[proposal] Mitochondrial Dna Variation Of Cultured And Wild Populations Of Asian Seabass In Thailand

MITOCHONDRIAL DNA VARIATION OF CULTURED AND WILD POPULATIONS OF ASIAN SEABASS (Lates Calcarifer) IN THAILAND

THESIS PROPOSAL

YUSMANSYAH

A THESIS PROPOSAL SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE MASTER DEGREE OF SCIENCE IN AQUATIC SCIENCE GRADUATE SCHOOL BURAPHA UNIVERSITY SEPTEMBER 2008 COPYRIGHT OF BURAPHA UNIVERSITY

THESIS PROPOSAL

Name

: Yusmansyah

Program

: Master of Science

Department

: Aquatic Science

Academic year : 2008

Examining Committee Wansuk Senanan, Ph.D. (Principal Committee) Chuta Boonphakdee, Ph.D. (Committee) Thadsin Panithanarak, Ph.D. (Committee)

Title : Mitochondrial DNA Variation of Cultured and Wild Populations of Asian Seabass (Lates calcarifer) in Thailand

CONTENTS CONTENTS LIST OF TABLES LIST OF FIGURES CHAPTER 1 INTRODUCTION 1.1.Background ……………………………………………………

1

1.2.

Objectives…………………………………………………..

3

1.3.

Cotribution to Knowledge …………………………………

3

1.4.

Scope of Study……………………………………………...

3

1.5.

Hypothesis.…………………………………………………

4

2 LITERATURE REVIEWS 2.1.

Biology of Asian seabass...………………………………….

7

2.2.

Ecology and Distribution...…………………………………

9

2.3.

Genetic Processes in Population……………………………

10

2.4.

Mitochondrial DNA as Genetic Marker for Population genetic13

Molecular Techniques for mtDNA Analysis……………….

15

2.5.1. Restriction Fragment Length Polymorphism…………...

15

2.5.2. Direct Sequencing………………………………………

16

2.5.

Analysis……………………………………………………

2.6.

Genetic diversity of Asian seabass and fish species with similar life 18

Genetic data analysis………………………………………..

23

2.7.1. Genetic diversity within populations…………………...

23

2.7.2. Genetic differentiation among Populations…………….

24

2.7.

Histories…………………………………………………….

3 RESEARCH METHODOLOGY 3.1.

Samples Collection ………………………………………...

29

3.2.

DNA Extraction…………………………………………….

30

3.3.

PCR Amplification…………………………………………

31

3.4.

Enzyme Digestion…………………………………………..

31

3.5.

Data Analysis……………………………………………….

33

REFERENCES

35

LIST OF TABLES Table 2.1

Page Genetic diversity of the mtDNA in L. calcarifer and species with similar life histories

2.2

21

General design for hierarchical analysis of molecular variance (AMOVA)

25

3.1

Population sources and number of samples

30

3.2

Recognition size sensitivity of restriction enzymes used

32

LIST OF FIGURES Figure 1.1

Page Linear regression plot of Tamura-Nei’s and delta mu squared genetic distance with coastal distance inferred from mtDNA sequencing and microsatellite using Mantel test

5

2.1

Morphology of Adult Seabass/barramundi (Lates calcarifer Bloch)

7

2.2

World geographic distribution of Asian seabass

2.3

Outcome of three modes of selection: (A) Directional selection,

3.1

10

(B) Stabilizing selection, and (C) Disruptive selection

13

A map of sampling locations

29

1

CHAPTER I INTRODUCTION 1.1. Background Asian seabass (Lates calcarifer Bloch), commonly called ‘giant sea perch’ or barramundi, has become economically important coastal, estuaries and marine fish species in the world, especially in the Indo-pacific region. In 2006, Food and Agriculture Organization (FAO) estimated the world aquaculture production of L. calcarifer to reach 31,909 tons with market value US$ 88,383,000. World demand for L. calcarifer is increasing overtime, not only for flesh but also for fingerlings. It stimulates rapid growth in aquaculture, particularly cage culture, of L. calcarifer in Asian countries, mainly Japan, Taiwan, China, and almost all of South East Asian countries (De Silva & Phillips, 2007). Aquaculture of L. calcarifer in Thailand, pond or cage culture as well as larval rearing, is also growing and spreading in almost all provinces along the Gulf of Thailand and Andaman Sea coastlines, (Department of Fisheries of Thailand, 2005). In Thailand, L. calcarifer fingerlings are produced mainly in hatcheries. The fingerling are not only distributed for local fish farms but also exported to several countries (Interviews with local hatcheries). Well managed breeding program plays an important role in producing good quality fingerling to meet industrial scale aquaculture needs. A successful breeding program depends greatly on broodstock management that maintains high level of genetic diversity of a hatchery population. Reduced genetic variation within population may lead to undesirable consequences on traits related to production (inbreeding depression), for example resistance to diseases stresses, and adaptive potential to environmental stresses (Frankham, 2005). Loss of genetic variation within-population in hatchery populations may be a result of inappropriate hatchery practices, such as mating a limited number of broodstock, and mass spawning causes unequal sex ratio and unequal contribution of each family (Frost, Evans and Jerry, 2006). Wild populations can usually serve as potential source of genetic variation for hatchery populations as they tend to contain higher genetic diversity than hatchery

2

populations. However, information regarding genetic diversity of hatchery and wild populations of L. calcarifer in Thailand is remaining unknown. Genetic diversity data will very useful for designing broodstock management strategies that maintain genetic diversity, such as broodstock collection, mating schemes, and rearing practices (Le Vay et al., 2006). A study in genetic of L. calcarifer in Southeast Asia using microsatellite marker shows relatively high genetic diversity within both hatchery and wild populations, but wild populations tended to have higher within-population genetic diversity than hatchery populations (Zhu et al., 2006). Lower genetic diversity in hatchery populations is commonly observed in other species, for example Japanese flounders Paralichthys olivaceus in Japan (Sekino, Hara and Taniguchi, 2002), Japanese scallops Patinopecten yessoensis in China (Li, Xu and Yu, 2007), Pacific oysters Crassotrea gigas in Australia (English, Maguire and Ward, 2000) and brown trout Salmo trutta (Hansen et al., 1997). Mitochondrial DNA (mtDNA) markers have been useful in describing genetic variation in hatchery and wild populations, and population structure of several species such as Giant Tiger Shrimp Penaeus monodon (Klinbunga et al., 2001), mahseer species (Nguyen et al., 2006b), Common Carp (Thai, Pham, & Austin, 2006), Asian Moon Scallop, Amusium pleuronectes (Mahidol et al., 2007). A mtDNA is a single ‘chromosome’, consisting 15-60 kilo base pair length (Burger et al., 2003). Characteristics of mtDNA, such as uniparental inheritance (Birky, Fuerst, & Maruyama, 1989), smaller effective population size than nuclear DNA (Avise, 2004), and higher mutation rates (Chenoweth et al., 1998), makes mtDNA a sensitive marker for detecting patterns of genetic structure in natural systems. A mitochondrial genome is considered as haplotype (Cavalli-Sforza, 1998). The haplotype diversity allows us to determine variation among individuals within a population and among populations (Billington, 2003). Restriction Fragment Length Polymorphisms (RFLP) combined with Polymerase Chain Reaction (PCR) technique has become a powerful tool to extract haplotypic frequency data from mitochondrial DNA (Bernatchez & Danzmann, 1993). Using this technique, I attempt to evaluate level of mitochondrial DNA variation within and among populations of cultured broodstock of L. calcarifer in

3

Thailand compared to wild populations. This information may help the management of genetic diversity of existing programs as well as the development of a selective breeding program.

