Immune and viral surveillance of HIV-1C reservoirs in an Indian cohort

Snehal Kaginkar; Shilpa Bhowmick; Nandini Kasarpalkar; Sharad Bhagat; Jyoti Sutar; Sapna Yadav; Sameen Khan; Pranay Gurav; Nandan Mohite; Namrata Neman; Nidhi Sehgal; Satyajit Musale; Varsha Padwal; Pratik Devadiga; Ranajoy Mullick; Priyanka Jayal; Tejaswini Pandey; Amit Kumar Singh; Shilpa Velhal; Sayantani Ghosh; Ritam Chatterjee; Varsha Kalsulkar; Vikrant Bhor; Gita Nataraj; Kavita Joshi; Nayana Ingole; Sachee Agrawal; Jayanthi Shastri; Sushma Gaikwad; Vidya Nagar; Priya Patil; Jayanta Bhattacharya; Vainav Patel

doi:10.1016/j.isci.2025.114278

. 2025 Nov 28;29(1):114278. doi: 10.1016/j.isci.2025.114278

Immune and viral surveillance of HIV-1C reservoirs in an Indian cohort

Snehal Kaginkar ¹, Shilpa Bhowmick ¹, Nandini Kasarpalkar ^1,², Sharad Bhagat ¹, Jyoti Sutar ^3,^8,⁹, Sapna Yadav ¹, Sameen Khan ¹, Pranay Gurav ¹, Nandan Mohite ¹, Namrata Neman ¹, Nidhi Sehgal ¹, Satyajit Musale ¹, Varsha Padwal ¹, Pratik Devadiga ², Ranajoy Mullick ^3,^8,⁹, Priyanka Jayal ^3,⁸, Tejaswini Pandey ¹, Amit Kumar Singh ¹, Shilpa Velhal ¹, Sayantani Ghosh ^1,¹¹, Ritam Chatterjee ¹, Varsha Kalsulkar ¹, Vikrant Bhor ², Gita Nataraj ⁴, Kavita Joshi ⁵, Nayana Ingole ⁴, Sachee Agrawal ⁶, Jayanthi Shastri ⁶, Sushma Gaikwad ⁷, Vidya Nagar ¹⁰, Priya Patil ¹⁰, Jayanta Bhattacharya ³, Vainav Patel ^1,^12,^∗

PMCID: PMC12768867 PMID: 41503215

Summary

Indian HIV-1 subtype C, infecting 2.6 million individuals, demonstrates unique geospatial diversity reflecting distinct evolution and host-pathogen interactions that may instruct the development of region-specific therapeutic strategies. An Indian PLHIV cohort was profiled for immune dysfunction, proviral load, broadly neutralizing antibody sensitivity, and drug resistance mutations in putative CD4⁺ T cell reservoirs. We demonstrate therapy state specific immune dysfunction, including in ART responding individuals, coincident but not correlated with stable proviral load, apparently enriched in CD4⁺ T memory subsets. Reservoir derived full length envs displayed distinct neutralization profiles against best-in-class broadly neutralizing antibodies, highlighting the need for a combinatorial approach to target potential breakthrough viruses. Surveillance of the archival repertoire demonstrated the occurrence of drug resistance conferring mutations (>10%) across therapy states, including instances of primary and acquired resistance to recently introduced integrase strand transfer inhibitors. Our data, underlines the need for incorporating reservoir diversity in intervention and management strategies.

Subject areas: Immunology, Microbiology, Virology

Graphical abstract

Highlights

•
Immune and virological features of a large Indian HIV-1C infected cohort
•
HIV-1C proviral load and associated immune dysfunction in CD4⁺ T cell reservoirs
•
Neutralization diversity of reservoir derived envelopes by best-in-class bnAbs
•
DTG resistance in reservoirs advocates the need for DRM surveillance in reservoirs

Immunology; Microbiology; Virology

Introduction

India, with a largely successful anti-retroviral therapy (ART) program to curtail HIV transmission, remains a major site for the ongoing HIV-1 pandemic, with 64000 new infections over the last year and with a PLHIV population of 2.6 million.¹ Indian HIV-1 subtype C (HIV-1C) is a constituent of the globally dominant subtype, but has been shown to have distinct genetic evolution, compared to African subtype C, with implications for therapeutic interventions such as anti-retroviral therapy and, more recently, broadly neutralizing antibodies (bnAbs).²^,³^,⁴ A further challenge to rationally guided approaches toward therapy and prevention is the paucity of Indian HIV-1C sequence information derived from circulating as well as reservoir viruses. HIV eradication or cure approaches face a significant impediment due to the persistence, even during extended ART, of amplifiable reservoirs that may be the source of breakthrough viremia due to drug resistance or treatment non-adherence.⁵^,⁶ Functional characterization of and viral diversity within putative CD4⁺ T cell reservoirs in the context of Indian HIV-1C have not been elucidated heretofore. In this study, we provide data pertaining to the activation and functional status of putative reservoirs across therapy states (therapy naive and receiving) from a large cohort of HIV-1C infected individuals from India. We also provide viral sequence diversity information relevant to drug resistance and validated bnAb neutralization profiles from CD4⁺ T cell reservoir derived full length envelopes. Our results highlight the importance of viral surveillance, both in the pre- and post-ART setting for India which must also include reservoir derived proviruses in order to optimize and rationally guide interventions, including bnAbs.

Results

Human immunodeficiency virus-1 subtype C disease progression states

As shown in Tables 1 and S1, a total of 235 HIV-1C infected participants, from clinical sites were recruited along with 57 HIV seronegative (SN) controls and studied cross-sectionally. HIV infected participants were further grouped as per their anti-retroviral therapy (ART) status into ART naive (AN, n = 94), 1^st line ART receiving individuals (FLAR, n = 107), and 2^nd line ART receiving individuals (SLAR, n = 34).

Table 1.

Clinical and virological characteristics of the participants

	HIV seronegative	HIV seropositive ART Naive (AN)	HIV seropositive 1^st line ART Receiving (FLAR)	HIV seropositive 2^nd line ART receiving (SLAR)
Number of participants	n = 57	n = 94	n = 107	n = 34
Age^A	31 (18–54)^a	38 (16–64)	42 (18–65)	43 (20–58)
Gender (M/F)	33/24	67/27	63/44	19/15
Absolute CD4 Count (cells/μL)^A	813 (286–1981)	279 (19–1199)	797 (256–2255)	689 (182–1311)
CD4/CD8^A	1.4 (0.68–3.8)	0.25 (0.02–3.5)	0.8 (0 0.19–2.6)	0.6 (0.2–3)
Viral load (copies/mL)^A	–	103738 (275–5948952)	118 (34–14543)	119 (35–1475)
PBMC Proviral load (copies/million cells)^A	–	10037 (241–55475)	5099 (95–289149)	5311 (328–214050)
Duration of ART (months)^A	–	–	93 (<1–198)	44 (2–76)^b

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A, Data is represented as median followed by range.

Viral load (<34 copies/ml) and proviral load (<66 copies/million genomic equivalents) was excluded for median and range.

Age of seronegative controls was significantly lower than all the infected groups.

Duration of ART for SLAR group represents duration on second line of ART.

Figure 1 highlights significantly altered disease progression states of the study groups, both in terms of absolute CD4⁺ T cell count and CD4/CD8 ratio. As expected, compared to the SN group, the AN group had significantly lower counts of CD4⁺ T cells (Figure 1A). CD4⁺ T cell rebound was noted in most of the participants after ART (Figure 1B). The CD4/CD8 ratio was the least in the AN group and remained unaltered in therapy receiving groups despite CD4⁺ T cell rebound (Figure 1C). Additionally, in spite of extended ART, viral load remained detectable (>34 copies/ml) in many individuals of FLAR and SLAR groups – 45% (48/106) and 45% (15/33) respectively (Figure 1D). Moreover, cell associated HIV proviral DNA remained persistently detectable in therapy receiving individuals, significantly lower than that in the AN group, for FLAR, but not for SLAR groups, where viral breakthrough had occurred (Figure 1E).

