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Integrated transcriptome and metabolome association analysis reveals the complex genetic architecture of tobacco bacterial wilt resistance

文献类型： 外文期刊

作者： Kamran, Muhammad 1 ; Liu, Jie 3 ; Lin, Feng 1 ; Ullah, Asad 1 ; Aqeel, Tania 4 ; Shahzad, Muhammad 1 ; Ren, Xueliang 3 ; Ren, Min 5 ; Lou, Xiangyang 6 ; Xu, Haiming 1 ; Yu, Shizhou 3 ;

作者机构： 1.Zhejiang Univ, Inst Crop Sci, Coll Agr & Biotechnol, Hangzhou 310058, Zhejiang, Peoples R China

2.Zhejiang Univ, Inst Bioinformat, Hangzhou 310058, Zhejiang, Peoples R China

3.Guizhou Acad Tobacco Sci, Mol Genet Key Lab China Tobacco, Guiyang 550081, Peoples R China

4.Bahauddin Zakariya Univ, Inst Mol Biol & Biotechnol, Multan 60800, Pakistan

5.Chinese Acad Agr Sci, Tobacco Res Inst, Key Lab Tobacco Gene Resources, Qingdao 266101, Peoples R China

6.Univ Florida, Dept Biostat, Gainesville, FL 32611 USA

关键词： Tobacco bacterial wilt; Ralstonia solanacearum; Resistance mechanisms; Transcriptomics; Metabolomics; Differentially expressed genes; Candidate genes

期刊名称：INDUSTRIAL CROPS AND PRODUCTS （ 影响因子：6.2； 五年影响因子：6.2 ）

ISSN： 0926-6690

年卷期： 2025 年 233 卷

页码：

收录情况： SCI

摘要： Tobacco bacterial wilt, caused by Ralstonia solanacearum, severely affects tobacco crops, leading to significant economic losses. We performed integrated transcriptomic and metabolomic analyses to identify key genes and metabolites that govern tobacco's resistance to bacterial wilt. Resistant (Qiongzhongwuzhishan, R1), moderately resistant (Fandisanhao-bing, R2), and susceptible (Honghuadajinyuan, S) genotypes were sampled at 3, 24, and 48 h post-inoculation. RNA-seq identified 16,295 differentially expressed genes (DEGs) (FDR < 0.05, |log(2)FC= > 1). In R1 (treated vs. control), 3046, 2332, and 2003 genes were upregulated, while 1403, 3099, and 2895 genes were downregulated at 3 h, 24 h, and 48 h, respectively. In R1 (treated) vs. R2 (treated), 1900, 3867, and 3553 genes were upregulated, while 1321, 6818, and 3490 genes were downregulated at 3 h, 24 h, and 48 h, respectively. Similarly, in R1 (treated) vs. S (treated), 1236, 1333, and 1689 genes were upregulated, and 836, 2496, and 1292 genes were downregulated at the corresponding time points. Time-course analysis using maSigPro identified 11,121 genes with significant temporal expression changes. Metabolomic profiling detected 69 differentially accumulated metabolites, from which the 20 most significant (p < 0.05) were selected. Weighted Gene Co-expression Network Analysis (WGCNA) clustered genes into 17 modules; five were significantly correlated with key metabolites and overlapped with DESeq2 and maSigPro gene sets. A total of 39 candidate genes were consistently identified through differential expression, functional enrichment, and network analyses, and six were validated by qRT-PCR. These results provide a foundational framework for future studies into the genetic architecture and molecular basis of bacterial wilt resistance in tobacco.

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