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.

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-bet^dimEomes^hi 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.

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 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.'

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

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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.

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

   "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

   "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

    "This ORCID iD identifies the author of this article:" 0000-0002-5439-4645

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