An Atlas of Immune Cell Exhaustion in HIV-Infected Individuals

bioRxiv preprint doi: https://doi.org/10.1101/678763; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Revealed by Single-Cell Transcriptomics

Shaobo Wang1,4, Qiong Zhang1,4, Hui Hui1,2, Kriti Agrawal1,2, Maile Ann Young Karris3,

and Tariq M. Rana1*

1Department of Pediatrics, Division of Genetics, Institute for Genomic Medicine, Program in Immunology, 2Department of Biology, and Bioinformatics Program, 3Division of Infectious Diseases, UCSD Center for AIDS Research, Department of Medicine, University of California San Diego School of Medicine, 9500 Gilman Drive MC 0762, La Jolla, California 92093, USA.

4These authors contributed equally to this work.

ABSTRACT KLRG1+ population to T cell exhaustion and providing a novel target for developing Chronic infection with human immunodeficiency immunotherapy to treat HIV chronic infection. virus (HIV) can cause progressive loss of immune Analysis of gene signatures also revealed cell function, or exhaustion, which impairs control impairment of B cell and NK cell function in HIV- of virus replication. However, little is known about infected donors. These data provide a the development and maintenance, as well as comprehensive analysis of gene signatures heterogeneity of immune cell exhaustion. Here, we associated with immune cell exhaustion during HIV investigated the effects of HIV infection on immune infection, which could be useful in understanding cell exhaustion at the transcriptomic level by exhaustion mechanisms and developing new cure analyzing single-cell RNA sequencing of peripheral therapies. blood mononuclear cells from two healthy subjects (15,121 cells) and six HIV-infected donors (28,610 INTRODUCTION cells). We identified nine immune cell clusters and More than 76 million people have been infected eight T cell subclusters according to their unique with human immunodeficiency virus (HIV) since the gene expression programs; three of these epidemic was first recognized in the 1980s. (exhausted CD4+ and CD8+ T cells and interferon- Approximately 37 million people worldwide are responsive CD8+ T cells) were detected only in currently living with HIV infection, of whom only ~21 samples from HIV-infected donors. An inhibitory million have access to antiretroviral therapy receptor KLRG1 was identified in the exhausted T (UNAIDS.org; cell populations and further characterized in HIV http://www.unaids.org/en/resources/fact-sheet). infected individuals. We identified a novel HIV-1 The development of potent combination specific exhausted CD8+ T cell population antiretroviral therapy (cART) has allowed HIV expressing KLRG1, TIGIT, and T-betdimEomeshi viremia to be controlled and significantly reduced markers. Ex-vivo antibody blockade of KLRG1 the mortality of HIV-infected individuals; however, restored the function of HIV-specific exhausted withdrawal of treatment leads to a rapid rebound of CD8+ T cells demonstrating the contribution of bioRxiv preprint doi: https://doi.org/10.1101/678763; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

viremia, indicating that the host immune system and prolonged survival 16,17. Similarly, treatment remains unable to control viral replication 1,2. HIV- with a PD-L1-blocking antibody suppresses viremia induced dysfunction of the host immune system is and increases CD4+ T cell counts in chronically one of the major causes of this recrudescence. HIV-infected humanized mice 18. Intriguingly, a Persistent exposure to viral antigens leads to recent report described that nivolumab (anti-PD-1) chronic activation of immune cells, progressive loss treatment of an HIV-infected patient with non-small- of function, and ultimately, to a state of exhaustion cell lung cancer restored the function of HIV- accompanied by complete loss of effector function specific CD8+ T cells and decreased the HIV 3-6. For example, exhausted CD8+ T cells lose their reservoir 19. A clinical trial is currently ongoing to cytotoxic effector function and the ability to evaluate the safety and efficacy of PD-1 blockade eradicate HIV-infected cells 7. In addition, CD8+ T in HIV-infected individuals with a low CD4+ T cell cell exhaustion prevents the differentiation of count (NCT03367754). effector cells into memory cells with the ability to Despite the great promise of reversal of T cell undergo rapid reactivation upon encounter with exhaustion as a curative strategy for HIV, little is antigen 7,8. Chronically activated CD4+ T cells also known about the mechanisms involved in immune lose their effector functions, including the cell exhaustion in HIV-infected individuals at the production of cytokines, such as IL-2 and IL-21, transcriptomic level. In addition, chronic HIV that sustain HIV-specific CD8+ T cells 9-11. infection is associated with exhaustion not only of Consequently, HIV-induced CD4+ T cell depletion T cells but also of B and NK cells 20-22. Therefore, it and exhaustion also results in dysfunction of HIV- is possible that these three exhausted cell specific CD8+ T cells, resulting in disease populations could cooperate in contributing to host progression 7,8. Therefore, understanding the immune system dysfunction, allowing unchecked mechanisms by which HIV infection leads to HIV replication and disease progression. immune exhaustion is critical for the development of vaccines and therapies for HIV and, possibly, for Single-cell RNA sequencing (scRNA-seq) is a other viral infections. powerful technological advance to analyze the transcriptomic profiles of individual cells, thus Sustained expression of high levels of enabling the heterogeneity of cells affecting inhibitory receptors such as PD-1, CTLA-4, CD160, biological processes to be analyzed 23. scRNA-seq TIGIT, and TIM-3 is a hallmark of T cell exhaustion. can reveal cellular identity as well as spatial In HIV-infected individuals, expression of multiple organization and clonal distribution in complex inhibitory receptors has been demonstrated to heterogeneous immune populations 24,25. The correlate positively with plasma viral load and complexity of the immune system is a major disease progression 6,12-15. Notably, neutralizing obstacle to understanding the host immune antibody-mediated blockade of these receptors response to HIV infection. The diversity and reverses T cell exhaustion by increasing the dynamic states of individual immune cells cannot production of effector molecules and the be precisely revealed by traditional bulk expression proliferation of HIV-specific CD4+ and CD8+ T cells profiles, which provide averaged values from highly 6,12-15. Blockade of PD-1 in simian heterogeneous populations 26. In addition, there immunodeficiency virus (SIV)-infected macaques are limitations to identifying, isolating, and results in expansion of SIV-specific CD8+ T cells, analyzing special or new cell populations, proliferation of memory B cells, decreased viremia,

