Program in Systems Biology; Graduate School of Biomedical Sciences
Cell Biology | Cellular and Molecular Physiology | Computational Biology | Molecular Biology | Systems Biology
Metabolism 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.
Caenorhabditis elegans, data integration, metabolic network, single-cell RNA-seq, tissue metabolism
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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.
DOI of Published Version
Mol Syst Biol. 2020 Oct;16(10):e9649. doi: 10.15252/msb.20209649. Link to article on publisher's site
Molecular systems biology
Yilmaz LS, Li X, Nanda S, Fox B, Schroeder F, Walhout AJ. (2020). Modeling tissue-relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels. Open Access Publications by UMMS Authors. https://doi.org/10.15252/msb.20209649. Retrieved from https://escholarship.umassmed.edu/oapubs/4430
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.