Studies on genetic relationships among six varieties of jackfruit in Kerala employing the "matK" gene using PCR technique and RFLP markers

Table of contents

Table of figures

Table of tables

List of abbreviations

Studies on genetic relationships among six varieties of jackfruit (Artocarpus heterophyllus Lam.) in Kerala employing matK gene using PCR technique and RFLP markers

Abstract

1. Introduction
1.1 Taxonomical classification

2. Review of literature

3. Hypothesis

4. Materials and Methods
4.1 Study area
4.2 Sample collection
4.3 Isolation of DNA
4.4 Quantification of DNA
4.5 PCR amplification
4.6 Data sequencing
4.7 Data analysis
4.8 Data collection
4.9 Proximate analysis
4.10 Statistical analysis

5. Results and discussion
5.1 Estimation of Dry matter & moisture content
5.2 Estimation of crude protein
5.3 Estimation of crude fat
5.4 Estimation of crude ash
5.5 Estimation of crude fibre
5.6 DNA isolation
5.7 Polymerase chain reaction
5.8 Molecular phylogentic analysis using ‘matK’ gene
5.9 Restriction fragment length polymorphism
5.10 Polymorphic sites
5.11 Analysis

6. Conclusions

Acknowledgements

References

ACKNOWLEDGEMENTS

Firstly we thank God Almighty whose blessing were always with us and helped us to complete this project work successfully.

We wish to thank our beloved Manager Rev. Fr. Dr. George Njarakunnel, Respected Principal Dr. V.J.Joseph, Vice Principal Fr. Joseph Allencheril, Bursar Shaji Augustine and the Management for providing all the necessary facilities in carrying out the study. We express our sincere thanks to Mr. Binoy A Mulanthra (lab in charge, Department of Biotechnology) for the support. This research work will not be possible with the co-operation of many farmers.

We are gratefully indebted to our teachers, parents, siblings and friends who were there always for helping us in this project.

Prem Jose Vazhacharickal*, Sajeshkumar N.K, Jiby John Mathew and Sophyiamol Jose

*Address for correspondence

Assistant Professor

Department of Biotechnology

Mar Augusthinsoe College

Ramapuram-686576

Kerala, India

premjosev@gmail.com

Table of figures

Figure 1. Mean monthly rainfall (mm), maximum and minimum temperatures (°C) in Kerala, India (1871-2005; Krishnakumar et al., 2009).

Figure 2. Map of Kerala showing the various sample collection points; Ku (A1), Va (A9), Uc (A7), Pv (A16), Tv (A3) and Sv (A20).

Figure 3. Sample Ku (A1) description a) tree bearing jackfruits, b) jackfruit with exposed pith, c) longitudinal section of Jackfruit, d) flake, e) fruit stalk leaf, branch leaf, flake, seeds and spine, f) pith, g) seed.

Figure 4. Sample Tv (A3) description a) jackfruit, b) cross section of jackfruit, c) reddish flake, d) fruit stalk leaf, branch leaf, flake, seeds and spine.

Figure 5. Sample Uc (A7) description a) tree bearing jackfruits, b) bunch of round jackfruit, c) spherical shaped jackfruit, d) longitudinal section of jackfruit, e) fruit stalk leaf, branch leaf, flake, seeds and spine, f) spines, g) depression in stalk attachment to fruit.

Figure 6. Sample Va (A9) description a) tree bearing jackfruits, male and female flower, b) fruit stalk leaf, branch leaf, flake, seeds and spine, c) flake and seed, d) leaf apex (retuse).

Figure 7. Sample Pv (A16) description a) jackfruit, b) leaf apex shape, c) fruit stalk leaf, branch leaf, flake, seeds, d) flake and seed.

Figure 8. Sample Sv (A20) description a) jackfruit, b) spine, c) longitudinal section of jackfruit, d) flake, e) fruit stalk leaf, branch leaf, flake, seeds and spine, f) inflated stock attachment to fruit.

Figure 9. Description and morphology of samples Ku (A1), Va (A9), Uc (A7), Pv (A16), Tv (A3) and Sv (A20).

Figure 10. DNA on agarose gel a) agarose gel electrophorosis of the DNA of samples; Ku (A1), Va (A9), Uc (A7), Pv (A16), Tv (A3) and Sv (A20), b) amplified DNA samples, c) amplified PCR-RFLP products.

Figure 11. MatK gene sequences of six different Artocarpus heterophyllus varities; Sv (A20), Va (A9), Tv (A3), Pv (A16), Uc (A7), Ku (A1).

Figure 12. Pairwise genetic distance between six Artocarpus varities; Sv (A20), Va (A9), Tv (A3), Pv (A16), Uc (A7), Ku (A1) based on matK gene sequence (Overall distance is 0.000).

Figure 13. Phylogenetic tree based on matK sequence by a) Maximum likelihood (ML) method b) Neghbour joining method (NJ) c) Unweighted pair group method with arithmetic mean (UPGMA) method.

Table of tables

Table 1. Phenolic, flavinoid content and antioxidant activity of araticum, papaya and jackfruit in undigested and digested extracts (Modified after; Pavan et al., 2011).

Table 2. Biochemical difference various jackfruit varieties in South India (Chrips et al., 2008).

Table 3. Uses of different jackfruit parts (Chrips et al., 2008).

Table 4. Different vernacular names of Artocarpus heterophyllus in India (Modified after; Baliga et al., 2011).

Table 5. Common names, uses and distribution of major Artocarpus species (Modified after; Jagtap and Bapat, 2010).

Table 6. Chemical composition of jackfruit (Modified after; Jagtap and Bapat, 2010).

Table 7. Description of the jackfruit variety samples; Sv (A20), Va (A9), Tv (A3) Pv (A16), Uc (A7), Ku (A1) and their special features.

