The clinical course of autoimmune and infectious disease varies greatly, even between individuals with the same condition. An understanding of the molecular basis for this heterogeneity could lead to significant improvements in both monitoring and treatment. During chronic infection the process of T-cell exhaustion inhibits the immune response, facilitating viral persistence1. Here we show that a transcriptional signature reflecting CD8 T-cell exhaustion is associated with poor clearance of chronic viral infection, but conversely predicts better prognosis in multiple autoimmune diseases. The development of CD8 T-cell exhaustion during chronic infection is driven both by persistence of antigen and by a lack of accessory ‘help’ signals. In autoimmunity, we find that where evidence of CD4 T-cell co-stimulation is pronounced, that of CD8 T-cell exhaustion is reduced. We can reproduce the exhaustion signature by modifying the balance of persistent stimulation of T-cell antigen receptors and specific CD2-induced co-stimulation provided to human CD8 T cells in vitro, suggesting that each process plays a role in dictating outcome in autoimmune disease. The ‘non-exhausted’ T-cell state driven by CD2-induced co-stimulation is reduced by signals through the exhaustion-associated inhibitory receptor PD-1, suggesting that induction of exhaustion may be a therapeutic strategy in autoimmune and inflammatory disease. Using expression of optimal surrogate markers of co-stimulation/exhaustion signatures in independent data sets, we confirm an association with good clinical outcome or response to therapy in infection (hepatitis C virus) and vaccination (yellow fever, malaria, influenza), but poor outcome in autoimmune and inflammatory disease (type 1 diabetes, anti-neutrophil cytoplasmic antibody-associated vasculitis, systemic lupus erythematosus, idiopathic pulmonary fibrosis and dengue haemorrhagic fever). Thus, T-cell exhaustion plays a central role in determining outcome in autoimmune disease and targeted manipulation of this process could lead to new therapeutic opportunities.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Wherry, E. J. T cell exhaustion. Nature Immunol. 12, 492–499 (2011)
McKinney, E. F. et al. A CD8+ T cell transcription signature predicts prognosis in autoimmune disease. Nature Med. 16, 586–591 (2010)
Lee, J. C. et al. Gene expression profiling of CD8+ T cells predicts prognosis in patients with Crohn disease and ulcerative colitis. J. Clin. Invest. 121, 4170–4179 (2011)
Baechler, E. C. et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc. Natl Acad. Sci. USA 100, 2610–2615 (2003)
West, E. E. et al. Tight regulation of memory CD8+ T cells limits their effectiveness during sustained high viral load. Immunity 35, 285–298 (2011)
Aubert, R. D. et al. Antigen-specific CD4 T-cell help rescues exhausted CD8 T cells during chronic viral infection. Proc. Natl Acad. Sci. USA 108, 21182–21187 (2011)
Urbani, S. et al. Outcome of acute hepatitis C is related to virus-specific CD4 function and maturation of antiviral memory CD8 responses. Hepatology. 44, 126–139 (2006)
Wherry, E. J. et al. Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670–684 (2007)
Rangachari, M. et al. Bat3 promotes T cell responses and autoimmunity by repressing Tim-3-mediated cell death and exhaustion. Nature Med. 18, 1394–1400 (2012)
Francisco, L. M., Sage, P. T. & Sharpe, A. H. The PD-1 pathway in tolerance and autoimmunity. Immunol. Rev. 236, 219–242 (2010)
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)
Blackburn, S. D. et al. Coregulation of CD8+ T cell exhaustion by multiple inhibitory receptors during chronic viral infection. Nature Immunol. 10, 29–37 (2009)
Sevilla, N. et al. Immunosuppression and resultant viral persistence by specific viral targeting of dendritic cells. J. Exp. Med. 192, 1249–1260 (2000)
Virgin, H. W., Wherry, E. J. & Ahmed, R. Redefining chronic viral infection. Cell 138, 30–50 (2009)
Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014)
Baitsch, L. et al. Exhaustion of tumor-specific CD8+ T cells in metastases from melanoma patients. J. Clin. Invest. 121, 2350–2360 (2011)
Lang, K. S. et al. Inverse correlation between IL-7 receptor expression and CD8 T cell exhaustion during persistent antigen stimulation. Eur. J. Immunol. 35, 738–745 (2005)
Mueller, S. N. & Ahmed, R. High antigen levels are the cause of T cell exhaustion during chronic viral infection. Proc. Natl Acad. Sci. USA 106, 8623–8628 (2009)
Barber, D. L. et al. Restoring function in exhausted CD8 T cells during chronic viral infection. Nature 439, 682–687 (2006)
