Identification of responsive genes to multiple abiotic stresses in rice (Oryza sativa): a meta-analysis of transcriptomics data

Abstract Abiotic stresses limit the quantity and quality of rice grain production, which is considered a strategic crop in many countries. In this study, a meta-analysis of different microarray data at seedling stage was performed to investigate the effects of multiple abiotic stresses (drought, sal...

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Main Authors: Mahnaz Azad, Masoud Tohidfar, Rahele Ghanbari Moheb Seraj, Mohammad Mehralian, Keyvan Esmaeilzadeh-Salestani
Format: Article
Language:English
Published: Nature Portfolio 2024-04-01
Series:Scientific Reports
Subjects:
Online Access:https://doi.org/10.1038/s41598-024-54623-7
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author Mahnaz Azad
Masoud Tohidfar
Rahele Ghanbari Moheb Seraj
Mohammad Mehralian
Keyvan Esmaeilzadeh-Salestani
author_facet Mahnaz Azad
Masoud Tohidfar
Rahele Ghanbari Moheb Seraj
Mohammad Mehralian
Keyvan Esmaeilzadeh-Salestani
author_sort Mahnaz Azad
collection DOAJ
description Abstract Abiotic stresses limit the quantity and quality of rice grain production, which is considered a strategic crop in many countries. In this study, a meta-analysis of different microarray data at seedling stage was performed to investigate the effects of multiple abiotic stresses (drought, salinity, cold situation, high temperature, alkali condition, iron, aluminum, and heavy metal toxicity, nitrogen, phosphorus, and potassium deficiency) on rice. Comparative analysis between multiple abiotic stress groups and their control groups indicated 561 differentially expressed genes (DEGs), among which 422 and 139 genes were up-regulated and down-regulated, respectively. Gene Ontology analysis showed that the process of responding to stresses and stimuli was significantly enriched. In addition, pathways such as metabolic process and biosynthesis of secondary metabolites were identified by KEGG pathway analysis. Weighted correlation network analysis (WGCNA) uncovered 17 distinct co-expression modules. Six modules were significantly associated with genes involved in response to abiotic stresses. Finally, to validate the results of the meta-analysis, five genes, including TIFY9 (JAZ5), RAB16B, ADF3, Os01g0124650, and Os05g0142900 selected for qRT-PCR analysis. Expression patterns of selected genes confirmed the results of the meta-analysis. The outcome of this study could help introduce candidate genes that may be beneficial for use in genetic engineering programs to produce more tolerant crops or as markers for selection.
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spelling doaj.art-4ff617d901834b0282096afc770e798e2024-04-07T11:17:14ZengNature PortfolioScientific Reports2045-23222024-04-0114111310.1038/s41598-024-54623-7Identification of responsive genes to multiple abiotic stresses in rice (Oryza sativa): a meta-analysis of transcriptomics dataMahnaz Azad0Masoud Tohidfar1Rahele Ghanbari Moheb Seraj2Mohammad Mehralian3Keyvan Esmaeilzadeh-Salestani4Department of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti UniversityDepartment of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti UniversityDepartment of Horticultural Sciences, Faculty of Agriculture and Natural Resources, University of Mohaghegh ArdabiliDepartment of Agriculture, Medicinal Plants and Drugs Research Institute, Shahid Beheshti UniversityChair of Crop Science and Plant Biology, Institute of Agricultural and Environmental Sciences, Estonian University of Life SciencesAbstract Abiotic stresses limit the quantity and quality of rice grain production, which is considered a strategic crop in many countries. In this study, a meta-analysis of different microarray data at seedling stage was performed to investigate the effects of multiple abiotic stresses (drought, salinity, cold situation, high temperature, alkali condition, iron, aluminum, and heavy metal toxicity, nitrogen, phosphorus, and potassium deficiency) on rice. Comparative analysis between multiple abiotic stress groups and their control groups indicated 561 differentially expressed genes (DEGs), among which 422 and 139 genes were up-regulated and down-regulated, respectively. Gene Ontology analysis showed that the process of responding to stresses and stimuli was significantly enriched. In addition, pathways such as metabolic process and biosynthesis of secondary metabolites were identified by KEGG pathway analysis. Weighted correlation network analysis (WGCNA) uncovered 17 distinct co-expression modules. Six modules were significantly associated with genes involved in response to abiotic stresses. Finally, to validate the results of the meta-analysis, five genes, including TIFY9 (JAZ5), RAB16B, ADF3, Os01g0124650, and Os05g0142900 selected for qRT-PCR analysis. Expression patterns of selected genes confirmed the results of the meta-analysis. The outcome of this study could help introduce candidate genes that may be beneficial for use in genetic engineering programs to produce more tolerant crops or as markers for selection.https://doi.org/10.1038/s41598-024-54623-7Meta-analysisMicroarrayRiceEnvironmental stressesqRT-PCR
spellingShingle Mahnaz Azad
Masoud Tohidfar
Rahele Ghanbari Moheb Seraj
Mohammad Mehralian
Keyvan Esmaeilzadeh-Salestani
Identification of responsive genes to multiple abiotic stresses in rice (Oryza sativa): a meta-analysis of transcriptomics data
Scientific Reports
Meta-analysis
Microarray
Rice
Environmental stresses
qRT-PCR
title Identification of responsive genes to multiple abiotic stresses in rice (Oryza sativa): a meta-analysis of transcriptomics data
title_full Identification of responsive genes to multiple abiotic stresses in rice (Oryza sativa): a meta-analysis of transcriptomics data
title_fullStr Identification of responsive genes to multiple abiotic stresses in rice (Oryza sativa): a meta-analysis of transcriptomics data
title_full_unstemmed Identification of responsive genes to multiple abiotic stresses in rice (Oryza sativa): a meta-analysis of transcriptomics data
title_short Identification of responsive genes to multiple abiotic stresses in rice (Oryza sativa): a meta-analysis of transcriptomics data
title_sort identification of responsive genes to multiple abiotic stresses in rice oryza sativa a meta analysis of transcriptomics data
topic Meta-analysis
Microarray
Rice
Environmental stresses
qRT-PCR
url https://doi.org/10.1038/s41598-024-54623-7
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