Table 8. General features of jackfruit variety samples; Sv (A20), Va (A9), Tv (A3) Pv (A16), Uc (A7), Ku (A1).

Table 9. Proximate composition of jackfruit variety samples on as such basis; Sv (A20), Va (A9), Tv (A3), Pv (A16), Uc (A7), Ku (A1).

Table 10. Proximate composition of jackfruit variety samples on dry matter basis; Sv (A20), Va (A9), Tv (A3), Pv (A16), Uc (A7), Ku (A1).

Table 11. Maximum composite likelihood estimate of the pattern of nucleotide substitution.

List of abbreviations

illustration not visible in this excerpt

Studies on genetic relationships among six varieties of jackfruit (Artocarpus heterophyllus Lam.) in Kerala employing matK gene using PCR technique and RFLP markers

Prem Jose Vazhacharickal1*, Sajeshkumar N.K1, Jiby John Mathew1 and Sophyiamol Jose1

* premjosev@gmailcom

1 Department of Biotechnology, Mar Augusthinose College, Ramapuram, Kerala, India-686576

Abstract

Artocarpus heterophyllus belong to the Moraceae family and seen abundant in Western Ghats. The fruit provide 2 MJ per kg/wet weight of ripe perianth and contain high levels of carbohydrates, protein, starch, calcium and vitamins. Jackfruit has diverse medicinal uses especially anti-oxidant, anti-inflammatory, antimicrobial, anti-cancer and anti-fungal activity. MatK’ (maturase K) genes are fast evolving highly variant regions of plant chloroplast DNA that can serve as potential biomarkers for DNA coding and also in generating primers for plants with identification of unique motif regions. Advances in the genetic markers such as RFLP and PCR based methods are more reliable for identification of genetic diversity than morphological markers although each technique has advantages and limitations. The objective of this research work was to estimate the level of genetic diversity and to assess genetic relationships among six varieties of jackfruit using ‘matK gene’ based on PCR technique and RFLP markers. The partial sequence of ‘matK’ gene of six different Artocarpus varities was used in the analysis. The size of amplified products was approximately 700 bp. After sequencing and sequence editing, sequence information on a 674 bp region was finally obtained for analysis. The alignment of sequences revealed two haplotypes out of 674 sites. The nucleotide frequencies are 30.00% (A), 37.69% (T/U), 17.93% (C), and 14.39% (G). Being one of the underutilized fruits in India, Artocarpus heterophyllus Lam. has promising leads to further scientific researches and livelihood strategies. The study of matK gene using PCR and RFLP seems to a promising tool in establishing genetic diversity among jackfruit varities. The tree indigenous to the Western Ghats is an important source of nutritious food during summer season. Encouragements should be done to the marketing as well as value added food products from this underutilized fruit tree.

Keywords: Anti-oxidant; matK; Jackalin; PCR; RFLP; Underutilized fruit.

1. Introduction

Jackfruit (Artocarpus heterophyllus Lam.) belongs to the family Moraceae which is the largest tree-borne fruit in the world. Jackfruit is grown mainly in Bangladesh, India, in many parts of Southeast Asia, in the evergreen forest zone of West Africa, Brazil, Myanmar, Thailand, Vietnam, China, the Philippines, Indonesia, Malaysia and Sri Lanka (Thaman and Ali. 1993; Jagadeesh et al., 2007a; Baliga et al., 2011; Jagadeesh et al., 2007b; Prakash et al., 2009; Wangchu et al., 2013). It is a medium sized tree that bears high yields of largest known edible fruit and typically reaching 8-25 m in height and up to 50 kg in weight, producing heavier yields, up to 700 fruits per year (Jagadeesh et al., 2007a; Baliga et al., 2011; Saxena et al., 2009; Hameed, 2009; Swami et al., 2012; Selvaraj and Pal, 1989).

Jackfruit is one of the most popular tropical fruits grown in India. It seeds make up around 10 to 15% of the total fruit weight and have high carbohydrate and protein contents, dietary fibre, vitamins, minerals and phytonutrients (Baliga et al., 2011; Prakash et al., 2009). The leaves and fruit waste provide valuable fodder for cattle, pigs and goats. Many parts of the plant includeing the bark, leaves, and fruit are attributed with medicinal properties (Baliga et al., 2011; Haq, 2006; Prakash et al., 2009; (Jagadeesh et al., 2007a; Saxena et al., 2009; Hameed, 2009; Swami et al., 2012; Selvaraj and Pal, 1989).

The tree can provide many environmental services. It is highly wind tolerant and therefore makes a good component in a windbreak or border planting. Despite numerous advantages, the populaeity of jackfruit as a commerical crop is very poor owing to wide variations in fruit quality, the long seed dormancy and the widespread belief that excessive consumption of jackfruit flakes leads to certain digestive ailments (Sammadar, 1985; Baliga et al., 2011; Prakash et al., 2009).

Mainly jackfruit can be classified into two groups varikka and koozha. There are no well-defined varieties in specific localities; local varieties have different names based on their variability in yeild, fruit shape, flake colour, total sugars, and so on (Wangchu et al., 2013; Jagadeesh et al., 2007a; Prakash et al., 2009). Boiled and cooked jackfruit seeds are included in the diets which have 77% starch content, which is exploited as a potent source of starch (Bobbio et al., 1978; Tulyathan et al., 2002; Mukprasirt and Sajjaanantakul, 2004; Odoemelam, 2005). Jackfruit is widely used in culinary preparation, baking, candid jackfruit, baby food, jams, jellies, juice, chips, deserts and the advances in food processing technologies further expanded the possibilities (Burkill, 1997; Swami et al., 2012; Selvaraj and Pal, 1989; Narasimham, 1990; Roy and Joshi, 1995; Haq, 2006). Jackfruit is widely accepted by consumers, researchers and food industries due to the presence of bioactive compounds and diversity products made out of it (Swami et al., 2012; Saxena et al., 2009; Dutta et al., 2011; Lin et al., 2009; Devalaraja et al., 2011). Various parts of jackfruit tree have been used for medicine and the hard wood is used for construction (Roy and Joshi, 1995).

