The Dynamic Host-Pathogen Interactions of Verticillium Dahliae: Comparative Genomics of V

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Master's Theses and Capstones Student Scholarship

Fall 2015

THE DYNAMIC HOST-PATHOGEN INTERACTIONS OF VERTICILLIUM DAHLIAE: COMPARATIVE GENOMICS OF V. DAHLIAE AND ITS INTERACTIONS IN COVER CROP SPECIES AND WILD STRAWBERRY (FRAGARIA VESCA)

Garrett Gleeson University of New Hampshire, Durham

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Recommended Citation Gleeson, Garrett, "THE DYNAMIC HOST-PATHOGEN INTERACTIONS OF VERTICILLIUM DAHLIAE: COMPARATIVE GENOMICS OF V. DAHLIAE AND ITS INTERACTIONS IN COVER CROP SPECIES AND WILD STRAWBERRY (FRAGARIA VESCA)" (2015). Master's Theses and Capstones. 1037. https://scholars.unh.edu/thesis/1037

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THE DYNAMIC HOST-PATHOGEN INTERACTIONS OF VERTICILLIUM DAHLIAE: COMPARATIVE GENOMICS OF V. DAHLIAE AND ITS INTERACTIONS IN COVER CROP SPECIES AND WILD STRAWBERRY (FRAGARIA VESCA)

Garrett Gleeson BS, Allegheny College, 2012

THESIS

Submitted to the University of New Hampshire in Partial Fulfillment of the Requirements for the Degree of

Master of Science

September, 2015

This thesis has been examined and approved in partial fulfillment of the requirements for the degree of Maters in Microbiology by:

Thesis Director, Dr. Kirk Broders, Assistant Professor (Biological Sciences)

Dr. Thomas Davis, Professor of Plant Biology (Biological Sciences)

Dr. Louis Tisa, Professor (Molecular, Cellular, & Biomedical Sciences)

On June 25th, 2015

Original approval signatures are on file with the University of New Hampshire Graduate School

ACKNOWLEDGMENTS

I would like to express my deep gratitude to Kirk Broders for his roles as thesis director and mentor. Kirk dedicated countless hours to bring invaluable insight and guidance to this project. I would like to offer my special thanks to my committee members, Louis Tisa and Tom

Davis, who provided necessary materials and critique.

I would like to thank all of my current and former lab mates: Andre Boraks, Matthew

Wallhead, Franz Lichtner, Kate Blasioli, Stephen Wyka, Taruna Aggarwal, Gloria Broders, and the numerous undergraduates that have come through the lab for their support and friendship. I would also like to thank those who assisted with this project outside the Broder’s lab: Mark

Townley, Jonathan Ebba, David Goudreault, Lise Mahoney, Kelley Thomas, Jordan Ramsdell, and Sheldon Hurst.

To the following organizations that provided funding towards my research: the United

States Department of Agriculture, University of New Hampshire Agriculture Experiment Station, and the Molecular, Cellular and Biomedical Sciences Department.

Finally I would like to thank my friends and family, especially my best friend Sabah, for all of their support.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... iii

FIGURES ...... vi

TABLES ...... viii

ABSTRACT ...... ix

INTRODUCTION ...... 1 1.1 Scope of Thesis ...... 1 1.2 Disease Management in Strawberry ...... 3 1.3 Verticillium dahliae ...... 4 1.4 Objectives ...... 6 CHAPTER 2: COVER CROP INFECTION ...... 7

2.1 Introduction ...... 7 2.2 Methods ...... 8 2.2.1 Cover Crop Germination ...... 8 2.2.2 Verticillium Preparation/Inoculation ...... 9 2.2.3 Confocal Imaging ...... 9 2.3 Results ...... 10 2.3.1 Greenhouse Infection Assay ...... 10 2.3.2 Confocal Imaging ...... 11 2.4 Discussion ...... 11 2.4.1 Infection of Tolerant Hosts ...... 11 2.4.2 Future Work ...... 13

CHAPTER 3: FRAGARIA VESCA-VERTICILLIUM DAHLIAE INTERACTIONS ...... 16

3.1 Introduction ...... 16 3.2 Methods ...... 17 3.2.1 Strawberry Growth Conditions ...... 17 3.2.2 Verticillium Growth Conditions ...... 17 3.2.3 Inoculation Assay ...... 18 3.2.4 Visual Disease Assessment ...... 18 3.2.5 Confocal Microscopy ...... 19 3.3 Results ...... 20 3.3.1 Lateral Root Infection ...... 20

3.3.2 Primary Root and Stem Infection ...... 20 3.3.3 Ordinal Rating ...... 21 3.4 Discussion ...... 21 3.4.1 Resistant Phenotypes ...... 21 3.4.2 Lateral Root Infection ...... 22

CHAPTER 4: COMPARATIVE GENOMICS OF VERTICILLIUM DAHLIAE ...... 31

4.1 Introduction ...... 31 4.2 Methods ...... 34 4.2.1 Isolates Information ...... 34 4.2.2 De novo Assembly ...... 34 4.2.3 Making Reads Compatible with BWA MEM ...... 34 4.2.4 Variant Discovery ...... 35 4.2.5 Generating Phylogenetic Tree ...... 35 4.2.6 Mauve Analysis ...... 36 4.3 Results ...... 36 4.3.1 De novo Assemblies ...... 36 4.3.2 Variant Discovery ...... 36 4.3.3 Phylogenetic Analysis ...... 37 4.3.4 SNP Distribution ...... 37 4.3.5 Mauve Analysis ...... 38 4.4 Discussion ...... 38 4.4.1 Synteny ...... 38 4.4.2 Host Specificity ...... 39

REFERENCES ...... 52

FIGURES

Figure 2.1: Infection of Cover Crops by V. dahliae. Confocal images of GFP-expressing V. dahliae overlaid with transmitted light images of Buckwheat (A&B), Field Pea (C), Crimson Clover (D), and Winter Rye (E) roots ten days post-inoculation. Figure 3.1: Strawberry growth conditions. Strawberry plants used in inoculation assays were initially harvested as plantlets from runners (A), then rooted in a mist room for two weeks (B) before being inoculated. Figure 3.2: Inoculation Assay Conditions. Strawberry plants were grown under artificial light at 72ͦF (A & B). Plants were arranged in random complete block design, with the two blocks being inoculated and negative control. All time points and replicates within the time points were arranged at random using a random number generator. Figure 3.3: Reference Spectra Used in UnMixing. Reference Spectra for GFP (1), yellow autofluorescence (2), and green autofluorescence (3) at 10x objective magnification were used to UnMix raw confocal images of primary root and stem cross sections. Figure 3.4: Lateral root infection. Lateral roots of F. vesca accessions F2-1, F2-13, F2-14, F2- 15, and F2-18 were observed for infection by V. dahliae expressing GFP. The % of lateral roots infected for each isolate was calculated from 3 lateral roots taken each of the four replicates for each accession. Lateral roots were observed over five time points of 1, 2, 3, 10, and 49 days post-inoculation. Figure 3.5: Lateral Root Infection of F. vesca accessions at time points 14 and 28 days post- inoculation. The % of lateral roots infected is shown along the y-axis for strawberry accessions BC30, BC3, and BC5 along with the F2 progeny of the BC30xTMD2 cross, F2-9 and F2-13. Figure 3.6: Vascular inclusions in F. vesca after infection by V. dahliae expressing Green Fluorescent Protein (GFP). Vascular occlusions were detected by confocal microscopy of primary root and stem cross sections. Two-dimensional images were taken of three separate roots and stems from each of the four replicates. The threshold to determine an occlusion was set at 50µm2 with a minimum GFP intensity of 50/255. Figure 3.7: Foliar stress severity. Plant foliage was rated on an ordinal scale (0-3). 0=healthy plant, 1,2=moderate stress symptoms, 3=dead plant. Disease symptoms observed for include wilting, chlorosis, and dieback. Each test group was observed in replicates of four. The graph represents the average ordinal rating for each test group. Error bars represent the standard deviation of each group. Figure 4.1: Commands to modify headers from paired-end read files using awk. Command A changes header formats from Illumina 1.8+ to Sanger. Command B changes header formats that were used in NCBI’s Sequence Read Archive to Sanger. Both commands replace the third line of the header to “+” to save disk space.

Figure 4.2: Generating a Variant Call File (VCF). Workflow that follows from aligning paired- end Illumina reads to a reference using BWA MEM, to generating a VCF. The function of this workflow is to generate a VCF from sequence reads that has fewer false positives. Figure 4.3: Workflow for taking VCF and generating a Multiple Sequence Aligment (MSA). The MSA generated from this workflow was inputted into MEGA6 to generate a neighbor- joining phylogenetic tree. Figure 4.4: Neighbor Joining Tree of V. dahliae isolates. SNP loci discovered by BWA MEM were aligned by Kalign, then inputted into MEGA 6.0 to generate a neighbor-joining tree. Bootstrap values are at each node. Figure 4.5: SNP distribution along the genome. Number of SNP loci/10Kb among 13 V. dahliae isolates along the reference genome, VdLs.17. Figure 4.6: Average TiTv ratio per 10Kb of SNP loci among 13 V. dahliae isolates along the reference genome, VdLs.17. Figure 4.7: Total SNPs shared between isolates of V. dahliae from progressive mauve multiple genome alignment. Figure 4.8: Inferred phylogeny based on gene order rearrangements. Branch distances showed above each branch

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TABLES

Table 2.1: Infection of cover crops by Verticillium dahliae. Cover crops of Field Pea, Crimson Clover, Winter Rye, and Buckwheat were root-dip inoculated with GFP3 (infectious to strawberry) and GFP9 (infectious to mint) strains of V. dahliae. The percent of plants within the 5 replicates with roots successfully infected ten days post-inoculation were recorded. Table 4.1: FastQC Reports on Sequence Reads. Quality Reports generated by FastQC showing each read file whether it Passes, Fails, or is near Failing (WARN) each of the parameters (Basic Statistics, Per base sequence quality, Per sequence quality scores, Per base sequence content, Per base GC content, Per base N content, Sequence Length Distribution, Sequence Duplication Levels, Overrepresented sequences, and Kmer Content). Each column in headed by the Isolate name and a 1 or 2 based on which member of the pair the reads belong to. Table 4.2: Report summary of de novo assemblies. GC content, number of reads used in the assembly (#Reads), N75, N50, N25, shortest contig (MIN), longest contig (MAX), average contig length (AVG), number of contigs (COUNT), genome size, originating host, sampling location of each isolate, and race for each isolate of V. dahliae. Table 4.3: Summary of variant discovery among 11 isolates of V. dahliae and the reference, VdLs.17. The number of total SNPs (nSNPs), transition:transversion mutation ration (TiTv Ratio), number of SNPs/1000 base pairs (SNPs/1Kb), and the average PHRED quality score (Avg Qual) are listed for each isolate. SNPs were discovered from a total of 33828453 total loci.

