Rice Chalky Grain 5 regulates natural variation for grain quality under heat stress.

Heat stress occurring during rice (Oryza sativa) grain development reduces grain quality, which often manifests as increased grain chalkiness. Although the impact of heat stress on grain yield is well-studied, the genetic basis of rice grain quality under heat stress is less explored as quantifying grain quality is less tractable than grain yield. To address this, we used an image-based colorimetric assay (Red, R; and Green, G) for genome-wide association analysis to identify genetic loci underlying the phenotypic variation in rice grains exposed to heat stress. We found the R to G pixel ratio (RG) derived from mature grain images to be effective in distinguishing chalky grains from translucent grains derived from control (28/24°C) and heat stressed (36/32°C) plants. Our analysis yielded a novel gene, rice Chalky Grain 5 (OsCG5) that regulates natural variation for grain chalkiness under heat stress. OsCG5 encodes a grain-specific, expressed protein of unknown function. Accessions with lower transcript abundance of OsCG5 exhibit higher chalkiness, which correlates with higher RG values under stress. These findings are supported by increased chalkiness of OsCG5 knock-out (KO) mutants relative to wildtype (WT) under heat stress. Grains from plants overexpressing OsCG5 are less chalky than KOs but comparable to WT under heat stress. Compared to WT and OE, KO mutants exhibit greater heat sensitivity for grain size and weight relative to controls. Collectively, these results show that the natural variation at OsCG5 may contribute towards rice grain quality under heat stress.

HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds.

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.

Global Analysis of Cereal microProteins Suggests Diverse Roles in Crop Development and Environmental Adaptation.

MicroProteins are a class of small single-domain proteins that post-translationally regulate larger multidomain proteins from which they evolved or which they relate to. They disrupt the normal function of their targets by forming microProtein-target heterodimers through compatible protein-protein interaction (PPI) domains. Recent studies confirm the significance of microProteins in the fine-tuning of plant developmental processes such as shoot apical meristem maintenance and flowering time regulation. While there are a number of well-characterized microProteins in Arabidopsis thaliana, studies from more complex plant genomes are still missing. We have previously developed miPFinder, a software for identifying microProteins from annotated genomes. Here we present an improved version where we have updated the algorithm to increase its accuracy and speed, and used it to analyze five cereal crop genomes - wheat, rice, barley, maize and sorghum. We found 20,064 potential microProteins from a total of 258,029 proteins in these five organisms, of which approximately 2000 are high-confidence, i.e., likely to function as actual microProteins. Gene ontology analysis of these 2000 microProtein candidates revealed their roles in stress, light and growth responses, hormone signaling and transcriptional regulation. Using a recently developed rice gene co-expression database, we analyzed 347 potential rice microProteins that are also conserved in other cereal crops and found over 50 of these rice microProteins to be co-regulated with their identified interaction partners. Overall, our study reveals a rich source of biotechnologically interesting small proteins that regulate fundamental plant processes such a growth and stress response that could be utilized in crop bioengineering.

Genome-Wide Transcriptome Analysis of Rice Seedlings after Seed Dressing with Paenibacillus yonginensis DCY84T and Silicon.

Plant-growth-promoting bacteria (PGPB) are beneficial microorganisms that can also protect against disease and environmental stress. Silicon (Si) is the second most abundant element in soil, and is known to increase plant growth, grain yield, resistance to biotic stress, and tolerance to abiotic stress. Combined treatment of PGPB and Si has been shown to further enhance plant growth and crop yield. To determine the global effects of the PGPB and Si on rice growth, we compared rice plants treated with Paenibacillus yonginensis DCY84T (DCY84T) and Si with untreated rice. To identify the genes that respond to DCY84T+Si treatment in rice, we performed an RNA-Seq transcriptome analysis by sampling treated and untreated roots on a weekly basis for three weeks. Overall, 576 genes were upregulated, and 394 genes were downregulated in treated roots, using threshold fold-changes of at least 2 (log2) and p-values < 0.05. Gene ontology analysis showed that phenylpropanoids and the L-phenylalanine metabolic process were prominent in the upregulated genes. In a metabolic overview analysis using the MapMan toolkit, pathways involving phenylpropanoids and ethylene were strongly associated with upregulated genes. The functions of seven upregulated genes were identified as being associated with drought stress through a literature search, and a stress experiment confirmed that plants treated with DCY84T+Si exhibited greater drought tolerance than the untreated control plants. Furthermore, the predicted protein-protein interaction network analysis associated with DCY84T+ Si suggests mechanisms underlying growth promotion and stress tolerance.

