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Research Article

Microbiome Data Distinguish Patients with Clostridium difficile Infection and Non-C. difficile-Associated Diarrhea from Healthy Controls

Alyxandria M. Schubert, Mary A. M. Rogers, Cathrin Ring, Jill Mogle, Joseph P. Petrosino, Vincent B. Young, David M. Aronoff, Patrick D. Schloss
Claire M. Fraser, Editor
Alyxandria M. Schubert
aDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
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Mary A. M. Rogers
bDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
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Cathrin Ring
bDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
cDivision of Infectious Diseases, University of Michigan, Ann Arbor, Michigan, USA
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Jill Mogle
bDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
cDivision of Infectious Diseases, University of Michigan, Ann Arbor, Michigan, USA
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Joseph P. Petrosino
dDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, USA
eAlkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, Texas, USA
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Vincent B. Young
aDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
bDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
cDivision of Infectious Diseases, University of Michigan, Ann Arbor, Michigan, USA
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David M. Aronoff
aDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
bDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
cDivision of Infectious Diseases, University of Michigan, Ann Arbor, Michigan, USA
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Patrick D. Schloss
aDepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
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Claire M. Fraser
University of Maryland, School of Medicine
Roles: Editor
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DOI: 10.1128/mBio.01021-14
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ABSTRACT

Antibiotic usage is the most commonly cited risk factor for hospital-acquired Clostridium difficile infections (CDI). The increased risk is due to disruption of the indigenous microbiome and a subsequent decrease in colonization resistance by the perturbed bacterial community; however, the specific changes in the microbiome that lead to increased risk are poorly understood. We developed statistical models that incorporated microbiome data with clinical and demographic data to better understand why individuals develop CDI. The 16S rRNA genes were sequenced from the feces of 338 individuals, including cases, diarrheal controls, and nondiarrheal controls. We modeled CDI and diarrheal status using multiple clinical variables, including age, antibiotic use, antacid use, and other known risk factors using logit regression. This base model was compared to models that incorporated microbiome data, using diversity metrics, community types, or specific bacterial populations, to identify characteristics of the microbiome associated with CDI susceptibility or resistance. The addition of microbiome data significantly improved our ability to distinguish CDI status when comparing cases or diarrheal controls to nondiarrheal controls. However, only when we assigned samples to community types was it possible to differentiate cases from diarrheal controls. Several bacterial species within the Ruminococcaceae, Lachnospiraceae, Bacteroides, and Porphyromonadaceae were largely absent in cases and highly associated with nondiarrheal controls. The improved discriminatory ability of our microbiome-based models confirms the theory that factors affecting the microbiome influence CDI.

IMPORTANCE The gut microbiome, composed of the trillions of bacteria residing in the gastrointestinal tract, is responsible for a number of critical functions within the host. These include digestion, immune system stimulation, and colonization resistance. The microbiome’s role in colonization resistance, which is the ability to prevent and limit pathogen colonization and growth, is key for protection against Clostridium difficile infections. However, the bacteria that are important for colonization resistance have not yet been elucidated. Using statistical modeling techniques and different representations of the microbiome, we demonstrated that several community types and the loss of several bacterial populations, including Bacteroides, Lachnospiraceae, and Ruminococcaceae, are associated with CDI. Our results emphasize the importance of considering the microbiome in mediating colonization resistance and may also direct the design of future multispecies probiotic therapies.

  • Copyright © 2014 Schubert et al.

This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Microbiome Data Distinguish Patients with Clostridium difficile Infection and Non-C. difficile-Associated Diarrhea from Healthy Controls
Alyxandria M. Schubert, Mary A. M. Rogers, Cathrin Ring, Jill Mogle, Joseph P. Petrosino, Vincent B. Young, David M. Aronoff, Patrick D. Schloss
mBio May 2014, 5 (3) e01021-14; DOI: 10.1128/mBio.01021-14

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Microbiome Data Distinguish Patients with Clostridium difficile Infection and Non-C. difficile-Associated Diarrhea from Healthy Controls
Alyxandria M. Schubert, Mary A. M. Rogers, Cathrin Ring, Jill Mogle, Joseph P. Petrosino, Vincent B. Young, David M. Aronoff, Patrick D. Schloss
mBio May 2014, 5 (3) e01021-14; DOI: 10.1128/mBio.01021-14
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