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ABSTRACT

THE DYNAMIC HOST-PATHOGEN INTERACTIONS OF VERTICILLIUM DAHLIAE: COMPARATIVE GENOMICS OF V. DAHLIAE AND ITS INTERACTIONS IN COVER CROP SPECIES AND WILD STRAWBERRY (FRAGARIA VESCA) by Garrett Gleeson University of New Hampshire, September, 2015

The evolutionary arms race between plants and their associated pathogens has created highly dynamic interactions. Understanding the mechanisms that drive these interactions and the phylogenetic history of the pathogens is key to developing effective disease management practices. Although there have been great advances in understanding the evolution of plant pathogens, they only scratch the surface of the true complexity of pathogens’ interactions with their hosts and their environment. In this study, the interactions between the fungal vascular wilt pathogen, Verticillum dahliae, and a variety of hosts including accessions of wild strawberry,

Fragaria vesca, and several cover crop species are investigated. With a transgenic strain of V. dahliae expressing Green Fluorescent Protein, infection of hosts was visualized using confocal microscopy. A comparative genomics study of 13 V. dahliae isolates was also done to investigate their phylogeny and to investigate the genetic structure between different isolates.

INTRODUCTION

1.1 Scope of Thesis

The challenge of ensuring food security in the future is becoming ever increasing with a growing population estimated to reach nine billion by 2050 (Andreev et al., 2013). This, coupled with a scarcity of arable land not already being cultivated, the necessary paradigm of obtaining food security is to intensify agricultural production while maintaining fertility and biodiversity.

During the Green Revolution, agriculture saw an unprecedented jump in crop yields through high yielding varieties, chemical fertilizers and pesticides, irrigation, and mechanization (Matson, et al., 1997). Since the Green Revolution, increases in crop yields have outpaced population growth; however, the current paradigm in unsustainable. Issues that have arisen from agricultural intensification include soil erosion, decreased soil fertility and biodiversity, pollution of ground water, eutrophication of bodies of water, over-extraction of water sources, and greenhouse gas emissions associated with climate change (Matson, et al., 1997, Millennium

Ecosystem Assessment, 2013). In order to sustain food security into the future, agricultural intensification needs to address these issues while closing the gap between maximum potential crop yield and actual crop yield.

An essential tool in mitigating the environmental and public health costs of disease management in food crops is the development of genetically resistant crop varieties. Disease resistant cultivars provide an alternative to the use of soil fumigants and pesticides in cases where disease pressure makes chemical-based disease management otherwise necessary

(Brookes and Barfoot, 2005; Brookes and Barfoot, 2012, Pray, et al., 2001; Oerke, 2006).

Although applying pesticides and fumigating soils may be extremely effective against pathogens, these treatments are relatively expensive and may be hazardous to the applicator or have other environmental impacts on ground water and biodiversity. Microbial diversity and abundance are essential to healthy soil, contributing to nutrient availability and disease suppression (Van

Bruggen & Semenov, 2000). Over time, continued chemical-based disease management can decrease the overall soil health and fertility, leaving subsequent crops more susceptible to disease. Implementing disease resistant cultivars decreases the necessity of chemical-based disease management, protecting the soils’ native microbial communities (Gerhardson, 2002).

To develop genetically resistant crop varieties, it is necessary to study plant-pathogen interactions and to understand the mechanisms that allow a compatible host-pathogen interaction.

The increased understanding of effector-mediated disease resistance greatly accelerated the development of monogenic resistance in crops (Hammond-Kosack & Jones, 1997). In these cases, disease resistance is “all or nothing”, meaning that a particular cultivar has either a resistant or susceptible phenotype to a pathogen of interest and little in the way of partial resistant phenotypes (Baker and Cook, 1974). As the mechanisms behind effector-mediated resistance have been elucidated, development of monogenic resistance in crops has greatly accelerated. On the other hand, there are cases of partial resistance to pathogens. These interactions are far more complex and relatively difficult to observe, so progress in developing partially resistant cultivars has been slow (Prioul et al., 2004).

Partial resistance differs in several key ways to monogenic resistance, making it more difficult to study. As the name implies, partial resistance manifests a variety of phenotypes along the disease resistance spectrum between highly susceptible and highly resistant. In

2 monogenic resistance, disease resistance is determined by one or a few genes whereas in partial resistance, disease resistance is determined by multiple genes. The action of partial resistance genes appears to be additive, meaning having more partial resistance genes confers higher disease resistance, while fewer resistance genes confers lower disease resistance (Geiger and

Huen, 1989). This additive quality of partial resistance genes gives rise to a range of disease resistance phenotypes dependent on a cultivar’s resistance gene makeup. Genes responsible for conferring partial resistance are most commonly genes related to basal plant defenses (Poland et al., 2009). Basal plant defenses are not geared towards any one specific pathogen; rather, they act as a general defense against a broad range of pathogens. Because partial resistance involves multiple genes and confers resistance to a broad range of pathogens, it offers the potential to develop durable resistance.

Given the variety of resistance phenotypes, it is necessary to employ methods of quantifying partial resistance in the field. One of the quickest and easiest way of quantifying partial resistance is to rate plant cultivars on an ordinal scale where one end of the scale represents highly susceptible phenotypes and the other highly resistant phenotypes (Straathof et al., 1993). Though the information from ordinal rating is relatively east to obtain and very useful, it has low resolution in defining phenotypes and provides little information on the host- pathogen interactions (Braun & Sinclair, 1979).

1.2 Disease Management in Strawberry

Commercial strawberry production is plagued by a variety of soil pathogens such as

Verticillium and Phytophthora species. Due to the paucity of resistant cultivars, soil pathogens have been commonly managed using soil fumigation (Wilhelm & Paulus, 1980). One of the primary fumigants used is methyl bromide used in conjugation with chloropicrin. During the

3 advent of the Green Revolution, methyl bromide quickly became the most popular fumigant used in strawberry cultivation due to its broad spectrum effectiveness and it’s relatively easy of application to soils (Wilhelm & Paulus, 1980). Methyl bromide ushered in a new era of strawberry cultivation, marked by the drastic increase in yield between pre-fumigation and post- fumigation implementation. Methyl bromide was used in conjugation with chloropicrin treat for a wide variety of soil pathogens and weeds (Johnson et al., 1962). These fumigants even stimulate root growth, increasing yields not only through weed and disease control, but also by directly promoting plant vigor (Johnson et al., 1962; Larson and Shaw, 1995; Yuen et al., 1991).

In 1993, the EPA set a timetable to phase out methyl bromide due to its destructive effects on the ozone layer. Methyl bromide was scheduled to be completely phased out by 2005, its use continues in cropping systems that qualify for a Critical Use Exemption (CUE), which includes California strawberries (United States EPA, 1993). CUEs are approved on the basis that there is no economically or technically feasible alternative to methyl bromide and that removal of methyl bromide from the system would cause a significant market disruption (Goodhue et al.,

2005). Since 1993, the impending need for an alternative to methyl bromide has led to several alternative fumigants and cultivars resistant to disease; however, no alternative has been found that can match the effectiveness of methyl bromide (Duniway, 2002).

1.3 Verticillium dahliae

Verticillium dahliae is a fungal vascular wilt pathogen found in temperate regions across the globe (Verticillium dahliae information sheet). Vascular wilt pathogens such as V. dahliae are naturally found in the soil, where they infect their host through the roots and invade the vascular tissue, a network of cells throughout a plant that transport water and nutrients. Once inside the vascular tissue, the pathogen can infect its host by following the flow of water. During

4 infection, the vascular tissue becomes occluded by hyphal growth, preventing the transport of water, causing wilt symptoms in the host (Berlanger & Powelson, 2000). V. dahliae has a broad host range, infecting over 400 plant species, including many important agricultural crops such as strawberry, lettuce, tomato, potato, and mint (Berlanger & Powelson, 2000). There are several characteristics of V. dahliae that make it a difficult pathogen to manage: 1) V. dahliae is persistent in soil even without a host 2) fungicides are not able to treat already infected plants and 3) development of resistant crops has only been partially successful (Rowe et al., 1987).

The persistence of V. dahliae in soil can be attributed to its asexual propagules, microsclerotia. Microsclerotia are microscopic, dense, melanated balls of hyphae. They are resistant to a variety of abiotic stresses such as desiccation, temperature, and UV radiation

(Jimenez Diaz and Millar, 1988). Even without a compatible host, microsclerotia can persist in soil for over a decade, though the majority only survive for a couple of years (Berlanger &

Powelson, 2000). Rotating non-host crops for a couple years can effectively diminish the density of microsclerotia, but the disease soon increases in abundance after a few seasons of susceptible host crop plantings. Long, several year crop rotations are not always an option for large industrial production, limiting the use of crop rotations in managing V. dahliae (Berlanger &

Powelson, 2000).

