Meta-Analysis of Expression of the Stress Tolerance Associated Genes and Uncover their Cis-Regulatory Elements in Rice (Oryza sativa L.)

Nikwan Shariatipour1
, Bahram Heidari1, *

1 Department of Crop Production and Plant Breeding, School of Agriculture, Shiraz University, Shiraz, 7144165186, Iran

© 2020 Shariatipour & Heidari

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: ( This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Crop Production and Plant Breeding, Shiraz University, School of Agriculture, Bajgah, Shiraz, Iran; Tel: 989177070889; E-mail:



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.


To identify the key genes involved in the tolerance mechanism for abiotic stresses and their regulatory networks in rice (Oryza sativa L.).


A total of 152 various microarray datasets associated with nine rice trials were retrieved for expression meta-analysis through various bioinformatics tools.


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.


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.

Keywords: Bioinformatics, Gene ontology, Abiotic stress, Rice, High salinity, Novel gene.


Agriculture is affected by environmental abiotic stresses including drought, high salinity, low and high temperatures. Responses of plants to abiotic stresses are being increasingly addressed on a genome-wide scale in order to find novel gene targets involved in the tolerance mechanisms [1]. The share of rice to the diet of almost half of the worlds’ population and its popularity as a post-genomic model crop has made it an important crop for meta-analysis of stress tolerance associated genes [2, 3]. The response of gene families to abiotic stresses has been assessed by transcriptome-wide analyses suggesting their role in response to multiple environmental stresses [4-7]. Genetic analysis of the tolerance mechanisms against environmental stresses in plants reveals that specific genes respond to such stresses at the transcriptional level [8-15]. At the molecular level, environmental stresses affect the expression of stress-responsive genes [16-21]. The products of the stress-inducible genes have been classified into two classes: one that directly protects against environmental stresses and the other that is associated with gene expression and signal transduction pathways. Detection of stress-inducible genes helps to improve the stress tolerance in plants through cross-breeding and genetic engineering tools [8-10, 14, 15, 22-24]. Identification of stress-inducible genes and expression changes in response to environmental stresses and a better understanding of the regulatory mechanism behind differentially expressed genes are important for the improvement of crop plants under abiotic stress conditions.

Regulation of gene expression in various tissues during physiological processes is controlled at the transcriptional, post-transcriptional and post-translational levels. Regulation at the transcriptional level that plays an important role in response to abiotic stresses is mainly associated with promoters and their contributing cis-acting regulatory elements (CARE) [25]. Promoters are DNA sequences located at the upstream of the gene coding region and included the CAREs as the binding sites for proteins in transcription events. In higher plants, CAREs act as enhancers, silencers or insulators [26]. Promoters have a core segment of 40 bp at the upstream of the transcription initiation site that compromises the TATA-box [27, 28]. Proximal and distal regions located at the upstream of the core promoters (regulatory sequence and cis-elements) play a significant role in the regulation of gene expression [28, 29]. Promoter analysis provides valuable information to identify the function and signalling of genes. Furthermore, CARE is the appropriate goal to dissect the molecular mechanisms of responses to abiotic stresses [30]. As a result, access to gene expression data helps to better understand the expression pattern of gene families or single genes at whole genome level and accommodates to identify the gene(s) contributed to biological processes [31]. DNA microarray representing high-throughput gene expression profiling has been used to discern the tolerance mechanisms in plants [32-41]. Besides, the gene expression databases and various bioinformatics tools help to determine cis-regulatory sites in coding and non-coding DNA sequences [42].

Analysis of expression patterns of stress responsive genes in rice associated trials resulted in a large volumes of expression data that are available as online databases. However, the exact molecular mechanism underlying stress responses is still poorly understood. Hence, there is a great demand to assess expression data through meta-analysis and identify commonly expressed genes under various abiotic stresses conditions that can be used in engineering stresses tolerance in rice. The aims of the present study were to (1) assess subsets of expression data associated with stress tolerance retrieved from online databases using bioinformatics tools; (2) meta-analysis of common expressed genes contributing to cold, heat, drought and salinity tolerance in rice; and to (3) uncover and characterize the regulatory mechanisms of abiotic tolerance responsive genes (Table 1).


2.1. Database Development and Expression Meta-Analysis

A database containing 152 microarray expression data sets corresponding to nine rice trials associated with cold, salt, drought and heat-stressed conditions was developed (Supplementary Table 1). The rice microarray data sets were retrieved from the ArrayExpress and NCBI GEO DataSets [43, 44]. The expression data have been annotated to the ArrayExpress and NCBI GEO DataSets. All the data sets, title, experiment type and the overall design for downloading the data can be found on the ArrayExpress ( arrayexpress/) and NCBI ( documentation pages [45-53]. Furthermore, the library (.CDF: rice_libraryfile) and annotation (.CSV: Rice.na35.annot.csv) files were retrieved from the Affymetrix database ( Selection of these data files was based on the type of Chips that have been used in microarray experiments. The Affymetrix raw data (.CEL) files were analyzed by FlexArray software version 1.6.3. Firstly, the CEL data and the library file were imported to the FlexArray software. The CEL data were normalized based on the Robust Multiarray Average (RMA) algorithm [54] and the RMA signal values were transformed into Log2. The RMA is a preprocessing algorithm used for background correction and data normalization in Affymetrix and Nimblegen gene expression microarray trials. In order to identify Differentially Expressed Genes (DEGs), the RMA expression values were analyzed based on a two-sample student’s t-test. Finally, the gene list of the expression data identified using t-test and the annotation for each gene were added based on a new rice annotation file. The up-regulated genes with above 1- symmetrical raw fold change (FC), the down-regulated genes of less than 0-symmetrical raw FC and the over-expressed genes with above 30- symmetrical raw FC were selected for further data analysis (Table 2). The P-values were adjusted in the false discovery rate (FDR) of less than 0.05.

2.2. Differentially Expressed Genes (DEGs)

To compare the results of expression data analyses, the UpSetR R package was used to identify the interrelationship between the DEGs and the representing interactions among the gene sets identified under cold, heat, salinity and drought stress conditions.