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especially for the rare cell subtypes 27. scRNA-seq novel target to develop immunotherapy for HIV has successfully identified new types of human infection. Finally, we performed integrated analysis blood dendritic cells (DCs), monocytes, and of PBMCs and showed that HIV infection also progenitors, resulting in a revised taxonomy of induces B and NK cell dysfunction, which, together blood cells (Villani et al., 2017). Inspired by these with T cell exhaustion, may contribute to HIV- studies, we reasoned that scRNA-seq could make related immunodeficiency. it possible to analyze the effects of HIV infection on RESULTS and DISCUSSION the landscape of immune cell types, including rare cell populations. Atlas of PBMCs in Healthy and HIV-Infected Donors Recently, scRNA-seq technology has been applied to investigate viral diversity, heterogeneity To examine the landscape of immune cell of infection states, latency and reactivation, and exhaustion induced by HIV infection, we isolated virus–host interactions 28-33. For example, Cohn et PBMCs from healthy and HIV-infected donors and al. identified unique signature genes in reactivated performed scRNA-seq to identify cell clusters by latent CD4+ T cells, isolated from HIV infected expression cell type-specific gene signatures. individuals, which allow cell division without trigging Previous studies have suggested that expression the cell death pathways induced by HIV-1 of exhaustion genes such as PD-1, CTLA-4, replication 28. In another model of HIV latency, CD160, TIGIT, and TIM-3 is associated with high scRNA-seq identified transcriptional heterogeneity HIV loads 12,13. Therefore, we included three in latent and reactivated CD4+ cells 29. Other donors with a high viral load and three with a low scRNA studies provided new insights into viral load (>100,000 and <1000 RNA copies/ml of transcriptional dynamics in Zika and dengue viral plasma, respectively) in the analysis. A total of infections 33 and during cytomegalovirus latency 32, 12,852 and 15,758 PBMCs were sequenced from and the heterogeneity of influenza virus infection 31. donors with high and low viral loads (hereafter These reports represent proof-of-concept that referred to as HL-HIV-infected and LL-HIV-infected scRNA technology can provide unique insights into donors), respectively (4000–5500 PBMCs/donor). the virus–host interaction. For comparison, we performed scRNA-seq of PBMCs from one healthy donor (ID HD_1) and In this study, we performed scRNA-seq on obtained scRNA-seq data from another healthy PBMCs from HIV-infected and healthy donors to donor (ID HD_2; 10X Genomics), to give a final determine how HIV infection shapes the landscape healthy donor dataset from 15,121 PBMCs. An of exhausted immune cells. We found that HIV overview of the approach is given in Figure 1A. infection induced the appearance of several cell Through unbiased analysis, we identified nine clusters with novel gene signatures, including major cell clusters present in the healthy and HIV- subsets of exhausted CD8+ and CD4+ T cells and infected donors based on expression of a unique CD8+ T cells that are highly responsive to interferon gene signature: CD4+ T cells (CD3D+ CD8A− (IFN). One of the exhausted T cell signature genes IL7Rhi), CD8+ T cells (CD3D+ CD8A+), natural killer was the inhibitory receptor KLRG1, which was co- cells (NK; CD3D− CD8A− IL7R− GNLYhi), B cells expressed with TIGIT in exhausted T cells. (MS4A1+), CD14+ monocytes (LYZ hi CD14hi), Blockade of KLRG1 efficiently restored HIV- CD16+ monocytes (LYZ hi FCGR3Ahi), conventional specific immune responses providing a promising dendritic cells (cDCs; LYZ hi FCER1Ahi),

