Characterization of Fraxinus Spp. Phloem Transcriptome Thesis

Characterization of Fraxinus spp. Phloem Transcriptome

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Loren J Rivera Vega, B.S.

Graduate Program in Entomology

The Ohio State University

2011

Thesis Committee

Omprakash Mittapalli, Advisor

Pierluigi E Bonello

Daniel A Herms

Abstract

Ash trees (Fraxinus spp.) are widely spread throughout eastern North America and represent an important tree species in urban landscape and natural settings. Since the accidental introduction of the invasive insect pest emerald ash borer (Agrilus planipennis

Fairmaire) millions of ash trees have been killed by this devastating pest. North American species such as black (F. nigra), white (F. americana), green (F. pennsylvanica), and to some extent blue (F. quadrangulata) are susceptible to this pest. However, in Asia, A. planipennis’ natural habitat, the damage to native ash is isolated and only observed in trees under stressed conditions, indicating some level of resistance, presumably due to a shared co-evolutionary history. To date various efforts to contain A. planipennis have been implemented, yet it continues to spread at an alarming rate within the US and

Canada. Despite the high impact status of A. planipennis, there is little information available at the molecular level for any Fraxinus species.

The main objective of this study was to characterize the transcriptome of ash phloem including North American and Manchurian species, which would then lay the foundation for future functional and applied studies. The first part of the study was to describe the phloem transcriptome and predict potential molecular markers using 454 pyrosequencing. A database of more than 50,000 sequences was obtained from a pooled sample of black, green, white, blue and Manchurian ash. The database was profiled using

Blast2GO software in order to annotate the sequences, determine gene ontology (GO), and identify associated metabolic pathways. Also, the expression of eight candidate genes was quantified using real time quantitative PCR (RTqPCR) in three ash species: black, green and Manchurian ash. Finally, more than 1,272 single nucleotide polymorphisms

(SNPs) and 980 microsatellites were predicted.

The information obtained from the first part of the study was used to carry out the second part of the study, which was profiling of housekeeping genes across various ash species. In total, ten housekeeping genes were analyzed in order to identify a reliable reference gene for use in gene expression techniques such as RTqPCR. This analysis identified translation elongation factor alpha (eEF1α) as the most stable gene, and therefore is recommended for current and future ash transcriptomics work.

The third and final part of the study was to compare and contrast constitutive gene expression profiles of black, green and Manchurian ash using RNA-Seq on an Illumina platform. From this study, three more databases were generated for ash, one for each of the three species analyzed. These databases were also profiled using Gene Ontology terms and KEGG pathways, results were similar to those observed in the first part of the study. More than 50,000 SNPs and 5,000 microsatellites were predicted from all three databases combined. Differential analysis revealed approximately 600 genes to be differentially expressed among the ash species out of the 8,691 orthologs used for the analysis. In order to validate the results obtained from these gene expression profiles, eight candidate genes were analyzed using RTqPCR.

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The data generated from these studies using next-generation sequencing strategies has revealed a wealth of genomic information on ash, the target host for A. planipennis.

The results obtained will clearly lay the foundation for future functional studies, ash breeding programs, candidate gene identification, and population genetic studies, as well as simply provide more (comparative) genomic information for deciduous trees.

Dedication

To my friends and family: Those far away who keep me grounded and those close who

keep me going.

Acknowledgements

I would like to thank:

My advisory committee members Dr Omprakash Mittapalli for his constant motivation,

Dr Daniel Herms for all his advices including those that didn‟t involve research

and Dr Enrico Bonello.

Ronald Batallas, Lucia Orantes, Alejandra Claure, and Nelson Davila, for those great first

days as interns during which we shared our hopes and dreams – truly the end of

an era.

The Mittapalli Lab: Priya Rajarapu, Ma Anita Bautista, Binny Bhandary and Praveen

Mamidala for their support both inside and outside of the laboratory.

The Herms Lab: Bryant Chambers, Diane Hartzler, Vanessa Muilenburg, Priya Loess,

and Jamie Imhoff for welcoming me into their lab and always considering me a

part of it.

AGEAP-OSU for their support and guidance, for showing me that despite being far away

from my country as long as a Zamorano is around we‟ll always feel at home.

Los padrinos – the Cañas-Acosta family – for opening the doors of their home and

allowing me to share with them so many special moments, for their invaluable

advices, and mainly for their friendship.

All my friends in Ohio for allowing me to be my crazy self and still love me and for

letting me be a part of their lives.

All my friends in Honduras/Latin America because I was always able to feel their love

and support despite the distance and time.

My amazing family for always supporting and trusting my decisions.

SEEDs Grant Program and USDA-APHIS for the funding provided for this research.

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Vita

2007……………………………………… B.S. Agricultural Science and Production Pan-American College of Agriculture Tegucigalpa, Honduras

2008-2009……………………………….. Research Aide Department of Entomology The Ohio State University, Wooster, OH

2009-present………………………………Graduate Research and Teaching Assistant The Ohio State University, Wooster, OH

Publications

Rivera-Vega L*, Mamidala P*, Koch JL, Mason ME, Mittapalli O. 2011. Evaluation of reference genes for expression studies in ash (Fraxinus spp). Plant Molecular Biology Reporter. DOI 10.1007/s11105-011- 0340-3. Bai X*, Rivera-Vega L*, Mamidala P, Bonello P, Herms DA, Mittapalli O. 2011. Transcriptomic signatures of ash (Fraxinus spp) phloem. PLoS ONE 6(1): e16368.

Mittapalli O, Rivera-Vega L, Bhandary B, Bautista M, Mamidala P, Michel A, Shukle R, Mian R. 2011. Cloning and characterization of mariner-like elements in the soybean aphid, Aphis glycines Matsumura. Bulletin of Entomological Research 12:1-8

Rivera-Vega L and Mittapalli O. 2010. Molecular characterization of mariner–like elements in emerald ash borer, Agrilus planipennis (Coleoptera, Polyphaga). Archives of Insect Biochemistry & Physiology. 74(4): 205-216.

Bhandary B, Rajarapu SP, Rivera-Vega L, Mittapalli O. 2010. Analysis of Gene Expression in Emerald Ash Borer (Agrilus planipennis) Using Quantitative Real Time-PCR. Journal of Visualized Experiments (39).

Saballos A, Vermerris W, Rivera L and Ejeta G. 2008. Allelic Association, Chemical Characterization, and Saccharification Properties of brown midrib Mutants of Sorghum (Sorghum bicolor (L.) Moench). Bioenergy Research. 1: 193-204 Field of Study

Major Field: Entomology

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Table of Contents

Abstract ...... ii Dedication ...... v Acknowledgements ...... vi Vita ...... viii Table of Contents ...... ix List of Tables ...... xii List of Figures ...... xiv

Chapter 1 ...... 1 Introduction ...... 1 1.1 Framework ...... 1 1.2 Research objectives ...... 7 1.3 References ...... 8

Chapter 2 ...... 15 Transcriptomic Signatures of Ash (Fraxinus spp.) Phloem ...... 15 2.1 Abstract ...... 15 2.2 Introduction ...... 16 2.3 Materials and Methods ...... 18 2.3.1 Sample Collection and RNA extraction ...... 18 2.3.2 cDNA library construction ...... 19 2.3.3 Roche 454 sequencing ...... 19 2.3.4 Bioinformatic Analysis ...... 20 2.3.5 Gene Mining and Quantitative Real Time PCR ...... 21 2.3.6 Microsatellites Analysis ...... 22 2.3.7 Data Deposition ...... 22

2.4 Results and Discussion ...... 23 2.4.1 Transcriptome Analysis ...... 23 2.4.2 Comparative analysis ...... 24 2.4.3 Gene Ontology ...... 25 2.4.4 Metabolic Pathways ...... 26 2.4.5 Protein Domains...... 29 2.4.6 Genes of Interest ...... 31 2.4.7 Molecular Markers ...... 35 2.5 Conclusions ...... 37 2.6 References ...... 38

Chapter 3 ...... 53 Evaluation of reference genes for expression studies in ash (Fraxinus spp.) ...... 53 3.1 Abstract ...... 53 3.2 Introduction ...... 54 3.3 Materials and methods ...... 55 3.4 Results and discussion ...... 57 3. 1 GeNorm Analysis...... 58 3.4.2 NormFinder Analysis ...... 59 3.5 References ...... 61

Chapter 4 ...... 63 Gene Expression Profile of Three Ash species (Fraxinus spp) using RNA-Seq...... 63 4.1 Abstract ...... 63 4.2 Introduction ...... 64 4.3 Materials and Methods ...... 66 4.3.1 Samples and RNA isolation ...... 66 4.3.2 cDNA library construction and Sequencing ...... 67 4.3.3 De novo assembly ...... 67 4.3.4 Functional annotation...... 68

4.3.5 Ortholog Identification and Expression analysis ...... 69 4.3.6 Gene validation and real time quantitative PCR (RT-qPCR) ...... 69 4.4 Results and Discussion ...... 71 4.4.1 Functional Comparisons ...... 72 4.4.2 Differential expression analysis ...... 73 4.4.3 Validation ...... 75 4.4.4 Molecular marker prediction ...... 77 4.5 References ...... 80

Chapter 5 ...... 86 Conclusions ...... 86 5.1 Summary ...... 86 5.2 Future Studies ...... 87 5.3 References ...... 89

Bibliography ...... 90 Appendices ...... 114 Appendix A: Supplementary Table 4.1-Differentially expressed orthologs ...... 114 A.1 F. mandshurica vs F. nigra ...... 114 A.2 F. mandshurica vs F. pennsylvanica ...... 122 A.3 F. nigra vs F. pennsylvanica ...... 140 Appendix B: Reactive Oxygen Species (ROS) in Fraxinus spp...... 153 B.1 Ascorbate Peroxidase ...... 153 B.2 Monodehydroascorbate reductase ...... 154 B.3 Ferritin...... 155 B.4 Dehydroascorbate Reductase (DHAR) ...... 156 B.5 Peroxiredoxin ...... 157 B.6 Thioredoxin ...... 158 B.7 Manganese Superoxide Dismutase ...... 159

List of Tables

Table 2. 1 Putative defense pathways identified in the phloem of Fraxinus mandshurica, F. americana, F. pennsylvanica, F. nigra, and F. quadrangulata pooled phloem database...... 28

Table 2. 2 Genes of interest recoverd from the Fraxinus mandshurica, F. Americana, F. pennsylvanica, F. nigra and F. quadrangulata phloem pooled transcriptomic database...... 31

Table 3. 1Primer sequences of the six potential reference genes for Fraxinus spp. The abbreviations stand for Cyp:cyclophilin, eEF1β: translation elongation factor beta, G6PD: glucose-6-phosphate dehydrogenase, RPL13: Ribosomal protein L13, eEF1α: translation elongation factor 1alpha and E3upl: ubiquitin ligase ...... 57

Table 3. 2Fraxinus reference genes ranking according to NormFinder and geNorm softwares. Numbers in parenthesis indicate stability values, smaller values indicate higher stability. Abbreviations stand for Cyp: Cyclophilin, eEF1β: translation elongation factor β, G6PD: Glucose-6-phosphate dehydrogenase, RPL13: Ribosomal protein L13, eEF1α: translation elongation factor 1α, E3upl: E3 ubiquitin protein ligase...... 60

Table 4. 1Primer sequences for eight genes used in real time quantitative PCR (RTqPCR) for RNA-Seq validation...... 70

Table 4. 2Statistics of the assembly of the transcriptomes of Fraxinus mandshurica (Manchurian ash), F. nigra (black ash) and F. pennsylvanica (green ash) phloem...... 71

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Table 4. 3Top protein domains in Fraxinus mandshurica (Manchurian ash), F. nigra (black ash) and F. pennsylvanica (green ash) phloem databases...... 73

Table 4. 4Top ten metabolic pathways in Fraxinus mandshurica (Manchurian ash), F. nigra (black ash) and F. pennsylvanica (green ash) phloem databases...... 73

Table 4. 5Candidate genes potentially involved in response to biotic and abiotic stimulus in both resistant (Fraxinus mandshurica) and susceptible (F. nigra and F. pennsylvanica) phloem according to gene ontology (GO) annotation...... 74

Table 4. 6Comparison of single nucleotide polymorphism types among Fraxinus mandshurica (Manchurian ash), F. nigra (black ash) and F. pennsylvanica (green ash) phloem...... 78

Table 4. 7Summary of microsatellite repeats predicted in Fraxinus mandshurica (Manchurian ash), F. nigra (black ash) and F. pennsylvanica (green ash) phloem...... 79

