PHYLOGENETIC AND POPULATION GENETIC STUDIES ON SOME INSECT AND PLANT ASSOCIATED NEMATODES

DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

By

Amr T. M. Saeb, M.S.

* * * * * The Ohio State University 2006

Dissertation Committee:

Professor Parwinder S. Grewal, Adviser Professor Sally A. Miller Professor Sophien Kamoun Professor Michael A. Ellis Approved by

Adviser Plant Pathology Graduate Program

Abstract:

Throughout the evolutionary time, nine families of nematodes have been found to have

close associations with insects. These nematodes either have a passive relationship with

their insect hosts and use it as a vector to reach their primary hosts or they attack and

invade their insect partners then kill, sterilize or alter their development. In this work I

used the internal transcribed spacer 1 of ribosomal DNA (ITS1-rDNA) and the

mitochondrial genes cytochrome oxidase subunit I (cox1) and NADH dehydrogenase

subunit 4 (nd4) genes to investigate genetic diversity and phylogeny of six species of the

entomopathogenic nematode Heterorhabditis. Generally, cox1 sequences showed higher levels of genetic variation, larger number of phylogenetically informative characters, more variable sites and more reliable parsimony trees compared to ITS1-rDNA and nd4.

The ITS1-rDNA phylogenetic trees suggested the division of the unknown isolates into two major phylogenetic groups: the HP88 group and the Oswego group. All cox1 based phylogenetic trees agreed for the division of unknown isolates into three phylogenetic groups: KMD10 and GPS5 and the HP88 group containing the remaining 11 isolates.

KMD10, GPS5 represent potentially new taxa. The cox1 analysis also suggested that

HP88 is divided into two subgroups: the GPS11 group and the Oswego subgroup. Our results suggest that the HP88 group is most closely related to H. downesi K122 followed by H. marelatus Oregon, H. zealandica X1, H. megidis (UK and Jun) and H. indica EG2,

respectively. Neither nd4 gene sequence- nor ND4 amino acid- based phenetic and

ii phylogenetic trees were able to completely resolve the phylogenetic relationships among

species of Heterorhabditis. We concluded that cox1 gene has a potential for molecular

differentiation and diagnosis between closely related species within Heterorhabditis

while nd4 is not an ideal target for reconstruction of phylogeny or molecular

differentiation. We also advocate for more extensive sampling and deposition of

representative molecular data in GenBank for all Heterorhabditis species to avoid

inaccurate molecular identification. We also used the major sperm protein gene (msp) to

investigate genetic diversity among 13 strains of Heterorhabditis bacteriophora. We

hypothesized that H. bacteriophora strains might show genetic diversity in sequence and

structure of msp genes due to large differences in their biological traits. Phenetic and

phylogenetic analysis showed the presence of high genetic structuring within H.

bacteriophora. Our results suggest that all the strains currently recognized as H.

bacteriophora may not belong to the same species. Finally, we investigated the

phylogeny and population structure of the nematode Bursaphelenchus conicaudatus and

its insect vector, the yellow spotted longicorn beetle Psacothea hilaris from the Japanese

islands. Phylogenetic and phenetic analyses indicated the existence of subspecies

structure in B. conicaudatus and support the current subspecies groupings of P. hilaris subspecies with few exceptions. All fixation indices indicated the presence of high genetic differentiation among the local populations of both the nematode and its insect vector. The effective numbers of migrants showed definite but limited gene flow among insect and nematode local populations.

iii

Dedicated to my father

iv ACKNOWLEDGMENTS

During the course of my thesis work, there were many people who were instrumental in helping me. Without their guidance, help and patience, I would have never been able to accomplish the work of this thesis. First, I wish to thank my adviser,

Parwinder S. Grewal, for intellectual support, encouragement, and enthusiasm, which made this thesis possible, and for his patience in correcting both my stylistic and scientific errors.

I thank my advisory committee members, Dr. Sophien Kamoun, Dr. Sally Miller and Dr. Michael Ellis for their guidance, help, academic support and constructive suggestions that opened my eyes to much deeper insight in my research project. Also I want to thank all members of Dr. Grewal’s lab for their compassion, friendship and help especially Dr. Gunpat Jagdale. Also I want to thank all the faculty members and staff of the Department of Plant Pathology for their help, time and the highest level of education.

I would like also, to express my deep respect and gratitude for all members of the

Department of Entomology, where I conducted my entire research project. I do not want to forget to thank my wife, daughter and son for any inconvenience I caused them during stressful times. Finally, I present my deep gratefulness and appreciation to my home country Egypt, for giving me the opportunity to come to the USA to obtain my Ph. D. through a governmental scholarship.

v VITA

EDUCATION April 2002 – Present: Ph.D. Candidate, Department of Plant Pathology. The Ohio State University.

February 1997 - June 2000: Master of Science, Major: Genetics. Genetic studies on some heavy metal resistance genes in bacteria. Faculty of Agriculture, Ain Shams University, Cairo, Egypt.

October 1990 – June 1994: Bachelor of Science (Very Good Grade). Department of Genetics, Faculty of Agriculture, Ain Shams University, Cairo, Egypt.

EMPLOYMENT June 2000 – until now: Position: Assistant Lecturer. Genetic Engineering and Biotechnology Research Institute (GEBRI), Minufiya University. Worked as a teaching member of Department of Molecular Biology and participate in the Institute scientific projects.

March 1998 - June 2000: Position: Demonstrator. Genetic Engineering and Biotechnology Research Institute (GEBRI), Minufiya University. Worked as a teaching member of Genetic and Microbial Genetics Department. I also participated in the Institute’s scientific projects.

October 1995 - February 1998: Position: Research Assistant. National Research Center, Dokki, Cairo, Egypt. Worked as a member of the team working on the genetic improvement of some economic microbes. I worked on heavy metal resistance in bacteria.

June 1994 - September 1995: Position: Research Assistant. The Environmental Mutagensis Research Unit (EMRU), Ain Shams University, Faculty of Agriculture. Worked as a member of the research group of the scientific project dealing with genetic characterization of blue green algae.

vi

PUBLICATIONS Ganpati. B. Jagdale, Amr T. M. Saeb, Nethi Somasekhar, and Parwinder S. Grewal. (2006). Genetic variation and relationships between isolates and species of the entomopathogenic nematode genus Heterorhabditis deciphered through isozyme profiles. Journal of Parasitology: Vol. 92, No. 3, pp. 509–516. Amr T. M. Saeb and Parwinder S. Grewal. (2006). Intraspecific genetic variation in the major sperm protein gene of the entomopathogenic nematode Heterorhabditis bacteriophora. (Submitted).

FIELDS OF STUDY Major: Plant Pathology

vii TABLE OF CONTENTS Page Abstract ...... ii Dedication ...... iv Acknowledgments...... v Vita...... vi List of Tables...... x List of Figures...... …xiii

Chapters:

1. Introduction...... 1

2. Inter and intra-specific genetic variation and phylogeny of Heterorhabditis: a comparison between two molecular markers………..…11 2.1 Introduction……………………………………………………..11 2.2 Materials and methods…. ……………………………………...14 2.3 Results…………………………………………………………..18 2.4 Discussion……………………………………………………....22 2.5 Summary………………………………………………………..28 2.6 Reference……………………………………………………….29

3. Investigation of the use of nd4 gene in phylogeny and molecular differentiation of entomopathognic nematodes (Rhabditida: Heterorhabditidae)…………………………………………………………50 3.1 Introduction…………………………………………………….50 3.2 Materials and methods…. ……………………………………..52 3.3 Results………………………………………………………….55 3.4 Discussion……………………………………………………...58 3.5 Summary……………………………………………………….62

viii 3.6 Reference……………………………………………………63

4. Intraspecific genetic variation in the major sperm protein gene of the entomopathogenic nematode Heterorhabditis bacteriophora…………..80 4.1 Introduction………………………………………………….80 4.2 Materials and methods…. …………………………………..83 4.3 Results……………………………………………………….87 4.4 Discussion…………………………………………………...90 4.5 Summary…………………………………………………….94 4.6 Reference…………………………………………………....95

5. Phylogeny, population structure, and gene flow in the insect tramsmitted nematode Bursaphelenchus conicaudatus and its vector Psacothea hilaris on Japanese islands…………………………………………………………110 5.1 Introduction………………………………………………….110 5.2 Materials and methods…. …………………………………..113 5.3 Results…………………………………………………….…116 5.4 Discussion…………………………………………………...122 5.5 Summary………………………………………………….…126 5.6 Reference……………………………………………………127

6. General Referances……………………………………………….……...150

ix LIST OF TABLES

Table Page

2.1. List of entomopathogenic nematode species and strains used in this study……..39

2.2. DNA sequence and parsimony analysis comparison between ITS1 and cox1…….40

2.3. ITS1-rDNA based pairwise distances between Heterorhabditis isolates using

Tamura-Nei substitution model…………………………………………………..41

2.4. Partial cox1 DNA sequence based Pairwise distances between Heterorhabditis

isolates using Jukes-Cantor substitution model………………………………….42

2.5. cox1 and ITS1-rDNA distance comparison for some species/isolate pairs………..43

3.1. List of entomopathogenic nematode species and strains used in this study……...71

3.2. Blastn search results for nd4 gene sequence of Heterorhabditis strains…………72

3.3. Nucleotide composition of the nd4 gene of each Heterorhabditis strain, broken

down by codon position…………………………………………………………..73

3.4. Codon usage of nd4 gene sequences for nd4 gene in Heterorhabditis strains…...74

3.5. Nucleotide Pair Frequencies of nd4 gene sequences for Heterorhabditis strains.

All frequencies are averages (rounded) over all taxa……………………….……75

3.6. nd4 based pairwise distances between Heterorhabditis strains using Tamura-Nei

substitution model………………………………………………………………..76

4.1. List of Heterorhabditis bacteriophora strains used in this study……………….101

x LIST OF TABLES

Table Page

4.2. Blast-n search results of msp gene sequences of Heterorhabditis bacteriophora

strains…………………………………………………………………………….102

4.3. Gene structure prediction of msp sequences of Heterorhabditis bacteriophora

strains…………………………………………………………………………….103

4.4. DNA based pairwise distance values among Heterorhabditis bacteriophora

strains…………………………………………………………………………….104

4.5. Translated amino acid based pairwise distance values among Heterorhabditis

bacteriophora strains………..…………………………………………………...105

5.1.The numbers of collected individuals with nematodes, origin of isolation and

nematode haplotypes.………………………………………………………………133

5.2. Pairwise distance values calculated using Nucleotide Kimura 2-parameter among

Bursaphelenchus conicaudatus isolates………………………………………..134

5.3. Pairwise distance values calculated using Nucleotide Kimura 2-parameter among

Psacothea hilaris isolates………………………………………………………135

5.4. Pairwise distance values between some representative isolates of Bursaphelenchus

conicaudatus and its sister species……………………………………………...136

5.5. Groups of Bursaphelenchus conicaudatus and Psacothea hilaris isolates………...137

5.6. Sequence analysis information for the eleven B. conicaudatus groups…………...138

xi LIST OF TABLES

Table Page

5.7. Sequence analysis information for the ten Psacothea hilaris groups…………….139

5.8. Genetic diversity statistics for the eleven B. concicaudatus local populations

and the ten Psacothea hilaris groups…………………………………………..140

5.9. Genetic Differentiation estimates for B. concicaudatus (local populations) and

Psacothea hilaris 10 phylogenetic group………………………………………141

5.10. Gene flow Estimates of Nm using Fst values according to Hudson, Slatkin and

Maddison (1992) for B. concicaudatus 11 Groups (A) and Psacothea hilaris

10 phylogenetic groups (B)……………………………………………………..142

5.11. Fst (A), Nst (B), δst (C) and γst (D) pairwise distance values for the eleven groups

of B.conicaudatus………………………………………………………………143

5.12. Fst (A), Nst (B), δst (C) and γst (D) pairwise distance values for the ten groups of P.

hilaris…………………………………………………………………………...144

xii LIST OF FIGURES

Figure Page

2.1. Multiple sequence alignment of ITS1-rDNA in-group (Heterorhabditis)………..44

2.2. Multiple sequence alignment of cox1 in-group (Heterorhabditis)……….………46

2.3. Phylograms depicting the degree of relationship between Heterorhabditis

species and isolates produced from ITS1-rDNA ……………………………….47

2.4. Phylograms depicting the degree of relationship between Heterorhabditis species

and isolates produced from cox1 DNA………………………………….……...48

2.5. Phylograms depicting the degree of relationship between Heterorhabditis species

and isolates produced from partial COX1 amino acid sequence……………….49

3.1. Multiple sequence alignment of nd4……………………………………………...77

3.2. Phylograms depicting the degree of relationship between Heterorhabditis species

and strains produced from partial nd4………………………………………….78

3.3. Phylograms depicting the degree of relationship between Heterorhabditis species

and strains produced from partial ND4 amino acid sequence………………….79

4.1. A picture showing the amplification product of H. bacteriophora strains

msp genes……………………………………………………………………...106

4.2. Multiple sequence alignment of Heterorhabditis bacteriophora msp gene. Hyphens

indicate alignment gaps or missing data………………………………………107

xiii LIST OF FIGURES

Figure Page

4.3. Phylograms depicting the degree of relationship among Heterorhabditis

bacteriophora strains produced from msp DNA sequences…………………...108

4.4. Phylograms depicting the degree of relationship among Heterorhabditis

bacteriophora strains produced from msp amino acid sequences……………..109

5.1. Phylogenetic tree constructed using the neighbor joining method for reconstructing

the phylogenetic relationships among B. concicaudatus isolates……………...145

5.2. Phylogenetic trees constructed using maximum parsimony for reconstructing the

phylogenetic relationships among B. concicaudatus isolates………………….146

5.3. Cladograms constructed using the Bayesian inference of phylogeny for

reconstructing the relationships among B. concicaudatus isolates…………….147

5.4. Phylogeny reconstruction showing the relationship between the eleven groups of B.

concicaudatus………………………………………………………………….148

5.5. Phylogeny reconstruction showing the relationship between the P. hilaris 10

phylogenetic groups…………………………………………………………....149

xiv CHAPTER 1

INTRODUCTION

Nematodes or roundworms inhabit virtually all ecosystems including marine, freshwater, and terrestrial environments. They encompass the phylum Nemata that includes plant, insect, animal and human parasites and predatory forms that consume microorganisms including bacteria, fungi, algae, and other nematodes (Platt,

1994). Nematodes are small, usually less than a millimeter in length, with exceptions of some animal parasitic nematodes, which are rather large and can be seen by the naked eye (Wallace et al., 1996). Animal, plant and insect parasitic nematodes are of importance to agriculture, veterinary and human health. On the other hand, beneficial nematodes feed on other organisms and play important role in recycling minerals and nutrients in the ecosystem (Woods, 1973). They also play important role in the decomposition of organic matter, biodegradation of toxic compounds and as indicators of environmental quality (Yeates, 1977; 1999; 2003). Insect-parasitic nematodes play a very important role in regulating insect populations and in the biological control of many insect pests (Kaya and Gaugler, 1993; Grewal et al., 2005).

The phylum Nemata was first suggested by Cobb (1919) to include all nematodes families. Although nematode morphology is rich in potentially valuable characters that can be used in nematode classification, its usage have been hindered by the lack of 1 convenient, cheap and easy to use observation instruments (Ley and Blaxter,

2002). In contrast, molecular tools, such as DNA sequencing, provide affluence of

characters that can be used in systematic studies at different taxonomic levels (Sidow and

Thomas, 1994; Aleshin et al., 1998; Adams et al., 1998; De Ley at al., 1999). However,

any classification of nematodes must attempt to combine morphological and molecular

data (De Ley and Blaxter, 2002). Generally, a small number of genes have been used for

reconstruction of phylogeny of the metazoan radiation and the initial placement of

nematodes among the metazoans was based on the sequence data of the small subunit of

the rDNA (SSU rDNA) of Caenorhabditis elegans (Ellis et al., 1986). However, determination of the relationship of nematodes with other metazoans was hindered by the lack of nematode fossil record older than 30 million years (Conway, 1981). In addition, gene families, such as HOX gene family, have been used to reconstruct phylogeny among the metazoan organisms (De Rosa et al., 1999).

The first nematode classification was proposed by Schneider (1866) that was based on somatic musculature. It was followed by Cobb (1919) who proposed a classification based on stoma armature. Baylis and Daubney (1926) proposed another classification system based on parasitism, allocating all zoo-parasitic nematode together in one group and all other nematodes in another group. Filipjev (1934) rejected the latter classification and suggested that marine nematodes are the ancestors of all nematodes.

