bioRxivMolecular BiologyPreprint
Identification of disease resistance genes from a Chinese wild grapevine (Vitis davidii) by analysing grape transcriptomes and transgenic Arabidopsis
Ying ZhangiD, Jia‐Long YaoiD, Feng Hu, Jianfu JiangiD, Xiucai Fan, Haisheng SuniD, Yunfei JiaiD, Yanyan Zhu, Ran WangiD, Chonghuai LiuiD
Abstract
HighlightTranscription profiles showed that 20 candidate genes were obviously co-expressed at 12 hpi in 30 Vitis davidii.\n\nVdWRKY53 trancription factor enhanced the resisitance in grapevine and Arabidopsis.\n\nAbstractThe molecular mechanisms underlying disease tolerance in grapevines remain uncharacterized, even though there are substantial differences in the resistance of grapevine species to fungal and bacterial diseases. In this study, we identified genes and genetic networks involved in disease resistance in grapevines by comparing the transcriptomes of a strongly resistant clone of Chinese wild grapevine (Vitis davidii cv. Ciputao 941, DAC) and a susceptible clone of European grapevine (Vitis vinifera cv. Manicure Finger, VIM) before and after infection with white rot disease (Coniella diplodiella). Disease resistance-related genes were triggered in DAC approximately 12 hours post infection (hpi) with C. diplodiella. Twenty candidate resistant genes were co-expressed in DAC. One of these candidate genes, VdWRKY53 (GenBank accession KY124243), was over-expressed in transgenic Arabidopsis thaliana plants and was found to provide these plants with enhanced resistance to C. diplodiella, Pseudomonas syringae pv tomato PDC3000, and Golovinomyces cichoracearum. This result indicates that VdWRKY53 may be involved in nonspecific resistance via interaction with fungal and oomycete elicitor signals and the activation of defence gene expression. These results provide potential gene targets for molecular breeding to develop resistant grape cultivars.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- approach: statistical model
- approach: experimental assay
- Citations
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