Impact of maternal HIV infection on the gut microbiome and metabolome of mothers and infants: the PRACHITi cohort in Pune, India

Domain: chemistry
Published: 2026-02-23
Editorial outcome: accept

Abstract
Human immunodeficiency virus (HIV) affects millions of reproductive-age women globally, and during pregnancy is associated with adverse birth and infant health outcomes. Research on how maternal HIV shapes the gut microbiota, a potentially modifiable factor, during pregnancy, postpartum, and in infancy remains limited. The PRACHITi cohort study was conducted in India among 244 pregnant women with and without HIV, who were followed along with their children through 1 year postpartum. Our study focuses on secondary objectives of the PRACHITi study related to gut microbiota, with longitudinal samples being collected in the full cohort and more frequent sampling in a sub-study. Here, our findings reveal gut dysbiosis (based on 16S rRNA sequencing) and distinct plasma metabolomic profiles across pregnancy, postpartum, and their infants among women with HIV compared with seronegative women. We show that specific taxa and metabolites are differentially abundant by HIV status, some of which are linked to adverse outcomes, including preterm birth, low birth weight, and inflammation, conditions that are more common among populations with HIV. These results suggest potential biological pathways through which HIV affects maternal and infant health.

Introduction
Human immunodeficiency virus (HIV) affects millions of women of childbearing age globally, with the highest burden in resource-limited settings where maternal morbidity and mortality are high1,2. With or without antiretroviral therapy (ART), HIV infection in pregnant women continues to be associated with increased adverse birth outcomes, including preterm birth, low birth weight, and small for gestational age3–5. Further, their children who are HIV-exposed uninfected (CHEU) also remain at increased risk for mortality, infectious morbidity, impaired growth, and neurodevelopment compared to children who are HIV-unexposed uninfected (CHUU), i.e., children born to mothers without HIV4,6.

The impact of HIV infection on the gut microbiome profile is an area of active investigation, as the gut microbiome can impact immunity, affect metabolic pathways (e.g., directly through production of metabolites or indirect impact on the pathways), and clinical outcomes7,8, and is potentially modifiable. HIV infection, and specifically HIV infection of immune cells in the gastrointestinal tract, results in gut dysbiosis and epithelial barrier dysfunction, leading to gut and systemic inflammation, and metabolic dysfunction that are linked to adverse clinical outcomes9. Studies in non-pregnant adults with HIV have shown microbial shifts, including depletion of taxa Akkermansia, Anaerovibrio, Bifidobacterium, and Clostridium, which may contribute to chronic inflammation and metabolic dysfunction; notably, ART does not fully restore the gut dysbiosis10. Studies on the gut microbiota and metabolome are limited in pregnant and postpartum women with HIV (WHIV) and their CHEU.

A cross-sectional study of third-trimester pregnant women in Zimbabwe found reduced abundance of several taxa, including Bifidobacterium in WHIV11, which was also noted in the microbiota of their CHEU in another study12. As the gut microbiota could change over the course of pregnancy and postpartum, with associated shifts in immunity and metabolism13, longitudinal studies over pregnancy and postpartum, and in their CHEU are needed. Microbiota changes during this period have been linked to maternal and infant health outcomes, thus differences by HIV status could potentially explain the higher rates of adverse events observed in WHIV and their CHEU14,15. Understanding the longitudinal change in the gut microbiota over time could reveal critical timepoints during pregnancy and postpartum when interventions would be most effective in preventing adverse outcomes.

In the general population, the overall gut microbiota composition has been shown to be consistent over mid-to-late gestation, as assessed by α-diversity in the second and third trimester16. However, it is not known whether a chronic infection, such as HIV infection, results in distinct temporal dynamics of the microbiota during pregnancy (e.g., decreasing α-diversity as gestation progresses).

In this study, to address gaps in understanding how maternal HIV affects the gut microbiota in the perinatal period, we assessed the relationship between maternal HIV status and gut microbiota composition during the second and third trimesters, at 6 months postpartum, and their infants’ gut microbiota at 6 months of age, using a longitudinal cohort of mothers with HIV on ART and without HIV, and their infants. We further studied the maternal plasma metabolomics during pregnancy in a subset to better understand differences in function and metabolic pathways in maternal HIV. To understand how the gut microbiota changes during pregnancy and by HIV status, we also nested a study in the cohort to conduct intensive bi-weekly (i.e., every 2 weeks) stool sampling during the second and third trimester in a subset of participants with and without HIV.

Our work shows pregnant and postpartum women with HIV, and their CHEU infants, exhibit gut dysbiosis and distinct metabolomic profiles compared with seronegative women, and their CHUU infants. Specific microbial taxa and metabolites are differentially abundant by HIV status, including those linked to adverse outcomes, such as preterm birth, low birth weight, and inflammation, conditions that are more frequently observed in populations with HIV. These findings identify potential biological pathways through which maternal HIV could affect both maternal and infant health.

Results
Sociodemographic characteristics and clinical features
Table 1 shows the sociodemographic and clinical characteristics by HIV status for the 207 pregnant women (75 WHIV (36%) and 132 seronegative for HIV (SN) (64%)) with available data at the third trimester. The median (interquartile range: IQR) age of these study participants was 23 (21–27) years. About 41% had anemia, 89% had an education of high school or less, 11% were past smokers, and 9% had gestational diabetes. There were significant differences for age by HIV status, with older age in WHIV (p=0.022). There were more individuals with anemia among WHIV (45%) than among SN (37%) (p=0.097), and more undernutrition (36% vs 28%, p=0.28). The median (IQR) CD4 count for women with HIV was 439 (306.5–698) cells/mm3. The median (IQR) HIV viral load (VL) for those with detectable VL was 460 (103.75-1584.25) copies/mL. All WHIV were on ART, with 73% on TDF/3TC/EFV, 11% on AZT/3TC/NVP, and the rest were on another type of ART regimen. The median number of sexual partners in a participant’s lifetime was 1 for WHIV and SN. WHIV had no partners newly diagnosed with HIV (Table 1), and a cross-tabulation of maternal HIV status at the third trimester by number of sexual partners in lifetime is shown in Supplementary Table 3. Of those with data, there were no participants who used drugs or had alcoholic drinks in the past 2 years (Table 1). Study population characteristics for mothers in the second trimester and their infants can be found in Supplementary Tables 1 and 2.

Temporal Dynamics of the gut microbiota α-diversity by HIV status during pregnancy
We first assessed data from the sub-study with intensive frequent sampling during the second and third trimester to understand the temporal dynamics of the gut microbiota during pregnancy overall, and by HIV status. In this sub-study, 71 pregnant women had a total of 474 samples taken every two weeks between enrollment in second trimester and their last third trimester study visit with a median of 7 samples per participant. Gestational age at sampling ranged from 14 to 37 weeks.

We assessed whether microbiota α-diversity, Shannon index specifically, was similar between the second and third trimester using the 71 pregnant women with frequent sampling. Using a paired t-test comparing mean α-diversity from all the second trimester samples to that of the mean of all third trimester samples (Supplementary Table 4), we observed no significant differences across all 71 pregnant women or by HIV status.

Using the same frequent samples, we also used linear mixed models (LMM) with longitudinal α-diversity measures through second and third trimesters as the outcome and gestational age (in weeks) as the explanatory variable, and treated subject as a random effect to examine whether α-diversity changes over time in pregnancy within SN and WHIV, separately. The coefficient of gestational age within WHIV is 0.005 (p=0.30) and within SN is <0.001 (p=0.93). This suggests that α-diversity does not change over time during the second and third trimester within either group.

With the conclusion that microbiota α-diversity does not differ between second and third trimesters, we then tested whether α-diversity differs by HIV status using all longitudinal samples of the same pregnant woman (n=71) from the second and third trimesters with LMMs, where we have repeated α-diversity measures as outcomes and HIV status as the predictor. We observed that WHIV had significantly lower α-diversity (p=0.015) than SN women between the second and third trimester (Fig. 1). We observed similar results with other α-diversity indices (e.g., Chao1 (p=0.002) and Fisher (p<0.001)). In summary, we observe that WHIV had lower α-diversity than SN pregnant women, with a consistent relationship through the second and third trimester of gestation.