Micoletzky (1922) classified nematodes into two major groups: Odentopharyngidae

(nematodes with teeth) and Tylenchidae (nematodes with stylets). Nevertheless, the most important nematode classification system was suggested by the Chitwoods (Chitwood

2 and Chitwood, 1933; Chitwood, 1937) which was based on the presence of phasmids, dividing nematodes into two major groups: Secrenentea (nematodes with an excretory system) and Adenophorea (nematodes with caudal glands). Meggenti (1963) suggested a classification system based on pharynx structure and excretory system, which maintained the bipartite system of nematodes, namely, Secrenentea and Adenophorea. Meggenti’s system suggested that plectids are not related to rhabditids because of structural differences in their basal bulbs. De Coninck (1965) suggested that class Adenophorea be divided into two subclasses: Chromadoria and Enoplia. Andrassy (1974) rejected class

Adenophorea as a cohesive nematode group and suggested that phylum Nemata should be divided into three groups namely: Secrenentea, Torquentia, and Penetrentia, based on the amphidial structure. Andrassy’s system was criticized and did not receive collective acceptance. Based on the morphological characters, Lorenzen (1981 and 1994) again divided phylum Nemata into two groups: Secrenentea and Adenophorea and the latter was divided into two groups Chromadoria and Enoplia.

Currently, the phylum Nemata contains about 2000 described species, and is divided into two classes Adenophorea and Secernentea. The class Adenophorea comprises two subclasses, namely Enoplia and Chromadoria, and 11 orders, while

Secernentea contains three subclasses, namely Rhabditia, Spiruria and Diplogasteria, and eight orders (Ley and Blaxter, 2002). Among the 19 orders of phylum Nemata, seven orders include nematodes that are parasites or associates of invertebrates including invertebrate animals, such as the Annelida, Mollusca and Arthropoda (Cobb, 1914; Platt,

1994). The invertebrate animals serve as a direct host for the parasitic nematodes, in

3 which they complete their live cycle, or as a vector to transfer them between vertebrate or

plant hosts. In many cases this transfer is passive.

An important nematode-insect association is Bursaphelenchus spp. insects,

especially bark beetles and woodborers. There are 49 described species of

Bursaphelenchus, most of which have a phoretic relationship with insects and feed on

fungi (Ryss et al., 2005). Cerambycid beetles of the genus Monochamus vector

Bursaphelenchus nematodes to their plant hosts, such as pine or mulberry trees

(Wingfield, 1983; Linit, 1987; Kanzaki et al., 2000). Transmitted dauer larvae invade

trees through wounds on twigs created by feeding of Monochamus beetles (Magnusson,

1986; McNamara and Stooen, 1988). These nematodes feed on epithelial tissue cause

death of the parenchyma cells and eventually the death of the tree, such as the case of the

plant parasitic nematode B. xylophilus, or feed on fungi that grow in tunnels created by the insect vector larvae (Mamiya and Endo, 1979; Linit, 1987; Kanzaki et al., 2000). In both cases, there is no known nutritional or physiological relation between the nematode and the insect vector (passive transmission) (Kanzaki and Kazuyoshi, 2001).

Nine families of nematodes, namely, Allantonematidae, Diplogasteridae,

Heterorhabditidae, Mermithidae, Neotylenchidae, Rhabditidae, Sphaerulariidae,

Steinernematidae and Tetradonematidae, are parasitic to insects (Smart, 1995). These groups of nematodes invade their insect hosts then kill or sterilize them or manipulate their development. Entomopathogenic nematodes (Heterorhabditidae and

Steinernematidae) invade the insect hosts through natural openings and use their hemolymph as a breeding place where they cultivate their symbiotic bacteria. When the

4 insect is dead or near death, growth and subsequent development of nematodes occur as

they utilize essential steroids supplied by the insect (Maggenti, 1981; Johnigk and Ehlers,

1999).

Entomopathogenic nematodes are very effective biocontrol agents of many insect

pests (Grewal et al., 2005). However, few species have been described in this group of

nematodes. In addition, there is discordance between the two available studies, ITS1 and

nd4 based phylogeny, which deal with the phylogenetic relationships among the species

of Heterorhabditis. Heterorhabditis is morphologically conserved thus building

phylogenetic relationships or differentiation among species based on morphological

features is very difficult (Hominick et al., 1996). Thus, molecular phylogeny, targeting

more genes and using more sophisticated computer based analysis, is very important to

solve the problematic relationships among species of Heterorhabditis. Also there is very little information about population structure and gene flow within insect associated nematodes systems. Understanding population structure and gene flow in B. conicaudatus and P. hilaris will provide valuable information that can be used in management and/or conservation programs.

In this thesis I aimed to investigate phylogeny and population genetic structure in two insect associated nematode groups, namely the entomopathogenic nematode

Heterorhabditis and the insect/plant associated nematode Bursaphelenchus conicaudatus.

My specific objectives are to: 1) investigate the amount and structure of inter- and intra- specific genetic variation and reconstruct phylogenetic in Heterorhabditis using ITS1- rDNA, cytochrome oxidase subunit I (cox1), NADH dehydrogenase nd4 and major sperm

5 protein gene(s) sequence data, 2) investigate the presence of subspecies structuring in H.

bacteriophora, 3) reanalyze the cox1 gene sequence characteristics for B. conicaudatus isolates and P. hilaris subspecies; 4) reconstruct phylogenetic relationship and presence of sub-species structure among groups of B. conicaudatus and P. hilaris using Bayesian analysis of phylogeny and compare it with other commonly used phenetic and phylogenetic methods and 5) quantify the extent of genetic differentiation and gene flow among B. conicaudatus local populations and P. hilaris subspecies.

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dwelling nematodes. Soil Biology and Biochemistry 5:593–601.

YEATES, G. W. 1977. Soil nematodes in New Zealand pastures. Soil Science 123: 415–

422.

10 CHAPTER 2

INTER- AND INTRA-SPECIFIC GENETIC VARIATION AND

PHYLOGENY OF HETERORHABDITIS: A COMPARISON

BETWEEN TWO MOLECULAR MARKERS

2.1 INTRODUCTION

Entomopathogenic nematodes of the genera Heterorhabditis and Steinernema

(Rhabditida: Heterorhabditidae, Steinernematidae) have shown high potential as biological control agents of many insect pests (Kaya and Gaugler, 1993; Grewal et al.,

2005). Heterorhabditis is cosmopolitan with species occurring throughout the world

(Adams et al., 1998; Hominick, 2002; Stock et al., 2002; Stock and Hunt, 2005).

However, only twelve species of Heterorhabditis have been described so far. They are:

H. bacteriophora (the type species), H. indica, H. baujardi, H. brevicaudis, H. downesi,

H. marelatus, H. megidis, H. poinari, H. taysearae H mexicana, H. floridensis and H. zealandica (Stock and Hunt, 2005; Nguyn et al., 2004; 2006). Of the ten species, H. bacteriophora is the most widely distributed having been found in North and South

America (Stock et al., 1999; Poinar, 1990; Stock, 1993), Southern Europe (Smits et al.,

1991; De Doucet and Gabarra, 1994), Australia (Poinar, 1990), and Asia (Li and Wang,

1989; Hominick et al., 2002).

11 Differentiation among species of Heterorhabditis is problematic due to the extreme morphological conservation and difficulties in performing cross breeding tests

(Hominick et al., 1996). Therefore, several molecular studies have been conducted to determine phylogenetic relationships and differentiation among the described species of

Heterorhabditis. These studies include protein polymorphism (Akhurst, 1987), isoenzyme patterns (Jagdale et al., 1996; 2006), random-amplified polymorphic DNA

(RAPD) (Liu and Berry 1996), DNA fingerprinting (Joyce et al., 1994; Pamjav et al.,

1999; Stack et al., 2000), restriction fragment length polymorphism (RFLP) analysis

(Nasmith et al., 1996), and the Internal Transcribed Spacer Region (ITS-rDNA) sequencing (Adams et al., 1998). To distinguish between nematode species, different

DNA sequences have also been investigated for significant rates of polymorphism.

These include ITS 1 and 2 regions, located between 18S and 26S rDNA genes within the rRNA operon and nd4 gene sequence of mitochondrial DNA (Adams et al., 1998;

Nasmith et al., 1996; Liu et. al., 1999; Blouin et al., 1999). However, there is little information available about intra-specific genetic variation and population structure of entomopathogenic nematodes except for a study by Blouin et al. (1999) which reported relatively low mtDNA diversity both at the population and the species level in H. marelatus. Mitochondrial DNA is widely used for studying population genetics, phylogeny and molecular evolution (Liu et al., 1999; Gasser et al., 2002). Mitochondrial

DNA (mtDNA) is highly variable, maternally inherited, lacks recombination and seems to be selectively neutral (Avise, 1994; Liu et al., 1999). High evolution rates of mtDNA genes permit their use to compare both inter- and intra-specific variation. Among mitochondrial genes, NADH dehydrogenase subunit 4 (nd4) and cytochrome c oxidase

12 subunit 1 (cox1) provide ideal markers for population genetic structure and molecular

evolution (Blouin 2002; Blouin et al., 2001).

The complex life cycle of Heterorhabditis species has consequences for genetic structuring, gene flow, and intraspecific variation in natural populations. Heterorhabditis nematodes enter their insect host through natural openings and use its hemocoel as a breeding site where they cultivate their symbiotic bacterium Photorhabdus luminescens as a source of food. Once inside, the infective juvenile, also called dauer juvenile, starts feeding on bacteria and develops into a hermaphrodite. The hermaphroditic adult lays about 300 eggs, which hatch into the first larval stage that undergoes three additional larval stages and eventually develop into males or females. When environmental conditions are unfavourable or the food source is depleted, first stage juveniles develop into dauer juveniles, which leave the insect cadaver to seek new hosts to repeat their life cycle (Johnigk and Ehlers, 1999). The alternation between sexual and asexual

(hermaphroditic) reproduction should lead to continuous change of allele frequency and population structure (Hebert, 1987 a, b; Carvalho, 1994). Sexual reproduction promotes genetic variability (Barton and Charlesworth, 1996), while asexual reproduction with selection lead to erosion of genotypic diversity (Gomez and Carvalho, 2000).

Although the nature of the complex life cycle of Heterorhabditis should promote low genetic diversity among Heterorhabditis populations (Blouin et al. 1999), previous studies indicate large variation in infective juvenile virulence, longevity, UV and desiccation tolerance (Bedding, 1984; Grewal et al., 2002 a, b), and isoenzymes (Jagdale et al., 2006). Therefore, our overall hypothesis is that there is a high inter- and intra- specific genetic diversity in Heterorhabditis. The specific objectives of the study were

13 to: 1) investigate the amount and structure of inter- and intra-specific genetic variation in

Heterorhabditis, 2) investigate the presence of subspecies structuring in H. bacteriophora and 3) compare among the resulting phylogenetic relationships inferred from ITS1-rDNA and cytochrome oxidase subunit I (cox1) sequence analysis.

2.2 MATERIALS AND METHODS

Nematode species and isolates

Eighteen isolates of six Heterorhabditis species (H. bacteriophora, H. indica, H. megidis, H. marelatus, H. downesi and H. zealandica) collected from various geographic

locations and used in this study are listed in Table 2.1. Only 6 species were available for

this research. In order to eliminate any confusion we considered isolate HP88 as the

representative of H. bacteriophora, while all the other isolates were considered unknown

Heterorhabditis isolates. All nematode isolates were propagated in the last instar wax

moth Galleria mellonella and maintained in our laboratory as described by Kaya and

Stock (1997).

Genomic DNA isolation

For genomic DNA extraction, infective juveniles of each isolate were concentrated by centrifugation at 3000 g for 10 min in EN buffer (100 Mm NaCl, 10 mM

EDTA) and then washed three times with sterile distilled water. The resulting nematode pellet was ground in liquid nitrogen and DNA was extracted using Qiagen® Genomic Tip

100/G (Valencia, California) according to the manufacturer’s recommendations.

14 ITS1-rDNA and cox1 PCR amplification

PCR amplification of ITS1- rDNA included an initial denaturation step for 4 min

at 94˚C followed by 35 cycles of 1 min at 94˚C, 50 seconds at 50˚C and 1 min at 72˚C.

The PCR was followed by a final extension step at 72˚C for 7 min at 94°C. Each 50-µL-

reaction mix contained 50 ng (2µL) of DNA, 1.25 (1µL) units of the enzyme blend

(FailSafe PCR enzyme mix, Epicentre®, Madison, Wisconsin), 1 µM of each primer

(µM10), 20-µL sterile water, and 25 µL of the FailSafe PCR 2X PriMix F. The primer set used for the amplification was ITS Forward: 5’-TTG AAC CGG GTA AAA GTC G--

3’ and ITS Reverse: 5’-TTA GTT TCT TTT CCT CCG CT-3’. PCR products were cleaned using Qiagen® PCR purification kit and prepared for sequencing. DNA sequencing was performed using Applied Biosystems 3730xl automated DNA sequencing instrument (University of Madison Wisconsin), using 50 cm capillary arrays and POP-7 polymer.

The following primer set was used for the amplification of the cox1 gene:

Forward: 5’-TTT TTT GGG CAT CCT GAG GTT TAT-3’ and Reverse: 5’-AAA GAA

AGA ACA TAA TGA AAA TG-3’. PCR conditions were optimized to clearly visible products on agarose gels stained with ethidium bromide. Approximately, 50 ng (4 µL) of

DNA was amplified using 1.25 (1µL) units of the enzyme blend (FailSafe® PCR enzyme mix). The final reaction volume was 50 µL containing 1 µM of each primer, 20µL sterile

water, and 25 µL of the FailSafe® PCR 2X Premix F (100 Mm Tris-HCl, 400 µM each

dNTP). PCR included an initial denaturation step at 94 °C for 4 min, followed by 35

cycles of 1 min at 94 °C, 60 sec at 55 °C and 1 min at 72 °C. The PCR was followed by

a final extension step at 72 °C for 10 min. After the PCR was completed, 10 µL of

15 amplified product from each sample was analyzed by electrophoresis in a 1.0 % (w/v) agarose gel. PCR amplification products were purified using either Qiagen® gel extraction kit or Qiagen® PCR purification kit (for ITS1-rDNA). Purified PCR products of cox1 were cloned using cloning Invetrogen® TOPO TA cloning kit. Ten bacterial clones, which have been successfully transformed, were isolated by white and blue selection, and 10 representative colonies from each isolate were submitted to PCR and then sequenced using M13 in one direction as recommended by the manufacturer

(Invitrogen, Carlsbad, California).

DNA sequence and phylogenetic analyses

All DNA sequences were edited to remove low quality sequences, using Bioedit sequence alignment editor (Hall, 1999) and subjected to BLAST (Basic Local Alignment

Search Tool) to perform sequence similarity searches. All sequence data were submitted to the GenBank at http://www.ncbi.nlm.nih.gov/. Sequences were initially aligned using the Bioedit built-in clustal W program (gap opening penalty = 10, gap extension penalty

= 5, delay divergent sequences = 40%). Additional sequence alignments were performed using MAFFT multiple sequence alignment program available online at http://align.genome.jp/mafft/ and with PRRN (the best-first iterative refinement strategy with tree-dependent partitioning) multiple sequence alignment program available online at http://align.genome.jp/prrn/.

Resulting alignments were compared and the final alignments were improved manually and prepared in FASTA, MEGA and NEXUS formats. In order to select the best substitution model, we used FindModel program available online at http://hcv.lanl.gov/content/hcv-db/findmodel/findmodel.html. We used Kimura 2-

16 parameter (AIC9 = 4399.298654 lnL = -2198.649327) as the best substitution model for

partial ITS-rDNA sequences as the best phylogenetic model that can then be used to

generate trees. For cox1 the software chose Kimura 2-parameter (AIC9 = 4399.298654 lnL = -2198.649327) but it resulted in many incalculable distance values between some isolates. Thus, we used Jukes-Cantor (AIC1 = 1209.691724 lnL = -604.845862) as the substitution model for cox1. Pairwise distance calculation was performed using Tamura-

Nei and Jukes-Cantor models, for partial rDNA and cox1 respectively, and gaps, marked as (-), and missing data and stop codons marked as (?), were taken into account using the complete deletion method in MEGA software (Kumar et al., 2004) and the default gap values in PAUP V4B10, MrBayes v3.1 and TreeBuzzel (Swofford, 2001; Strimmer and

Haeseler, 1996).

