Abstract
Systemic lupus erythematosus (SLE) is prototypical autoimmune disease driven by pathological T cell–B cell interactions1,2. Expansion of T follicular helper (TFH) and T peripheral helper (TPH) cells, two T cell populations that provide help to B cells, is a prominent feature of SLE3,4. Human TFH and TPH cells characteristically produce high levels of the B cell chemoattractant CXCL13 (refs. 5,6), yet regulation of T cell CXCL13 production and the relationship between CXCL13+ T cells and other T cell states remains unclear. Here, we identify an imbalance in CD4+ T cell phenotypes in patients with SLE, with expansion of PD-1+/ICOS+ CXCL13+ T cells and reduction of CD96hi IL-22+ T cells. Using CRISPR screens, we identify the aryl hydrocarbon receptor (AHR) as a potent negative regulator of CXCL13 production by human CD4+ T cells. Transcriptomic, epigenetic and functional studies demonstrate that AHR coordinates with AP-1 family member JUN to prevent CXCL13+ TPH/TFH cell differentiation and promote an IL-22+ phenotype. Type I interferon, a pathogenic driver of SLE7, opposes AHR and JUN to promote T cell production of CXCL13. These results place CXCL13+ TPH/TFH cells on a polarization axis opposite from T helper 22 (TH22) cells and reveal AHR, JUN and interferon as key regulators of these divergent T cell states.
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
Data availability
Bulk RNA-seq data of sorted T cell subsets and long-term in vitro stimulated cells, scRNA-seq of in vitro stimulated cells, scRNA-seq of T cells before and after anifrolumab treatment, and ATAC–seq data on in vitro stimulated cells, synovial fluid cells and tonsils are available through dbGAP as study phs003582.v1.p1. CUT&RUN sequencing data and bulk RNA-seq time-course of in vitro stimulation are available under accession no. GSE233050. scRNA-seq data of rheumatoid arthritis synovial T cells were obtained from Synapse (https://doi.org/10.7303/syn52297840)20. Bulk RNA-seq and ATAC–seq data of IFN-treated cells were obtained from GSE195543 (ref. 29). Source data are provided with this paper.
Change history
References
Tsokos, G. C. Autoimmunity and organ damage in systemic lupus erythematosus. Nat. Immunol. 21, 605–614 (2020).
Jenks, S. A., Cashman, K. S., Woodruff, M. C., Lee, F. E. & Sanz, I. Extrafollicular responses in humans and SLE. Immunol. Rev. 288, 136–148 (2019).
Bocharnikov, A. V. et al. PD-1hiCXCR5− T peripheral helper cells promote B cell responses in lupus via MAF and IL-21. JCI Insight 4, e130062 (2019).
He, J. et al. Circulating precursor CCR7lo PD-1hi CXCR5+ CD4+ T cells indicate Tfh cell activity and promote antibody responses upon antigen reexposure. Immunity 39, 770–781 (2013).
Havenar-Daughton, C. et al. CXCL13 is a plasma biomarker of germinal center activity. Proc. Natl Acad. Sci. USA 113, 2702–2707 (2016).
Rao, D. A. et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542, 110–114 (2017).
Morand, E. F. et al. Trial of anifrolumab in active systemic lupus erythematosus. New Engl. J. Med. 382, 211–221 (2020).
Choi, J. Y. et al. Circulating follicular helper-like T cells in systemic lupus erythematosus: association with disease activity. Arthritis Rheumatol. 67, 988–999 (2015).
Jenks, S. A. et al. Distinct effector B cells induced by unregulated Toll-like receptor 7 contribute to pathogenic responses in systemic lupus erythematosus. Immunity 49, 725–739 e726 (2018).
Wang, S. et al. IL-21 drives expansion and plasma cell differentiation of autoreactive CD11chiT-bet+ B cells in SLE. Nat. Commun. 9, 1758 (2018).
Crotty, S. Follicular helper CD4 T cells (TFH). Annu. Rev. Immunol. 29, 621–663 (2011).
Gunn, M. D. et al. A B-cell-homing chemokine made in lymphoid follicles activates Burkitt’s lymphoma receptor-1. Nature 391, 799–803 (1998).
Kobayashi, S. et al. TGF-β induces the differentiation of human CXCL13-producing CD4+ T cells. Eur. J. Immunol. 46, 360–371 (2016).
