Abstract

A defining feature of adaptive immunity is the development of long-lived memory T cells to curtail infection. Recent studies have identified a unique stem-like T-cell subset amongst exhausted CD8-positive T cells in chronic infection1,2,3, but it remains unclear whether CD4-positive T-cell subsets with similar features exist in chronic inflammatory conditions. Amongst helper T cells, TH17 cells have prominent roles in autoimmunity and tissue inflammation and are characterized by inherent plasticity4,5,6,7, although how such plasticity is regulated is poorly understood. Here we demonstrate that TH17 cells in a mouse model of autoimmune disease are functionally and metabolically heterogeneous; they contain a subset with stemness-associated features but lower anabolic metabolism, and a reciprocal subset with higher metabolic activity that supports transdifferentiation into TH1-like cells. These two TH17-cell subsets are defined by selective expression of the transcription factors TCF-1 and T-bet, and by discrete levels of CD27 expression. We also identify signalling via the kinase complex mTORC1 as a central regulator of TH17-cell fate decisions by coordinating metabolic and transcriptional programmes. TH17 cells with disrupted mTORC1 signalling or anabolic metabolism fail to induce autoimmune neuroinflammation or to develop into TH1-like cells, but instead upregulate TCF-1 expression and acquire stemness-associated features. Single-cell RNA sequencing and experimental validation reveal heterogeneity in fate-mapped TH17 cells, and a developmental arrest in the TH1 transdifferentiation trajectory upon loss of mTORC1 activity or metabolic perturbation. Our results establish that the dichotomy of stemness and effector function underlies the heterogeneous TH17 responses and autoimmune pathogenesis, and point to previously unappreciated metabolic control of plasticity in helper T cells.

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Data availability

Microarray data are available via GEO (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE107521. ATAC-seq and scRNA-seq data are available via GEO under accession number GSE121599.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

  2. 2.

    He, R. et al. Follicular CXCR5-expressing CD8+ T cells curtail chronic viral infection. Nature 537, 412–416 (2016); erratum 540, 470 (2016).

  3. 3.

    Leong, Y. A. et al. CXCR5+ follicular cytotoxic T cells control viral infection in B cell follicles. Nat. Immunol. 17, 1187–1196 (2016).

  4. 4.

    Gagliani, N. et al. Th17 cells transdifferentiate into regulatory T cells during resolution of inflammation. Nature 523, 221–225 (2015).

  5. 5.

    Lee, Y. K. et al. Late developmental plasticity in the T helper 17 lineage. Immunity 30, 92–107 (2009).

  6. 6.

    Hirota, K. et al. Fate mapping of IL-17-producing T cells in inflammatory responses. Nat. Immunol. 12, 255–263 (2011).

  7. 7.

    Hirota, K. et al. Plasticity of Th17 cells in Peyer’s patches is responsible for the induction of T cell-dependent IgA responses. Nat. Immunol. 14, 372–379 (2013).

  8. 8.

    Gaublomme, J. T. et al. Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell 163, 1400–1412 (2015).

  9. 9.

    Hendriks, J. et al. CD27 is required for generation and long-term maintenance of T cell immunity. Nat. Immunol. 1, 433–440 (2000).

  10. 10.

    Zhou, X. et al. Differentiation and persistence of memory CD8+ T cells depend on T cell factor 1. Immunity 33, 229–240 (2010).

  11. 11.

    Grayson, J. M., Zajac, A. J., Altman, J. D. & Ahmed, R. Cutting edge: increased expression of Bcl-2 in antigen-specific memory CD8+ T cells. J. Immunol. 164, 3950–3954 (2000).

  12. 12.

    Kalia, V. et al. Prolonged interleukin-2Rα expression on virus-specific CD8+ T cells favors terminal-effector differentiation in vivo. Immunity 32, 91–103 (2010).

  13. 13.

    Choi, Y. S. et al. Bcl6 expressing follicular helper CD4 T cells are fate committed early and have the capacity to form memory. J. Immunol. 190, 4014–4026 (2013).

  14. 14.

    Buck, M. D., Sowell, R. T., Kaech, S. M. & Pearce, E. L. Metabolic instruction of immunity. Cell 169, 570–586 (2017).

  15. 15.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

  16. 16.

    Choi, Y. S. et al. LEF-1 and TCF-1 orchestrate TFH differentiation by regulating differentiation circuits upstream of the transcriptional repressor Bcl6. Nat. Immunol. 16, 980–990 (2015).