Clinical characteristics of the study population

(A) Absolute counts of CD4⁺ T cells.

(B) Nadir CD4⁺ T cells count and its restoration after anti-retroviral therapy (ART).

(C) CD4/CD8 ratio.

(D) Plasma HIV viral load.

(E) Total HIV DNA copies in PBMC; HIV seronegative group (SN), ART naive group (AN), ART 1^st line receiving group (FLAR), and 2^nd line receiving group (SLAR). Number of individuals in each group is mentioned in the bracket. Study groups were compared with each other using the Kruskal-Wallis one-way ANOVA non-parametric test. Dunn’s corrected p values are mentioned in the graphs, while uncorrected p values are provided in Table S5. Restoration of CD4⁺ T cell count was calculated by comparing the nadir CD4⁺ T cell count with the CD4⁺ T cell count after ART initiation using the Wilcoxon matched-pairs signed rank test. TND individuals (viral and proviral load) were excluded from statistical comparisons. Data are represented as median with interquartile range.

Systemic cellular immune profiling

In addition to the analysis of CD4 counts and CD4/CD8 ratio, we concurrently analyzed other cellular immune subsets such as B cells, monocytes, and NK cells and their subsets (gating strategy provided in Figure S1A). B cell frequency, along with that of classical monocytes, was significantly reduced in the AN group compared to other groups (Figures 2A and 2B). Conversely, intermediate monocyte frequency was significantly increased in the AN group compared to other groups (Figure 2C). The putatively cytotoxic (CD16^brightCD56- & CD16^brightCD56^dim) NK cell subset appeared dysregulated in an opposing manner within the AN group (Figures 2E and 2F). This was in contrast to the “regulatory” subset (CD16^-/dimCD56^bright) that was relatively unperturbed across therapy states (Figures 2G and 2H).

To explore group specific signatures, if any, using an unsupervised approach, we performed the dimensionality reduction of detected markers on CD45⁺ lymphocytes and monocytes using t-Stochastic Neighbor Embedding (tSNE) along with clustering tool X-Shift and the Cluster Explorer tool (Figures S1B and S1C). This unsupervised analysis resulted in the generation of 11 clusters (Figures 2I–2K and S2). Clusters 1 and 10, with relative abundance of 2.5% and 24% respectively, resembled CD8⁺ T cells, probably expanded during infection, and were contributed to mainly by HIV infected groups in the order of AN > FLAR > SLAR. Similarly, Cluster 2 comprised of CD4^dimCD8^bright cells contributed mainly by HIV infected groups in the order of FLAR > AN > SLAR > SN. Surprisingly, Cluster 3 represented possibly non-classical monocytes contributed to by the therapy receiving FLAR group and with minor contribution from the SLAR group that had a history of ART failure. Also, Clusters 6 and 11 both resembled intermediate monocytes, in which representation by the AN group was clearly disparate, where Cluster 6 had the least contribution by this group and Cluster 11 the most. Clusters 5 and 7, comprising CD4⁺ T cells, were the least contributed to by the AN group as an expected consequence of pathogenic depletion. Cluster 9, which seemed to denote CD8⁺ NK cells was contributed less to HIV infected groups irrespective of therapy status, suggesting a possibly impaired restoration of this cytotoxic NK cell subset in spite of viral suppression.

Immune dysregulation of the putative reservoir

As chronic immune activation is a hallmark of HIV-1 infection, we monitored co-expression of HLA-DR and CD38 on CD4⁺ T cells and their subsets, including central memory (CM) and transition memory (TM) and effector CD4⁺ T cells (Figures 3A–3C and gating strategy shown in Figure S3A). Activation in the global CD4⁺ T cell compartment was higher in all the infected groups compared to seronegative controls, with the highest levels observed in the AN group (Figure S3B). Of note, this pattern was reflected in the CM subset, a putative circulating reservoir (Figure 3A). Similarly, we found a high level of activation in all the infected groups in TM and effector cells, and the highest in ART naive individuals (Figures 3B and 3C). Although the naive subset showed lower activation compared to the total CD4⁺ T cell compartment and its subsets, all infected groups had higher activation compared to seronegative controls (Figure S3C).

Concurrently monitored immune checkpoint marker, PD-1 expression was significantly higher in the ART naive group, but in contrast to results described above for HLA-DR and CD38 co-expression, was found to be similar in seronegative and ART receiving groups in total CD4⁺ T cells (Figure S3D). We observed high PD-1 expression in the AN group, which was restored in ART receiving groups in the CM subset (Figure 3D). High PD-1 expression in TM and effector cells in the AN group was restored in ART receiving individuals (Figures 3E and 3F). PD-1 expression in the naive subset, though overall lower in relation to other subsets, did trend higher within infected individuals compared to seronegative controls (Figure S3E). Similar and in fact, more prominent dysregulated patterns of activation characterized the CD8⁺ T cell compartment, with the highest activation and PD-1 expression observed in the AN group that showed evidence of partial restoration in ART receiving groups (Figures S3F–S3G). Activation and PD-1 expression in CD8⁺ NV T cell subsets were overall lower than the other subsets.

Next, to delineate disease specific signatures in the T cell compartment that integrated all our markers in an unsupervised fashion, we performed tSNE dimensionality reduction of CD3⁺ T cells (Figures S1B and S1C). Based on the marker expression clustering tool (X-shift) generated 8 clusters which were further analyzed using Cluster Explorer (Figures 3G–3I and S4A–S1D). Using this approach, we were able to identify clusters that apparently discriminated our cohort into disparate therapy stages. Clusters 1, 6, 7 which resembled a probable phenotype of CD8⁺ TM, CD8⁺ EMRA, and CD8⁺ effectors, respectively, were most contributed to by the AN group. Clusters 1 and 7 also exhibited gut homing potential, evinced through integrin β7 expression, with clear activation (1 > 7) and checkpoint marker expression. In contrast, Cluster 6 lacked β7 expression and showed lower activation/modulation profiles. Evidence for therapy mediated immune restoration was observed through Clusters 2, 3, and 5, which corresponded to naive populations of CD8⁺ T cells, CD4⁺ T cells, and notably CD4⁺ CM cells, respectively. Cluster 2, in contrast to Cluster 1 enriched in the AN group, also had gut homing characteristics but lacked activation or PD-1 expression. Cluster 3 probably represented naive peripherally repopulated CD4⁺ T cells that also lacked activation and PD-1 expression. Notably, Cluster 5, expressing low activation, PD-1, and gut homing markers, may have represented gut restorative homeostatic responses in therapy receiving individuals. Interestingly, evidence of persistent immune dysregulation in the CD8⁺ T cell compartment, possibly contributing to unrestored CD4/CD8 ratios in therapy receiving individuals, was observed through the occurrence of Clusters 4 and 6. These clusters, contributed to most by therapy receiving individuals and least by the seronegative group, corresponded to CD8⁺ RA+ effectors and CD8⁺ EMRA, respectively.

Expansion of regulatory T (Treg) cells

After establishing activation status of the CD4⁺ T cells, we sought to monitor dynamics of regulatory CD4⁺ T cells across therapy states (Figures 4 and S5A). The frequency of CD4⁺ Treg cells overall was found to be significantly higher in the AN group that exhibited active viral replication. Circulating frequencies of these cells seemed to be similar to those obtained for the SN group in conditions of viral suppression, as observed for the FLAR group (Figure 4A). Further resolution of Tregs into naive and non-naïve/memory subsets using CD45RA expression revealed disparate profiles based on therapy states (Figures 4B and 4C). We observed an inverse pattern of dysregulation in the AN group, where the frequency of naive and memory Treg cells was respectively reduced and increased compared to the SN and therapy receiving groups.

