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Watson E, Yilmaz LS, Walhout AJM. Understanding Metabolic Regulation at a Systems Level: Metabolite Sensing, Mathematical Predictions, and Model Organisms. Annu Rev Genet 2016; 49:553-75. [PMID: 26631516 DOI: 10.1146/annurev-genet-112414-055257] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Metabolic networks are extensively regulated to facilitate tissue-specific metabolic programs and robustly maintain homeostasis in response to dietary changes. Homeostatic metabolic regulation is achieved through metabolite sensing coupled to feedback regulation of metabolic enzyme activity or expression. With a wealth of transcriptomic, proteomic, and metabolomic data available for different cell types across various conditions, we are challenged with understanding global metabolic network regulation and the resulting metabolic outputs. Stoichiometric metabolic network modeling integrated with "omics" data has addressed this challenge by generating nonintuitive, testable hypotheses about metabolic flux rewiring. Model organism studies have also yielded novel insight into metabolic networks. This review covers three topics: the feedback loops inherent in metabolic regulatory networks, metabolic network modeling, and interspecies studies utilizing Caenorhabditis elegans and various bacterial diets that have revealed novel metabolic paradigms.
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Affiliation(s)
- Emma Watson
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01605; , ,
| | - L Safak Yilmaz
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01605; , ,
| | - Albertha J M Walhout
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01605; , ,
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Watson E, Olin-Sandoval V, Hoy MJ, Li CH, Louisse T, Yao V, Mori A, Holdorf AD, Troyanskaya OG, Ralser M, Walhout AJ. Metabolic network rewiring of propionate flux compensates vitamin B12 deficiency in C. elegans. eLife 2016; 5. [PMID: 27383050 PMCID: PMC4951191 DOI: 10.7554/elife.17670] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 06/20/2016] [Indexed: 12/20/2022] Open
Abstract
Metabolic network rewiring is the rerouting of metabolism through the use of alternate enzymes to adjust pathway flux and accomplish specific anabolic or catabolic objectives. Here, we report the first characterization of two parallel pathways for the breakdown of the short chain fatty acid propionate in Caenorhabditis elegans. Using genetic interaction mapping, gene co-expression analysis, pathway intermediate quantification and carbon tracing, we uncover a vitamin B12-independent propionate breakdown shunt that is transcriptionally activated on vitamin B12 deficient diets, or under genetic conditions mimicking the human diseases propionic- and methylmalonic acidemia, in which the canonical B12-dependent propionate breakdown pathway is blocked. Our study presents the first example of transcriptional vitamin-directed metabolic network rewiring to promote survival under vitamin deficiency. The ability to reroute propionate breakdown according to B12 availability may provide C. elegans with metabolic plasticity and thus a selective advantage on different diets in the wild.
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Affiliation(s)
- Emma Watson
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States.,Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
| | | | - Michael J Hoy
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States.,Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
| | - Chi-Hua Li
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States.,Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
| | - Timo Louisse
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States.,Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
| | - Victoria Yao
- Department of Computer Science, Princeton University, Princeton, United States.,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States
| | - Akihiro Mori
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States.,Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
| | - Amy D Holdorf
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States.,Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
| | - Olga G Troyanskaya
- Department of Computer Science, Princeton University, Princeton, United States.,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States.,Simons Center for Data Analysis, Simons Foundation, New York, United States
| | - Markus Ralser
- Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.,The Francis Crick Institute, Mill Hill Laboratory, London, United Kingdom
| | - Albertha Jm Walhout
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, United States.,Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
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