ArticleNPJ systems biology and applications2022
High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds.
Article in NPJ systems biology and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A geometric and probabilistic framework for mechanistic antifungal screening from colony morphology.Pest management science · 2026Article
- HTD1265 Disrupts GimC-Dependent Cellular Processes inPathogens (Basel, Switzerland) · 2026Article
- Yeast as a Model for Human Disease.International journal of molecular sciences · 2026Review
- Article
- Fusion of microscopic and diffraction images with VGG net for budding yeast recognition in imaging flow cytometry.Scientific reports · 2025Article
- Rational selection of morphological phenotypic traits to extract essential similarities in chemical perturbation in the ergosterol pathway.Scientific reports · 2024Article
- Article
- Application of unimodal probability distribution models for morphological phenotyping of budding yeast.FEMS yeast research · 2024Article
- QATS: an ImageJ plugin for the quantification of toroidal nuclei in biological images.Bioinformatics (Oxford, England) · 2024Article
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18 authors.
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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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