Lyons, P. A. et al. Microarray analysis of human leucocyte subsets: the advantages of positive selection and rapid purification. BMC Genom. 8, 64 (2007)
Taylor, M. W. et al. Changes in gene expression during pegylated interferon and ribavirin therapy of chronic hepatitis C virus distinguish responders from nonresponders to antiviral therapy. J. Virol. 81, 3391–3401 (2007)
Lauer, G. M. et al. Full-breadth analysis of CD8+ T-cell responses in acute hepatitis C virus infection and early therapy. J. Virol. 79, 12979–12988 (2005)
Vahey, M. T. et al. Expression of genes associated with immunoproteasome processing of major histocompatibility complex peptides is indicative of protection with adjuvanted RTS,S malaria vaccine. J. Infect. Dis. 201, 580–589 (2010)
Nakaya, H. I. et al. Systems biology of vaccination for seasonal influenza in humans. Nature Immunol. 12, 786–795 (2010)
Querec, T. D. et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nature Immunol. 10, 116–125 (2009)
Hoang, L. T. et al. The early whole-blood transcriptional signature of dengue virus and features associated with progression to dengue shock syndrome in Vietnamese children and young adults. J. Virol. 84, 12982–12994 (2010)
Shum, A. K. et al. BPIFB1 is a lung-specific autoantigen associated with interstitial lung disease. Sci. Translat. Med. 5, 206ra139 (2013)
Herazo-Maya, J. D. et al. Peripheral blood mononuclear cell gene expression profiles predict poor outcome in idiopathic pulmonary fibrosis. Sci. Translat. Med. 5, 205ra136 (2013)
Kodama, K. et al. Tissue- and age-specific changes in gene expression during disease induction and progression in NOD mice. Clin. Immunol. 129, 195–201 (2008)
Elo, L. L. et al. Early suppression of immune response pathways characterizes children with prediabetes in genome-wide gene expression profiling. J. Autoimmun. 35, 70–76 (2010)
Stone, J. H. et al. A disease-specific activity index for Wegener’s granulomatosis: modification of the Birmingham Vasculitis Activity Score. International Network for the Study of the Systemic Vasculitides (INSSYS). Arthritis Rheum. 44, 912–920 (2001)
Tan, E. M. et al. The 1982 revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum. 25, 1271–1277 (1982)
Isenberg, D. A. et al. BILAG 2004. Development and initial validation of an updated version of the British Isles Lupus Assessment Group’s disease activity index for patients with systemic lupus erythematosus. Rheumatology 44, 902–906 (2005)
Silverberg, M. S. et al. Toward an integrated clinical, molecular and serological classification of inflammatory bowel disease: report of a Working Party of the 2005 Montreal World Congress of Gastroenterology. Can. J. Gastroenterol. 19, 5A–36A (2005)
Harvey, R. F. & Bradshaw, M. J. Measuring Crohn’s disease activity. Lancet i, 1134–1135 (1980)
Walmsley, R. S., Ayres, R. C., Pounder, R. E. & Allan, R. N. A simple clinical colitis activity index. Gut 43, 29–32 (1998)
Whitney, A. R. et al. Individuality and variation in gene expression patterns in human blood. Proc. Natl Acad. Sci. USA 100, 1896–1901 (2003)
Le Brigand, K. et al. An open-access long oligonucleotide microarray resource for analysis of the human and mouse transcriptomes. Nucleic Acids Res. 34, e87 (2006)
Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 3 (2004)
Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004)
Huber, W., von Heydebreck, A., Sultmann, H., Poustka, A. & Vingron, M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18 (Suppl. 1). S96–S104 (2002)
Kauffmann, A., Gentleman, R. & Huber, W. arrayQualityMetrics–a bioconductor package for quality assessment of microarray data. Bioinformatics 25, 415–416 (2009)
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007)
Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998)
Renshaw, B. R. et al. Humoral immune responses in CD40 ligand-deficient mice. J. Exp. Med. 180, 1889–1900 (1994)
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformat. 9, 559 (2008)
Barabasi, A. L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999)
Langfelder, P. & Horvath, S. Eigengene networks for studying the relationships between co-expression modules. BMC Syst. Biol. 1, 54 (2007)
van der Laan, M. J. & Pollard, K. S. A new algorithm for hybrid hierarchical clustering with visualization and the bootstrap. J. Stat. Plann. Inf. 117, 275–303 (2002)
Ingenuity Pathway Analysis (Ingenuity Systems, 2003)
Breiman, L. Random forests. Machine Learn. J. 45, 5–32 (2001)
Gentleman, R. et al. Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Springer, 2005)
