Appl Biochem Biotechnol DOI 10.1007/s12010-015-1830-9
Achieving Crop Stress Tolerance and Improvement—an Overview of Genomic Techniques Saiema Rasool 1 & Parvaiz Ahmad 2 & Muneeb U Rehman 3 & Ahmad Arif 3 & Naser A. Anjum 4
Received: 15 February 2015 / Accepted: 2 September 2015 # Springer Science+Business Media New York 2015
Abstract The inexorable exposure of plants to the combinations of abiotic stresses has affected the worldwide food supply. The crop improvement against these abiotic stresses has been captivating approach to increase the yield and enhance the stress tolerance. By using traditional and modern breeding methods, the characters that confer tolerance to these stresses were accomplished. No doubt genetic engineering and molecular breeding have helped in comprehending the intricate nature of stress response. Understanding of abiotic stress-involved cellular pathways provides vital information on such responses. On the other hand, genomic research for crop improvement has raised new assessments in breeding new varieties against abiotic stresses. Interpretation of responses of the crop plants under stress is of great significance by studying the main role of crops in food and biofuel production. This review presents genomic-based approaches revealing the complex networks controlling the mechanisms of abiotic stress tolerance, and the possible modes of assimilating information attained by genomic-based approaches due to the advancement in isolation and functional analysis of genes controlling the yield and abiotic stress tolerance are discussed. Keywords Abiotic stresses . Genomics . EST . SAGE . NGS . SNP . QTL
* Saiema Rasool
[email protected] 1
Forest Biotech Laboratory, Department of Forest Management, Faculty of Forestry, University of Putra Malaysia, 43400 Serdang, Selangor, Malaysia
2
Department of Botany, S.P. College Srinagar, Srinagar 190001 Jammu and Kashmir, India
3
Molecular Biology Lab., Division of Veterinary Biochemistry, Faculty of Veterinary Science & Animal Husbandry, Sheri Kashmir University of Agricultural Science & Technology (SKUAST-K), Srinagar, Jammu & Kashmir 190006, India
4
CESAM-Centre for Environmental and Marine Studies & Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
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Introduction Currently, the agricultural world is under immense pressure to improve crop productivity in order to accomplish the ever-increasing demand of food by the rapidly growing population [1, 2]. The situation is further aggravated by the low crop productivity mostly caused by abiotic stresses such as drought, salinity, water logging, temperature variations, mineral deficiency, and toxicity [3]. Consequently, the major focus of current plant science research has been dissecting mechanisms which underline the crop plant’s response to previous abiotic stresses. It is now recognized that abiotic stresses in isolation and/or in association craft changes in the plant not only at morphological and physiological levels but also at biochemical and molecular levels as well [4]. Furthermore, as evidenced by various reports, hundreds of genes and their by-products have been found to act in response to these stresses both at transcriptional and translational levels [5–7]. However, due to the intricate nature of abiotic stress tolerance traits, it is difficult to know the function of abiotic stress-inducible genes and to work out the mechanisms of stress tolerance. Since these days, agriculture has to take up new approaches to overcome the evergrowing demand for food as the conventional approaches have attained their limits. With the aid of the recent technological advances, a number of tools are being implemented to explore the plant genome and thus improve crop yield and tolerance to abiotic stress. A considerable amount of literary information on genomic techniques aimed at achieving crop stress tolerance and improvement is available [8, 9]. Considering recent reports, this review presents a critical overview of all the classes of genomic approaches namely functional, structural, and comparative genomics (Fig. 1a). The outcomes may help both to understand the molecular genetics of plant abiotic stress responses as well as to apply the previous genomic approaches in crop breeding programs.
Fig. 1 Functional, structural, and comparative genomic approaches are highly interconnected
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Functional Genomics Before implementing this approach to untangle the function and interaction between genes in regulatory networks, the results of intergenic and intragenic species under stress response become useful to understand how the breeding lines in crop species have produced improved varieties with better yields. Functional genomics principally makes use of sequence or hybridization-based technique, making available vast inputs about the genes to be evaluated. Arabidopsis and rice genome sequence outcomes have cemented the means for analyzing the function of genes at genomic scale [10, 11]. A brief discussion about the methodologies is presented as under.
Sequence-Based Approaches The catalogue pertaining to expressed genes of a particular species is investigated through expressed sequence tags (ESTs). The genomic sequences of complementary DNA (cDNA) clones produced as a result single pass sequencing are usually incomplete [12, 13]. Focusing mainly on functional studies, EST techniques by means of fast and cost valuable manner identify genes clearly [12].The availability of EST compilations and cDNA sequences of Arabidopsis and rice has encouraged large-scale compilations for other crops as well [14–17]. However, not many studies focus on ESTs from plants exposed to abiotic stresses, and these compilations (as a result of functional genomics) are unmerited toward the high to moderate classes taken from diverse cells, tissues, or organs [5]. The main importance of EST technique is to recognize the vital function of the responsible genes. National Centre for Biotechnological Information (NCBI) database at the moment have about a million of EST, for crops like maize, rice, soy bean, and wheat along with other plants as well. In order to establish plant EST datasets with stress-responsive genes, it is required to build up sequencing programs at the developmental stages based on cDNA libraries from stress-treated plant tissues and organs from different plant species. In comparison to cDNA, ESTs are shorter, and their overlapping is more informative on the organization of parental cDNA that makes known the polymorphism. Since para-log genes may result in misassemble of sequences, it must be handled carefully particularly in species having polyploidy [18]. The technique is widely employed in crops comprising of lengthy and repetitive genomes. Owing to its prospect in gene discovery, latest reports have established EST sequencing method as suitable for evaluating the range of genotypes under controlled and stressed out conditions [9, 19]. Serial analysis of gene expression (SAGE) is an alternative but influential technique, exploited for global gene expression. The technique developed to quantify thousands of transcripts instantly and generates short sequences of 9–17 bp holding adequate information to recognize the transcripts [17, 20]. These transcripts once translated and sequenced provide entire information about gene expression [21, 22]. As established by reports, SAGE can construct an array of tags potentially able to identify >49 genomic sequences, far more than the expected figure of genes in Arabidopsis and rice. In a population, these tags offer explicit update about gene expression at the cellular level, but the scheme is only suited for those organisms whose genome has been fully sequenced. In plants, SAGE is typically applied with some modification to the actual methodology like Super-SAGE and Deep-SAGE [23–27]. The technique however has been extensively applied in humans [28], yeast [21], and mouse [29]. In plants, the approach has been extensively exploited to study gene-related stress-response [25]. In rice seedlings alone, about 10,122 tags from 5921 expressed genes were examined. As
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revealed by global gene expression in rice leaf and seed, out of 50,519 SAGE tags, 15,131 tags resembled to unique transcripts and 70 % occurred only once in both libraries [24]. SAGE approach has also been found valuable in unveiling plant-pathogen interactions in a number of plants including soybean [30] and tomato [31].
