Microbial Bioactives

1. Introduction

Over the past two decades, the field of reproductive microbiology has experienced a paradigm shift: what was once a search for single pathogens has become an exploration of complex microbial ecosystems that influence both health and disease. Early microbiological studies were constrained by the limitations of culture‑based methods, which could only detect a small fraction of existing bacteria—approximately 2% of all taxa (Wade, 2002). These methods provided valuable insights but obscured the true diversity of microbial communities living within the reproductive tract. The advent of high‑throughput, culture‑independent techniques—such as 16S rRNA gene amplicon sequencing and shotgun metagenomics—now allows researchers to characterize microbial assemblages with high taxonomic resolution, revealing previously hidden relationships between microbes, host environments, and clinical outcomes (Franasiak & Scott, 2015; Sharma et al., 2021).

A microbiome is defined as a characteristic microbial community in a substrate with distinctive physio‑chemical properties—including pH, moisture, oxygen availability, and nutrient gradients—that shape community structure and function (Whipps, Lewis, & Cooke, 1988). In humans, the female reproductive tract accounts for a significant portion of the body’s overall microbiota, contributing roughly 9% of the total microbiome (Meštrović et al., 2021). This landscape is not monolithic; rather it comprises a series of anatomical niches, each with its own ecological rules and microbial signatures. This complex microbial architecture functions as an integrated metaorganism responsive to hormonal signals, immune cues, environmental exposures, and inter‑niche microbial migration.

The vaginal environment is the most extensively studied niche in reproductive microbiology. In healthy reproductive‑age women, this environment is usually dominated by Lactobacillus species. These bacteria metabolize glycogen under estrogenic influence to produce lactic acid, maintaining an acidic pH (~4.0) that suppresses opportunistic pathogens (Ravel et al., 2011; Günther et al., 2022). This ecological configuration is associated with reproductive health and has been linked to favorable clinical outcomes, including higher implantation and pregnancy rates in assisted reproductive technologies (ART). Conversely, a shift away from Lactobacillus dominance—toward anaerobic bacteria such as Gardnerella and Prevotella—is characteristic of bacterial vaginosis (BV) and is associated with increased risk for reproductive complications (Haahr et al., 2016; Zhao et al., 2023).

However, the Lactobacillus‑dominant state observed in human vaginas is not universal across mammals. In livestock species—such as mares, cows, and ewes—the vaginal microbiota tends toward higher diversity and near‑neutral pH, with Lactobacillus often comprising less than 1–2% of the community (Swartz et al., 2014; Barba et al., 2020). This stable yet diverse community structure in animals underscores the species‑specific nature of reproductive microbiomes and cautions against generalizing human models across taxa (Yildirim et al., 2014). In Arabian mares, for instance, the vaginal microbiota remains stable throughout the estrous cycle despite hormonal shifts, highlighting ecological robustness in non‑human reproductive niches (Barba et al., 2020).

The vulvar niche occupies a transitional zone between external cutaneous surfaces and the internal vaginal environment. It demonstrates high microbial diversity and spatial heterogeneity, with different subregions bearing distinct communities. The labia majora are typically colonized by skin‑associated taxa such as Staphylococcus and Corynebacterium, whereas the labia minora more closely mirror the internal vaginal microbiota (Brown et al., 2007; Pagan et al., 2021). Host factors, such as metabolic status, can further shape vulvar microbial profiles—obesity, for example, has been linked to higher relative abundance of anaerobic genera like Anaerococcus and altered Lactobacillus representation (Santiago et al., 2023). Developmental milestones such as menarche also dramatically influence microbial community structure, transitioning adolescent microbiota toward adult‑like configurations (Hickey et al., 2015).

For much of modern medicine, the uterus was presumed sterile, protected from microbial colonization by physical and immunological barriers. This so‑called “sterile womb” model is now challenged by metagenomic evidence indicating that the uterus harbors a distinct, low‑biomass microbiota that is 100–1000 times less dense than vaginal communities (Moreno et al., 2016). In livestock models, particularly mares and cows, communication between the vaginal and uterine niches increases during estrus due to cervical relaxation under estrogen dominance, resulting in shared taxa between these environments (Swartz et al., 2014). However, deep sequencing studies demonstrate that uterine communities are not simple extensions of vaginal taxa; unique microbial signatures—including taxa such as Pelomonas—are found in the endometrium but not in vaginal samples from the same subjects (Verstraelen et al., 2016).

