News Feature | May 9, 2014

Virtual Bacteria Model Helps In Cystic Fibrosis Research

By Estel Grace Masangkay

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Researchers at the University of Virginia School of Medicine have reconstructed a virtual model of drug resistant bacteria in an effort to advance research in cystic fibrosis infections.

The new metabolic model allows the researchers to analyze Burkholderia cenocepacia and Burkholderia multivorans, the two bacteria responsible for the majority of infections in patients with cystic fibrosis. The bacteria’s resistance to treatments makes them difficult to eradicate and causes rapid deterioration of the lungs.

Dr. Jason Papin, principal investigator at UVA, said that the two bacteria remain largely unknown and have only recently been identified as significant pathogens in cystic fibrosis. “We want to be able to use the models to predict good drug targets, to try to understand why the pathogen behaves the way it does, to understand how it’s going to evolve under pressure of antibiotics. These bugs have a lot of natural antibiotic-resistance mechanisms – you give some antibiotics and the bug adapts and rewires its network in order to evade that drug, so you want to be able to come up with new targets,” he said.

With the new computer model, scientists are able to study the bacteria’s growth capability, reactions to antibiotics, virulence, and other characteristics. The bacteria’s behavioral data will be used as valuable indicators for the research and development of new therapeutic targets.

To make the virtual model, the team used the complete sequenced genome of the two bacteria, as well as a sample of B. cenocepacia from the lungs of a cystic fibrosis patient and a sample of B. multivorans taken from the soil. The computer models share 1,437 metabolic reactions, though each contains a unique set. Dr. Papin pointed out that the bacteria’s profiles present key differences which can be used as avenues for new research. “We use these models to delineate the functional impact of some of these genetic differences. Where one enzyme is present in one bug and absent in the other, what are the functional effects of that? Does it make one bug more capable of growing in a particular environment?”

The team’s models will help scientists understand the bacteria’s biological similarities and differences, which can help generate new hypotheses. It will also serve as a valuable tool in predicting phenotypes of pathogenesis as well as support research in cystic fibrosis treatments.

Dr. Papin stated, “We want to be able to use the models to predict good drug targets, to try to understand why the pathogen behaves the way it does, to understand how it’s going to evolve under pressure of antibiotics.”

The researchers’ bacteria model is described in the Journal of Bacteriology.

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