Conventional Breeding Approach for Heat Tolerance

Breeding for heat tolerance is still in its infancy stage and warrants more attention than it has been given in the past (Ortiz et al., 2008; Ashraf, 2010). It is unfortunate that the literature contains relatively little information on breeding for heat tolerance in different crop species. During the past two decades, the use of marker-assisted selection (MAS) approaches has contributed greatly to a better understanding of the genetic bases of plant stress-tolerance in tomato and maize (Liu et al, 2006; Sun et al., 2006; Momcilovic and Ristic, 2007) and, in some cases, led to the development of plants with enhanced tolerance to abiotic stress. Because of the general complexity of abiotic stress tolerance and the difficulty in phenotypic selection for tolerance, MAS has been considered as an effective approach to improve plant stress tolerance (Foolad, 2005). Comparatively, however, limited research has been conducted to identify genetic markers associated with heat tolerance in different plant species. Use of SSR markers revealed mapping of 2 QTLs for grain filling duration under heat stress in wheat (Yang et al, 2002).

Expressed Sequence Tags: A Molecular Approach to Study Growth Processes and Stress Mechanisms

Because of the relatively large size of most plant genomes and the associated high cost of sequencing, it is unlikely to have the full genomic sequence for many plant species in the near future. A less expensive alternative is to sequence or partially sequence cDNA clones, which can reveal a substantial portion of the expressed genes of a genome at a fraction of the cost of genomic sequencing. As a result, extensive efforts on expressed sequence tags (EST) are under way in a wide variety of plant species (National Science Foundation Plant Genome Research Program []; Peimisi, 1998; Adam, 2000; Paterson et al, 2000). ESTs can provide a basis for gene discoveiy and the determination of gene function.

ESTs are unedited, automatically processed, single read sequences produced from cDNAs (small DNA molecules reverse transcribed from cellular mRNA population). The concept of using cDNAs as a rout to expedited in early 1980s (Putney et al, 1983).

Expressed sequence tags are created by sequencing the 5’ and/or 3’ ends of randomly isolated gene transcripts that have been converted into cDNA (Adams et al., 1991). Despite the fact that a typical EST represents only a portion (approximately 200-900 nucleotides) of a coding sequence, the partial sequence data is of substantial utility. For example, EST collections are a relatively quick and inexpensive route for discovering new genes (Bourdon et al, 2002; Rogaev et al., 1995), confirm coding regions in genomic sequence (Adams et al., 1991), create opportunities to elucidate phylogenetic relationships (Nishiyama et al, 2003), facilitate the construction of genome maps (Paterson et al, 2000), can sometimes be interpreted directly for transcriptome activity (Ewing et al, 1999; Ogihara et al, 2003; Roiming et al, 2003), and provide the basis for development of expression arrays also known as DNA chips (Chen et al, 1998; Shena et al, 1995).

In addition, high-throughput technology and EST sequencing projects can result in identification of significant portions of an organism’s gene content and thus can serve as a foundation for initiating genome sequencing projects (Van der Hoeven

et al, 2002). Currently there are 60,923,778 ESTs in the NCBI public collection, 290,973 of which derived from tomato (http://www.ncbi.nhn. With many large-scale EST sequencing projects in progress and new projects being initiated, the number of ESTs in the public domain will continue to increase in the coming years. The sheer volume of this sequence data has and will continue to require new computer- based tools for systematic collection, organization, storage, access, analysis, and visualization of this data. Not surprisingly, despite the relative youth of this field, an impressive diversity of bioinformatics resources exists for these purposes.

As sequence and annotation data continue to accumulate, public databases for genomic analysis will become increasingly valuable to the plant science community. The Arabidopsis Information Resource (TAIR; http://www. html), the Salk Institute Genomic Analysis Laboratory (SIGnAL; http://signal.salk. edu/), the Solanaceae Genomics Network (SGN; http:/ /, and GRAMENE ( serve well as examples of these on-line resources.

By clustering genes according to then relative abundance in various EST libraries, expression patterns of genes across various tissues were generated and genes with similar patterns were grouped. In addition, tissues themselves were clustered for relatedness based on relative gene expression as a means of validating the integrity of the EST data as representative of relative gene expression.

EST collections from other species (e.g. Arabidopsis) were also characterized to facilitate cross-species comparisons where possible ( With the rapid expansion of available EST data (e.g.;;; http://;, opportunities for digital analysis of gene expression will continue to expand. Expressed sequence tag collections also have limitations when being used for genomic analysis from the perspectives of accurate representation of genome content, gene sequence, and as windows into transcriptome activity. The fact that ESTs reflect actively transcribed genes makes it difficult to use EST sequencing alone as a means of capturing the majority of an organism’s gene content. Despite many limitations, it has been shown that EST databases can be a valid and reliable source of gene expression data (Ewing et al, 1999; Ogihara et al, 2003; Ronning et al, 2003).

Defining the transcriptome of a complex, multicellular eukaryote is, however, a daunting challenge. The two most widely used and comprehensive approaches are whole genome sequencing coupled with application of gene prediction algorithms (Mathe et al, 2002) and single pass sequencing of cDNAs to obtain expressed sequence tags (ESTs; Adams et al, 1991). Among newer approaches that have not yet been used widely are targeted sequencing of gene-rich regions, identified either as being hypomethylated (Rabinowicz et al, 1999; Bedell et al, 2005) or enriched in single-copy sequences (Peterson et al, 2002), and serial analysis of gene expression (Velculescu et al., 1995). However, gene prediction algorithms are as yet imperfect (Mathe et al, 2002), while other methods are in a practical sense incapable of identifying every potentially expressed gene. Ultimately, a combination of strategies employed in parallel will be required to provide a near- complete description of any complex transcriptome.

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