Highlights
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We found 29 new stress responsive genes during the analysis of nine guide MATE genes.
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100 genetic interactions were found in the analysis of 38(29 + 9) new guide genes at the gene level with a good log likelihood score (LLS) implying that these genes were having some functional linkage among themselves while one gene among them was found to be invalid for the interaction by the programme itself.
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All the 37 genes were reported to be interacting at the protein level but 15 genes were found to be interacting with a high confidence value at the protein level.
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Apart from the aforesaid 37 genes, additional 10 genes were found to interact among themselves at protein level. Surprisingly, only 05 genes (Os05g33240, Os05g38530, Os09g19560, Os02g01500 and Os03g16920) were from the initial 37 genes were found to play a key role at protein level.
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These 15 genes were subjected to further analysis for their interaction at STITCH Version 5.0 (Search Tool for Interacting Chemicals) with soil available inorganic species of arsenic viz., arsenate [As(V)] and arsenite [As(III)] and organic arsenic species viz., monomethylarsonic acid (MAA) and dimethylarsinic acid (DMAA) and 03 genes (LOC_Os05g38530, LOC_Os03g16920, LOC_Os08g39140) were found to interact directly both with arsenate and arsenite whereas 03 genes (LOC_Os01g23610, LOC_Os1g22520, LOC_Os03g01770) were found to show direct interaction exclusively with arsenite only.
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Maximum expression levels (expression potential) of these 06 genes were found mostly at young inflorescence and seed development stage after the analysis at Rice eFP Browser (BAR) which was developed at the University of Toronto.
Keywords: Rice, Multidrug and toxic compound extrusion, Gene prioritization, Maximum expression level
Abstract
MATE genes play an important role in cellular detoxification processes. Nine MATE genes were identified by a transcriptomics study previously. Candidate gene prioritization was done where 29 new genes were found to interact with 09 guide genes. Therefore, a total of 38 genes were analyzed here to predict a concise model by gene prioritization study. Those genes were analyzed further in Rice Interactions Viewer programme, and based on high ICV, 10 new genes were found to interact among themselves at protein level. Surprisingly, only 05 genes were found to play a key role at protein level. These 15 genes were analyzed for their interaction with soil available inorganic arsenic species. Maximum expression levels were found mostly at young inflorescence and seed development stage for those genes. So, these genes may have a direct role in arsenic sequestration from cells and thereby providing safety to the developing embryo within the seed.
1. Introduction
According to the WHO, Arsenic (As) toxicity has been a well-known phenomenon and a matter of great concern for the world where at least 140 million people in 50 countries have been exposed to it. Arsenic, a toxic metalloid commonly present in subsoil has been reported to be a carcinogenic for human [1]. Many a times this carcinogenic metalloid is coming to human and ruminants through a number of agricultural produces being grown in the arsenic contaminated areas but the biological availability, transport, accumulation and toxicity of arsenic are principally decided by its speciation forms (methylated or inorganic) whereas inorganic arsenic species are known to be more toxic than organic arsenic species. Although, As (III) is graded to be more toxic than As (V) but the degree of toxicity can be reduced with the increase in methylation. Very often the symptoms of reduced root and shoot growth are observed in various plant species which are exposed to arsenate [2,3]. Inorganic arsenic species stimulates the production of reactive oxygen species (ROS) such as superoxide radical (O2−), hydroxyl radical (OH), and hydrogen peroxide (H2O2) in the plant cellular system whenever exposed with arsenic [4,5]. This reactive oxygen species chemically disrupt different proteins, amino acids and nucleic acids at cellular level and can damage membrane lipids by causing peroxidation [6].
Rice is known to accumulate higher quantity of arsenic than any other cereals [7]. So, there is every chance of entry of arsenic into the food chain through rice where it is the major staple crop. Like other plants, rice also permits the entry of different arsenic species such as arsenate (phosphate analogue) entering through root phosphate transporters and arsenite enters through silicic acid transporter [8]. There are reports of arsenic sequestration as well as uploading in different organs including grain of rice plant following several pathways [9]. It is evident that, neither arsenic contaminated soil nor contaminated irrigation water can be withdrawn from the reported contaminated zones; thereby it becomes compulsory either to breed low arsenic accumulating genotypes or inventing management technologies to ameliorate the physical sources of arsenic in the environment. In order to breed low arsenic accumulating genotypes, understanding the detoxification mechanism as well as revealing the genetic network induced by arsenic is necessary. Various mechanisms of detoxification have been found in plants like sequestration and binding of toxicants, enzyme induction, biotransformation of absorbed compounds, co-substrates and MATE (multidrug and toxic compound extrusion) genes [10]. Multidrug and toxic compound extrusion gene is one of the major detoxifying gene-families present in bacteria, fungi, plants and mammals [11]. Previously MATE genes were reported to detoxify secondary metabolites but presently they are known to extrude organic compound, transport a wide range of organic acids, plant hormones etc. [12]. In 2008, a study was done by Norton et al., to reveal Rice-arsenate interaction in hydroponics. In that study, a whole genome transcriptional analysis was conducted through micro-array where a large number of transcription factors, stress related proteins and several other classes of genes were found to show differential expression pattern. Strikingly, nine MATE transporters were also documented by them. Those nine MATE genes were taken into consideration as candidate genes in this present study to find out whether any functional linkage was there among them or with other cellular proteins. Exploring functional linkage would help to understand how these MATE genes are getting influenced by other genes. This would strongly help in defining all the relevant associated genes related to arsenic detoxification. Because targeting only a few key genes may become a futile exercise to create any desired phenotype in routine plant breeding experiments [13,14]. Advancement of molecular genetics suggests that, ultimate phenotype is determined by the genetic network rather than the mere activity of a single or few genes. In this back drop, the present experiments were designed and performed by in silico platforms.