1.2. Objectives 1. Estimate genetic variation within hatchery and wild populations of L. calcarifer using PCR-RFLP of the D-loop region of mitochondrial DNA. 2. Examine population differentiation among hatchery and wild populations.

1.3. Contribution to Knowledge 1. The genetic data will be useful in managing existing genetic diversity in hatchery populations of L. calcarifer in Thailand. 2. These data should be useful for breeders and government agencies in order to develop a selective breeding program for L. calcarifer. 3. Data for wild populations will provide basic information to aid conservation efforts of native genepool of L. calcarifer in Thailand.

1.4. Scope of Study This study will focus on the analyses of the D-loop region of mitochondrial DNA to obtain genetic variation within and among hatchery, and wild populations located around the Gulf of Thailand. The selected hatchery populations represent important broodstock supplying fingerlings for Thailand. In this study, I will use the RFLP technique with seven restriction enzymes that cut at specific sites within the mtDNA D-loop region. Polymerase Chain Reaction (PCR) will be used to amplify partial fragment of the D-loop region from extracted genomic DNA. For each individual, restriction fragment patterns generated all enzymes will be grouped into as composite haplotype. Haplotypic frequencies for each population will be used for further analysis.

4

1.5. Hypothesis 1.

Level of genetic variation within hatchery populations of L. calcarifer is lower than that of wild populations. I hypothesized that broodstock used in L. calcarifer hatcheries have lower genetic variation than the wild populations as consequence of aquaculture practices. Aquaculture practices that may lead to the reduction of genetic variation include using limited number of mating parents and employing mass spawning leading to unequal sex ratio and unequal contribution of each family (Miller & Kapuscinski, 2003; Frost, Evans, & Jerry, 2007). Reduced genetic variation has been observed in hatchery stocks relative to the wild population in fish species such as Oreochromis niloticus in Cameroon (Brummett et al., 2004), Common carp, Cyprinus carprio in Vietnam (Thai, Burridge, & Austin, 2007), Atlantic salmon, Salmo salar in northern Spain (Horreo et al., 2008).

2.

Genetic differentiation between wild L. calcarifer populations is high, but the differences among hatchery populations are low Lates calcarifer is catadromous fish, spawn in estuaries and moving vertically to feeding ground in the upper part of river (Moore, 1982). Based on a tagging and recaptured study, Russel & Garrett (1988) reported that barramundi can move to another river up to 17 km along north-eastern Australian from their origin habitat. A study employed the allozyme technique revealed significant genetic differences between areas separated by about 150 km (Salini & Shaklee, 1988). In absence of recombination, mtDNA should be more sensitive than nuclear DNA in detecting differentiation between populations because the mtDNA has smaller effective population size (Ne). Small Ne leads to high rate of reduction of genetic variation within population due to genetic drift, this process will allow for genetic divergence of disconnected populations (Allendorf & Luikart, 2007). A linear regression of genetic distances inferred from mtDNA sequence (Tamura-Nei distance) and from microsatellite data (delta mu

5

squared genetic distance), and geographic distance by Mantel test showed positive correlation of the genetic distance with coastal distance (Figure 1.1; Marshall, 2005). Based on mtDNA data, I hypothesized that the level of genetic differentiation between two wild populations (Chantaburi and Nakhon Si Thammarat) will be high because of its geographical distance (480 km a part measuring as straightline across South China Sea, or about 900 km of coastline distance).

Figure 1.1.

Linear regression plot of Tamura-Nei’s and delta mu squared genetic distance with coastal distance inferred from mtDNA sequencing (top) and microsatellite (bottom) using Mantel test. In contrast, I hypothesized that genetic differentiation among hatchery

populations are low. Based on informal interviews, hatchery managers prefer introducing

already

domesticated

broodstock

from

other

fisheries

development centers or fish breeders to introducing wild individuals because the convenience and lower mortality rate during transportation. This practice will lead to genetic homogenization of various hatchery populations. Based

6

on sequences of mtDNA control region and Single Strand Confirmation Polymorphisms (SSC), Thai, Pham, and Austin (2006) suggested variation among-population partitioned by AMOVA in Vietnamese common carp (Cyprinus carpio) hatcheries was not significant (19.53% at P < 0.01). The genetic homogenization among hatcheries populations was due to interbreeding among lineages in order to produce genetically improved strains.

7

CHAPTER 2 LITERATURE REVIEWS

2.1.

Biology of Asian seabass Asian seabass, (Lates calcarifer Bloch, 1790) are large centropomid fish

(Actinoperygii: Perciformes: Centropomidae: Latidae). The body is elongate and laterally compressed, with a relatively large mouth, slighty oblique upper jaw extending behind the eye. The head profile is clearly concave. The lower edge of the pre-operculum is serrated with a strong spine; the operculum has a small spine and a serrated flap above the origin of the lateral line and the scales are ctenoid. The first dorsal fin bears eight to nine spines and ten to eleven soft rays. The ventral fins have spines and soft rays; the paired pectoral and pelvic fins have soft rays only; and the caudal fin has soft rays and is rounded (Larson, 1999). Classification of barramundi could be described as follows: Kingdom: Phylum: Class: Order: Family: Genus: Species:

Animalia Chordata Actinopterygii Perciformes Latidae Lates Lates calcarifer

Figure 2.1. Morphology of Adult Seabass / Barramundi (Lates calcarifer Bloch) Source:www. fishase.org.

Based on recorded movement of tagged fish, Asian seabass or Barramundi is categorized as a catadromous-demersal teleost fish (Moore, 1982), inhabiting coastal areas, particularly estuaries and rivers (Russel & Garrett, 1988) with depth range 1040 m (Larson, 1999). In Papua New Guinea, larvae and young juveniles live in brackish temporary swamps associated with estuaries, and after 2-3 years they move into inland waters. After their third or fourth age, the fish migrates from inland to coastal waters (Moore & Reynolds, 1982). Based on tagging and recaptured study, movement of barramundi in north-eastern Australian coastline reached maximum 70

8

km from estuaries to upstream the river as vertical migration, and maximum 17 km to another river along coastline from their origin habitat (Russel & Garrett, 1988). The von Bertalanffy growth parameters for infinitive length (L∞) and growth (K) in L. calcarifer varies between populations. In Mary River and West Alligator River, both located at Van Diemen Gulf, Australia, L∞ was estimated as 1374 mm and 996 mm with K = 0.148 and 0.216, respectively (Davis & Kirkwood, 1984). In Fitzroy River, Queensland L∞ = 690 mm, growth K = 0.53 and t 0= 0.003 years (Stuart and McKillup, 2002). Although there was variation of L∞ between river populations in Northern Australian coastline, but the variation was not significant. Furthermore, mean length at age 1 to 5 years among river populations was not statistically different (Davis & Kirkwood, 1984). L. calcarifer is sexually protandrous hermaphrodites; individual is sexually matured as male in the first time. The gonad structure then develops as function as female at the following ages (Moore, 1979). Length at first maturity in males was observed at 250 mm in the Gulf of Carpentaria, Australia (Davis, 1982). In a wild population of the Northern Territory and South-eastern Gulf of Carpentaria-Australia, eggs can be optimally produced after 8 years old, therefore the proportion of mature female in a population is very low due to limited numbers of survivor reaching that age (Davis, 1982). However, the fecundity of L. calcarifer is very high (up to 46 x 106 eggs), so that the small proportion of females can maintain number of recruitment in a population (Davis, 1984). In sex-biased molecular marker such as mitochondrial DNA, limited number of females and high fecundity may result in low genetic variation in a population. The adult exhibits a single annual reproductive period, but the peak period varied among areas. In French Polynesia sea cages, a breeding season starts from October to February beginning with the dry and wet seasons (Guiguen et.al., 1994). In Van Diemen Gulf - Northern Australia, the fish spawned from September to February and in Gulf of Carpentaria, it starts from November to March (Davis, 1985).