Gut microbiota dysbiosis in WHIV during pregnancy
Next, we compared the microbiota profile during pregnancy utilizing data from the full cohort. 242 women out of 244 women in the full cohort had available samples at the second and/or third trimesters. In permutational multivariate analysis of variance (PERMANOVA) adjusting for age, mid-upper arm circumference (MUAC), and education, the overall gut microbiota profile, as assessed by β-diversity Bray Curtis distance, was different by HIV status in the second (p=0.05) and third (p=0.004) trimester (Supplementary Table 5).

We then assessed the differences in α-diversity as well as abundance of specific taxa during pregnancy by HIV status using longitudinal microbiota data available from the full cohort (n=242). These analyses included all samples available during pregnancy, i.e., 242 women from the full cohort with a sample collected at the second and third trimester each, and additional samples collected from the overlapping 71 women in the sub-study with frequent sampling, a total of 747 stool samples. Similar to the results from the sub-study with 71 pregnant women, in a LMM with repeated α-diversity, WHIV had lower α-diversity (although not significant) with HIV status having a coefficient of −0.059 (p=0.12) for Shannon index (Supplementary Table 6), with similar and significant results for Chao1 (p=0.005) and Fisher (p=0.002), adjusting for covariates (same as those for PERMANOVA).

To assess whether specific taxa at the genus level are different by HIV status using available repeated samples from second and third trimesters in the full cohort (n=242), we used the Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC2) method, where we included HIV status in the model, adjusted for age, MUAC, and education and treated subject as a random effect, to compare the bias-corrected log absolute abundance of taxa between the two groups. The results revealed 4 bacteria with differential abundance by HIV status at FDR<0.05 level (false discovery rate using Benjamini-Hochberg (BH) adjustment) (Fig. 2 and Supplementary Table 7). We observed that WHIV had significantly higher abundance of Fusobacterium, Megamonas, and Lachnoclostridium compared to SN (Fig. 2 and Supplementary Table 7). Conversely, the abundance of SCFA-producing Lachnospiraceae_NK4A136_group was lower in WHIV, and lower levels of this taxon have been associated with inflammation and poor immune function17 (Fig. 2 and Supplementary Table 7). ANCOM-BC2 analysis also identified structural zeros, defined as taxa that were differentially present/absent in WHIV compared to SN women. 16 taxa were differentially absent in WHIV but present in SN, while 12 taxa were differentially present in WHIV but absent in SN (Supplementary Table 8).

Prior studies have noted increased levels of pro-inflammatory Prevotella in HIV18, while Bacteroides, which includes many commensal gut bacteria important in SCFA production19, may be lower. We specifically assessed these two bacteria to further understand their levels in WHIV during pregnancy and to test our hypothesis on whether the ratio of Bacteroides to Prevotella would be lower in WHIV. However, mean abundance of Prevotella (0.0002 vs. 0.0001 in WHIV and SN) and Bacteroides (0.08 vs. 0.05 in WHIV and SN) were both higher in WHIV compared to SN, although this association was not statistically significant in FDR-adjusted ANCOM-BC2 analyses on genus level taxa. We further conducted LMM with log ratio of Bacteroides to Prevotella in longitudinal samples as the outcome, HIV status as the exposure, and subject as the random intercept, and observed higher log ratio of Bacteroides to Prevotella in WHIV compared to SN (p=0.08), which is not consistent with our hypothesis on Bacteroides to Prevotella ratio.

In an exploratory analysis, we also tested whether there was a difference in the third trimester microbiota of WHIV with detectable viral load (VL) as compared to WHIV with undetectable VL. We did not observe any significant differences in β-diversity (p=0.395) or taxa abundance, but observed that the α-diversity Shannon index was higher among WHIV with detectable VL than undetectable VL (p=0.001).

Further, in additional exploratory analyses, we compared the microbiota profile by HIV status in non-pregnant women and conducted another analysis comparing the microbiota between pregnant WHIV and non-pregnant WHIV to understand the impact of pregnancy among those with HIV (discussed further in Supplementary Results).

WHIV have a distinct plasma metabolome profile in pregnancy
To better understand the functional changes by HIV status, we then compared the third trimester plasma metabolome in an analysis within a subset of 100 participants with plasma metabolome measures (Fig. 3a). We identified 87 differentially abundant metabolites between WHIV and SN, with 40 metabolites at higher levels and 47 at lower levels in pregnant WHIV compared to SN. Some of the most significant metabolites included N2,N2-dimethylguanosine, cytosine, tetrahydrocortisol glucuronide, glucuronate, and methionine sulfone (Supplementary Table 9). These also included 5alpha-pregnan-3beta,20alpha-diol disulfate, 5alpha-pregnan-3beta-ol, 20-one sulfate, and 5alpha-pregnan-3beta,20alpha-diol monosulfate, all of which are steroid sulfates20. Higher levels of N2,N2-dimethylguanosine, which was elevated in WHIV, show strong associations with preterm birth21. Further details on all significant metabolites in each group can be found in Supplementary Table 9. Among the 40 metabolites elevated in WHIV, the significantly enriched metabolites were from the following sub-pathways: fatty acid metabolism, N-acyl amino acids, pyrimidine metabolism, purine metabolism, corticosteroids, and food component/plant (Supplementary Table 9). Among the 47 metabolites lower in WHIV, these were from sub-pathways including sphingomyelin metabolism, progestin steroids, secondary bile acid metabolism, dihydrosphingomyelin metabolism, hexosylceramides, and androgenic steroids (Supplementary Table 9).

Multi-omics among WHIV during pregnancy
Out of 100 pregnant women with plasma metabolome measures from the third trimester, 88 also have third trimester microbiota data. Using this subset of 88 pregnant women, we repeated the ANCOM-BC analysis and identified 14 differentially abundant genera with unadjusted p-values ≤ 0.1, and repeated a two-sample t-test and identified 87 significant metabolites. We then assessed correlations between abundance of the 14 microbes and metabolomic measures of the 87 significant metabolites (Fig. 3b and Supplementary Table 10). We found taxa Fusobacterium had the strongest correlations with several metabolites, including positive correlations with 2-hydroxyglutarate and phenol glucuronide, and inverse correlations with hydroxypalmitoylsphingomyelin (d18:1/16:0(OH)), glycosylceramide (d18:1/20:0, d16:1/22:0), palmitoylsphingomyelin (d18:1/16:0), and sphingomyelin (d18:2/23:1) (Fig. 3b and Supplementary Table 10). Taxa Ruminococcaceae_UCG-003 was positively correlated with metabolite 5alpha-pregnan-3beta,20alpha-diol monosulfate and pregnenolone sulfate (Fig. 3b and Supplementary Table 10). Additionally, the correlation of the 17 differentially present/absent structural zero genera from our ANCOM-BC analysis with the 87 significant metabolites is presented in Supplementary Table 11 and Supplementary Fig. 2.

Gut dysbiosis persists for WHIV in the postpartum period
In our postpartum analysis of the full cohort, α-diversity Shannon index between WHIV (N=62) and SN (N=120) at 6 months postpartum was not significantly different (p=0.36 using multivariable linear regression) (Supplementary Fig. 3). However, the overall gut microbiota assessed by β-diversity was significantly different between WHIV and SN (p=0.009) (Supplementary Table 5). Across the three time periods by HIV status, α-diversity remained unchanged from second to third trimester but decreased slightly at postpartum (Supplementary Fig. 1).

There were no differentially abundant taxa at 6 months postpartum by HIV status with significant BH-adjusted p-value. However, the top two ranked taxa, Lachnoclostridium and Megamonas, are also top ranked and significant in the full cohort pregnancy analysis, pooling samples from second and third trimesters samples, with higher abundance in WHIV. For the differentially present/absent taxa based on structural zeros, 34 bacteria were absent in postpartum WHIV but were present in SN women, including Corynebacterium, Erysipelotrichaceae, and Granulicatella (Table 2). Taxa Peptostreptococcus were differentially present in postpartum WHIV but absent in SN women (Table 2). In an additional analysis, we observed that the gut microbiota profile of postpartum WHIV more closely resembles that of non-pregnant WHIV, as no significant differences between groups were observed.

Maternal HIV status impacts CHEU gut microbiota profile at 6 months old
Analysis of infants’ gut microbiota at 6 months of age between CHEU (n=63) and CHUU (n=116) based on α- and β-diversity did not show significant differences (Supplementary Fig. 4 and Supplementary Table 5).