Yoshitomi, H. et al. Human Sox4 facilitates the development of CXCL13-producing helper T cells in inflammatory environments. Nat. Commun. 9, 3762 (2018).
Reshef, Y. A. et al. Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics. Nat. Biotechnol. 40, 355–363 (2022).
Trifari, S., Kaplan, C. D., Tran, E. H., Crellin, N. K. & Spits, H. Identification of a human helper T cell population that has abundant production of interleukin 22 and is distinct from TH-17, TH1 and TH2 cells. Nat. Immunol. 10, 864–871 (2009).
Eyerich, K., Dimartino, V. & Cavani, A. IL-17 and IL-22 in immunity: driving protection and pathology. Eur. J. Immunol. 47, 607–614 (2017).
Veldhoen, M. et al. The aryl hydrocarbon receptor links TH17-cell-mediated autoimmunity to environmental toxins. Nature 453, 106–109 (2008).
Ramirez, J. M. et al. Activation of the aryl hydrocarbon receptor reveals distinct requirements for IL-22 and IL-17 production by human T helper cells. Eur. J. Immunol. 40, 2450–2459 (2010).
Zhang, F. et al. Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes. Nature 623, 616–624 (2023).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Long, W. P., Pray-Grant, M., Tsai, J. C. & Perdew, G. H. Protein kinase C activity is required for aryl hydrocarbon receptor pathway-mediated signal transduction. Mol. Pharmacol. 53, 691–700 (1998).
MacPherson, L. et al. Aryl hydrocarbon receptor repressor and TiPARP (ARTD14) use similar, but also distinct mechanisms to repress aryl hydrocarbon receptor signaling. Int. J. Mol. Sci. 15, 7939–7957 (2014).
Gutierrez-Vazquez, C. & Quintana, F. J. Regulation of the immune response by the aryl hydrocarbon receptor. Immunity 48, 19–33 (2018).
Rothhammer, V. & Quintana, F. J. The aryl hydrocarbon receptor: an environmental sensor integrating immune responses in health and disease. Nat. Rev. Immunol. 19, 184–197 (2019).
Bennett, L. et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J. Exp. Med. 197, 711–723 (2003).
Panwar, B. et al. Multi-cell type gene coexpression network analysis reveals coordinated interferon response and cross-cell type correlations in systemic lupus erythematosus. Genome Res. 31, 659–676 (2021).
Furie, R. A. et al. Type I interferon inhibitor anifrolumab in active systemic lupus erythematosus (TULIP-1): a randomised, controlled, phase 3 trial. Lancet Rheumatol. 1, e208–e219 (2019).
Sumida, T. S. et al. Type I interferon transcriptional network regulates expression of coinhibitory receptors in human T cells. Nat. Immunol. 23, 632–642 (2022).
Frank, D. A., Robertson, M. J., Bonni, A., Ritz, J. & Greenberg, M. E. Interleukin 2 signaling involves the phosphorylation of Stat proteins. Proc. Natl Acad. Sci. USA 92, 7779–7783 (1995).
Zella, D. et al. IFN-α2b reduces IL-2 production and IL-2 receptor function in primary CD4+ T cells. J. Immunol. 164, 2296–2302 (2000).
Rothhammer, V. et al. Type I interferons and microbial metabolites of tryptophan modulate astrocyte activity and central nervous system inflammation via the aryl hydrocarbon receptor. Nat. Med. 22, 586–597 (2016).
Shinde, R. et al. Apoptotic cell-induced AhR activity is required for immunological tolerance and suppression of systemic lupus erythematosus in mice and humans. Nat. Immunol. 19, 571–582 (2018).
Hatzi, K. et al. BCL6 orchestrates Tfh cell differentiation via multiple distinct mechanisms. J. Exp. Med. 212, 539–553 (2015).
Seth, A. et al. AP-1-independent NFAT signaling maintains follicular T cell function in infection and autoimmunity. J. Exp. Med. 220, e20211110 (2023).
Park, J. et al. Integrated genomic analyses of cutaneous T-cell lymphomas reveal the molecular bases for disease heterogeneity. Blood 138, 1225–1236 (2021).
Liu, B., Zhang, Y., Wang, D., Hu, X. & Zhang, Z. Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade. Nat. Cancer 3, 1123–1136 (2022).