In order to test all possibilities and alternative hypotheses of evolution, phylogenetic trees were produced using neighbor joining (NJ), unweighted pair-group arithmetic mean (UPGMA), minimum evolution (ME), the pairwise distance based methods, Bayesian analysis of phylogeny (BAP), which uses prior models of evolution and mcmc search for the best tree, maximum likelihood (ML), which search for the most probable tree among all the area of possible trees, and maximum parsimony (MP), which search for the tree with the least amount of genetic mutations, methods using PAUP

V4B10, MEGA 3, MrBayes v3.1 and TreeBuzzle softwares. We used the default values of open and extended gaps in both of PAUP V4B10 and MrBayes v3.1 softwares.

The transition/transversion ratio was set to 2:1 for PAUP V4B10. Bootstrap analysis was performed using 1000 replicates to test the support for each branch of the tree. For the MP method, heuristic (with tree bisection-reconnection as the branch

17 swapping algorithm, TBR) and branch-and-bound searches were conducted. All

characters had the same weight, treated as unordered and the third position of the codon

was excluded. Bootstrap analysis with 1000 replicates was used to test the support for

the branches. We calculated a 75% majority rule MP consensus tree. For BAP method,

the selected evolutionary model was GTR model with gamma-distributed rate variation

across sites and a proportion of invariable sites. All codon positions were included for

phylogenetic analysis. The number of generations used is 1,000,000, sampling a tree

every 100 generations and then calculating a 50% majority rule consensus tree in

PAUP4.0b10. The value of the standard deviation of split frequencies is 0.007312.

Amino acid translations of partial nucleotide cox1 sequences were obtained and analyzed by MEGA 3.1 software. Poisson correction was used as amino acid substitution method.

DNA sequence polymorphism analysis was performed using DnaSP software (Rozas et al., 2003).

2.3 RESULTS

DNA sequence and parsimony analyses

DNA sequence and parsimony analyses indicated large differences between the

ITS1-rDNA and cox1 genes sequences in the quality of data obtained (Table 2.2). For example, 618 out of 990 (62%) studied characters missing in the ITS1-rDNA sequences

due to alignment gaps and missing data, whereas there were only 283 of 580 (49%) sites

with missing data in the cox1 sequences (Figs. 2.1 and 2.2). In general, cox1 sequences showed higher levels of variation, larger number of phylogeneticlly informative characters, more variable sites and more reliable parsimony trees compared to ITS1- 18 rDNA. For example, there were only 156 variable sites in the ITS1-rDNA sequences, while there were 241 variable sites in the cox1 gene sequences. Also there was twice as many parsimony informative sites in cox1 sequences than in the ITS1-rDNA. However, there were three times monomorphic (invariable) sites in cox1 compared to ITS1-rDNA sequences.

Blast-n search for ITS1-rDNA sequences revealed that all Heterorhabditis isolates evaluated belonged to their assigned species. All the unknown isolates included in this study had high similarity to H. bacteriophora ITS1-rDNA sequences with e-values for the species and isolates ranging between 0 to 9e-145. The cox1 sequence blast-n search revealed high similarity to several animal and plant parasitic nematodes with e-values ranging between 2e-33 and 1e-102 as there were no cox1 sequence data for

Heterorhabditis in the Genbank.

Phylogenetic and pairwise distance analyses

Phylogenetic analysis of ITS1-rDNA sequences revealed strong sub-structuring among the unknown isolates (Fig. 2.3). All phylogenetic trees agreed for the division of the unknown isolates into two major phylogenetic groups. The first group (termed the

HP88 group hereafter) contained HP88 (H. bacteriophora), Acows, NC1, Riwaka, GPS1,

GPS3, GPS5 and GPS11 isolates and the second group (termed the Oswego group) contained GPS2, KMD10, KMD19, Oswego and OH25. High bootstrap (98-100) and clade credibility (1.00) values supported the suggested grouping (Fig. 2.3). An additional subgroup was observed in the BAP tree within the HP88 group, termed NC1 subgroup, which contained both NC1 and Riwaka. This subgrouping was supported by a clade credibility value of 0.94. Within the Oswego group, a strong sub-division was observed

19 with OH25 and Oswego in one clade and GPS2, KMD 10 and KMD19 in another clade supported by a clade credibility value of 1.00. All trees suggested a close relationship between H. megidis UK and H. downesi K122 followed by H. marelatus Oregon and H. zealandica X1. The results also suggested that H. indica EG2 is the species most closely related species to H. bacteriophora. Surprisingly, the UPGMA tree showed that the out- group Steinernema carpocapsae was located within the two major groups of H. bacteriophora isolates (Fig. 2.3).

The overall ITS1-rDNA pairwise distance for 18 isolates was 0.5331 and for the

13 unknown isolates was 0.5658, indicating the presence of high genetic diversity among the isolates (Table 3). The highest value of pairwise distance, 1.1261, was observed between H. megidis UK and isolates GPS2, KMD10, KMD19, Oswego and OH25. In contrast, H. megidis UK showed relatively low pairwise distance (0.1401) with members of the HP88 group. Some isolates from the HP88 group, which contained the widely known and used isolates HP88 and NC1, showed lower values of pairwise distance with

H. indica EG2, 0.2055, than with isolates from the Oswego group such as KMD19 and

Oswego (1.0452) (Table 2.3).

All cox1 based phylogenetic trees supported the division of the unknown

Heterorhabditis isolates into three phylogenetic groups, KMD10, GPS5 and HP88 group containing the 11 isolates (Fig. 2.4). The HP88 group is further divided into two subgroups: GPS11 subgroup, containing GPS1, GPS2, GPS3, GPS11, HP88, Acows and

Riwaka and Oswego subgroup, containing NC1, KMD19, OH25 and Oswego. This relationship was supported by high bootstrap (98-100) and clade credibility (1.00) values.

According to the cox1 data, GPS5 is more related to H. marelatus than to the HP88

20 group. The cox1 results suggest that H. bacteriophora sensu stricto is most closely

related to H. downesi K122 followed by GPS5, H. marelatus Oregon, H. zealandica X1,

H. megidis (UK and Jun), and H. indica EG2, respectively. The cox1 DNA sequence

based trees provided topologies indicating that the HP88 group is more closely related to

H. marelatus Oregon than to H. megidis UK and Jun isolates. The translated COX1

amino acid based trees (Fig. 2.5) agreed with cox1 DNA sequence based relationships

and generally resulted in higher clade credibility values.

The overall average cox1 based pairwise difference including Dictyocaulus

viviparous, the animal parasitic nematode as an out-group, was 0.7311 and excluding the

out-group was 0.6447 (Table 2.4). The lowest value (0.0000) was observed 18 times

between nematode isolates suggesting a high level of similarity between the isolates. The

highest value (1.6479) of pairwise differences was observed between H. megidis UK and

the isolates NC1, KMD19 and OH25. The UK isolate of H. megidis showed relatively

high values of pairwise differences (1.6044 and 1.3551) with Oswego and GPS3. The

overall value of pairwise distance among Heterorhabditis species was 0.7900 when using

HP88 as a representative of H. bacteriophora. The value of pairwise distance between the two H. megidis isolates, UK and Jun, was only 0.0318.

Table 5 shows a comparison between cox1 and ITS1-rDNA pairwise distance

values of some closely related species and isolate pairs. Generally, cox1 showed higher

pairwise distance values between taxa as compared to the ITS1-rDNA. For example,

ITS1-rDNA and cox1 based pairwise distance values between H. megidis UK and H.

downesi K122 were 0.0410 and 1.3551, respectively. Also, ITS1-rDNA and cox1 based

21 pairwise distance values between H. megidis UK and H. marelatus Oregon were 0.0902 and 0.2730, respectively.

2.4 DISCUSSION

Generally mitochondrial genes have more advantages in phylogenetic and

population genetic studies over nuclear genes because they are maternally inherited, have

higher evolutionary rates, limited exposure to recombination, lack introns and are

inherited in haploid mode. Mitochondrial genomes also have highly conserved gene

content (Gasser et al. 2002; Hu et al. 2002). The mitochondrial gene cox1 provides a

greater range of signals than other mitochondrial genes, including ribosomal 12S and 16S

DNA. Although COX1 protein has a critical function in the process of oxidative

metabolism, it usually possesses substantial variation in its amino acid composition. Lunt

et al. (1996) reported the presence of 125 amino acid variable positions out of 522 when

comparing COX1 protein from an insect species. In our study, the sequence and

parsimony analyses showed that cox1 had fewer alignment gaps and low quality

sequences than ITS1-rDNA, which resulted in more information in resolving phylogenetic

relationships. Also, cox1 had a higher number of variable (polymorphic) and parsimony

informative sites that allowed building better parsimony, distance, and ML trees. The

translated COX1 amino acid based trees had the highest possible consistency index,

retention index and relative completeness index resulting in much trusted parsimony

trees. Adams et al. (1998) also reported large alignment gaps in the ITS1-rDNA

sequences of Heterorhabditis species which resulted in only 192 parsimony informative

sites out of 730 total sites and only 89 parsimony informative sites when including the 22 out group. Therefore, we conclude that cox1 is much better target than ITS1-rDNA for reconstructing phylogeny in Heterorhabditis.

Although, ITS1-rDNA may be a useful diagnostic target for differentiation between known species, cox1 is much better target for differentiating between closely related and cryptic species and for constructing phylogenetic relationships. The pairwise distances calculated using ITS1-rDNA were generally lower than those calculated using cox1 information. This was always the case when comparing between closely related species or isolates within the same species. However, for distantly related species, such as H. zealandica and H. marelatus, pairwise distance calculated using ITS1-rDNA was

higher than for cox1. Such results were obtained by Blouin (2002) who reported that

pairwise distance values between H. bacteriophora and H. megidis were 0.130 and 0.150

using the mitochondrial gene nd4 and ITS1-rDNA, respectively. However, in both cases

cox1 gene should give better resolution for the phylogenetic relationship since very low

and very high pairwise distance values hinder the construction of phylogenetic trees.

Reconstruction of phylogeny based on the cox1 gene sequences suggested the

presence of a close genetic relationship between the HP88 group and H. downesi K122,

with bootstrap values ranging between 99 and 100 and BAP clade credibility value of

1.00. This was not the case between the HP88 group and H. downesi’s sister species H.

megidis UK and Jun, which suggests that cox1 gene has a potential for molecular

differentiation and diagnosis between closely related species within Heterorhabditis.

Blouin (2002) showed that ITS1-rDNA sequences data may not be as useful as mtDNA

data for identifying closely related and cryptic species. He also illustrated that the cox1

could be used to differentiate between the animal parasitic nematode Haemonchus

23 contortus (barber pole worm) and its close sister species H. placei, while ITS1-rDNA failed to provide similar affirmation. Morgan and Blair (1998) showed that mtDNA evolves more rapidly than ITS1-rDNA, and thus is more useful for distinguishing among closely related species of trematodes. Blouin (2002) and Vilas et al. (2005) showed that mitochonderial genes such as nd4 and cox1 are the best choices to search for potential cryptic species when using sequence data on small numbers of individuals in nematodes, trematodes and cestodes. In fact, H. megidis showed two unique signature sequence regions in the cox1 that were absent in all other Heterorhabditis species (data not shown).

The cox1 DNA sequence based trees provided topologies that agreed with ITS1- rDNA relations indicating that the HP88 group is closely related to H. marelatus followed by H. zealandica and H. megidis, respectively. The amino acid sequence of cox1

encoded protein based trees provided better resolution for the relationships among

Heterorhabditis species. Translated amino acid results suggested that the HP88 group is

more closely related to H. downesi, followed by H. zealandica, H. megidis and H.

marelatus, respectively. The cox1 gene DNA sequence and amino acid based trees also

provided high resolution for determining intraspecific relationships compared to ITS1-

rDNA. In addition the cox1 data indicated that KMD10 and GPS5 may be new taxa

which was not possible using ITS1-rDNA.

We observed more than one haplotype in the cox1 data in some nematode isolates

such as, OH25, Acows, HP88 and Oswego (data not shown). Haplotypes from the same

isolate were very similar except for few exceptions. For example, Acows haplotype A

belonged to the HP88 group, whereas haplotype B belonged to the Oswego group. These

results indicate that cox1 can also show intra-population variation if we a large number of

24 representative individuals are sequenced. One extreme case, the commonly used NC1

isolate, contained two different haplotypes the most abundant haplotype (D) belonged to

the Oswego group, while the second was more related to H. zealandica and H. marelatus.

This case may be explained by the presence of a cryptic species within NC1 isolate.

Collins and Paskewitz (1996) identified cryptic species as those groups of closely related

species that are difficult or impossible to distinguish by morphological traits. Anderson

et al. (1998) reported that nematodes tend to be very conserved morphologically but

molecular techniques indicate that many presumed mono-specific species actually consist

of several cryptic species. Heterorhabditis species are shown to be morphologically

conservative and are difficult to differentiate based on morphology of the adult stages or

the free-living infective juvenile stages (Hunt, 1997; Liu et al., 1999). The concept of

cryptic species has been proven in many organisms, such as the insect Astraptes

fulgerator (Hebert et al., 2004), plant Grimmia laevigata (Fernandez et al., 2006), fungi

Amanita muscaria (Geml et al., 2006) and Aspergillus fumigatus (Pringle et al., 2005),

slugs, Arion spp (Pinceel et al., 2005), and in birds Phylloscopus spp (Olsson et al.,

2005), and is thus of common occurrence.

Reconstruction of phylogeny of Heterorhabditis has been done targeting several gene sequences, including partial 18S gene of rDNA (Liu et al., 1997), ITS1-rDNA

(Adams et al., 1998; Phan et al., 2003) and nd4 of mitochondrial DNA (Liu et al., 1999).

Liu et al. (1999) showed that 18S gene sequences were very conservative among

Heterorhabditis species, and thus are inappropriate for resolving the phylogenetic relationships within the genus. Adams et al. (1999) suggested that ITS1-rDNA provides useful phylogenetic characters to resolve phylogenetic relations among closely related

25 sister taxa (Joyce et al., 1994, Adams et al., 1999). In this analysis, Adams et al. (1999)

showed that H. bacteriophora strain Brecon is closely related to H. marelatus. They

observed different tree topologies were observed when using other tree building methods

such as UPGMA, which revealed that H. zealandica is more related to H. bacteriophora.

Thus, they rejected UPGMA relationship and showed it to be not suitable for resolving phylogenetic relationships in Heterorhabditis. We also observed some differences in tree

topologies when using different phylogenetic tree building methods. For example, ITS1- rDNA based UPGMA tree presented S. carpocapsae, the out-group, within the two groups of H. bacteriophora. Adams et al. (1999) resolved the relationships between the closely related species H. bacteriophora, H. hawaiiensis and H. indica but could not resolve relationships among the distantly related species, H. zealandica, H. megidis and

H. marelatus. Liu et al. (1999) using mitochondrial nd4 gene showed that H. bacteriophora is closely related to H. megidis contradicting Adams et al. (1999) who suggested that H. bacteriophora is closely related H. marelatus. Our ITS1-rDNA phylogenetic analysis agreed with Adams et al. (1999) that the HP88 group is closely related to H. marelatus, while cox1 results suggested that the HP88 group is more closely

related to H. downesi. We suggest reconstruction of the phylogeny of Heterorhabditis using information from several genetic loci to avoid problems of the non-correspondence between gene and species phylogeny, and using a larger number of representative isolates for each species.

Mitochondrial cox1 gene can be effectively used to differentiate between sister nematode species, such as H. downesi and H. megidis. The cox1 is more suitable for species differentiation in Heterorhabditis than ITS1-rDNA, since it accumulates

26 informative substitutions more rapidly than ITS1-rDNA, and shows larger genetic

distances between taxa. Two potentially new phylogenetic taxa KMD10 and GPS5 were

discovered using cox1 sequence information, which was not possible with ITS1-rDNA analysis. We also suggest that the HP88 group is subdivided into two subgroups: GPS11 subgroup and Oswego subgroup. Crossbreeding and morphological studies should be done to confirm the identity of these isolates. In fact, our suggestion that ITS1-rDNA identified H. bacteriophora as a species complex is also supported by other biological and ecological data. Cross breeding studies by Burnell et al. (1992) suggested that H. bacteriophora contains at least 2 species, when using HP88 and Brecon as representatives of the species. Boemare (2002) proposed the concept of co-speciation between the nematodes and their associated bacteria. He showed that H. bacteriophora

HP88 harbors the symbiotic bacteria P. luminescens subspecies laumondii, H. bacteriophora NC1 harbors P. luminescens temperata, while H. bacteriophora Brecon harbors P. luminescens luminescens. Significant differences have been observed between

NC1 and Oswego in biocontrol efficacy and long-term field persistence (Shields et al.

1999). Our cox1 data revealed significant differences between two haplotypes in NC1 isolate suggesting the presence of a cryptic species within this isolate. Also H. bacteriophora isolates NC1 and HP88 had significant differences. Similarly large differences in the infective juvenile longevity between KMD10 and HP88 strains were observed by Grewal et al. (2002). All these observation provide support to the cox1 based phylogenetic trees. Thus, we conclude that cox1 is a superior target for species differentiation and phylogeny reconstruction in Heterorhabditis.

27 2.5 SUMMARY

We compared the internal transcribed spacer 1 of ribosomal DNA (ITS1-rDNA) and the mitochondrial gene cytochrome oxidase subunit I (cox1) to investigate genetic diversity and phylogeny of the entomopathogenic nematode Heterorhabditis. Eighteen isolates representing Heterorhabditis bacteriophora, H. indica, H. marelatus, H. megidis, H. downesi, H. zealandica and several unknown populations were included in the study.

Generally, cox1 sequences showed higher levels of genetic variation, larger number of phylogeneticlly informative characters, variable sites and more reliable parsimony trees compared to ITS1-rDNA. The ITS1-rDNA phylogenetic trees suggested the division of the unknown isolates into two major phylogenetic groups: the HP88 group containing

HP88, Acows, NC1, Riwaka, GPS1, GPS3, GPS5 and GPS11 isolates and the Oswego group containing GPS2, KMD10, KMD19, Oswego and OH25 isolates. All cox1 based phylogenetic trees agreed for the division of the unknown isolates into three phylogenetic groups: KMD10, GPS5 and the HP88, group containing the remaining 11 isolates.

KMD10, GPS5 represent potentially new taxa. The cox1 analysis also suggested that

HP88 is divided into two subgroups: the GPS11 group containing HP88, GPS11, GPS2,

GPS3, GPS1, Acows and Riwaka and the Oswego subgroup containing OH25, NC1,

KMD19 and Oswego. Our results suggest that HP88 group is most closely related to H. downesi K122 followed by H. marelatus Oregon, H. zealandica X1, H. megidis (UK and

Jun) and H. indica EG2, respectively. Cox1 gene has a potential for molecular differentiation and diagnosis between closely related species within Heterorhabditis. We advocate for more extensive sampling and deposition of representative molecular data in the Genbank for all Heterorhabditis species to avoid inaccurate molecular identification.

28

2.6 REFERENCE

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of Heterorhabditis (Nemata: Rhabditidae) based on internal transcribed spacer 1

DNA sequence data. Journal of Nematology 30: 22-39.

AKHURST, R. J. 1987. Use of starch gel electrophoresis in the taxonomy of the genus

Heterorhabditis (Nematoda: Heterorhabditidae). Nematologica 33: 1-9.

ANDERSON, T. J. C., M. S. BLOUIN, AND R. N. BEECH. 1998. Population biology

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38

Nematode Species Strain/Isolate GenBank GenBank Original locality accession accession number (cox1) number(rDNA) H. indica EG2 DQ632003 DQ100269 Egypt

H. marelatus Oregon DQ643794 DQ100271 Oregon, USA

H. megidis UK DQ632002 DQ100266 Site 76, UK

H. zealandica X1 DQ643795 DQ100268 Australia

H. downesi K122 DQ643796 DQ100267 UK

H. bacteriophora HP88 DQ632001 DQ100264 Logan, Utah, USA

Heterorhabditis sp. NC1 DQ643797 DQ333348 Clayton, North Carolina,

USA

Heterorhabditis sp. Riwaka DQ643807 DQ333347 Riwaka, New Zealand

Heterorhabditis sp. Oswego DQ643806 DQ100255 Oswego, New York, USA

Heterorhabditis sp. OH25 DQ643808 DQ333349 Hermiston, Oregon, USA

Heterorhabditis sp. Acows DQ643805 DQ100265 Ogallala, Nebraska, USA

Heterorhabditis sp. KMD10 DQ643803 DQ100257 Akron, Ohio, USA

Heterorhabditis sp. KMD19 DQ643804 DQ100258 Union City, Ohio, USA

Heterorhabditis sp. GPS1 DQ643798 DQ100259 Jeromesville, Ohio, USA

Heterorhabditis sp. GPS2 DQ643799 DQ100260 Jeromesville, Ohio, USA

Heterorhabditis sp. GPS3 DQ643800 DQ100261 Jeromesville, Ohio, USA

Heterorhabditis sp. GPS5 DQ643801 DQ100262 Jeromesville, Ohio, USA

Heterorhabditis sp. GPS11 DQ643802 DQ100263 Dellroy, Ohio, USA

Table 2.1. List of entomopathogenic nematode species and strains used in this study.

39 Character ITS1-rDNA cox1 Original product size (bp) 560-670 420-460 Total number of studied sites after alignment with the out group 990 580 Alignment gaps or missing data 618 283 Total number of sites (excluding sites with gaps / missing data) 174 297 Total number of haplotypes 10 14 Haplotype (gene) diversity (Hd) 0.837 0.937 Variance of haplotype diversity 0.00417 0.00182 Standard deviation of haplotype diversity 0.065 0.043 Nucleotide diversity (Pi) 0.33212 0.35175 Average number of nucleotide differences (k) 57.789 104.468 Total number of invariable (monomorphic) sites 18 56 Variable (polymorphic) sites 156 241 Total number of mutations 235 354 Total number of singleton variable sites 34 29 Total number of parsimony informative sites 122 212 Number of singleton variable sites (two variants) 26 23 Number of singleton variable sites (three variants) 8 6 Number of singleton variable sites (four variants) 0 0 Number of parsimony informative sites (two variants) 61 123 Number of parsimony informative sites (three variants) 51 71 Number of parsimony informative sites (four variants) 10 18 Consistency index for consensus parsimony tree (for all sites) 0.824561 0.781879 *(0.902857) Consistency index for consensus parsimony tree (for parsimony 0.794239 0.771127 informative sites) *(0.900585) Retention index for consensus parsimony tree 0.963134 0.920147 *(0.955614) Relative completeness index consensus parsimony tree (for all 0.747210 0.719444 sites) *(0.862783) Relative completeness index for parsimony informative sites 0.719732 0.709550 *(0.860611) Total number of parsimony trees 172 78 *(115) Table 2.2. DNA sequence and parsimony analysis comparison between ITS1-rDNA and cox1. *: Numbers within brackets are values of the 75% cut of COX1 amino acid tree.

40

CHAPTER 3

INVESTIGATION OF THE USE OF nd4 GENE IN PHYLOGENY AND

MOLECULAR DIFFERENTIATION OF ENTOMOPATHOGNIC NEMATODES

(RHABDITIDA: HETERORHABDITIDAE)

3.1 INTRODUCTION

Heterorhabditis species, the lethal parasite of many insect pests, are distributed throughout the world (Hominick et al., 2002; Stock and Hunt, 2005). Twelve species of

Heterorhabditis have been described till now. These species are H. bacteriophora (the type species), H. indica, H. baujardi, H. brevicaudis, H. downesi, H. marelatus, H. megidis, H. poinari, H. taysearae, H. zealandica, H mexicana and H. floridensis (Stock and Hunt, 2005; Nguyn et al., 2004; 2006).

Differentiation among species of Heterorhabditis is difficult due to extreme morphological conservation and difficulties in performing cross breeding tests (Hominick et al., 1996). Therefore, several protein and DNA based studies have been conducted to determine phylogenetic relationships and differentiation among the described species of

Heterorhabditis (Akhurst, 1987; Jagdale et al., 1996; 2006; Liu and Berry 1996; Joyce et al., 1994; Pamjav et al., 1999; Stack et al., 2000; Nasmith et al., 1996; Adams et al.,

1998). For distinguishing Heterorhabditis species, different DNA sequences have also been investigated for significant rates of polymorphism. These include ITS 1 and 2 50 regions, located between 18S and 26S rDNA genes within the rRNA operon and

nd4 gene sequence of mitochondrial DNA (Adams et al., 1998; Nasmith et al., 1996; Liu

et. al., 1999; Blouin et al., 1999). Mitochondrial genes are widely used for studying

population genetics, phylogeny and molecular evolution (Keddie et al., 1998; Liu et al.,

1999; Gasser et al., 2002). Mitochondrial genes are thought to be selectively neutral and

highly variable compared to nuclear genes, and thus more useful for phylogenetic

analysis, differentiation between closely related species, and to compare inter- and intra-

specific variation (Keddie et al., 1998; Blouin 2002; Blouin et al., 2001). Mitochondrial

genes are also maternally inherited and are therefore not subjected to sexual re-

assortment and genetic recombination making them useful for reconstructing phylogeny

among distanly related taxa (Avise, 1994; Keddie et al., 1998; Liu et al., 1999). Among

mitochondrial genes, NADH dehydrogenase subunit 4 (nd4) and cytochrome c oxidase

subunit 1 gene (cox1) provide ideal markers for population genetic structure and

molecular evolution (Blouin, 2002; Blouin et al., 2001). Liu et al. (1999) used partial nd4

to investigate sequence variation and phylogenetic relationships among five

Heterorhabditis species and compare the nd4-inferred relationships with those based on

ITS1 region. They indicated that nd4 contains sequence divergence that makes it useful

for genetic differentiation between Heterorhabditis species and that it is better than ITS1- rDNA for reconstructing phylogeny because of the lack of alignment gaps.

In this study we aimed to investigate inter- and intra-specific genetic diversity in

Heterorhabditis and reconstruct phylogeny of 20 Heterorhabditis strains using the mitochondrial gene nd4. Liu et al. (1999) concluded the usefulness of the nd4 gene in both inferring the phylogenetic relationships and distinguishing between species of

51 Heterorhabditis. They disagreed with the ITS1-rDNA based phylogeny by Adams et al.

(1998). Therefore, our main goal of this study is to re-examine the usefulness of nd4 in species differentiation and phylogeny reconstruction in Heterorhabditis.

The specific objectives of the study were to: 1) investigate the amount and structure of inter- and intra-specific sequence variation in nd4 gene of Heterorhabditis, 2) investigate the presence of subspecies structuring in H. bacteriophora and 3) reconstruct phylogenetic relationships among Heterorhabditis using nd4 sequence data.

3.2 MATERIALS AND METHODS

Nematode species and strains

Eighteen strains of six Heterorhabditis species (H. bacteriophora, H. indica, H. megidis, H. marelatus, H. downesi and H. zealandica) collected from various geographic locations used in this study are listed in Table 3.1. All nematode strains were propagated in the last instar wax moth Galleria mellonella and maintained in our laboratory as described by Kaya and Stock (1997).

Genomic DNA isolation

For genomic DNA extraction, infective juveniles of each isolate were concentrated by centrifugation at 3000 g for ten min in EN buffer (100 Mm NaCl, 10 mM

EDTA) and then washed three times with sterile distilled water. The resulting nematode pellet was ground in liquid nitrogen and DNA was extracted using Qiagen® Genomic Tip

100/G according to the manufacturer’s recommendations.

52 nd4 PCR amplification

PCR conditions were optimized to obtain clear products that can be visualized on agarose gels stained with ethidium bromide. PCR amplification of nd4 included an initial denaturation step for 4 min at 94˚C followed by 35 cycles of 1 min at 94˚C, 50 seconds at

50˚C and 1 min at 72˚C. The PCR was followed by a final extension step at 72˚C for 7 min at 94°C. Each 50-µL-reaction mix contained 50 ng (2µL) of DNA, 1.25 (1µL) units of the Taq polymerase, 1 µM of each primer (µM10), 20-µL sterile water, and 25 µL of the FailSafe PCR 2X PriMix F. The primer set used for the amplification was Forward: 5َ

GGC TGG CTT ATT ATT AAA ATT AG-3َ and Reverse: 5-CAAَ AGA ATA ATA-

.(AAA AGA TAC CAA-3َ (Blouin et al. 1999; Liu et al., 1999

After the PCR was completed, 10 µL of amplified product from each sample was analyzed by electrophoresis in a 1.0 % (w/v) agarose gel. PCR amplification products were purified using either Qiagen® gel extraction kit or Qiagen® PCR purification kit.

Purified PCR products were cloned using cloning Invitrogen® TOPO TA cloning kit.

Several bacteria clones, which have been successfully transformed, were isolated by white and blue selection, and two representative colonies from each isolate were submitted to PCR and then sequenced using M13. DNA sequencing was performed using Applied Biosystems 3730xl automated DNA sequencing instrument (University of

Madison Wisconsin), using 50 cm capillary arrays and POP-7 polymer. For confirmation purified PCR products directly were sequenced from both directions using forward or reverse primers.

53 DNA sequence and phylogenetic analyses

All DNA sequences were edited to remove low quality sequences, using Bioedit sequence alignment editor (Hall, 1999) and subjected to BLAST (Basic Local Alignment

Search Tool) to perform sequence similarity searches. All sequence data were submitted to the GenBank. Sequences were initially aligned using the Bioedit built-in clustal W program (gap opening penalty = 10, gap extension penalty = 5, delay divergent sequences

= 40%). Additional sequence alignments were performed using MAFFT multiple sequence alignment program available online at http://align.genome.jp/mafft/ and with

PRRN (the best-first iterative refinement strategy with tree-dependent partitioning) multiple sequence alignment program available online at http://align.genome.jp/prrn/.

Resulting alignments were compared and the final alignments were improved manually and prepared in FASTA, MEGA and NEXUS formats. Pairwise distance calculation was performed using Tamura-Nei for partial nd4 and gaps, marked as (-), and missing data, marked as (?), were taken into account using the complete deletion method in MEGA software and the default gap values in PAUP V4B10, MrBayes v3.1 and

TreeBuzzel (Kumar et al., 2004; Swofford, 2001; Strimmer and Haeseler, 1996; Ronquist and Huelsenbeck 2003). In order to test different possibilities and alternative hypotheses of evolution, phylogenetic trees were produced using neighbor joining (NJ), unweighted pair-group arithmetic mean (UPGMA), minimum evolution (ME), Bayesian analysis of phylogeny (BAP), maximum likelihood (ML) and maximum parsimony (MP) methods using PAUP V4B10, MEGA 3, MrBayes v3.1and TreeBuzzle softwares. We used the default values of open and extended gaps in both of PAUP V4B10 and MrBayes v3.1 softwares. The transition/transversion ratio was set to 2:1 for PAUP V4B10. Bootstrap

54 analysis was performed using 1000 replicates to test the support for each branch of the

tree.

For the MP method, heuristic (with tree bisection-reconnection as the branch

swapping algorithm, TBR) and branch-and-bound searches were conducted. All

characters had the same weight, treated as unordered and the third position of the codon

was excluded. Bootstrap analysis with 1000 replicates was used to test the support for

the branches. We calculated a 75% majority rule MP consensus tree. For BAP method,

the selected evolutionary model was GTR model with gamma-distributed rate variation

across sites and a proportion of invariable sites. All codon positions were included for

phylogenetic analysis. The number of generations used is 1,000,000, sampling a tree

every 100 generations and then calculating a 50% majority rule consensus tree in

PAUP4.0b10. The value of the standard deviation of split frequencies is 0.007312.

Amino acid translations of partial nucleotide cox1 sequences were obtained and analyzed by MEGA 3.1 software. Poisson correction was used as amino acid substitution method.

DNA sequence polymorphism analysis has been performed using DnaSP software (Rozas et al., 2003).

3.3 RESULTS

nd4 sequence analysis

Nucleotide-nucleotide basic local alignment search tool (Blastn) search showed

that all the amplified products belong to the mitochondrial nd4 gene with high e-values

and hit scores (Table 3.2). The original product sizes, after removal of ambiguous

regions, ranged between 450 and 470 bp. The total number of studied sites after 55 alignment with the out-group was 511. The number of alignment gaps or missing data

sites was 221 and the total number of sites excluding sites with gaps and missing data

was 290 (Fig. 3.1). The values of nd4 DNA sequence similarity ranged between 80 and

100%. The number of observed haplotypes was 13 and the value of haplotype diversity

was 0.853 with a variance of 0.006 and standard deviation of 0.080. The value of

nucleotide diversity was 0.051 and the average number of nucleotide differences was

15.06. The total number of invariable (monomorphic) sites was 205, number of variable

sites was 85 and total number of mutations was 103. The number of parsimony

informative sites was 38. Generally high AT content was observed among nematode

strains (Table 3.3). The overall AT content was 74.2%. The highest AT content, 76.7%,

was observed with H. bacteriophora strains GPS1 and GPS11 and the lowest, 69.1%,

was observed with H. zealandica strain X1. In addition, codon usage analysis (Table 3.4)

showed that there was obvious bias towards the usage of A or T-containing genetic

codes. For example, genetic codes UUU and UUC encode the amino acid phenylalanine

but the usage of UUU (16.4) was five times higher than UUC (3.0). Similar usage bias

was found with all other codons except for UGA and UGG, which encode for the amino

acid tryptophan, where the usage value of the latter was higher than the first. We also

observed that the AT content is lower in the first and second-position than it is in the

third position, 73, 71 and 78 %, respectively.

