The human organism is a complex ecosystem of coexisting microbiomes, including those in the gut, skin, and vagina in women. They play a vital role in health and disease. However, much remains to be learned about them.
A new article recently published online in Trends in Microbiology the journal evaluates a systems biology approach to investigating the vaginal microbiome (VMB), helping to understand its composition and function and the mechanisms by which it interacts with the host.
Review: New insights into the vaginal microbiome with systems biology. Image credit: Design_Cells / Shutterstock
The VMB is vital to female fertility, and disorders can be associated with pregnancy disorders, gynecological diseases such as pelvic inflammatory disease (PID), and a variety of infections affecting the female genitourinary and reproductive tract. In addition, VMB may be a tool to influence drug efficacy in women.
However, VMB is little understood beyond the vague notion that prevails Lactobacillus it is associated with a “good” condition with a homogeneous community structure. Conversely, an undesirable state of VMB exists when more diverse species are identified in greater numbers.
This last suboptimal condition is often associated with bacterial vaginosis (BV), which occurs in one in three women during their reproductive period, which can have serious consequences on their fertility. As such, research in this area is needed to understand the directionality and magnitude of such associations.
Although many studies have been conducted in this area, it is difficult to understand what optimal VMB looks like due to the complex interactions between microbes and other host factors. This means that a healthy VMB can vary greatly from woman to woman and at different points in the life cycle of the same individual.
Such changes occur within days, in contrast to the much slower shift seen in gut, skin, and oral microbiomes, which can change over months or even years. Unfortunately, this makes cross-sectional data completely unrepresentative when it comes to studying the association of VMB composition, function, and disease—making most of this data less useful than it could be.
Again, human VMBs differ significantly from animal as well as from culture-based models. In the first case, even subhuman primates do not exhibit the characteristic conditions of the human vagina, including an acidic pH and Lactobacillus dominance.
In the latter case, some microbes are incredibly resistant to in vitro culture, while different laboratories use different culture conditions depending on the medium. This could make the growth environment quite different from that of the human cervix and vagina, invalidating the results of such experiments.
Clinical specimens from which vaginal microflora are cultured, identified and quantified form the primary source of information on human VMB. This information is colored by experimental and host variables that require sophisticated statistical adjustments to reach a valid conclusion.
“Although relevant to all sites of the microbiome, [this] is particularly applicable to VMB due to the lack of experimental models that allow interrogation of the vaginal microflora under controlled conditions.”
Such an impasse can be resolved by a systems biology approach, where quantitative analyzes are used to extract important factors influencing microbial community behavior and function. Such, “Utilizing systems biology techniques applied to other microbiomes, as well as developing new techniques and applying these methods to VMB, will have a significant impact on improving women’s health..”
The use of systems biology can overcome the challenges of such complex and multiple external and internal interactive networks. In addition, multiple approaches can be used depending on the type of information available and the objective of the study.
Thus, statistical or data-driven methods are ideal where there is an abundance of high-throughput data in a relatively new field. This can help indicate which microbial profiles are associated with disease or health. Since little is known about the VMB, data-driven models have so far prevailed.
Conversely, hypothesis-driven mechanistic methods are better when much is already known about the system or at least basic data are available and the mechanisms of cause-and-effect associations underlying biological function need to be understood. In addition, they help set the ranges over which microbial composition and interactions can occur in normal and abnormal situations.
Some mechanistic methods include mass action kinetic models or population dynamics models (based on differential equations), genome-scale metabolic models (GEMs) and agent-based models (ABMs).
What was achieved?
A systems biology approach has already helped to identify and categorize community state types (CSTs) associated with health, disease, or transitions between them. First, they were defined by microbial abundance, incorporating patient demographic and health data into hierarchical groups. In addition, other methods such as nearest centroid classification have been developed to overcome the inherent bias in the dataset with the previous approach.
CST groupings help to simplify the composition of VMBs and thus suggest connections with community composition and function. However, this comes at the cost of overlooking community-specific factors specific to different taxa.
Multi-omics approaches could be integrated with systems biology strategies to identify associations with different community types and, for example, specific metabolomics, transcriptomics and metagenomics profiles. In addition, random forest models and other advanced machine learning models are being deployed to help distinguish VMBs dominated by different microbes, such as L. crispatus vs. L. inert or Bifidobacteriaceae.
Interestingly, neural network models demonstrated the superiority of metabolomics in accurately describing the cervicovaginal environment compared to VMB composition or immunoproteomics. The integrated use of these strategies could help to select important drivers of VMB states in health and disease.
Of particular importance could be the knowledge gained regarding the risk of sexually transmitted infections (STIs) with increased amounts of “bad” microbes. For example, an increase L. inert appears to be associated with a higher risk of sexually transmitted diseases L. gasseri it is related to health. On the contrary, Gardnerella vaginalis and Prevotella species are associated with Chlamydia infection.
Mechanistic models include a technique called MIMOSA (Model-based Integration of Metabolite Observations and Species Abundances), which uses metabolic network modeling to understand community function through its gene content. This helped to identify species of Prevotella a Atopobia sheaths as key modulators of VMB, using a calculated community metabolite potential (CMP) score. CMP shows the turnover of each metabolite by any given community.
Similarly, genome-wide network reconstructions (GENREs) could help understand the role of fastidious microbes in VMB. Models based on ordinary differential equations (ODEs) are used to investigate how drugs can affect the VMB and the ecology of this system, showing how the composition fluctuates after exposure to various factors.
What lies in the future?
Many studies have focused on the gut microbiome, with nearly $150 million invested in developing and standardizing new tools to investigate it. VMB researchers can use them to serve their goals. This includes BURRITO, a web tool that helps visualize the microbiome community by relative abundance. This could be extended by examining VMB metagenomics to show how patient symptoms relate to CST.
Supervised machine learning approaches to better understand VMB include Data Integration Analysis for Biomarker Discovery using Latent Components (DIABLO), where omic datasets are integrated using correlation, and Sparse regularized generalized canonical correlation analysis (SRGCCA), used in Crohn’s disease.
Unsupervised learning strategies such as multi-omic factor analysis (MOFA) may be useful to overcome the limitations caused by the lack of knowledge about the functional classification of VMBs.
Many ODE models can also be used based on Generalized Lotka–Volterra (gLV) models. These include web-gLV, the Microbial Dynamic Inference System for Microbiome Time Series Analysis (MDSINE) and Learning Interactions from the Microbial Time Series Method (LIMITS), as well as more recent adaptations such as compositional Lotka–Volterra (cLV) and “Biomass Estimation and expectation maximization model inference’ (BEEM) that are not dependent on community cultivability or the availability of large longitudinal data sets.
Newer methods include algorithms such as the Constant yield expectation framework (conYE) and MMinte, which simulate conditions for metabolism and community growth based on dense interactions between species. Such sophisticated adaptations and approaches could help to understand the factors that shape the dynamic VMB in health and disease in different populations.
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