  17. 17.

    Xu, L. et al. The transcription factor TCF-1 initiates the differentiation of TFH cells during acute viral infection. Nat. Immunol. 16, 991–999 (2015).

  18. 18.

    Wirth, T. C. et al. Repetitive antigen stimulation induces stepwise transcriptome diversification but preserves a core signature of memory CD8+ T cell differentiation. Immunity 33, 128–140 (2010).

  19. 19.

    Muranski, P. et al. Th17 cells are long lived and retain a stem cell-like molecular signature. Immunity 35, 972–985 (2011).

  20. 20.

    Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017).

  21. 21.

    Zeng, H. et al. mTORC1 couples immune signals and metabolic programming to establish Treg-cell function. Nature 499, 485–490 (2013).

  22. 22.

    Nagashima, S. et al. Liver-specific deletion of 3-hydroxy-3-methylglutaryl coenzyme A reductase causes hepatic steatosis and death. Arterioscler. Thromb. Vasc. Biol. 32, 1824–1831 (2012).

  23. 23.

    Zeng, H. et al. mTORC1 and mTORC2 kinase signaling and glucose metabolism drive follicular helper T cell differentiation. Immunity 45, 540–554 (2016).

  24. 24.

    Yang, K. et al. Homeostatic control of metabolic and functional fitness of Treg cells by LKB1 signalling. Nature 548, 602–606 (2017).

  25. 25.

    Wang, X. et al. Transcription of Il17 and Il17f is controlled by conserved noncoding sequence 2. Immunity 36, 23–31 (2012).

  26. 26.

    Shi, L. Z. et al. HIF1alpha-dependent glycolytic pathway orchestrates a metabolic checkpoint for the differentiation of TH17 and Treg cells. J. Exp. Med. 208, 1367–1376 (2011).

  27. 27.

    Pepper, M. et al. Different routes of bacterial infection induce long-lived TH1 memory cells and short-lived TH17 cells. Nat. Immunol. 11, 83–89 (2010).

  28. 28.

    Bettelli, E. et al. Myelin oligodendrocyte glycoprotein-specific T cell receptor transgenic mice develop spontaneous autoimmune optic neuritis. J. Exp. Med. 197, 1073–1081 (2003).

  29. 29.

    Du, X. et al. Hippo/Mst signalling couples metabolic state and immune function of CD8α+ dendritic cells. Nature 558, 141–145 (2018).

  30. 30.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  31. 31.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

  32. 32.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  33. 33.

    Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

  34. 34.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  35. 35.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  36. 36.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

  37. 37.

    Bailey, T. L. & Gribskov, M. Combining evidence using p-values: application to sequence homology searches. Bioinformatics 14, 48–54 (1998).

  38. 38.

    Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

  39. 39.

    Cuellar-Partida, G. et al. Epigenetic priors for identifying active transcription factor binding sites. Bioinformatics 28, 56–62 (2012).

  40. 40.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  41. 41.

    Yang, K. et al. Metabolic signaling directs the reciprocal lineage decisions of αβ and γδ T cells. Sci. Immunol. 3, eaas9818 (2018).

  42. 42.

    Zhu, J. et al. The transcription factor T-bet is induced by multiple pathways and prevents an endogenous Th2 cell program during Th1 cell responses. Immunity 37, 660–673 (2012).

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Acknowledgements

We acknowledge M. Hendren and A. KC for animal work; the Immunology FACS core facility at St Jude Children’s Research Hospital for cell sorting; and N. Chapman and Y. Wang for editing of the manuscript. This work was supported by the National Institutes of Health (NIH; grants AI105887, AI101407, AI131703, CA176624 and NS064599 to H.C.; CA021765 to B.X. and Y.F.) and the National Multiple Sclerosis Society (to H.C.).

Author contributions

P.W.F.K. conceived, designed and performed in vitro and in vivo experiments and bioinformatic analyses, analysed data, and wrote the manuscript. X.C. performed scRNA-seq analysis and developed the latent cellular state analysis (LCA) algorithm. S.A.L., A.A.H. and T.-L.M.N. helped to perform immunological experiments. B.X., Y.C. and Y.F. helped with ATAC-seq analysis. Y.D. and G.N. helped with microarray and scRNA-seq analyses. S.R. performed ChIP assays. W.C., C.R. and J.E. helped with scRNA-seq analyses. K.Y. performed early in vivo studies. P.V. did histopathology analysis. H.C. helped to conceive and design experiments, co-wrote the manuscript, and provided overall direction.

