Population structure and patterns of genetic variation in a pearl oyster (Pinctada radiata) native to the Arabian Gulf

Amal Al-Saadi

School of Earth, Environmental and Biological Sciences

Science and Engineering Faculty

Queensland University of Technology

Brisbane, Australia

Submitted in fulfilment of the requirement of the degree of Master of Science

2013

Keywords

Pearl oyster, P. radiata, gene flow, population structure, Arabian Gulf, Qatar, GSSs, SSRs.

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Abstract

The pearl oyster (Pinctada radiata), a marine species native to the Arabian Gulf in the Indian Ocean is common in Qatari waters where it comprises 95% of pearl oyster stocks found naturally there. This species has been harvested for many centuries primarily for natural pearls but also for edible flesh and a lustrous shell. P. radiata possesses a relatively long-lived pelagic larval phase and adults at least, can tolerate hypersaline conditions. As sedentary relatively long-lived bivalves, pearl oyster species have the capacity in general, to accumulate metallic pollutants in their soft tissues to levels higher than surrounding background levels in sea water. P. radiata therefore, has been used extensively as a natural bio-indicator for monitoring pollution in the Arabian Gulf. Sustainability of P. radiata wild stocks are now significantly threatened in the Arabian Gulf region as a result of both natural (e.g. extreme salinity, high temperatures, high evaporation rates and low flushing rates) and anthropogenic factors (e.g. rapid development of coastline areas, overfishing and heavy exploitation). Long term sustainability and conservation of this local resource in the Arabian Gulf has therefore become a serious issue, but their survival can significantly contribute to maintenance of healthy marine ecosystems across the region.

The current study assessed natural levels and patterns of genetic variation in Arabian Gulf populations of the native pearl oyster, P. radiata to define wild population structure. Potential intrinsic (e.g. pelagic larval phase and life history traits) and extrinsic factors (e.g. water current, wave action, water temperature and prevailing wind direction) that could influence wild population structure were investigated. MtDNA (COI) sequences were used here to define levels and patterns of genetic diversity within and among three sample sites in Qatar territorial waters. Results of the statistical analysis that partitioned variation (AMOVA) were significant (P=0.0234) and showed that all variation was present within P. radiata sampled sites (94.61%) and differentiation among sites was relatively low (5.39%). Shallow differentiation of P. radiata wild populations in Qatar can largely be attributed to high levels of ongoing gene flow that results from dispersal mediated by a relatively long pelagic larval phase. Wild populations of P. radiata sampled from the Qatari coast were panmictic and therefore should be considered to constitute a single

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management unit. The present study was also the first to generate a massive genome survey sequence (GSS) dataset using next generation sequencing technology (NGST) for the Arabian pearl oyster P. radiata, the objective being primarily to identify microsatellite markers (SSRs) with potential to be applied in future population genetic studies after the failure of PCR cross-amplification trials with primers sets developed for other Pinctada species. The GSS dataset generated a large number of putative microsatellite motifs for P. radiata that may be useful in future population genetic studies. In addition, a number of putative genes were also identified after bioinformatics analysis with potential functional roles in toxicological and/or environmental stress responses. Some putative genes identified could also influence growth and survival of the target species. The partial genome (GSS) dataset for P. radiata potentially can have future applications in both pearl oyster farming and biotechnology.

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Statement of Original Authorship

The work contained in this thesis has not been previously submitted for a degree or diploma at any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.

QUT Verified Signature

Amal Al-Saadi

June 2013

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Acknowledgements

I would like to thank my supervisory team Professor Peter Mather and Dr David Hurwood. I really appreciate their mentorship, efforts, patience and assistance in all aspects of the project. Big thanks to my principal supervisor Professor Peter Mather for his constant support and encouragement along the way and honestly without his great support and faith, this journey would have been much more difficult. Special thanks to my associate supervisor Dr David Hurwood for his expertise in statistical analysis. Throughout this journey, I had lots of ups and downs but by the end, and with my supervisory team’s guidance, I was able to overcome those obstacles.

Special thanks to Vincent Chand for his laboratory assistance and technical support. Without his help and technical advices, I would never have achieved my research aims. Thank you to my postgrad fellows, for their time, sharing knowledge and companionship especially Feni Iranawati, Hyungtaek Jung, Matt Krosch, Litticia Bryant, Terrence Dammannagoda, Lalith Dammannagoda and Norainy Mohad Husin.

Thank you to all collaborators from Qatar especially, Dr Nehad El-Din, Masoud Al- Marri, Mohammed Al-Mohannadi and Biotechnology Centre at the Ministry of Environment for their assistance with sample collection.

Last but not least, my deepest appreciation goes to my husband Ali. Without his love, endless support, enormous encouragement and tolerance, I would not have the strength to succeed and this journey would have been very different and much harder task. Big thanks to my lovely children: Selham, Sara, Almayasa and our new baby Muhammed for their unconditional love and always wishing me luck. I appreciate sincerely my family: for their patience and unlimited endurance for the long hours being away from home. I would like to extend my gratitude to my parents in Qatar: for their ongoing support via frequent phone calls and instilling in me the motivation to never give up as there is no limit for realizing our dreams.

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Table of Contents Key Words...... i Abstract...... ii Statement of Original Authorship...... iv Acknowledgments...... v List of Figures...... ix List of Tables...... x CHAPTER 1: GENERAL INTRODUCTION ...... 3 1.1 General Background ...... 3 1.2 Why is Natural Genetic Diversity Important?...... 4 1.3 Using Molecular Markers to Assess Genetic Variation and Population Structure ...... 6 1.4 Assessing Natural Population Structure ...... 7 1.4.1 Phylogenetics ...... 7 1.4.2 Phylogeography ...... 8 1.5 Genome Wide Survey of Genetic Variation ...... 10 1.6 Pearl Oysters ...... 11 1.7 Pinctada radiata (Leach, 1814): the focus of the current study ...... 13 1.7.1 Distribution ...... 13 1.7.2 The Arabian Gulf ...... 13 1.7.3 Economic importance of P. radiata in the Arabian Gulf ...... 15 1.7.4 Environmental importance ...... 17 1.7.5 Life history traits of P. radiata ...... 18 1.7.6 Current status of P. radiata populations in the Arabian Gulf ...... 20 1.8 Previous Studies of Pinctada radiata ...... 22 1.9 The Current Study ...... 23 CHAPTER 2: POPULATION STRUCTURE INFERRED FROM MITOCHONDRIAL (COI) SEQUENCE DATA ...... 25 2.1 introduction ...... 25 2.2 Methods ...... 30 2.2.1 Sample collection and DNA extraction ...... 30 2.2.2 DNA marker selection ...... 31 2.2.3 PCR amplification of mtDNA (COI) ...... 32 2.2.4 MtDNA sequencing ...... 33 2.3 Data Analysis ...... 33 2.4 Results ...... 34 2.4.1 MtDNA diversity in sampled wild P. radiata populations ...... 34 2.4.2 Phylogeography ...... 37 2.4.3 Phylogenetic comparisons among Pinctada species ...... 39 2.4.4 Tests of neutrality ...... 40 2.4.5 Inferred population structure of the P. radiata samples ...... 41 2.5 Discussion ...... 41 CHAPTER 3: SSR MARKER DEVELOPMENT AND GENOME SURVEY SEQUENCE (GSSS) IN P. RADIATA ...... 46 3.1 Introduction ...... 46 3.1.1 Microsatellite genetic markers ...... 46 3.1.2 Traditional methods for microsatellite isolation ...... 47

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3.1.3 NGS approaches to SSRs discovery and development ...... 48 3.1.4 Traditional approaches vs. NGS for isolation of SSR markers ...... 49 3.1.5 NGST for isolation of SSRs in bivalves ...... 51 3.2 Methods ...... 52 3.2.1 Samples and DNA extraction ...... 52 3.2.2 Attempts to screen SSR markers ...... 52 3.2.3 Library construction and Ion Torrent sequencing ...... 53 3.3 Data analysis ...... 54 3.3.1 Sequence assembly ...... 54 3.3.2 Annotation ...... 54 3.3.3 Identification of SSR motifs ...... 54 3.4 Results ...... 55 3.4.1 Putative SSRs ...... 57 3.4.2 Comparative GSSs analysis ...... 58 3.4.3 Gene Ontology assignment ...... 59 3.4.4 KEGG analysis ...... 59 3.4.5 Protein domains ...... 60 3.4.6 Analysis of genes ...... 61 3.5 Discussion: ...... 63 3.5.1 Putative P. radiata microsatellite markers ...... 64 3.5.2 Comparative GSS analysis ...... 64 3.5.3 Gene Ontology assignment ...... 66 3.5.4 KEGG analysis ...... 67 3.5.5 Protein domains ...... 68 3.5.6 Analysis of genes identified ...... 69 CHAPTER 4: GENERAL DISCUSSION ...... 74 BIBLIOGRAPHY ...... 84 Appendix...... 106

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List of Figures

Figure 1: Map of the Arabian Gulf...... 14 Figure 2: Map of Qatar showing sampling sites for P. radiata...... 31 Figure 3: Haplotype frequencies among P. radiata sampled populations...... 36 Figure 4: Neighbour joining tree for P. radiata mtDNA COI haplotypes. Haplotypes in black colour from the current study while blue P. radiata sequences (haplotypes) from GenBank...... 38 Figure 5: Median-Joining Network of phylogenetic relationships between sampled P. radiata haplotypes and haplotypes sampled from GenBank. Coloured circles represent individual haplotypes, size of circles indicates relative haplotype frequencies, lines between haplotypes indicate single mutational changes and small black circle represents missing haplotype...... 39 Figure 6: Consensus tree from neighbour-joining analysis of Pinctada species estimated from mtDNA COI region...... 40 Figure 7: Summary of P. radiata Ion Torrent sequences...... 57 Figure 8: Distribution of simple sequence repeat (SSR) nucleotide classes among identified nucleotide types in P. radiata...... 58 Figure 9: Gene Ontology (GO) terms for contig sequences in P. radiata...... 59 Figure 10: Top 20 hit species distribution based on BLASTx from contigs E value cut-off is 1e- 5...... 66

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List of Tables

Table 1: P. radiata collection sites and further sampling details...... 30 Table 2: Variable sites for 8 haplotypes of 450 nucleotide bases of COI for P...... 35 Table 3: Sample site, (n) sample sizes and genetic variation indices for each site...... 35 Table 4: P. radiata mitochondrial COI haplotype frequencies for each site individually...... 36 Table 5: Neutrality test of mtDNA COI...... 40 Table 6: Results of analysis of molecular variance (AMOVA)...... 41

Table 7: MtDNA pairwise Φst among Pinctada radiata sampling sites. Above the line: Significance of pairwise comparison (P < 0.05) and below the diagonal: Φst estimates...... 41 Table 8: Summary of P. radiata Ion-Torrent sequences...... 56 Table 9: Frequency of KEGG pathways identified in P. radiata contigs...... 60 Table 10: Summary of the top 20 domains in P. radiata contigs...... 61 Table 11: Summary of the top 20 genes and putative genes identified as environmental stress bio-markers in P. radiata contigs...... 62

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Chapter: General Introduction 1

Chapter 1: General Introduction

1.1 GENERAL BACKGROUND

Biodiversity is defined as the variability among living organisms within a particular ecosystem or on the Earth as a whole (Harrington et al. 2010). Environments across the world are changing quickly and as a consequence, biological diversity is being depleted rapidly that results from direct and indirect impacts of anthropogenic factors. Composition of biological communities have been altered directly by human activities including as a result of human population expansion and overexploitation of natural resources, and indirectly by impacts of invasive species, habitat destruction, elevated pollution levels and climate change, to name but a few (Hooper et al. 2005; Frankham 2003). The UN has recognized that human actions have accelerated extinction rates by up to 1000 times higher than the natural background rate (UN 2010a). An increasing number of species are facing extinction in the near future, a loss that will be irreversible, while many other extant species show declining finite population sizes that make them increasingly vulnerable to extinction in the future (Frankham 2003; Allendorf and Luikart 2007).

Wild fauna and flora are exposed to ongoing effects of environmental change and other factors including pollution, habitat loss, parasites, diseases, pests etc. Species require genetic diversity to adapt to changes in environmental conditions and if they lose diversity, by definition this will reduce their potential for future adaptation (Frankham 2003). It has been recognized that many species are facing declines in their natural levels of genetic diversity and this is most obvious in many threatened species. Furthermore, loss of genetic diversity contributes directly to a reduction in adaptive potential and this in turn can be associated with a reduction in population reproductive fitness and long-term persistence. In large natural populations, genetic diversity levels are often relatively high where populations are naturally outbreeding, a characteristic that allows them potential to adapt to environmental change (Freeland 2005; Frankham, Ballou and Briscoe 2004; Frankham 1995). Genetic diversity therefore, is a basic requirement for

Chapter: General Introduction 3

species that provides evolutionary potential and the ability to adapt to contemporary and future environmental change.

With respect to aquatic species, human impacts on ecosystems have caused a severe decline in biodiversity around the world that can be measured by biological community homogenization, native species extinctions and natural population depletion (Sala and Knowlton 2006). The UN has declared that the cumulative impacts of anthropogenic factors including over-fishing, effects of invasive species and pollution will increase extinction risks significantly for many aquatic species across most ocean regions (UN 2010b). For example, overharvesting directly caused the regional extinction of the gray whale (Eschrichtius robustus) from the Atlantic (Sala and Knowlton 2006). Rapid development in many coastal regions has also caused major threats to many marine benthic communities (Sheppard et al. 2010; Snelgrove 1998).

Thus, conservation of biodiversity is a major global issue. With the rate of contemporary environmental change, many species require effective management to ensure their survival and long-term sustainability. Natural resource availability and sustainability cannot be achieved effectively however, without also conserving population (intra-specific) levels of genetic diversity (Allendorf and Luikart 2007; Frankham 2003). Thus, conservation of adequate levels of genetic diversity is a critical issue in order to rescue endangered/threatened species/populations from extinction or depletion and to maintain their long term adaptive potential against future environmental change.

1.2 WHY IS NATURAL GENETIC DIVERSITY IMPORTANT?

Genetic diversity in any species is a resource that is manifested at two different hierarchical levels. The first level is explicit and includes the genetic differences between individuals within a population, for example eye colour in humans, flowers colours in plants etc. The second level of genetic diversity is that which exists among the various populations (Allendorf 1983; Frankham, Ballou and Briscoe 2004). The IUCN (the premier international conservation body) has emphasized three fundamental priorities that include conservation of genetic diversity, species diversity, and ecosystem diversity (Frankham 1995). For

Chapter: General Introduction 4

effective conservation therefore, it is an imperative to preserve natural genetic diversity in populations as one of three global conservation priorities (Frankham, Ballou and Briscoe 2004). It is apparent that environments across the world are changing rapidly. Consequently, biodiversity around the planet is being depleted by a variety of factors including human exploitation, natural habitat loss, effects of increased pollution and introductions of exotic species, to name but a few. Therefore, genetic diversity levels are commonly declining in many threatened species. Furthermore, loss of genetic diversity contributes directly to a reduction in adaptive potential in any population and this in turn is also associated with a reduction in population reproductive fitness and persistence. In large populations, genetic diversity is commonly very high where populations are naturally outbreeding, a trait that provides them with the potential to adapt and cope with environmental change (Freeland 2005; Frankham, Ballou and Briscoe 2004; Frankham 1995).

Ultimately, genetic diversity is a basic requirement for any population that provides evolutionary potential to adapt to contemporary environmental change. Thus, conservation of genetic diversity is imperative in order to rescue endangered species/populations from the threat of extinction and to maintain their long term potential against future environmental change. This can be addressed by appropriate application of genetic theory and molecular DNA technologies to quantify and document levels and patterns of genetic variation within and among populations of conservation interest (Frankham, Ballou and Briscoe 2004; Frankham 2003; Feral 2002).

Since pearl oyster species have been identified to be of conservation interest, molecular methodologies (i.e. application of genetic markers) can assist their conservation by documenting patterns of variation and describing natural population structure. They can also provide an effective method for documenting relationships among pearl oyster populations and taxa. In contrast, use of morphological approaches for these purposes in the past has led to significant errors in oyster classification and systematics (Beaumont and Khamdan 1991; Southgate and Lucas 2008; Wang and Guo 2008; Masaoka and Kobayashi 2005). Shell colouration and pearl oyster morphology are highly variable traits that can

Chapter: General Introduction 5

be developmentally plastic (Southgate and Lucas 2008). Both sets of traits are susceptible to, and influenced markedly by, certain environmental factors including water flow (Southgate and Lucas 2008; Wang and Guo 2008; Beaumont and Khamdan 1991; Masaoka and Kobayashi 2005). Therefore, morphological characteristics are often considered to provide unreliable markers of genetic relationships in bivalves and other mollusc taxa.

1.3 USING MOLECULAR MARKERS TO ASSESS GENETIC VARIATION AND POPULATION STRUCTURE

Over recent decades, molecular approaches have been used widely to address many ecological questions (Freeland 2005). They have gained wide acceptance due to their efficiency, usefulness, relative power and flexibility (Selkoe and Toonen 2006). Thus, genetic markers have been useful for resolving a number of ecological questions. In addition, the array of genetic markers available has advanced rapidly in order to address individual limitations on some marker types (Feral 2002; Slatkin 1987). Molecular genetic technologies have the capacity to provide invaluable insights at diverse levels including, for assessing genetic diversity levels (within and among populations), assessing levels of gene flow, defining population structure, inferring patterns of evolutionary biogeography, estimating phylogenetic relatedness and for parentage assignment (Feral 2002). They can also address ecological questions including assessing parameters of interest such as population bottlenecks, effective population size and individual migration rates (Selkoe and Toonen 2006; Palumbi 1996; Avise et al. 1987). Beneficial application and widespread utilization has resulted in the emergence of a rich diversity of molecular markers that includes: allozymes, mtDNA, RFLPs, RAPDs, SNPs, ESTs and SSRs (microsatellites) to name but a few (Feral 2002; Selkoe and Toonen 2006). For instance, mitochondrial DNA (mtDNA) has become a very popular marker for characterizing phylogenetic relationships and for use in phylogeographic studies (Avise 2000; Bermingham and Moritz 1998). Even though there are limitations on inference from mtDNA data, ease of application has meant that for many applications, mtDNA has become the marker of choice for addressing many evolutionary and ecological questions (Selkoe and Toonen 2006).

Chapter: General Introduction 6

1.4 ASSESSING NATURAL POPULATION STRUCTURE

1.4.1 Phylogenetics

Edwards and Cavalli-Sforza (1963) defined Phylogenetics as the field of life science that deals with the evolutionary relatedness among different groups of organisms, which are revealed in morphological and molecular data.

In other words, a phylogeny is a tree-like diagram that describes hypothesized relationships among biological taxa. The tips of the branches of a tree represent observed species (either living or fossil) while all deeper parts of the tree are inferred. A basic assumption of this approach is that neither hybridization nor horizontal gene transfer will affect phylogenetic inference (Stearns and Hoekstra 2000). Therefore, a gene will be transmitted from generation to generation so that it can be traced backwards in time via a pedigree of relationships (i.e. a phylogeny) (Avise et al. 1987).

Until the late 20th century, phylogenetic relationships were commonly reconstructed using morphological, anatomical and/or behavioural characteristics etc. As molecular marker technologies became available later, much of phylogenetics is now based on protein and DNA sequences. This type of marker has been used increasingly to construct the evolutionary history of taxa or

populations in order to describe phylogenetic relationships (Cavalli-Sforza and Edwards 1967; De Queiroz and Gauthier 1992).

