Microbiome Data Distinguish Patients with <named-content content-type="genus-species">Clostridium difficile</named-content> Infection and Non-<named-content content-type="genus-species">C. difficile</named-content>-Associated Diarrhea from Healthy Controls

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...

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Autores principales:	Alyxandria M. Schubert, Mary A. M. Rogers, Cathrin Ring, Jill Mogle, Joseph P. Petrosino, Vincent B. Young, David M. Aronoff, Patrick D. Schloss
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2014
Materias:	Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/2d7b3ddbf27a42c4a34b319483b62a47
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spelling	oai:doaj.org-article:2d7b3ddbf27a42c4a34b319483b62a472021-11-15T15:47:38ZMicrobiome Data Distinguish Patients with <named-content content-type="genus-species">Clostridium difficile</named-content> Infection and Non-<named-content content-type="genus-species">C. difficile</named-content>-Associated Diarrhea from Healthy Controls10.1128/mBio.01021-142150-7511https://doaj.org/article/2d7b3ddbf27a42c4a34b319483b62a472014-07-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.01021-14https://doaj.org/toc/2150-7511ABSTRACT 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.Alyxandria M. SchubertMary A. M. RogersCathrin RingJill MogleJoseph P. PetrosinoVincent B. YoungDavid M. AronoffPatrick D. SchlossAmerican Society for MicrobiologyarticleMicrobiologyQR1-502ENmBio, Vol 5, Iss 3 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Microbiology QR1-502
spellingShingle	Microbiology QR1-502 Alyxandria M. Schubert Mary A. M. Rogers Cathrin Ring Jill Mogle Joseph P. Petrosino Vincent B. Young David M. Aronoff Patrick D. Schloss Microbiome Data Distinguish Patients with <named-content content-type="genus-species">Clostridium difficile</named-content> Infection and Non-<named-content content-type="genus-species">C. difficile</named-content>-Associated Diarrhea from Healthy Controls
description	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.
format	article
author	Alyxandria M. Schubert Mary A. M. Rogers Cathrin Ring Jill Mogle Joseph P. Petrosino Vincent B. Young David M. Aronoff Patrick D. Schloss
author_facet	Alyxandria M. Schubert Mary A. M. Rogers Cathrin Ring Jill Mogle Joseph P. Petrosino Vincent B. Young David M. Aronoff Patrick D. Schloss
author_sort	Alyxandria M. Schubert
title	Microbiome Data Distinguish Patients with <named-content content-type="genus-species">Clostridium difficile</named-content> Infection and Non-<named-content content-type="genus-species">C. difficile</named-content>-Associated Diarrhea from Healthy Controls
title_short	Microbiome Data Distinguish Patients with <named-content content-type="genus-species">Clostridium difficile</named-content> Infection and Non-<named-content content-type="genus-species">C. difficile</named-content>-Associated Diarrhea from Healthy Controls
title_full	Microbiome Data Distinguish Patients with <named-content content-type="genus-species">Clostridium difficile</named-content> Infection and Non-<named-content content-type="genus-species">C. difficile</named-content>-Associated Diarrhea from Healthy Controls
title_fullStr	Microbiome Data Distinguish Patients with <named-content content-type="genus-species">Clostridium difficile</named-content> Infection and Non-<named-content content-type="genus-species">C. difficile</named-content>-Associated Diarrhea from Healthy Controls
title_full_unstemmed	Microbiome Data Distinguish Patients with <named-content content-type="genus-species">Clostridium difficile</named-content> Infection and Non-<named-content content-type="genus-species">C. difficile</named-content>-Associated Diarrhea from Healthy Controls
title_sort	microbiome data distinguish patients with <named-content content-type="genus-species">clostridium difficile</named-content> infection and non-<named-content content-type="genus-species">c. difficile</named-content>-associated diarrhea from healthy controls
publisher	American Society for Microbiology
publishDate	2014
url	https://doaj.org/article/2d7b3ddbf27a42c4a34b319483b62a47
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Microbiome Data Distinguish Patients with <named-content content-type="genus-species">Clostridium difficile</named-content> Infection and Non-<named-content content-type="genus-species">C. difficile</named-content>-Associated Diarrhea from Healthy Controls

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