The Pennsylvania State University

The Graduate School

Intercollege Graduate Program in Plant Physiology

SUPPLY-SIDE REGULATION OF PHENOLIC PRODUCTION IN PLANTS:

A COMPARISON OF TWO MODEL SYSTEMS

A Thesis in

Plant Physiology

by

Toni M. Schaeffer

© 2004 Toni M. Schaeffer

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

August 2004

The thesis of Toni M. Schaeffer was reviewed and approved* by the following:

John C. Schultz Distinguished Professor of Entomology Thesis Adviser Chair of Committee

Gary W. Felton Professor of Entomology

John E. Carlson Associate Professor of Molecular Genetics

Surinder Chopra Assistant Professor of Maize Genetics

Klaus M. Herrmann Professor of Biochemistry, Purdue University Special Signatory

Teh-hui Kao Professor of Biochemistry and Molecular Biology Chair, Intercollege Graduate Program in Plant Physiology

*Signatures are on file in the Graduate School.

ABSTRACT

Many plants respond to environmental cues with increased production of phenolics. However, theories of carbon allocation and plant defense lack an examination of mechanism and do not explain induced defenses. Phenolics are biosynthetic products of the shikimate and phenylpropanoid pathways. The first committed steps of these pathways are 3-deoxy-D-arabino- heptulosonate 7-phosphate synthase (DAHP synthase, DS) and phenylalanine ammonia lyase (PAL), respectively, and their activities and gene expression are induced in response to many of the same environmental cues that increase phenolics. PAL activity has been linked to increases in phenolics, but the role of DS has been neglected. In the context of induced phenolics, few studies have examined DS or its coordinate regulation with PAL. We hypothesized that the regulation of this “core” pathway, from DS to PAL, is conserved across species and that regulation of DS has a direct effect on phenolic production. We also proposed that alternative substrate pools (such as quinic acid) could uncouple the regulation of DS activity and phenolic production. Two phylogenetically divergent species were chosen for study: tobacco (Nicotiana tabacum) and hybrid poplar (Populus nigra X P. deltoides). We examined their patterns of DS and PAL activity and phenolic production when challenged with insect and simulated herbivory (jasmonic acid, a wound signal; JA) under different light environments. We found that DS and PAL were not coordinately regulated in either tobacco or poplar. Regardless of light level, induction of DS was the rate-determining component in JA increased phenolic production in tobacco. PAL activity and photosynthesis were also related to phenolic production in low light in tobacco, but DS was still the rate-determining component. However, PAL activity was the rate- determining component in JA increased phenolic production for hybrid poplar, regardless of light level, and DS activity was not related to increases in phenolic production. We also found that translocated sucrose, shikimic acid, and quinic acid from source locations were all able to supply JA-increased phenolic production in hybrid poplar sink tissues, but this ability was constrained by light level. Our results help clarify the regulation of plant investment in phenolics by providing a mechanistic explanation for carbon allocation patterns.

iii TABLE OF CONTENTS

List of Tables…………………………………………………………………………...... vi

List of Figures………………………………………………………………………………...……….…..viii

Abbreviations………………………………………………………………………………………………..x

Acknowledgments………………………………………………………………………………….……...xii

Chapter 1. Supply-side regulation of phenolic production in plants………………………….………1

Predicting phenolic production…………………...…………………………….……...1

Regulation of phenylpropanoid biosynthesis……………..…….…………….……...2

Hypotheses and predictions…………………………………………………………...6

Research description…………………………………………………………………...7

Experimental design………………………………………………………………….…9

Experiments…………………………………………………………………………….10

Figures………………………………………………………………………………….13

References……………………………………………………………………………..15

Chapter 2: The effects of supply from the shikimate pathway on induced phenolic

synthesis in tobacco (Nicotiana tabacum)…………………………………………………………...…22

Introduction…………………………………………………………………………..…22

Materials and Methods……………………………………………………………..…27

Results………………………………………………………………………………….38

Discussion………………………………………………………………………………47

Tables…………………………………………………………………………………...54

Figures………………………………………………………………………………….61

References…………………………………………………………………………..…77

Chapter 3. Control of phenolic production by DAHP synthase and PAL in a Populus hybrid…...84

Introduction………………………………………………………………..……………84

Materials and Methods…………………………………………………………..……87

Results………………………………………………………………………………....96

Discussion………………………………………………………………………….....102

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Tables………………………………………………………………………………….110

Figures……………………………………………………………………………...…116

References……………………………………………………………………………146

Chapter 4. The effects of translocated carbon sources on induced phenolic production in young leaves of Populus………………………………………………………………..152

Introduction…………………………………………………………………………...152

Materials and methods………………………………………………………………155

Results ...……………………………………………………………………………..163

Discussion ……………………………………………………...…………………….165

Tables………………………………………………………………………………….173

Figures……………………………………………………………………………...…182

References……………………………………………………………………………200

Chapter 5. Summary and future directions…………………………………………………………..205

References………………………………………………………………………...….212

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LIST OF TABLES

Table 2.1 Model statistics from two-way ANOVAs for chemical and biochemical measurements of young leaves ………….……………………………..…54

Table 2.2 Model statistics from two-way ANOVAs for chemical and biochemical measurements of mature leaves ………………………………..…………55

Table 2.3 Model statistics from two-way ANOVAs for photosynthesis measurements during A) experiments I and II and B) experiment III…………………..…56

Table 2.4 Heteroscedastic t-tests comparing relative growth rates at each sampling date for experiments I, II, III and IV …………………...……..…57

Table 2.5 Statistics from significant regressions on phenolics for experiments I-III …….…58

Table 2.6 Statistics from optimized multiple regressions on young phenolics for experiments I-III ……………………………………………………..……....59

Table 2.7 Heteroscedastic t-tests comparing concentrations of specific phenolics three days after treatment during experiment III ……………………....…60

Table 2.8 Z-tests for mean comparison between measures of phenolic concentration quantified with the Folin-Denis assay versus phenolic peaks detectable at 320 nm with HPLC ……………………………….….60

Table 3.1 Model statistics from analysis of variance for chemical and biochemical measurements of young leaves: phenolic content, condensed tannins, DAHP synthase activity, and PAL activity……………..………110

Table 3.2 Model statistics from analysis of variance for chemical and biochemical measurements of mature leaves: phenolic content, condensed tannins, DAHP synthase activity, and PAL activity ………………….…111

Table 3.3 Model statistics from analysis of variance for chemical measurements of young leaves: phenolic glycosides and protein ………...………………112

Table 3.4 Model statistics from analysis of variance for chemical measurements of young leaves: phenolic glycosides and protein …………………………113

Table 3.5 Model statistics from analysis of variance for whole plant relative growth rate and photosynthesis measurements for young and mature leaves ………………………………..………………………………….….114

Table 3.6 Model statistics from repeated measures analysis of variance of relative leaf growth for leaf plastochron index leaves 0, 3, 6, and 8 …….……115

Table 4.1 Experiment I model statistics from analyses of dependent variables total phenolics, and the phenolic glycosides salicortin and HCH-salicortin …………………………………...... …173

Table 4.2 Experiment I model statistics from analyses of dependent variables condensed tannins, protein, and leaf relative growth rate……...……...174

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Table 4.3 Experiment II model statistics from analyses of dependent variables total phenolics and condensed tannins……………….…………………….….175

Table 4.4 Experiment II model statistics from analyses of dependent variables salicortin and HCH-salicortin………………………………………..….….176

Table 4.5 Experiment II model statistics from analyses of dependent variables protein content and relative leaf growth rate.………………………...….177

Table 4.6 Least-squares means for total phenolics and condensed tannins in LPI 3 for experiments I and II ……………..……………………………..…...….178

Table 4.7 Least-squares means for phenolic glycosides in LPI 3 for experiments I and II ………………………………………………………………….……179

Table 4.8 Least-squares means for protein and standard means for relative leaf growth rate for LPI 3 for experiments I and II …………….…………..…180

Table 4.9 Summary of statistics (p-values) for responses to JA treatments for LPI 3 in Populus at three light levels…………………………………….…..…..181

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LIST OF FIGURES

Figure 1.1 Diagram of the plant biosynthetic route to phenolics …….………………….....…13

Figure 1.2 Structure of major phenolics found in tobacco (Nicotiana spp.) and poplar (Populus spp.)………………………………………………………..…..…..15

Figure 2.1 Total phenolics by light and leaf age in Nicotiana tabacum ………………………61

Figure 2.2 DAHP synthase activity by light and leaf age in Nicotiana tabacum .……………63

Figure 2.3 PAL activity by light and leaf age in Nicotiana tabacum ……………………...…..65

Figure 2.4 Photosynthesis by light and leaf age for Nicotiana tabacum ……………………..67

Figure 2.5 Protein content by light and leaf age in Nicotiana tabacum ………….…………..69

Figure 2.6 Whole plant relative growth rate by light level for Nicotiana tabacum …………..71

Figure 2.7 High performance liquid chromatography (HPLC) analysis of Nicotiana tabacum chemistry from plants grown in high light……………..….…….73

Figure 2.8 Responses to mite herbivory by leaf age at high light in Nicotiana tabacum …..75

Figure 3.1 Total phenolics (Folin-reactives) by light and leaf age in a Populus deltoides X P. nigra hybrid ……….…………………………….…………116

Figure 3.2 Condensed tannins (N-butanol assay) by light and leaf age in a Populus deltoides X P. nigra hybrid …………………………………………….….118

Figure 3.3 High performance thin layer chromatography (HPTLC) analysis of phenolic Glycosides from Populus deltoides X P. nigra hybrid plants grown in low light……………………………………………………………..…….120

Figure 3.4 High performance thin layer chromatography (HPTLC) analysis of phenolic Glycosides from Populus deltoides X P. nigra hybrid plants grown in moderate light …………………………………………….……………..122

Figure 3.5 High performance thin layer chromatography (HPTLC) analysis of phenolic Glycosides from Populus deltoides X P. nigra hybrid plants grown in high light …………………………………………………….……………124

Figure 3.6 DAHP synthase activity by light and leaf age in a Populus deltoides X P. nigra hybrid ………………………………………………….………..126

Figure 3.7 PAL activity by light and leaf age in a Populus deltoides X P. nigra hybrid ..….128

Figure 3.8 Protein content by light and leaf age in a Populus deltoides X P. nigra hybrid …………………………………………………………………..……130

Figure 3.9 Whole plant relative growth rate by light level for a Populus deltoides X P. nigra hybrid ……………………………………………………….…..132

Figure 3.10 Leaf relative growth by light level and leaf age for a Populus deltoides X P. nigra hybrid ……………………………………….…………………..134

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Figure 3.11 Photosynthesis by light and leaf age for a Populus deltoides X P. nigra hybrid ……………………………………………………………………..…136

Figure 3.12 Responses to gypsy moth treatment by leaf age in Populus deltoides X P. nigra hybrid plants grown at high light……………...………………138

Figure 3.13 High performance thin layer chromatography (HPTLC) analysis of phenolic glycosides from Populus deltoides X P. nigra hybrid plants subjected to gypsy moth treatments at high light ……………….140

Figure 3.14 Salicortin and HCH-salicortin ……………………………..………………………..142

Figure 3.15 Illustrations of the leaf plastochron index (LPI) and orthostichous leaves for Populus spp.………………………………………………………..…….…144

Figure 4.1 Diagram of the plant biosynthetic route to phenolics ……………………….……182

Figure 4.2 Quinic acid metabolism as it relates to the shikimate pathway in both A) plants and B) fungi..…..…………………………………………………184

Figure 4.3 Diagram of phyllotaxy and the leaf plastochron index (LPI) in Populus spp...... 186

Figure 4.4 Total phenolics and phenolic glycosides from Populus deltoides X P. nigra hybrid plants at high light…………………………………...………..…….188

Figure 4.5 Condensed tannins from Populus deltoides X P. nigra hybrid plants at high light…………………………………………………………………..190

Figure 4.6 Protein content and leaf relative growth rate from Populus deltoides X P. nigra hybrid plants at high light …………………………………...... 192

Figure 4.7 Total phenolics and phenolic glycosides from Populus deltoides X P. nigra hybrid plants at low and moderate light ………………………………....194

Figure 4.8 Condensed tannins from Populus deltoides X P. nigra hybrid plants at low and moderate light …………………………...………………………..196

Figure 4.9 Protein content and leaf relative growth rate from Populus deltoides X P. nigra hybrid plants at low and moderate light….…………………..198

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ABBREVIATIONS

BSA Bovine serum albumin HPLC High performance liquid chromatography Buffer Distilled deionized water, brought to HPTLC High performance thin layer pH 7.0 with KOH and HCl chromatography C Carbon supply model term JA jasmonic acid, a wounding signal CGA Chlorogenic acid L Light level model term CHEM Samples for chemical analysis LI Leaf index, used for tobacco CT Condensed tannins LOW Experiments conducted at low light Ctrl Control LPI Leaf plastochron index, used for poplar hybrids D Day model term LRG Leaf relative growth DAHP 3-deoxy-D-arabino-heptulosonate M Mature leaves 7-phosphate DAHPS DAHP synthase MDS DS activity in mature leaves DDF Denominator degrees of freedom Mites Spider mites; Tetranychus urticae Koch DF Degrees of freedom MLEAF Correlations from parameters measured within a mature leaf DHQ 3-dehydroquinate MOD Experiments conducted at moderate light DS DAHP synthase MPAL PAL activity in mature leaves DTPG Diterpene glycoside MPN Protein content in mature leaves DW Dry weight MPS Photosynthesis in mature leaves E4P Erythrose 4-phosphate ND Not statistically different ENZ Samples for enzyme assays OPP Oxidative pentose phosphate pathway EPPS Also HEPPS; N-(2-hydroxyethyl) P P-value; test statistic piperazine-N’-3-propanesulfonic acid; buffer used throughout thesis F F-statistic, from the Fisher PAL Phenylalanine ammonia-lyase distribution FD Folin-Denis assay, or Folin- PCA Photosynthetic carbon assimilation reactives, a measure of total phenolic content GPDH Glucose 6-phosphate PCM Protein competition model for dehydrogenase phenolic production GM Gypsy moths; Lymantria dispar L. PEP Phosphoenol pyruvate HCH Hydroxy-cyclohexene-on-oyl; also PG Phenolic glycoside used for HCH-salicortin HIGH Experiments conducted at high light Phe Phenylalanine

x

PLANT Correlations between young and Solv Solvent control for jasmonic acid mature leaf values in a plant treatments PN Protein SP Shikimate pathway PP Phenylpropanoid pathway Suc Sucrose PPT PEP-phosphate translocator T Treatment model term PS Photosynthesis TPhen Total phenolics; refers to values determined with the FD assay QA Quinic acid; quinate tr Statistical trend (p < 0.10) QD Quinate dehydrogenase; QORase U Uptake from source supply QH Quinate hydrolase W Water spray treatment QORase Quinate oxidoreductase; QD WPCtrl Within-plant controls; LPI 2 for carbon supplemented LPI 3 leaves RG Whole-plant relative growth Y Young leaf RGR Whole-plant relative growth rate YDS DS activity in young leaves RPP Reductive pentose phosphate YLEAF Correlations from parameters pathway; Calvin cycle measured within a young leaf S Spray model term YPAL PAL activity in young leaves SAL Salicortin YPN Protein content in young leaves SK Shikimic acid; shikimate YPS Photosynthesis in young leaves

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ACKNOWLEDGMENTS

This thesis marks the end of a long journey, a meandering route that started when I was in high school. As with all good journeys, the destination has not always been the primary goal, and as with most travelers some of my most memorable moments have come from diversions encountered along the way. Nonetheless, when one’s destination is finally reached – it is satisfying and fulfilling and I am grateful to the many people who have helped me along the way.

As a biologist, I must give credit where credit is due – first, to my parents for their contributions to both the “nature” and “nurture” components of my upbringing. They never knew quite how to handle me, so they let me do my own thing and fostered my independence as well as my curiosity and have supported me in everything that I have chosen to do…or not to do. Along with my parents, I would like to thank my husband, Brad Bruno, for his help in finishing this thesis – namely, for allowing me to get out of any shared household duty with the simple utterance of the phrase “but I have to work on my thesis.” Although my two dogs (Bella and Arjuna) also offered emotional support in their own way, I also have to thank Brad for taking care of them and removing any of their unneeded “assistance.”

I was encouraged in my academic pursuits by many of my teachers, in both high school and college, but my experiences working with Dr. Mark Davis at Cedar Creek Natural History Area in Minnesota while an undergraduate at Macalester College are what led directly to my graduate work at Penn State (literally). When I couldn’t come up with a satisfying explanation for the patterns of herbivory that we were observing in our garden plot of oak seedlings, Mark challenged me to search through the scientific literature to find an answer. Not surprisingly, I was not able to explain our data with any existing ecological theories. However, I did like the approach that a few researchers were exploring – investigating the chemical ecology of plant- insect interactions. I remember excitedly marching into his office to discuss a paper I’d just read (Hunter and Schultz 1995). I had hoped to convince Mark to fund some laboratory work, and instead ended up with a plane ticket to State College, Pennsylvania, to spend the winter term visiting his buddy from grad school – Jack Schultz.

After a crash-course in plant chemistry at Penn State that winter and a hands-on introduction to chemical ecology research working as a research assistant that summer, I still wasn’t sure I wanted to go to graduate school. I told Jack that I had to go canoe a river first, and that I’d let him know what I decided when I finished. So, from the very beginning, I have to thank Jack for putting up with me. I managed to convince him that exploring the regulation of the shikimate pathway was a worthy avenue for investigating patterns of phenolic production. However, he still thinks that he was the one who convinced me to be more interested in

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mechanisms. At the very least, we do agree on the approach. Jack fosters a holistic approach to scientific education and PhD dissertations; during my time in the Schultz lab I have learned much more than the mere mechanics of scientific experimentation.

I also wish to acknowledge the time and effort put forth by the rest of my committee members, Professors Gary Felton, John Carlson, Surinder Chopra, as well as the plant physiology program chair, Professor Teh-hui Kao. Their contributions have made this thesis a better work. I am especially thankful to each of them because I know that I am one of the fortunate few graduate students to have a committee where every member actually read the thesis from cover to cover. The work in this thesis would not have been possible without the help of Klaus Herrmann from Purdue University who hosted me in his laboratory and taught me about 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase. I am also extremely grateful to Rick Lindroth from the University of Wisconsin-Madison for allowing me to use his lab facilities to analyze phenolic glycosides; the data generated in his lab put the finishing touches on this thesis.

Past and present members of the Schultz lab were (and are) instrumental in supporting, commiserating, mentoring, and advising. The list is long, but includes fellow graduate students Ahnya Redman, Tim Morton, Don Cipollini, Anne Walton, Brian Rehill, Irmgard Seidl-Adams, Sarah Melissa Witiak, and Nate McCarthy, as well as post-docs Tom Arnold and Inga Mewis, our technician during most of my time in the Schultz lab, Sharie Ketcho, and the lab manager (co- director, Jack’s better half) who kept us all together, Heidi Appel. Special thanks goes to Anne Walton for sharing her tobacco methods, Tom Arnold for introducing me to poplar phyllotaxy, Inga Mewis for allowing Bella and me to invade her office space, and Brian Rehill for teaching me the tricks of the phenolic glycoside trade. Of equal importance in terms of emotional support and sanity checks was my network of non-lab friends. Thelma Brodzina provided endless supplies of conversation and caffeine. Eric Lyons made the journey through plant physiology with me, and along with Mark Holowach made sure I drank enough beer. John Cramer provided the critical home away from home (as well as home-cooked meals) that allowed me to survive being a long- distance graduate student. Last, but certainly not least, Rachel Kantola and Rachel Minkin were my electronic sounding board.

Finally, I would like to acknowledge the National Science Foundation (NSF) Graduate Fellowship program, the NSF Research Training Grant in “Signaling in Plant Development and in Response to the Environment”, and the NASA Space Grant Consortium for financial support. I would also like to thank the NSF Doctoral Dissertation Improvement Grant (DDIG, #DEB- 9318073) program for the grant that paid for the research supplies that made this work possible. NSF grant #IBN-0104820 to Jack Schultz also paid for some preliminary experiments.

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“It is the mark of an instructed mind to rest satisfied with the degree of precision which the nature of the subject permits and not seek an exactness where only an approximation of the truth is possible.”

- Aristotle

“I have steadily endeavoured to keep my mind free so as to give up any hypothesis, however much beloved (and I cannot resist forming one on every subject), as soon as facts are shown to be opposed to it.”

- Charles Darwin

Chapter 1

Supply-side regulation of phenolic production in plants

PREDICTING PHENOLIC PRODUCTION

Many theories have been advanced to explain the structural differentiation and function of plant secondary metabolites, as well as the differential allocation of energy and materials to them (Feeny 1976, Rhoades and Cates 1976, Bryant, et al. 1983, Coley, et al. 1985, Berenbaum 1995). Published studies alternately confirm or refute the predictions of these theories and hypotheses, and the theories are often not mutually exclusive (Berenbaum 1995). Given the structural and functional diversity of plant secondary metabolites, it may be too simplistic to assume that mechanisms generating patterns in all secondary metabolites are identical. We simply do not understand how production of major metabolite classes is regulated (Berenbaum 1995).

Probably because of their ubiquity among plant species and functions in defense, phenolics are among the most frequently studied plant secondary metabolites (Schultz 1988), and figure prominently in the theories mentioned above. Phenolics are incredibly diverse, and include simple phenolics, such as hydroxybenzoic acids (including salicylic acid), coumarins, flavonoids, and gallic acid, as well as more complex polyphenolics, such as hydrolyzable tannins, condensed tannins, and lignin. Phenolics play diverse roles in the lives of all plants as structural and defensive elements, UV screens, antioxidants, colors, wound sealants, growth regulators, intra- and interplant signals, and signals with symbionts (Jones and Hartley 1999). In litter, they can regulate decomposition and soil fertility (Northrup et al. 1998). They are important nutriceuticals, pharmaceuticals, horticultural attributes, regulators of food and forage value, flavors and odorants, food colors, and influence wood products (Bonner and Jensen 1998). These roles place phenolics at the center of efforts to bioengineer

1 plants, in addition to their role in the development of defense and allocation theories (Coley et al. 1985, Hu et al. 1999, Jones and Hartley 1999).

Because plants cannot escape the many environmental circumstances in which phenolics may be adaptive, phenolic synthesis is highly dynamic and responsive to diverse environmental stimuli, including quantity and quality of light, touch, wind motion, wounding, herbivory, microbial cues, pathogen attack (Dyer et al. 1989, McCue and Conn 1989, Muday and Herrmann 1992, Mol et al. 1996, Dixon et al. 1996). It would appear axiomatic that sufficient carbon skeletons must be available to supply phenolic induction, which is likely to be limited by photosynthesis and hence light, or by stored substrates. While constitutive levels of phenolics are routinely shown to be reduced in low light conditions (e.g. Buttery et al. 1992, Nichols-Orians 1991, Shure and Wilson 1993, Jansen and Stamp 1997), there are few, if any, studies of induction as a function of light levels; most induction studies are done under ideal plant growth conditions. Yet we know that plants are frequently more susceptible to consumers when growing at low light levels (e.g. Augsburger 1984, Entry et al. 1986, Vergeer and van der Velde 1997). Can this be explained by reduced ability to respond defensively, and can constraints be ameliorated by drawing on stored reserves? Indeed, many phenolic biosynthetic enzymes are light-regulated, with greater activity in higher light (e.g. Henstrand et al. 1992, Homeyer and Schultz 1988, Reinbothe et al. 1994). But if plants need to increase phenolic synthesis for defense in the dark or the shade, how is this accomplished when PAL and other enzyme activities are diminished and photosynthesis is diminished? These fundamental ecological, physiological, and biochemical questions have not been addressed.

REGULATION OF PHENYLPROPANOID BIOSYNTHESIS

Most plant phenolics are products of the shikimate (SP) and general phenylpropanoid pathways (PP)(Fig 1.1). The SP is also responsible for the synthesis of the three aromatic amino acids, tryptophan, tyrosine, and

2 phenylalanine (Phe). Erythrose 4-phosphate (E4P) and phosphoenol pyruvate (PEP) are condensed by the first enzyme of the SP, 3-deoxy-arabino- heptulosonate 7-phosphate synthase (DS). Within the SP these linear carbon chains are cyclized and double bonds are sequentially added to form an aromatic ring, the core of a phenolic structure. While some defensive phenolics originate directly from the SP (e.g. gallic acid, hydrolyzable tannins), most phenolic products are synthesized via the PP, which begins with the deamination of Phe by PAL. Most approaches to understanding plant investment in phenolics focus on ‘downstream’ or ‘demand-based’ phenomena (PAL and other regulatory steps in the PP; Jones and Hartley 1999). Many PP genes have been sequenced and their regulation characterized (Mol et al. 1996). But these studies have not led to a comprehensive understanding of phenolic production. This may be because endpoint processes can be limited by upstream substrate supply and because research on the SP has focused on aromatic amino acid synthesis instead of its role in phenolic synthesis. However, the PP is dependent upon the SP and the production of Phe (Jones and Hartley 1999). I suggest that the SP plays a critical role in regulating phenolic production both directly and as the supply of substrates for the PP. We must understand the roles of both pathways in phenolic production in planta to account for feedback effects and the summed effects of transcriptional and post-transcriptional regulation.

Several potentially dominant regulatory steps in phenolic synthesis lie upstream of PAL and the PP. Most obviously, substrate supply from the SP to PP and gallic acid synthesis could be limited by DS activity (Fig 1.1), which is modulated by wounding, light, and fungal elicitors (Logemann et al. 2000, Herrmann and Weaver 1999, Gorlach et al. 1995, Henstrand et al. 1992, Dyer et al. 1989, McCue and Conn 1989). It is well established that PAL activity is inducible by wounding (Thamarus and Furnier 1998, Bucciarelli et al. 1998, Shaw et al. 1990, Jones 1984), but PAL requires Phe supplied by the SP. I propose that coordinate activity of DS and PAL comprises a two-step

3 regulatory mechanism for phenylpropanoid production. If DS failed to provide sufficient substrate (Phe via SP), PAL could not increase phenolic production when elicited.

Phenolic synthesis ultimately depends on the supply of the two substrates, E4P and PEP, from the chloroplast via photosynthetic carbon assimilation (PCA). If this were the only source, then phenolic synthesis and the ability to meet enhanced demand under induction would depend directly on PCA. Preliminary experiments in tobacco have found instead that phenolic concentrations were increased by jasmonic treatment (JA, a wound-response signal) at low as well as high light levels despite decreased carbon gain, although the degree of induction was reduced in the shade (see Walton 2003). In 4 replicated experiments with tobacco grown at 2 light levels, the ability of JA to induce phenolic increases was reduced 0-25% by low light, but carbon gain was reduced 87% at the lower light level (Walton, Schaeffer, and Schultz, unpublished). I hypothesize that tobacco can maintain substantial phenolic induction at extremely low carbon gain rates, and that the induced demand for PP precursors is probably supplied from a source other than concurrent PCA. If PCA (and hence, E4P and PEP supply) is not the sole limiting factor, what else controls phenolic production?

A primary role for coordinate regulation of DAHP synthase and PAL makes sense only if there is no alternative supply of substrates to PAL. If there were an alternative source of substrate for producing Phe, then DAHP synthase activity could be uncoupled from PAL activity. Weinstein et al (1959, 1961) suggested that an alicyclic SP byproduct, quinic acid (QA), may play an important role in aromatic biosynthesis in higher plants. These and other studies have demonstrated that labeled carbon provided to plants as quinic acid can enter aromatic amino acids. Several authors have also suggested that QA pools accumulated early in the life of a leaf can be drawn upon to ‘feed’ the SP to increase or maintain Phe production or meet phenolic synthesis demands

4 (Bonner and Jensen 1998, Osipov and Shein 1990, Osipov and Shein 1986, Ossipov and Aleksandrova 1982, Boudet 1972, 1973). Boudet et al. (1985) even suggested an unknown shikimate-independent route from carbohydrates to the synthesis of QA, duplicating some of the functions of the shikimate pathway in tracheophytes and woody angiosperms. This idea resembles the theory of dual shikimate pathways (one in the chloroplast and one located primarily in the cytosol; Morris et al. 1989). Both hypotheses suggest non-chloroplastic carbon supply to Phe production and the PP.

