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Metabolomic networks connect host-microbiome processes to human Clostridioides difficile infections
John I. Robinson, … , Peter J. Mucha, Jeffrey P. Henderson
John I. Robinson, … , Peter J. Mucha, Jeffrey P. Henderson
Published August 12, 2019
Citation Information: J Clin Invest. 2019;129(9):3792-3806. https://doi.org/10.1172/JCI126905.
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Research Article Gastroenterology Infectious disease

Metabolomic networks connect host-microbiome processes to human Clostridioides difficile infections

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Abstract

Clostridioides difficile infection (CDI) accounts for a substantial proportion of deaths attributable to antibiotic-resistant bacteria in the United States. Although C. difficile can be an asymptomatic colonizer, its pathogenic potential is most commonly manifested in patients with antibiotic-modified intestinal microbiomes. In a cohort of 186 hospitalized patients, we showed that host and microbe-associated shifts in fecal metabolomes had the potential to distinguish patients with CDI from those with non–C. difficile diarrhea and C. difficile colonization. Patients with CDI exhibited a chemical signature of Stickland amino acid fermentation that was distinct from those of uncolonized controls. This signature suggested that C. difficile preferentially catabolizes branched chain amino acids during CDI. Unexpectedly, we also identified a series of noncanonical, unsaturated bile acids that were depleted in patients with CDI. These bile acids may derive from an extended host-microbiome dehydroxylation network in uninfected patients. Bile acid composition and leucine fermentation defined a prototype metabolomic model with potential to distinguish clinical CDI from asymptomatic C. difficile colonization.

Authors

John I. Robinson, William H. Weir, Jan R. Crowley, Tiffany Hink, Kimberly A. Reske, Jennie H. Kwon, Carey-Ann D. Burnham, Erik R. Dubberke, Peter J. Mucha, Jeffrey P. Henderson

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Top CDI-associated metabolites selected during cross-validation of logis...

Top CDI-associated metabolites selected during cross-validation of logistic regression model


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