Abstract
Naive T cells undergo radical changes during the transition from dormant to hyperactive states upon activation, which necessitates de novo protein production via transcription and translation. However, the mechanism whereby T cells globally promote translation remains largely unknown. Here, we show that on exit from quiescence, T cells upregulate transfer RNA (tRNA) m1A58 ‘writer’ proteins TRMT61A and TRMT6, which confer m1A58 RNA modification on a specific subset of early expressed tRNAs. These m1A-modified early tRNAs enhance translation efficiency, enabling rapid and necessary synthesis of MYC and of a specific group of key functional proteins. The MYC protein then guides the exit of naive T cells from a quiescent state into a proliferative state and promotes rapid T cell expansion after activation. Conditional deletion of the Trmt61a gene in mouse CD4+ T cells causes MYC protein deficiency and cell cycle arrest, disrupts T cell expansion upon cognate antigen stimulation and alleviates colitis in a mouse adoptive transfer colitis model. Our study elucidates for the first time, to our knowledge, the in vivo physiological roles of tRNA-m1A58 modification in T cell-mediated pathogenesis and reveals a new mechanism of tRNA-m1A58-controlled T cell homeostasis and signal-dependent translational control of specific key proteins.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
Source data are provided with this paper. All the high-throughput sequencing data generated for this study have been deposited in the NCBI Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE184909). The ChIP sequencing data are accessible from GEO with the following accession IDs: GSE39756, GSE40918, GSE54191, GSE58075 and GSE102317. The mass spectrometry proteomics data are available via the PRIDE database (http://www.proteomexchange.org) under accession numbers PXD004367 and PXD005492.
Code availability
Detailed custom Python scripts are available at https://github.com/xting1996/Proj_tRNA_m1A_in_T_cell.
References
Tan, H. et al. Integrative proteomics and phosphoproteomics profiling reveals dynamic signaling networks and bioenergetics pathways underlying T cell activation. Immunity 46, 488–503 (2017).
Saravia, J., Chapman, N. M. & Chi, H. Helper T cell differentiation. Cell. Mol. Immunol. 16, 634–643 (2019).
Buck, M. D., O’Sullivan, D. & Pearce, E. L. T cell metabolism drives immunity. J. Exp. Med. 212, 1345–1360 (2015).
Jones, R. G. & Thompson, C. B. Revving the engine: signal transduction fuels T cell activation. Immunity 27, 173–178 (2007).
Paillard, F., Sterkers, G. & Vaquero, C. Transcriptional and post-transcriptional regulation of TcR, CD4 and CD8 gene expression during activation of normal human T lymphocytes. EMBO J. 9, 1867–1872 (1990).
Taniuchi, I. & Ellmeier, W. Transcriptional and epigenetic regulation of CD4/CD8 lineage choice. Adv. Immunol. 110, 71–110 (2011).
Hwang, S. S. et al. mRNA destabilization by BTG1 and BTG2 maintains T cell quiescence. Science 367, 1255–1260 (2020).
Loo, T. T., Gao, Y. & Lazarevic, V. Transcriptional regulation of CD4+ TH cells that mediate tissue inflammation. J. Leukoc. Biol. 104, 1069–1085 (2018).
Schimmel, P. The emerging complexity of the tRNA world: mammalian tRNAs beyond protein synthesis. Nat. Rev. Mol. Cell Biol. 19, 45–58 (2018).
Zaborske, J. M. et al. A nutrient-driven tRNA modification alters translational fidelity and genome-wide protein coding across an animal genus. PLoS Biol. 12, e1002015 (2014).
Oerum, S., Degut, C., Barraud, P. & Tisne, C. m1A post-transcriptional modification in tRNAs. Biomolecules 7, 20 (2017).
Song, J. et al. Differential roles of human PUS10 in miRNA processing and tRNA pseudouridylation. Nat. Chem. Biol. 16, 160–169 (2020).
Bjork, G. R., Wikstrom, P. M. & Bystrom, A. S. Prevention of translational frameshifting by the modified nucleoside 1-methylguanosine. Science 244, 986–989 (1989).
