Agrilus Planipennis)

The Nature and Role of Host Defenses in Forest Pest Invasions: A Case Study Using Emerald Ash Borer (Agrilus planipennis)

DISSERTATION

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

David Neil Showalter, M.S.

Graduate Program in Plant Pathology

The Ohio State University

2017

Dissertation Committee:

Dr. Pierluigi Bonello, Advisor

Dr. Donald F. Cipollini, Jr.

Dr. Daniel A. Herms

Dr. Jason C. Slot

Dr. Guo-Liang Wang

David Neil Showalter

2017 ABSTRACT

Emerald ash borer (EAB) is causing massive ecological and economic damage as it devastates North American ash (Fraxinus spp.) populations. Resistance of its coevolved hosts is thought to limit EAB outbreaks and ash mortality in its native Asia, but an understanding of resistance mechanisms is still developing. The research reported in this dissertation provides new understanding of host defenses in the EAB-ash system, in regards to both their nature (chemical or biological identity and function) and role

(contribution to larval performance and tree survival), in part as a case study for a larger group of tree killing invasive pests.

In Chapter 2 I argue that once exotic bark- and wood-boring insects, as well as canker- and wilt-inducing pathogens, are recognized as newly established and damaging, work should begin to transition forests into “defense-constrained space” by developing host resistance. I provide an overview of conservation, marker-assisted selection, and targeted genetic engineering techniques and approaches, which together offer great potential to accelerate development of genetically diverse populations of insect- and pathogen-resistant trees. I acknowledge that improved strategies are needed to overcome substantial challenges to deploying resistant germplasm in forest restoration and realizing the full potential of modern tree resistance development, and provide suggestions for further development.

In Chapter 3 I describe the use of controlled egg inoculations to show lower rates of larval survival and development in coevolved Manchurian ash (F. mandshurica) compared to evolutionarily naïve white ash (F. americana). Water stress imposed on the ii host trees increased larval performance in Manchurian ash, with no significant effect on the already more susceptible white ash. These results show that the higher EAB resistance of Manchurian ash results from phloem traits that decrease larval performance, in addition to the previously documented lower oviposition preference.

In Chapter 4, I build on the idea that the pro-oxidant potential of ash phloem may be associated with resistance to EAB larval feeding. I profiled phloem phenolics and pro- oxidant enzyme activities in response to EAB larval feeding in susceptible white and resistant Manchurian ash, and found novel patterns of verbascoside-related chemistry.

Additionally, the results of this study have informed a revised hypothesis emphasizing the importance of pro-oxidant-associated peroxidase activities more than polyphenol oxidase and β-glucosidase activities in ash resistance to EAB.

In Chapter 5, I describe patterns of gene expression in response to EAB larval feeding in resistant Manchurian (F. mandshurica) and susceptible white (F. americana) ash. I found little support for a previous hypothesis of impaired recognition and signaling in susceptible ash species, but also identified a rich complement of transcription factors requiring further analysis. Expression results support the association of proteins in the major allergen Bet v 1 family with ash resistance to EAB. New mechanisms for further investigation relating to EAB resistance suggested by this study include lectin binding and phosphorylation, oxidoreductase activity, leucine-rich repeat transmembrane protein kinase (LRRTK) activity, and terpene synthase activities.

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ACKNOWLEDGMENTS

I am grateful to the current and past members of my advisory committee, Brian

McSpadden Gardener, Omprakash Mittapalli, Guo-Liang Wang, Jason Slot, Don

Cipollini, Dan Herms, and my advisor Enrico Bonello, for guiding me through the academic research process. They provided me with challenging and open research questions early in my program, as well as critical and constructive feedback on the interpretation and presentation of early results and of the design of subsequent experiments. I would like to additionally thank Dan for his generous sharing of research resources in the form of trees, equipment and accommodations, as well as his own time and expertise and that of his lab members. Dan was also generous with probing questions that had a way of exposing half-formed ideas or hasty conclusions, for which I am also grateful. I thank Enrico for his role as a mentor. He has been a consistent advocate for me and my fellow students, provided insightful, if sometimes difficult, suggestions to improve research and broader scholarship, and showed the restraint to allow me to grow and find ways to motivate myself to pursue my research and professional development.

Most of all, I would like to thank Kelly and Jonah. Jonah gives me heart-melting smiles and belly laughs, as well as perspective, optimism, and a renewed sense of wonder. Kelly provides love and support without which I may not have begun, much less

iv completed, graduate study. She challenges and inspires me to be a more active, empathetic, and confident version of myself.

Pierluigi Bonello, Dan Herms, Jason Smith, Richard Sniezko, Sandy Liebhold, and Ken Raffa provided help in conceiving of, developing, and providing feedback on the ideas presented in Chapter 2. The attendees of the 2015 Conference on the Genetics of

Tree-Parasite Interactions in Orleans, France participated in discussion and debate of the concepts in this chapter. Caterina Villari and John Obrycki reviewed early drafts. This work was supported by state and federal funds appropriated to The Ohio State University, and the Ohio Agricultural Research and Development Center, and by the University of

Wisconsin Vilas and Sorenson Foundations.

Caterina Villari, Pierluigi Bonello, and Dan Herms assisted in designing and interpreting the experiments in Chapters 3-5, additionally with Don Cipollini and Chad

Rigsby for experiments in Chapter 4. Caterina Villari assisted in phenolic analysis of the experiment in Chapter 4, and Chad Rigsby performed enzyme activity assays. Asela

Wijeratne and Saranga Wijeratne provided advice and assistance in designing and implementing the bioinformatic analysis in Chapter 5, with additional consultation from

Tea Meulia on the experimental design, sequencing, and bioinformatics approaches.

Robert Hansen, Alejandro Chiriboga, Amilcar Vargas, Ellie Shoup, Diane Hartzler, Paul

Snyder, Sara Rudowsky, Christian Bonilla, Bryant Chambers, Amy Hill, Anna Conrad,

Patrick Sherwood, Michael Falk, and Amy Itnyre (The Ohio State University) assisted in setup, maintenance, and harvest of the experiments in Chapters 3 and 5. Jonathan Lelito and Scott Whitehead (USDA-APHIS-PPQ Biological Control Rearing Facility, Brighton,

MI) provided EAB eggs for experiments in Chapters 3-5. Funding was provided by an

OARDC SEEDS Graduate Research Enhancement Competitive Grant Award, an experiment.com crowdfunding campaign (DOI: 10.18258/2954), a grant from the USDA

APHIS/USDA Forest Service Accelerated Emerald Ash Borer Research Program

(GRT00011769/60016270), and by State and Federal funds appropriated to the Ohio

Agricultural Research and Development Center and The Ohio State University.

VITA

2009...... B.S. Biochemistry, Eastern Mennonite University

2011 to 2014 ...... Graduate Fellow, Department of Plant

Pathology, The Ohio State University

2014...... M.S. Plant Pathology, The Ohio State University

2014 to present ...... Graduate Research Associate, Department

of Plant Pathology, The Ohio State

University

PUBLICATIONS

Rigsby CM, Showatler DN, Herms DA, Koch JL, Bonello P, Cipollini D. 2015. Physiological responses of emerald ash borer larvae to feeding on different ash species reveal putative resistance mechanisms and insect counter-adaptations. Journal of Insect Physiology 78: 47-54.

Showalter DN, Troyer EJ, Aklu M, Jang EB, Siderhurst MS. 2010. Alkylpyrazines: alarm pheromone components of the little fire ant, Wasmannia auropunctata (Roger) (Hymenoptera, Formicidae). Insectes Sociaux 57:223-232.

Jang EB, Siderhurst MS, Hollingsworth RG, Showalter DN, Troyer EJ. 2010. Sex attractant for the banana moth, Opogona sacchari Bojer (Lepidoptera: Tineidae): provisional identification and field evaluation. Pest Management Science 66:454-460.

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Troyer EJ, Derstine NT, Showalter DN, Jang EB, Siderhurst MS. 2009. Field studies of Wasmannia auropunctata alkylpyrazines: towards management applications. Sociobiology 54:955-971.

FIELDS OF STUDY

Major Field: Plant Pathology

Specialization: Plant Molecular Biology and Biotechnology

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TABLE OF CONTENTS

ABSTRACT ...... ii

ACKNOWLEDGMENTS ...... iv

VITA ...... vii

PUBLICATIONS ...... vii

FIELDS OF STUDY ...... viii

TABLE OF CONTENTS ...... ix

LIST OF TABLES ...... xii

LIST OF FIGURES ...... xvii

CHAPTER 1: LITERATURE REVIEW ...... 1

EMERALD ASH BORER INVASION HISTORY AND ECOLOGY ...... 1

BIOLOGICAL INVASION HISTORY AND MECHANISMS ...... 2

HOST DEFENSE SYNDROMES ...... 7

CHAPTER 2: TREE RESISTANCE AS A PRIMARY TOOL FOR MANAGING

FOREST PATHOGEN AND INSECT INVASIONS IN DEFENSE-FREE SPACE...... 9

ABSTRACT ...... 9

INTRODUCTION ...... 10

TOWARDS A SOLUTION - THE CASE FOR HOST RESISTANCE ...... 23

RECENT ADVANCES IN HOST RESISTANCE DEVELOPMENT ...... 29

CONCLUSIONS ...... 41

CHAPTER 3: SURVIVAL AND DEVELOPMENT OF EMERALD ASH BORER

LARVAE IN WHITE AND MANCHURIAN ASH UNDER WATER STRESS

IMPLICATE COEVOLVED PHLOEM-BASED TRAITS IN RESISTANCE OF

MANCHURIAN ASH ...... 43

ABSTRACT ...... 43

INTRODUCTION ...... 44

MATERIALS AND METHODS ...... 46

RESULTS...... 51

DISCUSSION ...... 60

CHAPTER 4: PRO-OXIDANT-ASSOCIATED ENZYME ACTIVITIES AND

PHENOLIC PROFILES IN RESPONSE TO EMERALD ASH BORER LARVAL

FEEDING IN WHITE AND MANCHURIAN ASH ...... 66

ABSTRACT ...... 66

INTRODUCTION ...... 67

MATERIALS AND METHODS ...... 68

RESULTS...... 74 x

DISCUSSION ...... 87

CHAPTER 5: TRANSCRIPTOMIC PROFILES OF RESISTANT AND SUSCEPTIBLE

ASH IN RESPONSE TO EARLY FEEDING BY EAB LARVAE ...... 92

ABSTRACT ...... 92

INTRODUCTION ...... 93

MATERIALS AND METHODS ...... 94

RESULTS...... 101

DISCUSSION ...... 119

CHAPTER 6: CONCLUSIONS AND FUTURE DIRECTIONS ...... 126

REFERENCES ...... 133

APPENDIX A: SUPPLEMENTARY REFERENCES FOR PIP RESISTANCE

DEVELOPMENT PROGRAMS IN CHAPTER 2...... 150

LIST OF TABLES

Table 2.1. Overview of approaches to tree resistance development, relevant technologies, strengths, and weaknesses………………………………………………………………..31

Table 2.2. Selected examples of phytophagous insects and phytopathogens (PIPs) requiring early and sustained investment in host resistance discovery and development.

All PIPs are intimately associated with host stem tissues (phloem, cambium, or xylem) at one or more critical life stages…………………………………………………………...38

Table 3.1. Analysis of variance results for species, water availability, and nutrient availability effects and interactions on arcsine transformed gallery initiation proportions in experiment 2. df; degrees of freedom. Bolded P values < 0.05, underlined < 0.1. (n = 8 replicate trees per treatment combination; total df = 63)………………………………...53

Table 3.2. Analysis of variance results for species, water availability, and nutrient availability effects and interactions on arcsine transformed larval outcome proportions in experiment 2. df; degrees of freedom. Bolded P values < 0.05, underlined < 0.1. (n = 8 replicate trees per treatment combination; total df = 62)………………………………53

Table 3.3. Analysis of variance results for water availability and nutrient availability effects and interaction on arcsine transformed larval outcome proportions in Manchurian

xii ash in experiment 2. df; degrees of freedom. Bolded P values < 0.05, underlined < 0.1. (n

= 8 replicate trees per treatment combination; total df = 62)…………………………….54

Table 3.4. Analysis of variance results for trunk diameter growth of white and

Manchurian ash in response to water and nutrient availability (df, degree of freedom).

Bolded P values < 0.05, underlined < 0.1. (n = 8 replicate trees per treatment combination; total df = 63)………………………………………………………………55

Table 3.5. Analysis of covariance results for light saturated net photosynthesis (Anet) and stomatal conductance (gs) for white and Manchurian ash in response to water and nutrient availability on three dates (df, degree of freedom; VPD, vapor pressure deficit). Bolded P values < 0.05, underlined < 0.1. (n = 8 replicate trees per treatment combination; total df

= 63)……………………………………………………………………………………...56

Table 4.1. Analysis of variance of enzyme specific activities by substrate in control and

EAB-inoculated white ash and Manchurian ash. Bolded values are statistically significant at α=0.05 (n = 8-10 replicate trees per treatment combination. df*= degrees of freedom; denominator degrees of freedom approximated according to method of Kenward and

Roger (1997) for split-plot analysis with missing data…………………………………..75

Table 4.2. Specific activities of β-glucosidase (βG) and peroxidase (POX) against different substrates in phloem extracts of in white and Manchurian ash. Least squares means (±SE)……………………………………………………………………………...75

Table 4.3. Specific activity of polyphenoloxidase (PPO) against the substrate catechol in control and EAB inoculation treatments in white and Manchurian ash. Least squares means (±SE)……………………………………………………………………………75

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Table 4.4. UPLC PDA and MS data used for provisional identification of phenolic compounds in white and Manchurian ash………………………………………………..78

Table 4.5. Loading scores for each phenolic peak in the principal component (PC2) associated with sample separation by inoculation treatment in white ash and Manchurian ash (see Figs. 4.3 and 4.4). Bolded values met the absolute value > 0.2 threshold and indicate peaks retained for univariate ANOVA. Underlined peaks were also retained for relative quantitation due to spectral similarity to the phenolic glycosides oleuropein and verbascoside, potential substrates for β-glucosidases……………………………………82

Table 4.6. Analysis of variance of normalized peak areas for shared phenolic compounds

(peak ID number in parentheses) in control and EAB-inoculated white ash and

Manchurian ash. Bolded values are statistically significant at P = 0.05. n = 10 per treatment combination…………………………………………………………………83

Table 4.7. Analysis of variance of normalized peak areas for shared and unique phenolic compounds (peak ID number in parentheses) in control and EAB-inoculated white ash.

Bolded values are statistically significant at P = 0.05. n = 10 per treatment combination.

Note: the shared compound pinoresinol derivative (24) was not analyzed separately by species as no significant S x I interaction was present…………………………………..84

Table 4.8. Analysis of variance of normalized peak areas for shared and unique phenolic compounds (peak ID number in parentheses) in control and EAB-inoculated Manchurian ash. Bolded values are statistically significant at P = 0.05. n = 10 per treatment combination. Note: the shared compound pinoresinol derivative (24) was not analyzed separately by species as no significant S x I interaction was present…………………....84

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Table 4.9. LSmean (SE) of normalized peak areas for phenolic compounds with significant inoculation or species x inoculation effects, as well as verbascoside and oleuropein derivatives in white ash and Manchurian ash……………………………..…85

Table 5.1. Summary statistics for ash transcriptome assemblies. *N50 is the transcript length at which half of the assembled bases are in transcripts longer than this length and half are in transcripts shorter than this length. Longest isoform N50 is calculated using only the longest isoform from each Trinity gene model………………………………..110

Table 5.2. Log2 fold change and false-discovery-rate-adjusted p-values (FDRp) for pairwise contrasts of induction treatments for transcript annotations related to early jasmonic acid signaling in white and Manchurian ash. FDRp < 0.05 are bolded, < 0.1 are underlined. o-p= ortholog-paralog group with at least one member from each species and at least three total members. *The Manchurian ash member of group 13832 was annotated as JAZ3 while the white ash member was annotated as JAZ1. As its expression much more closely matches that of other transcripts with JAZ1 annotations, it is considered as such. Notations: EAB: inoculations with EAB larvae; W: wounding treatment; C: unwounded control……………………………………………………….112

Table 5.3 Log2 fold change and false-discovery-rate-adjusted P-values (FDRp) for pairwise contrasts of induction treatments for transcript annotations related to major allergen in white and Manchurian ash. Mal d 1 and Bet v 1 annotations are structurally related and have been associated with PR-10 function. FDRp < 0.05 are bolded , < 0.1 are underlined. ”-“ indicates transcripts that were filtered by edgeR based on low

xv transcription across all samples. Notations: EAB: inoculations with EAB larvae; W: wounding treatment; C: unwounded control…………………………………………....113

Table 5.4. Log2 fold change and false-discovery-rate-adjusted P-values (FDRp) for pairwise contrasts of induction treatments for selected transcript annotations related to phenolic biosynthesis and oxidation in white and Manchurian ash. FDRp < 0.05 are bolded, < 0.1 are underlined. ”-“ indicates transcripts that were filtered by edgeR based on low transcription across all samples. o-p= ortholog-paralog group with at least one member from each species and at least three total members. Abbreviations and total number of transcripts for each annotation (only significantly DE shown): SK= shikimate kinase family, 5; C4H= cinnamic acid 4-hydroxylase, 5; 4CL= 4-coumarate ligase family, 13; CCR= cinnamoyl-CoA reductase family, 10; BGlu= beta-glucosidase family,

31. Notations: EAB: inoculations with EAB larvae; W: wounding treatment; C: unwounded control……………………………………………………………………..114

Table 6.1. Summary of larval performance-based resistance phenotypes of Fraxinus americana and F. mandshurica cultivars and associated candidate defense traits described in this dissertation. Styled after summary by Villari et al. (2016)…………...128

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LIST OF FIGURES

(B) white pine blister rust. Early detection may be complicated by cryptic nature of infestations, such as (C) small, infrequent adult emergence holes and (D) non-descript canopy thinning seen in early emerald ash borer infestation, or (E) small needle lesions and (F) branch flagging seen in early white pine blister rust disease development.(G,H)

Damage to critical vascular tissues or meristems leads to rapid decline and death of trees without adequate defenses. Image credits: (A) Bill McNee, Wisconsin Dept of Natural

Resources, Bugwood.org.(B) Dave Powell, USDA Forest Service (retired),

Bugwood.org. (C,D) David Showalter, The Ohio State University. (E) USDA Forest

Service – Ogden, Bugwood.org. (F) Chris Schnepf, University of Idaho, Bugwood.org.

(G) Eric R. Day, Virginia Polytechnic Institute and State University, Bugwood.org. (H)

Frantisek Soukup, Bugwood.org…………………………………………………………11

xvii through up to four sequential stages (numbered circles) by overcoming barriers (lettered boxes), with the passage of time (thick arrows). Management interventions (upper thin lines) attempt to strengthen barriers between stages. PIP sources may be populations of

PIPs in native ranges or previously invaded naïve ranges (in stages 3 and 4). Within an ecosystem, invasions may cycle between the final two stages, PIP spread (3) and outbreak or epidemic (4), depending on the barriers provided by existing biotic and abiotic environmental factors (D). Note: The generalized invasion framework proposed by Blackburn et al (2011) provides additional detail, with the distinction that for forest

PIPs, impacts are strongly associated with high PIP population density in the outbreak/ epidemic stage………………………………………………………………………..…14

Figure 2.3. Response framework for established, alien forest phytophagous insects and phytopathogens (PIPs). (1) Once a PIP is identified as a threat to become established in a forest ecosystem, (2) research responses must (2A) assess cultural, economic, and ecological value of hosts in various settings, (2B) characterize the invasion ecology in coevolved and naïve ranges to asses impact risk, and (2C) outline available short- and long-term impact mitigation approaches. (3) Likely costs and benefits of various approaches are estimated with available data, and (4) goals are set within limits of current response capacity for the forest ecosystem under consideration. Response goals are periodically revised through the framework, with (5A) reevaluation of invasion progression and (5B) the development of new mitigation strategies or technologies…...17

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Figure 2.4. Effective plant defense strategies and associated phytophagous insect and phytopathogen (PIP) traits. (A) Plant defense strategies and corresponding PIP population influences and invasion mechanisms vary along a continuum. (B) Features of the PIP- host interaction, intimacy and damage, are associated with the strategy/ influence/ mechanism continuum, and with feeding guild, symptomology, phenology, and host tissue. Shading indicates the likely importance of host resistance development in managing invasions of defense-free space. The pests emplasized in this review, i.e. phloem- and wood-borers and cankers and vascular wilts, occupy the darkest shaded, upper right region. Synthesized and adapted from Mattson et al. (1988)………………..24

Manchurian ash. Patterned bars represent low water availability treatment in (B) and low

(30ppm) nutrient availability treatment in (C). Least squares means (± SE). Refer to results text and Table 3.2 for tests of statistical significance…………………………….52

Figure 3.2. Light saturated net photosynthesis (Anet) in experiment 2 for white ash (white bar) and Manchurian ash (grey bar) in response to water (A,C,E) and nutrient availability

(B,D,F) on three dates in 2013 (lsmeans ± SE). Refer to Table 3.5 for results of statistical tests of significance………………………………………………………………………57

Figure 3.3. Stomatal conductance (gs) in experiment 2 for white ash (white bar) and

Manchurian ash (grey bar) in response to water (A,C,E) and nutrient availability (B,D,F) xix on three dates in 2013 (lsmeans ± SE). Refer to Table 3.5 for results of statistical tests of significance………………………………………………………………………………58

Figure 4.1. Comparison of relative activities of pro-oxidant related enzymes in phloem extracts of black, white, and Manchurian ash in the current study and Rigsby et al.

2016...... 76

Figure 4.2. Phenolic metabolite data reduction workflow combining multivariate analysis and limited univariate analysis to reduce type I error and maintain statistical power…...77

Figure 4.3. Principal component biplot of phenolic peaks in white ash. Numbers correspond to individual trees with red text representing samples from control branches and blue text representing samples from EAB-inoculated branches. Vectors with “P#” labels represent loading scores of each numbered phenolic peak for the two plotted principal components (PCs). Note that PC2, and thus peaks with vertically oriented vectors, are primarily responsible for separating samples by inoculation treatment…….80

Figure 4.4. Principal component biplot of phenolic peaks in Manchurian ash. Numbers correspond to individual trees with red text representing samples from control branches and blue text representing samples from EAB-inoculated branches. Vectors with “P#” labels represent loading scores of each peak for the two plotted principal components

(PCs). Note that PC2, and thus peaks with vertically oriented vectors, are primarily responsible for separating samples by inoculation treatment…………………………....81

Figure 5.1. Schematic of bioinformatic workflow employed to provide species-specific de novo assembled transcriptomes while allowing for interspecific comparisons of gene expression patterns……………………………………………………………………...108

Figure 5.2. Multidimensional scaling (MDS) plots of leading log2 fold changes in expression between samples before and after trimming in Manchurian (a,b) and white ash

(c,d). Leading log2 fold changes are calculated as the root mean square average of the log2 fold changes in expression between top transcripts in each pair of samples to provide a measure of similarity of expression among samples. Sample naming convention: White ash or Manchurian ash; Control, Wound, or EAB treatment; High or

Low nutrient availability; and replicate 1-3…………………………………………….109

Figure 5.3. Estimates of white ash and Manchurian ash transcriptome assembly completeness using ortholog hit ratio (OHR) compared to Fraxinus excelsior genome- guided assembly from the British Ash Tree Genome Project. Ortholog hit ratio is calculated by dividing the length of the query transcript by the length of the orthologous transcript in the reference assembly. Bars (gray = Manchurian ash, white = white ash) represent counts of transcripts binned by OHR. Lines (solid= Manchurian ash, dashed = white ash) represent percentage of total transcripts with a given OHR or greater……..111

Figure.5.4. Principal components analysis plot based on counts per million mapped reads

(CPM) for 8,676 orthologous transcripts in each sample. Manchurian ash samples are represented in red, and white ash samples in blue. Control samples are represented by circles, wounding by triangles, and EAB induction by squares. Percentage of total variation encompassed by each principal component: PC1=34%, PC2=21%...... 116

Figure 5.5. Semi-quantitative comparison of EAB-induced ortholog transcription between species, plotted as log2 fold change of EAB vs. control treatments in white ash vs.