Author information

Affiliations

  1. Department of Immunology, St Jude Children’s Research Hospital, Memphis, TN, USA

    • Peer W. F. Karmaus
    • , Seon Ah Lim
    • , Andrés A. Herrada
    • , Thanh-Long M. Nguyen
    • , Yogesh Dhungana
    • , Sherri Rankin
    • , Kai Yang
    •  & Hongbo Chi
  2. Department of Computational Biology, St Jude Children’s Research Hospital, Memphis, TN, USA

    • Xiang Chen
    • , Beisi Xu
    • , Wenan Chen
    • , Celeste Rosencrance
    • , Yiping Fan
    •  & John Easton
  3. Department of Hematology, St Jude Children’s Research Hospital, Memphis, TN, USA

    • Yong Cheng
  4. Hartwell Center for Bioinformatics and Biotechnology, St Jude Children’s Research Hospital, Memphis, TN, USA

    • Geoffrey Neale
  5. Department of Pathology, St Jude Children’s Research Hospital, Memphis, TN, USA

    • Peter Vogel

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Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Hongbo Chi.

Extended data figures and tables

  1. Extended Data Fig. 1 CD27 expression on TH17 cells during autoimmunity, and cellular homeostasis of CD27+ and CD27 TH17 subsets.

    a, Analysis of publicly available single-cell transcriptomics data from IL-17–GFP+ cells in dLN compared with the CNS8. b, Flow cytometry analysis of putative memory or activation markers on CD4+ TCR-β+ YFP+ cells from the dLN and spleen of mice at day nine post MOG-immunization. c, Summary of CD27 expression on CD4+ TCR-β+ YFP+ cells in dLN (red; n = 5, day 5; n = 3, day 7; n = 7, day 9; n = 8, day 16) overlaid with clinical EAE score (black, n = 5). d, Ki-67 expression in CD27+ and CD27 cell populations. e, Flow cytometry analysis (left) and summary (right) of CD27 expression on transferred CD27+ or CD27 YFP+ cells, at day 14 after transfer into CD45.1+ hosts (n = 14, CD27+; n = 15, CD27). f, Flow cytometry analysis of Bcl-2 expression in CD27+ and CD27 populations. Data are means ± s.e.m. and representative of three (bd, f) or at least five (e) independent experiments. Numbers in plots represent frequencies of cells in gates; numbers within histograms represent mean fluorescence intensities. The Mann–Whitney U-test (two-sided; non-parametric) was used in e to determine statistical significance. Source data

  2. Extended Data Fig. 2 Gene-expression profiles associated with CD27+ and CD27 TH17 subsets.

    a, Volcano plot of transcriptomics data in CD27+ versus CD27 CD4+ TCR-β+ YFP+ cells. b, GSEA plots comparing CD27+ and CD27 populations using gene sets of antigen-specific CXCR5+ and CXCR5 exhausted CD8+ T cells from chronic infection. Gene-expression heat maps are normalized by row (z-score) for the top 30 leading-edge genes between microarray samples from CD27+ versus CD27 CD4+ TCR-β+ YFP+ cells, using gene sets derived from CXCR5+ and CXCR5 exhausted CD8+ T cells1. c, Gene-expression heat maps normalized by row (z-score) for the top 30 leading-edge genes in CD27+ versus CD27 CD4+ TCR-β+ YFP+ cells, using the apoptosis hallmark gene set. d, GSEA plots comparing CD27+ and CD27 populations using ‘hallmark’ gene sets, showing the enrichment of mTORC1 signalling, Myc targets, and selective metabolic pathways in CD27 TH17 cells. Data are from one experiment (ad). NES, normalized enrichment score.