Lowery, F. J. et al. Molecular signatures of antitumor neoantigen-reactive T cells from metastatic human cancers. Science 375, 877–884 (2022).
Lynn, R. C. et al. c-Jun overexpression in CAR T cells induces exhaustion resistance. Nature 576, 293–300 (2019).
Liu, Y. et al. IL-2 regulates tumor-reactive CD8+ T cell exhaustion by activating the aryl hydrocarbon receptor. Nat. Immunol. 22, 358–369 (2021).
Luther, S. A., Lopez, T., Bai, W., Hanahan, D. & Cyster, J. G. BLC expression in pancreatic islets causes B cell recruitment and lymphotoxin-dependent lymphoid neogenesis. Immunity 12, 471–481 (2000).
Botia-Sanchez, M. et al. Gut epithelial barrier dysfunction in lupus triggers a differential humoral response against gut commensals. Front. Immunol. 14, 1200769 (2023).
Manfredo Vieira, S. et al. Translocation of a gut pathobiont drives autoimmunity in mice and humans. Science 359, 1156–1161 (2018).
Arshad, T., Mansur, F., Palek, R., Manzoor, S. & Liska, V. A double edged sword role of interleukin-22 in wound healing and tissue regeneration. Front. Immunol. 11, 2148 (2020).
Aringer, M. et al. 2019 European League Against Rheumatism/American College of Rheumatology classification criteria for systemic lupus erythematosus. Arthritis Rheumatol. 71, 1400–1412 (2019).
Nowicka, M. et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Res 6, 748 (2017).
Fonseka, C. Y. et al. Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis. Sci. Transl. Med. 10, eaaq0305 (2018).
Hultquist, J. F. et al. CRISPR–Cas9 genome engineering of primary CD4+ T cells for the interrogation of HIV–host factor interactions. Nat. Protoc. 14, 1–27 (2019).
Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).
Meers, M. P., Tenenbaum, D. & Henikoff, S. Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling. Epigenetics Chromatin 12, 42 (2019).
Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).
Conesa, A., Nueda, M. J., Ferrer, A. & Talon, M. maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics 22, 1096–1102 (2006).
Kang, J. B. et al. Efficient and precise single-cell reference atlas mapping with Symphony. Nat. Commun. 12, 5890 (2021).
Zhang, F. et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat. Immunol. 20, 928–942 (2019).
Hollbacher, B. et al. Transcriptomic profiling of human effector and regulatory T cell subsets identifies predictive population signatures. Immunohorizons 4, 585–596 (2020).
Acknowledgements
We thank the Accelerating Medicines Partnership RA/SLE Network for generation of the rheumatoid arthritis synovial scRNA-seq data. We also thank the Northwestern University Flow Cytometry Core (funded by NCI grant CA060553), the Northwestern University Skin Biology & Diseases Resource-Based center (funded by NIH grant P30 AR075049), and Admera Health for their contributions. This work has been supported in part by funding from the Burroughs Wellcome Fund Career Award in Medical Sciences, NIAMS grant no. K08 AR072791, grant no. P30 AR070253, the Lupus Research Alliance Target Identification in Lupus award, the Rheumatology Research Foundation K Supplement (to D.A.R.) and NIAMS grant no. R01 AR078769 (to D.A.R. and J.C.); NIH grant 1DP2AI136599-01, the Bakewell Foundation, Leukemia & Lymphoma Society (LLS) Scholar Award (1377-21), and a Research Scholar Grant from the American Cancer Society (ACS) (RSG-20-050-01) (to J.C.); NIAMS grant no. R01 AR073290 (to M.B.B.); and NIAID grant no. R01 AI176599, grant no. P30 AI117943, grant no. R01 AI165236 and grant no. U54 AI170792 (to J.F.H.). C.L. was supported by an NCI F31 fellowship (grant no. F31CA268839). V.S.W. was supported by a Lupus Research Alliance Postdoctoral Award. Support for mass cytometry data was provided by Merck Sharpe & Dohme. Support for scRNA-seq of in vitro stimulated T cells was provided by Janssen Research & Development, LLC. We thank the BWH Center for Cellular Profiling for cell sorting and scRNA-seq data generation and G. Perdew for providing the AHR luciferase reporter cell line. The indicated graphics in Figs. 2a, 4c, and 5k and Extended Data Figs. 8a and 9a were created with Biorender.com.
