Aire‐dependent transcripts escape Raver2‐induced splice‐event inclusion in the thymic epithelium
2022; Springer Nature; Volume: 23; Issue: 3 Linguagem: Inglês
10.15252/embr.202153576
ISSN1469-3178
AutoresFrancine Padonou, Virginie Gonzalez, Nathan Provin, Sümeyye Yayilkan, Nada Jmari, Julia Maslovskaja, Kai Kisand, Pärt Peterson, Magali Irla, Matthieu Giraud,
Tópico(s)Hereditary Neurological Disorders
ResumoArticle17 January 2022free access Transparent process Aire-dependent transcripts escape Raver2-induced splice-event inclusion in the thymic epithelium Francine Padonou Francine Padonou Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France Institut Cochin, INSERM, CNRS, Paris Université, Paris, France Contribution: Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Virginie Gonzalez Virginie Gonzalez Institut Cochin, INSERM, CNRS, Paris Université, Paris, France Contribution: Investigation Search for more papers by this author Nathan Provin Nathan Provin Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France Contribution: Formal analysis, Investigation, Visualization Search for more papers by this author Sümeyye Yayilkan Sümeyye Yayilkan Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France Contribution: Formal analysis, Investigation, Visualization Search for more papers by this author Nada Jmari Nada Jmari Institut Cochin, INSERM, CNRS, Paris Université, Paris, France Contribution: Investigation Search for more papers by this author Julia Maslovskaja Julia Maslovskaja orcid.org/0000-0002-6836-9520 Molecular Pathology Research Group, University of Tartu, Tartu, Estonia Contribution: Investigation Search for more papers by this author Kai Kisand Kai Kisand Molecular Pathology Research Group, University of Tartu, Tartu, Estonia Contribution: Investigation Search for more papers by this author Pärt Peterson Pärt Peterson orcid.org/0000-0001-6755-791X Molecular Pathology Research Group, University of Tartu, Tartu, Estonia Contribution: Resources, Writing - review & editing Search for more papers by this author Magali Irla Magali Irla Aix-Marseille Université, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Marseille, France Contribution: Conceptualization, Resources, Methodology, Writing - review & editing Search for more papers by this author Matthieu Giraud Corresponding Author Matthieu Giraud [email protected] orcid.org/0000-0002-1208-9677 Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France Institut Cochin, INSERM, CNRS, Paris Université, Paris, France Contribution: Conceptualization, Software, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Francine Padonou Francine Padonou Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France Institut Cochin, INSERM, CNRS, Paris Université, Paris, France Contribution: Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Virginie Gonzalez Virginie Gonzalez Institut Cochin, INSERM, CNRS, Paris Université, Paris, France Contribution: Investigation Search for more papers by this author Nathan Provin Nathan Provin Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France Contribution: Formal analysis, Investigation, Visualization Search for more papers by this author Sümeyye Yayilkan Sümeyye Yayilkan Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France Contribution: Formal analysis, Investigation, Visualization Search for more papers by this author Nada Jmari Nada Jmari Institut Cochin, INSERM, CNRS, Paris Université, Paris, France Contribution: Investigation Search for more papers by this author Julia Maslovskaja Julia Maslovskaja orcid.org/0000-0002-6836-9520 Molecular Pathology Research Group, University of Tartu, Tartu, Estonia Contribution: Investigation Search for more papers by this author Kai Kisand Kai Kisand Molecular Pathology Research Group, University of Tartu, Tartu, Estonia Contribution: Investigation Search for more papers by this author Pärt Peterson Pärt Peterson orcid.org/0000-0001-6755-791X Molecular Pathology Research Group, University of Tartu, Tartu, Estonia Contribution: Resources, Writing - review & editing Search for more papers by this author Magali Irla Magali Irla Aix-Marseille Université, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Marseille, France Contribution: Conceptualization, Resources, Methodology, Writing - review & editing Search for more papers by this author Matthieu Giraud Corresponding Author Matthieu Giraud [email protected] orcid.org/0000-0002-1208-9677 Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France Institut Cochin, INSERM, CNRS, Paris Université, Paris, France Contribution: Conceptualization, Software, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration, Writing - review & editing Search for more papers by this author Author Information Francine Padonou1,2, Virginie Gonzalez2, Nathan