Pan, T. Modifications and functional genomics of human transfer RNA. Cell Res. 28, 395–404 (2018).
Chan, C. T. et al. A quantitative systems approach reveals dynamic control of tRNA modifications during cellular stress. PLoS Genet. 6, e1001247 (2010).
Cantara, W. A. et al. The RNA Modification Database, RNAMDB: 2011 update. Nucleic Acids Res. 39, D195–D201 (2011).
Boccaletto, P. et al. MODOMICS: a database of RNA modification pathways. 2017 update. Nucleic Acids Res. 46, D303–D307 (2018).
Li, X. et al. Transcriptome-wide mapping reveals reversible and dynamic N1-methyladenosine methylome. Nat. Chem. Biol. 12, 311–316 (2016).
Dominissini, D. et al. The dynamic N1-methyladenosine methylome in eukaryotic messenger RNA. Nature 530, 441–446 (2016).
Saikia, M., Fu, Y., Pavon-Eternod, M., He, C. & Pan, T. Genome-wide analysis of N1-methyl-adenosine modification in human tRNAs. RNA 16, 1317–1327 (2010).
Clark, W. C., Evans, M. E., Dominissini, D., Zheng, G. & Pan, T. tRNA base methylation identification and quantification via high-throughput sequencing. RNA 22, 1771–1784 (2016).
Liu, F. et al. ALKBH1-mediated tRNA demethylation regulates translation. Cell 167, 816–828 (2016).
Yamane, H. & Paul, W. E. Early signaling events that underlie fate decisions of naive CD4+ T cells toward distinct T-helper cell subsets. Immunol. Rev. 252, 12–23 (2013).
Michalek, R. D. & Rathmell, J. C. The metabolic life and times of a T-cell. Immunol. Rev. 236, 190–202 (2010).
Hopper, A. K. Transfer RNA post-transcriptional processing, turnover, and subcellular dynamics in the yeast Saccharomyces cerevisiae. Genetics 194, 43–67 (2013).
Yoshihisa, T. Handling tRNA introns, archaeal way and eukaryotic way. Front. Genet. 5, 213 (2014).
Reinhold-Hurek, B. & Shub, D. A. Self-splicing introns in tRNA genes of widely divergent bacteria. Nature 357, 173–176 (1992).
Lorenz, C., Lunse, C. E. & Morl, M. tRNA modifications: impact on structure and thermal adaptation. Biomolecules 7, 35 (2017).
Kurachi, M. et al. The transcription factor BATF operates as an essential differentiation checkpoint in early effector CD8+ T cells. Nat. Immunol. 15, 373–383 (2014).
Ciofani, M. et al. A validated regulatory network for Th17 cell specification. Cell 151, 289–303 (2012).
Chou, C. et al. c-Myc-induced transcription factor AP4 is required for host protection mediated by CD8+ T cells. Nat. Immunol. 15, 884–893 (2014).
Li, P. et al. STAT5-mediated chromatin interactions in superenhancers activate IL-2 highly inducible genes: functional dissection of the Il2ra gene locus. Proc. Natl Acad. Sci. USA 114, 12111–12119 (2017).
Ostanin, D. V. et al. T cell transfer model of chronic colitis: concepts, considerations, and tricks of the trade. Am. J. Physiol. Gastrointest. Liver Physiol. 296, G135–G146 (2009).
Gasper, D. J., Tejera, M. M. & Suresh, M. CD4 T-cell memory generation and maintenance. Crit. Rev. Immunol. 34, 121–146 (2014).
Finer-Moore, J., Czudnochowski, N., O’Connell, J. D. 3rd, Wang, A. L. & Stroud, R. M. Crystal structure of the human tRNA m1A58 methyltransferase-tRNA3Lys complex: refolding of substrate tRNA allows access to the methylation target. J. Mol. Biol. 427, 3862–3876 (2015).
Popmihajlov, Z., Xu, D., Morgan, H., Milligan, Z. & Smith, K. A. Conditional IL-2 gene deletion: consequences for T cell proliferation. Front Immunol. 3, 102 (2012).
