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Altered differentiation is central to HIV-specific CD4+ T cell dysfunction in progressive disease

Abstract

Dysfunction of virus-specific CD4+ T cells in chronic human infections is poorly understood. We performed genome-wide transcriptional analyses and functional assays of CD4+ T cells specific for human immunodeficiency virus (HIV) from HIV-infected people before and after initiation of antiretroviral therapy (ART). A follicular helper T cell (TFH cell)-like profile characterized HIV-specific CD4+ T cells in viremic infection. HIV-specific CD4+ T cells from people spontaneously controlling the virus (elite controllers) robustly expressed genes associated with the TH1, TH17 and TH22 subsets of helper T cells. Viral suppression by ART resulted in a distinct transcriptional landscape, with a reduction in the expression of genes associated with TFH cells, but persistently low expression of genes associated with TH1, TH17 and TH22 cells compared to the elite controller profile. Thus, altered differentiation is central to the impairment of HIV-specific CD4+ T cells and involves both gain of function and loss of function.

Fig. 1: Deep coverage transcriptome analysis of HIV-specific CD4+ T cells from untreated HIV-infected people with distinct disease status.
Fig. 2: HIV-specific CD4+ T cell polarization is associated with level of viral control.
Fig. 3: An atypical TFH-like transcriptional signature in CXCR5neg HIV-specific CD4+ T cells discriminates chronic progressors from elite controllers.
Fig. 4: TFH cytokine expression by CXCR5neg HIV-specific CD4+ T cells of chronic progressors.
Fig. 5: Increased expression of cytokines related to mucosal immunity in HIV-specific CD4+ T cells of elite controllers compared to chronic progressors.
Fig. 6: Distinct transcriptional imprint with reduction in TFH-associated gene expression but poor correction of TH1, TH17 and TH22 gene levels after suppression of viremia by ART.

Data availability

Microarray data generated during the current study were deposited in the Gene Expression Omnibus public depository with the accession number GSE128297 for the SuperSeries, and the following accession numbers for the Subseries: GSE129872 (HIV-specific CD4+ T cells samples from CPs, VCs and ECs), GSE128280 (CXCR5mem and CXCR5neg HIV-specific CD4+ T cells from CPs and ECs), GSE128296 (HIV-specific CD4+ T cells samples from CPs before/after ART and ECs). mRNA expression data by high-throughput RT–qPCR are available in the Supplementary Material. All the datasets that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. 1.

    Laidlaw, B. J., Craft, J. E. & Kaech, S. M. The multifaceted role of CD4(+) T cells in CD8(+) T cell memory. Nat. Rev. Immunol. 16, 102–111 (2016).

    CAS  Article  Google Scholar 

  2. 2.

    Swain, S. L., McKinstry, K. K. & Strutt, T. M. Expanding roles for CD4+ T cells in immunity to viruses. Nat. Rev. Immunol. 12, 136–148 (2012).

    CAS  Article  Google Scholar 

  3. 3.

    O’Shea, J. J. & Paul, W. E. Mechanisms underlying lineage commitment and plasticity of helper CD4+ T cells. Science 327, 1098–1102 (2010).

    Article  Google Scholar 

  4. 4.

    Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

    CAS  Article  Google Scholar 

  5. 5.

    Crawford, A. et al. Molecular and transcriptional basis of CD4+ T cell dysfunction during chronic infection. Immunity 40, 289–302 (2014).

    CAS  Article  Google Scholar 

  6. 6.

    Morou, A., Palmer, B. E. & Kaufmann, D. E. Distinctive features of CD4+ T cell dysfunction in chronic viral infections. Curr. Opin. HIV AIDS 9, 446–451 (2014).

    CAS  Article  Google Scholar 

  7. 7.

    Vella, L. A., Herati, R. S. & Wherry, E. J. CD4+ T cell differentiation in chronic viral infections: the Tfh perspective. Trends Mol. Med. 23, 1072–1087 (2017).

    CAS  Article  Google Scholar 

  8. 8.

    Fahey, L. M. et al. Viral persistence redirects CD4 T cell differentiation toward T follicular helper cells. J. Exp. Med. 208, 987–999 (2011).

