Modeling the Context-Dependent Associations between the Gut Microbiome, Its Environment, and Host Health

ABSTRACT Changes in the gut microbiome are often associated with disease. One of the major goals in microbiome research is determining which components of this complex system are responsible for the observed differences in health state. Most studies apply a reductionist approach, wherein individual...
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Autor*in:	Thomas J. Sharpton [verfasserIn] Christopher A. Gaulke [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Übergeordnetes Werk:	In: mBio - American Society for Microbiology, 2010, 6(2015), 5
Übergeordnetes Werk:	volume:6 ; year:2015 ; number:5

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1128/mBio.01367-15

Katalog-ID:	DOAJ070433054

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author	Thomas J. Sharpton
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topic_title	QR1-502 Modeling the Context-Dependent Associations between the Gut Microbiome, Its Environment, and Host Health
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title	Modeling the Context-Dependent Associations between the Gut Microbiome, Its Environment, and Host Health
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abstract	ABSTRACT Changes in the gut microbiome are often associated with disease. One of the major goals in microbiome research is determining which components of this complex system are responsible for the observed differences in health state. Most studies apply a reductionist approach, wherein individual organisms are evaluated independently of the surrounding context of the microbiome. While such methods have yielded valuable insights into the microbiome, they fail to identify patterns that may be obscured by contextual variation. A recent report by Schubert et al. [A. M. Schubert, H. Sinani, and P. D. Schloss, mBio 6(4):e00974-15, 2015, doi: 10.1128/mBio.00974-15] communicates an alternative approach to the study of the microbiome's association with host health. By coupling a multifactored experimental design with regression modeling, the authors are able to profile context-dependent changes in the microbiome and predict health status. This work underscores the value of incorporating model-based procedures into the investigation of the microbiome and illustrates the potential clinical transformations that may arise through their use.
abstractGer	ABSTRACT Changes in the gut microbiome are often associated with disease. One of the major goals in microbiome research is determining which components of this complex system are responsible for the observed differences in health state. Most studies apply a reductionist approach, wherein individual organisms are evaluated independently of the surrounding context of the microbiome. While such methods have yielded valuable insights into the microbiome, they fail to identify patterns that may be obscured by contextual variation. A recent report by Schubert et al. [A. M. Schubert, H. Sinani, and P. D. Schloss, mBio 6(4):e00974-15, 2015, doi: 10.1128/mBio.00974-15] communicates an alternative approach to the study of the microbiome's association with host health. By coupling a multifactored experimental design with regression modeling, the authors are able to profile context-dependent changes in the microbiome and predict health status. This work underscores the value of incorporating model-based procedures into the investigation of the microbiome and illustrates the potential clinical transformations that may arise through their use.
abstract_unstemmed	ABSTRACT Changes in the gut microbiome are often associated with disease. One of the major goals in microbiome research is determining which components of this complex system are responsible for the observed differences in health state. Most studies apply a reductionist approach, wherein individual organisms are evaluated independently of the surrounding context of the microbiome. While such methods have yielded valuable insights into the microbiome, they fail to identify patterns that may be obscured by contextual variation. A recent report by Schubert et al. [A. M. Schubert, H. Sinani, and P. D. Schloss, mBio 6(4):e00974-15, 2015, doi: 10.1128/mBio.00974-15] communicates an alternative approach to the study of the microbiome's association with host health. By coupling a multifactored experimental design with regression modeling, the authors are able to profile context-dependent changes in the microbiome and predict health status. This work underscores the value of incorporating model-based procedures into the investigation of the microbiome and illustrates the potential clinical transformations that may arise through their use.
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title_short	Modeling the Context-Dependent Associations between the Gut Microbiome, Its Environment, and Host Health
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