Grant: Cat Gr: Develop./validation of new biomedical techniques/technolo.- General Pool

Precision medicine for microbial infections requires understanding how the genetic makeup of microorganisms will impact the success and outcome of antimicrobial treatments. This includes detecting drug susceptibility and resistance from DNA sequences of the infecting microbes. Two percent of Canadians currently suffer from serious fungal infections, many of which could be difficult or impossible to cure because of antimicrobial resistance mutations. Although it is possible to quickly and cheaply sequence fungal DNA from infected patient samples, the interpretation of the sequences for drug resistance (or other phenotypes such as virulence) is not feasible, or imprecise at best, because of the lack of solid reference data for the interpretation. Such data is present in the literature but it is sparse and, most of the time, has yet to be validated experimentally, which significantly diminishes its usefulness. In principle, Artificial Intelligence-based tools could be implemented to interpret DNA sequences, but the lack of solid data currently limits their implementation. In this project, we will develop high-throughput genome editing technologies to produce such data for the most frequently encountered yeast pathogens. The tools will be adapted so mutations can be precisely engineered in any gene of interest to test hypotheses regarding their impact on drug resistance and thus on treatment outcomes.