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 September 3, 2019; First 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 2

Supervised metabolomic analyses comparing Cx+/EIA+ with Cx–/EIA– samples.

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Supervised metabolomic analyses comparing Cx+/EIA+ with Cx–/EIA– samples...
(A) Observed separation of Cx+/EIA+ and Cx–/EIA– samples under sparse partial least squares–discriminatory analysis (sPLS-DA). The data ellipses are drawn around each group of samples (at the 95% level). (B) Penalized logistic regression under repeated 5-fold cross-validation shows how the number of features used relates to the obtained accuracy, yielding high accuracy with a relatively small number of features. The maximum percent predicted is indicated by a star. (C) Using the penalty parameter associated with the maximum percent predicted, penalized logistic regression demonstrates good separation in the distribution of log-odds to be classified Cx+/EIA+ versus Cx–/EIA–. In the log-odds distribution shown here, only the test folds of Cx+/EIA+ and Cx–/EIA– for each randomized cross-validated run are shown (that is, the corresponding distribution of the training set is not shown). For comparison, the corresponding log-odds of the Cx+/EIA– samples are also shown. (D) Logistic regression (without penalty) to classify Cx+/EIA+ versus Cx–/EIA– was performed using only the 6 features most frequently used in the penalized logistic regressions. Fitting to all samples gives 96.7% ROC AUC. The 95% CI of 85.6%–100% AUC was obtained under repeated randomized 5-fold cross-validation using the same 6 features.