Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Racioethnic diversity in the dynamics of the vaginal microbiome during pregnancy

Nature Medicine volume 25, pages 1001–1011 (2019)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

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.

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.

References

  1. Ravel, J. et al. Vaginal microbiome of reproductive-age women. Proc. Natl Acad. Sci. USA 108, 4680–4687 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Ravel, J. et al. Daily temporal dynamics of vaginal microbiota before, during and after episodes of bacterial vaginosis. Microbiome 1, 29 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Younes, J. A. et al. Women and their microbes: the unexpected friendship. Trends Microbiol. 26, 16–32 (2017).

    Article  PubMed  Google Scholar 

  4. Srinivasan, S. et al. Temporal variability of human vaginal bacteria and relationship with bacterial vaginosis. PloS One 5, e10197 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Petrova, M. I., Lievens, E., Malik, S., Imholz, N. & Lebeer, S. Lactobacillus species as biomarkers and agents that can promote various aspects of vaginal health. Front. Physiol. 6, 81 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sobel, J. D. Bacterial vaginosis. Annu. Rev. Med. 51, 349–356 (2000).

    Article  CAS  PubMed  Google Scholar 

  7. Fettweis, J. M. et al. The vaginal microbiome and preterm birth. Nat. Med. https://doi.org/10.1038/s41591-019-0450-2 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Shah, R. et al. Incidence and risk factors of preterm birth in a rural Bangladeshi cohort. BMC Pediatr. 14, 112 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Tielsch, J. M. Global incidence of preterm birth. Nestle Nutr. Inst. Workshop Ser. 81, 9–15 (2015).

    Article  PubMed  Google Scholar 

  10. WHO. The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bull. World Health Org. 88, 31–8 (2010).

    Article  Google Scholar 

  11. Goldenberg, R. L. et al. The preterm prediction study: the value of new vs standard risk factors in predicting early and all spontaneous preterm births. NICHD MFMU Network. Am. J. Public Health 88, 233–238 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. York, T. P., Eaves, L. J., Neale, M. C. & Strauss, J. F. The contribution of genetic and environmental factors to the duration of pregnancy. Am. J. Obstet. Gynecol. 210, 398–405 (2014).

    Article  PubMed  Google Scholar 

  13. Barcelona de Mendoza, V. et al. A systematic review of DNA methylation and preterm birth in African American women. Biol. Res. Nurs. 19, 308–317 (2017).

    Article  CAS  PubMed  Google Scholar 

  14. Modi, B. P. et al. Mutations in fetal genes involved in innate immunity and host defense against microbes increase risk of preterm premature rupture of membranes (PPROM). Mol. Genet. Genom. Med. 5, 720–729 (2017).

    Article  CAS  Google Scholar 

  15. Fettweis, J. M. et al. Differences in vaginal microbiome in African American women versus women of European ancestry. Microbiolology 160, 2272–2282 (2014).

    CAS  Google Scholar 

  16. MacIntyre, D. A. et al. The vaginal microbiome during pregnancy and the postpartum period in a European population. Sci. Rep. 5, 8988 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Gajer, P. et al. Temporal dynamics of the human vaginal microbiota. Sci. Transl. Med. 4, 132ra52 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hyman, R. W. et al. Diversity of the vaginal microbiome correlates with preterm birth. Reprod. Sci. 21, 32–40 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ma, B., Forney, L. J. & Ravel, J. The vaginal microbiome: rethinking health and diseases. Annu. Rev. Microbiol. 66, 371–389 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hickey, R. J., Zhou, X., Pierson, J. D., Ravel, J. & Forney, L. J. Understanding vaginal microbiome complexity from an ecological perspective. Transl. Res. J. Lab. Clin. Med. 160, 267–282 (2012).

    Article  CAS  Google Scholar 

  21. Martin, D. H. & Marrazzo, J. M. The vaginal microbiome: current understanding and future directions. J. Infect. Dis. 214, S36–S41 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zhou, X. et al. Differences in the composition of vaginal microbial communities found in healthy Caucasian and black women. ISME J. 1, 121–133 (2007).

