Concurrent TB and HIV therapies control TB reactivation during co-infection but not chronic immune activation

Domain: biology
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
Most HIV-negative individuals exposed to Mycobacterium tuberculosis (Mtb) control infection as latent TB infection (LTBI), but HIV co-infection greatly increases progression to tuberculosis (TB), the leading cause of death in people living with HIV (PLHIV). Although combination antiretroviral therapy (cART) reduces LTBI reactivation, immune control of Mtb is not fully restored, as shown by persistent TB incidence in PLHIV on cART. In macaques, skewed pulmonary effector memory CD4+ T-cell (TEM) responses and new TB lesions persist despite cART. We hypothesize that concurrent anti-TB therapy with cART would improve bacterial control and immune restoration compared to cART alone. Using rhesus macaques (RM) with LTBI and Simian Immunodeficiency Virus (SIV) co-infection, we tested three months of weekly isoniazid and rifapentine (3HP) plus daily cART. Concurrent cART+3HP improves clinical and microbiological outcomes but fails to fully restore lung CD4+ T-cell immunity. Treated RMs retain caseous granulomas with high FDG uptake and incomplete CD4+ T-cell reconstitution, marked by persistent activation, exhaustion, and inflammation. CD4+ TEM cells remain depleted. Concurrent therapy induces Type I IFN signatures and enhances Mtb-specific TH1/TH17—but reduces TNFα—responses. These findings reveal persistent pulmonary immune defects underlying TB risk in HIV co-infection and identify potential targets for host-directed adjunctivetherapies.

Introduction
Combinatorial antiretroviral therapy (cART) reduces the risk of reactivation of latent tuberculosis infection (LTBI) in humans1–4. However, tuberculosis (TB) remains a major cause of morbidity and mortality in people living with HIV (PLHIV)5–7. Anti-tubercular treatment has been shown to reduce disease incidence by 30%–50%8,9 and is recommended for PLHIV in high-burden countries. Observational studies in humans suggest that concurrent administration of cART and isoniazid preventive therapy (IPT) for LTBI lowers the risk of developing TB compared to cART alone10. A randomized double-blind placebo-controlled trial8 showed that administering IPT in conjunction with cART resulted in significantly lower numbers of incident TB cases than cART plus placebo. Thus, concurrent cART+IPT leads to improved outcomes with clear protective effects and clinical benefit to HIV-infected individuals. Although the WHO recommends concurrent IPT and cART in TB-endemic settings, uptake remains poor, and the immune mechanisms underlying the benefits of concurrent cART and IPT have not been defined. Another caveat of this approach has been a lack of completion of the treatment regimen by the majority of those who initiate the 6-month course of daily isoniazid while on cART. To enable treatment adherence and completion, the World Health Organization (WHO) recommended a 12-dose weekly regimen of Isoniazid and Rifapentine (3HP) as a treatment-shortened option for treating TB. Concurrent administration of cART and 3HP was safe and well-tolerated, with over 95% completion rate11,12. In our RM model of Mycobacterium tuberculosis (Mtb)/SIV co-infection, though 3HP effectively reduced persistent LTBI12, it did not sterilize the lungs of a third of the treated RMs13. Recent work in Mtb/SIV co-infected RMs14 shows that, in addition to the depletion of CD4+ T cells, HIV-driven chronic immune activation correlates with LTBI reactivation14–18. Though cART controls viral replication, it leads to insufficient reconstitution of protective CD4+ T effector memory (TEM) responses in the lungs15 and fails to rescue from virus-driven immune activation19. Administration of anti-tubercular therapy (ATT), concurrently with cART, significantly reduces reactivation better than cART among individuals with LTBI11. However, long-term sterilization of bacteria and immune reconstitution in the lungs has not been shown in these individuals. As such, it is important to model these conditions of concurrent treatment of Mtb/HIV co-infection with cART/ATT. Such experiments have the potential to both demonstrate experimentally long-term Mtb sterilization in this setting, as well as uncouple bacterial and viral control from immune activation.

In this study, we leveraged our nonhuman primate (NHP) model of Mtb/SIV co-infection to study the effect of simultaneous cART and 3HP treatment on immune responses to Mtb. Performing these studies in humans is virtually impossible due to the lack of determination of the timing of Mtb and HIV co-infections, verification of bacterial and viral loads and performance of invasive longitudinal studies to investigate the lung compartment. Our very low-dose aerosol infection NHP model recapitulates the spectrum of human lung pathologic lesions, including LTBI and its reactivation to active TB by HIV13–15,17,20. We carried out detailed studies of the immune responses by longitudinal sampling of bronchoalveolar lavage (BAL) in the absence of cART as well as during and after cART and cART+3HP. Importantly, we investigated the functional capacity of antigen-specific T-cell immunity in the lung microenvironment. Our findings clearly show that while cART and 3HP control viral and bacterial replication, respectively, immune reconstitution is partial and the functional capabilities of the reconstituted CD4+ T cells remain highly impaired. In particular, skewed CD4+ TEM responses persist despite concurrent cART and anti-TB treatment in Mtb/SIV co-infected macaques and the ongoing inflammation in the lung is not ameliorated. It is well known that PLHIV and TB remain at risk for progression due to subsequent infections or TB reactivation even after improved clinical and microbiological attributes. We conclude that persisting immune activation/inflammation is the mechanisms that cause this susceptibility. Clearly, host-directed therapies against immune activation and lung inflammation, adjunctive to TB therapy and cART, must be developed to better treat PLHIV with TB.

Results
Concurrent treatment with cART and 3HP improves clinical and microbiological attributes of Mtb/SIV co-infection
To assess the impact of concurrent cART and 3HP therapy on LTBI reactivation in Mtb/SIV co-infection, we co-infected a total of 6 new Indian-origin RMs and treated them with cART+3HP for 12 weeks (n = 6). The data of RMs in groups: LTBI without SIV co-infection (n=4), cART-naive co-infected RMs (n=8), co-infected RMs treated with cART alone for 9 weeks (n=4) were used from historical animals from completed studies (refs. 15,19, Supplementary Data 1). The study design is outlined in Fig. 1A. All the RMs were infected with a low dose of Mtb (~10 CFU deposited in the lungs). cART naive, cART and cART +3HP group RMs were subsequently infected with SIV (300 TCID50 SIVmac239), intravenously. Infection was confirmed by a positive tuberculin skin test at weeks 3 and 5 after Mtb infection. All study RMs developed LTBI infection characterized by serum C-reactive protein (CRP) of 5 µg/mL or lower (Fig. 1B), no significant change in percentage body temperature (Supplemental Fig. 1A) and body weight (Supplementary Fig. 1B) up to 9 weeks after Mtb infection and <1–2 Log10 CFU of Mtb in the BAL at weeks 3, 5, and 7 post Mtb infection (Supplementary Fig. 1C). Upon establishment of latency at week 9 post Mtb-infection, RMs in cART naive, cART and cART+3HP groups were co-infected with 300 TCID50 SIVmac239 via the intravenous route14,15,20. RMs in the LTBI group were not co-infected with SIV. SIV infection was confirmed by measuring the plasma viral loads via reverse transcriptase quantitative PCR (RT-qPCR). Post-SIV co-infection, all the RMs except in the LTBI group were either treated with cART alone for 9 weeks or cART+3HP, once weekly orally for 12 weeks (Fig. 1A), as described21. All animals were euthanized by week 24 post Mtb infection, which was the predetermined study endpoint, or at earlier time points if they became clinically unwell and/or showed signs of TB reactivation, as assessed by veterinarians in accordance with humane endpoint criteria. Clinical, pathological and immunological response was compared in the four experimental groups: LTBI, cART naive, cART and cART+3HP.

Elevated serum CRP levels associate with active TB and an increase in bacterial burdens in NHPs15,17,20. CRP levels in RMs in the cART+3HP group were significantly lower than in the cART-treated RMs (P<0.001) at weeks 13 (P<0.001), 15 (P<0.0001) and 17 post Mtb infection (P<0.0001) (Fig. 1B). Importantly, CRP levels in cART+3HP RMs were not significantly different from the RMs in LTBI group (P=0.44) (Fig. 1B). To evaluate the impact of cART+3HP on bacterial burden, we cultured BAL fluid, lung tissue, bronchial lymph nodes, and lung granulomas on 7H11 agar plates, following previously established protocols14,15,17,20. Among the six RMs treated with cART+3HP, five had no detectable bacterial burden in their lungs at necropsy (Fig. 1C). In contrast, only one of four cART-treated RMs and two of 14 cART-naive RMs remained sterile at the end of the study (Fig. 1C). Therefore, RMs in the cART+3HP group (87.5% sterile) showed bacterial clearance comparable to the RMs in the LTBI group (75% sterile). Additionally, the six RMs in cART+3HP RM had no detectable bacilli in lung granulomas (Fig. 1D), BAL (Fig. 1E) and bronchial lymph nodes (Fig. 1F) at necropsy. Notably, the bacterial burden in cART+3HP RMs was significantly lower than cART-treated RMs in lung (P=0.01) (Fig. 1C), lung granulomas (P=0.001) (Fig. 1D) and bronchial lymph node (P<0.0001) (Fig. 1F). These findings suggest that cART+3HP significantly reduces bacterial burden in key sites of infection.

To assess the effectiveness of cART regimen in controlling viral replication when administered alongside 3HP treatment, viral loads were measured in the plasma of all six RMs. The viral loads in the cART+3HP-treated RMs were compared to those in the cART-only treated group at weeks 11, 13, 15 and necropsy (Supplementary Fig. 1D). No significant differences in the virus decay rate were observed between the groups, indicating that 3HP did not impact the efficacy of cART in controlling viral replication. We also evaluated cytotoxicity markers in blood samples collected at the end of the treatment to determine the safety of cART+3HP administration (Supplementary Data 2). Serum levels of the albumin/globulin (A/G) (g/dL) ratio, aspartate aminotransferase, glutamic-oxaloacetic transaminase (ALT/SGOT) (units per liter of serum), blood urea nitrogen/creatinine (BUN/creat) (µmol/L) ratio, and alkaline phosphatase (Alk phos) (units per liter) remained within normal range at treatment completion (Supplementary Data 2). To assess the impact of cART+3HP treatment on the lung pathology, lung tissue sections collected at necropsy were stained with hematoxylin and eosin (H&E) (Fig. 1G) and analyzed by board-certified pathologists. Pathological findings aligned with the clinical and microbiological observations. The cART+3HP-treated RMs displayed a few small, non-necrotizing caseous granulomas (approximately 0.5—1cm in size) scattered across the lung lobes, with rare aggregates of lymphocytes and macrophages (Fig. 1G). One RM showed multifocal lymphocyte accumulations and non-necrotizing active granulomas in the liver. In contrast, the hilar and bronchial lymph nodes, spleen, showed normal pathology, similar to LTBI-only controls. Notably, the cART+3HP-treated RMs had significantly reduced percentage lung involvement compared to the cART-only and cART naive RMs (Fig. 1H). The lung tissue of cART-only and cART-naive RMs exhibited numerous large granulomas with necrotic cores. Overall, cART+3HP administration was safe, effectively controlled the bacterial burden, and improved lung pathology compared to cART-only treatment.

We conducted Positron Emission Tomography with Computed Tomography (PET/CT), as described earlier13,22,23, to investigate lung lesions in three of the six RMs at four time points: weeks 6 (LTBI), week 12 (LTBI+SIV co-infection, 1 week of cART+3HP treatment), week 16 (LTBI+SIV co-infection, 5 weeks of cART+3HP treatment) and week 22 (LTBI+SIV co-infection, 11 weeks of cART+3HP treatment) (Fig. 1I). Lung lesions in all RMs remained stable, showing no or minimal progression in size by week 6 after Mtb infection (Fig. 1I). The stability of these lung lesions at week 6, suggestive of LTBI, was supported by additional diagnostic parameters (positive Tuberculin Skin Test, CRP levels, and bacterial load explained in Fig. 1 and Supplementary Fig. 1C), which together confirm the LTBI diagnosis in this macaque model. At week 12, there was a significant increase (P=0.01) in both the volume of lung lesions (Supplementary Fig. 1E) and 18F-fluorodeoxyglucose (18F-FDG) uptake (Supplementary Fig. 1F). However, by week 16 post Mtb infection (five weeks of cART+3HP treatment), 18F-FDG uptake decreased, though this reduction was not statistically significant (P=0.003). No further increase in lesion volume (Supplementary Fig. 1E) or 18F-FDG uptake (Supplementary Fig. 1E) was observed by week 22 post Mtb infection (ten weeks of cART+3HP treatment). These PET/CT results indicate a significant decrease in lesion volume post-cART+3HP treatment, though the metabolic potential of the lesions remained unchanged, suggesting that concurrent therapy led to a gradual resolution of caseous lesions formed after SIV co-infection (week 12), while the inflammation persisted in the few remaining lesions. The continued metabolic activity within these lesions could be interpreted in several ways. While we propose that it reflects ongoing inflammation rather than active bacterial replication, the presence of PET-avid lesions despite therapy raises the possibility of undertreated active tuberculosis. The distinction between “mild active TB” and “latent” TB is complex, and our macaque model represents LTBI as observed in humans. It is possible that a more intensive treatment regimen could yield different outcomes regarding lesion resolution and metabolic activity. Future studies should examine the impact of more aggressive anti-TB therapies on both lesion size and metabolic activity in this model, to better understand whether the observed lesions reflect a failure of immune control or indicate undertreated active TB, thus offering further insights into the transition between active and latent TB.

Immune reconstitution by cART+3HP in pulmonary compartment of Mtb/SIV co-infected RMs
Immunophenotyping of T cells was conducted to evaluate both the extent and quality of immune reconstitution in the pulmonary compartment of Mtb/SIV co-infected RMs treated with cART+3HP compared to those treated with cART alone. After 12 weeks of cART+3HP treatment, CD4+ T-cell frequency in the BAL was restored to levels similar to those observed in LTBI (Fig. 2A). However, in the lung tissue, CD4+ T-cell frequency remained significantly lower than in LTBI controls (Fig. 2B) (P=0.0021). A significant increase in the percentage of CD8+ T cells was observed in the BAL of cART+3HP-treated RMs compared to cART-only-treated RMs (Fig. 2C) (P=0.04), but no such difference was seen in the lung (Fig. 2D). Additionally, the percentage of CD8+ T cells in the lung was not significantly different across the LTBI, cART, and cART+3HP treatment groups in the Mtb/SIV co-infected RMs. We have previously shown that chronic immune activation drives LTBI reactivation upon SIV co-infection in RMs14,15,20. Hence, to assess the impact of cART+3HP on T-cell activation, we studied expression of HLA-DR and CD69 on CD4+ T cells in BAL at week 11 post Mtb infection (or 2 weeks post SIV co-infection, prior to initiation of cART+3HP) and at necropsy (end of 12 weeks of cART+3HP treatment) in all four study groups. All Mtb/SIV co-infected groups exhibited significantly higher frequencies of HLA-DR+- and CD69+-CD4+ T cells at week 11 (peak viremia) compared to the LTBI group (Fig. 2E, F). cART+3HP effectively reduced the percentage of CD4+ T cells expressing HLA-DR and CD69 compared to cART naive RMs, but not to the levels seen in LTBI or cART-treated RMs. These findings suggest that while cART+3HP treatment can partially restore T-cell frequencies in the pulmonary compartment, it does not fully normalize immune responses in the lung, highlighting the complexity of immune reconstitution in Mtb/SIV co-infection.

High expression of the PD-1 marker on T cells is commonly associated with increased exhaustion and dysfunction in chronic infections like HIV, even with cART treatment [21,22]. To investigate the effects of cART+3HP on T-cell exhaustion in Mtb/SIV co-infection, we assessed the percentage of PD-1+ T cells in BAL cells at week 11 (peak viremia) and necropsy (Fig. 2G). At necropsy, RMs treated with cART and cART+3HP showed significantly higher percentages of PD-1+ CD4+ T cells compared to LTBI RMs. However, the addition of 3HP to cART did not alleviate T-cell exhaustion in the pulmonary compartment, as there was no significant difference in PD-1+ CD4+ T-cell expression in BAL between cART and cART+3HP-treated RMs (Fig. 2G). This lack of improvement occurred despite near undetectable levels of Mtb and SIV at the protocol’s conclusion in the -treated RMs. Overall, our findings suggest that cART+3HP fails to control immune activation following SIV co-infection in LTBI, potentially leading to CD4+ T-cell exhaustion in the lungs. We hypothesize that the duration and intensity of immune activation may impair T cells’ ability to mount the typical effector responses in Mtb/SIV co-infection. Notably, no increase in macrophage turnover was observed (Fig. 2H, I). In fact, significantly lower (P<0.05) macrophage turnover was noted in the lungs of cART+3HP-treated RMs compared to those treated with cART alone or untreated RMs (Fig. 2I). A higher number of BrDU+ nuclei (green) within macrophages (red), indicated by white arrows, was observed in the lungs of cART-naive and cART-treated RMs, but absent in the lungs of cART+3HP-treated RMs (Fig. 2H). In conclusion, these findings suggest that cART+3HP treatment leads to a significant reduction in macrophage turnover in the lungs of rhesus macaques, indicating that this combined therapy may impair the typical immune cell dynamics observed in LTBI. The absence of BrDU+ macrophages in the lungs of cART+3HP-treated RMs further emphasizes the potential influence of this treatment on macrophage proliferation and overall immune function in the respiratory system.