Manchurian ash. Positive values represent increased expression in response to EAB

xxi feeding and negative values represent decreased expression. Solid line represents when white ash and Manchurian ash fold changes are equal. Dashed lines represent the thresholds of |log2 fold change| >2 in each species. Points are colored red when transcripts are significantly differentially expressed (FDRp < 0.05) and exhibit a |log2 fold change| >2 in both species, green when only in Manchurian ash, and blue when only in white ash. Point size is proportional to the average expression level (log2 counts per million mapped reads) across Manchurian ash samples of all induction treatments…...117

Figure.5.6. Patterns of differential expression for orthologous sequences between species and induction treatments. Numbers of transcripts in each species significantly differentially expressed (FDRp < 0.05), with a |log2 fold change| > 2 and a) up or b) down in EAB vs control treatments. c) Expression patterns including wound treatments for up- regulated transcripts vs. control. No orthologous transcripts were significantly differentially expressed and down-regulated in wound vs. control treatments, so panel b represents the full complement of significant patterns for transcripts down-regulated vs. control…………………………………………………………………………………..118

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CHAPTER 1: LITERATURE REVIEW

EMERALD ASH BORER INVASION HISTORY AND ECOLOGY

Emerald ash borer (EAB; Agrilus planipennis Fairmaire) (Coleoptera: Buprestidae) invasion history and ecology has recently been reviewed in detail (Herms and

McCullough 2014, Villari et al. 2016). Briefly, EAB was first detected in North America in 2002 in southeastern Michigan where it was associated with rapid decline and death of otherwise healthy ash trees (Fraxinus spp.) (Cappaert et al. 2005). It was subsequently discovered, through dendrochronological analysis, that the beetle had been present in the area at least since the early 1990s (Siegert et al. 2014). EAB additionally has been discovered in eastern Europe near Moscow, Russia (Baranchikov et al. 2008). The insect’s native range is northeastern Asia, including parts of China and Russia, the

Korean Peninsula and Japan, where it occurs as a secondary colonizer of stressed or declining coevolved Fraxinus species, and as a primarily colonizer of introduced North

American ash species (Wei et al. 2004, Liu et al. 2007). EAB adults lay their eggs on the outer bark of ash trees in late spring and early summer. Belonging to the phloem- and wood-boring insect feeding guild, EAB larvae hatch and chew their way into the inner bark, destroying the host tree’s phloem, cambium, and water-conducting outer xylem tissues as they excavate serpentine feeding galleries. Larvae grow and develop through 1 four larval instars, overwinter as prepupae, pupate, and then emerge as adults the following spring, when they briefly consume ash foliage for sexual maturation, mate, and oviposit (Cappaert et al. 2005).

Emerald ash borer is devastating populations of North American ash species

(Klooster et al. 2014, Morin et al. 2016), and also colonizes white fringtree (Chionanthus virginicus L.) (Cipollini and Rigsby 2015). In terms of economic, ecological, and societal impacts, this invasion is unprecedented for a forest insect (Aukema et al. 2011).

However, in many ways the ongoing EAB outbreak is a classic biological invasion, and so presents an opportunity, and perhaps even a mandate, to further understand biological invasion mechanisms and their applications to management.

BIOLOGICAL INVASION HISTORY AND MECHANISMS

Biological invasions likely predate recorded history, but the concept was first generally introduced by Charles Elton in 1958 in his book The Ecology of Invasions by Animals and Plants. The field of invasion biology has since been formalized and has matured into an active sub-discipline of ecology concerned with the transport, introduction, establishment, spread, and impacts of living organisms in novel geographic ranges or ecosystems (Davis 2009). Much of the impetus for the study of biological invasions is their occasional significant ecological and economic impacts, such that, in the United

States, “invasive species,” “introduction” and related terms have formal legal definitions via Presidential executive order or Congressional legislation (NISC 2005).

Known biological invasions of forest ecosystems include introductions of herbaceous and woody plants (Martin et al. 2009), animals (Pimentel 1986), and many other heterotrophic organisms. Of most relevance to the research reported herein are invasions by pests that directly consume the tissues or fluids of trees, including phytophagous insects and phytopathogenic microorganisms (PIPs). Such invasions have occurred in North America for well over a century (Aukema et al. 2010) and occasionally have significant economic and ecological impacts (Loo 2009, Holmes et al. 2009, Lovett et al. 2016) such as the iconic cases of chestnut blight (Anagnostakis 1987) and European gypsy moth (Lymantria dispar dispar L.) (Davidson et al. 1999). In contrast to the conspicuous invasions causing widespread tree mortality and associated impacts, many introductions of alien species cause little or no measurable damage (Aukema et al. 2011).

The mechanisms underlying forest pest invasion success and impacts are therefore of great interest.

Invasion biologists have described a number of generally recognized sequential stages of invasion for any given organism or population: transport, introduction, establishment and spread (Blackburn et al. 2011). At each stage, the invader must overcome a number of obstacles in order to progress to the next stage of invasion, including geographic distance, survival, reproduction and dispersal. Biotic and abiotic factors associated with forest ecosystems, together with biological characteristics of the insect or pathogen, determine the relative strength of each obstacle, and are thus potential targets for development of management interventions (Blackburn et al. 2011). Chapter 2

3 makes an argument for, and describes the process of, using invasion characteristics to guide management.

Factors affecting obstacles to invasion may be anthropogenic, and are often spatially and temporally dynamic. For example, high-volume, rapid, long-distance trade of infested raw wood (Haack et al. 2014) and live plants (Liebhold et al. 2012) can weaken the geographic barrier that prevents PIP introduction in the first place. For introduced PIPs, the relative unavailability of suitable hosts in the new environment may be a significant obstacle to survival and reproduction (Guyot et al. 2015). Furthermore, even when hosts for insects are available, species that need to find mates to reproduce

(Tobin et al. 2009) and/or require cooperative behaviors to overcome host defense (Raffa and Berryman 1983), may encounter Allee effects that pose significant barriers to survival and reproduction of small founder populations. Allee effects describe the density-dependent phenomenon by which small populations may go extinct due to the positive relationship between population density and population growth rate in founder populations (Courchamp et al. 1999, Tobin et al. 2011). Allee effects can also be operative with pathogens (Garrett and Bowden 2002), but have not been studied in great detail in these systems.

Just as long-distance trade facilitates PIP introduction, trade of infested goods within a formerly naïve forest ecosystem can weaken dispersal barriers and facilitate spread of established PIP populations within the range of their new host trees. For example, the movement of infested firewood and nursery stock enabled long-distance spread of EAB (Tobin et al. 2010). The presence of biotic vectors, particularly for

4 pathogens, can also weaken dispersal barriers, as is the case of native elm bark beetles

(Scolytus spp.) vectoring the introduced fungal causal agent of Dutch elm disease

(Ophiostoma novo-ulmi Brasier) in Europe (Webber 1990).

Finally, PIP outbreak dynamics are determined by a number of biotic and abiotic factors that affect PIP population size by enhancing or inhibiting PIP survival. Abiotic environmental factors, such as temperature or precipitation extremes, modify PIP populations in a density-independent manner, as is the case when a frost event kills a proportion of a PIP population in an area, or when relative humidity favors fungal pathogen sporulation, regardless of population size. Abiotic factors may alternatively, or additionally, modify or interact with the regulating effect of density-dependent biotic factors on PIP populations. These biotic factors include top-down forces exerted by the abundance and activity of natural enemies (predators, parasitoids, pathogens, and more generally antagonists of PIPs) and bottom-up forces exerted by the abundance of host trees and the efficacy of tree defenses. Both top-down and bottom-up drivers are idiosyncratically important for the development of individual invasive PIP outbreaks, and two non-exclusive hypotheses have emerged to explain release of invading herbivores from biotic resistance: the enemy-free space hypothesis (Jeffries and Lawton 1984) and the defense-free space hypothesis (Gandhi and Herms 2010). Along with many other determining factors, the degree that these hypotheses explain the population dynamics of an invading organism can inform how that invasion may best be managed.

Invasions by phloem and wood-boring insects are somewhat less common than those by insects of other feeding guilds, but rates of introduction appear to be increasing

5 in recent decades, probably due to increases in international trade volumes and raw wood utilization practices (Aukema et al. 2010). As with all biological invasions by heterotrophic organisms, both top-down and bottom-up factors affect invasion success and host tree damage by phloem- and wood-borers. However, host damage is associated with bottom-up factors of naïve or defense-compromised host trees regulating some members of this feeding guild. The majority of borer invasions have been by bark and ambrosia beetles of the family Curculionidae subfamily Scolytinae, which are commonly closely associated with fungi and/or other microbes. An example includes the invasive walnut twig beetle (Pityophthorus juglandis Blackman) with the associated fungus

Geosmithia morbida M. Kolarík, E. Freeland, C. Utley & Tisserat, which together cause thousand cankers disease on evolutionarily naïve black walnut (Juglans nigra L.) in

North America (Tisserat et al. 2011). There have also been a number of non-Scolytid wood-borer invasions by beetles in the subfamilies Cerambicidae and Buprestidae that have been associated with bottom-up factors, including eucalyptus longhorned borer

(Phoracantha semipunctata Fabricius) (Hanks et al. 1999), and goldspotted oak borer

(Agrilus auroguttatus Schaeffer) (Haavik et al. 2013). Patterns of resistance in naïve and coevolved hosts of EAB that suggest host defenses also play a role in determining invasive outbreaks, and this hypothesis is further developed in Chapters 2 and 3.

HOST DEFENSE SYNDROMES

Plant defenses against herbivores and phytopathogens are diverse and may be categorized by a number of characteristics including constitutive or induced patterns of expression, direct or indirect effects on herbivores or pathogens, physical or chemical manifestation, as well as many modes of action. Trees are sessile, long-lived organisms with long generation times that result in the exposure of individuals to a variety of biotic and abiotic stressors that may change or evolve on relatively shorter time scales (Petit and

Hampe 2006). Such a life history has likely contributed to the evolution of an enormous diversity of defense-associated metabolism in trees (Weng et al. 2012). Maintenance and expression of specialized functions, including defenses, likely incur fitness costs (Herms and Mattson 1992, Heil 2002) and so must be regulated and tailored to different circumstances. Defense expression, as well as growth and reproduction, is regulated in plants by a complex network of interconnected phytohormone pathways. The jasmonic acid (JA) and salicylic acid (SA) pathways are primarily responsible for coordinating defense responses to herbivores and biotrophic pathogens, respectively, through partially antagonistic crosstalk (Glazebrook 2005). However, it is becoming clear that specificity of defense responses to different combinations of biotic and abiotic stressors is a result of complex crosstalk between JA, SA, and other canonical phytohormone pathways as well, particularly ethylene (ET) and abscisic acid (ABA) (Verma et al. 2016). Several of the proteins and small molecules mediating this crosstalk have been described, including

7 transcription factors in the WRKY (Li 2004, Pandey and Somssich 2009), and ERF families (Lorenzo et al. 2003).

A variety of defense-associated compounds have been identified in the phloem of ash species. Abundant and well-characterized are phenolic compounds (Terazawa 1986,

Kostova and Iossifova 2007), which vary substantially between taxonomic subgroups of the genus Fraxinus (Eyles et al. 2007, Cipollini et al. 2011, Whitehill et al. 2012) but have not been shown to be particularly responsive to EAB feeding or strongly associated with expression of resistance (Chakraborty et al. 2014, Villari et al. 2016). It is possible, however, that combinations of phenolic compounds may contribute to effective defense against EAB, perhaps through inhibition of proteases, protein crosslinking, or generation of oxidative stress (Rigsby et al. 2015, 2016). The association of these activities with resistance is explored further in Chapter 4.

In summary, the objectives of the research in this dissertation are to investigate the importance of phloem-based defenses in determining ash resistance and survival to

EAB attack, provide information on possible genetic bases of previously identified resistance traits, and describe new defense associated traits responsive to EAB attack in resistant and susceptible ash species, including pro-oxidant activities of ash phloem associated with EAB resistance.

CHAPTER 2: TREE RESISTANCE AS A PRIMARY TOOL FOR MANAGING FOREST PATHOGEN AND INSECT INVASIONS IN DEFENSE-FREE SPACE

ABSTRACT

Global trade facilitates movement of tree-killing, alien phytophagous insects and phytopathogens (PIPs). Several alien PIPs have caused massive mortality of host trees due to the “defense-free space” they find in naïve forest environments. Such PIPs often exhibit cryptic host association that can impede early detection, particularly pathogens and those insects that lack long-distance pheromones, and have life histories that destroy host tissue with high fitness value. Examples include bark- and wood-boring insects, and canker and wilt-inducing pathogens. It is argued herein that once such PIPs are recognized as newly established and damaging, work should begin to transition forests into “defense-constrained space” by developing host resistance. Combinations of conservation, marker-assisted selection, and targeted genetic engineering offer great potential to accelerate development of genetically diverse populations of PIP-resistant trees. Improved strategies are needed to overcome substantial challenges to deploying resistant germplasm in forest restoration and realizing the full potential of modern tree resistance development.

INTRODUCTION

Invasions by non-native phytophagous insects and phytopathogens (henceforth, PIPs) have dramatically altered forests in North America and elsewhere, sometimes with enormous economic and ecological impacts (Fig. 2.1 A and B) (Loo 2009; Gandhi and

Herms 2010b; Hicke et al. 2012; Lovett et al. 2016). Particularly devastating have been invasions by some PIPs that are intimately and cryptically associated with their hosts, including pathogens and some insect groups such as bark- and wood- borers. These cryptic associations make detection during transport and early detection of invading populations difficult (Fig. 2.1 C-F), particularly for pathogens and those insects that lack long-distance pheromones. Once established, eradication and containment of such cryptic organisms is often difficult and sometimes impossible. Some of these same PIPs damage critical host tissues with high fitness value, the loss of which plants cannot tolerate (Fig.

2.1 G and H). Such PIPs thus proceed to kill large proportions of their host trees on range-wide scales. The functional extinction of American chestnut [Castanea dentata

(Marshall) Borkham], a foundational tree species, to chestnut blight caused by the introduced fungus Cryphonectria parasitica (Murrill) Barr is an iconic example

(Anagnostakis 1987), in which eastern North American forest ecosystems and rural

Appalachian economies were dramatically altered (Davis 2005). On the other side of the continent, white pine blister rust, caused by the introduced fungus Cronartium ribicola

Fisch., in combination with climate change-driven range expansion of mountain pine beetle (Dendroctonus ponderosae Hopkins) into semi-naïve host populations, threatens

Figure 2.1 Landscape-, tree-, and tissue-scale presentations of tree-killing phytophagous insect and phytopathogen (PIP) invasions. Forest landscapes may be completely transformed by tree mortality resulting from PIP invasions like (A) emerald ash borer and (B) white pine blister rust. Early detection may be complicated by cryptic nature of infestations, such as (C) small, infrequent adult emergence holes and (D) non-descript canopy thinning seen in early emerald ash borer infestation, or (E) small needle lesions and (F) branch flagging seen in early white pine blister rust disease development.(G,H) Damage to critical vascular tissues or meristems leads to rapid decline and death of trees without adequate defenses. Image credits: (A) Bill McNee, Wisconsin Dept of Natural Resources, Bugwood.org.(B) Dave Powell, USDA Forest Service (retired), Bugwood.org. (C,D) David Showalter, The Ohio State University. (E) USDA Forest Service – Ogden, Bugwood.org. (F) Chris Schnepf, University of Idaho, Bugwood.org. (G) Eric R. Day, Virginia Polytechnic Institute and State University, Bugwood.org. (H) Frantisek Soukup, Bugwood.org.

whitebark pine (Pinus albicaulis Engelm.) with endangerment (USFWS 2011). Similarly, emerald ash borer (Agrilus planipennis Fairmaire, EAB) is functionally eliminating North

American ash (Fraxinus spp.) from the Great Lakes region (Klooster et al. 2014; Aubin et al. 2015), with infestations and impacts still spreading (Herms and McCullough 2014).

Invasions by devastating forest PIPs in North America have occurred for well over a century (Aukema et al. 2010), facilitated by world travel, and expansive global trade (Liebhold et al. 2012); yet, strategies for range-wide preservation of forest tree species threatened by invasive PIPs remain elusive. The current chapter provides a brief overview of forest PIP invasion biology and the current framework for management responses to established PIPs. From this background, the chapter argues that deploying host resistance may be feasible in many cases, and should be a priority component of effective management strategies for many tree-killing, invasive forest PIPs. Specifically, the chapter maintains that: 1) bark- and wood- boring insects and canker- and wilt- inducing pathogens exhibit certain traits associated with coevolution of genetically-based host resistance and thereby with potential for widespread tree mortality in the “defense- free space” of evolutionarily naïve host populations (Gandhi and Herms 2010a); 2) rapid, severe host damage, and in many cases low detectability, exhibited by these PIPs limit the effectiveness of other management approaches, such as eradication, containment, and biocontrol; and 3) recent scientific and technological advances make the discovery and development of host resistance highly feasible and desirable, although significant challenges remain for the operational deployment of landscape-scale resistance. Finally,

12 the chapter calls for integrating host resistance development and deployment once these types of species have established and determined to be causing damage, and identifies challenges and knowledge gaps.

Overview of invasion progression

Before they significantly alter a naïve forest ecosystem, all alien forest PIPs progress through a well-characterized series of invasion stages that includes overcoming significant barriers (Fig. 2.2) (Blackburn et al. 2011). Biotic and abiotic factors of forest ecosystems, coupled with traits of the alien PIP, determine the relative strength of each invasion barrier and thereby PIP impacts. In general, such barriers allow only a small fraction of potential alien PIPs to establish and become destructive (Aukema et al. 2010).

Like all organisms, PIP population dynamics are affected by interacting biotic and abiotic factors that enhance or inhibit survival and reproduction (Fig. 2.2). Abiotic factors, such as temperature or precipitation extremes, modify PIP populations in a density-independent manner. Such is the case when a hard frost event kills phytophagous insects, or relative humidity favors fungal pathogen sporulation, independent of population size. Abiotic factors also modify or interact with density-dependent biotic factors that regulate PIP populations. These biotic factors include top-down forces exerted by natural enemies (predators, parasitoids, pathogens) and bottom-up forces exerted by tree defenses. Two non-exclusive hypotheses have emerged to explain release

Figure 2.2. Simplified progression of phytophagous insect and phytopathogen (PIP) invasions of naïve forest ecosystems, adapted from Blackburn et al (2011). Beginning with separated PIP populations and naïve forest ecosystems, invasions may progress through up to four sequential stages (numbered circles) by overcoming barriers (lettered boxes), with the passage of time (thick arrows). Management interventions (upper thin lines) attempt to strengthen barriers between stages. PIP sources may be populations of PIPs in native ranges or previously invaded naïve ranges (in stages 3 and 4). Within an ecosystem, invasions may cycle between the final two stages, PIP spread (3) and outbreak or epidemic (4), depending on the barriers provided by existing biotic and abiotic environmental factors (D). Note: The generalized invasion framework proposed by Blackburn et al (2011) provides additional detail, with the distinction that for forest PIPs, impacts are strongly associated with high PIP population density in the outbreak/ epidemic stage.

from biotic resistance by invading herbivores: enemy-free space (Jeffries and Lawton

1984) and defense-free space (Gandhi and Herms 2010a). Along with other determining factors, the degree to which each hypothesis explains the population dynamics of an invading organism can inform how to best manage that invasion.

Conceptual framework for management response

Invasion barriers provide useful targets for management activities (Fig. 2.2). Prevention measures, including phytosanitary treatments and quarantine of imported goods, aim to strengthen the geographic barriers to PIP introductions (Fig. 2.2, box A). Eradication efforts informed by early detection (surveillance) are meant to strengthen barriers to survival and reproduction and thus limit long-term PIP population establishment (Fig.

2.2, box B). In addressing the threat of potential invasive forest PIPs, these prevention, early detection, and eradication efforts are crucial and valuable investments (Leung et al.

2014; Liebhold et al. 2016). However, some PIPs are able to overcome these barriers, establish, and spread within naïve forest ecosystems [Fig, 2.2, stages 2-3]. When responding to established PIPs, strengthening barriers to outbreaks (Fig. 2.2, box D) provided by host defenses and natural enemies are critical for mitigating tree mortality and negative ecosystem impacts.

Historically, US forest health management agencies and policy-makers have responded rapidly to established high-impact exotic PIPs, by facilitating research to assess the threat, characterize PIP ecology, and assess mitigation options. Typically, agencies then work with stakeholders in an iterative adaptive management process to set sustainable goals given available resources, and periodically revising those goals guided by new information. This framework as a whole, represented in Fig. 2.3, can be applied on a national or international level, and most elements can be scaled down to individual forest managers. This framework reflects concepts that have been proposed through the

Forest Health Initiative (FHI 2012; Nelson et al. 2014), by the USDA Forest Service

(Dodd et al. 2005; Keane et al. 2012; Dix et al. 2013), USDA APHIS and other sources

(Jacobs et al. 2013; Millar and Stephenson 2015).

In this framework, an established exotic PIP is first identified as a threat (Fig. 2.3, box 1), either post-facto, after detection, or a priori, through risk assessment protocols.

Examples of the latter include threats posed by bronze birch borer (Agrilus anxius Gory) to Europe (Muilenburg and Herms 2011) and ash dieback to North America (Drenkhan and Hanso 2010). Once a threat is identified, a rapid research response can be initiated, with three general research goals pursued in parallel or prioritized as needed (Fig. 2.3, box 2).

The first is to determine the value of products and services provided by the threatened tree species (Fig. 2.3, box 2.B) so that a proportionate response can be implemented and relevant stakeholders identified. In urban and plantation forests, value may be ascribed to current and future timber yields, aesthetic and recreational value, and

ecosystem services provided by the individual trees and stands. In highly managed forests, the target tree species can sometimes be replaced with non-host species with minimal loss of value. However, in more natural (i.e., managed for a variety of ecosystem services with minimal inputs) forests, the collective value of the affected tree species at the population and landscape levels can be substantial and irreplaceable. This value may include unique cultural significance [e.g. black ash, Fraxinus nigra Marshall, in northeastern U.S. (Donnely 2016) and redbay (Persea borbonia Spreng.) in the southeast (Billie 2014)], and contributions to local, regional, and global biodiversity and ecosystem resilience (Gandhi and Herms 2010b; Freer-Smith and Webber 2015). These values are more difficult to estimate, but are particularly significant when entire populations of foundational tree species are threatened (Ellison et al. 2005).

The second research goal is to characterize the ecology of the PIP in its native and invaded ranges (Fig. 2.3, box 2.A). Little is known about many PIPs in their coevolved native range, where they typically are much less damaging and little studied (GAO 2006).

Among the most urgent information needed for risk assessment is PIP life history and behavior, dispersal mechanisms, taxonomic host range (including relative suitability/susceptibility), symbionts, and patterns of host damage and mortality over time and space. A thorough understanding of host and PIP reproduction biology and population genetics, generalist and specialist natural enemies, and any interacting stressors affecting host trees, are also important for devising successful mitigation strategies (Dodd et al. 2005). This information can help determine the relative importance

of top-down and bottom-up factors in the regulation of PIP populations, informing long- term management.