Snook, J. P., Kim, C. & Williams, M. A. TCR signal strength controls the differentiation of CD4+ effector and memory T cells. Sci. Immunol. 3, eaas9103 (2018).
Li, H. B. et al. m6A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature 548, 338–342 (2017).
Frauwirth, K. A. et al. The CD28 signaling pathway regulates glucose metabolism. Immunity 16, 769–777 (2002).
Marchingo, J. M., Sinclair, L. V., Howden, A. J. & Cantrell, D. A. Quantitative analysis of how Myc controls T cell proteomes and metabolic pathways during T cell activation. eLife 9, e53725 (2020).
Wang, R. et al. The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity 35, 871–882 (2011).
Hydbring, P., Castell, A. & Larsson, L. G. MYC modulation around the CDK2/p27/SKP2 axis. Genes 8, 174 (2017).
Yang, K. et al. T cell exit from quiescence and differentiation into Th2 cells depend on Raptor-mTORC1-mediated metabolic reprogramming. Immunity 39, 1043–1056 (2013).
MacIver, N. J., Michalek, R. D. & Rathmell, J. C. Metabolic regulation of T lymphocytes. Annu. Rev. Immunol. 31, 259–283 (2013).
Gupta, S., Seth, A. & Davis, R. J. Transactivation of gene expression by Myc is inhibited by mutation at the phosphorylation sites Thr-58 and Ser-62. Proc. Natl Acad. Sci. USA 90, 3216–3220 (1993).
Lee, D. H. & Goldberg, A. L. Proteasome inhibitors: valuable new tools for cell biologists. Trends Cell Biol. 8, 397–403 (1998).
McGlincy, N. J. & Ingolia, N. T. Transcriptome-wide measurement of translation by ribosome profiling. Methods 126, 112–129 (2017).
Rak, R. et al. Dynamic changes in tRNA modifications and abundance during T cell activation. Proc. Natl Acad. Sci. USA 118, e2106556118 (2021).
Wang, Y. et al. N1-methyladenosine methylation in tRNA drives liver tumourigenesis by regulating cholesterol metabolism. Nat. Commun. 12, 6314 (2021).
Jackson, R. et al. The translation of non-canonical open reading frames controls mucosal immunity. Nature 564, 434–438 (2018).
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
Esposito, A.M. et al. Eukaryotic polyribosome profile analysis. J. Vis. Exp. https://doi.org/10.3791/1948 (2010).
Acknowledgements
We thank M. Yang, K. Mao and Y. Zhou for technical and administrative assistance. We also thank all members of the H.-B.L. laboratory for discussion. This work was supported by the National Natural Science Foundation of China (82030042/32070917/82111540277 to H.-B.L., 31861143026/21825701/91940304 to C.Y., 81901580 to Y.L., 81901569 to J. Zhou and 32171283 to X.L.), the Chongqing International Institute for Immunology (2021YJC01 to H.-B.L.), the Ministry of Science and Technology of China (2021YFA1100800 to H.-B.L.), the National Key R&D Program of China (2019YFA0802200/2019YFA0110900 to C.Y.), Shanghai Science and Technology Commission (20JC1417400/201409005500 to H-B.L., 20JC1410100 to J. Zheng and 20ZR1472900 to Y.Y.), the Shanghai Super Postdoctoral Program (Y.L. and J. Zhou), the China Postdoctoral Science Foundation (2019M651525 to Y.L. and 2020M671149 to J. Zhou), the Postdoctoral Innovation Talent Support Program (BX20190214 to J. Zhou), the Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20212501 to H.-B.L. and SHSMU-ZDCX20212500 to J. Zhou) and the Howard Hughes Medical Institute (R.A.F.).
Author information
Authors and Affiliations
Contributions
Y.L. designed the research, performed experiments and drafted the manuscript; J.Zhou performed experiments and revised the manuscript; X.L., X.Z. and J.S. performed experiments and the bioinformatic analysis; S.M., X.H., L.D., X.W. and H.L. performed experiments; H.C. and Y.Y. performed the bioinformatic analysis; G.R.L., J.Zheng., R-J.L., B.S. and Y.Y. contributed to discussions and suggestions; R.A.F., C.Y. and Y.W. coordinated and revised the manuscript; and H.-B.L. conceived the project, designed the study, supervised the research, acquired the funding, and wrote and revised the manuscript.