There were no differentially abundant taxa between CHEU and CHUU in the ANCOM-BC analysis with significant BH-adjusted p-values. However, 42 taxa were differentially absent in CHEU but were present in CHUU (Table 3). These include Klebsiella (also differentially absent in postpartum WHIV), Ruminococcaceae_UCG-008, and several from the Prevotellaceae family. Two taxa were differentially present in CHEU but absent in CHUU, Epulopiscium and Neisseria (Table 3).

In our cohort, 86% of infants were being breastfed, and 34% exclusively breastfed at 6 months of age, with a slightly higher proportion of CHEUs (36%) exclusively breastfed at 6 months than CHUUs (33%). More information about taxa abundance related to exclusive breastfeeding can be found in the Supplementary Results.

Methods
Study design and population
From June 27, 2016, to December 9, 2019, we conducted a longitudinal cohort study, the PRACHITi study (Pregnancy Associated Changes in Tuberculosis Immunology) in Pune, India41. The study focused on pregnant women and was carried out at Byramjee Jeejeebhoy Government Medical College (BJGMC), a tertiary care hospital serving low-income populations and acting as a referral center for HIV care. We enrolled adult pregnant women between the ages of 18 and 40, who were between 13 and 34 weeks of gestation, as confirmed by early pregnancy ultrasonography. Eligibility criteria for exclusion of women from the study included active tuberculosis, severe anemia, ongoing use of antibiotics for more than 14 days, autoimmune or immunosuppressive diseases, or current use of immunosuppressive medication.

The primary objective of the PRACHITi study was to compare immune responses during pregnancy and postpartum based on HIV status. To address these objectives, pregnant women were enrolled in the study, stratified by their HIV status, based on convenience sampling of participants who met the eligibility criteria within each stratum. The mothers and their infants were followed through 1 year postpartum. For comparison purposes, non-pregnant women were also enrolled for one cross-sectional study visit. For the analysis of pregnancy gut microbiota by HIV status, we included all participating pregnant women (90 WHIV and 154 SN for HIV, during the second and third trimester, and then followed postpartum), and their infants (63 CHEU and 116 CHUU). For the analysis of gut microbiota of non-pregnant women, we have 88 WHIV and 85 SN for HIV from PRACHITi with available stool samples.

To better understand the temporal dynamics of the gut microbiota during pregnancy and how HIV status affects that, as a sub-study of PRACHITi, we conducted frequent stool sampling (every two weeks until 37 weeks of gestational age) in a subset of 71 participants (35 WHIV and 36 SN for HIV) that were enrolled in PRACHITi from 14 to 26 weeks of gestational age (second trimester).

Ethics
Approval for the study was obtained from the institutional ethics review boards of BJGMC, the Johns Hopkins University, Columbia University, and Weill Cornell Medicine, as well as the Health Ministry Screening Committee of the Indian Council of Medical Research, India. All guidelines required by the US Department of Health and Human Services for Human experimentation were followed. We received written informed consent from all participants. The reporting guideline for observational studies: Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) was followed42.

Data collection and procedures
Sociodemographic information (age, education, smoking history) was obtained from all pregnant women at enrollment, either during the second (13–27 weeks of gestation) or third (week 28 onwards) trimester. Clinical and other relevant data were also collected at enrollment (either at the second or third trimester), with additional data collected at the third trimester visit, delivery, and at 6 months postpartum. Newborns were followed from birth through 1 year of age. At these visits, we also measured the maternal mid-upper arm circumference (MUAC) to determine undernutrition (MUAC<23cm) during pregnancy, and in select visits assessed gestational diabetes and anemia43,44. Blood was collected in the third trimester from all participants, heparin plasma was isolated and extracted, and stored until further assessment. Non-pregnant women were enrolled for one cross-sectional visit, and similar clinical and sociodemographic information was collected. Stool samples were collected at the second trimester, third trimester, and 6 months postpartum visits for the mothers, 6 months of age for the infants, and one time cross-sectionally for the non-pregnant women. For the sub-study, we collected stool samples every two weeks until 37 weeks of gestational age and collected related clinical data at each visit.

HIV status and sociodemographic/clinical characteristics
The exposure variable in our study was maternal HIV status, categorized as WHIV and SN. Correspondingly, infant HIV status was classified as CHEU for infants born to WHIV mothers and CHUU for those born to SN mothers. For pregnant women enrolled from the antenatal clinic at Sassoon Hospital in Pune, India, HIV testing is a part of routine care, and information regarding these assays was abstracted from the medical chart. An HIV report within the last 30 days of entry was required for HIV-negative women. For non-pregnant women recruited from the antiretroviral clinic, general medicine, or Obstetrics and Gynecology clinics at Sassoon Hospital, documentation of HIV status was provided through medical charts. If HIV status was unknown, HIV testing and counseling were offered prior to enrollment in the study. We collected the final HIV status of participants at the end of the study, and the HIV status of all participants did not change from 2nd trimester enrollment to 12 months postpartum.

Gut microbiota
The primary outcome variable for this study was the gut microbiota profile. Stool samples, collected by participants using OMNIgene.GUT kits (DNA Genotek) were utilized to assess gut microbiota composition for pregnant women, non-pregnant women, and infants. Participants collected the stool samples at second trimester (only if enrolled at this timepoint), third trimester, and 6 months postpartum for mothers, and at 6 months of age for infants. For participants enrolled in the sub-study, intensive bi-weekly (i.e., every 2 weeks) stool samples were collected during the second and third trimester. Stool samples were collected by participants at home prior to their next study visit, stored at room temperature (RT) and brought to the clinic at their next visit in RT. These samples were aliquoted and stored at −80°C prior to shipment on dry ice to the University of Maryland Institute for Genome Sciences (IGS) Microbiome Service Laboratory (https://msl.igs.umaryland.edu/) for sequencing and bioinformatics analysis. Further details are in Supplementary Methods, but briefly, total DNA was extracted from the stool samples using the QIAamp DNA stool extraction kit (QIAGEN, Valencia, CA, USA). Amplicon sequencing of the V3-V4 regions of the 16S rRNA gene was conducted through a two-step PCR process45. Sequences were de-multiplexed, primer sequences removed, and DADA2 workflow was used for data processing. Taxonomic assignments were made using the RDP Classifier46 and the SILVA v138 database47.

Metabolomics
From a subset of 100 participants enrolled in PRACHITi (48 WHIV and 52 SN), heparin plasma samples were shipped to Metabolon to conduct unbiased metabolomics. Further details can be found in the Supplementary Methods, but in brief, sample preparation was performed using the automated MicroLab STAR® system (Hamilton Company), with methanol-induced protein precipitation followed by centrifugation. Extracts were aliquoted into four fractions for analysis via ultrahigh-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) under positive and negative ion modes, optimized for hydrophilic, hydrophobic, and ionic compounds using both reverse-phase and HILIC columns. Chromatography was performed on a Waters ACQUITY UPLC system, and mass detection was conducted using a Thermo Scientific Q-Exactive mass spectrometer with a HESI-II source and Orbitrap analyzer at 35,000 resolution. MS analysis alternated between full scans and data-dependent MSn across a 70–1000 m/z range. Raw data was processed for peak detection, compound identification, and quality control.

Statistical analysis
Descriptive statistics of population characteristics by HIV status were calculated using third trimester samples, which is the timepoint with the most participants. To compare these population characteristics by HIV status, we used Fisher’s Exact test for categorical variables and Wilcoxon rank-sum test for continuous variables. P-values are from two-sided tests with <0.05 being considered statistically significant.

For analysis of the sub-study with frequent stool sampling, we first tested whether the microbiota α-diversity was similar (or changing) within the same pregnant woman from the second to the third trimester by using a paired t-test within WHIV, SN, and combined, respectively, comparing second (i.e., mean of all longitudinal second trimester samples) vs. third trimester (i.e., mean of all longitudinal third trimester samples) Shannon α-diversity index. We also conducted linear mixed-effect models (LMM) on longitudinal α-diversities to examine if α-diversity changes across frequent samples from second and third trimesters where we set subjects as random intercepts. This is to understand the temporal dynamics of the α-diversity within WHIV and SN.

We then used combined frequent samples in the sub-study to understand the impact of HIV status on overall microbiota over the course of gestation, also using LMM, with HIV as the exposure variable, adjusting for gestational age with subject as a random effect, on α-diversity outcomes.