Cultivar identification and estimation of genetic diversity using morphological data are limited, as they are environmentally influenced, and there are few distinctive characters (Cavagnaro, et al., 2006). Jackfruit is a tetraploid; its somatic chromosome number is (4n) 56. Therefore, the basic chromosome number is 14 (Darlington and Wylie, 1956). Markers aided by polymorphisms in proteins and DNA structures have reduced the disadvantages of morphological markers to some extent (Sensoy et al., 2007), because they are unaffected by environmental factors (Dhanaraj et al., 2002).

The molecular technology has in directly improved the efficiency of plant breeding programs (Collard and Mackill, 2008; Rafalski and Tingey, 1993; Allard and Bradshaw, 1964; Dekkers and Hospital, 2002). A comprehensive understanding of genetic diversity and molecular characterization of jackfruit cultivars is needed for formulating appropriate sampling and management strategies (Shyamalamma et al., 2008; Normah et al., 2013). A detailed analysis of a large number of genetic markers will provide us with useful gene conservation strategies and help in popularizing this species as a commercial crop (Prashanth et al., 2002). The molecular markers are broadly classified into morphological markers, protein based markers, and DNA based markers like restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP) (Liu and Cordes, 2004). Molecular difference using DNA and protein based markers are more authentic and unaffected by environmental factors (Dhanraj et al., 2002).

The genetic diversity of jackfruit is a valuable resource for the present and future (Khan et al., 2010; Virchow, 1999). In addition to the loss of jackfruit trees due to logging and clearing land for agriculture, market demand for jackfruit may lead to the replacement of local diversity with uniform exotic genotypes and to the replacement of local consumption with sales to large urban markets (Khan et al., 2010; Virchow, 1999; Swami et al., 2012; Selvaraj and Pal, 1989; Narasimham, 1990; Roy and Joshi, 1995; Haq, 2006).

Agarose gel electrophoresis is a quick and easy molecular technique used to analyze and separate nucleic acids based on their size (Davis, 2012; Olive and Bean, 1999). Electrophoresis takes advantage of the fact that DNA’s phosphate backbone is negatively charged. Thus when DNA is placed in an electric field, it will migrate toward the positive electrode. The differential ability of DNA to move through a gel based on its size doesn’t really depend on the electric field or the charged properties of DNA, but more importantly on the composition of the gel. Agarose is a complex polymer that forms a matrix through which DNA travels when subjected to an electric field (Davis, 2012; Olive and Bean, 1999; Karp et al., 1996).

Molecular biology techniques, such as polymerase chain reaction (PCR), have become widely used for medical and forensic applications, as well as research, and detection and characterization of infectious organisms (Eisenstein, 1990; Valasek and Repa, 2005; Hill and Wachsmuth, 1996; Gilbride et al., 2006). PCR technique is used for the amplification of DNA molecules. In the virology field, it has been demonstrated that the employment of PCR technique offers the advantages of high sensitivity and reproducibility in viral genomic detection and strains characterization. However, the sensitivity in the detection of DNA fragments is also linked to the sensitivity of the electrophoresis matrix applied for PCR product development (Powledge, 2004; Hill and Wachsmuth, 1996; Gilbride et al., 2006).

Electrophoresis through agarose or polyacrylamide gels is a standard method used to separate, identify and purify nucleic acids, since both these gels are porous in nature (Peacock and Dingman, 1968a; Shi and Jackowski, 1998; Bishop et al., 1967; Westermeier, 2016). The matrix used for electrophoresis should have adjustable but regular pore sizes and be chemically inert, and the choice of which gel matrix to use depends primarily on the sizes of the fragments being separated (Peacock and Dingman, 1968a; Shi and Jackowski, 1998; Bishop et al., 1967; Westermeier, 2016).

The agarose gel electrophoresis is widely employed to estimate the size of DNA fragments after digesting with restriction enzymes, e.g. in restriction mapping of cloned DNA (Tenover et al., 1995; Williams et al., 1990; Vilgalys and Hester, 1990). It has also been a routine tool in molecular genetics diagnosis or genetic fingerprinting via analyses of PCR products. Separation of restricted genomic DNA prior to Southern blot and separation of RNA prior to Northern blot are also dependent on agarose gel electrophoresis. Agarose gel electrophoresis is commonly used to resolve circular DNA with different supercoiling topology, and to resolve fragments that differ due to DNA synthesis (Wang et al., 1983; Middaugh et al., 1998; Gellert, 1981). DNA damage due to increased cross-linking proportionally reduces electrophoretic DNA migration (Blasiak et al., 2000; Lu and Morimoto, 2009).

In addition to providing an excellent medium for fragment size analyses, agarose gels allow purification of DNA fragments (Wang et al., 1983; Middaugh et al., 1998). Since purification of DNA fragments size separated in an agarose gel is necessary for a number molecular techniques such as cloning, it is vital to be able to purify fragments of interest from the gel (Sharp et al., 1973). Increasing the agarose concentration of a gel decreases the migration speed and thus separates the smaller DNA molecules makes more easily (Fangman, 1978; Southern, 1975; Carle and Olson, 1984; Cantor et al., 1998; Birren et al., 1988). Increasing the voltage, however, accelerates the movement of DNA molecules. Nonetheless, elevating the currency voltage is associated with the lower resolution of the bands and the elevated possibility of melting the gel (Southern, 1975; Carle and Olson, 1984; Cantor et al., 1998; Birren et al., 1988).