9

2.2.

Ecology and Distribution Adults of Seabass are carnivorous, but juveniles are omnivorous

(Sirimontaporn, 1988). In Seabass larval rearing, juveniles can consume several zooplankton species including rotifer (B. rotundiformis), Artemia nauplii and water flea (Moina sp.) (Pechmanee, 1997). For European seabass, Dicentrarchus labrax, diet of 0-group mainly consist of crustaceans with the most important food organisms including Decapoda, Mysidacea, Isopoda (Cabral & Costa, 2001) and Amphipods (Laffaile et al., 2001). Feeding activity increased during summer. Short-term variations were related to the period of the day and tidal cycle (Cabral & Costa, 2001). Distribution of L. calcarifer spread along the Indo-West Pacific coasts: eastern edge of the Persian Gulf to China, Taiwan and southern Japan, southward to southern Papua New Guinea and northern Australia (Figure 2; Larson, 1999). A study based on microsatellite and cytochrome b of the mtDNA data data supported that L. calcarifer in the coastline of Australia originated from Indonesia, migrated to Western Australia, then spread east and west along the coastline (Marshall, 2005).

Figure 2.2.

World geographic distribution of Asian Seabass (Retrieved from FIGIS-FAO, http://www.fao.org/figis/)

10

2.3.

Genetic processes in populations Principally, genetic composition in nature changes continuously as respond

to the environmental change. In a long term, genetic changes will lead to two consequences: survival adaptation and extinction. In a shorter term, genetic changes affect population’s characteristics and demography (Avise, 2004). There are four major

microevolutionary

processes

that

could

change

population

genetic

characteristics, i.e. mutation, genetic drift, gene flow and natural selection (Allendorf and Luikart, 2007). •

Mutation

The ultimate source of genetic variation in populations is mutation. There are two major types of mutations: point (gene) mutations and chromosomal mutations. Point mutation is a change in one nucleotide or several nucleotides in a single gene. The change could be due to base pair substitutions, insertion or deletion. Chromosomal mutation is a change in the number of chromosome or gene arrangement in chromosomes (Hallerman & Epifano, 2003). In the maternally inherited haploid DNA such as mitochondrial DNA (mtDNA) where recombination does not occur, most of genetic variation comes from mutation events, particularly point mutations that creating or deleting nucleotide(s) in mtDNA sequences (Avise, 2004). •

Genetic drift

Genetic drift is the changes in allele frequency in a population in successive generations due to a random process. The magnitude of genetic drift in a population depends on the levels of deviation from an ideal population such as unequal number of male and female breeders, variance in family size, and different number of parents in successive generations (Hallerman, 2003). The outcome of genetic drift can not be predicted because of the random process. Effect of genetic drift, however, can be estimated through simulation and the result is highly depends on population size (Ne). The impacts of genetic drift are more obvious in small populations. Two major impacts on the genetic composition of small populations are (1) change of allele frequency, and (2) lost of genetic variation

11

(Allendorf and Luikart, 2007). Theoretically, mtDNA has fourfolds lower population size than nuclear DNA. Thus, mtDNA is more susceptible to genetic drift effects than nuclear DNA (Avise, 2004). Factors influencing the level of genetic drift in a hatchery population may include inappropriate aquaculture practices such as mating limited number of parents, and employing mass spawning leading to unequal sex ratio and unequal contribution of each family (Miller & Kapuscinski, 2003; Frost, Evans, & Jerry, 2007). Loss of within-population genetic variation in a hatchery population is caused by several processes such as genetic drift, inbreeding and extinction vortex. These processes can lead to fitness reduction that driven to greater inbreeding and loss of variation. Using adequate number of broodstock in an appropriate mating strategy will help breeders avoiding loss of within population genetic variation in hatchery (Miller & Kapuscinski, 2003). For example, Sriphairoj, Kamonrat and Na-Nakorn (2007) simulated alternative mating strategies for broodstock Mekong giant catfish Pangasianodon gigas in Thailand and suggested that mating equal number of broodstock (28 mature male) and (28 mature female) with lowest the mean genetic relatedness (rxy < 0.07) was able to prolong the reduction of genetic diversity for short term mating plan. In the same study, minimal kinship approach in the first generation then followed by a random mating scheme with Ne larger than 30 individuals is recommended to preserve genetic variation at least 90% for long term plan (over 100 years). •

Gene flow

Gene flow (or migration) is any movement of alleles from one population to another. Those alleles recombine with local alleles through sexual reproduction. Genetic interactions between two or more populations through gene flow will increase or maintain genetic variability within a population, but will decrease genetic distinctiveness among populations (Gharret & Zhivotovsky, 2003). In mitochondrial DNA, gene flow can be indicated by haplotypes shared between two or more genetically related populations (Avise, 2004). There are two major factors governing gene flow in nature population: intrinsic and extrinsic factors. Intrinsic factors cover the role of biological aspects of

12

the species such as reproductive system (e.g. asexual reproduction, autogamy, outcrossing, and ploidy), behavior and dispersal (e.g. gametic or zygotic dispersal, gender differences and breeding behavior), and historical processes, such as historical events in a populations. Extrinsic factors are including physical barriers, and environmental selection factors determining the survival of a particular species (Lowe, Harris and Ashton, 2004). Gene flow also can be resulted by human activities such as artificial culture including restocking, marine ranching and fish escaped from culture system (Gharret & Zhivotovsky, 2003). The movements of broodstock among hatcheries will increase or maintain within-population genetic variation, but will reduce among-population genetic variation (or population identity). Based on AMOVA, Thai, Pham and Austin (2006) indicated significantly high genetic variation within hatchery than among hatchery populations (80.47% and 19.53, respectively; P<0.01) of common carp in Vietnam as result of genetic improvement dissemination program that facilitate gene flow among hatcheries. Lack of genetic divergence among white shrimp Litopenaeus vannamei hatcheries in Brazil also has been identified as results of the gene flow effects due to shared ancestry of initial founder composition of broodstocks used in all hatcheries (Freitas, Calgaro and Galetti, 2007). To prevent genetic identity among hatcheries, establishment of genetic improvement programs through broodstock’s interchange among hatcheries is needed. Genetic lineage, life history patterns and ecology of originating environment data of broodstock is important to increase the adaptive chances of hatchery to the introduced broodstock (Miller and Kapuscinski, 2003). •

Natural selection

Genetic processes such as mutation, genetic drift and gene flow are can cause change overtime, but natural selection is the primary process of adaptive evolution. Natural selection is the process which favorable heritable traits become more common and unfavorable heritable traits become less common in successive generations due to differential survival or reproduction of phenotypes in a population (Haliburton, 2004). Since phenotypes are highly associated with genotypes, unequal probability of alleles survive or reproduce the future generation will determine their allele frequency in a population (Allendorf, 2007, pp. 171-196).

13

There are three modes of selection which alternatively change the genetic composition at the end of population’s distribution: (1) Directional selection, that produces one favorable phenotypes or fixed homozygote (either dominant or recessive) outcome, (2) Stabilizing selection or normalizing selection, that favors intermediate phenotype or fixed heterozygote outcome, and (3) Disruptive selection, that favors extreme phenotypes or both dominant and recessive homozygote (Hallerman, 2003). Outcome of three modes of selection can be illustrated as Figure. 2.3.

