Using Metabolomic and Transportomic Modeling and Machine Learning to Identify Putative Novel Therapeutic Targets for Antibiotic Resistant Pseudomonad Infections

Hospital acquired infections sicken or kill tens of thousands of patients every year. These infections are difficult to treat due to a growing prevalence of resistance to many antibiotics. Among these hospital acquired infections, bacteria of the genus Pseudomonas are among the most common opportunistic pathogens. Computational methods for predicting potential novel antimicrobial therapies for hospital acquired Pseudomonad infections, as well as other hospital acquired infectious pathogens, are desperately needed. Using data generated from sequenced Pseudomonad genomes and metabolomic and transportomic computational approaches developed in our laboratory, we present a support vector machine learning method for identifying the most predictive molecular mechanisms that distinguish pathogenic from nonpathogenic Pseudomonads. Predictions were highly accurate, yielding F-scores between 0.84 and 0.98 in leave one out cross validations. These mechanisms are high-value targets for the development of new antimicrobial therapies.