In silico identification of metabolic enzyme drug targets in Burkholderia pseudomallei

Abstract

The intracellular pathogen Burkholderia pseudomallei, which is endemic to parts of southeast Asia and northern Australia, causes the disease melioidosis. Although acute infections can be treated with antibiotics, melioidosis is difficult to cure, and some patients develop chronic infections or a recrudescence of the disease months or years after treatment of the initial infection. B. pseudomallei strains have a high level of natural resistance to a variety of antibiotics, and with limited options for new antibiotics on the horizon, new alternatives are needed. The aim of the present study was to characterize the metabolic capabilities of B. pseudomallei, identify metabolites crucial for pathogen survival, understand the metabolic interactions that occur between pathogen and host cells, and determine if metabolic enzymes produced by the pathogen might be potential antibacterial targets. This aim was accomplished through genome scale metabolic modeling under different external conditions: 1) including all nutrients that could be consumed by the model, and 2) providing only the nutrients available in culture media. Using this approach, candidate chokepoint enzymes were identified, then knocked out in silico under the different nutrient conditions. The effect of each knockout on the metabolic network was examined. When five of the candidate chokepoints were knocked out in silico, the flux through the B. pseudomallei network was decreased, depending on the nutrient conditions. These results demonstrate the utility of genome-scale metabolic modeling methods for drug target identification in B. pseudomallei.