RT Journal Article
SR Electronic
T1 <em>In silico</em> identification of metabolic enzyme drug targets in <em>Burkholderia pseudomallei</em>
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 034306
DO 10.1101/034306
A1 Jean F. Challacombe
YR 2015
UL http://biorxiv.org/content/early/2015/12/13/034306.abstract
AB 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, alternatives are needed.The aim of the present study was to characterize the metabolic capabilities of B. pseudomallei, identify metabolites and aspects of the metabolic network crucial for pathogen survival, understand the metabolic interactions that occur between pathogen and host cells, and determine if any of the metabolic enzymes produced by the pathogen might be potential antibacterial targets.This aim was accomplished through genome scale metabolic modeling of B. pseudomallei under different external conditions, including all nutrients that could be consumed by the model and only the nutrients available in culture media. Using this approach, candidate chokepoints were identified, then knocked out in silico under the different nutrient conditions. The effect of each knockout on the metabolic network was examined. When four 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 in silico studies of pathogen metabolism and for drug target identification in B. pseudomallei.