    Article  CAS  PubMed  Google Scholar 

  23. Beamer, M. A. et al. Bacterial species colonizing the vagina of healthy women are not associated with race. Anaerobe 45, 40–43 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Peterson, J. et al. The NIH Human Microbiome Project. Genome Res. 19, 2317–2323 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  25. The Integrative Human Microbiome Project. Dynamic analysis of microbiome–host omics profiles during periods of human health and disease. Cell Host Microbe 16, 276–289 (2014).

    Article  Google Scholar 

  26. Fettweis, J. M. et al. Species-level classification of the vaginal microbiome. BMC Genom. 13, S17 (2012).

    Google Scholar 

  27. Brooks, J. P. et al. The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies. BMC Microbiol. 15, 66 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Nelson, D. B. et al. Early pregnancy changes in bacterial vaginosis-associated bacteria and preterm delivery. Paediatr. Perinat. Epidemiol. 28, 88–96 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Fredricks, D. N., Fiedler, T. L., Thomas, K. K., Oakley, B. B. & Marrazzo, J. M. Targeted PCR for detection of vaginal bacteria associated with bacterial vaginosis. J. Clin. Microbiol. 45, 3270–3276 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lopes dos Santos Santiago, G. et al. Gardnerella vaginalis comprises three distinct genotypes of which only two produce sialidase. Am. J. Obstet. Gynecol. 204, 450.e1–7 (2011).

    Article  Google Scholar 

  31. Piot, P. et al. Biotypes of Gardnerella vaginalis. J. Clin. Microbiol. 20, 677–679 (1984).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Ingianni, A., Petruzzelli, S., Morandotti, G. & Pompei, R. Genotypic differentiation of Gardnerella vaginalis by amplified ribosomal DNA restriction analysis (ARDRA). FEMS Immunol. Med. Microbiol. 18, 61–66 (1997).

    Article  CAS  PubMed  Google Scholar 

  33. Ahmed, A. et al. Comparative genomic analyses of 17 clinical isolates of Gardnerella vaginalis provide evidence of multiple genetically isolated clades consistent with subspeciation into genovars. J. Bacteriol. 194, 3922–3937 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Onderdonk, A. B., Delaney, M. L. & Fichorova, R. N. The human microbiome during bacterial vaginosis. Clin. Microbiol. Rev. 29, 223–238 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Menard, J. P. et al. High vaginal concentrations of Atopobium vaginae and Gardnerella vaginalis in women undergoing preterm labor. Obstet. Gynecol. 115, 134–140 (2010).

    Article  PubMed  Google Scholar 

  37. Eastment, M. C. & McClelland, R. S. Vaginal microbiota and susceptibility to HIV. AIDS Lond. Engl. 32, 687–698 (2018).

    Article  Google Scholar 

  38. Stout, M. J. et al. Identification of intracellular bacteria in the basal plate of the human placenta in term and preterm gestations. Am. J. Obstet. Gynecol. 208, 226.e1–7 (2013).

    Article  Google Scholar 

  39. DiGiulio, D. B. et al. Temporal and spatial variation of the human microbiota during pregnancy. Proc. Natl Acad. Sci. USA 112, 11060–11065 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Goltsman, D. S. A. et al. Metagenomic analysis with strain-level resolution reveals fine-scale variation in the human pregnancy microbiome. Genome Res. 28, 1–14 (2018).

    Article  Google Scholar 

  41. Walther-António, M. R. S. et al. Pregnancy’s stronghold on the vaginal microbiome. PloS One 9, e98514 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Romero, R. et al. The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women. Microbiome 2, 4 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Brooks, J. P. et al. Effects of combined oral contraceptives, depot medroxyprogesterone acetate and the levonorgestrel-releasing intrauterine system on the vaginal microbiome. Contraception 95, 405–413 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Muhleisen, A. L. & Herbst-Kralovetz, M. M. Menopause and the vaginal microbiome. Maturitas 91, 42–50 (2016).

