Altmae, S. | bioRxiv Science

bioRxiv2026

Microbial-derived D-lactate and LPS shape growth and inflammatory signalling in endometrial glandular epithelium

The endometrium, the inner lining of the uterus, is a dynamic tissue that undergoes precise molecular and structural changes to achieve a receptive state capable of supporting embryo implantation. Although the uterine environment was long considered sterile, molecular studies have detected microbial signals and bioactive compounds that may influence endometrial function. Endometrial epithelial organoids (EEOs) provide a three-dimensional in vitro model that recapitulates the architecture, polarity, and hormonal responsiveness of native endometrial tissue. This study aimed to elucidate how bacterial-derived compounds, including D-lactate (D-lac), commonly associated with Lactobacillus communities, and lipopolysaccharides (LPS), a component of Gram-negative bacteria, affect the transcriptomic profile of the endometrial epithelium under a hormonally induced receptive state. EEOs were exposed to different concentrations of these compounds, and relative metabolic activity was monitored through resazurin-based assays, revealing no significant alterations across the conditions tested. Transcriptomics analysis of hormonally stimulated EEOs, mimicking the mid-secretory phase, revealed that D-lac modulated genes related to epithelial development, tissue remodelling and growth regulation, whereas LPS influenced genes associated with inflammatory signalling and immune response. While key markers of receptivity remained largely stable, small transcriptional changes suggest that microbial signals may modulate the functional balance of the receptive endometrium. These findings highlight a modulatory role of microbial signals on endometrial epithelial function and demonstrate that EEOs are a robust platform for exploring host-microbe interactions in the uterus, offering new insights into the mechanisms underlying uterine receptivity.

molecular biology↗

bioRxiv2025

Comprehensive 16S rRNA gene sequencing and meta-transcriptomic analyses in female reproductive tract microbiota: Two molecular profiles with different messages

In recent years, high-throughput sequencing technologies have revolutionised reproductive microbiome research. Our understanding of the endometrial microbiome is primarily based on DNA-based 16S rRNA gene profiling, but DNA detection does not imply alive microbe presence. While this method is cost-effective and widely used, it has notable limitations, including the underestimation of microbial diversity, abundance, and functionality, as well as limited species-level resolution. Meta-transcriptomic analysis, an RNA-based approach, addresses these shortcomings by capturing functional transcripts that are actively expressed in living microbes. This study aims to characterize the endometrial microbiota through a dual approach, by integrating 16S rRNA gene sequencing and meta-transcriptomic analyses. By simultaneously analysing microbial composition and gene expression within the female reproductive tract samples, we seek to provide a more comprehensive understanding of its microbiota and their functional potential. A total of 49 women, aged 27-42 years, were enrolled in the study. Vaginal swabs, endometrial brushings, and endometrial biopsy samples were collected from each participant during the mid-secretory menstrual cycle phase, 6-9 days post luteinizing hormone surge. Our findings suggest that in low-microbial-biomass environments like the endometrium, the correlation between 16S rRNA gene sequencing and meta-transcriptomics is relatively weak. This highlights the limitations of microbial analysis of low-microbial-biomass samples. Alternatively, it suggests that microbial functions and genome activity are tissue-specific and dependent on the host tissue environment. Moreover, RNA-based analysis provides higher resolution in detecting certain pathogens, even within the endometrium. In conclusion, our study underscores the need to integrate genomic and transcriptomic data in microbiota studies to better elucidate critical and health-related microbiota-host interactions.

genomics↗