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The limited number of available effective agents necessitates the development of new antifungals. We report that jervine, a jerveratrum-type steroidal alkaloid isolated from Veratrum californicum, has antifungal activity. Phenotypic comparisons of cell wall mutants, K1 killer toxin susceptibility testing, and quantification of cell wall components revealed that β-1,6-glucan biosynthesis was significantly inhibited by jervine. Temperature-sensitive mutants defective in essential genes involved in β-1,6-glucan biosynthesis, including BIG1 , KEG1 , KRE5 , KRE9 , and ROT1 , were hypersensitive to jervine. In contrast, point mutations in KRE6 or its paralog SKN1 produced jervine resistance, suggesting that jervine targets Kre6 and Skn1. Jervine exhibited broad-spectrum antifungal activity and was effective against human-pathogenic fungi, including Candida parapsilosis and Candida krusei. It was also effective against phytopathogenic fungi, including Botrytis cinerea and Puccinia recondita. Jervine exerted a synergistic effect with fluconazole. Therefore, jervine, a jerveratrum-type steroidal alkaloid used in pharmaceutical products, represents a new class of antifungals active against mycoses and plant-pathogenic fungi. IMPORTANCE Non-Candida albicans Candida species (NCAC) are on the rise as a cause of mycosis. Many antifungal drugs are less effective against NCAC, limiting the available therapeutic agents. Here, we report that jervine, a jerveratrum-type steroidal alkaloid, is effective against NCAC and phytopathogenic fungi. Jervine acts on Kre6 and Skn1, which are involved in β-1,6-glucan biosynthesis. The skeleton of jerveratrum-type steroidal alkaloids has been well studied, and more recently, their anticancer properties have been investigated. Therefore, jerveratrum-type alkaloids could potentially be applied as treatments for fungal infections and cancer.

Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes. [ABSTRACT FROM AUTHOR]