Global Transcriptome Analysis of Combined Abiotic Stress Signaling Genes Unravels Key Players in Oryza sativa L.: An In silico Approach

Abstract

Combined abiotic stress (CAbS) affects the field grown plants simultaneously. The multigenic and quantitative nature of uncontrollable abiotic stresses complicates the process of understanding the stress response by plants. Considering this, we analyzed the CAbS response of C3 model plant, Oryza sativa by meta-analysis. The datasets of commonly expressed genes by drought, salinity, submergence, metal, natural expression, biotic, and abiotic stresses were data mined through publically accessible transcriptomic abiotic stress (AbS) responsive datasets. Of which 1,175, 12,821, and 42,877 genes were commonly expressed in meta differential, individual differential, and unchanged expressions respectively. Highly regulated 100 differentially expressed AbS genes were derived through integrative meta-analysis of expression data (INMEX). Of this 30 genes were identified from AbS gene families through expression atlas that were computationally analyzed for their physicochemical properties. All AbS genes were physically mapped against O. sativa genome. Comparative mapping of these genes demonstrated the orthologous relationship with related C4 panicoid genome. In silico expression analysis of these genes showed differential expression patterns in different developmental tissues. Protein–protein interaction of these genes, represented the complexity of AbS. Computational expression profiling of candidate genes in response to multiple stresses suggested the putative involvement of OS05G0350900, OS02G0612700, OS05G0104200, OS03G0596200, OS12G0225900, OS07G0152000, OS08G0119500, OS06G0594700, and Os01g0393100 in CAbS. These potential candidate genes need to be studied further to decipher their functional roles in AbS dynamics.

Global analysis of threonine metabolism genes unravel key players in rice to improve the abiotic stress tolerance

“…Available public microarray hybridization DB of rice and their respective expression values confirmed the developmental tissue specific expression profiling of 16 AbSR – ThrMPG. The plants are simultaneously affected by stress in diverse developmental tissues 41 – 43 . Therefore, these 16 AbSR-ThrMPG reveal higher levels of AbSR - ThrMPG expression and thus a similar expression pattern in 41 different developmental tissues at different individual AbS.…”

“…Hence, our analysis suggests their diverse role in different molecular and physiological activities. For delineating their functional roles, this information may be used to choose potential candidate genes based on their improved expression patterns 43 . This data could be further used to enhance our understanding on rice metabolism and to improve the quality and quantity of rice grain by developing AbS tolerance plant through gene manipulation and genetic engineering.…”

Comparative transcriptome meta-analysis of Arabidopsis thaliana under drought and cold stress

“…There are several studies on single stress conditions, which do not provide enough information about expression profiles of stress-responsive genes in multiple stress conditions. Recent investigations on multiple stress-induced biological networks received much attention[ 5 – 8 ]. Additionally, comparison of molecular profiles of an organism under different stresses would make it possible to identify the conserved stress mechanisms [ 5 – 8 ].…”

“…Recent investigations on multiple stress-induced biological networks received much attention[ 5 – 8 ]. Additionally, comparison of molecular profiles of an organism under different stresses would make it possible to identify the conserved stress mechanisms [ 5 – 8 ]. Gene co-expression networks study is becoming increasingly popular as one of the approaches to identify sets of interacting genes.…”

ESTs as a Resource for Gene Discovery and Population Genetic Analysis of Crop Plants

“…Publicly available datasets are useful for future crop research, such as elucidating the molecular mechanisms of specific traits and understanding the complexity of the transcriptome, gene expression regulation, and gene networks, gene annotation and discovery, [12][13][14] comparative genomics, 9 development of molecular markers, 15,16 and population genomics studies of genetic variation associated with adaptive traits. 6,17 One can map ESTs to specific chromosome locations using physical mapping techniques, such as radiation hybrid mapping, happy mapping, or FISH.…”