The cytokine IL-6 controls the survival, proliferation and effector characteristics of lymphocytes through activation of the transcription factors STAT1 and STAT3. While STAT3 activity is an ever-present feature of IL-6 signaling in CD4+ T cells, prior activation via the T cell antigen receptor limits IL-6’s control of STAT1 in effector and memory populations. Here we found that phosphorylation of STAT1 in response to IL-6 was regulated by the tyrosine phosphatases PTPN2 and PTPN22 expressed in response to the activation of naïve CD4+ T cells. Transcriptomics and chromatin immunoprecipitation–sequencing (ChIP-seq) of IL-6 responses in naïve and effector memory CD4+ T cells showed how the suppression of STAT1 activation shaped the functional identity and effector characteristics of memory CD4+ T cells. Thus, tyrosine phosphatases induced by the activation of naïve T cells determine the way activated or memory CD4+ T cells sense and interpret cytokine signals.
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Microarray, ChiP-seq and RNA-seq data have been deposited in ArrayExpress under accession codes E-MTAB-7682, E-MTAB-6273 and E-MTAB-6141, respectively. Available p300 ChIP-seq fastq files from CD4 T cells, Th1, Th2 (GSE40463) and Th17 (GSE60482), and Stat1−/− and Stat3−/− T cells (GSE65621) were obtained from GEO (https://www.ncbi.nlm.nih.gov/geo/). Access to interactive datasets can be found at www.jones-cytokinelab.co.uk (addition information in relevant figure legends).
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This manuscript is dedicated in fond memory of Javier Uceda Fernandez, a dearly loved friend and colleague who was tragically taken from us on August 29, 2018. Remembered forever. Versus Arthritis (Reference 19796,20770 awarded to S.A.J., N.M.W. and G.W.J.), Hoffmann la Roche (to S.A.J.), Kidney Research UK (to S.A.J.) and the National Health and Medical Research Council of Australia (to T.T.) provided grant support for this project. G.W.J. is recipient of a Versus Arthritis Career Development Fellowship (Reference 20305). J.U.F. held a la Caixa PhD Studentship administered through the British Council, and B.C.C. is supported by a PhD studentship from the Systems Immunity University Research Institute at Cardiff. J.L. is recipient of a Rutherford Fellowship Grant. Analysis of synovial tissues was conducted with support from the Medical Research Council (to C.J.P.; Reference 36661) and the Versus Arthritis-funded Experimental Arthritis Treatment Centre (to C.J.P.; Reference 20022). P.R.T. is recipient of a Wellcome Trust Investigator Award (107964/Z/15/Z) and receives funding through the UK Dementia Research Institute. Bioinformatic analysis was developed with support from the Systems Immunity University Research Institute in Cardiff. The authors would like to thank Y. Kanno, C. Hunter, S. Turner, T. Nakayama, M. Czubala, R. Errington, A. Onodera, K. Hirahara and J. O’Shea for their advice, constructive opinions and kind support.
Integrated supplementary information
(a) Flow cytometry analysis for IFNγ, IL-4, IL-17A and IL-21 in naïve (TN) and effector memory (TEM) CD4+ T cells. Representative plot of n=3. (b) Representative histogram plots for IL-6RA (CD126), Gp130, CD62L and CD44 analysis in CD4+ TN (CD25−CD44lo-intCD62LhiCD127hi), central memory (CD25−CD44hiCD62LhiCD127hi) (TCM), effector (CD25−CD44lo-intCD62LloCD127lo-int) (TEff), and effector memory (CD25−CD44hiCD62LloCD127int-hi) (TEM) T cells. Bottom heat map shows the intensity expression of Sell (CD62L) and Il6ra genes in CD4+ TN and TEM cells. (c) Relative expression of pY-STAT1 (n=3), pY-STAT3 (n=12), pY-STAT5 (n=3), pS-STAT1 (n=3) and pS-STAT3 (n=3) in CD4+ TN, TCM, TEff and TEM cells sorted from human blood after 30 min stimulation with 20 ng/ml IL-6. **** P<0.0001, *** P<0.001 (One-way ANOVA test with Tukey multiple comparison test. Data are shown as mean ± s.e.m).
(a) Experimental design for TCR experienced effector-like CD4+ T cells (TEXP). CD4+ TN cells were activated with anti-CD3/CD28 (1 μgr/ml and 5 μgr/ml, respectively) for 3 days after 2 days rest in normal media (RPMI). (b) Representative plot of STAT1 and STAT3 activity of CD4+ TEXP cells exposed to IL-6 (20ng/ml) for 30 minutes (n=3). (c) Analysis of pY-STAT1 and pY-STAT3 (MFI) in CD4+ TN cells (Day 1; n=3) versus CD4+ TEXP cells (Day 5; n=4) derived from WT and IL6ra −/− mice in response to IL-6 (20 ng/ml), HDS (30.74ng/mL; equimolar concentration equivalent to 20 ng/ml IL-6) or IL-27 (20 ng/ml) for 30 minutes as indicated. (d) Mean fluorescence intensity (MFI) for pY-STAT1 in CD4+ TN, TCM, TEM and TEff cells after 20 ng/ml of IL-6, IL-7 or IL-10 stimulation for 30min (n=3). **** P<0.0001, *** P<0.001, ** P<0.01, * P<0.05 (One-way ANOVA with Tukey multiple comparison test (c,d). Data are shown as mean ± s.e.m).
