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Joint transcriptomic and metabolomic analysis provides new insights into drought resistance in watermelon (Citrullus lanatus)

文献类型： 外文期刊

作者： Chen, Sheng 1 ; Zhong, Kaiqin 2 ; Li, Yongyu 3 ; Bai, Changhui 1 ; Xue, Zhuzheng 1 ; Wu, Yufen 1 ;

作者机构： 1.Fujian Acad Agr Sci, Crops Res Inst, Fuzhou, Peoples R China

2.Fuzhou Inst Vegetable Sci, Fuzhou, Peoples R China

3.Fujian Agr & Forestry Univ, Coll Hort, Fuzhou, Peoples R China

关键词： watermelon; drought; RNA-Seq; metabolomic; WGCNA

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.6； 五年影响因子：6.8 ）

ISSN： 1664-462X

年卷期： 2024 年 15 卷

页码：

收录情况： SCI

摘要： Introduction: Watermelon is an annual vine of the family Cucurbitaceae. Watermelon plants produce a fruit that people love and have important nutritional and economic value. With global warming and deterioration of the ecological environment, abiotic stresses, including drought, have become important factors that impact the yield and quality of watermelon plants. Previous research on watermelon drought resistance has included analyzing homologous genes based on known drought-responsive genes and pathways in other species. Methods: However, identifying key pathways and genes involved in watermelon drought resistance through high-throughput omics methods is particularly important. In this study, RNA-seq and metabolomic analysis were performed on watermelon plants at five time points (0 h, 1 h, 6 h, 12 h and 24 h) before and after drought stress. Results: Transcriptomic analysis revealed 7829 differentially expressed genes (DEGs) at the five time points. The DEGs were grouped into five clusters using the k-means clustering algorithm. The functional category for each cluster was annotated based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database; different clusters were associated with different time points after stress. A total of 949 metabolites were divided into 10 categories, with lipids and lipid-like molecules accounting for the most metabolites. Differential expression analysis revealed 22 differentially regulated metabolites (DRMs) among the five time points. Through joint analysis of RNA-seq and metabolome data, the 6-h period was identified as the critical period for watermelon drought resistance, and the starch and sucrose metabolism, plant hormone signal transduction and photosynthesis pathways were identified as important regulatory pathways involved in watermelon drought resistance. In addition, 15 candidate genes associated with watermelon drought resistance were identified through joint RNA-seq and metabolome analysis combined with weighted correlation network analysis (WGCNA). Four of these genes encode transcription factors, including bHLH (Cla97C03G068160), MYB (Cla97C01G002440), HSP (Cla97C02G033390) and GRF (Cla97C02G042620), one key gene in the ABA pathway, SnRK2-4 (Cla97C10G186750), and the GP-2 gene (Cla97C05G105810), which is involved in the starch and sucrose metabolism pathway. Discussion: In summary, our study provides a theoretical basis for elucidating the molecular mechanisms underlying drought resistance in watermelon plants and provides new genetic resources for the study of drought resistance in this crop.

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