    CAS  Article  Google Scholar 

  9. 9.

    Greczmiel, U. et al. Sustained T follicular helper cell response is essential for control of chronic viral infection. Sci. Immunol. 2, eaam8686 (2017).

    Article  Google Scholar 

  10. 10.

    Kaufmann, D. E. et al. Upregulation of CTLA-4 by HIV-specific CD4+ T cells correlates with disease progression and defines a reversible immune dysfunction. Nat. Immunol. 8, 1246–1254 (2007).

    CAS  Article  Google Scholar 

  11. 11.

    Ferre, A. L. et al. HIV controllers with HLA-DRB1*13 and HLA-DQB1*06 alleles have strong, polyfunctional mucosal CD4+ T-cell responses. J. Virol. 84, 11020–11029 (2010).

    CAS  Article  Google Scholar 

  12. 12.

    Crotty, S. T follicular helper cell differentiation, function, and roles in disease. Immunity 41, 529–542 (2014).

    CAS  Article  Google Scholar 

  13. 13.

    Reiss, S. et al. Comparative analysis of activation induced marker (AIM) assays for sensitive identification of antigen-specific CD4 T cells. PLoS ONE 12, e0186998 (2017).

    Article  Google Scholar 

  14. 14.

    Wu, D. & Smyth, G. K. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 40, e133 (2012).

    CAS  Article  Google Scholar 

  15. 15.

    Ono, C. et al. Fluorescently activated cell sorting followed by microarray profiling of helper T cell subtypes from human peripheral blood. PLoS ONE 9, e111405 (2014).

    Article  Google Scholar 

  16. 16.

    Zhang, W. et al. Effector CD4+ T cell expression signatures and immune-mediated disease associated genes. PLoS ONE 7, e38510 (2012).

    CAS  Article  Google Scholar 

  17. 17.

    Ramesh, R. et al. Pro-inflammatory human Th17 cells selectively express P-glycoprotein and are refractory to glucocorticoids. J. Exp. Med. 211, 89–104 (2014).

    CAS  Article  Google Scholar 

  18. 18.

    Locci, M. et al. Human circulating PD-1+CXCR3CXCR5+ memory Tfh cells are highly functional and correlate with broadly neutralizing HIV antibody responses. Immunity 39, 758–769 (2013).

    CAS  Article  Google Scholar 

  19. 19.

    Porichis, F. et al. Responsiveness of HIV-specific CD4 T cells to PD-1 blockade. Blood 118, 965–974 (2011).

    CAS  Article  Google Scholar 

  20. 20.

    Porichis, F. et al. High-throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry. Nat. Commun. 5, 5641 (2014).

    CAS  Article  Google Scholar 

  21. 21.

    Baxter, A. E. et al. Multiparametric characterization of rare HIV-infected cells using an RNA-flow FISH technique. Nat. Protoc. 12, 2029–2049 (2017).

    CAS  Article  Google Scholar 

  22. 22.

    Brenchley, J. M. et al. Microbial translocation is a cause of systemic immune activation in chronic HIV infection. Nat. Med. 12, 1365–1371 (2006).

    CAS  Article  Google Scholar 

  23. 23.

    Favre, D. et al. Critical loss of the balance between Th17 and T regulatory cell populations in pathogenic SIV infection. PLoS Pathog. 5, e1000295 (2009).

    Article  Google Scholar 

  24. 24.

    Levy, M., Kolodziejczyk, A. A., Thaiss, C. A. & Elinav, E. Dysbiosis and the immune system. Nat. Rev. Immunol. 17, 219–232 (2017).

    CAS  Article  Google Scholar 

  25. 25.

    Vujkovic-Cvijin, I. et al. Dysbiosis of the gut microbiota is associated with HIV disease progression and tryptophan catabolism. Sci. Transl. Med. 5, 193ra191 (2013).

    Article  Google Scholar 

  26. 26.

    Shin, N. R., Whon, T. W. & Bae, J. W. Proteobacteria: microbial signature of dysbiosis in gut microbiota. Trends Biotechnol. 33, 496–503 (2015).