Provin1, Sümeyye Yayilkan1, Nada Jmari2, Julia Maslovskaja3, Kai Kisand3, Pärt Peterson3, Magali Irla4 and Matthieu Giraud *,1,2 1Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France 2Institut Cochin, INSERM, CNRS, Paris Université, Paris, France 3Molecular Pathology Research Group, University of Tartu, Tartu, Estonia 4Aix-Marseille Université, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Marseille, France *Corresponding author. Tel: +33 02 40 08 47 23; E-mail: [email protected] EMBO Reports (2022)23:e53576https://doi.org/10.15252/embr.202153576 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Aire allows medullary thymic epithelial cells (mTECs) to express and present a large number of self-antigens for central tolerance. Although mTECs express a high diversity of self-antigen splice isoforms, the extent and regulation of alternative splicing events (ASEs) in their transcripts, notably in those induced by Aire, is unknown. In contrast to Aire-neutral genes, we find that transcripts of Aire-sensitive genes show only a low number of ASEs in mTECs, with about a quarter present in peripheral tissues excluded from the thymus. We identify Raver2, as a splicing-related factor overexpressed in mTECs and dependent on H3K36me3 marks, that promotes ASEs in transcripts of Aire-neutral genes, leaving Aire-sensitive ones unaffected. H3K36me3 profiling reveals its depletion at Aire-sensitive genes and supports a mechanism that is preceding Aire expression leading to transcripts of Aire-sensitive genes with low ASEs that escape Raver2-induced alternative splicing. The lack of ASEs in Aire-induced transcripts would result in an incomplete Aire-dependent negative selection of autoreactive T cells, thus highlighting the need of complementary tolerance mechanisms to prevent activation of these cells in the periphery. SYNOPSIS Genes in the thymic epithelium sensitive to Aire expression are characterized by low levels of H3K36me3 marks and show few alternative splicing events (ASEs) in their transcripts, as they escape the splicing-related factor Raver2. Aire-sensitive genes in mTECs are devoid of H3K36me3 and escape Raver2. Aire-neutral genes show high levels of H3K36m3 and many ASEs in their transcripts. In peripheral tissues Aire-sensitive genes are enriched for H3K36me3 and show higher rates of alternative splicing. Aire-induced transcripts show a fraction of all ASEs, suggesting an incomplete Aire-dependent negative selection. Introduction Tolerance against self-tissues is an essential feature of the immune system. It is established in the thymus following the presentation of self-antigen peptides to developing T cells. T cells recognizing their cognate antigen either undergo negative selection by apoptosis, preventing the release of autoreactive T cells, or develop into regulatory T cells (Tregs) able to suppress potential autoreactive T cells in the periphery (Goodnow et al, 2005; Cowan et al, 2013; Klein et al, 2014). The subset of medullary thymic epithelial cells characterized by high levels of major histocompatibility complex class II (MHC II) molecules (mTEChi) is essential to the presentation of self-antigens to developing T cells. Indeed, mTEChi have the unique ability to load, onto MHC II molecules, antigenic peptides originated from a wide array of endogenously expressed self-antigens (Sansom et al, 2014; Danan-Gotthold et al, 2016), including those controlled by the autoimmune regulator (Aire) and restricted to specific peripheral tissues (Derbinski et al, 2001; Sansom et al, 2014). In mice, invalidation of the Aire gene results in a drop of Aire-sensitive gene expression in mTEChi, and the presence of autoantibodies and immune infiltrates directed at multiple peripheral tissues due to impaired negative selection of autoreactive T cells and their harmful activation in the periphery (Anderson et al, 2002). Mutations in the human AIRE gene also cause the multi-organ devastating autoimmune disorder named Autoimmune Polyendocrine Syndrome type 1 (APS1) (Nagamine et al, 1997; Peterson et al, 2004). The breadth of the repertoire of self-peptides presented by mTEChi does not only rely on the expression of a high number of self-antigen genes but also on the high diversity of their transcript isoforms whose translation produces multiple protein variants as a result of high rates of alternative splicing (Keane et al, 2015; Danan-Gotthold et al, 2016). Hence, a variety of alternative splicing events (ASEs) are expected to be spliced-in in mTEChi and the resulting processed peptides presented to developing T cells, thereby ensuring the efficient elimination of T cells capable to elicit autoreactive responses against peptides derived from these ASEs in the periphery. Although the expression of a wide range of transcript isoforms has