China) detected apoptotic cells. After 48 h of transfection, we seeded SKOV3 and A2780 cells (2 × 105 cells per well) into a six-well plate. After labeling with annexin V-FITC and PI, the solution was placed in a flow cytometer for analysis.
After 48 h of transfection, SKOV3 and A2780 cell suspension was introduced into the top chamber (24-well; Costar, Corning, NY, USA) precoated with Matrigel, while the bottom counterpart contained complete medium. Cells remaining bottom were fixed and stained after 24 h, and invasion pictures were obtained using a microscope (×100; Leica).
Angiogenesis capability was assessed by tube formation assay. In brief, when transfected cells (SKOV3 and A2780) reached 80% confluence, the supernatant was collected as the conditioned medium. Twenty-four-well dishes were coated with Matrigel in each well at 37 ℃ to polymerize. Next, HUVECs were seeded into Matrigel-coated wells under different conditioned media for 6 h. Finally, results were analyzed under a microscope and using ImageJ.
Total protein was extracted using RIPA lysis buffer (Solarbio, Beijing, China). After quantification of total protein using BCA protein assay kit (Solarbio), protein samples were loaded onto SDS–PAGE prior to being immunoblotted onto PVDF membranes (Millipore, Billerica, MA, USA). After incubation with primary antibodies, these membranes were incubated for 2 h with a corresponding secondary antibody (ab205718; 1:5000; Abcam). The combined signals were analyzed using enhanced chemiluminescence (ECL) (Vazyme, Nanjing, China). The primary antibodies were purchased from Abcam: SLC1A5 (ab237704; 1:1000), c-Myc (ab32072; 1:200), MMP9 (ab76003; 1:1000), and β-actin (1:2500; ab8227).
According to the manufacturer’s protocols, glutamine consumption, α-ketoglutarate production, and glutamate production were determined according to glutamine assay, α-ketoglutarate assay, and glutamate assay kits (Abcam), respectively.
These fragments of circ_0025033 or 3′ UTR of SLC1A5 with or without putative binding sites of hsa_miR-370-3p were introduced via pmirGLO vector (YouBia, Changsha, China), generating WT/MUT-circ_0025033 and WT/MUT-SLC1A5 3′ UTR. Then, SKOV3 and A2780 cells were transfected with hsa_miR-370-3p/miR-NC and reporter vectors, followed by analysis using dual-luciferase reporter gene assay kit.
After being transfected with biotinylated (bio)-hsa_miR-370-3p or miR-NC (GenePharma, Shanghai, China), harvested cells were lysed, followed by reaction with M-280 streptavidin (Invitrogen). Subsequently, beads were mixed with the biotinylated hsa_miR-370-3p for 10 min and analyzed via qRT-PCR.
Twelve 5-week-old BALB/c nude mice (female; Vital River, Beijing, China) were separated into two groups, followed by subcutaneous inoculation with A2780 cells with sh-circ_0025033 or sh-NC (RiboBio). Tumor volume was measured. After inoculation for 23 days, the excised tumors from these sacrificed mice were weighed and studied. Permission to perform this experiment was provided by the Animal Care and Use Committee of First Affiliated Hospital of Xi'an Jiaotong University.
GraphPad Prism 7.0 software was used to process all data in this work, presented as mean ± standard deviation. P-value below 0.05 was considered statistically significant. Student’s t-test or one-way analysis of variance (ANOVA) was adopted for comparisons. Survival curve was analyzed by Kaplan–Meier method. Pearson’s correlation coefficient was used to determine correlations in expression.
IHC analysis revealed higher Ki67 content in tumor tissue (Fig. 1A). circ_0025033 content was increased in ovarian cancer tissue and cells (HEY, OVCAR3, SKOV3, and A2780) (Fig. 1B and C). Among these ovarian cancer cells, circ_0025033 content was higher in SKOV3 and A2780 cells, so these two cell lines were selected for further analysis. Moreover, high level of circ_0025033 was predictive of poor prognosis in sufferers of ovarian cancer (Fig. 1D). In addition, linear FOXM1 mRNA was degraded by RNase R, but there was no change in circ_0025033 level (Fig. 1E and F). Localization of circ_0025033 in tumor cells was determined. Figure 1G and H shows that circ_0011298 was prominently located in tumor cell cytoplasm. Taken together, the findings show that circ_0025033 was upregulated in ovarian cancer.
As expected, circ_0025033 content was diminished in tumor cells via si-circ_0025033 (Fig. 2A). Functionally, circ_0025033 silencing reduced cell viability and DNA synthesis in SKOV3 and A2780 cells (Fig. 2B and C). As shown in Fig. 2D, SKOV3 and A2780 cell apoptosis was increased after circ_0025033 downregulation. Meanwhile, circ_0025033 silencing blocked tumor cell invasion (Fig. 2E and F). Angiogenesis is required for tumor growth and metastasis. Tube formation assay showed that circ_0025033 interference decreased angiogenesis (Fig. 2G). Next, circ_0025033 deficiency reduced levels of proliferation/metastasis-related proteins (c-Myc and MMP9) (Fig. 2H–J). Glutamine, a non-essential amino acid, can be converted into glutamate and then transformed into α-ketoglutarate, which is involved in the tricarboxylic acid cycle to provide energy for cells [27, 28]. Glutamine metabolism is indispensable for tumor development [29]. We found that circ_0025033 silencing reduced glutamine consumption, α-ketoglutarate production, and glutamate production (Fig. 2K–M), suggesting that circ_0025033 downregulation repressed glutamine metabolism. Together, circ_0025033 absence alleviated tumor cell malignancy glutamine metabolism.
It has been confirmed that circRNAs could exert their role by interacting with miRNAs [30]. Circinteractome software revealed that circ_0025033 shares binding sites with hsa_miR-370-3p (Fig. 3A), indicating their interaction. Figure 3B shows the overexpression efficiency of hsa_miR-370-3p (Fig. 3B), which exhibited an evident suppression in luciferase activity of WT-circ_0025033, instead of MUT-circ_0025033 (Fig. 3C and D). circ_0025033 was pulled down when using bio-hsa_miR-370-3p rather than bio-miR-NC (Fig. 3E and F). In addition, hsa_miR-370-3p content was downregulated (Fig. 3G), and its level was inversely correlated with circ_0025033 in ovarian cancer tissue (Fig. 3H). Similarly, an obvious decrease of hsa_miR-370-3p in tumor cells was found (Fig. 3I). The significant increase of hsa_miR-370-3p indicated the significant transfection efficiency of pCD-circ_0025033 (Fig. 3J). Next, hsa_miR-370-3p was upregulated via si-circ_0025033, and reduced via circ_0025033 (Fig. 3K), suggesting that circ_0025033 negatively regulates hsa_miR-370-3p expression. Overall, circ_0025033 sequestered hsa_miR-370-3p.
We found that circ_0025033 deletion promoted hsa_miR-370-3p expression, while anti-hsa_miR-370-3p abated the effect (Fig. 4A). hsa_miR-370-3p absence mitigated circ_0025033 deficiency-mediated tumor cell viability and DNA synthesis inhibition (Fig. 4B and C). Moreover, circ_0025033 knockdown-induced apoptosis was prevented via hsa_miR-370-3p downregulation (Fig. 4D). In addition, circ_0025033 silencing constrained cell invasion and angiogenesis, and hsa_miR-370-3p inhibition reversed the phenomenon (Fig. 4E and F). Meanwhile, hsa_miR-370-3p reduction might abolish downregulation of c-Myc and MMP9 protein levels caused via circ_0025033 absence (Fig. 4G and H). Further, hsa_miR-370-3p downregulation counteracted the si-circ_0025033-caused reduction in glutamine consumption, α-ketoglutarate production, and glutamate production (Fig. 4I–K). Together, circ_0025033 regulated ovarian cancer cell behaviors by targeting hsa_miR-370-3p.
starBase software revealed that hsa_miR-370-3p harbored some complementary binding sites with SLC1A5 3′ UTR (Fig. 5A). hsa_miR-370-3p overexpression strikingly reduced the luciferase activity of WT-SLC1A5 3′ UTR (Fig. 5B and C). A higher enrichment of SLC1A5 was observed in the captured fraction of bio-hsa_miR-370-3p (Fig. 5D and E). Additionally, SLC1A5 content was significantly reduced in ovarian cancer tissue (Fig. 5F), and its mRNA content was negatively correlated with the hsa_miR-370-3p level (Fig. 5G). Furthermore, SLC1A5 protein expression was notably enhanced in ovarian cancer tissue and cells (Fig. 5H and I). Transfection of anti-hsa_miR-370-3p reduced hsa_miR-370-3p expression in SKOV3 and A2780 cells (Fig. 5J). In addition, overexpression of hsa_miR-370-3p decreased SLC1A5 content in tumor cells, and hsa_miR-370-3p absence displayed the opposite effect (Fig. 5K). Taken together, the findings indicate that SLC1A5 was targeted by hsa_miR-370-3p.
Transfection of si-SLC1A5 reduced SLC1A5 content in tumor cells (Additional file 1: Fig. S1A). Functionally, deletion of SLC1A5 notably repressed proliferation, invasion, and angiogenesis while promoting apoptosis (Additional file 1: Fig. S1B–S1F). Moreover, SLC1A5 knockdown inhibited c-Myc and MMP9 protein expression (Additional file 1: Fig. S1G and S1H). Simultaneously, glutamine consumption, α-ketoglutarate production, and glutamate production were inhibited by downregulation of SLC1A5 in tumor cells (Additional file 1: Fig. S1I-S1K). These data suggested that SLC1A5 might be an oncogene in ovarian cancer.
hsa_miR-370-3p overexpression downregulated SLC1A5 protein expression, which was rescued by SLC1A5 upregulation (Fig. 6A). Apart from that, increased hsa_miR-370-3p resulted in a significant suppression in cell proliferation, while increased SLC1A5 reversed these impacts in tumor cells (Fig. 6B and C). Cell apoptosis was induced, and cell invasion and angiogenesis were inhibited, by hsa_miR-370-3p restoration, which were abated by SLC1A5 overexpression (Fig. 6D–F). Enhanced hsa_miR-370-3p reduced the protein levels of c-Myc and MMP9, while the re-introduction of SLC1A5 prevented this reduction (Fig. 6G and H). In addition, glutamine metabolism was decreased by overexpression of hsa_miR-370-3p, which was partly reversed via SLC1A5 enhancement (Fig. 6I–K). Overall, hsa_miR-370-3p inhibited ovarian cancer cell malignant behaviors via targeting SLC1A5.
As shown in Additional file 2: Fig. S2A and S2B, SLC1A5 content was dramatically downregulated via circ_0025033 absence, and hsa_miR-370-3p interference recovered the SLC1A5 content, supporting the regulatory role of the circ_0025033/hsa_miR-370-3p/SLC1A5 axis.
Mouse xenograft models of ovarian cancer were established. As shown in Fig. 7A and B, tumor growth was diminished in the sh-circ_0025033 group (Fig. 7A and B). Apart from that, we confirmed that circ_0025033 expression and SLC1A5 protein expression were remarkably reduced in the sh-circ_0025033 group, and the hsa_miR-370-3p level was increased (Fig. 7C and D). IHC analysis showed that circ_0025033 silencing suppressed SLC1A5, c-Myc, and MMP9 (Fig. 7E). Taken together, circ_0025033 knockdown repressed ovarian cancer growth in vivo.
Patients with ovarian cancer, a gynecologic malignancy, have a short survival time [31]. In this study, circ_0025033 knockdown repressed ovarian cancer cell proliferation, metastasis, angiogenesis, and glutamine metabolism and accelerated apoptosis through the hsa_miR-370-3p/SLC1A5 axis, which is expected to offer a promising treatment strategy for patients with ovarian cancer. circRNAs have been shown to be stable in general and aberrantly expressed in various diseases [32]. These characteristics make circRNAs potential therapeutic targets or biomarkers for many diseases, especially cancers. Regarding ovarian cancer, high-throughput sequencing has identified abnormal expression of an increasing number of circRNAs [33, 34]. Nevertheless, the majority of circRNAs in ovarian cancer still need further research. circ_0025033 has been shown to promote cell invasion by targeting the miR-1304/miR-1231 axis in papillary thyroid cancer [35]. Moreover, circ_0025033 was upregulated, and its knockdown inhibited ovarian cancer cell viability and metastasis through targeting the miR-330-5p/KLK4 axis [36]. In addition, Hou and Zhang report that circ_0025033 downregulation suppressed colony formation ability, mobility, and glycolysis metabolism in ovarian cancer cells via regulation of the LSM4/miR-184 axis [18]. However, the roles of circ_0025033 in angiogenesis and glutamine metabolism have not been reported. In line with previous research, high circ_0025033 levels in tumor specimens and cells were observed. Moreover, circ_0025033 deficiency limited tumor malignant phenotypes, indicating its promoting effect in ovarian cancer. Accumulating reports have indicated that circRNAs in the cytoplasm function as miRNA sponges, resulting in changes of target gene expression [23]. In this research, circ_0025033 was predominantly located in the cytoplasm. Hence, circ_0025033 was a hsa_miR-370-3p sponge. Cumulative evidence indicates that hsa_miR-370-3p has a strong ability to modulate tumor development. When hsa_miR-370-3p level is reduced, its increase might inhibit the development of bladder cancer [37], papillary thyroid carcinoma [38], gliomas [39], and acute myeloid leukemia [40]. However, hsa_miR-370-3p expression is enhanced and acts as a tumor-promoting miRNA in gastric carcinoma [41] and breast cancer [42]. In terms of ovarian cancer, hsa_miR-370-3p suppression abated circAGFG1 interference-mediated ovarian cancer cell growth and migration [43]. In addition, hsa_circ_0061140 absence repressed ovarian cancer cell metastasis through sponging miR-370 [44]. Herein, hsa_miR-370-3p showed a low level in ovarian cancer tissue samples and ovarian cancer cells. Rescue assays revealed that suppression of hsa_miR-370-3p counteracted circ_0025033 deficiency-triggered ovarian cancer cell proliferation, apoptosis, metastasis, angiogenesis, and glutamine metabolism inhibition, indicating that circ_0025033 promoted ovarian cancer cell progression via downregulating hsa_miR-370-3p. Online software Starbase indicated that SLC1A5 may be an hsa_miR-370-3p target. SLC1A5, a glutamine transporter, can control glutamine uptake and is essential for tumor growth [45, 46]. SLC1A5 plays as a vital role in prostate cancer [47], gastric cancer [48], lung cancer [49], and esophageal cancer [50]. Importantly, Huang and her colleagues stated that upregulation of miR-122-5p inhibited ovarian cancer process via targeting SLC1A5 [26]. High SLC1A5 levels were associated with poor prognosis for patients with ovarian cancer [51]. In this research, SLC1A5 silencing inhibited ovarian cancer cell malignant behaviors, indicating a cancer-promoting role of SLC1A5 in ovarian cancer cells. Furthermore, SLC1A5 upregulation could abrogate hsa_miR-370-3p-triggered anti-ovarian cancer. Mechanistically, circ_0025033 could regulate SLC1A5 expression in ovarian cancer cells via binding to hsa_miR-370-3p. Consistently, tumor growth in this research was also suppressed via circ_0025033 knockdown in vivo. In conclusion, circ_0025033 interference repressed ovarian cancer cell malignant behaviors and glutamine metabolism via the hsa_miR-370-3p/SLC1A5 axis, indicating an underlying therapeutic target for the tumor.
Additional file 1: Fig. S1. SLC1A5 and circ_0025033 had similar roles in ovarian cancer. (A-K) SKOV3 and A2780 cells were transfected with si-NC or si-SLC1A5. (A) Western blot analysis of SLC1A5 content. (B-E) Proliferation, apoptosis, and invasion were assessed using CCK-8, EdU, and flow cytometry assays, respectively. (F) Angiogenesis ability was evaluated using tube formation assay. (G and H) Western blot analysis of c-Myc and MMP9. (I-K) Glutamine metabolism was analyzed using special kits. ***P < 0.001, ****P < 0.0001.Additional file 2: Fig. S2. Circ_0025033 sponged hsa_miR-370-3p to regulate SLC1A5 expression. (A and B) Effects of si-circ_0025033 and anti-hsa_miR-370-3p on SLC1A5 content were monitored using western blot. **P < 0.01, ***P < 0.001, ****P < 0.0001. | true | true | true |
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PMC9588234 | Qian Xu,Zhaozhong Liao,Zunshuang Gong,Xiaokun Liu,Yuling Yang,Zhe Wang,Weiyan Yang,Lin Hou,Jiejie Yang,Junying Song,Wenjing Liu,Bin Wang,Junnan Hua,Mingyi Pu,Ning Li | Down-regulation of EVA1A by miR-103a-3p promotes hepatocellular carcinoma cells proliferation and migration | 22-10-2022 | EVA1A,miR-103a-3p,hepatocellular carcinoma,Apoptosis,TP53,Autophagy,Mitosis | Background EVA1A (Eva-1 homolog A), a novel protein involved in autophagy and apoptosis, functions as a tumor suppressor in some human primary cancers, including hepatocellular carcinoma (HCC). While it is consistently downregulated in several cancers, its involvement in hepatocarcinogenesis is still largely unknown. Methods We first detected the expression of EVA1A in HCC tissues and cell lines using RT‒qPCR, immunohistochemistry and western blotting and detected the expression of miR-103a-3p by RT‒qPCR. Then, bioinformatics prediction, dual-luciferase reporter gene assays and western blotting were used to screen and identify the upstream microRNA of EVA1A. After manipulating the expression of miR-103a-3p or EVA1A, wound healing, invasion, proliferation, colony formation, apoptosis, autophagy, mitosis and mitochondrial function assays, including mitochondrial membrane potential, ROS and ATP production assays, were performed to investigate the functions of miR-103a-3p targeting EVA1A in HCC cells. Apoptosis-related proteins were assessed by RT‒qPCR (TP53) or western blotting (TP53, BAX, Bcl-2 and caspase-3). Autophagy level was evaluated by observing LC3 puncta and examining the protein levels of p62, Beclin1 and LC3-II/I. Results We found that EVA1A expression was decreased while miR-103a-3p expression was increased in HCC tissues and cell lines and that their expression was inversely correlated in HCC patients. The expression of miR-103a-3p was associated with HCC tumor stage and poor prognosis. miR-103a-3p could target EVA1A through direct binding to its 3'-UTR and suppress its expression. Overexpression of miR-103a-3p significantly downregulated the expression of EVA1A, TP53 and BAX, upregulated the JAK2/STAT3 pathway and promoted HCC cell migration, invasion and proliferation, while repression of miR-103a-3p dramatically upregulated the expression of EVA1A, TP53, BAX and cleaved-caspase-3, inhibited HCC cell migration, invasion and proliferation, and caused mitochondrial dysfunction and apoptosis. Overexpression of EVA1A significantly attenuated the cancer-promoting effects of miR-103a-3p in HCC cells, while knockdown of EVA1A alleviated the mitochondrial dysfunction and apoptosis caused by miR-103a-3p inhibition. Overexpression of EVA1A did not induce significant changes in autophagy levels, nor did it affect G2/M transition or mitosis. Conclusion These findings indicate that the downregulation of the tumor suppressor EVA1A by miR-103a-3p potentially acts as a key mediator in HCC progression, mainly by inhibiting apoptosis and promoting metastasis. The miR-103a/EVA1A/TP53 axis provides a new potential diagnostic and therapeutic target for HCC treatment. Supplementary Information The online version contains supplementary material available at 10.1186/s11658-022-00388-8. | Down-regulation of EVA1A by miR-103a-3p promotes hepatocellular carcinoma cells proliferation and migration
EVA1A (Eva-1 homolog A), a novel protein involved in autophagy and apoptosis, functions as a tumor suppressor in some human primary cancers, including hepatocellular carcinoma (HCC). While it is consistently downregulated in several cancers, its involvement in hepatocarcinogenesis is still largely unknown.
We first detected the expression of EVA1A in HCC tissues and cell lines using RT‒qPCR, immunohistochemistry and western blotting and detected the expression of miR-103a-3p by RT‒qPCR. Then, bioinformatics prediction, dual-luciferase reporter gene assays and western blotting were used to screen and identify the upstream microRNA of EVA1A. After manipulating the expression of miR-103a-3p or EVA1A, wound healing, invasion, proliferation, colony formation, apoptosis, autophagy, mitosis and mitochondrial function assays, including mitochondrial membrane potential, ROS and ATP production assays, were performed to investigate the functions of miR-103a-3p targeting EVA1A in HCC cells. Apoptosis-related proteins were assessed by RT‒qPCR (TP53) or western blotting (TP53, BAX, Bcl-2 and caspase-3). Autophagy level was evaluated by observing LC3 puncta and examining the protein levels of p62, Beclin1 and LC3-II/I.
We found that EVA1A expression was decreased while miR-103a-3p expression was increased in HCC tissues and cell lines and that their expression was inversely correlated in HCC patients. The expression of miR-103a-3p was associated with HCC tumor stage and poor prognosis. miR-103a-3p could target EVA1A through direct binding to its 3'-UTR and suppress its expression. Overexpression of miR-103a-3p significantly downregulated the expression of EVA1A, TP53 and BAX, upregulated the JAK2/STAT3 pathway and promoted HCC cell migration, invasion and proliferation, while repression of miR-103a-3p dramatically upregulated the expression of EVA1A, TP53, BAX and cleaved-caspase-3, inhibited HCC cell migration, invasion and proliferation, and caused mitochondrial dysfunction and apoptosis. Overexpression of EVA1A significantly attenuated the cancer-promoting effects of miR-103a-3p in HCC cells, while knockdown of EVA1A alleviated the mitochondrial dysfunction and apoptosis caused by miR-103a-3p inhibition. Overexpression of EVA1A did not induce significant changes in autophagy levels, nor did it affect G2/M transition or mitosis.
These findings indicate that the downregulation of the tumor suppressor EVA1A by miR-103a-3p potentially acts as a key mediator in HCC progression, mainly by inhibiting apoptosis and promoting metastasis. The miR-103a/EVA1A/TP53 axis provides a new potential diagnostic and therapeutic target for HCC treatment.
The online version contains supplementary material available at 10.1186/s11658-022-00388-8.
Hepatocellular carcinoma (HCC), the most common pathological type of primary liver cancer, is the fourth leading cause of cancer-related death worldwide [1]. In recent years, significant progress has been made in the diagnosis and treatment of HCC, but the overall prognosis of patients remains poor, with an overall 5-year survival rate of 10.1% [2]. A large proportion of patients with HCC are diagnosed at a late stage, and recurrence or metastasis of HCC remains common after resection. Therefore, a better understanding of the molecular pathways involved in the etiology and progression of HCC is urgently needed and may lead to improved early diagnosis and treatments. EVA1A (Eva-1 homolog A), also known as TMEM166/FAM176A, is a novel protein involved in programmed cell death screened by high-throughput in 2007 [3]. As an ER-associated protein, EVA1A can regulate cellular autophagy and apoptosis [4–8]. Studies have shown that EVA1A is expressed in a cell-type-specific and tissue-type-specific manner, and compared with normal tissues, the expression of EVA1A is widely downregulated in tumor tissues [9, 10]. EVA1A has recently been reported to have antitumor activity in several carcinomas and is considered a tumor suppressor gene [5–7]. Our latest research shows that EVA1A expression was significantly decreased in HCC and was associated with advanced TNM clinical stage and poor clinical outcome of HCC patients. Overexpression of EVA1A inhibits HCC growth by upregulating TP53, which makes it possible for EVA1A to be a potential therapeutic target for HCC [10]. However, the regulatory mechanisms of EVA1A expression in HCC remain unclear. One study showed that EVA1A can be downregulated by miR-125b in HCC, thereby increasing the sensitivity of HCC to the chemotherapy drug oxaliplatin, which indicates that the decline in EVA1A expression in the development of HCC may be regulated by miRNAs [11]. The elucidation of the regulatory mechanisms of EVA1A expression in HCC will facilitate the discovery of the underlying mechanisms of HCC tumorigenesis. The miR-103/107 family has been found to participate in the regulation of many tumors [12]. Studies have shown that miR-103 is abnormally expressed in a variety of cancers. For example, miR-103 is downregulated in non-small cell lung cancer, where it functions as a tumor suppressor gene [13]. On the other hand, miR-103 is upregulated in numerous types of cancers, including neuroblastoma [14], gastric cancer [15], breast cancer [16] and colorectal cancer [17]. More precisely, the regulation of miR-103a-3p has been widely reported in many diseases. For example, miR-103a-3p suppresses cell proliferation and invasion by targeting tumor protein D52 in prostate cancer [18] but promotes human gastric cancer cell proliferation by targeting activating transcription factor 7 (ATF7) in vitro [19], and it also promotes tumor glycolysis through the Hippo pathway in colorectal cancer [20]. In addition, miR-103a-3p was reported to regulate proliferation and apoptosis by targeting RCAN1 in oral squamous cell carcinoma (OSCC) cell lines [21]. Thus, the miR-103/107 family is widely considered an oncogene. Based on these findings, miR-103 may represent a prospective target for both cancer diagnosis and therapy. However, the role of miR-103a-3p in HCC remains unclear. In this study, we confirmed that EVA1A was downregulated, while miR-103a-3p was notably upregulated in HCC tissues and HCC cell lines, and its high expression was associated with poor prognosis in patients with HCC. We identified that miR-103a-3p downregulates EVA1A by directly targeting its 3ʹ-untranslated region (3ʹ-UTR) to promote HCC cell growth and migration. EVA1A overexpression can significantly reduce the cancer-promoting effects caused by miR-103a-3p mimics. miR-103a-3p repression induces upregulation of EVA1A and TP53, which play an anticancer role by inducing mitochondrial dysfunction and triggering cell apoptosis. Our results indicate that repression of EVA1A by upregulated miR-103a-3p may contribute to HCC development and that the miR-103a-3p/EVA1A/TP53 axis may be a potential signaling mechanism for the tumorigenesis of HCC.
All tissue samples, including HCC tumor samples and matched distant non-cancerous samples, were collected from 25 patients with operable primary HCC who underwent surgery in 2021 at the Affiliated Hospital of Qingdao University. Informed consent was obtained from each patient, and the ethics committee of Medical College of the of Qingdao University approved the study. For analysis the correlation of the expression of EVA1A with the clinicopathological features of HCC patients, HCC tissue microarrays, containing samples from more than 900 patients, were purchased from SHANGHAI OUTDO BIOTECH Company and subjected to EVA1A IHC analyses according to previous study[10].
Fresh tissues were immobilized immediately after liver resection, paraffin-embedded and sliced. Then, the samples were stained with hematoxylin for 5 min, washed with running water for 5 min, soaked in xylene and alcohol, dyed with 0.5% eosin for 3 min and re-immersed in alcohol and xylene. Specimens were sealed using a synthetic resin.
Tissue sections were deparaffinized, rehydrated and incubated in 0.01 M citrate buffer (pH 6.0) for 30 min at 95 °C. Then, the sections were washed 3 times in PBS. Soak in 3% H2O2 for 10 min to inhibit endogenous peroxidase activity. Next, block with ready-to-use goat serum (AR0009) BOSTER for 1 h. Afterwards, the sections were incubated with TMEM166 (Abeam, UK) antibody overnight at 4 °C, followed by incubation with secondary antibody for 2 h at room temperature, and after 3 washes with PBS, were stained by using 3,3-diaminobenzidine (DAB). The original body was observed for immunoreactivity. Sections were stained with hematoxylin and examined under a microscope.were stained with hematoxylin and examined under a microscope.
Double-stranded siRNAs against EVA1A (si-EVA1A: sense 5ʹ- UGAUAAGGAUCUCUUGCCATT-3ʹ; antisense 5ʹ-UGGCAAGAGAUCCUUAUCATT-3ʹ) were designed chemically synthesized by GenePharma Corporation (Shanghai, China). The control siRNA had no sequence homology to any known human genes. The transfection of siRNA was performed by using Lipofectamine 2000 reagent (Invitrogen, Carlsbad, CA, USA).
The immortalized normal human liver cell line L02 and HCC cell lines Hccl-M3, QGY-7703 were obtained from our laboratory. HCC cell line PLC-PRF5 was kindly provided by Dr. Yingyu Chen (Peking University, Beijing). All cells were routinely grown in DMEM (HyClone, USA) with 10% FBS (BI, USA) at 37℃ in a humidified chamber under an atmosphere of 5% CO2. Transfection was performed using Lipofectamine 2000 according to the manufacturer’s protocol. 6 h after transfection, cells were cultured in normal medium. Analysis was performed within 72 h of micro-RNA transfection and 24 h after EVA1A transfection.
Total RNA was extracted from tissue samples and HCC cell lines using Trizol (TaKaRa, Japan) according to the manufacturer’s instructions. Complementary DNA was synthesized using the cDNA Reverse Transcription Kit (HiScript® II Q RT superMix, Vazyme, China), and PCR was performed with 10 pmol of primers and MonAnpTM2 × Taq Mix Pro (Monad). The expression of mRNA was also analyzed by quantitative real-time PCR with SYBR Green Master Mix (ChamQTM SYBR® Color qPCR Master Mix, Vazyme, China). Real‐time PCR was performed with a CFX96 Touch Real‐Time PCR Detection System (Bio‐Rad, Hercules, CA, USA). The relative miR-103a-3p and EVA1A mRNA samples was normalized to that of U6 and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), respectively. The primers used are listed below. miR-103a-3p-forward, 5ʹ-GCGAGCAGCATTGTACAGGG-3ʹ. miR-103a-3p- reverse, 5ʹ-AGTGCAGGGTCCGAGGTATT-3ʹ. U6-forward, 5ʹ-AAAGCAAATCATCGGACGACC-3ʹ. U6- reverse, 5ʹ-GTACAACACATTGTTTCCTCGGA-3ʹ. EVA1A-forward, 5ʹ-AGATGGCTTTGCTCAGCAACA-3ʹ. EVA1A-reverse, 5ʹ-GATGCACACGCCAGAAACAA-3ʹ. EVA1A-AS-forward, 5ʹ-CCTGCATCACTGCATTTCCG-3ʹ. EVA1A-AS-reverse, 5ʹ-TGCGAAAGAGTGGCACACAG-3ʹ. TP53-forward, 5ʹ-GAGAGCTGAATGAGGCCTTG-3ʹ. TP53-reverse, 5ʹ-TTATGGCGGGAGGTAGACTG-3ʹ. GAPDH-forward, 5ʹ-AACGGATTTGGTCGTATTGGG-3ʹ. GAPDH-reverse, 5ʹ-TCGCTCCTGGAAGATGGTGAT-3ʹ.
Tissue samples and HCC cells were lysed using RIPA lysis buffer supplemented with PMSF (1:100, G-CLONE) to obtain total proteins. The proteins concentration was estimated using the BCA protein quantification kit (Solarbio). Equal amounts of protein were subjected to SDS-PAGE, proteins were then transferred onto PVDF membranes (PerkinElmer). After blocking with skimmed milk or BSA, the membranes were incubated with the primary antibodies overnight at 4 ℃. Primary antibodies were as follows: anti-EVA1A (1:500, Abcam), anti-TP53 (1:1000, OriGene), anti-BAX(1:2000, OriGene), anti-BCL-2 (1:2000, Bioss), anti-p-JAK2 (1:1000, Cell Signaling Technology), anti-p-STAT3 (1:1000, Cell Signaling Technology), anti-MMP-9 (1:1000, Cell Signaling Technology), anti-LC3 (1:1000, MBL), anti-p62 (1:1000, Proteintech), anti-Beclin1 (1:2000, Proteintech) and anti-β-actin (1:1000, Servicebio). And then the blot was incubated with peroxidase-conjugated sheep anti-rabbit IgG (1:3000, Servicebio). Protein bands were detected using the ECL system. Each independent experiment was performed at least three times.
The EVA1A 3′-UTR (untranslated region) and mutant luciferase plasmids were obtained from Hanbio (Shanghai, China). HEK293T cells were co-transfected with miR-103a-3p mimics or mimic controls and wild-type or mutated EVA1A-3′UTR plasmids. 48 h after transfection, luciferase activity was measured using the Dual-Luciferase Assay System (Promega) according to the manufacturer’s instructions.
The proliferation of cells was measured by Cell Counting Kit (CCK-8) assay. Cells were seeded in a 96-well plate with 100 µL medium per well containing 2000 cells totally, then cells were conventionally incubated for 1 to 4 days at 37 ℃ in a carbon dioxide incubator. At the indicated times, 10 µL CCK8 reagent was added to each well and incubated at 37 ℃ for 4 h. The absorbance of the samples was measured at 450 nm by plate reader (Bio-Rad Laboratories). Each independent experiment was run in triplicate.
The migratory capacity of tumor cells was measured by wound healing assay. Cells were seeded in 6-well plates, 24 h after transfection, straight wounds were generated using a 200 µL sterile pipette tip. Then the floating cell debris was rinsed off by washing with PBS and readding serum free DMEM. The cells were conventionally incubated for 1 to 2 days at 37 ℃. Photographs were taken at different time points (0, 12, 24 and 48 h) to assess the wound healing area. The open wound area was measured using Image J software.
Transwell chambers (Corning, NY, USA) had a base membrane pore size of 8 μm, and the chambers were coated with Matrigel (Sigma) to determine invasive capacity. A cell suspension of 1 × 105 cells/mL was prepared, 200 µL of the suspension was inoculated into each upper chamber with serum-free medium. 12 h later, 600 µL of culture medium containing 10% fetal bovine serum was added to the lower chamber. After 24 h of incubation in 37 °C, the non-migrating cells on the top chamber were completely removed with a cotton swab. Cells that migrated to the bottom chamber were then fixed with methanol for 30 min, stained with 0.1% crystal violet for 15 min, and cells were counted and visualized in five random fields under a microscope.
Long-term survival of transfected cells was examined using a plate colony formation assay. Cells were plated into 6-well plates (2000 cells/well) and cultured at 37 ℃ under an atmosphere containing 5% CO2. 14 days later, cells were fixed by methyl alcohol for 20 min, and stained with 0.1% crystal violet for 20 min. Then colonies were counted and photographed. All assays were independently performed in triplicate.
Cell apoptosis rate was evaluated by the Annexin V-FITC/PI Apoptosis Detection Kit (BD, USA). Cells were harvested using trypsin without EDTA, washed twice with cold PBS, washed one time with cold binding buffer, and resuspended in 200 µL cold binding buffer. Then the cells were incubated with Annexin V-FITC staining solution and PI staining solution for 15 min at room temperature in the dark. Finally, the samples were immediately analyzed on a flow cytometer (Becton Dickinson, USA). Three independent experiments were performed.
Mitochondrial membrane potential was determined using the JC-1 assay kit (Solarbio, CN). The decrease of mitochondrial membrane potential is a landmark event in the early stage of apoptosis. The JC-1 dye aggregates in the mitochondria of healthy cells and emits red fluorescence. However, in unhealthy cells, due to the drop or loss of mitochondrial membrane potential, the JC-1 dye cannot aggregate in the mitochondria, and remains as monomers in the cytoplasm and emits green fluorescence. The transition of fluorescence is usually used as an indicator of early apoptosis. 72 h after transfection, cells were washed with PBS, stained with JC-1 for 20 min, observed under the fluorescence microscope (Nikon, Japan).
Intracellular ROS levels were determined by the non-fluorescent probe 2, 7-dichlorofluorescein diacetate (DCFH-DA) (Beyotime Biotechnology, CN). DCFH-DA can passively diffuse into cells and be hydrolyzed by esterase to form DCFH. In the presence of ROS, ROS reacts with DCFH to produce fluorescent DCF. Cells were washed two times with culture medium without serum, then incubated with 1 ml culture medium without serum, added DCFH-DA at a final concentration of 10 µM and incubated for 20 min at 37 ℃ in a humidified chamber. Then DCF fluorescence intensity was detected by fluorescence microscope (Nikon, Japan) at excitation wavelength 488 nm and at emission wavelength 525 nm.
Intracellular ATP levels were determined by the ATP Assay Kit (Beyotime Biotechnology, CN) according to the manufacturer's instructions. In brief, ATP detection working buffer (100 µL) was gently mixed with the cell lysate substrate, then the luminescence was measured using a micro-plate reader (Bio-Rad Laboratories).
Cells were seeded in 24-well plate with cell slides and cultured overnight. After transfection for 24 h, cells were washed with PBS, fixed with 4% paraformaldehyde for 15 min, permeabilized with PBS containing 0.1% Triton X-100 for 20 min, and blocked with ready-to-use goat serum (BOSTER, CN) for 1 h. According to the method in a recent report [22], cells were incubated with appropriate primary antibodies (for example, anti-β-Tubulin (1:200, affinity) overnight at 4 °C and then incubated with secondary antibodies (Alexa Fluor 594-conjugated Goat anti-Mouse IgG (#AS054, Abclonal)) for 1 h at room temperature. DAPI reagent was used to stain cell nuclei. Data were visualized and analyzed with confocal microscopy (STELLARIS 5, Leica) with a 63 × Plan Apochromat 1.4 NA objective.
All experiments were conducted for at least three independent times. Data are expressed as the mean ± standard deviation (SD). GraphPad Prism 6 (USA) was used for all statistical analyses, Comparisons were performed using a student’s t test or 1-way ANOVA, and differences were considered statistically significant at P < 0.05.
We collected 25 pairs of HCC tumor tissues and the corresponding adjacent noncancerous tissues and determined the expression of EVA1A by immunohistochemical staining, RT‒qPCR and western blotting. H&E staining and immunohistochemistry results clearly showed that EVA1A expression was significantly lower in HCC tissues than in matched adjacent nontumor tissues (Fig. 1A). In all 25 HCC samples, the EVA1A mRNA levels were significantly lower than those in their normal adjacent tissue pairs (P < 0.001; Fig. 1B). Consistent with the IHC results, western blot analysis showed that the EVA1A protein level in HCC tissues was markedly decreased (P < 0.001; Fig. 1C, D), confirming observations we made previously [10]. We further assessed EVA1A expression levels in HCC cell lines and obtained consistent results with HCC tissues. EVA1A mRNA levels and protein levels in PLC-PRF5, Hccl-M3, and QGY-7703 cells were significantly lower than those in the immortalized normal human liver cell line L02 (P < 0.001, Fig. 1E–G).
Previously, we reported that low expression of EVA1A is associated with the progression of HCC and might be a potential biomarker for poor prognosis of HCC. However, the regulation of EVA1A expression in HCC is not clear thus far. miRNA has been found to be abnormally expressed in HCC, whose central role is gene-expression regulation and some of which are involved in the progression of cancer. We explored the possibility that the differential EVA1A expression in HCC is regulated by miRNA. To identify the potential upstream miRNAs of EVA1A, we searched four databases, miRDB, TargetScan, STARbase and miRanda, and five miRNAs (miR-107, miR-103a-3p, miR-125a-5p, miR-125b-5p, miR-4319) were identified as candidate regulators of EVA1A (Fig. 2A). Next, we performed RT‒qPCR to analyze the expression levels of EVA1A and the five candidate miRNAs in 10 randomly selected pairs of clinical samples. The results showed that in HCC tissues, EVA1A was significantly downregulated, miR-107 and miR-103a-3p were significantly upregulated, while the other 3 miRNAs were downregulated (Fig. 2B), which was excluded according to the concept that miRNAs should have expression patterns that are opposite to those of their targets [23, 24]. In addition, the prediction results demonstrated that the 3′-UTR of EVA1A contains putative complementary binding sites for miR-103a-3p and miR-107 (Fig. 2D). To identify which one could target EVA1A, HEK293T cells were transfected with miR-103a-3p or miR-107 mimics and the luciferase reporter vector harbouring the wild-type or mutant 3ʹ-UTR of EVA1A, respectively. The results demonstrated that overexpression of miR-103a-3p significantly decreased the luciferase activity of cells expressing the wild-type but not the mutant 3′-UTR of EVA1A (Fig. 2E). Furthermore, upregulation of miR-103a-3p with synthetic miR-103a-3p mimics reduced the mRNA expression of EVA1A in Hccl-M3 cells (P < 0.001, Fig. 2F). In contrast, downregulation of miR-103a-3p with miR-103a-3p inhibitors had the opposite effect on the mRNA level of EVA1A (P < 0.01; Fig. 2F). Together with an analysis using the TCGA database, showing that miR-103a-3p had significant negative correlations with EVA1A (p = 0.001, r = − 0.186; Fig. 2C), it was confirmed that miR-103a-3p interacts with the EVA1A-3′UTR and is able to inhibit its promoter activity. In addition, western blot results showed that miR-103a-3p overexpression led to an obvious decline in the EVA1A protein level, whereas miR-103a-3p inhibition had the opposite effect on EVA1A expression in Hccl-M3 cells (P < 0.001; Fig. 2G, H). Another restoration experiment in which miR-103a-3p mimics/inhibitor were co-transfected with Myc-EVA1A plasmids or EVA1A siRNA in Hccl-M3 cells showed that exogenous Myc-EVA1A expression attenuated the decline in the EVA1A protein level induced by miR-103a-3p overexpression, and si-EVA1A counteracted the increase in the EVA1A protein level caused by inhibition of miR-103a-3p (P < 0.001; Fig. 2I, J). The above data indicated that Eva1a is one target gene of miR-103a-3p and that miR-103a-3p may regulate EVA1A expression both by mRNA degradation and by translational repression.
To further verify the expression of miR-103a-3p in HCC, we collected liver hepatocellular carcinoma data from TCGA public datasets and found that the expression level of miR-103a-3p was markedly increased in primary HCC tissues compared with normal tissues (P < 0.001, Fig. 3A), and with the progression of cancer, stage II–III tumors showed progressively higher expression of miR-103a-3p than stage I tumors in the TCGA cohort (P < 0.001, Fig. 3B). The prognostic value of miR-103a-3p for liver cancer, evaluated using an online database for prognostic analysis (Kaplan‒Meier Plotter, www.kmplot.com), indicated that high miR-103a-3p expression in patients with liver cancer was positively correlated with poor overall survival (P = 0.02, Fig. 3C). These results indicated that the expression of miR-103a-3p was upregulated in HCC and that increased expression of miR-103a-3p might serve as a prognostic factor for poor outcome in patients with HCC.
To explore the biological function of miR-103a-3p in HCC cells, we measured the expression levels of miR-103a-3p in HCC cell lines. The RT‒qPCR results showed significantly higher expression of miR-103a-3p in the HCC cell lines PLC-PRF5, QGY-7703 and Hccl-M3 than in the normal human liver cell line L02 (P < 0.001, Fig. 3D), which was opposite to the results for EVA1A expression in the HCC cell lines (Fig. 1E). Colony formation assays were performed to assess the roles of miR-103a-3p in the proliferation of HCC cells. The results revealed that overexpression of miR-103a-3p significantly promoted colony formation of Hccl-M3 cells and QGY-7703 cells (P < 0.01, Fig. 3E, F). In contrast, colony formation of HCC cells transfected with miR-103a-3p inhibitor was significantly suppressed (P < 0.01, Fig. 3G, H). CCK-8 assays also showed that upregulation of miR-103a-3p promoted the proliferation of Hccl-M3 cells and QGY-7703 cells and that downregulation of miR-103a-3p inhibited the proliferation of HCC cells (P < 0.01, Fig. 3I–L). In addition, we used wound healing and transwell assays to explore the effects of miR-103a-3p on the migration and invasion of Hccl-M3 and QGY-7703 cells. The results of the wound healing assay showed that the wound areas of cells transfected with miR-103a-3p mimics were significantly smaller than those of the control group, especially at 24 h (P < 0.01, Fig. 4A, B) and 48 h (P < 0.05, Fig. 4A, B) after transfection, while those transfected with miR-103a-3p inhibitors showed lower migratory capacities than the control group (P < 0.05, Fig. 4A, B). The results of the invasion assay demonstrated that the neoplasm invasiveness of HCC cells after transfection with miR-103a-3p mimics was significantly improved (P < 0.01, Fig. 4C, D), and those transfected with miR-103a-3p inhibitors showed lower invasiveness (P < 0.001, Fig. 4C, D). Together, these results demonstrated that miR-103a-3p promotes the proliferation, migration and invasion of HCC cells and implied that miR-103a-3p functions as a pro-oncogenic miRNA in HCC.
We have reported that EVA1A is a potential tumor suppressor gene for HCC and that its downregulation is associated with poor clinical outcomes for HCC patients [10], so we evaluated the effect of EVA1A on the malignant action of miR-103a-3p in HCC. We conducted wound healing, transwell, colony formation and CCK-8 assays in Hccl-M3 cells. As the results showed, there was an evident decrease in migration, invasion and proliferation activity in the group co-transfected with miR-103a-3p and Myc-EVA1A plasmids compared with the group transfected with miR-103a-3p overexpression alone (P < 0.01, Fig. 5). Upregulation of miR-103a-3p significantly promoted the migration, invasion and proliferation of Hccl-M3 cells (P < 0.01, Fig. 5), and overexpression of EVA1A significantly suppressed the migration, invasion and proliferation of Hccl-M3 cells (P < 0.001, Fig. 5), indicating that EVA1A attenuates the cancer-promoting roles that miR-103a-3p plays in Hccl-M3 cells. Taken together, these results suggest that EVA1A is a bona fide target of miR-103a-3p and that its downregulation is involved in the tumor-promoting action of miR-103a-3p in HCC. The epithelial-mesenchymal transition (EMT) is the critical factor for cancer cell metastasis [25, 26]. Recently, we have reported that EVA1A could inhibit EMT in HCC cells, by upregulating epithelial marker E-cadherin levels and downregulating mesenchymal markers N-cadherin and Vimen levels [10]. In the present study, we further investigated the molecular mechanisms involved. Studies have shown that abnormal activation of JAK/STAT3(Janus kinase/signal transducer and activator of transcription 3) signaling pathway can promote EMT and promote HCC progression [27–30], so we detected this pathway and found that the phospho-JAK2 levels, the phospho-STAT3 levels and the downstream gene MMP9 (matrix metallopeptidase 9) levels were significantly increased in miR-103a-3p overexpressing cells, while all of these protein levels dropped dramatically in EVA1A overexpressing cells, and the activation of JAK2/STAT3 signaling pathway was significantly attenuated in cells co-expressing EVA1A and miR-103a-3p compared with cells overexpressing miR-103a-3p alone (Additional file 1: Fig. S1). These data suggest that the pro-metastatic effect of miR-103a-3p in HCC may be mediated by EMT after JAK2/STAT3 signaling pathway activation by downregulating EVA1A. Furthermore, the correlation of the expression of EVA1A with the clinicopathological features of HCC patients was analyzed by HCC tissue microarray. According to the EVA1A IHC staining score, samples were divided into high and low expression group. Results showed that a low EVA1A expression was related to TNM stage (P = 0.032), tumor size (P = 0.0161), lymph node metastasis (P = 0.0168) and distant metastasis (P = 0.0185) (Additional file 1: Table S1). The associations of EVA1A expression with gender and age were not significant (Additional file 1: Table S1). The results indicate that EVA1A might be correlated with the development and progression of HCC and its low expression might be a potential biomarker for HCC metastasis.
We next investigated how EVA1A inhibits HCC cell proliferation. One previous study has reported that ectopic expression of EVA1A in HepG2 cells caused cell death during G2/M transition by microtubule catastrophe, resulting a G2 peak decline [31]. However, we didn't find changes in G2 phase cells proportion upon EVA1A overexpression, and our previous results showed an evident increase in G0/G1 phase proportion and an evident decrease in S phase proportion in EVA1A overexpressing Hccl-M3 and OGY-7703 cells [10], which was consistent with the study of Shen Xue et al. [7]. To determine whether EVA1A overexpression affects G2/M transition or mitosis progression, Hccl-M3 cells overexpressing EVA1A-GFP were synchronized to M phase with nocodazole, a chemical which could bind to β-tubulin in microtubules to interfere with microtubule dynamics and inhibit mitotic spindle function, inducing cell arrest in G2/M phase [32], and released from M phase by removal of nocodazole for different time to observe the mitosis and spindle state of the cells. As shown in Fig. 6A, after nocodazole treatment for 14 h, microtubules were partially depolymerized and many cells arrested in mitosis. Notably, cells overexpressing EVA1A were also able to enter mitosis, such as entering telophase and cytokinesis (Fig. 6A), and chromosomes were being pulled toward the poles by the spindle (Fig. 6A, second panel), suggesting that EVA1A overexpression does not affect microtubule assembly to form the correct spindle, nor does it affect mitosis. 0.5 h after removal of nocodazole, microtubules began to reassemble, and the cells overexpressing EVA1A had significantly enhanced signals in the contractile ring during cytokinesis and the spindle at metaphase (Fig. 6B). 1 h and 4 h after removal of nocodazole, microtubule assembly was basically restored, and cells overexpressing EVA1A completed mitosis and entered interphase (Fig. 6C, D). These results indicates that EVA1A has no effect on microtubule assembly and EVA1A overexpressing cells could enter and complete mitosis normally. Given the negative regulation of EVA1A by long antisense noncoding RNA, EVA1A-AS in HepG2 cells [31], we also checked the expression of EVA1A-AS in our cell system. In contrast to their findings, EVA1A-AS was also expressed in immortalized normal human liver cell line L02, and although the levels of EVA1A-AS in Huh7 and HepG2 cells were significantly higher than that in L02, the levels of EVA1A-AS in Hccl-M3 cells was almost the same as that in L02 cells (Additional file 1: Fig. S2), indicating that the suppressing effect of EVA1A-AS on EVA1A expression may be limited to some HCC cell lines. In addition, we also did not find the phenotype of lipid droplet accumulation in EVA1A overexpressing Hccl-M3 cells (Additional file 1: Fig. S3), suggesting that lipid droplet accumulation induce by depletion of EVA1A-AS in HepG2 cells is independent of EVA1A. Considering that EVA1A is an important autophagy regulator [8], and studies have shown that EVA1A inhibits GBM cell and other cancer cell growth by inducing autophagy [5, 7], so we examined the effect of overexpression EVA1A on autophagy activity in Hccl-M3 cells by measuring the LC3 isoform B (LC3B) autophagy marker. Unexpectedly, cells overexpressing EVA1A-GFP did not show a significant increase in endogenous LC3B puncta compared with control cells overexpressing GFP (Fig. 6E), and analysis by western blot showed similar effects, the autophagy membrane protein LC3-II/I levels, the autophagy initiation molecule Beclin1 levels and the autophagic substrate protein p62 levels showed little difference between Myc-EVA1A overexpressing cells and control cells (Fig. 6F), suggesting that overexpression of EVA1A could not induce autophagy in Hccl-M3 cells.
TP53 is an important anti-oncogene, and we previously reported that overexpression of EVA1A inhibits HCC cell proliferation by inducing apoptosis by upregulating TP53 [10]. Therefore, we further explored whether the anticancer effect of downregulating miR-103a-3p is related to apoptosis and whether EVA1A and TP53 are involved in this process. RT‒qPCR results showed that TP53 mRNA levels were significantly upregulated (P < 0.01) upon miR-103a-3p inhibition but significantly downregulated (P < 0.001) upon miR-103a-3p overexpression (Fig. 7A). Western blot analysis showed that the levels of TP53, BAX and EVA1A were significantly increased and the level of BCL-2 was dramatically decreased in miR-103a-3p-inhibited cells, while miR-103a-3p overexpression led to the opposite results (Fig. 7B, C), indicating that miR-103a-3p suppresses TP53/BAX-mediated apoptosis and that inhibition of miR-103a-3p promotes TP53/BAX-mediated apoptosis, as shown in the cell apoptosis profiles obtained by flow cytometry (P < 0.001, Fig. 7G, H) and caspase-3 activation by western blot (Fig. 7I). Importantly, co-expressing exogenous EVA1A evidently reversed the inhibitory effects of miR-103a-3p on TP53 expression, and knockdown of EVA1A greatly weakened the enhancement of miR-103a-3p inhibition on TP53 expression (P < 0.001, Fig. 7E, F). In addition, overexpressing EVA1A directly upregulated the mRNA level of TP53 (P < 0.001, Fig. 7D), which was consistent with our previous finding that overexpressing EVA1A upregulated the protein level of TP53 [10]. These results suggested that the negative regulation of TP53 expression by miR-103a-3p is primarily mediated by EVA1A. Consequently, knockdown of EVA1A significantly reduced cell apoptosis caused by miR-103a-3p inhibition (P < 0.001, Fig. 7G–I). Together, these results suggest that the cell apoptosis achieved by miR-103a-3p inhibition is dependent on upregulating the EVA1A/TP53 pathway and that miR-103a-3p potentially functions as a pro-oncogenic miRNA by targeting EVA1A and thereby inhibiting apoptosis and enhancing proliferation.
To further explore how inhibition of miR-103a-3p or upregulation of EVA1A affects cell apoptosis, we evaluated the mitochondrial membrane potential (MMP), intracellular ROS level and ATP level. In healthy cells, the JC-1 dye aggregates in the mitochondria and emits red fluorescence. When the MMP is lost, it depolymerizes into monomers and is released from the mitochondria into the cytoplasm, emitting green fluorescence. The results showed that inhibition of miR-103a-3p caused a significant enhancement in green fluorescence and a clear drop in red fluorescence (P < 0.001, Fig. 8A, B), indicating that inhibition of miR-103a-3p led to a significant decline in MMP, which was consistent with the finding that overexpressing EVA1A significantly reduced the MMP of HCC cells [10]. Not surprisingly, EVA1A knockdown greatly rescued the decline in MMP caused by miR-103a-3p inhibition, and EVA1A knockdown itself had almost no effect on MMP (P ≥ 0.05, Fig. 8A, B). In addition, inhibition of miR-103a-3p caused a significant increase in ROS production (P < 0.001, Fig. 8C, D) and a clear decrease in ATP production (P < 0.01, Fig. 8E), which suggested that it caused mitochondrial dysfunction. Likewise, EVA1A knockdown attenuated the changes in ROS and ATP levels induced by miR-103a-3p inhibition (Fig. 8C–E), while it had little effect on the production of ROS and ATP per se (P ≥ 0.05, Fig. 8C–E). In summary, inhibition of miR-103a-3p reduced cell MMP, promoted ROS production and reduced ATP production, and simultaneous knockdown of EVA1A reduced these effects, which revealed that the cell apoptosis induced by miR-103a-3p inhibition originates from mitochondrial dysfunction dependent of EVA1A upregulation.
