Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities.
anaerobic digestion
gut microbiota
metabolic modeling
metagenomics
metatranscriptomics
microbial community
Journal
Cell reports methods
ISSN: 2667-2375
Titre abrégé: Cell Rep Methods
Pays: United States
ID NLM: 9918227360606676
Informations de publication
Date de publication:
23 01 2023
23 01 2023
Historique:
received:
28
04
2022
revised:
07
10
2022
accepted:
12
12
2022
entrez:
23
2
2023
pubmed:
24
2
2023
medline:
24
2
2023
Statut:
epublish
Résumé
Multi-omics data integration via mechanistic models of metabolism is a scalable and flexible framework for exploring biological hypotheses in microbial systems. However, although most microorganisms are unculturable, such multi-omics modeling is limited to isolate microbes or simple synthetic communities. Here, we developed an approach for modeling microbial activity and interactions that leverages the reconstruction of metagenome-assembled genomes and associated genome-centric metatranscriptomes. At its core, we designed a method for condition-specific metabolic modeling of microbial communities through the integration of metatranscriptomic data. Using this approach, we explored the behavior of anaerobic digestion consortia driven by hydrogen availability and human gut microbiota dysbiosis associated with Crohn's disease, identifying condition-dependent amino acid requirements in archaeal species and a reduced short-chain fatty acid exchange network associated with disease, respectively. Our approach can be applied to complex microbial communities, allowing a mechanistic contextualization of multi-omics data on a metagenome scale.
Identifiants
pubmed: 36814842
doi: 10.1016/j.crmeth.2022.100383
pii: S2667-2375(22)00288-0
pmc: PMC9939383
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Pagination
100383Informations de copyright
© 2022 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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