植物免疫基因功能网络的构建与动态性分析

结合申请人在计算系统生物学方面的研究基础，本项目将针对拟南芥与丁香假单胞菌这一植物与病原微生物互作的模式体系，通过构建植物免疫基因功能网络来研究植物病理表型和网络动态性之间的关系。首先，通过概率模型整合不同类型的分子互作数据，构建一个具有高覆盖度的拟南芥免疫基因功能网络。借助免疫过程中的基因表达数据，采用基于机器学习的子网络生长策略，从免疫基因功能网络中划分出代表性子网络，鉴定出响应不同植物病理表型变化的关键子网络及其内部免疫组分间的关键调控关系。此外，我们还将采用相同的子网络划分策略，系统地分析病原物相关分子模式激发的免疫反应(PTI)与效应蛋白激发的免疫反应(ETI)之间的关系。最后，我们将使用基于图论的方法从免疫基因功能网络中推断一些未知免疫响应基因的潜在功能。本项目的实施将有助于系统地认识植物免疫网络响应病原微生物入侵的分子机制，进而为农业上选育优良抗病作物新品种提供新的线索。

In the proposed project, we will use plant Arabidopsis thaliana (A. thaliana) and bacterium Pseudomonas syringae (P. syringae) as the model pathosystem to construct a comprehensive plant immunity-related gene functional network and investigate the relationship between network dynamics and pathologic phenotypes. Firstly, we will integrate diverse molecular interaction information to construct a high-coverage probabilistic network of functionally associated plant pathogen-responsive genes. Through combining A.thaliana gene expression data in response to the infection of P. syringae, we are going to conduct a machine learning-based algorithm to assign sub-networks. Meanwhile, the key regulatory patterns amongst components in each sub-network will be learned. Furthermore, the assigned sub-networks and their corresponding regulatory patterns will be compared between resistant and susceptible strains in order to identify important sub-networks and find important regulatory difference between two pathologic phenotypes. We will also investigate the relationship between pathogen-associated molecular pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) through this machine learning-based sub-network detection strategy. Additionally, we plan to infer new function of unknown pathogen-responsive genes in the established network by utilizing some graph theory-based algorithms. It is hoped that the proposed project will help to improve our understanding of plant immunity at the systematic level and the results may provide some new hints for breeding new crop varieties that can effectively defense phytopathogens.