EVA1A, known for its autophagy regulation function, plays an important role in numerous physiological and pathological processes [33]. Recently, the downregulation of EVA1A and its tumor suppressor activity was shown to be a high profile. It has been found that EVA1A is significantly downregulated in human tumor tissues such as liver cancer, esophageal squamous cell carcinoma, gastric adenocarcinoma and pancreatic tumors [9, 10, 34], indicating that it may be involved in the occurrence or development of these tumors. Overexpression of EVA1A can inhibit tumor cell proliferation by inducing apoptosis in cervical cancer HeLa cells [3], non-small cell lung cancer H1299 cells [6], and glioblastoma SHG44, U87, and U251 cell lines [7]. Moreover, our recent research found that EVA1A can inhibit HCC cell migration, invasion, and proliferation and induce cell apoptosis and cell cycle arrest by upregulating TP53 [10]. In this study, we show for the first time that the tumor suppressive activity of EVA1A in HCC cells can be inhibited by miR-103a-3p. miR-103a-3p can target and negatively regulate EVA1A, which illuminates the regulatory mechanism of miR-103a-3p to promote HCC. We also reveal that miR-103a-3p promotes HCC cell growth and mobility by targeting EVA1A and further downregulating TP53; in turn, downregulation of miR-103a-3p induces HCC cell apoptosis by upregulating the EVA1A/TP53 pathway (Fig. 9). As a member of the miR-103/107 family, miR-103 shows abnormal expression in a variety of cancers and might act as an oncogene or tumor suppressor gene in different cancer types. For instance, miR-103 is upregulated in numerous types of cancers, including neuroblastoma [14], gastric cancer [15], breast cancer [16] and colorectal cancer [17], where it functions as an oncogene, but it is downregulated in non-small cell lung cancer, where it functions as a tumor suppressor gene [13]. Additionally, miR-103a-3p suppresses cell proliferation and invasion by targeting tumor protein D52 in prostate cancer [18] but promotes human gastric cancer cell proliferation by targeting activating transcription factor 7 (ATF7) [19], and it also promotes tumor growth and glycolysis through the Hippo pathway in colorectal cancer [20]. Moreover, miR-103a-3p regulates proliferation and apoptosis by targeting RCAN1 in oral squamous cell carcinoma (OSCC) cells [21], miR-103 promotes HCC growth by inhibiting AKAP12 [35] or by promoting glucose metabolism function [27], and miR-103 promotes metastasis and EMT by inhibiting LATS2 [36]. Thus, miR-103 usually targets different genes in different cancers or targets different genes in the same cancer but exerts different functions. To date, miR-103 has been identified as an oncogene in HCC; however, the relationship between miR-103a-3p and EVA1A in HCC remains unclear and merits further investigation. Our study adds miR-103a-3p to the list of regulators of EVA1A, which previously included miR-125b and antisense lncRNA EVA1A-AS [11, 31]. This study is the first to comprehensively analyze the role and molecular mechanism of miR-103a-3p-EVA1A interplay in HCC growth and metastasis. In HCC tissues and cell lines, we confirmed that the expression of EVA1A is downregulated, and the expression of miR-103a-3p in HCC tissues and cells is upregulated, which is consistent with previous studies [35–37]. We also analyzed the correlation of miR-103a-3p expression with HCC tumor stages and its prognostic prediction value and found that high miR-103a-3p expression was positively correlated with poor overall survival, so miR-103a-3p could serve as a potential biomarker and a prognostic factor for poor outcome in patients with HCC. Then, we proved that EVA1A is targeted by miR-103a-3p and downregulated in HCC cells. EVA1A upregulation attenuates the cancer-promoting effects of miR-103a-3p, and EVA1A downregulation greatly reduces cell apoptosis induced by miR-103a-3p inhibition. Our study adds weight to the notion that EVA1A functions as a tumor suppressor in hepatocytes. Furthermore, our data show that by regulating EVA1A expression, miR-103a-3p plays an oncogenic role in hepatocarcinogenesis. Combining our previous study [10], we found that TP53 could be positively regulated by EVA1A and negatively regulated by miR-103a-3p at both the mRNA and protein levels, and the negative regulation of TP53 expression by miR-103a-3p is primarily mediated by EVA1A. It is difficult to rule out that miR-103a-3p could also target TP53 and directly regulate its expression. If so, miR-103a-3p may act synergistically with EVA1A to regulate TP53 expression. Moreover, the mechanism by which EVA1A upregulates TP53 is unclear. In this study, overexpressing EVA1A significantly enhanced TP53 mRNA levels, implying that it is a direct stress response. As an endoplasmic reticulum-located protein, a high EVA1A protein load may cause ER stress, which can cause an increase in TP53 [38], the details of which should be investigated in the future. TP53, as a crucial anticancer gene, controls cell apoptosis, the cell cycle, and cell invasion [39, 40], and it was also reported to inhibit EMT and metastasis by negatively regulating several EMT-inducing transcription factors and regulatory molecules [7]. Whether miR-103a-3p regulates EMT and metastasis through TP53 signaling in HCC remains to be confirmed. Additionally, as we reported previously, EVA1A overexpression induces mitochondrial outer membrane permeabilization (MOMP), decreases MMP and initiates cell apoptosis [10]. Based on this, this study also explored the effect of miR-103a-3p on mitochondrial function, and the results showed that repression of miR-103a-3p can damage the normal physiological functions of mitochondria, causing a decrease in MMP and ATP production and an increase in ROS production, all of which are induction factors of apoptosis. Furthermore, we found that the cell apoptosis induced by miR-103a-3p inhibition depends on the EVA1A/TP53 pathway and that EVA1A also mediates the mitochondrial dysfunction caused by miR-103a-3p inhibition. One study with quantitative proteomics of EVA1A−/− mouse brains showed that the proteins with altered expression are related to ATP synthesis, oxidative phosphorylation and the TCA cycle, implying that EVA1A may be involved in mitochondrial energy generation [41]. Since mitochondria play an important role in cell energy metabolism, reactive oxygen generation and cell apoptosis, whether EVA1A and miR-103a-3p also affect the related disease process through mitochondrial quality control needs further research.
In summary, our study identifies miR-103a-3p as a potential oncogene and an inhibitor of EVA1A in HCC cells. We found that miR-103a-3p is upregulated in HCC tissues and that high expression of miR-103a is associated with poor patient prognosis. We also show that miR-103a-3p promotes HCC cell growth by downregulating TP53 and promotes HCC cell migration by upregulating JAK2/STAT3 pathway in an EVA1A-dependent manner, thereby inhibiting apoptosis and enhancing proliferation and metastasis. We also provided novel evidence that miR-103a-3p and EVA1A antagonistically regulate mitochondrial function. These findings provide a platform for investigating the signaling pathways in HCC that are mediated by EVA1A and modulated by miR-103a-3p, offering new insights into the miRNA regulatory network in the development of HCC. The identification of the miR-103a-3p/EVA1A/TP53 regulatory axis contributes to a better understanding of the molecular mechanisms of HCC progression. Therefore, our study reveals that the downregulation of EVA1A by miR-103a-3p may act as a key mediator in HCC progression. miR-103a-3p may represent a prospective target for both HCC diagnosis and therapy.
Additional file 1: Fig. S1. Western blot analysis of JAK2/STAT3 activation. Hccl-M3 cells were transfected with miR-103a-3p mimics, Myc-EVA1A plasmid or co-transfected with both, 72 h after transfection, the protein levels of phospho-JAK2, phospho-STAT3 and MMP9 were detected by western blot. Fig. S2. The EVA1A-AS expression level in HCC cell lines. Total RNAs from L02, Hccl-M3, Huh7 and HepG2 cells were supplied for EVA1A-AS and GAPDH specific semi-quantitative RT-PCR. Three independent experiments were performed. Fig. S3. Effect of overexpression EVA1A on lipid droplet distribution in Hccl-M3 cells. (A) The transfection efficiency of TMEM166-GFP or GFP vector in Hccl-M3 cells. (B) Hccl-M3 cells were transfected with GFP empty vector or EVA1A-GFP plasmid, 24 h later, cells were applied for oil red O staining. Bars represent 10 μm. Table S1. The association between EVA1A expression and clinicopathologic features in HCC patients. | true | true | true |
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PMC9588310 | Mohammad Reza Aslani,Zahra Jafari,Reza Rahbarghazi,Jafar Rezaie,Aref Delkhosh,Mahdi Ahmadi | Effects of crocin on T-bet/GATA-3 ratio, and miR-146a and miR-106a expression levels in lung tissue of ovalbumin-sensitized mice | 01-10-2022 | Asthma,GATA-3,microRNA-106a,microRNA-146a,T-box transcription factor | Objective(s): Although various studies have revealed the beneficial effects of crocin (derived from saffron), such as anti-inflammatory, anti-cancer, antioxidant, and immune modulator, however, its exact mechanism is unknown. The present study aimed to investigate the effect of crocin on the expression ratio of T-bet/GATA-3 as an indicator of altered immune responses in the lung tissue of ovalbumin (OVA)-sensitized mice. In addition, the effect of crocin on the expression level of miR-146a and miR-106a in the lung tissue OVA-sensitized mice was investigated. Materials and Methods: Mice were randomly divided into five groups (n=6): Control; OVA, OVA + Crocin 25, OVA + Cro 50, and OVA + Cro100 groups. Crocin was administrated intraperitoneally at doses of 25, 50, and 100 mg/kg for five consecutive days. One day after asthma induction, animals were euthanized, and lungs were sampled for pathological and gene expression analysis. Results: OVA-sensitization led to increased inflammation and histopathological changes in the lung tissue of mice. In addition, GATA-3 expression increased (P<0.001) and T-bet expression decreased (P<0.001) in OVA-sensitized groups. The T-bet/GATA3 ratio was also reduced markedly in asthma groups (P<0.001). Furthermore, increased expression of miR-146a and miR-106a levels was evident in the lung tissue of OVA-sensitized mice (P<0.001 for both). Intervention with high concentrations of crocin (50 and 100 mg/kg) significantly reduced airway inflammation, GATA-3 expression, miR-146a expression, and miR-106a expression and corrected the T-bet/GATA-3 ratio (P<0.05 to P<0.001). Conclusion: Treatment with crocin led to a decrease in the severity of lung inflammation in OVA-sensitized mice, which is probably through the reduction of the T-bet/GATA-3 ratio, and mir-146a and mir-106a expression level. | Effects of crocin on T-bet/GATA-3 ratio, and miR-146a and miR-106a expression levels in lung tissue of ovalbumin-sensitized mice
Although various studies have revealed the beneficial effects of crocin (derived from saffron), such as anti-inflammatory, anti-cancer, antioxidant, and immune modulator, however, its exact mechanism is unknown. The present study aimed to investigate the effect of crocin on the expression ratio of T-bet/GATA-3 as an indicator of altered immune responses in the lung tissue of ovalbumin (OVA)-sensitized mice. In addition, the effect of crocin on the expression level of miR-146a and miR-106a in the lung tissue OVA-sensitized mice was investigated.
Mice were randomly divided into five groups (n=6): Control; OVA, OVA + Crocin 25, OVA + Cro 50, and OVA + Cro100 groups. Crocin was administrated intraperitoneally at doses of 25, 50, and 100 mg/kg for five consecutive days. One day after asthma induction, animals were euthanized, and lungs were sampled for pathological and gene expression analysis.
OVA-sensitization led to increased inflammation and histopathological changes in the lung tissue of mice. In addition, GATA-3 expression increased (P<0.001) and T-bet expression decreased (P<0.001) in OVA-sensitized groups. The T-bet/GATA3 ratio was also reduced markedly in asthma groups (P<0.001). Furthermore, increased expression of miR-146a and miR-106a levels was evident in the lung tissue of OVA-sensitized mice (P<0.001 for both). Intervention with high concentrations of crocin (50 and 100 mg/kg) significantly reduced airway inflammation, GATA-3 expression, miR-146a expression, and miR-106a expression and corrected the T-bet/GATA-3 ratio (P<0.05 to P<0.001).
Treatment with crocin led to a decrease in the severity of lung inflammation in OVA-sensitized mice, which is probably through the reduction of the T-bet/GATA-3 ratio, and mir-146a and mir-106a expression level.
Asthma is a chronic inflammatory disease associated with various symptoms, including airway obstruction and hyper-responsiveness (1). Inflammation in asthma is caused by increased and decreased immune responses of Th2 and Th1 cells, respectively (2). Increased Th2 cell activity led to elevated IL-4 levels, and decreased Th1 cell activity led to decreased INF-γ levels, indicating altered immune responses in patients with asthma (2). GATA-3 transcription factor acts as the primary regulator of Th2 differentiation and increases its production of inflammatory cytokines such as IL-4, IL-5, and IL-13 (3, 4). On the other hand, transcription factor T-bet, a member of the T-box family, plays a vital role in the differentiation and effector function of Th1 cells (5, 6). An imbalance between T-bet and GATA-3 transcription factors has recently been reported in bronchial asthma (7). Therefore, one of the treatment goals in patients with asthma is to correct the Th1/Th2 imbalance, so increasing the Th1/Th2 ratio is an essential indicator of improving immune responses in asthmatic patients (8, 9). Micro-RNAs (miRs) are small non-coding RNAs that regulate gene expression after transcription by inhibiting mRNA or inducing its degradation (10, 11). Recently, miRs have been shown to play a key role in regulating immune and inflammatory responses, lymphocyte activation, and eosinophil evolution (12). However, the role of many of them in patients with asthma is not well understood. Elevated miR-146a levels have been observed in patients with asthma (13). Interestingly, in obese ovalbumin-sensitized rats, increased expression level of miR-146a was more evident in lung tissue (13). Increased expression of miR-106a has also been reported in experimental asthma. By inhibiting miR-106a activity, a reduction in airway inflammation and mucous secretions occurred in the lung tissue of OVA-sensitized mice by increasing IL-10 production and decreasing the infiltration of inflammatory cells into the airways (14, 15). Therefore, another therapeutic goal in patients with asthma can be to focus on the activity of miRs. The effectiveness of medicinal plants in chronic inflammatory diseases has been demonstrated in various human and animal studies (16-19). One recommended herbal medicine for asthma patients is saffron and its active ingredient crocin (20, 21). Experimental and clinical studies have revealed the effectiveness of crocin in chronic inflammatory diseases such as rheumatoid arthritis, heart disease, central nervous system, kidney disease, and lung disease (21-25). Although various mechanisms have been reported for the effectiveness of crocin in asthma conditions, such as decreased Th2 lymphocyte activity, modulated expression of endoplasmic reticulum stress genes, and improved oxidant/antioxidant imbalance (21, 22), the exact mechanism is not well understood. Therefore, the present study aimed to investigate the effect of crocin on the expression ratio of T-bet/GATA-3 as an indicator of altered immune responses in the lung tissue of ovalbumin-sensitive mice. The current study also evaluated the effects of crocin on miR-146a and miR-106a expression.
Chemicals Quantitative enzyme-linked immunosorbent assay (ELISA) kits for OVA-sensitive IgE were obtained from Crystal Day (Shanghai, China). The crocin standard (>98%) was obtained from Sigma-Aldrich (St. Louis, MO, USA). OVA was obtained from Sigma-Aldrich (St. Louis, MO, USA). Aluminum hydroxide gel was obtained from Thermo Fisher (Waltham, MA, USA). All RT-PCR chemicals were obtained from Yekta Tajhiz Co. (Tehran, Iran). All other chemicals used in the study were of analytical grade. Experimental design This study used 30 adult male mice weighing 25 to 30 g. The animals were obtained from the Animal House of Tabriz University of Medical Sciences and adapted to the environment for one week. A temperature of 22±2 °C, a light-dark cycle of 12 hr: 12 hr, and free access to water and food were provided for all animals during the study. All animal-related interventions were performed after approval by the TBZMED ethics committee (IR.TBZMED.VCR.REC.1399.059). The animals were divided into five groups (n = 6 in each group) according to Figure 1. For sensitization with ovalbumin (OVA), the model of previous studies was used (21). In summary, 10 μg of OVA and 2 mg of aluminum hydroxide (Al (OH)3) were injected intraperitoneally on days 0, 7, and 14. From the 28th to the 32nd day, the animals were then exposed to 1% OVA aerosol through the nose for 30 min. In the control group, the animals received normal saline instead of OVA. In the intervention groups, one hour before the challenge with OVA, the animals were treated with crocin (IP). Bronchoalveolar lavage fluid (BALF) collection In order to collect BALF of animals after anesthesia with ketamine and xylazine (100 mg/kg and 10 mg/kg, IP, respectively), tracheal cannulation was performed. Sample collection was performed by injecting and aspirating 0.5 ml of phosphate buffer saline (PBS) (three times). The supernatant prepared from the BALF sample was used for total white blood cell (WBC) and differential cell count (26). Total and differential white blood cell (WBC) count Total WBC count was performed using a hemocytometer and Wright-Giemsa staining. Differential cell count was performed by a light microscope with ×400 magnification and following the standard protocol (26). Tissue sampling and protein measurement Right lungs were frozen in liquid nitrogen and stored at -70 °C until OVA-sensitive IgE was measured. To prepare a supernatant, tissue samples were weighed and homogenized in PBS (pH 7.2–7.4) and centrifuged for 20 min at 4 °C at 3000 rpm (21). According to the manufacturer’s instructions, OVA-specific IgE (µg/gram total protein) was measured using mouse ELISA commercial kits (Crystal Day, Shanghai, China). Real-time polymerase chain reaction We performed real-time PCR analysis to evaluate GATA-3 and T-bet mRNA expression levels and miR-146a and miR-106a (10, 27). Table 1 shows the locked nucleic acid (LNA) forward and reverses mRNA’s primer sets (Exiqon). The PCR products were normalized with β-actin genes for mRNA samples. Results were expressed as fold change versus controls. Pathological assessment Isolated left lung tissue was fixed in 10% neutral buffered formalin (37%, Merck, Germany) and embedded with paraffin blocks. The paraffin blocks were then cut to 4 μm, stained with hematoxylin-eosin, and evaluated under a light microscope. Pathological changes included a detachment of epithelium, bronchioles infiltration of lymphocytes, and interstitial tissue pneumonia. Scoring for each pathological lung change was identified from 0 to 3 as follows: 0= normal; 1= patchy injury, 2= local injuries, and 3= scattered injuries (27, 28). Statistical analysis In the current study, results are reported as mean ±SEM. Comparisons among different groups were performed using variance (ANOVA) with Tukey-Kramer post hoc test. In addition, Kruskal-Wallis statistical test was used to analyze the pathological results. P<0.05 was considered the significance level.
Effects of crocin on the BALF cell infiltration The total number of WBCs in the sensitized group was significantly higher in comparison with the control group (P<0.001). Instead, the number of leukocytes in the crocin-treated groups was significantly lower than in the OVA group (P<0.001 for all) (Figure 2A). Here we reported that in the OVA group, levels of all inflammatory cells, including eosinophils, lymphocytes, neutrophils, and macrophages were significantly higher than in control animals (P<0.001 for all cases, Figure 2B-E). The significant improvement in the levels of all inflammatory cells in the treated groups (OVA-Cr25, OVA-Cr50, and OVA-cr100) was seen in comparison with the OVA group (P<0.001 for all cases, Figures 2B-E). However, total and differential leucocyte count indices in the BAL samples of all treated groups were still higher than in the healthy animals. Effect of crocin on GATA-3 and T-bet expression levels in the lung tissue OVA-sensitization increased the expression level of GATA-3 in the lung tissue of mice, which crocin at a concentration of 100 mg/kg significantly prevented (P<0.01, Figure 3A). The inhibitory effect of crocin at 100 mg/kg was significantly higher than that of 50 mg/kg (P<0.05). On the other hand, the expression of T-bet mRNA level in lung tissue of OVA-sensitized mice was significantly reduced compared with the control group (P<0.001 for all) (Figure 3B). Crocin at a 100 mg/kg concentration resulted in a significant increase in T-bet expression levels (P<0.001, Figure 3B). Furthermore, the results showed that the T-bet/GATA-3 ratio was significantly reduced in the OVA-sensitized groups compared with the control group (Figure 3C). Intervention with 50 and 100 mg/kg crocin concentrations prevented T-bet/GATA-3 reduction. Effect of crocin on miR-146a and miR-106a expression levels in the lung tissue The elevated expression level of miR-146a was significantly observed in the OVA-sensitized groups compared with the control group (P<0.001 for all). Concentration-dependent crocin-treatment (50 and 100 mg/kg) inhibited the increased expression of miR-146a (P<0.05 and P<0.01, respectively), which had a higher effect on crocin 100 than 50 mg/kg (P<0.05) (Figure 4A). The OVA-sensitization effects were also significantly associated with enhanced miR-106a expression levels in mice lung tissue compared with the control group (P<0.001 for all). Crocin 50 and 100 mg/kg significantly inhibited the increase in miR-106a expression compared with the OVA group (P<0.01 and P<0.001, respectively) (Figure 4B). Effects of crocin on the OVA-specific IgE protein levels in the lung tissue OVA-sensitization resulted in increased OVA-specific IgE protein levels (69.17 ± 9.92 µg) compared with the control group (36.50 ± 6.17 µg) (P<0.001, Figure 5). Crocin at a concentration of 100 mg/kg significantly reduced OVA-specific IgE protein levels (49.83 ± 5.18 µg) compared with the OVA group (P<0.01), while concentrations of 25 and 50 mg/kg had no effect (Figure 5). Effects of crocin on lung tissue pathological changes The pathological findings revealed that the severity of changes such as lymphocyte infiltration, epithelial layer detachment, and interstitial tissue pneumonia (interalveolar septal thickening) was significantly higher in the OVA-sensitized mice than in the control group (P<0.001 for all) (Table 2 and Figure 6). Intervention with 50 and 100 mg/kg of crocin concentrations significantly reduced histopathological changes (Table 2).
This study showed that OVA-sensitization led to an increase in inflammatory cells in the airways of mice and histopathological changes in the lung tissues that were prevented by treatment with crocin, especially at high concentrations. In addition, in OVA-sensitized mice, increased expression of GATA-3 and decreased T-bet occurred in lung tissue, and crocin with high concentrations (50 and 100) significantly prevented their changes. Finally, increased expression of mir-146a and mir-106a in lung tissue of OVA-sensitized mice was suppressed by crocin intervention. Asthma is an inflammatory disease characterized by AHR, the production of Th2 inflammatory cytokines (IL-4, IL-5, and IL-13), and the increased infiltration of inflammatory cells into the airways that results from the activation of Th2 cells (29, 30). In addition, histopathological changes in asthma patients are evident, including mucus secretion, airway smooth muscle hypertrophy, lymphocyte infiltration, epithelial detachment, interstitial tissue pneumonia, subepithelial fibrosis, and goblet cell hyperplasia (31, 32). One of the causes of AHR in asthmatic patients is the infiltration of eosinophils in the respiratory airways, which affects the severity of the disease by producing Th2 inflammatory cytokines (5). The present study results identified that increased levels of eosinophils, macrophages, and neutrophils were evident in the BALF samples of OVA-sensitized mice. In addition, OVA-induced histopathological changes such as lymphocyte infiltration, epithelial detachment, and interstitial tissue pneumonia also occurred, indicating induction of the asthma model in this study. Accordingly, one of the therapeutic goals in patients with asthma may be to prevent the eosinophil requirement (5). The anti-inflammatory, antioxidant, and anti-allergic effects of saffron and its active ingredients (crocin, crocetin, and safranal) have been reported in various animal and human studies (33, 34). The anti-inflammatory and immunomodulatory activities of saffron and crocin on leukocytes and lymphocyte cells have been demonstrated under OVA-sensitized animals (34). This study revealed that crocin prevented airway eosinophilia and lung inflammation. In addition, in previous studies, the reducing effects of saffron and crocin on Th2 cytokine levels (IL-4, IL-5, and IL-13) also have been reported (34). The results suggest that crocin had induced anti-inflammatory effects in OVA-sensitization status. In asthma, Th1/Th2 imbalance has been shown to play a critical role in the pathogenesis of the disease (35). CD4 + T cells are divided into Th1 and Th2 based on functional differences and the type of cytokines produced (35). Two transcription factors, GATA-3 and T-bet, determine the differentiation of T cells to Th2 and Th1, respectively (5). IL-4 and INF-γ levels in asthma patients have been associated with the T-bet/GATA-3 ratio, indicating an immune imbalance in asthma conditions (35). In addition, a decrease in the T-bet /GATA3 ratio has been shown in most animal and human studies (36). Another therapeutic goal in asthma patients could be to modify the T-bet/GATA-3 ratio. The results showed that GATA-3 and T-bet expression levels were significantly increased and decreased in OVA-sensitized mice. Interestingly, there was a significant decrease in the T-bet/GATA-3 ratio in OVA-sensitized mice, indicating a significant increase in GATA-3 expression level. The current study results were consistent with the previous study results, which showed a more significant increase in GATA-3 compared with a decrease in T-bet in asthmatic mice (5). In fact, the role of GATA-3 in controlling Th1/Th2 differentiation appears to be greater than that of T-bet, as the direct effect of GATA-3 on IL-5 expression has been reported (5). Crocin treatment prevented T-bet/GATA-3 ratio imbalance, especially at high concentrations. Although there are not many findings regarding the role of crocin on the T-bet/GATA-3 ratio, a recent study by Hosseinzadeh et al. reported the modifying effects of crocin on ConA-treated human lymphocyte proliferation (37). Crocin-treated cells showed slightly lower T-bet/GATA-3 and INF-γ/IL-4 ratios than untreated cells (37). In another study, the effects of crocin on GATA-3 and T-bet expression in mononuclear cells of patients with osteoarthritis (OA) were investigated (38). The results revealed that crocin treatment significantly increased GATA-3 expression in mononuclear cells (38). Contradictory results of crocin effects in inflammatory diseases need further study. Various studies have shown that mirRs are involved in immune regulation and play a vital role in the therapeutic aspects of immune-related diseases (39). Elevated miR-146a and miR-106a have been reported in OVA-sensitized mice (13, 14). The present study results revealed that OVA-sensitization increased the expression of miR-146a and miR-106a in the lung tissue of mice, which was consistent with previous findings (13, 14). Crocin treatment reduced the expression of miR-146a and miR-106a levels markedly at high concentrations. Inhibition of miR-146a and miR-106 expression in an asthmatic animal model has improved disease severity (40). MiR-146a may exacerbate the disease in asthma patients by increasing IL-1β production and miR-106a by decreasing IL-10 production (13, 14). Interestingly, miR-146a has been reported to play a dual role in asthma. Some studies have shown miR-146a as an anti-inflammatory factor, the reduction of which leads to an increase in neutrophil migration (41). On the other hand, miR-146a expression increases in response to IL-17A, TNF-α, and IL-4, which indicates its negative feedback effects on inflammatory cells such as neutrophils (41, 42). It seems that the results of the current study, on the other hand, confirmed the anti-inflammatory effects of miR-146a in conditions of ovalbumin sensitization. Intervention with crocin may have prevented the increase in miR-146a expression by reducing the number of leukocytes and inflammatory cytokines. In relation to the role of mir-106, it has also been shown that its increased expression was caused by IL-4, which led to inhibitory effects on the expression of Th2 cells (43). The results of the current study also showed that sensitization with ovalbumin led to an increase in the expression of miR-106a, the levels of which treatment with crocin reduced . In fact, reducing the severity of the disease affected the expression of miR-106a Little is known about the association between mirRs and transcription factors, especially in patients with asthma. In a study, Saki et al. showed that ectopic expression of miR-146a increased the expression of various transcription factors such as PU.1, c-Fos, CCAAT/enhancer-binding protein alpha (C/EBPα), GATA3, Foxp3, and Runx1 in lymphoblastic cells (44). The current study results were in line with the findings of the Saki study. Although the present study did not directly evaluate the effects of miR-146a on GATA-3 expression level, a significant positive correlation between GATA-3 and miR-146a reflects the role of miR-146a in GATA-3 expression, which requires further studies. On the other hand, the role of miR-146a in T-bet expression was observed as an increased expression in peripheral blood mononuclear cells in patients with acute coronary syndrome (45), which was different from the findings of the current study. The results revealed a significant negative association between miR-146a and T-bet expression in lung tissue of OVA-sensitized mice. The differences between the current study’s findings and the previous study may be due to differences like the diseases that have affected immune responses. GATA-3 and IL-4, which are two specific markers of Th2 cells, as well as miR-106a were revealed to be significantly increased in Th2 cells and unchanged or less expressed in Th17 cells, which actually confirms the specific differentiation (46). The relationship between miR-106a and the transcription factors T-bet and GATA-3 is not clear. Based on the findings of the current study, at least in part, it can be inferred that miR-106 may have indirect effects on the expression and function of the above transcription factors, which requires further studies.
The study had some limitations. First, the study results evaluated the expression of genes and did not specify their post-translation changes. It is better to study the change levels of target genes in future studies. Second, cell line studies should be designed in terms of mechanism evaluation to evaluate the association between miR-146a and miR-106a with transcription factors GATA-3 and T-bet. In summary, the present study results revealed changes in the expression of transcription factors associated with Th1 and Th2 cells (T-bet and GATA-3, respectively) occurring in OVA-sensitized mice, which treatment with crocin significantly prevented. In addition, increased expression of miR-146a and miR-106a was observed in the lung tissue of OVA-sensitized mice, which was prevented with crocin intervention. Interestingly, there was a significant positive correlation between miR-146a and miR-106a with GATA-3 and a significant negative correlation with T-bet, at least in part, reflecting the role of miRs in the expression of transcription factors.
MRA and MA Helped with proposal writing, literature search, data collection, interpretation of data, analysis of data, review of manuscript, and manuscript preparation. ZJ, RR, JR, and AD: Provided proposal writing, draft preparation, review of manuscript, and analysis of data.
This study was supported by a grant (IR.TBZMED.VCR.REC.1399.059) from the Stem Cell Research Center of Tabriz University of Medical Sciences.
The authors have declared that there are no conflicts of interest. | true | true | true |
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PMC9588325 | Hui Zhang,Jin Yuan,Yuehua Xiang,Yong Liu | Comprehensive Analysis of NPSR1-AS1 as a Novel Diagnostic and Prognostic Biomarker Involved in Immune Infiltrates in Lung Adenocarcinoma | 15-10-2022 | The incidence of lung adenocarcinoma (LUAD), the most common subtype of lung cancer, continues to make lung cancer the largest cause of cancer-related deaths worldwide. Long noncoding RNAs (lncRNAs) have been shown to have a significant role in both the onset and progression of lung cancer. In this study, we aimed to investigate the clinical significance and underlying mechanism of lncRNA NPSR1-AS1 (NPSR1-AS1) in LUAD. First, we performed an analysis on TCGA and identified 229 differentially expressed lncRNAs (DELs) (including 216 upregulated lncRNAs and 13 downregulated lncRNAs). Then, we carried out a screening of the lncRNAs associated with survival, and a total of 382 survival-related lncRNAs were found. 15 survival-related DELs were identified. Among them, our attention focused on NPSR1-AS1. We found that the expression of NPSR1-AS1 was much higher in LUAD specimens compared to nontumor tissues. According to the results of the ROC assays, high NPSR1-AS1 expression had an AUC value of 0.904 for LUAD, with a 95% confidence interval ranging from 0.881 to 0.927. The expression of NPSR1-AS1 was shown to be significantly elevated in a wide variety of cancers, according to the findings of a pancancer investigation. Functional enrichment analysis confirmed that NPSR1-AS1 was involved in LUAD progression via regulating several tumor-related pathways. Patients with high levels of NPSR1-AS1 expression were shown to have a shorter disease-specific survival (DSS) or overall survival (OS) than those with low levels of NPSR1-AS1 expression, according to the findings of a clinical investigation. It was determined by multivariate analysis that NPSR1-AS1 expressions served as an independent prognostic factor for the overall survival of LUAD patients. The results of immune cell infiltration revealed that the expressions of NPSR1-AS1 were negatively associated with CD8 T cells, pDC, cytotoxic cells, mast cells, iDC, neutrophils, NK CD56dim cells, DC, Th17 cells, Tgd, and macrophages, while they were positively associated with NK CD56bright cells and B cells. Overall, our findings revealed that NPSR1-AS1 could serve as a potential biomarker to assess the clinical outcome and immune infiltration level in LUAD. | Comprehensive Analysis of NPSR1-AS1 as a Novel Diagnostic and Prognostic Biomarker Involved in Immune Infiltrates in Lung Adenocarcinoma
The incidence of lung adenocarcinoma (LUAD), the most common subtype of lung cancer, continues to make lung cancer the largest cause of cancer-related deaths worldwide. Long noncoding RNAs (lncRNAs) have been shown to have a significant role in both the onset and progression of lung cancer. In this study, we aimed to investigate the clinical significance and underlying mechanism of lncRNA NPSR1-AS1 (NPSR1-AS1) in LUAD. First, we performed an analysis on TCGA and identified 229 differentially expressed lncRNAs (DELs) (including 216 upregulated lncRNAs and 13 downregulated lncRNAs). Then, we carried out a screening of the lncRNAs associated with survival, and a total of 382 survival-related lncRNAs were found. 15 survival-related DELs were identified. Among them, our attention focused on NPSR1-AS1. We found that the expression of NPSR1-AS1 was much higher in LUAD specimens compared to nontumor tissues. According to the results of the ROC assays, high NPSR1-AS1 expression had an AUC value of 0.904 for LUAD, with a 95% confidence interval ranging from 0.881 to 0.927. The expression of NPSR1-AS1 was shown to be significantly elevated in a wide variety of cancers, according to the findings of a pancancer investigation. Functional enrichment analysis confirmed that NPSR1-AS1 was involved in LUAD progression via regulating several tumor-related pathways. Patients with high levels of NPSR1-AS1 expression were shown to have a shorter disease-specific survival (DSS) or overall survival (OS) than those with low levels of NPSR1-AS1 expression, according to the findings of a clinical investigation. It was determined by multivariate analysis that NPSR1-AS1 expressions served as an independent prognostic factor for the overall survival of LUAD patients. The results of immune cell infiltration revealed that the expressions of NPSR1-AS1 were negatively associated with CD8 T cells, pDC, cytotoxic cells, mast cells, iDC, neutrophils, NK CD56dim cells, DC, Th17 cells, Tgd, and macrophages, while they were positively associated with NK CD56bright cells and B cells. Overall, our findings revealed that NPSR1-AS1 could serve as a potential biomarker to assess the clinical outcome and immune infiltration level in LUAD.
It is estimated that 1.76 million people die every year from lung cancer, making it the top cause of death resulting from cancer worldwide (18.4% of all cancer-related deaths) [1]. Approximately forty percent of all instances of lung cancer are classified as lung adenocarcinoma (LUAD), making it the most frequent type [2, 3]. The frequency of this form of lung cancer is rising worldwide. The primary cause of lung cancer is still smoking, as it has been for decades [4, 5]. Even though prolonged exposure to tobacco smoke is by far the most common cause of this form of cancer, nonsmokers account for anywhere from 15 to 20 percent of cases and are typically thought to have contracted the disease due to a confluence of hereditary and environmental factors [6, 7]. Despite the use of morphological analysis to classify patients into different risk groups, it is evident that the overall survival (OS) rates for LUAD patients with high invasiveness and early metastasis ranged from 13 to 58.3% at 5 years [8, 9]. This was the case despite the use of morphological analysis to classify patients. It is difficult to diagnose non-small-cell lung cancer in its early stages, when the disease is also tough to treat [10]. Consequently, it is of the utmost importance and a pressing necessity to discover innovative prognostic biomarkers in order to provide helpful therapy methods for LUAD. The discovery of the potential diagnostic usefulness of genetic biomarkers, such as long noncoding RNAs (lncRNAs), was made possible by the advent of high-throughput sequencing techniques and bioinformatics technologies [11, 12]. lncRNAs are becoming an increasingly important focus of attention in research on cancers [13]. lncRNAs are a category of nonprotein coding transcripts that are typically longer than 200 nucleotides without an open reading frame [14]. Their length is what defines them as “long noncoding RNAs.” Numerous studies have shed light on the function of lncRNAs in several biological processes, such as the silencing of X-chromosome genes, the remodeling of chromatin, and transcriptional activity [15, 16]. In addition, a growing number of studies have indicated a link between lncRNAs and the onset and progression of many malignancies, including LAUD [17, 18]. For instance, Zhang et al. indicated that the expressions of SNHG17 were highly elevated in LUAD specimens and cells, and high SNHG17 expression was related to advanced stages of tumor node metastases and a bad prognosis for patients who had LUAD. The targeting of the microRNA-193a-5p/NETO2 axis by SNHG17 knockdown resulted in an inhibition of the EMT process as well as cell migration, invasion, and proliferation [19]. Cong et al. showed that it is possible that the lncRNA known as linc00665, which was found to be significantly overexpressed in lung adenocarcinoma (LUAD) tissues, can act as an independent predictor of a bad prognosis. According to the results of functional tests, linc00665 promoted LUAD cell proliferation and metastasis both in vitro and in vivo through modulating the AKR1B10-ERK signaling pathway and by sponging miR-98 [20]. These studies suggested that lncRNAs have the potential to be turned into potentially valuable biomarkers that can aid in the diagnosis and prognosis of LUAD. Although immunotherapy has been used with promising results in the treatment of tumors, it is still only effective for a very small percentage of cancer patients [21]. This is despite the fact that it represents a unique approach to cancer treatment [22]. There is a strong correlation between the tumor microenvironment (TME) and the effectiveness of immunotherapy [23, 24]. The epigenetic differentiation of tumor cells and the metastasis and infiltration of the tumor are both linked to the suppression of the immune system caused by the tumor [25]. TME is a complex system that is made up of many distinct cell types, cytokines, and other extracellular components. Both the kind and the quantity of immune cells that invade a tumor are significant factors in establishing its development and evolution [26, 27]. Additionally, the make-up and proportion of TIICs and stroma can be used for the diagnosis, prognosis, and prediction of many cancers [28]. It is possible that new therapy targets for cancers could be found by mining related lncRNAs and then examining how those lncRNAs affect immune cell infiltration in TME and the prognosis of the tumor. In this study, NPSR1-AS1, a previously unknown long noncoding RNA associated to LUAD, was found to have abundant expression in LUAD. Previous researches from a number of different investigations have uncovered its roles in some cancers. For instance, Ni et al. revealed that NPSR1-AS1 was substantially expressed in thyroid cancer, and its overexpression boosted the proliferation and metastasis of thyroid cancer cells. It was accomplished by recruiting ELAVL1 to stabilize NPSR1 mRNA [29]. NPSR1-AS1 was found to be highly expressed in thyroid cancer. On the other hand, its expression and potential prognostic usefulness in LUAD have not been researched. The investigation of the immunological microenvironment in patients with LUAD has opened up new possibilities for the conventional therapy protocols that are now in use. Therefore, the improvement of patient survival is one of our primary objectives in the development of a universal immunodiagnostic marker.
Using the UCSC Xena browser, we were able to retrieve the gene expression data, phenotypic data, and extensive clinicopathological data for TCGA-LUAD. The Illumina HiSeq RNA-Seq platform was used to retrieve the sequence data that was needed. For the purposes of the subsequent studies, the HTSeq-FPKM gene expression data were converted into TPM. TPM produces results that are more comparable to those provided by an approach using microarrays, and it makes it easier to compare the results of different samples. In accordance with the associated annotation file, the probe ID was transformed into the gene symbol, and then, the average expression values for many probes that corresponded to the same gene were computed. The data were collected and analyzed in a way that was compliant with the publication standards provided by TCGA datasets. There was not a single study that directly involved human volunteers or animal testing that was included. The approval of the ethics committee and informed consent were not required.
All samples were compared using a differential expression analysis between LUAD and nontumor samples, and the Wald significance test (as specified by the nbinom Wald test function) was utilized to determine statistical significance. RNA-seq data can be trusted when analyzed using the DESeq2 package in R, which employs this test. This package was based on raw read counts for each gene, making it a robust way for assessing RNA-seq data. A statistical limit for significance was set at a false discovery rate (FDR) of less than 0.05 and a fold change of more than 4.
A high-expression group was defined as having an expression level that was higher than the median expression level across all samples, and a low-expression cohort was defined as having an expression level that was lower than the median expression level across all samples. It was performed in order to facilitate the screening process for survival-related genes. A log-rank test was applied to compare the Kaplan-Meier curves of the high-expression cohort with those of the low-expression cohort. In order to determine the factors that are linked with survival, a multivariate analysis using the Cox proportional hazard model was carried out. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) are reported. The level of significance for each test was two-sided and set at P less than 0.05. The “survival” package in R was utilized in order to successfully complete the procedure (https://cran.r-project.org/web/packages/survival/index.html).
We separated the tumor groups into high- and low-expression subgroups based on the median expression values of NPSR1-AS1, and we used the “limma” R package to screen for differentially expressed genes (DEGs) between the two subgroups. In order to be considered statistically significant, the |logFoldChange (logFC)| value needed to be greater than 2, and the false discovery rate (FDR) needed to be lower than 0.05. The next step was to conduct a study of GO and KEGG enrichment of MMP14 coexpressed genes. The procedure was carried out with the assistance of the R programming language and the clusterProfiler, Enrichment plot, and GGplot2 software programs.
To examine the relative expression levels of 22 different TIICs in LUAD samples, the CIBERSORT package of the R software was utilized in its version 3.6.3 form [30]. We determined the percentages of each of these 22 TIIC subpopulations that were present in each sample.
The statistical studies were carried out with the help of the R programming language. The Wilcoxon test was used to evaluate whether or not there were continuous variable differences between the two groups. The Kruskal-Wallis test was applied to make comparisons between more than two different groups. Either the chi-square test or Fisher's exact test was used to investigate the variations in frequency of occurrence between category variables. The log-rank test was utilized for the study of the variations in survival rates. In the analysis of disease-specific survival (DSS) or overall survival, the Cox assays was utilized for the purpose of calculating the hazard ratios (HRs) of variables together with their respective 95% confidence intervals (95% CIs). Moreover, Pearson's correlation and Spearman's correlation were used to assess the correlations between different genes. A P < 0.05 was considered statistically significant.
The transcriptional profiles of 535 tumor samples and 59 normal samples were first retrieved from TCGA databases and then reanalyzed by our team. It was possible to determine the levels of expression for all lncRNAs. We were able to collect a total of 229 DELs, 216 of which were upregulated lncRNAs and 13 of which were downregulated lncRNAs (Figure 1(a)). Then, we screened the lncRNAs associated with survival, and we found 382 survival-related lncRNAs that had a P value of less than 0.01 (Table S1). Venn diagram showed the overlapping lncRNAs between 229 DELs and 382 survival-related lncRNAs, and 15 survival-related DELs were identified, including FAM83A-AS1, LINC01833, LASTR, AC022784.1, AC068228.1, AC010343.3, NPSR1-AS1, LINC01559, AC005256.1, AC125603.2, AL365181.3, AL365181.2, AC125603.1, LINC00973, and LINC02535 (Figure 1(b)). NPSR1-AS1 was the primary focus of our study among the 15 survival-related DELs listed above. We observed that the expressions of NPSR1-AS1 were markedly elevated in LUAD tissues when compared to nontumor specimens (Figure 1(c)). Additionally, the diagnostic significance of NPSR1-AS1 for LUAD patients was investigated using data from TCGA datasets. According to the results of the ROC tests, high NPSR1-AS1 expression yielded an AUC value of 0.904 for LUAD with a 95% confidence interval ranging from 0.881 to 0.927 (Figure 1(d)). Moreover, based on the data from TCGA and GTEx data, the results of ROC assays indicated that high NPSR1-AS1 expression had an AUC value of 0.824 (95% CI: 0.801 to 0.847) for LUAD (Figure 1(e)).
In addition, we performed pancancer analysis of NPSR1-AS1 expression using TCGA and GTEx data. As shown in Figure 2, we found that the expression of NPSR1-AS1 was distinctly increased in many types of tumors, such as CHOL, COAD, and ESCA. Our findings suggested that NPSR1-AS1 upregulation may be a common event.
To further explore the roles of NPSR1-AS1 in LUAD, we used the “limma” R package to separate the tumor groups into high- and low-expression subgroups based on median expression values of NPSR1-AS1. Following that, 79 DEGs were found. Then, we performed GO analysis using 79 DEGs. As shown in Figure 3(a), we found that the 79 DEGs were mainly enriched in digestion, nucleosome assembly, cellular glucuronidation, uronic acid metabolic process, glucuronate metabolic process, apical plasma membrane, apical part of cell, neuronal cell body, nucleosome, DNA packaging complex, glucuronosyltransferase activity, bitter taste receptor activity, taste receptor activity, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding. Moreover, the results of KEGG assays indicated that the 79 DEGs were mainly enriched in olfactory transduction, bile secretion, biosynthesis of cofactors, ascorbate and aldarate metabolism, pentose and glucuronate interconversions, porphyrin metabolism, steroid hormone biosynthesis, retinol metabolism, viral carcinogenesis, and chemical carcinogenesis-receptor activation (Figure 3(b)).
In order to carry out statistical analysis, the level of NPSR1-AS1 expression was split between high- and low-expression groups. In individuals diagnosed with LUAD, an investigation was conducted to determine whether or not there was a correlation between the expression of NPSR1-AS1 and clinicopathological features. However, we find that the expressions of NPSR1-AS1 were not associated with age (Figure 4(a)), gender (Figure 4(b)), pathologic stage (Figure 4(c)), and smoker (Figure 4(d)). In addition, the results from the chi-square test also showed a similar finding (Table 1).
Further, we investigated whether or not the expression of NPSR1-AS1 was connected with the fate of LUAD patients. Patients who had high levels of NPSR1-AS1 had a lower overall survival rate than those who had low levels of NPSR1-AS1 (Figure 5(a), P = 0.003), as shown by the findings of a Kaplan-Meier survival analysis. In addition, the group with high levels of NPSR1-AS1 showed a considerably lower DSS than the group with low levels of NPSR1-AS1 expression (Figure 5(b), P = 0.043).
The next step was to conduct univariate and multivariate analysis to determine whether the NPSR1-AS1 expression level was an independent predictive indicator of LUAD patient outcomes. According to the findings of our study, both the pathologic stage and the expression of NPSR1-AS1 functioned as independent prognostic indicators for overall survival (Table 2). In addition, it was demonstrated that the pathologic stage is an independent prognostic indication for patients diagnosed with LUAD (Table 3). However, no additional evidence of NPSR1-AS1 expression could be found in DSS (Table 3).
The ssGSEA methodology was utilized to analyze the transcriptomes of TCGA-LUAD cohort in order to determine the degree to which immune cell infiltration was present. Twenty-four immune-related phrases were included in the study in order to determine the number of immune cells that are present in the microenvironment of a tumor. Our group observed that the expressions of NPSR1-AS1 were negatively associated with CD8 T cells, pDC, cytotoxic cells, mast cells, iDC, neutrophils, NK CD56dim cells, DC, Th17 cells, Tgd, and macrophages, while they were positively associated with NK CD56bright cells and B cells (Figure 6).
Tumor developments were dependent on the survivals and death of tumor cells [31]. The study of cell death can therefore assist us in understanding the underlying mechanisms that are responsible for the development of malignancies [32]. In addition to the well-known techniques of functional genes, researchers are uncovering other kinds of regulators that are involved in the progression of tumors. In recent years, for instance, lncRNAs have garnered a lot of attention from researchers [33, 34]. Research on lncRNAs has also become increasingly common. However, the majority of the attention has been directed toward conducting more in-depth basic studies. The question of whether lncRNAs can give doctors with some therapeutic insight has received very little attention in the published research. In the subject of LUAD research, there are likewise very few studies. Therefore, in the hopes of locating additional new approaches that may be utilized for clinical diagnosis and therapy, we decided to conduct research on the relationship that existed between lncRNAs and the clinical data associated with LUAD. In our study, we performed an analysis on TCGA datasets, and as a result, we found a total of 229 DELs, which included 216 upregulated lncRNAs and 13 downregulated lncRNAs. NPSR1-AS1 was the primary focus of our attention. In the past, a number of studies have indicated that NPSR1-AS1 served a function in a variety of cancers. For instance, He et al. revealed that the expressions of NPSR1-AS1 were shown to be increased in hepatocellular carcinoma tissues and cell lines. In the following step, the ectopic expression of NPSR1-AS1 regulated the MAPK/ERK pathway, which in turn accelerated the proliferation and glycolysis of hepatocellular carcinoma cells [35]. Dastjerdi et al. showed that NPSR1-AS1 had the ability to make a considerable distinction between the tumor and the normal samples. These findings might have repercussions for the early diagnosis and focused treatment of colorectal cancer in the future [36]. He et al. discovered that NPSR1-AS1 activated the MAPK pathway to promote the proliferation and metastasis of thyroid cancer cells by engaging ELAVL1 to stabilize NPSR1 mRNA. This was accomplished by facilitating the proliferation of thyroid cancer cells [35]. In the first part of our study, we observed that the level of NPSR1-AS1 was significantly higher in LUAD tissues compared to nontumor specimens. This finding was in line with findings from other studies. The findings of the ROC tests then revealed that NPSR1-AS1 may be utilized as an indicator to screen LUAD specimens vs. nontumor specimens. In addition, the expression of NPSR1-AS1 was shown to be significantly elevated in many other kinds of tumors, such as CHOL, COAD, and ESCA, according to the findings of a pancancer investigation. Our research led us to believe that an upregulation of NPSR1-AS1 is a rather typical occurrence. The GO and KEGG tests found evidence that NPSR1-AS1 may play a regulatory role in the course of LUAD by exerting an influence over a number of different tumor-related pathways. Patients who had a high level of NPSR1-AS1 expression in clinical studies were found to have a lower overall survival time and disease-free survival time than patients who had a low level of NPSR1-AS1 expression. Moreover, multivariate studies demonstrated that NPSR1-AS1 expression was an independent prognostic factor for overall survival of LUAD patients. Based on these findings, we hypothesized that NPSR1-AS1 could serve as a diagnostic and prognostic biomarker for patients with LUAD. The prognosis of LUAD will be further investigated in subsequent studies in which we will also further evaluate the association between NPSR1-AS1-associated genes and the prognosis. Tumor stromal cells are part of the tumor microenvironment and can influence how cancerous tumor cells behave [37]. One type of immune cell that plays a crucial role in tumor development and progression is called tumor-infiltrating lymphocytes (TILs). By building a complex intercellular contact network, TILs aid in the development and maintenance of an immunosuppressive environment, aid in immune escape, and eventually contribute to tumor progression [38]. Research on immune cell infiltration has revealed that there is a significant function for immune cells in the TME in the progression of cancer. New approaches to cancer immunotherapy may be easier to come up with if researchers had a better grasp of how immune cells infiltrate the immunological milieu. We found that the expression of NPSR1-AS1 was negatively associated with CD8 T cells, pDC, cytotoxic cells, mast cells, iDC, neutrophils, NK CD56dim cells, DC, Th17 cells, Tgd, and macrophages, while it was positively associated with NK CD56bright cells and B cells. It is possible that Th17 cells have an antitumor effect because the subgroup of patients with LUAD that has a greater infiltration of Th17 cells is less likely to develop lymph node metastases and more likely to have a better prognosis. Therefore, based on the findings of our study, immunosuppression, which is caused by the presence of less Th17 cells in the primary tumor microenvironment, may be the cause of a shorter survival rate at 10 years for patients with LUAD who have high levels of NPSR1-AS1. Despite the fact that our research showed a relationship between NPSR1-AS1 and LUAD, there were still several limitations to our investigation that need to be addressed. Firstly, the number of patients who participated in this study was rather low, which meant that additional research including a substantial number of participants was necessary to validate our findings. Secondly, most of our findings were obtained from bioinformatics analysis and TCGA datasets, which lack experimental verification in in vitro and in vivo experiments.
