A GENETIC DIVERSITY IN MAHSEER SPECIES IN AZAD JAMMU AND KASHMIR
NUZHAT SHAFI
85-Umdb-1449
Session: 2007-2010
DEPARTMENT OF ZOOLOGY FACULTY OF SCIENCES AND ENGINEERING UNIVERSITY OF AZADJAMMU AND KASHMIR MUZAFFARABAD
A GENETIC DIVERSITY IN MAHSEER SPECIES IN AZAD JAMMU AND KASHMIR
By
NUZHAT SHAFI
85-Umdb-1449
A thesis submitted in partial fulfillment of The requirements for the degree of
Doctor of Philosophy
In
Zoology
Department of Zoology Faculty of Sciences and Engineering The University of Azad Jammu and Kashmir, Muzaffarabad
Dedicated
To
My Loving
Parents, Husband and Children CONTENTS
LIST OF FIGURES ...... viii
LIST OF PLATES ...... xi
LIST OF TABLES ...... xiii
ACKNOWLEDGEMENT ...... xvii
ABSTRACT ...... xviii
INTRODUCTION ...... 1
1.1 BACKGROUND ...... 1
1.2 OBJECTIVES ...... 6
1.3 GOLDEN MAHSEER ...... 7
1.3.1 Taxonomy ...... 7
1.3.2 Morphological Features ...... 9
1.3.3 Distribution ...... 10
1.3.4 Habitat ...... 11
1.4 GENETIC DIVERSITY ...... 12
1.4.1 Rapid Amplified Polymorphic DNA Techniques ...... 15
1.4.2 Microsatellite Genetics ...... 17
LITERATURE REVIEW ...... 20
2.1 DNA EXTRACTION ...... 20
2.2 GENETIC DIVERSITY ...... 21
2.2.1 Choice of Marker Type ...... 22
iv
2.3 RAPD MARKERS ...... 24
2.4 MICROSATELLITE MARKERS ...... 28
MATERIALS AND METHODS ...... 31
STUDY AREA ...... 31
3.1 SAMPLING ...... 36
3.2 DNA EXTRACTION ...... 41
3.2.1 Preparation ...... 41
3.2.2 Extraction ...... 41
3.2.3 Quality and Quantity Assessment ...... 42
3.3 PCR OPTIMIZATION ...... 43
3.3.1 Primers ...... 43
3.3.2 PCR Amplification ...... 44
3.4 GENOMIC DNA ANALYSIS ...... 47
3.4.1 Binary Data Generation ...... 47
3.4.2 Statistical Analysis ...... 47
3.4.3 Discriminatory Power ...... 47
3.4.4 Polymorphism ...... 48
3.4.5 Genetic Diversity ...... 48
3.4.6 Genetic Variation and Population Analysis ...... 49
3.4.7 AMOVA ...... 49
3.4.8 UPGMA and PCA ...... 50
v
RESULTS AND DISCUSSION ...... 52
4.1 SAMPLE COLLECTION ...... 52
4.2 PCR OPTIMIZATION ...... 54
4.2.1 RAPD Markers ...... 54
4.2.1.1 Magnesium Chloride ...... 55
4.2.1.2 DNA template concentration ...... 55
4.2.1.3 Annealing temperature ...... 57
4.2.1.4 dNTPs and Taq DNA polymerase ...... 58
4.2.1.5 PCR cycle profile ...... 58
4.2.2 SSR Primers ...... 60
4.3 GENETIC ANALYSIS ...... 62
4.3.1 RAPD Markers ...... 62
4.3.1 Discriminatory Power ...... 67
4.3.2 Band Amplification Pattern ...... 70
4.3.3 Polymorphism ...... 76
4.3.4 Genetic Diversity ...... 81
4.3.5 Inter-population Genetic Distance ...... 95
4.3.6 Principal Component Analysis (PCA) ...... 100
4.4 MICROSATELLITE MARKERS ...... 103
4.4.1 Allelic Diversity ...... 103
4.4.2 Allelic Frequencies ...... 107
vi
4.4.3 Genetic Diversity ...... 108
4.5 GENERAL DISCUSSIONS ...... 113
4.6 CONCLUSION ...... 118
4.7 RECOMMENDATIONS ...... 119
SUMMARY ...... 121
LITERATURE CITED ...... 121
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LIST OF FIGURES
Figure 1.1: Fish production and utilization from 1950 to 2011 (FAO Statistics)...... 2
Figure 1.2: Aquaculture production in top ten countries (FAO,2011)…………………3
Figure1.3: Water system and Potential distribution range of Golden mahseer in Pakistan .... 4
Figure 1.4: Present distribution of Golden mahseer in the rivers of Pakistan and Azad
Jammu and Kashmir ...... 5
Figure 3.1: Map showing different Rivers of Azad Jammu and Kashmir ...... 32
Figure 3.2: Map showing rivers and lakes of inland water resources in Pakistan; (after Rafiq,
2007) ...... 33
Figure 3.3: Map showing different sites of mahseer population collection...... 37
Figure3.4: Map showing upper stretch of River Poonch Azad Kashmir, Collection sites for mahseer, Pop A ...... 38
Figure 3.5: Map showing Collection sites for mahseer, Pop B River Poonch (Poonch Mangla
Reservoir) Azad Kashmir ...... 38
Figure 3.6: Map showing Collection sites for mahseer, Pop C River Jhelum Azad Kashmir .... 39
Figure 3.7: Map showing, collection sites for mahseer, Pop D River Swat KPK...... 39
Figure 3.8: Map showing collection sites for mahseer, Pop E River Indus KPK ...... 40
Figure 3.9: Map showing Collection sites for mahseer, Pop F River Hingol (Balochistan)...... 40
Figure 4.1: Description of amplicon per RAPD markers for number of monomorphic, polymorphic and unique bands in Golden mahseer populations [T.A.L=total amplified loci;
U.B=unique bands; M.B = monomorphic band; P.B polymorphic bands] ...... 70
Figure 4.2: Frequency of amplification by different RAPD primers in Poonch River Golden
mahseer population (Pop A)...... 73
Figure 4.3: Frequency of amplification by different RAPD primers in Mangla Poonch River
Golden mahseer population (Pop B)...... 73
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Figure 4.4 :Frequency of amplification by different RAPD primers in Jhelum River Golden mahseer population (Pop C)...... 74
Figure 4.5: Frequency of amplification by different RAPD primers in Swat River Golden mahseer population (Pop D)...... 74
Figure 4.6: Frequency of amplification by different RAPD primers in Indus River Golden mahseer population (Pop E)...... 75
Figure 4.7: Frequency of amplification by different RAPD primers in Hingol River Golden mahseer population (Pop F)...... 75
Figure 4.8: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Poonch population (Pop A) of Golden mahseer...... 78
Figure 4.9: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Poonch (Mangla) population (Pop B) of Golden mahseer...... 78
Figure 4.10: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Jhelum population (Pop C) of Golden mahseer ...... 79
Figure 4.11: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Swat population (Pop D) of Golden mahseer ...... 79
Figure 4.12: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Indus population (Pop E) of Golden mahseer ...... 80
Figure 4.13: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Hingol population (Pop F) of Golden mahseer ...... 80
Figure 4.14: Overall percentage of polymorphism and monomorphism with different RAPD primers...... 81
Figure 4.15: Results of UPGMA cluster analysis based on genetic distance (Nei, 1972), calculated on band amplification at16 RAPD markers in different populations of Golden mahseer. Pop. 1= river Poonch (Pop A); Pop. 2 = River Jhelum (Pop C); Pop 3 = Mangla (Pop
B); Pop. 4 = River Swat (Pop D); Pop. 5 = River Indus (PopE); Pop. 6 = River Hingol (Pop F). .. 98
ix
Figure 4.16: Clustering of different RAPD generated genotypes of Golden mahseer (Jaccard similarities coefficient) based on (UPGMA) cluster analysis between Individuals of six populations. (Numbers represent the individuals of different populations; Pop A=1-12; Pop
B=17-28; Pop C=13-16; Pop D=29-33; Pop E=34-37; Pop F=38-39)...... 99
Figure 4.17: Clustering of different genotypes of Golden mahseer specimen collected from different parts of AJK and Pakistan based on genetic distances. Pop A = 1-12, Pop B = 13-16,
Pop. C = 17-28, Pop D = 29-33, Pop E = 34-37, Pop F = 38-39...... 100
Figure 4.18: Principle component analysis (PCA) output for different genotypes of Golden mahseer. Pop. A: 1-12; Pop B: 17-28; Pop C: 13-16; Pop D: 29-33; Pop E: 34-37 & Pop F: 38-39
...... 101
Figure 4.19: Allele’s frequency amplified by SSR markers, Series 1=TPF; 2=Barb 37; 3 =TTR
...... 108
Figure 4.20: Genetic distance (Nei, 1972) based on (UPGMA) cluster analysis by different
SSR markers between different populations of mahseer. [1= Pop A; 2= Pop C; 3= Pop B; 4=
Pop D; 5= Pop E; 6= Pop F] ...... 112
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LIST OF PLATES
Plate 1.1: Golden mahseer collected from River Poonch ...... 10
Plate 4.1-2: The agarose (1.5 %) gel profile of DNA isolation from different mahseer population………………………………………………………………………………………………………………….………. 59
Plate 4.3: PCR amplification of RAPD marker (FA-1) for Golden mahseer populations...... 63
Plate 4.4: PCR amplification of RAPD marker (FA-5) for Golden mahseer populations ...... 63
Plate 4.5: PCR amplification of RAPD marker (FA- 6) for Golden mahseer populations ...... 64
Plate 4.6: PCR amplification of RAPD marker (FA-7) for Golden mahseer populations ...... 64
Plate 4.7: PCR amplification of RAPD marker (OPA-4) for Golden mahseer populations ...... 65
Plate 4.8: PCR amplification of RAPD marker (OPA-17) for Golden mahseer populations ...... 65
Plate 4.9: PCR amplification of RAPD marker (OPN-11) for Golden mahseer populations ...... 66
Plate 4.10: PCR amplification of RAPD marker (OPN-20) for Golden mahseer populations ...... 66
Plate 4. 11:PCR amplification of Golden mahseer genome by TPF microsatellites marker in different populations. 1-2 = Jhelum, 3-8 = Poonch, 9-10 = Indus, 11-12 =
Swat, 13,14 = Hingol...... 105
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Plate 4.12: PCR amplification of Golden mahseer genome by TTR microsatellites marker in different populations. 1-2 = Jhelum, 3-8 = Poonch, 9-10 = Indus, 11-12 =
Swat, 13,14 = Hingol...... 106
Plate 4.13: PCR amplification of Golden mahseer genome by Barb37 microsatellites marker in different populations. 1-2 = Jhelum, 3-8 = Poonch, 9-10 = Indus, 11-12 =
Swat, 13,14 = Hingol...... 106
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LIST OF TABLES
Table 2.1:Broad types of molecular markers available for genetic diversity at species and sub-species level...... 23
Table 3.1: RAPD primers sequences tried for PCR amplification of Golden mahseer
DNA ...... 45
Table3.2: SSR markers sequences tried for PCR amplification of Golden mahseer
DNA ...... 46
Table 4.1: Optimized conditions for 16 RAPD primers amplifying Golden mahseer template DNA ...... 59
Table 4.2: PCR program (profile) used in amplification of RAPD markers for Golden mahseer template DNA ...... 60
Table 4.3: Optimized conditions for SSR markers for amplification of Golden mahseer genome...... 61
Table 4.4: PCR program (profile) amplification of Golden mahseer genome using
SSR primers ...... 62
Table 4. 5: Description of amplicon for RAPD markers for number of mono-morphic, poly morphic and unique bands in Golden mahseer populations...... 69
Table 4.6: Amplification of bands scores of different RAPD markers in different
Golden mahseer populations ...... 72
Table 4.7: Relative frequency (%) of polymorphism in different populations of
Golden mahseer ...... 77
xiii
Table 4.8: Genetic diversity constants for different RAPD marker loci in Poonch
River (Pop A) population of Golden mahseer. na = observed alleles, ne = effected alleles, h = Nei‘s index, I = Shannon index ...... 84
Table 4.9: Genetic diversity constants for different RAPD marker loci in Mangla
Reservoir (Pop B) population of Golden mahseer. na = observed alleles, ne = effected alleles, h = Nei‘s index, I = Shannon index ...... 85
Table 4.10: Genetic diversity constants for different RAPD marker loci in Jhelum
River (Pop C) population of Golden mahseer. na = observed alleles, ne = effected alleles, h = Nei‘s index, I = Shannon index ...... 86
Table 4.11: Genetic diversity constants for different RAPD marker loci in Swat River
(Pop D) population of Golden mahseer. na = observed alleles, ne = effected alleles, h =
Nei‘s index, I = Shannon index ...... 87
Table 4.12: Genetic diversity constants for different RAPD marker loci in Indus River
(Pop E) population of Golden mahseer. na = observed alleles, ne = effected alleles, h =
Nei‘s index, I = Shannon index ...... 88
Table 4.13: Genetic diversity constants for different RAPD marker loci in Hingol
River (Pop F) population of Golden mahseer. na = observed alleles, ne = effected alleles, h = Nei‘s index, I = Shannon index ...... 89
Table 4. 14: Summary of average values of genetic diversity constants generated for different RAPD loci for different populations of Golden mahaseer (AJK= Azad
Jammu and Kashmir, KPK = Khyber Pukhtoonkhwa, BLN = Balochistan) . Pol =
Polymorphism, na = observed alleles, ne = effective alleles, h = Nei‘s index, I =
Shannon index...... 90
Table 4.15: Genetic diversity, genetic differentiation and gene flow indices at different RAPD loci in different populations of Golden mahseer. Hs = genetic
xiv diversity within population, Dst =genetic diversity between population, Gst =genetic differentiation among population, Rst = genetic differentiation within population .... 93
Table 4.16: Overall Ewens-Watterson Test for Neutrality statistics for different RAPD loci for sample of Golden mahseer from Pakistan and AJK...... 94
Table 4. 17: Genetic similarities between different populations of Golden Mahseer . 95
Table 4.18: Genetic distances between different populations of Golden mahseer ..... 96
Table 4.19: Variability (%) attributed to different components under PCA...... 101
Table 4.20: Analysis of molecular variance (AMOVA) between and within populations of Golden mahseer...... 102
Table 4.21: Description of amplicon for microsatellite (SSR) markers in Golden mahseer populations...... 104
Table 4.22: Relative proportion (%) shared by different SSR loci in different populations of Golden mahseer...... 107
Table 4.23: Genetic diversity constants calculated for 3 SSR markers in different populations of Golden mahseer. Poly = polymorphism, Na = observed numbers
(alleles), Ne effective numbers, h =heterozygosity, I = Shannon index ...... 109
Table 4.24: SSR markers generated genetic diversity within and between different
Golden mahseer populations. Ht = total heterogeneity, Hs = mean genetic diversity,
Gst = genetic diversity, Nm = gene flow ...... 110
Table 4.25: Genetic distances and similarities between different populations of
Golden mahseer in SSR markers. A = Poonch River, B = Mangla Resevoir, C =
Jhelum River, D = Swat River, E = Indus River, F = Hingol River...... 111
xv
LIST OF ABBREVIATIONS
AJ&K Azad Jammu and Kashmir
AMOVA Analysis of Molecular Variance
GB Gilgit Biltistan
BLN Balochistan
KPK Khyberpukhtunkhwa
PCA Principal Component Analysis
Pop Population
Pop A population A
Pop B population B
Pop C population C
Pop D population D
Pop E population E
RAPD Random Amplified Polymorphic DNA
SEM Standred Error of Mean
SSR Simple Sequence Repeats
UPGMA Unweighted Pair Group methods with arithmatic mean
xvi
ACKNOWLEDGEMENT
All praises be to Allah, the Almighty, on Whom ultimately we depend for sustenance and guidance and many Darood to Hazarat Muhammad (PBUH).
The author acknowledges the University of Azad Jammu and Kashmir,
Muzaffarabad and the Department of Zoology for providing the opportunity for Ph D.
The author is grateful to supervisor Prof. Dr. Khurshid Anwar and Professor
Dr. Afsar Mian (Co-Supervisor) for their indomitable encouragement and guidance.
The author is equally indebted to Prof. Dr. Muhammad Nasim Khan, Prof. Dr.
Mushtaq Ahmed, (members supervisory committee) and Muhammad Siddique
Awan, Chairman, Department of Zoology, Prof. Dr. A. Q. Nayyar, Riaz Aziz
(Lecturer) and Inayatullah (Assistant professor) for helpful assistance.
The author is highly obliged to Director Bioresource Research Centre (BRC)
Islamabad, for providing required research facilities and enormous cooperation.
The author also extends thanks to Department of Fisheries of Azad Jammu and
Kashmir, Khyber Pukhtunkhwa, and Balochistan for making available fish samples and fishing points throughout the length of study.
Nuzhat Shafi
xvii
ABSTRACT
Golden mahseer (family Cyprinidae, resident of South and South East Asia) was once commonly distributed in different rivers of Pakistan; while recent ecological changes and over-harvesting has limited its populations only to rivers of Punjab
(Chenab, Soan and Harro), Khyber Pukhtunkwa (Swat and Indus), Azad Jammu and
Kashmir (Rivers Poonch, Jhelum and Mangla Reservoir) and Balochistan (River
Hingol). Present study has been designed to find out the genetic similarity existing between different populations by using 16 Random Amplified Polymorphic DNA
(RAPD) and 3 microsatellite (SSR) markers.
RAPD primers generated 197 bands with 87.73 percent (%) polymorphic loci and 43.75 percent (%) unique bands. The mean genetic diversity between the population was 0.13±0.04 SEM (Nei‘s index) and 0.20±0.05 SEM (Shannon index).
The population of Swat River with the highest level of polymorphism holds the highest genetic diversity (73 percent (%) followed by Mangla Reservoir (57 percent
(%), Indus River (54.31%), Jhelum River (44.67%), Poonch River (37.06 percent (%) and Hingol River (2.03 percent (%). Assuming populations under Hardy-Weinberg
Equilibrium, the values of heterogeneity (Ht, 0.19±0.02 SEM), genetic diversity within (HS, 0.13±0.01, SEM) and between populations (Dst, 0.05±0.02 SEM), and genetic differentiation constant (Gst, 0.022±0.04 SEM) were low. The gene flow between populations (3.22± 0.32 SEM) were high, pointed out that population was not isolated in to sub-populations. The analysis of molecular variance revealed higher genetic variation (79 percent) within population and lower (21 percent) noted between populations. UPGMA dendrogram based on Nei‘s genetic similarities and genetic distances separated three main clusters of populations; 1) Poonch River, Jhelum River
xviii and Mangla Reservoir; 2) Swat River and Indus River; 3) Hingol River. RAPD markers, with a higher number of loci analyzed, worked more efficiently in analysis of intra- and inter-population differences.
SSR markers produced 8 identifiable bands/ alleles. The average heterogeneity
(Ht) for the pooled sample (0.336±0.088 SEM), genetic diversity index within populations (Hs, 0.168±0.023 SEM), and genetic diversity (Gst, 0.403±0.148 SEM) were relatively lower as compared to higher rate of gene flow (Nm, 1.802±1.101
SEM) between populations. Dendrogram on the basis of similarities/differences generated two clusters, separating the River Swat population from all other populations. SSR markers due to small amplified loci did not effectively work in the inter-population and intra-population analysis of the Golden mahseer populations.
xix
1
Chapter 1
INTRODUCTION
1.1 BACKGROUND
Fish and fishery products present a valuable source of protein and essential micronutrients for balanced nutrition and good health, providing a source of good quality animal protein for human beings. Fish provides an excellent replacement of red meat, giving minerals and essential amino acids, like, vitamin A, vitamin D, iodine, phosphorus, potassium, iron, copper and unsaturated fat (especially Omega 3)
(Roos et al.,2007). This sector also has a role in the economy of the country, providing income generation activities and employment for common masses. Global freshwater biodiversity is threatened by over exploitation, water pollution, destruction of habitat, and invasion by exotic species (Hölker et al., 2007; Poff, 2009).
