Identification and characterization of a fungal-selective glutaminyl tRNA synthetase inhibitor with potent activity against Candida albicans

Abstract

Candida albicans is the leading cause of systemic candidiasis. Effective treatment is threatened by a dearth of antifungal options and the emergence of resistance. Thus, there is an urgent need to identify novel therapeutic targets to expand our antifungal armamentarium. A promising approach is the discovery of essential genes, as most antimicrobials target essential bioprocesses. Despite detailed characterization of gene essentiality in Saccharomyces cerevisiae,defining essential targets in the pathogen of interest is necessary due to the high level of divergence between these organisms. Thus, using a machine learning algorithm we generated a comprehensive prediction of all genes essential in C. albicans. We leveraged our essentiality predictions with high-throughput screening and chemogenomic datasets to assign the mechanism of action of a previously uncharacterized compound. We identified T-035897 as a molecule with potent bioactivity against C. albicans. Prior chemogenomic profiling in S. cerevisiae suggested that T-035897 targets the glutaminyl tRNA synthetase Gln4, whose homolog in C. albicans was predicted and verified to be required for viability. To confirm the mechanism of T-035897 in C. albicans, we performed haploinsufficiency profiling,which supported Gln4as the target. In parallel, selection of resistant mutants and targeted sequencing uncovered substitutions in the Gln4 catalytic domain. Moreover, T-035897 inhibited translation in afluorescence-based reporter assay. Finally, T-035897 selectively abrogated fungal cell growth in a co-culture model with mammalian cells. Thus, we highlight the power of leveraging essentiality datasets in order to characterize compounds with potent antifungal activity in an effort to unveil novel therapeutic strategies.