Hybridization-Based Approaches In addition to sequence-based methodology, hybridization-based gene expression has proved to be a promising technique for analyzing tens of thousands of genes in a single genome. This transcript profile has been encouraged by the development of micro-array-based technologies, cDNA micro-arrays [32] and oligo-nucleotide micro-arrays [33]. Micro-array technologies, based on selective and differential principle of nucleic acid hybridization, have completely restructured gene expression profiling globally. Both the probe and the target in this approach are hybridization partners. In microarray technology, “probe” is the extension of DNA specific to a particular gene attached to the solid surface, while assembling the microarray itself together with the labeled DNA or RNA strand in the solution is the “target.” The technology has evolved significantly by increasing the number of probes on an array and reducing the surface area of array. Hence, micro-arrays have become the perfect tool in functional genomics and global gene expression analysis. Globally, cDNA microarray profiling and oligonucleotide base chips are the two major types of microarrays employed to study the gene expression. During stress response studies, microarrays provide the quick and comprehensive evaluation of transcriptional activity by making new insights into the complicated world of signaling system governing these stress responses and assisting in recognition of relatively new genes. cDNA microarray is merely a PCR amplicon that results from precise amplification of genomic DNA by employing EST-based primers [34]. Schena et al. [32] for the first time used cDNA microarray to study the differential expression of 45 genes in roots and shoots of Arabidopsis. In addition to Arabidopsis cDNA, microarrays have also been developed for rice, strawberry, lima beans, etc. In response to stresses, microarrays have been effectively exploited to evaluate the gene regulation at different stages of development [35]. Seki et al. [36] prepared about 7000 full-length cDNA microarrays in Arabidopsis to establish the genes associated to stress conditions like cold, salinity, and drought. Out of the total identified genes, 53 were cold inducible, 194 were salinity inducible, and 277 were drought inducible. With only 22 genes found to be associated to cold, drought, and salinity inducible, 70 % of salinity inducible genes were associated with drought indicating a strong relationship between drought and salinity stress response. Nowadays, microarray profiling endows with information about prime DNA sequence both in coding and regulatory regions and their interactions as well, RNA expression during development, sub-cellular localization and intermolecular interactions of RNA molecule, polymorphic variation within a species, physiological response, and environmental stresses. Apart from this, the approach has been largely used in crops like barely [37], maize [38], and wheat [39], and in plant species that rather important both agriculturally and industrially such as cassava [40], tomato [41], and cotton [42] but are less emphasized to untangle the stress response. Fodor et al. [43] chemically generated the oligonucleotides straight from solid substrate by in situ production of arrays. Since the oligonucleotide information is already accessible through databases, there is no need to keep the compilation of cloned DNA molecules. In
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Arabidopsis, Genome Gene Chip array was applied to make out the plants response to a range of biotic and abiotic stresses [44]. In order to increase the specificity and to differentiate these arrays and full-length cDNA arrays as the two were similar, 50-mer and 70-mer probe arrays were designed [45, 46]. Zhu et al. [47] reported that 25-mer oligonucleotide (Gene Chip Rice Genome Array) denoting 21,000 genes of rice cultivar was used to study the gene expression during different stages and to categorize the genes involved in the synthesis and transport of carbohydrates, proteins, and fatty acids. Cooper et al. [48] using similar Gene Chip analyzed the gene expression silhouette during seed development and stress response. Several genes that might play a role in development through act in response to environmental signs and stresses were also found. Furthermore, the availability of complete genomic sequence of a genre might guide to the successful development of the total genomic tiling array based on transcript profiling [49]. Tiling arrays not only identify different splice sites and transcriptional units on chromosomes but also map these transcripts and methylation sites [50, 51]. These assortments have also been useful in studying abiotic stress responses in mock-up species [52–54]. Therefore, at the genomic level, microarrays provide ideal platform to evaluate gene expression and simultaneously create their functional data. In spite of being an improved tool in functional genomics and global gene expression analysis, their usefulness is limited to only those genes for which a probe, i.e., either a clone or sequence, is available. In précis, ESTS, cDNA libraries, and SAGE are the tools of current functional genomics to study global gene expression. The main aim of functional genomics is to study the stress tolerance, which is achieved by recognizing the function of a gene in plant species through its orthologue in other organisms.
Structural Genomics Unlike functional genomics, structural genomics emphasizes on the physical makeup of the genome like its identity, location, and order on the chromosome. These “omics” in sync illustrate the modern genomics to its full extent: Some of the techniques involved are discussed hereunder.
