Back

Adaptation of Enterococcus faecalis to intestinal mucus revealed by a human colonic organoid model

Mikhaleva, S.; Hsiao, P.-L.; Kurmashev, A.; Anderson, C. M.; Colomer-Winter, C.; Boos, J. A.; Choo, P. Y.; Willett, J. L. E.; Hierlemann, A.; Kline, K. A.; Persat, A.

2025-08-20 microbiology
10.1101/2025.08.20.670035 bioRxiv
Show abstract

The human gastrointestinal tract hosts a diverse population of microorganisms that have a significant impact on host health. Among this population, Enterococcus faecalis (Ef) represents a common member of intestinal microbiota colonizing humans early in life, but which can also opportunistically infect its host. Despite its importance in human health, investigations of its physiological adaptation to the mucosal environment remain limited. Building on recent advances in tissue engineering, we here leverage human colonic organoids (colonoids) to investigate the Efs mechanisms of mucosal surface colonization across space and time. Using high-resolution microscopy, we visualized Ef growth within the natively formed colonic mucus layer in colonoids. Leveraging a custom perfusion chamber, we tracked Ef growth within the mucus of live colonoids over time under flow, which revealed specific colonization strategies, including biofilm-like microcolony formation. To identify Ef fitness determinants in this niche, we implemented transposon insertion sequencing (Tn-seq) in the natively formed mucus of live colonoids. This approach revealed a large fitness rearrangement compared to typical liquid culture, mainly involving metabolic activity and regulatory response during mucosal colonization, as well as factors that may contribute to colony formation at the mucosal surface. Altogether, our results show important physiological and biophysical adaptation of Ef to the mucosal surface that are not captured by in vitro conditions and that cannot be revealed in vivo at high resolution.

Published in mSystems (predicted rank #6) · training set

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.