Gradual development of technology in freshwater fisheries and aquaculture, and organization of better distribution channels, world freshwater fish food supply has shown a dramatic growth during the last five decades. This increase can be largely ascribed to development of aquaculture (FAO, 2011; Figures 1.1; Figure 1.2).
However, increase in the fish production through aquaculture, in Pakistan during this period, has been relatively slow, which is still not in a position to meet the growing requirements of the human population and market (FAO fisheries statistics, 2011).
Under the increasing market pressure and competition the natural freshwaters resources of Pakistan are being over harvested resulting in stress on wild freshwater fish resources (Irshad et al., 2008). Changes in the environmental conditions and
2
pollution have also degraded biotic potentials of the important bodies of freshwater.
This has accelerated the rate of loss of freshwater biodiversity. Further the distribution ranges of many species have been affected, primarily resulting in contraction in the previous distribution ranges and isolations into different sub populations (Irshad et al.,
2008).
Figure 1.1: Fish production and utilization from 1950 to 2011 (FAO Statistics).
Figure 1.3: Aquaculture production in top ten countries (FAO, 2011).
3
Golden mahseer, Tor putitora (also called Himalayan mahseer or Indian mahseer), a freshwater carp, is the occupant of the Himalayan aquatic system. It can achieve a length of 2.7 m and can weigh 30 - 54 kg (Talwar and Jhingran, 1991;
Mortuza and Rahman, 2006) but specimens weighing 5-8 kg frequently appear in catch from wild stocks. Due to its size, weight and quality meat, the Golden mahseer has always been the object of challenging interest. It is a highly prized sport fish, presenting challenge to anglers (recognized as Indian salmon among British anglers).
It is a good source of protein and is regarded as a table delicacy. Golden mahseer fetches higher market price and hence is a source of livelihood for river dependent communities (Ingram et al., 2005).
Mahseer species is inhabitant of rapid streams of clear fast-moving cold fresh water rivers / streams with rocky bottoms of plains and sub-mountainous areas. This fish species is distributed generally all along the foothills of the Himalaya (Raina et al.,1999; Kausar and Salim, 2006), from South and East Asian countries, including,
Indonesia, Malaysia, Myanmar, India, Bangladesh, Nepal, Pakistan and Afghanistan to South China (Mortuza and Rahman, 2006; Chen et al., 2006; Nautiyal et al., 2008;
Jayram, 2010).
Owing to the size, color and taste, mahseer is overharvested throughout its distribution range which, beside some other factors, has pushed to decline in its numbers throughout the Himalayan stretch (Nautiyal et al., 2008), and is regarded as
Endangered in India (Ingram et al., 2005), Bangladesh (Ameen et al., 2000) and actively considered for Threatened status in Nepal (Shrestha, 1997; Rai, 2008).
The Golden mahseer was a common flourishing species in all five rivers and the associated tributaries of the Indus drainage system distributed in the Punjab, Azad
4
Jammu and Kashmir (AJK) and Khyber Pukhtunkhwa (KPK) till 1970s (Mirza et al.,
2004; HWF, 2012: ). Good isolated populations of Golden mahseer were also present in southern rivers of Balochistan, including, Rivers Hingol, Porali Gaj and Anamber, draining directly into the Arabian Sea (Mirza et al., 2004: Figure 1.3).
Figure1.3: Water system and potential distribution range of Golden mahseer in Pakistan
This species has a long way migration for the selection of breeding grounds in the northern colder waters, where its populations are restricted to pockets of favorable breeding habitat. The construction of dams for power generation, barrages and water diversion for irrigation, along with urban sewage pollution have emerged as barriers in the free seasonal migration of this species to and from breeding grounds. Over and above, the indiscriminate hunting of Golden mahseer, for commercial exploitation over the years, resulting in a rapid decline in its natural population in most parts of its previous distribution range and consequently resulted in contraction of the distribution range of this fish species (Asghar et al., 1993) while it is sharply declining in other
5
areas. The species has now been regarded as Endangered in most water bodies of the region (Bhatt et al., 2004; Vindhya et al., 2007) attributable to habitats loss, habitat degradation and overfishing (Srivastava et al., 2004; Lakra et al., 2010).
The distribution range of Golden mahseer (Tor putitora) is continuously squeezing in Pakistan also, and presently it is almost non–existent in the rivers of the
Punjab, and has been declared as an Endangered species, declining by more than 50% in the area (Jha and Rayamajhi, 2010; IUCN, 2011). The populations of Golden mahseer in Pakistan and associated parts of AJK is now reported from the waters of the Rivers Swat, Punjkora, Kurram, Orakzai, Tochi (KPK); Hingol, Porali Gaj,
Anamber (Balochistan), Poonch, Jhelum (AJK), Chenab, Soan and Harro (Punjab)
(Rafiq, 2003; Mirza, 2006: Figure 1.4).
Figure1.4: Present distribution of Golden mahseer in the rivers of Pakistan and Azad Jammu and Kashmir
The problem is supposed to be serious for freshwater fish, where populations are isolated by barriers to migration. Isolation and low species diversity have been
6
hypothesized to be the key ingredients in an adaptive radiation taken place in mahseer fish since the last glaciations. The present distribution of Golden mahseer indicates that different populations are segregated as isolated breeding subpopulations and probably this isolation is gradually increasing with the gradual decline in the population levels and creation of physic-chemical barrier under recent changes. Such isolation can add another factor to seal the fate of this species through genetic fixation, bottleneck effect and genetic drifts in smaller subpopulation, even if overharvesting is controlled through administrative measures and habitat is restored in selected pockets through wiser management. The recent development of molecular biology techniques has provided effective tools for the analysis of genetic structure of populations and level of isolation existing between the subpopulations (Ferguson,
1995; Çiftci, and Okumuú, 2002).
The present study has been designed to check genetic isolation existing between different viable Golden mahseer populations presently distributed in different water bodies of Pakistan and associated tracts of Azad Jammu and Kashmir (AJK).
The study also attempts to find out inter-population genetic diversity existing between different subpopulations of mahseer species.
1.2 OBJECTIVES
Distribution of Golden mahseer in different water bodies suggested that population of southern most rivers of Balochistan have remained separate from the population of Indus drainage system over a longer geological period. However, the populations of rivers of KPK and AJK of Indus drainage system probably maintained some genetic exchange, yet the absence of mahseer population in central and southern parts of the Punjab (Pakistan) suggest some level of isolation between such
7
populations. The present study is therefore based upon the hypothesis that populations of Golden mahseer distributed in different water bodies of Pakistan and AJK has isolated from one another to different degrees, and hence have sufficient levels of inter-population genetic diversity. The specific objectives of this research on Golden mahseer populations of Pakistan and AJK thus include:
1. Assessment of allelic diversity in different viable Golden mahseer
populations.
2. Assessment of genetic diversity within and between different
populations.
3. Analysis of inter-population genetic flow.
The present study will allow us to know the present level of homogeneity and genetic fixation present in different populations and the level of isolation existing between these populations. The results of this study will help us in analysis of the impact of the barriers created at different times in the distribution of mahseer species in this region. Such results will be of value for conservation biologists and managers interested in the future management of this species.
1.3 GOLDEN MAHSEER
1.3.1 Taxonomy
Mahseer is a common name used for big carps (class: Teleostemi; order:
Cypriniformes, family: Cyprinidae) belonging to genera Tor, Neolissochilus and
Naziritor; but often limited to species belonging to genus Tor (Sen and Jayaram,
1982). The group of fish in Indian subcontinent is called as mahasir or mahasaula in
Hindi, mahashir in Urdu, Punjabi and Kashmiri and sahar in Nepal. Golden mahseer
8
is one among the 11 species of genus Tor (mahseer) that occurs in Asia, and one of the 9 in India ( Jhingran, 1982;). Nautiyal et al. (2008) recognized 7 valid mahseer species (T. putitora, T. tor, T. khudree, T. mussullah, T. kulkarnii, T. progeneius and
T. mosal) distributed in the Indian subcontinent, of which 3 (T. putitora, T. tor and T. progenies) have been recognized from the Himalayas (Nautiyal et al., 2008). Tor putitora is prevalent in Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Nepal and even the North East, while latter two are restricted to Central and East Himalayas
(Menon, 1992).
There are two species of mahseer known from Indus river system, i.e., Zhobi mahseer (Naziritor zhobensis) and Golden mahseer (T. Putitora) (Mirza, 1967; Mirza and Alam, 1994). Mirza believes that Tor zhobensis is a new species then he accommodated this species in a new genus, Naziritor, and named as Naziritor zhobensis. Mirza and Javed (1985) studied 192 specimens of Golden mahseer collected from different water bodies of Pakistan and AJK to study the head length/ body depth ratio based upon criteria of Silas (1960) and reported that 64% of these specimens conform to T. putitora (head length greater than body depth), while 28% were close to T. mosal (head length and body depth equal) and 8% to T. tor (head length shorter than body depth). Later on, the mahseer of Indus system is proclaimd as
―Tor macrolepis” on the basis of hypertrophied lip structure than T. putitora by
Mirza (2004) in his book ―Pakistan Mein Taza Paani ki Machlian‖and point out that
Golden mahseer of Indus drainage system is different from the mahseer present in west Balochistan and Ganga-Braham Putra drainage system and suggested the name
Tor macrolepis (already present for this species in literature by Heckel, 1938) for
Indus Mahseer
9
Chatta and Ayub (2010) also suggested that the Golden mahseer distributed in the Indus river system is different from that present in other rivers systems of south Asian subcontinent and therefore proposed the name Tor macrolepis for Golden mahseer, which is also confirmed by Pervaiz (2012) in his work on the taxonomic and morphmetric analysis of this fish (Mirza, 2004; Pervaiz et al., a, b).
1.3.2 Morphological Features
Based upon the record of Silas (1960) and Mirza and Javed(1986), Zafar et al.
(2002) Jayaram, (2010), the diagnostic morphological features of Golden mahseer are as: It has a streamlined, compressed (flattened from side to side) body. Head length is greater than body depth. Eyes are located interiorly and provided with circular pupils; and are proportionately larger in the smaller individuals. Mouth is small, its gape does not extend to below the eyes, and the lips are fleshy having two pairs of barbels. Body is covered with large scales. There are 25-28 scales in a longitudinal series along the lateral line. Dorsal fin has 4-8 fin rays, and last spine of the dorsal fin is strong and bony. Pectoral fins are 1-14 in number, pelvic fin 1-8, and caudal fin has 17 fin rays
(Plate 1.1).
10
Plate 1.1: Golden mahseer of River Poonch
1.3.3 Distribution
Golden mahseer is migratory fish species, travelling to variable distances for spawning in relatively higher altitudes and during winters occupies a wider distribution in relatively lower altitudes. In Pakistan the breeding population of the species is limited to Azad Jammu and Kashmir (AJK), Khyber Pukhtunkhwa (KPK), and Balochistan; though small populations do persist in isolated localities of the
Punjab.
In AJK the sizable and stable population persists in the River Poonch which is unique in having warm water in its lower and middle reaches and cold water in its upper reaches. Many nullahs/streams/channels join the river Poonch in its way, giving Golden mahseer sideway pockets for breeding and feeding. Poonch River and its tributaries have been recently declared as Mahseer National Park, to afford protection to the breeding population of Golden mahseer and its habitat. All the confluence streams of upper reach of the River Poonch also provide breeding grounds
11
for Golden mahseer (HWF, 2012). In KPK, Swat and Indus rivers provide breeding and non-breeding grounds at higher and lower altitudes. In the Punjab, small isolated populations are known from Harro River, Soan River and Jhajjar stream of River
Chenab. Hingol River, in the Balochistan, also holds a good breeding population of this mahseer species.
1.3.4 Habitat
Golden mahseer survives in foothill sector of glacier-fed but non-freezing clear waters of streams and rivers with temperature ranging between 22 and 28 ºC, and pH of 6.6-7.3. In Himalayas Golden mahseer can ascent up to an elevation of 200-
2,000 m above sea level (asl) (Mirza, 2004). Nautiyal et al. (2008) remarked that distribution of mahseer is mostly controlled by the prevailing water temperature. It migrates to variable distance, into small tributaries during the breeding season. Initial flooding and snow melting initiate the upstream migration of Golden mahseer into the glacier-fed tributaries located at higher altitudes. During summer this species is scarce at lower altitudes. The emigrants stay in these streams till the start of summer monsoon when the brooders again ascend into the flooded streams for breeding while recruits of the year move down to the foothills. The brooders and new young descend as the flood drops. Golden mahseer thus exhibits a tri-phased migration (Esa et al.,
2008). The migration pattern suggests that, the mature Golden mahseer have two breeding seasons, minor breeding during January- February and major breeding during August-September (Cordington, 1946).
12
1.4 GENETIC DIVERSITY
Genetic diversity, total number of characteristics present in genetic makeup of population of a species, equips a population to adapt to the changing environment
(Zhang et al., 2000) and increases possibilities of its future survival under different environmental odds (Lynch and Milligan, 1994), while lower genetic diversity means reduced fitness of the population (Sueli et al., 2004). A population shares a part of its genetic diversity with the ancestral population, while random mutations, isolations and selection under different levels of biotic/ abiotic stresses on different populations results in changed genetic diversity. Thus comparative analysis of genetic diversity in different population suggests the extent of isolations existing between the population and the diversity level within population may indicate its size and possible level of inbreeding occurring in the population. The levels of intra- and inter-population variation can fluctuate with free gene flow or complete genetic isolation (Doebeli,
1999).
The studies on population isolations and evolution were initially based on the general analysis of distribution, possible biotic and abiotic barriers and possibilities of movement under dispersal potentials of the organisms between the populations.
Subsequently such studies were based upon the morphological differences between populations, and calculation of allelic frequencies under Hardy-Weinberg
Equilibrium, based upon the phenotypic expression of the genes marker. The use of statistical tools in such studies further improved the validity and precision of such studies. Recently developed technology of molecular markers (mitochondrial DNA, genomic DNA, etc.) has the advantage of identification of the origin of animals by assigning the population to their multi- locus genotypes (Animali et al., 2006). This technique can make out dispersal patterns, which in turn can specify the breaks in
13
gene flow across populations or re-connection of formerly isolated populations
(Fortin, 2010).
Biodiversity can be determined, by assessing the genetic diversity all the way through a level of genes (Crozier et al., 2003). For conservation of any endangered species, it is necessary to get the knowledge of levels of genetic diversity through mitochondrial and genomic DNA (Doebeli, 1999). Population fragmentation can potentially boost the probability of population extinction. Therefore, for conservation, the knowledge about gene pool and its diversity for threatened as well as endangered species is necessary. In nutshell, genetic diversity is an essential component of biodiversity, and has significant effect on population of a species. Molecular markers have now been developed and used as popular tools to analyze the genetic diversity and genetic relationships among threatening (Crozier et al., 2003).
Loss of genetic variation in small populations can be a consequence of genetic drift and inbreeding as observed in captive and isolated threatened stocks (Freitas et al., 2002). Low level of genetic variability may reduce the mean fitness of population and its viability (Sueli et al., 2004).
Widespread consideration, in recent time, have been focused on the conservation and continued development of natural living resources and its environmental protection. Conservation priorities are becoming the matter of concerns for threatening fish species, and conservation of freshwater biodiversity has been introduced as a distinctive field only in the 1980s (Geist, 2011). The main objective of biodiversity conservation is to reduce loss of extremely rare biodiversity.
Therefore, in conservation, the first step is to evaluate biodiversity at genetic, species and ecosystem levels.
14
Genetic diversity is the evaluation of variety of genotypes present in the population, species or group of species (Rez et al., 2001). Therefore, through molecular biology techniques, attempt is made to describe and monitor the distribution of genetic variability in natural populations of fish species. The molecular genetics approaches determined the extent to which individuals in a population share a common gene pool or differences arising through local adaptation, resulting in a discrete population structure (Glencross, 2008). Fishes are facing higher anthropogenic threats due to exploitation and habitat degradation that is why fish have received great consideration of the molecular biologists who studied stock structure
(Thorpe et al., 2000).
Freshwaters make up only very small portion (around 0.01%) of the world‘s water, but has high biodiversity (11,952 freshwater fish species, 43% of all fish species, belonging to 33 orders live exclusively in freshwater lakes and rivers)
(Ospina et al., 2008; Geist, 2011). Nevertheless, there are many threats to global freshwater biodiversity, such as, overexploitation, water pollution, destruction of habitat, and invasion by exotic species (Hölker et al., 2007; Geist, 2011).
In spite of controversies, the use of molecular techniques has contributed to the resolution of many systemic and phylogenetic problems (Helland and Holland,
1989; Solórzano et al., 2009). Molecular methodology, especially the random amplified polymorphic DNAs (RAPD) and microsatellite analysis have been frequently used in understanding structure of wild fish population and to discover and understand roles of different historical and existing factors in determining population‘s genetic structures.
15
1.4.1 Rapid Amplified Polymorphic DNA Techniques
Rapid amplified polymorphic DNA (RAPD) is an extensive and powerful method for genetic analysis. RAPD technique is one of the most frequently used molecular methods for taxonomic and systemic analyses of various organisms (Yoon and Park, 2000) by means of band sharing values. The RAPD marker have been extensively used in study of phylogeny and taxonomy of species (Santos-guerra,
2005; Tosun and Al, 2007; Suprabha et al., 1994). Such markers play an important role in analysis of population genetics of living organisms and systemic relationship existing among them (Sueli et al., 2004).
Polymerase chain reaction (PCR) randomly amplified polymorphic DNAs
(PCR-RAPD) has been particularly used for genetic and molecular studies as it is a simple and rapid method for determining genetic diversity and similarity in various organism. It also has the advantage that it requires no prior knowledge of genome under research (Gottelli and Colwell, 2001). Development of RAPD or arbitrarily primed PCR finger printing gave an advantage, as in such technique, preliminary molecular information of the species studied is not necessary (Sueli et al., 2004) and polymorphism patterns obtained usually varies among the species. RAPD-PCR has been successfully used for genetic finger printing and molecular mapping of many animal species, including man (Easteal, 1989).
DNA finger printing is the identification of the individuals based on DNA markers. The pattern detected in DNA finger printing is unique to each individual, with the exception of identical twin (Fairbanks and Andersen, 1999).
16
The RAPD technique can be used for the estimation of genetic diversity which detects polymorphic DNA fragments amplified in PCR with a single arbitrary primer
(8-10 base pair, bp). This method has been successfully used to detect genetic differentiation within and between related species and populations of different organisms, making it possible to conduct molecular phylogenetic studies (Sueli et al.,
2004) and is also used for detection of micro geographical differentiation.
RAPD markers are generally used because of their reliable and faster genotyping method with small amount of DNA for amplification of RAPD, in small low-tech laboratories (Yu et al., 2009) and appears to offer a cost and time effective alternative to restriction fragment length polymorphism (RFLP) analysis.
Advances in molecular biology techniques have provided the basis for uncovering the unlimited number of DNA markers. The utility of DNA based markers in generally determined by the technology used to reveal DNA based polymorphism.
Currently, the Restriction Fragment Length Polymorphism (RFLP) assay has been the choice for many species to measure genetic diversity and construct a genetic linkage map(Law, 2003). However, an RFLP assay is, in general, time consuming and laborious. Over the last decade, polymerase chain reaction (PCR) has become a widely used research technique and has led the development of several novel genetic assay based on selective amplification of DNA (Easteal, 1989).
The discovery of PCR with randomly distributed loci in any genome facilitated the development of genetic markers for a variety of purposes (Gotelli and
Colwell, 2001; Sueli et al., 2004) and perhaps the success of RAPD analysis is the gain of large number of genetic markers that require small amount of DNA without the requirement for cloning, sequencing or any other form of the molecular
17
characterization of the genome of the species in question. Nevertheless RAPD analyses have some limitations that must be considered. It shows dominant inheritance and homozygote cannot be distinguished from markers/null heterozygote.
