In silico Analysis and Model-Driven Optimization of the Biodesulfurization Process in Pseudomonas Putida

The removal of sulfur from fuels to avoid the toxic emissions of sulfur oxides is a key challenge of the current environmental technology. Biodesulfurization (BDS) is one of the most studied processes to remove sulfur-compounds from fuels by using enzymes and/or microorganisms. The most widely used metabolic pathway to carry out this transformation is the so-called 4S route, which consists of four serial reactions that convert the model sulfur-containing compound dibenzothiophene (DBT) into 2-hydroxybiphenyl (2HBP), a sulfur free compound.

Pseudomonas putida is a bacterium that possesses a remarkable metabolic versatility, exhibits a large resistance to environmental stresses, high tolerance to organic solvents, and is amenable for genome editing. For these features, P. putida has become in one of the most suitable organisms for bioremediations and biotransformations applications, including BDS.    

In this work, we present a complete and mass and charge balanced modeling of the reactions involved in the 4S route within the context of the high-quality genome-scale metabolic reconstruction of P. putida KT2440, iJN1441. The resulting recombinant in silico strain was used subsequently for: i) finding suitable carbon sources for the optimization of the BDS process, ii) identifying the essential reactions for DBT degradation, iii) studying the impact of BDS on the whole metabolism of P. putida while identifying the main metabolic bottlenecks and iv) performing an exhaustive in silico search for growth-coupled knock outs mutants to increase the yield of the bioprocess.

In summary, we show that constraint-based reconstruction and analysis is a powerful in silico approach to identify key metabolic bottlenecks in the biodesulfurization process and also for developing more efficient and stable biocatalysts.