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. 2020 Dec;9(1):2333-2347.
doi: 10.1080/22221751.2020.1826361.

An atlas of immune cell exhaustion in HIV-infected individuals revealed by single-cell transcriptomics

Shaobo Wang  1   2 , Qiong Zhang  1   2 , Hui Hui  1   3 , Kriti Agrawal  1   3 , Maile Ann Young Karris  4 , Tariq M Rana  1   2
Affiliations

An atlas of immune cell exhaustion in HIV-infected individuals revealed by single-cell transcriptomics

Shaobo Wang et al. Emerg Microbes Infect. 2020 Dec.

Abstract

Chronic infection with human immunodeficiency virus (HIV) can cause progressive loss of immune cell function, or exhaustion, which impairs control of virus replication. However, little is known about the development and maintenance, as well as heterogeneity of immune cell exhaustion. Here, we investigated the effects of HIV infection on immune cell exhaustion at the transcriptomic level by analyzing single-cell RNA sequencing of peripheral blood mononuclear cells from four healthy subjects (37,847 cells) and six HIV-infected donors (28,610 cells). We identified nine immune cell clusters and eight T cell subclusters, and three of these (exhausted CD4+ and CD8+ T cells and interferon-responsive CD8+ T cells) were detected only in samples from HIV-infected donors. An inhibitory receptor KLRG1 was identified in a HIV-1 specific exhausted CD8+ T cell population expressing KLRG1, TIGIT, and T-betdimEomeshi markers. Ex-vivo antibody blockade of KLRG1 restored the function of HIV-specific exhausted CD8+ T cells demonstrating the contribution of KLRG1+ population to T cell exhaustion and providing an immunotherapy target to treat HIV chronic infection. These data provide a comprehensive analysis of gene signatures associated with immune cell exhaustion during HIV infection, which could be useful in understanding exhaustion mechanisms and developing new cure therapies.

Keywords: HIV-1; KLRG1; NK cell impairment; T cell dysfunction; immune exhaustion; single-cell RNA-seq.

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Conflict of interest statement

T.M.R. is a founder of ViRx Pharmaceuticals and has an equity interest in the company. The terms of this arrangement have been reviewed and approved by the University of California San Diego in accordance with its conflict of interest policies.