Applying fungicides to crops infected with V. dahliae is ineffective because the pathogen resides in the vascular tissue of the plant, harboring it from foliar chemical applications. Soil fumigation, which involves sterilizing soil by injecting chemicals, is effective in reducing the density of microsclerotia in the soil (Duniway, 2002). This allows for several susceptible crop plantings before the disease resurfaces. Soil fumigation however, is expensive to apply and is environmentally damaging (Berlanger & Powelson, 2000). One of the most common fumigants

5 used against V. dahliae, methyl bromide, is toxic and leads to ozone depletion. Soil fumigants are being phased out and may no longer be an available option for disease management in the near future (Berlanger & Powelson, 2000).

Development of disease resistant crops has seen variable success between different crop species. In tomato, varieties that are highly resistant and even totally resistant to races of V. dahliae have been developed (Fradin et al., 2009). Complete resistance in tomato to V. dahliae is conferred by a single dominant gene, Ve, providing resistance to race 1 of V. dahliae (Diwan et al., 1999). On the other hand, crops such as lettuce, mint, and strawberry have only seen development of partially resistant commercial varieties. Typically, when disease resistant crops are developed, the disease resistance is expressed by one or a few genes. With the exception of tomato, there is no single gene that conveys total resistance; rather, resistance is dependent on several genes (Fradin et al., 2009). Having multiple genes responsible for resistance, each of which contributes partially towards disease resistance, gives rise to varieties within a species that have differing levels of disease resistance based on their genetic makeup (Geiger & Heun, 1989).

1.4 Objectives

This research investigated the host-pathogen interactions between wild strawberry

(Fragaria vesca) and V. dahliae. Its aim was to distinguish between tolerant and resistant strains of F. vesca and to characterize the mechanisms behind strawberry defense against vascular wilt pathogens. Understanding the host-pathogen interactions between F. vesca and V. dahliae ultimately will aid in the development of strawberries resistant to vascular wilt pathogens, providing an alternative to soil fumigation in large scale strawberry production. In addition, this research completed a comparative genomic analysis of 13 V. dahliae strains in order to understand the phylogenetic relationship between host specific strains.

CHAPTER 2: COVER CROP INFECTION

2.1 Introduction

The use of cover crops in agriculture confers a multitude of benefits to soil health.

Depending on the plant species, cover crops have been used for erosion control, nitrogen fixation, penetrating deeper soil layers, adding biomass/compost, disease management, and recently biofumigation (Wyland et al., 1996; Mattner et al., 2008). Different cover crop species are better suited for certain tasks than others, and are often used in mixes in orders take advantage specific benefits provided by each member of the species mix.

Disease management using cover crops in rotation with cash crops primarily relies on the concept of rotating in a crop that is not a compatible host for disease organisms infecting the cash crops. In the case of persistent soil pathogens such as V. dahliae, rotating with incompatible hosts can decrease inoculum levels drastically (Xiao et al., 1998; Huisman and

Ashworth, 1978); however, crop rotations would have to be exceedingly long to eliminate the pathogen altogether (references). The decreased concentration of disease propagules in soils allows for growth of susceptible hosts for a limited number of seasons with only minimal yield loss to disease before the concentration of disease propagules builds back up (Subbarao et al.,

2007).

This method of crop rotation in disease management is most effective when the crops in rotation are incompatible hosts for disease organisms of interest. In the case of V. dahliae, selecting an incompatible host can be difficult given the pathogen’s broad host range. Though V. dahliae is an economically devastating pathogen and has drawn a lot of attention from the

7 agricultural and scientific communities, the known host range of this pathogen is still being expanded. There has been discovery of several asymptomatic hosts, expanding the known host range of V. dahliae even further beyond commonly grown crops (Woolliams, 1966; Ligoxigakis,

2002). Tolerant hosts are relevant to disease management of V. dahliae by crop rotation because tolerant hosts, though asymptomatic, allow for growth and maintenance of inoculum levels in soil. Based on this information the objective of the present study was to screen several common cover crops species for susceptibility to infection by V. dahliae isolates from mint and strawberry.

2.2 Methods

2.2.1 Cover Crop Germination

Cover crops of cowpea, buckwheat, field pea, alfalfa variety “vernal”, tillage radish, new zealand white clover, sorghum-sudangrass, winter rye, rape, oat, piper sudangrass, mustard variety “pacific gold”, and crimson clover were germinated from seed on paper towels and kept moist with DI water. Paper towels were wrapped around the seeds and stood upright in a glass beaker with DI water in the bottom, allowing the water to wick up the towel, keeping the seeds moist. During germination, seedlings were left at room temperature near the window of the lab for sunlight.

Three varieties of field pea, crimson clover, winter rye, and buckwheat were successfully germinated. Sorghum sudangrass, rape, winter rye, alfalfa “vernal”, mustard, tillage radish, and oat suffered from fungal contamination during germination. Rape, mustard, cowpea, piper sudangrass, and sorghum sudangrass showed either limited or no germination and were excluded from the experiment.

2.2.2 Verticillium Preparation/Inoculation

Cultures of the Verticillium dahliae isolates, GFP3 and GFP9, were grown in Czapek’s

Broth (30g/L sucrose, 3g/L sodium nitrate, 0.5g/L magnesium sulfate, 0.5g/L potassium chloride, 1.0g/L potassium phosphate dibasic, 0.01g/L ferrous sulfate) amended with 25μg/mL and 45μg/mL hygromycin B, respectively. The two isolates were previously transformed with a

Green Fluorescent Protein (GFP) gene and a hygromycin resistance gene (Lorang et al., 2001).

Strain GFP3 was isolated from strawberry, and strain GFP9 was isolated from mint (Lorang et al.,

2001). Cultures were incubated at room temperature with shaking for 2 weeks. V. dahliae cultures were spun down at 2,500g for 5min. Supernatant was discarded and the pellets were resuspended in 10mL DI water. Spores were counted using a hemocytometer. The concentration of spores was determined from the average of 10 spore counts of 5 small cells within the grid of the counting chamber. Spore concentration was then adjusted to 1.0 x 106 spores/mL by adding sterile DI water.

Three varieties of field pea, crimson clover, winter rye, and buckwheat were root-dip

6 inoculated in a 1 x10 spores/mL suspension for 5min. Cover crops were inoculated with GFP3,

GFP9, or a negative control of DI water in replicates of 5. Following inoculation, plants were transferred to perlite in 8.5oz foam cups and grown in the greenhouse, exposed to daylight.

Plants were watered daily to saturation without fertilizer and allowed to drain.

2.2.3 Confocal Imaging

Ten days post-inoculation, cover crops were harvested. Perlite was rinsed off with DI water. Lateral roots and areas surrounding necrotic tissue of the roots were sampled using a razor blade and kept in a plastic bag until mounted on slides. Root sections of interest were

9 mounted on glass microscope slides and #1.5 glass coverslips in DI water. All samples were mounted the day of collection. Z-stack images were taken of infected areas of the roots using a confocal microscope. Scans were taken in lambda mode, capturing excitation at eight wavelengths 488.6nm-574.2 nm with 10.7 nm steps. The laser was set to 16% transmission.

Images were generated with a 1024 x 1024 frame size at a scan speed setting of seven, eight bit data depth, and a pinhole setting at 0.99 airy units. Scan average was set to line mode, mean method, and four passes.

Raw images of root tissues were unmixed using reference spectra collected from uninfected root tissue and GFP from V. dahliae in pure culture. Filters of GFP, yellow autofluorescence, and green autofluorescence at 10x objective lens magnification were used for unmixing samples. Maximum intensity projections of hyphae were overlaid with transmitted light images of the roots. Infection was determined by the presence of hyphae expressing GFP.

The number of replicates infected was recorded. The 3D Z-stack images were used to determine where along the roots infection occurred.

2.3 Results

2.3.1 Greenhouse Infection Assay

Of the cover crops inoculated, one variety of field pea and winter rye showed no infection by strain GFP3 while all three varieties of field pea were the only cover crops to show no infection by strain GFP9 (Table 2.1). Buckwheat showed the highest incidence of infection with all five replicates being infected by both strains GFP3 and GFP9 (Table 2.1). Field pea varieties

D and D2 showed low incidences of infection, with only one in five for both varieties showing infection by GFP3 (Table 2.1). Crimson clover showed one of five replicates infected with GFP3,

10 and three of five replicates infected with GFP9. Three of five replicates of Winter rye showed infection by GFP9 (Table 2.1).

2.3.2 Confocal Imaging

Infection of buckwheat and winter rye showed more extensive growth of hyphae within the vascular tissue whereas infection of the field pea varieties and crimson clover manifested as more sheath-like around the outer layers of the roots (Figure 2.1). In the case of crimson clover, where both isolated of V. dahliae were able to infect, the infection manifested itself on the exterior and outer layers of the root for both isolates GFP3 and GFP9. In buckwheat, infection by

GFP3 and GFP9 were both localized in the vascular tissue.

2.4 Discussion

2.4.1 Infection of Tolerant Hosts

Persistence in soil and its broad host range make V. dahliae a difficult pathogen to manage. Crop rotations that include plant species outside the host range of V. dahliae help limit its persistence (Xiao et al., 1998; Huisman and Ashworth, 1978). Although the host range of V. dahliae has been well-characterized, the realm of asymptomatic hosts has not been entirely explored. The diverse host range of V. dahliae makes the list of potential asymptomatic hosts expansive. Asymptomatic hosts of V. dahliae, though not showing disease, can still act as a source of inoculum by harboring the pathogen, potentially allowing disease to persist in soils or spreading to new fields by seed (Vallad et al., 2005; Evans, 1971). Characterization of the asymptomatic host range will allow growers to design crop rotations to select for non-hosts, increasing the efficacy of crop rotation in reclaiming V. dahliae infected soils.