QA is produced via the SP from dehydroquinate (DHQ) in the chloroplast and then is exported to vacuoles (Osipov and Shein 1986). Accumulation of quinic acid varies in different plant species and seasonally (Yoshida et al. 1975). Blocking EPSP synthase with glyphosate enhances incorporation of carbon into QA as well as SP intermediates (Stasiak et al. 1992, Lydon and Duke 1988, Ossipov and Aleksandrova 1982). QA serves as a ligand for many phenolics, including PP products (e.g. chlorogenic acid) and may be recovered from them (Maury et al. 1999, Hoffmann et al. 2003). Enzymatic interconversion of QA via quinate oxidoreductase (QORase) and quinate hydrolase (QH) permit it to re- enter the SP at two points above chorismate (Fig 1.1). The chloroplast is permeable to QA (Leuschner and Schultz 1991; a transporter has been identified in Neurospora) and the necessary enzyme systems (Leuschner et al. 1995, Kang and Scheibe 1993, Osipov and Shein 1986) exhibit appropriate dynamics to provide the chloroplast with QA as a substrate for the SP and eventually Phe production. This means that the plant could meet a demand for either Phe or phenolics by drawing upon stored QA pools (Boudet 1972, 1973). I hypothesize that recruiting stored QA to the SP comprises an alternative regulatory step in meeting demands for elicited increases in phenolics.

Taken together, I propose that these two major regulatory possibilities – coordinate regulation of DAHP synthase and PAL, and the ability to draw upon pools of stored QA to fuel the production of Phe (and supply PAL) – provide a

5 logical, general, and useful model of plant investment in phenolics. This ‘supply- side’ approach explains how plants might maintain flexibility and responsiveness while dealing with multiple stresses (especially restricted carbon gain) and how responses might change with tissue age and storage accumulation.

In the following chapters, I have used two model plant systems (tobacco and poplar) to assess the dependence of phenolic induction on regulatory steps in the SP and the ability of phenolic production carbon demands to be met by QA sources. I expected to find that the SP and PP were coordinately regulated, that resource supply by the SP could determine increases in phenolics, and that some plants could also utilize QA as a resource for phenolic induction. The simultaneous examination of the SP and PP and comparison of plants with different carbon allocation patterns furthers the mechanistic understanding of phenolic production and has implications for general theories of plant defense.

HYPOTHESES AND PREDICTIONS

H1: PHENOLIC INDUCTION IS DEPENDENT ON DS AND SP ACTIVITY

H1a: DAHP synthase activity is elevated in response to wound signals and herbivory. H1b: DAHP synthase activity is positively correlated with phenolic accumulation. H1c: Coordinate regulation of DAHP synthase and PAL are required for altered phenolic production. H1d: The activity increases of both DS and PAL are light dependent. H1e: The developmental state of a leaf (i.e. young or mature) can influence the induction responses of both DS and PAL.

H2: QA POOLS CAN SUPPLY SUBSTRATES NECESSARY FOR INDUCTION

H2a: Plants can meet demands for phenolic synthesis by utilizing stored quinate. H2b: Plants (or species) with greater quinate reserves (e.g. woody angiosperms) will be able to synthesize phenolics, even under photosynthetically limiting conditions.

6 H2c: Species with little or no quinate reserves (e.g. herbaceous angiosperms) will be less able to synthesize de novo phenolics under photosynthetically limiting conditions.

RESEARCH DESCRIPTION

Study Systems This research used two model plant systems representing two contrasting carbon allocation patterns: tobacco as a model herbaceous species, and poplar as a model woody species. The availability of full-sibs and clones, production of a variety of phenolic structures (Fig 1.2), established chemical and biochemical methods, and an extensive literature on induction and herbivory help make tobacco and poplar ideal study systems to address the role of the SP pathway and QA reserves in the production of phenolics, and helps ensure that my work remains as broadly relevant as possible.

Previous work has shown that both herbivory and application of the wound-response signal, jasmonic acid (JA), can cause increases in phenolic content in both tobacco and poplar (Arnold and Schultz, 2001, Lindroth and Hwang 1996). Chemical methods and enzyme assays have been established (Bi et al. 1997, Ahl Goy et al. 1993, Lindroth et al. 1987, Lindroth and Pajutee 1987) and fluxes and fluctuations of quinic acid metabolism have been measured in both species (Weinstein et al. 1961, Beaudoin-Eagan and Thorpe 1984, Boudet 1972). The translocation of assimilated carbon from older source to younger sink leaves has been characterized (Jones et al. 1959, Shiroya et al. 1961, Larson and Isebrands 1971, Larson and Gordon 1969, Larson and Dickson 1973, Arnold and Schultz 2001) and a leaf plastochron index (LPI) has been adapted to classify developmental and morphological leaf stages in both tobacco and poplar (Larson and Isebrands 1971)

Plants and Herbivores Full-sib Nicotiana tabacum cv. petite Havana plants were grown from seed in both growth chamber and greenhouse environments (depending on

7 experiment, see below). Plants were used approx. 40 d from germination, and were 30 cm tall with 15 leaves on average. The first fully uncurled was designated as an index leaf (0) and leaves were numbered consecutively downward. Leaves 4 and 8 were chosen as representative young sinks and mature source leaves, respectively (Masclaux et al. 2000, Shiroya et al. 1961). Genetically identical hybrid poplars (P. deltoides X P. nigra, OP-367, male, Segal Ranch, PA) were grown in a greenhouse from cuttings. Plants were used at 7-8 wks. At this stage, plants were 50-60 cm high with 25-30 leaves. Leaves at LPI 3 and LPI 8 were young sinks and mature source leaves respectively (Jones et al. 1993, Davis et al. 1991). All plant positions were randomized and rotated every 3d until manipulation. The growth chamber grown plants (two experiments in tobacco) were randomly assigned to treatments at the start of experiments. Greenhouse-grown plants (one tobacco experiment, and all poplar experiments) were assigned to treatments based on size distributions (Baldwin and Ohnmeiss 1994).

Spider mites (Tetranychus urticae) were used to generate herbivory on tobacco and gypsy moth (Lymantria dispar L.) larvae were used with poplar; both elicit phenolic induction (Scriber et al. 1999, Kielkiewicz 1994, Tomczyk 1994, 1992). Experiments were designed to avoid the use of containment controls. Spider mites were left on plants from the time of treatment for the duration of the experiment and removed with camel hair brushes at the time of harvest (see methods section in Chapter 2). Gypsy moth larvae were used to generate herbivory on mature poplar leaves and were tended during a 24-hr pulse of herbivory and repositioned to contain herbivory to specific leaves. Regurgitant was collected from gypsy moth larvae and used in conjunction with mechanical wounding for treatment of young poplar leaves (larvae consumption would have diminished leaf sample size, precluding necessary chemical analyses)(see methods section in Chapter 3). A small spider mite colony was supplied by John Sanderson (Cornell University, Ithaca, NY) a few days prior to the experiment.

8 The rearing of gypsy moths (obtained from USDA-ARS) and collection of larval regurgitant were conducted in our lab.

EXPERIMENTAL DESIGN

Regulation of Phenolic Production by DAHP synthase and PAL

To test the hypothesis that SP and DS activity can regulate (or constrain) phenolic synthesis, we elicited phenolic production with wounding signals and herbivory and measured DS and PAL activity, as well as phenolic accumulation in both tobacco and poplar. Preliminary experiments indicated that the between- plant variation inherent in experiments using real herbivory may mask subtle changes in enzyme activity, confound statistical tests, and result in leaf samples that do not yield enough tissue for the desired assays. Therefore, both real and simulated herbivory (JA treatment) were used. We were also interested in the light-dependence of enzymatic and phenolic responses, so experiments were conducted with varying light levels for each species. We expected that SP activity, as indicated by DS activity, in coordination with PAL, would be elevated in response to both simulated and real herbivory and that DS activity would be positively correlated with phenolic accumulation. Also, we anticipated that the induction responses of both DS and PAL would be light dependent and the developmental state of a leaf (i.e. young or mature) would influence the induction responses of both enzymes.

Utilization of QA for Phenolic Synthesis To test the hypothesis that phenolic induction was capable of using QA as a carbon source, we fed unlabelled QA as well as sucrose and shikimic acid to poplar plants (as in Minamikawa et al. 1969, 1972) via source leaf petioles and analyzed the ability of the external carbon supply to support induced phenolic synthesis in sink leaves. Originally, we had planned to use 13C-labeled QA (or 14C QA) for these experiments, but custom synthesis and analysis of 13C ratios in phenolic end-products were prohibitively expensive (~$80,000 for custom

9 synthesis; extensive equipment purchases and months of time for in planta production and isolation).

Source-sink connections were already characterized in this hybrid poplar clone (Arnold and Schultz 2001) and were verified with these cohorts of plants using phloem dyes. Based on previous studies with insects, JA, and 13CO2 labeling (Kleiner et al. 1999, Arnold and Schultz 2001), we provided external carbon supplies to the source leaf conduit just before treatments began and supplies were maintained until harvest to maximize the incorporation into induced phenolics in the sink leaves. We expected that phenolic induction in the sink leaves would be diminished when the source leaf was removed and that plants provided with QA (or sucrose) at their sources would be able to augment their induced phenolic production above those of controls. We predicted that plants would be able to use both externally supplied QA and sucrose to augment induced phenolic synthesis at all light levels, but especially when carbon assimilation was reduced at low light levels and if DS and PAL regulation were uncoupled.

Results of these studies should provide the mechanistic understanding necessary to account for inter- and intraspecific variation in plant phenolic responses to disease and herbivores, and to provide more predictive models of plant allocation to defense and interactions with the environment.

EXPERIMENTS

I conducted several experiments with both tobacco and poplar. One experiment for each species was a detailed time-course at high light levels designed to compare results from simulated and real herbivory. Other experiments (either separately, or as blocks within one experiment) investigated the effects of simulated herbivory at low and moderate light. QA utilization was studied at the same three light levels, but using only poplar. Methods for each

10 experiment are detailed in the following chapters. I conducted four experiments with tobacco to investigate DS and PAL activity. I conducted five experiments with poplar, three to investigate DS and PAL activity, and two to investigate the effects of supplying plants with external carbon supplies to support induced phenolic synthesis.

Chapter 2: Tobacco – investigation of DS, PAL and phenolic responses to treatment.

I: JA treatment effects at low light. - JA vs. solvent control treatments under shade cloth in a growth chamber, plants harvested one and three days after treatment (N = 36)

II: JA treatment effects at moderate light. - JA vs. solvent control treatments in a growth chamber, plants harvested one and three days after treatment (N = 36)

III: JA treatment effects at high light. - JA vs. solvent control and water misted treatments in a greenhouse, plants harvested one, two, three, and four days after treatment (N = 108)

IV: Mite herbivory effects at high light. - Leaves with mite feeding vs. controls in a greenhouse, plants harvested two and four days after treatment (N = 36)

Chapter 3: Poplar – investigation of DS, PAL and phenolic responses to treatment.

I: JA treatment effects at low and moderate light. - JA vs. solvent control treatments under shade cloth, or bordered by shade treatments in a greenhouse, plants harvested one and three days after treatment (split-split-plot design, N = 80)

11 II: JA treatment effects at high light. - JA vs. solvent control and water misted treatments in a greenhouse, plants were harvested one, two, three, and four days after treatment (N = 120)

III: Gypsy moth herbivory and regurgitant effects at high light. - Young leaves were treated with mechanical wounding plus gypsy moth larval regurgitant and mature leaves were treated with 3rd instar gypsy moth larval chewing in a greenhouse, plants were harvested two and four days after treatment (N = 40)

Chapter 4: Poplar – investigation of the effect of supplementation with external carbon supplies on induced phenolic production.

I: Four carbon supply treatments at high light. - Sucrose, shikimic acid, quinic acid, and a control solution were supplied to known source leaves at high light. Phenolic responses to JA treatment (and solvent and water misted controls) in connected and non-connected sink leaves were measured three days after treatment (N = 120)

II: Two carbon supply treatments at low and moderate light. - Sucrose and quinic acid were supplied to source leaves at low and moderate light. Phenolic responses to JA treatment (and solvent controls) in connected and non-connected sink leaves were measured three days after treatment. (split-split-plot design, N = 80)

12

Figure 1.1 Diagram of the plant biosynthetic route to phenolics from carbon assimilated by photosynthesis or mobilized from source tissues or storage (Walton 2003, Herrmann and Weaver 1999, Leuschner et al. 1995).

Abbreviations:

Metabolites - Enzymes -

CINN cinnamic acid DS DAHP synthase CTANNINS condensed tannins PAL Phenylalanine ammonia-lyase DAHP 3-deoxy-D-arabino- QD Quinate dehydrogenase heptulosonate 7-phosphate QH Quinate hydrolase DHQ 3-dehydroquinate E4P erythrose 4-phosphate A – DHQ synthase G6P glucose 6-phosphate B – DHQ dehydratase/ SK dehydrogenase, GPT glucose phosphate transporter bifunctional enzyme complex OPPP 0xidative pentose phosphate C – SK kinase

Pi inorganic phosphate D – 5-enolpyruvylshikimate 3-phosphate PEP phosphoenol pyruvate (EPSP) synthase 2-PGA 2-phosphoglycerate E – Chorismate synthase PHE phenylalanine F – Chorismate mutase PPT PEP-phosphate transporter G – Prephenate aminotransferase QA quinic acid, quinate H – Arogenate dehydratase SK shikimate I – Arogenate dehydrogenase TRP tryptophan TYR tyrosine

13

Figure 1.1

CO2 Pi G6P Starch GPT Light 2-PGA G6P CALVIN OPPP PEP CYCLE Pi PPT Sucrose P OCOOH

E4P PEP DS OH GLYCOLYSIS O P O

+ OCOOH P OH HO COOH

O P = Phosphate Group P DAHP P O i DHQ QA H2C OH

HO COOH HO COOH OH QD ?

A Pi DHQ OOH HO OH

OH OH HO COOH HO COOH DHQ OOH HO OH QA QA OH OH B ? QA HO COOH H O QH HOOC 2 COOH HO OH

COOH ATP C OH

OOH HO OH

OH OH B P OOH PEP SK OH D SHIKIMATE COOH Vacuole

PATHWAY CH2

P O OCOOH COOH OH

CH TRP 2 E OCOOHPi (multiple OH steps) F COOH HOOC

O

COOH HOOC

OH NH2 G I

TYR OH COOH H COOH NH2 PHE H2N CHLOROGENIC HO Chloroplast ACIDS

PHE PHENOLIC CINN GLYCOSIDES COOH PAL

H2N

LIGNIN CTANNINS Figure 1.2 Structures of major phenolics found in A) tobacco (Nicotiana spp.)(Keinänen et al. 2001, Robinson 1991) and B) poplar (Populus spp.)(Rehill et al. 2004, Lindroth et al. 1988).

15 Figure 1.2 Crypto-Chlorogenic Acid Chlorogenic Acid

A O O H HO COOH HO OH

COOH

HO HO OH O O O O

H O O H

+ H2N(CH2)4NH2 HO HO OH Caffeoyl Putrescine OH

Scopoletin Rutin

OH H3CO HO O OH

HO O O O RUTINOSE OH O ______

B Salicortin HCH-Salicortin OH OH CH2OH CH2OH O O O O HO O OH OH R HO CH2 HO CH2 OH O O O OH OH C O O C C O OH OH OH OH OH O O O HO O R n OH OH OH CH2OH CH2OH OH O O O O HO O OH OH R HO CH2 HO CH2OH O O O OH C O C O C O OH OH O Condensed Tannin Polymer

Tremulacin Tremuloidin

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21 Chapter 2

The effects of supply from the shikimate pathway on induced phenolic synthesis in tobacco (Nicotiana tabacum)

INTRODUCTION

Plants interpret a cascade of sometimes conflicting signals from their surroundings and integrate these signals into appropriate physiological responses in order to survive in a dynamic environment (Gibson 2004, Foyer and Noctor 2003, Karpinski et al. 2003, Rakwal and Agrawal 2003, Thum et al. 2003). The production of secondary metabolites, such as phenolics, is one such physiological response. The end-products of phenolic biosynthetic pathways arise from the integration of both abiotic and biotic signals (Dixon and Paiva 1995). Of the abiotic factors that influence phenolic production in plants, light has often been shown to have a consistent, stimulating effect on phenolic production (Hahlbrock and Scheel 1989, Dixon and Paiva 1995). Plants grown at higher light levels have higher levels of phenolics than plants grown at lower light levels (e.g. Wilkens et al. 1996, Jansen and Stamp 1997). Many biotic challenges, such as insect herbivory and its associated wounding events, have also been shown to increase phenolic production (Karban and Baldwin 1998).

Several authors (Bryant et al. 1983, Coley et al. 1985, Tuomi et al. 1988, Herms and Mattson 1992) have proposed that variation in phenolic production reflects the availability of light as well as nutrients, and arises from a trade-off in allocation by plants to growth and defense against herbivores. More recently, these widely-held concepts have been questioned (Nitao et al. 2002) and alternative hypotheses have been proposed, including the premise that the main role of many plant phenolics may be to protect leaves from photodamage, not herbivores, and that their levels vary with different risks of photodamage (Frankel and Berenbaum 1999, Close and McArthur 2002).

22 For much of plant evolutionary history, these selective pressures have not been mutually exclusive and predictive theories based on current phenolic functions are difficult to test. Manipulative studies have often led to inconsistent and contradictory results (Koricheva 2002; Nitao et al. 2002). Recent attempts to explain variation in concentrations of carbon-based secondary compounds using biosynthetic origins have been more promising (Haukioja et al. 1998, Jones and Hartley 1999, Ossipov et al. 2003). Using a similar ideology, we propose that detailed analyses of the biochemical regulation of the pathways responsible for the synthesis of phenolics can lead to the formation of predictive theories for phenolic production. When combined with metabolite analysis, this approach may also be able to answer questions about the adaptive significance of plant responses.

Phenylalanine ammonia-lyase (PAL), the first enzyme of the phenylpropanoid pathway and the branch point between primary (shikimate pathway, Herrmann and Weaver 1999) and secondary metabolism (phenylpropanoid pathway, Hahlbrock and Scheel 1989), has been implicated as a major regulatory enzyme in phenolic synthesis (Dixon et al. 2002, Dixon and Paiva 1995). PAL consists of multiple isozymes representing multi-gene families in many (most) plant species (e.g. Sarma et al. 1998, Wanner et al. 1995, Bolwell et al. 1985). PAL genes are often differentially expressed in response to stimuli such as wounding, light and elicitors (e.g. Shufflebottom et al. 1993, Yamada et al. 1992, Gowri et al. 1991, Liang et al. 1989) as well as in a tissue specific manner (e.g. Leyva et al. 1990, Ohl et al. 1990). PAL is also subject to post- translational modifications (Nishizawa et al. 1979). This means that for any plant species there is a broad range of gene-to-protein pathways for the stimulation (or repression) of PAL activity. Each specific stimulus may influence one or more of these gene-to-protein routes and contribute to total PAL activity, thus providing a mechanism for signal integration.

23 Phenylpropanoid production, PAL gene expression, and PAL enzyme activity respond to many of the same stimuli, including light (Creasy and Zucker 1974, and Lamb 1979, and Sarma et al 1998), fungal elicitors (Sharan et al. 1998), wounding (Pellegrini et al. 1994, Sarma et al. 1998, Campos-Vargas and Saltveit 2002), herbivores (Karban and Baldwin 1998), and plant hormones such as jasmonic acid and salicylic acid (Andi et al. 2001, Sharan et al. 1998, Campos-Vargas and Saltveit 2002). Correlations between biosynthetic enzyme activity and phenylpropanoid product accumulation exist (Dixon and Paiva 1995, Hahlbrock and Scheel 1989), and changes in phenylpropanoid production are sometimes accounted for by the activity of single enzymes (e.g. PAL), such as with studies investigating transgenic suppression (Bate el al. 1994) and overexpression (Howles et al. 1996) of heterologous PAL genes in tobacco plants (Nicotiana tabacum cv. Xanthi). Bate et al. (1994) conclude, “PAL appears to be the dominant control point in chlorogenic acid regulation” (the main phenylpropanoid produced in tobacco, Snook et al. 1986). These studies with transgenic plants have supported the widely held belief that PAL is the dominant control point in phenylpropanoid biosynthesis. However, these authors only analyzed PAL and other downstream enzymes. Even they state that, “it remains to be established over what interval PAL can be increased before the regulatory architecture of the pathway changes and downstream enzymes become more prominent sites of flux control” (Bate et al. 1994). These and other similar studies implicating a regulatory role for PAL do not rule out the possibility that upstream enzymes might be able to constrain or amplify phenolic production.

Upstream metabolism could influence phenolic production via the supply of phenylalanine (Phe) to PAL. This idea is not novel; others have also proposed that substrate control through changes in Phe pool size might be a major site of phenylpropanoid regulation (e.g. Da Cunha 1987, Margna 1977, Creasy and Zucker 1974). More recently, Jones and Hartley (1999) explain the often apparent trade-off between growth and defense using competition for phenylalanine precursors by PAL as a mechanism in their Protein Competition

24 Model (PCM). Although they discuss the potential for Phe to be a limiting factor (when protein and phenolic synthesis rates exceed rates of Phe synthesis), they do not explicitly consider Phe supply as a regulatory mechanism. They focus only on the predictive value of the mechanistic trade-off. The concept of control of phenolic production at the level of substrate supply to PAL has a new application within the context of maximizing PAL activity through the use of transgenic plants and in considering situations in which PAL activity may be maximized via the integration of multiple environmental stimuli. Improved tools in both molecular and biochemical techniques give new reasons to re-examine old pathways and basic regulatory mechanisms (sensu Browse and Coruzzi 2000).

We propose that substrate supply of Phe to PAL can control phenolic production and that enzymes upstream of PAL (constituting the shikimate pathway) play a prominent role in the control of phenolic synthesis in tobacco, through their supply of Phe. Even if Phe pools are static (for review see Creasy and Zucker 1974), the rate of input must equal or exceed the rate of use by PAL in order to support phenolic synthesis. In theory, points both before and after the Phe pool may be regulatory for phenolic production. We hypothesize that another enzyme, 3-deoxy-arabino-heptulosonate 7-phosphate (DAHP) synthase (DS), the first committed step in the shikimate pathway, may have either a stimulating or limiting effect on phenolic production in addition to the effects of PAL. DS uses erythrose 4-phosphate (E4P, from the pentose phosphate pool) and phosphoenol pyruvate (PEP, from glycolysis) as substrates (Figure 1.1).

Changes in DS gene expression and enzyme activity are elicited by many of the same environmental and developmental stimuli that have been shown to cause changes in PAL gene expression and enzyme activity. Increases in light stimulate DS gene expression (Henstrand et al. 1992) and enzyme activity (Mori et al. 2000). Expression of DS genes is developmentally regulated (Gorlach et al. 1994, and Tieman and Handa 1997), and is elicited by pathogens (McCue and Conn 1989, Keith et al. 1991, Henstrand et al. 1992, Gorlach et al. 1995,

25 Suzuki et al. 1995b) and mechanical wounding (Dyer et al. 1989, Keith et al. 1991, Muday and Herrmann 1992). In cell culture, hormones such as gibberellic acid and jasmonic acid also increase DS gene expression (Hara et al. 1994, and Suzuki et al. 1995a).

Metabolic regulation of DS occurs at the genetic level leading to an increase in the DS protein (Herrmann and Weaver 1999), although mechanisms for post-transcriptional regulation also exist (Entus et al. 2002). In many plants, DS activity is the result of two or more nucleus-encoded gene isoforms (Gorlach et al. 1993, 1995, Zhao and Herrmann 1992, Keith et al. 1991), and at least two forms of the enzyme (differentiated by their substrate use and metal requirements) exist in several species (Mori et al. 2000, Sharma et al. 1999, Gorlach et al. 1994, 1995, Henstrand et al. 1992, Keith et al. 1991, Morris et al. 1989). After protein assembly, the enzyme precursor is transported to the chloroplast where the transit sequence is removed (shown using a tomato cDNA, Zhao et al. 2002). Although the gene-to-protein relationships in DS are not fully defined (Schmid and Amrhein 1995), there is the potential for environmental stimuli to influence different gene-to-protein routes to contribute to total DS activity. As with PAL, this provides a mechanism for signal integration at the level of total protein activity.

Even though PAL has been a major focus of studies of flux and control of the phenylpropanoid pathway, other studies have implicated upstream enzymes and supply pathways (including DS and the shikimate pathway) as having roles in induced flavonoid and anthocyanin production (e.g. Logemann et al. 2000, Mori et al. 2000). These studies have demonstrated coordinate induction of DS with other enzymes leading to phenolic production (namely, PAL and chalcone synthase; CHS). This information has led to the assumption that DS is coordinately regulated with other enzymes, such as PAL. However, there are occasions where DS and other shikimate pathway enzymes are differentially expressed (e.g. Sharma et al. 1999).

26 In an effort to capture differential roles of DS and PAL, we conducted several experiments in tobacco (Nicotiana tabacum cv. Petit Havana) to examine the combined effects of stimuli that have already been shown to affect DS, PAL, and phenolic content individually. We examined the combined effects of light, the wounding signal jasmonic acid (JA), and development on phenolic production and the activities of DS and PAL. We also conducted an additional experiment using spider mites (Tetranychus urticae Koch) for comparison to results obtained with JA.

MATERIALS AND METHODS

Experimental design We carried out four experiments with tobacco in which we investigated the activity of the two targeted regulatory enzymes (DS and PAL) and related their activity to concurrent changes in phenolic production in both young and mature leaves. We also monitored other physiological parameters sometimes related to changes in phenolic synthesis (photosynthesis, growth, and protein content). Experiments I, II and III employed three light levels (low, moderate and high) to examine the effect of light on the responses of DS and PAL activity to wounding signals. We induced phenolic production in these three experiments by the application of the wounding signal, jasmonic acid (JA), which mimics the chemical signaling associated with insect herbivory (Creelman and Mullet 1997, Korth and Dixon 1997, Kessler and Baldwin 2002, Reymond et al. 2000). The responses of plants in experiment III to JA (at high light) were compared to plants in experiment IV which were challenged with spider mite herbivory (also at high light). Experiments I and II were conducted simultaneously in a growth chamber and experiments III and IV were conducted simultaneously in a greenhouse.

Study system (plant material & growth conditions) Full-sib seeds collected from one self-crossed Nicotiana tabacum cv petite Havana plant were germinated in Metro Mix 200 soilless media (Scotts-Sierra

27 Horticultural Products Co., Marysville, OH), grown for 10 d, and then transferred to 10 X 10 X 13 cm plastic pots (Geiger Co., Harleysville, PA) containing Metro Mix 200 supplemented with 120 g·m3 Osmocote 14:14:14 (Scotts-Sierra Horticultural Products Co., Marysville, OH). The same seed stock was used for all four experiments.

For each of experiments I and II, 36 plants were grown in a growth chamber in a controlled atmosphere building (24 °C day, 20 °C night; Pennsylvania State University, State College, PA ) under moderate light levels (MOD) of an average of 204 ± 24 SD µE (µmol photons·m-2·s-1) at plant height and a 16 h photoperiod. This light level was achieved by using one 1000W high- pressure sodium lamp (G3 series, Ruud Lighting, Racine, WI) for every 36 plants. At the time of the experiment, plants were either maintained in moderate light (Exp II – MOD) or transferred to low light (Exp I – LOW). Low light levels (26 ± 4 SD µE) were achieved by the addition of 80% shade cloth (PAK Unlimited, Inc., Cornelia, GA). Plants were 40 d old at the start of the experiments, were an average of 36.5 ± 4.4 SD cm tall and had an average of 15.9 ± 1.6 SD unrolled leaves.