Electrophoresis is the motion of colloidal particles relative to a fluid medium under the influence of an electric field that is uniformly spaced. This electro-kinetic phenomenon (Reuss, 1809) remains widely used in an array of practical devices and processes to produce macro-scale effects. Examples of applications and operations include measurements, electrophoretic deposition, electrophoretic fingerprinting, as well as gel electrophoresis (Meyers et al., 1976; Davis, 1964; Sarkar and Nicholson, 1996; Besra and Liu, 2007).

‘MatK’ (maturase K) genes are fast evolving highly variant regions of plant chloroplast DNA that can serve as potential biomarkers for DNA coding and also in generating primers for plants with identification of unique motif regions (Biswal et al., 2012; Stoeckle, 2003; Yokoyama et al., 2000; Koch et al., 2001; Hilu and Liang, 1997). From ‘matK’ dataset which can be further validated for coding and designing of PCR primers (Bafeel et al., 2011; Barthet and Hilu, 2007). In this analysis using a novel approach of sequence-structure alignment and phylogenetic reconstruction using molecular morphometrics congrue with different verities of jackfruits (Biswal et al., 2012).

Molecular techniques require isolation of genomic DNA of suitable purity (Xin et al., 2003; Pitcher et al., 1989; Mahuku, 2004). The isolation of good quality DNA is the prerequisite for molecular research (Eads et al., 2000; Aljanabi and Martinez, 1997; Lahiri and Nurnberger, 1991; Mathew, 1984). Successful application of PCR based applications requires efficient recovery of good quality and quantity of DNA (Wintzingerode et al., 1997; Aljanabi and Martinez, 1997; Lahiri and Nurnberger, 1991; Mathew, 1984). However, plant species belonging to the same or related genera can exhibit enormous variability in the complexity of pathways of dispensable functions (Li et al., 2006; Weng, 2014; Bergelson et al., 2001; Choudhary et al., 2008). The cetyl trimethyl ammonium bromide (CTAB) method and its modifications have been used to obtain good quality total DNA for PCR based applications (Allen et al., 2006; Lodhi et al., 1994; Zhang and Stewart, 2000; Demeke and Jenkins, 2010).

PCR-RFLP based analysis, also known as cleaved amplified polymorphic sequence (CAPS), is a popular technique for genetic analysis (Neff et al., 1998; Agarwal et al., 2008; Newton et al., 1999; Kumar et al., 2009). It has been applied for the detection of intraspecies as well as interspecies variation (Newton et al., 1999; Kumar et al., 2009). There exist several techniques that are related with PCR-RFLP and also involve gel electrophoresis including techniques for DNA fingerprinting and expression profiling (Lodhi et al., 1994; Zhang and Stewart, 2000; Demeke and Jenkins, 2010; Neff et al., 1998; Agarwal et al., 2008; Newton et al., 1999).

Analysis of RFLPs in DNA to study species relationships has been utilized (Botstein et al., 1980; Liu et al., 1997; Song et al., 1988; Laguerre et al., 1994). Polymerase chain reaction is widely used to amplify a special, relatively mutable fragment of the chloroplast genome (Ogihara et al., 1991; Jasieniuk and Maxwell, 2001; Cowperthwaite et al., 2002; Heinze, 2001; Weising et al., 2005). Following amplification of analogous fragments from species, the fragments are restriction digested to detect mutated sites which are in turn used for taxonomic analysis (Cowperthwaite et al., 2002; Heinze, 2001; Weising et al., 2005).

In qualitative analysis, organic compounds can be identified by use of spectrophotometer, if any recorded data is available, and quantitative spectrophotometric analysis is used to ascertain the quantity of molecular species absorbing the radiation (Behera et al., 2012; Thakur and Kumar, 2014; Cordonnier and Schaep, 2013). Spectrophotometric technique is simple, rapid, moderately specific and applicable to small quantities of compounds (Behera et al., 2012; Thakur and Kumar, 2014). The fundamental law that governs the quantitative spectrophotometric analysis is the Beer-Lambert law (Ghaedi et al., 2006; Gampp et al., 1988).

PCR techniques have become essential for many common procedures such as cloning specific DNA fragments, detecting and identifying genes in diagnostics and forensics, and in the investigation of gene expression patterns (Valasek and Repa, 2005; Kogan et al., 1987; Meyer and Candrian, 1996). More recently, PCR has allowed the investigation of new fields such as the control of the authenticity of foodstuff, the presence of genetically modified DNA and microbiological contamination (Meyer and Candrian, 1996; O'hanlon et al., 2000; Meyer et al., 1993; Meyer et al., 1994; Behera et al., 2008).

1.1 Taxonomical classification

Kingdom: Plantae-- planta, plantes, plants, vegetal

Subkingdom: Tracheobionta -- vascular plants

Division: Magnoliophyta -- angiosperms, flowering plants, phanerogames

Class: Magnoliopsida -- dicots, dicotyledones, dicotyledons

Subclass: Hamamelidae

Order: Urticales

Family: Moraceae - mulberries

Genus: Artocarpus - breadfruit

Species: Artocarpus heterophyllus Lam.

2. Review of literature

The jackfruits were classified based on their phonotypical and organoleptic characteristics with variation in bulb colour as well as shape, size, odour, flake size, flake colour and period of maturity (Haq, 2006; Prakash et al., 2009; Jagadeesh et al., 2007b; Jagadeesh et al., 2007a). Two types of ecotypes are recognised flake characteristics, one with soft and spongy while other with firm carpels which called different in regional languages (Baliga et al., 2011; Amma et al., 2011; Shyamalamma et al., 2008; Muralidharan et al., 1997; Odoemelam, 2005).