Molecular phylogenetics has many useful applications. It has played an increasingly significant role in conservation biology. For example, it provides a powerful tool for indentifying clades that have been subject to either rapid diversification or recent extinction. It also has the capability for evaluating an individual's contribution towards overall population genetic diversity (Moritz 1995). Molecular phylogenies correlated with allele frequencies within species can be used to specify evolutionary population units. These sort of molecular studies have yielded considerable insights into systematic relationships by documenting evolutionary attributes, historical patterns and evolutionary processes including divergence and adaptation over evolutionary time (Moritz 1995).

Chapter: General Introduction 7

The science of molecular phylogenetics has made substantial contributions in comparative studies by tracking genes within and among living taxa and assessing their historical evolution (Hillis and Huelsenbeck 1992). Rapid recent developments in the generation of genomic sequence data now allow these data to be combined together into a single phylogenetic analysis (Whelan, Li and Goldman 2001).

This approach is now considered advantageous for documenting evolutionary variation in species. The approach has some limitations however, for revealing the geographical structure of population(s) and population connectivity (Moritz 1995). To address these issues as well as to gain a better understanding and a broader view, a new sub-discipline (phylogeography) emerged in the 1980s that can act as a bridge between historical processes that produce diversity and contemporary patterns of genealogical lineages based on geographical distributions: this bridge is referred to as the science of Phylogeography.

1.4.2 Phylogeography

Phylogeography interprets the contemporary spatial distributions of genealogical lineages via understanding the historical processes (Avise 2000).

Essentially phylogeography is a relatively new sub-discipline of biogeography that studies the variable patterns of genealogical lineages correlated with their geographical distributions at the intraspecific level in order to test hypotheses regarding the history of populations over evolutionary time (Avise 2000; Riddle 1996; Avise et al. 1987; Arbogast and Kenagy 2001; Bermingham and Moritz 1998). Basically, phylogeography deals with two fundamental axes: space and time, to jointly map targeted gene genealogies (Avise 2000; Bermingham and Moritz 1998; Freeland 2005). Such analyses require comprehensive input from a variety of disciplines including; population genetics, molecular biology, demography, evolutionary geography and geology. As a consequence, phylogeography is considered to be an integrative approach that reaches its goals by integrating with other analytical approaches (Avise 2000; Arbogast and Kenagy 2001). From a

Chapter: General Introduction 8

molecular perspective, DNA sequences provide an important resource for inferring relationships between biotic and earth history. In other words, molecular sequences can be used to infer the current patterns of diversity in species or populations as well as their geographical distributions that are affected indirectly by evolutionary processes in deep time (e.g. speciation, habitat adaptation and biogeographical history) and in shallow time (e.g. range shifts). Thus, molecular sequence data can provide the rationale for constructing phylogeographic bridges between the past and present history of biota (Riddle 1996; Arbogast and Kenagy 2001; Bermingham and Moritz 1998).

Phylogeographic analysis has been used for many applications including to reveal the extent of relatedness among cryptic species by identifying genetic boundaries (Palumbi 1997; Arbogast and Kenagy 2001; Bermingham and Moritz 1998; Palumbi 1996). This approach has also been useful for identifying the origin of exotic species (e.g. invasive or introduced species) and as a way of tracing their paths of colonization and has also proven valuable for distinguishing between indigenous and introduced (by human activities) populations (Freeland 2005; Beebee and Rowe 2008; Palumbi 1996). In addition, phylogeography is also a powerful tool for assessing the timing of population and species divergence. This crucial role permits us to detect correlations between extrinsic or intrinsic factors that have influenced species formation. Phylogeographic studies are also useful for providing a better understanding of the environmental factors that impact gene flow among populations that can influence species formation (Palumbi 1997; Encalada et al. 1996; Arbogast and Kenagy 2001; Avise 2009).

Dispersal and vicariance are considered to be significant opposing mechanisms that affect phylogeographic patterns that will ultimately determine population structure (Encalada et al. 1996; Arbogast and Kenagy 2001). Molecular data assessed in a phylogeographic context can be used to reveal if dispersal has homogenized a population by gene flow (i.e. producing low genetic differentiation), or if vicariance has isolated previously connected populations (i.e. resulting in high genetic differentiation) (Avise et al. 1987; Avise 2000). Phylogeographic inference depends therefore, on evaluating genetic variation patterns that either enhance population

Chapter: General Introduction 9

connectivity or restrict natural gene flow (Balloux and Lugon-Moulin 2002; Avise 2009) .

1.5 GENOME WIDE SURVEY OF GENETIC VARIATION

Microsatellites or simple sequence repeats (SSRs) are tandem repeated segments of DNA. Repetitive motifs range in length from 1 to 6 nucleotide bases and are inherited as co-dominant, bi-parental markers. They are found at high frequency across the nuclear genome of most eukaryote taxa and show relatively high levels of allelic polymorphism (Selkoe and Toonen 2006; Jarne and Lagoda 1996; Chistiakov, Hellemans and Volckaert 2006; Zhang and Hewitt 2003). Such markers are highly susceptible to mutations and correspondingly have shown high levels of genetic variation in most natural populations (Feral 2002; Bruford, Bradley and Luikart 2003). Therefore, they can be used to provide insights into genetic variation both within and among populations (Zhang and Hewitt 2003).

Genome survey sequences (GSSs) have played an important role in providing a wealth of SSR markers by producing random fragments of sheared genomic DNA via next generation sequencing (NGS) platforms (Gardner et al. 2011; Bertozzi et al. 2012). GSSs possessing long sequence length and high similarity can also be an efficient technique for predicting the functions of putative genes in order to identify potential exon-intron boundaries, an approach applied in Siamese mud carp (Iranawati et al. 2012). Identification of putative genes is a major objective in most genome projects including in non-model species. An accepted approach for identifying genes in new species is to search in public database for similar sequences with Expressed Sequence Tags (ESTs) or protein sequences. The reliability of the putative genes identified as novel sequences can vary widely depending solely on the database screened. Obtaining putative genes identified from a database is mostly effective for; gDNA (GSS), cDNA (ESTs) and proteins (Burge and Karlin 1998). Depending on an EST library solely however, is not always reliable because gene expression possibly may not be recognized if there is an association with a specific developmental stage or a specific tissue, which in turn makes the identification of putative genes as novel sequences through GSS approach more efficient and as a compliment source to EST profiling. In addition,

Chapter: General Introduction 10

avoiding redundant mRNA from over-expressed genes can be achieved using the GSS approach. Thus the GSS approach is more efficient and productive compared with an EST approach in terms of gene identification (Strong and Nelson 2000).

1.6 PEARL OYSTERS

Pearl oyster is a traditional term that has been applied widely to bivalves that belong to two widely recognized genera; Pinctada (Roding, 1798) and Pteria (Scopoli, 1777), both sets of species belonging to the Family Pteriidae (Gray, 1874). Genera in the Family Pteriidae are characterized by possession of an oblique shell with a straight hinge. There are several criteria that distinguish members of the two genera including the arrangement of hinge teeth and, morphological and anatomical character variation (Hayes 1972; Lamprell, Whitehead and Healy 1998; Mikkelsen et al. 2004). Variation in shell colour and shape are influenced considerably by both genetic and environmental factors (Hynd 1960; Wada 1984). Pearl oysters occur widely across the globe in both tropical and subtropical waters where they normally inhabit shallow marine environments. Certain species are also found occasionally in deeper waters reaching a maximum depth of approximately 100m (Shirai 1994; Hayes 1972). Pearl oysters however, generally experience poor growth rates at depth as a result of lower temperatures, wave action and low availability of phytoplankton (natural food) (Yoo, Chang and Lim 1986). Many environmental variables determine the quality and colour of pearls including, depth, temperature and light (Ikenoue and Kafuku 1983). Water temperature in general however, is considered to be the main factor that influences pearl oyster natural distribution patterns.

Pinctada spp. distributions are restricted to a relatively high range of temperatures in marine waters ranging from approximately 29 to 32°C. Spawning seasonality and gonad development are both greatly influenced by ambient water temperature (Hynd 1955). During the winter season, high mortality rates have been reported by Yamashita (1986) as a result of pearl oyster exposure to cold temperatures that inhibits reproductive development, slows heart rate, decelerates growth rates and increases susceptibility to disease. A common phenomenon in many marine organisms, in particular species that live in tidal zones like pearl oysters, is that they are adapted to tolerating large variations in salinity (Alagarswami and Victor 1976).

Chapter: General Introduction 11

Pearl oysters in the genus Pinctada have a preference for hard substrates, including rocky outcrops, pebble bottoms, dead coral or shells and crevices. Some individuals however, are also found infrequently on sandy bottoms. They typically use their byssal threads to attach to diverse substratum types either individually or by forming bulky clusters (Gervis and Sims 1992; Hynd 1955; Lamprell, Whitehead and Healy 1998).

Pearl oysters in the Pinctada genus have been fished to harvest mother of pearl shell (MOP) from ancient times. The substantial potential for Pinctada oyster tissues to secrete MOP has been harnessed more recently to yield cultured pearls.

The following sections will describe the most-commercially significant species of Pinctada with respect to their geographical distributions around the world:

• Pinctada margaritifera (Black-lip): Pinctada margaritifera has a wide distribution across the Indian and Pacific Oceans which identifies them as an Indo-Pacific species. This species is present in relatively high densities in waters in the Arabian Gulf, the Red Sea, the Arabian Sea, Sri Lanka, India, Southeast Asia, French Polynesia, Australia and the Pacific Islands (Southgate and Lucas 2008; Bernard, Cai and Morton 1993; Sims 1992; Hynd 1955).

• Pinctada maxima (Silver or Gold-lip): Pinctada maxima has a wide distribution in the Indo-Pacific region that extends from Myanmar to French Polynesia, including the South China Sea, the Philippines, Southeast Asia, southern Japan, Papua New Guinea, Australia, Indonesia and Solomon Islands (Hu and Tao 1995; Shirai 1994; Gervis and Sims 1992; Swennen et al. 2001; Southgate and Lucas 2008).

• Pinctada mazatlanica (Panamanian): The geographical distribution of P. mazatlanica includes the western coast of the Americas from northern Peru to the Gulf of California, and includes Panama, Ecuador, the west coast of Mexico and Costa Rica (Arnaud et al.

Chapter: General Introduction 12

2000; Southgate and Lucas 2008; Shirai 1994; Saucedo and Monteforte 1997; Hayes 1972).

• Pinctada fucata: Pinctada fucata has a broad distribution that extends across the Mediterranean Sea, the Red Sea, the Arabian Gulf, Indian Ocean to the Western Pacific Ocean including, Korea, Japan and parts of southern China (Southgate and Lucas 2008; Shirai 1994; Gervis and Sims 1992).

1.7 PINCTADA RADIATA (LEACH, 1814): THE FOCUS OF THE CURRENT STUDY

1.7.1 Distribution

Pinctada radiata is abundant in the Arabian Gulf region of the north-western Indian Ocean and the Red Sea (Cunha et al. 2011; Beaumont and Khamdan 1991; Al- Khayat and Al-Ansi 2008). Although current evidence suggests that P. radiata has not established populations in the Mediterranean Sea, many records report that P. radiata is an invasive species that may have been introduced unintentionally to the latter region. Anthropogenic activity may have contributed to introductions of exotic species to this region and allowed them to colonize new habitats, particular in the Mediterranean Sea.

1.7.2 The Arabian Gulf

Southgate and Lucas (2008, p11) state that:

"Pearl grounds originally stretched on the Arabian side from Kuwait along the coast of Saudi Arabia to Bahrain, Qatar, the United Arab Emirates and the Sultanate of Oman. They also run along nearly the whole coast of the Persian side of the Gulf, from near Bander-e-Busehr (island of Khark) to Bander-e- Lengeh (island of Kish) in the south, and even further south to the Strait of Hormuz". (See Figure 1)

Chapter: General Introduction 13

Kuwait Bander-e-Busher Iran Persian/Arabain Gulf Bander-e-Lengeh Strait of Bahrain Hormuz

Qatar Saudi Arabia

United Arab Emirates Oman

Figure 1: Map of the Arabian Gulf.

Current importance of the Arabian Gulf region At present, the Arabian Gulf plays a major role in modern human society. It contains abundant living (e.g. fisheries) and non-living resources (e.g. oil and gas) that constitute the cornerstones of sustainability for Gulf states (Price, Sheppard and Roberts 1993). Fisheries in the region are also a significant industry (Price 1993) while the region's non-renewable resources, in particular oil, dominate the world market and have promoted remarkable economic development across the region (Din 1990). The Gulf is also the main source and supply of freshwater for humans after undergoing desalination since natural freshwater supplies are limited in the region (Price 1993; Price, Sheppard and Roberts 1993). Since the 1930s, after discovery of oil, the Gulf has played a vital role as the main channel for oil and gas export activities from the Arabian Gulf states to the rest of the world (Price 1993).

Physical features of the Arabian Gulf region The Arabian Gulf is situated in a subtropical belt (Sheppard et al. 2010; Sheppard 1993). The most prominent physical characteristics of the Gulf region are its semi- enclosed, shallow, hyper-arid setting that has very high evaporation and low flushing rates. An enclosed nature has resulted in relatively low water exchange as

Chapter: General Introduction 14

water is only exchanged with the greater Indian Ocean on average approximately once every five years (Sheppard 1993; De Mora et al. 2004; Hunter 1986). The Gulf also experiences extreme salinity that can typically reach over 40ppt that increases up to 50ppt in certain zones and occasionally, even to 70ppt in more enclosed embayments such as the Gulf of Salwah (Sheppard 1993). Extreme salinity as well as high ambient temperatures has influenced a number of marine ecosystems adversely in the Gulf region (Sheppard et al. 2010; Price, Sheppard and Roberts 1993; Price 1993; De Mora et al. 2004). As a result of the features described above, marine pollutants are less likely to be diluted and dispersed in the Gulf than they would be in most open marine systems (Sheppard 1993; De Mora et al. 2004).

The Gulf is basically a sedimentary basin with an old carbonate platform. The region experienced a complete drying out during the late Pleistocene, thus the region now is characterized by relatively low biological diversity of marine fauna and flora due to the fact that current marine conditions are only recent and stressful environmental conditions are now also present there (Sheppard 1993). Overall, it is apparent that Arabian Gulf waters constitute a comparatively fragile marine ecosystem that will require effective management and protection overtime in order to maintain biodiversity and sustainable natural marine communities (Sheppard 1993; Price 1993; Price, Sheppard and Roberts 1993; De Mora et al. 2004; Sheppard et al. 2010).

1.7.3 Economic importance of P. radiata in the Arabian Gulf Pinctada radiata (Leach, 1814) is the most common pearl oyster species found in the Arabian Gulf region. According to Mohammed (1995), P. radiata is very common on the western side of the Arabian Gulf, with populations extending from the coast of Kuwait in the north along the Emirates and Omani coasts to the south (Lorimer 1986; Al-Matar, Morgan and Hakim 1983). Al-Khayat and Al-Ansi (2008) confirm that P. radiata is also common in Qatari waters and comprises 95% of pearl oysters found there in comparison with P. margaritifera and Pteria marmorata that jointly comprise the remaining 5% of wild pearl oyster stocks in the region.

Chapter: General Introduction 15

Historically, the Arabian Gulf States have harvested pearl oysters, mainly P. radiata, (Gray, 1874). Exploitation has largely focused on harvest of wild pearls rather than harvest for meat (Mohammed and Yassien 2003; Al-Khayat and Al- Ansi 2008). The ability of pearl oyster tissues to secrete natural pearl and MOP provides significant commercial value. Consequently, Arabian Gulf countries have practiced pearl oyster fishing as a significant economic activity and the industry has contributed to livelihoods of local families for centuries (Al-Khayat and Al-Ansi 2008; Mohammed 1995; Southgate and Lucas 2008). Gulf pearl oyster fisheries have been large in the past and once dominated the world market, providing approximate 80% of the overall world production of natural pearls, due to their excellent quality and preferred shape (Bowen 1951; Al-Matar et al. 1993; Khamdan 1988; Lorimer 1986; Al-Mohannadi 2002). Following the discovery of oil in the Arabian region in the 1930s, however, and with the emergence of cultured pearls produced in Japan, the local pearl oyster fishing industry diminished considerably in the Arabian Gulf (Al-Matar et al. 1993; Southgate and Lucas 2008; Al-Khayat and Al-Ansi 2008; Sheppard, Price and Roberts 1992; Al-Mohannadi 2002).

Nowadays, pearl oyster wild fisheries and culture has significant potential to improve the economic status of a wide range of people, coastal communities, local economies, cooperatives and international companies (Southgate and Lucas 2008; Gervis and Sims 1992). This industry offers lucrative markets with relatively low capital input (Gervis and Sims 1992). Nevertheless, "Natural oyster populations are the principal source of seed collection for pearl culture and oyster hatcheries, despite the advanced hatchery and culture technologies that exist nowadays" (Yassien, El-Ganainy and Hasan 2009, p97).

Beside production of pearls for the jewellery industry, there is increasing commercial interest in pearl oyster edible flesh for local human consumption and shell to generate export revenue (Gervis and Sims 1992; Yassien, El-Ganainy and Hasan 2009; Carpenter 1997). Pearl shell is used for buttons and jewellery is considered a high value export commodity because it is non perishable and it is relatively low cost to ship (Gervis and Sims 1992).

Chapter: General Introduction 16

1.7.4 Environmental importance Pinctada radiata has also an important role to play in the local ecology of the Gulf waters and so has environmental significance. First, pearl oysters contribute substantially to influencing local sediment processing as individuals filter particulates from the water column due to their capacity as suspension feeders (Yassien, El-Ganainy and Hasan 2009). A second characteristic of pearl oysters from an environmental perspective is that as sedentary bivalves they have the capacity to accumulate metallic pollutants to various orders of magnitude above surrounding background levels in sea water. Pearl oysters can concentrate heavy metals and other chemicals in their soft tissues e.g. in mantle, gills, digestive gland and gonads (Al- Madfa, Abdel-Moati and Al-Gimaly 1998; Bu-Olayan and Subrahmanyam 1997; Goksu et al. 2005; Frias-Espericueta et al. 1999; Tlig-Zouari et al. 2009; De Mora et al. 2004; Yassien, El-Ganainy and Hasan 2009; Southgate and Lucas 2008). Goksu and co-researchers (2005) report that levels of cadmium bioaccumulation in mussels and oysters tissues can reach up to 100,000 times higher than background levels in sea water. Bivalves, in particular oysters, can therefore act as ideal bio-indicators of marine chemical contamination levels because of their sessile nature as adults, relative longevity and their tendency to form large population clusters. Consequently, P. radiata has been utilized extensively as a biological indicator organism for pollution monitoring programs in the Arabian Gulf, to assess local marine water quality in coastal areas and to reflect regional marine ecosystem quality (Bou-Olayan et al. 1995; Al-Madfa, Abdel-Moati and Al-Gimaly 1998; De Mora et al. 2004; Mohammed 1994). For example, Al-Madfa and colleagues (1998) observed elevated concentrations of cadmium in P. radiata tissues collected in Doha Harbour, Messaieed and Ras Laffan. These locations are subjected to intense, continuous dredging activities for harbour expansion. In another study, bioaccumulation of trace heavy metals in P. radiata was reported in Kuwait after the Gulf war to assess the ecological consequences of a very large oil spill linked to the conflict (Bou-Olayan et al. 1995; Bu-Olayan and Subrahmanyam 1997).

