Characterization of the Endometrial Microbiome in Patients with Recurrent Implantation Failure

Abstract

An abnormal endometrial microbiota has been associated with implantation failure; therefore, it may be important to evaluate it in order to improve reproductive outcomes in infertile patients. The main objective of our study was to compare the endometrial microbiome of patients with recurrent implantation failure (RIF) and control patients undergoing assisted reproduction treatment (ART). A prospective cohort study including forty-five patients with their own or donated gametes. The endometrial microbiome was analysed by massive sequencing of the bacterial 16S rRNA gene. Different bacterial communities were detected in RIF and control patients. Lactobacillus stands out as the most frequent genus, with 92.27% in RIF patients and 97.96% in control patients, and significant differences were reported between the two groups (p = 0.002). No significant differences were found regarding alpha diversity index. In beta diversity analysis, a significant trend was observed in the separation of the bacterial community between established groups (p < 0.07). Relative abundance analysis identified genera Prevotella (p < 0.001), Streptococcus (p < 0.001), Bifidobacterium (p = 0.002), Lactobacillus (p = 0.002) and Dialister (p = 0.003). Our results demonstrated the existence of an endometrial microbiota characteristic of RIF patients and showed that there might be a relationship between population of the endometrial microbiome and embryo implantation failure, providing us the possibility to improve clinical results in this patients.

Keywords: assisted reproduction; bacterial pathogens; biodiversity; embryo implantation; metagenomics; microbiome; microbiota; next generation sequencing.

Figure 1

Alpha diversity analysis in the study population. (A) Comparative analysis of the Shannon diversity index (p = 0.567) and (B) the Simpson diversity index (p = 0.410) for the RIF and control groups. MicrobiomeAnalyst MDP. Black blocks indicate the midpoint of the alpha-diversity indexes.

Figure 2

Beta diversity analysis. PCOA showing the clustering between the RIF (green) and control (red) groups, in which each dot represents a sample. PC1 is the principal coordinate component that generates the largest difference in the samples, with a value of 27.02%. PC2 and PC3, with a value of 13.8% and 8.8%, respectively (p value < 0.072). MicrobiomeAnalyst MDP.

Figure 3

Composition of the bacterial community. Bar chart of the relative frequency of the most abundant genera (A) and species (B), grouped by RIF and control group. MicrobiomeAnalyst MDP.

Figure 4

Bacterial network visualising the relationship between genus in all endometrial samples. Circle features: (1) size, proportional to bacterial relative abundances; (2) colour, communities for RIF group (orange) and control group (green). Line features: (1) thickness, from the most significant (thicker) to the less significant correlation (thinner); (2) colour, negative (red) and positive (grey) correlation between the genera. Each concurrent bacterial network was calculated with significant Pearson correlation coefficients.