Studies have proved that the nutritional and photochemical composition among jackfruit varies depending on the cultivar as well as region (Baliga et al., 2011; Arkroyd et al., 1966; Azad, 2000; Haq, 2006; Narasimham, 1990). It is a good source of vitamins (A, C, thiamine, riboflavin, niacin) and minerals ; calcium (Ca), potassium (K), iron (Fe), sodium (Na) and zinc (Zn) (Swami et al., 2012; Haq, 2006; Narasimham, 1990; Arkroyd et al., 1966; Azad, 2000). Protein and carbohydrate concentration also varied in seeds across India were some varieties contain 6.8% of protein content in seeds (Baliga et al., 2011; Chrips et al., 2008).

The Artocarpus species contain a diversity of compounds especially phenolic compounds, flavonoids, stilbenoids, arylbenzofurons, carotenoids, volatile acid sterols and tannins which varies depending on the variety (Jagtap and Bapat, 2010; Baliga et al., 2011; Hakim et al., 2006; Arung et al., 2006; Chandrika et al., 2005; De Faria et al., 2009; Ko et al., 1998; Venkataraman, 1972; Wong et al., 1992; Maia et al., 2004). Fructos, glucose and sucrose were the major sugars in jackfruit, while capric, myristic, lauric, palmitic, oleic, stearic, linoleic and arachidic acids were the major fatty acids (Chowdhury et al., 1997; Jagtap and Bapat, 2010; Rahman et al., 1999; Ong et al., 2006).

The jackfruit is believed to have originated in the Western Ghats region of India. It has been cultivated for centuries in the lowland rainforests of south-east Asia where it is widely grown commercially and in the home garden (Acedo, 1992; Samaddar, 1985; Soepadmo, 1991).

Jackfruit is gaining popularity in the United States due to the availability of superior germplasm, modern growing techniques, and emerging ethnic and mainstream marketing opportunities (Campbell and El-Sawa, 1998; Campbell and McNaughton, 1994). A small collection of jackfruit cultivars has been established at Fairchiled Tropical Garden (FTG) which is providing germplasm throughout tropical America (Campbell and McNaughton, 1994).

Amplified fragment length polymorphism markers are a recently developed system that combines the specificity of restriction enzyme analysis with the relative technical simplicity of the PCR (Vos et al., 1995). AFLP’s have been used to fingerprint accessions, for genetic linkage mapping, and for genetic diversity analysis (Russell et al., 1997; Mueller and Wolfenbarger, 1999; Milbourne et al., 1997; Hill et al., 1996; Powell et al., 1996). They have the advantages that they are easy to use, sample a large number of loci per reaction, and are reproducible between laboratories. They do have the same disadvantage as randomly amplified polymorphic DNA markers that they are inherited in a dominant manner (Mueller and Wolfenbarger, 1999; Milbourne et al., 1997; Hill et al., 1996; Powell et al., 1996).

Advances in the genetic markers such as RFLP and PCR based methods are more reliable for identification of genetic diversity than morphological markers although each technique has advantages and limitations (Mueller and Wolfenbarger, 1999; Bardakci, 2001; Lee, 1994; Bowles and McManus, 1993; Thurston et al., 2002; Liu and Cordes, 2004; Cacciò et al., 2002). These molecular markers can be linked to important traits, used for early selection of potentially desirable genotypes and individuals (Newbury and Ford-Lloyd, 1993; Lee, 1994; Bowles and McManus, 1993; Thurston et al., 2002; Liu and Cordes, 2004).

Polymorphism detected by RAPD has proved to be useful for identifying variation at different levels in different plants (Reddy et al., 2002; Howell et al., 1994; Pejic et al., 1998; Assigbetse et al., 1998; Hadrys et al., 1992; Crowhurst et al., 1991; Mueller and Wolfenbarger, 1999). The reproducibility of RAPD results may be overcome by optimizing experimental conditions and following precisely a chosen experimental protocol (Vanijajiva, 2011; Pushpakumara and Harris, 2010). Further, RAPD is a less expensive technique compared to others (Mueller and Wolfenbarger, 1999; Atienzar and Jha, 2006).

A comprehensive understanding of genetic diversity and molecular characterization of jackfruit cultivars is needed for formulating appropriate sampling and management strategies (Shyamalamma et al., 2008; Normah et al., 2013; ZongWen et al., 2001). A detailed analysis of a large number of genetic markers will provide us with useful gene conservation strategies and help in popularizing this species as a commercial crop (Shyamalamma et al., 2008). Among many available DNA-based markers, AFLP markers are more reliable, yield a large number of markers per reaction, are cost-effective and have wide genome coverage (Prashanth et al., 2002; Hansen et al., 1999). AFLP markers have been successfully used, in the last decade, as a satisfactory alternative as well as compliment to morphological data in a variety of plant and tree species (Basha et al., 2007; Mahmud et al., 2007; Sreekumar et al., 2007).