Seventy-two plants for experiments III, and 63 plants for experiment IV, were grown and manipulated in a greenhouse under high (HIGH) intensity light. High light levels of approx. 300-600 µE were achieved by supplementing natural greenhouse light with one 400W high-pressure sodium lamp (G3 series, Ruud Lighting, Racine, WI) for every 15 plants. The experiment was conducted in early December, when plants received natural light for approximately 9 h/d. Supplemental lighting was provided for 16 h/d, with a window of 3 h of only artificial light at the beginning and 4 h at the end of each day. Total light-levels were not constant during the HIGH light experiments due to the variability of natural light received in the greenhouse. However, light levels during sampling intervals were an average of 453 ± 20 SD µE. Plants were 40 d old at the start of

28 the experiments, were an average of 26 ± 3.5 SD cm tall and had an average of 15.5 ± 1.8 SD unrolled leaves.

Plants grown for experiments I and II in the growth chamber were watered by intermittent drip irrigation every three days to maintain soil that was moist to the touch. Greenhouse grown plants for experiments III and IV were hand watered to achieve similar moisture levels. While in pots in the growth chamber or the greenhouse, all plant locations were randomized every 3-4 days up until the time of the experiments using a random number generator (Excel, Microsoft Corporation, Redmond, WA).

Growth chamber-grown plants were first randomly assigned to either experiment I or II, and then to spray treatment and harvest day. This resulted in 4 treatment groups for each experiment that were not significantly different from one another in plant size on day 0 (data not shown). However, despite frequent randomization of plant locations, greenhouse-grown plants were more variable in size than growth chamber-grown plants and repeated attempts at randomly allocating to treatment categories failed to achieve treatment groupings that were not different from one another (data not shown). Therefore, for experiments III and IV, plants were allocated to treatment groups based on their size one day before the beginning of treatments. The allocation procedure was similar to that used by Ohnmeiss and Baldwin (1994) and consisted of sorting all plants by height and leaf number and randomly assigning plants to treatment groups by consecutive random halvings. This procedure produced 15 treatment groups with plant sizes that were not significantly different on day 0 (data not shown).

To ensure that individually sampled leaves were of the same physiological age, at the start of each experiment leaves were numbered from the top down. The first fully unfurled leaf from the apical bud was designated as leaf index 1 (LI 1)(sensu Larsons and Isebrands 1971). Photosynthetic profiles revealed that the transition to a fully-photosynthetically competent leaf occurs between LI 6 and 7

29 (data not shown). LI 4, as the first leaf large enough for all the desired analyses but not yet fully-developed, was chosen to be sampled to represent a young leaf (Y) and LI 8 was chosen to represent a mature leaf (M). All physiological, chemical, and biochemical measurements follow the profile of an individual leaf that was originally LI4 or LI8; in later sampling dates, leaves had progressed onwards to different physiological states (e.g. LPI 4 to LPI 5 or 6). Investigations of phyllotaxy in similar Nicotiana species (Jones et al. 1959, Shiroya et al. 1961) indicate that these leaves are not directly connected to one another.

Treatments Spray treatments of either solvent (Solv), or jasmonic acid (JA) were administered to each plant on day 0 for experiments I, II, and III. All spray treatments were concluded within a 2 hr time-period. Solv sprays consisted of 3% ethanol (v/v) and JA sprays consisted of 5mM jasmonic acid dissolved in the solvent. The spray was applied to designated plants in a fine mist to the adaxial side of all the leaves of the plant until the leaves just began to drip. Plants for experiments I and II were removed from the growth chamber and sprayed in a separate, larger room of the same building to avoid cross-contamination between treatments. The leaves were allowed to dry (15-30 minutes) before being returned to the experiment room. Plants for each treatment in the greenhouse were removed from greenhouse benches and placed on the floor for spraying, and also allowed to dry (15 minutes) before being returned to the greenhouse benches.

Spider mite herbivory was begun on day 0 (Mites) for experiment IV and allowed to continue for 24 hrs. Controls (Ctrl) for experiment IV received the same physical handling as Mite plants but received no treatment. A two-spotted spider mite (Tetranychus urticae Koch) colony (obtained from John Sanderson at Cornell University, Ithaca, NY) was maintained on common bean plants in a separate self-contained growth chamber (Model 352632, Hotpack, Philadelphia, PA). At the time of the experiment, fully-colonized leaflets on expiring bean

30 plants were excised and transferred to tobacco plants in the greenhouse. Colonized leaflets, containing 50-100 spider mites, were placed at the base of each tobacco leaf to be sampled (LI4 and LI8 for each plant). Tobacco plants in experiment IV were kept from touching other tobacco plants, the edges of their pots were coated with petroleum jelly, and each pot was surrounded by a home- made water moat to isolate the spider mite herbivory to designated plants. Spider mites were allowed to remain on the tobacco plants for 24 hours and then they were removed with an aspirator. The number of spider mites that had moved from the colonizing leaflet to the tobacco leaf was recorded at removal. Spider mites located on the tobacco leaves suffered high mortality (>90%, data not shown), but provided a pulse of low-level herbivory to the epidermal layer of the chosen leaves.

Plant sampling We harvested leaves at 24 hr intervals from treatment times to account for diurnal fluctuations in activities of the enzymes to be measured (e.g. Thain et al. 2002, Peter et al. 1991). We randomly harvested designated leaves between 2 and 4pm each day, immediately after measurements of photosynthesis and growth. The treatment X day X age class size was 9 leaves for all four experiments. Designated young and mature leaves were harvested on each of days 1 and 3 for each treatment class (Solv & JA) in experiments I and II, for a total of 36 leaves in each age class per experiment. Leaves were harvested on each of days 1, 2, 3, and 4 for each treatment class in experiment III, for a total of 72 leaves in each age class. Experiment IV had two harvest schedules: Ctrl leaves were harvested on days 1, 2, 3, and 4, but due to logistical constraints Mite leaves were only harvested on days 2 and 4 (corresponding to 24 and 72 h after the end of mite treatment). Additionally, leaves from Ctrl plants were harvested on day 0 to serve as starting point controls for both experiments III and IV.

31 We removed leaves at the petiole and separated the halves of each leaf blade from the midrib using a razor blade on a cutting board. The tissue from the left half of the leaf was reserved for enzyme analysis (ENZ), and the tissue from the right half of the leaf was reserved for analysis of protein and phenolic content (CHEM). Although chemistry is known to vary within a leaf, lateral within- leaf variation is minimal (data not shown), and less than intra- and inter-plant variation (Orians et al. 2002, Winn 1996). The variability introduced by sampling based on a lateral leaf split (< 10%) was significantly less than that from different plants (e.g. up to 50-70% variation in phenolic content in controls from these experiments). ENZ and CHEM samples were placed in separate paper coin envelopes, flash frozen in liquid nitrogen, and temporarily stored on dry ice. Samples were not allowed to thaw once frozen. ENZ samples were stored at - 80 °C and CHEM samples were stored at -20 °C. At the end of each experiment, all CHEM samples were lyophilized and then stored again at -20°C.

Photosynthesis and growth measurements Photosynthesis and growth measurements were performed on all plants at the start of the experiments and on their harvest day for experiments I and II. Photosynthesis and growth measurements were taken daily for a subsample of plants designated to be harvested on day 4 (n=3 for each treatment) for experiment III. Ctrl and Mite plants from experiment IV were subsampled for growth measurements, but the Mite treatment precluded photosynthetic measurements.

Photosynthetic carbon assimilation (PCA) was measured individually for both sample leaves on each plant (Y & M). Measurements were made using a LICOR 6400 Portable Photosynthesis System (LICOR, Lincoln, Nebraska). We used a LICOR external LED light source to provide the appropriate light level in the leaf chamber for each experiment: low light (26 µE), moderate light (200 µE) or high light (400 µE). PCA was expressed as carbon dioxide uptake (µmol·m- 2·s-1).

32 Chemical analysis: spectrophotometry CHEM samples were individually ground to a fine powder using porcelain mortars and pestles (Coorstek., Golden, CO). Using subsamples from each vial of leaf powder, total phenolic and protein content were determined. Protein was extracted and analyzed following the methods of Jones et al. (1989). Leaf powder (3 mg) was extracted for 2 hrs in 1.5 mL of 0.1 N sodium hydroxide at 100 °C and allowed to cool for 20 minutes before assaying. Bovine serum albumin (BSA) was used as the protein standard and results were expressed as mg of protein per mg of dry weight (DW).

Total phenolics were extracted into 50% (v/v) methanol and assayed using the Folin-Denis method as described in Appel et al. (2001), amended to include lithium sulfate (8% w/v) in the Folin-reagent to prevent precipitate formation (Singleton and Rossi, 1965). Leaf powder (10 mg) was extracted four times into a total volume of 2 mL of 50% (v/v) methanol. Each extraction involved vortexing, a 10 min sonication, and centrifugation at 2,000 X g for 3 min. The extracts were then partitioned 1:1 against hexane and immediately assayed (adapted from recommendations in Waterman and Mole 1994). Chlorogenic acid (CGA), one of the most prevalent phenolic moieties found in tobacco (Snook et al. 1986), was used as the standard and results were expressed as mg of CGA- equivalents per mg DW.

Chemical analysis: HPLC Extraction of phenolics for HPLC and HPLC procedures were adapted from Keinänen et al (2001). Leaf powder (10 mg) was extracted twice into a total volume of 1 mL of either 40% or 80% (v/v) methanol containing 0.5% (v/v) acetic acid. Each extraction involved vortexing, a 10 min sonication, and centrifugation at 11,000 X g for 1 min. Extracts were filtered through 0.2 µm nylon membrane SpinX Tubes (11,000 g for 1 min; CoStar by Corning Inc., Corning, NY) and stored in brown glass HPLC vials at 4 °C until analysis. Following the procedures developed in Keinänen et al (2001), 40% methanol (v/v) extractions

33 were used for experiments I and II to capture the full-range of secondary metabolites produced in tobacco: alkaloids, phenolics, and diterpene glycosides (DTPGs). For experiment III, 80% methanol (v/v) extractions were used. Although 80% methanol also extracts alkaloids and DTPGs, it provides greater recovery for phenolic moieties (see results).

Our HPLC system (Waters, Bedford, MA) consisted of a WISP 710 autosampler, a photodiode array detector, a SIM control module, and Waters Millenium 3.2 software. Phenolics, alkaloids, and DTPGs were separated on an Inertsil ODS-3 RP column (3 µm, 150 X 4.6 mm ID) monitoring the eluent between 190 and 400 nm. The column was attached to a Supelco Discovery RP guard column (2 cm X 4 mm). Elution solvents were as in Keinanen et al. (2001). The eluent was monitored at 210, 254, 320, and 365 nm.

Nicotine, rutin, and caffeoylquinic acid (chlorogenic acid, cryptochlorogenic acid, and neo-chlorogenic acid) identities were confirmed by comparing UV-vis spectral scans and co-elution times with authentic standards, in addition to comparing our elution times with those in Keinänen et al (2001). Caffeoylputrescine was confirmed by LC/MS using a Quattro II mass spectrometer (Micromass, Beverly, MA). Analyses of this molecule was performed using atmospheric pressure chemical ionization (APCI) in positive ion + + mode. The following ions were observed: m/z 251 ([M+H ]), 234 ([M+H-NH3] ), and 163 (caffeoyl cation)(Anne Walton and Dan Jones, Penn State Mass Spectrometry Facility, University Park, PA). Nicotine and rutin were quantified at 254 and 365 nm, respectively. Chlorogenic acid isomers and caffeoylputrescine were quantified at 320 nm as mg CGA-equivalents per mg DW. DTPGs were expressed as peak areas at 210 nm/g of DW as in Keinanen et al. (2001).

Enzyme analysis: extraction All operations were carried out at 4 °C. ENZ samples were removed from storage at -80 °C, quickly weighed, and then ground under liquid nitrogen with

34 porcelain mortars and pestles (Coorstek, Golden, Colorado). While still frozen, PVPP was added 1:6 (w/w) and thoroughly mixed into the leaf tissue. The tissue was allowed to thaw in the mortar and at the first appearance of liquid, extraction buffer [50 mM EPPS, pH 8.6, 1 mM PEP, 1 mM MgCl2, 1 mM MnCl2, 0.1% β- mercaptoethanol] in a ratio of 2 ml per g plant tissue was added. The slurry was ground until no particulate matter was visible and then centrifuged at 40,000 g for 30 min.

The supernatants were filtered through PD-10 columns containing Sephadex G25 (Supelco Inc., Bellefonte, PA) equilibrated with extraction buffer to remove low molecular weight compounds (Suzuki et al. 1995b, Biagioni et al. 1997). The protein-containing fraction of the filtrate was used for both the DS and PAL assays, as well as for the determination of protein content. The extracts were immediately assayed for DS activity. An aliquot was flash frozen and stored at -20 °C for protein determination, and the remainder was flash frozen and stored at -80 °C for PAL assays. Protein in enzyme extractions was determined by the method of Bradford (1976), with bovine serum albumin as the standard.

Enzyme analysis: DS assay DS was assayed by measuring the absorbance at 549 nm of the periodate degradation product of DAHP complexed with thiobarbituate (Suzich et al. 1985). The reaction mixture for each assay consisted of 50 mM EPPS, pH 8.6, containing 5 mM PEP, 2 mM E4P, 13.3 mM MgCl2, and suitably diluted enzyme in a total volume of 0.15 mL. The reaction was initiated by adding 100 µL of a reaction cocktail to 50 uL of enzyme extract. Incubations were at 37 °C for 30 min. The reactions were stopped by the addition of 0.3 mL of 10% (w/v) trichloroacetic acid. All assays were carried out in duplicate, with one control for background absorbance from which the substrate E4P was omitted. Since the product of the reaction, DAHP, is not commercially available for use as a standard, the molar extinction coefficient of 4.5 X 104 at 549 nm was used to

35 calculate DAHP concentrations (Jensen and Nester 1966). DS activity was expressed as µmol DAHP produced per mg protein. Frozen aliquots of a single tobacco enzyme extraction, as well as varying concentrations of gallic acid, which interacts with the color-development portion of the assay, were used as batch controls and correction factors were determined as in Jensen and Nester (1966).

Enzyme analysis: PAL assay Using previously prepared extracts stored at -80 °C, samples were slowly thawed on ice and assayed for the conversion of L-[14C] phenylalanine to [14C]trans-cinnamic acid using a method similar to that of Howles et al. (1997), modified from Legrand et al. (1976). 50 µL of extract (in extraction buffer) were combined with 50 µL of 2 mM unlabeled phenylalanine in 100 mM borate buffer, pH 8.8 with 0.03 µCi [U-14C] L-phenylalanine (496 mCi/mmol; ICN-Biomedicals, Irvine, CA). Incubations were for 3 hrs at 37 °C and were stopped with the

addition of 10 µL 10N H2SO4. Labeled cinnamic acid was extracted into 750 µL of toluene. Samples were centrifuged briefly and 500 µL of the toluene layer was added to 10 mL Ecoscint O scintillation cocktail (National Diagnostics, Atlanta, GA). Scintillation counting was performed by a Beckman LS 3801 (Beckman Instruments, Fullerton, CA) using Beckman 14C standards for quenching and calibration. All assays were run in duplicate and background radioactivity was

determined using protein extracts that were inactivated with H2SO4 prior to incubation. For calculating total PAL activity, the conversion of L-[14C]- phenylalanine to [14C]-trans-cinnamic acid was considered proportional to the overall conversion of phenylalanine to trans-cinnamic acid. PAL activity was expressed as nmol cinnamic acid produced per mg protein. Frozen aliquots of a single tobacco enzyme extraction were used as batch controls. However, the primary source of batch variation came from different stock solutions of L-[14C]- phenylalanine, so aliquots of the reaction cocktail were assayed separately and a correction factor similar to that of the DS assay was used.

36 Statistical Methods Statistical analyses were performed separately for each leaf age class for each experiment. Phenolic content and measures of enzyme activity were log transformed before analysis to meet the underlying assumptions of parametric tests (Zar 1999). Only untransformed means are graphed and error bars represent the standard error of the mean for untransformed data. However, error bars from transformed data represent the interval indicated by the standard error of the transformed mean as converted back to original units for graphical illustration; in some cases this results in asymmetrical error bars about the untransformed mean.

Two-way ANOVAs with treatments and sampling day as main effects were performed for measures of protein, phenolic content (Folin-reactives), DS activity, and PAL activity for each experiment. This approach was also used to analyze photosynthesis and growth data for experiments I and II. Repeated-measures analysis was used to analyze photosynthesis and growth for experiment III. Contrasts (Tukey method) were performed on the two-way ANOVAs if the interaction terms were significant. Heteroscedastic t-tests were used to perform statistical comparisons between treatments for individual phenolic compounds detected by HPLC and for comparison of relative growth rates on each sampling day. Z-tests were used to compare means of phenolic content detected with the Folin-Denis assay with phenolic content detected by HPLC, as well as for comparisons of other means between experiments (e.g. comparing between light levels).

We analyzed relationships among enzyme activity, photosynthesis, growth and phenolic content using first simple correlations and then multiple regression to determine which of these physiological factors best predict phenolic content.

Optimal regression models were chosen objectively using the Cp statistic for choosing from among the best 1-variable, 2-variable, etc. models as formed by stepwise multiple regression (Freund & Wilson 1998, Mallows 1973). The Cp

37 statistic is a measure of total squared error for each model, and Mallows’ method selects an optimum model based on number of variables, significance, overall error, and R-square values. We also explored simple correlations to compare correlations that differed in significance between experiments. All statistical analyses were performed with the SAS statistical package (Version 8.2, SAS Institute, Inc., Cary, NC).

Chemicals Unless otherwise mentioned, all chemicals used for the chemical analysis, HPLC, and enzyme assays were of the highest quality commercially available and were obtained from Sigma-Aldrich Co., St. Louis, MO.

RESULTS

Light and age effects on phenolic and biochemical measures. Both phenolic concentrations from treated and control plants at the beginning and end of each experiment were proportional to experiment light levels (Fig 2.1). Plants at higher light levels had more phenolics at the start of the experiments (Z-tests, young leaves all Ps < 0.03, mature leaves all Ps < 0.07) and higher induced phenolic content at the end of the experiments (Z-tests, young leaves all Ps < 0.001, mature leaves all Ps < 0.02) than did plants at lower light levels. Young and mature leaves were capable of reaching similar levels of phenolic content by day three when treated (all Ps > 0.30), although initial rates of accumulation differed in young and mature leaves.

We elicited increased production of phenolics with the application of JA at all three light levels tested, in both young and mature tobacco leaves. We detected significant changes in phenolic content due to treatment with JA by one day after treatment in young leaves and by three days after treatment in mature leaves at all three light levels (Fig 2.1; Tables 2.1 and 2.2).

38 DS activity from control plants was light-independent. DS activity in control (Solv) plants did not differ statistically with light level or age class one day after treatment (Fig 2.2, all P’s > 0.30). DS activity increased in response to JA treatment at all three light levels (Fig 2.2, Tables 2.1 and 2.2), concurrent with increases in phenolic content (Fig 2.1). However, there were slight differences in the timing and intensity of these changes between age classes at different light levels.

PAL activity was light-dependent, with the lowest PAL activity occurring in low light and the highest in high light (comparing controls). Control (Solv) PAL activity did not change during the course of the experiment in low light, increased slightly from day 1 to day 3 in moderate light, and fluctuated from day to day in high light. These patterns did not differ between young and mature leaves within each light environment (Fig 2.3; Tables 2.1 and 2.3). The response of PAL activity to JA treatment was also light-dependent. PAL activity was elevated by JA treatment only at low light levels (Fig 2.3A and B) and we measured either no changes or decreases in PAL activity in moderate and high light (Fig 2.3 C-F).

Experiment I: Effects of jasmonic acid treatments at low light. Both young and mature leaves increased rates of photosynthesis (as measured by carbon dioxide assimilation) in response to JA treatment in low light. There were 35% and 30% increases in photosynthesis above control levels by day three in young and mature leaves, respectively (Fig 2.4A and B; Table 2.3A). There were no changes in protein content in control plants during the course of the experiment. However, protein content decreased 25% in response to JA treatment in mature leaves (Fig 2.5B; Table 2.2). Plant growth, as measured by whole-plant relative growth over the course of the experiment, was not affected by JA treatment (Fig 2.6; Table 2.4). We elicited increased production of phenolics with the application of JA in both young and mature leaves (Tables 2.1 and 2.3). We detected a significant 35 % increase by one day

39 after treatment in young leaves (Fig 2.1A) and a significant 25% increase by three days after treatment in mature leaves (Fig 2.1B).

DS activity had increased in response to JA treatment by one day after treatment for both leaf age classes (Tables 2.1 and 2.2). DS activity had increased by 110 % in young leaves by one day after treatment and 130 % by three days after treatment (Fig 2.2A). Only 65-70 % elevation was present in mature leaves, detectable on both sampling dates (Fig 2.2B). PAL activity was also elevated by JA treatment at low light levels (Tables 2.1 and 2.2). However, the young leaf increase at low light was slight, only detectable at a P-level of 0.099, and was due largely to the response one day after treatment (70% increase over controls on day one, versus 10% on day three)(Fig 2.3A). The mature leaf increase of PAL activity due to JA treatment at low light was 45- 120% above controls and statistically significant on both sampling dates (Fig 2.3B).

We investigated the relationships among physiological, biochemical, and phenolic measurements to determine which of these metabolic parameters were most associated with phenolic production. We explored simple correlations and optimized regression models between physiological and biochemical measures, and phenolic production within a single leaf (young leaves, YLEAF; mature leaves, MLEAF). We also examined relationships between mature leaf physiological measures and young leaf phenolic production within the same plant (PLANT), because mature leaves can act as sources and provide carbon resources for young sink leaves.

Photosynthesis was significantly positively correlated with phenolic production within YLEAF, MLEAF, and PLANT at low light (Table 2.5). However, two additional parameters, RG and PAL activity, do explain some of the variation in phenolic production when used in regression models with photosynthesis (Table 2.6). The relationships between RG and mature leaf phenolics, and

40 young leaf PAL activity and young leaf phenolics were both positive, but not statistically significant when considered alone (Table 2.5). Although DS and PAL activity were significantly positively correlated within YLEAF, and MLEAF there were no significant correlations between the activity of either enzyme and phenolic production at low light (Table 2.5).

Experiment II: Effects of jasmonic acid treatments at moderate light. Photosynthesis did not change due to treatment in moderate light for either leaf age class (Fig 2.4C and D). Young leaves showed no changes in protein content in control plants during the course of the experiment (Fig 2.5C, Table 2.1). However, mature leaves exhibited a general decrease in protein content of about 10-20% over time that was not related to JA treatment (Fig 2.5D; Table 2.2). Plants in moderate light (this experiment) had similar growth rates to plants in low light (experiment I), but RG was reduced by about 30% by the end of the experiment by JA treatment (Fig 2.6B; Table 2.4).

The age-dependent induction of phenolic production by JA treatment that was observed in low light was also observed for plants grown at moderate light (Table 2.1 and 2.2). However, the relative increases in phenolic production were larger in moderate light. We detected a significant 45 % increase in phenolic content by one day after treatment in young leaves, and 150% by three days after treatment (Fig 2.1C). We detected a significant 45% increase three days after treatment in mature leaves (Fig 2.1D).

DS activity had increased in response to JA treatment by one day after treatment for young leaves, just as in low light (Fig 2.2C; Table 2.1). However, we did not detect a change in mature leaves until three days after treatment (Fig 2.2D; Table 2.2). DS activity increased by 130% over controls in young leaves by one day after treatment and 225% by three days after treatment (Fig 2.2C). A 110% elevation over controls was present three days after treatment in mature leaves (Fig 2.2D). We discovered either no change or decreases in PAL activity

41 due to JA treatment in moderate light (Tables 2.1 and 2.2). Young leaves in moderate light had significantly lower PAL activity than controls three days after treatment (Fig 2.3C), but there was no JA-related change for mature leaves in moderate light (Fig 2.3D).

DS activity was significantly positively correlated with phenolic production within YLEAF, MLEAF, and PLANT at moderate light (Table 2.5). There was also a significant negative correlation between protein and phenolic production within MLEAF and PLANT. However, optimized regression models indicate that DS activity was the primary predictor of phenolic production within YLEAF, MLEAF, and PLANT (Table 2.6). DS and PAL activity were significantly positively correlated within YLEAF and MLEAF, but the correlations were not as strong or as significant as in low light and only DS activity was associated with phenolic production (Table 2.5).

Experiment III: Effects of jasmonic acid treatments at high light. Photosynthesis decreased in both young and mature leaves by 20-35% in response to JA treatment in high light (Fig 2.4E and F). Although we controlled the light levels under which photosynthesis was measured, plants in the greenhouse (this experiment) also showed more day to day variability that was unrelated to treatment than did growth-chamber grown plants (experiments I and II). This variability was reflective of daily fluctuations in incident light received in the greenhouse (data not shown). Both young and mature leaves also exhibited a general decrease in protein content over time, but these decreases were not related to JA treatment (Fig 2.4 E and F; Table 2.3B). Greenhouse grown plants (exp III & IV) exhibited RG that was two to three times that of the growth chamber grown plants (exp I & II). However, JA reduced RG by 25% one day after treatment (Fig 2.6C; Table 2.4). The overall reduction in RG by the end of the experiment was also greater at high light than at moderate light (40 % reduction compared to 30% at moderate light).

42 We again observed age-dependent induction of phenolic production by JA treatment, as in low and moderate light (Tables 2.1 and 2.2). Induced phenolic production in high light reached absolute concentrations dramatically higher than either low or moderate light. We detected a significant 60 % increase in phenolic content above controls by one day after treatment in young leaves, and 100 % by three days after treatment (Fig 2.1E). We detected a significant 35-40 % increase by three days after treatment in mature leaves (Fig 2.1F).

DS activity had increased 150% in response to JA treatment by two days after treatment in young leaves and by 75% one day after treatment in mature leaves (Tables 2.1 and 2.2). DS activity reached 325% increase over controls by three days after treatment in young leaves, before dropping back to 160% over controls at the end of the experiment (Fig 2.2E). The 75 % elevation over controls one day after treatment in mature leaves was maintained through the end of the experiment (Fig 2.2F).

PAL activity fluctuated from day to day in high light (for all plants, regardless of treatment) (Fig 2.3E and F). These day to day changes were related to changes in ambient light levels in the greenhouse, and to changes in photosynthetic activity (cf. Fig 2.4E and F). However, even in the context of this fluctuating high light environment, PAL activity was again either unchanged or decreased in response to JA treatment (Tables 2.1 and 2.2), as in moderate light (exp II). PAL activity in young leaves was unchanged by JA until four days after treatment when a 60 % decrease compared to controls was seen (Fig 2.3E). Decreases in PAL activity of 65% compared to controls were measured by three days after treatment in mature leaves (Fig 2.3F).

DS activity was significantly positively correlated with phenolic production within YLEAF, MLEAF, and PLANT at high light (Table 2.5). There were significant negative correlations between RG and phenolic production within YLEAF and between protein and phenolic production within MLEAF. However,

43 optimized regression models indicate that DS activity was again the primary predictor of phenolic production within YLEAF, MLEAF, and PLANT (Table 2.6), as in moderate light. Contrary to results in low and moderate light, DS and PAL activity were not correlated within either YLEAF or MLEAF (Table 2.5).

Experiment IV: Effects of mite herbivory at high light. Observations revealed heavy mite damage on young leaves and mild to moderate damage on mature leaves. Young leaves that experienced mite herbivory had significant increases in protein content over controls, despite a general decrease in protein content over the course of the experiment that was not related to treatment (data not shown, cf. Fig 2.5 E and F; Table 2.1). Mature leaves were not analyzed for protein content due to difficulty in removing mites from the leaf tissue, and photosynthesis was not measured in this experiment to avoid transporting mites to other greenhouse experiments. Mite herbivory did not affect RG (data not shown), unlike treatment with JA.

