Selective loss of CD107a TIGIT+ memory HIV-1-specific CD8+ T cells in PLWH over a decade of ART

  1. Oscar Blanch-Lombarte
  2. Dan Ouchi
  3. Esther Jimenez-Moyano
  4. Julieta Carabelli
  5. Miguel Angel Marin
  6. Ruth Peña
  7. Adam Pelletier
  8. Aarthi Talla
  9. Ashish Sharma
  10. Judith Dalmau
  11. José Ramón Santos
  12. Rafick-Pierre Sékaly
  13. Bonaventura Clotet
  14. Julia G Prado  Is a corresponding author
  1. IrsiCaixa AIDS Research Institute, Spain
  2. Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
  3. Pathology Department, Case Western Reserve University, United States
  4. Lluita contra la SIDA Foundation, Hospital Universitari Germans Trias i Pujol, Spain
  5. Infectious Diseases Department, Hospital Universitari Germans Trias i Pujol, Spain
  6. Germans Trias i Pujol Research Institute (IGTP), Spain
  7. Faculty of Medicine, University of Vic - Central University of Catalonia (UVic-UCC), Spain
  8. CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Spain

Abstract

The co-expression of inhibitory receptors (IRs) is a hallmark of CD8+ T-cell exhaustion (Tex) in people living with HIV-1 (PLWH). Understanding alterations of IRs expression in PLWH on long-term antiretroviral treatment (ART) remains elusive but is critical to overcoming CD8+ Tex and designing novel HIV-1 cure immunotherapies. To address this, we combine high-dimensional supervised and unsupervised analysis of IRs concomitant with functional markers across the CD8+ T-cell landscape on 24 PLWH over a decade on ART. We define irreversible alterations of IRs co-expression patterns in CD8+ T cells not mitigated by ART and identify negative associations between the frequency of TIGIT+ and TIGIT+ TIM-3+ and CD4+ T-cell levels. Moreover, changes in total, SEB-activated, and HIV-1-specific CD8+ T cells delineate a complex reshaping of memory and effector-like cellular clusters on ART. Indeed, we identify a selective reduction of HIV-1 specific-CD8+ T-cell memory-like clusters sharing TIGIT expression and low CD107a that can be recovered by mAb TIGIT blockade independently of IFNγ and IL-2. Collectively, these data characterize with unprecedented detail the patterns of IRs expression and functions across the CD8+ T-cell landscape and indicate the potential of TIGIT as a target for Tex precision immunotherapies in PLWH at all ART stages.

Editor's evaluation

This important study shows that the expression of some inhibitory receptors on CD8 T cells is increased in people living with HIV (PLWH) and remains elevated even after years of viral suppression by antiretroviral therapy. The authors further provide convincing evidence that inhibition of TGIT partially restores the ability of CD8 T cells to produce CD107a but not the other functions and is relevant for researchers and clinicians interested in viral infections, especially HIV/AIDS.

https://doi.org/10.7554/eLife.83737.sa0

Introduction

The ART introduction has been the most successful strategy to control viral replication, transforming HIV-1 into a chronic condition. However, ART does not cure the infection, and treatment is required lifelong due to a stable viral reservoir, raising the need to find a cure for people living with HIV-1 (PLWH). A sterilizing or functional cure aims to eliminate or control HIV-1 in the absence of ART. In both scenarios, HIV-1-specific CD8+ T-cells are likely to play an essential role as they have been widely recognized as a critical factor in the natural control of viral replication (Borrow et al., 1994; Collins et al., 2020; McBrien et al., 2018; Cartwright et al., 2016; Goulder and Walker, 2012). Proliferative capacity, polyfunctionality, and ex vivo antiviral potency are features of HIV-1-specific CD8+T cells associated with spontaneous viral control (Sáez-Cirión et al., 2007; Migueles et al., 2008; Nguyen et al., 2019; Perdomo-Celis et al., 2019a).

Although ART in PLWH normalizes the levels of CD8+ T-cells and potentially preserves their functional characteristics (Perdomo-Celis et al., 2019a; Perdomo-Celis et al., 2019b; Tavenier et al., 2015; Serrano-Villar et al., 2014), microbial translocation and continuous immune activation lead to long-term CD8+ T-cell dysfunction and exhaustion (Tex), a critical barrier for HIV-1 curative interventions (Ruiz et al., 2018; Jiang et al., 2020; Hoffmann et al., 2016). CD8+ Tex is defined by the persistent co-expression of inhibitory receptors (IRs) and the progressive loss of immune effector functions linked to transcriptional, epigenetic and metabolic changes (Sekine et al., 2020; Buggert et al., 2014; Bengsch et al., 2016; Sen et al., 2016). In HIV-1 infection, IRs are continuously expressed despite long-term suppressive ART (Breton et al., 2013; Yamamoto et al., 2011; Cockerham et al., 2014; Rutishauser et al., 2017; Tauriainen et al., 2017; Chew et al., 2016; Trautmann et al., 2006) and have been associated with disease progression and immune status in PLWH (Chew et al., 2016; Day et al., 2006; Graydon et al., 2019; Gupta et al., 2015; Jones et al., 2008; Teigler et al., 2017; Tian et al., 2015). Thus, the expression of IRs is linked to diminished functionality and is a hallmark of CD8+ Tex in PLWH.

The interest in evaluating the blocking of IRs or immune checkpoint blockade (ICB) in PLWH as a therapeutic strategy to reverse CD8+ Tex increases (Trautmann et al., 2006; Day et al., 2006). Over the last years, several studies supported the recovery of proliferative capacity, cell survival, and cytokine production of HIV-1-specific CD8+ T-cells through ICB (Chen et al., 2020; Blanch-Lombarte et al., 2019). The blockade of the PD-1/PDL-1 axis has been extensively studied, demonstrating the functional recovery of HIV-1-specific CD8+ T-cells in PLWH (Trautmann et al., 2006; Day et al., 2006). Moreover, alternative pathways to PD-1 /PDL-1, including LAG-3, TIGIT, and TIM-3, have been explored as candidates for ICB therapies for HIV-1 infection (Chen et al., 2020; Sakuishi et al., 2010; Harjunpää and Guillerey, 2020; Maruhashi et al., 2020). Also, recent data support the combinatorial use of ICB to favour synergistic effects on the recovery of HIV-1-specific CD4+ and CD8+ T-cell function (Attanasio and Wherry, 2017; Chiu et al., 2022).

The unprecedented success of the clinical use of ICB in the cancer field (Vaddepally et al., 2020; Twomey and Zhang, 2021) has prompted the clinical evaluation of ICB in PLWH (Gonzalez-Cao et al., 2020) to boost immunity to reduce or eliminate the viral reservoir. However, clinical evidence on the impact of ICB as an HIV-1 cure intervention continues to be controversial (Blanch-Lombarte et al., 2019; Fromentin et al., 2019; Le Garff et al., 2017; Gay et al., 2017; Guihot et al., 2018; Uldrick et al., 2022). In this context, the simultaneous characterization of IRs co-expression and functional patterns across CD8+ T-cells is critical to understanding Tex regulation in PLWH. This information is essential to identify novel targets for precise immunotherapies in PLWH on ART (Deeks et al., 2021).

To address these questions, we performed supervised and unsupervised immunophenotypic analyses of IRs (PD-1, TIGIT, LAG-3, TIM-3, CD39), and functional markers (CD107a, IFNγ, and IL-2) across the landscape of CD8+ T-cells over a decade of ART in PLWH and compared to PLWH with early infection and healthy individuals. We profile changes of bulk, SEB-activated, and HIV-1-specific CD8+ T-cells in PLHW and unfold a selective decrease of memory-like HIV-1-specific CD8+ clusters sharing TIGIT expression and low CD107a in PLWH on ART. Moreover, TIGIT blockade rescues CD107a expression without changes in IFNγ or IL-2 production on HIV-1-specific CD8+ T-cells. Of note, the response to TIGIT, TIM-3, or TIGIT + TIM-3 blockade was heterogeneous across HIV-1-specific CD8+ T-cell differentiation stages and functions, indicating the plasticity and complexity of the IRs pathways as targets for immune-base cure interventions.

Results

Alterations in CD8+ T-cell IRs frequencies and expression patterns in PLWH are not mitigated by ART

Although the co-expression of IRs is a hallmark of CD8+ Tex in HIV-1 infection (Breton et al., 2013; Yamamoto et al., 2011; Cockerham et al., 2014; Rutishauser et al., 2017; Tauriainen et al., 2017; Chew et al., 2016; Trautmann et al., 2006), a detailed characterization of the combinatorial expression of IRs across CD8+ T-cell lineages in PLWH on long-term ART is still missing. To do this, we combined the analyses of IRs (PD-1, TIGIT, LAG-3, TIM-3, and CD39) and lineage markers (CD45RA, CCR7, and CD27) in CD8+ T-cells from longitudinal samples in PLWH on ART by flow-cytometry (Figure 1—figure supplement 1). We compare three groups; healthy controls (HC), PLWH with early infection (Ei), and PLWH on ART (S) with longitudinal samples available a median period of 2.2 (S1) and 10.1 (S2) years on fully suppressive ART (Figure 1A, Figure 1—source data 1). The epidemiological and clinical characteristics of the study groups are detailed in Supplementary file 1 and Figure 1—source data 1.

Figure 1 with 2 supplements see all
Patterns of IRs co-expression and correlations with CD4+ T-cell counts in PLWH.

(A) Overview of study design and study groups, healthy controls (HC), PLWH in early HIV-1 infection (Ei), and PLWH on fully suppressive ART (S) in S1 and S2 time points. (B) The expression of IRs summarized in the pie chart is none, one, two, or more than three IRs expressed in CD8+ T-cell subsets. For statistical analysis, we used permutation tests using SPICE software. (C) Scatter plots showing the median and interquartile ranges of IR combinations in CD8+ T- cell subsets. (D) Scatter plots of the frequencies of single TIGIT+ expression in CM and TIGIT+TIM-3+ expression in EM and effector EFF CD8+ T cells. (E–G) Correlations between CD4+ T-cell counts as a function of TIGIT+, TIGIT+TIM-3+, and combinations of IRs from total CD8+ T-cells and subsets in Ei (E), S1 (F), and S2 (G). The data in B to D represent the mean of two technical replicates. We used the Mann-Whitney U test for intergroup comparison (HC, Ei, S1, and S2) and the signed-rank test for intragroup comparison (S1 and S2). Holm’s method was used to adjust statistical tests for multiple comparisons. All possible correlations of the 32 Boolean IRs combinations are not shown. p-values: *<0.05, **<0.005 and ***<0.0005. Sample sizes in A: HC (24), Ei (24), S1(24), S2 (24). Sample sizes in B–G: HC (20), Ei (21), S1(18), S2 (21).

Figure 1—source data 1

Epidemiological and clinical data of study groups; healthy controls (HC), PLWH in early infection (Ei), and PLWH on fully suppressive ART (S) in S1 and S2 time points.

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Figure 1—source data 2

Frequencies of IRs expression shown in pie charts as 0 to >3 IRs expressed in CD8+ T-cell subsets and study groups.