The overall ratio of transitional/transversional (si/sv) changes was 1.4, with the

highest number of changes occurring in the third position (Table 3.5).

Pairwise comparison of the nd4 gene sequence divergence (Table 3.6) provided quantitative information about the genetic relationship among Heterorhabditis strains.

56 The overall average pairwise distance among Heterorhabditis strains is 0.0517. The

lowest value of pairwise distance, 0.000, was observed 36 times between some H.

bacteriophora strains such as GPS11 and HP88, and between some H. bacteriophora

strains, such as KMD19 and H. indica EG2 and H. megidis Jun. The highest pairwise

distance value, 0.2654, was observed between H. marelatus Oregon and H.

bacteriophora GPS2. Generally, H. downesi K122 showed the highest pairwise distance

values with all studied species and strains. For example, a value of pairwise distance of

0.18 was observed between H. downesi K122 and H. bacteriophora Acows. Same

pairwise distance, 0.18, was observed between H. downesi K122 and H. bacteriophora

NC1 and KMD10 and H. indica LN2. H. marelatus also showed high values of pairwise

distance with all studied species and strains.

Phylogenetic reconstruction and molecular differentiation within Heterorhabditis

The nd4 gene sequence based UPGMA phenetic tree and NJ, MP, ML and BAP phylogenetic trees did not resolve the phylogenetic relationships within Heterorhabditis.

Phylogenetic analysis resulted in poor trees with low values of bootstrap and clade

credibility. The nd4 gene also did not able to differentiate between different species,

such as H. bacteriophora, H. indica and H. megidis, which were placed together in one

clade (Fig. 3.2 and 3.3). Also, nd4 based phylogenetic analysis showed that H. marelatus

Oregon is more closely related to H. downesi K122 than to H. megidis UK and Jun

strains. This was supported by moderate bootstrap values of 52 and 53 and relatively

high clade credibility value of 0.78. H. zealandica is more related to H. bacteriophora

than to H. marelatus Oregon and H. downesi K122 with bootstrap values ranging

between 91 and 100 (Fig. 3.2 and 3.3) and strains of H. indica LN2 and EG2 and H.

57 megidis UK and Jun (Table 3.2 and Fig. 3.2 and 3.3). The nd4 sequences differentiated

H. marelatus Oregon, H zealandica X1 and H. downesi K122 from other Heterorhabditis

strains that included H. bacteriophora, H. indica and H. megidis.

3.4 DISCUSSION

Liu et al. (1999) used a partial nd4 gene sequences to construct phylogeny and

differentiate between 15 Heterorhabditis strains from different geographic regions. They concluded the usefulness of the nd4 gene in both inferring the phylogenetic relationships and distinguishing between species of Heterorhabditis. They disagreed with the ITS1- rDNA based phylogeny proposed by Adams et al. (1998) showing that H. bacteriophora is closely related to H. megidis and not to H. zealandica. They also advocated for greater utility of nd4 gene over ITS1 because of the higher number of alignment gaps frequently found in ITS1.

However, Liu and coworkers did not include the distantly related species H. zealandica or H. downesi in their study, thus they did not resolve the phylogenetic relationship among the distantly related species of Heterorhabditis. In this study we used 20 strains of Heterorhabditis representing six species from different geographic regions. ITS1-rDNA based phylogenetic analysis for these 20 strains resulted in phylogeny reconstruction identical to that of Adams at al. (1998). In order to accomplish our objectives we used similar nd4 gene amplification conditions including the primer set used by Liu et al. (1999). Our nd4 sequence analysis revealed the presence of 13 haplotypes among the studied 20 strains of Heterorhabditis. While, Liu et al. (1999) reported the presence of 7 haplotypes among the studied 15 strains. Haplotype analysis

58 revealed that H. bacteriophora, H. indica and H. megidis polymorphic. Similarly, Liu et al (1999) showed that H. bacteriophora, H. indica and H. marelatus are haplotypically polymorphic. We could not test if H. marelatus, H. downesi and H. zealandica are polymorphic since we did not have multiple strains for these species. The observed high

AT content and codon usage analysis bias of nd4 (Table 4) suggest that there is an A+T mutational bias in Heterorhabditis nd4 gene sequence and that the possibility of substitutions from C or G to A or T should always be higher than the opposite direction.

A+T mutational bias has been observed in many nematodes including genus

Caenorhabditis elegans (Thomas and Wilson, 1991) and Ascaris suum (Okimoto et al.,

1992), Meloidogyne hapla (Hugall et al., 1997) and other nematode species (Hyman and

Azevedo, 1996; Blouin et al., 1998).

Our observation in which we found that the A+T content is lower in the first and second-position than it is in the third position was also reported by Blouin et al. (1998) with mitochondrial nd4 gene of parasitic nematodes species in the family

Trichostrongylidae: Haemonchus placei, Haemonchus contortus, Teladorsagia circumcincta and Mazamastrongylus odocoilei. They found that the total average A+T percentage for these nematodes was 82.4, 72.1 and 87.1 for the first, second and third positions of the codons, respectively. This mutational pressure is consistent with the findings of Jukes and Bushan (1986) who suggested that changes in the nucleotide composition often occur in silent positions in codons, usually in the third position, which lead to neutral or near neutral changes, as a result of mutational pressure in the direction of increasing either A+T or G+C content. Similar to Blouin et al. (1998) we also found

59 that the lowest A+T content in the second letter of the codon, which is very critical for

recalling the correct amino acid in the active protein.

We observed relatively high ratio of transitional/transversional (si/sv) changes

with the highest number of changes occurring in the third position. The presence of high

number of transitional changes, which usually results in silent mutation, indicates the

selective pressure to maintain the protein product in a functional form. Similar value of

si/sv, 1.8, was observed by Qui et al., (unpublished data deposited in the GenBank) when

studying nd4 gene in some Heterorhabditis strains collected from different geographic regions in China. While, Liu et al. (1999) reported that si/sv was 1.3 for Heterohabditis species and strains, higher ratio of si/sv was observed for the parasitic nematode

Haemonchus contortus, 10.1, Haemonchus placei, 6.4, Teladorsagia circumcinta, 8.7,

Mazamastrongylus odocoilei, 10.2 (Blouin et al., 1998). Although the most common transversional changes are T↔A, these changes do not rapidly occur (Blouin et al.,

1998), thus the high TA content and AT mutational pressure does not explain the low

(si/sv). However, these ratios have been reduced when performing intraspecific comparison, H. contortus and H. placei, 1.1 (Blouin et al., 1998). Similar case of rapid drop of si/sv ratio was observed by Thomas and Wilson (1991) when moving from intra- to inter-specific comparison in Caenorhabditis. Thus, it is important to use only the most closely related species in order to infer the substitution patterns correctly in nematodes due to the transitional change saturation on the con-generic species level and the transversional changes plateau on the con-familial genera (Blouin et al., 1998).

Relatively low overall average pairwise distance was observed among

Heterorhabditis strains is 0.0517 (5.1%). Even lower overall average of pairwise

60 distance, 0.0371 (3.7%), among Heterorhabditis strains was observed by Liu et al.

(1999). This difference may be due to the non-inclusion of the distantly related

Heterorhabditis species H. zealandica and H. downesi and/or the usage of fewer isolates.

Qui et al. (deposited in the GenBank) data showed an overall average of pairwise distance among Chinese isolates of Heterorhabditis was 0.0478. These results indicate that relatively low values of genetic variation of nd4 gene among Heterorhabditis species is either due to the importance of NADH-reductase complex for the survival of the organisms or due to AT bias which leads eventually to over saturation and homoplasy.

No nd4 based phylogenetic trees resolved the phylogenetic relationships within

Heterorhabditis. They also did not able to differentiate between previously identified H. bacteriophora strains and strains of H. indica LN2 and EG2 and H. megidis UK and Jun,

based on ITS1-rDNA. In contrast, Liu et al. (1999) advocated for the potential use of nd4

gene in constructing phylogeny among Heterorhabditis spp. But in their analysis they

considered both NC1 and OH25 strains to belong to H. megidis, while according to our

ITS1-rDNA analysis both strains belong to H. bacteriophora. These results in addition to

the observed low genetic diversity of nd4, question its potentiality in both constructing

phylogeny and molecular differentiation among Heterorhabditis spp.

It has been found that the third position sites of mtDNA in nematodes are saturated with uninformative sites and should be excluded when constructing phylogeny.

This was the case for Meloidogyne, Ascaris, Romanomermis and Caenorhabditis cytochrome oxidase 11 (Nadler 1995). Blouin et al. (1998) stated that care must be taken when constructing phylogeny using ND complex encoding genes in con-familial genera and thus also in genera that contain very distant species such as observed in

61 Heterorhabditis. They also advocated for the use of slower evolving mitochondrial

genes, such as Cytochrome oxidase genes using only first and second positions or amino

acid. Highly A+T content mtDNA causes numerous problems for reconstruction

phylogeny since nucleotide composition can lead to the grouping of taxa based on the

shared characters, due to composition bias, not on shared history (Hasegawa and

Hashimoto 1993; Steel et al., 1993; Simon et al., 1994; Blouin et al., 1998). Also rapid

saturation of nucleotides reduces resolution when building distance and parsimony trees

(Wolfe et al., 1989; Wolfe and Sharp 1993; Brower and DeSalle 1998; Blouin et al.,

1998). Blouin et al. (1998) suggested that mtDNA and specifically the nd4 gene should be used in reconstruction of phylogeny among closely related species using the appropriate phylogenetic methods and nucleotide substitution rates and models. Thus, we can conclude from these results that the nd4 is not suitable for constructing phylogeny among species of the genus Heterorhabditis, which is characterized by including distally related species, or only use nd4 to study relationships between closely related species in the genus. Also our results suggested that nd4 is no a useful target for molecular identification of Heterorhabditis species.

3.5 SUMMARY

We investigated the potential of the mitochondrial gene nd4 in molecular

differentiation and phylogenetic relationship inference among Heterorhabditis strains.

Partial nd4 gene sequences of mitochondrial DNA were determined from 20 strains of

Heterorhabditis representing six different species collected from different geographic regions all over the world. Thirteen haplotypes were observed with a diversity index of

62 0.853 and standard deviation of 0.08 while the nucleotide diversity was only 0.05087.

The overall average of pairwise distance among Heterorhabditis strains was only 5%.

No nd4 gene sequence- and ND4 amino acid- based phenetic and phylogenetic trees

completely resolved the phylogenetic relationships among species of Heterorhabditis.

We conclude that nd4 is not an ideal target for reconstruction of phylogeny or molecular

differentiation among Heterorhabditis species.

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70

Nematode Species Strain/Isolate Original locality H. indica EG2 Egypt

H. marelatus Oregon Oregon, USA

H. megidis Jun Hungary

H. megidis UK Site 76, UK

H. zealandica X1 Australia

H. downesi K122 UK

H. bacteriophora HP88 Logan, Utah, USA

H. bacteriophora NC1 Clayton, North Carolina, USA

H. bacteriophora Riwaka Riwaka, New Zealand

H. bacteriophora Oswego Oswego, New York, USA

H. bacteriophora OH25 Hermiston, Oregon, USA

H. bacteriophora Acows Ogallala, Nebraska, USA

H. bacteriophora KMD10 Akron, Ohio, USA

H. bacteriophora KMD19 Union City, Ohio, USA

H. bacteriophora GPS1 Jeromesville, Ohio, USA

H. bacteriophora GPS2 Jeromesville, Ohio, USA

H. bacteriophora GPS3 Jeromesville, Ohio, USA

H. bacteriophora GPS5 Jeromesville, Ohio, USA

H. bacteriophora GPS11 Dellroy, Ohio, USA

Table 3.1. List of entomopathogenic nematode species and strains used in this study.

71 Isolate Best hit(s), accession no. score E-value

H. megidis UK H. bacteriophora strain NJ NADH dehydrogenase subunit 4 533 2e-148 H. indica EG2 H. bacteriophora strain NJ NADH dehydrogenase subunit 4 547 1e-152 H. marelatus Heterorhabditis sp. NP3 NADH dehydrogenase subunit 4 367 2e-98 Oregon H. zealandica Heterorhabditis sp. strain OH25 NADH dehydrogenase 410 1e-111 X1 subunit 4 H. downesi H. zealandica NADH dehydrogenase subunit 4 234 2e-58 K122 H. bacteriophora H. bacteriophora strain NJ NADH dehydrogenase subunit 4 555 4e-155 HP88 Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 432 4e-118 sp. NC1 Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 529 2e-147 sp. GPS1 Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 331 1e-87 sp. GPS2 Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 549 3e-153 sp. GPS3 Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 543 2e-151 sp. GPS5 Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 547 1e-152 sp. GPS11 Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 410 1e-111 sp. KMD10 Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 517 8e-144 sp. KMD19 Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 605 4e-170 sp. Acows Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 704 0.0 sp. Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 464 1e-127 sp. Riwaka Heterorhabditis H. bacteriophora strain NJ NADH dehydrogenase subunit 4 511 5e-142 sp. OH25 H. megidis Jun H. bacteriophora strain NJ NADH dehydrogenase subunit 4 492 5e-136 H. indica LN2 H. bacteriophora strain NJ NADH dehydrogenase subunit 4 662 0.0 Table 3.2. Blastn search results for nd4 gene sequence of Heterorhabditis strains.

72

CHAPTER 4

INTRASPECIFIC GENETIC VARIATION IN THE MAJOR SPERM PROTEIN

GENE OF THE ENTOMOPATHOGENIC NEMATODE HETERORHABDITIS

BACTERIOPHORA

4.1 INTRODUCTION

Major sperm protein (MSP) is the most abundant protein present in the nematode sperm, comprising 40% of total soluble protein and 15% of total protein (Bennett and

Ward 1986; Baker et al., 2002). MSP is a characteristic feature of the phylum Nemata, since this protein has never been detected in other organisms (Ammons 1990; Klass and

Ammons, 1988; Novistki et al., 1999; Scott et al., 1989a) except for a single report of a

tomato protein, VAP-33, in which the first half is homologous to the nematode msp

domain (Laurent et al., 2000). As the MSP-based spermatozoa crawling motility is a

characteristic feature of nematodes, MSP and its encoding genes are highly conserved

among nematodes (Bennett and Ward 1986; Scott et al., 1989; Ward et al., 1988).

Furthermore, sperm production is the limiting factor during fertilization, thus nematodes

with a higher number of intact genes encoding MSP would have a selective advantage

(Scott et al., 1989a).

MSP is encoded by a multigene family that contains a different number of gene

members in each nematode species. There are more than 50 members in C. elegans, one 80 in Ascaris sp., one to four in filarial nematodes, five to 12 in plant and insect

parasitic nematodes and one to 13 in mammalian intestinal parasites (Burke and Ward,

1983; Klass et al., 1984; Ward et al., 1988). Nucleotide sequence homology of msp genes among wide spectrum of nematode genera ranges between 72 and 83% and the

MSP amino acid sequence homology ranges between 82 and 90% (Scott et al., 1989a;

Hojas and Post, 2000). This allows the detection and amplification of msp genes from different nematode genera and species using the same primer set or cDNA probes (Hojas and Post, 2000). Also the msp genes are small in size and organized into two conserved exons with a variable size intron and show minimal number of insertions and deletions.

These characteristics of msp genes suggest their potential use in nematode molecular identification on different taxonomic levels (Hojas and Post, 2000).

Entomopathogenic nematodes belonging to the families Steinernematidae and

Heterorhabditidae (Rhabditida) are important biocontrol agents (Grewal et al., 2005).

These nematodes form a symbiotic relationship with the bacteria Photorhabdus spp (for

Heterohabditidae) and Xenorhabdus spp (for Steinernematidae) (Boemare, 2002). The infective stage juveniles penetrate the insect host through natural openings and release the symbiotic bacteria into the hemocoel, which multiply and produce toxins that kill the host

(Dowds and Peters, 2002). Heterorhabditis species undergo both automictic (asexual) and amphimictic (sexual) life cycles inside the host cadaver. The infective juveniles always develop into hermaphrodites, which lay 100-300 eggs each. The eggs hatch into the first larval stage that undergoes three additional larval stages to develop into adult males or females leading to sexual reproduction 2-3 times within the insect cadaver

(Johnigk and Ehlers 1999).

