UMMS Affiliation

Program in Systems Biology; Graduate School of Biomedical Sciences

Publication Date

2020-10-06

Document Type

Article

Disciplines

Cell Biology | Cellular and Molecular Physiology | Computational Biology | Molecular Biology | Systems Biology

Abstract

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.

Keywords

Caenorhabditis elegans, data integration, metabolic network, single-cell RNA-seq, tissue metabolism

Rights and Permissions

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

10.15252/msb.20209649

Source

Mol Syst Biol. 2020 Oct;16(10):e9649. doi: 10.15252/msb.20209649. Link to article on publisher's site

Journal/Book/Conference Title

Molecular systems biology

Related Resources

Link to Article in PubMed

PubMed ID

33022146

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Share

COinS