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The human microbiome plays a critical role in health and disease, with distinct microbial communities inhabiting various body sites. Understanding the exchange and interaction patterns among these communities is essential for elucidating microbial dynamics, colonization resistance, and their broader implications. This study employed the Fast Expectation-maximization microbial Source Tracking (FEAST) algorithm to quantitatively estimate the contribution of microbial sources from different body sites to target (sink) communities, utilizing 16S rRNA gene amplicon sequencing data from the Human Microbiome Project (HMP). Our analysis revealed intricate microbiome exchange patterns characterized by notable sex-specific differences. In male participants, a high bidirectional similarity was observed between the skin and nasal microbiomes (~58% reciprocal contribution), suggesting frequent microbial exchange driven by anatomical proximity and shared environmental exposures. The salivary microbiome also showed a substantial contribution from the nasal cavity (~35%). For female participants, a striking finding was the profound similarity between the vaginal and skin microbiomes (76.12% contribution from skin to vagina), indicating the skin as a primary source for vaginal colonization, potentially influenced by local anatomical contiguity. Consistent with male samples, the skin and nasal microbiomes in females also exhibited high bidirectional exchange. Furthermore, the skin emerged as a prominent multi-site source in females, contributing significantly to both vaginal and gut microbiomes. While core similarities—such as skin-nasal and saliva-nasal interactions—were conserved across sexes, distinct gender-specific ecological dynamics in overall source contributions were evident. These findings underscore the highly interconnected nature of the human microbiome, highlighting specific exchange routes and emphasizing the need to consider sex as a critical biological variable in microbiome research.

Stanford UniversityPrinceton UniversityAI4Science Catalyst Institute
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