数字农科院2.0

Identification of core candidate genes responding to Verticillium wilt(Verticillium dahliae) in cotton via integrated methods

文献类型: 外文期刊

作者: 孙国清

关键词: 0

期刊名称: International Journal of Biological Macromolecules

ISSN: 0141-8130

年卷期: 2025 年

页码:

收录情况: SCIE(2025版) ; ; EI(2025版)

摘要: Cotton is a vital natural fiber and oil crop, yet it is severely affected by verticillium wilt (VW), known as the ‘cancer’ of cotton, hindering the industry’s sustainable development. Upland cotton, which is widely cultivated, lacks effective resistance to VW, while most sea island cotton shows strong resistance. In this study, an F2:3 population was constructed by hybridizing the verticillium wilt-resistant island cotton variety ‘Hai7124’ with the susceptible variety ‘Xinhai14’. Using Bulked Segregant Analysis (BSA-seq), we identified 10 genetic intervals significantly associated with resistance. Additionally, two pathogenic strains of Verticillium dahliae, Vd592 (a strong pathogenic type) and VdKT (a weak pathogenic type), were used to infect the ‘Hai7124’ and ‘Xinhai14’ for RNA-seq analysis, focusing on differentially expressed genes and signaling pathways in samples treated with different resistant and susceptible materials and infected with different pathogens. By integrating BSA-seq and RNA-seq association analyses, the candidate gene range was further refined. Five genes (GBMYB102, GBWRKY65, GBRDA2, GBSOT16, and GBCWINV1) were validated through virus-induced gene silencing (VIGS). The results revealed that reduced expression of these genes significantly decreases plant disease resistance and leads to a reduction in the activity of defense-related enzymes (such as SOD, CAT or PAL) and secondary metabolites (including lignin or flavonoids). Based on the preliminary functional analysis of these candidate genes, we speculate that redox metabolism and secondary metabolites play crucial roles in the resistance of island cotton to Verticillium wilt, and that the resistance of island cotton to verticillium wilt is the result of multiple genes working together.

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