Transcript splicing optimizes the thymic self-antigen repertoire to suppress autoimmunity
Ryunosuke Muro, … , Kazuo Okamoto, Hiroshi Takayanagi
Ryunosuke Muro, … , Kazuo Okamoto, Hiroshi Takayanagi
Published October 15, 2024
Citation Information: J Clin Invest. 2024;134(20):e179612. https://doi.org/10.1172/JCI179612.
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Transcript splicing optimizes the thymic self-antigen repertoire to suppress autoimmunity

Abstract

Immunological self-tolerance is established in the thymus by the expression of virtually all self-antigens, including tissue-restricted antigens (TRAs) and cell-type–restricted antigens (CRAs). Despite a wealth of knowledge about the transcriptional regulation of TRA genes, posttranscriptional regulation remains poorly understood. Here, we show that protein arginine methylation plays an essential role in central immune tolerance by maximizing the self-antigen repertoire in medullary thymic epithelial cells (mTECs). Protein arginine methyltransferase-5 (Prmt5) was required for pre-mRNA splicing of certain key genes in tolerance induction, including Aire as well as various genes encoding TRAs. Mice lacking Prmt5 specifically in thymic epithelial cells exhibited an altered thymic T cell selection, leading to the breakdown of immune tolerance accompanied by both autoimmune responses and enhanced antitumor immunity. Thus, arginine methylation and transcript splicing are essential for establishing immune tolerance and may serve as a therapeutic target in autoimmune diseases as well as cancer immunotherapy.

Authors

Ryunosuke Muro, Takeshi Nitta, Sachiko Nitta, Masayuki Tsukasaki, Tatsuo Asano, Kenta Nakano, Tadashi Okamura, Tomoki Nakashima, Kazuo Okamoto, Hiroshi Takayanagi

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Figure 3

Transcriptome analysis of Prmt5-deficient mTECs.

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Transcriptome analysis of Prmt5-deficient mTECs. (A) PCA of RNA-Seq data...
(A) PCA of RNA-Seq data. The indicated TEC subsets isolated from control mice (Prmt5fl/fl, n = 3), Prmt5-cKO mice (Prmt5fl/fl Foxn1-Cre, n = 3), and Aire-KO mice (n = 3) were subjected to RNA-Seq analysis. The percentages on each axis indicate the variance contribution. (B) Scatter plots of differentially expressed genes (P < 0.05, and fold change >2 [red] or <0.5 [blue]) in Prmt5-deficient or Aire-deficient mTEChi cells. Data represent the mean of log2(reads per kilobase of exon per million mapped reads [RPKM]). Significance was determined using the unpaired, 2-tailed Student’s t test. (C) Venn diagram showing the overlap between the set of genes significantly downregulated in Prmt5-deficient mTEChi cells and Aire-deficient mTEChi cells. (D) Density plot showing the entropy score of individual genes expressed in mTEChi cells. The total genes expressed in mTEChi cells (mean of RPKM >0 in control mTEChi cells, 19,324 genes: black line), genes downregulated (P < 0.05, fold change <0.5) in Prmt5-deficient mTEChi cells (2,652 genes: red line), and genes downregulated (P < 0.05, fold change <0.5) in Aire-deficient mTEChi cells (3,677 genes: blue line). (E) Density plot showing gene expression of total TRAs (entropy score <3.0) in mTEChi cells from control mice (black), Prmt5-cKO mice (red), and Aire-KO mice (blue). (F) The diversity index of the TRAs (entropy score <3.0) expressed in mTEChi cells was evaluated using the Shannon-Weaver model. **P < 0.01, by 1-way ANOVA followed by Dunnett’s multiple-comparison test. (G) Expression heatmap of total TRA genes in mTEChi cells (entropy score <3.0). (H) Relative expression of representative transcriptional factors and TRAs in mTEChi cells shown as a heatmap. (I) Venn diagram showing the overlap between a set of TRA genes under the control of Prmt5, Aire, and Fezf2 (GSE144877).