In addition, without previous pedigree program, it is unable to assign bands to specific loci. It is assumed that populations are under the Hardy-Weinberg Equilibrium that polymorphic band segregate in the Mendelian way (Freitas et al., 2002).
1.4.2 Microsatellite Genetics
Microsatellite, also known as simple sequence repeats (SSR) or short tandem repeats (STRs), are repeating sequence of 2-6 bp of DNA. Microsatellites are typically co-dominant. They are small sized molecular markers used in genetics, for kinship, population and other studies. They can also be used for study of gene duplication or deletion. The common repeating units are (CA) n than (AT) n, where n varies between alleles (Schwenkenbecher et al., 2005). These markers often present high level of inter- and intra-specific polymorphism.
These sequences can be repeated 3 to 100 times and longer loci with more alleles generally have greater potential for slippage. These repeated units provide the successful application in applied fields of biology, medicine and molecular genetics and its mapping (Gjedrem and Baranski, 2009). Numerous methods exist for measuring genetic diversity at the genetic level, with microsatellites analysis the favored method.
Microsatellite markers are obtained by two methods: Either separated from the species itself or from cross-species amplification ( Scribner et al., 1996; Barbara et al
2007). Small length, abundance and co-dominant behavior of microsatellites have
18
made it effective in genetic mapping, DNA identification and ancestry inheritance in population genetics. It is only molecular markers used to provide evidences about which alleles are more closely related.
The variability of microsatellites is due to a higher rate of mutation during
DNA replication or due to recombination during meiosis. Microsatellites can be amplified by the PCR process, useful for direct estimation of patterns and sharing of genetic variability at the intra-specific level (Rodrigues et al., 2008). The microsatellites flanking sequences are highly conserved in species and have been used for genetic improvement, genetic propagation and conservation of endangered species ( O,Connell and Wright,1997) as well as in monitoring of genetic blockage in natural population (Thorpe et al., 2000). The assessment of these markers can be enhanced, when primers developed for one species also amplified resembling loci in another species (Farias et al., 2007). Microsatellites, as genetic markers, have been applied in conservation of population and for assessing the evolutionary trend (
O,Connell and Wright,1997). Microsatellite marker along with other molecular markers such as allozymes, mitochondrial DNA, random amplified polymorphic DNA
(RAPD) and single nucleotide polymorphism (SNP) (Vincenzi et al., 2009), have been used for the discrimination of hybrid and ancestry analysis in fish.
In the field of fisheries, SSR markers are widely used to assess population size and identification of stocks (Thorpe et al., 2000), level of inbreeding within population (Teissier et al., 1999), gene flow (Ser et al., 2010), and for quantification of traits (Gjedrem and Baranski, 2009). It has special value in migratory species where population size fluctuates due to migration of individuals there by complicating
19
the stocks assessment (Laptikhovsky et al., 2001) applied for assessing relationships, linkage mapping and the evolutionary study of fish (Kurt et al., 2011).
A lot of work has been carried out on genetic characterization of fishes by means of RAPD techniques or by using microsatellites DNA markers, including some in the Asian region and on mahseer (Tor). In Malaysia, efforts have been made to develop microsatellite DNA markers for the assessment of genetic diversity in mahseer and population structure of T. douronensis and T. tembroides.
Characterization of mahseer species through microsatellite DNA markers has also been done in Thailand (Esa et al., 2008). Assessment of genetic diversity in Tor putitora has also been carried out by employing RAPD markers as well as by isolation of microsatellites in India (Mohindra et al., 2004; Silas et al., 2005).
On the other hand, in Pakistan, assessment of genetic variation between populations or inter-population phylogenetic relationship and species identification by using mitochondrial DNA (mt DNA), Cytochrome Oxidase 1 (CO 1) gene, is still in its initial stage. The thesis present a preliminary study on inter and intra-population variations existing in Golden mahseer populations of Pakistan to save this important reserve, efficient conservation and for rehabilitation approaches. However, this is not practicable unless the data about its population structure and genetic variation from all over its distribution range will not be available.
20
Chapter 2
LITERATURE REVIEW
The mahseer has for all the time been a center of attention and unusual interest of biologists, but the documented information on any class of genetic markers or stock structure of the Golden mahseer is only few. Available researches about
Golden mahseer are limited to taxonomy (Das, 1979), migratory behavior and reproductive niches (Pathani, 1983), distribution (Menon, 1994; Mirza and Alam,
1994), ecological parameters (Nautiyal et al., 2008) and feeding behavior (Nautiyal and Lal, 1982). Taxonomy of the species has been based upon the morphological characters, leaving many unresolved problems. Genetic studies were restricted to its karyology only (Singh et al., 2010). This is despite the fact that without the genetic studies, the morphological characters cannot resolve taxonomic status of fishes (Esa,
2009).
2.1 DNA EXTRACTION
The molecular markers, like RAPD (random amplified polymorphic DNA) and
SSR (microsatellites)can put forward a more clear-cut exploration of genetic potentials of fish and its population, before appearance of its phenotype (Gjedrem and
Baranski, 2009). Through the use of these molecular markers genetic diversity can be assessed. The effective use of these techniques need extraction of superior quality and adequate quantity of DNA (Melandri, 2004).
Polymerase chain reaction (PCR) based studies require a suitable sampling, tissues preparation and DNA extraction protocols. Genomic DNA has been extracted
21 from different organs of fish, like, fins (Barrero et al., 2008), blood, scales (Ser et al.,
2010 ; Melandri, 2004 ; Thorpe et al., 2000), buccal cells (Livia et al., 2006), ovules
(Aranishi, 2006) and muscles (Chakraborty et al., 2006). For extraction of good quality DNA, phenol-chloroform method has been successfully exploited (Penzo et al., 1997; Sambrook and Russell, 2001). However, the use of primers and selection of isolation techniques for this purpose are species specific and need to be optimized for the specific species and specific laboratory conditions.
2.2 GENETIC DIVERSITY
Genetic diversity, within species and population, starts with evolution in the genetic material of a population (Liu, and Cordes., 2004)) and is determined by quantity and quality of alleles and genes present within and between populations
(Gottelli and Colwell, 2001). Population fitness depends on its genetic makeup of individuals and is controlled by evolutionary forces, like, mutation, selection, random genetic drift, migration and isolation (Chícharo and Chícharo, 2008). Level of genetic diversity was initially assessed as difference in allelic frequencies at different loci within populations, which were attributed to migration, mutation, selection and genetic drift (Gjedrem and Baranski, 2009).
Genetic diversity was assessed through measuring the phenotypic expression of different alleles. Nonetheless, now evolutionary biologist regularly take advantage of molecular markers, such as, SSR markers, AFLP (amplified fragment length polymorphism), RAPD and other DNA markers, directly or through protein polymorphisms (Ospina-Alvarez and Piferrer, 2008) and analyze the genetic differences in natural populations/ variety within species by calculating the number of polymorphic alleles and level of heterozygosis in a population. Through the
22 application of DNA based RAPD and RFLP (restricted fragment length polymorphism) analysis majority of species can be further sub-divided into separate entities, genetically different from one another(Karnik and Chakraborty,
2001). These molecular techniques have provided new means of approximating the genetic relatedness among organisms (Gottelli and Colwell, 2001), and helped speedy and accurate identification for inter- and intra-specific isolated population.
Larson et al. (1984) successfully analyzed the genetic structure of salamander
(Ambystoma californiense) to show level of gene flow among populations and pattern of habitat and population fragmentation. Rice and Hostert (1993) proposed that collective effect of gene flow and divergent natural selection pressures among populations of Drosophila spp. (laboratory experiments on speciation) in different habitats conditions increased genetic diversity.
A variety of molecular methods, are now routinely used in population genetics for phylogenetic studies (Thorpe et al., 2000; Gottelli and Colwell, 2001; Rashed et al., 2008). Basic genetic diversity analyses and its quantification within and between populations and its relationships and differentiation at the nucleotide level is measured by means of polymorphism, proportion of polymorphic loci, richness of allelic variants and average number of alleles per locus, effective number of alleles and average expected heterozygosity (Nei, 1978).
2.2.1 Choice of Marker Type
During the last decades of previous century, various molecular techniques, like, allozyme electrophoresis (Kramer, 1987), mt DNA sequencing, RFLP, RAPD,
AFLP and SSR, along with PCR were introduced and are now being effectively used in analysis of intra and inter-population variation. Molecular markers are universally
23 applicable and accessible for the comparative analysis of species, population and individual characteristics. However, the choices of marker have a remarkable effect
(Thorpe etal., 2000) and needs to be carefully planned (Ospina-Alvarez and Piferrer,
2008), considering its evolutionary time frame, mode of inheritance (maternal or bi parental; dominant or co-dominant). Table 2.1 presents a summary of different broad types of molecular markers available for use in study of gene flow, quantification of isolation between populations and latest speciation events (Peacock and Smith, 1993).
All these molecular biology techniques are though equally useful in related researches in fish (Sunnucks, 2000), yet RAPD and SSR markers have been more effectively used in analysis of genetic diversity and in resolving controversies associated with systematic and phylogenetics; because of their lower cost, high polymorphism, ease in scoring and their dominant (RAPD) and co-dominant(SSR) behavior (Thorpe et al.,
2000).
Table 2.1: Broad types of molecular markers available for genetic diversity at species and sub-species level
Markers Mode of Suitable for Prior type inheritan Information ce
Allozyme Mendelian Co-dominance Linkage mapping Yes
Mt DNA Meternal Lineage Evolutionary relationship Yes
REFLP Mendelian Co-dominance Linkage mapping Yes
RAPD Mendelian Dominant Finger printing; No Population study
AFLP Mendelian Dominant Linkage mapping NO
SNP Mendelian Co-dominance Genetic diversity Yes
SSR Mendelian Co-dominance Genetic diversity. and strain analysis
24
2.3 RAPD MARKERS
RAPD molecular technique has been widely used in species and subspecies identification (Bardakci, 2001), analysis of inter-species/inter-population gene flow
(Hadrys et al., 1992) and genetic fingerprinting (Brown and Epifanio, 2003). The usual DNA fingerprinting, via restriction fragment length polymorphism (RFLP), is laborious and time consuming, requiring pure and radio labeled DNA insertion, and using endo-nucleases (Kesseli et al., 1992). RFLP was substituted by PCR based
RAPD gene mapping in 1990s, which is now widely used for the study of genetic diversity from individuals level to closely related species, taking advantage of its potentials of finding DNA polymorphism, and haphazardly amplifying more than one fragments of genome by PCR, using single arbitrary primers (Butkauskas et al.,
2009).
RAPD analysis have been productively used in scrutiny of genetic diversity at species, subspecies and population levels for identification of different fish species, including guppy (Poecilia reticulata: Foo et al., 1995), tilapia (Oreochromis niloticus;
O. mossambicus: Bardakci, 2001), ictalurid catfish (Ictalurus punctatus: Edward et al., 2010), common carp (Cyprinus carpio:Bártfai et al., 2003) and Indian major carps
(Catla catla, Cirrhina mrigla and Labeo rohita: Barman et al., 2003, Prochildus marggravii: Hatanaka and Galetti,2003), and Silurus asotus (Yoon and Kim, 2001) in different parts of the globe.
RAPD analysis has special value in resolving the relatedness and closer relationship between species/ populations (Bardakci and Skibinski, 1994; Bartish et al.,2000; Lehmann et al., 2000; Kumar et al., 2003) . DNA amplification by PCR has opened up the prospect of examining genetic changes in different animal/ plant
25 populations, including fish populations (Ser et al., 2010). Through RAPD, genetic mapping of zebra fish (Danio rerio: Postlethwait et al., 1994), tilapia (Oreochromus niloticus and O. aureus; Ser et al., 2010) and rainbow trout (Oncorhynchus mykiss;
Gjedrem and Baranski, 2009) have been developed.
DNA sequencing can be important in analysis of diversity of organisms, together with that of fish (Theodorakis, 2001; Ali,et al., 2005), but needs more sophistication. Enzyme electrophoresis was previously used to quantify the genetic variation but Molina et al. (2008) could not detect genetic divergence in Prochilodus lineatus population. RAPD can identify allelic differences and DNA polymorphism, and hence is useful in constructing genetic markers (Ali et al., 2004). Vindhya et al.
(2007) used RAPD markers to record diversity within Oreochromis niloticus. It has been used in a number of taxonomic studies for the verification species status and in uncovering of population genetic inconsistency (Penzo et al., 1997; Ali et al.,
2004). RAPD has little reproducibility, therefore demands purified DNA, even in small quantities (Hadrys et al., 1992) for amplification by short arbitrary primers.
However, RAPD markers are not locus specific; therefore its band summary cannot be understood on equivalent ranged fragment (Bardakci, 2001).
RAPD - PCR has been used for the estimation of DNA polymorphism in color mutant in tiger barbs (Barbus tetrozona) and guppy (Poecilia reticulata) (Dinesh et al., 1996). Theodorakis (2001) used this method for the detection of radiation induced
DNA damages in Japanese madaka (Oryzias latipes). RAPD analysis discriminated
10 species of tropical and 2 species of temperate fishes through finger printings and amplifying 25-75 loci (Steffen, 2006).
26
RAPD primers were used successfully for screening and recognizing species specific loci in Neopomacentrus nemurus, N. cyanomus and Pomacentrus caeruleus, where 33% RAPD bands were species specific. Values of gene diversity (H) in N. nemurus (0.0652±0.0520), N. cyanomus (0.0354±0.0208), and P. caeruleus
(0.0283±0.0197) suggested that N. nemurus is farthest from P. caeruleus (Parveen et al., 2011).
Hatanaka and Galletti (2003) applied RAPD markers for the detection of the genetic variation in migratory freshwater fish, Prochilodus marggravii, collected from three different sites of river Brazil, and suggested that fish population downstream dam/ barrier had higher resemblance co-efficient than the upstream population. Sueli et al. (2004) assessed genetic variability in Astyanax altiparanae populations isolated by barrier (dam on river in Brazil) and recorded 43% variability in population from lower site and 75% from middle and upper sites of the dam suggesting that lower population was different from middle and upper populations. Dendrogram formed a large cluster which was subdivided into small sub-clusters with similarity co-efficient varying from 0.441 to 0.667. Similar studies on the cultured Korean cat fish (Cyprinus carpio singuonensis) created 199 RAPD fragments and identified, three sub- populations (Ali et al., 2004). Genetic variation between population of C. idella and
C. carpio sigauonensis was analyzed using 120 RAPD primers to detect inter- and intra-specific genomic changes between these species (Du et al., 2005).
Assessment of variations in three fish species of genus Garra inhabiting different rivers of South India using morphometric variation pattern and by RAPD lead to similar results (Anbalagan et al., 2012). Morphological analysis showed that
G. mullya and G. kalakadensis were more similar compared to G. gotyla
27 stenorhynchus. RAPD-Fingerprinting recorded 72 reliable fragments from 10 Operon primers, with molecular weights of 2600-3100. The common RAPD scores observed in G. mullya and G. kalakadensis were indicating a closer relationship.
Mohindra et al. (2008) discriminated five mahseer species from Indian peninsula using RAPD analysis. Extremely confusing taxonomic status of Tor malabaricus, a mahseer endemic to Western Ghats (India) was settled using RAPD markers (Vindhya et al., 2007). T. khudree shared many fragments (119 with 15
Operon) with T. malabericus. Fixed differences at RAPD loci indicated that two species were not interbreeding, as no RAPD locus showed significant disequilibrium
(P>0.05) in the two species. The genetic distance (0.3429) and UPGMA dendrogram also indicated that two species did not share common gene pool. Ghosh and Alam
(2008) used RAPD markers to assess the genetic variation in two Critically
Endangered mahseer species, viz., Tor tor and T. putitora, where primers generated
44 loci, of which 41 were polymorphic. Frequency of polymorphism was 86.36% (T. tor) and 31.83% (T. putitora).Gene diversity of 0.270 (T. tor) and 0.106 (T. pititora) revealed that T. tor has higher genetic variability. Colour difference (bronz and reddish) in different populations of T. tambroides in Malaysia (Zhou et al., 2000) generated 226 RAPD loci (100-1500 bp) which showed 100% polymorphism across
63 individuals. Ali et al. (2004) suggested that three population of T. tembroides from different rivers, were due to the influence of environment variation occurring during the end of the Pleistocene glaciations periods (Esa et al., 2008). Use of RAPD markers on Critically Endangered T. tor and T. putitora in Bangladesh (Ghosh and
Alam 2008) revealed that out of 44 loci 41 were polymorphic with 86.36% (T. tor) and 31.83% (T. putitora) similarity. The genetic diversity was higher in T. tor (0.270)
28 than in T. putitora (0.106) and genetic identity and genetic distance between the two species were placed at 0.863 and 0.147, respectively.
2.4 MICROSATELLITE MARKERS
SSRs can be present in both coding and non-coding regions of the genome and hence are ideal for study of genetic variation between closely related populations (Du etal., 2005). These repetitive DNA‘s represented and characterize the key elements of the heterochromatin (Miklos, 1985; Charlesworth et al., 1994). The repeated DNA sequences, along with its associative proteins, are believed to participate in stability of structure of the genome with respect of chromosomal variation, pairing, supra- chromosomal organization and genetic discrimination (Gotelli and Colwell, 2001).
SSR markers are used for parentage detection and strain analysis (NaNakorn et al., 2006). Due to their multi-allelic characters, i.e., small length, co-dominant inheritance and relative abundance, microsatellites have been effectively applied in genetic mapping, individual DNA identification and parentage inheritance and conservation genetics (Fiksen et al. 2002; Gjedrem and Baranski, 2009). SSR markers were also used for detection of hybridization in Hypophthalmichtysmolitrix and
Aristichthys nobilis, in hatchery stocks in Bangladesh. The investigation suggested that23.3% of the fish morphologically recognized as A. nobilis had H. molitrix alleles at one or more loci, revealing that some of these fish might be hybrid (Mazid et al.,
2005).
Evolutionary comparison through SSR indicated deviation from the major lineages of cichlid fishes. Cichlids from India and Malagasy represented basal paraphyletic group and cichlids collected from Neotropical region were the sister clad
29 to an African group. Results confirmed that these two novel DNA markers can disclose mutation within simple-sequence repeat loci over extensive periods of fish evolution (Zardoya et al., 1996).
SSR is polymorphic and show co-dominant inheritance, and hence useful for identification of many alleles at single locus. It estimates genetic variability and its patterns of distribution at specific level (Pepin, 1995). The identification of SSR is quite simple and more informative because within individuals of the linked taxa microsatellites flanking sequences are greatly conserved (Vindhya et al., 2007).
These replicated SSR sequences are longer than RAPD primers and are carried out effectively at higher temperatures, yielding more reproducible results and provided more precise genome probes (Fiksen et al., 2002). Microsatellite markers have been used for population genetic diversity in fish, shell fishes (Chauhan et al., 2007; Tong, et al., 2005) and other vertebrates (Vindhya et al., 2007; Stoltz et al., 2005). Through the use of SSR markers, Rodrigues et al. (2008) revealed that 10 types of C. carpio belonged to 6 strains. It is also used for conservation of endangered species
(Salgueiro et al., 2003; Na-Nakorn et al., 2006)
Polymorphic allozyme have been developed with microsatellite markers in
C.mrigala, which were capable of judging genetic differentiation in its natural populations (Vindhya et al., 2007;Chauhan et al., 2007).
Polymorphic loci (26) were developed in Golden mahseer (Tor putitora) and number of alleles observed per locus was 5-16. The observed and expected heterozygosity were 0.272-0.983 and 0.463-0.921, respectively. There was no significant linkage disequilibrium among the loci. Eight (8) loci showed significant deviation from Hardy-Weinberg Equilibrium, but none of these had evidence for null
30 alleles. Twenty four (24) primer cross amplified in other species of genus Tor, i.e.