We further studied the impact of cART+3HP on TH17 and TH1* phenotypes in the pulmonary compartment of Mtb/SIV co-infected RMs. A significantly higher percentage of CD4+ T cells expressing CCR6, a regulator of migration and function of TH17 cells was observed in BAL cells of cART and cART+3HP-treated RMs (Fig. 2J) compared to LTBI and cART naive RMs at necropsy. Similarly, we observed a significantly higher percentage of CD4+ T cells co-expressing CXCR3 and CCR6 in cART and cART+3HP-treated RMs compared to LTBI and cART naive RMs, in both, BAL and peripheral blood cells (Fig. 2K, L). Additionally, cART+3HP-treated RMs harbored a significantly higher percentage of CXCR3+CCR6+CD4+ T cells (TH1*) in local and peripheral compartments compared to cART-treated RMs (Fig. 2K, L). These findings align with our previous observation that higher frequencies of CD4+ T cells co-expressing CXCR3 and CCR6 associate with bacterial control in Mtb/SIV co-infection24. The TH1* subset has been previously reported to be the most frequent Mtb-specific T-cell subset in the lungs of latent TB donors and their numbers increase when compared to healthy subjects25. The higher percentage of CXCR3+CCR6+CD4+ T cells in local and peripheral compartments could also be attributed to cART mediated control of viral replication as CXCR3+CCR6+ cells are known to be preferential targets of HIV/SIV infection25,26. Further, a reduction in this subset could be attributed to higher rates of LTBI reactivation. Thus, treatment of Mtb/SIV co-infected RMs with cART+3HP increases migration of TH17 and TH1* cells into pulmonary compartment compared to cART naive RMs.

Poor recovery of effector memory T cells by cART+3HP in Mtb/SIV co-infected RMs
To investigate the impact of cART+3HP on functional immune reconstitution in the pulmonary compartment of Mtb/SIV co-infected RMs, we further immunophenotyped the partially replenished CD4+ T cells into central memory (CD28+/CD95+) (CD4+ TCM) and effector memory (CD28-/CD95+) T cells (CD4+ TEM) (Supplementary Fig. 2). SIV co-infection of latent Mtb infection caused a significant increase in percentage of CD4+ TCM in BAL at week 11 (peak viremia prior to cART+3HP treatment) (P<0.0001) (Fig. 3A; Supplementary Fig. 3A). The increased percentage of CD4+ TCM persisted during and till end of the 12-week-long concurrent cART+3HP treatment. On the contrary, a significant decline occurred in the frequency of CD4+ TEM in BAL at the time of peak viremia. While the frequency of this subset marginally increased at end of the treatment (Fig. 3B; Supplementary Fig. 3A), it was still significantly lower than that seen in LTBI phase of the study (week 3 post Mtb-infection) (P=0.002). These findings align with our previous observation that cART treatment fails to fully replenish the depleted CD4+ TEM in BAL and lung of Mtb/SIV co-infected RMs15. Immunophenotyping of BAL CD8+ T cells into CD8+ TCM and CD8+ TEM showed a significant increase (P=0.01) in percentage of CD4+ TCM at peak viremia (week 11 post-Mtb infection or 2 weeks post SIV co-infection). This increase was mitigated by cART+3HP as seen by marginally reduced percentage at necropsy (P=0.01) (Fig. 3C; Supplementary Fig. 3B). No significant change was observed in percentage of CD8+ TEM in BAL at weeks 3, 11 and 24 (Fig. 3D; Supplementary Fig. 3B) (P=0.2). Thus, cART+3HP expands the CD4+ and CD8+ TCM but is unable to replenish CD4+ TEM in pulmonary compartment of Mtb/SIV co-infected RMs.

We further compared the restoration of CD4+ TCM and CD4+ TEM in BAL and lung of Mtb/SIV co-infected RMs treated with cART or cART+3HP (Fig. 3E–L). Despite a similar percentage of CD4+ T cells in BAL at necropsy, there was a significantly higher percentage (P<0.0001) of CD4+ TCM in cART+3HP-treated RMs compared to cART-treated RMs (Fig. 3E). No significant difference was observed in lung CD4+ TCM (Fig. 3F), BAL CD4+ TEM (Fig. 3G) and lung CD4+ TEM (Fig. 3H) between cART and cART+3HP-treated RMs. Similar to CD4+ TCM, cART+3HP RMs exhibited significantly higher (P=0.009) percentage of CD8+ TCM in BAL (Fig. 3I) with a concurrent decrease in CD8+ TEM (P<0.0001) (Fig. 3K) compared to cART-treated RMs. However, there was no significant difference between lung CD8+ TCM (Fig. 3J) and CD8+ TEM (Fig. 3L) in cART and cART+3HP-treated RMs. Overall, there were dynamic changes in the memory phenotype of CD4+ and CD8+ T cells in BAL compared to lung in cART and cART+3HP-treated RMs. BAL is a critical resource to study longitudinal changes in pulmonary immune response and has been shown to be useful to evaluate local response to therapy27,28.

cART+3HP increases Mtb-specific TH1/TH17 response in pulmonary compartment of Mtb/SIV co-infected RMs
BAL samples were collected from study RMs at weeks 5, 11 and necropsy post Mtb infection using standard operating procedures by the veterinarian. Single-cell suspensions were prepared as per the standardized lab protocol29. BAL cells were stimulated ex vivo with Mtb-specific antigens, ESAT-6/CFP-10 and Mtb Cell Wall Fraction (Mtb CW) for 16 h and stained with flow cytometry antibodies to detect IFNγ, TNFα, and IL-17, as described earlier30,31 (Supplementary Fig. 4). All Mtb-specific responses were background corrected (Supplementary Fig. 5). A significantly higher percentage of IFNγ expressing Mtb-specific CD4+ T cells was observed in BAL of cART+3HP-treated RMs at end of treatment when stimulated with ESAT-6/CFP-10 (Fig. 4A) (P=0.04) and Mtb CW (Fig. 4B) (P=0.03) compared to cART-treated RMs. We hypothesize that cART+3HP treatment effectively control bacteria thus enhancing production of protective IFNγ by Mtb-specific CD4+ T cells in pulmonary compartment of Mtb/SIV co-infected RMs32. In contrast to IFNγ, cART+3HP treatment resulted in a significantly lower percentage of Mtb-specific CD4+ T cells to produce TNFα after stimulation with either ESAT-6/CFP-10 (Fig. 4C) (P=0.03) or Mtb CW (Fig. 4D) (P=0.009) compared to cART-treated RMs. During Mtb infection, both IFNγ- and TNFα-producing T cells have crucial but distinct protective functions. While the former population is critical for macrophage activation and the subsequent killing of Mtb33, the latter contributes to immunocyte recruitment and granuloma formation33,34. It has also been reported previously that T-cell derived TNFα can promote proliferation of effector T cells resulting in increased immunogenicity35,36. Thus, while the IFNγ producing T-cell population is restored by combinatorial treatment, the TNFα producting population is not. The antigen-specific expression of TNFα in the absence of IFNγ on CD4+ T cells in Mtb-infected patients has been demonstrated to strongly correlate with the potential to develop active TB, while the opposite phenotype (absence of TNFα in the presence of IFNγ on CD4+ T cells) promotes latent infection37,38. The lack of increased Mtb-specific TNFα during concurrent cART+3HP treatment could however have a detrimental impact on effector T-cell function needed for sustained protection. Similar to IFNγ, a significant increase in IL-17+ CD4+ T cells was also observed in BAL of cART+3HP-treated RMs upon ESAT-6/CFP-10 (Fig. 4E) (P=0.01) or Mtb CW (Fig. 4F) (P=0.005) stimulation, compared to cART-treated RMs. The trends were similar in lung with significantly higher percentage of CD4+ T cells expressing IFNγ (P=0.04) and IL-17 (P=0.01) when stimulated with ESAT-6/CFP-10 (Fig. 4G) or Mtb CW (Fig. 4H) compared to cART-treated RMs. While the role of TH1 cells is clearly associated with protection in Mtb infection through IFNγ production, the role of TH17 cells is complex and is associated with tissue damage on one hand and anti-inflammatory responses on the other. However, our findings align with the recent studies that show that Mtb-responsive IL-17-producing CD4+ T cells are preserved in humans with LTBI with HIV-ART and that IL-17 producing CD4+ T cells constitute the dominant response to Mtb antigen39. Moreover, we did not observe an increase in the levels of pro-inflammatory cytokines, IL-6 and IP-10 in cART+3HP-treated RMs compared to cART-treated RMs (Fig. 4I). Overall, there is an increased TH1/TH17 Mtb-specific response in cART+3HP-treated RMs that associates with protection.

To better understand immune responses after concurrent cART+3HP treatment, we assessed transcriptional profiles of lung cells collected at necropsy from Mtb/SIV co-infected, cART+3HP-treated RMs by RNA sequencing (Fig. 4J). An increased expression of Type I IFN genes, such as IFNA2, IFNA1/IFNA13 was seen in cART+3HP-treated RMs (Fig. 4J). 20 out of 38 IFN associated genes from the cART+3HP dataset were upregulated (Supplementary Data 3). The role of Type I IFN in TB is ambiguous. Both human and animal studies show evidence for the role of Type I IFN in Mtb expansion and TB pathogenesis40,41. Murine data particularly suggests that Type I IFN signaling promotes TB progression. Our own data from RMs suggest that pDC expressing Type I IFN associate with TB progression42,43. A human blood transcriptional signature also largely comprised of Type I IFN response genes was described in TB patients44 and validated in macaques with TB45. Together, these results suggest a pathological role for Type I IFN in TB. Type I IFN signatures are however enriched among the lymphoid cell clusters from the lungs of Mtb-infected mice46 and in the BAL T cells of macaques vaccinated with a leading preclinical vaccine candidate where it is associated with protection from TB31. Our finding of an increased Type I IFN signature therefore align with previously reported transcriptional signatures in humans and NHPs42,44 and could be predictive of better lymphoid responses engendered after combinatorial treatment relative to cART alone. Our results however also suggest that while clinical disease is controlled by concurrent therapy, these animals continue to harbor molecular signatures that can be associated with immune activation and lung pathology47.

Single-cell transcriptomics signature in pulmonary compartment of Mtb/SIV co-infected RMs
To better understand lung-specific immune responses to combinatorial treatment in Mtb/SIV co-infected macaques, we further investigated the transcriptional changes at single cell level in these macaques. We collected BAL at four critical time points from the same RMs during the study period; week 5 (represents the asymptomatic phase of Mtb infection), week 11 (represents 2 weeks post-SIV co-infection), week 13 (represents post-SIV co-infection and 4 weeks of cART treatment) and necropsy (study endpoint after 12 weeks of cART+3HP treatment) (Fig. 5A). Using this experimental design, we were able to track the early transcriptomic changes in defined populations of cells at four different stages of Mtb/SIV co-infection (Supplementary Fig. 6A, B). This negates the need for LTBI- and cART-naive controls since they are represented by week 5 and week 11 time points in the same animals in this study. All samples passed quality control in terms of cell quality (fraction reads in cells) and sequencing after which they were run on 10x chromium controller and the combined data filtered by removing cells with 1)>8000 detected genes or 2)>15% mitochondrial content (Supplementary Data 4; Supplementary Figs. 7–11). t-distributed Stochastic Neighbor Embedding (tSNE) clustering identified eight transcriptionally distinct cell clusters across all samples that could be broadly classified into lymphoid and myeloid cell subsets (Fig. 5B; Supplementary Data 5): macrophages, monocytes, CD4+ or CD8+ T cells, B cells, basophils, NK/NKT cells and mast cells (Fig. 5C). The total number of transcripts (nFeature_RNA) and molecules (nCount_RNA) detected within each cell increased in early phase of SIV co-infection compared to LTBI phase (Supplementary Fig. 7). Cells were filtered to detect genes within the range of 10-8000 to remove extreme outliers. scRNA-seq data unequivocally demonstrate a significant reduction in CD4+ T (Fig. 5D) (P=0.02) or NK/NKT (Fig. 5E) (P=0.004) cell counts and proportions following SIV co-infection (Supplementary Fig. 12A–F), which cART+3HP fail to restore to levels observed in LTBI. Contrarily, cell counts and proportions of CD8+ T-cell cluster (Fig. 5F) (P=0.002), macrophage (Fig. 5G) (P=0.008) and monocyte (Fig. 5H) (P=0.02) significantly increased post SIV co-infection (Supplementary Fig. 12A–F).

We further studied the distributions of 42 genes across cell clusters (color-coded) when comparing three time points (week 5, week 11, and necropsy). These genes were selected from the DGE list based on their association with TH1 (IFNG, IFNGR1, IFNGR2, TBX21, STAT1, TNFAIP2, TNFAIP3, TNFAIP8, TNFAIP8L1, TNFAIP8L3, IL2, IL2RA, and BHLHE40), TH17 (IL20, IL20RA, IL20RB, IL21, IL21R, IL22, IL22RA1, IL22RA2, IL23A, IL23R, RORA, and STAT3), and immune activation (IDO, IRF4, CD38, CD69, LAG3, ICOS, CTLA4, IL13, CXCL10, CXCR1, CXCL12, CXCL13, IL27, KLRD1, IL6, MMP9, and FCGR1A) (Supplementary Fig. 13). A significant difference was observed in the 42 genes when compared between time points in each cell cluster (Fig. 6A–C). Module scores were computed and differences between time points were assessed for TH1 (Fig. 6A), TH17 (Fig. 6B) and Immune activation (Fig. 6C)—associated genes (Supplementary Data 6). Significant differential expression of the TH1 module was observed across multiple immune cell clusters (Fig. 6D), with the most pronounced changes occurring in macrophages and monocytes (FDR<5×10−29), particularly between week 11 and necropsy conditions (e.g., macrophages: FDR=4.83×10−175; monocytes: FDR=6.61×10−115), while CD4+ T cells, CD8+ T cells, and mast cells also showed significant, though comparatively moderate, changes (FDR range: 1.03×10−7 to 4.15×10−6). TH17 module expression (Fig. 6E) was significantly altered across multiple immune cell clusters, with the most striking differences observed in macrophages, particularly between week 11 and the necropsy (FDR=3.35×10−302), as well as other time points (e.g., wk5 vs N: FDR=1.16×10−46; wk13 vs N: FDR=2.57×10−19). Monocytes also exhibited great TH17-associated changes between week 11 and necropsy conditions (FDR=1.11×10−34). CD4+ and CD8+ T cells showed robust differential expression, especially at week 11 vs necropsy (e.g., CD4+: FDR=8.19×10−81; CD8+: FDR=2.87×10−63), suggesting broad activation of the TH17 program across lymphoid populations. Significant but more modest changes were also detected in NK/NKT cells (FDR=0.0042) and mast cells (FDR=2.21×10−6). The immune activation module (Fig. 6F) showed significant differential expression across a wider range of immune cell clusters. Macrophages exhibited the most substantial changes, particularly between wk5 and wk11 (FDR=1.43×10−57). Monocytes also displayed robust modulation of the immune activation module across all time points, with especially strong effects in the wk5 vs wk13 contrast (FDR=5.58×10−77) and wk11 vs necropsy (FDR=6.40×10−46). CD4+ and CD8+ T cells demonstrated significant immune activation module activity, notably in wk5 vs wk13 (CD4+: FDR=1.08×10−17; CD8+: FDR=1.99×10−6), while NK/NKT cells and mast cells showed modest but statistically significant changes (FDR=0.03 and 0.032, respectively).

Analysis of gene expression across time points within individual animals suggested that most animals may follow similar temporal expression trends (Supplementary Figs. 14–21). Week 5 captures the LTBI phase prior to viral challenge, while week 11 represents the early stage of SIV co-infection—before the initiation of cART+3HP—allowing us to isolate the impact of viral infection on innate immune dynamics in the lung. This comparison is critical for understanding how the host’s early response to SIV shapes subsequent immune control and informs the effectiveness of cART+3HP. Based on findings from Fig. 6F, DGEs were identified from time course contrasts between week 5 versus week 11 in the macrophage cluster (Supplementary Fig. 22). Pathway analysis of DGEs in the macrophage cluster between wk5 and wk11 revealed significant enrichment for innate immune and inflammatory responses. The GO enriched pathways include response to type I IFN, IFN-mediated signaling pathway, cellular response to type I IFN and activation of the innate immune response (Supplementary Fig. 22A). GSEA enriched pathways in cART+3HP RMs include cytokine mediated signaling pathway, response to type I IFN and innate immune response (Supplementary Fig. 22B), coupled with increased expression of immune activation—associated CD69 (Supplementary Fig. 23). This data corroborates our findings in bulk RNAseq (Fig. 4J) wherein, we observed predicted activation of IFN associated genes as well as upregulation of 20 out of 38 IFN associated genes in the cART+3HP dataset (Supplementary Data 3).

In conclusion, the focus on macrophages in this comparison allows us to highlight early immune perturbations that are highly relevant to the host-pathogen interaction in TB/HIV co-infection. The observed enrichment of type I IFN and innate immune pathways suggests a central role for these mechanisms in shaping the trajectory of infection and treatment response. Moreover, these findings complement our broader analysis of differential gene expression across TH1, TH17, and immune activation-associated genes, underscoring the complexity of the immune response during co-infection. Understanding how these innate responses evolve in the context of cART+3HP can provide important insights for optimizing therapeutic strategies aimed at achieving durable immune control and TB sterilization in HIV-positive individuals.

Methods
Study approval
All infected animals were housed under Animal Biosafety Level 3 facilities at the Southwest National Primate Research Center, where they were treated according to the standards recommended by AAALAC International and the NIH guide for the Care and Use of Laboratory Animals. The study procedures were approved by the Animal Care and Use Committee of the Texas Biomedical Research Institute.

Animal infection
This study included macaque data from completed studies15,19,69. A total of 22 specific pathogen-free Indian-origin rhesus macaques (Macaca mulatta) (4 females and 18 males) were infected with a low dose of approximately 10 CFU M. tuberculosis CDC1551 (BEI Resources, catalog NR13649) via aerosol as described before29,70–72. All the animals were housed in ABSL3 containment during the entirety of the experiment. Tuberculin Skin Test was performed at weeks 3 and 5 post TB infection to confirm infection. All the RMs were monitored for CRP, percent body weight and body temperature weekly through the study period. 18 of the LTBI RMs were then co-infected with 300 TCID50 SIVmac239 via the intravenous route 9 weeks post-TB infection15,17,19,69 (provided by the Preston Marx Laboratory, TNPRC, Covington, Louisiana, USA). All the procedures were conducted by a board-certified veterinary clinician. The remaining four RMs served as LTBI controls for the study. The viral infection was confirmed through plasma viral loads via reverse transcription quantitative PCR (RT-qPCR). Upon confirmation of SIV infection, the 18 RMs were then divided into three groups: the first group of 8 RMs served as co-infected controls with no cART administration; the second group of 4 RMs was started on cART at 2 weeks post-SIV co-infection or 11 weeks post TB infection (cART at peak viremia), and the third group of 6 RMs started cART+3HP at 2 weeks post-SIV co-infection or 11 weeks post TB infection. 3HP was administered once weekly for 12 weeks, while cART was administered daily for the 12 weeks. The RMs in LTBI control group were euthanized at week 24 post Mtb infection. All the RMs in cART-naive group were euthanized between 17-24 weeks post Mtb infection due to clinical signs of LTBI reactivation. The RMs in the cART group were euthanized after 9 weeks of cART treatment or at week 20 post Mtb infection while the RMs in cART+3HP group were euthanized at end of 12-week treatment or at week 24 post Mtb infection. Overall, all the RMs were euthanized by week 24, which was the predetermined study endpoint, or at earlier time points if they became clinically unwell and/or showed signs of TB reactivation, as assessed by veterinarians in accordance with humane endpoint criteria.

cART+3HP regimen
Co-infected RMs received a drug regimen consisting of 20 mg/kg of (R)-9-(2-phosphonylmethoxypropyl)adenine (PMPA, tenofovir, Gilead Sciences), 30 mg/kg of 2’, 3’-dideoxy-5-fluoro-3’-thiacytidine (FTC, emtricitabine, Gilead Sciences) and 2.5 mg/mL of the integrase inhibitor, DTG, Dolutegravir (ViiV Healthcare). The drugs were administered daily via subcutaneous injection of a cocktail of these three drugs in the vehicle kleptose at previously published doses19. Co-infected RMs also received a weekly oral dose of 15 mg/kg isoniazid and 15 mg/kg 3HP for 12 weeks beginning week 12 after aerosol infection up to week 23 post-TB infection. Oral intake was monitored by veterinary staff to ensure consumption.

PET/CT imaging
Longitudinal CT and PET/CT scans were performed using MEDISO’s LFER150 PET-CT scanner at 3–6 week intervals, starting from week 6 post-Mtb infection, with the last scan prior to necropsy73. Briefly, we performed 18F-fluorodeoxyglucose (FDG) PET/CT scans for each anesthetized RM using the breath-hold technique. RMs were anesthetized and intubated under the supervision of a board-certified veterinarian as per approved IACUC protocols. All the RMs received an intravenous injection of 1 mCi per kg of body weight dose of 18F-FDG74, procured from Cardinal Health radiopharmacy. The single field of view (FOV) and/or double FOV lung CT scans were performed using breath-hold as described75. PET scans were acquired after completion of the 40–50 min FDG uptake period. Images were visualized using Interview Fusion 3.03 (Mediso) and reconstructed using Nucline NanoScan LFER 1.07 (Mediso) with parameters as described76. The lung segmentation, volumetric and SUV analysis was performed using Vivoquant 4.0 (Invicro, USA)73.

Viral load and bacterial burden measurement
Bacterial burden in BAL was measured throughout the study period as previously described17. Viable Mtb burden was also measured at necropsy in BAL, lung, spleen, bronchial lymph node and individual granulomas collected at necropsy17,69. Viral loads in acellular BAL supernatant and plasma were determined by RT-qPCR at peak viremia (2 weeks post-SIV or 11 weeks post TB-infection), week 13, week 15 post-Mtb infection and at necropsy. The measurements were performed by NIAID, DAIDS, NHP Core Virology Laboratory for AIDS Vaccine Research and Development). Qiagen QI Symphony DSP Virus/Pathogen Midi Kit (96)/QIAgility Applied Biosystems StepOnePlus Quantitative Real-Time PCR was used to measure the viral loads. A lower limit of 100 copies/sample was set for quantification of SIV copies in this assay.

High parameter flow cytometry
High parameter flow cytometry was performed on BAL cells at pre-infection, pre-SIV (wk 3, 5), post-SIV, pre-cART (wk 11), post-cART (wk 20 or necropsy) and post-cART+3HP (wk 24 or necropsy). Lung, bronchial lymph nodes and granulomas were harvested at necropsy and processed as described earlier15,17,69. Briefly, the tissues and granulomas were dematricized, filtered, RBC lysis was performed, and cell counts were obtained using an automated cell counter. The lung tissue was digested with Liberase TM/DL (Sigma-Aldrich), lung lysate was filtered, RBC lysed and cells were counted using an automated cell counter. The BAL was filtered through a 100 µm filter, RBCs were lysed, and cell counts were also measured with an automated cell counter. A total of 1×106 single cells were stained with surface markers to analyze different T-cell phenotypes (Supplementary Data 7). The single-cell suspension from tissues and BAL was incubated with the antibody cocktail for 25 minutes, washed, and fixed in BD Fixation and Stabilization Buffer. The samples were then run on the BD Symphony. To study the effect of treatment on T-cell function, the freshly collected BAL cells were stimulated ex vivo with Mtb-specific antigens, ESAT-6/CFP-10 and Mtb Cell Wall Fraction (BEI Resources, 10 µg/mL) for a total of 16 h. Brefeldin A (0.5 µg/mL, SIGMA) was added 2 h after the onset of stimulation. After stimulation, the cells were stained with LIVE/DEAD fixable Near-IR stain (Thermo Fisher) and stained subsequently with the surface antibodies: CD4-PerCP-Cy5.5 (BD Biosciences), CD8-APC (BD Biosciences), CD3-Alexa Flour 700 (BD Biosciences), CD95-BV421 (BD Biosciences), CD28-PECy7 (BD Biosciences) and CD45-BUV395 (BD Biosciences). Cells were then fixed, permeabilized and stained with intracellular antibodies: IFNγ - APC-Cy7 (Biolegend), IL-17-BV605 (Biolegend) and TNFα - BV650 (Biolegend). Cells were washed, suspended in BD stabilizing fixative buffer and acquired on BD FACS Symphony flow cytometer. Analysis was performed using FlowJo (v10.6.1) using previously published gating strategy15,17,19,72.

Gross pathology
The animals were euthanized for necropsy, and lung lobes, spleen, liver, and bronchial lymph nodes were collected. All the tissues were weighed at the time of collection. Tissues were fixed in 10% neutral-buffered formalin, paraffin-embedded, sectioned at 5 µm thickness and stained with hematoxylin and eosin (H&E) using standard methods. Lung tissues were collected stereologically at necropsy by a board-certified veterinary pathologist. The H&E-stained slides were scanned in Zeiss AxioScan Z1, and the images were analyzed using HALO 4.0 software. HALO scores served as an indication for the true percentage of lung affected (primary and secondary lesions from TB). The lesions in each lung lobe were scored for pleural thickening, intralobular septae inflammation, perivasculitis, pneumocyte hyperplasia and lymphadenitis. The score was then utilized to annotate the different disease types: active non-necrotizing, active suppurative, active caseous, latent sclerotic, and latent fibrocalcific.

Immunohistochemistry staining
Fluorescent immunohistochemistry was performed on formalin-fixed, paraffin-embedded lung and bronchial lymph node tissues as previously described15,16,19,69,77. The stained slides were scanned in the Axio Scan Z1, and the images were analyzed using HALO software. Immunohistochemistry was performed to study the impact of cART or cART+3HP on macrophage proliferation by staining BrDU+CD163+CD68+ macrophages in the lungs of the cART-naive, cART and cART+3HP groups (Fig. 2K). The presence of BrDU+ nuclei (green) within macrophages (red), as indicated by the white arrows, was observed in the lungs of cART naive and cART only treated macaques.

BAL cell isolation
BAL was performed with 80 mL of sterile saline in BSL3 on NHPs at weeks 5, 11, 15 and necropsy after Mtb infection. The BAL was filtered, centrifuged 400×g, 10 min at 4 °C), lysed for red blood cells (RBCs) using ACK lysis buffer (GibcoTM) and resuspended in 2 mL final volume of RPMI 1640 media (GibcoTM). The cells were counted and frozen in Cryostor in internally threaded cryovials in liquid nitrogen for downstream scRNA-seq processing using 10× Genomics platform. The BAL cells were frozen in Cryostor (Sigma-Aldrich). Cryostor is formulated to contain 2% dimethyl sulfoxide (DMSO). Cryostor is designed to preserve cell viability and functionality through modulation of cellular biochemical response to the cryopreservation process. Our lab routinely utilizes this reagent to preserve freshly isolated BAL cells.

Quality control for frozen BAL cells
Prior to running the BAL cells on 10× Genomics platform, the cells were analyzed for viability using i) automated cell counters, ii) manual counts using Trypan Blue and iii) microscopic evaluation. Briefly, cells were thawed on ice. 100 µL of cells was washed once in 1 mL warmed 1× phosphate-buffered saline (PBS) (Gibco), centrifuged, and resuspended in 1 mL of 1× PBS. Cells were mixed in a 1:1 ratio with Trypan blue and counted in an automated Countess as well as by a hemocytometer. Cellular morphology, including shape and size, was determined using a standard bright-field light microscope. Institutionally approved protocols were applied when removing samples from BSL3.

Single-cell RNA Library generation and sequencing
BAL cell suspensions were loaded onto Chromium instrument (10x Genomics) to generate single-cell beads in emulsion. Single-cell RNA-seq libraries were then prepared using Single-Cell 3’ Gel bead and library kit version 3.1 (10× Genomics). Single-cell barcoded cDNA libraries were quantified and sequenced on an Illumina NovaSeq 6000. Read lengths were 28 bp for read 1, 10 bp for index 1, 10 bp for index 2, and 100 bp for read 2. Cells were sequenced to about 50,000 reads per cell.

Single-cell data analysis
Cell Ranger Single-Cell Software suite (V7.0.1) from 10x was used to perform sample demultiplexing and generate fastq files. The resulting fastq files were aligned against reference genome mmul10 (Genebank, https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_003339765.1/) with cell ranger count. The targeted cell recovery per sample was set to 10,000 cells. The cell ranger counting results for 24 samples were further integrated and analyzed by R software with package Seurat (V5.0.1). The data matrix for each sample was read by Read10X. The Seurat objects of all 24 sample data were created and merged. The combined data were filtered by removing cells that had >8000 detected genes or more than 15% mitochondrial contents. Then the data were normalized by method “LogNormalize” with scale.factor=1e6, and the data were further filtered by keeping cells with CD45(PTPRC) expression as the final dataset for single cell clustering analysis. The most variable genes were detected by the FindVariableFeatures function with nfeatures=2000, then data were scaled by ScaleData, and principal component analysis was performed by RunPCA with npcs=50. The merged and filtered data were integrated by the function ‘IntegrateLayers’ with method “CCA Integration” and parameters orig.reduction=“pca”, k.weight=50. Data clustering was run by FindNeighbors (“integrated.cca”, dims=1:32) and FindClusters (resolution=0.155) to pre-select 15 clusters. To visualize the data, the RunTSNE dimensionality reduction was performed by reduction=“integrated.cca”, dims=1:32. To annotate the clusters, the package clustermole was applied and combined with the generally known immune makers. Differential gene expression between different time Course for each cluster were firstly identified using FindAllMarkers function in Seurat R package by (only.pos = FALSE, min.pct=0.0, logfc.threshold=0.0), then the marker genes were further studied and evaluated by different cut-off. GO enrichment analyses for all clusters for week 5 versus week 11 are provided in Supplementary Data 8–15. Differential gene expression for each cluster is provided in Supplementary Data 16–23. Only genes with a raw p value <0.05 and a log2 fold change >1 were retained for subsequent Gene Ontology (GO) and KEGG pathway enrichment analyses. In these analyses, enriched terms were defined as those with an FDR (Benjamini-Hochberg) corrected p value <0.05 and a Storey’s q value <0.05. All enrichment analyses were performed using the Bioconductor package clusterProfiler.

Bulk RNA sequencing
Lung tissue pieces from RMs were weighed for genomic DNA extraction, transferred to a petri dish, and tissue dissociation was performed using a tissue homogenizer. The Chelex method was used for Mtb DNA extraction from macaque lung tissue. Post dissociation, the lung samples were resuspended in 2 mL tubes containing lysing matrix B and Chelex. The bacteria were lysed mechanically using a bead beater for 30s at 3800 rpm for 3 cycles. The tubes were transferred to a heating block and boiled at 100 degrees Celsius for 15 mins. This was followed by water bath sonication for 15 mins. The prepared samples were then used for stranded mRNA-Seq library prep, and 75bp PE sequencing on Illumina NextSeq 500 Mid Output Platform. RNA quality control, library, and Agilent quality control were also performed. Qiagen’s Ingenuity Pathway Analysis (IPA) software was used to analyze the RNA-seq data to identify significant pathways. Raw, demultiplexed .fastq sequence files were uploaded to Partek Flow analysis software for standard RNA-seq analysis, including differential gene expression analysis. Briefly, bases with a Phred score less than Q30 were trimmed, aligned to RefSeq Mmul10 reference assembly (GCF_003339765.1) with STAR aligner v2.7.8a using default settings, and quantified using the Quantify to annotation model (Partek E/M) tool with the RefSeq Mmul10 annotation release as the transcript model. Raw gene counts were normalized by median ratio (transformed on samples), and subsequent DESeq2 differential expression analysis was carried out by comparing cART-treated vs. cART+3HP-treated samples with the cART+3HP-treated samples as reference levels. The details of the analysis are now provided in Supplementary Data 24. QC results are provided in Supplementary Data 25. Differentially expressed gene lists and gene set analysis results are now included in Supplementary Data 26. A list of enriched canonical pathways, along with associated p values for the cART+3HP samples, is provided in Supplementary Data 27. Accession for these SRA data: PRJNA1327945.