    Article  PubMed  Google Scholar 

  45. Cauci, S. et al. Prevalence of bacterial vaginosis and vaginal flora changes in peri- and postmenopausal women. J. Clin. Microbiol. 40, 2147–2152 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Pabich, W. L. et al. Prevalence and determinants of vaginal flora alterations in postmenopausal women. J. Infect. Dis. 188, 1054–1058 (2003).

    Article  PubMed  Google Scholar 

  47. Hillier, S. L. & Lau, R. J. Vaginal microflora in postmenopausal women who have not received estrogen replacement therapy. Clin. Infect. Dis. 25, S123–126 (1997).

    Article  PubMed  Google Scholar 

  48. Spear, G. T. et al. Human α-amylase present in lower-genital-tract mucosal fluid processes glycogen to support vaginal colonization by. Lact. J. Infect. Dis. 210, 1019–1028 (2014).

    Article  CAS  Google Scholar 

  49. Aagaard, K. et al. A metagenomic approach to characterization of the vaginal microbiome signature in pregnancy. PloS One 7, e36466 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Freitas, A. C. et al. The vaginal microbiome of pregnant women is less rich and diverse, with lower prevalence of Mollicutes, compared to non-pregnant women. Sci. Rep. 7, 9212 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Samant, S. et al. Nucleotide biosynthesis is critical for growth of bacteria in human blood. PLOS Pathog. 4, e37 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Janulaitiene, M. et al. Prevalence and distribution of Gardnerella vaginalis subgroups in women with and without bacterial vaginosis. BMC Infect. Dis. 17, 394 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Callahan, B. J. et al. Replication and refinement of a vaginal microbial signature of preterm birth in two racially distinct cohorts of US women. Proc. Natl Acad. Sci. USA 114, 9966–9971 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Brooks, J. P., Dulá, J. H. & Boone, E. L. A pure L1-norm principal component analysis. Comput. Stat. Data Anal. 61, 83–98 (2013).

    Article  PubMed  Google Scholar 

  55. Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Forney, L. J. et al. Comparison of self-collected and physician-collected vaginal swabs for microbiome analysis. J. Clin. Microbiol. 48, 1741–1748 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Parikh, H. I., Koparde, V. N., Bradley, S. P., Buck, G. A. & Sheth, N. U. MeFiT: merging and filtering tool for Illumina paired-end reads for 16S rRNA amplicon sequencing. BMC Bioinform. 17, 491 (2016).

    Article  Google Scholar 

  58. Brooks, J. P., Dulá, J. H. & Pakyz, A. L. Identifying hospital antimicrobial resistance targets via robust ranking. IISE Trans. Healthc. Syst. Eng. 7, 121–128 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  60. Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N. & Golani, I. Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125, 279–284 (2001).

    Article  CAS  PubMed  Google Scholar 

  61. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Ounit, R., Wanamaker, S., Close, T. J. & Lonardi, S. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genom. 16, 236 (2015).

    Article  Google Scholar 

  64. Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  66. Hahsler, M., Chelluboina, S., Hornik, K. & Buchta, C. The arules R-package ecosystem: analyzing interesting patterns from large transaction data sets. J. Mach. Learn. Res. 12, 2021–2025 (2011).

    Google Scholar 

Download references

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

Author notes

  1. These authors contributed equally: Myrna G. Serrano, Hardik I. Parikh.

Authors and Affiliations

  1. Department of Microbiology and Immunology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA

    Myrna G. Serrano, Hardik I. Parikh, Laahirie Edupuganti, Bernice Huang, Steven P. Bradley, Jamie L. Brooks, Molly R. Dickinson, Jennifer I. Drake, Robert A. Duckworth III, Sophonie Jean, Nicole R. Jimenez, Vishal N. Koparde, Ana M. Lara, Vladimir Lee, Andrey V. Matveyev, Shreni D. Mistry, Sarah K. Rozycki, Stephany C. Vivadelli, Ping Xu, Kimberly K. Jefferson, Jennifer M. Fettweis & Gregory A. Buck