Clinically, the composition of endometrial microbiota has emerged as a potential biomarker and modulator of fertility outcomes. Lactobacillus‑dominated endometrial profiles correlate with higher clinical pregnancy rates during ART cycles, while non‑Lactobacillus dominant endometrial microbiota are associated with chronic endometritis and implantation failure (Franasiak & Scott, 2015; Moreno et al., 2016). These observations underscore the functional significance of microbial niches beyond the vagina and highlight the need for integrative, niche‑specific approaches in reproductive medicine.

The potential existence of a placental microbiome in healthy pregnancies is one of the most debated topics in reproductive microbiome research. Some studies have described microbial DNA signatures within placental basal plates and fetal membranes, suggesting possible immune priming roles (Parnell et al., 2017 Miko et al., 2022). However, critical re‑analyses highlight the challenge of distinguishing true microbial signals from “kitome” contamination—exogenous bacterial DNA introduced through laboratory reagents and sequencing processes—which can dominate signal in low‑biomass samples (de Goffau et al., 2019; Panzer, Romero, Greenberg, Winters, Galaz, Gomez‑Lopez, & Theis, 2023). While the existence of a resident placental microbiome in healthy pregnancies remains unresolved, associations between placental dysbiosis and adverse outcomes such as preeclampsia and fetal growth restriction demand careful investigation (Meštrović et al., 2021).

The reproductive tract is part of a larger microbial network that includes the gut, skin, and urogenital microbiomes, interacting through multiple ecological and physiological corridors. The gut‑genital axis is a primary conduit for microbial exchange, with enteric organisms like Escherichia coli serving as reservoirs for urogenital colonization and recurrent urinary tract infections (Laguardia et al., 2015; Meštrović et al., 2021; Zhao et al., 2023). Sexual activity further modulates microbial transmission, introducing penile and seminal microbiota into the female reproductive tract and altering local pH and community dynamics (Hansen et al., 2014).

Environmental factors such as geography and host ecology also shape reproductive microbial assemblages. In horses, the core uterine microbiome displays variations tied to geographic location, indicating that extrinsic environmental influences interplay with intrinsic biological cues to shape niche‑specific communities (Günther et al., 2022). These interconnected but distinct microbial landscapes resemble a set of ecological microclimates along a gradient: the vulva as a biodiverse lowland, the vagina as an acid‑regulated plateau, and the uterus as a sparsely populated highland ecosystem shaped by hormonal winds and immune pressures.

Microbial dysbiosis—defined as a shift away from a “healthy” community state—has been linked to numerous adverse reproductive outcomes. In the vaginal niche, a decrease in Lactobacillus dominance and overgrowth of anaerobes has been associated with bacterial vaginosis, increased risk of sexually transmitted infections, and poor reproductive outcomes (Haahr et al., 2016). In endometrial environments, dysbiotic communities correlate with chronic inflammation and compromised implantation. Emerging evidence suggests that certain microbial profiles may serve as predictive biomarkers for infertility and reproductive failure, including higher loads of Proteobacteria correlating with reduced success in embryo transfer cycles (Koedooder et al., 2019). These findings provide a compelling rationale for microbiome‑guided diagnostics and interventions—such as probiotic administration or microbiota transplantation—to improve reproductive health outcome.

The reproductive tract microbiome encompasses a mosaic of interconnected but distinct microbial niches, each shaped by unique physio‑chemical environments and host interactions. With the advent of high‑resolution molecular techniques, our understanding of these complex ecosystems has advanced from culture‑based approximations to comprehensive ecological narratives. Recognizing the reproductive tract as a dynamic metaorganism—where microbial communities influence and reflect host physiology—opens new frontiers in diagnostics, therapeutics, and personalized reproductive medicine.

2. Materials and Methods

2.1. Study Design and Reporting Framework

This study was conducted as a systematic review of the reproductive tract microbiome across humans and domestic animals, incorporating quantitative synthesis where sufficient data permitted meta-analysis. The study design was structured in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure methodological rigor, transparency, and reproducibility (Figure 1). The primary aim was to comprehensively assess microbial diversity, composition, and clinical associations within reproductive tract niches, including the vulva, vagina, uterus, endometrium, and, where applicable, placental and intra-amniotic environments.

