基于蛋白质相互作用网络的茶树抗假眼小绿叶蝉研究

王芬; 裴会敏; 文狄; 陈志; 李静

doi:10.19303/j.issn.1008-0384.2021.03.011

基于蛋白质相互作用网络的茶树抗假眼小绿叶蝉研究

doi: 10.19303/j.issn.1008-0384.2021.03.011

黔南民族师范学院生物科学与农学院，贵州都匀 558000

基金项目: 贵州省科技厅基础研究计划项目（黔科合基础[2019]1298）；贵州省教育厅项目（黔教合KY字[2018]420、[2018]419和 [2016] 020）；国中医药办科技函（[2018]132号、[2019]186号）；贵州省植物学重点支持学科开放基金（qnsyzw1809）；黔南民族师范学院科研创新团队项目（QNYSKYTD2018011、Qnsyk201605）；黔南民族师范学院项目（QNSY2018ZJ006、2019xjg0303、QNSY2018BS018）

详细信息

作者简介:
王芬（1984−），女，博士研究生，副教授，主要从事生物信息学教学及研究工作（E-mail：fenmin521@163.com）

通讯作者:
裴会敏（E-mail：huiminpei@163.com）

中图分类号: S 435
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出版历程
- 收稿日期: 2020-07-06
- 修回日期: 2020-09-23
- 网络出版日期: 2021-02-08
- 刊出日期: 2021-03-31

Resistance of Tea Plants to Empoasca vitis Gothe Invasion Analyzed by PPIN

Department of Life Science and Agriculture, Qiannan Normal University for Nationalities, Duyun, Guizhou 　558000, China

摘要

摘要: 目的细胞生长的每个阶段都离不开蛋白质相互作用，研究假眼小绿叶蝉侵染茶叶后差异基因参与的蛋白质相互作用对解析抗虫分子机理具有指导意义。方法对有无受假眼小绿叶蝉侵害的都匀毛尖茶树鸟王种进行转录组测序，基于同源比对算法构建0 h与12 h、0 h与24 h的差异基因蛋白质相互作用网络。结果 0 h与12 h的差异基因共有212个互作，0 h与24 h的差异基因共有3 551个互作，合并网络后得到3 605个互作，并对网络进行拓扑属性分析，发现网络中蛋白质的度符合幂律分布。通过网络可以进行蛋白质的功能预测，并且对网络进行GO和KEGG分析发现差异基因的蛋白互作主要参与植物病原体互作，植物激素信号转导，DNA碱基切除修复、核酸切除修复、错配修复，亚麻酸和亚油酸代谢等生物过程，并且侵染24 h比侵染12 h需要启动更多的互作来抵御假眼小绿叶蝉的侵染。结论茶叶主要通过三个差异基因启动相应的互作来抵抗假眼小绿叶蝉的侵害，继续被侵害时将启动更多的互作，并通过产生相关次级代谢产物来抵御虫害。研究结果为探究鸟王种受假眼小绿叶蝉取食前后茶树抵御假眼小绿叶蝉侵染的代谢机理提供参考，为都匀毛尖病虫害防治、后续良种选育和引种奠定基础。
- 差异基因 /
- 蛋白质相互作用网络 /
- 假眼小绿叶蝉 /
- 鸟王种
Abstract: Objective The protein-protein interaction networks (PPINs) was applied to decipher the protective mechanism of tea plants in resisting the invasion by insects. Method RNA-seq was used to determine if a Camellia sinensis cv. Niaowang plant was infested by Empoasca vitis Gothe. PPINs of differential expression genes (DEGs) between 0 h and 12 h as well as 0 h and 24 h after the infestation were constructed based on Interolog. Result There were 212 PPIs between 0 h and 12 h, 3 551 PPIs between 0 h and 24 h, and 3 605 PPIs in the merged network. The topological properties of the PPINs showed the degree of the protein distribution following the power-law model, and hence, their functions predictable. It was found that DEGs mainly partook in the plant-pathogen interactions, plant hormone signal transduction, DNA base excision repair, nucleotide excision repair, mismatch repair, linolenic acid and alpha-linoleic acid metabolism, and other biological processes through GO and KEGG analysis. Furthermore, the tea leaves that were infected for 24 h would require greater amounts of PPIs than those affected for 12 h to develop the defense mechanism to ward off the ill-effect brought by Empoasca vitis Gothe. Conclusion Tea plants mainly resist the invasion of Empoasca Vitis through the three differential genes to promote the corresponding interaction, and when tea plants continue to be invaded, more interactions will be initiated, or various secondary metabolites will be produced to resist pests. To resist the invasion of Empoasca Vitis, tea plant mainly promoted the PPIs of three differential genes. When invasion continuing, more interactions would be initiated, and various secondary metabolites would be produced to resist pests.
- differential expression genes /
- protein-protein interaction network /
- Empoasca vitis Gothe /
- Camellia sinensis cv. Niaowang

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图 1 侵染0 h和侵染24 h差异基因蛋白质相互作用网络

Figure 1. PPINs of DEGs between 0 h and 24 h after infestation

下载: 全尺寸图片幻灯片

图 2 0 h与12 h的差异基因互作与0 h与24 h的差异基因互作的重叠数与差异数

Figure 2. Number of overlapping and different PPIs between 0-12 h and 0-24 h DEGs

下载: 全尺寸图片幻灯片

图 3 假眼小绿叶蝉侵染0 h和24 h的差异基因蛋白互作网络拓扑属性

注：A：中介中心性；B：接近中心性；C：拓扑系数；D：度分布。

Figure 3. Topological properties of PPINs in 0 h and 24 h DEGs

Note: A: Betweenness centrality; B: closeness to centrality; C: topological coefficient; D: degree distribution.

下载: 全尺寸图片幻灯片

表 1 差异基因相互作用数目

Table 1. Number of PPIs in DEGs

差异基因 DEG	蛋白质相互作用数目 PPI number
0 h vs 12 h	212
0 h vs 24 h	3 551
12 h vs 24 h	0

下载: 导出CSV

表 2 三个网络的全局特性

Table 2. Global properties of 3 networks

特性 Property	0 h与12 h的差异基因蛋白互作网络 0 h vs 12 h	0 h与24 h的差异基因蛋白互作网络 0 h vs 24 h	合并的网络 Merged network
节点 Nodes	112	499	531
聚合系数 Clustering coefficient	0.220	0.351	0.333
网络直径 Network diameter	6	7	7
网络半径 Network radius	1	1	1
最短路径 Shortest paths	32%	33%	32%
特征路径长度 Characteristic path length	2.912	2.625	2.668
平均邻居数 Avg.number of neighbors	3.786	14.232	13.578

下载: 导出CSV

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