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List of Figures

Figure 2. 1 (A) Summary of Fraxinus phloem database which includes F. mandshurica, F. americana, F. pennsylvanica, F. nigra, and F. quadrangulata phloem transcriptomic sequences. The singleton sequences are represented by clear bars and the contig sequences by shaded bars (insert). (B) A pie chart showing species distribution of the top BLAST hits of the Fraxinus phloem database to various plant species. (C) A venn diagram showing the comparisons of the sequences from Fraxinus phloem database with the genome sequences of Arabidopsis thaliana and Populus trichocarpa...... 23

Figure 2. 2 Depiction of Gene ontology (GO) terms for the transcriptomic sequences of Fraxinus mandshurica, F. americana, F. pennsylvanica, F. nigra, and F. quadrangulata phloem database (A) Biological process, (B) Cellular component, and (C) Molecular functions ...... 27

Figure 3. 1Average cycles to threshold (Ct) values for the six candidate reference genes used for all the Fraxinus spp. samples.*PY:phloem from young trees (<5 yrs old), PO: phloem from old trees (>5 yrs old), LY:leaves from young trees, LO: leaves from old trees, IL: immature leaves, ML: mature leaves, SH: shoots, RT: roots, Above: phloem samples above girdling, Below: phloem samples below girdling. The acronyms for the genes stand for: Cyp: Cyclophilin, eEF1β: translation elongation factor β, G6PD: Glucose-6-phosphate dehydrogenase, RPL13: Ribosomal protein L13, eEF1α: translation elongation factor 1α, E3upl: E3 ubiquitin protein ligase...... 58

Figure 4. 1 Venn diagram of Fraxinus mandshurica (Manchurian ash), F. nigra (black ash) and F. pennsylvanica (green ash) phloem ortholog comparisons. Sequences similar at an E-value cutoff of 1e-10 at the amino acid level using tBLASTx searches were considered orthologs...... 72

Figure 4. 2Expression comparisons between RNA-Seq and real time quantitative PCR in eight genes for a gene expression comparison between (A) resistant Manchurian (Fraxinus mandshurica) and susceptible black (F. nigra)ash and (B) resistant Manchurian and susceptible green (F. pennsylvanica) ash. Fold change was calculated by dividing the expression level of resistant by susceptible ash; if expression above X-axis then resistant ash was higher than susceptible, if expression below X-axis then susceptible ash was higher than resistant. The genes analyzed were: an allergen, an F-box protein, a Zinc finger CCCH transcription factor,

xiv phenylalanine ammonia lyase (PAL), suppressor of G2 allele of SKP1 (Sgt1), transcinnamante-4- monooxygenase (TC4M), universal stress protein (USP) and a WRKY transcription factor...... 76

Chapter 1

Introduction

1.1 Framework

Ash (Fraxinus spp) is a genus of deciduous trees from the Oleaceae family widely spread in eastern North America, parts of Canada and western United States. There are approximately 43 species of Fraxinus, out of which twenty can be found in North

America [Wallander, 2008]. White ash (F. americana) was probably the most commercially used species for manufacturing of products like tool handles, baseball bats, flooring, and furniture [MacFarlene and Meyer, 2005]. Green ash (F. pennsylvannica) is probably the most widely distributed of all North American ash [Fowells, 1965]. It is resistant to abiotic stress such as drought [Abrams et al, 1990] which has also made it a preferred ash species for plantation in urban areas [MacFarlane and Meyer, 2005].

Currently, it is considered that the survival of this genus in North America is threatened by the unstoppable spread of an invasive insect pest – the emerald ash borer (Agrilus plannipenis Fairmaire) [Poland and McCullough, 2006].

A. planipennis is a buprestid or metallic wood boring beetle native to northeastern

Asia. Among the countries where it has been found are China, Japan, Korea, Mongolia,

Russia and Taiwan. In Asia, its host range can be both native Fraxinus spp. such as F.

1 chinensis, F. mandshurica and F. rhynchophylla and introduced ash such as F. pennsylvanica and F. velutina, plus Juglans mandshurica, Pterocarya rhoifolia and

Ulmus davidiana [Liu et al 2003]. A. planipennis attacks all North American ash species it has encountered thus far, including green, white, black, and blue ash [Anulewicz et al,

2007; Rebek et al, 2008]. In contrast to the significant impact caused in North America, the incidence of damage in Asia has been reported to be isolated and limited to trees under stress. This difference in resistance has been attributed to defense mechanism developed by Asian species to A. planipennis in the course of their co-evolutionary history [Rebek et al, 2008]. It has been hypothesized that the mode of resistance against

A. planipennis in Asian ash could be due to induction and/or biosynthesis of phenolic compounds such as hydroxycoumarins and phenylethanoids [Eyles et al, 2007]. A rapid rate of wound browning, high soluble protein concentration, low trypsin inhibitor activities and peroxidase activity has also been identified in resistant ash [Cipollini et al,

2011].

Agrilus planipennis adults feed on foliage for a week or two. Then, mating occurs and female beetles lay about 50-100 eggs individually in bark crevices, cracks and or surface of trunk. Eggs hatch in about 10 days. The larvae bore through the bark and feed on the phloem and vascular cambium excavating serpentine galleries just beneath the bark [Wei et al, 2007]. This leads to girdling and ultimately killing of trees within 1-4 years post colonization [Rebek et al, 2008]. Late instars complete development prior to overwintering in the fall and pupate in the spring of the following year. Adult emergence occurs between May and August [Wei et al, 2007]. The devastation of this genus can have significant impact in ecosystems due to the formation of canopy gaps which can

2 have an impact on community structure as well as alter the microenvironment in the forest and impact nutrient cycles [Gandhi and Herms, 2010].

The impact of A. planipennis to North American natural and urban landscape is not only of ecological importance but also has significant economic implications. Losses in landscape value for ash trees in Ohio alone will be approximately $0.8-3.4 billion, assuming complete loss [Sydnor et al, 2007]. Recent predictions indicate that if the spread continues at the current pace, the mean discounted cost of treating, removing and replacing ash trees in developed communities will be approximately $10.7 billion

[Kovacs et al, 2010].

Agrilus planipennis continues to spread throughout the United States and Canada at an alarming rate [APHIS, 2011]. Despite its current and potential impact, little information on the molecular biology of either the pest or its host is available – specifically DNA (genetics and genomics) and RNA (transcriptomics). This lack of knowledge not only hinders our understanding of ash but also prevents the identification of genetic factors that could be vital to develop resistance against A. planipennis.

Transcriptomics, or the study of RNA molecules in a particular tissue or organism, has been revolutionized by the introduction of next generation sequencing

(NGS) [Morozova et al, 2009]. Sanger sequencing method is considered as first generation technology, techniques such as 454 pyrosequencing (Roche Diagnostics),

Illumina Solexa sequencing (Illumina, Inc.), SoLiD (Applied Biosystems), etc. are usually referred to as NGS. One of the most significant advances offered by NGS is the generation of high volumes of data at a relatively low cost [Metzker, 2010]. Also, it is

3 possible to identify and quantify rare transcripts without knowledge of a particular gene, transcriptome profiling, single nucleotide polymorphism discovery, mutation mapping, and alternative splicing identification, etc [Bentley, 2006; Novaes et al, 2008; Weber et al, 2007; Lister et al, 2009]. Another of the many advantages of using techniques such as these is that it allows for studies in non model organisms, for which little to no molecular knowledge exists (e.g. Cucumis sativus [Guo et al, 2010], Castanea dentata [Barakat et al, 2009], Pinus contorta [Parchman et al, 2010], Persea americana [Wall et al, 2009], etc.). Such techniques are invaluable to obtain as much information as possible in a cost and time effective manner for a non-model system like ash-emerald ash borer.

454 pyrosequencing was the first NGS released to the market. It uses an in vitro amplification method known as emulsion PCR, where a single fragment is amplified in a bead. Next, these beads are sequenced using a pyrosequencing reaction. This is a sequencing-by-synthesis method that measures the release of pyrophosphate. Solutions of dNTPs (A,T,G,C) are added one at a time and whenever a complementary base is incorporated a light is produced. A program with the order in which correct nucleotides were incorporated is used to determine the sequence [Morozova and Marra, 2008;

Margulies et al, 2005]. 454 pyrosequencing provides longer reads than most NGS platforms and has faster runs, nevertheless, the reagents are costly and it has a high error rate [Metzker, 2010].

Illumina (also known as SOLEXA) sequencing platform uses a flow cell rather than a bead to fix the transcripts for sequencing. Here a single fragment is attached to the surface using adapters. The molecule then bends over creating a “bridge” and this is used as template for amplification creating clusters of the same fragment. These clusters are

4 then sequenced using a sequence-by-synthesis technique in which different dNTPs are labeled with different colors to distinguish among them. The sequence is deduced by reading the color that illuminates after each base is added [Mortazavi and Marra, 2008;

Bennett, 2004]. This platform is perhaps the most used although it has low multiplexing capabilities (sequencing of several samples on same flow cell) and reads are shorter than the ones obtained through pyrosequencing [Metzker, 2010].

RNA-Seq refers to the comparison and contrast of transcriptomes through deep sequencing. This technique allows for identification of differentially expressed genes, gene variants, identification of rare transcripts, etc. and has become an alternative for microarrays. Unlike microarrays prior transcriptomic knowledge of the organism is suggested but not required. Also, microarrays are more limited to identify isoforms, they require more starting material (RNA) and the costs for analyzing large genomes is relatively higher[Wang et al 2009]. Nevertheless, because microarrays have been around for longer, the inherent biases and complications of the techniques have already been studied and thus accounted for in the analyses of the results. In RNA-Seq, these biases are still being identified (i.e. gene expression and gene ontology assignments differences due to transcript length) [Young et al, 2010; Oshlack and Wakefield, 2009; Hansen et al,

2010; Roberts et al, 2011; Wu et al, 2011].

One of the challenges of analyzing such high throughput data like the ones obtained with NGS is how to identify relevant information for the system of interest. A bioinformatic resource that has helped with assigning higher-order functional meaning to cells or organisms based on genetic information is the Kyoto Encyclopedia of Genes and

Genomes or KEGG [Kanehisa et al, 2004]. KEGG consists of nineteen databases for

5 genomic, chemical and network (metabolic) information including pathways maps, functional hierarchies, diseases, drugs, enzymes, metabolites, etc [Kanehisa et al, 2008].

Genes can be mapped to higher functions using these databases. KEGG databases are quite comprehensive, are curated by hand, and can be linked to other useful databases such as NCBI, Gene Ontology (GO), SwissProt, etc. [Bauer-Mehren et al, 2009;

Viswanathan et al, 2008]. KEGG uses information from multiple organisms in order to create the reference map of pathways, this allows for maps to usually be larger, although it is possible to isolate which reactions occur in a given organism [Green and Karp,

2006]. In many cases this can lead to redundancy, for example a gene that maps to

“biosynthesis of terpenoids” will also map to “biosynthesis of phenylpropanoids” given that the first can be considered part of the latter.

Another resource is the use of Gene Ontology (GO) to annotate sequences based on standardized terms (ontologies). The GO project was created by the GO consortium in an effort to provide structure, controlled vocabularies and classifications for genes, gene products and sequences freely available for the scientific community. Ontologies can be part of three main groups: molecular function (MF), biological process (BP), and cellular component (CC). MF describes activities such as catalytic and binding activities at the molecular level. These terms represent the activities but not where, when or in what context they take place. BP describes the goal accomplished by one or more molecular functions (i.e. defense). CC indicates locations at subcellular levels [GOConsortium,

2004]. GO annotations are widely used to identify which biological processes, functions or locations are significantly over or under represented in a group of genes [Rhee et al,

2008]. A simple count of terms is usually the most common way of representing this

6 data; however, statistics that account for biases (i.e. probability of picking a gene annotated to a specific term because the term is highly present in the reference) can provide the most accurate and reliable information. Software like GOseq account for these biases [Young et al, 2010].

A technique widely used in transcriptomic studies is the real time quantitative polymerase chain reaction (RTqPCR), a variation to Mullis PCR [1987]. This type of

PCR allows for the quantification of either relative or absolute quantities of nucleic acids in a sample. RTqPCR amplifies a specific target monitoring the amplification using fluorescent dyes. The number of cycles required to reach a threshold is calculated and this correlates with the initial amount in the sample [Valasek and Repa, 2005]. Some of the features that have made RTqPCR a widely used tool include its rapidity to obtain results, sensitivity, specificity, and no need for post PCR manipulation [Gachon et al,

2004]. Nevertheless, similar to other PCR variations, there could be potential inhibitors in the samples that might affect results, a stringent criterion for primer design is required and proper sample handling is primordial. RTqPCR has been used for gene expression studies, validation of DNA microarray results, identification of mutations, bacterial counts, etc [Morey et al, 2006; Matsuki et al 2004; Bai et al, 2004].