(a) CD4+ TN cells were cultured for 3 days with varying concentrations of CD3 (0.1-10.0 μg/ml) and CD28 antibodies (0.5-15.0 μg/ml). Cells were rested for 2 days before activation for 30 min with 20ng/ml IL-6. Intracellular flow cytometry for pY-STAT1 and pY-STAT3 is presented as a fold change in MFI (n=4). (b) Real-time PCR for Ahr, Bcl3, Bcl6, Il10, Il21, Kat2b, Pim1 and Stat3 in CD4+ TN and TEM cells after 30 min stimulation with 20 ng/ml IL-6 (n=3). (c) Hierarchical clustering, using the complete linkage method (row 1-spearman rank correlation, of all significant transcriptomic data (P<0.05, relative signal intensity of >150, and a >1.5 fold) in each CD4+ T cells population with and without treatment with 20ng/ml IL-6 for 6h. (d) Relative number of genes (up-regulated in red and down-regulated in blue), individual fold change and gene intensity expression for genes differentially regulated by IL-6 in CD4+ TN, TEM and TEXP cells (see Supplementary Fig. 2a) at 6h after stimulation in the presence or absence of CD3/CD28 antibodies. Analysis was confined to genes displaying both a relative signal intensity of >150 and >1.5-fold alteration in expression following IL-6 treatment (P<0.05). n.s – not significant. (One-way ANOVA (a). Data are shown as mean ± s.d).
(a) Intensity expression of selected genes involved in the IL-6 signaling cascade in CD4+ T cells subsets after 6h treatment with 20 ng/ml IL-6 or control (PBS). (b) Ptpn2 (n = 2), pY-STAT1 (n = 2) and pY-STAT3 (n = 3) positive cells in CD4+ TN and TEM cells after 30 min IL-6 stimulation (20ng/ml) determined by flow cytometry. (c) Intracellular cytokine staining for IL-21 and IL-17A in CD4+ TEM cells from Ptpn2fl/fl, Lck-Cre Ptpn2fl/fl and Ptpn22−/− mice (n = 3). *** P < 0.0001, ** P < 0.01 (One-way ANOVA (c). Data are shown as mean ± s.d).
(a) Representative peak alignment for STAT1 (top) an STAT3 (bottom) for selected target genes including: Irf9 (Chr14:55,602,633-55,611,556), Stat3 (Chr11:100,913,375-100,948,267), Nfat5 (Chr8:107,292,133-107,303,956, Il27ra (Chr8: 84,028,287-84,044,575), Stat3 (Chr11:100,931,206-100,942,360) and Cmtm6 (Chr9:114,729,054-114,740,438). (b) Panther analysis (http://pantherdb.org) for the differential expressed genes with a STAT1 or STAT3 binding in the promoter region for CD4+ TEM cells. (c) Enrichment of STAT1 and STAT3 to consensus binding motifs for specific transcription factors. Sequences were identified using MEME and STAMP/Jaspar software. For each condition, the two mostly highly predicted motifs are presented. Statistical significance of the motif prediction is shown as an E-value (MEME tool) (d) ChIP-qPCR analysis of STAT1, STAT3 and SP1 binding to promoter sequences from Irf1 and Socs3. CD4+ TN and TEM cells were treated with 20ng/ml IL-6 for 1 hour. For STAT1 and STAT3, ChIP-qPCR was normalised against binding to a control DNA sequence (NBS: non-binding sequence). To control for constitutive SP1 binding, analysis was normalised to untreated sample (UT) as described in Methods.
(a) Canonical pathway analysis of IL-6 regulated signatures in CD4+ TN and TEM cells. Statistical analysis shows the profile of canonical pathways that are common and distinct to both population after treatment with 20 ng/ul IL-6 for 30 min. The statistical significance assigned to each pathway is depicted by the size of the dot. A full list of the pathways identified is presented in Supplemental Table 3, and an interactive figure of additional information (http://jones-cytokinelab.co.uk/NI2019/sfigure6a.shtml). (b) ChIP-seq tracks shows p300 peaks for TH1(orange), TH2 (green) and TH17 (purple) CD4+ T cells overlapping with STAT1 and STAT3 peaks in sorted CD4+ TN (blue) and/or TEM (red) cells for the 8 distinct patters of STATs binding. Chromatin marks for Osm (chr11:4,219,039-4,241,349), Stat5b (chr11:100,796,667-100,852,538), Il4ra (chril217:125,551,189-125,572,632), Il21 (chr3:37,220,308-37,234,185), Plgrkt (chr19:29,352,142-29,370,256), Junb (chr:84,975,439-84,980,207), Socs1 (chr16:10,770,927-10,785,406) and Treml2 (chr17:48,289,565-48,303,569) are shown. (c) Prediction of consensus binding motifs for STAT1 or STAT3 transcription factors. Sequences were identified using MEME and STAMP/Jaspar software. Statistical significance of the motif prediction is shown as an E-value (MEME tool). Due to the small number of genes in some of the patterns, not significant motifs were found.
(a) FSC vs SSC gate was used to selected live lymphocyte population. Doublet discrimination was applied when we use FSC-H vs FSC-A. After selecting CD4 positive CD25 negative population, we used CD62L and CD44 difference to select CD4+ T cells subsets. (b) After sorting a sample of each population were run to check population purity.