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

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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Figure 9

Interrelationships between host- and C. difficile–associated metabolites.

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Interrelationships between host- and C. difficile–associated metabolites...
(A) Plotting bile acid PC1 (Figure 7) versus 4-methylpentanoic acid index (Figure 4) reveals that high PC1 score and high 4-methylpentanoic acid index values coincide in patients with CDI compared with control patients (n = 32 for each group). The dashed line marks the dividing line assigned 50% probability of being Cx+/EIA+ by a logistic regression model incorporating both PC1 and 4-methylpentanoic acid index. (B) Probabilities assigned to each patient by the logistic regression model (n = 32 per group). Higher values indicate higher certainty of Cx+/EIA+ status. The gray line marks the 50% probability cutoff above which samples are considered Cx+/EIA+. (C) ROC curve showing the performance of the logistic regression model in discriminating Cx–/EIA– patients from Cx+/EIA+ patients. The gray region represents 95% confidence intervals bootstrapped for the true-positive rate at each possible false-positive rate. The AUC and its 95% confidence interval are also reported. (D) Euler diagram showing the overlap between culture, EIA, and metabolome status. Samples were considered metabolome-positive if assigned a probability above 50% by the logistic regression model.