A web-based tool for the prediction of rice transcription factor function.

Transcription factors (TFs) are an important class of regulatory molecules. Despite their importance, only a small number of genes encoding TFs have been characterized in Oryza sativa (rice), often because gene duplication and functional redundancy complicate their analysis. To address this challenge, we developed a web-based tool called the Rice Transcription Factor Phylogenomics Database (RTFDB) and demonstrate its application for predicting TF function. The RTFDB hosts transcriptome and co-expression analyses. Sources include high-throughput data from oligonucleotide microarray (Affymetrix and Agilent) as well as RNA-Seq-based expression profiles. We used the RTFDB to identify tissue-specific and stress-related gene expression. Subsequently, 273 genes preferentially expressed in specific tissues or organs, 455 genes showing a differential expression pattern in response to 4 abiotic stresses, 179 genes responsive to infection of various pathogens and 512 genes showing differential accumulation in response to various hormone treatments were identified through the meta-expression analysis. Pairwise Pearson correlation coefficient analysis between paralogous genes in a phylogenetic tree was used to assess their expression collinearity and thereby provides a hint on their genetic redundancy. Integrating transcriptome with the gene evolutionary information reveals the possible functional redundancy or dominance played by paralog genes in a highly duplicated genome such as rice. With this method, we estimated a predominant role for 83.3% (65/78) of the TF or transcriptional regulator genes that had been characterized via loss-of-function studies. In this regard, the proposed method is applicable for functional studies of other plant species with annotated genome.

Infrastructures of systems biology that facilitate functional genomic study in rice.

Rice (Oryza sativa L.) is both a major staple food for the worldwide population and a model crop plant for studying the mode of action of agronomically valuable traits, providing information that can be applied to other crop plants. Due to the development of high-throughput technologies such as next generation sequencing and mass spectrometry, a huge mass of multi-omics data in rice has been accumulated. Through the integration of those data, systems biology in rice is becoming more advanced.To facilitate such systemic approaches, we have summarized current resources, such as databases and tools, for systems biology in rice. In this review, we categorize the resources using six omics levels: genomics, transcriptomics, proteomics, metabolomics, integrated omics, and functional genomics. We provide the names, websites, references, working states, and number of citations for each individual database or tool and discuss future prospects for the integrated understanding of rice gene functions.

Genome-wide analysis of root hair-preferential genes in rice.

BACKGROUND: Root hairs are valuable in taking up nutrients and water from the rhizosphere and serving as sites of interactions with soil microorganisms. By increasing the external surface area of the roots or interacting with rhizobacteria, root hairs directly and indirectly promote plant growth and yield. Transcriptome data can be used to understand root-hair development in rice. RESULT: We performed Agilent 44 K microarray experiments with enriched root-hair samples and identified 409 root hair-preferential genes in rice. The expression patterns of six genes were confirmed using a GUS reporter system and quantitative RT-PCR analysis. Gene Ontology (GO) analysis demonstrated that 13 GO terms, including oxygen transport and cell wall generation, were highly over-represented in those genes. Although comparative analysis between rice and Arabidopsis revealed a large proportion of orthologous pairs, their spatial expression patterns were not conserved. To investigate the molecular network associated with root hair-preferential genes in rice, we analyzed the PPI network as well as coexpression data. Subsequently, we developed a refined network consisting of 24 interactions between 10 genes and 18 of their interactors. CONCLUSION: Identification of root hair-preferential genes and in depth analysis of those genes will be a useful reference to accelerate the understanding of root-hair development in rice.

Meta-expression analysis of unannotated genes in rice and approaches for network construction to suggest the probable roles.