Another issue to consider, is that pearl oyster shells are used by large numbers of fouling organisms as a solid substrate for larval settlement (Mohammed 1994). Al- Khayat and Al-Maslamani (2001) recorded more than 100 species of marine bio- fouling organisms were associated with various pearl oyster beds along the Qatari

Chapter: General Introduction 17

coast in the Arabian Gulf. Therefore, pearl oyster beds likely play a remarkable role in enhancing and maintaining regional marine macroinvertebrate bio-diversity (Al- Khayat and Al-Maslamani 2001; Al-Khayat and Al-Ansi 2008).

It is apparent therefore that P. radiata is an important component of the marine ecosystem in the Arabian Gulf region and contributes both economically and as an important mediator for promoting biodiversity there.

1.7.5 Life history traits of P. radiata

The life cycle of Pinctada radiata is similar to that of a number of marine bivalves (Fletcher et al. 2006). Pinctada radiata is a protandrous hermaphrodite (Fletcher et al. 2006; Southgate and Lucas 2008; Herdman 1903; Gervis and Sims 1992) with individuals maturing first as a male then changing sex later to female after completing a number of reproductive cycles as a male (Southgate and Lucas 2008; Herdman and Hornell 1906; Al-Matar et al. 1993; Fletcher et al. 2006). This sex change is reversible and can occur more than once during an individual's lifetime (Southgate and Lucas 2008). This phenomenon is controlled by a variety of environmental factors in particular by stress and availability of food resources. Individuals can only perform one sexual function at any point in time, so this form of sex change is transitional but not functional (Southgate and Lucas 2008; Fletcher et al. 2006; Gervis and Sims 1992). Based on the fact that spawning occurs continuously under optimal water temperature conditions, each individual potentially can act within their own lifetime as both a male and female for many spawning seasons. The sex ratio of pearl oysters in the wild approaches 1:1 (males to females) (Khamdan 1998; Southgate and Lucas 2008; Gervis and Sims 1992; Fletcher et al. 2006; Al-Matar et al. 1993) at any one time.

Continuous presence of all developmental stages across the year indicates that spawning is periodical and regular with the greatest reproductive activity occurring in the warmest months (June/July). While spawning is continuous, two spawning peaks have been identified. The period of greatest activity or the major spawning period occurs in summer while a secondary spawning period occurs during the autumn (Khamdan 1998; Al-Matar et al. 1993; Mohammed and Yassien 2003; Tlig- Zouari et al. 2009). It appears that the primary spawning period (summer peak:

Chapter: General Introduction 18

April-August) is most likely induced by sudden changes in water temperature and extremes of salinity, while relative food availability is considered to be an important factor determining the timing of the secondary spawning peak (autumn peak: September-November) (Khamdan 1998; Gervis and Sims 1992). Spawning starts when individuals release gametes (either sperm or eggs) into the water column where fertilization takes place (Gervis and Sims 1992; Fletcher et al. 2006; Al-Mohannadi 2002). Females can produce huge quantities of eggs that can reach up to 12 million ova per individual (Fletcher et al. 2006).

The pelagic planktonic larval stage that results is considered to be the dispersal phase for young pearl oysters where they are potentially able to select favourable substrates to colonize and to settle in the wild. Only a very small proportion (≤ 1%) of planktonic veligers are likely to survive and metamorphose into the juvenile stage, a process that is influenced by a variety of extrinsic factors (Fletcher et al. 2006; Herdman 1903). These can include water temperature, predation, availability of sufficient food resources and suitable substrate for settlement (Fletcher et al. 2006; Gervis and Sims 1992). Usually, larval settlement and development occurs over 16 to 30 days (Gervis and Sims 1992; Southgate and Lucas 2008). As soon as an appropriate substrate is found, larvae settle after testing the area then start to metamorphose to the juvenile stage. If no suitable substrate has been found to settle within a short period of time, larvae will metamorphose and eventually die (Herdman 1903; Fletcher et al. 2006). When settlement is imminent, larvae enter a transitive phase between crawling and swimming, a process that requires development of a foot by which they can move away from unfavourable conditions (Southgate and Lucas 2008; Gervis and Sims 1992). Thereafter, during later phases of the life cycle (juvenile and adult), individuals use byssal threads in order to attach to a solid surface, that when made, provide a permanent attachment to the substrate (Fletcher et al. 2006; Carpenter 1997). Juveniles retain some mobility for a short time period that allows them to crawl away from unsuitable conditions and find more suitable habitat by ejecting their byssus and secreting new ones (Gervis and Sims 1992; Southgate and Lucas 2008). In general, pearl oyster larvae tend to settle more readily on dead or alive adult oyster shells. In Gulf waters, a large quantity of P. radiata spat frequently settle on pearl oyster shells, dead coral, rocky

Chapter: General Introduction 19

surfaces and sea grass rhizomes (Al-Khayat and Al-Ansi 2008; Al-Mohannadi 2002; Herdman and Hornell 1906; Mohammed 1994).

Growth rates are initially fast but this is influenced by ambient water environment, in particular food supply as well as quality of both food and water. Pearl oysters, like most bivalves, are filter feeders with small food particles sieved from the surrounding water column using their gills (Fletcher et al. 2006; Gervis and Sims 1992; Herdman 1903; Southgate and Lucas 2008; Yassien, El-Ganainy and Hasan 2009). In general, P. radiata is a nonselective feeder, and can ingest diverse organic and inorganic material including a large amount of mud, bivalve eggs and larvae (Gervis and Sims 1992). It is evident therefore, that long-term productivity and persistence of pearl oyster populations is determined by a complex interaction between endogenous behaviours (intrinsic factors) and environmental variables (extrinsic factors).

1.7.6 Current status of P. radiata populations in the Arabian Gulf

The spatial distribution of P. radiata and natural population sustainability are threatened as a result of both natural and anthropogenic factors including rapid development along coastlines (Price, Sheppard and Roberts 1993; Sheppard et al. 2010). The Arabian Gulf environment is a naturally stressed ecosystem because waters in the Gulf are essentially closed. An enclosed nature has resulted in low water exchange rates with the Indian Ocean and it takes an estimated five years for the Gulf waters to undergo complete water exchange (Sheppard 1993; De Mora et al. 2004; Hunter 1986). The Arabian Gulf experiences a number of stressful factors, including extreme salinity, high temperatures, very high evaporation rates and low flushing rates (Sheppard 1993; Carpenter 1997). These environmental stressors influence adversely marine ecosystems in the Gulf region (Sheppard et al. 2010; Price, Sheppard and Roberts 1993; Price 1993; De Mora et al. 2004). As a result of the above features, marine pollutants are less likely to be diluted and dispersed in the Gulf than they would be in other open marine systems (Sheppard 1993; De Mora et al. 2004). Furthermore, over the past three decades, offshore areas and coastal zones around the Gulf in particular, have been exposed to unprecedented anthropogenic development pressures. Rapid growth in human populations in

Chapter: General Introduction 20

coastal regions around the Gulf has led to increasing demand to address human economic, residential, industrial, social and recreational objectives. Construction activities related to landfills, dredging for oil exploration and for port construction, and for desalination plants have elevated threats to local marine ecosystems. Most benthic habitats in the region are deteriorating and their nature is altering rapidly as a result of anthropogenic activities (Sheppard et al. 2010; Price 1993). For instance, mortality rates of littoral invertebrates have risen near to 100% in Qatar and Bahrain due to oil spills and other human-generated impacts (Price 1993; Gerges 1993). Dredging and landfills have led to a significant increase in coastal sedimentation that has subsequently caused a permanent loss of natural habitat by smothering or limiting photosynthesis by some shallow water plant species (Vousden and Price 1985; IUCN 1987). Approximately 30km2 of the Bahrain coastline has been reclaimed recently to create an artificial island for recreational development (Madany, Ali and Akhter 1987). The existence of solid structures such as the Saudi-Bahrain causeway and oil platforms have also impacted dispersal and distributional ranges of some marine species that possess only short larval duration times (Basson 1977; Price 1993).

Human practices and activities, including fishing and over exploitation of marine resources can further contribute to alteration of the genetic integrity of target species and indirect declines in populations of non-target species. One indirect impact of instability of local marine environments are changes that may occur to the genetic composition of different communities (Law 1991; Smith, Francis and McVeagh 1991). In addition, along the northeast and southeast coast of Qatar, increased levels of cool water discharge have had a profound impact not only on the dispersion of benthic fauna but also on the composition of local marine communities. It is significant in the Qatari coastal area that the flow of thermal water has affected many non-mobile species and affected the distribution of sediment biota, bivalves and coral species. Cooling water has also had severe effects on the pelagic stages of certain marine species more so than on the adult phase due to the greater sensitivity of larval stages and their low tolerance to the discharge plume (El-Din 2004).

Chapter: General Introduction 21

To summarize, the Arabian Gulf littoral and offshore waters have become a significant repository for solid wastes, cooling water and oil industry discharge in recent times (Price, Sheppard and Roberts 1993; Price 1993; Sheppard et al. 2010). The Gulf is also subject to many natural stressors such as extreme temperature and high salinity. Based on these natural and anthropogenic factors, the Arabian Gulf is suffering from loss of natural habitats, loss of biodiversity and productivity, all of which indicate that there is a significant threat to the sustainability of wild P. radiata stocks in the region (Sheppard et al. 2010). Negative impacts on the Gulf region are also likely to increase with the many ambitious plans and projects indentified for implementation across the region in the future.

1.8 PREVIOUS STUDIES OF PINCTADA RADIATA

To date very few studies have examined genetic diversity at any scale in Pinctada radiata, so little is known about levels and patterns of variation in this species. Beaumont and Khamdan (1999) conducted an allozyme electrophoretic study that suggested P. radiata populations from the Arabian Gulf constituted a single genetic stock. In the same study, it was asserted that variation in Japanese pearl oyster species overlapped that of the Arabian pearl oyster P. radiata after examining a wide collection of Japanese pearl oyster (P. matensii/ fucata/ fucata martensii) specimens from the Natural History Museum, London. Arabian pearl oyster specimens, were discriminated later however, from Japanese specimens by Masaoka and Kobayashi (2006) based on ITS sequences taken from nuclear ribosomal DNA and mitochondrial 16S ribosomal RNA gene regions. This analysis showed that they were divergent and constituted a unique monophyletic clade. Another evolutionary study reported diversification patterns and modes of speciation in Pinctada species based on mtDNA (COI) and nDNA (18S rRNA) sequences (Cunha et al. 2011). No gene sequences studies to date, however have explicitly attempted to examine population variation in P. radiata. This is basically all that is known currently about P. radiata in terms of genetic diversity and genome structure and organization.

To date, few studies have been directed at documenting genetic diversity (using allozymes) or evolutionary relationships in P. radiata (Khamdan 1999). While other Pinctada species are relatively well studied in particular for population

Chapter: General Introduction 22

structure and phylogeographical purposes using several molecular markers including SSRs, no studies to date have screened SSR variation in P. radiata. Therefore, there is a significant gap in terms of assessing the natural levels and patterns of genetic diversity in P. radiata and defining the population structure of this taxon across its natural distribution. Anonymous nuclear (ANL) markers are random selected fragments of DNA, that can be found in non-coding regions of the genome and all eukaryotes possess non-coding regions in their genomes (Thomson et al. 2010). These markers are independent, less exposed to selection, fast-evolving and not biased to specific regions of the genome. As a consequence, they have proven to be useful genetic markers for population genetic, phylogenetic and phylogeographic studies (Bertozzi et al. 2012). Simple sequence repeat (SSR) markers generated from ANL in GSS can be used to assess the patterns of natural levels of genetic diversity in P. radiata populations across their natural distribution in the Arabian Gulf.

1.9 THE CURRENT STUDY

The specific aims of the current study therefore were to:

1) Assess the levels and patterns of genetic variation in the Arabian pearl oyster, Pinctada radiata, a species native to the Arabian Gulf in the Indian Ocean. The study also sought to define P. radiata population structure considering potential intrinsic (e.g. pelagic larval phase and life-history traits) and extrinsic factors (e.g. water temperature, wave action, prevailing wind variation and water currents) that could influence any wild structure detected. Considering these factors, this study determined whether sampled wild populations of P. radiata were differentiated or panmictic and described their natural levels of genetic diversity.

2) Develop SSR markers for the target species P. radiata using an NGST approach (Ion Torrent). In parallel, bioinformatics tools were used to analyze the large DNA sequence dataset generated and to annotate unique genes. Therefore, the current study was the first attempt to discover a large number of SSRs and to identify putative genes that may be affected by environmental stressors and possibly affect P. radiata growth and survival using an NGST approach.

Chapter: General Introduction 23

Chapter 2: Population Structure Inferred from Mitochondrial (COI) Sequence Data

2.1 INTRODUCTION

Genetic diversity and population structure of aquatic species can be influenced by many factors, some intrinsic to the organism (e.g. life-history traits) while others are extrinsic to them (e.g. physical barriers to dispersal). Gene flow is an important force that can affect population structure. Essentially gene flow is the amount of effective dispersal that occurs among subpopulations (demes) that in turn, will influence population structure and the amount of genetic variation that is maintained within and between natural populations or even demes (Slatkin 1985), over time.

Gene flow is the change in gene frequencies in subpopulations that results from dispersal of gametes, individuals, or groups of individuals from one population to another population followed by successful reproduction (i.e. effective dispersal) (Slatkin 1987). Dispersal has the capacity to introduce new alleles and/or to change the frequencies of existing alleles in populations (Bohonak 1999; Johnson and Gaines 1990). Therefore, gene flow can promote genetic diversity within wild populations (Freeland 2005) while limiting divergence among them.

Genetic drift in contrast, is considered to be an opposing force to gene flow. It is often considered to be the most powerful force that can erode genetic variation, particularly in small populations. Therefore, its impact is usually a function of effective population size (Ne), overtime. Genetic drift can be defined as random changes in allele frequencies from one generation to the next that results from sampling error (Freeland 2005). Such outcomes occur as a result of stochastic processes, when more surviving offspring are produced by chance by certain individuals compared with others in the population. As a result, some alleles can be lost or random fixation of particular alleles may occur in the next generation, a

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 25

process that can reduce genetic diversity levels in small populations overtime (Slatkin 1987; Bohonak 1999; Cook 1976; Allendorf 1983; Freeland 2005; Allendorf and Luikart 2007)

Mutation is also a process that can influence the levels of genetic diversity present in a population. Even though it contributes significantly to genetic diversity, mutation can also have lethal impacts on individuals that express specific, poorly- adapted phenotypes where carriers may not survive to reproduce (Cook 1976; Allendorf 1983; Hartl and Clark 1997).

Natural selection can also influence levels and patterns of genetic diversity in natural populations. Natural selection is defined as a process in nature that leads to differential reproductive success of individuals in a population (Freeland 2005). Natural selection can alter gene frequencies (i.e. either increase or decrease frequencies of alleles) via different ways (e.g. balancing vs. directional selection). Both balancing and directional selection can affect the levels of genetic diversity present in wild populations. Directional selection will decrease genetic diversity when selection favours certain alleles or genotypes under specific environmental conditions. As a result any favoured allele may reach fixation that in turn reduces overall diversity at the locus potentially leading to homozygosity. In contrast, genetic diversity at a locus can also be enhanced by balancing or stabilizing selection, a process that favours maintenance of multiple alleles at a locus within a population. This is due to higher fitness of heterozygotes over homozygotes (i.e. overdominance) or where more than a single allele at the locus is favoured in different sub-environments (multiple niche polymorphism) (Allendorf 1983; Freeland 2005; Cook 1976; Allendorf and Luikart 2007).

It is apparent therefore, that differences in the amount of genetic diversity present within and among populations is a function potentially, of many forces generated from the combined effects of processes including gene flow, genetic drift, mutation and various forms of natural selection. Genetic drift, mutation and natural selection generally enhance divergence among sub-populations while gene flow has the

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 26

opposing effect by homogenizing diversity across populations and inhibiting subpopulation development or population structure (Slatkin 1987; Bohonak 1999; Allendorf 1983; Balloux and Lugon-Moulin 2002).

Gene flow can be constrained when there is a barrier (e.g. physical or biological) to dispersal that in turn disrupts population connectivity (i.e. vicariance). This potentially will result in populations becoming isolated and subsequently they will evolve independently. Consequently, such changes in allele frequencies will enhance divergence among populations over time (Bohonak 1999; Palumbi 1996; Balloux and Lugon-Moulin 2002; Freeland 2005).

The extent of gene flow differs markedly among various taxa depending on both external (extrinsic) and internal (intrinsic) behavioural factors (i.e. life history traits) (Slatkin 1985; Johnson and Gaines 1990; Planes, Parroni and Chauvet 1998). The level and patterns of intraspecific population structure is influenced largely by a species’ life-history traits (Bay et al. 2004). For example, populations of many freshwater species are often structured at the level of among streams and/or drainages. This results from their stenohaline nature that limits dispersal and consequently obligates them at fine spatial scales (Bohonak and Jenkins 2003). In contrast to most freshwater species, many terrestrial and marine organisms often show shallow population structures due to extensive dispersal by their zygotes and/or gametes via various dispersal vectors (e.g. wind and water currents etc) (Jenkins et al. 2007).

A general perception widely held about population structure in marine species is that they often exhibit less spatial variation among populations than that present in equivalent freshwater or terrestrial species (Bernardi 2000; Riginos and Liggins 2013). This is due to greater potential to disperse larger distances and also that in the marine environment barriers that hinder gene flow are generally less common (Graves 1998; Penant et al. 2013). This can produce genetic homogeneity among populations of many marine species as has been reported for Atlantic cod (Pogson et al. 2001), some sessile taxa (Dias, Duarte and Solferini 2006), reef fishes (Lessios and Robertson 2006) and gastropods (Kano and Kase 2004).

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 27

Therefore, larval dispersal is a common characteristic of many marine species that strongly influences population connectivity and natural distribution patterns. For example, many aquatic species that possess relatively long-lived pelagic larval phases tend to exhibit extensive natural distributions, low genetic differentiation among sub-populations and high rates of gene flow due to extensive passive larval dispersal. Therefore, the length of the pelagic larval stage is a critical factor that can affect population structure and is often correlated positively with the extent of individual dispersal and negatively with recognition of discrete populations (Bohonak 1999; Bay et al. 2004). For instance, some marine species have high potential to disperse (including many oyster, sea urchin and starfish taxa) because they have long-lived planktonic larval phases (e.g. pelagic eggs or larvae). This is manifested by essentially passive movement (dispersal) in water currents for time intervals from several days to weeks or even longer time frames. Potentially this can result in dispersal over large open oceanic distances (even between oceans) and consequently this can homogenize populations when compared with sedentary species that lack a pelagic larval phase (Bohonak 1999; Freeland 2005; Jenkins et al. 2007).

Some studies have shown however, that trans-oceanic dispersal patterns can often be much more complex and non-intuitive than had previously been anticipated, as a result of the existence of unseen barriers to dispersal and/or behavioural traits that affect gene flow across many habitats and regions of open ocean (Freeland 2005; Bohonak 1999; Bay, Crozier and Caley 2006; Doherty, Planes and Mather 1995; Planes, Parroni and Chauvet 1998). A diverse array of marine taxa while possessing long lived larval phases have shown limited connectivity among populations including, some marine invertebrates (Sotka 2005), groupers (Craig et al. 2006) and starfish (Williams and Benzie 1998). Tracing dispersal patterns in some taxonomic groups across vast areas of open ocean was often a difficult task before the development of molecular markers as a method for indirect estimation of the levels and patterns of genetic diversity (Freeland 2005).

