Linking Gut Microbiome to HIV-1 Reservoir Size in People Living with HIV

Linking Gut Microbiome to HIV-1 Reservoir Size in People Living with HIV | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Linking Gut Microbiome to HIV-1 Reservoir Size in People Living with HIV Oscar Kieri, Aswathy Narayanan, Bianca B Jütte, Peter Svensson, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6646788/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The gut microbiome is altered during HIV-1 infection contributing to immune dysfunction and inflammation in people living with HIV (PLWH) that persists despite antiretroviral therapy (ART). We explored the associations between the gut microbiome and HIV-1 reservoir size in PLWH (n = 30) on long-term ART. The intact proviral DNA assay (IPDA) and shotgun metagenomic sequencing were performed to identify microbial species and metabolic pathways associated with the size of the HIV-1 reservoir. PLWH with a smaller intact reservoir exhibited lower alpha diversity compared to individuals with a larger intact reservoir. We found that Phocaeicola plebeius and Lachnospira sp000437735 were significantly enriched in individuals with a smaller intact reservoir and lower intact-to-total proviral ratio, respectively. We observed a negative association between Fecalibacterium prausnitzi and a positive association of Prevotella copri , with the intact proviral reservoir size. Additionally, the metabolic pathways of glycolysis and branched-chain amino acid biosynthesis were enriched in individuals with larger reservoir. HIV reservoir size in blood is associated with gut microbiome diversity, specific metabolic pathways and microbial signatures, including Lachnospira, Prevotella , and Faecalibacterium . These findings underscore the potential role of the gut microbiome in viral persistence and suggest that strategies targeting microbiome modulation could disrupt the HIV reservoir. Biological sciences/Immunology/Infectious diseases/Hiv infections Biological sciences/Microbiology/Virology/Viral reservoirs Health sciences/Diseases/Infectious diseases Health sciences/Diseases/Infectious diseases/Hiv infections Biological sciences/Immunology/Infection Biological sciences/Immunology/Infectious diseases Biological sciences/Immunology/Inflammation/Chronic inflammation Biological sciences/Microbiology/Communities/Microbiome Health sciences/Pathogenesis/Infection Figures Figure 1 Figure 2 Figure 3 Introduction The gut microbiome plays a critical role in modulating immune responses and systemic inflammation in people living with HIV (PLWH), and these two key factors influence the persistence of the HIV-1 reservoir 1 . During HIV-1 infection, the gut-associated lymphoid tissue (GALT) becomes a major site for viral replication and immune activation 2 , 3 . This process compromises gut barrier integrity, leading to microbial translocation and sustained systemic inflammation 4 Although antiretroviral therapy (ART) effectively suppresses viral replication and has dramatically reduced HIV-related morbidity and mortality, it does not fully restore gut immune homeostasis or repair the structural and functional damage within the GALT 5 , 6 . Additionally, the gut microbiome, which is profoundly altered during HIV infection, remains dysbiotic despite long-term ART 7 . Lastly, ART alone is insufficient to eliminate the latent HIV reservoir, which persists primarily in lymphoid tissues and represents the major obstacle to achieving a definitive cure 8 , 9 . Persistent gut barrier dysfunction allows microbial derived products such as lipopolysaccharides (LPS) and bacterial DNA to enter systemic circulation, driving chronic immune activation 4 , 10 . This persistent immune stimulation promotes CD4 + T cell activation, facilitating maintenance and potential expansion of the HIV reservoir, which is predominantly harbored within the GALT 11 . Additionally, microbial metabolites, including tryptophan catabolites like kynurenine, have been associated with the size of the HIV reservoir 12 . Despite these insights, the direct influence of gut microbiome composition and causal function on the size and dynamics of the HIV reservoir remains incompletely understood, and robust clinical data are lacking. Our previous research has shown distinct differences in gut microbiome composition and functional capacity among viremic individuals, ART-treated, and elite controllers 13 , 14 . These individuals also differ in reservoir size and suggest that the microbiome may shape the HIV reservoir 15 , 16 . To explore this question, the primary objective of our current study was to investigate the interplay between the gut microbiome and the HIV-1 reservoir. Specifically, we aimed to identify microbial taxa and functional pathways that correlate with the size of the intact HIV-1 reservoir, with the goal of uncovering novel microbiome-based targets for reservoir modulation. Materials and Methods Study Cohort Individuals included in this study were part of the COVAXID trial, an open-label, non-randomized prospective clinical trial at the Karolinska University Hospital, Stockholm, Sweden, to investigate the safety and clinical efficacy of the mRNA BNT162b2 vaccine (Comirnaty®, Pfizer/BioNTech) in immunocompromised individuals 17 . The ethical permit was granted by the Swedish Ethical Review Authority (ID 2021 − 00451), and all participants provided written informed consent. All methods of this study was performed in accordance with the Declaration of Helsinki and The Good Clinical Practice guidelines. The trial was registered at the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT 2021-000175-37) and clinicaltrials.gov (NCT04780659) by Feb 9, 2021, and Feb 19, 2021, respectively. The trial was also approved by the Swedish Medical Product Agency (ID 5.1-2021-5881). The original trial protocol is available via the SciLifeLab Data Repository. PLWH, aged 18–85, followed at the outpatient HIV clinic, eligible for COVID-19 vaccination, were screened for inclusion in the trial. Recruitment started on Feb 15, 2021 and follow-up ended Oct 15, 2021. The trial was fully recruited as intended in the study plan. Ninety PLWH on ART were enrolled in the trial, with fecal and blood samples for reservoir analysis collected from 39 participants. After excluding nine individuals who had received antibiotic treatment within three months prior to inclusion, the analysis was conducted on the remaining 30 participants. Clinical and laboratory characteristics were obtained from electronic health records. Basic dietary information (omnivorous, vegetarian) and body mass index (BMI) were collected at inclusion in the study. Reservoir analysis Blood samples were collected from the participants for reservoir analysis. PBMCs were isolated by density gradient separation and resting CD4 + T cells (rCD4 + T cells) were isolated from the obtained PBMCs by a two-step magnetic isolation using the Miltenyi Biotec magnetic associated cell sorting (MACS) platform. For the first step, the CD4 + negative cell isolation kit (Miltenyi Biotec, Cat# 130-096-533) was used according to the manufacturer’s protocol. Secondly, rCD4 + T cells were negatively selected with magnetic beads against the activation markers CD25, CD69 and HLA-DR. DNA was extracted either by Allprep (Qiagen, Cat#80204) or QIAamp DNA Mini Kit (Qiagen, Cat#51304). The DNA was used to quantify the HIV-1 reservoir (intact, 5′ ( Ψ) deleted, and 3′ ( env ) deleted/hypermutated proviruses) using the Intact Proviral DNA Assay (IPDA), which has been previously described 18 . For IPDA, digital droplet PCR (ddPCR) was performed with the QX200 Droplet Digital qPCR System (Bio-Rad). Each reaction consisted of 20 µL, containing 10 µL Supermix for Probes without dUTP (Bio-Rad, Cat# 1863024), 900 nM primers, 250 nM probe (labelled with HEX or FAM), and 8 µL undiluted cellular DNA. Droplets were generated using the QX200 droplet generator. Emulsified PCR reactions were performed with a C1000 Touch thermal cycler (Bio-Rad), with the following protocol: 95 ̊C for 10 min, followed by 40 cycles of 94 ̊C for 30 s and 57 ̊C for 60 s, and a final droplet cure step of 10 min at 98 ̊C. Each well was then read with a QX200 Droplet Reader (Bio-Rad). Droplets were analyzed with QuantaSoft version 1.5 (Bio-Rad) software in the absolute quantification mode. Fecal sample processing The fecal samples were collected in RNA/DNA shield (Stratec, Germany). DNA was extracted using QIAamp PowerFecal Pro DNA Kits – Stool/Gut DNA Extraction (Qiagen) and sequenced on the Illumina Novaseq6000 platform. Sequence analysis The paired-end sequences obtained from the sequencing platform were quality-checked using FastQC 19 . Adapter and low-quality reads (Phred score < 30) were removed using Trim Galore 20 . Further, host DNA contamination was removed from the fastq sequences using a combination of Bowtie2 21,22 , SAMtools 23 , and Bedtools 24 . Pre-processed reads were then analyzed for taxonomic classification using MetaPhlAn4 25 , and functional pathways were identified using HUMAnN3 26 . The alpha diversity indices such as Observed, Chao1, Shannon, Simpson and InvSimpson were performed to calculate the richness and evenness of the samples. Linear discriminant effect size (LEfSe) was employed to identify significant microbial compositions between groups. Spearman correlation was performed using the R package psych (v2.4.3) 27 to analyze the association between microbiota at the species level, and clinical parameters and HIV-1 reservoir. The plots were visualized using the R package ggplot2 (v3.5.1) 28 . Results Study Participants The study included 30 PLWH with a median age of 54 years (IQR 44, 68), 40% of whom were female. All participants were on ART at inclusion, with a median treatment duration of eight years (IQR 4, 15). The majority (83%) were treated with integrase strand transfer inhibitors (INSTI), and 90% had a HIV RNA viral load of less than 50 