Investigation of whole system determinants of antibiotic resistance in Pseudomonas Aeruginosa

Abstract

Pseudomonas aeruginosa has been declared as one of the “six top priority dangerous microbes” and has also been classified as one of the six ESKAPE organisms with emerging clinical importance. The organism possesses a large plastic genome which is considered the base for its high physiologic diversity and high metabolic adaptability. Ps. aeruginosa shows extreme adaptability to colonize different habitats including hospital environments. Although equipped with extensive intrinsic resistance machinery that leads to basal levels of lower susceptibility to many antibiotics, a complete understanding of core resistance mechanisms is considered challenging. High-throughput methods that explore for system-level resistance including transcriptional profiling, mutant library screening, and experimental evolution have many technical drawbacks which make them unreliable to predict clinical resistance. In the thesis, a novel strategy has been adopted to mine for the system level antibiotic susceptibility determinants using a sequence-based integrated genomics approach that combines cluster analysis, predictive modelling, and comparative behavioral genomics. Combining the existing body of knowledge about resistance-associated variants and results of comparative behavioral genomics has resulted into a new way to understand resistance and to prioritize system elements contributing to resistance. This new knowledge is expected to offer promising diagnostic and therapeutic potentials. The approach has interrogated the whole physiologic system resulting in the identification of a new group of candidate resistance predictor markers and new combinations showing up to 90% better performance. The approach has also created a new understanding about the combinatorial quantitative contribution of different resistance mechanism to quinolones and aminoglycoside groups of antibiotics focusing on gentamycin, amikacin, ciprofloxacin, and levofloxacin. It has also highlighted some novel functional groups and genes that contribute to resistance. This knowledge could offer improved genome-based directed antibiotic treatment. Recent advances in sequencing technologies are expected to provide a rich information resource and a promising diagnostic platform. The new knowledge is capable of providing a base for rapid point-of-care antibiotic resistance diagnostic platforms, thus increasing the spectrum and informative value of some diagnostic panels in current use.