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Figure 1—source data 3

Frequencies of the 32 possible combinations of expressions for TIGIT, PD-1, LAG-3, TIM-3, and CD39 in CD8+ T-cell subsets per study group.

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Figure 1—source data 4

Frequencies of IRs expression in total and CD8+ T-cell subsets per study group.

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Figure 1—source data 5

Correlations between CD4+ T-cell counts and TIGIT+, TIGIT+TIM-3+, and combinations of IRs in total and CD8+ T-cell subsets per study group.

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As shown in Figure 1 (Figure 1—source data 2), we found persistent alterations in the expression and co-expression of IRs in naïve, central memory (CM), and transitional memory (TM) CD8+ T-cells in PLWH on ART. These perturbations were maintained despite prolonged ART (S2) in naive and CM, but not TM cells compared to HC. Deconvolution of IRs co-expression patterns by the number of receptors expressed (0, 1, 2, >3) further delineates a significant reduction of Naïve, CM, and TM CD8+ T-cells lacking IRs expression concomitant with a significant increase of CD8+ T-cells expressing one (Naïve and CM) or >3 IRs (TM) on ART (Figure 1C). Moreover, we observed an augment in effector memory (EM), TM, and effector (EFF) CD8+ T-cells co-expressing >3 IRs in Ei and S1 that normalized in S2 (Figure 1C). Of note, out of the 32 possible combinations of IRs expression studied in CD8+ T-cell subsets (Figure 1—figure supplement 2, Figure 1—source data 3), single TIGIT+ expression in CM and dual TIGIT+TIM-3+ co-expression in EFF CD8+ T-cells accounted for continuous increases in frequency under suppressive ART (Figure 1D, Figure 1—source data 3). We confirmed similar increases in the frequency of TIGIT in total, CM, and TM CD8+ T-cells on ART. These data contrast with transient changes in other IRs upon infection normalizing with ART (Figure 1—figure supplement 2, Figure 1—source data 4).

These initial findings led us to postulate associations between the expression of IRs in CD8+ T-cells, persistent immune activation, and the degree of CD4+ T-cell immune recovery in PLWH on ART. For this purpose, we performed correlation analyses that revealed several negative associations between the frequency of CD8+ T-cells expressing IRs and CD4+ T-cell counts across study groups (Figure 1E–G, Figure 1—source data 5). Focusing on S1 (Figure 1F), we found significant negative correlations between CD4+ T-cell counts and frequencies of CM CD8+ T-cells expressing 2 (p=0.0054, r=−0.64) and >1 IRs (p=0.0087, r=−0.61). Focusing on S2 (Figure 1G), we observed significant negative correlations between CD4+ T-cell counts and the frequency of total CD8+ T cells expressing TIGIT+ (p=0.0157, r=−0.58), expressing 1 IRs (p=0.0386, r=−0.54) or >1 IRs (p=0.0386, r=−0.51). At the level of CD8+ T-cell subsets, the expression of 2 IRs in CM and >1 IR in TM negatively correlated with CD4+ T-cell counts (p=0.0072, r=−0.64; p=0.0346, r=−0.52, respectively; Figure 1G). These correlations further indicate a negative relationship between IRs expression patterns and immune status in PLWH on long-term suppressive ART.

In summary, these data support changes in IRs expression not mitigated by long-term ART in total and CD8+ T-cell subsets expressing one or >1 IRs, particularly TIGIT+ and TIGIT+TIM-3+. These findings also uncover negative associations between IRs expression in CD8+ T-cells and CD4+ T-cell levels in PLWH on ART.

Unsupervised phenotypic analyses of IRs across the CD8+ T-cell landscape in PLWH on ART

Next, to further characterize IRs expression across CD8+ T cells in PLWH on ART, we performed an unsupervised net-SNE analysis of flow-cytometry data. We concatenated 1,988,936 total CD8+ T-cells and analyzed the phenotypes with the topographical regions of each surface marker tested (Figure 2A-B, Figure 2—source data 1). CD8+ T cells were classified into 38 cellular clusters distributed according to the relative marker expression of 14 parameters and represented using net-SNE and heatmaps (Figure 2C-D).

Unsupervised net-SNE analyses of total CD8+ T-cells.

(A) Gating strategy for selecting total CD8+ T-cells (top), net-SNE plots of HC, Ei, S1, S2 and all merge groups. (B) Representative net-SNE visualization of surface markers. The colour gradient displays the relative marker expression. (C) Unsupervised KNN algorithm of 38 clusters colored according to the legend. Only clusters with statistical differences are represented in the legend. (D) Heatmap of the median biexponential-transformed marker expression normalized to a –3–3 range of respective markers. Asterisks represent the clusters with statistical differences. (E–F) Scatter plots of intergroup (HC, Ei, S1 and S2) and intragroup (S1 and S2) cluster comparisons. Data represent the median and interquartile ranges of cluster cell frequency. We used the Mann-Whitney U test for intergroup analyses and the signed-rank test for intragroup analyses. Holm’s method was used to adjust statistical tests for multiple comparisons. p-values: *<0.05, **<0.005 and ***<0.0005. Sample sizes for A–F: HC (20), Ei (21), S1(18), S2 (21).

Figure 2—source data 1

Unsupervised net-SNE analyses of total CD8+ T-cells.

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Figure 2—source data 2

Cluster cell frequencies from net-SNE analyses in total CD8+ T-cells per study group.

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Out of the 38 clusters identified, we found eight cellular clusters (#1, #2, #6, #7, #9, #10, #11, and #12) with significant differences by inter- and intragroup comparisons (Figure 2D–E). Most of the differentially expressed clusters shared memory-like phenotypes. Of note, clusters #6, #7, and #12 shared memory-like phenotypes and low expression of IRs. Meanwhile, clusters #9 and #10 shared effector-like phenotypes and co-expression of IRs, including TIGIT, LAG-3, and low TIM-3 (Figure 2D). Briefly, intergroup analyses demonstrated significant changes in composition and frequency with a decrease of #1, #6, and an increase of #10 in Ei compared with HC. Also, clusters #1 and #2 decreased in S1 and S2, respectively, and clusters #9, #10, #11, and #12 increased in S1 while tended to normalize in S2 compared with HC (Figure 2E, Figure 2—source data 2). Intragroup analyses further supported changes in cluster frequency and composition during long-term ART. Meanwhile, clusters #6 and #7 increased, and #9 and #11 decreased over time on ART (Figure 2F). These data support an expansion of memory-like clusters with low IR expression (#6 and #7) and a contraction of effector-like clusters sharing TIGIT expression (#9 and #11) during ART. Thus, unsupervised analyses support cellular clusters' continuous expansion and contraction in frequency and composition across the landscape of CD8+ T-cells in PLWH on ART.

Unsupervised phenotypic characterisation of SEB-activated CD8+ T cells in PLWH on ART

Then, we evaluate CD8+ T-cell responses by bacterial superantigen activation with Staphylococcal enterotoxin B (SEB). Using SEB can provide complementary information on T-cell activation in response to pathogens involved in the disease by stimulating TCR-VB clonotypes (Teigler et al., 2017; Kou et al., 1998; Chiu et al., 2022). In this context, we analysed IRs expression and functional markers using unsupervised net-SNE analysis. We defined SEB-activated CD8+ T cells by the expression of at least one functional marker (CD107a, IFNγ, IL-2) upon incubation with SEB, as previously described (Gaffen and Liu, 2004; Akdis et al., 2016; Voskoboinik et al., 2015; Aktas et al., 2009; Bhat et al., 2017, Figure 3A,B, Figure 3—source data 1). We concatenated 253,021 SEB-activated CD8+ T-cells and identified 29 unique clusters represented by net-SNE and heatmaps (Figure 3C-D). Only six of the 29 clusters showed statistical differences by inter- and intragroup comparisons (Figure 3D). All differential clusters shared memory-like phenotypes (#2, #3, #5, #8, and #14) except cluster #6, with effector-like phenotype and low TIGIT expression. In addition, we observed a functional exclusion of clusters expressing IL-2 (#5 and #6) and those expressing CD107a and IFNγ (#2, #3, and #8; Figure 3D). Intergroup comparisons identified increases in cluster #2 and a reduction of #14 and #5 with HIV-1 infection compared to HC. Additionally, ART was linked to the decrease of #5, #6, and #14 in S1 and #3 in S2 when compared with HC (Figure 3E, Figure 3—source data 2). Moreover, intragroup analyses identified an increase in clusters #6 and #14 and a reduction in #3 and #8 on ART. Of note, #6 characterizes by IL-2 expression in the without TIGIT expression. Meanwhile, clusters #3 and #8 express CD107a, IFNγ and variable expression of IRs (Figure 3F). In agreement with unsupervised clustering analyses, classical supervised analyses identified an augment of CD107a and IFNγ SEB-activated CD8+ T-cells with Ei that normalized over time on ART. Also, we delineate significant increases of IL-2 SEB-activated CD8+ T-cells across subsets and time on ART (Figure 3—figure supplement 1, Figure 3—source data 3).

Figure 3 with 1 supplement see all
Unsupervised net-SNE analyses of SEB-activated CD8+ T-cells.

(A) Gating representation of CD107a, IFNγ and IL-2 expression in HIV-1-specific CD8+ T-cells (top), net-SNE plots of HC, Ei, S1, S2 and merge groups of SEB-activated CD8+ T-cells (bottom). (B) Representative net-SNE visualization of IR expression, lineage, and functional markers. The color gradient displays relative marker expression. (C) Unsupervised KNN algorithm for 29 polyclonal clusters color-coded according to the legend. Clusters with statistical differences between groups are represented in the legend. (D) Heatmap of the median biexponential-transformed marker expression normalized to a –3–3 range of respective markers. Asterisks represent the clusters with intergroup statistical differences. (E–F) Scatter plots of intergroup (HC, Ei, S1 and S2) and intragroup (S1 and S2) cluster comparisons. Data represent the median and interquartile ranges of cluster cell frequency. We used the Mann-Whitney U test for intergroup analyses and the signed-rank test for intragroup analyses. Holm’s method was used to adjust statistical tests for multiple comparisons. p-values: *<0.05, **<0.005, ***<0.0005. Sample sizes: HC (20), Ei (21), S1(18), S2 (21).

Figure 3—source data 1

Unsupervised net-SNE analyses of SEB-activated CD8+ T-cells.

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Figure 3—source data 2

Cluster cell frequencies from net-SNE analyses of SEB-activated CD8+ T-cells per study group.

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Figure 3—source data 3

Supervised analyses of CD107a, IFNγ and IL-2 frequencies of SEB-activated CD8+ T-cells per study groups.

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These data support plasticity in the composition of SEB-activated CD8+ T-cell clusters with HIV-1 infection and ART. We observed the dominance of memory-like clusters with changes in composition and frequency in PLWH on ART, particularly IL-2 expression.

