Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India.
PLoS One. 2019 May 6;14(5):e0216068. doi: 10.1371/journal.pone.0216068. eCollection 2019.
Drought is a severe environmental stress. It is estimated that about 50% of the world rice production is affected mainly by drought. Apart from conventional breeding strategies to develop drought-tolerant crops, innovative computational approaches may provide insights into the underlying molecular mechanisms of stress response and identify drought-responsive markers. Here we propose a network-based computational approach involving a meta-analytic study of seven drought-tolerant rice genotypes under drought stress.
Co-expression networks enable large-scale analysis of gene-pair associations and tightly coupled clusters that may represent coordinated biological processes. Considering differentially expressed genes in the co-expressed modules and supplementing external information such as resistance/tolerance QTLs, transcription factors, network-based topological measures, we identify and prioritize drought-adaptive co-expressed gene modules and potential candidate genes. Using the candidate genes that are well-represented across the datasets as 'seed' genes, two drought-specific protein-protein interaction networks (PPINs) are constructed with up- and down-regulated genes. Cluster analysis of the up-regulated PPIN revealed ABA signalling pathway as a central process in drought response with a probable crosstalk with energy metabolic processes. Tightly coupled gene clusters representing up-regulation of core cellular respiratory processes and enhanced degradation of branched chain amino acids and cell wall metabolism are identified. Cluster analysis of down-regulated PPIN provides a snapshot of major processes associated with photosynthesis, growth, development and protein synthesis, most of which are shut down during drought. Differential regulation of phytohormones, e.g., jasmonic acid, cell wall metabolism, signalling and posttranslational modifications associated with biotic stress are elucidated. Functional characterization of topologically important, drought-responsive uncharacterized genes that may play a role in important processes such as ABA signalling, calcium signalling, photosynthesis and cell wall metabolism is discussed. Further transgenic studies on these genes may help in elucidating their biological role under stress conditions.
Currently, a large number of resources for rice functional genomics exist which are mostly underutilized by the scientific community. In this study, a computational approach integrating information from various resources such as gene co-expression networks, protein-protein interactions and pathway-level information is proposed to provide a systems-level view of complex drought-responsive processes across the drought-tolerant genotypes.
干旱是一种严重的环境胁迫。据估计，世界上大约 50%的水稻产量受到干旱的主要影响。除了传统的培育耐旱作物的策略外，创新的计算方法可能会提供对胁迫反应的潜在分子机制的深入了解，并确定耐旱性标记物。在这里，我们提出了一种基于网络的计算方法，涉及对七种耐旱水稻基因型在干旱胁迫下的元分析研究。
共表达网络能够大规模分析基因对关联和紧密耦联的簇，这些簇可能代表协调的生物学过程。考虑到共表达模块中差异表达的基因，并补充抗性/耐受性 QTL、转录因子、基于网络的拓扑度量等外部信息，我们确定并优先考虑耐旱性共表达基因模块和潜在的候选基因。使用在多个数据集上表现良好的候选基因作为“种子”基因，构建了两个上调和下调基因的干旱特异性蛋白质-蛋白质相互作用网络（PPIN）。对上调 PPIN 的聚类分析揭示了 ABA 信号通路是干旱响应的中心过程，可能与能量代谢过程发生交叉对话。鉴定了代表核心细胞呼吸过程上调和支链氨基酸和细胞壁代谢增强降解的紧密耦联基因簇。下调 PPIN 的聚类分析提供了与光合作用、生长、发育和蛋白质合成相关的主要过程的快照，其中大多数过程在干旱期间关闭。阐明了与生物胁迫相关的植物激素、细胞壁代谢、信号转导和翻译后修饰的差异调节。讨论了拓扑上重要的、耐旱的未表征基因的功能特征，这些基因可能在 ABA 信号转导、钙信号转导、光合作用和细胞壁代谢等重要过程中发挥作用。对这些基因的进一步转基因研究可能有助于阐明它们在胁迫条件下的生物学作用。
目前，水稻功能基因组学的大量资源存在，但科学界大多未得到充分利用。在这项研究中，提出了一种基于网络的计算方法，该方法集成了来自各种资源的信息，如基因共表达网络、蛋白质-蛋白质相互作用和途径水平信息，以提供对耐旱基因型中复杂耐旱反应过程的系统水平视图。