It is possible that NPSR1-AS1 is a predictive biomarker for LUAD, which is the factor that determines how well cancer immunotherapy works. The findings of the current research have the potential to offer fresh perspectives on the formulation of efficient therapy methods directed against LUAD. | true | true | true |
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PMC9588341 | Jinhong Wu | Pancreatic Cancer-Derived Exosomes Promote the Proliferation, Invasion, and Metastasis of Pancreatic Cancer by the miR-3960/TFAP2A Axis | 15-10-2022 | Background The microRNAs (miRNAs) in cancer-derived exosomes have the ability to change tumor microenvironment. This study aims to investigate the role of miRNA in cancer-derived exosomes in pancreatic cancer (PC). Methods Based on the analysis of PC-derived and healthy exosomes by bioinformatics analysis and quantitative real-time PCR validation, the miR-3960 was identified to be the most significantly different miRNA, and TFAP2A proved as its potential target gene. Besides, the exosomes were isolated from PANC-1 cells and identified. After that, PANC-1 cells were treated with the isolated exosomes or transfected with miR-3960 mimics or si-TFAP2A, the effect of PC-derived exosomes, as well as the miR-3960/TFAP2A axis in PC cells, were assessed by the CCK-8, EDU staining, Transwell, cell colony formation, and flow cytometry assays. Furthermore, the effects of exosomes and the miR-3960/TFAP2A axis on PC tumor growth were observed in tumor-bearing mice by the measurement of tumor weight and volume, and hematoxylin-eosin staining. Moreover, the expressions of TFAP2A/PTEN/AKT signaling proteins were detected by Western blot. Results PC-derived exosomes were isolated successfully and proved to have promotion effects on the proliferation, metastasis, and invasion of PC cells both in vitro and tumor growth in vivo. Also, the PC-derived exosomes upregulated the TFAP2A, Bcl-2, and p-AKT/AKT protein levels, and inhibited PTEN and Bax levels and PANC-1 cell apoptosis. Overexpression of miR-3960 antagonized the promotion effect of exosomes on PC cells and the TFAP2A/PTEN/AKT signaling pathway, inhibiting the growth of tumors. Besides, si-TFAP2A enhanced the inhibitory effect of miR-3960 in PC. Conclusion MiR-3960 antagonizes the promotion effect of tumor-derived exosomes on the proliferation, invasion, and metastasis of PC via suppressing TFAP2A. | Pancreatic Cancer-Derived Exosomes Promote the Proliferation, Invasion, and Metastasis of Pancreatic Cancer by the miR-3960/TFAP2A Axis
The microRNAs (miRNAs) in cancer-derived exosomes have the ability to change tumor microenvironment. This study aims to investigate the role of miRNA in cancer-derived exosomes in pancreatic cancer (PC).
Based on the analysis of PC-derived and healthy exosomes by bioinformatics analysis and quantitative real-time PCR validation, the miR-3960 was identified to be the most significantly different miRNA, and TFAP2A proved as its potential target gene. Besides, the exosomes were isolated from PANC-1 cells and identified. After that, PANC-1 cells were treated with the isolated exosomes or transfected with miR-3960 mimics or si-TFAP2A, the effect of PC-derived exosomes, as well as the miR-3960/TFAP2A axis in PC cells, were assessed by the CCK-8, EDU staining, Transwell, cell colony formation, and flow cytometry assays. Furthermore, the effects of exosomes and the miR-3960/TFAP2A axis on PC tumor growth were observed in tumor-bearing mice by the measurement of tumor weight and volume, and hematoxylin-eosin staining. Moreover, the expressions of TFAP2A/PTEN/AKT signaling proteins were detected by Western blot.
PC-derived exosomes were isolated successfully and proved to have promotion effects on the proliferation, metastasis, and invasion of PC cells both in vitro and tumor growth in vivo. Also, the PC-derived exosomes upregulated the TFAP2A, Bcl-2, and p-AKT/AKT protein levels, and inhibited PTEN and Bax levels and PANC-1 cell apoptosis. Overexpression of miR-3960 antagonized the promotion effect of exosomes on PC cells and the TFAP2A/PTEN/AKT signaling pathway, inhibiting the growth of tumors. Besides, si-TFAP2A enhanced the inhibitory effect of miR-3960 in PC.
MiR-3960 antagonizes the promotion effect of tumor-derived exosomes on the proliferation, invasion, and metastasis of PC via suppressing TFAP2A.
Pancreatic cancer (PC) has a poor prognosis and high mortality, and its incidence is rising globally [1]. It is predicted that PC will become the second cause of cancer-related death in the United States by 2030 [2]. The five-year survival rate of PC is about 6%, and the risk factors include genetics, smoking, diabetes, diet, inactivity, etc. [3]. Early PC is usually asymptomatic clinically, therefore, researchers are committed to developing markers for screening early curable pancreatic lesions [4]. It is essential to investigate the biological mechanism of the occurrence and development of PC to provide a direction for its prevention and treatment. Exosomes are important mediators of intercellular communication, in particular, they have a strong ability to modify the tumor microenvironment [5]. Exosomes are cup-shaped in morphology under transmission electron microscopy (TEM), with diameters ranging from 40 nm to 160 nm, and carry bioactive substances, such as proteins, microRNA (miRNA), and metabolites [6]. Exosomes of host cells can activate receptors, or regulate miRNA/RNA expression in adjacent cancer cells to change their biological phenotype. For example, the miRNA of hypoxia mesenchymal stem cells-derived exosomes advanced the metastasis of lung cancer cells [7]. miRNA can transport to target cancer cells through exosomes, affecting their metastasis, invasion, and apoptosis [8]. miRNA is a kind of small non-coding RNA, which plays a role in the post-transcriptional regulation of gene expression and regulating various cellular activities [9]. For example, miR-146a-5p regulated proliferation and chemoresistance via the TRAF6/NF-κB p65 signaling pathway in pancreatic ductal adenocarcinoma [10]. We screened genes by bioinformatics analysis and quantitative real-time PCR (qRT-PCR), and found the TFAP2A in PANC-1 cells was markedly inhibited by miR-3960. The transcription factor activating enhancer-binding protein 2 α (TFAP2A), promotes cell proliferation and apoptosis through LncRNA [11, 12]. Beck's team found that the knockout of TFAP2A decreased AKT phosphorylation in colon cancer and promoted drug resistance [13]. Gene Expression Omnibus (GEO) database is mostly used for the prediction of target miRNA [14]. So, in this study, we investigated the effects of exosomes from PC cells on the proliferation, invasion, and metastasis of PC cells based on bioinformatics analysis.
Through GSE50632 datasets in the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50632), the serum exosome samples of 2 patients and 2 healthy individuals were obtained in the GPL16294 platform. The original data file was analyzed by GEO2R analysis software, and the parameter P <0.05 and |log2 (FC)| >2.0 was set as the cut-off standard. The top 30 miRNAs were shown in a bar chart and the most differentially expressed miRNA was selected for this study. Genes targeted by miRNAs were predicted by the four bioinformatics algorithms, miRanda v3.3a [15], miRTargetLink (https://ccb-compute.cs.uni-saarland.de/mirtargetlink2/) [16], miRDB (http://mirdb.org/) [17] and Targetscan (https://www.targetscan.org/vert_72/) [18]. And the Gene Expression Profiling Interactive Analysis (GEPIA) [19] and quantitative real-time PCR (qRT-PCR) were used to compare the expression level of target genes in PC and normal tissues.
The immortalized human pancreatic duct epithelial cell line, HPDE6-C7, and human PC cell lines (MIA, PANC-1, and BxPC-3 cell lines) were purchased from iCell Bioscience Inc (Shanghai, China). They were cultured with 10% fetal bovine serum (FBS) (11011-8615, Tianhang, CHN) and 90% Dulbecco's modified eagle medium (DMEM) (SH30243.01, Hyclone, US) at 37°C, 5% CO2.
The PANC-1 cells were cultured for 48 h with a medium that has been removed from exosomes by centrifugation, and the supernatant was collected. The supernatant was cleared off suspended cells and cell debris by stepwise centrifugation. After filtration by 0.22 μm, exosome precipitation was obtained by centrifuging the supernatant at 120,000 g at 4°C for 2 h. The resuspended exosomes were visualized by transmission electron microscope (TEM, H-7650, Hitachi, JPN). The Nanoparticle Tracking Analysis (NTA) was carried out by a particle size analyzer (N30E, NanoFCM, Xiamen, CHN).
After constructing the plasmid expressing miR-3960 and si-TFAP2A [20, 21], according to the instructions, the PANC-1 cells with 90% confluence were transfected by Lipofectamine 2000 (11668-027, Invitrogen, US) to construct the miR-3960-overexpressed and TFAP2A-knockdown cell lines. The cells were divided into miR-NC, miR-3960, Exo + miR-NC, Exo + miR-3960, si-NC, si-TFAP2A, miR-NC + si-NC, miR-NC + si-TFAP2A, miR-3960 + si-NC, and miR-3960 + si-TFAP2A groups.
The supernatant was obtained from the lysed-cell suspension by centrifugation and was treated with chloroform and isopropanol successively to obtain precipitation by centrifugation, and finally, the precipitation was rinsed with 75% ethanol. The RNA was dissolved in 40 μL DEPC water (R0021, Beyotime, CHN) and stored in a -80°C refrigerator. The reverse transcription was performed by HiFiScript cDNA Synthesis Kit (CW2569, cwbio, Beijing, CHN). Reaction conditions: 42°C for 15 min; and 85°C for 5 min. At last, the qRT-PCR was carried out by the SYBR Premix Ex TaqII (RR820A, Takara, JPN) according to the following procedure: 95°C for 10 min and 95°C for 15 s, then at 60°C for 60 s, performed 40 cycles. The data were processed by the relative quantitative method (2-△△Ct). The information on the primers was shown in Table 1.
After the lysis and centrifugation, the protein extracted from each group of cells was diluted to equal concentrations. After denaturation, the proteins were electrophoresed and the bands transferred to PVDF membranes (10600023, GE Healthcare Life, US). The blots were blocked by 5% nonfat milk and washed by TBST. Then, the membranes were incubated with the following antibodies overnight. The antibodies were anti-TSG10 (1 : 2000, DF8427), Alix (1 : 2000, DF9027), CD81 (1 : 2000, DF2306), CD9 (1 : 1000, AF5139), GAPDH (1 : 5000, AF7021), CD63 (1 : 100, AF5117), HAP70 (1 : 1000, AF5466), C-myc (1 : 2000, AF0358), Bax (1 : 2000, AF0120), Bcl-2 (1 : 1000, AF6139), TFAP2A (1 : 1000, AF0535), PTEN (1 : 1000, AF6351), p-AKT (1 : 1000, AF0016), and AKT (1 : 1000, AF6261), all purchased from Affinity Biosciences Co., Ltd, US. After washing by TBST, the membranes were incubated with corresponding secondary antibodies. The protein bands were imaged and observed by the Chemiluminescence imaging analysis system (610020-9Q) and ImageJ.
The 96-well plates were used to culture cells in the logarithmic growth phase. At 48 h after transfection, cells and CCK-8 (HY-K0301, MedChemExpress, US) reagent (10 μL/well) were incubated for 2 h in an incubator. The optical density was measured at 450 nm with the microplate reader (CMaxPlus, MD, US). The percentage of the ratio for the experimental to control groups, which is the value after subtracting separately the value of the blank-control, was used to evaluate the level of cell proliferation.
The cells with 90% confluence were cultured on 12-well plates. At 48 h after transfection, the cells were processed by the BeyoClick™ EdU Cell Proliferation Kit with Alexa Fluor 594 (C0078s, Beyotime, CHN) according to the instructions, and the slides were observed and photographed under the fluorescence inverted microscope (Ts2-FC, Nikon, JPN).
The Transwell chambers were coated with Matrigel that was diluted 3 : 1 in serum-free DMEM. The upper Transwell chamber (3422, Coring, US) was added with 200 μL cell suspension. After 24 h, the 4% paraformaldehyde-fixed cells at the bottom chamber of the Transwell membrane were stained by the Crystal Violet Staining (548-62-9, Sunqiang, CHN). Finally, the number of deep purple cells was measured by the microscope.
1000 cells were inoculated into the 6-well plates and were incubated with 70% DMEM and 30% FBS medium. The medium was refreshed every 3 days and the colony was observed until the number of cells of each colony was greater than 50. Each well was added with 1 mL of 4% paraformaldehyde at 4°C, and the plate was rested for 60 min. And paraformaldehyde-fixed cells were cleaned gently with PBS once. Last, the cells were stained with 1000 μL crystal violet for 2 min.
The 1 × 106 cells/mL were treated with the Annexin V FITC Apoptosis Detection Kit I (556547, BD, US), and the apoptosis rate was measured by flow cytometry (C6, BD, US).
According to the instructions, the lysis cells were processed by the Dual-Luciferase Reporter Gene Assay Kit (D0010-100 T, Solarbio, CHN), and the relative light unit was measured at 560 and 465 nm with the microplate reader.
BALB/c male nude mice (6 weeks) were obtained from the Beijing Vital River Laboratory Animal Technology Co., Ltd (SCXK (Jing) 2016-0011). Mice were raised in a standard Specific-Pathogen-Free environment and all animal tests were approved by the Animal Experimentation Ethics Committee of Zhejiang Eyong Pharmaceutical Research and Development Center (SYXK (zhe) 2021-0033). 1 × 106/100 μL PANC-1 cells that were treated with blank-vector/miR-3960 plasmid or si-TFAP2A/siRNA control, were subcutaneously injected into the right groin of nude mice. The volume of tumor that was in vivo or resected was calculated according to the formula (Volume =0.5 × long diameter × short diameter2). After four weeks, the mice were euthanized, and the tumors were removed, then the size and weight were measured [22]. Then, the tumor was fixed with 10% formaldehyde and embedded in paraffin. The livers and lungs were isolated and the metastatic nodules were counted.
After 4 μm tumor paraffin slices were dewaxed and washed with distilled water, and they were stained with hematoxylin (MD911467, MDL, CHN) and eosin (613101, BaSO, CHN). Then sections were dehydrated, cleared and sealed. Finally, the microscope was used for observation. Semi-quantitative scoring (0-4 points) was performed according to the degree of inflammatory cell infiltration and the tumor cell metastatic area proportion, the extent of tumor metastasis and inflammatory cell infiltration were scored [23, 24].
Statistical analysis was performed by SPSS 16.0. One-way ANOVA followed by the Turkey test was used if the multi-group data were normally distributed and conformed to the homogeneity of variance test; the paired Student's t-test was used if the two-group data were normal distribution but unequal variance. All data were expressed as mean ± standard deviation (), P <0.05 means the difference was statistically significant.
The bioinformatics analysis results were shown in Figures 1(a)–1(c). The miR-3960 was the most significantly different miRNA in tumor and normal tissues, and the log2(FC) is 8.90065 (Figure 1(a)). Combined with four platform analyses and literature review, we obtained ten potential target genes (Figure 1(b)). They are PEG10, HOXB8, EN1, NRARP, TFAP2A, PCDHA6, EGR3, PCDHA2, PCDHA4, and PCDHA11. Based on TCGA normal and GTEx database, the expressions of these genes in PC were analyzed, and the levels of PEG10, TFAP2A and EGR3 was remarkably greater in tumor compared to normal group (P <0.01) (Figure 1(c)). In order to determine the PC cell lines used for the experiments, the expression of miR-3960 in HPDE6-C7, PANC-1, BXPC-3, and MIA cell lines was analyzed by qRT-PCR (Figure 1(d)). The miR-3960 expression levels in PANC-1, BXPC-3, and MIA cell lines were immensely lower in comparison to it in HPDE6-C7 cell line (P <0.01), and the level in PANC-1 was the lowest. Thus, PANC-1 cell line was used for subsequent experiments. The expression levels of PEG10, HOXB8, EN1, NRARP, PCDHA4, TFAP2A, PCDHA6, EGR3, PCDHA2, and PCDHA11 mRNA were measured by qRT-PCR in PANC-1 cells transfected with miR-3960 mimics. Among them, in the miR-3960 group, the PEG10, TFAP2A, and EGR3 mRNA levels were markedly inhibited in comparison with the miR-NC group, with TFAP2A as the most significant one (P <0.01, P <0.05) (Figures 1(e), 1(j), and 1(l)). The mRNA levels of HOXB8, EN1, NRARP, PCDHA4, PCDHA6, PCDHA2, and PCDHA11genes were not significantly different (P >0.05) (Figures 1(f)–1(i), 1(k), 1(m) and 1(n)).
The TEM and NTA analyses were performed to identify the exosomes in PANC-1. The morphology of exosomes with typical bilayer structures in the form of discs- or cup-shaped was observed by TEM (Figure 2(a)), suggesting the high purity of the samples. And the NTA analysis showed that the exosomes were spherical in shape and the average diameter is 177 nm, the size range is 12-300 nm, with a relatively normal distribution (Figures 2(b) and 2(c)). WB indicated the presence of typical exosome marker proteins in exosomes (Figure 2(d)). In the exosome group, the TSG101, Alix, CD81, CD9, CD63, and HSP 70 protein expression levels were remarkably greater in comparison to the cell group (P <0.01). And the nuclear protein C-myc was not expressed in exosomes.
The xenograft tumor model was established to observe the proliferation of PC. Compared with the physiological saline group, the volume and weight of the tumor were markedly greater in the exosomes group (P <0.01). (Figures 3(a)–3(c)). The invasion and metastasis of PC were observed by the HE staining. The morphology of the lung in mice of the physiological saline group was clear and complete, and there were no tumor metastases. While in the exosomes group, there was marked tissue expansion with thickening of the alveolar septa and permeation of inflammatory cells in the lung. And the semi-quantitative score was greater in the exosomes group in comparison with the physiological saline group (P <0.01) (Figures 3(e) and 3(f)). Similarly, in the exosomes group, the liver was fragmented and not visible in morphology, with prominent inflammatory cell infiltration and increased metastatic foci, and the semi-quantitative score was higher in comparison with the physiological saline group (P <0.01) (Figures 3(e) and 3(f)). The Bax and Bcl-2 proteins in the liver of exosomes treatment mice were tested by WB. The Bax protein level of the exosomes group was immensely decreased in comparison to the physiological saline group (P <0.01), and the level of Bcl-2 protein was the opposite (P <0.05). (Figures 3(g)–3(i)).
The inhibition of exosomes on the miR-3960 expression level was examined by qRT-PCR. In comparison to the miR-NC group, the level in the miR-3960 group was markedly raised (P <0.01), and in the Exo + miR-NC was the opposite. Comparing the Exo + miR-3960 group with the Exo + miR-NC group, the miR-3960 level was notably raised (P <0.01) in miR-3960 group, suggesting that exosome treatment can significantly inhibit miR-3960 expression (Figure 4(a)). To analyze the effect of exosome on miR3960-overexpressed PANC-1 cell proliferation, we carried out the CCK-8, EdU, and cell colony formation assays (Figures 4(b)–4(d)). The viability of PANC-1 cells in the miR-3960 group was notably repressed in comparison to the miR-NC group (P <0.01). On the contrary, in the Exo + miR-NC group, the viability was notably raised in comparison with the miR-NC group (P <0.01). Additionally, the viability of cells was repressed significantly in the Exo + miR-3960 group compared to the Exo + miR-NC group. The results of EdU and cell colony formation analysis was in consistent with the CCK-8 results (Figures 4(c)–4(f)). In comparison with the miR-NC group, the EdU positive cells and relative colony number were inhibited in the miR-3960 group and was raised in the Exo + miR-NC group (P <0.05, P <0.01). In the Exo + miR-3960 group, the percentage of EdU positive cells was remarkably lower compared to the Exo + miR-NC group (P <0.05, P <0.01). The abilities of PANC-1 cell metastasis and invasion was tested by the Transwell assay (Figures 5(a) and 5(b)). Compared to the miR-NC group, the numbers of migrating and invading cells were notably reduced in the miR-3960 group (P <0.01). And the cells were both markedly increased in the Exo + miR-NC group compared with the miR-NC group (P <0.05 or P <0.01). In the Exo + miR-3960 group, the numbers of migrated and invaded cells were repressed significantly in comparison to the Exo + miR-NC group (P <0.01). The apoptosis levels in PANC-1 cells were inversely correlated with the viability of the cells (Figure 5(c)). The apoptotic rate was notably enhanced in the miR-3960 group and was immensely reduced in the Exo + miR-NC group, both in comparison to the miR-NC group (P <0.01). The percentage of apoptotic cells in the miR-3960 exosome treatment cells had a marked increase (P <0.01). Tumor-derived exosomes regulated the expression of Bax and Bcl-2 proteins in PANC-1 via miR-3960. We measured the expression of Bax and Bcl-2 in PANC-1 by WB. The Bax expression was markedly increased in the miR-3960 group in comparison to the miR-NC group (P <0.05). In the Exo + miR-NC group, the Bax protein was immensely reduced compared to the Exo + miR-3960 group (P <0.01). The Bax protein level was notably greater in the Exo + miR-3960 group than in the Exo + miR-NC group (P <0.01). The expression of Bcl-2 was opposite to that of Bax (P <0.05). (Figures 5(d)–5(f)).
Compared to the Exo + miR-NC group, the volume and weight of the tumors were notably decreased in the Exo + miR-3960 group (P <0.01) (Figures 6(a)–6(c)). The HE staining was performed to observe the invasion and metastasis of PC. Compared to the Exo + miR-NC group, the morphology of the lung and liver in the Exo + miR-3960 group were complete and clear, and the tumor metastases were few. The semi-quantitative score of the lung and liver were lower in the Exo + miR-3960 group in comparison with the Exo + miR-NC group (P <0.01) (Figures 6(d)–6(f)). The Bax, Bcl-2, TFAP2A, PTEN, and p-AKT/AKT proteins expression in the lung were observed by WB. The Bax and PTEN protein levels in the Exo + miR-3960 group were immensely increased in comparison to the Exo + miR-NC group (P <0.05), and the Bcl-2, TFAP2A, and p-AKT/AKT expression decreased (P <0.01). (Figures 6(g) and 6(h)).
Based on the results of our bioinformatics and literature analysis, we investigated TFAP2A in PC. We silenced TFAP2A in vivo to observe the tumor proliferation and invasion. The volume and weight of the tumor were both notably decreased in the si-TFAP2A group (P <0.01) (Figures 7(a)–7(c)). In comparison to the si-NC group, the lung and liver morphology in the si-TFAP2A group were normal and complete, and the tumor metastases were less. The semi-quantitative score of the lung and liver were both decreased in the TFAP2A-silenced PC mice compared to the si-NC group (P <0.01) (Figures 7(d)–7(f)). In the liver, the expression of Bax and Bcl-2 proteins in TFAP2A-silencing mice were measured by WB. In the si-TFAP2A group, the Bax protein level was notably increased (P <0.01, P <0.05), while the Bcl-2 level was decreased (P <0.01) in comparison with the si-NC group (Figures 7(g)–7(i)).
We investigated the interaction of miR-3960 and TFAP2A through the dual-luciferase reporter assay. The luciferase activity of the miR-3960 transfected cells was markedly decreased in the WT group compared to the miR-NC group, while this decrease was blocked by mutation of the 3 ‘UTR plasmid sequence of the TFAP2A gene (Figure 8(a)). The proliferation of PANC-1 cells was measured by EdU (Figures 8(b) and 8(c)) and cell colony formation assays (Figures 8(d) and 8(e)). The relative proportion of EdU positive cells and colony number were notably lower in the miR-3960 and miR-NC + si-TFAP2A group than those in the miR-NC group (P <0.05, P <0.01). The number of EdU positive cells and cell colonies in the miR-3960 + si-TFAP2A group was significantly reduced compared to the miR-NC + si-TFAP2A group (P <0.05, P <0.01). The flow cytometry was used to test the apoptosis in PANC-1 cells. The proportion of apoptotic cells in the miR-3960 and miR-NC + si-TFAP2A groups was markedly higher compared to the miR-NC group (P <0.01). Also, comparing the miR-3960 + si-TFAP2A group with the miR-NC + si-TFAP2A groups, the proportion of apoptotic cells was markedly raised in the miR-3960 + si-TFAP2A groups (P <0.05). (Figures 8(f) and 8(g)). In addition, the TFAP2A, Bax, and Bcl-2 protein in the PANC-1 cells were measured by WB (Figures 8(h)–8(k)). In comparison to the miR-NC group, the TFAP2A level was reduced in the miR-3960 and miR-NC + si-TFAP2A groups (P <0.05 or P <0.01). Similarly, the TFAP2A level in the miR-3960 + si-TFAP2A group was notably decreased in comparison to the miR-NC + si-TFAP2A group (P <0.01). (Figure 8(i)). In the miR-3960 group, the Bax protein level was immensely greater than it in the miR-NC group (P <0.01), while the Bcl-2 expression level was lower compared to the miR-NC group (P <0.01). In the miR-NC + si-TFAP2A group, the level of Bax protein was markedly increased and the Bcl-2 protein was immensely reduced, compared to the miR-NC group (P <0.01 or P <0.05). The Bax protein was markedly greater and the Bcl-2 protein was notably lower in the miR-3960 + si-TFAP2A group in comparison to the miR-NC + si-TFAP2A group (P <0.01 or P <0.05). (Figures 8(j) and 8(k)).
The TFAP2A and PTEN/Akt signaling proteins in the miR-3960-overexpressed and TFAP2A-silenced PANC-1 cells were measured (Figures 8(l)–8(o)). In comparison with the control group, the expression TFAP2A and p-AKT/AKT was markedly raised in the Exo group (P <0.01, P <0.05), while the PTEN level was notably decreased (P <0.01). The TFAP2A and p-AKT/AKT levels in the miR-3960 group were reduced in comparison to the miR-NC group (P <0.01). Conversely, the PTEN protein level was raised significantly (P <0.05). The expression of TFAP2A and p-AKT/AKT was markedly decreased and the PTEN protein was markedly higher in the TFAP2A-silenced cells than that in the control cells (P <0.01).
Exosomes are mediators of signal and molecular transmission between cells. By secreting exosomes, cells participate in a variety of biological activities [25]. In tumors, exosomes play a major role in the formation and development of different cancers [26]. It was found that exosomes were involved in liver metastasis of PC by activating the TGF-β in PC mouse model [27]. Therefore, the study on the mechanism of bioactive substances in exosomes of PC is helpful to understand the occurrence, development, and metastasis of PC. In this study, we used miR-3960 plasmid transfection in PANC-1 cells and the xenograft tumor model in BALB/C nude mouse to study the biological mechanism of miR-3960 in the exosomes of PC cells. For the first time, we demonstrated that miR-3960 in exosomes of PC inhibits tumor proliferation, metastasis, and invasion via TFAP2A. We found that there was a big difference in miR-3960 expression between patients with PC and healthy individuals. Mo proved that knockdown of miR-3960 antagonized the apoptosis of small-cell lung cancer H446 cells induced by Caffeic Acid Phenethyl Ester [28]. While Yang's team believed that miR-3960 promoted drug resistance in head and neck squamous cell carcinoma [29]. MiR-3960 has different biological effects on different kinds of tumor cells. This study examined the changes in the miRNA spectrum in exosomes derived from PC and revealed the antitumor effect of miR-3960, that overexpression of miR-3960 could inhibit the proliferation, metastasis, and invasion of PC cells and promote apoptosis. In PC cells, the low expression of miRNA 3960 may be a biological marker of abnormal proliferation, metastasis and invasion. In recent years, TFAP2A has been proved to promote the proliferation and metastasis of lung adenocarcinoma, cervical cancer, and other cancer cells, mainly by affecting EMT to promote tumor metastasis [30, 31]. This study demonstrated that knockdown of TFAP2A could block the apoptosis of PC cells, and this process involved the regulation of apoptosis-related proteins. In this study, Bcl-2, as an anti-apoptotic protein [32], was significantly down-regulated after overexpression of miR-3960, and this effect could be achieved by the knockdown of TFAP2A. The raise of Bax, the pro-apoptotic protein, further prove the role of miR-3960 in promoting tumor cell apoptosis. Exosomes may antagonize PC cell apoptosis and promote its proliferation by inhibiting the expression of miR-3960. Research reported that the effect of miR-3196 on the JAK2/STAT3 pathway by TFAP2A [33], and the JAK2/STAT3 pathway was proved that was related with apoptosis [34]. This may be a relevant biological mechanism for the regulation of Bax and Bcl2 by TFAP2A and deserves further exploration. In addition, we explored the expression of molecules in the PTEN/Akt signaling pathway. PTEN/AKT is a pathway related to tumor metastasis. Cheng et al. discovered that low PTEN expression was related to the poor survival in PC patients [35]. Moreover, a study has proved that the exosomes of high metastatic liver cancer cells inhibited PTEN to activate the Akt signaling pathway and promoted the EMT and metastasis of cancer cells [36]. Our study found that PC-derived exosomes inhibited PTEN and activated Akt, while miR-3960-overexpression and TFAP2A-knockdown antagonized this effect, suggesting that the inhibitory effect of miR-3960-overexpressed on PC cell metastasis by inhibiting TFAP2A may be closely related to the regulation PTEN/Akt signaling pathway. This signaling pathway may be one of the key biological mechanisms for PC cells-derived exosomes to advance tumor metastasis, and invasion. Further studies are worth unfolding in the future.
In summary, this study investigated miR-3960-containing exosomes in regulating the proliferation, metastasis and invasion of PANC-1cells. By constructing the xenograft tumor model, the promoting effect of exosomes on tumor proliferation, metastasis and invasion was observed and verified. Overexpression of miR-3960 can antagonize this effect by inhibiting TFAP2A. This study proved that the inhibition of miR-3960 on PC via the TFAP2A/PTEN/AKT pathway and provides the ideas and methods to study the biological mechanism of exosomes in promoting tumor development and treatment of PC. | true | true | true |
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PMC9588363 | DongHua Hou,Qi Wu,SiYu Wang,Shuo Pang,Hui Liang,HuiYan Lyu,Lu Zhou,Qiao Wang,Lirong Hao | Knockdown of miR-214 Alleviates Renal Interstitial Fibrosis by Targeting the Regulation of the PTEN/PI3K/AKT Signalling Pathway | 15-10-2022 | The microRNA-214 (miR-214) precursor is formed by the DNM3 gene on human chromosome 1q24.3, which is encoded and transcribed in the nucleus and processed into mature miR-214 in the cytoplasm. Association of miR-214 with the interstitial fibrosis of the kidney has been reported in existing research. Renal interstitial fibrosis is considered necessary during the process of various renal injuries in chronic kidney disease (CKD). One of the important mechanisms is the TGF- (transforming growth factor-) β1-stimulated epithelial interstitial transformation (EMT). The specific mechanisms of miR-214-3p in renal interstitial fibrosis and whether it participates in EMT are worthy of further investigation. In this paper, we first demonstrated modulation of the downstream PI3K/AKT axis by miR-214-3p through targeting phosphatase and tension protein homologues (PTEN), indicating the miRNA's participation in unilateral ureteral obstruction (UUO) nephropathy and TGF-β1-induced EMT. We overexpressed or silenced miR-214-3p and PTEN for probing into the correlation of miR-214-3p with PTEN and the downstream PI3K/AKT signalling pathways. According to the results of the study, miR-214-3p overexpression silenced PTEN, activated the PI3K/AKT signalling pathway, and exacerbated EMT induced by TGF-β1, while miR-214-3p knockdown had the opposite effect. In miR-214-3p knockdown mice, the expression of PTEN was increased, the PI3K/AKT signalling pathway was inhibited, and fibrosis was alleviated. In conclusion, miR-214-3p regulates the EMT of renal tubular cells induced by TGF-β1 by targeting PTEN and regulating the PI3K/AKT signalling pathway. Furthermore, miR-214-3p knockdown can reduce renal interstitial fibrosis through the PTEN/PI3K/AKT pathway. | Knockdown of miR-214 Alleviates Renal Interstitial Fibrosis by Targeting the Regulation of the PTEN/PI3K/AKT Signalling Pathway
The microRNA-214 (miR-214) precursor is formed by the DNM3 gene on human chromosome 1q24.3, which is encoded and transcribed in the nucleus and processed into mature miR-214 in the cytoplasm. Association of miR-214 with the interstitial fibrosis of the kidney has been reported in existing research. Renal interstitial fibrosis is considered necessary during the process of various renal injuries in chronic kidney disease (CKD). One of the important mechanisms is the TGF- (transforming growth factor-) β1-stimulated epithelial interstitial transformation (EMT). The specific mechanisms of miR-214-3p in renal interstitial fibrosis and whether it participates in EMT are worthy of further investigation. In this paper, we first demonstrated modulation of the downstream PI3K/AKT axis by miR-214-3p through targeting phosphatase and tension protein homologues (PTEN), indicating the miRNA's participation in unilateral ureteral obstruction (UUO) nephropathy and TGF-β1-induced EMT. We overexpressed or silenced miR-214-3p and PTEN for probing into the correlation of miR-214-3p with PTEN and the downstream PI3K/AKT signalling pathways. According to the results of the study, miR-214-3p overexpression silenced PTEN, activated the PI3K/AKT signalling pathway, and exacerbated EMT induced by TGF-β1, while miR-214-3p knockdown had the opposite effect. In miR-214-3p knockdown mice, the expression of PTEN was increased, the PI3K/AKT signalling pathway was inhibited, and fibrosis was alleviated. In conclusion, miR-214-3p regulates the EMT of renal tubular cells induced by TGF-β1 by targeting PTEN and regulating the PI3K/AKT signalling pathway. Furthermore, miR-214-3p knockdown can reduce renal interstitial fibrosis through the PTEN/PI3K/AKT pathway.
In the course of CKD (chronic kidney disease) evolution, most kidney damage undergoes the same pathological stage, namely, renal interstitial fibrosis. The typical characteristics involve extracellular matrix (ECM) protein overproduction and deposition, including increased levels of collagen I (COL1) and fibronectin (FN) [1, 2]. Epithelial mesenchymal transformation (EMT) refers to transforming growth factor β (TGFβ) driving the process by which renal tubular epithelial cells (TECs) acquire mesenchymal cell features after losing epithelial features. The EMT process in TECs exerts a pivotal function in the interstitial fibrosis of the kidney [3]. However, we lack an understanding of the underlying molecular mechanisms. Studies on the molecular mechanism of renal interstitial fibrosis can provide new possibilities for inhibiting fibrosis [4, 5]. miRNAs (microRNAs) refer to small 19–25 nucleotide-long molecules of noncoding RNAs that are highly conserved. By combining with the 3′UTR (3′-untranslated region) of the target posttranscriptional genes, the effect of regulating gene expression is realized [6]. miRNAs are associated with a variety of kidney diseases, especially fibrotic diseases, by regulating cell proliferation, apoptosis, differentiation, and development [7, 8]. miRNAs participate in various kidney diseases by regulating corresponding signalling pathways and cell activities [9]. For example, miR-107 induces TNF-α secretion by targeting bispecific phosphatase 7 (DUSP7) in endothelial cells, inducing renal tubular cell damage in septic AKI [10]; miR-21 inhibits the inflammatory response and apoptosis of renal tubular epithelial cells by stimulating the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathway, improves AKI induced by I/R [11], and inhibits apoptosis of AKI renal cells induced by sepsis through the PI3K/AKT signalling pathway [12]. miR-214-3p is a microRNA encoded by the DNM3 gene in the q24.3 region of chromosome 1, which is highly conserved in vertebrates [13]. In the nucleus, pri-miR-214-3p is produced by transcription initiated by RNA polymerase II or III. Subsequently, the precursor miR-214-3p is formed from pri-miR-214-3p due to the action of endonuclease Drosha and its cofactor [14–16]. The precursor miR-214-3p is transported to the cytoplasm and processed by the endonuclease Dicer to gradually form mature miR-214-3p [17–19]. miR-214-3p exists in multiple organs and is involved in different kidney diseases (Human miRNA Expression Database). For example, previous research demonstrated prominent miR-214-3p overexpression in the kidney injury models and was associated with the development of fibrosis [9, 20]. miR-214-3p has been confirmed to increase expression in renal interstitial fibrosis in vivo, and this change does not depend on the TGF-β pathway [9, 20]. miR-214-3p antagonism can be regarded as a new treatment for renal tubulointerstitial fibrosis [9, 20]. Therefore, the specific mechanism of miR-214-3p in renal tubulointerstitial fibrosis needs further study. There is increasing evidence that phosphatase and tensin homologue (PTEN) is a negative fibrosis regulator in diverse organs, including the lungs, heart, and liver [21–23]. PTEN can reduce the activity of the PI3K (phosphatidylinositol 3-kinase)/AKT (protein kinase B) axis. PTEN inhibits the effect of this pathway on cells and is an endogenous negative regulator [24–26]. The PI3K/AKT axis initiation is linked to the pathogenesis of various tumours [27] and is an important mediator related to organ fibrosis [28–30]. Accumulating evidence shows that PTEN in kidney cells can be targeted and regulated by miRNAs, which in turn affects AKT activation [31]. Flufenidone suppresses the functionality of nicotinamide adenine dinucleotide phosphate oxidase during renal interstitial fibrosis by relying on the PI3K/AKT axis [30]. These studies confirm that the EMT process of renal tubulointerstitial fibrosis is related to PTEN/PI3K/AKT. In a diabetic environment, low miR-214-3p expression leads to PTEN overexpression by targeting PTEN, thus improving renal glomerular hypertrophy [32, 33]. However, there are few studies on the mechanism of miR-214-3p during renal interstitial fibrosis. The above studies confirm the relationship between the loss of PTEN in the renal interstitium and renal interstitial fibrosis. The increased expression of miR-214-3p can promote EMT of renal tubular epithelial cells and renal interstitial fibrosis. Previous research and miRNA databases have shown that miR-214-3p targets PTEN directly [32–34]. Therefore, we assume that miR-214-3p regulates the PI3K/AKT signalling pathway through targeting PTEN and promotes EMT and renal interstitial fibrosis. Intervening with the expression of miR-214-3p and PTEN may be a new method to alleviate renal fibrosis. Thus, we first used the unilateral ureteral obstruction (UUO) mouse model and human tubuloepithelial (HK-2) to study the relationship between miR-214-3p and PTEN in renal interstitial fibrosis. We demonstrate that miR-214-3p reduces PTEN levels by binding to the 3′-UTR of PTEN mRNA, thereby acting on the PI3K/AKT signalling pathway to reduce the EMT of renal tubular cells triggered by TGF-β1. Knockdown of miR-214-3p can relieve renal interstitial fibrosis through the PTEN/PI3K/AKT pathway.
We chose DMEM/F12 (Gibco, USA) as the human tubuloepithelial cell (HK-2) culture medium. The medium was supplemented with 1% penicillin, 1% streptomycin, and 10% foetal bovine serum (Gibco, USA) according to the ratio. The cells were placed in a humidified incubator (5% CO2, 37°C). HK-2 cells at a confluency of 50-60% were placed in a 24-well plate and starved for 48 h (0.2% foetal bovine serum) (Biological Industries, Israel). After starvation, the medium was changed, and 10 ng/mL TGF-β1 (Solarbio, China) was added to the culture medium for 48 h. Then, LV-miR-214-OE (a lentivirus expressing the sequence of human pri-miR-214-3p), LV-miR-214-KD (a lentivirus having an oligonucleotide against the mature sequence of human miR-214-3p), or LV-NC-OE/LV-NC-KD negative controls were transfected into HK-2 cells for 12 h. The medium containing the lentivirus was replaced with fresh medium. The lentivirus used was from Wanlei Biological Technology (Shenyang, China).
To investigate the role of PTEN, we chose Lipofectamine 2000 (Invitrogen, USA) for transient transfection of TGF-β1-stimulated logarithmic HK-2 cells with 50 nM si PTEN (PTEN siRNA) and scr-siRNA (scrambled siRNA; both from GenePharma, China) for a duration of 8 h. The same method was used to transfect pcDNA3.1-PTEN overexpression plasmids (including human PTEN cDNA, PTEN-OE plasmid, Gene, China) or blank control pcDNA3.1 plasmid (BC plasmid, Gene, China) in LV-miR-214-OE or LV-NC-OE cells for 8 h.
Each group of cells was spread on sterile coverslips, subsequently immobilized in paraformaldehyde (4%) and, 25 min later, washed away. A 5 min infiltration of the cells proceeded using Triton X-100 (0.15%) at ambient temperature, followed by a 1 h blockage using goat serum (10%; Beyotime, China) for 1 h. Primary antibody (anti-PTEN, 1 : 50 dilution, Wanlei; anti-PI3K 1 : 200 dilution, Abcam) was incubated overnight at 4°C. At 37°C, the cells were incubated with secondary antibody (Cy3-labeled goat anti-rabbit IgG, 1 : 500 dilution, Proteintech) for 1 h after overnight incubation. Finally, a 5 min staining of the cellular nuclei was accomplished using DAPI (Solarbio, China). For the image assessment, the Eclipse Ti-s microscopes (Nikon) adopting constant exposure were utilized. The statistical analysis of fluorescence intensity was accomplished via the Image-Pro Plus 6.0 (Media Cybernetics).
The experimental animals came from the Harbin Medical University's Laboratory Animal Center. The experimental protocol was approved by the Ethics Committee on the Laboratory Animal Use and Welfare of the First Hospital Affiliated to Harbin Medical University in China's Harbin. We selected wild-type male C57BL/6 mice for experiments and obtained mice from specific pathogen-free (SPF) conditions. The mice were 8 weeks old and weighed 20-25 g. The experimental mice were randomized into 4 groups, with 6 mice per group. In addition to a sham operation group, the other three groups were UUO groups. The UUO group was anaesthetized with pentobarbital sodium (40 mg/kg, IP), and 4-0 silk ligate was used to ligate the left ureter. In the sham group, the mice were treated in a similar way but did not undergo ureteral ligation. Mice were sacrificed 14 days after UUO to collect obstructed kidneys for real-time PCR, Western blotting, histology, and immunohistochemistry.
C57BL/6 mice had ad libitum access to food and water and were kept under conventional experimental conditions (light and dark cycle for 12 h; 22-24°C). Two groups of UUO mice were injected with 100 μL of lentivirus (LV-miR-214-KD) (1 × 109 TU/mL) and 100 μL of control lentivirus (LV-NC-KD) (1 × 109 TU/mL) via the tail vein 7 and 14 days before the operation. On the 14th day after UUO, half of the left kidney of the sacrificed mice was fixed in 10% formalin. After tissue dehydration, paraffin embedding, and sectioning, histological and immunohistochemical analyses were carried out.
To evaluate histological changes in the kidney, such as interstitial fibrosis, paraffin sections of renal tissue (3 μm) were subjected to haematoxylin-eosin (HE) and Masson's trichrome staining. We detected collagen fibre deposition by a Masson staining kit (Baso, China). The blue–purple collagen deposition area indicated the severity of interstitial fibrosis. We deparaffinized renal sections, hydrated them, and rinsed them with distilled water. A 25 min treatment proceeded with 3% hydrogen peroxide (H2O2) at ambient temperature. Following a 30 min blockage using goat serum (10%; Solarbio, China), the cells were bound to primary antibodies (anti-PTEN, 1 : 200 dilution, Wanlei; anti-PI3K, 1 : 600 dilution, Absin) overnight at 4°C. After overnight incubation, the sections were subjected to a 1 h incubation using a 1 : 500 diluent of peroxidase-labeled secondary antibody (Jackson ImmunoResearch) under a 37°C condition. Then, we developed immunohistochemically stained tissue sections with DAB for 30 min and counterstained them with haematoxylin. Two blinded researchers independently observed all slides and chose five fields of view in a random manner from every section. The percentage of positively stained areas was estimated via the Image-Pro Plus 6.0.
Cell and tissue proteins were extracted with a RIPA extraction kit (Wanlei, China). A 10% SDS–PAGE gel was used to isolate the same amount of protein (50 μg). The proteins were transferred to a PVDF membrane (Roche, USA). At room temperature, a 2 h sealing was accomplished using nonfat milk (5%). At 4°C, the primary antibodies were incubated overnight, including anti-PTEN (1 : 1000 dilution, Proteintech), anti-PI3K (1 : 1000 dilution, Abmart), anti-p-AKT (1 : 1000 dilution, Abmart), anti-AKT (1 : 2000 dilution, Abmart), anti-α-SMA (1 : 1000 dilution, Wanlei), anti-COL1 (1 : 500 Wanlei), anti-FN (1 : 1000 dilution, Wanlei), and anti-GAPDH (1 : 3000 dilution, Abmart) antibodies. The membranes were nurtured for 1 h using horseradish peroxidase (Wanlei, China) under ambient temperature. The protein bands were detected via an advanced ECL kit (Beyotime, China).
The membranes were nurtured for 1 h using horseradish peroxidase (Wanlei, China) under ambient temperature. The protein bands were detected via an advanced ECL kit (Beyotime, China). Reverse transcription of the overall RNA as template cDNA proceeded through PrimeScriptTM RT Master Mix (TOYOBO, Japan) for mRNA. The qRT-PCR amplification was assessed with a real-time PCR system from Applied Biosystems. All amplifications were repeated three times. We used the comparative Ct (2-ΔΔCt) method to measure the related gene levels. The internal reference genes were GAPDH and U6. According to the primer design principle, primers were designed by Primer 5.0 operation software. Three sets of forward and reverse primers were designed for each target gene primer, which were synthesized by Generay Biotechnology Company (China). The appropriate primers of the target gene were selected by pre-experiments (Table 1).
When the HK-2 cells were in the log phase, we used Lipofectamine 2000 (Invitrogen, USA) to cotransfect mimics of miR-214-3p or NC plus MUT (mutant) or WT (wild-type) PTEN 3′-UTR. The HK-2 cells treated with different transfections after 48 h were subjected to the luciferase potential assessment through the dual-luciferase reporter assay (Abcam, UK).
We used GraphPad Prism 7.0 to perform statistical analysis on the experimental data. The data represent three independent repeated experiments, rendered as the means ± SDs (standard deviations). Student's t test was employed to make the pairwise comparisons, while univariate ANOVA was adopted for comparisons among 3 or more groups. And then, Tukey's multiple comparison post hoc test was applied. Usually, differences were regarded as significant with the P values being less than 0.05.
For the exploration of the miR-214-3p and PTEN levels, we selected HK-2 cells and C57BL/6 mice as the research objects. The results revealed that HK-2 cells exhibited typical mesenchymal cell morphology after 48 h of TGF-β1 treatment, and the morphology changed significantly in contrast to the control (Figure 1(a)), implying the EMT elicitation by TGF-β1 in the HK-2 cells based on cell morphology. The UUO model is a commonly used experimental model of renal interstitial fibrosis. We established the UUO mouse model by obstructing its left renal ureter and performed histochemical staining to observe renal pathological changes. Compared with the sham group, HE and Masson staining revealed that UUO mice showed the following typical characteristics: inflammatory cell infiltration, tubular dilatation, and interstitial fibrosis (Figure 1(b)), indicating that the UUO group developed renal interstitial fibrosis. The miR-214-3p, PTEN, COL1, FN, and α-SMA expressions were examined through qRT–PCR analysis, showing that the level of miR-214-3p in HK-2 cells stimulated with TGF-β1 was greatly upregulated in relative to the control (Figure 1(c)). The miR-214-3p level in the fibrosis group (UUO) was also significantly increased in the in vivo experiment (Figure 1(d)). Additionally, the mRNA level of PTEN was downregulated, and fibrosis-related mRNA levels, such as α-SMA, COL1, and FN, were upregulated in HK-2 cells after stimulation with TGF-β1 and in UUO mice (Figures 1(e) and 1(f)). The Western blot results also confirmed this finding (Figures 1(g)–1(j)).
According to Figure 2(a), binding of miR-214-3p to the PTEN mRNA's 3′UTR was validated according to the TargetScan and miRanda, two bioinformatics software. Transfection of HK-2 cells was accomplished using mimics of miR-214-3p and NC. The miR-214-3p expression was upregulated greatly after 48 hours for the miR-214-3p group (Figure 2(b)). Based on the dual-luciferase reporter assay outcomes, when HK-2 cells were cotransfected using miR-214-3p/NC mimics plus WT/Mut PTEN 3′-UTR, the luciferase activity of the WT PTEN 3′-UTR decreased by the mimic of miR-214-3p, while did not in the case of the Mut PTEN 3′-UTR (Figure 2(c)). Furthermore, Western blot results showed that miR-214-3p greatly reduced PTEN expression in either the TGF-β1 treatment group or the negative control group (Figure 2(d)).
To probe deeper into the miR-214-3p's specific mechanisms in the EMT event of TGF-β1-processed HK-2 cells, LV-miR-214-KD, LV-miR-214-OE, LV-NC-KD, or LV-NC-OE were used for cellular transfection. As revealed by the results, miR-214-3p was obviously enhanced for the LV-miR-214-OE group, while notably decreased for the LV-miR-214-KD group (Figure 3(a)). miR-214-3p upregulation led to elevated levels of fibrosis biomarkers, including α-SMA, COL1, and FN, while its silencing resulted in declines of these levels, suggesting its role in adjusting the EMT of HK-2 cells triggered by TGF-β1 (Figures 3(c), 3(d) and 3(f)). Upregulating miR-214-3p reduced PTEN levels but increased PI3K and p-AKT levels (Figures 3(b), 3(d), and 3(e)). Silencing miR-214-3p upregulated PTEN levels but decreased PI3K and p-AKT levels (Figures 3(b), 3(d), and 3(e)). qRT–PCR and Western blot methods confirmed that miR-214-3p regulated the PTEN/PI3K/AKT pathway (Figures 3(b)–3(f)). Furthermore, immunofluorescence staining of PTEN and PI3K revealed the same results (Figures 3(g)–3(j)). This experiment confirmed that miR-214-3p mediated EMT induced by TGF-β1 via the PTEN/PI3K/AKT pathway.
In order to show the function of PTEN in TGF-β1-induced EMT, transfection of HK-2 cells was accomplished using si-PTEN and scr-siRNA after 48 hours of TGF-β1 treatment. PTEN expression in the si-PTEN group was greatly reduced, accompanied by upregulated synthesis of α-SMA, COL1, and FN and upregulated activity of the PI3K/AKT pathway proteins (Figures 4(a) – 4(e)). As validated by qRT–PCR plus WB, PTEN downregulation enhanced the levels of fibrosis-related indicators and PI3K/AKT pathways in TGF-β1-induced EMT (Figures 4(a)–4(e)). Immunofluorescence staining of PTEN and PI3K also confirmed these trends (Figures 4(f)–4(h)). These findings indicated that PTEN downregulation could promote the TGF-β1-triggered EMT through the PI3K/AKT axis initiation.
To further confirm that PI3K/AKT pathway activation and EMT of HK-2 cells caused by miR-214-3p overexpression were achieved through PTEN downregulation, EMT cells stimulated by TGF-β1 were divided into four groups and cotransfected with LV − NC − OE + BC, LV − NC − OE + PTEN OE, LV − miR − 214 − OE + BC, and LV − miR − 214 − OE + PTEN OE. PTEN expression was lower, the PI3K/AKT pathway was overactivated, and the levels of fibrosis markers were upregulated in the LV − miR − 214 − OE + BC group (Figures 5(a) – 5(e)). However, the overexpression of PTEN in the LV − miR − 214 − OE + PTEN − OE group significantly eliminated the abovementioned abnormal changes caused by miR-214-3p overexpression (Figures 5(a)–5(e)). Immunofluorescence analysis showed the same results (Figures 5(f)–5(h)). These data illustrated that miR-214-3p activates the PI3K/AKT signalling pathway and TGF-β1-induced EMT mainly through a PTEN-dependent mechanism.
Finally, we used animal experiments to explore whether knockdown of miR-214-3p could relieve renal interstitial fibrosis and was linked to the PTEN/PI3K/AKT axis. LV-miR-214-KD and LV-NC-KD were injected via the tail vein into UUO mice. Compared with the UUO group, the LV-miR-214-KD group exhibited significantly decreased expression of miR-214-3p (Figure 6(a)). In accordance with the results of HE and Masson staining, we found that compared with UUO mice, UUO mice with miR-214-3p knockdown had significantly reduced interstitial fibrosis (Figure 6(b)). In UUO mice with miR-214-3p knockdown, reduced collagen deposition and low expression of fibrosis-related markers (α-SMA, COL1 and FN) were observed (Figures 6(c), 6(e), 6(f), and 6(h)). Furthermore, PTEN expression increased and PI3K and p-AKT levels decreased after treatment using the miR-214-3p inhibitor, and these results were the same as in the in vitro experiments (Figures 6(d), 6(f), and 6(g)). PTEN and PI3K were found mainly in renal tubular cells of the renal interstitium according to immunohistochemistry. The kidney PTEN level of UUO mice declined in contrast to that in the sham mice; however, silencing miR-214-3p reversed this change, and the PTEN level increased (Figures 6(i) and 6(j)). PI3K expression was opposite to that of PTEN (Figures 6(k) and 6(l)). This indicated that knockdown of miR-214-3p reduced renal fibrosis by overexpressing PTEN and inhibiting the PI3K/AKT pathway.