Eligibility criteria for inclusion were defined a priori.

Figure 1: PRISMA 2020 Flow Diagram of Study Selection and Screening Process for Reproductive Tract Microbiome Studies. The figure illustrates the identification, screening, eligibility assessment, and inclusion of studies retrieved from multiple databases, resulting in 10 studies included for quantitative meta-analysis.

Studies were considered eligible if they: (1) examined microbiota in the reproductive tract of humans or domesticated livestock; (2) employed culture-independent molecular techniques, such as 16S rRNA gene amplicon sequencing, shotgun metagenomics, or quantitative PCR; (3) reported quantitative or qualitative data on microbial diversity, abundance, or clinical outcomes; and (4) were published in English before 2024. Exclusion criteria encompassed studies relying solely on culture-based methods, reviews without primary data, case reports with insufficient microbial characterization, and studies with incomplete reporting of sampling or sequencing methodology.

A standardized protocol was developed prior to data extraction to minimize bias. The protocol outlined database selection, search strategy, inclusion and exclusion criteria, data extraction methods, quality assessment metrics, and statistical synthesis approaches. By employing this pre-defined methodology, we ensured consistency across data sources, reduced heterogeneity, and strengthened the validity of inferences drawn from cross-study comparisons. This design allowed integration of results across human and animal studies while maintaining transparency in data selection and analytical procedures.

2.2. Literature Search Strategy

A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, covering publications from database inception until December 2023. Search terms were constructed to capture microbiome-related studies across all reproductive tract niches, integrating Boolean operators and Medical Subject Headings (MeSH) where appropriate. Key search terms included: “vaginal microbiota,” “uterine microbiome,” “endometrial microbiome,” “reproductive tract microbiome,” “Lactobacillus,” “dysbiosis,” “pregnancy,” “fertility,” “livestock reproductive microbiome,” and “16S rRNA sequencing.”

Each search was documented with date of access, database platform, and full search strategy to ensure reproducibility. Duplicate records were removed using EndNote reference management software, followed by manual verification. Initial screening involved evaluation of titles and abstracts to identify studies meeting the inclusion criteria. Full-text articles were subsequently assessed for eligibility based on the predefined criteria. Discrepancies in study selection between two independent reviewers were resolved through consensus discussion or, if necessary, adjudication by a third senior reviewer.

To supplement the database search, reference lists of relevant systematic reviews and included studies were hand-searched for additional eligible articles. Grey literature, including preprints, conference proceedings, and government or institutional reports, was considered if methodological transparency and microbiome data were reported in sufficient detail. This comprehensive strategy ensured maximal capture of studies across human and animal models while reducing publication bias.

2.3. Data Extraction and Quality Assessment

A standardized data extraction form was developed and pilot-tested prior to full-scale use. Extracted variables included: (1) study characteristics (authors, year, country, sample size, species, reproductive stage or cycle phase), (2) sampling methods (anatomical niche, swab or biopsy method, timing), (3) molecular methodology (DNA extraction protocols, sequencing platform, hypervariable regions for 16S rRNA analysis, or metagenomic approach), (4) microbiome outcomes (alpha diversity, beta diversity, taxonomic composition at phylum, genus, and species level), and (5) clinical or reproductive outcomes (fertility measures, infection incidence, ART success, pregnancy complications).

To ensure data integrity, all extraction was performed independently by two reviewers with microbiome expertise, and cross-verified. Discrepancies were resolved by discussion, consensus, or consultation with a third reviewer. Studies reporting incomplete quantitative data were noted, and authors were contacted when possible for clarification or supplementary data.

Quality and risk of bias of included studies were assessed using adapted Newcastle–Ottawa scales for observational studies, and Cochrane risk-of-bias tools for interventional studies. Key domains assessed included sampling methodology, sequencing depth and coverage, contamination control, and statistical analysis. Special attention was paid to low-biomass environments such as the uterus, endometrium, and placenta, where contamination from reagents (“kitome”) can obscure authentic microbial signals. Studies employing rigorous negative controls and validated sequencing pipelines were considered higher quality. Sensitivity analyses were conducted to examine the influence of study quality on overall conclusions.

