Integrative identification of key genes governing Verticillium wilt resistance in Gossypium hirsutum using machine learning and WGCNA
IntroductionVerticillium 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 determinant...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1621604/full |
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| author | Yufeng Lei Jing Zhao Siyuan Hou Fufeng Xu Chongbo Zhang Dongchen Cai Xiaolei Cao Zhaoqun Yao Sifeng Zhao |
| author_facet | Yufeng Lei Jing Zhao Siyuan Hou Fufeng Xu Chongbo Zhang Dongchen Cai Xiaolei Cao Zhaoqun Yao Sifeng Zhao |
| author_sort | Yufeng Lei |
| collection | DOAJ |
| description | IntroductionVerticillium 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.MethodsWe 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 discussionA 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. |
| format | Article |
| id | doaj-art-2a4f0ddeda23408db56bdeda343063c9 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-2a4f0ddeda23408db56bdeda343063c92025-08-20T03:56:50ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-07-011610.3389/fpls.2025.16216041621604Integrative identification of key genes governing Verticillium wilt resistance in Gossypium hirsutum using machine learning and WGCNAYufeng Lei0Jing Zhao1Siyuan Hou2Fufeng Xu3Chongbo Zhang4Dongchen Cai5Xiaolei Cao6Zhaoqun Yao7Sifeng Zhao8Key Laboratory at the Universities of Xinjiang Uygur Autonomous Region for Oasis Agricultural Pest Management and Plant Protection Resource Utilization, Agriculture College, Shihezi University, Shihezi, ChinaCotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, ChinaKey Laboratory at the Universities of Xinjiang Uygur Autonomous Region for Oasis Agricultural Pest Management and Plant Protection Resource Utilization, Agriculture College, Shihezi University, Shihezi, ChinaKey Laboratory at the Universities of Xinjiang Uygur Autonomous Region for Oasis Agricultural Pest Management and Plant Protection Resource Utilization, Agriculture College, Shihezi University, Shihezi, ChinaKey Laboratory at the Universities of Xinjiang Uygur Autonomous Region for Oasis Agricultural Pest Management and Plant Protection Resource Utilization, Agriculture College, Shihezi University, Shihezi, ChinaKey Laboratory at the Universities of Xinjiang Uygur Autonomous Region for Oasis Agricultural Pest Management and Plant Protection Resource Utilization, Agriculture College, Shihezi University, Shihezi, ChinaKey Laboratory at the Universities of Xinjiang Uygur Autonomous Region for Oasis Agricultural Pest Management and Plant Protection Resource Utilization, Agriculture College, Shihezi University, Shihezi, ChinaKey Laboratory at the Universities of Xinjiang Uygur Autonomous Region for Oasis Agricultural Pest Management and Plant Protection Resource Utilization, Agriculture College, Shihezi University, Shihezi, ChinaKey Laboratory at the Universities of Xinjiang Uygur Autonomous Region for Oasis Agricultural Pest Management and Plant Protection Resource Utilization, Agriculture College, Shihezi University, Shihezi, ChinaIntroductionVerticillium 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.MethodsWe 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 discussionA 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.https://www.frontiersin.org/articles/10.3389/fpls.2025.1621604/fullVerticillium wiltGossypium hirsutumRNA-SeqWGCNAmachine learningdisease resistance |
| spellingShingle | Yufeng Lei Jing Zhao Siyuan Hou Fufeng Xu Chongbo Zhang Dongchen Cai Xiaolei Cao Zhaoqun Yao Sifeng Zhao Integrative identification of key genes governing Verticillium wilt resistance in Gossypium hirsutum using machine learning and WGCNA Frontiers in Plant Science Verticillium wilt Gossypium hirsutum RNA-Seq WGCNA machine learning disease resistance |
| title | Integrative identification of key genes governing Verticillium wilt resistance in Gossypium hirsutum using machine learning and WGCNA |
| title_full | Integrative identification of key genes governing Verticillium wilt resistance in Gossypium hirsutum using machine learning and WGCNA |
| title_fullStr | Integrative identification of key genes governing Verticillium wilt resistance in Gossypium hirsutum using machine learning and WGCNA |
| title_full_unstemmed | Integrative identification of key genes governing Verticillium wilt resistance in Gossypium hirsutum using machine learning and WGCNA |
| title_short | Integrative identification of key genes governing Verticillium wilt resistance in Gossypium hirsutum using machine learning and WGCNA |
| title_sort | integrative identification of key genes governing verticillium wilt resistance in gossypium hirsutum using machine learning and wgcna |
| topic | Verticillium wilt Gossypium hirsutum RNA-Seq WGCNA machine learning disease resistance |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1621604/full |
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