A gap-filling algorithm for prediction of metabolic interactions in microbial communities.
Algorithms
Bacteroidetes
/ metabolism
Bifidobacterium adolescentis
/ metabolism
Computational Biology
Computer Simulation
Databases, Factual
Escherichia coli
/ metabolism
Faecalibacterium prausnitzii
/ metabolism
Gastrointestinal Microbiome
/ physiology
Humans
Metabolic Networks and Pathways
Microbiota
/ physiology
Models, Biological
Peptococcaceae
/ metabolism
Synthetic Biology
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
received:
04
05
2021
accepted:
05
10
2021
revised:
11
11
2021
pubmed:
2
11
2021
medline:
18
12
2021
entrez:
1
11
2021
Statut:
epublish
Résumé
The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.
Identifiants
pubmed: 34723959
doi: 10.1371/journal.pcbi.1009060
pii: PCOMPBIOL-D-21-00834
pmc: PMC8584699
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1009060Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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