Statistics and reproducibility
All statistical analyses were performed using appropriate methods as indicated in the figure legends. Sample sizes were based on prior experience with rhesus macaque studies of similar design and were sufficient to detect biologically meaningful differences in immune responses. No data were excluded from the analyses. The experiments were not randomized. The investigators were not blinded to group allocation during the experiments or outcome assessments. All attempts at replication were successful, and consistent results were obtained across biological replicates. Data and analyses have been verified for accuracy and reproducibility using independent computational pipelines.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Sharan et al. evaluate the impact of simultaneous initiation of cART and a three-month regimen of 3HP in a rhesus macaque model of LTBI with SIV co-infection. The primary outcomes of interest are immunologic, including CD4 T cell and CD8 T cell abundance in BAL and lung tissue and macrophage turnover. There are some exciting aspects of this study- in particular, this model offers bacteriologic and immunologic insights into a clinical intervention (addition of 3HP upon initiation of cART in individuals with LTBI) currently in clinical trials. However, there are significant limitations to the study that overall diminish my enthusiasm.\nMajor comments:\n1. Limited scope of new investigations: As transparently acknowledged by the authors in the text of the manuscript, many of the experimental results of this work were part of their previous work/have previously been published. This includes all of the immunologic comparisons of BAL and tissue T cells and macrophages in RMs with LTBI and SIV co-infection who do or do not receive cART. The new experimental condition introduced in this work is the addition of 3HP upon initiation of cART. There are some differences in that experimental group noted in the BAL T cells, including activated T cells, and some differences in cytokines in 3HP-treated RMs. However, it’s not entirely clear what the significance of these differences are for reactivation of any remaining viable Mtb or what the significance is in terms of future immunologic protection from reinfection. The overall insights gleaned from the work are limited, and this seems to be reflected in the abstract and discussion- reading them both carefully, it’s hard to pin down what the most significant or impactful findings are in the work.\n2. Limited information offered from the scRNAseq analysis: scRNAseq analysis can potentially offer substantial insight, in particular into subpopulation shifts under different conditions and the dominant expression modules in those subpopulations. Two of the six figures in the paper are devoted to scRNAseq- a substantial proportion of the paper. However, the scRNAseq analysis offered here provides minimal complementary insight into immune dynamics during treatment. There’s no text description of a key takeaway from Figure 5D aside from the number of clusters identified, and 5E and 5F seem to provide no interpretable information at all. Figure 6 is slightly more interpretable, but it’s still hard to find a clear takeaway from the data.\nMinor comments:\n-From 1A and the text, it’s hard to tell if the LTBI population of RMs are exposed to SIV or not\n-It’s hard to know what to make of the data offered in 1K and the accompanying text, as only RMs from a single experimental group received PET scans- there’s no comparator\n-There is very limited methodologic detail included for most experiments, somewhat limiting the ability to interpret the data\n-1H would be better represented as a supplementary table with an additional column showing the normal range for each value\n-5B and C are not necessary as the process is standard and can be briefly described in the methods\n-The discussion section primarily resummarizes the results of the study rather than using those results to reflect on context or how the results concretely move the field forward", "reviewer_2": "In this manuscript, Sharan et. al. examine the impact of concurrent cART and Isoniazid and Rifapentine (3HP) treatment during MtB and SIV co-infection in a NHP model. Following 12 weeks of combined treatment cART+3HP animals demonstrate decreased bacterial burden. Despite this significant and promising finding, the authors report incomplete immune reconstitution and continued evidence for immune exhaustion and inflammation. These findings expand upon the groups previous findings and use historical data for comparison to their cART+3HP contemporary cohort. Although the results presented are of interest, there are gaps in the methodology and data that, in its current state, limit the data interpretation.\nMajor Comments:\n1. Historical data from animals enrolled in the LTBI, cART naïve, and cART group were used as comparators for the experimental cART+3HP group. While leveraging historical samples is important for reducing the number of NHPs enrolled in the study, it is a limitation of the study. Additional scrutiny and information is needed to confirm the validity and rigor of the findings:\na. Prior to the start of the combined treatment regimen (Week 11) how comparable are the clinical disease and bacterial burden between the cART+3HP and historical groups used for comparison? Lines 532-533 indicate that “Bacterial burden in BAL was measured throughout the study”, please provide this data in comparison to historical groups.\nb. How do animal demographics (Supplemental Table 2) influence the interpretation of the results? e.g. age, sex, MHC class. Sex differences are inadequately addressed. The cART naïve and LTBI groups are exclusively male, cART+3HP is mostly male and cART treated is mostly female.\n2. Details regarding necropsy need to be clear and consistent.\na. Based on Supplemental Figure 2C, necropsies between the groups appear to occur at different timepoints (LTBI Wk23-25), cART naïve (Wk17-23), cART (Wk19-20) and cART+3HP (Wk23-24). How might bacterial burden or immune responses differ between these varying timepoints?\nb. What were the humane endpoints used (Lines 128-129)? If some of the necropsies were planned as a study endpoints then the survival data in Supplemental Figure 2C is misleading.\nc. Necropsy timepoints are inconsistently described and the timing is critical for data interpretation. 1) Line 129 indicates some animals were euthanized “2 weeks post SIV co-infection”, which according to Figure 1A would correspond to Wk11, yet the Supplemental Figure 1C indicates Week 17 as the earliest necropsy. 2) Lines 507-509 indicate that “All the RMS in the cART-naïve group had to be euthanized within 2-4 weeks of cART treatment”, which according to Figure 1A would correspond to Wks13-15, yet the Supplemental Figure 1C indicates Week 17-23 as the necropsy timepoints. 3) Lines 509-510 “the RMs incART+3HP group were euthanized at end of 12-week treatment at week 24,” but Figure 1 C shows that 1 animal went to necropsy at Week 23\n3. In the current submission, an insufficient amount of details is provided for the experiments to be reproduced.\na. Details of how samples were collected, processed, and/or stored prior to analysis should be provided. 1)Immune responses in Figures 2 and 3 between the BAL and lung are not consistent. Why might this be? 2) In Figure 4 how were cells processed and stored before being thawed for the single cell experimentation?\nb. What statistical tests were used for Figure 1B and G. In Figure 1B what is being compared?\nc. The method details and flow cytometry panel used in Figure 2 should be provided.\nd. Line 115: As the LTBI group did not receive SIV, this statement is incorrect.\ne. The gating scheme in Supplemental Figure 2 identifies Tem as CD28- CD95-, but are defined correctly by the authors as CD28-CD95+. Please explain this discrepancy.\nf. Lines 587 and 590 indicate that 16 samples were used in analysis for Figure 6, but 24 samples were included in the original pipeline of Figure 5. What is the rationale for this? Additional details should be provided in the Figure 6 legends.\ng. The number of animals and timepoints should be explicitly described in all Figure legends\n4. Statistics to support data interpretations should be provided.\na. Given the small sample size of n=4 for the LTBI and cART groups, nonparametric statistical analysis seems more appropriate.\nb. Line 156: Where is the correlational data being referenced?\nc. Where are the statistics to support lines 196-198, 217-219, 219-221\nd. The statistical test for each panel should be explicit in the Figure Legends. Currently they are lumped together, so it is unclear which tests are being applied.\nMinor Comments:\n1. A description of Figure 3 I-J is not provided in the figure legend\n2. Labels for the legends of Figure 3K, 4E/F, and 5 should be provided\n3. The term LTBI is sometimes used in reference to the experimental group and at other times to the phase of the study and can be confusing at times.\n4. The information in lines 331-341 may be more useful in a table format.\n5. The accession number on line 603 is missing", "reviewer_3": "This is a well-executed study that combines several investigative modalities to demonstrate the effect of concurrent TB (3HP) therapy and HIV ART (emtricitabine/tenofovir/dolutegravir) in a non-human primate model of Mycobacterium tuberculosis (Mtb) and simian immunodeficiency virus (SIV) co-infection. Using techniques such as histopathology/immunohistochemistry, PET/CT, flow cytometry, RNA sequencing, and ELISA, these authors demonstrate novel insights into immune reconstitution and persistent inflammation in the lungs despite viral and bacterial control with combination therapy.\nOverall, they demonstrated that ART+3HP certainly reduced the burden of pulmonary TB burden at study endpoint compared to cart alone and untreated animals. TH1/TH17 CD4 T cel responses also increased in ART+3HP treated animals but incomplete immune reconstitution and persistent immune-mediated inflammation persisted regardless of LTBI txt and concomitant ART. Overall, this is an interesting finding that suggests that in the setting of SIV (and perhaps HIV) initiation of ART with 3HP certainly has benefits but does not eliminate TB (or HIV) associated inflammation which can hinder TB responses.\nOne general comment is how the TB infection model adequately recapitulates LTBI in the human population, especially those in endemic areas with multiple long-term exposures. I realize there are limitations to all animals models of LTBI, but some discussion on this and addressing how LTBI in the macaque differ from human LTBI would be essential in understanding and extrapolating these results.\nWith particular regard to the imaging component of this study, FDG PET/CT scans were used to study lung lesions in a subset of the rhesus macaque subjects at certain timepoints: Week 6 (LTBI phase), Week 12 (1 week post cART+3HP initiation), Week 16 (4 weeks post cART+3HP initiation), and Week 22 (10 weeks post cART+3HP initiation). The authors used the lack of lung lesion progression at week 6 to demonstrate an LTBI state, which as above, is questionable to how this equates to LTBI in humans. For example, a suspicion lesion with any evidence of mTB on BAL, sputum culture or gene applification would be considered active TB. Further clarification of how stability of lesions equates to LTBI should be povided.\nAt week 12, a significant increase in 18F-FDG uptake in the lungs was ascribed to progression of TB pathology in the setting of SIV co-infection. Following therapy with ART and 3HP, there was no progression of the size of the lung lesions, however metabolic activity by PET did not decrease. The authors suggest that combination treatment aided in treatment of caseous lesions but did not reduce the ongoing inflammation in the lungs.\nOverall, the use of PET/CT in this study is a strength which serves to supplement the researchers’ other investigative modalities, offering a longitudinal visual element. Further, the use of both CT and PET helps to demonstrate both structural changes as well as metabolic activity. It is interesting to note that the lesions that did persist on ART+3HP remained PET avid, suggesting ongoing increase metabolic processes in these lesions, which, in some ways, would be expected if 3HP and ART railed to fully suppress or eradicate these lesions. One wonders, however, given the limitations of understanding how this LTBI model holes up to human disease, if these foci represent undertreated active TB rather than failure to control inflammation or immune responses in “latent” associated lesions. It seems from images and from other direct lung data that the “LTBI” here is more on the end of mild active TB disease. Further discussion on this would significantly strengthen the paper. E.g. would actually treating the monkey with at least 2 active drugs for a full treatment course result in different outcomes- one would assume so given the data presented.\nAreas in which this study could be improved include sample size and lack of an ART-only control group. The small sample size of 3 macaques for the imaging component of the study carries a limitation in power with the understanding that these are expensive and logistically difficult studies. The lack of a 3HP control arm also makes it somewhat difficult to know the full extent of 3HP on TB infection.\nThe text is quite dense and often difficult to follow without consant referral to study images. Streamlining in organization and textual presentation would make this manuscript more accessible to the general scientific reader. E.g. it is often difficult to know what each RM group refers to in the text or which group underwent which therapy or had which outcome. The figures are much more clear.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "Brief summary of findings:\nThe manuscript by Sharan et. al. examines the effect of combined TB and HIV therapies in a rhesus macaque model for latent TB infection (LTBI) and Simian Immunodeficiency Virus (SIV) co-infection. The authors studied the effect of cART and isoniazid and rifapentine (3HP) treatment in the coinfected animals on the components of TB immunity in lung, control of bacterial infection and clinical presentation of TB compared to coinfected animals with or without cART and untreated LTBI animals. Although the combination treatment showed lower levels of bacterial infection compared to other conditions, CD4+ T cells were not fully restored and the reconstituted CD4+ T cells also showed activated, exhausted and inflamed phenotype. Additionally, enrichment of Type 1 interferon related transcriptional signature and TB specific TH1/TH17 responses were seen but TB specific TNFa was reduced in the combination treatment. Overall, the study showed that combination cART and 3HP treatment in itself was not sufficient to prevent activation of TB because of certain immune mechanisms in the lung compartment.\nOverall assessment of quality and importance:\nThe study addresses an important issue of latent TB activation in the context of HIV infection and seeks to examine if combination of cART and 3HP would be effective in preventing progression of TB in an in vivo model. The results showing reduction of bacteria after the combination treatment were quite promising. Data showing suboptimal reconstitution of immune cells in the lungs although disappointing are informative in guiding future studies on treating concurrent HIV and activation of latent TB. The experiments are well designed with sufficient numbers of replicates for each condition and the methods are well-suited to the questions being asked. The interpretations of results are generally reasonable and the manuscript is easy to read. The use of transcriptomic methods to understand what biological mechanisms might be impairing immune function is justified. However, this reviewer has major concerns about how the transcriptomic data (especially from single-cell sequencing) are presented and interpreted in the manuscript. This part of the paper requires significant improvement before being ready for publication. Specific concerns are described below.\nSpecific major concerns and suggested additional experiments:\n• There is an imbalance in age and sex of animals across different conditions; two conditions only have male animals. The manuscript should address how the results could be affected by this.\n• Information about bulk RNA-seq is very sparse. Method section is missing information about bulk-RNA-seq, especially how results in Figure 4 were generated. The legend for this figure seems not to match the figure. It would be helpful to include additional results including PCA to define relatedness of transcriptomic profiles in different animals; differential gene expression; and GO/pathway analysis. List of genes from differential gene expression or from the selected pathways highlighted in the results (e.g. apoptosis) should be included as supplementary material.\n• Rationale for doing single-cell RNA-seq should be better explained in the manuscript. What information can be obtained from single-cell analysis that cannot be obtained from a more straightforward bulk RNA-seq? Were the authors looking for transcriptomic signatures in specific population of cells?\n• The message of figure 5 is unclear. The contents of the figure do not show a clear transcriptomic signature (as the figure legend mentions). Moreover, the sizing of the cartoon is out of proportion to the rest of the figure. Consider decreasing the size of the instruments shown as they are just part of outline and not actual results. Consider also incorporating other results from the analysis (e.g. violin plots of cluster markers, or even feature plots as shown in supplementary fig 7) to make the figure more informative. Results from differential gene expression between different conditions in each cluster could also be also helpful. The figure legend also needs to be clarified. The violin plots for the n_feature_counts and n_RNA_counts are not very informative as the differences in these could arise from differences in library sizes, presence of empty droplets or multiplets etc. Overall, arguments and statistics related to this figure were insufficient to persuade this reviewer of relevant biological findings in the data.\n• The lower limit on for the number of detected genes per cell is on a lower side (10). The default for Seurat is 200. Why use such a low threshold? Including cells with such low numbers of detected genes could affect validity of downstream results. Perhaps a low threshold was chosen due to excessive loss of cells at higher thresholds? If this is the case, then including a violin plot showing distribution of number of genes detected/cell in each sample could be helpful.\n• Were data filtered for % of mitochondrial reads? Filtering on this could remove signals from dead cells.\n• No details are provided about how results in figure 6 were generated. Are the results statistically significant? What is the figure really trying to show? Are the gene lists included here curated from literature or databases?\n• Additional analysis that may be helpful: GO/Pathways, GSEA, changes in abundance of certain population of cells across conditions, plot expression values of Th1/Th17, IFN 1, TNF-alpha related genes between conditions or in a feature plot.\nOther minor concerns\n• Fig 1 C. Are there multiple samples from the same animal? It seems there are more dots than the number of animals esp for cART naïve.\n• Figure 4 legend doesn’t have information about the panels I and J.\n• Figure 4 panel J. How many pathways shown in the heatmap are significant (z-score and p-value cut-off).\n• Single cell method details missing details (e.g. no. of cells loaded).\n• Why is read length of R1 read only 28 bp in single cell sequencing?\n• Units are missing from supplementary table headers.\n• Statistical information from differential gene expression is not provided.\n• Supplementary fig 1 C, p-values not provided. Are the differences significant?\n• Inconsistency in information presented in methods and elsewhere (e.g. TSNE vs UMAP).\n• Why are estimated number of cells diff than target cells in supplementary table for 10X experiment?", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "POINT-BY-POINT RESPONSES TO REVIEWER COMMENTS\nReviewer #1:\nIn this manuscript, Sharan et al. evaluate the impact of simultaneous initiation of cART and a three-month regimen of 3HP in a rhesus macaque model of LTBI with SIV co-infection. The primary outcomes of interest are immunologic, including CD4 T cell and CD8 T cell abundance in BAL and lung tissue and macrophage turnover. There are some exciting aspects of this study- in particular, this model offers bacteriologic and immunologic insights into a clinical intervention (addition of 3HP upon initiation of cART in individuals with LTBI) currently in clinical trials. However, there are significant limitations to the study that overall diminish my enthusiasm.\nResponse: Thank you for your insightful feedback on our manuscript. We appreciate your positive comments on the exciting aspects of the study, especially in terms of providing both bacteriologic and immunologic insights into the clinical intervention of adding 3HP upon initiation of cART in individuals with LTBI. We have now provided more detailed explanations and context regarding these limitations in both the Discussion and the Methods sections to ensure the study's findings are appropriately framed. We hope that the revisions will address your concerns, making the study more robust and informative. Thank you again for your constructive critique.