T. Khudree and T. masal mahandicus (Chauhan et al., 2007).
Primers previously developed for three cyprinid fished were also tested through cross-species amplification in Golden mahseer and identified 7 polymorphic microsatellites DNA. Study revealed that allelic frequencies diverged appreciably from the expected Hardy Weinberg Equilibrium (Mohindra et al., 2004) by means of observed heterozygosity values ranged from 0.29 - 0.40, indicating that sampled collected from different water bodies were different.
31
Chapter 3
MATERIALS AND METHODS
STUDY AREA
The State of Azad Jammu and Kashmir (AJK: 33-36oN, 73-75 oE; area around
13,200 km2) is a basically hilly country, representing the mountain spurs of western extremities of the Himalayan range. The State presents the western parts of the
Kashmir (eastern parts being the Indian Held Kashmir). The mountain series are confluent with eastwardly located Indian Held Kashmir, northwardly located Gilgit-
Baltistan (GB), westwardly located Khyber-Pukhtunkhwa (KPK) and southwardly located Punjab (GB, KPK and Punjab being the provinces/administrative units of
Pakistan). Mountain ranges have a general east-west orientation in northern parts and north-south orientation in southern parts, and are associated with narrow valleys / ravine, representing streams/ rivers, receiving rain water, snow melt and/ or spring water coming from mountain slopes.
The altitudes in the State gradually rise from around 500 m above sea level
(asl) in south to some 6,500 m asl in the northern parts. The area receives moderate rainfall. Summer monsoons (July-September) are more frequent and result in flash floods in the drainage system. Winter rains (December-May) are scarce but are important source of snow at higher altitudes and have a role in recharging the ground water resources and continued flow of springs, ensuring continuous flow of water in almost all streams of the State throughout the year. Variation in altitude is associated with variation in temperatures, colder with longer winters at higher altitudes and hotter with longer summers at lower altitudes.
32
The areas under AJK are catchment of the river Jhelum. Tributaries and hill streams join together at different places with the river Neelam, entering AJK in northern parts. The river Jhelum enters AJK from the Held Kashmir in the central parts and receives river Neelam at Muzaffarabad. On its way the river Mahl drains into the river Jhelum. Different streams coming from southeastern AJK and associated hills of the Held Kashmir join at different places to form the river Poonch. River
Jhelum and river Poonch independently drain into Mangla Reservoir, from where the river Jhelum enters the Punjab (Figure 3.1).
Figure 3.1: Map showing different Rivers of Azad Jammu and Kashmir
All the rivers and streams are basically hill torrents, swelling immediately after precipitation, fast moving with rocky bed, and generally maintaining lower temperatures (>20 ºC), except at lower altitudes where it is relatively moderate.
33
Turbidity and depth of water in the river system is variable, depending upon the location and erosion in the associated mountain slopes (Figure 3.2).
Figure 3.2: Map showing rivers and lakes of inland water resources in Pakistan; (after Rafiq, 2007)
The river Jhelum after leaving the Mangla reservoir enters Indus Plain, where it joins the river Chenab (coming directly from the Held Kashmir) which later receives rivers Ravi and Sutluj at different places. The river Indus, originating from
Himalayas travels long distance to enter GB and then through KPK drain into
34
Terbella Reservoir, receiving the drains of a number of hill streams/ river at different points. Rivers Swat and Punjkhora emerge from hills of more northwestern hills of
KPK and join river Kabul, which ultimately meets the river Indus at Attock Khurd
(Punjab) and it leaves Terbella Reservoir. River Indus then travels and receives river
Chenab in the southern Punjab; from where on it travels through Sindh to drain into the Arabian Sea. Smaller rivers (Hingol, Basol, Hab, Porali and Dasht) emerge from the southwestern coastal mountain ranges of Pakistan and drain directly into the
Arabian Sea. The rivers and streams of western parts of Pakistan have internal drainage into some landlocked lakes and are isolated (Wellcome, 1985; Rafiq, 2007;
Rafiq and Najmul Huda, 2012: Figure 3.2).
Geologically AJK and Pakistan are located at the juncture of Oriental and
Eurasian tectonic plates, which met some 40-45 million years ago when the Oriental land mass slided northwards and struck Eurasian landmass, resulting in emergence of the Himalayas and western mountains and the drainage pattern (Shroder, 1993;
Fairbanks, 1989; Le Fort, 1996; Rowley, 1996). The river Indus is believed to represent the line of attachment between the two tectonic plates, the areas falling in the east of the river Indus representing oriental origin and those in the west the
Eurasian (Palaearctic) origin. The rivers and streams of the Indus drainage system emerged after this collision, and still maintain some degree of link. The rivers of the southwestern Pakistan, directly falling into the Arabian Sea, are located in the area falling on Eurasian plate do not indicate a direct link with the rivers of the Indus drainage system.
Almost all the rivers originate from the area located at higher altitude as a small stream of water, and move in westward or southward direction, swelling after receiving different streams. In hilly areas the streams are mostly turbid, and torrential,
35
having cold water and rocky beds. In the sub-mountainous tracts the river channels are broader and larger, associated with gradual increase in temperature of water. The river downstream become still wider and slow with higher temperature and increased turbidity (Rafiq, 2007). The rivers and streams of the Indus drainage are swollen during July-September, receiving flooded flow of the hill torrents after the summer monsoons. The temperature of water is higher during summer (May-October) compared with the winters. Based upon the variation in ecological conditions, Rafiq
(2007) identified 6 ichthyo-ecological zones in Pakistan, viz. Karakoram (extreme northern mountains, temperature <13 ºC during summer, average 10 ºC, mostly turbid), Himalayas-Hindukush (northern mountains, summer temperature < 17 ºC),
Abasin (northern and western low mountains, temperature average 22 ºC, range 14-28
ºC), Indus Plain (eastern plain areas; temperature 18-33 ºC, average 28 ºC), southwestern Balochistan (average 28 ºC, range 19-33 ºC) and coastal (average 32 ºC, range 30-33ºC) (Figure 3.2).
Golden mahseer has been reported from Abasin (frequency of occurrence
45%), Indus Plain (26%) and southwestern Balochistan (6%) zones, where the temperature remains within 22-28ºC. Breeding populations of the species have been reported from tributaries of Poonch River (some 90 km from western foot hills of
Pir Punjal, extending up to Mangla reservoir; recently declared as Mahseer National
Park to afford special protection), Arja stream (Mahl Nullah, river Jhelum), sub- mountainous parts of Indus river (stretch extending up to Jinnah barrage), rivers Swat and Punjkhora (Chakdara, confluence of two rivers), and Hingol river (southern
Balochistan). Some small isolated populations have also been reported by anglers in
Soan River, Harro River, Jhajjar Stream (river Chenab) and Porali River (southern
Balochistan). The populations of Golden mahseer extend over a wider range during
36
non-breeding winter season to occupy comparatively southern reaches of these rivers
(Figure 3.3).
3.1 SAMPLING
A total of 60 freshly captured specimens of Golden mahseer were collected between October, 2010 and March, 2012 from 3 different localities of AJK, viz. northern reaches of the Poonch River and its tributaries (n=22), Pooch River pocket of
Mangla Reservoir (n=21 and Arja Stream (Mahl Nullah, River Jhelum; n = 4), the known mahseer breeding localities of AJK, through the courtesy of local fishermen and anglers. Additional 11 freshly caught Golden mahseer specimens were received from Swat River (from Dir Nullah and Malakand, KPK; n = 6), Indus River
(upstreams of the Turbela Reservoir, KPK; n = 5) and Hinglo River (Balochistan; n =
2) through the courtesy of KPK and Balochistan Fisheries Departments. The specimens, after their collection, were packed/ sealed individually in polythene bags, labeled with location of capture and transported in the ice box to the Molecular
Ecology Laboratory, Bioresource Research Centre, (BRC), Islamabad (Pakistan).
Each of the specimen was identified as Tor putitora, using morphological characters (Mirza and Javed 1985, 1986; Mirza and Alam, 1994 and Jayaram, 1999) and confirmed through bar coding (Khaliq, 2011: M.Sc thesis: Published in 2015) As the number of specimen received from Poonch were very large, therefore 12 specimens of identified T. putitora each from Poonch River tributaries and Poonch pocket of Mangla Reservoir were randomly selected for molecular analysis. Thus 39 specimens were finally selected for molecular analysis, which were given consecutive reference number and grouped into 6 populations, designated as A (Poonch River, reference No.: 1-12, B (Mangla Reservoir, Reference No. 17-28), C (Jhelum River,
37
reference No. 13-16), D (Swat River, reference No. 29-33), E (Indus River, reference
No. 34-37) and F (Hingol River, reference No. 38-39) (Figures 3.3 - 3.9).
Figure 3.3: Map showing different sites of Golden mahseer population collection.
38
Figure3.4: Map showing upper stretch of River Poonch Azad Kashmir, Collection sites for Golden mahseer, Pop A
Figure3.5: Map showing Collection sites for Golden mahseer, Pop B River Poonch (Poonch Mangla Reservoir) Azad Kashmir
39
Figure3.6: Map showing Collection sites for Golden mahseer, Pop C River Jhelum Azad Kashmir
Figure3.7: Map showing, collection sites for Golden mahseer, Pop D River Swat KPK.
40
Figure3.8: Map showing collection sites for Golden mahseer, Pop E River Indus KPK
Figure3.9: Map showing Collection sites for Golden mahseer, Pop F River Hingol (Balochistan)
41
3.2 DNA EXTRACTION
3.2.1 Preparation
Each fish sample was thoroughly washed with tap water, cut into pieces (100 -
150 gram) of muscles tissues using a sterilized knife, placed in labeled zip bags
(locality, reference number) and stored at -20ºC, following (Paetkau,2003). DNA was extracted within 6 months of the storage as suggested by Lda et al. (2007).
3.2.2 Extraction
The frozen muscle tissues were thawed at room temperature (25-30ºC) and
DNA extracted by phenol-chloroform protocol (Penzo et al., 1997; Sambrook and
Russell, 2001). Thawed muscle tissue (100 mg) was macerated and placed in clean pre sterilized Eppendorf tubes (autoclaved at 121ºC for 20 minutes), to which 500 µL of the lysis buffer [200 mM NaCl + 50 mM Tris- HCl + 20 mM EDTA + 1% SDS
(pH 8.0], 40 uL proteinase K (2 mg/ml) and 25 µL DTT (Dithiothreitol was added.
This mixture was kept overnight at room temperature, when it was incubated in a water bath (56oC) for 3 hours, whirl-pooled it on Vortex intermittently. The incubated material was then centrifuged (Sigma1-14, Germany) at 10,000 rpm for 10 minutes, the aqueous phase remained at the top, interface with whitish in color, contained the denatured proteins and carbohydrates. The aqueous layer was removed and transferred to the new sterilized Eppendorf tubes, to which equal volume of buffered phenol was added and again centrifuged at 12,000 rpm for15minutes. The upper aqueous layer was then poured out, which contain DNA floating in this aqueous layer.
The uppermost layer, containing floating DNA, was carefully pipette off and saved in pre-sterilized (autoclaved at 121oC for 20 minutes) micro-centrifuge tubes. Extracted
DNA was washed with phenol-chloroform-isoamyl (25:24:1) mixture, and
42
centrifuged at 12,000 rpm for 12 minutes. The upper layer was once again taken off and washed with an equal volume phenol-chloroform (1:1) mixture, and tubes centrifuged at 14,000 rpm for 5 minutes, when clear whitish DNA appeared on the top. DNA remained separate from the phenol-chloroform mixture layer, which was pipetted off and saved the DNA containing upper layer. Once again this upper layer was washed with equal volume of chloroform, and again centrifuged for 8 minutes at
14,000 rpm. The top layer was pipetted off and placed into a labeled, pre-weighed micro-centrifuge tube, to which equal volume of iso-propanol plus 20 µL of sodium acetate was added and left overnight at -20 ºC. Afterward the tubes were centrifuged for 10 minutes at 14,000 rpm, when the liquid was carefully poured off. DNA pellet was saved and washed with 70% ethanol, and centrifuged at 12,000 rpm for 10 minutes. Ethanol was carefully poured out; saving rambling pellet of DNA, which was air dried at room temperature. Dried pellets were re-dissolved in 50-100 µL of TE
[10 mMTris (HCl) and 1mM EDTA] buffer.
3.2.3 Quality and Quantity Assessment
Presence of DNA was confirmed by agarose gel electrophosis following
Sambrook and Rusell (2001). Agrose gel was prepared by dissolving 1.5 g of agaros in 100 ml of 5 X TBE (tris borate 10mM, EDTA 1mM, pH 8.0).; The solution was boiled to crystal clear, allowed to cool up to 50 ºC, and added 2-3 drops of ethidium bromide. The mixture was poured into a gel electrophoresis tray (Gibco BRL horizontal electrophoresis) and placed comb into the gel and allowed it to solidify under room temperature. TBE buffer (200-300 ml) was then poured on the gel to fill the tray. The comb was then lifted, leaving wells in the gel.
43
Extracted genomic DNA (4 µL) mixed with 2uL of loading dye (25 mg of bromophenol blue + xylene cyanol and3 ml glycerin, mixed in 10 ml distilled water), was loaded into the wells. The electrophoresis was run at 75 volt for 1 hour
(Sambrook and Rusell, 2001). Gel was observed on a gel-doc system (Pro-Alpha enotech, 200) for bands and image photographed for documentation. Gels were later properly disposed off.
Purity of extracted DNA was determined by spectrophotometer (8415A Diode
Array, USA) at 260 nm to 280 nm wave length following and Linacero et al. (1998).
DNA concentration was determined using formula:
DNA concentration (µg/µL) = [(A (260) × DF × 50)]/ 10,000, where: A (260) = absorbance at 260 nm and DF = dilution factor.
Ratio of absorbance at 260 nm and 280 nm was calculated for the quality of the extracted DNA. DNA samples with absorbance ratios of 1.7-2.0 were used for PCR amplification.
DNA solution was then diluted with de-ionized water to obtain final working
DNA concentration (25ng/ µL).
3.3 PCR OPTIMIZATION
3.3.1 Primers
PCR conditions were optimized for 20 RAPD (FA and Operon series,
Fermentas, USA; randomly selected based on 60% GC contents; Table 3.1; some
Operon series markers previously used effectively for genetic analysis of Tor khudree
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and T. malabericus: (Silas et al., 2005) and 8 SSR (MFW and Barb series obtained from Fermentas, USA; originally designed for Cyprinus carpio and Barbus barbus, used for studies on Golden mahseer (Mohindra et al., 2004; Chauhan etal., 2007);
TPF and TTR developed under present study exploiting Tor putitora and Tor tor
DNA sequences (Table 3.2).
3.3.2 PCR Amplification
PCR amplification of reproducible DNA was carried out in sprint thermo cycler (Thermo Hybaid,SPR 220362, USA) using different concentrations of DNA template (20, 30,…..,100ng/ uL), dNTPs (0.10, 0.15, 0.20 mM), Taq DNA polymerase (1 units; Fermentas, USA) , MgCl2 (1.5- 2.5 mM) and 1 X reaction buffer
(50mM KCl, 1.5mM MgCl2, 100mM Tris HCl, pH 9, 0.1% triton X-100 and primers
(5 pmol). PCR amplification was performed at initial denaturation temperature of
95ºC for 5 minutes (stage 1of cycle 1), and subsequent denaturation at 94 ºC for 20 seconds, followed by annealing at different temperatures (optimized for each primer) for 20 seconds, and extension at 72ºC for 25 second (stage 2 for 39 to 42 cycles
(showed variation for different primers), and then final extension at 72ºC for 5 minutes(Esa et al., 2008). PCR conditions for each microsatellite primer were also optimized. Concentration of MgCl2and annealing temperatures were totally different from the concentration of RAPD markers
The optimal conditions / concentrations for amplification of PCR product were then selected for genomic analyses. Quality of PCR products were ensured by gel electrophoresis on 2 percent (%) agarose.
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Table 3.1: RAPD primers sequences tried for PCR amplification of Golden mahseer DNA
G+C (%) Primer Sequence Content
FA-1 5‘---CAATCGCCGT---3‘ 60
FA-2 5---ACCTGAACGG---3 60
FA-3 5---CTCTGGAGAC—3 60
FA-4 5---AGCGCCATTG---3 60
FA-5 5---GGGGTGACGA---3 70
FA-6 5---CTTCCCCAAG---3 60
FA-7 5---ACCCGGTCAC---3 70
FA-8 5---TTCGAGCCAG ---3 60
FA-9 5---GGGGGTCTTT---3 60
FA-10 5---GTGCCTAACC---3 60
OPA – 4 5---AATCGGGCTG—3 60
OPA -11 5---CAATCGCCGT---3 60
OPA-17 5---GACCGCTTGT---3 60
OPA-19 5---TCTGTGCTGG---3 60
OPA- 20 5---GTTGCGGATCC--3 70
OPN -04 5---GACCGACCCA---3 70
OPN -11 5---TCGCCGCAAA---3 60
OPMN-13 5---AGCGTCACTC---3 60
OPN -19 5---GTCCGTACTG---3 70
OPN -20 5---GGTGCTCCGT---3 70
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Table3.2: SSR markers sequences tried for PCR amplification of Golden mahseer DNA
Locus Primer sequence Repeat motif
TPF GGCGCCACAGAAAAAAGTCG
GTTTCGTTTCGAGCTTTTGC
TTR AGCAAAAGAGGCCCTAGCTC
AGCAAAAGAGGCCCTAGCTC
Barb 37 AAATACGCTCTCCTCATTAC ATTT
GTACAAAAGCAAAAATAAATTA
Barb 54 GTTGTTTTGATTCACACTGAG CA
TACCATCTGCTGCTGCTTC
MFW 1 GTCCAGACTGTTCATCAGGAG CA
GAGGTGTACACTGAGTCACGC
MFW 2 CACACCGGGCTACTGCAGAG CA
GTGCAGTGCAGGCAGTTTGC
MFW 7 TACTTTGCTCAGGACGGATGC CA
ATCACCTGCACATGGCCACTC
MFW11 GCATTTGCCTTGATGGTTGTG CA
TCGTCTGGTTTAGAGTGCTGC CA
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3.4 GENOMIC DNA ANALYSIS
PCR amplification data was generated for genomic DNA of each specimen and for each marker under the optimized conditions. RAPD and SSR markers were separately analyzed, following the conventionally used techniques.
3.4.1 Binary Data Generation
After the scanning of gel pictures in the computer, its format was changed from file JPEG to TIFF. The software (POPGENE version 1.31) was applied to compare the scores for the scrutiny of fragments. Binary matrix of each entity was documented as presence ―1‖ and absence ―0‖ of bands. DNA ladders run along with test DNA during electrophoresis helped in estimation of the molecular size of all bands in gel descriptions of an individual sample. From the binary matrix the number of RAPD/ SSR fragments and polymorphic bands were estimated for each primer.
3.4.2 Statistical Analysis
Bivariate (1-0) data matrix created for study were arranged in excel table for each specimen and counted markers scores. These banding model for binary data were used to put up the pair-wise matrix of genetic distances (Fitzpatrick, 2009; Nei and Li, 1979). A precise input file is made by binary matrix data and computed against different software programs, for studying according to our objectives.
3.4.3 Discriminatory Power
For each marker, the discriminatory power was determined by three
48
parameters:
Polymorphic information content (PIC) = 2fi (1-fi) (Silva et al., 2013) where: fi = Frequency of marker band present; and
1-fi = Frequency of marker bands absent
Resolving power (Rp) = ∑ IB (Pérez et al., 2006)
where: IB =for Band information
Maker index (MI) = PIC × EMR (Maras et al., 2008))
where: PIC = Diversity index, and EMR = Effective multiple ratio.
Computer POPGENE (version1.31) software (Yeh et al., 1999) was used to estimate the degrees of similarities and dissimilarities of RAPD fragments between individuals and populations. For single and multi-population analysis, descriptive statistics were carrying out in binary data of dominant markers (Kassam et al., 2005)
3.4.4 Polymorphism
From total estimated numbers of amplified bands and band frequency, percentage of polymorphic loci and polymorphic information constant (PIC) were analyzed at each locus for each population. Monomorphic and polymorphic bands of each markers were measured using excel data.