Next, we assessed data from the full PRACHITi cohort to assess the associations between maternal HIV status and (1) maternal microbiota during pregnancy (repeated sample analysis combining all second and third trimester samples – analysis 1), (2) maternal microbiota at 6 months postpartum (analysis 2), (3) infant microbiota (i.e. CHEU vs. CHUU) at 6 months of age (analysis 3). In exploratory analyses, we also assessed associations between (4) maternal HIV status and microbiota in non-pregnant women (analysis 4), and (5) pregnancy status (third trimester vs. non-pregnant women) and microbiota in WHIV (analysis 5).

For these various analyses in the full cohort, we analyzed microbiota data as α-diversity, β-diversity, and taxa abundance. First, we assessed microbiota α-diversity by comparison groups. For the first comparison of maternal HIV status and maternal microbiota during pregnancy (analysis 1), we conducted LMMs with subject as a random effect, α-diversity as outcome, and HIV status as exposure variable, and adjusting for maternal age, education, and MUAC, for model 1. To determine the association between the exposure variable (i.e., HIV status (analysis 2–4) or pregnancy status (analysis 5)) and α-diversity for the above comparisons, we conducted t-tests for model 0 and multivariable linear regression for model 1. In these multivariable analyses, we adjusted for age (years), education (none to high school or post high school to postgraduate), and anthropometrics (i.e., mid-upper arm circumference (MUAC) in cm for mothers and body mass index Z-scores for infants) based on a directed acyclic graph (DAG).

Next, we compared overall microbiota profile by HIV status in the full cohort using PERMANOVA on β-diversity (based on Bray-Curtis distance) for the above comparisons48. For the pregnancy PERMANOVA analysis (analysis 1), we conducted separate analysis for each trimester and only used one sample (based on protocol-assigned second or third trimester sample) per trimester.

Finally, we conducted analysis comparing individual taxa at the genus level with above-defined comparison groups in the full cohort using the ANCOM-BC2 method, version 2.10.1, with pseudocount of 0.1 and structural zero as true49. For analysis 1, the ANCOM-BC2 model was fitted to compare the bias-corrected log absolute abundance of each taxon between WHIV and SN groups over longitudinal samples from the second and third trimesters, where the bias correction is due to sampling fraction, the group status is an explanatory variable, and the subject is treated as a random effect, along with additional covariates included in the model. The log fold change of the bias-corrected log absolute abundance by the group status was calculated for each taxon. The p-values for taxa were adjusted for multiple comparisons using either the BH method to calculate adjusted p-values (which is the focus of our interpretations) or the Bonferroni method to calculate 95% confidence intervals (which is being used primarily for visualization purposes). In addition to differentially abundant taxa, we also report structural zero taxa as differentially present/absent bacteria (i.e., differentially present in one group but not another). Of note, differentially abundant taxa and differentially present/absent taxa refer to distinct analytical approaches used in ANCOM-BC to deal with each type of taxa. However, both are considered together for interpretation of differences between study groups. We analyzed two different ANCOM-BC models: crude (model 0 with no covariates) and adjusted model 1 with the same covariates, maternal age, education, and MUAC, as in analysis with α-diversity. Microbiota samples with less than 1000 total reads were removed from all microbiota analyses. Analogously, for analyses 2–5, we fit ANCOM-BC2 using cross-sectional data and reported similar quantities of log fold changes and p-values for taxa to compare the two groups. Further, using a linear mixed model (LMM), we assessed the log ratio of Bacteroides to Prevotella during pregnancy as the outcome variable, HIV status as the exposure variables and subject as random effect and generated an ANCOM-BC generatable with pseudocount of 0.1.