    CAS  Article  Google Scholar 

  27. 27.

    Richman, D. D., Wrin, T., Little, S. J. & Petropoulos, C. J. Rapid evolution of the neutralizing antibody response to HIV type 1 infection. Proc. Natl Acad. Sci. USA 100, 4144–4149 (2003).

    CAS  Article  Google Scholar 

  28. 28.

    Petrovas, C. et al. CD4 T follicular helper cell dynamics during SIV infection. J. Clin. Invest. 122, 3281–3294 (2012).

    CAS  Article  Google Scholar 

  29. 29.

    Cubas, R. A. et al. Inadequate T follicular cell help impairs B cell immunity during HIV infection. Nat. Med. 19, 494–499 (2013).

    CAS  Article  Google Scholar 

  30. 30.

    Lindqvist, M. et al. Expansion of HIV-specific T follicular helper cells in chronic HIV infection. J. Clin. Invest. 122, 3271–3280 (2012).

    CAS  Article  Google Scholar 

  31. 31.

    Perreau, M. et al. Follicular helper T cells serve as the major CD4 T cell compartment for HIV-1 infection, replication, and production. J. Exp. Med. 210, 143–156 (2013).

    CAS  Article  Google Scholar 

  32. 32.

    Miyauchi, K. et al. Protective neutralizing influenza antibody response in the absence of T follicular helper cells. Nat. Immunol. 17, 1447–1458 (2016).

    CAS  Article  Google Scholar 

  33. 33.

    Kahan, S. M., . & Zajac, A. J. Late arising T follicular helper cells cultivate the B cell crop during chronic infections. Sci. Immunol. 2, eaap9339 (2017).

    Article  Google Scholar 

  34. 34.

    Rao, D. A. et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542, 110–114 (2017).

    CAS  Article  Google Scholar 

  35. 35.

    Gu-Trantien, C. et al. CD4+ follicular helper T cell infiltration predicts breast cancer survival. J. Clin. Invest. 123, 2873–2892 (2013).

    CAS  Article  Google Scholar 

  36. 36.

    Neff, C. P. et al. Fecal microbiota composition drives immune activation in HIV-infected Individuals. EBioMedicine 30, 192–202 (2018).

    Article  Google Scholar 

  37. 37.

    Kim, C. J. et al. A role for mucosal IL-22 production and Th22 cells in HIV-associated mucosal immunopathogenesis. Mucosal Immunol. 5, 670–680 (2012).

    CAS  Article  Google Scholar 

  38. 38.

    Hasegawa, M. et al. Interleukin-22 regulates the complement system to promote resistance against pathobionts after pathogen-induced intestinal damage. Immunity 41, 620–632 (2014).

    CAS  Article  Google Scholar 

  39. 39.

    Klase, Z. et al. Dysbiotic bacteria translocate in progressive SIV infection. Mucosal Immunol. 8, 1009–1020 (2015).

    CAS  Article  Google Scholar 

  40. 40.

    El Hed, A. et al. Susceptibility of human Th17 cells to human immunodeficiency virus and their perturbation during infection. J. Infect. Dis. 201, 843–854 (2010).

    CAS  Article  Google Scholar 

  41. 41.

    Favre, D. et al. Tryptophan catabolism by indoleamine 2,3-dioxygenase 1 alters the balance of TH17 to regulatory T cells in HIV disease. Sci. Transl. Med. 2, 32ra36 (2010).

    Article  Google Scholar 

  42. 42.

    Ortiz, A. M. et al. IL-21 and probiotic therapy improve Th17 frequencies, microbial translocation, and microbiome in ARV-treated, SIV-infected macaques. Mucosal Immunol. 9, 458–467 (2016).

    CAS  Article  Google Scholar 

  43. 43.

    R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2014).

  44. 44.

    Carvalho, B. S. & Irizarry, R. A. A framework for oligonucleotide microarray preprocessing. Bioinformatics 26, 2363–2367 (2010).

    CAS  Article  Google Scholar 

  45. 45.

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

    Article  Google Scholar 

  46. 46.

    Larsson, J. eulerr: area-proportional Euler and Venn diagrams with ellipses. R package version 4.1.0. (2018).