A

B

C

Figure 2.3. Outcome of three modes of selection: (A) Directional selection, (B) Stabilizing selection, and (C) Disruptive selection (Hallerman, 2003).

2.4.

Mitochondrial DNA as Genetic Marker for Population genetic Analyses Genetic variation in natural populations has been studied by using various

markers following the development of molecular techniques. Chromosome and protein electrophoresis were commonly used for genetic variation studies because of its fast and simplicity to observe polymorphisms and heterozygosity to estimate level of variation (Allendorf & Luikart, 2007). Development in molecular techniques allows us not only to infer variation from nuclear DNA but also cytoplasm DNA such as mitochondrial DNA (mtDNA), chloroplast DNA, and ribosomal DNA. The variation can be obtained from direct sequence and indirect methods to infer nucleotide sequences including Restriction Fragment Length Polymorphisms (RFLP), Amplified

Fragment

Length

Polymorphisms

(AFLP),

Random

Amplified

Polymorphic DNA (RAPD), Single-Strand Conformational Polymorphisms (SSCP), DNA Microarrays and Microsatellites (Halliburton, 2004). Nuclear DNA and mtDNA

14

contain rich information for variation studies on animal variation, particularly for fish species (Billington, 2003; Brown & Epifano, 2003). Animal mtDNA is a relatively small circular molecule of 15 to 20 kb, comprising of 37 genes. These genes code for 22 tRNAs, 2 rRNAs, and 13 mRNAs. The mRNA is protein coding involved in electron transfer and oxydative phosphorilation in the mitochondria. The genome lacks introns and contains small intergenic spaces in which the reading frames sometime overlap. The control region is the only major non coding area of the mitochondrial genome. It is approximately 1 kb in size, involved in the regulation and initiation of replication and transcription of the mitochondrial genome (Terzioglu & Larson, 2007). The use of mtDNA in recent years has been increasingly popular, especially in phylogenetic and population genetic studies. Analysis techniques for mtDNA have evolved from the use of restriction enzymes to detect differences in nucleotides of the whole mtDNA genome (Lansman et al, 1981), to the use of polymerase chain reaction (PCR) to amplify a particular region of mtDNA (Kocher et al., 1989). Characteristics of mtDNA such as uniparental inheritance (Birky, Fuerst, & Maruyama, 1989), smaller effective population size than nuclear DNA, and higher mutation rates (Chenoweth et al., 1998), make mtDNA a sensitive marker for detecting patterns of genetic structure in natural systems. Different regions of mtDNA may be appropriate for different applications because of the varied mutation rates among regions. For example, cytochrome b and 16S rDNA are suitable for describing phylogenetic relationships (Irwin, Kocher & Wilson, 1991), while control region is appropriate for population genetic studies (Brown et. al., 1986) because of its hypervariable nature. The use of PCR-amplified mtDNA control region has been commonly used for genetic variation studies at population levels, for example Asian Nile and red hybrid tilapia (Romana-Eugia et al., 2004), Asian seabass or barramundi in Australia (Marshall 2005), and common carp in Vietnam (Thai, Pham and Austin, 2006).

15

2.5.

Molecular Techniques for mtDNA Analysis Mitochondrial DNA analysis requires molecular techniques to obtain

information related to nucleotide variation and its relative changes at an individual or population level. Principally, there are two techniques commonly used for mitochondrial DNA analysis, i.e. restriction fragment length polymorphisms (RFLP) and direct sequencing. Both restriction pattern analysis and direct sequencing could be performed on whole mtDNA or particular region within mtDNA genome.

2.5.1. Restriction Fragment Length Polymorphisms (RFLP) Restriction Fragment Length Polymorphisms of a DNA genome can be obtained using one or more restriction endonucleases. Fragments can, then, be observed by electrophoresis gel separation based on their molecular weight. Variation in the RFLP patterns may result from nucleotide substitution within restriction sites, insertions or deletions of DNA, or sequence rearrangements (Avise, 2004, 67-78). RFLP technique is commonly used in aquaculture genetics studies (Liu & Cordes, 2004). RFLP method can be applied on whole mtDNA genome or a specific region such as control region or cytochrome b. RFLP of a whole mtDNA genome is a powerful tool to provide a comprehensive map for mtDNA variation e.g., mtDNA diversity in wild and culture population of Brycon opalinus in Brazil (Hilsdorf et al., 2002). However, RFLP for mtDNA genome needs large quantity and high quality of purified mtDNA, involving methods that use either radioisotopes or complex biochemical material. Alternatively, RFLP of a specific region through Polymerase Chain Reaction (PCR) offer a rapid, simple, and low cost method to detect polymorphisms (Kocher, 1992). This method provides vast utility in genetic variation study for both nuclear and organelles DNA (Liu & Cordes, 2004). Furthermore, sampling for genetic markers that are assayed using PCR-based technology can be carried out using small amount of samples (e.g., scales or 10-3 g of tissue) so that lethal sampling can be avoided (Scribner et al., 1998). PCR amplification from a specific region requires appropriate primers. Bernatchez and Danzmann (1993) designed LN20 and HN20 for amplification of ~1 kb control region of brook charr Salvelinus fontinalis Mitchill. These primers can also

16

be applied in several fish species such as scad mackerel Decapterus russelli (Arnaud, Bonhomme, & Borsa, 1999), Nile and hybrid tilapia (Romana-Eugia et al., 2004), Moroccan sardines Sardina pilchardus (Atarhouch et. al., 2006) and Asian seabass Lates calcarifer (Marshall, 2005) to seek population structure and differentiation. MtDNA assay using the PCR-RFLP technique produces high sensitivity in population genetic study. Bernatchez and Danzmann (1993) compared the level of congruence of RFLP and sequencing data in describing genetic diversity and phylogenetic relationships among mitochondrial DNA haplotypes of wild and hatchery populations of brook charr Salvelinus fontinalis Mitchill from Ontario. This report showed high congruent between both techniques in terms of mtDNA variation detected per number of nucleotide sampled and their ability to describe phylogenetic relationships among haplotypes. Basic assignment of RFLP method is constructed from presence-absence matrix of restriction sites (Nei & Miller, 1990). A pattern of fragment produced after enzyme digestion notated by a specific letter. A composite of fragment patterns generated by a set of restriction enzymes is described as haplotype. Frequency of haplotypes within samples is the key source for data analysis (Nei & Tajima, 1981).

