Tag Archives: Vaginal

Babies’ gut microbiome is not influenced by vaginal microbiome, new study suggests

New research suggests that exposure to the vaginal microbiome during birth may not influence babies’ gut microbiome as has long been assumed. A new study conducted by a team of Canadian scientists has revealed that the composition of the maternal vaginal microbiome has no significant influence on the microbiome composition found in infant stool during early life.

Mother and Baby

Image Credit: Prostock-studio/Shutterstock.com

Challenging the assumption that vaginal birth corresponds to better gut health for the baby

For many years, it has been strongly believed that vaginal birth, as opposed to birth by cesarean section, is beneficial to the baby’s health. This is because it has been assumed that in moving through the vaginal microbiome, the baby is exposed to microbiota that influences the development of its own gut microbiome.

This assumption has been backed up by recent studies that have suggested that the mode of delivery significantly influences the gut microbiomes of babies and that those born by cesarean section are missing key microbes. A new study published in the journal Frontiers in Cellular and Infection Microbiology, however, has revealed evidence that contradicts this long-standing assumption.

No relationship found between vaginal microbiome composition and babiesstool microbiome

The study was one of the biggest ever mother-infant cohort studies. It recruited over 600 Canadian women expecting to give birth either vaginally or via cesarean section. Researchers took vaginal swabs from the mothers prior to delivery to assess their vaginal microbiome. Stool samples were also taken from the babies within 72 hours of birth, at 10 days old, and finally at three months old.

Analysis of the samples revealed that the mother’s vaginal microbiome composition was not a predictor of the babies’ stool microbiome composition at any of the three time points.

“It does not appear that exposure to maternal vaginal microbiota at the time of vaginal birth establishes the infant stool microbiome”,

Dr. Deborah Money, Professor of Obstetrics, University of British Colombia

Scott Dos Santos, a PhD candidate at the University of Saskatchewan responsible for the study’s lab work and data analysis, said, “from this study and other follow-up work, we were able to show that transfer of vaginal bacteria to the infant’s gut is limited”. It is theorized that other sources, such as the environment and breast milk, may play a more important role in establishing the baby’s gut microbiome. Further research is needed to understand these potential relationships further.

The role of antibiotics on the microbiome

The study did find that at 10 days and three months, there were statistically significant differences between the stool microbiome composition of babies born vaginally compared with those born by cesarean section. Money hypothesizes that this difference might be related to antibiotic use “the differences we found between infants’ stool microbiome composition by mode of delivery in early life seemed to be primarily influenced by exposure to antibiotics around the time of birth”. However, more research is needed to fully understand the impact of antibiotics on the development of babies’ gut microbiota.

More research is needed to understand the gut microbiome fully

Given the increasing importance attributed to the gut microbiome in the pathology of numerous diseases, it is vital that we understand the true relationship between the mode of birth and babies’ gut microbiome.

More research is needed to further understand the findings of this study. Researchers pointed out its limitations in that stool microbiome samples were not collected from the mothers. Future studies that analyze this type of information may provide a valuable perspective. In addition, further studies are needed to explore the impact of antibiotic use on the development of the gut microbiome of infants.


Vaginal sex can shape the composition of urethral microbiome in healthy men

Contrary to common beliefs, your urine is not germ free. In fact, a new study shows that the urethra of healthy men is teeming with microbial life and that a specific activity-;vaginal sex-;can shape its composition. The research, published March 24 in the journal Cell Reports Medicine, provides a healthy baseline for clinicians and scientists to contrast between healthy and diseased states of the urethra, an entrance to the urinary and reproductive systems.

We know where bugs in the gut come from; they primarily come from our surroundings through fecal-oral transfer. But where does genital microbiology come from?”

David Nelson, co-senior author, microbiologist at Indiana University

To flush out the answer, the team of microbiologists, statisticians, and physicians sequenced the penile urethra swabs of 110 healthy adult men. These participants had no urethral symptoms or sexually transmitted infections (STIs) and no inflammation of the urethra. DNA sequencing results revealed that two types of bacterial communities call the penile urethra home-;one native to the organ, the other from a foreign source.