\nMajor comments:\n1. Limited scope of new investigations: As transparently acknowledged by the authors in the text of the manuscript, many of the experimental results of this work were part of their previous work/have previously been published. This includes all of the immunologic comparisons of BAL and tissue T cells and macrophages in RMs with LTBI and SIV co-infection who do or do not receive cART. The new experimental condition introduced in this work is the addition of 3HP upon initiation of cART. There are some differences in that experimental group noted in the BAL T cells, including activated T cells, and some differences in cytokines in 3HP-treated RMs. However, it’s not entirely clear what the significance of these differences are for reactivation of any remaining viable Mtb or what the significance is in terms of future immunologic protection from reinfection. The overall insights gleaned from the work are limited, and this seems to be reflected in the abstract and discussion- reading them both carefully, it’s hard to pin down what the most significant or impactful findings are in the work.\nResponse: The authors thank the reviewer for their feedback. Modeling antiretroviral therapy (ART) and anti-tuberculosis (anti-TB) therapy in M. tuberculosis (Mtb) and HIV co-infection in nonhuman primates (NHPs) is crucial from a human perspective because it provides a more accurate representation of the complex interactions between these two infections, which is challenging to fully replicate in human clinical studies. NHPs, particularly rhesus macaques, are genetically similar to humans and offer a unique opportunity to study the immune and microbiological responses to co-infection in a controlled environment. This model allows for the evaluation of therapeutic strategies such as the simultaneous initiation of ART and anti-TB treatment, which is currently being tested in clinical trials for individuals with latent TB and HIV co-infection. Understanding the pulmonary immune responses pre-and post-therapy, and potential drug interactions in NHPs provides critical insights into the unique challenges posed by HIV co-infection. Moreover, NHP models are essential for exploring the safety, efficacy, and timing of combined therapies, ultimately leading to better clinical outcomes in people living with both HIV and TB. Although the WHO recommends concurrent 3HP and cART in TB-endemic settings, uptake remains poor and the immune mechanisms underlying the benefits of concurrent cART and 3HP have not been defined. Identifying the components of TB immunity that remain impaired after ART, versus those that are restored by concurrent ART and 3HP is an innovative approach to identify correlates of immune control versus reactivation. Such studies will also aid in targeting potential targets for host-directed therapies that further improve treatment regimens for TB/HIV and provide insights into designing therapeutic vaccines that prevent reactivation of LTBI.\nLines 398-413.\nIn this manuscript, we used historical controls to minimize the numbers of animals used, consistent with standard practices in NHP research encouraging the use of archived samples or control cohorts to reduce the number of study animals for repeated measures of same readouts. We wanted to compare the T , T , immune activation H1 H17 markers that were reconstituted in cART+3HP groups compared to cART only and cART naïve group and to ascertain if the reconstitution was comparable to the immune responses seen in LTBI. Hence, we chose to use previously completed cohorts. We determined that concurrent treatment of Mtb/SIV co-infected rhesus macaques with cART and 3HP did not rescue the immune defect (initiated as early as the first 2 weeks of SIV co-infection) better than cART alone. Specifically, cART+3HP did not control virus-driven chronic immune activation in the pulmonary compartment (HLA-DR+CD4+ T and CD69+CD4+ T cells), or periphery (CXCR3+CCR6+CD4+ T cells). Additionally, the global frequency of CD4+ T cell as well as the Mtb-specific CD4+ effector memory T cells remained significantly lower at the end of cART and cART+3HP treatment compared to LTBI only macaques demonstrating failure of treatment to reconstitute the functional responses in the pulmonary compartment. These are critical novel findings in a biologically relevant preclinical animal model and has important clinical implications for humans who receive these treatments. Lines 417-431.\nThe main finding of this study is the incomplete immune reconstitution and persistent immune-mediated inflammation, even with LTBI treatment and concomitant cART. This is a crucial finding and we believe it underscores the complexity of TB and HIV co-infection, particularly in the context of treatment. While cART+3HP clearly provides benefits, our findings highlight the persistent challenges in eliminating inflammation, which may continue to hinder TB responses despite the intervention. Lines 452-456.\n2. Limited information offered from the scRNAseq analysis: scRNAseq analysis can potentially offer substantial insight, in particular into subpopulation shifts under different conditions and the dominant expression modules in those subpopulations. Two of the six figures in the paper are devoted to scRNAseq- a substantial proportion of the paper. However, the scRNAseq analysis offered here provides minimal complementary insight into immune dynamics during treatment. There’s no text description of a key takeaway from Figure 5D aside from the number of clusters identified, and 5E and 5F seem to provide no interpretable information at all. Figure 6 is slightly more interpretable, but it’s still hard to find a clear takeaway from the data.\nResponse: We introduced significant changes to how scRNAseq data was analyzed and interpreted. As senior author, Dr. Kaushal recruited Dr. Hulin Wu, Professor and Director of Biostatistics at University of Texas Health Sciences Center at Houston, and his team of bioinformaticians/biostatisticians. We have now significantly revised both the Figures. Figure 5 includes the study design, tSNE plot representing the clusters, feature plots of selected genes representing annotation, and plots representing the cell counts per cluster per time point. We identified eight clusters in the BAL collected at weeks 5, week 11, week 13 and necropsy; macrophages, monocytes, CD4+ T cells, CD8+ T cells, B cells, Basophils, NK/NKT cells and mast cells. scRNAseq data validates our previous findings of a significant reduction in CD4+ T cell counts and proportions post SIV co-infection that cART+3HP does not restore to levels seen in LTBI. Newer analyses further inform us about the dynamics of other immunocytes critical to TB immunity, that we did not study using the traditional flow cytometry technique: macrophages, monocytes, NK/NKT cells and mast cells (Figure 5F-5J, Supplementary Figure 8).\nFigure 6A includes Violin plots displaying the distributions of 42 differentially expressed genes across cell clusters (color-coded) when comparing three time points (week 5, week 11, and necropsy). These genes were associated with TH1 (IFNG, IFNGR1, IFNGR2, TBX21, STAT1, TNFAIP2, TNFAIP3, TNFAIP8, TNFAIP8L1, TNFAIP8L3, IL2, IL2RA, and BHLHE40), TH17 (IL20, IL20RA, IL20RB, IL21, IL21R, IL22, IL22RA1, IL22RA2, IL23A, IL23R, RORA, and STAT3), and immune activation (IDO-1, IRF4, CD38, CD69, LAG3, ICOS, CTLA4, IL13, CXCL10, CXCR1, CXCL12, CXCL13, IL27, KLRD1, IL6, MMP9, and FCGR1A). As shown in Figure 6A and 6B, there was a significant difference in DGE in macrophage, monocyte and CD8+ T cell clusters.\nOnly genes with a raw p-value < 0.05 and a log2 fold change > 1 were retained for subsequent Gene Ontology (GO) (Figure 6C) and GSEA pathway enrichment analyses (Figure 6D). In these analyses, enriched terms were defined as those with a FDR (Benjamini-Hochberg) corrected p-value < 0.05 and a Storey’s q-value < 0.05. All enrichment analyses were performed using the Bioconductor package clusterProfiler. The GO enriched pathways include response to type I interferon, interferon-mediated signaling pathway, cellular response to type I interferon and activation of the innate immune response. The GSEA enriched pathways include cytokine mediated signaling pathway, response to type I interferon and innate immune response.\nLastly, In Figure 6E and 6F, we have now included the ridge plots from the scRNAseq showing expression levels of genes associated with immune activation; CD38 and CD69. We report an increased immune activation-associated CD38 and CD69 gene expression in both lymphoid and myeloid clusters.\nIn conclusion, the activation of innate immune pathways, particularly those related to type I interferon responses, plays a crucial role in the immune dynamics of TB/HIV co-infection. The differential expression of genes associated with T and T responses, as well as immune activation markers, underscores the complexity of the immune response during co-infection. Importantly, the enrichment of pathways related to type I interferon and innate immunity highlights the significance of these pathways in modulating the host's defense against both tuberculosis and HIV. Understanding how these immune responses evolve over time could provide valuable insights into potential therapeutic strategies for managing TB/HIV co-infection (Lines 376-382).\nMinor comments:\n-From 1A and the text, it’s hard to tell if the LTBI population of RMs are exposed to SIV or not\nResponse: The authors have now clearly described the group at the first mention at Line 114. Fig. 1A is modified to represent the same.\n-It’s hard to know what to make of the data offered in 1K and the accompanying text, as only RMs from a single experimental group received PET scans- there’s no comparator\nResponse: Figure 1K is now represented as Fig. 1I. This figure is now revised to include four different phases of the disease within the same macaque. It represents the lung lesions in three of the six RMs at weeks 6 (LTBI), 12 (LTBI+SIV co-infection, one week of cART+3HP treatment), week 16 (5 weeks of cART+3HP treatment) and week 22 (11 weeks post cART+3HP treatment). We have further elaborated on the main findings from Figure 1I and its translational impact at Lines 164-188.\n-There is very limited methodologic detail included for most experiments, somewhat limiting the ability to interpret the data\nResponse: The authors have now added additional details to the methodology (Lines 585-591, 618-620, 626-633, 648-653, 658-661, 668-675, 694-710; 715-723).\n-1H would be better represented as a supplementary table with an additional column showing the normal range for each value\nResponse: 1H is now added a Supplementary Table 1 with an additional column showing the normal range for each parameter studied.\n-5B and C are not necessary as the process is standard and can be briefly described in the methods\nResponse: Figures 5B and 5C of the previous version of the manuscript are now moved to Supplementary Figure 7E and 7F. Figure 5 is now revised to include critical findings as outlined above.\n-The discussion section primarily resummarizes the results of the study rather than using those results to reflect on context or how the results concretely move the field forward.\nResponse: Discussion is now revised in various sections to reflect the context and discuss how results concretely move the field forward. Lines 384-395, 398-413, 417-431, 437-443, 451-456, 493-495, 509-512, 533-552.\nReviewer #2:\nIn this manuscript, Sharan et. al. examine the impact of concurrent cART and Isoniazid and Rifapentine (3HP) treatment during MtB and SIV co-infection in a NHP model. Following 12 weeks of combined treatment cART+3HP animals demonstrate decreased bacterial burden. Despite this significant and promising finding, the authors report incomplete immune reconstitution and continued evidence for immune exhaustion and inflammation. These findings expand upon the groups previous findings and use historical data for comparison to their cART+3HP contemporary cohort. Although the results presented are of interest, there are gaps in the methodology and data that, in its current state, limit the data interpretation.\nResponse: We sincerely appreciate the reviewer’s positive assessment of our study and the recognition of the promising findings regarding the impact of concurrent cART and 3HP treatment in the context of Mtb and SIV co-infection in a non-human primate (NHP) model.\nWe recognize that some aspects of our study design and data collection may not have been fully explained in the initial manuscript. In particular, we have provided more detailed descriptions of the study cohorts (Revised Figure 1A), the treatment protocols, and the timing of key measurements (e.g., bacterial burden, immune reconstitution, and inflammation markers) Lines 109-128. Additionally, we have expanded on the rationale for using historical data for comparison with the cART+3HP cohort (Lines 417-431) and clarified the methodological approach to ensure that these comparisons are robust (Lines 585-591, 618-620, 626-633, 648-653, 658-661, 668-675, 694-710; 715-723).\nMajor Comments:\n1. Historical data from animals enrolled in the LTBI, cART naïve, and cART group were used as comparators for the experimental cART+3HP group. While leveraging historical samples is important for reducing the number of NHPs enrolled in the study, it is a limitation of the study. Additional scrutiny and information is needed to confirm the validity and rigor of the findings:\na. Prior to the start of the combined treatment regimen (Week 11) how comparable are the clinical disease and bacterial burden between the cART+3HP and historical groups used for comparison? Lines 532-533 indicate that “Bacterial burden in BAL was measured throughout the study”, please provide this data in comparison to historical groups.\nResponse: The authors thank the reviewer for this valuable comment. In response, we have now included the BAL CFU counts from each individual macaque per group at pre-infection and at weeks 3, 5, and 7 post-Mtb infection (Supplementary Figure 1C). This addition ensures a comprehensive comparison of CFU data throughout the latent phase of the experiment. Based on our previous studies, we have consistently observed stable latent CFU across all experiments conducted to date with no significant difference between study cohorts for this parameter. Line 119-120.\nb. How do animal demographics (Supplemental Table 2) influence the interpretation of the results? e.g. age, sex, MHC class. Sex differences are inadequately addressed. The cART naïve and LTBI groups are exclusively male, cART+3HP is mostly male and cART treated is mostly female.\nResponse: We appreciate the reviewer’s insightful comment regarding the influence of animal demographics, particularly sex and MHC class, on the interpretation of our results. We recognize that demographic factors can potentially affect the outcomes of studies and the generalizability of findings. However, given that the primary focus of this study is not specifically sex differences, we think that while sex may influence certain aspects of the immune response, the main conclusions of the study remain valid. Sex differences are difficult to study in the NHP model. Since the COVID-19 pandemic, an already existing shortage of research macaques in the Western world has been exacerbated, limiting the choices available to research groups like ours. Nonetheless, we emphasize the need for further research to explore how sex differences interact with treatment regimens in similar models. Lines 533-537.\n2. Details regarding necropsy need to be clear and consistent.\na. Based on Supplemental Figure 2C, necropsies between the groups appear to occur at different timepoints (LTBI Wk23-25), cART naïve (Wk17-23), cART (Wk19-20) and cART+3HP (Wk23-24). How might bacterial burden or immune responses differ between these varying timepoints?\nResponse: We thank the reviewer for their feedback. We agree that the differences in necropsy timepoints across the groups could potentially influence bacterial burden and immune responses. However, all four groups were euthanized before week 24, which was the consistent humane endpoint determined in consultation with the veterinarians. We appreciate the concern regarding the varying time points, and we believe the observed differences in bacterial burden and immune responses can be explained by the mechanisms underlying each group’s treatment protocol. As expected, the bacterial burden in the LTBI and cART+3HP groups were comparable, given that both rely on distinct mechanisms for bacterial control (host control in the LTBI group and drug control in the cART+3HP group). In contrast, the cART naïve animals demonstrated a higher bacterial burden with extrapulmonary spread, consistent with LTBI reactivation and dissemination upon SIV co-infection. The bacterial burden in the cART-only group is also as expected, given the absence of anti-TB therapy. As we have previously shown, cART alone does not result in bacterial control (PMID: 34855621). Regarding immune responses, they differed across the four groups, as described in Figures 2-4. We do not think the differences were due to necropsy time points but due to the different conditions of treatment in these four cohorts. However, there were some comparable observations, including an expansion of the central memory response in both CD4 and CD8 T cells in both cART and cART+3HP groups. Additionally, we observed partial reconstitution of CD4 T cells and increased immune activation, exhaustion, and inflammation in the cART naïve, cART, and cART+3HP groups compared to the LTBI group. These findings are consistent with what we would expect from the immune dynamics in these cohort based on the treatment. Lines 437-443.\nb. What were the humane endpoints used (Lines 128-129)? If some of the necropsies were planned as a study endpoints then the survival data in Supplemental Figure 2C is misleading.\nResponse: We agree with the reviewer’s comment and have now added details on the necropsy time points in the Methods section “Animal infection” at Lines 585-591 to reflect the specific details of necropsy time points per group. Additionally, we have provided the explanation in text to better contextualize the necropsy details with the manuscript findings, Lines 124-127.\nc. Necropsy timepoints are inconsistently described and the timing is critical for data interpretation. 1) Line 129 indicates some animals were euthanized “2 weeks post SIV co-infection”, which according to Figure 1A would correspond to Wk11, yet the Supplemental Figure 1C indicates Week 17 as the earliest necropsy. 2) Lines 507-509 indicate that “All the RMS in the cART-naïve group had to be euthanized within 2-4 weeks of cART treatment”, which according to Figure 1A would correspond to Wks13-15, yet the Supplemental Figure 1C indicates Week 17-23 as the necropsy timepoints. 3) Lines 509-510 “the RMs incART+3HP group were euthanized at end of 12-week treatment at week 24,” but Figure 1 C shows that 1 animal went to necropsy at Week 23.\nResponse: The authors have now revised the methods section “Animal infection” at Lines 585-591 to reflect the specific details of necropsy time points per group. The RMs in LTBI control group were euthanized at week 24 post Mtb infection. All the RMs in cART-naive group were euthanized between 17-24 weeks post Mtb infection due to clinical signs of LTBI reactivation. The RMs in the cART group were euthanized after 9 weeks of cART treatment or at week 20 post Mtb infection while the RMs in cART+3HP group were euthanized at end of 12-week treatment or at week 24 post Mtb infection. All animals were euthanized by week 24, which was the predetermined study endpoint, or at earlier time points if they became clinically unwell and/or showed signs of TB reactivation, as assessed by veterinarians in accordance with humane endpoint criteria.\n3. In the current submission, an insufficient amount of details is provided for the experiments to be reproduced.\na. Details of how samples were collected, processed, and/or stored prior to analysis should be provided.\n1)Immune responses in Figures 2 and 3 between the BAL and lung are not consistent. Why might this be?\nResponse: The authors have provided the total BAL CD4 (Fig. 2A) and Lung CD4 (Fig. 2B) and total BAL CD8 (Fig. 2C) and Lung CD8 T (Fig. 2D) cell percentage in Figure 2. Figure 3 represents CD4+ T and CD8+T cell subsets – CD4+ T central memory response and CD4+T effector memory response in the BAL and lung. Further Figure 2 represents markers of immune activation, exhaustion, trafficking and T * phenotype while Figure 3 represents the longitudinal changes in central and effector response of CD4 and CD8 in BAL (Fig. 3A-D) and comparison of central and effector memory response at necropsy between cART only and cART+3HP groups in BAL and lung (Fig. 3E-L). Hence there are 2 different sets of data presented in Figures 2 and 3.\n2) In Figure 4 how were cells processed and stored before being thawed for the single cell experimentation?\nResponse: The authors have now added the BAL cell isolation protocol at Lines 668-675. BAL was performed with 80 mL of sterile saline in BSL3 on NHPs at weeks 5, 11 and necropsy after Mtb infection. The BAL was filtered, centrifuged (1,200 rpm, 10 min at 4C), lysed for red blood cells (RBCs) using ACK lysis buffer (GibcoTM) and resuspended in 2 mL final volume of RPMI 1640 media (GibcoTM). The cells were counted and live cells were used to perform ex-vivo antigen stimulation to measure Mtb-specific response. The prepared single cells were stained for surface and intracellular markers to study various cell phenotypes. The freshly collected BAL cells were stimulated ex vivo with Mtb-specific antigen, ESAT-6/CFP-10 (BEI Resources, 10 μg/mL), for a total of 16 hours. Brefeldin A (0.5 μg/mL, Sigma-Aldrich) was added 2 hours after the onset of stimulation. Additionally, the cells were counted and frozen in Cryostor in internally threaded cryovials in liquid nitrogen for downstream scRNA-seq processing using 10x Genomics platform. The BAL cells were frozen in Cryostor (Sigma Aldrich). Cryostor is formulated to contain 2% dimethyl sulfoxide (DMSO). Cryostor is designed to preserve the cell viability and functionality through modulation of cellular biochemical response to the cryopreservation process. Our lab routinely utilizes this reagent to preserve freshly isolated BAL cells.\nb. What statistical tests were used for Figure 1B and G. In Figure 1B what is being compared?