been unambiguously established in mTEChi, the impact and the regulation mechanisms of alternative splicing on the genes controlled by Aire remain to be determined. Notably, it is unknown whether they encode a high transcript-isoform diversity similarly to total mTEChi, or whether the ASEs included in their transcripts equal the diversity of spliced-in ASEs detected for the same genes in their tissues of expression. We therefore investigated the pattern of ASE inclusion for transcripts of Aire-sensitive genes in WT and Aire-KO mTEChi, as well as in peripheral tissues. Comparison of ASE inclusion for transcripts of Aire-sensitive versus neutral genes in mTEChi revealed an unexpected conservation of alternative splicing regulation between Aire-positive and Aire-negative subsets. Differences in the pattern of ASE inclusion with peripheral tissues provided clues on the relative importance of the role of negative selection, in comparison to peripheral mechanisms, for the establishment and maintenance of immunological tolerance against Aire-dependent self-antigens. Finally, we identified an epigenetic mechanism, involving the splicing-related factor Raver2 that sustains the regulation of alternative splicing in mTECs and explains the patterns of ASE inclusion for transcripts of Aire-sensitive and neutral genes in these cells. Results Aire-sensitive genes encode a low diversity of transcript isoforms in mTEChi To characterize the alternative splicing complexity of Aire-sensitive genes in mTEChi sorted following the outline strategy (Appendix Fig S1 and S2), we determined the diversity of Aire-induced transcript isoforms that result from alternative splicing through the calculation of the median splicing entropy of these genes (Ritchie et al, 2008). For a given gene, splicing entropy was calculated using the levels of transcript-isoform expression obtained by assignment of paired-end sequencing reads to the RefSeq mRNA isoform annotations (Fig 1A). The more diverse the transcript isoforms, the greater the associated splicing entropy. Since a sufficient number of mapped junction-spanning reads is necessary for accurate characterization of transcript isoforms, we looked for the minimum expression value below which transcript isoform diversity could not be reliably captured in our mTEChi RNA-seq data. For that purpose, we binned the genes based on expression and calculated their median splicing entropy (Fig 1B). We found stable values of splicing entropy for genes with expression levels over 1 FPKM and highly degraded values for genes showing lower expression levels. This prevented the detection of minor transcript isoforms and therefore prompted us to exclude weakly expressed genes for subsequent splicing analyses. Next, we selected the Aire-sensitive genes characterized by a twofold expression increase in WT versus Aire-KO mTEChi (Fig 1C) and found that their median splicing entropy was significantly lower than those of Aire-neutral genes and of all genes taken together (Fig 1D). This finding thus revealed that Aire-sensitive genes encode a lower diversity of transcript isoforms in mTEChi, denoting lower rates of alternative splicing at these genes. Figure 1. Low splicing entropy of Aire-sensitive genes in mTEChi Schematic representation of a hypothetical gene with mapped paired-end sequencing reads identifying transcript isoforms. The isoform diversity of this gene is evaluated by calculation of its splicing entropy. Median splicing entropy of genes binned according to their expression levels. FPKM of 1 corresponds to the threshold over which the transcript isoform diversity can be accurately characterized in our RNA-seq dataset. (three mTEChi biological replicates [combined]; error bars show the 95% confidence interval of the medians). Identification of Aire-sensitive genes upregulated by Aire in WT versus Aire-KO mTEChi (FC > 2) and matching the threshold of 1 FPKM in WT mTEChi (red dots, n = 2,608). Aire-neutral genes (0.5 < FC < 2) with expression levels over 1 FPKM in WT mTEChi are represented by dark gray dots between the dashed lines. (n = 3 WT and three Aire-KO mTEChi biological replicates). Median splicing entropy of Aire-sensitive genes, Aire-neutral genes (equal numbers), and all genes in each of the three mTEChi biological replicates (rep). Error bars show the 95% confidence interval of the medians. **P < 10−3, ***P < 10−4 (Wilcoxon test, n = 2,608, performed in each of the three biological replicates). Download figure Download PowerPoint Aire-induced transcripts show low ASE inclusion in mTEChi We next sought to determine whether the low diversity of transcript isoforms induced by Aire was sustained by a biased inclusion of ASE. To this end, we first identified all ASEs from the RefSeq mRNA annotation database in parsing its