This study has shown V. dahliae is capable of infecting cover crops of Buckwheat,

Winter Rye, Field Pea, and Crimson Clover; none of which are listed as known hosts (Berlanger

& Powelson, 2000). Though the assay was not carried out long term to determine if V. dahliae can cause disease in these cover crops, it does highlight the potential for V. dahliae to grow and reside in tissues of plant species traditionally thought of as outside its host range.

The two isolates of V. dahliae used in this experiment demonstrated some host specificity. Strain GFP3 infected field pea, but not winter rye whereas strain GFP9 was not able to infect field pea, but was able to infect winter rye. Interestingly, winter rye belongs to the monocots, which are typically considered non-host to Verticillium dahliae. Further characterization of monocot cover crop susceptibility to V. dahliae is necessary to determine if infection of this group is common. Carrying out the assay to several weeks would be show if the infection would progress to cause disease or not.

Crimson clover and field pea, both legumes, were infected by V. dahliae. In the case of field pea, only the strain GFP3 of V. dahliae was able to infect. Because field pea was not a host to both isolates, it shows potential as being used as a cover crop in V. dahliae infested field. To use a cover crop such as field pea or winter rye in managing V. dahliae, it would be necessary to characterize what strains do infect the cover crop species and what strains of V. dahliae are present in the soil of interest. If the isolates present in the soil are not able to infect the cover crop species, then the cover crop is essentially a non-host species.

The host specificity of V. dahliae strains could be used to inform the decision of cover crop species to use in managing the disease. Selecting plants that may be within the host range of the entire species of V. dahliae, but out of the host range for strains in a field of interest, could

12 be an effective means of crop rotation to control disease (Bhat and Subbarao, 1999; Subbarao et al., 1995).

2.4.2 Future Work

Results from this study need to be replicated for confirmation and the screening of cover crops needs to include more species. Brassica species, which have shown antagonistic activity to

V. dahliae (Berg et al., 2005), are commonly used as cover crops and were not covered in this study. Characterizing interactions between a variety of cover crop species and different isolates of V. dahliae will allow improve management of infested soils through selection of non-host species.

Field plots are needed to confirm results of the greenhouse studies, making sure that interactions that occur in the greenhouse are also observed in the field. Differences between greenhouse conditions and field plots such as soil structure and weather could impact the ability of V. dahliae to infect its hosts’ or the ability of the hosts to resist infection. The actual impact of planting cover crops with known resistance against those that are tolerant on V. dahliae populations needs to be studied over several years in field plots. Refined screening methods using confocal screening that quantify infection load and plating assays will allow for higher resolution characterization of tolerance in cover crops. Characterizing the host range of V. dahliae isolates on cover crops will allow for more effective crop selection when managing V. dahliae infected soils.

Cover Crop % Plants Infected GFP3 % Plants Infected GFP9 Field Pea* 0 0 Field Pea D* 20 0 Field Pea D2* 20 0 Crimson Clover 20 40 Winter Rye 0 60 Buckwheat 100 100

Table 2.1: Infection of cover crops by Verticillium dahliae. Cover crops of field pea, crimson clover, winter rye, and buckwheat were root-dip inoculated with GFP3 (infectious to strawberry) and GFP9 (infectious to mint) strains of V. dahliae. The percent of plants within the 5 replicates with roots successfully infected ten days post-inoculation were recorded.

B C

D E

Figure 2.1: Infection of cover crops by V. dahliae. Confocal images of GFP-expressing V. dahliae overlaid with transmitted light images of buckwheat (A&B), field pea (C), crimson clover (D), and winter rye (E) roots ten days post-inoculation. Infection of buckwheat (A, B) and winter rye (E) is localized in the vascular tissue whereas infection of crimson clover (D) and field pea (C) manifests in the outer tissues of the root.

CHAPTER 3: FRAGARIA VESCA-VERTICILLIUM DAHLIAE INTERACTIONS

3.1 Introduction

Disease resistance in strawberry to the fungal pathogen V. dahliae appears to be through partial or quantitative resistance, as indicated by previous research (Shaw et al., 1996). Breeding strawberry cultivars resistant to V. dahliae is dependent on reliable screening methods that can characterize and quantify partial resistance. One common method of measuring partial resistance in strawberry is to rate foliar symptoms of disease after inoculation on an ordinal scale and compare to a negative, uninfected control (Straathof et al., 1993). Though rating on an ordinal scale is relatively low cost and easy to implement, it lacks resolution (Braun & Sinclair,

1979). Foliar symptoms of verticillium wilt are similar to a number of other stresses including abiotic stresses such as drought or nutrient deficiency, and biotic stresses such as caused by other wilt pathogens (Berlanger et al., 2001). These other stresses can blur the lines when determining the extent of foliar symptoms caused by V. dahliae.

There is also the issue of tolerant hosts. A cultivar of strawberry resistant to V. dahliae will have the same phenotype as a cultivar tolerant to V. dahliae. The difference is that the tolerant host can still harbor the pathogen within its tissues, maintaining inoculum levels of the disease in the soil (Robb et al., 2007). Therefore, the objective of this study was to monitor infection of several accessions of F. vesca by V. dahliae using confocal microscopy and an isolate transformed to express GFP. Observing interactions between resistant accessions of F. vesca and V. dahliae may provide insight into the resistance mechanisms against vascular wilt pathogens.

3.2 Methods

3.2.1 Strawberry Growth Conditions

Mother plants of strawberry accessions were kept in greenhouse conditions and allowed to runner (Figure 3.1). F. vesca accessions BC3, BC5, and BC30 were grown along with F2 progeny from a cross between F. vesca accessions BC30 and TMD2. Accession BC30 shows high susceptibility to V. dahliae while accession TMD2 shows high resistance from previous screening performed by the Davis lab at UNH. F2 progeny F2-1, F2-6, F2-9, F2-13, F2-14, F2-

15, and F2-18 were used in experimental trials. Strawberry plants used in experimental trials were cut from runners and rooted in sterile potting soil under mist for two weeks. Once rooted, plant roots were cleaned in tap water. For each plant accession, forty plants were grown for each inoculation assay. F2 progeny F2-1, F2-13, F2-14, F2-15, and F2-18 were observed at five time points of 1, 2, 3, 10, and 49 days post-inoculation while all other F. vesca accessions were observed at time points 14 and 28 days post-inoculation.

3.2.2 Verticillium Growth Conditions

For strawberry inoculation assays, V. dahliae cultures used were transformed with Green

Fluorescent Protein (GFP) and hygromycin resistance to allow for the visualization of fungal colonization in the vascular tissue by confocal microscopy (Lorang et al., 2001). V. dahliae cultures were subcultured from Czapeks agar amended with 25µg/mL hygromycin B to Czapeks broth amended with 25µg/mL hygromycin B. Broth cultures were shaken at 290rpm for three weeks at room temperature.

After three weeks of incubation, broth cultures of V. dahliae were filtered through three layers of cheese cloth into 50mL falcon tubes, then spun down at 2,500g for 10 minutes.

Solutions were decanted, leaving a pellet of conidia. Conidia were resuspended in sterile DI

H20. Concentrations of conidial suspensions was determined using a hemocytometer. After spore concentrations were determined, the final volume was increased with sterile DI H20 to adjust for a final concentration of 1x107 conidia/mL. Conidial suspensions were used the day of being spun down.

3.2.3 Inoculation Assay

After rinsing roots in tap water, strawberry plants were root-dip inoculated in 1x107 conidia/mL V. dahliae conidial suspensions for 5 min. Strawberry plants were repotted in 8.5oz foam cups in perlite. Negative controls were treated the same as inoculated plants except they were root-dipped in sterile DI H2O. The experiment was set up in randomized complete block design with 4 replicates, arranging negative controls and inoculated plants as two separate blocks. All replicates and all time points were arranged in random order within their block using a random number generator. Blocks were rotated 180ͦ once a day to mitigate any differences in light coverage, air flow, etc. Plants were kept under controlled temperature and lighting of 72°F and 16hr light/8hr dark cycle (Figure 3.2). Plants were watered to saturation daily with sterile water with no fertilizer amendments.

3.2.4 Visual Disease Assessment

At each time point, plants were rated for foliar symptom development on an ordinal rating scale. The scale ranged from 0 to 3, where 0=healthy, 1=low stress, 2=moderate stress,

3=heavy stress/dead. Plants were rated on the ordinal scale at the time of inoculation and 49 days post-inoculation.

3.2.5 Confocal Microscopy

At each time point, plants were sampled for viewing by confocal microscopy. From each individual plant, 1cm length samples of three lateral roots, three primary roots, and three stems were taken. Wet mounts of lateral roots were prepared with standard glass slides, sterile DI H2O, and #1.5 glass coverslips. To mount lateral root and stem samples, three cross sections of each sample were taken, then mounted in the same fashion as for lateral roots. Root samples were taken at 1, 2, 3, 10, and 49 days post-inoculation. For the rapid detection assay, only two time points of 14 and 28 days post-inoculation were observed.

Lateral root samples were observed for infection under the confocal microscope. The number of infected roots and the region of tissue (root surface, intracellular spaces, or vascular tissue) were recorded. Primary root and stem cross sections were scanned. Scans were taken in lambda mode, capturing excitation at eight wavelengths ranging from 488.6nm to 574.2 nm with

10.7 nm steps. The laser was set to 16% transmission. Images were generated with a 1024 x

1024 frame size at a scan speed setting of seven, eight bit data depth, and a pinhole setting at

0.99 airy units. Scan average was set to line mode, mean method, and four passes.

