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Racioethnic diversity in the dynamics of the vaginal microbiome during pregnancy

Abstract

The microbiome of the female reproductive tract has implications for women’s reproductive health. We examined the vaginal microbiome in two cohorts of women who experienced normal term births: a cross-sectionally sampled cohort of 613 pregnant and 1,969 non-pregnant women, focusing on 300 pregnant and 300 non-pregnant women of African, Hispanic or European ancestry case-matched for race, gestational age and household income; and a longitudinally sampled cohort of 90 pregnant women of African or non-African ancestry. In these women, the vaginal microbiome shifted during pregnancy toward Lactobacillus-dominated profiles at the expense of taxa often associated with vaginal dysbiosis. The shifts occurred early in pregnancy, followed predictable patterns, were associated with simplification of the metabolic capacity of the microbiome and were significant only in women of African or Hispanic ancestry. Both genomic and environmental factors are likely contributors to these trends, with socioeconomic status as a likely environmental influence.

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Data availability

16S rRNA sequence data and metadata for each sample have been deposited in the HMP DACC (https://portal.hmpdacc.org/). Data that are of controlled access (that is, metagenomic and metatranscriptomic sequence data, which can include some sensitive human sequence and subject metadata) have been deposited at NCBI’s controlled-access dbGaP (study accession IDs phs001523 and phs000256) and Sequence Read Archive (SRA; BioProject IDs PRJNA326441, PRJNA430481, PRJNA430482, PRJNA74947, PRJNA51443 and PRJNA46877). Additional metadata have been deposited in, and are available through, the RAMS Registry (https://ramsregistry.vcu.edu). Project information is also available at the project website (http://vmc.vcu.edu).

Code availability

Custom code is available at https://github.com/Vaginal-Microbiome-Consortium/TBS. Open-source software is described in the text.

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Acknowledgements

The study team would like to gratefully acknowledge the participants who contributed specimens and data to the study. The authors would also like to acknowledge other members of the Vaginal Microbiome Consortium whose contributions made the study possible, including the team of research coordinators, the team of sample processors and the team of clinicians and nurses who assisted with sample collection. This study was funded by NIH grants UH3AI083263 and U54HD080784 to G.A.B., K.K.J. and J.F.S. We would also like to thank the Common Fund, the National Center for Complementary and Integrative Health, the Office of Research on Women’s Health, the Eunice Kenedy Shriver National Institute of Child Heatlh and Human Development, and the National Institute of Allergy and Infectious Disease at NIH for their generous support of this project. Other grants that provided partial support include a GAPPS BMGF PPB grant to G.A.B. and J.M.F. and NIH grant R21HD092965 to J.M.F. and E. P. Wickham and 1R01HD092415 to G.A.B. and T.J.A. N.R.J. was supported by grant R25GM090084 for the VCU Initiative For Maximizing Student Development (IMSD) programme. All sequence analysis reported herein was performed in the Nucleic Acids Research Facilities at VCU, and all informatics analysis was performed in servers provided by the Center for High Performance Computing at VCU.

Author information

G.A.B., K.K.J., J.F.S. and J.M.F. comprised the executive committee. M.G.S., H.I.P., J.M.F., J.P.B., D.J.E., T.J.A., P.H.G., B.H., L.E., K.D.H.-M, K.K.J., J.F.S. and G.A.B. designed this study. P.H.G., S.C.V., S.H.M., S.K.R., M.R.D., J.K., A.L.S., M.G.G., C.E.R., N.R.W., K.D.H.-M and J.F.S. comprised the clinical team M.G.S., B.H., V.L., A.M.L., J.X., A.V.M., J.L.B., S.J., R.A.D., J.I.D., S.D.M., K.K.J., J.M.F. and G.A.B. generated the 16S rRNA, metagenomic and metatranscriptomics data. M.G.S., H.I.P., J.M.F., L.E., A.L.G., N.R.J., N.U.S., S.P.B., V.N.K., A.V.M., P.X., S.S.F. and G.A.B. managed the bioinformatics and data. H.I.P., D.J.E., T.J.A., S.S.F., Y.A.B., E.S. and J.P.B. performed statiscal analysis. C.E.R., M.G.G., D.O.C. and A.L.S. comprised the GAPPS Team.

Correspondence to Gregory A. Buck.