content using rMATS and considered the types of ASEs whose inclusion is susceptible to add amino acid content to the encoded protein isoforms without removing some, that is, skipped exon (SE), alternative 5′ splicing site (5SS), alternative 3′ splicing site (3SS), and intron retention (IR) events. We then computed their percent splicing inclusion (PSI), that is, the relative expression of transcript isoforms spliced in versus spliced in or out for each ASE (Fig 2A). Comparison of PSI values for Aire-sensitive versus neutral genes in mTEChi revealed a significant imbalance toward lower values (< 0.1), therefore showing a reduced inclusion of ASEs in the transcripts induced by Aire(Aire-sensitive: PSI < 0.1 [n = 163], PSI > 0.1 [n = 265]; Aire-neutral: PSI < 0.1 [n = 93], PSI > 0.1 [n = 332]; Chisq = 3.6 × 10−7; Fig 2B). We then calculated, for Aire-sensitive and neutral genes, the levels of ASE inclusion imbalance, that is, the number ratio of ASEs showing some level of active inclusion (PSI > 0.1) to ASEs showing no or background inclusion (PSI < 0.1), and found similar reduced levels for Aire-sensitive genes across three replicates (Fig 2C, Left). Comparison of the levels of ASE inclusion imbalance for Aire-neutral genes between mTEChi and peripheral tissues for which we collected RNA-seq datasets (Li et al, 2017) revealed higher levels in mTEChi, which is in line with reports showing higher rates of alternative splicing in mTEChi (Keane et al, 2015; Danan-Gotthold et al, 2016). In addition, the breadth of the reduction of ASE inclusion between transcripts of Aire-neutral and Aire-sensitive genes in mTEChi is striking since it is much wider than the reduction observed for Aire-neutral genes between mTEChi and each peripheral tissue (Fig 2C, Right). Since alternative splicing was reported to be influenced by gene expression in some systems (Kornblihtt et al, 2013), we calculated the median expression level of neutral genes in each mTEChi replicate and peripheral tissues. We found no significant correlation with the levels of ASE inclusion imbalance, therefore ruling out gene expression as a primary factor responsible for variation of ASE inclusion in our tested samples (Appendix Fig S3). Figure 2. Low levels of ASE inclusion imbalance for Aire-sensitive genes in mTEChi Schematic representation exemplifying the characterization of two transcript isoforms defined by a spliced in (i1) and a spliced out (i2) ASE, respectively, as well as, of an additional isoform (i3) unrelated to the ASE. The PSI value of the ASE is calculated using the specific expression of the transcript isoforms having the ASE spliced in or out. Distribution of ASEs according to their PSI values for Aire-sensitive (Left) and neutral genes (Right) in mTEChi. ASEs with PSI < 0.1 are considered as excluded, whereas ASEs with PSI > 0.1 as present, with some level of active inclusion. Levels of ASE inclusion imbalance for Aire-sensitive and neutral genes (equal numbers) in each of the three mTEChi biological replicates (rep; Left) and for Aire-neutral genes in peripheral tissues (unique samples; Right). Inclusion imbalance of each type of ASE in each of the three mTEChi biological replicates (rep). Data information: In (C, D) *** P < 10−4, **P < 10−3, *P < 0.05 (chi-squared test). Download figure Download PowerPoint Next, we calculated in mTEChi, the inclusion imbalance of the different types of considered ASEs, and found decreased levels for each type of ASE, with SE, 5SS, and IR events reaching statistical significance (Fig 2D). Finally, to address whether the transcripts induced by Aire also show reduced inclusion of ASEs in human mTEChi, we isolated mTEChi from human thymic tissues obtained during pediatric cardiac surgery and performed RNA-seq experiments. We identified the minimum expression values over which transcript isoforms could be accurately characterized in these samples, as shown by the example of two individuals (Fig EV1A). We then identified all ASEs from human RefSeq mRNA annotations and selected human Aire-sensitive and neutral ortholog genes for which we calculated the PSI values of their associated ASEs. Comparison of ASE inclusion imbalance for the Aire-sensitive and neutral genes in one male (indiv 1) and four female (indiv 2–5) human individuals showed a marked reduction for Aire-sensitive genes similar to what we found in mice, therefore revealing conservation of ASE inclusion in mTEChi between mice and humans (Fig EV1B). Click here to expand this figure. Figure EV1. Low levels of ASE inclusion imbalance for Aire-sensitive genes in human mTEChi Median splicing entropy of genes binned according to their expression values. FPKM of 0.5 corresponds to the threshold over which the transcript isoform diversity can be accurately characterized in our RNA-seq