For our analysis of metabolomics data, we normalized each metabolite by dividing the metabolite values with its median. We then imputed metabolite below the detection limit by the minimum divided by two for each metabolite. The values were then natural log transformed. We conducted logistic regression with HIV status as the outcome, and metabolite measures as the predictor, adjusting for maternal age, education, and MUAC, the same covariates in the microbiota analysis. Multiple comparisons were corrected using FDR with BH adjustment. In the sub-sample with both microbiota and metabolome data, we assessed correlations between microbes (with raw p-value ≤ 0.1 in ANCOM) and metabolites (BH-adjusted p-value < 0.05 using a t-test). All statistical analyses were two-sided and performed using R (version 4.5.1).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Please see the attached report", "reviewer_2": "Mandell et al examined how maternal HIV infection impacts gut microbiota composition across second and third trimesters in\npregnancy as well as the systemic metabolome. They also assessed how HIV exposure impacts gut microbiota in infants.\nOverall, the manuscript is well written, but there are areas that can be improved. It would be helpful to the reader if the\nanalyses can be simplified and time points clearly stated to allow the reader to easily follow the manuscript. Authors should\nalso integrate the plasma metabolome with the microbiota to strengthen insights into functional pathways impacted by HIV.\nMajor comments:\nMetabolomics analyses was only done in the third trimester while microbiota analyses were done in the second and third\ntrimesters. Can authors also look at the metabolome in the second trimester if samples are available?\nAs presented, the work is very descriptive. It would help to link the changes in microbiota to the metabolites. For example, do\nwe know which of the metabolites measured in plasma are microbe versus host derived? These analyses would strengthen\nthe connection between changes observed in gut composition and the systemic metabolome.\nMinor comments:\nHow often was sampling done from the full cohort?\nProvide refence for the STROBE guidelines\nIs there any reason why you have two multivariable models (1 and 2 which differ in covariates) for analyses?This just\ncomplicates the analyses making it difficult for the reader. Authors should show the univariable (unadjusted) and the\nmultivariable (adjusted for all covariates)\nFor taxa filtering, what does an average of 0.0005% across samples mean?Is this mean abundance?median abundance?\nWhat % of samples did the taxa have to be present on to be kept?\nPlease include equations for the various linear mixed models.\nSince authors included women with detectable HIV viral load, is there a difference in microbiome between those with and\nwithout detectable VL?\nDiscuss other papers that have done similar work ie PMID 24034618; 38230936; 39707483; 30657007\nGenerally, in the figures where you are doing differential abundance testing, please show directionality on the figure itself so\nit is easy to visualize which bacteria are abundant in what group. Unless the plots are only showing what is up or down\nwithin one group?\nWhat method was used to analyze the differentially abundant metabolites across groups?\nWhat exactly is Figure 3 showing? The legend says metabolites but the results section say pathways. Also, the legend says\nbacteria yet this is supposed to be metabolites?\nSphingomyelins are metabolites, if pathway do you mean sphingomyelin metabolism?"}, "author_response": "December 24, 2025\nWe thank the reviewers for the constructive and helpful comments on our manuscript titled\n“Impact of maternal HIV infection on gut microbiota and metabolome of maternal-infant\npopulations.” We have revised our manuscript addressing each comment from the reviewers as\nshown in detail below with a point-by-point response. Please note that the number lines refer to\nthe highlighted version of the manuscript.\nReviewer Comments:\nReviewer #1:\n1. Models: The authors interpreted their data using three different statistical models. Model\n0 is the crude model unadjusted for any covariates, Model 1 adjusted for age, education,\nand anthropometrics, and Model 2 further adjusted Model 1 for smoking status,\ngestational diabetes (GDM) and anemia. While, I can understand Model 1, but to me\nModel 2 does not seem appropriate. Please note that HIV condition can disrupt the gut\nmicrobiome which in turn can lead to anemia or HIV may have a direct effect leading to\nanemia. Thus, anemia is potentially a collider and not a confounder. Anemia does not\naffect HIV status and not sure if it causes changes in the microbial ecology. It will be\nhelpful, if the authors draw a DAG to explain why they should adjust for anemia. For the\nsame reasons, I am not sure about GDM either. Furthermore, the two groups don’t seem\nto be significantly different (Table 1). Since smoking is not very common in this\npopulation (Table 1) and the two groups don’t appear to be significantly different, I think\nit is better not to adjust for it in the model. It is sufficient to present results from Model 1\nonly.\nResponse: Thank you for pointing this out. Based on a similar suggestion from Reviewer #2, we\nhave removed model 2 from all tables, figures and the manuscript. We now only focus on model\n1 as we agree that anemia and GDM do not have a direct effect on HIV and could be colliders\nrather than confounders.\n2. Sexual activity and substance use. Major contributors to HIV infection are sexual\nactivity and substance use. It would be helpful if the authors provided information on\nnumber of sexual partners the women had and whether any of the partners are known to\nbe people with HIV. If such data are available, then summarize these data in Table 1.\nAlso, cross-tabulate the HIV status with number of sexual partners. This can be relegated\nto supplementary text. Similarly, it would be useful to provide information on substance\nuse and needle sharing.\nResponse: Thank you for this suggestion. We have data on substance use (drugs and alcohol),\nand in a subset data on number of sexual partners and HIV partner exposure. This has been\nadded to Table 1, and a supplementary table on HIV status and the number of sexual partners\nhas also been added. Supplementary Table 3 has the cross-tabulation of maternal HIV status at\nthird trimester by number of sexual partners.\nLines 264-268: “The median number of sexual partners in a participant’s lifetime was 1 for\nWHIV and SN. WHIV had no partners newly diagnosed with HIV (Table 1), and a cross-\ntabulation of maternal HIV status at third trimester by number of sexual partners in lifetime is\nshown in Supplementary Table 3. Of those with data, there were no participants who used drugs\nor had alcoholic drinks in the past 2 years (Table 1).”\n3. Spaghetti plots: While the spaghetti plots tracking the alpha diversity over the\ngestational age between second and third trimesters is interesting to visualize, I find them\nto be difficult to interpret. Secondly, in a normal pregnancy (without HIV infection or\nany other health condition), the alpha diversity as well as the microbial compositions are\nexpected to change during the course of pregnancy (Kore et al., Cell, 2012). The alpha\ndiversity is expected to decrease with gestational age in a normal pregnancy (Mesa et al.,\nNutrients 2020). In view of this, it would be interesting to test if this hypothesis is true by\nHIV status. The authors could use the software package CLME (Constrained Linear\nMixed Effects) to test whether the mean alpha diversity is decreasing within each HIV\ngroup. The CLME software is designed for testing such hypotheses.\nResponse: Thank you for the suggestion to test whether the mean alpha diversity is decreasing\nover time, and whether there are differences by HIV status. We now present data in\nSupplementary Table 4 that addresses this point. Paired t-test of the mean alpha diversity of\nsecond trimester samples and mean of third trimester samples did not show a decreasing trend of\nalpha diversity with gestational age in analyses of the combined population (WHIV+SN) as well\nas analyses within each group (WHIV and SN). We also present data from a LMM to show the\ncoefficient of gestational age in WHIV and SN. This suggests stable alpha diversity during this\nperiod of second and third trimester in both groups. We have added the following text in the\nmethod section lines 715-727 to detail the methods:\nLines 715-727: “For analysis of the sub-study with frequent stool sampling, we first\ntested whether the microbiota α-diversity was similar (or changing) within the same pregnant\nwoman from the second to the third trimester by using a paired t-test within WHIV, SN, and\ncombined, respectively, comparing second (i.e. mean of all longitudinal second trimester\nsamples) vs. third trimester (i.e. mean of all longitudinal third trimester samples) Shannon α-\ndiversity index. We also conducted linear mixed-effect models (LMM) on longitudinal α-\ndiversities to examine if α-diversity changes across frequent samples from second and third\ntrimesters where we set subjects as random intercepts. This is to understand the temporal\ndynamics of the α-diversity within WHIV and SN.\nWe then used combined frequent samples in the sub-study to understand the impact of\nHIV status on overall microbiota over the course of gestation, also using LMM, with HIV as the\nexposure variable, adjusting for gestational age with subject as a random effect, on α-diversity\noutcomes.”\nWe also added the following lines in the results and results section lines 278-299 and also\nimproved our explanation of the spaghetti plot in the legends for the reader to better understand\nthese figures:\nLines 278-299: “We assessed whether microbiota α-diversity, Shannon index specifically,\nwas similar between the second and third trimester using the 71 pregnant women with frequent\nsampling. Using a paired t-test comparing mean α-diversity from all the second trimester\nsamples to that of the mean of all third trimester samples (Supplementary Table 4), we observed\nno significant differences across all 71 pregnant women or by HIV status.\nUsing the same frequent samples, we also used linear mixed models (LMM) with\nlongitudinal α-diversity measures through second and third trimesters as the outcome and\ngestational age (in weeks) as the explanatory variable, and treated subject as a random effect to\nexamine whether α-diversity changes over time in pregnancy within SN and WHIV, separately.\nThe coefficient of gestational age within SN is 0.005 (p=0.30) and within WHIV is <0.001\n(p=0.93). This suggests that α-diversity does not change over time during the second and third\ntrimester within either group. We observed similar results using other α-diversity indices (e.g.\nChao1 and Fisher (data not shown)).