Many studies on kidney-related diseases, such as diabetic nephropathy and acute kidney injury models, have revealed the role of miR-214 [9, 35, 36], but the mechanism of miR-214 in renal interstitial fibrosis has rarely been investigated. According to our research, we confirmed that miR-214-3p levels were upregulated, PTEN expression was decreased in UUO mice, and this effect was related to renal interstitial fibrosis (Figure 1), which was consistent with existing reports [20, 37]. We found that the expression of miR-214 and PTEN during the EMT process of HK2 cells induced by TGF-β1 was the same as that observed in vivo (Figure 1). Through online prediction software analysis and dual luciferase experiments, miR-214-3p was confirmed to target PTEN (Figure 2). We found that after si-PTEN silencing of PTEN, the PI3K/AKT axis was initiated, and the levels of fibrosis-related indicators increased. The level of PTEN was linked to the PI3K/AKT axis activity, as well as the TGF-β1-elicited EMT in HK-2 cells (Figure 4), suggesting that PTEN downregulation could promote EMT induced by TGF-β1 through activating the PI3K/AKT pathway. In terms of the exogenous modulation of the miR-214-3p level, treatment of these models with LV-miR-214-KD and LV-miR-214-OE was utilized, and its role in TGF-β-induced EMT was further studied. miR-214-3p upregulation intensified TGF-β-induced EMT, and silencing miR-214-3p had completely different effects (Figure 3). Combining the changes in PTEN and PI3K/AKT, we proved miR-214-3p's function in the EMT stimulated by TGF-β1, and this effect was achieved through the PTEN/PI3K/AKT pathway. By increasing the levels of PTEN and miR-214-3p, we demonstrated that the recovery of PTEN expression significantly eliminated the increase in PI3K/AKT pathway protein expression and fibrosis indicator upregulation resulted from the overexpression of miR-214-3p (Figure 5). This result suggested that PTEN was necessary for the EMT process induced by the miR-214-3p expression elevation. Finally, we verified through animal experiments that the miR-214-3p knockdown mitigated the interstitial fibrosis of the kidney through the PTEN/PI3K/AKT axis targeting (Figure 6). PTEN is considered to be a tumour suppressor gene and has also been confirmed to have antifibrotic effects. Studies have confirmed that PTEN is related to glomerular hypertrophy in diabetic nephropathy [32, 33, 38–40] and can negatively regulate PI3K/AKT signalling activity [14]. Although there have been reports that PTEN and its regulation of PI3K/AKT activity play a role in various kidney diseases [41–43], the specific effect of the miR-214-3p/PTEN/PI3K/AKT axis on renal interstitial fibrosis remains to be explored and verified. In this article, miR-214-3p targeting of PTEN affected the downstream PI3K/AKT pathway, thereby regulating renal interstitial fibrosis and TGF β-induced EMT (Figure 4). miR-214 was proven to upregulate the PI3K/AKT pathway by targeting PTEN, thereby protecting cells from damage induced by hypoxia/reoxygenation (H/R) and reducing myocardial damage induced by ischaemia/reperfusion (I/R) and cardiomyocyte apoptosis [44, 45]. According to the results of this experiment, there may be a feedback regulatory loop between PTEN and PI3K/AKT in renal fibrosis induced by miR-214, but this conclusion needs further experimental confirmation. miR-214 is abnormally expressed in tumours of many organs, including nasopharyngeal, pulmonary, hepatic, colorectal, pancreatic, cervical, and vesical carcinomas [46–48]. The pathogenesis and metastasis of these tumours are closely associated with the expression of miR-214 [49–52]. Knockdown of miR-214 was involved in the increase in apoptosis and fibrosis induced by cardiac I/R injury [44]. Previous studies and our research confirmed that miR-214 knockdown has a protective effect on the apoptosis of renal tubular epithelial cells, as well as the fibrosis of the kidney [20] (Figure 6). This result does not explain the specific mechanism in previous studies. TGF-β and downstream Smad signalling are generally considered to be the main signalling pathways in the process of fibrosis [53]. However, in the improvements in fibrosis after UUO induced by blocking the expression of miR-214 through gene knockout or drug inhibition, it has been confirmed that the regulation of miR-21 and miR-214 was independent of typical TGF-β signalling and the improvements in fibrosis [20]. In our study, we concluded that through the PTEN/PI3K/AKT axis targeting, the anti-miR-214 reduced TGF-β1-induced tubuloepithelial interstitial transformation and renal interstitial fibrosis. Studies have found that there are some non-Smad-mediated pathways of TGF-β for the TGF-β-elicited EMT in human pulmonary carcinoma cells, renal TECs, and renal sarcoma cells, and the PI3K/AKT pathway is a very important pathway [54]. Our conclusion was consistent with the existing research. There are still some shortcomings in this research. The miR-214 downregulation was noted in the kidney following the miR-214 inhibitor administration intravenously, so the kidney may be the target organ of miRNA therapy. However, we did not explore the level of miR-214 in other tissues or organs, such as the liver, pancreas, and heart. These organs and tissues may have potential targets that are regulated by miR-214, which needs further research and verification. The study did not further explore the relationship between the classical pathway (TGF-β/Smad pathway) of renal interstitial fibrosis and the regulatory pathway of miR-214/PTEN/PI3K/AKT. Furthermore, we did not measure the expression of miR-214 and PTEN in human tissues, which would be needed to better determine expression changes in renal interstitial fibrosis from the three levels of cells, animals, and humans. The results in Figure 1 of this study show the changes in miR-214-3p and PTEN in cell and animal renal fibrosis models, and Figure 2 verified the targeting relationship between miR-214-3p and PTEN. Based on these results, we further explored the changes in the PI3K/AKT signalling pathway and fibrosis during the changes in upstream miR-214-3p and downstream PTEN in Figures 3 and 4 to explore their internal regulatory relationship. Figure 5 shows that PTEN was a necessary factor for miR-214-3p to regulate the PI3K/AKT signalling pathway and fibrosis. Finally, the results in Figure 6 are from in vivo experiments to confirm the above results. This study first confirmed that silencing miR-214 could alleviate renal interstitial fibrosis. miR-214-3p targeted PTEN to regulate the PI3K/AKT signalling pathway and participated in regulating the regulation of renal interstitial fibrosis. This research provides a new theoretical basis for exploring the role of renal tubular epithelial cells in renal interstitial fibrosis and a new target for treating TGF-β-induced renal interstitial fibrosis. Targeted intervention of renal miR-214-3p and PTEN will be of great significance for the clinical treatment of renal fibrosis.
In summary, this research shows that knockdown of miR-214-3p can reduce TGF-β1-induced tubulointerstitial interstitial transformation and renal interstitial fibrosis by upregulating PTEN and inhibiting the PI3K/AKT signalling pathway. Inhibiting miR-214-3p expression may offer a new therapeutic approach to inhibit renal interstitial fibrosis. | true | true | true |
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PMC9588640 | 36198801 | Hua Su,Fei Yang,Rao Fu,Brittney Trinh,Nina Sun,Junlai Liu,Avi Kumar,Jacopo Baglieri,Jeremy Siruno,Michelle Le,Yuhan Li,Stephen Dozier,Ajay Nair,Aveline Filliol,Nachanok Sinchai,Sara Brin Rosenthal,Jennifer Santini,Christian M. Metallo,Anthony Molina,Robert F. Schwabe,Andrew M. Lowy,David Brenner,Beicheng Sun,Michael Karin | Collagenolysis-dependent DDR1 signalling dictates pancreatic cancer outcome | 05-10-2022 | Cancer metabolism,Cancer microenvironment,Cancer metabolism,Nutrient signalling | Pancreatic ductal adenocarcinoma (PDAC) is a highly desmoplastic, aggressive cancer that frequently progresses and spreads by metastasis to the liver. Cancer-associated fibroblasts, the extracellular matrix and type I collagen (Col I) support or restrain the progression of PDAC and may impede blood supply and nutrient availability. The dichotomous role of the stroma in PDAC, and the mechanisms through which it influences patient survival and enables desmoplastic cancers to escape nutrient limitation, remain poorly understood. Here we show that matrix-metalloprotease-cleaved Col I (cCol I) and intact Col I (iCol I) exert opposing effects on PDAC bioenergetics, macropinocytosis, tumour growth and metastasis. Whereas cCol I activates discoidin domain receptor 1 (DDR1)–NF-κB–p62–NRF2 signalling to promote the growth of PDAC, iCol I triggers the degradation of DDR1 and restrains the growth of PDAC. Patients whose tumours are enriched for iCol I and express low levels of DDR1 and NRF2 have improved median survival compared to those whose tumours have high levels of cCol I, DDR1 and NRF2. Inhibition of the DDR1-stimulated expression of NF-κB or mitochondrial biogenesis blocks tumorigenesis in wild-type mice, but not in mice that express MMP-resistant Col I. The diverse effects of the tumour stroma on the growth and metastasis of PDAC and on the survival of patients are mediated through the Col I–DDR1–NF-κB–NRF2 mitochondrial biogenesis pathway, and targeting components of this pathway could provide therapeutic opportunities. | Collagenolysis-dependent DDR1 signalling dictates pancreatic cancer outcome
Pancreatic ductal adenocarcinoma (PDAC) is a highly desmoplastic, aggressive cancer that frequently progresses and spreads by metastasis to the liver. Cancer-associated fibroblasts, the extracellular matrix and type I collagen (Col I) support or restrain the progression of PDAC and may impede blood supply and nutrient availability. The dichotomous role of the stroma in PDAC, and the mechanisms through which it influences patient survival and enables desmoplastic cancers to escape nutrient limitation, remain poorly understood. Here we show that matrix-metalloprotease-cleaved Col I (cCol I) and intact Col I (iCol I) exert opposing effects on PDAC bioenergetics, macropinocytosis, tumour growth and metastasis. Whereas cCol I activates discoidin domain receptor 1 (DDR1)–NF-κB–p62–NRF2 signalling to promote the growth of PDAC, iCol I triggers the degradation of DDR1 and restrains the growth of PDAC. Patients whose tumours are enriched for iCol I and express low levels of DDR1 and NRF2 have improved median survival compared to those whose tumours have high levels of cCol I, DDR1 and NRF2. Inhibition of the DDR1-stimulated expression of NF-κB or mitochondrial biogenesis blocks tumorigenesis in wild-type mice, but not in mice that express MMP-resistant Col I. The diverse effects of the tumour stroma on the growth and metastasis of PDAC and on the survival of patients are mediated through the Col I–DDR1–NF-κB–NRF2 mitochondrial biogenesis pathway, and targeting components of this pathway could provide therapeutic opportunities.
Retrospective clinical studies suggest that patients with PDAC whose tumours have a fibrogenic but inert stroma (defined by extensive extracellular matrix (ECM) deposition, low expression of the myofibroblast marker α-SMA and low levels of matrix metalloprotease (MMP) activity) have improved progression-free survival compared to patients whose tumours are populated by a fibrolytic stroma (defined by a low content of collagen fibres, high expression of α-SMA and high levels of MMP activity). How the stromal state affects clinical outcome is unknown. Moreover, previous investigations of the influence of the stroma on the growth and progression of PDAC have yielded conflicting results, assigning stroma and cancer-associated fibroblasts (CAFs) as either tumour-supportive or tumour-restrictive. It is likely that the failure of stromal-targeted PDAC therapies is due, in part, to unrecognized pathways that result in tumour-promoting or tumour-suppressive stromal subgroups; successful treatments may thus require precision medicine rather than one-size-fits-all approaches.
To investigate how the fibrolytic stroma affects PDAC outcome, we compared survival between patients with high and low collagenolysis, using a panel of collagen-cleaving MMPs (MMP1, MMP2, MMP8, MMP9, MMP13 and MMP14), and found that high mRNA expression of MMPs correlated with poor survival (Extended Data Fig. 1a). Single-cell RNA sequencing (scRNA-seq) revealed that MMP1, MMP14 and MMP2 mRNAs were the most abundant MMP family members, and were expressed in epithelial-tumour cells, M2-like macrophages and fibroblastic cells (Extended Data Fig. 1b). The main target of MMPs in desmoplastic tumours is Col I, the prevalent ECM protein. Using antibodies that distinguish iCol I from cCol I (3/4 Col I; Fig. 1a), we stratified a cohort of 106 patients with PDAC whose tumours had been resected (see below), and correlated the tumour Col I state with survival data. These results also pointed to Col I remodelling as a strong prognostic factor, as patients whose tumours were enriched for cCol I had poorer median survival (Fig. 1b). To understand the basis for these results and mimic a cCol Ilow inert tumour stroma, we used mice expressing either wild-type Col1a1+/+ (Col IWT), or a MMP-resistant version of Col I generated by two amino acid substitutions in the 1α1 subunit that block the cleavage of Col I by MMPs, Col1a1r/r (Col Ir/r). Col Ir/r mice develop more-extensive hepatic fibrosis than Col IWT mice, but despite the hepatocellular carcinoma (HCC)-supportive functions of hepatic fibrosis, they poorly accommodate HCC growth, through unknown mechanisms. Col IWT and Col Ir/r mice were either orthotopically or intrasplenically (to model liver metastasis) transplanted with mouse PDAC KPC960 (KPC) or KC6141 (KC) cells. Col Ir/r mice poorly supported the growth of primary pancreatic tumours or hepatic metastases, even though their pancreata were more fibrotic than Col IWT pancreata. These differences persisted in mice that were pretreated with the pancreatitis inducer caerulein (CAE), which stimulated liver metastasis in Col IWT pancreata (Fig. 1c,d and Extended Data Fig. 1c–f). After intrasplenic transplantation, KPC or KC tumours in Col IWT livers were larger in mice pretreated with CCl4 to induce liver fibrosis, whereas the number and size of tumours were lower in Col Ir/r livers, regardless of CCl4 pretreatment (Fig. 1e,f and Extended Data Fig. 1g). As expected, Col Ir/r livers were more fibrotic than Col IWT livers, regardless of CCl4 pretreatment (Extended Data Fig. 1h). Primary PDAC and liver metastases were confirmed by staining with ductal (CK19), progenitor (SOX9) or proliferation (Ki67) markers (Extended Data Fig. 1e,f,i). Enhanced tumour growth in CAE- or CCl4-pretreated Col IWT mice suggested that tumour suppression in Col Ir/r mice was not simply due to a space limitation imposed by a build-up of Col I. To determine how Col I remodelling affects human PDAC, we subcutaneously co-transplanted wild-type and R/R fibroblasts with a patient-derived xenograft cell line (1305) into immunocompromised Nu/Nu mice. Wild-type fibroblasts enhanced tumour growth, whereas R/R fibroblasts inhibited tumour growth but lost their inhibitory activity after ablation of Col1a1 (Fig. 1g) whose loss did not affect the stimulatory activity of wild-type fibroblasts, suggesting a specific inhibitory function of noncleaved Col I.
To determine the basis for reduced tumorigenesis in Col Ir/r mice, we plated KPC cells on ECM deposited by wild-type and R/R fibroblasts, incubated them in low-glucose (LG) medium (to model nutrient restriction) and performed RNA sequencing (RNA-seq). Bioinformatic analysis revealed marked differences between cells cultured on wild-type and cells cultured on R/R ECM, with the former showing an upregulation of signatures related to sulfur amino acid metabolism, mammary gland morphogenesis, telomere maintenance and RNA processing, and the latter showing an upregulation of mRNAs related to innate immunity and inflammation (Extended Data Fig. 2a). The most notable differences were in nuclear and mitochondrial genes that encode components of the mitochondrial electron transfer chain (ETC) and ribosome subunits, and macropinocytosis-related genes, which were upregulated by wild-type and suppressed by R/R ECM (Fig. 2a–c). Consistent with the upregulation of macropinocytosis-related genes by wild-type ECM, IKKα-deficient KC cells, which have high macropinocytosis activity, grew better than parental cells in Col IWT livers, but grew as poorly as parental KC cells in Col Ir/r livers (Extended Data Fig. 1g). To assess the effects of Col I on metabolism, we labelled wild-type and R/R fibroblasts with [3H]-proline or [U-13C]-glutamine for five days, during which period the cells coated the plates with Col I-containing ECM. After decellularization, KPC or KC cells and variants thereof were plated and cultured for 24 h in LG medium. The uptake of [3H] in cells plated on wild-type ECM was dependent on macropinocytosis, as indicated by sensitivity to macropinocytosis inhibitors (EIPA (an NHE1 inhibitor), IPI549 (a PI3Kγ inhibitor) or MBQ-167 (a CDC42 and RAC inhibitor)) and to the knockdown of NHE1 or SDC1, and enhancement by the ULK1 inhibitor MRT68921 (MRT). By contrast, cells plated on R/R ECM showed a negligible uptake of [3H] that was unaffected by the inhibition of macropinocytosis (Extended Data Fig. 2b–e). Notably, ablation of Col1a1 or overexpression of cleavable Col I in ECM-laying R/R fibroblasts restored [3H] uptake (Extended Data Fig. 2b). Cells that were cultured on 13C-glutamine-labelled wild-type ECM took up glutamine and metabolized it, but cells that were plated on 13C-glutamine-labelled R/R ECM exhibited minimal glutamine uptake and metabolism (Fig. 2d,e). Congruently, cells that were cultured on wild-type ECM had higher levels of ATP and a higher amino acid content than cells that were cultured on R/R ECM, and this effect was further increased by treatment with MRT and reduced by blockade of macropinocytosis; by contrast, cells that were cultured on R/R ECM had low levels of ATP and amino acids, which were barely affected by the inhibition of macropinocytosis (Fig. 2f and Extended Data Fig. 2f–j). Ablation of Col I or overexpression of wild-type Col I prevented the decline in ATP and amino acids (Extended Data Fig. 2h,j), suggesting that cCol I is a key signalling molecule that stimulates PDAC metabolism and energy generation.
KPC or human MIA PaCa-2 cells plated on wild-type ECM or co-cultured with wild-type fibroblasts in LG or low-glutamine (LQ) medium exhibited high rates of macropinocytosis, as measured by their uptake of tetramethylrhodamine-labelled high-molecular-mass dextran (TMR-DEX), whereas cells plated on R/R ECM or co-cultured with R/R fibroblasts exhibited low rates of macropinocytosis (Fig. 3a and Extended Data Fig. 3a). Furthermore, KPC cells cultured on wild-type ECM showed a marked upregulation of macropinocytosis-related proteins and NRF2 relative to plastic-cultured cells, but culturing on R/R ECM had the opposite effect (Fig. 3b). Similar differences in macropinocytosis activity, NRF2 and macropinocytosis-related mRNAs and proteins were shown by KPC tumours in Col IWT or Col Ir/r pancreata or livers (Extended Data Fig. 3b–d). Mitochondria are important for cancer growth in that they generate energy for macromolecular synthesis. Consistent with the RNA-seq data, mitochondria and ETC proteins were decreased in PDAC cells grown on R/R ECM or in Col Ir/r pancreata (Fig. 3c,d and Extended Data Fig. 3e). The human PDAC stroma consists of intact and cleaved collagens. To recapitulate this setting and determine how the balance of iCol I to cCol I affects PDAC metabolism, we mixed R/R fibroblasts with wild-type (R:W) or Col IΔ (knockout) (R:KO) fibroblasts to generate ECM with different amounts of iCol I and cCol I, and confirmed this with isoform-specific antibodies. KPC cells were plated on the ECM preparations and kept in LG medium for 24 h, and their rates of macropinocytosis, numbers of mitochondria and levels of nuclear NRF2 were evaluated. Nondegradable Col I at 6:4 (R:W) or 4:6 (R:KO) ratios and higher ratios inhibited macropinocytosis and reduced mitochondria numbers and nuclear NRF2 (Extended Data Fig. 3f,g). We conclude that iCol I inhibits macropinocytosis and mitochondrial biogenesis, which are stimulated by different cleaved collagens, not just cCol I. To investigate how Col I regulates macropinocytosis and mitochondrial biogenesis, we systematically ablated (Extended Data Fig. 4a) all known collagen receptors expressed by KPC cells—MRC2, DDR1, LAIR1 and β1 integrin (ITGB1). The only receptor whose ablation inhibited macropinocytosis activity and mitochondrial biogenesis (Fig. 4a) was DDR1, a collagen-activated receptor tyrosine kinase (RTK), which scRNA-seq showed was highly expressed in primary and liver-metastatic human PDAC epithelial-tumour cells, marked by the mRNA expression of EPCAM and KRT19 (Extended Data Fig. 4b). Other collagen receptor mRNAs were either not expressed in PDAC (LAIR1 and MRC2) or had a broad distribution (ITGB1). Whereas wild-type ECM stimulated the expression and phosphorylation of DDR1, R/R ECM strongly downregulated DDR1 and its downstream effector NF-κB, as well as p62 (Fig. 4b), an NF-κB target. The inhibitory effect of iCol I was not observed in previous DDR1 signalling studies, which used artificially fragmented acid-solubilized collagens as ligands. Consistent with the induction of p62, wild-type ECM decreased KEAP1 and upregulated NRF2, whereas R/R collagen had the opposite effect (Fig. 4b). We wondered whether cCol I affects macropinocytosis and mitochondrial biogenesis through the DDR1–NF-κB–p62–NRF2 cascade. Indeed, R/R ECM and inhibition or ablation of NRF2, DDR1 or IKKβ decreased macropinocytosis activity, 3/4 Col I fragment uptake, NRF2 nuclear localization, mitochondria number and expression of macropinocytosis-related and mitochondrial ETC proteins (Fig. 4c,d and Extended Data Figs. 4c–g and 5a–e). Overexpression of an activated NRF2(E79Q) variant reversed the inhibitory effects of R/R ECM, DDR1 inhibition or IKKβ inhibition but did not restore or affect DDR1 expression or phosphorylation and p65 nuclear localization. Consistent with these data, pancreatic and liver tumours from Col Ir/r mice showed more-extensive expression of iCol I but no cCol I and lower levels of DDR1, p65, p62, NRF2, NHE1 and SDHB (a mitochondrial marker), as compared to tumours from Col IWT mice (Fig. 4e and Extended Data Fig. 5f,g). These results suggest that Col I controls macropinocytosis and mitochondrial biogenesis through the DDR1–NF-κB–p62–NRF2 axis. As myofibroblast-specific ablation of Col I enhances intrahepatic PDAC growth, we examined how Col IΔ ECM affects macropinocytosis and DDR1 signalling. Notably, Col IΔ ECM behaved like wild-type ECM, stimulating macropinocytosis, mitochondrial biogenesis and DDR1 phosphorylation, which were blocked by the ablation of DDR1 (Extended Data Fig. 6a–c). However, collagen-free ECM generated by Col IΔ fibroblasts and treatment with bacterial collagenase no longer activated DDR1 and its downstream effectors (Extended Data Fig. 6d). These results are consistent with DDR1 being a general collagen receptor, with other collagens in Col IΔ fibroblasts acting as ligands.
The expression and function of DDR1 vary in different cancer stages and types. Levels of mouse Ddr1 mRNA were increased by culturing KPC cells on R/R ECM (Extended Data Fig. 6e), implying that the diminished expression of DDR1 protein in these cultures is post-transcriptional. Indeed, MG132, a proteasome inhibitor, but not the lysosomal inhibitor chloroquine, rescued DDR1 expression but not autophosphorylation (Fig. 4f). Notably, GFP–DDR1 showed cell-surface localization and little polyubiquitin colocalization in human 1305 cells that were co-cultured with wild-type fibroblasts, but was cytoplasmic and colocalized with polyubiquitin in R/R fibroblast cocultures (Fig. 4g). Unlike DDR1 in triple-negative breast cancer (TNBC), no shedding of the DDR1 extracellular domain was detected (Extended Data Fig. 6f). Our results therefore reveal a new mode of DDR1 regulation in PDAC and probably in other desmoplastic cancers.
ECM from fibroblasts treated with the FDA-approved MMP inhibitor Ilomastat behaved like R/R ECM (Extended Data Fig. 6g,h), indicating that the results were not unique to the Col IR variant. R/R ECM also decreased the number of mitochondria in autophagy-deficient PDAC cells (Extended Data Fig. 6i), which suggests that the reduced mitochondrial content is not mediated by mitophagy. Moreover, colocalization of mitochondria and polyubiquitin, which marks mitophagy, was rarely observed (Extended Data Fig. 6j). Expression of TFAM, a key activator of mitochondrial DNA transcription, replication and biogenesis, was downregulated in PDAC cells cultured in R/R ECM, but Nrf1 (unrelated to NRF2) mRNA, PGC1α protein and AMPK activity, which also stimulate mitochondrial biogenesis, were upregulated (Extended Data Fig. 6e,k). The latter results match the low ATP content of R/R-ECM-cultured cells. In silico analysis revealed putative NRF2-binding sites in the Tfam promoter region, to which NRF2 was recruited in cells plated on wild-type ECM or in NRF2(E79Q)-expressing cells (Extended Data Fig. 6l,m), confirming that NRF2 mediates cCol I-stimulated macropinocytosis and mitochondrial biogenesis.
Immunohistochemistry (IHC) of surgically resected human PDAC showed that most tumours (77/106) contained high amounts of 3/4 Col I and most of them exhibited higher levels of staining for DDR1 (58/77), NF-κB p65 (55/77), NRF2 (60/77), SDC1 (53/77), CDC42 (52/77), SDHB (62/77), α-SMA (56/77) and MMP1 (52/77) than did cCol Ilow tumours (Fig. 5a and Extended Data Fig. 7a,b), suggesting that PDAC tumours with fibrolytic stroma have higher macropinocytosis activity and mitochondrial content than do tumours with inert stroma. Moreover, DDR1 and p65, DDR1 and NRF2, p65 and NRF2, NRF2 and macropinocytosis proteins (NHE1, SDC1 or CDC42), and NRF2 and SDHB showed strong positive correlations (Extended Data Fig. 7b), suggesting that the fibrolytic stroma stimulates macropinocytosis and mitochondrial biogenesis through the DDR1–NF-κB–NRF2 axis in human PDAC. Increased levels of cCol I also correlated with high expression of inflammatory markers (Extended Data Fig. 7c), supporting the notion that inflammation may drive Col I remodelling. Notably, patients with cCol Ihigh and DDR1high, cCol Ihigh and NRF2high or DDR1high and NRF2high tumours had a considerably worse median survival than did patients with low expression of these markers (Fig. 5b). These results are consistent with those obtained in our preclinical PDAC models, suggesting that the fibrolytic stroma may drive the recurrence of human PDAC through NRF2-mediated macropinocytosis and mitochondrial biogenesis.
Increasing iCol I in the ECM inhibited cellular DNA synthesis (Extended Data Fig. 8a). Parental, NRF2E79Q or IKKα-knockdown (IKKαKD) PDAC cells were plated on wild-type or R/R ECM, incubated in LG medium and treated with inhibitors of DDR1 (7rh), IKKβ (ML120B), NRF2 (ML385) or macropinocytosis (NHE1KD or EIPA, IPI549 or MBQ-167). Whereas wild-type ECM increased and R/R ECM decreased parental PDAC cell growth, inhibition of macropinocytosis, DDR1, IKKβ or NRF2 decreased growth on wild-type ECM (Fig. 6a and Extended Data Fig. 8b–f). NRF2(E79Q)-expressing cells grew faster than parental cells and were resistant to R/R ECM, DDR1 inhibition or IKKβ inhibition but not NRF2 inhibition. IKKαKD cells with high rates of macropinocytosis and high levels of nuclear NRF2 also grew faster than parental cells on wild-type ECM but were more sensitive to R/R ECM and macropinocytosis inhibitors (Extended Data Fig. 8b,c). Inhibition of macropinocytosis, DDR1, IKKβ or NRF2 did not decrease the low growth of parental cells on R/R ECM (Fig. 6a and Extended Data Fig. 8b–f). Moreover, parental KPC or 1305 cells that were plated on wild-type ECM were more sensitive to the mitochondrial protein synthesis inhibitor tigecycline than cells plated on R/R ECM or DDR1KD cells grown on wild-type ECM (Extended Data Fig. 8g). NRF2E79Q cells showed higher rates of oxygen consumption and mitochondrial ATP production than did parental cells; these rates were diminished by R/R ECM but only in the parental cells (Fig. 6b and Extended Data Fig. 8h). Thus, the fibrolytic stroma may support PDAC cell growth through Col I-stimulated macropinocytosis and mitochondrial biogenesis. R/R fibroblasts inhibited human PDAC (MIA PaCa-2) tumour growth, but wild-type fibroblasts were stimulatory. NHE1 ablation or EIPA inhibited tumour growth with or without co-transplanted wild-type fibroblasts or in wild-type livers, but had little effect on tumours growing with R/R fibroblasts or in Col Ir/r livers (Fig. 6c and Extended Data Fig. 8i). Tumours growing with wild-type fibroblasts were more fibrotic than tumours without added fibroblasts, and small tumours growing with R/R fibroblasts had the highest collagen content (Extended Data Fig. 8j), indicating that deposition of Col I enhances the growth of PDAC only when Col I is cleaved by MMPs. NRF2E79Q cells in Col Ir/r hosts exhibited similar growth, NRF2, NHE1 and SDHB expression and liver metastases to cells growing in Col IWT hosts, despite low expression of DDR1 and p65 (Fig. 6d–f and Extended Data Fig. 9a). In TNBC, DDR1 aligns collagen fibres to exclude immune cells. By measuring second-harmonic generation (SHG), we observed no change in collagen fibre alignment and CD8+ T cell content between tumours from Col IWT and Col Ir/r pancreata or between parental and DDR1KD tumours, although CD45-, F4/80- or CD4-expressing cells were reduced in tumours from Col Ir/r pancreata (Extended Data Fig. 9b,c). Accordingly, ablation of DDR1 inhibited tumour growth, p65, p62, NRF2, NHE1 and SDHB expression in Col IWT pancreata but did not reduce it further in Col Ir/r pancreata (Fig. 6g and Extended Data Figs. 9d,e and 10a). NRF2(E79Q) rescued tumour growth and the expression of NHE1 and SDHB—but not p65 or p62—in DDR1KD cells, regardless of Col I status. Similar results were observed in immunodeficient mice (Extended Data Fig. 10b), indicating that the effects of Col I–DDR1 interaction differ between PDAC and TNBC. Notably, inhibition of IKKβ, mitochondrial protein synthesis, TFAM or NRF2 decreased the growth of tumours that were co-transplanted with wild-type fibroblasts or grown in Col IWT pancreata, but had no effect on tumours that were co-transplanted with R/R fibroblasts or grown in Col Ir/r pancreata (Fig. 6h and Extended Data Fig. 10c,d), illustrating different ways of targeting PDAC with fibrolytic stroma.
We show here that Col I remodelling is a prognostic indicator for the survival of patients with PDAC. In preclinical models, Col I remodelling modulated tumour growth and metabolism through a DDR1–NF-κB–p62–NRF2 cascade that is activated by cCol I and inhibited by iCol I. The activation of DDR1 by collagens and downstream activation of NF-κB have been described before. However, it was previously unknown—to our knowledge—that iCol I triggers the polyubiquitylation and proteasomal degradation of DDR1. This indicates that DDR1 distinguishes cleaved from intact collagens, and that the latter are capable of restraining the metabolism and growth of tumours. Although inhibition of DDR1 reduces the growth of mouse PDAC, the ability of DDR1 to control tumour metabolism by stimulating macropinocytosis and mitochondrial biogenesis was unknown. It is unclear, however, why DDR1—a rather weak RTK—exerts such profound metabolic effects on PDAC cells that express more potent RTKs, such as EGFR and MET. Perhaps this is due to high concentrations of cCol I in the PDAC tumour microenvironment and the stronger NF-κB-activating capacity of DDR1 relative to other RTKs. Indeed, IKKβ inhibition was as effective as the blockade of mitochondrial protein synthesis in curtailing the growth of PDAC with fibrolytic stroma. The differential effects of fibrolytic and inert tumour stroma on PDAC growth and metabolism explain much of the controversy that surrounds the effects of CAFs and Col I on the progression of PDAC in mice. Most notably, our findings extend to humans and suggest that Col I remodelling is linked to tumour inflammation. We thus propose that treatments that target DDR1–IKKβ–NF-κB–NRF2 signalling and mitochondrial biogenesis should be evaluated in prospective clinical trials that include stromal state—an important modifier of tumour growth—as an integral biomarker. Given that three Col I-cleaving MMPs were highly expressed in the human PDAC samples we analysed, and that this situation may differ from patient to patient, specific MMP inhibitors are additional candidates for precision therapy. A deeper understanding of whether stromal state is affected by neoadjuvant chemotherapy and how it affects metastasis is another area of priority for further investigation. Although our results do not apply to TNBC, they provide mechanistic insight into SPARC-mediated PDAC progression, and may be applicable to other desmoplastic and fibrolytic cancers.
All cells were incubated at 37 °C in a humidified chamber with 5% CO2. MIA PaCa-2 (MIA), UN-KPC-960 (KPC) and UN-KC-6141 (KC) cells, wild-type and R/R fibroblasts were maintained in Dulbecco’s modified Eagle’ s medium (DMEM) (Invitrogen) supplemented with 10% fetal bovine serum (FBS) (Gibco). MIA cells were purchased from ATCC. KPC and KC cells were generated at the laboratory of S. K. Batra. Wild-type and R/R fibroblasts were generated at the laboratory of D.B.. The 1305 primary human PDAC cells were generated by the A.M.L. laboratory from a human PDAC patient-derived xenograft and were maintained in RPMI (Gibco) supplemented with 20% FBS and 1 mM sodium pyruvate (Corning). All media were supplemented with penicillin (100 mg ml−1) and streptomycin (100 mg ml−1). All cells were partially authenticated by visual morphology. Wild-type and R/R fibroblasts were partially authenticated by ECM production and collagen type I alpha 1 cleavage. KPC and KC cells were partially authenticated by orthotopic tumour formation in mouse pancreas. MIA and 1305 cells were partially authenticated by subcutaneous tumour formation in nude mice. Cells were not further authenticated. Cell lines were tested for mycoplasma contamination. LG medium: glucose-free DMEM medium was supplemented with 0.5 mM glucose in the presence of 10% dialysed FBS and 25 mM HEPES. LQ medium: glutamine-free DMEM medium was supplemented with 0.2 mM glutamine in the presence of 10% dialysed FBS and 25 mM HEPES.
For gene ablations, the target cDNA sequences (Supplementary Table 1) of mouse Ddr1, Mrc2, Itgb1, Lair1, Nrf2, Col1a1 and human DDR1 were cloned into a lentiCRISPR v2-Blast vector or lentiCRISPR v2-puro vector, respectively using BsmBI. For gene knockdowns, pLKO.1-puro-Ddr1 (TRCN0000023369), pLKO.1-puro-DDR1 (TRCN0000121163), pLKO.1-puro-Sdc1 (TRCN0000302270), pLKO.1-puro-Nrf2 (TRCN0000054658) and pLKO.1-puro-Tfam (TRCN0000086064) were ordered from Sigma. pCDH-CMV-MCS-EF1-puro-Col1α1-6XHis and pLVX-IRES-Puro-NRF2E79Q-Flag were made by Sangon Biotech (Shanghai, China). pLKO.1-blast-Ikkα, pLKO.1-puro-Nhe1, pLKO.1-puro-NHE1, pLKO.1-puro-NRF2, and lentiCRISPR v2-Puro-p62/Sqstm1 have been described previously. LentiCRISPR v2-Blast-ATG7 (ref. ) was a gift from S. Ghaemmaghami.
Lentiviral particles were generated as before. MIA, 1305, KPC or KC cells and fibroblasts were transduced by combining 1 ml of viral particle-containing medium with 8 μg ml−1 polybrene. The cells were fed 8 h later with fresh medium and selection was initiated 48 h after transduction using 1.25 μg ml−1 puromycin or 10 μg ml−1 blasticidin. IKKαKD KC, NRF2KD MIA and ATG7Δ MIA cells have been described previously.
Female homozygous Nu/Nu nude mice and C57BL/6 mice were obtained at six weeks of age from Charles River Laboratories and The Jackson Laboratory, respectively. Col1a1+/+ (Col IWT) or Col1a1r/r (Col Ir/r) mice on a C57BL/6 background were obtained from D.B. at UCSD and were previously described. Mice matched for age, gender and equal average tumour volumes were randomly allocated to different experimental groups on the basis of their genotypes. No sample size pre-estimation was performed but as many mice per group as possible were used to minimize type Ι/II errors. Both male and female mice were used unless otherwise stated. Blinding of mice was not performed except for IHC analysis. All mice were maintained in filter-topped cages on autoclaved food and water at constant temperature and humidity and in a pathogen-free controlled environment (23 °C ± 2 °C, 50–60%) with a standard 12-h light–12-h dark cycle. Experiments were performed in accordance with UCSD Institutional Animal Care and Use Committee and NIH guidelines and regulations. Animal protocol S00218 (M.K.) was approved by the UCSD Institutional Animal Care and Use Committee. The number of mice per experiment is indicated in the figure legends and their age is indicated in Methods.
Col IWT or Col Ir/r mice were pretreated with or without 50 μg kg−1 CAE by intraperitoneal injections every hour, six times daily on the first, fourth and seventh days. On day 11, parental, NRF2E79Q, DDR1KD, DDR1KD + NRF2E79Q, NRF2KD or TFAMKD KPC or KC cells were orthotopically injected into three-month-old Col IWT or Col Ir/r mice as described. After surgery, mice were given buprenorphine subcutaneously at a dose of 0.05–0.1 mg kg−1 every 4–6 h for 12 h and then every 6–8 h for 3 additional days. Mice were analysed after four weeks.
Three-month-old Col IWT or Col Ir/r mice were treated with or without an oral gavage of 25% CCl4 in corn oil twice a week for two weeks. After two weeks of recovery, parental, NHE1KD or IKKαKD KPC or KC cells (106 cells in 50 μl phosphate-buffered saline; PBS) were adoptively transferred into the livers of Col IWT or Col Ir/r mice by intrasplenic injection, followed by immediate splenectomy. Mice were analysed 14 days after treatment with or without 10 mg kg−1 EIPA (Sigma) by intraperitoneal injection every other day.
Homozygous BALB/c Nu/Nu female mice were injected subcutaneously in a single flank or in both flanks at 7 weeks of age with 5 × 105 parental, NHE1KD, DDR1KD or DDR1KD + NRF2E79Q MIA cells or 1305 cells mixed with or without 5 × 105 wild-type, R/R, Col IΔ wild-type or Col IΔ R/R fibroblasts diluted 1:1 with BD Matrigel (BD Biosciences) in a total volume of 100 μl. Tumours were collected after four weeks. To evaluate the effect of IKKβ or mitochondrial protein synthesis inhibition on tumour growth, mice were treated with vehicle (dimethyl sulfoxide in PBS), ML120B (60 mg kg−1) twice daily through oral gavage or tigecycline (50 mg kg−1) twice daily through intraperitoneal injection for three weeks. Therapy was started one week after tumour implantation. Volumes (1/2 × (width2 × length)) of subcutaneous tumours were calculated on the basis of digital caliper measurements. Mice were euthanized to avoid discomfort if the tumour diameter reached 2 cm.
Survival analysis of patients expressing high and low levels of Col I–MMP was performed using The Cancer Genome Atlas (TCGA) data and the GEPIA2 platform. The collagen-cleaving signature consisted of MMP1, MMP2, MMP8, MMP9, MMP13 and MMP14. Overall survival was determined in the TCGA cohort of 178 patients with PDAC using a median cut-off. A total of 106 specimens of human PDAC were acquired from patients who were diagnosed with PDAC between January 2017 and May 2021 at The Affiliated Drum Tower Hospital of Nanjing University Medical School. All patients received standard surgical resection and did not receive chemotherapy before surgery. Paraffin-embedded tissues were processed by a pathologist after surgical resection and confirmed as PDAC before further investigation. Overall survival duration was defined as the time from the date of diagnosis to that of death or last known follow-up examination. Survival information was available for 81 of the 106 patients. The study was approved by the Institutional Ethics Committee of The Affiliated Drum Tower Hospital with IRB 2021-608-01. Informed consent for tissue analysis was obtained before surgery. All research was performed in compliance with government policies and the Helsinki declaration.
Pancreata or liver were dissected and fixed in 4% paraformaldehyde in PBS and embedded in paraffin. Five-micrometre sections were prepared and stained with H&E or sirius red. IHC was performed as before. Slides were photographed on an upright light/fluorescent Imager A2 microscope with AxioVision Rel. 4.5 software (Zeiss). Antibody information is shown in Supplementary Table 2.
IHC scoring was performed as before. Negative and weak staining was viewed as a low expression level and intermediate and strong staining was viewed as a high expression level. For cases with tumours with two satisfactory cores, the results were averaged; for cases with tumours with one poor-quality core, results were based on the interpretable core. On the basis of this evaluation system, a chi-squared test was used to estimate the association between the staining intensities of Col I–DDR1–NRF2 signalling proteins. The number of evaluated cases for each different staining in PDAC tissues and the scoring summary are indicated in Extended Data Fig. 7a.
Wild-type or R/R fibroblasts were seeded on 6, 12 or 96-well plates. One day after plating, cells were switched into DMEM (with pyruvate) with 10% dialysed FBS supplemented with or without 500 μM [3H]-proline or [U-13C]-glutamine and 100 μM vitamin C. Cells were cultured for five days with renewal of the medium every 24 h. Then fibroblasts were removed by washing in 1 ml or 500 μl or 100 μl per well PBS with 0.5% (v/v) Triton X-100 and 20 mM NH4OH. The ECM was washed five times with PBS before cancer cell plating. The following day, cancer cells were switched into the indicated medium for 24 or 72 h.
Cells were cultured on coverslips coated with or without ECM and fixed in 4% paraformaldehyde for 10 min at room temperature or methanol for 10 min at −20 °C. Macropinosome visualization in cell and tissue and immunostaining were performed as previously described. Images were captured and analysed using a TCS SPE Leica confocal microscope with Leica Application Suite AF 2.6.0.7266 software (Leica). Antibody information is shown in Supplementary Table 2.
Mouse pancreatic tumour tissue was fixed in 4% paraformaldehyde in PBS and embedded in paraffin. Five-micrometre sections were prepared and deparaffinized in xylene, rehydrated in graded ethanol series as described, mounted using an aqueous mounting medium and sealed with a coverslip. All samples were imaged using a Leica TCS SP5 multiphoton confocal microscope and an HC APO LC 20× 1.00W was used throughout the experiment. The excitation wavelength was tuned to 840 nm, and a 420 ± 5-nm narrow bandpass emission controlled by a prism was used for detecting the SHG signal of collagen. SHG signal is generated when two photons of incident light interact with the non-centrosymmetric structure of collagen fibres, which leads to the resulting photons being half the wavelength of the incident photons. SHG measurements were performed using CT-Fire software (v.2.0 beta) (https://loci.wisc.edu/software/ctfire). The tumour area was confirmed by H&E staining.
Preparation of protein samples from cells and tissues, immunoblotting and immunoprecipitation were performed as before. Immunoreactive bands were detected by an automatic X-ray film processor or a KwikQuant Imager. Antibody information is shown in Supplementary Table 2.
Cells were cross-linked with 1% formaldehyde for 10 min and the reaction was stopped with 0.125 M glycine for 5 min. The chromatin immunoprecipitation assay was performed as described. Cells were lysed and sonicated on ice to generate DNA fragments with an average length of 200–800 bp. After pre-clearing, 1% of each sample was saved as the input fraction. Immunoprecipitation was performed using antibodies that specifically recognize NRF2 (CST, 12721). DNA was eluted and purified from complexes, followed by PCR amplification of the target promoters or genomic loci using primers for mouse Tfam: 5′-GAGGCAGGGTCTCATG-3′ and 5′-CAAGCTGAGTTCTATC-3′; 5′- TCTGGGCCATCTTGGG-3′ and 5′- CCATGGGCCTGGGCTG-3′.
Total RNA and DNA were extracted using the All Prep DNA/RNA Mini Kit (Qiagen). RNA was reverse-transcribed using a Superscript VILO cDNA synthesis kit (Invitrogen). Quantitative (q)PCR was performed as described. Primers obtained from the NIH Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome) are shown in Supplementary Table 3.
Total RNA was isolated as described above from KPC samples grown on wild-type (n = 3) or R/R (n = 3) ECM as indicated. RNA purity was assessed by an Agilent 2100 Bioanalyzer. Five hundred nanograms of total RNA was enriched for poly-A-tailed RNA transcripts by double incubation with Oligo d(T) Magnetic Beads (NEB, S1419S) and fragmented for 9 min at 94 °C in 2× Superscript III first-strand buffer containing 10 mM DTT (Invitrogen, P2325). The reverse-transcription reaction was performed at 25 °C for 10 min followed by 50 °C for 50 min. The reverse-transcription product was purified with RNAClean XP (Beckman Coulter, A63987). Libraries were ligated with dual unique dual index (UDI) (IDT) or single UDI (Bioo Scientific), PCR-amplified for 11–13 cycles, size-selected using one-sided 0.8× AMPure clean-up beads, quantified using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific) and sequenced on a HiSeq 4000 or NextSeq 500 (Illumina). RNA-seq reads were aligned to the mouse genome (GRCm38/mm10) using STAR. Biological and technical replicates were used in all experiments. Quantification of transcripts was performed using HOMER (v.4.11). Principal component analysis (PCA) was obtained on the basis of transcripts per kilobase million (TPM) on all genes from all samples. Expression value for each transcript was calculated using the analyzeRepeats.pl tool of HOMER. Differential expression analysis was calculated using getDiffExpression.pl tool of HOMER. Pathway analyses were performed using the Molecular Signature Database of GSEA.
Samples from five primary tumours from patients with PDAC and one PDAC liver metastasis were obtained and analysed separately to better identify cell heterogeneity and clusters. The datasets were processed in R (v.4.0.2) and Seurat (v.4.0.5) and cells with at least 200 genes and genes expressed in at least 3 cells were retained for further quality control analysis for the percentage of mitochondrial genes expressed, total genes expressed and unique molecular identifier (UMI) counts. The gene–cell barcode matrix obtained after quality control analysis was log-normalized and 3,000 variable genes were identified and scaled to perform PCA. The five PDAC primary patient samples were then batch-corrected and integrated using a reciprocal PCA (RPCA) pipeline in Seurat using ‘FindIntegrationAnchors’ and ‘IntegrateData’ functions. The ‘integrated’ assay was again scaled to perform PCA. The top significant principal components of PCA were identified using ‘ElbowPlot’ in each dataset. To cluster and visualize the cells, ‘FindNeighbours’, ‘FindClusters’ and ‘RunUMAP’ functions were used on the top identified principal components in each dataset. The cell types were identified by manual annotation of well-known makers, namely: epithelial-tumour cells (EPCAM and KRT8), pancreatic epithelial cells (CPA1 and CTRB1), T cells (CD3D and IL7R), myeloid cells (CD14, CD68, FCGR3A and LYZ), NK cells (NKG7 and GNLY), B cells (CD79A and MS4A1), dendritic cells (FCGR1A and CPA3), endothelial cells (PECAM1, KDR and CDH5), fibroblasts (ACTA2, COL1A1, COLEC11 and DCN), vascular smooth muscle cells (MYH11 and ACTA2), hepatocytes (ALB, APOE and CPS1), cholangiocytes (ANXA4, KRT7 and SOX9), plasma cells (JCHAIN and IGKC) and cycling cells (TOP2A and MKI67). M1/M2 macrophages were designated as described: M1-like macrophages (AZIN1, CD38, CXCL10, CXCL9, FPR2, IL18, IL1B, IRF5, NIFKBIZ, TLR4, TNF and CD80) and M2-like macrophages (ALOX5, ARG1, CHIL3, CD163, IL10, IL10RA, IL10RB, IRF4, KIF4, MRC1, MYC, SOCS2 and TGM2). The mean expression score for the M1 and M2 signatures were computed for each macrophage subcluster using ‘AddModuleScore’ function and clusters with a higher M1 or M2 signature score were assigned M1-like or M2-like annotation, respectively.
Cells grown on a 12-well plate coated with or without ECM. Metabolite extraction and analysis were performed as before. Gas chromatography-mass spectrometry (GC-MS) analysis was performed using an Agilent 6890 gas chromatograph equipped with a 30-m DB-35MS capillary column connected to an Agilent 5975B mass spectrometer operating under electron impact ionization at 70 eV. For measurement of amino acids, the gas chromatograph oven temperature was held at 100 °C for 3 min and increased to 300 °C at 3.5 °C per min. The mass spectrometer source and quadrupole were held at 23 °C and 150 °C, respectively, and the detector was run in scanning mode, recording ion abundance in the range of 100–605 m/z. Mole per cent enrichments of stable isotopes in metabolite pools were determined by integrating the appropriate ion fragments and correcting for natural isotope abundance as previously described.
Cells were plated in 96-well plates coated with or without ECM at a density of 3,000 cells (MIA, 1305) or 1,500 cells (KPC or KC) per well and incubated overnight before treatment. 7rh (500 nM), ML120B (10 μM), EIPA (10.5 μM), IPI549 (600 nM), MBQ-167 (500 nM), MRT68921 (600 nM) or ML385 (10 μM), or their combinations, were added to the wells in the presence of complete medium (CM), LG medium or LQ medium for 72 h. Cell viability was determined with a Cell Counting Kit-8 assay (Glpbio). Optical density was read at 450 nm and analysed using a microplate reader with SoftMax 6.5 software (FilterMax F5, Molecular Devices). For all experiments, the medium was replaced every 24 h.
KPC or KC cells were grown on 96-well plates coated with or without the indicated ECM in the presence of 100 μl CM or LG medium with or without EIPA (10.5 μM), MBQ-167 (500 nM), MRT68921 (600 nM) or their combinations for 24 h. Then the cell number was measured. Intracellular ATP was determined with a luminescence ATP detection assay system (PerkinElmer) according to the manufacturer’s protocol. Finally, luminescence was measured and normalized to cell number.
KPC or KC cells were grown on six-well plates coated with or without the indicated ECM in the presence of 100 μl LG medium with or without EIPA (10.5 μM), MRT68921 (600 nM) or their combinations for 24 h. Total amounts of free l-amino acids (except for glycine) were measured using an L-Amino Acid Assay Kit (Colorimetric, antibodies) according to the manufacturer’s protocol. The concentration of l-amino acids was calculated within samples by comparing the sample optical density to the standard curve and normalized to cell number.
Macropinosomes or mitochondria were quantified by using the ‘Analyze Particles’ feature in Image J (NIH). Macropinocytotic uptake index or mitochondria number was computed by the macropinosome or mitochondria area in relation to the total cell area for each field and then by determining the average across all the fields (six fields). Tumour area (%) was quantified by using the ‘Polygon’ and ‘Measure’ feature in Fiji Image J and was computed by tumour area in relation to total area for each field and then by determining the average across all the fields (five fields). Positive area of protein expression in tumour (%) was quantified by using ‘Colour Deconvolution’, ‘H DAB’, and ‘Analyze Particles’ feature in Fiji Image J and was computed by the protein-positive area in relation to the tumour area for each field and then by determining the average across all the fields (5–6 fields). These measurements were done on randomly selected fields of view. A two-tailed unpaired Student’s t-test was performed for statistical analysis using GraphPad Prism software. Data are presented as mean ± s.e.m. Kaplan–Meier survival curves were analysed by log-rank test. Statistical correlation between Col I–DDR1–NRF2 signalling proteins in human PDAC specimens was determined by two-tailed chi-squared test. (****P < 0.0001, ***P < 0.001, **P < 0.01 and *P < 0.05). All experiments except the IHC analysis of 106 human specimens were repeated at least 3 times.
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PMC9588814 | Xue-Qin Zhao,Chuan-Bei Ao,Yi-Tong Yan | The circular RNA circ_0099630/miR-940/receptor-associated factor 6 regulation cascade modulates the pathogenesis of periodontitis | 25-04-2022 | Circ_0099630,MiR-940,Periodontitis,TRAF6,circRNAs, circular RNAs,miR-940, microRNA-940,TNF, tumor necrosis factor,TRAF6, tumor necrosis factor receptor-associated factor 6,qRT-PCR, quantitative real-time polymerase chain reaction,ELISA, Enzyme-linked immunosorbent assay,CCK8, Cell Counting Kit-8,EdU, 5-ethynyl-2′-deoxyuridine,NF-κB, nuclear factor kappa-B,RIP, RNA immunoprecipitation,LPS, lipopolysaccharides,ceRNA, competing endogenous RNA,Cdr1as, circRNA CDR1 antisense RNA,FOXM1, forkhead box M1,ATG14, autophagy related 14,3′UTRs, 3′ untranslated regions,GAPDH, glyceraldehyde-3-phosphate dehydrogenase,U6, U6 small nuclear RNA,PDLCs, periodontal ligament cells,RIPA, radioimmune precipitation assay | Background/purpose Periodontitis is one of the highly prevalent chronic inflammatory conditions in adults. The importance of circular RNAs (circRNAs) in the regulation of inflammation has been gradually reported in recent years, but the role of circRNA circ_0099630 in periodontitis has not been reported. Materials and methods The contents of circ_0099630, microRNA-940 (miR-940) and tumor necrosis factor (TNF) receptor-associated factor 6 (TRAF6) were measured using quantitative real-time polymerase chain reaction (qRT-PCR) or Western blot. Inflammatory factor secretion, cell proliferation, and apoptosis were analyzed under the application of Enzyme-linked immunosorbent assay (ELISA), Cell Counting Kit-8 (CCK8), 5-ethynyl-2′-deoxyuridine (EdU) and flow cytometry, respectively. The Western blot also analyzed the phosphorylation levels of RELA proto-oncogene (P65) and IkappaBalpha (IκBα), key molecules of the nuclear factor kappa-B (NF-κB) pathway. The relationship between miR-940 and circ_0099630 or TRAF6 was verified by luciferase reporter system and RNA immunoprecipitation (RIP) assay. Results Higher abundance of circ_0099630 and TRAF6 and lower miR-940 expression were observed in periodontitis, and circ_0099630 knockdown attenuated the damage of human PDL cells (PDLCs) induced by lipopolysaccharides (LPS). The relationship between miR-940 and circ_0099630 or TRAF6 was evidenced, while miR-940 downregulation diminished the repair effect of si-circ_0099630 on overexpression LPS-induced damage in PDLCs. Similarly, TRAF6 upregulation impaired the mitigating effect of miR-940 overexpression on LPS-induced injury in PDLCs. Circ_0099630 silencing evidently curbed the phosphorylation levels of P65 and IκBα and thus attenuating the inflammatory response by acting on the miR-940/TRAF6 axis. Conclusion Silencing circ_0099630 alleviates LPS-induced periodontal ligament cell injury via targeting miR-940/TRAF6/NF-κB in periodontitis. | The circular RNA circ_0099630/miR-940/receptor-associated factor 6 regulation cascade modulates the pathogenesis of periodontitis
Periodontitis is one of the highly prevalent chronic inflammatory conditions in adults. The importance of circular RNAs (circRNAs) in the regulation of inflammation has been gradually reported in recent years, but the role of circRNA circ_0099630 in periodontitis has not been reported.