2.5.2. Direct Sequencing DNA sequencing is a biochemical method to determine the order of the nucleotide bases: adenine (A), guanine (G), cytosine (C) and thymine (T) in a DNA strand. Main purpose of this method is seeking variation differences of nucleotides within particular or whole DNA genome. Direct sequencing method allows us to detect a nucleotide base changes (or differentiation) due to insertion, deletion or substitution between two individuals. Two general protocols for DNA sequencing include (1) Radioactive end label or Maxam-Gilbert method and (2) Anneal primer or Sanger method (Avise, 2004, pp. 98-100). Radioactive end label method uses radioactive chemical

32

P to

label one end of a DNA strand. DNA is then cleaved at base-specific position using four chemical reagents for four DNA subsamples (Maxam & Gilbert, 1977). Sanger method uses the 2’, 3’-dideoxy and arabinonucleoside replacing normal deoxynucleoside triphosphates that act as specific chain-terminating inhibitors of

17

DNA polymerase (Sanger, Nicklen & Coulsen, 1977). Both methods using polyacrilamide gel electrophoresis to separate fragments at corresponding nucleotide sequence. Recent advance for sequencing technology has shifted from flat – polyacrylamide gel electrophoresis to capillary electrophoresis, a method that measures electric current of ion conduction of nucleotide molecule passing through the 1 nanometer pores. Electric current specific to a nucleotide charge then read by a detector, and then translated and recorded by computerized machine (Ewing, Wallingford & Olefirowicz, 1989). To compare DNA sequences between two or more individuals, sequence data need to be edited and aligned so that differences between sequences can be proceed to the next analysis. Mitochondrial DNA sequence data has been widely used for population genetic study in aquaculture species, for example, mtDNA control region sequence data was very informative to determine the level of genetic diversity within and among population of cultured and wild Vietnamese common carp Cyprinus carprio, and relationships with common carp strains from China, Indonesia, Japan, Hungary, and India (Thai, Pham, & Austin, 2006). The mtDNA control region sequence data also powerful to trace evolutionary relationship among 30 fish species from 14 suborders that closely related with L. calcarifer taken from Australia and Singapore (Lin et al., 2005). Although sequencing has higher sensitivity in detecting nucleotide variation than RFLP analysis, magnitude of variation and differentiation resulted from both analysis are congruent. Bernatchez and Danzmann (1993) provided an evidence of congruence in terms of amount of variation and nucleotide differentiation between the control region sequence and restriction site variation in mtDNA control region of brook charr (Salvelinus fontinalis). This evidence included: (1) there was only one different sequence haplotype undetected by RFLP analysis if some of rare haplotypes from RFLP analyses (of 27 haplotypes) are converted into the same number of sequence haplotype (11 haplotypes), (2) the number of mutations per nucleotide detected in sequence analysis was approximately twice that of the number detected from RFLP analysis, but linear relationships between the number estimated through

18

both methods showed strong positive relationship, suggested identical efficiency in sequence and RFLP analyses to detect mtDNA variations.

2.6.

Genetic diversity of Asian seabass and species with similar life histories Genetic diversity of Asian seabass (L. calcarifer) and other species with

similar life history has been studied for decades. Most studies has focused on L. calcarifer populations in Australia, where L. calcarifer is highly abundant in the wild. Genetic markers used to describe genetic diversity of L. calcarifer in various geographic locations include allozyme, mtDNA and microsatellite DNA markers. Genetic variation in wild populations of L. calcarifer is relatively high compared with other species with similar life histories. Using allozyme analysis, Salini and Shaklee (1988) observed high genetic variations in L. calcarfer populations in Northern Australia (average expected heterozygosity (He) = 0.120±0.185) compared to European seabass Dicentrarchus labrax populations in Mediterranean Sea (He = 0.041 and He = 0.113±0.015) (Lemaire et al., 2000; Allegrucci, Fortunato, & Sbordoni, 2007), and wild brown trout Salmo trutta populations from Sourge river (He = 0.007) and Orb river (He = 0.053), both located in the Mediterranean drainage basin (Poteaux, Berrebi, & Bonhomme, 2001). MtDNA marker also indicated high genetic variation in wild populations of L. calcarifer. Using mtDNA control region sequences, Chenoweth et al. (1988) observed high genetic diversity in term of haplotype diversity (ĥ) in populations of L. calcarifer in Northern Australia and Western Arafura Sea (ĥ = 0.763 – 0.933). Similarly, Doupe, Horwitz and Lymbery (1999) detected high haplotype diversity (ĥ = 0.711 - 0.897) in populations of L. calcarifer in Western-Northern Australia. The ĥ values of L. calcarifer were higher than, for example, wild populations of Salmo trutta in Danube drainage-Austria (ĥ = 0.356 – 0.642) inferred from mtDNA control region sequences analysis (Weiss et al., 2000). Using nuclear DNA microsatellite data, the level of genetic diversity, represented as expected heterozigosity (He), of the wild populations of L. calcarifer were found to be high in the Northern Australia (He = 0.518 – 0.728); and Thailand

19

(He = 0.75) (Marshall, 2005; Zhu et al., 2006). Differences result in obtained by different studies may be influenced by characteristic and sensitivity of marker used, number individuals sampled, and/or genetic composition has changed overtime in corresponding populations. Genetic variation in hatchery populations is relatively lower than wild populations. Using Microsatellite and mtDNA sequence method, Sekino, Hara and Taniguchi (2002) observed lower genetic variation within population in hatchery (He = 0.59-0.71; h = 0.692-0.798) than wild population (He = 0.75-0.76; h=0.998) of Japanese flounders Paralichthys olivaceus. Genetic variation study in wild and hatchery populations of brown trout (Salmo trutta) indicated significant differences of within-population genetic variation (Hansen et al, 1997). In a hatchery population, low variation within population may indicate occurrence of genetic drift and inbreeding (Kapuscinski and Miller, 2003). In wild populations, low genetic variation may be facilitated by the lack of migration or gene flow, founding effect or bottleneck process in the past (Allendorf & Luikart, 2007). Genetic variation in hatchery population, however, can be higher than wild population. In some cases, Zhu et al. (2006) observed the hatchery/stock I were slightly higher (He = 0.76) than wild population because it consisted of individuals from genetically distinct sources. Strong population structure among wild populations of L. calcarifer has been indicated by high percentage (most values significant at P<0.001) of variation among population (analyzed by analysis of molecular variance /AMOVA), clear clustering pattern based on genetic distant (high distribution or FST supportive values on a node), or exact test (Table 2.1). Recent study in wild populations of L. calcarifer in Northern Australia (Marshall, 2005) showed a significant variation differences (P < 0.001) among river populations within province (percentage of variation = 28% and 61% from mtDNA and microsatellite, respectively) and among provinces (percentage of variation = 3.1% and 47% from mtDNA and microsatellite, respectivelly). High variation among populations and among groups indicated the existence of population structure of L. calcarifer in Northern Australia. A PCR-RFLP study in the mtDNA of brown trout Salmo trutta also indicated existence of population structure because of high percentage of variation (88.83%; P<0.0001) among five lineage groups: Atlantic, Danubian, Marmoratus, Mediterranean, and Adriatic (Bernatchez, 2001).

20

The presence of population structure in L. calcarifer populations in Northern Australia likely indicated stepping-stone migration model (Salini and Shaklee, 1988; Marshall, 2005), where populations within group are genetically similar. The stepping stone model is a spatial model of population structure where adjacent populations are the primary sources of genetic material. Adjacent populations would them be more genetically similar compared to more distant populations (Gharrett & Zhivotovsky, 2003). Linear stepping-stone model of migration may fit to describe L. calcarifer movement in the wild, because they migrate along the river in one direction, out to coastal areas, and return to original rivers. This migration pattern will preserve the within population genetic diversity (Marshall, 2005). A similar genetic diversity pattern also has been observed in brown trout Salmo trutta populations within five lineage groups (Atlantic, Danubian, Marmoratus, Mediterranean, and Adriatic), where populations within a lineage group were more genetically similar compared to populations in different lineage. The lower variation among populations within lineages than among lineages (8.67% and 88.83% at P<0.0001 for the levels of variation within and among lineages, respectivelly) not only indicated a onedimensional migration but also the existence of evolutionary lineages as a result of geographic isolation during Pleistocene period (Bernatchez, 2001).

