Characterization of the follicular fluid microbiome in endometriosis patients undergoing in vitro fertilization

Background Endometriosis (EMs) is a common gynecological disorder associated with infertility. EMs patients often require assisted reproductive technology (ART) but exhibit lower success rates. This study aimed to characterize the follicular fluid microbiome in EMs patients undergoing in vitro fertilization (IVF) and provide insights into mechanisms underlying lower pregnancy rates.

Follicular fluid samples were collected from EMs patients and control subjectsundergoing IVF. Microbial DNA was subjected to 16S rRNA gene sequencing. Bioinformatic analyses, including alpha and beta diversity analysis, microbial composition profiling and biomarker identification, were performed.

The follicular fluid microbiome in EMs patients exhibited altered alpha and beta diversity compared to controls. Distinct microbial compositions were observed at various taxonomic levels. Differentially abundant taxa were identified as potential biomarkers for EMs. Microbial profiles were associated with clinical parameters such as oocyte quality and fertilization rates. Models based on microbial profiles were constructed to elucidate the relationship between EMs and IVF outcomes. Functional predictions suggested alterations in metabolic pathways in the follicular fluid microbiome of EMs patients.

This study revealed significant alterations in the follicular fluid microbiome of EMs patients, providing a basis for further research into the role of the microbiome in EMs-related infertility.

Endometriosis (EMs) is a prevalent gynaecological disorder affecting 10-15% of women of reproductive age and up to 50% of women with infertility [Citation1,Citation2]. It is characterised by the growth of endometrial-like tissue outside the uterus, leading to chronic pelvic pain and infertility [Citation3]. A significant proportion of EMs patients (10−25%) require assisted reproductive technology (ART) to conceive [Citation4]. However, EMs patients often experience suboptimal outcomes following ART, including lower oocyte quality, embryo developmental potential, and pregnancy rates [Citation5,Citation6]. Endometriosis-associated infertility is driven by a multifaceted pathophysiological continuum that integrates structural, inflammatory, endocrine, and immunological derangements [Citation7]. Pelvic adhesive disease and endometriotic cysts deform the tubo-ovarian architecture, mechanically obstructing oocyte capture and embryo transport. Persistent peritoneal inflammation—fuelled by ectopic endometrial lesions—disrupts folliculogenesis, accelerates follicular atresia, and compromises oocyte cytoplasmic and genomic integrity. Within the endometrium, local oestrogen excess coupled with progesterone resistance attenuates decidualization, alters pinopode formation, and generates a hostile implantation milieu. These focal perturbations are amplified by systemic immune dysregulation: heightened macrophage activation and a skewed cytokine profile intensify oxidative stress, promote sperm DNA fragmentation, and exert direct embryotoxicity, thereby impairing fertilisation and early embryonic development. In addition, recent studies have implicated the role of the microbiome in reproductive health and ART outcomes [Citation8]. The human microbiome, which refers to the collective genomes of microorganisms residing in the human body, has been shown to play a crucial role in various physiological processes [Citation9]. Dysbiosis, or alterations in the microbial composition, has been associated with numerous pathological conditions, including gynaecological disorders [Citation10]. In the context of EMs, previous studies have demonstrated distinct microbiome profiles in the endometrium and vaginal fluid of EMs patients compared to healthy controls [Citation11,Citation12]. These findings suggest that microbial dysbiosis in the reproductive tract may contribute to the pathophysiology of EMs and its associated infertility. However, the microbiome of follicular fluid, which constitutes the microenvironment for oocyte development and maturation, has not been well-characterised in EMs patients. Follicular fluid is a complex biological fluid that contains various metabolites, hormones, and growth factors essential for oocyte growth and folliculogenesis [Citation13]. The composition of follicular fluid has been shown to influence oocyte quality, fertilisation rates, and embryo development [Citation14,Citation15]. Therefore, investigating the microbiome of follicular fluid in EMs patients may provide valuable insights into the potential microbiological mechanisms underlying the lower success rates of ART in this population. This study aimed to characterise the follicular fluid microbiome in EMs patients undergoing in vitro fertilisation (IVF) using 16S rRNA gene sequencing. We hypothesised that EMs patients would exhibit a distinct microbial profile in the follicular fluid compared to control subjects, and that these alterations would be associated with clinical outcomes of IVF. The findings of this study could pave the way for developing microbiome-based strategies to improve ART success rates in EMs patients.