Concurrently, unsupervised analysis was performed focusing on the Treg compartment using tSNE as described above (Figures S1B and S1C). Here, we observed 7 clusters (Figures 4D–4F and S5B–S5E) of which two represented Treg like phenotype and were contributed to predominantly the AN group. Both these clusters, however, were distinct in terms of the expression of gut homing marker integrin β7. Interestingly, of these 2 memory Treg like populations, Cluster 1, the dominant cluster, also had high gut homing potential, which probably indicated pathology driven mitigatory homing. Conversely, Clusters 2, 4, and 6, minimally contributed to by the AN group, consisted of naive, mucosally homing (integrin β7+) non-Treg CD4⁺ T cells repopulating the gut following ART. Interestingly, Cluster 3, which seemed to consist of mucosally homing memory CD4⁺ T cells, was maximally contributed to by the FLAR group, indicative of viral suppression mediated immune restoration.

Delineation of therapy state specific immune signatures

To explore relationships between the multiple cellular immune markers that were assessed, a correlation matrix was generated for each of the study groups (Figures 5 and S6). Unique to the AN group, strong negative correlations were observed between activated CD4⁺ T subsets and disease progression markers such as CD4, CD8 counts, and CD4/CD8 ratio. Also, activation (HLA-DR, CD38 co-expression) of CD4⁺ T cells was positively and significantly correlated with PD-1 expression in CM, TM, and effectors of this compartment in the AN group. However, within the therapy receiving groups, this association was found to be notably weakened. Interestingly, PD-1 expression on CD4⁺ NV cells was significantly and positively correlated with the activation of CD4⁺ CM in the FLAR group. Similarly, the association of activation and exhaustion within the CD8⁺ T compartment showed a change in the degree but not the direction of correlation across the study groups.

As expected, memory Treg frequency negatively and significantly correlated with CD4 count and CD4/CD8 ratio in the AN group; however, this association was weak in the FLAR group, probably due to ART mediated CD4 recovery. Notably, the strong association was restored in the SLAR group, probably recapitulating the effects of viral breakthrough. Also, a strong positive association between Treg frequency and activation/exhaustion was not retained in the therapy receiving settings. Indeed, while NV Treg frequency was inversely correlated with PD-1 expression in CD4⁺ T cells in the AN group, this association transitioned toward positive correlation in ART receiving settings.

Further, we observed unique correlative patterns in NK subsets and monocyte subsets in the SLAR group, suggestive of immune sequelae consequent to virological breakthrough. Of note, intermediate monocyte frequency, a key marker of innate immune activation, was significantly and positively correlated with the activation of CD8⁺ T cells in the AN group, while the strength of this association was weak in ART receiving groups, indicative of partial immune restoration.

Proviral burden in the putative CD4⁺ T cell reservoir

Pertinent to cure and eradication strategies, PBMC proviral burden in our cohort was evaluated as described in Table 1. To assess the enrichment of putative reservoir burden, if any, within CD4⁺ T cell subsets, levels of proviral DNA in PBMC were compared with those in cognate FACS sorted subsets, including resting CD4⁺ CM, NV, and TM cells (gating strategy in Figure S7A). Overall, across infected study groups, we observed enrichment, albeit not significant, in CM and TM subsets compared to PBMC but not in the naive subset (Figures 6A–6C).

Enrichment of proviral load in CD4⁺ T cell subsets

(A–C) Proviral load in PBMC was compared with proviral load in CM, TM, and NV subsets, respectively, from all infected groups using a non-parametric Wilcoxon test.

(D) Proviral DNA load was compared within all CD4⁺ T cell subsets and PBMC whenever data were available in the AN group, FLAR group, and SLAR group, respectively. Comparison was performed using Friedman’s one-way ANOVA test.

(E) Relationship of proviral DNA with immune parameters represented as a correlation matrix generated using non-parametric Spearman’s two tailed test. p values ≤0.05 are represented as asterisks.

(F) Hierarchical clustering of HIV proviral DNA in PBMCs was performed using R Studio.

Further, to resolve HIV DNA distribution in putative reservoirs across various stages of therapy, we compared individual matched HIV-1 DNA levels in AN, FLAR, and SLAR groups within the aforementioned sorted CD4⁺ T cell subsets (Figure 6D). Here, a significant enrichment within the TM of the AN group and the CM and TM of the ART receiving individuals was observed compared to the NV subset. To delineate possible immune based correlates for reservoir size in our study groups, correlation matrices of proviral load with disease progression markers were generated (Figure 6E). In the AN setting, probably consequent to active viral replication and accompanying increased T cell dysregulation, total HIV-1 proviral DNA was significantly and positively correlated with levels of activation and PD-1 expression both in CM and TM CD4⁺ T cells. Contrastingly, proviral load in the FLAR setting did not correlate with activation levels of CD4⁺ T cells, but interestingly, it did significantly and negatively associate with PD-1 expression on CD4⁺ CM and TM subsets. This observation most likely reflected the induction of PD-1 expression on repopulated naive CD4⁺ T cells exposed to the milieu of chronic activation that persisted in spite of significant virological suppression. In the SLAR group, a unique and apparently opposing relationship between proviral load and NV and memory Treg compartments was observed, reflective of virological breakthrough and immune restoration driven NV Treg expansion (significant positive correlation) and dilution of memory Tregs (significant negative correlation). Further, a congruent pattern of association between proviral load and B cell count, as well as frequency of cytotoxic (CD56^dimCD16^bright) NK cells, was observed in AN and SLAR groups.

As the putative HIV reservoir burden was apparently reduced in the therapy receiving groups, significantly so in the case of the FLAR group (Figure 1E), we assessed the utility of proviral load as an independent clustering variable to segregate our study groups in an unsupervised approach. Supporting our results of the correlation analysis, HIV-1proviral load within PBMC (Figure 6F) or putative CD4⁺ T cell reservoir subsets (Figure S7B) was unable to demarcate our study groups using this approach, highlighting the persistence of HIV-1 DNA harboring circulating reservoirs following successful ART.

In vitro validation of the broadly neutralizing antibodies based neutralization of env pseudoviruses

In light of the impending deployment of broadly neutralizing antibodies (bnAbs) as both preventive strategies for infection and therapeutic adjuncts to ART, we aimed to assess neutralization diversity and sensitivity of putative reservoir derived full length HIV-1C envelopes, where possible (N = 12) in PBMC and FACS sorted resting CD4⁺ CM cells. First, full length env sequences of amplicons derived from primary samples were queried against the CATNAP database to assess their potential sensitivity against a wide class of 14 broadly neutralizing antibodies (bnAbs) targeting the V2 apex region, CD4 binding site, V3 glycan, MPER & Interface of HIV-1 env (Table S2). Residues conferring resistance were further classified based on their position within or outside of contact sites, and this preliminary analysis indicated disparate resistance profiles at the individual level in CD4⁺ CM T cell putative reservoir compared to the PBMC compartment. Next, the validation of in-silico predicted bnAb resistance was performed in-vitro by the neutralization of pseudoviruses generated from 8 individuals (Figure 7). To begin with, we observed distinct intra-individual neutralization patterns for the PBMC and CM subsets. Reservoir derived viruses from 7 out of 8 individuals, across PBMC and CM compartments, were found to be resistant to V2 apex targeted bnAbs, with the highest occurrence of resistance observed for PGDM1400. Improved neutralization sensitivity to V3 glycan directed antibodies was observed in four out of 8 (50%) participants, where resistance against any given antibody from this class occurred in no more than 3 out of 8 individuals (∼38%). Within this limited cohort, uniformly high sensitivity to CD4 binding site directed bnAbs was observed except in the case of antibodies VRC01 and 3BNC117. Interestingly, 2 out of 8 (25%) individuals exhibited resistance to VRC01, but complete sensitivity to the 2^nd generation bnAb VRC07. Additionally, neutralization sensitivity was observed against both MPER and interface directed bnAbs in 5 of 8 individuals. Of note, intra-individual compartmental neutralization diversity was clearly evident in the case of most bnAbs, where resistance often occurred only in either the CM or PBMC compartment. Taken together, these results support the use of a combination of CD4 binding site directed bnAbs such as VRC07, N6, and MPER & interface directed 10E8 for therapeutic applications against Indian HIV-1C reservoir derived viruses.