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plasmacytoid dendritic cells (pDCs; LYZ low IGJhi), identified based on the relative enrichment or and megakaryocytes (Mk; PPBP+). This depletion of signature genes as naïve CD4+ T cells information is displayed as t-Distributed Stochastic (CD4-Tn: CD8A− CCR7+ IL7Rhi), effector memory + − hi − Neighbor Embedding (tSNE) plots of the cell CD4 T cells (CD4-Tem: CD8A IL7R CCR7 clusters in Figures 1B–D and S1B-F, and GZMA+) 34, and a cluster that we defined as expression of the gene signatures defining each precursor memory cells (CD4-Tpm: CD8A− IL7Rhi cluster is shown as heatmaps and violin plots in CCR7low LTBhi). The putative CD4-Tpm cluster Figure S1A. The absolute numbers and showed a similar signature to that of CD4-Tn cells percentages of cell subsets in the PBMC samples (e.g., TCF7, FOXP1) but they did not express for each donor are given in Figure S1G.The healthy effector function-associated genes (e.g., GZMA, donor PBMC samples contained the expected CCR5, NKG7), suggesting that they may represent proportions of major white blood cell classes: ~50% a transitional state between naïve and effector T cells (CD4+:CD8+ T cells ~2:1), 10–15% B cells, memory status (Gattinoni et al., 2011; Youngblood ~10% NK cells, ~20% monocytes, and ~3% DCs et al., 2017). This cluster also harbored high (Figure S1G). expression of the TNF family molecule lymphotoxin β (LTB; Figure S2A), which is consistent with a Consistent with the known effects of HIV recently described CD4-Tpm cytotoxic cell cluster infection, the absolute number and percentage 35. In addition to the three CD4+ T clusters, PBMCs CD4+ T cell counts were considerably lower in the from the healthy donors also contained clusters three HL-HIV-infected donors (18.1%, 25.2%, 3.6%) consistent with naïve CD8+ T cells (CD8-Tn: CD8A+ compared with the two healthy donors (33.9%, CCR7hi) and effector memory CD8+ T cells (CD8- 34.0%). In concert, the percentage (but not Tem: CD8A+ IL7R− CCR7− GZMA+ NKG7+; Figures absolute cell numbers) of CD8+ T cells was 1G, S2A, and S2C). increased in the HL-HIV-infected donors (40.8%, 36.1%, 32.7% versus 22.0%, 20.6%; Figures 1E Notably, the composition and proportion of T and S1G). We also observed a striking increase in cell subtypes in PBMCs were markedly altered by the percentage CD4+ T cells in the LL-HIV-infected HIV infection. Samples from the donors with high donor samples (60.7%, 64.3%, 63.1%) (Figures 1E viral loads contained dramatically smaller and S1G). Importantly, the absolute CD4+ T cell populations of CD4-Tem and CD8-Tn, and three counts in the clinical blood samples and the new cell clusters with unique gene signatures could numbers estimated from the scRNA-seq analysis be discerned; namely, exhausted memory CD8+ T showed a strong correlation (R2=0.8963, Figure cells (CD8-Tex), exhausted memory CD4 + T cells 1F), indicating that the scRNA-seq datasets (CD4-Tex), and a population of CD8 + Tem cells with accurately reflect the cell clusters present in the marked upregulation of IFN-response genes, which original blood samples. we termed CD8-Tem-IFNhi (Figures 1H, S2B, S2D, and S2E). The Tex subpopulations were classified To identify which T cell subsets are selectively based on previous work showing that CD8+ T cells depleted or expanded in the HIV-infected donors, expressing high levels of the exhaustion markers we performed unbiased clustering based on CD160 and TIGIT were enriched in HIV-infected subset-specific marker genes. In the healthy donor individuals 14,15. In the three high-load HIV-infected PBMCs, three CD4+ T cell and two CD8+ T cell donors, we found that 18.1%, 10.1%, and 33.9% of clusters (Figures 1G, S2A, and S2C) were total CD8+ T cells carried the Tex gene signature.