Study design and participants This prospective study was conducted at the Reproductive Medicine Centre of Weifang People's Hospital. The study protocol was approved by the Institutional Review Board of Weifang People's Hospital (approval number: KYLL20220422-1), and written informed consent was obtained from all participants. EMs patients and control subjects undergoing IVF were recruited between June 2022 and June 2024. The inclusion criteria for EMs patients were: (1) age between 20 and 37 years; (2) diagnosed with EMs by laparoscopy or histopathology; (3) undergoing IVF with a fresh embryo transfer cycle. The control group consisted of age-matched women with tubal factor infertility (as diagnosed by tubal obstruction, tubal adhesion or salpingectomy) undergoing IVF. Exclusion criteria for both groups included: (1) presence of other endocrine or metabolic disorders; (2) use of hormonal or antibiotic medications within the past three months; (3) history of pelvic inflammatory disease or sexually transmitted infections. Sample collection and processing Follicular fluid samples were collected during oocyte retrieval under transvaginal ultrasound guidance. The first follicular aspirate from the largest follicle of each ovary was collected, and the cumulus-oocyte complex was removed. The follicular fluid samples were centrifuged at 3,000 rpm for 10 minutes to remove cellular debris, and the supernatant was stored at –80 °C until further analysis. DNA extraction and 16S rRNA gene sequencing Microbial DNA was extracted from the follicular fluid samples using the QIAamp DNA Microbiome Kit (Qiagen, Germany) according to the manufacturer's instructions. The V3-V4 region of the 16S rRNA gene was amplified using the forward primer 338F (5'-ACTCCTACGGGAGGCAGCA-3') and the reverse primer 806 R (5'-GGACTACHVGGGTWTCTAAT-3'). PCR amplification, library preparation, and sequencing on the Illumina MiSeq platform (Illumina, USA) were performed as described previously [Citation16]. Bioinformatic analysis The raw sequencing data were processed using the QIIME2 pipeline (version 2021.4) [Citation17]. Briefly, the paired-end reads were demultiplexed and quality-filtered using the DADA2 algorithm [Citation18]. The filtered sequences were then clustered into amplicon sequence variants (ASVs) at a 99% similarity threshold. Taxonomy was assigned to the ASVs using the Silva 138 reference database [Citation19]. Alpha diversity metrics, including observed ASVs, Shannon index, and Faith's phylogenetic diversity (PD), were calculated to assess the microbial diversity within each sample. Beta diversity was evaluated using the Bray-Curtis dissimilarity matrix and visualised by principal coordinate analysis (PCoA). Permutational multivariate analysis of variance (PERMANOVA) was performed to test for differences in microbial community composition between the EMs and control groups. Differential abundance analysis was conducted using the linear discriminant analysis effect size (LEfSe) method to identify taxa that were significantly enriched in the EMs or control group [Citation20]. The threshold for the logarithmic LDA score was set at 2.0. Random forest analysis was performed to identify microbial biomarkers that could discriminate between the EMs and control groups [Citation21]. The predictive accuracy of the random forest model was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). Functional prediction of the follicular fluid microbiome was performed using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) [Citation22]. The KEGG (Kyoto Encyclopaedia of Genes and Genomes) database was used as the reference for functional annotation [Citation23]. Differentially abundant KEGG pathways between the EMs and control groups were identified using the Wilcoxon rank-sum test with Benjamini-Hochberg correction for multiple comparisons. Statistical analysis Continuous variables were expressed as mean ± standard deviation or median (interquartile range) depending on the data distribution. Categorical variables were presented as frequencies and percentages. Comparisons between the EMs and control groups were performed using the Student's t-test or Mann-Whitney U test for continuous variables and the chi-square test or Fisher's exact test for categorical variables. Correlations between microbial taxa and clinical parameters were assessed using Spearman's rank correlation coefficient. Statistical analyses were performed using R software (version 4.0.3), and a p-value < 0.05 was considered statistically significant.

Participant characteristics A total of 60 participants, including 30 EMs patients and 30 control subjects, were enroled in this study. The demographic and clinical characteristics of the participants are summarised in . There were no significant differences in age, body mass index (BMI), duration of infertility, or baseline hormonal levels between the two groups. Sequencing data and alpha diversity After quality filtering and preprocessing, a total of 3,422,734 high-quality sequences were obtained, with an average of 57,045 ± 8,236 sequences per sample. The rarefaction curves reached a plateau, indicating that the sequencing depth was sufficient to capture the microbial diversity in the follicular fluid samples (). The alpha diversity metrics, including observed ASVs, Shannon index, and Faith's PD, were significantly lower in the EMs group compared to the control group (P < 0.05) (). These results suggest a decreased microbial diversity in the follicular fluid of EMs patients. Beta diversity and microbial composition The PCoA plot based on the Bray-Curtis dissimilarity matrix revealed a clear separation between the EMs and control groups (). PERMANOVA analysis confirmed that the microbial community composition was significantly different between the two groups (R2 = 0.085, P = 0.001). At the phylum level, Firmicutes, Bacteroidetes, and Proteobacteria were the predominant taxa in both groups (). However, the relative abundance of Firmicutes was significantly higher in the EMs group compared to the control group (P < 0.05), while the relative abundances of Bacteroidetes and Proteobacteria were significantly lower (P < 0.05). At the genus level, the EMs group was characterised by a higher abundance of Lactobacillus, Gardnerella, and Prevotella, while the control group had a higher abundance of Pseudomonas, Acinetobacter, and Bifidobacterium (). Microbial biomarkers and predictive models LEfSe analysis identified 18 differentially abundant taxa between the EMs and control groups (). The genera Firmicutes, Bacilim, and Lactobacillus were enriched in the EMs group, while Gardnerella, Sphingomonas, and Lysobacter were enriched in the control group. Random forest analysis revealed that a combination of 12 microbial taxa could discriminate between the EMs and control groups with an AUC-ROC of 0.65 (95% CI: 0.44−0.86) (). The top five discriminatory taxa were Catenibacterium, Gardnerella, Pseudomonas, Parabacteroides, and Marvinbryantia. Functional prediction and correlation analysis PICRUSt2 analysis predicted significant differences in the functional profiles of the follicular fluid microbiome between the EMs and control groups (). Several KEGG pathways, including ‘Energy production and conversion,’ ‘Amino acid transport and metabolism,’ and ‘Inorganic ion transport and metabolism,’ were significantly enriched in the EMs group (P < 0.05). Spearman's correlation analysis revealed significant associations between microbial taxa and clinical parameters (). The abundance of Lactobacillus was negatively correlated with oocyte maturation rate (r = –0.42, P = 0.02) and fertilisation rate (r = –0.45, P = 0.01), while the abundance of Bifidobacterium was positively correlated with these parameters (r = 0.39, P = 0.03 and r = 0.41, P = 0.02, respectively).