\nResponse: The authors have added the statistical tests used for Figure 1B and 1G (now supplementary Figure 1D) in the Figure legend (Lines 1002-1004, Line 1102). Nonparametric Mann-Whitney 2-tailed Student’s t test was used to compare 2 groups at individual time points in Figure 1B. Specifically, cART naïve group RMs were compared with cART+3HP RMs at weeks 13, 15 and 17. cART only group RMs are also compared with cART+3HP group RMs at weeks 13, 15 and 17. CRP levels in RMs in the cART+3HP group were significantly lower than in the cART-treated RMs (P < 0.001) at weeks 13 (P < 0.001), 15 (P < 0.0001) and 17 post Mtb infection (P < 0.0001) (Figure 1B). Nonparametric Mann-Whitney 2-tailed Student’s t test was used to compare plasma viral loads in cART only group RMs and cART+3HP at weeks 13, 15 and necropsy (Supplementary Figure 1D). No significant difference was observed in the plasma viral groups indicating comparable levels of viral control in both the groups.\nc. The method details and flow cytometry panel used in Figure 2 should be provided.\nResponse: The authors have provided method details of lung tissue and BAL processing as well as the flow cytometry protocol used at Lines 626-633. The panel used has been provided in Supplementary Table 5.\nd. Line 115: As the LTBI group did not receive SIV, this statement is incorrect.\nResponse: The authors thank the reviewer for the correction. The statement is now corrected at Line 114.\ne. The gating scheme in Supplemental Figure 2 identifies Tem as CD28- CD95-, but are defined correctly by the authors as CD28-CD95+. Please explain this discrepancy.\nResponse: The revised visual representation of the gates has been added to reflect the gating strategy used to analyze the data (Tem is defined as CD28-CD95+) in Supplementary Figure 2.\nf. Lines 587 and 590 indicate that 16 samples were used in analysis for Figure 6, but 24 samples were included in the original pipeline of Figure 5. What is the rationale for this? Additional details should be provided in the Figure 6 legends.\nResponse: The authors thank the reviewer for pointing this out. Indeed, 24 samples were used for all the analysis (BAL samples were taken from six rhesus macaques at four time points = 24 samples). This has been corrected in both methods (Line 694, 1093, 128, 135, 144, 158).\ng. The number of animals and timepoints should be explicitly described in all Figure legends\nResponse: The authors have added animals and timepoints to all the Figure legends.\n4. Statistics to support data interpretations should be provided.\nResponse: The statistical analyses have now been provided as requested in points a-d below.\na. Given the small sample size of n=4 for the LTBI and cART groups, nonparametric statistical analysis seems more appropriate.\nResponse: The authors agree with the reviewer. Nonparametric Mann-Whitney 2-tailed Student’s t test was used when comparing 2 groups in Figures 1, 2, 3, 4, Supplementary Figure 1. The details are added to the respective Figure Legends.\nb. Line 156: Where is the correlational data being referenced?\nResponse: The sentence is now modified to “The pathological findings aligned with the clinical and microbiological observations”. Line 154.\nc. Where are the statistics to support lines 196-198, 217-219, 219-221.\nResponse: Nonparametric Mann-Whitney 2-tailed student’s t test was used to compare LTBI with cART naïve, cART and cART+3HP groups in Figures 2E, 2F and 2G. The statistical details are now added to the respective Figure legend (Lines 1017-1036).\nd. The statistical test for each panel should be explicit in the Figure Legends. Currently they are lumped together, so it is unclear which tests are being applied.\nResponse: The statistical analysis per panel in Figures 1-4 and Supplementary Figure 1 are now added to the Figure legends.\nMinor Comments:\n1. A description of Figure 3 I-J is not provided in the figure legend – Response: Provided in Lines 1043-1044, 1055-1061.\n2. Labels for the legends of Figure 3K, 4E/F, and 5 should be provided Response: Provided in Lines 1044, 1050-1051, 1063-1076\n3. The term LTBI is sometimes used in reference to the experimental group and at other times to the phase of the study and can be confusing at times. Response: The authors have used asymptomatic instead of LTBI for the scRNAseq transcriptomics part of the manuscript (Lines 338-339).\n4. The information in lines 331-341 may be more useful in a table format. It is now added as Suppl. Table 4.\n5. The accession number on line 603 is missing. Accession number is now added at Line 714.\nReviewer #3:\nThis is a well-executed study that combines several investigative modalities to demonstrate the effect of concurrent TB (3HP) therapy and HIV ART (emtricitabine/tenofovir/dolutegravir) in a non-human primate model of Mycobacterium tuberculosis (Mtb) and simian immunodeficiency virus (SIV) co-infection. Using techniques such as histopathology/immunohistochemistry, PET/CT, flow cytometry, RNA sequencing, and ELISA, these authors demonstrate novel insights into immune reconstitution and persistent inflammation in the lungs despite viral and bacterial control with combination therapy.\nResponse: Thank you for the positive feedback. We appreciate your recognition of the combination of investigative techniques we used to explore the effects of concurrent TB therapy (3HP) and cART in the non-human primate model of Mtb and SIV co-infection. We’re glad that our findings on immune reconstitution and persistent inflammation despite viral and bacterial control were insightful and valuable to you. Thank you again for your thoughtful comments.\nOverall, they demonstrated that ART+3HP certainly reduced the burden of pulmonary TB burden at study endpoint compared to cart alone and untreated animals. TH1/TH17 CD4 T cel responses also increased in ART+3HP treated animals but incomplete immune reconstitution and persistent immune-mediated inflammation persisted regardless of LTBI txt and concomitant ART. Overall, this is an interesting finding that suggests that in the setting of SIV (and perhaps HIV) initiation of ART with 3HP certainly has benefits but does not eliminate TB (or HIV) associated inflammation which can hinder TB responses.\nResponse: We also acknowledge your observation regarding the incomplete immune reconstitution and persistent immune-mediated inflammation, even with LTBI treatment and concomitant ART. This is a crucial point, and we believe it underscores the complexity of TB and HIV co-infection, particularly in the context of SIV. While ART+3HP clearly provides benefits, our findings highlight the persistent challenges in eliminating inflammation, which may continue to hinder TB responses despite the intervention.\nOne general comment is how the TB infection model adequately recapitulates LTBI in the human population, especially those in endemic areas with multiple long-term exposures. I realize there are limitations to all animals models of LTBI, but some discussion on this and addressing how LTBI in the macaque differ from human LTBI would be essential in understanding and extrapolating these results.\nResponse: We appreciate the reviewer’s insightful comment regarding the relevance of our macaque model in recapitulating latent tuberculosis infection (LTBI) in humans, particularly in individuals from endemic areas with multiple long-term exposures. We acknowledge the limitations inherent in animal models of LTBI, and we agree that it is crucial to highlight the distinctions between macaque models and human LTBI to properly contextualize our findings.\nIn response to this, we have revised the manuscript to include a more thorough discussion of the differences between LTBI in macaques and humans. Specifically, we emphasize that while macaques exhibit a form of LTBI that shares key characteristics with human infection—such as the persistence of Mycobacterium tuberculosis in a non-replicating state—there are notable differences. For example, macaques tend to develop a more robust immune response compared to humans, potentially leading to differences in the dynamics of infection and latency. Furthermore, the frequency of multiple, chronic exposures in endemic human populations may differ from the controlled exposures in macaque models, which could affect the progression and persistence of the infection. While the macaque model provides valuable insights, we recognize the need for careful extrapolation when translating these findings to human LTBI, particularly in highly endemic regions. Thank you again for this constructive feedback, which has helped strengthen our manuscript. Lines 533-552.\nWith particular regard to the imaging component of this study, FDG PET/CT scans were used to study lung lesions in a subset of the rhesus macaque subjects at certain timepoints: Week 6 (LTBI phase), Week 12 (1 week post cART+3HP initiation), Week 16 (4 weeks post cART+3HP initiation), and Week 22 (10 weeks post cART+3HP initiation). The authors used the lack of lung lesion progression at week 6 to demonstrate an LTBI state, which as above, is questionable to how this equates to LTBI in humans. For example, a suspicion lesion with any evidence of mTB on BAL, sputum culture or gene applification would be considered active TB. Further clarification of how stability of lesions equates to LTBI should be povided.\nResponse: We thank the reviewer for the valuable feedback regarding the interpretation of lung lesion stability in the context of LTBI.\nIn our study, FDG PET/CT imaging was used to monitor lung lesions in rhesus macaque subjects at several time points, including Week 6 (LTBI phase), Week 9 (LTBI+SIV), Week 12 (LTBI+SIV+cART+3HP), and Week 23 (End of cART+3HP treatment) (Revised Figure 1I). At Week 6, we observed no progression of lung lesions, which was interpreted as evidence of a stable, LTBI-like state. However, we acknowledge that the absence of lesion progression alone does not equate to a definitive diagnosis of LTBI, particularly given the complex nature of TB diagnosis in humans.\nTo address this, we incorporated additional diagnostic measures to confirm LTBI in our study. Infection was confirmed through a positive tuberculin skin test (TST) at weeks 3 and 5 post Mtb infection. All study RMs developed LTBI, which was characterized by serum C-reactive protein (CRP) levels of 5 µg/mL or lower (Figure 1B), no significant changes in body temperature (Supplemental Figure 1A) or body weight (Supplemental Figure 1B) up to 9 weeks post-Mtb infection, and less than 1 to 2 Log10 CFU of Mtb in bronchoalveolar lavage (BAL) at weeks 3, 5, and 7 post-infection (Supplementary Figure 1C). Lines 109-127.\nAdditionally, we measured Mtb-specific immune responses by isolating BAL cells at week 5 post-low-dose Mtb infection. These cells were stimulated ex vivo with ESAT-6/CFP-10 antigens, and cytokine responses (IFN-g, IL-2, IL-17, and TNF-a) were measured to assess Mtb-specific response in the pulmonary compartment of macaques during the LTBI phase of the study.\nIn response to the reviewer’s concern, we have now clarified in the manuscript that the stability of lung lesions at Week 6, while suggestive of LTBI, was corroborated with these other diagnostic parameters (TST, CRP levels, immune responses, and bacterial load) that together support the LTBI diagnosis in this macaque model. Lines 116-120. We recognize that more comprehensive microbiological testing and longer follow-up would be required to confirm a definitive LTBI diagnosis in both non-human primates and humans.\nAt week 12, a significant increase in 18F-FDG uptake in the lungs was ascribed to progression of TB pathology in the setting of SIV co-infection. Following therapy with ART and 3HP, there was no progression of the size of the lung lesions, however metabolic activity by PET did not decrease. The authors suggest that combination treatment aided in treatment of caseous lesions but did not reduce the ongoing inflammation in the lungs.\nOverall, the use of PET/CT in this study is a strength which serves to supplement the researchers’ other investigative modalities, offering a longitudinal visual element. Further, the use of both CT and PET helps to demonstrate both structural changes as well as metabolic activity. It is interesting to note that the lesions that did persist on ART+3HP remained PET avid, suggesting ongoing increase metabolic processes in these lesions, which, in some ways, would be expected if 3HP and ART railed to fully suppress or eradicate these lesions. One wonders, however, given the limitations of understanding how this LTBI model holes up to human disease, if these foci represent undertreated active TB rather than failure to control inflammation or immune responses in “latent” associated lesions. It seems from images and from other direct lung data that the “LTBI” here is more on the end of mild active TB disease. Further discussion on this would significantly strengthen the paper. E.g. would actually treating the monkey with at least 2 active drugs for a full treatment course result in different outcomes- one would assume so given the data presented.\nResponse: We appreciate the reviewer’s insightful comments regarding the interpretation of the PET/CT imaging data. We acknowledge the reviewer’s comment about the persistence of PET-avid lesions during the cART and 3HP treatment period, despite no significant progression in lesion size. This observation suggests that while combination therapy with cART and 3HP might have reduced the caseous components of the lesions, it did not fully suppress the ongoing metabolic activity, which can be interpreted as indicative of persistent inflammation in these lesions.\nAs noted by the reviewer, the continued metabolic activity within the lesions could be interpreted in different ways. While we suggested that this might reflect ongoing inflammation rather than active bacterial replication, we agree that the presence of PET-avid lesions despite therapy may also raise the possibility of undertreated active TB, rather than a true LTBI state. The distinction between “mild active TB” and “latent” TB is indeed a nuanced one, and as the reviewer points out, our macaque model may better represent a transitional state between active and latent TB, rather than fully quiescent LTBI as understood in humans. Lines 180-188.\nIn response to this suggestion, we have revised the manuscript to discuss this issue more thoroughly.\nSpecifically, we highlight that while the macaque model is an important tool for studying LTBI, its precise equivalence to human disease remains uncertain. The persistence of PET-avid lesions despite the initiation of ART and 3HP treatment raises valid questions about the ability of these therapies to fully suppress infection in this model. As the reviewer suggests, it is possible that a more aggressive treatment regimen, including at least two active drugs over a full treatment course, could lead to different outcomes in terms of lesion resolution and metabolic activity. Lines 180-188\nWe have added this discussion to the manuscript, proposing that future studies could explore the effects of more intensive anti-TB therapy on the resolution of both lesion size and metabolic activity in this model. This would help determine whether the observed lesions represent a failure of immune control or are indeed indicative of undertreated active TB, providing further insight into the transition between active and latent TB. Lines Lines 180-188.\nWe believe these revisions strengthen the manuscript by addressing the complexities of interpreting PET/CT data in the context of TB therapy and LTBI, as well as emphasizing the need for further investigation into the best treatment strategies for LTBI in non-human primates.\nAreas in which this study could be improved include sample size and lack of an ART-only control group. The small sample size of 3 macaques for the imaging component of the study carries a limitation in power with the understanding that these are expensive and logistically difficult studies. The lack of a 3HP control arm also makes it somewhat difficult to know the full extent of 3HP on TB infection.\nResponse: We thank the reviewer for raising this important point. The authors would like to clarify that we do have a cART only control group (n = 4) (PMID: 34855621) that we included in this study. Additionally, we have published impact of 3HP on low dose TB infection previously (PMID: 35862216). A once-weekly oral dose of isoniazid and rifapentine for 3 months (3HP) is recommended by the CDC for treatment of latent tuberculosis infection (LTBI). The aim of this study was to assess 3HP-mediated clearance of M. tuberculosis bacteria in macaques with asymptomatic LTBI. Twelve Indian-origin rhesus macaques were infected with a low dose (~10 CFU) of M. tuberculosis CDC1551 via aerosol. Six animals were treated with 3HP and 6 were left untreated. The animals were imaged via PET/CT at frequent intervals. Upon treatment completion, all animals except 1 were coinfected with SIV to assess reactivation of LTBI to active tuberculosis (ATB). Four of 6 treated macaques showed no evidence of persistent bacilli or extrapulmonary spread until the study end point. PET/CT demonstrated the presence of significantly more granulomas in untreated animals relative to the treated group. The untreated animals harbored persistent bacilli and demonstrated tuberculosis (TB) reactivation following SIV coinfection, while none of the treated animals reactivated to ATB. 3HP treatment effectively reduced persistent infection with M. tuberculosis but did not sterilize the bacteria. This has implications of individuals in TB-endemic countries who are potentially still susceptible to TB infection or reactivation.\nWe appreciate the reviewer’s feedback regarding the small sample size of 3 macaques in the imaging component of our study. We fully recognize the limitation this presents. Given the high cost and logistical challenges associated with conducting imaging studies in non-human primates, including the time, resources, and specialized expertise required for such experiments, we were constrained by the number of macaques that could be included. However, despite this limitation, we believe the findings provide valuable preliminary insights into [the topic of study], and we hope that this work will lay the foundation for future studies with larger sample sizes. We have noted this limitation in the revised manuscript and discussed it as a point for consideration in future research directions Lines 545-552.\nThe text is quite dense and often difficult to follow without constant referral to study images. Streamlining in organization and textual presentation would make this manuscript more accessible to the general scientific reader. E.g. it is often difficult to know what each RM group refers to in the text or which group underwent which therapy or had which outcome. The figures are much clearer.\nResponse: Thank you for your valuable feedback. We appreciate your observation regarding the text’s density and its accessibility to a broader audience. We agree that clarity and ease of understanding are crucial, and we have made efforts to streamline the organization and presentation of the manuscript. In response to your specific points: We have revised the manuscript to better clarify the different RM groups and their corresponding therapies and outcomes. Where applicable, we have added more explicit references to specific groups in the text to avoid any ambiguity. Textual Presentation: We have worked to reduce the complexity of the text, breaking down longer sentences, using simpler language where appropriate, and improving transitions between sections to make the narrative more cohesive.\nReviewer #4:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: Thank you for your thoughtful and detailed review of the manuscript. Your feedback is greatly appreciated and will contribute to the improvement of the work. We value your time and effort in providing such insightful comments.\nReviewer #5:\nBrief summary of findings:\nThe manuscript by Sharan et. al. examines the effect of combined TB and HIV therapies in a rhesus macaque model for latent TB infection (LTBI) and Simian Immunodeficiency Virus (SIV) co-infection. The authors studied the effect of cART and isoniazid and rifapentine (3HP) treatment in the coinfected animals on the components of TB immunity in lung, control of bacterial infection and clinical presentation of TB compared to coinfected animals with or without cART and untreated LTBI animals. Although the combination treatment showed lower levels of bacterial infection compared to other conditions, CD4+ T cells were not fully restored and the reconstituted CD4+ T cells also showed activated, exhausted and inflamed phenotype. Additionally, enrichment of Type 1 interferon related transcriptional signature and TB specific TH1/TH17 responses were seen but TB specific TNFa was reduced in the combination treatment. Overall, the study showed that combination cART and 3HP treatment in itself was not sufficient to prevent activation of TB because of certain immune mechanisms in the lung compartment.\nOverall assessment of quality and importance:\nThe study addresses an important issue of latent TB activation in the context of HIV infection and seeks to examine if combination of cART and 3HP would be effective in preventing progression of TB in an in vivo model. The results showing reduction of bacteria after the combination treatment were quite promising. Data showing suboptimal reconstitution of immune cells in the lungs although disappointing are informative in guiding future studies on treating concurrent HIV and activation of latent TB. The experiments are well designed with sufficient numbers of replicates for each condition and the methods are well-suited to the questions being asked. The interpretations of results are generally reasonable and the manuscript is easy to read. The use of transcriptomic methods to understand what biological mechanisms might be impairing immune function is justified. However, this reviewer has major concerns about how the transcriptomic data (especially from single-cell sequencing) are presented and interpreted in the manuscript. This part of the paper requires significant improvement before being ready for publication. Specific concerns are described below.