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The oral cavity of chronically homeless adults is home for unusual extremophile environmental bacteria

Ruiz-Coronel, A.; Sandoval-Motta, S.; Melendez-Sanchez, D.; Larios-Serrato, V.; Torres, R. C.; Torres, J.

2025-04-21 bioinformatics
10.1101/2025.04.16.649188 bioRxiv
Show abstract

Chronically homeless adults (CHA) often face limited healthcare access, poor nutrition, frequent substance use, and close contact with stray animals. These factors can significantly alter their oral microenvironment. This study aimed to characterize the oral microbiota of this vulnerable and understudied population. We analyzed saliva samples from 60 chronically homeless men and 40 asymptomatic men with no history of homelessness, all living in Mexico City. DNA was extracted, and the V1-V3 regions of the 16S rRNA gene were sequenced. Taxonomic classification was performed using human and non-human databases. Diversity metrics were calculated using the vegan v2.6-4 package in R, and informative species were identified through machine learning and statistical approaches. Microbial correlation networks were inferred using NetCoMi in RStudio. Bacterial diversity was significantly higher in the CHA group. While both groups shared 369 species, only eight were exclusive to the control group, and 149 were unique to CHA. Several of these taxa had never been reported in humans and are typically found in extreme environments. For example, Megasphaera cerevisiae, adapted to high ethanol and low pH, was the most abundant species. Syntrophocurvum alkaliphilum, from a hypersaline lake in Siberia, and Sinanaerobacter chloroacetimidivorans, from anaerobic bioreactors, were also prevalent. Marked differences in microbial network structure were observed between groups. These findings highlight the adaptability of extremophile bacteria to human environments under chronic stress and poor hygiene. This study provides a first look into the oral microbiota of individuals experiencing chronic homelessness, emphasizing the need for more research into marginalized populations.

Published in iScience (predicted rank #20) · training set

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