*In vitro* validation of bnAb based neutralization of env pseudoviruses from primary samples

14 bnAbs were tested for their efficiency to neutralize pseudoviruses containing full length envelopes directly generated from CD4⁺ T central memory (CM) and PBMC (PS) compartments within 8 participants (AN-N002, N015; FLAR-S030, S100, S007; SLAR-S055, S062, S027). bnAbs are classified as per their target epitopes into V2 apex targeting bnAbs (CAP256-VRC26.25, PGDM400, PG9, PGT145), CD4 binding site directed bnAbs (VRC01, VRC07, 1–18, N6, and 3BNC117), V3 glycan directed bnAbs (PGT121, 10–1074, BG18) and MPER & Interface targeting bnAbs (10E8, VRC34.01). IC₅₀ values indicate concentrations of bnAbs that conferred 50% virus neutralization in TZM-bl cells. IC₅₀ value of 5 μg/mL was considered as neutralization sensitivity threshold. Viruses that showed IC₅₀ values > 25 μg/mL are considered resistant. Color gradation from light to dark indicates increasing resistance.

Drug resistance mutation analysis of primary putative CD4⁺ T cell reservoirs

Due to error prone activity of RT, the evolution of resultant quasispecies under ART selection could contribute to eventual treatment failure or viral breakthrough upon therapy interruption. Thus, in a subset of participants where archival (cell associated DNA) data were available, we monitored both presence and abundance of DRMs against the four widely used classes of ART -protease inhibitors (PrIs), nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleotide reverse transcriptase inhibitors (NNRTIs), and integrase strand transfer inhibitors (INSTIs) - within PBMC and CD4⁺ T cell compartments across therapy states. An integrated representation of both abundance and level of resistance is presented for each group from a total of 41 individuals in (Figures 8 and S8; Table S3). In spite of our limited sample size, we detected DRMs occurring at frequencies of 10% or greater across all groups, highlighting their possible clinical significance. The AN group reflected its anti-retroviral drug naive setting overall, though 2/15 participants (13%) did show the presence of DRMs against the current regimen of ART (NNRTI and INSTI classes, Figure 8A). In this group, compartment data were available for 3 participants, and no DRMs were detected within their putative reservoirs. As expected, within the therapy exposed FLAR group, a larger and significant proportion, 6/20 (33%), of participants showed the presence of DRMs that included 2 participants where compartment data (PBMC, NV, CM, TM for F2 and PBMC, CM for F18) was available (Figure 8B). In the case of F2, a clearly distinct DRM profile was observed in the TM subset where NRTI resistant mutations were absent and a unique mutation (E138A) conferring potential low to intermediate resistance to NNRTI class was present at a frequency of 47%. Also, in the NNRTI class for PBMC, NV and CM subsets in this individual, a clear enrichment of a DRM (M41L), confirming intermediate to high level resistance, occurred within the NV subset where abundance was 78%. Interestingly, when we analyzed compartment data from participant F18, we observed unique DRMs occurred, albeit at the same position, within the PBMC (E138K) and CM (E138A) compartments. Notably, the frequency of the latter was 98% compared to that of the former, which was 25%, but levels of resistance conferred were higher (potentially low to intermediate) in the PBMC compartment. Of the remaining 4 individuals in this group that showed presence of DRMs, NRTI class resistance was observed in F8 and F14, NNRTI class resistance was observed in F3, F8, and F14, INSTI class resistance was observed in F7 and F14. Classwide resistance was observed in F14.

Drug resistance mutation (DRM) analysis of primary putative CD4⁺ T cell reservoirs

Circos heatmap of residue wise major drug resistance mutations (DRMs) in pol sequences in (A) AN group (N = 15), (B) FLAR group (n = 20), and (C) SLAR group (n = 6). Selection criteria of read depth >50 and mutation detection threshold >10% applied to predict DRMs using the HIVDB tool. Drug resistance summary represented in circos format with a total of 23 concentric tracks of the heatmap representing 23 ARVs from PI (pink), NRTI (blue), NNRTI (yellow), INSTI (green) classes viz from periphery to center (PI- ATV/r, DRV/r, FPV/r, IDV/r, LPV/r, NFV, SQV/r, TPV/r; NRTI- 3 TC, ABC, AZT, D4T, DDI, FTC, TDF; NNRTI- DOR, DPV, EFV, ETR, NVP, RPV; INSTI-BIC, CAB, DTG, EVG, and RAL). Each section represents a participant with its ID and CD4⁺ T cell subset. Major mutations are shown as colored lines indicating their relative position in the gene, level of resistance, and frequency of mutation as described in the key. White color indicates data non-availability.

Finally, a limited analysis in only 6 participants of the SLAR group (Figure 8C), which was comprised of participants with cumulative exposure to ARV, revealed the presence of DRMs in 2/6 (33%) individuals-S1 and S4. Moreover, in both these participants, where compartment data were available, DRMs were detected within multiple CD4⁺ T cell subsets. Resistance mutations in Participant S1, where NV, CM, TM, and PBMC data were available, were identical for CM and PBMC except for differences in the abundance of mutations (CM > PBMC) at amino acid positions 70 and 184 of pol conferring resistance to NRTI class. DRMs at amino acid positions 70 and 219 were not detectable in the TM compartment. In contrast, the NV subset showed the absence of DRMs in the NRTI class. For the NNRTI class, DRM profiles were identical in CM and TM subsets but differed from PBMC in the abundance of mutations (CM and TM > PBMC). Whereas the NV subset showed DRM only at position 138 as opposed to other subsets. Participant S4, with compartment data available for NV, CM, and PBMC compartments, had identical DRMs for the NRTI class, but apparent enrichment of these mutations in CM and NV subsets compared to PBMC. For the NNRTI class, CM and NV subsets showed identical DRMs which differed in abundance at aa position 348 (CM > NV). Interestingly, only a single DRM at aa position 348 was detectable in PBMC. To our surprise, 4 participants within this group did not show any detectable DRMs within the putative reservoir but were apparently yet switched to second line therapy. Further, none of the participants in this group demonstrated DRMs against the INSTI class, which probably reflected the modest sampling that was available from this group, as well as their exposure to a prior non-INSTI therapy regimen (Tenofovir, Lamivudine, and Atazanavir/Ritonavir).

Overall, in the 41 individuals where DRMs were assessed, plasma viral load was available in 40 individuals, and occurrence of DRM was not correlated with detectable viremia, as shown in Figure S8B. Interestingly, none of the participants showed major DRMs against protease inhibitors. We observed disparate DRM patterns in the context of mutation positions or frequency of occurrence at intra as well as inter-individual levels.

Discussion

In this study, we report on persistent cellular immune dysregulation, accompanied by stable proviral load and accumulation of current therapy resistant drug resistance mutations (DRMs) in putative reservoirs, across HIV-1 disease progression in the largest HIV-1C cohort reported on from India so far. We also evaluate broadly neutralizing antibodies (bnAbs) sensitivity of CD4⁺ T cell reservoir derived full length envelopes against best-in-class therapeutic candidates.