\nWith the conclusion that microbiota does not differ between second and third trimesters,\nwe then tested whether α-diversity differs by HIV status using all longitudinal samples of the\nsame pregnant woman (n=71) from the second and third trimesters with LMMs, where we have\nrepeated α-diversity measures as outcomes and HIV status as the predictor. We observed that\nWHIV had significantly lower α-diversity (p=0.015) than SN women between the second and\nthird trimester (Figure 1). We observed similar results with other α-diversity indices (e.g. Chao1\nand Fisher (data not shown)). In summary, we observe that WHIV had lower α-diversity than SN\npregnant women, with a consistent relationship through the second and third trimester of\ngestation.”\nOf note, the Koren paper that showed changes was comparing the first trimester to the third\ntrimester, while our data is focused on the second and third trimester with consistent results from\nanother paper studying this time period (PMID: 26283357). We have added the following text in\nlines to clarify this difference:\nLines 497-502: “The temporal dynamics of gut microbiota α-diversity, however, was consistent\nthrough gestation and did not differ by HIV status. Similar results were observed in populations\nwithout HIV, where the gut microbiota α-diversity did not change over gestation16. Although\nsome studies have noted decreasing α-diversity as pregnancy progresses, there are important\ndifferences in the time-point of comparison data (i.e. notable differences between the first and\nthird trimester while ours compared second and third trimester)33, 34.”\n4. Figure 2 (and Supplementary tables 2 and 3): As noted above in my point #2, the gut\nmicrobiota are continuously evolving during the course of pregnancy. In view of that, it\ndoes not seem appropriate to combine data from trimesters 2 and 3 and compare the HIV\ngroups as the primary analysis (Figure 2) and treat differential abundance analysis of\nindividual trimesters as secondary analysis (Supplementary tables 2 and 3). Also, it\nappears some taxa are reported at the family level and others at genus level. Is there a\nreason to do so? Also, I was surprised to see very few taxa to be differentially abundant,\nwhereas a large number of taxa are differentially abundant at postpartum.\nResponse: Thank you for the feedback. We have addressed this in the response above by\nshowing that the gut microbiota is stable during the second and third trimester. This motivates us\nto keep the second and third trimester samples together in our analysis, as it is justified by these\nnew analyses, consistent with our original approach, and helps with the power of the analyses.\nRelated to taxa reporting at the family level and genus level, we want to note that all of the taxa\nanalyzed and reported for ANCOM-BC are classified at the genus level according to SILVA\nphylogeny. We used the SILVA v132 dataset to assign taxonomy to ASVs. In the results section,\nwe refer to certain families only to group multiple significant genera that belong to the same\nfamily, but the analysis itself was performed at the genus level.\nBased on reviewer comment on the differences between pregnancy (few taxa significant) and\npostpartum (larger number of taxa significant), we have reanalyzed our data using the latest\nversion of ANCOM-BC. This version is more conservative, especially in how it analyzes\npseudocounts, resulting in fewer taxa that are significant. As a result, while the pregnancy results\nremain consistent, there are changes to both the postpartum and infant results and we have\nupdated these sections accordingly in the results and discussion section.\n5. Bacteroides and Prevotella: Many Bacteroides species are commensal gut bacteria and\nsome of them play an important role in the production of short chain fatty acids. On the\ncontrary Prevotella species are known to be proinflammatory and reported in the\nliterature to have increased abundance in people with HIV. Hence, I was surprised that\nthese taxa were not differentially abundant in this pregnancy cohort. It will be interesting\nto see the log ratio of Bacteroides to Prevotella is significantly different by HIV status in\nthis population.\nResponse: Thank you for this comment. As suggested, we further looked at the mean abundance\nof each taxa and the log ratio of Bacteroides to Prevotella by HIV status. As noted by the\nreviewer, Prevotella had a greater mean log abundance in WHIV compared to SN (0.0002 vs.\n0.0001) but this association was not statistically significant in FDR-adjusted analyses. However,\nBacteroides mean log abundance was also higher, but not statistically significant, in WHIV\ncompared to SN (0.08 vs. 0.05).\nRelated to the log ratio, the mean abundance of Bacteroides was higher than the mean abundance\nof Prevotella in both WHIV and SN, with a higher log ratio of Bacteroides to Prevotella in\nWHIV (7.39) compared to SN women (6.59). Further, we fit a linear mixed effect model:\nlog(Bacteroides) – log(Prevotella) ~ HIV status + subject (random effect). When we used the\nANCOM-BC bias corrected genera table, we observed higher log ratio of Bacteroides to\nPrevotella in WHIV compared to SN (p=0.08). Thus, this is in the reverse direction as\nhypothesized by the reviewer.\nWe added a line about this analysis in the methods:\nLines 768-771: “Further, using a linear mixed model (LMM), we assessed the log ratio\nof Bacteroides to Prevotella during pregnancy as the outcome variable, HIV status as the\nexposure variables and subject as random effect and generated an ANCOM-BC genera table\nwith pseudocount of 0.1.”\nWe also noted these findings in the results section lines 360-370:\n“Prior studies have noted increased levels of pro-inflammatory Prevotella in HIV27, while\nBacteroides, which includes many commensal gut bacteria important in SCFA production28, may\nbe lower. We specifically assessed these two bacteria to further understand their levels in WHIV\nduring pregnancy and to test our hypothesis on whether the ratio of Bacteroides to Prevotella\nwould be lower in WHIV. However, mean abundance of Prevotella (0.0002 vs. 0.0001 in WHIV\nand SN) and Bacteroides (0.08 vs. 0.05 in WHIV and SN) were both higher in WHIV compared\nto SN, although this association was not statistically significant in FDR-adjusted ANCOM-BC2\nanalyses on genus level taxa. We further conducted LMM with log ratio of Bacteroides to\nPrevotella in longitudinal samples as the outcome, HIV status as the exposure and subject as the\nrandom intercept and observed higher log ratio of Bacteroides to Prevotella in WHIV compared\nto SN (p=0.08), which is not consistent with our hypothesis on Bacteroides to Prevotella ratio.”\n6. Figure 4: These are interesting postpartum results. I was surprised to see Akkermansia to\nbe the most differentially abundant taxa with higher abundance in women with HIV. It is\nwell-known that Akkermansia Muciniphila plays an important role in the gut health by\ncontributing to the production of short chain fatty acids. I would have expected it to have\nhigher abundance in women without HIV. It will be useful to perform CLME analysis, by\nHIV status, to see if the alpha diversity decreased from 2nd trimester to third trimester\nand then increased 6 months postpartum. Thus, a “U” shaped response in mean alpha\ndiversity.\nResponse: Thank you for this suggestion. We created side-by-side boxplots by HIV status for\nalpha diversity for the second trimester, third trimester and 6 months postpartum\n(Supplementary Figure 2). Our results did not show a “U” shaped response in mean alpha\ndiversity for the Shannon index. For both WHIV and SN samples, 2nd and 3rd trimester mean\nalpha diversity remained constant and decreased slightly at postpartum. The WHIV mean alpha\ndiversity remained lower than the SN mean alpha diversity for all 3 timepoints. We have added\nthe following text in lines 427-429 of the results section to detail this comparison:\n“Across the three time periods by HIV status, diversity remained unchanged from second\nto third trimester but decreased slightly at postpartum (Supplementary Figure 1).”\nRelated to Akkermansia results, after using the more updated conservative ANCOM-BC version\nmentioned above, the abundance of Akkermansia was not significantly different by HIV status.\n7. Metabolomics: Although it appears the authors obtained data from both positive and\nnegative ion channels, it is not clear if they analyzed them separately (which is the\npreferred approach) or did they combine them into one? Also, in the methods section,\nthey indicate that they adjusted for the “same covariates as in the microbiota analysis”.\nHowever, it is not clear to me if the results presented in Supplementary Table 4 are based\non Model 1 or Model 2? I suggest presenting results only from Model 1.\nResponse: Thank you for pointing this out. For our metabolomics analysis, we followed the\nstandard procedures used by Metabolon (the lab which conducted the metabolomics) and in their\nvarious collaborations using this platform. Metabolon uses multiple arms (both negative positive\nion channels) (LC-MS/MS method) within a platform to try to maximize biochemical coverage.\nData from all arms are then combined into a single dataset and analyzed together. Although\ncertain metabolites can be detected across multiple arms of the platform, data from only one arm\nis reported for each metabolite and is selected by them based on sensitivity and reproducibility\nparameters, which is consistent across all samples. Each metabolite is identified by matching the\nion chromatographic retention index, accurate mass, and mass spectral fragmentation patterns\nwith a reference library created from standard metabolites analyzed with identical instrumental\nprocedures. PMID: 32445384 explains these methods in detail as well. Of relevance to our data,\nthe global discovery platform that we used is an untargeted method that uses relative\nquantification. Thus, statistical comparisons are only made between study groups (i.e. WHIV and\nSN), and not between individual metabolites.\nRegarding the models, we have now made it clearer in both the manuscript and Supplementary\nTable 5-8, and 10 that we are only presenting results from model 1.\nLines 763-765: “We analyzed two different ANCOM-BC models: crude (model 0 with no\ncovariates) and adjusted model 1 with the same covariates, maternal age, education and MUAC,\nas in analysis with α-diversity.”\n8. Table 1: shows the distributions at 3rd trimester. Did the HIV status of any participant\nchange from 2nd to 3rd trimester of pregnancy. This might be important to consider\nbecause the authors combined 2nd and 3rd trimesters together. Is there a similar table\navailable for 2nd trimester? What about infants?