3.4.5 Genetic Diversity
Genetic diversity was projected by using POPGENE 1.32 software with special parameters of (Nei, 1978; Kassam et al., 2005). Total observed numbers of allele (na) and effective number of alleles (ne) sharing in gene diversity were examined. Mean expected heterozygosity (observed and expected) and Shannon‘s
49
index (h) and trend line analysis for Nei‘ diversity was also found out (Gaudeul et al.,
2004).
All amplified markers were also tested for its Neutrality by using the Ewens–
Watterson test (Bagley et al., 1999)
3.4.6 Genetic Variation and Population Analysis
Heterozygosity in the genome of different populations was determined by using of POPGENE 32 version 3.1. Homozygosity and heterozygosity (H), under consideration of total heterozygosity (Ht), genetic variation within population (Hs) and among populations (Dst) and genetic variation coefficient (Gst) were also calculated.
Ht- Hs= Dst
Similarly, levels of gene flow among populations were estimated as:
Nm = 0.5*(1 - Gst)/Gst (Setti et al., 2012)
3.4.7 AMOVA
Analysis of molecular variance (AMOVA) on data was analyzed for population differentiation (Fitzpatrick, 2009), the Nei, clustering of populations was described on the basis of genetic identity and distances (Kassam et al., 2005). On the base of AMOVA, variance of components and their significance level, among individuals, populations and geographical regions were calculated through Gen AlEx
6.4 (Gaudeul et al., 2004).
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By using of software FAMD.1.5 (Schlüte, 2006; Delphi 2009/2010) significance level of variance in pair-wise genetic distances (phist ΦST) among all and each population were obtained from the AMOVA. Phist ΦST in turn applied to estimate the migration level (Gene flow = Nm) of individuals from one population to other or between all populations.
Nm = (1/ΦST - 1) – 1 (Frankham et al., 2010)
Nm = number of migrant from one population to other.
3.4.8 UPGMA and PCA
Cluster analysis was done by constructing dendrogram by putting data into
NSYST pc.2.1 program (Andrew and Whitehead, 2003) using un-weighed pair group mathematical averages (UPGMA), based on the genetic similarities and distances for population structure. Nested cluster of a dendrogram was used for Cophenetic correlation (CC) analysis for pair-wise genetic distance and genetic similarities through Mantel test. Principal component analyses (PCA) described the percentage of variation based on axis of coordinates and allocate the grouping of individual and population.
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Chapter 4
RESULTS AND DISCUSSION
4.1 SAMPLE COLLECTION
Golden mahseer though has a very wide distribution, yet has restricted area of
occupancy, living in freshwater rivers/ streams/ riverine pools and associated lakes
having moderately cold (22 - 28 ºC) and highly oxygenated water and with rocky bed.
Under the recent anthropologic changes the present population of this mahseer has
contracted to certain well defined pockets (Rafiq, 2007: relative distribution in different
ecological zones). The casual information collected through the staff of Fisheries
Department and the anglers suggests that the major surviving population of AJK is now
limited to the Poonch river (recently declared as Mahseer National Park), while a
smaller population survives in the Arja stream, a tributary of the river Jhelum. Majority
of mahseer samples (56) were thus received from different parts of the river Poonch,
which were divided into two populations, i.e., stream (called Poonch sample, Pop A) and
Poonch river pocket of the Mangla Reservoir (called Mangla Reservoir sample, Pop B),
believing that the Mangla pool sample may represent a different ecotype. Only 4
specimens were available from Arja stream (called Jhelum River sample, Pop C). As the
species has been declared as Endangered (IUCN, 2011; HWF, 2012) therefore we did
not opt for special sampling of this pocket. The ‗Endangered‘ status of the Himalayan
mahseer population, however, is debatable as there are still some pockets where this
species predominates. The number of specimen received from the Poonch River were
53
very large (56), therefore we opted to randomly select 24 specimens (12 each from Pop
A and Pop B) for the molecular analysis.
To compare the genetic diversity of AJK Golden mahseer populations with the other populations of the area, we tried to collect Golden mahseer samples from other parts of the Indus drainage system. Golden mahseer population is known from the rivers
Indus and Swat, and smaller limited populations in rivers Soan and Harro and Jhajjar stream (Chenab River). We received Golden mahseer specimen from rivers Swat (n = 5, designated as Swat River, Pop D), and Indus (n = 4, Indus River, Pop E). We did not receive any specimen from other three localities, and did not try special sampling effort, keeping to endangered status of the species. A small isolated Golden mahseer population survives in the river Hingol (directly draining into the Arabian Sea). We received 2 specimens from this population, which were included in the present study to draw some inference on origin of this population.
All the specimens, included in the present molecular study, were belonging to the same species, based upon the taxonomic characters (Sen and Jayaram, 1982; Mirza and
Alam, 1994; Zafar et al., 2002) and confirmed by Pakistan National History Museum,
Islamabad, Pakistan. Further confirmation was ensured using bar coding facility of
MEGA5 computer program (Tamura et al., 2011). Amplified fragment of 650 bp of COI genes of mahseer approximated 0.5 Percent (%) genetic differences between populations
(A, B, C, D and E) collected from river Indus drainage system and those from the River
Hingol (Balochistan) (Khaliq et al., 2015). It is supposed that different populations of
Golden mahseer present in Indus (AJK) water diverse from Balochistan population by point 5 percent (0.5%). Bar coding results were acceptable on the basis of Mirza (2004)
54
and Pervaiz (2012) and in addition to, the Indus river system and west Balochistan
Rivers have been separated since Pleistocene epoch, due to Himalayan barrier, from
Indus fauna (Harrison et al., 2012; Le Fort, 1996; Rowley, 1996).
4.2 PCR OPTIMIZATION
The studies on the analysis of genetic diversity using PCR based molecular markers require optimal amplification of the template DNA. Over amplification may produce dark strips with non-specific multiple bands, while under-amplification reduces the PCR product, creating difficulty in identification of the band. PCR amplification varies with amplification conditions, the primer and the species of template genome.
Therefore optimization of PCR conditions is essential for different primers and species.
4.2.1 RAPD Markers
Of the 20 RAPD markers tested (Table 3.1), 16 (80%, Tale 4.1) amplified with well-marked band scores. Golden mahseer genome did not amplify for 4 (FA 2, FA 9,
OPA 20, OPN 19) RAPD markers, used under present study. This was expected because we used randomly selected markers based upon GC contents, which were not specific for T. putitora.
Table 4.1 presents a summary of the optimal conditions standardized for each of the 16 RAPD markers, responding to PCR-amplification of template DNA extracted from Golden mahseer muscle tissue.
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4.2.1.1 Magnesium Chloride
Of the 16 RAPD primers, used for amplification of segments of Golden mahseer
DNA, 7 (43.75%) primers optimally amplified at magnesium chloride (MgCl2) concentration of 1.5 mM, 4 (25.00%) at 2.0 mM, and 5 (31.25%) at 2.5 mM.
++ Magnesiumion (Mg ) in any form (MgCl2or MgSO4) generally stabilizes primer- template complexes, and hence has a role in PCR amplification of template DNA.MgCl2 concentrations of 0.1 - 2.5 mM have been used for PCR amplification of template genome of different species and for different primers (Tower et al., 2007).
4.2.1.2 DNA template concentration
Of the Golden mahseer template DNA concentrations tested in the present study
(20, 30, 40, 50, 60 and 70 ng/µL in volume of 25µl of reaction), DNA template concentrations between 30 to 70 ng/µL produced clearer bands. Template DNA concentrations below or above this range either did not produce clear bands or increased the non-specific amplification. Optimum amplifications were achieved at50 ng/µL for
10 (62.50%) markers and at 40 ng/µL for 6 (37.50%) markers (Table 4.1). Importance of using a balanced concentration of DNA template has been indicated by Blom and
Dabrowski (1995).
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1 2 3 4 5 6
Plate 4.1: The agarose (1.5 %) gel profile of DNA isolation from different
Golden mahseer population.
7 8 9 10
Plate 4.2: The agarose (1.5 %) gel profile of DNA isolation from different
Golden mahseer population.
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4.2.2.2 Primers
Different primers used under present study showed optimum PCR amplification of Golden mahseer template DNA at different amounts, ranging between 0.15 and 0.25 pmol. Template DNA amplified for majority of RAPD primers (10/16; 62.50%) at 0.25 pmol of the primer, while 3 (18.75%) primers amplified at 0.20 pmol and 3 (18.75%) at
0.15 pmol (Table 4.1). Higher amounts of primers increase probability of mis-priming and thence manifestation of nonspecific PCR product.
4.2.1.3 Annealing temperature
Optimal annealing temperature is variable with the primer used for DNA amplification. It is an important factor during amplification in PCR system. After denaturation at high temperature, amplification proceeds again at low temperature, i.e., the annealing temperature. Annealing temperatures for different RAPD markers (Table
4.2) remained at 33 ºC and 34 ºC; but optimal amplification was achieved at 34 ºC for 10
(FA1, FA3, FA5, FA7, FA10 OPA11, OPA17, OPA19, OPN 11, OPN20) RAPD primers, while the others could optimally amplify Golden mahseer DNA template at 33
ºC.
58
4.2.1.4 dNTPs and Taq DNA polymerase
All the 16 RAPD markers optimally amplified with 0.15 mM / µL dNTP and
0.02 units /µL of Taq polymerase (Table 4.2).
4.2.1.5 PCR cycle profile
The amplification for RAPD maker can change reproducibility due to change in profiles of thermo cycle PCR (Bakht et al., 2013). Different PCR profiles were tested for template Golden mahseer DNA for different primers and the PCR profile (Table 4.2; representative gel pictures Plates 1-8) finally worked effectively for optimal amplification for all 16 RAPD primers.
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Table4.1: Optimized conditions for 16 RAPD primers amplifying Golden mahseer template DNA
DNA Primer Annealing Taq MgCl dNTPs S.N Primer 2 template temperature polymerase (mM/µL) (mM/µL) (ng/µL) (pmol) ( ºC) (unit/µL)
1 FA-1 2.0 50 0.25 34 0.15 0.2
2 FA-3 2.0 50 0.25 34 0.15 0.2
3 FA-4 2.0 50 0.25 33 0.15 0.2
4 FA-5 2.5 40 0.25 34 0.15 0.2
5 FA-6 2.5 40 0.25 33 0.15 0.2
6 FA-7 2.5 40 0.25 34 0.15 0.2
7 FA-8 2.0 40 0.25 33 0.15 0.2
8 FA-10 2.5 50 0.25 34 0.15 0.2
9 OPA-4 1.5 40 0.20 33 0.15 0.2
10 OPA-11 1.5 50 0.15 34 0.15 0.2
11 OPA-17 2.0 50 0.15 34 0.15 0.2
12 OPA-19 1.5 50 0.15 34 0.15 0.2
13 OPN-04 1.5 50 0.20 33 0.15 0.2
14 OPN-11 1.5 40 0.20 34 0.15 0.2
15 OPMN-13 1.5 50 0.25 33 0.15 0.2
16 OPN-20 1.5 50 0.25 34 0.15 0.2
60
Table4.2: PCR program (profile) used in amplification of RAPD markers for Golden mahseer template DNA
Step Stages TemperatureºC Time Cycles
Denaturation 1 95 3min 1
2 95 20 Sec
Primer annealing 30-45 20 Sec
Primer Extension 3 72 25 Sec 35-42
Final extension 72 5 min
Hold temperature 4
4.2.2 SSR Primers
Total of 8 SSR (short sequence repeats) markers (Table 3.3) were selected for amplification of homologous loci of Golden mahseer genome. Of the 8 SSR markers used, only 3 (TPF, TTR, Barb 37) amplified the template DNA, while the other 5 primers did not amplify mahseer genome. The primers originally designed for Cyprinus carpio (MFW1, MFW2, MFW7, MFW8) and one designed for Barbus barbus (Barb54) did not amplify the Golden mahseer template DNA, collected from different mahseer populations of AJK and Pakistan, despite the fact, that these effectively amplified
Golden mahseer DNA samples collected from India, Barb37, however, effectively amplified Golden mahseer DNA template sampled from India (Chauhan et al., loc cit.) and Pakistan/AJK. The effective amplification of Golden mahseer DNA by the primer
61
specifically designed by us, based upon the known DNA sequence of Tor putitora (TPF) is understandable. The amplification of SSR primer designed by us based upon the known DNA sequence of Tor tor (TTR) indicates some homologies in the genome of T. tor and T. putitora.
The conditions required for optimal PCR amplification for 3 SSR markers have been presented in Table 4.3. Optimal amplification was achieved at MgCl2 concentration of 1.5 mM for the all 3 primers amplifying Golden mahseer genome under present study. Annealing temperature for different markers was remarkably different for different primers, ranging from 45ºC (Barb37) to 56ºC (TPF), TTR optimally amplifying at 54ºC. Numbers of PCR cycles required for final extension also vary between 30 (for
TPF and Barb37) and 37 for TTR primer. For SSR markers amplification, 30 ng /µl of the template DNA were used for successful amplification.
The PCR programme standardized for optimal amplification of Golden mahseer genome using SSR primers has been summarized in Table 4.4 (gel pictures Plate: 9 to
11).
Table4.3: Optimized conditions for SSR markers for amplification of Golden mahseer genome
Markers Temperature MgCl2 Cycle ( ºC ) (mM)
TPF 56 30 1.5
TTR 54 37 1.5
Barb 37 45 30 1.5
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Table4.4: PCR program (profile) amplification of Golden mahseer genome using SSR primers
Temperature Steps Stages Time Cycles (ºC)
Denaturation 1 94 5min
2 94 30 Sec 1
Primer annealing 45 – 56 45 Sec
Primer Extension 3 72 50 Sec 30- 42
Final extension 72 10 min
Hold temperature 4
4.3 GENETIC ANALYSIS
4.3.1 RAPD Markers
RAPD markers have been used to analyze the population structure and for estimation of inter- and intra-population genetic variation in different animal groups
(Wolf et al., 2010). RAPD markers are completely dominant markers, used for genome analysis in cases having no previous genetic information. Primer‘s nucleotide sequences are very important for production of number and size of DNA bands. Simple clear bands were counted and considered and scored for further genetic analysis. Different degrees of band amplification with some of the RAPD primers in different populations appear in gel pictures (Plates 3-10).
63
M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Plate4.3: PCR amplification of RAPD marker (FA-1) for Golden mahseer populations. [Lane2= popA; Lane 3, 4= pop C; Lane 5-8=Pop B; Lane9-11Pop D; lane12- 14=Pop E; Lane15=Pop F]
M 1 2 3 4 5 6 7 8 9 10 11 12
Plate4.4: PCR amplification of RAPD marker (FA-5) for golden mahseer populations
[Lane2 -9= River Poonch Pop; Lane 10-12= River Swat Pop]
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M 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Plate4.5: PCR amplification of RAPD marker (FA- 6) for Golden mahseer populations [Lane2-3= popA; Lane 4= pop C; Lane 5-9=Pop B; Lane9-12 Pop D; Lane13- 14=Pop E]
M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Plate4.6: PCR amplification of RAPD marker (FA-7) for Golden mahseer populations [Lane2-4= popA; Lane 5-7= pop C; Lane 8-10=Pop B; Lane11-13Pop D; Lane14- 15=Pop E; Lane16=Pop F].
65
M 1 2 3 4 5 6 7 8 9 10 11 12 13
Plate4.7: PCR amplification of RAPD marker (OPA-4) for golden mahseer populations [Lane2-6= Pop B, (River Poonch); Lane7-8= Pop D, (Indus); Lane10-12=Pop E(River Swat); Lane13-Pop F(Hingol)].
1 2 3 4 5 6 7 8 9 10 11 12 13 14 M
Plate4.8: PCR amplification of RAPD marker (OPA-17) for Golden mahseer populations [Lane1-4= PopA; Lane 5-7= Pop C; Lane 8-12=Pop B; Lane13-Pop D; Lane14-=Pop E].
66
1 2 3 4 5 6 7 8 9 10 11 12 13
Plate4.9: PCR amplification of RAPD marker (OPN-11) for Golden mahseer populations [Lane1-2= PopA; Lane 3-5= Pop C; Lane 6-8=PopD ; Lane9-12-Pop E; Lane13-=Pop F].
1 2 3 4 5 6 7 M 9 10 11 12 13 14 15 M
Plate4.10: PCR amplification of RAPD marker (OPN-20) for Golden mahseer populations [Lane1-2= PopB; Lane 3-6= Pop D; Lane7=Pop E; Lane9-10 Pop B; Lane11-13Pop D =Lane14 Pop E; Lane15=Pop F].
67
4.3.1 Discriminatory Power
The total number of RAPD fragm ents, polymorphic bands and unique bands were calculated for each primer and for different Golden mahseer populations of AJK and Pakistan by the binary matrix. Table 4.5 summarizes the genetic diversity in the amplicons produced for 16 RAPD primers for the pooled sample of 39 Golden mahseer genomes collected from AJK and Pakistan. Gel.doc software identified 197 score-able bands, with a mean of 12.31±0.90 bands marker (Table 4.5) ranging between 8 (OPA19; 4.06% of the gene pool) and 21 (OPA4; 10.66% of gene pool). Band sizes varied between 2000 bp to 114 bp. Majority of bands (171/197;
87.73%; with average of 10.81/ marker) were polymorphic, while a lower frequency
(25/197, 12.69%; with average of 1.56/ marker) of score-able bands were monomorphic.
The polymorphism at different loci ranged between 66.67% and 100% (at 3 loci).
Monomorphic bands appeared at 14 loci with the highest numbers (4) at OPA 11 and the lowest (1) at 6 (FA5, FA6, FA7, FA8, FA10) RAPD loci.
The present study identified 13 unique bands, which appeared in specific individuals/ populations. These unique bands appeared on 7/16 (43.75%) RAPD loci, with band size ranging from 145 bp to 2,000 bp. Number of unique loci observed at different loci were 1 (2 markers: FA5 and OPN20); 2 (4 loci: FA1, FA4, OPA19,
OPN04); 3 (1 locus: OPA 17). The maximum number (5; one each on FA1, FA4, FA5,
OPN04 and OPA17 markers; band sizes: 1,000 - 2,000 bp) of the unique loci were appeared in the Indus River Population (Pop E). One unique band was recorded each in
Poonch River (Pop A; OPN04,), Mangla Reservoir (Pop B; OPN20) and Jhelum River
68
(Pop C; FA5). Swat River (Pop D) presented 2 unique bands appearing on OPA 17 and
OPA19 (360 bp) RAPD markers (Table 4.5; Fig 4.1).
The amplification of a higher number of RAPD markers (16/20) in 6 different
Golden mahseer populations and a higher level of polymorphism at many loci suggest that the presently exploited conditions and RAPD makers have sufficient potentials to differentiate different geographically/ genetically isolated populations and can also validate the existence of locally adapted populations of a species. There was also remarkable differences in the band frequencies/ patterns in the Golden mahseer populations collected from different localities, indicating good discriminatory potentials of these markers as previously reported for different populations of the migratory freshwater fish species of Brazil (Sanches et al., 2012).
69
Table4.5: Description of amplicon for RAPD markers for number of mono-morphic, poly morphic and unique bands in Golden mahseer populations.
