Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.
Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.
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DOI：
10.1371/journal.ppat.1001011
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发表时间：
2010-07-22
期刊：
影响因子：
6.7
通讯作者：
Katagiri F
中科院分区：
文献类型：
--
作者：
Sato M;Tsuda K;Wang L;Coller J;Watanabe Y;Glazebrook J;Katagiri F
生物信号传导过程可以由复杂网络介导，其中网络组件和网络部分以复杂的方式相互作用。复杂网络的研究得益于在网络的背景下考虑单个组件的作用的方法。植物免疫信号网络就是这样一个复杂的网络，它控制着植物对病原菌攻击的诱导反应。我们研究了拟南芥免疫信号网络的挑战与细菌病原体假单胞菌的菌株表达的效应蛋白AvrRpt 2（Pto DC 3000 AvrRpt 2）。这种细菌菌株向信号网络提供多个输入，允许网络的许多部分同时被激活。在接种Pto DC 3000 AvrRpt 2后6小时收集22个拟南芥免疫突变体和野生型的571个免疫应答基因的mRNA谱。分析mRNA谱，作为遗传扰动导致的网络状态变化的详细描述。通过递归地将非线性降维过程应用于mRNA谱数据来推断对应于突变的基因之间的调控关系。由此产生的静态网络模型准确地预测了文献中报道的25种调控关系中的23种，这表明对新型调控关系的预测也是准确的。该网络模型揭示了两个显著的特征：（i）网络的组成部分是高度互连的;（ii）信号部门之间普遍存在负监管关系。进一步验证了复杂的调控关系，包括早期微生物相关分子模式触发的信号部门和水杨酸部门之间的新型负调控关系。我们建议，普遍存在的负调控信号部门之间的关系，使植物免疫信号网络的“部门切换”网络，有效地平衡两个明显相互冲突的要求，对病原干扰的鲁棒性和适度的负面影响的免疫反应对植物的健身。当植物检测到病原体攻击时，这些信息通过分子信号网络传递，以启动各种各样的免疫反应。我们研究了这种植物免疫信号网络是如何组织使用模式植物拟南芥。用病原体攻击具有免疫信号传导缺陷的野生型和突变体植物。然后，使用微阵列测量许多基因的表达水平。详细分析突变对基因表达的影响，使我们能够建立一个由突变对应的基因组成的信号网络模型。该模型预测网络组件是高度互连的，并且介导不同信令事件的网络组件彼此抑制是非常常见的。网络中普遍存在的信号抑制表明，通常只使用部分信号网络，但如果这一部分受到病原体的攻击，其他部分就会启动并支持受攻击部分的功能。我们推测，植物免疫信号是高度耐受病原体的攻击，由于这种备份机制。我们还推测，在任何时候只使用网络的一部分有助于最大限度地减少免疫反应对植物适应性的负面影响。
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. 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 for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the 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 static network model accurately predicted 23 of 25 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; and (ii) negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were 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. When a plant detects pathogen attack, this information is conveyed through a molecular signaling network to turn on a large variety of immune responses. We investigated how this plant immune signaling network was organized using the model plant Arabidopsis. Wild type and mutant plants with defects in immune signaling were challenged with a pathogen. Then, expression levels of many genes were measured using microarrays. Detailed analysis of the mutation effects on gene expression allowed us to build a signaling network model composed of the genes corresponding to the mutations. This model predicted that the network components are highly interconnected and that it is very common for network components that mediate different signaling events to inhibit each other. The prevalent signaling inhibitions in the network suggest that only part of the signaling network is usually used but that if this part is attacked by pathogens, other parts kick in and back up the function of the attacked part. We speculate that plant immune signaling is highly tolerant to pathogen attack due to this backup mechanism. We also speculate use of only part of the network at any one time helps minimize negative impacts of the immune response on plant fitness.
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