2. Materials and methods
2.1. Mate genes
Norton et al., conducted whole genome transcriptional analysis to know rice-arsenate interaction in hydroponics and the work was published in journal of experimental botany in 2008. This group of researchers conducted the Affymetrix (52 K) Gene Chip Rice Genome array of two rice varieties which were arsenic tolerant and sensitive respectively. After analysis of the genome wide gene expression pattern, several transporter genes were found to be differentially expressed where there were nine identified MATE transporters also. Apropos to that report, those MATE transporters were found to be up and down regulated in rice genotypes were induced by the presence of arsenic. In this present study, these MATE genes were considered as candidate genes of interest for finding the genetic network behind arsenic detoxification. So, these Nine MATE genes happened to be used as guide gene for this study. The guide genes were subjected for analysis through computational biology for the evaluation of the information on functional linkage among the arsenic induced MATE genes (Table 1).
Table 1.
List of the nine guide MATE genes and their functions.
| Locus Name | Function | Reference |
|---|---|---|
| LOC_Os03g37490.1 (MATE efflux family protein) | Transport, membrane, vacuole, cellular process, transporter activity | UniProtKB - Q10HY1 |
| LOC_Os05g48040.1 (MATE efflux family protein) | Transport, membrane, vacuole, cellular process, transporter activity | Rice Genome annotation project |
| LOC_Os08g37432.1 (MATE efflux family protein) | Transport, membrane, ripening, cellular process, transporter activity | UniProtKB - Q6ZB84 |
| LOC_Os10g20450.1 (MATE efflux family protein) | Transport, membrane, ripening, cellular process, transporter activity, response to biotic stimulus | UniProtKB - Q7XFI4 |
| LOC_Os10g20470.1 (MATE efflux family protein) | Transport, membrane, ripening, cellular process, transporter activity, response to biotic stimulus | UniProtKB - Q7XFI3 |
| LOC_Os12g03260.1 (MATE efflux family protein) | Transport, membrane, cellular process, transporter activity | UniProtKB-Q2QYB0 |
| LOC_Os04g48290.1 (MATE efflux family protein) | Transport, membrane, cellular process, transporter activity | UniProtKB - Q7XU48 |
| LOC_Os09g29284.1 (MATE efflux family protein) | Transport, membrane, ripening, cellular process, transporter activity | UniProtKB - Q6K5E6 |
Information regarding LOC_Os08g37430 could not be retrieved.
Among these different MATE genes, LOC_Os05g48040 was reported to have some function in arsenic sequestration in stem and in lowering the level of arsenic in grain. LOC_Os10g20470 and LOC_Os12g03260 were reported to have some role in arsenic resistance in rice (Debnath et. al., 2016).
2.1.1. Candidate gene prioritization by Rice netV2
Prioritization of genes helps to predict new candidate gene networks for different phenotypes and biological pathways. In a genetic network, genes which are connected to one another are hypothesized to be functionally associated as candidate genes of the pathway. There are two complementary network prioritization algorithms which are provided by the RiceNet v2 (https://www.inetbio.org/ricenet/) based on network direct neighbourhood and context-associated hubs.
RiceNet maintained by the National Research Foundation of Korea grant (2010-0017649, 2012M3A9B4028641, 2012M3A9C7050151) is supported as a part by the Joint Bioenergy Institute, Office of Biological and Environmental Research, U. S. Department of Energy Under Contract No. DE-aC02-05CH11231, and the Department of Energy Systems Biology Knowledgebase (KBase) is an advanced platform for inventing network prioritization of rice genes. The rice interactome is predicted on the basis of conserved interactions among the proteins across species over the course of evolution where a confidence value (CV) as an internal quality control is assigned. The improved quality of the network and prediction power is given by RiceNet V2 (p-value = 1.11e-6, specified by Wilcoxon signed rank sum test). It is useful for the prediction of both major subspecies of rice, japonica and Indica.