2.3. Gene Ontology (GO) and Functional Categorization

Annotations of gene for overexpressed genes and clus-tering functions, location and biological roles were accomp-lished by the GO Tutorial-TAIR at https://www.arabidopsis. org/help/tutor-ials/go6.jsp. Furthermore, the GO annotation and the functional categorizations derived from the TAIR ( database. Functional categorization was performed followed by the below equation:

Table 1. The list of genes overexpressed under various stress conditions.
Condition Probeset Gen Bank Acc. Fold Change T Statistic P-value
Cold Stress Os.52451.1.A1_at AK067195.1 83.19 11.17 3.60E-04
Os.32366.1.S1_at AK105196.1 78.70 15.90 1.99E-08
Os.52280.1.S1_at AK066054.1 33.45 33.07 4.98E-06
Drought Stress Os.25497.1.S1_at CA765994 955.99 306.68 1.06E-05
Os.51718.1.S1_at AK063517.1 926.56 36.38 3.41E-06
Os.47732.1.S1_at BI809490 797.08 61.51 2.60E-04
Os.49245.1.S1_at AK063685.1 746.08 74.90 1.90E-07
Os.5325.1.S1_at AK107930.1 738.38 20.21 3.54E-05
Os.8668.1.S1_x_at AK066459.1 637.73 163.33 3.75E-05
Os.12551.1.S1_s_at U57641.1 585.38 142.02 1.47E-08
Os.12633.1.S1_s_at U60097.2 553.39 41.28 1.35E-08
Os.42784.1.S1_at NM_189885.1 521.36 22.12 5.58E-07
Os.28200.1.S1_x_at AK099709.1 511.55 39.61 1.73E-08
Os.37717.1.A1_s_at BU673746 473.89 6.87 2.30E-03
Os.11271.2.S1_at CB666821 374.63 138.28 5.23E-05
Os.12415.1.S1_at AK063582.1 335.29 44.93 4.90E-04
Os.11260.1.S1_at AK102039.1 156.35 237.82 1.77E-05
Os.47625.1.A1_s_at BX901098 117.62 8.18 1.40E-02
Salt Stress Os.49245.1.S1_at AK063685.1 413.38 62.04 4.04E-07
Os.23092.1.S1_at CA755805 319.17 126.75 6.22E-05
Os.5325.1.S1_at AK107930.1 154.66 32.22 5.53E-06
Os.12633.1.S1_s_at U60097.2 100.73 26.57 1.19E-05
Os.56004.1.S1_at AK109114.1 99.61 29.87 1.22E-08
Os.12415.1.S1_at AK063582.1 81.91 26.16 1.27E-05
Os.12703.1.S1_at AK070417.1 63.08 15.75 1.01E-06
Os.51718.1.S1_at AK063517.1 62.40 24.61 1.62E-05
Heat Stress Os.11039.3.S1_at AK105370.1 661.77 193.18 4.31E-09
Os.11039.1.S1_s_at AK063751.1 619.97 75.44 1.85E-07
Os.47625.1.A1_s_at BX901098 112.82 7.19 1.18E-02
Os.10038.1.S1_s_at AU082861 100.98 10.83 8.40E-03
Table 2. Identified Cis-regulatory elements and associated function in the promoter region of abiotic stress responsive genes.
Cis-Regulatory Element Sequence Function
AAGAA-motif GAAAGAA Unknown
A-box CCGTCC Cis-acting regulatory element
ABRE TACGTG Cis-acting element involved in the abscisic acid responsiveness
ACE AAAACGTTTA Cis-acting element involved in light responsiveness
ARE TGGTTT Cis-acting regulatory element essential for the anaerobic induction
Box 4 ATTAAT Part of a conserved DNA module involved in light responsiveness
CAAT-box CAAT Common cis-acting element in promoter and enhancer regions
CAT-box GCCACT Cis-acting regulatory element related to meristem expression
CCGTCC-box CCGTCC Cis-acting regulatory element related to meristem specific activation
CGTCA-motif CGTCA Cis-acting regulatory element involved in the MeJA-responsiveness
Circadian CAANNNNATC Cis-acting regulatory element involved in circadian control
GAG-motif GGAGATG Part of a light responsive element
GA-motif AAAGATGA Part of a light responsive element
G-box TACGTG Cis-acting regulatory element involved in light responsiveness
G-Box CACGTA Cis-acting regulatory element involved in light responsiveness
HSE AAAAAATTTC Cis-acting element involved in heat stress responsiveness
MBS TAACTG MYB binding site involved in drought-inducibility
O2-site GATGATATGG Cis-acting regulatory element involved in zein metabolism regulation
SKn-1-motif GTCAT Cis-acting regulatory element required for endosperm expression
Sp1 CC(G/A)CCC Light responsive element
TATA-box TATAAA Core promoter element around -30 of transcription start
TC-rich repeats ATTTTCTTCA Cis-acting element involved in defense and stress responsiveness
TCT-motif TCTTAC Part of a light responsive element
TGACG-motif TGACG Cis-acting regulatory element involved in the MeJA-responsiveness
Fig. (1). The upset plot of up-regulated genes differentially expressed in response to drought, cold, salinity and heat stresses.

Where, GO slims (subsets) are cut-down versions of the gene ontology containing a subset of the terms. This structure represents a subset of the ontology that was designed specifically for plants and can be used for organizing sets of genes according to broad GO ontology categories. The nominator of the equation represents annotation count of a specific functional category (nucleus, cell, nuclease activity) in each GO category (cellular component, molecular function or biological process) and the denominator stands for the total annotation count of the functional category in each GO category.

2.4. Analysis of Cis-Acting Regulatory Element (CAREs)

The accession numbers of the complete nucleotide sequence (FASTA format) of the over-expressed genes contributing to transcription regulatory activities were obtained from the National Center for Biotechnology Information (NCBI) ( database. For CAREs analysis, the 1.5 kbp sequences of 5´ upstream of each gene were retrieved from the Phytozome (http://www.phytozome. net/) database and subsequently were subjected to the PlantCare ( care/html/) database to identify common CAREs in the responsive genes against the abiotic stresses [55].