1.2 Research objectives

The overarching objective of this thesis was to characterize the transcriptome of ash (Fraxinus spp), including North American and Asian species, in order to learn the genetic makeup of ash phloem, which would then lay the foundation for future functional

7 and applied studies. This overall objective was achieved through the following specific objectives:

1. Describe the phloem transcriptome of Fraxinus spp. and predict potential molecular markers using 454 pyrosequecing technology (Chapter 21).

2. Identify a reliable set of reference genes for gene expression studies in ash

(Chapter 32).

3. Compare and contrast constitutive gene expression profiles of black, green and

Manchurian ash using RNASeq (Chapter 4).

1.3 References

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Chapter 2

Transcriptomic Signatures of Ash (Fraxinus spp.) Phloem

2.1 Abstract

Ash (Fraxinus spp.) is a dominant tree species throughout urban and forested landscapes of North America (NA). The rapid invasion of NA by emerald ash borer

(Agrilus planipennis), a wood-boring beetle endemic to eastern Asia, has resulted in the death of millions of ash trees and threatens billions more. Larvae feed primarily on phloem tissue, which girdles and kills the tree. While NA ash species including black (F. nigra), green (F. pennsylvannica) and white (F. americana) are highly susceptible, the

Asian species Manchurian ash (F. mandshurica) is resistant to A. planipennis perhaps due to their co-evolutionary history. Little is known about the molecular genetics of ash.

Hence, we undertook a functional genomics approach to identify the repertoire of genes expressed in ash phloem. Using 454 pyrosequencing we obtained 58,673 high quality ash sequences from pooled phloem samples of green, white, black, blue and Manchurian ash.

Intriguingly, 45% of the deduced proteins were not significantly similar to any sequences in the GenBank non-redundant database. KEGG analysis of the ash sequences revealed a high occurrence of defense related genes. Expression analysis of early regulators potentially involved in plant defense (i.e. transcription factors, calcium dependent protein

15 kinases and a lipoxygenase 3) revealed higher mRNA levels in resistant ash compared to susceptible ash species. Lastly, we predicted a total of 1,272 single nucleotidepolymorphisms and 980 microsatellite loci, among which seven microsatellite loci showed polymorphism between different ash species. The current transcriptomic data provide an invaluable resource for understanding the genetic make-up of ash phloem, the target tissue upon which A. planipennis feeds. These data along with future functional studies could lead to the identification/characterization of defense genes involved in resistance of ash to A. planipennis, and in future ash breeding programs for marker development.

2.2 Introduction

Ash (Fraxinus) is a dominant tree genus in many urban and forest landscapes of

North America (NA) [MacFarlane and Meyer, 2005; Raupp et al, 2006]. The emerald ash borer (Agrilus planipennis Fairmaire, EAB), which is indigenous to eastern Asia, has killed millions of ash trees since its accidental introduction to NA, primarily in the midwestern United States and southeastern Ontario [Herms et al, 2004; Poland and

McCullough, 2006]. Larvae feed on phloem and outer xylem of trees of all sizes, girdling the tree and ultimately killing it within 1–4 years after symptoms become apparent

[Herms et al, 2004; Poland and McCullough, 2006]. Black (F. nigra Marshall), green (F. pennsylvanica Marshall), and white ash (F. americana L.) are known to be highly susceptible [Smith, 2006], while blue ash (F. quadrangulata Michx) appears to be less preferred [Anulewicz et al, 2007]. If the pattern of invasion continues, A. planipennis has

16 the potential to decimate ash throughout NA with substantial economic and ecological impact [Kovacs et al, 2009; Gandhi and Herms, 2010].

Conversely, A. planipennis is not reported to be a major pest in Asia, where

Manchurian ash (F. mandshurica Rupr) is a primary host [Yu, 1992]. In a common garden experiment, Manchurian ash was found to be much more resistant to A. planipennis than were NA green and white ash, perhaps by virtue of the co-evolutionary history shared by A. planipennis and Manchurian ash [Rebek et al, 2008]. Phloem tissue of Manchurian ash was found to have high constitutive concentrations of phenolic-based hydroxycoumarins, phenylethanoids and calceloariosides, which may contribute to its resistance to A. planipennis [Eyles et al, 2007].

Second generation sequencing technologies such as 454 pyrosequencing have been applied to a wide variety of studies like transcriptome sequencing, single nucleotide polymorphism (SNP) discovery, mutation mapping, alternative splicing identification etc.

[Bentley, 2006, Novaes et al, 2008; Weber et al, 2007; Lister et al, 2009]. In particular, gene discovery via transcriptome analysis has greatly helped in genomic analysis of several non-model organisms including plants viz., Cucumis sativus [Guo et al, 2010],

Eucalyptus grandis [Novaes et al, 2008], Castanea dentata and C. mollisima [Barakat et al, 2009], and Pinus contorta [Parchman et al, 2010]. Roche 454 GS FLX Titanium is a high throughput sequencing platform that makes it possible to generate massive amounts of information in a short period of time with unprecedented high sequencing depth and low cost [Moore et al, 2006]. The generated expressed sequenced tags (ESTs) databases are invaluable for gene mining and annotation [Wicker et al, 2006; Cheung et al, 2008;

Seki et al, 2002; Emrich et al, 2007; Mao et al, 2008], phylogenetic analysis [Nishiyama

17 et al, 2003], discovery of molecular markers [Gonzalo et al, 2005] and expression analysis [Barbazuk et al, 2007].

Given the status of A. planipennis as an aggressive pest of NA ash trees, we undertook a functional genomics approach to identify the repertoire of genes expressed in phloem tissue of different ash species including green, white, black, blue, and

Manchurian ash. This study will enable us to identify genes that are potentially involved in A. planipennis resistance of Manchurian ash, and to characterize the genetic makeup of ash phloem for future studies. Results stemming from this study could be used in future ash targeted breeding programs and increase fundamental understanding of interaction between ash trees and wood-borers such as A. planipennis.

2.3 Materials and Methods

2.3.1 Sample Collection and RNA extraction

Two 10 mm in diameter phloem plugs from un-infested (by A. planipennis) green

(F. pennsylvanica cv Patmore, Cimmaron, Summing), white (F. americana cv Sparticus,

Autumn Purple, Autumn Applause and seedling), black (F. nigra cv Fallgold and seedlings), blue (F. quadrangulata seedlings) and Manchurian ash (F. mandshurica seedlings) were collected in February 2009 from a common garden established at Novi,

MI. The trees sampled did not have D-shaped exit holes and/or vertical splits on the trunk which are indicators of EAB infestation [Poland and McCullough, 2006]. At least three different trees were sampled per species and the phloem plugs were immediately wrapped in aluminum foil and stored in liquid nitrogen. Approximately 70 mg of phloem tissue

(phloem plugs homogenized in liquid nitrogen) per species was used for RNA extraction.

Total RNA was extracted using Trizol® Reagent (Invitrogen, Carlsberg, CA) following manufacturer‟s protocol and stored at -80oC until further use.

2.3.2 cDNA library construction

The RNA extracted from the five Fraxinus species described above was aliquoted and pooled to construct a cDNA library. RNA isolated from different ash species was pooled in order to capture a diverse population of transcripts. Further, pooling of the

RNA samples represents a cost effective transcriptomic approach to build an EST database for closely related species of a non-model organism. A SMART cDNA library construction kit (Clontech, Mountain View, CA) was used following manufacturer‟s protocol with modifications according to protocol followed at Purdue Genomics Facility: i) A modified CDSIII/3‟ primer (5‟-TAG AGG CCG AGG CGG CCG ACA TGT TTT

GTT TTT TTT TCT TTT TTT TTT VN-3‟; PAGE purified) and SuperScript II reverse transcriptase (Invitrogen, Carlsberg, CA) were used for first-strand cDNA synthesis, ii) cDNA size fractionation was excluded and final products were cleaned and eluted using a

QIAquick PCR purification kit (Qiagen, Valencia, CA).

2.3.3 Roche 454 sequencing

cDNA was sheared by nebulization and DNA fragments of approximately 500–

800 bp were isolated by agarose gel electrophoresis and subsequent extraction. The isolated DNA was blunt ended, ligated to adapters and immobilized on beads. Single stranded DNA was later isolated from these beads. The isolated library was subjected to

Quality Control using RNA 6000 (Agilent Technologies). Concentration and ligation of

19 adapters were estimated using quantitative real-time PCR (qPCR). The emPCR reactions were performed to amplify a single template onto a single sequencing bead. One-quarter of a pico-titer plate was sequenced at the Purdue Genomics Core Facility (West

Lafayette, IN) using the GS FLX Titanium chemistry (Roche Diagnostics, Indianapolis,

IN).

2.3.4 Bioinformatic Analysis

The 454 transcriptome reads were assembled using Newbler software package

(Roche Diagnostics) after the removal of adapter sequences. To achieve better consistency, the contigs and singletons were renamed in the format of

„„ASH454ONE000001‟‟ where „„ASH‟‟ stands for the ash genus, „„454‟‟ for 454 sequencing technology, „„ONE‟‟ for the first trial, and „„000001‟‟ for an arbitrarily assigned number. The ash transcriptome sequences were annotated by searching against

GenBank non-redundant database using the BLASTx algorithm [Altschul et al, 1990].

Also, the sequences were compared to the protein sequences of A. thaliana in TAIR9 release from The Arabidopsis Information Resource (http://www.arabidopsis.org/) and

P.trichocarpa v1.1 (http://genome.jgi-psf.org/Poptr1_1/Poptr1_1.home.html) using

BLASTx algorithm. Protein domains were identified by searching against the Pfam database release 24.0 [Cogill et al, 2008] using HMMER v3 program [Eddy, 1998]. The

Blast2GO software [Conesa et al, 2005; Gotz et al, 2008] was used to predict the functions of the sequences, assign Gene Ontology terms, and predict the metabolic pathways in Kyoto Encyclopedia of Genes and Genome (KEGG) [Kanehisa et al, 2008;

Kanehisa et al, 2006; Kanehisa and Goto, 2000]. Microsatellite markers were identified

20 using the Msatfinder version 2.0.9 program [Thurston and Field, 2005]. SNPs in the library were predicted using gsMapper software (Roche Diagnostics) with an arbitrary criterion of at least 4 reads supporting the consensus or variant.

2.3.5 Gene Mining and Quantitative Real Time PCR

The ash transcriptome database was mined for genes potentially involved in plant defense. The constitutive gene expression profiles of potential early regulators:

CDPK349, CDPK361, MYB, LOX, WRKY and ERF were analyzed using qPCR. cDNA was synthesized from green, black and Manchurian ash using a SuperScriptTM First-

Strand synthesis kit (Invitrogen, Carlsberg, CA) following manufacturer‟s protocol. We selected these three ash species to include one genetically close to Manchurian (black ash) and a species distantly related (green ash) which show different levels of susceptibility to A. planipennis [Anulewicz et al, 2007; Smith, 2006;Wallander, 2008].

Primers were designed using Beacon Designer 7 software (Supplementary Table 2.73).

The cycling parameters were 95oC for 5 min followed by 39 cycles of 95oC for 10 s and

60oC for 30 s ending with a melting curve analysis (65oC to 95oC in increments of 0.5oC every 5 s) to check for nonspecific product amplification. Relative gene expression was analyzed by the 2-∆∆CT method [Livak and Schmittgen, 2001]. An ash glucose-6- phosphate dehydrogenase (G6PD) was used as the internal reference gene, which has been previously shown to serve as a good internal control in plants [Jian et al, 2008].

3 For Supplementary material in this chapter refer to Bai et al 2011.

2.3.6 Microsatellites Analysis

Samples from eight individual trees of green, white and Manchurian ash were collected from the U.S. Forest Service, Northern Research station experimental plot

Delaware, OH. Genomic DNA was extracted using E.Z.N.A. DNA kit (Omega Bio-Tek,

Northcross, GA). Primers were designed for 25 of the predicted microsatellite markers of which only seven were used for genotyping (Supplementary Table 2.8). Amplifications were performed in 10 µl reactions. Each reaction contained 5 µl of 2X-Failsafe PCR mix

(Epicentre Biotech, Madison, WI), 0.5 U Taq polymerase, 2 pmol reverse primer, 4 pmol modified forward primer (M13 sequence at 5‟ end) and 10 ng of DNA. M13-tagging protocol was followed using 4 pmol of M13 fluorescently-tagged primer (5‟-CAC GAC

GTT GTA AAA CGA C-3‟) [Schuelke, 2000]. Thermocycling conditions were as follows: 94oC for 5 min, 35 cycles of 94oC for 20 s, 59oC for 20 s, 72oC for 30 s followed by eight cycles of 94oC for 30 s, 53oC for 15 s and 72oC for 30 s with a final extension at

72oC for 10 minutes [Michel et al, 2009]. PCR products were genotyped using Beckman-

Coulter CEQ8800XL (Fullerton, CA) at the Molecular and Cellular Imaging Center

(OARDC, Wooster, OH). Alleles were determined using CEQ Fragment Analysis software (Beckman Coulter, Inc., Indianapolis, IN).

2.3.7 Data Deposition

The Roche 454 reads of Fraxinus species were submitted to NCBI Sequence Read

Archive under the accession number of SRA020745.3

2.4 Results and Discussion

2.4.1 Transcriptome Analysis

So far, one cDNA library has been developed for F. excelsior (European ash;

NCBI database), and no EST sequences were available in public databases for NA and

Manchurian ash as of October, 2010. The 454 pyrosequencing has made possible genomic studies in non-model organisms because it overcomes the limitations of conventional Sanger sequencing [Parchman et al, 2010]. Our pyrosequencing study contributes a significant number of ESTs for future functional genomic studies in ash, yielding 203,718 total reads and 63,096,022 bases from which 79% and 60% were aligned respectively with an inferred read error of 2.21%. Assembled contigs had an average size of 649 bp with the largest contig being 5,662 bp. The singletons had an average size of 329 bp with the largest being 964 bp. Overall, we obtained 58,673 high quality ash sequences totaling 23,580,430 bp (Figure 2.1a). To our knowledge, this is the first comprehensive study on the transcriptome of ash phloem.

A B C

A sequence similarity search was done using BLASTx algorithm. This analysis revealed that 45% of the ash transcriptomic sequences had no significant matches (E value cutoff of 1e-5) to protein sequences found in the GenBank nr database. Ninety- nine percent of the sequences with significant matches matched to plant sequences

(Figure 2.1b), out of which 41.5% matched with Vitis vinifera L., 16.3% with Populus trichocarpa (Torr. & Gray) and 16.9% with Ricinus communis L. In our dataset, 9 sequences matched to viral sequences, 18 to artificial sequences, and 42 to bacterial sequences. These were excluded from further analysis due to possible contamination.

2.4.2 Comparative analysis

The ash transcriptomic sequences were compared to protein sequences of the model plant Arabidopsis thaliana (Brassicaceae) and black cottonwood P. trichocarpa

(Salicaceae) (also known as western balsam poplar or California poplar). These two species were chosen as they represent model plant systems whose genomes have been sequenced. Of the total 58,604 ash sequences, 24,980 (42.6%) had no significant similarity to any protein identified within the genomes of A. thaliana or P. trichocarpa

(Figure 2.1c). Similar observations of species specific transcript sequences were observed in other transcriptomic studies and were attributed to the presence of novel sequences or transcripts of 5‟ and 3‟ untranslated regions or genes with homologs in other species whose biological functions are not yet assigned. [Mittapalli et al, 2010; Lian et al, 2008;

Faara et al, 2008]. About 2,634 (4.5%) sequences were shared between P. trichocarpa and Fraxinus spp., but not with A. thaliana, suggesting that they are potential tree-

24 specific sequences. Only 809 (1.4%) sequences were shared between A. thaliana and

Fraxinus spp (Supplementary Table 2.1). There were 30,182 sequences (51.5%) that were shared among all three plant species under comparison.

Comparative genomics explore similarity with transcriptomes of other species, which can reveal species specific details and define genes which are conserved or diverging [Quesada et al, 2008; Caicedo and Purugganan, 2005]. For such species functional and comparative genomics is possible upon obtaining a good EST database.

2.4.3 Gene Ontology

The derived Fraxinus phloem transcripts were assigned to three functional groups based on Gene Ontology (GO) terminology: biological process, molecular function and cellular component (Supplementary Table 2.2). The software assigned 1,248 biological process terms to 19,958 transcripts (Figure 2.2a), 396 cellular component terms to 17,977 transcripts (Figure 2.2b), and 1,386 molecular function terms to 24,294 transcripts

(Figure 2.2c). The most represented biological process terms were related to development

(36.87%) and carbon utilization (35%). Finally, the majority of terms represented in molecular function were for binding (49.57%) and catalytic activity (36.24%) suggesting a high degree of basal metabolic activity. A previous study documented a higher percentage of transcripts involved in binding and catalytic activity in phloem sap compared to other parts of the plant [Omid et al, 2007]. We identified 417 Fraxinus transcripts encoding for proteins that are potentially involved in stress responses, including 120 transcripts encoding for heat-shock proteins. These proteins could be

25 involved in responding to external stimuli, including biotic and abiotic factors [Wang et al, 2006]. Functional annotation is a prerequisite to better understand transcriptomic data

(especially of non-model systems). The GO facilitates functional characterization of genes, transcripts and proteins of any organism with respect to cellular component, biological process and molecular function in a species independent manner as reported in several other studies [Conesa and Gotz, 2008; Botton et al, 2008; Sathiyamoorthy et al,

2010].

2.4.4 Metabolic Pathways

Overall 4,667 sequences were assigned to 142 KEGG metabolic pathways and the number of transcripts in the different pathways ranged from 1 to 1,422 (Supplementary

Table 2.3). The highest number of transcripts (1,422) corresponded with secondary metabolites biosynthesis pathway (Table 2.1), this is expected given that this is a broader pathway which includes others like phenylpropanoid biosynthesis, terpenoid synthesis, etc. Several transcripts that are involved in 7 biosynthetic pathways for alkaloids including indole alkaloid, isoquinoline alkaloid, tropane, piperidine and pyridine alkaloids were predicted in the ash sequences. Alkaloids are important components of the plant defense system against insect herbivory and so far 12,000 different alkaloids have been reported in plants which are involved in plant defense, growth and development

[Adler et al, 2001; Ziegler and Facchini, 2008]. Nevertheless, to date alkaloids have not been identified in ash phloem [Cipollini et al, 2011; Eyles et al, 2007; Rowe and Conner,

1979].It is important to keep in mind that genes might be involved in more than one

27 pathway, so these genes that are being mapped to alkaloid pathways might in fact be part of other pathways such as biosynthesis of phenylpropanoids or other secondary metabolism. This is one of the reasons why it is vital to analyze the pathways of interest in closer detail.

Table 2. 1 Putative defense pathways identified in the phloem of Fraxinus mandshurica, F. americana, F. pennsylvanica, F. nigra, and F. quadrangulata pooled phloem database.

Pathway # of ESTs Biosynthesis of secondary metabolites 1422 Biosynthesis of plant hormones 668 Biosynthesis of phenylpropanoids 531 Thiamine metabolism 232 Arginine and proline metabolism 191 Cysteine and methionine metabolism 169 Valine, leucine and isoleucine degradation 151 Phenylalanine metabolism 127 Lysine degradation 123 Tryptophan metabolism 123 Tyrosine metabolism 112 Flavonoid biosynthesis 96 Drug metabolism (cyt P450) 93 Metabolism of xenobiotic by cyt P450 72 Anthocyanin biosynthesis 22 Isoflavonoid biosynthesis 10

In this study, we recovered a high number of transcripts (531) that were mapped to the phenylpropanoid biosynthesis pathway. This pathway leads to the production of several phenolic compounds (flavonoids, tannins, coumarins etc.,) that play an important role in plant defense against herbivores, microbes, as well as response to wounding

[Hahlbrock and Scheel, 1989; Treutter, 2005; Bernays, 1981; Dixon et al, 2002]. This correlates with previous ash studies in which a number of these compounds were found in the phloem of both resistant and susceptible ash [Eyles et al, 2007; Cipollini et al, 2011].

Although not all of the major genes reported in the pathway were found in this study, this information provides a good basis for further analysis and to better understand the potential role of phenylpropanoids in ash defense against biotic stress.

2.4.5 Protein Domains

A domain search using HMMER3 software identified 2,534 distinct domains in

19,291 ash transcriptomic sequences (Supplementary table 2.4). Among the top Pfam domains, the most abundant were protein kinase domains (588) and protein tyrosine kinase domains (464). Protein kinases are primarily involved in plant signal transduction pathways [Hirt and Scheel, 2000; Tena et al, 2001] and also participate in plant defense responses wherein they play an important role in signaling during pathogen recognition and activation of other plant defense mechanisms [Zhang and Klessig, 2001; Romeis,

2001; Nurnberger and Scheel, 2001]. On the other hand, proteins containing tyrosine kinase domains and protein tyrosine phosphatases (PTPs) regulate abscisic acid (ABA) transduction pathways in plants [Ghelis et al, 2008]. The role of PTPs has been largely ignored; however, a few tyrosine specific phosphatases were reported in A. thaliana

[Kerk et al, 2008; Rayapureddi et al, 2005]. PTPs are documented in Daucus carota,

Mimosa pudica, Arabidopsis hypocotyls and suspension cells [Barizza et al, 1999;

Kameyama et al, 2000; Huang et al, 2003; Sugiyama et al, 2008]. Other abundant domains included metallothionein (263) and RNA recognition motif (RRM, 231). While metallothioneins are primarily involved in copper detoxification [Roosens et al, 2004],

RRM (also known as RNA binding domain or Ribonucleoprotein domain) plays an important role in post transcriptional events and in particular is involved in the 3‟ end

29 processing of chloroplast mRNA, [Schuster and Gruissem, 1991; Maris et al, 2005]. In a recent study, RRMs have emerged as key players in plant morphogenesis and RNA metabolism in chloroplast and mitochondria [Kroeger et al, 2009]. Further, we identified

154 RAS family members, which constitute RAS, RHO, RAB/YPT, ARF and RAN.

RAS and RHO GTPases are considered to be important components in signaling cascades

[Wu et al, 2000].

Interestingly, 153 cytochrome P450 domains were predicted in the derived ash sequences. Plant cytochrome P450 monoxygenases are thought to be involved in many biochemical pathways including the biosynthesis of secondary metabolites (e.g. phenylpropanoids, alkaloids, terpenoids, glucosinolates etc.), which have been well studied in plant-insect interactions [Schuler, 1996]. However, plant cytochrome P450s are also involved in the biosynthesis of brassinosteroids and plant growth regulators

[Tanabe et al, 2005; Chapple, 1998].

In total, 92 PPR (pentatricopeptide repeat) domains were identified in the ash sequences. The PPR repeat domain of 35 amino acids are well-known protein family members of both prokaryotes and eukaryotes [Small and Peeters, 2000] and appear to function as sequence-specific RNA-binding proteins involved in post-transcriptional processes within organelles and during translation initiation [Delannoy et al, 2007;

Meierhoff et al, 2003; Mili and Pinol-Romma, 2003; Kotera et al, 2005; Nakamura et al,

2004; Schmitz-Linneweber et al, 2005]. We also identified the PIWI domain in 18 transcripts and the PAZ domain in 6 transcripts. These domains are reported to be up regulated in the egg cell of A. thaliana and the presence of these domains suggests a role in epigenetic regulation through small RNA pathways [Wuest et al, 2010].

2.4.6 Genes of Interest

Plants being sessile overcome various biotic and abiotic stress conditions through controlled gene expression. Immediate recognition of the biotic or abiotic factors/stimuli

(i.e. in the early stages) is one of the key factors in plant defense [Maffei et al, 2007]. Of the potential genes of interest listed in Table 2.2, we are particularly interested in those genes that participate in the early stages of plant defense including calcium dependent protein kinases (CDPKs); the transcription factors (TFs) WRKYs, MYBs and ethylene response factor (ERF); and a lipoxygenase (LOX3) Gene expression was quantified using real time quantititative PCR for these genes (Table 2.3).

Table 2. 2 Genes of interest recoverd from the Fraxinus mandshurica, F. Americana, F. pennsylvanica, F. nigra and F. quadrangulata phloem pooled transcriptomic database.

Candidate genes Number of occurrence Proteases 282 cytochrome P450 192 Lipase 94 WRKYs* 47 CDPKs 43 MYB 37 Hydroxyproline-rich glycoprotein 29 Protease/proteinase inhibitors 16 Phytoalexin deficient 4 (PAD4) 11 Hypersensitive-induced response protein 9 DREB 7 Myrosinase 7 Lipoxygenase 6 Jasmonic acid-amino conjugating enzyme 3 Pathogen-related protein 3 ERF 2 *Candidate genes assayed in this study (in bold).

In the current study, both CDPKs (CDPK 349 and CDPK 361) showed the highest mRNA levels in Manchurian ash followed by black ash and green ash. Interestingly, both

CDPKs of ash significantly matched with CDPK3 of Nicotiana tabacum and P. trichocarpa (1e-14 and 6e-53). In a recent study, it was reported that CDPK3 and

CDPK13 are involved in herbivory-induced signaling network via the regulation of defense related transcriptional machinery in A. thaliana [Kanchiswamy et al, 2010].