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Extended data

Extended Data Fig. 1 Differences in microbiome diversity in pregnant and non-pregnant women of different ancestry.

a, Differences in alpha diversities of the vaginal microbiomes in 613 pregnant and 1,969 non-pregnant women of different racial descendance due to pregnancy. b, Differences in alpha diversities of the vaginal microbiomes of 300 pregnant and 300 non-pregnant women of different racial descendance case-matched for race, age and socioeconomic status due to pregnancy. c, Differences in alpha diversities of the vaginal microbiomes of 300 pregnant and 300 non-pregnant women of different racial descendance case-matched for race, age and socioeconomic status. Box plots were generated in R using standard approaches. The bar represents the median and the boxes indicate interquartile ranges. Significant differences are indicated (*P < 0.05).

Extended Data Fig. 2 Effects of pregnancy on the vaginal microbiome in different racial backgrounds.

a, Microbiome profiles of 304 pregnant women (upper panel) and 1,184 non-pregnant women of African ancestry. b, Microbiome profiles of 111 pregnant women of European ancestry and 682 non-pregnant women of European ancestry. c, Microbiome profiles of 198 pregnant women of Hispanic ancestry and 103 non-pregnant women of Hispanic ancestry. Legend is as shown for Fig. 2. The blue bars denote the Lactobacillus taxa (L. crispatus, L. jensenii, L. gasseri and L. iners).

Extended Data Fig. 3 Vaginal microbiome profiles of 90 women, 49 of African and 41 of non-African ancestry.

a, Microbiome profiles of all samples (421 total, 175 from women of non-African ancestry and 246 from women of African ancestry) from each of these 90 women. Taxa are color-coded as indicated. b, Microbiome profiles of these same samples from women of non-African (top) and African ancestry (bottom). Taxa are color-coded as in a. c, Alpha diversity measures of richness (species counts) and evenness (Shannon index) of these samples (described in a) from women of non-African (n-Afr) and African (Afr) ancestry, measured using the vegan package. Alpha diversities and statistical analysis were calculated as indicated in the Methods. Box plots were generated in R using standard approaches. The bar represents the median and the boxes indicate interquartile ranges. d, L1-Norm PCA analysis of the same samples (see Methods). Legend of vagitypes is as indicated. See Supplementary Table 5 for sequence read statistics for data presented in this figure.

Extended Data Fig. 4 Longitudinal changes in microbiome profiles across trimesters during pregnancy.

a, Vaginal microbiome profiles of 41 pregnant women of African (n = 22) or non-African (n = 19) ancestry who provided at least 1 sample from each of 3 trimesters. b, Alpha diversity measures of richness (species counts) and evenness (Shannon index) of samples from a. Diversity measures calculated using the vegan package (see Methods). Box plots were generated in R using standard approaches. The bar represents the median and the boxes indicate interquartile ranges. Asterisks indicate statistical significance (*P < 0.05; **P < 0.01). c, L1-Norm PCA analysis (see Methods) of samples from a. Legends are indicated. n-Afr, women of non-African ancestry; Afr: women of African ancestry. See Supplementary Table 5 for sequence read statistics for data presented in this figure.

Extended Data Fig. 5 Changes in abundance of taxa across pregnancy.

a, Relative abundances of L. crispatus and L. iners in 1 early and 1 late sample from each of 90 participants, 41 of non-African (n-Afr) and 49 of African (Afr) ancestry. b, Longitudinal differences in relative abundance of select taxa—L. crispatus, L. iners, L. jensenii, L. gasseri, G. vaginalis, BVAB1, A. vaginae, S. amnii, Prevotella cluster 2 and TM7_OTU-H1, from 1 sample collected in each trimester from 90 participants, 41 of non-African (n-Afr) and 49 of African (Afr) ancestry. For both a and b, the medians for each group were compared using a two-sided Wilcoxon test, with FDR adjustments for multiple comparisons where applicable (ns, not significant; *P < 0.05; **P < 0.01).

Extended Data Fig. 6 Stability of vagitypes in pregnancy showing the variation of the microbiomes of each woman across all samples collected during that pregnancy.

a, Vaginal microbiome profiles from 41 women of non-African ancestry. Each facet represents the data from a single participant across all vaginal samples collected during her pregnancy. The samples, within each facet, are ordered from left to right based on their gestational age at sampling; same as Fig. 3a,b. The bars below each stacked bar indicate the strain of L. crispatus (1 or 2), L. jensenii (1 or 2), L. gasseri (1 or 2), L. iners (1 or 2), BVAB1 (1 or 2) or G. vaginalis (1, 2, 3 or 4). b, Vaginal microbiome profiles from 49 women of African ancestry. As for Extended Data Fig. 7, each facet represents the data from a single participant across all vaginal samples collected during her pregnancy. The samples, within each facet, are ordered from left to right based on their gestational age at sampling; same as Fig. 3a,b. The bars below each stacked bar indicate the strain of L. crispatus (1 or 2), L. jensenii (1 or 2), L. gasseri (1 or 2), L. iners (1 or 2), BVAB1 (1 or 2) or G. vaginalis (1, 2, 3 or 4).