Cramp, M. E. et al. Hepatitis C virus-specific T-cell reactivity during interferon and ribavirin treatment in chronic hepatitis C. Gastroenterology 118, 346–355 (2000)
Bienkowska, J. R. et al. Convergent random forest predictor: methodology for predicting drug response from genome-scale data applied to anti-TNF response. Genomics 94, 423–432 (2009)
Toonen, E. J. et al. Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis. PLoS ONE 7, e33199 (2012)
This work was supported by The National Institute of Health Research (NIHR), Cambridge Biomedical Research Centre and funded by the Wellcome Trust (project 094227/Z/10/Z and program grants 083650/Z/07/Z), a Lister Prize Fellowship (K.G.C.S) and the Lupus Research Institute (Distinguished Innovator Award, K.G.C.S). E.F.M. is a Wellcome–Beit Research Fellow supported by the Wellcome Trust and Beit Foundation (104064/Z/14/Z). The Cambridge Institute for Medical Research is in receipt of a Wellcome Trust Strategic Award (079895). We thank A. Kaser and J. Todd for reviewing the manuscript, staff of the NIHR Cambridge Biomedical Research Centre Cell Phenotyping hub and the patients who provided samples.
The authors declare no competing financial interests.
Extended data figures and tables
a, Messenger RNA derived from purified leucocyte subsets sampled during active, untreated autoimmune disease is labelled and hybridized to a microarray platform (both HsMediante 25k and Affymetrix Gene 1.0 ST used here). Genes are then combined into modules (b, coloured blocks) based on the similarity of their expression profile in all samples. c, Detail for the ‘black’ module. Each horizontal black line represents expression of a single gene within the given module; y axis, gene expression; x axis, patient samples; red bar, eigengene profile which effectively summarizes the expression of all genes constituting the black module. d, Each modular profile is related to all others in a hierarchy that can itself be visualized by plotting correlation of all module eigengenes, such as in the heat map shown here. Coloured blocks represent individual modules, defined as in a. Modules are aligned in identical order on x and y axes with heat-map colour representing the correlation between each. Note that the diagonal (top left to bottom right) therefore represents the correlation of each eigengene profile with itself, and is always 1. Distance metric is the Euclidean distance. e, As each module is summarized by a representative eigengene profile, each may then be correlated against a range of clinical variables, allowing visualization of how the transcriptome relates to clinical variables, again in the form of a correlation heat map. Pearson correlation, r. f, Heat map showing gene expression modules (y axis) correlated against clinical variables (x axis) for the CD4 transcriptome in AAV. Pearson correlation, r. g, Heat map illustrating significance of correlations identified in f. P value threshold at Bonferroni-corrected P < 0.05. Colour bar indicates actual P value of correlations deemed significant; grey shading, corrected P > 0.05. Significance for co-stimulation (black) module from Fig. 1 is also shown (P = 0.0005).
Extended Data Figure 2 Weighted gene co-expression network analysis of the T-cell transcriptome and its correlation with clinical phenotype in SLE.
a, e, Heat maps illustrating the correlation of co-expression modules (coloured blocks, y axis) derived from the CD8 (a) and CD4 (e) transcriptomes of 23 SLE patients with clinical traits (x axis). Overlap of the previously described prognostic signature with co-expression modules, along with the distribution of a random signature of equivalent size, shown to the right of a (overlap = signature genes/module genes, as a percentage). Overlap of the CD4 T-cell co-stimulation ‘black’ module (defined in Fig. 1) shown to the right of e, with a randomly derived module and a type 1 IFN response signature previously shown to associate with active SLE4. Overlap shown as percentage representation of the signature within each module. b, d, Linear plots illustrating the ‘charcoal’ (b) and ‘grey’ (d) modules in detail; y axis, gene expression; x axis, individual patients; coloured lines (red, blue), module eigengenes. c, Correlation of SLE CD4 T-cell co-stimulation module eigengene (x axis, blue) against SLE CD8 T-cell prognostic signature (y axis, red). Pearson correlation, r, with P = two-tailed significance. f, Expanded detail from e, illustrating that modules corresponding to type 1 IFN response and co-stimulation signatures correlate with disease activity and outcome respectively but not vice versa.