The third research goal is to delineate all available approaches to mitigate impacts based on an assessment of their cost and the value of the threatened host. As described for other disturbances (Millar and Stephenson 2015), forest health managers employ combinations of two basic impact mitigation approaches: short-term ecosystem maintenance and long-term ecosystem transition (Fig. 2.3, box 2C). Maintenance seeks to temporarily or continually strengthen barriers to PIP spread, suppress populations below outbreak/epidemic levels and preserve populations of healthy trees, thereby delaying costs associated with tree death and transitions to a new forest community. Transition seeks to facilitate conversion of the forest ecosystem to one that is less prone to outbreaks, and restore cultural, ecological and/or economic value lost to PIP invasion.

In response to an established PIP, maintenance may include suppression of PIP populations through direct or indirect measures. Direct measures include the use of chemical or biological pesticides, pheromone-based mating disruption or trap-out, and removal of infested hosts (sanitation). Indirect measures include augmentative/non- persistent introductions of competitors or natural enemies, and/or silvicultural practices intended to enhance existing host defenses (Fig. 2.3, box 2.C-I).

Transition-based approaches to managing established invasive PIPs revolve around identifying, developing, and deploying resistant populations of the affected host tree species, facilitating the establishment of forest communities with alternative non-host

species, and/or establishing self-sustaining populations of antagonists or other natural enemies of invasive PIPs (classical biocontrol) (Fig. 2.3, box 2.C II).

Assessments of the diverse values of host trees and the ecology of the progressing invasion can be the basis for predicting the effectiveness, feasibility, integration, and acceptability of proposed mitigation approaches (Fig. 2.3, box 3). Effectiveness represents the benefit conferred by a mitigation strategy, i.e. the ability to reduce invasion impact by preserving, delaying, or restoring value lost to tree damage or mortality.

Effectiveness is both relative and quantitative, as host tree values may be measured on diverse continuous scales (Kovacs et al. 2010; Meldrum et al. 2013). For example, resistant tree restoration can preserve biodiversity and/or restore cultural value associated with a species, but is generally not effective in preventing costs associated with tree mortality, such as costs of removal or lost timber yield.

As argued in detail below, the effectiveness of individual management strategies is dependent on the ecology of the invasion. While biological control has been both successful and self-sustaining for some invasive forest insects, mainly folivores

(MacQuarrie et al. 2016), there are few examples of effective biological control of subcortical insects and pathogens. Noted exceptions include using parasitic nematodes against the woodwasp Sirex noctilio Fabricius which has worked to variable degrees in different regions (Slippers et al. 2015), and control of chestnut blight in Europe using mycovirus-induced hypovirulence (Milgroom and Cortesi 2004).

For purposes of this overview, feasibility represents the capacity required to implement effective mitigation, including time, funding, institutional support, and

political will. Integration of, and synergy among, different mitigation approaches can improve effectiveness and/or feasibility. Examples of synergy could be as simple as combining pesticide application and detection trap collection into a single site visit. A more complex example is the SLow Ash Mortality (SLAM) approach used for EAB

(McCullough and Mercader 2012), which combines numerous tactics to reduce and delay costs associated with ash mortality in urban forests.

Finally, acceptability is the reckoning by both public and private stakeholders

(Freer-Smith and Webber 2015) of cost and benefit in the face of short- and long-term risk and uncertainty (Seidl 2014). A balance is required between maintenance and transition approaches that reflects the short- and long-term value of the affected tree and forest. For urban or plantation forests, this may require a maintenance approach that preserves existing trees and delays costs associated with their mortality. However, in natural forests, the importance of long-term resilience of the forest ecosystem may more strongly favor a transition approach.

For any approach, the acceptability of management strategies must be evaluated.

For example, if a decision to pursue ecosystem transition via restoration with resistant hosts is made, then the utility of specific methodologies must be considered (see “Recent advances” section and Table 1 for examples). Restoring forest communities with transgenic resistant trees may require less time than using traditional selective breeding or hybrid breeding with exotic hosts, but the perceived risks associated with transgenic trees may be unacceptable to some. Determining acceptable mitigation approaches requires weighing tradeoffs associated with competing interests. Goals can be set within the

response capacity for a given forest and assessed using criteria of effectiveness, feasibility, integration and acceptability (Fig. 2.3, box 4). The research and goal-setting process can be repeated as new information is gathered as the invasion progresses (Fig.

2.3, box 5.A) and/or as new mitigation tools and technologies are developed (Fig. 2.3, box 5.B).

Ideally, this adaptive management process is evidence-based. Very often, however, little information on the PIP biology and ecology is available in the crucial early stages of invasion. Furthermore, regulatory and management agencies experience social, political, and legal pressures to respond quickly to a dramatic invasion. This can lead to the development and adoption of measures that can be useful for continuous monitoring of a given PIP, and with it substantial short-term benefits, but which may have little long-term effect on the pest’s eventual distribution and ecological impact. For instance, in the case of EAB, considerable capacity has been devoted since 2002 to monitoring (i.e. trapping) and biocontrol, while fewer resources have been allocated to development of host resistance. However, it is now clear that EAB will continue to cause widespread mortality of undefended trees as it continues to spread across North America

(e.g. Klooster et al. 2014). A goal setting process that follows a negative feedback loop approach (Fig. 2.3, box 5.A), and recognizes substantial population-level value of ash species, would lead to early and sustained investment in development of resistance and long-term transition toward a resilient system. In addition to advocating for periodic reevaluation as an invasion progresses, early invasion characteristics that may help determine when host resistance development should be a priority are identified.

TOWARDS A SOLUTION - THE CASE FOR HOST RESISTANCE

As discussed above, decisions about how to manage an invasive forest PIP should be guided by support for either of two mechanistic hypotheses explaining an invasive outbreak: enemy-free space (Jeffries and Lawton 1984) and defense-free space (Gandhi and Herms 2010). These two hypotheses are not exclusive, but rather describe a continuum of top-down and bottom-up biotic factors regulating the population dynamics of all heterotrophic organisms (Fig. 2.4 A). Mattson et al. (1988) associated certain plant defense strategies, and thus importance of top-down or bottom-up factors, with a number of host and insect pest characteristics. The current work proposes these same concepts can be applied to phytopathogens (Fig. 2.4).

At one end of the continuum, plants tolerate PIP damage while environmental factors or natural enemies reduce PIP populations and associated damage (Fig. 2.4 A). Under this scenario, PIP outbreaks may occur in naïve forest ecosystems if effective natural enemies are not present, i.e. in enemy-free space. At the other end of the plant/PIP interaction continuum, plant defense traits kill or reduce growth and reproduction of the PIP, thereby limiting damage (Fig. 2.4 A). When PIPs that have evolved under these constraints become established in a naïve forest ecosystem, outbreaks may occur in the absence of effective host defenses, i.e. in defense-free space. Host resistance traits are diverse, and belong to well-recognized classes of anatomical or biochemical defenses (Franceschi et

al. 2005; Eyles et al. 2010). In many cases natural enemies and plant defenses interact, such as when plants respond to herbivory by attracting parasitoids (Turlings et al. 1995), form structures whose primary function is to provide shelter or nutrients to predators

(Palmer et al. 2010), or exude genotype-specific compounds that favor bacterial antagonists of phytopathogenic fungi (Dunn et al. 2003). Plant defense traits can come with fitness costs of varying magnitude, and so a corresponding fitness benefit such as reduced herbivory or resource depletion must be present for the traits to persist in a host population (Herms and Mattson 1992; Heil 2002). The ability of a tree to tolerate damage decreases with the severity or fitness cost of that damage. Thus, the inability to tolerate herbivory or infection is the first useful criterion for estimating the likelihood that host resistance traits are an important feature in effective management of invasions of defense- free space (Fig. 2.4 B).

PIP traits associated with outbreaks in defense-free space

A number of recognized traits in a PIP can influence its potential for host damage, but the type of host tissues it affects is among the most important (Mattson et al. 1988). This trait is generally described as feeding guild for insects, but the same concept applies for many phytopathogens. Folivores are among the least damaging, as defoliation can be tolerated relatively easily, especially in deciduous plants. Next are those that impact twigs, branches, or peripheral roots, either through nutrient/sap removal or tissue destruction.

The most damaging PIPs are those that affect main stem and/or primary root vasculature

which can lead to rapid decline and death. Particularly damaging pests, regardless of tissue, are those that interfere with the potential for recovery, either through the intensity, phenological or ontogenic timing of damage, by damaging meristematic tissue (e.g. buds, root tips, or vascular cambium), or in the case of insects, by vectoring phytopathogens or creating infection courts. Bark and wood-boring insects, as well as vascular wilt- or canker-inducing pathogens, are among the most damaging classes of invasive PIPs, evidenced by their ability to rapidly kill host trees with compromised or non-coevolved, ineffective defenses.

The intimacy of the association between host and PIP is another useful criterion for predicting defense coevolution (Fig. 2.4 B). Intimacy of the interaction can be understood as the proportion of the PIP lifecycle during which it is in direct contact with the host plant, the proportion of PIP tissues in direct contact with the host, and the extent to which plant losses extend beyond the point of actual nutrient transfer between host and parasite (e.g., some galls represent highly intimate but relatively benign associations)

(Mattson et al. 1988). For example, some less intimately associated PIPs may interact with host tissues for a large portion of their life cycle but only superficially via feeding structures, be they mouth-parts and digestive tracts of leaf chewing insects, or appressoria and haustoria of powdery mildew fungi. In more intimate associations, PIPs may be entirely surrounded by host tissue for a large portion of their life cycles, with greater opportunity for exchange of specific molecular information (elicitors, effectors, phytotalexins, etc.) that may be integrated into signaling, defense, and counter-defense

responses. Again, bark and wood-boring insects as well as vascular wilt- or canker- inducing pathogens are among the most intimately associated PIPs.

Other PIP traits that limit management strategies

The effectiveness of eradication and containment efforts is strongly associated with the ability to detect PIPs with high sensitivity and specificity (Harwood et al. 2011; Tobin et al. 2014). PIP traits that limit detectability and the effectiveness of eradication/containment approaches include cryptic associations with host tissues, the absence of long-distance pheromones, and delayed, temporary, or non-descript presentation of symptoms (Liebhold and Tobin 2008, Tobin et al. 2014). Many wood- borers, adelgids, and canker- and wilt-inducing pathogens that invade defense-free space are also difficult to detect and monitor. The eradication of the wood-boring Asian longhorned beetle (Anoplophora glabripennis Motschulsky) from several U.S. cities and adjoining areas represents one of the few successes for such cryptic pests (Smith et al.

2016). In contrast, the sudden oak death epidemic caused by the canker pathogen

Phytophthora ramorum Werres in northern California and southern Oregon is an example of how low detectability can initially complicate and ultimately preclude containment of an invasive tree-killing PIP (Cunniffe et al. 2016). The ability to efficiently detect PIPs is important through all phases of management response, including eradication, delimitation of infestation boundaries for defining quarantine zones, containment, and informing various management or mitigation tactics.

Along with informing eradication efforts, features of PIP ecology and invasion biology may help predict the effectiveness of other ecosystem transition approaches, such as biological control. Classical biological control has been an effective ecosystem transition response against 13 (primarily folivorous) phytophagous insects, and a partial success against 4 more, by limiting outbreaks and reducing tree mortality (Van Driesche et al. 2010; MacQuarrie et al. 2016). However, there is less evidence that biological control is effective against bark- and wood-boring insects and canker- and wilt-inducing pathogens that have a cryptic life history, intimate host associations, and ability to kill trees rapidly by damaging essential, irreplaceable tissues. When available, specialist natural enemies are preferred for biological control due to their limited non-target effects on other organisms (MacQuarrie et al. 2016). These specialist natural enemies, whether they are parasitoids, pathogens or other agents, often exert negative density-dependent effects on PIP populations (Elkinton 2008). In such systems, time lags between relatively rapid tree mortality, and associated PIP reproduction and dispersal, versus relatively slower natural enemy increase, yield a relationship in which enemy-induced mortality to

PIPs does not often scale up to population regulation or higher tree survival.

For example, it appears unlikely that biocontrol agents of EAB will allow North

American ash to persist as dominant tree species in the invaded range without improved host resistance or tolerance. While woodpeckers and native and introduced parasitoids have caused relatively high levels of EAB larval mortality, their impacts have been primarily in late stages of localized EAB infestation or heavily infested individual trees

(Duan et al. 2014: Jennings et al. 2015). Furthermore, EAB has caused high mortality of

North American ash species planted in Asia, even in the presence of coevolved natural enemies (Yang et al. 2010; Duan, Yurchenko, and Fuester 2012; Duan et al. 2015). This suggests that even a full complement of natural enemies may not suppress outbreaks in the absence of host resistance (Liu et al. 2003; Wei et al. 2004; Herms and McCullough

2014). Models of EAB population dynamics predict that increased rates of parasitism by natural enemies and higher levels of host resistance are required to maintain EAB populations at equilibrium levels low enough to maintain reproducing populations of

North American ash species (Duan et al. 2015).

As a further example, biological control of Sirex notilio by entompathogenic nematodes (Slippers et al. 2015), is especially successful when integrated with silviculture which improves innate host defenses. In this system the natural enemy reproduces much more quickly than the PIP and thus the negative density-dependent effects are less delayed. As such, biological control is best applied to systems in which the hosts effectively resist or tolerate PIP attack, especially if negative density-dependent effects of natural enemies are weak or delayed. Therefore, biological control may act synergistically with effective tree defenses to prevent or reduce tree mortality caused tree-killing PIPs (Raffa et al. 2006, Duan et al. 2015).

RECENT ADVANCES IN HOST RESISTANCE DEVELOPMENT

Following PIP invasions, the objective of long-term ecosystem transition via restoration with resistant hosts is increased resilience of tree populations. This is achieved by

increasing the frequency of resistant individuals while maintaining sufficient genetic diversity for evolutionary adaptation of loci associated with resistance and other traits affecting long-term success. This approach takes different forms across diverse forest settings, summarized in Table 1.

Deployment challenges likewise vary across socioeconomic and ecological land covers. In urban and forest plantation settings, trees are physically or mechanically planted, and amenable to additional inputs, so deployment of resistant germplasm poses few if any unique challenges. Although these systems may provide multiple values, they are primarily focused on the single attributes of aesthetics and agronomic return, respectively. Naturally regenerating forests pose much more significant challenges, because they are less amenable to planting and they are large-scale (whether within single ownerships or in aggregate), which imposes high operational and potential environmental costs. Further, they provide and are often managed for diverse and competing values, occur largely on publicly owned lands, and owe much of their existing resistance and resiliency against native insects and pathogens to their structural diversity, which would incur additional costs to replicate in out-plantings. Wilderness areas pose some of the most paradoxical challenges (Keane et al. 2012): Their stated philosophy is to minimize human intervention, to allow natural ecosystem processes to function unimpinged. Yet some invasive PIPs threaten to completely transform these systems in the absence of intervention. The extent to which the public will fund protection of these economically nonproductive systems on the one hand, or permit introduction of new genotypes and deployment-associated disturbance on the other, will be driven by complex

Table 2.1. Overview of approaches to tree resistance development, relevant technologies, strengths, and weaknesses.

Source(s) of Approach Resistance Technologies Strengths Weaknesses Conservation & Affected Marker discovery Little intervention required, Dependent on trait combinations occurring in local Recruitment populations particularly planting; germplasm; germplasm conserved in situ particularly little development time susceptible to other mortality factors; dependent on in before action situ rates of host reproduction Selection & Affected Marker discovery; May combine multiple traits Dependent on traits present in local germplasm; requires Breeding populations mechanistic of interest; may initiate with planting for deployment understanding little mechanistic understanding Hybrid Affected and Marker discovery; May combine multiple traits Requires multiple generations to reduce linkage drag of 31 Backcross coevolved mechanistic of interest; may initiate with non-target traits; backcrossing may limit genetic populations understanding little mechanistic diversity; requires planting for deployment understanding Cisgenesis Affected and Mechanistic May combine multiple traits Requires precise identification of genetic loci conferring coevolved understanding; of interest; more acceptable resistance; potential challenges to acceptability of populations genetic form of genetic engineering deployment; requires multiple transformations or engineering crosses to maintain diversity; requires planting for Transgenesis Any Mechanistic May combine multiple traits deployment understanding; of interest; may exploit genetic novel mechanisms of engineering resistance

socioeconomic processes. Future research into deployment strategies from a landscape perspective can help inform and reconcile these decisions.

Conservation and recruitment

In naturally regenerating forests, development of enhanced resistance primarily involves genetic conservation and promoting recruitment of resistant genotypes or families.

Additionally, resistant trees and genetically diverse but more susceptible breeding partners must be protected from other mortality agents, such as fire (Budde et al. 2016).

For example, natural regeneration and recruitment of limber pine (Pinus flexilis James) and whitebark pine (P. albicaulis Engelm.) seedlings are being encouraged in advance of blister rust infection in high-elevation ecosystems where other management interventions are difficult (Schoettle and Sniezko 2007, Sniezko et al 2014). Concurrently, the anti- aggregation pheromone verbenone is applied to trees showing documented or putative resistance against blister rust, in order to protect these trees from mountain pine beetle to which they are particularly susceptible (Gillette et al. 2012). Increasing or maintaining population size improves the chances of selection, survival and reproduction of rare resistant individuals.

Knowledge of the frequencies of resistant individuals within a population can guide proactive management and restoration activities. Such estimates have traditionally been inferred from patterns of mortality in other invaded areas. However, the identification of molecular markers associated with resistance may enable identification

of individual trees with resistance (Conrad et al. 2014), which may facilitate pro-active management at the tree as well as population level (i.e. preferential protection of trees with resistance markers during fire suppression or pre-infestation salvage cuttings, etc.).

The development of molecular markers is discussed below, and represents a point of integration between resistance breeding and forest conservation.

In planted urban or plantation forests, development of resistant genotypes usually takes the form of selection, breeding, and planting of improved germplasm. Tree breeding programs exploit naturally occurring variation in traits conferring resistance to pests and pathogens (Telford et al. 2015), through four main approaches: classical selection, hybrid backcrossing, cisgenesis, and/or transgenesis. These activities vary among programs and the specific attributes of each system, but can generally be divided into three phases: trait discovery, trait development, and trait deployment. Recent conceptual and technological advances have improved the feasibility (speed and cost) of the first two phases for Eucalyptus, a model tree genus used extensively in plantation forestry (Resende et al. 2012). Examples of resistance breeding programs targeting cryptic tree-killing PIPs are summarized in Table 2 and Appendix A.

Classical selection

In classical selection, naïve host species or populations are screened for allopatric resistance (Harris 1975) to tree-killing PIPs, the frequency of which, by definition, is very low (Budde et al. 2016). However, some successful programs have employed this

approach (Table 2, with more detailed summary in FAO 2008), including breeding North

American white pines for resistance to white pine blister rust resistance (Sniezko 2006).

Resistance traits may be discovered by identifying healthy individuals in forests where the PIP has killed or damaged a large proportion of hosts. Host germplasm also may be screened for resistance using controlled inoculations in common garden plantations or laboratories. A major recent advance in tree breeding has been the development of increasingly inexpensive molecular markers (Neale and Kremer 2011; Parent et al. 2015).

Marker assisted selection requires studies to associate genomic (Grattapaglia and

Resende 2011; Boshier and Buggs 2015), transcriptomic (Harper et al. 2016), and/or chemical markers (Conrad et al. 2014) with resistant phenotypes. The application of such

”omics” technologies to tree breeding programs is still in its early stages, but merits further research and development, as the markers discovered can enable rapid, non- destructive screening of germplasm, thereby accelerating trait discovery and development.

In the trait development phase, identified resistance traits are combined with other traits which enhance resistance, silvicultural value, and/or local adaptation by crossing desirable individuals, screening offspring for desired phenotypes or markers, and repeating the process if necessary. Advantages of classical selection, in comparison to hybrid backcrossing, include relative avoidance of linkage drag (co-transfer of undesirable traits) and the extended backcrossing necessitated in the development phase.

The second major recent advance in tree breeding has been an improved mechanistic understanding of resistance traits, which can lead to identification of genetic

Table 2.2. Selected examples of phytophagous insects and phytopathogens (PIPs) requiring early and sustained investment in host resistance discovery and development. All PIPs are intimately associated with host stem tissues (phloem, cambium, or xylem) at one or more critical life stages. Resistance breeding program Disease or pest Co-evolved host(s) Naïve host(s) of highest interest in Molecular name (symptom) Agent(s)/ PIP(s) in region of origin invaded regions Statusa Approachesb resourcesc White pine blister Cronartium ribicola Pinus spp. in Asia High elevation Pinus spp. in 1,2,3,4 S A,B,C,D rust (canker) western N. Amer. Sudden oak death Phytophthora ramorum Unknown hosts, Coast live oak (Quercus agrifolia), 3,4 S A,B,C,D (canker) likely Asian origin tanoak (Notholithocarpus densiflorus) in N. Amer., Japanese larch (Larix kaempferi) in UK plantations Laurel wilt Raffaelea lauricola, Unknown hosts in Persea spp. including redbay (P. 2-3 S Xyleborus glabratus Southeast Asia borbonia) and avocado (P. americana) in N. Amer.

Chestnut blight Cryphonectria Chinese chestnut American (C. dentata) and 1-2 S,H,G A,B,C,D (canker) parasitica (Castanea molissma) European chestnut (C. sativa) Dutch elm disease Ophiostoma novo-ulmi, Asian elms (Ulmus North American and European 1-2 S,H,G C,D (wilt) Scolytus spp., spp.) elms (Ulmus spp.) Hylurgopinus spp. Ash dieback Hymenoscyphus Asian ash species European ash (F. excelsior) 3 S A,B,C,D (canker) fraxineus (Fraxinus spp.) Emerald ash borer Agrilus planipennis Asian ash species N. Amer. ash (Fraxinus spp.) 3 S,H,G B,C,D (Fraxinus spp.) Note: Reference literature summarized in Appendix A. a. Program status. Multiple statuses may refer to different hosts or regions:1=deployed resistant material; 2=developing/breeding for resistance, no deployed material; 3=resistance detected in genetic/provenance trials; 4=evidence of genetic variation in resistance in seedling or clonal screens. b. Program approach, excluding conservation: S=classical selection; H=hybrid backcross; G=genetic engineering (cis/transgenesis). c. Molecular resources (lettering unrelated to any hierarchical order): A=validated host resistance markers (genetic, chemical, etc.); B=reference genome of host or close relative; C=infected/infested host transcription profiles; D=PIP molecular resources.

loci, chemical compounds, biochemical pathways, and enzymatic or receptor proteins associated with resistance (e.g. Villari et al. 2016), as well as qualitative or quantitative nature of trait inheritance. Qualitative, or major-gene/R-gene, resistance traits make large contributions to resistance but are prone to evolution of counter-adaptations by PIPs. For example, the white pine blister rust pathogen has evolved to overcome the single major- gene resistance traits deployed in sugar pine (P. lambertiana Douglas) and western white pine (P. monticola Douglas) (Kinloch et al. 2004). Quantitative, or polygenic, resistance traits individually contribute less to the overall resistance phenotype, and in combination often prove to be more evolutionarily durable. For example, C. ribicola evolved virulence on western white pine carrying major-gene, but not polygenic, resistance (Sniezko et al.

2014). Such understanding can enable combinations of multiple unique traits with diverse mechanisms that promote the durability of resistance by decreasing the likelihood of evolution of counter-resistance (Telford et al. 2015).