  2. Center for Microbiome Engineering and Data Analysis, Virginia Commonwealth University, Richmond, VA, USA

    Myrna G. Serrano, J. Paul Brooks, David J. Edwards, Tom J. Arodz, Laahirie Edupuganti, Philippe H. Girerd, Stephen S. Fong, Nicole R. Jimenez, Vladimir Lee, Andrey V. Matveyev, Stephany C. Vivadelli, Jie Xu, Ping Xu, Karen D. Hendricks-Muñoz, Kimberly K. Jefferson, Jerome F. Strauss III, Jennifer M. Fettweis & Gregory A. Buck

  3. Supply Chain Management and Analytics, School of Business, Virginia Commonwealth University, Richmond, VA, USA

    J. Paul Brooks

  4. Department of Statistical Sciences and Operations Research, College of Humanities and Sciences, Virginia Commonwealth University, Richmond, VA, USA

    David J. Edwards

  5. Department of Computer Science, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA

    Tom J. Arodz, Yahya A. Bokhari & Gregory A. Buck

  6. Department of Obstetrics and Gynecology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA

    Philippe H. Girerd, Sarah H. Milton, Jerome F. Strauss III & Jennifer M. Fettweis

  7. Department of Chemical and Life Science Engineering, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA

    Stephen S. Fong

  8. VCU Life Sciences, Virginia Commonwealth University, Richmond, VA, USA

    Abigail L. Glascock, Nihar U. Sheth & Ping Xu

  9. Division of General Pediatrics, Department of Pediatrics, School of Medicine, Children’s Hospital of Richmond at Virginia Commonwealth University, Richmond, VA, USA

    Joseph Khoury, N. Romesh Wijesooriya, Jie Xu & Karen D. Hendricks-Muñoz

  10. Division of Neonatal Medicine, Department of Pediatrics, School of Medicine, Children’s Hospital of Richmond at Virginia Commonwealth University, Richmond, VA, USA

    Joseph Khoury, Jie Xu & Karen D. Hendricks-Muñoz

  11. Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA

    Ekaterina Smirnova

  12. Philips Institute for Oral Health Research, Virginia Commonwealth University, Richmond, VA, USA

    Ping Xu

  13. Global Alliance to Prevent Prematurity and Stillbirth, Seattle, WA, USA

    Donald O. Chaffin, Amber L. Sexton, Michael G. Gravett & Craig E. Rubens

  14. Department of Obstetrics & Gynecology, University of Washington, Seattle, WA, USA

    Michael G. Gravett

Authors
  1. Myrna G. Serrano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hardik I. Parikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Paul Brooks
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David J. Edwards
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tom J. Arodz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laahirie Edupuganti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bernice Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Philippe H. Girerd
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yahya A. Bokhari
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Steven P. Bradley
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jamie L. Brooks
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Molly R. Dickinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Jennifer I. Drake
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Robert A. Duckworth III
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Stephen S. Fong
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Abigail L. Glascock
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Sophonie Jean
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Nicole R. Jimenez
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Joseph Khoury
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Vishal N. Koparde
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Ana M. Lara
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Vladimir Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Andrey V. Matveyev
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Sarah H. Milton
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Shreni D. Mistry
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Sarah K. Rozycki
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Nihar U. Sheth
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Ekaterina Smirnova
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Stephany C. Vivadelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. N. Romesh Wijesooriya
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Jie Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Ping Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Donald O. Chaffin
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Amber L. Sexton
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Michael G. Gravett
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Craig E. Rubens
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Karen D. Hendricks-Muñoz
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Kimberly K. Jefferson
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Jerome F. Strauss III
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Jennifer M. Fettweis
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Gregory A. Buck
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding author

Correspondence to Gregory A. Buck.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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. b–f, 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.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Serrano, M.G., Parikh, H.I., Brooks, J.P. et al. Racioethnic diversity in the dynamics of the vaginal microbiome during pregnancy. Nat Med 25, 1001–1011 (2019). https://doi.org/10.1038/s41591-019-0465-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-019-0465-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing