A2 Vertaisarvioitu katsausartikkeli tieteellisessä lehdessä
Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview
Tekijät: Partho Sen, Matej Orešič
Julkaisuvuosi: 2019
Journal: Metabolites
Tietokannassa oleva lehden nimi: Metabolites
Lehden akronyymi: Metabolites
Vuosikerta: 9
Numero: 2
Sivujen määrä: 15
ISSN: 2218-1989
eISSN: 2218-1989
DOI: https://doi.org/10.3390/metabo9020022
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/39302396
Tiivistelmä
There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.
There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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