Another important breakthrough is advances in genetic engineering of woody plants. In addition to enabling a novel approach to resistance development (discussed below), genetic engineering technologies, including Agrobacterium-mediated transformation (Fillatti et al. 1987; Du and Pijut 2009), electrical discharge particle acceleration (MCown et al. 1991), and CRISPR/Cas9 methods (Fan et al. 2015; Zhou et al. 2015), can contribute to mechanistic understanding of resistance by enabling manipulative trait expression experiments (knock down/out) during the trait discovery and development phases (Bortesi and Fischer 2015).

The final phase of a resistance breeding program is trait deployment. Resistant germplasm, either seedlots or clonal cultivars, must be successfully established in forests.

There are considerable challenges and limitations to deployment of resistant trees, some of which are being alleviated by technological and conceptual advances. Landscape resistance concepts seek to identify and explain factors that contribute to PIP resistance across broad spatial scales (Haas et al. 2015), including stand demography, genetic diversity, and climatic or site characteristics. Associational resistance and susceptibility may operate at smaller scales whereby community composition affects herbivory or infection of individual plants (Tahvanainen and Root 1972; Plath et al. 2012; Guyot et al.

2015). For example, whitebark pine has inferior defenses to mountain pine beetle than the historically more coevolved lodgepole pine (P. contorta Douglas), yet this insect prefers and disproportionately attacks lodgepole pine, yielding lower mortality to whitebark pine in mixed stands (Bentz et al. 2015, Esch et al. 2016).

Understanding and incorporating landscape-level resistance concepts may improve durability of resistance and long-term forest resilience by allowing deployment of genetically diverse resistant germplasm, rather than only a few highly resistant genotypes. It also may be possible to deploy less resistant individuals that possess other desirable traits if they are protected by their association with resistant individuals and/or other landscape-scale resistance factors. This could create a larger genetic pool that facilitates ongoing adaptation to changing environments.

Hybrid backcross

Hybrid backcrossing introgresses resistance traits from reproductively compatible populations or species that have coevolved with the PIP into the naïve susceptible host.

This approach has been used successfully to enhance pest resistance to crop varieties by incorporating resistance traits from wild hosts and cultivated land races that have coevolved with key pests (Warschefsky et al. 2014).

The chestnut blight and Dutch elm disease resistance breeding programs are the most prominent examples of hybrid backcrossing to generate pest resistant trees

(Burnham 1981; Smalley and Guries 1993; Nelson et al. 2014). However, this approach requires multiple generations of backcrossing hybrids to the parent(s) to eliminate undesirable traits resulting from linkage drag, which can greatly increase the time and expense required to breed long-lived trees. Such backcrossing can additionally lead to inbreeding and reduced genetic diversity of eventual resistant germplasm. Trait discovery, development and deployment in the hybrid backcross approach benefit from all of the current technologies and concepts described in the classical selection approach.

Cisgenesis

Cisgenesis is the use of genetic engineering technology to transfer genetic material between plants that are reproductively compatible (as opposed to conventional genetic engineering that transfers genes from one type of organism to another). This strategy

requires some understanding of resistance mechanisms, and regulation of genetic loci associated with resistance (Telem et al. 2013). By transferring only the resistance loci, cisgenesis can eliminate linkage drag and the need for extensive backcrossing, dramatically decreasing the time required for trait development. Cisgenesis may be more acceptable to policymakers and the public (Hou et al. 2014), as it is much faster than traditional crossing while avoiding more controversial transgenesis (discussed below).

This may be especially true when the technique is combined with CRISPR/Cas9 gene- editing technology that transforms trees without introducing foreign genetic material.

Such trees would be genetically identical to those that could be generated by hybrid backcrossing, but produced much faster.

Transgenesis

Advances in genetic engineering have enabled incorporation into trees of traits from reproductively incompatible hosts, non-host organisms, or even organisms from different kingdoms. Examples include an insectidical protein from an entomopathogenic bacterium

(Kleiner et al. 1995) and a proteinase inhibitor from rice conferring lepidopteran and coleopteran resistance, respectively, in hybrid Populus (Zhang et al. 2011), a tobacco anionic peroxidase gene increasing resistance of sweetgum (Liquidambar stryacifula) to

European gypsy moth (Lymantria dispar dispar L.) (Dowd et al. 1998), and an oxalic acid degrading enzyme from wheat conferring blight resistance to American chestnut

(Newhouse et al. 2014). As with other development approaches, transformed trees are

subjected to resistance screenings and may be crossed to incorporate other desirable traits, as in the Populus examples.

The trait development phase must include studies of how resistance genes interact with the forest ecosystem. The development of resistance to the target invasive PIP may also impact the ecological relationships between the tree and other native and introduced phytophagous insects, endophytic or mycorrhizal fungi, and other microorganisms (Raffa

1989). A major challenge in deployment of pest resistant transgenic (and cisgenic) trees is maintaining adequate genetic diversity (Strauss et al. 2009). The trait, or traits, of interest should be incorporated into as many genotypes as possible through multiple transformations and/or crosses of transformed trees with diverse breeding partners. In agriculture, inclusion of substantial proportions of non-transformed plants in plantings, rapid plant turnover to incorporate new host genotypes and transgenes, and other practices aimed at delaying evolution of pest biotypes capable of overcoming resistance, have yielded considerable stability (Tabashnik et al. 2013). More research aimed explicitly at forest ecosystems is needed to access the feasibility and operational practicality of various strategies employed in agriculture, contend with the much larger ratio of plant to PIP generation times in trees than most crops as it relates to biotype evolution, and exploit the opportunities that come with the higher species, genetic, and demographic diversity of forest- than agro-ecosystems.

CONCLUSIONS

Restoration of bottom-up regulation through deployment of host resistance, and establishment of resilience at the landscape scale through genotypic, species, and phenotypic diversity, are essential to preserving the long-term value of healthy forests in the face of invasive tree-killing PIPs. To achieve genetically diverse, resilient tree populations in the future, resistance development programs should be integrated into response frameworks as soon as established invasive insects or pathogens are determined to be causing widespread mortality of susceptible hosts. Timely study of PIP ecology in the native and introduced ranges can help determine when defense-free space is the primary driver behind unmitigated damage following invasions, and thereby prioritize interventions. In addition to protecting trees directly, enhanced, durable resistance may also improve the effectiveness of density-dependent biological control as part of an integrated response (Duan et al. 2015).

Early, long-term, and sustained support of resistance programs through trait discovery, development, and deployment is required for successful management of alien tree pests that establish and spread in defense-free space. Such support will require greater public and governmental commitment to sustaining biodiversity and ecosystem services of resilient forest ecosystems. Technology exists to economically develop and deploy pest resistant trees, but continued incorporation into breeding programs is required. Knowledge gaps include challenges associated with the integration of improved germplasm into naturally regenerating forest ecosystems that are managed by diverse

stakeholders. As such, resistance development programs should be closely linked to restoration research and planning to ensure that improved germplasm is ecologically and silviculturally matched to management objectives (Jacobs et al. 2013). Increased integration of host resistance development into responses against invasive forest pests should be the foundation to resolving a hitherto largely intractable problem that pervades an increasing array of urban, plantation, and forest ecosystems affected by tree-killing

PIPs that have escaped regulation by coevolved defenses.

CHAPTER 3: SURVIVAL AND DEVELOPMENT OF EMERALD ASH BORER LARVAE IN WHITE AND MANCHURIAN ASH UNDER WATER STRESS IMPLICATE COEVOLVED PHLOEM-BASED TRAITS IN RESISTANCE OF MANCHURIAN ASH

ABSTRACT

Emerald ash borer (EAB) is causing massive ecological and economic damage as it devastates North American ash (Fraxinus spp.) populations. Resistance of its coevolved hosts is thought to limit EAB outbreaks and ash mortality in its native Asia, but an understanding of resistance mechanisms is still developing. In this study controlled egg inoculations were used to isolate mechanisms impacting larval performance, and characterize inter- and intra-specific resistance phenotypes based on larval outcomes.

Larvae exhibited lower rates of survival and development in coevolved Manchurian ash

(F. mandshurica Rupr.) than in evolutionarily naïve white ash (F. americana L.). Water stress imposed on the host trees increased larval performance in Manchurian ash, with no significant effect on the already more susceptible white ash. These results show that the higher EAB resistance of Manchurian ash results from phloem traits that decrease larval performance, as well as previously documented lower oviposition preference.

INTRODUCTION

Emerald ash borer (Agrilus planipennis Fairmaire, EAB) is devastating North American ash (Fraxinus spp.) populations, with economic and ecological impacts that are unprecedented for an invasive forest insect pest (Aukema et al. 2011). North American ash species, particularly black (F. nigra Marshall), green (F. pennsylvanica Marshall), and white ash (F. americana L.), as well as European ash (F. excelsior L.), experience extremely high levels of mortality in areas invaded by EAB (Orlova-Bienkowskaja, 2014;

Klooster et al. 2014; Herms & McCullough 2014). Manchurian ash (F. mandshurica

Rupr.), which shares a coevolutionary history with EAB in Asia, was much more resistant in common garden studies than these evolutionarily naïve species (Rebek et al.

2008; Herms 2015; Tanis & McCullough 2015).

EAB rarely outbreaks on coevolved ash species native to eastern Asia, where it appears to function as a secondary colonizer of stressed or declining trees (Wei et al.

2004, 2007), which is a common feature of other buprestid wood-borers (Evans 2007).

For example, outbreaks of bronze birch borer (Agrilus anxius Gory) have been associated anecdotally with drought stress (Muilenburg & Herms 2012). In one of the few experimental studies, Chakraborty et al. (2014) found that drought stress increased performance of EAB larvae on Manchurian ash.

Host traits thought to be important for tree resistance to EAB and other wood- borers include those that influence adult oviposition preference and/or larval performance

(Hanks et al. 1999; Villari et al. 2016). However, the majority of comparative studies of

ash resistance to EAB relied on natural infestations, and therefore cannot distinguish between effects of oviposition preference and larval performance, i.e. larval growth and survival. Compared to susceptible North American ash species in host preference experiments, Manchurian ash experienced lower levels of foliage feeding (Pureswaran &

Poland, 2009) and oviposition (Rigsby et al. 2014) by EAB adults. In initial studies isolating larval performance from oviposition preference by using standardized artificial egg inoculations, Manchurian ash supported lower levels of larval survival and growth compared to North American ash species, but comparisons were either not tested statistically (Koch et al. 2015), or varied somewhat by measure of performance (survival vs. growth) (Chakraborty et al. 2014).

To clarify the role of phloem-based defenses in resistance of Manchurian ash to

EAB, experiments were conducted to test the hypotheses that 1) larvae would survive and develop better in susceptible, evolutionarily naïve white ash than in resistant coevolved

Manchurian ash, and that 2) water and nutrient stresses would increase larval performance to a greater degree in Manchurian ash than in white ash, which will remain susceptible even when healthy owing to lack of targeted defenses.

MATERIALS AND METHODS

Experiment 1 - Plant material and experimental design

In April 2006, three-year-old clonal cultivars of white (F. americana cv. ‘Autumn

Purple’) and Manchurian ash (F. mandshurica cv. ‘Mancana’) (Bailey Nurseries, Inc.

Newport, MN) were planted in a common garden at the Ohio Agricultural Research and

Development Center (OARDC) in Wooster, OH (40°46'50.9"N 81°55'34.6"W). In June

2014, when the trees 11 years old, five Manchurian ash and five white ash were selected for the study from the population of 18 trees of each species planted in a randomized complete block design, and a single branch on each tree was chosen to be inoculated with

EAB eggs (mean diameters ± SE: trunk = 11.4 ± 0.38 cm; branch = 4.3 ± 0.19 cm).

Experiment 2 - Plant material, experimental design, water and nutrient availability treatments

On 3 April 2012, bare-root individuals of clonal cultivars of white ash (‘Autumn Purple’) and Manchurian ash (‘Mancana’) (Bailey Nurseries, Inc. Newport, MN) were planted in

51L pots containing pine bark mulch and compost (2:1) at OARDC’s outdoor Landscape

Nursery Crop Engineering Research Laboratory. Mean ± SE stem diameter 25 cm above soil line = 2.46 ± 0.52 cm.

On 4 May 2012, 64 total trees were arranged into eight blocks based on height and trunk diameter. A full 2 x 2 x 2 factorial combination of two host species, two water availability levels, and two nutrient availability levels was applied in a randomized complete block design, with one replicate of the eight treatment combinations per block.

Nutrient levels of 30 or 150 ppm N, with N:P2O5:K2O in a ratio of 3:1:2 from Ca(NO3)2,

NH4H2PO4, and KNO3 were provided via a computer-controlled irrigation and fertilization system (Argus Control Systems Ltd, White Rock, BC, Canada) equipped with 15 cm soil tensiometers (Irrometer Company, Inc. Riverside, CA) (Hansen et al.

2011). Two levels of water availability were achieved by controlling the potting medium moisture tension threshold (PMMTT, -5 kPa for high water availability or -10 kPa for low water availability) at which the pots would receive a pulse of nutrient solution. After

3 October, nutrient delivery was halted and trees received differential delivery of municipal water until 3 November, when water delivery was terminated for the season.

Differential nutrient and water availability treatments were resumed on 3 May 2013 and continued until harvest.

Experiment 2 - Plant growth and gas exchange measurements

Stem diameter 25 cm above the soil line was measured on 8 June 2012 and 3 May 2013 and diameter growth was calculated by subtracting the initial from the final diameter. Gas

-2 - exchange parameters, including light saturated net photosynthesis (Anet, µmol CO2 m s

1 -2 -1 ), stomatal conductance (gs, mmol H2O m s ), and potential covariates including air

vapor pressure deficit (VPD), were measured on all trees using an open infrared gas analyzer system (LI-COR 6400, Li-Cor Biosciences, Lincoln, NE) (Field et al. 1989). On each tree, measurements were taken on a randomly selected, non-terminal, fully expanded, sun-exposed leaflet large enough to fill the sampling chamber (6 cm2). The leaf sampling chamber provided photosynthetically active radiation (PAR) of 1500 µmol

-2 -1 m s from a red/blue LED light source (LI6400-02B), and a CO2 concentration of 400

µmol mol-1 dry air. Measurements were made in 2013 under ambient temperature and relative humidity conditions, which varied across sampling dates (min °C, max °C, min

RH%, max RH% = 24.9, 28.5, 54.8, 61.9, respectively, on 29 May; 27.6, 31.4, 67.1, 82.5, respectively, on 17 July; and 23.1, 26.0, 87.5, 97.1, respectively, on 8 August.

Egg inoculation, harvest, and larval outcome assessments

On 23 June 2014 (experiment 1) and 18 June 2013 (experiment 2) trees were inoculated as in Rigsby et al. (2015) using EAB eggs laid on coffee filters obtained from the USDA-

APHIS-PPQ Biological Control Rearing Facility, Brighton, MI. Mean inoculation densities of 90 (experiment 1) and 330 (experiment 2) eggs m-2 bark surface were achieved by placing four eggs at each of three sites separated by 50 cm along the branch

(experiment 1) or stem (experiment 2), and wrapping lightly with gauze secured on the edges with duct tape. On 25 August 2014 (experiment 1) and 27 August 2013

(experiment 2), an estimated 65-70 days following egg hatch (based on subsample of eggs incubated and observed for hatch), trees were debarked, and larvae were assigned

one of five unique outcomes (first through fourth instar or killed larva) using a method modified from Duan et al. (2013) and Koch et al. (2015). Larvae for which a head capsule could be recovered were assigned an instar according to Loerch and Cameron

(1983) and Chamorro et al. (2012).

For larvae that were accidentally damaged during harvest, instars were estimated based on the terminal width of their galleries (MacQuarrie & Scharbach, 2015).

Specifically, a loge equation was fit to the relationship between larval epistomal width

(mm) and gallery terminal width (mm) of recovered measured larvae and used to estimate unrecovered larvae. The percentages of total outcomes that were estimated (rather than measured) were 12% for experiment 1, and 11% for experiment 2. There were no signs of larval mortality from microbial or parasitoid activity, nor of intraspecific competition

(e.g. cannibalism or depletion of phloem), and inoculation densities were lower than those at which intraspecific competition has been observed in other studies (Duan et al.

2013). Therefore, all galleries terminating in browned or calloused phloem tissue and/or containing no observable larva were considered to correspond to host-killed larvae (Duan et al. 2012, 2014; Koch et al. 2015). To assess treatment effects on gallery initiation, each egg was noted as positive or negative for the presence of a gallery, with positives having a larval outcome, either killed or any instar.

Statistical analysis

Although one of four distinct instars was determined for each larvae (via measurement or estimation as above), these were pooled into two functional groups for statistical analysis: first or second instar larvae corresponding to those which would be unlikely to pupae and emerge as adults in the following year, and third or fourth instar larvae that were likely to emerge the following year. This resulted in three larval outcome groups: host killed; first or second instar; and third or fourth instar. Proportions of total larvae in each tree belonging to each of these outcome groups were calculated and proportions were arcsine transformed for variance stabilization prior to parametric statistical analysis. Analysis of variance in PROC MIXED in SAS 9.4 (SAS Institute, Inc., Carey, NC) was used to test the random effect of block in both experiments and fixed effect of species (experiment 1) or fixed effects and interactions of species, water availability, and nutrient availability

(experiment 2). Type 3 sums of squares were used for variance component estimation

(Littell et al. 2006). Tests of treatment effects on proportions of eggs resulting gallery initiation were performed in the same manner as larval outcome groups.

In experiment 2, the random effect of block, and fixed effects and interactions of species, water, and nutrient availability on trunk diameter growth were tested using analysis of variance as described for larval outcome. Initial stem diameter was used as a covariate but was not significant and was removed from the model. Effects on light saturated net photosynthesis (Anet) and stomatal conductance (gs) on each sampling date were tested in the same manner as trunk diameter growth, but with VPD as a covariate.

The significance threshold of all tests was set at α = 0.05, but marginally significant effects below the threshold of α = 0.10 are also discussed. All data met assumptions of normality and homoscedasticity as assessed by residual plots. One tree in the experiment 2 design was not inoculated due to insufficient numbers of EAB eggs and was thus excluded from analyses of gallery initation and larval outcome, but retained for analyses of diameter growth and gas exchange. Data are reported as least squares (LS) means ± standard error, with gallery initiation and larval outcome proportions resulting from back-transformed LSmeans of arcsine transformed data.

RESULTS

Experiment 1 - Species effects on gallery initiation and larval outcome

In experiment 1 (common garden experiment), no significant effect of species on gallery initiation was observed (F1, 4 = 1.22, P = 0.2749), with a mean (± SE) of 40 ± 7% of eggs resulting in gallery initiation. Species did have significant effects on measures of larval performance. Compared to white ash, greater proportions of larvae recovered from

Manchurian ash were killed by the host (F1, 4 = 7.47, P = 0.0523), similar proportions were first or second instar (F1, 4 = 0.13, P = 0.7386), and smaller proportions were third or fourth instar (F1, 4 = 15.06, P = 0.0178) (Fig. 3.1 A).

A White Ash Manchurian Ash 1

0.8 per Tree per

0.6 Proportion of Proportion Larvae Larvae 0.4 0.2 0 Killed First or Second Instar Third or Fourth Instar White Low Water White High Water B Manchurian Low Water Manchurian High Water 1

per Tree per 0.8

0.6

Proportion of Proportion Larvae Larvae 0.4 0.2 0 Killed First or Second Instar Third or Fourth Instar

C White 30 ppm N White 150 ppm N

Manchurian 30 ppm N Manchurian 150 ppm N 1

per Tree per 0.8

0.6

Proportion of Proportion Larvae Larvae 0.4 0.2 0 Killed First or Second Instar Third or Fourth Instar

Figure 3.1. Proportions of total larvae per tree belonging to three survival and development outcome groups compared between ash species in experiment 1 (A), and between species x water availability (B) and species x nutrient availability (C) treatment combinations in experiment 2. White bars represent white ash. Grey bars represent Manchurian ash. Patterned bars represent low water availability treatment in (B) and low (30ppm) nutrient availability treatment in (C). Least squares means (± SE). Refer to results text and Table 3.2 for tests of statistical significance. 52

Gallery initiation Effect df F P Species (S) 1, 47 1.22 0.2749 Water (W) 1, 47 0.98 0.3282 Nutrient (N) 1, 47 0.89 0.3507 S x W 1, 47 0.28 0.5982 S x N 1, 47 0.22 0.6408 W x N 1, 47 0.01 0.9387 S x W x N 1, 47 2.35 0.1319 Block 7, 47 0.98 0.4591

Killed Larvae 1st-2nd Instar 3rd-4th Instar Effect df F P F P F P Species (S) 1,47 18.23 <0.0001 32.81 <0.0001 63.83 <0.0001 Water (W) 1,47 3.56 0.0654 0.79 0.3774 2.48 0.122 Nutrient (N) 1,47 2.10 0.1536 0.32 0.5733 3.69 0.0607 S x W 1,47 2.25 0.1402 1.43 0.2373 0.89 0.3504 S x N 1,47 0.57 0.4538 3.53 0.0663 0.42 0.5178 W x N 1,47 0.88 0.3537 1.70 0.1992 0.20 0.655 S x W x N 1,47 0.46 0.5008 0.36 0.5493 0.49 0.4858 Block 7,47 1.03 0.4260 1.38 0.2368 2.19 0.0523

Table 3.3. Analysis of variance results for water availability and nutrient availability effects and interaction on arcsine transformed larval outcome proportions in Manchurian ash in experiment 2. df; degrees of freedom. Bolded P values < 0.05, underlined < 0.1. (n = 8 replicate trees per treatment combination; total df = 62).

Killed Larvae 1st-2nd Instar 3rd-4th Instar Effect df F P F P F P Water (W) 1,21 4.94 0.0373 1.31 0.2645 3.08 0.0938 Nutrient (N) 1,21 0.21 0.6529 1.81 0.1934 3.22 0.0873 W x N 1,21 1.14 0.2984 1.11 0.3047 0.65 0.4282 Block 7,21 0.65 0.7136 1.13 0.3833 1.46 0.2345

Experiment 2 - water, nutrient and species effects on larval outcomes and survival

In experiment 2 (fertigator experiment), the overall gallery initiation rate was 39 ± 2% with no significant effects or interactions of species, water availability, or nutrient availability (Table 3.1). Species again showed significant effects on measures of larval performance (Table 3.2). Compared to white ash, greater proportions of larvae recovered from Manchurian ash were either killed by the host or reached only the first or second instar, and a smaller proportion was recovered as third or fourth instar (Fig. 3.1 B).

No other treatment effects or interactions were significant at P < 0.05 for any of the three larval outcome proportions. However, marginally significant (P < 0.1) effects of water availability on proportion of killed larvae, of species x nutrient availability interaction on proportion of first or second instar larvae, and of nutrient availability on proportion of third or fourth instar larvae were observed (Table 3.2, Fig. 3.1B,C).

A very large and consistent proportion (0.97 ± 0.03) of larvae recovered from white ash was third or fourth instar. This limited the variation available to statistically 54

test treatment effects in white ash using analysis of variance. As such, the effects and interaction of water availability and nutrient availability on larval outcome proportions were additionally tested in Manchurian ash alone. Low water availability decreased the proportion of killed larvae and increased the proportion of third and fourth instar larvae

(Table 3.3, Fig. 3.1B). High nutrient availability (150 ppm N) slightly increased the proportion of third and fourth instar larvae (Table 3.3, Fig. 3.1C).

Experiment 2 - treatment effects on tree growth and gas exchange

In experiment 2, low water availability decreased trunk diameter growth (1.67 ± 0.14 mm and 2.58 ± 0.13 mm for low and high water availability, respectively), as did low nutrient availability (1.65 ± 0.14 mm and 2.61 ± 0.13 mm for low and high nutrient availability, respectively (Table 3.4).