Usually a dramatic change in cytosolic Ca2+ is observed through signaling pathways mediated by CDPKs in plants upon biotic and abiotic stress [Pandey et al, 2000; Ludwig et al, 2005; Romeis et al, 2001]. These findings along with the expression analysis could suggest that the recovered Fraxinus CDPKs (349 and 361) may regulate the transcriptional machinery involved in defense response. We posit that the observed

(constitutive) high mRNA levels for both CDPKs in Manchurian ash (compared to the susceptible black and green ash) may represent an enhanced capability to defend against

A. planipennis.

Table 2. 3 Fold change of eight candidate genes in three Fraxinus species. Species with lowest expression used as calibrator. Glucose-6-phosphate dehydrogenase used as reference gene for normalization. Numbers in parenthesis indicate fold change range according to standard deviation (N=2). Abbreviations stand for: CDPK- calcium dependent protein kinase, LOX3- lipoxygenase 3, MYB-myeloblast transcription factor, ERF-ethylene response factor and WRKY-WRKY transcription factor. Gene F. mandshurica F. nigra F. pennsylvanica CDPK349 13786 5640 1 (10839-17535) (5106-6231) (0.31-3.28) 11 1.22 1 CDPK361 (7.42-16.12) (0.72-2.04) (0.92-1.09) 24.87 14.71 1 LOX3 (22.07-28.03) (12.09-17.89) (0.77-1.30) 3.28 1.05 1 MYB10337 (2.67-4.03) (0.99-1.11) (0.82-1.22) 4419 1022 1 ERF (3570-5471) (949-1102) (0.60-1.66) 5.18 1 2.75 WRKY21 (4.47-6.00) (0.62-1.62) (2.39-3.16) 3.69 1.05 1 MYB8679 (3.07-4.42) (0.93-1.20) (0.74-1.36) 1.44 1 8.99 WRKY7 (0.25-8.34) (0.89-1.13) (7.34-11.01)

Upon physiological and environmental stimuli TFs (sequence specific DNA binding proteins) modulate transcription of specific target genes by binding to cis- elements located in gene promoters and/or introns [Lee and Young, 2000; Martinez,

2002; Zhang and Wang, 2005]. TFs represent potential candidate genes for developing novel traits in crop plants [Century et al, 2008]. In this transcriptomic study, we found several WRKYs, which are key regulators in higher plants, representing the top ten largest families of transcription factors, and are found throughout the green plants

[Rushton et al, 2010]. These early regulators are involved in modulating defense responses, abiotic stress and biosynthesis of secondary metabolites [Ulker and Somssich,

2010; Dong et al, 2003; Mare et al, 2004; Eulgem et al, 2000].

Expression analysis of two WRKYs (WRKY 7 and WRKY21) revealed no difference in mRNA levels for WRKY 7 among species and higher levels for WRKY21 in Manchurian ash compared to the other ash species assayed. As reviewed in many previous studies, overexpression of OsWRKY resulted in enhanced salt and drought tolerance and the AtWRKY 25 mutants exhibited increased thermosensitivity [Rushton et al, 2010; Li et al, 2009]. Besides these biotic and abiotic stress responses, WRKY proteins are reported to be involved in sugar signaling and seed development [Sun et al,

2005; Sun et al, 2003; Zhou et al, 2009; Ishida et al, 2007].

Both of the MYBs (MYB8679 and MYB10337) assayed were more highly expressed in Manchurian ash compared to the mRNA levels observed for green and black ash (Figure 5E and 5F). MYBs are reported to be involved in several physiological and biochemical processes including defense and stress response, regulation of secondary

33 metabolism, and signaling pathways [Borevitz et al, 2000; Abe et al, 1997; Gocal et al,

1999; Dubos et al, 2010; Newman et al, 2004].

In this study, we found an ethylene response factor (ERF) that showed higher mRNA levels in Manchurian ash than in green and black ash. ERFs are important TFs that bind to the GCC motif of the promoter region of ethylene regulated genes [Pirrello et al, 2006]. Plants usually show an ethylene burst in response to insect attack, which eventually activates polyphenol oxidase, peroxidase, and proteinase inhibitor activities

[von Dahl and Baldwin, 2001]. Ethylene is known to regulate a large number of genes related to defensive proteins and other secondary metabolites [Harfouche et al, 2006;

Winz and Baldwin, 2001]. In a recent study it was shown that several genes involved in ethylene signaling were upregulated during forest tent caterpillar (Malacosma disstria) feeding on hybrid poplar leaves [Philippe et al, 2010].

Expression analysis of an ash lipoxygenase 3 (LOX3) revealed higher mRNA levels in Manchurian ash than in black and green ash. LOXs are versatile catalysts that participate in various physiological processes and are ubiquitous in nature [Kolomiets et al, 2000]. In particular, LOXs in higher plants play a central role in lipid peroxidation processes during defense responses, as precursors for biosynthesis of jasmonic acid related products, and in growth, development, senescence, and during abiotic stress

[Kolomiets et al, 2000; Veronesi et al, 1996; Hwang and Hwang, 2009]. Although it is thought that LOX genes are upregulated upon insect attack and/or wounding, perhaps the already high levels of this LOX3 in Manchurian ash might help for a faster response to the attack.

2.4.7 Molecular Markers

We identified 1,272 single nucleotide polymorphisms (SNPs) in 410 ash transcriptome sequences (Table 2.4 and Supplementary table 2.5)including, 823 transitions, (i.e., changes from one purine to another purine or one pyrimidine to another pyrimidine) and 449 transversions (changes between purines or pyrimidines). This ratio of transitions to transversions (2:1) of SNP occurrence in ash corresponds with other systems [Collins and Jukes, 1994]. About 94% of the microsatellite loci predicted were dinucleotide (389) and tri-nucleotide repeats (532) followed by quad-nucleotide (37), hexa-nucleotide (17) and penta-nucleotide (5) repeats (Table 2.5 and Supplementary table

2.6). In general, EST-derived microsatellites are shorter than the genomic microsatellites

[Thiel et al, 2004], however, long dinucleotide microsatellites (CT) 24 were predicted in the current study. Primers were designed for 25 microsatellites (10 primers for dinucleotide repeats and 15 primers for trinucleotide repeats) from the microsatellites predicted. Seventeen of the 25 primers showed single band amplification in a PCR run, of which seven were genotyped to check for polymorphism among three ash species (white, green, and Manchurian). Results indicate all seven loci to be polymorphic among the three species studied, providing a valuable resource of molecular markers for ash (Table

2.6). Similar patterns were reported in transcriptomic studies of C. sativus and E. grandis, wherein 454 pyrosequencing was shown to be an excellent method for large scale prediction of molecular markers for future genetic linkage and QTL analysis in non- model organisms [Novaes et al, 2008; Guo et al, 2010]. Given that these microsatellite and SNP markers were predicted from transcriptomic sequences, they are likely linked to protein coding genes, and therefore might have substantial physiological implications.

Table 2. 4 Summary of putative SNPs in Fraxinus mandshurica, F. americana, F. pennsylvanica, F. nigra, and F. quadrangulata phloem database.

SNP types Number Transition A-G 411 C-T 412 Transversion A-C 123 A-T 137 C-G 80 G-T 109 Total 1272

Table 2. 5 Summary of microsatellite loci predicted in Fraxinus mandshurica, F. americana, F. pennsylvanica, F. nigra, and F. quadrangulata phloem transcriptomic sequences. Penta- Hexa- Number of Dinucleotide Trinucleotide Quad nucleotide nucleotide repeats repeats repeats nucleotices repeats repeats 5 312 22 4 14 6 128 9 3 7 52 2 1 8 129 14 1 9 86 10 10 46 3 1 11 34 2 2 12 29 8 13 10 1 14 12 15 8 1 16 8 17 3 1 18 6 19 3 20 9 21 1 22 2 23 1 24 2 Subtotal 389 532 37 5 17

Table 2. 6 Number of alleles in seven loci of three ash species (Fraxinus americana- White, F. mandshurica- Manchurian and F. pennsylvanica- Green). - Locus Species ASH1502 ASH2429 ASH7867 ASH9764 ASH35207 ASH43402 ASH53476 White 4 7 4 2 1 5 4 Manchurian 2 1 1 1 2 3 1 Green 5 3 1 3 3 4 2

The basic understanding of host resistance mechanisms in Angiosperm trees, including resistance of ash against wood borers is very limited. To date there is no evidence that any native NA ash species possesses resistance to EAB, which makes the entire ash population highly vulnerable to EAB invasion. Thus, results stemming from this functional genomics approach to discovery of host resistance factors in ash, could inform future ash breeding/genetic improvement programs. Nevertheless, the candidate genes from this study need to be studied more in depth before they can be used in such studies.

2.5 Conclusions

The utilization of second generation sequencing for ash species has identified various metabolic pathways in ash that may contribute to its resistance to A. planipennis.

We found higher constitutive expression of early gene regulators in Manchurian ash than in the NA ash species. Results of this study lay the foundation for future differential gene expression analysis of ash species, and for deciphering secondary metabolic pathways related to plant defense. Molecular markers predicted by this study will inform population genomics and gene-based association studies, and contribute to the

37 development of ash species with resistance to A. planipennis through breeding and/or the application of transgenic technology.

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Chapter 3

Evaluation of reference genes for expression studies in ash (Fraxinus spp.)

3.1 Abstract

Ash (Fraxinus spp.) is a dominant tree species in North America, in both managed and natural landscapes. However, due to the rapid invasion by the emerald ash borer (Agrilus planipennis), an exotic invasive insect pest, millions of North American ash trees have been killed. Real-time quantitative polymerase chain reaction (RTq-PCR) is widely used for validating transcript levels in gene expression studies for which a good reference gene is mandatory. In the current study, we evaluated the stability of ten reference genes in at least five different tissues (phloem, roots, shoots, immature leaves, and mature leaves), and two developmental stages (young and old) among three ash species including the A. planipennis resistant Asian Manchurian ash (F. mandshurica) and two susceptible North American ash species (green - F. pennsylvanica and white - F. americana). Of the examined genes, the translation elongation factor alpha (eEF1α) was observed to be most stable and thus is recommended for RTq-PCR based gene expression studies in Fraxinus species. To our knowledge, this is the first report on the stability of reference genes across ash species (in different tissues and during development).

3.2 Introduction

Real time quantitative PCR (RTq-PCR) is a widely used technique for assessing gene expression, which allows fast and accurate quantification of even low expressed transcripts [Bustin, 2002]. RTq-PCR quantifies the relative expression of mRNA in real time through the detection of fluorescence after every PCR cycle, avoiding post PCR processing [Ginzinger, 2002]. For the accurate quantification of mRNA transcripts using

RTq-PCR, identification of stable reference genes is crucial to normalize the target‟s levels [Phillips et al, 2009; Dheda et al 2005]. Most commonly used reference genes are housekeeping genes or endogenous control genes, which are thought to be non-regulated.

However, several studies revealed large variation of these under different experimental conditions and thus to date no universal reference gene has been identified in plants or animals [Gutierrez et al, 2008]. Given the variation of reference gene expression among different experimental conditions, it is essential to validate a set of reference genes (~ 2-

3) [Bustin and Nolan, 2004; Li et al, 2010].

Ash (Fraxinus spp.) is a dominant tree species with widespread distribution throughout the world‟s temperate forests, including North America. The accidental introduction of emerald ash borer (Agrilus planipennis Fairmaire), an exotic invasive wood boring beetle into North America, has resulted in death of millions of ash trees including white (F. americana L.), green (F. pennsylvanica Marshall) and black ash (F. nigra Marshall) with significant economic and ecological impact [Kovacs et al, 2010].

However, in Asia A. planipennis is not considered as a major insect pest perhaps due to their co-evolutionary history with Manchurian ash (F. mandshurica Rupr)[Rebek et al,

2008]. In an on-going study, we have attempted to decipher the transcriptome of ash

54 phloem in the hope to unravel candidate resistance genes to A. planipennis [Bai et al,

2011]. This study has laid the foundation for several gene expression/functional genomic studies, for which there is an urgent need to identify/validate reference genes to be included in real-time quantitative polymerase chain reaction (RTq-PCR) experiments. In this study we evaluated the stability of 10 reference genes among different samples

(tissues and development stages) of Fraxinus species including green, white and

Manchurian.