2.2. Rice interaction viewer (bar)
It is well evidenced that protein-protein interactions (PPI) were found to play an important roles in the cellular environment where Co-immunoprecipitation, co-sedimentation and two-hybrid systems were convetionally used to characterize interactions at single protein complex level [15]. Nowadays, high-throughput methods are available for large-scale detection of pairwise interactions (two-hybrid systems, the split-ubiquitin method) [[16], [17], [18]] or multi-protein complexes (TAP-TAG, HMS-PCI) [[19], [20], [21]] and [22]. The Bio-Analytic Resource for Plant Biology (BAR) is a web platform (http://bar.utoronto.ca/interactions/cgi-bin/rice_interactions_viewer.cgi) to access enormous data sets from about 15 different plant species to successfully analyze transcriptomics, protein-protein interaction and promoter data.
2.3. Stitch V5.0
The knowledge about the interactions among the proteins and small molecules is crucial for a better understanding of the molecular and cellular functions like metabolism, signaling, drug treatments as the cases may be. However, information of such interactions is widely available crossways a number of databases and the literatures. STITCH (search tool for interactions of chemicals) has been so developed to integrate information regarding the interactions from metabolic pathways, crystal structures, binding protein experiments and drug–target relationships [23]. This is an integrated platform (http://stitch.embl.de/) for the discovery of interactions which is connected over 300,000 chemicals and 2.6 million proteins from 1133 organisms. [24]
2.4. Rice eFP browser
The electronic fluorescent pictograph (eFP) software is a famous tool for visual display of the transcriptome data and is extensively used for different model organisms. [25] It was developed at the University of Toronto to aid visual examination of gene expression. The function of this software (http://bar.utoronto.ca/transcriptomics/efp_rice/cgi-bin/efpWeb.cgi?dataSource=rice_leaf_gradient) is to exhibit cartoon images for illustrating various tissue types where each tissue is represented with distinguished colors indicating the level of expression for a targeted gene. [25]
3. Results and discussion
3.1. Functional linkage of the mate genes
Finding functionally related genes help in determining the presence of epistasis at molecular level. Such gene interaction models have already been exploited for drug target discovery. In rice, system level gene interaction model may open up the ways for prediction and discovery of genes as well as associated pathways related to environmental stress tolerance and resistance [13,14]. In this present study, novel genetic interaction were predicted from gene prioritization based on context associated hub analysed at RiceNet V2 platform. The nine MATE genes were subjected to gene prioritization study based on context associated hub before network direct neighbourhood study because we assumed them (MATE genes) as differentially expressed genes. According to the protocol of RiceNet V2 prioritization based on context associated hub should be the option for abiotic and biotic stress response gene. Here, 29 new genes were found to interact rather functionally associated with the nine candidate MATE genes which were put as guide genes (Table 2)
Table 2.
List of 29 new candidate genes interacting with 9 MATE gene.
| Rank | RGAP locus ID | paralog | gene description | p- value |
|---|---|---|---|---|
| 1 | LOC_Os03g16920 | response to stress;response to virus;response to heat;response to bacterium;response to cadmium ion | 8.16E-10 | |
| 2 | LOC_Os03g60620 | response to stress;response to virus;response to heat;response to cadmium ion | 9.16E-10 | |
| 3 | LOC_Os03g16860 | LOC_Os05g38530 | response to stress;response to virus;response to hydrogen peroxide;response to heat;response to bacterium;response to temperature stimulus;response to high light intensity;response to cadmium ion;protein ubiquitination | 1.00E-09 |
| 4 | LOC_Os05g38530 | DnaK family protein, putative, expressed | 1.19E-09 | |
| 5 | LOC_Os01g62290 | LOC_Os05g38530 | response to stress | 1.23E-09 |
| 6 | LOC_Os11g47760 | LOC_Os12g38180 | response to stress;response to heat;response to cadmium ion | 1.37E-09 |
| 7 | LOC_Os05g33240 | RNA splicing, via endonucleolytic cleavage and ligation;transcription, DNA-dependent;transcription from RNA polymerase II promoter | 0.000137 | |
| 8 | LOC_Os07g10840 | metabolic process | 0.000151 | |
| 9 | LOC_Os09g19560 | protein amino acid methylation;methylation;peptidyl-arginine N-methylation | 0.000353 | |
| 10 | LOC_Os03g45370 | LOC_Os12g42910 | transmembrane transport | 0.000407 |
| 11 | LOC_Os09g30130 | cellulose biosynthetic process | 0.000482 | |
| 12 | LOC_Os09g30120 | cellulose biosynthetic process | 0.000489 | |
| 13 | LOC_Os07g22720 | pyruvate metabolic process;metabolic process | 0.00054 | |
| 14 | LOC_Os07g44410 | proteolysis;toxin catabolic process;response to ethylene stimulus;abscisic acid mediated signaling pathway;response to cyclopentenone;methylglyoxal catabolic process to D-lactate | 0.000547 | |
