MOLECULAR PHYLOGENETICS OF ELAPID AND VIPERID IN

MUHAMMAD RIZWAN ASHRAF 2009-VA-702

A THESIS SUBMITTED IN THE PARTIAL FULFILLMENT OF THE

REQUIREMENTS FOR THE DEGREE

OF

DOCTOR OF PHILOSOPHY

IN

MOLECULAR BIOLOGY AND BIOTECHNOLOGY

UNIVERSITY OF VETERINARY AND SCIENCES, LAHORE

2019

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To, The Controller of Examinations,

University of Veterinary and Animal Sciences,

Lahore.

We, the Supervisory Committee, certify that the contents and form of the thesis, submitted by

Muhammad Rizwan Ashraf Reg. No. 2009-VA-702, have been found satisfactory and recommend that it be processed for the evaluation by the External Examiner (s) for award of the Degree.

Supervisor ______

Dr. Asif Nadeem

Member ______

Prof. Dr. Tahir Yaqub

Member ______

Dr. Abu Saeed Hashmi

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DEDICATION

“I dedicate my this piece of work to my Parents, Wife and My Daughters Sabeen & Tasmia who are always with me in my Life”

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ACKNOWLEDGEMENT All praises to “ALLAH”, the Almighty, Most Gracious, the most Merciful and the Sustainer of the worlds, who sent us “MUHAMMAD (PBUH)” as a blessing for whole universe and the best teacher along with the ultimate source of wisdom “HOLLY QURAN”. I deem it as my utmost pleasure to avail this opportunity to express the heartiest gratitude and deep sense of obligation to my reverend supervisor, Dr. Asif Nadeem, Associate Professor, Institute of Biochemistry and Biotechnology, University of Veterinary & Animal Sciences (UVAS), Lahore. His skillful guidance, unfailing patience, masterly advice and inspiring attitude made it very easy to undertake this work and to write this manuscript. I also have the honor to express my deep sense of gratitude and profound indebtedness to Prof. Dr. Tahir Yaqub and Dr. Abu Saeed Hashmi, Institute of Biochemistry and Biotechnology, UVAS, Lahore, member of the supervisory committee, for his help and guidance all the time. I would like to thank Dr. Eric Nelson Smith from The University of Texas at Arlington. Texas, USA. He has been great all the time during my visit to the university with immense guidance and help during my stay there. I learnt a lot from him. I would also appreciate the role of Dr. Utpal Smart and Panupong Thamchoti and all the other lab fellows from the University of Texas at Arlington. They were always with me for every moment whenever I needed their help in my research work. I am also thankful of my friends whose active support and cooperation turned my dream into reality. Last, but not least, I must acknowledge my indebtness to my loving and helpful parents, my brothers, Hafiz Muhammad Adnan Ashraf, Muhammad Irfan Ashraf and my sisters, who always supported me throughout my education and without their support. I would have not been able to achieve the present position in life. And I appreciate the part of my dear wife Saira and my daughter Sabeen Rizwan and Tasmia Rizwan who are always with me in all the ups and downs during my study and research work. No acknowledgement could ever adequately express my obligation to my affectionate parents for leading their children into intellectual pursuits. “May Allah give a long, prosperous and happy life to my family.

MUHAMMAD RIZWAN ASHRAF

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CONTENTS DEDICATION (i) ACKNOWLEDGEMENT (ii) LIST OF TABLES (iv) LIST OF FIGURES (v) LIST OF GRAPHS (vi)

SR. NO. CHAPTERS PAGE NO. 1. INTRODUCTION 01 2. REVIEW OF LITERATURE 05 3. MATERIALS AND METHODS 18 4. RESULTS 39 5. DISCUSSION 125 6. SUMMARY 135 7. LITERATURE CITED 136

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LIST OF TABLES

TABLE NO. TITLE PAGE NO. 3.1 Recipe for the amplification reaction 19 3.2 General Protocol for PCR Cycles 20 Evolutionary Models for Maximum likelihood and Bayesian 3.3 20 Phylogenetics Common krait (Bungarus caeruleus) Samples and their location 3.4 26 information 3.5 Black Cobra ( naja) Samples and their location information 27 Russell’s Viper (Daboia russelli) Samples and their location 3.6 28 information Saw-Scaled Viper ( carinatus sochureki) Samples and their 3.7 29 location information 3.8 Mitochondrial DNA primers for Common krait (Bungarus caeruleus) 32 3.9 Mitochondrial DNA primers for Black Cobra (Naja naja) 33 3.1 Mitochondrial DNA primers for Russell's Viper (Daboia russelli) 34 Mitochondrial DNA primers for Saw-Scaled Viper (Echis carinatus 3.11 35 sochureki) Mitochondrial and Nuclear Protein Coding DNA primers for Nuclear 3.12 36 protein coding genes Accession Numbers for Mitochondrial genes used in the study for data 3.13 37 analyses Accession Numbers used for Nuclear Protein Coding used in the study 3.14 38 for data analyses DNA Polymorphism in mitochondrial DNA Genes in Common Krait 4.1 47 (Bungarus caeruleus) DNA Polymorphism in Protein Coding Nuclear Genes in Common 4.2 48 Krait (Bungarus caeruleus) DNA Polymorphism in mitochondrial DNA Genes in Black Cobra 4.3 69 (Naja naja) DNA Polymorphism in nuclear protein coding genes in Black Cobra 4.4 70 (Naja naja) DNA Polymorphism in mitochondrial DNA Genes in Russell’s Viper 4.5 91 (Daboia russelli) DNA Polymorphism in nuclear protein coding genes in Russell’s Viper 4.6 92 (Daboia russelli) DNA Polymorphism in mitochondrial DNA Genes in Saw-scaled 4.7 111 Viper (Echis carinatus sochureki) DNA Polymorphism in nuclear DNA Genes in Saw-scaled Viper 4.8 112 (Echis carinatus sochureki)

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LIST OF FIGURES

FIGURE NO. TITLE PAGE NO.

Sample collection sites of Pakistan for Common Krait (Bungarus 3.1 22 caeruleus)

3.2 Sample collection sites of Pakistan for Black Cobra (Naja naja) 23

Sample collection sites of Pakistan for Russell’s Viper (Daboia 3.3 24 russelli)

Sample collection sites of Pakistan for Saw-Scaled Viper (Echis 3.4 25 carinatus sochureki)

3.5 Dorsal View of Common krait (Bungarus caeruleus) 30

3.6 Dorsal View of Black Cobra (Naja naja) 30

3.7 Dorsal View of Russell’s Viper (Daboia russelli) 31

3.8 Dorsal View of Saw-Scaled Viper (Echis carinatus sochureki) 31

Maximum Likelihood phylogeny for Common Krait (Bungarus 4.1 60 caeruleus)

4.2 Bayesian Phylogeny for Common Krait (Bungarus caeruleus) 61

4.3 Maximum Likelihood Phylogeny for Black Cobra (Naja naja) 83

4.4 Bayesian Phylogeny for Black Cobra (Naja naja) 84

4.5 Maximum Likelihood Phylogeny for Russell’s Viper (Daboia russelli) 104

4.6 Bayesian Phylogeny for Russell’s Viper (Daboia russelli) 105

Maximum Likelihood Phylogeny for Saw-scaled Viper (Echis 4.7 111 carinatus sochureki)

4.8 Bayesian Phylogeny for Saw Scaled Viper (Echis carinatus sochureki) 112

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LIST OF GRAPHS

GRAPH NO. TITLE PAGE NO. 4.1 Common Krait (Bungarus caeruleus) NADH4 percent identity 49 4.2 Common Krait (Bungarus caeruleus) Cytochrome b percent identity 49 4.3 Common krait (Bungarus caeruleus) 12S rRNA percent identity 49 4.4 Common Krait (Bungarus caeruleus) 16S rRNA percent identity 50 4.5 Common Krait (Bungarus caeruleus) C-mos percent identity 50 4.6 Common Krait (Bungarus caeruleus) RAG-1 percent identity 50 4.7 Common Krait (Bungarus caeruleus) NT3 percent identity 51 Common Krait (Bungarus caeruleus) combined mitochondrial genes 4.8 51 percent identity Common Krait (Bungarus caeruleus) combined nuclear genes percent 4.9 51 identity 4.10 Pairwise Number. of Differences for Bungarus caeruleus NADH 4 Gene 52 4.11 Pairwise Number of Differences in Bungarus caeruleus Cytochrome b 53 4.12 Pairwise Number of Differences in Bungarus caeruleus 12S rRNA Gene 54 4.13 Pairwise Number of Differences in Bungarus caeruleus 16S rRNA Gene 55 4.14 Pairwise Number of Differences in Bungarus caeruleus C-mos 56 Pairwise Number of Differences in Common Krait (Bungarus caeruleus) 4.15 57 RAG-1 Gene 4.16 Pairwise Number of Differences in Bungarus caeruleus NT3 Gene 58 4.17 Black Cobra (Naja naja) NADH4 percent identity 71 4.18 Black Cobra (Naja naja) Cytochrome b percent identity 71 4.19 Black Cobra (Naja naja) 12S rRNA percent identity 71 4.20 Black Cobra (Naja naja) 16S rRNA percent identity 72 4.21 Black Cobra (Naja naja) combined mitochondrial genes percent identity 72 4.22 Black Cobra (Naja naja) C-mos percent identity 72 4.23 Black Cobra (Naja naja) RAG-1 percent identity 73 4.24 Black Cobra (Naja naja) BDNF percent identity 73 4.25 Black Cobra (Naja naja) NT3 percent identity 73 4.26 Black Cobra (Naja naja) combined nuclear genes percent identity 74

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4.27 Pairwise Number of Differences in Black Cobra (Naja naja) ND4 Gene 75 Pairwise Number of Differences in Black Cobra (Naja naja) 4.28 76 Cytochrome b Gene Pairwise Number of Differences in Black Cobra (Naja naja) 12S rRNA 4.29 77 Gene Pairwise Number of Differences in Black Cobra (Naja naja) 16S rRNA 4.30 78 Gene Pairwise Number. of Differences in Black Cobra (Naja naja) C-mos 4.31 79 Gene Pairwise Number of Differences in Black Cobra (Naja naja) RAG-1 4.32 80 Gene Pairwise Number of Differences in Black Cobra (Naja naja) BDNF 4.33 81 Gene 4.34 Pairwise Number of Differences in Black Cobra (Naja naja) NT3 Gene 82 4.35 Russell’s Viper (Daboia russelli) NADH4 percent identity 93 4.36 Russell’s Viper (Daboia russelli) Cytochrome b percent identity 93 4.37 Russell’s Viper (Daboia russelli) 12S rRNA percent identity 93 4.38 Russell’s Viper (Daboia russelli) 16S rRNA percent identity 94 Russell’s Viper (Daboia russelli) combined mitochondrial genes percent 4.39 94 identity 4.40 Russell’s Viper (Daboia russelli) C-mos percent identity 94 4.41 Russell’s Viper (Daboia russelli) RAG-1 percent identity 95 4.42 Russell’s Viper (Daboia russelli) BDNF percent identity 95 4.43 Russell’s Viper (Daboia russelli) NT3 percent identity 95 Russell’s Viper (Daboia russelli) combined nuclear genes percent 4.44 96 identity Pairwise Number of Differences in Russell’s Viper (Daboia russelli) 4.45 97 ND4 Gene Pairwise Number of Differences in Russell’s Viper (Daboia russelli) 4.46 98 Cytochrome b Gene Pairwise Number of Differences in Russell’s Viper (Daboia russelli) 4.47 99 12S rRNA Gene Pairwise Number of Differences in Russell’s Viper (Daboia russelli) 4.48 100 16S rRNA Gene Pairwise Number of Differences in Russell’s Viper (Daboia russelli) C- 4.49 101 mos Gene Pairwise Number of Differences in Russell’s Viper (Daboia russelli) 4.50 102 RAG-1 Gene Pairwise Number of Differences in Russell’s Viper (Daboia russelli) 4.51 103 NT3 Gene 4.52 Saw-scaled Viper (Echis carinatus sochureki) NADH4 percent identity 113

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Saw-scaled Viper (Echis carinatus sochureki) Cytochrome b percent 4.53 113 identity Saw-scaled Viper (Echis carinatus sochureki) 12S rRNA percent 4.54 113 identity 4.55 Saw-scaled Viper (Echis carinatus sochureki)16S rRNA percent identity 114 4.56 Saw-scaled Viper (Echis carinatus sochureki) COI percent identity 114 Saw-scaled Viper (Echis carinatus sochureki) combined mitochondrial 4.57 114 genes percent identity 4.58 Saw-scaled Viper (Echis carinatus sochureki) C-mos percent identity 115 4.59 Saw-scaled Viper (Echis carinatus sochureki) RAG-1 percent identity 115 4.60 Saw-scaled Viper (Echis carinatus sochureki) BDNF percent identity 115 4.61 Saw-scaled Viper (Echis carinatus sochureki) C-mos percent identity 116 Saw-scaled Viper (Echis carinatus sochureki) combined nuclear genes 4.62 116 percent identity Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus 4.63 117 sochureki) ND4 Gene Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus 4.64 118 sochureki) Cytochrome b Gene Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus 4.65 119 sochureki) 12S rRNA Gene Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus 4.66 120 sochureki) 16S rRNA Gene Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus 4.67 121 sochureki) COI Gene

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CHAPTER 1 INTRODUCTION Biology is making its appearance as computational analytical science. At molecular level, DNA sequencing has enabled scientist to accumulate sequence data, computing methods (Lander et al. 1991). Massively parallel sequencing has reduced the cost for data interpretation. Till the first complete genome sequencing project of a bacteriophage (Sanger et al. 1977) almost 95% sequencing of the human genome (Lander et al. 2001; Venter et al. 2001) has been completed using many different latest methodologies and rapid automation and computational facilities (Venter et al. 2003). This complete genome sequencing has helped in developing new genome analyzing techniques like techniques for measuring global mRNA abundance, systematically knocking out of all genes and comprehensively generating clone collections (Ghaemmaghami et al. 2003; Reboul et al. 2003). Studies consisting of variation among and between help in understanding complex diseases to evaluation of products produced in experimental evolution. The complete potential of this science can be realized when hundreds of samples could have been sequenced completely in short period of time. Research community is trying to develop new and efficient techniques but even then, this has been a challenge for all biologists. The methods for determining genetic differences in structural rearrangements, polymorphisms or mutations are used for only a fraction of the entire genome in most of the laboratories. DNA microarrays has been an efficiently used for the detecting interspecific variation in their genomes. Several molecular biology tools like DNA hybridization, polymerase chain reaction, randomly amplified polymorphic DNA, Restriction fragment length polymorphism, allozymes, microsatellite and many others are contributing to vary fast development and improvement in the field of molecular systematics. Biologists have been using these techniques and allozyme data instead DNA sequences in parasitology, genetics, and for resolving issues regarding the phylogenetic relationships in a fast and cheap way as DNA sequencing requires time and money. Nevertheless, DNA sequencing is the best method to infer variations and relationships among organisms. Molecular systematics uses thus produced molecular data for inferring phylogenetic relationships among organisms (O'Brein et al. 1991; Bernatchez and Danzmann, 1993). Before the discovery of DNA sequencing, systematics and taxonomy used to infer phylogenies among species to explain their relationships. Now every field in biology is using phylogenies for paralogues relationships, population history, dynamics of pathogens with respect to their evolution and epidemiology (Marra et al. 2003; Grenfell et al. 2004), ancestory of body cells when being differentiated, development of tumors (Salipante and Horwitz 2006). Variations in nucleotide sequences can construct phylogenies for inferring relationships in the compared sequences. Topology of phylogeny gives some estimates about the mutation rate and time scale of evolutionary events and prehistoric movement among different geographical regions (Avise, 1986). Mitochondrial DNA studies for animal evolution has become a powerful tool in last decade. Molecular biology has helped in these mitochondrial DNA studies give an insight into structure of a population, gene flow, hybridization, biogeography, and phylogenetics (Attardi, 1985; Brown, 1985; Wilson et al. 1985; Avise, 1986). Evolutionary studies give comparison of mitochondrial genome organization and function while molecular studies help in the improvement of these evolutionary studies. Animal mitochondrial genome is a double helical covalently closed circular genome with conserved gene contents and maternal inheritance. Mitochondrial genome consists of 22 tRNA, two rRNA coding genes. Remaining 13 genes code enzyme subunits that function in ATP synthesis (Chomyn et al. 1985, 1986). Only Ascaris mitochondrial DNA lacks ATPase8 genes (Van Leuven et al. 2019). Animal mitochondrial genome is without introns. Many of the vertebrate and invertebrate mitochondrial genome have been sequenced completely (Zhang et al. 2019; Tang et al. 2018; Van Leuven et al. 2019). Gene order for mitochondrial genome is conserved and rearrangements occur in the circular genome but amplification of full genome has become trivial. DNA from poorly preserved, feces or ancient sample can be easily used for phylogenetic information. In these types of sample, long low copy strands of nuclear genome are usually too fragmented to amplify. One more issue with nuclear genes is that they have non-conserved intergenic sequences between them. Due to these Intergenic regions, nuclear genes must be amplified from internal conserved regions which are difficult and challenging. Mitochondrial genes have no introns which make it easier to amplify them and nuclear genes with introns should have been amplified

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Introduction in parts. Mitochondrial genes have high rate of substitutions but even then, they have some conserved regions that can be amplified through targeted PCR primers (Simon et al. 1994; Castresana, 2000). Intergenic regions are very small or absent. It has control region (CR) that contains regions responsible for replication and transcriptional initiation but lacks structural genes. Mitochondrial genome has unidirectional and highly asymmetric replication (Clayton, 1982). and nematodes have AT rich control region (Tang et al. 2018; Van Leuven et al. 2019). One displacement loop of sequence is present in Echinoids and vertebrates in control region. Mammalian mitochondrial genome transcription starts from this nucleotide sequence till the end (Clayton, 1984; Attardi, 1985). Most of the vertebral mitochondrial genes are separated by tRNAs that are considered as signals for the processing of polycistronic RNA (Ojala et al. 1980). There are almost eleven complete mtDNA that have been sequenced and available in NCBI database. mitochondrial genome has many different features as compared to other vertebrates. The most significant difference is the presence of one control region in blind snakes while alethinophidians have duplicated control region. The two control regions in alethinophidians are identical to each other. The original one is present at upstream region of 12S rRNA. On the other hand, one copy of this control region (CR) is in between ND1 and ND2 genes. Control region has high evolution rate as compared to other mtDNA genes. A note-able divergence between the two CR is expected. However, these expectations have not been encouraged by the observers as concerted evolution explains this divergence. When this type of evolution should occur on frequent basis to remove the substitutional differences between two CRs. The presence of two control regions have not been answered yet but it gives snake mtDNA more efficient replication and transcription through the two CRs. Snake mtDNA has small rRNA, tRNA and protein coding genes as compared to vertebrates other than snakes. It has no or reduced noncoding regions between adjacent genes. Most of tRNAs reduction occurred on D-loop that is contributing in the stability of clover leaf structure of tRNAs. In complete sequenced mitochondrial genomes, eight snakes out of eleven have CR of 1000 bp while the other species like B. constrictor, X. unicolor, and T. reticulatus have1500bp in their control region. This difference in length of CR is due to the presence of many tandem repeats. Genome wide length reduction has no effect on the length of snake CRs but tandem repeats do. Vertebrate other than snakes have conservation in control region (CR) length while aves, testudines and crocodylia have longer CRs as compared to mammalia and lacertilia. Snake mitochondrial genome has no origin of replication for replication of light strand in blind snakes. This origin of replication is also absent in birds. How these are able to complete replication without this light chain origin of replication is still not clear but tRNA in WANCY region is thought to serve as the starting point of light (L) strand DNA replication. Mitochondrial genome in vertebrates has been a model for many biological studies like molecular evolution, phylogenies and genome structures of organisms. The genome is a promising model for replication, transcription and diversity in coding genes. Vertebral mitochondrial genome has zero recombination making its genome conserved. There are some examples that have intramolecular recombination abilities (Tsaousis et al. 2005) and rearrangements (Mindell et al. 1998a; Cooper et al. 2001). Nuclear encoded genes seem to be strong source of phylogenetic information. They can be more useful for the divergence of those genes whose multiple substitutions may cause destruction of phylogenetic signal. But due to high copy number and easy to amplify makes mtDNA more promising for phylogenetic information. Moreover, mitochondrial sequence variations can exist in same organism or even same cell due to higher rate of mutation; they have similar sequence as they are inherited maternally. On the other hand, nuclear genes exist in two distinct forms called alleles. These alternative forms of genes are present on maternal and paternal chromosomes. This complicates the process of getting phylogenetic reconstruction as amplification becomes more difficult. Now molecule based phylogenetics is an indispensable tool for comparing genomes. It can be used for comparing metagenomic sequences (Brady and Salzberg 2011.), identification of genes, regulatory sequences and non-coding RNAs (Pedersen et al. 2006; Lindblad-Toh et al. 2011), interpretation of modern and ancient individual and (Gronau et al. 2011; Li and Durbin (2011) ancestral genome reconstruction (Paten et al. 2008; Ma, 2011). In early 1990s, serious efforts have been made to sequence the whole genomes of many different organisms. (Adams et al. 2000).

2

Introduction

Snakes are legless carnivorous of suborder serpents. They have no eyelids and external ears distinguishing them from legless (Reeder et al. 2015). All squamates including snakes are ectothermic and amniotic with overlapping scales. Except Antarctica and some other large island like Ireland, Iceland, Greenland, Hawaiian archipelage and New Zealand islands, snake are found everywhere in the world. Small islands like Atlantic and central Pacific oceans are also covered by snakes (Roland, 1994). Currently, snakes have 20 families with 500 genera and 3400 species (Annonymous 2008, 2012). It is believed that snakes have originated from some pre- ancestors and are monophyletic (Riepple, 1979a). For many years, Anguimorphic varanoid and monitor lizards have been believed to be the snake ancestors on the basis of similarity in skeleton and some bony structures along with dental features and tongue (McDowell, 1972). Similarities, however, in skull and tongue are opposed as this is thought an adaptation for swallowing larger prey in both the reptiles i.e., monitor lizards and snakes (cf. Smith, 1982; Riepple, 1983). The suborder of all snakes is Serpentes but sometimes snakes are classified as ophidians. But some extinct members which are not actually serpentes are also classified in Ophidia. According to Ripple (1988), serpents are classified in two infra-orders. First one is Scolecophidia also known as worm or blind snakes while the second infraorder is Althinophidia or true snakes. Scolecophidia have three families with snakes having reduced eyes and burrowing. All other snakes are althinophidia divided into Anilioidea and Macrostomata. Most of the semi fossorial (semi-burrowing) snakes are aniliodea while Macrostmatans are larger one of all snakes. Macrostomatans are further divided into two main groups i.e., Booidea and Caenophidia. Boas and pythons are booidea with many other lesser known families. , cobras and vipers are included in Caenophidia. Many basal families like Scolecophidians, Booidea and Aniliidae have traces of pelves and hind limbs (Rage and Escuillie 2003). But not any extant snakes have vestiges of forelimbs. There are many debates within this group, for example, many researchers accept boas and pythons as one separate family and Elapids and Hydrophiidae as separate family for some practical reasons. Snake have always been a fear factor, worshipped and loathed in South . Hindus and Buddhists always tell stories and myths about cobras as they accept cobras as sacred. But snakes have always been a pain for millions of people in the world. Even when sufficiently efficient is available, many of them loose their lives and this makes morbidity and mortality high. Despite of many attempts to figure out exact measure of global snake bites envenoming, this issue is still debatable except some countries (Gutierrez et al. 2006; Kasturiratne et al. 2008). South Asia is most affected region with snake bites as World Health Organization reports 35000 to 50000 deaths annually in (Chippaux, 1998; Kasturiratne et al. 2008). Pakistan reports 40,000 snake bites every year that result in 8200 fatalities (Kasturiratne et al. 2008; Ali, 1990). Nepal loses 1,000 lives every year with 20,000 envenoming cases WHO (1987) while reports 33,000 cases of snake bites every year. The annual incidents of snake-bites in are 4.3 per 100,000 people with 20% deaths (Sarker et al. 1999). But, current epidemiological data is still not very compact and is underestimated. Surveys in rural areas of Sri Lanka have shown less than 50% deaths in hospitals caused by snake (De Silva, 1981; Sawai, 1984; Fox et al. 2006). In most of the developing countries, almost 80% people with snake bite immediately consult a practitioner before a medical doctor (Chippaux, 1988; Snow et al. 1994). The highest number snake bite incidents in Asia came from southeast Nepal in 2002 through a community based survey (Sharma et al. 2004). Mostly the farmers, herders and fisherman are frequently victimized by snakes. In Pakistan, out of total 300 species of land snakes, only 40 of them are venomous. Commonly found venomous snakes in Pakistan are cobras, kraits and vipers (Iliyas et al. 1997). But there is no reliable statistics regarding snake bite incidents, morbidity and mortality except virtual statistics based on hospital cases. The people from rural areas having rarely go to the hospital but use some home remidies (Hati et al. 1992; Nhachi and Kasilo 1994; Lalloo et al. 1995). In Pakistan, the annual mortality estimate following envenomation soars as high as 20,000 (Quraishi et al. 2008; Alirol et al. 2010). The hidden toll of suffering continues to affect the families of the deceased, and patients who survived with crippling deformity. With today’s medical advancement, snakebite envenomation is supposedly a preventable and treatable condition. Unfortunately, over the years, various challenges remain unresolved, hindering the solution for envenomation in many countries (Williams et al. 2011). One of these challenges is pertaining

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Introduction to the production and distribution of an effective antivenom tailored to the region that requires it. Countries that do not have local antivenom manufacturing plant such as Sri Lanka, and those with limited local antivenom production such as Pakistan, resort to importing antivenom from India (Quraishi et al. 2008; Williams et al. 2011). The Indian assume the “Big Four” formulation using venoms of the black cobra, common krait, Russell’s viper and saw-scaled viper, sourced from a restricted area in southeastern India. These snakes although can be found in Pakistan, their venom profiles can vary geographically within the same species as demonstrated in several other venoms, attributed mainly to ecological factor (Alape-Giron et al. 2008; Mackessy, 2010; Tan et al. 2015). By the same token, the antigenicity of toxins can vary substantially too, thus limiting the efficacy of the imported antivenom against local species (Ali et al., 2013; Sintiprungrat et al., 2016). Here, a very pertinent concern arises when imported antivenoms are not vigorously evaluated against the venoms of local species. This contributes to uncertainty on the indication and dosing of the foreign antivenom, thereby exposing the patients to high doses of antivenom (and the risk of anaphylaxis) in which itself is probably ineffective to begin with. In Pakistan, the landscapes differ greatly from fertile plains to deserts, forests, mountains, plateaus, and coastal lines. The extremely diverse bioclimatic and topographic profiles create multifarious habitats that cultivate unique fauna and flora with an exotic mixture of Palaearctic realms, Indo-Malaya, and Ethiopia (Khan, 1999). These include many venomous snakes of great medical importance, some of which are shared with the (the Big Four aforementioned), while many more are unique endemic species or those overlap with the Middle Eastern and Himalayan species (Khan, 2002b). As with most developing countries, snakebite envenomation in Pakistan occurs following increased human contact with snakes during agricultural activities (Alirol et al. 2010). This is particularly obvious with anthropometrically adapted snakes such as cobras (: Naja) (Tan et al. 2015). Phylogenetics shows relationships among organism and genes (Johnston et al. 1998). It gives a clearer picture of biodiversity, biogeography and evolution of many characters in related groups (Ortolani, 1996; Zamudio et al. 1997; da Silva and Patton, 1998). This study is such one attempt to assess the genetic biodiversity, characterization and phylogenetic relationships of Pakistani elapids and viperids which will help in developing effective strategies to manage snake bites through effective antivenom development.

