Infectious Microbes & Diseases (Dec 2019)

Multi-omic Microbiome Profiles in the Female Reproductive Tract in Early Pregnancy

  • Sophonie Jean,
  • Bernice Huang,
  • Hardik I. Parikh,
  • David J. Edwards,
  • J. Paul Brooks,
  • Naren Gajenthra Kumar,
  • Nihar U. Sheth,
  • Vishal Koparde,
  • Ekaterina Smirnova,
  • Snehalata Huzurbazar,
  • Philippe H. Girerd,
  • Dayanjan S. Wijesinghe,
  • Jerome F. Strauss, III,
  • Myrna G. Serrano,
  • Jennifer M. Fettweis,
  • Kimberly K. Jefferson,
  • Gregory A. Buck,
  • Stijn van der Veen

DOI
https://doi.org/10.1097/IM9.0000000000000007
Journal volume & issue
Vol. 1, no. 2
pp. 49 – 60

Abstract

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Abstract. The vaginal microbiome likely influences host signaling compounds within the reproductive tract, including pro-inflammatory signals, which may play an important role during pregnancy. Vaginal lactobacilli are associated with positive pregnancy outcome, whereas bacterial vaginosis, a dysbiosis of the vaginal microbiome, is associated with an increased risk of adverse pregnancy outcomes including preterm birth. If the host response could be predicted based on the taxonomic composition of the vaginal microbiome, particularly early in pregnancy, then those predictions could potentially be used to personalize intervention methods to reduce preterm birth and other adverse events. In this proof of principle study, we apply multivariate strategies to analyze 16S rRNA-based taxonomic surveys in conjunction with targeted immuno-proteomic and lipidomic data from vaginal samples from 58 women enrolled in the Multi-Omic Microbiome Study-Pregnancy Initiative during early pregnancy. Relationships between the vaginal microbiome and the vaginal lipidome have not been previously reported. Results from this study reveal significant multiple pairwise associations between microbial taxa, specific eicosanoids and sphingomyelins, and cytokines. While the biologic significance of these associations is not yet known, these results support the utility of such multi-omic approaches as a means to predict the impact of the microbiome on the host.