copies/ml. The median CD4 + T cell count was 630 cells/mm³ (IQR 290, 720), with a CD4 + /CD8 + ratio of 1.05 (IQR 0.55, 1.40). Nearly half (47%) had a nadir CD4 + T cell count below 200 cells/mm³, with a median nadir CD4 + T cell count of 260 cells/mm³ (IQR 80, 410) (Table 1 ). Table 1 Characteristics according to intact HIV-1 proviral reservoir size at inclusion Characteristics High N = 15 1 Low N = 15 1 Overall N = 30 1 p-value 2 q-value 3 Sex 0.14 0.3 Male 11 (73%) 7 (47%) 18 (60%) Female 4 (27%) 8 (53%) 12 (40%) Age (years) 54 (44, 68) 54 (41, 63) 54 (44, 68) 0.8 > 0.9 CD4 + T-cell count 320 (240, 720) 642 (570, 770) 630 (290, 720) 0.042 0.089 Nadir CD4 + T-cell count 140 (58, 280) 370 (220, 470) 261 (80, 410) 0.007 0.028 CD4 + /CD8 + ratio 0.58 (0.37, 1.31) 1.29 (0.96, 2.10) 1.05 (0.55, 1.40) 0.036 0.089 HIV RNA 0.9 ART regimen > 0.9 > 0.9 INSTI-based 12 (80%) 13 (87%) 25 (83%) NNRTI-based 3 (20%) 2 (13%) 5 (17%) BMI (kg/m 2 ) 24.0 (21.0, 27.0) 25.0 (22.9, 29.0) 25.0 (22.9, 27.0) 0.8 > 0.9 Diet > 0.9 > 0.9 Omnivorous 14 (93%) 14 (93%) 28 (93%) Others/Unknown 1 (6.7%) 0 (0%) 1 (3.3%) Vegetarian 0 (0%) 1 (6.7%) 1 (3.3%) Ethnicity > 0.9 > 0.9 Asian 3 (20%) 3 (20%) 6 (20%) Black 2 (13%) 2 (13%) 4 (13%) Caucasian 10 (67%) 9 (60%) 19 (63%) Latino 0 (0%) 1 (6.7%) 1 (3.3%) Route of transmission 0.5 0.7 Heterosexual 8 (53%) 10 (67%) 18 (60%) MSM 7 (47%) 5 (33%) 12 (40%) Intact provirus 196 (87, 391) 34 (27, 45) 60 (34, 196) < 0.001 < 0.001 5´defective provirus 335 (142, 1,069) 211 (125, 347) 281 (142, 832) 0.15 0.3 3´defective provirus 318 (202, 902) 120 (91, 169) 190 (104, 342) < 0.001 0.006 Total defective provirus 641 (385, 2,097) 318 (243, 624) 527 (272, 1,099) 0.037 0.089 Total provirus 729 (522, 2,542) 363 (272, 677) 632 (307, 1,138) 0.011 0.038 Ratio intact-to-total provirus 0.18 (0.15, 0.29) 0.08 (0.04, 0.12) 0.12 (0.08, 0.19) < 0.001 < 0.001 ART, antiretroviral therapy; INSTI, integrase strand transfer inhibitor; NNRTI, non-nucleoside analogue reverse transcriptase inhibitor; MSM, men who have sex with men; BMI, body mass index. CD4 + T-cell count (cells/mm 3 ); Nadir CD4 + T-cell count (cells/mm 3 ); provirus (proviral HIV DNA copies/million resting CD4 + T-cells). 1 n (%); Median (Q1, Q3) 2 Pearson’s Chi-squared test; Wilcoxon rank sum test; Fisher’s exact test; Wilcoxon rank sum exact test 3 False discovery rate correction for multiple testing Reservoir analysis We quantified the intact and defective (5′ deleted and 3′ deleted/hypermutated) proviral HIV-1 reservoir per million rCD4 + T cells in blood using IPDA (Supplementary Fig. 1A). The median intact reservoir size was 60 (IQR 34, 198) copies/million rCD4 + T cells, while the defective reservoir confined 282 (IQR 141, 874) copies of 5′-deleted proviruses and 190 (IQR 103, 350) copies of 3′-deleted/hypermutated proviruses per million rCD4 + T cells. The estimated median total reservoir size (intact and defective) was 632 (301–1144) and correlated to the amount of intact provirus (Spearmans’s, r = 0.68, p = < 0.0001) (Supplementary Fig. 1D), with a median ratio of intact-to-total provirus of 0.13 (IQR 0.08, 0.2) (Supplementary Fig. 1C). In order to elucidate relationships between the HIV-1 reservoir size and the gut microbiome, we stratified PLWH based on higher and lower levels of intact proviral HIV-1 DNA and ratio of intact-to-total proviruses, a potential indicator of reservoir decay, using the median as divider (Supplementary Fig. 1B, 1C). PLWH with a larger intact reservoir size had a significantly lower CD4 + T cell count, with a median of 320 (IQR 240, 720) cells/mm 3 , compared to individuals with a smaller intact reservoir size, who had a median of 642 (IQR 570, 770) cells/mm 3 (p = 0.04). Similar relationships were observed for the nadir CD4 + T cell count and the CD4 + /CD8 + ratio. In the groups of higher and lower ratio of intact-to-total provirus a similar trend was observed. Despite the differences in immune status, the duration and type of ART did not differ between the reservoir groups, suggesting that these factors are unlikely to account for the observed differences. Additionally, there were no significant differences in age, sex distribution, BMI, diet, ethnicity, or mode of transmission. Antibiotic use was absent, as individuals who had received antibiotics within three months prior to inclusion were excluded from the study. The detailed characteristics are presented in Table 1.3 Alterations in bacterial diversity and composition in relation to intact proviral reservoir and intact-to-total proviral ratio Individuals within the group of higher intact proviral reservoir size showed significantly greater alpha diversity, compared to individuals in the group of lower intact reservoir size (Shannon p = 0.03; Simpson p = 0.03) (Fig. 1 A). However, there were no significant differences in richness (Observed p = 0.88; Chao1 p = 0.88) and we did not find any associations between alpha diversity and intact reservoir size in regression analysis (Supplementary Fig. 2). We observed a significantly greater abundance of Lachnospira rogosae A (p = 0.04), Muricomes contortus B (p = 0.04), Copromorpha sp900066305 (p = 0.02) in the group with a high intact reservoir. On the contrary, in the group with a low intact reservoir, we found significant enrichment of Phocaeicola plebeius A (p = 0.04), Lachnospira sp000437735 (p = 0.02) and Eubacterium G sp000434315 (p = 0.02) (Fig. 1 B). Conversely, we did not observe any significant differences in alpha diversity indices between the groups with high and low ratio of intact-to-total reservoir (Fig. 1 C). Furthermore, Prevotella sp002265625 (p = 0.02), Alloprevotella sp900539625 (p = 0.01), and Desulfovibrio sp900556755 (p = 0.04) were significantly abundant in individuals with a high ratio. On the other hand, individuals with a low ratio showed significant increase in Phocaeicola plebeius A (p = 0.04), Lachnospira sp000437735 (p = 0.003), Blautia A faecis (p = 0.04), and Gemmiger formicilis (p = 0.04) (Fig. 1 D). Association between microbiota and intact proviral reservoir size and intact-to-total proviral ratio We found several significant correlations between different bacterial species and the level of intact HIV-1 DNA. Notably, we observed a negative association of Faecalibacterium prausnitzii D (p = 0.001), Lachnospira sp000437735 (p = 0.01), and Bacteriodes clarus (p = 0.05) with intact proviral reservoir size. On the contrary, a significant positive association was observed with Oscillospiraceae CAG-83 sp000435975 (p = 0.004), Bifidobacterium bifidum (p = 0.01), Prevotella copri A (p = 0.02), and Sutterella wadsworthensis (p = 0.04) (Fig. 2 A). Furthermore, we observed a significant positive association of the ratio of intact-to-total proviruses with Ventrimonas sp003481825 (p = 0.01 ) , Prevotella bivia (p = 0.02) and Lachnospira rogosae A (p = 0.03) while taxa such as Lachnospira sp000437735 (p = 0.01), Phocaeicola plebeius A (p = 0.02), and Phocaeicola massiliensis (p = 0.04) were negatively associated with the ratio of intact-to-total proviruses (Fig. 2 B). Relationship of metabolic pathways with HIV-1 reservoir size and intact-to-total proviral ratio We further investigated if the functional pathways of the metabolic traits were related to the intact proviral HIV-1 reservoir size and intact-to-total proviral ratio. Our analysis revealed a significant higher abundance of L-valine biosynthesis (p = 0.02), glycolysis IV (p = 0.04) and super-pathway of branched-chain amino acid biosynthesis (p = 0.04) in PLWH within the group with a high intact proviral reservoir (Fig. 3 A). Additionally, we observed a significant abundance of CDP-diacylglycerol biosynthesis I and II (p = 0.04) in PLWH within the high ratio group (Fig. 3 B). Bacteroidales/Clostridiales ratio in PLWH with respect to intact proviral reservoir and intact-to-total proviral ratio As a previous study found an inverse correlation between the Bacteroidales/Clostridiales ratio and HIV-1 reservoir size [22], we explored if similar patterns were present in our cohort. However, we did not observe significant differences in Bacteroidales, Clostridiales , and Bacteroidales/Clostridiales ratio between the groups of higher and lower intact reservoir size and ratio of intact-to-total proviruses in our study cohort (Fig. S3 A, B). Discussion In this study, we explored the relationship between the gut microbiome and the HIV-1 reservoir in PLWH on long term ART, identifying microbial patterns and metabolic pathways associated with reservoir size. Interestingly, we observed lower bacterial diversity in individuals with a smaller intact proviral reservoir size in blood. Furthermore, Lachnospira sp000437735 and Phocaeicola spp. , species related to gut health, were more abundant in individuals with a smaller intact reservoir and smaller intact-to-total proviral ratio, respectively, and negatively correlated to reservoir size and intact-to-total proviral ratio. Conversely, Prevotella spp. , species associated with chronic inflammation, were positively correlated to intact reservoir size, and were also enriched in the group with a high ratio. The metabolic pathways of glycolysis and branched-chain amino acid biosynthesis were significantly enriched in individuals with a larger intact reservoir. Following the initiation of ART, the intact proviral reservoir declines slowly over time, accompanied by an accumulation of defective proviruses, resulting in a progressively lower intact-to-total proviral ratio. The most pronounced decay of the intact reservoir typically occurs within the first four to seven years after ART initiation, after which the decline reaches a plateau 29 – 32 . In our cohort, individuals with a smaller intact reservoir size had a significantly higher current and nadir CD4 + T cell count and CD4 + /CD8 + ratio compared to those with a larger intact reservoir, however, the duration of ART did not differ between the groups. We observed lower alpha diversity, as measured by Shannon and Simpson indices, in individuals with smaller intact proviral reservoirs. Yet, regression analysis did not reveal a statistically significant association between individual intact reservoir size and alpha diversity. This trend aligns with findings from a previous study, which also reported reduced diversity in individuals with viral control and presumed smaller reservoir size 33 . Nevertheless, the associations between microbial diversity and reservoir remain poorly defined, and the potential mechanisms are not yet understood 13 , 15 . Furthermore, the diversity is generally lower in PLWH and is influenced by several factors including diet and therapeutics like ART 1 , 14 . Moreover, we identified several bacterial taxa associated with a lower intact HIV-1 reservoir size and a reduced intact-to-total proviral ratio. Notably, we observed a significant enrichment of Phocaeicola plebeius in individuals with a smaller intact HIV-1 reservoir and a lower intact-to-total proviral ratio. Additionally, Phocaeicola massiliensis and Phocaeicola plebeius were both negatively correlated with the intact-to-total proviral ratio. The role of Phocaeicola in HIV pathogenesis and reservoir dynamics remains poorly understood. A previous study 34 reported positive associations between Phocaeicola abundance and HIV-1 reservoir size, as well as inflammatory protein levels, though these findings were limited to treatment-naïve individuals. Conversely, Phocaeicola plebeius has been shown to decline in abundance with increasing HIV disease severity 35 , highlighting potential context-dependent effects. Importantly, Phocaeicola species are known for their anti-inflammatory function and roles in gut health with dietary fiber fermentation and polysaccharide metabolism 36 , 37 . Furthermore, we found that Eubacterium G sp000434315 was more abundant in individuals with a smaller intact reservoir and ratio of intact provirus. This observation is consistent with previous studies showing enrichment of Eubacterium species in elite controllers 38 . Similarly, Faecalibacterium prausnitzii — a prominent butyrate-producing bacterium 39 frequently depleted in PLWH — demonstrated a negative correlation with intact reservoir size in our cohort. Taken together, these findings support the hypothesis that butyrate-producing bacteria, such as Eubacterium spp. 40 and F. prausnitzii , may play a protective role in modulating HIV reservoir dynamics, potentially by reducing systemic immune activation 41 and promoting mucosal integrity and immune homeostasis. Additionally, we found that Lachnospira sp000437735 was enriched and negatively correlated with both intact HIV-1 reservoir size and the intact-to-total proviral ratio. In contrast, Lachnospira rogosae was more abundant in individuals with a larger intact reservoir and positively correlated with the intact proviral ratio. While the functional roles of these species remain poorly characterized, their opposing associations suggest distinct influences on reservoir dynamics within the same genus. The Lachnospiraceae family is known for fermenting complex carbohydrates and producing short-chain fatty acids (SCFAs), particularly butyrate, which supports gut barrier integrity and immune regulation. However, Lachnospiraceae is functionally diverse — some taxa have been linked to inflammation and immune dysregulation — and its overall abundance is often reduced in PLWH, reflecting HIV-associated gut dysbiosis 42 , 43 . Moreover, Prevotella has frequently been found enriched in HIV infection and is associated with chronic inflammation, microbial translocation, and reduced CD4 + T cell counts 44 – 48 . Our results further support this profile, as Prevotella copri was positively correlated with larger intact HIV-1 reservoir, while Prevotella bivia was associated with a higher intact-to-total proviral ratio. These results reinforce the concept that certain Prevotella species may contribute to reservoir persistence by promoting immune activation and compromising gut barrier integrity. In addition to microbial taxa, we also identified metabolic pathways such as glycolysis IV, super-pathway of branched-chain amino acid biosynthesis (BCAA biosynthesis), and L-valine biosynthesis, which were significantly enriched in individuals with a high intact proviral reservoir size. Previous studies suggest that glycolysis plays a dual role in HIV-1 infection. Kang et al. 49 reported that increased glycolysis in CD4 + T cells facilitates viral invasion, latency formation, and inflammation. Conversely, Shytaj et al. 50 demonstrated that glycolysis upregulation supports initial HIV-1 replication in activated cells, but the virus downregulates glycolysis during the transition to latency, enabling it to evade immune responses and antiretroviral therapy. Furthermore, L-valine is one of the three products of BCAA biosynthesis from pyruvate, one of the end products of glycolysis, along with L-leucine and L-isoleucine. Bacterial BCAA biosynthesis depends on the availability of pyruvate, so increased glycolysis can fuel BCAA production. Moreover, BCAA accessibility plays a pivotal role in the activation and function of T cells, influencing the mechanistic target of rapamycin (mTOR) signaling pathway. Activation of mTOR by BCAAs enhances T cell activation and cytokine production, modulating immune responses 51 . HIV relies heavily on the mTOR pathway, which regulates key signaling and metabolic processes critical for viral entry, replication, and the establishment of latent reservoirs. However, clinical trials targeting mTOR inhibition have not demonstrated a reduction in reservoir size but have shown a decrease in HIV transcription within gut-resident T cells 52 – 54 . Similarly, CDP-diacylglycerol biosynthesis pathways I and II were enriched in individuals with a high ratio of intact provirus. CDP-diacylglycerol is a key precursor in the biosynthesis of major phospholipids, including phosphatidylglycerol, cardiolipin, and phosphatidylethanolamine 55 . Emerging evidence suggests that alterations in phospholipid metabolism may contribute to a cellular environment conducive to rapid HIV rebound following ART interruption 56 and has also been associated with individuals who fail to control HIV replication. Our work did not replicate the results reported by Borgognone et al. 33 , where the authors described an association between a higher Bacteroidales / Clostridiales ratio and a smaller HIV-1 reservoir. In our cohort, we observed a positive trend between both Bacteroidales and Clostridiales individually — as well as their ratio — and a higher intact-to-total proviral ratio. The differences in cohort size, treatment history, and disease course between the two studies may account for the observed discrepancies. We acknowledge several limitations in our study. First, while we only collected basic dietary information, we did not assess detailed dietary components, such as specific nutrient intake, or other lifestyle factors (e.g., physical activity, other medications) that could influence gut microbiome composition and, in turn also, the HIV reservoir. Second, our analysis of the reservoir was limited to peripheral blood, whereas the majority of the reservoir is thought to reside within lymphoid tissues, including GALT. Finally, although our final sample size was sufficient after applying exclusion criteria, a larger cohort would increase the statistical power and further enhance the generalizability of our results. Overall, our findings demonstrate a link between gut microbiome composition and HIV-1 reservoir size. We show that microbial diversity is lower in individuals with a smaller intact proviral reservoir and observe certain microbial patterns associated with the size and the ratio of intact provirus in the blood reservoir. Additionally, we identify key metabolic pathways linked to reservoir size, which provide potential mechanistic relationship. The association between the microbial markers and HIV reservoir size in PLWH suggests that variations in the microbiome composition in the gut could shape the immune status and vice versa. These findings highlight the complex interplay between microbial communities and viral persistence. Further research is needed to elucidate these relationships, which could offer valuable insights into microbiome-targeted interventions aimed at modulating the HIV reservoir. Declarations Author contributions P.N., S.R., A.N., and O.K. conceptualized the study. O.K., S.A., and P.N. recruited study participants. A.N., B.B.J., and P.S. extracted and analyzed samples. O.K., A.N., S.R., and P.S. collected and analyzed data. P.N., S.R., and A.S., supervised and made intellectual input. O.K., and A.N. wrote the original draft. All authors reviewed, revised and approved the final manuscript. Data availability The datasets generated and analysed during the current study, including both the gut metadata and the raw 16S rRNA sequences, are available in the NCBI SRA repository, BioProject ID: PRJNA1263627. Acknowledgments We express our gratitude to all the participants enrolled in this study. We would like to thank the research and clinical nurses at the Karolinska University Hospital, especially Douglas Carrick and Katarina Stigsäter at the HIV outpatient clinic for their hard work in contributing to the study and data collection. Financial support This work was supported by the Stockholm County Council [SLL-KI FoUI-995225 and CIMED FoUI-1002100 to P.N., CIMED FoUI-1002923 and ALF 2023-2883 to A.S.], the Swedish Research Council [2020-02129 to A.S.], the Physicians Against AIDS Research Foundation [FOa2022-0001 and FOa2023-0003 to B.B.J.] and the Karolinska Institutet-KID funding [2021–00552 to O.K.]