Corresponding authors
Ethics declarations
Competing interests
R.A.F. is a consultant for GSK and Zai Lab Ltd. All other authors declare no competing interests.
Peer review
Peer review information
Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: L. A. Dempsey, in collaboration with the Nature Immunology team. Peer reviewer reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 The transcriptional and translational regulation during T cell activation.
a. The schematic diagram of the TCR, IL-2R, and IL-7R pathways during T cell activation. b. GO analysis of the genes regulated by transcription factors MYC, c-JUN, FOSL2, STAT3, and STAT5B within T cell according to the published ChIP-seq data, pathways involved in translational regulation were listed, and the gene counts in different pathways were also indicated. Hypergeometric test (adjustments method: BH). c. IGV snapshots showing the alignment data (BigWig format), including IP and input, around the Trmt61a gene loci for ChIP‐seq experiments from public datasets. Labels indicate some genomic regions with peaks. d. Validation of the interaction between c-JUN or FOSL2 and the Trmt61a gene. c-JUN or FOSL2 binding was quantified after normalization to input DNA (%input = 100*2^ (Ctadjusted-Cttarget)). Error bars represent mean ± s.e.m., n = 3 biologically independent samples. ****P < 0.0001, two-tailed, unpaired t-test. e. Quantification of the m1G9/G9, m1G37/G37, m3C/C ratios in total tRNA purified from naïve CD4+ T cells, in vitro activated CD4+ T cells (24 hours), BMDMs, heart, kidney, liver, lung, and spleen by tRNA-sequencing. Data are shown as box plots (The n number is displayed above boxes and boxes show median, upper and lower quartiles, whiskers show 1.5× IQR on either side, and the points show outlier).
Extended Data Fig. 2 Characterization of Trmt61a conditional knockout mice.
a. The schematic diagram of the generation of Trmt61a conditional knockout mice. b. The mRNA level of Trmt61a was analyzed by real-time PCR in Trmt61a-KO and WT naïve CD4+ T cells. Error bars represent mean ± s.e.m., n = 3 biologically independent samples from 3 independent experiments. **** P < 0.0001; two-tailed, unpaired t-test. c. tRNA-seq detection of the level of each tRNA in activated Trmt61a-KO CD4+ T cells and WT CD4+ T cells (6 hours). Each dot represents one tRNA. Two biologically independent samples are shown (R1, R2). d. Quantification of the m1A/A ratio in tRNA and mRNA by liquid chromatography-mass spectrometry. Error bars represent mean ± s.e.m., n = 3 biologically independent samples. **** P < 0.0001; NS: non-significant; two-tailed, unpaired t-test. e. The development of T cells in the thymus of 8-week-old Trmt61a-KO mice and littermate control mice. Representative data of six independent experiments are shown. f. The percentage and cell number of T cells (CD3+ TCRβ+) were quantified. Error bars represent mean ± s.e.m., n = 4 biologically independent animals. *** P = 0.0008, * P = 0.0111; two-tailed, unpaired t-test. g. The percentage and cell number of CD4+ or CD8+ T cells in spleen were quantified. * P = 0.0100 (CD4), * P = 0.0385 (CD8), NS: non-significant; two-tailed, unpaired t-test. h. The percentage and cell number of CD4+ or CD8+ T cells in lymph nodes were quantified. *** P = 0.0008, ** P = 0.0046, NS: non-significant; two-tailed, unpaired t-test. i. The fractions of effector memory cells (CD44hiCD62Llo) and naïve cells (CD44loCD62Lhi) in spleen were quantified. ** P = 0.0069, * P = 0.0356; two-tailed, unpaired t-test. j. The fractions of effector memory cells (CD44hiCD62Llo) and naïve cells (CD44loCD62Lhi) in lymph nodes were quantified. **** P < 0.0001, *** P = 0.0008, * P = 0.0732; two-tailed, unpaired t-test. Boxes show median, upper and lower quartiles, whiskers show 1.5× IQR on either side (g-j). n = 4 biologically independent animals (g-j).