3.1. Identification of DEGs

The results of the t-test in meta-analysis suggested 29593, 202798, 73224 and 25241 gene accessions with significant differential expression under cold, drought, salinity and heat stresses, respectively (Supplementary Table 2). These data suggested that drought and salinity stresses were more restrictive than cold and heat stresses for rice growth.

Differences and cross-talk of gene expression among drought, salinity, cold and heat stress responses were analyzed using Upset plots. As shown in Fig. (1), 10363, 4399, 1660 and 901 gene accessions represented up-regulation under drought, salt, cold and heat stresses, respectively. Furthermore, the expression of 1238 genes in the four stresses was increased. A number of 8550, 2417, 520 and 130 genes showed an increase in expression level under drought-salinity, drought-cold, salinity-heat and cold-heat stress conditions, respectively (Fig. 1) suggesting the existence of greater crosstalk between drought and salinity stress signaling processes in rice.

Fig. (2). The upset plot of down-regulated genes differentially expressed in response to drought, cold, salinity and heat stresses.

Analysis of down/up-regulated stress related genes helps a better understanding of the basis of molecular responses to abiotic stresses [38]. In the present study, the results of the meta-analysis revealed that a total of 11328, 2982, 1613 and 845 gene accessions were down-regulated under drought, salinity, cold and heat stresses (Fig. 2). The results indicated that 8258, 2713, 503 and 187 DEGs showed crosstalk in response to drought-salinity, drought-cold, salinity-heat and cold-heat stress combinations, respectively. The number of down-regulated genes was higher than that of up-regulated showing the majority of genes have been switched off under drought and salinity stresses in rice (Figs. 1 and 2). Similar results were obtained in arabidopsis tested under stressed conditions [38].

Of the up-regulated genes, 47% were associated with potential candidate genes contributed to abiotic stress tolerance. Some of the key genes involved in stress-induced proteins, brassinosteroid-regulated proteins, binding proteins, pathogenesis-related proteins, transcription factors, photo-synthetic proteins and transporter proteins. The up-regulation of these genes under drought, cold and salinity stresses has been reported previously [38]. A number of 24 genes over-expressed in various environmental conditions were selected for further analysis to provide a valuable resource of information for use in breeding programs under abiotic stresses (Table 1).

3.2. GO and Functional Annotation of DEGs

Functional groups of overexpressed genes in the four stresses tested are presented in Fig. (3). The results of the functional annotation of DEGs showed that the nucleus, other membranes and plasma membrane gene groups had the highest percentages in cellular component category. Several genes with specific products in certain places of cell (cell membranes) showed up-regulation in response to abiotic stresses. The results of gene expression analysis revealed that trehalose that protects membranes and proteins in cells exposed to drought stress conditions was accumulated under various abiotic stresses [56, 57].

Transporter activity, DNA and RNA-binding, transcription factor activity and kinase activity were the most prevalent gene groups among the identified over-expressed genes (Fig. 3) demonstrating their roles in alleviating the adverse effects of abiotic stress conditions in rice. RNA-binding proteins have an important role in post-transcriptional gene regulation. Most of the RNA-binding proteins are plant-specific with known functions. RNA-binding proteins that regulate pre-mRNA splicing, polyadenylation, RNA stability and RNA export are important for the adaptation of plants to various environments [58, 59]. The functional gene group for DNA binding activity contributes to tolerance to multiple stresses, generally in an ABA-independent manner through DRE/CRT cis-elements and the AP2/ERF DNA binding domain [60].

Various products of genes with kinase activities (Fig. 3) contribute to abiotic stress tolerance. Among the protein kin-ases involved in stress signal transduction, mitogen-activated protein kinases (MAPKs) [61-63], glycogen synthase kinase 3 (GSK3) [64, 65], S6 kinase (S6K) [55], calcium-dependent protein kinases (CDPKs) [66-68] and most of the SNF1-related kinases (SnRKs) are common among all eukaryotic organisms [69]. Furthermore, SnRK2 family members are plant-specific serine/threonine kinases contributed to plant response to abiotic stresses and abscisic acid (ABA)-dependent plant development [69].

Some of the identified overexpressed genes belonged to the Transcription Factors (TF) category (Fig. 3). It has been shown that TFs belonging to the dehydration-responsive element-binding proteins (DREB), C-repeat-binding factors (CBF), ABA-Binding Factors (ABF), myelocytomatosis oncogenes (MYC) and myeloblastosis oncogenes (MYB) respond to drought stress conditions [70-72]. Genes involved in kinase activity help plants deal with abiotic stresses. One of the most important plant TFs is DREB that regulates the expression of many stress-inducible genes mostly in an ABA-independent manner. The DREBs play a critical role in improving the abiotic stress tolerance of plants by interacting with a DRE/CRT cis-element present in the promoter regions of various abiotic stress-responsive genes [60]. The DREB TFs contain a highly conserved AP2/ERF DNA-binding domain across the plant kingdom including arabidopsis, rice, soybean, chickpea, tomato, tobacco, and millets [73]. Busk and Pages [74] also reported that phosphorylation is necessary for the activation of proteins under drought-stress conditions, thus enhancing the DNA-binding activity of several transcription regulators. Furthermore, a large portion of biological processes of the overexpressed genes under abiotic stresses was related to response to stress, response to abiotic and biotic stimuli, developmental processes and other cellular processes (Fig. 3). These results suggest the role of key genes responsible for the regulation of the most important biological processes under various abiotic stress conditions.

3.3. Identification of CAREs in the Promoter Region of Transcription Factors

The detected CAREs at the upstream of the DEGs under drought, heat, cold and salinity stress conditions were associated with a light response, hormonal regulation and stress responses in rice (Table 2 and Fig. 4). The hormonal regulatory elements included methyl jasmonate (MeJA) and abscisic acid (ABA) responsive motifs such as ABRE, CGTCA-motif and TGACG- elements were presented in the majority of the DEGs identified under salinity, cold, heat and drought stress conditions (Table 2).