Author information
Authors and Affiliations
Consortia
Contributions
C.L. and V.S.W. designed and performed experiments, analysed data and wrote the manuscript; Y.C. performed computational analysis and wrote the manuscript. C.L. organized CRISPR screen libraries and performed CRISPR arrayed screens. C.L. performed ELISA assays for the CRISPR screens with assistance from A.P., B.H. and V.S.W. C.L. performed CUT&RUN assays, T cell functional assays, time-course RNA-seq assays, computational analyses, and organizing and designing of figures for the manuscript. A.P. performed co-immunoprecipitation assays. V.S.W. and J.S. performed T cell functional and phenotyping analyses with assistance from V.S., I.A., I.J.B. and D.P.N. Y.C. and C.L. performed bulk RNA-seq and D.P.S. participated in bulk RNA-seq analyses. V.S. performed luciferase assays. T.B., P.Z.B. and H.A.-M. provided and analysed anifrolumab clinical trial data. J.K., S.E.A., Y.Q. and J.A.L. generated mass cytometry data, and A.H. analysed mass cytometry data. S.B. generated and analysed in vitro scRNA-seq data, and A.H., J.S., L.-Y.H., B.J. and N.R. participated in analysis of these data. M.B.B. and A.H.J. contributed to generation and analysis of AMP T cell scRNA-seq analysis. K.H.C., E.M., R.A.V., K.S.S., E. Dillon, A.H. and L.C. participated in evaluation of SLE patient clinical data. J.F.H. contributed to design and execution of CRISPR array experiments. D.A.R. and J.C. conceived and supervised the study, analysed and interpreted data and wrote the manuscript. All authors contributed to editing the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The work was performed in part with grant support from Merck Sharpe & Dohme and from Janssen Research & Development, LLC. D.A.R. reports personal fees from Pfizer, Janssen, Merck, GlaxoSmithKline, AstraZeneca, Scipher Medicine, HiFiBio and Bristol-Myers Squibb, and grant support from Bristol-Myers Squibb and Merck outside the submitted work. D.A.R. and M.B.B. are co-inventors on a patent on TPH cells as a biomarker of autoimmunity. T.B. and P.Z.B. are employees of AstraZeneca. H.A.-M. was an employee of AstraZeneca during the study period. L.-Y.H., B.J. and N.R. are employees of Janssen Research & Development, LLC. S.E.A. and Y.Q. are employees of Merck & Co., Inc. J.C. holds patents outside the submitted work and is a co-founder and board member of Moonlight Bio. A patent application has been submitted based on this work.
Peer review
Peer review information
Nature thanks Marcus Clark, Lucy Walker and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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 figures and tables
Extended Data Fig. 1 Clinical associations of PD-1+/ICOS+ and CD96hi cell clusters.
a, Differentially expressed proteins on memory CD4+ T cells from SLE patients compared to controls. p-values from t-test with Bonferroni correction. b, Heatmap of marker expression on mass cytometry cell clusters (left) and MASC association statistics for each cluster comparing SLE vs controls (right). SLE OR = odds ratio of representation in SLE vs control. CI = confidence interval. Adj. p-value by FDR. c, Correlation plot of PD-1/ICOS+ cluster and CD96hi cluster abundances in SLE patients and controls. Spearman statistics shown. d, Association of indicated cluster proportions with serum anti-dsDNA antibody level in SLE patients (n = 19). e, Association of indicated cluster proportions with SLE disease activity by SLEDAI-2K (n = 19). f, Association of indicated cluster proportions with prednisone dose or equivalent at time of sample collection. g, Cluster proportions of PD-1+/ICOS+ (left) and CD96hi (right) clusters in SLE patients stratified by immunosuppressant drug use at time of sample collection (no, n = 7; yes, n = 12). Spearman correlation statistics shown in d-f. Boxes in g show median ± interquartile, with bars indicating min/max values within 1.5x interquartile range. Statistics by Mann-Whitney test.