dataset. Example of two individuals is shown. Levels of ASE inclusion imbalance for Aire-sensitive and neutral genes in mTEChi of five different individuals, *** P < 10−4 (chi-squared test). Download figure Download PowerPoint Together, these findings revealed that the transcripts induced by Aire in mTEChi show conserved low ASEs in contrast to those that are neutral to Aire. Low ASE inclusion in transcripts of Aire-sensitive genes is a general feature of TECs To discriminate whether the reduced inclusion of ASEs in the transcripts induced by Aire was directly associated with the Aire’s induction of gene expression or was also observed in the absence of Aire, we selected the genes with twofold increased expression in WT versus Aire-KO mTEChi and with levels of expression over 1 FPKM in both WT and Aire-KO mTEChi (Fig 3A). We noted that meeting the threshold of 1 FPKM in Aire-KO mTEChi shrank the selection of Aire-sensitive genes since most of Aire-sensitive genes are inactive or expressed at very low level in the absence of Aire. Figure 3. Low levels of ASE inclusion imbalance for Aire-sensitive genes in Aire-negative TECs Identification of Aire-sensitive genes upregulated by Aire in WT versus Aire-KO mTEChi (FC > 2) and matching the threshold of 1 FPKM in WT and Aire-KO mTEChi (red dots, n = 1,010). (three WT and three Aire-KO mTEChi biological replicates). 3D representation of the distribution of ASEs of Aire-sensitive (Left) and neutral genes (Right) according to their PSI values calculated from three WT (combined) and three Aire-KO mTEChi replicates (combined). Levels of ASE inclusion imbalance for Aire-sensitive and neutral genes (equal numbers) based on PSI values calculated from three WT (combined) and three Aire-KO mTEChi replicates (combined). Differential gene expression of Aire-sensitive genes between mTEChi and mTEClo. Red dots show the Aire-sensitive genes with FPKM > 1 in mTEChi and mTEClo (n = 1,589). (three mTEChi biological replicates and one mTEClo dataset). 3D representation of the distribution of ASEs of Aire-sensitive (Left) and neutral genes (Right) according to their PSI values calculated from three mTEChi replicates (combined) and one mTEClo dataset. Levels of ASE inclusion imbalance for Aire-sensitive and neutral genes (equal numbers) based on PSI values calculated from three mTEChi replicates (combined) and one mTEClo dataset. Differential gene expression of Aire-sensitive genes between mTEChi and cTEC. Red dots show the Aire-sensitive genes with FPKM > 1 in mTEChi and cTEC (n = 879). (three mTEChi and three cTEC biological replicates). 3D representation of the distribution of ASEs of Aire-sensitive (Left) and neutral genes (Right) according to their PSI values calculated from three mTEChi replicates (combined) and three cTEC replicates (combined). Levels of ASE inclusion imbalance for Aire-sensitive and neutral genes (equal numbers) based on PSI values calculated from three mTEChi replicates (combined) and three cTEC replicates (combined). Download figure Download PowerPoint We calculated the PSI values of ASEs of the selected Aire-sensitive and neutral genes and compared their distributions in WT and Aire-KO mTEChi using three-dimensional representations (Fig 3B). We observed similar PSI values for Aire-sensitive genes in WT and Aire-KO mTEChi, as well as for Aire-neutral genes. We then calculated the values of ASE inclusion imbalance and identified similar low levels for Aire-sensitive genes in mTEChi from WT and Aire-KO mice, showing that the reduced inclusion of ASEs in the transcripts of Aire-sensitive genes was independent of Aire expression (Fig 3C). Since mTEChi correspond to a stage of differentiation derived from precursor cells in mTEClo that lack Aire expression (Gäbler et al, 2007; Hamazaki et al, 2007; Dhalla et al, 2020), we asked whether the low ASE inclusion in transcripts of Aire-sensitive genes could be a feature already present in mTEClo. To this end, we selected the genes with expression values over 1 FPKM in mTEChi and mTEClo for calculation of PSI values of their associated ASEs (Fig 3D). As for the WT versus Aire-KO mTEChi comparison, we observed similar PSI values for Aire-sensitive genes in mTEChi and mTEClo, as well as for Aire-neutral genes (Fig 3E). Finally, calculation of the values of ASE inclusion imbalance revealed similar low levels for Aire-sensitive genes in mTEChi and mTEClo (Fig 3F). This finding thus shows that the low number of ASEs in the transcripts of Aire-sensitive genes in mTEChi is also a feature of mTEClo. Next, we sought to definitively confirm this observation in analyzing an independent RNA-seq dataset of mTEChi/lo sorted as CD45−EpCAM+UEA1+Ly51−CD80highMHCIIhigh/CD80lowMHCIIlow (St-Pierre et al, 2015). We first showed that the level of expression of the