| 15 | LOC_Os06g45480 | purine base metabolic process;proteolysis;metabolic process;ureide catabolic process;chlorophyll catabolic process | 0.000593 | |
| 16 | LOC_Os02g01500 | LOC_Os06g01630 | pyruvate metabolic process;metabolic process | 0.000605 |
| 17 | LOC_Os06g08600 | metabolic process;oxidation reduction | 0.000804 | |
| 18 | LOC_Os08g05910 | transport;oligopeptide transport;response to water deprivation;response to nitrate;nitrate transport | 0.000831 | |
| 19 | LOC_Os07g20150 | translation;methylation | 0.000975 | |
| 20 | LOC_Os02g02110 | Alg9-like mannosyltransferase protein, putative, expressed | 0.001015 | |
| 21 | LOC_Os02g36870 | LOC_Os08g33680 | YGL010w, putative, expressed | 0.001025 |
| 22 | LOC_Os01g14440 | OsWRKY1v2 - Superfamily of TFs having WRKY and zinc finger domains, expressed | 0.00113 | |
| 23 | LOC_Os01g40070 | LOC_Os05g51520 | expressed protein | 0.001388 |
| 24 | LOC_Os01g31610 | tRNA aminoacylation for protein translation;tyrosyl-tRNA aminoacylation;phosphatidylglycerol biosynthetic process;chloroplast organization;embryonic development ending in seed dormancy;thylakoid membrane organization;vegetative to reproductive phase transition of meristem;iron-sulfur cluster assembly;ovule development | 0.002289 | |
| 25 | LOC_Os06g49120 | translational initiation | 0.002504 | |
| 26 | LOC_Os02g07160 | aromatic amino acid family metabolic process;vitamin E biosynthetic process;plastoquinone biosynthetic process;carotenoid biosynthetic process;oxidation reduction | 0.004493 | |
| 27 | LOC_Os03g55240 | oxidation reduction | 0.00496 | |
| 28 | LOC_Os01g55740 | proteolysis | 0.006715 | |
| 29 | LOC_Os10g40570 | glucosinolate biosynthetic process;glucosinolate biosynthetic process from homomethionine;oxidation reduction;defense response to fungus | 0.006968 |
The p-value <0.01 indicated that, the model was statistically highly significant. Therefore all these 29 genes were taken into consideration as target genes for designing gene based molecular markers for utilization in marker assisted breeding for further studies.
LOC_Os03g16920 gene encoding the heat shock cognate 70 kDa protein was located in cytoplasm. It is involved in ATP binding, misfolded protein binding and unfolds protein binding. It was also found to show cellular response to heat (UniProtKB - Q10NA1). Protein encoded by the gene LOC_Os03g60620 was also belong to heat shock 70 family and involved in ATP binding activity. It was found to show response to heat stress, cadmium ion (UniProtKB - A0A0E0D8U9). LOC_Os03g16860 gene represents 70 kDa heat shock protein. It give response towards heat stress, cadmium ion stress, response to high light intensity, protein ubiquitination (UniProtKB - A0A0D3FGU6). LOC_Os05g38530 gene encode the Os05g0460000 protein and it was found to play roles in molecular activities like DNA binding, DNA directed 5′-3′ RNA polymerase activity, protein dimeritization. It was also involved in transcription and DNA templating (UniProtKB - Q6L509). LOC_Os01g62290 gene was reported to show response to stress and played role in ATP binding (UniProtKB - Q943K7). LOC_Os11g47760 gene represents HSP 70 kDa protein which involving in ATP binding activity and give response to heat, stress and cadmium ion (UniProtKB - Q2QZ41). Gene belonging to HSP 70 family were reported to show highly additive gene expression at the m RNA and protein level on high exposure to As and heat stress [26]. Heat shock proteins were found to be responding during abiotic stresses [27]. The K-exchanger protein encoded by the gene LOC_Os03g45370 was an integral component of membrane. It was a putative and expressed protein (UniProtKB - Q7Y0B2). LOC_Os08g05910 gene was involved in transport activities like oligopeptide transport, transport of nitrate. It was also found to show response towards water depriviation in the cell (UniProtKB-A2YRC4). LOC_Os10g40570 gene encodes flavin containing monooxygenase protein. This protein was reported to play major role in NADP binding, flavin adenine dinucleotide binding, N,N-dimethylaniline monooxygenase activity. It was also involved in biosynthesis of glucosinolate from homomethionine, oxidation reduction process. This gene was found to show defensive response towards fungal infection in the plant. So, it may be concluded that triggering MATE gene expression is associated with a numbers of transporter/defence/ATP binding activities and these findings corroborated with the findings of [28].
A set of 38 genes (29 newly found genes with 09 guide genes) were analyzed further by putting them as guide genes in Rice Net V2 to predict a concise model by gene prioritization based on network direct neighbourhood where LOC_Os08g37430 was found to be invalid as guide gene by the programme itself. Hence, the model was prepared for a total of 37 genes that were identified by RGAP7.0 as well as in RiceNet having AUC value of 0.9712 and p = 6.684656e- 100 (Fig. 1). The model was found to show AUC (area under the ROC curve) >0.7 i.e. 0.9. As the value of AUC was >0.9, it indicated perfect prediction rather than random [13,14].
Fig. 1.
genetic network of 37 guide genes.
