Dynamics of Mutations during Development of Resistance by Pseudomonas aeruginosa against Five Antibiotics

Abstract

cPseudomonas aeruginosa is an opportunistic pathogen that causes considerable morbidity and mortality, specifically during intensive care. Antibiotic-resistant variants of this organism are more difficult to treat and cause substantial extra costs compared to susceptible strains. In the laboratory, P. aeruginosa rapidly developed resistance to five medically relevant antibiotics upon exposure to stepwise increasing concentrations. At several time points during the acquisition of resistance, samples were taken for whole-genome sequencing. The increase in the MIC of ciprofloxacin was linked to specific mutations in gyrA, parC, and gyrB, appearing sequentially. In the case of tobramycin, mutations in fusA, HP02880, rplB, and capD were induced. The MICs of the beta-lactam compounds meropenem and ceftazidime and the combination of piperacillin and tazobactam correlated linearly with beta-lactamase activity but not always with individual mutations. The genes that were mutated during the development of beta-lactam resistance differed for each antibiotic. A quantitative relationship between the frequency of mutations and the increase in resistance could not be established for any of the antibiotics. When the adapted strains are grown in the absence of the antibiotic, some mutations remained and others were reversed, but this reversal did not necessarily lower the MIC. The increased MIC came at the cost of moderately reduced cellular functions or a somewhat lower growth rate. In all cases except ciprofloxacin, the increase in resistance seems to be the result of complex interactions among several cellular systems rather than individual mutations.T he medical consequences of antibiotic resistance, such as fewer options for and increased costs of treating infectious diseases, are well recognized. The pathway to resistance consists of sequential mutations or acquisition of resistance genes driven by the selective pressure caused by antibiotic exposure (1). Once resistance has been acquired, the cell rarely reverses to become sensitive again, compensating for the metabolic costs instead (2, 3). The increased level of resistance caused by an antibiotic treatment typically prescribed by primary care physicians is very noticeable when subsequent further treatment is necessary (4). Hence, in order to limit the development of resistance when antibiotics have to be used, treatment protocols need to be devised to prevent this side effect. Rational design of such protocols requires knowledge of the molecular mechanisms that cause resistance. One of the central questions is whether similar mechanisms are operational for all drugs or whether resistance to each drug is induced in a distinct manner. Other basic questions center on evolutionary pathways to clinically significant resistance and the persistence of molecular changes after treatment.Molecular changes that cause the development and persistence of drug resistance can be identified by combining experimental evolution and whole-genome sequencing (WGS), provided that the proper c...

History of Antibiotic Adaptation Influences Microbial Evolutionary Dynamics During Subsequent Treatment

“…Thus, for these 3 treatments, removal of the antibiotic pressure can maintain the high resistance or lead to resensitization in a drug-specific manner. Similar trends were seen in a recent adaptive evolution study whereby P. aeruginosa was evolved to tobramycin, ciprofloxacin, piperacillin/tazobactam, meropenem, and ceftazidime, followed by subsequent adaptation in the absence of the drug (growth medium only) to determine the effects of removing the drug selection pressure [34]. Similar to the patterns seen in our study, they observed that the tobramycin-resistant cultures partially resensitized, the ciprofloxacin-resistant cultures had a modest resensitization, and the 3 beta-lactam-evolved cultures maintained high levels of resistance.…”

“…While we observed clear cases of collateral sensitivity develop to piperacillin and tobramycin during the course of ciprofloxacin adaptation (S4 Fig), other adaptive evolution studies of P. aeruginosa evolved to ciprofloxacin showed mixed results. In one study, the adaptation of P. aeruginosa ATCC 27853 to ciprofloxacin showed no change in the MIC of 3 different beta-lactams (including piperacillin-tazobactam), nor of tobramycin [34]. In another study, while no statistical significances were assigned, adaptation of P. aeruginosa PAO1 to ciprofloxacin appeared to result in slight collateral sensitivities to piperacillin-tazobactam and tobramycin in some of their replicates.…”

“…The most Drug sequence influences evolution of resistance commonly mutated gene was fusA1, which encodes elongation factor G and was mutated in 11 different replicate lineages adapted to tobramycin. fusA1 has been observed to be mutated in clinical isolates of P. aeruginosa [39][40][41], as well as in adaptive evolution studies to aminoglycosides in P. aeruginosa [34] and E. coli [12,16,18]. Mutations in fusA1 may also contribute to altered intracellular (p)ppGpp levels, which may modulate virulence in P. aeruginosa [41].…”

“…These include how historical context can influence the frequency of mutations in certain genes, the varying contributions of adaptive and acquired resistance to total resistance, and specific cases of inverse correlation of the expression of different efflux pumps. While mutations are likely not the sole determinants of the differences [34,59], many of the observed genomic mutations can partially explain the drug-order-specific effects.…”

Time-Resolved Tracking of Mutations Reveals Diverse Allele Dynamics during Escherichia coli Antimicrobial Adaptive Evolution to Single Drugs and Drug Pairs

“…Studies of such evolutionary trajectories have identified the population genetic dynamics underlying adaptation to different diverse perturbations, such as glucose being a limiting nutrient (Barrick et al, 2009; LaCroix et al, 2015) and antibiotic exposure (Toprak et al, 2011; Zhang et al, 2015; Feng et al, 2016). …”