RiceMetaSys for salt and drought stress responsive genes in rice: a web interface for crop improvement

Sandhu, Maninder; Sureshkumar, V.; Prakash, Chandra; Dixit, Rekha; Solanke, Amolkumar U.; Sharma, Tilak Raj; Mohapatra, Trilochan; Mithra, S. V. Amitha

doi:10.1186/s12859-017-1846-y

Cited by 32 publications

(28 citation statements)

References 38 publications

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“…Comparing the expression profiles of two contrasting genotypes for a specific trait under different treatments is supposed to help in identification of causal genes when genetic analysis (mapping) of such traits are undertaken [ 20 , 53 ]. Here we have identified 87 such candidate genes (62 from root and 28 from shoot) in the major QTLs regions for NUE in rice.…”

Section: Discussionmentioning

confidence: 99%

Transcriptome Analysis of Two Rice Varieties Contrasting for Nitrogen Use Efficiency under Chronic N Starvation Reveals Differences in Chloroplast and Starch Metabolism-Related Genes

Sinha

Sevanthi

Chaudhary

et al. 2018

Genes

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The nitrogen use efficiency (NUE) of crop plants is limited and enhancing it in rice, a major cereal crop, would be beneficial for farmers and the environment alike. Here we report the genome-wide transcriptome analysis of two rice genotypes, IR 64 (IR64) and Nagina 22 (N22) under optimal (N+) and chronic starvation (N−) of nitrogen (N) from 15-day-old root and shoot tissues. The two genotypes were found to be contrasting in their response to N−; IR64 root architecture and root dry weight remained almost equivalent to that under N+ conditions, while N22 showed high foraging ability but a substantial reduction in biomass under N−. Similarly, the photosynthetic pigments showed a drastic reduction in N22 under low N, while IR64 was more resilient. Nitrate reductase showed significantly low specific activity under N− in both genotypes. Glutamate synthase (GOGAT) and citrate synthase CS activity were highly reduced in N22 but not in IR64. Transcriptome analysis of these genotypes revealed nearly double the number of genes to be differentially expressed (DEGs) in roots (1016) compared to shoots (571). The response of the two genotypes to N starvation was distinctly different reflecting their morphological/biochemical response with just two and eight common DEGs in the root and shoot tissues. There were a total of 385 nitrogen-responsive DEGs (106 in shoots and 279 in roots) between the two genotypes. Fifty-two of the 89 DEGs identified as specific to N22 root tissues were also found to be differentially expressed between the two genotypes under N−. Most of these DEGs belonged to starch and chloroplast metabolism, followed by membrane and signaling proteins. Physical mapping of DEGs revealed 95 DEGs in roots and 76 in shoots to be present in quantitative trait loci (QTL) known for NUE.

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Section: Discussionmentioning

confidence: 99%

Transcriptome Analysis of Two Rice Varieties Contrasting for Nitrogen Use Efficiency under Chronic N Starvation Reveals Differences in Chloroplast and Starch Metabolism-Related Genes

Sinha

Sevanthi

Chaudhary

et al. 2018

Genes

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“…Since the genomic constitution of the modern day varieties, bred after 1960s, is completely different from that of the traditional cultivars, mutations in their alleles could be an altogether a new resource from the rest of the resources available. Further, N22 is an important resource for drought tolerance and heat tolerance in rice, and is supported by a large number of QTLs, miRNAs, expression data, and databases, which would be useful in characterizing the mutants ( Kumar et al, 2014 ; Kansal et al, 2015 ; Mutum et al, 2016 ; Mangrauthia et al, 2017 ; Sandhu et al, 2017 ; Shanmugavadivel et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

“…Rice ( Oryza sativa L.) is a genomic model crop species for monocots, and is the main staple food for more than 50% of the world’s population ( Gross and Zhao, 2014 ). Structural genomics in rice is well established with multifarious resources (IRGSP, 2005; 3000 rice genome project; Kumar et al, 2015 ; Singh et al, 2016 ; Mansueto et al, 2017 ; Sandhu et al, 2017 ), while only 2,000 genes have been functionally characterized in rice, indicating that progress in functional genomics is much more laborious and time and resource consuming than initially envisaged. The functionally characterized genes were either identified by traditional genetic- and map-based cloning approaches ( Ashikari and Matsuoka, 2006 ; Miura et al, 2011 ; Yamamoto et al, 2012 ; Liu et al, 2015 ; Neelam et al, 2016 ) or from mutant resources.…”