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Similarly, CD4-Tex cells were characterized by donors also showed up-regulated expression of expression of the exhaustion markers TIGIT and another inhibitory receptor, killer cell lectin-like CTLA4. This is consistent with an earlier receptor subfamily G member 1 (KLRG1), which demonstration that CTLA4 is enriched in CD4-Tex contains an immunoreceptor tyrosine-based cells during HIV chronic infection 13. Finally, cells inhibitory motif in the cytoplasmic domain (Figure within the CD8-Tem-IFNhi cluster showed 2D). KLRG1 is known to be a T cell differentiation enrichment of IFN-stimulated genes such as OASL, marker 41. Other genes specifically up-regulated in ISG15, IFIT2, and IFIT3, consistent with expansion CD8-Tex cells from HL-HIV-infected donors of the host antiviral immune response (Figures 1H, included CMC1, SH2D1A, COMMD6, TRAPPC1, 1I, S2E). Interestingly, analysis of samples from the and COTL-1 (Figures 2B and 2E). Conversely, LL-HIV-infected donors showed a reduction in the there were 15 genes commonly down-regulated in CD4-Tem and CD8-Tn clusters and the CD8-Tex cells from HL-HIV-infected donors, appearance of a CD8-Tem-IFNhi cluster, similar to including IRF1, ITGB1, and genes encoding the the observations in samples from HL-HIV-infected cytotoxic T cell effector molecules granzyme B donors; however, there were no obvious CD4+ or (GZMB), perforin 1 (PRF1), and granulysin (GNLY) CD8+ Tex cell populations in samples from these (Figures 2C and 2E). These observations strongly donors (Figures 1I, 1J, S2E, and S2F). suggest that CD8-Tex cells are less cytotoxic than normal CD8+ Tem cells. Collectively, these data identify the major PBMC and T cell subsets affected by HIV infection, Other immune response genes differentially including CD4+ and CD8+ Tex populations and expressed in HL-HIV-infected donor-derived Tex highly IFN-responsive CD8+ Tem cells, each of cells compared with Tem cells included COMMD6, which have unique signature profiles. an NF-κB-inhibiting protein, and the IFN- responsive transcription factor IRF1, which were Identification of Novel Genes Associated with T up-regulated and down-regulated, respectively, in Cell Exhaustion CD8-Tex cells, consistent with HIV-induced Since little is known about the gene signatures of suppression of immune signaling (Figure 2E) 42. In Tex cells in HIV-infected donors, we further support of this, SH2D1A, which encodes the characterized the CD8+ Tex subsets by comparing signaling inhibitor SLAM-associated protein (SAP), their gene expression patterns with those of their was also up-regulated in Tex compared with Tem unexhausted counterparts, which are typically Tem cells 43. SAP has important roles in signaling for T cells (Figure 2A and Table 1). From this analysis, cell differentiation and the antiviral immune we identified a total of 39 genes that were response 43. Tex cells exhibited reduced commonly altered in CD8-Tex cells from at least expression of the integrin β1-encoding gene ITGB1, two of the three HL-HIV-infected donors (p < 0.01, which has many crucial immune functions and roc test; Figures 2B and 2C). The 24 up-regulated mediates cell–cell interactions 44, suggesting genes in HL-HIV-infected donors included the another mechanism that may contribute to the known exhaustion markers TIGIT and CD160 exhaustion phenotype. Interestingly, Tex cells (Figure 2D) 14,15. These inhibitory receptors have showed altered expression of genes involved in also been identified as T cell exhaustion markers in cytoskeletal function (TMSB4X, PFN1, ACTG1, pathogen-infected and tumor-bearing animals COTL1, ARPC1B), energy metabolism (CMC1), 14,15,36-40. CD8-Tex cells from the HL-HIV-infected and vesicle transportation (TRAPPC1) (Figure 2E).

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These pathways have also been associated with significantly increased in PBMCs from HIV-infected CD8+ T cell exhaustion during lymphocytic individuals (Low VL, 10.8%; High VL, 18.1%) choriomeningitis virus infection 40. Gene Ontology compared with healthy donors (6.6%). Since analysis revealed that the differentially expressed PBMCs from higher viral load PBMCs showed genes common to Tex cells in HIV-infected donors severe exhaustion, we did see higher ratio of were enriched in pathways involved in pathogen KLRG1+TIGIT+ populations in high viral load infection, actin cytoskeleton regulation, cell PBMCs. KLRG1 is widely used as a terminal adhesion, antigen processing and presentation, differentiation marker in lymphocytes 47. It is and TNF signaling (Figure 2F). necessary to clarify the exact exhausted KLRG1+ subpopulation induced by HIV chronic infection. T- The development of normal CD8 Tem cell into bet and Eomes had been proved to be highly exhausted T cell is was analyzed by “pseudotime” associated with exhausted phenotype induced by analysis as the cells differentiate asynchronously HIV infection in a reciprocal pattern 48. T-betdim and heterogeneously. CD8 effector memory cells Eomeshi bulk or HIV-specific CD8+ T cells showed were ordered based on single cell transcriptomics poor functional characteristics 48. Therefore, we by pseudotime analysis using monocle 3 45,46. Root further analyzed the percentages of T-betdim state is defined as normal effector memory. CD8 Eomeshi population in KLRG1+TIGIT+ CD8+ T cells. effector memory cells branched into interferon high The result showed that the frequency was much effector memory and exhausted cells, revealing a higher in HIV-infected PBMCs (Low VL, 23.3%; bifurcating trajectory of CD8 differentiation in HIV High VL, 23.3%) compared with that from healthy infected individuals. Exhausted cells are at the end donors (HD, 6.0%) (Figure 3C). In addition, in both of pseudotime, suggesting a terminal differentiation high viral load and low viral load group, the state (Figures 2G and H). Importantly, this percentage of T-betdim Eomeshi cells was much exhaustion sub-branch was occupied by cells higher in KLRG1+TIGIT+ population (potential expressing high levels of exhaustion genes, CD160, exhausted) compared with KLRG1-TIGIT- (non- KLRG1, and TIGIT. (Figure 2I). Taken together, exhausted) population (Figure 3C). This data these results identified novel signatures of KLRG1, suggests that KLRG1+TIGIT+ T-betdim Eomeshi CD160, and TIGIT characterizes exhausted CD8+ population in CD8+ T cells represent a novel T cells in HIV-infected donors. exhausted T cell population in HIV chronic infection. KLRG1 blockade effectively restores the To delineate the relationship between up- function of HIV-specific CD8+ T cells regulated KLRG1 and T cell dysfunction, we The observation that KLRG1 was strongly up- evaluated whether KLRG1 blockade on HIV- regulated and co-expressed with two known HIV specific CD8+ T cells could restore the antiviral induced exhaustion markers TIGIT, CD160 in Tex immune responses. PBMCs from HIV-infected cell clusters from all three HL-HIV-infected donors individuals were stimulated with HIV Gag/Nef raised the possibility that KLRG1 may be involved peptide pool and treated with KLRG1 blocking or in T cell exhaustion. To test this hypothesis, we isotype antibodies, and then IFN-γ and TNFα analyzed KLRG1 expression in PBMCs from expressing cells were evaluated to determine the healthy and HIV-infected donors by flow cytometry restoration of HIV-specific T cell function. In the (Figure 3A and B). We found the frequency of representative sample, incubation with KLRG1 KLRG1 and TIGIT expressing CD8+ T cells blocking antibody (20ug/mL) significantly