  47. 47.

    Andersen, C. L., Jensen, J. L. & Orntoft, T. F. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 64, 5245–5250 (2004).

    CAS  Article  Google Scholar 

  48. 48.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  Article  Google Scholar 

  49. 49.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  Google Scholar 

  50. 50.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Article  Google Scholar 

  51. 51.

    Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).

    CAS  Article  Google Scholar 

  52. 52.

    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  Article  Google Scholar 

  53. 53.

    O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).

    Article  Google Scholar 

  54. 54.

    Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    CAS  Article  Google Scholar 

  55. 55.

    Oksanen, J. et al. vegan: community ecology package. R package version 2.4-3 (2017).

  56. 56.

    Warnes, G. et al. gplots: various R programming tools for plotting data. R package version 3.0.3 (2005).

Download references

Acknowledgements

We thank J. Girouard, the clinical staff at the McGill University Health Centre in Montreal, the Ragon/MGH clinical and technical staff and all study participants for their invaluable role in this project; B. Walker for providing clinical samples; D. Gauchat and the CRCHUM Flow Cytometry Platform for technical assistance; N. Hamel and McGill University and Génome Québec Innovation Centre for microarray analysis; and J. Boss and S. Crotty for their input on this manuscript. The following reagent was obtained through the NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH: HIV-1 IIIB p24 Recombinant Protein from ImmunoDX, LLC. This study was supported by the National Institutes of Health grants (no. HL092565 to D.E.K.; no. AI100663 CHAVI-ID to D.E.K. and R.T.W. (Consortium PI: D. Burton); nos. OD011103 and OD011132 to R.P.J.); the Canadian Institutes for Health Research grants (nos. 137694 and 152977 to D.E.K.; no. MOP-93770 to C.T.; and foundation no. 352417 to A.F.); the Canada Foundation for Innovation Program Leader grant (no. 31756 to D.E.K.); and the FRQS AIDS and Infectious Diseases Network. This work was funded in part by the Intramural Program of the National Institutes of Health (D.C.D., S.D.). D.E.K. and C.T. are supported by Senior Research Scholar Awards of the Quebec Health Research Fund (FRQS). J.-P.R. is the holder of the Louis Lowenstein Chair in Hematology & Oncology, McGill University. A.F. is a Canada Research Chair on Retroviral Entry.

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A.M. and D.E.K. designed the studies. A.M., E.B.-R., R.C., N.B., S.A., G.G.-L., L.Y. and P.A.R. performed experiments. M.D. provided input on manuscript content and data representation. A.M., E.M., S.D. and F.L. performed bioinformatics analyses. K.N.-M., J.N., A.E.B., J.M.B., R.P.J., R.T.W., A.F. and D.C.D. provided technical expertise. C.T. and J.-P.R. obtained institutional review board approval and managed study participant recruitment. A.M. and D.E.K. interpreted the data and wrote the paper with all co-authors’ assistance.

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Correspondence to Daniel E. Kaufmann.

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Supplementary Figure 1 Analysis of HIV-specific CD4+ T cells identified by the CD69/CD40L (CD154) Activation-Induced Marker (AIM) assay.

(a) Gating strategy for sorting HIV-specific CD4+ T cells based on co-upregulation on the cell surface of the activation markers CD69 and CD154 9h after stimulation with an HIV Gag peptide pool. (b) Flow cytometry plots comparing the use of CD69 alone or in combination with CD154 as AIM markers for detection of antigen-specific CD4+ T cells. Numbers in plots represent the percentage of CD69+CD154+ Gag-specific CD4+ T cells over unstimulated cells. (c) Statistical comparison of frequencies of CD69+ CD154+ CD4+ T cells between unstimulated and Gag-stimulated PBMCs in the three groups of HIV-1+ infected people (two-tailed Wilcoxon matched paired test). Bars represent median with interquartile range. For (a,b,c) n= (11 CPs, 11 VCs, 13 ECs). (d) Statistical comparison of frequencies of CD69+ IL-2+ or CD69+ IFNγ+ CD4+ T cells detected by intracellular staining 6h after Gag stimulation with the frequencies of CD69+ CD154+ CD4+ T cells detected by surface staining 9h after Gag stimulation (two-tailed Wilcoxon matched paired test) (n=17 subjects). (e,f) Two-tailed Spearman correlation of net frequencies of Gag-specific CD4+ T cells (background in No Antigen condition subtracted) with (e) CD4+ T cell count (cells/μl) and (f) Viral Load (vRNA copies/ml) (n=38 subjects).

