Back

Carbohydrate consumption drives adaptive mutations in Escherichia coli associated with increased risk for systemic infection

Hecht, A. L.; Mahmud, N.; Chaudhry, S.; Cao, J. Y.; Branigan, G. P.; Lee, J.; Theiller, E.; Roggiani, M.; Friedman, E. S.; Herman, L.; Gallis, B. E.; Jones, S. M.; Planet, P. J.; Zackular, J. P.; Kaplan, D. E.; Serper, M.; Reddy, K. R.; Moustafa, A. M.; Goulian, M.; Wu, G. D.

2025-03-26 microbiology
10.1101/2025.03.26.645536 bioRxiv
Show abstract

Dissemination of organisms from the gut microbiota is a major contributor to sepsis and critical illness. Patients with cirrhosis are prone to systemic infections and are commonly prescribed the carbohydrate lactulose to manage hepatic encephalopathy (HE) 1. Commensal metabolism of lactulose is believed to reduce pathobiont colonization through short-chain fatty acid production, but its direct effects on gut pathobionts remain unexplored 2. Here, we show that lactulose consumption unexpectedly selects for mutations in Escherichia coli lactose (lac) operon regulation, enhancing its metabolic fitness and colonization capacity. This is mediated by selection for constitutive expression of the lac operon through mutations in its regulatory components. Using in vitro systems, murine models, and clinical samples, we demonstrate that these mutations enable E. coli to exploit lactulose as a carbon source, bypassing host carbohydrate metabolism and increasing its intestinal colonization. Despite its long-standing use in HE treatment, we find that lactulose has a paradoxical association with risk of infection hospitalization in patients with cirrhosis in a large epidemiologic study. The emergence of lactulose-adapted E. coli strains could be suppressed by a dietary oligosaccharide that competitively inhibits lactulose uptake. These findings reveal a mechanism by which dietary substrates exert selective pressure on the microbiome, with implications for diet-based strategies to modulate microbial evolution and infection risk.

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.