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Background:The development of genome microarrays of the model plant;Arabidopsis thaliana, with increasing repositories of publicly available data and high-throughput data analysis tools, has opened new avenues to genome-wide systemic analysis of plant responses to environmental stresses.Objective:To identify differentially expressed genes and their regulatory networks inArabidopsis thalianaunder harsh environmental condition.Methods:Two replications of eight microarray data sets were derived from two different tissues (root and shoot) and two different time courses (control and 24 hours after the beginning of stress occurrence) for comparative data analysis through various bioinformatics tools.Results:Under drought stress, 2558 gene accessions in root and 3691 in shoot tissues had significantly differential expression with respect to control condition. Likewise, under salinity stress 9078 gene accessions in root and 5785 in shoot tissues were discriminated between stressed and non-stressed conditions. Furthermore, the transcription regulatory activity of differentially expressed genes was mainly due to hormone, light, circadian and stress responsivecis-acting regulatory elements among which ABRE, ERE, P-box, TATC-box, CGTCA-motif, GARE-motif, TGACG-motif, GAG-motif, GA-motif, GATA- motif, TCT-motif, GT1-motif, Box 4, G-Box, I-box, LAMP-element, Sp1, MBS, TC-rich repeats, TCA-element and HSE were the most important elements in the identified up-regulated genes.Conclusion:The results of the high-throughput comparative analyses in this study provide more options for plant breeders and give an insight into genes andcis-acting regulatory elements involved in plant response to drought and salinity stresses in strategic crops such as cereals.

Comparative genomics and meta-quantitative trait loci (MQTLs) analysis are important tools for the identification of reliable and stable QTLs and functional genes controlling quantitative traits. We conducted a meta-analysis to identify the most stable QTLs for grain yield (GY), grain quality traits, and micronutrient contents in wheat. A total of 735 QTLs retrieved from 27 independent mapping populations reported in the last 13 years were used for the meta-analysis. The results showed that 449 QTLs were successfully projected onto the genetic consensus map which condensed to 100 MQTLs distributed on wheat chromosomes. This consolidation of MQTLs resulted in a three-fold reduction in the confidence interval (CI) compared with the CI for the initial QTLs. Projection of QTLs revealed that the majority of QTLs and MQTLs were in the non-telomeric regions of chromosomes. The majority of micronutrient MQTLs were located on the A and D genomes. The QTLs of thousand kernel weight (TKW) were frequently associated with QTLs for GY and grain protein content (GPC) with co-localization occurring at 55 and 63%, respectively. The co- localization of QTLs for GY and grain Fe was found to be 52% and for QTLs of grain Fe and Zn, it was found to be 66%. The genomic collinearity within Poaceae allowed us to identify 16 orthologous MQTLs (OrMQTLs) in wheat, rice, and maize. Annotation of promising candidate genes (CGs) located in the genomic intervals of the stable MQTLs indicated that several CGs (e.g., TraesCS2A02G141400, TraesCS3B02G040900, TraesCS4D02G323700, TraesCS3B02G077100, and TraesCS4D02G290900) had effects on micronutrients contents, yield, and yield-related traits. The mapping refinements leading to the identification of these CGs provide an opportunity to understand the genetic mechanisms driving quantitative variation for these traits and apply this information for crop improvement programs.

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Background: Rice contributes to the staple food of more than half of the world’s population. However, its productivity is influenced by various biotic and abiotic stresses. Genetic engineering and plant breeding tools help to overcome the adverse effects of environmental stresses. The advanced bioinformatics tools provide information for a better understanding of the mechanisms underlying stress tolerance, gene expression profiles and functions of the important genes and cis-regulatory elements involved in better performance under abiotic stresses. Objective: To identify the key genes involved in the tolerance mechanism for abiotic stresses and their regulatory networks in rice (Oryza sativa L.). Methods: A total of 152 various microarray datasets associated with nine rice trials were retrieved for expression meta-analysis through various bioinformatics tools. Results: The results indicated that 29593, 202798, 73224 and 25241 genes represented significant differential expression under cold, drought, salinity and heat stress conditions compared with the control condition, respectively. Twenty three highly overexpressed genes were identified under the evaluated abiotic stresses. The transcription regulatory activity of differentially expressed genes was mainly due to hormone, light and stress-responsive cis-acting regulatory elements among which ABRE, ARE, CGTCA-motif, GARE-motif, TGACG-motif, G-box, G-Box, GAG-motif, GA-motif, TCT-motif, Box 4, Sp1, HSE, MBS and TC-rich repeats were the most important in the promoter sites of the identified up-regulated genes. The results of cis-acting regulatory analysis suggest that 15 cis-acting regulatory elements were contributed to the tolerance mechanisms for abiotic stresses. Conclusion: The result of expression meta-analysis in this study provides an insight for plant breeders for better understanding the function of the genes and their regulatory mechanism in plants (especially cereals) exposed to different abiotic stresses. The outcome of this study suggests practical approaches for designing unified breeding programmes to breed multi-abiotic stress-tolerant species.