Raw images of primary root and stem cross sections were unmixed using reference spectra collected from uninfected root and stem tissue and GFP from V. dahliae in pure culture

(Figure 3.3). Filters of GFP, yellow autofluorescence, and green autofluorescence at 10x objective lens magnification were used for unmixing samples. The presence of occlusions in the vascular tissue caused by growth of V. dahliae was determined by counting the number of regions fluorescing at the same wavelength as GFP within a cross section of root that took up more than 50µm2. Regions also had to be fluorescing at a higher intensity than all other filters combined to account for overlap of reference spectra.

3.3 Results

3.3.1 Lateral Root Infection

Infection by V. dahliae was observed in the BC30 x TMD2 progeny F2-1, F2-13, F2-14,

F2-15, and F2-18. Growth of V. dahliae at time points 1, 2, and 3 days post-inoculation was limited to elongation of the conidia, with no penetration into the lateral root tissue (Figure 3.4).

By 10 days post-inoculation, infection was observed in the intercellular spaces of the root cortex and early infection of the vascular tissue. Variation in infection was observed between progeny.

For the five progeny where infection was observed, there ranged from 33% of the lateral roots infected in F2-15 to 100% of the lateral roots infected in F2-13 at 10 days post-inoculation

(Figure 3.4). At 7 weeks days post-inoculation, only one incidence of infection was observed in the lateral roots of F2-15 and there was no observable infection in the roots of the remaining progeny. This trend can be observed from time points 3 days to 35 days, as there is a steady decline in the percentage of roots with observable infection by V. dahliae (Figure 3.4).

3.3.2 Primary Root and Stem Infection

Infection of F. vesca accessions F2-1, F2-13, F2-14, F2-15, and F2-18 appears to have been limited to the lateral roots with the exception of accession F2-1 and F2-13 at 10 days post- inoculation in the stems and primary roots, respectively (Figure 3.6). In both cases, infection was observed in one of the four replicate plants within the time point, with all other replicates showing no observable infection. In the case of the primary roots of F2-13 at 10 days post- inoculation, the one infected root cross section showed 13 occlusions by V. dahliae. In the case of the stems of F2-1 at 10 days post-inoculation, the one infected stem cross section showed one occlusion by V. dahliae. Accessions BC3, BC5, and BC30, along with F2 progeny from the

BC30 x TMD2 cross F2-6, F2-9, and F2-13 had observable infections at time points 14 and 28 days post-inoculation (Figure 3.5). All of the accessions showed no signs of infection of the primary root or stem tissue.

3.3.3 Ordinal Rating

None of the observed F. vesca accessions, F2-1, F2-13, F2-14, F2-15, and F2-18 showed any elevated stress symptoms between the time of inoculation and 49 days post-inoculation

(Figure 3.7). There was also little to no difference in stress symptoms between different F. vesca accessions.

3.4 Discussion

3.4.1 Resistant Phenotypes

The inability of isolate GFP3 of V. dahliae to systemically infect and the lack of disease symptoms in any of the F. vesca accessions observed represents an incompatible host-pathogen interaction. This incompatible host-pathogen interaction could be a result of several factors. For a compatible host-pathogen interaction to occur, there needs to be: i) a pathogen ii) a compatible host iii) and an environment conducive to infection. Accession BC30 of F. vesca has known susceptibility to V. dahliae from previous screenings done by the Davis lab at UNH, so under the right conditions, BC30 can show systemic infection. The conditions of the assay were successful in previous trials when infecting cover crops, showing the environment allows for growth and infection by isolate GFP3 of V. dahliae, suggesting the culprit may be the host, F. vesca, is not compatible with the pathogen V. dahliae isolate GFP3. The host ranges of V. dahliae can vary greatly between different isolates, and it may be possible that F. vesca was not within the host range of isolate GFP3.

Another possibility is that the transformation process impacted the ability of isolate GFP3 to infect F. vesca, making it less pathogenic. Depending on how many times and the location of insertion into the genome, there is a possibility that the transformation disrupted genes responsible for pathogenicity, lowering isolate GFP3’s ability to infect F. vesca. The rare exceptions where V. dahliae isolate GFP3 was able to systemically infect F. vesca may have occurred because of a slight change in the conditions such as a wound in the primary root that allowed for infection to occur. In the future, multiple isolates of V. dahliae transformed with

GFP could be used in the assay, increasing the potential for a compatible host-pathogen interaction.

The first step in determining the root cause of the incompatible host-pathogen interaction between V. dahliae isolate GFP3 and F. vesca would be to find appropriate controls. Without a positive control, it can’t be ruled out that there may have been something wrong with the V. dahliae culture itself. Buckwheat showed consistent infection under the same conditions as the

F. vesca assays, making it a potential positive control. To show whether or not the system is conducive to infection, running the assay in perlite alongside an assay in soil (same conditions as the screenings done by the Davis labs prior).

3.4.2 Lateral Root Infection

Infection of F. vesca by V. dahliae isolate GFP3 occurred in the lateral roots, but never went on to become a systemic infection. The incidence of infected lateral roots decreased over time, showing almost no lateral roots with infection by 49 days post-inoculation. This suggests a resistance mechanism in F. vesca that limits infection to the lateral roots, and over time those lateral roots are replaced by the growth of new lateral roots. A similar resistance mechanism was seen in lettuce infected with V. dahliae (Vallad and Subbarao, 2008).

Observing infection of F. vesca by V. dahliae in resistant accessions may provide insight into the resistance mechanisms at work. Though the F. vesca isolates in this study did not show a compatible interaction with V. dahliae isolate GFP3, strawberry cultivars that can limit infection to their lateral roots may be key in developing strawberry cultivars resistant to virulent isolates of V. dahliae.

A B

Figure 3.1: Strawberry growth conditions. Strawberry plants used in inoculation assays were initially harvested as plantlets from runners (A), then rooted in a mist room for two weeks (B) before being inoculated.

B C

Figure 3.2: Inoculation Assay Conditions. Strawberry plants were grown under artificial light at 72ͦF (A & B). Plants were arranged in random complete block design, with the two blocks being inoculated and negative control. All time points and replicates within the time points were arranged at random using a random number generator.

Figure 3.3: Reference Spectra Used in UnMixing. Reference Spectra for GFP (1), yellow autofluorescence (2), and green autofluorescence (3) at 10x objective magnification were used to UnMix raw confocal images of primary root and stem cross sections.

120

100

40 % Lateral Roots Infected Roots Lateral % 20

0 1.00 6.00 11.00 16.00 21.00 26.00 31.00 36.00 41.00 46.00 Days Post-Inoculation

F2-1 F2-13 F2-14 F2-15 F2-18

Figure 3.4: Lateral root infection. Lateral roots of F. vesca accessions F2-1, F2-13, F2- 14, F2-15, and F2-18 were observed for infection by V. dahliae expressing GFP. The % of lateral roots infected for each isolate was calculated from 3 lateral roots taken each of the four replicates for each accession. Lateral roots were observed over five time points of 1, 2, 3, 10, and 49 days post-inoculation.

100 90 80 2 Wks 4 Wks 70 60 50 40 30

% Lateral Lateral % Roots Infected 20

10 0 F2-9 F2-13 BC30 BC3 BC5 Strawberry Accession

Figure 3.5: Lateral Root Infection of F. vesca accessions at time points 14 and 28 days post-inoculation. The % of lateral roots infected is shown along the y-axis for strawberry accessions BC30, BC3, and BC5 along with the F2 progeny of the BC30xTMD2 cross, F2-9 and F2-13.

Figure 3.6: Vascular occlusions in F. vesca after infection by V. dahliae expressing Green Fluorescent Protein (GFP). Vascular occlusions were detected by confocal microscopy of primary root and stem cross sections. Two-dimensional images were taken of three separate roots and stems from each of the four replicates. The threshold to determine an occlusion was set at 50µm2 with a minimum GFP intensity of 50/255.

2.5 t0 Control t0 Inoculated Week 7 Control Week 7 Inoculated

1.5 Stress Stress Severity 1

0.5

0 F2-1 F2-13 F2-14 F2-15 F2-18 Strawberry Accession

Figure 3.7: Foliar stress severity. Plant foliage was rated on an ordinal scale (0-3). 0=healthy plant, 1,2=moderate stress symptoms, 3=dead plant. Disease symptoms observed include wilting, chlorosis, and dieback. Each test group was observed in replicates of four. The graph represents the average ordinal rating for each test group. Error bars represent the standard deviation of each group.

CHAPTER 4: COMPARATIVE GENOMICS OF VERTICILLIUM DAHLIAE

4.1 Introduction

Sexual reproduction in fungal plant pathogens provides a means of genetic recombination, a necessity to overcome host plant defenses in an evolutionary arms race between host and pathogen (Ma et al., 2010). Although sexual reproduction is considered the primary means by which organisms generate genetic diversity, approximately 20% of fungal plant pathogens primarily reproduce asexually (de Jonge et al., 2013). Some asexual pathogens are thought to not be truly asexual, undergoing a cryptic sexual stage, or to have lost the ability to sexually reproduce only recently (Milgroom et al., 2014). Without sexual recombination, asexual organisms are typically viewed as evolutionary dead ends (Muller, 1932); however the high occurrence of asexual fungal phytopathogens suggests sexual reproduction is not necessary to adapt to ever-evolving host plant defenses (Taylor et al., 1999).

A potential means by which asexual organisms can produce genetic diversity is through transposable element (TE) mediated rearrangement. First discovered in maize by Barbara

McClintock (McClintock, 1950), TE’s are genetic elements capable of self-replication, reinserting themselves in another location in the genome and sometimes taking adjacent DNA sequences with them. TE’s are classified into two classes; DNA transposons, which have a DNA intermediate; and RNA transposons (retrotransposons) which have an RNA intermediate. TE’s were initially thought of as parasitic or junk DNA, given that they replicated themselves while serving no known purpose for the organism (Orgel & Crick, 1980). Recently however, TE’s have been investigated for their potential role in genome rearrangement (Rouxel et al. 2011;

Manning et al. 2013; Neafsey et al. 2010; Biémont & Viera 2006; Fedorova et al. 2008; Gout et al. 2006, Schardl et al. 2013).