3.2. Validation of the paralogi based functional linkage at protein level in rice interaction viewer (BAR)
The previous model (Fig no.03) was prepared on the basis of paralogous relationship which means that these genes are having homology as they may have been evolved by the course of evolution indicating their belongings to a common ancestral gene. For a further validation of this network model at protein level, same set of genes were analyzed in Rice Interaction Viewer (BAR) at http://bar.utoronto.ca/interactions/cgi-bin/rice_interactions_viewer.cgi, and based on high confidence value (as set by the programme itself) we found a total of 178 interactions at the protein level among those 37 genes with having low, medium and high interolog confidence value. Apart from the aforesaid 37 genes, 10 other genes were found to interact among themselves at protein level (Fig. 2). Surprisingly, only 05 genes (Os05g33240, Os05g38530, Os09g19560, Os02g01500 and Os03g16920) from the initial 37 genes were found to play a key role at protein level.
Fig. 2.
Interactions at the protein level.
3.3. Interaction of identified genes with different species of arsenic at protein level at stitch v5.0 platform
These 15 genes were subjected to analysis for their interaction at STITCH Version 5.0 with soil available inorganic species of arsenic viz., arsenate [As (V)] and arsenite [As (III)] and organic arsenic species viz., monomethylarsonic acid (MMA) and dimethylarsinic acid (DMAA). Finally, among those 15 candidate genes, 03 genes (LOC_Os05g38530, LOC_Os03g16920, LOC_Os08g39140) were found to interact directly both with arsenate and arsenite whereas 03 genes (LOC_Os01g23610, LOC_Os1g22520, LOC_Os03g01770) were found to show direct interaction exclusively with arsenite only (Table 3, Table 4; Fig. 3, Fig. 4).
Table 3.
Interaction of arsenate with the identified genes at protein level.
| Node1 | node2 | node1 annotation | score |
|---|---|---|---|
| 4326849 (LOC_Os01g23610.1) | Arsenate (233) sodium arsenate | dihydrolipoyl dehydrogenase, putative, expressed | 0.514 |
| 4326980 (LOC_Os01g22520.1) | Arsenate (233) sodium arsenate | dihydrolipoyl dehydrogenase 1, mitochondrial precursor, putative, expressed | 0.455 |
| 4331335 (LOC_Os03g01770.1) | Arsenate (233) sodium arsenate | rhodanese, putative, expressed; Possesses arsenate reductase activity in vitro. Catalyzes the reduction of arsenate [As(V)] to arsenite [As(III)]. May play a role in arsenic retention in roots | 0.606 |
Table 4.
Interaction of arsenite with identified genes at protein level.
| Node 1 | Node 2 | Function of Node 1 genes | Score |
|---|---|---|---|
| 4326849 | Arsenite | Dihydrolipoyl dehydrogenase | 0.652 |
| LOC_Os01g23610.1 | |||
| 4326980 | Arsenite (544) | Dihydrolipoyl dehydrogenase 1, mitochondrial precursor | 0.454 |
| LOC_Os01g22520.1 | |||
| 4327388 | Arsenite (544) | DnaK family protein | 0.627 |
| LOC_Os05g38530.1 | |||
| 4331335 | Arsenite (544) | Rhodanese, arsenate reductase activity | 0.54 |
| LOC_Os03g01770.1 | |||
| 4332420 | Arsenite (544) | DnaK family protein | 0.627 |
| LOC_Os03g16920.1 | |||
| 4345951 LOC_Os08g39140.1 |
Arsenite (544) | Heat shoch protein, molecular chaperon | 0.5 |
Fig. 3.
Direct interaction with arsenate.
Fig. 4.
Direct interaction with arsenite.
Arsenate reductase 2.2 (ARC 2.2) protein encoded by the gene LOC_Os03g01770 located in both nucleus and cytoplasm have tyrosine protein phosphatase activity. It was also involved in arsenate reductase activity and act as a catalyst for reduction of arsenate [As (V)] to arsenite [As (III)]. (UniProtKB - B8ALE5).This indicated that it may play some roles in controlling the entry of arsenic to the seed and other plant parts by retaining it in the roots and help in reducing the accumulation of more arsenic in grains. Accumulation of arsenic in grain was less than ten percent of the arsenic accumulated in stem [29].