A number of molecular studies have investigated levels and patterns of genetic variation and related this to natural distributions in pearl oyster species from diverse regions including Australia, China, Japan and Bahrain. These studies have

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 28

employed a variety of molecular markers but most commonly; allozymes, mtDNA and microsatellite makers (Beaumont and Khamdan 1991; Masaoka and Kobayashi 2005; Southgate and Lucas 2008). In general, the studies have reported low genetic differentiation and high levels of gene flow suggesting regular exchange of migrants between Japanese and Australian populations for P. imbricata (Colgan and Ponder 2002). Another study reported a similar pattern for P. fucata between Australian, Japanese and three Chinese populations (Yu and Chu 2006b). Similarly, Beaumont and Khamdan (1991) suggested that P. radiata populations in the Arabian Gulf constituted a single genetic stock based on allozyme variation.

Lind et al. (2007) examined some potential factors that have influenced genetic diversity in P. maxima populations across the Indo-Australian Archipelago (IAA) using six polymorphic microsatellite loci to estimate levels of genetic diversity among locations. They reported that genetic diversity levels increased towards central Indonesia and decreased at greater geographical distances away from this region. A number of factors were proposed that could have influenced this pattern. The first factor suggested that the IAA may provide more favoured habitat for P. maxima that would in turn, result in more temporally stable populations and ones that were less subject to demographic effects for example, sequential population bottlenecks. The second factor was that central Indonesia is surrounded by significant natural biogeographical barriers (e.g. Wallace’s Line) that enhance genetic diversity across the region due to high capacity to isolate populations, geographically (Lind et al. 2007).

Just as with any species, there are a number of extrinsic (environmental) factors that have potential to influence P. radiata population structure in the wild, some obvious ones include; water temperature, wave action, prevailing wind patterns and water currents. Considering these factors, the specific aims of this chapter were to determine if sampled wild populations of P. radiata collected from Qatari waters in the Arabian Gulf were structured and to describe natural levels of population genetic diversity. By addressing these issues, patterns of genetic variation in sampled populations can be explored and wild population structure (if any) can be defined. Understanding the natural levels of genetic variation will be important for

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 29

long term sustainability of P. radiata in the Arabian Gulf waters and as basic data required for effective management of wild stocks across the region.

2.2 METHODS

2.2.1 Sample collection and DNA extraction

Mantle tissues samples from, Pinctada radiata, individuals were collected from the following three locations; Doha Harbour, Ras Bu Fontas and Al-aseeri Bouy in Qatar (Figure 2). Detailed information for each site including sampling dates, site codes and sample sizes are presented in Table 1. Species identification was verified initially in the field via examination of external morphological traits. Fresh mantle tissue samples were first rinsed with 95% ethanol then stored in 99% ethanol in a Biotechnology lab in Qatar before being transported to QUT (Molecular Genetics Research Facility) for genomic DNA (gDNA) extraction. Some samples were extracted using a modified salt extraction method (Miller et al. 1988) and other samples using a NucleoSpin Tissue extraction kit (MACHEREY-NAGEL).

Table 1: P. radiata collection sites and further sampling details.

Collection site Site Latitude and Sampling Country Total code longitude dates samples Doha Harbour DH 25 18 37 N May 2008 Qatar 42 51 18 56 E Ras Abu Fontas RF 25 18 37 N May 2008 Qatar 40 51 33 19 E Al-aseeri Bouy AB 25 12 992 N Feb 2011 Qatar 25 51 37 352 E

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 30

Figure 2: Map of Qatar showing sampling sites for P. radiata.

2.2.2 DNA marker selection

A region of the Cytochrome Oxidase subunit I (COI) gene from the mitochondrial DNA genome (mtDNA) was chosen to assess levels of genetic diversity within and between Qatari P. radiata populations. Mitochondrial DNA has become one of the most popular genetic markers for use in phylogeographic and population genetic studies. It has been applied in more than 80% of phylogeographic studies to date (Beebee and Rowe 2008). MtDNA possesses several attributes that has led to its wide application in ecological and evolutionary studies (Liu and Cordes 2004). First, mtDNA regions can be readily amplified using universal PCR primers because of the often conserved arrangement of gene fragments (Feral 2002; Freeland 2005). Second, mtDNA exists in high copy numbers in the majority of

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 31

tissues, a factor that assists PCR amplification from a wide range of tissue sources including even archaeological remains and museum samples (Beebee and Rowe 2008; Feral 2002). Third, because of a relatively rapid molecular evolutionary rate, geographical distributions of evolutionary lineages can be examined successfully at a broad array of taxonomic levels (Encalada et al. 1996; Giannasi, Malhotra and Thorpe 2001; Avise 2009). Mitochondrial DNA can also be used to estimate divergence times from the contemporary to the more distant past (Avise 2000; Palumbi 1996). Fourth, mtDNA is a single circular molecule that lacks recombination. Because it is inherited uniparentally (maternal inheritance) and is haploid in most eukaryote species, genetic material is transmitted directly from mother to offspring. Therefore, it has an effective population size one quarter that of the diploid nuclear genome causing this single locus to drift rapidly (4 times faster than nDNA loci) resulting in genetic differentiation among populations even over relatively short periods of evolutionary time where gene flow is constrained. Thus mtDNA has potential to be an important indicator for detecting demographic events over a population's history such as those that result from genetic bottlenecks (Freeland 2005; Feral 2002; Beebee and Rowe 2008; Ward and Grewe 1994). Finally, mtDNA at the intraspecific level usually manifests neutrality due to its nature of not contributing to the expressed phenotype of the individual. Therefore, deviations from mutation/drift or mutation/gene flow equilibrium will likely be signatures of past demographic events rather than direct effects of selection (Beebee and Rowe 2008; Avise et al. 1987; Slatkin 1987).

2.2.3 PCR amplification of mtDNA (COI)

Approximately 450bp of the mtDNA COI gene was amplified using universal primers developed by Folmer et al. (1994). Following unsuccessful mtDNA (COI) amplification in some P. radiata individuals, the degenerate primer (LCO 1490) was replaced with a specific internal primer designed (ACO specific) for the current study using Primer3 (v.0.4.0) software (Rozen and Skaletsky 2000) in order to ensure specificity during PCR amplification. PCRs were run in an EPPENDORF Mastercycler to amplify the target region mtDNA (COI). Agarose gel electrophoresis was used to verify either amplification success or failure. Gels were run for (30-40) mins at 100V and PCR products viewed under UV light.

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 32

Cytochrome Oxidase subunit I primers (Folmer et al. 1994) (710bp)

LCO 1490: F (5’-GGTCAACAAATCATAAAGATATTGG-3’)

HCO 2198: R (5’-AACTTCAGGGTGACCAAAAAATCA-3’)

ACO (this study): F (5’- AGCGCATGCTCTGGTTATG-3’)

2.2.4 MtDNA sequencing

Successfully amplified products were purified using a PCR isolation kit from BIOLINE following the manufacture protocols. After purification, amplified products were sequenced in both directions using an EPPENDORF Mastercycler. An Ethanol/EDTA protocol was used to clean up products from sequence reactions. Sequences were generated using an ABI (Applied Biosystems) 3500 Genetic Analyser.

2.3 DATA ANALYSIS

Raw sequence data were aligned using ClustalW as implemented in BioEdit v.7.0.9 (Hall 1999). Chromatographs were used to edit sequence data manually and to verify any unambiguous bases. MEGA v4.0.2 (Tamura et al. 2007) was utilized to estimate evolutionary distance by calculating pairwise nucleotide differences among haplotypes. MEGA software was also used to identify all variable sites including singleton and parsimony-informative sites in mtDNA sequences.

A haplotype phylogenetic tree was constructed following the Neighbour-Joining (NJ) method (Saitou and Nei 1987) based on Kimura 2-parameter model (Kimura 1980). Median joining network (MJ) was another approach used to construct evolutionary relationships among haplotypes using NETWORK v.4.5.1 (Bandelt, Forster and Röhl 1999). Conformation to neutral expectations was tested using Tajima's D (Tajima 1989) and Fu’s Fs (Fu 1997) in DnaSP v.5.10 (Rozas and Rozas 1999). Significant deviations from neutrality were assessed using the coalescent simulations method in DnaSP. Genetic diversity indices were computed in ARLEQUIN v.3.1 (Excoffier, Laval and Schneider 2005). Several measures of diversity were calculated including: (h) number of haplotypes, (s) polymorphic

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 33

sites, (Hd) haplotype diversity, (π) nucleotide diversity (mean number of pairwise nucleotide differences). To describe the partitioning of genetic variation at different hierarchical levels (within/among populations), AMOVA (Analysis of Molecular Variance) was used as implemented in ARLEQUIN (Excoffier, Laval and

Schneider 2005). Statistical significance of P. radiata population pairwise Φst estimates were calculated based on 10,000 iterations of a non-parametric permutation process.

2.4 RESULTS

2.4.1 MtDNA diversity in sampled wild P. radiata populations A total fragment length 450 bp of the mtDNA COI gene was amplified and screened for variation in a total of 107 individuals of P. radiata from three sample sites. Overall, 8 unique haplotypes were identified among the samples that contained 8 polymorphic sites including 2 parsimony informative sites (positions 90; 276), while other sites showed singleton mutations (Table 2). Table 2 indicates each unique haplotype identified in the current study compared against a single reference haplotype (H1). Fragment lengths of mtDNA COI gene were edited down to 384 bp, when other P. radiata sequences available from GenBank were included in order to align them appropriately.

The presence of nuclear mitochondrial DNA (Numts) in the mtDNA datasets is problematic. False phylogenetic relationships can be inferred when constructing phylogenies if Numts are present (Buhay 2009). To avoid these nonfunctional copies of nuclear mtDNA in the present study, specific primers were designed to ensure specificity during PCR amplification. MtDNA sequence alignments in BioEdit were homologous and they also showed 99% matches to P. radiata and Pinctada species mtDNA sequences in GenBank when Blast searches were undertaken.

Unique haplotypes, nucleotide diversity, polymorphic sites and haplotype diversity per sampling site are shown in Table 3. Haplotype diversities per site ranged from 0.094 at Doha Harbor to 0.470 at Al-aseeri Bouy. Nucleotide diversity estimates among haplotypes were low (0.00032-0.00178). While the Al-aseeri Bouy site had the smallest sample size, it showed the highest haplotype diversity estimate (0.470)

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 34

and nucleotide diversity estimate (0.17%) among the three samples sites (Table 3).

In contrast, the lowest haplotype diversity (Hd=0.094) and nucleotide diversity (π=0.00032) estimates were found at the Doha Harbour site.

Table 4 and Figure 3 represent complete haplotype frequencies at each site and among sampled populations. Of the 8 unique haplotypes identified, the most common haplotype (H1) was detected at all three sampled sites. The second most- common haplotype (H2) was also shared among the three locations. Haplotype (H7) was present in two populations (RF and DH) and absent at the Al-aseeri Bouy site (AB). Several private alleles (haplotypes; H3, H4, H5, H6 and H8) were observed at only a single site and these rare haplotypes were distributed independently among the three populations.

Table 2: Variable sites for 8 haplotypes of 450 nucleotide bases of COI for P. radiata. Parsimony informative sites are shown in bold.

Sites Hap 90 132 192 261 276 288 324 369 Majority T G C A T C G T Hap1 ...... Hap2 G . . . C . . . Hap3 G . T . C . . . Hap4 ...... C Hap5 ...... A . Hap6 . . . . . T . . Hap7 . . . G . . . . Hap8 . A ......

Table 3: Sample site, (n) sample sizes and genetic variation indices for each site. (h): Number of haplotypes, (S): Number of polymorphic sites, (Hd):Haplotype diversity, (π): Nucleotide diversity.

Sample site n h S Hd π

AB 25 5 5 0.470 0.00178 (Al-aseeri Bouy)

RF 40 5 5 0.278 0.00126 (Ras Abu Fontas)

DH 42 3 3 0.094 0.00032 (Doha Harbour)

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 35

Total 107 8 8 0.258 0.00102

Table 4: P. radiata mitochondrial COI haplotype frequencies for each site individually.

Haplotype AB RF DH Total H1 18 34 40 92 H2 4 2 1 7 H3 2 2 H4 1 1 H5 1 1 H6 1 1 H7 1 1 2 H8 1 1

Arabian Gulf H1

H2

H3 H4

H5

H6 Qatar H7 H8

Figure 3: Haplotype frequencies among P. radiata sampled populations.

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 36

2.4.2 Phylogeography

A neighbour–joining tree for all P. radiata COI haplotypes was constructed in MEGA as shown in Figure 4. Bootstrap values were low among nodes. All haplotypes in the network were closely-related (maximum of 4bp difference between the most divergent haplotypes) and formed a single clade. Four additional P. radiata COI sequences (haplotypes) were available on GenBank sampled from the United Arab Emirates (UAE) taken from another study (Cunha et al. 2011). The NJ tree did not show any evidence for distinct clades when the additional COI haplotypes were included suggesting that all P. radiata haplotypes (those sampled here and from GenBank) formed a single monophyletic group. Moreover, they all shared a single ancestor that was the common haplotype (H1). One of the additional four haplotypes (P. radiata4) shared the derived haplotype H4.

A median joining (MJ) network was used to construct the evolutionary relationships among the 8 unique haplotypes identified here using NETWORK as shown in Figure 5. In Figure 5, circles represent individual haplotypes (different colours) and sizes of circles indicate relative frequencies of each haplotype in the sampled populations (Table 4 contains actual frequencies). Single mutational changes are presented as lines among haplotypes while small black circles represent missing haplotypes. The resolved network of phylogenetic relationships among the 8 COI haplotypes formed a star-like pattern (Figure 5). (H1) constituted the central haplotype in the star pattern and was also the most-common haplotype present among the samples. All haplotypes were closely related and connected to the central haplotypes (H1) or (H2) by a maximum of two base pairs. Therefore, the resolved network for Qatar samples did not show evidence for phylogenetic structure, however there was clear evidence for a recent population expansion.

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 37

41 P. radiata 2 88 P. radiata 1

P. radiata 3 Hap8 24 Hap5 Hap6 Hap4/ P. radiata 4

Hap7 Hap1 Hap2

88 Hap3

0.001

Figure 4: Neighbour joining tree for P. radiata mtDNA COI haplotypes. Haplotypes in black colour from the current study while blue P. radiata sequences (haplotypes) from GenBank.

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 38

Hap6

Hap3

Hap5

Hap1 Hap2 Hap4

Hap7 AB Hap8 RF DH P. radiata GenBank

Figure 5: Median-Joining Network of phylogenetic relationships between sampled P. radiata haplotypes and haplotypes sampled from GenBank. Coloured circles represent individual haplotypes, size of circles indicates relative haplotype frequencies, lines between haplotypes indicate single mutational changes and small black circle represents missing haplotype.

2.4.3 Phylogenetic comparisons among Pinctada species A consensus tree from neighbour-joining analysis of Pinctada species estimated solely from mtDNA COI region failed to resolve clear systematic relationships among the taxa (paraphyletic), except for the P. radiata, P. imbricata, P. maxima and P. margaritifera nodes (see Figure 6). Results did show however, a single monophyletic group for P. radiata. The topology of the NJ tree did support a sister relationship between P. martensi and P. fucata, and between P. albina and P. maculata. In general, evolutionary relationships among some Pinctada species represent polytomies (colour-coded) that are partially unresolved. Pteria sterna was used as the outgroup taxa for this analysis.

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 39

2.4.4 Tests of neutrality Results of neutrality tests including Tajima’s D and Fu’s Fs are shown in Table 5 and were performed in DnaSP. All neutrality values were not significant at all sites, suggesting that P. radiata COI sequences were evolving as per a neutral model. Neutrality estimates however, when sites were combined, were significant and they indicated a recent population expansion.

P. radiata 2 P. radiata 3 Hap5 45 Hap7 P. radiata 1 64 Hap1 Current study Hap6 99

Hap8

Hap2

99 64 Hap3

P. imbricata 100

P. Imbricata AB 56 40 P. martensi

97 P. fucata 30 41 P. martensi AB P. maculata AB 65 P. albina 100 P. maculata P. maxima 100 P. maxima AB

P. margaritifera 100 P. margaritifera AB

Pteria sterna 0.05

Figure 6: Consensus tree from neighbour-joining analysis of Pinctada species estimated from mtDNA COI region.

Table 5: Neutrality test of mtDNA COI.

Test DH AB RF Total Tajima’s D -1.7123 -1.340 -1.3179 -1.6772 Fu’s Fs -2.059 -1.420 -1.858 -5.471

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 40

2.4.5 Inferred population structure of the P. radiata samples

Sources of differences within and among the three sites sampled in Qatar were analyzed using AMOVA. Results showed only 5.39% of the variation was present among P. radiata sites while 94.61% of the variation was present within sites (see

Table 6). The mean fixation index (Φst) was 0.054 among sites which was not significantly different from zero. Results indicate that no obvious genetic structure was apparent among the sampled P. radiata populations based on the mtDNA COI region sequence data and most genetic variation was present within sampled

populations. P. radiata population pairwise Φst estimates were calculated based on 10,000 permutations (Table 7), and suggested that gene flow was ongoing among sites.

Table 6: Results of analysis of molecular variance (AMOVA).

Source of d.f Sum of Variance Percentage of Variation squares components variation Among sites 2 1.239 0.01183 Va 5.39 Within sites 104 21.570 0.20741 Vb 94.61 Total 106 22.810 0.21923 FST 0.05394 (P=0.0234)

Table 7: MtDNA pairwise Φst among Pinctada radiata sampling sites. Above the line: Significance of pairwise comparison (P < 0.05) and below the diagonal: Φst estimates.

AB RF DH AB ns ns RF 0.11712 ns DH 0.0000 0.44144

2.5 DISCUSSION

Analysis of genetic diversity in three wild P. radiata populations at three sites in Qatar based on mtDNA (COI) sequences indicated that no significant structure was evident and the three populations formed a single monophyletic clade but there was obvious evidence for population expansion. This implies that gene flow has been ongoing among the sampled sites. When the same data were compared with

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 41

haplotypes of P. radiata sampled previously from UAE waters in the Arabian Gulf (Cunha et al. 2011), this also confirmed the absence of distinct P. radiata clades suggesting no spatial variation at this geographical scale (approximately 300km). Larval dispersal is a common characteristic of many marine species that can influence population connectivity and natural distribution patterns. For example, many aquatic species possess a long pelagic larval phase and tend to exhibit low genetic differentiation among populations and high rates of gene flow due to potential for extensive larval dispersal (Bohonak 1999). Therefore, the length of the pelagic larval stage is an important functional characteristic of Pinctada spp. and is often correlated positively with the extent of individual dispersal and negatively with recognition of discrete populations (Bohonak 1999; Bay et al. 2004). Ecological studies indicate that many marine species have high potential to disperse (including many oyster, sea urchin and starfish taxa) due to possession of a planktonic larval phase (e.g. pelagic eggs or larvae) that is manifested by essentially passive movement (dispersal) in water currents for time intervals from several days to weeks or even longer time frames (Bohonak 1999). Potentially this can result in dispersal over large open ocean distances (even between oceans) and consequently, can homogenize populations when compared with equivalent sedentary species that lack a pelagic larval phase (Jenkins et al. 2007; Freeland 2005). The general pattern for P. radiata appears to reflect extensive dispersal among all sites examined here.