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increased the percentage of IFN-γ expressing HIV- HIV Infection Perturbs Immune Cell specific CD8+ T cells from 0.21% to 2.65% (Figure Composition and Induces B and NK Cell 3D). Meanwhile, the percentage of TNFα Dysfunction expressing HIV-specific CD8+ T cells increased To extend our investigation to non-T cells, we from 0.26% to 1.31% (Figure 3D). On the other performed an integrated analysis of the hand, PD-1 blocking antibody slightly increased the composition of PBMCs from the two healthy donors percentages. The statistical analysis for all of the and three HL-HIV-infected donors. Whereas the PBMCs isolated from 8 HIV-infected donors cell clusters identified in PBMCs from the healthy showed that KLRG1 blockade could significantly donors were similar and overlapping, suggestive of restore cytokine responses (Figure 3E and F). a homogeneous cell composition (Figure S3A), we Previous study described that repetitive observed marked cluster heterogeneity in the HIV- and persistent antigen stimulation induced the up- infected donors (Figure S3B). This heterogeneity regulation of KLRG1 in virus-specific CD8+ T cells could reflect variations in the individual donor 49. KLRG1, as an inhibitory receptor, inhibits T cell immune response to HIV infection or to different and NK cell function when activated by its legends stages of HIV pathogenesis. Interesting, a 50,51. In addition, another inhibitory receptor TIGIT completely separated tSNE plot with rarely which marks exhausted T cells in HIV infection co- overlapping cells was generated when the healthy expressed with KLRG1. Also, KLRG1+ CD8+ T cells donors and the high viral load individuals were showed decreased ability to secret cytokines upon compared. Nine clusters were identified with HIV stimulation compared with KLRG1- CD8+ T common marker genes in the integrated plots cells 52. Therefore, it is reasonable that KLRG1 (Figures 3A and S3C). Among the varied genes in expression was induced by HIV chronic infection to the integrated plots, IFN-stimulated genes, such as impair the immune system. T-bet and Eomes are T- IFIH1, IFIT2, IFI6, IFI27, IFI44, IFI44L, MX1 and box transcription factors which regulate the ISG15, were significantly up-regulated in expression of inhibitory receptors and the monocytes and CD4+, CD8+, B, and NK cells exhaustion of CD8+ T cells53. HIV chronic infection (Figure 3B). Sustained IFN signaling has been down-regulates T-bet and up-regulates Eomes reported to be a key driver of immunosuppression, which contributes to poor HIV-specific CD8+ T cell as demonstrated by CD4+ T cell depletion and functionality 48. The significant increase of T-betdim CD8+ T cell expansion during HIV infection 55,56. Eomeshi population in KLRG1+TIGIT+ CD8+ T cells Blocking of the IFNα/β receptor in cART- in HIV-infected PBMCs compared with healthy suppressed HIV-infected humanized mice PBMCs was consistent with these conclusions. Our reversed HIV-induced type-I IFN and rescued the T KLRG1 blockade functional experiments further cell response 3. Therefore, suppression of IFN validate the contribution of KLRG1+ population to signaling is a potential therapeutic strategy in HIV- CD8+ T cell exhaustion. Although, KLRG1 was a infected patients with chronic inflammation. well-known differentiation maker for lymphocytes, it HIV infection is known to hyperactivate B cells was not dispensable for T cell and NK cell but, paradoxically, it also suppresses the antibody development and function after virus infection in response 55. We found that PBMCs from HIV- vivo 54. It suggests that KLRG1 is a promising and infected donors contained an increased novel therapeutic target for HIV infection. percentage of B cells compared with healthy