Phytohormones play a key role in response to prioritization stresses [75]. Phytohormone signaling mediated by ABA is an evolutionarily conserved mechanism that promotes abiotic stress tolerance in plants [76]. The process of plants perceives, response and adaptation to abiotic stresses are controlled mainly by ABA that regulates plant water situation as an endogenous messenger [77]. Abscisic acid is a plant stress hormone because it induces under various stresses [78, 79]. Induction of the ABA hormone often relies on the presence of a cis-acting element called ABRE (ABA-responsive) element [8, 10, 80]. The ABRE elements play a key role in abscisic acid response that induced abiotic stresses, seed dormancy and maturation processes [1]. The ABRE elements are one of the most important CRE in rice and located near the transcription start site (TSS) [81, 82].

Fig. (3). Functional categorization by GO annotation for (A) cellular components, (B) molecular function and (C) biological process of common over-expressed genes in rice in response to drought, salinity, cold and heat stresses.

Both MeJA and Jasmonic acid (JA) contribute to a wide range of environmental conditions and physiological events comprising of seed germination and leaf senescence [83]. Jasmonic acid participates in plant growth and plays critical roles in both biotic and abiotic stress responses [84-86]. The application of MeJA alleviates the adverse effects of environmental stresses [87]. The MeJA suppresses the absorption of toxic ions, and reduces the adverse effects of osmotic stress through regulating inorganic penetrating ions or organic [87]. The results of the current study suggest that TGACG and CGTCA motifs are involved in MeJA response and regulation of plant defense against abiotic stresses. The MeJA activates antioxidant systems to detoxify Reactive Oxygen Species (ROS) in stressed plants [87, 88]. It has been shown that the signaling networks related to ABA and JA hormones are correlated [89].

The light-responsive elements (LREs) including ACE, BOX4, GAG-motif, G-box, G-Box, GA-motif, TCT-motif and Sp1 were identified in the promoter regions of the expressed salinity, cold, heat and drought-responsive genes (Table 2). These regulatory elements play a critical role in the regulation of transcriptional activity [90]. The LREs such as G-box, Box-4, GAG, GAP, GA motifs have been identified in the regulatory regions of the light-regulated genes that are needed for light-controlled transcriptional activities [91, 92]. The role of the G-Box element in arabidopsis; as part of the response mechanism against abiotic stresses has been uncovered. The role of GAG motif and G-Box element in response to abiotic stresses has been reported in studies with tobacco and wheat [93, 94].

A circadian element was detected in the drought, cold, heat and salinity responsive genes (Table 2). The circadian clock coordinates the responses of plants to multiple environmental challenges. The results of the present study indicated that the circadian clock may reinforce the plant’s ability to reduce the adverse effects of abiotic stresses. Results of transcriptomic analyses have shown that circadian clock controls several genes associated with response to salinity, drought and cold stresses [95, 96].

The stress-responsive elements such as TC-rich repeats, heat shock elements (HSE) and the myeloblastosis binding sequence (MBS) were presented in the majority of responsive genes (Table 2). The role of TC-rich repeats in responses to environmental stresses has been documented in previous reports [97-100]. Heat shock element is a transcription factor that binds to HSE cis-acting elements in the promoter of stress-inducible genes and plays central roles in the acquisition of plant tolerance against abiotic stresses [101]. MBS, which is a binding site for MYB transcription factors, controls many abiotic stress responses [102, 103]. Molecular approaches discerned the functional characterization of MYB domain proteins, particularly the R2R3-type members in various plant species, including rice, maize and soybean [103, 104]. A genome-wide comparative analysis of MYB genes and their expression in arabidopsis and rice suggested the potential role of MYB domain proteins in plant stress responses [105]. Several members of R2R3-type MYB transcription factors are involved in the regulation of the phenylpropanoid pathway and the production of various secondary metabolic compounds under abiotic stress conditions. The role of MBS elements against drought stress in common bean [106] and maize [107] has been previously reported. Furthermore, the effects of MYB TFs in low-temperature, light and osmotic stress induction responses have been uncovered [108-111].

Fig. (4). Distribution of major cis-acting regulatory elements in the promoter site of stress responsive genes.

The results of the present study showed that ARE regulatory elements are essential for the induction of anaerobic respiration (Table 2). Moreover, the Skn-1 motif and the O2-site were present in the majority of responsive genes specifically those that act in the endosperm or involved in the zein metabolism regulation events. The role of these elements against abiotic stresses is restricted. However, the results of our study indicated the possibility of the role of these elements in tolerance against abiotic stresses in rice.


The expression pattern and regulation network of rice as a post-genomic crop under different abiotic stresses (heat, salt, cold and drought) were assessed on the basis of meta-analysis methods. The higher number of differentially expressed genes (DEGs) in drought and salinity environments compared to heat and cold stresses suggested greater crosstalk between drought and salinity stress signalling processes in rice. The higher number of co-expressed genes under drought and salinity stresses demonstrated strong correlations between the responses of rice to these stresses compared to the other cross-talks tested. Furthermore, 24 common over-expressed genes were identified in response to cold, drought, heat and salt stresses. These uncovered genes had DNA and RNA-binding role, transcription factor activity and kinase activity that were associated with both abiotic and biotic stimuli. Moreover, the identified CAREs at the upstream of common DEGs under drought, heat, cold and salinity stress conditions were related to light response, hormonal regulation and stress-related responses in rice. Overall, the outcome of this study helps to better understand the regulation mechanisms of stress responses at the transcriptional level.


RMA algorithm  = Robust Multiarray Average Algorithm
DEGs  = Differentially Expressed Genes
CBF  = C-repeat-Binding Factor
GO  = Gene Ontology
FDR  = False Discovery Rate
MAPKs  = Mitogen-Activated Protein Kinases
NCBI  = National Center for Biotechnology Information
DREB  = Dehydration Responsive Element Binding
CAREs  = Cis-Acting Regulatory Element
MeJA  = Methyl Jasmonate
GA  = Gibberellin
GSK3  = Glycogen Synthase Kinase3
CDPKs  = Calcium-Dependent Protein Kinases
S6K  = S6 Kinase
SnRKs  = SNF1-Related Kinases
ABA  = Abscisic Acid
ABF  = Abscisic Acid-Binding Factor
MYC  = Myelocytomatosis oncogene
MYB  = Myeloblastosis Oncogene
TFs  = Transcription Factors


Not applicable.


