Dataset Information

M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models.

ABSTRACT: The gut microbiota is the human body's largest population of microorganisms that interact with human intestinal cells. They use ingested nutrients for fundamental biological processes and have important impacts on human physiology, immunity, and metabolome in the gastrointestinal tract. Here, we present M2R, a Python add-on to cobrapy that allows incorporating information about the gut microbiota metabolism models to human genome-scale metabolic models (GEMs) like RECON3D. The idea behind the software is to modify the lower bounds of the exchange reactions in the model using aggregated in- and out-fluxes from selected microbes. M2R enables users to quickly and easily modify the pool of the metabolites that enter and leave the GEM, which is particularly important for those looking into an analysis of the metabolic interaction between the gut microbiota and human cells and its dysregulation. M2R is freely available under an MIT License at https://github.com/e-weglarz-tomczak/m2r. Supplementary data are available at Bioinformatics online.

SUBMITTER: Weglarz-Tomczak E

PROVIDER: S-EPMC8428599 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models.

Weglarz-Tomczak Ewelina E Tomczak Jakub M JM Brul Stanley S

Bioinformatics (Oxford, England) 20210901 17

<h4>Motivation</h4>The gut microbiota is the human body's largest population of microorganisms that interact with human intestinal cells. They use ingested nutrients for fundamental biological processes and have important impacts on human physiology, immunity and metabolome in the gastrointestinal tract.<h4>Results</h4>Here, we present M2R, a Python add-on to cobrapy that allows incorporating information about the gut microbiota metabolism models to human genome-scale metabolic models (GEMs) lik ...[more]

PMID: 33523116

Similar Datasets

Project description:BackgroundThe genomic information of a species allows for the genome-scale reconstruction of its metabolic capacity. Such a metabolic reconstruction gives support to metabolic engineering, but also to integrative bioinformatics and visualization. Sequence-based automatic reconstructions require extensive manual curation, which can be very time-consuming. Therefore, we present a method to accelerate the time-consuming process of network reconstruction for a query species. The method exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks.ResultsWe have evaluated the method using Lactococcus lactis IL1403, for which a genome-scale metabolic network was published recently. We recovered most of the gene-reaction associations (i.e. 74 - 85%) which are incorporated in the published network. Moreover, we predicted over 200 additional genes to be associated to reactions, including genes with unknown function, genes for transporters and genes with specific metabolic reactions, which are good candidates for an extension to the previously published network. In a comparison of our developed method with the well-established approach Pathologic, we predicted 186 additional genes to be associated to reactions. We also predicted a relatively high number of complete conserved protein complexes, which are derived from curated metabolic networks, illustrating the potential predictive power of our method for protein complexes.ConclusionWe show that our methodology can be applied to accelerate the reconstruction of genome-scale metabolic networks by taking optimal advantage of existing, manually curated networks. As orthology detection is the first step in the method, only the translated open reading frames (ORFs) of a newly sequenced genome are necessary to reconstruct a metabolic network. When more manually curated metabolic networks will become available in the near future, the usefulness of our method in network prediction is likely to increase.

Dataset Information

M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models.

Publications

M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets