Exposure and resistance to lantibiotics impact microbiota composition and function.

The intestinal microbiota is composed of hundreds of distinct microbial species that interact with each other and their mammalian host. Antibiotic exposure dramatically impacts microbiota compositions and leads to acquisition of antibiotic-resistance genes. Lantibiotics are ribosomally synthesized and post-translationally modified peptides produced by some bacterial strains to inhibit the growth of competing bacteria.

Bifidobacteria metabolize lactulose to optimize gut metabolites and prevent systemic infection in patients with liver disease.

Progression of chronic liver disease is precipitated by hepatocyte loss, inflammation and fibrosis. This process results in the loss of critical hepatic functions, increasing morbidity and the risk of infection. Medical interventions that treat complications of hepatic failure, including antibiotic administration for systemic infections and lactulose treatment for hepatic encephalopathy, can impact gut microbiome composition and metabolite production.

Virulence and genomic diversity among clinical isolates of ST1 (BI/NAP1/027) Clostridioides difficile.

Clostridioides difficile produces toxins that damage the colonic epithelium, causing colitis. Variation in disease severity is poorly understood and has been attributed to host factors and virulence differences between C. difficile strains.

Virulence and genomic diversity among clinical isolates of ST1 (BI/NAP1/027) .

, a leading cause of nosocomial infection, produces toxins that damage the colonic epithelium and results in colitis that varies from mild to fulminant. Variation in disease severity is poorly understood and has been attributed to host factors (age, immune competence and intestinal microbiome composition) and/or virulence differences between strains, with some, such as the epidemic BI/NAP1/027 (MLST1) strain, being associated with greater virulence. We tested 23 MLST1(ST1) clinical isolates for virulence in antibiotic-treated C57BL/6 mice.

Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes.

Cellular behaviors emerge from layers of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be defined from the statistical pattern of proteome variation measured across thousands of diverse bacteria and that these networks reflect the emergence of complex bacterial phenotypes. Our results are validated through gene-set enrichment analysis and comparison to existing experimentally derived databases.