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CHAPTER 2 REVIEW OF LITERATURE 2.1. Snakes: Snakes are carnivores without legs belonging to Serpentes. Lack of legs, eyelids and external ears distinguishes snakes from legless lizards (Reeder et al. 2015). including snakes are ectothermic and amniotic with overlapping scales. Greater number of skull joints than lizard ancestors makes snakes able to swallow large preys more efficiently. They have highly mobile jaws. Organs like kidneys have been accommodated in front of each other. Except Antarctica and some other large island like Ireland, Iceland, Greenland, Hawaiian archipelage and New Zealand islands, snake are found everywhere in the world. Small islands like Atlantic and central Pacific oceans are also covered by snakes (Roland, 1994). Sea snakes are wide spread throughout Indian and Pacific Oceans. Currently snakes have 20 families with 500 genera and 3400 species (Anonymous 2008, 2012). Range of size for snake is 10.4cm long thread snake (Hedges, 2008) to reticulated python with a length of 6.95meters (22.8 feet) (Fredriksson, 2005). Jurassic period is thought to be period of origin of snakes from burrowing or aquatic lizards with earliest fossils dating between 143 and 167MYA (Perkins and Sid 2015). Most of the snake species are nonpoisonous and poisonous snakes use their venom to kill the prey. Many snakes have potential venom to cause pain or even death to humans. Constriction to kill and alive prey swallowing are non-’s characteristics. Tetrapodophis shows affinities with snake with respect to skull, axial skeleton, limbs, integument and behavior. It shows short rostrum, long brain case and nasal descending lamina as snake like features. The bowed mendible with deep sub-dental ridge and intra-mandibular joint is similar as in Dinilysia. The teeth are uni-capsid and hooked like an ophidian. The implantation being subacordont while teeth are separated by interdental ridges. In case of snake like axial skeleton, a trunk made up of 150 vertebrae, sygosphenezygantrum articulation. The ilium is ling and selender like other snakes have bowed fibula like in Najash (Apesteguia and Zaher 2006) and Simoliophidae (Lee et al. 1998). Due to venom biomolecules, snakes are medically important. They have ecological roles and important for evolutionary and developmental research. Snake diversity and evolution needs to be explored through genomic characterization. Recent attempts to find snake relationships has been well supported by molecular data since morphological classification. Monophyly of snakes and their relationship with lepidosaurian has been recognized. Morphological similarities like overall structural reductions and body plan simplification can answer problems and uncertainties about relationships among the closest relative of lizard to snake and relationship among snake lineages. Systematics gives a system connecting all extant and extinct organisms through morphological, developmental, physiological attributions or ultimately genetic and epigenetic processes. Order Squamata includes 4900 species of lizards, 3070 of snakes and almost 200 species of amphisbaenians. Including two tuataras from New Zealand Lepidosauria is formed (Pough et al. 2003; Uetz, 2010). This widely accepted arrangement suggests the evolutionary switching of squamates from muscular tongue gripping of food by tuataras and iguanians to teeth and jaw gripping through keratinized tongue to scleroglossans. Thus, Scleroglossans were able to inhabit even those habitats that were not available to iguanians. Everyone knows a snake with elongated limbless body with forked tongue but this does not distinguished snakes from lizards and amphisbaenians because lizards and amphisbaenians are also legless and elongated body and forked tongues (Riepple, 1988). Some geckos have also immoveable eyelids like snakes (Underwood, 1970) while earless monitor lizards (Lanthanotus borneensis) and skinks have no external ears like snakes (Greene, 1997; Greer, 2002). Snakes are believed to be originated from some pre- lizard ancestors and are monophyletic (Riepple, 1979). According to some researchers, family Dibamidae representatives are thought to be the most suspected animals that lie close to snake origin. This is based on some structure of forebrain, midbrain and some others (Riepple, 1984; Riepple in litt.). The dibamides have similar features to that amphisbaenians and scincid lizards (Riepple, 1984; Greer, 1985). According to Estes et al. (1988), snakes share several unique characters with varanids, lanthanotus and anguimorphs but nothing with scincomorphs. Iguanids are the closest group to ancestral squamates (Carroll, 1975). So, if snakes were derived from iguana, many similar characters can be found in both the snakes and iguana like

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Review of literature the presence of labial glands on two jaws which is also shared with amphisbaenians. On the other hand, lizards have these labial glands inly on lower jaws. These glands are thought to be well developed in venomous snakes and anguimorphs (Kochva, 1974). The gland location does not favoures Anguimorph and Ophidian relationship. According to Varanoid/mosasauroid hypothesis, snakes are the closest relative of varanoid lizards. These lizards belong to anguimorphs lizards that have extant monitor lizards and extinct mosasaurs. Shape of teeth, articulation of lower jaw flexibility, positioning of skull bones and vertebrae are the similarities between snakes and mosasaurs (Cope, 1869, 1878). Intra-mendibular joint is one strong character that connects snakes and mozasauroids. Camp, (1923a, b) suggested grass living ancestors for snakes instead of marine hypothesis. Nopcsa (1923, 1925) associated snakes with dolichosaurs. Dolicosaurs is basal mosasauroid that lived in mid-Cretaceous period. In 1923, Nopcsa referred Pachyophis as aquatic snakes that lived in mid-Cretaceous period. According to him, Pachyophis is an incomplete fossil and a missing connection between snakes and long lizard. In 1925, he gave some evidences about the marine ancestors of snakes. Many authors claim an independent origin of snakes than the other squmates. Fejevary, (1918) rejected the varanoid hypothesis about the ancestory of snakes. According to him, snakes have no lizards as ancestors. He suggested pro-lacertilians as ancestors of snakes. Walls (1940, 1942) studied squmates eyes finding many differences between the eyes in snakes and lizards. He proposed that there was eye degeneration during the burrowing phase of their evolution with revolution of new structures. Underwood, (1979) gave some skeletal and other characters showing variation among lizards and snakes. Kochva (1978, 1987) suggested the presence of labial glands in both jaws while lizards and anguimorphs had labial gland only in lower jaw. Iguanians which are thought to be basal to lizards had labial gland in both the jaws. He also noted that amphisbaenians had labial glands in both jaws. Burrowing scincomorphs and snakes and dibamids have some similar structures in their brain (Senn and Northcutt 1973). This is the only Dibamus that was found to be closely related to snakes. A synapomorphy was proposed between snakes and dibamids. Many studies not only support close relation between snakes and dibamids but also between them and amphisbaenians. Dibmaids have many characters similar to amphisbaenians and snakes as compared to scincomorphs or anguimorphs or gekkotans (Greer, 1985). Phylogenetic studies for Dibamus and amphisbaenians showed close phylogenetic relationship (Rieppel and Zaher 2000a; Kearney, 2003). They also inferred that this similarity might be due to burrowing related characters. In a study, Hallermann, (1998) found a group consisting of dibamids, amphisbaenians along with snakes. He found certain characters but not associated with burrowing. In a study by Estes et al. (1988), snakes and worm lizards were nested in Anguimorphs but rejected this nesting by saying that characters supporting this relationship were losses correlated with fossorial. Ripple in 1988, divided snakes into two groups. First group includes worm or blind snakes while other are true snake. True snakes or Scolecophidia have three families with snakes having reduced eyes and burrowing. All other snakes are althinophidia divided into Anilioidea and Macrostomata. Mostly of semi fossorial (semi- burrowing) snakes are aniliodea while Macrostmata means “big mouth” is the largest group of snakes. Flexion in jaws and skull kinesis is maximum in these snakes. These snakes have increased area of mouth because of absence of hinge that connects the lower jaws. Thus, macrostmatans were able to engulf the prey that is bigger than the diameter of their mouth. Macrostomatans are further divided into two main groups i.e., Booidea and Caenophidia. Boas and pythons are booidea with many other lesser known families. Colubridae, cobras and vipers are included in Caenophidia. 2.2. : Snake have always been a fear factor, worshipped and loathed in South Asia. Hindus and Buddhists always tell stories and myths about cobras as they accept cobras as sacred. But snakes have always been a pain for millions of people in the world. Even when sufficiently efficient antivenom is available, many of them loose their lives and this makes morbidity and mortality high. Despite of many attempts to figure out exact measure of global snake bites envenoming, this issue is still debatable except some countries (Gutierrez et al. 2006; Kasturiratne et al. 2008). South Asia is most affected region with snake bites as India has 35000 to 50000 deaths annually (Kasturiratne et al. 2008). Pakistan reports 40,000 snake bites every year that result in 8200 fatalities (Kasturiratne et al. 2008; Ali and Begum 1990). In most of the developing countries, almost 80% people with snake bite immediately consult a practitioner before a

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Review of literature medical doctor (Chippaux, 1988; Snow et al. 1994). The highest number snake bite incidents in Asia came from southeast Nepal in 2002. In 2007, floods in Bangladesh, snake bite was the second highest cause of deaths after diarrhea and respiratory diseases. Rural area usually with poor basic health facilities face snake bites commonly. Some 500 species of atractaspididae, Elapidae, hydrophildae and . Of the 40 venomous snakes of Pakistan, cobra, krait and vipers are common (Iliyas et al. 1997). But there is no reliable statistics regarding snake bite incidents, morbidity and mortality except virtual statistics based on hospital cases. The people from rural areas having snakebite rarely go to the hospital but consult a local or native doctor. According to World Health Orgnization estimates, about one million snakebites every year throughout the world cause 30000 to 40,000 deaths. South East Asia with highest number of snake bites (Iliyas et al.1997; Aubert, 1996). Pakistan faces more incidents of snakebites while working in their crop fields with primitive methods of watering and ploughing (Iliyas et al.1997). Snake bites needs management through specialized knowledge, starting from immediate first aid to victim to delay the life-threatening effects of snakebite. The victim of snake bit is terrified so needs reassurance. Bitten limb should be immobilized if possible and should be moved to the health facility. The local incision and suction of the wound may cause infection than removal. Bands to stop the are not thought to be reliable in humans but a broad firm constricting band has shown the delay in venom spread but this also can lead to songestion and edema of limb confusing signs. A tight arterial occlusive tourniquet is effective for preventing venom spread and delay in death (Sutherland, 1983). The snake bite envenoming treatment is anti-venom/anti venin/anti snake bite serum treatment. Proper measure like no attacking, disturbance or handling of snake should be taken. Small population, less area, few natural jungles and few poisonous species are responsible for low incidence rates of snake bites in Pakistan than India. Northern and northwestern highland is thin in population and southwestern parts of Baluchistan, Punjab and Sindh are deserts. Only exceptional cases of snake bites are seen in these areas (Khan, 2014). Indus valley Punjab Indus valley and Indus Delta of Sindh Indus Delta reports most of the snake bite cases probable because of 95% agricultural activity of the country. 2.3. Venomous Snakes of Pakistan: Pakistan has three families of venomous land snake including Elapidae, viperdae and Crotalidae: Elapid includes Bungarus caeruleus, Bungarus sindanus sindanus, Bungarus sindanus razai, Naja oxiana and Naja naja. Viperdae includes Echis carinatus sochureki, Echis carinatus astolae, E. c. multisquamatus, E. c. sochureki, Daboia russelii, Eristicophis macmahoni, P. persicus, Macrovipera lebetina obtusa. The third family Crotalidae has Gloydius himalayanus. Only one poisonous family hydrophiidae is found in waters. Indus Valley is agriculturally active area throughout the whole year due to its prevailing aridity. Untended growth of bushes and hedges around the houses makes these places ideal for many fossorial and secretive venomous and nonvenomous snakes. These snakes eat rodents living in the holes around these houses occupying these holes and becoming constant threat of human snake bite incident. The estimates tell that increase in envenomation could reach up to 5 million per year resulting in nearly 50,000 to 400,000 developing severe sequelae (Jones and Karalliedde 2006). Pakistan has many misconceptions and misunderstandings about snake bite incidents about prevalence and mortality rates. Warrell, (1999) estimated 20,000 deaths due to snake bites but Health Department gave Pop. 0.9 million morbidity and mortality figures in Tharparker District. In 2003, the morbidity and mortality rates were 24.41 and 1.1 per 100,000 (Qurashi et al. 2008). Almost 15% of 3000 species of snakes are venomous. Snake envenomation is termed technically as ophitoxemia (Sheikh et al. 2008; Rawlins and Vale 2006). Anti-snake venom neutralizes the snake venom. Pakistan has anti snake venom whose initial doze is incorrect or misleading (Simpson and Norris 2009). Correct dosage can be determined by correct estimation of injected venom quantity by a snake in an average bite. Russell’s viper and cobra usually inject 60mg venom in the average bite. One Anti snake venom (ASV) vial can neutralize 6mg of cobra and Russell’s viper venom. Thus, initial dose of antivenom should be 8-10 vials with clear evidence of envenomation (Hazra, 2003). 2.4. Animal Mitochondrial DNA: Mitochondrial DNA studies for animal evolution has become a powerful tool in last decade. Molecular biology has helped in these mitochondrial DNA studies give an insight into structure of a

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Review of literature population, gene flow, hybridization, biogeography, and phylogenetics. Avise, (1986) (Attardi, 1985) Wilson et al. (1985) (reviewed in Attardi, 1985; Brown, 1985). Evolutionary studies give comparison of mitochondrial genome organization and function while molecular studies helps in the improvement of these evolutionary studies. Animal mitochondrial genome is a double helical covalently closed circular genome with conserved gene contents. Genes code many proteins and RNAs (Chomyn et al. 1986). Only Ascaris mitochondrial DNA lacks ATPase8 genes (Van Leuven et al. 2019). Animal mitochondrial genome is without introns. Many of the vertebrate and invertebrate mitochondrial genome have been sequenced completely (Zhang et al. 2019; Van Leuven et al. 2019; Tang et al. (2018). Intergenic regions are very small or absent. It has control region that contains regions responsible for replication and transcriptional initiation but lacks structural genes. Mitochondrial genome has unidirectional and highly asymmetric replication (Clayton, 1982). Insects and nematodes have AT rich control region (Wolstenholm et al. 1987; Tang et al. 2018). Echinoids and vertebrates have a displacement loop sequence. Mammalian mitochondrial genome transcription starts within this control region and proceeds to the end of rRNA genes or for the full length of coding sequences (Attardi, 1985; Clayton, 1984). Most of the vertebral mitochondrial genes are separated by tRNAs that are considered as signals for the processing of polysistronic RNAs (Battey and Clayton 1978). Perhaps two billion years ago mitochondrion was predating the separation of multicellular animals, plants and fungi (Lang et al. 1999) and thus the heart of Hydrogen- hypothesis for the first eukaryote (Martin and Muller 1998). According to this hypothesis, an archeo-bacterium developed a symbiotic relationship with an organism capable of producing hydrogen and CO2 through carbohydrate metabolism. These Carbon dioxide and hydrogens were used by this autotrophic archeo-bacterium with subsequent genomic rearrangements and merger into a third archaic organism (Doolittle,1999), there developed a trimeric symbiotic relationship into a more aerobic, oxidizing environment that further developed to multicellular animals, plants and fungi from a protist. Such symbiosis suggest that karyotes with mitochondria have been derived from a single symbiosis. Reclimonas americana has 69034 bp in its mitochondrial genome with a total of 97 genes. These 97 genes include 62 protein coding genes which are greater than those of 37 protein coding genes of primitive nonvascular land plants like Marchantia (Lang et al. 1997). Mitochondrion is thought to be partly genetically independent. The reason of this independence is thought to be the drift in tRNA code for amino acid that occurred in proto-mitochondrion during Cambrian period (Anderson et al. 1981). Like mitochondria, chloroplasts have also their own DNA which is circular but only found in higher plants. This chloroplast DNA also takes some of the nuclear genome’s responsibilities of metabolism but unlike mitochondria, chloroplasts have universal genetic code. In vertebrates, genes have retained their order in mitochondrial genome circle for more than 400 million years (Shadel and Clayton 1997). Mitochondrial DNA does not obey Mendelian inheritance patterns but passes from mother to a child i.e., maternally inherited. Paternal mtDNA is destroyed after two cell stage (Sutovsky et al. 2000; Cummins et al.1999). As mitochondrial DNA is kept in oocyte, its replication stops when it is fertilized (Jansen, 2000). 2.5. Evolutionary Rates in mitochondrial DNA: In 1979, Brown and his colleagues proposed that mitochondrial DNA evolutionary rate is 5-10 times greater than that of nuclear genome. Thermal stability differences (Delta tm) between homo and heteroduplex mtDNA was measured from man and an African monkey. The thermal stability was compared of single copy primate nuclear DNA. About 22% divergence was found between man and guenon mtDNA. This divergence value was consistent with four primate species whose mtDNA was restricted to make a restriction map and compared. On the other hand, thermal stability values for nuclear DNA were found to be only 1.4-6.3% from the same species. Understanding evolution in mtDNA has become easier through sequence comparisons. Many complete mitochondrial genome sequences are now available (Brown and Simpson 1982; Wolstenholme et al. 1982). The rate of evolution is variable across sites. In protein coding genes, synonymous substitution rate was 4-6 times higher than the non-synonymous substitution. A similarity was found between tRNA and protein coding genes with respect to their replacement substitution. Brown et al. (1982) assayed tRNA genes in mitochondria that showed 100 times faster evolutionary rate than the nuclear counterparts.

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Displacement loop shows a very rapid evolution. Now a day, there are two hypotheses that are under consideration for explaining mtDNA evolution. According to the first hypothesis, there is an enhanced mutation rate while the other hypothesis is favors relaxed and selective checks on products. Lack of editing or repairing mechanisms for DNA replication is one reason for higher mutation rate (Brown, 1981). A constant rate of mutation over per replication round with rapid turnover of molecules is another reason for higher mutation pressure. Point mutations and rearrangements (particularly deletions) cause the deterioration of mtDNA with passing age in most of the body tissues (Wallace, 1995). This rate of mutation is much faster than that of nuclear genome. The mutation fixation rate for mtDNA has 15-20 times greater fixation rate of mutations than nucDNA. This high fixation rate is because of lack of histone proteins, single stranded replication intermediates during mtDNA replication for a longer period and lack of dsDNA repair mechanisms (Wallace et al. 1987). Mitochondrial DNA has no introns but only tightly packed exons. This means that if any mutation occurs along the mtDNA gene sequence, it will affect cellular metabolism. The load of mutations in mtDNA is responsible for aging and many familial maternally inherited diseases (Wallace, 1995; Zeviani and Antozzi 1997). Electron micrographs show that mitochondria can form a dynamic network with many repeated fission and fusion between the organelles. Mitochondrial DNA replication occurs near the nucleus and new mtDNA finds the way to peripheral organelles using this network (Davis and Clayton 1996). Comparisons of mtDNA can provide insights into the base substitution patterns among closely related groups (Brown 1983, 1985). Homionoid primates mitochondrial DNA evolution rate has been studied thoroughly that has shown approximate difference of 0.5 to 1% per million years per lineage. Beyond about 15% overall sequence divergence, the apparent rate of substitution slows dramatically, until at approximately 30% divergence, it is reduced by at least an order of magnitude. Parts of the mtDNA show different rates of changes. Control region shows rapid change at inter and intraspecific level (Aquadro and Greenberg 1983; Fauron and Wolstenholme 1980; Greenberg et al. 1983) and between species (Brown, 1985; Fauron and Wolstenholme 1980) while rRNA genes change slowly and some parts retain their identity with homologous parts of eukaryotes and prokaryotes (Hixson and Brown 1986). The rate of evolution of mitochondrial genome varies between groups of organisms relative to that of nuclear genome. Mitochondrial DNA evolves faster than nuclear genomes in rodents (Miyata et al. 1982) and (Carr et al. 1987; Dawid, 1972). But in sea urchin and flies, mtDNA and nuclear DNA have similar rate of evolution (Powell et al. 1986; Zhang et al. 2019; Solignac et al. 1986a; Vawter and Brown 1986). The predominance of transitions in closely related sequences and decreases as the sequences diverge (Brown and Simpson 1982; Brown et al. 1982). Transitions show a high frequency at all codon positions including tRNAs and rRNAs which is probably because of non-random mutation but not selection (Brown et al. 1982). The differences in mtDNA length have been observed which are thought to because of presence of noncoding sequences particularly because of control region (Brown, 1985; Cann and Wilson 1983; Hauswirth and Clayton 1985; Monnerot et al. 1985). Duplications or deletions may cause the large differences in size. Large differences may also be caused by replication slippage (Albertini et al. 1982; Levinson and Gutman 1987). Intense selection for a small genome is responsible for extreme economy animal mtDNAs (Sederoff, 1984). Molecular and evolutionary studies on different sized but identical mtDNAs have provided the proofs. Short tandem repeats of almost 500bp in CR may be shared between closely related species (Densmore et al. 1985; Solignac et al. 1986b). It is believed that mtDNA has evolved from more complex genome and genetic functions have been transferred to nucleus (Wallace, 1982) therefore mitochondrial metabolism relies upon` interaction between mitochondrial DNA and nuclear DNA (Tzagoloff and Myers 1986). The interaction between mitochondrial and nuclear DNA includes complete dependence of mitochondrial nucleic acid metabolism on enzymes encoded by nuclear DNA and mitochondrial and nuclear encoded proteins interact to form intermediates of respiratory chain. Enzymes for DNA replication, transcription and translation are encoded by nuclear DNA (Attardi, 1985) while mitochondrial ribosomes are synthesized by mitochondrial rRNAs and proteins encoded by nuclear DNA (Matthews et al. 1982). Polymerases replicating or transcribing mtDNA may differ in efficiency in different species. Closely related species polymerases showed efficient transcription but inefficient in distantly related species (Chang et al. 1985). The two strands of mtDNA have differences

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Review of literature with respect to nucleotide composition and that is why termed as heavy and light strands. Heavy strand of mtDNA is because of high content of guanine while light (L) strand is deficient in guanine (Anderson et al. 1981). The replication starts from control region. When two third of the heavy (H) strand replication is completed, light strand starts forming new light strand. The start of replication for light (L) strand is found within the cluster of tRNA. This cluster of tRNA if often termed as WANCY region (tRNATrp-tRNAAla- tRNAAsn-tRNACys-tRNATyr). During the asymmetric replication, heavy chain is exposed as single strand for some time (Tanaka and Ozawa 1994) that gives mutations to accumulate in this strand (Clayton, 1982). 2.6. Features of snake mtDNA: Almost eleven complete mtDNAs of different snakes have been sequenced and are available on NCBI GenBank database. Snake mitochondrial genome has many different features as compared to other vertebrates. The most significant difference is the presence of one control region in blind snakes while alethinophidians have duplicated control region. The two control regions in alethinophidians are identical to each other. The original one is present at 5’ end of 12S rRNA gene while the copy of original CR is in between ND1 and ND2 genes. The presence of two control regions have not been answered yet but it gives snake mtDNA more efficient replication and transcription through the two CRs. Snake mtDNA has small rRNA, tRNA and protein coding genes as compared to vertebrates other than snakes. It has no or reduced noncoding regions between adjacent genes. Most of tRNAs reduction occurred on D-loop that is contributing in the stability of clover leaf structure of tRNAs. In complete sequenced mitochondrial genomes, 8 snakes out of eleven has CR of 1000bp while others have1500 bp in their control region. This difference in length of CR is due to the presence of many tandem repeats. Genome wide length reduction has no effect on the length of snake CRs but tandem repeats do. Vertebrate other than snakes have conservation in CR length. Snake mitochondrial genome has no origin of replication for replication of light strand in blind snakes. This origin of replication is also absent in birds. How these animals are able to complete replication without this light chain origin of replication is still not clear but tRNA in WANCY region starts DNA replication in light (L). Vertebrate mitochondrial genome has been a model for many biological studies like molecular evolution, phylogenies and genome structures of organisms. Vertebral mitochondrial genome has zero recombination making its genome conserved. There are some examples that have intramolecular recombination abilities (Tsaousis et al. 2005). Very little is known about genomic architecture even when there are numerous molecular studies on function and evolution of mitochondrial genome. Now patterns have been emerging about these studies to take insight into mtDNA structure, function and nucleotide evolution (Krishnan et al. 2004; Raina et al. 2005). Classical model of replication of mtDNA suggests heavy strand has its origin of replication in mitochondrial control region. After two third of inner standard of heavy chain, replication of light strand starts from WANCY region (Tanaka and Ozawa 1994). Deamination of cytosine to uracil are more in single strand DNA than adenine to hypoxanthine (Frederico et al. 1990; Impellizzeri et al. 1991). Both de- aminations lead to mutations that count for synonymous substitutions (Faith and Pollock 2003; Krishnan et al. 2004; Raina et al. 2005). Most of the protein coding genes except use heavy strand of mtDNA as template therefore, mutations biases in light strand parallel the biases in most protein producing mRNA (Faith and Pollok 2003). This results in T>C substitutions thus producing C/T nucleotide frequency gradient. Snake mtDNA has many unique and unusual features among vertebrates. Snake mtDNA has high evolutionary rates with truncated tRNAs. All snake species except blind snakes Leptotyphlops dulcis have two control regions that are located between ND1 and ND2 genes. (Kumazawa et al. 1996; Dong and Kumazawa 2005). 2.7. Mitochondrial and nuclear genes in phylogeny reconstruction: Nuclear encoded genes seem to be strong source of phylogenetic information. They can be more useful for the divergence of those genes whose multiple substitutions may cause destruction of phylogenetic signal. But due to high copy number and easy to amplify makes mtDNA more promising for phylogenetic information. Moreover, mitochondrial sequence variations can exist in same organism or even same cell due to higher rate of mutation; they have similar sequence as they are inherited maternally. On the other hand, nuclear genes exist in two distinct forms called alleles. These alternative forms of genes are

