Probing the bypassability of genetic constraints in drug-resistance enzymes

The fast evolution of bacterial pathogens towards antibiotic resistance is estimated by 2050 to be killing 10 million people every year. Consequently, much interest is being directed towards finding ways to curb or even arrest this evolutionary process.

Of note, sequencing efforts are revealing that many of the genetic changes that drive resistance evolution are often repeatable.

Understanding what drives this repeatability is of foremost importance if we ever want to develop interventions aimed at anticipating and preventing the evolution of undesirable variants.

Here, I will aim at advancing our understanding of evolutionary repeatability in several clinically-relevant, drug-resistance enzymes spanning a range of GC compositions.

I will use this relevant model system to empirically test recent predictions on the roles of mutation bias and GC content in shaping mutational pathways.

To this end, I will conduct high-throughput 'in vitro' Directed Evolution experiments to explore the potential adaptive paths available via single step mutations among the drug-resistance enzymes.

Next, I will compare these 'in vitro' predictions with the outcomes of highly-parallel antibiotic adaptation experiments conducted with bacterial strains with strong mutation biases (e.g., mutators) carrying the same panel of enzymes.

By producing important insights into some of the key determinants of evolutionary repeatability, PROBYDE aspires to form a knowledge base that may help harness evolution not only in bacterial pathogens, but also in other human-relevant systems such as cancer, crop pests and industrial microbes.