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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Society for Microbiology
2014-07-01
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| Series: | mBio |
| Online Access: | https://journals.asm.org/doi/10.1128/mBio.01021-14 |
| _version_ | 1818346753841168384 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T17:23:17Z |
| format | Article |
| id | doaj.art-2d7b3ddbf27a42c4a34b319483b62a47 |
| institution | Directory Open Access Journal |
| issn | 2150-7511 |
| language | English |
| last_indexed | 2024-12-13T17:23:17Z |
| publishDate | 2014-07-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | mBio |
| spelling | doaj.art-2d7b3ddbf27a42c4a34b319483b62a472022-12-21T23:37:15ZengAmerican Society for MicrobiologymBio2150-75112014-07-015310.1128/mBio.01021-14Microbiome 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 ControlsAlyxandria M. Schubert0Mary A. M. Rogers1Cathrin Ring2Jill Mogle3Joseph P. Petrosino4Vincent B. Young5David M. Aronoff6Patrick D. Schloss7Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USADepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USADepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USADepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USADepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, USADepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USADepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USADepartment of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USAABSTRACT 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.https://journals.asm.org/doi/10.1128/mBio.01021-14 |
| spellingShingle | 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 mBio |
| 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_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_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_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 |
| url | https://journals.asm.org/doi/10.1128/mBio.01021-14 |
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