No animals/humans were used for studies that are the basis of this research.


Not applicable.


PRISMA Guideline and methodology were followed.


The data supporting the findings of the article is available in the Dropbox cloud storage at: qlz9ybgoka2dyt2/Rice%20Raw%20Data_Library%20and%20Annotation%20File.rar?dl=0.




The authors declare no conflict of interest, financial or otherwise.


The authors gratefully acknowledge the support from Shiraz University.


Supplementary material is available on the publishers web site along with the published article.

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[1] Pandey S, Subramanaym RC, Yaqoob U, Negi YK, Arora S. Insilico analysis of cis acting regulatory elements CAREs in upstream regions of ascorbate glutathione pathway genes from oryza sativa. Biochem Physiol 2015; 4(159): 2.
[2] Jangam AP, Pathak RR, Raghuram N. Microarray analysis of rice d1 (RGA1) mutant reveals the potential role of G-protein alpha subunit in regulating multiple abiotic stresses such as drought, salinity, heat, and cold. Front Plant Sci 2016; 7: 11.
[3] Arif M, Jan T, Riaz M, et al. Advances in rice research for abiotic stress tolerance: Agronomic approaches to improve rice production under abiotic stress.Advances in Rice Research for Abiotic Stress Tolerance 2019; 585-614.
[4] Wang D, Pan Y, Zhao X, Zhu L, Fu B, Li Z. Genome-wide temporal-spatial gene expression profiling of drought responsiveness in rice. BMC Genomics 2011; 12(1): 149.
[5] Ray S, Dansana PK, Giri J, et al. Modulation of transcription factor and metabolic pathway genes in response to water-deficit stress in rice. Funct Integr Genomics 2011; 11(1): 157-78.
[6] Venu RC, Sreerekha MV, Madhav MS, et al. Deep transcriptome sequencing reveals the expression of key functional and regulatory genes involved in the abiotic stress signaling pathways in rice. J Plant Biol 2013; 56(4): 216-31.
[7] Jiang SY, Ma A, Ramamoorthy R, Ramachandran S. Genome-wide survey on genomic variation, expression divergence, and evolution in two contrasting rice genotypes under high salinity stress. Genome Biol Evol 2013; 5(11): 2032-50.
[8] Thomashow MF. Plant cold acclimation: Freezing tolerance genes and regulatory mechanisms. Annu Rev Plant Physiol Plant Mol Biol 1999; 50(1): 571-99.
[9] Hasegawa PM, Bressan RA, Zhu JK, Bohnert HJ. Plant cellular and molecular responses to high salinity. Annu Rev Plant Physiol Plant Mol Biol 2000; 51(1): 463-99.
[10] Shinozaki K, Yamaguchi-Shinozaki K. Molecular responses to dehydration and low temperature: Differences and cross-talk between two stress signaling pathways. Curr Opin Plant Biol 2000; 3(3): 217-23.
[11] Wan J, Dunning FM, Bent AF. Probing plant-pathogen interactions and downstream defense signaling using DNA microarrays. Funct Integr Genomics 2002; 2(6): 259-73.
[12] Zhu JK. Salt and drought stress signal transduction in plants. Annu Rev Plant Biol 2002; 53(1): 247-73.
[13] Narusaka Y, Narusaka M, Seki M, et al. The cDNA microarray analysis using an Arabidopsis pad3 mutant reveals the expression profiles and classification of genes induced by Alternaria brassicicola attack. Plant Cell Physiol 2003; 44(4): 377-87.
[14] Seki M, Kamei A, Satou M, et al. Transcriptome analysis in abiotic stress conditions in higher plants.Plant Responses to Abiotic Stress 2003; 271-308.
[15] Shinozaki K, Yamaguchi-Shinozaki K, Seki M. Regulatory network of gene expression in the drought and cold stress responses. Curr Opin Plant Biol 2003; 6(5): 410-7.
[16] Hadiarto T, Tran LS. Progress studies of drought-responsive genes in rice. Plant Cell Rep 2011; 30(3): 297-310.
[17] Hirayama T, Shinozaki K. Research on plant abiotic stress responses in the post-genome era: Past, present and future. Plant J 2010; 61(6): 1041-52.
[18] Cooper B, Clarke JD, Budworth P, et al. A network of rice genes associated with stress response and seed development. P Natl A Sci 2003; 100(8): 4945-50.
[19] Lan L, Li M, Lai Y, et al. Microarray analysis reveals similarities and variations in genetic programs controlling pollination/fertilization and stress responses in rice (Oryza sativa L.). Plant Mol Biol 2005; 59(1): 151-64.
[20] Rabbani MA, Maruyama K, Abe H, et al. Monitoring expression profiles of rice genes under cold, drought, and high-salinity stresses and abscisic acid application using cDNA microarray and RNA gel-blot analyses. Plant Physiol 2003; 133(4): 1755-67.
[21] Zhou J, Wang X, Jiao Y, et al. Global genome expression analysis of rice in response to drought and high-salinity stresses in shoot, flag leaf, and panicle. Plant Mol Biol 2007; 63(5): 591-608.
[22] Salmeron JM, Vernooij B. Transgenic approaches to microbial disease resistance in crop plants. Curr Opin Plant Biol 1998; 1(4): 347-52.
[23] Hammond-Kosack KE, Parker JE. Deciphering plant-pathogen communication: Fresh perspectives for molecular resistance breeding. Curr Opin Biotechnol 2003; 14(2): 177-93.
[24] Zhang JZ. Overexpression analysis of plant transcription factors. Curr Opin Plant Biol 2003; 6(5): 430-40.
[25] Zou C, Sun K, Mackaluso JD, et al. Cis-regulatory code of stress-responsive transcription in Arabidopsis thaliana. P Natl A Sci 2011; 108(36): 14992-7.