Table 3.4. Analysis of variance results for trunk diameter growth of white and Manchurian ash in response to water and nutrient availability (df, degree of freedom). Bolded P values < 0.05, underlined < 0.1. (n = 8 replicate trees per treatment combination; total df = 63).

Source of variation df F P Species (S) 1,48 0.89 0.3489 Water (W) 1,48 20.48 <0.0001 Nutrient (N) 1,48 23.05 <0.0001 S x W 1,48 0.69 0.4106 S x N 1,48 0.03 0.8641 W x N 1,48 1.29 0.2624 S x W x N 1,48 0.74 0.3951 Block 7,48 0.82 0.5742

-2 -1 Anet (µmol CO2 m s ) 29-May 17-Jul 8-Aug Source of variation df F P F P F P Species (S) 1,48 0.08 0.7719 6.4 0.0148 0.69 0.4096 Water (W) 1,48 4.33 0.0428 1.19 0.2809 2.84 0.0982 Nutrient (N) 1,48 2.24 0.1414 0.03 0.8566 6.11 0.0171 S x W 1,48 0.60 0.4414 1.85 0.1803 0.14 0.7101 S x N 1,48 1.39 0.2437 4.12 0.0480 0.13 0.7187 W x N 1,48 0.43 0.5173 1.47 0.2313 0.13 0.7217 S x W x N 1,48 1.91 0.1734 0.03 0.8700 0.16 0.6932 VPD 1,48 120.24 <0.0001 19.52 <0.0001 130.80 <0.0001 Block 7,48 16.17 <0.0001 4.74 0.0004 16.91 <0.0001

-2 -1 gs (mmol H2O m s ) Species (S) 1,48 1.06 0.3092 3.80 0.0572 0.84 0.3648 Water (W) 1,48 5.09 0.0286 0.05 0.8230 1.88 0.1768 Nutrient (N) 1,48 0.10 0.7952 0.47 0.4953 1.94 0.1698 S x W 1,48 1.91 0.1728 0.75 0.3915 0.24 0.6254 S x N 1,48 4.15 0.0471 1.71 0.1974 0.00 0.9679 W x N 1,48 0.61 0.4404 3.02 0.0886 0.71 0.4031 S x W x N 1,48 1.69 0.1994 0.00 0.9550 1.06 0.3091 VPD 1,48 143.19 <0.0001 35.26 <0.0001 1071.72 <0.0001 Block 7,48 18.84 <0.0001 5.82 <0.0001 92.35 <0.0001

12 A B

) ) 29 May 1

- 10

- 8

2 6

4 (µmolCO

2 net A 0

12 C D

) ) 1 17 July

- 10 2 s 2

- 8

2 6

mol (µ

2 net A 0

12 E F

) ) 8 Aug 1

- 10

2 -

m 8

2 6

4 (µmol CO (µmol

2 net A 0 Low High 30 ppm N 150 ppm N Water availability Nutrient availability White ash Manchurian ash

Figure 3.2. Light saturated net photosynthesis (Anet) in experiment 2 for white ash (white bar) and Manchurian ash (grey bar) in response to water (A,C,E) and nutrient availability (B,D,F) on three dates in 2013 (lsmeans ± SE). Refer to Table 3.5 for results of statistical tests of significance.

A D

250

) ) 1

- 29 May

2 200 -

O m O 150 2 100

(mmol H (mmol 50

s g 0

250 B E

) )

1 -

s 17 July

200

2 -

O m O 150 2

100 (mmol H (mmol

s g 0

C F

) ) 250 1

- 8 Aug

2 200 -

150

O m O 2 100

(mmol H (mmol

g 0 Low High 30 ppm N 150 ppm N Water availability Nutrient availability

White ash Manchurian ash

Figure 3.3. Stomatal conductance (gs) in experiment 2 for white ash (white bar) and Manchurian ash (grey bar) in response to water (A,C,E) and nutrient availability (B,D,F) on three dates in 2013 (lsmeans ± SE). Refer to Table 3.5 for results of statistical tests of significance. 58

Effects of water and nutrient availability treatments on net photosynthesis and stomatal conductance varied between species and sampling dates in 2013 (Table 3.5, Figs. 3.2 and

3.3). On 29 May, low water availability decreased net photosynthesis (Fig. 3.2 A) and stomatal conductance (Fig. 3.3 A). However, there was a significant interaction between species and nutrient availability for stomatal conductance (Table 3.5). Stomatal conductance was higher in the high nutrient availability treatment for white ash, but higher in the low nutrient availability treatment for Manchurian ash (Fig. 3.3 D).

However, the magnitude of the effect was small.

On 17 July, white ash had a higher rate of net photosynthesis than Manchurian ash, with the magnitude of the effect influenced by nutrient availability (significant main effect of species coupled with an interaction between species and nutrient availability

(Table 3.5). Net photosynthesis of white ash was much greater than that of Manchurian ash under low nutrient availability, but there was little difference under high nutrient conditions (Fig. 3.2 B and E). A similar pattern between species was observed for stomatal conductance, which was higher in white ash (Table 3.5) (Fig 3.3 B and E), but the species x nutrient interaction was not significant (Table 3.5).

On August 8, only nutrient availability had a significant effect on net photosynthesis (Table 3.5), which was higher in the high nutrient availability treatment

(Fig 3.2 F). Overall levels of stomatal conductance were higher than on the other sampling dates (Fig 3.3 C and F), but no significant treatment effects were observed

(Table 3.5).

DISCUSSION

The results of experiments 1 and 2 clearly support hypothesis 1, i.e. larval performance is much greater on susceptible white ash than on resistant Manchurian ash, as much higher proportions of larvae were killed by the host or were first or second instar Manchurian ash, and a much higher proportion reached third and fourth instar in white ash.

Lower rates of larval survival and development in Manchurian ash compared to white ash are consistent with results of previous studies measuring numbers of adult exit holes after natural infestation (Rebek et al. 2008; Whitehill et al. 2014). Herms (2015) observed the same infestation and ash mortality pattern in a common garden study when comparing an open-pollinated cohort of Manchurian ash against diverse green, white, and black ash cultivars, suggesting that the high EAB resistance of Manchurian ash cultivar

‘Mancana’ is representative of the species as a whole. Manchurian ash has also been observed to be much more resistant to EAB than North American species in field plantings in China (Wei et al. 2004, 2007).

Such resistance patterns could have resulted from decreased oviposition preference for Manchurian ash (e.g. Rigsby et al. 2014) and/or decreased larval survival.

The measures of larval outcome in response to standardized egg inoculations provide insights into potential resistance mechanisms, because oviposition preference was bypassed in the current experiments. The data show that phloem-based traits contribute to differential larval survival and development after gallery initiation between resistant

Manchurian ash and susceptible white ash. Chakraborty et al. (2014) and Koch et al.

(2015) found that larval performance was lower on Manchurian ash than black and green ash, respectively, which is consistent with the current findings with white ash and the hypothesis that Manchurian ash is inherently more resistant to EAB than these North

American ash species. Specifically, Koch et al (2015) found greater proportions of killed larvae in resistant green ash genotypes compared to susceptible genotypes. If similar proportions of gallery initiation across species are assumed for Chakraborty et al (2014) then the variable “larval count” reported in the study roughly translates to larval survival.

If this is the case, then Chakraborty et al (2014) also found higher proportions of killed larvae in resistant Manchurian ash than in black ash or water-stressed Manchurian ash, as was observed in the current study.

Most larval mortality in Manchurian ash in the current study occurred during the first or second instars. Of the tree-killed larvae for which instars could be estimated, 60% were likely killed as first instars, while 40% were killed as second instars. No third or fourth instars were observed killed by the host, which suggests that effective host defenses act rapidly on the invading insect. Results of multiple studies of larval performance following egg inoculation together suggest that host mechanisms affecting mortality of early EAB larval instars are important for resistance.

A small proportion of surviving larvae in Manchurian ash were fourth instars when the study was terminated, and probably would have survived to emerge as adults.

This is consistent with the very low density of exit holes observed on Manchurian ash in common garden studies (e.g. Rebek et al. 2008; Whitehill et al. 2014; Herms 2015).

Rigsby et al. (2014) found that Manchurian ash was also less preferred by EAB as an

oviposition host than white, green, and black ash. Collectively, this suggests that oviposition preference or other antixenotic defenses may interact additively or synergistically with antibiotic phloem defenses to result in the high resistance of

Manchurian ash relative to white, green, and black ash. Larvae recovered from

Manchurian and white ash in experiment 2 of this study and analyzed by Rigsby et al.

(2015) exhibited differences in enzymatic activities associated with oxidative stress, suggesting one possible mechanism of antibiosis contributing to decreased larval growth and survival in Manchurian ash (Rigsby et al. 2015, 2016).

The prediction of hypothesis 2, that low water availability would increase larval performance to a greater degree in Manchurian ash than white ash, was expected to result in a statistically significant species x water interaction. Such significance was not observed for any larval outcome group, and a marginally significant (P = 0.065) main effect was water seen for killed larvae (Table 3.2). However, while the trends between water availability treatments (low water resulting in fewer killed larvae and more third or fourth instar larvae) were consistent across species, the biological significance of these patterns may differ between species. That is, while the proportion of killed larvae approximately halved with low water availability in both species, the biological effect of an decrease in a very small proportion of killed larvae (0.02 to 0.01) in white ash is likely negligible compared to the decrease in Manchurian ash with low water availability (0.47 to 0.15) (Fig. 3.1B). A similar pattern, in terms of larval performance, was observed for the proportions of third and fourth instar larvae between water availability treatments and across species. The proportion of third and fourth instar larvae was very high in white ash

and increased slightly with low water availability (0.96 to 0.98) and was moderate in

Manchurian ash and increased more substantially (0.19 to 0.43) (Fig. 3.1B). The hypothesized larger effect of water availability in Manchurian ash compared to white ash is consistent with decreased P values for ANOVA F tests in Manchurian ash alone compared to species combined (Table 3.3 compared to Table 3.2). ANOVAs were not performed for white ash alone due to strong skewing of killed and first and second instar proportions towards zero and third and fourth instar proportions towards one, resulting in heteroscedasticity that could not be sufficiently mitigated with transformation. Such heteroscedasticity was not as severe when data from both species were included.

A biological interpretation, rather than a strict statistical interpretation, of the current results is consistent with hypothesis 2, i.e. that larval performance is higher when

Manchurian ash is stressed by low water availability, and that this effect is greater in

Manchurian ash than white ash. Such stress is evidenced by a substantially lower trunk diameter growth rate (Table 3.4). Photosynthesis and stomatal conductance also decreased with low water availability, but only on the first sampling date (29 May)

(Table 3.5, Figs. 3.2 and 3.3), which is consistent with other studies that found growth of trees to be more sensitive than photosynthesis to drought stress and nutrient availability

(Körner 1991; Luxmoore 1991; Lambers and Poorter 1992; Glynn et al. 2003). These patterns are also consistent with observations that EAB appears to be a secondary colonizer of stressed trees when infesting coevolved hosts (Wei et al. 2004, 2007), as well as the experimental results of Chakraborty et al. (2014), who also found that low water availability decreased the resistance of Manchurian ash to EAB. Conversely, water

availability had a negligible effect on larval performance in white ash, which is consistent with EAB causing greater than 99% mortality of white ash (and green and black ash) in forests of southeast Michigan across a range of site conditions, encompassing a range of water availabilities (Klooster et al. 2014; Smith et al. 2015). These evolutionarily naïve

North American hosts are highly susceptible even when healthy.

High nutrient availability substantially increased tree growth but only affected photosynthesis on the third sampling date, when photosynthesis increased slightly. High nutrient availability marginally increased larval performance (P = 0.06 for third and fourth instars with species combined, P = 0.09 for Manchurian alone), which is consistent with numerous studies that have found that increased nutrient availability generally increases herbivore performance across multiple feeding guilds, by decreasing host defenses and/or increasing nutritive quality of host tissues (Kytö et al. 1996; Herms

2002). However, in a field study, Tanis & McCullough (2015) found that fertilization had no effect on resistance of Manchurian, white, or other ash species tested.

In summary, this study found evidence suggesting that the higher resistance of

Manchurian ash observed in common garden field studies (Rebek et al. 2008; Whitehill et al. 2014; Tanis & McCullough 2015) is associated with phloem-based defensive traits, in addition to previously documented lower oviposition preference (Rigsby et al. 2014).

Furthermore, increased EAB larval performance (decreased host resistance) in coevolved

Manchurian ash with low water availability was associated with physiological stress. This is consistent with the hypothesis and observations that EAB primarily acts as a secondary

colonizer of stressed trees when infesting coevolved hosts in Asia, but is a primary colonizer of healthy, evolutionarily naïve North American hosts.

CHAPTER 4: PRO-OXIDANT-ASSOCIATED ENZYME ACTIVITIES AND PHENOLIC PROFILES IN RESPONSE TO EMERALD ASH BORER LARVAL FEEDING IN WHITE AND MANCHURIAN ASH

ABSTRACT

The oxidative stress generated in insect herbivores by ingested plant material has been shown to be an effective defense in some plant systems. Initial studies on ash species resistant and susceptible to EAB have shown that the enzyme and substrate components of such a system are constitutively present and differentially active. Building on these results, the current study profiled phloem phenolics and pro-oxidant enzyme activities in response to EAB larval feeding in susceptible white and resistant Manchurian ash.

Potential substrates for pro-oxidant enzyme activities, like verbascoside in white ash and a verbascoside derivative in Manchurian ash, were found to be induced in response to

EAB larval feeding. A novel interspecific pattern of phloem verbascoside concentration was also observed. The results of this study have informed a revised hypothesis that pro- oxidant-associated peroxidase enzyme activities make greater contributions to EAB resistance than do polyphenol oxidase and β-glucosidase activities, but that biological effects of enzyme activities may be dependent on substrate composition.

INTRODUCTION

As expected for a trait hypothesized to be polygenic, ash (Fraxinus spp.) resistance to emerald ash borer (Agrilus planipennis Fairmaire, EAB) has not been strongly associated with any individual or class of phenolic compounds, protein, or other molecule, and different patterns have emerged depending on species compared (Villari et al. 2016).

Perhaps as a result, recent ash studies have focused on multivariate analyses

(Chakraborty et al. 2014) or integrated targeted univariate approaches (Rigsby et al.

2015, 2016) to identify defense syndromes or mechanisms based on common function rather than individual compounds, as is the case with other plant-herbivore systems

(Agrawal and Fishbein 2006). Pro-oxidant qualities of diverse phloem components such as phenolic acids, polyphenols, and proteins may act as anti-herbivore defenses (Appel

1993, Summers and Felton 1994, Konno et al. 1999). Based on observations of rapid oxidation and browning of damaged Manchurian ash (F. mandshurica Rupr.) phloem

(Cipollini et al. 2011) several studies have profiled pro-oxidant activities of ash phloem differing in resistance to EAB (Cipollini et al. 2011, Rigsby et al. 2016), as well as antioxidant counter-responses in EAB larvae (Rajarapu 2013, Rigsby et al. 2015).

Comparisons of ash phloem pro-oxidant activity have been limited to phloem of susceptible black (F. nigra Marshall) and resistant Manchurian ash (Rigsby et al .2016), with the exception of some basic profiling in white (F. americana L.) and green ash (F. pennsylvanica Marshall) (Cipollini et al. 2011). Profiles of anti-oxidant activities in EAB have used comparisons of larvae fed on susceptible white and green ash vs. resistant

Manchurian ash (Rigsby et al. 2015). Additionally, pro-oxidant activity of ash phloem in response to EAB larval feeding has not been evaluated. To address these knowledge gaps, constitutive and EAB-induced phloem phenolic profiles and selected enzyme activities associated with oxidant activity were investigated in white and Manchurian ash.

MATERIALS AND METHODS

Ash material, insect application and phloem sampling

In April 2006, three-year-old clones of white (F. americana cv. ‘Autumn Purple’) and

Manchurian ash (F. mandshurica cv. ‘Mancana’) (Bailey Nurseries, Inc. Newport, MN) were planted in a common garden at the Ohio State University’s Ohio Agricultural

Research and Development Center (OARDC) in Wooster, OH (40°46'50.9" N

81°55'34.6" W). In June 2014, 10 Manchurian ash and 10 white ash were selected for the study from the population of 18 trees of each species, according to a randomized complete block design, and a single branch on each tree was chosen for inoculation

(mean diameters ± SE: trunk = 11.4 ± 0.38 cm; branch = 4.3 ± 0.19 cm).

On 23 June 2014 trees were inoculated with EAB eggs oviposited on coffee filters

(USDA-APHIS-PPQ Biological Control Rearing Facility, Brighton, MI) following

Rigsby et al. (2015). Briefly, eggs were incubated at 26°C in the laboratory and observed for hatching. Upon hatching of first eggs, remaining eggs were applied to branches by placing four eggs at each of three sites separated by 50 cm along the branch and wrapping

lightly with gauze secured on the edges with duct tape. Mean inoculation density was 90 eggs m-2 bark. Seven days following inoculation and presumed initiation of larval feeding, nascent feeding galleries were sampled by removing the outer bark with a razor blade and sampling phloem immediately surrounding the gallery using a 1.5 cm diameter cork borer. Phloem plugs where immediately placed in liquid nitrogen and later stored at

-80 C until they were ground in liquid nitrogen in a mortar.

Extraction of native proteins

Native protein was extracted for enzyme activity analysis using methods modified from

Cipollini et al. (2011) and Rigsby et al. (2016). Approximately 200 mg of ground tissue was extracted in 1 mL extraction buffer (50 mM NaPO4, pH 6.5, 10% glycerol, 7%

PVPP, 10 mM β-mercaptoethanol, 1 mM EDTA, 1 mM ascorbate, and 1 mM PMSF) on ice for 1 hr. Homogenates were centrifuged at 7,000 g (10 min, 4 oC) and protein was precipitated from 800 µL supernatant using 4 mL cold (-20 oC) acetone. After a 1 hr incubation at -20 oC, tubes were centrifuged at 20,000 g (5 min, 4oC), decanted, and pellets were allowed to dry in a fume hood for 15-30 min. Pellets were resuspended in

20% of the original supernatant volume in native protein assay buffer (50 mM NaPO4, pH 8.0, 10% glycerol) and stored at -20oC until used in assays (within 48 h). The differences in pH between extraction and assay buffers was intentional, to avoid loss of the polyphenol adsorbing function of PVPP, which occurs at pH ≥ 7 (Makkar et al. 1995).

Enzyme activity assays

The activities of guaiacol and syringaldazine peroxidases (POX), β-glucosidases (βG), and polyphenol oxidases (PPO) in phloem extracts were quantified using guaiacol, syringaldazine, oleuropein, and catechol substrates, respectively, as described by

Cipollini et al. (2011) and Rigsby et al. (2016). Guaiacol POX activity was quantified by monitoring the change in absorbance at 470 nm for 2 min at 30 oC of reaction mixtures containing 10 µL protein extract and 190 µL substrate solution consisting of 0.25% guaiacol and 0.375% H2O2 in assay buffer. Guaiacol POX activity was expressed as

µmoles oxidized guaiacol using the extinction coefficient of 26.6 mM-1 cm-1 (Meisrimler et al. 2011). For syringaldazine POX activity, 10 µL protein extract and 180 µL 0.2 mM of syringaldazine in assay buffer (10 mM stock solution prepared in warmed methanol) were allowed to incubate at room temperature for one min, after which 10 µL of 1%

H2O2 in assay buffer were added to initiate the reaction and the change in absorbance was monitored at 530 nm for one min at 30 oC. Syringaldazine POX activity was expressed as

µmoles oxidized syringaldazine using the extinction coefficient of 27 mM-1 cm-1 (Lee et al. 2007). The oleuropein-hydrolyzing activity of βG was quantified by mixing 10 µL protein extract with 90 µL 3 mM oleuropein in assay buffer for 30 min at 30 oC. The amount of glucose in the reaction mixture was determined using the color reagent described by Siemens and Mitchell-Olds (1998). The absorbance at 492 nm of reaction mixtures after 1 h of incubation with the color reagent was used to determine glucose concentration and the amount of glucose liberated in this procedure was used to express

βG activity. Finally, PPO activity was quantified by mixing 10 µL protein extract with

190 µL 3 mM catechol and monitoring the change in absorbance at 400 nm at 30 oC for three min. Activity was expressed as µmoles o-quinone produced using the extinction coefficient 3,450 M-1 cm-1 (Saeidian 2012). All assays were performed in duplicate wells of a 96-well microplate with appropriate controls and blanks.

To compare relative enzyme activities on substrates shared between the current study and Rigsby et al. (2016), constitutive enzyme activities from black ash in Rigsby et al. (2016) were all set equal to 1. Constitutive enzyme activities of Manchurian ash from

Rigsby et al. (2016) were normalized relative to constitutive enzyme activities in black ash. To compare between studies, constitutive enzyme activities of Manchurian ash from the current study were normalized to constitutive enzyme activities in Rigsby et al.

(2016) which were of the same order of magnitude. Enzyme activities of white ash

(constitutive and EAB-induced) and EAB-induced enzyme activities of Manchurian ash in the current study were normalized using the between-study normalization factor for each enzyme determined from constitutive Manchurian samples.

Extraction and analysis of phloem phenolics

Phloem phenolics were extracted as in Chakraborty et al. (2014). Briefly, 100 mg FW of ground phloem tissue was extracted twice for 24 hrs at 4 oC in 500 µL HPLC-grade methanol (Fisher Scientific, Pittsburgh, PA, USA) containing 5 mM butylated hydroxyanisole (Sigma-Aldritch, St. Louis, MO, USA) as an internal standard. The two

extracts were then pooled in a new 1.5 mL microcentrifuge tube and stored at -20 oC until analysis.

Phenolic compounds were quantified using a Waters Acquity H-class 1200 series ultra high performance liquid chromatograph (UPLC) equipped with a temperature- controlled autosampler and a photodiode array detector (PDA) (Waters, Milford, MA).

Analytes were separated on an Acquity BEH C18 2.1×100 mm, 1.7 µm diameter particle column (Waters). The binary mobile phase consisted of 0.1% HPLC-grade acetic acid

(Fisher Scientific) in HPLC-grade water (solvent A), and 0.1% HPLC-grade acetic acid in HPLC-grade methanol (Solvent B), with a flow rate of 0.5 ml min-1. Total run time was 11.14 min. The following linear gradient [cumulative run time (min), % solvent A] was used: 0.0, 95.0; 1.70, 85.0; 3.97, 70.0; 4.53, 60.0; 5.67, 40.0; 6,23, 10.0; 6.80, 0.0;

7.03, 95.0; 11.14, 95.0. The autosampler and column temperatures were set at 24 and 50 °

C, respectively, and the injection volume was 0.8 µL. UV spectral data were recorded from 210 to 400 nm, and peak areas were integrated at 280 nm (Bonello & Blodgett

2003) using the apex-track algorithm. Minimum integrated peak area was set at 35,000 mV*s. Data acquisition was performed using the Empower 3 software (Waters). Peak area of each compound was normalized by dividing it by the peak area of the internal standard, and expressed as internal standard-normalized peak area per gram fresh phloem weight. Peaks of interest were further characterized and putatively identified by LC-

MS/MS using an Agilent 1290 UHPLC equipped with an AB Sciex QTRAP 5500 hybrid triple-quadrupole/ion-trap mass spectrometer. LC-MS/MS data were acquired and processed using Analyst 1.6.1 software.