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Review of literature present on maternal and paternal chromosomes. This complicates the process of getting phylogenetic reconstruction as amplification becomes more difficult. Mitochondrial genome gene order is conserved and rearrangements occur in the circular genome, amplification of full genome has become trivial. Poorly preserved, feces or ancient DNA sample can be easily used for phylogenetic information. In these types of sample, long low copy strands of nuclear genome is usually too fragmented to amplify. One more issue with nuclear genes is that they have non-conserved intergenic sequences between them. Due to these intergenic regions, nuclear genes must be amplified from internal conserved regions which is difficult and challenging. Mitochondrial genes have no introns which makes it easier to amplify them and nuclear genes with introns should have been amplified in parts. Mitochondrial genes have high rate of substitutions but even then, they have some conserved regions that can be amplified through targeted PCR primers (Simon et al. 1994; Castresana, 2000). In nuclear genes, exact function and evolutionary pressures highly variable regions are difficult to amplify and align. Mitochondrial and nuclear genes have been used for phylogenetic inferences (Friedrich and Tautz 1995). Mitochondrial genes have been used for mammalian, avian phylogenetics (Waddell et al. 1999). They were also used for divergence of deuterostomes and ancient (Castresana et al. 1998; Hwang et al. 2001). Phylogenies from mitochondrial and nuclear genes often disagree but this disagreement can be resolved through some other analysis of mitochondrial genes (Gibson et al. 2005; Hassanin, 2006). Although mitochondrial phylogenies have been accepted but at some instances, they are unable to solve phylogenetic problems. This inability raises question on their use for phylogenetic inference (Lin and Danforth 2004; Zink and Barrowclough 2008). The mechanism that works on mitochondrial evolution suggests that mitochondrial DNA is less informative than nuclear genome on per site basis. This is because of low sequence complexity (AT bias) and multiple substitutions. When mitochondrial DNA is compared to nuclear DNA, mitochondrial DNA performs poorly on per site basis. The fact that many mitochondrial gene trees rely on only a small subset of available mitochondrial genes compounds the problem by not compensating for decreased per site informativeness with an increased number of sites. Current model based phylogenetic inferences are statistically consistent. Greater the amount of data greater will be the probability of giving right phylogenetic topology approaches to 1 (Fisher, 1922). So, if the number of samples is increases with increased number of mitochondrial genes, mitochondrial gene phylogenies show improved phylogenies. The performance of larger amounts of mitochondrial gene data (up to the full mitochondrial gene complement) may provide a level of phylogenetic utility greater than is suggested by its per site performance. Rigorous testing of complete mitochondrial sequence data against comparably sized nuclear gene data sets is an area that requires further exploration. 2.8. Phylogenetics: Phylogenies infer relationship between paralogous in a gene family (Maser et al. 2001) population histories (Edwards, 2009). It is now being used in identification of genes, regulatory sequences, non-coding RNAs in newly sequenced genomes (Lindblad et al. 2011) classification of metagenomics sequences (Brady and Salzberg 2011), modern and ancient genome interpretations (Gronau et al. 2011; Li and Durbin 2011), and reconstruction of ancestral genomes (Paten et al. 2008; Ma, 2011). The gene trees are highly uncertain lacking valuable information. In species trees (Edwards, 2009; Than and Nakhleh 2009), the gene trees at individual loci may conflict with species tree (Rannala and Yang 2008). Snakes are one of the most diverse and familiar group of animals without limbs. They have almost more than 2900 species that occupy terrestrial, arboreal and fossorial and aquatic environments except Antarctics (Pough et al. 2004). A very few molecule based studies addressed higher level phylogenetic relationships and most of them were concerned with supera-familial relationships. (Macey et al. 2000; Whiting et al. 2003). They were unable to infer comprehensive phylogenies of squamates at higher level. Ast (2001), inferred monophyly of Varanoidea relative to anguids and annielids but not used Xenosaurus and Shinisaurus as outgroups. A strong support was shown for New World Xantusiidae and African Cordylidae as sister taxon. Harris (2003), used 162 sequences for major squamate clades. They did not use Dibamidae and used c-mos gene to infer higher squamate relationships. Vidal and Hedges (2004) used c- mos and RAG-1 (500bp) to infer relationships among major snake taxa and their position within squamata. They used representative of all squamate families and found strong support placing snakes outside

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Review of literature

Anguimorpha. But most of basal squamat relationships were not found to be well supported. Townsend et al. (2004) used 69 species of squmates with 4600bp from RAG-1 and c-mos and ND2. They found that species from Sclerglossa, Varanoidea and many other higher taxa are not monophyletic. They found that amphisbaenians are sister to Lacerids and dibamid lizards. Zaher et al. (2009) used two mitochondrial ribosomal RNAs i.e., 12S and 16S with one nuclear gene c-mos to infer the phylogentics of advanced snake. They used 1681bp of DNA and 131 terminal taxa from major caenophidia mainly focusing Neotropical xenidontines. Parsimony analysis gave well supported phylogenies corroborating some clade that were identified in previous studies. Their study suggested some taxonomic changes in Xenodontines. They also suggested the new taxonomic status for alsophis elegans and liophis amarali. They also revised caenophidians classification on molecular basis. Many decades have faced a lot of debates on classification and phylogenetics of advanced snakes. The diversity of species and limitations in taxonomic and genomic sampling for molecule based studies are the main hurdle in understanding of advanced snake’s phylogenetics. Rieppel, (1988) gave many historical views about the progress in phylogenetics of snake. Recent molecular studies focusing on higher level phylogenies while some just limiting themselves to some restricted assemblages like elapids, homalopsines, xenodontines, pseudoxyrhophiines etc have helped in understanding higher level phylogeny of caenophidians e.g., Vidal et al. 2008; Kelly et al. 2003). All such and other studies have developed some consensus, Xenodontinae, Colubrinae and Boodontinae. For the first time, basal rooting of Xenodermatinae was determined through DNA analysis. Vidal et al. 2007a, 2008). Colubridae was defined as restrictive group and families and superfamilies were devised from subfamilies recognized by Zaher, 1999 (Vidal et al. 2007b, 2008). Lawson et al. (2005) conducted a study on molecular phylogeny of 100 caenophidians. These caenophidians were from all those subfamilies that were recognized by Zaher, (1999). They showed Acrochordus as sister to all other caenophidia and recognized Colubridae and Elapidae, and viperidae as families. Keogh, (1998) inferred elapid phylogeny and bio-geographical history. Keogh used 16S rRNA and cytochrome b gene with 17 species of elapids from the whole world and two non-elapids as out-groups. A total of 181 sites were found to be informative for parsimony analysis. Separate analysis of the data gave good phylogenies as compared to combined data analysis. Elapids are advanced snakes comprising more than 80% of the snake species of the World. There are four divisions of advanced snakes. They include non-venomous colubrids, venomous atractaspids and independently evolved viperids and elapids. Elapids have been variously kept in family Elapidae or hydrophiidae and Elapidae. Elapids are mainly characterized to define with unique presence of two permanently erected canaliculated front fangs (McCarthy, 1985). They comprise of almost 300 species and 61 genera with tropical and subtropical distribution along with America, Africa, Asia, Melansia, and Austrailia. India and Pacific oceans (Golay et al. 1993a). Many lineages within elapids are putative monophyletic with uneven distribution around the world species and genus level. Terrestrial elapids include Austrailian elapids (Mengden, 1983; Hutchinson, 1990). Asian Elapids show diversity with cobras, Asian coral snakes and terrestrial and sea kraits. Sea kraits have two groups which are Laticauda and hydrophiid. Laticauda spend most of their life in water but lay eggs on dry surface while hydrophid are fully aquatic and viviparous. They are also called as true sea snakes. Many molecular studies have focused on relationships of elapids. Mao et al. 1977 used transferrin immunological distance, peptide fingerprinting of haemoglobins and protein albumin immunological distance to elucidate relationships of elapids (Mao et al. 1983). Minton, (1981) and Schwaner et al. (1985) also showed relationship between Laticauda and true sea snakes with their independent evolution from terrestrial Australian elapids. Slowinski et al. (1997) investigated the venome protein sequence. This study did not show monophyly of Laticauda and true sea snakes. It was found two sister clades. One is elpine that consists of African and Asian species. The second clade consisted of four species of Austrailian elapids and four Laticauda with sea snakes. Heise et al. (1995) inferred higher level snake phylogeny from mitochondrial 12S rRNA and 16S rRNA genes. A total of 36 species of living snakes with tuatara and three families of lizards were used in their study, Scolecophidians were found to be most basal clade with snake being monophyletic with 96% confidence probability. This showed that snake went through a subterranean period during their evolution. Caenophidians were found to be monophyletic with confidence probability of 99%. Furthermore, viperids

12

Review of literature were found to be sister to elapids and colubrids and monophyletic. True vipers and pit vipers along with azemiops were found to be monophyletic among vipers. Elapids formed a monophyletic group with atructpis. From colubrids, Holarctic Colubrinae made a monophyletic group while Xenodontinae was found to be polyphyletic. Henophidia showed a weak resolution, therefore their monophyly was not rejected nor accepted except for the clustering of Colubaria with Uropeltid Rhinophis. Dessauer et al. (1987) reviewed molecular data for snake relationship with his own addition. Immunological techniques gave most of the data. They also found snakes to be monophyletic diverged from scolecophidia and henophidia and caenophidia. Scolecophidians and caenophidians had common ancestor but henophidians were not. No close relationship was found between pythons and boids. Vipers showed themselves as sister to elapids and colubrids. Sea snake were found to be a member of elapids. Monophyly of colubrids and phylogenetic status of Acrochordtls and Atractaspis was not resolved by their study. Micro-complement fixation was used to find relationship of advanced snakes by Cadle, (1988). They found four clades of advanced snakes as vipers, elpids, colubrids and Atractaspis. Monophyly of colubrids could not be resolved here also. Knight and Mindell (1994) used 12S rRNA and 16S rRNA genes to elucidate the relationship among Elapids, viperids and Clubrids.One species from each group of snakes was used. Gloydius is group of venomous snakes found in Asia (David and Ineich 1999; Gumprecht et al. 2004. This is an ecologically and morphologically diverse group (Zhao, 2006). There are many studies on ecology, taxonomy and evolution of Gloydius (Wuster et al. 2008; Ding et al. 2011). Skull morphology was studied by Guo et al. (1998) for the validation of many Gloydius species. Morphology divided Gloydius into three groups i.e., brevicaudus, intermedius and strauchi. The arrangements of apscies in these three groups have been different in many different studies (Gumprecht et al. 2004; Zhao, 2006; Jiang and Zhao 2009). Xu et al. (2012) used cytochrome b and ND4 from mitochondrial genome for inferring relationships within Asian pit vipers genus Gloydius. They also included a nuclear c-mos gene. With 23 samples from 10 species, phylogentics proved Gloydius as monophyletic group with two major clades made up of G. intermedius, G. saxatilis, G. halys and G. shedaoensis. They were found to be diverged 15Ma while the genus diverged 9.89Ma. Taxonomic studies help in the identification and development of antivenom against snake bites. Similar study was conducted by Alshammari, (2011). He inferred phylogenies for four venomous species of snakes in Saudi Arabia on the basis of 16S rRNA. Echis coloratus from Saudi Arabia formed a clade with those from and Yemen with insignificant relation. Cerastes and Bitis arietans were found to be sister while cerastes did not show any correlation with Egyptian or Israeli samples. Phylogeography based on molecules links biogeography and population genetics. This linkage provides great insight in many evolutionary studies (Avise, 1994). Comparative phylogeography works for adaptive radiation and diversity patterns (e.g. Bermingham and Moritz, 1998; Moritz and Faith 1998). Many researchers have used molecular phylogeography for many North American vertebrates. North America is showing great progress in phylogeography of different animal taxa (e.g. Rodriguez-Robles et al. 1999; Pook et al. 2000; Nielsen et al. 2011; Rodriguez-Robles et al. 2001). Common Garter snakes are excellent models for studying phylogeography in western North America. It is a widely distributed species from Central Canada to Gulf Coast except much of southwestern United States (Conant and Collins 1991). Partial cytochrome b, ND4 and complete sequence of ND2 were used to study phylogeography of Thamnophis sirtalis (Fredric et al. 2002). Thamnophis sirtalis has a wide distribution in North America and variation in phenotype is observed in westernmost part. The patterns for phylogeography agreed with other studies in the region linking historical vicariant processes occurred during Pleistocene. The topology was also found to be consistent with hypothesis from southern and northen refugia. They also identified genetic breaks among the three clades. California Clade had well supported branching while the relationship among other two clades was difficult to distinguish. They found that evolutionary forces did more for phenotypic variations in western T. sirtalis, thus morphology was considered invalid as it did not show reciprocal monophyly of mtDNA sequences. Keogh et al. (1998) used cytochrome b and 16S rRNA to infer phylogenetic relationship among elapids including 19 terrestrial Australian genera and 6 terrestrial Melanwsian genera. They also included one sea krait and true sea snake. Their study addressed many issues related to elapid phylogeny. The first

13

Review of literature one was that Melanesian elapids were sister to Australian species suggesting the radiation of Australian ancestors came via Asia. Secondly, sea kraits and true sea snakes showed the independent invasion to marine eaters. They could not find any relationship among Australian radiation through parsimony analysis. The previous data suggests that elapids have undergone an adaptive radiation process after immediate arrival to Australia. This divergence is even older within the genera. Interagenic and intergenic divergence were found equally larger. Drysdalia and Simoselaps were paraphyletic. The sequence data supported many previous studies results that used different data. Phylogeography helps in finding the historical and contemporary processes. These are the processes that usually affect geographic distribution of genealogical lineages especially at intraspecific level. Phylogeography has a place between micro-evolutionary and macro-evolutionary fields (Avise, 2000). It also helps in revealing the difficult patterns which are difficult to find using other approaches. For example, it can detect cryptic genetic diversity in a region leading to the discovery of a new species (Feldman and Spicer 2002; Castoe et al. 2005). So phylogeography can give us some clues about new species in geographic range that can be helpful in conservation priorities. Unfortunately, only a few snake species have been used in phylogeographic studies as compared to other vertebrate particularly birds and mammals. Only 3 out of 148 species (Burbrink et al. 2000; Burbrink, 2002) of snake in southwestern United States have been used in comparative phylogeographic studies (Soltis et al. 2006) while more than 30 species of snakes are found between Mississippi River and Florida panhandle (Crother and Guyer 1996; Gibbons and Dorcas 2005). Only 3% of total snake species have been studied through phylogeographic studies even this field has ability to infer information about taxonomy, biodiversity and evolution of snakes. Inferring phylogenetic relationship among closely related groups helps in correct inference of patterns of community structure, biogeography and character evolution (Zamudio et al. 1997; da Silva and Patton 1998; Roderick and Gillespie 1998). Well supported phylogenies can prove the evolutionary model diversification, (Autumn et al. 1997; Vogler and Kelley 1998) and evolution of a trait within a lineage (Benabib et al. 1997; Mueller et al. 1998). Alfaro and Arnold (2001) used 32 species of Thamnophiine in a phylogenetic study. They used cytochrome b, ND2 and 12S rRNA genes for phylogeny inference. They found three clades consisting of garter, water and a novel semi-fossorial group. Genus Regina of crayfish eating snake was found to be polyphyletic. All over the world, people are publishing a huge number of phylogenetic studies to get an insight into evolution. Most of these studies have used mitochondrial genes. The reason for this use is high rate of evolution (Brown et al. 1979) and universal primers (Brown, 1985). Due to no recombination, all mt DNA genes are inherited as a single unit. Phylogenetic tree from this single unit do not give an independent estimate of phylogenetic relationship among organisms (Moore, 1995; Pagel, 1999). Mitochondrial DNA based phylogenies may not be in agreement with species phylogenies (Moore, 1995). The solution of all these problems is the addition of nuclear genome sequences for inferring phylogenies (Wu, 1991). A number of introns have been used recently for phylogenetic analysis. Introns surrounded with exons have been used for studying different vital proteins in different organisms (Palumbi and Baker 1994; Prychitko and Moore 1997; Friesen et al. 1997). Species tree for snake are important for many reasons. The first reason is snake bite treatment (Theakston, 1997). Monovalent anti-venom are not efficient if there is a difference in venom composition of snake taxa. Thus, knowledge about snake species with their geographic distribution become more important for solution of these problems. Venom variation is important for biomedical and toxin studies (Chippaux et al. 1991; Thorpe et al. 1997; Theakston, 1997). Mistake in taxonomic status of the animals can lead to useless studies if the wrong name is assigned to a model for study or comparison with wrong material. Giannasi et al. (2001) sequenced intron 7 of beta fibrinogen. They used 25 vipers and 8 Asian pit-viper species. Maximum parsimony and maximum likelihood phylogenies were generated and compared to those generated from mitochondrial cytochrome b DNA sequences. Separate and combined analyses were used for both mt and nuclear gene phylogenies. Thomas et al. (2002) used 12S, 16S rRNA and intervening tRNA-Val to infer phylogenetic relationship of dwarf boa. They used 23 species of snakes along with 4 genera of New World Dwarf boas. Their study did not show any monophyly of Dwarf boas. They also inferred phylogenies of basal snakes.

14

Review of literature

They found that Exiliboa and Ungaliphis are more close to Erycinae and Boinae and cenophidea. Tropidiphis and Trachyboa showed independent separation during early snake radiation forming here a separate clade. For example, Pythons showed closed relationship with Loxocemus and Xenopeltis. Uropeltids and Cylindrophis cluster together and embedded in macrostomatan radiation. Bootstraping ad Bayesian analysis supported these relationships. Simulations prove Bayesian support value giving more accurate estimate of the relationship. Many studies are discovering new species using morphological and molecular data with different techniques but species boundaries are not a part of these studies always even there is always a chance that single gene/locus tree or morphological data can mislead the phylogenetics (Bossu and Near 2009; Smith et al. 2012). This leaves species as un-decribed and unsampled (Smith et al. 2012). Impact of missing taxa on comparative analyses is one of the major problems in phylogenetics. Incomplete sampling can increase overall divergence time estimation (Cusimano and Renner 2010). Measure for quantifying diversification will give wrong indication of speciation rates. They will show a decrease towards the present (Pybus and Harvey 2000; Rabosky and Lovette 2008; Cusimano and Renner 2010; Smith et al. 2013). Genes divergence predated species divergence, gene based trees will yield incorrect estimation of older branching times as compared to species (Carstens and Knowles 2007; Burbrink and Pyron 2011). Pook et al. (2000) inferred phylogeography of Western rattle snake using mtDNA sequences of cytochrome b and ND4. The phylogenetic analyses showed two clades. East and south of Rocky Mountains rattle snakes formed one clade while the other clade consisted of west side snakes. Population from southern Arizonais sister to remaining western side snakes. The analysis revealed the evolution of small sized body twice and the ability to secrete toxin arose within the complex. Monophyly of elapids is challenged in two ways. There are many studies on phylogenetic of elapids but most of them include few taxa of elapids. However, biochemical and pharmacological studies of snake venom is creating opportunities for generating molecular databases with amino acid sequence of venom proteins. Wuster et al. (2007) used mitochondrial genes sequences for inferring phylogenetics of cobras. They found evolution of spitting venom occurred three times during their evolution. Asiatic Naja are monophyletic originating in Asia from Africa. African spitting cobras radiated in early period of Miocene. Their data analysis suggested the inclusion of Boulengerina and Paranaja multifasciata within the cobras. It also generated a clade that included African rainforest cobras (N. melanoleuca, Paranaja multifasciata and Boulengerina) which was sister to African open formation non-spitting cobras. Naja nigricollis was found polyphyletic thus they considered Naja nigricincta as a separate species that was closely related to N. ashei and N. mossambica than N. nigricollis. Mitochondrial DNA based studies have contributed a lot for phylogenetic and phylogeographic inference. This also helps in studying evolutionary history and distribution of different animal taxa with demographic processes involved (Avise, 2000). The phylogeographic studies have also been used to infer the history about habitat fragmentation and connections (Wuster et al. 2005a). But these studies are biased towards extant organisms of northern temperate parts without Asian and African parts. In Africa, these kinds of studies involve most of the mammals and birds but not the ectothermic vertebrates. For inferring biogeographical history of any part of the world is dependent on the wide range of taxonomic investigations. It is now clear that species range is changes individually and independently of each other even in the presence of ecological and geological events (Hewitt, 2004). With respect to species diversity, Sub Saharan Africans has been divided into two parts. The one part is called tropical forest of central and Western Africa together with coastal forests of Eastern Africa. The other part is the part separating these two forest lands. Many of the species or species complex of these forests regions inhabit one of these two parts (Hughes, 1983; Spawls and Branch 1995). Palentological and palynological evidences recognize Miocene-Pliocene boundary as the major event in the formation and spread of open grassland area. Evolution of present day mammals of these grassland are the evidence of this formation (Eggert et al. 2002). Unfortunately, this has never been tested many times and presence of spitting and non- spitting cobras in Asia is suggested to because of separated colonization of two different African stocks in Asia (Minton, 1986; Ineich, 1995). This suggests the paraphyly of spitting cobras from Asian spitting cobras. The spitting has been the hot issue of many studies (Westhoff et al. 2005). Cobras and other related elapids need special phylogenetic studies for answering these types of questions. Many

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Review of literature of the studies have identifies a monophyletic group named as “core cobra group” through phylogenetic studies (Slowinski and Keogh 2000). In this core cobra group, all spitting elapids are grouped comprising of Naja, Boilengerina, Paranaja, Aspidelaps, Walterinnesia and Hemachatus genera (Slowinski and Keogh 2000). But limited number of samples has not helped in inferring high resolutions of these relationships within this core cobra group. Limited number of sample for this core group has not helped in resolution of the phylogenetic relationship even many studies have shown Naja as non-monophyletic because of exclusion of Boulengerina and Paranaja (Slowinski et al. 1997; Slowinski and Keogh 2000; Nagy et al. 2005). More studies involving venom composition and phylogenetics are required for sub Saharan African populations (e.g., Daltry et al. 1996; Fry et al. 2003). Snakes have been the part of many phylogenetic studies for over a century but higher-level relationships are still to be resolved. Many different phylogenies of snakes were generated a few decades ago but recent morphological analysis (Tchernov et al. 2000; Lee and Scanlon 2002) showed a similar description of snake evolution but they have some opposite views about some particular taxa or regions of tree. Recent molecular studies (Vidal and Hedges 2002a, 2004a; Lawson et al. 2004) have tried to resolve this issue by generating well supported clades. This suggests the need of more morphological and molecular studies. All these morphologically well supported clades within snakes show scolecophidians as monophyletic and most basal living snakes however, they are still in question in terms of their relations. The other clade of snakes was Alethinophidia. Anilioids are the most basal alethinophidians but their relationship and monophyly is still not clear. Remaining other alethinophidians are monophyletic macrostomatans (Lee and Scanlon 2002a). Lawson et al. (2004) and Vidal and Hedges (2002b, 2004b) used combinations of mitochondrial and nuclear genes to infer phylogenetic relationships among snakes which agreed with previous morphological studies. They agreed with respect to basal position of scolecophidians in snakes, monophyletic status of alethinohidians, caenophidians. All the aniliods were found to be basal to alethinophidiana while anulus were very basal. The most conspicuous for diversity in serpent fauna of North America are colubrid snakes in Lampropeltini tribe (Dowling et al. 1983). Lampropeltini found to be monophyletic through morphological (Keogh, 1996), immunological (Dowling et al. 1996) and mitochondrial DNA data (Lopez and Maxson 1996). Almost 25 lampropeltinines are oviparous and non- venomous, diurnalnoctural, fossorial, terrestrial and aemiarbreal. They live in deserts, grasslands and woodlands (Greene, 1997). Rodriguez-Robles and De Jesus‐Escobra (1999) used mitochondrial DNA for inferring phylogenetic relationship among North American snakes of colubrid tribe Lamporpeltini. Maximumm likelihood analysis found Rhinocheilus sister to other lampropeltinines and found Lampropeltinines, New World Elaphe and Pituophis as monophyletic.

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Review of literature

Statement of Problem: In Pakistan, out of total 300 species of land snakes, only 40 are poisonous. The common poisonous snakes are elapids and viprids (Iliyas et al. 1997). They account for a large number of snake bites and deaths. Correct identification of the snake and snake bite is important for the development of effective antivenom is required which will also be helpful. Mitochondrial and nuclear genomes of such vertebrates are thought to be the best markers for such kind of studies. Among vertebrates, snakes have always been obviously unique and interesting. Information on the genetic relationships of organisms is deficient on these unique and distinctive species. Aims and Objectives: The study included Pakistani elapids and viperids with the objective to assess genetic variation and phylogenetic relationship through mitochondrial (Cytochrome b, 16S rRNA, 12S rRNA, ND4 and COI) and nuclear protein coding (C-mos, RAG-1, BDNF and NT3) genes in Elapid (Common Krait, Black Cobra) and Viperid (Russell’s viper, Saw-scaled vipers) snakes in Pakistan.

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CHAPTER 3 MATERIALS AND METHODS 3.1. Sampling: Venomous snakes which include Krait (Bungarus caeruleus), Russel’s viper (Doboia russelii), Saw-scaled viper (Echis carinatus) and Black Cobra (Naja naja) were used for this study. Samples were collected from tail tip biopsies and shed skin of the snakes. A total of 100 samples [Twenty five of each species i.e., Krait (Bungarus caeruleus), Black Cobra (Naja naja), Russel’s viper (Daboia russelii) and Saw-scaled viper (Echis carinatus)] were obtained from private breeders and different people who found any of these snakes, dead or killed around their living areas after certain morphological measurements and observations. Tissue Samples were transferred to 70% ethanol before the DNA extraction. The sampling localities were plotted on the Pakistan map obtained from (https://www.ezilon.com/maps/asia/pakistan-maps.html) as shown in the figures 3.1 to 3.4. The coordinated of these sites are shown in the Tables 3.4 to 3.7. The samples of four species were collected from different cities of Pakistan. The localities and coordinates of the animals were recorded and all the samples were given specific identities. Two samples of Naja naja were collected from Department of Herpetology, California Academy of Sciences. One of the sample was from Khuzdar Baluchistan Pakistan, while the other from Museum of Vertebrate Zoology, University of California Berkeley, collected from Thatta Sindh Pakistan. One sample of Daboia russelli was also provided by Museum of vertebrate Zoology collected from Thatta Sindh. The tissue samples were preserved in 70% ethanol which were later used for DNA extraction. DNA samples were preserved in 10mM Tris buffer at -200C. Suitable PCR primers were used to amplify the mitochondrial and nuclear gene sequences and nucleotide data was obtained after Sanger sequencing. 3.2. DNA Extraction: Following different methods of DNA extraction were used in this study. 3.2.1. AMPure Magnetic Beads (Agencourtt, Bioscience, Beverly, Massachusetts, USA): 1. About 3mg of tissue was lysed in 100uL of lysis buffer and 5uL of proteinase K (4X) in 1.5mL micro- centrifuge tube. 2. The mixture was incubated at 55ᵒC until the tissue dissolves. 3. Then the lysate was cooled at room temperature. 4. Then 180µL of Serapure was add to the mixture and mixed by gentle pipetting. 5. The mixture was kept at room temperature for 2 minutes so that DNA might be able to bind with magnetic beads. 6. Sample tubes were then placed on a magnetic rack and Serapure beads were allowed to congregate for at-least 2 minutes. 7. After 2 minutes the supernatant was removed leaving the Serapure beads holding the DNA with the walls of the tubes. 8. The beads were washed twice with 70% ethanol for almost 30 seconds each. 9. As much as possible 70% ethanol was removed. Then, the beads were allowed to dry for 2 minutes off the magnet. 10. The dried beads were suspended in 50-200uL of 10mM Tris (pH=8.0) with gentle mixing and incubated at room temperature for 5 minutes. 11. Again, the samples tubes were kept on magnetic plate so that beads get separated from supernatant. The supernatant contains the DNA. 3.2.2. Phenol Chloroform-Isoamy alcohol (PCI) Method: DNA from some tissues samples was also extracted through standard organic method (Sambrook and Russel 2001). 1. Small piece of tissue (3mg) was taken in 1.5mL eppendorf tube and about 400µL of buffer A1, 150µL of 10%SDS and 25µL of Proteinase K was added to this tube. The contents were mixed well through vortex. 2. Then all tubes were placed in water bath with shaking, for overnight incubation at 56oC. 3. After overnight incubation, equal volume of PCI (phenol, chloroform, isoamyl alcohol; taken in 25:24:1 ratio) was added to all the tubes.