Next-Generation Sequencing Innovations in the DNA-sequencing tools have endowed us the information about the complete genomic sequences. Next-generation sequencing (NGS) has offered us the stage with the facility to undergo sequencing economically in contrast to conventional Sanger sequencing approach [55]. NGS has tiled a way by making sequencing and re-sequencing of large genomes feasible, a way to exploit plant genomes for breeding improved varieties. In recent times, complete genomic sequences of a number of crops such as maize, rice, sorghum, and soybean along with some model species like Arabidopsis thaliana and Brachypodium distachyon have been published [56]. Although whole genomic sequences provide thorough description about coding and noncoding regions, repetitive elements and regulatory sequences in a gene, manipulation of genes is only possible through functional studies [51]. In the course of molecular breeding, genomic sequencing is well thought-out to be a fundamental means for crop development. In general, the impact of NGS-mediated shotgun sequences has been due to their widespread influence on the development of molecular markers. In comparison to
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morphological markers, molecular markers are not affected by the environment [57] and indicate the diagnostic polymorphisms at the genomic level.
Single Nucleotide Polymorphism, Quantitative Trait Loci, and Marker-Assisted Selection Single nucleotide polymorphisms referred to as SNPs are so far the most dominating molecular markers used [58, 59] as recommended by majority of software data related to SNP discovery [55]. SNPs are usually identified by transcript re-sequencing approaches or by comparing the different genotypes where reference genomic sequences are available. However, bioinformatics approaches also assist in the assortment of SNPs from associated sequences [60–62]. Transcriptome re-sequencing in complex genome not only prevents repetitive sequences but also detects the SNPs within transcripts [63]. NGS platforms sometimes result in low-quality sequences, hence to distinguish SNPs from sequence errors over-sampling is desired [55]. Despite successful attempts of SNP detection on monotonous portions of genomes, efforts are presently undergoing to efforts that are undergoing to expand SNP identification to gene-poor regions [64]. While mapping the important vernalization gene Vrn-D4 to the centromic region of chromosome 5D in wheat, gene-poor regions have also been found to be functional [65, 66]. Molecular genetics technologies have greatly improved the potential of the traditional breeding techniques. Quantitative trait loci (QTL), an important part of modern genomics, involve multiple genomic regions which control overall performance like growth rate and disease resistance and production of the traits that are very important to breeders. According to Tanksley and Nelson [67], QTL discovers and its relocation from an unadapted to a selected germplasm simultaneously is termed as “Advanced Backcross QTL Analysis.” With no information on the trait determining genetic design, molecular genetic analysis of these traits is based on apparent phenotype and anonymous polymorphism linked to QTL. QTL position on a chromosome can be predicted either by constructing a linkage map that requires polymorphic DNA markers or by their segregation relationship. In breeding studies, linkage maps are used in the assessment of a quantitative trait for the identification and documentation of a marker associated to QTL and also allow QTL to be traced on the specified linkage maps. While mapping QTL to a chromosomal region, selective mapping can be accompanied by polymorphic markers located near chromosomal regions having QTL. In tomato [68], soybean [69], genetic linkage, and QTL mapping have been remarkably complex. Flanking markers together with the assortment for recombination reduce the linkage haul which otherwise decreases the crop performance due to cotransfer of undesirable traits located in the vicinity of the trait of interest [70]. A resolution interval of 15 cm approximately containing hundreds of genes is provided by conventional QTL analysis [71]. The current genomic approaches have encouraged researchers around the globe to study the genes and networks involved in abiotic stress responses; trailing QTL effects both in response to salt response [72] and submergence tolerance [73] in rice to a single gene. The availability of whole genomic sequence of plants offers a superior source for QTL analysis. The approach has been very successful in many breeding populations and lines in many crops especially rice and in certain ecotypes of Arabidopsis thaliana [74–76]. As proposed by Jansen and Nap [77] QTL manifestation, an important technology in molecular breeding can be used to develop trait linked molecular markers and transgenic scheme (Fig. 1b). Marker-assisted selection (MAS) involves the use of molecular markers in breeding purposes to propagate better quality variants with respect to desired characters like high yield
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and tolerance to abiotic stresses [70]. MAS can predict phenotypes with regard to genotype of the species [59], but for efficient and accurate use of MAS, the trait of interest should either be tightly linked to molecular markers or at least be flanked by two close markers [78]. To implement MAS resourcefully, researchers need to produce high-resolution maps and understand the QTL traits, their mode of inheritance, determine the linkage and potential interactions of different QTL for the trait and for other traits, and evaluate importance of each trait. To make MAS efficient for breeding programs, markers used in germplasm should be highly polymorphic or used at multiple levels. MAS involves the assortment of traits that are highly effectual to score phenotypically that are under complex genetic control. In the cases where genotyping by MAS is cheap, the number of plants to be screened in secondary steps is reduced significantly [70, 78]. For efficient and precise selection, QTL mapping and genomic research ultimately lead to MAS. Hence, the designing of high-density linkage maps is longterm goal. Consequently to increase the resolution of linkage maps, other methodologies should be adopted. MAS requires the support of QTLs in diverse genetic backgrounds. So, the only option that may possibly overcome the issue of QTL validation are functional markers [78]. MAS has been effective in improving stress tolerance in some crops that have been improved against abiotic stress tolerance by using the MAS establishing the fact that responsible genetic elements are accurately defined for high tolerance traits like drought [79], salinity [80], and water logging [81].