3.3 Materials and methods

Mature leaves and phloem plugs of 5 mm in diameter were collected from un- infested cultivars of green, white, and Manchurian ash in June 2010 from a common garden at the Ohio Agricultural and Research Development Center (OARDC, Wooster,

OH). Two young (~5 years old) and two old (~15 years old) trees were sampled per species. In June 2011, samples from shoots, roots, immature and mature leaves were obtained from 4-year old potted seedlings of the same three species at the US Forest

Service (Delaware, OH) following the same protocol as the previous year. In addition to these samples, phloem plugs were also collected from girdled ash trees. Samples were obtained from above and below the girdled region from three trees per species. The post- girdled samples were obtained in July 2010 21 days after girdling from a common garden at Novi, MI. Girdling was performed by peeling a 5 cm band of the bark as per Baldwin

[1934]. All samples were immediately placed in liquid nitrogen and kept at -80oC until further processing. Approximately 70 mg of tissue was ground to powder in liquid

55 nitrogen for RNA extraction. Total RNA was extracted using Trizol® Reagent

(Invitrogen, Carlsberg, CA) following manufacturer‟s protocol. After RNA extraction, samples were treated with TURBO DNaseTM (AMBION, Inc., Austin, TX) following manufacturer‟s protocol, in order to eliminate genomic DNA contamination and were stored at -80oC until further use.

The sequences of the potential reference genes including a cyclophilin (Cyp), elongation factor 1-beta (eEF1β), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), glucose-6-phosphate dehydrogenase (G6PD), histone H3 (HIS), ribosomal protein L13

(RPL13), RNA polymerase II (RNAPII), translation elongation factor alpha (eEF1α), beta-tubulin (TUB), and ubiquitin protein ligase (E3upl) were mined from an on-going phloem transcriptomic study of ash [Bai et al, 2011]. All the sequences pertaining to the current study are available at the NCBI Sequence Read Archive under the accession number of SRA020745.3.

One microgram of RNA from each sample was used for cDNA synthesis using the SuperScript™ First-Strand synthesis kit (Invitrogen, Carlsberg, CA) following the manufacturer‟s protocol. Ten pairs of gene specific primers were designed using Beacon

Designer 7 software. RTq-PCR reactions were carried out in a total volume of 10 µl containing 5 µl of 2X SybrGreen (BIORAD, Hercules, CA), 0.5 µl of each primer (10

µM) and 2 µl of cDNA template (40 ng/µl) and 2 µl of nuclease free water. All reactions were performed in duplicate. The cycling parameters were 95oC for 5 min followed by 40 cycles of 95oC for 10 s and 60oC for 30 s ending with a melting curve analysis (65oC to

95oC in increments of 0.5oC every 5 s) to check for nonspecific product amplification.

Primer efficiencies were calculated using a standard curve that consisted of 5-fold

56 dilutions over four points. Each point was measured in triplicate. Three standard curves were done per primer set. The primer efficiency (10-1/slope) and R2 presented in Table 3.1 are an average of these three curves. Two software programs, GeNorm [Vandesompele et al, 2002] and NormFinder [Andersen et al, 2004] were used for the selection of stable constitutively expressed reference genes. Cycles to threshold (Ct) values were converted according to the requirements of the software used.

Product E Gene* Primer Sequence (5’-3’) size Tm(oC) R2 (%) (bp) Cyp Fcyclo-F TTC GGT CTT ATA CTC TTC CTC AG 76 53.3 92.5 0.99 Fcyclo-R GGG TCA CTT CCT TCA AAT CTT C 53.5 eEF1β FEF1-F GTC TAC AGC AGG AGG AGT TG 116 54.7 93.2 0.96 FEF1-R ACC ACA TTG AAG CAC TAT TGA G 53.1

G6PD G6PD-F AGG GCA GGT TAT GTT CAA ACA C 117 55.6 94.1 0.96 G6PD-R CAC ACG ACC TTA TTG ACA GAG C 55.7

RPL13 FRPL-F CAC AAG ACT AAG CGA GGA GCA G 107 57.5 96.0 0.95 FRPL-R TTG AGA GCA TCA GGA ATG ACC ATC 56.8 eEF1α FTEF-F ACC AGC AAG TCC CAG TTG AGA TG 77 59.2 91.9 0.96 FTEF-R TGA GCC AGG TTC AGC TTC CAA TG 59.7

E3upl FubiL-F CAA GCA CAT CCT CGC GTA AAG 108 58.5 99.3 0.96 FubilL-R GGT ATG CCA CTC ACA TCA TTC TCC 57.5

3.4 Results and discussion

Since ash phloem is the target tissue of A. planipennis larvae, and the adult beetle feeds on leaf tissue, we included both phloem and leaf tissue (of different ash species) from two developmental stages. Additionally, we included phloem samples from girdled trees, immature and mature leaves, shoots and roots with the goal of identifying reference

57 genes with stable expression levels both within and across the species. Initial analysis of the ten reference genes indicated a high rate of variation; GAPDH, RNAPII, HIS and

TUB were found to be the most unstable and hence were removed from further analysis.

The Ct values for the remaining six genes ranged between 17 and 33 (highest Ct value obtained for eEF1α and lowest for RPL13), the average of the Ct values is represented in

Figure 3.1.

3. 1 GeNorm Analysis

The stability of the reference genes was calculated using GeNorm, a visual basic application (VBA) for Excel. GeNorm determines the pairwise variation of all control genes as the standard deviation of the logarithmically transformed expression ratios. It measures a gene expression stability value (M), which is the average pairwise variation of a gene compared to the other control genes included in the same analysis. Genes with the

58 lowest M value are considered to be the most stable [Vandesompele et al, 2002].

GeNorm suggests M=1.5 as a cutoff value, meaning that genes with M values higher than

1.5 should not be used as reference genes.

The value of M for the six reference genes showed eEF1α as a potential reference gene across all the samples assayed with E3upl and RPL13 as the least stable genes

(Table 3.2). When all the species were considered as a pool, the most stable genes were eEF1α, eEF1β and G6PD with M values of 1.038, 1.102, and 1.222, respectively. When each species was considered as a separate subgroup, eEF1α was the most stable for all ash species. In order to discard the possibility of eEF1α and eEF1β being co-regulated, an exclusion analysis was performed wherein each gene was excluded in separate analyses.

The ranking of the genes was still the same with very similar M values, indicating no co- regulation between eEF1α and eEF1β (data not shown).

3.4.2 NormFinder Analysis

NormFinder is also an Excel add-in that uses a mathematical model which performs separate analysis of sample subgroups, estimates intra- and inter-expression variation and calculates a stability value [Andersen et al, 2004]. Genes with a lower stability value are considered to be more stable i.e. there is lower variation of gene expression across the samples. NormFinder indicated eEF1α as the most stable gene for all the samples with a stability value of 0.028. When analyses were done per species, eEF1α was the most stable gene for Manchurian ash (0.029) while eEF1β and RPL13 were the most stable genes for green (0.020) and white ash (0.028), respectively. eEF1α

59 ranked as the second most stable gene for both the latter species (Table 3.2). The reference gene Cyp displayed the highest stability value (least stable) in the overall analysis. Despite the separate analysis, we noticed some similarities such as: 1) eEF1α was consistently among the top three genes both with and without subgroups, and 2) Cyp was consistently among the lowest three genes in all the analyses performed. This trend in the stability values obtained for eEF1α and Cyp is in agreement with other studies in plants, albeit within a species [Tong et al, 2009].

NormFinder GeNorm Overall Manch Green White Overall Manch Green White

eEf1α eEf1α eEF1β RPL13 eEf1α eEf1α eEf1α eEf1α (0.028) (0.029) (0.020) (0.028) (1.038) (1.225) (0.850) (0.713) eEF1β eEF1β eEf1α eEf1α eEF1β Cyp eEF1β eEF1β (0.035) (0.041) (0.027) (0.031) (1.102) (1.252) (0.886) (0.741) E3upl E3upl G6PD eEF1β G6PD eEF1β G6PD RPL13 (0.042) (0.051) (0.027) (0.031) (1.222) (1.370) (0.906) (0.780) G6PD Cyp E3upl E3upl Cyp G6PD Cyp E3upl (0.044) (0.051) (0.032) (0.035) (1.231) (1.454) (0.976) (0.800) RPL13 G6PD Cyp G6PD E3upl E3upl E3upl Cyp (0.058) (0.056) (0.047) (0.038) (1.327) (1.753) (1.002) (0.836) Cyp RPL13 RPL13 Cyp RPL13 RPL13 RPL13 G6PD (0.068) (0.065) (0.071) (0.062) (1.472) (1.762) (1.589) (0.952)

Analysis of potential reference genes for the three species indicated a high variability. RTq-PCR is often used to analyze the relative expression of a gene under different conditions in the same species. However, on-going functional studies include analysis of gene expression in different Fraxinus spp., thus increasing the need to find good and reliable reference genes across species. It was possible to observe that

60 irrespective of the samples pertaining to different Fraxinus spp., eEF1α was observed to be highly consistent. We thus recommend eEF1α as a candidate reference gene for gene expression studies in ash. To our knowledge this is the first report on validating reference genes across different tree species.

3.5 References

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Chapter 4

Gene Expression Profiles of Three Ash species (Fraxinus spp) Using RNA-Seq.

4.1 Abstract

Fairmaire), millions of ash trees have been killed. However, in Asia, A. planipennis’ natural habitat, the damage to native ash is isolated and only observed in trees under stressed conditions, indicating some level of resistance, presumably due to a shared co- evolutionary history. Despite the high impact status of A. planipennis, there is little information available about the molecular biology of any of the North American and

Asian Fraxinus species. Fundamental molecular biology knowledge on both susceptible and resistant ash could provide the tools to mitigate the A. planipennis invasion. In this study, constitutive gene expression profiles among susceptible ash species, including black (Fraxinus nigra) and green ash (F. pennsylvanica), and resistant Manchurian ash

(F. mandshurica) were compared using RNA-Seq on an Illumina platform. A total of

37,516 high quality expressed sequence tags (ESTs) for Manchurian, 41,885 for black and 35,676 for green ash, were generated. Out of these transcripts, 8,691 were found to

63 be orthologs for all three species. Differential gene expression analysis was done using these orthologs, between Manchurian and black ash and Manchurian and green ash, separately. These comparisons revealed 230 genes differentially expressed between

Manchurian and black ash and 527 differentially expressed genes between Manchurian and green ash. Out of these comparisons, it was possible to identify 84 genes which were significantly differentially expressed between resistant and susceptible ash. Several of these genes are potentially involved in plant response to both biotic and abiotic stimulus.

The expression of eight differentially expressed genes was validated using real time quantitative PCR (RT-qPCR). Finally, molecular marker prediction for each species revealed an average of 30,000 SNPs and more than 1,000 microsatellites. The information obtained in this study will be of great importance in future functional studies and ash breeding programs.

4.2 Introduction

Ash trees (Fraxinus spp.) are widely spread in eastern North America and extensively planted in urban landscapes [MacFarlane and Meyer, 2005]. Haack et al

[2002] described a new exotic invasive pest of ash - the emerald ash borer (Agrilus planipennis) - a buprestid native to eastern Asia [Akiyama and Ohmomo, 2000]. Since then, it has been reported in managed and natural landscapes in the United States and Canada.

A. planipennis has killed millions of ash trees in North America, threatening the very survival of the entire genus and causing great economical, environmental and ecological damage (Poland and McCullough, 2006). A. planipennis attacks trees of different sizes,

64 from saplings to fully mature trees, and both healthy and stressed [Cappaert et al, 2005], of all North American ash species, including green (F. pennsylvanica Marshall), white

(F. americana L.), black (F. nigra Marshall), and to some extent blue ash (F. quadrangulata Michaux) [Rebek et al, 2008, Anulewicz et al, 2007]. Freshly hatched larvae bore through the bark and begin feeding on the phloem excavating “S-shaped” galleries just beneath the bark [Wei et al, 2007], finally killing the trees within 1-4 years

[Poland and McCullough, 2006].

In contrast to the economic losses caused by this pest in North America, incidences of damage have been reported to be isolated in some of the native Asian ash species (F. chinensis Roxb, F. mandshurica Rupr and F. rynchophylla L.), usually only in trees under stressed conditions, suggesting coevolution of the pest with the native species [Herms et al, 2004]. It is likely, therefore, that Asian ash species express constitutive defenses to this pest.

More recently, transcriptomic studies have become easier and more affordable to carry out. The use of technologies such as RNA-Seq have allowed for a global understanding of organisms such as: Anopheles funestus [Crawford et al, 2010], Persea americana [Wall et al, 2009], Eucalyptus grandis [Novaes et al, 2008], Camellia sinensis

[Shi et al, 2011], Vitis vinifera [Zenoni et al, 2010], among others. RNA-Seq refers to the sequencing of whole transcriptomes of different tissues or organisms in order to reveal transcriptomic adjustments (gene expression studies), discovery of novel transcripts, identification of alternate splicing, and molecular marker discovery [Zhang et al, 2011;

Crawford et al, 2010;Yang et al, 2011;Shi et al, 2011;Pitts et al, 2011]. It uses deep sequencing to quantify levels of transcripts in different samples [Wang et al 2009]. Given

65 that the transcriptomic information for ash is limited and that the primary goal of this study is to compare expression profiles among different ash species, RNA-Seq was applied. Such comparisons can shed light into the constitutive gene expression in ash.