KEY MESSAGE: This work suggests 2020 potential candidates in rice for the functional annotation of unannotated genes using meta-analysis of anatomical samples derived from microarray and RNA-seq technologies and this information will be useful to identify novel morphological agronomic traits. Although the genome of rice (Oryza sativa) has been sequenced, 14,365 genes are considered unannotated because they lack putative annotation information. According to the Rice Genome Annotation Project Database ( http://rice.plantbiology.msu.edu/ ), the proportion of functionally characterized unannotated genes (0.35%) is quite limited when compared with the approximately 3.9% of annotated genes with assigned putative functions. Researchers require additional information to help them investigate the molecular mechanisms associated with those unannotated genes. To determine which of them might regulate morphological or physiological traits in the rice genome, we conducted a meta-analysis of expression data that covered a wide range of tissue/organ samples. Overall, 2020 genes showed cultivar-, tissue-, or organ-preferential patterns of expression. Representative candidates from featured groups were validated by RT-PCR, and the GUS reporter system was used to validate the expression of genes that were clustered according to their leaf or root preference. Taking a molecular and genetics approach, we examined meta-expression data and found that 127 genes were differentially expressed between japonica and indica rice cultivars. This is potentially significant for future agronomic applications. We also used a T-DNA insertional mutant and performed a co-expression network analysis of Sword shape dwarf1 (SSD1), a gene that regulates cell division. This network was refined via RT-PCR analysis. Our results suggested that SSD1 represses the expression of four genes related to the processes of DNA replication or cell division and provides insight into possible molecular mechanisms. Together, these strategies present a valuable tool for in-depth characterization of currently unannotated genes.

Integrated omics analysis of root-preferred genes across diverse rice varieties including Japonica and indica cultivars.

Plant root systems play essential roles in developmental processes, such as the absorption of water and inorganic nutrients, and structural support. Gene expression is affected by growth conditions and the genetic background of plants. To identify highly conserved root-preferred genes in rice across diverse growth conditions and varieties, we used two independent meta-anatomical expression profiles based on a large collection of Affymetrix and Agilent 44K microarray data sets available for public use. We then identified 684 loci with root-preferred expression, which were validated with in silico analysis using both meta-expression profiles. The expression patterns of four candidate genes were confirmed in vivo by monitoring expression of ß-glucuronidase under control of the candidate-gene promoters, providing new tools to manipulate agronomic traits associated with roots. We also utilized real-time PCR to examine the root-preferential expression of 14 genes across four rice varieties, including japonica and indica cultivars. Using a database of rice genes with known functions, we identified the reported functions of 39 out of the 684 candidate genes. Sixteen genes are directly involved in root development, while the remaining are involved in processes indirectly related to root development (i.e., soil-stress tolerance or growth retardation). This indicates the importance of our candidate genes for studies on root development and function. Gene ontology enrichment analysis in the 'biological processes' category revealed that root-preferred genes in rice are closely associated with nutrient transport-related genes, indicating that the primary role of roots is the uptake of nutrients from soil. In addition, predicted protein-protein interaction analysis suggested a molecular network for root development composed of 215 interactions associated with 44 root-preferred or root development-related genes. Taken together, our data provide an important foundation for future research on root development in rice.

Comparative Expression Analysis of Rice and Arabidopsis Peroxiredoxin Genes Suggests Conserved or Diversified Roles Between the Two Species and Leads to the Identification of Tandemly Duplicated Rice Peroxiredoxin Genes Differentially Expressed in Seeds.