Extended Data Figure 3 Identification and validation of genes involved in CD4 co-stimulation that correlate with clinical outcome, and how that relationship changes after treatment.
a, A knowledge-based network analysis of 336 probes composing the ‘black’ expression module (Fig. 1e) identifies a network of co-stimulation signalling (Supplementary Table 3). Individual genes are shown in circles with the ‘strength’ of their connections indicated by the weight of the black bar linking them. Pathways of TCR signalling, inducible T-cell co-stimulator and its ligand (ICOS–ICOSL) signalling and CD28 signalling are all significantly enriched in this module (FDR P < 0.05). b–e, Scatter plots showing the outcome of multiple linear regression models testing the association of four signatures (red symbols) as indicated, directly compared with clinical markers of disease activity (black symbols); x axis, magnitude of association (regression coefficient, change in normalized flare rate (flares per number of days follow-up) per unit change in each variable tested); y axis, significance of association in multiple regression model; P, significance threshold (dashed red line, P = 0.05). b, CD8 turquoise module eigengene in AAV, (c) CD4 co-stimulation (black) module eigengene in AAV, (d, e) CD8 exhaustion signature (Supplementary Table 6) in AAV/SLE (d) and IBD (e). Clinical variables incorporated vary owing to differing relevance in each case but include some of the following: disease activity score (BVAS/BILAG/CDAI/Harvey–Bradshaw score), C-reactive protein, autoantibody titre (PR3/MPO, dsDNA), lymphocyte count, neutrophil count, platelet count, IgG, IgA, IgM, erythrocyte sedimentation rate, age. f, Line plot showing mean expression of a CD8 T-cell exhaustion signature in 38 patients with AAV measured at presentation during active, untreated disease (t0) and 12 months later when disease activity was quiescent and patients were on maintenance immunosuppressive therapy (t12). Patients are grouped into those falling above (red) and below (blue) median expression of the exhaustion signature eigengene at entry. P = Mann–Whitney test comparing t12 and t0 values. The difference between the groups that is easily apparent at enrolment with active, untreated disease (t0) is no longer apparent when disease is treated and quiescent 12 months later (t12). g–i, Scatter plots showing inverse correlation between individual eigenvalues of the CD4 co-stimulation signature (x axis, red) and the CD8 exhaustion signature (y axis, blue) defined as in Fig. 2, for AAV (g), SLE (h) and IBD (i) cohorts. Pearson correlation, r2, two-tailed significance.
Extended Data Figure 4 Wind rose plots showing relative GSEA enrichment of immune signatures in autoimmune disease and melanoma.
Wind rose plots showing relative enrichment (GSEA FDR q value) of distinct immune signatures between subgroups of patients (as defined as in Fig. 2). a, b, AAV; c, d, SLE; e, f, IBD. a, c, e, Enrichment of immune signatures from selected CD8 T-cell phenotypes; b, d, f, enrichment of signatures specifically up-/downregulated by CD8 T-cell subsets derived from the LCMV model of T-cell exhaustion (acute LCMV Armstrong versus chronic LCMV Cl13 (ref. 8)). Detailed information on genes included in each signature is provided in Supplementary Table 6. g, h, Wind rose plots showing relative enrichment (GSEA FDR q value) of distinct immune signatures between CD8 T cells from patients with melanoma, comparing CD8 from tumour-infiltrated lymph node with circulating CD8 T cells16. g, Enrichment of immune signatures from selected CD8 T-cell phenotypes; h, enrichment of signatures specifically up-/downregulated by CD8 T-cell subsets derived from the LCMV model of T-cell exhaustion (acute LCMV Armstrong versus chronic LCMV Cl13 (ref. 8)). Specific enrichment is seen for genes downregulated by exhausted cells but not for all genes upregulated by exhausted cells. c, Heat map showing differential expression of selected canonical co-inhibitory receptors (as for Fig. 2c (ref. 12)) in the LCMV exhaustion model, between prognostic subgroups identified in Fig. 2d, g, j and between exhausted CD8 T cells from melanoma-infiltrated lymph node compared with circulating tumour-specific CD8 T cells16. Blue, up in exhausted; red, up in non-exhausted; grey, no significant change (FDR P < 0.05).