Extended Data Fig. 7 Functional metabolic potential and transcriptional activity in vaginal microbiomes cluster according to vagitype.

a, Sparse partial least squares discriminant analysis (PLS-DA) of pathways derived from metagenomic sequence analysis of all 373 samples (147 samples from the 41 women of non-African ancestry, and 226 samples from the 49 women of African ancestry) from the 90 women in this study. Samples are color-coded according to vagitype (see legend). b, Sparse PLS-DA of pathways derived from metatranscriptomic sequence analysis of 1 sample from each pregnancy taken in the second or early third trimester (20 samples from the women of non-African ancestry and 28 from the women of African ancestry). c, Heat map of pathways from metagenomic analysis of samples as for a. Samples are sorted according to major vagitype (see legend). Samples from women of African ancestry (African) and from prior to 26 weeks’ gestation (early) are indicated. Alpha diversity is shown. d, Heat map of pathways from metatranscriptomic analysis of samples as for b. Samples are sorted as in c. Abundance and alpha diversity value scales are indicated. Sparse PLS-DA is a technique for fitting classification models that simultaneously selects features (via an L1 norm penalty term) that best describe group separation. The resulting model is sparse so that only a small subset of bacteria is included; the discriminant functions allow for visualization of the classification rule.

Extended Data Fig. 8 Association of G. vaginalis, L. crispatus, L. jensenii, L. gasseri, L. iners and Lachnospiracea BVAB1 strains with ancestry and other taxa.

Samples with these taxa were analysed in parallel with known reference strains using PanPhlan software to discriminate strain designations using default parameters of -min_coverage 1 (see Methods). a, G. vaginalis. Using these parameters, 121 samples provided sufficient numbers of G. vaginalis reads to provide accurate strain designations. Strain designations, which were previously reported by Ahmed et al.33 or Callahan et al.53, are indicated by the colored bars below the heat map. Note that G1 of Callahan et al. is within Set B of Ahmed et al., which also overlaps clades 3 and 4, and G2 of Callahan et al. includes Set A of Ahmed et al., which is also subdivided into clades 1 and 2. G3 of Callahan et al. classifies in clade 1 of Ahmed et al. The ancestry of each participant is indicated in the bar above the heat map, where blue indicates non-African and gray indicates African ancestry, and orange indicates a reference strain genome. Note: several samples contained multiple strains of different lineage. The black bar indicates two samples that contained three strains from clades 2, 3 and 4. bf, L. crispatus, L. jensenii, L. gasseri, L. iners, and Lachnospiracea BVAB1. Analyses similar to that done for G. vaginalis above were performed with samples containing sufficient presence of these taxa (see above, and Methods). The ancestry of each participant is indicated in the bar above the heat map, where blue indicates non-African and gray indicates African ancestry, and white indicates a reference strain genome. Clades are differentiated by pink and light brown bars under each heat map.

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Fig. 1: Overview of the VaHMP study and the MOMS-PI Term Birth study.
Fig. 2: Pregnant and non-pregnant women of different ancestry exhibit different vaginal microbiome profiles.
Fig. 3: Vaginal microbiome profiles of women of African ancestry change early in pregnancy.
Fig. 4: Temporal dynamics of vagitype transitions during pregnancy.
Fig. 5: Metagenomic, metatranscriptomic and pathway analyses of vaginal microbiome samples support metabolic differences among vagitypes of pregnant women of African and non-African ancestry.
Extended Data Fig. 1: Differences in microbiome diversity in pregnant and non-pregnant women of different ancestry.
Extended Data Fig. 2: Effects of pregnancy on the vaginal microbiome in different racial backgrounds.
Extended Data Fig. 3: Vaginal microbiome profiles of 90 women, 49 of African and 41 of non-African ancestry.
Extended Data Fig. 4: Longitudinal changes in microbiome profiles across trimesters during pregnancy.
Extended Data Fig. 5: Changes in abundance of taxa across pregnancy.
Extended Data Fig. 6: Stability of vagitypes in pregnancy showing the variation of the microbiomes of each woman across all samples collected during that pregnancy.
Extended Data Fig. 7: Functional metabolic potential and transcriptional activity in vaginal microbiomes cluster according to vagitype.
Extended Data Fig. 8: Association of G. vaginalis, L. crispatus, L. jensenii, L. gasseri, L. iners and Lachnospiracea BVAB1 strains with ancestry and other taxa.