The (MJ) network (Figure 5) generated for P. radiata shows evidence for a population expansion in Qatar waters because a star like pattern was evident with common haplotypes (H1 and H2) in the centre. Common haplotypes were shared among all sampled locations suggesting ongoing historical gene flow due to high potential for P. radiata larval dispersal at least across the sampled region (Southgate and Lucas 2008). All haplotypes were very closely related (including rare haplotypes) and connected to central haplotypes (H1 and H2) by only a small number of base pairs. When P. radiata COI haplotypes from GenBank were included, one of the UAE haplotypes (P. radiata 4) was shared with the derived haplotypes (H4) from Qatar and both Qatari and UAEs’ P. radiata haplotypes shared a common ancestor (H1). Relative high haplotype diversity, a star-like pattern, significant neutrality values when sites were combined and low number of nucleotide differences among rare haplotypes indicate in general, potential for a

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 42

recent population expansion. Sheppard (1993) reported that the Arabian Gulf region experienced a complete drying out during the late Pleistocene. Thus, the region now is characterized by relatively low biological marine fauna and flora biodiversity due to the fact that marine conditions there are only recent and are very stressful (high temperatures and hypersaline) (Sheppard 1993). The Al-aseeri Bouy site while it had the smallest sample size, showed the highest haplotype diversity estimate (0.470) as well as nucleotide diversity (0.00178) among the sampled sites (DH and RF) (Table 3). As a result of the water current circulation pattern in the Arabian Gulf (anti-clockwise rotation) this would tend potentially to increase gene diversity and hence genetic variation towards the south as alleles move passively downstream in marine currents (Sheppard et al. 2010). The RF site however, showed only moderate haplotype diversity (0.278) compared with other sites. This less than expected level of diversity could be affected by presence of the largest desalination plant in Qatar at RF. This desalination plant has been reported to adversely affect the local marine biota and the general ecology of the local environment there. High water temperatures and salinity affect the growth and health of benthic invertebrates in the vicinity of the desalination plant, which as a consequence shows reduced benthic community abundance.

Unfavourable local environmental conditions have reduced the survival rate of eggs and pelagic larvae of the marine taxa and increased significantly the mortality rate of sedentary adult organisms due to their lack of mobility (Areiqat and Mohamed 2005; Miri and Chouikhi 2005). Thus, low water quality has likely changed the

species composition and diversity. The lowest haplotype diversity (Hd=0.094) and nucleotide diversity (π=0.00032) estimates were found at the Doha Harbour site. Doha Harbour is now considered to be one of the most polluted sites along the Qatari coast due to the amount of shipping that disturbs and muddies local waters. This area simultaneously also receives large quantities of ground water loaded with suspended solids and inorganic nutrients resulting in high eutrophication impacts (Al-Ansari 1998). Al-Madfa and colleagues (1998) observed elevated concentrations of cadmium in P. radiata individuals collected in Doha Harbour, Messaieed and Ras Laffan. These locations are subjected to intense, continuous dredging activities for harbour expansion. High concentrations of Pb were also found as a result of the harbour being close to the main highway as well as to boat

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 43

anchorages and to fishing boat harbours that all can be a major source of leaded gasoline used as fuel. Moreover, dead P. radiata beds have been found in the Doha Harbour site due to continuous dredging activities for shipping a process that can hinder oyster settlement (Al-Madfa, Abdel-Moati and Al-Gimaly 1998). The Doha Harbour showed lowest mtDNA diversity of all three sites examined here, and this site is also the site most impacted by human activities.

The most prominent physical characteristics of the Arabian Gulf region are its semi-enclosed, shallow, hyper-arid setting where evaporation is extreme and flushing rates are low. An enclosed nature has resulted in relatively low water exchange as water is estimated to only be exchanged with the greater Indian Ocean on average approximately once every five years (Sheppard 1993; De Mora et al. 2004; Hunter 1986). Most benthic habitats in the region are deteriorating and their nature is altering rapidly as a result of increased anthropogenic activity (Sheppard et al. 2010; Price 1993). Thus, it would appear that both natural and anthropogenic factors may have contributed significantly to shaping the contemporary P. radiata population diversity in the region. These factors combined with ongoing gene flow resulting most likely from larval drift have been sufficient to hinder any potential population differentiation in P. radiata wild populations in Qatar since modern populations have evolved.

Analysis of Molecular Variance (AMOVA) analysis results showed only 5.39% of the variation was present among P. radiata sites while 94.61% of the variation was

present within sites and the mean fixation index (Φst) among sites in Qatar was not significantly different from zero. The results, although limited in scale, suggest that it is unlikely real divergent wild populations of P. radiata are present in the Qatari region and suggest that P. radiata populations are essentially homogenous there. Low levels of genetic differentiation among sampled sites in Qatar can be attributed largely to high levels of gene flow suggesting relative high exchange of migrants among sites where the species occurs naturally.

The pattern of population structure observed here for P. radiata showed no evidence for genetic differentiation among populations within the Qatari region and also when populations were included from another geographical area in the Gulf

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 44

(UAE). Low levels of population differentiation have also been reported in several pearl oyster species and other marine organisms. A number of molecular studies have investigated levels and patterns of genetic variation and related this to natural distributions in pearl oyster species from diverse regions including Australia, China, Japan and Bahrain. In general, studies of other Pinctada species have reported extensive genetic homogeneity and high levels of gene flow suggesting regular exchange of migrants for example; between Japanese and Australian populations of P. imbricata (Colgan and Ponder 2002). Another study found a similar result for P. fucata between Australian, Japanese and three Chinese populations (Yu and Chu 2006b). Thus, similar life history traits in various Pinctada species results in large outbred populations that experience extensive gene flow over large geographical scales in most instances.

Thus, data here are clear and indicate that P. radiata populations sampled from the Qatari coast are panmictic and therefore should be considered as a single management unit. At a larger spatial scale (approximately 300km) in the Arabian Gulf, there was no significant genetic differentiation between Qatar’s P. radiata stock and that present in UAE’s based on mtDNA (COI). Thus, the pearl oyster fishery in the Gulf should probably consider these populations as a single stock for management purposes until wild populations that occur between the two regions have been evaluated to determine if they are genetically differentiated.

Chapter: Population Structure Inferred from Mitochondrial (COI) Sequence Data 45

Chapter 3: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata

3.1 INTRODUCTION

3.1.1 Microsatellite genetic markers Microsatellites or simple sequence repeats (SSRs) are tandem repeated segments of DNA. Repetitive motifs range in length from 1 to 6 nucleotide bases and are inherited as co-dominant, bi-parental markers. They are found at high frequency across the nuclear genome of most eukaryote taxa and in general, show relatively high levels of allelic polymorphism (Selkoe and Toonen 2006; Jarne and Lagoda 1996; Chistiakov, Hellemans and Volckaert 2006; Zhang and Hewitt 2003).

Due to their relative small size and unique characteristics, SSRs have proven to be powerful genetic markers for many applications. As a consequence, they have been used widely in a variety of fundamental and applied fields including in both biology and medicine (Chistiakov, Hellemans and Volckaert 2006). Microsatellites have been used to study some genetic diseases because they follow simple laws of Mendelian inheritance (Jarne and Lagoda 1996; Selkoe and Toonen 2006). Such markers are highly susceptible to mutations and correspondingly often show high levels of genetic variation in natural populations (Feral 2002; Bruford, Bradley and Luikart 2003). They can be used therefore to provide insights into genetic variation both within and among populations (Zhang and Hewitt 2003). Microsatellite genetic markers have also been used successfully for testing pedigrees, for estimating genetic diversity levels, for gene mapping studies, and for defining population structure (Zhang and Hewitt 2003; Jarne and Lagoda 1996).

Microsatellite repetitive sequences are frequently subject to mutation by either insertion or deletion of repeats resulting from slippage or failed proof-reading during DNA replication (Feral 2002; Li et al. 2002; Selkoe and Toonen 2006). In general, they have high mutation rates that can reach 5×10-4 on average per locus per generation. Therefore, they have potential to generate high levels of allelic diversity

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 46

in particular, in large wild populations. Both high allelic diversity and availability of many loci have contributed significantly to identifying fine scale population structure in many species (Liu and Cordes 2004; Li et al. 2002; Selkoe and Toonen 2006; Chistiakov, Hellemans and Volckaert 2006). Microsatellite markers potentially provide very high resolution power for addressing many questions commonly raised in both terrestrial and aquatic species (Chistiakov, Hellemans and Volckaert 2006).

3.1.2 Traditional methods for microsatellite isolation Traditionally, isolating microsatellite loci for new species was achieved via developing a partial genomic library. Restriction enzymes were used to fragment gDNA to obtain high quality restricted genomic DNA. Small fragments, preferably (300-700bp), in size were then selected from the sheared DNA. Plasmid vectors were then used to ligate directly the DNA fragments after adaptors were first attached to the fragment. Bacterial colonies containing SSR inserts were then blotted onto nylon membranes. Positive clones were then identified using a colony hybridization approach to identify repeat-containing clones. There are two ways to transfer a colony: classical replica or picking and ordering single colonies onto a new arrayed plate. Genomic clones with microsatellite loci were then isolated and ready for screening. Labelling the hybridization probes can be carried out using both radioactive and nonradioactive methods. Specific primers can then be designed and PCR conditions optimized before amplification of a microsatellite locus for various individuals in a population (Zane, Bargelloni and Patarnello 2002).

Factors that limit use of the traditional SSR development approach Developing SSR markers using the traditional approach can be problematic due to cost, time and intensive labour requirements. The major limitation is the need for prior identification of sequences with SSR repeats to design specific primers for amplification. The second limitation is the requirement for library construction via isolation of genomic clones containing SSRs repeats, followed by sequencing each one. A third problem can occur when designing optimal primers for a single polymorphic microsatellite locus. Collectively, high cost, high expertise requirements and significant effort are all required to yield useable SSR markers for a target species using the traditional approach (Zalapa et al. 2012). These issues have

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 47

often limited development of SSR markers for new species particularly when appropriate molecular research labs have limited facilities or are not available.

3.1.3 NGS approaches to SSRs discovery and development Next generation sequencing (NGS) is revolutionizing applied science by allowing massive data collection from DNA sequencing at relatively low-cost with high performance (Metzker 2010). In parallel, bioinformatics tools have improved rapidly in recent years and now allow large datasets to be generated and annotated rapidly in most laboratories (Mehinto et al. 2012). Regardless of the diversity of DNA synthesis systems, NGSTs require highly sophisticated pre-sequencing and post- sequencing (Hui 2012). The pre-sequencing phase involves two main steps: target enrichment and library constriction. Target DNA enrichment can be acquired either by using amplification and sequencing methods or via hybridization and selection methods. NGS library preparation can be accomplished using the following procedures: (1) the enriched DNA is fragmented by physical methods into preferable lengths of (150-500) base pairs, (2) small DNA fragments are ligated to adaptor primers, and (3) clonal amplification of the NGS library (by emulsion bead PCR or cluster amplification). While procedures for sequencing reactions and methods for capturing sequence data are different among NGS platforms (Roche, lllumina, AB SOLiD, and Ion-Torrent), billions of read can be generated using each technology from the sequencing reaction. Lengths of reads are typically 100 bases in length and each read contains a single sequence clone. The billions of reads can then be assembled as longer sequences referred to as contigs using post-sequencing bioinformatics analysis. Assembled contigs can then be screened to identify SSR motifs using relevant bioinformatics tools (Hui 2012). Search and filter are the two main phases in most search tools for detecting tandem repeats (Merkel and Gemmell 2008; Domaniç and Preparata 2007). Users of search algorithms can modify sorting parameters in order to suit their particular applications. Outcomes however, are highly changeable according to the settings used (Merkel and Gemmell 2008; Leclercq, Rivals and Jarne 2007; Richard, Kerrest and Dujon 2008; Sharma, Grover and Kahl 2007). Several recent studies have demonstrated the efficient use of NGSTs for large-scale discovery of SSR loci in non-model species (Zalapa et al. 2012; Franchini, Van der Merwe and Roodt-Wilding 2011; Hou et al. 2011; Lim et al. 2012).

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 48

3.1.4 Traditional approaches vs. NGS for isolation of SSR markers

Sanger and colleagues introduced conventional DNA-sequencing technology in 1977 (Sanger, Nicklen and Coulson 1977). This technique allows individual specimens to be sequenced for up to 1Kb of DNA sequence data. Automated Sanger sequencer, an advance on the manual approach is now considered to be the most advanced version of the technology, and has the capability to sequence up to 1Kb at a time from 384 individual specimens. New advanced sequencing technologies however, have been introduced rapidly over the last few years. These high throughput-sequencing platforms differ only in the chemistry they employ and the amount of sequence data they can capture. In parallel, NGS devices have the potential to recover DNA sequence reads from hundred of thousand up to tens of millions of genes. Thus enormous volumes of sequence reads can be generated using three approaches: (i) genome sequencing (i.e. fragmented libraries of target genome); (ii) RNA sequencing or transcriptome sequencing (i.e. cDNA pooled library produced by reverse transcription of RNA; or amplicon sequencing (i.e. a pool of amplified molecules generated via PCR). In all approaches, sequence reads have been obtained without the requirement to use classical cloning procedures that are commonly utilized to isolate DNA templates and for amplification. In this case, conventional cloning bias issues can be avoided such as unevenness in sequencing. There are some specific limitations however, associated with each NGS platform (Mardis 2008).

Although chemistry, nucleotide incorporation and searching tools differ among the available NGSTs, they still have two common steps: library fragmentation/amplicon preparation and detection of incorporated bases into the sequence (Glenn 2011; Zhang et al. 2011). NGSTs can be categorised into two main types: (1) PCR-based technologies including four commercial platforms (Roche, lllumina, AB SOLid and Ion-Torrent); or (2) single molecule sequencing (SMS) systems including two recently announced (HeliScope and pacBio RSSMART), which do not require PCR amplification before sequencing (Shokralla et al. 2012)

In 2010, the Ion Personal Genome Machine (PGM) was introduced by Life Technologies that is considered to be a post-light sequencing system for basic and applied science. This technology depends on the instant detection of hydrogen ion

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 49

release, with the product released when a nucleotide base is incorporated into a DNA strand during polymerase sequencing. In parallel techniques, high-density micro- wells are utilized in Ion Torrent to perform the biochemical process. Each micro- machined well contains a single DNA template. There are two types of sensors underneath the wells: (1) an ion-sensitive layer and (2) a proprietary ion sensor. The function of these sensors is detection of the hydrogen ion change when a nucleotide base is incorporated (Miller et al. 2011; Rothberg et al. 2011). This technique does not require either nucleotide labelling or optical detection. The system has some common features with many others including that; it is able to test simultaneously multiplexed samples by addition of bar-coding adaptors. Chip sizes range (from 314 to 318) and in each run can capture 10-1,000 MB of sequence data. In spite of labour intensive operation and one-touch library preparation, the Ion Torrent system has rapidly scaled up the sequencing process (Shokralla et al. 2012; Hui 2012).

Since the introduction of high-throughput NGSTs in 2005, a number of limitations and challenges have been identified (Shokralla et al. 2012; Delseny, Han and Hsing 2010). First, most NGSTs produce only short reads compared with the Sanger approach. Secondly, abundant repeat regions are difficult to assemble unambiguously. Since sequences generated from NGSTs are short reads, it can often be a real challenge to assemble short reads into longer sequences (contigs). Thirdly, sequencing errors and artefacts arising from homopolymers can be a common problem. The fourth, while the cloning procedure has been eliminated, several steps are still involved including; fragmentation, ligation and amplification that can make library construction difficult (Mardis 2008; Shendure and Ji 2008). The fifth, is that a new way of storing and mining data needed to be developed for NGSTs. Such a computational challenge is attributed to the fast rate of generating raw data compared with the relatively low storage capacity of computers (Stein 2010). While there has been massive improvement in sequencing output, in terms of read length, accuracy, cost-effectiveness, and reduced labour requirements, challenges still exist with instruments, chemistry and protocols that will require improvement in the future (Mardis 2008; Quail et al. 2008).

The comparison between traditional approaches for SSRs isolation vs. NGST approaches manifests clear differences for non-model species. Although Sanger,

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 50

lllumina and 454 technologies can generate potentially similar numbers of polymorphic loci, they vary significantly in the total number of SSR loci possible. The fundamental feature of NGSTs over Sanger sequencing is the discovery of SSR total number (Zalapa et al. 2012). According to Zalapa et al. (2012), the gross number of SSRs detected from genome and transcriptome data via NGSTs can be very large. Although this study focused only on plant species, there is potential to yield much larger numbers of SSRs using NGST compared with approaches in non- model species. A substantial advantage of NGSTs is the length of reads that can simplify the primer design procedure for SSRs under (low-medium) sequencing coverage.

3.1.5 NGST for isolation of SSRs in bivalves

Traditionally, a genomic library approach has been used to develop SSR markers in most species and has been done through selecting and sequencing individually SSR- carrying clones. The traditional approach however, is comparatively difficult in the case of large and complex genomes which occur in most eukaryotic non-model species (Bertozzi et al. 2012; Echt et al. 2011; Jennings and Edwards 2005). Alternative methods that have potential to develop SSR markers include: (1) public genomic DNA and EST databases and (2) NGS de novo genomic DNA generation and EST libraries (cDNA) (Bertozzi et al. 2012; Echt et al. 2011). The advent of NGSTs has allowed study of the transcriptomes and genomes of pearl oyster Pinctada species. Joubert et al. (2010) conducted a pyrosequencing study to analyse the first EST library produced from the calcifying mantle of P. margaritifera. Kinoshita et al. (2011) reported ESTs from P. fucata using 454 GS FLX transcriptome analysis to study shell-formation tissues including mantle edge, pallium, and pearl sac. McGinty et al. (2012) also identified some putative biomineralisation genes expressed in pearl sac from P. maxima and P. margaritifera tissues using Illumina sequencing. More recently, the first draft genome of the pearl oyster P. fucata was completed to provide a platform for identification of selection markers and genes for calcification, knowledge of which will be important in the pearl industry (Takeuchi et al. 2012). While several studies have confirmed that NGSTs can be an effective approach for large-scale discovery of SSRs in non-model species (Zalapa et al. 2012; Wang et al. 2011; Lim et al. 2012; Franchini, Van der Merwe and Roodt-Wilding 2011; Hou et al. 2011), most studies have focused on identifying putative genes using mRNA converted to cDNA as

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 51

resources to address their research goals. Simple sequence repeats (SSRs) are commonly located however, in nuclear DNA regions, in both protein-coding and non- coding regions. A significant source of SSR markers in non-model organisms will therefore be present in genomic (often non-coding) DNA sequences (Abdelkrim et al. 2009; Echt et al. 2011). Genome survey sequence (GSS) can play an important role in providing a wealth of SSR markers by producing random fragments of sheared genomic DNA using NGST platforms (Bertozzi et al. 2012; Gardner et al. 2011). To date however, no published studies have applied Ion-Torrent approaches for SSR discovery or identification in non-model species. The current study aimed to develop potential SSR markers for the target species P. radiata that will be useful for population genetic studies in the future and to generate GSS dataset using Ion Torrent. In parallel, bioinformatics tools were used to analyze the large amounts of data generated, to screen for SSR motifs and to annotate unique genes detected. The current study is the first attempt to discover a large number of SSRs and identify putative genes that may be affected by environmental stressors and possibly affect P. radiata growth and survival using an NGST approach.