. Competing interests The authors declare no competing interests. References Marín-Sánchez, N. et al. Exploring potential associations between the human microbiota and reservoir of latent HIV. Retrovirology 2024 21:1 21 (2024). -11-29 https://doi.org/10.1186/s12977-024-00655-w Thompson, C. G., Gay, C. L. & Kashuba, A. D. HIV Persistence in Gut-Associated Lymphoid Tissues: Pharmacological Challenges and Opportunities. AIDS Res. Hum. Retroviruses . 33 https://doi.org/10.1089/aid.2016.0253 (2017 Jun 1). Nelson, J. et al. 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Gut colonization of Bacteroides plebeius suppresses colitis-associated colon cancer development. Microbiol. Spectr. 13 , 02–4. https://doi.org/10.1128/spectrum.02599-24 (2025). Nascimento, W. M. et al. Gut Microbiome Profiles and Associated Metabolic Pathways in HIV-Infected Treatment-Naïve Patients. Cells 2021 . 10 -02-13 (2021). L, Z. et al. Faecalibacterium prausnitzii Produces Butyrate to Maintain Th17/Treg Balance and to Ameliorate Colorectal Colitis by Inhibiting Histone Deacetylase 1 - PubMed. Inflammatory bowel diseases 24 (08/16/2018). https://doi.org/10.1093/ibd/izy182 Mukherjee, A., Lordan, C., Ross, R. P. & Cotter, P. D. Gut microbes from the phylogenetically diverse genus Eubacterium and their various contributions to gut health. Gut Microbes . 12 https://doi.org/10.1080/19490976.2020.1802866 (2020 Aug 23). Dubourg, G. et al. Gut microbiota associated with HIV infection is significantly enriched in bacteria tolerant to oxygen. BMJ Open. Gastroenterol. 3 https://doi.org/10.1136/bmjgast-2016-000080 (2016 Jul 28). Vacca, M. et al. The Controversial Role of Human Gut Lachnospiraceae. Microorganisms Vol. 8, Page 573 8 (2020-04-15). (2020). https://doi.org/10.3390/microorganisms8040573 Z, G. & B, L. B, N., BG, S. Gut Microbiome Alteration in HIV/AIDS and the Role of Antiretroviral Therapy-A Scoping Review - PubMed. Microorganisms 12 (11/01/2024). https://doi.org/10.3390/microorganisms12112221 CA, L. et al. Alterations in the gut microbiota associated with HIV-1 infection - PubMed. Cell host & microbe 14 ( 09/11/2013 ). https://doi.org/10.1016/j.chom.2013.08.006. Kaur, U. S. et al. High Abundance of genus Prevotella in the gut of perinatally HIV-infected children is associated with IP-10 levels despite therapy. Scientific Reports 2018 8:1 8 (2018). -12-05 https://doi.org/10.1038/s41598-018-35877-4 SM, D. et al. Gut dendritic cell activation links an altered colonic microbiome to mucosal and systemic T cell activation in untreated HIV-1 infection - PubMed. Mucosal immunology 9 (2016). Jan https://doi.org/10.1038/mi.2015.33 JM, L. The immune response to Prevotella bacteria in chronic inflammatory disease - PubMed. Immunology 151 https://doi.org/10.1111/imm.12760 (2017 Aug). M, N.-J. et al. Gut Microbiota Linked to Sexual Preference and HIV Infection - PubMed. EBioMedicine 5 ( 01/28/2016 ). https://doi.org/10.1016/j.ebiom.2016.01.032. Kang, S. & Tang, H. Frontiers | HIV-1 Infection and Glucose Metabolism Reprogramming of T Cells: Another Approach Toward Functional Cure and Reservoir Eradication. Frontiers Immunology 11 (2020/10/07). https://doi.org/10.3389/fimmu.2020.572677 Shytaj, I. L. et al. Glycolysis downregulation is a hallmark of HIV-1 latency and sensitizes infected cells to oxidative stress. EMBO Mol. Med. 13 https://doi.org/10.15252/emmm.202013901 (2021 Jul 20). Soliman, G. A. The Role of Mechanistic Target of Rapamycin (mTOR) Complexes Signaling in the Immune Responses. Nutrients 5 https://doi.org/10.3390/nu5062231 (2013 Jun 19). Crater, J. M., Nixon, D. F. & Furler O’Brien, R. L. HIV-1 replication and latency are balanced by mTOR-driven cell metabolism. Front. Cell. Infect. Microbiol. 12 /11/17 (2022). TJ, H. et al. Everolimus, an mTORC1/2 inhibitor, in ART-suppressed individuals who received solid organ transplantation: A prospective study - PubMed. Am. J. transplantation: official J. Am. Soc. Transplantation Am. Soc. Transpl. Surg. 21 https://doi.org/10.1111/ajt.16244 (2021 May). D, P. et al. LILAC pilot study: Effects of metformin on mTOR activation and HIV reservoir persistence during antiretroviral therapy - PubMed. EBioMedicine 65 (2021). Mar https://doi.org/10.1016/j.ebiom.2021.103270 NJ, B., CDP-Diacylglycerol Synthases, S. C. & CDS. Gateway to Phosphatidylinositol and Cardiolipin Synthesis - PubMed. Front. cell. Dev. biology . 8 https://doi.org/10.3389/fcell.2020.00063 (2020). 02/07/. LB, G. et al. Phospholipid Metabolism Is Associated with Time to HIV Rebound upon Treatment Interruption - PubMed. mBio 12 (02/23/2021). https://doi.org/10.1128/mBio.03444-20 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6646788","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":465498371,"identity":"29f544d1-268e-4ead-ac4b-497a7bc230c5","order_by":0,"name":"Oscar Kieri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYPCCBB4GBsbGhw0GJGppNiRJC4hgk2wgRi0//+GHnysY0mT4pQ+3Vc4oYLDnJ6RFckaaseQZhhweyb7EtpsbDBgSZxKyyuAGDwPQPRU8BmcY224+MGBIMDhASMv5M8w/QVrsgVoKgVrs7QlqOZAD8nUOjwEPYxsj0GGMG4jwi5llg0Eaj8QZxmbJGQYSiTMI2QIMscc3GyqS7fl72B9+7PljY8/fQMgaiPPgLAmi1I+CUTAKRsEoIAAAiHM3wQtZ8PAAAAAASUVORK5CYII=","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Oscar","middleName":"","lastName":"Kieri","suffix":""},{"id":465498373,"identity":"0dc5ca3d-bd66-4558-b296-11e439ce16f0","order_by":1,"name":"Aswathy Narayanan","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Aswathy","middleName":"","lastName":"Narayanan","suffix":""},{"id":465498375,"identity":"8ee371fb-78c5-479a-9e29-ef40e6b257f7","order_by":2,"name":"Bianca B Jütte","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Bianca","middleName":"B","lastName":"Jütte","suffix":""},{"id":465498378,"identity":"eebce05d-1d12-4c09-b0b7-21ddeee2fb62","order_by":3,"name":"Peter Svensson","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Svensson","suffix":""},{"id":465498381,"identity":"eb81eb9a-1329-461b-9eeb-c370ea39e40e","order_by":4,"name":"Soo Aleman","email":"","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Soo","middleName":"","lastName":"Aleman","suffix":""},{"id":465498383,"identity":"e1e23a17-96e1-4183-ac76-cb9d8b86ce5f","order_by":5,"name":"Anders Sönnerborg","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Anders","middleName":"","lastName":"Sönnerborg","suffix":""},{"id":465498385,"identity":"5d067269-7b2f-486e-9434-34350f4f2e7d","order_by":6,"name":"Shilpa Ray","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Shilpa","middleName":"","lastName":"Ray","suffix":""},{"id":465498386,"identity":"b3bf5475-2d1c-4a83-aae0-cb55e0d08e67","order_by":7,"name":"Piotr Nowak","email":"","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Piotr","middleName":"","lastName":"Nowak","suffix":""}],"badges":[],"createdAt":"2025-05-12 12:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6646788/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6646788/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83839218,"identity":"c9d361bf-effe-4c40-bf02-3be7938bebb1","added_by":"auto","created_at":"2025-06-03 13:50:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77923,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in the bacterial diversity and richness according to intact proviral reservoir size (A) and ratio of intact-to-total proviruses (C) between groups of high and low. Linear discriminant effect size analysis (LefSe) at the species level showing the significantly abundant organisms between high and low groups based on the intact proviral reservoir size (B) and ratio of intact-to-total proviruses (D).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6646788/v1/bb3dedb2b6d4e51836aef15c.png"},{"id":83840151,"identity":"4dd44a89-ad24-4eae-90d7-6b9dc0f9f6bb","added_by":"auto","created_at":"2025-06-03 14:06:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43801,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation analysis between microbiota at species level and intact proviral reservoir size (A) and ratio of intact-to-total proviruses (B).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6646788/v1/599873e89886e38393144290.png"},{"id":83839215,"identity":"f7becc4b-b152-47c6-8f4e-600714db72a3","added_by":"auto","created_at":"2025-06-03 13:50:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25191,"visible":true,"origin":"","legend":"\u003cp\u003eLinear discriminant effect size analysis (LefSe) showing the significantly abundant pathways in high and low groups based on intact proviral reservoir size (A) and intact-to-total proviruses (B).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6646788/v1/f7ebae6221dd98568fd0467a.png"},{"id":94826516,"identity":"39597286-be95-49e6-b879-5c155ace312c","added_by":"auto","created_at":"2025-10-31 06:51:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1015324,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6646788/v1/79a06727-7e7e-4a97-9d6c-e024763ef3bc.pdf"},{"id":83839212,"identity":"fafcb8be-103a-4155-a63c-4a9f49a45ecd","added_by":"auto","created_at":"2025-06-03 13:50:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":524315,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6646788/v1/3653028e3c47d37e2f620e82.