Bands % Loci Marker Total Monomorphic Polymorphic Gene Range Unique (#) # (%) # (%) Pool (bp) FA1 11 2 3 27.27 8 72.73 5.58 400-2000 FA3 14 0 2 14.29 12 85.71 7.11 333- FA4 15 2 0 0 13 100.00 7.61 142 FA5 9 1 1 11.11 8 88.89 4.57 466- FA6 10 0 1 10.00 9 90.00 5.08 250- FA7 9 0 1 11.11 8 88.89 4.57 236- FA8 12 0 1 8.33 11 91.67 6.09 248- FA10 12 0 0 0.00 12 100 5.58 281- OPMN13 16 0 3 18.75 13 81.25 8.12 229- OPA4 21 0 2 9.52 19 90.48 10.66 114- OPA11 13 0 4 30.77 9 69.23 6.60 464- OPA17 9 3 3 33.33 6 66.67 4.57 426- OPA19 8 2 2 25 6 75.00 4.06 360- OPN04 9 2 1 11.11 8 88.89 4.57 205- OPN11 17 0 0 0 17 100.00 8.63 445- OPN20 13 1 0 0 12 100.00 6.60 254- Total 197 13 25 171 6.25 Mean 12.31 1.56 10.69 6.25 SEM 0.90 0.26 0.30 0.92 0.46
70
Figure4.1: Description of amplicon per RAPD markers for number of monomorphic, polymorphic and unique bands in Golden mahseer populations [T.A.L=total amplified loci; U.B=unique bands; M.B = monomorphic band; P.B polymorphic bands]
4.3.2 Band Amplification Pattern
RAPD bands reproduced independently and scored on the basis of their presence or absence of DNA (―1‖ or ―0‖; binary matrix). Molecular weights of amplified fragments were ranged between 2000 and 114. Reproducibility of RAPD in the present study has been arranged for different populations in Figures 4.2 to 4.7. Variations were noticed among primers reproducibility in each Golden mahseer population, the Hingol population (Pop F) amplified the maximum number of DNA bands (15) followed by
Indus River population (Pop E: n 14) and Swat River population (Pop D: n 12). On the other hand, Poonch River (Pop A), Swat River (Pop D) and Hingol River (Pop F) populations did not respond to primers 5 (OPN 04, OPN 11 FA4, FA3 and FA5) RAPD
71
primers. Similarly primer OPA4 amplified with highest band score (21), while the lowest bands were generated by the primers OPA 17 OPA 19 and FA7 (Table 4.6).
Each population exhibited a specific pattern of relative frequencies of the amplified bands for different RAPD markers. In Poonch River population (Pop A) the highest frequency of bands amplified for OPMN 13 (11.48%) and the lowest frequencies for OPA17 and OPA19 (1.64%). The Jhelum River population (Pop C) showed the higher frequencies (10.26%) of amplified bands for three markers, i.e., OPMN13,
OPA11 and OPA19; and the lowest frequency for OPA17 (1.28%). The highest frequencies of bands (10.39%) amplified for FA7 and the lowest frequency of the bands amplified (1.30%) for OPA19 in samples collected from Mangla Reservoir population
(Pop B). Three populations sampled from other parts of Pakistan (Swat River, Pop D
14.46%; Indus River, Pop E 12.50 %; Hingol River, Pop F, 15.46 %) exhibited the highest frequencies of band amplifications for OPA4 locus, while the lowest frequencies of amplified bands were recorded for OPA17 (1.03%) for Swat River (Pop D), for
OPA19 for Indus River, (Pop E) and Hingol River (Pop F) populations. Thus, from the
RAPD primers used under present study, OPA4 amplified the highest frequencies of bands in three populations from Pakistan (Pop D, Pop E and Pop F); while OPMN13 amplified the highest frequency of bands in two populations of AJK (Poonch River, Pop
A and Jhelum River, Pop C) and FA7 in the (Mangla Reservoir, Pop B) AJK populations.
Reproducibility of RAPD analysis within and between laboratories has remains in doubt (Bakht et al., 2013), each laboratory amplifying different size range and reproducibility decreasing with increasing size of amplified fragment (Perry et al.,
72
2012). However, the present study has been conducted simultaneously for all the different populations under the similar laboratory conditions and for the same set of loci, therefore the results of the study can have a predictive values for the samples analyzed through RAPD markers. The study indicates that AJK populations had more identical pattern of band amplification which is different from those exhibited by other Golden mahseer populations of Pakistan.
Table4.6: Amplification of bands scores of different RAPD markers in different Golden mahseer populations
Markers Pop A Pop B Pop C Pop D Pop E Pop F FA1 7 6 6 6 6 7 FA3 7 7 7 2 2 6 FA4 7 6 6 0 5 5 FA5 5 7 5 8 5 5 FA6 1 5 7 8 9 7 FA7 6 8 7 4 8 4 FA8 2 5 2 6 9 6 FA10 3 3 3 6 9 9 OPMN13 8 10 9 10 10 10 OPA4 5 6 5 12 11 15 OPA11 4 5 8 4 6 5 OPA17 3 5 3 5 9 5 OPA19 3 3 2 5 4 3 OPN04 3 8 6 5 6 4 OPN11 9 9 9 0 8 7 OPN20 3 4 2 9 7 6 Total 76 97 87 90 114 104 Max 9 10 9 12 11 15 Mini 1 3 2 0 2 3
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Figure4.2: Frequency of amplification by different RAPD primers in Poonch River Golden mahseer population (Pop A).
Figure4.3: Frequency of amplification by different RAPD primers in Mangla Poonch River Golden mahseer population (Pop B).
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Figure 4.4 :Frequency of amplification by different RAPD primers in Jhelum River Golden mahseer population (Pop C).
Figure 4.5: Frequency of amplification by different RAPD primers in Swat River Golden mahseer population (Pop D).
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Figure 4.6: Frequency of amplification by different RAPD primers in Indus River Golden mahseer population (Pop E).
Figure 4.7: Frequency of amplification by different RAPD primers in Hingol River Golden mahseer population (Pop F).
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4.3.3 Polymorphism
Relative frequency of polymorphism at different RAPD loci presented different patterns in different Golden mahseer populations (Table 4.6; Figure 4.8 - 4.13). Highest polymorphism was recorded by FA6 locus (90.00) from Swat River population (Pop D).
Three populations of AJK (Pop A, Pop B and Pop C) showed the highest frequency of polymorphism for FA7 locus, i.e., 67%, 89%, and 78%, respectively. The lowest polymorphic frequency was also recorded for FA7 locus (9.09%) for Hingol River population (Pop F), followed by locus OPA 17 (11.11%) for Poonch River population
(Pop A) and Jhelum River population (Pop C) and by OPA 19 locus (12.50%) in
Poonch (Pop A), Jhelum (Pop B) and Hingol (Pop F) populations. On the other hand, locus OPN04 did not show any polymorphic band for Poonch River (Pop A) and Indus
River (Pop E) populations, and FA3 locus for Hingol population (Pop F).
Genetic identity between Tor malabaricus and T. khudree was indicated by
using 11 RAPD markers of Operon series to separate the stocks of these two mahseer
species. The frequencies of polymorphic bands for different primers ranged between
10.0% and 71.43% (Tiwari et al., 2013). A similar level of polymorphism has been
exhibited for different RAPD markers in different populations of Golden mahseer
populations of AJK and Pakistan, indicating a good level of genetic diversity existing
in the present sample of populations.
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Table 4.7: Relative frequency (%) of polymorphism in different populations of Golden mahseer
Populations Primers A B C D E F
FA1 18.18 36.36 36.36 45.45 27.27 27.27 FA3 42.86 28.57 42.86 42.86 35.71 0.00 FA4 40.00 26.67 33.33 40.00 0.00 36.36 FA5 55.56 77.78 66.67 55.56 88.89 0.00 FA6 50.00 70.00 70.00 90.00 80.00 36.36 FA7 66.67 88.89 77.78 88.89 44.44 9.09 FA8 16.67 41.67 16.67 66.67 50.00 41.67 FA10 18.18 18.18 18.18 72.73 54.55 81.82 OPMN13 43.75 37.50 50.00 56.25 50.00 56.25 OPA4 23.81 23.81 23.81 66.67 57.14 71.43 OPA11 30.77 38.46 61.54 46.15 61.54 38.46 OPA17 11.11 33.33 11.11 77.78 33.33 55.56 OPA19 12.50 12.50 25.00 25.00 50.00 12.50 OPN04 0.00 55.56 44.44 44.44 0.00 44.44 OPN11 35.29 35.29 47.06 47.06 0.00 41.18 OPN20 23.08 38.46 23.08 53.85 69.23 46.15
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Figure 4.8: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Poonch population (Pop A) of Golden mahseer.
Pop B
Figure 4.9: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Poonch (Mangla) population (Pop B) of Golden mahseer
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Pop C
Figure 4.10: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Jhelum population (Pop C) of Golden mahseer
Pop D
Figure 4.11: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Swat population (Pop D) of Golden mahseer
Pop E
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Figure 4.12: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Indus population (Pop E) of Golden mahseer
Pop F
Figure 4.13: Relative frequencies (%) of polymorphic bands amplified for different RAPD primers in River Hingol population (Pop F) of Golden mahseer
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Figure 4.14: Overall percentage of polymorphism and monomorphism with different RAPD primers.
4.3.4 Genetic Diversity
Individuals RAPD markers amplification patterns were compared between and within different populations of Golden mahseer, under present analysis. The number and the size of the fragments generated generally depend upon the nucleotide sequence of the primer and source of DNA template, consequently resulting in the genome specific fingerprints of random DNA fragments (Welsh et al., 1990). To determine the genetic variation, the band amplification data was used in binary form for statistical analysis to calculate observed and effective number of alleles at different loci followed by calculation of two different diversity indices, i.e., Shannon and Nei. The present statistical analyses showed considerable genetic variation among populations.
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Table 4.8 presents the summary of the result on genetic diversity constants in the Pooch River (Pop A) of AJK. The number of observed alleles varied between 1.000
(FA5 and OPA19) to 1.667 (FA7 and OPMN13) for different RAPD loci. The lowest effective number of alleles were also recorded for FA5 and OPA19 (1.000), though the highest number of effectives alleles were presented for FA4 (1.165). The mean numbers of observed and effective alleles at different loci were 1.313±0.053 (SEM) and
1.058±0.011 (SEM), respectively.
The Mangla population (Pop B) showed variation in the mean numbers of observed and effected alleles (observed = 1.743±0.042 SEM and effective =
1.414±0.038 SEM), with the lowest number of observed alleles at FA4 (1.333); and the highest at FA 5, FA7 and OPN04 (1.889). The numbers of effective alleles ranged between 1.095 (OPN04) to 1.438 (FA1). Values of Nei‘s index ranges between 0.071
(OPN20) and 0.287 (OPN04) and those of Shannon index between 0.119 (OPN 20) and
0.441 (OPN04) (Table 4.9).
The number of observed alleles in Jhelum River (Pop C) population ranged between 1.000 (FA5) and 1.778 (FA7), while the number of effective alleles fell between 1.000 (FA5) and 1.559 (OPA11) (Table 4.10). The mean of the observed and effective alleles in this population has been placed at 1.439±0.060 SEM and
1.213±0.037 SEM, respectively. Nei and Shannon index showed variation with 0.00
(FA5) to 0.228 (OPN11) to 0.352 (OPA11) and 0.329 (OPN11) to 0.486 (OPA11) respectively.
For Swat River (Pop D) population, the highest number of observed and effective alleles were 2.00 (OPA17) and 1.69 (FA7), respectively. The minimum
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number of observed and effective alleles were 1.47 (OPN11) and 1.11 (OPN11), respectively. The average values for observed and effective numbers were calculated as
1.740±0.042 SEM and 1.414±0.038 SEM. The values of Nei‘s and Shannon indices ranged between 0.089 and 0.388 and 0.159 to 0.557, respectively; the lowest values noted for OPN11 and highest for FA7 (Table 4.11).
Indus River (Pop E)population showed the lowest observed and effected genetic diversity as 1.000 among FA4, OPN04 and OPN11, while the highest number of observed alleles were 1.952 (OPA4) and the higher number of effective alleles were
1.626 (OPN20). Nei‘s and Shannon indices indicated no diversity for primers FA4,
OPN04 andOPN11, while higher values of these indices appeared at OPA4 (Nei‘s =
0.444 and Shannon = 0.626) locus (Table 4.12).
The population from Hingol River (Pop F) exhibited the higher number of observed and effective alleles at FA5 locus (observed = 1.222 and effective =1.157), and at FA8 locus (observed = 1.154 and effective = 1.109). All the other primers showed minimum values of observed and effected loci (1.000) indicating no diversity.
Consequently, FA5 (Nei = 0.092 and Shannon = 0.134) and FA8 (Nei = 0.064 and
Shannon = 0.093) produced higher values of Nei‘s and Shannon indices, the other primers showing no allelic diversity (Table 4.13).
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Table 4.8: Genetic diversity constants for different RAPD marker loci in Poonch River (Pop A) population of Golden mahseer. na = observed alleles, ne = effected alleles, H = Nei‘s index, I = Shannon index
Markers na ne H I
FA 1 1.200 1.018 0.016 0.035
FA 3 1.500 1.095 0.080 0.148
FA 4 1.600 1.165 0.122 0.208
FA 5 1.000 1.000 0.000 0.000
FA 6 1.100 1.019 0.016 0.030
FA 7 1.667 1.094 0.080 0.155
FA 8 1.154 1.029 0.024 0.046
FA 10 1.182 1.045 0.036 0.063
OPMN13 1.667 1.086 0.075 0.149
OPN04 1.222 1.031 0.027 0.052
OPN11 1.329 1.058 0.048 0.089
OPN20 1.385 1.075 0.060 0.110
OPA4 1.286 1.079 0.058 0.099
OPA11 1.385 1.065 0.055 0.105
OPA17 1.333 1.063 0.053 0.099
OPA19 1.000 1.000 0.000 0.000
Mean 1.313 1.058 0.047 0.087
SEM 0.053 0.011 0.008 0.015
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Table 4.9: Genetic diversity constants for different RAPD marker loci in Mangla Reservoir (Pop B) population of Golden mahseer. na = observed alleles, ne = effected alleles, H = Nei‘s index, I = Shannon index
Markers na ne H I
FA 1 1.533 1.22 0.146 0.231
FA 3 1.571 1.427 0.238 0.344
FA 4 1.533 1.336 0.193 0.287
FA 5 1.556 1.268 0.171 0.267
FA 6 1.9 1.466 0.297 0.456
FA 7 1.889 1.698 0.388 0.557
FA 8 1.846 1.512 0.303 0.454
FA 10 1.909 1.239 0.186 0.324
OPMN13 1.667 1.579 0.31 0.438
OPN04 1.778 1.384 0.254 0.393
OPN11 1.471 1.11 0.089 0.159
OPN20 1.846 1.439 0.269 0.414
OPA4 1.81 1.483 0.297 0.446
OPA11 1.716 1.397 0.242 0.367
OPA17 2 1.577 0.357 0.539
OPA19 1.875 1.48 0.289 0.44
Mean 1.743 1.414 0.252 0.383
SEM 0.042 0.038 0.020 0.027
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Table 4.10: Genetic diversity constants for different RAPD marker loci in Jhelum River (Pop C) population of Golden mahseer. na = observed alleles, ne = effected alleles, H =
Nei‘s index, I = Shannon index
Markers na ne H I
FA 1 1.545 1.165 0.127 0.215
FA 3 1.500 1.151 0.116 0.197
FA 4 1.333 1.236 0.138 0.202
FA 5 1.000 1.000 0.000 0.000
FA 6 1.700 1.212 0.162 0.276
FA 7 1.778 1.435 0.26 0.396
FA 8 1.154 1.046 0.036 0.061
FA 10 1.273 1.082 0.063 0.107
OPMN13 1.600 1.235 0.164 0.264
OPN04 1.444 1.257 0.153 0.232
OPN11 1.706 1.356 0.228 0.352
OPN20 1.231 1.132 0.082 0.123
OPA4 1.333 1.139 0.095 0.151
OPA11 1.846 1.559 0.329 0.486
OPA17 1.333 1.333 0.167 0.231
OPA19 1.250 1.076 0.058 0.098
Mean 1.439 1.213 0.136 0.212
SEM 0.060 0.037 0.021 0.032
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Table 4.11: Genetic diversity constants for different RAPD marker loci in Swat River (Pop D) population of Golden mahseer. na = observed alleles, ne = effected alleles, H = Nei‘s index, I = Shannon index
Markers na ne H I
FA 1 1.533 1.22 0.146 0.231
FA 3 1.571 1.427 0.238 0.344
FA 4 1.533 1.336 0.193 0.287
FA 5 1.556 1.268 0.171 0.267
FA 6 1.9 1.466 0.297 0.456
FA 7 1.889 1.698 0.388 0.557
FA 8 1.846 1.512 0.303 0.454
FA 10 1.909 1.239 0.186 0.324
OPMN13 1.667 1.579 0.31 0.438
OPN04 1.778 1.384 0.254 0.393
OPN11 1.471 1.11 0.089 0.159
OPN20 1.846 1.439 0.269 0.414
OPA4 1.81 1.483 0.297 0.446
OPA11 1.716 1.397 0.242 0.367
OPA17 2 1.577 0.357 0.539
OPA19 1.875 1.48 0.289 0.44
Mean 1.743 1.414 0.252 0.383
SEM 0.042 0.038 0.020 0.027
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Table 4.12: Genetic diversity constants for different RAPD marker loci in Indus River (Pop E) population of Golden mahseer. na = observed alleles, ne = effected alleles, H = Nei‘s index, I = Shannon index
Markers na ne H I
FA 1 1.636 1.376 0.23 0.346
FA 3 1.500 1.209 0.142 0.227
FA 4 1.000 1.000 0 0
FA 5 1.889 1.359 0.247 0.397
FA 6 1.900 1.474 0.300 0.460
FA 7 1.667 1.381 0.236 0.356
FA 8 1.385 1.179 0.117 0.184
FA 10 1.545 1.238 0.16 0.253
OPMN13 1.667 1.201 0.155 0.263
OPN04 1.000 1.000 0.00 0.00
OPN11 1.000 1.000 0.00 0.00
OPN20 1.846 1.626 0.349 0.506
OPA4 1.952 1.841 0.444 0.626
OPA11 1.615 1.186 0.143 0.242
OPA17 1.444 1.134 0.103 0.175
OPA19 1.75 1.415 0.253 0.386
Mean 1.55 1.289 0.18 0.276
SEM 0.079 0.058 0.032 0.046
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Table 4.13: Genetic diversity constants for different RAPD marker loci in Hingol River (Pop F) population of Golden mahseer. na = observed alleles, ne = effected alleles, H = Nei‘s index, I = Shannon index
Markers na ne H I
FA1 1.000 1.000 0.000 0.000
FA 3 1.000 1.000 0.000 0.000
FA4 1.000 1.000 0.000 0.000
FA5 1.222 1.157 0.092 0.134
FA6 1.000 1.000 0.000 0.000
FA 7 1.000 1.000 0.000 0.000
FA8 1.154 1.109 0.064 0.093
FA10 1.000 1.000 0.000 0.000
OPMN13 1.000 1.000 0.000 0.000
OPN04 1.000 1.000 0.000 0.000
OPN11 1.000 1.000 0.000 0.000
OPN20 1.000 1.000 0.000 0.000
OPA4 1.000 1.000 0.000 0.000
OPA11 1.000 1.000 0.000 0.000
OPA17 1.000 1.000 0.000 0.000
OPA19 1.000 1.000 0.000 0.000
Mean 1.0235 1.0166 0.0097 0.0143
SEM 0.016 0.012 0.007 0.010
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Table 4.14: Summary of average values of genetic diversity constants generated for different RAPD loci for different populations of Golden mahaseer (AJK= Azad Jammu and Kashmir, KPK = Khyber Pukhtoonkhwa, BLN = Balochistan) . Poly = Polymorphism, na = observed alleles, ne = effective alleles, H = Nei‘s index, I = Shannon index.