81 The family Heterorhabditidae consists of one genus, Heterorhabditis (Poinar,

1976) that contains 12 species (Stock and Hunt, 2005). Heterorhabditis species are cosmopolitan and generally dominate tropical and subtropical environments (Griffin et al., 1990; 1991; Hominick et al., 1996). H. bacteriophora is the most widely distributed species, having been reported from North and South America (Poinar, 1990; Stock,

1993), Southern Europe (Smits et al., 1991; De Doucet and Gabarra, 1994), Australia

(Poinar, 1990) and China (Li and Wang, 1989). However, many strains or populations of

H. bacteriophora reported from around the world (Poinar, 1990; Hominick, 2002; Grewal et al., 2002) show substantial genetic diversity for important biological traits including infective juvenile longevity, environmental stress tolerance and virulence (Shields et al.

1999; Grewal et al. 2002ab). These large intra-specific differences and association of certain strains with distinct sub-species of the symbiotic bacteria Photorhabdus luminescens (Boemare, 2002) calls into question the validity of H. bacteriophora as a single species.

Heterorhabditis nematodes are morphologically more conserved than

Steinernema (Hominick et al., 1996) and cross breeding is difficult due to their complex

life cycle (Hominick, 2002; Johnigk and Ehlers, 1999). Currently the most accepted

method for identification of Heterorhabditis species is based on the ITS1-rDNA sequence

information (Adams et al., 1998). While the ITS1-rDNA may be a useful taxonomic

candidate for nematode identification, only a few sequences have been deposited in the

GenBank, which hinders accurate differentiation between sister and cryptic species.

Therefore, there is a need for other molecular markers for differentiation of

Heterorhabditis species and strains. We evaluated the potential use of msp gene to

82 determine intraspecific variability in the most widely distributed Heterorhabditis species

H. bacteriophora. We hypothesized that there is large diversity in msp among

Heterorhabditis strains currently recognized as H. bacteriophora. Our specific objectives were to investigate genetic diversity in msp genes of H. bacteriophora strains from different geographic regions and to determine their phylogenetic relationships.

4.2 MATERIALS AND METHODS

Nematodes strains

Thirteen strains of H. bacteriophora used in this study are listed in Table 4.1. All the strains used in this study have been reported to belong to H. bacteriophora in the literature (Shields et al. 1999; Grewal et al. 2002ab). In addition, ITS1-rDNA and Blast-n similarity searches confirmed that all strains belonged to H. bacteriophora (Saeb and

Grewal, 2006, unpublished data).

Template DNA Preparation, PCR, Cloning and Sequencing

Nematodes were maintained and propagated in last instar wax moth Galleria mellonella using the methods described by Kaya and Stock (1997). Nematodes were collected by centrifugation at 3000 g for 10 min in EN buffer (100 Mm NaCl, 10 mM

EDTA), and then washed three times with sterile distilled water. The nematode pellets were crushed into powder in liquid nitrogen and DNA was extracted using QiagenR

Genomic Tip 100/G according to the manufacturer’s recommendations. The following primer set was used for the amplification of the msp gene: Forward: 5’-TGGCGC

83 AATCGGTTCCACC-3’ and Reverse: 5’-CTTAAGATTTTTGCGACGAACCAT-3’

(Hojas and Post, 2000).

Each 100 µL PCR reaction contained 100 ng (6-8µL) of DNA, 2.5 units of the

Fermentus® recombinant Taq polymerase, 2 µM of each primer, 40µL sterile water and

50 µL of the FailSafe® PCR 2X PriMix F (100 Mm Tris-HCl, 400 µM each dNTP). The

PCR cycle included an initial denaturation step at 94 °C for 4 min, followed by 40 cycles of 1 min at 94 °C, 50 seconds at 53-55 °C and 1 min at 72 °C. The PCR was followed by a final extension step at 72 °C for 10 minutes.

After the PCR was completed, 10 µL of amplified product from each sample was analyzed by electrophoresis in a 1.0 % in TBE (w/v) agarose gel. Then the remaining 90

µL of the PCR reaction were loaded into big wells of 1.0 % in TBE (w/v) agarose gel and separated by electrophoresis. Gel slices containing the desired PCR product were cut out using new sharp razor blades and purified using Qiagen® gel extraction kit. Purified PCR products were sequenced, from both ends, using Applied Biosystems 3730xl automated

DNA sequencing instrument, using 50 cm capillary arrays and POP-7 polymer

(University of Madison Wisconsin). For comparison GPS1 and GPS5 PCR products were cloned using Invitrogen® TOPO TA cloning kit (Invitrogen, Carlsbad, California).

Five bacterial clones that had been successfully transformed were isolated by white and blue selection, submitted to PCR and then sequenced using msp forward primer as stated before. Identical DNA sequences were observed from PCR-amplified and cloned msp products.

84 DNA sequence, genetic distance and phylogenetic analysis

The msp sequences were edited to remove low quality sequences, using Bioedit sequence alignment editor (Hall, 1999) and subjected to BLAST (Basic Local Alignment

Search Tool) to perform sequence similarity searches. All msp sequence data were

submitted to the GenBank and the assigned accession numbers are given in Table. 1.

Sequences were initially aligned using the Bioedit built-in clustal W program (gap

opening penalty = 10, gap extension penalty = 5, delay divergent sequences = 40%).

Additional sequence alignments were performed using MAFFT multiple sequence

alignment program available online at http://align.genome.jp/mafft/ and with PRRN (the best-first iterative refinement strategy with tree-dependent partitioning) multiple sequence alignment program available online at http://align.genome.jp/prrn/.

Resulting alignments were compared and the final alignments were improved

manually and prepared in FASTA, MEGA and NEXUS formats. We used Kimura 2-

parameter (AIC9 = 4399.298654 lnL = -2198.649327) substitution model to calculate

pairwise distance and to generate trees. In order to investigate the genetic similarity

between the different sequences among H. bacteriophora isolates a phenetic tree was

constructed using the unweighted pair group method with arithmetic averages (UPGMA)

in PAUP V4B10 and MEGA 3.1 softwares (Swofford, 2001; Kumar et al., 2004). In

order to establish phylogenetic relationships within species, trees were constructed using

neighbor joining (NJ), minimum evolution (ME), Bayesian Analysis of Phylogeny

(BAP), and maximum parsimony (MP) methods using PAUP V4B10, MEGA 3.1

software and MrBayes v3.1 (Swofford, 2001; Huelsenbeck et al., 2002; Kumar et al.,

2004). We used Kimura 2-parameter model, and gaps and missing data were taken into

85 account using the complete deletion option in MEGA 3.1 software. We used the default

values of open and extended gaps in both of PAUP V4B10 and MrBayes v3.1 softwares.

We set the transition/transversion ratio set to 2:1 for PAUP V4B10. Bootstrap analysis

was performed using 1000 replicates to test the support for each branch in the tree.

For the MP method, heuristic (with tree bisection-reconnection as the branch

swapping algorithm, TBR) and branch-and-bound searches were conducted. All

characters had the same weight, treated as unordered and the third position of the codon

was excluded. Bootstrap analysis with 1000 replicates was used to test the support for

the branches. We calculated a 75% majority rule MP consensus tree. For BAP method,

the selected evolutionary model was GTR model with gamma-distributed rate variation

across sites and a proportion of invariable sites. All codon positions were included for

phylogenetic analysis. The number of generations used is 1,000,000, sampling a tree

every 100 generations. The value of the standard deviation of split frequencies is

0.007312. Amino acid translations of partial nucleotide cox1 sequences were obtained

and analyzed by MEGA 3.1 software. Poisson correction was used as amino acid

substitution method. Dictyocaulus viviparous (Accession number S64873.1) and

Oesophagostomum dentatum (AJ627870.1 and AJ627871.1) were used as out-groups.

For gene structure prediction we used FGENESH software available at:

http://sun1.softberry.com/. Sequences were examined against Cenorhabditis elegans, the free-living nematode, and against the filarial nematode Brugia malayi if no prediction was obtained against C. elegans. Sequences were also compared with Onchocerca volvulus msp exon 1 and 2 (Scott et al., 1989).

86 4.3 RESULTS

Sequence and structural analysis

For all H. bacteriophora strains, we observed a single product of approximately

480 bp on 1% agarose gel using 1kb DNA ladder as DNA size marker (Fig. 4.1). After

editing, the size of the gene sequences ranged from 409 to 413 bp. Pairwise sequence

alignment scores ranged from 86 to 99% (Fig. 4.2). We treated the external alignment

gaps as missing data. The total number of used sequences was 13, number of haplotypes

was 13, haplotype diversity was 1.000, with a variance of 0.00091 and standard deviation

of 0.030. The value of nucleotide diversity was 0.05560 and average number of

nucleotide differences was 22.462. Total number of sites excluding sites with gaps and

missing data was 404 and number of sites with alignment gaps or missing data was 21.

The number of invariable (monomorphic) sites was 331, variable (polymorphic) sites was

73 and total number of mutations was 88. The number of singleton variable sites was 29

and the number of parsimony informative sites was 44. The number of singleton

variable sites with two variants was 28, three variants was one and four variants was 0.

The number of parsimony informative sites with two variants was 38, three variants was

eight and four variants was three.

Blast-n results for H. bacteriophora msp showed similarities with msp genes from different animal parasitic nematodes with relatively high E-values (Table 4.2). Gene prediction analysis revealed that the msp sequences represent one gene that contains two exons and one intron with few exceptions (Table 4.3). In general, the length of the first exon was 99 bp, but it was 123 bp for HP88 and 120 bp for GPS5 and GPS11. For the second exon, the common length was 96 bp but it was 93 bp for both GPS5 and GPS11.

87 No second exon was detected in Oswego, OH25 and GPS3 within the sequenced PCR

product. The common intron size was 81 bp and ranged from 81 to 84. Differences in

the structure of the exons and introns are given in Table 4.4.

Genetic distance

The overall average pairwise distance among H. bacteriophora strains was 0.0572

with a range of 0.0059 (Riwaka and GPS2) to 0.0973 (Acows and HP88) (Table 4.5).

The overall average pairwise difference for amino acid sequences among H.

bacteriophora strains was 0.0906 with a range of 0.0000 (Riwaka and NC1) to 0.1335

(Acows and HP88) (Table 4.6).

Sub-species structuring and phylogenetic analysis

The msp gene sequence based UPGMA phenetic tree and NJ, MP and BAP phylogenetic trees showed the presence of high genetic structuring within H. bacteriophora. UPGMA phenetic tree identified two evolutionary groups in H. bacteriophora (Fig. 4.2A); the HP88 group and Oswego group. The HP88 group was further divided into two subgroups, NC1 containing NC1, Riwaka and GPS2 strains, and

HP88 containing only the HP88 strain. The Oswego group was also divided into two subgroups, the Oswego subgroup, comprising Oswego and OH25 strains and the KMD19 subgroup comprising KMD19 and Acows but with lower bootstrap value of 78 (Fig.

4.2A). NJ tree also identified the same two groups. Again, within the HP88 group, there are 2 additional subgroups, HP88 subgroup comprising KMD10, GPS1, GPS2 GPS3,

GPS5, GPS11 and HP88 strains and NC1 subgroup comprising NC1 and Riwaka (Fig.

4.3B). The NJ tree also suggests that Oswego group contains Oswego subgroup comprising Oswego and OH25 strains.

88 MP tree suggests that H. bacteriophora strains are divided into three major

groups; HP88, KMD19 and Oswego. The HP88 group contains three further subgroups,

HP88, Riwaka and NC1 with a bootstrap value of 100. The Oswego group containing

Oswego and OH25 strains was supported by a bootstrap value of 100, while KMD19

group that contained KMD19 and Acows was supported by a bootstrap value of 92. The

HP88 subgroup is further sub-divided into four sub-sub-groups: HP88, GPS11, KMD10

and GPS2. The MP tree also suggests that Riwaka and NC1 are stand alone phylogenetic

taxa.

BAP tree suggests that H. bacteriophora strains are divided into two major groups, HP88 and Acows. This division is supported by a perfect clade credibility value of 1.00. The HP88 group is further divided into two subgroups: HP88 and Oswego (Fig.

3D) with a perfect clade credibility value of 1.00. The Oswego subgroup contains

Oswego and OH25 strains, while the HP88 subgroup is further divided into two sub- subgroups, HP88 and KMD19. The HP88 sub-sub-group contains three further lineages,

HP88, KMD10 and NC1, each containing three strains (Fig. 4.3D). All these divisions were also supported by high clade credibility values.

The MSP amino acid-based trees showed a slightly lower degree of genetic

structuring within H. bacteriophora compared to the msp gene based trees (Fig. 4.4). The

UPGMA phenetic tree suggested the division of H. bacteriophora into two major

evolutionary groups; the Acows and HP88 with a perfect bootstrap value of 100 (Fig.

4.4A) as suggested by the BAP msp gene based tree. The MSP amino acid based NJ tree

suggested the division of H. bacteriophora strains into two groups, HP88 group and

Oswego group with a perfect bootstrap value of 100 (Fig. 4.4A). The Oswego group

89 contained KMD19, Acows, Oswego and OH25, while the HP88 group contained the

remaining nine strains. The MSP amino acid based MP tree suggested that H.

bacteriophora contains three groups, KMD10, HP88 and NC1 in addition to five isolates

GPS11, Acows, Oswego, OH25 and KMD19 as stand alone taxa. The amino acid based

BAP tree is in complete agreement with msp gene based BAP tree with only one

exception that HP88 sub-sub-group contains one additional lineage, GPS11.

For the MP analyses, the retention index (RI) was 0.88. The consistency index

(CI) was 0.85 for all sites and 0.83 for parsimony informative sites. The relative

completeness index (RCI) was 0.75 for all sites and 0.73 for parsimony informative sites.

The total number of parsimony trees was 14 and the consensus tree was calculated with a

cut-off value of 75%. For the amino acid MP tree, the RI value was 0.96 and the CI

value was 0.95 for all sites and 0.95 for parsimony informative sites. The RCI was 0.91

for all sites and 0.91 for parsimony informative sites. The total number of parsimony

trees was 97 and the consensus tree was calculated with a cut-off value of 75%.

4.4 DISCUSSION

This study suggests the presence of high sub-structuring in H. bacteriophora with

an overall genetic distance of 5% in the msp gene sequences. Two major lineages were observed in the human parasitic nematode Onchocerca volvulus with an overall genetic distance of 3.96% but only one lineage was observed in the human pathogenic nematode

Mansonella ozzardi with an overall genetic distance of 0.56% (Hojas and Post, 2000).

The DNA sequence similarity score range for H. bacteriophora is wider (86-99%) compared to O. volvulus (95-100%; Scott et al., 1989b) and M. ozzardi (98-100%; Hojas

90 and Post, 2000). Our calculations of the DNA sequence similarity score ranges for many

other parasitic nematodes, using the available information in the GenBank, further

supported that the DNA sequence similarity score for H. bacteriophora is unusually low.

The scores for the parasitic nematode species were; 96-99% for Ascaris suum, 95-99%

for Oesophagostomum dentatum, 91-93% for Pratylenchus penetrans, 91-99% for

Pratylenchus scribneri, 91-95% for Globodera rostochiensis and 94-100% for

Teladorsagia circumcincta (Saeb and grewal, unpublished data). Ward et al. (1988)

reported that C. elegans showed sequence similarity range of 87-98%, using 14 clones.

Setterquist (1997), in his major nucleotide sequence survey of msp genes in nematodes

showed that sequence similarity range for C. elegans was 91-100% using 40 sequences.

This sequence similarity percentage was reduced to 75-78% when including

nonfunctional genes (pseudogenes). He also showed that the sequence similarities

between C. elegans msp56 and its sister species C. briggsae msp (seven sequences)

ranged between 85-88%. Pseudogenes have not been reported in other nematode species

(Scott et al., 1989a; Klass and Ammons, 1988; Ward et al., 1988). Setterquist and Fox

(1995) reported that many of the msp genes in C. elegans appear to be defective but the

redundancy provided by the presence of a large number of gene copies prevents the

organism from being harmed. Also this may be the reason for the higher level of genetic

variation in C. elegans msp gene sequences reported by Ward et al. (1998). In contrast,

A. suum contained only one copy of the msp gene (Scott et al., 1989a), but had a very

high level of genetic conservation (96-99%) since any change in this gene copy can

threaten the survival of the nematode. Thus we conclude that our observed low msp

sequence similarity in H. bacteriophora may be due to the presence of different

91 taxonomic identities within H. bacteriophora that may be difficult to distinguish

morphologically.

