Department of Agriculture and Natural Resources, Higher Education Center of Eghlid, Eghlid, Iran.
School of Bioscience, University of Skovde, Skovde, Sweden.
Physiol Plant. 2020 Sep;170(1):46-59. doi: 10.1111/ppl.13102. Epub 2020 Apr 13.
Studying the drought-responsive transcriptome is of high interest as it can serve as a blueprint for stress adaptation strategies. Despite extensive studies in this area, there are still many details to be uncovered, such as the importance of each gene involved in the stress response as well as the relationship between these genes and the physiochemical processes governing stress tolerance. This study was designed to address such important details and to gain insights into molecular responses of barley (Hordeum vulgare L.) to drought stress. To that, we combined RNA-seq data analysis with field and greenhouse drought experiments in a systems biology approach. RNA-sequence analysis identified a total of 665 differentially expressed genes (DEGs) belonging to diverse functional categories. A gene network was derived from the DEGs, which comprised of a total of 131 nodes and 257 edges. Gene network topology analysis highlighted two programmed cell death (PCD) modulating genes, MC1 (metacaspase 1) and TSN1 (Tudor-SN 1), as important (hub) genes in the predicted network. Based on the field trial, a drought-tolerant and a drought-susceptible barley genotype was identified from eight tested cultivars. Identified genotypes exhibited different physiochemical characteristics, including proline content, chlorophyll concentration, percentage of electrolyte leakage and malondialdehyde content as well as expression profiles of MC1 and TSN1 genes. Machine learning and correspondence analysis revealed a significant relationship between drought tolerance and measured characteristics in the context of PCD. Our study provides new insights which bridge barley drought tolerance to PCD through MC1 and TSN1 pathway.
研究干旱响应转录组具有重要意义,因为它可以作为适应应激策略的蓝图。尽管在这一领域进行了广泛的研究,但仍有许多细节有待揭示,例如参与应激反应的每个基因的重要性,以及这些基因与控制应激耐受的生理化学过程之间的关系。本研究旨在解决这些重要的细节问题,并深入了解大麦(Hordeum vulgare L.)对干旱胁迫的分子反应。为此,我们采用系统生物学方法,将 RNA-seq 数据分析与田间和温室干旱实验相结合。RNA-seq 分析共鉴定出 665 个差异表达基因(DEGs),属于多种功能类别。从 DEGs 中得出一个基因网络,共包含 131 个节点和 257 个边。基因网络拓扑分析突出了两个程序性细胞死亡(PCD)调节基因,MC1(metacaspase 1)和 TSN1(Tudor-SN 1),作为预测网络中的重要(枢纽)基因。基于田间试验,从 8 个测试品种中鉴定出一个耐旱和一个耐旱大麦基因型。鉴定出的基因型表现出不同的生理化学特性,包括脯氨酸含量、叶绿素浓度、电解质渗漏百分比和丙二醛含量,以及 MC1 和 TSN1 基因的表达谱。机器学习和对应分析揭示了 PCD 背景下耐旱性与测量特征之间的显著关系。我们的研究通过 MC1 和 TSN1 途径为大麦耐旱性与 PCD 之间提供了新的见解。