Abstract
N6-methyladenosine (m6A) is the most common and abundant messenger RNA modification, modulated by ‘writers’, ‘erasers’ and ‘readers’ of this mark1,2. In vitro data have shown that m6A influences all fundamental aspects of mRNA metabolism, mainly mRNA stability, to determine stem cell fates3,4. However, its in vivo physiological function in mammals and adult mammalian cells is still unknown. Here we show that the deletion of m6A ‘writer’ protein METTL3 in mouse T cells disrupts T cell homeostasis and differentiation. In a lymphopaenic mouse adoptive transfer model, naive Mettl3-deficient T cells failed to undergo homeostatic expansion and remained in the naive state for up to 12 weeks, thereby preventing colitis. Consistent with these observations, the mRNAs of SOCS family genes encoding the STAT signalling inhibitory proteins SOCS1, SOCS3 and CISH were marked by m6A, exhibited slower mRNA decay and showed increased mRNAs and levels of protein expression in Mettl3-deficient naive T cells. This increased SOCS family activity consequently inhibited IL-7-mediated STAT5 activation and T cell homeostatic proliferation and differentiation. We also found that m6A has important roles for inducible degradation of Socs mRNAs in response to IL-7 signalling in order to reprogram naive T cells for proliferation and differentiation. Our study elucidates for the first time, to our knowledge, the in vivo biological role of m6A modification in T-cell-mediated pathogenesis and reveals a novel mechanism of T cell homeostasis and signal-dependent induction of mRNA degradation.
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 51 print issues and online access
$199.00 per year
only $3.90 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
Accession codes
References
Cao, G., Li, H. B., Yin, Z. & Flavell, R. A. Recent advances in dynamic m6A RNA modification. Open Biol. 6, 160003 (2016)
Fu, Y., Dominissini, D., Rechavi, G. & He, C. Gene expression regulation mediated through reversible m6A RNA methylation. Nat. Rev. Genet. 15, 293–306 (2014)
Geula, S. et al. Stem cells. m6A mRNA methylation facilitates resolution of naive pluripotency toward differentiation. Science 347, 1002–1006 (2015)
Batista, P. J. et al. m6A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014)
Collins, A. & Littman, D. R. Selection and lineage specification in the thymus: commitment 4-stalled. Immunity 23, 4–5 (2005)
Esplugues, E. et al. Control of TH17 cells occurs in the small intestine. Nature 475, 514–518 (2011)
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)
Martin, C. E., Frimpong-Boateng, K., Spasova, D. S., Stone, J. C. & Surh, C. D. Homeostatic proliferation of mature T cells. Methods Mol. Biol. 979, 81–106 (2013)
Takada, K. & Jameson, S. C. Naive T cell homeostasis: from awareness of space to a sense of place. Nat. Rev. Immunol. 9, 823–832 (2009)
Sprent, J. & Surh, C. D. Normal T cell homeostasis: the conversion of naive cells into memory-phenotype cells. Nat. Immunol. 12, 478–484 (2011)
Yoshimura, A., Naka, T. & Kubo, M. SOCS proteins, cytokine signalling and immune regulation. Nat. Rev. Immunol. 7, 454–465 (2007)
Palmer, D. C. & Restifo, N. P. Suppressors of cytokine signaling (SOCS) in T cell differentiation, maturation, and function. Trends Immunol. 30, 592–602 (2009)
Surh, C. D. & Sprent, J. Homeostasis of naive and memory T cells. Immunity 29, 848–862 (2008)
Chong, M. M. et al. Suppressor of cytokine signaling-1 is a critical regulator of interleukin-7-dependent CD8+ T cell differentiation. Immunity 18, 475–487 (2003)
Cacalano, N. A., Sanden, D. & Johnston, J. A. Tyrosine-phosphorylated SOCS-3 inhibits STAT activation but binds to p120 RasGAP and activates Ras. Nat. Cell Biol. 3, 460–465 (2001)
Matsumoto, A. et al. A role of suppressor of cytokine signaling 3 (SOCS3/CIS3/SSI3) in CD28-mediated interleukin 2 production. J. Exp. Med. 197, 425–436 (2003)
Matsumoto, A. et al. Suppression of STAT5 functions in liver, mammary glands, and T cells in cytokine-inducible SH2-containing protein 1 transgenic mice. Mol. Cell. Biol. 19, 6396–6407 (1999)
