Isabelle Gagnon‐Arsenault

This study uses population genomics and machine learning to identify genetic markers predictive of antifungal resistance in Candida albicans, highlighting the need for expanded resistance catalogues.

This study shows that gene duplication can lead to non-optimal expression levels that still confer a fitness advantage in yeast.

This paper develops a heterologous expression system in yeast to rapidly test and discover resistance-conferring mutations from multiple pathogenic fungi.

The paper curates AMR mutations in fungi to create FungAMR, identifies universal resistance mutations, and provides a tool to screen genomes for AMR mutations.

The paper shows that paralog interference enhances negative selection on loss-of-function mutations, preserving genetic redundancy over longer timescales.

The paper presents a cost-effective method for constructing barcoded libraries for genes of any length using low-cost DNA synthesis and standard cloning technologies.

This study investigates the role of REV1, an error-prone polymerase, in DNA damage and antifungal drug resistance in Candida albicans, finding that its deletion increases mutation rates and resistance.

The paper shows that hybrid yeast adaptation is slowed by Haldane's sieve due to slower loss of heterozygosity and incomplete dominance of adaptive mutations.

The paper evaluates 4,000 Erg11 variants to determine azole resistance and fitness costs, finding 33% resistance, 88% cross-resistance, and minimal fitness cost.