Reduction of HIV-1-specific CD8+ T-cell clusters sharing memory-like phenotypes, TIGIT expression and low CD107a

Next, we characterize HIV-1-specific CD8+ T-cells in PLWH on long-term ART compared to Early infected individuals (Ei), aiming to identify signatures of cellular dysfunction. We performed unsupervised net-SNE analyses in 53,751 cells concatenated, based on the production of at least one functional marker (CD107a, IFNγ, IL-2) in response to HIV-1-Gag peptides combined with lineage markers and IRs (Figure 4A-B, Figure 4—source data 1). We defined 26 HIV-1-specific clusters by net-SNE analysis. Of note, only three showed significant differences between groups (#1, #2, and #3; Figure 4C-D). All three clusters decreased in frequency on ART and shared memory-like features (Figure 4E, Figure 4—source data 2). Clusters #1 and #2 shared co-expression of IRs, mainly PD-1 and TIGIT, low CD107a, IFNγ, and higher expression of IL-2 than #3. Meanwhile, cluster #3 co-express (TIGIT, PD-1, and TIM-3) low expression of IL-2 and higher expression of CD107a and IFNγ (Figure 4D). Although CD107a, IFNγ, and IL-2 expression differed between clusters #1, #2, and #3, all shared TIGIT expression in the context of variable levels of TIM-3. These findings in memory-like clusters, together with the initial ones, accounting for increases in the frequency of TIGIT+ and TIGIT+TIM-3+ in CM and EFF cells on ART, led us to postulate then as potential markers of HIV-1-specific CD8+ T-cell dysfunction in PLWH on ART.

Figure 4 with 1 supplement see all
Unsupervised and supervised analyses of HIV-1-specific CD8+ T-cells.

(A) Gating representation of CD107a, IFNγ, and IL-2 expression in HIV-1-specific CD8+ T-cells (top), net-SNE plots of Ei, S1, S2 and merge groups for HIV-1-specific CD8+ T-cells (bottom). (B) Representative net-SNE plots for surface and functional markers. The color gradient displays relative marker expression. (C) Unsupervised KNN algorithm for 26 HIV-1-specific clusters color-coded according to the legend. Only clusters with statistical differences are represented in the legend. (D) Heatmap of the median biexponential-transformed marker expression normalized to a –3–3 range of respective markers. Asterisks represent the clusters with intergroup statistical differences. (E) Scatter plots of intergroup (Ei, S1 and S2) cluster comparisons with significant statistical differences. Data represent the median and interquartile ranges of cluster cell frequency. (F) CD107a, IFNγ, and IL-2 frequency of expression in TIGIT+ (upper panel) and TIGIT +TIM-3+ (bottom panel) HIV-1-specific memory CD8+ T-cell subsets. Scatter plots represent the median and interquartile ranges. (G) Polyfunctional analyses of CD107a, IFNγ, and IL-2 expression in CM TIGIT HIV-1-specific CD8+ T-cells. Scatter plots represent median and interquartile ranges. We used the Mann-Whitney U test for intergroup analyses and the signed-rank test for intragroup analyses. Holm’s method was used to adjust statistical tests for multiple comparisons. p-values: *<0.05, ***<0.0005, and ****<0.0001. Sample sizes: Ei (21), S1(18), S2 (21).

Figure 4—source data 1

Unsupervised net-SNE analyses of HIV-1-specific CD8+ T-cells.

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Figure 4—source data 2

Cluster cell frequencies from net-SNE analyses of HIV-1-specific CD8+ Tcells per Ei and S (S1–S2) study groups.

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Figure 4—source data 3

Supervised analyses of CD107a, IFNγ and IL-2 frequencies of HIV-1-specific CD8+ T-cells in Ei and S (S1–S2) study groups.

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Figure 4—source data 4

Frequencies of CD107a, IFNγ, and IL-2 expression in TIGIT+ and TIGIT+ TIM-3+HIV-1-specific memory CD8+ T-cell subsets in Ei and S (S1–S2) study groups.

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Despite no significant changes observed in the total frequency of CD107a, IFNγ and IL-2 HIV-1-specific CD8+ T-cell responses between groups (Figure 4—figure supplement 1A–B, Figure 4—source data 3). The analyses of TIGIT and TIGIT +TIM-3 HIV-1-specific memory subsets revealed decreases in the frequency of CD107a TIGIT HIV-1-specific CD8+ T-cells limited to the CM compartment (Figure 4F, Figure 4—source data 4). No changes in IFNγ and IL-2 were observed. Furthermore, polyfunctional analysis of TIGIT HIV-1-specific CM CD8+ s identified a decrease in monofunctional CD107a+ as well as in bifunctional CD107a+IFNy+ and CD107+IL-2+ cells overtime on ART (Figure 4G). Overall, these data support a reduction of HIV-1-specific CD8+ T-cell clusters sharing memory-like phenotypes, TIGIT expression and low CD107a in PLWH on ART.

TIGIT blockade restores CD107a expression but not IFNγ or IL-2 production in HIV-1-specific CD8+ T cells

Then, we decided to explore the blockade of TIGIT and TIM-3 pathways as targets for the recovery of CD107a and potentially IFNγ and IL-2 production in HIV-1-specific CD8+ T-cells. We performed short-term ICB experiments using monoclonal antibodies αTIGIT, αTIM-3, and αTIGIT+ αTIM-3 in PBMC from PLWH on suppressive ART (S) with previous immunophenotype.

After short-term ICB, we monitored changes in CD107, IFNγ, and IL-2 in total and subsets of HIV-1-specific CD8+ T-cells by flow cytometry (Figure 5A). The Net-SNE TIGIT and TIM-3 projections are represented in Figure 5B (Figure 5—source data 1). At the level of total HIV-1-specific CD8+ T-cells, short-term ICB experiments demonstrate a specific increase of CD107a expression by αTIGIT (isotype vs. αTIGIT; p<0.05) and αTIGIT + αTIM-3 (isotype vs. αTIGIT + αTIM-3; p<0.05) blockade (Figure 5B, left). Moreover, the recovery of CD107a by αTIGIT was consistent across HIV-1-specific CD8+ T-cell subsets, being particularly marked for CM (isotype vs. αTIGIT; in CM p<0.005; Figure 5C) in agreement with our previous findings. Of note, αTIM-3 blockade did not show any effect, and dual blockade of αTIM-3+αTIGIT did not reveal an additive effect (Figure 5C). No changes in IFNγ and IL-2 production were observed for any conditions tested (Figure 5D–E, Figure 5—source data 2).

Effect of TIGIT, TIM-3, and TIGIT +TIM-3 mAb blockade in HIV-1-specific CD8+ T-cell responses in PLWH on ART.

(A) Representative flow cytometry plots gated on CD8+ T-cells, in the absence of HIV-1 Gag stimulation (basal condition) and presence of HIV-1 Gag stimulation with isotype control, αTIGIT, αTIM-3, and αTIGIT+αTIM-3 antibodies for CD107a, IFNγ and IL-2 expression. (B) Representative net-SNE plots for HIV-1-specific CD8+ T-cells from PLWH concatenated and merged according to the condition. (C–E) Frequency of CD107a, IFNγ, and IL-2 expression in total and HIV-1-specific CD8+ T-cell subsets for the various conditions tested. The Wilcoxon matched-pairs signed ranked test calculated statistical differences. The data represent the mean of two technical replicates. p-values:=0.05, *<0.05, **<0.005 and ***<0.0005. Sample sizes: S1(10), S2 (10).

Figure 5—source data 1

Unsupervised net-SNE analyses for HIV-1-specific CD8+ T-cells in PLWH on ART.

https://cdn.elifesciences.org/articles/83737/elife-83737-fig5-data1-v1.xlsx
Figure 5—source data 2

Frequencies of CD107a, IFNγ, and IL-2 expression in total and subsets of HIV-1-specific CD8+ T-cells in PLWH on ART.

https://cdn.elifesciences.org/articles/83737/elife-83737-fig5-data2-v1.xlsx

These data support heterogeneity in the functional recovery of HIV-1-specific CD8+ T-cells by differentiation stage based on αTIGIT, αTIM-3, and αTIGIT+αTIM-3 blockade. Overall, these results identify the targeting of TIGIT to recover the degranulation in HIV-1-specific CD8+ T-cells, particularly within the CM compartment in PLWH on ART.

Discussion

CD8+ Tex displays a range of functional defects in PLWH early in HIV-1 infection and during ART (Sekine et al., 2020; Buggert et al., 2014; Bengsch et al., 2016; Sen et al., 2016). The expression of IRs is a hallmark of Tex (Breton et al., 2013; Cockerham et al., 2014; Rutishauser et al., 2017; Jiang et al., 2020), and co-expression of IRs has been associated with HIV-1 disease progression (Hoffmann et al., 2016; Chew et al., 2016; Gupta et al., 2015; Jones et al., 2008; Tian et al., 2015) and cancer severity (Chauvin et al., 2015; Anderson et al., 2016; Joller and Kuchroo, 2017). Although ICB has demonstrated promising results in cancer remission (Vaddepally et al., 2020; Twomey and Zhang, 2021), its applicability in HIV-1 as a cure intervention remains unclear (Blanch-Lombarte et al., 2019; Fromentin et al., 2019; Le Garff et al., 2017; Gay et al., 2017; Guihot et al., 2018; Uldrick et al., 2022). Therefore, it is essential to understand the patterns of IRs expression and function across the CD8+ T-cell landscape to identify targets for ICB broadly applicable to PLWH on ART (Deeks et al., 2021).

Here, we immunophenotype CD8+ T-cells using five IRs and three functional markers in PLWH over the ten years of ART. In this way, we overcome previous study limitations based on single IRs expression, bulk CD8+ T-cells, and cross-sectional data (Breton et al., 2013; Rutishauser et al., 2017; Chew et al., 2016; Gupta et al., 2015; Tian et al., 2015). Our results demonstrated a marked and significant increase in TIGIT CD8+ T-cells, particularly within the central memory compartment, not ameliorated by long-term ART. Also, TIGIT CD8+ T-cells negatively correlated with CD4+ counts in PLWH on ART. These data support continuous expression of TIGIT despite ART in agreement with previous studies (Tauriainen et al., 2017; Chew et al., 2016; Jones et al., 2008; Anderson et al., 2016; Schildberg et al., 2016) and uncover novel associations between TIGIT expression in CD8+ T-cells and poorer immune status in PLWH on ART. Thus, these data indicate a specific contribution of TIGIT expression to persistent immune activation and poor CD4+ recovery on ART.

In contrast, we observed transient increases of PD-1, LAG-3, TIM-3, and CD39 expression in total, memory and effector CD8+ T-cells that normalize over time on ART. The potential biological implications of such a difference may relate to the nature of each receptor and the specific immune regulatory pathway activated during HIV-1 infection and ART. Also, these divergences may be influenced by the presence of γ-chain cytokines, such us IL-2, IL-15, and IL-21, in plasma able to upregulate the TIGIT expression in CD8+ T-cells (Chew et al., 2016). A previous study demonstrated an association between high levels of IL-15 and high TIGIT expression in CD4+ T-cells with suboptimal CD4+ recovery in PLWH on ART (Pino et al., 2021).