3.2 METHODS

3.2.1 Samples and DNA extraction Pearl oyster samples, Pinctada radiata, were collected from mantle tissue from the following two locations; Doha Harbour and Ras Bu Fontas in Qatar. Species identification was verified initially in the field through examination of morphological traits then confirmed via mtDNA (COI) sequence analysis. Fresh mantle tissue samples were first rinsed with 95% ethanol then stored in 99% ethanol in the Biotechnology lab in Qatar before being transported to QUT (Molecular Genetics Research Facility) for genomic DNA (gDNA) extraction using a NucleoSpin Tissue extraction kit (MACHEREY-NAGEL).

3.2.2 Attempts to screen SSR markers At the beginning of the current project no SSR markers were available for the target species P. radiata so the first approach adopted was to trial cross-species amplification. SSR cross-species amplification is a desirable approach in terms of saving time and money (López-Vinyallonga et al. 2011). Simple sequence repeat (SSR) primer sets were available for other Pinctada species but failed (Shi et al.

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 52

2009) to amplify in P. radiata. Failure probably resulted from high mutation rate in priming sites and high allelic diversity associated with SSR cross-amplification primers. Moreover, availability of only low quality DNA for the target species led to non-specific amplification of P. radiata samples here (Smith et al. 2000). Next, a traditional approach to SSR development (microsatellite isolation with Dynabeads) (Glenn and Schable 2003) was trialled to develop species-specific SSR markers, but this also failed due to the relatively low quality of the extracted DNA sample. Therefore, a quick and effective method to develop a large number of SSR markers rapidly was trialled using next generation sequencing (NGS). Fresh mantle tissues were obtained to overcome the low DNA quality issue.

3.2.3 Library construction and Ion Torrent sequencing Two different kits: (1) Ion Plus Fragment Library and (2) Ion Xpress Plus Fragment Library (Life Technology) were used to obtain high quality genomic libraries from gDNA following the manufacture’s protocols. Two gDNAs of high quality were obtained and pooled at equimolar concentrations. Pooled gDNAs were then fragmented to a selected size of (250bp). Ion adaptors were used then to ligate the blunt- ends of DNA fragments. Subsequently, Ion-compatible adapters and nick repair were used as a linkage ring between the DNA inserts and adaptors. To quantify gDNA yields, a Quant-iT RiboGreen fluorometer (Invitrogen) was used. Bioanalyzer (Aglient) was then used to determine the average sequence length reads. Analysis of aliquots (1 µl) on a Bioanalyzer was also used to determine average sequence length.

Next, the ligated library underwent size selection to produce an optimal target read length of 250 bp. All successful libraries were amplified following the manufacturer’s protocol (Life Technology) for the Ion Xpress Template kit and the automated Ion OneTouch System on Sphere particles. After successful amplification and ligation, pooled gDNAs were sequenced using a 314 semiconductor chip and PGM chemistry according to (Life Technology) the manufacturer’s protocol.

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 53

3.3 DATA ANALYSIS

3.3.1 Sequence assembly

After obtaining sequence reads from the PGM, all reads were run via the Ion Torrent server in order to remove any poor sequences and sequencing adaptors. Data were then captured from the three different chips (Ion Chip Sequencing protocol). Default parameters in the Software SeqMan NGen 10.1(DNASTAR) were used to assemble (de novo) trimmed sequences. Both singleton and contig datasets were generated after quality filtering in SeqMan NGen. All genome survey sequences (GSSs) for P. radiata were captured and then analysed further.

3.3.2 Annotation The most common data used to predict putative genes in BLASTx comes from expressed sequenced tags (ESTs) obtained from cDNA. GSS data can also be used however, to identify functional genes due to their high sequence similarity (i.e. E- value) and long sequence length. Despite potential for introns that can hinder such analyses, GSSs can identify potential exon-intron boundaries in sequences that have high similarity with subject sequences. To identify functional genes (E- value threshold<1e-5), the BLASTx search non-redundant (nr) database (Altschul et al. 1997) in GenBank was searched based on P. radiata contig sequences. This search is hosted by the National Centre for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/). Predicting functions for GSSs was completed using Blast2GO software (Götz et al. 2008) in order to assign Gene Ontology (GO) terms (The Gene Ontology Consortium 2008). The Kyoto Encyclopaedia of Genes and Genomes (KEGG) (Kanehisa et al. 2006) was used to identify metabolic pathways. InterProScan tool was utilised to interrogate all translated sequences against the InterPro database (http://www.ebi.ac.uk/Tools/pfa/iprscan/) to predict protein domains. Quantification and drawing of each GO term for all annotated contigs was done using Blast2GO (Götz et al. 2008).

3.3.3 Identification of SSR motifs Msatcommander (Faircloth 2008) was then employed to search all P. radiata GSS sequences for SSR motifs. To detect different SSR nucleotide motifs (di, tri, tetra, penta and hexa), default parameters were applied. A minimum of 8 repeats was required to assign di-nucleotide SSRs while 6 repeats were need to identify larger

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 54

nucleotide motifs. 100 bp were set as a maximum interruption between 2 adjacent SSRs to consider them a compound SSR. PCR primers were designed for each unique SSR identified using primer3 software (Rozen and Skaletsky 2000).

3.4 RESULTS

Genomic DNA from P. radiata was purified and isolated from pooled mantle tissue samples then run via Ion Torrent sequencing to generate a large dataset of sequence reads. De novo assembly was used to cluster and assemble sequence reads that passed minimum quality standards. A total of 278,932 GSS sequences (total=55.95Mb and Q20=36.96Mb) were produced in the Ion Torrent run. The longest GSS read was 399 bp and average length was 201 bp. 898 contigs were generated from a high quality assembly of P. radiata GSSs and the average length was 709 bp from 28265 GSSs (Table 8). While contig N50 was 792bp, nine contigs exceeded more than 2Kb in length. Contigs varied in length from 229bp to 4021bp (average 709bp), with 674 (75%) being >500bp in length as shown in (Figure 7). Singleton lengths ranged from 5bp to 387bp with average length 179bp.

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 55

Table 8: Summary of P. radiata Ion-Torrent sequences.

1 Description Total number of bases (Mb) 55.95Mb Number of Q17 bases (Mb) 41.37Mb Number of Q20 bases (Mb) 36.96Mb Average read length (bp) 201 Longest read length (bp) 399

2 Number of reads Total reads 278,932 Assembled 28265 Singleton 250667 Repeat 337

3 Number of contigs Total contigs 898 Average contig read length (bp) 709bp Largest contig (bp) 4021bp Number of large contigs >500bp 674 Number of large contigs >1000bp 104 Average sequences per contig 31

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 56

180000

160000

140000

120000

100000 Singleton 80000 Contig Frequency

60000

40000

20000

0 100 200 300 >400 Length (bp)

Figure 7: Summary of P. radiata Ion Torrent sequences.

3.4.1 Putative SSRs A total of 782 microsatellite motifs or simple sequence repeats (SSRs) were identified in the P. radiata GSS dataset comprising 37.2% dinucleotide repeats, 23.5% trinucleotide repeats, 35.4% tetranucleotide repeats and 3.7% penta/hexanucleotide repeats (Figure 8).

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 57

Others (Penta/Hexa) 29 3.7% ATCG 1 ATCC 5 AGAT 6 ACTG 7 ACTC 7 ACGG 56 ACCT 7 ACCG 4 ACAT 1 ACAG 61 AATT 1 AATG 59 AATC 10 AAGT 2 AACG 5 AAAT 34 AAAG 2 AAAC 9 35.4% Tet ra-nucleotide 277 ATC 63

repeated nucleotide types AGG 2 AGC 7 ACT 4 ACG 56 AAT 29 AAG 10 AAC 13 23.5% Tri-nucleotide 184 AT 66 AG 182 AC 43 37.2% Di-nucleotide 291 0 50 100 150 200 250 300 Perfect SSR motif numbers

Figure 8: Distribution of simple sequence repeat (SSR) nucleotide classes among identified nucleotide types in P. radiata.

3.4.2 Comparative GSSs analysis BLASTx searches for GSS sequences obtained from P. radiata indicated that 315 of the 898 (35%) contigs identified had significant similarity (E value <1e-5) with non- redundant proteins (nr) available in the GenBank database.

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 58

3.4.3 Gene Ontology assignment

Based on BLAST match results, 898 of the P. radiata contigs were assigned to proteins with known functions (Figure 9). As shown in Figure 9, GSS coding sequences for P. radiata were assigned to terms as following: 502 sequences to molecular function, 501 to biological processes and 190 sequences to cellular components.

38(Organelle) 43(macromolecular complex) 5(extracellular region) 187(metabolic process) 11(membrane enclosed lumen)

119(cell) 48(localization)

Cellular 4(multi-organism process) component

3(biological adhesion) Biological process 15(cellular component biogenesis) Molecular function 2(multicellular organismal process) 6(singaling) 155(binding) 3(reproduction)

183(cellular process)

37(structural molecule activity) 5(molecular transducer activity) 8(electron carrier activity) 1(antioxidant activity) 20(response to stimulus) 1(locomotin) 19(biological regulation) 7(carbon utliziation) 7(cellular component organization) 2(cell wall organization or 1(growth) 2(viral reproduction) 1(developmental process) biogenesis)

Figure 9: Gene Ontology (GO) terms for contig sequences in P. radiata.

3.4.4 KEGG analysis

Pinctada radiata contig sequences were mostly identified as existing in the following KEGG pathways; purine metabolism (n=19), alcohol dehydrogenase (n=13), oxidative phosphorylation (n=11), and nitrogen metabolism (n=10). Apart from these, several additional pathways were also recovered including; pyrimidine,

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 59

pyruvate, starch and sucrose, amino sugar and nucleotide sugar, fructose and mannose, and pentose phosphate pathways (Table 9).

Table 9: Frequency of KEGG pathways identified in P. radiata contigs.

Candidate pathway Contig IDs Occurrence 239;778;056;583;676;771;781;194;285; Purine metabolism 649;611;776;759;310;783;208;343;591;614 19 229;269;754;312;061;130;212;235;268; alcohol dehydrogenase 026;783;559;230 13 048;079;224;231;321;568;598;671;756;195;6 Oxidative phosphorylation 29 11 Nitrogen metabolism 048;079;224;231;321;568;209;756;195;629 10 Pyrimidine metabolism 239;600;632;771;781;194;285;649;809 9 Pyruvate metabolism 348;238;679;026;783;304;229;077;269 9 Starch and sucrose metabolism 061;130;212;235;268;242;587;690;230 9 Amino sugar and nucleotide sugar metabolism 061;130;212;235;268;348;582;690;230 9 Fructose and mannose metabolism 061;130;212;235;268;690;234 7 Pentose phosphate pathway 784;765;611;776;186;297;230 7 Butanoate metabolism 283;141;135;229;269;077 6 Aminoacyl-tRNA biosynthesis 263;650;128;674;578 5 Ascorbate and aldarate metabolism 061;130;212;235;268 5 Carbon fixation in photosynthetic organisms 783;186;297;599;748 5 Arginine and proline metabolism 209;267;225;748 4 Fatty acid metabolism 229;269;754;283 4 Glycine, serine and threonine metabolism 229;269;754;549 4 Tyrosine metabolism 229;269;754;748 4 Alanine, aspartate and glutamate metabolism 310;209;748 3 Benzoate degradation 283;229;269 3

3.4.5 Protein domains Based on InterProScan, 956 protein domains were identified among the P. radiata contigs. This result is consistent with data from analyses of other GSS non-model

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 60

species screened for SSR motifs (Iranawati et al. 2012; Romana-Eguia et al. 2004; Milano et al. 2011). Dominant protein domains detected here include; reverse transcriptase, ribonuclease H-like, integrase catalytic core (Table 10).

Table 10: Summary of the top 20 domains in P. radiata contigs.

IPR accession Domain name Domine description Occurrence IPR000477 RVT Reverse transcriptase 12 Ribonuclease H-like IPR012337 RNaseH-like_dom domain 10 IPR001584 Integrase_cat-core Integrase, catalytic core 6 IPR003439 ABC_transporter-like ABC_transporter-like 3 ATPase, P-type, cytoplasmic transduction IPR023300 ATPase_P-typ_cyto_domA domain A 3 ATPase_P-typ_ATPase- ATPase, P-type, ATPase- IPR008250 assoc-dom associated domain 3 Glycoside hydrolase, IPR017853 Glycoside_hydrolase_SF superfamily 3 IPR025948 HTH-like_dom HTH-like domain 3 IPR016181 Acyl_CoA_acyltransferase Acyl_CoA_acyltransferase 2 Alcohol dehydrogenase, IPR001670 ADH_Fe iron-type 2 IPR015902 Alpha_amylase Alpha amylase 2 Amino acid/polyamine IPR002293 AA/rel_permease1 transporter I 2 Aminoacyl-tRNA IPR006195 aa-tRNA-synth_II synthetase, class II 2 DNA polymerase, palm IPR023211 DNA_pol_palm_dom domain 2 Glycoside hydrolase, IPR013781 Glyco_hydro_catalytic_dom catalytic domain 2 IPR000182 GNAT_dom GNAT domain 2 IPR001650 Helicase_C Helicase, C-terminal 2 Helicase, superfamily 1/2, IPR014001 Helicase_ATP-bd ATP-binding domain 2 IPR000358 Ribonucl_redctse Ribonucleotide reductase 2 IPR001207 Transposase_mutator Transposase, mutator type 2

3.4.6 Analysis of genes The most abundant GSS sequences identified in the P. radiata GSS dataset were homologous to abc atp-binding protein, phage protein, surface antigen, transposase, and zinc finger protein. Putative genes related to environmental and/or toxicological stresses in the P. radiata genome were also recognized (Table 11).

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 61

Table 11: Summary of the top 20 genes and putative genes identified as environmental stress bio-markers in P. radiata contigs.

Top 20 genes Gene list Contig ID Length (bp) E-value range abc atp-binding protein 560;208;164;585 598;764;980;723 1.56E-116-2.87E-56 hypothetical protein SELMODRAFT_419703 807;409;810;817;618;359 845;641;2051;2033;1438 [Selaginella 871;452;858 ;644;963;3156;932 moellendorffii] 2.81E-36-2.22E-06 hypotheical protein 123;218;319;309;043 713;446;798;351;762 1.52E-87-1.47E-35 phage protein 669;646;148;642 909;860;847;558 surface antigen 619;252;662;076 756;606;551;529 1.83E-77-1.22E-57 130;086;308;061;036;338 025;534;013;222;283;291 991;828;719;900;856 transposase ;179 ;905;646;688; 585;747;730;421;566;500 5.60E-176-2.60E-21 zinc finger protein 068;024;311;605 832;741;432;745 3.15E-81-3.93E-38 cation transport atpase 648;806;233 881;900;496 5.32E-115-7.95E-59 cell wall surface anchor family protein 262;258;575 748;1040;559 1.69E-123-2.93E-58 conserved protein 008;017;041 1615;1398;867 4.16E-67-3.25E-35 cytochrome b 228;527;088 398;915;597 5.94E-76-3.77E-53 cytoplasmic alpha-amylase 242;587;672 1133;529;767 4.49E-144-9.55E-84 atp-dependent metallopeptidase 614;343 1098;934 1.18E-125-2.04E-122 cell wall-associated hydrolase 098;227 590;392 3.19E-56-2.56E-42 cold shock protein 704;752 703;722 1.10E-13-4.09E-23 cytochrome oxidase subunit i 224;079 858;401 1.82E-76-6.73E-31 nadh dehydrogenase subunit 4 060;629 702;769 2.44E-25-2.38E-11 nadh dehydrogenase subunit 5 195;293 356;600 7.93E-17-3.42E-06 dna-directed rna polymerase subunit beta 285;194 732;750 2.37E-105-5.81E-30 domain-containing cell surface protein 601;342 820;576 3.86E-98-9.95E-80 Putative genes identified as environmental stress bio-markers Gene list Contig ID Length(bp) E-value range alkaline phosphatase superfamily protein 780 1077 3.96E-91 glucose-6-phosphate 1- dehydrogenase 784 712 5.20E-32 glucose-6-phosphate 230 487 2.57E-64 atp synthase b subunit isomerase 108 1032 5.44E-75

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 62

Table 11: Continued.

Gene list Contig ID Length (bp) E-value range nadh dehydrogenase subunit 5 293 600 3.42E-06 nadh dehydrogenase subunit 4 60 702 2.44E-25 cytochrome oxidase subunit i 79;224 401;858 6.73E-31- 1.82E-76 cytochrome c oxidase subunit 2 48 648 1.30E-34 cytochrome c oxidase subunit 3 321 1026 8.13E-40 cytochrome c oxidase subunit iii 568 508 6.91E-57

3.5 DISCUSSION:

Ion Torrent and contig assembly were used to obtain a GSS dataset from gDNA isolated from P. radiata. A mean of 201 bp GSS read length retrieved was expected because 200 bp library preparation kits were utilized here. Currently, only 100 bp and 200 bp library preparation kits are available for Ion-Torrent. The total average GSS read length of 201 bp was similar however to previous studies that have reported NGST runs for ESTs in non-model species including for Glanville fritillary (197 bp Roche 454) (Vera et al. 2008) European hake (206 bp Roche 454) (Milano et al. 2011), but lower than for giant freshwater prawn (311 bp Roche 454) (Jung et al. 2011) and channel catfish (292 bp Roche 454) (Jiang et al. 2011). In bivalves, average EST read lengths include; 413 bp in 454 pyrosequencing of Meretrix meretrix (Huan, Wang and Liu 2012), 313 bp in 454 run of Patinopecten yessoensis (Hou et al. 2011), 234 bp in Roche 454 pyrosequencing of P. margaritifera (Joubert et al. 2010), 347 ~ 361 bp in Roche 454 run of P. fucata (Kinoshita et al. 2011), and 286 ~ 341 bp in 454 run and 50 bp in Illumina run of P. fucata (Takeuchi et al. 2012). More recently, (Iranawati et al. 2012) generated a partial GSS dataset to develop SSR markers from the mud carp; Henicorhynchus siamensis using 454 pyrosequencing that resulted in production of a 264 bp average read length. Average read lengths varies according to the target material (gDNA vs. mRNA) used for sequencing and depending on the raw read total numbers generated from the NGST used. While 454 technologies produce longer read lengths compared with other NGSTs, the overall sequences generated from a single run are often much lower in

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 63

number than for other (i.e. Illumina and Ion-Torrent) technologies. In particular, the Ion-Torrent is the most cost-effective approach because it is less time consuming and labour intensive compared with other commercialised NGSTs. When the 400 bp library preparation kit is available for Ion Torrent, the disadvantages associated with relative short read lengths should be reduced significantly.

3.5.1 Putative P. radiata microsatellite markers A total of 782 microsatellite motifs were detected among the P. radiata GSS sequences. Most of the SSRs identified in the P. radiata genome were dinucleotide repeats (37.2%). This result concords in particular with reports from other pearl oyster species (Jones, Zenger and Jerry 2011) and previous studies of aquatic organisms in general (Jung et al. 2011; Iranawati et al. 2012; Wang et al. 2012). A total of 98 SSR primer sets were designed successfully for P. radiata that included 37.7% dinucleotide repeat primers, 23.4% trinucleotide repeat primers, 35.7% tetranucleotide repeat primers, and 3.06% penta/hexanucleotide repeat primers. This number is however, relatively small compared with some previous outcomes from NGST studies (Iranawati et al. 2012; Jones, Zenger and Jerry 2011). While the number of SSRs detected in P. radiata contig sequences was relatively low, the majority of SSRs detected and primers designed were in singletons suggesting that P. radiata GSS sequences mostly are present in non-coding regions which embraced many polymorphic sites or as a consequence of homopolymer issues which are common in Ion-Torrent sequencing.

