Modeling regulatory relationships in Arabidopsis immune signaling network.. Arabidopsis thaliana

Expression profiles of 22 reference Arabidopsis immunity mutants were collected using the Arabidopsis Pathoarray 464_001 (GPL3638) in order to build a network model predicting the Arabidopsis immune signaling network. Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. Here, we demonstrate that use of mRNA profiling to collect and analyze detailed descriptions of changes in the network state resulting from specific network perturbations is a powerful and economical strategy to elucidate regulatory relationships among the components of a complex signaling network. Specifically, we studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with the Pto DC3000 AvrRpt2 and used as detailed descriptions of the network states resulting from specific genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting network model accurately predicted 22 of 23 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; (ii) negative regulatory relationships are common between signaling sectors. One case of a novel negative regulatory relationship, between the early microbe-associated molecular pattern (MAMP)-activated sector and the salicylic acid (SA)-mediated sector, was further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector-switching" network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness. Keywords: Responses of reference Arabidopsis immunity mutants to Pseudomonas syringae pv. tomato DC3000 carrying avrRpt2 Overall design: This experiment consists of two (group00) or three (group01-04) biological replicates of each genotype (total 25 genotypes [3 are multiple mutants, which were removed for the network modeling, but were used for normalization]). For each genotype, two leaves per plant were pooled from three pots to prepare total RNA.

为构建拟南芥免疫信号调控网络的预测模型，本研究采用拟南芥病原基因芯片（Arabidopsis Pathoarray 464_001，GPL3638）采集了22株参考拟南芥免疫突变体的表达谱。
生物信号转导过程往往由复杂网络介导，网络组分及功能模块间以复杂方式相互作用。对复杂网络的研究需结合整体网络背景，分析单个组分的功能角色。植物免疫信号网络负责调控病原菌侵染后的诱导型免疫响应，正是这类复杂网络的典型代表。
本研究证实，通过mRNA表达谱分析采集并解析特定网络扰动后网络状态变化的详细特征，是阐明复杂信号网络各组分间调控关系的高效且经济的研究策略。具体而言，本研究针对表达效应蛋白AvrRpt2的丁香假单胞菌番茄致病变种DC3000（Pto DC3000 AvrRpt2）侵染后的拟南芥免疫信号网络展开研究。该菌株可向信号网络导入多种上游输入信号，能够同时激活网络的多个区域。
我们在接种Pto DC3000 AvrRpt2 6小时后，采集了22株拟南芥免疫突变体及野生型植株的mRNA表达谱，以此作为特定遗传扰动后网络状态的详细表征。通过对该表达谱数据递归应用非线性降维算法，我们推断出对应突变基因间的调控关系。所得网络模型可准确预测已发表文献中23个调控关系中的22个，表明该模型对新型调控关系的预测同样具有可靠性。
该网络模型揭示了两个显著特征：(i) 网络各组分间存在高度互联；(ii) 信号功能模块间普遍存在负调控关系。我们进一步验证了其中一个新型负调控关系——早期微生物相关分子模式（microbe-associated molecular pattern, MAMP）激活模块与水杨酸（salicylic acid, SA）介导模块间的相互抑制。我们提出，信号模块间普遍存在的负调控关系使植物免疫信号网络成为一种“模块切换型”网络，该网络可有效平衡两项看似矛盾的需求：抵御病原菌侵染的鲁棒性，以及缓解免疫响应对植物适合度造成的负面影响。
关键词：参考拟南芥免疫突变体对携带avrRpt2的丁香假单胞菌番茄致病变种DC3000的响应
整体设计：本实验包含每个基因型的2个（group00组）或3个（group01-04组）生物学重复，总计25个基因型（其中3个为多突变体，用于标准化分析但未纳入网络建模）。对于每个基因型，从3个培养盆中选取每株植物的2片叶片混合以提取总RNA。