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Constraint-Based Model of Shewanella oneidensis MR-1 Metabolism: A Tool for Data Analysis and Hypothesis Generation
Authors:Grigoriy E Pinchuk  Eric A Hill  Oleg V Geydebrekht  Jessica De Ingeniis  Xiaolin Zhang  Andrei Osterman  James H Scott  Samantha B Reed  Margaret F Romine  Allan E Konopka  Alexander S Beliaev  Jim K Fredrickson  Jennifer L Reed
Institution:1.Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States of America;2.Burnham Institute for Medical Research, La Jolla, California, United States of America;3.Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America;4.Department of Earth Sciences, Dartmouth College, Hanover, New Hampshire, United States of America;Medical College of Wisconsin, United States of America
Abstract:Shewanellae are gram-negative facultatively anaerobic metal-reducing bacteria commonly found in chemically (i.e., redox) stratified environments. Occupying such niches requires the ability to rapidly acclimate to changes in electron donor/acceptor type and availability; hence, the ability to compete and thrive in such environments must ultimately be reflected in the organization and utilization of electron transfer networks, as well as central and peripheral carbon metabolism. To understand how Shewanella oneidensis MR-1 utilizes its resources, the metabolic network was reconstructed. The resulting network consists of 774 reactions, 783 genes, and 634 unique metabolites and contains biosynthesis pathways for all cell constituents. Using constraint-based modeling, we investigated aerobic growth of S. oneidensis MR-1 on numerous carbon sources. To achieve this, we (i) used experimental data to formulate a biomass equation and estimate cellular ATP requirements, (ii) developed an approach to identify cycles (such as futile cycles and circulations), (iii) classified how reaction usage affects cellular growth, (iv) predicted cellular biomass yields on different carbon sources and compared model predictions to experimental measurements, and (v) used experimental results to refine metabolic fluxes for growth on lactate. The results revealed that aerobic lactate-grown cells of S. oneidensis MR-1 used less efficient enzymes to couple electron transport to proton motive force generation, and possibly operated at least one futile cycle involving malic enzymes. Several examples are provided whereby model predictions were validated by experimental data, in particular the role of serine hydroxymethyltransferase and glycine cleavage system in the metabolism of one-carbon units, and growth on different sources of carbon and energy. This work illustrates how integration of computational and experimental efforts facilitates the understanding of microbial metabolism at a systems level.
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