Integrative Identification of Key Genes Governing Verticillium Wilt Resistance in Gossypium hirsutum Using Machine Learning and WGCNA

Yufeng Lei¹Jing Zhao²Siyuan Hou¹Fufeng Xu¹Chongbo Zhang¹Dongchen Cai¹Xiaolei Cao¹Zhaoqun Yao^1*Sifeng Zhao^1*

• ¹Key Laboratory at the Universities of Xinjiang Uygur Autonomous Region for Oasis Agricultural Pest Management and Plant Protection Resource Utilization, Agriculture College, Shihezi, China
• ²Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, China

Verticillium wilt, caused by Verticillium dahliae, is one of the most devastating diseases threatening 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. Using a Gossypium hirsutum cultivar Jimian 11, we conducted time-course RNA-Seq on root and leaf tissues post-inoculation. Comparative transcriptome analysis revealed that roots displayed more rapid and intense transcriptional responses than leaves. Weighted gene co-expression network analysis (WGCNA) identified infection time-correlated modules enriched in defense-related pathways. Furthermore, machine learning algorithms including LASSO, Random Forest, and SVM were applied to select key candidate genes from DEGs and network modules. 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.