MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

ABSTRACT Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metab...

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Autores principales:	Christian Diener, Sean M. Gibbons, Osbaldo Resendis-Antonio
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	flux balance analysis gut microbiome metagenome systems biology Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/3b1f026b12644edaa7b60fd33f68e052
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spelling	oai:doaj.org-article:3b1f026b12644edaa7b60fd33f68e0522021-12-02T18:15:47ZMICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota10.1128/mSystems.00606-192379-5077https://doaj.org/article/3b1f026b12644edaa7b60fd33f68e0522020-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00606-19https://doaj.org/toc/2379-5077ABSTRACT Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular, MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in an open-source Python package, which is available at https://github.com/micom-dev/micom. IMPORTANCE The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. Here, we present a mathematical modeling framework (named MICOM) that can recapitulate the growth rates of diverse bacterial species in the gut and can simulate metabolic interactions within microbial communities. We show that MICOM can unravel the ecological rules that shape the microbial landscape in our gut and that a given dietary or probiotic intervention can have widely different effects in different people.Christian DienerSean M. GibbonsOsbaldo Resendis-AntonioAmerican Society for Microbiologyarticleflux balance analysisgut microbiomemetagenomesystems biologyMicrobiologyQR1-502ENmSystems, Vol 5, Iss 1 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	flux balance analysis gut microbiome metagenome systems biology Microbiology QR1-502
spellingShingle	flux balance analysis gut microbiome metagenome systems biology Microbiology QR1-502 Christian Diener Sean M. Gibbons Osbaldo Resendis-Antonio MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota
description	ABSTRACT Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease, and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM, a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization, we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short-chain fatty acids (SCFAs) was heterogeneous and highly individual specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular, MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in an open-source Python package, which is available at https://github.com/micom-dev/micom. IMPORTANCE The bacterial communities that live within the human gut have been linked to health and disease. However, we are still just beginning to understand how those bacteria interact and what potential interventions to our gut microbiome can make us healthier. Here, we present a mathematical modeling framework (named MICOM) that can recapitulate the growth rates of diverse bacterial species in the gut and can simulate metabolic interactions within microbial communities. We show that MICOM can unravel the ecological rules that shape the microbial landscape in our gut and that a given dietary or probiotic intervention can have widely different effects in different people.
format	article
author	Christian Diener Sean M. Gibbons Osbaldo Resendis-Antonio
author_facet	Christian Diener Sean M. Gibbons Osbaldo Resendis-Antonio
author_sort	Christian Diener
title	MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota
title_short	MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota
title_full	MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota
title_fullStr	MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota
title_full_unstemmed	MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota
title_sort	micom: metagenome-scale modeling to infer metabolic interactions in the gut microbiota
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/3b1f026b12644edaa7b60fd33f68e052
work_keys_str_mv	AT christiandiener micommetagenomescalemodelingtoinfermetabolicinteractionsinthegutmicrobiota AT seanmgibbons micommetagenomescalemodelingtoinfermetabolicinteractionsinthegutmicrobiota AT osbaldoresendisantonio micommetagenomescalemodelingtoinfermetabolicinteractionsinthegutmicrobiota
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MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota

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