[26] Kumari S, Ware D. Genome-wide computational prediction and analysis of core promoter elements across plant monocots and dicots. PLoS One 2013; 8(10: )e79011
[27] Molina C, Grotewold E. Genome wide analysis of Arabidopsis core promoters. BMC Genomics 2005; 6(1): 25.
[28] Lee TI, Young RA. Transcription of eukaryotic protein-coding genes. Annu Rev Genet 2000; 34(1): 77-137.
[29] Hernandez-Garcia CM, Finer JJ. Identification and validation of promoters and cis-acting regulatory elements. Plant Sci 2014; 217-218: 109-19.
[30] Urano K, Kurihara Y, Seki M, Shinozaki K. ‘Omics’ analyses of regulatory networks in plant abiotic stress responses. Curr Opin Plant Biol 2010; 13(2): 132-8.
[31] Rensink WA, Buell CR. Microarray expression profiling resources for plant genomics. Trends Plant Sci 2005; 10(12): 603-9.
[32] Ma L, Chen C, Liu X, et al. A microarray analysis of the rice transcriptome and its comparison to Arabidopsis. Genome Res 2005; 15(9): 1274-83.
[33] Oono Y, Seki M, Satou M, et al. Monitoring expression profiles of Arabidopsis genes during cold acclimation and deacclimation using DNA microarrays. Funct Integr Genomics 2006; 6(3): 212-34.
[34] Mantri NL, Ford R, Coram TE, Pang EC. Transcriptional profiling of chickpea genes differentially regulated in response to high-salinity, cold and drought. BMC Genomics 2007; 8(1): 303.
[35] Fernandez P, Di Rienzo J, Fernandez L, Hopp HE, Paniego N, Heinz RA. Transcriptomic identification of candidate genes involved in sunflower responses to chilling and salt stresses based on cDNA microarray analysis. BMC Plant Biol 2008; 8(1): 11.
[36] Kawasaki S, Borchert C, Deyholos M, et al. Gene expression profiles during the initial phase of salt stress in rice. Plant Cell 2001; 13(4): 889-905.
[37] Seki M, Narusaka M, Abe H, et al. Monitoring the expression pattern of 1300 Arabidopsis genes under drought and cold stresses by using a full-length cDNA microarray. Plant Cell 2001; 13(1): 61-72.
[38] Seki M, Narusaka M, Ishida J, et al. Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold and high-salinity stresses using a full-length cDNA microarray. Plant J 2002; 31(3): 279-92.
[39] Seki M, Ishida J, Narusaka M, et al. Monitoring the expression pattern of around 7,000 Arabidopsis genes under ABA treatments using a full-length cDNA microarray. Funct Integr Genomics 2002; 2(6): 282-91.
[40] Chen W, Provart NJ, Glazebrook J, et al. Expression profile matrix of Arabidopsis transcription factor genes suggests their putative functions in response to environmental stresses. Plant Cell 2002; 14(3): 559-74.
[41] Fowler S, Thomashow MF. Arabidopsis transcriptome profiling indicates that multiple regulatory pathways are activated during cold acclimation in addition to the CBF cold response pathway. Plant Cell 2002; 14(8): 1675-90.
[42] Lescot M, Déhais P, Thijs G, et al. PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences. Nucleic Acids Res 2002; 30(1): 325-7.
[43] Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: Archive for functional genomics Data sets--update. Nucleic Acids Res 2013; 41(Database issue): D991-5.
[44] Rustici G, Kolesnikov N, Brandizi M, et al. Array express update-trends in database growth and links to data analysis tools. Nucleic Acids Res 2013; 41(Database issue): D987-90.
[45] Ambavaram MM, Basu S, Krishnan A, et al. Coordinated regulation of photosynthesis in rice increases yield and tolerance to environmental stress. Nat Commun 2014; 5: 5302.
[46] Hu W, Hu G, Han B. Genome-wide survey and expression profiling of heat shock proteins and heat shock factors revealed overlapped and stress specific response under abiotic stresses in rice. Plant Sci 2009; 176(4): 583-90.
[47] Lv Y, Guo Z, Li X, Ye H, Li X, Xiong L. New insights into the genetic basis of natural chilling and cold shock tolerance in rice by genome-wide association analysis. Plant Cell Environ 2016; 39(3): 556-70.
[48] Pabuayon IM, Yamamoto N, Trinidad JL, Longkumer T, Raorane ML, Kohli A. Reference genes for accurate gene expression analyses across different tissues, developmental stages and genotypes in rice for drought tolerance. Rice (N Y) 2016; 9(1): 32.
[49] Tyagi AK, Kapoor S, Khurana JP, Ray S. Expression data for stress treatment in rice seedlings. NCBI Availiable at: http://www. ncbi. nlm. nih. gov/projects/geo/query/acc. cgi 2007
[50] Wang WS, Zhao XQ, Li M, et al. Complex molecular mechanisms underlying seedling salt tolerance in rice revealed by comparative transcriptome and metabolomic profiling. J Exp Bot 2016; 67(1): 405-19.
[51] Ding X, Li X, Xiong L. Insight into differential responses of upland and paddy rice to drought stress by comparative expression profiling analysis.Int J Mol Sci 2003; 14(3): 5214-38.
[52] Krishnan A, Gupta C, Ambavaram MM, Pereira A. RECoN: Rice environment Coexpression network for systems level analysis of abiotic-stress response.Front Plant Sci 2017; 8: 1640.
[53] Mishra P, Singh N, Jain A, Jain N, Mishera V. Identification of cis-regulatory elements associated with salinity and drought stress tolerance in rice from co-expressed gene interaction networks.Bioinformation 2018; 14(3): 123-131.
[54] Bolstad BM, Irizarry RA, Åstrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003; 19(2): 185-93.
