Leveraging genome and transcriptome sequencing to decipher fungicide resistance mechanisms in crop pathogenic fungi: current status and prospects
IF 3.8
1区 农林科学
Q1 AGRONOMY
Alexander Dumbai Joe, Runze Liu, Xiao Luo, Xianhang Meng, Xiaojie Fang, Ziting Ding, Meng Liu, Zhitian Zheng
求助PDF
{"title":"Leveraging genome and transcriptome sequencing to decipher fungicide resistance mechanisms in crop pathogenic fungi: current status and prospects","authors":"Alexander Dumbai Joe, Runze Liu, Xiao Luo, Xianhang Meng, Xiaojie Fang, Ziting Ding, Meng Liu, Zhitian Zheng","doi":"10.1002/ps.70168","DOIUrl":null,"url":null,"abstract":"Fungicide resistance in crop‐pathogenic fungi presents a major threat to global agriculture, jeopardizing food security and sustainable crop production. This review thoroughly explores the use of genomic and transcriptomic sequencing technologies to uncover the molecular and genetic mechanisms underlying fungicide resistance in various phytopathogens. Resistance develops through both target‐site mutations, such as changes in <jats:italic>CYP51</jats:italic> and <jats:italic>β‐tubulin</jats:italic> genes, and nontarget‐site mechanisms, including metabolic detoxification, efflux pump overexpression and stress response modulation. Advances in whole‐genome sequencing (WGS), genome‐wide association studies (GWAS) and RNA‐seq have led to the discovery of novel resistance alleles, differentially expressed genes (DEGs) and critical regulatory networks. Combining multi‐Omics approaches improves our understanding of resistance mechanisms and aids the development of targeted diagnostics, breeding of resistant crop varieties and precise application of fungicides. Furthermore, machine learning and bioinformatics tools offer predictive insights into the development of resistance and potential countermeasures. This review highlights the importance of using genomic and transcriptomic data to design sustainable and integrated strategies for managing crop diseases and combating fungicide resistance in agriculture. © 2025 Society of Chemical Industry.","PeriodicalId":218,"journal":{"name":"Pest Management Science","volume":"27 1","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pest Management Science","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1002/ps.70168","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
引用
批量引用
Abstract
Fungicide resistance in crop‐pathogenic fungi presents a major threat to global agriculture, jeopardizing food security and sustainable crop production. This review thoroughly explores the use of genomic and transcriptomic sequencing technologies to uncover the molecular and genetic mechanisms underlying fungicide resistance in various phytopathogens. Resistance develops through both target‐site mutations, such as changes in CYP51 and β‐tubulin genes, and nontarget‐site mechanisms, including metabolic detoxification, efflux pump overexpression and stress response modulation. Advances in whole‐genome sequencing (WGS), genome‐wide association studies (GWAS) and RNA‐seq have led to the discovery of novel resistance alleles, differentially expressed genes (DEGs) and critical regulatory networks. Combining multi‐Omics approaches improves our understanding of resistance mechanisms and aids the development of targeted diagnostics, breeding of resistant crop varieties and precise application of fungicides. Furthermore, machine learning and bioinformatics tools offer predictive insights into the development of resistance and potential countermeasures. This review highlights the importance of using genomic and transcriptomic data to design sustainable and integrated strategies for managing crop diseases and combating fungicide resistance in agriculture. © 2025 Society of Chemical Industry.
利用基因组和转录组测序破译作物病原真菌抗杀菌剂机制:现状与展望
作物致病真菌的杀菌剂抗性对全球农业构成重大威胁,危及粮食安全和可持续作物生产。这篇综述深入探讨了基因组和转录组测序技术的应用,揭示了各种植物病原体对杀菌剂抗性的分子和遗传机制。耐药通过靶位点突变(如CYP51和β微管蛋白基因的变化)和非靶位点机制(包括代谢解毒、外排泵过表达和应激反应调节)产生。全基因组测序(WGS)、全基因组关联研究(GWAS)和RNA - seq的进展导致了新的抗性等位基因、差异表达基因(DEGs)和关键调控网络的发现。多组学方法的结合提高了我们对抗性机制的理解,并有助于开发靶向诊断、抗性作物品种的育种和杀菌剂的精确应用。此外,机器学习和生物信息学工具提供了对耐药性发展和潜在对策的预测性见解。这篇综述强调了利用基因组和转录组学数据设计可持续和综合的战略来管理作物病害和对抗农业中的杀菌剂抗性的重要性。©2025化学工业协会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。