Model-based assessment of mammalian cell metabolic functionalities using omics data

Anne Richelle; Benjamin P. Kellman; Alexander T. Wenzel; Austin W.T. Chiang; Tyler Reagan; Jahir M. Gutierrez; Chintan Joshi; Shangzhong Li; Joanne K. Liu; Helen Masson; Jooyong Lee; Zerong Li; Laurent Heirendt; Christophe Trefois; Edwin F. Juarez; Tyler Bath; David Borland; Jill P. Mesirov; Kimberly Robasky; Nathan E. Lewis

Cell Reports: Methods (Jul 2021)

Model-based assessment of mammalian cell metabolic functionalities using omics data

Anne Richelle,
Benjamin P. Kellman,
Alexander T. Wenzel,
Austin W.T. Chiang,
Tyler Reagan,
Jahir M. Gutierrez,
Chintan Joshi,
Shangzhong Li,
Joanne K. Liu,
Helen Masson,
Jooyong Lee,
Zerong Li,
Laurent Heirendt,
Christophe Trefois,
Edwin F. Juarez,
Tyler Bath,
David Borland,
Jill P. Mesirov,
Kimberly Robasky,
Nathan E. Lewis

Affiliations

Anne Richelle: Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
Benjamin P. Kellman: Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
Alexander T. Wenzel: Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Medicine, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
Austin W.T. Chiang: Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
Tyler Reagan: Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
Jahir M. Gutierrez: Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
Chintan Joshi: Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
Shangzhong Li: Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
Joanne K. Liu: Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
Helen Masson: Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
Jooyong Lee: Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
Zerong Li: Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
Laurent Heirendt: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
Christophe Trefois: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
Edwin F. Juarez: Department of Medicine, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
Tyler Bath: Department of Biomedical Informatics, UC San Diego Health, University of California, San Diego, La Jolla, CA 92093, USA
David Borland: Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
Jill P. Mesirov: Department of Medicine, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
Kimberly Robasky: Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Carolina Health and Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Nathan E. Lewis: Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Corresponding author

Journal volume & issue: Vol. 1, no. 3
p. 100040

Abstract

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Summary: Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org—CellFie). Motivation: The existence of complex interdependencies between genes, proteins, and metabolites challenge the interpretation of omics experiments. Data-driven approaches have been particularly useful for identifying gene sets of interest. However, it remains difficult to gain a mechanistic understanding of and to quantify a cell's functions from enriched ontology terms. Genome-scale systems biology models can be used to analyze these datasets, but they require specialized training and can take extensive effort to deploy. Here, we developed a framework to directly predict how changes in omics experiments correspond to cell or tissue functions, thereby facilitating phenotype-relevant interpretation of these complex datum types.

Published in Cell Reports: Methods

ISSN: 2667-2375 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Technology: Chemical technology: Biotechnology; Science: Chemistry: Organic chemistry: Biochemistry
Website: https://www.cell.com/cell-reports-methods

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