Free-living nematodes, such as C. elegans, C. briggsae, C. remanei and

Panagrellus redivivus, contain larger numbers of msp gene family members, 30-60, than

do parasitic nematodes (Scott et al. 1989a; Setterquist, 1997). Scott et al. (1989) showed

that filarial nematodes, such as Brugia malayi and A. suum, contained 1-4 members and

insect, Steinernema (=Neoplectana) carpocapsae, and plant parasitic, Meloidogyne

incognita, nematodes contained 5-12 members. Also we should mention the presence of

relatively high similarity of msp genes among distantly related species of nematodes. For

example, A. suum showed msp sequence similarity of 72% with msp from Caenorhabditis

spp and O. volvulus showed more than 80% similarity with the distantly related genera

Ascaris and Caenorhabditis (Scott et al., 1989a; Hojas and Post, 2000. Therefore, the

wide msp DNA similarity range that we observed among H. bacteriophora strains

suggest that H. bacteriophora is not a single species.

The msp gene sequence pairwise distances among H. bacteriophora strains

ranged between 0.59% (Riwaka and GPS2) and 9.73% (Acows and HP88), while the

pairwise distance values among O. volvulus and in M. ozzardi isolates ranged between

0.42 and 7.2% and 0 and 1.7%, respectively (Hojas and Post, 2000). The overall average

pairwise difference for amino acid sequences among H. bacteriophora isolates was 9.1%

with a range of 0 (Riwaka and NC1) to 23% (Acows and HP88). The overall average

pairwise difference for amino acids within O. volvulus isolates was 4.9% with a range of

0-12%. This difference was 1.1% for M. ozzardi with a range of 0% - 1.1%. These results show that H. bacteriophora contains higher values of genetic diversity compared

92 to the studied human parasitic nematodes. There is no available information for insect

parasitic nematodes except for Steinernema carpocapsae, in which only the approximate

number of msp gene was presented (Scott et al., 1989a).

Our results also showed the presence of structural differences in msp gene

sequences among H. bacteriophora strains. Although we used equal amount of template

DNA, differences in the PCR band intensities were observed, suggesting the presence of different gene copy numbers among H. bacteriophora strains (Fig. 1). While there were no differences in the msp PCR products, structural differences of msp were also observed among the strains of H. bacteriophora such as exon length and intron length. Generally msp genes of H. bacteriophora strains contained two exons and one intron. The length of the first exon was 99 bp except for strains HP88 (132 bp), GPS5 (120 bp) and GPS11

(120 bp). The length of the first exon was 99 bp in human parasitic nematode O. volvulus and M. ozzardi (Hojas and Post, 2000). Length of the second exon was 96 bp in H. bacteriophora except for GPS5 (93 bp), GPS11 (93 bp) and Acows (75 bp). The length of the sequenced part of the second exon in both O. volvulus and M. ozzardi was 267

(Hojas and Post, 2000). We did not observe the second exon in strains GPS3, OH25 and

Oswego probably due to the longer intron sizes (> 200 bp) and thus the second exon may have been outside the sequenced part of the gene. Generally, the intron length in H. bacteriophora strains ranged from 81 to 84 bp while it was 233 bp in M. ozzardi and 153-

156 in O. volvulus (Hojas and Post, 2000), no introns were observed in msp genes of C. elegans (Scott et al., 1989a; Ward et al., 1999).

In conclusion, our results suggest that strains currently recognized as H.

bacteriophora may contain more than one phylogenetic species. This conclusion is

93 further supported by studies on cross breeding and symbiotic bacteria in H.

bacteriophora. For example, Burnell et al. (1992) suggested that H. bacteriophora contains at least two species, HP88 and Brecon, using cross breeding techniques. Also,

Boemare (2002) suggested that H. bacteriophora may contains three phylogenetic taxa,

Brecon, HP88 and NC1, using information on their associated symbiotic bacteria. He showed that H. bacteriophora HP88 harbors the symbiotic bacteria P. luminescens subspecies laumondii, H. bacteriophora NC1 harbors P. luminescens temperata, while H. bacteriophora Brecon harbors P. luminescens luminescens. Furthermore, large differences in biocontrol efficacy and long-term field persistence have also been observed between NC1 and Oswego strains (Shields et al. 1999) and in infective juvenile longevity, stress tolerance and virulence in other strains (Grewal et al., 2002ab). All these observations question the true identity of strains currently recognized as H. bacteriophora and suggest the need for additional crossbreeding and molecular studies to confirm their identity.

4.5 SUMMARY

We used major sperm protein gene (msp) to investigate genetic diversity among

13 strains of Heterorhabditis currently recognized as H. bacteriophora. We hypothesized that H. bacteriophora strains might show genetic diversity in sequence and structure of msp genes due to large differences in their biological traits. Blast-n similarity searches showed homology between H. bacteriophora msp and animal and human parasitic nematode msp genes. Pairwise sequence alignment scores of H. bacteriophora msp genes varied between 86 and 99%, which were considerably higher than those

94 typically observed for parasitic nematode species. We found 13 haplotypes with

haplotype diversity of 1.00 and nucleotide diversity of 0.05560. The H. bacteriophora

msp gene sequences contained two exons (99 and 96 bp) and one intron with some

exceptions. The overall average pairwise differences in msp gene and amino acid

sequences were 0.0572 and 0.0906, respectively among H. bacteriophora strains.

Phenetic and phylogenetic analysis showed the presence of high genetic structuring within H. bacteriophora. Our results suggest that all the strains currently recognized as

H. bacteriophora may not belong to the same species.

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100

CHAPTER 5

PHYLOGENY, POPUALTION STRUCTURE, AND GENE FLOW IN THE

INSECT TRAMSMITTED NEMATODE BURSAPHELENCHUS

CONICAUDATUS AND ITS VECTOR PSACOTHEA HILARIS IN JAPANESE

ISLANDS

5.1 INTRODUCTION

Metapopulations are groups of a species that occur in patchy habitats, isolated from one another but connected via a limited exchange of individuals (Levins, 1970;

Ovaskainen and Hanski, 2004). There are two suggested models of the metapopulation dynamics. The classical metapopulations model which predicts high rates of extinction and re-colonization of groups and the mainland-island model which suggests lower levels of extinction but frequent migration events that make a group’s complete turnover a rare event (Rowe et al., 2000). There are several advantages for populations that exist in a metapopulation structure over a panmictic population including their greater genetic diversity (Lande, 1992) and ease with which they can track suitable environmental conditions enabling them to outlive panmictic populations (Thomas, 1994; Wright,

1982).

Numerous natural species are subdivided into local breeding units, a condition that can lead to genetic differentiation of local demes (Tero et al., 2003). Hanski (2004) 110 stated that birds, mammals, vascular plants, insects and a very large portion of

small-bodied species live in habitats that occur in small and disconnected patches.

However, detailed studies on metapopulation dynamics have been conducted only on few

species. Nematodes are ideal model systems to apply the concept of metapopulation

dynamics as their populations occur in discrete patches and they have very limited self

dispersal capability. Although studies have investigated population fragmentation and

gene flow (Blouin et al., 1999; Blouin et al., 1999, Hu et al., 2002; Gasser et al., 2002),

no studies have directly addressed the metapopulation structure and dynamics in

nematodes probably due to difficulties in tracking the movement of individuals.

Here we explore the presence of metapopulation structure in the insect transmitted

nematode Bursaphelenchus conicaudatus and its insect vector Psacothea hilaris by

estimating gene flow among geographically isolated subpopulations. B. conicaudatus

was first described from Japan by Kanzaki et al. (2000) and it is closely related to B.

xylophilus, the causal agent of the pine wilt disease. B. conicaudatus has a wider host range than B. xylophilus, including fig and mulberry trees and 10 species of fungi

(Kanzaki and Futai, 2001). The nematode is vectored by the yellow-spotted longicorn P. hilaris beetle, which is a severe pest of mulberry and fig trees Ficus carica L.,

(Moraceae) in Japan. Larvae of the beetle tunnel into tree trunks, ingesting living wood resulting in substantial damage to the trees (Scrivener et al., 1997; Kanzaki et al., 2000;

Sugimura et al., 2003). The beetles feed on leaves of mulberry trees.

P. hilaris is found in the easternmost parts of Asia, including southern China,

Taiwan and Japanese islands (Shinitani and Ishikawa, 1999 and Kanzaki and Futai,

2002). P. hilaris has been divided into 13 subspecies based on morphological differences

111 in the beetles from different localities. The subspecies P. h. hilaris, found in Honshu

Island, is further divided into two types, West Japan type and East Japan type, based on differences in spot patterns and adult pronotum shape (Iba 1980; Makihara, 1986), esterase isozyme patterns, larval diapause characteristics, seasonal life cycles, and photoperiodic responses (Sakakibara and Kawakami, 1992; Shinitani et al., 1996;

Shintani and Ishikawa, 1997).

Relationship between B. conicaudatus and P. hilaris is commensal, though the economic importance of the nematode species is not known. The nematodes are found in the tracheae of both female and male beetles with 71.4 -100% of the beetles carrying the nematodes (Kanzaki et al., 2000). Dauer (non feeding) juvenile nematodes carried by the beetles have no nutritional relationship to their vector. The number of nematodes per beetle range from 0 to 2800 (Kanzaki et al., 2000). Only one haplotype was observed within each nematode strain (Kanzaki et al., 2000; Kanzaki and Futai, 2001; 2002) (Table

1 and Fig 1). Kanzaki and Futai (2001) performed a phylogenetic study in order to compare between the evolutionary patterns of B. conicaudatus and P. hilaris. They employed sequences of partial cytochrome c oxidase subunit I (cox1) in the mitochondrial DNA. They concluded that the phoretic relationship between the nematode and the beetle has been established before the divergence of P. hilaris subspecies. The experimental design of the study is ideal for investigation of population structure, diversity and gene flow among groups of both the nematode and its insect vector, which are geographically isolated on different islands. Based on the findings of

Kanzaki and Futai (2001) we hypothesized that the Japanese population of B. conicaudatus is fragmented in small and relatively homogenous local sub-populations

112 (groups), which are connected by restricted gene flow aided by the vector. Due to the

coexistence of both nematode and its vector, we also hypothesized that P. hilaris

subspecies exhibit a parallel population structure. Our specific objectives were to: 1)

reanalyze the cox1 gene sequence characteristics for B. conicaudatus isolates and P. hilaris subspecies; 2) reconstruct phylogenetic relationship and presence of sub-species structure among groups of B. conicaudatus and P. hilaris using Bayesian analysis of phylogeny and compare it with other commonly used phenetic and phylogenetic methods and 3) quantify the extent of genetic differentiation and gene flow among B. conicaudatus local populations and P. hilaris subspecies. We used the cox1 sequences data deposited in GenBank by Kanzaki and Futai (2001) for this investigation.

5.2 MATERIALS AND METHODS

Partial base sequences of cytochrome c oxidase subunit I (cox1) in the mitochondrial DNA of B. conicaudatus and P. hilaris were acquired from the GenBank.

Sequences for the nematode (accessions from AB083711- AB083739) and for insect

(accessions from AB083741.1 - AB083779.1) were aligned using the clustal W program and prepared for phylogenetic analysis using MEGA3 and PAUP 4.0 software (Kumar et al., 2004; Swofford, 2001). Sequence dissimilarity comparisons (D) were calculated using the formula D = 1–(M/L), where M is the number of alignment positions at which the two sequences have a base in common, and L is the total number of alignment positions over which the two sequences are compared. Pairwise distances among nematode isolates and insect subspecies were calculated using nucleotide Kimura 2- parameter including both transition and transversion substitutions.

113 Sequences were aligned using the Bioedit built-in clustal W program (gap opening penalty = 10, gap extension penalty = 5, delay divergent sequences = 40%).

Resulting alignments were compared and the final alignments were improved manually and prepared in FASTA, MEGA and NEXUS formats. In order to establish phylogenetic relationships within species, trees were constructed using the unweighted pair group method with arithmetic averages (UPGMA), neighbor joining (NJ), minimum evolution (ME), maximum parsimony (MP) and Bayesian Analysis of Phylogeny (BAP) methods using PAUP V4B10, MEGA 3.1 software and MrBayes v3.1 (Swofford, 2001;

Huelsenbeck et al., 2002; Kumar et al., 2004). We used Kimura 2-parameter model, and gaps and missing data were taken into account using the complete deletion option in

MEGA 3.1 software. We used the default values of open and extended gaps in both

PAUP V4B10 and MrBayes v3.1 softwares. The transition/transversion ratio was set at

2:1 for PAUP V4B10. Bootstrap analysis was performed using 1000 replicates to test the support for each branch of the tree.

For the MP method, heuristic (with tree bisection-reconnection as the branch swapping algorithm, TBR) and branch-and-bound searches were conducted. All characters had the same weight, treated as unordered and the third position of the codon was excluded. Bootstrap analysis with 1000 replicates was used to test the support for the branches. We calculated a 50% majority rule MP consensus tree. For BAP method, the selected evolutionary model was GTR model with gamma-distributed rate variation across sites and a proportion of invariable sites. All codon positions were included for phylogenetic analysis. The number of generations used is 1,000,000, sampling a tree every 100 generations. The value of the standard deviation of split frequencies is

114 0.007312. Amino acid translations of partial nucleotide cox1 sequences were obtained

and analyzed by MEGA 3.1 software. Since this is a bio-geographic and population

genetic study, both nematode and insect groups were designed according to general

agreement of between phylogenetic and geographical information.

The number of transitions and transversions and the transition: transversion ratios

(Ts:Tv) were also determined. Codon bias index (CBI) was calculated according to

Morton (1993). Number of haplotypes, haplotype diversity (H), and genetic

differentiation and gene flow quantification among the sub-populations were determined

using the DnaSP 4.0 program (Rozas et al., 2003). Genetic differentiation was also

analyzed by calculating nucleotide sequence-based statistics: Ks, Kst, Ks*, Kst*, Zs, Zs*

(Hudson et al., 1992a) and Snn (Hudson 2000) and haplotype-based statistics Hs and Hst

(Hudson et al., 1992a). We also calculated both the F statistics and N statistics that serve

as indicators for genetic differentiation and gene flow among populations. The

coefficient of group differentiation which estimates the proportion of interpopulational

diversity was calculated using the following formula: Nst – δst / πT, where Nst is the value of the N statistics, δst is the value of the delta statistic and πT is the value of total nucleotide diversity (Kumar et al., 2004). In order to test the significance of nucleotide sequence-based statistics, the permutation test with a replication value of 1000 was performed.

The mean diversity for the entire population was calculated according to the

q q following formula: πT - q/q-1 Σ i-1 Σ j-1 Xi Xj dij, where Xi is the estimate of average frequency of the i-th allele in the entire population, and q is the number of different sequences in the entire sample (Kumar et al., 2004). The mean interpopulational

115 diversity was calculated using the following formula: δst – Nst / πs, where Nst is the

value of the N statistics, δst is the value of the Delta statistic and πs is the value of single sequence nucleotide diversity (Kumar et al., 2004). Since we can not estimate the gene flow by directly counting migrants, we indirectly determined the effective number of migrants (Nm) using the Fst and Nst values. Both Fst and Nst use DNA sequence information and are calculated according to Lynch and Crease (1990) and Hudson et al.

(1992).

5.3 RESULTS

Genetic distance

The cox1 sequence dissimilarity values ranged between 0 and 11% for B. conicaudatus isolates and 0 and 3% for P. hilaris. In case of B. conicaudatus the overall average pairwise difference was 0.0534 (5.34%). The lowest value (0.0000) was observed eight times between different nematode isolates suggesting high level of similarity between some isolates (Table 2). Generally, the isolates from the same or close geographic localities tended to have lower pairwise distances. For example, the lowest value of pairwise differences was observed between Miyako 2 and Miyako 3, Kuchi 2 and Takara 2, Kyoto 2 and both Kuchi 2 and Takara 2, Chiba and both Kuchi 2 and

Takara 2 and Tsukuba 2 and Kuchi 2 and Takara 2 and Chiba. The highest pairwise distance, 0.1117, was between Takara 1 and Toku 1. The second highest distance,

0.1116, was observed between Toku 1 and Yonaguni 2.

Isolates of B. conicaudatus showed the highest pairwise distances with the unrelated nematode Ascaris caninum, 0.7567 (Table 4) reflecting the distant genetic 116 relationship between the two taxa. However, some B. conicaudatus isolates had even

lower values of pairwise distances with the sister species B. mucronatus (Table 4) than

those observed within the same species.