21

Table 2.1. Genetic diversity of L. calcalifer and species with similar life histories Species - Habitat

Marker/ Technique

Number of Enzymes used (for RFLP)

Number of Haplotype

Haplotype diversity ( ĥ )/ Expected Heterozygosity (He)

-

He 0.120±0.185

Lates calcariferNorthern Australia

Allozyme

-

Lates calcarifer Northern Australia - Coral Sea - Gulf of Carpentaria - Western Arafura sea

mtDNA/ Sequencing

-

Nucleotide diversity

Reference

Partitioned Variation

Within Among Among group populations populations (%) (%) within group (%) Mean FST 8.7 * Salini & Shaklee, 1988 ( P < 0.001) AMOVA, Haplotype based

ĥ 17 22 24

0.763±0.044 0.915±0.014 0.933±0.012

0.0296±0.0054 0.0369±0.0055 0.0598±0.0093

8.6

10.4

( P < 0.001)

(P < 0.001)

Chenoweth, (1998)

et

AMOVA, Sequence based 3.5 (P = 0.009)

30.3 (P = 0.002)

Lates calcarifer - Darwin (D) - Fitzroy river (F) - Ord river (O) Lates calcariferNorthern Australia

mtDNA/ Sequencing

ĥ 4 17 25

mtDNA/ Sequencing

-

Nuclear DNA/ Microsatellite

-

AMOVA 0.711±0.239 0.897±0.045 0.836±0.057 -

0.006±0.001 0.017±0.006 0.040±0.006 -

52.6 (P < 0.001) AMOVA, mtDNA 10.9* 28.0*

Doupe, Horwitz, & Lymbery (1999) 61.1* Marshall (2005)

He 0.518(±0.073) – 0.728(±0.067)

-

AMOVA, Microsatellite 49.8* 3.1* All at P < 0.001

47.0*

al.

22

Lates calcariferSingapore - Wild (Thailand) - Stock I - Stock II Dicentrarchus labrax Mediterranean Sea

Nuclear DNA/ Microsatellite

Nuclear DNA/ Allozyme

-

Salmo trutta/ Denmark - Wild - Hatchery Salmo trutta/ Mediterranean Sea - Atlantic lineage - Danubian lineage - Marmoratus lineage - Mediterranean lineage - Adriatic lineage Salmo trutta Mediterranean Sea

mtDNA-ND-1 ND-5/ND6/ RFLP

6 5

mtDNAControl region/ RFLP

6

Sorgue drainage Orb drainage

Nuclear DNA/ Allozyme

Sorgue drainage Orb drainage

mtDNAcontrol region / RFLP

Sorgue drainage Orb drainage

Microsatellite

-

He

-

30 (P < 0.05)

He 0.113±0.015

-

Zhu et al. (2006) Allegrucci, Fortunato, & Sbordoni (1997)

FST

34 ( P < 0.05)

ĥ 11 9

Hansen et al. (1997) 0.764±0.104 0.572±0.173 AMOVA

ĥ 0.582(±0.011) 0.931(±0.005) 0.851(±0.009) 0.523(±0.011) 0.511(±0.007)

2

FST

0.75 0.76 0.72

He 0.0007 - 0.087 0.0532- 0.076 ĥ 0.06 -0.25 0.20 -0.49 He 0.4- 0.73 0.59-0.84

0.0020 0.0045 0.0018 0.0005 0.0005

62.42 78.48 93.73 75.07 90.42

-

-

8.67* (P < 0.0001)

88.83* (P < 0.0001)

-

-

Bernatchez (2001)

Poteaux, Berrebi, Bonhomme (2001)

&

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2.7.

Genetic Data Analysis Analysis of genetic data is crucial for population genetic studies. The aim of

genetic data analysis is to provide objective, stable classification of individuals into groups (Shaklee and Currens, 2003). Principally, there are two mathematical models to characterize population genetics in terms of RFLP technique data, i.e. genetic diversity within populations and genetic differentiation among populations (Nei and Tajima, 1981).

2.7.1. Genetic diversity within populations Genetic variation in wild population reflects the occurrence of evolutionary events, such as population bottlenecks, and presence of population structure (Avise, 2004, 478-515). In aquaculture populations, low genetic diversity within population reflects the impacts of genetic drift or aquaculture practices that lead to reduction of genetic variation (Billington, 2003). Populations with high genetic variation within population may have high potential to adapt to environmental changes or disturbance (Nguyen et al., 2006a). Two important calculations for describing genetic diversity within populations, are haplotype diversity and nucleotide diversity (Billington, 2003). Haplotype diversity (ĥ), or gene diversity is subject to random variation due to stochastic changes of haplotype frequencies. This can be estimated by the Nei and Tajima method

(1981; Equation 1). ………………………(1)

where n is the number of sample in a group, xi is the frequency of ith haplotype composite and l is the number of haplotype composite. Haplotype diversity is analogous to expected heterozygosity for nuclear, codominant DNA markers. Nucleotide diversity ( ) is the average of nucleotide differences per site between two randomly choosen DNA sequences (Nei and Li, 1979), and can be estimated by Equation 2; …………………………(2)

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where xi and xj are the frequencies of ith and jth haplotype in a population, πij is the difference between haplotype i and j, and n is the number of individuals in the population (Nei and Tajima, 1981). Nucleotide diversity will indicate haplotype variation within populations. Using the Arlequin program, calculations of haplotype and nucleotide diversity could be performed simultaneously (Excoffier, Laval, & Schneider, 2005).

2.7.2. Genetic Differentiation among Populations Genetic differentiation is determined from nucleotide differences between individuals or populations. Analysis of genetic differentiation among populations provides information in genealogy at a microevolutionary scale such as parentage and kinship, historical population size, geographic distribution and gene flow (Avise, 2004, pp. 491–497). In aquaculture populations, genetic differentiation among populations analysis is very useful for identifying populations, assessing gene flow and identifying its potential origin, and designing selection criteria of broodstock (Miller and Kapuscinski, 2003). Levels of genetic differentiation including estimation of within population, among populations within groups, and among populations among groups. Genetic differentiation at a population level relies upon haplotypic frequency differences inferred from several restriction site patterns. A method for assessing genetic differentiation between populations was proposed by Nei and Tajima (1981); it is analogous to genetic differentiation in sequence data. Consider pi and qi as frequency of the ith haplotype in population X and Y, respectively, mean number of genetic differentiation between two randomly chosen haplotypes (

, one each

from populations X and Y can be described as follow: ………..……………………(3) Where vij = restriction site difference between haplotype i and j. If the sample frequencies of the ith haplotype are xi and yi in population X and Y, respectively, the value in Equation (3) can be estimated with Equation (4): …………….………………(4)

25

This estimation, however, assumes that there are polymorphisms of the restriction site differences within populations, and the amount of restriction-site differences within populations as described by equation (3) and equation (4). If populations to be compared are closely related, the assumption will not be valid. Thus, the number of net restriction site differences, or genetic differences should be subtracted from the total differences among population, can be estimated by Equation (5): ………..………………………….(5) where

and

are the values of

described in Equation (4) in population X and Y,

respectively (Nei and Li, 1979). Genetic differentiation also can be indicated by Φ-statistics developed by Excoffier, Smouse & Quattro (1992) by calculating ΦST, ΦSC, and ΦCT, as the correlation of random haplotypes among groups, among populations within a group, and among individuals within a population, respectively. These equations can be described as follows: ΦSC