Figures

None
Graphical abstract
Figure 1.
Figure 1.
Distinct cell clusters are identified by scRNA-seq of PBMCs from healthy and HIV-infected donors. (A) Overview of workflow. PBMCs were isolated from healthy donors and HIV-infected donors (three each with high and low viral loads [>100,000 and <20 RNA copies/ml plasma, respectively]). Single cells were captured by gel beads with primers and barcoded oligonucleotides and subjected to deep RNA-seq. (B–D) t-Distributed Stochastic Neighbor Embedding (t-SNE) projection of PBMCs from healthy donor HD_1 (B), high-load HIV-infected donor ID_717 (C), and low-load HIV-infected donor ID_876 (D), showing major cell clusters based on normalized expression of cell type-specific markers. NK, natural killer cells; CD14 mono, CD14+ monocytes; CD16 mono: CD16+ monocytes; cDC, conventional dendritic cell; pDC, plasmacytoid dendritic cell; Mk, megakaryocytes. (E) Pie charts showing the percentage CD4+ T cells, CD8+ T cells, and other PBMC subsets in the healthy and HIV-infected donors. (F) Linear regression analysis showing the correlation between CD4+ T cell counts calculated from scRNA analysis (cells/1000 PBMCs) vs flow cytometry (cells/μl) of PBMCs from HIV-infected donors. (G–I) tSNE projections for T cell subsets from healthy donor HD_1 (G), HL-HIV-infected donor ID_717 (H), and LL-HIV-infected donor ID_876 (I). Tn, naïve; Tpm, precursor memory; Tem, effector memory; Tex, exhausted; IFNhi, highly IFN-responsive. (J) Percentage of the indicated subclusters of CD4+ and CD8+ T cells from four healthy donor samples (HD_1, 2, 3, 4), three HL-HIV-infected donors (ID_529, _717, and _168), and LL-HIV-infected donors (ID_876, _630, and _471). See also Figure S1 and S2.
Figure 2.
Figure 2.
Identification of novel signature genes in CD8-Tex cells from HIV-infected donors. (A) Heatmap showing differentially expressed genes in CD8-Tem and CD8-Tex cells from HL-HIV-infected donor ID_717. The signature genes are indicated to the right of the heatmap. The colour code below the map indicates the relative expression levels. (B and C) Venn graphs of conserved up-regulated (B) and down-regulated (C) genes in CD8-Tex cells from the three HL-HIV-infected donors. (D) Violin plots of the conserved up-regulated exhaustion-associated signature genes in CD8-Tex compared with CD8-Tem cells from the three HL-HIV-infected donors. Each dot represents a single cell and the shapes represent the expression distribution. The remaining cells are depicted on the x axis. (E) Violin plots of the up-regulated and down-regulated genes associated with the indicated functions in CD8-Tex compared with CD8-Tem cells from HIV-infected donor (ID_717). Each dot represents a single cell and the shapes represent the expression distribution. The remaining cells are depicted on the x axis. (F) GO analysis of the common genes in exhausted CD8+ memory T cells. (G and H) The trajectory plots of the pseudotime analysis shows two distinct trajectories of the CD8-Tem cells in HIV infected individuals. The start of the pseudotime was set to be CD8-Tem cells. The trajectory plots were visualized and coloured by cell types (G) and pseudotime (H) separately. (I) Three representative genes CD160, KLRG1, and TIGIT were identified by pseudotime analysis to be significantly enriched in the CD8-Tex cells.
Figure 3.
Figure 3.
KLRG1 blockade effectively restores the function of HIV-specific CD8+ T cells. (A) Flow cytometry of KLRG1- and TIGIT-expressing CD8+ T cells from the indicated healthy and HIV-infected donors. Numbers indicate the percentage of KLRG1+ TIGIT+, KLRG1− TIGIT−, KLRG1+ TIGIT− and KLRG1− TIGIT+, -expressing cells. (B) KLRG1+ TIGIT+ population is increased in HIV HL-individuals. The percentage of KLRG1+ TIGIT+ cells was analysis by flow in healthy donors (n = 4), HIV LL- (n = 9) and HL- (n = 6) individuals. Mean ± SD, *p < 0.05, student’s t test. (C) A novel KLRG1+ TIGIT+T-betdimEomeshi CD8+ T cell population is significant up-regulated in HIV infected individuals. KLRG1 and TIGIT double negative or double positive cells was extracted from B. Expression of T-bet and Eomes was analyzed. The percentage of KLRG1+ TIGIT+T-betdimEomeshi was shown. Mean ± SD, *p < 0.05, ***p < 0.001, ns, not significant, student’s t test. (D-F) Blocking KLRG1 restores the activation of T cells to HIV peptides stimuli. Ex vivo PBMCs from chronically HIV-infected individuals were stimulated with HIV Gag/Nef peptide pool in the presence of isotype, KLRG1 blocking antibodies. (D) Representative flow cytometry plots with gating for CD8 T cells, showing IFN-γ responses of PBMCs from two HIV-infected individuals (PID 233 and 208). No HIV-1 Gag stimulation with an isotype control is shown as a negative control. A positive control with PMA and ionomycin (1:500) treatment is shown. (E and F). The frequency (%) of IFN-γ (E) and TNFα (F) positive CD8+ T cells (n = 8) is shown. p values were calculated by Wilcoxon matched-pairs signed ranked test. See also Figure S3.
Figure 4.
Figure 4.
Integrated analysis of HIV-infected individuals revealed heterogeneity of exhausted CD8+ T cells and immune cell dysfunction induced by HIV infection. (A) tSNE plots of integrated datasets from three high viral load HIV-infected individuals. Tn, naïve; Tpm, precursor memory; Tem, effector memory; Tex, exhausted; IFNhi, highly IFN-responsive. (B) Heatmap of the scRNA-seq dataset from three high viral load HIV-infected individuals showing differentially expressed genes in in CD8-Tem and CD8-Tex cells. Colour bar below the map indicates the expression level. (C) tSNE plots of integrated datasets from the healthy and HIV-infected donors (left) and identification of nine major cell subpopulations (right). NK, natural killer cells; CD14 mono, CD14+ monocytes; CD16 mono: CD16+ monocytes; cDC, conventional dendritic cells; pDC, plasmacytoid dendritic cells; Mk, megakaryocytes. (D) Expression of “variable genes” in six of the cell clusters in healthy and HIV-infected donors. The colour intensity indicates the average expression level in a cluster and the circle size reflects the percentage of expressing cells within each cluster. See also Figure S4 and S5.
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References

    1. Chun TW, Justement JS, Murray D, et al. . Rebound of plasma viremia following cessation of antiretroviral therapy despite profoundly low levels of HIV reservoir: implications for eradication. AIDS. 2010 Nov 27;24(18):2803–2808. - PMC - PubMed
    1. Cheng L, Ma J, Li J, et al. . Blocking type I interferon signaling enhances T cell recovery and reduces HIV-1 reservoirs. J Clin Invest. 2017 Jan 3;127(1):269–279. - PMC - PubMed
    1. Haas A, Zimmermann K, Oxenius A.. Antigen-dependent and -independent mechanisms of T and B cell hyperactivation during chronic HIV-1 infection. J Virol. 2011 Dec;85(23):12102–12113. - PMC - PubMed
    1. Jones RB, Ndhlovu LC, Barbour JD, et al. . Tim-3 expression defines a novel population of dysfunctional T cells with highly elevated frequencies in progressive HIV-1 infection. J Exp Med. 2008 Nov 24;205(12):2763–2779. - PMC - PubMed
    1. Zhen A, Rezek V, Youn C, et al. . Targeting type I interferon-mediated activation restores immune function in chronic HIV infection. J Clin Invest. 2017 Jan 3;127(1):260–268. - PMC - PubMed

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