Sub-communities of the vaginal microbiota in pregnant and non-pregnant women

Abstract

Diverse and non-Lactobacillus-dominated vaginal microbial communities are associated with adverse health outcomes such as preterm birth and acquisition of sexually transmitted infections. Despite the importance of recognizing and understanding the key risk-associated features of these communities, their heterogeneous structure and properties remain ill-defined. Clustering approaches have been commonly used to characterize vaginal communities, but they lack sensitivity and robustness in resolving community substructures and revealing transitions between potential sub-communities. We used a more highly resolved approach based on mixed membership topic models with multi-domain longitudinal data from cohorts of pregnant and non-pregnant subjects to identify several non-Lactobacillus-dominated sub-communities common to women regardless of reproductive status. These sub-communities correlated with clusters of metabolites. In non-pregnant subjects, we identified a few sub-communities that were more common during menses but did not predict an increased likelihood of non-Lactobacillus-dominated communities during the rest of the menstrual cycle. The menstrual cycle was a strong driver of transitions between sub-communities and was correlated with changes in levels of cytokines, for example, elevated TNF-α concentrations at the time of ovulation, and metabolites, for example, elevated kynurenine concentrations during menses. In pregnant women, some metabolite clusters were predictive of changes in vaginal microbiota structure. Overall, our results show that the vaginal community substructure is shaped by the menstrual cycle and that specific sets of metabolites are associated with community instability during pregnancy.

Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism

“…Most recently, Symul et. al consider how we might use approaches like topic modeling to identify sub-communities of samples within this continuous microbiome space in the vaginal ecosystem Symul et al (2022) . To our knowledge, the approach of topic modeling to define microbial sub-communities has been reported only for 16S data, with other omic data as supporting evidence.…”

“…However, while this categorization serves as an important dimensionality reduction tool for these complex datasets, it tends to oversimplify the community structure, and hides complexity in the microbial communities that exist in the space between enterotypes Knights et al (2014) . Moreover, while clustering approaches can identify frequently co-occurring species, they do not identify species that share similar co-occurrence patterns but do not themselves directly co-occur (as might be seen in species with similar function) Symul et al (2022) . Newer work argues that host-associated microbiomes should be considered to be on a spectrum, and that mixed membership models, and in particular topic models, are powerful and robust tools to learn and define that spectrum Knights et al (2014); Sankaran and Holmes (2018); Deek and Li (2021); Breuninger et al (2021); Sommeria-Klein et al (2020); Okui (2020) .…”

Celiac Disease Predisposition and Genital Tract Microbiota in Women Affected by Recurrent Pregnancy Loss

“…Relative abundances at Phylum and Genus level were reported and statistical significance was assessed by Mann–Whitney U test. Based on ASVs intrinsic characteristics and following previously used experimental setting [ 17 , 18 , 19 , 20 ], Lactobacillacae family was selected, and the relative abundance of Lactobacillus species was investigated. ASVs annotated as Lactobacillus species were additionally compared to the NCBI database to corroborate the previously obtained identification.…”

Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism

“…Most recently, Symul et. al consider how we might use approaches like topic modeling to identify sub-communities of samples within this continuous microbiome space in the vaginal ecosystem 23 . To our knowledge, the approach of topic modeling to define microbial sub-communities has been reported only for 16S data, with other omic data as supporting evidence.…”

“…However, while this categorization serves as an important dimensionality reduction tool for these complex datasets, it tends to oversimplify the community structure, and hides complexity in the microbial communities that exist in the space between enterotypes 22 . Moreover, while clustering approaches can identify frequently co-occurring species, they do not identify species that share similar co-occurrence patterns but do not themselves directly co-occur (as might be seen in species with similar function) 23 . Newer work argues that host-associated microbiomes should be considered to be on a spectrum, and that mixed membership models, and in particular topic models, are powerful and robust tools to learn and define that spectrum 22,[24][25][26][27][28] .…”