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Introduction 1. INTRODUCTION 1.1 Sesame Sesame (Sesamum indicum L.), of family Pedaliaceae, is one of the oldest and most nutritious oilseed crops known to humankind. It has been cultivated in Indian subcontinent since 1500 B.C. (Bedigian, 2003) and was a highly prized oilseed in the ancient world because of its resistance to drought, the ease to extract oil from seeds and the high stability of the oil (Langham & Wiemers, 2002). 1.1.1 Oil quality of sesame In sesame oil, oleic (C18:1) and linoleic (C18:2) acids are predominant and make up more than 80% of the total fatty acids. Linolenic acid (C 18:3) is found in traces in sesame oil. The high levels of unsaturated (UFA) and polyunsaturated fatty acids (PUFAs) increase the quality of oil for human consumption. Moreover, high level of PUFAs in sesame oil is claimed to reduce blood cholesterol, high blood pressure and play an important role in preventing atherosclerosis, heart diseases and cancers (Ghafoorunissa, 1994; Hibasami et al., 2000; Miyahara et al., 2001). Among PUFAs, linolenic acid play very important roles in physiology, especially during foetal and infant growth, in particular in the formation of central nervous system and retina (Bourre, 2003; Bowen and Clandinin, 2005) and for the prevention of cardiovascular diseases, being antithrombotic, anti-inflammatory, antiarhythmic and flavouring plaque stabilization (Hu et al., 1999; SAS, 1999) . The nutritional value of this PUFA in human diet is well recognized and increased consumption of this fatty acid has been recommended (Department of health, 1994). As the demand for beneficial polyunsaturated fatty acids (PUFAs) has drastically increased in recent years, there are increasing efforts to find plant sources of PUFAs that are economical and sustainable, unlike animal sources. The amount of unsaturated and polyunsaturated fatty acids, in a plant species, depends upon the efficiency with which the process of desaturation and elongation takes place in the biosynthetic pathway. Therefore, desaturation of fatty acids is also an important aspect in oil biochemistry as it determines the 1 Introduction level of unsaturation and the economic value of oil (Knutzon et al., 1992; Mikkilineni and Rocheford, 2003). 1.2 Fatty acid biosynthetic pathway A detailed knowledge of the metabolic pathways involved in the biosynthesis of fatty acids is a prerequisite for genetic engineering of the seed fatty acid composition. Although the pathway for sesame is not documented, the fatty acid profile suggests synthesis via the known route common to most major oil crops. The fatty acid biosynthetic pathway is a primary metabolic pathway because it is found in every cell of the plant and is essential to growth (Ohlrogge and Browse, 1995). The synthesis of PUFA in plant cells are accomplished by sequential desaturation of saturated fatty acids. Plant genetics and biochemistry have so far identified over 10 genes involved in the fatty acid production (Ohlrogge and Browse, 1995). But our present work was limited to genes involved directly in biosynthesis of linolenic acid as it is an essential fatty acid but present in traces in sesame. As an initial step for C18:3 fatty acid synthesis, first double bond is introduced by a soluble stearoyl acyl carrier protein deasturase (sad) in stearic acid (C18:0). It is the chloroplastic enzyme which catalyzes the conversion of C18:0 to oleic acid C 18:1. Hence their activity primarily regulates the ratio of saturated to monounsaturated fatty acids (Ohlroggeav and Browseb, 1995). Fatty acid desaturase 2 (fad2) encodes endoplasmic reticulum 18:1 desaturase that controls the conversion of oleic C 18:1 to linoleic acid C18:2. Finally, linoleic acid C 18:2 is converted to linolenic acid C 18:3 by omega 3 fatty acid desaturase (o3fad). Stearic acid (a) Oleic acid (b) Linoleic acid (c) Linolenic acid (a) Stearoyl acyl desaturase (Sad) (b) Fatty acid desaturase 2 (Fad2) (c) Omega 3 fatty acid desaturase (O3fad) The three enzymes involved in the biosynthesis of linolenic acid are encoded by the genes sad, fad2 and o3fad, respectively. 2 Introduction 1.3 Modification of fatty acid composition in plantstorage oils Development of crop varieties producing oils, with quality appropriate for specific market needs, presents a better alternative to chemical modification of vegetable oils and a means to circumvent the short-comings associated with the technology. One way to achieve this is by domesticating wild plants that accumulate oil with characteristics of interest. However, the long time scale (of over 20 years) needed to adapt them to cultivation and the requirement for remodelling of agricultural machinery and processing equipment becomes a major limitation to development of novel oil crops. Induced mutagenesis has been used to create additional diversity in seed fatty acid composition, as was done when developing high linoleic acid linseed (Linola) from a high linolenic acid variety (Green, 1986). However, induced mutagenesis is disadvantageous as it lacks precision, generating many plants with defects and entails extensive screening of lines to eliminate the bulk of abnormal ones. Undesirable traits such as late flowering, reduced vigour and low seed yield are obtained alongside the phenotype of interest in mutant lines. This method is therefore unreliable for creating variants in which only one locus influencing synthesis of a specific fatty acid is disrupted. Current research effort is directed towards creating plant oils having diverse fatty acid composition by genetic engineering. This approach is superior to those previously used owing to its precision and applicability across taxa. By using molecular techniques, it is possible to modify specifically the seed oil quality while keeping the rest of the genetic background of the plant intact. 