3.4. Expression potential of the genes which were found to interact with different species of arsenic
All the six genes were subjected to analyze individually to know their expression potential. The level of expression potential of LOC_Os01g22520 was high at seedling root and low in the shoot apical meristem (Table 5) indicating its involvement in some activity of controlling the entry of arsenic to the root at seedling stage (UniProtKB - A0A0E0HNB8). Besides, the expression potential was observed to be high for LOC_Os01g23610 in the inflorescence P2 (meiotic stage) and low in the embryo maturation stage. (Table 6). Strikingly, LOC_Os03g01770 gene encoding arsenate reductase 2.2 protein which was reported to be observed only in roots and found to be responsible for reduction of arsenate to arsenite. In the present study, (Table 7) the observed expression potential for the said gene was found to be high in the matured seedling as well as vacuolated pollen stage (inflorescence P5). LOC_Os03g16920 (Table 8) was shown to have high expression potential during matured embryo stage which indicated that, it may restrict the entry of arsenic to the embryo while in the shoot apical meristem, it was found to show less expression. In some other studies it was found that arsenic accumulation is significantly higher in shoot as compared to the root [7]. The accumulation of arsenic in grain is usually less than ten percent of the stem accumulation and majority of the grain arsenic is uploaded by phloem [30,29]. The maximum expression potential of the gene LOC_Os05g38530 was shown in the embryo morphogenesis stage and minimum in the shoot apical meristem (Table 9). This indicated that, LOC_Os05g38530 may have some role in reduction in arsenic content in grain as less grain accumulation is evident from other studies. This gene was also involved in the activity of DNA binding and protein dimerization (UniProtKB - Q6L509). The expression of LOC_Os08g39140 was high both at embryo morphogenesis and maturity stage (Table 10) indicating it involvement in some activity of restricting the entry of the arsenic to the embryo of the seed. LOC_Os08g39140 is known to encode a heat shock protein (HSP81-1) located in cytoplasm and act as molecular chaperon which promotes the structural maintenance, maturity and regulating several target proteins (UniProtKB - A2YWQ1). Additionally, the expression was found to be low at matured leaf stage. Therefore, maximum expression levels (expression potential) of these 06 genes were found mostly at young inflorescence and seed development stage after the analysis at Rice eFP Browser (BAR) which was developed at the University of Toronto. So, these six genes may have a direct role in arsenic sequestration from cells and thereby providing safety to the developing embryo within the seed.
Table 5.
Expression potential of the LOC_Os01g22520 gene.
| Group # | Tissue | Expression Level | Standard Deviation | Samples | Links |
|---|---|---|---|---|---|
| 1 | Seedling Root | 16899.39 | 513.75 | Seedling_Root_Rep1,Seedling_Root_Rep2,Seedling_Root_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Mature Leaf | 9060.44 | 922.76 | MatureLeaf_Rep1,MatureLeaf_Rep2,MatureLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Young Leaf | 6867.62 | 946.85 | YoungLeaf_Rep1,YoungLeaf_Rep2,YoungLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 3 | SAM | 4997.96 | 484.6 | SAM_Rep1,SAM_Rep2,SAM_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Young Inflorescence | 6490.03 | 681.19 | YoungInflorescence_Rep1,YoungInflorescence_Rep2,YoungInflorescence_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P2 | 6995.48 | 1017.12 | InflorescenceP2_Rep1,InflorescenceP2_Rep2,InflorescenceP2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P3 | 6499.46 | 317.02 | InflorescenceP3_Rep1,InflorescenceP3_Rep2,InflorescenceP3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P4 | 7178.98 | 594.35 | InflorescenceP4_Rep1,InflorescenceP4_Rep2,InflorescenceP4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P5 | 8500.99 | 310.84 | InflorescenceP5_Rep1,InflorescenceP5_Rep2,InflorescenceP5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P6 | 9305.58 | 676.05 | InflorescenceP6_Rep1,InflorescenceP6_Rep2,InflorescenceP6_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S1 | 10128.84 | 867.01 | Seed_S1_Rep1,Seed_S1_Rep2,Seed_S1_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S2 | 9486.12 | 808.74 | Seed_S2_Rep1,Seed_S2_Rep2,Seed_S2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S3 | 7764.57 | 502.79 | Seed_S3_Rep1,Seed_S3_Rep2,Seed_S3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S4 | 5933.47 | 187.33 | Seed_S4_Rep1,Seed_S4_Rep2,Seed_S4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S5 | 6086.38 | 530.49 | Seed_S5_Rep1,Seed_S5_Rep2,Seed_S5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
Table 6.
Expression potential of the LOC_Os01g23610 gene.
| Group # | Tissue | Expression Level | Standard Deviation | Samples | Links |
|---|---|---|---|---|---|
| 1 | Seedling Root | 1246.59 | 39.47 | Seedling_Root_Rep1,Seedling_Root_Rep2,Seedling_Root_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Mature Leaf | 1567.78 | 222.41 | MatureLeaf_Rep1,MatureLeaf_Rep2,MatureLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Young Leaf | 1649.46 | 53.54 | YoungLeaf_Rep1,YoungLeaf_Rep2,YoungLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 3 | SAM | 1149.82 | 206.98 | SAM_Rep1,SAM_Rep2,SAM_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Young Inflorescence | 1617.58 | 148.65 | YoungInflorescence_Rep1,YoungInflorescence_Rep2,YoungInflorescence_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P2 | 2784.01 | 201.14 | InflorescenceP2_Rep1,InflorescenceP2_Rep2,InflorescenceP2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P3 | 1557.54 | 182.34 | InflorescenceP3_Rep1,InflorescenceP3_Rep2,InflorescenceP3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P4 | 1435.78 | 153.26 | InflorescenceP4_Rep1,InflorescenceP4_Rep2,InflorescenceP4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P5 | 1006.91 | 104.72 | InflorescenceP5_Rep1,InflorescenceP5_Rep2,InflorescenceP5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P6 | 1088.79 | 26.81 | InflorescenceP6_Rep1,InflorescenceP6_Rep2,InflorescenceP6_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S1 | 918.56 | 55.62 | Seed_S1_Rep1,Seed_S1_Rep2,Seed_S1_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S2 | 1035.12 | 140.05 | Seed_S2_Rep1,Seed_S2_Rep2,Seed_S2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S3 | 1202.26 | 190.18 | Seed_S3_Rep1,Seed_S3_Rep2,Seed_S3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S4 | 869.77 | 116.99 | Seed_S4_Rep1,Seed_S4_Rep2,Seed_S4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S5 | 1876.85 | 538.6 | Seed_S5_Rep1,Seed_S5_Rep2,Seed_S5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
Table 7.