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Linking Gut Microbiome to HIV-1 Reservoir Size in People Living with HIV","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe gut microbiome plays a critical role in modulating immune responses and systemic inflammation in people living with HIV (PLWH), and these two key factors influence the persistence of the HIV-1 reservoir \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. During HIV-1 infection, the gut-associated lymphoid tissue (GALT) becomes a major site for viral replication and immune activation \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This process compromises gut barrier integrity, leading to microbial translocation and sustained systemic inflammation \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlthough antiretroviral therapy (ART) effectively suppresses viral replication and has dramatically reduced HIV-related morbidity and mortality, it does not fully restore gut immune homeostasis or repair the structural and functional damage within the GALT \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Additionally, the gut microbiome, which is profoundly altered during HIV infection, remains dysbiotic despite long-term ART \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Lastly, ART alone is insufficient to eliminate the latent HIV reservoir, which persists primarily in lymphoid tissues and represents the major obstacle to achieving a definitive cure \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePersistent gut barrier dysfunction allows microbial derived products such as lipopolysaccharides (LPS) and bacterial DNA to enter systemic circulation, driving chronic immune activation \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This persistent immune stimulation promotes CD4\u003csup\u003e+\u003c/sup\u003e T cell activation, facilitating maintenance and potential expansion of the HIV reservoir, which is predominantly harbored within the GALT \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Additionally, microbial metabolites, including tryptophan catabolites like kynurenine, have been associated with the size of the HIV reservoir \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Despite these insights, the direct influence of gut microbiome composition and causal function on the size and dynamics of the HIV reservoir remains incompletely understood, and robust clinical data are lacking.\u003c/p\u003e \u003cp\u003eOur previous research has shown distinct differences in gut microbiome composition and functional capacity among viremic individuals, ART-treated, and elite controllers \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These individuals also differ in reservoir size and suggest that the microbiome may shape the HIV reservoir \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. To explore this question, the primary objective of our current study was to investigate the interplay between the gut microbiome and the HIV-1 reservoir. Specifically, we aimed to identify microbial taxa and functional pathways that correlate with the size of the intact HIV-1 reservoir, with the goal of uncovering novel microbiome-based targets for reservoir modulation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Cohort\u003c/h2\u003e \u003cp\u003eIndividuals included in this study were part of the COVAXID trial, an open-label, non-randomized prospective clinical trial at the Karolinska University Hospital, Stockholm, Sweden, to investigate the safety and clinical efficacy of the mRNA BNT162b2 vaccine (Comirnaty\u0026reg;, Pfizer/BioNTech) in immunocompromised individuals\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The ethical permit was granted by the Swedish Ethical Review Authority (ID 2021\u0026thinsp;\u0026minus;\u0026thinsp;00451), and all participants provided written informed consent. All methods of this study was performed in accordance with the Declaration of Helsinki and The Good Clinical Practice guidelines. The trial was registered at the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT 2021-000175-37) and clinicaltrials.gov (NCT04780659) by Feb 9, 2021, and Feb 19, 2021, respectively. The trial was also approved by the Swedish Medical Product Agency (ID 5.1-2021-5881). The original trial protocol is available via the SciLifeLab Data Repository. PLWH, aged 18\u0026ndash;85, followed at the outpatient HIV clinic, eligible for COVID-19 vaccination, were screened for inclusion in the trial. Recruitment started on Feb 15, 2021 and follow-up ended Oct 15, 2021. The trial was fully recruited as intended in the study plan. Ninety PLWH on ART were enrolled in the trial, with fecal and blood samples for reservoir analysis collected from 39 participants. After excluding nine individuals who had received antibiotic treatment within three months prior to inclusion, the analysis was conducted on the remaining 30 participants. Clinical and laboratory characteristics were obtained from electronic health records. Basic dietary information (omnivorous, vegetarian) and body mass index (BMI) were collected at inclusion in the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eReservoir analysis\u003c/h3\u003e\n\u003cp\u003eBlood samples were collected from the participants for reservoir analysis. PBMCs were isolated by density gradient separation and resting CD4\u003csup\u003e+\u003c/sup\u003e T cells (rCD4\u003csup\u003e+\u003c/sup\u003e T cells) were isolated from the obtained PBMCs by a two-step magnetic isolation using the Miltenyi Biotec magnetic associated cell sorting (MACS) platform. For the first step, the CD4\u003csup\u003e+\u003c/sup\u003e negative cell isolation kit (Miltenyi Biotec, Cat# 130-096-533) was used according to the manufacturer\u0026rsquo;s protocol. Secondly, rCD4\u003csup\u003e+\u003c/sup\u003e T cells were negatively selected with magnetic beads against the activation markers CD25, CD69 and HLA-DR. DNA was extracted either by Allprep (Qiagen, Cat#80204) or QIAamp DNA Mini Kit (Qiagen, Cat#51304). The DNA was used to quantify the HIV-1 reservoir (intact, 5\u0026prime; (\u003cem\u003eΨ)\u003c/em\u003e deleted, and 3\u0026prime; (\u003cem\u003eenv\u003c/em\u003e) deleted/hypermutated proviruses) using the Intact Proviral DNA Assay (IPDA), which has been previously described \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor IPDA, digital droplet PCR (ddPCR) was performed with the QX200 Droplet Digital qPCR System (Bio-Rad). Each reaction consisted of 20 \u0026micro;L, containing 10 \u0026micro;L Supermix for Probes without dUTP (Bio-Rad, Cat# 1863024), 900 nM primers, 250 nM probe (labelled with HEX or FAM), and 8 \u0026micro;L undiluted cellular DNA. Droplets were generated using the QX200 droplet generator. Emulsified PCR reactions were performed with a C1000 Touch thermal cycler (Bio-Rad), with the following protocol: 95 ̊C for 10 min, followed by 40 cycles of 94 ̊C for 30 s and 57 ̊C for 60 s, and a final droplet cure step of 10 min at 98 ̊C. Each well was then read with a QX200 Droplet Reader (Bio-Rad). Droplets were analyzed with QuantaSoft version 1.5 (Bio-Rad) software in the absolute quantification mode.\u003c/p\u003e\n\u003ch3\u003eFecal sample processing\u003c/h3\u003e\n\u003cp\u003eThe fecal samples were collected in RNA/DNA shield (Stratec, Germany). DNA was extracted using QIAamp PowerFecal Pro DNA Kits \u0026ndash; Stool/Gut DNA Extraction (Qiagen) and sequenced on the Illumina Novaseq6000 platform.\u003c/p\u003e\n\u003ch3\u003eSequence analysis\u003c/h3\u003e\n\u003cp\u003eThe paired-end sequences obtained from the sequencing platform were quality-checked using FastQC \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Adapter and low-quality reads (Phred score\u0026thinsp;\u0026lt;\u0026thinsp;30) were removed using Trim Galore \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Further, host DNA contamination was removed from the fastq sequences using a combination of Bowtie2 \u003csup\u003e21,22\u003c/sup\u003e, SAMtools \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and Bedtools \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Pre-processed reads were then analyzed for taxonomic classification using MetaPhlAn4 \u003csup\u003e25\u003c/sup\u003e, and functional pathways were identified using HUMAnN3 \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe alpha diversity indices such as Observed, Chao1, Shannon, Simpson and InvSimpson were performed to calculate the richness and evenness of the samples. Linear discriminant effect size (LEfSe) was employed to identify significant microbial compositions between groups. Spearman correlation was performed using the R package psych (v2.4.3) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e to analyze the association between microbiota at the species level, and clinical parameters and HIV-1 reservoir. The plots were visualized using the R package ggplot2 (v3.5.1) \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Participants\u003c/h2\u003e\n \u003cp\u003eThe study included 30 PLWH with a median age of 54 years (IQR 44, 68), 40% of whom were female. All participants were on ART at inclusion, with a median treatment duration of eight years (IQR 4, 15). The majority (83%) were treated with integrase strand transfer inhibitors (INSTI), and 90% had a HIV RNA viral load of less than 50 copies/ml. The median CD4\u003csup\u003e+\u003c/sup\u003e T cell count was 630 cells/mm\u0026sup3; (IQR 290, 720), with a CD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e ratio of 1.05 (IQR 0.55, 1.40). Nearly half (47%) had a nadir CD4\u003csup\u003e+\u003c/sup\u003e T cell count below 200 cells/mm\u0026sup3;, with a median nadir CD4\u003csup\u003e+\u003c/sup\u003e T cell count of 260 cells/mm\u0026sup3; (IQR 80, 410) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics according to intact HIV-1 proviral reservoir size at inclusion\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;15\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;15\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;30\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eq-value\u003csup\u003e\u003cem\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (44, 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (41, 63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (44, 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e T-cell count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320 (240, 720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e642 (570, 770)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e630 (290, 720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNadir CD4\u003csup\u003e+\u003c/sup\u003e T-cell count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (58, 280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370 (220, 470)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261 (80, 410)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58 (0.37, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29 (0.96, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (0.55, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIV RNA\u0026thinsp;\u0026lt;\u0026thinsp;50 copies/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration of ART (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (4, 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (4, 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (4, 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eART regimen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINSTI-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNNRTI-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.0 (21.0, 27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0 (22.9, 29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0 (22.9, 27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOmnivorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetarian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLatino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoute of transmission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeterosexual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntact provirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196 (87, 391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (27, 45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (34, 196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026acute;defective provirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e335 (142, 1,069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211 (125, 347)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e281 (142, 832)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026acute;defective provirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e318 (202, 902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (91, 169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190 (104, 342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal defective provirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e641 (385, 2,097)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e318 (243, 624)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e527 (272, 1,099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal provirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e729 (522, 2,542)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e363 (272, 677)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e632 (307, 1,138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRatio intact-to-total provirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18 (0.15, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08 (0.04, 0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12 (0.08, 0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eART, antiretroviral therapy; INSTI, integrase strand transfer inhibitor; NNRTI, non-nucleoside analogue reverse transcriptase inhibitor; MSM, men who have sex with men; BMI, body mass index. CD4\u003csup\u003e+\u003c/sup\u003e T-cell count (cells/mm\u003csup\u003e3\u003c/sup\u003e); Nadir CD4\u003csup\u003e+\u003c/sup\u003e T-cell count (cells/mm\u003csup\u003e3\u003c/sup\u003e); provirus (proviral HIV DNA copies/million resting CD4\u003csup\u003e+\u003c/sup\u003eT-cells).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cem\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e n (%); Median (Q1, Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cem\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e Pearson\u0026rsquo;s Chi-squared test; Wilcoxon rank sum test; Fisher\u0026rsquo;s exact test; Wilcoxon rank sum exact test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cem\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e False discovery rate correction for multiple testing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eReservoir analysis\u003c/h3\u003e\n\u003cp\u003eWe quantified the intact and defective (5\u0026prime; deleted and 3\u0026prime; deleted/hypermutated) proviral HIV-1 reservoir per million rCD4\u003csup\u003e+\u003c/sup\u003e T cells in blood using IPDA (Supplementary Fig. 1A). The median intact reservoir size was 60 (IQR 34, 198) copies/million rCD4\u003csup\u003e+\u003c/sup\u003e T cells, while the defective reservoir confined 282 (IQR 141, 874) copies of 5\u0026prime;-deleted proviruses and 190 (IQR 103, 350) copies of 3\u0026prime;-deleted/hypermutated proviruses per million rCD4\u003csup\u003e+\u003c/sup\u003e T cells. The estimated median total reservoir size (intact and defective) was 632 (301\u0026ndash;1144) and correlated to the amount of intact provirus (Spearmans\u0026rsquo;s, r\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Supplementary Fig. 1D), with a median ratio of intact-to-total provirus of 0.13 (IQR 0.08, 0.2) (Supplementary Fig. 1C).\u003c/p\u003e\n\u003cp\u003eIn order to elucidate relationships between the HIV-1 reservoir size and the gut microbiome, we stratified PLWH based on higher and lower levels of intact proviral HIV-1 DNA and ratio of intact-to-total proviruses, a potential indicator of reservoir decay, using the median as divider (Supplementary Fig.\u0026nbsp;1B, 1C). PLWH with a larger intact reservoir size had a significantly lower CD4\u003csup\u003e+\u003c/sup\u003e T cell count, with a median of 320 (IQR 240, 720) cells/mm\u003csup\u003e3\u003c/sup\u003e, compared to individuals with a smaller intact reservoir size, who had a median of 642 (IQR 570, 770) cells/mm\u003csup\u003e3\u003c/sup\u003e (p\u0026thinsp;=\u0026thinsp;0.04). Similar relationships were observed for the nadir CD4\u003csup\u003e+\u003c/sup\u003e T cell count and the CD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e ratio. In the groups of higher and lower ratio of intact-to-total provirus a similar trend was observed. Despite the differences in immune status, the duration and type of ART did not differ between the reservoir groups, suggesting that these factors are unlikely to account for the observed differences. Additionally, there were no significant differences in age, sex distribution, BMI, diet, ethnicity, or mode of transmission. Antibiotic use was absent, as individuals who had received antibiotics within three months prior to inclusion were excluded from the study. The detailed characteristics are presented in Table 1.3\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAlterations in bacterial diversity and composition in relation to intact proviral reservoir and intact-to-total proviral ratio\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIndividuals within the group of higher intact proviral reservoir size showed significantly greater alpha diversity, compared to individuals in the group of lower intact reservoir size (Shannon p\u0026thinsp;=\u0026thinsp;0.03; Simpson p\u0026thinsp;=\u0026thinsp;0.03) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). However, there were no significant differences in richness (Observed p\u0026thinsp;=\u0026thinsp;0.88; Chao1 p\u0026thinsp;=\u0026thinsp;0.88) and we did not find any associations between alpha diversity and intact reservoir size in regression analysis (Supplementary Fig. 2). We observed a significantly greater abundance of \u003cem\u003eLachnospira rogosae A\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.04), \u003cem\u003eMuricomes contortus B\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.04), \u003cem\u003eCopromorpha sp900066305\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.02) in the group with a high intact reservoir. On the contrary, in the group with a low intact reservoir, we found significant enrichment of \u003cem\u003ePhocaeicola plebeius A\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.04), \u003cem\u003eLachnospira sp000437735\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.02) and \u003cem\u003eEubacterium G sp000434315\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.02) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Conversely, we did not observe any significant differences in alpha diversity indices between the groups with high and low ratio of intact-to-total reservoir (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). Furthermore, \u003cem\u003ePrevotella sp002265625\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.02), \u003cem\u003eAlloprevotella sp900539625\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.01), and \u003cem\u003eDesulfovibrio sp900556755\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.04) were significantly abundant in individuals with a high ratio. On the other hand, individuals with a low ratio showed significant increase in \u003cem\u003ePhocaeicola plebeius A\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.04), \u003cem\u003eLachnospira sp000437735\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.003), \u003cem\u003eBlautia\u003c/em\u003e A \u003cem\u003efaecis\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.04), and \u003cem\u003eGemmiger formicilis\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.04) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cem\u003eAssociation between microbiota and intact proviral reservoir size and intact-to-total proviral ratio\u003c/em\u003e\u003c/div\u003e\n\u003cp\u003eWe found several significant correlations between different bacterial species and the level of intact HIV-1 DNA. Notably, we observed a negative association of \u003cem\u003eFaecalibacterium prausnitzii D\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.001), \u003cem\u003eLachnospira sp000437735\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.01), and \u003cem\u003eBacteriodes clarus\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.05) with intact proviral reservoir size. On the contrary, a significant positive association was observed with \u003cem\u003eOscillospiraceae CAG-83 sp000435975\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.004), \u003cem\u003eBifidobacterium