High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds

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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number3
Journalnpj Systems Biology and Applications
Issue number1
Publication statusPublished - 2022 Dec

ASJC Scopus subject areas

  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Drug Discovery
  • Computer Science Applications
  • Applied Mathematics


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