A molecular marker is any measure character and molecular characteristic that is inherited in a simple mendalian fashion (Avise, 2012; Hadrys et al., 1992; Rieseberg and Brunsfeld, 1992). The discovery of molecular markers in recent years has greatly enhanced the scope for detailed genetic analysis and approaches to improve crop plants (Collard and Mackill, 2008; Egan et al., 2012; Snowdon and Friedt, 2004; Reynolds et al., 2009). The molecular technology has indirectly improved the efficiency of plant breeding programs (Collard and Mackill, 2008; Egan et al., 2012; Snowdon and Friedt, 2004). Molecular markers play two main roles in plant breeding programs, firstly as a source of genetic finger prints and as a selected marker linked to phenotyphic traits of interest to breeder (Koebner et al., 2001; Eagles et al., 2001; Skøt et al., 2005; Haussmann et al., 2004; Perez-de-Castro et al., 2012). Markers are broadly classified into morphological markers, protein based markers and DNA based markers (Eagles et al., 2001; Liu and Cordes, 2004; Avise, 2012; Fukami et al., 2004; Halward et al., 1991). DNA based markers like, RFLP, RAPD and AFLP can act as excellent tools to study the genetic diversity eliminate duplicate in germplasm to study the genetic relationships, gene tagging, genome mapping and to use in plant variety rights (PVR) (Karp, 1997; Meudt and Clarke, 2007; Hegarty and Hiscock, 2005; Sharma et al., 2008; Langridge et al., 2001). These markers measure, diversity at DNA level in all tissue at all of plant and are seldom influenced by environmental condition (Gopalsamy et al., 2012; Hegarty and Hiscock, 2005; Sharma et al., 2008).

Molecular differences using DNA and protein based markers are more authentic and unaffected by environmental factors (Dhanraj et al., 2002). Hence, characterization of genotypes at the genetic level supplemented by phenotypic characters and it provides the first step towards more efficient conservation maintenance and utilization of existing genetic diversity (Prakash et al., 2002). Among DNA based molecular markers, RAPD provide an excellent tool for studying genetic diversity and genetic relationships (Williams et al., 1990; Meudt and Clarke, 2007; Hegarty and Hiscock, 2005; Sharma et al., 2008). RAPD is relatively simple, versatile, inexpensive to detect slight genetic differences and help to identify duplicates in the population (Simon et al., 2007; Agarwal et al., 2008; Rastogi et al., 2007; Algabal et al., 2011; Narayanaswamy et al., 2008). RAPD markers have been used successfully to study genetic diversity and relatedness among perennial fruit crops such as mango (Ravishankar et al., 2000; Rao, 2004; Prakash et al., 2002; Krishna and Singh, 2007; Cregan and Schaap, 2010; Simon et al., 2007; Bhat et al., 2010; Litz and Gómez-Lim, 2005).

Genetic diversity is important in plant breeding and is commonly measured by genetic distance or genetic similarity (Weir, 1990; Nybom, 2004; Mohammadi and Prasanna, 2003; Loveless and Hamrick, 1984; Ellstrand and Elam, 1993; Riaz et al., 2001). Morphological and biochemical markers tend to be restricted to relatively few traits, display a low degree of polymorphism, are often environmentally variable in their manifestation and can depend on the expression of several unlinked genes (Melchinger et al., 1991; Schulman, 2007; Behera et al., 2008; Williams and Clair, 1993; Kumar et al., 2009). In contrast, molecular marker-based genetic diversity analysis has potential for assessing changes in genetic diversity over time and space (Ravi et al., 2003; Collard and Mackill, 2008; Ouborg et al., 2010; Koebner et al., 2003; Allendorf et al., 2010). A molecular marker is a nucleotide sequence corresponding to a particular physical location in the genome (Sorrells et al., 2003; McCouch et al., 2003; Agarwal et al., 2008). It plays an essential role today in the study of variability and diversity (Ravi et al., 2003; Collard and Mackill, 2008; Ouborg et al., 2010; Koebner et al., 2003; Sorrells et al., 2003; McCouch et al., 2003; Agarwal et al., 2008).

Analysis of RFLPs in cpDNA to study species relationships has been utilized since the early 1980s. Arnold et al. (1991), Liston (1992), Rieseberg et al. (1992), and Badenes and Parfitt (1995) have used PCR to amplify a special, relatively mutable fragment of the chloroplast genome, described by Ogihara et al. (1991). Following amplification of analogous fragments from species, the fragments are restriction digested to detect mutated sites which are in turn used for taxonomic analysis. In this study, we applied their methods and analyzed mutation-site variation in this cpDNA region in order to determine phylogenetic relationships among the edible Artocarpus spp. and their relatives (Shinya et al., 1997).

The nutritional quality a fruit may vary due variety of factors including variety feature, soil and climatic parameters, management practices (Dale, 1997; Jackson and Lombard, 1993; Lanyon et al., 2004; Pieper and Barrett, 2009). The nutritional quality may also differ in parts other than fruit especially leaves, seeds, flowers, stem and bark. Based on the morphological diversity among jackfruit varieties in Kerala, it was assumed that nutritional properties may vary. The objective of this research work was to estimate the level of genetic diversity and to assess genetic relationships among six varieties of jackfruit using ‘matK gene’ based on PCR technique and RFLP markers.

3. Hypothesis

The current research work is based on the following hypothesis

1) Morphological variations among jackfruit varieties in Kerala would be reflected in genetic diversity among them
2) Gentetic relationships among Jackfruit varieties could be established using ‘matk gene’.
3) RFLP and other bioinformatics tools would provide a better understabability and establish relationship among different morphologically identified jackfruit varities.

4. Materials and Methods

4.1 Study area

Kerala state covers an area of 38,863 km2 with a population density of 859 per km2 and spread across 14 districts. The climate is characterized by tropical wet and dry with average annual rainfall amounts to 2,817 ± 406 mm and mean annual temperature is 26.8°C (averages from 1871-2005; Krishnakumar et al ., 2009). Maximum rainfall occurs from June to September mainly due to South West Monsoon and temperatures are highest in May and November (Figure 1).