BACKGROUND: Peroxiredoxins (PRXs) have recently been identified as plant antioxidants. Completion of various genome sequencing projects has provided genome-wide information about PRX genes in major plant species. Two of these -- Oryza sativa (rice) and Arabidopsis -- each have 10 PRX members. Although significant progress has been made in understanding their biological roles in Arabidopsis, those functions in rice, a model crop plant, have not been well studied. RESULTS: We performed a comparative expression analysis of rice and Arabidopsis PRXs. Our phylogenetic analysis revealed that one subgroup contains three rice and three Arabidopsis Type-II PRXs that are expressed ubiquitously. This suggests that they are involved in housekeeping functions to process reactive oxygen species (ROS). Within the second subgroup, expression of Os1-CysPrxA (LOC_Os7g44430) and AtOs1-CysPrx is conserved in seeds while Os1-CysPrxB (LOC_Os7g44440) shows a root-preferential pattern of expression. We used transgenic plants expressing the GUS reporter gene under the control of the promoters of these two tandem duplicates to confirm their meta-expression patterns. Our GUS expression data from developing seeds and those that were germinating indicated that Os1-CysPrxB is involved in root development, as initiated from the embryo, while Os1-CysPrxA has roles in regulating endosperm development near the aleurone layer. For the third and fourth subgroups, the rice PRXs are more likely to show leaf/shoot-preferential expression, while those from Arabidopsis are significantly expressed in the flowers and seeds in addition to the leaf/shoot. To determine the biological meaning of those expression patterns that were dominantly identified in rice PRXs, we analyzed three rice genes showing leaf/shoot-preferential expression in a mutant of the light-responsive 1-deoxy-D-xylulose 5-phosphate reductoisomerase (dxr) gene and found that two of them were significantly down-regulated in the mutant. CONCLUSION: A global expression analysis of the PRX family in rice identified tandem duplicates, Os1-CysPrxA and Os1-CysPrxB, in the 1-CysPrx subgroup that are differentially expressed in developing seeds and germinating seeds. Analysis of the cis-acting regulatory elements (CREs) revealed unique CREs responsible for embryo and root or endosperm-preferential expression. In addition, the presence of leaf/shoot-preferential PRXs in rice suggests that they are required in that crop because those plants must tolerate a higher light intensity in their normal growth environment when compared with that of Arabidopsis. Downregulation of two PRXs in the dxr mutant causing an albino phenotype, implying that those genes have roles in processing ROS produced during photosynthesis. Network analysis of four PRXs allowed us to model regulatory pathways that explain the underlying protein interaction network. This will be a useful hypothetical model for further study.

Updated Rice Kinase Database RKD 2.0: enabling transcriptome and functional analysis of rice kinase genes.

BACKGROUND: Protein kinases catalyze the transfer of a phosphate moiety from a phosphate donor to the substrate molecule, thus playing critical roles in cell signaling and metabolism. Although plant genomes contain more than 1000 genes that encode kinases, knowledge is limited about the function of each of these kinases. A major obstacle that hinders progress towards kinase characterization is functional redundancy. To address this challenge, we previously developed the rice kinase database (RKD) that integrated omics-scale data within a phylogenetics context. RESULTS: An updated version of rice kinase database (RKD) that contains metadata derived from NCBI GEO expression datasets has been developed. RKD 2.0 facilitates in-depth transcriptomic analyses of kinase-encoding genes in diverse rice tissues and in response to biotic and abiotic stresses and hormone treatments. We identified 261 kinases specifically expressed in particular tissues, 130 that are significantly up- regulated in response to biotic stress, 296 in response to abiotic stress, and 260 in response to hormones. Based on this update and Pearson correlation coefficient (PCC) analysis, we estimated that 19 out of 26 genes characterized through loss-of-function studies confer dominant functions. These were selected because they either had paralogous members with PCC values of <0.5 or had no paralog. CONCLUSION: Compared with the previous version of RKD, RKD 2.0 enables more effective estimations of functional redundancy or dominance because it uses comprehensive expression profiles rather than individual profiles. The integrated analysis of RKD with PCC establishes a single platform for researchers to select rice kinases for functional analyses.

Genome-wide identification and analysis of rice genes preferentially expressed in pollen at an early developmental stage.