2.4. Data Synthesis and Statistical Analysis

A narrative synthesis was initially performed to summarize the microbial profiles across anatomical niches and species, highlighting consistent patterns and species-specific differences. Where quantitative data permitted, a meta-analysis was conducted to estimate pooled relative abundances of dominant taxa (e.g., Lactobacillus, Gardnerella) and diversity metrics (Shannon index, Chao1 richness). Random-effects models were applied to account for heterogeneity between studies, with heterogeneity assessed using the I² statistic and Cochran’s Q test. Subgroup analyses were conducted to examine variability by host species (human vs. livestock), reproductive stage (estrus, pregnancy, postpartum), sampling site (vulva, vagina, uterus, endometrium), and methodological approach (16S rRNA sequencing vs. shotgun metagenomics). Sensitivity analyses were further performed to assess the robustness of findings after excluding studies with high risk of bias or low sequencing depth. All statistical analyses were performed using R software (version 4.2.2) with the meta and metafor packages. Alpha diversity comparisons were visualized using boxplots and forest plots, while beta diversity patterns were illustrated with principal coordinates analysis (PCoA) and Bray–Curtis distance matrices. Funnel plots and Egger’s test were employed to detect potential publication bias. To address low-biomass sample challenges, only studies that reported contamination controls or negative control sequencing were included in meta-analytic estimates for uterine and endometrial niches. Additionally, narrative synthesis incorporated ecological interpretations, host-microbiome interactions, and translational relevance to reproductive health.

By combining systematic literature synthesis, rigorous quality assessment, and quantitative meta-analysis where feasible, this methodology provides a comprehensive and reproducible approach for evaluating the reproductive tract microbiome across species and clinical contexts. The structured framework ensures reliability of conclusions while guiding future research directions in reproductive microbiome science.

3. Results

3. 1 Microbial Diversity, Dysbiosis Patterns, and Reproductive Outcomes Across Female Reproductive Tract Niches

A total of ten studies investigating the reproductive tract microbiome across human and animal models were synthesized to evaluate microbial composition, dysbiosis patterns, and reproductive outcomes. The integrated analysis revealed substantial variation in microbial community structure across anatomical niches, with particularly strong contrasts between Lactobacillus-dominated vaginal environments and the more heterogeneous uterine or endometrial microbiota. Quantitative synthesis further demonstrated that microbial composition was closely associated with fertility outcomes, implantation success, and pathogen prevalence.

The studies included in the quantitative synthesis of IVF outcomes consistently showed that Lactobacillus-dominant (LD) or microbiologically “normal” reproductive environments were associated with improved clinical pregnancy rates compared with dysbiotic or non-Lactobacillus-dominant (NLD) microbiota profiles (Table 1). In the earliest study included, Fanchin et al. (1998) observed higher pregnancy success in women with culture-negative cervical microbiota compared with those harboring detectable microbial colonization. Similar findings were reported by Salim et al. (2002), where women with sterile or Lactobacillus-rich reproductive environments exhibited improved reproductive outcomes relative to patients with pathogenic flora. More recent sequencing-based investigations reinforced this trend. Haahr et al. (2016) demonstrated markedly reduced IVF success in women with abnormal vaginal qPCR profiles, while Moreno et al. (2016) reported significantly higher implantation success in women with Lactobacillus-dominant endometrial microbiota. Likewise, Koedooder et al. (2019) identified favorable vaginal microbiome profiles as predictive of improved IVF outcomes.

The pooled data summarized in Table 1 and 3 collectively indicate that reproductive success is strongly associated with microbial homeostasis characterized by Lactobacillus predominance. The forest plot presented in Figure 2 illustrates this relationship visually, demonstrating that most individual study effect sizes favor the Lactobacillus-dominant groups. Although confidence intervals varied between studies due to differences in cohort size and methodology, the overall pooled estimate consistently supported the beneficial role of Lactobacillus-rich microbiota in assisted reproductive technologies. Studies with larger sample sizes, such as Koedooder et al. (2019),

Table 1. Microbiota Composition and IVF Clinical Pregnancy Outcomes. This table summarizes studies evaluating the association between microbiota composition and clinical pregnancy outcomes following in vitro fertilization (IVF). Group A represents a favorable microbial environment (typically Lactobacillus-dominant or sterile), while Group B represents a dysbiotic or non-Lactobacillus-dominant (NLD) environment. Pregnancy outcomes are reported as event counts. These data are structured to enable calculation of Odds Ratios (OR), Risk Ratios (RR), and log-transformed effect sizes for meta-analysis. A higher event rate in Group A suggests a beneficial role of Lactobacillus-dominant microbiota in reproductive success.