India harbors the third largest population of PLHIV globally,⁷ which are infected with a distinct HIV-1C subtype⁸ whose replication kinetics, disease progression, and response to therapy are distinct from HIV-1 subtype B.⁹ Furthermore, we have recently demonstrated¹⁰ that HIV-1C isolates from Africa and India showed distinct phylogenetic clustering based on env evolution. Moreover, neutralization susceptibility of primary circulating viral envelopes to bnAbs was found to be significantly different due to changes in contact site residues, length/charge in variable loop regions, and env glycosylation patterns driven by disparate viral evolution and host genetics. We therefore believe that these factors may also govern region-specific pathogenesis profiles. A study addressing neurotoxicity caused by HIV subtypes has shown increased monocyte chemotaxis and neurotoxicity of African HIV-1C due to Tat dicysteine motif (CC, commonly found in subtype B isolates), which is rare in Indian isolates.¹¹ Additionally, sequence variations in gp120 were implicated as one of the factors responsible for increased neurovirulence of South African HIV-1C compared to Indian HIV-1C.¹² This may reflect distinct evolution and host pathogen interactions that may instruct the development of India specific therapeutic strategies.

Thus, assessing factors that govern reservoir dynamics as well as surveillance of putative breakthrough viruses, particularly in India, where sequence data remains limited, is imperative to successfully manage what has been heretofore an effective AIDS control national program.¹³ Our cross-sectional cohort of 235 PLHIV encompassing therapy naive and receiving (including history of failure) individuals was recruited from major tertiary ART centers that drained Mumbai as well as Maharashtra, both high HIV-1 prevalence areas from Western India.¹³

As demonstrated previously by us and others,¹⁴^,¹⁵^,¹⁶^,¹⁷ impaired restoration of CD4/CD8 ratios following extended ART was observed in these individuals, suggesting that low levels of ongoing viral replication with accompanying dysregulation is an impediment to complete immune reconstitution. Indeed, we and others have shown the persistence of significant HIV-1C specific responses, probably due to reservoir driven viral production, following extended ART previously.¹⁸^,¹⁹ Deep immune profiling, in this study, of putative reservoirs within the CD4⁺ T cell compartment revealed unrestored immune activation, in contrast to PD-1 expression, to be a signature associated with non-naïve CD4⁺ T cells, including potential reservoirs such as central memory (CM), transitional memory (TM), and effector T cells. Whether such residual activation observed in circulating CD4⁺ T cell subsets plays a role in reservoir dynamics within PLHIV is as yet conclusively not determined. Circulating, activated CD4⁺ T cells in PLHIV on ART have been shown to contain a high frequency of genetically intact HIV genomes and could mediate the amplification of these genomes through cellular proliferation during therapy.²⁰ However, an earlier robust, longitudinal study²¹ demonstrated that there was no “consistent association” between inflammation or T cell activation and changes or levels of HIV-1B reservoirs following ART. Our results correlating CD4⁺ T cell activation and PD-1 expression with PBMC proviral loads across therapy states did show strong positive correlations in ART naive infected individuals, but did not associate positively in the group of treatment receiving individuals, which included those that had experienced virological breakthrough. Indeed, both therapy receiving conditions showed evidence of immune restoration driven PD-1 expression on repopulated CD4⁺ T cell subsets known to occur in the setting of chronic viral exposure.²² Other than the CD4⁺ T cell compartment, monocyte and NK cell subsets represent important components of both viral clearance and putative sanctuaries harboring latent infection.²³^,²⁴ Unsupervised analysis of immune monitoring data in our study did delineate unconventional subsets such as dual positive CD4^dimCD8+ T cells and CD14, CD16 dual positive intermediate monocytes known to express CCR5²³^,²⁵ that, upon further study, could emerge as targets for eradication therapy. Interestingly, a recent longitudinal study (N = 28) has highlighted the role of plasmacytoid dendritic cells and NK cells in possibly controlling proviral DNA levels.²⁶ Our results support this role through strong negative correlations obtained in both therapy naive and receiving groups between cytotoxic NK frequency and proviral load. These results also extended a previously reported observation of increased circulating gut homing integrin beta 7 expressing CD8⁺ memory T cells in therapy naive PLHIV that we observed to persist in therapy receiving settings, probably reflective of continuing viral clearance.²⁷ In concordance with the high abundance of gut homing CD8⁺ T cells in the therapy naive setting, we also observed concurrently higher frequencies of gut homing memory (CD45RA-) Tregs in this setting that represented a dysregulated suppressive response to ongoing viral replication in the gut.

The HIV-STAR trial demonstrated that the anatomical source of rebound viremia is probably heterogeneous, however, enriched with viral repertoires, suggestive of clonal proliferation by cellular reservoirs.²⁸ These plausibly include resting CD4⁺ memory T cell subsets with variable proviral loads and decay rates. Our analysis of such sorted populations across our cohort suggested that all analyzed CD4⁺ T cell compartments, including naive, CM, and TM subsets, harbored detectable proviral DNA at variable levels with a trend toward relative enrichment in TM and CM compartments both in therapy naive and receiving individuals. This is in concordance with earlier reports highlighting these subsets as major contributors to the HIV reservoir.²⁹ Significantly, within these subsets, only a small fraction of intact proviruses has proven to be inducible, suggesting the need to exhaustively surveil their proviral repertoire to identify potential breakthrough viruses.³⁰ In light of the aforementioned, our study includes heretofore unreported data with respect to Indian HIV-1C in terms of proviral distribution in putative CD4⁺ T cell reservoirs. While these insights represent integrated immune profiling and reservoir dynamics exclusively from Indian HIV-1C viruses, considering the disparate host genetic background and evolution of African HIV-1C viruses, it would be highly informative to perform direct comparative studies of these parameters to establish strain specific attributes. The humanized mouse model represents a suitable avenue to explore such questions.⁹

HIV specific broadly neutralizing antibodies are an important upcoming prophylactic as well as therapeutic intervention, adjunctive to ART.³¹ The recently concluded AMP trial³² showed the effectiveness of VRC01 bnAb against sensitive viruses and, by implication, highlighted the importance of surveilling target viral sequences prior to the administration of future therapeutic candidates. In fact, our own recent work has highlighted distinct intra subtype neutralization profiles of patient derived circulating HIV-1C envelopes from Africa and India.⁴^,¹⁰ Few studies and none from individuals infected with the Indian HIV-1 subtype C have attempted to profile reservoir neutralization diversity encompassing a broad range of specificities.⁴^,³³^,³⁴ A single report by Sutar et al.³⁵ attempted to provide a neutralization profile of circulating viruses against 4E10 and 10E8 bnAbs. Here we report on the efficacy of 14 bnAbs directed against a range of viral targets present in reservoir derived full length envs using pseudotyped viral neutralization assays. Our results, albeit with a limited sample size and not capturing complete viral diversity, indicate prevalent resistance (of major variants) within these envelopes to a wide range of bnAb classes, with distinct neutralization profiles observed in the CD4⁺ CM subset compared to total PBMC. These results further highlight the importance of carrying out robust surveillance of putative reservoirs, a potential source of rebound/breakthrough viremia, before the finalization of candidate bnAbs. Also, a preferential sensitivity to CD4 binding site directed bnAbs such as N6, 1–18 and VRC07 was observed by us, advocating for the development and use of this class of antibodies in human trials being considered for PLHIV in India.