Data filtering and statistical analysis

Datasets were subjected to a data reduction workflow summarized in Fig. 4.2 and based on that of Chakraborty et al. (2014). The purpose of the workflow was to to limit the number of univariate ANOVAs performed and thus control type I error while maintaining the highest possible statistical power. In order to limit error associated with technical variation, only peaks that were above a minimum integrated peak area threshold (35,000 mV*s) in at least six of ten (60%) individual replicates of at least one species x inoculation treatment combination were retained in the analysis, resulting in eight peaks being excluded due to the replication requirement. To be conservative in identifying peaks that were induced by EAB treatment and/or differed between species, samples with peak area outputs equal to zero (either truly zero or not calculated because they were below the minimum integrated peak area threshold of35,000 mV*s) were assigned a value equal to the sample-specific limit of quantitation (LOQ). LOQ was calculated in the same manner as normalized peak areas for each sample; LOQ = minimum integrated peak area threshold / peak area of BHA internal standard * phloem fresh weight).

Because species dominated the total variation in the dataset, and differences in phloem phenolic profiles between these two species have already been documented, data was split by species for data reduction steps, in order to focus on variation between control and EAB-inoculated samples. For each species the second principle component

(PC2) separated samples by inoculation treatment (Figs. 4.3 and 4.4). Peaks were sorted

by PC2 loading scores, and a threshold of |loading score| > 0.2 was selected for peak retention and univariate analysis (Table 4.5). The threshold of absolute value > 0.2 retained about 1/3 of the peaks in each species. In addition to retention by loading score, peaks having spectra matching the possible pro-oxidant enzyme substrates verbascoside or oleuropein were retained for univariate ANOVA.

The random effect of block and fixed effects of species (tree level) and inoculation (branch level) within species on individual phenolic peaks and enzyme activities were tested using analysis of variance in PROC MIXED SAS 9.4 (SAS

Institute, Inc., Carey, NC). Type III sum of squares were used for variance component estimation (Littell et al. 2006). The significance threshold of all tests was α = 0.05. All data met assumptions of normality and homoscedasticity as assessed visually using residual plots.

RESULTS

Constitutive POX activities (per mass total protein) were 3-22 fold higher, depending on substrate, in Manchurian ash than in white ash, with no effect of inoculation in either species (Tables 4.1 and 4.2). PPO activity was higher in white ash and significantly increased in both species with larval feeding, though much more so in white ash than in

Manchurian ash (5-fold vs. 2-fold, respectively) (Tables 4.1 and 4.3). βG activity was 3- fold higher in white ash than Manchurian ash, with no effect of larval feeding in either species (Tables 4.1 and 4.2).

A total of 27 phenolic compounds met the replication criterion to be included in the analysis (Table 4.4). Of these, 15 were retained following data reduction, either due to high loading scores in principal components responsible for separating inoculation treatments (PC2 in Figs. 4.3 & 4.4, Table 4.5), or due to spectral similarity to the potential βG substrates verbascoside and oleuropein (Table 4.5). Of the 15 compounds subjected to univariate ANOVA, concentrations of five of six shared compounds differed significantly between species (Table 4.6), three compounds were unique to white ash

(Tables 4.7 and 4.9), and six compounds were unique to Manchurian ash (Tables 4.8 and

4.9). A pinoresinol derivative (24) was lower in EAB-inoculated samples in both species, while the remaining five shared compounds displayed significant species x inoculation interactions (Tables 4.6 - 4.9). Two shared compounds, verbascoside (23) and verbascoside derivative 2 (27), were higher in EAB-inoculated than in control white ash samples (Tables 4.7 and 4.9), and three compounds unique to Manchurian ash, peak 18, calceolarioside A/B (19), and verbascoside derivative 1 (26), were higher in EAB- inoculated than in control samples (Tables 4.8 and 4.9).

Table 4.1. Analysis of variance of enzyme specific activities by substrate in control and EAB-inoculated white ash and Manchurian ash. Bolded values are statistically significant at α=0.05 (n = 8-10 replicate trees per treatment combination. df*= degrees of freedom; denominator degrees of freedom approximated according to method of Kenward and Roger (1997) for split-plot analysis with missing data.

Enzyme Substrate Factor df* F P PPO Catechol Species 1,9.16 6.41 0.0318 Inoculation 1,14 64.61 <0.0001 S x I 1,14 27.55 0.0001 βG Oleuropein Species 1,9.59 51.25 <0.0001 Inoculation 1,13 1.78 0.2047 S x I 1,13 0.51 0.4879 POX Guaiacol Species 1,9.22 69.89 <0.0001 Inoculation 1,13 1.76 0.2077 S x I 1,13 1.66 0.2196 Syringaldazine Species 1,8.84 25.37 0.0007 Inoculation 1,11 0 0.9995 S x I 1,11 0.06 0.8043

Table 4.2. Specific activities of β-glucosidase (βG) and peroxidase (POX) against different substrates in phloem extracts of in white and Manchurian ash. Least squares means (±SE).

Enzyme βG POX Species Oleuropein Guaiacol Syringaldazine White ash 16.39 (0.84) 23.58 (4.50) 171.4 (538.5) Manchurian ash 5.34 (0.73) 66.20 (4.31) 3768.2 (454.0)

Table 4.3. Specific activity of polyphenoloxidase (PPO) against the substrate catechol in control and EAB inoculation treatments in white and Manchurian ash. Least squares means (±SE).

PPO Species Inoculation Catechol White ash control 1.9 (1.02) EAB 10.4 (1.08) Manchurian ash Control 1.8 (1.02) EAB 3.9 (0.98)

Figure 4.1. Comparison of relative activities of pro-oxidant related enzymes in phloem extracts of black, white, and Manchurian ash in the current study and Rigsby et al. 2016.

Ground phloem samples extracted in methanol with internal standard

LC-MS PDA

Peak integration above minimum threshold and normalization to internal standard and sample mass

Peak filtering by detection in ≥ 60% of replicates of any treatment combination

Sample-specific LOQ calculation and substitution for missing peak areas

Principal component analysis and selection of principal component capable of grouping inoculation treatments in each species (PC2)

Peak filtering by |loading score| > 0.2 for selected principal component (PC2) and/or by spectral similarity to substrate of interest

ANOVA to model species and inoculation effects and interaction on shared peak areas and inoculation effects on peaks unique to species

Figure 4.2. Phenolic metabolite data reduction workflow combining multivariate analysis and limited univariate analysis to reduce type I error and maintain statistical power.

Table 4.4. UPLC PDA and MS data used for provisional identification of phenolic compounds in white and Manchurian ash.

a Peak Species RT λmax (nm) [M-H]- ions Putative ID Reference 1 Fm 2.063 280 329 148,121 3 Fm, Fa 2.708 221, 275 299 119,179 Tyrosol hexoside Whitehill et al. 2012 5 Fa 3.152 221, 275 431 251,299 Tyrosol hexoside pentoside Whitehill et al. 2012 6 Fm, Fa 3.493 219, 265 371 209,179,151 Syringin Whitehill et al. 2012 7 Fm 3.568 230, 295 (sh), 346 369 207,192 Fraxin Whitehill et al. 2012 8 Fm 3.880 229, 324 383 221,191,163 Mandshurin Whitehill et al. 2012 9 Fm 4.028 292, 334 (sh) 221 207,163 Fraxidin B Whitehill et al. 2012 10 Fm, Fa 4.160 268 12 Fm, Fa* 4.438 249, 324 477 161,135,179 Calceolarioside A Eyles et al. 2007 13 Fm 4.704 226, 276 Pinoresinol hexoside Whitehill et al. 2012 14 Fa 4.798 251, 330

79 16 Fm 5.087 278, 318 (sh)

17 Fa 5.149 228, 278 18 Fm 5.154 229, 280 (sh), 315 (sh) 19 Fm 5.211 250, 284 (sh), 327 (sh) 477 161,133,179,203 Calceolarioside A/B Whitehill et al. 2012 20 Fa 5.217 276 22 Fa 5.327 279 23 Fm, Fa 5.424 245, 282 (sh), 327 623 135,179,311 Verbascoside Whitehill et al. 2012 24 Fm, Fa 5.515 227, 278 519 357,136,151 Pinoresinol derivative Whitehill et al. 2012 25 Fa 5.571 221, 248 (sh)

RT = Retention time; (sh) = Shoulder; Fm = Fraxinus mandschurica; Fa = F. americana; *Present below limit of quantitation in all sample. aPutative identification was performed by matching PDA and MS spectral data to literature in “Reference” column, with the exception of peak 23 verbascoside, which was matched to an external standard.

Continued

Table 4.4. Continued

a Peak Species RT λmax (nm) [M-H]- ions Putative ID Reference 26 Fm 5.577 293 (sh), 328 623 161,133,461 Verbascoside derivative 1 Whitehill et al. 2012 27 Fm, Fa 5.652 280, 328 (sh) 653 623,161,133 Verbascoside derivative 2 Whitehill et al. 2012 28 Fa 5.725 229, 280 607 461 Verbascoside derivative 3 Whitehill et al. 2014 29 Fa 5.780 226, 248 (sh) 539 377,291,275 Oleuropein derivative Eyles et al. 2007 32 Fm, Fa* 6.007 232, 281 Oleuropein Whitehill et al. 2012 34 Fm, Fa 6.300 225, 248 (sh), 279 (sh) 523 361,291,259 Ligustroside derivative 1 Eyles et al. 2007 35 Fm 6.353 232, 279 Ligustroside derivative 2 Eyles et al. 2007

All White Peaks 212

P24

406 2.5 P5 P6 Inoculation 101 106 502 P14 507 a control 303 209 306 P20 P10 a EAB 0.0 P34P17 209403 P29 212 P25 502 P22P28 306 P27 106

PC2 (22.3% explained PC2var.) (22.3% 403 406 101P23507 -2.5 303 -5.0 -2.5 0.0 2.5 5.0 PC1 (49.2% explained var.)

All Manchurian Peaks

107 308 P2 304 2 P7P9 P1 109 201510 211 P35P12P23 P24 304 Inoculation P32P27 201404 407 506 P13 0 a control 109 308 P8 a EAB 404 P10 -2 P3 510 P16 407 P34 211 107

PC2 (18.4% explained PC2var.) (18.4%

P26 506 P18 P19 -5 0 PC1 (54.5% explained var.)

White ash Manchurian ash Peak PC2 PC2 P1 0.177974591 P3 0.5105799 -0.226481608 P5 0.36285167 P6 0.31732856 P7 0.19975776 P8 -0.141665798 P9 0.215233692 P10 0.04590036 -0.197379528 P12 0.123599872 P13 0.020172023 P14 0.14677774 P16 -0.230793244 P17 0.00116405 P18 -0.39464257 P19 -0.442509735 P20 0.13632928 P22 -0.1286464 P23 -0.3288993 0.114175562 P24 0.51718626 0.079083475 P25 -0.1142078 P26 -0.362266931 P27 -0.1868126 0.029051125 P28 -0.1482835 P29 -0.046039 P32 0.044594511 P34 0.02134422 -0.306252559 P35 0.102883726

Table 4.6. Analysis of variance of normalized peak areas for shared phenolic compounds (peak ID number in parentheses) in control and EAB-inoculated white ash and Manchurian ash. Bolded values are statistically significant at P = 0.05. n = 10 per treatment combination.

Shared Peak Factor df F P Tyrosol hexoside (3) Species 1,9 0.61 0.4565 Inoculation 1,18 11.63 0.0031 S x I 1,18 26.73 <0.0001 Syringin (6) Species 1,9 473.19 <0.0001 Inoculation 1,18 8.8 0.1966 S x I 1,18 2.03 0.171 Verbascoside (23) Species 1,9 34.76 0.0002 Inoculation 1,18 5.82 0.0267 S x I 1,18 15.1 0.0011 Pinoresinol derivative (24) Species 1,9 223.05 <0.0001 Inoculation 1,18 11.66 0.0031 S x I 1,18 0.5 0.4891 Verbascoside derivative 2 (27) Species 1,9 72 <0.0001 Inoculation 1,18 7.95 0.0113 S x I 1,18 7.42 0.0139 Ligustroside derivative 1 (34) Species 1,9 35.25 0.0002 Inoculation 1,18 0 0.9456 S x I 1, 18 5.94 0.0255

Table 4.7. Analysis of variance of normalized peak areas for shared and unique phenolic compounds (peak ID number in parentheses) in control and EAB-inoculated white ash. Bolded values are statistically significant at P = 0.05. n = 10 per treatment combination. Note: the shared compound pinoresinol derivative (24) was not analyzed separately by species as no significant S x I interaction was present.

Shared Peak Factor df F P Tyrosol hexoside (3) Inoculation 1,9 3.41 0.0977 Verbascoside (23) Inoculation 1,9 18.68 0.0019 Verbascoside derivative 2 (27) Inoculation 1,9 10.08 0.0113 Ligustroside derivative 1 (34) Inoculation 1,9 2.09 0.1821 Syringin (6) Inoculation 1,9 1.92 0.1995 Unique White Ash Peak Tyrosol hexoside pentoside (5) Inoculation 1,9 4.24 0.0696 Verbascoside derivative 3 (28) Inoculation 1,9 2.7 0.1350 Oleuropein derivative (29) Inoculation 1,9 0.08 0.783

Shared Peak Factor df F P Tyrosol hexoside (3) Inoculation 1,9 3.41 0.0977 Verbascoside (23) Inoculation 1,9 1.16 0.3102 Verbascoside derivative 2 (27) Inoculation 1,9 0.01 0.9235 Ligustroside derivative 1 (34) Inoculation 1,9 4.75 0.0572 Syringin (6) Inoculation 1,9 Unique Manchurian Ash Peak Fraxidin B (9) Inoculation 1,9 3.11 0.1116 P16 Inoculation 1,9 1.40 0.2668 P18 Inoculation 1,9 23.1 0.0010 Calceolarioside A/B (19) Inoculation 1,9 33.3 0.0003 Verbascoside derivative 1 (26) Inoculation 1,9 7.01 0.0265 Oleuropein (32) Inoculation 1,9 0.7 0.4248

White ash Manchurian ash Peak Putative ID Control EAB Control EAB 3 Tyrosol hexoside 0.570 (0.032) a 0.371 (0.032) b 0.485 (0.032) a 0.525 (0.032) a 5 Tyrosol hexoside pentoside 2.023 (0.108) 1.814 (0.108) ND ND 6 Syringin 16.441 (0.849) a 15.253 (0.849) a 0 (0.601) b 0.036 (0.601) b 9 Fraxidin B ND ND 1.115 (0.072) 0.948 (0.072) 16 ND ND 0.367 (0.018) 0.395 (0.018)

18 ND ND 0.418 (0.038) b 0.598 (0.038) a

19 Calceolarioside A/B ND ND 6.683 (0.904) b 11.260 (0.904) a 23 Verbascoside 3.70 (0.807) c 8.429 (0.807) b 11.787 (0.807) a 10.651 (0.807) ab 24 Pinoresinol derivative 0.869 (0.196) c 0.461 (0.196) c 4.293 (0.196) a 3.672 (0.196) b

86 26 Verbascoside derivative 1 ND ND 0.347 (0.045) b 0.519 (0.045) a

27 Verbascoside derivative 2 1.192 (0.134) b 1.809 (0.134) a 0.442 (0.134) c 0.453 (0.134) c 28 Verbascoside derivative 3 0.660 (0.075) 0.791 (0.075) ND ND 29 Oleuropein derivative 1.843 (0.175) 1.894 (0.175) ND ND 32 Oleuropein below LOQ below LOQ 1.141 (0.116) 1.014 (0.116) 34 Ligustroside derivative 1 1.780 (0.141) a 1.598 (0.141) a 0.717 (0.141) b 0.910 (0.141) b Letters next to LSmeans indicate significant differences. LSD test performed only if main effect of species, inoculation or interaction was significant. ND=not detected. Below LOQ= Below limit of quantitation.

DISCUSSION

The significantly higher (and EAB-inducible) PPO and βG activities observed in white compared to Manchurian ash, and the relatively higher inducibility of PPO by larval feeding in white ash (Tables 4.1 and 4.3, Fig. 4.1), together with comparable levels between the two species of some possible phenolic-glycoside substrates [total peak areas of verbascoside-related and olueropein-related compounds (Table 4.9)], suggest that these activities cannot explain the levels of Manchurian ash resistance. POX activities however, were strongly associated with resistance (Tables 4.1 and 4.2, Fig. 4.1), as was also found in Rigsby et al. (2016). It appears that white and Manchurian ash express the same three functional POX enzymes based on native PAGE staining (data not shown) as was the case for black and Manchurian ash (Rigsby et al. 2016).

The relative activities of these enzymes between species and across studies (Fig.

4.1) inform a revised hypothesis that PPO and βG activities may contribute in small part to EAB resistance, as reflected in the comparison between white and black ash. The results also suggest that POX activities make larger contributions to resistance, as higher activities in Manchurian ash compared to the susceptible species may outweigh the lower

PPO and βG activities in Manchurian ash. It is also possible that the enzyme activities contribute similarly to resistance, or not at all, and additional or alternative traits are responsible for the observed patterns of resistance.

However, the hypothesis that POX activity contributes more to resistance than does PPO activity is supported by the data available on putative modes of action of these enzymes in the ash-EAB system. Compared to PPO, POX activities in Manchurian ash result in greater and more rapid protein crosslinking, which may result in EAB larval midgut damage and/or reduced availability of dietary protein (Rigsby et al. 2016). As indicated by oxidation of the monolignol analog syringaldazine in this and prior work

(Rigby et al. 2016), high POX activities may also enhance lignification in Manchurian ash compared to susceptible species. Differences in total phloem lignin content have been associated with MeJA-induced EAB resistance by Whitehill et al. (2014). The current results call for targeted analysis of lignification surrounding nascent EAB feeding galleries in differentially resistant ash phenotypes.

Though comparisons of closely related black and Manchurian ash have been fruitful in reducing the number of candidate phenolic compounds and proteins relative to comparisons of Manchurian ash with more dissimilar white and green ash (Cipollini et al.

2011, Whitehill et al. 2012, Chakraborty et al. 2014, Villari et al. 2016), the differences in relative enzyme activities between resistant and susceptible species in Rigsby et al. 2016 and the current study highlight the importance of studying diverse ash germplasm to understand candidate resistance traits.

Consistent with previous work, the current study found white and Manchurian ash to have distinct phloem phenolic profiles (Eyles et al. 2007, Cipollini et al. 2011,

Whitehill et al. 2012, 2014, Villari et al. 2016), and that ash phenolic profiles do not change dramatically in response to EAB larval feeding (Chakraborty et al. 2014).

However, several specific results related to verbascoside in the current study differ from previous work. This and previous studies have identified verbascoside by matching retention time and spectra with an external standard, and assigning spectrally similar compounds with different retention times as verbascoside-related compounds or derivatives. All previous studies of constitutive ash phenolics have found higher levels of standard-matched verbascoside in white ash than in Manchurian ash (Eyles et al. 2007,

Whitehill et al. 2012, 2014). However, the current study found the opposite pattern; higher constitutive verbascoside levels in Manchurian ash (Table 9). Absolute amounts of verbascoside are as yet unknown, due to lack of a standard curve. Absolute quantification of verbascoside in the current study is critical to clarify interspecific patterns and contrast with previous work.

Exact matching of compounds between studies without external standards can be difficult, as similar compounds can be difficult to distinguish based on PDA and MS spectra alone. The changes in retention times between studies due to variation in columns, instruments, and/or solvent batches make compound matching using elution order unfeasible across small retention time intervals. The ash phenolic chromatogram is particularly crowded in the region where putative verbascoside derivatives are found. As such, relating this study’s constitutive verbascoside results to previous studies is difficult.

However, the relative abundances of verbascoside-related compounds within and between species are consistent with those documented by Whitehill et al. (2012).

Standard-matched verbascoside was the most abundant verbascoside-related compound in both species in both studies. Verbascoside B was next most abundant in Whitehill et al.

(2012) as was verbascoside derivative 2 in this study, both of which are found in both species and more abundant in white ash. Third most abundant was verbascoside A in

Whitehill et al (2012) and derivative 3 in this study, both of which are found only in white ash.

Verbascoside derivative 1 (peak 26) was detected only in EAB-induced

Manchurian ash samples in this study (though reported conservatively as LOQ in control samples to allow for statistical analysis). No Manchurian-specific verbascoside-related compound has been found in constitutive samples previously (Eyles et al. 2007, Cipollini et al. 2011, Whitehill et al. 2012, 2014, Chakraborty et al. 2014), consistent with this pattern. However, the only prior study of Manchurian phloem phenolic chemistry in response to larval feeding did not identify any verbascoside-related compounds to be important for distinguishing between control and EAB treatments 14 days following egg inoculation (Chakraborty et al. 2014).

In summary, the results of this study have informed a revised hypothesis relating pro-oxidant-associated enzyme activities and ash resistance to EAB, emphasizing the importance of POX activities over PPO and βG activities. This hypothesis is based on enzyme activities on small molecule substrates and previous assays of their indirect effects on protein crosslinking in vitro (Rigsby et al 2016). As in previous work, this study showed clear differences between white and Manchurian ash phenolic profiles and the absence of dramatic changes following EAB feeding. However, this study also reported novel induction of verbascoside in white ash and verbascoside derivative 1 in

Manchurian ash in response to EAB larval feeding, as well as a novel interspecific

verbascoside pattern. Absolute quantification of verbascoside in this study will address the hypothesis that verbascoside levels are positively associated with resistance in North

American ash species.

CHAPTER 5: TRANSCRIPTOMIC PROFILES OF RESISTANT AND SUSCEPTIBLE ASH IN RESPONSE TO EARLY FEEDING BY EAB LARVAE

ABSTRACT

Numerous biochemical traits have been associated with resistance of Fraxinus species to emerald ash borer (Agrilus planipennis Fairmaire, EAB), but little information is available on their underlying genetic basis. The current study examined patterns of gene expression in response to wounding and EAB larval feeding in resistant Manchurian (F. mandshurica Rupr.) and susceptible white ash (F. americana L.). Expression patterns of major allergen Mal d 1 and Bet v 1 transcripts support the association of proteins in this family with ash resistance to EAB. Early transcriptional responses related to jasmonate signaling were similar in the two species, but complex responses of transcription factor families require further analysis. Among new transcript annotations of interest suggested by this study are lectins and lectin kinases, oxidoreductase activity, leucine-rich repeat transmembrane protein kinase (LRRTK) activity, and terpene synthase activities. This study additionally provides the resources for multiple sequence alignments and GO enrichment analysis that will further strengthen and expand its results.

INTRODUCTION

As predicted in Chapter 2, and demonstrated and discussed in Chapter 3, phloem-based traits appear to be very important for ash (Fraxinus spp.) resistance to emerald ash borer

(Agrilus planipennis Fairmaire, EAB). Known and hypothesized traits have been recently summarized in Villari et al. (2016), and include those related to phenolics (Eyles et al.

2007, Whitehill et al. 2012, 2014, Chakraborty et al. 2014, Chapter 4), defense-associated proteins (Whitehill et al. 2011), and oxidative metabolism or “browning reaction”

(Cipollini et al. 2011, Rajarapu et al. 2011, Rajarapu and Mittapalli 2013, Rigsby et al.

2015, 2016, Chapter 4). Additionally, Whitehill et al. (2014) found that latent defenses and actual resistance can be induced in susceptible white ash (F. americana L.) by treatment with methyl jasmonate, suggesting that white ash may be deficient in its ability to recognize EAB and/or unable to mount an effective defense signaling response (Villari et al. 2016).

Molecular resources are being developed to help understand the genetic bases of putative resistance traits and mechanisms. These include a genus-level transcriptome built from pooled constitutive phloem samples of multiple Fraxinus species (Bai et al.

