Chronic endometritis and the endometrial microbiota: implications for reproductive success in patients with recurrent implantation failure

Abstract

Background: Chronic endometritis (CE) is associated with poor reproductive outcomes, yet the role of endometrial microbiota in patients with recurrent implantation failure (RIF) and CE remains unclear. This study aims to characterize endometrial microbiota in RIF patients with CE and assess its implications for reproductive outcomes.

Methods: In this prospective study, we enrolled RIF patients both with and without CE. Endometrial and cervical samples were collected for 16 S rRNA gene sequencing. Microbiota composition was compared between groups using diversity indices, phylum, and genus-level analysis. Canonical correlation analysis (CCA) and Spearman's correlation coefficients were used to assess relationships between CE, reproductive outcomes, and microbiota. Predictive functional profiling was performed to evaluate metabolic pathways associated with CE.

Results: Endometrial microbiota in CE patients exhibited greater diversity and evenness compared to non-CE patients. Principal coordinates analysis (PCoA) revealed distinct clustering between CE and non-CE groups. Linear discriminant analysis (LDA) identified Proteobacteria, Aminicenantales, and Chloroflexaceae as characteristic of CE, while Lactobacillus, Acinetobacter, Herbaspirillum, Ralstonia, Shewanela, and Micrococcaceae were associated with non-CE. CCA demonstrated associations between CE, adverse reproductive outcomes, and specific bacterial taxa. Microbial metabolic pathways significantly differed between CE and non-CE groups, with enrichment in pathways related to cofactors, vitamins, secondary metabolites, and the immune system in CE patients.

Conclusion: RIF patients with CE exhibit distinct endometrial microbiota compositions associated with adverse reproductive outcomes. The increased microbial diversity and altered metabolic pathways in CE suggest a potential correlation with reproductive outcomes, although further studies are necessary to elucidate the causal relationship between microbiota alterations and fertility. Modulating the endometrial microbiome may represent a novel therapeutic strategy to improve IVF outcomes in patients with CE.

Keywords: Chronic endometritis; Endometrial microbiota; Microbial diversity; Recurrent implantation failure; Reproductive outcomes.

Fig. 1

Flow diagram of the study All patients with RIF underwent vaginal smear examination, and those with a dominant Lactobacillus flora were selected for inclusion. A total of 83 RIF patients were enrolled. These patients underwent hysteroscopy and endometrial biopsy to confirm the presence of uterine structural normalities and endometritis. No uterine structural abnormalities were detected. Based on the examination results, patients were categorized into endometritis and non-endometritis groups. Endometritis group patients were treated with antibiotics and underwent repeat endometrial biopsy. Three patients with persistent endometritis were excluded. The remaining endometritis and non-endometritis group patients had their endometrial flushing fluid collected for 16 S rRNA sequencing analysis RIF, recurrent implantation failure; CE, chronic endometritis

Fig. 2

Bar charts showing mean values of 10 most abundant genera in endometrium and cervix of 20 paired samples from 10 enrolled patients. Analysis and plotting were performed using R software (version 3.3.1)

Fig. 3

Microbial diversity in the non-CE and CE groups (A) Rarefaction curve for observed species (Sob). Comparison of Chao index (B), Shannon index (C), Pieou’s evenness index (D) and Phylogenetic diversity (Pd) index (E) between the non-CE and CE groups. Statistical analysis was performed using mothur software (version v.1.30.2), and plotting was performed using R software (version 3.3.1). Data were represented as mean ± SD, n = 40. *P < 0.05, **P < 0.01, ***P < 0.001 by two-tailed Wilcoxon rank-sum tests followed by FDR corrections

Fig. 4

Bacterial communities in endometrial microbiota of the non-CE and CE groups Bar charts showing showing mean values of 10 most abundant phyla (A) and genera (B) in the non-CE and CE groups. Analysis and plotting were performed using R software (version 3.3.1)

Fig. 5

Identification of the discriminatory genera (A) PCoA plots of uterine samples from the non-CE and CE patients. R software (version 3.3.1) was used for PCoA analysis and visualization (B) Linear discriminant analysis (LDA) effect size analysis of the differentially abundant genera, which indicated their contribution to group differentiation. The green bars indicate that the genera were more abundant in the non-CE group, while the red bar indicates that the genus was more abundant in the CE group. LEfSe analysis was employed to confirm the significance of the LDA-selected genera, ensuring that only those with the strongest contribution to group separation were identified

Fig. 6

Relationships between endometrial microbiota and reproductive outcomes (A) Canonical correspondence analysis (CCA) diagram showing the associations between CE, endometrial microbiota and reproductive outcomes. Vegan package (version 2.4.3) in R software (version 3.3.1) was used for the CCA analysis and visualization. Statistical significance was determined by P values: clinical pregnancy (CP, P = 0.01), miscarriage (MISC, P = 0.01), and non-pregnancy (NP, P = 0.01) (B) Spearman correlation heatmap analysis between endometrial microbiota and reproductive outcomes. Red represents a positive correlation, and blue represents a negative correlation. The heatmap was generated using the pheatmap (version 1.0.8) package in R software (version 3.3.1)

Fig. 7

The functional properties predicted by PICRUSt2 (B) (A) Heatmap showing the predicted microbial metabolism pathways of the endometrial microbiota of the CE and non-CE groups. Relative proportions of the predicted functions with significant difference between the two groups. *P < 0.05, **P < 0.01, ***P < 0.001 by two-tailed Wilcoxon rank-sum tests followed by FDR corrections The functional analysis was performed using PICRUSt2 software