Transcriptomic differences were determined between susceptible (black and green ash) and resistant (Manchurian ash) species. These efforts will allow for gene discovery, uncovering the genetic landscape of ash genome and eventually leading to the identification of candidate genes involved in resistance to A. planipennis attack.

4.3 Materials and Methods

4.3.1 Samples and RNA isolation

Two 10 mm diameter phloem plugs from green (F. pennsylvanica), black (F. nigra), and Manchurian ash (F. mandshurica) were collected from a common garden established in 2003 at Novi, MI and from the Ohio Agricultural Research and

Development Center at Wooster, OH. The trees sampled did not have D-shaped exit holes and/or vertical splits on the trunk which are indicators of A. planipennis infestation

[Poland and McCullough, 2006]. Phloem plugs were immediately wrapped in aluminum foil and stored in liquid nitrogen. Approximately 70 mg of phloem tissue homogenized in liquid nitrogen were used for RNA extraction. Total RNA was extracted using Trizol

Reagent (Invitrogen, Carlsberg, CA) following manufacturer‟s protocol and stored at -

80oC until further use. RNA integrity was checked using the Agilent 2100 Bioanalyzer TM

(Agilent Technologies, Palo Alto, CA).

4.3.2 cDNA library construction and Sequencing

cDNA library preparation and sequencing were conducted at the Molecular

Cellular and Imaging Center (Wooster, OH). There were two biological replicates per species, with each biological replicate representing different locations (Novi. MI and

Wooster, OH). Library preparation was done following Illumina mRNA sequencing protocol. Briefly, mRNA was purified from ~1 µg of total RNA using poly-T oligos attached to magnetic beads and then fragmented under elevated temperature. Next, the mRNA was used as template for first-strand cDNA synthesis using random primers. This was followed by a second strand synthesis using DNA polymerase I. After that, a single

A base was added to the ends of the strands and ligated to adapters. Finally, the products were enriched through PCR and sequenced on an Illumina GAII platform at MCIC

(OARDC, Wooster, OH). One capillary lane per sample was subjected to paired-end sequencing.

4.3.3 De novo assembly

The resulting Illumina reads for each Fraxinus sp. were assembled de novo. The paired-end reads were processed first to remove low-quality reads (at least 80% of an entire read with a PHRED score of less than 20) and low complexity reads (high number of repetitive bases). The processed reads were then assembled using a combination of

Velvet [Zerbino and Birney, 2008] and Oases (http://www.ebi.ac.uk/~zerbino/oases/) programs with k-mer lengths of 41, 43, 45, 47, 49, 51, 53, and 55. The resultant sequences were combined, processed to remove duplicates, and further assembled after

67 examining the overlapped regions identified by Vmatch program [Kurtz, 2011]. The resulting contigs were then further assembled using PHRAP program [Green, 2008] to obtain the final transcriptome with a cutoff of 100 bp. These transcriptome assemblies were used as a reference for gene expression mapping.

4.3.4 Functional annotation

The Fraxinus transcriptomes were annotated by searching against the Swissprot database for similar sequences. The sequences that were potentially of archaea, bacterial, fungal or viral origins were considered potential contaminants and therefore removed from further analyses. Sequence similarity information was used in the BLAST2GO program [Gotz et al, 2008] to assign Gene Ontology terms [Ashburner et al, 2000] and

KEGG (metabolic) pathways [Kanehisa et al, 2008]. The deduced protein sequences were subjected to a protein domain search using HMMER3 program [Eddy, 1998].

Molecular markers were also predicted from the library. The Msatfinder v2.0.9 program

[Thurston and Field, 2005] was used for microsatellite repeat prediction with 9 repeats as a cutoff for di-nucleotide units and 5 repeats for tri-, tetra-, penta-, and hexa-nucleotide units. Single nucleotide polymorphisms (SNPs) were predicted separately by aligning their respective sets of reads to the transcriptome for SNP calling with MAQ program

(http://maq.sourceforge.net/index.shtml). SNPs were called at the positions that were covered by at least 100 Illumina reads.

4.3.5 Ortholog Identification and Expression analysis

Sequence similarity searches were used to identify the orthologous transcripts among the Fraxinus transcriptomes. An E value cutoff of 1e-10 at the amino acid level was used in the tBLASTx searches. The original Illumina reads for different Fraxinus sp. were mapped to the corresponding assembled transcriptomes using bowtie aligner

[Langmead et al, 2009]. Only the uniquely mapped reads were retained for further analysis. To assess differential gene expression, the expression levels of the transcripts that were considered as orthologs among all three Fraxinus sp. were compared as described above. The expression level of each transcript in different Fraxinus transcriptomes was calculated as number of reads per kilobase of the transcript per million of total reads (RPKM) [Mortazavi et al, 2008]. The differential expression was analyzed using the edgeR package using common dispersion estimation [Robinson et al,

2010]. Differences in transcript levels were considered as statistically significant at P <

0.05, after Benjamini and Hochberg adjustment [Benjamini and Hochberg, 1995] to account for false discovery rate (FDR).

4.3.6 Gene validation and real time quantitative PCR (RT-qPCR)

Eight genes that were differentially expressed were validated in samples obtained from a nursery at the US Forest Service, Delaware, OH (Manchurian and green ash) and collections at OARDC, Wooster, OH (black ash). We included three biological replicates per species, i.e. three individual trees from resistant Manchurian ash, three from susceptible green ash and three from susceptible black ash. Sample collection and RNA isolation were done as previously described. Turbo DNase (Ambion, Austin, TX) was

69 used to eliminate any potential genomic contamination. First strand cDNA was synthesized using a Super ScriptTM First-Strand synthesis kit (Invitrogen, Carlsber, CA) following manufacturer‟s protocol. Primers were designed using Beacon Designer 7 software (Table 4.1). The cycling parameters were 95oC for 5 min followed by 39 cycles of 95oC for 10 s and 60oC for 39 s ending with a melting curve analysis (65oC to 95oC in increments of 0.5oC every 5 s) to check for nonspecific product amplification. Relative gene expression was analyzed by the standard curve method (ABI Bulletin No. 2).

Transcription elongation factor (eEF1α) was used as the internal reference gene [Rivera-

Vega et al, 2001]. A standard curve was done for each set of primers to check for primer efficiency.

Table 4. 1Primer sequences for eight genes used in real time quantitative PCR (RTqPCR) for RNA-Seq validation.

Size Gene Name Sequence (bp) R2 Major Allergen FALLERqF GCC TTC GTT CTT GAT GCT GAT AA 100 0.99 FALLERqR TGA CTG TTC CAA CTC CAC CAT

F-Box FFBOXqF AAT CTG GTG GCT TGA AGT 186 0.99 FFBOXqR TGT CCG TCT GAA GTT GAG

Zinc Finger CCCH FC3HqF CCT AAT ACT TCA ACT CCA CCA AT 158 0.98 FC3HqR TCC ATA TCC AAC TCC ATA TCT CT

Phenylalanine- FPALqF GCT GCT GCT ATA ATG GAA CA 161 0.99 ammonia-lyase FPALqR GCT GAA CGA ATG ACC TCT ATT SGT1 FSGT1qF ACA GGT CAA CTC AAG TCT CA 197 0.99 FSGT1qR TCG GCG TCG TAA GGA TTA

Transcinnamate-4- FTC4MqF CCA CCA CAC GAC CAC CATT 113 0.99 monooxygenase FTC4MqR AGA GAC TAC AGC GGC GAC TAT Universal stress FUSPqF GAC ACT GCT GGT AGA CAA 172 0.99 protein FUSPqR CAT CAC ATA ATT GGT CAC ACT WRKY transcription FWRKYqF GGC AAC GGA ACT ACC TCT T 115 0.99 factor FWRKYqR TCA TCT CCT TCA CCT TCA TAA CC

4.4 Results and Discussion

To date, only two EST databases are available for ash: one for European ash

(Fraxinus excelsior; available at NCBI EST database), and one from a pooled library that included white, green, black, blue and Manchurian ash (SRA020745.3). However, since the latter database was prepared from a pooled sample, it made it difficult to identify transcriptomic differences among the species and cannot be used for building a reference contig file. This study addresses that limitation and provides insights into transcriptomic adjustments (expression differences) among three ash species – two susceptible (F. pennsylvanica; green and F. nigra; black) and one resistant (F. mandshurica;

Manchurian). Sequencing yielded a total of 49,702,070 bp for Manchurian ash,

51,926,637 bp for black ash, and 45,307,941 bp for green ash, assembled into 37,516,

41,885, and 35,676 high quality transcripts for Manchurian, black and green ash, respectively (Table 4.2). An ortholog comparison revealed that 20% of the transcripts were orthologous among all three species (Fig. 4.1). In pairwise comparisons, on average about 40% of the transcripts were orthologs to one of the other species.

Table 4. 2Statistics of the assembly of the transcriptomes of Fraxinus mandshurica (Manchurian ash), F. nigra (black ash) and F. pennsylvanica (green ash) phloem.

F. mandshurica F. nigra F. pennsylvanica Total number of 76-bp Illumina reads 62,593,508 75,760,784 66,487,298 Total bases of assembled transcriptomes 49,702,070 51,926,637 45,307,941 (bp) Number of transcripts in the 37,516 41,885 35,676 transcriptome N50 transcriptome (bp) 2,033 1,861 1,916 Minimal transcript length (bp) 108 108 101 Maximal transcript length (bp) 15,157 17,131 9,696

4.4.1 Functional Comparisons

A total of 20,816 (55%) sequences were annotated for Manchurian ash, 22,805

(54%) for black ash, and 19,583 (55%) for green ash. The search for protein domains identified an average of 3,000 domains for each of the ash databases. In general, the top domains were similar for all species and included: protein kinase, protein tyrosine kinase,

RNA recognition motif, PPR repeat, WD Domain, helicase conserved C terminal, Myb- like DNA binding, Zinc finger, and Cytochrome p450 (Table 4.3). As expected, the metabolic pathways associated with each library were also highly similar (Table 4.4). The predominant pathways deduced are related to secondary metabolism including: biosynthesis of phenylpropanoids and terpenoids, etc. However, the pathway for microbial metabolism in diverse environments was highly represented (second in abundance) within the databases. These functional results are in accordance with previous ash phloem transcriptome studies (Bai et al 2011).

Table 4. 3Top protein domains in Fraxinus mandshurica (Manchurian ash), F. nigra (black ash) and F. pennsylvanica (green ash) phloem databases.

Number of sequences Domain Name Description F. F. F. mandshurica nigra pennsylvanica Pkinase Protein kinase domain 1228 1405 1093 Pkinase_Tyr Protein tyrosine kinase 1163 1366 1033 RRM_1 RNA recognition motif 610 526 532 PPR PPR repeat 384 266 301 WD40 WD domain, G-beta repeat 269 263 261 Helicase_C Helicase conserved C-terminal domain 221 194 209 Myb_DNA- Myb-like DNA-binding domain 207 239 210 binding zf-C3HC4 Zinc finger, C3HC4 type (RING finger) 194 250 219 DEAD DEAD/DEAH box helicase 173 131 146 p450 Cytochrome P450 166 207 146

Table 4. 4Top ten metabolic pathways in Fraxinus mandshurica (Manchurian ash), F. nigra (black ash) and F. pennsylvanica (green ash) phloem databases.

KEGG Pathway Number of transcripts F. F. F. mandshurica nigra pennsylvanica Biosynthesis of secondary metabolites 739 941 719 Microbial metabolism in diverse environments 410 511 438 Biosynthesis of plant hormones 348 413 358 Biosynthesis of phenylpropanoids 232 332 258 Starch and sucrose metabolism 307 318 264 Biosynthesis of alkaloids derived from shikimate pathway 203 273 223 Biosynthesis of terpenoids and steroids 213 256 233 Biosynthesis of alkaloids derived from ornithine, lysine and nicotinic acid 186 242 215 Biosynthesis of alkaloids derived from histidine and purine 180 239 201 Biosynthesis of alkaloids derived from terpenoid and polyketide 192 231 200

4.4.2 Differential expression analysis

Comparisons for identifying differential expressions were done in pairs –

Manchurian vs black ash (Fm vs Fn), and Manchurian vs green ash (Fm vs Fp). For Fm vs Fn, 230 transcripts were found to be significantly differentially expressed, wherein 141 transcripts were expressed significantly higher in Manchurian ash. The Fm vs Fp

73 comparison revealed 527 significantly different transcripts, of which 279 of the transcripts were higher in expression in Manchurian ash. A comparison between green and black ash – Fp vs Fn – was also done, but is not further discussed in this paper due to its little relevance to this study (Supplementary Table 4.1-4.34).

