Yen, P. | bioRxiv Science

bioRxiv2018

Network analysis reveals differential metabolic functionality in antibiotic-resistant Pseudomonas aeruginosa

Metabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To interrogate this relationship, we profiled the growth of lab-evolved antibiotic-resistant lineages of the opportunistic pathogen Pseudomonas aeruginosa across 190 unique carbon sources. We semi-automatically calculated growth dynamics (maximum growth density, growth rate, and time to mid-exponential phase) of over 2,800 growth curves. These data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. Drug-resistant lineages predominantly displayed decreased growth relative to the ancestral lineage; however, resistant lineages occasionally displayed enhanced growth on certain carbon sources, indicating that adaption to drug can provide a growth advantage in certain environments. A genome-scale metabolic network reconstruction (GENRE) of P. aeruginosa strain UCBPP-PA14 was paired with whole-genome sequencing data of one of the drug-evolved lineages to predict genes contributing to observed changes in metabolism. Finally, we experimentally validated in silico predictions to identify genes mutated in resistant P. aeruginosa affecting loss of catabolic function. Our results build upon previous mechanistic knowledge of drug-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens. Robust drug-driven changes in bacterial metabolism have the potential to be exploited to select against antibiotic-resistant populations in chronic infections.

systems biology↗

bioRxiv2016

History of Antibiotic Adaptation Influences Microbial Evolutionary Dynamics During Subsequent Treatment

Antibiotic regimens often include the sequential changing of drugs to limit development and evolution of resistance of bacterial pathogens. It remains unclear how history of adaptation to one antibiotic can influence the resistance profiles when bacteria subsequently adapt to a different antibiotic. Here, we experimentally evolved Pseudomonas aeruginosa to six two-drug sequences. We observed drug order-specific effects whereby: adaptation to the first drug can limit subsequent adaptation to the second drug, adaptation to the second drug can restore susceptibility to the first drug, or final resistance levels depend on the order of the two-drug sequence. These findings demonstrate how resistance not only depends on the current drug regimen but also history of past regimens. These order-specific effects have profound clinical implications and provide support for the need to consider history of past drug exposure when designing strategies to mitigate resistance and combat bacterial infections.

systems biology↗