Mitochondrial Dna Variation Of Asian Seabass

MITOCHONDRIAL DNA VARIATION OF CULTURED AND WILD ASIAN SEABASS (Lates calcarifer) IN THAILAND Yusmansy a ID. h 50912419 Department of Aquatic Science, Faculty of Science, Burapha University

I.

Introduction

Statement and Significance of The Problem Objectives Contribution to Knowledge Scope of Study Hypothesis

II. Literature Review Biology of Asian Seabass Ecology and distribution Genetic Processes in Populations mtDNA as genetic marker for population genetic analyses Molecular techniques for mtDNA analysis

I. Introduction •Statement

and Significance of The Problem

•Objectives •Contribution to Knowledge •Scope of Study •Hypothesis

I. Introduction

Background

FAO, 2006

I. Introduction

Background

• Thailand is a major producer of Asian seabass (Lates calcarifer) fingerlings, mainly produced in hatcheries • Well managed breeding program is important to produce good quality fingerlings • Success of a breeding program highly depends on broodstock management maintaining genetic diversity

I. Introduction

Problem

Loss genetic diversity in hatcheries due to inapropriate hatchery practises, such as:

- Mating limited number of broodstock - Mass spawning that lead to unequal sex ratio and contribution of each families

I. Introduction

Consequences of the Problem Reduced genetic diversity may lead to undesirable consequences on traits reated to production, for instance:

- Resistance to the disease stress - Adaptive potential to the environmental stress

How to overcome the Problem Managing broodstock that able to maintain genetic diversity. Broodstock collection Design of mating scheme Rearing practice Introducing variation from Wild populations. Wild populations are potential source of genetic variation, because wild populations usually have larger population size (Ne) -> likely contain higher genetic variation

To implement both alternatives, genetic data

Molecular tools to evaluate

genetic data

Heterozygosity of nuclear DNA and genetic distance Allozyme Sequencing Microsatellite Haplotype and nucleotide diversity of mtDNA Genomic RFLP PCR -RFLP Direct sequencing

Molecular tools to evaluate

genetic data (cont.) Smaller genome size than nuclear DNA mtDNA is generally very sensitive to detect population structure due to lower Ne than nuclear DNA Nucleotide substitution rate higher than nuclear DNA Not subject to recombination

Objective of the Study 1

Estimate genetic variation within hatchery, and wild

populations of L. calcarifer using PCR-RFLP of the Dloop control region of mitochondrial DNA

2

Examine population differentiation among hatchery

and wild populations

Contribution to knowledge 1 The genetic data will be useful in managing existing genetic diversity in hatchery populations of L. calcarifer in Thailand

2 This data should be useful for breeders and government in order to develop a selective breeding program for L. calcarifer species

3

Data for wild populations will provide basic information to aid conservation efforts of

native genepool of L. calcarifer in Thailand

Scope of study

1 Analysis: mtDNA control region 2 Samples taken from 3 hatchery and 2 wild populations located around Gulf of Thailand, represent broodstock supplying fingerling in Thailand

3

Technique: Polymerase Chain Reaction (PCR) – Restriction Fragment Length

Polymorphisms (RFLP)

Hypothesis 1 Level of genetic variation within hatchery populations of L. calcarifer is lower than of wild populations.

2 Genetic differentiation among wild L. calcarifer populations is high, but the differences among hatchery populations are low

II. Literature Review •

Biology of Asian Seabass

•

Ecology and distribution

•

Genetic Processes in Populations

•

mtDNA as genetic marker for population genetic analyses

•

Molecular techniques for mtDNA analysis

•

Genetic Diversity of Asian seabass and fish species with similar life history

Literat Rure eview

Biology of Asian Seabass

Classification Kingdom: Animalia Phylum: Chordata Class: Actinopterygii Order: Perciformes Family: Centropomidae Genus: Lates Species: Lates calcarifer

Source: fishase.org

Literat Rure eview

Biology of Lates calcarifer Catadromous-demersal (Moore, 1982) Larvae and young juveniles live in estuaries, older juveniles inhabit the upper reaches of rivers (Moore & Reynolds, 1982) Vertical migration reaches maximum 70 Km, horizontal migration maximum 17 Km (Russel & Garrett, 1988)

Literat Rure eview

Biology of Lates calcarifer Growth Parameter:

Protandrous hermaphrodites

Individual is sexualy matured as male in the first time, then gonad structure develops into female in the following ages (Moore, 1979)

Literat Rure eview

Ecology & Distribution

Single annual reproductive period (Davis, 1985; Guiguen et al., 1994)

Adult -> Carnivorous; Juveniles -> Omnivorous (Sirimontaporn, 1988) Important food: Crustacean, decapoda, mysidacea, Isopoda (Cabral & Costa, 2001), and Amphipods (Laffaile et al., 2001)

Literat Rure eview

Ecology & Distribution Asian seabass distributed spread along Indo-West Pacific: eastern edge of the Persian Gulf to China, Taiwan and southern Japan, southward to southern Papua New Guinea (Larson, 1999), Indonesia and Australia (Marshall, 2005)

World geographic distribution of L. calcariifer (Retrieved from FIGIS-FAO, http://www.fao.ofg/figis/)

Literat Rure eview

Genetic Processes In Populations

Mutations

Primary source of genetic variation in populations

Two major types of mutations: Point (gene) mutations and Chromosomal mutations

Change could be due to base pair substitution, insertion or deletion Chromosomal mutation

Literat Rure eview

Genetic Processes In Populations

Genetic drift

Is change in allele frequency in a population in succesive generations due to random process

Magnitude of genetic drift in a population depend on deviation level from an ideal condition

In general, genetic drift occurs when the founder population size is small Mitochondrial DNA has lower population size (Ne) than nuclear DNA, therefore more susceptible to genetic drift effects

Literat Rure eview

Genetic Processes In Populations

Gene flow

Is any movement of alleles from one population to another through recombination of sexual reproduction

Gene flow will increase or maintain genetic variation in a population, but decrease distinctiveness among populations.

Gene flow in mtDNA can be indicated by haplotype share between genetically related populations

Literat Rure eview

Genetic Processes In Populations

Natural selection

Unequal probability of survived or reproduced alleles to the future generation causes genetic variation change

Major factor leads to the natural selection process in a populations the differential survival or reproduction of genotypes

Alleles associated with favorable heritable traits become more common, while unfavorable ones become less common.

Literat Rure eview

Mitochondrial DNA as genetic marker for Population genetic Analysis

• Uniparental inheritance • Relatively small effective population size than nucleus DNA • Higher mutation rate • Powerful for detecting patterns of genetic structure in natural systems

courses.washington.edu/fish340/Lab%20Pro ject.htm

Literat Rure eview

Molecular Techniques for Mitochondrial DNA Analysis Restriction Fragment Length Polymorphisms (RFLP) Detecting nucleotide variation based on specific restriction site recognition of : • Whole genomic DNA, or • Specific region, e.g. control region, cytochrome b

Direct Sequencing Determination of the order of nucleotide bases, i.e. Adenine(A), Guanine (G), Cytosine (C) and Thymine (T). Direct sequencing has higher sensitivity in detecting nucleotide

Bernatchez & Danzmann (1993) suggested congruence of the magnitude of variation and differentiation resulted from RFLP and sequencing analysis

Literat Rure eview

Genetic diversity of L. calcarifer And fish species with similar life history

Genetic variation within population • Genetic variation in wild population of L. calcarifer is relatively high

Literat Rure eview

Genetic diversity of L. calcarifer And fish species with similar life history

Genetic variation within population • Genetic variation in hatchery population is lower than wild populations

• In some cases, genetic variation within hatchery may higher than wild because of it consist of individual coming from genetically distinct sources

Literat Rure eview

Genetic diversity of L. calcarifer And fish species with similar life history

Genetic differentiation among populations • Genetic differentiation among wild population is much lower than within population, suggesting presence of population structure

Literat Rure eview

Genetic diversity of L. calcarifer And fish species with similar life history

Migration pattern • Indicated stepping stone model of migration dimensional linear structure along the river

one

• This migration pattern will preserve the genetic diversity rather among population

Picture reference: Neal, D. (2004) Introduction to population biology. Cambridge University Press. Cambridge - UK

Literat Rure eview

Genetic data

Analysis

Genetic diversity within population Haplotype diversity Nucleotide diversity

Genetic differentiation among population Mean number of genetic differentiation between two randomly chosen haplotypes

Genetic differentiation also can be indicated by Φstatistics by using Analysis of Molecular Variation (AMOVA) developed by Excoffier, Smouse & Quattro (1992)

Literat Rure eview

Genetic data

Analysis

Genetic distance

Genetic distance can be estimated from 3 ways:

• From the proportion of nucleotide substitution • Pairwise FST values derived based on drift model • Average number of restriction site differences between populations (D)

Relationship among populations Relationship among population can be constructed using Neighbor-Joining (NJ) method based on pairwise genetic distance values.