Yue, Y., Liu, J. & He, C. RNA N6-methyladenosine methylation in post-transcriptional gene expression regulation. Genes Dev. 29, 1343–1355 (2015)
Schwartz, S. et al. Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5′ sites. Cell Reports 8, 284–296 (2014)
Tani, H. & Akimitsu, N. Genome-wide technology for determining RNA stability in mammalian cells: historical perspective and recent advantages based on modified nucleotide labeling. RNA Biol. 9, 1233–1238 (2012)
Rabani, M. et al. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nat. Biotechnol. 29, 436–442 (2011)
Rabani, M. et al. High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategies. Cell 159, 1698–1710 (2014)
Duffy, E. E. et al. Tracking distinct RNA populations using efficient and reversible covalent chemistry. Mol. Cell 59, 858–866 (2015)
Henao-Mejia, J. et al. Generation of genetically modified mice using the CRISPR–Cas9 genome-editing system. Cold Spring Harb. Protoc. http://dx.doi.org/10.1101/pdb.prot090704 (2016)
Gagliani, N. et al. TH17 cells transdifferentiate into regulatory T cells during resolution of inflammation. Nature 523, 221–225 (2015)
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013)
Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010)
Wang, J., Duncan, D., Shi, Z. & Zhang, B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 41, W77–W83 (2013)
Wickham, H. ggplot2: elegant graphics for data analysis. http://dx.doi.org/10.1007/978-0-387-98141-3 (2009)
Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-seq data. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014)
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2ΔΔCt Method. Methods 25, 402–408 (2001)
Dominissini, D., Moshitch-Moshkovitz, S., Salmon-Divon, M., Amariglio, N. & Rechavi, G. Transcriptome-wide mapping of N6-methyladenosine by m6A-seq based on immunocapturing and massively parallel sequencing. Nat. Protocols 8, 176–189 (2013)
Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008)
Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011)
S´ledz´, P. & Jinek, M. Structural insights into the molecular mechanism of the m6A writer complex. eLife 5, e18434 (2016)
Clancy, M. J., Shambaugh, M. E., Timpte, C. S. & Bokar, J. A. Induction of sporulation in Saccharomyces cerevisiae leads to the formation of N6-methyladenosine in mRNA: a potential mechanism for the activity of the IME4 gene. Nucleic Acids Res. 30, 4509–4518 (2002)
Wang, P., Doxtader, K. A. & Nam, Y. Structural basis for cooperative function of Mettl3 and Mettl14 methyltransferases. Mol. Cell 63, 306–317 (2016)
Duffy, E. E. & Simon, M. D. Enriching s4U-RNA using methane thiosulfonate (MTS) chemistry. Curr. Protoc. Chem. Biol. 8, 234–250 (2016)
Rosenbloom, K. R. et al. The UCSC Genome Browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2015)
Kersey, P. J. et al. Ensembl Genomes: an integrative resource for genome-scale data from non-vertebrate species. Nucleic Acids Res. 40, D91–D97 (2012)
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015)
de Pretis, S. et al. INSPEcT: a computational tool to infer mRNA synthesis, processing and degradation dynamics from RNA- and 4sU-seq time course experiments. Bioinformatics 31, 2829–2835 (2015)
Nueda, M. J., Tarazona, S. & Conesa, A. Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics 30, 2598–2602 (2014)
Acknowledgements
We thank R. Flynn, R. Jackson, Y. Yang, M. Vesely, R. Paiva, N. Palm and all the other members of the Flavell laboratory for discussions and comments. We thank J. Alderman, C. Lieber, C. Hughes and J. Stein for technical support. H.-B.L. was supported by NIH T32 2T32DK007356. S.Z was supported by a fellowship from Helen Hay Whitney Foundation-Howard Hughes Medical Institute. This work was supported by the Howard Hughes Medical Institute (R.A.F.), NSF Major International Joint Research Program of China - 31420103901 (Z.Y. and R.A.F.) and ‘111’ project (Z.Y.), R01-HG004361 (H.Y.C.), NIH New Innovator Award DP2 HD083992-01 (M.D.S.), and a Searle scholarship (M.D.S.).
