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

Area of Science:

• Systems Biology
• Metabolic Engineering
• Microbial Biotechnology

Background:

• Rhodococcus opacus PD630 is a robust bacterium capable of tolerating aromatic compounds and accumulating triacylglycerol (TAG).
• Accurate metabolic models are crucial for understanding and engineering microbial capabilities for biotechnological applications.

Purpose of the Study:

• To construct the first genome-scale metabolic model (GSM) for R. opacus PD630, named iGR1773.
• To evaluate the model's predictive accuracy for growth rates and metabolic fluxes using transcriptomics data.
• To assess the model's utility in predicting aromatic substrate utilization and guiding computational strain design.

Main Methods:

• Genome-scale model reconstruction using CarveMe, incorporating genomic data for R. opacus PD630.
• Model validation using Metabolic Model tests (MEMOTE).
• Integration with Constraint-Based Reconstruction and Analysis (COBRA) methods, specifically E-Flux2 and SPOT, utilizing transcriptomics data.
• Comparison of predicted fluxes with experimental 13C-Metabolic Flux Analysis (13C-MFA) data for glucose and phenol metabolism.

Main Results:

• The iGR1773 model comprises 1773 genes, 3025 reactions, and 1956 metabolites.
• E-Flux2 demonstrated superior prediction of growth rates and metabolic fluxes compared to FBA and pFBA, particularly when incorporating transcriptomics data.
• High R2 values (0.54 for glucose, 0.96 for phenol) were achieved with E-Flux2, significantly outperforming pFBA (0.28 for glucose, 0.93 for phenol).
• Similar relative ATP maintenance costs were observed for both glucose and phenol metabolism.

Conclusions:

• The iGR1773 GSM provides a valuable computational tool for R. opacus metabolic engineering.
• The integration of transcriptomics data via E-Flux2 significantly enhances metabolic flux prediction accuracy.
• This model facilitates predictions of aromatic substrate utilization and supports computational strain design for improved microbial functions.