markers used to sort these cells and of a set of genes specific to mTEChi and mTEClo were identical or very close to those measured in our mTEChi and mTEClo data (Appendix Fig S4A and B). Then, we analyzed the extent of ASE inclusion in the independent mTEChi/lo dataset and confirmed similar low levels of ASE inclusion imbalance for Aire-sensitive genes in mTEChi and mTEClo (Appendix Fig S4C–E). To evaluate the levels of ASE inclusion in cTECs, we analyzed RNA-seq data of cTECs that were also generated in (St-Pierre et al, 2015) and showed that the low ASE inclusion in transcripts of Aire-sensitive genes was also a feature of cTECs (Fig 3G–I), suggesting that it could stem from a common thymic epithelial progenitor. Together these findings revealed that the low inclusion of ASEs in the transcripts of Aire-sensitive genes is a general feature of TECs, independent of Aire expression and conserved upon maturation of mTEClo into mTEChi. Transcripts of Aire-sensitive genes show enhanced ASE inclusion in those tissues where they are expressed Since transcripts of Aire-sensitive genes are subject to low ASE inclusion in mTEChi, we asked whether they could exhibit a stronger inclusion when their expression is driven by tissue-specific transcriptional mechanisms in the periphery. To address this question, we selected for each tissue in our dataset, the Aire-sensitive genes showing a specific or selective expression by using the Specificity Measurement (SPM) method (Pan et al, 2013) as in Guyon et al (2020), and determined the levels of ASE inclusion imbalance. Comparison with mTEChi revealed overall stronger ASE inclusion in peripheral tissues (Fig 4A), indicating that ASE inclusion for transcripts of Aire-sensitive genes is differentially regulated in the periphery. We further identified for each ASE, its PSI values in mTEChi and in the peripheral tissue(s) of specific/selective expression of its corresponding Aire-sensitive gene (Fig EV2A). We selected the ASEs with PSI values < 0.1 in mTEChi and > 0.1 in the periphery, as exemplified in (Figs EV2A–C), and found that nearly a quarter of ASEs present in the tissues of specific Aire-sensitive gene expression, were excluded in mTEChi (Fig 4B, Left). This exclusion of ASEs in mTEChi would therefore increase the risk of release of autoreactive T cells. Conversely, a significantly lower percentage of ASEs present in mTEChi were excluded in the tissues of specific Aire-sensitive gene expression (Fig 4B, Right). We noted that among the latter ASEs, only a minority (about 2%) showed a full inclusion in mTEChi (PSI > 0.9 in mTEChi and < 0.1 in the periphery; Fig EV2A), indicating that only few autoreactive T cells specific for the antigenic epitopes generated upon the exclusion of these ASEs, would leave the thymus and contribute to the risk for autoimmunity. Figure 4. Higher levels of ASE inclusion imbalance for Aire-sensitive genes in their tissues of expression A. Levels of ASE inclusion imbalance for Aire-sensitive genes in their tissues of expression. Open red bars are for particular peripheral tissues, whereas the solid red bar is for mTEChi. The ASE inclusion imbalance is calculated for each of the three mTEChi biological replicates on an identical set of ASEs (n = 3, error bars show mean ± STD). B. Percentage of ASEs of Aire-sensitive genes that are excluded in mTEChi among ASEs showing some level of inclusion in the tissues of specific expression (Left). The percentage of ASEs excluded in specific tissues among ASEs present in mTEChi is shown (Right). C, D. Percentage of ASE exclusion for Aire-neutral genes in mTEChi among ASEs present in each peripheral tissue (C) or (D) in all 1–14 permutations of the 14 peripheral tissues in our database, as a mean percentage. The dashed line represents the percentage of ASE exclusion shown in (B, Left) and (C), **P < 10−3 (pnorm (cumulative distribution function) to the normal distribution defined by the mean and STD of ASE exclusion percentages in the 14 peripheral tissues). Download figure Download PowerPoint Click here to expand this figure. Figure EV2. PSI values of ASEs of Aire-sensitive genes in mTEChi and in their tissues of expression Each ASE is represented by a circle. ASEs excluded in mTEChi or in the periphery (PSI < 0.1) are shown on a white background, otherwise on a salmon-colored background. The ASEs present in the periphery (PSI > 0.1) and excluded in mTEChi (PSI < 0.1) are framed by an orange square. Level of expression of Fhod3, Ablim2, Dsc1, and Reep6 (taken as examples of Aire-sensitive genes with a specific or selective peripheral expression) from RNA-seq data of mTEChi and 14 mouse tissues. Asterisks indicate the tissue(s) of specific/selective expression: Fhod3 is specific to the heart, Dsc1 to the forestomach; Abli
Referência(s)