Extended Data Fig. 2 CD96hi cells are a Th22 cell population.
a, Example of flow cytometry sorting of CD4+ T cell subsets for bulk RNA-seq analysis. b, PCA plot of bulk RNA-seq profiles of CD4+ T cell subsets sorted from SLE (n = 6) or healthy control (n = 5) donors. Colors indicate cell subsets and shapes indicate clinical group. c, Multi-set Venn diagram of the number of differentially expressed genes between CD96hi cells and indicated CD4+ T cell subsets. d, Expression of IL22 and CXCL13 by qPCR in T cell populations from SLE patients (n = 6), plotted relative to expression in CD96hi cells. IL22 expression p-values from left to right: 0.0063, 0.0075. CXCL13 expression p-values from left to right: 0.0128, 0.0012, 0.0283. e, Flow cytometry detection of IL-22 and IL-17A in PMA/ionomycin-stimulated CD96hi CD4 T cells (left) and quantification of IL-22+ IL-17A+ cells (right) in cell subsets from controls (n = 6). Boxes indicate median bounded by 1st and 3rd quartile; bars indicate min/max. f,g, Flow cytometry quantification of cytokines from PMA/ionomycin stimulated CD4+ T cell subsets sorted from healthy donors (f, n = 5, p = 0.0012 for Th17 versus Tph) and base chemokine receptor expression (g, n = 6-7). p-values for g from left to right, all comparing to CD96hi subset, for CCR6: 0.0156, 0.0156, 0.0156, for CXCR5: 0.0313, 0.0313, 0.0313, for CXCR3: 0.0156, 0.0156, 0.0156. Data for f and g are shown as mean ± S.D. P-values (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001) were obtained by ratio paired t-test in d, f, g or by Wilcoxon test in e.
Extended Data Fig. 3 AHR controls T cell production of CXCL13.
a, CXCL13 quantification by ELISA from cells in CRISPR screen without TGF-β. Results from 2 independent experiments using different donors. b, Western blot for CBLB in memory CD4+ T cells treated with control or sgCBLB CRISPR guide (left) and ELISA quantification of CXCL13 from indicated cells (n = 4, 2 biological donors each with 2 technical replicates, p = 0.031). c, Western blot for AHR in cells nucleofected with sgAHR and sgCD8a control. d, ELISA quantification of cytokines from memory CD4+ T cells nucleofected with sgAHR or sgCD8 (n = 12 donors). For CXCL13 p = 4.88e-4 and IL-22 p = 4.88e-4. e, CXCL13 quantification by ELISA in supernatants of memory CD4+ T cells nucleofected with sgAHR or sgCD8a in the presence or absence of TGF-β (n = 8). From left to right, p = 0.0078, 0.0078, 0.0078, 0.0156. f,g, Normalized (to DMSO control) ELISA quantification of indicated cytokines in supernatants of memory (f) or naive CD4+ T cells (g) stimulated under indicated conditions (n = 5–7). For AHRinh and TCDD in f, respectively, p = 7.31e-4 and 0.00304 for CXCL13, and p = 0.00159 and 0.0124 for IL-22. For AHRinh and TCDD in g, respectively, p = 0.0679 and 0.00108 for CXCL13, and p = 0.0192 and 0.0157 for IL-22. h, Normalized (to DMSO control) ELISA quantification of indicated cytokines in supernatants of memory CD4+ T cells stimulated with AHR agonist FICZ, AHR inhibitor GNF-351, or DMSO control (n = 3-4). For FICZ and GNF-351, respectively, p = 0.0109 (GNF-351 only) for CXCL13, and p = 0.0084 and 0.0393 for IL-22. i, Effects of AHR CRISPR deletion (left, n = 10) and pharmacological modulation (middle[n = 9] and right[n = 3]) on IFNγ production measured by ELISA. AHR modulators as in g and h were tested. Results shown normalized to DMSO control. j, Flow cytometry quantification of indicated cytokines in memory CD4 + T cells cultured in polarizing conditions as indicated (n = 6). p = 0.0316 for IL-17. k, ELISA data for CXCL13 (left) and IL-22 (right), normalized to control (DMSO) condition, in supernatants of CD4 + T cells stimulated and cultured with indicated factors. Each dot represents a donor (n = 4-5). l, ELISA data for CD8+ T cells stimulated in the presence of TGF-β with indicated AHR modulators, normalized to DMSO condition per donor (n = 6). For AHRinh and TCDD compared to DMSO, respectively, p = 0.0021 and 0.0038 for CXCL13, and p = 0.0103 and 0.0032 for IL-22. m, ELISA measurement for CXCL13 in supernatants of memory CD8+ T cells nucleofected with sgAHR or control CRISPR guide (n = 6, P = 0.0312). n, Expression of ICOS (left) and CD96 (right) by flow cytometry in memory CD8+ T cells stimulated in indicated conditions, normalized to DMSO condition (n = 8). For AHRinh and TCDD, respectively, p = 0.0158 (AHRinh only) for ICOS, and p = 7.09e-3 and 0.0371 for CD96. Data for f, g, h, i, l and n are shown as mean ± S.D. p-values (NS ≥ 0.05, *p < 0.05, **p < 0.01, ***p < 0.001) by ratio paired t-test for b, f-j, l, n, Wilcoxon test in e, m.