Our study combined high-dimensional supervised and unsupervised analysis providing an unprecedented deep immunophenotype of the CD8+ T-cell landscape in PLWH over a decade on ART. We explored complementary levels of complexity for the characterization of CD8+ T-cells, including an absence of stimuli (bulk), the presence of antigen-independent stimuli (SEB), and the presence of antigen-specific stimuli (HIV-1).

In the absence of stimuli, supervised analyses confirmed heterogeneous and complex patterns of IRs co-expression across CD8+ T-cell lineages altered by HIV-1 infection and shaped by ART (Sekine et al., 2020; Yamamoto et al., 2011; Jones et al., 2008; Noyan et al., 2018; Avery et al., 2018). Furthermore, unsupervised analyses added complexity to previous data by delineating in profound detail early and continuous changes of contraction and expansion of cellular clusters with HIV-1 infection and time on ART. We tracked memory-like expansion and effector-like cluster contraction over time on ART according to the establishment of memory responses. Similarly, in the presence of antigen-independent stimuli, we identified continuous changes in cellular cluster composition by contraction and expansion of CD8+ cellular cluster with infection and treatment. We observed a dominance of memory-like clusters with changes in composition and frequency tracked by an augment of IL-2 expressing clusters on ART both by supervised and unsupervised analyses. The IL-2 expression regulates proliferation and homeostasis and contributes to the generation of long-term memory responses (Teigler et al., 2017; Kou et al., 1998; Chiu et al., 2022), suggesting a partial functional remodelling of CD8+ T-cell populations independent of antigen in PLWH over time on ART. Thus, our findings support the contribution of IRs co-expression in CD8+ Tex and T-cell activation favouring a continuous reshaping of memory and effector-like CD8+ cellular clusters. These findings indicate the enormous plasticity and constant homeostasis of CD8+ T-cells in PLWH during a decade of ART (Warren et al., 2019).

In the presence of HIV-1-specific stimuli, supervised and unsupervised analyses delineate a reduction of HIV-1-specific CD8+ T cells sharing memory phenotypes, TIGIT expression and low CD107a. Our findings focused on the memory compartment of TIGIT expressing HIV-1-specific CD8+ T-cells demonstrating a decrease in monofunctional CD107a+ and bifunctional CD107a+IFNγ+ and CD107a+IL-2+ cells. Previous studies support a direct correlation between monofunctional CD107a+ and Eomes intensity in exhausted HIV-1-specific CD8+ T-cells (Buggert et al., 2014). Although this study did not include the expression of TIGIT across analyses, it may suggest an association between TIGIT expression and the T-bet /Eomes axis in charge of regulating the exhaustion and memory cell fate of CD8+ T-cells (Doering et al., 2012).

Our findings further support the role of TIGIT as a signature of dysfunctional and Tex antiviral responses (Chew et al., 2016). Indeed, the blockade of the TIGIT pathway restored CD107a expression in HIV-1-specific CD8+ T-cells across cellular compartments with a marked effect in the central memory compartment according to our findings from HIV-1-specific immunophenotype of TIGIT CD8+ T-cells. To our knowledge, our study is the first to demonstrate the recovery of CD107a expression in HIV-1-specific CD8+ T-cells by TIGIT blockade. These data contrast with Chew et al., which observed the recovery of IFNγ production by TIGIT blockade. However, they also reported a reduction in CD107a expression in TIGIT+ CD8 T-cells in response to aCD3/aCD28 activation, supporting a dysfunctional profile of TIGIT+ CD8+ T-cells. Differences between study groups accounting for time on ART, samples tested and interindividual variability of in vitro ICB experiments may account for some of the differences observed.

Although the mechanistic behind TIGIT signalling and CD107a expression are not fully understood, low CD107a expression has been linked to the terminal T-betdimEomeshi exhausted phenotype, and HIV-1-specific CD8+ T-cells expressing TIGIT can degranulate to a certain extent (Buggert et al., 2014; Tauriainen et al., 2017). Moreover, our data did not support an additive effect recovering HIV-1-specific CD8+ T-cells function of TIGIT and TIM-3 combinatorial blockade over TIGIT blockade. The redundancy and promiscuity of TIM-3 for several ligands, including Gal-9, CEACAM-1, PtdSer, and HMGB-1 (Andrews et al., 2019; Sabatos-Peyton et al., 2018), may be associated with these results. We cannot exclude the impact of TIGIT and TIM-3 blockade in other cell types (Anderson et al., 2016; Joller and Kuchroo, 2017; Pende et al., 2006).

We acknowledge several study limitations; First, the sample size of study groups and the use of peripheral blood samples underestimate the potential contribution of TIGIT expression to Tex in lymphoid tissues. Second, the use of only Gag as stimuli for the characterization of HV-1-specific CD8+ T- cell responses in the absence of TCR sequencing. Using alternative HIV-1 antigens such as Nef, Env or Pol may provide additional information on the profile of CD8+ T-cell functional responses against early and late-expressed viral proteins in PLWH on ART (Kløverpris et al., 2013; Stevenson et al., 2021). Third, limited ICB experiments to CD107a, INFγ and IL-2 functional markers without complementary cytotoxic markers (perforin, granzyme B). Forth, complementary transcriptomic, epigenetic, and metabolic markers are needed for a complete description of Tex’s immune signatures linked to TIGIT expression in HIV-1 specific CD8+ T-cells in PLWH on ART.

In summary, our study profile with unprecedented detail continuous reshaping of memory-like and effector-like of CD8+ T cellular clusters in PLWH over a decade of ART. The study identifies the TIGIT as a critical target for Tex associated with the loss of CD107a expression in HIV-1-specific CD8+ T-cells. These findings support targeting the TIGIT/CD155 axis for Tex precision immune-base curative interventions in PLWH at all ART stages.

Materials and methods

Study groups

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This retrospective study analyzed clinical data and biological sample availability from 3000 patients assigned to the HIV-1 clinical unit of the Germans Trias i Pujol University Hospital. We included individuals with cryopreserved PBMCs available in our collection. We identified 24 chronically HIV-1-infected individuals who had been treated mainly with a combination of NNRTI and NRTI for more than ten years with sustained virological suppression (<50 HIV-1-RNA copies/ml) and with longitudinal biological samples at timepoint 1 (S1), 2.2 (1.8–2.8) years undetectable on ART, and at time point 2 (S2), 10.1 (7.4–12.9) years undetectable on ART (Supplementary file 1, Figure 1A, Source data Figure 1A). We excluded individuals with integrase inhibitors, ART as monotherapy, and treatments with mitochondrial toxicity, including Trizivir, d4T, ddI, AZT and blips over the ART period (S1- S2) to ensure homogeneous treatment over time. For comparative purposes, we included 24 early HIV-1-infected individuals (Ei) defined in a window of 1.3 (0.77–17.8) weeks after seroconversion in the absence of ART and 24 healthy controls (HC). The groups were balanced by age to the S2 samples to avoid confounding effects on IR expression.

CD8+ T-cell immunophenotype

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Cryopreserved PBMCs from the study groups were thawed and rested overnight at 37 °C in a 5% CO2 incubator. The following day, PBMCs were incubated for six hours at 37 °C in a 5% CO2 incubator under RPMI complemented medium 10% FBS in the presence of CD28/49d co-stimulatory molecules (1 μl/ml, BD), Monensin A (1 μl/ml, BD Golgi STOP), and anti-human antibody for CD107a (PE-Cy5, clone H4A3, Thermo Fisher Scientific). PBMCs were left unstimulated, stimulated with SEB (1 μg/ml, Sigma-Aldrich), and stimulated with HIV-1-Gag peptide pool (2 μg/peptide/ml, EzBiolab). After six hours of stimulation, cells were rested overnight at 4 °C as previously described (Blanch-Lombarte et al., 2019). The next day, PBMCs were washed with PBS 1 X and stained for 25 min with the Live/Dead probe (APC-Cy7, Thermo Fisher Scientific) at RT to discriminate dead cells. Cells were washed with PBS 1 X and surface stained with antibodies for 25 minutes at RT. We used CD3 (A700, clone UCHT1, BD), CD4 (APC-Cy7, clone SK3, BD), CD8 (V500, clone RPA-T8, BD), CD45RA (BV786, clone HI100, BD), CCR7 (PE-CF594, clone 150503, BD), CD27 (BV605, clone L128, BD), TIGIT (PE-Cy7, clone MBSA43, Labclinics SA), PD-1 (BV421, clone EH12.1, BD), LAG-3 (PE, clone T47-530, BD), TIM-3 (A647, clone 7D3, BD) and CD39 (FITC, clone TU66, BD) antibodies. Afterwards, cells were washed twice in PBS 1 X, fixed, and permeabilized with Fix/Perm kit (A and B solutions, Thermo Fisher Scientific) for intracellular cytokine staining with anti-human antibodies of IFNγ (BV711, clone B27, BD) and IL-2 (BV650, clone MQ1-17H12, BD). Finally, stained cells were washed twice with PBS 1 X and fixed in formaldehyde 1%.

TIGIT, TIM-3 and TIGIT+TIM-3 short-term checkpoint blockade

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We selected cryopreserved PBMCs from S1 (n=10) and S2 (n=10). Samples were previously characterized by the expression of TIGIT and TIM-3 on total CD8+ T-cells. PBMCs were thawed and rested for four hours at 37 °C in a 5% CO2 incubator. Next, cells were incubated under RPMI complemented medium 10% FBS with 1 μl/ml of anti-CD28/CD49d and 1 μl/ml of Monensin A overnight at 37 °C in a 5% CO2. PBMCs are divided in the following conditions; (1) unstimulated, (2) SEB (1 μg/ml, Sigma-Aldrich) and (3) HIV-1-Gag peptide pool (2 μg/peptide/ml) in the absence or presence of αTIGIT and/or αTIM-3, and its respective isotype antibodies. For the single blockade of TIGIT (αTIGIT), we included Ultra-LEAF purified anti-human TIGIT antibody (10 μg/ml, clone A15153G, Biolegend) or its control isotype Ultra-LEAF purified mouse IgG2a antibody (10 μg/ml, MOPC-173, Biolegend). For single TIM-3 blockade (αTIM-3), we used Ultra-LEAF purified anti-human TIM-3 antibody (10 μg/ml, clone F38-2E2, Biolegend) or its respective isotype Ultra-LEAF purified mouse IgG1 antibody (10 μg/ml, MOPC-21, Biolegend). Finally, we included αTIGIT+αTIM-3 or their respective IgG2 + IgG1 isotypes for a combinational blockade. The next day, PBMCs were surface and intracellularly stained with the panel of antibodies and the methodology described in the section above.