18

Materials and methods

4. Tubes were gently inverted and then and centrifuged at 8,000 rpm for 5 min. at 4oC. 5. Three layers were formed in the tube, upper layer having nucleic acid; middle layer contains proteins and bottom layer having cellular debris and proteins. Upper layer was carefully picked with the help of pipette and shifted in new labeled eppendorf tube. 6. An equal volume of chilled isopropanol was added to this upper layer containing DNA of the cell. After some gentle shaking DNA was visible in the tube. 7. Then the tubes were centrifuged at 8,000 rpm for 5 mins. at 4oC. 8. The pallet was obtained at the bottom while the supernatant was discarded. 9. Then the pallet was washed with 70% ethanol and centrifuged as 8,000 rmp for 5 mins. 10. After ethanol precipitation, the tubes were air dried to remove all ethanol residue. 11. DNA was gently dissolved in 70 µl of sterile water. 12. Heat shock was given at 70oC for 30 min. in water bath, to inactivate any remaining nuclease. 13. Heat shocked DNA was stored at -20oC for further use. 3.2.3. DNeasy Blood & Tissue Kit Method: 1. The tissue piece was cut into small pieces in a 1.5mL eppendorf tube. Proteinase K (20µL) and buffer ATL (180 µL) were added. 2. Contents were mixed well and incubated 56oC in shaking water bath until the tissue was lysed completely. 3. Then another buffer AL (200µL) was added and mixed well with addition of 200µL absolute ethanol with subsequent mixing by vortex. 4. Then the mixture was pipet into DNeasy Mini spin column already placed in 2mL collection tube. The column was centrifuged at 6,000 rpm for 3 mins. 5. Then column was put in a new collection tube and 500µL of buffer AW1 and AW2 were added with centrifugation with each buffer at 6,000 rpm. 6. Then placed the tube in a new collection tube and added 200µL of buffer AE to the column membrane directly. The column was centrifuged at 6,000 rpm for 1 min. This elute contains extracted DNA. 3.3. Storage and Quantification of DNA Samples: Extracted DNA was dissolved in 10mM Tris buffer (Including 0.1% Tween 20) and stored at -20ºC freezer. DNA quantification of the samples was done with the help of NanoDrop 1000 Spectrophotometer. Some of the DNA samples showed low quantities. 3.4. Primers Designing: Different accession numbers of mitochondrial genes Cytochrome b, 16S rRNA, 12S rRNA, ND4, COI and nuclear genes C-mos, RAG1, BDNF and NT3 were used for designing the PCR primers for amplification of selected regions as shown in the Tables 3.8 to 3.11. Some already reported PCR primers for mitochondrial and nuclear genes were also used as shown in the Table 3.12. 3.5. Polymerase Chain Reaction (PCR): Polymerase chain reactions used 0.01% bovine serum albumin and GoTaq® Flexi DNA polymerase master mix. Thermocycler GeneAmp® 9700 was used for Cycle sequencing reactions and DNA sequencing. 3.5.1. Steps for Polymerase Chain Reaction (PCR): The recipe for the PCR reaction is shown in Table 3.1. Touch-down PCR was performed with 650C- 500C as shown in the Table 3.2 with subsequent maintenance of final temperature at 40C. Table 3.1: Recipe for the amplification reaction No. Contents Master Mix/reaction (µL) 1 Green Taq Master Mix 14.0 2 Forward Primer 1.0 3 Reverse Primer 1.0 4 DNA Sample 3.0 5 DEPC Water 6.0 Total Volume 25.0

19

Materials and methods

Table 3.2: General Protocol for PCR Cycles Steps Temperatures Time (Sec, Min) Initial denaturation 940 C 2 minutes Denaturation 940 C 30 Seconds Annealing 50-600C 30-60 Seconds Extension 720 C 30 Seconds Denaturation 94°C 30 Seconds Final extension 720 C 1:30 Minutes. Storage 4oC ∞ 3.6. Agarose Gel Electrophoresis: Amplified DNA was detected through 1.2% agarose gel electrophoresis with 100bp DNA ladder at 120v for 35 minutes. 3.7. Precipitation of Amplified DNA: Following were the steps for the precipitation of amplified DNA. 1. About 20 µL of distilled water and 80µL of absolute ethanol was added to the amplified DNA. 2. After 20 minutes, the product was centrifuged at 13,500 rpm for 10 minutes 3. The DNA pellet was obtained and air dried for complete removal ethanol residues. 4. The DNA pellet was dissolved in 12µL of distilled deionized water. 5. Then product was sent for DNA Sanger sequencing. 3.8. Data Analysis: Analyses of the sequences were done through appropriate software. MEGA v6.0 was used to align the sequenced sequences along with other reported sequences at NCBI database of the same mitochondrial and nuclear genes (Accession numbers are given in the table 3.12) through ClustalW (Thompson et al. 1994). DnaSP (v. 5.0 Librado and Rozas 2009) was used for polymorphic DNA polymorphisms analyses and Pairwise number of differences. Pairwise number of differences were also represented in the form of graphs. Using Multiple Sequence Comparison by Log-Expectation (MUSCLE), homology was measured among the gene sequences of the different snake species in this study. 3.8.1. Phylogenetic Analysis: All the DNA sequences were uploaded on Sequencher v5.0 (Gene Codes, Ann Arbor, Michigan, USA). Contigs of each forward and reverse sequence chromatogram were made to get a consensus sequence making sure that there is no stop codon. Sequences with low quality than 70% were not used to avoid any ambiguity. The sequences were aligned through MEGA (v6.0; Tamura et al. 2011) software using ClustalW (Larkin et al. 2007) multiple sequence alignment tool. Nucleotide data was translated into amino acids using the vertebrate mitochondrial and universal genetic code. All the contiged sequences were concatenated by using Sequence Matrix software (v1.7.8; Vaidya, 2010) getting files used later for Bayesian Inference and Maximum Likelihood analyses. Concatenated data was partitioned using PartitionFinder (v1.10; Lanfear et al. 2012) that gave best partition scheme for partitioning the data and best models (Table 3.3) of evolution for maximum likelihood and Bayesian analyses using greedy search algorithm for examining phylogenetic relationship of Pakistani elapids and Viperids with other species from NCBI.

Table 3.3: Evolutionary Models for Maximum likelihood and Bayesian Phylogenetics

Maximum Likelihood Bayesian Inference Species Models of Evolution InL LnL Common Krait (Bungarus GTR+G, HKY+I, TrN+ -5788.13964 -5812.563 caeruleus) G Black Cobra (Naja naja) TrN+I+G, HKY+I, TrN -8008.97556 -8045.494 Russell’s Viper (Daboia russelli) TrN+G, HKY+I, TIM -3610.50707 -3837.12 Saw Scaled Viper (Echis GTR, GTR+G, -4498.85875 -6143.527 carinatus) GTR+I+G

20

Materials and methods

3.8.1.1. Maximum Likelihood Analysis: Maximum likelihood analyses were conducted using RaxML (v8.00 Stamatakis, 2014) on CIPRES Science Gateway server (v3.2; Miller et al. 2010). Nodal support was provided by bootstrapping (BS; 1,000 pseudo-replicates); with bootstrap values ≥70 or 0.7 were considered as strong supports (Hillis and Bull 1993). 3.8.1.2 Bayesian Inference: MrBayes (v3.3 Ronquist et al. 2012) was used for Bayesian Markov chain Monte Carlo (MCMC) phylogenetic analyses. Two simultaneous runs of four MCMC analyses with total four chains (one cold plus three incrementally heated chains) were run with trees for 5 ×3 106 total generations (sampled every 500 generations). Burnin value of 25% was set that discarded 2500 generations. Trace plots and ESS value (>200) was used to examine stationarity on TRACER (v1.5 Rambaut and Drummond 2009). Posterior probability (PP) values ≥0.95 were considered as strong supports (Mulcahy et al. 2011). Figtree (Rambaut, 2007) software was used to edit the resulting phylogeny of RaxML and MrBayes analyses. 3.9. Work Place: Molecular Biology and Genomics Laboratory, Institute of Biochemistry and biotechnology, University of Veterinary & Animal Sciences, Lahore. Smith’s Lab, Department of Biology, University of Texas at Arlington, Texas, United States of America (USA).

21

Materials and methods

Figure 3.1: Sample collection sites of Pakistan for Common Krait (Bungarus caeruleus)

22

Materials and methods

Figure 3.2: Sample collection sites of Pakistan for Black Cobra (Naja naja)

23

Materials and methods

Figure 3.3: Sample collection sites of Pakistan for Russell’s viper (Daboia russelli)

24

Materials and methods

Figure 3.4: Sample collection sites of Pakistan for Saw-Scaled Viper (Echis carinatus)

25

Materials and methods

Table 3.4: Common krait (Bungarus caeruleus) samples and their location information

Sample ID Locality Latitude Longitude BC-1 Jallo Park, Lahore, Punjab, Pakistan 31°34'17.29"N 74°28'36.78"E

BC-2 Balochanwali, Bahawalpur, Punjab, Pakistan 29°28'56.90"N 71°59'41.86"E

BC-3 Qila Ram Qaur, Hafiz Abad, Punjab, Pakistan 32° 4'57.12"N 73°40'49.02"E Yazman Housing Society, Yazman, Punjab, BC-4 29° 7'3.00"N 71°45'6.73"E Pakistan BC-5 Changa Manga Forest, Kasur, Punjab, Pakistan 31° 4'54.19"N 73°59'53.49"E Lal Suhanra National Park, Bahawalpur, Punjab, BC-6 29°19'1.36"N 71°54'16.43"E Pakistan Rahim Yar Khan Zoo, Rahim Yar Khan, Punjab, BC-7 28°24'14.30"N 70°15'32.63"E Pakistan BC-8 Chak Risalwala, Faisalabad, Punjab, Pakistan 31°22'4.90"N 73° 1'24.80"E

BC-9 Qila Ram Qaur, Hafizabad, Punjab, Pakistan 32° 4'57.12"N 73°40'49.02"E New City Housing Society, Jaranwala, Punjab, BC-10 31°19'16.60"N 73°23'21.44"E Pakistan Chak 126 GB Pind Janjua, Jaranwala, Punjab, BC-11 31°21'38.48"N 73°25'28.74"E Pakistan Rahim Yar Khan Zoo, Rahim Yar Khan, Punjab, BC-12 28°24'14.30"N 70°15'32.63"E Pakistan Yazman Housing Scheme, Yazman, Punjab, BC-13 29° 6'54.39"N 71°45'17.40"E Pakistan Ayub National Park, Jehlam Road, Punjab, BC-14 33°34'19.00"N 73° 4'59.00"E Pakistan BC-15 Tibbi Balochan, Sadiqabad, Punjab, Pakistan 28°16'35.01"N 70° 8'6.58"E

BC-16 Maraghzar Colony, Lahore, Punjab, Pakistan 31°30'8.71"N 74°14'55.48"E

BC-17 , Punjab, Pakistan 31°33'23.78"N 74°19'33.73"E

BC-18 Lahore Zoo, Punjab, Pakistan 31°33'23.78"N 74°19'33.73"E

BC-19 Raza Garden Phase 1, Sargodha, Punjab, Pakistan 32° 2'51.23"N 72°37'31.68"E

BC-20 Pir wala, Jhang, Punjab, Pakistan 31° 1'42.61"N 72°16'45.51"E

BC-21 Noor Garden, Okara, Punjab, Pakistan 30°48'48.38"N 73°28'38.33"E

BC-22 Chenab Park, Multan, Punjab, Pakistan 30° 4'29.90"N 71°18'51.93"E

BC-23 Kalarwala, Chiniot, Punjab, Pakistan 31°28'26.21"N 72°33'56.11"E

BC-24 Qadir Abad Tiba, Sadiqabad, Punjab, Pakistan 28°16'54.33"N 70° 7'45.48"E

BC-25 Chenab Park, Gujranwala, Punjab, Pakistan 30° 4'29.90"N 71°18'51.93"E

26

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Table 3.5: Black Cobra (Naja naja) samples and their location information

Sample ID Locality Latitude Longitude

NN-1 Bahawalnagar Zoo, Bahawalnagar, Panjab, Pakistan 30° 0'11.20"N 73°16'22.26"E Yazman Housing Society, Yazman, Punjab, NN-2 29° 7'3.00"N 71°45'6.73"E Pakistan NN-3 Ayub National Park, Jhelum Road, Punjab, Pakistan 33°34'19.00"N 73° 4'59.00"E

NN-4 Basti Miani, Bahawalpur, Punjab, Pakistan 29°26'24.13"N 71°41'51.86"E

NN-5 Dijkot, Faisalabad, Panjab, Pakistan 31°12'55.90"N 72°59'57.12"E

NN-6 Lahore Zoo, Lahore, Punjab, Pakistan 31°33'23.78"N 74°19'33.73"E

NN-7 Fateh Pul, Hasilpur, Punjab, Pakistan 29°39'12.14"N 72°34'17.40"E

NN-8 Mian Town, Haroonabad, Punjab, Pakistan 29°36'4.41"N 73° 8'23.12"E

NN-9 Mauza Ali wala, Multan, Punjab, Pakistan 30° 5'58.03"N 71°23'39.52"E

NN-10 Hiran Minar Park, Sheikhupura, Punjab, Pakistan 31°44'34.88"N 73°57'18.64"E

NN-11 Noor Garden, Okara, Punjab, Pakistan 30°48'48.38"N 73°28'38.33"E

NN-12 Budla Sant, Multan, Punjab, Pakistan 30° 9'13.65"N 71°42'42.59"E

NN-13 Tauheedabad, Chiniot, Punjab, Pakistan 31°43'37.38"N 72°59'43.84"E

NN-14 Qadir Abad Tiba, Sadiqabad, Punjab, Pakistan 28°16'54.33"N 70° 7'45.48"E

NN-15 Chenab Park, Gujranwala, Punjab, Pakistan 30° 4'29.90"N 71°18'51.93"E

NN-16 Malkhanwala, Faisalabad, Panjab, Pakistan 31°21'21.69"N 73° 6'32.91"E

NN-17 Lahore Zoo, Lahore, Punjab, Pakistan 31°33'23.78"N 74°19'33.73"E

NN-18 Setellite Town, Hasilpur, Punjab, Pakistan 29°41'18.72"N 72°33'27.34"E

NN-19 Tiba Sharqiya, Haroonabad, Punjab, Pakistan 29°36'33.18"N 73° 8'49.67"E

NN-20 Green City, Okara, Punjab, Pakistan 30°49'23.90"N 73°28'2.54"E

NN-21 Khajiwala, Multan, Punjab, Pakistan 30° 8'32.90"N 71°22'53.94"E

NN-22 Chiniot Road, Chiniot, Punjab, Pakistan 31°43'29.21"N 73° 0'39.39"E

NN-23 Tibbi Balochan, Sadiqabad, Punjab, Pakistan 28°16'35.01"N 70° 8'6.58"E

NN-S Makli, 4km S, Thatta Sindh 24.60247”N 67.8205E

NN-B Khuzdar, , Pakistan 7 43.34 N 66 55.24 E

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Table 3.6: Russell’s viper (Daboia russelli) samples and their location information

Sample ID Locality Latitude Longitude

DR-1 25°51'27.64"N 69°28'42.43"E Sanghar, Sindh, Pakistan DR-2 Buddh Village, Thatta, Sindh, Pakistan 24.605306 68.077217

DR-3 Nawanpind Chak 255 RB, Faisalabad, Punjab, Pakistan 31°15'7.49"N 73° 3'13.71"E

DR-4 Chak 38 JB Dabbora, Faisalabad, Punjab, Pakistan 31°24'1.38"N 72°52'27.98"E

DR-5 Rahwali, Gujranwala, Punjab, Pakistan 32°14'52.44"N 74°10'4.92"E

DR-6 Pirkot, Sheikhupura, Punjab, Pakistan 31°40'52.97"N 73°59'59.63"E

DR-7 Dera Kalar Wala, Sheikhupura, Punjab, Pakistan 31°43'39.77"N 73°56'50.54"E

DR-8 Kot Jann, Okara, Punjab, Pakistan 30°47'22.28"N 73°26'37.69"E

DR-9 Budla Sant, Multan, Punjab, Pakistan 30° 9'13.65"N 71°42'42.59"E

DR-10 Lahore Zoo, Lahore, Punjab, Pakistan 31°33'23.78"N 74°19'33.73"E

DR-11 Chak 169 P, Sadiqabad, Punjab, Pakistan 28°16'48.63"N 70° 8'54.39"E

DR-12 Mauza Talib Chiniot, Punjab, Pakistan 31°41'18.14"N 73° 1'16.67"E

DR-13 Saad City, Okara, Punjab, Pakistan 30°49'34.90"N 73°27'52.00"E

DR-14 Noor Garden, Okara, Punjab, Pakistan 30°48'48.38"N 73°28'38.33"E

DR-15 Bahawalnagar Zoo, Bahawalnagar, Punjab, Pakistan 30° 0'11.20"N 73°16'22.26"E

DR-16 New City Housing Society, Jaranwala, Punjab, Pakistan 31°19'16.60"N 73°23'21.44"E

DR-17 New City Housing Society, Jaranwala, Punjab, Pakistan 31°19'16.60"N 73°23'21.44"E Rahim Yar Khan Zoo, Rahim Yar Khan, Punjab, DR-18 28°24'14.30"N 70°15'32.63"E Pakistan DR-19 Rehman City, Yazman, Punjab, Pakistan 29° 8'25.95"N 71°44'54.72"E

DR-20 Ayub National Park, Jhelum Road, Punjab, Pakistan 33°34'19.00"N 73° 4'59.00"E

DR-21 Lahore Wildlife Park, Lahore, Panjab, Pakistan 31°23'3.62"N 74°12'34.39"E Rahim Yar Khan Zoo, Rahim Yar Khan, Punjab, DR-22 28°24'14.30"N 70°15'32.63"E Pakistan DR-23 Gillanwala, Gujrat, Punjab, Pakistan 32°34'1.84"N 74° 6'38.89"E

DR-24 Sanghar, Sindh, Pakistan 25°51'27.64"N 69°28'42.43"E

DR-25 Gujrat, Punjab, Pakistan 32°34'23.06"N 74° 6'1.82"E

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Table 3.7: Saw-scaled Viper (Echis carinatus) samples and their location information

Samples ID Locality Latitude Longitude EC-1 Rehmat Pura, Okara, Punjab, Pakistan 30°49'41.78"N 73°26'57.58"E

EC-2 Chak 56/2 L Okara, Punjab, Pakistan 30°49'36.05"N 73°29'26.05"E

EC-3 Noor Garden, Okara, Punjab, Pakistan 30°48'48.38"N 73°28'38.33"E Tiba Maqsoodpura, Bahawalnagar, Punjab, EC-4 29°59'28.08"N 73°15'51.74"E Pakistan EC-5 Aziz Town, Bahawalnagar, Punjab, Pakistan 29°58'39.26"N 73°15'6.63"E

EC-6 Lahore Zoo, Lahore, Panjab, Pakistan 31°33'23.78"N 74°19'33.73"E

EC-7 Budla Sant, Multan, Punjab, Pakistan 30° 9'13.65"N 71°42'42.59"E

EC-8 Model Town, Multan, Punjab, Pakistan 30°14'39.35"N 71°29'58.88"E

EC-9 Changa Manga Forest, Kasur, Punjab, Pakistan 31° 4'54.19"N 73°59'53.49"E

EC-10 Lahore Zoo, Lahore, Panjab, Pakistan 31°33'23.78"N 74°19'33.73"E

EC-11 Jharianwala, Hafizabad, Punjab, Pakistan 32° 5'26.11"N 73°40'29.85"E

EC-12 Zafar Abad, Sheikhupura, Punjab, Pakistan 31°41'13.28"N 74° 3'27.19"E

EC-13 Dera Kalar wala, Sheikhupura, Punjab, Pakistan 31°43'39.77"N 73°56'50.54"E

EC-14 Jharianwala, Hafizabad, Punjab, Pakistan 32° 5'26.11"N 73°40'29.85"E

EC-15 Qadirabad, Sahiwal, Punjab, Pakistan 30°43'22.36"N 73°14'59.99"E Chak 225 RB Malkhanwala, Faisalabad, Punjab, EC-16 31°21'35.97"N 73° 6'46.19"E Pakistan EC-17 Nawanpind, Faisalabad, Punjab, Pakistan 31°15'7.49"N 73° 3'13.71"E

EC-18 Dogranwala, Gujranwala, Punjab, Pakistan 32°13'22.56"N 74° 5'29.38"E

EC-19 Hiran Minar Park, Sheikhupura, Punjab, Pakistan 31°44'34.88"N 73°57'18.64"E

EC-20 Dera Kalar wala, Sheikhupura, Punjab, Pakistan 31°43'39.77"N 73°56'50.54"E

EC-21 Chak 7/4-L, Okara, Punjab, Pakistan 30°44'43.12"N 73°25'52.61"E

EC-22 Khaji wala, Multan, Punjab, Pakistan 30° 8'32.90"N 71°22'53.94"E

EC-23 Mouza Talib Chiniot, Punjab, Pakistan 31°41'18.14"N 73° 1'16.67"E

EC-24 Chak 169 P, Sadiqabad, Punjab, Pakistan 28°16'48.63"N 70° 8'54.39"E

EC-25 Tauheedabad, Chiniot, Punjab, Pakistan 31°43'37.38"N 72°59'43.84"E

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Figure 3.5: Dorsal View of Common krait (Bungarus caeruleus)

Figure 3.6: Dorsal View of Black Cobra (Naja naja)

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Materials and methods

Figure 3.7: Dorsal View of Russell’s viper (Daboia russelli)

Figure 3.8: Dorsal View of Saw-Scaled Viper (Echis carinatus)

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Table 3.8: Mitochondrial DNA primers for Common krait (Bungarus caeruleus) Sr. No. Gene Name Primer ID Primer Sequence Accession No. Position 1 Cytochrome b Cytb BC F1 CATGACCAGGGCAGCAAAC DQ343648 14702 2 Cytb BC R1 GCGGGTTAGCTTTGGAGAAA 15679 3 Cytb BC F2 TCATTCTGAGCAGCAACAGT 15329 4 Cytb BC R2 TCTCGTTTAATAGGCGTGAGG 16308 5 ND4 ND4 BC F1 TATTTCTGTGTGCGGAGCAG DQ343648 10978 6 ND4 BC R1 TTGCTAGGAAGGCGGTTAGA 11643 7 ND4 BC F2 GGTTTCACCTCATCAGCACTATT 12004 8 ND4 BC R2 TACCACTTGGATTTGCACCA 12580 9 12S rRNA 12S BC F1 CAAGCCTCACCACAACAGTG DQ343648 115 10 12S BC R1 TTTATTTCTTTCACAAGGTAAGCTG 677 11 12S BC F2 CGATTCAACCAACCCACTCT 629 12 12S BC R2 TTCTAAGCGCACCTTCCAGT 975 13 16S rRNA 16S BC F1 CCATACTCCTACTGTGACCCC DQ343648 1762 14 16S BC R1 TCGGTCCTTTCGTACTGGGT 2438 16 16S BC F2 CACAACAGCCACCGTATGAC 1427 17 16S BC R2 CGGCCGTTAAATTGTTCACT 1907 18 16S BC F3 GTACCGCAAGGGAACCACTA 1164 19 16S BC R3 GCCAGCTATCTCCAGATTCG 1373 20 COI COI BC F1 ATCGCCCGTTGACTGTTTTC DQ343648 6227 21 COI BC R1 ACAATGCCAAATCCCGGTAAG 6961 22 COI BC F2 CGGGATTTGGCATTGTATCT 6966 23 COI BC R2 CGGTGTTTCATGTGGTGAAG 7558

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Table 3.9: Mitochondrial DNA primers for Black Cobra (Naja naja)

Sr. No. Gene Name Primer ID Primer Sequence Accession No. Position

1 Cytochrome b Cytb NN F1 CATGACCAGGGCAGCAAAC 14702 DQ342648 2 Cytb NN R1 GCGGGTTAGCTTTGGAGAAA 15679

3 Cytb NN F2 TCATTCTGAGCAGCAACAGT 15329

4 Cytb NN R2 TCTCGTTTAATAGGCGTGAGG 16308

5 ND4 ND4 NN F1 TATTTCTGTGTGCGGAGCAG 10978 DQ342648 6 ND4 NN R1 TTGCTAGGAAGGCGGTTAGA 11643

7 ND4 NN F2 GGTTTCACCTCATCAGCACTATT 12004

8 ND4 NN R2 TACCACTTGGATTTGCACCA 12580

9 12S rRNA 12S NN F1 CAAGCCTCACCACAACAGTG 115 DQ342648 TTTATTTCTTTCACAAGGTAAGCT 10 12S NN R1 677 G 11 12S NN F2 CGATTCAACCAACCCACTCT 629

12 12S NN R2 TTCTAAGCGCACCTTCCAGT 975

13 16S rRNA 16S NN F1 CCATACTCCTACTGTGACCCC 1762 DQ342648 14 16S NN R1 TCGGTCCTTTCGTACTGGGT 2438

16 16S NN F2 CACAACAGCCACCGTATGAC 1427

17 16S NN R2 CGGCCGTTAAATTGTTCACT 1907

18 16S NN F3 GTACCGCAAGGGAACCACTA 1164

19 16S NN R3 GCCAGCTATCTCCAGATTCG 1373

20 COI COI NN F1 ATCGCCCGTTGACTGTTTTC 6227 DQ342648 21 COI NN R1 ACAATGCCAAATCCCGGTAAG 6961

22 COI NN F2 CGGGATTTGGCATTGTATCT 6966

23 COI NN R2 CGGTGTTTCATGTGGTGAAG 7558

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Table 3.10: Mitochondrial DNA primers for Russell's viper (Daboia russelli)

Sr. No. Gene Name Primer ID Primer Sequence Accession No. Position

1 Cytochrome b Cytb DR F1 TCCTGTGGGATCAAACATTTC EU913478 15064

2 Cytb DR R1 GGCAAGTTGGCTAATTTCTGTG 16042

3 ND4 ND4 DR F2 ACGCCTCTTCCTCCATAACC 10994

4 ND4 DR R2 TGCCGGACATCAGTTGAATA 12271

5 ND4 DR F1 TAGCTAGCCTGACCGCATTT 11707

6 ND4 DR R1 GGATGGTTCTGTTGAGTTGTTTAAT 12377

7 ND4 DR F2 ATGAGGACAACAAGCCGAAC 11531

8 ND4 DR R2 GGTGGAGCCCAAAGATAGGT 11738

9 12S rRNA 12S DR F1 CGGTGAAACAGCCATACAAA EU913478 129

10 12S DR R1 ACGGGGAGGAGGTAGAAAAA 1478

11 12S DR F2 CGCATCCCTCCACTAGACAC 1647

12 12S DR R2 GATTGCTCCGGTCTGAACTC 2342

13 16S rRNA 16S DR F1 TAACCCATGAGAGGGCAAAG EU913478 722

14 16S DR R1 ACGGGGAGGAGGTAGAAAAA 1478

15 16S DR F2 CACCACCCATCAACACAAAG 49

16 16S DR R2 TATGTCCCGGCTTTATCGAG 437

17 16S DR F3 ACAGTCAAAACAAAGTACCGCA 1119

18 16S DR R3 GCCCCAACCGAAACATAGTT 2079

19 COI COI DR F1 AACTTCATTACCACATGCATTAACA EU913478 6786

20 COI DR R1 CTCATACTACAAACCCAAGAATTGC 7140

21 COI DR F2 TTAAGCATCCTGATACGAATAGAGC 6396

22 COI DR R2 CTCATACTACAAACCCAAGAATTGC 7140

23 COI DR F3 CCATTGCAATTCTTGGGTTT 7135

24 COI DR R3 TGAGTAACGGCGAGGCATAC 7603

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Materials and methods

Table 3.11: Mitochondrial DNA primers for Saw-Scaled Viper (Echis carinatus)

Sr. No. Gene Name Primer ID Primer Sequence Accession No. Position

1 Cytochrome b Cytb EC F1 TCCTGTGGGATCAAACATTTC EU913478 15064 Cytb EC 2 GGCAAGTTGGCTAATTTCTGTG 16042 R1 3 ND4 ND4 EC F2 ACGCCTCTTCCTCCATAACC 10994 ND4 EC 4 TGCCGGACATCAGTTGAATA 12271 R2 5 ND4 EC F1 TAGCTAGCCTGACCGCATTT 11707 ND4 EC 6 GGATGGTTCTGTTGAGTTGTTTAAT 12377 R1 7 ND4 EC F2 ATGAGGACAACAAGCCGAAC 11531 ND4 EC 8 GGTGGAGCCCAAAGATAGGT 11738 R2 9 12S rRNA 12S EC F1 CGGTGAAACAGCCATACAAA EU913478 129