  3. Extended Data Fig. 3 Cell-intrinsic requirement for Raptor in TH17 cells.

    a, Naive CD4+ T cells were differentiated under TH17-polarizing conditions and analysed for cytokine expression after PMA/ionomycin stimulation in vitro (left), or for proliferation (CellTrace Violet) and ROR-γt expression (right). b, Flow cytometry analysis (left) and total number of CD4+ T cells (right) from spinal cord at day 16 post-immunization (n = 10, WT; n = 8, Rptorll17aCre). c, Experimental design for the generation (left) and clinical scores (right) of WT and RptorIl17aCre (R26ReYFP) bone-marrow (BM) chimaeras for restriction of Raptor deficiency specifically to TCR-α-expressing IL-17+ T cells (n = 5 per genotype). Specifically, a 5/1 ratio of Tcra–/– and Rptorll17aCre (or WT control) BM cells was transferred into sublethally irradiated Tcra−/− recipients, followed by reconstitution. In this system, all T cells in the chimaeras were derived from Rptorll17aCre or WT BM cells, whereas most non-T-cell compartments were derived from WT cells. d, Experimental design for the generation (left) and clinical scores (right) of WT and RptorIl17aCre (R26ReYFP) BM chimaeras for equal inflammatory conditions. Specifically, mixed BM chimaeras were generated using a 1/1 ratio of congenically marked CD45.2+ RptorIl17aCre (or WT control) and CD45.1+ WT BM-derived cells, which mediated CNS inflammation in EAE (n = 3, WT; n = 4, Rptorll17aCre). ek, The equi-inflammatory chimaeric mice generated in panel d were analysed at day 18 post-MOG immunization, for the frequencies of CD4+ T cells positive for CD45.2 or YFP (e; n = 12, WT; n = 14, Rptorll17aCre) and YFP+ CD4+ T cells expressing IL-17 or IFN-γ within the spinal cord (f; n = 6, WT; n = 7, Rptorll17aCre); the expression of Ki-67 (g; n = 8, WT; n = 10, Rptorll17aCre), T-bet (h; as fold-change in mean fluorescence intensity after normalization to WT cells) (n = 10 per genotype), and active caspase-3 in splenic YFP+ CD4+ T cells (I; n = 6 per genotype); and the expression of CCR6 (j; n = 7, WT; n = 6, Rptorll17aCre) and CXCR3 (k; n = 8, WT; n = 10, Rptorll17aCre) in YFP+ cells from different organs. Data are means ± s.e.m. and representative of three (a, c, ik) or four (b, dh) independent experiments. Numbers in plots represent frequencies of cells in gates or quadrants. Student’s t-test (two-sided) was used in h and k, and Mann–Whitney U-test (two-sided) was used in b, e, f and g, to determine statistical significance. Source data

  4. Extended Data Fig. 4 Raptor deficiency induces selective phenotypic changes in fate-mapped TH17 cells.

    ag, WT and RptorIl17aCre (R26ReYFP) mice were immunized with MOG, and YFP+ cells from dLN were analysed at day nine post-immunization. a, Frequency of YFP+ cells (n = 7 per genotype). b, Efficiency of Rptor deletion (left; n = 7 per genotype) and flow cytometry analysis of phosphorylated S6 (S235/236) and 4E-BP1 (T37/46) (right) in YFP+ cells. c, Flow cytometry analysis of active caspase-3 and 7-AAD staining in YFP+ cells. d, Flow cytometry analysis of CXCR3 and CCR6 expression on YFP+ cells. e, Cytokine production by YFP+ cells from dLN (n = 7 per genotype). f, Real-time PCR analysis of Tbx21 (n = 8 per genotype), Rorc (n = 6 per genotype), Il12rb2 (n = 7 per genotype) and Il23r (n = 7 per genotype) expression in YFP+ cells. g, Flow cytometry analysis of Foxp3 expression. h, i, Cytokine production (h, i) and proliferation (h) of YFP+ cells from dLN of the indicated mice after four days of stimulation with MOG alone (h) or with MOG plus IL-23 (i) (n = 7 per genotype). j, Sorted YFP+ cells were stimulated with IL-23 or IL-12 for 30 min in vitro and stained with specific antibodies to phosphorylated STAT3 (left), phosphorylated STAT4 (right), or isotype controls. Data are means ± s.e.m. and representative of seven (a), three (bf, j), two (g), or five (h, i) independent experiments. Numbers in plots represent frequencies of cells in quadrants; numbers within histograms represent mean fluorescence intensities. Student’s t-test (two-sided) was used in b, and Mann–Whitney U-test (two-sided) in a, e, i, to determine statistical significance. Source data

  5. Extended Data Fig. 5 Altered gene-expression profiles in Raptor-deficient cells, and shared functional pathways with CD27+ TH17 cells.