Supervised immunophenotype data analysis

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Stained PBMCs were acquired on an LSR Fortessa cytometer using FACSDiVa software (BD). Approximately 1,000,000 events of PBMCs were recorded per specimen. Antibody capture beads (BD) were used for single-stain compensation controls. Flow cytometry data were analyzed with FlowJo software v10.6.1, and fluorescence minus one (FMO) was used to set manual gates. We analyzed CD8+ T-cells by excluding dump and CD4+ T-cells. We excluded patients with <20% viability in lymphocytes and total CD8+ T-cells (Source data Figure 1A). As previously described, we measured by supervised and classical analyses the IRs expression in CD8+ T-cell subsets, including Naive, central memory, transitional memory, effector memory, and effector CD8+ T-cells (Breton et al., 2013; Blanch-Lombarte et al., 2019). We performed two technical replicates for SEB-activated and HIV-1-specific CD8+ T-cell cytokine production. We considered the cytokine response positive after background subtraction (mean of two technical replicates) used as the cut-off value. For each independent sample, we recorded a median of 1,000 events and 50 events positive for cytokines for total and CD8+ T-cell subsets, respectively.

Unsupervised immunophenotype data analysis

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The phenotypic and functional characterization of cellular populations was analyzed by using t-Distributed Stochastic Neighbor Embedding (t-SNE; van der Maaten, 2008) and net-SNE (Cho et al., 2018) dimensionality reduction algorithms to visualize single-cell distributions in two-dimensional maps. Briefly, cell intensity was z-normalized, and a randomly selected subset of cells, at least 1000 cells per sample, was passed through the t-SNE algorithm. The resulting t-SNE dimension was then used to predict the position of all remaining CD8+ T-cells acquired per sample from each group using the net-SNE algorithm based on neural networks. For functional analysis, we selected polyclonally activated and HIV-1-specific CD8+ T-cells producing at least one cytokine CD107a, IFNγ, or IL-2 under SEB or HIV-1 conditions, respectively. In parallel, we discovered cell communities using the Phenograph clustering technique. It operates by computing the Jaccard coefficient between nearest neighbours, which was set to 30 in all executions, and then locating cell communities (or clusters) using the Louvain method. The method creates a network indicating phenotypic similarities between cells. The netSNE maps included representations of the identified cell communities, and additionally, we built a heatmap with the clusters in the columns and the markers of interest in the rows to better comprehend the phenotypical interpretation of each cluster. The color scale displays each marker’s median intensity on a biexponential scale. We calculated quantitative assessments of cellular clusters in the percentage of cells for each sample to analyze and compare the distribution between HC, Ei, S1, and S2 groups, similar to the classical flow cytometry analysis.

Statistics

Bivariate analysis was conducted using nonparametric methods as follows: Mann-Whitney U test for independent median comparison between groups, Wilcoxon signed-rank test for paired median changes over time, permutation test for composition distribution between groups, Kruskal-Wallis test for comparison between more than two groups, and spearman linear correlation coefficient to study the association between continuous variables. Holm’s method was used when appropriate to adjust statistical tests for multiple comparisons with a significance level of 0.05. All statistics and single-cell analyses were conducted using the R statistical package (R Development Core Team, 2008). The selection of clusters was performed through the significant differences obtained by inter- and intragroup comparisons between groups. Moreover, pattern distribution and graphical representations of all possible Boolean combinations for IRs co-expression and functional markers were conducted using the data analysis program Pestle v2.0 and SPICE v6.0 software (Roederer et al., 2011). Graph plotting was performed by GraphPad Prism v8.0 software and R packages. The data represents the mean of two technical replicates.

Study approval

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The study was conducted according to the principles of the Declaration of Helsinki (World Medical Association, 2013). The Hospital Germans Trias i Pujol Ethics Committee approved all experimental protocols (PI14-084). For the study, subjects provided written informed consent for research purposes of biological samples taken from them.

Data availability

The data supporting the findings of this study are available within the paper and its supplementary information files. Source data are provided in this paper.

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Decision letter

  1. Frank Kirchhoff
    Reviewing Editor; Ulm University Medical Center, Germany
  2. Murim Choi
    Senior Editor; Seoul National University, Republic of Korea

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Single-cell CD8+ dynamics uncover the reduction of CD107a TIGIT+ memory HIV-1-specific cells in PLWH over a decade of ART" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Murim Choi as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. Revise the statistical analyses and consider correction for multiple comparisons and verify the validity of the use of mixing paired and unpaired comparisons in the same plots.

2. More primary data need to be included to prove the reliable detection of specific markers, such as Lag-3, Tim3, and CD39 and the potential biological of subtle differences should be critically discussed. Levels of baseline IR expression on naïve cells should be shown where appropriate.

3. The rationale for the selection of clusters for tSNE analysis needs to be provided.

4. Concerns about the utilization of a super-antigen as activators without knowing the absolute numbers of potential responding cells (Figure 3) and the interpretation of results shown in Figures 4 and 5 (see reviewer 3, points 4 and 5) need to be addressed.

5. Results from two data points do not allow us to conclude that there are continuous increases. Information on the magnitude of the HIV-specific CD8 T cell responses in the different cohorts should be provided.

6. It needs to be explained why the author directly focused on the CM component of CD8 HIV-specific responses instead of first looking at total HIV-specific CD8 T cells?

Reviewer #1 (Recommendations for the authors):

1. Please put this work in context with reference 25. That study demonstrated very similar results with respect to CD8 T cell expression of TIGIT; however, the prior study did show enhancement of IFN-g responses when TIGIT was blocked (CD107a was not analyzed).

2. For figure 1A, I believe there were 48 total PLWH analyzed not 24 as is currently shown (24 in S1 and 24 in S2). Also, the female/male ratio should be separated for both S1 and S2.

3. For the data demonstrated in figures 2-4, were the comparisons statistically corrected for multiple comparisons?

4. Please explain the importance of looking at SEB-stimulated T cells.

5. Please explain why only the three functional parameters were analyzed (CD107a, IFN-g, and IL-2).

6. It is unclear from the text, the effect of ART on these functional parameters. It appears that patients on ART for prolonged periods (S2) have some restoration of their HIV-specific CD8 T cells with respect to polyfunctionality. Please clarify as this is an important aspect with respect to the studies done for figure 5.

7. It appears that most of the analysis in figures 2-4 was unsupervised with the exception of supervised data shown for the HIV-specific data; however, it is unclear as to what parameters were supervised.

8. The following statement in the discussion "These data may support further investigations 309 on the potential use of TIGIT expression in CD8+ T cells as a biomarker of immune activation through residual replication in PLWH on ART" needs more justification. There are numerous publications showing that residual replication does not significantly occur and several demonstrate that continued immune activation is due to microbial translocation. The latter is supported by the current work whereby CD4 T cell counts correlate with TIGIT expression.

Reviewer #2 (Recommendations for the authors):

Statistics (all figures):

The statistical analyses should be revised, ideally with a statistician. Correction for multiple comparisons should be considered, and the validity of the use of mixing paired and unpaired comparisons in the same plots verified.

Figure 1: The levels of baseline expression of IRs on phenotypically naïve cells should be presented as well. While they are expected to be low, different cytokines can upregulate IRs on T cells in the absence of TCR signaling. Figure 1FandG: The authors focus on S2 in the text. They should consider also mentioning that they found significant negative correlations between CD4+ T-cell counts and the frequency of CM CD8+ T Cells expressing 2 and >1 IRs in the S1 condition.

Figure 2: Lines 198 -200 and at other places in the manuscript. The authors mention a continuous increase of effector-like clusters (and elsewhere, of other changes). It is not possible to confirm that these changes are a continuous process with only two time points, this statement should be revised.

Figure 3: The interpretation of the SEB-responsive cells seems to be "cellular clusters susceptible to TCR activation". However, SEB as a superantigen will stimulate only some Vbeta families. This should be clarified.

Figure 4: Some information should be given on the magnitude of the HIV-specific CD8 T cell responses in the different cohorts, and how this magnitude over time for S1 and S2 pairs. It is important, because it may change the interpretation of the shift in the relative proportions of the clusters observed (absolute attrition of some? Or the expansion of others?).

The authors directly focus on the CM component of these HIV-specific responses, it is unclear why and it would be important to look first at total HIV-specific CD8 T cells. If there is a shift in the memory differentiation pattern (e.g, in relative proportions of CM vs EM and TM over time), this may change the findings.

The authors stimulate with HIV-1 GAG to select for HIV-1 specific CD8+ T cells. Have they also tested other antigens (Nef, Env, Pol) in a subset of patients? Would they expect the results to be similar?

Figure 5: The functional assays with TIGIT blockade are limited and do not include other markers of cytotoxic cells (perforin, granzyme B expression…). It is not clear how these subsets compare to the other CD8 clusters in terms of CD107 expression.

Does the short-term ICB result in any changes to cell viability?

Reviewer #3 (Recommendations for the authors):

1. Data for Lag-3, Tim3, and CD39 shown in supplementary figure 1A does not appear to demonstrate reliable detection. Additional data should be shown to demonstrate convincing detection of these markers. Importantly, such raw data also needs to be shown for each of the stimulation conditions, and in the context of the functional outputs.

2. For the data shown in Figures 2,3,4, it is unclear why the stated number of clusters was chosen for the tSNE analysis. Whether this leads to the detection of meaningless clusters is unclear. In addition, in some cases, populations are grouped together, yet some of these grouped clusters appear disparate.

3. Many many statistical comparisons are made, yet there is no discussion of correction for multiple comparisons.

4. The differences between many groups appear very subtle despite being statistically different (pending adjustment for multiple comparisons). The authors should consider carefully what may be biologically relevant in the discussion.

5. The data analysis in Figure 3 is fundamentally flawed because the authors used super antigen as a 'polyclonal' activator. This is a great T cell activator but has to be interpreted carefully because every donor has an inherently different 'maximal' response based on the proportion of T cells bearing the appropriate TCR-BV to respond to SEB. This means that directly comparing total responding cells between groups is not particularly informative. Furthermore, without knowing the absolute number of potential responding cells (which was not measured here), it is not appropriate to interpret functional deficiencies within the population. Also, it is not correct to conclude polyclonal activation using SEB, because the clonality measure requires TCR assessment – not performed here. Within any given SEB-responding memory subset it is formally possible that a monoclonal activation could occur.

6. In Figures 4 and 5, it is difficult to interpret the data without knowing the actual magnitude of the responses to HIV, and the number of responding events recovered in any given subset examined. Did the authors have a cutoff for a minimum number of events to consider a positive response- both overall, and also within the subset populations?

7. Figure 5, the use of tSNE analysis does not seem necessary when memory subsets are simply examined with or without blockade. Also, how the memory subsets were defined should be described.

Stylistic comments.

1. The use of 'single-cell analysis' in the title and abstract (as well as several times in the paper) seems somewhat inappropriate at times given the more broad use of this term when referring to single-cell genomic studies. This manuscript is simply a flow cytometry study, which by definition is a single cell, but rarely described as such.

2. There are numerous stylistic and grammatical errors that should be fixed after careful reading; additionally:

– Lines 101-103 and 105-106 basically say the same thing.

– Lines 65-66, and 230-231 are not sentences.

– Line 205. define ICB.

– Line 267. fix 'PBCM.'

https://doi.org/10.7554/eLife.83737.sa1

Author response

Essential revisions:

1. Revise the statistical analyses and consider correction for multiple comparisons and verify the validity of the use of mixing paired and unpaired comparisons in the same plots.