4.2 Sample collection

Sampling locations were selected in Kerala based on an elaborative baseline survey conducted during February 2015 to March 2015. The samples were collected based on an elaborative iterative survey as well as traditional knowledge from local people. Six samples were collected from different parts of Kerala, locations of the sample collection areas were recorded using a Trimble Geoexplorer II (Trimble Navigation Ltd, Sunnyvale, California) and data were transferred using GPS pathfinder Office software (Trimble Navigation Ltd, Sunnyvale, California). Fresh leaves were collected and transferred to polyethylene zipper bags placed along with frozen ice packs; transported immediately to the laboratory. The leaves were washed with sterile distilled water and placed in deep freezers till further analysis. The six different varieties (koozha, varikka, undachakka, paathi varikka, thaen varikka, Singapore varikka) of jackfruit leaves were collected and abbreviated as koozha (Ku; A1), varikka (Va; A9), Undachakka (Uc; A7 paathivarikka (Pv; A16), theanvarikka (Tv; A3) and singapore varikka (Sv; A20).

4.3 Isolation of DNA

The total genomic samples from jackfruit leaves were isolated using a modified Cetyl trimethyl ammonium bromide (CTAB) method with the addition of β-mercaptoethanol (Simon et al., 2007; Shyamalamma et al., 2008). Three gram leaves samples were wiped with 70% alcohol and chopped into fine pieces and later homognized along with 10 ml extraction buffer using a pre-chilled pestile and motar. The extraction buffer contains 100 mM Tris-HCl, pH 8.0, 20 mM EDTA (ethylene diamine tetracetic acid), 1.4 M sodium chloride (NaCl), 3% (w/v) CTAB, 2% polyvinyl pyrrolidine (PVP) and 1% β-mercaptoethanol. The contents were slowly mixed and incubated in water bath at 65°C for 1 hr with slight shaking. The contents were brought to room temperature after incubation and 5 ml chloroform: isoamyl alcohol mixture (24:1) was added. The tubes were centrifuged at 8000 rpm for 20 min at 4°C till a clear supernatant was obtained. After the final spin, the DNA was precipitated using ice-cold isopropanol for overnight at 4°C. The tubes were further centrifuged at 5000 rpm and the pellet washed with 2-3 drops of 70% alcohol, air dried and redissolved in 50 µl Tris-EDTA (TE) buffer and stored at -20°C till further analysis (Simon et al., 2007; Shyamalamma et al., 2008; Xiaoming and Xiuxin, 2001).

4.4 Quantification of DNA

The purity of the isolated DNA was checked spectrophometrically in a UV-Vis spectrophotometer by checking the 260/280 values (Thompson and Dvorak, 1989; Muller et al., 2003). Concentration of DNA (ng/µl) were calculated using the formula

DNA (ng/µl) = OD @ A260 x 50 x 100 x 0.1

Where OD @ A260 is the optical density at absorbance 260 nm

50 is the calculation factor

100 is the dilution factor

0.1 is the total volume of DNA

4.5 PCR amplification

The polymerase enzymes, adaptors and primers were purhasec from Genie life science technology, Bangalore, India. The PCR reaction was perfomed with a Biorad MJPt100 thermocycler (Bio-Rad Laboratories, Bangalore, India). PCR amplifications were performed on two stages. The pre-selecctive amplification was performed with an amplification profile of 94°C for 30 s, anneling at 56°C for 1 min, extension at 72°C for 1 min, repeated for 20 cycles, and then cooling at 10°C for 30 min. Further amplification was performed with a cycling profile of 94°C for 30 s, 65°C for 30 s, 72°C for 24 cycles followed by a cooling of 10°C for 30 min. The primers used for the selective amplification ended with three nucleotide extensions at 3’ ends. The PCR products were stored at -20°C in a deepfreezer. Electrophoresosi of the samples was carried out on agarose gels, by loading 10 µl of each DNA samples at 50 V for 3 hrs tracking dye has properly moved across the gel. The gels were lated viewed on a gel docuing sataion and photographed.

4.6 Data sequencing

The PCR products were cleaned up using GenEluteTM PCR Clean-Up Kit (Sigma-Aldrich). Purified PCR products were sequenced by dideoxy chain termination method (Sanger et al., 1977) using AB3730XL capillary sequencer for the six jackfruit samples.

4.7 Data analysis

Taxonomical identification of the Artocarpus varities collected from different locations were carried out using molecular techniques. The sequences were aligned using the ClustalW algorithm (Thompson et al., 1994) in Bioedit 7.0 (DNA Sequence Analysis Software 224 package). Molecular phylogeny of Artocarpus heterophyllus varieties were studied using the partial sequence of the rbcl gene. Phylogenetic trees were constructed by the maximum likelihood (ML), neighbour joining (NJ) and unweighted pair group method with arithmetic mean (UPGMA) analysis with the software MEGA version 6 (Tamura et al., 2007), and using partial sequence of rbcl gene of the six Artocarpus heterophyllus varieties with 1,000 times boots trapping. Pair wise genetic distances between the Artocarpus heterophyllus varieties were calculated based on Kimura 2 parameter model and was used to calculate estimates of nucleotide diversity (Tajima, 1989), singleton variation, parsimony informative sites, and haplotype diversity. Statistical significance of Artocarpus heterophyllus varieties within the inferred trees was evaluated using the bootstrap of 1,000 replications.

4.8 Data collection

Various jackfruit varieties in Kerala were selected based on a baseline survey, information’s collected from various data bases and beneficiaries. Seven different jackfruit varieties were selected across Kerala for nutritional analysis. Samples of jackfruits were collected and covered with sterile polyethylene sheets and transported to the laboratory as fast as possible. The fruits were opened, and parts were separated carefully with surgical blades and stainless steel knives. The different parts include flake, seed, and skin were taken for the nutritional analysis. Additional leave samples were also collected in polyethylene zipper bags and processed in the laboratory. The samples were dried in hot air oven at 60°C for 48 hrs. The samples were finely powered using a kitchen blender (Preethi Kitchen Magic V2, Preethi Industries, Mumbai) and later stored in airtight polyethylene tubes till analysis. Locations of the sample collection points were recorded using a Trimble Geoexplorer II (Trimble Navigation Ltd, Sunnyvale, California) and data were transferred using GPS Pathfinder Office software (Trimble Navigation Ltd, Sunnyvale, California).