Section: Introductionmentioning

confidence: 99%

Whole Genome Characterization of a Few EMS-Induced Mutants of Upland Rice Variety Nagina 22 Reveals a Staggeringly High Frequency of SNPs Which Show High Phenotypic Plasticity Towards the Wild-Type

et al. 2018

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The Indian initiative, in creating mutant resources for the functional genomics in rice, has been instrumental in the development of 87,000 ethylmethanesulfonate (EMS)-induced mutants, of which 7,000 are in advanced generations. The mutants have been created in the background of Nagina 22, a popular drought- and heat-tolerant upland cultivar. As it is a pregreen revolution cultivar, as many as 573 dwarf mutants identified from this resource could be useful as an alternate source of dwarfing. A total of 541 mutants, including the macromutants and the trait-specific ones, obtained after appropriate screening, are being maintained in the mutant garden. Here, we report on the detailed characterizations of the 541 mutants based on the distinctness, uniformity, and stability (DUS) descriptors at two different locations. About 90% of the mutants were found to be similar to the wild type (WT) with high similarity index (>0.6) at both the locations. All 541 mutants were characterized for chlorophyll and epicuticular wax contents, while a subset of 84 mutants were characterized for their ionomes, namely, phosphorous, silicon, and chloride contents. Genotyping of these mutants with 54 genomewide simple sequence repeat (SSR) markers revealed 93% of the mutants to be either completely identical to WT or nearly identical with just one polymorphic locus. Whole genome resequencing (WGS) of four mutants, which have minimal differences in the SSR fingerprint pattern and DUS characters from the WT, revealed a staggeringly high number of single nucleotide polymorphisms (SNPs) on an average (16,453 per mutant) in the genic sequences. Of these, nearly 50% of the SNPs led to non-synonymous codons, while 30% resulted in synonymous codons. The number of insertions and deletions (InDels) varied from 898 to 2,595, with more than 80% of them being 1–2 bp long. Such a high number of SNPs could pose a serious challenge in identifying gene(s) governing the mutant phenotype by next generation sequencing-based mapping approaches such as Mutmap. From the WGS data of the WT and the mutants, we developed a genic resource of the WT with a novel analysis pipeline. The entire information about this resource along with the panicle architecture of the 493 mutants is made available in a mutant database EMSgardeN22 (http://14.139.229.201/EMSgardeN22).

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“…With the accumulation of a large number of genome-wide gene expression data sets in the public domain, different databases were developed in rice on either a specific stress or all stresses like RiceSRTFDB for salinity stress (http://www.nipgr.res.in/RiceSRTFDB.html), drought stress gene database (http://pgsb.helmholtz-muenchen.de/droughtdb/) and plant stress gene database (http://ccbb.jnu.ac.in/stressgenes/). Recently, we have also developed RiceMetaSys database for identification and analysis of differentially expressed genes (DEGs) under drought and salt stresses in rice (24). All these databases catered to one or more abiotic stresses.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, there is no database that comprehensively covers gene expression profiles of major rice diseases across pathogen strains and host genotypes. Even those databases, which cater to all stresses and developmental stages, such as OryzaExpress, RicePLEX and RiceXPro, did not allow meta-analysis of multiple expression data sets under one common theme (24). Though ROAD database allowed meta-analysis, it is no more functional (24).…”

Section: Introductionmentioning

confidence: 99%

RiceMetaSysB: a database of blast and bacterial blight responsive genes in rice and its utilization in identifying key blast-resistant WRKY genes

et al. 2019

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Nearly two decades of revolution in the area of genomics serves as the basis of present-day molecular breeding in major food crops such as rice. Here we report an open source database on two major biotic stresses of rice, named RiceMetaSysB, which provides detailed information about rice blast and bacterial blight (BB) responsive genes (RGs). Meta-analysis of microarray data from different blast- and BB-related experiments across 241 and 186 samples identified 15135 unique genes for blast and 7475 for BB. A total of 9365 and 5375 simple sequence repeats (SSRs) in blast and BB RGs were identified for marker development. Retrieval of candidate genes using different search options like genotypes, tissue, developmental stage of the host, strain, hours/days post-inoculation, physical position and SSR marker information is facilitated in the database. Search options like ‘common genes among varieties’ and ‘strains’ have been enabled to identify robust candidate genes. A 2D representation of the data can be used to compare expression profiles across genes, genotypes and strains. To demonstrate the utility of this database, we queried for blast-responsive WRKY genes (fold change ≥5) using their gene IDs. The structural variations in the 12 WRKY genes so identified and their promoter regions were explored in two rice genotypes contrasting for their reaction to blast infection. Expression analysis of these genes in panicle tissue infected with a virulent and an avirulent strain of Magnaporthe oryzae could identify WRKY7 , WRKY58 , WRKY62 , WRKY64 and WRKY76 as potential candidate genes for resistance to panicle blast, as they showed higher expression only in the resistant genotype against the virulent strain. Thus, we demonstrated that RiceMetaSysB can play an important role in providing robust candidate genes for rice blast and BB.

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RiceMetaSys for salt and drought stress responsive genes in rice: a web interface for crop improvement

Cited by 32 publications

References 38 publications

Transcriptome Analysis of Two Rice Varieties Contrasting for Nitrogen Use Efficiency under Chronic N Starvation Reveals Differences in Chloroplast and Starch Metabolism-Related Genes

Transcriptome Analysis of Two Rice Varieties Contrasting for Nitrogen Use Efficiency under Chronic N Starvation Reveals Differences in Chloroplast and Starch Metabolism-Related Genes

Whole Genome Characterization of a Few EMS-Induced Mutants of Upland Rice Variety Nagina 22 Reveals a Staggeringly High Frequency of SNPs Which Show High Phenotypic Plasticity Towards the Wild-Type

RiceMetaSysB: a database of blast and bacterial blight responsive genes in rice and its utilization in identifying key blast-resistant WRKY genes

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