Region Population N Poly (%) na ne H I
Poonch (A) 12 37.06 1.37 1.07 0.05 0.10
AJK Mangla (B) 12 57.36 1.57 1.21 0.14 0.23
Jhelum (C) 4 44.67 1.45 1.22 0.14 0.22
Swat (D) 5 73.10 1.73 1.41 0.25 0.38
KPK Indus (E) 4 54.31 1.54 1.30 0.18 0.28
Hingol (F) 2 2.03 1.02 1.01 0.01 0.01 BLN
Mean 44.76 1.45 1.20 0.13 0.20
S.E 0.10 0.06 0.04 0.05
Summary of genetic diversity constants for 6 populations of Golden mahseer
sampled from AJK and Pakistan has been presented in Table 4.14. The results suggest a
high dissimilarity in genetic composition of different populations. The Golden mahseer
populations exhibited an average of <50% (44.76%) polymorphism in all the different
population on RAPD loci, ranging between 2.03% (Hingol River, Pop E) to 73% (Swat
River , Pop D)., The population of Swat River (73%, Pop D), with the highest level of
polymorphism, bears the highest genetic diversity, compared with medium genetic
diversity present in Mangla Reservoir (57%, Pop B) and Indus River (54.31%, Pop E)
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and lower polymorphism/ genetic diversity in Jhelum River (44.67%, Pop C) and
Poonch River (37.06%, Pop A) populations and the lowest genetic diversity (2.03%) was recorded in Hingol River (2.03%, Pop F) population. A similar pattern was followed by the other genetic diversity indicators, i.e., observed and effective allele frequencies, and also in the values of Nei and Shannon diversity indices (Table 4.14).
Genetic differentiation constants determined among 6 Golden mahseer populations across 16 RAPD loci by using POPGENE 32 (Suresh et al., 2013) has been presented in Table 4.15. Genetic diversity (Ht) or heterogeneity in the overall Golden mahseer population under present study as estimated under Hardy-Weinberg
Equilibrium is 0.19±0.02 while mean genetic diversity within populations (HS) is calculated as 0.13 ± 0.01). The genetic diversity between populations remained at 0.05±
0.02. The value of genetic differentiation constant (Gst) for overall population was placed at 0.022 0.04 indicating very low genetic variation among all the different populations of Golden mahseer. The overall value of gene flow constant between populations was higher (3.22 0.32, Values of gene flow constant (Nm), falling at above 1 (Nm > 1) is an indication of a high level of gene flow present between populations (Mallet et al., 1990). Low values of Gst is also indicative of a higher gene flow between populations, and hence a lower levels of isolation between populations.
Summary of genetic similarity indices between different populations suggest a high level (80%) of inter-population similarity (Table 4.15) in confirmation with a higher rate of gene flow and lower heterogeneity within population. The values generated for Ewens-Watterson test for neutrality for different loci (Tab 4.16) showed
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that the allele frequencies at all loci were selectively neutral in the overall study population of Golden mahseer. However, the similarity is very high between three populations of AJK (97- 99%; Pooch River, Jhelum River, Mangal Reservoir). These three populations of AJK showed exhibited almost equally high genetic similarities (94-
95%) with two populations (Swat River and Indus River) of KPK (Table 4.17). The similarities of the 5 populations present in the northern parts of Pakistan showed more similarities among each other (> 94%) but have relatively lower similarities and higher distance with Hingol River population, present in southern parts of the Pakistan.
Golden mahseer is migratory fish species which moves longer distances for the breeding purposes (Nautiyal et al., 2008). Under such movements fair amount of inter population gene flow probably persisted till recent times between populations occupying different rivers of Indus river system in northern parts of Pakistan (including three populations of AJK). Damming of water at different places is a recent event; therefore effects of such isolations do not appear in present analysis. Hingol River population appears to have a longer isolation from other Golden mahseer populations of the Indus river system. Similar studies using RAPD markers in Prochilodus marggravii (Hatanaka and Galletti, 2003) and Korean cat fish (Silurus asotus; Yoon and Kim, 2001) indicated significant differences between different population, attributed to different habitat conditions of different sampling sites and thence ecological isolations. Such isolations resulted in high rates of inbreeding within population in damsels fishes of family
Pomacentridae (Amang et al, 2012).
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Table 4.15: Genetic diversity, genetic differentiation and gene flow indices at different RAPD loci in different populations of Golden mahseer. Hs = genetic diversity within population, Dst =genetic diversity between population, Gst =genetic differentiation among population, Rst = genetic differentiation within population
Heterozygosity Genetic diversity Genetic Gene flow Marker (Ht) differentiation (Nm) Hs Dst Gst Rst
FA1 0.16 0.14 0.02 0.15 0.88 3.26
FA3 0.13 0.11 0.02 0.15 0.85 4.01
FA4 0.12 0.1 0.02 0.15 0.83 5.04
FA5 0.13 0.12 0.01 0.1 0.92 5.42
FA6 0.17 0.15 0.02 0.12 0.88 4.21
FA7 0.23 0.19 0.04 0.16 0.83 2.90
FA8 0.17 0.11 0.06 0.23 0.65 3.85
FA10 0.30 0.09 0.21 0.65 0.30 0.95
OPMN13 0.34 0.15 0.19 0.51 0.44 1.09
OPN04 0.15 0.12 0.03 0.16 0.80 3.20
OPN11 0.10 0.09 0.01 0.10 0.90 4.65
OPN20 0.18 0.14 0.04 0.20 0.78 2.69
OPA4 0.22 0.16 0.06 0.24 0.73 1.75
OPA11 0.19 0.16 0.03 0.17 0.84 2.69
OPA17 0.17 0.14 0.03 0.17 0.82 2.94
OPA19 0.22 0.12 0.10 0.27 0.55 2.95
Mean 0.19 0.13 0.05 0.22 0.75 3.22
S.E 0.02 0.01 0.02 0.04 0.04 0.32
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Table 4.16: Overall Ewens-Watterson Test for Neutrality statistics for different RAPD loci for sample of Golden mahseer from Pakistan and AJK.
Markers Obs. F Min F Max L95* U95*
FA1 0.827 0.500 0.950 0.770 0.025 0.503 0.95
FA3 0.874 0.500 0.950 0.770 0.026 0.502 0.95
FA4 0.868 0.500 0.950 0.769 0.026 0.501 0.95
FA5 0.874 0.500 0.950 0.768 0.026 0.501 0.95
FA6 0.862 0.500 0.950 0.766 0.026 0.502 0.95
FA7 0.784 0.500 0.950 0.770 0.026 0.503 0.95
FA8 0.846 0.500 0.950 0.770 0.026 0.502 0.95
FA10 0.848 0.500 0.959 0.800 0.024 0.441 0.95
OPMN13 0.762 0.500 0.950 0.769 0.026 0.502 0.95
OPN4 0.838 0.500 0.950 0.768 0.026 0.503 0.95
OPN11 0.874 0.500 0.950 0.768 0.026 0.502 0.95
OPN20 0.822 0.500 0.950 0.768 0.026 0.502 0.95
OPA4 0.817 0.500 0.950 0.769 0.026 0.501 0.95
OPA11 0.810 0.500 0.950 0.769 0.026 0.502 0.95
OPA17 0.843 0.500 0.950 0.769 0.026 0.502 0.95
OPA19 0.807 0.500 0.950 0.769 0.026 0.501 0.95
Mean 0.835 0.500 0.951 0.771 0.026 0.498 0.95 L= Lowest; U= upper
95
Table 4.17: Genetic similarities between different populations of Golden Mahseer
Population Poonch Mangla Jhelum Swat Indus Hingol
Poonch (A) ****
Mangla (B) 0.9831 ****
Jhelum (C) 0.9830 **** 0.9713 Swat (D) 0.9536 0.9604 0.9458 ****
Indus (E) 0.9472 0.9385 0.9369 0.9529 ****
Hingol (F) 0.8758 0.8724 0.8576 0.8799 0.8551 ****
4.3.5 Inter-population Genetic Distance
The genetic distances calculated between different Golden mahseer populations
(Table 4.18) suggest low (<16%) genetic distances between different populations.
Hingol River (Pop F) population showed the highest genetic distance with Indus River population (0.1565, Pop E), followed by those with Jhelum River (pop C), Mangla
Reservoir (Pop B), Poonch River (pop A) and Swat River (Pop D). Three populations of
AJK (Poonch, Mangla and Jhelum) revealed the lowest values of genetic distances
(0.0171- 0.0291) between these populations.
Construction of Unweighted Pair-Group Method with Arithmetic Mean
(UPGMA) dendrogram of 6 Golden mahseer populations under the present analysis based on Nei‘s genetic similarities and genetic distances between these populations
(Parveen et al., 2011), developed three main clusters of populations. First cluster represented Poonch River (Pop A), Jhelum River (Pop C) and Mangla Reservoir (Pop B) populations, while the second cluster included two populations, i.e., Swat River (Pop D)
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and Indus River (Pop E) populations, while the third cluster singled out the Hingol River
(Pop F) population as single isolated population (Figure 4.14).
Table 4.18: Genetic distances between different populations of Golden mahseer
Population Poonch Mangla Jhelum Swat Indus Hingol
Poonch ****
Mangla 0.0171 ****
Jhelum 0.0171 0.0291 ****
Swat 0.0475 0.0404 0.0558 ****
Indus 0.0543 0.0634 0.0652 0.0482 ****
Hingol 0.1326 0.1365 0.1536 0.1280 0.1565 ****
The genetic distance between populations of Golden mahseer was calculated based on an unbiased measure, i.e., Nei‘s diversity index (Nei, 1972; Parveen et al.,
2011). Dendrogram was constructed using ―Unweighted Pair-Group Method with
Arithmetic Mean‖ (UPGMA) to illustrate the relations between different geographic samples using POPGENE 1.32from the combined data generated for 16 RAPD loci. The dendrogram divided 6 populations into two clusters. The first cluster is divided into two sub-clusters, having 3 populations (Poonch River, Mangla Reservoir and Jhelum River, three populations of AJK) into one group and the second group included Swat River and
Indus River populations (two populations of KPK). The second main cluster included the Hingol River population only (Figure 4.14, Figure 4.15). These clusters were created on the basis of genetic similarities and distances pattern at different RAPD loci. The
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clustering of the populations appears natural, as expected under the geographic locations of different populations and possible present/ past barriers. Thus on the basis of genetic similarities and distances, lower level of gene flow and higher value of genetic variation were seen in Hingol River (pop F) population compared with rest of the populations and therefore emerged as a separate cluster. It is suggested that Hingol River System has remained isolated from Indus River system of Pakistan since millions of years. Similar evidence was given by Esa and Rahim (2013) to indicate that a common lineage for
Tor douronensis distributed in the Endau-Rompin River was possible due to historical interconnection with majority of the major river systems of Peninsular Malaysia, during the Tertiary and Quaternary periods (10–5 Million years ago), via the North Sunda
River. The eventual separation Borneo from mainland during the Pleistocene period ultimately resulted in the isolation of T. douronensis of the Endau-Rompin populations from their counterpart Borneo populations (Voris, 2000: Fairbanks, 1989). Similar evidence of a closer genetic relationship between freshwater fishes of Borneo and mainland Asia in relation to their biogeographical history was observed in Hemibagrus nemurus and Hampala macrolepidota (Ryan and Esa, 2006; Dodson et al., 1995). Such type of clustering of populations of migratory fish species, Brycon lundii, was also determined and suggested 100% sharing of bands between un isolated populations and
27.3% sharing with the recently isolated population (Sanches et al., 2012)
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Figure 4.15: Results of UPGMA cluster analysis based on genetic distance (Nei, 1972), calculated on band amplification at16 RAPD markers in different populations of Golden mahseer. Pop. 1= river Poonch (Pop A); Pop. 2 = River Jhelum (Pop C); Pop 3 = Mangla (Pop B); Pop. 4 = River Swat (Pop D); Pop. 5 = River Indus (PopE); Pop. 6 = River Hingol (Pop F).
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1 2 20 4 17 7 5 11 12 8 10 29 35 19 9 16 3 6 15 10 30 25 26 13 27 28 38 39 23 18 21 22 24 14 34 36 37 31 32 33
0.70 0.55 0.39 0.23 0.07 Coefficient
Figure4.16: Clustering of different RAPD generated genotypes of Golden mahseer (Jaccard similarities coefficient) based on UPGMA cluster analysis between Individuals of six populations. (Numbers represent the individuals of different populations; Pop A=1-12; Pop B=17-28; Pop C=13-16; Pop D=29-33; Pop E=34-37; Pop F=38-39).
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1 4 5 8 2 3 7 11 35 25 17 30 9 38 10 39 13 27 19 10 28 12 31 18 20 37 34 29 33 36 6 15 16 14 21 22 24 26 23 32
1.58 1.18 0.79 0.39 0.00 Coefficient
Figure4.17: Clustering of different genotypes of Golden mahseer specimen collected from different parts of AJK and Pakistan based on genetic distances. Pop A = 1-12, Pop B = 13-16, Pop. C = 17-28, Pop D = 29-33, Pop E = 34-37, Pop F = 38-39.
4.3.6 Principal Component Analysis (PCA)
Three dimensional principal component analysis (PCA) accounted for 37.94%,
18.89% and 14.50 % of the variability to three different principal component of the total
71.33% accounted variability (Table 4.15). This suggests that first principal component, i.e., molecular variance among genotypes, accounts for the maximum variability. PCA output has produced three clusters or coordinates as three populations of AJK (genotype
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# 1-28), Indus-Swat population (genotype # 29-37) and Hingol population(genotype #
38-39) (Figure 4.18). PCA generated clustering is close to that generated by dendrogramme developed through similarity/ genetic distance analysis.
Figure4.18: Principle component analysis (PCA) output for different genotypes of Golden mahseer. Pop. A: 1-12; Pop B: 17-28; Pop C: 13-16; Pop D: 29-33; Pop E: 34-37 & Pop F: 38-39
Table 4.19: Variability (%) attributed to different components under PCA.
1 2 3 Axis
37.94 18.89 14.50 %
37.94 56.83 71.32 Cum %
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Table4.20: Analysis of molecular variance (AMOVA) between and within populations
of Golden mahseer.
Est. Source df SS MS Var. % Phist P
Between Populations 5 270.555 54.111 5.592 21
Within Populations 33 676.650 20.505 20.505 79 0.214 0.010
Total 38 947.205 26.096 100
Analysis of molecular variance (AMOVA) showed 21% variance between
populations and 79% variance within populations. The variance within and between
populations was (Fst 0.214) significant (p < 0.01) (Table 4.20). AMOVA thus suggested
an appreciably higher variance contributed by individual variation present within
populations, and relatively lower variation coming from the inter population variability.
Pair wise estimation of 퐹st for over all loci was 0.214. Similar pair wise estimate results
showed significant genetic differentiation within the Kelantan mahseer population,
showing the higher level of differentiation from all other populations (퐹st = 0.1811–
0.6494, 푃< 0.05) (Esa and Rahim, 2013).
AMOVA used on RAPD marker between Tor khudree and Tmalabaricus
suggested that genetic differences were not significant between the two species. Intra-
species genetic difference is expected to be lower than interspecies genetic differences
(Tewari et al., 2013). Genetic distances calculated using Operon markers ranged from
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0.154 to 0.460 among 7 groups of species sampled form the Indian waters. The
variability was attributed to varying nature of genomes (Kuusipalo, 1999)
4.4 MICROSATELLITE MARKERS
Out of 8 microsatellite markers (SSR markers) used in this study only 3 (TPF,
TTR, and Barb37) could successfully amplify template DNA sample of Golden mahseer
used under the present study (Plate 11-13). Other 5 (Barb54MFW1, MFW2, MFW7 and
MFW11) did not amplify the Golden mahseer template DNA. Four (4) of the SSR
primers not amplifying Golden mahseer tamplate DNA successfully which amplified
Tor pitutora genome of Indian origin (Mohindra et al., 2004), however MFW1 and
MFW11 were regarded as a duplicate loci while MFW2 and MFW7 produced mono-
bands, and hence were not consider for genotyping. Primers pairs which successfully
amplified 4 bands (Table 4.4.1)) were Barb 37, in Mangla Reservoir (Pop B) population
and Swat River (Pop D) populations. Rest of the populations (Pop A, Pop C, Pop E and
Pop F) generated 3 bands, the third allele missing. This primer did previously amplify T.
pitutora genome and generated 3 bands (Mohindra et al., 2004).Two newly developed
and applied primer pair also successfully generated two bands each in most of the
individuals of the six different populations of Golden mahseer under the present study.
4.4.1 Allelic Diversity
Three microsatellite (SSR) markers, amplified in Golden mahseer populations
under present study, produced 8 different identifiable bands/ alleles. Number of alleles at
different loci were 2 (TPF and TTR, each) and 4 (BARB37) (Table 4.21). The pattern of
distribution of alleles was similar in the three geographical regions (AJK, KPK,
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Balochistan). The size of alleles amplified ranged between 176 bp (TTR) and 720 bp
(TPF). The identified alleles were relatively smaller at TTR locus, followed by Barb37 locus, and larger alleles were recorded at TPF locus. MFW1 and MFW7 loci could not amplify Golden mahseer genome under the present study, though both these loci were used to assess genetic variation in T. putitora (Lal et al., 2004). This indicates some level of genetic difference between the Golden mahseer populations of India and
Pakistan, associated with different river drainages systems
Table4.21: Description of amplicon for microsatellite (SSR) markers in Golden mahseer populations
Amplified Amplified Proportion Allele Marker Alleles Size of total Frequency (#) (bp) alleles (%)
TPF 2 582-720 25 0.25
BARB 37 4 209-354 50 0.50
TTR 2 176-191 25 0.25
TOTAL 8 100 1
The distribution of alleles varied in different populations. Three populations of
AJK, i.e. Poonch River (Pop A), Mangla Reservoir (Pop B) and Jhelum River Pop C) did not exhibit the 3rd allele (1101) at Barb37 locus. While in Indus River (Pop E) and
Hingol River (Pop F) populations 4th allele (1110) was missing. Alleles amplified at TPF locus had a comparatively smaller amplicon size (566-655 bp) in Poonch River (Pop A),
Swat River (Pop D) and Indus River (Pop E) populations; compare to amplicon size
(582-720 bp) amplified in other three populations, i.e., Jhelum River (Pop C), Managla
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Reservoir (Pop B) and Hingol River (Pop F) populations. Relative proportion of the gene pool shared by different SSR loci also varied in different populations (Table 4.21).
The number of alleles (n 26) and range of alleles per locus (5-16) identified in the
Indian population of Golden mahseer (T. putitora) was higher (Chauhan etal., 2007) than those exhibited for SSR markers in the present sample of Golden mahseer of
Pakistan genome. It is difficult to propose that this variation is due to homogeneity in
Golden mahseer population of AJK and Pakistan or due to characteristic of differences in the loci being amplified in the two studies.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 M
Plate4.11: PCR amplification of Golden mahseer genome by TPF microsatellites marker in different populations. 1-2 = Jhelum, 3-8 = Poonch, 9-10 = Indus, 11-12 = Swat, 13, 14 = Hingol.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 M
Plate4.1: PCR amplification of Golden mahseer genome by TTR microsatellites marker in different populations. 1-2 = Jhelum, 3-8 = Poonch, 9-10 = Indus, 11-12 = Swat, 13,14 = Hingol.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 M
Plate4.2: PCR amplification of Golden mahseer genome by Barb37 microsatellites marker in different populations. 1-2 = Jhelum, 3-8 = Poonch, 9-10 = Indus, 11-12 = Swat, 13, 14 = Hingol.
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Table4.22: Relative proportion (%) shared by different SSR loci in different populations of Golden mahseer.
Markers Poonch Mangla Jhelum Swat Indus Hingol River (A) Reservoir River (C) River River (E) River (B) (D) (F)
TPF 33.33 25.00 33.33 33.33 33.33 33.33
BARB 37 50.00 50.00 50.00 50.00 50.00 50.00
TTR 16.67 25.00 16.67 16.67 16.67 16.67
TOTAL 100.00 100.00 100.00 100.00 100.00 100.00
4.4.2 Allelic Frequencies
The allelic frequencies of different alleles at three loci in 6 populations were ranged from 16.67 to 50.00, with highest alleles from Barb 37 followed by TPF and
TTR, (Table 4.22and Figures 4.18). Highest allele frequency of the alleles was amplified by Barb37 loci (which was followed by TPF and TTR (Figure 4.24). The present results showed that allele frequencies in different populations were closely similar to one another; except for Mangla Reservoir (Pop B) and Swat River (Pop D) populations, where four bands were amplified.
