Temporal Dynamics of the Human Vaginal Microbiota

By Pawel Gajer, R. Brotman, Guoyun Bai, J. Sakamoto, Ursel M. E. Schütte, Xue Zhong, Sara S. K. Koenig, Li Fu, Z. Ma, Xia Zhou and 3 more. Science Translational Medicine, 2012

1,411 citations recorded by the source corpus.

The vaginal microbiome is dynamic, varying over time in composition and function with implications for women’s health. What’s Up with Vaginal Microbes? The ability to properly identify women at risk of acquiring sexually transmitted infectious diseases or who might suffer from adverse obstetric sequelae is a critical first step in reducing their incidence and the unnecessary use of antibiotics. Currently, patients undergo a clinical examination of the vagina that includes measuring the pH and evaluating the amount and type of discharge and the presence of odor. These criteria are thought to be surrogates for the presence of an “abnormal” vaginal microbiota. Although these kinds of tests, done only once, could be used to diagnose conditions such as bacterial vaginosis, it is debatable whether they are accurate predictors of risk because little is known about how the composition and function of the vaginal microbiome changes over time. Previous studies have established that in healthy asymptomatic women, five types of vaginal microbiota exist that differ in the kinds of microbes they contain. It was thought that each type carries its own risks and particular response to environmental disturbances, such as sexual activity or hygiene practices. In an exciting new study, Gajer and colleagues now describe changes in the identity and abundance of bacteria in the vaginal communities of 32 women by analyzing vaginal samples obtained twice weekly over a 16-week period. The kinds of bacteria present in the samples were identified by classifying thousands of 16S rRNA gene sequences in each sample using high-throughput next-generation sequencing. The authors further characterized vaginal community function by determining the metabolites produced throughout the 16-week period. Gajer and colleagues found that there were five longitudinal patterns of change in vaginal microbial community composition. Moreover, in some women, the vaginal microbial community composition changed markedly and rapidly over time, whereas in others it was relatively stable. Using statistical modeling, the authors showed that the menstrual cycle influenced the stability of the vaginal communities. In many cases, the metabolite profiles indicated that vaginal community function was maintained despite changes in bacterial composition. Intervals of increased susceptibility to disease may occur because the vaginal microbiota varies over time. The authors envision that better knowledge of the causes and consequences of these changes to the host will lead to the development of new strategies to manage vaginal microbiomes in ways that promote health and minimize the use of antibiotics. Elucidating the factors that impinge on the stability of bacterial communities in the vagina may help in predicting the risk of diseases that affect women’s health. Here, we describe the temporal dynamics of the composition of vaginal bacterial communities in 32 reproductive-age women over a 16-week period. The analysis revealed the dynamics of five major classes of bacterial communities and showed that some communities change markedly over short time periods, whereas others are relatively stable. Modeling community stability using new quantitative measures indicates that deviation from stability correlates with time in the menstrual cycle, bacterial community composition, and sexual activity. The women studied are healthy; thus, it appears that neither variation in community composition per se nor higher levels of observed diversity (co-dominance) are necessarily indicative of dysbiosis.

Subfields: Epidemiology, Microbiology, Rheumatology

View Paper: https://www.semanticscholar.org/paper/b33b68dbc86f18cd360b4e08050831d91eded7a9

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In Epidemiology

In Microbiology

Robert C. Edgar

Citations in the corpus

Computational meta'omics for microbial community studies (2013, background): “…be needed to ensure the correctness of these challenging models; some studies of the latter with respect to natural long-term dynamics (McCarren et al , 2010; Gajer et al , 2012; Zhao et al , 2012) and short-term perturbations (Dethlefsen and Relman, 2011; Ubeda et al , 2013) have already begun.”
Mother-to-Infant Microbial Transmission from Different Body Sites Shapes the Developing Infant Gut Microbiome (2018, background): “The diversity of the infant microbiome was significantly lower than all the maternal microbiomes except for the Lactobacillus-dominated vaginal microbiome (Figure 1B), which is known to have a low diversity (Gajer et al., 2012; Human Microbiome Project Consortium, 2012b; Ravel et al., 2011), and was”
Host lifestyle affects human microbiota on daily timescales (2014, background): “Such observational studies have recently been used to show that menstrual cycles are the primary driver of vaginal microbial dynamics in women [16], and to show that infant gut microbiota begin transitioning towards adult communities after weaning [17].”
Temporal and spatial variation of the human microbiota during pregnancy (2015, background): “The relative stability of each Lactobacillus-dominated CST in our study was similar to that reported in nonpregnant women (25).”
The human microbiome (2017, background): “While some vaginal communities frequently fluctuate between several of the five different bacterial profiles, others are more stable [48].”
Temporal Stability of the Human Skin Microbiome (2016, neutral): “This suggested that while skin microbial communities overall exhibit site-speciﬁc trends in stability, stability itself may be largely an individual trait that has been observed previously in skin, gut, and vaginal communities (Flores et al., 2014; Gajer et al., 2012).”
Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment (2021, methodology): “…9 papers related to the development of ML methods for microbiome longitudinal analysis that are mainly based on the reuse of ﬁve datasets (Caporaso et al., 2011; Gajer et al., 2012; David et al., 2014; La Rosa et al., 2014; DiGiulio et al., 2015) with Gajer et al. being reused in four of them.”
The composition and stability of the vaginal microbiota of normal pregnant women is different from that of non-pregnant women (2014, methodology): “These studies have established that at least six types of vaginal microbiota exist, referred to as community state types (CSTs) [100,109,112,113].”
Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences (2013, methodology): “Finally, we assessed 993 samples from time courses covering ~16 weeks each from the vaginal microbiomes of 34 individual subjects 43 .”