Modeling tissue-relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels
UMass Chan AffiliationsGraduate School of Biomedical Sciences
Program in Systems Biology
Document TypeJournal Article
Cellular and Molecular Physiology
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AbstractMetabolism is a highly compartmentalized process that provides building blocks for biomass generation during development, homeostasis, and wound healing, and energy to support cellular and organismal processes. In metazoans, different cells and tissues specialize in different aspects of metabolism. However, studying the compartmentalization of metabolism in different cell types in a whole animal and for a particular stage of life is difficult. Here, we present MEtabolic models Reconciled with Gene Expression (MERGE), a computational pipeline that we used to predict tissue-relevant metabolic function at the network, pathway, reaction, and metabolite levels based on single-cell RNA-sequencing (scRNA-seq) data from the nematode Caenorhabditis elegans. Our analysis recapitulated known tissue functions in C. elegans, captured metabolic properties that are shared with similar tissues in human, and provided predictions for novel metabolic functions. MERGE is versatile and applicable to other systems. We envision this work as a starting point for the development of metabolic network models for individual cells as scRNA-seq continues to provide higher-resolution gene expression data.
Mol Syst Biol. 2020 Oct;16(10):e9649. doi: 10.15252/msb.20209649. Link to article on publisher's site
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/41639
RightsCopyright 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as Copyright 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.