Comparative Genomics Comparative genomics help us to understand similarities between model species and their corresponding crop species and how model species can be applied in agriculture [82, 83]. The valuable source for this approach is the availability of large-scale plant genomic sequences together with their expression data and the number of stress-related cDNA libraries. However, plant genomes also share wide similarities among distantly related species [84]. Conservation of gene sequences, order, and distribution between the species besides the survival of the related functional genes in morphologically similar organs enables high quality of putative gene [85]. Among the species evolved from common ancestors, the genes that are preserved normally perform the similar gene functions [86]. Stress-related transcription factors (TFs), gene expression patterns, and resemblance in sequences of orthologs in different plant species help to predict genes with similar functions in newly sequenced crop species. Comparative analytical studies of known responsive TFs in Arabidopsis and rice in turn helped to predict the stress responsive TFs in maize, soybean, barley, sorghum, and wheat, and comparative analysis of known stress-responsive TFs helped to predict the stress responsive TFs in maize, soybean, barely, sorghum, and wheat [87–89]. Walia et al. [90] reported that comparative genomics can be used to analyze stress-related expressions of less studied plants to recognize stress-related genes and compare their gene expression profiles with the known ones. The approach will successively help researchers to predict and understand the function of genes in newly sequenced species and provide appropriate prospect to detect species-specific stress responsive genes and regulatory mechanisms within and among model species. Therefore, through this approach, possible transfer of information from model species to other species important to food production is made possible. It is usually difficult to compare variation in transcriptomics among closely related species under varied stress conditions, as the treatments performed are diverse in response to different
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tissues, time exposure, intensities, and by different techniques. Hence, related plants with different degrees of tolerance to stress are very informative. For example, the transcriptomic comparisons of winter and spring wheat cultivars have variable tolerance to cold, when exposed to low temperatures. This highlights the importance of sampling in functional genomics which is altogether ignored while establishing the relationship between gene expression and tolerance to low temperatures [91, 92]. Synteny is an innovative breakthrough in the discovery of gene content and gene order that are usually conserved in discovery of the gene content and gene order that are conserved in distantly related species [93]. As recently tested in shotgun rice sequences, synteny is the most clearly documented in grasses [10]. Grasses due to their high economic importance have been the focus of comparative genomics. Over the past 60 million years, the genomic map of grasses has been developed including cereal crop species [94]. Comparative genomic mapping has made the conservation of genomes evident proposing collinear order of genes and markers shared by genomes of different species. The genomic similarity is restricted to genes themselves, but the intergenic regions differ significantly even between closely related species as shown in Fig. 2, giving rise to variations in the genomic size as found in grasses [95] (Fig. 3). However, studies have revealed changes at the molecular level like, inversions, deletions and translocations, and colinearity across grass genomes that have been exploited for gene discovery and isolation [96]. In addition to syntenic genes found in colinear blocks, the presence of nonsyntenic genes also provides valuable understanding about genome evolution and speciation. Similarly, other than the transposable elements, recent studies have focused on nonconserved genomic portions of wheat and barley suggesting novel mechanisms by dogging the size and evolution of these genomes (Fig. 3). Pseudo-gene characteristics revealed by nonsyntenic genes might have effects for gene content, based on survey sequences evaluating large genome Fig. 2 Proposed scheme of bridging the gap between and genomic approaches for abiotic stress tolerence
Appl Biochem Biotechnol Fig. 3 Rice and barley are having conserved gene content and gene order
sequences [97]. About 240 million years ago, grasses separated from broad-leafed plants, and dicots degraded the precise map equivalent to the point where preserved synteny does not have predictive utility [10, 98]. For example, the sequence and genomic research studies of Arabidopsis are available and relevant to broad-leafed tomato crop [99]. Similarly, the genomic relationship between Cruciferae family including Arabidopsis and brassica crops is very close. However, genomic organization of Arabidopsis can be helpful in genetic analysis in tomato [100]. Breeders ought to have accessibility to Arabidopsis sequence to establish the syntenic relationship in broadleafed in case the genomic relationship with the plant is not known. Hence, for the support of synteny to crop improvement, bioinformatics tools are needed. “Genome Zipper,” one of the recent approaches to comparative genomic analysis, has been helpful in determining the implicit order of genes in a partially sequenced genome. The approach in turn has been helpful in comparing less studied species with full sequences and marked genomes of crops like Brachypodium, rice, and sorghum [101]. In the case of Triticeae, genome zipper reveals the evolutionary relationship by providing its close approximation to a reference genome sequence [102]. Since genome zipper depends on synteny, it cannot be used to explore newly evolved genes and small-scale rearrangements. As apparent from the utility of comparative genomics and genome zipper, genomic features specific to species can be made accessible only through total sequence of a reference genome. While representing the barley genome, comparative genomics can be utilized to a limited extent [102]. Therefore, for optimum exploitation of crop genomes, construction of reference genome sequences by physical and genetic map is necessary. Genomic approaches although still in its early stages have shown its potential in some plant species. In future, the information gathered so far from these approaches will be helpful in accelerating the success for different agronomic traits including abiotic stress tolerance.
Conclusions and Future Perspectives Though it is well-known that abiotic stresses limit the crop productivity, the available knowledge for abiotic stress is insufficient to foster sustainable farming. For instance, a vast gap exists in the acquaintance about the level of stress tolerance to be developed in crops
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planned and to be grown on a battered environment, and so, this type of knowledge will definitely be helpful in selecting the measures and techniques for the improvement of stress tolerance. In the recent years, molecular studies have contributed a lot to the fundamental biology involved in tolerance to abiotic stresses and yield control. Genomic-based approaches have helped in identifying the genomic systems that are available for improvement in the existing varieties. Large scale of related resources like germplasm lines and inbred lines can be developed with extensive knowledge about genetic markers by using the genomic techniques more effectively. The expression of stress-tolerant genes in many cases has resulted in some unwanted effects, so it is highly desirable to attain organ-specific and stress-responsive expression of the introduced gene by introducing specific promoters. Therefore, the stress-induced genes and their promoters recognized through genomic tools can be tested methodically for specificity. Hence, gene silencing can be prevented by these promoters. No doubt the genomic-based approaches are involved in identifying the pathways and their relationship in abiotic stress responses, and the pathway regulators are also identified at the same time. Therefore, thorough research and breeding efforts are needed to yoke the side effects potentially avoidable by controlling the regulatory genes. So, in order to sort out and understand the data fall irrespective of the availability of large-scale DNA sequencing devices, transcriptomic factors and proteins analytes in a cell, we must also learn to utilize bioinformatics tools that are essential as we have approached the threshold allowing analysis of plants stress response at the entire genomic level. Acknowledgments The authors would like to thank Dr. Tariq Omar Siddiqui (Department of Botany, Jamia Hamdard New Delhi, India) who provided useful guidelines, instructions, and comments on an earlier draft of this article.