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donors (22.1%, 26.9%, 33.7% versus 8.7% and increased expression of BIRC3, TNF, and IFNG, 15.1%; Figure S1G). However, compared with which have been proposed to be NK cell activation healthy donors, expression of immunoglobulin markers 22. Despite the beneficial roles of NK cells genes, including IGKC, IGLC2, IGHM, IGLC3, in antiviral responses, their function is known to be IGHD, IGHG1, FCER2, and JCHAIN, the naïve B impaired by chronic HIV infection 60. Consistent cell marker TCL1A, and the activated B cell marker with this, we found a reduction in the expression of CD27 was abolished or dramatically down- genes involved in cytokine signaling proteins (e.g., regulated in HIV-infected donors (Figure 3B). In IL12RB and IL18RAP). Costanzo et al. also contrast, the B cell inhibitory receptor LILRB1 was observed a reduction in IL18RAP expression in NK up-regulated in PBMCs from HIV-infected donors cells in HIV-infected donors, and this was rescued (Fig 3B). in individuals inoculated with an HIV vaccine 22. These data indicate that chronic HIV infection is In HIV-infected individual ID_168, infection associated with an impaired NK cell response. induced a marked expansion of naïve B cells (TCL1A+ CD27−), reduced the memory B cell Collectively, our results underscore the ability population (TCL1A− CD27+), and virtually of scRNA-seq to overcome obstacles presented by eliminated plasmablasts (TCL1A− CD27− XBP1hi) the complexity and heterogeneity of the immune (Figures 3C and 3D). However, the HL-HIV- system to enable investigation of infected donor PBMC also contained a novel pathophysiological changes in very small cell subcluster of B cells characterized by a lack of populations 26. In this study, we applied scRNA-seq TCL1A or CD27 and high expression of inhibitory to analyze how HIV infection perturbs the immune receptor LILRB1 (also known as CD85j) 20, cell transcriptome, and we identified marked suggesting that this subcluster may represent changes in the composition and function of CD4+, exhausted B cells (Figures 3C and 3D)57. These CD8+ T, and B cell subsets as well as the function observations indicate that HIV infection induces B of NK cells. Importantly, exhausted CD8+ T cell cell dysfunction, possibly secondary to the chronic populations expressing CD160, TIGIT, and KLRG1 inflammation and abnormal T cell responses in were identified here in three HL-HIV-infected these patients 58,59. individuals. KLRG1 co-expressing with TIGIT marked a new exhausted population and KLRG1 Growing evidence supports a role for NK cells blocking antibody reversed the cytokines response in the antiviral response to HIV-1 60. NK cell of virus-specific CD8+ T cells. This validated the cytokine secretion and cytotoxic functions are contribution of KLRG1+ population to exhaustion controlled by multiple activating and inhibitory and suggested KLRG1 could be a novel target for receptors 61. Of note, we found that HIV infection immunotherapy against HIV infection. markedly altered the expression of several NK receptor genes (Figure 3B). Among the activating The immune cell exhaustion landscapes receptor genes, CD160, NCR3, and CD69 were demonstrated by this and previous studies 62 down-regulated while SLAMF7, CD2, and CD84 provide a clearer illustration of HIV-induced were up-regulated. Conversely, the inhibitory immune deficiency. Chronic proinflammatory receptor genes LILRB1, LAG3, and TIGIT were up- signaling induced by HIV infection and replication regulated and KLRB1, LAIR2, and SIGLEC7 were leads to activation of the immune system; however, down-regulated. In addition, we observed the accompanying depletion of CD4+ T cells

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reduces their effector functions and, in turn, impairs following read length: read 1, 26 bp; read 2, 98 bp; the CD4+ T cell-dependent functions of CD8+ T and i7 index, 8 bp. The libraries were sequenced to cells and B cells. Thus, despite the fact that CD8+ reach ~50,000 reads per cell. T cell and B cell populations can be activated and scRNA-seq Mapping expanded during HIV infection, the cells cannot differentiate into effector cells to counter the The scRNA-seq dataset was mapped against the infection. Moreover, T and B cell exhaustion human hg19 reference genome using CellRanger exacerbates the immune deficiency 55,56. Taken (v2.1; 10x Genomics). GRCh38-1.2.0 was used as together, our data support a vital role for chronic a gene model for the alignment and was provided inflammation and immune cell exhaustion in the as part of the CellRanger pipeline as a compatible pathogenesis of HIV infection. transcriptome reference. The Cell Ranger count function from the CellRanger pipeline was used to EXPERIMENTAL PROCEDURES generate scgene counts for a single library. This Ethical Statement pipeline was also used for alignment, filtering, and UMI counting. Written informed consent was obtained from all donors or a parent under a study protocol approved Single-Cell Differential Gene Expression by the Human Research Protection Program at Analysis UCSD. Secondary analysis on the raw counts generated Human Subjects and PBMC Isolation from the Cell Ranger pipeline was performed using Blood from healthy donors and PBMCs from HIV- Seurat (v.2.2.0), an R package for single-cell infected donors were obtained from the UCSD genomics. The analysis was based on the pipeline AntiViral Research Center. Blood samples were presented in the Guided Clustering Tutorial by the isolated by Ficoll density centrifugation (Invitrogen) Sajita Lab. First, the cells were filtered to exclude and PBMCs were removed, washed, and frozen cells with a large number of genes or with a large until analyzed. scRNA-seq was performed on amount of mitochondrial DNA. The data were then PBMCs from six HIV-infected donors, three each normalized using a global-scaling method known with high or low viral loads, and one healthy donor. as “LogNormalize,” which normalizes gene In addition, we also analyzed a published scRNA- expression measurements for each cell by total seq dataset (from 8381 PBMCs) from another expression, multiplies the values by a scale factor, healthy donor (10x Genomics). and log-transforms the results. The average scRNA-seq Library Construction expression for each gene was calculated to identify genes with highly variable expression and then PBMCs from the seven donors were thawed, and scaled to remove background variation. Linear dead cells were removed using magnetic beads dimensional reduction principal component (AMSBIO, CB002) to ensure cell viability >80%. analysis was performed on the identified variable scRNA-seq libraries were prepared on the 10X genes. A graph-based clustering approach was Genomics platform using Chromium™ Single Cell used to cluster the cells by type. The resolution 3' v2 Reagent Kits and a Chromium instrument parameter was altered for each sample according according to the manufacturer’s protocol. Libraries to cell number. Typically, a resolution >0.8 was were sequenced on a HiSeq2500 platform to obtain used to identify a large number of clusters so that 100-bp and 32-bp paired end reads using the