1.3.1 Molecular aspect of modifying seed oil composition Marker-assisted selection (or molecular-assisted), MAS breeding can provide a dramatic improvement in the efficiency with which breeders can select plants with desirable combinations of genes. MAS is gaining considerable importance as it would improve the efficiency of plant breeding through precise transfer of genomic regions of interest (foreground selection) and accelerate the recovery of the recurrent parent genome (background selection). They can be used to monitor DNA sequence variation in and among the species and create new sources of genetic 3 Introduction variation by introducing new and favourable traits from landraces, wild relatives and related species. This will help to fasten the time taken in conventional breeding, germplasm characterization, genetic mapping, gene tagging and gene introgression from exotic and wild species. For successful MAS, a molecular breeder needs a lot of information like: position of the markers, details of candidate gene, mapping of those markers alongwith the phenotypic data and domestication history of the candidate gene beside other minor information of the crop germplasm. 1.4 Molecular markers Molecular markers are now widely used to track loci and genome regions in several cropbreeding programmes, as molecular markers tightly linked with a large number of agronomic and disease resistance traits are available in major crop species (Phillips and Vasil, 2001; Jain et al., 2002; Gupta and Varshney, 2004). These molecular markers include: (i) hybridization-based markers such as restriction fragment length polymorphism (RFLP), (ii) PCR-based markers: random amplification of polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP) and microsatellite or simple sequence repeat (SSR), and (iii) sequence-based markers: single nucleotide polymorphism (SNP). The majority of these molecular markers has been developed either from genomic DNA libraries (e.g. RFLPs and SSRs) or from random PCR amplification of genomic DNA (e.g. RAPDs) or both (e.g. AFLPs). These DNA markers can be generated in large numbers and can prove to be very useful for a variety of purposes relevant to crop improvement. Amongst all the markers, the third generation molecular marker with highest frequency is SNP. 1.4.1 SNP In the simplest form, a single nucleotide polymorphism (SNP) is an individual nucleotide base difference between two DNA sequences. SNPs can be categorized according to nucleotide substitution as either transitions (C/T or G/A) or transversions (C/G, A/T, C/A, or T/G). As a nucleotide base is the smallest unit of inheritance, SNPs provide the ultimate form of molecular genetic marker. They also represent the most frequent type of genetic polymorphism, and the potential number of such markers is enormous in comparison with any but the most closely 4 Introduction related genotypes within a given species (Rafalski, 2002). They can occur in any position within or outside of genes and accordingly can have very different effects. SNPs present within the protein encoding regions of a gene may result in incorporation of an alternative amino acid in the protein for which the gene serves as the blueprint, or template. Depending on where this occurs within the protein and to what extent the alternative amino acid differs from the normally incorporated one; such an amino acid exchange can have a profound influence on the function of the protein. 1.4.1.1 SNP: advantages and uses The main advantages of SNPs are: they are very common and evenly-distributed in the genome, and secondly, methods of detecting (or "assaying") SNPs can be easily automated. This ease of automation is what makes SNPs "high- throughput" markers. It can be applied to various purposes including rapid identification of crop cultivars, construction of ultra-high density genetic maps and association with genetic disorders (in humans and livestock) and agronomic traits (in crop plants). 1.4.1.2 SNP discovery in plants The two main strategies which can be followed for SNP searching to obtain genetic correlation data are whole-genome scans and candidate gene-based approaches. 1.4.1.2.1 Whole genome scan This requires scanning the whole-genome with a very large number of genetic loci (in the region of 10,000–100,000 or higher). This objective is difficult to achieve as it requires an extremely detailed knowledge of the genome under consideration, the availability of a large number of independent SNP markers, and a high throughput detection method that can ideally be multiplexed on a very large-scale. For plant species, in which genomes can be relatively complex, for which linkage disequilibrium may only extend over short molecular distances because of the influence of reproductive systems, and for which SNP frequencies may be low (Rafalski and Morgante, 2004), this approach can be difficult to apply. 5 Introduction 1.4.1.2.2 Candidate gene approach This approach consists of the characterization of SNPs present in a subset of specific genes identified using various strategies such as bioinformatics-based data mining, QTL analysis and linkage mapping, expression studies, transgenic modification by antisense RNA expression or RNA interference (RNAi), or positional cloning and physical mapping. The idea is to find the single base polymorphism that is directly causal of functional variation in the trait of interest (which is often termed the qualitative or quantitative trait nucleotide, QTN), or at least to find a SNP located within the functional gene or at a small physical distance from the gene. This strategy provides a good solution to the problems raised by the rapid decline of linkage disequilibrium observed in plant genomes (Rafalski and Morgante, 2004), as the chances that linkage disequilibrium may be dissipated by a recombination event are extremely low in generational time (c. 10 6per meiosis) when assaying a SNP located in a candidate gene, compared with much higher probabilities when using a more distant marker in a low-resolution genome scan. This numerically discrete strategy may consequently be applied to a large number of individuals (such as those present within germplasm collections). 1.5. Mapping Two of the most commonly used tools for dissecting complex traits are Quantitative trait loci (QTL) mapping and association mapping (Risch and Merikangas, 1996; Mackay, 2001). 1.5.1 QTL mapping The acronym QTL refers to Quantitative Trait Locus. Quantitative traits refer to phenotypes (characteristics) that vary in degree and can be attributed to polygenic effects, i.e., product of two or more genes, and their environment. Quantitative trait loci (QTLs) are stretches of DNA containing or linked to the genes that underlie a quantitative trait. Mapping regions of the genome that contain genes involved in specifying a quantitative trait is done using molecular tags such as AFLP or, more commonly SNPs. This is an early step in identifying and sequencing the actual genes underlying trait variation. QTLs are detected through QTL mapping experiments. In crop plants, these experiments utilize experimental pedigrees, usually produced from crossing two inbred lines. 6 Introduction 1.5.2 Association mapping In association mapping, the genetic markers usually must lie within (or directly upstream or downstream of) candidate genes suspected to contribute to the variation in that trait, and the goal is to identify the actual genes affecting that trait, rather than just (relatively large) chromosomal segments. In contrast to QTL mapping, which is performed in the context of a pedigree, association mapping is performed at the population level: the genotypes of the candidate gene markers and the phenotypes of the corresponding trait are determined in a set of unrelated or distantly-related individuals sampled from a population. Association mapping relies on linkage disequilibrium (LD) between the candidate gene markers and the actual causative polymorphism in that gene (i.e., the actual polymorphism that causes the differences in the phenotypic trait). Hence association mapping is also referred to as 'LD mapping'. Although the goal of both association mapping and linkage mapping is to find associations between phenotypes and genes (or molecular markers), there are some important differences. Linkage mapping, is usually done in the context of closely related individuals having known relationships, such as the offspring of a controlled cross or the members of a family where the pedigree is known. Since the number of recombination events in these cases is relatively small, genes or quantitative traits are mapped to large chromosomal blocks, and the resolution is low (Mb scale). On the other hand, association mapping is done using distantly related individuals with unknown relationships, randomly chosen from a natural population. If the population has a long history of inbreeding, LD will decay slowly and the resolution of the association mapping study will be quite low. 1.6 SNP genotyping SNP genotyping is the measurement of genetic variations of single nucleotide polymorphisms (SNPs) between members of a species. Several methods are employed to study SNP genotyping like which fall under two categories broadly, hybridization based methods and enzyme based methods. Several applications have been developed that interrogate SNPs by hybridizing complementary DNA probes to the SNP site. The challenge of this approach is reducing crosshybridization between the allele-specific probes. This challenge is generally overcome by 7 Introduction manipulating the hybridization stringency conditions (Rapley and Harbron, 2004). In enzyme based techniques a broad range of enzymes including DNA ligase, DNA polymerase and nucleases are employed to generate high-fidelity SNP genotyping methods. PCR based method falls under enzyme based genotyping. It employs two pairs of primers to amplify two alleles in one PCR reaction. The primers are designed such that the two primer pairs overlap at a SNP location but each match perfectly to only one of the possible SNPs. As a result, if a given allele is present in the PCR reaction, the primer pair specific to that allele will produce product but not to the alternative allele with a different SNP. The two primer pairs are also designed such that their PCR products are of a significantly different length allowing for easily distinguishable bands by gel electrophoresis. In examining the results, if a genomic sample is homozygous, then the PCR products that result will be from the primer which matches the SNP location to the outer, opposite strand primer as well from the two opposite, outer primers. If the genomic sample is heterozygous, then products will result from the primer of each allele to their respective outer primer counterparts as well as from the two opposite, outer primers. It is also called allele-specific (AS) Polymerase Chain Reaction, and is a convenient and inexpensive method for genotyping SNPs and mutations. It is applied in many recent studies including population genetics, molecular genetics and pharmacogenomics. The present study The aim of this study was to develop conventional and biotechnological tools that could be used in sesame improvement programs towards diversifying the fatty acid composition of the seed oil. Such work would help expand the market niche for sesame oil, thereby contributing to increased cultivation of the crop. In addition, a better market competitiveness would translate to having a sure source of income for sesame farmers, thereby improving their livelihood. From a long-term perspective the present work, through development of SNP markers those are currently lacking and their association mapping to the fatty acid composition will help in future for marker assisted selection. Moreover domestication history of the trait of interest will help plant breeders in the discovery and utilization of rare but potentially important alleles present in the genetic resources. Till date, no such work has been reported not only in Indian germplasm of sesame but also worldwide. Moreover, there is insufficient variability in the fatty acid composition of sesame oil. 8 Introduction This study will identify cultivars with high linolenic acid content and a composition different from the rest that will later be developed further by genetic modification for the production of novel oils. Genetic transformation of sesame with certain genes involved in fatty acid synthesis will provide a means to effect changes in oil composition. 9