Extended Data Fig. 3 Trmt61a KO T cells failed to induce adoptive transfer colitis.
a. Representative flow cytometry gating strategy for thymocyte. b. Representative flow cytometry gating strategy for CD4+ T cell and CD8+ T cell in spleen or lymph nodes. c. Representative image of the colons from Rag2−/− mice receiving WT or Trmt61a-KO naïve CD4+ T cells 12 weeks post-transfer. Scale bars, 1 cm. d. The length of colons from Rag2−/− mice receiving WT or Trmt61a-KO naïve CD4+ T cells 12 weeks post-transfer. Error bars represent mean ± s.e.m., n = 8 biologically independent animals. *** P = 0.0002; two-tailed, unpaired t-test.
Extended Data Fig. 4 Characterization of Trmt61a KO T cells in vitro.
a. The T cell activation was assessed by flow cytometry analysis of CD69 and CD44 staining. Quantification of the fraction of activated T cells (CD69hiCD44hi) was shown in the right graph. Error bars represent mean ± s.e.m., n = 4 biologically independent samples. NS: non-significant; two-tailed, unpaired t-test. b. Representative flow cytometry gating strategy for CellTrace dilution in CD4+ T cell. c. Apoptosis was assessed by flow cytometry after staining by Annexin V and 7-AAD. The percentage is listed in the right graph. Error bars represent mean ± s.e.m., n = 4 biologically independent samples. NS: non-significant; two-tailed, unpaired t-test. d. Naïve CD4+ T cells were differentiated into Th1 subset under defined optimal conditions. The percentage is listed in the right graph. Error bars represent mean ± s.e.m., n = 4 biologically independent samples. **** P < 0.0001; two-tailed, unpaired t-test. e. Naïve CD4+ T cells were differentiated into Th17 subset under defined optimal conditions. The percentage is listed in the right graph. Error bars represent mean ± s.e.m., n = 4 biologically independent samples. **** P < 0.0001; two-tailed, unpaired t-test. f. Naïve CD4+ T cells were differentiated into iTreg subset under defined optimal conditions. The percentage is listed in the right graph. Error bars represent mean ± s.e.m., n = 5 biologically independent samples. ** P = 0.0022; two-tailed, unpaired t-test. g. Retrovirus-mediated expression of TRMT61A-WT and TRMT61A-Dead in WT and Trmt61a-KO CD4+ T cells. The protein level of TRMT61A was quantified by immunoblot. G: EGFP, W: TRMT61A-WT, D: TRMT61A-Dead. Representative data of three independent experiments are shown. h. Flow cytometric analysis of cell counts. Data are shown as box plots (boxes show median, upper and lower quartiles, whiskers show 1.5× IQR on either side). n = 3 biologically independent samples. **** P < 0.0001, * P = 0.0267, NS: non-significant; two-tailed, unpaired t-test. i. Up left: CellTrace dilution in WT and Trmt61a KO CD4+ T cells before transfer; Up right: flow cytometric analysis of CellTrace dilution in different treatments; Down: flow cytometry shows CellTrace dilution in different treatments. Error bars represent mean ± s.e.m., n = 5 biologically independent samples. **** P < 0.0001, ** P = 0.0045, NS: non-significant; two-tailed, unpaired t-test.
Extended Data Fig. 5 Characterization of Trmt6 conditional knockout mice.