As has been noted in a recent WHO report,³⁶ real world prevalence of ART resistance, especially to Dolutegravir (DTG), the integrase strand transfer inhibitor (INSTI) of globally disseminated ART regimens, is higher than anticipated from clinical trials, prompting the recommendation by the world body to conduct regular surveillance of drug resistance among virologically unsuppressed PLHIV. Significantly, this report did not contain, to our knowledge, any data from India, where ∼2.6 million PLHIV were switched to an ART regimen (TLD) consisting of Tenofovir, Lamivudine, and DTG from 2020 onwards.³⁷ Such surveillance, coupled with effective therapy management, would be paramount toward achieving 95:95:95 national targets. The HIV reservoir is also believed to be a major source for the emergence of drug-resistant viruses as well as rebound viremia accompanying treatment non-adherence.³⁸^,³⁹^,⁴⁰ In the continuation of our efforts to evaluate the landscape of Indian HIV-1C viral diversity in putative reservoirs, we assessed the prevalence of DRMs in PBMC, as well as when available, sorted CD4⁺ T cell compartments from a subset of infected individuals across therapy states. In our cohort of 41 individuals, we observed a 20% prevalence of intermediate to high level DRMs, including one case of primary resistance (high level) present in archival DNA. Focusing on DTG, 3 individuals (7%), including one with primary resistance, showed the presence of archival DRMs. These preliminary results obtained less than 5 years post initiation of the TLD regimen in India complement recently reported circulating DRM surveillance by Sutar et al., 2025, and studies reporting primary resistance in circulating viruses to NRTI and NNRTI drug classes in therapy naive PLHIV.¹⁰^,⁴¹^,⁴² Taken together, our findings underscore the need for reservoir surveillance in the management of ART. Interestingly, a very recent study, utilizing humanized mice and an Indian HIV-1C virus, demonstrated disparate HIV-1C DRM evolution compared to that of a prototypical HIV-1B virus.⁹ Notably, in this study, the Indian HIV-1C virus uniquely exhibited acquisition of DRM against the INSTI bictegravir in circulating viruses post ART administration.

In conclusion, our findings demonstrate persistent CD4⁺ T cell dysfunction in ART responding HIV-1C infected individuals that is coincident but not correlated with stable proviral burdens in circulating reservoirs. Also, currently proposed bnAb combinations for circulating viruses, as per our preliminary results, would need modification to target reservoir HIV-1C envelopes in the population of PLHIV in India. Finally, our DRM data highlights the need for continued surveillance of the proviral repertoire during ART.

Limitations of the study

Our study is limited in terms of sample size with respect to bnAb analysis, as well as by the use of gross proviral loads and bulk PCR approach rather than single-genome amplification to estimate reservoir burden and env diversity, respectively. Also, the longitudinal evaluation of reservoir dynamics and immune restoration is not presented here. However, we believe this report is an important contribution toward concurrently and systematically characterizing the immune dysfunction of and viral diversity within reservoirs of Indian HIV-1C.

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Vainav Patel (patelv@nirrch.res.in).

Materials availability

This study did not generate new unique reagents.

Data and code availability

•
Raw fastq datasets generated as a part of this study can be downloaded from NCBI Sequence Read Archive (SRA) using PRJNA1231361 accession ID or https://www.ncbi.nlm.nih.gov/sra/PRJNA1231361 link, also listed in the key resources table.
•
This study did not generate new code.
•
Any additional information required to reanalyze the data reported in this article is available from the lead contact upon request.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies

FITC Mouse Anti-Human CD45	BD	Cat# 347463; RRID:AB_400306
APC-Cy™7 Mouse Anti-Human CD3	BD	Cat# 557832; RRID:AB_396890
BV480 Mouse Anti-Human CD4	BD	Cat# 746541; RRID:AB_2743832
BV605 Mouse Anti-Human CD8	BD	Cat# 564116; RRID:AB_2869551
BV421 Mouse Anti-Human CD19	BD	Cat# 562440; RRID:AB_11153299
BV786 Mouse Anti-Human CD56	BD	Cat# 564058; RRID:AB_2738569
PE anti-human CD16 Antibody	Biolegend	Cat# 302008; RRID:AB_314208
PE/Cyanine7 anti-human CD14 Antibody	Biolegend	Cat# 301814; RRID:AB_389353
PE-Cy™7 Mouse Anti-Human CD28	BD	Cat# 560684; RRID:AB_1727459
PerCP-Cy™5.5 Mouse Anti-Human CD45RA	BD	Cat# 563429; RRID:AB_2738199
PE-CF594 Mouse Anti-Human CD197 (CCR7	BD	Cat# 562381; RRID:AB_11153301
Brilliant Violet 421™ anti-human HLA-DR Antibody	Biolegend	Cat# 307636; RRID:AB_2561831
BUV395 Mouse Anti-Human CD38	BD	Cat# 740294; RRID:AB_2740033
PE Mouse anti-Human CD279 (PD-1)	BD	Cat# 560795; RRID:AB_2033989
BB515 Rat Anti-Integrin β7	BD	Cat# 565331; RRID:AB_2739188
Alexa Fluor® 647 Mouse anti-Human CD127	BD	Cat# 558598; RRID:AB_647113
PE anti-human/mouse Integrin β7 Antibody	Biolegend	Cat# 321204; RRID:AB_571971
FITC Mouse Anti-Human HLA-DR	BD	Cat# 347363; RRID:AB_400291

Biological samples

10 mL whole peripheral blood of HIV seronegative and positive participants	Recruited at KEM hospital, B.Y.L Nair hospital and Sir JJ group of hospitals at Mumbai	–

Chemicals, peptides, and recombinant proteins

FACS Lyse	BD	Cat# 349202; RRID:AB_2868862
BD Liquid counting beads	BD	Cat# 335925; RRID:AB_2868699
FACS Accudrop beads	BD	Cat# 661612; RRID:AB_2870528
DMEM	Gibco	Cat# 11965-092
RPMI-1640	HiMedia Laboratories	Cat# AL060A
Fetal bovine serum	HiMedia Laboratories	Cat# RM9955
DPBS	HiMedia Laboratories	Cat# TL1023
Lymphocyte Separation medium	HiMedia Laboratories	Cat# LS001
DEAE-dextran	Sigma	Cat# D9885

Critical commercial assays

Qiagen DNA extraction kit	Qiagen	Cat# 51306
QIAmp Viral RNA Mini kit	Qiagen	Cat# 52906
AltoStar® HIV RT-PCR Kit 1.5	Altona Diagnostics	Cat# AS0221513
QIAseq® FX DNA library kit	Qiagen	Cat# 180477
FuGENE6 transfection kit	Promega	Cat# E2691
Britelite plus luciferase substrate	PerkinElmer	Cat# 396501

Deposited data

DNA sequences generated using Illumina	This study	https://www.ncbi.nlm.nih.gov/sra/PRJNA1231361 accession ID-PRJNA1231361

Experimental models: Cell lines

J-Lat 8.4	BEI_Resources	Cat# HRP-9847; RRID:CVCL_8284
293 T	ATCC	Cat# CRL-3216; RRID:CVCL_0063
TZM-bl cells	BEI_Resources	Cat# ARP-8129; RRID:CVCL_B478

Oligonucleotides

See Table S4: Oligonucleotides	See Table S4: Oligonucleotides	–

Recombinant DNA

Plasmid: pSG3ΔEnv	NIH AIDS reagent program	Cat# ARP11051
HIV-1 envelope expressing plasmid	NIH AIDS reagent program	Cat# ARP11505
HIV-1 envelope expressing plasmid	NIH AIDS reagent program	Cat# ARP13419

Softwares and algorithms

Prism version 10.1.0	GraphPad	https://www.graphpad.com/
FlowJo version 10.10	BD	https://www.flowjo.com/solutions/flowjo
Rstudio version 2025.5.1.513	Posit Software	https://posit.co/
R version4.4.2	R core Team	https://www.R-project.org/
Guppy basecaller (v6.3.7)	Oxford Nanopore Technologies	https://github.co/nanoporetech/rerio
Prowler	Simon Lee, 2021	https://github.com/ProwlerForNanopore/ProwlerTrimmer
Minimap2	Li H, 2018⁴³	https://github.con/ih3/minimap2
Samtools	Li H,2009	https://github.com/samtools/samtools
Picard tools	Broad Institute	https://broadinstitute.github.io/picard/
iVar	Anderson lab	https://github.com/andersen-lab/ivar
Pilon	Broad Institute	https://github.com/broadinstitute/pilon

Others

HIVdb program	Stanford University	https://hivdb.stanford.edu/hivdb/by-patterns/
CATNAP Tools	LANL HIV database	https://www.hiv.lanl.gov/components/sequence/HIV/neutralization/

Open in a new tab

Experimental model and study participant details

Ethics statement

The study protocol was approved by ethics committee for human studies at ICMR-NIRRCH (410/2020, 356/2019, 348/2018), Institutional Ethics committee Seth GS Medical College and KEM Hospital (EC/OA-152/2019), Ethics Committee for the Academic Research and Projects at Nair hospital (ECARP/2020/109), Institutional Ethics Committee, JJ Group of hospitals (IEC/Pharm/RP/183/Oct/2020). Study participants were recruited from KEM hospital and B.Y.L. Nair Charitable hospital and JJ hospital in accordance with the approved protocol. Written informed consent was received prior to participation.

