Laboratorio de Bioingeniería, Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile.
Millennium Nucleus Center for Plant Systems and Synthetic Biology, Santiago, Chile.
BMC Bioinformatics. 2020 Apr 15;21(1):142. doi: 10.1186/s12859-020-3472-3.
An important process for plant survival is the immune system. The induced systemic resistance (ISR) triggered by beneficial microbes is an important cost-effective defense mechanism by which plants are primed to an eventual pathogen attack. Defense mechanisms such as ISR depend on an accurate and context-specific regulation of gene expression. Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRNs). Here, we explore the regulatory mechanism of the ISR defense response triggered by the beneficial bacterium Paraburkholderia phytofirmans PsJN in Arabidopsis thaliana plants infected with Pseudomonas syringae DC3000. To achieve this, a GRN underlying the ISR response was inferred using gene expression time-series data of certain defense-related genes, differential evolution, and threshold Boolean networks.
One thousand threshold Boolean networks were inferred that met the restriction of the desired dynamics. From these networks, a consensus network was obtained that helped to find plausible interactions between the genes. A representative network was selected from the consensus network and biological restrictions were applied to it. The dynamics of the selected network showed that the largest attractor, a limit cycle of length 3, represents the final stage of the defense response (12, 18, and 24 h). Also, the structural robustness of the GRN was studied through the networks' attractors.
A computational intelligence approach was designed to reconstruct a GRN underlying the ISR defense response in plants using gene expression time-series data of A. thaliana colonized by P. phytofirmans PsJN and subsequently infected with P. syringae DC3000. Using differential evolution, 1000 GRNs from time-series data were successfully inferred. Through the study of the network dynamics of the selected GRN, it can be concluded that it is structurally robust since three mutations were necessary to completely disarm the Boolean trajectory that represents the biological data. The proposed method to reconstruct GRNs is general and can be used to infer other biologically relevant networks to formulate new biological hypotheses.
植物生存的一个重要过程是免疫系统。有益微生物触发的诱导系统抗性（ISR）是植物对最终病原体攻击进行预先防御的一种重要且具有成本效益的防御机制。ISR 等防御机制依赖于基因表达的准确和特定于上下文的调节。基因及其产物之间的相互作用产生了被称为基因调控网络（GRN）的复杂电路。在这里，我们探索了有益细菌 Paraburkholderia phytofirmans PsJN 触发拟南芥植物对丁香假单胞菌 DC3000 感染的 ISR 防御反应的调节机制。为此，使用某些防御相关基因的基因表达时间序列数据、微分进化和阈值布尔网络来推断 ISR 反应的 GRN。
推断出 1000 个满足所需动态限制的阈值布尔网络。从这些网络中，获得了一个共识网络，有助于发现基因之间的合理相互作用。从共识网络中选择一个代表性网络，并对其施加生物学限制。所选网络的动态表明，最大吸引子是一个长度为 3 的极限环，代表防御反应的最后阶段（12、18 和 24 小时）。此外，通过网络的吸引子研究了 GRN 的结构鲁棒性。
设计了一种计算智能方法，使用被 P. phytofirmans PsJN 定殖的 A. thaliana 的基因表达时间序列数据和随后感染 P. syringae DC3000 来重建植物中 ISR 防御反应的 GRN。使用微分进化，成功推断出 1000 个来自时间序列数据的 GRN。通过对所选 GRN 的网络动态研究，可以得出结论，它的结构是鲁棒的，因为需要三个突变才能完全破坏代表生物数据的布尔轨迹。所提出的用于重建 GRN 的方法是通用的，可以用于推断其他具有生物学相关性的网络，以形成新的生物学假设。