    ad, WT and RptorIl17aCre (R26ReYFP) mice were immunized with MOG, and sorted YFP+ cells from dLN were analysed by microarray. a, Expression of individual genes, with vertical lines indicating −1.5-fold and +1.5-fold cut-offs and a horizontal line indicating the P = 0.05 cut-off; gene-ontology (GO) gene sets for acetylation and cholesterol biosynthesis are coloured and Hmgcr is indicated. b, GSEA reveals significant ‘hallmark’ gene sets that are downregulated in RptorIl17aCre compared with WT cells (P < 0.05). c, d, Comparison of functional enrichment of coregulated gene sets in RptorIl17aCre and CD27+ YFP+ cells. We used the downregulated genes in CD27+ versus CD27 TH17 cells and in RptorIl17aCre versus WT TH17 cells (with an FDR of less than 0.05 and the top 200 genes, based on fold change) for functional enrichment using hallmark (c) and KEGG pathway (d) gene sets. Keys indicate FDR values and enrichment scores. e, Comparison of microarray analyses of CD27+ versus CD27 and RptorIl17aCre versus WT samples from mice at day nine post MOG-immunization. Shown are empirical cumulative distribution functions for the changes in expression (log2 values) of all genes expressed in RptorIl17aCre (R26ReYFP) TH17 cells (red line; changes are relative to expression in WT TH17 cells) and for subsets of genes downregulated (green line) or upregulated (blue line) in CD27+ versus CD27 TH17 cells (with a FDR of less than 5%). P-values were calculated using the Kolmogorov–Smirnov test. f, Real-time PCR analysis of Cd27 expression in WT and RptorIl17aCre YFP+ cells (n = 5 per genotype). Data are means ± s.e.m. and from one experiment (ae) or representative of two independent experiments (f). Student’s t-test (two-sided) was used in f to determine statistical significance. Source data

  6. Extended Data Fig. 6 Anabolic metabolism promotes the transdifferentiation of TH17 cells into TH1-like IFN-γ-producing cells.

    a, Ingenuity pathway analysis (IPA) of upstream transcriptional regulators in RptorIl17aCre versus WT samples. b, WT and RptorIl17aCre (R26ReYFP) mice were immunized with MOG, and YFP+ cells from dLN were analysed by flow cytometry for intracellular expression of Myc. c, d, WT and MycIl17aCre (R26ReYFP) mice were immunized with MOG, and YFP+ cells from dLN were analysed by real-time PCR at day nine (c; n = 4, Tbx21; n = 2, Rorc; n = 4, Il12rb2; n = 4, Il23r), or alternatively dLN cells were cultured with MOG, MOG plus IL-12, or MOG plus IL-23 for four days in order to analyse cytokine expression within YFP+ cells (d). e, f, WT and HmgcrIl17aCre (R26ReYFP) mice were immunized with MOG; YFP+ cells were isolated from dLN and analysed by real-time PCR (e; n = 4, Tbx21; n = 2, Rorc; n = 4, Il12rb2; n = 4, Il23r), or dLN cells were cultured with MOG, MOG plus IL-12, or MOG plus IL-23 for four days in order to analyse cytokine expression within YFP+ cells (f). g, Flow cytometry analysis of CD27 expression on YFP+ cells from WT, RptorIl17aCre, MycIl17aCre and HmgcrIl17aCre mice at day nine post MOG-immunization. h, i, WT (R26ReYFP) mice were immunized with MOG, and dLN cells were stimulated with MOG and IL-12 in the presence of vehicle, PF-4708671 (an inhibitor of S6K phosphorylation) or Cbz-B3A (an inhibitor of 4E-BP phosphorylation, which in turn suppresses eIF4E-dependent protein translation) at the indicated concentrations for four days to analyse cytokine expression within YFP+ cells (h; right, summary plots) (n = 9, vehicle; n = 7, PF-4708671 (5 μM); n = 9, PF-4708671 (10 μM); n = 7, Cbz-B3A (5 μM); n = 9, Cbz-B3A (10 μM)) and for CellTrace Violet dilution (i). Data are means ± s.e.m. and from one experiment (a), or representative of four (bf, h, i) or three (g) independent experiments. Numbers in plots represent frequencies of cells in gates or quadrants; numbers within histograms represent mean fluorescence intensities. Student’s t-test (two-sided; parametric) was used to determine statistical significance in panel h. Source data