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Figure 4.19: Allele‘s frequency amplified by SSR markers, Series 1=TPF; 2=Barb 37; 3 =TTR
4.4.3 Genetic Diversity
Population genetic diversity indices, viz. Nei‘s genetic diversity index and
Shannon diversity index, and polymorphism analysis revealed inter-population variability. There was 100% polymorphism in Mangla Reservoir (Pop B) population, followed by that in Indus River (Pop E; 62 %) and Swat River (Pop D; 50.0%) populations.
Data generated for number of observed and effective SSR alleles, as well as for Shanon index (I) and genetic heterozygosity/diversity (h) (Table 4.23) suggested the maximum values for population of Mangla Reservoir (Pop B) and the lowest values for the population of Jhelum River (Pop C). Number of observed (Na) alleles ranged between 2.00 (Mangla Reservoir; Pop B) to 1.13 (Jhelum River; Pop C) and number of effective alleles (Ne) ranged between and 1.39 (Mangal Reservoir; Pop B) to 1.09
(Jhelum River; Pop C). Values of Shannon diversity index ranged between 0.39 (Jhelum
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River; Pop C) to 0.08 (Mangla Reservoir; Pop B), while heterozygosity index (h) ranged between 0.24 (Mangal Reservoir; Pop B) and 0.04 (Jhelum River; Pop C).
Table4.23: Genetic diversity constants calculated for 3 SSR markers in different populations of Golden mahseer. Poly = polymorphism, na = observed numbers (alleles), ne effective numbers, H =Heterozygosity, I = Shannon index
Size Poly Populations na ne H I (No.) (%)
River Poonch (A) 7 37.5 1.38 1.22 0.14 0.20
Mangla (B) 12 100.0 2.00 1.39 0.24 0.39
River Jhelum(C) 2 12.5 1.13 1.09 0.05 0.08
River Swat (D) 3 50.0 1.50 1.21 0.15 0.24
River Indus (E) 3 62.5 1.63 1.33 0.21 0.32
River Hingol (F) 2 37.5 1.38 1.27 0.16 0.23
Mean 2.00 1.47 0.29 0.45 50 SEM 0.00 0.33 0.16 0.20
The average heterogeneity (Ht) for the pooled sample for SSR markers was calculated as 0.336±0.088; remaining between 0.200 (TTR) and 0.500 (TPF). The mean genetic diversity index within populations (Hs) remains at 0.168±0.023. The computed average values of genetic diversity (Gst = 0.403±0.148) was relatively lower as compared to gene flow (Nm= 1.802±1.101) between different populations of Golden mahseer (Table 4.24). In the present microsatellite analyses, high gene flow between different Golden mahseer populations has been calculated indicating a low level of
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genetic isolation present between different geographic populations. This suggests strong genetic differences between populations (Paetkau et al., 2004).Similar results were previously indicated by Mohindra et al. (2004) by comparing cross-species amplification in Golden mahseer (primers developed for three cyprinid fishes) where they identified 7 polymorphic microsatellites DNA. The studies revealed that allelic frequencies diverged appreciably from the expected Hardy Weinberg Equilibrium and heterozygosity values ranged between 0.29 and 0.40, indicating that samples collected from different water bodies were different.
Table4.24: SSR markers generated genetic diversity within and between different Golden mahseer populations. Ht = total heterogeneity, Hs = mean genetic diversity, Gst = genetic diversity, Nm = gene flow
Markers Ht Hs Gst Nm*
TPF 0.500 0.201 0.598 0.336
Barb 37 0.307 0.125 0.498 1.111
TTR 0.200 0.179 0.113 3.959
Overall 0.336 0.168 0.403 1.802 SEM 0.088 0.023 0.148 1.101
Genetic similarity, calculated on the basis of allelic difference, Nei‘s genetic distances and similarity index between different Golden mahseer populations (Table
4.24), suggest that Swat River (Pop D) population showed larger distance from other populations (range: 0.9098 - 0.4026) between Swat and Jhelum populations, followed
(distance) by those with Poonch (0.5679), Mangla (0.5117), Hingol (0.5088) and Indus
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(0.3608). Hingol population (Pop F) showed the highest allelic similarities (0.9935) with Jhelum (Pop C) and Mangla (Pop. B) (0.9195), which was in sharp contraction to the results suggested by RAPD markers in the present study. Estimation of Nei‘s (1978) genetic distance demonstrated sufficient genetic divergence to discriminate the samples of different populations of mahseer (Table 4.24).
The analysis of overall genetic similarities and differences between different populations using SSR marker generated data showed a mixed pattern (Table 4.25).
Very low genetic difference of Hingol River (Pop F) with Jhelum River (Pop C; 0.0065) and Mangla Reservoir (Pop B; 0.0839) populations is difficult to explain under present distribution.
Table 4.25: Genetic distances and similarities between different populations of Golden mahseer in SSR markers. A = Poonch River, B = Mangla Resevoir, C = Jhelum River, D = Swat River, E = Indus River, F = Hingol River.
Pop A B C D E F
A **** 0.9998 0.8736 0.5667 0.9749 0.8439
Genetic Similarities
B 0.0002 **** 0.9295 0.5995 0.9457 0.9195
C 0.1351 0.0731 **** 0.4026 0.7192 0.9935
D 0.5679 0.5117 0.9098 **** 0.6971 0.6012
Genetic Distances Distances Genetic E 0.0254 0.0558 0.3296 0.3608 **** 0.6963
F 0.1698 0.0839 0.0065 0.5088 0.3619 ****
Dendrogram generated on the basis of similarities / differences between different populations using POPGENE 32 computer program(Nei, 1972; Parveen et al., 2011) identified two main clusters of populations. Cluster one has two sub-clusters; Poonch
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River (Pop A), Mangla Reservoir (Pop B) and Indus River (Pop E) appearing in one sub- cluster, and Jhelum River (Pop C) and Hingol River (Pop F) appearing in the second sub cluster. Swat River (Pop D) populations appeared as a separate cluster (Figure 4.25 and Figure 4.19). The grouping generated through the genetic data using SSR markers, does not fit in the model expected under geographic distribution. Under the sharing of
Jhelum River drainage in AJK (Poonch River, Mangal Reservoir, and Jhelum River) populations are expected to have closer affinity, and two populations sharing Indus
River drainage system (Swat River and Indus River) also expected having higher affinity; while the Hingol River population known to have independent drainage was expected to appear in a separate lineage. Grouping of Hingol River population with
Jhelum River, and wider separation of Swat River population as separate lineage, looks un-natural.
Figure 4.20: Genetic distance (Nei, 1972) based on (UPGMA) cluster analysis by different SSR markers between different populations of mahseer. [1= Pop A; 2= Pop C; 3= Pop B; 4= Pop D; 5= Pop E; 6= Pop F]
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4.5 GENERAL DISCUSSIONS
Fishes are known to exhibit high levels of population differentiation across
geographic areas (Gottelli and Colwell, 2001; Hänfling et al., 1998), caused by
irreversible shifts in genetic structure and diversity (Gonzalo et al., 2006). It is therefore,
important to describe and monitor the fish population structure and genetic diversity
threats attributable to the human activity. Measure of genetic diversity and magnitude of
genetic variability in a population is a fundamental source of biodiversity.
Due to such reasons, wild populations of fishes have received the foremost
attention from molecular biologists. For the purpose, over the recent years variety of
techniques has been developed for the analysis of population discreteness and related
models for analysis of genetic distribution and gene flow. The most accepted application
in molecular biology is the PCR – based tools for the investigation of population
structure together with micro-geographic isolation and the molecular description of
allelic variants among population (Tewari et al., 2013).
Distribution of genetic variability in a natural population of fish depends upon
migration and mating occurring between this population and the adjacent population and
these behaviors ultimately decides the level of sharing between the gene pools and
possibly creating a common gene pool. Thus when, only a few individuals exchange the
genes between population under the migratory potentials of the species and the
presence4 of physical or ecological barriers, chance of genetic segregation of
populations increase. And population will be exposed to higher level of inbreeding,
resulting in genetic fixation and loss of heterozygosity in the population. Each
population will then have a distinct population structure (Thorpe et al., 2000). Molecular
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techniques can help the biologists in the analysis of heterozygosity in the populations and thence genetic distances and gene flow between sub-populations.
RAPD is one such techniques used by molecular biologists for the analysis of population structure and allelic diversity, and wide range of applications in the study of population genetics, and genetic variation in the species of both animals and plants
(Neale et al., 1992) with little expenditure, time and effort (Zohar et al., 2008). RAPD –
PCR has been used by many workers for the detection of genetic polymorphism in mixture of animals by using special primers ( Carvalho and Hauser, 1995; Leuzzi et al.,
2004) and in proposing genetic relationship between populations ( Scheffer et al., 2001;
Ramon et al., 2008). RAPD markers can be applied for nuclear genome to estimate the level of polymorphism for assessing of genetic variation in different endangered species
(Nei, 1978). RAPD analysis has also been successfully used in fisheries research to find out the genetic diversity present at species, subspecies and population levels in many different species, including, guppy (Poecilia reticulata; Suresh et al., 2013), brown trout (Salmo trutta; Parveen et al., 2011) and Atlantic salmon (Salmo salar; Parveen et al., 2011), largemouth bass (Micropterus salmoides ; Wang et al., 2012), ictalurid catfishes (Gjedrem and Baranski, 2009), common carp (Cyprinus carpio; Chiu et al.,
2009) and Indian major carps ( Labeo rohita; Parveen et al., 2011). Sanches et al. (2012) also applied RAPD technique for the detection of the genetic variation in a migratory freshwater fish, Prochilodus marggravii.
The present study was designed to analyze the genetic structure of Golden mahseer populations of AJK and Pakistan. The study revealed a low level (21%) of intra- and a high level (79%) of inter-population variations as indicated by AMOVA
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analysis (Table 4.20). Similar results were also reported by Nguyen et al. (2006) while working on the genetic variation of Tor douronensis sampled from Limbang River tributaries, Bunan River and Layar River of Sarawak (Malaysia). This study resolved the unexpected inter-population variation within populations of this mahseer species (Wei and Cui, 1996; Roberts, 1999).
Similarly, even with facing difficulty in free movement due tosome types of barriers, the overall gene flow between Golden mahseer populations was higher (3.22 ±
0.32) indicated that frequently occurring floods and opening of spill ways may make chances for fish migration across drainage systems, as explained by Wang et al. (2000) about the inter-stream migration that may led to the heterogeneity of populations of
Arossocheilus paradoxus. Hence, study revealed that the majority of population of
Golden mahseer did not separate into breeding sub-populations.
Recently, studies has been undertaken on mahseer (Tor sp.) genetic characterization by the use of RAPD and microsatellite markers in the Malaysia (Esa,
2009), Bangkok, Thailand (Keyse et al., 2014), India (Tewari et al., 2013) and in
Bangladesh (Ghosh and Alam, 2008).
In the current study, by means of RAPD markers, 197 score able bands were identified from six different populations collected from different rivers. From these bands 172 were polymorphic and only 25 were monomorphic with the ratio of 6.88 to
1.00. Seven of these bands were unique bands (Table 4.1). High level of polymorphism is an indication of variation in genetic structure of Golden mahseer, as is indicated by the Nei and Shannon diversity indices. However, the value of gene flow (Nm) was high
116
(3.22; i.e., >1), giving some indication of the migration of individuals between populations and a low probability of inbreeding and thence lower chances of the genetic drift in these populations. This allows us to suggest that the barriers have not been worked effectively to isolate Golden mahseer populations till the recent past. The area faces heavy/low floods (flood of 1992 and super flood of 2010) when the ecological/ or physical barriers may break, reducing the efficacy of the ecological barriers created under the anthropogenic influences.
Similarly, the results of genetic distances based on (UPGMA) cluster analysis exploiting 16 RAPD markers (POP Gene analysis) separated into two clear cut clades or clusters, 1st clusters have two sub-groups or sub-clusters. Three populations of AJK
(Poonch River, Pop A; Mangla Reservoir, Pop B, and Jhelum River, Pop C) constituted one sub-cluster of one population and both populations of KPK (Swat River, Pop D and
Indus River, Pop E) formed the second sub-groups. Hingol River Population (Pop F) emerged as an independent cluster). Population F has larger genetic distance with other fives (0.1565) and lower genetic similarity (0.8551). These findings were also confirmed by Chatta and Ayub, (2010).
As a part of present study, the analysis of mitochondrial C Oxidase 1 (mt COI) of
Golden mahseer populations revealed a difference of 0.5% between the populations of
KPK, AJK and Hingol River of Baluchistan (Khaliq et al., 2015) this difference is might be due to longer isolation as the Hingol River population separated from other five populations possibly from Pleistocene glaciations periods (Harrison et al., 2012; Le
Fort, 1996; Rowley, 1996). Similar longer separation of Malaysian population of Golden mahseer has been indicated previously by Esa (2009).
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Uniformly and abundantly dispersed microsatellite markers are co- dominant and highly polymorphic in nature. Microsatellite markers are helpful in mapping the quantitative trait loci, marker-assisted selective breeding, and understanding the evolution of aquatic organisms (Kang et al., 2011).These markers are useful to generate the maximum genetic information by means of PCR. The three SSR markers which effectively amplified Golden mahseer genome, also generated two main clusters of populations, but these clusters appeared un-natural assemblages of populations as predicted under existing physic-chemical barriers, clustering the Indus River (Pop E) with two AJK populations, i.e., Poonch River (Pop A), Mangla Reservoir (Pop. B).
Similarly Jhelum River (Pop C) population clustered with Hingol River (Pop F), and population of Swat River (Pop D) emerged as independent cluster.
SSR markers suggested low value of genetic diversity (Gst = 0.403) and low level of gene flow (Nm= 1.802) between different populations of Golden mahseer. This also indicates some degree of controversial results generated through SSR markers for
Golden mahseer population of AJK and Pakistan. Neverthe less, SSR results support the
Khare et al (2014) work, who studied the taxonomic instability in Mahseer species of
India through D-loop and Cytochrome C Oxidase 1(CO1) of mtDNA sequences and found that out of seven, five (5/7) are valid species while two (Tor mosal mahandicus and T. macrolepis) are not distinct from T. putitora.
SSR marker though did not work effectively in a natural grouping of Golden mahseer population, yet worked fairly well in effectively separating different ecologically separate populations of A. fasiatus in Tunesia (Annabi et al., 2012) and in
Tor tambroides that suggested a range of heterozygosity (0.0472 to 0.7745) in different
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populations (Bhassu et al., 2008) and high level of intra-population variability (Esa and
Rahim, 2013). The present inconsistency in the results generated by SSR markers can be
attributed to a smaller number of loci used in the analysis and also probably to smaller
size of samples. The effect of smaller size of the samples was probably overcome by the
simultaneous dependence of a larger number of RAPD loci.
4.6 CONCLUSION
The present study leads us to the conclusion:
i. RAPD markers, with a higher number of loci analyzed, worked more efficiently in
analysis of intra-population and inter-population differences. SSR markers though
proved efficient yet did not effectively work in the inter-population and intra-
population analysis of the Golden mahseer of Azad Jammu and Khashmir and
Pakistan. ii. There is still sufficient heterogeneity in the individual population and different
populations are not seriously isolated from one another, and inbreeding within
population is not a serious, immediate problem for future survival of maheer in the
area. iii. Pollution and man-made reservoirs and dams, are also not yet caused effective
population isolations and floods in river systems during different parts of the year
probably break such isolations. iv. Hingol population has a more distant relation with other populations of Golden
mahseer present in the Indus River system.
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v. Three populations of AJK represented a single population, with very frequent
interpopulation movements of individuals.
4.7 RECOMMENDATIONS
The present study allows us to recommend:
i. Attempt be made to ensure maintenance of genetic diversity within and
between Golden mahseer populations to save the species from bottleneck effects
and its genetic fixation. The maintenance of different populations at present
levels, and facilitating interpopulation breeding and interpopulation movement of
breeding individuals can be the presently available options. Continuous genetic
monitoring is also required to judge the smooth execution of the future mahseer
conservation strategy ii. Small isolated populations are probably still present at inaccessible sites, like,
rivers Chenab, Soan, Haro, Tochi, Porali Gaj, Anamber and Orakzai, from which
population and genetic data can not be managed. The status and genetic diversity
levels in these populations needed possible interpopulation gene flow level
known to provide an ample base for future continued survival of the Golden
mahseer in the area. iii. Limited size of the samples resulting in problems with the present analysis.
Further attempt be made to collect/analyze the samples of adequate size from
different subpopulations of Golden mahseer for confirming and adding reliability
to the present findings. Continuous genetic monitoring is also required to judge
the smooth execution of the future mahseer conservation strategy.
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iv. A larger number of SSR and other molecular genetic markers be developed and studies
for a better analysis. SSR markers are though good genetic markers yet selection of
markers having higher level of polymorphism can increase their effectiveness.
v. International cooperation between research organizations/countries falling in the
distribution range of Golden mahseer can provide a wide based data on Golden
mahesser diversity, variability patterns can help in a better planning of species survival
and future possible threats to species endurance.
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SUMMARY
Golden mahseer (family Cyprinidae) was some time ago extensively distributed in different rivers of Pakistan; however constant ecological changes, over-exploitation and manmade barrier has limited its populations merely to rivers of Punjab (Chenab, Soan and
Harro) Khyber Pukhtunkwa (Swat and Indus), Azad Jammu and Kashmir (Rivers Poonch,
Jhelum and Mangla Reservoir) and Balochistan (River Hingol). While this is a migratory fish and has a stretched course of migration for the selection of breeding grounds towards higher altitudes (200-2000m asl) and during winters brooders (spent fish) and juvenile travels in the direction of relatively lower altitudes (warm waters). Consequently its population is segregated in isolating breeding sub-populations. Possibly this isolation is gradually increasing with the gradual decline in the population level. Ultimately, this isolation becomes the source of low genetic diversity.
In the present study, genetic similarity existing between different populations was finded out by means of 16 Random Amplified Polymorphic DNA (RAPD) and 3 microsatellite (SSR) markers.
RAPD primers generated 197 bands with 87.73 percent polymorphic loci and
43.75 percent unique bands. The mean genetic diversity between the populations was
0.13±0.04), (Nei‘s index) and 0.20±0.05) (Shannon index). The population of Swat River with the highest level of polymorphism holds the highest genetic diversity (73%) followed by Mangla Reservoir (57 %), Indus River (54.31%), Jhelum River (44.67 %), Poonch
River (37.06 %) and Hingol River (2.03 %). Assuming populations under Hardy-
Weinberg Equilibrium, the values of heterogeneity (Ht, 0.19±0.02), genetic diversity within (HS, 0.13±0.01) and between populations (Dst, 0.05±0.02), and genetic
122
differentiation constant (Gst, 0.022±0.04) were low. The gene flow between populations
(3.22± 0.32) is high. The analysis of molecular variance revealed higher genetic variation
(79 %) within population and lower (21%) noted between populations. UPGMA dendrogram based on Nei‘s genetic similarities and genetic distances separated three main clusters of populations: 1). Poonch River, Jhelum River and Mangla Reservoir: 2) Swat
River and Indus River; 3) Hingol River. RAPD markers, with a higher number of loci analyzed, worked more efficiently in analysis of intra- and inter-population differences.
SSR markers produced 8 identifiable bands/ alleles. The average heterogeneity
(Ht) for the pooled sample (0.336±0.088), genetic diversity index within populations (Hs,
0.168±0.023), and genetic diversity (Gst, 0.403±0.148) had been relatively lower as compared to higher rate of gene flow (Nm, 1.802±1.101) between populations. On the basis of similarities and differences, dendrogram generated two clusters, separating the
River Swat population from all other populations which also reflected the highest variation measured on the basis of RAPD loci. On the other hand, Population F of River
Hingole made mix cluster with other populations of Indus drainage system clearly indicates that yet no significant changes has taken place or there is no major genetic drift observed which might led to a sub specie.
121
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