Wang Yibo, Zhang Ke, Chen Dan, Liu Kai, Chen Wei, He Fei, Tong Zhijun, Luo Qiaoling
Key Laboratory of Tobacco Biotechnological Breeding, Yunnan Academy of Tobacco Agricultural Sciences, National Tobacco Genetic Engineering Research Centre, Kunming, 650021, China.
State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
Arch Microbiol. 2024 May 2;206(5):241. doi: 10.1007/s00203-024-03925-5.
The epidemic of stripe rust, caused by the pathogen Puccinia striiformis f. sp. tritici (Pst), would reduce wheat (Triticum aestivum) yields seriously. Traditional experimental methods are difficult to discover the interaction between wheat and Pst. Multi-omics data analysis provides a new idea for efficiently mining the interactions between host and pathogen. We used 140 wheat-Pst RNA-Seq data to screen for differentially expressed genes (DEGs) between low susceptibility and high susceptibility samples, and carried out Gene Ontology (GO) enrichment analysis. Based on this, we constructed a gene co-expression network, identified the core genes and interacted gene pairs from the conservative modules. Finally, we checked the distribution of Nucleotide-binding and leucine-rich repeat (NLR) genes in the co-expression network and drew the wheat NLR gene co-expression network. In order to provide accessible information for related researchers, we built a web-based visualization platform to display the data. Based on the analysis, we found that resistance-related genes such as TaPR1, TaWRKY18 and HSP70 were highly expressed in the network. They were likely to be involved in the biological processes of Pst infecting wheat. This study can assist scholars in conducting studies on the pathogenesis and help to advance the investigation of wheat-Pst interaction patterns.
由条形柄锈菌小麦专化型（Pst）病原体引起的条锈病流行会严重降低小麦（普通小麦）产量。传统实验方法难以发现小麦与Pst之间的相互作用。多组学数据分析为有效挖掘宿主与病原体之间的相互作用提供了新思路。我们使用140个小麦 - Pst RNA测序数据筛选低感和高感样本之间的差异表达基因（DEG），并进行基因本体论（GO）富集分析。在此基础上，我们构建了基因共表达网络，从保守模块中识别核心基因和相互作用基因对。最后，我们检查了共表达网络中核苷酸结合和富含亮氨酸重复序列（NLR）基因的分布，并绘制了小麦NLR基因共表达网络。为了为相关研究人员提供可访问的信息，我们构建了一个基于网络的可视化平台来展示数据。基于分析，我们发现TaPR1、TaWRKY18和HSP70等抗性相关基因在网络中高表达。它们可能参与了Pst感染小麦的生物学过程。本研究可为学者开展发病机制研究提供帮助，并有助于推进对小麦 - Pst相互作用模式的研究。