Author information
Authors and Affiliations
Contributions
H-B. L. conceived the project. H.-B. L., J. T., S. Z., P.B., E.E.D., W.B., G.C., Y. C., G.W., J.P.B. and Y.G.C. performed the experimental work. J.Z., L.K., M.D.S. and P.B. analysed the RNA-seq, ribo-profiling, s4U-seq and m6A-seq data and performed the statistical analysis. H.Y.C., M.D.S., Y.K., and Z.Y. provided key suggestions. H.-B. L. and R.A.F designed the study, analysed the data and wrote the manuscript. R.A.F. supervised the study.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Additional information
Reviewer Information Nature thanks F. Fuks, J. H. Hanna and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Figure 1 Abnormal T cell homeostasis in generated Mettl3 CD4-Cre conditional knockout mice.
a, The two lox sites were inserted into the first and last introns by CRISPR technology. b, Protein levels of METTL3 and its associated METTL14 were analysed by western blot in the naive T cells and in in vitro differentiated TH1, TH2 and TH17 cells from Mettl3-KO and wild-type mice. c, Overall levels of RNA m6A methylation in naive T cells from Mettl3-KO and wild-type mice (n = 3, P = 0.0032). d, Naive T cells increased in all lymphoid organs from Mettl3-KO mice compared to littermate control wild-type mice. Cells from spleen (SPL), mesenteric lymph node (mLN), and peripheral lymph node (pLN) were analysed by FACS by staining with CD4/CD44/CD62L. e, f, The percentage of CD4+CD44loCD62+ naive T cells increased in all three lymphoid organs in Mettl3-KO mice (e) and the total number of naive T cells in mLN and pLN also increased in knockout mice (f; n = 3). g, The cell population in the thymus did not change in Mettl3-KO (n = 3). n = number of biological replicates. Repeated three times and one set of data is shown. **P < 0.01, ***P < 0.001.
Extended Data Figure 2 Naive T cells from Mettl3-KO mice differentiated into fewer TH1 and TH17 cells and more TH2 cells ex vivo compared to wild-type naive T cells.
a, Naive T cells isolated from Mettl3 wild-type and knockout mice were differentiated into effector subsets under defined optimal conditions. b, The percentages of each T cell subtype over total CD4+ T cells were analysed by FACS (n = 3). c, No apoptosis defects were found in ex vivo cultured cells from wild-type and Mettl3-KO naive T cells by FACS staining of annexin V and 7AAD. Double-negative stained cells are live cells, and the remaining are apoptotic cells. The percentage is listed in the right graph (n = 2). d, No proliferation differences were found in ex vivo cultured cells from wild-type and Mettl3-KO naive T cells. Naive T cells labelled with CellTrace were cultured ex vivo under different concentrations of anti-CD3/CD28 beads for 4 days. The percentages of proliferating cells are listed in the right graph (n = 2). n = number of biological replicates. Repeated three times and one set of data is shown. ***P < 0.001.