The contents of circ_0099630, microRNA-940 (miR-940) and tumor necrosis factor (TNF) receptor-associated factor 6 (TRAF6) were measured using quantitative real-time polymerase chain reaction (qRT-PCR) or Western blot. Inflammatory factor secretion, cell proliferation, and apoptosis were analyzed under the application of Enzyme-linked immunosorbent assay (ELISA), Cell Counting Kit-8 (CCK8), 5-ethynyl-2′-deoxyuridine (EdU) and flow cytometry, respectively. The Western blot also analyzed the phosphorylation levels of RELA proto-oncogene (P65) and IkappaBalpha (IκBα), key molecules of the nuclear factor kappa-B (NF-κB) pathway. The relationship between miR-940 and circ_0099630 or TRAF6 was verified by luciferase reporter system and RNA immunoprecipitation (RIP) assay.
Higher abundance of circ_0099630 and TRAF6 and lower miR-940 expression were observed in periodontitis, and circ_0099630 knockdown attenuated the damage of human PDL cells (PDLCs) induced by lipopolysaccharides (LPS). The relationship between miR-940 and circ_0099630 or TRAF6 was evidenced, while miR-940 downregulation diminished the repair effect of si-circ_0099630 on overexpression LPS-induced damage in PDLCs. Similarly, TRAF6 upregulation impaired the mitigating effect of miR-940 overexpression on LPS-induced injury in PDLCs. Circ_0099630 silencing evidently curbed the phosphorylation levels of P65 and IκBα and thus attenuating the inflammatory response by acting on the miR-940/TRAF6 axis.
Silencing circ_0099630 alleviates LPS-induced periodontal ligament cell injury via targeting miR-940/TRAF6/NF-κB in periodontitis.
Periodontitis is a complex infectious disease that occurs mostly in early adulthood and is reflected in pathological changes in the periodontal ligament and alveolar bone. Periodontitis is usually caused by infections such as herpes virus, aggregatibacter actinomycetemcomitans and porphyromonas gingivalis, and it interacts with the immune system of the body. Periodontitis can be divided into resting periodontitis, which contains a large number of latent herpes viruses, and active periodontitis, in which these viruses are activated, which in turn causes pathogenic bacterial overgrowth and immunosuppression to develop. In addition, activated herpes viruses can promote the progression of gingivitis to periodontitis. The diagnosis of periodontitis is an ongoing clinical challenge, usually accompanied by under- or over-diagnosis, with over-diagnosis being relatively frequent. Therefore, a rapid and accurate diagnosis of periodontitis is now a priority. The treatment of mild periodontitis usually does not require surgical treatment, and the elimination of pathogenic bacteria and calculus by using antibacterial drugs combined with ultrasonic therapy can effectively suppress the number of pathogenic bacteria. Severe periodontitis is usually treated surgically in combination with antibiotic application, but the high cost of surgical treatment and the phenomenon of small treatment differences with antibiotic treatment are the reasons why surgical treatment is not commonly used today. Therefore, it is more urgent to find new ways of diagnosing and treating periodontitis. Circular RNAs (circRNAs) are a common class of closed-loop RNA molecules among non-coding RNAs. CircRNAs are demonstrated to competitively binding microRNAs (miRNAs) to play regulatory roles by acting as competing endogenous RNAs (ceRNAs). A great number of circRNAs have been identified to be dysregulated in various types of diseases.9, 10, 11, 12 In cancer, numerous circRNAs have been observed to be dysregulated. For instance, circRNA CDR1 antisense RNA (Cdr1as) was revealed to be abnormally upregulated in hepatocellular carcinoma (HCC) cells, and circRNA Cdr1as upregulation acting on the miR-1270/alpha fetoprotein (AFP) axis accelerated the proliferation and metastasis of hepatocellular carcinoma cells, which revealed that circRNA Cdr1as had the promise to become a molecular marker for HCC diagnosis. CircRNAs were also demonstrated to play a role in preeclampsia (PE), a multisystem disorder. For example, circ_0003496 was less abundant in placental tissues and acted on the miR-1244/forkhead box M1 (FOXM1) axis to regulate PE progression. This indicated that circ_0003496 might be a future molecular marker in the diagnostic and therapeutic process of PE. Further, dysregulation of circRNAs was also found in various types of inflammatory diseases. Zhang et al. established that circ_0005567 competitively bound miR-495 to regulate autophagy related 14 (ATG14) level and thus modulated osteoarthritis (OA) progression. Circ_0005567 upregulation attenuated apoptosis in OA model cells, while circ_0005567 knockdown did the opposite. In periodontitis, using RNA sequencing analysis of periodontitis tissues and normal tissues, 1304 differentially expressed circRNAs were found. Among these circRNAs, circ_0138960 was confirmed to be greatly upregulated in periodontitis tissues, but no validation of the mechanism was performed. Herein, our study explored the role and mechanism of circ_0099630 in periodontitis. MiRNAs are a class of tiny RNAs with fragment lengths of 18–25 nt and are members of regulatory non-coding RNAs. MiRNAs are synthesized by different mechanisms in plants and animals and can play a regulatory role by acting on the 3′ untranslated regions (3′UTRs) of target genes to inhibit the initiation and progression of translation. MiRNAs not only play a role in physiological processes such as cell growth and differentiation and death in the body, but have also been identified to be aberrantly expressed in diseases such as cancer, neurological disorders, and inflammation.15, 16, 17, 18 In periodontitis, miR-21 was demonstrated to repress inflammatory responses both in vitro and in vivo and had potential as a target for periodontitis treatment. Guo et al. demonstrated that miR-218 oppressed the inflammatory response of periodontitis cells by targeting Mmp9. MiR-940 was found to be dysregulated in multiple human diseases., For instance, miR-940 upregulation contributed to the proliferation and invasion of breast cancer cells. Conversely, in esophageal squamous cell carcinoma, miR-940 might function as an anti-tumor agent by repressing cell growth and enhancing apoptosis. MiR-940 was also demonstrated to be downregulated in mice with spinal cord injury (SCI), and upregulation of miR-940 restrained the levels of inflammatory factors toll like receptor 4 (TLR4) and myeloperoxidase (MPO), thereby suppressing the inflammatory response and promoting SCI recovery. However, no studies showed whether miR-940 was involved in the pathogenesis of periodontitis. We therefore analyzed the regulatory mechanism of miR-940 in periodontitis. Tumor necrosis factor receptor-associated factor 6 (TRAF6) is a member of the TRAFs family, which currently has seven members characterized by the C-terminal TRAF domain. TRAF6 was first cloned by Ishida in 1996, and it was demonstrated that upregulation of TRAF6 activated NFkappaB and that TRAF6 mediated CD40 signaling to regulate B-cell function. It was demonstrated that TRAF6 had a 530 amino acid composition, was less homologous than other TRAFs family members, and had been detected as expressed in a variety of human tissues. Yuan et al. identified that TRAF6 upregulation was notably associated with apoptosis rates in stroke cell models and also enhanced inflammatory responses. The action mechanism of circ_0099630/miR-940/TRAF6 in periodontitis was investigated in this study.
Periodontal tissues from 26 patients with periodontitis (9 cases mild, 10 cases moderate and 7 cases severe divided according to the severity of periodontitis) and 21 volunteers without periodontitis were collected from Stomatological Hospital of Jingmen Second People's Hospital, volunteers in both groups were free of other diseases and were not treated with antibiotics for one month prior to sample collection. Sample collection and follow-up tests were approved by Stomatological Hospital of Jingmen Second People's Hospital ethics committee along with informed consent and written confirmation from all participants.
RNA from periodontal ligament (PDL) tissues and cells was extracted with the help of TRIzol (TaKaRa, Dalian, China). Immediately afterwards, reverse transcription (RT) was executed adhering to the instructions of the AMV Reverse Transcriptase (Solarbio, Beijing, China) and miScript RT Kit (TaKaRa). The quantitative real-time polymerase chain reaction (qRT-PCR) with cDNA as template was carried out exactly as described in the SYBR Green (TaKaRa) instructions, and information on the primers used in this procedure was listed in Supplement Table 1. We normalized to the qRT-PCR data by glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and U6 small nuclear RNA (U6).
RNA was mixed with 3U/μg of RNase R (Solarbio) was mixed and co-incubated at 37 °C for 30 min. We analyzed circ_0099630 and GAPDH contents in this RNA with reference to the above steps.
Human PDL cells (PDLCs) were obtained from the China center for type culture collection (Wuhan, China). PDLCs were grown in Dulbecco's Modified Eagle Medium (DMEM) medium supplemented with 10% fetal bovine serum (FBS) and 1% double antibodies and were placed at 37 °C in 5% CO2. Subsequently, PDLCs were induced by 0 μg/mL, 2 μg/mL, 4 μg/mL, and 8 μg/mL of lipopolysaccharides (LPS) (Sigma–Aldrich, St. Louis, MO, USA) for 24 h to obtain periodontitis model cells. The circ_0099630 small interfering RNA (si-circ_0099630) and negative control (si-NC), miR-940 mimic (miR-940), miR-940 inhibitor (anti-miR-940) and negative controls (miR-NC, anti-miR-NC), TRAF6 overexpression vector (TRAF6) and negative control (pcDNA) all from Songon (Shanghai, China) were transfected into PDLCs following the Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) guidelines.
Cellular levels of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were assessed by enzyme-linked immunosorbent assay (ELISA) following the instructions of the corresponding ELISA kits (Beyotime, Shanghai, China). Cells were first lysed, then the supernatant of the lysate was incubated with detection reagents protected from light, and finally an enzyme marker was used for quantitative analysis.
Cell viability of PDLCs was estimated by Cell Counting Kit-8 (CCK8) assay and the whole procedure was carried out following the instructions of Cell Counting Kit-8 (Beyotime). 96-well plates of PDLCs were incubated with 10 μL of CCK8 solution 24 h after transfection and then the absorbance at 450 nm was measured.
The proliferation of PDLCs was analyzed by 5-ethynyl-2′-deoxyuridine (EdU) assay accompanied by the use of the EdU Cell Proliferation Assay Kit (Beyotime). PDLCs were inoculated in 96-well plates for 24 h. PDLCs were first labeled with 10 μL EdU. After the cells were washed with PBS, 100 μL of click reaction solution was added for 30 min, and 20 μL DAPI was applied to the labeled cells for staining, which were then immediately placed under a fluorescent inverted microscope for observation.
Assessment of apoptosis with the Annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) Apoptosis Detection Kit (Solarbio) was done. The digested PDLCs were collected and mixed with binding buffer, 5 μL Annexin V-FITC and PI were added and mixed with the obtained single cell suspensions, placed in the dark for 15 min, and the apoptosis of PDLCs was analyzed using flow cytometry.
PDLCs were lysed in the presence of radioimmune precipitation assay (RIPA) buffer (Beyotime), then total protein was captured and the protein was quantified by Bicinchoninic acid (BCA) Protein Assay Kit (Beyotime). Equal amounts of proteins were utilized for Western blot. After sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), the proteins were transferred to polyvinylidene fluoride (PVDF) membranes (Beyotime), and the blocking solution (Beyotime) was mixed with the membranes for 2 h. After that, the membranes were co-incubated with anti-Cleaved-caspase-3 (ab32042, 1:500, Abcam, Cambridge, UK), anti-BCL2 associated X (anti-Bax, ab32503, 1:1000, Abcam), anti-GAPDH (ab8245, 1:1000, Abcam), anti-TRAF6 (ab33915, 1:2000, Abcam), anti-RELA proto-oncogene (anti-P65, ab32536, 1:1000, Abcam), anti-phosphorylated-P65 (anti-p-P65, ab31624, 1:1000, Abcam), anti-IkappaBalpha (anti-IκBα, ab32518, 1:1000, Abcam) and anti-p-IκBα (ab133462, 1:1000, Abcam) at 4 °C overnight. The secondary antibody goat anti-rabbit immunoglobulin G (IgG) (ab205718, 1:20,000, Abcam) was mixed and this membrane on the second day, the proteins were then developed and analyzed in the FluorChem E system (Protein Simple, Silicon Valley, CA, USA).
After the target sites of circ_0099630 and miR-940 were predicted by Circinteractome (https://circinteractome.nia.nih.gov/) and Targetscan (http://www.targetscan.org/vert_80/). The wild-type (WT) circ_0099630 and TRAF6 3′UTR sequences containing the miR-940 complementary sites and the mutant (MUT) sequences after mutating the binding sites were cloned. The successfully constructed WT-circ_0099630, MUT-circ_0099630, WT-TRAF6 3′UTR and MUT-TRAF6 3′UTR were then co-transfected into PDLCs with miR-940 mimic. 24 h later, the cells were lysed and the supernatant was collected, and the luciferase activity of each group was measured using Dual-Lucy Assay Kit (Solarbio).
The RNA immunoprecipitation (RIP) assay was completely carried out as per the RNA Immunoprecipitation Kit (Sigma–Aldrich) guidelines, PDLCs lysate was gathered after the addition of RIP lysate. Then the lysate was added with magnetic beads coupled to the Argonaute 2 (Ago2) or IgG antibody (Sigma–Aldrich) and subsequently the RNA in the complex on the beads was captured, circ_0099630, miR-940 and TRAF6 levels were detected.
For all experiments repeated three times at least, the obtained data were processed by SPSS 19.0 (SPSS Inc., Chicago, IL, USA). Student's t-test or analysis of variance (ANOVA) was employed in this study for the analysis of the differences between groups. P-value < 0.05 was statistically significant.
Fig. 1A demonstrated that circ_0099630 content was higher in periodontitis tissues than that in normal tissues, while the expression of circ_0099630 in PDLCs was increased with increasing LPS concentration (Fig. 1B). Moreover, with the progression of the severity of periodontitis, the expression of circ_0099630 was markedly increased (Fig. 1A). We observed essentially no difference in circ_0099630 level before and after RNase R treatment, while GAPDH was substantially degraded after RNase R treatment, strong resistance of circ_0099630 to RNase R was displayed by qRT-PCR results (Fig. 1C). The level of circ_0099630 amplified by Oligo (dT)18 primers was lower than that of circ_0099630 amplified by random primers, whereas this difference was not present in the GAPDH group, showing the ring structure feature of circ_0099630 (Fig. 1D). These data suggested that circ_0099630 had higher abundance in periodontitis tissues and LPS-induced PDLCs.
Cellular levels of inflammatory factors IL-6 and TNF-α were increased with increasing LPS concentration (Fig. 2A and B). The viability and proliferation capacity of PDLCs became progressively weaker with increasing LPS concentration (Fig. 2C and D). Flow cytometry results also confirmed that LPS exhibited a concentration-dependent promotion of apoptosis in PDLCs (Fig. 2E). Protein levels of the apoptosis marker proteins Cleaved-caspase-3 and Bax were promoted with increasing LPS concentration as observed by Western blot (Fig. 2F). Combined with the results of this study, we selected 4 μg/mL of LPS for the follow-up test and confirmed that LPS curbed the viability and proliferation of PDLCs and enhanced apoptosis and inflammatory response in a concentration-dependent manner.
Circ_0099630 knockdown abated the promotion of LPS on circ_0099630, IL-6 and TNF-α level in PDLCs (Fig. 3A–C). In addition, the inhibitory effects of LPS on the viability and proliferation of PDLCs were recovered with the transfection of si-circ_0099630 (Fig. 3D and E). Meanwhile, LPS-enhanced apoptosis and the protein levels of Cleaved-caspase-3 and Bax were reversed after si-circ_0099630 was transfected into PDLCs (Fig. 3F–H). In conclusion, the negative effects of LPS on the function of PDLCs were mitigated by knockdown of circ_0099630.
The complementary sequences of circ_0099630 and miR-940 predicted by Circinteractome were displayed in Fig. 4A. MiR-940 level was markedly enhanced with the introduction of miR-940 mimic in PDLCs (Fig. 4B). The luciferase activity in the WT-circ_0099630 group was greatly curbed by miR-940 mimic, however, no change appeared in the MUT-circ_0099630 group (Fig. 4C). Also, RIP assay also indicated that circ_0099630 and miR-940 were dramatically enriched in the Ago2 group relative to the IgG group, further supporting their targeting relationship (Fig. 4D). Next, low abundance of miR-940 was observed in periodontitis tissues and negatively correlated with circ_0099630 level (Fig. 4E and F). Additionally, the abundance of miR-940 was reduced in periodontitis tissues with the progression of the severity of periodontitis (Fig. 4E). Furthermore, we found that miR-940 level was decreased in PDLCs with increasing LPS concentration (Fig. 4G). The reciprocal relationship between circ_0099630 and miR-940 was evidenced by these data.
The promotion of si-circ_0099630 on miR-940 level and the inhibition on IL-6 and TNF-α levels were diminished by co-transfection with anti-miR-940 in LPS-induced PDLCs (Fig. 5A–C). Si-circ_0099630-enhanced the impacts of cell viability and proliferation were receded by silencing miR-940 in LPS-induced PDLCs (Fig. 5D and E). Meanwhile, anti-miR-940 mitigated si-circ_0099630-mediated suppression on apoptosis and Cleaved-caspase-3, Bax protein levels in LPS-induced PDLCs (Fig. 5F–H). Collectively, miR-940 and circ_0099630 jointly regulated the malignant behavior of LPS-induced PDLCs.
Targetscan software discovered the potential binding sites for miR-940 and TRAF6, as illustrated in Fig. 6A. Luciferase activity was greatly downregulated in the WT-TRAF6 3′UTR and miR-940 mimic co-transfection group, but no difference was observed in the MUT-TRAF6 3′UTR group (Fig. 6B), tentatively proving the relationship between miR-940 and TRAF6. The phenomenon that miR-940 and TRAF6 were heavily adsorbed in the Ago2 group further demonstrated the association between miR-940 and TRAF6 (Fig. 6C). High level of TRAF6 was found in periodontitis tissues compared to normal tissues and was negatively correlated with miR-940 level (Fig. 6D and E). Also, TRAF6 expression was significantly elevated in periodontitis tissues with the progression of the severity of periodontitis (Fig. 6D). The high abundance of TRAF6 in periodontitis tissues and LPS-induced PDLCs was also observed by Western blot results (Fig. 6F and G). TRAF6 was identified as a target of miR-940 by these results.
TRAF6 overexpression reinstated the downregulation of TRAF6 level induced by miR-940 mimic in LPS-induced PDLCs (Fig. 7A). The depressed effects of miR-940 upregulation on IL-6 and TNF-α levels were recuperated by TRAF6 overexpression in LPS-induced PDLCs (Fig. 7B and C). Enhancing effects of miR-940 mimic on the viability and proliferation of LPS-induced PDLCs were diminished with TRAF6 co-transfection (Fig. 7D and E). Besides, miR-940 upregulation impeded apoptosis and Cleaved-caspase-3 and Bax protein expression in LPS-induced PDLCs, but TRAF6 upregulation ameliorated this phenomenon (Fig. 7F–H). Also, we observed that miR-940 downregulation mitigated the inhibition of TRAF6 protein level in LPS-induced PDLCs by si-circ_0099630 (Fig. 8A). Si-circ_0099630-repressed phosphorylation levels of P65 and IκBα in LPS-induced PDLCs were restored accompanied by co-transfection of anti-miR-940 or TRAF6 (Fig. 8B). These illustrated that miR-940 silencing and TRAF6 overexpression activated the si-circ_0099630-suppressed NF-κB signaling pathway, which in turn facilitated the inflammatory response. Overall, LPS-induced PDLCs injury was co-regulated by circ_0099630/miR-940/TRAF6 axis.
The inaccuracy of periodontitis diagnosis had accelerated the study of its molecular diagnostic markers., LPS was used to stimulate the inflammatory conditions of periodontitis, and LPS-induced periodontitis in vitro and in vivo models had been widely used for investigation of the molecular mechanisms of periodontitis pathogenesis.28, 29, 30 Our study validated the expression profile and action mechanism of circ_0099630/miR-940/TRAF6 axis in periodontitis tissues and model cells induced by LPS. CircRNAs were more abundantly expressed in various types of tissues while being more stable, conditions that support their use as molecular markers for diagnosis and treatment of various diseases. We observed a higher abundance of circ_0099630 in periodontitis tissues and model cells compared to normal tissues and cells, which suggested that circ_0099630 was expected to be a more rapid and ready diagnostic marker for periodontitis in the future, and the analysis of the association between circ_0099630 level and clinical symptoms of periodontitis patients should be enhanced in the future. Consistent with our findings, Li et al. unveiled that circ_0138960 were greatly upregulated in periodontitis tissues. We found after a series of validation that circ_0099630 knockdown effectively alleviated the inhibition of proliferation of PDLCs by LPS. In addition, IL-6 and TNF-α were identified as inflammatory markers, and circ_0099630 knockdown also suppressed the cellular levels of IL-6 and TNF-α in LPS-induced PDLCs. All these data pointed to circ_0099630 as a very promising molecular target for the diagnosis and treatment of periodontitis in the future. In this study, miR-940 was identified as a target of circ_0099630 to co-regulate LPS-induced injury in PDLCs. MiR-940 had established a critical role in human diseases. For example, miR-940 could enhance the progression of breast cancer and endometrial carcinoma., MiR-940 might function as a protective factor in lung adenocarcinoma and esophageal squamous cell carcinoma., Moreover, miR-940 participated in the inflammatory response of IL-1β-stimulated chondrocytes in a MyD88-dependent manner, indicating its regulation in osteoarthritis. MiR-940 downregulation in periodontitis tissues and model cells was detected in the study. Meanwhile, miR-940 downregulation abrogated the mitigating effect of circ_0099630 knockdown on LPS-induced damage in PDLCs. Combined regulation of periodontitis process by circ_0099630 and miR-940 was confirmed in our study. The targeting relationship between TRAF6 and miR-940 was established in this study, and TRAF6 had a high abundance in periodontitis tissues and model cells. In previous studies, TRAF6 was detected in higher abundance in tissues with myocardial hypertrophy. Xie et al. identified that TRAF6 combined with miR-125/124 axis was involved in the regulation of the nuclear factor kappa-B (NF-κB) signaling pathway and chemoresistance. The NF-κB signaling was an inflammation-associated signaling pathway. In our study, the TRAF6/circ_0099630/miR-940 axis jointly regulated the phosphorylation levels of P65 and IκBα and thus participating in the regulation of NF-κB signaling pathway activation. Meanwhile, TRAF6 level was co-regulated by circ_0099630/miR-940 axis, and the circ_0099630/miR-940/TRAF6 axis jointly regulated periodontitis progression. In conclusion, silencing circ_0099630 alleviated LPS-induced periodontal ligament cell injury via targeting miR-940/TRAF6/NF-κB axis in periodontitis, which provides a promising target for future diagnosis and treatment of periodontitis.
The authors have no conflicts of interest relevant to this article. | true | true | true |
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PMC9588925 | Chidambra D. Halari,Pinki Nandi,Jasmin Sidhu,Maria Sbirnac,Michael Zheng,Peeyush K. Lala | Decorin–induced, preeclampsia-associated microRNA-512-3p restrains extravillous trophoblast functions by targeting USF2/PPP3R1 axis 10.3389/fcell.2022.1014672 | 10-10-2022 | decorin,preeclampsia,microRNA,extravillous trophoblast,USF2,PPP3R1 | Decorin (DCN) is a leucine-rich proteoglycan produced by chorionic villus mesenchymal cells anddecidual cells during human pregnancy. Studies from our laboratory demonstrated that decidua-derived DCN restrains multiple trophoblast functions including proliferation, migration, invasion andendovascular differentiation, mediated by DCN-binding to multiple tyrosine kinase receptors; expressed by the trophoblast. Furthermore, DCN was shown to be selectively over-produced by thedecidua in preeclampsia (PE) subjects and elevated in the second trimester maternal plasma in PE, before the appearance of clinical signs, presenting as a predictive biomarker for PE. Micro (mi)RNAs are single-stranded non-coding RNAs (17–25 nucleotides) that typically downregulate target genes by repressing translation or facilitating degradation of mRNAs. The human; placenta expresses many miRNAs, some of which are exclusively expressed by the trophoblast. Many; of these miRNAs are dysregulated in PE-associated placentas and some appear in the maternal blood as PE biomarkers. However, little is known about their contribution to the pathogenesis of PE, a multi-factorial disease associated with a hypo-invasive placenta. The objective of the present study was to examine whether exposure of extravillous trophoblast (EVT) to DCN affects expression of specific miRNAs, and to test the role of these miRNAs in altering EVT functions. We identified miR-512-3p, as one of the DCN-induced miRNAs, also upregulated in PE placentas. It was shown to be elevated in ectopic DCN-over-expressing or exogenous DCN-treated first trimester human trophoblast cell line HTR-8/SVneo. Use of miRNA-mimics and inhibitors revealed that miR-512-3p compromised trophoblast migration, invasion and VEGF-dependent endovascular differentiation. Finally, Protein Phosphatase 3 Regulatory Subunit B, Alpha (PPP3R1), a known target of miR-512-3p, was paradoxically elevated in miR-512-3p-overexpressing trophoblast and PE-associated placentas. Using Enrichr, a tool that consists of both a validated user-submitted gene list and a search engine for transcription factors, we found that PPP3R1 elevation resulted from the miRNA binding to and targeting Upstream Transcription Factor 2 (USF2) which targeted PPP3R1. These findings reveal a novel aspect of pathogenesis of PE and biomarker potentials of this miRNA in PE. | Decorin–induced, preeclampsia-associated microRNA-512-3p restrains extravillous trophoblast functions by targeting USF2/PPP3R1 axis 10.3389/fcell.2022.1014672
Decorin (DCN) is a leucine-rich proteoglycan produced by chorionic villus mesenchymal cells anddecidual cells during human pregnancy. Studies from our laboratory demonstrated that decidua-derived DCN restrains multiple trophoblast functions including proliferation, migration, invasion andendovascular differentiation, mediated by DCN-binding to multiple tyrosine kinase receptors; expressed by the trophoblast. Furthermore, DCN was shown to be selectively over-produced by thedecidua in preeclampsia (PE) subjects and elevated in the second trimester maternal plasma in PE, before the appearance of clinical signs, presenting as a predictive biomarker for PE. Micro (mi)RNAs are single-stranded non-coding RNAs (17–25 nucleotides) that typically downregulate target genes by repressing translation or facilitating degradation of mRNAs. The human; placenta expresses many miRNAs, some of which are exclusively expressed by the trophoblast. Many; of these miRNAs are dysregulated in PE-associated placentas and some appear in the maternal blood as PE biomarkers. However, little is known about their contribution to the pathogenesis of PE, a multi-factorial disease associated with a hypo-invasive placenta. The objective of the present study was to examine whether exposure of extravillous trophoblast (EVT) to DCN affects expression of specific miRNAs, and to test the role of these miRNAs in altering EVT functions. We identified miR-512-3p, as one of the DCN-induced miRNAs, also upregulated in PE placentas. It was shown to be elevated in ectopic DCN-over-expressing or exogenous DCN-treated first trimester human trophoblast cell line HTR-8/SVneo. Use of miRNA-mimics and inhibitors revealed that miR-512-3p compromised trophoblast migration, invasion and VEGF-dependent endovascular differentiation. Finally, Protein Phosphatase 3 Regulatory Subunit B, Alpha (PPP3R1), a known target of miR-512-3p, was paradoxically elevated in miR-512-3p-overexpressing trophoblast and PE-associated placentas. Using Enrichr, a tool that consists of both a validated user-submitted gene list and a search engine for transcription factors, we found that PPP3R1 elevation resulted from the miRNA binding to and targeting Upstream Transcription Factor 2 (USF2) which targeted PPP3R1. These findings reveal a novel aspect of pathogenesis of PE and biomarker potentials of this miRNA in PE.
Preeclampsia (PE) is a severe pregnancy-specific disorder that affects around 5% of pregnancies worldwide. It is characterized by new onset hypertension and (typically) proteinuria after 20 weeks of gestation (Gao et al., 2018). If left untreated, PE may lead to multisystem organ damage such as renal failure, pancreatitis and hemolytic anemia (Sheikh et al., 2016). PE is one of the leading causes of maternal and perinatal morbidity and mortality (Duley, 2009). It has long been recognized that the pathogenesis of PE lies within the placenta because removal of the placenta eradicates the clinical manifestations of PE (Zhu et al., 2009; Chen and Wang, 2013). During normal placental development, trophoblast stem cells contained within the cytotrophoblast (CTB) layer of the chorionic villi differentiate into two subpopulations: syncytiotrophoblast (STB) and extravillous trophoblast (EVT). STB arises by cell fusion and EVT as migratory cell columns. EVT cells proliferate at the villus base and invade uterine decidua and spiral arteries (Knöfler and Pollheimer, 2013). Some of the EVT cells undergo endothelial-like (endovascular) differentiation to invade and remodel distal segments of the arteries into low-resistance tubes that allow steady flow of maternal blood for fetal nourishment (Kaufmann et al., 2003; Cartwright et al., 2010). While both EVT and STB differentiation pathways are dysregulated in PE, poor EVT invasion is thought to be the root cause of the pathological manifestation such as defective uterine arterial remodeling, resulting in a hypo-perfused placenta (Kaufmann et al., 2003; Lala and Chakraborty, 2003; Burton et al., 2009; Cartwright et al., 2010). A hypoxic placenta releases toxic lipid peroxides and inflammatory chemokines into the maternal circulation, leading to vascular damage in multiple maternal organs (Roberts and Lain, 2002). Currently, there is no known cure for PE except delivering the fetus and the placenta (Backes et al., 2011). However, premature delivery increases perinatal morbidity and mortality rates (Backes et al., 2011). As such, further research to understand the mechanisms underlying PE and to identify the novel biomarkers for its early detection is urgently needed. Decorin (DCN), a leucine rich-proteoglycan produced by various mesenchymal cells including decidual stromal cells, is overexpressed by the PE-associated decidua, as shown by in situ hybridization for mRNA and immuno-localization of the protein (Siddiqui et al., 2016). Additionally, plasma DCN levels in second trimester patients are elevated in PE compared to control (non-PE) patients matched for body-mass index (Siddiqui et al., 2016). DCN controls EVT cell proliferation, migration, invasion (Xu et al., 2002; Iacob et al., 2008) and endovascular differentiation (Lala et al., 2012), events needed for uterine spiral artery remodeling (Kaufmann et al., 2003; Lala and Nandi, 2016). Furthermore, DCN is a key regulator of human trophoblast stem cell self-renewal and differentiation (Nandi et al., 2018) and plays an autocrine role in decidual cell differentiation from human endometrial stromal cells (HESC) (Halari et al., 2020). These findings reveal that a balanced DCN production by the decidua is essential for a healthy pregnancy. Decreased DCN production may result in decidual maturation defects, whereas increased levels may result in compromised trophoblast functions associated with PE. MicroRNAs (miRNAs) are single-stranded non-coding RNAs (17–25 nucleotides) that typically bind to the 3′ untranslated region of target mRNAs to block translation and facilitate degradation (Dexheimer and Cochella, 2020). The human placenta expresses many miRNAs, some of which are exclusively expressed by trophoblasts and not by other normal human tissues (Baek et al., 2008). These placenta-specific miRNAs are clustered in three groups: chromosome 14 miRNA cluster (C14MC), chromosome 19 miRNA cluster (C19MC), and miR-371–373 cluster (Miura et al., 2010; Morales-Prieto et al., 2012). Some of them have been detected in maternal circulation throughout gestation with a significant decline after delivery (Miura et al., 2010; Kotlabova et al., 2011). Many of the miRNAs are dysregulated in PE-associated placentas (Ishibashi et al., 2012; Hong et al., 2014; Xu et al., 2021) and some may appear in the maternal blood as PE biomarkers (Munaut et al., 2016; Jelena et al., 2020; Xu et al., 2021). MiR-512-3p is part of the C19MC (Morales-Prieto and Markert, 2011) and multiple studies have reported upregulation of miR-512-3p in PE patients (Wang et al., 2012; Martinez-Fierro et al., 2018; Martinez-Fierro and Garza-Veloz, 2021). A recent study showed elevated level of this miR-512-3p at 20 weeks of gestation in the serum of women who later developed severe PE (Martinez-Fierro and Garza-Veloz, 2021). Whether this miRNA contributes to the pathology in PE has never been investigated. The objective of the present study was to determine whether exposure of EVTs to DCN affects expression of specific miRNAs, and to test the role of these miRNAs in altering EVT functions. We identified miR-512-3p, as one of the DCN-induced miRNAs, also upregulated in PE placentas. It was shown to be elevated in ectopic DCN-over-expressing or exogenous DCN-treated first trimester human trophoblast cell line HTR-8/SVneo. Use of miRNA-mimics and inhibitors revealed that miR-512-3p compromised trophoblast migration, invasion and VEGF-dependent endovascular differentiation. Surprisingly, PPP3R1, a known target of miR-512-3p, was paradoxically elevated in miR-512-3p overexpressing trophoblast and PE-associated placentas. Using Enrichr, a tool that consists of both a validated user-submitted gene list and a search engine for transcription factors, we found that PPP3R1 elevation resulted from the miRNA binding to and targeting a transcription factor USF2 which targeted PPP3R1. These findings suggest that induction of this miRNA may be one mechanism for the pathogenesis of PE by DCN overproduction by the decidua.
HTR-8/SVneo cells, originally produced by immortalization of EVTs derived from explant outgrowths (Graham et al., 1993), and commonly used as a model of invasive EVTs, were maintained in RPMI- 1640 supplemented with 10% fetal bovine serum (FBS), 100 units/ml penicillin, and 100 μM streptomycin. Cells were passaged via light trypsinization prior to reaching confluency and were maintained at 37°C in an atmosphere consisting of 5% CO2 for no more than twenty sequential passages.
Flash-frozen placenta samples collected from normotensive and preeclampsia were obtained from the Research Centre for Women’s and Children’s Health Biobank (RCWIH, Mount Sinai Hospital, Toronto, ON, Canada, http://biobank.lunenfeld.ca). All samples were collected from caesarean section deliveries with informed consent and were approved by the Mount Sinai Hospital and University of Western Ontario research ethics boards. Jeyarajah et al., 2019, have reported the details of these subjects including the gestational age of the pregnancies and parameters used to define preeclampsia.
Prior to transfection, HTR-8/SVneo cells were plated in a 12-well plate and grown to 70%–80% confluency. Following the removal of culture media, the cells were incubated with 800 μl/well Opti-MEM media (supplemented with 7.5% FBS) for 1 h. Cells were then treated with Lipofectamine 2000 RNAiMax (Invitrogen, 2.5 μl Lipofectamine/100 μl of Opti-MEM media) and oligonucleotides (2 μg/well) and incubated for 4 h. Subsequently, cells were washed two times with RPMI-1640 complete media and incubated overnight. Successful transfection was verified by determining miRNA levels in cells 24 h after exposure to lipofectamine. MiRNA overexpression and knockdown were achieved via transfection with miRIDIAN miR-512-3p mimic (Dharmacon, cat no. C-300769-03-0005) and miR-512-3p Inhibitor (Dharmacon, Cat No. IH-300769-05-0005). Controls for overexpression and knockdown experiments consisted of transfection with miRIDIAN™ Dharmacon mimic (Dharmacon, Cat No. CN-001000-01-05) and inhibitor (Dharmacon, Cat No. IN-001005-01-05), respectively. The sequences of these control oligonucleotides do not target any known mammalian transcript.
Transient transfection of PPP3R1 siRNA oligomers (Thermofisher, cat no. AM16708) and negative control (Thermofisher, cat no AM4641) was carried out using lipofectamine 2000 RNAiMax (Invitrogen, Thermo Fisher). All plasmid transfections (1 μg) were carried out similar to the protocol described above in miRNA transfection. Successful transfection was verified by determining PPP3R1 mRNA levels in cells 24 h after transfection using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Experiments with PPP3R1 knockdown cells were conducted within 3 days of transfection.
MiRNA was extracted from HTR-8/SVneo cells using the miRNeasy Kit (Qiagen) according to the manufacturer’s instructions (Qiagen, 2020). Briefly, HTR-8/SVneo cells were lysed using 700 µl of TRIzol (Thermofisher) and collected in 1.5 ml Eppendorf tubes. The resulting lysate was treated with 140 µl of chloroform, agitated for 15 s, and centrifuged at 12,000 rpm for 15 min (4°C). The upper aqueous phase was then transferred to a new collection tube and mixed in a 1:1 ratio with 70% ethanol solution. The resulting mixture was then put through the RNeasy minielute spin column (Qiagen, Cat No. 1026497) in increments of 700 µl and centrifuged at 10,000 rpm for 1 min (25°C). Flowthrough was collected, mixed with 0.65 volume of 100% ethanol, and passed through the miRNeasy column at 10,000 rpm for 1 min (25°C) (Qiagen, Cat No. 74104) to isolate miRNA. Once done, the resulting flowthrough was discarded and the isolated miRNA was washed in two steps: 500 μl of RPE buffer (Qiagen) was added, the column was centrifuged at 10,000 rpm for 1 min (25°C) and flow through was discarded; 500 μl of 80% ethanol was added, the column was centrifuged at 10,000 rpm for 1 min (25°C), and flowthrough was discarded. The column was then centrifuged at 10,000 rpm for 5 min (25°C) to dry. Lastly, the column was transferred to a new 1.5 ml Eppendorf collection tube, 12 μl of RNase-free water (Qiagen) was added, and the new tube was centrifuged at 10,000 rpm for 2 min at 25°C. Following purification, the sample concentration and 260/230-absorbance purity ratio were quantified using the Epoch/Take Multi-Volume Spectrophotometer System (BioTek Instruments, Inc.).
MiRNA was extracted from placental tissue samples using the miRNeasy Kit (Qiagen) according to manufacturer’s instructions (Qiagen). Briefly, a small amount of flash-frozen tissue was placed in a 15 ml collection tube with 700 µl of TRIzol (Invitrogen Life Technologies) and homogenized for 40 s using the Omni TH Tissue Homogenizer (Omni International, Inc.). The tube was then left to sit at room temperature for 5 min to promote nucleoprotein complex dissociation. After 5 min, 140 µl chloroform was added to the tube and the isolation proceeded with the same protocol as the HTR-8/SVneo cellular miRNA extraction mentioned above.
Purified miRNA was utilized for cDNA synthesis using the qScript miRNA cDNA Synthesis Kit (Quantabio) according to manufacturer’s instructions (Quantabio, 2020). Briefly, 2 µl of Poly(A) Tailing Buffer (5X), 1 µl of Poly(A) Polymerase, and 7 µl of 500 ng/μl purified miRNA solution were added to a PCR tube to give a final volume of 10 µl. The resulting mixture was then incubated in the C1000 thermal cycler (Bio-Rad) at 37°C for 60 min followed by incubation at 70°C for 5 min. Once finished incubating, 9 µl of miRNA cDNA Reaction Mix and 1 µl of qScript Reverse Transcriptase were added to the PCR tube to give a final volume of 20 µl. The resulting mixture was then placed back in the Thermal Cycler at 42°C for 20 min, followed by incubation at 85°C for 5 min. The resulting cDNA was stored at −20°C prior to qRT-PCR analyses.
qRT-PCR was performed using the qScript miRNA cDNA Synthesis Kit (Quanta bio, Cat No. 95107-025) according to manufacturer’s instructions. Briefly, 2 μl of 500 ng/μl cDNA sample (diluted 1:10) was added to PCR tubes containing 18 μl of master mix (10 µl PerfeCTa SYBR Green SuperMix, 0.4 µl Custom miRNA Assay Primer (2 µM), 0.4 µl PerfeCTa Universal PCR Primer (10 µM), 7.2 µl Nuclease-Free Water). Samples were then run through the Rotor-Gene 3000 (Corbett Research) thermal cycler using custom designed primers (Tables 1, 2). Cycling conditions involved initial holding step (95°C for 13 min), followed by 45 cycles of a two-step PCR (95°C for 15 s and 60°C for 60 s) and a dissociation phase. Fold-changes in miRNA and mRNA expression in treated samples compared to control samples were calculated using the 2−ΔΔCt method. RNU6 was used as a reference miRNA and the geometric mean of GAPDH and 18S rRNA (RNA18SN1) as reference RNA for HTR-8/SVneo and placental tissue samples.
To measure migration, we used wound-healing assay. Cells were treated with mitomycin C (500 ng/ml, Sigma, cat: M4287) for 1 h to block cell proliferation, scratched multiple times (eight linear scratches each, vertically and horizontally) in the absence or presence of exogenous DCN (250 nM, a concentration previously shown to have the highest anti-migratory effect, Sigma, cat: D8428) for 24 h. The wound area was recorded using light microscopy (Leica Microsystems) at 0 and 24 h. Migration was recorded as the percentage wound closure at 24 h. To calculate the area of the wound, images were imported into ImageJ (version 1.5.3), where cell frontiers bordering the wound were traced. The percentage of wound closure was determined using the following equation: [(A0−A24)/A0] × 100%, where A0 represents the initial area of the wound at 0 h and A24 represents the area of the wound after incubating for 24 h.
A transwell migration assay was performed using a 24 well plate and transwell inserts (Corning, CLS3464) containing microporous (8 μm pores) membranes. 40,000 cells were resuspended in serum free media, placed on top of each transwell, and allowed to migrate through the microporous (8 μm pore) membrane for 24 h towards the complete FBS-containing media. After 24 h, non-migratory cells on top of the membrane were removed using a cotton bud and membranes were stained with hematoxylin and eosin. Once dried, membranes were imaged at ×40 total magnification using the Leica Inverted Light Microscope and cells were counted using ImageJ software. For the invasion assay, transwells were coated with a thin layer of matrigel (BD Biosciences, 400 μg/ml diluted in serum free RPMI-1640 medium) for 4 h before performing the assay same as above for 48 h.
This assay allows one to quantify the invasion of HTR-8/SVneo cells into Matrigel at various time points (Siddiqui et al., 2016). Spheroids were formed using 24-well AggreWell 800 plates (Stemcell Technologies) containing 800 μm microwells according to manufacturer’s protocol (Stemcell Technologies, 2017). Briefly, AggreWell 800 plate wells were pre-treated with 500 μl of Anti- Adherence Rinsing Solution (Stemcell Technologies, Cat No. 07010) and then the plate was centrifuged at 1,300 g for 5 min. Following microscope verification that no bubbles remained, the Anti-Adherence Rinsing Solution was aspirated from the wells. Each well was then rinsed with 2 ml of basal RPMI-1640 media prior to addition of 1 ml of complete RPMI-1640 media. Next, 900,000 HTR-8/SVneo cells suspended in 1 ml complete media were plated per well and the AggreWell plate was centrifuged at 100 g for 3 min. This resulted in approximately 3,000 HTR-8/SVneo cellscaptured in each microwell. The plate was then incubated at 37°C and 5% CO2 for 24 h. After 24 h, spheroids were collected using a P1000 pipette and passed through a 37 μm reversible strainer into a 15 ml conical tube. Next, 1 ml of basal RPMI-1640 media was dispensed across the surface of the well to dislodge any remaining spheroids, then media was collected and passed again over the strainer. This washing step was repeated three times prior to inverting the strainer, placing it over a well in a 6-well plate and rinsing with 4 ml of complete RPMI-1640 media. The spheroids were then collected individually using a P200 pipette and two spheroids were plated per well in a 12-well plate pre-coated with 200 μl of 8 mg/ml growth factor-reduced matrigel. The plated spheroids were then incubated at 37 °C with 5% CO2 for 48 h. Images were taken every 24 h under ×100 magnification using the Leica Inverted Light Microscope. Spheroid area and percent invasion (measured as the area of sprouts invading Matrigel) relative to the spheroid area was quantified using ImageJ software.
Cellular proliferation was measured using EdU (5-ethynyl-2 deoxyuridine) incorporation. Briefly, glass coverslips, which had been sterilized via treatment with 70% ethanol solution for 15 min, were placed at the bottom of each well in a 12 well plate. Next, 50,000 cells suspended in 1 ml of RPMI- 1640 complete media were plated into each well and incubated overnight. The following day the medium was changed with fresh media containing 1 μl/ml EdU. The cells were then incubated at 37°C with 5% CO2 for 72 h and media was replaced with fresh EdU supplemented media every 24 h. After 3 days, cells were fixed using 4% paraformaldehyde solution and stained using Hoechst 33342 dye and the Click-iT EdU Cell Proliferation Kit for Alexa Fluor 488 dye (ThermoFisher Scientific, Cat No. C10337) according to manufacturer’s protocol. The cells were then imaged using fluorescence microscopy (Hoechst 33342 excitation/emission: 350/461 nm; Alexa Fluor 488 excitation/emission: 495/519 nm) and percent EdU-positive cells in each representative image were quantified using ImageJ software.
Tube formation capabilities of HTR-8/SVneo cells were measured using an endothelial-like tube formation assay. Accelerated tube formation, conducted in the presence of VEGF-A is a measure of endovascular differentiation (Lala et al., 2012). Briefly, 20,000 HTR-8/SVneo cells were diluted in RPMI-1640 complete media supplemented with 30 ng/ml VEGF121 (Sigma, cat no. H9041), plated on 100 μl of 8 mg/ml Growth Factor Reduced Matrigel (Corning, Cat No. 354230) in a 24 well plate, and incubated at 37°C with 5% CO2 overnight. Cells were then imaged at 24 h under ×100 total magnification using the Leica Inverted Light Microscope. Tube length and branch points were quantified using ImageJ software.
Statistical analyses were performed using GraphPad Prism Software version 8. Student’s t-test was used to measure differences between two means. All figures present mean data with error bars extending to ± the standard error of the mean (SEM). Significance is represented by: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, and ****p ≤ 0.0001.
Trophoblast cells do not produce DCN. To make trophoblast cell exposed to DCN continuously, we generated DCN overexpressing HTR-8/SVneo cell line (WT-HTR-DCN), which steadily produces substantial amounts of DCN. Since we did not have an a priori knowledge of the local concentration of DCN produced by the decidua to which trophoblast is exposed, we reasoned that a sustained exposure to DCN resulting from ectopic introduction of DCN gene into the trophoblast would provide us with a good means of identifying the DCN-induced miRNAs. Next, we used a concentration of exogenous DCN (250 nM), based on the DCN concentration found in the supernatant of DCN over-expressing trophoblast confirmed by qPCR and ELISA (160–275 nM/24 h) and our earlier findings that at 250 nM, maximal migration inhibition was reached, to treat wild-type trophoblast cells. The latter approach narrowed down the number of DCN-induced miRNAs as presented in Figure 1A. We conducted a differential gene/miRNA micro-array analysis using WT-HTR-DCN and the control (mock- transfected) cell line. Of the large number of DCN-dysregulated miRNAs, we selected some which showed 1.5-fold upregulation or downregulation and were reported to be dysregulated in PE (Table 3). To determine whether exogenous DCN altered expression of these miRNAs, HTR-8/SVneo cells were treated with 250 nM DCN for 24 h, and expression of select miRNAs was evaluated using qRT-PCR. This concentration was based on a pilot experiment conducted by Nandi et al., 2018 showing a maximal inhibitory effect of DCN was observed between 200 nM and 300 nM. We found that only miR-512-3p, out of 12 miRNAs tested, was significantly upregulated (p < 0.05, Figure 1A).
To validate literature reports of increased miR-512-3p expression in PE, miRNA was extracted from gestational age matched placental tissues derived from healthy normotensive (control) pregnancies and PE complicated pregnancies. Purified miRNA was then used to synthesize cDNA for qRT-PCR analysis of miRNA expression level. miR-512-3p expression levels in PE placentas was found to be significantly higher than in control placentas (p ≤ 0.05, n = 5 biological replicates, Figure 1B).
To investigate the role of miR-512-3p on various EVT functions, a miR-512-3p mimic was transfected into HTR-8/SVneo cells. Compared to mock-transfected cells, HTR-8/SVneo cells transfected with the miR-512-3p mimic had a 50-fold increased expression of miR-512-3p (Figure 2A, p ≤ 0.05, n = 4). Cells with increased miR-512-3p had reduced capacity to migrate through a transwell membrane compared to control cells (Figure 2B, p < 0.05, n = 4), showing that miR-512-3p has an inhibitory effect on trophoblast motility. To study the role of miR-512-3p on EVT invasion, a 3D spheroid invasion assay was done. Quantification of percent invasion (based on area of sprouts) relative to spheroid area revealed a significant decrease in the invasion capacity of HTR-8/SVneo cells over-expressing miR-512-3p at 24 h (Figure 2C, p ≤ 0.001, n = 4 replicates). Cells transfected with the miR-512-3p mimic also showed decreased numbers of proliferating EVT cells in comparison to the mock-transfected control cells, as indicated by reduced EdU fluorescence signals (Figure 2D, p ≤ 0.0001, n = 4 replicates). To investigate the role of miR-512-3p on EVT endovascular differentiation, an endothelial-like tube formation assay was performed. Extensive tube formation was observed under the control conditions; however, this was drastically disrupted in cells over-expressing miR-512-3p, including a significant decrease in both the total tube length and the number of branch points (Figure 2E, p ≤ 0.01, n = 3 replicates). Collectively, these results indicate that miR-512-3p compromised proliferative, migratory, invasive and tubulogenesis functions of EVT cells.
Knockdown of miR-512-3p was performed by transfecting HTR-8/SVneo cells with a specific miR- 512-3p antisense oligonucleotide (inhibitor). Using this strategy, we achieved approximately 50% knockdown of miR-512-3p (Figure 3A, p < 0.05, n = 4). Migration, invasion, and tube formation did not show any significant change between the control and knockdown cells (Figures 3B,C,E, n = 3, 4). For the tube formation assay, there was some disruption in tube formation with the inhibitor, but the quantification as presented made no significant difference. However, cells with reduced miR-512-3p expression showed significant increase in proliferation as determined by the number of cells that incorporated EdU (Figure 3D, p < 0.01, n = 4). Collectively, these findings of an absence of significant changes in most EVT functions by miRNA knockdown may have resulted from the possibility that miR-512-3p is a DCN-inducible miRNA, expressed at relatively low levels in native EVT cells.
PPP3R1 is a known target for miR-512-3p (Kurashina et al., 2014). The authors showed an downregulation in PPP3R1 in miR-512-3p overexpressing BeWo choriocarcinoma cells. Paradoxically, our qRT-PCR data consistently showed an increase in PPP3R1 mRNA in miR-512-3p overexpressing cells (Figure 4A, p < 0.05, n = 7). We proceeded with knocking down PPP3R1 in HTR-8/SVneo cells (60% knockdown) and performed migration and invasion assays. There was a 50% increase in percent wound closure after 24 h in PPP3R1-knockdown cells in comparison to control cells (p < 0.05, n = 3). Invasion assay was performed using matrigel coated transwells at 48 h. There was a significant increase in the number of cells invading through the membrane in PPP3R1-knockdown cells in comparison to control (Figure 4B, p < 0.05, n = 3). Therefore, PPP3R1 function in EVT cells was consistent with the upregulation of this gene by miR-512-3p overexpression. This finding called for investigating the possibility of an intermediary miR-512-3p binding molecule which targets PPP3R1.
To the best of our knowledge, there is no information on the levels of PPP3R1 in placentas from PE. Therefore, we decided to measure PPP3R1 transcript levels using gestation age-matched control and PE placental tissues and found a significant increase of PPP3R1 mRNA expression in PE-associated placentae (Figure 4C, p < 0.05, n = 5). Furthermore, since exposure of HTR-8/SVneo to DCN increased levels of miR-512-3p, and miR-512-3p overexpression increased PPP3R1 expression, we also checked the expression level of PPP3R1 after treatment with exogenous DCN for 24 h. We found a significant increase in PPP3R1 mRNA after DCN treatment (Figure 4D, p < 0.05, n = 4).
Elevated expression of PPP3R1 in miR-512-3p overexpressing cells indicated that this miRNA might be targeting negative regulators of PPP3R1. To identify these transcription factors, we used Enrichr, a tool that consists of both a validated user-submitted gene list and a search engine for transcription factors. By comparing miRNA target genes from TargetScan (https://www.targetscan.org/vert_80/), we identified PPP3R1 regulatory transcription factors. Of these transcription factors, we identified USF2 as the only negatively regulated transcription factor targeting PPP3R1. PPP3R1 was found to be a direct target of USF2. (https://maayanlab.cloud/Harmonizome/gene_set/USF2/ENCODE+Transcription+Factor+Targets) We validated this prediction by comparing USF2 mRNA expression in control and miRNA-512-3p over-expressing trophoblast cells. There was a robust downregulation of USF2 with a concomitant upregulation of PPP3R1 in miR-512-3p over-expressing trophoblast cells (Figure 5A). Similarly exogenous DCN treatment (250 ng/ml for 24 h) tended to downregulate USF2, although this did not reach statistical significance (Figure 5B, p = 0.065).