Young leaves did not increase production of phenolics in response to mite herbivory; there was actually a decrease in phenolic content in response to treatment (Fig 2.8E, Table 2.1). Mature leaf phenolics were not analyzed (for the same reasons given above). Increases in DS activity were significant at P = 0.0657 for young leaves and P = 0.0900 for mature leaves (Fig 2.8A and B). PAL activity exhibited significant decreases due to mite herbivory in both young and mature leaves (Tables 2.1 and 2.2)(Fig 2.8C and D). The changes to both DS and PAL activity due to mite herbivory were in the same directions as the changes due to JA treatment (increases and decreases, respectively), but the absolute and relative magnitude of the mite changes was much smaller (cf. Fig 2.3 and 2.4 with Fig 2.8). An exploration of correlations between physiological measurements for experiment IV did not reveal any significant relationships (data not shown).

44 Comparison of detection methods for phenolics. HPLC analysis of total phenolics confirmed the patterns detected with the Folin-Denis assay for all three light levels (phenolic results discussed above) and provided qualitative information regarding overall changes in phenolic content (see below). HPLC measurements of total phenolics (sums of significant peaks detectable at 320 nm) were nearly identical to results obtained by the Folin-Denis assay (using chlorogenic acid as a standard) when 80% methanol was used for HPLC extractions (z-tests of means, Ps all > 0.30). A single extraction with 80% methanol resulted in concentrations detectable by HPLC that agreed with concentrations found with the Folin-Denis assay for four sequential extractions of plant material with 50% methanol (e.g. Fig 2.7A and B; Table 2.8). Extractions using 40% methanol for HPLC analysis revealed similar patterns of phenolic content to those seen with the Folin-Denis assay, but tended to underestimate total concentrations. We found that 80% methanol extractions also significantly increased recovery of all analyzed categories of secondary metabolites, not just those containing phenolic structures (all Ps <0.05). For example, extraction with 80% methanol instead of 40% methanol increased the recovery of caffeoylquinic acid, caffeoyl putrescine, and nicotine by 110%, 39, and 9% respectively in extractions of young tissue.

Qualitative changes in plant chemistry. In addition to confirming the relative differences in phenolic content due to treatments, HPLC analysis revealed qualitative changes underlying total phenolic concentrations. The pattern for caffeoyl putrescine content was most striking; it was found almost exclusively in plants that had been treated with JA. Although it was detected in both young and mature JA-treated leaves at all three light levels, concentrations always reached much higher levels in young leaves (e.g. at high light Fig 2.7A and B).

At high light, young leaves dramatically increased production of caffeoyl putrescine when treated with JA (Fig 2.7A, Table 2.7). This molecule was

45 primarily responsible for the overall increase in phenolic content for young leaves at high light. Mature leaves also increased production of caffeoyl putrescine in response to JA, but overall increases in phenolic content in mature leaves were largely explained by increases in chlorogenic acid isomers (Fig 2.7B, Table 2.7). We observed similar patterns for caffeoylputrescine at low and moderate light (data not shown).

JA treatment also caused significant increases in caffeoylquinic acids in both young and mature leaves, at both low and moderate light levels (data not shown, all Ps <0.05). However, increases in caffeoylquinic acids in high light were only statistically significant in mature leaves. Rutin, a phenolic glycoside, was detectable at low concentrations, but did not change in response to treatment (Fig 2.7 A and B, Table 2.7). We were not able to detect scopolin or scopoletin, two other phenolics commonly found in tobacco, on any of our HPLC chromatograms although generation of standard curves demonstrated that our detection methods were capable of quantifying these molecules (data not shown).

JA treatment also caused tobacco plants to respond with changes in non- phenolic secondary plant chemistry. We detected changes in nicotine and diterpene glycosides (DTPGs) with the same HPLC analyses used to quantify phenolic changes. Nicotine production was elevated by JA treatment in both young and mature leaves by day 3, at both low and moderate light levels (all Ps <0.05, data not shown). However, at high light only mature leaves showed higher concentrations of nicotine in response to JA treatment (Fig 2.7B; Table 2.7). HPLC peaks in the DTPG range could not be resolved for plants from low and moderate light experiments, but at high light both young and mature leaves increased production of DTPGs when treated with JA. JA-treated young leaves exhibited a four-fold increase, while mature leaves showed a two-fold increase in DTPG content (Fig 2.7B; Table 2.7).

46 DISCUSSION

The interaction of light and wounding signals on the regulation of phenolic production. Enzyme activity (DS or PAL) did not regulate phenolic production at low light, although both DS and PAL activity increased significantly in response to treatment with JA. DS and PAL activity were also strongly correlated at low light. The supply of substrates to the shikimate pathway, as indicated by photosynthetic activity both within leaf and within plant, appeared to be the main factor determining phenolic production at low light levels in tobacco. Phe may have been limiting at low light, causing phenolic production to compete with protein synthesis for the limited resource; protein content was reduced when phenolic production was elevated. Thus, the proposed mechanism of the PCM model, PAL competing for Phe, is supported by our findings at low light. However, Jones and Hartley (1999) stated that when Phe was limiting, protein synthesis would take priority and this prediction is not supported.

DS activity was the main predictor of phenolic production at moderate light, and only DS activity (not PAL) increased significantly in response to treatment with JA. DS and PAL activity were still somewhat correlated at moderate light, but the association was less than at low light. Photosynthetic activity was not a predictive variable for phenolic production at moderate light, indicating that the supply of substrates (E4P and PEP) to the shikimate pathway was not driving phenolic production. Also, when phenolic production increased, there was no concurrent reduction in protein content. Thus, DS regulated phenolic production and the supply of Phe to PAL was non-limiting in moderate light. This finding could expand the scope of the PCM model beyond Phe-limiting conditions, i.e. no tradeoffs occur between protein synthesis and phenolic production when Phe is not limiting. However, tradeoffs may have occurred at the leaf-level because growth was reduced when phenolic production was

47 stimulated with JA; DS activity could have been diverting substrate supplies to the shikimate pathway at the expense of other biosynthetic pathways.

DS activity was also the main predictor of phenolic production at high light, and only DS activity (not PAL) increased significantly in response to treatment with JA. There was no correlation between DS and PAL activity at high light, demonstrating that the two enzymes are independently regulated. Photosynthesis had no predictive role for phenolic production at high light. There was no reduction of protein in response to treatment with JA, but protein did enter regression models as a significant negative variable; DS predicted phenolic production, but in plants with higher protein content, increased phenolic production was constrained. The supply of Phe to PAL appears to be non- limiting at high light, but just as at moderate light, reductions in whole plant growth occurred with increases in phenolic production indicating larger scale metabolic trade-offs.

We have established that PAL activity is not always the rate-determining step in phenolic production. Furthermore, evidence from our experiments indicated that when PAL activity was unresponsive to stimuli that elicit increases in phenolic production, changes in phenolic production were associated with changes in DS activity. Although these two enzymes respond to similar stimuli, they are clearly independently regulated.

The Role of PAL vs. DS PAL has been considered to be the rate-limiting or rate-determining enzyme in general phenylpropanoid metabolism, leading to the production of precursors for many classes of secondary metabolites (Bate et al. 1994, Howles et al. 1996). Our findings in tobacco at first seem to contradict this general understanding of PAL regulation. However, an often overlooked component of our knowledge is that we have yet to establish the interval (for each stimuli) over which PAL is the key regulatory mechanism for downstream metabolites (Bate et

48 al. 1994, Biagioni et al. 1997). We know that the regulation of metabolism can be plastic (e.g. glycolysis, Plaxton 1996; lignin biosynthesis, Sewalt et al. 1997); different regulatory mechanisms (or even different biosynthetic pathways) can be important in different environments, in different species, and even in different tissue types.

There are several situations in which dynamic regulatory mechanisms become important when examining the effects of multiple stimuli. First, we know that light can elevate PAL activity, but there are few reports studying the effects on PAL of any stimuli at light levels higher than 200 µE. Most studies are done in growth chambers, where light levels at best are similar to our “moderate” light conditions. The few studies found that were conducted in greenhouse conditions do not report light levels, or even time-of-year of experiments (in our greenhouses, we routinely experience winter light levels as low as 3-400 µE compared to summer levels near 1600 µE). Second, most PAL-induction studies in tobacco study the effects of elicitors or viral infection. When wounding has been studied, it has been noted that increases in PAL mRNA are very moderate and transient, with changes almost disappearing after 48 h (Pellegrini et al. 1994). Third, studies dealing with the effects of light on PAL activity usually focus on the effects of presence/absence or changes in light. Enzyme activity is rarely studied in the context of light environments that could affect constitutive expression. Fourth, a major problem in comparing across studies is variation in tissue type – many experimenters use extremely young tissue, such as cotyledons, in studies of enzyme activity, or cell cultures which may not reflect regulation in mature plants.

The strongest evidence for a direct relationship between PAL activity and phenolic accumulation in tobacco comes from several excellent studies of transgenic plants under or over-expressing PAL genes. Many correlations between biosynthetic enzyme activity and phenylpropanoid product accumulation have been reported (Dixon and Paiva 1995, Hahlbrock and Scheel 1989), but

49 until Bate et al. (1994) and Howles et al. (1996) there was little direct evidence that changes in phenylpropanoid production could be accounted for by changes in biosynthetic enzymes. These studies focused on transgenic suppression (Bate el al. 1994) and overexpression (Howles et al. 1996) of PAL genes in tobacco plants (Nicotiana tabacum cv. Xanthi). In the suppression study, PAL activity was highly correlated with CGA and rutin content. A direct relationship between PAL activity and CGA content was also found in the overexpression study. However, the transgenic plants used in these studies were designed for constitutive heterologous expression; PAL genes from Phaseolis vulgaris were expressed under constitutive promoters in tobacco. Even when just gene promoter expression is studied in heterologous systems, there are important species-specific differences (Gray-Mitsumune et al. 1999). Constitutive overexpression of enzymes can reveal potential points of regulation within a pathway, but does not necessarily elucidate rate-limiting steps (Fell 1998). In the suppression studies, PAL activity was probably limiting, leading to a high correlation with product accumulation. However, in the overexpression study PAL was also correlated with product accumulation, indicating that it was driving product formation. Thus, even in these genetically controlled situations, the role for PAL in phenolic production was metabolically plastic.

Our experiments differ significantly in several ways from previous approaches to studying the role of PAL in the regulation of phenolic production. Our goal was to study the combined effects of light, development, and herbivory- related signals on the regulatory mechanisms affecting increases in phenolic production. These factors had each been studied previously from the point of view of how each individually induces changes in phenolics, enzyme activity, or gene expression. Our interest was in whether inducible phenolic production (by herbivory-related signals) could be mediated by constitutive metabolic activity (determined by both light and development, in this case).

50 Thus, our work complements current understanding of PAL regulation in two major ways. First, we have demonstrated that PAL and DS activity are not coordinately regulated, as previously thought (at least in tobacco). Second, we have shown that in planta biotic stimuli may not be able to modulate total phenylpropanoid production by affecting PAL activity. It is likely that physiological controls exist that restrict maximal PAL activity (such as developmental status, or stimulation with other biotic and abiotic signals). In these cases, the rate-determining steps in phenolic synthesis can occur before or after PAL. There appears to be a hierarchy of responses to stimuli. The abiotic environment (as exemplified by the effects of light in our experiments) and the developmental status of the plant tissue mediate constitutive metabolic activity and biotic stimuli effect changes as constrained by these pre-existing metabolic conditions.

Biotic signals may affect quantitative and qualitative phenolic production by altering pre-PAL as well as post-PAL control points, through increases in enzyme activity or shifts in isozyme ratios. In our experiments, we made observations consistent with metabolic control occurring both pre-PAL and post- PAL. We have demonstrated a role for substrate supply (via photosynthesis) as well as DS activity, but JA treatment also led to a qualitative shift in phenolic production – CGA content was reduced compared to controls and de novo production of caffeoyl putrescine was observed. This regulatory change could have occurred through differential control of PAL isozymes or via changes in other post-PAL enzymes.

Adaptive Role of Specific Metabolites A detailed understanding of the isoform-specific metabolic pathways leading to individual metabolites would provide a better framework for testing theories regarding the adaptive value of phenolic production. The answer to whether or not “phenolics” have evolved as defenses against enemies (e.g. herbivory, pathogens) or as protection against photodamage is dependent upon

51 the individual metabolites in question, not the entire class of phenolics. However, even this approach can be problematic. Eichenseer et al (1998) and Bi et al. (1997) have demonstrated that in tobacco, increased concentrations of CGA do not provide for increased resistance from either a generalist herbivore (Heliothis virescens) or a specialist herbivore (Manduca sexta). In contrast, treatment of tobacco with methyl jasmonate has led to inhibition of feeding by cabbage looper (Trichoplusia ni) (Avdiushko et al. 1997) and insect grazing is capable of inducing increased systemic resistance in tobacco (in the same plants where elevated basal levels of CGA have no effect; Felton et al. 1999). However, Felton et al. (1999) did demonstrate that CGA played a rule in pathogen-related systemic acquired resistance, a suite of responses mediated by salicylic acid (SA) signaling. Thus, it is crucial to understand which subsets of a plant’s metabolome confer specific functional responses (i.e. cf. functional metabolomics to functional genomics). Just as studies of the responses of individual metabolites can give us clues to pathway regulation, pathway regulation and metabolic response to environmental signals can inform our understanding of metabolite function and generate new hypotheses and predictive theories that are directly testable.

A New Role for DS as a Control Point for Phenolic Synthesis Our results reveal a new role for DS as a rate-determining component in phenolic production, not just for the production of the aromatic amino acids (phenylalanine, tyrosine, and tryptophan). Henstrand et al. (1992) were the first to indicate that a supply pathway might be coordinately regulated with phenolic production by observing that DS mRNA accumulated in response to light in cultured parsley cells. Logemann et al. (2000) then demonstrated that mRNAs for glucose 6-phosphate dehydrogenase (from carbohydrate metabolism, supplying substrates to DS) and for DS were coinduced by UV light with PAL and chalcone synthase (an enzyme involved in post-PAL flavonoid synthesis). More recently, Entus et al. (2002) have shown that modification of DS proteins by the ferredoxin/thioredoxin system in chloroplasts may play a role in light-mediated

52 post-translational regulation of DS in Arabidopsis. This type of post-translational regulation was demonstrated for PAL over two decades ago (Creasy and Zucker 1974, Nishizawa et al. 1979). Investigators who have noted the elevation of DS activity and the accumulation of DS mRNA in response to mechanical wounding (Dyer et al. 1989, Keith et al. 1991, Muday and Herrmann 1992) and in response to jasmonic acid treatment (Suzuki et al. 1995a) have presumed that DS was coordinately regulated with PAL in these instances as well. In fact, Suzuki et al. (1995a) noted that phenolic production increased along with DS activity, but PAL activity was not measured. Thus, our study is the first to our knowledge to demonstrate the induction of DS activity in conjunction with the absence of induction of PAL activity, revealing that the two enzymes are not always coordinately regulated. Our study is also the first to directly implicate in planta DS activity as having a rate-determining role in phenolic production in response to herbivory-related signals.

53 Table 2.1. Model statistics from two-way ANOVAs for chemical and biochemical measurements of young leaves: protein content, phenolic content (Folin-reactives), DAHP synthase activity, and PAL activity. All analyses, except protein content, were performed on log transformed data.

Protein Content Phenolic Content DS Activity PAL Activity df F P df F P df F P df F P LOW LIGHT (Experiment I)

Treatment (T) 1 1.29 0.2654 1 9.59 0.0043 1 26.46 <0.0001 1 2.84 0.0992 Day (D) 1 0.64 0.4293 1 9.43 0.0046 1 5.10 0.0314 1 0.00 0.9737 T*D 1 0.05 0.8241 1 0.13 0.7207 1 0.09 0.7709 1 1.59 0.2173 Error 31 29 30 31

MODERATE LIGHT (Experiment II)

Treatment (T) 1 1.68 0.2042 1 53.22 <0.0001 1 25.55 <0.0001 1 2.66 0.1130 54 Day (D) 1 0.09 0.7720 1 20.86 <0.0001 1 1.98 0.1696 1 4.31 0.0459 T*D 1 0.04 0.8488 1 10.53 0.0028 1 0.81 0.3749 1 4.91 0.0339 Error 30 31 31 32

HIGH LIGHT (Experiment III)

Treatment (T) 1 0.04 0.8334 1 318.96 <0.0001 1 114.61 <0.0001 1 7.74 0.0071 Day (D) 3 36.44 <0.0001 3 6.15 0.0010 3 9.22 <0.0001 3 21.37 <0.0001 T*D 3 1.61 0.1961 3 3.27 0.0271 3 4.78 0.0046 3 4.12 0.0099 Error 61 61 63 63

MITES at HIGH LIGHT (Experiment IV)

Treatment (T) 1 13.75 0.0008 1 1.24 0.2733 1 3.63 0.0657 1 53.16 <0.0001 Day (D) 1 58.94 <0.0001 1 7,72 0.0091 1 4.56 0.0405 1 217.68 <0.0001 T*D 1 0.22 0.6449 1 0.00 0.9891 1 0.23 0.6357 1 9.85 0.0036 Error 32 32 32 32

Table 2.2. Model statistics from two-way ANOVAs for chemical and biochemical measurements of mature leaves: protein content, phenolic content (Folin-reactives), DAHP synthase activity, and PAL activity. All analyses, except protein content, were performed on log transformed data.

Protein Content Phenolic Content DS Activity PAL Activity df F P df F P df F P df F P LOW LIGHT (Experiment I)

Treatment (T) 1 0.10 0.7500 1 0.77 0.3878 1 6.55 0.0162 1 7.59 0.0102 Day (D) 1 4.53 0.0414 1 9.91 0.0036 1 9.89 0.0039 1 4.73 0.0382 T*D 1 6.27 0.0177 1 5.12 0.0308 1 0.00 0.9661 1 0.85 0.3635 Error 31 31 28 28

MODERATE LIGHT (Experiment II)

Treatment (T) 1 0.45 0.5078 1 3.01 0.0930 1 8.09 0.0080 1 0.68 0.4167 Day (D) 1 13.79 0.0008 1 8.13 0.0078 1 44.77 <0.0001 1 22.79 <0.0001 55 T*D 1 0.64 0.4295 1 11.38 0.0021 1 3.00 0.0935 1 0.05 0.8302 Error 32 30 30 29

HIGH LIGHT (Experiment III)

Treatment (T) 1 0.47 0.4953 1 37.86 <0.0001 1 80.81 <0.0001 1 6.83 0.0112 Day (D) 3 4.47 0.0066 3 17.23 <0.0001 3 10.26 <0.0001 3 26.63 <0.0001 T*D 3 0.73 0.5351 3 3.48 0.0210 3 0.15 0.9291 3 8.25 0.0001 Error 63 63 63 72

MITES at HIGH LIGHT (Experiment IV)

Treatment (T) Results not available Results not available 1 3.06 0.0900 1 5.45 0.0260 Day (D) 1 14.81 0.0005 1 40.33 <0.0001 T*D 1 0.02 0.8936 1 0.53 0.4734 Error 32 32

Table 2.3A. Model statistics from two-Way ANOVAs for photosynthesis measurements during experiments I and II. Measurements taken on each plant (n=36 for each experiment) were grouped by experiment and leaf age class. Young and mature leaf measurements were taken rom the same plants for all three experiments, and are not independent – therefore, each age class was analyzed separately.

YOUNG LEAVES MATURE LEAVES df F P F P LOW LIGHT (Experiment I)

Treatment (T) 1 2.09 0.1587 1.92 0.1761 Day (D) 1 38.24 <0.0001 12.63 0.0013 T * D 1 3.45 0.0729 4.85 0.0354 Error

MODERATE LIGHT (Experiment II)

Treatment (T) 1 0.77 0.3869 0.28 0.6023 Day (D) 1 0.04 0.8461 5.5 0.0256 T * D 1 0.75 0.3921 2.18 0.1502 Error

Table 2.3B. Model statistics from repeated measures ANOVA for photosynthesis measurements during Experiment III. Measurements were repeated on leaves on a subset of plants throughout the experiment (n=6). Day and Day * Treatment effects indicate Wilk’s Lambda values and numerator/denominator degrees of freedom.

YOUNG LEAVES MATURE LEAVES df F P F P HIGH LIGHT (Experiment III)

Treatment 1 21.86 0.0095 32.34 0.0047 Overall Error 4 Day 0 1 10.71 0.0307 0.40 0.5624 Day 1 1 54.41 0.0018 168.55 0.0002 Day 2 1 6.74 0.0603 9.31 0.0380 Day 3 1 2.33 0.2018 3.37 0.1403 Day 4 1 13.66 0.0209 7.76 0.0495 Error 5 Day Effect 4/1 14.05 0.1972 196.72 0.0534 D * T Effect 4/1 139.26 0.0635 6.27 0.2900

56 Table 2.4. Heteroscedastic t-tests (one-tailed) comparing relative growth rates at each sampling date for experiments I, II, III, and IV. Relative growth rate was calculated as cm of growth/cm original height at time of treatment designation. In the case of experiments I and II, original height was at day 0. For experiments III and IV, original height was measured on day -1. a

df t P LOW LIGHT (Experiment I) Day 1 16 1.5886 0.0658 Day 3 14 0.9083 0.1895

MODERATE LIGHT (Experiment II) Day 1 16 1.3151 0.1035 Day 3 14 3.2074 0.0032

HIGH LIGHT (Experiment III) Day 1 16 3.4781 0.0016 Day 2 12 3.1517 0.0042 Day 3 13 2.3155 0.0188 Day 4 15 6.1842 <0.0001

MITES at HIGH LIGHT (Experiment IV) Day 2 11 - 0.1644 1.0217 Day 4 13 - 0.2965 0.5480 a n=9 for each treatment mean, so uncorrected t-statistics would have had df = 16.

57 Table 2.5. Statistics from significant regressions on phenolic content (Folin-reactives, FD) for experiments I-III, testing the following as predictors: photosynthesis (PS), protein (PN), whole plant relative growth (RG), DAHP synthase activity (DS), and phenylalanine ammonia-lyase activity (PAL). Within leaf correlations refer to measurements taken from the same leaf – but not necessarily from the same sample tissue. Within plant correlations refer to relationships between measurements of phenolics in young leaves with the predictors from mature leaves sampled from the same plant. The relationships between DS and PAL activity are also indicated. Parentheses indicate negative relationships with phenolic content. R2 <0.05 or p-values > 0.15 are not shown. Additional abbreviations are for young leaves (Y), mature leaves (M).

Within-Leaf Correlations for Within-Leaf Correlations for Young Leaf Phenolics Correlated with Young Leaves Mature Leaves Mature Leaf Predictors N F R2 P N F R2 P N F R2 P LOW LIGHT (Experiment I)

YPN MPN MPN YPS 32 5.20 0.1476 0.0299 MPS 34 7.56 0.1911 0.0097 MPS 32 20.87 0.4103 <.0001 YDS 32 2.70 0.0825 0.1109 MDS MDS YPAL MPAL MPAL YDS&YPAL 34 15.96 0.3327 0.0004 MDS&PAL 30 20.14 0.4184 0.0001 MFD 32 21.96 0.4147 <.0001 58 RG 32 2.97 0.0875 0.0947 RG 34 3.37 0.0926 0.0756

MODERATE LIGHT (Experiment II)

YPN MPN 34 5.18 (0.1393) 0.0297 MPN 35 2.75 (0.0770) 0.1066 YPS MPS MPS YDS 34 3,92 0.1091 0.0565 MDS 32 5.95 0.1656 0.0208 MDS 33 15.80 0.3377 0.0004 YPAL MPAL MPAL YDS&YPAL 35 2.40 0.0678 0.1308 MDS&PAL 31 4.40 0.1318 0.0448 MFD 32 17.51 0.3609 0.0002 RG RG

HIGH LIGHT (Experiment III)

YPN MPN 71 7.99 (0.1038) 0.0061 MPN YDS 69 31.69 0.3211 <.0001 MDS 71 14.89 0.1775 0.0003 MDS 69 49.03 0.4226 <.0001 YPAL MPAL MPAL YDS&YPAL MDS&PAL MFD 68 49.57 0.4252 <.0001 RG 68 7.54 (0.1012) 0.0077 RG

Table 2.6. Statistics from optimized multiple regressions on young phenolics (Folin-reactives, FD) for experiments I-III, testing the following as predictors: photosynthesis (PS), protein (PN), whole plant relative growth (RG), DAHP sythase activity (DS), and phenylalanine ammonia-lyase activity (PAL). Within leaf correlations refer to measurements taken from the same leaf – but not necessarily from the same sample tissue. Within plant correlations refer to relationships between measurements of phenolics in young leaves with the predictors from both young and mature leaves sampled from the same plant. Parentheses indicate negative relationships with phenolic content. Sample sizes are noted below each list of variables. The Cp statistic was used for appropriate model selection, from all possible models, according to Mallows (1973) as discussed in Freund & Wilson (1998) (see text for further description). Additional abbreviations are for young leaves (Y), and matureleaves (M).

Within-Leaf Model for Within-Leaf Model for Within-Plant Modelfor Young Leaves Mature Leaves Young Leaves with Mature Leaf Predictors 2 2 2 Cp F R P Cp F R P Cp F R P

LOW LIGHT (Experiment I) 59 YPS 2.2388 5.20 0.1672 0.0299 MPS 0.3648 3.94 0.2325 0.0321 MPS 3.3266 8.63 0.4184 0.0015 31 RG YPAL 28 26

MODERATE LIGHT (Experiment II)

YDS 2.3212 7.80 0.3422 0.0019 MDS 1.7456 6.74 0.2059 0.0153 MDS 4.2008 8.46 0.5138 0.0005 (YPN) 27 YDS 32 (YPN)

HIGH LIGHT (Experiment III)

YDS 5.0000 23.79 0.5979 <.0001 MDS 5.0000 10.20 0.3857 <.0001 MDS 5.6327 29.89 0.6549 <.0001 (YPAL) (MPAL) YDS (YPN) (MPN) (YPN) (RG) RG (RG) 68 69 67

Table 2.7. Heteroscedastic t-tests (one-tailed) comparing concentrations of specific phenolics three days after treatment during Experiment III,. CGA = chlorogenic acid, DTPGs = diterpene glycosides. a

YOUNG LEAVES MATURE LEAVES df t P df t P HIGH LIGHT (Experiment III)

Total Phenolics 6 -8.9176 <0.0001 6 -5.3776 0.0008

Caffeoyl Putrescine 5 -10.9981 <0.0001 5 -3.8642 0.0059 Unknown at 15.8 min 5 -12.5396 <0.0001 5 -6.1486 0.0008

Total CGA 6 -1.1726 0.1427 6 -3.3619 0.0076 Neo CGA 8 0.7746 0.2304 6 -3.1521 0.0099 Crypto CGA + CGA 6 -1.2531 0.1284 6 -3.2328 0.0089

Rutin 10 1.7718 0.0534 9 -0.2839 0.3898

Nicotine 7 -0.0045 0.4983 9 -1.8943 0.0454

DTPGs 6 -8.1081 <0.0001 6 -2.0504 0.0431 a n=6 for each treatment mean, so uncorrected t-statistics would have had df = 10.

Table 2.8. Z-tests (one-tailed) for mean comparison between measures of phenolic concentration quantified with the Folin-Denis assay versus phenolic peaks detectable at 320 nm with HPLC.b

YOUNG LEAVES MATURE LEAVES z P z P HIGH LIGHT (Experiment III) Treatment Means Compared

Solvent -2.4660 0.0068 -4.3676 <0.0001 JA -0.4648 0.3211 0.1645 0.4347

b n=6 for each treatment mean for HPLC and n=9 for each treatment mean for the Folin-Denis assay.