bifidum\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.01), \u003cem\u003ePrevotella copri A\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.02), and \u003cem\u003eSutterella wadsworthensis\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.04) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Furthermore, we observed a significant positive association of the ratio of intact-to-total proviruses with \u003cem\u003eVentrimonas sp003481825\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.01\u003cem\u003e)\u003c/em\u003e, \u003cem\u003ePrevotella bivia\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.02) and \u003cem\u003eLachnospira rogosae A\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.03) while taxa such as \u003cem\u003eLachnospira sp000437735\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.01), \u003cem\u003ePhocaeicola plebeius A\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.02), and \u003cem\u003ePhocaeicola massiliensis\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.04) were negatively associated with the ratio of intact-to-total proviruses (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eRelationship of metabolic pathways with HIV-1 reservoir size and intact-to-total proviral ratio\u003c/h2\u003e\n \u003cp\u003eWe further investigated if the functional pathways of the metabolic traits were related to the intact proviral HIV-1 reservoir size and intact-to-total proviral ratio. Our analysis revealed a significant higher abundance of L-valine biosynthesis (p\u0026thinsp;=\u0026thinsp;0.02), glycolysis IV (p\u0026thinsp;=\u0026thinsp;0.04) and super-pathway of branched-chain amino acid biosynthesis (p\u0026thinsp;=\u0026thinsp;0.04) in PLWH within the group with a high intact proviral reservoir (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, we observed a significant abundance of CDP-diacylglycerol biosynthesis I and II (p\u0026thinsp;=\u0026thinsp;0.04) in PLWH within the high ratio group (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cem\u003eBacteroidales/Clostridiales ratio in PLWH with respect to intact proviral reservoir and intact-to-total proviral ratio\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eAs a previous study found an inverse correlation between the \u003cem\u003eBacteroidales/Clostridiales\u003c/em\u003e ratio and HIV-1 reservoir size [22], we explored if similar patterns were present in our cohort. However, we did not observe significant differences in \u003cem\u003eBacteroidales, Clostridiales\u003c/em\u003e, and \u003cem\u003eBacteroidales/Clostridiales\u003c/em\u003e ratio between the groups of higher and lower intact reservoir size and ratio of intact-to-total proviruses in our study cohort (Fig. S3 A, B).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we explored the relationship between the gut microbiome and the HIV-1 reservoir in PLWH on long term ART, identifying microbial patterns and metabolic pathways associated with reservoir size. Interestingly, we observed lower bacterial diversity in individuals with a smaller intact proviral reservoir size in blood. Furthermore, \u003cem\u003eLachnospira sp000437735\u003c/em\u003e and \u003cem\u003ePhocaeicola spp.\u003c/em\u003e, species related to gut health, were more abundant in individuals with a smaller intact reservoir and smaller intact-to-total proviral ratio, respectively, and negatively correlated to reservoir size and intact-to-total proviral ratio. Conversely, \u003cem\u003ePrevotella spp.\u003c/em\u003e, species associated with chronic inflammation, were positively correlated to intact reservoir size, and were also enriched in the group with a high ratio. The metabolic pathways of glycolysis and branched-chain amino acid biosynthesis were significantly enriched in individuals with a larger intact reservoir.\u003c/p\u003e \u003cp\u003eFollowing the initiation of ART, the intact proviral reservoir declines slowly over time, accompanied by an accumulation of defective proviruses, resulting in a progressively lower intact-to-total proviral ratio. The most pronounced decay of the intact reservoir typically occurs within the first four to seven years after ART initiation, after which the decline reaches a plateau\u003csup\u003e\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In our cohort, individuals with a smaller intact reservoir size had a significantly higher current and nadir CD4\u003csup\u003e+\u003c/sup\u003e T cell count and CD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e ratio compared to those with a larger intact reservoir, however, the duration of ART did not differ between the groups.\u003c/p\u003e \u003cp\u003eWe observed lower alpha diversity, as measured by Shannon and Simpson indices, in individuals with smaller intact proviral reservoirs. Yet, regression analysis did not reveal a statistically significant association between individual intact reservoir size and alpha diversity. This trend aligns with findings from a previous study, which also reported reduced diversity in individuals with viral control and presumed smaller reservoir size\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the associations between microbial diversity and reservoir remain poorly defined, and the potential mechanisms are not yet understood \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Furthermore, the diversity is generally lower in PLWH and is influenced by several factors including diet and therapeutics like ART \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, we identified several bacterial taxa associated with a lower intact HIV-1 reservoir size and a reduced intact-to-total proviral ratio. Notably, we observed a significant enrichment of \u003cem\u003ePhocaeicola plebeius\u003c/em\u003e in individuals with a smaller intact HIV-1 reservoir and a lower intact-to-total proviral ratio. Additionally, \u003cem\u003ePhocaeicola massiliensis\u003c/em\u003e and \u003cem\u003ePhocaeicola plebeius\u003c/em\u003e were both negatively correlated with the intact-to-total proviral ratio. The role of \u003cem\u003ePhocaeicola\u003c/em\u003e in HIV pathogenesis and reservoir dynamics remains poorly understood. A previous study \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e reported positive associations between \u003cem\u003ePhocaeicola\u003c/em\u003e abundance and HIV-1 reservoir size, as well as inflammatory protein levels, though these findings were limited to treatment-na\u0026iuml;ve individuals. Conversely, \u003cem\u003ePhocaeicola plebeius\u003c/em\u003e has been shown to decline in abundance with increasing HIV disease severity\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, highlighting potential context-dependent effects. Importantly, \u003cem\u003ePhocaeicola\u003c/em\u003e species are known for their anti-inflammatory function and roles in gut health with dietary fiber fermentation and polysaccharide metabolism\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, we found that \u003cem\u003eEubacterium G sp000434315\u003c/em\u003e was more abundant in individuals with a smaller intact reservoir and ratio of intact provirus. This observation is consistent with previous studies showing enrichment of \u003cem\u003eEubacterium\u003c/em\u003e species in elite controllers \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Similarly, \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e \u0026mdash; a prominent butyrate-producing bacterium\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e frequently depleted in PLWH \u0026mdash; demonstrated a negative correlation with intact reservoir size in our cohort. Taken together, these findings support the hypothesis that butyrate-producing bacteria, such as \u003cem\u003eEubacterium\u003c/em\u003e spp. \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eF. prausnitzii\u003c/em\u003e, may play a protective role in modulating HIV reservoir dynamics, potentially by reducing systemic immune activation \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and promoting mucosal integrity and immune homeostasis.\u003c/p\u003e \u003cp\u003eAdditionally, we found that \u003cem\u003eLachnospira sp000437735\u003c/em\u003e was enriched and negatively correlated with both intact HIV-1 reservoir size and the intact-to-total proviral ratio. In contrast, \u003cem\u003eLachnospira rogosae\u003c/em\u003e was more abundant in individuals with a larger intact reservoir and positively correlated with the intact proviral ratio. While the functional roles of these species remain poorly characterized, their opposing associations suggest distinct influences on reservoir dynamics within the same genus. The \u003cem\u003eLachnospiraceae\u003c/em\u003e family is known for fermenting complex carbohydrates and producing short-chain fatty acids (SCFAs), particularly butyrate, which supports gut barrier integrity and immune regulation. However, \u003cem\u003eLachnospiraceae\u003c/em\u003e is functionally diverse \u0026mdash; some taxa have been linked to inflammation and immune dysregulation \u0026mdash; and its overall abundance is often reduced in PLWH, reflecting HIV-associated gut dysbiosis \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, \u003cem\u003ePrevotella\u003c/em\u003e has frequently been found enriched in HIV infection and is associated with chronic inflammation, microbial translocation, and reduced CD4\u003csup\u003e+\u003c/sup\u003e T cell counts \u003csup\u003e\u003cspan additionalcitationids=\"CR45 CR46 CR47\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Our results further support this profile, as \u003cem\u003ePrevotella copri\u003c/em\u003e was positively correlated with larger intact HIV-1 reservoir, while \u003cem\u003ePrevotella bivia\u003c/em\u003e was associated with a higher intact-to-total proviral ratio. These results reinforce the concept that certain \u003cem\u003ePrevotella\u003c/em\u003e species may contribute to reservoir persistence by promoting immune activation and compromising gut barrier integrity.