2011), a draft genome and genome-guided transcriptome assembly of European ash (F. excelsior L.) (Harper et al. 2016, ashgenome.org), and a transcriptome of green ash (F. pennsylvanica Marshall) assembled from trees subjected to a variety of stress treatments, including EAB larval feeding (Lane et al. 2016). However, no genomic or transcriptomic sequence information is currently available for Manchurian (F. mandshurica Rupr.) or

white ash, two of the best-studied ash species with regards to potential resistance mechanisms. In order to provide sequence information for previously identified putative resistance traits, generate evidence for or against hypothesized resistance mechanisms, and identify new genes of interest, an experiment was conducted to compare gene expression profiles across control, mechanical wounding and EAB-induction treatments in white and Manchurian ash.

MATERIALS AND METHODS

Plant material, design, and nutrient application

On 3 April 2012, bare-root clones (mean ± SE stem diameter 25 cm above soil line =

2.46 ± 0.52 cm) of EAB-susceptible white ash (Fraxinus americana cv. ‘Autumn

Purple’) and resistant Manchurian ash (F. mandshurica cv. ‘Mancana’) (Bailey

Nurseries, Inc. Newport, MN) were planted in a 2:1 mixture of pine bark mulch and compost in 51 L pots outdoors at the Landscape Nursery Crop Engineering Research

Laboratory of the Ohio State University Ohio Agricultural Research and Development

Center (OARDC) in Wooster, OH.

On 4 May 2012, 16 trees of each species were arranged into three blocks by height and trunk diameter. A full factorial combination of two species, two nutrient availability levels, and three induction treatments (control, mechanical wounding, EAB larval feeding, described below) were assigned according to a randomized block design.

Each treatment combination was replicated once per block, with the exception of non- induced control treatments, which were only present in two of three blocks. Nutrient availability levels of 30 or 150 ppm N, with N:P2O5:K2O in a ratio of 3:1:2 from calcium nitrate, mono-ammonium phosphate, and potassium nitrate, were provided by a computer-controlled irrigation and fertilization system (Argus Control Systems Ltd,

White Rock, BC, Canada) equipped with 15 cm soil tensiometers (Irrometer Company,

Inc. Riverside, CA) (Hansen et al. 2011). The pots received a pulse of nutrient solution when potting medium moisture tension (PMMT) exceeded -5 kPa. After 3 October, nutrient delivery was halted and trees received municipal water until 3 November, when water delivery was terminated for the season. Differential nutrient treatments were resumed on 3 May 2013 and continued until sample collection.

Induction treatments and sample collection

Over three days, on 18-20 June 2013, three induction treatments, i.e. non-induced control, mechanical wounding, and EAB larval feeding, were applied to one experimental block per day. Mechanical wounding was implemented by creating a 1 mm hole through the outer bark and phloem to the outer xylem using a drill bit. EAB larval feeding treatments were implemented by inoculating each tree with four neonate larvae retained on moistened filter paper and wrapping lightly with gauze secured on the edges with duct tape. EAB neonates were obtained by incubating eggs laid on coffee filters at 26°C

(USDA-APHIS-PPQ Biological Control Rearing Facility, Brighton, MI) and observing

hatching twice daily. Freshly hatched neonates were transported to the experimental site on moistened filter paper in plastic petri dishes in indirect contact with ice in coolers.

One experimental block was sampled each day for transcriptional profiling over three days, forty-eight hours after induction treatments were applied (20-22 June 2013).

Successful gallery initiation by neonate EAB larvae was confirmed by removing the outer bark with a razor blade and observing a nascent feeding gallery. Any ambiguous gallery initiations were noted to assist in interpretation of transcriptomic results. A 12 mm diameter cork borer was used to sample phloem immediately surrounding the induction site, either control phloem, around the drill hole, or around the nascent gallery. All phloem samples were immediately frozen in liquid nitrogen, and stored at -80 °C until processing.

Phloem sample processing, RNA isolation, cDNA library preparation, and sequencing

Phloem samples were ground in a mortar chilled with liquid nitrogen. Total RNA was extracted from 75 mg ground phloem tissue using the Concert™ Plant RNA Reagent kit

(Life Technologies, Carlsbad, CA) according to manufacturer’s instructions. Total RNA quantity and quality were assessed using an Agilent 2100 Bioanalyzer (Agilent

Technologies; Santa Clara, CA). Poly(A) RNA was selected from total RNA by binding to poly(T) oligonucleotide-coated magnetic beads and used as template for cDNA synthesis. RNA (1 µg) was used to generate adaptor-ligated, strand-specific double- stranded cDNA libraries for RNA-Seq using the TruSeq Stranded mRNA Sample Prep

Kit (Illumina, San Diego, CA), according to the manufacturer’s protocol. Quantification of ds-cDNA was carried out using the Qubit 2.0 Fluorometer (Life Technologies,

Carlsbad, CA) and quality was assessed using the Experion Automated Electrophoresis

System (Bio-Rad, Hercules, CA). Indexed samples were pooled to generate a multiplexed cDNA library.

The cDNA libraries were sequenced on two flow-cell lanes using the Illumina

HiSeq 2000 platform at the Ohio State University Comprehensive Cancer Center. The mean library insert sequence size was 382 bp and both ends of the library were sequenced to generate 100-bp raw paired-end reads. The Illumina Analysis Package CASAVA was used to perform bcl conversion and demultiplexing. Image deconvolution and quality value calculations were carried out using the Illumina GA pipeline.

Species-specific transcriptome assembly and annotation

A general overview of the workflow for samples through sequencing and bioinformatic analysis is provided in Fig. 5.1. Poly(A) sequences and adapters were trimmed from raw demultiplexed reads using cutadapt (Martin 2011). Low quality reads were removed using “Trimmer” function from popoolation2 (Kofler et al. 2011). Trimmed reads were split into species-specific datasets from which transcriptomes were independently assembled de novo using Trinity (Grabherr et al. 2011, Haas et al. 2013). Transcripts were annotated using Blast2GO (Conesa et al. 2005) with a minimum e-value of 1x10-3 against a custom reference database of 15,115 protein sequences generated from

combined Arabadopsis thaliana and Populus trichocarpa RefSeq proteomes. Assembly completeness was assessed using ortholog hit ratio (OHR), i.e. the ratio of transcript length to the length of an orthologous transcript in a reference transcriptome, with a value of 1 indicating a complete transcript (O’Neil et al. 2010). Reference orthologs were identified from top blastx results against the F. excelsior genome-guided transcriptome assembly from the British Ash Tree Genome Project (ashgenome.org).

Interspecific sequence comparisons and intraspecific differential gene expression analysis

Reads were mapped to the assembled transcriptome of the corresponding species using

Bowtie2 (Langmead and Salzberg 2012). Gene and isoform counts for each sample were estimated using RSEM (Li and Dewey 2011). Because Trinity gene models are simply a collection of related isoforms, they do not have a single representative sequence.

Therefore, predicted proteomes for each species were generated from sequences of the most abundant isoform of each gene using TransDecoder (Haas et al. 2013) and redundant sequences (≥ 99% similarity) were removed using CD-HIT (Li and Godzik

2006). Predicted proteomes of the two species were then clustered using OrthoMCL

(Fischer et al. 2011) to infer ortholog and paralog relationships among transcripts between and within species (Zhou et al. 2016). Ortholog and paralog relationships were then used to assign each transcript to a relational group: 1-to-1 orthologs, ortholog- paralog group, paralog-only group, or singletons (Fig. 5.1). When searching for

differences between relatively recently-diverged congeners such as white and

Manchurian ash, paralogs may be the most intuitive candidates, since paralogs are thought to represent more recent evolution, while orthologs are thought to be inherited from a common ancestor. However, 1-to-1 ortholog groups were prioritized for interspecific comparisons in this study due to the relative simplicity of expression pattern interpretation; interspecific comparisons of individual ortholog-paralog group members must account for the expression patterns of other group members, a conceptually challenging task. Additionally, since the gene models used for ortholog and paralog assignment were generated from de novo transcriptome assemblies, paralogs cannot be entirely distinguished from transcriptional isoforms, as the true genomic sequence is unknown.

Normalization and differential expression analysis between induction treatments within species was performed in R version 3.2.1 (R Core Team 2015) using the packages

RUVseq (Risso et al. 2014) and edgeR (Robinson et al. 2010), respectively. Overall expression patterns of samples were assessed using multi-dimensional scaling of leading log2 fold changes in expression between samples. Leading log2 fold changes are calculated as the root mean square average of the log2 fold changes in expression between top genes in each pair of samples. One white ash sample was extremely dissimilar from other white ash samples, and thus trimmed (Fig. 5.2 sample WWL1). Trimming of one white ash and three Manchurian ash samples that did not group with others receiving

EAB treatment (Fig.5.2, samples WEL2, MEH1, MEH2, MEL3) was supported by notes taken at time of sample collection indicating ambiguous signs of successful gallery

initiation. Due to sample trimming, as well as observations in a parallel experiment suggesting that nutrient availability had little impact on resistance to EAB (Chapter 3), nutrient availability treatments were pooled to increase replication of induction treatments. Trimmed sample sets with nutrient availability levels pooled provided 4 biological replicates for induction control treatments in each species, 5 replicates in white and 6 in Manchurian ash for mechanical wounding treatments, and 4 replicates in white and 3 in Manchurian ash for EAB treatments.

To assess the impact of nutrient availability treatment on transcriptional profiles, samples were alternatively analyzed with two nutrient availability levels, pooling induction treatments. Insufficient replication was available to model interactions between induction and nutrient availability treatments, but the interaction could still be evaluated by comparing the relationships between samples belonging to combinations of induction and nutrient treatments in sample-wise multidimensional scaling (MDS) plots.

Quasi-likelihood F-tests (Lun et al. 2016) of pairwise contrasts between treatment groups in a generalized linear model were used to test for differential gene expression with a Benjamini-Hochberg false discovery rate-adjusted P (FDRp) threshold of 0.05.

Transcripts with FDRp < 0.1 are also discussed when complementary to transcript patterns with FDRp < 0.05 transcripts. Transcripts with annotations related to a priori hypotheses about resistance mechanisms were identified as genes of interest based exclusively on statistical significance, with log2 fold change values informing interpretation and discussion. Differentially expressed genes from unguided screening were selected only when their log2 fold change absolute value was greater than 2.

100

RESULTS

Transcriptome assembly, annotation, and interspecific comparison

Basic assembly statistics are summarized in Table 5.1. Annotation completeness was acceptable for both species, as indicated by OHR distributions (Fig. 5.3) comparable to other de novo assembled transcriptomes used for differential expression analysis (Finseth and Harrison 2014, Posnien et al. 2014). Provisional annotation resulted in 15,184 annotated transcripts from Manchurian ash and 16,194 from white ash. Of these annotations, 1,707 in Manchurian ash and 1,822 in white ash were “hypothetical proteins”. This resulted in 13,477 meaningfully annotated transcripts in Manchurian ash and 14,327 in white ash.

Transcripts encoding predicted proteins (indicated by the presence of an ORF resulting in a minimum protein length of 100 amino acids) for which expression data was available were primarily of a putative one-Manchurian-to-one-white ash orthologous interspecific relationship (Table 5.1). The next most common putative relationship was that of an ortholog-paralog group, with multiple members from each species sharing sequence similarity. The remaining sequences were either paralogs or singletons, i.e. with members of each group being identified only in one species. Being paralogs and singletons does not necessarily mean that such sequences are not present in the genome or possible transcriptome of the other species, but rather that the sampling of the given

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time point and subsequent data filtering did not find a sufficiently similar sequence in the other species.

Expression patterns of genes related to JA signal propagation, attenuation, and response

Six orthologous transcripts and one singleton annotated as jasmonate ZIM-domain 1

(JAZ1) were significantly induced in white ash in response to EAB attack, compared to control or wounding, with only the JAZ1-annotated singleton exhibiting non-statistically significant increased expression. Of the six orthologous transcripts, all were also induced in Manchurian ash in response to EAB attack compared to the control at FDRp < 0.1 level, and four at a FDRp < 0.05 level (Table 5.2). None of the differences between EAB and wound treatments in Manchurian ash were statistically significant for JAZ1 transcripts. No transcripts annotated as JAZ3 showed significantly different expression between any induction treatments in either species (Table 5.2). Transcripts with jasmonic acid-amido synthetase (JAR1) annotations were part of a balanced ortholog-paralog group; each species showed one transcript significantly more highly expressed in EAB vs. control and wound vs. control treatments, and one transcript that was not significantly differentially expressed (Table 5.2).

A total of 51 transcripts had one of 25 unique WRKY transcription factor-related annotations. Among these transcripts, nine were differentially expressed between at least one pair of induction treatments. A total of 96 transcripts had one of 29 unique basic helix-loop-helix (bHLH) transcription factor-related annotations. Among these

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transcripts, 25 were differentially expressed between at least one pair of induction treatments. Annotations for transcription factors were often general (e.g. “basic helix- loop-helix family protein”) and thus not summarized in more detail.

Major allergen Bet v 1 family expression patterns

Of the 10 observed members of the major allergen Bet v 1 family, which includes the structurally similar Mal d 1 and Bet v 1 proteins, four were differentially expressed between induction treatments, with their patterns differing between species (Table 5.3).

One Mal d 1-annotated transcript was significantly higher in EAB and wound treatments vs. control in Manchurian ash, with no significant differential expression among white ash induction treatments. Conversely, three Bet v 1-annotated transcripts were significantly higher in EAB than wound or control treatments in white ash, with no significant differential expression between Manchurian ash induction treatments. Three orthologous Manchurian ash transcripts did not pass the quantitation filter in edgeR, likely due to overall low expression, but where available, some transcript patterns (e.g.

Group ID 17561) differed between Manchurian and white ash (Table 5.3).

Expression patterns of transcripts related to phenylpropanoid biosynthesis and oxidation

Shikimate kinase (SK), an enzyme involved in the biosynthesis of phenylalanine and subsequent phenylpropanoid compounds, was represented by five transcripts in the

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dataset. Only one orthologous transcript was significantly higher in EAB vs. control treatments in both species and additionally higher in wound vs. control treatments in white ash (Table 5.4).

The annotation cinnamic acid 4-hydroxylase (C4H), an enzyme involved in early stages of phenylpropanoid biosynthesis, was represented by five transcripts: a balanced ortholog-paralog group of two white and two Manchurian ash transcripts as well as a

Manchurian ash singleton with varying levels and significance of induction. Both white ash members were higher in EAB vs. wound treatments, one significantly so at FDRp =

0.03 and one marginally significant at FDRp = 0.06. The Manchurian ash singleton was marginally induced in EAB vs. control treatments at FDRp = 0.06 (Table 5.4).

The annotation 4-coumarate-CoA ligase (4CL), involved in middle stages of phenylpropanoid biosynthesis, was represented by 13 transcripts in the dataset. The only significant pattern in Manchurian ash 4CL transcripts was the significantly higher levels of one ortholog in EAB vs. control samples. Three white ash 4CL transcripts were significantly higher in EAB vs. wound and control samples, and one was significantly lower in EAB vs. wound samples.

The annotation cinnamoyl-CoA reductase (CCR), involved in late stages of phenylpropanoid biosynthesis, was represented by 10 transcripts. One ortholog was significantly higher in Manchurian ash EAB and wound treatments vs. control, while its white ash counterpart remained unchanged. No other differences were significant at

FDRp < 0.05, and positive or negative relationships between induction treatments were

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the same across species for two orthologs with significant differences at FDRp < 0.1

(Table 5.4).

There were a total of 31 β-glucosidase annotations between the species, excluding endoglucanase-related annotations. Of these, only one ortholog showed significantly higher levels in Manchurian ash EAB vs. control treatments while levels in white ash remained unchanged (Table 5.4).

Summary of discovery-based differential gene expression analysis of orthologs

Expression patterns of orthologous sequences varied between species and induction treatments, with the greatest separation provided by species (Fig. 5.4). The diversity of orthologous transcript expression patterns between Manchurian and white ash EAB and control treatments is shown in Fig. 5.5, with patterns including wound treatments summarized in Fig. 5.6. A total of 45 orthologs were significantly more highly expressed in EAB vs control samples in both species and had a log2 fold change of at least 2 (Figs.

5.5, 5.6A). These transcripts represented 37 unique annotations including: mitogen- activated protein kinase kinase (MAPKK), ethylene-responsive transcription factor, zinc finger family protein, terpene synthase cyclase family protein, as well as eight transcripts with either no or only hypothetical protein annotation.

A total of 50 orthologs were significantly more highly expressed in EAB vs control samples and had a log2 fold change of at least 2 in only Manchurian ash (Figs.

5.5, 5.6A). These transcripts represented 46 unique annotations including: ACC oxidase,

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ethylene response factor family, concanavalin a-like lectin kinase-like protein, jacalin- like lectin domain-containing protein, pathogenesis-related protein, pollen Ole e 1 allergen and extensin family protein, as well as five transcripts with either no or only hypothetical protein annotation. The pollen Ole e 1 allergen and extensin family protein is neither structurally nor functionally related to the major allergens Bet v 1 or Mal d 1, but simply shares the ability to sometimes cause an allergic reaction in humans (Villalba et al. 1993).

A total of nine orthologs were significantly less expressed in EAB vs control samples and had a log2 fold change of at least -2 in only Manchurian ash (Figs. 5.5,

5.6B). These transcripts represented nine unique annotations including: leucine-rich repeat-containing protein, ferredoxin I family protein, as well as one transcript with only hypothetical protein annotation.

A total of 97 orthologs were significantly more highly expressed in EAB vs control samples and had a log2 fold change of at least 2 in only white ash (Figs. 5.5,

5.6A). These transcripts represented 89 unique annotations including: disease resistance- responsive family protein, leucine-rich repeat transmembrane protein kinase (LRRTK), membrane-associated kinase regulator, ethylene-responsive transcription factor, terpene synthase cyclase family protein, as well as 14 transcripts with only hypothetical protein annotation.

A total of 76 orthologs were significantly less expressed in EAB vs control samples and had a log2 fold change of at least -2 in only white ash (Figs. 5.5, 5.6B).

These transcripts represented 73 unique annotations including: concanavalin a-like lectin

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kinase-like protein, several transcription factor annotations, a receptor-like kinase, as well as 10 transcripts with either no or only hypothetical protein annotation.

Effects of soil nutrient availability

To try and account for the effects of pooling two soil nutrient availability levels during the analysis of induction effects, samples were also analyzed for the effects of nutrient availability level with induction treatments pooled. A total of 67 transcripts were significantly differentially expressed between soil nutrient availability treatments in either white or Manchurian ash. Of these, only six transcripts were significantly differentially expressed between nutrient availability treatments and at least one pair of induction treatments, and none were genes of interest discussed in this study. While the interaction between induction treatment and soil nutrient availability is not sufficiently replicated in this dataset to test statistically, it is expected to be negligible, considering the relatively few transcripts responsive to nutrient availability treatment and the similar relationship between samples of each nutrient availability level within a given induction treatment in sample-wise MDS plots (Fig. 5.2).

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EAB-susceptible white ash EAB-resistant Manchurian ash

Control Wound EAB EAB Wound Control Most Most abundant abundant Sequencing sample- isoform per isoform per Sequencing sample- specific libraries gene gene specific libraries

Quality trim Quality trim Manchurian reads White reads gene gene sequences sequences White ash Manchurian ash transcriptome assembly transcriptome assembly White Manchurian predicted predicted

108 Provisional annotation proteins proteins Provisional annotation

Read mapping OorthoMCL: Read mapping and counting Interspecific relational lists for and counting putative orthologs and paralogs Differential gene expression Differential gene expression analysis between analysis between induction treatments induction treatments

Singletons Paralog 1-to-1 Ortholog- groups orthologs paralog groups

Sets of candidate transcripts

Figure 5.1. Schematic of bioinformatic workflow employed to provide species-specific de novo assembled transcriptomes while allowing for interspecific comparisons of gene expression patterns.

a b

c d

Figure 5.2. Multidimensional scaling (MDS) plots of leading log2 fold changes in expression between samples before and after trimming in Manchurian (a,b) and white ash (c,d). Leading log2 fold changes are calculated as the root mean square average of the log2 fold changes in expression between top transcripts in each pair of samples to provide a measure of similarity of expression among samples. Sample naming convention: White ash or Manchurian ash; Control, Wound, or EAB treatment; High or Low nutrient availability; and replicate 1-3.

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Manchurian ash White ash

Reads 209,394,318 199,355,845 % Reads assembled 93.6 91.6 Trinity gene models 165,272 180,943 Isoforms 230,285 259,435 Isoform N50* 1,404 1,182 Longest isoform per gene N50* 913 848 Predicted proteins (ORFs) 28,334 29,664 ORFs with orthologs 8,676 8,676 expression ortholog-paralog groups 4,827 4,565 data, grouped paralogs 40 81 by putative singletons 881 1,491 relationship

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Manchurian ash White ash Group Group EAB vs. C EAB vs. W W vs. C EAB vs. C EAB vs. W W vs. C

ID Annot. type log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp 12728 JAZ1 ortho 3.863 0.004 1.139 0.305 2.723 0.011 6.723 0.002 2.608 0.011 4.115 0.065

112 11833 JAZ1 ortho 3.111 0.040 1.758 0.200 1.352 0.593 6.384 0.000 3.046 0.000 3.339 0.015

8261 JAZ1 ortho 2.250 0.004 1.074 0.087 1.176 0.062 3.788 0.004 1.532 0.016 2.256 0.098 5942 JAZ1 ortho 1.736 0.066 0.673 0.587 1.064 0.339 3.281 0.007 1.525 0.015 1.756 0.207 10083 JAZ1 ortho 1.603 0.052 0.674 0.474 0.928 0.325 5.241 0.004 2.078 0.015 3.163 0.092 13832 JAZ1* ortho 2.623 0.015 1.000 0.323 1.623 0.077 3.932 0.007 1.980 0.012 1.951 0.286 NA JAZ1 single NA NA NA NA NA NA 3.197 0.002 1.995 0.001 1.202 0.275 NA JAZ1 single NA NA NA NA NA NA 2.371 0.173 1.212 0.202 1.159 1.000 13330 JAZ3 ortho 0.212 0.893 0.226 0.936 -0.014 1.000 0.582 0.763 0.294 0.683 0.288 1.000 14704 JAZ3 ortho -0.433 0.679 -0.127 0.981 -0.306 1.000 0.029 0.995 0.361 0.583 -0.332 1.000 8533 JAZ3 ortho -0.143 0.936 -0.054 0.995 -0.088 1.000 -0.134 0.980 -0.123 0.891 -0.011 1.000 1895 JAR1 o-p NA NA NA NA NA NA 7.463 0.002 1.237 0.180 6.226 0.015 6.157 0.002 1.581 0.152 4.576 0.002 NA NA NA NA NA NA NA NA NA NA NA NA 0.908 0.639 -0.058 0.962 0.966 1.000 0.823 0.236 0.936 0.129 -0.114 1.000 NA NA NA NA NA NA

Table 5.3. Log2 fold change and false-discovery-rate-adjusted P-values (FDRp) for pairwise contrasts of induction treatments for transcript annotations related to major allergen in white and Manchurian ash. Mal d 1 and Bet v 1 annotations are structurally related and have been associated with PR-10 function. FDRp < 0.05 are bolded , < 0.1 are underlined. ”-“ indicates transcripts that were filtered by edgeR based on low transcription across all samples. Notations: EAB: inoculations with EAB larvae; W: wounding treatment; C: unwounded control.