According to the reviewers, we have revised the statistical analyses and included the correction for multiple comparisons, modifying the results across Figures and Supplemental Figures. Regarding the statistics, we changed Figures 2, 3, and 4 to have the intergroup and intragroup comparisons in separate graphs to clarify the information. In addition, we have included in the material and methods section and figure legends information regarding the statistical analyses and the correction for multiple comparisons (Holm’s method) performed when appropriate. Specific questions raised by the reviewers are included in the following sections.

2. More primary data need to be included to prove the reliable detection of specific markers, such as Lag-3, Tim3, and CD39 and the potential biological of subtle differences should be critically discussed. Levels of baseline IR expression on naïve cells should be shown where appropriate.

In agreement with the reviewers, we have provided primary data on reliable detection of LAG-3, TIM-3, and CD39 in Figure 1 —figure supplement 1, and data on baseline IR expression in naïve cells in Figure 1 and Figure 1 —figure supplement 2. Also, the potential biological implications of subtle and transient differences between IRs expression in CD8+ have been now included in the Discussion section (page 13).

Regarding the reliable detection of LAG-3, TIM-3, and CD39 markers. These markers have lower frequencies and fluorochrome intensities than PD-1 and TIGIT, comprising < 10% of total CD8+ T cells and subsets. To support the reliable detection, we have now provided source data of flowcytometry intensities for each fluorochrome compared with the Fluorescent minus one (FMO) intensity included now in Figure 1 —figure supplement 1. As shown in the figure, we can observe reliable detection for all the IRs markers despite differences in the intensities between fluorochromes compared with FMOs.

Regarding the baseline expression of IRs in naïve CD8+ T cells, we have now included data when appropriate, as suggested by reviewers. Information regarding the baseline expression of IR in naïve cells is now included in Figure 1 and Figure 1 —figure supplement 2. These data indicate very low or undetectable levels of baseline IR expression in naïve CD8+ T cells and subsets for all the IRs studied (TIGIT, PD-1, LAG-3, TIM-3, and CD39). This information further supports the reliable detection of IRs expression in total and CD8+ cellular subsets. Including the information on naïve CD8+ T cells brought new information regarding the composition of naïve populations by permutation analyses and demonstrated an interesting the reduction of CD8+ naïve cells expressing no IRs and the increase of naïve cells expressing one IR (Figure 1C).

3. The rationale for the selection of clusters for tSNE analysis needs to be provided.

In agreement with the reviewers, we now provided the rationale for cluster identification in the Material and Methods under the Unsupervised immunophenotype data analysis section (pages 20-21): “We discovered cell communities using the Phenograph clustering technique. It operates by computing the Jaccard coefficient between nearest neighbours, which was set to 30 in all executions, and then locating cell communities (or clusters) using the Louvain method. This creates a network indicating phenotypic similarities between cells. The netSNE maps included representations of the identified cell communities, and additionally, we built a heatmap with the clusters in the columns and the markers of interest in the rows to better comprehend the phenotypical interpretation of each cluster.”

4. Concerns about the utilization of a super-antigen as activators without knowing the absolute numbers of potential responding cells (Figure 3) and the interpretation of results shown in Figures 4 and 5 (see reviewer 3, points 4 and 5) need to be addressed.

We appreciate the reviewers' concerns about using superantigen Staphylococcal enterotoxin B (SEB) as an activator and the need for information about the absolute number of potential responding cells. Considering these comments, we have now included all the information required under de Results section. Unsupervised phenotypic characterisation of SEB-activated CD8+ T cells in PLWH in ART. In this section, we now justified the use of SEB to obtain complementary information on T-cell activation in response to pathogens involved in the disease by stimulating TCR-VB clonotypes, and previous studies in HIV-1 support the use of SEB to evaluate the effect of ICB in the recovery of T cell function (31,49,50). In addition, we provided information on the supervised analyses regarding the total frequency of SEB-activated CD8+ T cells presented in Figure 3 —figure supplement 1, revised the Results section accordingly, and included the lack of TCR sequencing as a limitation for data interpretation in the Discussion section.

5. Results from two data points do not allow us to conclude that there are continuous increases. Information on the magnitude of the HIV-specific CD8 T cell responses in the different cohorts should be provided.

We agree with the reviewers that the analyses of two time-points may not allow to conclude continuous increases. According with the comment, we have now rephrased the content of the manuscript when appropriate, including the title of the article, excluding the concept of dynamics. In agreement with the comment, we have now included all the information on the supervised analyses of the magnitude of total HIV-1-specific CD8 T cells in Figure 4 —figure supplement 1.

6. It needs to be explained why the author directly focused on the CM component of CD8 HIV-specific responses instead of first looking at total HIV-specific CD8 T cells?

According with the reviewers’ comments, we have now rephrased the Results section “Reduction of HIV-1-specific CD8+ T-cell clusters sharing memory-like phenotypes, TIGIT expression and low CD107a” include the analyses of all memory subsets, now in Figure 4F. We now have also included the supervised analyses of total HIV-1-specific CD8+ T cells (Figure 4 —figure supplement 1).

The focus on the memory compartment comes from identifying alterations of memory-like clusters by unsupervised analyses of HIV-1-specific CD8+ T cells and detecting continuous and significant increases of TIGIT CM cells in PLWH on ART (Figure 1D). This information is now summarised in the manuscript as follows (page 10): “These findings in memory-like clusters, together with the initial ones, accounting for increases in the frequency of TIGIT+ and TIGIT+TIM-3+ in CM and EFF cells on ART, led us to postulate then as potential markers of HIV-1-specific CD8+ T-cell dysfunction in PLWH on ART. Despite no significant changes observed in the total frequency of CD107a, IFNγ and IL-2 HIV-1-specific CD8+ T-cell responses between groups (Figure 4 —figure supplement 1A-B, Figure 4 – source data 3). The analyses of TIGIT and TIGIT+TIM-3 HIV-1-specific memory subsets revealed decreases in the frequency of CD107a TIGIT HIV-1-specific CD8+ T cells limited to the CM compartment (Figure 4F, Figure 4 – source data 4). No changes in IFNγ and IL-2 were observed. Furthermore, polyfunctional analysis of TIGIT HIV-1-specific CM CD8+s identified a decrease in monofunctional CD107a+ as well as in bifunctional CD107a+IFNγ+ and CD107+IL-2+ cells overtime on ART (Figure 4G). Overall, these data support a reduction of HIV-1-specific CD8+ T-cell clusters sharing memory-like phenotypes, TIGIT expression and low CD107a in PLWH on ART.”

Reviewer #1 (Recommendations for the authors):

1. Please put this work in context with reference 25. That study demonstrated very similar results with respect to CD8 T cell expression of TIGIT; however, the prior study did show enhancement of IFN-g responses when TIGIT was blocked (CD107a was not analyzed).

According to this comment, we have now put in context our work with reference 25 in the Discussion section as follows (page 15): “These data contrast with Chew et al., which observed the recovery of IFNγ production by TIGIT blockade. However, they also reported a reduction in CD107a expression in TIGIT+ CD8 T cells in response to aCD3/aCD28 activation, supporting a dysfunctional profile of TIGIT+ CD8+ T cells. Differences between study groups accounting for time on ART, samples tested and interindividual variability of in vitro ICB experiments may account for some of the differences observed.”

2. For figure 1A, I believe there were 48 total PLWH analyzed not 24 as is currently shown (24 in S1 and 24 in S2). Also, the female/male ratio should be separated for both S1 and S2.

To clarify this point, we have accordingly revised Figure 1A, including subjects, samples, and female/male ratio, Supplementary Table 1 and the Study groups section in the manuscript. In brief, we analysed 48 PLWH, 24 in Early infection and 24 in ART. From those 24 in ART, we analysed 48 samples, 24 at the S1 and 24 at the S2 time points. The female/male ratio is the same for S1 and S2 samples as all samples come from the same 24 individuals selected in ART.

3. For the data demonstrated in figures 2-4, were the comparisons statistically corrected for multiple comparisons?

According to the reviewer, we have revised all the statistical analysis for the manuscript and corrected for multiple comparisons when appropriate using Holm's method. The information regarding the statistical method is included in the Materials and methods section under Statistics (page 20-21) and figure legends when appropriate.

4. Please explain the importance of looking at SEB-stimulated T cells.

Considering this comment, we have now included all the information required under de Results section, Unsupervised phenotypic characterisation of SEB-activated CD8+ T cells in PLWH in ART (page 8). Briefly, the use of Staphylococcal enterotoxin B (SEB) allows to characterise T-cell responses (in magnitude and function) to superantigen stimulation providing a complementary read out of response to pathogens involved in disease to antigen-specific T-cell responses. SEB has been previously used to monitor antigen-independent TCR activation through the cross-link of MHC class II and TCR VB of the T cell lymphocytes (Kou et al. 1998) and in vitro effect of the ICB (Lewin et al. 2022, and Teigler et al. 2017). We acknowledge the limitations of this kind of analysis in terms of TCR response and the functional profile observed, but also the interest of SEB-activated CD8+ T cells as complementary information to the antigenspecific responses to potentially detect functional defects.

5. Please explain why only the three functional parameters were analyzed (CD107a, IFN-g, and IL-2).

We prioritise the analyses of five IRs (TIGIT, PD-1, LAG-3, TIM-3 and CD39) and lineage markers (CD45RA, CCR7 and CD27) in our panel, adding relevant functional markers such as CD107a, IFNγ and IL-2 that provide general and complementary aspects of CD8+ T-cell functionality. From the functional parameters, we selected CD107a because it is widely used to detect cytolytic activity in CD8+ T cells as an early marker based on degranulation (Voskoboinik, et al. 2015, Aktas et al. 2009), we selected IFNγ because it is a classical moderator of cellmediated immunity with pro-inflammatory actions and enhances the antiviral effects of CD8+ T cells (Bhat et al. 2017) and selected IL-2 because it plays a crucial role in the expansion of CD8+ T cells, regulates proliferation and homeostasis, and long-term memory responses of T-cell responses (Gaffen and K. D. Liu 2004, Akdis et al. 2016). The relevant references to support using these three functional parameters have been included in the manuscript (references 51 to 55).

6. It is unclear from the text, the effect of ART on these functional parameters. It appears that patients on ART for prolonged periods (S2) have some restoration of their HIV-specific CD8 T cells with respect to polyfunctionality. Please clarify as this is an important aspect with respect to the studies done for figure 5.

According to the reviewer, we revised the functional data of HIV-1-specific CD8+ T cell responses. We have clarified the information in the Results section, new Figure 4, and new Figure 4 —figure supplement 1. All the information required is under de Results section, Reduction of HIV-1-specific CD8+ T-cell clusters sharing memory-like phenotypes, TIGIT expression and low CD107a (page 9). Briefly, Figure 4F included scatter plots showing median and interquartile ranges of CD107a, IFNγ, and IL-2 expression in TIGIT and TIGIT+TIM-3 HIV1-specific memory CD8+ T cell subsets. This analysis revealed decreased CD107a expression limited to the CM compartment in TIGIT HIV-1-specific CD8+ T cells. In addition, we have now included in Figure 4G the polyfunctional analyses of CD107a, IFNγ, and IL-2 expression for TIGIT HIV-1-specific CM cells. The new polyfunctional analysis identified monofunctional CD107a+ cells as the most common phenotype reduced in TIGIT HIV-1-specific CM cells, followed by bifunctional CD107a+IFNγ+ and CD107a+IL-2+ double-positive cells overtime on ART. These data do not support the restoration of polyfunctional responses over prolonged periods of ART (S2).