Microspore production using endogenous developmental programs has not been well studied. The main limitation is the difficulty in identifying genes preferentially expressed in pollen grains at early stages. To overcome this limitation, we collected transcriptome data from anthers and microspore/pollen and performed meta-expression analysis. Subsequently, we identified 410 genes showing preferential expression patterns in early developing pollen samples of both japonica and indica cultivars. The expression patterns of these genes are distinguishable from genes showing pollen mother cell or tapetum-preferred expression patterns. Gene Ontology enrichment and MapMan analyses indicated that microspores in rice are closely linked with protein degradation, nucleotide metabolism, and DNA biosynthesis and regulation, while the pollen mother cell or tapetum are strongly associated with cell wall metabolism, lipid metabolism, secondary metabolism, and RNA biosynthesis and regulation. We also generated transgenic lines under the control of the promoters of eight microspore-preferred genes and confirmed the preferred expression patterns in plants using the GUS reporting system. Furthermore, cis-regulatory element analysis revealed that pollen specific elements such as POLLEN1LELAT52, and 5659BOXLELAT5659 were commonly identified in the promoter regions of eight rice genes with more frequency than estimation. Our study will provide new sights on early pollen development in rice, a model crop plant.

Genome-wide transcriptome analysis of expression in rice seedling roots in response to supplemental nitrogen.

Nitrogen (N) is the most important macronutrient for plant growth and grain yields. For rice crops, nitrate and ammonium are the major N sources. To explore the genomic responses to ammonium supplements in rice roots, we used 17-day-old seedlings grown in the absence of external N that were then exposed to 0.5mM (NH4)2SO4 for 3h. Transcriptomic profiles were examined by microarray experiments. In all, 634 genes were up-regulated at least two-fold by the N-supplement when compared with expression in roots from untreated control plants. Gene Ontology (GO) enrichment analysis revealed that those upregulated genes are associated with 23 GO terms. Among them, metabolic processes for diverse amino acids (i.e., aspartate, threonine, tryptophan, glutamine, l-phenylalanine, and thiamin) as well as nitrogen compounds are highly over-represented, demonstrating that our selected genes are suitable for studying the N-response in roots. This enrichment analysis also indicated that nitrogen is closely linked to diverse transporter activities by primary metabolites, including proteins (amino acids), lipids, and carbohydrates, and is associated with carbohydrate catabolism and cell wall organization. Integration of results from omics analysis of metabolic pathways and transcriptome data using the MapMan tool suggested that the TCA cycle and pathway for mitochondrial electron transport are co-regulated when rice roots are exposed to ammonium. We also investigated the expression of N-responsive marker genes by performing a comparative analysis with root samples from plants grown under different NH4(+) treatments. The diverse responses to such treatment provide useful insight into the global changes related to the shift from an N-deficiency to an enhanced N-supply in rice, a model crop plant.

Functional classification of rice flanking sequence tagged genes using MapMan terms and global understanding on metabolic and regulatory pathways affected by dxr mutant having defects in light response.

BACKGROUND: Rice is one of the most important food crops for humans. To improve the agronomical traits of rice, the functions of more than 1,000 rice genes have been recently characterized and summarized. The completed, map-based sequence of the rice genome has significantly accelerated the functional characterization of rice genes, but progress remains limited in assigning functions to all predicted non-transposable element (non-TE) genes, estimated to number 37,000-41,000. RESULTS: The International Rice Functional Genomics Consortium (IRFGC) has generated a huge number of gene-indexed mutants by using mutagens such as T-DNA, Tos17 and Ds/dSpm. These mutants have been identified by 246,566 flanking sequence tags (FSTs) and cover 65 % (25,275 of 38,869) of the non-TE genes in rice, while the mutation ratio of TE genes is 25.7 %. In addition, almost 80 % of highly expressed non-TE genes have insertion mutations, indicating that highly expressed genes in rice chromosomes are more likely to have mutations by mutagens such as T-DNA, Ds, dSpm and Tos17. The functions of around 2.5 % of rice genes have been characterized, and studies have mainly focused on transcriptional and post-transcriptional regulation. Slow progress in characterizing the function of rice genes is mainly due to a lack of clues to guide functional studies or functional redundancy. These limitations can be partially solved by a well-categorized functional classification of FST genes. To create this classification, we used the diverse overviews installed in the MapMan toolkit. Gene Ontology (GO) assignment to FST genes supplemented the limitation of MapMan overviews. CONCLUSION: The functions of 863 of 1,022 known genes can be evaluated by current FST lines, indicating that FST genes are useful resources for functional genomic studies. We assigned 16,169 out of 29,624 FST genes to 34 MapMan classes, including major three categories such as DNA, RNA and protein. To demonstrate the MapMan application on FST genes, transcriptome analysis was done from a rice mutant of 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) gene with FST. Mapping of 756 down-regulated genes in dxr mutants and their annotation in terms of various MapMan overviews revealed candidate genes downstream of DXR-mediating light signaling pathway in diverse functional classes such as the methyl-D-erythritol 4-phosphatepathway (MEP) pathway overview, photosynthesis, secondary metabolism and regulatory overview. This report provides a useful guide for systematic phenomics and further applications to enhance the key agronomic traits of rice.