\u003c/p\u003e \u003cp\u003eIn addition to microbial taxa, we also identified metabolic pathways such as glycolysis IV, super-pathway of branched-chain amino acid biosynthesis (BCAA biosynthesis), and L-valine biosynthesis, which were significantly enriched in individuals with a high intact proviral reservoir size. Previous studies suggest that glycolysis plays a dual role in HIV-1 infection. Kang et al. \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e reported that increased glycolysis in CD4\u003csup\u003e+\u003c/sup\u003e T cells facilitates viral invasion, latency formation, and inflammation. Conversely, Shytaj et al. \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e demonstrated that glycolysis upregulation supports initial HIV-1 replication in activated cells, but the virus downregulates glycolysis during the transition to latency, enabling it to evade immune responses and antiretroviral therapy. Furthermore, L-valine is one of the three products of BCAA biosynthesis from pyruvate, one of the end products of glycolysis, along with L-leucine and L-isoleucine. Bacterial BCAA biosynthesis depends on the availability of pyruvate, so increased glycolysis can fuel BCAA production. Moreover, BCAA accessibility plays a pivotal role in the activation and function of T cells, influencing the mechanistic target of rapamycin (mTOR) signaling pathway. Activation of mTOR by BCAAs enhances T cell activation and cytokine production, modulating immune responses \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. HIV relies heavily on the mTOR pathway, which regulates key signaling and metabolic processes critical for viral entry, replication, and the establishment of latent reservoirs. However, clinical trials targeting mTOR inhibition have not demonstrated a reduction in reservoir size but have shown a decrease in HIV transcription within gut-resident T cells \u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Similarly, CDP-diacylglycerol biosynthesis pathways I and II were enriched in individuals with a high ratio of intact provirus. CDP-diacylglycerol is a key precursor in the biosynthesis of major phospholipids, including phosphatidylglycerol, cardiolipin, and phosphatidylethanolamine \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Emerging evidence suggests that alterations in phospholipid metabolism may contribute to a cellular environment conducive to rapid HIV rebound following ART interruption \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e and has also been associated with individuals who fail to control HIV replication.\u003c/p\u003e \u003cp\u003eOur work did not replicate the results reported by Borgognone et al. \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, where the authors described an association between a higher \u003cem\u003eBacteroidales\u003c/em\u003e/\u003cem\u003eClostridiales\u003c/em\u003e ratio and a smaller HIV-1 reservoir. In our cohort, we observed a positive trend between both \u003cem\u003eBacteroidales\u003c/em\u003e and \u003cem\u003eClostridiales\u003c/em\u003e individually \u0026mdash; as well as their ratio \u0026mdash; and a higher intact-to-total proviral ratio. The differences in cohort size, treatment history, and disease course between the two studies may account for the observed discrepancies.\u003c/p\u003e \u003cp\u003eWe acknowledge several limitations in our study. First, while we only collected basic dietary information, we did not assess detailed dietary components, such as specific nutrient intake, or other lifestyle factors (e.g., physical activity, other medications) that could influence gut microbiome composition and, in turn also, the HIV reservoir. Second, our analysis of the reservoir was limited to peripheral blood, whereas the majority of the reservoir is thought to reside within lymphoid tissues, including GALT. Finally, although our final sample size was sufficient after applying exclusion criteria, a larger cohort would increase the statistical power and further enhance the generalizability of our results.\u003c/p\u003e \u003cp\u003eOverall, our findings demonstrate a link between gut microbiome composition and HIV-1 reservoir size. We show that microbial diversity is lower in individuals with a smaller intact proviral reservoir and observe certain microbial patterns associated with the size and the ratio of intact provirus in the blood reservoir. Additionally, we identify key metabolic pathways linked to reservoir size, which provide potential mechanistic relationship. The association between the microbial markers and HIV reservoir size in PLWH suggests that variations in the microbiome composition in the gut could shape the immune status and vice versa. These findings highlight the complex interplay between microbial communities and viral persistence. Further research is needed to elucidate these relationships, which could offer valuable insights into microbiome-targeted interventions aimed at modulating the HIV reservoir.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP.N., S.R., A.N., and O.K. conceptualized the study. O.K., S.A., and P.N. recruited study participants. A.N., B.B.J., and P.S. extracted and analyzed samples. O.K., A.N., S.R., and P.S. collected and analyzed data. P.N., S.R., and A.S., supervised and made intellectual input. O.K., and A.N. wrote the original draft. All authors reviewed, revised and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study, including both the gut metadata and the raw 16S rRNA sequences, are available in the NCBI SRA repository, BioProject ID: PRJNA1263627.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to all the participants enrolled in this study. We would like to thank the research and clinical nurses at the Karolinska University Hospital, especially Douglas Carrick and Katarina Stigsäter at the HIV outpatient clinic for their hard work in contributing to the study and data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Stockholm County Council [SLL-KI FoUI-995225 and CIMED FoUI-1002100 to P.N., CIMED FoUI-1002923 and ALF 2023-2883 to A.S.], the Swedish Research Council [2020-02129 to A.S.], the Physicians Against AIDS Research Foundation [FOa2022-0001 and FOa2023-0003 to B.B.J.] and the Karolinska Institutet-KID funding [2021–00552 to O.K.].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMar\u0026iacute;n-S\u0026aacute;nchez, N. et al. 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Phospholipid Metabolism Is Associated with Time to HIV Rebound upon Treatment Interruption - PubMed. \u003cem\u003emBio\u003c/em\u003e 12 (02/23/2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/mBio.03444-20\u003c/span\u003e\u003cspan address=\"10.1128/mBio.03444-20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6646788/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6646788/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe gut microbiome is altered during HIV-1 infection contributing to immune dysfunction and inflammation in people living with HIV (PLWH) that persists despite antiretroviral therapy (ART). We explored the associations between the gut microbiome and HIV-1 reservoir size in PLWH (n\u0026thinsp;=\u0026thinsp;30) on long-term ART. The intact proviral DNA assay (IPDA) and shotgun metagenomic sequencing were performed to identify microbial species and metabolic pathways associated with the size of the HIV-1 reservoir. PLWH with a smaller intact reservoir exhibited lower alpha diversity compared to individuals with a larger intact reservoir. We found that \u003cem\u003ePhocaeicola plebeius\u003c/em\u003e and \u003cem\u003eLachnospira sp000437735\u003c/em\u003e were significantly enriched in individuals with a smaller intact reservoir and lower intact-to-total proviral ratio, respectively. We observed a negative association between \u003cem\u003eFecalibacterium prausnitzi\u003c/em\u003e and a positive association of \u003cem\u003ePrevotella copri\u003c/em\u003e, with the intact proviral reservoir size. Additionally, the metabolic pathways of glycolysis and branched-chain amino acid biosynthesis were enriched in individuals with larger reservoir. HIV reservoir size in blood is associated with gut microbiome diversity, specific metabolic pathways and microbial signatures, including \u003cem\u003eLachnospira, Prevotella\u003c/em\u003e, and \u003cem\u003eFaecalibacterium\u003c/em\u003e. These findings underscore the potential role of the gut microbiome in viral persistence and suggest that strategies targeting microbiome modulation could disrupt the HIV reservoir.\u003c/p\u003e","manuscriptTitle":"Linking Gut Microbiome to HIV-1 Reservoir Size in People Living with HIV","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 13:49:58","doi":"10.21203/rs.3.rs-6646788/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"abbbfc54-501a-4315-8878-26017d6593c6","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49417018,"name":"Biological sciences/Immunology/Infectious diseases/Hiv infections"},{"id":49417019,"name":"Biological sciences/Microbiology/Virology/Viral reservoirs"},{"id":49417020,"name":"Health sciences/Diseases/Infectious diseases"},{"id":49417021,"name":"Health sciences/Diseases/Infectious diseases/Hiv infections"},{"id":49417022,"name":"Biological sciences/Immunology/Infection"},{"id":49417023,"name":"Biological sciences/Immunology/Infectious diseases"},{"id":49417024,"name":"Biological sciences/Immunology/Inflammation/Chronic inflammation"},{"id":49417025,"name":"Biological sciences/Microbiology/Communities/Microbiome"},{"id":49417026,"name":"Health sciences/Pathogenesis/Infection"}],"tags":[],"updatedAt":"2025-11-20T14:23:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 13:49:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6646788","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6646788","identity":"rs-6646788","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}