Study participants

57 HIV seronegative and a total of 235 HIV-1 seropositive individuals were recruited (clinical parameter details of the participants provided in Table S1). Participants were screened by HIV TRIDOT test to confirm HIV-1 serostatus and stratified as per their therapy status into ART naive group (AN, n = 94), 1^st line ART receiving group (FLAR, n = 107) and 2^nd line ART receiving (SLAR, n = 34). 8–10 mL blood was drawn in EDTA coated vacutainers. Gender distribution across the study groups was skewed with more males than females (182 + 110). Though gender-based influences have been reported in the previous studies our recruitment was stochastic and simply reflects demographic of participants visiting the clinical sites.

Method details

Enumeration of immune cells

50 μL of blood was stained with anti-human CD45-FITC (Clone: 2D1), CD3-APC-Cy7 (SK7), CD4-BV480 (RPA-T4), CD8-BV605 (SK1), CD19- BV421 (HIB19/SJ25C1), CD56-BV786 (NCAM16.2), CD16- PE (3G8), CD14-PE- Cy7 (M5E2) antibodies and incubated in dark for 20 min. Stain/Lyse/no wash technique was used. 50 μL of BD liquid counting beads (BD cat no. 335925) were added and data acquisition was carried out on BD FACS Aria Fusion or BD FACSymphony A3. Data analysis was carried out using FlowJo v10.10 Software. Absolute counts were calculated as per the manufacturer’s datasheet.

Whole blood immunophenotyping

200 μL of whole blood sample was processed using stain/lyse/wash technique. To determine activation status of T cell subsets, anti-human CD3-APC-Cy7 (SK7), CD4-BV480 (RPA-T4), CD8-BV605 (SK1), CD28-PE-Cy7 (28.2), CD45RA-Per-CP-Cy5.5 (HI100), CCR7-PECF594 (150503), HLADR-BV421 (L243), CD38-BUV395 (HIT2) PD-1-PE (EH12.1) and Β7 FITC (FIB 504) antibodies were used. Frequency of regulatory T cells (Treg) was determined using anti human CD3-APC-Cy7 (SK7), CD4-BV480 (RPA-T4), CD8-BV605 (SK1), CD25-PE-Cy7 (M-A251), CD127-AF647 (HIL-7R-M21), CD45RA-Per-CP-Cy5.5 (HI100) and Β7 PE (FIB 504).

Fluorescence-activated cell sorting of putative reservoirs

CD4⁺ T cell subsets were sorted from PBMCs using BD FACS Aria Fusion. Briefly, PBMCs were stained with anti-human CD3-APC-Cy7 (SK7), CD4-BV480 (RPA-T4), CD28-PE-Cy7 (28.2), CD45RA-Per-CP-Cy5.5 (HI100), CCR7-PECF594 (150503), HLADR-BV421 (L243) to sort resting CD4⁺ T naive (NV), central memory (CM), transition memory (TM) and effector memory (EM) cells. DNA was extracted from the sorted cells using Qiagen DNA extraction kit (Cat- 51306).

Proviral load estimation

Proviral HIV DNA copy number was estimated using HIV-1 specific gag PCR.⁴⁴ Briefly, 100 ng DNA template corresponding to 15151 cells (normalised for GAPDH copies using qPCR⁴⁵) from the sorted cells/PBMC was subjected to nested gag PCR. Copy number standards were prepared by mixing 1:10 serially diluted J-Lat 8.4 cells DNA with DNA of uninfected PBMC to keep final DNA concentration 100 ng. HIV DNA copies were expressed as copies per million cells. (Primer sequences are provided in Table S4).

Plasma viral load estimation

Viral nucleic acid was extracted from plasma samples using QIAmp Viral RNA Mini kit (Cat- 52906). Viral load (expressed as copies/ml) was quantified using AltoStar HIV RT-PCR Kit 1.5 (Altona Diagnostics, AS0221513) with detection limit of 34 copies/ml of plasma.

Drug resistance mutation (DRM) analysis

DNA was extracted from PBMC or sorted CD4⁺ T cell subsets and used to generate gag-pol amplicons using primers mentioned in⁴³ (Primer sequences are provided in Table S4). Amplicons were prepared for sequencing using QIAseq FX DNA library kit. Paired end data was generated using NovaSeq 6000 with PE150 chemistry. Reads were trimmed using Trimmomatic and checked for their quality. DRM data was generated from sequences having read depth >50 and mutation detection threshold >10% using Stanford HIVDB Algorithm v9.8.

Env diversity analysis

Full length env amplicons were generated from DNA of PBMC or sorted CD4⁺ T cell subsets as described previously with modified primer set in the 2^nd round of a nested PCR¹⁰ (Primer sequences are provided in Table S4). env amplicons were sequenced using both long read Oxford Nanopore (ON) and short read Illumina (IL) platforms. Next generation sequencing was performed for 5′ fragments using the Illumina platform while 3′ fragments were sequenced using both Illumina and Oxford nanopore platforms. The raw data obtained from the nanopore sequencing was converted to fastq files using Guppy basecaller (v6.3.7). Raw reads were filtered for quality and read length using Prowler (Flags: -l 1500 -q 12 -c “LT” -g “F1” -m “S”).⁴⁶ The reads were aligned to the HIV-1 subtype C reference sequence [JB1] using Minimap2⁴⁷^,⁴⁸ and processed for read sorting and filtration with samtools.⁴⁹ Reads encompassing the entire gene were extracted from the binary alignment maps using Picard tools (https://broadinstitute.github.io/picard/). Reads were further clustered and corrected using isONclust and isONcorrect respectively into quasispecies clusters.⁵⁰^,⁵¹ Consensus sequences were generated for each quasispecies clusters using iVar.⁵² Quasispecies thus constructed were further corrected with the help of Illumina reads using Pilon.⁵³

bnAb contact site assessment

Broadly neutralizing antibodies (bnAbs) specific epitope contact positions as well as documented sensitivity/resistance imparting variants at each position were retrieved from CATNAP database (https://www.hiv.lanl.gov/components/sequence/HIV/neutralization/main.comp) and assessed for the presence of sensitive/resistant/undefined mutation at each of these positions using custom bash scripts.