While the majority of PCR primer sets designed here for P. radiata comprised polymorphic SSRs, further validation will be required in the future to report these primers as genetic bio-markers to examine ecological and adaptive processes in P. radiata as has been reported in previous studies of non-model species (Panhuis et al. 2011; Wang et al. 2012). In addition, SSR markers detected here have the potential to be transferred to other pearl oyster species (Wang et al. 2012; Jones, Zenger and Jerry 2011).

3.5.2 Comparative GSS analysis A low rate of gene identification is common for mollusc databases, which usually range from only 15 to 40% (Craft et al. 2010; Clark et al. 2010). The outcome in the current study however, was lower still. Large number of contigs matched well to

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 64

reported mollusc and invertebrate sequences (Figure 10) that coincide with previous bivalve studies including for pearl oysters (Craft et al. 2010; Clark et al. 2010; Joubert et al. 2010; Kinoshita et al. 2011; Takeuchi et al. 2012). The majority of contigs matched in the current study however, were from metagenomic sequences. Considering the filter feeding behaviour of oysters, discovering a large number of bacterial sequences in the current study was not a surprise. Although the lack of annotation can result from difficulty with annotating some short length sequences from GSS, it can largely be explained by the lack of sequences available for mollusc species, and by the fact that a vast majority of genes on public databases currently are available largely from model taxa. In the case of Pinctada species, P. fucata was identified as the major species match for P. radiata sequences largely due to availability of many sequences in the public databases. GSSs identified here did not match any P. radiata published sequences because of the low number of sequences available for P. radiata in the NCBI database (only a few mtDNA sequences) (Cunha et al. 2011). The number of identified genes for P. radiata species will be enhanced significantly due to the GSS sequences generated here. Most P. radiata GSSs lack coding sequences resulting in reduction of potential to match with any sequence available on GenBank. This is not surprising nor uncommon in uncharacterized genome sequence survey studies (Iranawati et al. 2012; Takeuchi et al. 2012). Some P. radiata GSS sequences generated here may not be identified as a result of assembly problems from homopolymers as been reported in NGS and EST analyses which in turn can be considered as novel genes unique to the target species and/or to molluscs (Wang et al. 2004; Mittapalli et al. 2010; Jung et al. 2011).

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 65

Top 20 hit species distribution

Others Lactococcus lactis Selaginella moellendorffii Strongylocentrotus purpuratus Pinctada fucata Escherichia coli Lactococcus garvieae Lactococcus phage

Providencia stuartii Nasonica vitripennis Danio rerio

Species Streptococcus thermophilus Nematostella vectensis Lactobacillus plantarum Lactobacillus brevis Haemophilus parainfluenzae Branchiostoma floridae Chlamydomonas reinhardtii Streptococcus phage Streptococcus downei 0 50 100 150 200 250 BLAST Hit

Figure 10: Top 20 hit species distribution based on BLASTx from contigs E value cut-off is 1e-5.

3.5.3 Gene Ontology assignment

Based on Gene Ontology functions for the P. radiata GSSs many were related to both binding and structural molecular activity. These sequences constitute either enzyme regulators or molecular transducers. Biological process assignments showed that many hits were likely involved in metabolic processes and cellular process, while those coding sequences assigned to cellular components were mostly assumed to contribute to both macromolecular complexes and cell functions. Transcripts

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 66

reported in previous sequence analyses for other pearl oyster species and non-model species commonly possess similar metabolic function domains (Iranawati et al. 2012; Joubert et al. 2010; Kinoshita et al. 2011; Wang et al. 2011).

3.5.4 KEGG analysis KEGG pathways identified that GSS coding sequences from P. radiata were largely confined to; purine metabolism, alcohol dehydrogenase, oxidative phosphorylation, and nitrogen metabolism. The highest number of GSS sequences identified were assigned to purine metabolism, a process that plays an important role in many cellular and biochemical pathways and contributes potentially to glutamine synthetase that is required mostly during embryonic development as well as during embryogenesis and oogenesis in oysters (Tanguy, Boutet and Moraga 2005; Ng et al. 2009). Purine metabolic pathways are potentially involved in interactions with GTP levels and ATP when there is a cellular stress (e.g. oxidative stress or oxygen depletion) (Mommsen and Hochachka 1988; Hardie and Hawley 2001). Developmental defects including; pupal lethality, bristle, eye-growth, and eye- pigment disorders may occur as a result of mutations occurring in these essential pathways (Ng et al. 2009; Tiong et al. 1989). A total of 13 GSSs were attributed to alcohol dehydrogenase (ADH) which is considered to be one of the primary enzymes involved largely in alcohol metabolism in partnership with aldehyde dehydrogenase (Edenberg 2007). According to Park and Kwak (2009), ADH3 gene expression can be suppressed by heavy metal stress in Chironomus riparius larvae and so was suggested as a possible biomarker for monitoring aquatic environments. Several GSS sequences identified in P. radiata were related to oxidative phosphorylation and assumed to be involved in producing ATP in mitochondria (Birceanu et al. 2011; Wallace and Starkov 2000). A total of 10 P. radiata GSSs also recovered in the current study mapped to nitrogen metabolism. P. radiata secretes ammonia that is important to cope with eutrophication resulting from phytoplankton production (Saucedo et al. 2004; Newell, Cornwell and Owens 2002). While not all essential genes recovered previously in putative KEGG pathways in other species were found in P. radiata in the current study, the information provides a foundation for understanding molecular processes more broadly in P. radiata metabolism, including functions and responses.

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 67

3.5.5 Protein domains Protein domains identified in P. radiata GSSs in general included; reverse transcriptase, ribonuclease H-like, integrase catalytic core.

Reverse transcriptase (RVT) is considered as a cellular gene that plays a critical role in polymerizing DNA on RNA templates (Gladyshev and Arkhipova 2011). RVT is a component of retrotransposons, however a single copy of the RVT genes possessing introns in evolutionarily conserved positions tends to exist in syntenic regions and evolves under purifying selection. Cellular genes (RVTs) are found in all major taxonomic groups and were also identified among the P. radiata contigs (12) in the current study.

Ten domains containing ribonuclease H-like were also recovered in the P. radiata GSSs. Ribonucleases (RNases) are known to be a type of nuclease that play a catalyzing role in RNA degradation. RNases exist in various forms of life ranging from prokaryotes to eukaryotes (Fang and Ng 2011). Members of the ribonuclease domains are thought to be involved in metabolic pathways as well as carbohydrate metabolism (Cheek, Zhang and Grishin 2002).

A total of six integrase catalytic cores were also recognized among the P. radiata GSS sequences. Integrase enzymes are known as virus-encoding and play a catalytic role in viral integration events (Craigie 2002; Gao and Voytas 2005).

In addition, a total of three ATP-binding cassette (ABC) transporter-like domains were identified. ABC transporter-like genes are a type of transporter that participate in microcystin transportation (Pearson et al. 2004) and toxin localization in thylakoids (Shi, Carmichael and Miller 1995; Young et al. 2005) or toxin extrusion under some growth conditions (Kaebernick et al. 2000). ABC transporters have an essential role in resisting multi-xenobiotics and have been identified in aquatic species using immunohistochemistry (Luckenbach and Epel 2008; Valério, Chaves and Tenreiro 2010). A few early studies suggested that ABC transporter activity could protect mussels against toxicants due to their regulatory roles in absorbing allelochemicals in the gut of consumers, a process that results in changing diet choice and foraging patterns in these aquatic organisms (Whalen et al. 2010; Sotka and

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 68

Whalen 2008). Therefore, where studies are focused on understanding detoxifying mechanisms against various toxic agents in aquatic organisms in particular bivalves, ABC transporters must be considered as a primary candidate gene due to their significance in bivalves’ gills as an active physiological and selective-permeability barrier that act as a defence mechanism against environmental toxicants (Kingtong, Chitramvong and Janvilisri 2007; Luckenbach and Epel 2008).

Glycoside hydrolase (GH), which belongs to the family 18 chitinases and is widely expressed in prokaryotes and eukaryotes, has a fundamental role in physiological processes including T-cell mediation via allergenic reactions and inflammation responses (Funkhouser and Aronson 2007; Zhu et al. 2004; Elias et al. 2005). Despite the critical role that chitinase plays in chitin degradation, GHs act as key players in the degradation of polysaccharides. Glycosylation is an important chemical reaction for compound modification and in biological activities. Biotechnologically, glycoside hydrolases are considered to be attractive enzymes that can be useful components in synthetic reactions in order to assemble glycosidic linkages (Trincone et al. 2008; Rebuffet et al. 2011). GHs are responsible largely for nutrient acquisition, while the presence of GHs in cells is involved in splitting glycosidic linkages. A small amount of glycosidases contain specific enzymes that are capable of polysaccharide degradation in marine species. The physiological role of the GH gene family identified in molluscs has been shown to be involved in immunity reactions and also in embryonic development. In crustacean species, GH is known to be involved in defence against pathogens and also has a role in ecdysis (Bai et al. 2009; Badariotti et al. 2011; Huang et al. 2010). Other common domains also identified in the P. radiata GSS sequences include; ATPase-P-type-cytoplasmic transduction domain A, HTH-like domain, Acyl_CoA_acyltransferse and Alpha amylase.

3.5.6 Analysis of genes identified The main focus of the present study was to identify putative GSS-SSR motifs in P. radiata and putative genes related to environmental and/or toxicological stress in the P. radiata genome, however many putative functional sequences were also identified here which provide fundamental information for future genetic studies. Since very little is known currently about the P. radiata genome, putative functional GSS

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 69

sequences provide a foundation for understanding the potential roles of novel genes in different tissue types in this and other Pinctada species.

Zinc finger proteins exist abundantly in eukaryotic genomes and have varied functions including; transcriptional activation, protein folding, DNA recognition, lipid binding, RNA packaging and protein assembly (Laity, Lee and Wright 2001). The possible role of zinc finger proteins in bivalves has been suggested to include, as a defence mechanism (Moreira et al. 2012; Venier et al. 2011).

Cation transport atpase is responsible for anion exchange in bivalves and crustaceans in the gills when there is cellular incorporation of many ions (Grosell, Nielsen and Bianchini 2002; Bianchini and Wood 2002). For example, calcium-ATPase active transport protein plays an important role in transferring divalent cations of calcium and heavy metals from the cytoplasm to the vacuoles (Ahearn, Mandal and Mandal 2004). An organic cation transporter, which is a major facilitator superfamily, has been suggested to be involved in regulating cell growth and differentiation in Drosophila (Herranz, Morata and Milán 2006) and also in osmoregulatory ion transport across crustacean gills (Towle, Henry and Terwilliger 2011).

Laminarinase and α-amylase are two important digestive enzymes in bivalves. They are involved in carbohydrase activity as well as hydrolysis of (α-1,4) and (β-1,3) internal glucosidic bonds in starch and laminaran (Huvet et al. 2003). α-amylase is considered to be a key enzyme in molluscs due to its capability for assimilating carbohydrate in their digestive tracts (Huvet et al. 2003; Moal et al. 2000). It has also a metabolic role, as it catalyses starch hydrolysis. Thus, α-amylase is responsible for carbohydrate storage reserve mobilization in many organisms (Jie et al. 2009).

Adaptation to environmental change is an essential factor affecting survival in all living organisms, in particular against dramatic stressors and changes that include; cold shock, pressure, heat shock, osmotic stress and acid shock that can be fatal to most organisms (Thieringer, Jones and Inouye 1998). Cold shock proteins (CSPs) are expressed when there is an abrupt downshift in temperature (Graumann and Marahiel 1998; Thieringer, Jones and Inouye 1998; Phadtare, Alsina and Inouye 1999; Kumari Garapati and Suryanarayana 2012). CSPs are evolutionarily conserved proteins from

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 70

archaea to eukaryotes and are known to be involved in diverse cellular processes including cellular growth, cellular division, nutrient stress adaptation to low temperature, and the stationary phase (Kumari Garapati and Suryanarayana 2012; Yang et al. 2012).

Alkaline phosphatase (ALP) is known as a polyfunctional enzyme that is found in the plasma membrane of most cells. In bivalve and mollusc, ALP can be used as an early indicator of cell differentiation and in osteogenic lineages (Pereira Mouriès et al. 2002). In addition, ALP has been involved in bivalve immunity responses and various important metabolic activities including; growth, shell formation, protein synthesis, molecule permeability and steroidogenesis (Chen et al. 2005; Ram 1985). For bivalves, in particular for pearl oysters, ALP activity has been reported to be sensitive to several heavy metal pollutants as an indicator of heavy metal exposure because marine pollution can disrupt artificial production of pearls (Regoli and Principato 1995; Xiao et al. 2002).

Glucose-6-phosphate (G6P) is considered to be one of a group of antioxidant enzymes and is also an important enzyme in the pentose phosphate pathway. G6P enzymes have also been reported to function as oxidative stress biomarkers in bivalves (Romero-Ruiz et al. 2003) under heavy metal exposure and temperature change (Hall 1985; Viselina and Luk'yanova 2000). For genetic monitoring against aquatic pollution, this gene could be a good potential candidate for identification of optimum conditions (Nevo et al. 1986).

NADH dehydrogenase, ATP synthase and cytochrome C oxidase are major genes involved in oxidative stress and energy metabolism (Tomanek 2012), in particular ATPase molecules are considered to be ion transporters that bind to cell membranes. They play key roles in ion transport and energy metabolism because they are engaged in energy production and consumption (Liu et al. 2007). A number of studies have indicated that heat shock or stress can inhibit the translation of several non heat shock proteins (non-HSPs) (Holcik and Sonenberg 2005). For example in bivalves, the lifespan of some crucial proteins responding to heat stress can be prolonged as a result of down-regulation of many proteasome isoforms (Tomanek 2012). This activity has been indicated as well in oxidative stress proteins (Bieler et

Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 71

al. 2009; Anestis et al. 2007). Metabolic depression in marine bivalves can cause a reduction in ATP consumption or turnover as a result of shifting to anaerobiosis that occurs as a consequence of hypoxia (Anestis et al. 2007). In abalone, the expression of cytochrome c oxidase genes related to energy and cellular metabolism were inhibited in the presence of heavy metal exposure (Silva-Aciares et al. 2011). A reduction in the expression of the encoded gene for ATP synthase will decrease the stored energy required for growth and consequently disrupt energy pathways (production and conversion), suggesting that a large amount of the energy has been diverted as a response to metal stress (Silva-Aciares et al. 2011; Zhou et al. 2010). In American oyster, comparisons of physiological and biochemical results have provided evidence suggesting that metabolic depression due to a shortage of energy substrate such as proteins and lipids occurs in moribund larvae because of higher demand for energy and limited feeding resulting from antioxidant defences and the activation of the immune response before massive mortality results (Genard et al. 2012).

Massive mortality in summer and winter in oysters is considered a major problem for their sustainable management, so the genes identified immediately above potentially could be informative bio-markers to report on the physiological and molecular processes in bivalve larvae that have been reared in diverse microbial environments where aquatic pollutants and massive mortality have occurred. Thus, in general the approach adopted here (NGST generation of SSR markers for P. radiata) has been very successful. Not only were a large number of potential SSR markers generated from a single Ion Torrent run, but the resulting data set consisting of a multitude of random sequences from right across the genome of this species, identified a large number of putative coding genes, many of which have important functions in other model and non-model species. In particular, given the stressful environmental conditions that P. radiata individuals are exposed to both naturally and as a result of the impacts of anthropological activates in the region, a number of the gene sequences recovered in the GSS survey here provide potentially important markers for managing stress and as bio-markers for detecting sub-lethal stress in wild populations. The data in combination also constitutes in all possibility the largest single genome resource for any pearl oyster species, and so has broader applications in this important mollusc group.

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Chapter: SSR Marker Development and Genome Survey Sequence (GSSs) in P. radiata 73

Chapter 4: General discussion

Pinctada radiata, a pearl oyster species native to the Arabian Gulf in the Indian Ocean, is the most common pearl oyster found in the Arabian Gulf region with natural populations occurring from the coast of Kuwait in the north along the Emirates and Omani coasts to the south. Currently P. radiata stocks comprise 95% of wild pearl populations found naturally in Qatari waters.

Pearl oysters (mainly P. radiata) have significant commercial value in the Arabian Gulf because of their potential to secrete natural pearl. Consequently, Arabian Gulf countries have harvested pearl oysters for many centuries largely for their wild pearls. Arabian Gulf natural pearls once constituted 80% of the world commercial pearl market due to their excellent quality and preferred shape. Pearl oyster fishing therefore has generated significant economic activity and has been practiced by Arabian Gulf states. In spite of development in recent decades of modern pearl culture technologies, wild oyster populations are considered the main source for seed collection and pearl culture in the region. Nowadays, pearl oyster wild and culture fisheries are considered lucrative markets that require relatively low capital investment. This industry therefore, is a significant contributor to indigenous coastal communities, local economies and international companies in terms of improving their economic status. There is also a commercial value in pearl oyster edible flesh (meat) for human consumption by local people and shell to gain export revenue. Apart from the economic significance of pearl oysters to the region, they have an environmental importance as well.

Pearl oysters are sedentary bivalves and hence naturally accumulate metallic and chemical pollutants in their soft tissues to levels higher than surrounding background levels in sea water due to bioaccumulation. Thus, P. radiata has been used extensively in the Arabian Gulf as an ideal natural bio-indicator for monitoring pollution and for assessing local marine water quality. Moreover, pearl oysters play an important role in promoting and enhancing regional biodiversity as their shells can be used as a solid substrate for fouling organisms (invertebrates etc) and for

Chapter: General discussion 74

larval settlement. It is apparent therefore that P. radiata is an important natural component of the marine ecosystem in the Arabian Gulf region and conservation of this resource will contribute long-term to ecosystem health across the region.

Sustainability of local P. radiata natural populations in the Arabian Gulf region is currently threatened as a result of both natural and anthropogenic factors. A number of natural factors, including low tidal flushing rates, very high evaporation rates, high ambient temperatures that produce extreme salinity levels, are factors that contribute to a naturally stressed ecosystem in the Arabian Gulf. Offshore and littoral waters in the Gulf region are also subjected to increasing anthropogenic development pressures to fulfil local human population needs and ambitious development objectives. The combination of natural and anthropogenic induced stress has adversely influenced most benthic habitats in the Gulf region altering and affecting species diversity and densities. Furthermore, over exploitation of some natural resources by humans has contributed and altered the genetic composition of target high value species. Alteration of the genetic structures of local species is one indication of the instability of local marine ecosystems across the region. Based on all these factors, P. radiata wild pearl oyster stock sustainability is significantly threatened in the Arabian Gulf region.

Prior to the current study, there had been no detailed molecular genetic studies of the target species here (P. radiata) and the genome was poorly known. In contrast, a number of other Pinctada spp. have been studied in more depth particularly in terms of wild population structure using highly variable markers (SSRs) (Benzie and Smith-Keune 2006; Benzie, Smith and Sugama 2003). Little is also known currently about the extent of natural genetic diversification in P. radiata. Although this issue has also been well documented in some other pearl oyster species notably across the Pacific and South East Asia regions (Benzie and Ballment 1994; Southgate and Lucas 2008).