a. The schematic diagram of the generation of Trmt6 conditional knockout mice. b. The mRNA level of Trmt6 was analyzed by real-time PCR in Trmt6-KO and WT naïve CD4+ T cells. Error bars represent mean ± s.e.m., n = 3 biologically independent samples from 3 independent experiments. **** P < 0.0001; two-tailed, unpaired t-test. c. The protein level of TRMT6 was quantified by immunoblot in Trmt6-KO and WT naïve CD4+ T cells. Representative data of three independent experiments are shown. d. CD4+ T cell subpopulations from the spleens of 8-week-old Trmt6-KO mice and littermate control mice were analyzed by flow cytometry. The fraction of CD4+ effector memory cells (CD44hiCD62Llo) was quantified. Error bars represent mean ± s.e.m., n = 3 biologically independent animals. * P = 0.0140; two-tailed, unpaired t-test. e. The proliferation of WT naïve CD4+ T cells and Trmt6-KO naïve CD4+ T cells in the presence of anti-CD3 and anti-CD28 antibodies, as measured by CellTrace dilution after 3 days. Error bars represent mean ± s.e.m., n = 4 biologically independent samples. *** P = 0.0001; two-tailed, unpaired t-test.
Extended Data Fig. 6 Deletion Trmt61a did not affect the TCR/ IL-2 signaling pathway.
a. Naïve CD4+ T cells obtained from the spleens of Trmt61a-KO and littermate control mice were stimulated for the indicated minutes with anti-CD3 and anti-CD28 antibodies. Phosphorylated proteins of the TCR signaling pathway were detected by immunoblot. Representative data of two independent experiments are shown. b. Naïve CD4+ T cells obtained from the spleens of Trmt61a-KO and littermate control mice were stimulated for 24 hours with anti-CD3 and anti-CD28 antibodies. The expression of IL-2 was quantified by flow cytometry. Error bars represent mean ± s.e.m., n = 4 biologically independent samples. NS: non-significant; two-tailed, unpaired t-test. c. CD4+ T cells obtained from the spleens of Trmt61a-KO and littermate control mice were stimulated for the indicated times with IL-2. The phosphorylation of STAT5 in the IL-2 signaling pathway was detected by immunoblot. Representative data of two independent experiments are shown. d. Naïve CD4+ T cells were obtained from the spleens of Trmt61a-KO and littermate control mice. The expression of CD127 was quantified by flow cytometry. Error bars represent mean ± s.e.m., n = 3 biologically independent samples. NS: non-significant; two-tailed, unpaired t-test.
Extended Data Fig. 7 mTOR signaling and MYC pathways.
a. Naïve CD4+ T cells obtained from the spleens of Trmt61a-KO and littermate control mice were stimulated for the indicated times with anti-CD3 and anti-CD28 antibodies. Phosphorylated proteins of the mTORC1 signaling pathway were detected by immunoblot. Representative data of four independent experiments are shown. b. Heatmap showing the expression of proteins in activated CD4+ T cells for indicated timepoint, data from the PRIDE database under accession numbers PXD004367. c. Gene-set enrichment analysis showing the down-regulated genes in Trmt61a-KO cells involved in MYC signaling. d. Heatmap showing the expressions of transcripts of the cholesterol biosynthesis and amino acid biosynthesis pathways in activated Trmt61a-KO CD4+ T cells vs. activated WT CD4+ T cells (anti-CD3 and anti-CD28 antibodies for 48 hours). e. Naïve CD4+ T cells obtained from the spleens of Trmt61a-KO and littermate control mice were stimulated for 3 hours with anti-CD3 and anti-CD28 antibodies and then treated by MG132 for 3 hours. The level of MYC protein was quantified by immunoblot. Representative data of two independent experiments are shown.
Extended Data Fig. 8 The design principle of codon switch assay.
a. tRNA-m1A-seq detection of the m1A58 modification level of tRNAiMet in activated Trmt61a-KO CD4+ T cells and WT CD4+ T cells (6 hours). n = 2 biologically independent samples. b. The decrease in magnitude (the tRNA-m1A58 level in WT CD4+ T cells minus the level in KO CD4+ T cells) of the tRNA-m1A58 level in each tRNA after TRMT61A deletion. c. The codon frequency of mouse Myc mRNA. In the Myc codon switch assay, the codons marked with red boxes were replaced with the codon marked with blue box, and the codons marked with purple boxes were replaced with the codon marked with green box.