ΦST where

,

,

and

ΦCT

……..(6)

are the associated covariance component for groups,

populations within a group, within populations, and total variation, respectively. These calculations can be performed by Analysis of Molecular Variance provided in the Arlequin program (Excoffier, Laval, & Schneider, 2005), an analogous to Analysis of Variance (ANOVA) of a variable. AMOVA partitions existing genetic variation into different hierarchical levels (Table 1). Table. 1 General design for hierarchical analysis of molecular variance (AMOVA). Source of variation d.f MSD Expected MSD MSD/(AG) Among groups G–1 Among populations within groups

MSD/(AP/WG)

Among individual within populations

MSD/(WP)

Total

N–1

Note: MSD = Mean squared deviations ; G = number of group ; Ig = number of populations in g group ; N = number of overall sample ; n, n’, n’’ = average sample size of particular hierarchical level as described at Excoffier et.al, (1992)

26

Permutation analysis method can be used for testing the significance of the variance component (

,

,

and

) analysis and Φ-Statistics (Equation 6) to

obtain the null distribution and test for the significance of among groups (ΦST and ); among populations within group (ΦSC and populations (ΦSC and

); and among individual within

). This method requires fewest assumptions, that is null

hypothesis, where each samples assumed as part of global population and variation was due to random sampling in the structure of populations (Excoffier, Smouse and Quattro, 1992). The mtDNA restriction fragment data can be used to measure genetic distance based on relatedness estimation between populations. Upholt (1977) first described the relationship between the proportion of fragments shared in mtDNArestriction fragments and nucleotide sequence divergence (Avise, Lansman, & Shade, 1978). If Nx and Ny are the number of restriction fragments observed in sequences X and Y, and Nxy is the number of shared fragment in both corresponding sequences, the overall shared fragments proportion (F) is calculated by the Equation (7): …..……………………………….(7) Fragment patterns produced from enzyme digestion can be used to estimate sequence divergence between each pair of samples based on the percentage nucleotide substitution (p), that is the proportion of fragments shared between two digested sequences (F), and number of base pairs recognized by restriction enzyme (r) by the following equation: …………..(8) Genetic distance is expressed from the proportion of nucleotide substation

by

construct pairwise matrix between observed samples (Avise, Lansman, & Shade, 1978). Genetic distance calculation based on Equation (8), however, requires the number of base pair recognized by enzyme restriction (r), where this value is more complex with recent development of multi-recognition restriction enzymes.

27

Alternativelly, genetic distance can be derived by calculating pairwise FST values. The FST values can be derived based on drift model, including mutation and other factors affecting gene frequencies (Reynolds, Weir, & Cockerham, 1983) and Nei’s average number of restriction site differences between populations (Nei and Li, 1979). Reynold’s distance is defined as D = - log (1- FST), where FST = 1 – (1-1/N)t = 1 – e-t/N………..……………(9) where N is the initial size of reference population, and t is the number of known generation from the initial population to the present population, assumed that the reference population is randomly mating, completely isolated and constant in size. Distances based on coancestry coefficient - the probability that two homologous genes from two individual that are identical by decent - is designed to measure the divergence FST between populations of size N that is caused by drift during t generations ago. For short divergence times, the genetic distance D is approximately proportional to t/N (Reynolds, Weir, & Cockerham, 1983). Nei’s average number of restriction site differences between populations is provided as Nei’s raw (D) and net ( DA). Nei’s raw (D) is the difference number of restriction site between populations A and B, while Nei’s net is the average number of net nucleotide substitutions per site (Nei & Li, 1979). The difference number of restriction site between populations A and B (

) can be calculated by Equation

(10): ………..(10) and

……….…..……..(11) where k and k’ are the number of distinct haplotypes in populations A and B, respectively, xAi is the frequency of the i-th haplotype in population A, and δij is the number of restriction site differences between haplotype i and haplotypes j. Relationship tree among populations can be constructed using NeighborJoining (NJ) Method (Saitou and Nei, 1987) based on pairwise genetic distance values. The NJ method calculates and combines closely related number of operational

28

taxonomic units (OTU’s) into a pair of neighbor. Each pair of neighbor, or tree branch, is connected by a node, and length of branch is determined based on the number of OTU of corresponding neighbor joined. Thus, distance between population 1 (DOTU-1) and population 2 (DOTU-2) is: D(1-2) = DOTU-1 + DOTU-2. For neighbor joining of three populations or more, the distance between combined OTU of population 1 and population 2, and another OTU j can be written as Equation (12): ) ………………(12)

where D1j and D2j are the distance between OTU 1 and OTU j ; and OTU 2 - OTU j, respectively. The small distance value indicates closely related pair. Usually first draw of branch length started from those least values following by larger values. Distance values, however, often more complicated to be constructed and have various relationship possibilities. Hence, statistical method known as the ‘bootstrap’ should be employed to assess reliability of tree construction by resampling the tree. Majority consensus trees can be use to construct a tree inferred from majority of bootstrap tree samples (Felsenstein, 1985).

29

CHAPTER 3 RESEARH METHODOLOGY 3.1. Samples Collection This study focuses on the mitochondrial DNA (mtDNA) variation of Asian seabass populations along the Gulf of Thailand, where most of Asian seabass aquaculture are located (Department of Fisheries of Thailand, 2006). Fin clips will be collected from cultured and wild populations. Samples represented cultured broodstock will come from three locations: Rayong Coastal Fisheries Reseach and Development Center hatchery – Rayong (RA) , a private hatchery - Chonburi (CB) and Nakhon Si Thammarat Coastal Fisheries Reseach and Development Center Nakhon Si Thammarat (NK), and the wild samples will come from two locations: Chantaburi (CH) and Nakhon Si Thammarat (PN) (Figure 3.1).

Figure 3.1. A map of sampling locations: Chantaburi (CH), Rayong (RA) and Chonburi (CB), Nakhon Si Thammarat (NK and PN). Circle and triangle notation indicates cultured and wild sampling populations, respectively.

30

Each samples consist of 39 - 67 individuals with size varied from 4 to 7 kilograms weight (Table 3.1). For cultured broodstock, small amount of caudal fin clips will be cut without further harm to the fish. Wild samples will be collected from local fisherman and fish distributors. Fin clips will be preserved with 95% ethanol. Table 3.1. Population source and number of sample Sample/Population Code Chantaburi Rayong Chonburi Nakhon Si Thammarat/Sichon Nakhon Si Thammarat/Phak Nakhon

CH RA CB NK PN

Wild/ Cultured Wild Cultured Cultured Cultured Wild

Number of Individuals 55 67 56 39 65

3.2. DNA extraction Total DNA will be extracted from the samples using a universal and rapid salt-extraction protocol described by Aljanabi and Martinez (1997). A small piece of fin clip will be submerged in 400 µl lysis buffer (0.4 M NaCl; 10 mM Tris-HCL pH 8.0; 2 mM ethylenediamene tetraaceticacid (EDTA) pH 8.0; 20% sodium dodecyl sulfate/SDS (2% final concentration)) and 20 mg/ml Proteinase K (USBiological, USA). The fin clip samples will then be crushed and incubated in waterbath at 55oC overnight. Under this condition, SDS will digest cell membrane lipid layer for cell lysis, and proteinase K will be at optimum condition for protein digestion. After protein digestion, DNA can be separated from cell debris by adding 6 M NaCl to each sample tube, and centrifugation at 10,000 rotate per minute (rpm) for 30 minutes. About 450 µl of the supernatant containing DNA to be transferred into a new tube. DNA is precipitated using equal volume of 450 µl Isopropanol, then incubated at -20oC for an hour and followed by centrifugation at 10,000 rpm for 20 minutes. Supernatant will be quickly poured. The salt remains will be washed with 1000 µl 70% Ethanol, and spun at 10,000 rpm for 20 minutes. After supernatant is poured, the pellet containing DNA is then dried for an hour. Dried DNA pellet is resuspended in 50 µl 0.1 X TE.