\nResponse: Thank you for your thoughtful and constructive feedback on our manuscript. We appreciate your positive comments regarding the importance of our study, the promising results from the combination treatment, and the value of our experimental design and methods. We are also grateful for your acknowledgment of the significance of the suboptimal immune reconstitution findings, which we believe will be important for guiding future studies on concurrent HIV and latent TB activation. We have collaborated with Drs. Xi Luo, Hulin Wu and Guanlin Guo from UTHealth Houston School of Public Health to address the comments on scRNAseq data. We have ensured that the transcriptomic data are presented more clearly and that the interpretation aligns more closely with the experimental evidence. We believe these changes have strengthened the manuscript and make the findings more accessible and convincing to the readers.\nSpecific major concerns and suggested additional experiments:\n• There is an imbalance in age and sex of animals across different conditions; two conditions only have male animals. The manuscript should address how the results could be affected by this.\nResponse: We appreciate the reviewer’s insightful comment regarding the influence of animal demographics, particularly sex and MHC class, on the interpretation of our results. We recognize that demographic factors can potentially affect the outcomes of studies and the generalizability of findings. However, given that the primary focus of this study is not specifically sex differences, we argue that while sex may influence certain aspects of the immune response, the main conclusions of the study remain valid. Nonetheless, we emphasize the need for further research to explore how sex differences interact with treatment regimens in similar models (Lines 533 - 537).\n• Information about bulk RNA-seq is very sparse. Method section is missing information about bulk-RNA-seq, especially how results in Figure 4 were generated. The legend for this figure seems not to match the figure. It would be helpful to include additional results including PCA to define relatedness of transcriptomic profiles in different animals; differential gene expression; and GO/pathway analysis. List of genes from differential gene expression or from the selected pathways highlighted in the results (e.g. apoptosis) should be included as supplementary material.\nResponse: The authors have now added the bulk RNA sequencing methods at Lines 715-723 and the visualization of the analysis has been improved.\n• Rationale for doing single-cell RNA-seq should be better explained in the manuscript. What information can be obtained from single-cell analysis that cannot be obtained from a more straightforward bulk RNA-seq? Were the authors looking for transcriptomic signatures in specific population of cells?\nResponse: The authors have now revised both the Figures. Figure 5 includes the study design, tSNE plot representing the clusters, feature plots of selected genes representing annotation, and plots representing the cell counts per cluster per time point. We identified eight clusters in the BAL collected at weeks 5, week 11, week 13 and necropsy; macrophages, monocytes, CD4+ T cells, CD8+ T cells, B cells, Basophils, NK/NKT cells and mast cells. scRNAseq data validates our previous findings that there is a reduction in CD4+ T cell counts and proportions post SIV co-infection that cART+3HP does not restore to levels seen in LTBI. It also informs us about the counts and proportions of other cell types, critical in TB immunity, that we did not study using the traditional flow cytometry technique: macrophages, monocytes, NK/NKT cells and mast cells (Figure 5F-5J, Supplementary Figure 8).\nFigure 6 includes Violin plots displaying the distributions of 42 differentially expressed genes across cell clusters (color-coded) when comparing three time points (week 5, week 11, and necropsy). These genes were associated with TH1 (IFNG, IFNGR1, IFNGR2, TBX21, STAT1, TNFAIP2, TNFAIP3, TNFAIP8, TNFAIP8L1, TNFAIP8L3, IL2, IL2RA, and BHLHE40), TH17 (IL20, IL20RA, IL20RB, IL21, IL21R, IL22, IL22RA1, IL22RA2, IL23A, IL23R, RORA, and STAT3), and immune activation (IDO-1, IRF4, CD38, CD69, LAG3, ICOS, CTLA4, IL13, CXCL10, CXCR1, CXCL12, CXCL13, IL27, KLRD1, IL6, MMP9, and FCGR1A). As shown in Figure 6A and 6B, there was a significant difference in DGE in macrophage, monocyte and CD8+ T cell clusters.\nOnly genes with a raw p-value < 0.05 and a log2 fold change > 1 were retained for subsequent Gene Ontology (GO) (Figure 6C) and GSEA pathway enrichment analyses (Figure 6D). In these analyses, enriched terms were defined as those with a FDR (Benjamini-Hochberg) corrected p-value < 0.05 and a Storey’s q-value < 0.05. All enrichment analyses were performed using the Bioconductor package clusterProfiler. The GO enriched pathways include response to type I interferon, interferon-mediated signaling pathway, cellular response to type I interferon and activation of the innate immune response. The GSEA enriched pathways include cytokine mediated signaling pathway, response to type I interferon and innate immune response.\nLastly, In Figure 6E and 6F, we have now included the ridge plots showing expression levels of immune activation markers, CD38 and CD69 in scRNAseq clusters. There is an increased immune activation-associated CD38 and CD69 gene expression in all clusters.\nIn conclusion, the activation of innate immune pathways, particularly those related to type I interferon responses, plays a crucial role in the immune dynamics of TB/HIV co-infection. The differential expression of genes associated with T and T responses, as well as immune activation markers, underscores the complexity of the immune response during co-infection. Importantly, the enrichment of pathways related to type I interferon and innate immunity highlights the significance of these pathways in modulating the host's defense against both tuberculosis and HIV. Understanding how these immune responses evolve over time could provide valuable insights into potential therapeutic strategies for managing TB/HIV co-infection (Lines 376-382).\n• The message of figure 5 is unclear. The contents of the figure do not show a clear transcriptomic signature (as the figure legend mentions). Moreover, the sizing of the cartoon is out of proportion to the rest of the figure. Consider decreasing the size of the instruments shown as they are just part of outline and not actual results. Consider also incorporating other results from the analysis (e.g. violin plots of cluster markers, or even feature plots as shown in supplementary fig 7) to make the figure more informative. Results from differential gene expression between different conditions in each cluster could also be also helpful. The figure legend also needs to be clarified. The violin plots for the n_feature_counts and n_RNA_counts are not very informative as the differences in these could arise from differences in library sizes, presence of empty droplets or multiplets etc. Overall, arguments and statistics related to this figure were insufficient to persuade this reviewer of relevant biological findings in the data.\nResponse: The authors thank the reviewer for their valuable feedback. In response, Figures 5 and 6 have been revised. Additionally, Supplementary Figures 6, 7 and 8 have been added. Please see the response to the previous comment. Overall, scRNA-seq provides a valuable tool to track the abundance of cells expressing specific transcripts over time in the same study macaque, allowing us to gain deeper insights into the transcriptomic landscape of these animals.\n• The lower limit on for the number of detected genes per cell is on a lower side (10). The default for Seurat is 200. Why use such a low threshold? Including cells with such low numbers of detected genes could affect validity of downstream results. Perhaps a low threshold was chosen due to excessive loss of cells at higher thresholds? If this is the case, then including a violin plot showing distribution of number of genes detected/cell in each sample could be helpful.\nResponse: We selected a lower detection limit due to the type of sample: BAL (bronchoalveolar lavage) cells. To accommodate animals that had lower number of cells, we chose the threshold of 10. To maintain sample integrity and avoid missing relevant data, we are using a CD45 filter to specifically identify and select the appropriate cell population. We have now provided the Violin plots per animal per time point to depict the same in Supplementary Figure 7A-D.\n• Were data filtered for % of mitochondrial reads? Filtering on this could remove signals from dead cells.\nResponse: The data matrix for each sample was read by Read10X, The Seurat objects of all 24 samples data were created and merged. The combined data were filtered by removing cells which have more than 8000 detected genes or more than 15% mitochondrial contents. Then the data were normalized by method “LogNormalize” with scale.factor=1e6, and the data were further filtered by keeping cells with CD45(PTPRC) expression as final dataset for single cell clustering analysis. Supplementary Figure 7 includes the per animal per time point wise mitochondrial filtration violin plots.\n• No details are provided about how results in figure 6 were generated. Are the results statistically significant? What is the figure really trying to show? Are the gene lists included here curated from literature or databases?\nResponse: Figure 6 is now substantially revised through the input of Dr. Wu and his colleagues who are expert bioinformaticians/biostatisticians. The Figure legend has been accordingly updated. Differentially expressed genes across cell clusters and time points. (A) Violin plots displaying the distributions of 42 differentially expressed genes across cell clusters (color-coded) when comparing three time points (week 5, week 11, and necropsy). These genes were associated with T (IFNG, IFNGR1, IFNGR2, TBX21, STAT1, TNFAIP2, TNFAIP3, TNFAIP8, TNFAIP8L1, TNFAIP8L3, IL2, IL2RA, and BHLHE40), T (IL20, IL20RA, IL20RB, IL21, IL21R, IL22, IL22RA1, IL22RA2, IL23A, IL23R, RORA, and STAT3), and immune activation (IDO-1, IRF4, CD38, CD69, LAG3, ICOS, CTLA4, IL13, CXCL10, CXCR1, CXCL12, CXCL13, IL27, KLRD1, IL6, MMP9, and FCGR1A). Statistical differences were evaluated using paired Wilcoxon signed rank tests, and statistically significant (uncorrected) p-values, after false discovery rate control at 0.1, are indicated on the plots (n = 6). (B) FDR corrected p values <0.10 for panel A. Differentially expressed genes that were identified from time-course contrasts between week 5 versus week 11 in the macrophage cluster. Only genes with a raw p-value < 0.05 and a log2 fold change > 1 were retained for subsequent (C) Gene Ontology (GO) and (D) GSEA pathway enrichment analyses. In these analyses, enriched terms were defined as those with a FDR (Benjamini-Hochberg) corrected p-value < 0.05 and a Storey’s q-value < 0.05. All enrichment analyses were performed using the Bioconductor package clusterProfiler. Ridge plots showing (E) CD38 and (F) CD69 gene expression with X axis representing expression levels and Y axis representing cell clusters. A total of 24 samples were analyzed (six RMs x four time points = 24 samples in total).\n• Additional analysis that may be helpful: GO/Pathways, GSEA, changes in abundance of certain population of cells across conditions, plot expression values of Th1/Th17, IFN 1, TNF-alpha related genes between conditions or in a feature plot.\nResponse: The authors have now included the suggested data interpretations in Figures 5 and 6 as explained above.\nOther minor concerns\n• Fig 1 C. Are there multiple samples from the same animal? It seems there are more dots than the number of animals esp for cART naïve. Response: The figure is now revised to reflect each dot as each animal.\n• Figure 4 legend doesn’t have information about the panels I and J. The authors have added them Lines 1055-1061.\n• Figure 4 panel J. How many pathways shown in the heatmap are significant (z-score and p-value cut-off). Response: Activation Z-score is provided in the Figure 4J at the bottom of the heatmap. The figure provides details on which pathways are upregulated when comparing cART with cART+3HP in the lung tissue at necropsy and has been revised for ease of interpretation.\n• Single cell method details missing details (e.g. no. of cells loaded). Response: The targeted cell recovery per sample was set to 10,000 cells. The sample wise cell counts are provided in Supplementary Table 3.\n• Why is read length of R1 read only 28 bp in single cell sequencing? scRNAseq methods have been updated at Lines 691-710.\n• Statistical information from differential gene expression is not provided. Response: The details are provided at Lines 1078-1093.\n• Supplementary fig 1 C, p-values not provided. Are the differences significant? Response: The Supplementary Figure 1 is now revised with updated statistics provided in the figure legend at Lines 1095-1105.\n• Inconsistency in information presented in methods and elsewhere (e.g. TSNE vs UMAP). Response: This has now been corrected.\n• Why are estimated number of cells diff than target cells in supplementary table for 10X experiment? Response: This type of variation (targeted cells vs actual sequenced cells) is mostly due to sample quality, such as single cell solution background, cell shape/morphology and cell viability.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nIn this revised manuscript, Sharan et al have addressed some of the points raised in initial reviews.\nThere is somewhat better contextualization of the work, significantly more methodological information,\nand the discussion is significantly improved. The two core issues with the manuscript are incompletely\naddressed.\nResponse: We thank the reviewer for acknowledging the improvements in contextualization,\nmethodological detail, and the overall discussion in the revised manuscript. We also appreciate the\ncontinued engagement and thoughtful critique. Regarding the two core issues mentioned as being\nincompletely addressed, we have revisited these concerns carefully in this revision. Below, we provide\na detailed explanation of how we have further clarified and expanded on these points in the manuscript.\nMajor comments:\n1. Limited scope of studies- The authors offer a compelling description of how their current work makes\nuse of historic control RM studies, explaining why data for many of the comparative conditions has\nbeen previously published. They posit that it’s very important to understand the impact of simultaneous\ntreatment for LTBI and HIV (SIV in this case). This is certainly overall true, but the argument that it’s\nnecessary important to understand whether the addition of LTBI treatment to cART changes the specific\nimmunologic features they look at is less compellingly made. With the exception of the CRP in Figure\n1, measurements do not include an uninfected control, which further limits how the data can be\ninterpreted.\nResponse: We thank the reviewer for making this important point. We have now added text to this effect\nboth in the Introduction (lines 88-91) and Discussion (lines 391-401). Regarding the exclusion of\ncontrols, the authors believe that the science is well established on that issue, with multiple labs\nincluding ours (e.g. PMID: 21781131, PMID: 27601645, PMID: 31479428) as well as Joanne Flynn’s,\nPhilana Lin’s, showing extensive reactivation due to SIV co-infection in macaques with prior, controlled\nMtb infection. As a result, we did not include macaques in the untreated groups in order to reduce the\ntotal number of macaques assigned to the project. It would be virtually impossible, to get an IACUC\nprotocol approved at a Primate Center, with uninfected macaques undergoing procedures as a control\ngroup, as the justification to have these animals undergo procedures is not going to be accepted.\n2. Limited information gleaned from the scRNAseq experiment: The authors engaged additional\nexpertise in scRNAseq analysis, which improved the technical analysis and visualization of the work.\nHowever, there’s still no biological interpretation, take-away, or implication offered for the scRNAseq\ndata, which makes it hard to understand what these experiments and figures add to the overall\nnarrative.\nResponse: We appreciate the reviewer’s feedback regarding the biological interpretation of our scRNA-\nseq data. In response, we would like to clarify the biological insights derived from this analysis and how\nthey contribute to the overall narrative of our study.\nTo understand the lung-compartment–specific immune dynamics during Mtb/SIV co-infection and\ntreatment, we conducted longitudinal single-cell transcriptomic profiling of BAL cells at four critical\nstages in the same animals. This design allowed us to monitor immune shifts within individual animals,\navoiding the need for separate control groups.\nOur analysis revealed a clear immunological signature associated with SIV co-infection, including a\nsignificant reduction in the presence of CD4+ T cells (which is highly expected) as well as NK/NKT\ncells, with a concomitant increase in CD8+ T cells, macrophages, and monocytes. These shifts reflect\nhallmark features of HIV-induced immune dysregulation and demonstrate the lung-specific impact of\nco-infection. Importantly, these alterations were not reversed by cART+3HP, indicating persistent\nimmune dysregulation despite treatment.\nFurther, differential gene expression and pathway enrichment analyses revealed robust activation of\ninnate immune pathways—particularly type I IFN responses—following SIV co-infection. These findings\nsuggest that type I IFN signaling plays a central role in modulating the local immune environment in the\nlung during co-infection. Moreover, changes in expression of TH1-, TH17-, and immune activation–\nrelated genes across cell subsets underscore the functional remodeling of host immunity over time.\nIn summary, our scRNA-seq data provide critical biological insights into the temporal evolution of\nimmune responses during Mtb/SIV co-infection and treatment. The enrichment of interferon and innate\nimmune pathways supports the concept that immune activation, rather than restoration of protective\nimmunity, may dominate in the lung despite therapy. These findings offer mechanistic context to our\nbroader conclusions and highlight potential therapeutic targets for improving outcomes in TB/HIV co-\ninfection.\nAs will be seen below in response to other questions, we have significantly enhanced both the quantity\nand quality of scRNAseq analyses through our collaborators at UT Houston Department of\nBioinformatics and Biostatistics.\nMinor comments:\nFigures 5B and C are not needed, as this is standard experimental work flow for scRNAseq. The\nauthors indicated that they moved these subfigures to a supplementary figure, but it looks like it’s still\nin 5B and 5C.\nResponse: Figure 5B and 5C have been removed from Figure 5.\nFigure 4J doesn’t make sense as a heatmap, as there are only 2 values/colors.\nResponse: Figure 4J has been updated. We have replaced the original heatmap it with a graphical\nsummary generated by IPA–Partek analysis of the cART+3HP samples.\nReviewer #2 (Remarks to the Author):\nThis is an improved manuscript submission and the authors have effectively addressed the majority of\nthis reviewer’s initial concerns. While the added content enhances clarity overall, it also contributes to\nincreased textual density. Below are mostly minor comments that require clarification.\nResponse: We sincerely thank the reviewer for their thoughtful assessment and positive feedback on\nthe revised manuscript. We appreciate the recognition of our efforts to improve clarity and address the\ninitial concerns. We have addressed each of the minor comments in detail below and hope the revisions\nfurther improve the manuscript’s clarity and readability.\n1. Displaying the data as means with SEM is inappropriate for the nonparametric statistical tests that\nwere performed.\nResponse: We have now presented data as SD where nonparametric statistical tests were performed\n(Figs. 1B, 1C-F, 1H; Figs. 2A-D, 2E-G, 2J-L, Fig. 4I).\n2. Greater transparency that this study leverages historical data should be provided, including in the\nresults section and by explicitly stating in Supplemental Table 1, which animal comes from which study.\nResponse: The study detail pertaining to each animal has now been included in the Supplementary\nTable 1, clarifying the use of historical controls in detail.\n3. The text provided on lines 585-591 provides more clarity as to when animals went to necropsy.\nHowever, the reviewer still has concerns regarding accuracy of the description based on the original\nSupplemental Figure 1C was removed from the resubmission. Adding the week of necropsy to\nSupplemental Table 1 would be useful for transparency.\nResponse: The details of necropsy week are now added to the Supplemental Table 1.\n4. A citation to support the text on lines 541-543 should be provided: “For example, macaques tend to\ndevelop a more robust immune response compared to humans, potentially leading to differences in the\ndynamics of infection and latency.”\nResponse: The sentence is now modified, and the citation is provided: For example, macaques tend to\ndevelop a different immune response compared to humans, potentially leading to differences in the\ndynamics of infection and latency. (PMID: 31736945, PMID: 21187902).\n5. Text added to the results section may be better suited for the discussion (e.g. Lines 177-188, 290-\n299, 325-33).\nResponse: We appreciate the reviewer’s suggestion. However, we feel that the text in the Results\nsection adds critical perspectives that are directly tied to the data presentation and are important for\ninterpreting the findings in real time. Including these observations here helps maintain a clear and\ncoherent narrative that connects the data with its immediate implications. Additionally, as the\nDiscussion section already includes multiple broader points, we are concerned that integrating these\nspecific insights there might dilute their impact or cause them to be overlooked. For these reasons, we\nbelieve that retaining this text in the Results section enhances the overall clarity and flow of the\nmanuscript.