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Figure 1. Mean monthly rainfall (mm), maximum and minimum temperatures (°C) in Kerala, India (1871-2005; Krishnakumar et al., 2009).

4.9 Proximate analysis

The powered jackfruit samples were subjected various proximate analysis using standard protocols. The analysis includes estimation of dry matter and moisture content, determination of minerals using crude ash (CA) method, estimation of crude fat, fibre, crude protein and nitrogen free extract (NFE).

4.9.1 Estimation dry matter and moisture

The dry matter (DM) is calculated using oven dry methods where fresh samples were kept hot air oven at 85°C for 48 hrs. The values are calculated based on the initial and final weight of the samples using the equation given below

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Figure 2. Map of Kerala showing the various sample collection points; Ku (A1), Va (A9), Uc (A7), Pv (A16), Tv (A3) and Sv (A20).

4.9.2 Estimation of crude ash and insoluble ash

The weight of clean dry empty silica crucible is determined as ‘W’ gms approximately 3 gms of the dried powdered sample is weighed noting the exact weight of crucible + sample as W1. Ignite it in the muffle furnace at 600°C for 3 hrs, allow to cool overnight. Take the weight of silica crucible + crude ash as W2. Digest the ash in the crucible with 25 ml of 5N HCl, boiling it for 10 minutes, cool, filter through Whatmann no 42 ashless filter paper and make paper and crucible acid free. Transfer the paper with residue to respective crucible. Dry in hot air oven and ignite in the muffle furnace at 600°C for 3 hrs. Cool overnight and take the weight of the crucible ‘W3’gms, and acid insoluble ash is calculated as

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4.9.3 Estimation of crude fat

The crude fat is done using solvent extraction with petroleum ether. The extraction is done on soxtec fat analyser. Clean dry aluminium cups marked appropriately are weighed W1. Dry powdered sample are weighed approximately 3 gms of sample, noting exact sample weight, W in marked thimbles. The thimbles are attached in correct order on the adaptors of soxtec extractor. 60 ml of petroleum ether is taken in the aluminium cups and assembled seeing that markings of thimble, cup and sample numbers tally. Condenser water supply is switched on. The heating bench is turned on using the ‘power on’ button on control unit of soxtec unit and when the temperature reaches 100°C, the thimbles are dipped into boiling ether and boiling cycle is done for 15 minutes. The thimbles are raised and rinsed with condensed ether in the rinsing cycle for 30 minutes. This is followed by 10 minutes of recovery cycle where pure unsaturated ether is collected back and recovered. The fat containing cups with residual ether is then dried in hot air at 100°C for 1hr, cooled in desiccators and weighed, W2 gms. The crude fat is calculated as.

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4.9.4 Estimation of crude fibre

The thimbles containing fat free extract from the forgoing estimation are dried in hot air oven at 50°C for overnight. Approximately 0.8 gms of fat free sample is weighed exactly ‘W’ gms into gooch crucibles provided with fibretec extraction assembly. They are set on the assembly and two digestions, acid and alkali digestions in 1.25% sulphuric acid (H2SO4) and 1.25% sodium hydroxide (NaOH) are done one after the other for 30 minutes. With draining of acid and alkali and flushing of hot distilled water done in between each digestion. The residue containing crucibles are removed, over dried at 60°C for overnight, weighed ‘W1’gms.They are ashed at 600°C for 3 hours in muffle furnace overnight, cooled and weighed ‘W2’ gms.

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4.9.5 Estimation of crude protein

Estimation of crude protein consists of two parts: digestion and distillation. Weigh approximately 0.25 gm of dried powdered sample noting the exact weight, ‘W’gms, into clean dry digestion tubes. Add approximately 1 gm of digestion mixture (potassium sulphate & copper sulphate, 9:1 by weight) into each tube. Add 12ml of con.H2SO4 into each tube, place on the digester (Kjeltec) assembly and digest at 400°C for 11 to 12 hrs. Cool down to room temperature.

Place on distillation unit (Kjeltec) and set the program (water-70 ml, alkali-70 ml, receiver-30 ml, tube drain) and distil it with steam in the unit. The instrument estimates the crude protein on entering the weight of sample W as

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4.10 Statistical analysis

The survey results were analyzed and descriptive statistics were done using SPSS 12.0 (SPSS Inc., an IBM Company, Chicago, USA) and graphs were generated using Sigma Plot 7 (Systat Software Inc., Chicago, USA).

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Figure 3. Sample Ku (A1) description a) tree bearing jackfruits, b) jackfruit with exposed pith, c) longitudinal section of Jackfruit, d) flake, e) fruit stalk leaf, branch leaf, flake, seeds and spine, f) pith, g) seed.

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Figure 4. Sample Tv (A3) description a) jackfruit, b) cross section of jackfruit, c) reddish flake, d) fruit stalk leaf, branch leaf, flake, seeds and spine.

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Figure 5. Sample Uc (A7) description a) tree bearing jackfruits, b) bunch of round jackfruit, c) spherical shaped jackfruit, d) longitudinal section of jackfruit, e) fruit stalk leaf, branch leaf, flake, seeds and spine, f) spines, g) depression in stalk attachment to fruit.

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Figure 6. Sample Va (A9) description a) tree bearing jackfruits, male and female flower, b) fruit stalk leaf, branch leaf, flake, seeds and spine, c) flake and seed, d) leaf apex (retuse).

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