  7. Extended Data Fig. 7 Analysis of histone acetylation, and ATAC-seq overview and specific gene loci.

    a, ChIP qRT–PCR of pan-acetyl-histone bound to the Ifng promoter of WT or RptorIl17aCre (R26ReYFP) YFP+ cells from dLN (n = 4 per genotype). b, c, WT and RptorIl17aCre (R26ReYFP) mice were immunized with MOG, and YFP+ cells from dLN and spleen at day nine post-immunization were analysed by ATAC-seq. b, Density plot and heat maps of a representative individual ATAC-seq sample, demonstrating separation into different fragment lengths indicative of nucleosome-free, mononucleosome, dinucleosome and trinucleosome patterns, consistent with ref. 15. TSS, transcription start site. c, Correlation plot of nucleosome-free fragments. d, Nucleosome-free ATAC-seq tracks at the Tbx21 and Il12rb2 gene loci, with immediate promoter regions indicated by red boxes. e, Summary of ATAC-seq motif-enrichment data, showing log2 (odds ratio) and log10 (Fisher P-value) of cells from dLN. f, Tn5 insert sites from ATAC-seq analysis of dLN were aligned to motifs for transcription factors from the TRANSFAC database, and the binding profiles of selected transcription factors of the TCF–LEF family are shown. Data are means ± s.e.m. and representative of three independent experiments (a), or from one experiment (bf). Student’s t-test (two-sided; parametric) was used in panel a to determine statistical significance. Source data

  8. Extended Data Fig. 8 ATAC-seq in-depth analyses, TCF-1 binding activity, and effects of 2-DG on cytokine expression.

    a, Analysis of common regions in ATAC-seq and ChIP-seq peaks for motifs that bind to TCF–LEF family and T-bet transcription factors, from spleen samples. Numbers of motif matches and associated Fisher’s exact test P-values and log2 (odds ratios) are shown; a positive log2 (odds ratio) value indicates that a motif is more likely to occur in RptorIl17aCre than in WT samples; a negative value indicates that the chance of occurrence is lower in the RptorIl17aCre group. ‘E − x’ denotes ‘×10x’. b, Nucleosome-free ATAC-seq tracks at the Il6ra and Lrig1 gene loci, with TCF-1-binding sites indicated by red boxes, based on alignment with TCF-1-binding sites from published data (GEO accession numbers are shown). c, ChIP assay to measure TCF-1 binding to Il6ra and Lrig1 gene loci (Il6ra, n = 2 per genotype; Lrig1, n = 6 for WT, n = 5 for RptorIl17aCre). d, Cells from dLN of the indicated mice at day nine post-MOG immunization were cultured for four days with MOG plus IL-12 and sorted on the YFP+ population before intracellular staining. Flow cytometry was used to analyse T-bet and TCF-1 expression in YFP+ cells from WT and MycIl17aCre (R26ReYFP) mice. e, Tn5 insert sites from ATAC-seq analysis of YFP+ cells from PBS- or 2-DG-treated mice were aligned to motifs for transcription factors from the TRANSFAC database, and the binding profiles of selected TCF–LEF family transcription factors are shown. f, Cytokine expression in dLN YFP+ cells from MOG-immunized Il17aCre (R26ReYFP) mice after culture with MOG and IL-12 for four days in the presence of vehicle (PBS) or 2-DG (1 mM). g, Cytokine expression in splenic YFP+ cells from MOG-immunized Il17aCre (R26ReYFP) mice after treatment with 2-DG (2 g per kg of body weight) or PBS. Numbers in plots represent frequencies of cells in gates or quadrants. Data are means ± s.e.m. and from one experiment (a, b, e), or representative of three independent experiments (c, d, f, g). Student’s t-test (two-sided) was used to determine statistical significance in c. Source data