10 12S EC R1 ACGGGGAGGAGGTAGAAAAA 1478

11 12S EC F2 CGCATCCCTCCACTAGACAC 1647

12 12S EC R2 GATTGCTCCGGTCTGAACTC 2342

13 16S rRNA 16S EC F1 TAACCCATGAGAGGGCAAAG EU913478 722

14 16S EC R1 ACGGGGAGGAGGTAGAAAAA 1478

15 16S EC F2 CACCACCCATCAACACAAAG 49

16 16S EC R2 TATGTCCCGGCTTTATCGAG 437

17 16S EC F3 ACAGTCAAAACAAAGTACCGCA 1119

18 16S EC R3 GCCCCAACCGAAACATAGTT 2079

19 COI COI EC F1 AACTTCATTACCACATGCATTAACA EU913478 6786

20 COI EC R1 CTCATACTACAAACCCAAGAATTGC 7140

21 COI EC F2 TTAAGCATCCTGATACGAATAGAGC 6396

22 COI EC R2 CTCATACTACAAACCCAAGAATTGC 7140

23 COI EC F3 CCATTGCAATTCTTGGGTTT 7135

24 COI EC R3 TGAGTAACGGCGAGGCATAC 7603

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Table 3.12. Mitochondrial and Nuclear Protein Coding (NPC) DNA primers for Common Krait, Black Cobra, Russell’s viper and Saw-scaled Viper

Sr. No Gene Name Primer Sequence Reference

1 Cyt.b 5'-TGACTTGAARAACCAYCGTTG-3' Palumbi, 1996

5'-TGAGAAGTTTTCYGGGTCRTT-3' Parkinson et al., 2002

2 16S rRNA 5’-CGCCTGTTTAYCAAAAACAT-3 Vences et al. 2001

5’-CCGGTCTGAACTCAGATCACGT-3’ Vences et al. 2001

3 12S rRNA 5’GTACACTTACCTTGTTACGACTT 3’ Knight and Mindell, 1993

5’ AAACTGGGATTAGATACCCCACTAT3’ Knight and Mindell, 1993

4 COI 5’ACY TCR GGR TGA CCA AAA AAT CA -3′ Che et al., 2012

5’AYT CAA CAA ATC ATA AAG ATA TTG3’ Che et al., 2012

5 ND4 5’-CATTACTTTTACTTGGATTTGCACCA-3’ Arevalo, 1994

5’-CACCTATGACTACCAAAAGCTCATGTAAGC-3’ Arevalo, 1994

6 RAG-1 5’AGCTGCAGYCARTAYCAYAARATGTA3’ Chiari et al., 2004

5’AACTCAGCTGCATTKCCAATRTCA3’ Chiari et al., 2004

7 NT3 5’ATATTTCTGGCTTTTCTCTGTGGC3’ Townsend et al., 2008

5’GCGTTTCATAAAAATATTGTTTGACC3’ Townsend et al., 2008

5’GACCATCCTTTTCCTKACTATGGTTATTTCATAC 8 BDNF Townsend et al., 2008 TT3’

5’CTATCTTCCCCTTTTAATGGTCAGTGTACAAAC3’ Townsend et al., 2008

9 C-mos 5' CATGGACTGGGATCAGTTATG 3' Lawson et al., 2005

5'CCTTGGGTGTGATTTTCTCACCT 3' Lawson et al., 2005

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Materials and methods

Table 3.13: Accession Numbers for mitochondrial gene used in the study for data analyses

Museum Cytochrome Species Locality ND4 12S rRNA 16S rRNA number b Naja annulifera 881 Zimbabwe GQ359586 GQ359504 GQ359667 GQ3597753 Naja ashei 1430 Watamu Kenya GQ359575 GQ359493 GQ359656 GQ359742 Naja haje haje 1542 Mali GQ359585 GQ359503 GQ359666 GQ359752 Naja haje haje 1078 Egypt GQ359581 GQ387094 GQ359662 GQ359748 Naja haje haje 1262 Kenya GQ359580 GQ387095 GQ359661 GQ359747 Naja haje Arabic 1681 Saudi Arabia GQ359582 GQ359500 GQ359663 GQ359749 Naja Kaouthia 585 Thailand EU624209 GQ359507 EU624235 GQ359757 Naja katiensis 1540 Doussoudiana Mali GQ359576 GQ359494 GQ359657 GQ359743 Marromeu Naja mossambica WW191 GQ359577 GQ359495 GQ359658 DQ359744 Mosambique Naja naja 595 Nepal AY713378 GQ359506 EU624236 DQ359756 Naja nubiae 837 GQ359579 GQ359497 GQ359660 GQ359746 Naja pallida 1080 Tanzania GQ359578 GQ359496 GQ359659 GQ359745 Naja siamensis Thailand AF155214 JN687926 JN687927 Naja nubiae Herp:589595 AJ830244 Bungarus caeruleus UKH7 Pakistan AJ830220 AJ749305 Bungarus candidus Bmnam Vietnam AJ830232 AJ749330. Bungarus candidus FMNH255260 Loas AJ830247 AJ749338 Bungarus candidus Bcba Indonesia AJ830245 AJ749339. Bungarus flaviceps JAM1946 AJ830251 AJ749351 Bungarus flaviceps MNHN AJ830219 AJ748686 Bungarus niger Niger AJ830241 Bungarus sindanus Bsin1 Pakistan AJ830242 AJ749346 Bungarus ceyloniucs RS-135 KC347501 KC347457 Bungarus multicinctus Bm9204 Taiwan AJ830249 Bungarus fasciatus UKB24 Java Indonesia AJ830244 Bungarus candidus UKBT1 Thailand AJ830239 AJ749334 Bungarus bungaroides KIZ98R0186 China AY973270 Bungarus ceylonicus RS-135 KC347316.1 KC347350.1 Bungarus fasciatus JN687935.1 Bungarus candidus JN687933.1 Bungarus multicinctus A12 HM439979.1 Bungarus multicinctus EF520682.1 Bungarus multicinctus A13 HM439995.1 Bungarus multicinctus A7 HM439994.1 Bungarus fasciatus EU547184.1 Bungarus fasciatus Z46501.1 Bungarus fasciatus JF357944.1 Bungarus multicinctus AF236684.2 Bungarus fasciatus U96793.1 Bungarus fasciatus EU547135.1 Bungarus fasciatus Z46466.1 Bungarus fasciatus JN687934.1 Bungarus candidus JN687932.1 Cerastes cerastes Egypt EU624222 EU852317 EU624288 Echis coloratus Yemen GQ359562 GQ359643 GQ359727 Echis coloratus Oman GQ359549 GQ359630 GQ359714 Echis coloratus Egypt GQ359548 gq359629 GQ359713 Echis coloratus Israel GQ359546 GQ359627 GQ359711 Echis coloratus 29 Saudi Arabia HQ658438 HQ267791 Chennai Tamil Nadu Echis carinatus 596 GQ359521 GQ359601 GQ359682 India Echis carinatus 1612 UAE GQ359524 GQ359604 GQ359685 Echis carinatus 1628 Pakistan GQ359526 GQ359606 GQ359687 Echis jogeri 2004 Senegal GQ359567 GQ359648 GQ359732 2014 Mali GQ359568 GQ359649 GQ359733 Echis leucogaster DSMZleu Mauritania GQ359545 GQ359626 GQ359709 Echis leucogaster DSMZ8102 Morocco GQ359543 GQ359624 GQ359707 Echis leucogaster DSMZ478 Senegal GQ359540 GQ359621 GQ359704 Echis leucogaster 1650 Niger GQ359538 GQ359619 GQ359702 Echis omanensis 1691 Oman GQ359558 GQ359639 GQ359723 Echis omanensis 1688 UAE GQ359555 GQ359636 GQ359720 Echis ocellatus 1544 Mali GQ359516 GQ359595 GQ359677 Echis ocellatus 1631 Cameroon GQ359518 GQ359597 GQ359679 Echis ocellatus 1607 Benin GQ359519 GQ359598 GQ359680 Echis ocellatus DSMZ407 Niger GQ359520 GQ359599 GQ359681 Echis pyramidum 1776 Kenya GQ359559 GQ359640 GQ359724

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Materials and methods

Echis ocellatus 1978 Togo GQ359566 GQ359647 GQ359731 Echis pyramidum 1634 Egypt GQ359529 GQ359609 GQ359692

Poipet Sisphon Daboia siamensis AY165055 AY165081 Cambodia Daboia siamensis Guangdong China AY165056 AY165082 Daboia siamensis 2 Tuban AY165058 AY165084 Tononggurambang Daboia siamensis 1 AY165059 AY165085 Flores Indonesia Daboia russellii HLMDRA2899 Pakistan AY165061 AJ275723 Thayur Tamil Nadu Daboia russellii AY165062. AY165087 India Daboia russellii Gampola Sri Lanka AY165063 AY165088 Fongshan Pingtung Daboia siamensis AY165064 AY165089 Taiwan Daboia russellii 1 Thailand Southern AY165065 AY165090 Daboia russellii CAS205253 Myanmar DQ305477 AF471076 DQ305413 DQ305436 Daboia russellii CIB093926 EU913478 Daboia russellii GQ225676.1 Daboia russelii isolate A4 AY352773.1 AY352712 Daboia russelii CAS205253 DQ305413 Daboia russelii GQ225676.1 Daboia russelli

Table 3.14: Accession Numbers used for Nuclear Protein Coding Genes used in the study for data analysis

Museum Species Locality C-mos RAG-1 BDNF NT3 number Bungarus fasciatus ABTC85504 EU366447 EU366438 Bungarus fasciatus ROM 35253 KX694826 Bungarus ceylonicus RS-135 KC347390 KC347428 Bungarus fasciatus AF544732 CAS HERP- Bungarus fasciatus AY058924 207988 Bungarus multicinctus AF435021 Bungarus fasciatus FJ434090 Bungarus fasciatus ROM 35253 KX694998 Naja atra ROM 30851 KX695032 Daboia russelli CAS 213576 EU402843 EU402636 Daboia russelli GQ225671 Daboia russelli CAS 205255 Myanmar AF471156 Daboia russelli EU390916

38

CHAPTER 4 RESULTS Molecular Phylogenetics and Analyses of Polymorphism Common Krait (Bungarus caeruleus) 4.1.1. Analyses of Polymorphic Sites in mitochondrial and nuclear genes of Common Krait 4.1.2. DNA Polymorphism in mitochondrial and nuclear genes of Common Krait 4.1.3. Percent Identity Matrix graphs for mitochondrial and nuclear genes of Common Krait 4.1.4. Pairwise Number of Differences in mitochondrial and nuclear genes of Common Krait 4.1.5. Phylogenetic Analyses (Maximum Likelihood and Bayesian Inference) of Common Krait Black Cobra (Naja naja) 4.2.1. Analyses of Polymorphic Sites in mitochondrial and nuclear genes of Black Cobra 4.2.2. DNA Polymorphism in mitochondrial and nuclear genes of Black Cobra 4.2.3. Percent Identity Matrix graphs for mitochondrial and nuclear genes of Black Cobra 4.2.4. Pairwise Number of Differences in mitochondrial and nuclear genes of Black Cobra 4.2.5. Phylogenetic Analyses (Maximum Likelihood and Bayesian Inference) of Black Cobra Russell’s viper (Daboia russelli) 4.3.1. Analyses of Polymorphic Sites in mitochondrial and nuclear genes of Russell’s viper 4.3.2. DNA Polymorphism in mitochondrial and nuclear genes of Russell’s viper 4.3.3. Percent Identity Matrix graphs for mitochondrial and nuclear genes of Russell’s viper 4.3.4. Pairwise Number of Differences in mitochondrial and nuclear genes of Russel’s Viper 4.3.5. Phylogenetic Analyses (Maximum Likelihood and Bayesian Inference) of Russell’s viper Saw-scaled Viper (Echis carinatus sochureki) 4.2.1. Analyses of Polymorphic Sites in mitochondrial and nuclear genes of Saw-scaled Viper 4.2.2. DNA Polymorphism in mitochondrial and nuclear genes of Saw-scaled Viper 4.2.3. Percent Identity Matrix graphs for mitochondrial and nuclear genes of Saw-scaled Viper 4.2.4. Pairwise Number of Differences in mitochondrial and nuclear genes of Russel’s Viper 4.2.5. Phylogenetic Analyses (Maximum Likelihood and Bayesian Inference) of Saw-scaled Viper

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4.1. Data analyses for Common krait (Bungarus caeruleus) 4.1.1: Analyses of Polymorphic Sites: Analyses of polymorphic sites in the amplified regions were performed using DnaSP software in mitochondrial and nuclear protein coding genes of common krait. 4.1.1.1. Polymorphic Sites in Common Krait (Bungarus caeruleus) NADH dehydrogenase 4 (ND4) Gene:

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Results

4.1.1.2. Polymorphic Sites in Bungarus caeruleus Cytochrome b (Cyt. B) Gene:

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4.1.1.3. Polymorphic Sites in Common Krait (Bungarus caeruleus) 12S ribosomal RNA (12S rRNA) Gene:

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Results

4.1.1.4. Polymorphic Sites in Common Krait (Bungarus caeruleus) 16S rRNA Gene:

4.1.1.5. Polymorphic Sites in Common Krait (Bungarus caeruleus) Oocyte Maturation Factor (C- mos) Gene:

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Results

4.1.1.6. Polymorphic Sites in Common Krait (Bungarus caeruleus) Recombination Activating Gene 1 (RAG-1) Gene:

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4.1.1.7. Polymorphic Sites in Common Krait (Bungarus caeruleus) Neurotrophin-3 (NT3) Gene:

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Results

4.1.2. DNA Polymorphism: DnaSP software was used for detecting DNA polymorphism in mitochondrial and protein coding genes of Common Krait (Bungarus caeruleus) as shown in the Tables 4.1 and 4.2. Table 4.1: DNA Polymorphism in mitochondrial DNA Genes in Common Krait (Bungarus caeruleus) Parameters Cytochrome b ND4 12S rRNA 16S rRNA Number of Sequences 40 41 33 36 Selected No. of Sites 702 619 650 520 Total Number of sites (Excluding gaps/missing data) 527 614 333 238 No. of Polymorphic sites (S) 202 239 63 47 Total number of mutations (Eta) 245 302 71 62 No. of haplotypes (h) 16 17 8 10 Haplotype (gene) diversity (Hd) 0.615 0.866 0.686 0.521 Variance of haplotype diversity 0.00853 0.00153 502 0.01028 Standard deviation of haplotype diversity 0.092 0.039 0.071 0.101 Nucleotide diversity (Pi) 0.08243 0.09528 0.03617 0.03451 Theta (Per site) from Eta 0.1093 0.11496 0.05254 0.6282 Theta (Per site) from S, Theta-W 0.09011 0.09528 0.04662 0.04762 Variance of Theta (no recombination) 0.00007041 0.0007048 0.0002254 0.000239 Standard deviation of theta (no recombination) 0 0.02655 0.01501 0.01546 Variance of Theta (Free recombination) 0.0000402 0.0000346 0.0000345 0.000483 Standard deviation of theta (Free recombination) 0.00634 0.00588 0.00587 0.00695 Finite site model Theta per site from Pi 0.09261 0.10914 0.03801 0.03617 Theta (per sites) from S 0.11557 0.11714 0.05238 0.05376 Theta (Per sites) from Eta 0.12506 0.13256 0.05588 0.06769 Average nucleotide differences, (k) (no recombination) 43.441 58.5 12.045 8.213 Stochastic variance of k (no recombination) Vst (k) 351.565 632.23 29.319 14.349 Sampling variance of k (no recombination) Vs(k) 18.677 32.732 1.904 0.848 Total variance of k (no recombination) 370.242 664.963 31.224 15.197 Stochastic variance of k (Free recombination) Vst (k) 14.48 19.5 4.015 2.738 Sampling variance of k (Free recombination), Vs(k) 0.743 0.975 0.251 0.156 Total variance of k (free recombination) V(k) 15.223 20.475 4.266 2.894 Theta (Per sequence) from S, Theta W 47.49 55.86 15.523 11.334 Variance of Theta (no recombination) 195.548 265.718 24.99 13.538 Variance of theta (Free recombination) 11.165 13.056 3.825 2.733

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Table 4.2: DNA Polymorphism in Protein Coding Nuclear Genes in Common Krait (Bungarus caeruleus):

Parameters C-mos RAG-1 NT3 Number of Sequences 32 27 28 Selected No. of Sites 586 802 415 Total Number of sites (Excluding gaps/missing data) 431 685 356 No. of Polymorphic sites (S) 12 20 31 Total number of mutations (Eta) 12 20 32 No. of haplotypes (h) 4 3 5 Haplotype (gene) diversity (Hd) 0.333 0.145 0.754 Variance of haplotype diversity 0.01 0.00806 0.00132 Standard deviation of haplotype diversity 0.1 0.09 0.036 Nucleotide diversity (Pi) 0.00288 0.00226 0.03389 Theta (Per site) from Eta 0.00691 0.00757 0.0231 Theta (Per site) from S, Theta-W 0.003 0.00757 0.02238 Variance of Theta (no recombination) 0.059 0.0000082 0.0000627 Standard deviation of theta (no recombination) 0.00282 0.00286 0.00792 Variance of Theta (Free recombination) 0.0000079 0.0000029 0.0000162 Standard deviation of theta (Free recombination) 0.002 0.00169 0.00402 Finite site model Theta per site from Pi 0.00289 0.00227 0.0355 Theta (per sites) from S 0.00703 0.00771 0.02359 Theta (Per sites) from Eta 0.00679 0.00764 0.02372 Average nucleotide differences, (k) (no recombination) 1.24 1.55 12.066 Stochastic variance of k (no recombination) Vst (k) 0.61 0.847 29.348 Sampling variance of k (no recombination) Vs(k) 0.4 0.067 2.274 Total variance of k (no recombination) 0.65 0.914 31.623 Stochastic variance of k (Free recombination) Vst (k) 0.413 0.517 4.022 Sampling variance of k (Free recombination), Vs(k) 0.027 0.04 0.298 Total variance of k (free recombination) V(k) 0.44 0.556 4.32 Theta (Per sequence) from S, Theta W 2.98 5.189 7.966 Variance of Theta (no recombination) 1.476 3.843 7.944 Variance of theta (Free recombination) 0.74

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4.1.3. Percent Identity Matrix: Percent Identity Matrix were created by MUSCLE online tool for mitochondrial and nuclear protein coding genes of Common Krait (Bungarus caeruleus), as shown in the graphs.

120 100 80 60 40 20 Similarity 0

Species

Graph 4.1: Common Krait (Bungarus caeruleus) NADH4 percent identity

150 100 50

0 Similarity

Species

Graph 4.2: Common Krait (Bungarus caeruleus) Cytochrome b percent identity

150 100

50 Similarity 0

Species

Graph 4.3: Common krait (Bungarus caeruleus) 12S rRNA percent identity

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150 100 50 0 Similarity

Species

Graph 4.4: Common Krait (Bungarus caeruleus) 16S rRNA percent identity

120 100 80 60

Similarity 40 20 0

Species

Graph 4.5: Common Krait (Bungarus caeruleus) C-mos percent identity

120 100 80 60

40 Similarity 20 0

Species

Graph 4.6: Common Krait (Bungarus caeruleus) RAG-1 percent identity

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101 100 99 98 97 96 Similarity 95 94

Species

Graph 4.7: Common Krait (Bungarus caeruleus) NT3 percent identity

120 100 80 60 40 Similarity 20 0

Cytb ND4 12S 16S

Graph 4.8: Common Krait (Bungarus caeruleus) combined mitochondrial genes percent identity

101 100 99 98 97

Similarity 96 95 94

Cmos RAG-1 NT3 Species

Graph 4.9: Common Krait (Bungarus caeruleus) combined nuclear genes percent identity

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4.1.4. Pairwise Number of Differences: DnaSP (v 5.0) was used to find the pairwise number of differences in mitochondrial and nuclear protein coding genes. 4.1.4.1. Pairwise Number.of Differences in Common Krait (Bungarus caeruleus) NADH 4 Gene:

Graph 4.10: Pairwise Number of differences for Bungarus caeruleus NADH 4 Gene

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4.1.4.2. Pairwise Number of Differences in Common Krait (Bungaruus caeruleus) Cytochrome b Gene:

Graph 4.11: Pairwise Number of differences in Bungarus caeruleus Cytochrome b

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4.1.4.3. Pairwise Number of Differences in Common Krait (Bungarus caeruleus) 12S rRNA Gene:

Graph 4.12: Pairwise Number of Differences in Bungarus caeruleus 12S rRNA Gene

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4.1.4.4. Pairwise Number of Differences in Common Krait (Bungarus caeruleus) 16S rRNA Gene:

Graph 4.13: Pairwise Number of Differences in Bungarus caeruleus 16S rRNA Gene

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4.1.4.5. Pairwise Number of Differences in Common Krait (Bungarus caeruleus) C-mos Gene:

Graph 4.14: Pairwise Number of Differences in Bungarus caeruleus C-mos

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4.1.4.6. Pairwise Number of Differences in Common Krait (Bungarus caeruleus) RAG-1 Gene:

Graph 4.15: Pairwise Number of Differences in Common Krait (Bungarus caeruleus) RAG-1 Gene

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4.1.4.7. Pairwise Number of Differences in Common Krait (Bungarus caeruleus) NT3 Gene:

Graph 4.16: Pairwise Number of Differences in Bungarus caeruleus NT3 Gene

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4.1.5. Phylogenetic Analyses for Common krait (Bungarus caeruleus): Phylogenetic analysis of common krait (Bungarus caeruleus) from Pakistan were conducted using mitochondrial and nuclear protein coding genes. In this study, Naja naja from Thatta Sindh was used as outgroup for constructing maximum likelihood and Bayesian phylogenies. The best partition scheme and evolutionary models were used to construct character based maximum likelihood and Bayesian phylogenies to infer the phylogenetic relationship of Pakistani Bungarus caeruleus with other members of genus Bungarus. Maximum likelihood and Bayesian Inference gave almost similar phylogenies. All Bungarus species were divided into four main clades. The first clade included Bungarus candidus (Indonesia, Thailand, Vietnam, and Laos), Bungarus multicinctus (China, Taiwan, and Burma) and Bungaru niger (Nepal). The second clade included Bungarus sindanus, Bungarus caeruleus and Bungarus ceylonicus (Pakistan and Sri Lanka) respectively. The third clade included Bungarus fasciatus (Thailand and Indonesia). While the fourth clade included Bungarus bungroides (China) and Bungarus flaviceps (Malaysia and Indonesia). The first and second clade showed sister clade relationship with strong support (ML BS=100, BI PP= 1). Bungarus candidus and B. multicinctus probably diverged recently as separate species. Bungarus candidus, B. multicinctus and Bungarus niger showed highly supported sister relationship with complex pattern of divergence (BI PP=1.0 ML BS= 90). The second clade including Bungarus sindanus, Bungarus caeruleus (Pakistan) and Bungarus ceyloniucs (Sri Lanka) from Pakistan and Sri Lanka. Bungarus sindanus and B. caeruleus have been reported from Pakistan thus both being sympatric species while B. ceyloniucs also showed significant difference with strong support through Maximum likelihood and Bayesian inference phylogenies (PP= 1.0 and BS= 100). Pyron et al. 2012 also revealed the same relationship between Bungarus caeruleus and Bungarus sindanus and Bungarus ceylonicus.

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)

caeruleus Bungarus

( Krait Common for phylogeny Likelihood 4.1: Maximum Figure

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Bungarus caeruleus) Bungarus

Figure 4.2: Bayesian Phylogeny for Common Krait ( Krait for Common Phylogeny 4.2: Bayesian Figure

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4.2: Data analyses for Black Cobra (Naja naja): 4.2.1. Analyses of Polymorphic Sites: Analyses of polymorphic sites in the amplified regions were performed using DnaSP software in mitochondrial and nuclear protein coding genes of Black Cobra (Naja naja). 4.2.1.1. Polymorphic Sites in Black Cobra (Naja naja) NADH dehydrogenase 4 (NADH4) Gene:

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4.2.1.2. Polymorphic Sites in Black Cobra (Naja naja) Cytochrome b (Cyt. b) Gene:

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4.2.1.3. Polymorphic Sites in Black Cobra (Naja naja) 12S ribosomal RNA (12S rRNA) Gene:

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4.2.1.4. Polymorphic Sites in Black Cobra (Naja naja) 16S ribosomal RNA (16S rRNA) Gene:

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4.2.1.5. Polymorphic Sites in Black Cobra (Naja naja) Oocyte Maturation Factor (C-mos) Gene:

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4.2.1.6. Polymorphic Sites in Black Cobra (Naja naja) Recombination activating Gene 1 (RAG-1) Gene:

4.2.1.7. Polymorphic Sites in Black Cobra (Naja naja) Brain-Derived Neurotrophic factor (BDNF) Gene:

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4.2.1.8. Polymorphic Sites in Black Cobra (Naja naja) Neurotrophin-3 (NT3) Gene:

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4.2.2. DNA Polymorphism: DnaSP software was used for detecting DNA polymorphism in mitochondrial and protein coding genes of Black Cobra (Naja naja) as shown in the Tables 4.3 and 4.4.