Extended Data Fig. 4 Effects of chronic AHR modulation in CD4+ T cells.
a, ELISA data for indicated cytokines in supernatants of memory (top) and naïve (bottom) CD4+ T cells re-stimulated each week for 3 weeks, normalized to DMSO 1 week result for each donor (n = 3-4 donors). b, UMAP of RA synovial T cell clusters and expression of CXCL13. c, UMAP of cells from Fig. 2e mapped to RA synovial T cell UMAP. d, Cluster abundance of in vitro cultured memory CD4+ T cells from Fig. 2e mapped to RA synovial T cell clusters (n = 3). Compared to DMSO condition, from top to bottom, for TGF-β + DMSO p = 0.0092, 0.0023, 0.0275, 0.0323, and for TGF-β + AHRinh P = 0.0188, 0.0023, 0.0323. ANOVA with Holm-Sidak test. e, CXCL13 expression (by fragments per kilobase of transcript per million mapped reads, FPKM) in bulk RNA-seq samples of cell stimulated under indicated conditions (n = 3). f, GSEA enrichment plots of Tph gene signature in naïve or memory CD4+ T cells stimulated with TGF-β plus either AHR agonist TCDD or inhibitor (AHRinh) CH-223191. g, GSEA enrichment plots for Tph gene signature in T cells stimulated with or without TGF-β, under indicated conditions of AHR agonist TCDD, AHR inhibitor (AHRinh) CH223191, or DMSO control. Mean ± SD shown in a, e.
Extended Data Fig. 5 Effects of AHR and TGF-β on CD4+ T-cell subsets.
a, ELISA measurement of CXCL13 in supernatants of sorted CD4+ T cell subsets from healthy donors (n = 10), stimulated under indicated conditions. Statistical comparisons compare AHR agonist/inhibitor to DMSO within presence or absence of TGF-β, and TGF-β versus no TGF-β within each treatment. p-values from left to right for Naïve: 0.0188, 0.0188, Tph: 0.0032, 4.7e-5, Tfh: 0.0123, 0.0050, CD96hi: 0.0063, 0.0003, 0.0079, Th17: 0.0231, 0.0032, 0.0188, Th1: 0.0050, 0.0050, 0.0421. b, ELISA measurement of CXCL13 from CD4+ T cell subsets nucleofected with either sgAHR or sgCD8a CRISPR guides (n = 4). From left to right, p = 0.0331, 0.0507, 0.0539, 0.0154, 0.0127.c, TGF-β gene signature score in bulk RNA-seq data of T cell subsets as in Fig. 1h. Comparisons made against Tph subset, from left to right p = 1.14e-4, 1.85e-3, 0.0197, 1.85e-3. d, ELISA measurement for IL-22 in supernatants of indicated CD4+ T cell subsets stimulated under indicated conditions (n = 10). Statistical comparisons performed as in (a). p-values from left to right in each subset is as follows, Naïve: 0.0123, CD96hi: 0.0188, 7.24e-4, 1.96e-3, Th17; 4.31e-4, 0.028, 3.17e-3, 0.0188, Th1: 0.002. e, Surface expression of indicated markers in CD4+ T cell subsets by flow cytometry, normalized (to DMSO w/o TGF-β) mean fluorescence intensity (MFI), after stimulation as indicated (n = 4-5). Statistical comparisons performed as in (a). For ICOS, p-value from left to right in each subset is as follows, Naïve: 0.0188, 8.98e-3, 0.0264, 3.57e-3, Tph: 2.73e-3, 5.21e-3, 5.57e-3, 0.0104, 0.0109, 0.0293, Tfh: 2.20e-4, 1.54e-3, 3.34e-4, 8.89e-4, 5.49e-5, 1.31e-3, CD96hi: 5.17e-3, 6.66e-3, 4.52e-4, 0.0154, 2.10e-3, 3.14e-7, 4.39e-3. For CD96, p-value from left to right in each subset is as follows, Naïve: 0.0112, Tph: 0.0312, 0.0205, 6.75e-3, 5.57e-3, Tfh: 0.0315, CD96hi: 0.0463, 5.93e-3, 0.0296, 0.0224. For TIGIT, p-value from left to right in each subset is as follows, Naïve: 0.0160, 0.0203, Tph: 2.41e-3, 0.0115, 0.0165, 1.69e-3, Tfh: 3.26e-3, 0.0238, 0.0321, 4.35e-3, 8.51e-3, 6.84e-3, CD96hi: 