7. It appears that most of the analysis in figures 2-4 was unsupervised with the exception of supervised data shown for the HIV-specific data; however, it is unclear as to what parameters were supervised.

In agreement with the comment, we have now included whether we use supervised or unsupervised analyses in the text and figure legends for analyses corresponding to Figures 2-4. For SEB-activated CD8+ T cells, we used unsupervised net-SNE analyses and supervised classical analyses (Figure 3 and Figure 3 —figure supplement 1, respectively). Similarly, for HIV-1 -specific CD8+ T-cells, unsupervised net-SNE analyses and supervised classical analyses were combined across data sets (Figure 4 and Figure 4 —figure supplement 1, respectively).

8. The following statement in the discussion "These data may support further investigations 309 on the potential use of TIGIT expression in CD8+ T cells as a biomarker of immune activation through residual replication in PLWH on ART" needs more justification. There are numerous publications showing that residual replication does not significantly occur and several demonstrate that continued immune activation is due to microbial translocation. The latter is supported by the current work whereby CD4 T cell counts correlate with TIGIT expression.

According to the reviewer's comment, we have modified the content of the corresponding paragraph to clarify our statement focused on the association between TIGIT expression, poor immune recovery and persistent immune activation. We have now rewritten the content in the discussion as follows (page 12): “These data support continuous expression of TIGIT despite ART in agreement with previous studies (24,25,30,57,59) and uncover novel associations between TIGIT expression in CD8+ T cells and poorer immune status in PLWH on ART. Thus, these data indicate a specific contribution of TIGIT expression to persistent immune activation and poor CD4+ recovery on ART.”

Reviewer #2 (Recommendations for the authors):

Statistics (all figures):

The statistical analyses should be revised, ideally with a statistician. Correction for multiple comparisons should be considered, and the validity of the use of mixing paired and unpaired comparisons in the same plots verified.

According to the reviewer, we have revised all the statistical analysis to correct our findings for multiple comparisons when appropriate using Holm's method. The information regarding the statistical method is included in the statistics section of the Material and Methods and the figure legends when appropriate.

Figure 1: The levels of baseline expression of IRs on phenotypically naïve cells should be presented as well. While they are expected to be low, different cytokines can upregulate IRs on T cells in the absence of TCR signaling.

Data on baseline levels of IRs expression in naïve CD8+ T cells have been included in Figure 1 B-C and Figure 1 —figure supplement 2 B-C. These data demonstrate very low basal levels of IR expression in naïve CD8+ T cells across the IRs analysed (TIGIT, PD-1, LAG-3, TIM-3 and CD39). Including the information of basal IRs expression in naïve CD8+ T cells supports a reliable detection of IRs and uncovered new information of IRs expression now included in the Results section under, Alterations in CD8+ T-cell IRs frequencies and expression patterns in PLWH are not mitigated by ART (page 5).

Figure 1F and G: The authors focus on S2 in the text. They should consider also mentioning that they found significant negative correlations between CD4+ T-cell counts and the frequency of CM CD8+ T Cells expressing 2 and >1 IRs in the S1 condition.

According to the reviewer, we have clarified this point in the Results section under, Alterations in CD8+ T-cell IRs frequencies and expression patterns in PLWH are not mitigated by ART (page 6) as follows: “Focusing on S1 (Figure 1F), we found significant negative correlations between CD4+ T-cell counts and frequencies of CM CD8+ T cells expressing 2 (p=0.0054, r=0.64) and > 1 IRs (p=0.0087, r=-0.61). Focusing on S2 (Figure 1G), we observed significant negative correlations between CD4+ T-cell counts and the frequency of total CD8+ T cells expressing TIGIT+ (p=0.0157, r=-0.58), expressing 1 IRs (p=0.0386, r=-0.54) or >1 IRs (p=0.0386, r=-0.51). At the level of CD8+ T-cell subsets, the expression of 2 IRs in CM and >1 IR in TM negatively correlated with CD4+ T-cell counts (p=0.0072, r=-0.64; p=0.0346, r=-0.52, respectively) (Figure 1G)”.

Figure 2: Lines 198 -200 and at other places in the manuscript. The authors mention a continuous increase of effector-like clusters (and elsewhere, of other changes). It is not possible to confirm that these changes are a continuous process with only two time points, this statement should be revised.

In agreement with the comment, the statement has been revised across the manuscript. We agree with the reviewer that the analyses of two time points may not allow us to conclude a continuous increase of effector-like clusters. Accordingly, we have rephrased the manuscript when appropriate.

Figure 3: The interpretation of the SEB-responsive cells seems to be "cellular clusters susceptible to TCR activation". However, SEB as a superantigen will stimulate only some Vbeta families. This should be clarified.

In agreement with the comment, we have clarified the information and removed the term TCR activation by SEB-activated CD8+ T cells. We have included a paragraph under the Results section, Unsupervised phenotypic characterisation of SEB-activated CD8+ T cells in PLWH in ART (page 8) as follows: “Then, we evaluate CD8+ T-cell responses by bacterial superantigen activation with Staphylococcal enterotoxin B (SEB). Using SEB can provide complementary information on T-cell activation in response to pathogens involved in the disease by stimulating TCR-VB clonotypes (31,49,50).”

Figure 4: Some information should be given on the magnitude of the HIV-specific CD8 T cell responses in the different cohorts, and how this magnitude over time for S1 and S2 pairs. It is important, because it may change the interpretation of the shift in the relative proportions of the clusters observed (absolute attrition of some? Or the expansion of others?).

The authors directly focus on the CM component of these HIV-specific responses, it is unclear why and it would be important to look first at total HIV-specific CD8 T cells. If there is a shift in the memory differentiation pattern (e.g, in relative proportions of CM vs EM and TM over time), this may change the findings.

According to the reviewer, we have revised the information on total HIV-1-specific CD8+ T-cell responses and evaluated the magnitude of the response base on CD107a, IFNγ and IL-2 expression between study groups (Figure 4 —figure supplement 1). No differences in total HIV1-specific responses were observed between groups and over time on ART (S1 vs S2). Based on the unsupervised analyses, we focused on the memory compartment and TIGIT and TIGIT+TIM3 HIV-1-specific CD8+ T cells and found differences by classical supervised analysis on the CM compartment (Figure 4F). In addition, we characterise the TIGIT CM HIV-1-specific CD8+ T cells in terms of CD107a expression and polyfunctional profile with IFNγ and IL-2 (Figure 4G). We have clarified the information in the Results section, new Figure 4, and new Figure 4 —figure supplement 1. All the information required is under de Results section, Reduction of HIV-1-specific CD8+ T-cell clusters sharing memory-like phenotypes, TIGIT expression and low CD107a (page 9-10).

The authors stimulate with HIV-1 GAG to select for HIV-1 specific CD8+ T cells. Have they also tested other antigens (Nef, Env, Pol) in a subset of patients? Would they expect the results to be similar?

We acknowledge the interest in testing additional antigens to select for HIV-1-specific CD8+ T cells. We have not performed in this study stimulations with other antigens, including Nef, Env, and Pol, in a subset of patients. We acknowledge this point as study limitation and the potential interest of testing complementary antigens to select HIV-1-specific CD8+ T cells against early (Nef) and late virally-expressed proteins (Gag, Env). Indeed, previous studies by our group (Kloverpis et al., 2013, Ruiz et al. 2019) support the differences in the early vs late antigen recognition of HIV-1 proteins in the efficacy and kinetics of killing infected cells by CD8+ T cells. We have included this information in the Discussion section (page 15): “We acknowledge several study limitations; First, the sample size of study groups and the use of peripheral blood samples underestimate the potential contribution of TIGIT expression to Tex in lymphoid tissues. Second, the use of only Gag as stimuli for the characterization of HV-1-specific CD8+ T cell responses in the absence of TCR sequencing. Using alternative HIV-1 antigens such as Nef, Env or Pol may provide additional information on the profile of CD8+ T-cell functional responses against early and late-expressed viral proteins in PLWH on ART (68, 69).

Figure 5: The functional assays with TIGIT blockade are limited and do not include other markers of cytotoxic cells (perforin, granzyme B expression…). It is not clear how these subsets compare to the other CD8 clusters in terms of CD107 expression.

Does the short-term ICB result in any changes to cell viability?

We acknowledge the interest in testing additional markers, including perforin and granzyme B, for a more complete characterisation of the recovery of the cytotoxic potential of HIV-1-specific CD8+ T cells. We have now acknowledged this point as a study limitation and included this information in the Discussion section (page 15): “Third, limited ICB experiments to CD107a, INFγ and IL-2 functional markers without complementary cytotoxic markers (perforin, granzyme B). Forth, complementary transcriptomic, epigenetic, and metabolic markers are needed for a complete description of Tex's immune signatures linked to TIGIT expression in HIV-1 specific CD8+ T cells in PLWH on ART”.

Additionally, we have revised the information regarding changes in cell viability by short-term ICB. We have based our analysis on the frequency of total live lymphocytes using live/dead probe by flow cytometry and compared the viability of the PBMCs in the presence of HIV-1 (HIV-1 Gag peptide pool) and in the presence of HIV-1+ IgG isotypes alone or combined. A similar comparison was performed between PBMCs in the presence of HIV-1 and the presence of blockade antibodies aTIGIT and aTIM-3 alone or combined. We have performed paired analysis (Wilcoxon matched-pairs signed rank test) correcting for multiple comparisons (Holm´s method) between conditions. Our results indicated no changes in cellular viability by short-term ICB as represented in Author response image 1:

Author response image 1
Graphs represent paired comparisons of the frequency of live cells in PBMC samples in short-term ICB studies.

A. Graphs indicate the percentage of live cells in HIV-1 condition (Gag peptide pool) compared to HIV-1 + IgG2 isotype, HIV-1 + IgG1 isotype and HIV-1 + IgG2a+IgG1. B. Graphs indicate the % of live cells in HIV-1 compared to HIV-1 + aTIGIT, HIV-1 + aTIM-3 and HIV-1 + aTIGIT+ aTIM-3.

Reviewer #3 (Recommendations for the authors):

1. Data for Lag-3, Tim3, and CD39 shown in supplementary figure 1A does not appear to demonstrate reliable detection. Additional data should be shown to demonstrate convincing detection of these markers. Importantly, such raw data also needs to be shown for each of the stimulation conditions, and in the context of the functional outputs.