Artifacts of TE activity can shed light on the extent of TE-mediated recombination occurring within an organism, and also if TE-mediated recombination confers increased pathogenicity. One artifact of TE activity is a higher occurrence of genetic rearrangements and inversions (de Jonge et al., 2013). Because transposons in fungi are commonly retrotransposons that contain long terminal repeats (LTR), a high occurrence of these transposons lowers the GC content of regions where they are present (Grandaubert et al. 2014). Finally, because transposons are self-replicating, they expand the genome in size, resulting in genomes with higher C-values (weight of haploid genome in picograms). A prime example of this would be within the Leptosphaeria maculans - Leptosphaeria biglobosa fungal species complex than causes blackleg on brassica crops (Grandaubert et al. 2014). The most economically devastating among the species complex, Leptosphaeria maculans brassicae (Lmb), shows higher occurrence of TE-mediated recombination in comparison to other, less pathogenic, members of the species complex (Grandaubert et al. 2014). Grandaubert et al hypothesize the ability of Lmb to infect oilseed rape and its aggressiveness can be at least in part due to TE-mediated rearrangements.

Genetic recombination by TE in fungal pathogens does not seem to be random; rather,

TE activity is highest in regions of the genome that predominately contain genes related to pathogenicity (Ma et al., 2010; de Jonge et al., 2012). This limits the deleterious effects of TE activity near housekeeping genes. TE-mediated rearrangement allows asexual pathogens to develop novel gene combinations and disrupt effector expression, creating diversity and making it possible to overcome evolving host plant defenses (Klosterman et al., 2011). These highly dynamic genomic regions, often referred to as Lineage Specific (LS) regions, are found in a wide

32 range of phytopathogens such as Phytophthora ramorum, Phytophthora infestans, Leptoshpaeria maculans, Fusarium solani, and Verticillium dahiliae (Grandaubert et al., 2014; de Jonge et al.,

2013). The presence and activity of TE in these LS regions is much higher than throughout the rest of the genome. This results in LS regions with are highly dynamic and vary between isolates, contributing to these phytopathogens ability to overcome host defenses and also infect novel hosts (Ma et al., 2010).

How TE activity is regulated in phytopathogens is a topic still under investigation. Zeh et al. (2009) proposed that TE-mediated recombination is triggered by the environment and leads to events of punctuated equilibria. Phytopathogen stress in this scenario is triggered by changes in host availability, such as an introduction of new, closely related host species, host species developing resistance, or expansion of the phytopathogen’s host range. These stresses create an environment where epigenetic regulation of TE activity is lifted, causing a sudden increase in

TE-mediated rearrangement, enabling the phytopathogen to adapt to its new host environment

(Fedoroff, 2012; Raffaele et al., 2010).

In the case of V. dahliae, TE-mediated recombination and loss of the ability to sexually reproduce seems to have occurred recently, resulting in the rise of clonal lineages (Gurung et al.,

2014). This possibly coincides with the change in host availability due to the rise in agriculture

(Milgroom et al., 2014). In this study, the phylogeny of 13 V. dahliae isolates originating from a variety of hosts and from different geographic areas was investigated to understand the relationship between host specific strains. The study also mapped the SNP distribution across the genome of V. dahliae, showing regions that have higher incidences of mutation.

4.2 Methods 4.2.1 Isolates Information Illumina paired-end read data of the nine V. dahliae isolates CBS, DVD-161, DVD-3,

DVD-31, DVD-s26, DVD-s29, DVD-s94, St 100, and St 14.01 were imported from NCBI

BioProject PRJNA169154 (de Jonge et al., 2013) using the SRA Toolkit fastq-dump command

(Wheeler et al., 2007). Paired-end libraries of Verticillium isolates Y4, STD_STB, and GFP2 were sequenced in half a lane on an Illumina HiSeq 2000 at the University of New Hampshire.

The final data set included whole genome sequence data from isolates of V. dahliae originating from hosts of mint, strawberry, potato, pistachio, tomato, lettuce, and soil (Table 4.2). Paired- end reads were quality checked using FastQC (Table 4.1).

4.2.2 De novo Assembly

Paired-end read data of Verticillium dahliae whole genomes was acquired from NCBI

(BioProject PRJNA169154) and strains sequenced at UNH (Y4, DSA ERA, and STD STB). De novo assemblies were constructed in CLC Genomics Workbench 7.5. Paired read data was imported as Illumina Hi-Seq Paired-end data. After trimming adapter sequences, de novo assemblies were constructed for each isolate using default settings (Table 4.2).

4.2.3 Making Reads Compatible with BWA MEM

Quality scores of fastq files from BioProject PRJNA169154 isolates were adjusted from

PHRED +64 to PHRED +33 quality scores using Galaxy FASTQ Groomer tool (Blankenberg et al., 2010). Read header format from BioProject PRJNA169154 isolates were changed from their original SRA format to Sanger using awk. Using another awk command, read header formats from UNH isolates were changed from Illumina 1.8+ format to Sanger (lexnederbragt, 2011)

(Figure 4.1).

4.2.4 Variant Discovery

V. dahliae isolates were aligned to the reference genome, Vdls.17, using bwa mem (Li,

2013). To make the alignment compatible with picard tools, shorter split hits were marked as secondary (-M). SAM files generated from the BWA alignment were converted to BAM files using samtools. Read Group information was added to BAM file headers using picard tools

AddOrReplaceReadGroups. Read Group information added included the library name, platform, platform name, and sample name. AddOrReplaceReadGroups was also used to sort bam files by coordinate. Duplicates were marked in bam files using picard tools MarkDuplicates. BAM files were indexed using picard tools BuildBamIndex. Indexed BAM files were validated by picard tools ValidateSamFile.

BAM files were realigned using Genome Analysis Tool Kit’s (GATK) IndelRealigner.

Target interval files used during the realignment were generated using GATK’s

RealignerTargetCreator, requiring a reference indexed by picard tools

CreateSequenceDictionary. Realigned bam files were jointly genotyped using GATK’s

UnifiedGenotyper (standard minimum confidence to call = 250; standard minimum confidence to emit = 50), creating a variant call file (VCF) (Figure 4.2). The variant discover pipeline operated under the assumption that V. dahliae is haploid (Collins et al., 2003). The transition to transversion mutation ratio (TiTv ratio) was exported from the VCF for each isolate in a GATK evaluation report.

4.2.5 Generating Phylogenetic Tree

Genotype data from the VCF file was extracted using GATK’s VariantsToTable tool

(Table 4.3). Tabular data of each isolate’s genotype at each SNP position was concatenated into

35 a FASTA file for each isolate using vcf_tab_to_fasta (Bergey, 2012). The FASTA files were used to generate a multiple sequence alignment in Kalign (Figure 4.3). The multiple sequence alignment generated by Kalign was inputted into MEGA6. A neighbor-joining tree was created with default settings and a 1000 bootstrap replications.

4.2.6 Mauve Analysis

Genomes of JR2 and VdLs assembled in a previous study (Klosterman et al., 2011) and de novo assemblies of the remaining isolates were aligned in progressive Mauve. Mauve generated signed gene order permutation files and synteny maps of the isolates. Progressive

Mauve also generated a file of the number of SNPs, which was used to compare the number of

SNPs between isolates (Figure 4.7).

4.3 Results

4.3.1 De novo Assemblies

De novo assemblies were generated for all isolates of V. dahliae with the exception of

VdLs and JR2, which were previously assembled (Klosterman et al., 2011). Genome sizes from the assemblies and the two previously sequenced isolates, VdLs.17 and JR2, range from 32Mb to

36Mb. GC contents are all similar, ranging from 53.6% to 55.9%. Contig N50 values ranging from 35Kb to 69Kb with an average N50 value of 45.6Kb and a standard deviation of 10Kb among the 11 newly sequenced V. dahliae isolates (Table 4.2).

4.3.2 Variant Discovery

A total of 345,606 different SNP sites were discovered among the 11 isolates of V. dahliae. With the exception of isolates GFP2 and Y4, which have 12,220 and 26,561 SNPs,

36 respectively, and St_100, which has 212,663 SNPs, the number of SNPs for each individual isolate ranges from 158,787 to 166,116. The TiTv ratio ranges from 2.14 to 2.19 with the exception of isolates GFP2 and Y4, which has TiTv ratios of 0.98 and 1.68, respectively. TiTv ratios around 2.1 show the expected level of bias towards transitional mutations, meaning isolates GFP2 and Y4 manifest higher rates of transversion mutations than expected. The TiTv ratio of all the SNP sites combined is 2.31. The number of SNPs/1000 basepairs ranged from

4.79 to 6.49 with the exception of GFP2 and Y4 which had 0.35 and 0.75 SNPs/1000 basepairs, respectively.

4.3.3 Phylogenetic Analysis

The neighbor-joining tree of the 11 V. dahliae isolates and the reference, VdLs.17, shows separation into several distinct clades. Isolate St_100, originally isolated from soil (de Jonge et al., 2013), shows no relatively close relation to any other isolate. The reference strain isolated from lettuce and isolates GFP2 and Y4 isolated from strawberry and mint, respectively, form a clade distinct from all of the other isolates. The remaining isolates are more closely related to each other than they are to reference/Y4/GFP2 clade or isolate St_100. Isolate St_100 is the most distantly related to the all other V. dahliae isolates and happens to be the most distantly separated geographically, originating from Belgium whereas all other isolates originated in North

America.

