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The identification of candidate radio marker genes using a coexpression network analysis in gamma‐irradiated rice
- Kim, Sun‐Hee, Hwang, Sun‐Goo, Hwang, Jung Eun, Jang, Cheol Seong, Velusamy, Vijayanand, Kim, Jin‐Baek, Kim, Sang Hoon, Ha, Bo‐Keun, Kang, Si‐Yong, Kim, Dong Sub
- Physiologia plantarum 2013 v.149 no.4 pp. 554-570
- algorithms, biochemical pathways, cluster analysis, cold, cooling, correlation, drought, gamma radiation, gene expression, gene expression regulation, genes, genetic markers, heat, hypoxia, oxidative stress, plant biochemistry, plant physiology, radiation resistance, reverse transcriptase polymerase chain reaction, rice, roots, salt stress, salts, transcriptomics
- Plant physiological and biochemical processes are significantly affected by gamma irradiation stress. In addition, gamma‐ray (GA) differentially affects gene expression across the whole genome. In this study, we identified radio marker genes (RMGs) responding only to GA stress compared with six abiotic stresses (chilling, cold, anoxia, heat, drought and salt) in rice. To analyze the expression patterns of differentially expressed genes (DEGs) in gamma‐irradiated rice plants against six abiotic stresses, we conducted a hierarchical clustering analysis by using a complete linkage algorithm. The up‐ and downregulated DEGs were observed against six abiotic stresses in three and four clusters among a total of 31 clusters, respectively. The common gene ontology functions of upregulated DEGs in clusters 9 and 19 are associated with oxidative stress. In a Pearson's correlation coefficient analysis, GA stress showed highly negative correlation with salt stress. On the basis of specific data about the upregulated DEGs, we identified the 40 candidate RMGs that are induced by gamma irradiation. These candidate RMGs, except two genes, were more highly induced in rice roots than in other tissues. In addition, we obtained other 38 root‐induced genes by using a coexpression network analysis of the specific upregulated candidate RMGs in an ARACNE algorithm. Among these genes, we selected 16 RMGs and 11 genes coexpressed with three RMGs to validate coexpression network results. RT‐PCR assay confirmed that these genes were highly upregulated in GA treatment. All 76 genes (38 root‐induced genes and 38 candidate RMGs) might be useful for the detection of GA sensitivity in rice roots.