Integrative identification of key genes governing Verticillium wilt resistance in Gossypium hirsutum using machine learning and WGCNA

文献类型： 外文期刊

第一作者： Lei, Yufeng

作者： Lei, Yufeng;Hou, Siyuan;Xu, Fufeng;Zhang, Chongbo;Cai, Dongchen;Cao, Xiaolei;Yao, Zhaoqun;Zhao, Sifeng;Zhao, Jing

作者机构：

关键词： Verticillium wilt; Gossypium hirsutum; RNA-Seq; WGCNA; machine learning; disease resistance

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCI

摘要： Introduction Verticillium wilt, caused by Verticillium dahliae, is one of the most devastating diseases affecting global cotton (Gossypium hirsutum) production. Given the limited effectiveness of chemical control measures and the polygenic nature of resistance, elucidating the key genetic determinants is imperative for the development of resistant cultivars. In this study, we aimed to dissect the temporal transcriptional dynamics and regulatory mechanisms underlying Gossypium hirsutum response to V. dahliae infection.Methods We employed a time-course RNA-Seq approach using the susceptible upland cotton cultivar Jimian 11 to profile transcriptomic responses in root and leaf tissues post-V. dahliae inoculation. Differentially expressed genes (DEGs) were identified, followed by weighted gene co-expression network analysis (WGCNA). To prioritize key candidate genes, we applied machine learning algorithms including LASSO, Random Forest, and Support Vector Machine (SVM).Results and discussion A robust set of core genes involved in pathogen recognition (GhRLP6), calcium signaling (GhCIPK6, GhCBP60A), hormone response, and secondary metabolism (GhF3'H) were identified. Our findings provide novel insights into the spatiotemporal regulation of immune responses in cotton and offer valuable candidate genes for molecular breeding of Verticillium wilt resistance.

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