A Guard Cell Abscisic Acid (ABA) Network Model That Captures the Stomatal Resting State
A Guard Cell Abscisic Acid (ABA) Network Model That Captures the Stomatal Resting State
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DOI：
10.3389/fphys.2020.00927
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发表时间：
2020-08-13
影响因子：
4
通讯作者：
Albert, Reka
中科院分区：
文献类型：
--
作者：
Maheshwari, Parul;Assmann, Sarah M.;Albert, Reka
气孔在控制植物的碳同化和水分状况方面起着核心作用。保卫细胞对边界的每个孔整合来自环境和内源性信号的信息，并相应地膨胀或缩小，从而增加或减少气孔孔径。先前的研究表明，这一过程背后有一个复杂的细胞网络。我们以前已经构建了一个信号转导网络和布尔动态模型描述气孔关闭响应信号，包括植物激素脱落酸（阿坝），钙或活性氧（ROS）。在这里，我们改进的布尔网络模型，使其捕获的生物学预期的响应的警卫细胞在没有或以下的关闭诱导信号，如阿坝或外部Ca2+的去除。生物系统的期望是可逆性，即，在关闭信号被去除之后气孔应该重新打开。我们发现，该模型的可逆性是由先前假设的四个节点的持续活动阻碍。通过为这些节点引入时间依赖的布尔函数，该模型概括了去除信号后的气孔重新开放。由于多个网络节点的初始条件的不确定性，该模型的先前版本在没有任何信号的情况下预测类似于20%的闭合。我们系统地测试和调整这些初始条件，以找到最低限度的限制性组合，适当地导致开放气孔在关闭信号的情况下。我们支持这些结果的连续稳定的反馈图案在网络中的分析，照亮系统的动态进展，朝着开放或关闭的气孔状态。这一分析特别强调了胞质钙振荡在引起和维持气孔关闭中的作用。总体而言，我们说明了布尔网络建模框架的力量，有效地捕捉细胞表型作为细胞内生物过程的紧急结果。
Stomatal pores play a central role in the control of carbon assimilation and plant water status. The guard cell pair that borders each pore integrates information from environmental and endogenous signals and accordingly swells or deflates, thereby increasing or decreasing the stomatal aperture. Prior research shows that there is a complex cellular network underlying this process. We have previously constructed a signal transduction network and a Boolean dynamic model describing stomatal closure in response to signals including the plant hormone abscisic acid (ABA), calcium or reactive oxygen species (ROS). Here, we improve the Boolean network model such that it captures the biologically expected response of the guard cell in the absence or following the removal of a closure-inducing signal such as ABA or external Ca2+. The expectation from the biological system is reversibility, i.e., the stomata should reopen after the closing signal is removed. We find that the model's reversibility is obstructed by the previously assumed persistent activity of four nodes. By introducing time-dependent Boolean functions for these nodes, the model recapitulates stomatal reopening following the removal of a signal. The previous version of the model predicts similar to 20% closure in the absence of any signal due to uncertainty regarding the initial conditions of multiple network nodes. We systematically test and adjust these initial conditions to find the minimally restrictive combinations that appropriately result in open stomata in the absence of a closure signal. We support these results by an analysis of the successive stabilization of feedback motifs in the network, illuminating the system's dynamic progression toward the open or closed stomata state. This analysis particularly highlights the role of cytosolic calcium oscillations in causing and maintaining stomatal closure. Overall, we illustrate the strength of the Boolean network modeling framework to efficiently capture cellular phenotypes as emergent outcomes of intracellular biological processes.