ArticlePloS one2010
High-content, image-based screening for drug targets in yeast.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- The power of many: when genetics met yeasts and high-throughput.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Advanced genetic techniques in fungal pathogen research.mSphere · 2024Review
- Application of unimodal probability distribution models for morphological phenotyping of budding yeast.FEMS yeast research · 2024Article
- Assignment of unimodal probability distribution models for quantitative morphological phenotyping.BMC biology · 2022Article
- Jerveratrum-Type Steroidal Alkaloids Inhibit β-1,6-Glucan Biosynthesis in Fungal Cell Walls.Microbiology spectrum · 2022Article
- High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds.NPJ systems biology and applications · 2022Article
- Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge.PLoS computational biology · 2021Article
- Image-based profiling for drug discovery: due for a machine-learning upgrade?Nature reviews. Drug discovery · 2021Review
- Influence of Ethyl Caproate on the Size of Lipid Vesicles and Yeast Cells.Biomimetics (Basel, Switzerland) · 2020Article
- Multi-Faceted Systems Biology Approaches Present a Cellular Landscape of Phenolic Compound Inhibition inFrontiers in bioengineering and biotechnology · 2020Review
- Systematic exploration of cell morphological phenotypes associated with a transcriptomic query.Nucleic acids research · 2018Article
- Article
- Global study of holistic morphological effectors in the budding yeast Saccharomyces cerevisiae.BMC genomics · 2018Article
- Chemical genetics in drug discovery.Current opinion in systems biology · 2017Review
- Single-cell phenomics in budding yeast.Molecular biology of the cell · 2015Review
- Plant-derived antifungal agent poacic acid targets β-1,3-glucan.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Carpatamides A-C, cytotoxic arylamine derivatives from a marine-derived Streptomyces sp.Journal of natural products · 2014Article
- Distinct roles of cell wall biogenesis in yeast morphogenesis as revealed by multivariate analysis of high-dimensional morphometric data.Molecular biology of the cell · 2014Article
- Innovation in academic chemical screening: filling the gaps in chemical biology.Current opinion in chemical biology · 2013Review
- Light microscopy applications in systems biology: opportunities and challenges.Cell communication and signaling : CCS · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug discovery and development are predicated on elucidation of the potential mechanisms of action and cellular targets of candidate chemical compounds. Recent advances in high-content imaging techniques allow simultaneous analysis of a range of cellular events. In this study, we propose a novel strategy to identify drug targets by combining genetic screening and high-content imaging in yeast. METHODOLOGY: In this approach, we infer the cellular functions affected by candidate drugs by comparing morphologic changes induced by the compounds with the phenotypes of yeast mutants.
conclusionsUsing this method and four well-characterized reagents, we successfully identified previously known target genes of the compounds as well as other genes involved with functionally related cellular pathways. This is the first demonstration of a genetic high-content assay that can be used to identify drug targets based on morphologic phenotypes of a reference mutant panel.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.