A Framework for Engineering Stress Resilient Plants Using Genetic Feedback Control and Regulatory Network Rewiring

Crop disease leads to significant waste worldwide, both pre- and postharvest, with subsequent economic and sustainability consequences. Disease outcome is determined both by the plants’ response to the pathogen and by the ability of the pathogen to suppress defense responses and manipulate the plant to enhance colonization. The defense response of a plant is characterized by significant transcriptional reprogramming mediated by underlying gene regulatory networks, and components of these networks are often targeted by attacking pathogens. Here, using gene expression data from Botrytis cinerea-infected Arabidopsis plants, we develop a systematic approach for mitigating the effects of pathogen-induced network perturbations, using the tools of synthetic biology. We employ network inference and system identification techniques to build an accurate model of an Arabidopsis defense subnetwork that contains key genes determining susceptibility of the plant to the pathogen attack. Once validated against time-series data, we use this model to design and test perturbation mitigation strategies based on the use of genetic feedback control. We show how a synthetic feedback controller can be designed to attenuate the effect of external perturbations on the transcription factor CHE in our subnetwork. We investigate and compare two approaches for implementing such a controller biologicallydirect implementation of the genetic feedback controller, and rewiring the regulatory regions of multiple genesto achieve the network motif required to implement the controller. Our results highlight the potential of combining feedback control theory with synthetic biology for engineering plants with enhanced resilience to environmental stress.作物病害在全球范围内造成了收获前与收获后的大量浪费，并随之带来经济与可持续性层面的负面影响。病害的发生结果同时取决于植物对病原菌的响应，以及病原菌抑制植物防御反应并操控植物以增强定殖能力的能力。植物的防御反应以显著的转录重编程为特征，该过程由底层的基因调控网络（gene regulatory network）介导，而这些网络的组分往往会被攻击的病原菌作为靶向目标。本文中，我们利用灰葡萄孢（Botrytis cinerea）侵染的拟南芥植株的基因表达数据，借助合成生物学（synthetic biology）工具开发了一种系统性方法，以缓解病原菌诱导的网络扰动带来的影响。我们采用网络推断与系统辨识技术，构建了一个精准的拟南芥防御子网络（Arabidopsis defense subnetwork）模型，该子网络包含决定植物对病原菌侵染敏感性的关键基因。在通过时间序列数据（time-series data）验证后，我们利用该模型设计并测试了基于遗传反馈控制（genetic feedback control）的扰动缓解策略。我们展示了如何设计合成反馈控制器（synthetic feedback controller），以减弱该子网络中转录因子CHE（transcription factor CHE）所受外部扰动的影响。我们研究并比较了两种在生物学上实现该控制器的途径——直接实施遗传反馈控制器，以及重编程多个基因的调控区域——以构建实现控制器所需的网络基序（network motif）。我们的研究结果凸显了将反馈控制理论与合成生物学相结合，用于培育具备更强环境胁迫抗性的工程化植物的潜力。