\n6. It is unclear what timepoint(s) are represented in Figures 6E-F\nResponse: As suggested, we have added ridge plots representing CD69 expression across 4 time\npoints (week 5, 11, 13 and necropsy) in Supplementary Figure 12 as they represent the more significant\nchange across time points.\n7. Supplemental Figure 1E-F, why are only 3/6 animals represented?\nResponse: The graphs represent n=3 rhesus macaques that underwent PET-CT scans and are shown\nin Fig. 1I.\n8. Supplemental Figure 2\na. If this is a representative gating scheme for whole blood, then this should be indicated in the legend.\nResponse: This information is added to figure legend of Supplementary Figure 2.\nb. The y-axis label for “Live cells” is occluded\nResponse: The y-axis for Live cells is now corrected.\n9. Supplemental Figure 7\na. Panels A-D are unreadable\nResponse: The panels are now divided into Supplementary Figure 7A-D making the text clearer.\nb. A legend for panel F is needed\nResponse: The figure is now revised as Supplementary Figure 7.\n10. Supplemental Table 5 does not include all antibodies shown in the gating schemes of Supplemental\nFigures 2-5, this should be provided.\nResponse: The antibody list is now updated (Supplementary Table 5).\nReviewer #5 (Remarks to the Author):\nThe revised manuscript includes significant changes based on the reviewers’ comments. The changes\nhave strengthened the overall narrative. The text and figures look much improved and include details\nmissing in the previous versions. The newly revised transcriptomics analysis, especially the scRNA-\nseq, looks better with the presented results supporting the narrative of the paper. However, some minor\nconcerns remain, and these make interpretation of the results confusing for the reader. Some figures\nincluded in both the main and supplementary sections are of poor resolution and missing sample or\nother labels.\nResponse: We sincerely thank the reviewer for their thoughtful and encouraging feedback. We are\npleased to hear that the revisions have strengthened the manuscript, particularly the improvements in\nthe transcriptomics and scRNA-seq analyses. We also appreciate the reviewer’s note regarding the\nremaining minor concerns.\nIn response, we have carefully reviewed all figures in both the main and supplementary sections. We\nhave replaced low-resolution images with higher-quality versions and ensured that all relevant labels,\nincluding sample identifiers and axis titles, are clearly indicated. These changes are aimed at improving\nthe clarity and interpretability of the figures for the reader. We have detailed these specific figure\nrevisions below and believe these updates address the reviewer’s concerns.\nReviewer comments: The bulk RNA-seq methods do not include details of the how the data were\nanalyzed. Ingenuity pathways analysis is only used for detecting pathways from a list of differentially\nexpressed genes. How were the raw data pre-processed, aligned (to which version of reference\ngenome), how was the differential expression analysis performed, what were the cut-offs and statistical\ntests used? This information should be provided to ensure reproducibility of analysis results from the\nraw data (which should be shared publicly). Legend for figure 4J also only mentions ingenuity pathway\nanalysis. Additional details should be included here as well. Additionally, supporting data for the bulk\nRNA-seq are missing from the manuscript (ex., QC results such as number of input reads, % mapped\nreads etc. should be mentioned.\nResponse: The details of the analysis are now provided in Supplementary Table 23. We have also\nadded details to methods at Lines 728-735.\nWhat is the second panel in 4J. Are these top enriched transcription factors/upstream regulators?\nMaybe 4J can be split into two as they represent two different immune responses. Also figure 4 is about\nMtb-specific TH1/TH17 in response to cART+3HP. The relationship between apoptosis and the\nTH1/TH17 response should be explained.\nResponse: Figure 4J has been updated, as a heatmap was not appropriate given the presence of only\ntwo values/colors. We have replaced it with a graphical summary generated by IPA–Partek analysis of\nthe cART+3HP samples.\nlist of differentially expressed results, tables including information about pathways/transcription factors\nand the genes supporting their enrichment).\nResponse: Differentially expressed gene (DEG) lists and gene set analysis (GSA) results are now\nincluded in Supplementary Tables 24 and 25. A list of enriched canonical pathways, along with\nassociated p-values for the cART+3HP samples, is provided in Supplementary Table 26.\nPresumably, bulk RNA-seq data should be deposited publicly, but information about this is not provided\nin the Availability of data section.\nResponse: The authors will deposit the raw data and sequencing FASTQ files in the NCBI Sequence\nRead Archive (SRA) and will include the accession number in the Availability of Data section upon\nacceptance.\nReviewer comments: The revised figures look much better than those in the previous version. The\nauthors can consider including panel 5A, B and C as a single panel. As I understand the viability metrics\nfor the QC results as mentioned in 5B are reported in supplementary table. This should be mentioned\nin the results section in text as well.\nResponse: The authors thank the reviewer for the suggestion. As per the suggestion also by Reviewer\n1, we have removed the panel 5B and 5C since these are the standard experimental workflow for\nscRNAseq.\nWhat are the genes depicted in 5E. Are these cluster markers? What markers uniquely distinguish\nwhich cell types? The legend should be edited for redundancy. There are also extra lines for panel E\nand F, perhaps included by mistake.\nResponse: The authors have revised the legend of Figure 5 (Lines 1073-1075) to mark which gene\nuniquely identifies which cluster. Additional information is also provided in Supplementary Table 4.\nHow were the 42 DEGs identified? Were they genes that were differentially expressed across\ntimepoints without regards to the cluster identity or the animal ID? Details of how the analysis were\nperformed (specifically related to how the differentially expressed genes were obtained) should be\nincluded in the methods section. Methods mention information about heatmaps, however, there are no\nheatmaps in any of the figures.\nResponse: The authors thank the reviewer for the feedback. After data integration and final clustering\nidentification, the differential expressed genes (DEGs) were analyzed by FindMarkers between\ntimepoints based on clusters following Seurat DEGs analysis pipeline, where default method “Wilcoxon\nrank sum test” was applied. DEGs were analyzed by timepoints based on clusters after data integration\nanalysis including all animals. We would like to clarify that the current figures focus exclusively on the\n42 pre-specified candidate genes. The proteins encoded by these genes are associated with and well-\nknown in the field to be representative and canonical markers of - TH1 (IFNG, IFNGR1, IFNGR2,\nTBX21, STAT1, TNFAIP2, TNFAIP3, TNFAIP8, TNFAIP8L1, TNFAIP8L3, IL2, IL2RA, and BHLHE40),\nTH17 (IL20, IL20RA, IL20RB, IL21, IL21R, IL22, IL22RA1, IL22RA2, IL23A, IL23R, RORA, and STAT3)\nand immune activation (IRF4, CD38, CD69, LAG3, ICOS, CTLA4, IL13, CXCL10, CXCR1, CXCL12,\nCXCL13, IL27, KLRD1, IL6, MMP9, FCGR1A, and IDO1), responses.\nFigure 6A, shows a violin plot showing distribution of log2 fold changes of 42 genes in each pairwise\ncomparison of timepoints in each cluster and then compares the distributions across different contrasts.\nThis presentation was hard to digest. Wouldn’t comparing enrichment or module score of the 42 genes\n(or even subsets within these genes e.g. Th1, Th17) across different timepoints in each cluster would\nbe a more straightforward result to present?\nResponse: As suggested, we computed module scores and assessed differences between time points\nusing the Wilcoxon rank-sum test, stratified by cell cluster and module (T , T , and Immune\nH1 H17\nactivation) (Supplementary Figure 10A-C). A summary table (Supplementary Table 6) of the statistical\ntest results for each module and cluster is also provided.\nIt would also be helpful to plot expression values of individual genes as violin plots of each animal and\ncompare it across timepoints in each cluster. Additionally, it would also be interesting to see the\nexpression dynamics of these genes across timepoints within the same animal to see if most animals\nfollow the same trend in expression across timepoints. Alternatively, a heatmap or a dot plot can be\nconsidered. The additional plots could be added in the main figure or supplementary, as appropriate.\nResponse: As requested, we generated dot plots of average gene expression for each cluster, for\neach animal at each time point (Supplementary Figures 11A-H).\nIn the table in fig 6B, I assume these are comparisons in specific clusters that yielded significant p-\nvalues. However, this doesn’t match up with what’s shown in the violin plots where four comparisons\nare marked as significant while the table shows seven comparisons. The table could also be just\nincluded as part of figure 6B.\nResponse: The table contained additional raw p-values that were significant before correction but\ninsignificant after correction. The plot only shows p-values (FDR) significant after correction. As\nsuggested, we have now added the table as part of Figure 6B.\nAre the GO and GSEA analyses in fig 6C and D done for the 42 differentially expressed genes? The\ntext mentions results are only depicted for the macrophage cluster for week 5 and 11. There is no\nexplanation provided why the authors only showed results for this cluster/timepoints. Did the other\ncluster not yield any statistically significant results?\nResponse: Thank you for your comment. We focused on the macrophage cluster at weeks 5 and 11\nbecause our primary interest lies in understanding the pathways impacted by SIV co-infection in the\ncontext of LTBI. Week 5 represents the LTBI phase, while week 11 captures the SIV co-infection phase\nprior to the initiation of cART+3HP. This targeted approach allows us to highlight key pathway changes\nrelevant to our study objectives. We have also provided week 5 vs week 11 GO enrichment analysis as\nSupplementary Tables 7-14.\nIt is also not clear what the ridgeplots are depicting in 6E and F. These plots just seem to be showing\nthe distribution of expression values of CD38 and CD69 in different clusters in cART+3HP. However, a\nbetter depiction would be to include ridgeplots for these genes in each cluster and timepoints. The\nauthors can consider presenting the most significant results in the main figure and include the rest as\nsupplementary.\nResponse: As suggested, the authors have added ridge plots representing CD69 expression across\ntime points in Supplementary Figure 12 as they represent the more significant change across time\npoints.\nThe authors should also consider adding differential gene expression as well as GO/GSEA results as\nsupplementary tables.\nResponse: The authors now provide week 5 vs week 11 GO enrichment analysis as Supplementary\nTables 7-14. Differential gene expression results are provided in Supplementary Tables 15 – 22.\nReviewer #6 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of\nthe Nature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nResponse: Thank you. The authors of this manuscript fully support the development ESIs/ECRs in\nquality review process. The first author of this manuscript is an ESI (former post-doc in Kaushal lab,\nnow independent Assistant Professor, with own grants, albeit still an NIH-ESI).\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nIn this revised manuscript, the authors have addressed comments made in previous rounds of reviews.\nResponse: We thank the reviewer for acknowledging the revisions made in this manuscript. We appreciate the\nconstructive feedback provided throughout the review process, which has helped us improve the quality and\nclarity of our work.\nSpecific points incompletely addressed:\n1. Controls- the authors make a compelling argument that it would be unethical to include experimental\ncontrols- they argue in part that these controls have been performed frequently enough before that they do not\nmerit repetition. This is a very reasonable response- but given that other data included in the paper is historical\ndata, it is not totally clear to me why those historical experimental controls are not also included.\nResponse: The authors thank the reviewer for the feedback. We have included historical control groups—LTBI,\ncART-naïve, and cART-only—in Figures 1 (panels A–H) and 2 (A–L) to provide broader context. This is now\nspelled out clearly at the beginning of the results section (please see lines 112-118). For Figures 3 and 4, in\nparticular, our focus is to compare the two treatment arms: cART only versus cART+3HP. These figures\nexamine the percentages of CD4⁺ T , CD4⁺ T , CD8⁺ T , and CD8⁺ T in BAL and lung tissue at necropsy\nCM EM CM EM\nto determine whether immune cell replenishment is comparable or improved with the addition of 3HP to cART.\nIncluding only these two groups in Figures 3 and 4 allows for a clearer, more direct comparison of treatment\nefficacy. Additionally, Figures 5 and 6 present longitudinal data collected from four key time points within the\nsame macaque, eliminating the need for additional control samples. These time points include: week 5\n(representing LTBI), week 11 (LTBI+SIV), week 13 (LTBI+SIV+cART+3HP), and necropsy (marking the end of\n12 weeks of treatment).\n2. Analysis and information provided from the scRNAseq experiments: The analysis of the scRNAseq\nexperiment is markedly improved. Figure 5 is nicely presented and is a standard way of showing how cells\ncluster in single cell analysis.\nResponse: We thank the reviewer for the positive feedback on the scRNA-seq analysis and presentation. We\nare glad to hear that the improvements were helpful and that Figure 5 effectively conveys the clustering of cell\npopulations in a standard and interpretable manner. We appreciate your acknowledgment of the clarity and\nquality of the revised analysis.\nFigure 6 would ideally provide the main take-away messages from the scRNAseq experiment, but for me it\ndoes not meet this bar. In 6A, it's not clear why gene subsets from multiple immune pathways are combined\n(the way the data is shown in S9, looking at modules of genes that reflect individual pathways, would be a\nmore standard and interpretable way to show the data). There are no statistics shown in S9, so it may be that\nthere are no statistically significant differences in module expression for individual modules, and modules were\ncombined for 6A to meet a statistical threshold. It would be helpful for some explanation or justification for that\nchoice to be made given that it's hard to interpret the combination biologically. For 6B, there are a small number of cell subsets that are significantly differentially present after correction for multiple\ncomparisons- what distinguishes those subsets? How does that compare with the flow data in earlier figures? For C and D, pathway analysis is shown only for macrophages, comparing the LTBI and SIV/TB conditions. Given that the paper primarily seeks to understand how addition of 3HP to cART changes TB sterilization and immune activation, the focus on this particular comparison in this rich dataset is not intuitive, and some explanation of that choice would be helpful.\nResponse: Week 5 captures the LTBI phase prior to viral challenge, while week 11 represents the early stage\nof SIV co-infection—before the initiation of cART+3HP—allowing us to isolate the impact of viral infection on\ninnate immune dynamics in the lung. This comparison is critical for understanding how the host’s early\nresponse to SIV shapes subsequent immune control and informs the effectiveness of cART+3HP. Lines 387-392.\nPathway analysis of differentially expressed genes in macrophages between these time points revealed\nsignificant enrichment for innate immune and inflammatory responses. Specifically, GO enrichment analysis\n(old Figure 6C, new Figure S10, panel A) showed upregulation of pathways such as response to type I\ninterferon (IFN), IFN-mediated signaling, cellular response to type I IFN, and activation of the innate immune\nresponse. GSEA further confirmed enrichment of cytokine-mediated signaling pathways, type I IFN responses,\nand innate immune activation (old Figure 6D, new Figure S10, panel B). These transcriptional changes were\naccompanied by increased expression of immune activation markers, including CD69 (Supplementary Figure\n12). This data corroborates our findings in bulkRNAseq (Figure 4J) wherein, we observed predicted activation\nof IFN associated genes as well as upregulation of 20 out of 38 IFN associated genes in the cART+3HP\ndataset (Supplementary Table 3). Lines 392 – 402.\nIn conclusion, the focus on macrophages in this comparison allows us to highlight early immune perturbations\nthat are highly relevant to the host-pathogen interaction in TB/HIV co-infection. The observed enrichment of\ntype I IFN and innate immune pathways suggests a central role for these mechanisms in shaping the trajectory\nof infection and treatment response. Moreover, these findings complement our broader analysis of differential\ngene expression across TH1, TH17, and immune activation-associated genes, underscoring the complexity of\nthe immune response during co-infection. Understanding how these innate responses evolve in the context of\ncART+3HP can provide important insights for optimizing therapeutic strategies aimed at achieving durable\nimmune control and TB sterilization in HIV-positive individuals. Lines 403-410.\nSupplementary Tables 8–15 now include the full GO enrichment results to support this analysis.\nMinor comments:\nThe results section for the scRNAseq now includes text that would be better suited to the methods section.\nResponse: We have now moved the text pertaining to methodology to the methods section. Lines 739-742.\nReviewer #2 (Remarks to the Author):\nThe authors addressed most comments provided by this reviewer. The only comment that was inadequately\naddressed is regarding the transparency surrounding the use of historical controls. This should be explicitly\nmentioned early with the results section instead of being buried as citations or as a singular mention within the\ndiscussion section.\nResponse: The authors have added the statement addressing use of historical controls in Results section.\nLines 112-116.\nReviewer #5 (Remarks to the Author):\nThe revised manuscript covers most of the concerns raised by this reviewer. However, some points from\nconcern 1 were not fully addressed. Additionally figure labels and table captions were missing in the main as\nwell as supplementary sections.\nConcern 1\nReviewer comment:\nDescription of bulk RNA-seq in the revised methods section is sufficient. However, the results and figures\nrelated to these data still need improvement.\nFigure 4J doesn’t correspond to what is described in the text in lines 316-321 about cell death pathways.\nThese results were present in the previous version of the manuscript but seem to have been omitted in the\ncurrent revision. This reviewer would recommend adding back the figure or removing the text.\nResponse: The cell death pathways text is removed.\nThe Type I IFN genes, such as IFNA2, IFNA1/IFNA13 are shown to have predicted activation in the network\nplot depicted in 4J. Are these genes enriched or upregulated in cART+3HP treated RMs compared to cART?\nOr are the genes they regulate differentially expressed? This reviewer would suggest plotting expression levels\nof these genes or the genes that support their activation as a boxplot or heatmap. Genes supporting the\nactivation of IFN genes can be obtained from IPA results.\nResponse: The genes associated with IFN signaling and their activated status represented in Figure 4J are\nnow shown in Supplementary Table 3. 20 out of 38 IFN associated genes from the cART+3HP dataset are\nupregulated as shown in Supplementary Table 3. Lines 320-321.\nAdditionally, two points that this reviewer raised previously regarding bulk RNA-seq weren’t addressed.\nSpecifically, supporting data for the bulk RNA-seq are missing from the manuscript (e.g.., QC results such as\nnumber of input reads, % mapped reads, list of differentially expressed results, tables including information\nabout pathways/transcription factors and the genes supporting their enrichment). Secondly, information about\nwhere the data is deposited for public access should be added to the ‘Availability of data’ section.\nResponse: We have now included the relevant supporting data for the bulk RNA-seq analysis in the\nsupplementary materials as follows:\n QC results (e.g., number of input reads, percentage of mapped reads) are provided in Supplementary\nTable 25.\n List of differentially expressed genes is included in Supplementary Table 26.\n Pathway and transcription factor enrichment analyses, along with the supporting gene lists, are\nprovided in Supplementary Tables 27.\nAdditionally, information regarding data availability has been added to the ‘Availability of Data’ section of the\nmanuscript. The bulk RNA-seq data have been deposited in the Sequence Read Archive (SRA), NCBI under\naccession number: PRJNA1327945. Lines 763-767.\nConcern 2\nReviewer comment:\nThe changes made in the revised version have greatly improved this section and the reviewer doesn’t have\nany additional comments. Figure 6E and 6F are missing from the submitted draft.\nResponse: Figure 6E and 6F have been replaced with Supplementary Figure 12.\nReviewer #6 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of\nthe Nature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nResponse: Thank you. The authors of this manuscript fully support the development ESIs/ECRs in\nquality review process. The first author of this manuscript is an ESI (former post-doc in Kaushal lab,\nnow independent Assistant Professor, with own grants, albeit still an NIH-ESI)."}]

Decision letter