References 1. Waraich, E. A., Ahmad, R., Ashraf, M., Saifullah, Y., & Ahmad, M. (2011). Improving agricultural water use efficiency by nutrient management in crop plants. Acta Agriculturae Scandinavica, Plant and Soil Science, 61(4), 291–304. 2. Waraich, E. A., Ahmad, R., Halim, A., & Aziz, T. (2012). Alleviation of temperature stress by nutrient management in crop plants: a review. Journal of Soil Science and Plant Nutrition, 12(2), 221–244. 3. Mahajan, S., & Tuteja, N. (2005). Cold, salinity and drought stresses: an overview. Archives of Biochemistry and Biophysics, 444, 139–158. 4. Wang, B. Q., Zhang, Q. F., Liu, J. H., & Li, G. H. (2011). Over-expression of Pt ADC confers enhanced dehydration and drought tolerance in transgenic tobacco and tomato: effect on ROS elimination. Biochemical and Biophysical Research Communications, 413, 10–16. 5. Cushman, J. C., & Bohnert, H. J. (2000). Genomic approaches to plant stress tolerance. Current Opinion in Plant Biology, 3, 117–124. 6. Yamaguchi-Shinozaki, K., & Shinozaki, K. (2005). Organization of cis-acting regulatory elements in osmotic- and cold-stress-responsive promoters. Trends in Plant Science, 10, 88–94. 7. Umezawa, T., Fujita, M., Fujita, Y., Yamaguchi-Shinozaki, K., & Shinozaki, K. (2006). Engineering drought tolerance in plants: discovering and tailoring genes to unlock the future. Current Opinion in Biotechnology, 17, 113–122. 8. Bohnert, H. J., Gong, Q., Li, P., & Ma, S. (2006). Unraveling abiotic stress tolerance mechanisms-getting genomics going. Current Opinion in Plant Biology, 9, 180–188. 9. AkpJnar, B.A., Stuart, J., & Budak, L.H. (2013).Genomics approaches for crop improvement against abiotic stress. The Scientific World Journal, Article ID 361921, 9 pages.doi.org/10.115/2013/361921.
Appl Biochem Biotechnol 10. Goff, S. A., Ricke, D., Lan, T. H., Presting, G., Wang, R., Dunn, M., et al. (2002). A draft sequence of the rice genome (Oryza sativa L ssp. japonica). Science, 296, 92–100. 11. Yu, J., Hu, S., Wang, J., Wong, G. K. S., Li, S., Liu, B., et al. (2002). A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science, 296, 79–92. 12. Bouchez, D., & Hofte, H. (1998). Functional genomics in plants. Plant Physiology, 118(3), 725–732. 13. Iqbal, M., Khan, M. A., Naeem, M., Aziz, U., Afzal, J., & Latif, M. (2013). Inducing drought tolerance in upland cotton (Gossypium hirsutum L.), accomplishments and future prospects. World Applied Sciences Journal, 21(7), 1062–1069. 14. Bevan, M., Bancroft, I., Mewes, H. W., Martienssen, R., & Mc Combie, R. (1999). Clearing a path through the jungle: progress in Arabidopsis genomics. Bioassay, 21(30), 110–120. 15. Goff, S. A. (1999). Rice as a model for cereal genomics. Current Opinion in Plant Biology, 2(31), 86–89. 16. Walbot, V. (1999). Genes, genomes, genomics. What can plant biologists expect from the 1998 national science foundation plant genome research program? Physiology, 119(32), 1151–1155. 17. Tyagi, A. K., Vij, S., & Saini, N. (2006). ). Functional genomics of stress tolerance. In: Rao, K.V.M., Raghavendra, A.S., and Janardhan Reddy, K. (eds.). Physiology and Molecular Biology of Stress Tolerance in Plants, 301–334. 18. Rudd, S. (2003). Expressed sequence tags: alternative or complement to whole genome sequences. Trends in Plant Science, 8(7), 321–329. 19. Brenner, S., Johnson, M., Bridgham, J., Golda, G., Lloyd, D. H., Johnson, D., et al. (2000). Gene expression analysis by massively parallel signature sequencing (MPSS) on micro-bead arrays. Nature Biotechnology, 18(6), 630–634. 20. Saha, S., Sparks, A. B., Rago, C., Akmaev, V., Wang, C. J., Vogelstein, B., et al. (2002). Using the transcriptome to annotate the genome. Nature Biotechnology, 20(5), 508–12. 21. Velculescu, V. E., Zhang, L., Vogelstein, B., & Kinzler, K. W. (1995). Serial analysis of gene expression. Science, 270(5235), 484–487. 22. Vega-Sanchez, M. E., Gowda, M., & Wang, G. L. (2007). Tag-based approaches for deep transcriptome analysis in plants. Plant Science, 173(4), 371–380. 23. Lorenz, W. W., & Dean, J. F. D. (2002). SAGE profiling and demonstration of differential gene expression along the axial developmental gradient of lignifying xylem in loblolly pine (Pinus taeda). Tree Physiology, 22(5), 301–310. 24. Gibbings, J. G., Cook, B. P., Dufault, M. R., Madden, S. L., Khuri, S., Turnbull, C. J., et al. (2003). Global transcript analysis of rice leaf and seed using SAGE technology. Plant Biotechnology Journal, 1(4), 271– 285. 25. Lee, J. Y., & Lee, D. H. (2003). Use of serial analysis of gene expression technology to reveal changes in gene expression in Arabidopsis pollen under-going cold stress. Plant Physiology, 132(2), 517–529. 26. Matsumura, H., Reich, S., Ito, A., Saitoh, H., Kamoun, S., Winter, P., et al. (2003). Gene expression analysis of plant host-pathogen interactions by Super SAGE. Proceedings of the National Academy of Sciences of the United States of America, 100(26), 15718–15723. 27. Nielsen, K. L., Hogh, A. L., & Emmersen, J. (2006). Deep SAGE-digital transcriptomics with high sensitivity, simple experimental protocol and multiplexing of samples. Nucleic Acids Research, 34(19), e133. 28. Chen, J., Sun, M., Lee, S., Zhou, G., Rowley, J. D., & Wang, S. M. (2002). Identifying novel transcripts and novel genes in the human genome by using novel SAGE tags. Proceedings of the National Academy of Sciences, 99, 12257–12262. 29. Gunnersen, J. M., Augustine, C., Spirkoska, V., Kim, M., Brown, M., & Tan, S. S. (2002). Global analysis of gene expression patterns in developing mouse neocortex using serial analysis of gene expression. Molecular and Cellular Neuroscience, 19, 560–573. 30. Schupp, J. M., DicK, A. B., Zinnamon, K. N., & Keim, P. (2003). Serial analysis of gene expression applied to soybean. Plant and animal genomes XI conference, january 11-15, 2003. San Diego: Town and Country Convention, Center. 31. Mysore, K. S., Tuori, R. P., & Martin, G. B. (2001). Arabidopsis genome sequence as a tool for functional genomics in tomato. Genome Biology, 2, 1003. 32. Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995). Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270, 467–470. 33. Lockhart, D. J., Dong, H., Byrne, M. C., Follettie, M. T., Gallo, M. V., Chee, M. S., et al. (1996). Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnology, 13, 1675–1680. 34. Jiao, Y., Yang, H., Ma, L., Sun, N., Yu, H., Liu, T., et al. (2003). A genome-wide analysis of blue-light regulation of Arabidopsis transcription factor gene expression during seedling development. Plant Physiology, 133, 1480–1493.
