The evolution of antibiotic resistance
The injudicious use of antibiotics has led to the emergence of antibiotic resistant bacteria. Finding novel treatments represents a significant medical challenge.
Inhibitory drugs put an enormous selective pressure on bacteria, which
often respond by spontaneous mutations that confer resistance.
Antibiotic resistance can also occur through the horizontal transfer of
resistance-conferring genes to bacteria from other organisms.
Delineating the mechanisms governing drug resistance evolution is
central for preventing this phenomenon.
The EU-funded RARE (Revealing antibiotic resistance evolution) initiative worked to find novel ways of using antibiotics more efficiently and stop or slow down evolution of antibiotic resistance. In this context, researchers set out to measure the evolutionary rate of resistance emergence under single and combinatorial drug treatments.
The consortium developed an innovative device, the Morbidostat, to study the evolution of bacterial drug resistance. With this device, scientists maintained a nearly constant selection pressure using clinically relevant drugs on evolving populations.
Over 22 drugs were tested under mild and strong selection settings in a wild type drug-sensitive Escherichia coli strain. Propagated populations that survived in the highest drug concentration were analysed at the genetic level for changes associated with the emergence of resistance. When scientists phenotyped these clones, they discovered that often bacteria had acquired resistance to other drugs as well.
Measurement of the epistatic interactions between the different mutations alongside the whole genome sequencing data of the evolved strains led to a list of mutations responsible for resistance and cross-resistance. Furthermore, scientists discovered that strains which became resistant against aminoglycosides developed hypersensitivity against almost all other antibiotic classes.
Taken together, the results of the RARE study clearly indicate that strong selection promotes cross-resistance in bacteria. The generated data set can form the basis for designing therapies to stop or slow down the rate of evolution of resistance.
published: 2016-02-08