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Home-Journal Online-2024 No.11

Analysis of drought resistance of kiwifruit rootstocks based on physiology and transcriptome

Online:2024/12/11 15:35:48 Browsing times:
Author: ZHOU Kangyu, HE Chengyong, XU Zihong, WANG Lingli, ZHAO Ke, SONG Haiyan, LIU Pu, TU Meiyan
Keywords: Kiwifruit; Drought stress; Physiological mechanism; Transcriptome
DOI: 10.13925/j.cnki.gsxb.20240484
Received date: 2024-09-13
Accepted date: 2024-10-24
Online date:
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Abstract: ObjectiveThis study aimed to investigate the physiological and molecular mechanisms of drought tolerance of different kiwifruit rootstock materials, with the goal of providing a foundation for future breeding and genetic improvement efforts to enhance drought resistance in kiwifruit.MethodsFour kiwifruit cultivars with varying degrees of drought tolerance were selected: Bruno (Actinidia deliciosa), XD-GZ-7 (A. polygama), XD-RZ-1 (A. eriantha) and DJY-DE-1 (A. valvata). These cultivars were chosen based on previous observations on their drought tolerance and represent diverse genotypes from the collected germplasm. Drought stress was simulated using 25% PEG- 6000, applied at five times (0, 2, 4, 6 and 8 days). Phenotypic assessments included observations on leaf wilting, plant dehydration and overall drought response. Physiological parameters, such as malondialdehyde (MDA), proline (Pro), hydrogen peroxide (H2O2), superoxide dismutase (SOD) and catalase (CAT) were measured. In addition, transcriptome sequencing of the roots of DJY-DE-1 and Bruno at 0, 4 and 8 days was performed to identify differentially expressed genes (DEGs). Then, the Weighted Gene Co-expression Net-work Analysis (WGCNA) algorithm was employed for module construction. The core steps of this algorithm involved calculating the similarity between genes to construct a gene clustering tree, in which each branch represented an independent module. To more precisely delineate these modules, a dynamic tree cutting method was utilized to slice the gene clustering tree. To further quantify the co-expression similarity among the modules, the module eigengenes (MEs) for each module were calculated, and these eigengenes were then used to merge modules that exhibited similarity. Validation of key genes was conducted using quantitative real- time PCR (qRT- PCR).ResultsSignificant differences in drought responses were observed between the materials. Bruno exhibited early and severe symptoms of drought stress, with noticeable leaf wilting by day 4 (S3), and widespread dehydration by day 8, with a drought index of 87%, the highest among all cultivars. In contrast, DJY-DE-1 showed delayed drought symptoms, with minimal wilting and a low drought index of 33% , indicating superior drought tolerance. The intermediate cultivars, XD-GZ-7 and XD-RZ-1, displayed moderate wilting and dehydration, with drought indices of 67% and 60%, respectively. Physiological measurements supported these observations. Bruno had significantly higher MDA levels under drought stress, indicating greater lipid peroxidation and cellular damage. Conversely, the more drought- tolerant cultivars, especially DJY- DE- 1, showed elevated levels of proline and higher activities of antioxidant enzymes (SOD and CAT), suggesting better protection against oxidative damage. A fuzzy membership function analysis ranked the cultivars drought tolerance as follows: DJY-DE-1XD-RZ-1XD-GZ-7Bruno, which was consistent with the phenotypic and physiological data. Transcriptome analysis identified 435 047 transcripts across the three time points, with 102 588, 100 951 and 104 974 DEGs identified at 0, 4 and 8 days, respectively. These DEGs revealed significant expression differences between the drought-tolerant DJY-DE-1 and the drought-sensitive Bruno. Validation of eight highly expressed DEGs using qRT-PCR confirmed the accuracy of the transcriptome data. Gene Ontology (GO) analysis showed that the DEGs were enriched in processes related to cellular metabolism, energy and stress responses. KEGG pathway analysis indicated that these DEGs were involved in key pathways such as signal transduction, carbohydrate metabolism and protein folding, which were critical for maintaining cellular homeostasis under drought stress. Weighted Gene Co-expression Network Analysis (WGCNA) further identified five key DEGs (TRINITY_DN11629_c0_g1, TRINITY_DN257031_c0_g1, TRINITY_DN3814_c0_g1, TRINITY_DN9194_c0_g1 and TRINITY_DN16120_c0_g1) as potential regulators of drought tolerance, offering valuable targets for future genetic improvement.ConclusionThis study employed a comprehensive approach, integrating physiological with transcriptomic data, to explore the mechanisms of drought tolerance in kiwifruit. The findings provide important insights into the molecular basis of drought response and pave the way for breeding and genetic strategies to enhance drought resistance in kiwifruit. The identification of key drought-responsive genes highlights potential avenues for improving crop adaptation to changing environmental conditions.