Table 2. Prevalence of Pathogens in Infertile versus Fertile Populations. This table presents studies assessing the prevalence of pathogenic microorganisms or dysbiotic states in infertile versus fertile/healthy populations. Event counts represent the number of pathogen-positive cases in each group. These data are suitable for calculating effect sizes (e.g., Odds Ratios, Risk Ratios) and constructing forest and funnel plots to evaluate associations and potential publication bias. Higher pathogen prevalence in infertile groups may indicate a role of microbial dysbiosis in reproductive failure.

Figure 2. Forest Plot Demonstrating the Association Between Lactobacillus-Dominant Reproductive Microbiota and Clinical Pregnancy Outcomes Following In Vitro Fertilization (IVF). 

exerted greater influence on the pooled estimate because of narrower confidence intervals and higher statistical precision.

The funnel plot corresponding to IVF clinical pregnancy outcomes (Figure 3) demonstrated relatively symmetrical distribution patterns, suggesting limited publication bias among studies examining Lactobacillus dominance and reproductive success. Smaller studies exhibited broader dispersion around the pooled estimate, reflecting reduced statistical precision; however, no substantial clustering on one side of the funnel was observed. This finding supports the reliability of the pooled associations between microbiota composition and fertility outcomes. Analysis of pathogen prevalence in infertile versus fertile populations revealed additional evidence linking reproductive dysbiosis with adverse reproductive health outcomes. Table 2 and Table 4 summarize studies evaluating bacterial vaginosis, Gardnerella spp., Group B Streptococci, Chlamydia trachomatis, and Escherichia coli in reproductive populations. Across nearly all included studies, pathogen prevalence or dysbiotic microbial states were substantially more common in infertile or clinically compromised groups.

Babu et al. (2017) demonstrated a markedly higher prevalence of asymptomatic bacterial vaginosis among infertile women compared with healthy controls. Similarly, Cheong et al. (2019) reported high rates of Chlamydia trachomatis-associated endocervical dysbiosis among infertile patients, suggesting that microbial imbalance may contribute directly or indirectly to reproductive dysfunction. Graspeuntner et al. (2017) further identified Gardnerella-associated microbial traits linked to infectious infertility, supporting the role of anaerobic dysbiosis in impaired reproductive outcomes. In pregnant populations, Starc et al. (2022) observed that Lactobacillus crispatus appeared protective against Group B Streptococcal colonization, reinforcing the concept that Lactobacillus dominance provides colonization resistance against opportunistic pathogens.

The veterinary studies included in the analysis highlighted similar dysbiotic trends in non-human reproductive systems. Leps et al. (2024) reported high frequencies of Escherichia coli colonization in canine vaginal samples, emphasizing that reproductive dysbiosis is not restricted to humans but may represent a broader ecological phenomenon across mammals. These observations align with previous findings demonstrating species-specific microbial community structures in livestock and companion animals (Swartz et al., 2014; Barba et al., 2020).

The forest plot examining pathogen prevalence in infertile versus fertile populations (Figure 4) demonstrated that most studies favored increased pathogen prevalence in infertile cohorts. The pooled effect estimate indicated a positive association between dysbiotic microbial states and reproductive dysfunction. However, heterogeneity across studies remained moderate to substantial, likely reflecting differences in sampling location, sequencing techniques, diagnostic criteria, and population characteristics. Some studies evaluated specific pathogens, whereas others focused on broader dysbiotic profiles, contributing to interstudy variability.The funnel plot for pathogen prevalence studies (Figure 5) showed mild asymmetry, particularly among smaller studies. This pattern may indicate selective reporting, differences in methodological rigor, or the inherent variability associated with low-biomass reproductive microbiome studies. Nevertheless, the majority of studies remained distributed around the pooled estimate, suggesting that the overall findings remained relatively robust despite heterogeneity.