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even small cell populations could be identified. Seurat data matrices were imported into monocle3 Clusters of the same cell type were recombined for pseudotime analysis 45,46. The single-cell when the clusters were labeled. Non-linear dataset was normalize and pre-processed using dimension reduction (tSNE) was used to visualize the DelayedArray packages in Bioconductor and the data, and the plots were color-coded by cell the preprocessCDS function from monocle3, with a type. num_dim = 10. The dataset then went through one round of dimensionality reduction using UMAP to Unsupervised Clustering of PBMCs and T Cells eliminate noise. The cells in the dataset were by Specific Markers with Seurat partitioned into supergroups for future distinct Genes enriched in a specific cluster were identified trajectory recognition. The supergroups were by the mean expression of each gene across all organized into trajectories using SimplePPT. The cells in the cluster. Then the expression of each trajectory plots were visualized and colored by cell gene from the cluster was compared to the median types and pseudotime separately, with the start of expression of the same gene from cells in all other the pseudotime set to be CD8 T effector memory clusters. Genes were ranked based on the cells. expression level, and the most enriched genes Integrated Analysis of PBMCs from Healthy and from each cluster were selected. HIV-Infected Donors Identification of Signature Genes in Exhausted Using the Seurat integration pipeline 63, the PBMC T Cells from HIV-Infected Donors datasets from three donors with high HIV load and T lymphocyte clusters were selected in each two healthy donors were combined by executing sample using Seurat vignettes. Additional rounds of the following steps. Individual datasets were calculating highly variable genes, scaling the data, filtered, log-normalized, and scaled using the same and re-clustering the cells were performed on the T strategies and parameters as above. After pre- cell subsets. Upregulated and down-regulated processing the data and selecting genes, canonical differentially expressed genes for each cluster correspondence analysis (CCA) analysis was were identified. T cell subsets were identified by the performed to define the shared correlation space expression of cell type-specific markers in feature between the two healthy donor samples. Multi-CCA plots and heatmaps. Exhausted CD8+ T cell was performed on the three samples from high viral clusters were also identified using known load donors. After non-overlapping subpopulations exhaustion markers. Exhausted CD8+ T cell and were identified, the CCA subspaces were aligned effector memory CD8+ T cells from three donors with dimensional reduction. Another round of with high HIV loads were separately analyzed. All clustering was then performed on the two Seurat pre-processing procedures and clustering were objects. The two integrations were then combined also performed on the distinct subpopulations. using the same procedures as above. To detect Analysis of differentially expressed genes in these conserved and differentially expressed genes two cell types enabled identification of signature markers across datasets, differential expression genes from the heatmap and feature plots of these testing was performed on the integrated datasets subpopulations. from all five samples. By performing these two steps, further information was obtained about Pseudotime Analysis exhaustion markers, and dot plots were generated