Research methodology III. Research Methodology Samples collection DNA extraction

PCR amplification Enzyme digestion Data Analysis

Collecti

Research methodology

Sampl es

on

3 2 4

5

1

Collecti

Research methodology

Sampl es

Sampling Design

on

30-40 individuals per sample

Collecti

Research methodology

Sampl es

on

• Cut small amount caudal fin • Sample preserved in 95% Ethanol • Labeling

Digestio Cell

n

• Cut small amount 0,5 mm sample. • Insert to Lysis buffer + Proteinase K • Incubate overnight at 55oC

Extraction

Research methodology

DNA

Salting out method

NaCl saturated H2O

Amplification

Research methodology PCR

PCR-Reaction

• Primers – 2 pmole each LN20 (5’-ACC ACT AGC ACC CAA AGC TA-3’) HN20 (5’-GTG TTA TGC TTT AGT TAA GC-3’)

• • • • • •

PCR-Profile

Initial denaturing 94oC 1 min

1x PCR Buffer 2mM dNTPs, 0.4 mM MgCl2, Taq Polymerase, dH2O DNA template

35 Cycles 9 4 o 2 C 0oC 60s 60s

7 2oC 90s

Final extention 72oC 10 min

Digestion enzym e

Research methodology

7 Restriction enzymes will be used: • HindII, FauI, EcoRV, EcoRI, BstENI, Bse1I, EcoNI 10 µl RFLP-Reaction contains: • 1 µl - 1x Digestion Buffer • 2 µl - PCR product template • 0.1 µl – 2 u/ µl restriction enzyme • 6.9 µl dH2O Incubate overnight at 37oC (FauI); 37oC (HindII, EcoRI, EcoRV, EcoNI); and 65oC(Bse1I & BstENI)

Digestion enzym e

Research methodology

Scoring design for composite haplotype assignment

Composite haplotype is then rearranged to assigned the haplotype frequency

Digestion enzym e

Research methodology

Scoring for present/absence of the fragment position

The composite of present/absence (0/1 binary data format) is required for data analysis in Arlequin program, for example RA1 01100011 RA2 10001000 RA3 01100110

Analysis

Research methodology

data

Genetic diversity within populations haplotype diversity within and population, derived from formula where xi2 = frequency of sample of the ith haplotype; n = number of sample, l=number of nucleotide (Nei and Tajima, 1980)

Nucleotide diversity derived from formula proposed by Nei and Li (1990) where xi and xj = frequency of ith and jth haplotype; Πij = number of nucleotide differences per nucleotide sites between ith and jth haplotype.

Π = Σij xi xj Πij Haplotype and nucleotide diversity will be performed using Analysis of Molecular variation (AMOVA) in the Arlequin program

Analysis

Research methodology

data

Genetic differentiation among populations Genetic differentiation 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.

ΦST

ΦSC

ΦCT

Where , , and are the associated covariance component for groups, populations within a group, within populations, and total variation.

These calculations can be performed by AMOVA in the Arlequin program

Analysis

Research methodology

data

Genetic differentiation among populations Design for hierarchical Analysis of Molecular Variance (AMOVA)

Analysis

Research methodology

data

Genetic differentiation among populations Design for hierarchical Analysis of Molecular Variance (AMOVA) in the Arlequin program

Analysis

Research methodology

data

Genetic distance

Genetic distance can be calculated based on average number of restriction site differences between populations (D) (Nei & Li, 1979). The difference between populations A and B ( AB) where k & k’ = 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

Distance between population 1 and 2, and other OTU j can be calculated by where D1j and D2j are the distance between OTU 1 and OTU j; and OTU 2 OTU j, respectively.

Analysis

Research methodology

data

Population relationships will be constructed using Neighbor-Joining method based on Nei genetic distance and visualized by a consensus (bootstrap 1000 replicates) using PHYLIP program Consensed dendogram then will be visualized using TreeView Program

Thank You

Thank You