Abstract:Antibiotic resistance is an important public health problem.Understanding the genetic changes responsible for bacterialdrug resistance is therefore an important task for developingnovel strategies that can stop or slow down evolution of drug resistance. In the first part of my talk, I will introduce a novel automated microbial selection device, the “morbidostat”, which we developed to study the long term evolution of antibiotic resistance. The morbidostat dynamically adjusts drug concentrations to maintain nearly constant inhibition of bacterial growth even as bacterial populations become more and more resistant. Using the morbidostat and whole genome sequencing, we investigatedthe genetic trajectories underlying the evolution of resistanceto three clinically important drugs: chloramphenicol, doxycycline and trimethoprim. Chloramphenicol and doxycycline resistance evolved smoothly through diverse sets of mutations. Conversely, trimethoprim resistance evolved in a stepwise manner, through mutations almost exclusively restricted to the gene encoding the target enzyme dihydrofolate reductase (DHFR). The evolution of trimethoprim resistance displayed three striking properties:(1) a limited set of mutations were acquired repeatedly in similar orders; (2) multiple resistant endpoints with differentcombinations of mutations existed; and (3) some genetic trajectories included reversion and conversion of mutations.
In the second part of my talk, I will summarize the synthetic construction and analysis of all possible combinations of trimethoprim resistance conferring mutations targeting DHFRgene. We found that trimethoprim resistance evolves throughan extremely rugged fitness landscape with direct and indirect paths leading to multiple resistance peaks. Our findings suggests that high-order interactions between adaptive mutations create this rugged landscape, where the distributions of most mutations’ effects are indistinguishable from increasing or decreasing resistance by flipping a coin.
1. E. Toprak, A. Veres, S. Yildiz, J.M. Pedraza, R. Chait, J. Paulsson, R. Kishony, “Building a Morbidostat: An automated high-throughput fluidic system for studying bacterial drug resistance in dynamically sustained drug environments”, Nature Protocols, 2013.
2. E. Toprak, A. Veres, J. B. Michel, R. Chait, D.L. Hartl, R. Kishony, “Evolutionary paths to strong antibiotic resistance under dynamically sustained drug stress”, Nature Genetics, 2012.