Generation of env pseudoviruses

Briefly, 293 T cells were co-transfected with an HIV-1 backbone plasmid (pSG3ΔEnv) and an HIV-1 envelope expressing plasmid using FuGENE6 transfection kit (Promega). Culture supernatants containing the Env-pseudotyped viruses were harvested from culture supernatant 48 h post-transfection and stored at −80°C. Pseudoviruses’ infectivity was assessed with a luciferase-based assay using TZM-bl cells (10⁵ cells/ml). Virus titers were determined by addition of Britelite plus luciferase substrate (PerkinElmer) to the assay plate followed by luciferase activity measurement based on relative luminescence unit (RLU) using a Victor X2 luminometer (PerkinElmer) as described.⁵⁴

Pseudoviruses neutralization assay

Briefly, Env-pseudotyped viruses were pre-incubated in 96-well tissue culture plates with various concentrations of bnAbs (IgG) for an hour at 37°C in a CO₂ incubator under humidified conditions. Subsequently, 1 × 10⁴ TZM-bl cells were added to the mixture in the presence of 25 μg/mL DEAE-dextran (Sigma, Inc.). The degree of virus neutralization was assessed 48 h post infection by measuring reduction in relative luminescence units (RLU) in a luminometer (Victor X2; PerkinElmer Inc.).

Data analysis

All FCS files were analyzed using FlowJo v10.10 Software. Non-linear dimensionality reduction of multiparametric flow cytometry data was performed using DownSample, t-Stochastic Neighbor Embedding (tSNE, dimensionality reduction algorithm), X-Shift (Clustering algorithm) and Cluster Explorer plugins (a tool to generate interactive plots) in FlowJo.⁵⁵^,⁵⁶ Hierarchical clustering analysis and circos plots were generated using R studio.

Quantification and statistical analysis

Statistical analysis was carried out using GraphPad version 10.1.0 for Windows, GraphPad Software. Data is represented as scatter dot plots with median and interquartile range. Descriptive statistics was used for median and range. Study groups were compared with each other using Kruskal-Wallis one-way ANOVA non-parametric test with and without applying Dunn’s correction for multiple comparisons. Correlation between various parameters was calculated using Spearman’s two tailed non parametric test. p values ≤0.05 were considered as significant and represented as asterisks in the correlograms. Statistical information is provided in the respective figure legends.

Acknowledgments

We are grateful to the study participants and to the National AIDS control Organisation (NACO), Ministry of Health & Family Welfare, Govt. of India, for facilitating recruitment for this study. We appreciate the consistent help and support of our Infectious core flow cytometry facility staff, Ms. Fiza Shaikh, Denna Prabhin. We appreciate the help of Mr. Naresh Mali in sample recruitment. We are grateful to Ms. Kalyani Karandikar for her guidance in performing Oxford Nanopore Sequencing.

This work was supported by the Wellcome Trust DBT India Alliance grant, India, awarded to J.B. and V.P.1 (IA/TSG/19/1/600019). S.K.1 and N.M.2 were recipients of Junior Research Fellowship from University Grants Commission (UGC), Govt. of India. N.K. received a young researcher award from the ICMR-IAVI joint investigator-initiated research program. P.D. received a Lady Tata Memorial Trust Fellowship. S.G.1 was supported by a Department of Science and Technology (DST) Inspire Young Investigator Award. The funding agencies had no role in study design, sample collection, data analysis, or preparation of the article.

Author contributions

V.P.1., J.B., and J.S.1. designed the research study. S.K.1., N.K., S.B.1., S.Y., J.S.1., R.M., P.J., N.M., P.G., V.P.2, S.M., P.D., S.V., S.G.1., V.K., N.S., A.K.S., and V.B. conducted the experiments. S.K., N.K., S.B.1., S.K.2, and T.P. carried out data acquisition. S.K., N.K., S.B.1., S.B.2., J.S.1., R.M., P.J., N.M.2, and R.C. performed data analysis. V.P.1., S.K., N.K., S.B.1, and J.S.1 wrote the article, including comments from all the co-authors. J.S2., S.G2., S.A., G.N., K.J., N.I., V.N., P.P., and V.P1. facilitated participant recruitment at the clinical sites. V.P.1. is Vainav Patel, V.P.2. is Varsha Padwal, J.S1. is Jyoti Sutar, J.S2. is Jayanthi Shastri, S.K1. is Snehal Kaginkar, S.K2. is Sameen Khan, S.B1. is Shilpa Bhowmick, S.B.2. is Sharad Bhagat, N.M. is Namrata Nemad, N.M2. is Nandan Mohite, S.G1. is Sayantani Ghosh, S.G2. is Sushma Gaikwad.

Declaration of interests

The authors declare no competing interests.

Published: November 28, 2025

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2025.14278.

Supplemental information

Document S1. Figures S1–S8 and Tables S2 and S4

mmc1.pdf^{(1.8MB, pdf)}

Table S1. Clinical history of the study participants

mmc2.xlsx^{(81.8KB, xlsx)}

Table S3. Drug resistance mutations across study participants

mmc3.xlsx^{(24.2KB, xlsx)}

Table S5. p values for multiple comparisons without correction and after Dunn’s correction

mmc4.xlsx^{(16.3KB, xlsx)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S8 and Tables S2 and S4

mmc1.pdf^{(1.8MB, pdf)}

Table S1. Clinical history of the study participants

mmc2.xlsx^{(81.8KB, xlsx)}

Table S3. Drug resistance mutations across study participants

mmc3.xlsx^{(24.2KB, xlsx)}

Table S5. p values for multiple comparisons without correction and after Dunn’s correction

mmc4.xlsx^{(16.3KB, xlsx)}

Data Availability Statement

•
Raw fastq datasets generated as a part of this study can be downloaded from NCBI Sequence Read Archive (SRA) using PRJNA1231361 accession ID or https://www.ncbi.nlm.nih.gov/sra/PRJNA1231361 link, also listed in the key resources table.
•
This study did not generate new code.
•
Any additional information required to reanalyze the data reported in this article is available from the lead contact upon request.

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PERMALINK

Immune and viral surveillance of HIV-1C reservoirs in an Indian cohort

Snehal Kaginkar

Shilpa Bhowmick

Nandini Kasarpalkar

Sharad Bhagat

Jyoti Sutar

Sapna Yadav

Sameen Khan

Pranay Gurav

Nandan Mohite

Namrata Neman

Nidhi Sehgal

Satyajit Musale

Varsha Padwal

Pratik Devadiga

Ranajoy Mullick

Priyanka Jayal

Tejaswini Pandey

Amit Kumar Singh

Shilpa Velhal

Sayantani Ghosh

Ritam Chatterjee

Varsha Kalsulkar

Vikrant Bhor

Gita Nataraj

Kavita Joshi

Nayana Ingole

Sachee Agrawal

Jayanthi Shastri

Sushma Gaikwad

Vidya Nagar

Priya Patil

Jayanta Bhattacharya

Vainav Patel

Summary

Graphical abstract

Highlights

Introduction

Results

Human immunodeficiency virus-1 subtype C disease progression states

Table 1.

Figure 1.

Systemic cellular immune profiling

Figure 2.

Immune dysregulation of the putative reservoir

Figure 3.

Expansion of regulatory T (Treg) cells

Figure 4.

Delineation of therapy state specific immune signatures

Figure 5.

Proviral burden in the putative CD4+ T cell reservoir

Figure 6.

In vitro validation of the broadly neutralizing antibodies based neutralization of env pseudoviruses

Figure 7.

Drug resistance mutation analysis of primary putative CD4+ T cell reservoirs

Figure 8.

Discussion

Limitations of the study

Resource availability

Lead contact

Materials availability

Data and code availability

STAR★Methods

Key resources table

Experimental model and study participant details

Ethics statement

Study participants

Method details

Enumeration of immune cells

Whole blood immunophenotyping

Fluorescence-activated cell sorting of putative reservoirs

Proviral load estimation

Plasma viral load estimation

Drug resistance mutation (DRM) analysis

Env diversity analysis

bnAb contact site assessment

Generation of env pseudoviruses

Pseudoviruses neutralization assay

Data analysis

Proviral burden in the putative CD4⁺ T cell reservoir

Drug resistance mutation analysis of primary putative CD4⁺ T cell reservoirs