PE is a life-threatening maternal pregnancy complication arising from abnormal placentation (Roberts and Lain, 2002). Poor placentation and uterine invasion by the trophoblast in PE severely reduce oxygen supply to the placenta and developing fetus, resulting in placental hypoxia and subsequent secretion of toxic factors into maternal circulation (Roberts and Lain, 2002). Recently, the proteoglycan DCN was shown to be overproduced by the decidua associated with PE and elevated in the plasma of PE patients during the second trimester predating PE (Siddiqui et al., 2016). DCN has been shown to facilitate development of placental hypoxia by impairing trophoblast proliferation, migration, invasion, and endovascular differentiation (Xu et al., 2002; Iacob et al., 2008; Khan et al., 2011; Lala et al., 2012). While these observations have been attributed to DCN interaction with several tyrosine kinase receptors EGFR, IGFR2, VEGFR2 (Iacob et al., 2008; Khan et al., 2011), the role of miRNAs in DCN actions on EVT cells remained unknown. With the observation that several miRNAs are dysregulated in PE pregnancies, the contribution of miRNAs to PE development has been gathering attention in recent years (Gao et al., 2018; Xu et al., 2021). The current study aimed to identify DCN dysregulated miRNAs that could play a role in DCN mediated compromise in trophoblast functions associated with PE. We found multiple miRNAs that were altered in the miRNA microarray analysis with DCN-overexpressing trophoblast and selected those that were reported to be dysregulated in PE. Upon validation of selected miRNAs, we identified miR-512-3p that showed significant upregulation in DCN-treated native HTR-8/SVneo cells. We analyzed the expression of miR-512-3p miRNA in gestation age matched PE vs. control placental tissues (n = 5 for each). We found that this miRNA was significantly upregulated in placentas derived from PE pregnancies compared to normotensive controls. Although ours is a relatively small sample size, our findings are consistent with previous studies that reported upregulated expression of miR-512-3p in PE patients (Wang et al., 2012; Timofeeva et al., 2018). The current study is first report on the roles of miR-512-3p on trophoblast functions such as migration, invasion, proliferation and tube formation (endovascular differentiation). We found that all these functions were variably downregulated by miR-512-3p. To ascertain that the transwell migration or invasion assay was not influenced by cell proliferation, we used mitomycin C treated cells in our assay. Similarly, to ascertain that spheroid invasion assay was not influenced by proliferation, we quantified the average spheroid area between 24 and 48 h for both control and over-expression conditions and did not find any significant change in spheroid area. Since Zou et al. (2015) reported that DCN can induce apoptosis, we ascertained that in all our functional assays the cell viability was 98%–99% as noted from trypan blue exclusion. We acknowledge that reduced cell viability is an indirect measure of apoptosis. Endothelial-like tube formation gave the same results both measured as total tube length and branching points. The effects of miR-512-3p on EVT functions have not been previously reported, other members of the C19MC cluster, such as miR-515-5p, have shown to may play inhibitory roles in trophoblast differentiation processes (Zhang et al., 2016). Knocking down miR-512-3p did not show any meaningful change in migration, invasion or tube formation. This may reflect relatively low expression of this miRNA in trophoblast cells in the absence of DCN. However, we observed a significant increase in trophoblast proliferation on miR-512-3p downregulated cells, indicating its dominant anti-proliferative role relative to other effects on the trophoblast. We searched for potential targets of miR-512-3p using literature search and TargetScan. We narrowed down the targets by selecting those that are known to play a role in pregnancy or placenta development. We made a list of 18 targets and tried to validate them by qRT-PCR of control versus respective miRNA overexpressing cells. We validated a target (PPP3R1) for miR-512-3p. PPP3R1 is a gene coding for calcineurin-B, and a known target of miR-512-3p (Kurashina et al., 2014). Calcineurin-B pathway has been reported to cause renal podocyte injury in PE (Yu et al., 2018). Traditionally, miRNAs negatively regulate gene expression by repressing translation or directing sequence- specific degradation of target mRNAs (He and Hannon, 2004). But contrary to the traditional view, we consistently found an increase of PPP3R1 in cells overexpressing miR-512-3p. Indeed, there is an increasing number of recent reports suggesting that miRNAs can also induce or promote the expression of target genes (Place et al., 2008; Rusk, 2008). For example, Huang et al. (2012) reported miR-744 and miR-1186 induced transcriptional activation by targeting promoter of Cyclin B1 gene. Another study by Xiao et al. (2017) showed that in HEK293T cell line, miR-24-1 overexpression increased histone 3 lysine 27 acetylation by targeting enhancers. Since PPP3R1 was increased in HTR- 8/SVneo cells overexpressing miR-512-3p, the role of PPP3R1 on trophoblast functions was tested. We found a significant increase migration and invasion in PPP3R1-knockdown cells in comparison to control cells. This suggested that PPP3R1 is an anti-migratory and anti-invasive molecule which could contribute to the anti-migratory actions observed in HTR-8/SVneo cells overexpressing miR-512-3p. Calcineurin acts as a crucial connection between calcium signaling and the phosphorylation states of numerous important substrates. These substrates include, but are not limited to, transcription factors, receptors and channels, proteins associated with mitochondria, and proteins associated with microtubules (reviewed by Creamer, 2020). Using bioinformatics, we compared miR-512-3p target genes and PPP3R1 regulatory transcription factors to search for potential intermediaries. The analysis revealed that miR-512-3p is a putative negative regulator of USF2, a transcription factor which represses expression of PPP3R1. Decreased expression of USF2 and increased expression of PPP3R1 in miR-512-3p overexpressing cells indicate that PPP3R1 upregulation may result from an intermediary transcription factor USF2. It has been reported that DNA binding activity of USF2 mediates the inhibitory effects of hypoxia on CYP19 gene expression to restrain cyotrophoblast differentiation into syncytiotrophoblast (Jiang and Mendelson, 2003). Present results reveal additional roles of USF2 in regulating trophoblast functions. Whether PE- associated DCN over-expression is the cause of miR-512-3p upregulation in PE remains to be unequivocally established by an examination of the same subject population in a larger sample size. Taken together, our findings reveal a novel mechanism in miR-512-3p action (schema shown in Figure 6). While the binding partner for DCN-mediated miRNA induction in the trophoblast is currently unknown, it is also possible that DCN is a cargo carried by extracellular vesicles released from decidual cells (Ma et al., 2022) and endocytosed by EVT cells in vivo. | true | true | true |
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PMC9588990 | Weihua Ye,Yiyong Huang,Guanghui Zhu,An Yan,Yaoxi Liu,Han Xiao,Haibo Mei | miR-30a inhibits the osteogenic differentiation of the tibia-derived MSCs in congenital pseudarthrosis via targeting HOXD8 | 20-10-2022 | Congenital pseudarthrosis of the tibia,miR-30a,HOXD8,RUNX2,ADSCs, adipose-derived mesenchymal stem cells,ALP, alkaline phosphatase,CPT, congenital pseudarthrosis of the tibia,ChIP, chromatin immunoprecipitation,HOXD8, Homeobox D8,miRNAs, MicroRNAs,RT-qPCR, Quantitative reverse transcription PCR,MSCs, mesenchymal stem cells,OCN, osteocalcin,OPN, osteopontin,RUNX2, runt-related transcription factor 2,ARS, Alizarin Red S,α-MEM, α-minimum essential medium,DMEM, Dulbecco's modified Eagle's medium,FBS, fetal bovine serum,3′-UTR, 3′-untranslated region,wt, wild-type,mut, mutant,SD, standard deviation | Background Congenital pseudarthrosis of the tibia (CPT) is an uncommon congenital deformity and a special subtype of bone nonunion. The lower ability of osteogenic differentiation in CPT-derived mesenchymal stem cells (MSCs) could result in progression of CPT, and miR-30a could inhibit osteogenic differentiation. However, the role of miR-30a in CPT-derived MSCs remains unclear. Methods The osteogenic differentiation of CPT-derived MSCs treated with the miR-30a inhibitor was tested by Alizarin Red S staining and alkaline phosphatase (ALP) activity. The expression levels of protein and mRNA were assessed by Western blot or quantitative reverse transcription-polymerase chain reaction (RT-qPCR), respectively. The interplay between miR-30a and HOXD8 was investigated by a dual-luciferase reporter assay. Chromatin immunoprecipitation (ChIP) was conducted to assess the binding relationship between HOXD8 and RUNX2 promoter. Results CPT-derived MSCs showed a lower ability of osteogenic differentiation than normal MSCs. miR-30a increased in CPT-derived MSCs, and miR-30a downregulation promoted the osteogenic differentiation of CPT-derived MSCs. Meanwhile, HOXD8 is a direct target for miR-30a, and HOXD8 could transcriptionally activate RUNX2. In addition, miR-30a could inhibit the osteogenic differentiation of CPT-derived MSCs by negatively regulating HOXD8. Conclusion miR-30a inhibits the osteogenic differentiation of CPT-derived MSCs by targeting HOXD8. Thus, this study might supply a novel strategy against CPT. | miR-30a inhibits the osteogenic differentiation of the tibia-derived MSCs in congenital pseudarthrosis via targeting HOXD8
Congenital pseudarthrosis of the tibia (CPT) is an uncommon congenital deformity and a special subtype of bone nonunion. The lower ability of osteogenic differentiation in CPT-derived mesenchymal stem cells (MSCs) could result in progression of CPT, and miR-30a could inhibit osteogenic differentiation. However, the role of miR-30a in CPT-derived MSCs remains unclear.
The osteogenic differentiation of CPT-derived MSCs treated with the miR-30a inhibitor was tested by Alizarin Red S staining and alkaline phosphatase (ALP) activity. The expression levels of protein and mRNA were assessed by Western blot or quantitative reverse transcription-polymerase chain reaction (RT-qPCR), respectively. The interplay between miR-30a and HOXD8 was investigated by a dual-luciferase reporter assay. Chromatin immunoprecipitation (ChIP) was conducted to assess the binding relationship between HOXD8 and RUNX2 promoter.
CPT-derived MSCs showed a lower ability of osteogenic differentiation than normal MSCs. miR-30a increased in CPT-derived MSCs, and miR-30a downregulation promoted the osteogenic differentiation of CPT-derived MSCs. Meanwhile, HOXD8 is a direct target for miR-30a, and HOXD8 could transcriptionally activate RUNX2. In addition, miR-30a could inhibit the osteogenic differentiation of CPT-derived MSCs by negatively regulating HOXD8.
miR-30a inhibits the osteogenic differentiation of CPT-derived MSCs by targeting HOXD8. Thus, this study might supply a novel strategy against CPT.
Congenital pseudarthrosis of the tibia (CPT) is one of the most challenging problems in pediatric orthopedics [1,2]. The incidence rate of CPT is between 1:140,000 and 1:250,000 births. Clinically, CPT mostly manifests as progressive varus and antecurvation malformation of the tibia in infancy and childhood. CPT is associated with neurofibromatosis or fibrous dysplasia [3,4]. Accumulating evidence has revealed that a pathological alteration of the periosteum in pseudarthrosis may be crucially responsible for CPT [4,5]. Tibial intramedullary fixation is recommended to maintain the stability of pseudarthrosis [6]. However, current surgical approaches for CPT are not met clinically due to the challenges in realizing and keeping bone healing [7]. Therefore, it is urgent to explore new strategies for CPT therapy. Inhibition of osteogenic differentiation in CPT-derived mesenchymal stem cells (MSCs) could result in CPT progression [8]. MSCs therapy provides an approach to boost conventional surgical treatments [9,10]. In addition, MSCs derived from patients with CPT often exert a lower ability of osteogenic differentiation compared to MSCs from healthy people [11]. However, the related mechanism remains unclear. Thus, it is essential to investigate how CPT-derived MSCs display a lower ability of osteogenic differentiation. MicroRNAs (miRNAs), as endogenic noncoding small RNAs, are widely expressed and involved in regulating gene expression [12]. Accumulating evidence indicated that miRNAs play essential roles in osteogenic differentiation [13,14]. miR-30a is one of the miRNAs that has multiple functions in the cellular process. Liu et al. reported that the miR-30a inhibitor could induce osteogenic differentiation of bone marrow-derived MSCs by targeting Notch1 [15]. Guo et al. demonstrated that miR-30a participates in CircRNA-23525-mediated osteogenic differentiation of adipose-derived MSCs [16]. miR-30a is involved in osteogenic differentiation [15,17]. However, the function of miR-30a in CPT and its underlying mechanism remains mysterious. mRNAs are involved in CPT progression by modulating the osteogenic differentiation of MSCs [2]. HOXD8 is the pivotal gene in cancer-related pathways and could regulate the osteogenic differentiation of MSCs [18]. Nevertheless, the function of HOXD8 in CPT is largely unknown. This study illustrated the role of miR-30a in the osteogenic differentiation of MSCs during the progression of CPT. This research might supply a new strategy against CPT.
Patients with CPT and developmental dysplasia of the hip (DDH) were hospitalized at the Division of Orthopedics of Hunan Children's Hospital from June to December 2020. The groups were as follows: CPT group (3 CPT patients with periosteum lesion) and control group (normal iliac periosteum of DDH in 3 cases). This work was approved by the Medical Ethics Committee of Hunan Children's Hospital. Patients and their parents obtained and signed the informed consent. Periosteal tissues were harvested from 3 patients from the CPT or normal group during surgical procedures of osteosynthesis. MSCs were isolated by enzymatic digestion. The unwanted tissues were cut off, and residual blood clots were removed. Then tissue fragments were washed with Hanks' balanced salt solution containing calcium and magnesium. Furthermore, the tissue fragments were cut to ∼1 mm. And then the tissues were digested by 2 mg/mL collagenase type II (Gibco, USA) in α-minimum essential medium (α-MEM; Gibco) at 37 °C for 24 h. Digested tissues were centrifuged at 2000 rpm for 5 min, and cells were resuspended by α-MEM containing 10% fetal bovine serum (FBS). Then, cells were plated on a 100 mm dish and cultured. The medium was refreshed every 2–3 days. MSCs were identified by CD44, CD90, CD31, and CD34 detection, as described previously [19]. Briefly, third-generation periosteal MSCs were digested and resuspended with PBS. Subsequently, the cells were centrifuged at 1500 rpm for 5 min and resuspended with PBS. Cells (100 μL; 1 × 106/mL) were incubated with CD90 (328109), CD44 (397517), CD34 (343519), and CD31 (303115) antibodies (BioLegend, USA). After incubation for 20 min, cells were added with l.5 mL PBS and centrifuged at 2000 rpm for 5 min. After discarded the supernatant, the cells were added with 500 μL PBS for flow cytometry assay (CytoFLEX cytometer, Beckman, USA). 293T cells were derived by the American Type Culture Collection (ATCC, Manassas, VA, USA) and cultured in Dulbecco's modified Eagle's medium (DMEM) (Thermo Fisher, Shanghai, China) including 10% FBS.
si-NC, si-HOXD8, miR-NC, miR-30a mimic, and miR-30a inhibitor were obtained from GenePharma (Shanghai, China). MSCs were transfected with si-NC, si-HOXD8, miR-NC, miR-30a mimic, or miR-30a inhibitor by Lipofectamine 2000 (Invitrogen) for 48 h. For HOXD8 overexpression, HOXD8 overexpression lentiviruses (lenti-PLVX-HA-HOXD8) or the corresponding control (lenti-PLVX-HA) were provided by Hanbio Biotechnology Co., Ltd. (Shanghai, China). MSCs (5 × 106/well) were infected with the control (lenti-PLVX-HA) or HOXD8 overexpressed lentiviruses.
Cells were seeded in 96-well plates at a density of 1000 cells per well. CCK-8 reagents (10 μL; C0037; Beyotime Biotechnology, Shanghai, China) were added and further incubated for 2 h. The absorbance (450 nm) was tested by a microplate reader (Thermo Fisher, USA).
Osteogenesis induction was performed using an osteogenic medium (HUXMX-90021, Cyagen Biosciences, China). After 7 days, early osteogenesis was tested via an alkaline phosphatase (ALP) assay kit. After 21 days later, late osteogenic differentiation was detected using the Alizarin Red S (ARS) staining kit (C0148S; Beyotime Technology).
ALP activity was assessed by colorimetric assay and histochemical staining. The ALP kit assay was used to determine ALP activity (MAK447-1 KT; Millipore, Billerica, MA, USA). Cells were washed with PBS and lysed with a lysis buffer. Cell lysates were treated with p-nitrophenyl phosphate at 37 °C. Absorbance was tested by a microplate reader at 405 nm (Thermo Fisher). For ALP staining, the cells were immobilized in 4% paraformaldehyde at 25 °C for 30 min. The ALP staining kit (M039; Shanghai Gefan Biotechnology Co., Ltd., Shanghai, China) was used to perform ALP staining according to the manufacturer's instructions. Stained cells were examined under a microscope (BX51; Olympus, Tokyo, Japan).
To detect the matrix mineralization of MSCs, MSCs were fixed in 4% paraformaldehyde at 25 °C for 30 min. Subsequently, the cells were rinsed with PBS thrice and dyed with ARS (C0148S) at 37 °C for 0.5 h [20]. Finally, cells were observed using a microscope (BX51).
Total RNA was extracted by TRIzol reagent (TaKaRa, Tokyo, Japan). The PrimeScript RT Reagent Kit (TaKaRa) was used to synthesize first-strand cDNA. Finally, qPCR was measured by the ABI7500 PCR System (Thermo Fisher) with SYBR Green (TaKaRa). In this study, the primers were provided by GenePharma (China). qPCR was performed using the following primers: HOXD8: GACCGTTGTTAGCACGCCTT (forward) and CACGTATCGGTCCGTGTTGG (reverse), miR-30a: TTCCATACTGCAACGCCATACC (forward) and GCAATCCGCCCTTAGTCCAA (reverse), RUNX2: GAACTTTCTGCTGTCTTGGGTG (forward) and GGCAGTAGCTGCGCTGATAG (reverse), β-actin: CCTGCGAAACACCTTGATCG (forward) and TCGTCATGTTCCCCACTTCG (reverse), and U6: CGTCTTCCCAGGACCGTA (forward) and CGAATCCTGACATTAAGTCG (reverse). β-actin and U6 were adopted as a reference to quantify the mRNA and miR-30a levels, respectively. The 2−ΔΔCT method was applied for data quantification.
Total protein was homogenized in RIPA buffer (Cell Signaling Technology, Danvers, MA, USA). Next, protein was separated on an SDS-PAGE and transferred to polyvinylidene difluoride membranes. The membranes were incubated with 5% bovine serum albumin in Tris-buffered saline with Tween 20. The membranes were probed with primary antibodies, such as HOXD8 (ab228450; 1:1000; Abcam), RUNX2 (ab236639; 1:1000; Abcam), osteopontin (OPN; ab214050; 1:1000; Abcam), osteocalcin (OCN; ab93876; 1:1000; Abcam), and GAPDH (ab9485; 1:1000; Abcam), and IBSP (#5468; 1:1000; Cell Signaling Technology) at 4 °C overnight. After washing thrice, the membranes were incubated in corresponding horseradish peroxidase-conjugated goat anti-rabbit IgG polyclonal antibody (ab136636; 1:5000; Beyotime Biotechnology) at 25 °C for 1 h. Next, the blots were dyed by the ECL blot kit (Amersham, Cytiva, China) and visualized by the GEL imaging system (Bio-Rad, USA). Finally, protein bands were measured by ImageJ.
The wild-type (wt) and mutant (mut) constructs of the HOXD8 3′-untranslated region (3′-UTR) were linked with psiCHECK-2 vector (Promega, Madison, WI, USA). wt or mut HOXD8 3′-UTR and miR-NC or miR-30a mimics were transfected into 293T cells by Lipofectamine 3000. The luciferase activities were detected by a dual-luciferase reporter assay (Promega). The wt and mut constructs of the RUNX2 promoter were cloned into psiCHECK-2 vector (Promega). 293T cells were cot-ransfected with the wt or mut RUNX2 promoter vector and HOXD8 overexpression vector by Lipofectamine 3000. The relative luciferase activities were tested by a dual-luciferase reporter assay.
ChIP was performed according to the instructions provided by the ChIP Assay Kit (Millipore). Briefly, the cells were treated with a lysis buffer on ice for 10 min. Subsequently, the cell lysates were sonicated for seven 5 s pulses on ice using Sonicator 3000 (Misonix, Farmingdale, NY, USA) and acquired 200 to 1000 bp DNA fragments. The cell lysates were precleared with ChIP buffer, agarose beads and protease inhibitor cocktail on ice for 1 h. The cleared lysates were then incubated with antibodies against HOXD8 (ab228450; Abcam) or normal mouse IgG (ab37355; Abcam) at 4 °C for 12 h. Next, 60 μL of Protein A agarose/salmon sperm DNA slurry was added with rotation for 1 h at 4 °C. The beads were washed sequentially with low-salt wash buffer (150 mM), high-salt wash buffer (500 mM), LiCl wash buffer, and TE buffer. Subsequently, the mixture was washed with eluate buffer. The cross-linking was reversed using 5 M NaCl and incubated at 65 °C for 4 h. Finally, the samples were treated with RNase A and used for qPCR to detect the level of the RUNX2 promoter.
GraphPad Prism 7 was applied to analyze the data. All values were conducted at least three times and the data were expressed as means ± standard deviation (SD). Comparisons between two groups were analyzed using the unpaired Student's t-test. Comparisons of more than two groups were analyzed by one-way analysis of variance, followed by Tukey's post hoc test. p < 0.05 was considered statistically significant.
MSCs were derived from the periosteum of CPT patients, and flow cytometry was applied to identify MSCs. As revealed in Fig. 1A, flow cytometry results showed that CD44 (>99.61%) and CD90 (>99.57%) were significantly expressed in the isolated cells, whereas CD31 (<0.06%) and CD34 (<0.13%) were rarely detected, indicating that MSCs were successfully isolated. The ALP activity in CPT-derived MSCs was significantly lower than in normal iliac periosteum-derived MSCs (Fig. 1B and C). Consistently, CPT-derived MSCs showed a lower ability of osteogenic differentiation than normal iliac periosteum-derived MSCs (Fig. 1D). The cell viability of CPT-derived MSCs was slightly lower than normal iliac periosteum-derived MSCs, but there was no obvious difference (Supplementary Fig. S1). Furthermore, the protein expressions of osteogenic markers (OCN, IBSP, RUNX2, and OPN) [21,22] in CPT-derived MSCs on day 14 were significantly downregulated compared to those in normal iliac periosteum-derived MSCs (Fig. 1E). In sum, the ability of osteogenic differentiation in CPT-derived MSCs was much lower than in normal iliac periosteum-derived MSCs.
Accumulating evidence suggested that miR-30a plays crucial functions in the osteogenic differentiation of MSCs [23]. miR-30a expression was detected in CPT-derived MSCs or normal iliac periosteum-derived MSCs. In Fig. 2A, miR-30a was upregulated in CPT-derived MSCs. To evaluate the function of miR-30a in CPT, CPT-derived MSCs were transfected with the miR-30a inhibitor. miR-30a in MSCs was markedly decreased by the miR-30a inhibitor (Fig. 2B), and the miR-30a inhibitor remarkably upregulated the ALP activity of CPT-derived MSCs (Fig. 2C and D). Consistently, downregulation of miR-30a significantly promoted the osteogenic differentiation of CPT-derived MSCs (Fig. 2E). Furthermore, the levels of OCN, IBSP, RUNX2 and OPN in CPT-derived MSCs were remarkably increased in the presence of miR-30a inhibitor (Fig. 2F). In sum, downregulation of miR-30a remarkably promoted the osteogenic differentiation of CPT-derived MSCs.
Next, we aimed to explore the underlying mechanism of miR-30a in regulating the osteogenic differentiation of CPT-derived MSCs. HOXD8 was involved in modulating the osteogenic differentiation of MSCs [18]. In Fig. 3A and B, the expression level of HOXD8 in CPT-derived MSCs was notably lower than in normal MSCs. A sequence of the targeted sites in the miR-30a and HOXD8 3′-UTR regions was predicted by the TargetScan database (Fig. 3C). Luciferase assay confirmed that overexpression of miR-30a significantly inhibited luciferase activity in the wt HOXD8 3′-UTR group but had no effect on luciferase activity in the mut HOXD8 3′-UTR group (Fig. 3D). Furthermore, the downregulation of miR-30a upregulated the expression of HOXD8 in MSCs (Fig. 3E and F). Taken together, HOXD8 was identified to be the downstream mRNA of miR-30a.
A previous study indicated that HOXD8 could promote osteogenic differentiation via the activation of RUNX2 in adolescent idiopathic scoliosis [18]. Thus, the relation between HOXD8 and RUNX2 in CPT was further investigated. In Fig. 4A and B, HOXD8 had a potential binding sequence to the region of the RUNX2 promoter by using JASPAR prediction. Meanwhile, the luciferase activity in wt-RUNX2 was significantly promoted by pcDNA3.1-HOXD8 (Fig. 4C). HOXD8 was found to bind to the promoter of RUNX2 (Fig. 4D), and overexpression of HOXD8 notably upregulated the levels of RUNX2 and HOXD8 in MSCs (Fig. 4E and F). In sum, HOXD8 could upregulate RUNX2 by transcriptionally activating RUNX2.
To further confirm the mechanism of miR-30a in modulating the osteogenic differentiation of CPT-derived MSCs, CPT-derived MSCs were transfected with the miR-30a inhibitor or HOXD8 siRNA. In Fig. 5A and B, the level of miR-30a in CPT-derived MSCs was significantly reduced by the miR-30a inhibitor. In contrast, miR-30a downregulation upregulated the expression of HOXD8, which was abolished by HOXD8 knockdown. In addition, miR-30a inhibitor-induced upregulation of ALP activity and osteogenic differentiation were reversed by HOXD8 knockdown (Fig. 5C–E). Furthermore, miR-30a downregulation-induced upregulation of OPN, OCN, RUNX2, and IBSP in MSCs was rescued by HOXD8 knockdown (Fig. 5F). To sum up, miR-30a inhibited the osteogenic differentiation of CPT-derived MSCs by negatively regulating HOXD8 (Fig. 6).
CPT-derived MSCs play important roles in progression of CPT [8,24]. In this study, miR-30a downregulation promoted the osteogenic differentiation of CPT-derived MSCs, and miR-30a could directly target HOXD8. In addition, HOXD8 could transcriptionally activate RUNX2. Thus, this study investigated the role of miR-30a in CPT and found that miR-30a could act as a crucial mediator in CPT. miRNAs are involved in osteogenic differentiation [25,26]. miR-22 could induce the osteogenic differentiation of valvular interstitial cells by downregulation of CAB39 during aortic valve calcification [27]. miR-339-5p could inhibit osteogenic differentiation during the development of osteoporosis [28]. Zhang et al. have reported that miR-30a suppressed BMP9-induced osteogenic differentiation [23]. It was suggested that miR-30a-5p modulated the osteogenic differentiation potential in HUVECs by downregulation of SLUG, VIMENTIN and RUNX2 [17]. This study revealed that the miR-30a inhibitor could promote the osteogenic differentiation of CPT-derived MSCs. Thus, results further supplemented the function of miR-30a in CPT. HOXD8 is a crucial mediator in cellular processes, especially tumor progression [29,30]. Additionally, HOXD8 has been reported to act as a promoter in the osteogenic differentiation of MSCs [18]. This study indicated that HOXD8 was the downstream mRNA of miR-30a in CPT, and miR-30a could regulate the osteogenic differentiation of CPT-derived MSCs by negatively regulating HOXD8. Runx2 plays a vital role in osteogenic differentiation [31,32]. For instance, blocking the NLRP3 inflammasome could reduce osteogenic differentiation via inhibition of RUNX2 [33]. IRF4 could suppress osteogenic differentiation of BM-MSCs by inactivating RUNX2 [34]. CircRNA-23525 modulated RUNX2 expression by targeting miR-30a-3p, leading to positive regulation in the osteoblastic differentiation of adipose-derived mesenchymal stem cells (ADSCs) [16]. This study proved that HOXD8 could bind to RUNX2, and RUNX2 was transcriptionally activated by HOXD8. Data confirmed that upregulation of HOXD8 could induce the osteogenic differentiation of CPT-derived MSCs via RUNX2 activation. In summary, this study demonstrated that miR-30a inhibits the osteogenic differentiation of CPT-derived MSCs by targeting HOXD8, which might provide new ideas for discovering new methods for CPT treatment.
This work was approved by the Medical Ethics Committee of Hunan Children's Hospital.
This work was supported by Major science and technology project for collaborative prevention and control of birth defects in Hunan Province (2019SK1010). Key Research and Development Program of Hunan Province(2020SK2113) Clinical Research Center for Limb Deformity of Children in Hunan Province(2019SK4006). National Key Clinical Specialty Construction Project - Pediatric Surgery of Hunan Children's Hospital (XWYF [2022] No. 2).
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. | true | true | true |
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PMC9589166 | Shahrad Soghala,Kiana Harsiny,Parto Momeni,Mahsa Hatami,Vahid Kholghi Oskooei,Bashdar Mahmud Hussen,Mohammad Taheri,Soudeh Ghafouri-Fard | Down-regulation of LINC-ROR, HOXA-AS2 and MEG3 in gastric cancer | 18-10-2022 | Gastric cancer,LINC-ROR,HOXA-AS2,MEG3,HOTTIP,lncRNA | Long non-coding RNAs (lncRNAs) have been identified as modulators of gastric carcinogenesis. Evaluation of expression amounts of these transcripts is a primary but essential step for recognition of the role of lncRNAs in the carcinogenesis. Therefore, we compared expressions of LINC-ROR, HOXA-AS2, MEG3 and HOTTIP lncRNAs in gastric cancer samples and nearby non-cancerous samples. Expression levels of LINC-ROR, HOXA-AS2 and MEG3 lncRNAs have been lower in gastric cancer samples compared with nearby non-cancerous samples (Expression ratios = 0.26, 0.37 and 0.36; P values = 0.021, 0.015 and 0.032, respectively). However, expression levels of HOTTIP were not significantly different between gastric cancer tissues and nearby tissues (P value = 0.43). HOTTIP expression was associated with tumor size (P value = 0.04). In addition, MEG3 expression was associated with site of primary tumor (P = 0.0003). Expressions of LINC-ROR and HOXA-AS2 were not associated with any clinical or pathological parameter. ROC curve analysis revealed that HOXA-AS2 and LINC-ROR could significantly differentiate between gastric cancer samples and nearby non-cancerous tissues (AUC values = 0.68 and 0.64; P values = 0.01 and 0.04, respectively). Taken together, the current investigation provides clues for contribution of LINC-ROR, HOXA-AS2 and MEG3 lncRNAs in gastric carcinogenesis and warrants further mechanistical assays. | Down-regulation of LINC-ROR, HOXA-AS2 and MEG3 in gastric cancer
Long non-coding RNAs (lncRNAs) have been identified as modulators of gastric carcinogenesis. Evaluation of expression amounts of these transcripts is a primary but essential step for recognition of the role of lncRNAs in the carcinogenesis. Therefore, we compared expressions of LINC-ROR, HOXA-AS2, MEG3 and HOTTIP lncRNAs in gastric cancer samples and nearby non-cancerous samples. Expression levels of LINC-ROR, HOXA-AS2 and MEG3 lncRNAs have been lower in gastric cancer samples compared with nearby non-cancerous samples (Expression ratios = 0.26, 0.37 and 0.36; P values = 0.021, 0.015 and 0.032, respectively). However, expression levels of HOTTIP were not significantly different between gastric cancer tissues and nearby tissues (P value = 0.43). HOTTIP expression was associated with tumor size (P value = 0.04). In addition, MEG3 expression was associated with site of primary tumor (P = 0.0003). Expressions of LINC-ROR and HOXA-AS2 were not associated with any clinical or pathological parameter. ROC curve analysis revealed that HOXA-AS2 and LINC-ROR could significantly differentiate between gastric cancer samples and nearby non-cancerous tissues (AUC values = 0.68 and 0.64; P values = 0.01 and 0.04, respectively). Taken together, the current investigation provides clues for contribution of LINC-ROR, HOXA-AS2 and MEG3 lncRNAs in gastric carcinogenesis and warrants further mechanistical assays.
Gastric cancer is regarded as an important neoplasm throughout the world being responsible for 26,560 new cases in 2021 and approximately 11,180 demises in the United States [1]. The pathoetiology of this kind of cancer signifies a typical model of gene-environment interactions [2]. Chronic infection with Helicobacter pylori (H. pylori) is regarded as the main basis of noncardia tumors, with nearly all cases resulting from this kind of infection [3]. Consumption of alcohol, tobacco smoking, and salt-preserved food are other risk factors for gastric cancer [4]. Genetic factors participate in gastric tumorigenesis through changing expression patterns of genes and the resultant malignant transformation [5]. The most prevalent genetic aberrations in this type of cancer are activation of β-catenin and K-ras oncogenes, amplification of the c-erbB2 and c-met genes, mutations in p53 and E-cadherin as well as microsatellite instability [2]. Meanwhile, epigenetic changes such as hypermethylation of promoter CpG islands, particularly in hMLH1 and p16 genes have been reported in gastric cancer [2]. This type of cancer has also been associated with abnormal expression of several long non-coding RNAs (lncRNAs) [6]. LncRNAs are one of the principal regulatory mechanisms in the human genome. They have sizes more than 200 nt and share several features with mRNA coding genes, yet they normally do not have open reading frames [7]. These transcripts have been shown to influence genome stability, cell cycle progression, apoptotic pathways and angiogenic processes, thus affecting gastric carcinogenesis from different points [6]. Recent studies have identified several cancer-related lncRNAs in bio-fluids of cancer patients proving these transcripts as particularly valuable tools for cancer diagnostic methods [8]. Moreover, detection of lncRNAs has been used as a strategy for prediction of prognosis of patients with different types of cancers [8]. Most notably, lncRNAs have been found in cancer-derived exosomes. The amount of these transcripts in the circulatory exosomes reflects their expression in the original tissues and can be used as diagnostic and prognostic tools in gastric cancer [9]. These circulatory particles can also promote metastasis of gastric cancer [9]. Due to inter-population heterogeneity in gastric cancer risk factors and course, expression analysis of lncRNAs in each population is a prerequisite for design of diagnostic panels for each population. HOXA distal transcript antisense RNA (HOTTIP) is an lncRNA which controls the activity of several 5′ HOXA genes encoding critical regulators of development [10]. Expression of this lncRNA has been shown to be elevated in gastric cancer samples in a cohort of Chinese patients [11]. LincRNA-Regulator of Reprogramming (LINC-ROR) is another lncRNA whose abnormal expression has been associated with cell proliferation, invasiveness, and cancer progression [12]. Moreover, this lncRNA participates in DNA damage response [13]. HOXA cluster antisense RNA 2 (HOXA-AS2) is an oncogenic lncRNA that promotes malignant features of glioma through modulating RND3 [14]. Finally, maternally expressed 3 (MEG3) is an lncRNA known to affect several aspects of carcinogenesis ranging from apoptosis and proliferation to invasiveness and epithelial-mesenchymal transition [15]. In the current investigation, we compared expression levels LINC-ROR, HOXA-AS2, MEG3 and HOTTIP lncRNAs between gastric cancer samples and nearby non-cancerous samples.
The study included 30 patients. Thirty pairs of gastric cancer tissues and nearby non-cancerous tissues were purchased from tumor bank of National Cancer Institute, Imam Khomeini Hospital, Tehran, Iran. The study protocol was approved by the ethical committee of Shahid Beheshti University of Medical Sciences (IR.SBMU.RETECH.REC.1398.218).
Total RNA was isolated from gastric tissue specimens using TRIzol reagent (Invitrogen, Carlsbad, CA). The concentration and purity of the extracted RNA was assessed by photospectrometer. The absorbance of RNA samples was measured at 260 and 280 nm. After treatment with DNase I, RNA samples were subjected to cDNA synthesis using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA). B2M was selected as the reference gene. Each run consisted of a negative control sample (no template control). All experiments were run in duplicate with similar amounts of the template from each sample. LncRNA quantification was performed using SYBR-Green. The sequences of primers are shown in Table 1. Primers were similar to a previous study [16].
Relative expressions of LINC-ROR, HOXA-AS2, MEG3 and HOTTIP lncRNAs in gastric cancer samples versus nearby tissues were measured using the Relative Expression Software Tool-RG-version 3 (QIAGEN, Qiagen Germany Bloomberg, Korea). The mathematical model in this tool is based on the PCR efficiencies and the mean crossing point deviation between sample and control group. Then, the expression ratios are examined for significances by a randomisation test. The statistical significance was appraised using the Student paired t test. The association between clinical/pathological parameters and relative expressions of LINC-ROR, HOXA-AS2, MEG3 and HOTTIP was judged using the χ2 test. The correlation between relative expressions of LINC-ROR, HOXA-AS2, MEG3 and HOTTIP was measured using the regression model. Diagnostic power of lncRNAs in differentiating between cancerous and non-cancerous tissues was appraised by plotting the receiver operating characteristic (ROC) curves.
Mean age (± standard deviation) of patients recruited for this study was 42.53 (±10.1). Other clinical data of these patients are demonstrated in Table 2.
Expression levels of LINC-ROR, HOXA-AS2, MEG3 and HOTTIP lncRNAs in gastric cancer samples and nearby non-cancerous samples are depicted in Figure 1. Expression levels of LINC-ROR, HOXA-AS2 and MEG3 lncRNAs have been lower in gastric cancer samples compared with nearby non-cancerous samples (Expression ratios = 0.26, 0.37 and 0.36; P values = 0.021, 0.015 and 0.032, respectively). However, expression levels of HOTTIP were not significantly different between gastric cancer tissues and nearby tissues (P value = 0.43). Table 3 shows the statistical parameters of expression assays. Expression levels of LINC-ROR, HOXA-AS2, MEG3 and HOTTIP lncRNAs were correlated with each other in both gastric cancer samples and nearby non-cancerous samples (Table 4). The most robust correlations were detected between HOTTIP and MEG3 (r = 0.94) and between HOTTIP and HOXA-AS2 (r = 0.91) in gastric cancer tissues. HOTTIP expression was associated with tumor size (P value = 0.04). In addition, MEG3 expression was associated with site of primary tumor (P = 0.0003). Expressions of LINC-ROR and HOXA-AS2 were not associated with any clinical or pathological parameter (Table 5). ROC curve analysis revealed that HOXA-AS2 and LINC-ROR could significantly differentiate between gastric cancer samples and nearby non-cancerous tissues (AUC values = 0.68 and 0.64; P values = 0.01 and 0.04, respectively) (Figure 2). Combination of expression levels of HOXA-AS2 and LINC-ROR enhanced the diagnostic power (P value = 0.009). Table 6 shows the detailed statistical parameters of ROC curve analysis.
LncRNAs have appreciated roles in the carcinogenic processes [6]. These transcripts regulate cancer stem cells properties, cell cycle progression, epithelial-mesenchymal transition and cell apoptosis/proliferation [17]. Therefore, assessment of expression of these transcripts would provide important mechanistical clues in cancer research. In the current project, we compared expressions of LINC-ROR, HOXA-AS2, MEG3 and HOTTIP lncRNAs in gastric cancer samples and nearby non-cancerous samples. Expression levels of LINC-ROR, HOXA-AS2 and MEG3 lncRNAs have been lower in gastric cancer samples compared with nearby non-cancerous samples. Yu et al. have reported down-regulation of LINC-ROR in gastric cancer tissues compared with their nearby non-tumor tissues. Notably, expression of this lncRNA has been associated with tumor differentiation [18]. LINC-ROR expression has been previously assessed in a cohort of Iranian patients with diverse types of cancers revealing its up-regulation in esophageal, ovarian, and cervical cancers, while being down-regulated in breast, sarcoma, colon, and melanoma patients [19]. Although we detected down-regulation of this lncRNA in tumoral samples, we could not detect any association between its levels and histopathological parameters. A recent overview of LINC-ROR function in diverse cancers has indicated close relation between dysregulation of this lncRNA and advanced clinicopathological features showing a poor clinical outcome [20]. Thus, lack of association between expression of this lncRNA and clinical data in the current study might be explained by small sample size of the study. HOXA-AS2 has been previously reported to be an oncogenic lncRNA in glioma, as its silencing has inhibited cell proliferation and invasiveness, and induced cell apoptosis [14]. Moreover, this lncRNA has an oncogenic role in acute myeloid leukemia through binding with EZH2 and decreasing expression of LATS2 [21]. The current investigation proposes a different role for this lncRNA in gastric carcinogenesis and suggests that HOXA-AS2 might have tissue-specific functions. Such tissue-specific roles have been formerly proposed for LINC-ROR [19]. Our data regarding expression pattern of MEG3 in gastric cancer tissues is in line with the previously reported function for this lncRNA in this tissue [22], since MEG3 has been shown to inhibit gastric carcinogenesis through regulation of epithelial-mesenchymal transition [22]. Consistent with these studies, another study has indicated the role of MEG3 in inhibition of proliferation and metastasis of gastric cancer cells through modulating expression of miR-21 [23]. We also reported association between MEG3 expression and site of primary tumor. ROC curve analysis revealed that HOXA-AS2 and LINC-ROR could significantly differentiate between gastric cancer samples and nearby non-cancerous tissues. The obtained AUC value for LINC-ROR in the current study is comparable with Yu et al. study [18], yet the specificity of this marker in our study is far beyond their study [18]. However, the AUC value obtained for combination of two lncRNAs was not high enough. We also detected robust correlations between HOTTIP and MEG3 and between HOTTIP and HOXA-AS2 in gastric cancer tissues which might imply their coordinated function in the development of this kind of cancer. We did not detect any significant difference in expression of HOTTIP between gastric cancer samples and nearby non-cancerous samples. Yet, expression of this lncRNA was associated with tumor size. Over-expression of HOTTIP has been formerly shown to be linked with some determinants of gastric cancer invasiveness such as greater tumor size, deep tumor penetration, lymph node involvement, high TNM stage, and shorter overall survival [24]. Moreover, a recent review about the role of this lncRNA in gastrointestinal cancers has suggested superiority of HOTTIP expression levels over currently used diagnostic markers for these types of cancers [25]. However, data regarding the expression pattern of this lncRNA in gastric cancer tissues versus nearby tissues are not consistent [26]. The observed similar levels of HOTTIP between cancerous and non-cancerous tissues in this study and the former inconsistencies cast doubt on the appropriateness of this lncRNA as diagnostic marker for gastric cancer. Moreover, these data indicate the necessity of conduction of expression profiling experiments in different ethnic groups to find the best cancer biomarkers in each population. Taken together, the current investigation provide clues for contribution of LINC-ROR, HOXA-AS2 and MEG3 lncRNAs in gastric carcinogenesis and warrants further mechanistical assays. Our study has some limitations, namely small sample size and lack of validation of results in an independent cohort.
Shahrad Soghala and Mohammad Taheri: Conceived and designed the experiments. Kiana Harsiny, Parto Momeni and Mahsa Hatami: Performed the experiments. Vahid Kholghi Oskooei: Analyzed and interpreted the data. Bashdar Mahmud Hussen: Conceived and designed the experiments; Wrote the paper. Soudeh Ghafouri-Fard: Analyzed and interpreted the data; Wrote the paper.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data will be made available on request.
The authors declare no conflict of interest.
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PMC9589204 | Semiu Folaniyi Bello,Haiping Xu,Kan Li,Lijin Guo,Siyu Zhang,Ridwan Olawale Ahmed,Endashaw Jebessa Bekele,Ming Zheng,Mingjian Xian,Bahareldin Ali Abdalla,Adeniyi Charles Adeola,Adeyinka Abiola Adetula,Raman Akinyanju Lawal,Weijian Zhu,Dexiang Zhang,Xiquan Zhang,Congliang Ji,Qinghua Nie | Research Note: Association of single nucleotide polymorphism of AKT3 with egg production traits in White Muscovy ducks (Cairina moschata) | 28-09-2022 | AKT3,mRNA expression,egg production traits,variation | Prior studies on transcriptomes of hypothalamus and ovary revealed that AKT3 is one of the candidate genes that might affect egg production in White Muscovy ducks. The role of AKT3 in the uterus during reproductive processes cannot be overemphasized. However, functional role of this gene in the tissues and on egg production traits of Muscovy ducks remains unknown. To identify the relationship between AKT3 and egg production traits in ducks, relative expression profile was first examined prior to identifying the variants within AKT3 that may underscore egg production traits [age at first egg (AFE), number of eggs at 300 d (N300D), and number of eggs at 59 wk (N59W)] in 549 ducks. The mRNA expression of AKT3 gene in high producing (HP) ducks was significantly higher than low producing (LP) ducks in the ovary, oviduct, and hypothalamus (P < 0.05 or 0.001). Three variants in AKT3 (C-3631A, C-3766T, and C-3953T) and high linkage block between C-3766T and C-3953T which are significantly (P < 0.05) associated with N300D and N59W were discovered. This study elucidates novel knowledge on the molecular mechanism of AKT3 that might be regulating egg production traits in Muscovy ducks. | Research Note: Association of single nucleotide polymorphism of AKT3 with egg production traits in White Muscovy ducks (Cairina moschata)
Prior studies on transcriptomes of hypothalamus and ovary revealed that AKT3 is one of the candidate genes that might affect egg production in White Muscovy ducks. The role of AKT3 in the uterus during reproductive processes cannot be overemphasized. However, functional role of this gene in the tissues and on egg production traits of Muscovy ducks remains unknown. To identify the relationship between AKT3 and egg production traits in ducks, relative expression profile was first examined prior to identifying the variants within AKT3 that may underscore egg production traits [age at first egg (AFE), number of eggs at 300 d (N300D), and number of eggs at 59 wk (N59W)] in 549 ducks. The mRNA expression of AKT3 gene in high producing (HP) ducks was significantly higher than low producing (LP) ducks in the ovary, oviduct, and hypothalamus (P < 0.05 or 0.001). Three variants in AKT3 (C-3631A, C-3766T, and C-3953T) and high linkage block between C-3766T and C-3953T which are significantly (P < 0.05) associated with N300D and N59W were discovered. This study elucidates novel knowledge on the molecular mechanism of AKT3 that might be regulating egg production traits in Muscovy ducks.
Muscovy ducks are widely reared due to their unique adaptation to local environments, high fertility rate, and increasing meat productivity (Cui et al., 2019). However, improvement of egg production remains a major concern among duck breeders. Egg production is a major economic trait of poultry species which declines with ovarian aging as a result of a decrease in the levels of secreted reproductive hormones. Molecular techniques are one of the significant methods to improve egg production (Sato et al., 2016). Specifically, the identification of a candidate single nucleotide polymorphism (SNP) within a gene can be adopted to understand the relationship that exist between a specific gene and quantitative trait loci (QTL) (Bello et al., 2022). AKT3 was identified as one of the candidate genes responsible for egg production in White Muscovy ducks (Bello et al., 2021). Although, several candidate genes have been identified as essentials in egg production but the role of AKT3 in the uterus during reproductive processes can not be overemphasized. This forms the basis for further investigation of AKT3. AKT3 (AKT serine/threonine kinase 3) is one of the three associated serine/threonine-protein kinases (AKT1, AKT2, and AKT3) called AKT kinase. It plays an important role in many cytokines- and hormone-driven processes, AKT3 functions in the uterus (Fabi and Asselin, 2014). Studies revealed that SNPs within AKT3 are related with pig litter size (Getmantseva et al., 2020) and on myofiber characteristics (indispensable indicators of meat quality) in broiler chicken (Chen et al., 2013). Despite many efforts to understand AKT3 variants and their association with reproductive tissues, their expression level in these tissues and relationship with egg production traits in these ducks are not yet investigated. This finding elucidates the significance of AKT3 polymorphisms in molecular breeding for egg production in ducks.
One thousand five hundred thirty-seven Muscovy laying ducks raised in breeding farm of Guangdong Wenshi Southern Poultry Breeding Co., Ltd Renma Farm were used. These ducks have also been utilized in the previous study (Bello et al., 2021). Egg recording was done from 28 wk to 59 wk of age. Age at the first egg (days) (AFE), number of egg at 300 d (N300D) and number of egg at 59 wk (N59W)] were recorded for all experimental ducks. The consent of South China Agricultural University Institutional Animal Care and Use Committee (Guangzhou, People's Republic of China) was obtained prior to sample collection. All experimental animals were handled with maximum care during blood collection and euthanization for tissue collection.
Two milliliter of blood was collected from 1,467 individual ducks through their wing-web into EDTA (Ethylenediamine tetraacetic acid) tubes at 56 wk of age and stored at −20°C till further use.
Genomic DNA was extracted from 10 μL whole blood of individual ducks using E.Z.N.A. NRBC Blood DNA kit (OMEGA, Bio-tek, Norcross, GA) according to the manufacturer's instructions. The quality and integrity of genomic DNA samples were checked using Nanodrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA). All DNA samples were diluted to a working concentration of 50 ng/μL and stored at −20°C for further use.
Based on N59W, 4 lowest (LP) and 4 highest producing (HP) ducks within the same egg number from each group were euthanized for tissues collection. Eighteen tissue samples that include reproductive (hypothalamus, pituitary, ovary [excluding both the white and yellow follicles], and oviduct), and non-reproductive organs [brain (cerebrum, cerebellum), fat (abdominal and subcutaneous), (heart, kidney, gizzard, stomachus gladularis, lung, liver, spleen, small intestine, breast muscle, and leg muscle)] were collected. However, only 8 tissue samples relating to egg production were used for the expression profile analysis. All tissues were washed with RNA-free water, wrapped in nylon polybags, frozen in liquid nitrogen, and then stored at −80°C.
The primer used for qRT-PCR was designed according to NCBI database sequences of AKT3 with Accession number XM_027453844.2 via Primer premier version 5.0 software (Applied Biosystems, Norcross, GA) (Table 1). RNA extraction, cDNA synthesis, and qRT-PCR conditions were similar to those used in our previous study (Bello et al., 2021). The 2−ΔΔCT method was used to calculate target gene expression.
AKT3 is located on chromosome 3 (28648609-28801623bp) (Accession number of 101804556) of Anas platyrhynchos (mallard) genome and possess 15 exons. Three pairs of primers were designed to amplify different regions of AKT3 (Table 1). DNA mixed pool was constructed with 10 μL DNA sample of 20 randomly selected individuals with equal concentration. The PCR products were performed in 35 μL volume consisting of 31.5 μL Golden Star T6 Super PCR Mix (Tsingke Biological Technology, China), 1 μL (10 μmol/L) each of forward and reverse primers and 1.5 μL of DNA mixed pool using T100 Thermal Cycler (BioRad, Singapore). The PCR reactions were performed on two steps conditions. First, an initial denaturation at 98°C for 3 min was done, followed by 15 cycles of denaturation at 98°C for 10 sec, annealing at 60°C for 10 sec and extension at 10°C. Second, 30 cycles of denaturation at 98°C for 10 sec, annealing at 50°C for 10 sec, extension at 72°C for 15 sec, and final extension at 72°C for 3 min were done. The quality of PCR products was checked on 1% agarose gel electrophoresis before sequencing. All PCR products were sequenced directly using ABI-3730XL DNA analyzer (USA) by Sangon Biotech Company (Guangzhou, China). Trimming, alignment of sequences and SNP discovery were conducted using SnapGene 4.3.6 software.
The region containing SNPs was amplified using the same thermocycler with necessary reagents through one pair of primer (Table 1). Considering dam effect of duck population based on available breeding records, only 549 individuals were selected for amplification of SNP sites. The PCR protocols were similar to those used in SNP discovery. All primers were synthesized by Tian Yi Hui Yuan Gene Technology Company (Guangzhou, China).
Linkage disequilibrium was measured using Haploview software version 4.2 (BROAD, Cambridge UK). The significant differences between average expression of LP and HP tissues were examined with a t-test using SPSS 19.0 statistical software (IBM, Chicago, IL). Association analyses of SNPs with egg production traits of 549 Muscovy laying ducks were analyzed using the GLM procedure in SPSS 21.0. For each egg production trait, the least-squares mean and standard error of means (SEM) were calculated. Differences between the genotypes were analyzed. The difference with P-value ≤0.05 was considered significant.
In the 3 tissues (ovary, oviduct, and hypothalamus), mRNA expression of AKT3 gene in HP ducks is higher than LP, with AKT3 expression being the highest in oviduct (Figure 1a). There was significant difference (P < 0.05) in expression level of AKT3 in ovary of HP and LP ducks while its level is lowest in their Hypothalami (Figure 1a). Interestingly, cerebrum, cerebellum, and pituitary tissues had similar expression trends of AKT3 gene in HP ducks, while cerebrum and pituitary of LP ducks showed low expression levels of AKT3 (Figure 1b). There was significant higher expression of AKT3 in HP's subcutaneous and abdominal fats compared to LP counterpart (P < 0.001). Although, abdominal fat of LP had a lower expression of AKT3 when compared to its subcutaneous fat (Figure 1c). This result reveals that there is variation in AKT3 expression in the 8 selected tissues of HP and LP. The expression of AKT3 in human fetal brain was higher than other tissues sampled, emphasizing its important role in brain development ( Wu et al., 2009). The high expression of AKT3 in ovary, oviduct, and hypothalamus of HP corroborates a report by Bionaz and Loor (2011). In cerebrum, cerebellum and pituitary, mRNA expression of AKT3 was higher in HP than LP duck which ratifies a finding that expression of AKT3 is the most expressed isoform in the brain (Yang et al., 2006).
