Back

Comparison of oral and gut microbiome highlights role of oral bacteria in systemic inflammation in HIV

Fulcher, J. A.; Newman, K.; Pham, B.; Li, F.; Cho, G. D.; Elliott, J.; Tobin, N. H.; Shoptaw, S.; Gorbach, P. M.; Aldrovandi, G. M.

2025-05-16 microbiology
10.1101/2025.05.16.654362 bioRxiv
Show abstract

BackgroundChronic HIV-1 infection is associated with increased inflammation-related comorbidities, despite effective viral suppression with antiretroviral therapy. While the role of the gut microbiome in inflammation is well-studied, the contribution of the oral microbiome remains less clear. This study investigates the relationship between the oral and gut microbiomes in driving systemic inflammation in persons with HIV. MethodsThis cross-sectional study utilized archived samples from 198 participants (99 with HIV and 99 without HIV). Oral microbiome composition was analyzed via 16S rRNA sequencing and systemic inflammatory biomarkers were measured using multiplex assays. Gut microbiome data from previous studies were integrated for comparative analyses. Bacterial inflammatory potential was assessed through in vitro co-culture and epithelial barrier permeability assays. ResultsThe oral microbiome in HIV was characterized by increased Veillonella, Capnocytophaga, and Megasphaera, and several decreased genera including Fusobacterium. Using PERMANOVA, we found that the oral microbiome was a significant driver of cytokine variation in HIV compared to the gut microbiome, and identified specific associations with oral Veillonella and Megasphaera. We found no differences in anti-Veillonella parvula serum IgG by HIV status, but IgG titers did correlate with microbial translocation markers sCD14 and LBP in HIV. In vitro studies demonstrated that Veillonella parvula increased oral epithelial barrier permeability and induced monocyte activation. ConclusionsThe oral microbiome, particularly Veillonella parvula, may contributes to systemic inflammation in HIV through mechanisms involving epithelial barrier disruption, oral translocation, and monocyte activation.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.