To our knowledge, this is the first study to characterise the follicular fluid microbiome in EMs patients undergoing IVF using 16S rRNA gene sequencing. Our findings revealed significant alterations in the microbial diversity, composition, and functional profiles of the follicular fluid microbiome in EMs patients compared to control subjects. The decreased alpha diversity observed in the follicular fluid of EMs patients suggests a state of microbial dysbiosis. This finding is consistent with previous studies reporting reduced microbial diversity in the endometrium and vaginal fluid of EMs patients [Citation11,Citation12]. The altered microbial composition, characterised by a higher abundance of Lactobacillus, Gardnerella, and Prevotella and a lower abundance of Pseudomonas, Acinetobacter, and Bifidobacterium, indicates a shift in the microbial balance in the follicular fluid of EMs patients. Follicular fluid of endometriosis-affected women exhibits elevated IL-6, attributable to augmented secretion by patient-derived granulosa-luteal cells, concomitant with diminished VEGF accumulation. These compositional deviations denote a pathologically altered follicular microenvironment, implicating oocyte-intrinsic perturbations that precipitate compromised embryonic developmental competence and impaired implantation potential [Citation24]. The enrichment of Lactobacillus in the follicular fluid of EMs patients is particularly interesting. While Lactobacillus is generally considered a beneficial genus in the vaginal microbiome, its role in the upper reproductive tract remains controversial [Citation25]. Some studies have suggested that Lactobacillus dominance in the endometrium may be associated with adverse reproductive outcomes [Citation26–29]. Our correlation analysis showed a negative association between Lactobacillus abundance and oocyte maturation and fertilisation rates, supporting the notion that an overabundance of Lactobacillus in the follicular fluid may have detrimental effects on oocyte quality and developmental competence. The functional prediction analysis revealed significant differences in the metabolic pathways of the follicular fluid microbiome between the EMs and control groups. The enrichment of lipid, amino acid, and carbohydrate metabolism pathways in the EMs group suggests that the altered microbial composition may influence the metabolic environment of the follicular fluid, which could in turn impact oocyte development and quality. In addition, the recent identification of kisspeptin as a key integrator of internal and external signals to the hypothalamic–pituitary–gonadal axis has redefined upstream neuroendocrine control of human reproduction and revealed its role in endometriosis pathophysiology [Citation30]. The microbial biomarkers identified in this study, including Lactobacillus, Gardnerella, Pseudomonas, Acinetobacter, and Bifidobacterium, could potentially serve as diagnostic markers for EMs-related infertility. The random-forest model achieved high accuracy with these microbial taxa, highlighting the diagnostic value of microbiome profiling in endometriosis patients undergoing ART.Our study has several strengths, including the prospective design, the use of high-throughput sequencing technology, and the comprehensive bioinformatic analysis. However, there are also limitations that should be acknowledged. First, the study is limited by its small sample size and the consequent instability of the AUC (0.65); validation in larger cohorts is required to obtain a reliable estimate. Second, controls were women with tubal-ligation infertility, a condition linked to prior pelvic inflammation and attendant microbiome shifts that may have influenced our findings. Third, follicular fluid can become contaminated by vaginal microbiota during aspiration; although an aseptic technique was used, residual contamination cannot be ruled out and must be considered when interpreting the data. In conclusion, this study provides novel insights into the follicular fluid microbiome in EMs patients undergoing IVF. The findings suggest that microbial dysbiosis in the follicular fluid may contribute to the pathophysiology of EMs-related infertility and the suboptimal outcomes of ART in this population. The identified microbial biomarkers and models, if validated in larger cohorts, may have diagnostic and prognostic value in the management of EMs patients seeking ART. Future research should focus on elucidating the mechanisms underlying the interactions between the follicular fluid microbiome, oocyte quality, and embryo development, as well as exploring microbiome-targeted interventions to improve ART success rates in EMs patients. Ethic approval and consent for publication All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study protocol was approved by the Institutional Review Board of Weifang People's Hospital (approval number: KYLL20220422-1), and written informed consent was obtained from all participants. Consent for publication Not applicable.

None. Disclosure statement The authors declare that they have no competing interests. Data availability statement The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Additional information Funding

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