Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

Ci Fu,Cassandra Wong,Teresa R O’Meara,Sean D Liston,Matthew J O’Meara,Alice Xue,Suzanne M Noble ,Chad L Myers,Amanda O Veri,Benjamin Vandersluis,Nicole Robbins,Yoko Yashiroda,Jing Hou,Nicole M Revie,Kali R Iyer,Elizabeth J Polvi,Emma Lash,Yan Huang ,Charles Boone,Zhen‐Yuan Lin ,Xiang Zhang,Anne‐Claude Gingras ,Leah E Cowen

doi:10.1038/s41467-021-26850-3

Abstract

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln4 as the target of N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), an antifungal compound.

Highlights

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers
In order to generate a comprehensive assessment of genes with evidence for essentiality in C. albicans, we first collected a set of functional genomic features for use in training a machine-learning model (Supplementary Data 1)
It encompassed a codon adaptation index (CAI), which measures the bias in codon usage across each gene[21]; the number of SNPs per nucleotide for each gene across a set of sequenced C. albicans strains[23]; the presence of an essential ortholog in S. cerevisiae[12]; and the presence of a duplicated set of paralogs in S. cerevisiae that exhibited a synthetic sick/lethal genetic interaction[24]

Summary

Introduction

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. We build a machine learning model to generate genomewide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln[4] as the target of N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), an antifungal compound. This work leverages machine learning, functional genomics, and chemical genomics to characterize essential genes and defines additional targets to advance antifungal drug development

Methods

Results

Conclusion