“It is important to set this baseline,” says co-senior author Qunfeng Dong, a bioinformatician at Loyola University Chicago. “Only by understanding what health is can we define what diseases are.”

The researchers found that most of the healthy men had a simple, sparse community of oxygen-loving bacteria in the urethra. In addition, these bacteria probably live close to the urethral opening at the tip of the penis, where there is ample oxygen. The consistent findings of these bacteria suggest that they are the core community that supports penile urethra health.

But some of the men also had a more complex secondary group of bacteria that are often found in the vagina and can disturb the healthy bacterial ecosystem of the vagina. The team speculates that these bacteria reside deeper in the penile urethra because they thrive in oxygen-scarce settings. Only men who reported having vaginal sex carry these bacteria, hinting at the microbes’ origins.

Delving into the participant’s sexual history, the team found a close link between this second bacterial community and vaginal sex but not other sexual behaviors, such as oral sex and anal sex. They also found evidence that vaginal sex has lasting effects. Vagina-associated bacteria remained detectable in the participants for at least two months after vaginal sex, indicating that sexual exposure to the vagina can reshape the male urinary-tract microbiome.

“In our study, one behavior explains 10% of the overall bacterial variation,” says Nelson, when discussing the influence of vaginal sex. “The fact that a specific behavior is such a strong determinant is just profound.”

Although current findings from the study show that vaginal bacteria can spread to the penile urethra, the team’s next plan is to test whether the reverse is true. Using the newly established baseline, the researchers also hope to offer new insights into bacteria’s role in urinary- and reproductive-tract diseases, including unexplained urethral inflammation and STIs.

“STIs really impact people who are socioeconomically disadvantaged; they disproportionately impact women and minorities,” says Nelson. “It’s a part of health care that’s overlooked because of stigma. I think our study has a potential to dramatically change how we handle STI diagnosis and management in a positive way.”

This work was supported by the National Institute of Allergy and Infectious Diseases.

Journal reference:

Toh, E., et al. (2023). Sexual behavior shapes male genitourinary microbiome composition. Cell Reports Medicine. doi.org/10.1016/j.xcrm.2023.100981

Chemicals accumulated in the vagina may contribute to spontaneous preterm birth

Chemicals that accumulate in the vagina, potentially originating from personal care products, may contribute to spontaneous preterm birth, according to a new study by researchers at Columbia University Vagelos College of Physicians and Surgeons.

The study of 232 pregnant women found that a handful of non-biological chemicals previously found in cosmetics and hygiene products are strongly associated with preterm birth.

Our findings suggest that we need to look more closely at whether common environmental exposures are in fact causing preterm births and, if so, where these exposures are coming from. The good news is that if these chemicals are to blame, it may be possible to limit these potentially harmful exposures.”

Tal Korem, PhD, study co-leader, assistant professor in the Program for Mathematical Genomics and the Departments of Systems Biology and Obstetrics and Gynecology at Columbia University

The study was published January 12 in Nature Microbiology.

Preterm birth, childbirth before 37 weeks of pregnancy, is the number one cause of neonatal death and can lead to a variety of lifelong health issues. Two-thirds of preterm births occur spontaneously, but despite extensive research, there are no methods for predicting or preventing spontaneous preterm birth.

Several studies have suggested that imbalances in the vaginal microbiome play a role in preterm birth and other problems during pregnancy. However, researchers have not been able to reproducibly link specific populations of microorganisms with adverse pregnancy outcomes.

The research team, co-led by Korem and Maayan Levy, PhD, of the University of Pennsylvania, decided to take a more expansive view of the vaginal microenvironment by looking at its metabolome. The metabolome is the complete set of small molecules found in a particular biological niche, including metabolites produced by local cells and microorganisms and molecules that come from external sources. “The metabolome can be seen as a functional readout of the ecosystem as a whole,” Korem says. “Microbiome profiling can tell us who the microbes are; metabolomics gets us close to understanding what the microbes are doing.”