We identified three significant SNP sites when aligned with reference genome of Anas platyrhynchos (mallard) (Figure 2a). The three SNPs (C-3631A, C-3766T, and C-3953T) identified in intron 15 of AKT3 are on an average of 150bp apart. Moreover, no synonymous amino acid substitution was observed at these SNPs sites. Haplotype analysis showed a high linkage block between C-3766T and C-3953T of AKT3 suggesting that SNPs might have been inherited together (linkage disequilibrium) (Figure 2b). There is a significant difference (P < 0.05) in C-3766T genotypes with TT recording values of 102.10 ± 1.94 and 189.28 ± 1.06 in N300D and N59W, respectively than CC and TT genotypes (Table 2). The TT genotype individuals of C-3953T laid five to six eggs more than individuals with genotypes CC and CT at 300 days of laying. Furthermore, the number of eggs laid in individuals with TT genotype at N59W was more than their CT and CC genotypes with 13 and 14 eggs, respectively. The wide difference between N300D and N59W by TT genotype individuals might be due to smaller number of individual ducks with TT genotype at these SNP sites. It was observed that high linkage sites of C-3766T and C-3953T are significantly (P < 0.05) associated with N300D and N59W. This justifies the findings on polymorphic sites at A-1864G and C-1704G of IGF2 in ducks having high linkage disequilibrium (Ye et al., 2017). Although, TT genotype individuals in the 2 SNPs sites (C-3766T and C-3953T) had the highest N300D and N59W. In the C-3631A polymorphic site, there is no significant difference (P > 0.05) in three egg production traits considered across three genotypes (CC, CA, and AA). This finding is similar with previous studies that had no significant difference (P > 0.05) on association analysis of SNPs at C-1704G and A-1864G of IGF2 with FEA and E300D (Ye et al., 2017), A-227G and C-320T of FSHR associated with E33W and E59W, and AFE and E33W, respectively (Ye et al., 2017), and g.3270 A > G of GH with AFE (Wu et al., 2014) in ducks. The identified molecular markers which were significantly related to egg production parameters could be used by Muscovy duck breeders to improve egg production. However, due to limitation of data from antibodies for protein expression of AKT3 gene, further conclusions could not be made from the quantitative real-time PCR result. Therefore, future studies should incorporate antibodies for protein expression of AKT3 gene in Muscovy ducks. | true | true | true |
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PMC9589305 | 35765958 | Xuebing Li,Baohua Lu,Lina Zhang,Jing Yang,Yurong Cheng,Dong Yan | Mechanism of OTUD5 in non-small cell lung cancer cell proliferation, invasion, and migration | 27-06-2022 | Invasion,miR-652-3p,non-small cell lung cancer,ovarian tumor protease deubiquitinase 5,phosphatase and tensin homolog,proliferation,migration,ubiquitination | Ovarian tumor protease deubiquitinase 5 (OTUD5) has been discussed as a regulator of cancer development. Herein, the present study set out to explore the molecular mechanism of OTUD5 in non-small cell lung cancer (NSCLC) cell proliferation, invasion, and migration. First, the expression patterns of OTUD5, phosphatase, and tensin homolog (PTEN), as well as microRNA (miR)-652-3p in cells were detected by qRT-PCR and Western blot. Cell viability, migration, and invasion were assessed with the help of cell-counting kit-8 and Transwell assays, in addition to the measurement of the ubiquitination and protein levels of PTEN. The binding relations between OTUD5 and PTEN, and miR-652-3p and OTUD5 were testified by coimmunoprecipitation or dual-luciferase assays. Cells were further treated with GSK2643943A (inhibitor of deubiquitinase) or miR-652-3p-inhibitor to explore the role of PTEN ubiquitination and miR-652-3p in NSCLC cells. OTUD5 and PTEN were both poorly-expressed, and miR-652-3p was highly-expressed in NSCLC cells. On the other hand, over-expression of OTUD5 suppressed NSCLC cell proliferation, invasion, and migration. OTUD5 deubiquitinated and stabilized PTEN, and miR-652-3p targeted and inhibited OTUD5 expression. Augmenting the ubiquitination levels of PTEN promoted NSCLC cell growth, whereas miR-652-3p inhibition promoted the tumor-suppressing effects of the OTUD5/PTEN axis in NSCLC. Altogether, our findings highlighted that miR-652-3p restrained the role of OTUD5 in deubiquitinating PTEN to improve PTEN protein level, thereby promoting NSCLC cell proliferation, invasion, and migration. | Mechanism of OTUD5 in non-small cell lung cancer cell proliferation, invasion, and migration
Ovarian tumor protease deubiquitinase 5 (OTUD5) has been discussed as a regulator of cancer development. Herein, the present study set out to explore the molecular mechanism of OTUD5 in non-small cell lung cancer (NSCLC) cell proliferation, invasion, and migration. First, the expression patterns of OTUD5, phosphatase, and tensin homolog (PTEN), as well as microRNA (miR)-652-3p in cells were detected by qRT-PCR and Western blot. Cell viability, migration, and invasion were assessed with the help of cell-counting kit-8 and Transwell assays, in addition to the measurement of the ubiquitination and protein levels of PTEN. The binding relations between OTUD5 and PTEN, and miR-652-3p and OTUD5 were testified by coimmunoprecipitation or dual-luciferase assays. Cells were further treated with GSK2643943A (inhibitor of deubiquitinase) or miR-652-3p-inhibitor to explore the role of PTEN ubiquitination and miR-652-3p in NSCLC cells. OTUD5 and PTEN were both poorly-expressed, and miR-652-3p was highly-expressed in NSCLC cells. On the other hand, over-expression of OTUD5 suppressed NSCLC cell proliferation, invasion, and migration. OTUD5 deubiquitinated and stabilized PTEN, and miR-652-3p targeted and inhibited OTUD5 expression. Augmenting the ubiquitination levels of PTEN promoted NSCLC cell growth, whereas miR-652-3p inhibition promoted the tumor-suppressing effects of the OTUD5/PTEN axis in NSCLC. Altogether, our findings highlighted that miR-652-3p restrained the role of OTUD5 in deubiquitinating PTEN to improve PTEN protein level, thereby promoting NSCLC cell proliferation, invasion, and migration.
Non-small cell lung cancer (NSCLC), the most prevalent form of lung cancer (LC), is primarily classified into two subtypes, namely, lung adenocarcinoma and squamous cell carcinoma [1]. NSCLC treatment is improved with the clinical implementation of targeted therapies, including the adoption of small-molecule tyrosine kinase inhibitors and antibodies targeting T-cell receptor programmed cell death-1 and its ligand [2,3]. Despite the advent of such therapeutic modalities, the prognoses of NSCLC patients remain dismal, which can be attributed to late diagnosis and high metastatic potential of these tumors [4]. Therefore, it is prudent to advance the search for effective molecules that could target NSCLC cell growth and metastasis to reduce the plight of NSCLC. The process of ubiquitination, a ubiquitin-mediated protein post-translational modification, carries out the critical function of regulating protein degradation, localization, or function, whereas deubiquitinases (DUBs) reverse ubiquitination by removing ubiquitin from ubiquitinated proteins [5,6]. Together, ubiquitination and deubiquitination, induced by ubiquitin ligases and DUBs, are involved in the stabilization of cancer-related proteins and altering their oncogenic or anti-oncogenic functions [7-10]. Meanwhile, the superfamily of ovarian tumor protease deubiquitinases (OTUD) plays a role in a myriad of biological processes, including virus infection, bone remodeling, tumorigenesis, stem cell differentiation, and DNA repairing [11]. What’s noteworthy, OTUD5, a member of the OTUD superfamily, was previously shown to serve as a tumor suppressor in NSCLC by deubiquitinating anti-oncogenes, including Tripartite motif 5, p53, and programmed cell death 5 [12,13]. Another key focus of the present study, phosphatase and tensin homolog (PTEN), has emerged as a well-established tumor suppressor gene, such that its inactivation or mutation is implicated in a plethora of malignancies, such as breast, thyroid, colon, and endometrial cancers [14,15]. Moreover, PTEN is known to be capable of negatively regulating the PI3K/mTOR/Akt oncogenic pathway, thus conferring a profound role in targeted therapy of LC [16]. Interestingly, ubiquitination/deubiquitination serves as one of the major regulatory mechanisms of PTEN in cancer by influencing its stability, subcellular localization, and activity [17]. Furthermore, the study performed by Qin et al. revealed that ubiquitination-mediated degradation of PTEN hijacks cell growth rheostat control for neoplastic remodeling [18]. However, it has not been reported whether OTUD5 can stabilize PTEN by deubiquitination, and thereby influence the fate of NSCLC cells. MicroRNAs (miRNAs), a class of small transcripts with about 22 nucleotides, are well-known to be dysfunctional in tumorigenesis, in addition to their roles in cancer diagnosis and tailored therapy [19]. What’s more, various miRNAs have been identified as promising targets for lung cancer treatment [20]. In addition, some miRNAs possess the ability to interact with ubiquitin ligases or DUBs to form feedback loops that regulate cancer development [21-24]. Therefore, exploring the interaction between miRNA and ubiquitin-proteasome system may provide a novel strategy for efficacious treatment of NSCLC. Of note, miR-652-3p, a well-documented onco-miRNA, was previously documented to be up-regulated in NSCLC and facilitate NSCLC proliferation and metastasis [25]. Herein, initial database prediction and a dual-luciferase assay revealed that miR-652-3p functions as an upstream target of OTUD5. Nevertheless, whether miR-652-3p regulates that the OTUD5/PTEN axis has not been discussed before and warrants further exploration. In light of the abovementioned evidence, we put forth a hypothesis that OTUD5 deubiquitinates and stabilizes PTEN to suppress NSCLC cell proliferation, invasion, and migration, whereas this mechanism could be counteracted by miR-652-3p overexpression. Consequently, the present study was carried out to unveil the role of miR-652-3p/OTUD5/PTEN and their interactions in NSCLC and provide a novel theoretical basis for NSCLC treatment.
Human bronchial epithelioid cell lines 16HBE (procured from Millipore, Bedford, MA, USA) and NSCLC cell lines A549 and NCI-H292 (procured from ATCC, Manassas, VA, USA) and PC9 cells (procured from Tongpai Biotechnology Co., Ltd, Shanghai, China) were cultured in Roswell Park Memorial Institute 1640 medium (RPMI) containing 10% fetal bovine serum (FBS), 100 μg/ml streptomycin, and 100 U/ml penicillin (Invitrogen, Carlsbad, CA, USA) in a 5% CO2 incubator at 37°C.
First, pcDNA3.1-OTUD5, miR-652-3p-inhibitor, and their control plasmids were all supplied by GenePharma (Shanghai, China). In accordance with the provided instructions, the Lipofectamine 3000 reagent (Invitrogen, Carlsbad, CA, USA) was adopted to transfect the above plasmids into A549 or NCI-H460 cells. Meanwhile, 160 nM GSK2643943A (MedChemExpress Co., Ltd., Monmouth Junction, NJ, USA) or 100 nM MG132 (MCE) was added to the cell culture medium, with equal amounts of dimethylsulfoxide serving as the control.
CCK-8 assay was carried out to assess cell viability in accordance with the manufacturer’s instructions. Briefly, transfected cells (N = 2.0 × 103) were seeded into 96-well plates. At 24 h, 48 h, and 72 h post cell seeding, the cells were treated with 10 μL CCK-8 solution. Afterward, the cells were cultured at 37°C for 2.5 h, followed by absorbance measurement at a wavelength of 450 nm using a microplate reader.
Transwell assays were carried out in accordance with a previously published method [26]. In brief, about 1 × 104 transfected cells were suspended in 200 mL serum-free culture medium and placed in the apical chamber. The filter was covered with Matrigel (BD Biosciences, San Jose, CA, USA) in the invasion assay, not in the migration assay. The basolateral chamber was supplemented with RPMI 1640 medium containing 10% FBS as the chemical attractant. Cells were subsequently subjected to 48-h culture for the invasion assay and 24-h culture for the migration assay. Next, cells in the apical chamber were removed using cotton swabs, and cells on the lower surface of the filter were fixed with 0.1% crystal violet. Afterward, the number of cells on the optical filter in three random areas was counted with the help of an optical microscope (Olympus, Tokyo, Japan).
In accordance with the manufacturer’s instructions, total RNA content was extracted from cells using the TRIzol reagent. Complementary DNA was synthesized by PrimeScript reverse transcription kits (Invitrogen), and qRT-PCR was conducted with SYBR Premix Ex Taq II (Takara, Tokyo, Japan) and a 7500 real-time RT PCR system (Applied Biosystems, Inc., Carlsbad, CA, USA). Primer sequences are illustrated in Table 1. GAPDH and U6 were adopted as standardized controls of OTUD5 mRNA and miR-652-3p, respectively [27]. The relative gene expression was calculated by means of the 2-ΔΔCt method.
Total protein content was extracted from cells using a radioimmunoprecipitation assay lysis buffer (Invitrogen) and quantified with the bicinchoninic acid method. Subsequently, 50 μg proteins were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and then transferred to nitrocellulose membranes. The membranes were subsequently blockaded with 5% non-fat milk and incubated with antibodies anti-OTUD5 (dilution ratio of 1:2000, ab176727, Abcam, Cambridge, MA, USA), anti-PTEN (dilution ratio of 1:1000, ab267787, Abcam), and anti-GAPDH (dilution ratio of 1:1000, ab9485, Abcam) at 4°C overnight. Following rinsing with Tris Buffered Saline Tween (TBST) thrice (15 min each time), the membranes were cultured with horseradish peroxidase (HRP)-coupled secondary antibody (dilution ratio of 1:2000, ab6721, Abcam) at room temperature for 1 h. Afterward, the membranes were washed with TBST thrice (15 min each time), reacted with an enhanced electrochemical luminescence reagent, and imaged. The gray value was analyzed using the Image J software, with GAPDH serving as the internal reference.
Cells were re-suspended in the lysis buffer (50 nM Tris-HCl pH = 7.6–8.0, 0.5% NP40, 1 nM ethylene diamine tetraacetic acid, and 1 mM b-mercaptoethanol) containing a protease inhibitor, and then subjected to ultrasonic lysing. Following 10-min centrifugation at 14000 g, the supernatant was incubated overnight with antibody anti-PTEN (ab267787, Abcam) or anti-IgG (ab172730, Abcam) and protein A/G agarose beads at 4°C overnight. After washing with the lysis buffer thrice, the beads were boiled in 40 μL 2 × SDS-PAGE sample buffer and then subjected to SDS-PAGE, followed by Western blot assay with the anti-OTUD5 (ab176727, Abcam) and HRP-coupled secondary antibody (ab6721, Abcam). Afterward, the protein bands were visualized using the chemiluminescence method.
Ubiquitination assay was carried out following a previously published method [28]. Briefly, the cells were resuspended in the lysis buffer containing protease inhibitor and subjected to ultrasonic lysing. Following 10-min centrifugation at 15000 g, the supernatant was incubated with anti-PTEN (ab267787, Abcam) or anti-IgG (ab172730, Abcam) antibody-coupled agarose beads at 4°C and rotated overnight. After extensive washing, the beads were boiled in 2 × loading buffer for 5 min and isolated using SDS-PAGE, followed by Western blot assay with the anti-Ub antibody (ab6721, Abcam).
The expression pattern of OTUD5 in NSCLC was predicted with the help of the GEPIA database (http://gepia.cancer-pku.cn/) [29]. The downstream miRNAs of OTUD5 were predicted with the help of the StarBase database (https://starbase.sysu.edu.cn/) [30]. In addition, the binding sites of miR-652-3p and OTUD5 were also predicted using the StarBase database.
Wild or mutant types of OTUD5 3’ UTR sequences containing complementary sites of miR-652-3p were inserted into pmiGLO vectors (Promega, Madison, WI, USA) to construct the wild-type luciferase reporter plasmid (OTUD5 3’UTR-WT) and the mutant derivative (OTUD5 3’UTR-MUT). In accordance with the provided instructions, the aforementioned luciferase reporter plasmids were cotransfected into A549 or HCI-H460 cells with miR-652-3p-mimic or mimic-NC using the Lipofectamine 3000 reagent (Invitrogen). Afterward, the relative luciferase activity was analyzed with the help of the dual-luciferase assay system (Promega).
Data analyses and graphing were performed using the GraphPad Prism 8.0 software (GraphPad Software Inc., San Diego, CA, USA). Measurement data were represented as mean ± standard deviation (SD). Analysis of pairwise comparisons were processed using the t test and those of multi-group comparisons were processed by means of one-way or two-way analysis of variance (ANOVA), followed by Tukey’s post-hoc test. A value of p < 0.05 was indicative of statistical significance.
To analyze the role of OTUD5 in NSCLC, we first determined the expression patterns of OTUD5 in NSCLC. The GEPIA database predicted that OTUD5 was poorly expressed in NSCLC (Figure 1A). Subsequently, we detected OTUD5 expression levels in human bronchial epithelioid cells 16HBE and NSCLCs (A549, NCI-H292, NCI-H460, and PC9) using qRT-PCR and Western blot analysis, the results of which revealed that OTUD5 expression levels in NSCLC cells were significantly lower compared to those in 16HBE cells (p < 0.05, Figure 1B and C). Together, these findings highlighted that OTUD5 was poorly-expressed in NSCLC.
To further explore the influence of OTUD5 on NSCLC cell proliferation, migration, and invasion, we selected A549 cells presenting with the lowest OTUD5 expression, and HCI-H460 cells presenting with the highest OTUD5 expression for further analyses. A549 and HCI-H460 cells were transfected with pcDNA3.1-OTUD5 to over-express OTUD5 (p < 0.05, Figure 2A and B). Subsequent results elicited that cell viability of A549 and HCI-H460 cells was significantly decreased following OTUD5 overexpression (p < 0.05, Figure 2C), in addition to a decline in the number of migrated and invaded cells (p < 0.05, Figure 2D and E). Overall, these findings suggested that overexpression of OTUD5 suppressed NSCLC cell proliferation, invasion, and migration.
Thereafter, we explored the downstream target genes of OTUD5 and their respective influence in regard to NSCLC. OTUD5, as a deubiquitinating enzyme, is capable of stabilizing the protein level by means of deubiquitination [31]. Existing evidence further suggests that PTEN can be deubiquitinated to stabilize its expression [32], while PTEN is also known to be poorly-expressed in NSCLC [33]. Accordingly, we detected the protein levels of PTEN in NSCLC cells with a Western blot assay, which revealed that PTEN protein levels were significantly down-regulated in NSCLC cells (p < 0.05, Figure 3A). Therefore, we conjectured that OTUD5 stabilizes the protein level of PTEN through deubiquitination. Subsequent results of Co-IP assay demonstrated the presence of a binding relationship between OTUD5 and PTEN (Figure 3B). We further detected the protein levels of PTEN in A549 and HCI-H460 cells with another Western blot assay and uncovered that overexpression of OTUD5 promoted the protein level of PTEN, while PTEN protein levels were further increased on treatment with MG132 (a proteasome inhibitor) (p < 0.05, Figure 3C), indicating that OTUD5 may regulate PTEN expression through the proteasome pathway. Furthermore, we quantified the ubiquitination levels of PTEN in A549 and HCI-H460 cells and documented that OTUD5 overexpression reduced the ubiquitination level of PTEN (Figure 3D). Altogether, these findings indicated that OTUD5 stabilized the protein levels of PTEN through deubiquitination.
To further validate that OTUD5 executes deubiquitination to regulate NSCLC cell proliferation, invasion, and migration, the ubiquitination levels in A549 and HCI-H460 cells were enhanced using GSK2643943A (GSK). Subsequent experimentation elicited that the protein levels of PTEN were significantly declined (p < 0.05, Figure 4A), while the ubiquitination levels of PTEN were significantly increased (p < 0.05, Figure 4B) as a result of GSK treatment. Thereafter, we carried out a collaborative experiment with a combination of GSK and pcDNA3.1-OTUD5 treatments and uncovered that cell viability of A549 and HCI-H460 cells was significantly enhanced (p < 0.05, Figure 4C), and the number of migrated and invaded cells was augmented (p < 0.05, Figure 4D and E) following GSK treatment. Overall, these findings suggested that augmenting the ubiquitination level of PTEN reversed the inhibition of OTUD5 overexpression on NSCLC cell proliferation, invasion, and migration.
Furthermore, we explored the upstream target genes of OTUD5. The study carried out by Bai et al. reported that OTUD5 expression can be inhibited by its target miRNAs [34]. We predicted the upstream miRNAs of OTUD5 on the StarBase database and focused our efforts on miR-652-3p. Interestingly, miR-652-3p was previously documented to be highly-expressed in NSCLC [25]. Accordingly, we postulated that miR-652-3p serves as an upstream target of OTUD5. Subsequent dual-luciferase assay revealed a binding relationship between miR-652-3p and OTUD5 3’UTR (p < 0.05, Figure 5A). In addition, we detected miR-652-3p expression patterns in NCSLC cell lines by means of qRT-PCR, which revealed that miR-652-3p was strongly expressed in NSCLC cells (p < 0.05, Figure 5B). Altogether, these findings indicated that miR-652-3p served as an upstream target of OTUD5 and miR-652-3p was highly-expressed in NSCLC cells.
Finally, we explored whether miR-652-3p regulated NSCLC cell proliferation, invasion, and migration through the OTUD5/PTEN axis. Briefly, A549 and HCI-H460 cells were transfected with miR-652-3p-inhibitor to inhibit miR-652-3p expression (p < 0.05, Figure 6A). Subsequent experimentation revealed that miR-652-3p inhibition brought about an increase in OTUD5 expression levels (p < 0.05, Figure 6B and C) and PTEN protein levels (p < 0.05, Figure 6D and E), while simultaneously reducing the ubiquitination level of PTEN, cell viability (p < 0.05, Figure 6F), and the number of migrated and invaded cells (p < 0.05, Figure 6G and H). Collectively, these findings suggested that downregulation of miR-652-3p suppressed NSCLC cell proliferation, invasion, and migration through the OTUD5/PTEN axis.
NSCLC, the major subtype of LC, contributes to high mortality and morbidity rates across the globe [25]. Meanwhile, the hard-done work of our peers has shown that deubiquitinases (DUBs) play a key role in cancer progression by improving protein stability [35-37]. Moreover, some miRNAs possess the ability to function as regulators of DUBs to alter the ubiquitination levels of cancer-related proteins and further influence the malignant characteristics of cancer cells [24,38]. Herein, we carried out a series of experiments to uncover the role of miR-652-3p/OTUD5/PTEN and their interactions in NSCLC, in an effort to provide a novel theoretical basis for NSCLC treatment. The obtained findings revealed that OTUD5 deubiquitinated and stabilized PTEN to suppress NSCLC proliferation, invasion, and migration, and miR-652-3p serving as an upstream target of OTUD5 abolished the tumor-suppressing role of OTUD5/PTEN in NSCLC. There is a plethora of evidence to highlight the correlation between decreased levels of OTUD5 and poor prognoses in NSCLC patients, such that OTUD5 knockdown exerts an enhancing effect on NSCLC cell proliferation, migration, and chemoresistance [13]. On the other hand, high expression of OTUD5 was associated with improved overall survival in Stage II NSCLC, further underscoring the beneficial role of OTUD5 for tumor repression in NSCLC [39]. In our study, we came across downregulated levels of OTUD5 in NSCLC cell lines (A549, NCI-H292, NCI-H460, and PC9), which is in line with the prediction results from the GEPIA database. To elaborate our understanding of OTUD5 functionality in NSCLC cells, we further overexpressed OTUD5 in A549 and HCI-H460 cells and learnt that OTUD5 overexpression led to a reduction in cell viability, migration, and invasion. In accordance, OTUD5 is also negatively correlated with clinicopathologic characteristics of liver and cervical cancers [12,34]. In lieu of these findings and evidence, it would be plausible to suggest that OTUD5 suppresses NSCLC cell proliferation, invasion, and migration. The process of ubiquitination is known to serve as a vital post-translational modification and consequently exerts control over tumor-suppressing and tumor-promoting proteins in cancer [40]. OTUD5 as a DUB removes ubiquitin from target proteins, thus reversing ubiquitin-dependent degradation and improving protein stability [41,42]. More importantly, DUBs, such as ubiquitin-specific protease 10, 13, and 20, have been previously documented to restore the protein stability of PTEN in cancers [43-46]. The latter is particularly important as PTEN is lauded as a promising therapeutic target in cancer therapy [47-49]. Additional experimentation in our study illustrated the down-regulation of PTEN protein levels in NCSLC cells and validated the binding relationship between OTUD5 and PTEN. Besides, a prior study demonstrated that OTUD5 overexpression and MG132 treatment bring about an elevation in the protein levels of PTEN in A549 and HCI-H460 cells, along with decreased ubiquitination levels of PTEN, suggesting that OTUD5 stabilized PTEN by means of deubiquitination. Thereafter, we promoted the total ubiquitination levels in A549 cells with the help of GSK2643943A (GSK), which led to decreased PTEN protein levels and increased ubiquitination levels. In addition, a collaborative experiment was carried out with GSK and pcDNA3.1-OTUD5 in A549 cells and brought about an enhancement in cell viability, migration, and invasion. In accordance with our findings, there is much evidence to suggest that epigenetic silencing of PTEN facilitates NCSLC cell growth, mobility, and chemoresistance [50-52]. Besides, elevated ubiquitination or degradation of PTEN was previously shown to drive the progression of bladder, prostate, brain, and pancreatic cancers [53-56]. Altogether, the abovementioned findings and valuable evidence highlighted that OTUD5 exerts an anti-tumor function by means of deubiquitinating and stabilizing PTEN, while enhancing the ubiquitination of PTEN reverses the inhibition of OTUD5 overexpression on NSCLC progression. Furthermore, we focused our efforts on the upstream targets of OTUD5. A prior bioinformatics analysis reported that a number of miRNAs, such as miR-137, miR-1913, miR-937, miR-607, and miR-3149, are capable of negatively-regulating the mRNA expression of OTUD5 [34]. In our study, one such mRNA, namely, miR-652-3p, was predicted as an upstream miRNA of OTUD5, which was further validated by means of a dual-luciferase assay. Moreover, there is also evidence to suggest that miR-652-3p is involved in the oncogenic ceRNA network for NSCLC, which further emphasizes its participation in the pathogenesis of NSCLC [57]. Hence, we silenced miR-652-3p expression in A549 cells with a miR-652-3p-inhibitor and uncovered that silencing miR-652-3p led to a reduction in NSCLC cell viability, migration, and invasion, along with increased OTUD5 and PTEN protein levels and diminished ubiquitination levels. Consistently, a prior study reported that miR-652-3p is highly-expressed in NSCLC, such that overexpressed miR-652-3p accelerates cell proliferation and metastasis and restricts apoptosis by modulating Lgl1 [25]. Above all, our findings demonstrated that miR-652-3p may abolish the tumor-suppressing role of OTUD5/PTEN, thereby facilitating NSCLC cell proliferation, invasion, and migration.
In summary, our study was the first-of-its-kind to shed light on the interplay of miR-652-3p/OTUD5/PTEN in NSCLC cells and provide novel insight into the possible application of OTUD5 in NSCLC treatment. However, we failed to perform analysis of clinical samples or in vivo assays with xenograft tumors. In addition, except OTUD5 and miR-652-3p, it remains unknown whether other DUBs and upstream miRNAs of OTUD5 are involved with NSCLC cell behaviors, which requires further experimentation. We shall strive to validate our conclusions with clinical analysis, in vivo assays, and experiments about other DUBs in our future endeavors. | true | true | true |
PMC9589348 | Zheren Huang,Yilin Bai,Qin Wang,Xue Yang,Tiejun Zhang,Xuan Chen,Hongning Wang | Persistence of transferable oxazolidinone resistance genes in enterococcal isolates from a swine farm in China | 10-10-2022 | Enterococci,oxazolidinone,poxtA2-cfr(D) co-harboring,optrA,poxtA,genomic analyses,phylogenetic analysis,genetic environment | The appearance of transferable oxazolidinone resistance genes poses a major challenge to public health and environmental safety. These genes not only lead pathogenic bacteria to become resistant to linezolid but also reduce sensitivity to florfenicol, which is widely used in the veterinary field. To verify the dissemination of oxazolidinone resistance genes in enterococcal isolates from pigs at different production stages in a swine farm in China, we collected 355 enterococcal isolates that were resistant to florfenicol from 600 (150 per stage) fresh fecal swabs collected from a swine farm. Through initial PCR screening and whole-genome sequencing, 175 isolates harboring different oxazolidinone resistance genes were identified. All isolates carried the optrA gene. A total of 161 (92%, 161/175) isolates carried only the optrA gene. Three (1.71%, 3/175) isolates carried both the optrA and poxtA genes, and 11 (3.1%, 11/175) isolates contained the optrA gene and poxtA2 and cfr(D) variants. A total of 175 isolates that harbored oxazolidinone resistance genes included 161 E. faecalis, 6 E. faecium, and 8 E. hirae. By sequencing the whole genomes, we found that the 161 isolates of E. faecalis belonged to 28 different STs, including 8 new STs, and the 6 isolates of E. faecium belonged to four different STs, including one new ST. The phylogenetic tree based on SNPs of the core genome showed that both clonal spread and horizontal transfer mediated the diffusion of oxazolidone resistance genes in enterococcal isolates at specific stages in pig farms. Moreover, enterococcal isolates carrying oxazolidone resistance genes could spread from breeding pigs to fattening pigs, while transferable oxazolidone resistance genes in enterococcal isolates could persist on a pig farm throughout all production stages. Representative enterococcal isolates with different oxazolidinone resistance genes were further studied through Nanopore sequencing. We identified a novel plasmid, pM4-80 L4 (15,008 bp), carrying the poxtA2 and cfr(D) genes in enterococcal isolates at different stages. We also found three different plasmids harboring the poxtA gene with high genetic variation, and all poxtA genes were flanked by two copies of IS1216E elements. In addition, four genetically distinct plasmids carrying the optrA gene were identified, and Tn554 was found to mediate chromosome-localized optrA gene transfer. Our study highlighted that transferable oxazolidinone resistance genes in enterococcal isolates could persist throughout all production stages on a pig farm, and the prevalence and dissemination of oxazolidinone resistance genes in enterococcal isolates from animal farms should be continually monitored. | Persistence of transferable oxazolidinone resistance genes in enterococcal isolates from a swine farm in China
The appearance of transferable oxazolidinone resistance genes poses a major challenge to public health and environmental safety. These genes not only lead pathogenic bacteria to become resistant to linezolid but also reduce sensitivity to florfenicol, which is widely used in the veterinary field. To verify the dissemination of oxazolidinone resistance genes in enterococcal isolates from pigs at different production stages in a swine farm in China, we collected 355 enterococcal isolates that were resistant to florfenicol from 600 (150 per stage) fresh fecal swabs collected from a swine farm. Through initial PCR screening and whole-genome sequencing, 175 isolates harboring different oxazolidinone resistance genes were identified. All isolates carried the optrA gene. A total of 161 (92%, 161/175) isolates carried only the optrA gene. Three (1.71%, 3/175) isolates carried both the optrA and poxtA genes, and 11 (3.1%, 11/175) isolates contained the optrA gene and poxtA2 and cfr(D) variants. A total of 175 isolates that harbored oxazolidinone resistance genes included 161 E. faecalis, 6 E. faecium, and 8 E. hirae. By sequencing the whole genomes, we found that the 161 isolates of E. faecalis belonged to 28 different STs, including 8 new STs, and the 6 isolates of E. faecium belonged to four different STs, including one new ST. The phylogenetic tree based on SNPs of the core genome showed that both clonal spread and horizontal transfer mediated the diffusion of oxazolidone resistance genes in enterococcal isolates at specific stages in pig farms. Moreover, enterococcal isolates carrying oxazolidone resistance genes could spread from breeding pigs to fattening pigs, while transferable oxazolidone resistance genes in enterococcal isolates could persist on a pig farm throughout all production stages. Representative enterococcal isolates with different oxazolidinone resistance genes were further studied through Nanopore sequencing. We identified a novel plasmid, pM4-80 L4 (15,008 bp), carrying the poxtA2 and cfr(D) genes in enterococcal isolates at different stages. We also found three different plasmids harboring the poxtA gene with high genetic variation, and all poxtA genes were flanked by two copies of IS1216E elements. In addition, four genetically distinct plasmids carrying the optrA gene were identified, and Tn554 was found to mediate chromosome-localized optrA gene transfer. Our study highlighted that transferable oxazolidinone resistance genes in enterococcal isolates could persist throughout all production stages on a pig farm, and the prevalence and dissemination of oxazolidinone resistance genes in enterococcal isolates from animal farms should be continually monitored.
Gram-positive enterococcal bacteria occur widely in the intestines of humans and animals. Enterococci in animals used for food production may contaminate food and the environment, creating a risk of human infection through anthropozoonosis (He et al., 2016). Enterococcus faecalis and Enterococcus faecium often cause urinary tract and soft tissue infections, and in severe situations, they may cause septicemia or meningitis (Yi et al., 2022). Notably, E. faecium and E. faecalis are important nosocomial pathogens worldwide (Bender et al., 2018). It has been reported that enterococci can not only exhibit inherent resistance to some antibiotics, but also easily acquire new resistance genes located on mobile genetic elements from other sources, including other bacteria. Thus, multidrug resistance can arise readily in enterococci, resulting in limited clinical treatment options (He et al., 2016). In addition, vancomycin-resistant E. faecium (VRE) has been listed by the World Health Organization as a pathogen requiring high vigilance since it was discovered at the end of the last century (WHO, 2017). Oxazolidinone antibiotics, mainly linezolid and tedizolid, have a bactericidal effect on various gram-positive bacteria (Wilson et al., 2008; Moellering, 2014). In 2000, the US FDA introduced linezolid for clinical treatment. Linezolid is usually used to treat severe clinical infections [Methicillin-resistant Staphylococcus aureus (MRSA) and Vancomycin-resistant Enterococcus faecium (VRE)] and is known as the last line of defense for the treatment of gram-positive bacteria (Bender et al., 2018). With the extensive clinical use of linezolid in the clinic, linezolid-resistant enterococci have gradually emerged. The emergence of linezolid-resistant enterococci poses a great challenge to human clinical treatment and public health (Schwarz et al., 2021). Linezolid resistance mechanisms are associated with ribosomal mutations in the 23S rRNA and/or L3, L4 and L22 ribosomal proteins (Wang et al., 2020). However, the appearance of transferable oxazolidinone-resistant determinants in enterococci or other bacteria in many regions of the world should not be ignored (Ewa, 2018). Cfr is the first reported transferable oxazolidinone resistance gene and encodes a 23S rRNA methyltransferase. Bacteria carrying the cfr gene exhibit resistance to phenicols, oxazolidinones, lincosamides, pleuromutilins, and streptogramin A (PhLOPSA phenotype; Long et al., 2006; Shen et al., 2013). Since the cfr gene was originally discovered, four cfr-like genes have been reported worldwide, namely, cfr(B), cfr(C), cfr(D) and cfr (E) (Deshpande et al., 2015; Zhang et al., 2017; Stanley et al., 2019). OptrA is the second oxazolidinone resistance gene, reported in 2015, and encodes a ribosomal protection protein of the ABC-F family. Bacteria carrying the optrA gene exhibit resistance to oxazolidinones and phenicols (Wang et al., 2015). Since the initial description of the optrA gene, it and its many variants have been reported in several countries, revealing that it is more difficult to treat oxazolidinone-resistant bacteria (Schwarz et al., 2021). Unfortunately, another ribosomal protection protein of the ABC-F family, poxtA, was reported in MRSA of clinical origin in 2018 (Antonelli et al., 2018; Crowe-McAuliffe et al., 2022). The poxtA2 variant was also reported in 2021 (Baccani et al., 2021). Now, poxtA has been detected in clinical samples, animal samples and even marine plankton samples in many countries (Papagiannitsis et al., 2019; Dejoies et al., 2021). Although oxazolidinone antibiotics have never been approved for use in livestock breeding, we often detect the transferable determinants of oxazolidinone resistance in enterococci isolated from animals used in food production (Wang et al., 2020). Florfenicol is a phenicol compound that has broad spectrum antibacterial activity and few side effects and is used to treat respiratory and intestinal bacterial infections in animals used in food production (Crowe-McAuliffe et al., 2022). It was reported that the abundance of oxazolidinone resistance genes in livestock feces was related to florfenicol residue (Kim, 2021). Given that phenicol resistance could be caused by the extensive use of florfenicol, the widespread use of florfenicol in the veterinary field will not only promote the spread of antibiotic resistance genes of florfenicol but also promote the spread of oxazolidone resistance genes, which will cause great public health concerns. Transferable oxazolidinone resistance genes are common in enterococci isolated from pig farms, as has been reported worldwide recently (Hao et al., 2019; Lei et al., 2019, 2021; Fioriti et al., 2021). However, there are few reports on the dissemination mode of transferable oxazolidinone resistance genes in pigs at different production stages in large-scale pig farms. In this study, we investigated the presence of oxazolidinone resistance genes in florfenicol-resistant enterococci isolated from a large-scale swine farm in China and determined the dissemination mechanisms of oxazolidinone resistance genes in enterococci isolated from healthy pigs in different production stages.
The large-scale swine farm (approximately 1,000 breeding pigs and 10,000 fattening pigs) is located in Sichuan, one of China’s major pig raising provinces. In October and November 2021, Fresh fecal swabs were taken from 150 breeding pigs in the breeding pig section of the farm and from 450 fattening pigs aged between approximately 2, 4, and 6 months (150 fattening pigs each stage) in the fattening pig section of the farm. The swab samples were inoculated into 3 ml of Buffer Peptone Water (Oxoid, Basingstoke, United Kingdom) containing 8 mg/l florfenicol. The culture was incubated overnight at 37°C and 180 RPM, and following incubation, 0.1 ml aliquots of culture were streaked onto Pfizer Enterococcus Selective Agar (Qingdao Hope Bio-Technology, China) supplemented with florfenicol (8 mg/l), and the selective plate was incubated overnight at 37°C. If the plate had colonies, we randomly selected one enterococcal colony from the plate. Then, we used an automated system (BD Diagnostic Systems, Sparks, MD, United States) to identify the enterococcal isolates.
Initial PCR screening was performed for all florfenicol-resistant enterococcal isolates using generic primers directed against the optrA, cfr and poxtA genes (Bender et al., 2019), and variants of optrA, cfr and poxtA were then identified by whole-genome sequencing. We sequenced the positive PCR products with Sanger (Chengdu Sangon, China). According to Clinical and Laboratory Standards Institute (CLSI) guidelines (Clinical and Laboratory Standards Institute, 2020), the minimum inhibitory concentrations (MICs) of linezolid, tedizolid, florfenicol, vancomycin, ampicillin, doxycycline, chloramphenicol and tetracycline were determined by the broth microdilution method. E. faecium ATCC29212 was used as a quality control strain for antimicrobial susceptibility testing.
The MiniBEST bacterial genomic DNA Extraction Kit (Takara, Dalian, China) was used to extract the genomic DNA of enterococcal isolates containing oxazolidone resistance genes (optrA, poxtA and cfr). The entire genomes were sequenced using the Illumina HiSeq platform (150-bp paired-end reads, with an average coverage of approximately 200-fold). We used SPAdes_3.13.0 software to collect original sequencing data to map the genomes. Antimicrobial resistance genes were identified by using ResFinder 4.1. Linezolid resistance determinants were searched by LRE-Finder 1.0. To investigate the genetic environments of different oxazolidinone resistance genes, we selected 12 enterococcal isolates for further research, and the basis of our selection is described below. First, among the 11 enterococcal isolates carrying the optrA gene and the poxtA2 and cfr(D) variants, there were three ST-type isolates, and we selected one isolate for each ST (M2-9, M4-54, and M4-80). Second, we selected all enterococcal isolates carrying the optrA and poxtA genes, for a total of three isolates (B6, B54, and M2-95). Third, because there have been many reports on the genetic environment of optrA gene, and our manuscript mainly wanted to focus on the genetic environment of poxtA gene, poxtA2 and cfr(D) variants, we selected four representative enterococcal isolates out of 161 enterococcal isolates carrying only the optrA gene (M6-97, M6-130, B83, and B126). Finally, to study the phenomenon of heteroresistance, we selected two enterococcal isolates carrying only the optrA gene (M2-77 and M2-82). A Nanopore MinION Rapid Sequencing Kit was used to further sequence the genomes of representative enterococcal isolates with different oxazolidinone resistance genes. Using Nanopore sequencing data combined with Illumina sequencing data, the complete genome sequences were assembled by Unicycler. PCR linkage confirmed that there was a circular plasmid containing a transferable oxazolidinone resistance gene. Easyfig v2.2.2 was used for comparative analyses of the plasmids.
Sequence types (STs) were assigned on the basis of conventional MLST loci. STs of enterococcal isolates were determined by PubMLST, and the new STs were also assigned by PubMLST. MLST-based minimum spanning trees were created by GrapeTree v1.3.2. The core genome is present in all individuals of the same species, and the genes in the core genome are generally related to the stable biological function and phenotypic characteristics of the species, most of which are housekeeping genes (Tettelin et al., 2005; Yang and Gao, 2022). Using the core genomes of enterococci to construct phylogenetic trees can reduce the influence of variable genomes on genetic phylogenetic relationships. Sequences of isolates that harbored oxazolidinone resistance genes were annotated using Prokka v1.12 software, and the core genome was identified using Roary v3.11.2, then the SNPs of the core genome were extracted by Harvest tools (Seemann, 2014; Page et al., 2015; Dejoies et al., 2021). A phylogeny based on SNPs of the core genome was constructed by FastTree v2.1.11. The Newick file for the phylogenetic tree was modified in iTOL.
Using the criteria described previously in “Whole-genome sequencing and genomic analyses of enterococcal isolates that harbored oxazolidinone resistance genes,” eight isolates (M4-80, B6, B54, M2-95, M2-77, M2-82, M6-97, and M6-130) carrying different plasmids that were representative of the 175 enterococcal isolates carrying the oxazolidinone resistance gene in this study were selected as donors. The rifampicin-resistant E. faecalis JH2-2 was used as the recipient. Conjugal transfer was performed on a filter membrane as described previously (Brenciani et al., 2016). The donor and recipient bacteria were mixed in a 1:1 ratio on the filter membrane. Transconjugants were selected on Brain Heart Infusion Agar (Oxoid, Basingstoke, United Kingdom) plates containing 2 mg/l linezolid and 25 mg/l rifampicin. The transfer frequency was expressed as the ratio of the cell number (cfu/ml) of the transconjugant to that of the recipient. Transconjugants were evaluated for their susceptibility to linezolid, tedizolid, chloramphenicol, florfenicol, tetracycline and doxycycline. Then, the detection of transferable oxazolidinone resistance genes, 16S rDNA sequencing and whole genome sequencing were also used to confirm transconjugants.
The genomes of enterococcal isolates that harbored oxazolidinone resistance genes in this study have been deposited in the National Center for Biotechnology Information and registered BioProject number PRJNA856057. The complete nucleotide sequences of nine plasmids harboring different transferable oxazolidinone resistance genes have been deposited in GenBank and assigned accession numbers OP046170–OP046178.
In this study, 355 isolates of florfenicol-resistant enterococcal isolates were collected from 600 fresh fecal swabs taken from pigs at different stages from breeding pig section and fattening pig section of the pig farm. Through initial PCR screening and whole-genome sequencing, 175 isolates harboring different oxazolidinone resistance genes were identified (Table 1). All isolates carried the optrA gene. A total of 161 (92%, 161/175) isolates carried only the optrA gene. Three (1.71%, 3/175) isolates carried both the optrA and poxtA genes, and eleven (3.1%, 11/175) isolates contained the optrA gene and poxtA2 and cfr(D) variants (Table 1). A total of 175 isolates that harbored oxazolidinone resistance genes included 161 E. faecalis, 6 E. faecium, and 8 E. hirae (Table 1). With respect to the isolation rate of enterococci carrying oxazolidinone resistance genes in each production stages, 49 (32.67%, 49/150) isolates came from the breeding pig section of the pig farm. The remaining isolates came from the fattening pig section of the pig farm, with 47 (31.33%, 47/150) from 2-month fattening pigs, 40 (26.67%, 40/150) from 4-month fattening pigs, and 37 (24.67%, 37/150) from 6-month fattening pigs. Antimicrobial susceptibility testing indicated that the MIC values of linezolid against 161 enterococcal isolates that harbored only the optrA gene ranged from 4 to 8 mg/l, and the MIC values of tedizolid ranged from 0.125 to 1 mg/l (Table 1). Three isolates that carried the optrA and poxtA genes were resistant to linezolid and tedizolid. The MICs of the three isolates to linezolid and tedizolid were 8–16 mg/l and 1 mg/l, respectively (Table 1). In addition, 11 isolates carrying both the optrA gene and the poxtA2 and cfr(D) variants showed resistance to linezolid and tedizolid, and the MICs for these two antibiotics were 8 and 2 mg/l, respectively (Table 1). A total of 175 isolates exhibited a multidrug resistance phenotype and were resistant to chloramphenicol, florfenicol, tetracycline and doxycycline. Moreover, three isolates were resistant to penicillin (MICs ≥ 16 mg/l).
By sequencing the whole genomes of 175 isolates that carried oxazolidinone resistance genes, we found that the cfr and poxtA genes carried by 11 E. faecalis isolates were the cfr(D) and poxtA2 variants. The homology of the cfr(D) gene and wild type (accession number NG_067192) was 99.9%. The poxtA2 variant was recently detected in two E. faecalis isolates and one E. casseliflavus isolate from manure of a swine farm in Italy (Baccani et al., 2021). Unlike poxtA, the poxtA2 variant (accession number MZ171245) was not truncated by an IS1216 insertion at the 3′ end; thus, a new sequence consisting of eight amino acids (TPEEEQKY) replaced the six amino acids (GSVAKF) of the wild-type protein. Six different optrA variants were found by alignment of the optrA amino acid sequences with those found in E. faecalis E349 (Supplementary Table 1; Wang et al., 2015). Five of these variants have been reported previously (Cai et al., 2018). One variant, DKD (G40D, I287K, G393D), has not been reported previously (Supplementary Table 1). Different linezolid MICs were also present in isolates harboring different optrA variants (Supplementary Table 1). In addition, all 175 isolates that contained the phenicol resistance gene fexA and five isolates also carried the fexB gene. All the isolates carried the tetracycline resistance genes tet(L) and/or tet(M) and macrolide resistance genes erm(A) and/or erm(B). Five isolates carried the macrolide resistance gene msr(C), and the M2-95 strain harbored both the mosaic tetracycline resistance gene tet(S/M) and the macrolide resistance gene msr(C). Four isolates of E. faecalis of ST1257 contained the mosaic tetracycline resistance gene tet(O/W/32/O) and chloramphenicol resistance gene cat. Thirteen to nineteen mutations in the pbp5 gene were found in six isolates of E. faecium. Two isolates carried the clpL gene.
MLST showed that the 161 isolates of E. faecalis that harbored oxazolidinone resistance genes belonged to 28 different STs, including ST21, ST59, ST256, ST314, ST506, ST632, ST634, ST868, ST902, and ST982, and eight new STs were assigned by PubMLST, including ST1250, ST1251, ST1252, ST1253, ST1254, ST1255, ST1256, and ST1257 (Table 1). Six E. faecium isolates that harbored oxazolidinone resistance genes belonged to 4 different STs, including ST184, ST323, ST1630 and one new ST, ST2165, assigned by PubMLST (Table 1). The 161 E. faecalis isolates were widely isolated from pigs in different production stages (Figure 1A). It is worth noting that isolates ST506 and ST632 were found in the samples from all four stages (Figure 1A). However, the distribution of the six E. faecium isolates was relatively homogeneous across different stages (Figure 1B). A phylogenetic tree based on SNPs of the core genome showed that the 175 enterococcal isolates carrying oxazolidone resistance genes were divided into 35 branches (Figure 2). The isolates with the same ST type were clustered into the same branch in the phylogenetic tree (Figure 2). For the isolates isolated from a certain stage, all ST902 isolates isolated from breeding pigs belonged to one branch, and the ST21 isolates isolated from 2-month fattening pigs also clustered together in the phylogenetic tree. Similar phenomena were found in 4-month fattening pigs and 6-month fattening pigs (Figure 2). For the isolates isolated from different stages, ST506 isolates carrying the optrA gene belonged to one clade and were isolated from samples in all four production stages (Figure 2). ST632 isolates carrying the optrA gene were clustered in the phylogenetic tree, and they were prevalent in all four production stages (Figure 2). The poxtA gene was carried by two isolates of E. faecium and one strain of E. hirae, and the poxtA2 and cfr(D) variants were carried by ST256, ST1251 and ST1255 isolates of E. faecalis (Figure 2).
The genetic structures of plasmids containing oxazolidinone resistance genes and the genetic environments of different oxazolidinone resistance genes are shown in Figure 3. In three E. faecalis isolates (M2-9, M4-54 and M4-80) that carried the optrA gene and the poxtA2 and cfr(D) variants, bioinformatic data revealed that the optrA gene of three isolates was localized to the chromosome at Tn554, and a novel plasmid carrying poxtA2 and cfr(D) variants, named pM4-80 L4, was found concomitantly in these three isolates. This 150,008 bp plasmid (35.5% GC content) contained 17 ORFs. BLASTN analysis revealed that pM4-80 L4 was 98.15% identical (coverage 99%) to pV386 (33,48 kb in size; accession number MZ603802.1) carrying cfr(D) and poxtA2 variants. pV386 was recently found in an E. faecalis strain that was isolated from a swine environment in central Italy (Cinthi et al., 2022). Similar to pV386, pM4-80 L4 showed that the poxtA2 gene was closely associated with the cfr(D) gene (Figure 3A). Upstream of the poxtA2 gene, the fexA gene was found to be surrounded by two copies IS1216E in the same direction, and the rep1 gene (belonging to the Inc18 family) was also detected downstream of the cfr(D) gene (Figure 3A). Three isolates (B6, B54, and M2-95) harbored both the optrA and poxtA genes. In these three isolates, we found that the optrA gene was localized on the chromosome, whereas the poxtA gene was located in a plasmid in all isolates. Three different poxtA-carrying plasmids with sizes ranging from 21.2 to 61.8 kb were obtained after hybrid assembly (Table 2). Analysis of the poxtA nucleotide sequence location in each isolate showed that they exhibited 100% identity to that of S. aureus AOUC-0915, in which poxtA was initially described (Antonelli et al., 2018). Every poxtA gene was flanked by IS1216E at the left and/or right ends (Figure 3B). Two poxtA-carrying plasmids from two E. faecium isolates (B6 and M2-95) belonged to the same type that harbored rep2 and repUS12 replicons (Figure 3B). Two plasmids exhibited high genetic variation, and both of them carried the tetracycline resistance genes tet(L) and tet(M). pB6-poxtA also carried the phenicol resistance gene fexB, macrolide resistance gene erm(B) and aminoglycoside resistance gene aph(3′)-III; IS3 and IS30 were also found in pB6-poxtA (Figure 3B). The final poxtA-carrying plasmid, pB54-poxtA, from the E. hirae strain (B-54), carried three replicons (rep2, repUS12, and repUS1) and harbored the fexB, tet(L), tet(M), aph(3′)-III and erm(B) resistance genes, and IS1252, ISL3, IS30, and Tn916 were also identified in pB54-poxtA (Figure 3B). Among the 161 enterococcal isolates that carried only the optrA gene, 27 isolates had the optrA gene localized to the chromosome at Tn554, while 134 isolates had the optrA gene localized to a plasmid. From the four optrA-carrying isolates that we selected (M2-77, M2-82, M6-97, and M6-130), four different optrA-carrying plasmids were identified, three of which belonged to the same type and carried rep9a, repUS43, rep33 and repB replicons (Table 2). The remaining plasmid carried the rep9a, repUS43 and repB replicons (Table 2). The genetic structure of the optrA-carrying plasmids is shown in Figure 3C. All optrA genes were associated with the phenicol resistance gene fexA and the macrolide resistance gene erm(A), and a similar genetic context IS1216E–fexA–optrA–erm(A)–IS1216E was found in four different optrA-carrying plasmids (Figure 3C). In the two optrA-carrying isolates we selected (B83 and B126) and the previous six isolates with optrA gene localization on chromosomes (M2-9, M4-54, M4-80, B6, B54, and M2-95), four different chromosomal optrA gene clusters were found. Three of these optrA genes were associated with Tn554 inserted into the radC gene, and the fexA—optrA segment was detected upstream of the Tn554 transposon (Figure 3D). The M4-54 isolate also carried the macrolide resistance gene erm(B) and the aminoglycoside resistance gene ant(9)-Ia upstream of the fexA-optrA segment, and IS3 inserted into the fexA-optrA segment was also found in M2-95 (Figure 3D). The genetic context IS1216E–fexA–optrA–erm(A)–IS1216E on the chromosome was identified in the remaining isolate (B54; Supplementary Figure 1). Interestingly, the clonally related isolates had the heteroresistant genotype in ST506 and ST256, specifically in the ST506 isolates M2-77 and M2-82 (genotype M2-77 lacked aph(3′)-III, aac(6′)-aph(2″), ant(6)-Ia, lsa(E), lnu(B) and dfrG genes compared with M2-82) and the ST256 isolates M4-75 and M4-80 (genotype M4-75 lacked aph(3′)-III, erm(B) and dfrG genes compared with M4-80). Bioinformatics’ results revealed the genetic environments of differential resistance genes in clonally related isolates. Compared with M2-77, IS1216E and Tn916 might mediate the insertion of a 21,511 bp fragment that contained differential resistance genes into plasmids (pM2-82) of M2-82 (Figure 3C). However, compared with M4-75, M4-80 probably received a complete plasmid (pM4-80L3, 57.9 kb in size) that harbored differential resistance genes from the external environment (Supplementary Figure 1).
Using antimicrobial susceptibility testing, the detection of transferable oxazolidinone resistance genes, 16S rDNA sequencing and whole-genome sequencing, nine transconjugants containing transferable oxazolidinone resistance genes were obtained, with frequencies ranging from 7.1 × 10−6 to 2.5 × 10−7 (Table 2). The MICs of linezolid against those transconjugants varied from 4 to 8 mg/l, and the MICs of tedizolid varied from 0.25 to 1 mg/l (Table 2).
