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

Rhodococcus opacus is a bacterium thathas a hightolerance to aromatic compounds and can produce significant amountsof triacylglycerol (TAG). Here, we present iGR1773, the first genome-scalemodel (GSM) of R. opacus PD630 metabolism based onits genomic sequence and associated data. The model includes 1773genes, 3025 reactions, and 1956 metabolites, was developed in a reproduciblemanner using CarveMe, and was evaluated through Metabolic Model tests(MEMOTE). We combine the model with two Constraint-Based Reconstructionand Analysis (COBRA) methods that use transcriptomics data to predictgrowth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlationwith Transcriptomic data). Growth rates are best predicted by E-Flux2.Flux profiles are more accurately predicted by E-Flux2 than flux balanceanalysis (FBA) and parsimonious FBA (pFBA), when compared to 44 centralcarbon fluxes measured by C-13-Metabolic Flux Analysis (C-13-MFA). Under glucose-fed conditions, E-Flux2 presents an R (2) value of 0.54, while predictions based onpFBA had an inferior R (2) of 0.28. We attributethis improved performance to the extra activity information providedby the transcriptomics data. For phenol-fed metabolism, in which thesubstrate first enters the TCA cycle, E-Flux2's flux predictionsdisplay a high R (2) of 0.96 while pFBA showedan R (2) of 0.93. We also show that glucosemetabolism and phenol metabolism function with similar relative ATPmaintenance costs. These findings demonstrate that iGR1773 can helpthe metabolic engineering community predict aromatic substrate utilizationpatterns and perform computational strain design.