In the current study, the researchers measured over 700 different metabolites in the second-trimester metabolome of 232 pregnant women, including 80 pregnancies that ended prematurely.

The study found multiple metabolites that were significantly higher in women who had delivered early than in those who delivered at full term.

“Several of these metabolites are chemicals that are not produced by humans or microbes-;what we call xenobiotics,” says Korem. “These include diethanolamine, ethyl-beta glucoside, tartrate, and ethylenediaminetetraacetic acid. While we did not identify the source of these xenobiotics in our participants, all could be found in cosmetics and hygiene products.”

Algorithm predicts preterm birth

Using machine learning models, the team also developed an algorithm based on metabolite levels that can predict preterm birth with good accuracy, potentially paving the way for early diagnostics.

Though the predictions were more accurate than models based on microbiome data and maternal characteristics (such as age, BMI, race, preterm birth history, and prior births), the new model still needs improvement and further validation before it could be used in the clinic.

Despite the current limitations, Korem says, “our results demonstrate that vaginal metabolites have the potential to predict, months in advance, which women are likely to deliver early.”

Journal reference:

Kindschuh, W.F., et al. (2023) Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome. Nature Microbiology. doi.org/10.1038/s41564-022-01293-8.

The vaginal microbiome through the lens of systems biology

The human organism is a complex ecosystem of coexisting microbiomes, including those in the gut, the skin, and the vagina in females. These play a crucial role in health and disease. However, a great deal remains to be learned about them.

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

Review: New perspectives into the vaginal microbiome with systems biology. Image Credit: Design_Cells / ShutterstockReview: New perspectives into the vaginal microbiome with systems biology. Image Credit: Design_Cells / Shutterstock


The VMB is vital in female fertility, and disruptions can be associated with pregnancy disorders, gynecologic diseases such as pelvic inflammatory disease (PID), and an array of infections involving the female genitourinary and reproductive tract. In addition, the VMB may be instrumental in affecting drug efficacy in women.  

However, the VMB is little understood beyond a vague idea that a preponderance of Lactobacillus is associated with a ‘good’ state with a homogeneous community structure. Conversely, an undesirable state of the VMB exists when more diverse species are identified in greater abundance.

This latter suboptimal state is often linked to bacterial vaginosis (BV), found in one in three women during their reproductive period, which can have severe consequences on their fertility. As such, research in this area is required to understand the directionality and magnitude of such associations.

The problem

While many studies have been performed in this area, it is difficult to understand what an optimal VMB looks like because of the complex interactions between microbes and other host factors. This means that the healthy VMB can differ considerably from woman to woman and at different points in the same individual’s life cycle.

Such changes occur within days, which contrasts with the much slower shift seen with the gut, skin, and oral microbiomes, which may change over months or even years. Unfortunately, this makes cross-sectional data quite non-representative when it comes to studying the association of VMB composition, function, and disease – and thus makes most of this data less useful than it could be.

Again, the human VMB differs significantly from that of animals, as well as from culture-based models. In the former, even non-human primates fail to show the characteristic conditions of the human vagina, including the acidic pH and Lactobacillus dominance.

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

As such, clinical samples from which vaginal microflora are cultured, identified, and quantified form the primary source of information about the human VMB. This information is colored by experimental and host variables, which require sophisticated statistical adaptations to achieve a valid conclusion.

While relevant to all microbiome sites, [this] is particularly applicable to the VMB because of its lack of experimental models that allow for interrogation of vaginal microbiota under controlled conditions.”

The solution

Such an impasse can be solved with a systems biology approach, where quantitative analyses are used to extract the important factors affecting the behavior and function of a microbial community. As such, “Leveraging systems biology techniques applied to other microbiomes, as well as developing novel techniques and applying these methods 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 interactive networks. Furthermore, multiple approaches can be used, depending on the type of information available and the aim 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 what microbial profiles are linked to disease or health. Since little is known so far about the VMB, data-driven models have predominated so far.

Conversely, based on hypotheses, mechanistic methods are better when much is already known about a system, or at least the fundamental data is available, and the need is to understand the mechanisms of cause-effect associations underlying biological function. In addition, they help to set the ranges within which microbial composition and interactions can occur in normal and abnormal situations.

Some mechanistic methods include mass-action kinetic or population dynamics models (based on differential equations), genome-scale metabolic models (GEMs), and agent-based models (ABMs).

What has been achieved?

The systems biology approach has already helped to identify and categorize community state types (CSTs) associated with health, disease, or transitions between the two. First defined by microbial abundance, they incorporated patient demographic and health data to form hierarchical clustering groups. In addition, other methods like nearest centroid classification have been developed to overcome the inherent variation in the dataset with the former approach.

CST groupings help simplify VMB composition and thus suggest associations with community composition and function. But this is 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 types of community and specific metabolomics, transcriptomics, and metagenomics profiles, for instance. In addition, random forest models and other advanced machine learning models are being pressed into service to help distinguish VMBs with a predominance of different microbes, such as L. crispatus vs. L. iners or Bifidobacteriaceae.

Interestingly, neural network models have shown the superiority of metabolomics in describing the cervicovaginal environment accurately compared to either VMB composition or immunoproteomics. The integrated use of these strategies could help pick out the important drivers of VMB states in health and disease.

Especially important could be the insights obtained regarding sexually transmitted infection (STI) risk with an increased abundance of ‘bad’ microbes. For instance, an increase in L. iners seems to be associated with a higher risk for STIs, while L. gasseri is associated with health. Conversely, Gardnerella vaginalis and Prevotella species are linked to Chlamydia infection.

Mechanistic models include the technique called MIMOSA (Model-based Integration of Metabolite Observations and Species Abundances) that uses metabolic network modeling to understand community function via its gene content. This helped identify Prevotella species and Atopobium vaginae as key modulators of the VMB, using a calculated community-based metabolite potential (CMP) score. The CMP shows the turnover of each metabolite by any given community.

Similarly, genome-scale network reconstructions (GENREs) could help understand the role of fastidious microbes in the VMB. Ordinary differential equation (ODE)-based models are being used to examine how drugs can affect the VMB and the ecology of this system, showing how the composition fluctuates following exposure to different factors.

What lies in the future?

A multitude of studies has focused on the gut microbiome, with almost $150 million being poured into developing and standardizing new tools for its exploration. VMB researchers may be able to use these to serve their aims. This includes BURRITO, a web tool that helps visualize a microbiome community by relative abundance. This could be extended to examine VMB metagenomics, showing how patient symptoms relate to the CSTs.

Supervised machine learning approaches to understand the VMB better include Data Integration Analysis for Biomarker Discovery using Latent cOmponents (DIABLO), where omics datasets are integrated by correlation, and Sparse regularized generalized canonical correlation analysis (SRGCCA), used in Crohn’s disease.

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

Many ODE models can also be used based on the Generalized Lotka–Volterra (gLV) models. These include web-gLV, Microbial dynamical systems inference engine for microbiome time-series analysis (MDSINE), and the learning interactions from microbial time series (LIMITS) method, as well as newer adaptations like the compositional Lotka–Volterra (cLV) and the ‘Biomass Estimation and Model Inference with an Expectation Maximization’ algorithm (BEEM), that are not dependent on the culturability of the community or on the availability of extensive longitudinal datasets.

Newer methods include algorithms like Constant yield expectation framework (conYE) and MMinte, that simulate conditions for community metabolism and growth based on dense interactions between the species. Such ingenious adaptations and approaches could help understand the factors that shape the dynamic VMB in health and disease in different populations.

Journal reference: