The vaginal microbiome through the lens of systems biology

The human organism is a complex ecosystem of commensal microbiomes, including those found in the gut, skin, and female vagina. These play an important role in health and disease. However, there is still a lot to learn about it.

A new research paper was recently published online in Trends in Microbiology The journal reviews a systems biology approach to exploring the vaginal microbiome (VMB), helping to understand its composition, function and the mechanisms through which it interacts with the host.

Review: New perspectives on the vaginal microbiome with systems biology.  Image credit: Design_Cells/Shutterstock

reconsidering: New perspectives on the vaginal microbiome with systems biology. Image credit: Design_Cells/Shutterstock

an introduction

VMB is vital to female fertility, and disorders can be associated with pregnancy and gynecological disorders such as pelvic inflammatory disease (PID) and a group of infections involving the female genitourinary and reproductive systems. In addition, VMB may be beneficial in affecting medication effectiveness in women.

However, the VMB is not much understood other than the vague notion that pervades it lactobacillus “Good” condition with a homogeneous community structure. Conversely, an undesirable case of VMB exists when more diverse species are recognized in greater abundance.

This last case is often suboptimal Associated with bacterial vaginosis (BV), which is present in one in three women during their childbearing period, which can have serious consequences for their fertility. As such, research in this area is required to understand the direction and magnitude of these associations.

the problem

While many studies have been conducted in this field, it is difficult to understand the optimal form of VMB 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, which contrasts with the much slower transformation observed in the gut, skin, and oral microbiome, which may change over months or even years. Unfortunately, this makes the cross-section data quite unrepresentative when it comes to studying the association of VMB formation, function, and disease—and thus makes most of this data less useful than it could be.

Again, human VMB differs greatly from animals, as well as from culture-based models. Previously, even non-human primates fail to exhibit conditions characteristic of the human vagina, including acidic pH and lactobacillus dominance.

In the latter, some microbes are incredibly resistant to in vitro cultivation, while different culture conditions are used in different laboratories, depending on the media. This can make the developmental environment very different from that of the human cervix and vagina, invalidating the results of such experiments.

As such, clinical samples from which the vaginal microflora is cultured, identified, and quantified constitute the primary source of information on human VMB. This information is colored by experimental variables and host variables, which require complex statistical adjustments to achieve a valid result.

While relevant to all microbiome sitesAnd the [this] It is particularly applicable to the VMB because of its lack of experimental paradigms that allow for the interrogation of the vaginal microbiota under controlled conditions.. “

The solution

This quandary can be resolved with a systems biology approach, in which quantitative analyzes are used to extract important factors that influence microbial community behavior and function. as such, “Leveraging systems biology techniques applied to other microbiomes, as well as developing new technologies and applying these approaches to the 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 interaction networks. Furthermore, multiple approaches may be used, depending on the type of information available and the objective of the study.

Thus, statistical or data-driven methods are ideal when high-throughput data are abundant in a relatively new field of study. This can help suggest microbial profiles associated with disease or health. Since not much is known yet about the VMB, data-driven models have so far prevailed.

On the contrary, based on hypotheses, mechanistic approaches are better when much is already known about a system, or at least basic data are available, and the need is to understand the mechanisms of cause-effect association underlying biological function. In addition, they help identify the ranges in which components and microbial interactions can occur in normal and abnormal situations.

Some of the mechanistic approaches include models of mass motion or population dynamics (based on differential equations), genome-wide metabolic models (GEMs), and agent-based models (ABMs).

What has been achieved?

The systems biology approach has already helped identify and classify the types of community states (CSTs) associated with health, disease, or transitions between the two. First identified by microbial abundance, they combined patient demographic and health data to form hierarchical aggregation groups. In addition, other methods such as nearest centroid classification have been developed to overcome the inherent divergence of the data set with the previous approach.

CSTs help simplify VMB formation and thus suggest a link to community formation and function. But this is at the cost of ignoring the factors specific to the specific community of different species.

Multiscale approaches can be combined with systems biology strategies to identify associations with different types of community and specific metabolomes, transcriptomes, and metagenomics profiles, eg. In addition, random forest models and other advanced machine learning models are being pressed into service to help characterize VMBs with a predominance of different microbes, such as Crispatos against. L. insiders or Bifidobacteriaceae.

Interestingly, neural network models have shown superiority of metabolic processes in accurately describing the cervix-vaginal environment compared to VMB formation or immunoproteomics. The integrated use of these strategies can help identify important drivers of VMB states in health and disease.

The insights gained on the risks of sexually transmitted infection (STI) can be particularly important as the abundance of ‘bad’ microbes increases. For example, an increase in L. insiders It appears to be associated with an increased risk of STDs L. Gasseri associated with health. On the other hand Gardnerella vaginalis and Prevotella species are associated with chlamydia infection.

Mechanistic models include a technique called MIMOSA (Model-Based Integration for Monitoring Metabolism and Species Abundance) that uses metabolic network modeling to understand community function via its genetic content. This helped identify Prevotella species and Vaginal atopopium As principal adapters for the VMB, using a community-level computed score (CMP). CMP shows the turnover of each metabolite by any given population.

Similarly, genome-wide network reconstructions (GENREs) can help understand the role of hypersensitive microbes in the VMB. Models based on ordinary differential equation (ODE) are used to examine how drugs affect the VMB and the environment of this system, showing how the combination fluctuates after exposure to different agents.

What lies ahead?

Many studies have focused on the gut microbiome, with nearly $150 million pumped into developing and standardizing new tools to explore it. VMB researchers may be able to use these to serve their goals. This includes BURRITO, a web tool that helps visualize the microbiome community by its relative abundance. This could be extended to examine VMB genomics, showing how a patient’s symptoms relate to CSTs.

Supervised machine learning approaches to better understand the VMB include data integration analysis for biomarker detection using latent cOmponents (DIABLO), in which omics datasets are combined by correlation, and regular and sparse generalized canonical correlation analysis (SRGCCA), used in Crohn’s disease.

To overcome the limitations posed by the lack of knowledge about the functional classification of the VMB, unsupervised learning strategies, such as multiple factor analysis (MOFA), may be useful.

Several ODE models based on the generic Lotka-Volterra (gLV) models can also be used. These include web-gLV, the microbial dynamic systems inference engine for microbiome time-series analysis (MDSINE), and learning interactions from the microbial time-series method (LIMITS), as well as newer modifications such as Lotka-Volterra (cLV) and constructs. “Biomass estimation and model inference using the Expectation Maximization Algorithm” (BEEM), which does not depend on community culture or on the availability of large-scale longitudinal datasets.

Newer approaches include algorithms such as the Constant Yield Prediction Framework (conYE) and MMinte, which simulate metabolic and community growth conditions based on dense interactions between species. Such modifications and innovative approaches can help in understanding the factors shaping the dynamic VMB in health and disease in different populations.

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