High-throughput Platform for Yeast Morphological Profiling Predicts the Targets of Bioactive Compounds

Overview

Journal NPJ Syst Biol Appl

Specialty Biology

Date 2022 Jan 28

PMID 35087094

Authors

Shinsuke Ohnuki

Itsuki Ogawa

Kaori Itto-Nakama

Fachuang Lu

Ashish Ranjan

Mehdi Kabbage

Abraham Abera Gebre

Masao Yamashita

Sheena C Li

Yoko Yashiroda

Satoshi Yoshida

Takeo Usui

Jeff S Piotrowski

Brenda J Andrews

Charles Boone

Grant W Brown

John Ralph

Yoshikazu Ohya

Affiliations

Soon will be listed here.

Abstract

Morphological profiling is an omics-based approach for predicting intracellular targets of chemical compounds in which the dose-dependent morphological changes induced by the compound are systematically compared to the morphological changes in gene-deleted cells. In this study, we developed a reliable high-throughput (HT) platform for yeast morphological profiling using drug-hypersensitive strains to minimize compound use, HT microscopy to speed up data generation and analysis, and a generalized linear model to predict targets with high reliability. We first conducted a proof-of-concept study using six compounds with known targets: bortezomib, hydroxyurea, methyl methanesulfonate, benomyl, tunicamycin, and echinocandin B. Then we applied our platform to predict the mechanism of action of a novel diferulate-derived compound, poacidiene. Morphological profiling of poacidiene implied that it affects the DNA damage response, which genetic analysis confirmed. Furthermore, we found that poacidiene inhibits the growth of phytopathogenic fungi, implying applications as an effective antifungal agent. Thus, our platform is a new whole-cell target prediction tool for drug discovery.

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