Table 4.3: DNA Polymorphism in mitochondrial DNA genes in Black Cobra (Naja naja)

Parameters Cyt.b ND4 12S rRNA 16S rRNA Number of Sequences 39 38 39 39 Selected region 1-851 1-584 1-414 1-531 Total Number of sites (Excluding gaps/missing data) 353 471 345 404 No. of Polymorphic sites (S) 123 178 235 60 Total number of mutations (Eta) 157 211 264 70 No. of haplotypes (h) 17 17 16 15 Haplotype (gene) diversity (Hd) 0.756 0.791 0.657 0.595 Variance of haplotype diversity 0.0051 0.00428 0.00792 0.00903 Standard deviation of haplotype diversity 0.071 0.065 0.089 0.095 Nucleotide diversity (Pi) 0.07422 0.06751 0.30998 0.03111 Theta (Per site) from Eta 0.1052 0.10662 0.18099 0.04098 Theta (Per site) from S, Theta-W 0.08241 0.08995 0.16111 0.03513 Variance of Theta (no recombination) 0.0006147 0.000721 0.0022569 0.0001213 Standard deviation of theta (no recombination) 0.02479 0.02685 0.04751 0.01102 Variance of Theta (Free recombination) 0.0000552 0.0000455 0.0001105 0.0000206 Standard deviation of theta (Free recombination) 0.00743 0.00674 0.01051 0.00453 Finite site model Theta per site from Pi 0.08238 0.07419 0.52834 0.03246 Theta (per sites) from S 0.10309 0.115 0.26498 0.03841 Theta (Per sites) from Eta 0.11969 0.12152 0.22864 0.04301 Average nucleotide differences, (k) (no recombination) 26.201 31.797 106.942 12.57 Stochastic variance of k (no recombination) Vst (k) 130.668 190.552 2087.811 31.839 Sampling variance of k (no recombination) Vs(k) 7.124 10.684 114.039 1.732 Total variance of k (no recombination) 137.792 201.236 2201.85 33.571 Stochastic variance of k (Free recombination) Vst (k) 8.734 10.599 35.647 4.19 Sampling variance of k (Free recombination), Vs(k) 0.46 0.573 1.876 0.221 Total variance of k (free recombination) V(k) 9.193 11.172 37.523 4.41 Theta (Per sequence) from S, Theta W 29.092 42.365 55.583 14.191 Variance of Theta (no recombination) 76.6 159.948 268.633 19.804 Variance of theta (Free recombination) 6.881 10.083 13.147 3.357

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Table 4.4: DNA Polymorphism in nuclear protein coding genes in Black Cobra (Naja naja)

Parameters C-mos RAG-1 BDNF NT3 Number of Sequences 28 26 28 26 Selected region 1-556 1-686 1-696 1-426 Total Number of sites (Excluding gaps/missing data) 451 640 665 340 No. of Polymorphic sites (S) 31 15 5 3 Total number of mutations (Eta) 31 15 5 3 No. of haplotypes (h) 5 4 2 4 Haplotype (gene) diversity (Hd) 0.508 0.483 0.071 0.582 Variance of haplotype diversity 0.00921 0.00925 0.00425 0.00354 Standard deviation of haplotype diversity 0.096 0.096 0.065 0.059 Nucleotide diversity (Pi) 0.00567 0.00283 0.00054 0.00319 Theta (Per site) from Eta 0.01766 0.00614 0.00193 0.00231 Theta (Per site) from S, Theta-W 0.01766 0.00614 0.00193 0.00231 Variance of Theta (no recombination) 0.0000391 0.000006 0.000001 0.0000021 Standard deviation of theta (no recombination) 0.00625 0.00245 0.00102 0.00146 Variance of Theta (Free recombination) 0.0000101 0.0000025 0.0000007 0.0000018 Standard deviation of theta (Free recombination) 0.00317 0.00159 0.00086 0.00133 Finite site model Theta per site from Pi 0.00572 0.00284 0.00054 0.00321 Theta (per sites) from S 0.01841 0.00623 0.00194 0.00232 Theta (Per sites) from Eta 0.01802 0.00618 0.00194 0.00232 Average nucleotide differences, (k) (no recombination) 2.558 1.812 0.357 1.086 Stochastic variance of k (no recombination) Vst (k) 1.862 1.075 0.119 0.503 Sampling variance of k (no recombination) Vs(k) 0.142 0.088 0.009 0.041 Total variance of k (no recombination) 2.004 1.164 0.128 0.544 Stochastic variance of k (Free recombination) Vst (k) 0.853 0.604 0.119 0.362 Sampling variance of k (Free recombination), Vs(k) 0.063 0.048 0.009 0.029 Total variance of k (free recombination) V(k) 0.916 0.652 0.128 0.391 Theta (Per sequence) from S, Theta W 7.966 3.931 1.285 0.786 Variance of Theta (no recombination) 7.944 2.462 0.457 0.247 Variance of theta (Free recombination) 2.047 1.03 0.33 0.206

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4.2.3. Percent Identity Matrix: Percent Identity Matrix were created by MUSCLE online tool for mitochondrial and nuclear protein coding genes of Common Krait (Naja naja), as shown in the line graphs.

120 100 80 60 40

Similarity 20 0

Species

Graph 4.17: Black Cobra (Naja naja) NADH4 percent identity

120 100 80 60

Similarity 40 20 0

Species Graph 4.18. Black Cobra (Naja naja) Cytochrome b percent identity

105 100 95 90

Similarity 85 80

Species

Graph 4.19. Black Cobra (Naja naja) 12S rRNA percent identity

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102 100 98 96 94 92

Similarity 90 88 86

Species

Graph 4.20. Black Cobra (Naja naja) 16S rRNA percent identity

105 100 95 90 85

Similarity 80 75

Species

12S 16S Cytb ND4

Graph 4.21: Black Cobra (Naja naja) combined mitochondrial genes percent identity

120 100 80 60

40 Similarity 20 0

Species

Graph 4.22. Black Cobra (Naja naja) C-mos percent identity

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120 100 80 60 40 Similarity 20 0

Species

Graph 4.23. Black Cobra (Naja naja) RAG-1 percent identity

120 100 80 60 40 Similarity 20 0

Species

Graph 4.24. Black Cobra (Naja naja) BDNF percent identity

120 100 80 60

40 Similarity 20 0

Species

Graph 4.25. Black Cobra (Naja naja) NT3 percent identity

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102 100 98 96 94

Similarity 92 90 88

C-mos RAG-1 BDNF NT3

Graph 4.26: Black Cobra (Naja naja) combined nuclear genes percent identity 4.2.4. Pairwise Number of Differences: DnaSP (v 5.0) was used to find the pairwise number of differences in mitochondrial and nuclear protein coding genes. 4.2.4.1. Pairwise Number of Differences in Black Cobra (Naja naja) ND4 Gene:

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Graph 4.27. Pairwise Number of Differences in Black Cobra (Naja naja) ND4 Gene 4.2.4.2. Pairwise Number of Differences for Black Cobra (Naja naja) Cytochrome b Gene:

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Graph 4.28. Pairwise Number of Differences in Black Cobra (Naja naja) Cytochrome b Gene

4.2.4.3. Pairwise Number of Differences in Black Cobra (Naja naja) 12S rRNA Gene:

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Graph 4.29. Pairwise Number of Differences in Black Cobra (Naja naja) 12S rRNA Gene 4.2.4.4. Pairwise Number of Differences in Black Cobra (Naja naja) 16S rRNA Gene:

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Graph 4.30. Pairwise Number of Differences in Black Cobra (Naja naja) 16S rRNA Gene 4.2.4.5. Pairwise Number of Differences in Black Cobra (Naja naja) C-mos Gene:

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Graph 4.31. Pairwise Number of Differences in Black Cobra (Naja naja) C-mos Gene 4.2.4.6. Pairwise Number of Differences in Black Cobra (Naja naja) RAG-1 Gene:

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Graph 4.32. Pairwise Number of Differences in Black Cobra (Naja naja) RAG-1 Gene 4.2.4.7. Pairwise Number of Differences in Black Cobra (Naja naja) BDNF Gene:

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Graph 4.33. Pairwise Number of Differences in Black Cobra (Naja naja) BDNF Gene 4.2.4.8. Pairwise Number of Differences in Black Cobra (Naja naja) NT3 Gene:

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Graph 4.34. Pairwise Number of Differences in Black Cobra (Naja naja) NT3 Gene 4.2.5. Phylogenetic analyses for Black Cobra (Naja naja): In the present study, mitochondrial and nuclear protein coding genes were amplified and sequenced to infer the phylogenetic relationship of Naja naja in Pakistan.genes were concatenated through SequenceMatrix software (v1.7.8 Vaidya, 2010). The nucleotide data was partitioned through Partition- Finder that gave the best partition scheme and best models of evolution. The models of evolution were TrN+I+G, HKY+I and TrN. Using the concatenated nucleotide data, two character based phylogenies i.e., maximum likelihood and Bayesian phylogenies were constructed using RaxML and MrBayes software respectively. Maximum likelihood phylogeny was constructed with InL= -8008.97556 and Bayesian phylogeny was constructed with LnL= -8045.494. Bungarus caeruleus 4 was used as outgroup in the study from Yazman Pakistan. The Naja naja used in this study were collected from different parts of Pakistan provinces like Punjab, Sindh and Baluchistan. The Naja naja from Baluchistan was provided by California Academy of Sciences, USA. After DNA extraction, genes were amplified with suitable PCR primers and all the amplified gene sequences were sequenced through genetic analyzer. The gene sequences were aligned by ClustalW in MEGA v.6.0 and the uncorrected p-distances were calculated as shown in the tables at the end. Almost similar phylogenies were constructed by RaxML and Bayesian phylogenies dividing all the Pakistani Naja naja and other members of Naja from other parts of the world that were already available on NCBI database with their accession numbers of gene sequences into 3 major clades. Bayesian phylogeny showed strong support for the divergence of the three clades but maximum likelihood did not show any kind of support at basal node. First clade includes Naja naja from Sindh and Baluchistan Pakistan and Nepal, Naja kaouthia and N. siamensis were from Thailand. The second clade included Naja pallida (Tanzania), N. nubiae and N. ashei (Kenya), Naja moambica (Mosambique) and Naja katiensis (Mali). The third clade consisted of only two species Naja haje (Mali, Saudi Arabia, Kenya) and Egypt and Naja annulifera (Zimbabwe). Second and third clade showed strongly supported sister group relationship with (ML BS=85; BI PP= 0.94). The first clade showed strong support for sister group relationship with both second and third clade strongly supported by Bayesian phylogeny (BI PP= 1) but had not good support by Maximum likelihood phylogeny. The first clade consisting of Naja naja from Pakistan and Nepal showed recent divergence which is strongly supported by ML and BI (BS=100 and PP=1) while Naja siamensis and Naja kaouthia and Naja naja c/f Naja oxiana from Baluchistan Pakistan showed deep divergence from Naja naja.

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)

Naja naja Naja

( Black Cobra for Phylogeny Likelihood Maximum

Figure 4.3: Figure

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)

naja Naja

( Black Cobra for Phylogeny 4.4. Bayesian Figure

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4.3: Data analyses for Russell’s viper (Daboia russelli): 4.3.1. Analyses of Polymorphic Sites: Analyses of polymorphic sites in the amplified regions were performed using DnaSP software in mitochondrial and nuclear protein coding genes of Russell’s viper (Daboia russelli). 4.3.1.1. Polymorphic Sites in Russell’s viper (Daboia russelli) NADH dehydrogenase subunit 4 (ND4) Gene:

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4.3.1.2. Polymorphic Sites in Russell’s viper (Daboia russelli) Cytochrome b (Cyt. b) Gene:

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4.3.1.3. Polymorphic Sites in Russell’s viper (Daboia russelli) 12S ribosomal RNA (12S rRNA) Gene:

4.3.1.4. Polymorphic Sites in Russell’s viper (Daboia russelli) 16S ribosomal RNA (16S rRNA) Gene:

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4.3.1.5. Polymorphic Sites in Russell’s viper (Daboia russelli) Oocyte Maturation Factor (C-mos) Gene:

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4.3.1.6. Polymorphic Sites in Russell’s viper (Daboia russelli) Recombination Activating Gene-1 (RAG-1) Gene:

4.3.1.7. Polymorphic Sites in Russell’s viper (Daboia russelli) Brain-Derived Neurotrophic factor (BDNF) Gene:

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4.3.1.8. Polymorphic Sites in Russell’s viper (Daboia russelli) Neurotrophin-3 (NT3) Gene:

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4.3.2. DNA Polymorphism: DnaSP software was used for detecting DNA polymorphism in mitochondrial and protein coding genes of Russell’s viper (Daboia russelli) as shown in the Tables 4.6 and 4.7.

Table 4.6: DNA Polymorphism in mitochondrial DNA Genes in Russell’s viper (Daboia russelli)

Parameters Cyt.b ND4 12S rRNA 16S rRNA Number of Sequences 36 33 32 29 Selected region 1-576 1-609 1-422 1-492 Total Number of sites (Excluding gaps/missing data) 527 537 382 385 No. of Polymorphic sites (S) 116 144 24 46 Total number of mutations (Eta) 126 153 24 49 No. of haplotypes (h) 12 12 4 5 Haplotype (gene) diversity (Hd) 0.77 0.818 0.339 0.261 Variance of haplotype diversity 0.00259 0.00239 0.01081 0.0113 Standard deviation of haplotype diversity 0.051 0.049 0.104 0.106 Nucleotide diversity (Pi) 0.04329 0.06949 0.01381 0.0123 Theta (Per site) from Eta 0.05766 0.0702 0.0156 0.03241 Theta (Per site) from S, Theta-W 0.05308 0.06607 0.0156 0.03042 Variance of Theta (no recombination) 0.0002643 0.0004173 0.0000312 0.0001057 Standard deviation of theta (no recombination) 0.01626 0.02043 0.00559 0.01028 Variance of Theta (Free recombination) 0.0000243 0.0000303 0.0000101 0.0000201 Standard deviation of theta (Free recombination) 0.00493 0.00551 0.00318 0.00449 Finite site model Theta per site from Pi 0.04594 0.07659 0.01407 0.0125 Theta (per sites) from S 0.06082 0.07828 0.01619 0.03272 Theta (Per sites) from Eta 0.06174 0.07631 0.01588 0.03364 Average nucleotide differences, (k) (no recombination) 22.813 37.318 5.274 4.734 Stochastic variance of k (no recombination) Vst (k) 99.767 260.3 6.418 5.294 Sampling variance of k (no recombination) Vs(k) 5.92 16.973 0.428 0.392 Total variance of k (no recombination) 105.687 277.273 6.846 5.686 Stochastic variance of k (Free recombination) Vst (k) 7.604 12.439 1.758 1.578 Sampling variance of k (Free recombination), Vs(k) 0.435 0.777 0.113 0.113 Total variance of k (free recombination) V(k) 8.039 13.217 1.871 1.691 Theta (Per sequence) from S, Theta W 27.974 35.481 5.959 11.713 Variance of Theta (no recombination) 73.416 120.322 4.559 15.668

Variance of theta (Free recombination) 6.746 8.742 1.48 2.983

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Table 4.7: DNA Polymorphism in nuclear protein coding genes in Russell’s viper (Daboia russelli) Parameters C-mos RAG-1 BDNF NT3 Number of Sequences 27 26 26 26 Selected region 1-494 1-847 1-670 1-543 Total Number of sites (Excluding gaps/missing data) 346 847 670 477 No. of Polymorphic sites (S) 22 1 0 5 Total number of mutations (Eta) 22 2 0 5 No. of haplotypes (h) 3 2 1 2 Haplotype (gene) diversity (Hd) 0.145 0.077 0 0.077 Variance of haplotype diversity 0.00806 0.00486 0 0.00486 Standard deviation of haplotype diversity 0.09 0.07 0 0.07 Nucleotide diversity (Pi) 0.00511 0.00009 0 0.00081 Theta (Per site) from Eta 0.0165 0.00031 0 0.00275 Theta (Per site) from S, Theta-W 0.0165 0.00031 0 0.00275 Variance of Theta (no recombination) 3.77E-05 0.0000001 0 0.0000021 Standard deviation of theta (no recombination) 0.00614 0.00031 0 0.00145 Variance of Theta (Free recombination) 1.24E-05 0.0000001 0 0.0000015 Standard deviation of theta (Free recombination) 0.00352 0.00031 0 0.00123 Finite site model Theta per site from Pi 0.00514 0.00009 0 0.00081 Theta (per sites) from S 0.01714 0.00031 0 0.00276 Theta (Per sites) from Eta 0.01681 0.00031 0 0.00276 Average nucleotide differences, (k) (no recombination) 1.766 0.077 0 0.385 Stochastic variance of k (no recombination) Vst (k) 1.034 0.022 0 0.13 Sampling variance of k (no recombination) Vs(k) 0.081 0.002 0 0.01 Total variance of k (no recombination) 1.116 0.023 0 0.14 Stochastic variance of k (Free recombination) Vst (k) 0.589 0.026 0 0.128 Sampling variance of k (Free recombination), Vs(k) 0.045 0.002 0 0.01 Total variance of k (free recombination) V(k) 0.634 0.028 0 0.138 Theta (Per sequence) from S, Theta W 5.708 0.262 0 1.31 Variance of Theta (no recombination) 4.517 0.069 0 0.48

Variance of theta (Free recombination) 1.481 0.069 0 0.343

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4.3.3. Percent Identity Matrix: Percent Identity Matrix were created by MUSCLE tool for mitochondrial and nuclear protein coding genes of Russell’s viper (Daboia russelli), as shown in the line graphs.

120 100 80 60 40

Similarity 20 0

Species

Graph 4.35. Russell’s viper (Daboia russelli) NADH4 percent identity

105 100 95 90 85 Similarity 80 75

Species

Graph 4.36. Russell’s Viper (Daboia russelli) Cytochrome b percent identity

120 100 80 60 40

Similarity 20 0

Species

Graph 4.37. Russell’s viper (Daboia russelli) 12S rRNA percent identity

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100 100 100 100 100 100 100 100 99.88 120 100 80 60 40

Similarity 20 0 0 0 0 0 0 0 0 0 0

Species

Graph 4.38. Russell’s viper (Daboia russelli) 16S rRNA percent identity 105

100 95 90

Similarity 85 80

ND4 Cytb 12S rRNA 16S rRNA

Graph 4.39: Russell’s viper (Daboia russelli) combined mitochondrial genes percent identity

120 100 80 60 40 SImilarity 20 0

Species

Graph 4.40. Russell’s Viper (Daboia russelli) C-mos percent identity

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120 100 80 60 40

Similarity 20 0

Species

Graph 4.41. Russell’s Viper (Daboia russelli) RAG-1 percent identity

120 100 80 60

40 Similarity 20 0

Species

Graph 4.42. Russell’s viper (Daboia russelli) BDNF percent identity

120 100 80 60

40 Similarity 20 0

Speccies

Graph 4.43. Russell’s viper (Daboia russelli) NT3 percent identity

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100.2 100 99.8 99.6 99.4 99.2

Similarity 99 98.8 98.6 98.4

C-mos RAG-1 BDNF NT3 Species

Graph 4.44: Russell’s viper (Daboia russelli) combined nuclear genes percent identity

4.3.4. Pairwise Number of Differences DnaSP (v 5.0) was used to find the pairwise number of differences in mitochondrial and nuclear protein coding genes. 4.3.4.1. Pairwise Number of Differences in Russell’s viper (Daboia russelli) ND4 Gene

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Graph 4.45. Pairwise Number of Differences in Russell’s viper (Daboia russelli) ND4 Gene 4.3.4.2. Pairwise Number of Differences in Russell’s viper (Daboia russelli) Cytochrome b Gene

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Graph 4.46. Pairwise Number of Differences in Russell’s viper (Daboia russelli) Cytochrome b Gene 4.3.4.3. Pairwise Number of Differences in Russell’s viper (Daboia russelli) 12S rRNA Gene:

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Graph 4.47. Pairwise Number of Differences in Russell’s viper (Daboia russelli) 12S rRNA Gene 4.3.4.4. Pairwise Number of differences in Russell’s viper (Daboia russelli) 16S rRNA Gene:

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Graph 4.48. Pairwise Number of Differences in Russell’s viper (Daboia russelli) 16S rRNA Gene 4.3.4.5. Pairwise Number of Differences in Russell’s viper (Daboia russelli) C-mos Gene:

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Graph 4.49. Pairwise Number of Differences in Russell’s viper (Daboia russelli) C-mos Gene 4.3.4.6. Pairwise Number of Differences in Russell’s viper (Daboia russelli) RAG-1 Gene:

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Graph 4.50. Pairwise Number of Differences in Russell’s viper (Daboia russelli) RAG-1 Gene 4.3.4.7. Pairwise Number of Differences in Russell’s viper (Daboia russelli) BDNF Gene: NO polymorphism was found in Brain Derived Neurotrophic Factor (BDNF) gene, so software did not compute pairwise number of Differences. 4.3.4.8. Pairwise Number of Differences in Russell’s viper (Daboia russelli) NT3 Gene

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Graph 4.51. Pairwise Number of Differences in Russell’s viper (Daboia russelli) NT3 Gene 4.3.5. Phylogenetic analyses for Russell’s viper (Daboia russelli): In the present study, mitochondrial and nuclear protein coding genes were used to construct maximum likelihood and Bayesian inference phylogenies in figure 4.5 and 4.6. Vipera seoanei (Portugal) was used as outgroup for all Russell’s vipers. Both the phylogenies showed almost similar topologies with (LnL= -3837.12 and InL= 3610.50707) dividing Daboia into three clades. The one clade included eastern Daboia and the other one was western Daboia. While the third clade comprised of Daboia deserti and D. mauritanica. Daboia russelli (Western) and Daboia siamensis (Eastern) showed strong support (ML BS= 100, BI PP= 1) for sister group relationship among them in maximum likelihood analysis but weak support was found for sister group relation between Daboia deserti and D. mauritanica group with eastern and western Daboia. The eastern clade included Daboia from Cambodia, Myanmar, Thailand, China, Taiwan and Indonesia. The western clade of Daboia included Daboia from Pakistan India and Sri Lanka. Two Daboia russelii samples were used in this study from Sanghar and Thatta Sindh Pakistan. Eastern and Western clades showed strongly supported sister group relationship (ML BS=100 and BI PP= 1) among them but relatively weaker support for their sister group relationship with the third clade. The similar divergence pattern was shown by Wuster et al. (1992). They conducted a study on population systematics of Russel’s viper through multivariate analyses. According to them Russell’s viper in one of the most venomous Asiatic snake ranging from Pakistan to Sri Lanka Taiwan and Southern Indonesia, India, Bangladesh, Burma, Thailand, western Cambodia and parts of mainland China.

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)

Daboia russelli Daboia

s vipers (

ikelihood Phylogeny for Russell’ for Phylogeny ikelihood

Figure 4.5: Maximum L 4.5: Maximum Figure

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)

ia russelli

Dabo

Figure 4.6: Bayesian Phylogeny for Russell’s viper ( viper for Russell’s Phylogeny 4.6: Bayesian Figure

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4.4. Data analyses for Saw-scaled Viper (Echis carinatus sochureki): 4.4.1. Analyses of Polymorphic Sites Analyses of polymorphic sites in the amplified regions were performed using DNaSP software in mitochondrial of Saw-scaled Viper (Echis carinatus sochureki). 4.4.1.1. Polymorphic Sites in Saw-scaled Viper (Echis carinatus sochureki) NADH dehydrogenase subunit 4 (ND4) Gene

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4.4.1.2. Polymorphic Sites in Saw-scaled Viper (Echis carinatus sochureki) Cytochrome b (Cyt.b) Gene

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4.4.1.3. Polymorphic Sites in Saw-scaled Viper (Echis carinatus sochureki) 12S ribosomal RNA (12S rRNA) Gene

Number of protein coding regions (exons): 0

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4.4.1.4. Polymorphic Sites in Saw-scaled Viper (Echis carinatus sochureki) 16S ribosomal RNA (16S rRNA) Gene

4.4.1.5. Polymorphic Sites in Saw-scaled Viper (Echis carinatus sochureki) Cytochrome Oxidase I (COI) Gene

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4.2.2. DNA Polymorphism: DnaSP software was used for detecting DNA polymorphism in mitochondrial and nuclear protein coding genes of saw-scaled viper (Echis carinatus sochureki) as shown in the Tables 4.7 and 4.8.

Table 4.7: DNA Polymorphism in mitochondrial genes of Saw-scaled Viper

Parameters ND4 Cyt.b 12S rRNA 16S rRNA COI Number of Sequences 51 30 52 52 29 Selected region 1-507 1-597 1-343 1-483 1-585 Total Number of sites (Excluding gaps/missing data 298 561 338 352 305 No. of Polymorphic sites (S) 121 185 98 63 175 Total number of mutations (Eta) 159 234 131 79 140 No. of haplotypes (h) 26 21 22 21 24 Haplotype (gene) diversity (Hd) 0.764 0.915 0.767 0.727 0.0464 Variance of haplotype diversity 0.00438 0.00212 0.00394 0.00458 0.04 Standard deviation of haplotype diversity 0.066 0.046 0.063 0.068 0.02 Nucleotide diversity (Pi) 0.09451 0.10564 0.06942 0.04215 0.0876 Theta (Per site) from Eta 0.11859 0.10529 0.08577 0.05379 0.00923 Theta (Per site) from S, Theta-W 0.09025 0.08324 0.06416 0.0429 0.08509 Variance of Theta (no recombination) 0.0006676 0.00067 0.000343 0.0001627 0.000556 Standard deviation of theta (no recombination) 0.02584 0.02606 0.01851 0.01276 0.01954 Variance of Theta (Free recombination) 0.0000673 0.00003 0.000042 0.0000292 0.000056 Standard deviation of theta (Free recombination) 0.0082 0.00612 0.00648 0.0054 0.00694 Finite site model Theta per site from Pi 0.10814 0.12297 0.0765 0.04466 0.09231 Theta (per sites) from S 0.11723 0.10316 0.07677 0.04819 0.010987 Theta (Per sites) from Eta 0.13772 0.11957 0.09536 0.05741 0.1098 Average nucleotide differences (k) (No recombination 28.164 59.267 23.463 13.7 22.56 Stochastic variance of k (no recombination) Vst (k) 150.774 646.31 105.719 37.586 144.56 Sampling variance of k (no recombination) Vs(k) 6.197 6.734 4.257 1.511 5.761 Total variance of k (no recombination) 156.971 153.043 109.975 39.098 144.341 Stochastic variance of k (Free recombination) Vst (k) 9.388 9.651 7.821 4.567 8.251 Sampling variance of k (Free recombination), Vs(k) 0.376 1.362 0.307 0.179 0.298 Total variance of k (free recombination) V(k) 9.763 11.325 8.128 4.746 8.354 Theta (Per sequence) from S, Theta W 26.894 28.325 21.687 13.942 23.654 Variance of Theta (no recombination 59.283 55.324 39.126 17.19 54.156

Variance of theta (Free recombination) 5.977 5.912 4.799 3.085 4.943

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Table 4.8: Polymorphism in DNA Polymorphism in nuclear DNA Genes in Saw-scaled Viper (Echis carinatus sochureki) Parameters C-mos RAG-1 BDNF NT3 Number of Sequences 25 25 25 25 Selected region 1-494 1-847 1-670 1-543 Total Number of sites (Excluding gaps/missing data) 346 847 670 477 No. of Polymorphic sites (S) 0 0 0 0 Total number of mutations (Eta) 0 0 0 0 No. of haplotypes (h) 1 1 1 1 Haplotype (gene) diversity (Hd) 0 0 0 0 Variance of haplotype diversity 0 0 0 0 Standard deviation of haplotype diversity 0 0 0 0 Nucleotide diversity (Pi) 0 0 0 0 Theta (Per site) from Eta 0 0 0 0 Theta (Per site) from S, Theta-W 0 0 0 0 Variance of Theta (no recombination) 0 0 0 0 Standard deviation of theta (no recombination) 0 0 0 0 Variance of Theta (Free recombination) 0 0 0 0 Standard deviation of theta (Free recombination) 0 0 0 0 Finite site model Theta per site from Pi 0 0 0 0 Theta (per sites) from S 0 0 0 0 Theta (Per sites) from Eta 0 0 0 0 Average nucleotide differences, (k) (no recombination) 0 0 0 0 Stochastic variance of k (no recombination) Vst (k) 0 0 0 0 Sampling variance of k (no recombination) Vs(k) 0 0 0 0 Total variance of k (no recombination) 0 0 0 0 Stochastic variance of k (Free recombination) Vst (k) 0 0 0 0 Sampling variance of k (Free recombination), Vs(k) 0 0 0 0 Total variance of k (free recombination) V(k) 0 0 0 0 Theta (Per sequence) from S, Theta W 0 0 0 0 Variance of Theta (no recombination) 0 0 0 0 Variance of theta (Free recombination) 0 0 0 0

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4.4.3. Percent Identity Matrix: Percent Identity Matrix were created by MUSCLE tool for mitochondrial genes of Saw-scaled Viper (Echis carinatus sochureki), as shown in the line graphs. 120 100 80 60 40 Similarity 20 0

Graph 4.52. Saw-scaled Viper (Echis carinatus sochureki) NADH4 percent identity

120 100 80 60

40 Similarity 20

0

Echiscarinatus…

AJ275708Echis… AJ275707Echis… AJ275709Echis…

KX233722Echis…

GQ359463Echis… GQ359462Echis… GQ359435Echis… GQ359439Echis… GQ359437Echis… GQ359440Echis… GQ359438Echis… GQ359434Echis… GQ359436Echis… GQ359482Echis… GQ359481Echis… GQ359453Echis… GQ359455Echis… GQ359460Echis…

