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

• Published in: ACS synthetic biology 2023-06, Vol.12 (6), p.1632-1644
• Main Authors: Roell, Garrett W., Schenk, Christina, Anthony, Winston E., Carr, Rhiannon R., Ponukumati, Aditya, Kim, Joonhoon, Akhmatskaya, Elena, Foston, Marcus, Dantas, Gautam, Moon, Tae Seok, Tang, Yinjie J., García Martín, Hector
• Format: Article
• Language: English
• Subjects: 13C-metabolic flux analysis; aromatic compounds; ATP maintenance; BASIC BIOLOGICAL SCIENCES; carbohydrates; fluxes; genome-scale models; Metabolic Engineering - methods; Metabolic Flux Analysis - methods; metabolism; omics data; peptides and proteins; Phenols - metabolism; predictive biology; Rhodococcus - genetics; Rhodococcus - metabolism
• ISSN: 2161-5063; 2161-5063
• DOI: 10.1021/acssynbio.2c00618

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 R 2 value of 0.54, while predictions based on pFBA had an inferior R 2 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 R 2 of 0.96 while pFBA showed an R 2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.