4.3.4 SNP Distribution

Distribution of SNP loci along the reference genome shows areas with a higher incidence of SNP loci (Figure 4.5). Supercontigs eight and nine and the end of supercontig 3 show higher incidents of SNP loci, reaching maxima of 228, 123, and 174 SNP loci/Kb, respectively.

Distribution of SNP loci also correlates with the TiTv ratio of the SNP loci (Figure 4.6). Areas with a higher incidents of SNP loci also have a higher TiTv ratio, reaching 2.87 and 2.83 in supercontigs 8 and 9, respectively.

4.3.5 Mauve Analysis

Synteny mapping between isolates of V. dahliae shows extensive genomic rearrangements, even within clades with the exception of VdLs.17 and GFP2. Using the signed gene order permutation files generated by progressive Mauve, an inferred phylogenetic tree based on syntenic similarity between isolates. The inferred phylogenetic tree places isolates in clades differently from the SNP data, showing isolates from tomato (JR2, DVD-31, and CBS), potato (DVD-3, DVD-161), and lettuce (VdLs) form a clade distinct from isolates from soil

(St_100, DVD-s26, DVD-s94), Pistachio (St_14.01), strawberry (GFP2, STD STB), and mint

(Y4). The one exception being isolate DVD-s29 from soil which was placed in the clade with isolates from tomato, potato, and lettuce.

Discussion

4.4.1 Synteny

Among the 13 isolates of V. dahliae, all of the genomes show similar qualities with respect to size and GC content. Genome size and GC content are also similar when compared to the closely related species, Verticillium albo-atrum (Klosterman et al., 2011). Interestingly, synteny mapping between isolates, even within clades, shows extensive genomic rearrangements

(de Jonge et al., 2013). The breaks in synteny between closely related isolates suggests TE- mediated recombination, however the genomes do not exhibit large, low GC genomes associated with high TE activity (Grandaubert et al., 2014). The inferred phylogenetic tree (Figure 4.9)

38 based on gene order shows isolates separating into clades differently from that of the SNP data.

The difference in phylogenies between SNP data and gene order data may be an artifact of V. dahliae only recently becoming asexual. The changes in collinearity and synteny between clonal isolates of V. dahliae probably arose only recently with the loss of sexual reproduction whereas the accumulation of SNPs has occurred over a longer span on time. Highly divergent isolates of

V. dahliae based on gene order data may represent isolates that are under higher selection pressure from their hosts. Given the variable host specificities of V. dahliae isolates, this creates the potential for isolates with varying rates of genome rearrangement.

The TiTv ratios of each isolate are at or above the expected value of roughly 2.1 (Babbitt et al., 2011) with the exception of isolates GFP2 and Y4, with much lower TiTv ratios of .98 and

1.68, respectively. The higher rate of transversion mutations in isolates GFP2 and Y4 suggests the possibility of either false positives in the VCF or selection pressure for transversion mutations (Yang et al., 2000). Given the high quality scores for the SNPs found in isolates GFP2 and Y4, it is unlikely that they contain an over-representation of false positives. Interestingly, both isolates GFP2 and Y4 are transformed with GFP and hygromycin resistance genes, providing a characteristic that sets these two isolates apart from the rest. Future work could involve comparing genomes of isolates before and after transformation to see if there is an impact of transforming V. dahliae on variants. Also, finding the number of times the gene construct was inserted into the genome may provide insight into whether or not it can impact

SNP calls.

4.4.2 Host Specificity

Interestingly, the observed isolates of V. dahliae separate into distinctive clades, but not based on host specificity. A striking example of this would be the close relationship between

39 isolates VdLs.17, GFP2, and Y4, which were originally isolated from lettuce, strawberry, and mint, respectively. Isolates STD_STB and GFP2, both originally isolated from strawberry, are in different clades. Isolate DVD-s26, a member of race 2, is more closely related to race 1 isolates

CBS and St_14.01 than to other race 2 isolates based off of SNP data. Different isolates from the same species of host being phylogenetically distant suggests pathogenicity on a particular host can arise independently. There is also the possibility the host ranges of different isolates from the same host overlap, but are not entirely the same (Bhat et al., 1999). Because isolates of V. dahliae appear to have arisen clonally (Milgroom et al., 2014), this suggests a mode of evolving pathogenicity asexually. Prior studies in Fusarium oxysporum also show that host specificity is not always defined by the clonal lineage and that races can be distributed apparently at random within clonal lineages if those determinants are simple or mutable (Kistler, 1997).

Given that lineages of V. dahliae appear to have arisen clonally (Milgroom et al., 2014), the occurrence of different host ranges within lineages suggests there is a mode of asexual genetic recombination. The lack of synteny between closely related isolates (de Jonge et al.,

2013) could potentially be an artifact of TE-mediated recombination in V. dahliae, however the evidence is not supported by average sized genomes and a high GC content. Further work to investigate the possibility of TE-mediated recombination in V. dahliae would include finding TE themselves in the genome.

A) UNH Isolate Header => Sanger Header Format awk ‘{if (NR % 4 == 1) {split ($1$2, arr, “:”); printf “@Isolate_id:%s:%s:%s:%s#0/1or2\n”, arr[2], arr[3], arr[4], arr[5], $0} else if (NR % 4 == 3) {print “+”} else {print $0} }’ fileinput.fastq > fileoutput.fastq

B) BioProject Header => Sanger Header Format awk ‘{if (NR % 4 == 1) {split ($1, arr, “:”); printf “%s_%s:%s:%s:%s:%s#0%s\n”, arr[1], arr[3], arr[4], arr[5], arr[6], arr[7], substr($2,1,1), $0} else if (NR % 4 == 3) {print “+”} else {print $0} }’ fileinput.fastq > fileoutput.fastq

Isolate_id – Isolate identifier. Must be unique for each isolate 1or2 – enter “1” or “2” depending on which member of mate pair fileinput.fastq – fastq file being modified fileoutput.fastq – name of output file

Figure 4.1: Commands to modify headers from paired-end read files using awk. Command A changes header formats from Illumina 1.8+ to Sanger. Command B changes header formats that were used in NCBI’s Sequence Read Archive to Sanger. Both commands replace the third line of the header to “+” to save disk space.

BWA Alignment bwa mem -M ~/Pathway to indexed reference ~/Pathway to read file 1 ~/Pathway to read file 2 > outputfile.sam

SAM => BAM samtools view -bS Input_file.sam > Output_file.bam

Adding Read Group Information java -jar ~/../../fungi/tools/opt/picard/AddOrReplaceReadGroups.jar I=Input_file.bam O=Output_file_newreadgroups.bam SORT_ORDER=coordinate RGLB=library_name RGPL=sequencing_platform RGPU=sequence_platform_name RGSM=sample_name

Mark Duplicates java -jar ~/../../fungi/tools/opt/picard/MarkDuplicates.jar I=Input_file.bam O=Output_file_dedup.bam Metrics_File=metrics.txt

Index BAM java -jar ~/../../fungi/tools/opt/picard/BuildBamIndex.jar I=Input_file.bam

Quality Check of BAM File java -jar ~/../../fungi/tools/opt/picard/ValidateSamFile.jar I=Input_file.bam

Realigner Target Creator java -jar ~/../../usr/bin/GenomeAnaysisTK -T RealignerTargetCreator -R Indexed_Reference.fasta I=Input_file.bam o=Target_intervals.intervals

Realignment java -jar ~/../../usr/bin/GenomeAnalysisTK -T IndelRealigner -R Indexed_Reference.fasta -I Input_file.bam -targetIntervals Target_intervals.intervals - o=Output_file_realigned.bam

Generating VCF java -jar ~/../../usr/bin/GenomeAnalysisTK -T UnifiedGenotyper -R Indexed_Reference.fasta -I Input_file_1.bam [-I Input_file_2.bam...-I [Input_file_n.bam] -o [Raw_SNPs.vcf] --stand_call_conf [250] --stand_emit_conf [50] -dcov 200 -ploidy [1] --output_mode EMIT_ALL_SITES Figure 4.2: Generating a Variant Call File (VCF). Workflow that follows from aligning paired-end Illumina reads to a reference using BWA MEM, to generating a VCF. The function of this workflow is to generate a VCF from sequence reads that has fewer false positives.

VCF => Tab Delimited SNP File java -jar ~/../../usr/bin/GenomeAnalysisTK -T VariantsToTable -R Indexed_Reference.fasta -F CHROM -GF GT -o [SNPs_table.table]

Tab Delimited SNPs => FASTA perl vcf_tab_to_fasta_alignment.pl -i [SNPs_table.table] -o [SNPs.fasta]

Multiple Sequence Alignment kalign -i [SNPs.fasta] -o [Mulitple_Sequence_Alignment.fasta]

Figure 4.3: Workflow for taking VCF and generating a Multiple Sequence Aligment (MSA). The MSA generated from this workflow was inputted into MEGA6 to generate a neighbor-joining phylogenetic tree.