Appl Biochem Biotechnol 35. Kehoe, D. M., Villand, P., & Somerville, S. (1999). DNA microarrays for studies of higher plants and other photosynthetic organisms. Trends in Plant Science, 4, 38–41. 36. Seki, M., Narusaka, M., Yamaguchi-Shinozaki, K., Carninci, P., Kawai, J., Hayashizaki, Y., et al. (2001). Arabidopsis encyclopedia using full-length cDNAs and its application. Plant Physiology Biochemistry, 39, 211–220. 37. Close, T. J., Wanamaker, S. I., Caldo, R. A., Turner, S. M., Ashlock, D. A., Dickerson, J. A., et al. (2004). A new resource for cereal genomics: 22K Barley Gene Chip comes of age. Plant Physiology, 134(3), 960– 968. 38. Luo, M., Liu, J., Lee, R. D., Scully, B. T., & Guo, B. (2010). Monitoring the expression of maize genes in developing Kernels under drought stress using oligo-microarray. Journal of Integrative Plant Biology, 52(12), 1059–1074. 39. Ergen, N. Z., Thimmapuram, J., Bohnert, H. J., & Budak, H. (2009). Transcriptome pathways unique to dehydration tolerant relatives of modern wheat.Functional and. Integrative Genomics, 9(3), 377–396. 40. Utsumi, Y., Tanaka, M., Morosawa, T., Kurotani, A., Yoshida, T., Mochida, K., et al. (2012). Transcriptome analysis using a high-density oligo-microarray under drought stress in various genotypes of cassava: an important tropical crop. DNA Research, 19(4), 335–345. 41. Loukehaich, R., Wang, T., Ouyang, B., Ziaf, K., Li, H., Zhang, J., et al. (2012). SpUSP, an annexin interacting universal stress protein, enhances drought tolerance in tomato. Journal of Experimental Botany, 63(15), 5593–5606. 42. Ranjan, A., Pandey, N., Lakhwani, D., Dubey, N. K., Pathre, U. V., & Sawant, S. V. (2012). Comparative transcriptomic analysis of roots of contrasting Gossypium herbaceum genotypes revealing adaptation to drought. BMC Genomics, 13, 680. 43. Fodor, S. P. A., Read, J. L., Pirrung, M. C., Stryr, L., Lu, A. T., & Solas, D. (1991). Light-directed, spatially addressable parallel chemical synthesis. Science, 251, 767–773. 44. Cheong, Y. H., Chang, H. S., Gupta, R., Wang, X., Zhu, T., & Luan, S. (2002). Transcriptional profiling reveals novel interactions between wounding, pathogen, abiotic stress, and hormonal responses in Arabidopsis. Plant Physiology, 129, 661–677. 45. Kane, M. D., Jatkoe, T. A., Stumpf, C. R., Lu, J., Thomas, J. D., & Madore, S. J. (2000). Assessment of the sensitivity and specificity of oligonucleotides (50mer) microarrays. Nucleic Acids Research, 28, 4552– 4557. 46. Ten-Bosch, J., Seidel, C., Batra, S., Lam, H., Tuason, N., Saljoughi, S., et al. (2001). Operon® array- ready OligoSetsTMprovides sequence-optimized 70mers for DNA microarrays. QIAGEN News, 4, 1. 47. Zhu, J. K. (2003). Regulation of ion homeostasis under salt stress. Current Opinion in Plant Biology, 6, 441–445. 48. Cooper, B., Clarke, J. D., Budworth, P., Kreps, J., Hutchison, D., Park, S., et al. (2003). A network of rice genes associated with stress response and seed development. Proceedings of the National Academy of Sciences of the United States of America, 100, 4945–4950. 49. Rensink, W. A., & Buell, C. R. (2005). Micro array expression profiling resources for plant genomics. Trends in Plant Science, 10(12), 603–609. 50. Yazaki, J., Gregory, B. D., & Ecker, J. R. (2007). Mapping the genome landscape using tiling array technology. Current Opinion in Plant Biology, 10(5), 534–542. 51. Mochida, K., & Shinozaki, K. (2010). Genomics and bioinformatics resources for crop improvement. Plant and Cell Physiology, 51(4), 497–523. 52. Zeller, G., Henz, S. R., Widmer, C. K., Sachsenberg, T., Rätsch, G., Weigel, D., et al. (2009). Stress-induced changes in the Arabidopsis thaliana transcriptome analyzed using whole-genome tiling arrays. Plant Journal, 58(6), 1068–1082. 53. Matsui, A., Ishida, J., Morosawa, T., Okamoto, M., Kim, J. M., et al. (2010). Arabidopsis tiling array analysis to identify the stress-responsive genes. Methods in Molecular Biology, 639, 141–155. 54. Verelst, W., Bertolini, E., de Bodt, S., Vandepoele, K., Demeulenaere, M., Enrico Pé, M., et al. (2013). Molecular and physiological analysis of growth-limiting drought stress in Brachypodium distachyon leaves. Molecular Plant, 6(2), 311–322. 55. Varshney, R. K., Hiremath, P. J., Lekha, P., Kashiwagi, J., Balaji, J., Deokar, A. A., et al. (2009). A comprehensive resource of drought and salinity responsive ESTs for gene discovery and marker development in chickpea (Cicer arietinum L.). BMC Genomics, 10, 523. doi:10.1186/1471-2164-10-523. 56. Morrell, P. L., Buckler, E. S., & Ross-Ibarra, J. (2011). Crop genomics: advances and applications. Nature Reviews. Genetics, 13(2), 85–96. 57. Mohan, M., Nair, S., Bhagwat, A., Krishna, T. G., Yano, M., Bhatia, C. R., et al. (1997). Genome mapping, molecular markers and marker-assisted selection in crop plants. Molecular Breeding, 3(2), 87–103. 58. Rafalski, A. (2002). Applications of single nucleotide polymorphisms in crop genetics. Current Opinion in Plant Biology, 5, 94–100.