Expression potential of the LOC_Os03g01770 gene.
| Group # | Tissue | Expression Level | Standard Deviation | Samples | Links |
|---|---|---|---|---|---|
| 1 | Seedling Root | 2905.35 | 244.75 | Seedling_Root_Rep1,Seedling_Root_Rep2,Seedling_Root_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Mature Leaf | 1042.59 | 76.24 | MatureLeaf_Rep1,MatureLeaf_Rep2,MatureLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Young Leaf | 4050.5 | 359.89 | YoungLeaf_Rep1,YoungLeaf_Rep2,YoungLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 3 | SAM | 5097.81 | 576.57 | SAM_Rep1,SAM_Rep2,SAM_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Young Inflorescence | 2415.9 | 480.4 | YoungInflorescence_Rep1,YoungInflorescence_Rep2,YoungInflorescence_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P2 | 2680.78 | 417.52 | InflorescenceP2_Rep1,InflorescenceP2_Rep2,InflorescenceP2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P3 | 2703.51 | 84.16 | InflorescenceP3_Rep1,InflorescenceP3_Rep2,InflorescenceP3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P4 | 2599.58 | 198.27 | InflorescenceP4_Rep1,InflorescenceP4_Rep2,InflorescenceP4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P5 | 3689.42 | 439.91 | InflorescenceP5_Rep1,InflorescenceP5_Rep2,InflorescenceP5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P6 | 2750.63 | 218.28 | InflorescenceP6_Rep1,InflorescenceP6_Rep2,InflorescenceP6_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S1 | 3200.68 | 418.02 | Seed_S1_Rep1,Seed_S1_Rep2,Seed_S1_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S2 | 2134.29 | 160.11 | Seed_S2_Rep1,Seed_S2_Rep2,Seed_S2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S3 | 1363.85 | 175.65 | Seed_S3_Rep1,Seed_S3_Rep2,Seed_S3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S4 | 1041.95 | 390.84 | Seed_S4_Rep1,Seed_S4_Rep2,Seed_S4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S5 | 902.81 | 47.52 | Seed_S5_Rep1,Seed_S5_Rep2,Seed_S5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
Table 8.
Expression potential of the LOC_Os03g16920 gene.
| Group # | Tissue | Expression Level | Standard Deviation | Samples | Links |
|---|---|---|---|---|---|
| 1 | Seedling Root | 42.25 | 5.03 | Seedling_Root_Rep1,Seedling_Root_Rep2,Seedling_Root_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Mature Leaf | 29.72 | 22.09 | MatureLeaf_Rep1,MatureLeaf_Rep2,MatureLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Young Leaf | 81.82 | 83.51 | YoungLeaf_Rep1,YoungLeaf_Rep2,YoungLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 3 | SAM | 19.12 | 14.71 | SAM_Rep1,SAM_Rep2,SAM_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Young Inflorescence | 26.37 | 30.23 | YoungInflorescence_Rep1,YoungInflorescence_Rep2,YoungInflorescence_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P2 | 53.12 | 8.05 | InflorescenceP2_Rep1,InflorescenceP2_Rep2,InflorescenceP2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P3 | 21.46 | 12.11 | InflorescenceP3_Rep1,InflorescenceP3_Rep2,InflorescenceP3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P4 | 23.36 | 12.09 | InflorescenceP4_Rep1,InflorescenceP4_Rep2,InflorescenceP4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P5 | 40.15 | 5.63 | InflorescenceP5_Rep1,InflorescenceP5_Rep2,InflorescenceP5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P6 | 58.81 | 13.82 | InflorescenceP6_Rep1,InflorescenceP6_Rep2,InflorescenceP6_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S1 | 3903.05 | 758.55 | Seed_S1_Rep1,Seed_S1_Rep2,Seed_S1_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S2 | 1446.98 | 593.3 | Seed_S2_Rep1,Seed_S2_Rep2,Seed_S2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S3 | 6426.1 | 1243.72 | Seed_S3_Rep1,Seed_S3_Rep2,Seed_S3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S4 | 9732.32 | 2173.79 | Seed_S4_Rep1,Seed_S4_Rep2,Seed_S4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S5 | 9529.92 | 300.4 | Seed_S5_Rep1,Seed_S5_Rep2,Seed_S5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
Table 9.