CBS_1 DVD-161_1 DVD-3_1 DVD-31_1 DVD-s26_1 DVD-s29_1 DVD-s94_1 St_100_1 St_14.01_1 DSA_ERA_1 STD_STB_1 Y4_1 Basic Statistics PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS Per base sequence quality PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS Per sequence quality scores PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS Per base sequence content PASS PASS WARN PASS PASS PASS PASS PASS PASS WARN WARN PASS Per base GC content PASS PASS PASS PASS PASS PASS PASS PASS PASS WARN WARN PASS Per sequence GC content FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL Per base N content PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS Sequence Length Distribution PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS Sequence Duplication Levels WARN WARN WARN WARN WARN PASS PASS PASS PASS FAIL FAIL WARN Overrepresented sequences PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS Kmer Content PASS PASS WARN PASS PASS PASS PASS PASS PASS WARN WARN WARN

CBS_2 DVD-161_2 DVD-3_2 DVD-31_2 DVD-s26_2 DVD-s29_2 DVD-s94_2 St_100_2 St_14.01_2 DSA_ERA_2 STD_STB_2 Y4_2 Basic Statistics PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS

44 Per base sequence quality FAIL PASS PASS PASS PASS FAIL PASS PASS PASS PASS PASS PASS

Per sequence quality scores PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS Per base sequence content PASS PASS WARN PASS PASS PASS PASS PASS PASS FAIL FAIL FAIL Per base GC content PASS PASS PASS PASS PASS PASS PASS PASS PASS FAIL FAIL FAIL Per sequence GC content FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL

Per base N content PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS Sequence Length Distribution PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS Sequence Duplication Levels WARN WARN WARN WARN WARN PASS PASS PASS PASS FAIL WARN WARN Overrepresented sequences PASS PASS PASS PASS PASS PASS PASS PASS PASS WARN WARN WARN

Kmer Content WARN PASS WARN PASS PASS PASS PASS PASS PASS WARN WARN WARN

Table 4.1: FastQC Reports on Sequence Reads. Quality Reports generated by FastQC showing for each read file whether it Passes, Fails, or is near Failing (WARN) each of the parameters (Basic Statistics, Per base sequence quality, Per sequence quality scores, Per base sequence content, Per base GC content, Per base N content, Sequence Length Distribution, Sequence Duplication Levels, Overrepresented sequences, and Kmer Content). Each column in headed by the Isolate name and a 1 or 2 based on which member of the pair the reads belong to.

Isolate GC # Reads N75 N50 N25 MIN MAX AVG Count Genome Originating Location Race DVD-3 54.8 1.10x107 21434 39435 64803 200 177807 14381 2279 32774299Size PotatoHost Canada 2 DVD-31 55.2 1.13x107 18309 35962 62045 94 176929 13334 2414 32188276 Tomato Canada 2 DVD-161 55.3 1.10x107 21321 41021 66014 100 227349 14856 2168 322078083 Potato ON, 2 DVD-s94 55.2 1.16x107 25961 51220 80053 100 222423 17209 1902 32731518 Soil Canada 2 DVD-s26 55 1.20x107 21870 42591 76908 100 241630 14293 2334 33359862 Soil Canada 2 DVD-s29 55.4 1.10x107 24086 43741 74207 82 185691 16595 1931 32044945 Soil Canada 2 St_100 54.2 1.23x107 24119 47422 82729 100 232934 16099 2034 32745366 Soil Belgium 7 45 CBS381_66 54.2 1.29x10 29083 59215 105424 100 323547 18775 1748 32818700 Tomato QC, 1

ST_14_01 54.2 1.10x107 22766 46100 75397 100 195115 15517 2106 32678802 Pistachio CA,Canada 1 GFP2 54 8.53x107 17716 35142 61310 130 212482 12122 2858 34644676 Strawberry USA STD STB 53.9 5.43x107 36989 69147 105001 120 373311 24532 1388 34050416 Strawberry Y4 53.6 5.98x107 15837 36936 64048 100 185152 3991 8890 35479990 Mint VdLs.17 55.9 33900324 Lettuce CA, 2 JR2 36150287 Tomato ON,USA 1 Canada

Table 4.2: Report summary of de novo assemblies. GC content, number of reads used in the assembly (#Reads), N75, N50, N25, shortest contig (MIN), longest contig (MAX), average contig length (AVG),

number of contigs (COUNT), genome size, originating host, sampling location of each isolate, and race for each isolate of V. dahliae.

Isolate nSNPs TiTv Ratio SNPs/1Kb Avg Qual Total 345606 2.31 - 7792.68 CBS 159065 2.14 4.85 13190.58 DVD-161 163535 2.18 5.08 14173.37 DVD-3 165549 2.19 5.05 1408.38 DVD-31 161919 2.18 5.03 14259.15 DVD-s26 165108 2.14 4.95 12870.22 DVD-s29 161020 2.16 5.02 13124.48 DVD-s94 166116 2.18 5.08 14057.86 St_100 212663 2.14 6.49 7798.51 St_14.01 158787 2.15 4.86 13203.08 GFP2 12220 0.98 0.35 16585.12 STD_STB 163043 2.15 4.79 14141.58 Y4 26561 1.68 0.75 11438.81

Table 4.3: Summary of variant discovery among 11 isolates of V. dahliae and the reference, VdLs.17. The number of total SNPs (nSNPs), transition:transversion mutation ration (TiTv Ratio), number of SNPs/1000 base pairs (SNPs/1Kb), and the average PHRED quality score (Avg Qual) are listed for each isolate. SNPs were discovered from a total of 33828453 total loci.

Figure 4.4: Neighbor Joining Tree of V. dahliae isolates. SNP loci discovered by BWA MEM were aligned by Kalign, then inputted into MEGA 6.0 to generate a neighbor-joining tree. Bootstrap values are at each node.

4500 4000 3500 3000 2500 2000 1500 SNP Count 1000 500 0

Supercontig_1.1 Supercontig_1.5 Supercontig_1.8 Supercontig_1.1 Supercontig_1.1 Supercontig_1.1 Supercontig_1.1 Supercontig_1.1 Supercontig_1.2 Supercontig_1.2 Supercontig_1.2 Supercontig_1.2 Supercontig_1.3 Supercontig_1.3 Supercontig_1.3 Supercontig_1.3 Supercontig_1.4 Supercontig_1.4 Supercontig_1.4 Supercontig_1.4 Supercontig_1.5 Supercontig_1.5 Supercontig_1.5 Supercontig_1.6 Supercontig_1.6 Supercontig_1.6 Supercontig_1.7 Supercontig_1.7 Supercontig_1.7 Supercontig_1.8 Supercontig_1.9 Supercontig_1.9 Supercontig_1.9 Supercontig_1.10 Supercontig_1.10 Contig

Figure 4.5: SNP distribution along i genome. Number of SNP loci/10Kb among 13 V. dahliae isolates along the reference genome, VdLs.17.

8 7 6 5 4

Ts/Tv 3 2 1

Supercontig_1.1 Supercontig_1.4 Supercontig_1.8 Supercontig_1.1 Supercontig_1.1 Supercontig_1.1 Supercontig_1.1 Supercontig_1.1 Supercontig_1.2 Supercontig_1.2 Supercontig_1.2 Supercontig_1.2 Supercontig_1.2 Supercontig_1.3 Supercontig_1.3 Supercontig_1.3 Supercontig_1.3 Supercontig_1.4 Supercontig_1.4 Supercontig_1.4 Supercontig_1.5 Supercontig_1.5 Supercontig_1.5 Supercontig_1.5 Supercontig_1.6 Supercontig_1.6 Supercontig_1.6 Supercontig_1.7 Supercontig_1.7 Supercontig_1.7 Supercontig_1.8 Supercontig_1.8 Supercontig_1.9 Supercontig_1.9 Supercontig_1.9

Supercontig_1.10 Supercontig_1.10 Contig

Figure 4.6: Average TiTv ratio per 10Kb of SNP loci among 13 V. dahliae isolates along the reference genome, VdLs.17.

Total SNPs DVD- DVD- DVD- DVD- Total VdLs.17 JR2 Y4 STD_STB St. 100 St. 14.01 GFP2 s94 s29 s26 161 DVD-31 DVD-3 CBS381.66 VdLs.17 0 39776 45304 174851 224466 169529 33014 175471 166007 177301 168508 167694 176943 172089 JR2 39776 0 16020 148806 206688 143241 21126 151402 144888 148736 147949 148300 150547 144713 Y4 45304 16020 0 147031 202439 146798 49119 151235 144658 150434 143819 144862 149396 147382 STD_STB 174851 148806 147031 0 192052 69033 159649 17942 62411 72751 12668 13135 15385 72340 St. 100 224466 206688 202439 192052 0 190173 214993 196044 185776 199935 189428 188780 196489 191581 St. 14.01 169259 143241 146798 69033 190173 0 157941 72514 48898 13633 67887 68872 70616 14473

49 GFP2 33014 21126 49119 159649 214993 157941 0 166390 154487 167208 157883 158920 165705 160389

DVD-s94 175471 151402 151235 17942 196044 72514 166390 0 64152 79953 13393 15619 16077 72756 DVD-s29 166007 144888 144658 62411 185776 48898 154487 64152 0 46362 61634 62819 62580 47398 DVD-s26 177301 148736 150434 72751 199935 13633 167208 79953 48362 0 72320 72929 78326 15585 DVD-161 168508 147949 143819 12668 189428 67887 157883 13393 61634 72320 0 15400 18544 67664 DVD-31 167694 148300 144862 13135 188780 68872 158920 15619 62819 72929 15400 0 15738 70307 DVD-3 176943 150547 149396 15385 196489 70616 165705 16077 62580 78326 18544 15738 0 71445 CBS381.66 172089 144713 147382 72340 191581 14473 160389 72756 47398 15585 67664 70307 71445 0

Table 4.4: Total SNP count shared between isolates of V. dahliae from progressive mauve multiple genome alignment.

Total SNPs 3000000

2500000

2000000

1500000

1000000

500000

0 VdLs.17 JR2 Y4 STD_STB St. 100 St. 14.01 GFP2 DVD-s94 DVD-s29 DVD-s26 DVD-161 DVD-31 DVD-3 CBS381.66

VdLs.17 JR2 Y4 STD_STB St. 100 St. 14.01 GFP2 DVD-s94 DVD-s29 DVD-s26 DVD-161 DVD-31 DVD-3 CBS381.66

Figure 4.7: Total SNPs shared between isolates of V. dahliae from progressive mauve multiple genome alignment.

Figure 4.8: Inferred phylogeny based on gene order rearrangements. Isolate name is given with its originating host to the right. Isolates of race 1 are shown in red, isolates of race 2 are in blue, and isolates where the race in not known are in black.

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