{"title":"A high-throughput differential chemical genetic screen uncovers genotype-specific compounds altering plant growth","authors":"Côme Emmenecker , Jingqi Dai , Sandrine Lefranc , Ayoub Ouddah , Julie Guerin , Simine Pakzad , Philippe Andrey , Rajeev Kumar","doi":"10.1016/j.isci.2025.112375","DOIUrl":null,"url":null,"abstract":"<div><div>The identification of chemical compounds regulating plant growth in a genetic context can greatly enhance our understanding of biological mechanisms. Here, we have developed a high-throughput phenotype-directed chemical screening method in plants to compare two genotypes and identify small molecules inducing genotype-specific phenotypes. We used <em>Arabidopsis thaliana</em> wild type and <em>mus81</em>, a DNA repair mutant, and screened off-patent drugs from the Prestwick library to selectively identify molecules affecting <em>mus81</em> growth. We developed two complementary convolutional neural networks (CNN)-based image segmentation and classification programs to quantify <em>Arabidopsis</em> seedling growth. Using these approaches, we detected that about 10% of Prestwick molecules cause altered growth in both genotypes, suggesting their toxic effects on plant growth. We identified three Prestwick molecules specifically affecting <em>mus81</em>. Overall, we developed a straightforward, accurate, and adaptable methodology for performing high-throughput screening of chemical libraries in a time-efficient manner, accelerating the discovery of genotype-specific chemical regulators of plant growth.</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"28 5","pages":"Article 112375"},"PeriodicalIF":4.6000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"iScience","FirstCategoryId":"103","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589004225006364","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The identification of chemical compounds regulating plant growth in a genetic context can greatly enhance our understanding of biological mechanisms. Here, we have developed a high-throughput phenotype-directed chemical screening method in plants to compare two genotypes and identify small molecules inducing genotype-specific phenotypes. We used Arabidopsis thaliana wild type and mus81, a DNA repair mutant, and screened off-patent drugs from the Prestwick library to selectively identify molecules affecting mus81 growth. We developed two complementary convolutional neural networks (CNN)-based image segmentation and classification programs to quantify Arabidopsis seedling growth. Using these approaches, we detected that about 10% of Prestwick molecules cause altered growth in both genotypes, suggesting their toxic effects on plant growth. We identified three Prestwick molecules specifically affecting mus81. Overall, we developed a straightforward, accurate, and adaptable methodology for performing high-throughput screening of chemical libraries in a time-efficient manner, accelerating the discovery of genotype-specific chemical regulators of plant growth.
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