Appl Biochem Biotechnol 59. Edwards, D., & Batley, J. (2010). Plant genome sequencing: applications for crop improvement. Journal of Plant Biotechnology, 8(1), 2–9. 60. Barker, G., Batley, J., O'Sullivan, H., Edwards, K. J., & Edwards, D. (2003). Redundancy based detection of sequence polymorphisms in expressed sequence tag data using auto-SNP. Bioinformatics, 19, 421–422. 61. Kwok, P. Y., & Duan, S. (2003). SNP discovery by direct DNA sequencing. Methods in Molecular Biology, 212, 71–84. 62. Weil, M. M., Pershad, R., Wang, R., & Zhao, S. (2004). Use of BAC end sequences for SNP discovery. Methods in Molecular Biology, 256, 1–6. 63. Mammadov, J., Aggarwal, R., Buyyarapu, R., & Kumpatla, S. (2012). SNP markers and their impact on plant breeding. International Journal of Plant Genomics, 2012, Article ID 728398, 11 pages, 2012. 64. Simola, D. F., & Kim, J. (2011). Sniper: improved SNP discovery by multiply mapping deep sequenced reads. Genome Biology, 12(6), 55. 65. Feuillet, C., Stein, N., Rossini, L., Praud, S., Mayer, K., Schulman, A., et al. (2012). Integrating cereal genomics to support innovation in the Triticeae. Functional and Integrative Genomics, 12(4), 573–583. 66. Yoshida, T., Nishida, H., Zhu, J., Nitcher, R., Distelfeld, A., Akashi, Y., et al. (2010). Vrn-D4 is a vernalization gene located on the centromeric region of chromosome 5D in hexaploid wheat. Theoretical and Applied Genetics, 120(3), 543–552. 67. Tanksley, S. D., & Nelson, J. C. (1996). ). Advanced backcross QTL analysis: a method for the simultaneous discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines. Theoretical and Applied Genetics, 92, 191–203. 68. Weller, J. I. (1987). Mapping and analysis of quantitative trait loci in Lycopersicon (tomato) with the aid of genetic markers using approximate maximum likelihood methods. Heredity, 59, 413–421. 69. Caetano-Anolles, G., Bassam, B. J., & Grresshoff, P. M. (1993). Enhanced detection of polymorphic DNA by multiple arbitrary amplicon profiling of endonuclease-digested DNA: identification of markers tightly linked to the supernodulation locus in soybean. Molecular and General Genetics, 241, 57–64. 70. Collard, B. C. Y., & Mackill, D. J. (2008). Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philosophical Transactions of the Royal Society B, 363(1491), 557–572. 71. Tuberosa, R., Salvi, S., Sanguineti, M. C., Landi, P., Maccaferri, M., & Conti, S. (2002). Mapping QTLS regulating morpho-physiological traits and yield: case studies, shortcomings and perspectives in droughtstressed maize. Annals of Botany, 89, 941–963. 72. Ren, Z. H., Gao, J. P., Li, L. G., Cai, X. L., Huang, W., Chao, D. Y., et al. (2005). A rice quantitative trait locus for salt tolerance encodes a sodium transporter. Nature Genetics, 37, 1141–1146. 73. Xu, S., Li, J., Zhang, X., Wei, H., & Cui, L. (2006). Effects of heat acclimation pretreatment on changes of membrane lipid peroxidation, antioxidant metabolites, and ultrastructure of chloroplasts in two cool-season turf grass species under heat stress. Environmental and Experimental Botany, 56, 274–285. 74. Koornneef, M., Alonso-Blanco, C., & Vreugdenhil, D. (2004). Naturally occurring genetic variation in Arabidopsis thaliana. Annual Review of Plant Biology, 55, 141–172. 75. Sanchez, A. C., Subudhi, P. K., Rosenow, D. T., & Nguyen, H. T. (2002). Mapping QTLs associated with drought resistance in sorghum (Sorghum bicolor L Moench). Plant Molecular Biology, 48, 713–726. 76. Cattivell, L., Baldi, P., Crosatti, C., Di Fonzo, N., Faccioli, P., Grossi, M., et al. (2002). Chromosome regions and stress-related sequences involved in resistance to abiotic stress in Triticeae. Plant Molecular Biology, 48, 649–665. 77. Jansen, R. C., & Nap, J. P. (2001). Genetical genomics; the added value from segregation. Trends in Genetics, 17, 388–391. 78. Edmeades, G. O., McMaster, G. S., White, J. W., & Campos, H. (2004). Genomics and the physiologist: bridging the gap between genes and crop response. Field Crops Research, 90(1), 5–18. 79. Ashraf, M. (2010). Inducing drought tolerance in plants: recent advances. Biotechnology Advances, 28(1), 169–183. 80. Yamaguchi, T., & Blumwald, E. (2005). Developing salt-tolerant crop plants: challenges and opportunities. Trends in Plant Science, 10(12), 615–620. 81. Ahmed, F., Rafii, M. Y., Ismail, M. R., Juraimi, A. S., Rahim, H. A., Asfaliza, R., et al. (2013). Water logging tolerance of crops: breeding, mechanism of tolerance, molecular approaches, and future prospects. Bio Med Research International, 2013(963525), 10. 82. Paterson, A. H., Freeling, M., & Sasaki, T. (2005). Grains of knowledge: genomics of model cereals. Genome Research, 15, 1643–1650. 83. van de Mortel, J. E., & Aarts, M. G. M. (2006). Comparative transcriptomics - model species lead the way. New Phytologist, 170, 199–201. 84. Guyot, R., Lefebvre-Pautigny, F., Tranchant-Dubreuil, C., Rigoreau, M., Hamon, P., Leroy, T., et al. (2012). Ancestral synteny shared between distantly-related plant species from the asterid (Coffea canephora) and Solanum Sp.) and rosid (Vitis vinifera) clades. BMC Genomics, 13(103).
