A comprehensive meta-analysis to identify transcriptional signatures of abiotic stress responses in barley ( Hordeum vul

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ORIGINAL PAPER

A comprehensive meta‑analysis to identify transcriptional signatures of abiotic stress responses in barley (Hordeum vulgare) Zahra Zinati1   · Sima Sazegari2 · Ahmad Tahmasebi3 · Azar Delavari4 Received: 5 December 2019 / Accepted: 6 November 2020 © Akadémiai Kiadó Zrt. 2020

Abstract Barley, as one of the major cereals, possesses natural tolerance to major abiotic stresses such as drought and salinity, so that it is an outstanding model in abiotic stresses research. The study focuses on meta-analysis by combining different datasets of barley abiotic stress-related microarray data to identify stress-responsive genes and pathways. In addition to a thorough investigation to determine the up and downregulated gene sets under stress conditions, other analyses including gene ontology (GO) enrichment and protein–protein interaction network analysis were performed for a comprehensive study of differentially expressed genes under abiotic stresses. A total of 256 microarray samples from 14 different experiments were analyzed and 3723 probe sets were identified. The metabolic processes, cellular process, localization, biological regulation and regulation of biological process were the top enriched GO terms. Interestingly, the response to abiotic stress in the functional group contained the highest number of upregulated genes. In addition, the photosynthesis biological category included only downregulated genes. Fourteen genes in this category were related to photosystem II while only six genes belonged to photosystem I. Network analysis of DEGs revealed 52 and 57 genes as critical genes in down and upregulated networks, respectively; module analysis unveiled 28 and 23 clusters for up and downregulated networks. Regarding the GO analysis of modules, one upregulated cluster and two downregulated clusters exhibited a direct response to abiotic stress. Keywords  Barley · Abiotic stress tolerance · Meta-analysis · Microarray data · Gene ontology (GO) enrichment · Network analysis

Zahra Zinati and Sima Sazegari equally contributed to this study. Electronic supplementary material  The online version of this article (https​://doi.org/10.1007/s4297​6-020-00107​-z) contains supplementary material, which is available to authorized users. * Zahra Zinati [email protected] Sima Sazegari [email protected] Ahmad Tahmasebi [email protected] Azar Delavari [email protected] 1



Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran

2



Institute of Biotechnology, Shiraz University, Shiraz, Iran

3

Department of Crop Production and Plant Breeding, College of Agriculture, Shiraz University, Shiraz, Iran

4

Independent Scholar, Tehran, Iran



Abbreviations GO Gene ontology LEA DEG Embryogenesis abundant proteins HSP Heat shock proteins ROS Reactive oxygen species RMA Robust multichip average

Introduction Throughout their growth and development, plants encounter a combination of damaging abiotic stresses (Wania et al. 2016). To tackle a br