[55] Mahfouz MM, Kim S, Delauney AJ, Verma DP. Target of rapamycin nteracts with raptor, which regulates the activity of S6 kinase in response to osmotic stress signals. Plant Cell 2006; 18(2): 477-90.
[56] Singer MA, Lindquist S. Multiple effects of trehalose on protein folding in vitro and in vivo. Mol Cell 1998; 1(5): 639-48.
[58] Yamada T, Takatsu Y, Manabe T, Kasumi M, Marubashi W. Suppressive effect of trehalose on apoptotic cell death leading to petal senescence in ethylene-insensitive flowers of gladiolus. Plant Sci 2003; 164(2): 213-21.
[59] Shariatipour N, Heidari B. Investigation of drought and salinity tolerance related genes and their regulatory mechanisms in arabidopsis (Arabidopsis thaliana). Open Bioinforma J 2018; 11: 1.
[60] Lorković ZJ. Role of plant RNA-binding proteins in development, stress response and genome organization. Trends Plant Sci 2009; 14(4): 229-36.
[61] Lata C, Prasad M. Role of DREBs in regulation of abiotic stress responses in plants. J Exp Bot 2011; 62(14): 4731-48.
[62] Cho K, Agrawal GK, Jwa NS, Kubo A, Rakwal R. Rice OsSIPK and its orthologs: A “central master switch” for stress responses. Biochem Biophys Res Commun 2009; 379(3): 649-53.
[63] Mishra NS, Tuteja R, Tuteja N. Signaling through MAP kinase networks in plants. Arch Biochem Biophys 2006; 452(1): 55-68.
[64] Pitzschke A, Schikora A, Hirt H. MAPK cascade signalling networks in plant defence. Curr Opin Plant Biol 2009; 12(4): 421-6.
[65] Jonak C, Hirt H. Glycogen synthase kinase 3/SHAGGY-like kinases in plants: An emerging family with novel functions. Trends Plant Sci 2002; 7(10): 457-61.
[66] Koh S, Lee SC, Kim MK, et al. T-DNA tagged knockout mutation of rice OsGSK1, an orthologue of Arabidopsis BIN2, with enhanced tolerance to various abiotic stresses. Plant Mol Biol 2007; 65(4): 453-66.
[67] Das R, Pandey GK. Expressional analysis and role of calcium regulated kinases in abiotic stress signaling. Curr Genomics 2010; 11(1): 2-13.
[68] Hrabak EM, Chan CW, Gribskov M, et al. The arabidopsis CDPK-SnRK superfamily of protein kinases. Plant Physiol 2003; 132(2): 666-80.
[69] Wurzinger B, Mair A, Pfister B, Teige M. Cross-talk of calcium-dependent protein kinase and MAP kinase signaling. Plant Signal Behav 2011; 6(1): 8-12.
[70] Kulik A, Wawer I, Krzywińska E, Bucholc M, Dobrowolska G. SnRK2 protein kinases--key regulators of plant response to abiotic stresses. OMICS 2011; 15(12): 859-72.
[71] Abe H, Yamaguchi-Shinozaki K, Urao T, Iwasaki T, Hosokawa D, Shinozaki K. Role of arabidopsis MYC and MYB homologs in drought- and abscisic acid-regulated gene expression. Plant Cell 1997; 9(10): 1859-68.
[72] Bartels D, Sunkar R. Drought and salt tolerance in plants. Crit Rev Plant Sci 2005; 24(1): 23-58.
[73] Sakuma Y, Maruyama K, Osakabe Y, et al. Functional analysis of an Arabidopsis transcription factor, DREB2A, involved in drought-responsive gene expression. Plant Cell 2006; 18(5): 1292-309.
[74] Lata C, Bhutty S, Bahadur RP, Majee M, Prasad M. Association of an SNP in a novel DREB2-like gene SiDREB2 with stress tolerance in foxtail millet [Setaria italica (L.)]. J Exp Bot 2011; 62(10): 3387-401.
[75] Busk PK, Pagès M. Regulation of abscisic acid-induced transcription. Plant Mol Biol 1998; 37(3): 425-35.
[76] Berens ML, Berry HM, Mine A, Argueso CT, Tsuda K. Evolution of hormone signaling networks in plant defense. Annu Rev Phytopathol 2017; 55: 401-25.
[77] Berens ML, Wolinska KW, Spaepen S, et al. Balancing trade-offs between biotic and abiotic stress responses through leaf age-dependent variation in stress hormone cross-talk. P Natl A Sci 2019; 116(6): 2364-73.
[78] Tuteja N. Abscisic Acid and abiotic stress signaling. Plant Signal Behav 2007; 2(3): 135-8.
[79] Mahajan S, Tuteja N. Cold, salinity and drought stresses: An overview. Arch Biochem Biophys 2005; 444(2): 139-58.
[80] Swamy PM, Smith BN. Role of abscisic acid in plant stress tolerance. Curr Sci 1999; 1220-7.
[81] Uno Y, Furihata T, Abe H, Yoshida R, Shinozaki K, Yamaguchi-Shinozaki K. Arabidopsis basic leucine zipper transcription factors involved in an abscisic acid-dependent signal transduction pathway under drought and high-salinity conditions. P Natl A Sci 2000; 97(21): 11632-7.
[82] Gómez-Porras JL, Riaño-Pachón DM, Dreyer I, Mayer JE, Mueller-Roeber B. Genome-wide analysis of ABA-responsive elements ABRE and CE3 reveals divergent patterns in Arabidopsis and rice. BMC Genomics 2007; 8(1): 260.
[83] Berendzen KW, Stüber K, Harter K, Wanke D. Cis-motifs upstream of the transcription and translation initiation sites are effectively revealed by their positional disequilibrium in eukaryote genomes using frequency distribution curves. BMC Bioinformatics 2006; 7(1): 522.
[84] Concha CM, Figueroa NE, Poblete LA, Oñate FA, Schwab W, Figueroa CR. Methyl jasmonate treatment induces changes in fruit ripening by modifying the expression of several ripening genes in Fragaria chiloensis fruit. Plant Physiol Biochem 2013; 70: 433-44.
[85] Heil M, Ibarra-Laclette E, Adame-Álvarez RM, et al. How plants sense wounds: Damaged-self recognition is based on plant-derived elicitors and induces octadecanoid signaling. PLoS One 2012; 7(2)e30537
[86] Hou X, Ding L, Yu H. Crosstalk between GA and JA signaling mediates plant growth and defense. Plant Cell Rep 2013; 32(7): 1067-74.