Loose Plant Architecture1 (LPA1) determines lamina joint bending by suppressing auxin signalling that interacts with C-22-hydroxylated and 6-deoxo brassinosteroids in rice.

Lamina inclination is a key agronomical character that determines plant architecture and is sensitive to auxin and brassinosteroids (BRs). Loose Plant Architecture1 (LPA1) in rice (Oryza sativa) and its Arabidopsis homologues (SGR5/AtIDD15) have been reported to control plant architecture and auxin homeostasis. This study explores the role of LPA1 in determining lamina inclination in rice. LPA1 acts as a positive regulator to suppress lamina bending. Genetic and biochemical data indicate that LPA1 suppresses the auxin signalling that interacts with C-22-hydroxylated and 6-deoxo BRs, which regulates lamina inclination independently of OsBRI1. Mutant lpa1 plants are hypersensitive to indole-3-acetic acid (IAA) during the lamina inclination response, which is suppressed by the brassinazole (Brz) inhibitor of C-22 hydroxylase involved in BR synthesis. A strong synergic effect is detected between lpa1 and d2 (the defective mutant for catalysis of C-23-hydroxylated BRs) during IAA-mediated lamina inclination. No significant interaction between LPA1 and OsBRI1 was identified. The lpa1 mutant is sensitive to C-22-hydroxylated and 6-deoxo BRs in the d61-1 (rice BRI1 mutant) background. We present evidence verifying that two independent pathways function via either BRs or BRI1 to determine IAA-mediated lamina inclination in rice. RNA sequencing analysis and qRT-PCR indicate that LPA1 influences the expression of three OsPIN genes (OsPIN1a, OsPIN1c and OsPIN3a), which suggests that auxin flux might be an important factor in LPA1-mediated lamina inclination in rice.

Genome-wide identification and analysis of Japonica and Indica cultivar-preferred transcripts in rice using 983 Affymetrix array data.

BACKGROUND: Accumulation of genome-wide transcriptome data provides new insight on a genomic scale which cannot be gained by analyses of individual data. The majority of rice (O. sativa) species are japonica and indica cultivars. Genome-wide identification of genes differentially expressed between japonica and indica cultivars will be very useful in understanding the domestication and evolution of rice species. RESULTS: In this study, we analyzed 983 of the 1866 entries in the Affymetrix array data in the public database: 595 generated from indica and 388 from japonica rice cultivars. To discover differentially expressed genes in each cultivar, we performed significance analysis of microarrays for normalized data, and identified 490 genes preferentially expressed in japonica and 104 genes in indica. Gene Ontology analyses revealed that defense response-related genes are significantly enriched in both cultivars, indicating that japonica and indica might be under strong selection pressure for these traits during domestication. In addition, 36 (34.6%) of 104 genes preferentially expressed in indica and 256 (52.2%) of 490 genes preferentially expressed in japonica were annotated as genes of unknown function. Biotic stress overview in the MapMan toolkit revealed key elements of the signaling pathway for defense response in japonica or indica eQTLs. CONCLUSIONS: The percentage of screened genes preferentially expressed in indica was 4-fold higher (34.6%) and that in japonica was 5-fold (52.2%) higher than expected (11.1%), suggesting that genes of unknown function are responsible for the novel traits that distinguish japonica and indica cultivars. The identification of 10 functionally characterized genes expressed preferentially in either japonica or indica highlights the significance of our candidate genes during the domestication of rice species. Functional analysis of the roles of individual components of stress-mediated signaling pathways will shed light on potential molecular mechanisms to improve disease resistance in rice.