Extended Data Figure 5 T-cell co-stimulation with CD2, but not type 1 IFN or anti-CD40, prevents development of an exhausted IL-7RloPD1hi phenotype during prolonged anti-CD3/28 T-cell stimulation.
a–d, Representative scatter plots showing IL-7R expression (y axis) by cell division (CFSE dilution, x axis) in (a) unstimulated cells and following each of three different co-stimulation cultures: b, anti-CD3/CD28 alone; c, anti-CD2/3/28; d, anti-CD40/3/28. IL-7Rhi-expressing subset indicated in black gate with the percentage of live cells shown. e–g, Line and scatter plots showing absolute number of IL-7Rhi cells (e), PD-1 expression (f) and cell death (g) (death = AquaFluorescent dye+) during CD8 T-cell differentiation (x axis, number of divisions undergone by day 6 of culture measured by CFSE dilution) after anti-CD3/28 (blue) or anti-CD2/3/28 (red) stimulation. P = paired t-test, n = 5 paired samples. h, i, Hierarchical clustering of 44 patients with AAV (left panels) and 23 patients with SLE (right panels) using 336 genes composing a CD4 T-cell co-stimulation module (black module, Fig. 1) identifies two subgroups of patients (high co-stimulation, red; low co-stimulation, blue) in CD4 T-cell expression data defined by the first major division in the patient dendrogram. j, k, Scatter plots illustrating selected co-stimulatory and co-inhibitory receptors for the subgroups identified in h and i. Selected receptors were chosen on the basis of their inclusion in networks derived from the co-stimulation and exhaustion signatures as illustrated in Extended Data Fig. 3a. l, m, Line and scatter plots showing absolute number of IL-7Rhi cells (y axis) by number of divisions undergone at day 6 (x axis) after polyclonal stimulation with anti-CD3/28 (blue) or anti-CD3/28 plus anti-CD40 (l, green) or IFN-α (m, green) co-stimulation. n, Line and scatter plot showing extent of proliferation occurring (percentage of live cells on day 6 having undergone each of zero to four divisions) after polyclonal stimulation of primary human CD8 T cells with CD3/28 alone (blue) or with additional anti-CD2 co-stimulation (red), confirming no difference in the extent of live cell proliferation between groups. o, Absolute live (AquaFluorescent Dye−) cell counts (y axis) by the number of divisions undertaken (x axis) by day 6 after polyclonal stimulation of primary human CD8 T cells with CD3/28 alone (blue) or with additional anti-CD2 co-stimulation (red), illustrating increased cell survival with CD2 co-stimulation despite equivalent proliferation. P values = two-way ANOVA of four paired stimulations.
Extended Data Figure 6 CD2 co-stimulation results in functionally distinct subpopulations showing enhanced survival after in vitro re-stimulation but no preferential expansion of CD8 memory subsets.
a, Representative flow cytometry density plots of CD8 T cells showing BCL2 expression on day 7 after stimulation with anti-CD3/28 (blue) or anti-CD2/3/28 (red). Figures are the percentage of total CD8 T cells. b, Quantification of BCL2 expression in CD8 T cells stimulated as in a. P = Mann–Whitney, n = 5 paired biological replicates per group. c, Scatter plots showing cytokine levels (y axis, picograms per millilitre) measured in supernatants of CD8 T cells on day 7 after in vitro stimulation with either anti-CD3/28 (left column, blue) or CD2/3/28 (right column, red). Samples represent paired stimulations of primary CD8 T cells from the same individual using either stimulation protocol (n = 6 biological replicates per group). d, Scatter plots illustrating populations sorted after polyclonal anti-CD3/28 (left panel) and anti-CD2/3/28 (right panel) stimulation of primary CD8 T cells. e, Percentage of live cells (AquaFluorescent dye−) remaining 7 days after re-stimulation of each sorted subpopulation of CD8 cells. Cells were rested for 6 days in complete RPMI1640 medium without IL-2 before being re-stimulated with anti-CD2/3/28 for a further 7 days. P = Mann–Whitney; error bars, mean ± s.e.m. f, Representative scatter plot illustrating CD8 T-cell memory populations isolated by flow cytometric sorting and stimulated in g, h. g, Scatter plot showing absolute number of IL-7Rhi cells (y axis) on day 6 after anti-CD3/28 (blue) or anti-CD2/3/28 (red) stimulation of purified CD8 T-cell memory populations (x axis). *P < 0.05, Mann–Whitney test (n = 5 paired biological replicates per group). h, Scatter plots showing percentage CD8 T-cell memory subsets (y axis) resulting from stimulation of purified central memory (Tcm), naive (Tn), effector memory (Tem) and effector memory-RA (Temra) populations with anti-CD3/28 (blue) or anti-CD2/3/28 (red) for 6 days (n = 4 paired biological replicates per group).