Knowledge of the distribution of antimicrobial-resistant isolates in the food chain and edible animals is important in determining the potential risk of antimicrobial-resistant isolates to human health (Yoon et al., 2020). Although oxazolidinones have been approved for human use only, the cfr, optrA and poxtA genes have been detected in enterococcal isolates of animal and environmental origin and recently were even found in enterococci from coastal seawater samples (Freitas et al., 2021; Schwarz et al., 2021). Enterococci in edible animals that carry oxazolidinone resistance genes poses a threat to public health and the surrounding environment. In this study, we investigated the prevalence and genetic characteristics of oxazolidinone resistance genes in enterococcal isolates to better understand their resistance profiles and the dissemination of oxazolidinone resistance genes in enterococcal isolates obtained from pigs at different stages on a swine farm. Since the three oxazolidinone resistance genes were discovered, these genes have been detected in enterococcal isolates from hospital patients, as well as livestock in the community and veterinary hospital (Carmen et al., 2018). In this study, we reported the presence of optrA, poxtA, poxtA2 and cfr(D) in 49.3% (175/355), 1.71% (3/175), 3.1% (11/355) and 3.1% (11/355) of florfenicol-resistant enterococcal isolates, respectively. The detection rate of the optrA gene was higher than that reported by Wang et al., who found that 24.8% (37/149) of enterococcal isolates of swine origin harbored the optrA gene (Wang et al., 2015). The prevalence of the poxtA gene was lower than that observed by Hao et al., who reported that the poxtA gene was present in 57.9% (66/114) of the florfenicol-resistant enterococcal isolates from two swine farms in Henan Province in China (Hao et al., 2019). The prevalence of the poxtA gene was close to that in florfenicol-resistant enterococci of swine origin in Italy (4.14%, 6/145; Fioriti et al., 2020). Moreover, Cinthi et al. reported that two E. faecalis isolates and one E. casseliflavus isolate from a pig farm environment carried poxtA2 and cfr(D) variants (Cinthi et al., 2022). These results indicated that the prevalence of three oxazolidinone resistance genes in enterococcal isolates from different swine farms exhibited huge differences, which might be associated with different veterinary antimicrobial agent usage schemes. This study confirmed that WGS played an indispensable role in understanding the resistance profiles of oxazolidinone resistance genes and monitoring the dissemination of oxazolidinone resistance genes in enterococcal isolates. Searching for resistance genes among 355 florfenicol-resistant enterococcal isolates indicated no mutations in 23S rRNA (G2576T and G2505A) or in the L3, L4 and L22 ribosomal proteins in 175 enterococcal isolates carrying oxazolidinone resistance genes (Baccani et al., 2021); the remaining 180 florfenicol-resistant enterococcal isolates were not resistant to oxazolidinone, and no chromosomal mutations associated with oxazolidinone resistance were detected. In this study, three enterococcal isolates carried two oxazolidinone resistance genes, and 11 isolates carried three oxazolidinone resistance genes. Eleven E. faecalis harbored both the cfr-like variant cfr(D) and the poxtA2 variant. The poxtA2 variant was first identified from an isolate of E. gallinarum from a healthy child in Bolivia (Baccani et al., 2021). We also found six optrA variants, one of which has not been reported in E. faecalis. The presence of multiple oxazolidinone resistance genes in one strain might enhance resistance to oxazolidinones, and different variants of different oxazolidinone resistance genes and other factors in the different enterococcal species contribute to the level of linezolid resistance, which requires further investigation (Cai et al., 2018). We also found other antibiotic resistance genes in these 175 enterococcal isolates that harbored resistance genes for oxazolidines, such as tetracycline (tet(M), tet(S/M) and tet(O/W/32/O)), and macrolides (erm(A), erm(B) and msr(C)). Two isolates also carried the clpL gene, which encodes a chaperone family protein of HSP100/Clp (caseinolytic protease). It was found mainly in gram-positive bacteria, which might be associated with decreased penicillin susceptibility (Tran et al., 2011). Although we did not find vancomycin resistance genes, more attention should be given to monitoring the VRE that carries transferable oxazolidinone resistance genes (Yi et al. 2022). A total of 175 enterococcal isolates harbored multiple resistance genes, which indicated a broad antibiotic resistance spectrum of enterococcal isolates. This might make it difficult to treat antibiotic-resistant enterococcal isolates on swine farms. On the basis of conventional MLST loci, we classified 175 enterococcal isolates harboring oxazolidinone resistance genes. A total of 161 E. faecalis isolates belonging to 28 different STs, including eight new STs, were identified. Of those clones, the most prevalent was ST506 (24.84%, 40/161). ST902 (11.18%, 18/161) was the second most prevalent clone. Moreover, four different STs and one new ST were found in six E. faecium isolates. Using WGS, MLST-based minimum spanning trees of E. faecalis isolates (161) and E. faecium isolates (6) were constructed. We found that the STs of E. faecalis were distributed across multiple production stages. In particular, ST506 and ST632 isolates were found in pigs at four production stages, indicating that these ST isolates may spread in pigs of different stages on swine farms. However, the distribution of E. faecium (6) isolates was simple because the number of E. faecium isolates was relatively small. A phylogenetic tree of the 175 enterococcal isolates was constructed based on SNPs of the core genome. The isolates with the same STs were clustered into the same branch on the phylogenetic tree, suggesting that there was a clonal correlation among the isolates. This result was consistent with the MLST results. For the isolates isolated from a certain stage, 18 isolates of ST902 from breeding pigs showed high similarity, and four isolates of ST21 from 2-month fattening pigs also showed high similarity. In contrast, different ST isolates isolated from the same production stage showed high diversity and did not cluster together in the phylogenetic tree. These results suggest that both clonal spread and horizontal transfer might mediate the diffusion of oxazolidone resistance genes in enterococcal isolates at specific stages in pig farms. For the isolates isolated from different production stages, we found that ST506 and ST632 isolates isolated from four stages showed high similarity, and the same ST isolates were also found to have clonal correlation between two adjacent stages (BP to 2M-FP, 2M-FP to 4M-FP, 4M-FP to 6M-FP). This meant that enterococcal isolates carrying oxazolidone resistance genes could spread from breeding pigs to fattening pigs, while transferable oxazolidone resistance genes in enterococcal isolates could persist throughout all production stages on a pig farm. Finally, the poxtA2 and cfr(D) variants were always carried by ST256 isolates isolated from the 2-month fattening pigs and 4-month fattening pigs. In addition, ST1251 and ST1255 isolates isolated from the 2-month fattening pigs and 4-month fattening pigs also carried the poxtA2 and cfr(D) variants, which were also close to ST256 isolates in the phylogenetic tree. This result indicated that ST1251 and ST1255 may have evolved from ST256. Representative enterococcal isolates carrying different types of oxazolidinone resistance genes were further sequenced to investigate the locations and genetic environments of different oxazolidinone resistance genes. Mobile genetic elements contribute significantly to the transmission of resistance genes. For the 11 isolates carrying the optrA gene and the poxtA2 and cfr(D) variants, we identified a novel plasmid, named pM4-80 L4, that was prevalent in different STs of E. faecalis isolates from 2-month fattening pigs and 4-month fattening pigs. The plasmid harboring the poxtA2 and cfr(D) variants was similar to pV386 (accession number MZ603802.1), an isolate of swine origin from Italy, indicating that these two plasmids might have the same origin (Cinthi et al., 2022). The emergence of pM4-80 L4 harboring the poxtA2 and cfr(D) variants demonstrates that intense genetic exchanges between enterococcal isolates promoted the spread of oxazolidinone resistance determinants. The poxtA gene shares 32% homology with the optrA gene (Dejoies et al., 2021). To date, there is no report on poxtA in enterococcal chromosomes. Among three enterococcal isolates carrying the optrA and poxtA genes, we identified three different plasmids carrying the poxtA gene. All poxtA genes were flanked by two IS1216E element copies in the same direction, which was consistent with other reports. The IS1216E—poxtA—IS1216E segment in our study is similar to that found in S. aureus AOUC-0915 and clinical E. faecium isolates from Italy and France, indicating that the genetic background of poxtA is relatively unique (Bender et al., 2018; Carmen et al., 2018; Dejoies et al., 2021; Coccitto et al., 2022). Moreover, a variety of antibiotic resistance genes were found on these plasmids, revealing co-transmission of antibiotic resistance genes. The main differences among insert sequences (ISs) are that they have different transposase properties and catalyze different chemical reactions (Wilson et al., 2008). The three different plasmids exhibited high genetic variation. Three to eight copies of IS1216E were found in the three plasmids, and we also identified other types of ISs. Different ISs might promote the diversity of poxtA-harboring plasmids. Early studies suggested that the transposition of ISs in the genome was random, while recent experiments confirmed that ISs are more inclined to insert into the plasmid in bacteria, which is conducive to the spread of ISs with plasmids as vectors (Siguier et al., 2006). Therefore, more attention should be given to bacterial antibiotic resistance mediated by ISs. We also described the environment of optrA genes in different locations in this study. Because there have been many reports on the genetic environment of optrA gene, and our manuscript mainly wanted to focus on the genetic environment of poxtA gene, poxtA2 and cfr(D) variants, we selected four representative isolates from 161 isolates carried only optrA gene (M6-97 and M6-130 represent isolates with optrA gene localized to a plasmid, B83 and B126 represent isolates with optrA gene localized to the chromosome). We obtained two optrA-carrying plasmids by hybrid assembly in two isolates (M6-97 and M6-130). By comparing the sequences of the two optrA-carrying plasmids with the sequences of 134 isolates in which the optrA gene was localized to the plasmid, we found that regardless of the plasmid, its sequence coverage with the sequence of 134 isolates reached more than 85%. This showed that the optrA-carrying plasmids among 134 isolates were similar to these two plasmids obtained by hybrid assembly. In our study, we selected four isolates carrying only the optrA gene (M2-77, M2-82, M6-97, and M6-130), and four genetically distinct optrA-carrying plasmids were identified. A similar genetic context, IS1216E–fexA–optrA–erm(A)–IS1216E, was found in four plasmids, which was consistent with E. faecalis from human and animal origin (He et al., 2016). This indicates that IS1216E might promote the cotransfer of optrA, fexA and erm(A) among plasmids. In summary, IS1216E in Gram-positive bacteria (Enterococcus, Streptococcus suis and Listeria monocytogenes) belongs to the IS6/IS26 family of bacterial ISs and plays an important role in mobilizing antimicrobial resistance genes (Shan et al., 2020). Tn554 mediation of optrA gene transfer was identified in the chromosome carrying the optrA gene, consistent with other reports (He et al., 2016; Kang et al., 2019). The optrA gene was flanked by transposon (Tn554) or insertion sequences (IS1216E), indicating that the optrA gene can be transferred among different bacterial genera and species. In addition, the phenomenon of heteroresistance complicates the analysis of antibiotic resistance in bacteria. We found heteroresistance of clonally related isolates in ST506 and ST256 by genomic analyses and phylogenetic tree construction. Then, we preliminarily clarified the mechanism of heteroresistance emergence, which was likely that mobile genetic elements mediated the insertion of DNA fragments or the acquisition of external plasmids. Last, but not least, these results suggest that caution must be taken to avoid the dissemination of oxazolidinone resistance genes in the environment as they reveal the genetic environment of different oxazolidinone resistance genes.
Our study highlighted that transferable oxazolidinone resistance genes in enterococcal isolates could persist throughout all production stages on a pig farm. Different mobile genetic elements, such as plasmids (pM4-80 L4), IS1216E and Tn554, mediated the dissemination of oxazolidinone resistance genes in enterococcal isolates in the swine farm. These results indicate that oxazolidinone resistance genes in enterococcal isolates had diverse dissemination characteristics in different production stages of large-scale pig farms. Although few data show that oxazolidinone-resistant enterococci could be directly transmitted from animals to humans, enterococci in animals used for food production could be an important repository of transferable oxazolidinone resistance genes. The prevalence and dissemination of oxazolidinone resistance genes in enterococcal isolates from animal farms should be continually monitored.
The data presented in this study are deposited in the National Center for Biotechnology Information. The complete nucleotide sequences of nine plasmids harboring different transferable oxazolidinone resistance genes have been deposited in GenBank and assigned accession numbers OP046170–OP046178.
HW and ZH designed the study and supervised the work. YB, QW, XY, TZ, and XC participated, coordinated, and analyzed the data. ZH and YB wrote the manuscript. All authors contributed to the article and approved the submitted version.
This work was supported by the National Natural Science Foundation of China (grant no. U21A20257).
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. | true | true | true |
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PMC9589540 | 36269411 | Yanzhi Lu,Min Long,Zhaowei Gao,Chong Liu,Ke Dong,Huizhong Zhang | Long non-coding RNA ENST00000469812 promotes Enterovirus type 71 replication via targeting the miR-4443/NUPR1 axis in rhabdomyosarcoma cells | 21-10-2022 | Enterovirus type 71,Long non-coding RNA,microRNA,Nuclear protein 1,Viral replication | Hand, foot, and mouth disease (HFMD) caused by Enterovirus type 71 (EV71) is a serious threat to children’s health. However, the pathogenic mechanism of EV71 is still unclear. Long non-coding RNAs (lncRNAs), some of which bind to miRNA as competitive endogenous RNAs (ceRNA) and weaken the silencing effect on the mRNA of downstream target genes, play a key role in regulating the viral infection process. In this study, through experimental verification, we found miR-4443 to be downregulated in cells infected with EV71. Next, by predicting lncRNAs that potentially regulate miR-4443, we found that EV71 infection induced upregulation of lncRNA ENST00000469812 and then further downregulated miR-4443 expression by direct interaction. We also demonstrated that nuclear protein 1 (NUPR1) is one of the target genes of miR-4443 and is involved in the ENST00000469812/miR-4443/NUPR1 regulatory axis. Finally, the ENST00000469812/miR-4443/NUPR1 regulatory axis exhibited a positive effect on EV71 replication. Here, we lay a foundation for exploring the pathogenic mechanism of EV71 and identify potential targets for HFMD treatment. Supplementary Information The online version contains supplementary material available at 10.1007/s00705-022-05596-3 | Long non-coding RNA ENST00000469812 promotes Enterovirus type 71 replication via targeting the miR-4443/NUPR1 axis in rhabdomyosarcoma cells
Hand, foot, and mouth disease (HFMD) caused by Enterovirus type 71 (EV71) is a serious threat to children’s health. However, the pathogenic mechanism of EV71 is still unclear. Long non-coding RNAs (lncRNAs), some of which bind to miRNA as competitive endogenous RNAs (ceRNA) and weaken the silencing effect on the mRNA of downstream target genes, play a key role in regulating the viral infection process. In this study, through experimental verification, we found miR-4443 to be downregulated in cells infected with EV71. Next, by predicting lncRNAs that potentially regulate miR-4443, we found that EV71 infection induced upregulation of lncRNA ENST00000469812 and then further downregulated miR-4443 expression by direct interaction. We also demonstrated that nuclear protein 1 (NUPR1) is one of the target genes of miR-4443 and is involved in the ENST00000469812/miR-4443/NUPR1 regulatory axis. Finally, the ENST00000469812/miR-4443/NUPR1 regulatory axis exhibited a positive effect on EV71 replication. Here, we lay a foundation for exploring the pathogenic mechanism of EV71 and identify potential targets for HFMD treatment.
The online version contains supplementary material available at 10.1007/s00705-022-05596-3
Hand, foot, and mouth disease (HFMD) is an acute viral infectious disease to which children under 5 years are susceptible. Since its discovery in California in 1969, there have been several outbreaks of HFMD worldwide [1]. In recent decades, HFMD has occurred mainly in China, Singapore [2], Malaysia [3], Thailand [4], Japan [5], South Korea [6], and other Western Pacific countries [7]. Between 2010 and 2018, the child mortality rate of HFMD in China was 1.79 per 10,000 patients [8]. Although the prevention and control of COVID-19 have reduced the incidence of HFMD in the spring and summer [9, 10], its incidence in the winter has continued to increase significantly [11]. Therefore, HFMD remains a serious threat to children’s health. Enterovirus type 71 (EV71), a single-stranded positive-sense RNA virus, is the main pathogen causing severe HFMD with aseptic meningitis, encephalomyelitis, acute flaccid paralysis, or even death [12]. The pathogenesis of EV71 infection, however, has not been fully elucidated. For decades, non-coding RNAs (ncRNA) including microRNA (miRNA), long non-coding RNA (lncRNA), and small nucleolar RNA, have emerged as key regulators mediating the pathogenesis of virus infection [13]. As one class of non-coding RNAs longer than 200 nucleotides, some lncRNAs function as competitive endogenous RNAs (ceRNA) that bind to miRNAs, which silence downstream target mRNA genes, thus forming a lncRNA/miRNA/mRNA regulatory axis in viral infectious diseases [13, 14], such as those caused by influenza virus [15], HIV [16], hepatitis C virus [17], and SARS-CoV-2 [18]. A large number of lncRNAs and miRNAs have been found to be differentially expressed in host cells during EV71 infection [19–24], suggesting that lncRNAs and miRNA may function together to regulate the pathogenetic process of EV71 infection. A previous study showed that lncRNA IRAK3-3 was significantly downregulated in EV71-infected rhabdomyosarcoma (RD) cells and upregulated miR-891b via the ceRNA mechanism, which itself ultimately downregulated DNA damage-inducing gene 45 β (GADD45β) and decreased apoptosis [25]. This suggests that the ceRNA mechanism mediates the EV71 infection process. However, the involvement of the ceRNA mechanism in the regulation of EV71 replication is poorly understood. Here, we identified key lncRNAs, miRNAs, and mRNAs that regulate the pathogenesis of EV71 and examined the effects of EV71 infection on their expression. Then, the three RNA elements forming a ceRNA axis were identified, and their affect on EV71 replication was assessed. This study will provide a basis for elucidating the regulation mechanism of EV71 replication and lay a theoretical foundation for exploring therapeutic targets for HFMD.
In investigating the lncRNAs regulating miR-4443, we used the DIANA (http://www.microrna.gr/LncBase) online software to predict lncRNAs interacting with miR-4443. Then, we searched “EV71 lncRNA” in PubMed and selected the literature PMID23220233 with the most differentially expressed lncRNAs in RD cells after EV71 infection [20]. Where we found an intersection with the upregulated lncRNAs in PMID23220233 and in DIANA, we selected 30 candidate lncRNAs potentially regulating miR-4443. Finally, we selected lncRNAs with prediction scores over 0.8, and those associated with over 2.85-fold upregulation were selected as final candidate lncRNAs. The target genes of miR-4443 were predicted using the online software TargetScan (http://www.targetscan.org), miRWalk (http://mirwalk.umm.uni-heidelberg.de), miRanda (http://miranda.org.uk/downloads), and DIANA (https://diana.e-ce.uth.gr/home).
The miR-4443 mimic (5’-UUGGAGGCGUGGGUUUU-3’), the miR-4443 inhibitor (5’-AAAACCCACGCCUCCAA-3’), siNUPR1, siENST00000469812, the respective negative controls (NC), the ENST00000469812 overexpressing plasmid (pcDNA-ENST00000469812), the dual luciferase reporter plasmids for ENST00000469812 (pGL3-ENST00000469812), the ENST00000469812 mutant (pGL3-ENST00000469812mut), 3’ UTR NUPR1 (pGL3-NUPR1WT), and the 3’ UTR NUPR1 mutant (pGL3-NUPR1mut) were synthesized by Sangon Biotech (Shanghai, China). The pcDNA3.1, pGL3-basic, and pRL-TK-Renilla plasmids were stored in our lab beforehand. The NUPR1 overexpressing plasmids were constructed using a molecular cloning technique. Briefly, the NUPR1 gene (NM_012385) was amplified from cDNA of RD cells, using the following primers: forward, 5’-CGCGGATCCATGGCCACCTTCCCACCAGCAAC-3’; reverse, 5’- TGCTCTAGATCAGCGCCGTGCCCCTCG-3’. Gene fragments were then cloned into pcDNA3.1 after digestion with BamHI and XbaI.
Human rhabdomyosarcoma (RD) cells and human embryonic kidney 293 (HEK293) cells were obtained from ATCC. They were all cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum (FBS) at 37°C in 5% CO2. Transfection of cells with miRNAs, siRNAs, or plasmids was performed using Lipofectamine 2000 reagent (Invitrogen, CA) according to the manufacturer’s protocol.
The EV71 virus strain (87-2008 Xi’an Shaanxi (GenBank accession number HM003207.1) [26] was kindly donated by Dr. Wei Ye from the Department of Microbiology and Pathogen Biology, Air Force Medical University. RD cells were seeded into 24-well plates at 1×104 cells/well for virus infection. The next day, the cells were infected with EV71 in DMEM at the multiplicity of infection (MOI) indicated in the text or figure legend for 1 h for virus attachment. Then, the virus supernatants were removed and replaced with DMEM containing 2% FBS. For mock treatment, RD cells were treated with medium without virus. The RNA, whole-cell proteins, and supernatants were collected at the indicated time points. EV71 virus titers in supernatants of RD cells were determined by 50% tissue culture infectious dose (TCID50) assay using the Reed-Muench endpoint calculation method in RD cells. At least three independent experiments were performed for each treatment.
Viral RNA, endogenous cellular lncRNA, mRNA, and miRNA were extracted from cells using TRIzol Reagent (Takara, Japan). Then, 1 µg of total mRNAs and miRNAs were reverse transcribed to cDNAs using a PrimerScript RT Reagent Kit (Takara, Japan) and a Mir-X miRNA First-Strand Synthesis Kit (Takara, Japan), respectively. Real-time quantitative polymerase chain reaction (qRT-PCR) was performed using FastStart SYBR Green Master Mix (Roche, Switzerland). The PCR reaction conditions were as follows: initial denaturation at 95°C for 2 min, followed by 40 cycles of 95°C for 20 s, 58°C (for lncRNA, mRNA and viral RNA) or 60°C (for miRNA) for 20 s, and 72°C for 20s. Primers used in these reaction systems are listed in Supplementary Tables S1, S2, and S3. U6 primers and miRNA universal reverse primer were provided in the Mir-X miRNA First-Strand Synthesis Kit. For absolute quantification of miRNA, we performed reverse transcription (abm, Canada) on mimics of miR-4443 of 20 pmol, and the products were serially diluted for qRT-PCR. The Ct values and pmol values were plotted to form a standard curve, which was used for absolute quantification of miR-4443 in RD, HEK293, and A549 cells.
A Nuclear and Cytoplasmic Fraction Extraction Kit (Beyotime, China) was used for separating nuclear and cytosolic RNAs. Briefly, RD cells were washed using PBS and digested with trypsin. Then, RD cells were treated with buffer A containing RNase inhibitor (Beyotime, China) and vortexed. After freezing on ice, the suspended cells were treated with buffer B and vortexed, followed by centrifugation at 16,000 × g for 15 min at 4°C. The supernatants were collected as cytoplasmic fractions. The pellets were lysed using nuclear fraction extraction reagent, followed by vortexing and centrifugation, and the supernatants were collected as nuclear fractions. The RNAs in the cytoplasmic and nuclear fractions were extracted using TRIzol Reagent as described above.
A mouse monoclonal antibody against EV71 VP1 (~ 36 kDa, Abnova, Taiwan, China), a rabbit polyclonal antibody against NUPR1 (~ 10 kDa, Novus, USA), and a mouse monoclonal antibody against β-actin (~ 42 kDa, Abcam, UK) were used as primary antibodies. Goat anti-mouse and anti-rabbit IgG conjugated with horseradish peroxidase (Zhongshanjinqiao, China) were used as secondary antibodies. RD cells were washed with PBS and lysed in RIPA buffer containing the protease inhibitor PMSF at 4°C for 20 min. The cell lysates were obtained by centrifugation at 12,000 rpm for 5 min, and the total protein concentration was measured using the BCA method. The total proteins in 10 µg of lysate were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to a polyvinylidene fluoride (PVDF) membrane (Roche, Switzerland). The membranes were blocked for 1 h with 5% non-fat milk in Tris-buffered saline containing 0.05% Tween 20 (TBST). Next, the membranes were incubated with specific primary antibodies, followed by secondary antibodies conjugated with horseradish peroxidase. The blots were developed using chemiluminescent substrates (ECL, Millipore, USA).
The luciferase reporter plasmids for ENST00000469812 (pGL3-ENST00000469812), ENST00000469812 mutant (pGL3-ENST00000469812mut), 3’UTR of NUPR1 (pGL3-NUPR1WT), and 3’UTR of NUPR1 mutant (pGL3-NUPR1mut) were synthesized as described above. HEK293 cells or RD cells were seeded into 24-well plates at 1 × 105 cells per well and transiently cotransfected with plasmids and miRNA mimics, including pGL3-ENST00000469812, pGL3-ENST00000469812mut, pGL3-NUPR1WT, pGL3-NUPR1mut, pRL-TK-Renilla, miR-4443 mimics, miR mimics NC, miR-4443 inhibitor, miRNA inhibitor NC, and pcDNA-NUPR1. After 48 h, the cells were collected and luciferase activity was measured using a Dual-Luciferase Reporter Assay System (Promega, USA).
Statistical analysis was performed using the software GraphPad Prism 9.0. Statistical differences between two groups were analyzed by t-test, and statistical differences among multiple groups were analyzed by one-way ANOVA. A p-value less than 0.05 was considered statistically significant. *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001
To find miRNAs related to the EV71 infection process, we searched PubMed and found that six miRNAs, including miR-451a, miR-3960, miR-4516, miR-320c, miR-4443, and miR-3665, were significantly differentially expressed in several studies (Supplementary Table S4) [27–33]. Thus, we regarded these miRNAs as potentially involved in the EV71 infection process. Furthermore, in order to determine which miRNAs were closely related to EV71 infection, we infected RD cells with EV71 at an MOI of 0.1 and 1 and then performed qRT-PCR detection. After 24 h, cytopathic effects were observed (Fig. 1A), and the qRT-PCR results showed that the expression levels of miR-4443 and miR-3665 were altered significantly in an MOI-dependent manner (Fig. 1B). Since EV71 mainly invades the nervous system [34], we also examined changes in the intracellular levels of miR-451a, miR-3960, miR-320c, miR-4443, and miR-3665 after infection of the neural cell line SH-SY5Y with EV71. It was found that the intracellular expression of miR-4443 was still downregulated in an MOI-dependent manner (Fig. 1C). Furthermore, downregulation of miR-4443 also could be observed in RD cells infected with EV71 at an MOI of 0.5, 1, or 2 (Fig. 1D and E), indicating the potential role played by miR-4443 in the regulation of EV71 infection.
lncRNAs, by ‘sponging’ miRNAs, regulate the expression of downstream mRNA. To investigate the regulation of miR-4443 expression by lncRNAs, we used the bioinformatics software DIANA to predict the interaction between lncRNAs and miR-4443. Taking the intersection of these lncRNAs and those reportedly upregulated by EV71 infection [20], we identified 30 lncRNAs of interest. Then, by selecting lncRNAs with prediction scores of over 0.8 that were associated with over 2.85-fold upregulation, we selected three lncRNAs – ENST00000469812, ENST00000451940, and ENST00000418747 – for further study (Fig. 2A, Supplementary Table S6). To examine the effect of lncRNAs, we performed qRT-PCR on RD cells infected with EV71. The results showed that EV71 infection at an MOI of 2 for 12 h significantly increased the expression of all three lncRNAs (Fig. 2B). The expression of ENST00000469812 was significantly upregulated at 12 h and 24 h postinfection and showed the highest level of upregulation and the strongest MOI dependence (Fig. 2C). The expression of ENST00000451940 was upregulated at 12 h and 24 h after EV71 infection, but no correlation with infection time was observed (Fig. 2D). ENST00000418747 expression was negatively correlated with MOI (Fig. 2E). Considering that EV71 induced upregulation of ENST00000469812 (Fig. 2C) and downregulation of miR-4443 (Fig. 1H) and that ENST00000469812 had the highest prediction score (Supplementary Table S6), we inferred that ENST00000469812 negatively regulates miR-4443 expression. The gene for lncRNA ENST00000469812, also known as AC121756.1, LINC02046-201, RP11-501O2.5, and NONHSAT092602.2, is located at position 148276682–148279681 on human chromosome 3, with a total length of 378 nt. This long intergenic lncRNA is distributed mainly in the cytoplasm. To confirm this, the cytoplasmic and nuclear components of RD cells were extracted, and the cellular distribution of ENST00000469812 was analyzed by qRT-PCR. The results showed that most of the ENST00000469812 molecules were distributed in the cytoplasm (Fig. 2F), where ENST00000469812 could potentially interact with miRNAs. Sequence alignment was used to identify potential binding sites between ENST00000469812 and miR-4443 (Fig. 2G). A luciferase reporter assay showed that miR-4443 reduced the luciferase activity of wild-type ENST00000469812 but did not influence that of an ENST00000469812 variant with a mutation at nt 318–325 (‘GCCUCCAA’) (Fig. 2G). In addition, an miR-4443 inhibitor reversed the inhibitory effect of miR-4443 on ENST00000469812 in the luciferase assay, indicating that miR-4443 interacts with ENST00000469812 in RD cells and HEK293 cells (Supplementary Fig. S1A). To investigate whether the luciferase activity of ENST00000469812 could be affected by endogenous miR-4443, we performed absolute quantification of miR-4443 in RD, HEK293, and A549 cells by qRT-PCR. The results showed that the expression level of miR-4443 was low (Supplementary Table S5). Then, we transfected RD or HEK293 cells with the pGL-ENST00000469812 plasmid or its mutant. The results showed that endogenous miR-4443 had a stronger inhibitory effect on the luciferase activity of ENST00000469812 than on the ENST00000469812 mutant in HEK293 cells (Supplementary Fig. S1B), despite the low level of endogenous miR-4443 in the cells, further suggesting that endogenous miR-4443 interacts with ENST00000469812. Next, we transfected RD cells with an ENST00000469812 overexpression plasmid (Supplementary Fig. S2A) or small interfering RNA (siRNA) (Supplementary Fig. S2B) and found miR-4443 expression to be downregulated and upregulated, respectively (Fig. 2H). We therefore concluded that ENST00000469812 binds to miR-4443 and negatively regulates miR-4443 expression.
In order to identify the target genes of miR-4443, four online programs, including TargetScan, miRWalk, miRanda, and DIANA were used for prediction. Taking the intersection of the above four sets, we identified 19 target gene candidates (Fig. 3A). Furthermore, qRT-PCR of RD cells transfected with miR-4443 mimics or inhibitors (Supplementary Fig. S3) revealed that miR-4443 mimics and inhibitors induced downregulation and upregulation of NUPR1 transcription, respectively, while the other 18 genes did not exhibit the predicted effect (Fig. 3B). Western blot assay also confirmed that miR-4443 downregulated translational expression of NUPR1 in RD cells (Fig. 3C). A luciferase reporter assay performed in HEK293 cells demonstrated that miR-4443 reduced luciferase activity of wild-type NUPR1-3'UTR, but not that of NUPR1-3'UTR with a “GCCUCCAA” mutation at nt position 4743–4750 of NUPR1 mRNA (Fig. 3D). Likewise, the miR-4443 inhibitor reversed the inhibitory effect of miR-4443 on NUPR1 luciferase activity (Supplementary Fig. S4A), indicating that NUPR1 is one of the target genes of miR-4443. In addition, endogenous miR-4443 affected the NUPR1 luciferase activity significantly (Supplementary Fig. S4B), suggesting that low levels of miR-4443 are sufficient to reduce the NUPR1 luciferase activity. Since EV71 infection induced regulation of miR-4443 expression by ENST00000469812, it was of interest to explore whether EV71 influenced NUPR1 expression through ENST00000469812 and miR-4443. The results of qRT-PCR and Western blot assays confirmed that the transcriptional and translational levels of NUPR1 were significantly increased in RD cells 12 and 24 h after EV71 infection (Fig. 3E and F). Using a luciferase reporter assay in HEK293 cells, we confirmed that ENST00000469812 overexpression significantly weakened the inhibitory effect of miR-4443 on NUPR1-3'UTR (Fig. 3G), suggesting an effect of ENST00000469812 on the interaction between miR-4443 and NUPR1-3'UTR. Finally, using qRT-PCR and Western blot, we also found that miR-4443 overexpression in RD cells abolished the stimulation of NUPR1 transcription and translation by ENST00000469812 (Fig. 3H and I), indicating that EV71 infection affects the ENST00000469812/miR-4443/NUPR1 regulatory axis.
The enhancement of EV71 replication is one of the factors leading to severe HFMD [35–37]. It is therefore necessary to verify that the ENST00000469812/miR-4443/NUPR1 regulatory axis affects EV71 replication. Here, we used qRT-PCR, Western blot, and TCID50 assay to evaluate EV71 replication at the RNA genome transcriptional level, protein translational level, and extracellular viral particle level, respectively. The results showed that ENST00000469812 overexpression and silencing enhanced and reduced EV71 replication (Fig. 4A and B), respectively. Meanwhile, miR-4443 overexpression and inhibition decreased and increased EV71 replication, respectively (Fig. 4C and D). To verify that the promotion of EV71 replication by ENST00000469812 is mediated by miR-4443, we simultaneously transfected RD cells with ENST00000469812 overexpression plasmid and miR-4443 mimics. Our results revealed not only that miR-4443 mimics eliminated the positive effect of ENST00000469812 on EV71 replication but also that ENST00000469812 reversed the negative effect of miR-4443 on EV71 replication (Fig. 4E and F), suggesting that ENST00000469812 promotes EV71 replication via binding to miR-4443 and inhibiting its expression. Furthermore, overexpression and knockdown of NUPR1 in RD cells increased and decreased EV71 replication, respectively (Fig. 4G and H). When RD cells were transfected with ENST00000469812 overexpression plasmid and siNUPR1 simultaneously, siNUPR1 eliminated the positive effect of ENST00000469812 on EV71 replication and ENST00000469812 reversed the negative effect of siNUPR1 on EV71 replication (Fig. 4I and J), indicating that ENST00000469812 promotes EV71 replication via NUPR1 expression. It was therefore concluded that ENST00000469812 upregulates NUPR1 by inhibiting miR-4443 and ultimately promotes the replication of EV71.
lncRNA is an important nucleic acid element that regulates the pathogenic process of viruses [13], the cellular immune response, metabolic processes, and other functions. Li et al. [19] performed transcriptomic analysis to find 23 dysregulated lncRNAs in RD cells and 104 dysregulated lncRNAs in infant mice infected with EV71. Studies using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes cluster analysis of the lncRNA–mRNA coexpression network showed the differentially expressed lncRNAs to be closely associated with pathogenic process of EV71 infection. Yin et al. [20] found that over 4800 lncRNAs were differentially expressed in RD cells infected with EV71. Meng et al. [21] reported that, in peripheral blood mononuclear cells (PBMCs) of children with HFMD, there were 8541 differentially expressed lncRNAs, all of which were related to the host’s immune and inflammatory response. In this study, we investigated the role of lncRNA ENST00000469812 in the EV71 infection process. An earlier study showed that a homologous lncRNA, RP11-501O2.5, was highly expressed in endometrial cancer and might promote the occurrence and development of tumors [38]. However, the detailed function of ENST00000469812 has not yet been clarified. In this study, EV71 infection upregulated the expression of ENST00000469812, which further promoted EV71 replication, suggesting a role of ENST00000469812 in EV71 infection through an as yet undiscovered mechanism. Our data suggest that overexpression of an ENST00000469812 mutant might have an opposite effect from ENSTW00000469812, but the mechanisms still need to be explored. miR-4443 is differentially expressed in a variety of tumor cells and is involved in the occurrence and development of cancers such as breast cancer [39–41], non-small-cell lung cancer [42], ovarian cancer [43], and hepatocellular carcinoma [44]. miR-4443 disables T cells and macrophages by inhibiting tumor necrosis factor receptor (TNFR)-related factor 4 (TRAF4) [45], suggesting that miR-4443 regulates the replication of EV71 by affecting immune responses. Two studies have indicated that miR-4443 is involved in regulation of EV71 infection. Xun et al. [33] first reported that miR-4443 was downregulated by EV71 infection and that 27 dysregulated miRNAs mediated changes in Wnt, MAPK, TGF-β, and mTOR signaling pathways after EV71 infection in RD cells. Zhu et al. [32] found that miR-4443 was significantly downregulated by EV71 and coxsackievirus A16 (CVA16) infection, which might activate the MAPK signaling pathway. EV71 infection has been shown to alter the expression of cytokines by activating the MAPK pathway, and this affects replication of EV71 [46, 47]. When the MAPK pathway was inhibited, cytokine release was inhibited, and the EV71 replication level was reduced [48, 49]. Thus, the MAPK pathway seems to mediate the pathogenesis of EV71 or the immune response of the host. The role of miR-4443 is complex and depends on the downstream target genes. Through prediction and experimental verification, NUPR1 was found to be one of the target genes of miR-4443, as was already shown in a previous study [50], and this affects the proliferation, migration, and invasion of osteosarcoma cells [51]. p38 MAPK has been shown to induce the expression of NUPR1 in astrocytes and pancreatic cancer cells [52], and silencing of NUPR1 reduces the expression level of p38 MAPK [53], suggesting that NUPR1 interacts with the MAPK pathway. Our study showed that miR-4443 regulated the expression of NUPR1, thereby possibly affecting the MAPK pathway and the replication of EV71. NUPR1, a small protein related to the stress response, is upregulated and associated with endoplasmic reticulum and oxidative stress in cells after external stimulation [54]. There is considerable evidence that stress-induced upregulation of NUPR1 maintains and enhances cell metabolism [55]. Upregulation of NUPR1 promotes autophagy, which ultimately promotes tumor cell migration and invasion [56] and also has a protective effect on cells and tissues [57]. However, other studies showed that NUPR1 had the opposite effect on autophagy [58, 59], suggesting that NUPR1 might perform different functions under different conditions. EV71 infection causes a stress response in susceptible cells. For example, EV71 infection induces endoplasmic reticulum stress, which further promotes EV71 replication [60]. The VP1 protein of EV71 has been shown to induce endoplasmic reticulum stress in cells, increase the expression of peripheral myelin protein 22 (PMP22), and promote cellular autophagy [61], which is a key factor enhancing EV71 replication [61, 62]. Thus, it was inferred that EV71 induces the activation of the ENST00000469812/miR-4443/NUPR1 axis, which further causes cellular stress and promotes other activities such as autophagy and facilitates EV71 replication. According to the information on the Expression Atlas website (https://www.ebi.ac.uk/gxa/home), miR-4443 is mainly expressed at low levels in the testes and choroid plexus. Information in miRBase (https://www.ebi.ac.uk/) and LNCediting (http://bioinfo.life.hust.edu.cn/LNCediting/mirna/) also indicates that miR-4443 is expressed at medium or low levels in cell lines and tissues. When we performed absolute quantification of miRNA in cell lines, we also found that the expression levels of miR-4443 were low. However, we still found that miR-4443 affected the replication of EV71, which indicates that endogenous miR-4443 levels are sufficient to affect the ENST00000469812/miR-4443/NUPR1 regulatory axis. In summary, our study showed that EV71 infection results in upregulation of lncRNA ENST00000469812, which binds to and downregulates miR-4443 and upregulates the target gene NUPR1. The ENST00000469812/miR-4443/NUPR1 regulatory axis further promotes EV71 replication. This study provides a theoretical basis for the elucidation of the pathogenic mechanism of EV71 and lays a foundation for the treatment of HFMD.
Supplementary table for Tables (PDF 38 kb) | true | true | true |
PMC9589693 | 36272047 | Maria D. Politi,Angelo Gallo,Georgios Bouras,Maria Birkou,Bruno Canard,Bruno Coutard,Georgios A. Spyroulias | 1H, 13C, 15N backbone resonance assignment of apo and ADP-ribose bound forms of the macro domain of Hepatitis E virus through solution NMR spectroscopy | 22-10-2022 | Macro domain,Hepatitis E virus,ADP-ribose,Solution NMR spectroscopy,Secondary structure | The genome of Hepatitis E virus (HEV) is 7.2 kilobases long and has three open reading frames. The largest one is ORF1, encoding a non-structural protein involved in the replication process, and whose processing is ill-defined. The ORF1 protein is a multi-modular protein which includes a macro domain (MD). MDs are evolutionarily conserved structures throughout all kingdoms of life. MDs participate in the recognition and removal of ADP-ribosylation, and specifically viral MDs have been identified as erasers of ADP-ribose moieties interpreting them as important players at escaping the early stages of host-immune response. A detailed structural analysis of the apo and bound to ADP-ribose state of the native HEV MD would provide the structural information to understand how HEV MD is implicated in virus-host interplay and how it interacts with its intracellular partner during viral replication. In the present study we present the high yield expression of the native macro domain of HEV and its analysis by solution NMR spectroscopy. The HEV MD is folded in solution and we present a nearly complete backbone and sidechains assignment for apo and bound states. In addition, a secondary structure prediction by TALOS + analysis was performed. The results indicated that HEV MD has a α/β/α topology very similar to that of most viral macro domains. | 1H, 13C, 15N backbone resonance assignment of apo and ADP-ribose bound forms of the macro domain of Hepatitis E virus through solution NMR spectroscopy
The genome of Hepatitis E virus (HEV) is 7.2 kilobases long and has three open reading frames. The largest one is ORF1, encoding a non-structural protein involved in the replication process, and whose processing is ill-defined. The ORF1 protein is a multi-modular protein which includes a macro domain (MD). MDs are evolutionarily conserved structures throughout all kingdoms of life. MDs participate in the recognition and removal of ADP-ribosylation, and specifically viral MDs have been identified as erasers of ADP-ribose moieties interpreting them as important players at escaping the early stages of host-immune response. A detailed structural analysis of the apo and bound to ADP-ribose state of the native HEV MD would provide the structural information to understand how HEV MD is implicated in virus-host interplay and how it interacts with its intracellular partner during viral replication. In the present study we present the high yield expression of the native macro domain of HEV and its analysis by solution NMR spectroscopy. The HEV MD is folded in solution and we present a nearly complete backbone and sidechains assignment for apo and bound states. In addition, a secondary structure prediction by TALOS + analysis was performed. The results indicated that HEV MD has a α/β/α topology very similar to that of most viral macro domains.
Hepatitis E virus (HEV) is the most common cause of acute viral hepatitis worldwide (Chandra et al. 2010). HEV is quasi-enveloped virus with a positive single-stranded RNA genome. It is the only member of the genus Orthohepevirus of the family Hepeviridae (LeDesma et al. 2019). According to World Health Organization (WHO), every year there are 20 million estimated cases of HEV infection, with 3.3 million symptomatic cases. The virus is transmitted via fecal–oral or zoonotic route. The latest is caused by close contact with infected animals or consumption of contaminated undercooked animal products (Doceul et al. 2016; Izopet et al. 2012; Yan et al. 2016). In general, HEV is self-limiting illness which lasts a few weeks. The incubation period is 2 to 6 weeks and the symptoms of hepatitis develop, with fever and nausea followed by abdominal pain, vomiting, anorexia, malaise, and hepatomegaly. About 40% of patients develop jaundice (Aslan and Balaban 2020). It is worth mentioning that there is a mortality excess in pregnant females and patients with chronic diseases (Chaudhry et al. 2015). In addition to the classical hepatic manifestations, HEV is responsible for extrahepatic disorders such as neurological disorders associated with Guillain—Barré syndrome and neuralgic amyotrophy (Narayanan et al. 2019; Sooryanarain and Meng 2019). No specific antiviral drug or vaccine is licensed globally for chronic hepatitis, underlining the necessity in the development of potent viral inhibitors. The HEV genome is 7.2 kb long with a 7-methylguanosine cap at the 5′ end and is polyadenylated at the 3′ end. HEV consists of four open reading frames: ORF1, ORF2, ORF3 and ORF4. ORF4 is overlapped with ORF1 and its transcription is controlled by an IRES-like RNA structure with an essential role in HEV RNA polymerase proper function (Kenney and Meng 2019). ORF3 codes a 13 kDa small phosphoprotein, which enhances RIG-I signaling (VP13) (Nan et al. 2014a). ORF2 encodes a N-glycosylated 72 kDa protein important for the capsid formation, a protein that is an attractive target for HEV infection diagnostics and vaccine development (Nan and Zhang 2016). The larger ORF is the ORF1 that occupies about the 2/3 of the genome, encoding the non—structural protein crucial for viral replication, and composed of several functional domains. A methyltransferase (MeT/MTase), a Y undefined domain, a papain—like cysteine protease (PCP), a proline—rich hinge/hypervariable region (PPR/HVR), a macro domain, a helicase (Hel/NTPase) and an RNA-dependent RNA polymerase (Ojha and Lole 2016b; Wang and Meng 2021). The HEV macro domain was identified as a putative interferon (INF) antagonist (Nan et al. 2014b). In addition, its C—terminal region displays direct interaction with both MTase and ORF3 proteins (Anang et al. 2016). HEV MD specifically interacts with the light chain subunit of human ferritin, and suppress its secretion in cultured cells (Ojha and Lole 2016a). HEV MD belongs to the ADP-ribose-1’’-monophosphatase (Appr-1''-pase family) that catalyses conversion of ADP-ribose-1′′-monophosphate (Appr-1′′-p) to ADP-ribose (Allen et al. 2003). Recent studies on protein ADP-ribosylation suggested that viral macro domains are able to de-ADP-ribosylate Asp or Glu side chain of host proteins, which brought them into focus as promising therapeutic targets (Fehr et al. 2018; Li et al. 2016). In the last decade, the progress in the understanding of the crucial functions carried out by viral MDs, suggests that the MD could be a relevant antiviral target and stimulate the development of drug design efforts (Brosey et al. 2021; Dasovich et al. 2022; Fu et al. 2021; Ni et al. 2021; Rack et al. 2020). Here, we present for the first time a 1H, 13C and 15N almost complete resonance assignment of the apo and ADP-ribose bound forms of HEV MD. These assignments should contribute to the understanding of the molecular mechanisms of de-ribosylation and provide starting points for inhibition or protein–protein interaction studies by NMR.
The coding sequence of the HEV macro domain (HEV MD) (residues 772–926, Uniprot ID P29324) was synthesized, codon optimized (GenScript) and subcloned using NdeI and XhoI restriction enzymes into pET20b (+). The MD coding sequence is fused to an artificial ATG initiation codon in 5′ and to a sequence coding for an Hexahisitine preceded by a short linker (LE). Rosetta2 (DE3) (pLysS) Escherichia coli cells (Novagen) were transformed with the pET20b (+)—HEV MD. Precultures were grown overnight at 37 °C in 5 mL LB suppled with chloramphenicol and ampicillin, 180 revolutions per minute (rpm). Cells were then grown in 0.75 L minimal medium containing 15NH4Cl (1 g/L) and d-[13C6] glucose (4 g/L), NaCl (0.5 g/L), 1 mM MgSO4, 1.5 mL Solution Q [40 mM HCl, FeCl2·4H2O (50 mg/L), CaCl2·2H2O (184 mg/L), H3BO3 (64 mg/L), CoCl2·6H2O (18 mg/L), CuCl2·2H2O (4 mg/L), ZnCl2 (340 mg/L), Na2MoO4·2H2O (605 mg/L), MnCl2 4H2O (40 mg/L)] and was inoculated with the preculture and antibiotics (0.1 mg/mL ampicillin and chloramphenicol). Cell culture was grown at 37 °C, 200 rpm and when the Optical Density (OD) 600 reached 0.6–0.8, isopropyl β-d-1-thiogalactopyranoside (IPTG) was added to final concentration of 0.1 mM. After induction, the culture was incubated at 16 °C for seventeen hours (17 h). The cells were harvested by centrifugation at 8000 rpm for 10 min and pellet stored at − 80 °C until use. Cell suspension was supplemented with 5% glycerol, 1 mM Tris (2-carboxyethyl) phosphine (TCEP) and EDTA-free protease cocktail (Sigma-Aldrich). Three freeze–thaw cycles (liquid N2 – 42 °C) were performed before the sonication step. Cells were then lysed by sonication and the cell debris was cleared by centrifugation (21.000 × g, 45 min, 4 °C). Supernatant was filtered through a 0.25 μm filter and loaded on a 5 mL His-Trap HF column (GE Healthcare) charged with Ni2+. The HEV MD was purified by immobilized metal affinity chromatography (IMAC) and eluted with 200 mM imidazole, 20 mM Na2PO4, pH 8.0, 500 mM NaCl, 1 mM TCEP, 1 mM phenylmethylsulfonyl fluoride (PMSF). The eluted HEV MD was gradually introduced to the NMR buffer (10 mM Sodium Acetate, 5 mM EDTA pH 5.4), using an Amicon Ultra 15 mL Centrifugal Filter membrane (Merck Millipore) and concentrated to a final volume of 1 mL. The protein was further purified by size exclusion chromatography using FPLC ÄKTA Purifier System (GE Healthcare) with Superdex® Increase 75 10/300 GL (GE Healthcare) pre-equilibrated with buffer 10 mM Sodium Acetate, 5 mM EDTA at pH 5.4. The protein was eluted according to its molecular weight, indicating a monomer. The fractions containing the HEV MD were collected and concentrated to a final volume of 500 μL and stored at − 80 °C. For the ADP-ribose bound state, a 100 mM stock solution of ADP-ribose sodium salt (Sigma A0752) was prepared in water. This stock solution was used to prepare the HEV MD—ADP-ribose complex by adding a tenfold molar excess to the protein.
For the NMR experiments 15N and 13C/15N labelled samples prepared with a concentration of 0.4 mM for HEV MD in the apo form and 0.5 mM in the ADP-ribose bound form with protein to ADP-ribose ratio 1:10. All samples were in a mixed solvent of 90% H2O and 10% D2O (10 mM Sodium Acetate, 5 mM EDTA at pH 5.4). 1H chemical shifts were referenced on DSS methyl signal at 0.0 ppm. 0.25 mM 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS) were used as internal standard. 13C and 15 N chemical shifts were referenced indirectly to the 1H standard using a conversion factor derived from the ratio of NMR frequencies (Wishart et al. 1995). All NMR experiment were recorded on a Bruker Avance III HD 700 MHz NMR spectrometer equipped with a four-channel 5 mm cryogenically cooled TCI gradient probe at 298 Κ. All NMR data were processed with TOPSPIN 4.1.1 software and analysed with CARA 1.9.2a4 (Keller 2004). The acquired NMR experiments used for sequence specific assignment are summarized in Table 1. Backbone assignments and sidechains for HEV MD in the free and in the ADP-ribose bound form were obtained from the following series of heteronuclear experiments: 2D [1H,15N]–HSQC and 2D [1H,15N]–TROSY, 3D HN(CO)CA, 3D HNCA, 3D TROSY CBCA(CO)NH, 3D TROSY CBCANH, 3D HN(CA)CO, 3D HNCO, 3D HBHA(CO)NH, HCCH-TOCSY (Table 1).
The HEV macro domain shares a low sequence homology with other MDs (i.e., AF1521, VEEV, CHIKV, SARS-COV1, SARS-COV2) as shown in Fig. 1. Indeed, the percentage of identity between HEV MD and other viral MD is surprisingly low and found around 20% (23.44% with VEEV MD). The NMR 1H–15N HSQC spectrum showed well-dispersed amide signals and narrows line widths, indicative of a well-folded monomeric polypeptide as shown in Fig. 2a for apo and in Fig. 2b for ADP-ribose bound form of HEV MD, respectively. In addition, the superposition of 1H–15N HSQC spectra of HEV MD in apo and bound state indicated significant chemical shift changes of the 1H–15N HSQC cross-peaks upon binding with ADPR, as shown in Fig. 3. For the apo form of HEV MD, the analysis of the heteronuclear NMR experiments of the double isotopically labelled sample with the conventional backbone and sidechains methodology, results in the sequence specific assignment of 93.93% the resonances of the backbone atoms (HN, N, CO, Cα and Cβ) and 58.41% the resonances of the sidechains atoms. For the ADP-ribose bound form of HEV MD, we were able to assign 95.22% and 61.63% of the resonances of the backbone and sidechains atoms respectively. The unassigned HN and N resonances of free HEV MD belong to D810, R812, L817, C818, H819, F821, T846. All the missing residues belong to loop regions or to unstructured regions or part of loops indicating some differences in their conformational dynamics features that hampers their detection. By contrary, the signals missing in the assignment of the ADP-ribose bound form of HEV MD belong to regions spanning only the residues S807, L817, C818, H819, F821. The disappearance of the above—mentioned set of resonances in the two forms might suggest conformational variability and flexibility upon binding. In order to identify the secondary structure elements of the HEV MD apo and ADP-ribose forms, chemical shift assignments of backbone atoms (HN, Hα, Cα, Cβ, CO, N) for each residue in the sequence were analysed by TALOS + software (Shen et al. 2009). The secondary structure elements for free HEV MD protein are organized in an α/β/α sandwich-like fold with β/β/α/β/α/β/β/β/α/β/α/β/α topology from N- to C-terminal residues of the native sequence, graphically presented in Fig. 4. The order of the secondary structure segments are pretty similar to that of the other viral and human MDs ((Melekis et al. 2015), (Makrynitsa et al. 2015), (Lykouras et al. 2018), (Tsika et al. 2022)). We also report that upon interaction with ADPR no significant change in secondary structure elements has been identified (Fig. 3b). TALOS + analysis indicates also that HEV MD adopts a similar folding to that of many viral macro domains despite its low sequence similarity (Fig. 1), (Makrynitsa et al. 2019; Tsika et al. 2022). Chemical shift values for the 1H, 13C and 15N resonances of HEV macro domain in the free state and in the ADPR bound state have been deposited at the BioMagResBank (https://www.bmrb.wisc.edu) under accession numbers 51470, and 51471, respectively. To summarize, we present in this work a biological method to produce and purify in high yield the native form of recombinant HEV MD. NMR analysis indicated that the polypeptide is well folded and in monomeric state. These results will contribute to its 3D structure determination and open opportunities for the development of inhibitors with potential antiviral properties. | true | true | true |
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