Quantifying the human vaginal community state types (CSTs) with the species specificity index

PeerJ. 2017 Jun 27:5:e3366. doi: 10.7717/peerj.3366. eCollection 2017.

Abstract

The five community state types (CSTs) first identified by Ravel et al. (2011) offered a powerful scheme to classify the states of human vaginal microbial communities (HVMC). The classification is a significant advance because it devised an effective handle to deal with the enormous inter-subject heterogeneity and/or intra-subject temporal variability, the quantification of which is extremely difficult but of critical importance such as the understanding of BV (bacterial vaginosis) etiology. Indeed, arguably the most plausible ecological hypothesis for interpreting the BV etiology heavily depends on the CST classification (Gajer et al., 2012; Ma, Forney & Ravel, 2012; Ravel et al., 2011). Nevertheless, the current form of CSTs is still qualitative and lacks a quantitative criterion to determine the CSTs. In this article, we develop a quantitative tool that can reliably distinguish the CSTs by applying the species specificity of Mariadassou, Pichon & Ebert (2015) and the specificity aggregation index (SAI) we propose in this study. The new tool accurately characterized the classifications of the five CSTs with both 400-crosssectional cohort (Ravel et al., 2011) and 32-longitudinal cohort (Gajer et al., 2012) studies originally utilized to develop the CST scheme. Furthermore, it offers a mechanistic interpretation of the original CST scheme by invoking the paradigm of specificity continuum for species adaptation and distribution. The advances we made may not only facilitate the accurate applications of the CST scheme, but also offer hints towards an effective tool for microbiome typing such as classifying gut enterotypes.

Keywords: Community diversity; Community state type (CST); Human vaginal microbial community; Species specificity; Specificity aggregation index (SAI).

Grants and funding

This research received funding from the National Science Foundation of China (Grants No. 61175071, 71473243), Open Grant (GREKF14-06) of the State Key Laboratory of Genetic Resources and Evolution, The Exceptional Scientists Program and Top Oversea Scholars Program of Yunnan Province, and The Yun-Ridge Industrial Leadership Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.