Expression potential of the LOC_Os05g38530 gene.
| Group # | Tissue | Expression Level | Standard Deviation | Samples | Links |
|---|---|---|---|---|---|
| 1 | Seedling Root | 512.95 | 96.75 | Seedling_Root_Rep1,Seedling_Root_Rep2,Seedling_Root_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Mature Leaf | 703.76 | 77.61 | MatureLeaf_Rep1,MatureLeaf_Rep2,MatureLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Young Leaf | 349.1 | 114.65 | YoungLeaf_Rep1,YoungLeaf_Rep2,YoungLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 3 | SAM | 126.48 | 26.84 | SAM_Rep1,SAM_Rep2,SAM_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Young Inflorescence | 829.22 | 427.86 | YoungInflorescence_Rep1,YoungInflorescence_Rep2,YoungInflorescence_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P2 | 1277.77 | 135.91 | InflorescenceP2_Rep1,InflorescenceP2_Rep2,InflorescenceP2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P3 | 1428.59 | 368.4 | InflorescenceP3_Rep1,InflorescenceP3_Rep2,InflorescenceP3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P4 | 871.31 | 186.87 | InflorescenceP4_Rep1,InflorescenceP4_Rep2,InflorescenceP4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P5 | 330.06 | 63.87 | InflorescenceP5_Rep1,InflorescenceP5_Rep2,InflorescenceP5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P6 | 1331.87 | 85.09 | InflorescenceP6_Rep1,InflorescenceP6_Rep2,InflorescenceP6_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S1 | 8756.77 | 2270.0 | Seed_S1_Rep1,Seed_S1_Rep2,Seed_S1_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S2 | 3925.47 | 1657.18 | Seed_S2_Rep1,Seed_S2_Rep2,Seed_S2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S3 | 10169.83 | 2145.6 | Seed_S3_Rep1,Seed_S3_Rep2,Seed_S3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S4 | 8080.99 | 1916.94 | Seed_S4_Rep1,Seed_S4_Rep2,Seed_S4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S5 | 3980.54 | 511.99 | Seed_S5_Rep1,Seed_S5_Rep2,Seed_S5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
Table 10.
Expression potential of the LOC_Os08g39140 gene.
| Group # | Tissue | Expression Level | Standard Deviation | Samples | Links |
|---|---|---|---|---|---|
| 1 | Seedling Root | 17879.66 | 978.71 | Seedling_Root_Rep1,Seedling_Root_Rep2,Seedling_Root_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Mature Leaf | 5995.85 | 402.62 | MatureLeaf_Rep1,MatureLeaf_Rep2,MatureLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 2 | Young Leaf | 6824.24 | 913.83 | YoungLeaf_Rep1,YoungLeaf_Rep2,YoungLeaf_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 3 | SAM | 9195.96 | 1203.2 | SAM_Rep1,SAM_Rep2,SAM_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Young Inflorescence | 8947.63 | 2032.27 | YoungInflorescence_Rep1,YoungInflorescence_Rep2,YoungInflorescence_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P2 | 9974.24 | 1067.34 | InflorescenceP2_Rep1,InflorescenceP2_Rep2,InflorescenceP2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P3 | 10031.8 | 1646.06 | InflorescenceP3_Rep1,InflorescenceP3_Rep2,InflorescenceP3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P4 | 8748.31 | 1388.24 | InflorescenceP4_Rep1,InflorescenceP4_Rep2,InflorescenceP4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P5 | 10849.74 | 1535.85 | InflorescenceP5_Rep1,InflorescenceP5_Rep2,InflorescenceP5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 4 | Inflorescence P6 | 10471.43 | 592.63 | InflorescenceP6_Rep1,InflorescenceP6_Rep2,InflorescenceP6_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S1 | 16985.9 | 2014.02 | Seed_S1_Rep1,Seed_S1_Rep2,Seed_S1_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S2 | 8728.46 | 2571.56 | Seed_S2_Rep1,Seed_S2_Rep2,Seed_S2_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S3 | 22379.66 | 1486.43 | Seed_S3_Rep1,Seed_S3_Rep2,Seed_S3_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S4 | 20331.83 | 4730.64 | Seed_S4_Rep1,Seed_S4_Rep2,Seed_S4_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
| 5 | Seed S5 | 14113.16 | 2492.14 | Seed_S5_Rep1,Seed_S5_Rep2,Seed_S5_Rep3, | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6893 |
4. Conclusion
This result which was found from this current investigation concluded that, these six genes may have a direct role in arsenic sequestration from cells and thereby providing safety to the developing embryo as well as the seed. So, those genes can be used to breed rice genotypes having low arsenic in grain using them as target for finding and designing gene based molecular markers. Additionally, the results would focus towards the successful use of computational biology in the field of plant breeding by reducing cost, labour and time of biological experiments.
Declaration of Competing Interest
The authors declare that, they have no potential conflict of interest in the publication of this manuscript
Footnotes
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.btre.2019.e00390.
Appendix A. Supplementary data
The following are Supplementary data to this article:
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