0.0105, 0.0157. For PD-1, p-value from left to right in each subset is as follows, Naïve: 9.17e-3, 4.63e-3, 1.04e-3, 2.33e-3, 8.55e-4, 0.0331, Tph: 0.0133, 0.0119, 3.49e-3, 0.0104, Tfh: 8.49e-4, 5.38e-3, 3.73e-4, 1.03e-3, 1.75e-3, 0.0292, 6.66e-3, CD96hi: 9.75e-3, 0.0485, 0.0129, 0.0197, 2.94e-3, 2.07e-3, 0.0328. Boxes indicate median bounded by 1st and 3rd quartile, with bars indicating min/max for a and d, and as mean ± S.D for c and e. p-values (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001) by Friedman’s test with post-test by Dunn’s test for a, c and d, and by ratio paired t-test for b and e.
Extended Data Fig. 6 ATAC-seq analysis of Tph cells, Tfh cells, and AHR inhibitor-treated cells.
a,b, Example flow cytometry cell sorting of CD4+ T cell populations from RA synovial fluid (a) or tonsil (b) mononuclear cells. c, PCA plot of ATAC-seq data from CD4 T cell populations sorted from RA synovial fluid or from tonsil based on PD-1 expression level. d, PCA plot of ATAC-seq data from blood CD4+ T cells of healthy donors cultured with DMSO, AHR agonist TCDD or AHR inhibitor (AHRinh) CH-223191 in the presence of TGF-β. e, GSEA plots of annotated genes of DARs from synovial fluid Tph cells (top, p = 0.001) and tonsil Tfh cells (bottom, p = 0.001) in CD4+ T cells treated with AHRinh versus TCDD in presence of TGF-β for 1 week. f, Differentially accessible regions (red square) near the CXCL13 gene locus from ATAC-seq of each indicated cell type/culture condition. TCDD = AHR agonist; AHRinh = AHR inhibitor CH-223191.
Extended Data Fig. 7 Detection of PD-1+ Tph cells in SLE PBMC.
Gating strategy for flow cytometry detection of PD-1+ CXCR5- Tph cells and CD96hi cells in PBMC from SLE patient after treatment with AHR inhibitor (AHRinh) CH-223191.
Extended Data Fig. 8 Transcriptomic and epigenetic evaluation of AHR activation in T cells and association with AP-1 family members.
a, Schematic of RNA-Seq time course experiment to identify early transcriptomic events of AHR modulation. b, PCA plots of RNA-seq samples after 12 h (left) and 48 h (right) of stimulation with TGF-β and either AHR agonist (TCDD) or AHR inhibitor (AHRinh) CH-223191. c,d, Volcano plots of DESeq2 results from RNA-Seq analysis of memory CD4+ T cells cultured for 12 h (c) and 48 h (d) in TGF-β and either TCDD or AHRinh. The samples used for DESeq2 analysis correspond with the PCA plots in b. e, Pathway enrichment analysis of genes upregulated in TCDD-treated CD4+ T cells at 48 and 72 h of culture, based on Elsevier pathway collection. f, Transcription factor enrichment analysis of samples at 12 h using EnrichR databases TRRUST Transcription factors 2019 (left) and EnrichR Transcription factor Co-occurence (right). g, AHR CUT&RUN binding signal (top) and heat map (bottom). h, Volcano plot of AHR CUT&RUN Diffbind analysis comparing samples with and without AHR CRISPR knockout (top) and HOMER motif analysis of all upregulated peaks found in AHR WT samples (bottom). i, Representative AHR binding regions. j, Comparison of AHR binding in cells treated with AHR agonist or AHR antagonist. k, Venn diagram of overlapped genes bound by AHR with Th22 signature genes as shown in Fig. 1e, hypergeometric P-value is shown. l, Pathway enrichment analysis of AHR-bound peak associated genes. TCDD = AHR agonist; AHRinh = AHR inhibitor, CH-223191. Panel a created with Biorender.com.