Extended Data Fig. 9 The protein levels of TE down-regulated mRNAs.
a. Naïve CD4+ T cells were activated with 5 μg/mL anti-CD3 antibody and 2 μg/mL anti-CD28 antibody for 6 hours. The expression of Rhoa was verified by RNA sequencing. n = 2 biologically independent samples. NS: non-significant; two-tailed, unpaired t-test. b. Naïve CD4+ T cells obtained from the spleens of Trmt61a-KO and littermate control mice were stimulated for the indicated times with anti-CD3 and anti-CD28 antibodies (6 hours). The representative target with TE-downregulation was verified by immunoblot. Representative data of three independent experiments are shown. c. Retrovirus-mediated expression of RHOA-WT and RHOA-Mutant (All Leu codons replaced by CTT, and all Gly codons replaced by GGT) in WT and Trmt61a-KO CD4+ T cells. The protein level of RHOA was quantified by immunoblot. Representative data of two independent experiments are shown. d. Retrovirus-mediated expression of CDK2-WT and CDK2-Mutant (All Leu codons replaced by CTT, all His codons replaced by CAT, and all Gly codons replaced by GGT) in WT and Trmt61a-KO CD4+ T cells. The protein level of CDK2 was quantified by immunoblot. Representative data of two independent experiments are shown. e. Heatmap showing the protein expression of TE-down, TE-up, and TE-unchanged transcripts in Trmt61a-KO activated CD4+ T cells (6 hours), data obtained from the PRIDE database under accession numbers PXD004367. f. Venn diagram showed the intersection of the TE-down genes in RiboTag RNA-seq and genes in WPC-2 or WPC-3 (WPC-2 and WPC-3 were obtained from PMID: 28285833). The P value of intersection was calculated by the fisher test (two-sided).
Extended Data Fig. 10 The m1A58 in tRNA promotes Myc mRNA translation in activated CD4+ T cells.
Proposed mechanistic model depicting tRNA-m1A58 mediated translational control during T cell activation.
Supplementary information
Source data
Source Data Fig. 1
Statistical source data.
Source Data Fig. 2
Statistical source data.
Source Data Fig. 2
Unprocessed western blots.
Source Data Fig. 3
Statistical source data.
Source Data Fig. 4
Statistical source data.
Source Data Fig. 4
Unprocessed western blots.
Source Data Fig. 5
Statistical source data.
Source Data Fig. 5
Unprocessed western blots.
Source Data Fig. 6
Statistical source data.
Source Data Extended Data Fig. 1
Statistical source data.
Source Data Extended Data Fig. 2
Statistical source data.
Source Data Extended Data Fig. 3
Statistical source data.
Source Data Extended Data Fig. 4
Statistical source data.
Source Data Extended Data Fig. 4
Unprocessed western blots.
Source Data Extended Data Fig. 5
Statistical source data.
Source Data Extended Data Fig. 5
Unprocessed western blots.
Source Data Extended Data Fig. 6
Statistical source data.
Source Data Extended Data Fig. 6
Unprocessed western blots.
Source Data Extended Data Fig. 7
Statistical source data.
Source Data Extended Data Fig. 7
Unprocessed western blots.
Source Data Extended Data Fig. 8
Statistical source data.
Source Data Extended Data Fig. 9
Statistical source data.
Source Data Extended Data Fig. 9
Unprocessed western blots.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, Y., Zhou, J., Li, X. et al. tRNA-m1A modification promotes T cell expansion via efficient MYC protein synthesis. Nat Immunol 23, 1433–1444 (2022). https://doi.org/10.1038/s41590-022-01301-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41590-022-01301-3
This article is cited by
-
RNA modification-mediated mRNA translation regulation in liver cancer: mechanisms and clinical perspectives
Nature Reviews Gastroenterology & Hepatology (2024)
-
Epitranscriptomic modifications in mesenchymal stem cell differentiation: advances, mechanistic insights, and beyond
Cell Death & Differentiation (2024)
-
m6A/m1A/m5C-Associated Methylation Alterations and Immune Profile in MDD
Molecular Neurobiology (2024)
-
RNA modification: mechanisms and therapeutic targets
Molecular Biomedicine (2023)
-
Non-coding RNA methylation modifications in hepatocellular carcinoma: interactions and potential implications
Cell Communication and Signaling (2023)