A Genome‐Scale Modeling Approach to Quantify Biofilm Component Growth of Salmonella Typhimurium

Abstract

Salmonella typhimurium (S. typhimurium) is an extremely dangerous foodborne bacterium that infects both animal and human subjects, causing fatal diseases around the world. Salmonella's robust virulence, antibiotic-resistant nature, and capacity to survive under harsh conditions are largely due to its ability to form resilient biofilms. Multiple genome-scale metabolic models have been developed to study the complex and diverse nature of this organism's metabolism; however, none of these models fully integrated the reactions and mechanisms required to study the influence of biofilm formation. This work developed a systems-level approach to study the adjustment of intracellular metabolism of S. typhimurium during biofilm formation. The most advanced metabolic reconstruction currently available, STM_v1.0, was 1st extended to include the formation of the extracellular biofilm matrix. Flux balance analysis was then employed to study the influence of biofilm formation on cellular growth rate and the production rates of biofilm components. With biofilm formation present, biomass growth was examined under nutrient rich and nutrient deficient conditions, resulting in overall growth rates of 0.8675 and 0.6238 h respectively. Investigation of intracellular flux variation during biofilm formation resulted in the elucidation of 32 crucial reactions, and associated genes, whose fluxes most significantly adapt during the physiological response. Experimental data were found in the literature to validate the importance of these genes for the biofilm formation of S. typhimurium. This preliminary investigation on the adjustment of intracellular metabolism of S. typhimurium during biofilm formation will serve as a platform to generate hypotheses for further experimental study on the biofilm formation of this virulent bacterium.

Biomedical applications of genome-scale metabolic network reconstructions of human pathogens

“…Virulence factors with production mechanisms independent of pathogen growth have the potential to be effective therapeutic targets with limited selective pressure for resistance [49]. Genome-scale metabolic models of bacterial and fungal human pathogens have been used to identify novel virulence genes as well as to evaluate the interconnectivity between virulence factor synthesis, pathogen metabolism, and growth [20,30,50] (Table 1).…”

Neural network model for growth of Salmonella Typhimurium in brain heart infusion broth

“…Physiological state is an important variable that affects growth of Salmonella when it is shifted to a new environment (Oscar, ; Ribaudo et al ., ). Physiological state is an important variable for growth of other human bacterial pathogens as well (Skandamis et al ., ; Hereu et al ., ).…”