Extended Data Figure 3 m6A methylation function of Mettl3 controls naive T cell homeostatic expansion.
a, Mettl3−/− recipients had normal colon length, and Mettl3+/+ recipients had shorter colon length. b, Mettl3+/+ recipients had enlarged spleens indicative of normal homeostatic expansion, while Mettl3−/− recipients had very small spleens. c, All lymph organs had many fewer transferred knockout cells compared to wild-type cells analysed by FACS. d, The percentage of transferred Mettl3-KO and wild-type cells in Rag2−/− host mice (n = 3). e, No apoptosis defects were found in in vivo cells recovered from peripheral lymph nodes of Mettl3 wild-type and knockout recipient mice by FACS staining of annexin V and 7AAD. Double-negative stained cells are live cells, and the remainder are apoptotic cells. The percentage is listed in the right graph (n = 3). f–g, Wild-type Mettl3 constructs, but not m6A catalytic dead Mettl3 constructs, could rescue Mettl3-KO phenotypes in vivo. Empty construct (N103), catalytic dead Mettl3 construct (N103-CD), and wild-type Mettl3 construct (N103-M3) were electroporated into Mettl3-KO naive T cells, and then transferred into Rag2−/− mice. Four weeks after transfer, the cell number (proliferation) and CD45RB marker (differentiation) were analysed by FACS. Representative images are shown in f, and the numbers of cells are shown in g (n = 3). n = number of biological replicates. Repeated twice and one set of data is shown. **P < 0. 01, ***P < 0.001.
Extended Data Figure 4 Mettl14-KO naive T cell adoptive transfer pheno-copies Mettl3-KO cells.
a, Mettl14-KO recipient mice have smaller lymphoid organs, including spleen, peripheral lymph nodes, and mesenteric lymph nodes. b, c, The percentage and the number of transferred Mettl14-KO cells in Rag2−/− recipient mice were much lower than those of wild-type cells 4 weeks after transfer in all lymphoid organs (n=3). d, The MFI (median fluorescence intensity) of naive cell marker CD45RB is much higher in knockout than wild type, suggesting Mettl14 naive T cells were locked in naive state, whereas wild-type naive T cells differentiated after 4 weeks in Rag2−/− mice (n = 3). n = number of biological replicates. Repeated twice and one set of data is shown. ***P < 0.001.
Extended Data Figure 5 Socs genes are the m6A targets that contribute to the observed phenotypes.
a, Upregulated KEGG pathways in Mettl3-KO cells over wild-type cells based on RNA-seq data. b, Downregulated pathways in Mettl3-KO cells over wild-type cells. c, RT–qPCR validated the RNA-seq data, showing that the mRNA expression levels of other genes and regulators in IL-7 pathways did not change in Mettl3-KO naive T cells compared to wild-type cells (n = 6). d, Socs1 siRNAs knock down Socs1 gene expression by half in vitro (n = 3). Naive T cells were incubated with Socs1 or control siRNA in vitro for 3 days, and RT–qPCR was used to measure the mRNA levels of Socs1. e, Socs1, Socs3 and Cish mRNA 3′ UTRs are enriched with m6A peaks from published ES cell and dendritic cell m6A-RIP genome mapping. Red denotes the IP RNA counts, and grey denotes input. n = number of biological replicates. Repeated three times and one set of data is shown. **P < 0.01.
Extended Data Figure 6 Ribosome profiling does not reveal any ribosome occupancy differences in IL-7 and TCR signalling related genes.
a, Overall statistical analysis for all genes. Socs genes and other IL-7 pathway genes are highlighted. The y-axis is the log2 fold change of Mettl3-KO over wild type, and the x-axis plots the P value of the fold change value. b, Calculated translation efficiency for all genes, and the IL-7 and TCR pathway genes, do not show differences in translation efficiency between Mettl3-KO and wild-type naive T cells. c, Overall levels of RNA m6A methylation in naive T cells from Mettl3-KO and wild-type mice. c, d, Example ribosome profiles of Socs1 and Socs3 mRNAs, which do not show any significant differences between wild-type (right panel) and Mettl3-KO (left panel) samples. The RNA-seq for the inputs are shown below the ribosome profiles, which also demonstrate enhanced mRNA expression for Socs genes.
