Back

Female-enriched Eggerthella lenta drives neuroinflammation and IFN-γ via host receptor TLR2

Rock, R. R.; Alexander, M.; Noecker, C.; Trepka, K.; Upadhyay, V.; Ortega, E.; Ramirez, L.; Siewart, L.; Olson, C.; Halsey, T.; Probstel, A.-K.; Baranzini, S.; Turnbaugh, P. J.

2026-03-19 microbiology
10.64898/2026.03.16.711194 bioRxiv
Show abstract

Women are at increased risk of autoimmune diseases, including multiple sclerosis (MS); however, the degree to which sex differences in the gut microbiota impact autoimmunity remains largely unexplored. Our 27-cohort meta-analysis revealed 60 sex-associated gut bacterial species. Leveraging an independent clinical cohort, we demonstrate that female-enriched species significantly associate with MS status and clinical disability (EDSS). Top female-enriched species Eggerthella lenta drove disease in the experimental autoimmune encephalomyelitis (EAE) MS model, consistent with brain and gut lamina propria T cell infiltration and MS-associated T helper (Th) signatures. E. lenta induced intestinal Th1 and Th17 in healthy mice, independent of bacterial viability. Mechanistically, we demonstrate that TLR2 directly drives E. lenta-induced IFN-{gamma} production in Th cells and is necessary for exacerbation of EAE. Together, we identify a causal host-microbe axis contributing to sex differences in autoimmunity and provide a framework for evaluating sex as a biological variable in human microbiome research. HIGHLIGHTSO_LI27-cohort meta-analysis identifies a robust sex-signature in human gut microbiota. C_LIO_LIFemale-enriched species are associated with MS risk and severity. C_LIO_LIFemale-enriched Eggerthella lenta exacerbates the EAE model. C_LIO_LIE. lenta impacts neuroinflammation via toll-like receptor 2. C_LI

Matching journals

The top 4 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.