Supplementary Figure 2 Transcriptional expression and correlation of genes associated with Thelper differentiation and function.

(a) Heatmap of two-sided CAMERA enrichment analysis for CD4+ T cell lineage or exhaustion signatures in CP (n=11), VC (n=9) and EC (n=12) subjects. Color and intensity indicate the directionality of the enrichment and -log10FDR by the Benjamin Hochberg procedure. White boxes represent results with FDR>0.05. (b) Statistical comparison of transcriptional levels of master regulators of CD4+ T cell subsets in sorted HIV-specific CD4+ T cells of CPs (n=11) compared to ECs (n=12) as assessed by RT-qPCR (Fluidigm) by Mann-Whitney test. Bars represent median with interquartile range. (c,d) Correlograms illustrating gene correlations between (c) functional factors and surface molecules, and (d) functional factors and transcriptional factors in sorted HIV-specific CD4+ T cells. Transcriptional expression values (−ΔCt values) assessed by RT-qPCR (Fluidigm) were used for generation of the correlograms. Blue and red circles denote positive and negative correlation (two-sided Spearman), respectively. Color intensity and the size of the circle are proportional to the correlation coefficients. Only gene correlations with p<0.05 are displayed. (n= 11 CPs, 9 VCs, 12 ECs).

Supplementary Figure 3 Isolation and characterization of CXCR5mem and CXCR5neg HIV-specific CD4+ T cells.

(a) Gating strategy for the identification and live-cell sorting of CXCR5mem vs CXCR5neg HIV-specific specific CD4+ T cells 9 h after stimulation with a Gag peptide pool (n= 6 CPs, 6 ECs). (b) Statistical comparison of the frequencies of CXCR5mem in total CD4+ T cells compared to HIV-specific CD4+ T cells in CPs (n=10 CPs, upper panel) and ECs (n=9 ECs, lower panel) by two-tailed Wilcoxon matched paired test. Bars represent median with interquartile range. (c) Transcriptional expression of TFH-associated genes in sorted CXCR5mem or CXCR5neg HIV-specific CD4+ T cells assessed by real-time quantitative reverse transcription PCR on a Fluidigm platform. (n = 6 CPs, 6 ECs) Bars represent median with interquartile range. Statistical comparison by two-tailed Mann Whitney or two-tailed Wilcoxon matched paired test were used to verify significance. Only p ˂ 0.05 are displayed for clarity.

Supplementary Figure 4 TFH, TH17 and TH22 cytokine expression by HIV-specific CD4+ T cells in CPs compared to ECs.

(a) Statistical comparison of transcriptional levels of TFH cytokines and TH17 and TH22 cytokines in sorted HIV-specific CD4+ T cells compared to CMV-specific CD4+ T cells from the same donors, 9 h after stimulation with a gag or pp65 peptide pool (two-tailed Wilcoxon matched paired test). Transcriptional expression was assessed by RT-qPCR (Fluidigm). Bars represent median +/- interquartile range transcriptional expression (n= 9 CPs, 10 ECs). (b,c) Representative flow cytometry plots of CXCL13 and IL22 mRNA+ CD69+ CD4+ T cells after 12 h stimulation with a gag peptide pool, SEB or no antigen. Numbers in plots represent the percentage of CD69+ mRNA+ CD4+ T cells (n= 8 CPs, 8 ECs). (d-i) Statistical comparison of frequencies of CD69+ mRNA+ CD4+ T cells after stimulation with gag peptide pool or SEB detected by RNA-Flow-FISH Cytometry. (two-tailed Mann Whitney test, n= 8 CPs, 8 ECs).

