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Title: A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism

Journal Article · · ACS Synthetic Biology
 [1]; ORCiD logo [2];  [3];  [1];  [1];  [4];  [5]; ORCiD logo [1]; ORCiD logo [3];  [1];  [1]; ORCiD logo [6]
  1. Washington University in St. Louis, MO (United States)
  2. BCAM - Basque Center for Applied Mathematics, Bilbao (Spain); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  3. Washington University in St. Louis School of Medicine, MO (United States)
  4. DOE Agile BioFoundry, Emeryville, CA (United States); DOE Joint BioEnergy Institute, Emeryville, CA (United States)
  5. BCAM - Basque Center for Applied Mathematics, Bilbao (Spain); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); IKERBASQUE, Basque Foundation for Science, Bilbao (Spain)
  6. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); BCAM - Basque Center for Applied Mathematics, Bilbao (Spain); DOE Joint BioEnergy Institute, Emeryville, CA (United States)
+ Show Author Affiliations

Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2’s flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. Furthermore, these findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE
Grant/Contract Number:
SC0018324
OSTI ID:
1973702
Alternate ID(s):
OSTI ID: 1985647
Journal Information:
ACS Synthetic Biology, Vol. 12, Issue 6; ISSN 2161-5063
Publisher:
American Chemical Society (ACS)Copyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (10)

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
ATP maintenance
genome-scale models
omics data
13C-metabolic flux analysis
predictive biology
aromatic compounds
carbohydrates
fluxes
metabolism
peptides and proteins