GQ359477_Echis…

Echis_carinatus_2_P… Echis_carinatus_3_P… Echis_carinatus_4_P… Echis_carinatus_5_P… Echis_carinatus_6_P… Echis_carinatus_7_P… Echis_carinatus_8_P… Echis_carinatus_9_P… Echis_carinatus_1_P… Graph 4.53. Saw-scaled Viper (Echis carinatus sochureki) Cytochrome b percent identity

105 100 95 90 85

Similarity 80

75

Echis_carinatus_soch…

Echis_pyramidum_py… Echis_carinatus_carin…

Echis_ocellatus_Cam… Echis_ocellatu_Niger Echis_leucogaster_M… Echis_leucogaster_M… Echis_carinatus_soch… Echis_carinatus_soch… Echis_carinatus_soch… Echis_carinatus_soch…

Echis_jogeri_Senegal Echis_ocellatus_Niak…

Echis_leucogaster_Ni… Echis_leucogaster_Se…

Echis_carinatus_carin…

Echis_pyramidum_ali…

Echis_ocellatus_Togo Echis_leucogaster_Tu… Echis_coloratus_Saud…

Echis_coloratus_Israel

Echis_ocellatus_Benin

Echis_coloratus_Egypt

Echis_coloratus_Oman

Echis_omanensis_UAE

Echis_leucogaster_Mali

Echis_coloratus_Yemen Echis_omanensis_Oman Cerastes_cerastes_Egypt Graph 4.54. Saw-scaled Viper (Echis carinatus sochureki) 12S rRNA percent identity

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105 100 95 90

Similarity 85

80

Echis_ocellatu…

Echis_pyramid…

Echis_coloratu…

Echis_ocellatu… Echis_ocellatu… Echis_ocellatu… Echis_ocellatu…

Echis_jogeri_S…

Echis_carinatu… Echis_carinatu… Echis_carinatu… Echis_carinatu… Echis_carinatu… Echis_carinatu… Echis_carinatu… Echis_carinatu… Echis_carinatu… Echis_carinatu…

Echis_omanen… Echis_omanen…

Echis_pyramid…

Echis_coloratu… Echis_coloratu… Echis_coloratu… Echis_coloratu…

Echis_leucogas… Echis_leucogas… Echis_leucogas… Echis_leucogas… Echis_leucogas… Echis_leucogas… Cerastes_ceras… Species

Graph 4.55. Saw-scaled Viper (Echis carinatus sochureki) 16S rRNA percent identity

100.05 100 99.95 99.9 99.85 99.8 99.75 Similarity 99.7 99.65 99.6

Species Graph 4.56. Saw-scaled Viper (Echis carinatus sochureki) COI percent identity

105 100 95 90 85

Similarity 80 75

12S 16S COI ND4 Cytb

Graph 4.57: Saw-scaled Viper (Echis carinatus sochureki) combined nuclear genes percent identity

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120 100 80 60 40 Similarity 20 0 Echis Echis Echis Echis Echis Echis Echis carinatus 1 carinatus 2 carinatus 3 carinatus 4 carinatus 5 carinatus 6 carinatus 7

Species

Graph 4.58. Saw-scaled Viper (Echis carinatus sochureki) C-mos percent identity

120

100

80

60

Similarity 40

20

0 Echis Echis Echis Echis Echis Echis Echis carinatus 1 carinatus 2 carinatus 3 carinatus 4 carinatus 5 carinatus 6 carinatus 7 Species

Graph 4.59. Saw-scaled Viper (Echis carinatus sochureki) RAG-1 percent identity

120

100

80

60

Similarity 40

20

0 Echis Echis Echis Echis Echis Echis Echis carinatus 1 carinatus 2 carinatus 3 carinatus 4 carinatus 5 carinatus 6 carinatus 7 SPECIES

Graph 4.60. Saw-scaled Viper (Echis carinatus sochureki) BDNF percent identity

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120 100 80 60

Similarity 40 20 0 Echis Echis Echis Echis Echis Echis Echis carinatus 1 carinatus 2 carinatus 3 carinatus 4 carinatus 5 carinatus 6 carinatus 7 Species

Graph 4.61. Saw-scaled Viper (Echis carinatus sochureki) C-mos percent identity

200

150

100 Similarity 50

0 63 79 21 0.727 0.00458 0.068 0.04215

305 Total Number of sites (Excluding gaps/missing data) 346 847 670

Graph 4.62: Saw-scaled Viper (Echis carinatus sochureki) combined nuclear genes percent identity

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4.4.4. Pairwise Number of Differences: DnaSP (v 5.0) was used to find the pairwise number of differences in mitochondrial and nuclear genes. 4.4.4.1. Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus sochureki) ND4 Gene:

Graph 4.63. Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus sochureki) ND4 Gene

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4.4.4.2. Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus sochureki) Cytochrome b Gene:

Graph 4.64. Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus sochureki) Cytochrome b Gene

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4.4.4.3. Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus sochureki) 12S rRNA Gene

Graph 4.65. Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus sochureki) 12S rRNA Gene

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4.4.4.4. Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus sochureki) 16S rRNA Gene:

Graph 4.66. Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus sochureki) 16S rRNA Gene

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4.4.4.5. Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus sochureki) COI Gene:

Graph 4.67. Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus sochureki) COI Gene 4.4.4.6. Pairwise Number of Differences in Saw-scaled Viper (Echis carinatus sochureki) nuclear genes No polymorphism was found in C-mos, RAG-1, BDNF and NT3 genes, so software did not compute pairwise number of differences.

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4.4.5. Phylogenetic Analyses for Saw-Scaled Viper (Echis carinatus sochureki): In this study, Maximum likelihood (ML) and Bayesian phylogenies (BI) were constructed for saw scaled viper from Pakistan. The mitochondrial and nuclear genes were used for the analyses. Cerastes cerastes (Egypt) was used as an out-group for all Echis that showed a well-supported sister group relationship to Genus Echis. Maximum likelihood and Bayesian phylogenies showed almost similar topologies with best likelihood score (InL= -4498.85875) and (LnL= -6143.527). All Echis were divided into four distinct clades. These clades were Echis carinatus, E. ocellatus, E. pyramidum and E. coloratus. Although interrelationship among the four clades was not found to be very well resolved. While Echis pyramidum and Echis coloratus groups showed well supported sister group relationship through (ML BS=91 and BI PP= >98). On the other hand, both E. pyramidum and E. coloratus did not show well supported relationship with Echis carinatus and Echis ocellatus. Barlow et al. 2009 used mitochondrial and nuclear genes finding the same four clades of Echis with the same interrelationship among them. They inferred Bayesian phylogenies that showed Echis carinatus as sister to all other three Echis clades using cytochrome b, ND4, 12S rRNA, 16S rRNA and RAG-1. They used only one representative of all four Echis species and found highly supportive posterior probability value for monophyly of E. coloratus, E. pyramidum and E. ocellatus. Echis coloratus and E. pyramidum showed strong support for (ML BS=91, BI PP= 0.98) sister group relationship among them. Furthermore, both the groups (coloratus and pyramidum) showed low support values for sister relationship with Echis ocellatus (ML BS=58, BI PP= 0.5966.

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)

Echis carinatus sochureki carinatus Echis

scaled Viper (

-

Figure 4.7: Maximum Likelihood Phylogeny for Saw for Phylogeny Likelihood 4.7: Maximum Figure

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Echis carinatus sochureki carinatus Echis

scaled Viper ( Viper scaled

-

Figure 4.8: Bayesian Phylogeny for Saw Phylogeny 4.8: Bayesian Figure

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CHAPTER 5 DISCUSSION

Complex evolutionary models for sequence enable us to get phylogenetic signal from the given data in situations like where base substitution is common (Castoe et al. 2005; Castoe and Parkinson 2006). In this study, twenty five samples of four species of elapids and viperids (Common krait. Polymerase chain Reaction (PCR) primers were designed through Primmer3 tool using different accession numbers from NCBI database. Some already reported primers were also used for the amplification of mitochondrial (ND4, Cyt.b, 12S rRNA, 16S rRNA, COI) and nuclear protein coding genes (C-mos, RAG-1, BDNF, NT3). The amplified sequences were assembled and cleaned through Sequencher (v 5.0) and aligned through MEGA software. Due to unknown reason of short read length and poor quality base-calls, COI and BDNF sequences were not included in the analysis. The alignments were used for analyses of polymorphism and phylogenetic tree construction. DnaSP was used for the analyses of polymorphic sites, DNA polymorphism and pairwise number of differences. Homology analyses between different species of the same genera were conducted through online tool MUSCLE and presented in the form of line graphs. For phylogenetic analyses, concatenated nucleotide data through SequenceMatrix software was partitioned through PartitionFinder software that gave best partition scheme and evolutionary models. The models were used for the construction of maximum likelihood and Bayesian phylogenetic trees through RaxML (v 8.0) and MrBayes (v 3.2) softwares. 5.1. Common Krait (Bungarus caeruleus): Genus Bungarus also known as “kraits” are moderate to large sized elapids distributed in Pakistan eastward through southern Asia to Indonesia. A mid-dorsal row of enlarged hexagonal scales usually distinguish the kraits from other elapids found on terrestrial habitat. (Smith, 1943). Furthermore, the kraits are characterized by distinct vertebrae having prezygapophysial and postzygaphysial processes and high neural processes (Hoffstetter and Gasc, 1969). These raised neural processes make the raised mid-dorsal ridge that gives a characteristic triangular appearance in cross section. This proves Bungarus as monophyletic. No other elapids have such raised appearance (Hoffstetter and Gasc, 1969). Out of 2500 species of snakes, elapids make 300 of them (Golay et al. 1993b). Uncertain phylogenetics of elapids has been a major factor for varying number of identified species of elapids in the past (Cogger, 1985). Africa, Australia, America, Asia and many of the oceanic islands have been reported as the habitat of elapids with most abundantly found in tropical and subtropical areas. Elapids include African mambas, Afro Asian cobras, American coral snakes, Australian taipans, death adders, brown snakes and sea snakes. Southern Asian elapids include cobras (Naja and Ophiophagus), Kraits (Genus Bungarus), Maticora (long-glanded snakes), Calliophis (Asian coral snakes) (Minton, 1981). This study was aimed to characterize Bungarus caeruleus as there is more unpublished data on Bungarus caeruleus. Here mitochondrial and nuclear protein coding genes were used to construct phylogeny of common krait (Bungarus caeruleus) from Pakistan that also includes morphological characterization. One of the characters is the number of ventral scales that ranges from 207-218. All the snakes were collected from different cities of Pakistan. Khan, (1985) wrote note on taxonomic status of common krait (Bungarus caeruleus) (Schineider) and Sindh krait (Bungarus sindanus) comparing 46 specimens of bungrine snakes. Out of these 46 snakes, 37 were Bungarus caeruleus and 10 were Bungarus sindanus. Khan also noted almost similar range of ventral scales of 207-218. The Bungarus caeruleus in this study has 15 rows of mid-body scales which is same as described by Khan 1985. B. caeruleus and B. sindanus has 15 and 17 rows of mid-body scales with the central larger one being hexagonal and white in color. Average number of subcaudals observed is 41-47 which is somewhat within the range to that observed by Khan, (1985). He observed 40-54 in males and 30-54 in females. Boulenger, (1896) also observed 15 rows of mid-body scales in Indo-Pakistan common krait while 17 mid-body scale rows were reported in Bungarus sindanus from Indus Basin. Boulenger, (1897) reported small eyes with round pupil which is the like that of used in this study. There are also the reports about the distribution of Bungarus caeruleus in Indo-Pakistan subcontinent. Eastward it is found in Assam and Bengal (Wall, 1908; Whittaker, 1978), Westward to Pakistan Border, Shockley, (1949) and Karl, (1969) reported it in , Smith, (1943) to Southward in Peninsular India and Andaman Islands. In 1981, de Silva also reported it in

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Sri-Lanka. This study is one attempt to infer phylogenetics of Bungarus caeruleus in Pakistan but suggest more studies from the above-mentioned parts of the B. caeruleus distribution as there are almost no studies from other parts of Indo-Pakistan subcontinent on phylogenetics of common krait Bungarus caeruleus. Venomous snakes in Southeast Asia belong to Elapidae (Cobras and Kraits) and Vipers (typical vipers and pit vipers). Kraits, identified by alternating black and white cross-bands across body are found in all South Asian countries except Philippines. Currently, 12 species of kraits are recognized in the world (Slowinski, 1994a). In Pakistan, three species of kraits are identified. Common kraits (B. caeruleus) are reported throughout Punjab, Khyber Pakhtoonkhwa (KPK), Azad Kashmir, Sindh and Southern Balochistan. Common in Indus valley, this is the only species of kraits found in Rawalpindi and Islamabad (Khan, 2002a). Sindhi krait (B. sindanus) is prevalent in Tharparkar, Bahawalnagar and Bahawalpur. Northern Punjab krait (Bungarus s. razai) is reported from Mianwali (Khan, 2002b). Study of admitted snakebite cases in Pakistan revealed less than 5% neurotoxic snakebites, rest were viper bites (Nisar et al. 2009). The relationship among the members of Elapidae can help in understanding of distribution and diversity of elapids. Many studies have been focusing on evolutionary relationships of elapids. This study is one of those studies that focused on the molecular phylogenetics of Pakistani Bungarus caeruleus. The best partition scheme and evolutionary models were used to construct character based maximum likelihood and Bayesian phylogenies to infer the phylogenetic relationship of Pakistani Bungarus caeruleus with other members of genus Bungarus. Maximum likelihood and Bayesian Inference gave almost similar phylogenies. All Bungarus species were divided into four main clades. The first clade included Bungarus candidus (Indonesia, Thailand, Veit-nam, and Laos), Bungarus multicinctus (China, Taiwan, and Burma) and Bungarus niger (Nepal). The second clade included Bungarus sindanus, Bungarus ceyloniucs and Bungarus caeruleus (Sri Lanka and Pakistan) respectively. The third clade included Bungarus fasciatus (Thailand and Indonesia). While the fourth clade included Bungarus bungroides (China) and Bungarus flaviceps (Malaysia and Indonesia). The first and second clade showed sister clade relationship with strong support (ML BS=100, BI PP= 1). Bungarus candidus and B. multicinctus probably diverged recently as separate species. Bungarus candidus, B. multicinctus and Bungarus niger showed highly supported sister relationship with complex pattern of divergence.BI (PP=1.0), ML (BS= 90). The second clade including Bungarus sindanus, Bungarus caeruleus (Pakistan) and Bungarus ceyloniucs (Sri Lanka) from Pakistan and Sri Lanka. Bungarus sindanus and B. caeruleus have been reported from Pakistan thus both being sympatric species while B. ceyloniucs also showed significant difference with strong support through Maximum likelihood and Bayesian inference phylogenies (PP= 1.0 and BS= 100) Pyron et al. 2012 also revealed the same relationship between Bungarus caeruleus and Bungarus sindanus and Bungarus ceyloniucs. Pyron et al. presented a large-scale phylogeny of squamate reptiles for future comparative studies. They revised classification of squamates at family and subfamily level so that taxonomy might be brought in a line with new phylogenetic studies. Their phylogeny shows the same relationship (ML BS= 100, BI= 1) between Bungarus caeruleus, B. sindanus and B. ceyloniucs as it is shown in this study through Maximum Likelihood and Bayesian phylogenies based on mitochondrial genes. Kraits are found in the whole south Asia except Philippines. Cobras have been the dangerous snakes in Indian sub-continent (Ahuja and Singh 1956). Twelve species are currently recognized (Slowinski, 1994b; David and Ineich 1999) the majority of which are rare and poorly understood. Species causing thousands of snake bites throughout the region. Commonly krait (Bungarus caeruleus), Sindh Krait (B. sindanus) and B. walli in Pakistan, India, and Nepal. In Sri Lanka, the common krait (B. caeruleus) is widely distributed throughout the dry zone. 5.2. Black Cobra (Naja naja): Pakistan landscape has a wide range of variation including fertile plains to deserts, forests, mountains, plateaus and coastal lines. Extreme diverse bioclimatic and topographic profile has led to the creation of multifarious habitats cultivating very unique flora and fauna that have a blend of Palaearctic, Indomalaya and Ethiopian forms (Khan, 1999). This blend has introduced many venomous snakes in Pakistan. These venomous snakes have great medical importance. Some of these snakes are shared with Indian subcontinent and some of them are unique endemic species or some overlap with Middle Eastern and Himalyan species (Khan, 2002). In Pakistan, farmers face cobras during their agricultural activities.

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According to (Khan, 2002) there are two species of cobra in Pakistan. One of them is Naja naja also known as spectacled cobra. The spectacled cobra is distributed across Southern and estern Pakistan that includes Punjab, Baluchistan and Sindh provinces. The other species of cobra known as Naja oxiana also known as brown ox cobra is restricted to northern Pakistan at the areas at high elevation. In the present study, cobras from Punjab, Sindh and Baluchistan were used for phylogenetic study through mitochondrial and nuclear genes. These genes based maximum likelihood and Bayesian phylogenies have shown that cobra from Baluchistan is Naja oxiana while cobras from Sindh and Punjab has revealed them as Naja naja. As Khan, (2002b) has proposed the presence of Naja oxiana in Punjab, Sindh and Baluchistan present study suggests further sampling from Punjab and Sindh as these was no Naja oxiana found from these two provinces. Eleven samples for Naja naja from Punjab and Sindh but not even one of them has shown any kind of variation to be as Naja oxiana. One sample of Naja naja from California academy of Sciences USA had been collected from Khuzdar Baluchistan has been proved to be as Naja oxiana as it claded with Naja kaouthia (Thailand) and Naja siamensis (Thailand) while Naja from Sindh and Punjab fall into the same clade with Naja naja from Nepal. As Naja naja from Sindh and Punjab did not shown significant variation from each other and proved to be the same species when uncorrected p-distance was calculated, so in this study only used Naja naja sequences from Sindh province. This suggests that there is more need of sample collection from Punjab and Sindh so that the presence of Naja oxiana may be proved to be as suggested by Khan (2014). The one that was called as Naja naja from Baluchistan is cladding with Naja kaouthia and Naja siamensis. Wuster, (1994) reported Naja naja naja (Spectacled or binocellate cobra) to be found throughout India, Pakistan, Sri Lanka and Bangladesh. Naja naja kaouthia (monocellate or monoclad cobra) were reported in north-eastern India (Gangetic Plain, Orissa Bengal Asssam) as well as from Bangladesh, Malaysia, southern Veitnam and south-western China. Naja naja oxiana (Central Asian cobra) was reported from Kashmir and some areas of north-western India. Furthermore, N. n. sagittifera (Andaman cobra) was reported from Andaman Islands (Wuster, 1998). Asiatic cobras of genus Naja make a complex and widespread group of venomous snakes. They have many years of controversial systematics. Many researchers thought them to belong a single species, N. naja (Boulenger, 1896: Golay, 1985: Klemmer, 1963: Leviton, 1968) but recently taxa from Indonesia and Malayan Peninsula have been assigned as full species, N. sumatrana and N. Sputatrix (Wuster and Thorpe 1989). Two endemic Philippine taxa samarensis and Philippinensis are also full species (Wuster and Thorpe, 1990). There is much less confusion in and Indian subcontinent about the systematics of Naja than anywhere else. Recently three subspecies were recognized of Naja naja by (Golay, 1985: Harding and Welch 1980: Klemmer, 1963: Leviton, 1968: Whitker, 1978). The populations with spectacle-shaped hood mark from India, Pakistan and Sri Lanka are named as Naja naja naja (Linnaeus, 1758). The other populations of Naja from North-Estern India, Burma, Thailand, Indo-china and North Malaysia with monocellate hood mark are named as Naja naja Kaouthia (Lesson, 1831). Soviet central Asia, Iran Afghanistan and Northern Pakistan has Naja naja oxiana without a hood mark (Eichwald, 1831). The absence of hood mark had been observed in many specimen of N. n. oxiana from northern and western parts of India. This absence is made a base of assigning them as N. n. oxiana but in some parts of Indian except Kashmir, the presence of this subspecies is not accepted universally (e.g., jogar 1984). The Naja in this study (Naja naja from Baluchistan) had 68 or 69 subcaudals within the oxiana count. It has no hood markings above or below, like oxiana. The specimen has 200 ventrals (and 4 pre-ventrals), well beyond the Naja naja range. Also, oxiana tends to have weak bands on the dorsum and venter. The others should be Naja naja, Naja naja 5 from Faisalabad Punjab Pakistan has 191 ventrals and looks like a male; 191 is just the count you can expect for male or female Naja naja. Juveniles of these many times are obscured in their markings. Derniyagala, (1945, 1960, 1961) assigned both Naja oxiana and Naja Kaouthia as full species reviewing the systematics of Asiatic cobras. He also restricted the type locality of Naja naja to Sri Lanka and described 5 subspecies. 1: Naja naja from Indian subcontinent, 2: Naja naja madrasiensis South India 3: Naja naja gangetica from northeast, 4: Naja naja Indusi from Punjab, karachensis from southern Pakistan and northwestern India, 5: Naja naja bombaya from and neighboring areas. Naja naja is found throughout India, Pakistan, Nepal and Bangladesh while Naja oxiana is found in Soviet central Asia, northeastern Iran, Afghanistan, Northern Pakistan and northwestern India. Delhi east to

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Assam and south to Vietnam and northern Malaysia has Naja Kaouthia. They found central Asian and Indian sub-continental cobras and monocellate cobras from Indochina as three well defined taxa. Central Asian is clearly distinguishable from the other two spectacled and monocellate. Sympatry was found between Central Asian and Spectacled one with fairly large evidence. The spectacled cobra was found to occur in Chitral valley and Afghanistan. Chitral Valley drains into Kabul river valley at Jalalabad in Nangarhar province of Afghanistan. Because of mountains around Chitral valley, the only rout for species to reach is through Nangerhar and Konarha provinces of Afghanistan. One can assume the species dispersal during Pleistocene cold phase as these valleys had high altitudes with too low temperatures to support cobra populations. Therefore, the spectacled cobra may still occur in extreme eastern Afghanistan, from where it has not been recorded before. There are evidences about central Asian and Indian sub-continental cobras about their sympatric occurrence in some parts of northeastern Baluchistan from a cobra from Duki. Similarly, the Central Asian cobra has been found at Quetta and Sibbi and been reported from Suleiman Range (Joger, 1984: Minton, 1966). There are many reports that assign spectacled cobra as Naja naja naja and central Asian Taxon as Naja naja oxiana. But there is still a need of more careful studies as absence of hood mark in some spectacled taxon populations creates confusions. Khan, (1977, 1983) also observed sympatry between spectacled and central Asian cobra at Ahmed Nagar (Jhang Sadar District Punjab, Pakistan), in some parts of Punjab and Northwest Frontier Province. This is a clearer evidence that spectacled (Naja naja) and Central Asian cobra (Naja oxiana) are sympatric in many parts of northern Pakistan. As the two cobras are clearly distinguishable and sympatric, they are accepted as two species. Linnaeus, (1758) found the specimen of Naja naja with locality as India, but Deraniyagala, (1945) restricted its locality to Sri Lanka, and thus Indian spectacled cobra should be assigned as Naja naja. Eichwald in 1891 collected a central Asian cobra from Transcaspia (Soviet Central Asia) and named it as Tomyris oxiana which is the oldest name of central Asian cobra. Then the correct name was assigned as Naja oxiana. The range of N. oxiana from east is still not very well known. Joger in 1984 mentioned Gilgit as its east side locality but did not give any clue about its occurrence in India but only some northern Kashmir areas. Later, Murthy and Sharma (1976) and Murthy et al. (1979) collected specimens from Punch Vally, Northwest Jammu. Mahajan and Agrawal (1976) collected some immature specimens that had cross bands that increased the confidence that they were Naja oxiana. In areas like southern Pakistan, spectacled cobras show ontogenic change in their color as young are gray and with or without spectacled hood mark. But as they grow older the color darkens and develops more or less uniformly black dorsum. This ontogenic change leads many of the workers that assume adult belong to different taxa than the young one. Sundersingh, (1960) assigned a young specimen as Naja naja naja from Pilani Rajasthan. He assigned the other two adults as Naja naja oxiana as they were brown or black in color from Pilani Rajasthan. Biswas and Sanyal, 1977, also named the uniform brown cobra specimens from Rajasthan as Naja naja oxiana. The scale count of these Naja corresponds to those of Naja naja sensu stricto in Northern India. Thus, it shows that adult Naja oxiana are more or less uniformly medium or light brown in color Wuster and Thorpe (1992). On the other hand, some specimens of spectacled cobras from southern Pakistan have uniformly black dorsum and most of the Naja oxiana retain at least a trace of conspicuous juvenile banding pattern till the early adulthood at least. One of the Naja naja included in present study has 68 or 69 subcaudals and 200 ventrals (4 pre-ventrals) which is beyond the range of Naja naja. Minton, 1960 described Naja naja oxiana as more or less uniformly jet black, dark olive or dark brown with pale gray to butter yellow ventrals. The specimen from Baluchistan is brown in color while Naja naja from Sindh (NNS) and Punjab has dark black ventral side with some pale or yellow dorsal side near the head. Minton, 1960 the male specimen of Naja naja has 182-192 and female has 183-196 ventrals with 61-68 subcaudals in male and 57-62 in females. Naja naja has uniformly jet black, dark olive or dark olive brown above while ventrally, it is pale grey to butter yellow more or less heavily suffused with slate grey or dark brown uniformly jet black, dark olive or dark or dark brown above while ventrally, it is pale grey to butter yellow more or less heavily suffused with slate gray or dark brown from posterior and ventral side. Juveniles of Naja oxiana show a pattern of wide dark transverse bars. The adult is almost uniformly brown with no spectacles or ocellus mark on the hood. The ventral count of Naja oxiana ranges from 195 or more. The subcaudals are 62-70

