Computer Reconstruction of Drug-Resistant Bacteria Valuable to Cystic Fibrosis Research

cystic fibrosis and drug-resistant bacteriaScientists will now be able to investigate the drug-resistant bacteria Burkholderia cenocepacia and Burkholderia multivorans without first setting foot into a laboratory, thanks to a metabolic computation model developed by researchers at University of Virginia School of Medicine and Emory University School of Medicine. The two bacteria account for the majority of infections in patients with cystic fibrosis and wreak havoc on the lungs. Since they are drug-resistant, they are difficult to eradicate and lead to rapid deterioration of the lungs both directly and by contributing to other infections.

“For these two particular bugs, there’s not a lot known and they have only recently been appreciated as important pathogens in the cystic fibrosis lung,” said Jason Papin, PhD, principal investigator at UVa, in a press release. “There are a lot of similarities between these bugs, but there are also some notable differences, and it’s not really understood why those differences exist. So we use these models to delineate the functional impact of some of these genetic differences.”

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The model is described in Journal of Bacteriology and was created through collaborations among Jennifer Bartell and Phillip Yen from UVa and John Varga and Joanna Goldberg of Emory. To create the model, the team used the complete sequenced genome of B. cenocepacia and B. multivorans, as well as a sample of B. cenocepacia isolated from the lungs of a cystic fibrosis patient and a sample of B. multivorans from the soil. The computer reconstructions share 1,437 metabolic reactions, but each has a unique set the other does not (67 and 36 metabolic reactions, respectively). This prompts Dr. Papin to ask, “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?” He 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.”

Scientists will be able to answer these and other questions using the team’s model, but the goal is not for the model to always be correct. “The model makes some predictions correctly and some predictions incorrectly,” said Dr. Papin, but this is certainly not devastating. “What’s really neat about computer modeling of these biochemical networks and systems is it’s nice when the models are right but it’s really interesting when they’re wrong, because that helps point to aspects of the biology we don’t understand, which helps to generate new hypotheses, new ideas that can be tested.” The model is expected to be a valuable tool to aid in identifying novel therapeutic targets and strategies to treat cystic fibrosis, as well as predict phenotypes of pathogenesis.

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