\nResponse: Thank you for this suggestion. The HIV status did not change for any participant\nfrom 2nd trimester to 3rd to postpartum and mentioned this in the results at lines 671-673:\n“We collected the final HIV status of participants at the end of the study, and all participants\nHIV status did not change from 2nd trimester enrollment to 12 months postpartum.”\nAlso, we have now added a table 1 for 2nd trimester and 3rd trimester into the supplementary\nmaterials. They are Supplementary Table 1 and 2 and referred to in the text lines 268-269:\n“Study population characteristics for mothers in the second trimester and their infants can be\nfound in Supplementary Table 1 and 2.”\n9. Breastfeeding: Given that this is a low-income population, due to affordability, I\nexpected almost all babies to breastfed, but it appears the opposite is the case (exclusively\nbreastfed = 60 and not exclusively breastfed = 116).\nResponse: Thank you for pointing this out. In our manuscript, we referred to exclusive\nbreastfeeding. However, when it comes to breastfeeding in general, in fact, 86% of infants are\nbreastfeeding at 6 months postpartum. Given their age of 6 months, it is common for\ncomplementary feeds to be introduced in this setting, despite recommendation to exclusively\nbreastfeed through 6 months, which is why we decided to use the exclusive breastfeeding\nvariable instead. We have now added the following text to specify the breastfeeding rates in\naddition to the exclusive breastfeeding % as follows in lines 466-468:\n“In our cohort, 86% of infants were being breastfed, and 34% exclusively breastfed at 6 months\nof age, with a slightly higher proportion of CHEUs (36%) exclusively breastfed at 6 months than\nCHUUs (33%).”\n10. Measures of alpha diversity: While it is impressive to consider so many different\nmeasures of alpha diversity, I think it is sufficient to consider just one measure of their\nchoice and mention that other measures also considered with similar results (data not\nshown).\nResponse: Thank you for your suggestion. We agree with your suggestion and have decided to\nonly show and discuss the Shannon diversity index in our manuscript and figures, while\nmentioning that similar results were observed for the other indices.\n11. Non-pregnant women and third trimester: I am not sure why the authors compared the\nnon-pregnant women with third trimester women. If anything, I would have like to see\nthe results by comparing post-partum data with non-pregnant women data by HIV status.\nThis would tell us whether the postpartum gut ecology is closer to that of non-pregnant\nwomen, in the respective HIV groups.\nResponse: Thank you for your suggestion. We conducted the analysis between non-pregnant\nWHIV women and third trimester WHIV women because we wanted to understand the impact of\npregnancy among those with HIV on the microbiota. A line has been added in the manuscript to\nclarify this (lines 366-369). We also conducted the suggested analysis between non-pregnant\nWHIV women and postpartum WHIV women and found no significant differences in alpha\ndiversity, beta diversity and ANCOM-BC (bacteria abundance) analyses. This would suggest\nthat the postpartum gut ecology is closer to that of non-pregnant women’s gut microbiome. A\nline describing this has also been added to the manuscript (lines 435-438).\nLines 378-381: “Further, in additional exploratory analyses, we compared the\nmicrobiota profile by HIV status in non-pregnant women and conducted another analysis\ncomparing the microbiota between pregnant WHIV and non-pregnant WHIV to understand the\nimpact of pregnancy among those with HIV (discussed further in Supplementary Results).”\nLines 446-449: “In an additional analysis (results not shown), we also observed that the\ngut microbiota profile of postpartum WHIV more closely resembles that of non-pregnant WHIV,\nas no significant differences between groups were observed.”\nReviewer #2:\nMajor comments:\n1. Metabolomics analyses was only done in the third trimester while microbiota analyses\nwere done in the second and third trimesters. Can authors also look at the metabolome in\nthe second trimester if samples are available?\nResponse: We appreciate this comment. However, since our shipment of third trimester\nplasma samples to the US, the Indian government regulations related to sample shipment out\nof the country have changed. They only allow limited samples for quality control purposes to\nbe shipped out of country. Doing the metabolomics analyses in India would also provide\nchallenges related to comparisons of the number and types of metabolites identified, as the\nthird trimester samples have been run with the Metabolon platform in the US. Along with\nother logistical challenges – e.g. third trimester had the most samples, hence was our focus,\nwhile second trimester had fewer samples, some of which have been used for other assays –\nobtaining second trimester metabolomics data is not feasible at this time. We agree that it\nwould have been helpful to obtain second trimester data, and have specified this as a\nlimitation our study in lines 593-595:\n“Further, intensive sampling in the post-partum period and in infants for microbiota\nassessment, along with additional metabolomic profiles at other time points, would also be\nhelpful to better understand the temporal dynamics by HIV status in these populations.”\n2. As presented, the work is very descriptive. It would help to link the changes in\nmicrobiota to the metabolites. For example, do we know which of the metabolites\nmeasured in plasma are microbe versus host derived? These analyses would strengthen\nthe connection between changes observed in gut composition and the systemic\nmetabolome.\nResponse: Thank you for this helpful suggestion. We conducted analyses to link the\nsignificant genera in our combined second and third trimester microbiome analysis with the\nsignificant metabolites in our metabolomics analysis. We have added a figure and\nsupplementary table to show the multi-omics relationship between the microbiota and the\nmetabolome (Figure 3b, Supplementary Table 9, and Supplementary Figure 2).\nWe have also added a few lines in the results and discussion that mention these findings:\nLines 406-420: “Out of 100 pregnant women with plasma metabolome measures from the\nthird trimester, 88 also have third trimester microbiota data. Using this subset of 88 pregnant\nwomen, we repeated the ANCOM-BC analysis and identified 14 differentially abundant genera\nwith unadjusted p-values  0.1, and repeated a two-sample t-test and identified 87 significant\nmetabolites. We then assessed correlations between abundance of the 14 microbes and\nmetabolomic measures of the 87 significant metabolites (Figure 3b and Supplementary Table\n9). We found taxa Fusobacterium had the strongest correlations with several metabolites,\nincluding positive correlations with 2-hydroxyglutarate and phenol glucuronide, and inverse\ncorrelations with hydroxypalmitoyl sphingomyelin (d18:1/16:0(OH)), glycosyl ceramide\n(d18:1/20:0, d16:1/22:0), palmitoyl sphingomyelin (d18:1/16:0), and sphingomyelin\n(d18:2/23:1) (Figure 3b and Supplementary Table 9). Taxa Ruminococcaceae_UCG-003 was\npositively correlated with metabolite 5alpha-pregnan-3beta,20alpha-diol monosulfate and\npregnenolone sulfate (Figure 3b and Supplementary Table 9). Additionally, the correlation of\nthe 17 differentially present/absent structural zero genera from our ANCOM-BC analysis with\nthe 87 significant metabolites is presented in Supplementary Figure 2.”\nLines 534-548: “In our multi-omics analysis, there were several notable correlations\nbetween specific microbes and metabolites that were significantly different between WHIV and\nSN. The strongest positive correlation was observed between Fusobacterium and 2-\nhydroxyglutarate, with both having higher levels than SN. HIV infection is known to affect\nglutamate and glutamine metabolism, which subsequently impacts immunity and neurotoxicity40,\n41. Increase in Fusobacterium and 2-hydroxyglutarate within WHIV might reflect this altered\nmetabolism since Fusobacterium is known to ferment glutamate through the hydroxyglutarate\npathway42. Further research is needed to better understand these relationships, and their impact\nin pregnancy. The strongest negative correlations were observed between Fusobacterium and\nseveral sphingomyelin and related metabolites. Fusobacterium contain sphingolipids43, thus\nhigher levels of Fusobacterium could mean increased use of sphingomyelin by the bacteria and\nthus lower circulating levels of sphingomyelin. Alternatively, these and other relationships (e.g.\npositive correlation between Ruminococcaceae_UCG-003 and 5alpha-pregnan-3beta,20alpha-\ndiol monosulfate) could reflect indirect relationships between microbes and metabolites due to a\nshared pathway (e.g. inflammation39, 44, 45) impacted by HIV.”\nMinor comments:\n1. How often was sampling done from the full cohort?\nResponse: Stool sampling was done at the second trimester (for those who were enrolled at this\ntimepoint), third trimester, and 6 months postpartum for mothers, and at 6 months of age for\ninfants. We have now added this specific information in the methods section along with how\nparticipants store their samples.\nLines 679-684: “Participants collected the stool samples at second trimester (only if enrolled at\nthis timepoint), third trimester, and 6 months postpartum for mothers, and at 6 months of age for\ninfants. For participants enrolled in the sub-study, intensive bi-weekly (i.e. every two weeks)\nstool samples were collected during the second and third trimester. Stool samples were collected\nby participants at home prior to their next study visit, stored at room temperature (RT) and\nbrought to the clinic at their next visit in RT.”\n2. Provide refence for the STROBE guidelines\nResponse: We have now added a reference for the STROBE guidelines at line 643.\n3. Is there any reason why you have two multivariable models (1 and 2 which differ in\ncovariates) for analyses?This just complicates the analyses making it difficult for the\nreader. Authors should show the univariable (unadjusted) and the multivariable (adjusted\nfor all covariates)\nResponse: Thank you for this comment and we agree with it. Based on previous comments from\nReviewer #1, we have decided to only use multivariable model 1 in our analyses which adjusts\nfor age, MUAC, and education. Please see response #1 under reviewer #1 comments.\n4. For taxa filtering, what does an average of 0.0005% across samples mean?Is this mean\nabundance?median abundance?What % of samples did the taxa have to be present on to\nbe kept?\nResponse: Thank you for this comment. We have removed this criteria in the latest ANCOM\nanalyses as the default setting of ANCOM removes taxa that present in less than 10% of the\nsamples.\n5. Please include equations for the various linear mixed models.\nResponse: We have now included the equations for the linear mixed models in the methods\nsection.