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.

中文翻译：

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使用遗传反馈控制和监管网络重新布线的工程抗压植物框架

作物病害在全球范围内导致大量浪费，包括收获前和收获后，以及随后的经济和可持续性后果。疾病结果取决于植物对病原体的反应以及病原体抑制防御反应和操纵植物以增强定殖的能力。植物的防御反应的特点是由潜在基因调控网络介导的显着转录重编程，这些网络的组成部分通常是攻击病原体的目标。在这里，使用来自灰霉病菌感染的拟南芥的基因表达数据植物，我们使用合成生物学工具开发了一种系统方法来减轻病原体引起的网络扰动的影响。我们采用网络推理和系统识别技术来构建拟南芥的准确模型包含决定植物对病原体攻击易感性的关键基因的防御子网络。一旦针对时间序列数据进行了验证，我们就使用该模型来设计和测试基于遗传反馈控制使用的扰动缓解策略。我们展示了如何设计合成反馈控制器来减弱外部扰动对子网络中转录因子 CHE 的影响。我们研究并比较了两种在生物学上实现这种控制器的方法——直接实现遗传反馈控制器，并重新连接多个基因的调节区域——以实现实现控制器所需的网络基序。