  9. Extended Data Fig. 9 Single-cell transcriptomics analysis.

    WT and RptorIl17aCre (R26ReYFP) mice were immunized with MOG, and YFP+ cells were analysed by single-cell transcriptomics at day nine post-immunization. a, Membership of individual cell clusters in two-dimensional tSNE projections from scRNA-seq data. b, tSNE visualization of nine clusters partitioned by unsupervised clustering. c, Frequencies of WT and RptorIl17aCre cells in different clusters (n = 3 per genotype). d, Top three enriched gene sets for each of the clusters using ‘hallmark’, ‘canonical’ and GO gene sets. For example, genes enriched in cluster 1 by comparison with other clusters were associated with proliferative events. e, Summary of cluster-specific functional enrichment analysis via Fisher’s exact test, using the signatures of ‘T-bet targets’, ‘late memory’, ‘memory TFH overlap DOWN’, ‘HALLMARK_GLYCOLYSIS’, ‘memory TFH overlap UP’ and ‘early memory’ as described in the Methods. f, tSNE visualization of signature scores of ‘T-bet targets’ and ‘HALLMARK_GLYCOLYSIS’ expressed in individual cells. g, Violin plots of Bcl2, Cd27 and Tcf7 gene expression amongst the nine clusters. A violin plot combines the box plot and the local density estimation into a single display. The black bars and thin lines within the violin plots indicate, respectively, the interquartile range (first quantile to third quantile) and the entire range of the data (up to 1.5-fold of the interquartile range from first to third quantile); the white dots in the centre indicate the median values. Data are from one experiment (ag) (n = 3 per genotype). Source data

  10. Extended Data Fig. 10 Pseudotime analysis and experimental validation.

    a–h, WT and RptorIl17aCre (R26ReYFP) mice were immunized with MOG, and YFP+ cells were analysed by single-cell transcriptomics analysis at day nine post-immunization. a, Empirical dispersion and mean expression using the single-cell-analysis toolkit Monocle 2, including the genes used for temporal ordering in black; each grey or black dot represents one gene. The red line shows Monocle’s expected dispersion, with more- and less-dispersed genes based on average expression above and below the red line, respectively. b, Pseudotime densities for each individual cluster. For example, cluster 1, associated with the proliferative signature, was in the centre of the pseudotime spectrum, while clusters 2, 3 and 8 (early in pseudotime; predominantly Raptor-deficient cells) and clusters 7 and, to a lesser extent, 4 and 5 (late in pseudotime; predominantly WT cells) were on the opposite end of the spectrum. c, Projection of signature scores for ‘early memory’, ‘late memory’ and ‘T-bet targets’ onto pseudotime trajectory; the keys indicate the relative scores per cell. d, tSNE visualization of Tbx21 and Ifng gene expression. e, Tbx21 and Ifng gene expression during pseudotime; cells that did not express Tbx21 or Ifng were filtered out in their respective graphs. f, Pseudotime assignment for WT and RptorIl17aCre cells, coloured by genotype; each dot represents one cell. g, Cd27 and Tcf7 gene expression across pseudotime, coloured by genotype. h, tSNE visualization of Cd27 and Tcf7 expression. i, Flow cytometry analysis of T-bet expression in freshly isolated CD27+ and CD27 cells from dLN of Il17aCre (R26ReYFP) mice at day nine post MOG-immunization. j, Fold change in the percentage of the IL-17 IFN-γ+ cells amongst CD27+ or CD27 YFP+ cells stimulated with MOG plus IL-12 as compared with freshly isolated cells (n = 12). k, CD27+ YFP+ cells from MOG-immunized WT and RptorIl17aCre mice were sorted and transferred into CD45.1+ hosts. The following day, CD45.1+ host mice were immunized with MOG; four days later, YFP+ cells were analysed by flow cytometry for surface CD27 expression (left; a summary plot is at the right: n = 6, WT; n = 5, RptorIl17aCre). l, CD27+ YFP+ cells from MOG-immunized WT and RptorIl17aCre mice were stimulated with MOG plus IL-12 for four days, and then CD27 expression was analysed. m, TH17 cells are functionally and metabolically heterogeneous, and are composed of a subset with stemness features but lower anabolic metabolism, and a reciprocal subset with higher metabolic activity that supports transdifferentiation into TH1 cells. These two subsets are further distinguished by selective expression of the transcription factors TCF-1 and T-bet, respectively, and discrete levels of CD27 expression. mTORC1 activation drives reprogramming of anabolic metabolism, favouring transcription that is mediated by T-bet rather than TCF-1; consequently, TH17 transdifferentiation into TH1-like TH17 cells occurs. Memory/stem-like TH17 cells can become reactivated and have the potential to undergo terminal differentiation and acquire TH1-like phenotypes. Data are means ± s.e.m. and from one experiment (ah), or are representative of three (i) or five (jl) independent experiments. Numbers in plots represent frequencies of cells in gates; numbers within histograms represent mean fluorescence intensities. Mann–Whitney U-test (two-sided) was used in panel j to determine statistical significance. Source data

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