In agreement with the reviewer, we have demonstrated reliable detection of IRs in two ways; we have added Figure 1 —figure supplement 1B, including the comparison for the staining of all markers (TIGIT, PD-1, LAG-3, TIM-3 and CD39) in CD8+ T cells with the corresponding FMO in cryopreserved PBMCs. This information supports the reliable detection of all the markers data is similar for the SEB or HIV-1 condition. Also, including the information on basal IRs expression in naïve CD8+ T cells in Figure 1 and Figure 1 —figure supplement 2, requested by the previous reviewer, provides further evidence of the reliable detection of these markers.

2. For the data shown in Figures 2,3,4, it is unclear why the stated number of clusters was chosen for the tSNE analysis. Whether this leads to the detection of meaningless clusters is unclear. In addition, in some cases, populations are grouped together, yet some of these grouped clusters appear disparate.

We have revised and clarified the information regarding the tSNE analyses and eliminated cluster aggrupation to avoid problems in the interpretation of data analysis and representation. We now provided the rationale for cluster identification in the Material and Methods under the Unsupervised immunophenotype data analysis section (pages 19-20): “We discovered cell communities using the Phenograph clustering technique. It operates by computing the Jaccard coefficient between nearest neighbours, which was set to 30 in all executions, and then locating cell communities (or clusters) using the Louvain method. This creates a network indicating phenotypic similarities between cells. The netSNE maps included representations of the identified cell communities, and additionally, we built a heatmap with the clusters in the columns and the markers of interest in the rows to better comprehend the phenotypical interpretation of each cluster.”

3. Many many statistical comparisons are made, yet there is no discussion of correction for multiple comparisons.

We have revised the statistical analyses and included the correction for multiple comparisons, modifying the results across Figures and Supplemental Figures. Regarding the statistics, we changed Figures 2, 3, and 4 to have the intergroup and intragroup comparisons in separate graphs to clarify the information and correct for multiple comparisons. We have included information in the material and methods section and Figure legends regarding the statistical analyses and the correction for multiple comparisons (Holm’s method) performed when appropriate.

4. The differences between many groups appear very subtle despite being statistically different (pending adjustment for multiple comparisons). The authors should consider carefully what may be biologically relevant in the discussion

After adjusting for multiple comparisons, we revised the manuscript's analyses, figures and corresponding text. Additionally, we have carefully revised the discussion pointing to the potential biological significance of our findings.

5. The data analysis in Figure 3 is fundamentally flawed because the authors used super antigen as a 'polyclonal' activator. This is a great T cell activator but has to be interpreted carefully because every donor has an inherently different 'maximal' response based on the proportion of T cells bearing the appropriate TCR-BV to respond to SEB. This means that directly comparing total responding cells between groups is not particularly informative. Furthermore, without knowing the absolute number of potential responding cells (which was not measured here), it is not appropriate to interpret functional deficiencies within the population. Also, it is not correct to conclude polyclonal activation using SEB, because the clonality measure requires TCR assessment – not performed here. Within any given SEB-responding memory subset it is formally possible that a monoclonal activation could occur.

We appreciate the reviewer concerns about using superantigen Staphylococcal enterotoxin B (SEB) as an activator and the need for information about the absolute number of potential responding cells. Considering these comments, we have now included all the information required under de Results section, Unsupervised phenotypic characterization of SEB-activated CD8+ T cells in PLWH in ART and removed the concept of polyclonal activator from the manuscript. In this section, we now justified the use of SEB to obtain complementary information on T-cell activation in response to pathogens involved in the disease by stimulating TCR-VB clonotypes, and previous studies in HIV-1 support the use of SEB to evaluate the effect of ICB in the recovery of T cell function (31,49,50). In addition, we provided information on the supervised analyses regarding the total frequency of SEB-activated CD8+ T cells presented in Figure 3 —figure supplement 1, and revised the Results section carefully according to the findings. Also, we included the lack of TCR assessment by sequencing as a limitation for data interpretation in the Discussion section.

6. In Figures 4 and 5, it is difficult to interpret the data without knowing the actual magnitude of the responses to HIV, and the number of responding events recovered in any given subset examined. Did the authors have a cutoff for a minimum number of events to consider a positive response- both overall, and also within the subset populations?

We appreciate the reviewer's concerns regarding Figures 4 and 5. For Figure 4, we have analysed the information on total HIV-1-specific CD8+ T-cell responses and evaluated the magnitude of the response base on CD107a, IFN and IL-2 expression between study groups by supervised analyses (Figure 4 —figure supplement 1). Also, the information for the cut-off values and minimum numbers of events to consider a positive CD8+ T cell functional response based on CD107a, IFN and IL-2 by flow cytometry analyses is now included in the Material and Methods section (page 19) as follows: “We performed two technical replicates for SEB-activated and HIV1-specific CD8+ T-cell cytokine production. We considered the cytokine response positive after background subtraction (mean of two technical replicates) used as the cut-off value. For each independent sample, we recorded a median of 1,000 events and 50 events positive for cytokines for total and CD8+ T cell subsets, respectively.”

7. Figure 5, the use of tSNE analysis does not seem necessary when memory subsets are simply examined with or without blockade. Also, how the memory subsets were defined should be described.

In agreement with the comment, we have now modified Figure 5 and include representative dot plots of the ICB experiments for the different conditions tested (basal, Isotypes, αTIGIT, αTIM3, and αTIGIT+αTIM-3 blockade) and functional markers monitored (CD107a, IFNγ and IL-2) (Figure 5A). Also, we included specific net-SNE projections to represent the extent of mAb blockade (Figure 5B).

The definition of memory subsets CD8+ T cells and the gating strategy followed for flow cytometry analysis is included in Figure 1 —figure supplement 1 and the Materials and methods section.

Stylistic comments.

1. The use of 'single-cell analysis' in the title and abstract (as well as several times in the paper) seems somewhat inappropriate at times given the more broad use of this term when referring to single-cell genomic studies. This manuscript is simply a flow cytometry study, which by definition is a single cell, but rarely described as such.

According to the comment, we have rephrased the manuscript's content when appropriate, including the title and excluding the concept of single-cell analysis across the manuscript.

2. There are numerous stylistic and grammatical errors that should be fixed after careful reading; additionally:

According to the comment, we have revised the complete manuscript for stylistic and grammatical errors

– Lines 101-103 and 105-106 basically say the same thing.

Revised.

– Lines 65-66, and 230-231 are not sentences.

Revised.

– Line 205. define ICB.

Revised.

– Line 267. fix 'PBCM.'

Revised.

https://doi.org/10.7554/eLife.83737.sa2

Article and author information

Author details

  1. Oscar Blanch-Lombarte

    1. IrsiCaixa AIDS Research Institute, Barcelona, Spain
    2. Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, Spain
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8317-7535
  2. Dan Ouchi

    IrsiCaixa AIDS Research Institute, Barcelona, Spain
    Contribution
    Data curation, Software, Formal analysis, Validation, Methodology
    Competing interests
    No competing interests declared
  3. Esther Jimenez-Moyano

    IrsiCaixa AIDS Research Institute, Barcelona, Spain
    Contribution
    Data curation, Formal analysis, Methodology
    Competing interests
    No competing interests declared
  4. Julieta Carabelli

    IrsiCaixa AIDS Research Institute, Barcelona, Spain
    Contribution
    Formal analysis, Methodology
    Competing interests
    No competing interests declared
  5. Miguel Angel Marin

    IrsiCaixa AIDS Research Institute, Barcelona, Spain
    Contribution
    Formal analysis, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5294-6007
  6. Ruth Peña

    IrsiCaixa AIDS Research Institute, Barcelona, Spain
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  7. Adam Pelletier

    Pathology Department, Case Western Reserve University, Cleveland, United States
    Contribution
    Software, Methodology
    Competing interests
    No competing interests declared
  8. Aarthi Talla

    Pathology Department, Case Western Reserve University, Cleveland, United States
    Contribution
    Software, Methodology
    Competing interests
    No competing interests declared
  9. Ashish Sharma

    Pathology Department, Case Western Reserve University, Cleveland, United States
    Contribution
    Software, Methodology
    Competing interests
    No competing interests declared
  10. Judith Dalmau

    IrsiCaixa AIDS Research Institute, Barcelona, Spain
    Contribution
    Funding acquisition, Investigation, Project administration
    Competing interests
    No competing interests declared
  11. José Ramón Santos

    1. Lluita contra la SIDA Foundation, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain
    2. Infectious Diseases Department, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
    Contribution
    Recruited the study participants
    Competing interests
    No competing interests declared
  12. Rafick-Pierre Sékaly

    Pathology Department, Case Western Reserve University, Cleveland, United States
    Contribution
    Software, Methodology
    Competing interests
    No competing interests declared
  13. Bonaventura Clotet

    1. IrsiCaixa AIDS Research Institute, Barcelona, Spain
    2. Lluita contra la SIDA Foundation, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain
    3. Infectious Diseases Department, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
    4. Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain
    5. Faculty of Medicine, University of Vic - Central University of Catalonia (UVic-UCC), Catalonia, Spain
    Contribution
    Recruited the study participants
    Competing interests
    No competing interests declared
  14. Julia G Prado

    1. IrsiCaixa AIDS Research Institute, Barcelona, Spain
    2. Germans Trias i Pujol Research Institute (IGTP), Badalona, Spain
    3. CIBER Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    jgarciaprado@irsicaixa.es
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5439-4645

Funding

Institute of Health Carlos III (PI17/00164)

  • Julia G Prado

Catalan Government and the European Social Fund (AGAUR-FI_B 00582 PhD fellowship)

  • Oscar Blanch-Lombarte

Redes Temáticas de Investigación en SIDA (ISCIII RETIC RD16/0025/0041)

  • Esther Jimenez-Moyano

Grifols

  • Julia G Prado

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the Flow Cytometry Core Facility from the Germans Trias i Pujol Research Institute (IGTP).

Ethics

Human subjects: The study was conducted according to the principles expressed in the Declaration of Helsinki (Fortaleza, 2013). The Hospital Germans Trias i Pujol Ethics Committee approved all experimental protocols (PI14-084). For the study, subjects provided their written informed consent for research purposes of biological samples taken from them.

Senior Editor

  1. Murim Choi, Seoul National University, Republic of Korea

Reviewing Editor

  1. Frank Kirchhoff, Ulm University Medical Center, Germany

Version history

  1. Preprint posted: July 14, 2022 (view preprint)
  2. Received: September 27, 2022
  3. Accepted: August 30, 2023
  4. Version of Record published: September 19, 2023 (version 1)

Copyright

© 2023, Blanch-Lombarte et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Oscar Blanch-Lombarte
  2. Dan Ouchi
  3. Esther Jimenez-Moyano
  4. Julieta Carabelli
  5. Miguel Angel Marin
  6. Ruth Peña
  7. Adam Pelletier
  8. Aarthi Talla
  9. Ashish Sharma
  10. Judith Dalmau
  11. José Ramón Santos
  12. Rafick-Pierre Sékaly
  13. Bonaventura Clotet
  14. Julia G Prado
(2023)
Selective loss of CD107a TIGIT+ memory HIV-1-specific CD8+ T cells in PLWH over a decade of ART
eLife 12:e83737.
https://doi.org/10.7554/eLife.83737

Share this article

https://doi.org/10.7554/eLife.83737

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