60

Figure 2.1 Total phenolics by light and leaf age in Nicotiana tabacum. Folin-reactives are expressed as equivalents of chlorogenic acid (CGA, used as a standard) in mg per mg dry weight (DW). N ≈ 9 for each graph symbol. Young leaves and mature leaves were the fourth and eighth leaves from the plant apex, respectively, and were sampled from the same plants. Plants in low light received ~ 20 µE (experiment I), moderate light received ~ 200 µE (experiment II), and high light ~ 300-600 µE (experiment III). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

61 Figure 2.1

YOUNG LEAVES MATURE LEAVES 0.06 A B 0.05 Solv JA 0.04 LOW LIGHT 0.03

0.02 ND *

0.01 mg CGA-equivalents/mg DW } * 0.00 0 1 2 3 4 5 0 1 2 3 4 5

0.06 C D 0.05

0.04 MODERATE 0.03 LIGHT *** 0.02 *** * ND 0.01 mg CGA-equivalents/mg DW

0.00 0 1 2 3 4 5 0 1 2 3 4 5

0.06 E F 0.05

0.04 HIGH LIGHT 0.03 ND *** *** 0.02 ND *** *** 0.01 *** *** mg CGA-equivalents/mg DW

0.00 0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 2.2 DAHP (3-deoxy-D-arabino-heptulosonate 7-phosphate) synthase (DS) activity by light and leaf age in Nicotiana tabacum, expressed as µmol DAHP produced per mg protein (PN; Bradford 1976). Assays were run for 30 minutes. N ≈ 9 for each graph symbol. Young leaves and mature leaves were the fourth and eighth leaves from the plant apex, respectively, and were sampled from the same plants. Plants in low light received ~ 20 µE (experiment I), moderate light received ~ 200 µE (experiment II), and high light ~ 300-600 µE (experiment III). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

63 Figure 2.2 YOUNG LEAVES MATURE LEAVES

0.5 A B 0.4 Solv JA

0.3 LOW } LIGHT 0.2 * } *** 0.1 DS Activity (umol DAHP/mg PN) 0.0 0 1 2 3 4 5 0 1 2 3 4 5

0.5 C D 0.4

0.3 MODERATE } LIGHT 0.2 **

0.1 } *** DS Activity (umol DAHP/mg PN) 0.0 0 1 2 3 4 5 0 1 2 3 4 5

0.5 E F 0.4

0.3 HIGH 0.2 LIGHT

0.1 *** 0.0 ND *** *** } DS Activity (umol DAHP/mg PN) ***

0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 2.3 Phenylalanine ammonia-lyase (PAL) activity by light and leaf age in Nicotiana tabacum, expressed as nmol phenylalanine (PHE) produced per mg protein (PN; Bradford 1976). Assays were run for 3 hours. N ≈ 9 for each graph symbol. Young leaves and mature leaves were the fourth and eighth leaves from the plant apex, respectively, and were sampled from the same plants. Plants in low light received ~ 20 µE (experiment I), moderate received ~ 200 µE (experiment II), and high ~ 300-600 µE (experiment III). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

65 Figure 2.3 YOUNG LEAVES MATURE LEAVES

0.04

A B Solv

0.03 JA

0.02 LOW LIGHT 0.01

PAL Activity (nmol PHE/mg PN) } tr } * 0.00 0 1 2 3 4 5 0 1 2 3 4 5

0.04 C D

0.03

0.02 MODERATE Day 3 > Day 1 LIGHT * 0.01 ND PAL Activity (nmol PHE/mg PN) 0.00 0 1 2 3 4 5 0 1 2 3 4 5

0.06 E F

0.04 HIGH LIGHT ND *** ND 0.02 ND ND ***

PAL Activity (nmol PHE/mg PN) ND * 0.00 0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 2.4 Photosynthesis by light and leaf age in Nicotiana tabacum, expressed as µmol 2 CO2 assimilated per m leaf area per second. N ≈ 9 for each graph symbol in low and moderate light with different plants sampled on each day. N=3 for each graph symbol in high light using repeated measures on the same plants. Young leaves and mature leaves were the fourth and eighth leaves from the plant apex, respectively, and were sampled from the same plants. Plants in low light received ~ 20 µE (experiment I) and were measured under 20 µE from a LiCor external LED source. Plants in moderate light received ~ 200 µE (experiment II), and were measured under 200 µE. Plants in high light received ~ 300-600 µE (experiment III), and were measured under 400 µE. Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

67 Figure 2.4 YOUNG LEAVES MATURE LEAVES 1.2 A B 1.0

0.8

LOW assimilation 2 0.6 LIGHT * /s CO tr 2 0.4 ND Solv 0.2 JA

umol/m ND

0.0 0 1 2 3 4 5 0 1 2 3 4 5

10 C D 8

6 assimilation

MODERATE 2 LIGHT No differences 4 /s CO 2 Day 1 > Day 3 2 umol/m

0 0 1 2 3 4 5 0 1 2 3 4 5

20 E F

15

ND HIGH assimilation 2 10 LIGHT ND ND /s CO 2 * *** 5 *** * * tr

umol/m *

0 0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 2.5 Protein by light and leaf age in Nicotiana tabacum, expressed as mg of protein per mg dry weight (DW). Bovine serum albumin was used as a standard. N ≈ 9 for each graph symbol. Young leaves and mature leaves were the fourth and eighth leaves from the plant apex, respectively, and were sampled from the same plants. Plants in low light received ~ 20 µE (experiment I), moderate received ~ 200 µE (experiment II), and high ~ 300-600 µE (experiment III). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

69 Figure 2.5

YOUNG LEAVES MATURE LEAVES 0.22

0.20 A B

0.18

0.16 * LOW No differences 0.14 LIGHT 0.12

0.10 Protein (mg/mg DW) Solv 0.08 JA

0.06 0 1 2 3 4 5 0 1 2 3 4 5

0.22

0.20 C D

0.18

0.16 MODERATE 0.14 LIGHT No differences 0.12 Day 1 > Day 3 0.10 Protein (mg/mg DW)

0.08

0.06 0 1 2 3 4 5 0 1 2 3 4 5

0.22

0.20 E F

0.18 Overall decline is significant 0.16 HIGH ** LIGHT 0.14 0.12

0.10 Protein (mg/mg DW) *** 0.08 Overall decline is significant 0.06 0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 2.6 Whole plant relative growth by light level for Nicotiana tabacum, expressed as centimeters of growth since the start of the experiment per centimeter of original height on day 0. N ≈ 9 for each graph symbol. Plants in low light received ~ 20 µE (experiment I), moderate received ~ 200 µE (experiment II), and high ~ 300- 600 µE (experiment III). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

71 Figure 2.6

0.8 A Solv JA 0.6 LOW LIGHT 0.4

ND tr 0.2

RGR (cm growth/cm height at day 0) 0.0 0 1 2 3 4 5

0.8 B

0.6 MODERATE LIGHT 0.4 *** 0.2 ND

RGR (cm growth/cm height at day 0) 0.0 0 1 2 3 4 5

0.8 C

0.6 HIGH LIGHT 0.4 * *** 0.2 *** ***

RGR (cm growth/cm height at day 0) 0.0 0 1 2 3 4 5

Day

Figure 2.7 High performance liquid chromatography (HPLC) analysis of Nicotiana tabacum plants grown in high light. Folin-reactives, total chlorogenic acid (CGA), caffeoyl putrescine, and rutin are expressed as equivalents of chlorogenic acid (used as a standard for the Folin-assay as well as HPLC) in mg per mg dry weight (DW). Nicotine is expressed as mg per mg DW (nicotine was used as a standard) and diterpene glycosides (DTPGs) are expressed as area under the peak measured at 210 nm per g DW (no standard was available). N ≈ 9 for each graph symbol. Young leaves and mature leaves were the fourth and eighth leaves from the plant apex, respectively, and were sampled from the same plants. Plants in high light received ~ 300-600 µE (experiment III). Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

73 CGA-equivalents mg/mg DW CGA-equivalents mg/mg DW Figure 2.7 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.00 0.01 0.02 0.03 0.04 0.05 0.06 B A *** *** Folin-Denis ND ** Total CGA YOUNG LEAVES MATURE LEAVES *** ** Caff. Put. ND Rutin tr * ND Nicotine *** *

DTPGs JA Solv 0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.000 0.002 0.004 0.006 0.008 0.010 0.012

Nicotine mg/mg DW Nicotine mg/mg DW 0 50 100 150 200 250 0 100 200 300 400 500 600

DTPGs (Area at 210 nm)/g DW DTPGs (Area at 210 nm)/g DW

Figure 2.8 Responses to mite herbivory by leaf age at high light in Nicotiana tabacum. DAHP (3-deoxy-D-arabino-heptulosonate 7-phosphate) synthase (DS) activity is expressed as µmol DAHP produced per mg protein (PN; Bradford 1976). Assays were run for 30 minutes. Phenylalanine ammonia-lyase (PAL) activity is expressed as nmol phenylalanine (PHE) produced per mg PN. Folin-reactives are expressed as equivalents of chlorogenic acid (CGA, used as a standard). N ≈ 9 for each graph symbol. Young leaves and mature leaves were the fourth and eighth leaves from the plant apex, respectively, and were sampled from the same plants. Samples for phenolic analysis for mite treated mature leaves were not analyzed due to damage during storage. Plants in this experiment received ~ 300-600 µE (high light). Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

75 Figure 2.8 YOUNG LEAVES MATURE LEAVES

0.12 A B Ctrl 0.10 Mites

Mites > Ctrl tr 0.08 tr DS } Mites > Ctrl 0.06 { } 0.04 Day 2 < Day 4* {

DS Activity (umol DAHP/mg PN) Day 2 < Day*** 4 0.02 0 1 2 3 4 5 0 1 2 3 4 5

0.06 C D 0.05 Ctrl > Mites Ctrl > Mites * 0.04

PAL 0.03 ** } 0.02

0.01 * PAL Activity (nmol PHE/mg PN) { Day 2 < Day 4 0.00 0 1 2 3 4 5 0 1 2 3 4 5

0.04 E F

0.03

Folin- 0.02 Reactives Day 2 < Day 4 Mite treatment 0.01 data not available CGA-equivalents mg/mg DW

0.00 0 1 2 3 4 5 0 1 2 3 4 5

Day Day REFERENCES

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83 Chapter 3

Control of phenolic production by DAHP synthase and PAL in a Populus hybrid

INTRODUCTION

The susceptibility of plants to attack by enemies is strongly influenced by leaf chemistry. Plants constitutively produce secondary metabolites that affect (both positively and negatively) their desirability to insects and pathogens and are also able to respond to environmental cues by increasing the production of secondary metabolites when threatened (Karban and Baldwin 1998). The responses of plant phenolics, a ubiquitous category of secondary metabolites, have been extensively studied. Many biotic and abiotic environmental factors, including light intensity, light wavelength, wounding, pathogen attack, low nutrient availability and even low temperature (e.g. Lamb 1979, Hahlbrock et al. 1979, Hahlbrock and Scheel 1989, Liang et al. 1989, Dixon and Paiva 1995), can influence a plant’s constitutive phenolic chemistry. However, the extent to which environmental factors affect inducible phenolic chemistry has been less studied (Hartley and Jones 1999).

Several theories have been advanced to explain the patterns of secondary metabolites found in terrestrial plants (e.g. Feeny 1976; Rhoades and Cates 1973; Coley et al. 1985), and much of the data supporting (or refuting) these theories has come from the study of phenolics (e.g. Haukioja et al. 1998; Koricheva et al. 1998). However, even theories that are functionally based (e.g. Bryant et al. 1983; Herms and Mattson 1992) lack a biochemical framework. One theory that was developed from biochemical principles is the protein competition model (PCM) for predicting patterns of carbon allocation to phenolics (Jones and Hartley 1999). The PCM is based on the structure of the biochemical pathways leading to phenolic production and on the premise of competition for a limiting precursor, phenylalanine. One of the key elements of the PCM model that requires further investigation is the relationship

84 of the biosynthesis of phenylalanine to downstream metabolic networks. We need a better understanding of regulation of the biosynthetic pathways leading to the production of phenolics to build useful models of carbon allocation that can explain constitutive as well as inducible phenolic production.

We have undertaken several experiments designed to investigate the role of two putative rate-limiting enzymes in the synthesis of phenolics, 3-deoxy-arabino- heptulosonate 7-phosphate synthase (DAHP synthase; DS) and phenylalanine ammonia-lyase (PAL). DS is the first enzyme of the shikimate pathway, which provides carbon from sugar metabolism for the production of the three aromatic amino acids, phenylalanine, tryptophan, and tyrosine. Phenylalanine (Phe) serves as a precursor for many putative phenolic defenses (Herrmann and Weaver 1999). PAL modulates the entry of Phe into the phenylpropanoid pathway for the production of secondary metabolites such as chlorogenic acid, condensed tannins, and phenolic glycosides (Dixon and Paiva 1995).

Results from our experiments in tobacco (see Chapter 2) revealed that at low light levels both DS and PAL activities increased in response to a wounding signal (jasmonic acid, JA) that also increased phenolic production. PAL activity was related positively to light levels but at moderate and high light levels it was DS, and not PAL, activity which responded to JA and was associated with increases in phenolic production. Thus, in tobacco, DS is wound-responsive but less light responsive and PAL is light-responsive but less wound-responsive. The coordinate activity of both enzymes appeared to regulate phenolic production at low light levels, but DS and PAL activity were uncoupled at higher light levels and DS activity (and the supply of Phe to PAL) may have been rate-limiting. These findings provide evidence steering our conceptual understanding of phenolic production away from the concept of a single “rate-limiting step.” There are intervals for DS and PAL, of light at least, where they each can play a key regulatory role.

85 However, tobacco is a relatively fast growing plant with low investment in carbon-based defensive compounds and may not be representative of plants with other investment strategies. We were interested in the extent to which regulation patterns identified in tobacco would be applicable to other plant species, particularly those with higher investments in carbon-based defensive compounds. The plant selected for comparison to tobacco was poplar, a woody perennial with higher investments in carbon-based phenolic defenses than tobacco. Poplar manufactures two categories of phenolics: condensed tannins and phenolic glycosides. Significant variation of both phenolic glycosides and condensed tannins has been shown in natural systems (Lindroth et al. 1987, and Osier et al. 2000), and each has been shown to have antiherbivore effects (Lindroth et al. 1988, Lindroth and Bloomer 1991, and Ayres et al. 1997). However, as in tobacco, we are only just beginning to understand the regulation of their production (e.g. Ruuhola 2001, and Peters and Constabel 2002).

We used clonal hybrid poplars as a model system in which to examine phenolic and biochemical responses to wounding-related treatments, because of their ready availability as well as our desire to minimize the effects of genotypic variation (Lindroth and Hwang 1996, Havill and Raffa 1999, and Osier and Lindroth 2001). The wounding hormone JA was used in most of the experiments to precisely control the magnitude and location of the wound stimulus, but also to conserve the plant tissue needed for multiple assays. Two additional treatments were included to compare the JA responses to various aspects of natural feeding. We subjected young leaves to mechanical wounding plus the application of regurgitant collected from gypsy moth (Lymantria dispar L., GM) larvae, and exposed mature leaves to 24 hr pulses of chewing by 3rd instar GM larvae. Using these wounding-related treatments in a series of experiments at different light levels, we tested the hypothesis that these treatments elevate DS as a means of directing more carbon into the shikimate pathway to supply enhanced synthesis of condensed tannins and phenolic glycosides. We predicted that, as in tobacco, DS and PAL would both

86 contribute to phenolic production at lower light levels, but that at higher light levels DS would regulate phenolic output.

MATERIALS AND METHODS

Experimental design Three experiments were conducted using clonal hybrid poplar (Populus deltoides X P. nigra) saplings. In each experiment, we investigated the response to herbivory-related treatments of the two targeted regulatory enzymes (DS and PAL) and their associated increases in phenolic production in both young and mature leaves. We also monitored other physiological parameters sometimes affected by changes in phenolic synthesis (photosynthesis, growth, and protein content). Experiments I and II were designed to examine the potential constraining effect of light level on the elicitation of phenolic production. In these two experiments, phenolic production was induced by the application of the wounding signal, jasmonic acid (JA), intended to mimic the effects of insect herbivory (Creelman and Mullet 1997, Kessler and Baldwin 2002). In experiment III, conducted simultaneously with experiment II at high light, plants were challenged with gypsy moth treatments. Young leaves in experiment III were treated with tracing wheel damage and the application of larval regurgitant, while mature leaves were exposed to feeding by 3rd instar larvae. All experiments were conducted in a greenhouse during May 2000 (I) and May 2001 (II & III).

Study system (plant material & growth conditions) Hybrid poplar saplings (Populus deltoides X P. nigra; clone OP-367, Segal Ranch, Wash.) were grown from cuttings in 9 L pots containing Metro Mix 250 (with starter nutrient charge; Scotts-Sierra Horticultural Products Co., Marysville, OH). Plants were grown with supplemental lighting from one 400W high-pressure sodium lamp for every 9 plants (14 h day). Using this lighting arrangement, high light levels of approx. 800-1200 µE (HIGH) were maintained for all plants until the start of each experiment. For experiment I, light levels were manipulated by the addition of shade

87 treatments in a randomized split-split plot design. Five blocks were used, with light as the whole-plot factor within each block (5 each of low and moderate light), and treatment and day of harvest as a 2X2 factorial on each subplot. Light levels that were an average of 100-200 µE at plant height were achieved for low light (LOW) with 80% shade cloth that covered the shade whole-plots in each block (PAK Unlimited, Inc., Cornelia, GA). The shade tent structures also bordered the light plots on three sides, reducing the light received in these plots to 400-700 µE (MODERATE). For experiments II & III, all plants were maintained at HIGH light.

During sapling growth, plant locations on greenhouse benches were randomized every 3-4 days up until the time of the experiments using a random number generator (Excel, Microsoft Corporation, Redmond, WA). Plants were allocated to treatment groups based on their size one day before the beginning of treatments. The allocation procedure was similar to that used by Ohnmeiss and Baldwin (1994) and consisted of sorting all plants by height and leaf number and randomly assigning plants into treatment groups by consecutive random halvings. This procedure produced treatment groups with plant sizes that were not significantly different on day 0 (data not shown). Experiment I (LOW & MODERATE) used 80 plants in 8 treatment groups with a grouping size of N=10: Light (2) X Treatment (2) X Harvest Day (2). Experiment II (HIGH) used 108 plants in 12 treatment groups with a grouping size of N=9: Treatment (3) X Harvest Day (4). Experiment III (HIGH-GM) used 36 plants in 4 treatment groups with a grouping size of N=9: Treatment (2) X Harvest Day (2).

To ensure that individually sampled leaves were of the same physiological age they were numbered from the top down according to the plastochron index method of Larson and Dickson (1973) and Larson and Isebrands (1971). The first leaf from the apical bud that was longer than 2.5 cm was designated as leaf plastochron index 0 (LPI 0), and leaves were numbered consecutively downward. Photosynthetic profiles revealed that the transition to a fully-photosynthetically competent leaf occurs between LPI 5 and 7. We chose LPI 3 to represent a young

88 leaf (Y) and LPI 8 to represent a mature leaf (M). All physiological, chemical, and biochemical measurements follow the profile of an individual leaf that was originally LPI 3 or LPI 8; in later sampling dates, individual leaves had progressed onwards to different physiological states (e.g. LPI 3 to LPI 4 or 5). However, by the end of the experiments all mature leaves were still acting as source leaves and young leaves were either still acting as sink leaves (not fully photosynthetically competent) or were just beginning the transition. In this hybrid clone, and in related Populus species, LPI 3 and LPI 8 are directly connected by vasculature. Each leaf is supplied by three vascular bundles, and in this case LPI 3 is connected to LPI 5, 6 and 8 (with 8 as a source leaf; Larson et al, 1972, Arnold and Schultz 2001). Therefore, the simultaneous elicitation of phenolic production in LPI 3 and LPI 8 on the same plant creates potentially competing demands for carbon within LPI 8, between within-leaf needs and export demands from LPI 3.

Treatments JA sprays consisted of 5mM jasmonic acid (±JA) dissolved in 3% (aq.) ethanol, with 0.125% (v/v) Triton-X 100 detergent to help penetrate the waxy leaf cuticle. Solvent controls (Solv) were the same, minus JA. Distilled, deionized water was used for water treatments (W). The sprays were applied to designated plants in a fine mist. The mist was applied to the adaxial side of chosen leaves (LPI 3 and LPI 8) of the plant until the leaves just began to drip. To avoid cross-contamination, plants were removed from the greenhouse and sprayed in an entryway of the same building. The leaves were allowed to dry (15-30 minutes) before being returned to the experiment room.

Gypsy moth larvae (Lymantria dispar L., obtained from Animal and Plant Health Inspection Service - APHIS, USDA, Otis, MA) were raised from egg masses on artificial wheat germ diet (O’Dell et al. 1985). Newly molted third instar larvae were chosen for use in the experiment and transferred to poplar leaves (from non- experimental plants reared simultaneously in the same greenhouse room as the experimental plants) 3 days before use in the experiment. The larvae were removed

89 from their food source and starved for 24 hrs before the start of the experiment to encourage immediate feeding. Two larvae were placed on LPI 8 of each plant in the GM treatment (GM-Mature). Larvae were confined to desired leaves and plants by collecting and repositioning as needed for the duration of the chewing treatment.

We collected gypsy moth regurgitant from third instar larvae reared on artificial diet and transferred to poplar leaf tissue for several days as described above. Regurgitation was encouraged by gentle squeezing with forceps. The regurgitant was collected in small tubes and large particulate matter was removed by centrifugation. The collection tubes were stored at -20°C until use (for 3 to 4 weeks). For experiment III, we pooled several tubes of regurgitant and thoroughly mixed them to acquire the volume necessary to treat 18 young leaves. A pattern tracing wheel was used to perforate young leaves (LPI 3) in two lines parallel to each side of the midvein, and halfway between midvein and the leaf margin. We applied regurgitant (5 µl per line) directly to perforations (GM-Young).

On day 0 for experiments I and II, spray treatments (JA, Solv, or W) were administered to pre-identified individual leaves (LPI 3 & 8) on designated plants. All spray treatments for each experiment were concluded within a 2 hr time-period. For experiment III, regurgitant application to young leaves was completed (GM-Young) on day 0, but larval feeding was begun on day 0 (GM-Mature) and allowed to continue for 24 hrs. Controls (Ctrl) for experiment III were handled briefly to mimic minor mechanical stimulation incidental to the application of the GM treatments.

Plant sampling We set harvest times at 24 hr intervals from treatments to account for diurnal fluctuations in activities of the enzymes to be measured (e.g. Thain et al. 2002, Peter et al. 1991). We randomly harvested designated leaves for each sampling day between 2 and 4pm, immediately after measurements of photosynthesis and growth. For experiment I, designated young and mature leaves were harvested 24 hrs (Day 1) and 72 hrs (Day 3) after treatment. For experiment II, leaves were harvested on

90 each of Days 1, 2, 3, and 4 (again, 24 hr intervals). Due to logistical constraints, we staggered the harvest of experiment III plants (GM and Ctrl) from the concurrent experiment II harvest; GM and Ctrl treatments were harvested on days 2 and 4. However, because the chewing treatment (GM-Mature) was 24 hrs, this harvest interval was physiologically equivalent to 1 and 3 days after the end of treatment.

We removed leaves at the petiole and separated the halves of each leaf blade from the midrib using a razor blade on a cutting board. The tissue from the left half of the leaf was reserved for enzyme analysis (ENZ), and the tissue from the right half of the leaf was reserved for analysis of protein and phenolic content (CHEM). Although chemistry is known to vary within a leaf, lateral within-leaf variation is minimal (data not shown), and less than intra- and inter-plant variation (Orians et al. 2002, Winn 1996). Any variability introduced by sampling based on a lateral leaf split (< 5%) was less than that from different plants (e.g. up 10-20% variation in phenolic content in controls from these experiments). ENZ and CHEM samples were placed in separate paper coin envelopes, flash frozen in liquid nitrogen, and temporarily stored on dry ice. Samples were not allowed to thaw once frozen. At the end of each harvest, ENZ samples were stored at -80 °C and CHEM samples were stored at -20 °C. At the end of each experiment, all CHEM samples were lyophilized and then stored again at -20°C.

Photosynthesis and growth measurements We monitored whole plant relative growth rate for all plants in all three experiments. Leaf lamina expansion for LPI 0, 3, 6 and 8 was measured daily for a subsample of plants designated to be harvested on the last day of each experiment (n=3 for each treatment combination). These same plants were also used to monitor photosynthetic changes on day 0, 1, and 3 for experiments I and II and on days 1 and 3 for experiment III (larvae were on plants for day 0). Photosynthetic carbon assimilation (PCA) was measured individually for both sample leaves on each plant (Y & M) using a LICOR 6400 Portable Photosynthesis System (LICOR, Lincoln, Nebraska). We used a LICOR external LED light source to provide 400µE for all

91 three experiments. This light level reflects the MODERATE light treatment. Preliminary trials revealed that the time required for the establishment of equilibrium for each data point at extreme light levels (LOW & HIGH) would have prevented completion of photosynthetic measurements before the required harvest time each day. Thus, these photosynthesis measurements are a reflection of potential photosynthetic capacity rather than actual photosynthetic rates in the greenhouse. -2 -1 PCA was expressed as CO2 uptake (µmol·m ·s ).

Chemical analysis: spectrophotometry CHEM samples were individually ground to a fine powder using porcelain mortars and pestles (Coorstek., Golden, CO). Using subsamples from each vial of leaf powder, protein, total phenolic, condensed tannin, and phenolic glycoside content were determined. Protein was extracted and analyzed following the methods of Jones et al. (1989). Leaf powder (3 mg) was extracted for 2 hrs in 1.5 mL of 0.1 N sodium hydroxide at 100 °C and allowed to cool for 20 minutes before assaying. Bovine serum albumin (BSA) was used as the standard.

Leaf powder (10 mg) was washed with ether (3 X 500 µl) and then phenolics were extracted with 70% (aq.) acetone containing 1 mM ascorbate (3 X 250 µl, with 10 min sonication for each extraction). Samples were micro-rotovapped to remove

acetone and supplemented with ddH2O for a final extraction volume of 500 µl. Total phenolics were assayed using the Folin-Denis method as described in Appel et al. (2001), amended to include lithium sulfate (8% w/v) in the Folin-reagent to prevent precipitate formation (Singleton and Rossi, 1965). Condensed tannin concentrations were measured as extracted proanthocyanidins by the acid-butanol method (Hagerman and Butler 1989). Standard curves for both the Folin assay and the acid-butanol assay were constructed using purified standards from non-experimental leaves from the experiments. Purification procedures were as in Hagerman and Klucher (1986) and Appel et al. (2001). The results from these assays are expressed as mg Folin-reactives/mg DW and mg condensed tannins/mg DW, respectively. The results from these two assays should not be viewed as absolute

92 values, because the constraints of the purification procedure favor the enrichment of condensed tannins over phenolic glycosides in the final mixture of purified phenolics. However, because the same standards were used for all three experiments, comparisons among treatments and experiments are still valid.

Chemical analysis: High performance thin layer chromatography Only leaf samples from the last day of each experiment were analyzed for phenolic glycosides. We measured concentrations of salicortin (SAL) and HCH- salicortin (hydroxy-cyclohexene-on-oyl salicortin, HCH), the most abundant phenolic glycosides in this hybrid poplar clone (data not shown), using high performance thin layer chromatography (HPTLC). Trace amounts of two other phenolic glycosides, tremuloidin and tremulacin, were also identified but not quantified. Extraction of phenolic glycosides consisted of extracting leaf power (12.5 mg) directly into ice cold methanol (500 µl, HPLC quality). Samples were sonicated for 15 min and leaf powder was pelleted in the sample tubes by centrifugation. Silica gel HPTLC plates were spotted in duplicate (1 µl) directly from these extracts. HPTLC procedures and equipment were as in Lindroth et al. (1993) and were performed in Rick Lindroth’s lab (Entomology Dept., University of Wisconsin, Madison, WI). Briefly, plates were developed [solvent CH2Cl2:MeOH:THF (30:5:5)], then scanned and analyzed with a Camag TLC scanner and associated software (Camag Scientific, Wrightsville Beach, NC). Salicortin and HCH-salicortin standards were purified from aspen leaves (Populus tremuloides) by Brian Rehill (United States Naval Academy, Annapolis, MD; Rehill et al. 2004).