Extended Data Figure 7 Socs genes are signal-inducible degradation-controlled genes.
a, Upregulated genes in Mettl3-KO naive T cells are significantly enriched in the degradation-controlled group of genes from LPS-stimulated dendritic cells. We compared the genes that were differentially regulated by m6A in naive T cells to degradation-controlled genes in dendritic cells. We can assign cluster information to 5,784 genes in our sequencing data set. We looked at the clusters where fast degradation played a key role (clusters 2, 4 and 6), and tested whether the number of genes upregulated was significant with a chi square test. The P value was <0.0001. ‘Fast deg’, genes in clusters 2,5,6; ‘not fast deg’, all other clusters; ‘up’, genes upregulated (marked as significant and positive fold change); ‘not up’, genes that did not change or were downregulated. b, Socs1, Socs3 and Cish, but not Socs2, degraded faster upon IL-7 treatment in wild-type cells, and the faster degradation with IL-7 stimulation was abrogated in Mettl3-KO naive T cells. The naive T cells isolated from both wild-type and Mettl3-KO mice were pre-treated with actinomycin-D for 1 h to fully stop transcription before IL-7 stimulation, and the residual mRNAs at different time points were normalized back to t = 0 (100%).
Extended Data Figure 8 Summary of s4U-seq data.
a, Analysis of reads mapping to introns demonstrates high intronic read density in s4U-enriched samples. The ratio of reads mapping to introns is expressed as a ratio to the total number of reads that map to each transcript in each sample. b, Plot illustrating the Spearman correlations of the transcript-level read frequencies in total and s4U-enriched samples for wild-type and Mettl3-KO cells at various times after IL-7 stimulation. c, Changes in transcript frequencies after IL-7 stimulation for wild-type or Mettl3-KO cells with and without s4U enrichment on the basis of s4U-seq data. Expression levels are presented relative to the transcript levels of wild-type cells before IL-7 stimulation. Shown are cluster-3 Socs genes and a control gene Xist.
Extended Data Figure 9 Working model for m6A-controlled naive T cell homeostasis.
a, Mettl3-KO naive T cell molecular mechanism: loss of m6A leads to slower Socs mRNA degradation and increased SOCS protein levels, which blocks the IL7 pathway. b, Revised T cell differentiation model: m6A targets Socs1, Socs3 and Cish for inducible and rapid mRNA degradation upon IL-7 stimulation, allowing IL-7–JAKs signalling to activate the downstream target STAT5, to initiate the re-programming of the naive T cells for differentiation and proliferation.
Supplementary information
Supplementary information
This file contains Supplementary Figure 1, the uncropped blots.
Supplementary Table 1
This table contains a list of all the antibodies and reagents used in this study.
Supplementary Table 2
This table contains a list of all the primers used in this study.
Supplementary Table 3
This table contains two lists of up-regulated and down-regulated genes by RNA-Seq of Mettl3 KO and WT Naïve T cells.
Supplementary Table 4
This table contains a list of top 20 up-regulated and down-regulated Ribosome occupancy of Mettl3 KO and WT mRNAs by Ribosome profiling in this study.
Supplementary Table 5
This table contains lists of all the clusters revealed by s4U-Seq of Mettl3 KO and WT naïve T cells in response to IL-7 stimulation over times in this study. Each tab contains one cluster determined by similar time-dependent expression profile changes after IL-7 induction.
Rights and permissions
About this article
Cite this article
Li, HB., Tong, J., Zhu, S. et al. m6A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature 548, 338–342 (2017). https://doi.org/10.1038/nature23450
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nature23450
This article is cited by
-
The epigenetic downregulation of LncGHRLOS mediated by RNA m6A methylase ZCCHC4 promotes colorectal cancer tumorigenesis
Journal of Experimental & Clinical Cancer Research (2024)
-
Comprehensive analysis of m6A modification in immune infiltration, metabolism and drug resistance in hepatocellular carcinoma
Cancer Cell International (2024)
-
Regulation of inflammatory diseases via the control of mRNA decay
Inflammation and Regeneration (2024)
-
RNA methylation patterns, immune characteristics, and autophagy-related mechanisms mediated by N6-methyladenosine (m6A) regulatory factors in venous thromboembolism
BMC Genomics (2024)
-
Methylation in cornea and corneal diseases: a systematic review
Cell Death Discovery (2024)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.