Back

Temporally variable drug profiles select for diverse adaptive pathways despite conservation of efflux-based resistance mechanism

Akanksha, ; Mehra, S.

2023-05-22 evolutionary biology
10.1101/2023.05.19.541537 bioRxiv
Show abstract

Antibiotic resistance is a global health concern with emergence of resistance in bacteria out-competing the discovery of novel drug candidates. While Adaptive Laboratory Evolution (ALE) has been used to identify bacterial resistance determinants, most studies investigate evolution under stepwise increasing drug profiles. Thus, bacterial adaptation under long-term constant drug concentration, a physiologically relevant profile, remains underestimated. Using ALE of Mycobacterium smegmatis subjected to a range of Norfloxacin concentrations under both constant and stepwise increasing drug dosage, we investigated the impact of variation of drug profiles on resistance evolution. All the evolved mutants exhibited a drug concentration dependent increase in resistance accompanied with an increase in the number of mutations. Mutations in an efflux pump regulator, LfrR, were found in all the evolved populations suggesting conservation of an efflux-based resistance mechanism. The selection of these mutations was tightly coupled to the presence of its regulated gene in the genetic background. Further, lfrR mutations appeared early during the adaptive trajectory and imparted low-level resistance. Subsequently, sequential acquisition of other mutations, dependent on the drug profile, led to high-level resistance emergence. While divergent mutational trajectories led to comparable phenotype, populations evolved under constant drug exposure accumulated mutations in dehydrogenase genes whereas in populations under increasing drug exposure, mutations in additional regulatory genes were selected. Our data also shows that irrespective of the evolutionary trajectory, drug target mutations were not selected up to 4X drug concentration. Overall, this work demonstrates that evolutionary trajectory is strongly influenced by the drug profile.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.