Therefore, the current study sought to define natural levels and patterns of genetic diversity in the Arabian Gulf populations of the pearl oyster P. radiata and to detect wild population structure (if present) considering potential intrinsic (e.g. pelagic larval phase and life-history traits) and extrinsic factors (e.g. water temperature,

Chapter: General discussion 75

wave action, prevailing wind variation and water currents) that could impact any wild structure detected across the natural distribution. The current study also was the first attempt to generate a massive GSS dataset for the target species P. radiata primarily to identify SSR markers that have the potential to be applied in future population genetic studies of this species. Outcomes of the GSS analysis extended beyond characterisation of SSRs and also identified some putative gene fragments in the GSS dataset that may be influenced by environmental stressors and possibly that affect P. radiata growth and survival. These gene sequences could be useful in future studies that examine the impacts of anthropogenic factors on wild pearl oyster stocks which are important local invertebrates.

Results here provide valuable information about natural levels of genetic variation and stock structure for the most common pearl oyster, P. radiata, in the Arabian Gulf in the Indian Ocean. This information will be important for long term sustainability of P. radiata across the region and for management of the wild fishery.

Genetic diversity present in the P. radiata populations sampled here based on mtDNA (COI) sequences indicated clearly that P. radiata populations in Qatari waters were essentially homogenous (panmictic) across the three sites sampled (AB, RF and DH), and also at a larger geographical scale, when data were compared with that from a previous study of the same species sampled from UAE waters in the Arabian Gulf region (Cunha et al. 2011). Thus, at least at this geographical scale, P. radiata populations are essentially homogeneous and they can be considered to constitute a single population for management purposes as a result of high ongoing gene flow among sampled sites. Apparent high dispersal potential in P. radiata probably results from a relatively long-lived pelagic larval phase. Many other Pinctada spp. have also been reported to possess extensive dispersal and low genetic differentiation among wild populations indicating regular exchange of migrants between geographically separated sites (Colgan and Ponder 2002; Yu and Chu 2006a). The length of the pelagic larval phase has often been correlated positively with the extent of individual dispersal across the natural geographical distribution of marine taxa and negatively with recognition of discrete populations (Bay et al. 2004; Bohonak 1999). For example the larvae of many marine species (including oysters,

Chapter: General discussion 76

starfish and sea urchins) often exhibit passive movement in water currents over different time frames from days to weeks, a life history trait that tend to homogenize genetic diversity across populations when compared with species that are more sedentary in nature at all life-history stages (Freeland 2005; Jenkins et al. 2007). There are examples however, where even marine species with relatively long-lived pelagic larval stages can be structured (differentiated) at relatively small geographical scales due to action of often undetected extrinsic factors (e.g. major currents) (Barber et al. 2000) or intrinsic factors (e.g. behavioural traits) (Bohonak and Jenkins 2003). Analysis of population diversity in the current study showed that wild P. radiata populations contained relatively high haplotype diversity and low numbers of nucleotide differences among rare haplotypes suggesting that wild populations have most probably experienced a recent demographic expansion which reflects insufficient evolutionary time and isolation to accumulate divergent lineages. The natural water current circulation pattern in the Arabian Gulf is an anti- clockwise rotation and this would in theory likely, increase genetic diversity downstream of the current towards the south. While the AB site had the smallest sample size, it showed the highest levels of haplotype diversity. In contrast to the DH site that contained the lowest levels of genetic diversity perhaps because this location has been exposed intensely to continuous dredging and construction activities for harbor expansion as well as to other human activities including fishing. Anthropogenic factors and natural stressors (including high temperature and extreme salinity) therefore could have played significant roles in shaping the local natural population structure of P. radiata across the geographical scale of the current study. These factors along with ongoing historical gene flow apparently have been sufficient to hinder any potential for local population differentiation.

From a stock management perspective therefore, recognition that genetic diversity levels are relatively high and wild populations at least at the geographical scale sampled here of Arabian pearl oyster P. radiata are panmictic, is beneficial for local stock conservation and potential for a stock enhancement program. Populations of P. radiata sampled from the Qatari coastal zone should based on this evidence, be considered to be a single management unit. At a broader spatial distribution, there is no apparent significant genetic differentiation between wild populations in Qatar and populations that exist in UAE’s waters based on mtDNA (COI) sequence data.

Chapter: General discussion 77

For management purposes at least based on these data, the two populations should probably be considered to constitute a single stock for the local pearl oyster fishery across the Gulf region or until intermediate geographical populations have been investigated to confirm that they are not genetically isolated.

The extent of the natural distribution of other Pinctada spp. is relatively well studied and their levels and patterns of genetic variation from diverse regions have also been documented. Findings concord with results observed here for P. radiata. Given that, the natural distribution of P. radiata extends beyond the Arabian Gulf into adjacent regions of the Indian Ocean and the Red Sea, future studies should extend population differentiation studies to these regions,

Studies of Pinctada species to date however, have primarily relied solely on data from mtDNA markers. Depending solely on mitochondrial DNA markers however is often not sufficient in isolation to assess the real scale of population differentiation and to define patterns of population structure because a complex of factors may be in play. For example, sex biased dispersal is a specific life history trait present in certain taxa where dispersal mainly is mediated by males not females. Such dispersal can impact patterns of genetic differentiation depending on the marker type employed. Consequently, patterns of genetic differentiation may not be concordant with nuclear DNA (Zhang and Hewitt 2003; Liu and Cordes 2004; Avise et al. 1987). MtDNA will potentially provide biased information if males are the dispersing sex while females are philopatric to their natal areas (Beebee and Rowe 2008). Furthermore, mtDNA is a single locus and therefore only provides a single estimate of population structure and history. The current study aimed therefore to also develop nuclear markers (namely SSRs) since no SSR markers were available for the target P. radiata previously. A detailed analysis of these markers in the same P. radiata populations would allow a more complete picture of natural diversity to be obtained.

The approach to SSR development used here was novel and is the first simple sequence repeat (SSR) dataset generated for the Arabian pearl oyster P. radiata using a modern NGST approach (Ion-Torrent). A large number of microsatellite motifs were identified in the GSS data set of (55.95 Mb) for the target species P. radiata. A

Chapter: General discussion 78

total of 98 SSR primers consisting of 37.7% dinucleotide repeat primers, 23.4% trinucleotide repeat primers, 35.7% tetranucleotide repeat primers, and 3.06% penta/hexanucleotide repeat primers, were designed successfully for the target species and were highly polymorphic and most sequences were located in non- coding gene regions. Further validation of the polymorphic SSR primers will be required in the future however, before they can be used as genetic markers for evolutionary and ecological studies of P. radiata populations. Moreover, there is potential for the SSR markers discovered here to be transferable to other pearl oyster species via cross amplification (Wang et al. 2012; Jones, Zenger and Jerry 2011). As a significant by product of the GSS approach to SSR development for P. radiata, we also generated 55.95 Mb of gene sequence data from across the P. radiata genome. Gene annotation identified many putative gene sequences, some of which matched known functional genes in gene bank databases while others were new and potentially may represent novel genes. This partial genome dataset for P. radiata potentially, can have applications in biotechnology and pearl oyster farming in the future.

Chapter: General discussion 79

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Appendix 1: Microsatellite primers designed for P. radiata.

Primer Sequence Annealing Temp. GC Percent Product Left Right Left Right Left Right Size 1 TGTTGGCTTATTTAGTCGGGCG CCACTTCGGGATTGGTTAGTAATG 62 60 50 45.833 117 2 GGACTTGCCGAATATTAGATCATGG TTCTATCATCGTCATCTCTCCGAC 60 60 44 45.833 100 3 TTGTATGGCGGGAAACAATCTTC AATTAGGACGTGCACATCGACG 60 62 43.478 50 146 4 CGAAGCATAGCGACACTTAGGG GTAGTACAGCGACTCAGGATCAC 61 60 54.545 52.174 120 5 CCAAGCGGTTTCAGAGGAGAAG AGCTATTGTCATACCGGATTGTCC 61 61 54.545 45.833 101 6 CACAGCGACACTTAGGGATCAC ACTGCACGAAGGAATACACTATGC 61 61 54.545 45.833 210 7 CGGGTCGTATTATGGTTTGGCG ATTGCGGTTCATTACGGACGAG 62 62 54.545 50 131 8 TCCTCTGACTGCATGCAACAC TCTTGTCACAACTACCTCCATTCG 61 61 52.381 45.833 158 9 CTCATCCATTCTGCTAAGTCTTTCG TCAGATCATGCAGGTTTCGGTAC 60 61 44 47.826 161 10 TCGTTGTCTCCATTTGTTTGCC TACCATATACCGACCACAGCCG 60 62 45.455 54.545 113 11 GGAATCCGTCTGCTATGTCCAC GTAGTAACTCAGCTGGATGTGGG 61 61 54.545 52.174 167 12 GGAGGAATCCGTCTGTTGCTTG GTTGAACGTCATGATTAGCTTGGG 62 61 54.545 45.833 116 13 GAGTACTTCACACCTACCAAGTTTC AGCTTGTAACATCGATCGGTGC 60 62 44 50 306 14 AGCAACACCTGTCAATTAAGCC TTGATTGAGAGATATTGTGTCGTCG 60 60 45.455 40 136 15 GTCCAGTCGTTTAGTCAGTCGC TCGAATCGACAAACCTCAGACTC 61 61 54.545 47.826 160 16 TATGGTGATGTCAGTTGCGCAG GTGCTTCGTTATCAACAAACCAGG 62 61 50 45.833 127 17 TTCTCAATCCCAAAGTCGACATTG CCATGTTGTCCATTAAAGGTCAGAC 60 60 41.667 44 129 18 CTATTGTCATAGCGCGCCGTC GGCGGCACTAGTTGATTGATCC 62 62 57.143 54.545 213 19 TGCCTCAACCAGAAACTTTAACC TGCAAGTCAATTCCGGTCTATACG 60 61 43.478 45.833 118 20 CGAAGCATAGCGACACTTAGGG GGTATGAGTAGGAAGGTCTTGTCAC 61 61 54.545 48 193 21 GATCTCACATTTCGGACTGCGG GTGCTGTACTGTCGATGCTCAG 62 62 54.545 54.545 217 22 AAATCCGGTCTATGAAGCGGC TCGACGGACTGAAACTACACAC 61 61 52.381 50 139 23 TTACAGTCCTGCAACCCACTTG GTCGAGTGGATGAGGATTGAGTG 61 61 50 52.174 215 24 GGGTTGGGTTAGGGTTAGGG AGCTACTCACGCTCGTACCTAC 60 62 60 54.545 145 25 CGAAGCATAGCGACACTTAGGG GTCACCAAGCGATGACAGACAG 61 62 54.545 54.545 100 26 TAACGTCTCTGGATTGGCTTGG GTGTACAATTCGGACGGAGCTC 61 62 50 54.545 190

Appendix 107

Appendix 1: Continued.

Primer Sequence Annealing Temp. GC Percent Product Left Right Left Right Left Right Size 27 TTTAGATCACCTGAGCCAACGG TCGAACAACAACTTGATACGGGAC 61 62 50 45.833 223 28 AGGATCCCTTCTCAGAATTGGTAC ACCTGCTCTTCATACATCGTGTC 60 61 45.833 47.826 122 29 TCAATAAACGCCATCAATGCACATC TGTAGACTGTGCTCGACCTCAG 61 62 40 54.545 172 30 CTGGCGTCCGTATGTCCGTC ACACGACAACAGACAGTGAGTTC 63 62 65 47.826 122 31 GCATTTCTGACAAAGTTTAAGTCCG TCTGTCATTCGTCCGATTGCAC 60 62 40 50 236 32 TCATTTGACAGGTGTGAGTTCCC AGTTTAGCGATATGAGTGAGCGAC 61 62 47.826 45.833 137 33 CGAAGCATAGCGACACTTAGGG GTTGAGTGGACTATGGACACCG 61 61 54.545 54.545 123 34 AAATCTTTGCCAGTGTGTCCGG GTCAGACGTCGTGCTGTACATC 62 62 50 54.545 132 35 CAAAGGAGGGTACTATTCGTCACC CGACCTCTTCTTCAGAACCAGTG 61 62 50 52.174 172 36 TGAAAGCCCTATCACTTCCCATTC GTGTACACGATCGGATGCTCAG 61 62 45.833 54.545 163 37 GGTGTGACCCAAAGTGTGACAG ATCACTTGTCCGTTGTTCCGTC 62 62 54.545 50 103 38 TCATTTGACAGGTGTGAGTTCCC GCGCATCTGTTGTATAAAGGCC 61 61 47.826 50 149 39 TCGTGTATCCAGAGGGCTCAAG TCGTAGCTGTACATCGATCCGG 62 62 54.545 54.545 244 40 AACGTCACTGGATTAGCTTGGG AAGGAGGAATCGTCTCGTTTGC 61 62 50 50 104 41 ACTTCATGCCTACCAAGTTTCATC ATTGTCATAGTGTTGCGTCTGTC 60 60 41.667 43.478 140 42 AGTTGGACTATAAGGCGAGACAG GCCTTGTCAACTGTCTACCTCG 60 62 47.826 54.545 251 43 AGTGGTTTGGAATAATTTGTTCGCC CCCAAAGTTTCACGTCCGAATTC 61 61 40 47.826 125 44 GGGTTAGGGTTAGGGTAGGTAGG TCAGTCTGTGTACGTGAGCAAC 61 61 56.522 50 265 45 AAGCTGTCATTTGTAAAGCCTGG GAAGGCTCGTGAAGAATAGTGTATC 60 60 43.478 44 173 46 ACTGGTCGTATTATGGTATGGCG GCTTACCGTCCATCCAGTCAAC 61 62 47.826 54.545 163 47 CTTGTGTTCCAGTCCGCACAAG CGAGGAAAGGTGGCGTAGATATG 62 62 54.545 52.174 208 48 ACTGAACTCATTACACTGCCGG CTCAGCCTCCTTGCCTATGGG 61 63 50 61.905 201 49 ACGCTCTGTAAACATGTCGTCTAAC GTATATGTCCACTAGATAACGGCGC 62 62 44 48 101 50 AATCGTTGCTGTACCCTTCCAC AGATGTAGGACCGAGACGTCTTC 61 62 50 52.174 172 51 CTGTCGTCCGCCCGTAAGTC CACTCAGTGGCGCTTGAGAAC 63 63 65 57.143 111 52 GTAAGGCACCAGTCTACCGC CTGTAGTTCTGACCGGCTGTTC 61 62 60 54.545 270

Appendix 108

Appendix 1: Continued.

Primer Sequence Annealing Temp. GC Percent Product Left Right Left Right Left Right Size 53 TGCTGGTGATAATACGGGTCC TATTTGCTATCAGAACCCGCCC 60 61 52.381 50 118 54 AACTTTGATGACCTCTAGCGGG GATCGATGCTCAGACGTACGAG 61 61 50 54.545 245 55 CCATAACCCGTAAACAATGATCGAC TTTGGGATTATGCAGCAAAGGG 61 60 44 45.455 158 56 CCTCAGCATCGTCATCCACTAC GCTGCTTCTATCGCTATGGTGG 61 62 54.545 54.545 146 57 GGTCTGATCACAAGGAATACACATG CTTTGTCCGTTCTGTCCGTCTG 60 62 44 54.545 155 58 CTATTGTCATAGCGCGCCGTC GTTGCAGAGGAGATGTCGAAATG 63 61 57.143 47.826 104 59 ACCCTATTTACCTATCTTCCTGCC TAATGCGATTGGTGGAGTGTGC 60 62 45.833 50 116 60 GTGTATGGCGGCTAATTTGTGC AGTCAACATTGGACAGTACAGACAG 62 62 50 44 131 61 AGCTCGATCGGTACCTAAATTCC GATAGTGTGGCAGACGGACGAG 61 63 47.826 59.091 139 62 CTATTGTCATAGCGCGCCGTC GCAGTGGAGGATGTCGAAAGTAG 63 62 57.143 52.174 125 63 CTATTGTCATAGCGCGCCGTC GATCGATGCTCAGACGTACGTG 63 62 57.143 54.545 156 64 AAAGTTATGGGCTGTGACACCC AGTGCCGTTCCTGAATTTGACC 62 62 50 50 172 65 AGGGCACATGTTCAGAGATTAAAC AAGCAATGTCTCGATAAATGCCC 60 60 41.667 43.478 114 66 TCGCTTTCAGTACCTTCAGTGC TGTCATATGCTGCTGAGGAACG 62 61 50 50 207 67 CATATTGCTTTGCCGCTGTCTG CCACTCCAGAGGTATTGAACGG 62 61 50 54.545 100 68 TGTTAACCAGTTATATGACATGCCG CATCCAGCCAATTCAGAGTCGG 60 62 40 54.545 138 69 TCCGAGGAAGGTCTGATAAAGGAG CGTCAATCTAAATGTTGCGTTAGGG 62 61 50 44 118 70 TGACGTGCTATTTCCATGATCCTTC TCTACGATGCTGTAGGAGTCTAGC 62 62 44 50 160 71 TGGTGCTCGTCGTTTGAAATATTAC TCATCGACCAAGGACCGTAGTG 61 63 40 54.545 264 72 CAGTCTAGCTTATGGGATCGCC TCCATCACAAACAGTTTCAGAGGAC 61 62 54.545 44 143 73 TAACGTCTCTGGATTGGCTTGG ATTCGCGTTCATGAATCCTTTCTG 61 61 50 41.667 115 74 TCGTTGCCTTGTGTATTGTTTAGC TTCTCTGTTGGAATTTAGCACGG 61 60 41.667 43.478 124 75 CTATTGTCATAGCGCGCCGTC ATGAAATTCTGTGCAGCGGTTG 63 61 57.143 45.455 154 76 TAAGGGCAGCGATCTCTTTCAC AGACGAGTTTGGGAATAAAGTAAGC 61 60 50 40 124 77 AAGACTCGATTGCAGCATCCAC TCTTGTTCCCTTTCCTGTTTCTCC 62 61 50 45.833 111 78 AATACGGGTCCATAATTGTTCACG CTCCTGCTAATCACCAACCACC 60 62 41.667 54.545 115

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Appendix 1: Continued.

Primer Sequence Annealing Temp. GC Percent Product Left Right Left Right Left Right Size 79 ACATAGAGGTGGTTTGGTGTCG AACATAGTCTGACGAGAGGGCC 61 62 50 54.545 138 80 CCACTCTGTCATCGCGCTCC AGGTCACGTGATCAGGTAGGAC 64 62 65 54.545 142 81 CAGTGACTGGAACGATCTGGG GGAGGATCCGATCTGTTGCTTG 61 62 57.143 54.545 107 82 AAAGTTATGGGCTGTGACACCC CGAAGCATAGCGACACTTAGGG 62 62 50 54.545 121 83 TTGTTTGCAAAGAAACCATCCAATG TGCGAGTACAATTATCAGCGAGC 60 62 36 47.826 124 84 GCAGCACAACAAACGAAAGGAC GGTTTAAGGGCCTGGGTAATTCC 62 62 50 52.174 273 85 TCGGCGAAGATGGGAAGAGTAC GACGCGGATGGATAAGTCAGAC 63 62 54.545 54.545 130 86 AATTGAAAGGTCTTGTGATGGGC GGATTGCAAAGTCATTCCGTACG 61 61 43.478 47.826 189 87 AATCCCTTCAGCCGTTCTTGAG ATTGTCATTAGTGTGGCGTCCG 61 62 50 50 107 88 CATGATCTGATTGCGTTCCTGC TCGGCCATTGGTTCATCTAGTG 61 61 50 50 229 89 ATCGCTGCCATCCTTCTGTTTG TTATCCTGGATTACGTGCGCAC 62 62 50 50 103 90 CGAAGCATAGCGACACTTAGGG TACGAACTAGTGGCTCGATGAC 62 60 54.545 50 121

Appendix 110