基于传递熵的基因调控网络(GRNTE):一种重构基因调控相互作用的新方法,应用于植物病原菌疫霉的案例研究。

Q1 Mathematics
Juan Camilo Castro, Ivan Valdés, Laura Natalia Gonzalez-García, Giovanna Danies, Silvia Cañas, Flavia Vischi Winck, Carlos Eduardo Ñústez, Silvia Restrepo, Diego Mauricio Riaño-Pachón
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引用次数: 16

摘要

背景:基因组学数据的增加有助于理解复杂系统的分子动力学,如植物和动物疾病。然而,尽管转录调控在细胞系统的决策过程中起着核心作用,但人们对其仍然知之甚少。在这项研究中,我们将表达数据与数学模型联系起来,以推断基因调控网络(GRN)。我们提出了一种简单而有效的方法来估计转录因子的grn从转录数据。方法:我们将基因对之间的相互作用(GRN中的边)定义为这些基因之间的部分互信息,该信息考虑了时间和一个基因相对于另一个基因可能存在的时间滞后。我们将这种方法称为传递熵基因调控网络(GRNTE),它对应于自回归模型中高斯变量的格兰杰因果关系。为了评估我们方法的重建准确性,我们从真核酵母模型Saccharomyces cerevisae的GRN中生成了几个子网络。然后,我们将该方法应用于植物病原菌疫霉的实验数据。本研究采用RT-qPCR技术,对病原菌与黄马铃薯(Solanum tuberosum group Phureja)互作过程中48个转录因子的转录表达水平进行了评价。利用这些数据,我们重建了病原菌与这些寄主相互作用过程中的调控网络。结果:我们首先评估了基于传递熵(GRNTE)的方法在酵母酵母grn的真核数据集上的性能。结果表明,当边缘检测参数适当调整时,GRNTE与最先进的方法相当。在感染假单胞菌的情况下,本研究中考虑的大多数基因在相互作用开始(接种后0小时- hpi)到接种后较晚的时间点表现出显著的表达变化。表达数据的分层聚类区分了侵染过程中的两个不同时期:中等抗性和敏感品种的侵染期分别为12 ~ 36 hpi和48 ~ 72 hpi。这些不同的时期可能与病原体感染寄主植物时生命周期的两个阶段有关:生物营养期和坏死性营养期。结论:本文提出了一种解决时间序列数据中网络重构问题的算法。这种分析视角利用了时间序列数据的动态特性,因为它与转录调节等内在动态过程有关,即细胞的多个元素(例如转录因子)同时起作用并随时间变化。我们应用该算法研究了两种对病原菌抗性水平不同的寄主相互作用过程中病原菌的调控网络。虽然基因表达分析没有显示出两种宿主之间的差异,但GRN分析的结果证明,根据宿主的抗性水平,基因的相互作用会重新布线。这表明不同的调节过程被激活以响应不同的环境线索。我们的方法的应用表明,它可以可靠地预测在转录网络和子网络中放置边缘的位置。这里使用的实验方法可以帮助我们了解这些相互作用在诸如致病性等复杂过程中的生物学作用。所使用的代码可在https://github.com/jccastrog/GRNTE上获得,遵循GNU通用公共许可证3.0。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Gene regulatory networks on transfer entropy (GRNTE): a novel approach to reconstruct gene regulatory interactions applied to a case study for the plant pathogen Phytophthora infestans.

Gene regulatory networks on transfer entropy (GRNTE): a novel approach to reconstruct gene regulatory interactions applied to a case study for the plant pathogen Phytophthora infestans.

Gene regulatory networks on transfer entropy (GRNTE): a novel approach to reconstruct gene regulatory interactions applied to a case study for the plant pathogen Phytophthora infestans.

Gene regulatory networks on transfer entropy (GRNTE): a novel approach to reconstruct gene regulatory interactions applied to a case study for the plant pathogen Phytophthora infestans.

Background: The increasing amounts of genomics data have helped in the understanding of the molecular dynamics of complex systems such as plant and animal diseases. However, transcriptional regulation, although playing a central role in the decision-making process of cellular systems, is still poorly understood. In this study, we linked expression data with mathematical models to infer gene regulatory networks (GRN). We present a simple yet effective method to estimate transcription factors' GRNs from transcriptional data.

Method: We defined interactions between pairs of genes (edges in the GRN) as the partial mutual information between these genes that takes into account time and possible lags in time from one gene in relation to another. We call this method Gene Regulatory Networks on Transfer Entropy (GRNTE) and it corresponds to Granger causality for Gaussian variables in an autoregressive model. To evaluate the reconstruction accuracy of our method, we generated several sub-networks from the GRN of the eukaryotic yeast model, Saccharomyces cerevisae. Then, we applied this method using experimental data of the plant pathogen Phytophthora infestans. We evaluated the transcriptional expression levels of 48 transcription factors of P. infestans during its interaction with one moderately resistant and one susceptible cultivar of yellow potato (Solanum tuberosum group Phureja), using RT-qPCR. With these data, we reconstructed the regulatory network of P. infestans during its interaction with these hosts.

Results: We first evaluated the performance of our method, based on the transfer entropy (GRNTE), on eukaryotic datasets from the GRNs of the yeast S. cerevisae. Results suggest that GRNTE is comparable with the state-of-the-art methods when the parameters for edge detection are properly tuned. In the case of P. infestans, most of the genes considered in this study, showed a significant change in expression from the onset of the interaction (0 h post inoculum - hpi) to the later time-points post inoculation. Hierarchical clustering of the expression data discriminated two distinct periods during the infection: from 12 to 36 hpi and from 48 to 72 hpi for both the moderately resistant and susceptible cultivars. These distinct periods could be associated with two phases of the life cycle of the pathogen when infecting the host plant: the biotrophic and necrotrophic phases.

Conclusions: Here we presented an algorithmic solution to the problem of network reconstruction in time series data. This analytical perspective makes use of the dynamic nature of time series data as it relates to intrinsically dynamic processes such as transcription regulation, were multiple elements of the cell (e.g., transcription factors) act simultaneously and change over time. We applied the algorithm to study the regulatory network of P. infestans during its interaction with two hosts which differ in their level of resistance to the pathogen. Although the gene expression analysis did not show differences between the two hosts, the results of the GRN analyses evidenced rewiring of the genes' interactions according to the resistance level of the host. This suggests that different regulatory processes are activated in response to different environmental cues. Applications of our methodology showed that it could reliably predict where to place edges in the transcriptional networks and sub-networks. The experimental approach used here can help provide insights on the biological role of these interactions on complex processes such as pathogenicity. The code used is available at https://github.com/jccastrog/GRNTE under GNU general public license 3.0.

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来源期刊
Theoretical Biology and Medical Modelling
Theoretical Biology and Medical Modelling MATHEMATICAL & COMPUTATIONAL BIOLOGY-
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6-12 weeks
期刊介绍: Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, philosophers and historians of science are all contributing to the emergence of novel concepts in an age of systems biology, bioinformatics and computer modelling. This is the field in which Theoretical Biology and Medical Modelling operates. We welcome submissions that are technically sound and offering either improved understanding in biology and medicine or progress in theory or method.
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