Clostridioides difficile (C. difficile), 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 C. difficile strains, with some, such as the epidemic BI/NAP1/027 (MLST1) strain, being associated with greater virulence. We tested 23 MLST1(ST1) C. difficile clinical isolates for virulence in antibiotic-treated C57BL/6 mice. All isolates encoded a complete Tcd pathogenicity locus and achieved similar colonization densities in mice. Disease severity varied, however, with 5 isolates causing lethal infections, 16 isolates causing a range of moderate infections and 2 isolates resulting in no detectable disease. The avirulent ST1 isolates did not cause disease in highly susceptible Myd88-/- or germ-free mice. Genomic analysis of the avirulent isolates revealed a 69 base-pair deletion in the N-terminus of the cdtR gene, which encodes a response regulator for binary toxin (CDT) expression. Genetic deletion of the 69 base-pair cdtR sequence in the highly virulent ST1 R20291 C. difficile strain rendered it avirulent and reduced toxin gene transcription in cecal contents. Our study demonstrates that a natural deletion within cdtR attenuates virulence in the epidemic ST1 C. difficile strain without reducing colonization and persistence in the gut. Distinguishing strains on the basis of cdtR may enhance the specificity of diagnostic tests for C. difficile colitis.

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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. We demonstrate the biological utility of our approach by creating a model of motility in Pseudomonas aeruginosa and using it to identify a protein that affects pilus-mediated motility. Our method, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), may be useful for interrogating genotype-phenotype relationships in bacteria.

Progression of chronic liver diseases 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, impact gut microbiome composition and metabolite production. Using a multi-omics approach on 850 fecal samples from 263 patients with acute or chronic liver disease, we demonstrate that patients hospitalized for liver disease have reduced microbiome diversity and a paucity of bioactive metabolites. We find that patients treated with the orally administered but non-absorbable disaccharide lactulose have increased densities of intestinal Bifidobacteria and reduced incidence of systemic infections and mortality. Bifidobacteria metabolize lactulose, produce high concentrations of acetate and acidify the gut lumen, which, in combination, can reduce the growth of antibiotic-resistant pathobionts such as Vancomycin-resistant Enterococcus faecium. Our studies suggest that lactulose and Bifidobacteria serve as a synbiotic to reduce rates of infection in patients with severe liver disease.