Abstract
MotivationThe 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.ResultsHere, 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.Availability and implementationM2R is freely available under an MIT License at https://github.com/e-weglarz-tomczak/m2r.Supplementary informationSupplementary data are available at Bioinformatics online.
Highlights
The human cell metabolism is regulated by cell-intrinsic and cell-extrinsic factors
Genome-wide modeling provides a useful approach to uncover the molecular basis of metabolism (Mendoza et al, 2019)
Human genome-scale metabolic models (GEMs) have been extensively used for analyzing mRNA expression data to elucidate how changes in gene expression impact cell physiology (Pandey et al, 2019)
Summary
The human cell metabolism is regulated by cell-intrinsic and cell-extrinsic factors. For instance, cell-intrinsic factors are genes and hormones, while cell-extrinsic factors are associated with the environment, and include nutrients availability, tissue-specific context and microbiota that resides on or within human tissues. Most comprehensive human genome-wide model (GEM) Recon3D constitutes a computational resource that includes three-dimensional (3D) metabolite and protein structure data, and enable accurate integrated analyses of metabolic functions in humans (Brunk et al, 2018). Integration of the nutrients availability data into genome-wide models improves prediction of metabolic phenotypes (WReglarz-Tomczak et al, 2020; Zampieri et al, 2019).
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