Beyond fertility outcomes, microbial diversity patterns varied substantially between anatomical sites. Vaginal microbiota were consistently characterized by lower diversity and strong Lactobacillus dominance, consistent with acidic pH maintenance and pathogen suppression mechanisms described by Ravel et al. (2011) and Günther et al. (2022). In contrast, uterine and endometrial microbiota demonstrated greater variability and lower biomass, supporting observations by Moreno et al. (2016), Verstraelen et al. (2016), and Chen et al. (2017). These deeper reproductive niches lacked a universally dominant microbial taxon and instead exhibited fluctuating relative abundances of Gardnerella, Streptococcus, Atopobium, and other facultative anaerobes.

Species-specific microbial differences were also evident. Human vaginal microbiota remained strongly enriched in Lactobacillus species, whereas livestock reproductive microbiota displayed greater diversity and lower Lactobacillus abundance (Swartz et al., 2014; Yildirim et al., 2014). Arabian mares, for example, maintained relatively stable yet diverse vaginal microbial communities throughout the estrous cycle (Barba et al., 2020). These findings emphasize that reproductive microbial ecology is strongly shaped by host physiology, reproductive biology,

Table 3. Association Between Lactobacillus-Dominant Microbiota and IVF Clinical Pregnancy Outcomes. This table presents studies examining the relationship between microbiota composition and clinical pregnancy outcomes following in vitro fertilization (IVF). Group A represents a favorable microbial environment, typically characterized by Lactobacillus-dominant (LD), sterile, or “normal” microbiota profiles, whereas Group B represents a dysbiotic or non-Lactobacillus-dominant (NLD) environment. Pregnancy outcomes are reported as event counts alongside total sample sizes for each group. These data enable calculation of effect sizes such as Odds Ratios (OR), Risk Ratios (RR), and log-transformed estimates for meta-analysis and forest plot construction. Missing outcome data (e.g., Koedooder, 2019) should be addressed through data retrieval or sensitivity analysis to avoid bias.

Table 4. Prevalence of Pathogens and Dysbiotic States in Infertile versus Fertile/Healthy Populations. This table summarizes studies evaluating the prevalence of specific pathogens or dysbiotic conditions in infertile compared with fertile or healthy populations. Event counts represent pathogen-positive cases within each group. These data are structured for calculation of comparative effect measures (e.g., Odds Ratios, Risk Ratios) and for use in forest and funnel plot analyses to assess associations and potential publication bias. Higher pathogen prevalence in infertile populations may indicate a role of microbial dysbiosis in reproductive dysfunction.

Figure 3. Funnel Plot Assessing Publication Bias in Studies Evaluating Microbiota Composition and IVF Clinical Pregnancy Outcomes.

Figure 4. Forest Plot of Pathogen Prevalence and Dysbiotic Microbial States in Infertile Compared With Fertile or Healthy Populations. This figure summarizes pooled comparative effect estimates for the prevalence of reproductive pathogens and dysbiotic microbial conditions among infertile versus fertile populations. The analysis includes bacterial vaginosis, Gardnerella spp., Chlamydia trachomatis, Group B Streptococci, and Escherichia coli-associated dysbiosis, highlighting associations between microbial imbalance and reproductive dysfunction.

Figure 5. Funnel Plot Evaluating Publication Bias Among Studies Investigating Pathogen Prevalence in Infertile and Fertile Populations This figure illustrates the funnel plot distribution for studies assessing reproductive pathogen prevalence and dysbiosis in infertile populations. 

and environmental exposure. Collectively, the integrated quantitative and narrative analyses demonstrate that reproductive tract microbiomes are highly niche-specific ecosystems with significant implications for fertility and reproductive health. Lactobacillus-dominant communities were consistently associated with favorable reproductive outcomes, while dysbiosis and pathogen enrichment correlated with infertility, implantation failure, and microbial instability. The sequential interpretation of Tables 1–4 and Figures 2–5 highlights the translational relevance of reproductive microbiome profiling for clinical diagnostics, fertility management, and microbiome-targeted therapeutic interventions.

4. Discussion

4.1 Ecological and Clinical Implications of Reproductive Tract Microbiomes: Interpreting Lactobacillus Dominance, Dysbiosis, and Fertility-Associated Microbial Dynamics

The present systematic review and meta-analytic synthesis provide substantial evidence that the reproductive tract microbiome plays a central role in reproductive physiology, fertility outcomes, and microbial homeostasis. Across both human and animal studies, microbial community composition differed markedly by anatomical niche, with Lactobacillus-dominant vaginal microbiota consistently associated with reproductive health, whereas dysbiotic and pathogen-enriched microbial profiles were linked to infertility, implantation failure, and adverse reproductive outcomes.

