Taxonomic and functional shifts in the perinatal gut microbiome of rhesus macaques

Abstract

Pregnancy and the postpartum period result in some of the most dramatic metabolic, hormonal, and physiological changes that can be experienced by an otherwise healthy adult. The timing and magnitude of these changes is key for both maternal and fetal health. One of the factors believed to critically modulate these physiological changes is the maternal gut microbiome. However, the dynamic changes in this community during the perinatal period remain understudied. Clinical studies can be complicated by confounding variables such as diet and other drivers of heterogeneity in the human microbiome. Therefore, in this study, we conducted a longitudinal analysis of the fecal microbiome obtained during the perinatal and post-partum periods in 25 rhesus macaques using 16S rRNA gene amplicon sequencing and shotgun metagenomics. Shifts at both the taxonomic and functional potential level were detected when comparing pregnancy to postpartum samples. Taxonomically, Alloprevotella, Actinobacillus, and Anaerovibrio were enriched during pregnancy while Treponema, Lachnospiraceae, and Methanosphaera were more abundant post-partum. Functionally, pregnancy was associated with increased abundance in the pathway to produce the beneficial short chain fatty acid (SCFA), butyrate, while pathways associated with starch degradation and folate transformation were more abundant postpartum. These data demonstrate dramatic changes in the maternal gut even in the absence of dietary changes and suggest that rhesus macaques could provide a valuable model to determine how changes in the microbiome correlate to other physiological changes in pregnancy.

Maternal-Child Microbiome and Impact on Growth and Neurodevelopment in Infants and Children: A Scoping Review

“…Studies employed different technologies and statistical methods to characterize microbiome which limits the direct comparison of results across different studies. Use of different methodologies such as 16S rRNA sequencing and shotgun sequencing can produce slightly differing results even if applied on the same samples (Rhoades et al, 2022), and hence, results across several studies cannot be directly compared. Authors used a myriad of alpha- and beta-diversity metrics and statistical tests (e.g., correcting for multiple comparisons) which can create difficulty in generalizing any results.…”