[87] Wasternack C, Hause B. Jasmonates: Biosynthesis, perception, signal transduction and action in plant stress response, growth and development. An update to the 2007 review in Annals of Botany. Ann Bot 2013; 111(6): 1021-58.
[88] Yu X, Zhang W, Zhang Y, Zhang X, Lang D, Zhang X. The roles of methyl jasmonate to stress in plants. Funct Plant Biol 2019; 46(3): 197-212.
[89] Takahashi I, Hara M. Enhancement of starch accumulation in plants by exogenously applied methyl jasmonate. Plant Biotechnol Rep 2014; 8(2): 143-9.
[90] Sun W, Chen H, Wang J, et al. Expression analysis of genes encoding mitogen-activated protein kinases in maize provides a key link between abiotic stress signaling and plant reproduction. Funct Integr Genomics 2015; 15(1): 107-20.
[91] Gilmartin PM, Sarokin L, Memelink J, Chua NH. Molecular light switches for plant genes. Plant Cell 1990; 2(5): 369-78.
[92] Lam E, Chua NH. ASF-2: a factor that binds to the cauliflower mosaic virus 35S promoter and a conserved GATA motif in Cab promoters. Plant Cell 1989; 1(12): 1147-56.
[93] Ibraheem O, Botha CE, Bradley G. In silico analysis of cis-acting regulatory elements in 5′ regulatory regions of sucrose transporter gene families in rice (Oryza sativa Japonica) and Arabidopsis thaliana. Comput Biol Chem 2010; 34(5-6): 268-83.
[94] Behnam B, Iuchi S, Fujita M, et al. Characterization of the promoter region of an Arabidopsis gene for 9-cis-epoxycarotenoid dioxygenase involved in dehydration-inducible transcription. DNA Res 2013; 20(4): 315-24.
[95] Zhang HB, Bokowiec MT, Rushton PJ, Han SC, Timko MP. Tobacco transcription factors NtMYC2a and NtMYC2b form nuclear complexes with the NtJAZ1 repressor and regulate multiple jasmonate-inducible steps in nicotine biosynthesis. Mol Plant 2012; 5(1): 73-84.
[96] Grundy J, Stoker C, Carré IA. Circadian regulation of abiotic stress tolerance in plants. Front Plant Sci 2015; 6: 648.
[97] Nakamichi N, Takao S, Kudo T, et al. Improvement of Arabidopsis biomass and cold, drought and salinity stress tolerance by modified circadian clock-associated Pseudo-Response Regulators. Plant Cell Physiol 2016; 57(5): 1085-97.
[98] Merlot S, Gosti F, Guerrier D, Vavasseur A, Giraudat J. The ABI1 and ABI2 protein phosphatases 2C act in a negative feedback regulatory loop of the abscisic acid signalling pathway. Plant J 2001; 25(3): 295-303.
[100] Rowland O, Ludwig AA, Merrick CJ, et al. Functional analysis of Avr9/Cf-9 rapidly elicited genes identifies a protein kinase, ACIK1, that is essential for full Cf-9-dependent disease resistance in tomato. Plant Cell 2005; 17(1): 295-310.
[101] Diévart A, Clark SE. LRR-containing receptors regulating plant development and defense. Development 2004; 131(2): 251-61.
[102] Qin YX, Qin F. Dehydrins from wheat x Thinopyrum ponticum amphiploid increase salinity and drought tolerance under their own inducible promoters without growth retardation. Plant Physiol Biochem 2016; 99: 142-9.
[103] Guo M, Liu JH, Ma X, Luo DX, Gong ZH, Lu MH. The plant heat stress transcription factors (HSFs): Structure, regulation, and function in response to abiotic stresses. Front Plant Sci 2016; 7: 114.
[104] Dai X, Xu Y, Ma Q, et al. Overexpression of an R1R2R3 MYB gene, OsMYB3R-2, increases tolerance to freezing, drought, and salt stress in transgenic Arabidopsis. Plant Physiol 2007; 143(4): 1739-51.
[105] Zhang T, Zhao Y, Wang Y, Liu Z, Gao C. Comprehensive analysis of MYB gene family and their expressions under abiotic stresses and hormone treatments in Tamarix hispida. Front Plant Sci 2018; 9: 1303.
[106] Roy S. Function of MYB domain transcription factors in abiotic stress and epigenetic control of stress response in plant genome. Plant Signal Behav 2016; 11(1)e1117723
[107] Katiyar A, Smita S, Lenka SK, Rajwanshi R, Chinnusamy V, Bansal KC. Genome-wide classification and expression analysis of MYB transcription factor families in rice and Arabidopsis. BMC Genomics 2012; 13(1): 544.
[108] Wu J, Chen J, Wang L, Wang S. Genome-wide investigation of WRKY transcription factors involved in terminal drought stress response in common bean. Front Plant Sci 2017; 8: 380.
[109] Zhang T, Tan D, Zhang L, Zhang X, Han Z. Phylogenetic analysis and drought-responsive expression profiles of the WRKY transcription factor family in maize. Agri Gene 2017; 3: 99-108.
[110] Hong SH, Kim HJ, Ryu JS, et al. CRY1 inhibits COP1-mediated degradation of BIT1, a MYB transcription factor, to activate blue light-dependent gene expression in Arabidopsis. Plant J 2008; 55(3): 361-71.
[111] Yang SW, Jang IC, Henriques R, Chua NH. FAR-RED ELONGATED HYPOCOTYL1 and FHY1-LIKE associate with the Arabidopsis transcription factors LAF1 and HFR1 to transmit phytochrome A signals for inhibition of hypocotyl elongation. Plant Cell 2009; 21(5): 1341-59.
[112] Gao JJ, Zhang Z, Peng RH, et al. Forced expression of Mdmyb10, a myb transcription factor gene from apple, enhances tolerance to osmotic stress in transgenic Arabidopsis. Mol Biol Rep 2011; 38(1): 205-11.
[113] Soltész A, Vágújfalvi A, Rizza F, et al. The rice Osmyb4 gene enhances tolerance to frost and improves germination under unfavourable conditions in transgenic barley plants. J Appl Genet 2012; 53(2): 133-43.