Extended Data Figure 7 Top PBMC surrogate markers reflect expression of CD4 co-stimulation/CD8 exhaustion modules within CD4 and CD8 data respectively.
Top PBMC-level predictors (n = 13) were selected as indicated in Fig. 4a, and data are shown comparing expression of the optimal predictor (KAT2B, a, e) and of each other top predictor gene (d, h) in PBMC data compared with expression of the CD4 co-stimulation module eigengene in CD4 data (a–d) and the CD8 exhaustion signature eigengene in CD8 data (e–h) for n = 44 patients with AAV. Significance of correlation: *P < 0.05, **P < 0.01, ***P < 0.001. b, f, Scatter plots showing the outcome of multiple linear regression models testing the association of KAT2B expression in CD4 (b) and CD8 (f) data (red symbols) directly compared with clinical markers of disease activity (black symbols); x axis, magnitude of association (regression coefficient, change in normalized flare rate (flares per number of days follow-up) per unit change in each variable tested); y axis, significance of association in multiple regression model; P, significance threshold (dashed red line, P = 0.05). Clinical variables incorporated were disease activity score (BVAS), C-reactive protein, lymphocyte count, neutrophil count, IgG. c, g, Heat maps reproduced from Fig. 1a, i, respectively, showing overlap of top PBMC-level predictors with the modular analysis presented for CD4 (c) and CD8 (g) data in Fig. 1. As expected, surrogate markers showed stronger correlation with the CD4 than the CD8 signature as the algorithm was trained to detect the CD4 co-stimulation module.
Extended Data Figure 8 Immune cell subset expression pattern of top PBMC-level surrogate markers of CD4 co-stimulation/CD8 exhaustion signatures.
Dot plots showing expression (median ± s.e.m.) of KAT2B (a) and for each of 12 other top PBMC-level surrogate predictors of CD4 co-stimulation/CD8 exhaustion signatures (from Fig. 4a) in a range of 22 immune cell subsets. Genes showing significant correlation of expression with KAT2B across all cell types are indicated (**P < 0.001).
Extended Data Figure 9 Hierarchical clustering of multiple data sets using 13 top PBMC-level surrogate markers of CD4 co-stimulation/CD8 exhaustion modules identifies subgroups of patients with distinct clinical outcomes.
Replication of association between surrogate markers of CD4 co-stimulation/CD8 exhaustion signatures and clinical outcome (as shown in Fig. 4c–k) but using all top 13 PBMC-level surrogates rather than KAT2B alone. a, c, e, g, i, k, m, Heat maps showing hierarchical clustering of gene expression data of 13 top PBMC-level surrogate predictors of CD4 co-stimulation/CD8 exhaustion signatures (from Fig. 4a) in patients with chronic HCV (a), during malaria vaccination (c), influenza vaccination (e), yellow fever vaccination (g), dengue fever infection (i), IPF (k) and pre-T1D (m). Subgroups were defined using a major division of the cluster dendrogram and group 1 allocated on the basis of KAT2B expression (highest in group 1). Clinical outcome associated with each subgroup identified is shown in b (HCV, percentage of responders to IFN-α/ribavirin therapy), d (percentage showing protection versus no protection from malaria vaccine), f (percentage response to influenza vaccination), h (yellow fever antibody-titre after vaccination), j (percentage progression to DHF), l (percentage of patients progressing to need for transplantation or death) and n (percentage of samples from patients with previous or subsequent progression to islet-cell antibody seroconversion or to a diagnosis of T1D).