Manchurian ash White ash Group Group EAB vs. C EAB vs. W W vs. C EAB vs. C EAB vs. W W vs. C

ID Annot. type log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp 11798 Mald1 ortho 5.641 0.004 0.078 0.996 5.562 0.003 1.563 0.639 -1.867 0.119 3.430 0.228

113 5778 Mald1 ortho 1.122 0.105 0.814 0.264 0.309 1.000 0.041 0.994 0.076 0.937 -0.035 1.000

17408 Betv1 ortho ------10.363 0.004 5.633 0.001 4.730 0.415 8327 Betv1 ortho ------5.789 0.022 3.734 0.004 2.054 1.000 17561 Betv1 ortho 0.446 0.838 1.650 0.162 -1.204 0.643 5.658 0.008 4.413 0.001 1.246 1.000 10590 Betv1 ortho -0.893 0.447 -0.299 0.946 -0.593 1.000 -0.845 0.959 -0.205 0.944 -0.640 1.000 11964 Betv1 ortho -0.019 0.996 -1.296 0.400 1.276 0.654 -0.260 0.973 -1.038 0.331 0.778 1.000 12528 Betv1 ortho ------0.117 0.982 0.264 0.733 -0.381 1.000 4269 Betv1 ortho -3.254 0.116 -2.370 0.333 -0.884 1.000 -3.966 0.487 -2.755 0.246 -1.212 1.000 NA Betv1 single NA NA NA NA NA NA 3.525 0.094 -0.302 0.843 3.827 0.118

Manchurian ash White ash Group Group EAB vs. C EAB vs. W W vs. C EAB vs. C EAB vs. W W vs. C ID Annot. type log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp 13903 SK ortho 2.020 0.032 1.213 0.174 0.807 0.668 2.890 0.007 1.964 0.002 0.926 0.838

114 8200 SK ortho ------5.570 0.001 3.569 0.000 2.002 0.263

1867 C4H o-p NA NA NA NA NA NA 1.984 0.352 2.113 0.031 -0.130 1.000 NA NA NA NA NA NA 2.468 0.130 1.551 0.061 0.917 1.000 0.241 0.883 -0.013 0.998 0.254 1.000 NA NA NA NA NA NA 1.550 0.226 1.070 0.425 0.480 1.000 NA NA NA NA NA NA NA C4H single 3.802 0.064 2.825 0.131 0.976 1.000 NA NA NA NA NA NA 7784 4CL ortho 1.223 0.038 0.439 0.698 0.783 0.312 2.403 0.003 1.490 0.001 0.913 0.916 17266 4CL ortho -0.319 0.798 -0.282 0.896 -0.037 1.000 -2.141 0.111 -1.810 0.014 -0.330 1.000 14140 4CL ortho 0.009 0.997 0.137 0.979 -0.127 1.000 -0.321 0.916 -0.485 0.425 0.164 1.000 1597 4CL o-p NA NA NA NA NA NA 1.832 0.046 1.528 0.005 0.305 1.000 0.902 0.336 0.340 0.892 0.562 0.998 NA NA NA NA NA NA

Continued

Table 5.4 Continued

Manchurian White Group Group EvsC EvsW WvsC EvsC EvsW WvsC ID Annot. type log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp log2FC FDRp 1713 4CL o-p NA NA NA NA NA NA 1.832 0.046 1.528 0.005 0.305 1.000 NA NA NA NA NA NA 2.901 0.164 1.823 0.081 1.078 1.000 1.311 0.158 0.812 0.420 0.499 1.000 NA NA NA NA NA NA 1.516 0.350 0.835 0.714 0.681 1.000 NA NA NA NA NA NA 14733 CCR ortho 1.450 0.019 0.072 0.994 1.378 0.022 -0.531 0.800 -0.572 0.321 0.041 1.000 7881 CCR ortho 1.061 0.122 0.428 0.714 0.632 0.655 2.372 0.051 0.836 0.180 1.536 0.415 14016 CCR ortho -1.366 0.078 -0.747 0.404 -0.619 0.787 -1.433 0.175 -1.026 0.058 -0.407 1.000

115 7701 BGlu ortho 2.715 0.038 1.543 0.207 1.172 0.677 -0.692 0.913 0.222 0.899 -0.914 1.000

PC2 0

-50

-40 0 40 80 PC1

Figure.5.4. Principal components analysis plot based on counts per million mapped reads (CPM) for 8,676 orthologous transcripts in each sample. Manchurian ash samples are represented in red, and white ash samples in blue. Control samples are represented by circles, wounding by triangles, and EAB induction by squares. Percentage of total variation encompassed by each principal component: PC1=34%, PC2=21%.

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Figure 5.5. Semi-quantitative comparison of EAB-induced ortholog transcription between species, plotted as log2 fold change of EAB vs. control treatments in white ash vs. Manchurian ash. Positive values represent increased expression in response to EAB feeding and negative values represent decreased expression. Solid line represents when white ash and Manchurian ash fold changes are equal. Dashed lines represent the thresholds of |log2 fold change| >2 in each species. Points are colored red when transcripts are significantly differentially expressed (FDRp < 0.05) and exhibit a |log2 fold change| >2 in both species, green when only in Manchurian ash, and blue when only in white ash. Point size is proportional to the average expression level (log2 counts per million mapped reads) across Manchurian ash samples of all induction treatments.

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Figure 5.6. Patterns of differential expression for orthologous sequences between species and induction treatments. Numbers of transcripts in each species significantly differentially expressed (FDRp < 0.05), with a |log2 fold change| > 2 and a) up or b) down in EAB vs control treatments. c) Expression patterns including wound treatments for up- regulated transcripts vs. control. No orthologous transcripts were significantly differentially expressed and down-regulated in wound vs. control treatments, so panel b represents the full complement of significant patterns for transcripts down-regulated vs. control.

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DISCUSSION

The expression patterns of transcripts encoding proteins in the major allergen Bet v 1 family were among the most interesting in the study. Constitutive levels of a protein annotated as Mal d 1 (a Bet v 1 family member) were 35 to 56-fold higher in EAB- resistant Manchurian ash compared to susceptible white, green, and black ash (Whitehill et al. 2011). The transcriptional induction observed in the current study of Mal d 1 exclusively in Manchurian ash and Bet v 1 exclusively in white ash in response to EAB larval feeding provides further support for the importance of this protein family in ash resistance to EAB. Additionally, Lane et al. (2016) found significantly higher expression of transcripts with Pru av 1 and Pru ar 1 annotations, which also belong to the Bet v 1 family (Ivanciuc et al. 2002, 2003), in EAB vs control treatments and between resistant and susceptible ash genotypes after larval feeding. Various proteins in the Bet v 1 family exhibit pathogenesis related (PR)-10/ribonuclease activity and have been associated with resistance to pathogens (Xie et al. 2010, Liu et al. 2013). However, the precise function and activity of PR-10 proteins can vary with small changes in primary sequence (Zubini et al. 2009). As such, more precise annotation of proteins in this family is required and may be aided by multiple sequence alignment of members from various organisms to assess structure-function relationships.

Early transcriptional responses to herbivory and wounding are relatively well characterized in model and some non-model plant systems. JAZ proteins act as repressors of JA-associated gene expression by binding to and blocking JA-responsive transcription

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factors until they are degraded in a jasmonyl-isoleucine (JA-Ile)-dependent manner

(Chini et al. 2007). Upon degradation, JAZ proteins are rapidly re-expressed in order to attenuate the JA response (Thines et al. 2007). JAZ transcriptional induction has been observed from 5 min to 24 hours following mechanical wounding and insect feeding in

Arabidopsis thaliana (L.) Heynh. (Chung et al. 2008) and hybrid poplar (Major and

Constabel 2006), with transcriptional induction continuing longer with continuous herbivory compared to discrete wounding. Additionally, different JAZ transcripts respond to different degrees; JAZ1 has been shown to be highly responsive to both herbivory and wounding, with JAZ3 less so to both stimuli (Chung et al. 2008).

These patterns are consistent with the observation of higher transcriptional induction of JAZ1 and not JAZ3 in EAB treated vs mechanically wounded phloem (and each vs. control) 48h after initial induction (Table 5.2). Lane et al (2016) found various

JAZ proteins differentially expressed between green ash genotypes and in response to eight weeks of EAB larval feeding. The JAZ transcript annotations in Lane et al (2016) are derived from organisms other than Arabadopsis thaliana and Populus tricocarpa used in the current study, but the different annotations could be assigned to homologous functional groups for comparison based on multiple sequence alignments (Zhang 2008,

Oh et al. 2012, Yang et al. 2015). This approach shows that expression of an AtJAZ1 homolog is significantly higher following 8 weeks of larval feeding in green ash, and

AtJAZ3 homologs are significantly lower in resistant vs. susceptible genotypes of green ash, but not responsive to larval feeding. Additionally, an AtJAZ3 homolog was significantly lower 24h after mechanical wounding vs. control (Lane et al. 2016). With

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respect to JAZ1, these results are consistent with the observations in white and

Manchurian ash in the current study; however, the current study showed no differential expression of JAZ3. Such difference could be a result of differences in species, timing of sample collection, or grouping of similar but functionally distinct JAZ annotations based on homology.

Transcriptional induction of JAR1, the gene encoding the enzyme that catalyzes the conjugation of jasmonic acid and isoleucine into JA-Ile, the active form of JA

(Staswick et al. 2002), has also been shown within 1 hour of mechanical wounding in

Arabidopsis (Suza and Staswick 2008) . Induction of one JAR1 transcript 48h after both wounding and EAB feeding in both white and Manchurian ash was observed. No JAR1 annotations were found differentially expressed between any treatments in green ash by

Lane et al. (2016). Taken together, early JA transcriptional responses involving JAZs and

JAR1 appear to be very similar between the resistant and susceptible ash in the current study. This does not support the hypothesis that recognition and signaling of EAB attack differ between resistant Manchurian and susceptible white ash, as proposed in Villari et al. (2016). However, signaling downstream of early jasmonate responses may still differ between resistant and susceptible species.

Various WRKY and bHLH (MYC) transcription factors are known to mediate

JA-dependent and other defense responses (Chini et al. 2007, Pandey and Somssich 2009,

Sasaki-Sekimoto et al. 2013, Schluttenhofer et al. 2014). While differential expression of more than 30 WRKY and bHLH annotations at the 48h time point was observed in the current study, full characterization of the complex transcription factor responses was not

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feasible, but may still hold clues to resistance mechanisms associated with recognition and signaling. Additionally, several ethylene-responsive factors (ERFs) were differentially expressed between the species in this study. ERFs are known to mediate crosstalk between the JA and ethylene hormone pathways and are thought to fine-tune defense responses (Verma et al. 2016). Improved annotations and further exploration of gene expression profiles of WRKYs, bHLHs, ERFs, and other transcription factors in ash are warranted.

Based on pathways characterized in other plant species (Ferrer et al. 2008) the biosynthetic pathway for phenylpropanoid compounds in ash is likely complex, involving dozens of enzymes and metabolite intermediates. Consequently, it was not feasible to individually examine expression patterns of all genes involved. Instead, shikimate kinase

(SK) from the first step of the shikimic acid pathway responsible for phenylalanine synthesis, was chosen along with enzymes from early, mid, and late stages of phenylypropanoid biosynthesis: cinnamic acid 4-hydroxylase (C4H), 4-coumarate-CoA ligase (4CL), and cinnamoyl-CoA reductase (CCR), respectively. Additionally, β- glucosidase expression patterns were examined, due to its potential role in activating phenolic glycosides into more toxic oxidative forms (Rigsby et al. 2016).

No clear interspecific differences in expression related to phenylpropanoids at the three biosynthetic stages investigated in this study were observed at the annotation level, with species either responding similarly and/or different transcripts with the same annotation responding differently. However, with regards to hypothesized phenolic glycoside hydrolysis and pro-oxidation, the only significant change in β-glucosidase

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transcription was higher expression in Manchurian ash EAB vs. control treatments. This is in contrast to the results of enzyme activity assays of Manchurian and white ash 7 days after initiation of EAB larval feeding (Chapter 4), wherein β-glucosidase activity on the substrate oleuropein was inducible in white ash and not in Manchurian ash, and at higher levels in white ash control and EAB samples compared to Manchurian ash samples.

These differences may be due to the differences in timing between the studies (48 h transcription analysis vs. 7 day enzyme activity), and/or due to protein turnover, whereby transcription level is not closely related to steady-state protein level or enzyme activity, and/or due to some substrate specificity not captured by oleuropein in the in vitro enzyme activity assay.

Lane et al. (2016) also found differential expression of related annotations in green ash. Two transcripts with C4H annotations were higher in EAB vs. control; two transcripts with 4CL annotations were also higher in EAB vs. control, and one was lower

24 h post wounding vs. control; four transcripts with CCR annotations were also higher in EAB vs. control, one lower in EAB vs. control, and one lower in resistant vs. susceptible genotypes prior to EAB feeding. No transcripts with SK annotations were found differentially expressed in green ash. Many transcripts with (non-endoglucanase)

β-glucoside annotations were differentially expressed in green ash. 34 transcripts were lower and 33 higher in EAB vs. control; three were lower in resistant vs. susceptible genotypes prior to EAB feeding; two were higher in resistant vs. susceptible genotypes after feeding; and 14 were lower in wound treatments (5 h or 24 h) vs. control. The dramatic difference in responses of β-glucosidase annotations in green ash vs. the current

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study, as well as differences in other transcriptional patterns, may be due to differences in the timing of sampling and experimental design of the two experiments, as well as the different ash species used. Lane et al. (2016) sampled phloem before and 8 weeks after initiation of EAB larval feeding, confounding time and inoculation effects, whereas the current study sampled inoculated and non-inoculated trees at the same time 48 hours after initiation of larval feeding. Also, green ash wounding treatments were sampled 5 and 24 h after wounding, whereas the current study sampled 48 hours after wounding.

Another way to assess the differential transcriptional responses of phenylpropanoid pathways in Manchurian and white ash would be to perform an enrichment analysis for GO terms related to phenylpropanoid biosynthesis, as in Lane et al. (2016). However, annotations in the current study should first be improved in order to maximize the usefulness of such an enrichment analysis, by expanding the reference protein database used for transcript annotation. Indeed, improved annotation leading to enrichment analyses, in addition to previously mentioned multiple sequence alignments for closely related protein families, are two straightforward ways in which the results and conclusions of this study may be both strengthened and expanded.

In summary, the current study profiled gene expression patterns using species- specific transcriptome assemblies for EAB-resistant Manchurian ash and EAB- susceptible white ash, and implemented a method of interspecific comparison of expression patterns between induction treatments. This approach provided further support for the involvement of proteins in the Bet v 1/PR-10 family in ash resistance to

EAB and have generated species-specific sequence information that will aid in

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characterizing individual members of this family in each species. Little support was found for the hypothesis that white and Manchurian ash differ in early transcriptional responses related to JA signaling, but comparisons of responses of JA-associated transcription factor families was not performed. The study showed active transcriptional responses in phenylpropanoid biosynthetic pathways and the potential phenolic activating enzyme β-glucosidase. This rich expression dataset also provided new genes of interest with differential expression between Manchurian ash and white ash, including those with annotations related to jacalin-like lectin domains, concavalin a-like lectin kinase-like domains, ferredoxin 1 famliy proteins, LRRTKs, receptor-like kinases, MAPKKs, and terpene synthase cyclases.

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CHAPTER 6: CONCLUSIONS AND FUTURE DIRECTIONS

The research in this dissertation provides new understanding of host defenses in the ash-

EAB system, in regards to both their nature (chemical or biological identity or function) and role (contribution to larval performance and tree survival), in part as a case study for a larger group of tree killing invasive pests, i.e. phloem and wood-boring insects.

Chapter 2 made a case for increased use of host resistance based management approaches for invasive pests that are intimately associated with vascular tissues of trees, such as phloem and wood-borers, and canker and wilt pathogens. I argued that host defenses have a significant role in determining the severity of these pests’ outbreaks.

Chapter 2 additionally profiled some of the approaches that are successfully being used to develop and deploy host resistance as well as some of the technological and conceptual advances that are still being integrated, and identified specific knowledge gaps.

Chapter 3 showed that experimental patterns of larval performance in the phloem of healthy and water-stressed, resistant and susceptible ash genotypes match observed patterns of field resistance to EAB. These patterns strongly suggest that phloem based defenses, in addition to other factors, such as oviposition preference and, to a lesser degree, natural enemy pressure, are important for determining invasion progression.

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Chapters 3-5 focused on two ash genotypes that have been relatively well characterized in past studies, the EAB-susceptible ‘Autumn Purple’ cultivar of white ash

(Fraxinus americana) and the EAB-resistant ‘Mancana’ cultivar of Manchurian ash (F. mandshurica). A summary of the larval performance-based resistance phenotypes and associated defense traits described in this dissertation is presented in Table 6.1, and discussed below.

F. mandshurica cv. ‘Mancana’ killed a high proportion (0.47 - 0.56) of larvae under normal water conditions, with small proportions of first or second instar (0.16 -

0.21) and third or fourth instar (0.08 - 0.18) larvae surviving 65-70 days post hatch and gallery initiation. In contrast, F. americana cv. ‘Autumn Purple’ supported survival and development to third or fourth instar of a large proportion (0.69 - 0.96) of larvae, and killed a very small proportion (0.00 - 0.02) of larvae. However, Manchurian ash killed a much smaller proportion (0.15) of larvae when the trees were water-stressed. Defense traits were not profiled in this phenotype, and should be targeted in future research.

Resistance to larval survival and development in healthy Manchurian ash was associated with higher constitutive phloem POX activities, verbascoside concentration, and concentration of a pinoresinol derivative, compared to white ash. Manchurian ash also exhibited moderate constitutive phloem βG and PPO activities, and these were greater than those of the closely related by highly susceptible black ash (F. nigra) but lower than those of susceptible white ash.

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Constitutive resistance Larval-feeding-induced resistance Water stressed Normal water Pheno - Defense Pheno- Defense Pheno- Defense Ash cultivar typea trait typea trait typea traitb F. americana 'Autumn Purple' S3* βGc,4 S3 No data S3 βGc,4, PPOc,4

F. mandshurica 128 'Mancana' R3 Verbascoside4 I3 No data R3 Verbascoside derivative 14 Pinresinol derivative4 Unknown phenolic peak 184 POX4 Major allergen Mal d 1/ PR-105 Lectins and lectin kinases5 Terpene synthase cyclases5

*Superscripted numbers refer to dissertation chapters aPhenotypes: R- resistant; I- intermediate; S- susceptible bInterspecific comparisons of candidate defense traits in chapters 4 and 5 performed on normally watered trees cCompared to F. nigra in Rigsby et al. (2016) βG, β-glucosidase activity; PPO, polyphenol oxidase activity; POX, peroxidase activity

In response to feeding by EAB larvae, normally watered Manchurian ash exhibited sustained high levels of constitutive traits discussed above, as well as induction of an unidentified phenolic compound (“Peak 18” from Ch. 4), and a compound found only in EAB-induced Manchurian ash samples and putatively identified as verbascoside derivative 1. Distinct from white ash, Manchurian ash also exhibited transcriptional induction within 48 h of larval feeding of genes homologous to major allergen Mal d 1, lectins and lectin kinases, a β-glucosidase, and a terpene synthase cyclase.

Chapter 4 described the concept of functional or activity-based screening in addition to identity-based screening of ash defenses. Building on the work of Rigsby et al. (2015, 2016) and Rajarapu (2013), Chapter 4 proposes a revised hypothesis for the role of enzymes with pro-oxidant function in ash resistance to EAB, emphasizing protein crosslinking and hypothesized lignification promoting activities of POX over activities of

βG and PPO oxidases.

Activity-based defense profiling should be expanded in the ash-EAB system.

Specifically, additional and alternative ways of characterizing and quantifying pro- oxidant quality of ash phloem and or effects on larval midgut should be developed.

Hydrogen peroxide and/ or free radical generation assays, lipid and/or protein

(per)oxidation assays would be a place to start, along with other oxidative stress-related markers in larvae, such as malondialdehyde (Hyršl et al. 2007). Additionally, more information is needed on the mechanisms by which pro-oxidant quality may decrease larval performance. Research could attempt to determine effects on bioavailability of nutrients in pro-oxidant environments. One possible approach would be to amend

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phloem-free diets with either partial amounts of ash phloem differing in pro-oxidant activity or with a known protein cross-linking agent like glutaraldehyde or pro-oxidants like hydrogen peroxide ± POX, depending on the specific mechanism under evaluation.

The hypothesis would be that supplemental protein or amino acids in the diets would at least partially mitigate anti-nutritive effects of pro-oxidant or protein cross-linking amendments.

The identity vs. function concept translates to the future directions arising from the transcriptomic study in Chapter 5 as well. GO and other annotation/pathway enrichment analyses of transcriptomic data will provide a more systematic way to perform functional or activity-based screening of resistance mechanisms, which is likely more robust than the identity/description based screening of annotations performed up to this point.

However, identity-based screening of ash transcriptomes in Chapter 5 has provided some compelling targets for further study, particularly by confirming those previously suggested in other ash experiments. The Bet v 1 major allergen/PR-10 protein family is one such example. Multiple sequence alignment and structure-function modeling of predicted protein sequences from white and Manchurian ash transcriptomes,

European ash transcriptome and/or genome, and the green ash transcriptome should be performed to classify these proteins into structurally similar subfamilies within and between species that may share unique functions. It may also be useful to design semi- selective primers to amplify and sequence genes related to this protein family from

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genomic DNA of various ash species and genotypes, so that the classifications of orthologs, paralogs, and isoforms can be assigned to candidate sequences.

Enzyme activity assays in Ch. 4 and transcriptomic profiling in Ch. 5 provided opposite patterns for the relationship between βG and resistance. This discrepancy may arise from a number of factors including the timing of sampling (7 d in Ch. 4 vs. 4 h in

Ch. 5), size or age of trees (11-yr old vs. 5-yr old), processes of transcript and protein turnover by which transcriptional activity may not translate to enzymatic activity, as well as artificiality of single-substrate in vitro enzyme assays or imprecision of transcript annotations.

Factors contributing to the above discrepancy could be investigated as part of an experiment to address the knowledge gap related to biochemical mechanisms of drought- induced susceptibility in Manchurian ash. Such an experiment could profile pro-oxidant enzyme activities, (per)oxidation of phenolic substrates like verbascoside or oleuropein, and lignification in local tissues at a series of time points (48 h, 4 d, 7 d, 14 d) in healthy and water-stressed Manchurian ash. The study could include a susceptible ash species such as black or white ash, and optionally profiling selected gene transcription, including phenolic biosynthesis, Mal d 1 family proteins, and transcription factors associated with

JA signaling. Such a study could help link results from previous work including 14 d resistance phenotypes and phenolic profiles (Chakraborty et al. 2014), Ch. 3 resistance phenotypes, 7 d phenolic profiles and enzyme activities in Ch. 4, and transcription profiles at 48 h in Ch. 5. Additionally, the activity of ash βG on verbascoside and its derivatives should be tested. Improved identification and investigations into possible

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biochemical interconversion between derivatives and their associated biological activity in resistant and susceptible phenotypes is also desirable.

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150

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3. Laurel wilt

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Hughes, MA, Smith, JA, Ploetz, RC, Kendra, PE, Mayfield III, AE, Hanula, JL, Hulcr, J, Stelinski, LL, Cameron, S, Riggins, JJ, Carrillo, D, Rabaglia, R, Eickwort, J, Pernas, T. 2015. Recovery plan for laurel wilt on redbay and other forest species caused by Raffaelea lauricola and disseminated by Xyleborus glabratus. Plant Health Progress 16: 174-210.

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5. Dutch elm disease

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6. Ash dieback www.ashgenome.org

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