Evaluation of Transcription Response to Drought Stress in Rice Using RNA-seq Meta-analysis

Document Type : Research Paper


1 Ph.D. Student, Department of Plant Breeding and Biotechnology, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.

2 Professor, Department of Plant Breeding and Biotechnology, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.

3 Assistant Professor, Department of Plant Breeding and Biotechnology, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.

4 Assistant Professor, Department of Biology, Faculty of Sciences, Razi University, Kermanshah, Iran.

5 Assistant Professor, University of California Riverside School of Medicine, Riverside, California, USA.


Understanding the molecular mechanisms of response to stress such as drought can significantly improve the science of plant molecular breeding. Transcriptomic studies can make a large amount of information available to researchers. Integrating such data from different sources through advanced statistical methods such as meta-analysis provides a new opportunity to overcome biological complexity, identify differentially expressed genes (DEGs), and obtain more reliable results. The present study aimed to identify DEGs in response to drought stress using transcriptomic data through a meta-analysis of RNA-seq data.
Materials and methods
RNA-seq data were downloaded from the EMBL-EBI database and after preprocessing, high-quality reads were mapped on the rice reference genome with the STAR software. Differential expression genes were evaluated separately for each dataset using the edgeR package. The outputs were used for meta-analysis using the metaRNAseq package. Genes with different and significant expressions in response to drought stress were examined for functional enrichment, biological pathways, and protein interaction. Finally, hub genes were identified.
According to the meta-analysis results, 6607 differential expression genes with average log2FC≥|1| And FDR≤0.05 were detected. 3313 and 3294 of them were regulated up and down, respectively, and 162 genes were not identified as DEG in individual analyzes and were identified only by meta-analysis, which shows the statistical power of this method in identifying new genes. The results of functional enrichment of DEGs indicate the induction of various metabolic pathways under stress including biosynthesis of secondary metabolites and amino acids, carbohydrate metabolism, and plant hormone signal transduction. Investigation of protein interaction and identification of hub genes also showed their role in stress response, oxidoreductase activity, and amino acid metabolism.
This study can increase our understanding of the molecular mechanisms of rice response to drought stress and be useful in identifying key and new genes, even as molecular markers to improve drought stress tolerance in rice breeding programs.


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