Extended Data Figure 10 Kinetics of KAT2B expression during treatment of chronic HCV, malaria and influenza vaccination, during T1D development in the NOD mouse and in PBMC data from patients with IBD and rheumatoid arthritis.
a, Expression of a type 1 IFN response signature (average eigenvalue of type 1 IFN response signature plotted for each response group at each time point, A, signature as defined in ref. 4) in a cohort of 54 patients during treatment of chronic HCV infection with pegylated IFN-α and ribavirin (as described in ref. 53 and Fig. 4c), including 28 showing a marked response (red line, HCV titre decrease >3.5 log10(IU ml−1) by day 28) and 26 a poor response (HCV titre decrease <1.5 log10(IU ml−1) by day 28). P = two-way ANOVA. b, Schematic representation of the vaccination (black) and transcriptome profiling (red) schedule for the adjuvanted RTS,S malaria vaccine trial23 (as shown in Fig. 4d). b–d, Heat map (b) and line plots (c, d) illustrating temporal changes in expression of 404 genes representing the GO ‘inflammatory response’ module (c) or KAT2B expression (d) at each time-point during vaccination in patients with above- (red) and below- (blue) median KAT2B expression throughout the vaccination schedule outlined in b. Subgroups defined at T2, immediately after booster vaccination as this equates to the period of most ‘active’ immune response. Plots are mean ± s.e.m. e, Schematic representation of the vaccination (black arrows) and transcriptome profiling (red arrows) schedule for 28 vaccinees receiving the 2008 seasonal influenza vaccination (combined trivalent inactivated influenza vaccine24 as shown in Fig. 4e) with response assessed at day 28 by haemagglutination inhibition titre (green arrow). f, Linear plot illustrating temporal changes in expression of 404 genes representing the GO ‘inflammatory response’ module at each time-point during vaccination (d0–d7 corresponding to microarray bleed points in e for patients showing above- (red) or below- (blue) median expression of KAT2B at day 3 after vaccination; y, expression log2; x, time-point, days after vaccination; P = two-way ANOVA. g, Linear plot showing ratio of Kat2b expression in peripheral blood of NOD mice (y axis, n = 37 mice in total across six time points) before and during the induction and onset of insulitis and the development of overt diabetes (illustrated by black bars below); x axis, age (days); y axis, Kat2b expression log2 ratio versus B10 controls29. h, Kaplan–Meier censored survival curve showing flare-free survival (y axis) during follow-up (x axis) of n = 58 patients with IBD stratified by KAT2B expression (red, above median; blue, below median). P = log-rank test. i, j, Box plots showing clinical response (percentage responders) 3 months after treatment with anti-TNF therapy in two independent cohorts (I54 and J55) of patients with rheumatoid arthritis (RA). P = Fisher’s exact test. Linear plots show mean ± s.e.m. throughout.
This file contains a Supplementary Discussion and legends for Supplementary Tables 1-7 (see separate excel files). (PDF 176 kb)
This file contains Supplementary Table 1. (XLSX 38 kb)
This file contains Supplementary Table 2. (XLSX 68 kb)
This file contains Supplementary Table 3. (XLSX 35 kb)
This file contains Supplementary Table 4. (XLSX 52 kb)
This file contains Supplementary Table 5. (XLSX 35 kb)
This file contains Supplementary Table 6. (XLSX 16 kb)
This file contains Supplementary Table 7. (XLSX 11 kb)
About this article
Cite this article
McKinney, E., Lee, J., Jayne, D. et al. T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection. Nature 523, 612–616 (2015). https://doi.org/10.1038/nature14468
Presumption of guilt for T cells in type 1 diabetes: lead culprits or partners in crime depending on age of onset?
Journal of the Formosan Medical Association (2021)
Plasma miRNAs improve the prediction of coronary atherosclerosis in patients with rheumatoid arthritis
Clinical Rheumatology (2021)
On the impact of hepatitis C virus and heterologous immunity on alloimmune responses following liver transplantation
American Journal of Transplantation (2021)
Single-cell transcriptome analysis of CAR T-cell products reveals subpopulations, stimulation, and exhaustion signatures