31

To quantify extracted DNA, about 2µl of the DNA and 1 µl of loading dye is then electrophoresed in 1 % Agarose gel with dimension of the gel is 15 x 10 cm, and 2x20-well of 1.5 mm fixed-height comb (BIO-RAD SubcellGT, Italy). The mobility of extracted DNA was compared with 1 kb size standard and λ (lambda) DNA (Invitrogen, USA) with known quantity of 50 ng, 100 ng, and 200 ng. DNA concentration will be use as reference in determining PCR ingredients.

3.3. PCR Amplification Polymerase Chain Reaction (PCR) is used to amplify a 1 kb of the mitochondrial control region using primer LN20 (5’-ACC ACT AGC ACC CAA AGC TA-3’) and HN20 (5’-GTG TTA TGC TTT AGT TAA GC-3’) designed by Bernatchez

and Danzzman (1993). A PCR reaction contains 1x PCR buffer, 0.4 mM MgCl2, 2 mM dNTPs, 2 pmole each primers, 0.5 U Taq Polymerase (Vivantis), 15-50 ng DNA template and distilled water up to 20 µl of the solution. PCR is performed in a thermal cycler (Hybaid Touchdown) for 30 cycles (92oC, 60 s; 50oC, 60 s; 72oC,90s). The initial cycle is a 1 minute denaturing step at 92oC and followed by a 10 minutes final extension at 72oC. PCR products will be electrophoresed run in 1% Agarose (Vivantis) at 65 V for 45 minutes. After which the gel will be stained in Ethidium bromide for 10 minutes. DNA fragments will be visualized on a UV transluminator (VILBER LOURMAT ETX-40M, France).

3.4. Enzyme Digestion As a preliminary assessment of potential restriction enzymes and restriction sites for samples included in this study, seven individuals from Chantaburi (CH), Rayong (RA) and Chonburi (CB) were sequenced to obtain potential restriction sites of the mtDNA control region. PCR products were cleaned using RBC-Bioscience kit following the manufacture instructions, and sequenced (ABI I.6.0, Macrogen Inc. South Korea). Sequences were checked using Sequence scanner v1.0, compared with sequences of L. calcarifer available in the GenBank database using BLAST (http://blast.ncbi.nlm.nih.gov/Blast.cgi), alingned using BioEdit (Hall, 2001) and

32

adjusted by eyes for accuracy. Restriction site analysis was determined using the software Restriction Mapper (http://www.restrictionmapper.org). Sequences of L. calcarifer obtained from the preliminary assessment had 96 – 97% sequence identity to L. calcarifer sequences available in GenBank (accession number DQ012430.1, DQ012429.1, DQ012425.1 and DQ012432.1. Based on the sequence differences, I identified seven restriction enzymes that would reveal distinct DNA patterns of mtDNA control region of L. calcarifer: FauI (New England Biolabs, England), HindII, EcoRI, EcoRV, VneI, Bse1I and BstENI (Vivantis, Malaysia) (Table 3.2). About 100 ng of PCR-DNA template will be digested by 0.1 unit enzyme restriction in 10x buffer and water. The mixture then incubated for 16 hour at 55oC for FauI; 37 oC for HindII, EcoRI, EcoRV, and EcoNI; and 65oC for Bse1I and BstENI. Table 3.2. Recognition size sensitivity of restriction enzymes used Enzyme Cut position Site length Number (bp) of cut FauI CCCGC(N)4↓ 5 1 GGGCG(N)6↑ HindII GTY↓RAC 6 1 CAR↑YTG EcoRI G↓AATTC 6 1 CTTAA↑G EcoRV GAT↓ATC 6 1 CTA↑TAG VneI/ ApaLI G↓TGCAC 6 1 CACGT↑G Bse1I / BsrI ACTGGN↓ 6 1 TGAC↑CN BstENI/EcoNI CCTNN↓NNNAGG 6 1 GGANN↑NNNTCC

Expected size of fragments (bp)* 439 & 332 513 & 258 414 & 357 606 & 165 498 & 273 449 & 322 436 & 335

* Expected size of fragment obtained from L. calcarifer D-loop mtDNA from GenBank (accesion number DQ012430.1).

Digested PCR-DNA will be loaded into 1.5 % Agarose and run at 65 V for 70 minutes. A 50 bp standard ladder (Invitrogen, USA) and ~1 kb undigested PCR product will be used as measurement references. The gel will be stained in Ethidium bromide for 10 minutes and washed in water. DNA fragment will be visualized on UV transluminator (Vilber Lourmat, France) and photographed for manual measurement and documentation.

33

Size of DNA fragments (basepairs, bp) will be measured relative to standard ladder and undigested PCR product size. Unique size and number of fragments of each enzyme digestion for all samples will be notated with specific letter such as A, B, C, etc. Size of fragment data will be used to construct binary data representing present (1) and absence (0) fragment for analysis of nucleotide diversity. Notation of fragment pattern data will be used to construct haplotype composite for the analysis of haplotype diversity.

3.6. Data Analysis Each pattern of fragment produced after a enzyme digestion will be assigned to a specific letter representing each cut position. I construct a composit haplotype based on combinations of unique patterns generated by each enzyme. Haplotype data will be analyzed for genetic diversity within populations and genetic differentiation among populations. -

Genetic Diversity Within populations I will describe genetic diversity within populations in terms of haplotype

diversity and nucleotide diversity (Billington, 2003). Haplotype diversity (ĥ), or gene diversity, can be estimated by Equation (1) in Chapter 2, while Nucleotide diversity ( ) can be estimated by of Equation (2) in Chapter 2 (Nei & Tajima,1981). Calculations of haplotype diversity and nucleotide could be performed simultaneously by Analysis of Molecular Variation (AMOVA) using computer programs Arlequin (Excoffier, Laval, & Schneider, 2005) and GenAlex (Peakal and Smouse, 2006). To test the homogeneity of haplotype frequencies between samples, I will use program MONTE implemented in the Restriction Enzyme Analysis Package (REAP) (McElroy et al., 1992). Significant level for multiple tests significance levels will be adjusted by the sequential Bonferoni technique (Rice, 1989). -

Genetic Differentiation Among Populations I will use two approaches to analyze genetic differentiation among

populations, analysis of molecular variation (AMOVA) and cluster analysis based on genetic distances implemented in the Arlequin program (Excoffier, Laval, &

34

Schneider, 2005). This routine will search variation partitioned to two levels: among individuals within a population, and among populations within groups. Significance level of the covariance components for among populations (σ2b), within populations (σ2c), and total variation (σ2T) also will be tested using Φ-statistics and the P values will be generated by 1000 random permutation in AMOVA. Differences among populations may reflect the demographic history of a population such as level of migration rate or gene flow. This data may detect evolutionary event such as bottlenecks and founder events in the past (Avise, 2004). Information of variation differences among population data can also be used for stock identification in wild and hatchery populations, particularly in the broodstock management program (Billington, 2003). Neighbor-Joining tree for population relationships will be constructed based on a Nei genetic distance matrix as described in Equation (12) of Chapter 2 (Saitou and Nei, 1987). Program Neighbor from Phylogeny Inference Package (PHYLIP) can be used to perform bootstrapping to test the reliability of clusters on the tree (Felsenstein, 1985; 2008). Constructed consensus dendogram will be visualized on the TreeView program (Page, 2001).

35

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