\nLines 737-740: “For the first comparison of maternal HIV status and maternal microbiota\nduring pregnancy (analysis 1), we conducted LMMs with subject as a random effect, α-diversity\nas outcome and HIV status as exposure variable, and adjusting for maternal age, education, and\nMUAC, for model 1.”\n6. Since authors included women with detectable HIV viral load, is there a difference in\nmicrobiome between those with and without detectable VL?\nResponse: Thank you for this question. Based on your suggestion, we completed an analysis to\nassess microbiota differences between third trimester WHIV with detectable viral load and\nWHIV with undetectable viral load. Beta diversity and taxa abundance were not different by\ngroup. However, alpha diversity was significant with the Shannon index higher among pregnant\nWHIV with detectable viral load compared to pregnant women WHIV with undetectable viral\nload. We added a line with these results.\nLines 373-377: “In an exploratory analysis, we also tested whether there was a difference in the\nthird trimester microbiota of WHIV with detectable viral load (VL) as compared to WHIV with\nundetectable VL. We did not observe differences in β-diversity or taxa relative abundance but\nobserved that the -diversity Shannon index was higher among WHIV with detectable VL than\nundetectable VL (data not shown).”\n7. Discuss other papers that have done similar work ie PMID 24034618; 38230936;\n39707483; 30657007\nResponse: Thank you for suggesting these papers. We incorporated the papers we felt aligned\nbest with our results in the discussion section (PMID: 39707483 and 30657007).\nLines 564-567: “Finally, our comparison of CHEU and CHUU revealed no differences in \nand diversity, similar to previous studies49; however, there were differences in the microbiota\nprofile by maternal HIV status based on differential presence/absence of various taxa, consistent\nwith prior findings of dysbiosis14, 50.”\nLines 570-571: “Lachnospiraceae have previously been reported as one of the most abundant\nbacterial families in CHEU49”\n8. Generally, in the figures where you are doing differential abundance testing, please show\ndirectionality on the figure itself so it is easy to visualize which bacteria are abundant in\nwhat group. Unless the plots are only showing what is up or down within one group?\nResponse: Thank you for this comment. The figures of differential abundance testing show log\nfold change dots and confidence interval bars for the WHIV group only. If the bar is on the right\nside of zero, there is high abundance of that bacteria in the WHIV group compared to SN group.\nIf the bar is on the left side of zero, there is lower abundance of that bacteria in the WHIV group\ncompared to SN group. We added some clarification to the legends of these figures in both the\nmain and supplementary files for readers to better understand these plots.\n9. What method was used to analyze the differentially abundant metabolites across groups?\nResponse: We used logistic regression by using a binomial generalized linear model (GLM)\nwith a Wald z-test treating HIV status as the outcome, and metabolite measures as the predictor\nand adjusting for age, education, and MUAC, to analyze the differentially abundant metabolites\nacross groups. We have now added this at lines 774-777 in the manuscript, and we have also\nadded it in the legend of Supplementary Table 8.\nLines 774-777: “We conducted logistic regression with HIV status as the outcome, and\nmetabolite measures as the predictor, adjusting for maternal age, education, and MUAC, the\nsame covariates in the microbiota analysis.”\n10. What exactly is Figure 3 showing? The legend says metabolites but the results section say\npathways. Also, the legend says bacteria yet this is supposed to be metabolites?\nResponse: Thank you for pointing this out. Figure 3 is showing the significant metabolites in the\nthird trimester metabolomics analysis. We have now updated Figure 3 legend to say\n“metabolites” as that is what our plot is showing as well as the image.\n11. Sphingomyelins are metabolites, if pathway do you mean sphingomyelin metabolism?\nResponse: Thank you for your comment. We have now changed this sub-pathway name in our\nmain manuscript text to be “sphingomyelin metabolism” as well as any other ones that would\narise confusion.\nLines 394-402: “Among the 40 metabolites elevated in WHIV, the significantly enriched\nmetabolites were from the following sub-pathways: fatty acid metabolism, N-acyl amino acids,\npyrimidine metabolism, purine metabolism, corticosteroids, and food component/plant\n(Supplementary Table 8). Among the 47 metabolites lower in WHIV, these were from sub-\npathways including sphingomyelin metabolism, progestin steroids, secondary bile acid\nmetabolism, dihydrosphingomyelin metabolism, hexosylceramides, and androgenic steroids\n(Supplementary Table 8).”\n12. Link has accession numbers of deposited 16S raw fastq sequences from the study.\nThere is no analysis code provided.\nResponse: We have now included our analysis code for your reference in lines 815-817.\nWe hope we have successfully addressed the reviewer’s concerns. Please let me know if you\nhave further questions or comments.\nSincerely,\nRupak Shivakoti, PhD\nJanuary 29, 2026\nWe thank you and the reviewers for the constructive and helpful comments on our manuscript\ntitled “Impact of maternal HIV infection on the gut microbiome and metabolome of mothers and\ninfants: The PRACHITi Cohort in Pune, India.” We have revised our manuscript addressing the\nadditional minor comments from the reviewers as shown in detail below with a point-by-point\nresponse. Please note that the number lines refer to the highlighted version of the manuscript.\nReviewer Comments:\nReviewer #2:\n1. Is the full cohort made of 245 or 242? The number in the abstract is different from the\none in text.\nResponse: Thank you for asking this question. After checking carefully, our full cohort is\nactually made of 244 participants (we removed a duplicate third trimester visit), with 242\nof them having samples available during pregnancy (with 2 additional women having\nsamples during postpartum but not pregnancy). We have updated lines 131-133 to clarify\nthis further:\n“242 women out of 244 women in the full cohort had available samples at the second\nand/or third trimesters.”\n2. The manuscript has several sections saying “data/results not shown”. Could this maybe\nbe shown as supplementary or removed if not important?\nResponse: This was also an editorial comment in the checklist. We have updated these\nsections with results (e.g. p-values) where necessary and removed any sections that are\nnot as relevant to include.\n3. Is there a reason why in the LMM models, you use different methods for p value\nadjustment? In others you use Bonferroni and BH in others.\nResponse: As we state in our paper, the BH adjusted p-values is the focus of our\ninterpretation. However, we also wanted to present the point estimates and 95%\nconfidence intervals, which is useful for visualization purposes. It is not straightforward\nto adjust the confidence intervention when using BH. Thus, following recommendations\nfrom this paper based on European Medical Agency guidelines\n(https://link.springer.com/article/10.1186/s12874-019-0754-4), we use point estimates\nand Bonferroni correction-based confidence intervals only for visualization purposes. Our\ntext in the methods is referring to this in lines 553-555:\n“The p-values for taxa were adjusted for multiple comparisons using either the BH\nmethod to calculate adjusted p-values (which is the focus of our interpretations) and\nBonferroni method to calculate 95% confidence intervals (which is being used primarily\nfor visualization purposes).”\n4. You say in line 314 that WHIV had a lower alpha diversity compared to controls, but the\np value is 0.12. You mention that Chao1 and Fisher was significant, but that data is not\nshown.\nResponse: Thank you for this comment. WHIV has lower alpha diversity based on the\nnegative coefficient, but we added in parentheses that these results are not significant. We\nalso now present the significant results for Chao1 and Fisher as shown in lines 143-147:\n“Similar to the results from the sub-study with 71 pregnant women, in a LMM with\nrepeated diversity, WHIV had lower α-diversity (although not significant) with HIV\nstatus having a coefficient of -0.059 (p=0.12) for Shannon index (Supplementary Table\n6), with similar and significant results for Chao1 (p=0.005) and Fisher (p=0.002),\nadjusting for covariates (same as those for PERMANOVA).”\n5. Is the heatmap in Supp Fig 2 complete? You mention 17 taxa but only 3 are shown on\nrows on the heatmap. The metabolites on columns are also absent and the grouping\ninformation on the heatmap is missing.\nResponse: We have added in our legends for Supplementary Figure 2 the following line\nto clarify why we only mention a few bacteria and metabolites:\n“Due to the large number of metabolites and microbes, we only label select bacteria and\nmetabolites with high positive or negative correlations. The specific correlations with an\nabsolute correlation ≥ ±0.3 between microbes and metabolites are shown in\nSupplementary Table 9b.”\n6. Line 625–628, the discussion of the two taxa here that were differentially abundant in the\nsecond and third trimester appear misplaced. This section is for differentially abundant\ntaxa in the postpartum period.\nResponse: We purposely mentioned these two taxa in the postpartum section because\nthey were the top two taxa in the postpartum results with a significant raw p-value but not\nadjusted p-value. We thought it was interesting that they were top taxa in the combined\nsecond and third trimester results along with the postpartum results. We rephrased the\nsentence to be clearer for the audience to understand:\nLines 337-340: “Although not statistically significant, postpartum WHIV had a higher\nabundance of Megamonas and Lachnoclostridium, bacteria that were significantly higher\nin WHIV during pregnancy.”\n7. Include references in the discussion, line 723\nResponse: Thank you for pointing this out. We have now updated our bibliography to\nreflect only the references we cited in our manuscript.\n8. Since you mention the differentially present/absent taxa in the results (and these are\ndifferent from differentially abundant taxa), could you please discuss in a bit more detail\nwhat the implications of such taxa might be for WHIV or their CHEU?\nResponse: Thank you for this comment and opportunity to clarify further. We have\nadded the following text in lines 557-560 to describe that they are both used together for\ninterpretation:\n“Of note, differentially abundant taxa and differentially present/absent taxa refer to\ndistinct analytical approaches used in ANCOM-BC to deal with each type of taxa.\nHowever, both are considered together for interpretation of differences between study\ngroups.”"}]

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