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to display conserved and differentially expressed blocked with isotype IgG (BioXcell), anti-KLRG1 gene markers. (13F12F2) (Thermo Fisher), or anti-PD1 (Nivolumab) (Selleck) blocking antibody at 20 Flow Cytometry ug/ml. All of the cells were treated with Brefeldin A For phenotyping analysis of PBMC, cryopreserved to inhibit the secretion of cytokines. After PBMCs were rapidly thawed at 37°C and stimulation and blocking for 12 h, the cells were resuspended in complete RPMI 1640 medium (Life washed twice and then stained for viability with Technologies) supplemented with 10% fetal bovine Aqua and CD8a. Followed by permeabilization and serum (FBS), 1% penicillin-streptomycin (Hyclone), intracellular staining of CD3, IFN-γ (EH12.2H7), and 100 U/ml DNase I (StemCell). The cells were and TNFα (MAb11) and acquisition on the flow washed in complete medium and stained with the cytometer as above. viability dye Zombie Aqua (BioLegend), AF700- Author Contributions conjugated anti-CD3 (OKT3), APC-Cy7-anti-CD8a (RPA-T8) from BioLegend and FITC-anti-TIGIT S.W. and Q.Z. designed and performed the (MBSA43), PE-anti-KLRG1 (13F12F2) (from experiments, analyzed the data, and wrote the first Thermo Fisher). Cells were washed with staining manuscript draft; H.H. and K.A. analyzed the data; buffer (BioLegend) and permeabilized by M.A.Y.K. contributed to obtaining the PBMCs, and intracellular staining permeabilization wash buffer data analysis and interpretation; and T.M.R. (BioLegend). Intracellular factors, T-bet and Eomes, contributed to the experimental design, data were stained by APC-anti-T-bet (4B10) and PE- analysis and interpretation, and manuscript writing. eFluor 610-Eomes (WD1928) antibody. After all the All authors contributed to manuscript writing and staining, cells were washed with staining buffer for approved the final version. twice and fixed with FluoroFix buffer (BioLegend), Acknowledgments and analyzed on a custom four-laser FACSCanto flow cytometer (BD Biosciences, San Jose, CA, We thank Drs. Doug Richman, Mario Stevenson, USA). Fluorescence-minus-one (FMO) or isotype and Jon Karn for helpful discussions, Kristen staining controls were prepared for gating, and Jepsen at the IGM Genomics Center for help with UltraComp beads (Thermo Fisher) were scRNA-seq, Celsa Spina of UCSD CFAR for flow individually stained with each antibody to allow analysis and members of the Rana lab for helpful compensation. Data were analyzed using FlowJo discussions and advice. We also thank Dr. Song version 10 (Tree Star, Ashland, OR, USA). Chen for advice and help in sample preparation and data analysis. This work was supported in part KLRG1 Functional Assay by grants from the National Institutes of Health. For KLRG1 functional analysis, cryopreserved Access to retrospectively collected blood samples PBMCs were rapidly thawed and resting in from HIV-1-infected donors was made possible by complete RPMI 1640 medium for 12 h. Cells were the University of California, San Diego Center for counted and seeding at a concentration of 1 AIDS Research, an NIH-funded program (P30 million/ml. Pooled Gag and Nef peptides (National AI036214), which is supported by the following NIH Institute of Health AIDS Reagent Program) were Institutes and Centers: NIAID, NCI, NIMH, NIDA, added into the cells to stimulate HIV-specific cells NICHD, NHLBI, NIA, NIGMS, and NIDDK. at a 2.5 ug/ml each peptide. Meanwhile, cells were

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Figure 1. Distinct Cell Clusters can be identified Figure 2. Identification of Novel Signature by scRNA-seq of PBMCs from Healthy and HIV- Genes in CD8-Tex Cells from HIV-Infected Infected Donors. Donors.

(A) Overview of workflow. PBMCs were isolated (A) Heatmap showing differentially expressed from healthy donors and HIV-infected donors (three genes in CD8-Tem and CD8-Tex cells from HL-HIV- each with high and low viral loads [>100,000 and infected donor ID_717. The signature genes are <1000 RNA copies/ml plasma, respectively]). indicated to the right of the heatmap. The color Single cells were captured by gel beads with code below the map indicates the relative primers and barcoded oligonucleotides and expression levels. subjected to deep RNA-seq. (B and C) Venn graphs of conserved up-regulated (B–D) t-Distributed Stochastic Neighbor (B) and down-regulated (C) genes in CD8-Tex cells Embedding (t-SNE) projection of PBMCs from from the three HL-HIV-infected donors. healthy donor HD_1 (B), high-load HIV-infected (D) Violin plots of the conserved up-regulated donor ID_717 (C), and low-load HIV-infected donor exhaustion-associated signature genes in CD8-Tex ID_876 (D), showing major cell clusters based on compared with CD8-Tem cells from the three HL- normalized expression of cell type-specific markers. HIV-infected donors. Each dot represents a single NK, natural killer cells; CD14 mono, CD14+ cell and the shapes represent the expression monocytes; CD16 mono: CD16+ monocytes; cDC, distribution. The remaining cells are depicted on conventional dendritic cell; pDC, plasmacytoid the x axis. dendritic cell; Mk, megakaryocytes. (E) Violin plots of the up-regulated and down- (E) Pie charts showing the percentage CD4+ T cells, regulated genes associated with the indicated CD8+ T cells, and other PBMC subsets in the functions in CD8-Tex compared with CD8-Tem healthy and HIV-infected donors indicated in (B–D). cells from HIV-infected donor (ID_717). Each dot (F) Linear regression analysis showing the represents a single cell and the shapes represent correlation between CD4+ T cell counts calculated the expression distribution. The remaining cells are from scRNA analysis (cells/1000 PBMCs) vs flow depicted on the x axis. cytometry (cells/μl) of PBMCs from HIV-infected (F) GO analysis of the common genes in exhausted donors. CD8+ memory T cells. (G–I) tSNE projections for T cell subsets from (G and H) The trajectory plots of the pseudotime healthy donor HD_1 (G), HL-HIV-infected donor analysis shows two distinct trajectories of the CD8- ID_717 (H), and LL-HIV-infected donor ID_876 (I). Tem cells in HIV infected individuals. The start of Tn, naïve; Tpm, precursor memory; Tem, effector the pseudotime was set to be CD8-Tem cells. The memory; Tex, exhausted; IFNhi, highly IFN- trajectory plots were visualized and colored by cell responsive. types (G) and pseudotime (H) separately. (J) Percentage of the indicated subclusters of CD4+ and CD8+ T cells from two healthy donor samples (I) Three representative genes CD160, KLRG1, (HD_1, _2), three HL-HIV-infected donors (ID_529, and TIGIT were identified by pseudotime analysis _717, and _168), and LL-HIV-infected donors to be significantly enriched in the CD8-Tex cells. (ID_876, _630, and _471).