The overall average pairwise difference for P. hilaris was 0.0251 (2.51%). The

lowest value (0%) was always observed between isolates from the same subspecies from

geographically close localities. For example P. h. hilaris tsukuba 1 and tsukuba 2 and P.

h. insularis tane 2 and tane 3 had pairwise distance value of 0.00 (Table 3). Some

isolates, which belonged to the same subspecies but were isolated from distant localities,

showed relatively high distance values. For example, P. h. hilaris tsukuba 1 and P. h.

hilaris Kyoto 2 showed a pairwise distance of 0.0279 (2.79%), which is the same value

observed between isolates for different subspecies recovered from distant localities such

as P. h. hilaris Kyoto 2, located in Honshu island, and P. h. miyakoana miyako 3 from

Miyakojima Island. On the contrary, isolate Miyake 1, belonging to subspecies P. h.

miyakejimana, showed no pairwise distance from P. h. hilaris chiba 1. Miyake 1 also

showed lower pairwise distance (0.0178) from other isolates of P. h. hilaris such as

Kyoto 1 and Kyoto 2.

Phylogenetic analysis and sub-species structuring

All the phenetic and phylogenetic trees showed high degree of sub-structuring with values of bootstrap ranging between 95 and 100% and clade credibility values ranging between 0.70 and 0.98 for both B. conicaudatus and P. hilaris (Figures 1 – 4).

Information extracted from UPGMA phenetic tree and phylogenetic trees constructed using NJ, MP, ML, ME and BAP showed that the isolates of B. conicaudatus are divided into eleven groups, while P. hilaris was divided into ten groups (Table 5). In general, the

117 phylogenetic classification agreed with the geographic locality in both cases.

Phylogenetic analysis showed that the isolate Okinawa one representing subspecies P. h.

tenebura is closely related to subspecies P. h. intermedia. In fact P. h. tenebura isolate

Okinawa one showed a pairwise distance value with P. h. intermedia OE3 and OE4 of only 0.0059 and with P. h. intermedia OE2 of only 0.0039. While pairwise distance

value between P. h. intermedia OE1 and OE4 was 0.0078 (Table 3). Phylogenetic

analysis also suggested that isolate P. h. ishigakiana Iriomote two is a variant of the other

isolates belonging to subspecies P. h. ishigakiana. Phylogenetic analysis also suggested

that there should be a split within the subspecies P. h. macronatata to Takara type and

Kushi type isolates. This was supported by a bootstrap value of 100 in the MP consensus

tree and NJ tree and with 0.97 a clade credibility value in the BAP tree.

DNA sequence analysis

Sequence analyses data for B. conicaudatus and P. hilaris are presented in Tables

6 and 7, respectively. We identified 24 haplotyes in B. conicaudatus sequences and the total haplotype diversity (H) was 0.97241. In case of P. hilaris, we identified 18 haplotyes out of the studied 39 sequences. The haplotype diversity (H) was 0.94231.

Generally for both nematode and insect groups the number of transitional changes (si) was higher than transversional changes (sv) thus the si/sv ratio (R) is higher than 1. The si:sv ratio was significantly different (X2 test; P<0.01) among the studied groups of both nematode and its vector. Several variations were observed among the suggested B. conicaudatus eleven groups. The highest number of conserved sites, 958, was observed for group 4, while the lowest number, 920, was observed for group 9. The highest number of variable sites was 40 for group 9 while the lowest number was two for group

118 4. The highest number of conserved sites was observed for P. hilaris, groups 4, 5, 6 and

7, with 100% sequence similarity in all the 513 nucleotides studied. The lowest number,

504, was observed for group 1.

All B. conicaudatus groups, except for group 7, showed higher number of si than sv. For group 7, the number of si was eight while the number of sv was 17. As expected the lowest value of R, 0.5, was observed for group 7, while the highest number, 3.8, was observed for group 6. In case of P. hilaris, the overall R-value was 4. Group 1 showed the highest number of transitional changes, five, while group 11 showed the lowest number, 1. Groups 1 and 3 are the only groups that showed sv, only one change. All other groups showed neither type of changes.

High codon usage bias was observed for both of B. conicaudatus and P. hilaris.

For example, 97% of the codons encoding for phenylalanine were UUU, 85% of the

codons encoding for proline were CCU and 100% of the codons encoding for arginine

were CGU in case of B. conicaudatus. The codon bias index (CBI) was 0.740 for B. conicaudatus and 0.769 for P. hilaris. Both initiation codons, AUA (M) and AUG (M),

were observed but AUA represented 84% and 99.5% of the usage for B. conicaudatus

and P. hilaris, respectively. No stop codons were observed which suggests that all the

sequences represented exons or open reading frames. All nematode groups and insect

subspecies showed relatively similar percentage of use of initiation codons. The average

number of used codons was 320 for B. conicaudatus and 171 for P. hilaris.

Genetic diversity statistics

Among the B. conicaudatus groups, the haplotype diversity values were relatively high and ranged from 0.3333 to 1.0000 except for group 11, which only had a single

119 haplotype (Table 8). The average value of haplotype diversity was 0.9774. In contrast,

nucleotide diversity values were and ranged from 0.0007 to 0.0429 with an overall

average of 0.05294. The lowest value of nucleotide diversity was observed in group 4

while the highest value was observed in group 9.

The haplotype diversity values for P. hilaris ranged between 0 and 0.93333 with

the average value of 0.94231 (Table 8). The highest value of haplotype diversity,

0.93333, was observed for group 3 while the lowest value, 0, for groups 4, 5, 6 and 7.

Group 9 had only one haplotype. In this case also nucleotide diversity values were lower

and ranged from 0 to 0.01183 with an overall average of 0.02442. The lowest values of

the nucleotide diversity were observed in-group 4, 5, 6 and 7 while the highest values

were observed in-group 1.

Gene flow quantification and genetic differentiation

All nucleotide sequence and haplotype based genetic differentiation analyses

suggested that B. conicaudatus (Table 9A) and P. hilaris (Table 9B) groups are highly

differentiated from one another and the permutation test results confirmed the

significance of Hs, Hst, Ks, Kst, Ks*, Kst*, Zs, Zs* and Snn indices. Also, Fst and Nst

values showed that the genetic diversity was highly differentiated and distributed among

different groups of nematodes and insects. In all cases P. hilaris groups were genetically more differentiated compared to B. conicaudatus groups For example, Snn values for P.

hilaris and B. conicaudatus were 1.000 and 0.9666, respectively.

Low values of Gamma, Nst and Fst based Nm values for both B. conicaudatus

(0.04-0.08) and P. hilaris (0.02) suggest limited but definite gene flow among the groups.

The highest amount of gene flow 0.35 was observed among B. conicaudatus groups of

120 Okinawa-jima, northern Amami and southern Amami islands. Also a gene flow of 0.25

occurred between northern Amami islands and southern Amami islands. No evidence of

gene flow was observed between Housh, Osumi and Miyakojima islands and Izu islands

(Table 10A). In case of P. hilaris, generally the amount of gene flow was lower than B. conicaudatus (Table 10B). The highest amount of gene flow, 0.32, occurred between northern Amami and southern Amami islands and Okinawa islands and Miyakojema islands. The mainland P. h. insularis group 1 located on Honshu Island and its close neighbor miyakejima island showed considerable amount of gene flow, 0.07-0.10, with all other groups. On the contrary P. h. insularis isolates, located in Osumi Island, did not show any gene flow to any other group except for Yonaguni-jima Island group. The same was observed with Takarajima and Kushinojima islands.

The relative genetic relationship between the 11 groups of B. conicaudatus calculated using the Fst, Nst, Delta statistics (δst) and Gamma statistics (γst) are given in

Table 11 and Figure 4. All used methods agreed on the close relationship between northern and southern Amami islands and Okinawa islands groups, 6 and 9. The observed relative genetic relationship values of Fst, Nst, Delta statistics (δst) and Gamma statistics (γst) were 0.21333, 0.21718, 01185 and 0.43541, respectively. While the most divergent groups were group 4 and 11 isolated from Honshu and Izu island, respectively, with observed values of Fst, Nst, Delta statistics (δst) and Gamma statistics (γst) of

0.99588, 0.99612, 0.04362 and 0.98642, respectively. The relative genetic relationship

between the 10 groups of P. hilaris calculated using the same methods are presented in

Table 12 and Figure 5. Group 2 and 3, located in Amami (northern and southern),

Okinawa and Miyakojema islands were the closest with Fst, Nst, Delta (δst) and Gamma

121 (γst) statistics values of 0.43636, 0.43631, 0.38213 and 0.00116, respectively. Groups 6 and 9 located on Kushinojima Islands and Iriomotejima Islands, respectively, were the most divergent insect groups (Table 12). Similarly, Groups 4 and 5 also showed high divergence value with other P. hilaris groups.

5.4 DISCUSSION

The COX1 protein has a critical function in the process of oxidative metabolism.

However, it usually possesses substantial variation in its amino acid composition. Lunt et al. (1996) reported the presence of 125 amino acid variable positions out of 522 when comparing COX1 protein from an insect species. For B. conicaudatus among 323 amino acid position 121 variable positions were observed, while only seven amino acid position variable positions out of studied 171 positions were observed for P. hilaris. Our sequence analysis data showed that cox1 in both B. conicaudatus and P. hilaris has been maintained in an active form despite several genetic changes in its sequence. The occurrence of majority of mutations in the third position of the codon, some in the first position and only one in the second position, will not affect the function of the encoded protein. The high si:sv ratio supports this conclusion since transitional changes usually have less severe effect on the encoded protein than the transversional changes.

Phylogenetic and distance analyses results showed that there is a high level of subspecies structuring in both B. conicaudatus and its insect vector P. hilaris. We suggest that the species B. conicaudatus is structured into eleven phylogenetic groups.

Our suggested grouping fits with the geographical distribution of the nematode over the

Japanese Islands. Our results for P. hilaris supported its current subspecies taxonomic 122 division with some exceptions. Our results suggest that P. hilaris is phylogenetically

divided into ten groups as opposed to the currently recognized 13 subspecies. In both

cases the suggested sub- structuring was supported by genetic distance data, phylogenetic

data, sequence structure and codon usage data, genetic diversity levels and genetic

differentiation values. Overall P. hilaris showed relatively lower levels of genetic diversity but relatively higher levels of genetic differentiation than B. conicaudatus.

Almost perfect matching of phylogenetic classification and geographic distribution of B. concicaudatus isolates and P. hilaris subspecies suggests co-evolution between the nematode and its insect vector. This is interesting since the nematodes do not parasitize the insect vector and there is no evidence of nutritional relationship between the insect and the nematode (Kanzaki et al., 2000).

Phylogenetic analysis of cox1 gene revealed that P. hilaris subspecies generally matched the present classification of the insect with few exceptions. We propose lumping the subspecies P. h. miakejimana and P. h. hilaris, P. h. maculate and P. h. miyakoana, and P. h. intermedia and P. h. tenrbursa. Also the results suggest the presence of obvious sub-structuring within some P. hilaris subspecies. For example, P. h. hilaris tuskuba isolates are completely differentiated from the other isolates of the same subspecies isolated from different geographic localities. Furthermore the subspecies P. h. macronatata in which all the Takara groups are highly differentiated from Kushi isolates with Fst, Nst and Snn values of 1.000 and Nm value of 0.00. Our observations also suggest that P. h. hilaris tsukuba 1 and 2 may belong to a distinct subspecies other than P. h. hilaris. These disagreements between the phylogenetic

subspecies substructure and the currently used morphological and physiological

123 characteristics call for of further studies involving more genetic loci to resolve the

subspecies classification.

Similarly, genetic differentiation among insect populations, distributed in

discontinuous landscapes, has been reported in many cases. For example, Roslin (2001)

indicated the presence of genetic differentiation and limited gene flow between sub-

populations of the dung beetle, Aphodius fossor, isolated in Aland Islands and those

located on the Finish mainland. To the contrary the mainland island sub-population was

highly homogenous and showed high levels of gene flow. Carisio et al. (2004) showed

that three populations of dung beetles, isolated from 15 Italian localities, showed

moderate but significant genetic differentiation, (Fst=0.215, 0.173 and 0.199), despite the

presence of high inter-population genetic diversity.

We used different methods to investigate genetic differentiation of the 11 groups

of B. conicaudatus. The high values of the commonly used fixation indices Fst and Nst,

(0.72287 and 0.73014, respectively) indicate high degree of genetic differentiation of the

10 sub-populations. Similar values were observed by Blouin et al. (1999) for H.

marelatus (Fst = 0.78 and Nst = 0.86) and Hu et al. (2002) for Dicatyocaulus viviparus,

(Fst = 0.77 and Nst = 0.65). We also used statistical methods to detect geographical subdivision and genetic differentiation using Ks, Kst, Ks*, Kst*, Zs, Zs*, since most estimates of genetic differentiation are not useful statistical tests because of their unknown null hypothesis. The null hypothesis for the used tests is that there is no genetic differentiation between sub-populations at different localities. All statistical tests produced significant values when using 0.01

124 differentiation between sub-populations at different localities. We also used the new

statistical method suggested by Hudson (2000). This method, referred to as the nearest-

neighbor statistics (Snn), measures how often nearest neighbor sequences are those of

individuals or isolates isolated from the same geographic locality Hudson (2000). In Snn method, if the population is strongly structured we should find that the nearest neighbor of sequence for all our sequences is from the same locality, thus the Snn is near to one and

vice versa. Our results showed that the Snn value for the 11 groups of B. conicaudatus is

1.00 thus suggesting that our population is strongly genetically differentiated. The

calculated coefficient of differentiation between local populations was, 0.693, which

further supported our results. In practice rare haplotypes are lumped together so that the

expected number of haploypes for each locality is very small (Hudson et al., 1992a). In

haplotype based methods a single change in the DNA sequence can lead to the creation of

a new haplotype (Hudson et al., 1992b). While in the sequence-based statistics the

number of nucleotide changes between sequences is taken into consideration therefore

many researchers are now advocating the use of sequence-based statistics. Also except

for group 1, all insect groups showed no parsimony informative changes that advocate

that all the changes occurred in each group were identical, which further supports the idea

of the presence of high genetic differentiation among 10 groups of P. hilaris.

Limited gene flow between the 11 groups of B. conicaudatus was also evident

from the low values of Nm, 0.10 and 0.09, calculated using both Fst and Nst values

respectively. Limited gene flow values were also detected for the bovine lung worm D.

vivparus in Sweden, the plant parasitic nematode Meloidogyne arenaria (Hu et al., 2002)

and the entomopathogenic nematode H. marelatus (Blouin et al., 1999) but the exact Nm

125 values were not presented. The patterns of genetic diversity structure revealed in our

study are exactly what one expects under restricted gene flow as stated by Templeton et

al. (1995).

B. conicaudatus showed low genetic diversity within populations (0.016) and

relatively high genetic diversity between sub-populations (0.035) and the entire

population (0.051). Although we found 24 distinct haplotypes out of 29 sequences in the

entire sample we only found at most four haplotypes in any local population. Also, on

the nucleotide level the differences between these haplotypes are small.

Our results indicate that the movement of the nematode is linked by the insect

vector, but it is not playing a dominant role in the spread of the nematode throughout the

Japanese Islands. In conclusion, this study indicates that the B. conicaudatus population

represents a classical metapopulation which is similar in the structure of the plant and

insect parasitic nematodes but not the animal parasitic nematodes (Hu et al., 2002), which

usually exhibit higher degrees of genetic diversity and gene flow.

5.5 SUMMARY

We investigated the phylogeny and population structure of the nematode

Bursaphelenchus conicaudatus and its insect vector, the yellow spotted longicorn beetle

Psacothea hilaris on the Japanese islands. We hypothesized that both B. conicaudatus and P. hilaris populations on the Japanese islands represent classic metapopulations due

to geographical isolation of sub-populations connected with a limited but definite gene

flow. Partial sequences of cytochrome c oxidase subunit I in the mitochondrial DNA of

B. conicaudatus and P. hilaris were acquired from the GenBank. Sequence similarities

126 ranged from 89 to 100% and 97 to 100% for B. conicaudatus and P. hilaris, respectively.

Phylogenetic and phenetic analysis, using neighbor joining, unweighted pair group

method with arithmetic mean, maximum parsimony, minimum evolution, maximum

likelihood and Bayesian analysis and population genetic analysis, indicate the existence

of subspecies structure in B. conicaudatus and support the current subspecies groupings

of P. hilaris subspecies with few exceptions. The fixation indices, Nst and Fst, for both

B. conicaudatus and P. hilaris indicated significant genetic differentiation. The values of

nucleotide sequence-based statistics Ks, Kst, Ks*, Kst*, Zs, Zs* and Snn, confirmed the

presence of high genetic differentiation among the local populations of both the nematode

and its insect vector. The effective numbers of migrants calculated using Nst and Fst

values were 0.08 for B. conicaudatus and 0.02 for P. hilaris indicating a definite but limited gene flow among local populations. These results demonstrate the presence of a metapopulation structure in B. conicaudatus sub-populations and P. hilaris subspecies on

the Japanese islands and suggest that P. hilaris is not the sole source of nematode dispersal.

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