Extended Data Fig. 9 Overexpression of JUN in human CD4+ T cells.
a, Illustration of JUN-targeting sgRNAs used in CRISPR screens and validation experiments, and Western blot detection of JUN in T cells nucleofected with control (sgCtrl) or JUN-targeting guide (JUN-sg5), western blot has been reproduced in at least 5 different biological donors. b, Flow cytometry detection of CXCL13 in memory CD4+ T cells nucleofected with control (upper left) or JUN-sg5 (lower left), and quantification after stimulation under indicated conditions (n = 4, right), p-value from left to right: 3.81e-4, 0.0372. c, Flow cytometry detection of IL-22 detection and quantification of IL-22 as for CXCL13 in b. For flow cytometry data (right, n = 4, p = 3.6e-5). d, Top 10 HOMER motifs (left) from JUN CUT&RUN peaks in TCDD-treated memory CD4+ T cells with pathway enrichment analysis (middle and right). e, Venn diagram of CUT&RUN peaks bound by AHR and JUN. f,g, Verification of AHR and JUN as interactors. Immunoblot (WB) analysis of HA (f) or Flag (g) immunoprecipitates from the indicated cell lysates probed with the indicated antibodies, both have been repeated two times in HEK293T cells. h, Cytoplasmic or nuclear extracts (as indicated on bottom) from HEK293T cells stably expressing HA-AHR or vector control treated with AHRinh, TCDD or vehicle control (DMSO) were immunoblotted for AHR, JUN and respective controls (β-tubulin for cytoplasmic extract, Histone H3 for nuclear extract), this has been repeated 3 times in HEK293T cells. i, JUN expression by Western blot in T cells transduced with JUN overexpression construct or control vector. j, Example of flow cytometry sorting to obtain JUN-overexpressing cells based on GFP positivity. k, JUN overexpression (JUN OE) CUT&RUN assessment by peak density on total JUN bound peaks compared to vector control as in Fig. 3k and top HOMER motifs for each respective condition. p-values (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001) by ratio paired t-test for b and c. TCDD = AHR agonist; AHRinh = AHR inhibitor, CH-223191. Panel a created with Biorender.com.
Extended Data Fig. 10 Increased IFN in SLE patients inhibit AHR signaling.
a, IFN signature score in RNA-seq data of CD4+ T cell subsets from SLE and control patients as in Fig. 1g. Median ± interquartile range shown. b, Elsevier pathway enrichment from annotated genes of DAR in No IFN-β control treated CD4+ T cells. c, Normalized relative expression of CYP1A1 measured by qPCR in CD4+ T cells cultured in DMSO or TCDD with the addition of vehicle control or IFN-α (n = 8, p = 0.0016 by paired t-test). TCDD = AHR agonist.
Supplementary information
Supplementary Table 1
Clinical information of patient samples used in this study.
Supplementary Table 2
Mass cytometery panel.
Supplementary Table 3
Significant gene expression profiles of Treg, TPH and CD96hi cells sorted from SLE and HD.
Supplementary Table 4
TPH CRISPR array screen targets and respective guide sequences.
Supplementary Table 5
Rheumatoid arthritis synovial fluid gene expression by clusters.
Supplementary Table 6
Differential transcripts at each time point of stimulation with AHR agonist/inhibitor.
Supplementary Table 7
AHR modulated gene expression over time.
Supplementary Table 8
AHR unanimous bound regions and annotated peaks.
Supplementary Table 9
AP-1 family transcription factor CRISPR array screen targets and respective guide sequences.
Supplementary Table 10
Annotation of AHR and JUN co-bound peaks.
Supplementary Table 11
Annotation of JUN bound peaks reduced in IFNα-treated CD4 T cells.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) 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
Law, C., Wacleche, V.S., Cao, Y. et al. Interferon subverts an AHR–JUN axis to promote CXCL13+ T cells in lupus. Nature 631, 857–866 (2024). https://doi.org/10.1038/s41586-024-07627-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-024-07627-2
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.