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Discussion in females and male has 65-75 subcaudals. Minton collected a juvenile specimen from the areas around Peshawar and two specimens from Kach in Baluchistan Mountains between Quetta and Ziarat. Juvinile specimen had 198 ventrals and 66 subcaudals. The specimen from Kach had 202 ventrals and 65 subcaudals. This specimen showed almost the same counts as it has 68 or 69 subcaudals while it has 200 ventrals and 4 pre-ventrals. Naja naja specimen from Sindh and Punjab has 191 ventrals and looks as a male Naja naja which differentiates Naja naja from Naja oxiana. The other characteristic feature of Asian naja populations is the presence of one or many small scales between the edge of mouth and between fourth and fifth infra-labials. Sometimes they are between fifth and sixth infra-labials. According to Khan 1977, Naja oxiana lacks a cuneate scale unlike Naja naja. This has been observed in Soviet central Asian and Iranian populations but Naja from Pakistan, India and Afghanistan has a cuneate scale on each side of the head. Number of Maxillary teeth in Central Asian cobra also creates some confusion as N. oxiana has one solid maxillary tooth consistently. Bogert, (1943) describes N. oxiana having 2 solid maxillary teeth which is a misinterpretation of Eicfhwald’s original description. Deraniyagala, 1960 stated no solid tooth behind poison fang while Wuster and Thorpe (1992) reported one solid maxillary tooth in Naja oxiana. The sympatry of Naja naja and Naja oxiana is of great importance in treating snake bite and antivenin usage. The antigenic qualities of Naja oxiana and Asiatic Naja show major difference among neurotoxins (Karlsson and Eaker 1972). Kankonar et al. (1972) described about neutralizing effect of Indian polyvalent antivenom (Haffkine) from acellate cobras (they called as N. n. oxiana). Status of monocellate cobra is similar to Central Asian Cobra. Monocellate and Spectacled cobras are clearly distinct from each other morphologically and widespread sympatry is found between them. It is also clear that monocellate populations from Indo-china and northern Malaysia are part of same taxon as the monocellate from Northern India (Wuster and Thorp 1992). A wide range of localities have also been noted for monocellate and spectacled cobras in northern India. Many studies show the sympatry between spectacled and monocellate cobras as Fayrer, 1874 reported both cobras from Calcutta and Puruliya Bengal. In the present study, mitochondrial and nuclear genes were amplified and sequenced to infer the phylogenetic relationship of Naja naja in Pakistan. Mitochondrial and nuclear protein coding (NPC) genes were concatenated through SequenceMatrix software (v.1.7.8). The models of evolution were TrN+I+G, HKY+I and TrN. Using the concatenated nucleotide data, two-character based phylogenies i.e., maximum likelihood and Bayesian phylogenies were constructed using RaxML and MrBayes softwares respectively. One maximum likelihood phylogeny was constructed with InL= -8008.97556 and Bayesian phylogeny was constructed with LnL= -8045.494. Bungarus caeruleus 4 was used as outgroup in the study from Yazman Pakistan. The Naja naja used in this study were collected from different parts of Pakistan provinces like Punjab, Sindh and Baluchistan. The Naja naja from Baluchistan was provided by California Academy of Sciences, USA. Almost similar phylogenies were constructed by RaxML and Bayesian phylogenies dividing all the Pakistani Naja naja and Naja from other parts of the world that were already available on NCBI database into 3 major clades. Bayesian phylogeny showed strong support for the divergence of the three clades but maximum likelihood did not show any kind of support at basal node. The clade one includes Naja naja from Sindh and Baluchistan Pakistan and Nepal, Naja kaouthia and N. siamensis were from Thailand. The second clade included Naja pallida (Tanzania), N. nubiae and N. ashei (Kenya), Naja moambica (Mosambique) and Naja katiensis (Mali). The third clade consisted of only two species Naja haje from Mali, Saudi Arabia, Kenya and Egypt and Naja annulifera (Zimbabwe). Second and third clade has strongly supported sister group relationship (ML BS=85 and BI PP= 0.94). The first clade showed strong support for sister group relationship with both second and third clade strongly supported by Bayesian phylogeny (PP=1) but had not good support by Maximum likelihood phylogeny. The first clade consisting of Naja naja from Pakistan and Nepal showed recent divergence which is strongly supported by ML and BI (BS=100 and PP=1) while Naja siamensis and Naja kaouthia and Naja naja c/f from Baluchistan Pakistan showed deep divergence from Naja naja. Snake bites cause high mortality rates all over the world. In Pakistan, 20,000 annual deaths are reported due to snake envomation (Luzia et al. 2009). Snakes belonging to genus Naja are represented by two species in Pakistan: Naja naja naja (Indian cobra) and Naja naja oxiana (Brown cobra). Naja naja karachiensis (Black Pakistan cobra) is a subspecies of Naja naja oxiana and found widespread in southern

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Pakistan (Razi et al. 2011). Several enzymes present in cobra venom specifically act by degeneration of fibrinogen. The most important groups of these include metalloproteinases and serine proteinases. The metalloproteinases (α-fibrinogenase or β-fibrinogenase) act by physical cleaving of fibrinogen while serine proteinases may be thrombin like enzymes (fibrinogenolytic) or plasminogen activating that cleaves fibrin and fibrinogen (Kumar et al. 2010). Wuster et al. (2007) divided cobras of genus Naja into three distinct lineages. One of them was with two major sublineages. One clade was Asiatic clade comprising of Naja kaouthia, Naja naja and Naja siamensis and Naja sputatrix. African -non-spitting lineage made up of two sister lineages that includes Naja annulifera, Naje haje and Naja nivea. The other one includes Naja meanoleuca and Bolengerina annulata and paranaja mutifasciata. The third evolutionary lineage is African spitting cobras that comprise of Naja ashei, Naja katiensis, Naja mossambica, Naja nigricincta, Naja nigricollis, Naja nubiae and Naja pallida. Asiatic cobras of genus Naja are a widespread group of snakes ranging from Caspian Sea to China, Indonesia and Philippines. Many workers propose Asiatic Naja population as subspecies of single species Naja naja. Taxonomy of Naja have always been with confusion and controversy particularly the south-eastern i.e., Southern Thailand, Malaysia and Indonesia. De Rooij, (1917) and Haas, (1950) recognized not less than three subspecies from Sumatra and Borneo while two from Jawa and Sulawesi. On the other side, Klemer (1963), Leeviton, (1968) and Golay, (1983) recognized four subspecies from the same area. Lesson, (1931) recognized N.n. kaouthia (type localty: Bengal) ranging from India to northern Peninsular Malaysia. Boie, (1827) recognized N. n. sputatrix (type locality: Java) from Peninsular Malaysia, Java, Sulawesi, Bangka, Belitung, the Lesser Sunda Islands and the Riau Archipelago. Muller, 1890 recognized N. n. sumatrana (type locality: Solok, south-western Sumatra) from Sumatra and Boulenger (1896) recognized Naja species known as N. n. miolepis from Borneo and Palawan. In 1960 and 1961, Deraniyagala gave specific status to N. kaouthia as he found sympatry with Naja naja in Bengal and to N. sputatrix on the basis of some poorly substained and ill-defined characters. He classified N. kaouthia kaouthia in Thailand and northern Malaysia. N. sputatrix sputatrix in Sumatra and Java, N.n. malayaea in Peninsular Malaysia, N.s. miolepis in Borneo and N.s. celebensis in Sulawesi. He also suggested that there is a wide zone of introgression between N. kaouthia and N. sputatrix malayae in Peninsular Malaysia that made a contradiction to his earlier statement of proposing the two of them as spate species. Viravan et al. (1986) and Warrell. (1986) accorded N. kaouthia the specific status. Warrell, (1986) and Theakston (personal communication) found a heterogeneous group of spitting cobras in central and northern Thailand. These cobras were previously reported as N. n sputatrix. Bannerman and Pocha, (1905) and Sight, (1949) reported them in Medinipur West Bengal. A difference between the localities is also found such as monocellate cobras are also found in wetter areas than spectacled (Fayrer, 1874: Murthy, 1986: Sights, 1949 and Whittaker, 1978). This gives an unambiguous evidence that spectacled and monocellate cobras show sympatry but are clearly distinct from each other and has reproductive isolation naturally. Thus, they are separate species but mating during captivity and production of hybrids does not allow the distinction between the two distinct species (Campbel and Quinn, 1975). The authors report that parents were also sibling hybrids. Suggesting the weak reproductive isolation, Pro-copulatry reproductive isolating mechanisms can break in unnatural conditions producing fertile hybrids can be produced from interspecific mating for example in genus Crotalus (e.g., Arid et al. 1989). The oldest available name of monocellate cobra is Naja Kaouthia (Lesson, 1831). 5.3. Russell’s viper (Daboia russelii): In the present study, mitochondrial and nuclear genes were used to construct maximum likelihood and Bayesian phylogenetic trees. Vipera seoanei was used as outgroup. The basal node did not show any support for divergence of three clades. The one clade included eastern Daboia and the other one was western Daboia. While the third clade comprised of Daboia deserti and D. mauritanica. Daboia russelii and Daboia siamensis showed strong support for sister group relationship among them in maximum likelihood analysis but weak support was found for sister group relation between Daboia deserti and D. mauritanica group with eastern and western Daboia. The eastern clade included Daboia from Cambodia, Myanmar, Thailand, China, Taiwan and Indonesia. The western clade of Daboia included Daboia from Pakistan India and Sri Lanka. Two Daboia russelii samples were used in this study from Sanghar and

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Thatta Sindh Pakistan. The Bayesian and maximum likelihood analyses of combined sequence inferred phylogenetic trees with the same topology for Daboia russelli in this study. For all trees the primary split is between east and west clades have very strong node support. Previously, the discontinuous distribution of genus Daboia, lead to different number of subspecies as they were being described in different regions of the world. Harding and Welch 1980 and Leviton, (1968) recognized five subspecies of Russell’s viper. They were V. r. russelii that ranged from India, Pakistan and Bangladesh. The second one was V. r pulchella (Gray, 1842) from Sri Lanka. Third one V. r siamensis (Smith, 1917) from Burma, Thailand, Cambodia and southern China. V. r formosensis (Maki, 1931) from Taiwan and the fifth was V. r. limitis (Mertens, 1927) from Java, Komodo, Flores and Lomblen. Kopstein in 1936 studied Javan populations V. r. sublimitis while Deraniyagala, 1945 described northern Indian populations as V. r. nordicus. Kopstein’s findings about Javan population were supported by Hoesel, (1954, 1958) but many ignored that V. r. nordicus. In 1958, Brongersma suggested these subspecies were described primarily on the basis of morphological aspects like number of dorsal spoted pattern with few color pattern differences but pronounced similarities have been observed between some of the populations that have been assigned as different subspecies. Warrell, (1989) also confirmed these similarities in some of the population previously described as subspecies. Eastern and western clades showed strongly supported sister group relation among them but the divergence within the members of each clade is relatively low. This is the similar divergence pattern that was shown by Wuster et al. 1992. They conducted a study on population systematics of Russel’s viper through multivariate analyses. This study was based on scalation and color pattern characters. The viper population was divided into 2 clades. One of them was western form that included population from Indian subcontinent while other was eastern form that included populations from Eastern side of Bay of Bengal. Eastern and western taxa showed similar divergence pattern as that was found in the present study. Thorpe et al. (2007) studied the phylogeography of Russel’s viper. The study focused on the variations in color pattern and symptoms of envenoming. Based on cytochrome b, ND2 and ND4 genes sequencing data, they conducted Bayesian and maximum likelihood and parsimony phylogenetic analysis with similar topologies dividing all Daboia into eastern and western clades. Based on multivariate morphology, Wuster et al. (1992) named these subspecies, recognizing that more study was required before raising them to species status. This molecule based phylogeographic study fulfils the requirements thus leading to two forms to a full species status. Hence, in the west, D. r. russelii, sensu Wuster et al. (1992), is a species, D. russelii, and incorporates pulchella and nordicus, while in the east, D. r. siamensis, sensu Wuster et al. 1992, is raised to full species status, D. siamensis, and incorporates limitis, sublimitis and formosensis. The full synonomy is given in Wuster, (1998) and McDiarmid et al. (1999) and the basic colour pattern are diagnostic. Wuster et al. (1992) named these two eastern and western forms as subspecies and suggested the requirement of more studies. The present study is one addition to those studies that is based on mitochondrial genes. Thorpe et al. (2007) also added their part and named these two forms as two species. They suggested western D. r. russelii, sensu Wuster et al. 1992 to a full species status i.e., Daboia russelii incorporating pulchella and nordicus. They raised Eastern Daboia russelii siamensis sensu Wuster et al. (1992) to a full species as Daboia siamensis incorporating limitis, sublimitis and formosensis. West form, D. siamensis shows a patchy distribution with lack of divergence among them. The long undivided branches reflect the bottlenecking in the primary split of eastern and western Daboia. To time these time periods with a molecular clock looks problematic as only an approximation is needed with relative, rather than absolute times (Wuster et al. 2002). Thorpe et al. (2007) also suggested the simultaneous divergence of the D. siamensis approximately 2-3 myb probably during mainland range expansion. In the eastern side, Sunda shel exposed when at lower sea level (Karns et al. 2000). This exposure would have allowed the colonization of Java from the ancestors colonizing in China and Taiwan. The overland colonization of Java suggests about Malayan pitvipers and white lipped pitviper (Daltry et al. 1996; Giannasi et al. 2001) Taiwan has joined China at times over Pleistocene (Huang 1984). The discontinuous range of Russell’s viper has been under discussion for many years. The isolated populations from Indonesia are problematic as Russel’s viper has been reported from Sumatra and Java. The elicited comments on discontinuous range of Russel’s viper was based on some material from India (Brongersma, 1958). It was reported in Lesser Sunda Islands in 1927. Later, it was reported from Java by Neuhaus, 1935.

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The absence of Russel’s viper on Sumatra, Malayan Peninsula or Borneo suggests the isolation of Indonesian Russell’s viper over 200 km in Thailand. The Russel’s viper from Burmeses, Thai, China and Taiwan have also been found to have discontinuous localities. There are no records from Laos or Vietnam but only Cambodia reports near the border of Thailand (Saint Girons, 1972). On macro scale, western Russell’s viper has continuous distribution on Indian subcontinent but on the finer scale distribution is discontinuous and irregular (Smith, 1943). As Russell’s viper like to inhabit the open and dry areas avoiding rainforest explaining the absence in Malayan Peninsula, Sumatra, Borneo and Western Java as well as from Assam and western Burma mountains. This area separates Eastern and western forms of Russell’s viper geographically. Tsai et al. (1996) compared the Phospholipase A2 present in the venom of four commercially available venom samples through HPLC and partial sequence analysis. A difference in the Venom of Daboia russelli from South India and Sri Lanka and all others was the absence of heterodimeric PLA neurotoxin was found in Daboia russelii from South India and Sri Lanka. Previously, South India venom PLA2s of Russel’s viper has been found to be same as that of Daboia russelli puchella from Sri Lanka. On the other hand, venom from Daboia russelli russelli of Pakistan and D. russelli siamensis from Burma and Thailand was found to be similar to that of D. r. formosensis from Taiwan. Tsai et al. 1996 found the two types of Russell’s viper based on venom containing PLA2s. One type included Daboia russelli formosensis (Taiwan), D. r. siamensis (Thailand and Burma) and D. r. russelli (Pakistan) while other type was D. r. pulchella from South India and Sri Lanka. The present study has also found the difference with respect to mitochondrial genes as the maximum likelihood and Bayesian inference phylogenies clearly differentiated the Daboai russelli from Pakistan from those from South India and Sri Lanka. But these phylogenies could not find any support for interrelationship of Daboia russelli of Pakistan and D. r siamensis from Burma, Thailand and Taiwan. Thorpe et al. (2007) and Wuster et al. (1992) had clearly shown the difference in Daboia russelii from East and west and assigned them as Daboia russelli and Daboia siamensis. Bio-geographically, Dabia russelii from North India shows closer relationship to that of Pakistan, Thailand and Burma as compared to that from Southern India. Deserts, pleatues and oceans show the independent evolution of D. r. pulchella. This relationship is confirmed through present study as South Indian and Sri Lankan Daboia russelii shows distinct sister group relationship with that from Pakistan but even then, cladding in the same clade of western Daboia (Daboia russelli). But more studies involving venom and mitochondrial genes are required to clearly differentiate between the Daboia russelli populations from south Indian and Sri Lanka. A much better addition can be the studies involving Daboia russelli from North of India and southwestern region of Pakistan (Baluchistan). The southwestern region of Pakistan is one of the main partitions of Pakistan as Pakistan has been divided into three main geographical regions. One is northern highlands; Indus River plain while other two including provinces of Punjab and Sindh and Balochistan Plateau. More morphological and phylogenetic studies based on mitochondrial and nuclear gene are required as there has been a clear-cut difference in both the Daboia found in Pakistan and South India as separate species status with more number of samples. 5.4. Saw Scaled Viper (Echis carinatus sochureki): Echis, the Saw-scaled vipers, are found in a very large area from West Africa to India, including most of the countries. Their systematics and taxonomy has been a discussion for decades. Klemmer (1963) recognized only two species: Echis carinatus in most of the range and E. coloratus in Arabia. Joger (1984, 1987) added E. pyramidum for southwestern Arabia. Cherlin, (1990) described many new species and subspecies and increased the total number of Echis species significantly. A new species within the E. coloratus group was described by Babocsay, (2004). Pook et al. (in press), using molecular genetic methods, have now clarified the complicated situation. Based on their results and additional data, they recognized the following six species in the Near and Middle East: Echis carinatus group (Asian group): E. (carinatus) sochureki (Oman, UAE, Iran, Central Asia, Afghanistan, Pakistan) Echis coloratus group (Arabian group): E. coloratus (Egypt, Arabian Peninsula) E. omanensis (Oman, UAE) Echis pyramidum group (one of two African groups): E. pyramidum (Egypt, Sudan, East Africa) E. khosatzkii (western Oman, Yemen) E. sp. (cf. borkini) (Yemen, SW Saudi Arabia). E. borkini was originally described as a subspecies of the East African E. varia by Cherlin, 1990. As they did not find a close phylogenetic relationship between Echis populations from Yemen and Ethiopia, they

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Discussion consider borkini a separate species. There is a strong zoogeographical division in Arabian Echis, E. sochureki and E. omanensis being found in the eastern part of the peninsula only, whereas southwestern Arabia (including Dhofar province, Oman) is inhabited by E. coloratus, E. khosatzkii and E. cf. borkini (see also Joger, 1987). As Echis bites frequently cause death and successful bite treatment depends on choosing a species-specific antivenom (if available), it is of great importance to know which species of Echis occur in which area. There is still need for additional research in this genus. It is also time for an effort to study the venom of species like E. khosatzkii and E. cf borkini, and start production of antivenom against their bites. The present study gave phylogenies of Echis. Previously, there has been confusion in taxonomy of this genus but this study gave an important phylogenetic key for interpretation of previous hypothesis. Being a complex genus, there is a lot to unfold for comprehensive understanding of phylogenetics of this genus. Mitochondrial and nuclear genes were used in this study to infer the molecular phylogenetics of Saw scaled viper (Echis carinatus) from different parts of Pakistan. The sequences were used to infer phylogenetic relationship of Pakistani Echis carinatus with other species of genus Echis. Maximum likelihood and Bayesian inference phylogenies were constructed through RaxML and MrBayes softwares. Maximum likelihood phylogeny of the Echis did not show any support on basal level but Bayesian phylogeny gave PP=1. Both phylogenies were based on mitochondrial and nuclear protein coding genes. Cerastes cerastes (Egypt) was used as an out-group for all Echis that showed a well-supported sister group relationship to Genus Echis. Joger and Courage (1999), and Wuster et al. (2008) inferred the same sister group relationship between Echis and Cerastes. Similarly, Pook et al. (2009) used cytochrome b, ND4, 12S rRNA and 16S rRNA genes for inferring phylogenies of medically important and taxonomically unresolved genus Echis of vipers. They used Cerastes as outgroup and found the same sister group relationship with genus Echis. Maximum likelihood and Bayesian phylogenies divided all Echis into four distinct groups. Maximum likelihood phylogenies showed not very good support for phylogeny at basal node but Bayesian inference gave highly supported relationship among all Echis dividing them into four clades. These clades were Echis carinatus, E. ocellatus, E. pyramidum and E. coloratus group. Interrelationship among the four clades was not found to be very well resolved. While Echis pyramidum and Echis coloratus groups showed well supported sister group relationship (ML BS= 91; BI PP >98). On the other hand, both E. pyramidum and E. coloratus did not show well supported relationship with Echis carinatus and Echis ocellatus. There is still a need to resolve this relationship. Barlow et al. 2009 used mitochondrial and nuclear genes finding the same four clades of Echis with the same interrelationship among them. They inferred Bayesian phylogenies that showed Echis carinatus as sister to all other three Echis clades using Cytochrome b, ND4, 12S rRNA, 16S rRNA, and RAG-1. Only one representative of all four Echis species and found highly supportive posterior probability value for monophyly of E. coloratus, E. pyramidum and E. ocellatus. The present study showed good support for sister group relationship of Echis carinatus with other three Echis groups in Bayesian analyses but not good resolution was found in Maximum likelihood. Echis coloratus and E. pyramidum showed strong support for (ML BS=91, BI PP= 0.98) sister group relationship among them. Furthermore, both of these groups (coloratus and pyramidum) showed low support values for sister relationship with Echis ocellatus (ML BS= 58, BI PP= 0.5966). Rhadi et al. (2016) also used two mitochondrial genes (16S rRNA and Cytochrome b) for measuring phylogenetic affinities of Iraqi population of saw-scaled viper of genus Echis. They used 1,105 bp for inferring Maximum likelihood and Bayesian inference with similar topologies. Genus Echis was divided into 4 main clades with good support. In the present study, Maximum likelihood phylogeny did not show resolution for four clades but good support was shown by Bayesian inference analysis (BI PP= 1). Arnold et al. (2009) inferred phylogenetic somewhat similar relationship of Echis through mitochondrial DNA sequences using 1,117bp of two genes (731bp from cytochrome b and 386 bp from 16S rRNA). Genus Echis was divided into four distinct clades through Maximum likelihood (ML), Maximum parsimony (MP) and Bayesian inference (BI) analyses. They had phylogenies with similar topologies. In the present study, Echis ocellatus group included Echis jogeri showing good support for sister group relation (ML BS=100, BI PP= 1) which is in agreement with that of Pook et al. and Rhadi et al. 2016. Furthermore, Echis ocellatus included in this study showed significant phylogeographic structure

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Discussion as shown in the Pook et al. 2006 and Rhadi et al. 2016. The three studies including the present one, divided Echis ocellatus into 3 phylo-groups. One of the group ranges from West to east, a single haplotype from southern Mali. The second one ranges from Togo, Benin and Western Niger while third group was from Northern Cameroon. According to Arnold et al. (2009) Echis carinatus originated from Oman and Southern Iran and then dispersed to east and west of India and respectively. In this study only Echis carinatus was used from Pakistan, UAE and India thus could not say anything sure about the origin of Echis carinatus. On the other hand, Pook et al. (2009) suggested the origin from India and dispersal to north and west. Echis carinatus used in this study and those from NCBI database were divided into different groups. One group was from South India (Chennai Tamil Nadu), other one from Eastern and western India. Both of these groups included two haplotypes i.e., Echis carinatus carinatus (Mahrashtra India) and Echis carinatus sochureki from Pakistan, UAE and Northern India (Rajastan). Echis carinatus carinatus have also been found in southern Indian part Tamil Nadu. Pook et al. (2009) also found the same division of Echis carinatus. Their haplotypes included E. c. carinatus from India and Echis carinatus sochureki from Pakistan, Northeastern Arabian Peninsula. The haplotypes from northern part of range (Arabia, Pakistan, Central Asia and Northwestern India made a tightly packed cluster without having any clear phylogeographic structure showing recent divergence and dispersal. The basal sister groups have been shown to be originated from Maharashtra (Western India) and Tamil Nadu (Southern India). The results of this study for Echis carinatus show agreement with Pook et al. 2009 and Rhadi et al. 2016. The Echis carinatus group is the clade containing with minimum divergence between the populations. Cherlin identified three species i.e., E. multisquamatus (Central Asia, Iran), E. carinatus (southern India, Sri Lanka), E. sochureki (northern India to Pakistan, and. Auffenberg, (1991) found clinal variation across the range of the group, and considered all as subspecies of E. carinatus, and Lenk et al. (2001) found minimum divergence between E. carinatus sochureki from Pakistan and E. multisquamatus from . In the present study, Echis coloratus group included Echis coloratus and Echis omanensis as sister groups with strong support (ML BS= 99 and BI PP= 1) proving them to be the two different species of Echis. Babocsay, 2004 differentiated Echis coloratus and Echis omanensis through morphological differences but this study has focused on molecular basis that agrees with Babocsay, 2004 morphological basis and Pook et al. 2009 molecular basis. Echis coloratus included in the present study from Southern Oman showed sister group relationship with other Middle Eastern Echis coloratus probably suggesting it as another species. Similar relationship was found by Pook et al. 2009. Echis pyramidum in present ML and BI phylogenies showed a sister group relationship with other Echis coloratus group with strong support. The group includes Echis pyramidum and Echis leucogaster as sister groups but Echis pyramidum aliaborri from Kenya appeared to be a different one from all other E. pyramidum and E. leucogaster. E. pyramidum is polyphyletic with the one from Kenya being basal to the one from Egypt and to E. leucogaster. Depending on which is the type locality, either the Kenyan or the Egyptian animal will be true pyramidum, with the other one being a distinct taxon. Conclusion and Recommendations: Out of four snake species used in this study, two species (Black Cobra, Russell’s viper) are entirely different from Indian species from molecular evidence as Pakistan have two Cobras i.e., Black cobra (Naja naja) and Brown cobra (Naja oxiana). Saw scaled viper (Echis carinatus sochureki) is similar to northern but different from south Indian subspecies (Echis carinatus carinatus). More studies might claim Russell’s viper and saw-scaled viper as complete species. Common krait needs to be studied as there are no studies from other regions. So, this study finds genetic diversity and phylogenetic relationships of Pakistani elapid and viperid snakes showing the considerable inter and intra specific variations from different geographical regions of the world. More diverse and greater number of samples is recommended for more resolution in the genetic biodiversity and phylogenetic relationships of these snakes so that correct identification with authenticity might help not only in genetic conservation of such species but also in the development of effective antivenom against venomous snake bites.

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CHAPTER 6 SUMMARY

Among vertebrates, snakes have always been obviously unique and interesting. Major venomous snakes (Elapidae and Viperidae) are found in Pakistan. Information on the genetic relationships of organisms is deficient on this unique and distinctive species. The present study was designed for the molecular phylogeny of Elapidae (Common Krait, Black Cobra) and Viperidae (Russell's viper, saw-scaled vipers) snakes in Pakistan. A total of 100 samples [Twenty five of each species i.e., Krait (Bungarus caeruleus), Black Cobra (Naja naja), Russel’s viper (Daboia russelii) and Saw-scaled viper (Echis carinatus)] were obtained. Samples were collected from tail tip biopsies and shed skin of the snakes. After DNA extraction, PCR primers of mitochondrial genes (ND4, Cytochrome b, 12S rRNA, 16S rRNA, and COI) and nuclear genes (C-mos, RAG1, BDNF and NT3) were designed by Primer3 software. Some already reported primers in previous studies were also used. Selected regions of the genes were amplified by Polymerase Chain Reaction. PCR products were sequenced bi-directionally by Big DyeTM Terminator on ABI 3130XL Genetic analyzer. Forward and reverse sequences from a given sample was assembled through Sequencher 5.0 software. The resulting contigs were given specific identities. These contigs (sequences) were then aligned with other reported sequences from NCBI database through MEGA 6.0 using ClustalW tool for further data analyses. The nucleotide data for every gene was concatenated using SequenceMatrix 1.7.8 software. The concatenated data was partitioned through PartitionFinder 1.1.1 giving best partition scheme and evolutionary models for phylogenetic analyses. Two types of phylogenetic analyses i.e., Maximum Likelihood (ML) and Bayesian inference (BI) were performed through RaxML 8.0 and MrBayes 3.2 softwares. The resulting phylogenetic trees were visualized and saved by Figtree 1.4.3 software. DnaSP 5.0 was used for analyses of polymorphic sites, DNA polymorphism and pairwise number of differences for accessing the variation and genetic biodiversity in four snake species with other species of the respected genera (Bungarus, Naja, Daboia and Echis). Percent identity matrix were also constructed by comparing different species of every snake genus using online tool MUSCLE. The homology among the snake species was presented as line graphs. Black Cobra and Russel’s viper used in this study were found to be different from those found in India while saw scaled viper was similar to northern but different from south Indian one. Common krait needs more studies from Indian subcontinent. So, this study gives an insight in the genetic biodiversity and phylogenetic relationships of the four venomous snake species of Pakistan showing considerable inter and intra specific variations from different geographical regions of the world. Inclusion of greater number of samples and diverse sampling is recommended for more resolution in the genetic biodiversity and phylogenetic relationships of these snakes so that correct identification with authenticity might help not only in genetic conservation of such species as well as in the development of effective antivenom against venomous snake bites.

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