Supplementary Figure 5 TFH,TH17 and TH22 cytokine expression by HIV-specific CD4+ T cells compared to CMV-specific CD4+ T cells in CPs and ECs and in the absence of CD40 blockade and CD40L pre-gating.

(a) Comparison of CXCL13 protein expression in the supernatant of CD8-depleted PBMCs of CPs versus ECs by two-tailed Mann-Whitney test. The cells were stimulated with a Gag-peptide pool for 48 h and CXCL13 protein concentration was measured by ELISA. Bars represent median with +/- interquartile range (n= 8 CPs, 7 ECs). (b,c) Statistical comparison of frequency of PD-1/TIGIT subpopulations in (b) CXCL13 mRNA+ or (c) IL21 mRNA+ HIV-specific CD4+ T cells in CPs compared to ECs by two-tailed Mann-Whitney test (b,c; n = 6 CPs, 5 ECs). Bars represent median with +/- interquartile range. (d) Correlation of quantification of p24-specific antibodies in plasma by Biolayer Interferometry (BLI) binding analysis or ELISA (two-tailed Spearman correlation test, n = 12 CPs, 15 ECs). Semi-quantitive scale for anti-p24 antibody binding index: -, -/+, +, ++, +++ symbols correspond to peak values at <0.2, 0.2-0.3 0.3-0.5, 0.5-0.8, >0.8 nm, respectively (two-tailed Spearman correlation test, n=12 CPs, 15 ECs). (e,f) Correlation of weighted Mean Fluorescence Intensity for IL22 and IL17F detected by RNA-Flow-FISH cytometry to protein concentration detected by a magnetic bead-based assay (Luminex) (n= 6 CPs, 6 ECs) (two-tailed Spearman correlation). (g) Detection of IL-17A protein in the supernatant of CD8-depleted PBMCs 48 h after stimulation with a Gag-peptide pool by Luminex. Bars represent median with interquartile range and statistical comparison was performed by two-tailed Mann-Whitney test (n = 8 CPs, 8 ECs). (h) SPICE analysis of phenotyping of IL22 mRNA+ and IL17F mRNA+ antigen-specific CD4+ T cells from the CP group. Pie charts represent median percentages of CCR6/CXCR3 subpopulations (n= 6 CPs, 6 ECs). (i,j) Correlation of IL22 and IL17F mRNA levels assessed by RT-qPCR (Fluidigm) with the protein expression of the activation markers HLA-DR and CD38 on total unstimulated CD8+ T cells by two-tailed Spearman correlation (n = 11 CPs, 6 VCs, 12 ECs). (k,l) Correlation of bacterial diversity (Shannon Entropy) with the protein expression of the activation markers HLA-DR and CD38 on total unstimulated CD4+ and CD8+ T cells by two-tailed Spearman correlation (n = 8 CPs, 6 ECs). (m) Hierarchical clustering of Morisita-Horn dissimilarity indexes illustrates that samples largely segregate by disease status based on Genus TPM values (n= 12 CPs, 8 ECs).

Supplementary Figure 6 Impact of ART therapy on CD4+ T helper differentiation.

(a) Barcode plots of enrichment of the LCMV exhaustion signature in comparisons CPpre vs. EC, CPpost vs EC and CPpost vs EC by CAMERA. Red and blue lines denote positive and negative enrichment. Barcode plots were generated using microarray data by sorted HIV-specific CD4+ T cells from 8 CPs before and after ART and 12 ECs. (Exhaustion Signature; GSE41866, LCMV Clone 13 D30 vs. Armstrong D30 - Exhausted vs Memory) (two tailed p-values by CAMERA followed by the Benjamin Hochberg method). (b) Top 50 DEGs in HIV-specific CD4+ T cells of CPs before and after treatment compared to ECs. Red and purple denote logarithmic fold change for comparisons CPpre vs EC and CPpost vs EC, respectively, by microarray analysis in 8 CPs before/after ART and 12 ECs.

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Morou, A., Brunet-Ratnasingham, E., Dubé, M. et al. Altered differentiation is central to HIV-specific CD4+ T cell dysfunction in progressive disease. Nat Immunol 20, 1059–1070 (2019). https://doi.org/10.1038/s41590-019-0418-x

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