In order to narrow down to genes potentially involved in resistance, only those genes which were differentially expressed between Manchurian ash (resistant) and both white and green ash (susceptible) were analyzed. From this comparison, 43 genes were expressed more highly in resistant ash and 41 in susceptible ash. Gene ontology terms were assigned to these 84 genes and based on their annotation, 14 of these differentially expressed genes were categorized into biological processes such as response, including response to abiotic, biotic and chemical stimulus (Table 4.5).

Annotation p-value Higher in: MLO-like protein 1 0.049239 Resistant Zinc finger CCCH domain-containing protein 66 4.41E-06 Resistant Putative late blight resistance protein homolog R1A-6 0.00076 Resistant Dehydrin COR47 1.65E-05 Resistant Protein SGT1 homolog 0.006335 Resistant Receptor-like serine/threonine-protein kinase ALE2 0.031636 Resistant Disease resistance protein RPM1 0.001154 Resistant Probable aquaporin PIP1-5 0.002338 Resistant Nucleolysin TIAR 0.001349 Susceptible GDP-L-galactose phosphorylase 1 0.000124 Susceptible Aquaporin PIP2-7 2.13E-05 Susceptible Disease resistance response protein 206 0.000484 Susceptible Protein MAM3 0.020836 Susceptible Lactoylglutathione lyase 2.90E-05 Susceptible

4 Supplementary tables for this chapter in Appendix A.1-3.

4.4.3 Validation

In order to validate the accuracy of the RNA-Seq results, gene expression was quantified for eight candidate genes using RT-qPCR on new samples obtained from a nursery in Delaware, OH and a collection at Wooster, OH. These candidate genes included some highly expressed in both resistant and susceptible ash. The genes analyzed were related to biotic (2) and abiotic stress (4), or involved in the phenylpropanoid pathway (2). The genes involved in biotic stress were deduced as a major allergen and a

SGT1 (suppressor of G2 allele of SKP1) homolog. Allergens can belong to a series of protein families such as pathogenesis-related protein 10, thaumatin-like proteins, lipid transfer proteins, profilins, etc. [Chen et al, 2008], and used by plants as a means of defense [McGee et al, 2001]. Also, an allergen protein was previously reported as being differentially expressed among ash species [Whitehill et al, in press]. The SGT1 homolog is a protein that usually functions along with RAR1 and HSP90 proteins in plant response to microbes [Wang et al, 2008]. The genes related to abiotic response were: a universal stress protein. i.e. a small cytoplasmic protein that has been found to upregulate during stress, such as drought [Maqbool et al, 2007]; a WRKY transcription factor which has also been seen to respond to stress, including cold and drought [Mare et al, 2004]; a zinc finger CCCH transcription factor [Wang et al, 2008] and an F-BOX protein, which are proteins originally characterized as component of the SCF ubiquitin-ligase complex involved in ubiquitin-mediated proteolysis [Kipreos and Pagano, 2000]. Finally, the last two genes were phenylalanine ammonia lyase (PAL) and trans-cinnamate-4- monooxygenase, which are involved in the phenylpropanoid pathway, and thus

75 participates in the synthesis of secondary compounds such as coumarins, flavonoids, benzoic acids, etc. [Dixon et al, 2002].

In a comparison between Manchurian and black ash, five out of the eight genes showed differences in the same direction (higher in both methods, or lower in both methods; Figure 4.2a), meanwhile in the comparison between Manchurian and green ash, seven out of the eight genes were validated (Figure 4.2b). The difference in the results obtained with the two methods could be explained by the different sensitivities of the techniques (background noise) and/or the variation within the samples (e.g. age, environment, time of sampling, etc). These results are similar to those seen in other system such as Vitis vinifera where 80% of the validated genes were in agreement with

RNA-Seq data [Zenoni et al, 2010].

400

RNASeq qPCR 150 300

200 100

100 50

Fold change Fold 0 change Fold

Allergen F-box Zinc finger PAL SGT1 TC4m USP WRKY Allergen F-box Zinc finger PAL SGT1 TC4m USP WRKY

An intriguing result obtained from these validations is the difference observed in gene expression for an allergen protein from a previous study (Whitehill et al, in press).

In the proteomic study an allergen was seen to be significantly higher expressed in resistant Manchurian ash compared to the susceptible black ash. Nevertheless, our study indicates the opposite. It is important to take into consideration that these could be two different proteins that belong to the same family. Before definitive conclusions can be made with regards to these proteins, it is highly recommendable to fully sequence the gene and protein and compare them. Also, expression quantification with a higher number of samples is advisable, followed by more functional studies including gene silencing. Similar precautions need to be taken with the rest of the candidate genes in this and previous studies. However, a platform for future research has been developed.

4.4.4 Molecular marker prediction

The databases were also analyzed to identify potential molecular markers including single nucleotide polymorphisms (SNPs) and microsatellites. SNPs were found both in the form of transitions (changes within the same nitrogenous bases) and transversions (changes from purines to pyrimidines or vice versa). A total of 42,944

SNPs for Manchurian ash, 42,023 for black ash, and 32,580 for green ash were detected.

More than 60% of the SNPs detected in each of the libraries were transitions (Table 4.5).

Msatfinder v2.0.9 was used to detect potential microsatellite markers. This search discovered approximately 2,000 potential microsatellites in Manchurian ash, 2262 in black ash, and 1,768 in green ash. These microsatellites range from di-nucleotide to octa-

77 nucleotide repeats, with the repeats ranging from five to twenty-eight (Table 4.6).

Molecular markers are widely used in population genetics, plant breeding programs, and development of linkage maps, among other studies [Julier et al, 2003; Sledge et al, 2005;

Vignal et al, 2002] and because of these potential uses, validation of these molecular markers would be of great importance for future studies in this system.

These results confirm that RNA-Seq is a suitable method for high throughput gene expression studies. We must keep in mind that this is a relatively new technique and as such there are still a number of biases inherent to it which haven‟t been fully dealt with

[Wang et al, 2009; Oshlack and Wakefield, 2009; Young et al, 2010; Hansen et al, 2010].

Further, the ash genome is yet to be sequenced. Nevertheless, an advantage of using a technique such as RNA-Seq is that it allows for a global understanding of the biology of the organism. Also, these results have expanded our previous knowledge on the ash transcriptome by increasing the number of ESTs and molecular markers available, as well as providing a number of differentially expressed genes [Bai et al, 2011].

Table 4. 6Comparison of single nucleotide polymorphism types among Fraxinus mandshurica (Manchurian ash), F. nigra (black ash) and F. pennsylvanica (green ash) phloem.

F. mandshurica F. nigra F. pennsylvanica Transitions A-G (R) 14,217 13,484 10,297 C-T (Y) 14,228 13,789 10,683 Transversions G-T (K) 3,612 3,767 2,988 A-C (M) 3,662 3,865 2,914 C-G (S) 2,880 2,612 2,236 A-T (W) 4,345 4,506 3,462 Total 42,944 42,023 32,580

Table 4. 7Summary of microsatellite repeats predicted in Fraxinus mandshurica (Manchurian ash), F. nigra (black ash) and F. pennsylvanica (green ash) phloem. F. mandshurica F. nigra F. pennsylvanica

Di- Tri- Tetra- Oct- Hex- Di- Tri- Tetra- Oct- Hex- Di- Tri- Tetra- Oct- Hex- 5 703 15 8 67 716 45 21 66 549 27 11 46

6 275 5 3 22 311 6 4 40 242 5 4 10

7 75 6 119 1 13 82 4 3

21 8 51 3 194 45 7 177 58 3 4 16 9 17 2 148 33 132 26 1 6 11 10 9 138 15 87 7 0 11 52 5 75 4 78 11

12 41 3 51 3 47 7

79 13 45 3 52 4 34

14 20 22 1 33 3

15 16 28 14 2

16 24 1 28 30 2

17 22 40 16

18 10 14 12

19 4 13 5

20 2 4

21 1

… 28 1

72 1,25 Subtotal 1,142 20 11 100 808 51 26 126 665 989 36 15 63 7 1

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Chapter 5

Conclusions

5.1 Summary

High throughput sequencing has become of great importance for the generation of molecular information in a relatively short period of time – especially for non-model organisms. This study clearly showcases the application of next generation sequencing

(454 pyrosequencing and Illumina) to a classical scenario of insect-plant interaction, wherein little to no genetic knowledge existed just a couple of years ago. Our datasets have now laid a solid foundation for identification of potential ash resistance factors to A. planipennis and has provided glimpses of the genetic makeup of ash phloem – expressed sequence tags (ESTs), metabolic pathways, molecular markers, etc.

Chapter 2 showcases the constitution of an ash phloem transcriptome, which was represented by ~58,000 unigenes (including white, green, black, blue and Manchurian ash). This information was obtained with the use of 454 pyrosequencing. The transcriptome was characterized by performing metabolic pathway analysis, Gene

Ontology (GO) term assignment, domain detection, and molecular marker (SNPs and microsatellites) prediction. Various metabolic pathways and candidate resistance genes/factors potentially associated to plant defense were identified in this database such

as phenylpropanoid biosynthesis pathway, transcription factors, proteases, lipases, PR- proteins, etc.

With a database now available for ash, it was possible to identify various housekeeping genes and analyze their expression in a series of tissues and ash species in order to identify a reliable reference gene, which is required in gene expression studies.

This part of the study indicated that transcription elongation factor 1α (eEF1α) is best suited to be used in real time quantitative PCR (RT-qPCR) based gene expression studies.

Comparison of gene expression between resistant and susceptible ash species can provide vital information on the modus operandi of resistance against A. planipennis. For this, a transcriptome wide gene expression analysis was done for green, black, and

Manchurian ash using the cutting-edge tool – RNA-Seq. The information obtained from this study also validated the phloem characteristics previously observed in Chapter 2.

Three more EST databases are now available for green, black and Manchurian ash, which in contrast to the first one are each specific to the species. Further, a higher number of molecular markers were also predicted in these databases. Several genes potentially involved in response to external stimulus, both biotic and abiotic, were observed to be differentially expressed between susceptible and resistant ash.

5.2 Future Studies

Overall, the acquired knowledge has provided critical insights into tree phloem composition in general. Moreover, this study has provided snapshots of ash transcriptome prior to A. planipennis attack (i.e. constitutive expression). The next ideal

step would be to profile ash phloem post A. planipennis attack in order to provide insights into induced mechanisms of ash resistance, which may be even more critical in the interaction. In order to carry out such studies, it will first be necessary to identify the best sampling time (hours, days, months or years post initial infestation). Given the feeding habit of A. planipennis (wood borer) it is difficult to properly identify an early infestation, which is vital for studies that wish to analyze the induced responses of ash to A. planipennis attack. Much effort has been put into identifying trees which are in the early stages of infestation [McCullough et al, 2009; Crook et al, 2008; Marshall et al, 2010].

Also, researchers have begun to artificially infest trees by pasting eggs on the bark

(Muilenburg; Rajarapu, personal communication). Despite these difficulties it is important to notice that progress is being made and a proper protocol to test for induced response is underway.

With regards to the candidate genes identified in this and other studies, several functional and bioassays need to be conducted for considering their incorporation into breeding programs. For example, full length sequence should be obtained for the genes in order to properly characterize them and confirm annotation. Also, functional studies such as gene silencing can provide the confirmation necessary that these genes are indeed functioning as predicted and that they are involved in plant response to herbivore attack, in particular A. planipennis. Finally gene expression analyses should be carried out in larger sample sizes as well as in trees which are currently under A. planipennis attack preferably early stages. Studies that emphasize the cloning of full-length candidate genes

(See Appendix B.1-7) that might participate in the overall resistance mechanism of ash to

A. planipennis are in progress. Lastly, the purified proteins of the candidate genes need to be tested for their efficacy in artificial feeding bioassays.

There is plenty of opportunity for research with the molecular markers predicted in this study. These markers could potentially be used to not only distinguish resistance phenotypes but also to allow identify different ash species. However, first these need to be validated, not only to identify those which might be due to sequencing error but also to identify markers which are polymorphic (intra- and inter-specific). These would allow for population genetic studies. Next, given that these molecular markers were obtained from ESTs it would be interesting to correlate the markers with the different genes and finally associate them with potential phenotypes in the population, including resistance levels to herbivore attack.

5.3 References

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