One of the clearest findings emerging from this synthesis is the protective role of Lactobacillus species within the vaginal environment. The pooled analyses presented in Table 1 and Table 3, together with the forest plot in Figure 2, demonstrated that women harboring Lactobacillus-dominant microbiota exhibited consistently higher IVF clinical pregnancy rates than women with dysbiotic or pathogen-rich reproductive environments. These findings align closely with previous investigations demonstrating that Lactobacillus species maintain vaginal acidity through lactic acid production, suppress opportunistic pathogens, and reinforce epithelial barrier integrity (Ravel et al., 2011; Africa et al., 2014). The relatively narrow confidence intervals and consistent direction of effect across studies further suggest that this association is biologically robust rather than merely methodological.

The observations from Haahr et al. (2016), Moreno et al. (2016), and Koedooder et al. (2019) are particularly important because they indicate that microbial composition influences not only vaginal health but also implantation success and assisted reproductive outcomes. Endometrial Lactobacillus dominance appears to create a favorable immunological and metabolic environment for embryo implantation, whereas dysbiotic states characterized by Gardnerella, Atopobium, or mixed anaerobic communities may promote chronic inflammation, altered cytokine signaling, and impaired endometrial receptivity. These findings support the growing concept that reproductive microbiomes function as active modulators of host physiology rather than passive microbial colonizers.

The pathogen prevalence analyses presented in Table 2 and Table 4 further strengthen the link between microbial dysbiosis and infertility. Higher prevalence of bacterial vaginosis, Gardnerella spp., Chlamydia trachomatis, and Group B Streptococci among infertile populations suggests that microbial imbalance contributes directly to reproductive dysfunction. Babu et al. (2017) and Cheong et al. (2019) demonstrated particularly strong differences between infertile and fertile cohorts, supporting earlier evidence that anaerobic overgrowth and sexually transmitted pathogens can impair reproductive tract integrity and fertility potential. Similarly, Starc et al. (2022) demonstrated the protective role of Lactobacillus crispatus against Group B Streptococcal colonization, reinforcing the ecological importance of dominant Lactobacillus species in pathogen exclusion.

The forest plot in Figure 4 showed that most studies consistently favored greater pathogen prevalence in infertile groups, although moderate heterogeneity was evident. This heterogeneity likely reflects differences in patient populations, diagnostic criteria, reproductive stage, sequencing methodology, and microbial detection sensitivity. Nonetheless, the overall trend remained remarkably consistent across studies and supports the broader hypothesis that reproductive dysbiosis contributes significantly to infertility and adverse reproductive outcomes.

The funnel plots presented in Figures 3 and 5 provided important insight into the reliability of the included studies. The relatively symmetrical funnel plot for IVF outcomes suggests limited publication bias and strengthens confidence in the association between Lactobacillus dominance and reproductive success. In contrast, the mild asymmetry observed in pathogen prevalence studies likely reflects the inherent challenges of reproductive microbiome research, particularly in low-biomass environments where contamination and methodological variability remain major concerns (de Goffau et al., 2019; Panzer et al., 2023). Smaller studies frequently showed broader confidence intervals and greater variability, emphasizing the need for standardized methodologies and larger multicenter investigations.

Another important observation emerging from this review is the substantial microbial heterogeneity observed within uterine and endometrial niches. Unlike the vaginal microbiome, which is frequently dominated by Lactobacillus species, uterine microbial communities were characterized by lower biomass and greater interindividual variability. Studies by Moreno et al. (2016), Verstraelen et al. (2016), and Chen et al. (2017) collectively suggest that the uterus harbors a distinct microbial ecosystem rather than remaining completely sterile. However, the precise composition and functional significance of these low-abundance microbial communities remain controversial.

The debate surrounding the existence of a true uterine or placental microbiome illustrates the broader methodological challenges facing reproductive microbiome research. Low microbial biomass increases susceptibility to contamination from laboratory reagents, environmental DNA, and sequencing artifacts, often referred to as “kitome” contamination (de Goffau et al., 2019). Panzer et al. (2023) further demonstrated that several previously reported placental microbiome signatures may reflect contamination rather than authentic resident microbial populations. These findings underscore the critical importance of rigorous contamination controls, negative sequencing controls, and standardized sampling procedures in future investigations.