Appl Biochem Biotechnol 85. Brady, S. M., Long, T. A., & Benfey, P. N. (2006). Unraveling the dynamic transcriptome. Plant Cell, 18, 2101–2111. 86. Tran, L. S., & Mochida, K. (2010). A platform for functional prediction and comparative analyses of transcription factors of legumes and beyond. Plant Signaling and Behavior, 5, 550–552. 87. Mochida, K., Yoshida, T., Sakurai, T., Yamaguchi-Shinozaki, K., Shinozaki, K., & Tran, L. S. (2009). In silico analysis of transcription factor repertoire and prediction of stress responsive transcription factors in soybean. DNA Research, 16, 353–369. 88. Tran, L. S., & Mochida, K. (2010). Identification and prediction of abiotic stress responsive transcription factors involved in abiotic stress signaling in soybean. Plant Signaling and Behavior, 5, 255–257. 89. Mochida, K., Yoshida, T., Sakurai, T., Yamaguchi–Shinozaki, K., Shinozaki, K., & Tran, L. S. (2011). In silico analysis of transcription factor repertoires and prediction of stress-responsive transcription factors from six major gramineae plants. DNA Research, 18, 321–332. 90. Walia, H., Wilson, C., Ismail, A. M., Close, T. J., & Cui, X. (2009). Comparing genomic expression patterns across plant species reveals highly diverged transcriptional dynamics in response to salt stress. BMC Genomics, 10, 398. 91. Gulick, P., Drouin, S., Yu, Z., Danyluk, J., Poisson, G., Monroy, A. F., et al. (2005). Transcriptome comparison of winter and spring wheat responding to low temperature. Genome, 48, 913–923. 92. Monroy, A. F., Dryanova, A., Malette, B., Oren, D. H., Farajalla, M. R., Liu, W. C., et al. (2007). Regulatory gene candidates and gene expression analysis of cold acclimation in winter and spring wheat. Plant Molecular Biology, 64, 409–423. 93. Kellogg, E. A. (1998). Relationships of cereal crops and other grasses. Proceedings of the National Academy of Sciences, 95, 2005–2010. 94. Gale, M. D., & Devos, K. M. (1998). Plant comparative genetics after ten years. Science, 282, 656–659. 95. Dubcovsky, J., Ramakrrishna, W., SanMiguel, P. J., Busso, C. S., Yan, L., Shiloff, B. A., et al. (2001). Comparative sequence analysis of colinear barley and rice bacterial artificial chromosomes. Plant Physiology, 125, 1342–1353. 96. Li, F., Ma, C., Chen, Q., Liu, T., Shen, J., Tu, J., et al. (2012). ). Comparative mapping reveals similar linkage of functional genes to QTL of yield-related traits between Brassica napus and Oryza sativa.Journal of. Genetics, 91(2), 163–170. 97. Wicker, T., Mayer, K. F. X., Gundlach, H., Martis, M., Steuernagel, B., Scholz, U., et al. (2011). Frequent gene movement and pseudogene evolution is common to the large and complex genomes of wheat, barley, and their relatives. Plant Cell, 23(5), 1706–1718. 98. Mayer, K., Murphy, G., Tarchini, R., Wambutt, R., Volckaert, G., Pohl, T., et al. (2001). Conservation of microstructure between a sequenced region of the genome of rice and multiple segments of the genome of Arabidopsis thaliana. Genome Research, 11, 1167–1174. 99. Ku, H. M., Vision, T., Liu, J., & Tanksley, S. D. (2000). Comparing sequenced segments of the tomato and Arabidopsis genomes: large-scale duplication followed by selective gene loss creates a network of synteny. Proceedings of the National Academy of Sciences, 97, 9121–9126. 100. Ku, H. M., Liu, J., Doganlar, S., & Tanksley, S. D. (2001). Exploitation of Arabidopsis-tomato synteny to construct a high-resolution map of the ovate-containing region in tomato chromosome 2. Genome, 44, 470– 475. 101. Mayer, K. F. X., Taudien, S., Martis, M., Šimková, H., Suchánková, P., Gundlach, H., et al. (2009). Gene content and virtual gene order of barley chromosome 1H. Plant Physiology, 151(2), 496–505. 102. Mayer, K. F. X., Martis, M., Hedley, P. E., Simková, H., Liu, H., Morris, J. A., et al. (2011). Unlocking the barley genome by chromosomal and comparative genomics. Plant Cell, 23(4), 1249–1263.