Species-specific microbial variation was also evident throughout the review. Human vaginal microbiota are strongly enriched in Lactobacillus species, whereas livestock and non-human primates frequently exhibit more diverse reproductive microbial communities with near-neutral pH conditions (Swartz et al., 2014; Yildirim et al., 2014). Arabian mares, for example, maintain relatively stable but diverse vaginal microbiota across estrous stages (Barba et al., 2020). Similarly, bovine reproductive microbiota display microbial structures substantially different from those observed in humans (Laguardia Nascimento et al., 2015). These observations emphasize that reproductive microbial ecology is shaped by evolutionary adaptation, host physiology, endocrine regulation, and environmental exposure.

Methodological differences between studies also contributed substantially to heterogeneity. Variability in DNA extraction methods, sequencing depth, hypervariable region selection, and bioinformatic pipelines can profoundly influence microbial detection and taxonomic classification (Graspeuntner et al., 2018; Sharma et al., 2021). In addition, cross-sectional study designs limited the ability to assess temporal microbial dynamics throughout reproductive cycles, pregnancy, and postpartum periods. Longitudinal investigations are therefore essential to determine whether microbial shifts precede reproductive dysfunction or merely accompany pathological changes.

Clinically, the findings from this review have important translational implications. Reproductive microbiome profiling may eventually serve as a predictive biomarker tool for IVF success, implantation potential, and reproductive disease risk. Microbiome-guided interventions, including probiotics, prebiotics, antimicrobial modulation, and microbiota transplantation, may offer novel therapeutic approaches for restoring reproductive microbial homeostasis. However, before such interventions can become standardized clinical practice, greater methodological consistency and mechanistic understanding are required. Animal reproductive microbiome studies further show that aerobic bacterial communities in breeding bitches may persist over time and vary by reproductive condition, supporting the need for species-specific interpretation of reproductive microbial ecology (Bjurström & Linde Forsberg, 1992).

Overall, the present synthesis demonstrates that reproductive tract microbiomes are highly dynamic ecological systems intimately connected to reproductive physiology and fertility outcomes. Lactobacillus-dominant vaginal microbiota consistently support reproductive health, whereas dysbiotic microbial states correlate strongly with infertility and adverse reproductive conditions. The integration of Tables 1–4 and Figures 2–5 provides a coherent quantitative and ecological framework for understanding microbial influences on reproductive biology across species. These findings reinforce the importance of microbiome-centered approaches in reproductive medicine and establish a foundation for future personalized reproductive healthcare strategies.

5. Limitations

Despite the comprehensive synthesis presented, several limitations of this systematic review and meta-analysis warrant consideration. First, heterogeneity across studies—driven by differences in sampling methods, DNA extraction protocols, sequencing platforms, and hypervariable region selection in 16S rRNA analyses—may have influenced microbial detection and diversity estimates. Second, low-biomass environments such as the uterus and endometrium are particularly susceptible to contamination and methodological artifacts, which could contribute to variability observed in forest and funnel plots. Third, many studies relied on cross-sectional designs, limiting the ability to infer temporal dynamics or causal relationships between microbial composition and reproductive outcomes Fourth, small sample sizes in animal models and rare human reproductive tract studies reduced statistical power and increased confidence interval widths, especially for less dominant taxa. Finally, publication bias, though minimized in vaginal microbiota studies, cannot be entirely excluded in uterine and endometrial research, where fewer studies exist. These limitations highlight the need for standardized protocols, larger longitudinal studies, and robust contamination controls to generate more reliable, reproducible insights into reproductive tract microbiomes.

6. Conclusion

This review underscores the critical role of the female reproductive tract microbiome in health and reproductive outcomes. Vaginal microbial communities are stable and Lactobacillus-dominated, supporting host defense and reproductive success, whereas uterine and endometrial microbiota are more variable and sensitive to methodological and biological factors. Recognizing these patterns informs clinical interventions, particularly in assisted reproduction and infection prevention. Future research should prioritize standardized sampling, rigorous sequencing methods, and longitudinal designs to advance understanding of microbiome-mediated reproductive health.

References