Enzyme analysis: extraction All operations were carried out at 4 °C. ENZ samples were removed from storage at -80 °C, quickly weighed, and then ground under liquid nitrogen with porcelain mortars and pestles (Coorstek, Golden, Colorado). While still frozen, PVPP was added 1:6 (w/w) and thoroughly mixed into the leaf tissue. The tissue was allowed to thaw in the mortar and at the first appearance of liquid, extraction buffer [50 mM EPPS, pH 8.6, 1 mM PEP, 1 mM MgCl2, 1 mM MnCl2, 0.1% β-

93 mercaptoethanol] in a ratio of 2 ml per g plant tissue was added. The slurry was ground until no particulate matter was visible and then centrifuged at 40,000 g for 30 min.

The supernatants were filtered to remove low molecular weight compounds (such as small phenolic molecules not bound by PVPP that interact with both the DS and PAL assays) through PD-10 columns containing Sephadex G25 (Supelco Inc., Bellefonte, PA) equilibrated with extraction buffer (Suzuki et al. 1995 and Biagioni et al. 1997). The protein-containing fraction of the filtrate was used for both the DS and PAL assays, as well as for the determination of protein content. The extracts were immediately assayed for DS activity. An aliquot was flash frozen and stored at -20 °C for protein determination, and the remainder was flash frozen and stored at -80 °C for PAL assays. Protein in enzyme extractions was determined by the method of Bradford (1976), with bovine serum albumin as the standard.

Enzyme analysis: DS assay DS was assayed by measuring the absorbance at 549 nm of the periodate degradation product of DAHP complexed with thiobarbiturate (Suzich et al. 1985). The reaction mixture for each assay consisted of 50 mM EPPS, pH 8.6, containing 5 mM PEP, 2 mM E4P, 13.3 mM MgCl2, and suitably diluted enzyme in a total volume of 0.15 mL. The reaction was initiated by adding 100 µL of a reaction cocktail to 50 µL of enzyme extract. Incubations were at 37 °C for 30 min. The reactions were stopped by the addition of 0.3 mL of 10% (w/v) trichloroacetic acid. All assays were carried out in duplicate, with one control for background absorbance from which the substrate E4P was omitted. Since the product of the reaction, DAHP, is not commercially available for use as a standard, the molar extinction coefficient of 4.5 X 104 at 549 nm was used to calculate DAHP concentrations (Jensen and Nester 1966). Frozen aliquots of a single tobacco enzyme extraction, as well as varying concentrations of gallic acid, which interacts with the color-development portion of the assay, were used as batch controls and correction factors were determined as in Jensen and Nester (1966).

94 Enzyme analysis: PAL assay Using previously prepared extracts stored at -80 °C, samples were slowly thawed on ice and assayed for the conversion of L-[14C] phenylalanine to [14C]trans- cinnamic acid using a method similar to that of Howles et al. (1997), modified from Legrand et al. (1976). 50 µL of extract (in extraction buffer) were combined with 50 µL of 2 mM unlabeled phenylalanine in 100 mM borate buffer, pH 8.8 with 0.03 µCi [U-14C] L-phenylalanine (496 mCi/mmol; ICN-Biomedicals, Irvine, CA). Incubations

were for 3 hrs at 37 °C and were stopped with the addition of 10 µL 10N H2SO4. Labeled cinnamic acid was extracted into 750 µL of toluene. Samples were centrifuged briefly and 500 µL of the toluene layer was added to 10 mL Ecoscint O scintillation cocktail (National Diagnostics, Atlanta, GA). Scintillation counting was performed by a Beckman LS 3801 (Beckman Instruments, Fullerton, CA) using Beckman 14C standards for quenching and calibration. All assays were run in duplicate and background radioactivity was determined using protein extracts that

were inactivated with H2SO4 prior to incubation. For calculating total PAL activity, the conversion of L-[14C]-phenylalanine to [14C]-trans-cinnamic acid was considered proportional to the overall conversion of phenylalanine to trans-cinnamic acid. Frozen aliquots of a single tobacco enzyme extraction were used as batch controls. However, the primary source of batch variation came from different stock solutions of L-[14C]-phenylalanine, so aliquots of the reaction cocktail were assayed separately and a correction factor similar to that of the DS assay was used.

Statistical Methods Statistical analyses were performed separately for each leaf age class for each experiment. Some chemical and biochemical measures were log transformed before analysis to meet the underlying assumptions of parametric tests (Zar 1999). Data for each physiological measure were tested for outliers and in most cases, where no known reason for exclusion existed, the Winsorization method was used for statistical analysis (Sokal and Rohlf 1995). For illustration of untransformed data, error bars represent the standard error of the mean. For data that was transformed for analysis, the transformed mean and the interval indicated by the standard error of

95 the transformed mean were converted back to original units for graphical illustration, in some cases resulting in asymmetry.

All statistical analyses were performed with the SAS statistical package (Version 8.2, SAS Institute, Inc., Cary, NC). The split-split-plot experiment (I) was analyzed using the Mixed procedure, as described in Littell et al. (1996), with light as the whole-plot within each block, and treatment and harvest day as a factorial on each subplot. Completely randomized fixed-effects analysis of variance, with treatments and sampling day as main effects, was performed for experiments II and III using the GLM procedure. Repeated-measures analysis was used to analyze photosynthesis and leaf growth measurements using the Mixed procedure for each experiment. Contrasts (using either Tukey or Bonferroni adjustments, as required) were performed on the two-way ANOVAs if the interaction terms were significant. Z- tests were used to compare means between experiments (e.g. comparing between light levels).

Chemicals All chemicals used for the chemical analyses, and enzyme assays were obtained from Sigma-Aldrich Co., St. Louis, MO and were the highest quality available.

RESULTS

Light and age effects on phenolic and biochemical measures. Total phenolic concentrations (TPhen, measured as Folin reactives) in young control leaves in high light were higher than those in moderate light (Z-test, P < 0.05), and similarly TPhen in young control leaves in moderate light were higher than those in young control leaves in low light (Table 3.1). There were no differences for TPhen in mature control leaves among the three light levels (all Ps > 0.15; Table 3.2). Because young leaf TPhen were related to light levels while mature leaf concentrations were not, mature leaves in low light had higher levels of constitutive

96 TPhen than young leaves (Z-test, P < 0.05), had the same levels at moderate light (Z-test, P > 0.15), and had lower levels at high light than young leaves (Z-test, P < 0.05)(Fig. 3.1).

Condensed tannin concentrations (CT) in young control leaves were somewhat related to light level; they were greater in high light than in both moderate and low light (Z-tests, all Ps < 0.05), but young control leaves in moderate and low light were not different from each other (Table 3.1). CT in mature control leaves were more directly related to light level; they were greater in high light than in moderate light (Z-test, P < 0.05), and those in moderate light were higher than those in low light (Table 3.2). A steady increase in CT occurred over the course of the experiments at each light level and for both age classes, but the initial relationships between CT at the three light levels were maintained (Fig. 3.2). However, this pattern differed for phenolic glycosides. End-of experiment HCH content (day four for high light, day three for moderate and low light) for both age classes of control leaves was less in high light than in either moderate or low light (Z-tests, all Ps < 0.05), but HCH contents in moderate and low light were similar (Z-tests, all Ps > 0.15; Tables 3.3 and 3.4). End-of-experiment SAL content for mature control leaves exhibited the same pattern, less in high light than in both moderate and low light (Z- tests, all Ps < 0.05) with no differences between moderate and low light (Z-test, P > 0.15; Table 3.4). However, young control leaves in moderate light had the highest SAL content, followed by those in high light, and then those in low light (Table 3.3)(cf. Figs 3.3, 3.4, and 3.5).

DS activity was somewhat responsive to light level. DS activity in control leaves did not differ between low and moderate light for either age class (Tables 3.1 and 3.2), but was lower than DS activity in control leaves in high light (Z-tests, all Ps < 0.05). Mature leaves had lower DS activity than young leaves at all three light levels (Z-tests, all Ps < 0.05) (Fig. 3.6). PAL activity in control leaves was light- dependent, with the lowest PAL activity occurring in low light and the highest in high light. The light-dependence of PAL was consistent between young and mature

97 leaves (Table 3.1 and 3.2; Z-tests, all Ps < 0.05) even though mature leaves had lower PAL activity in moderate and high light than young leaves (Z-tests, all Ps < 0.05) and there was no age difference in PAL activity at low light (Z-test, P >0.15) (Fig. 3.7).

Protein content (PN) on day one in control leaves did not differ between low and moderate light for young leaves, but by day three young control leaves in low light had higher PN than young control leaves in moderate light (Table 3.3, Fig 3.8). Mature control leaves in low light had higher PN than mature control leaves in moderate light on both sampling dates (Table 3.4, Fig 3.8). Young control leaves in high light initially had higher PN than moderate and low light (Z-tests, all Ps < 0.05), but by day three PN in young controls was the same as in moderate light (all Ps > 0.15) , and by day four PN in young controls was lower than in young leaves in moderate light (P < 0.05). Mature control leaves in high light did not differ from mature control leaves in moderate light on either sampling date (Ps > 0.15)(Fig 3.8). Whole plant relative growth rate (RGR) for control plants and leaf relative growth (LRG) for control leaves (LPI 0, 3, 6, and 8) did not differ among the three light levels (Table 3.5 and 3.6, Fig 3.9 and 3.10). Potential photosynthesis (PS, as measured with an external light source at 400 µE) also did not differ among the three light levels (Table 3.5, Fig 3.11). However, younger leaves had higher LRG and lower PS than mature leaves (Z-tests, all Ps > 0.30, comparing LPI 3 and 8)(Figs 3.10 and 3.11).

Experiment I: Effects of jasmonic acid treatments at low and moderate light. Application of JA at low light failed to elicit increased production of TPhen, CT, or phenolic glycosides (SAL and HCH) (Figs. 3.1, 3.2, and 3.3, respectively). However, all three of these categories were elevated by JA treatment in moderate light (Fig 3.1, 3.2, and 3.4). We detected a significant 20% increase in TPhen on day one in young leaves in moderate light due to JA treatment, and a 40% increase by day three (Table 3.1). There was also a significant 30% increase in TPhen due to JA treatment by day three in mature leaves in moderate light (Table 3.2) (Fig 3.1).

98 CT were elevated 15% by JA treatment in young leaves by day one and 25% by day three in moderate light (Table 3.1). Mature leaves had 20% increased levels of CT by day three due to JA (Table 3.2) (Fig 3.2). Both SAL and HCH were also elevated 80% by JA treatment in young leaves in moderate light (Table 3.3), and by 20-50% in mature leaves (Table 3.4) (Fig 3.4).

DS activity did not change significantly in response to JA treatment at either low or moderate light for either age class (Tables 3.1 and 3.2, Fig 3.6). In contrast to DS activity, PAL activity was transiently elevated by JA treatment at both low and moderate light levels for both age classes. Both young and mature leaves showed elevated levels of PAL activity by one day after treatment. However, the relative induction of PAL activity was greater for mature leaves, and by three days after treatment only mature leaves still showed elevated PAL activity (Tables 3.1 and 3.2, Fig 3.7).

JA-treated young leaves in low and moderate light had higher levels of PN than controls (Table 3.3), but there were no changes in PN in JA-treated mature leaves in low and moderate light (Table 3.4) (Fig 3.8). RGR was reduced in JA- treated plants in both low and moderate light (Table 3.5, Fig 3.9), but LGR and PS were not different between control and JA-treated leaves (Table 3.6 and 3.5, Fig 3.10 and 3.11).

Experiment II: Effects of jasmonic acid treatments at high light. Application of JA at high light increased TPhen, CT, and phenolic glycosides (SAL and HCH) (Fig 3.1, 3.2, and 3.5, respectively). These increases were much greater (absolutely and relatively) than those detected in moderate light, but changes in TPhen and CT due to treatment were only found in young leaves (Tables 3.1 and 3.2, Figs. 3.1 and 3.2). We detected a significant 50% increase in TPhen by two days after treatment in young leaves that was maintained through the end of the experiment (Table 3.1, Fig 3.1). There was a significant 30% increase in CT by two days after treatment in young leaves, and this difference was also maintained

99 through the end of the experiment (Table 3.1, Fig 3.2). Although TPhen and CT were only elevated by JA treatment in young leaves, phenolic glycosides (SAL and HCH) were elevated by JA treatment in both age classes (Tables 3.3 and 3.4, Fig 3.5). Absolute elicited concentrations of phenolic glycosides were not different from those in moderate light (Z-tests, all Ps > 0.30). However, because control levels of phenolic glycosides in high light were relatively lower than those at low and moderate light, the changes due to JA treatment in high light represented 2-3 fold increases above controls in high light compared to a not quite 2-fold increase in moderate light (cf. Figs 3.4 and 3.5).

DS activity did not change significantly in response to JA treatment for either age class in high light, as in low and moderate light (Fig 3.6). Again, as in low and moderate light, PAL activity was elevated by JA treatment in high light for both age classes. Both young and mature leaves showed elevated levels of PAL activity by one day after treatment. The relative induction of PAL activity was similar in young and mature leaves. In contrast to PAL activity in low and moderate light, the change due to treatment was maintained longer in young leaves than in mature leaves. The more frequent sampling dates in experiment II reveal day to day fluctuations in PAL activity that were significant, but that do not affect the relative elicitation of PAL activity due to treatment (Fig 3.7).

Treatment with JA had no effect on PN in young (Table 3.3) or mature (Table 3.4) leaves in high light (Fig 3.8) and whole plant RGR was not affected by JA treatment in high light (Table 3.5), contrary to the reductions seen in RGR due to JA treatment in low and moderate light (Fig 3.9). LGR in sprayed leaves (LPI 3 and 8) was not affected by JA treatment in high light, but we did observe a reduction in LRG in LPI 0(Table 3.6, Fig 3.10). PS was not different between control and JA-treated leaves in high light, as in low in moderate light (Table 3.5, Fig 3.11).

100 Experiment III: Effects of gypsy moth treatments at high light. In general, the phenolic and biochemical results of gypsy moth treatments were similar to the results achieved with JA treatment in high light. For both young and mature leaves, the direction of the changes was the same as with JA treatment, but many were not statistically significant. TPhen were induced by larval feeding in mature leaves (Table 3.2), but changes due to treatment with larval regurgitant in young leaves were not statistically significant (Table 3.1). However, CT were not significantly increased by either regurgitant application to young leaves or larval chewing on mature leaves and we actually observed a significant reduction in CT due to the application of larval regurgitant to young leaves (Table 3.1 and 3.2)(Fig 3.12). The regurgitant application to young leaves and larval chewing on mature leaves both elicited increased production of SAL, but no changes in HCH production. This was in contrast to SAL and HCH responses to JA, where both were elevated by treatment (Tables 3.3 and 3.4, Fig 3.13).

Application of larval regurgitant led to a reduction in DS activity in young leaves (Table 3.1), while larval chewing on mature leaves had no effect on DS activity (Table 3.2). Larval chewing led to an increase in PAL activity in mature leaves (Table 3.2), but the application of larval regurgitant had no effect on PAL activity in young leaves (Table 3.1) (Fig 3.12).

PN, whole plant RGR, and LRG did not change in response to either larval regurgitant application to young leaves or larval chewing on mature leaves (Tables 3.3, 3.4 and 3.6). However, unlike the results found with JA treatment in high light, larval chewing in high light did cause a slight decrease in photosynthetic capacity in one instance; chewed mature leaves had lower PS on day three than controls (Table 3.5). Otherwise, there were no changes in PS due to regurgitant or chewing treatments.

101 DISCUSSION

Impacts of JA as constrained by light We found that the application of JA increased phenolic production in poplar saplings in moderate and high light, but not in low light, and the intensity of the phenolic response to treatment was mediated by both light level and leaf age. Young leaves in both moderate and high light, and mature leaves in moderate light increased production of TPhen, CT and both phenolic glycosides in response to JA treatment. However, JA-treated mature leaves in high light only increased production of the two prominent phenolic glycosides, not TPhen or CT.

DS activity did not respond significantly to JA treatment at any light level in either leaf age class. However, PAL activity consistently increased in response to JA treatment regardless of leaf age or light level. The magnitude of the change in PAL activity was related to light level, but was not greatly affected by leaf age. Increases in PAL activity due to treatment were consistently associated with elevations in condensed tannins and phenolic glycosides, except at low light levels. At low light, PAL activity increased in response to treatment, but condensed tannins and phenolic glycosides did not. This indicates that in poplar the lack of light can prevent induced phenolic synthesis, even with the induction of PAL activity.

PN was elevated in response to JA treatment in both low and moderate light (but not high light), concurrent with reductions in whole plant RGR. This occurred despite the lack of increased phenolic production in response to JA treatment. Poplar, with high levels of phenolic defenses (up to 4-5% condensed tannins, and up to 12% phenolic glycosides in this species) also makes some protein-based defenses, namely proteinase inhibitors, chitinase, and polyphenol oxidase (Bradshaw et al. 1991; Constabel et al. 2000; Haruta et al. 2001). The light- dependent increase in PN and reduction in RGR may be an indication that light-level can also mediate the production of these protein-based defenses.

102 Regulation of phenolic production in Populus Our results indicate that DS activity and PAL activity are not coordinately regulated in Populus. PAL activity is both light-dependent and responsive to JA treatment. DS activity is not responsive to JA treatment in Populus. although it may be somewhat light-dependent. As discussed in Logemann et al. (2000), mRNA levels associated with the two enzymes are routinely increased by the same stimuli in parsley. Ours is the first investigation of DS in Populus (or any tree species), although Populus PAL gene expression has been shown to be increased by some of the same stimuli implicated in coordinate regulation of DS and PAL in other species (Moniz de Sa et al. 1991; Subramaniam et al. 1993; Bucciarelli et al. 1998; Koch et al. 1998; Kao et al. 2002).

Increases in PAL activity were associated with increases in phenolic production under most, but not all, conditions. PAL activity was elevated by JA treatment in both young and mature leaves in low light, but no significant increases in phenolic production were measured. Also, PAL activity increased in response to JA treatment in mature leaves in high light, but with no increases in TPhen or CT (but some increases in SAL and HCH). These results indicate that at low light carbon availability (entering the shikimate pathway via DS) may limit phenolic production (due to lower PS, Lambers et al. 1998). So, although PAL activity is stimulated by JA treatment at low light, this response seems irrelevant for the regulation of phenolic production. Carbon in general may not be limiting at high light, but there may be a maximal amount of carbon diverted to the shikimate pathway; in essence, carbon supply to PAL could be metabolically limited at high light, and PAL up regulation again seems irrelevant. Alternatively, the diversion of carbon supplies to young sink leaves (with concurrent increases in sink strength due to increases in phenolic production, Arnold and Schultz 2001) may also be a limiting factor in high light.

Phenolic synthesis could be regulated by substrate supply to the shikimate pathway (erythrose 4-phosphate and phosphoenol pyruvate) and eventual Phe

103 supply to PAL (Herrmann and Weaver 1999), given that sufficient enzymatic machinery within the phenolic production pathway exists in both these cases (as evidenced by the plant’s ability to increase PAL activity at low light and the similarity of DS activity between low and moderate light). The concept of substrate regulation for phenolic production was originally proposed by Margna (1977) and da Cunha (1987) in the context of Phe as a limiting substrate for PAL, and the concept has recently been expanded to include substrate supply to the shikimate pathway. The transport of sugars into the chloroplast can be a limiting factor for constitutive and induced phenolic production in Arabidopsis (Voll et al. 2003, but see Walton 2003) and photosynthetic rates can be related to phenolic production (Chapter 2; Bassham and Dickmann 1983; Morrison and Reekie 1995; Oleskyn et al. 1998). However, the role of supply, from photosynthesis or from storage reserves, and the relationship of the shikimate pathway to sugar and starch reserves (both within and outside of the chloroplast, the location of the shikimate pathway) is still not well understood.

Carbon limitation of phenolic induction has several biological and ecological implications. Plants growing in low light may be more susceptible to prolonged insect attacks, due to their inability to respond with increases in carbon-based chemical defenses. Species able to modulate their carbon allocation patterns to allow for increased production of phenolics could have a competitive advantage in this scenario. However, the benefits of protection by the phenolic defenses would be offset by the cost of biochemical tradeoffs (at the expense of growth and/or protein production). Plants faced with competing needs for carbon among sink and source tissues may be forced to prioritize carbon resources, either by limiting or preventing the synthesis of defenses or increasing the senescence of older leaves or by aborting or reducing growth in younger tissues.

Effects of light on phenolic composition Light level affected the types of phenolic glycosides made by Populus, as well as the amounts. SAL and HCH were found in about a 1 to 4 ratio in young leaves in low light, and about a 2 to 3 ratio in mature leaves in low light; HCH content was

104 consistently higher than SAL. We found the reverse at moderate and high light; SAL content was consistently higher than HCH (usually in a 3 to 2 ratio). Structurally, HCH has two functional phenolic-based sidechains, while SAL has only one (SAL = glucose + a salicyl alcohol carboxylated to a cyclohexanone ring, HCH has an additional cyclohexanone ring carboxylated to glucose, Fig 3.14). Much of the biological activity associated with phenolic glycosides apparently comes from the cyclohexanone ring portion (Rehill et al. 2004), so HCH may effectively provide more biological activity than SAL while using less carbon. This could be another indicator that phenolic synthesis is carbon-limited at low light. However, the putative enzymes responsible for the interconversions between various phenolic glycosides have not been studied (Ruuhola 2001), and are another potential site of light-mediated regulation for the production of specific phenolic glycosides.

Impacts of JA versus GM treatments Larval gypsy moth chewing on mature leaves and the application of larval regurgitant to young leaves did not elicit the same responses obtained from the JA treatments. We expected that the application of larval regurgitant to young leaves might result in a weaker response than larval chewing on mature leaves (Havill and Raffa 1999), but also suspected that chewing treatments on older leaves could have had a systemic amplifying effect on young leaves (Davis et al. 1991), because the young leaves (LPI 3) were directly connected to the mature leaves being chewed (LPI 8)(Larson et al. 1972; Larson and Dickson 1973, Fig 3.15). The young and mature leaves could also have been in direct competition for resources due to this connection (Arnold et al. 2004). The design of our experiments does not allow us to speculate about either of these complications, so the larval regurgitant and larval chewing results should be examined separately and compared to responses to JA treatment, not each other.

Young leaves only significantly increased one type of phenolic glycoside (SAL) in response to the application of larval regurgitant. However, young treated leaves maintained consistent CT levels through the end of the experiment, while

105 young control leaves were reducing their CT content. So, in young leaves the application of larval regurgitant may have actually prevented a decrease in CT rather than cause an increase. Changes in TPhen and HCH were not significant in young leaves, but these categories tended to increase in treated leaves versus controls; so any changes were in the same direction as those caused by JA treatment. DS activity was reduced in response to the application of regurgitant in young leaves, but PAL did not change. Although the PAL response to larval regurgitant differs from the response to JA treatment, this result does reinforce the lack of coordinate regulation for these two enzymes. Mature leaves increased both TPhen and SAL in response to larval chewing. There were no significant changes in HCH or CT in mature leaves due to treatment, but CT tended to increase in treated leaves versus controls. Again, this indicates that any subtle changes were in the same direction as those caused by JA treatment. Enzyme activity responses to larval chewing on mature leaves were similar to responses to JA treatment; DS activity was not changed, but PAL activity increased.

The responses to both types of larval gypsy moth treatments were weaker than the responses to JA treatments, but generally similar. However, there are significant differences suggesting that responses to larval regurgitant, larval chewing, and JA differ in subtle but potentially significant ways. First, both SAL and HCH increased in response to JA treatment (at moderate and high light), but only SAL increased in response to application of larval regurgitant to young leaves and larval chewing on mature leaves. SAL is thought to be a structural precursor to HCH –salicortin (Lindroth and Pajutee 1987, Ruuhola 2001). The 6-HCH moiety is known to be particularly labile in phenolic glycosides. The rupturing of leaf compartments during herbivory could lead to the degradation of phenolic glycosides and the release of 6-HCH (6-HCH itself can also act as deterrent to herbivory, Clausen et al. 1989, Reichardt et al. 1990). Our results could mean either that synthesis of phenolic glycosides occurred more slowly in response to the gypsy-moth related treatments or that all HCH was being degraded to SAL and 6-HCH (detectable to us only as increases in SAL). Second, although the increase in PAL activity in chewed

106 mature leaves was associated with increases in TPhen and SAL (as expected from results with JA), there were some unexpected associations of phenolic production to enzyme activity in young leaves that received larval regurgitant treatment. Namely, the decrease in DS activity in young leaves was associated with a decrease in CT content and SAL content increased in young leaves despite the lack of increased PAL activity.

Evaluating the Protein Competition Model (PCM) The prominence of the association between PAL activity and increased phenolic production in our experiments led us to compare our results with the protein competition model (PCM) of Jones and Hartley (1999). The PCM is a mechanistic model for phenolic allocation based on the premise that phenolic production and protein synthesis compete for the same resource: phenylalanine. The PCM makes no assumptions about the regulatory mechanisms for phenylalanine biosynthesis; it merely assumes that phenylalanine is limiting. The PCM predicts that in plants that have no protein defenses, increases in phenolics (whether through herbivore induction, pathogen elicitation, light, etc.) will result in decreases in protein and eventual decreases in growth (see also Haukioja et al. 1998). The converse should also be true; when growth is rapid and the rate of protein synthesis is high, phenolic production should be low.

One of the apparent strengths of the PCM is that it claims to be valid for both constitutive and inducible defenses – a breadth that previous theories did not attempt. However, a major weakness for the application of the PCM is the constraint of being only relevant for plants without protein-based defenses. All plants make proteinase inhibitors, peroxidases, glucanases, chitanases, and oxidative enzymes directly or indirectly related to defense. Most plants with prominent carbon-based defenses probably have polyphenol oxidases with direct defensive roles as well. While producing high levels of phenolic defenses (up to 4-5% condensed tannins, and up to 12% phenolic glycosides in this species), poplar also makes significant commitments to protein-based defenses, namely proteinase inhibitors, chitinase,

107 and polyphenol oxidase (Bradshaw et al. 1991; Constabel et al. 2000; Haruta et al. 2001).

However, acknowledging the broad-based protein-based defenses disclaimer, our results do not support the PCM model. We did detect increases in protein in response to JA treatment in low light and these were associated with decreases in whole plant growth (but no changes in leaf growth or phenolic content). This was consistent with the PCM assertion that protein synthesis takes precedence over phenolic synthesis (Jones and Hartley 1999). However, competition for Phe did not prevent the concurrent increase of both protein and phenolic content in response to JA treatment in moderate light. Although there was a reduction in whole plant growth in moderate light, this tradeoff occurred at the plant level, not the leaf level, and was likely the result of competition for carbon and nutrients. The greatest increases in phenolic production occurred in high light, but they were also not associated with decreases in either protein content or leaf growth.

An alternative biochemical explanation also proposed by Jones and Hartley (1999) is that rather than competition for phenylalanine, pathways could share phenylalanine “…so that for every X molecules allocated to pathway A, nX molecules are allocated to pathway B.” This fixed allocation model at first seems more plausible for explaining the allocation strategies of a plant such as poplar, with several pathways competing for Phe, including both phenolic and protein-based defenses. However, this scenario would lead to the expectation that eliciting treatments would cause similar responses in both protein and phenolic content. Concurrent increases in both protein and phenolic production occurred only in moderate light in our experiments. Thus, neither the competition nor the fixed allocation model presented by Jones and Hartley (1999) explain the results observed in our experiments.

Our results in both tobacco and poplar indicate that the supply of Phe to PAL can sometimes be a control mechanism. We have shown that in tobacco, the

108 activity of DS is strongly associated with constitutive and induced phenolic production (Chapter 2), but that in poplar it is not. In poplar, carbon limitation seems to regulate both constitutive and induced phenolic production, but the mechanism for carbon limitation is an avenue for further investigation (and probably occurs before DS). PAL activity does play a regulatory role for induced total phenolic and phenolic glycoside production in poplar, but is unassociated with CT content under the same conditions. A true predictive model for phenolic production may lie in our understanding the regulation of the many isoforms for the PAL gene that exist in poplar (Osakabe et al. 1995) and other species with both high and low investment in carbon-based defenses (bean, Mavandad et al. 1990; alfalfa, Gowri et al. 1991; pine, Whetten and Sederoff 1991; pea, Yamada et al. 1994; Arabidopsis, Wanner et al. 1995; tobacco, Fukasawa-Akada et al. 1996; pine, Butland et al. 1998 ). Associating PAL isoforms with the production of specific phenolic categories could provide a gene-based mechanism for modeling the patterns we have observed.

109 Table 3.1. Model statistics from analysis of variance for chemical and biochemical measurements of young leaves: phenolic content (Folin- reactives), condensed tannins, DAHP synthase (DS) activity, and PAL activity. Experiment I was analyzed as a split-split plot experiment with light as the whole plot factor (in five blocks) and treatment X day as a factorial on the subplots. Experiments II and III were analyzed as completely randomized fixed effects ANOVAs. Abbreviations are as follows: Light (L), Treatment (Trtmnt, T), and Day (D).

Phenolic Content Condensed Tannins DS Activity PAL Activity Num Den F P N. D. F P N. D. F P N. D. F P DF DF DF DF DF DF DF DF LOW and MODERATE LIGHT (Experiment I)

Light 1 4 32.77 0.0046 1 4 0.32 0.6011 1 4 3.15 0.1507 1 4 218.81 0.0001 Trtmnt 1 62 5.87 0.0183 1 63 3.00 0.0882 1 60 1.59 0.2120 1 63 14.43 0.0003 L * T 1 62 15.03 0.0003 1 63 3.05 0.0852 1 60 2.52 0.1173 1 63 0.54 0.4666 110 Day 1 62 2.27 0.1366 1 63 64.45 <0.0001 1 60 30.18 <0.0001 1 63 70.15 <0.0001 L * D 1 62 4.67 0.0346 1 63 1.55 0.2179 1 60 13.83 0.0004 1 63 21.51 <0.0001 T * D 1 62 0.49 0.4851 1 63 0.18 0.6735 1 60 1.05 0.3103 1 63 7.73 0.0072 L * T * D 1 62 1.70 0.1975 1 63 0.18 0.6742 1 60 1.46 0.2316 1 63 0.00 0.9490

HIGH LIGHT (Experiment II)

Trtmnt 1 61 49.89 <0.0001 1 60 4.79 0.0325 1 61 0.01 0.9129 1 61 63.61 <0.0001 Day 3 61 16.12 <0.0001 3 60 9.04 <0.0001 3 61 2.17 0.1006 3 61 3.00 0.0372 T*D 3 61 2.69 0.0543 3 60 1.24 0.3035 3 61 0.63 0.5990 3 61 0.94 0.4249

Gypsy Moths at HIGH LIGHT (Experiment III)

Trtmnt 1 29 2.15 0.1529 1 29 11.59 0.0020 1 29 5.48 0.0263 1 29 0.68 0.4158 Day 1 29 2.02 0.1656 1 29 2.02 0.1661 1 29 2.81 0.1047 1 29 12.13 0.0016 T*D 1 29 0.46 0.5009 1 29 4.04 0.0537 1 29 1.14 0.2940 1 29 0.43 0.5196

Table 3.2. Model statistics from analysis of variance for chemical and biochemical measurements of mature leaves: phenolic content (Folin- reactives), condensed tannins, DAHP synthase (DS) activity, and PAL activity. Experiment I was analyzed as a split-split plot experiment with light as the whole plot factor (in five blocks) and treatment X day as a factorial on the subplots. Experiments II and III were analyzed as completely randomized fixed effects ANOVAs. Abbreviations are as follows: Light (L), Treatment (Trtmnt, T), and Day (D).

Phenolic Content Condensed Tannins DS Activity PAL Activity Num Den F P N. D. F P N. D. F P N. D. F P DF DF DF DF DF DF DF DF LOW and MODERATE LIGHT (Experiment I)

Light 1 4 3.01 0.1578 1 4 15.23 0.0175 1 4 0.87 0.4032 1 4 49.73 0.0021 Trtmnt 1 63 7.27 0.0090 1 63 0.63 0.4302 1 61 3.16 0.0804 1 63 84.55 <0.0001 L * T 1 63 3.45 0.0679 1 63 0.37 0.5465 1 61 0.51 0.4786 1 63 0.15 0.7029 111 Day 1 63 1.09 0.3012 1 63 19.79 <0.0001 1 61 15.33 0.0002 1 63 18.06 <0.0001 L * D 1 63 1.58 0.2130 1 63 2.36 0.1295 1 61 0.99 0.3227 1 63 0.18 0.6710 T * D 1 63 5.22 0.0258 1 63 3.79 0.0560 1 61 1.64 0.2046 1 63 12.61 0.0007 L * T * D 1 63 2.85 0.0965 1 63 0.00 0.9605 1 61 0.12 0.7275 1 63 0.02 0.8974

HIGH LIGHT (Experiment II)

Trtmnt 1 61 39.56 <0.0001 1 61 0.15 0.6996 1 61 0.32 0.5747 1 61 59.10 <0.0001 Day 3 61 16.65 <0.0001 3 61 26.71 <0.0001 3 61 1.46 0.2343 3 61 18.02 <0.0001 T*D 3 61 3.02 0.0363 3 61 1.08 0.3623 3 61 1.33 0.2733 3 61 2.57 0.0627

Gypsy Moths at HIGH LIGHT (Experiment III)

Trtmnt 1 29 7.25 0.0115 1 29 1.59 0.2174 1 29 0.00 0.9921 1 29 8.29 0.0073 Day 1 29 0.01 0.9043 1 29 5.48 0.0261 1 29 4.28 0.0474 1 29 86.71 <0.0001 T*D 1 29 0.04 0.8440 1 29 1.12 0.2985 1 29 0.21 0.6483 1 29 5.54 0.0253

Table 3.3. Model statistics for analysis of variance for chemical measurements of young leaves: phenolic glycosides (salicortin and HCH- salicortin) and protein. Experiment I was analyzed as a split plot experiment with light as the whole plot factor (in five blocks) and treatment by day as a factorial on the sub-plot for protein and just treatment as the sub-plot factor for phenolic glycosides. Experiments II and III were analyzed as completely randomized fixed effects ANOVAs. Only leaves harvested on day 3 for experiment I and day 4 for experiments II and III were analyzed for phenolic glycosides, thus day (D) was not part of the model structure. Abbreviations are as follows: Light (L), Treatment (Trtmnt, T), and Day (D).

Protein Salicortin HCH-Salicortin Num Den F P N. D. F P N. D. F P DF DF DF DF DF DF LOW and MODERATE LIGHT (Experiment I)

Light 1 4 3.39 0.1393 1 4 84.50 0.0008 1 4 18.25 0.0129 Trtmnt 1 58 7.84 0.0069 1 28 18.62 0.0002 1 28 14.07 0.0008 112 L * T 1 58 0.36 0.5535 1 28 15.43 0.0005 1 28 19.43 0.0001 Day 1 58 23.63 <0.0001 L * D 1 58 18.92 <0.0001 T * D 1 58 0.04 0.8446 L * T * D 1 58 0.93 0.3395

HIGH LIGHT (Experiment II)

Trtmnt 1 60 1.65 0.2042 1 15 21.91 0.0003 1 15 21.51 0.0003 Day 3 60 16.68 <0.0001 T*D 3 60 0.26 0.8572

Gypsy Moths at HIGH LIGHT (Experiment III)

Trtmnt 1 29 0.63 0.4341 1 15 7.56 0.0176 1 15 1.16 0.2989 Day 1 29 4.21 0.0493 T*D 1 29 2.26 0.1435

Table 3.4. Model statistics from analysis of variance for chemical measurements of mature leaves: phenolic glycosides (salicortin and HCH- salicortin) and protein. Experiment I was analyzed as a split plot experiment with light as the whole plot factor (in five blocks) and treatment by day as a factorial on the sub-plot for protein and just treatment as the sub-plot factor for phenolic glycosides. Experiments II and III were analyzed as completely randomized fixed effects ANOVAs. Only leaves harvested on day 3 for experiment I and day 4 for experiments II and III were analyzed for phenolic glycosides, thus day (D) was not part of the model structure. Abbreviations are as follows: Light (L), Treatment (Trtmnt, T), and Day (D).

Protein Salicortin HCH-Salicortin Num Den F P N. D. F P N. D. F P DF DF DF DF DF DF LOW and MODERATE LIGHT (Experiment I)

Light 1 4 91.81 0.0007 1 4 7.21 0.0550 1 4 2.92 0.1628 Trtmnt 1 63 2.62 0.1104 1 28 33.50 <0.0001 1 28 2.67 0.1133 L * T 1 63 0.51 0.4790 1 28 15.76 0.0005 1 28 0.96 0.3353 113 Day 1 63 0.04 0.8436 L * D 1 63 0.24 0.6242 T * D 1 63 1.18 0.2813 L * T * D 1 63 0.07 0.7951

HIGH LIGHT (Experiment II)

Trtmnt 1 61 0.02 0.8819 1 15 38.91 <0.0001 1 15 36.11 <0.0001 Day 3 61 1.33 0.2717 T*D 3 61 0.45 0.7206

Gypsy Moths at HIGH LIGHT (Experiment III)

Trtmnt 1 30 1.05 0.3126 1 15 8.60 0.0125 1 15 0.46 0.5098 Day 1 30 1.69 0.2038 T*D 1 30 2.96 0.0958

Table 3.5. Model statistics from analysis of variance (ANOVA) for whole plant relative growth rate (RGR) and photosynthesis measurements for young and mature leaves. The ANOVA for RGR was analyzed as a split-split plot design for experiment I, with light levels as the whole plot factor (in five blocks) and treatment by day as a factorial on the subplots. RGR for experiments II and III was analyzed as a completely randomized factorial ANOVA. For photosynthesis, a subset of plants (N=3 for each treatment combination) were measured on each day and repeated measures analysis of variance was used. Abbreviations are as follows: Light (L), Treatment (Trtmnt, T), and Day (D).

RGR PS for LPI 3 PS for LPI 8 Num Den F P N. D. F P N. D. F P DF DF DF DF DF DF LOW and MODERATE LIGHT (Experiment I)

Light 1 4 1.72 0.2599 1 8 2.64 0.1427 1 8 0.08 0.7833 Trtmnt 1 63 8.30 0.0054 1 8 0.36 0.5635 1 8 0.02 0.9051 L * T 1 63 0.02 0.8879 1 8 1.73 0.2252 1 8 0.07 0.7973 Day 1 63 1.51 0.2238 2 16 144.39 <0.0001 2 16 28.61 <0.0001 114 L * D 1 63 0.30 0.5840 2 16 0.64 0.5411 2 16 2.35 0.1278 T * D 1 63 0.10 0.7574 2 16 0.14 0.8696 2 16 0.27 0.7680 L * T * D 1 63 1.44 0.2349 2 16 1.19 0.3291 2 16 1.14 0.3447

HIGH LIGHT (Experiment II)

Trtmnt 1 61 0.76 0.3865 1 4 0.34 0.5891 1 4 0.02 0.8851 Day 3 61 1.64 0.1897 3 12 28.56 <0.0001 3 12 16.56 0.0001 T*D 3 61 0.87 0.4610 3 12 2.11 0.1523 3 12 0.50 0.0889

Gypsy Moths at HIGH LIGHT (Experiment III)

Trtmnt 1 32 0.67 0.4176 1 4 5.05 0.0879 1 4 4.17 0.1107 Day 1 32 0.15 0.7013 3 12 34.66 <0.0001 3 12 25.85 <0.0001 T*D 1 32 1.93 0.1747 3 12 0.10 0.9557 3 12 4.33 0.0275

Table 3.6. Model statistics from repeated measures analysis of variance of relative leaf growth for leaf plastochron index (LPI) leaves 0, 3, 6, and 8. A repeated measures amendment was made to the same model structure used to analyze chemical and biochemical measurements, i.e. experiment I was analyzed as a split-split plot and experiments II and III were analyzed as completely randomized fixed effects ANOVAs. A subset of plants (N=3 for each treatment combination) were measured from each experiment. Abbreviations are as follows: Light (L), Treatment (Trtmnt, T), and Day (D).

LPI 0 LPI 3 LPI 6 LPI 8 Num Den F P N. D. F P N. D. F P N. D. F P DF DF DF DF DF DF DF DF LOW and MODERATE LIGHT (Experiment I)

Light 1 8 2.22 0.1748 1 8 0.07 0.7961 1 8 2.27 0.1705 1 8 0.84 0.3853 Trtmnt 1 8 0.33 0.5838 1 8 0.02 0.8785 1 8 0.09 0.7710 1 8 1.36 0.2772 L * T 1 8 0.04 0.8552 1 8 1.18 0.3096 1 8 1.92 0.2032 1 8 0.25 0.6336 Day 2 16 136.87 <0.0001 2 16 197.79 <0.0001 2 16 17.49 <0.0001 2 16 2.61 0.1041 L * D 2 16 7.13 0.0061 2 16 1.70 0.2149 2 16 1.15 0.3425 2 16 0.20 0.8207 115 T * D 2 16 0.93 0.4153 2 16 0.37 0.6980 2 16 0.11 0.8923 2 16 0.96 0.4023 L * T * D 2 16 0.44 0.6547 2 16 1.29 0.3012 2 16 4.43 0.0294 2 16 0.23 0.7994

HIGH LIGHT (Experiment II)

Trtmnt 1 4 17.79 0.0135 1 4 2.44 0.1935 1 4 0.89 0.3986 1 4 0.36 0.5786 Day 2 8 128.99 <0.0001 2 8 106.43 <0.0001 2 8 17.11 0.0013 2 8 5.63 0.0298 T*D 2 8 25.31 0.0003 2 8 0.68 0.5322 2 8 0.99 0.4114 2 8 2.48 0.1455

Gypsy Moths at HIGH LIGHT (Experiment III)

Trtmnt 1 4 1.85 0.2451 1 4 1.32 0.3147 1 4 0.05 0.8386 1 4 0.21 0.6739 Day 2 8 126.97 <0.0001 2 8 125.54 <0.0001 2 8 13.38 0.0028 2 8 4.32 0.0535 T*D 2 8 1.31 0.3227 2 8 0.20 0.8215 2 8 0.14 0.8715 2 8 0.25 0.7859

Figure 3.1 Total phenolics (Folin-reactives) by light and leaf age in a Populus deltoides X P. nigra hybrid. Folin-reactives are expressed as equivalents of a poplar phenolic standard (purified as in Appel et al. 2001) in mg per mg dry weight (DW). N ≈ 10 for each graph symbol. Young leaves and mature leaves were LPI 3 and LPI 8 respectively (see text), and were sampled from the same plants. Plants in low light received ~ 100-200 µE, moderate light received ~ 400-700 µE (in blocks, experiment I), and high light ~ 800-1200 µE (experiment II). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

116 Figure 3.1

YOUNG LEAVES MATURE LEAVES A 0.3 B

0.2 No Differences LOW LIGHT

0.1 Solv Folin Reactives mg/mg D W Day 1 > Day 3 * JA 0.0 0 1 2 3 4 5 0 1 2 3 4 5

0.3 C D

MODERATE 0.2 LIGHT } *** } *** 0.1 Folin Reactives mg/mg D W

0.0 0 1 2 3 4 5 0 1 2 3 4 5

E F 0.3

HIGH 0.2 * LIGHT *** ** ND ** ** 0.1 ND *** Overall Increase Folin Reactives mg/mg D W Overall Increase *** 0.0 *** 0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 3.2 Condensed tannins (N-butanol assay) by light and leaf age in a Populus deltoides X P. nigra hybrid. Condensed tannins are expressed as equivalents of a poplar phenolic standard (purified as in Appel et al. 2001) in mg per mg dry weight (DW). N ≈ 10 for each graph symbol. Young leaves and mature leaves were LPI 3 and LPI 8 respectively (see text), and were sampled from the same plants. Plants in low light received ~ 100-200 µE, moderate light received ~ 400-700 µE (in blocks, experiment I), and high light ~ 800-1200 µE (experiment II). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

118 Figure 3.2

YOUNG LEAVES MATURE LEAVES 0.04

A B Solv JA 0.03

LOW 0.02 LIGHT ND * 0.01

Day 1 < Day 3 Condensed Tannins mg/mg D W *** Day 1 < Day 3 0.00 *** 0 1 2 3 4 5 0 1 2 3 4 5

0.04 C D

0.03

MODERATE 0.02 LIGHT * } ND 0.01 *

Condensed Tannins mg/mg D W Day 1 < Day 3 Day 1< Day 3 0.00 *** *** 0 1 2 3 4 5 0 1 2 3 4 5

0.05 E F 0.04 Treatment Difference 0.03 HIGH * { LIGHT 0.02 Overall Increase 0.01 Overall Increase ***

Condensed Tannins mg/mg D W *** 0.00 0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 3.3 High performance thin-layer chromatography (HPTLC) analysis of phenolic glycosides from Populus deltoides X P. nigra hybrid plants grown in low light (~100-200 µE, experiment I). Salicortin and HCH-salicortin, the two prominent phenolic glycosides found in this hybrid, were quantified with standards provided by the Lindroth lab (University of Wisconsin, Madison, WI) and are expressed as mg phenolic glycoside (PG) per mg dry weight (DW). N ≈ 10 for each graph symbol. Young leaves and mature leaves represent LPI 3 and LPI 8, respectively (see text), and were sampled from the same plant. Leaves were harvested three days after treatment. Folin-reactives and condensed tannin concentrations are shown for comparison, and are the same data presented in Figures 3.1 and 3.2 for low light. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

120 Figure 3.3 Total Phenolics mg/mg DW Total Phenolics mg/mg DW 0.0 0.1 0.2 0.3 0.0 0.1 0.2 0.3

Condensed Tannins mg/mg DW Condensed Tannins mg/mg DW 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.00 0.01 0.02 0.03 0.04 0.05 0.06 B A

Folin-Denis MATURE LEAVES No treatmentdifferences No treatmentdifferences YOUNG LEAVES Cond. Tannins

SAL

HCH JA Solv 0.00 0.02 0.04 0.06 0.08 0.10 0.00 0.02 0.04 0.06 0.08 0.10

Phenolic Glycosides mg/mg DW Phenolic Glycosides mg/mg DW

Figure 3.4 High performance thin-layer chromatography (HPTLC) analysis of phenolic glycosides from Populus deltoides X P. nigra hybrid plants grown in moderate light (~400-700 µE, experiment I). Salicortin and HCH-salicortin, the two prominent phenolic glycosides found in this hybrid, were quantified with standards provided by the Lindroth lab (University of Wisconsin, Madison, WI) and are expressed as mg phenolic glycoside (PG) per mg dry weight (DW). N ≈ 10 for each graph symbol. Young leaves and mature leaves represent LPI 3 and LPI 8, respectively (see text), and were sampled from the same plant. Leaves were harvested three days after treatment. Folin-reactives and condensed tannin concentrations are shown for comparison, and are the same data presented in Figures 3.1 and 3.2 for moderate light. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

122 Figure 3.4 Total Phenolics mg/mg DW Total Phenolics mg/mg DW 0.0 0.1 0.2 0.3 0.0 0.1 0.2 0.3

Condensed Tannins mg/mg DW Condensed Tannins mg/mg DW 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.00 0.01 0.02 0.03 0.04 0.05 0.06 B A *** *** Folin-Denis MATURE LEAVES YOUNG LEAVES * Cond. Tannins * *** *** SAL *** ND HCH JA Solv 0.00 0.02 0.04 0.06 0.08 0.10 0.00 0.02 0.04 0.06 0.08 0.10

Phenolic Glycosides mg/mg DW Phenolic Glycosides mg/mg DW

Figure 3.5 High performance thin-layer chromatography (HPTLC) analysis of phenolic glycosides from Populus deltoides X P. nigra hybrid plants grown in high light (~800-1200 µE, experiment II). Salicortin and HCH-salicortin, the two prominent phenolic glycosides found in this hybrid, were quantified with standards provided by the Lindroth lab (University of Wisconsin, Madison, WI) and are expressed as mg phenolic glycoside (PG) per mg dry weight (DW). N ≈ 10 for each graph symbol. Young leaves and mature leaves represent LPI 3 and LPI 8, respectively (see text), and were sampled from the same plant. Leaves were harvested four days after treatment. Folin-reactives and condensed tannin concentrations are shown for comparison, and are the same data presented in Figures 3.1 and 3.2 for high light. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

124 Figure 3.5 Total Phenolics mg/mg DW Total Phenolics mg/mg DW 0.0 0.1 0.2 0.3 0.0 0.1 0.2 0.3

Condensed Tannins mg/mg DW Condensed Tannins mg/mg DW 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.00 0.01 0.02 0.03 0.04 0.05 0.06 B A *** *** Folin-Denis MATURE LEAVES YOUNG LEAVES * Cond. Tannins ND *** *** SAL *** *** HCH JA Solv 0.00 0.02 0.04 0.06 0.08 0.10 0.00 0.02 0.04 0.06 0.08 0.10

Phenolic Glycosides mg/mg DW Phenolic Glycosides mg/mg DW

Figure 3.6 DAHP (3-deoxy-D-arabino-heptulosonate 7-phosphate) synthase (DS) activity by light and leaf age in a Populus deltoides X P. nigra hybrid, expressed as µmol DAHP per mg protein (PN; Bradford 1976). Assays were run for 30 minutes. N ≈ 10 for each graph symbol. Young leaves and mature leaves were LPI 3 and 8 respectively (see text), and were sampled from the same plants. Plants in low light received ~ 100-200 µE (experiment I), moderate light received ~ 400-700 µE (experiment I), and high light ~ 800-1200 µE (experiment II). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

126 Figure 3.6

YOUNG LEAVES MATURE LEAVES 0.20 B A Solv 0.15 JA

LOW LIGHT 0.10 Day 1 < Day 3 JA < Solv ** 0.05 } * } JA > Solv

DS Activity (umol DAHP/mg PN) tr 0.00 0 1 2 3 4 5 0 1 2 3 4 5

0.20 C D

0.15

MODERATE LIGHT 0.10 Day 1 > Day 3 ** Day 1 < Day 3** 0.05 JA > Solv } ND } DS Activity (umol DAHP/mg PN) tr 0.00 0 1 2 3 4 5 0 1 2 3 4 5

0.20 E F Overall Decrease 0.15 tr No Differences

HIGH 0.10 LIGHT

0.05 DS Activity (umol DAHP/mg PN) 0.00 0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 3.7 Phenylalanine ammonia-lyase (PAL) activity by light and leaf age in a Populus deltoides X P. nigra hybrid, expressed as nmol phenylalanine (PHE) per mg protein (PN; Bradford 1976). Assays were run for 3 hours. N ≈ 10 for each graph symbol. Young leaves and mature leaves were LPI 3 and LPI 8 (see text) respectively, and were sampled from the same plants. Plants in low light received ~100-200 µE (experiment I), moderate received ~ 400-700 µE (experiment I), and high ~ 800-1200 µE (experiment II). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

128 Figure 3.7 YOUNG LEAVES MATURE LEAVES

0.6 A B 0.5 Solv JA

0.4 LOW 0.3 LIGHT Day 1 > Day 3 *** Day 1 > Day 3 *** 0.2

0.1 ** ND ** PAL Activity (nmol PHE/mg PN) * 0.0 0 1 2 3 4 5 0 1 2 3 4 5

0.6 C D 0.5

0.4 Day 1 > Day 3*** MODERATE 0.3 *** LIGHT ND 0.2

0.1 *** **

PAL Activity (nmol PHE/mg PN) Day 1 > Day 3 0.0 ** 0 1 2 3 4 5 0 1 2 3 4 5

0.6 E F 0.5

0.4 *** { HIGH 0.3 *** LIGHT ** *** ND 0.2

0.1 Daily Fluctuations* PAL Activity (nmol PHE/mg PN) Daily Fluctuations 0.0 *** 0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 3.8 Protein by light and leaf age in a Populus deltoides X P. nigra hybrid, expressed as mg of protein per mg dry weight (DW). Bovine serum albumin was used as a standard. N ≈ 10 for each graph symbol. Young leaves and mature leaves were LPI 3 and LPI 8 (see text), respectively, and were sampled from the same plants. Plants in low light received ~ 100-200 µE (experiment I), moderate received ~400-700 µE (experiment I), and high ~ 800-1200 µE (experiment II). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

130 Figure 3.8

YOUNG LEAVES MATURE LEAVES 0.22

0.20 A B Solv JA 0.18 } 0.16 ** LOW No Differences LIGHT 0.14 0.12

0.10 Protein (mg/mg DW) Day 1 < Day 3

0.08

0.06 0 1 2 3 4 5 0 1 2 3 4 5

0.22

0.20 C D

0.18 MODERATE 0.16 LIGHT 0.14 } ** No Differences 0.12

0.10 Protein (mg/mg DW)

0.08

0.06 0 1 2 3 4 5 0 1 2 3 4 5

0.22

0.20 E F

0.18 Overall decrease

HIGH 0.16 *** 0.14 LIGHT No Differences 0.12

0.10 Protein (mg/mg DW)

0.08

0.06 0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 3.9 Whole plant relative growth rate by light level for a Populus deltoides X P. nigra hybrid, expressed as centimeters of growth since the start of the experiment per centimeter of original height on day 0. N ≈ 10 for each graph symbol. Plants in low light received ~100-200 µE (experiment I), moderate received ~400-700 µE (experiment I), and high ~800-1200 µE (experiment II). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

132 Figure 3.9

0.10 A 0.08 LOW 0.06 LIGHT } Solv ** JA 0.04

0.02

RGR (cm growth/cm orig height/day) 0.00 0 1 2 3 4 5

0.10 B 0.08 MODERATE 0.06 } LIGHT **

0.04

0.02

RGR (cm growth/cm orig height/day) 0.00 0 1 2 3 4 5

0.10 C 0.08 HIGH No Differences LIGHT 0.06

0.04

0.02

RGR (cm growth/cm orig height/day) 0.00 0 1 2 3 4 5

Day

Figure 3.10 Leaf relative growth by light level and leaf age for a Populus deltoides X P. nigra hybrid, expressed as centimeters of growth since the start of the experiment per centimeter of original height on day 0. Repeated measures were performed daily on a subset of plants. N ≈ 3 for each graph symbol. LPI 0 and 3 were young leaves, LPI 6 was a transitional leaf (just becoming photosynthetically competent) and LPI 8 was a mature leaf. All leaves were from the same plants, but only LPI 3 and 8 received treatment. Plants in low light received ~100-200 µE (experiment I), moderate received ~400-700 µE (experiment I), and high ~800- 1200 µE (experiment II). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

134 Figure 3.10 Solv LPI 0 & 3 JA LPI 6 & 8 0.25 1.4 A B No treatment differences 1.2 0.20

1.0 No treatment } LPI 6 differences } LPI 0 0.15 LOW 0.8 LIGHT 0.6 0.10 } LPI 3 0.4 0.05 0.2 } LPI 8 (cm growth)/(cm originial length) 0.0 0.00 0 1 2 3 4 5 0 1 2 3 4 5

0.25 1.4 C D 1.2 } LPI 0 0.20

1.0 No treatment No treatment differences differences 0.15 MODERATE 0.8 LIGHT 0.6 0.10 } LPI 3 } LPI 6 0.4 0.05 0.2

(cm growth)/(cm original length) } LPI 8 0.0 0.00 0 1 2 3 4 5 0 1 2 3 4 5

0.25 1.4 E No treatment F differences 1.2 for LPI 3 0.20 No treatment differences 1.0 *** 0.15 HIGH 0.8 ** LIGHT 0.6 0.10 ND 0.4 LPI 0 { 0.05 LPI 6 { 0.2

(cm growth)/(cm original length) LPI 3 { LPI 8 { 0.0 0.00 0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 3.11 Photosynthesis by light level and leaf age for a Populus deltoides X P. nigra 2 hybrid, expressed as µmol CO2 assimilated per m leaf area per second. Repeated measures were performed daily on a subset of plants. N ≈ 3 for each graph symbol. Young leaves and mature leaves were LPI 3 and LPI 8 respectively (see text), and were measured on the same plants. Plants in low light received ~100-200 µE (experiment I), moderate received ~400-700 µE (experiment I), and high ~800-1200 µE (experiment II). Dotted lines are for presentation purposes and do not imply known values for the intervals between graphed data points. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

136 Figure 3.11 YOUNG LEAVES MATURE LEAVES 20 A B

15

No treatment differences assimilation LOW 2 10 LIGHT /s CO 2

5 Solv No treatment differences JA umol/m

0 0 1 2 3 4 5 0 1 2 3 4 5

20 C D

15

assimilation No treatment differences

MODERATE 2 LIGHT 10 /s CO 2

5 No treatment differences umol/m

0 0 1 2 3 4 5 0 1 2 3 4 5

20 E F

15 assimilation HIGH 2 10 LIGHT /s CO 2 No treatment differences 5 No treatment differences umol/m

0 0 1 2 3 4 5 0 1 2 3 4 5

Day Day

Figure 3.12 Chemical and biochemical responses to gypsy-moth treatments by leaf age in Populus deltoides X P. nigra hybrid plants (grown in high light, ~800-1200 µE, experiment III). Folin-reactives and condensed tannins are expressed as equivalents of a poplar phenolic standard (purified as in Appel et al. 2001) in mg per mg dry weight (DW). DAHP (3-deoxy-D-arabino-heptulosonate 7-phosphate) synthase (DS) activity is expressed as µmol DAHP per mg protein (PN; Bradford 1976). Phenylalanine ammonia-lyase (PAL) activity is expressed as nmol phenylalanine (PHE) per mg PN. N ≈ 10 for each graph symbol. Young leaves and mature leaves were LPI 3 and LPI 8 respectively (see text), and were sampled from the same plants. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

138 Figure 3.12 YOUNG LEAVES MATURE LEAVES (GM = Regurgitant) (GM = Chewing) 0.30 0.10 A ND B 0.25 ND 0.08

Folin 0.20 Reactives 0.06 0.15 { & Ctrl > GM * 0.04 Condensed ND 0.10 * ND ND Tannins 0.02 0.05 Folin Reactives mg/mg D W Day 2 < Day 4* Condensed Tannins mg/mg D W FD-Ctrl 0.00 0.00 FD-GM 1 2 3 4 5 1 2 3 4 5 CT-Ctrl CT-GM

0.14 C D DS - Ctrl 0.12 DS - GM

0.10 Ctrl > GM DS 0.08 0.06 * { ND 0.04 ND

0.02 Day 2 > Day 4 DS Activity (umol DAHP/mg PN) 0.00 * 1 2 3 4 5 1 2 3 4 5

0.5 E F PAL - Ctrl 0.4 PAL - GM

ND 0.3 PAL Day 2 > Day 4 ND *** 0.2 * ND 0.1

PAL Activity (nmol PHE/mg PN) Day 2 > Day 4 0.0 *** 1 2 3 4 5 1 2 3 4 5

Day Day

Figure 3.13 High performance thin-layer chromatography (HPTLC) analysis of phenolic glycosides from Populus deltoides X P. nigra hybrid plants subjected to gypsy- moth treatments (grown in high light, ~800-1200 µE, experiment III). Salicortin and HCH-salicortin, the two prominent phenolic glycosides found in this hybrid, were quantified with standards provided by the Lindroth lab (University of Wisconsin, Madison, WI) and are expressed as mg phenolic glycoside (PG) per mg dry weight (DW). N ≈ 10 for each graph symbol. Young leaves and mature leaves represent LPI 3 and LPI 8, respectively (see text), and were sampled from the same plant. Leaves were harvested four days after the start of the experiment. Folin-reactives and condensed tannin concentrations are shown for comparison, and are the same data presented in Figures 3.12 for experiment III. Error bars represent one standard error from the mean.

Abbreviations representing statistical differences (p-values): ND = no statistical difference tr < 0.10 * < 0.05 ** < 0.01 *** < 0.001

140 Figure 3.13 Total Phenolics mg/mg DW Total Phenolics mg/mg DW 0.0 0.1 0.2 0.3 0.0 0.1 0.2 0.3

Condensed Tannins mg/mg DW Condensed Tannins mg/mg DW 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.00 0.01 0.02 0.03 0.04 0.05 0.06 B A * ND Folin-Denis MATURE LEAVES YOUNG LEAVES ND ND Cond. Tannins * * SAL ND ND HCH JA Solv 0.00 0.02 0.04 0.06 0.08 0.10 0.00 0.02 0.04 0.06 0.08 0.10

Phenolic Glycosides mg/mg DW Phenolic Glycosides mg/mg DW

Figure 3.14 Salicortin and HCH-salicortin (hydroxyl-cyclohexene-on-oyl salicortin), the two prominent phenolic glycosides found in the Populus deltoides X P. nigra hybrid used in these experiments.

142 Figure 3.14

Salicortin

6 5 HO 6' 1 5' O O 4 4' OH 2 1' 3 2' 3' 7 O OH 8 OH O OH

10 9 O 14

11 13

12 HCH-Salicortin 12' 13' 11'

14' 10' O 9' O 6 5 HO 8' 6' O 1 5' O O 4 4' OH 2 1' 3 2' 3' 7 O OH 8 OH O OH

10 9 O 14

11 13 12

Figure 3.15 Illustrations of the leaf plastochron index (LPI) and orthostichous leaves (with a direct, vertical vascular connection) for Populus spp. as described in Larson and Dickson (1973) and Larson and Isebrands (1971) and verified for this Populus deltoides X P. nigra clone (by using dyes, data not shown). Leaves follow a 2-3-5 spiral phyllotaxy; each leaf has a connection to leaves 2, 3, and 5 places above it and leaves spaced five positions apart are orthostichous. Thus, following a spiral pattern from the first index leaf (LPI 0) down the stem, a movement of five leaf positions completes a full rotation around the stem (each leaf is offset ~ 72° from the one above it, see arrows in diagram). Leaves with the same pattern in the diagram are orthostichous. LPI 3 and 8 were the leaves treated and harvested in these experiments. The transition from sink to source status occurs somewhere LPI 5 and 7 in this hybrid. The plants in these experiments had ~20-25 leaves per plant, although only 10 leaves are illustrated here.

144 Figure 3.15

0

1

2

3

Sink 4 Leaves

5

6 Transition Zone

7 Source 8 Leaves

9

10 LPI 0, 5, and 10

LPI 1 and 6

LPI 2 and 7

LPI 3 and 8 LPI 4 and 9 REFERENCES

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151 Chapter 4

The effects of translocated carbon sources on induced phenolic production in young leaves of Populus

INTRODUCTION

The ability of plants to respond to herbivory can be influenced by source-sink dynamics and the modular nature of plants can be used to explain why herbivory sometimes elicits responses from plants that seem erratic and unpredictable (Haukioja 1990, Honkanen et al. 1994). Active shifts in resource allocation can influence the translocation of carbohydrates in plants within these modules (Funk et al. 2004). Investigations of source-sink dynamics in Populus have revealed that young leaves deprived of assimilate from their source leaves are unable to augment phenolic production when challenged with wounding signals or herbivory (Arnold and 13 Schultz 2001, Arnold et al. 2004). Carbon dioxide ( CO2) assimilated by source leaves was found in phenolics produced in young sink leaves and the absence of source supply prevented phenolic induction. Induced invertase activity was also implicated as a mechanism for increasing sugar uptake by young leaves to supply induced phenolic synthesis (cleaving sucrose, a major phloem transport sugar, into glucose and fructose for use in young leaves, Arnold et al. 2004). However, the nature of the transported carbohydrates is still unclear.

Sucrose supply is important for supplying carbon to young leaves that are not photosynthetically competent. The route for inclusion of transported sucrose into phenolics in young leaves is presumably via glycolysis (PEP) and the pentose phosphate pathway (erythrose 4-phosphate) supplying substrates for 3-deoxy-D- arabino-heptulosonate 7-phosphate synthase (DAHP synthase, DS) and the shikimate pathway (SP, Herrmann and Weaver 1999)(Fig 4.1). We have previously demonstrated that DS is unresponsive to stimuli that elicit increased PAL activity in Populus and elevated PAL activity was associated with increased levels of phenolics

152 (condensed tannins and phenolic glycosides) in moderate and high light when plants were treated with a wounding signal, jasmonic acid (JA). However, at low light, even though PAL activity was elevated, no increases in phenolics were measured (Chapter 3). These observations indicate that regulation of phenolic production in low light could occur upstream of DS (within carbon supply pathways) or that DS is a rate-limiting step and its activity is determined by other signaling mechanisms. The phenylalanine (Phe) demands of increased PAL activity could be met without coordinate regulation of DS if an alternative supply of substrates was available to PAL, a non-DS route for the supply of Phe.

Although sucrose is a major transport sugars in plants, there are other candidate carbohydrates that could feed directly into phenolic metabolism. Weinstein et al (1959, 1961) suggested that an alicyclic SP byproduct, quinic acid (QA), may play an important role in aromatic biosynthesis in higher plants (Fig 4.1). QA exists at high levels in some plant species (particularly woody species; Boudet 1972, 1973; Yoshida et al. 1975), it can exhibit seasonal (Afzalpurkar and Lakshminarayana 1981, Osipov and Shein 1986; Gebre et al. 1998) as well as diurnal fluctuations (Gebre et al. 1997), and it can be transported in the phloem (Kluge 1964, 1970). QA pools accumulated early in the life of a leaf can be drawn upon to ‘feed’ the SP to increase or maintain Phe production or meet PP synthesis demands (Bonner and Jensen 1998, Osipov and Shein 1990, Osipov and Shein 1986, Ossipov and Aleksandrova 1982, Boudet 1972, 1973). Boudet et al. (1985) even suggested an unknown shikimate-independent route from carbohydrates to the synthesis of QA, duplicating some of the functions of the shikimate pathway in tracheophytes and woody angiosperms. This idea resembles the suggestion that there may be dual shikimate pathways (one in the chloroplast and one located primarily in the cytosol; Morris et al. 1989). Both ideas suggested non-chloroplastic carbon supply to Phe production and the phenylpropanoid pathway. Theoretically, QA supply from source leaves could bypass potentially rate-limiting steps in sink leaves within carbohydrate metabolism pathways or early in the SP (e.g. DS).

153 QA is presumably produced via the SP from dehydroquinate (DHQ) in the chloroplast and then is exported to vacuoles (Osipov and Shein 1986)(Fig 4.1 and 4.2A). Blocking EPSP synthase with glyphosate enhances incorporation of carbon into QA as well as SP intermediates (Stasiak et al. 1992, Lydon and Duke 1988, Ossipov and Aleksandrova 1986). QA serves as a ligand for many PPs, including phenylpropanoid products (e.g. chlorogenic acid) and may be recovered from them (Maury et al.1999, Hoffmann et al. 2003). Enzymatic interconversion of QA via quinate oxidoreductase (QORase; also quinate dehydrogenase, QD) and quinate hydrolase (QH) permit it to re-enter the SP at two points above chorismate (Fig 4.1 and 4.2A). The chloroplast is permeable to QA (Leuschner and Schultz 1991) and the necessary enzyme systems (Leuschner et al. 1995, Kang and Scheibe 1993, Osipov and Shein 1986) exhibit appropriate dynamics to provide the chloroplast with QA as a substrate for the SP and eventually Phe production. This means that the plant could meet a demand for either Phe or phenylpropanoids by drawing upon stored QA pools (Boudet 1972, 1973), as well as by relying on sucrose supplied by source leaves.

Another cyclic carbon intermediate could be supplied from source leaves to directly supply phenolic synthesis in sink leaves; shikimate (SK), an intermediate of the shikimate pathway, can also pass the chloroplast membrane (Bicket et al. 1978, Leuschner and Schultz 1991) to directly enter SP metabolism (Weinstein et al. 1959, 1961, Minamikawa and Yoshida 1972). Although the characterization of SP enzymes has only been undertaken in a few plant species, the evidence for the existence of the SP in all plants is strong (Herrmann and Weaver 1999). The enzymatic systems necessary for the entry of QA into the SP have been demonstrated in even fewer species, and are not well characterized (QA metabolizing enzymes are unexplored in Populus). Sucrose supply from source to sink leaves would serve as a general carbon source to support induced phenolic synthesis. QA supply would serve both as a phenolic-specific carbon supply and as a means of bypassing earlier regulation steps by late-entry into the SP if the enzyme

154 systems are present, but SK supply would have the same benefits as QA and we can presume that the appropriate enzyme systems are already present.

We hypothesized that sucrose, QA, and SK can act as transported carbon sources and supply the carbon demands for phenolic synthesis in young leaves. Additionally, if QA or SK serve as alternate entry points for carbon into the shikimate pathway (bypassing DS), leaves that are supplied with QA or SK should be able to support greater levels of phenolic induction than those fed sucrose. This ability would allow for metabolic plasticity in situations where PAL responds to eliciting stimuli, but DS does not (and potentially constrains the potential flow of carbon to PAL) as we found in Populus. As preliminary investigations, we exploited the phyllotaxy of Populus and conducted two experiments to explore the role of supply- based regulation on phenolic production via in situ supplementation with external carbon supplies.

MATERIALS and METHODS

Experimental design We conducted two experiments using clonal hybrid poplar (Populus deltoides X P. nigra) saplings. In each experiment, we used the known phyllotaxy of Populus spp. (Larson et al. 1972) to investigate the effects of external carbon sources on induced phenolic production in young leaves. We were also interested in the effect of light level on a plant’s ability to use the external carbon sources, because of the lack of phenolic induction in previous experiments with Populus in low light (Chapter 3). Experiment I was conducted at high light and was designed to examine the effects of three different external carbon sources (sucrose, QA, and SK, with a control for the method of delivery). Experiment II was designed to compare the effects of two external carbon sources (sucrose and QA) at two light levels – low and moderate. In both of these experiments, we elicited phenolic production by the application of the wounding signal, jasmonic acid (JA), intended to mimic the effects of insect herbivory (Creelman and Mullet 1997, Kessler and Baldwin 2002). Both

155 experiments were conducted in a greenhouse during May (exp. I) and July (exp. II) 2002.

Study system (plant material & growth conditions) We grew hybrid poplar saplings (Populus deltoides X P. nigra; clone OP-367, Segal Ranch, Wash.) from cuttings in 9 L pots containing Metro Mix 250 (with starter nutrient charge; Scotts-Sierra Horticultural Products Co., Marysville, OH). Plants were grown with supplemental lighting from one 400W high-pressure sodium lamps for every 9 plants (14 h day, coinciding with sunrise and sunset). Using this lighting arrangement, high light levels of approx. 800-1200 µE (HIGH) were maintained for all plants until the start of each experiment. For experiment I, all plants were maintained at HIGH light. For experiment II, light levels were manipulated by the addition of shade treatments in a randomized split-split plot design. Five blocks were used, with light as the whole-plot factor within each block (5 each of low and moderate light), and treatment and day of harvest as a 2X2 factorial on each subplot. Light levels that were an average of 100-200 µE at plant height were achieved for low light (LOW) with 80% shade cloth that covered the shade whole- plots in each block (PAK Unlimited, Inc., Cornelia, GA). The shade tent structures also bordered the light plots on three sides, reducing the light received in these plots to 400-700 µE (MODERATE).

During sapling growth, we randomized plant locations on greenhouse benches every 3-4 days up until the time of the experiments using a random number generator (Excel, Microsoft Corporation, Redmond, WA). Plants were allocated to treatment groups based on their size one day before the beginning of treatments. The allocation procedure was similar to that used by Ohnmeiss and Baldwin (1994) and consisted of sorting all plants by height and leaf number and randomly assigning plants into treatment groups by consecutive random halvings. This procedure produced treatment groups with plant sizes that were not significantly different on day 0 (data not shown). Experiment I (HIGH) used 120 plants in 12 treatment groups with a grouping size of N=10: Carbon Treatment (4) X Spray Treatment (3).

156 The four carbon treatments were 50 mM sucrose, 50 mM shikimic acid, and 50 mM quinic acid, with a control for feeding method. The two spray treatments were a 5 mM JA spray, and its solvent control spray (see below for descriptions). Experiment II (LOW & MODERATE) used 80 plants in 8 treatment groups with a grouping size of N=10: Light (2) X Carbon Treatment (2) X Spray Treatment (2). The two carbon treatments were 50mM sucrose and 50 mM quinic acid and the spray treatments were JA and its control. Plants were ~30 cm tall and had 25-28 leaves at the time of experiments.

To ensure that individually sampled leaves were of the same physiological age, they were numbered from the top down at the start of each experiment leaves according to the plastochron index method of Larson and Dickson (1973) and Larson and Isebrands (1971). The first leaf from the apical bud that was longer than 2.5 cm was designated as leaf plastochron index 0 (LPI 0), and leaves were numbered consecutively downward. We chose to investigate the effects of external carbon supply on induced phenolic production in LPI 3 and chose LPI 2 for analysis as a within-plant control because of its parallel vascular connections. Each leaf is supplied by three vascular bundles, and in this case LPI 3 is connected to LPI 5, 6 and 8 and LPI 2 is connected to LPI 4, 5, and 7 (Larson et al. 1972, Arnold and Schultz 2002). Photosynthetic profiles revealed that the transition to a fully- photosynthetically competent leaf occurs between LPI 5 and 7, so both LPI 2 and 3 acted as sink leaves, and both were connected to a mid-plant source leaf (LPI 7 and 8, respectively). The removal of LPI 6 for supplementing with artificial carbon sources left LPI 3 with one intact mid-plant source leaf (LPI 8), and one transitional sink-source leaf (LPI 5). LPI 2 shares no direct connection to either LPI 3 or 6, but has a similar source leaf structure; it is connected to one mid-plant source leaf (LPI 7) and the same transitional sink-source leaf as LPI 3 (i.e. LPI 5)(Fig 4.3). We verified all connections (and the lack of connections) with studies of tracer dyes (data not shown).

157 Treatments We treated all sink leaves (LPI 3 and above) with JA to elicit phenolic production. Sprays consisted of 5mM jasmonic acid (±JA) dissolved in 3% (aq.) ethanol, with 0.125% (v/v) Triton-X 100 detergent to help penetrate the waxy leaf cuticle. Solvent controls (Solv) were the same, minus JA. Distilled, deionized water was used for water treatments (W). The sprays were applied to designated plants in a fine mist until the leaves just began to drip. The mist was applied to the adaxial side of leaves located at LPI 3 and above, using shielding for spray administration.

To identify potential carbon sources for transport from source to sink leaves in support of phenolic induction, we supplemented plants with several artificial carbon supplies. We used sucrose, SK, QA, and a buffer control for experiment I in high light, and just sucrose and QA for experiment II in low and moderate light. We prepared 50 mM stock solutions for the external carbon sources at the beginning of each experiment. This concentration was chosen based on extrapolation from previous feeding experiments with plant cuttings (Minamikawa and Yoshida 1972) as well as a rough calculation of what would be required to supply carbon precursors for the amount of induced phenolic production seen in our previous experiments with this hybrid. Carbon sources were brought to pH 7.0 with KOH (forming potassium quinate at neutral pH; Minamikawa and Yoshida 1972). The carbon feeding method control (Buffer) consisted of the same amount of KOH in ddH2O brought to pH 7.0 (adjusted with HCl).

We removed LPI 6 from every plant used in both experiments by breaking the leaf-petiole juncture, leaving the petiole attached to the stem to act as a conduit for carbon supplementation. Petioles were immediately inserted into pre-drilled holes in the lids of 1.5 mL Eppendorf tubes containing either 50 mM sucrose, SK, QA, or the feeding control solution (Buffer). During the course of each experiment, tubes were refilled to maintain liquid levels that covered the petioles. We accomplished this without removing the petioles from their feeding tubes through the use of syringes inserted between the lid and the top of the tube (caps were cracked slightly, tubes

158 were not punctured). Liquid levels in feeding tubes were monitored 24-hr/day and supplemented as needed until just before harvest. The volume of external carbon source taken up by each plant was recorded.

Plant sampling We harvested all designated leaves three days after treatment, between 2 and 4pm, to coincide with sampling intervals from previous experiments (Chapter 3, Arnold and Schultz 2001, Arnold et al. 2004). We removed leaves at the petiole, excised stems, placed the leaf material in coin envelopes, flash froze them in liquid nitrogen, and temporarily stored them on dry ice while in the greenhouse. We transferred samples to storage at -20 °C at the end of the harvest and lyophilized them two days later. The samples were stored in airtight containers with desiccant at -20°C until analysis.

Growth and photosynthesis measurements We monitored plant growth, leaf growth, and photosynthesis in experimental plants because these physiological parameters are sometimes affected by changes in phenolic synthesis and JA treatment. We measured whole plant growth rate and relative leaf growth rate for LPI 0, 1 and 3 for all plants in both experiments, but photosynthesis was monitored only for a subset of plants (N=36 for experiment I, N=24 or experiment II, 3 per treatment combination) on day 0, 1, 2, and 3 for both experiments. We measured photosynthetic carbon assimilation (PCA) for LPI 3 using a LICOR 6400 Portable Photosynthesis System (LICOR, Lincoln, Nebraska) and a LICOR external LED light source to provide 400µE for both experiments. This light level reflects the MODERATE light treatment. Preliminary trials revealed that the time required for establishment of equilibrium for each data point at extreme light levels (LOW & HIGH) would have prevented completion of photosynthetic measurements before the desired harvest time. Thus, these photosynthesis measurements are a reflection of potential photosynthetic capacity rather than actual photosynthetic rates in the greenhouse. PCA was expressed as CO2 uptake

159 (µmol·m-2·s-1). Results for whole plant relative growth rate and photosynthesis were not significantly changed by JA treatment (data not shown).

Chemical analysis: spectrophotometry We ground plant samples to a fine powder using porcelain mortars and pestles (Coorstek., Golden, CO) for analysis of leaf chemicals. Using subsamples from each vial of leaf powder, we determined protein, total phenolic, condensed tannin, and phenolic glycoside content. Protein was extracted and analyzed following the methods of Jones et al. (1989). Leaf powder (3 mg) was extracted for 2 hrs in 1.5 mL of 0.1 N sodium hydroxide at 100 °C and allowed to cool for 20 minutes before assaying. Protein samples were quantified using bovine serum albumin (BSA) as the standard.

To extract phenolics, we washed leaf powder (10 mg) with ether (3 X 500 µl) and then extracted with 70% (aq.) acetone containing 1 mM ascorbate (3 X 250 µl, with 10 min sonication for each extraction). Samples were micro-rotovapped to remove acetone and supplemented with ddH2O for a final extraction volume of 500 µl. Total phenolics were assayed using the Folin-Denis method as described in Appel et al. (2001), amended to include lithium sulfate (8% w/v) in the Folin-reagent to prevent precipitate formation (Singleton and Rossi, 1965). Condensed tannin concentrations were measured as extracted proanthocyanidins by the acid-butanol method (Hagerman and Butler 1989). We constructed standard curves for both the Folin assay and the acid-butanol assay using purified standards from non- experimental leaves from the experiments. Purification procedures were as in Hagerman and Klucher (1985) and Appel et al. (2001). The results from these assays are expressed as mg Folin-reactives/mg DW and mg acid-butanol- reactives/mg DW, respectively (shorthand mg/mg DW). The results from these two assays should not be viewed as absolute values, because the constraints of the purification procedure favor the enrichment of condensed tannins over phenolic glycosides in the final mixture of purified phenolics. However, because the same

160 standards were used for all three experiments, comparisons among treatments and experiments are still valid.

Chemical analysis: High performance thin layer chromatography We analyzed leaf samples for phenolic glycosides only from LPI 3, due to limited sample material from LPI 2 leaves. We measured concentrations of salicortin (SAL) and HCH-salicortin (hydroxy-cyclohexen-on-oyl salicortin, HCH), the most abundant phenolic glycosides in this hybrid poplar clone (data not shown), using high performance thin layer chromatography (HPTLC). We also identified, but did not quantify, trace amounts of two other phenolic glycosides in this Populus hybrid, tremuloidin and tremulacin. Extraction of phenolic glycosides consisted of extracting leaf power (12.5 mg) directly into ice cold methanol (500 µl, HPLC quality). Samples were sonicated for 15 min and leaf powder was pelleted in the sample tubes by centrifugation. Silica gel HPTLC plates were spotted in duplicate (1 µl) directly from these extracts. HPTLC procedures and equipment were as in Lindroth et al. (1993) and were performed in Rick Lindroth’s lab (Entomology Dept., University of Wisconsin, Madison, WI). Briefly, plates were developed [solvent

CH2Cl2:MeOH:THF (30:5:5)], then scanned and analyzed with a Camag TLC scanner and associated software (Camag Scientific, Wrightsville Beach, NC). Salicortin and HCH-salicortin standards were purified from aspen leaves (Populus tremuloides) by Brian Rehill (United States Naval Academy, Annapolis, MD; Rehill et al. 2004).

Statistical Methods Some chemical measures were log transformed before analysis to meet the underlying assumptions of parametric tests (Zar 1999). Data for each physiological measure were tested for outliers and in most cases, where no known reason for exclusion existed, the Winsorization method was used for statistical analysis (Sokal and Rohlf 1995). For illustration of untransformed data, error bars represent the standard error of the mean. For data that was transformed for analysis, the transformed mean and the interval indicated by the standard error of the transformed

161 mean were converted back to original units for graphical illustration, in some cases resulting in asymmetry.

We used the Mixed procedure in the SAS statistical package (Version 8.2, SAS Institute, Inc., Cary, NC) for all statistical analyses. Measurements from LPI 2 and 3 were performed separately, but within-plant LPI 2 measurements and the uptake from the external carbon sources (volume measurements) were explored as covariates for LPI 3 measurements. We only included the covariate terms in the model statements if they were significant (P<0.05) and determined appropriate model statements from the data structure (i.e. common or unequal slopes). When covariate terms were used, we also used the Mixed procedure to generate least- squares means (adjusted for the covariates).

Since we do not know all the effects, even on patterns of carbon allocation, of removing LPI 6 (e.g. Larson and Whitham 1997), it was important to use LPI 2 values as covariates in analysis of LPI 3 measurements. We presumed that any unintended effects on LPI 2 were the result of shifting carbon allocation patterns and that these effects would be shared by LPI 3. Thus, using LPI 2 as a covariate for LPI 3 removes the effects of LPI 6 removal and statistical differences in LPI 3 responses can be attributed to the carbon fed at LPI 6.

Experiment I was analyzed as a completely randomized fixed-effects analysis of variance, with carbon treatment and spray treatment as main effects. After we determined that there was no difference between water (W) and solvent (Solv) treatments, all W data points were removed from the analysis of experiment I to simplify the interpretation of results. Experiment II was analyzed as a split-split-plot design as described in Littell et al. (1996), with light as the whole-plot within each block, and carbon treatment and spray treatment as a factorial on each subplot. Pairwise contrasts (JA vs. Solv) were explored if the model interaction terms (e.g. Carbon X Spray) had p-values less than 0.25. Unadjusted p-values are given for these pairwise comparisons solely to indicate the individual contributions to

162 significant model terms. For the same reason, several non-significant pairwise comparisons are indicated in appropriate figures.

Chemicals Unless otherwise noted, all chemicals used for the chemical analyses were obtained from Sigma-Aldrich Co., St. Louis, MO and were the highest quality available.

RESULTS

The effects of carbon feeding method on within-plant controls We expected that the removal of LPI 6 would have no effect on the ability of LPI 2 (within-plant control, WPCtrl) leaves to increase phenolic production in response to JA treatment, because LPI 6 is not directly connected to LPI 2. However, in high light (experiment I) LPI 2 leaves on plants that had LPI 6 removed and replaced with vials containing buffer, were unable to increase phenolic production significantly (either total phenolics or condensed tannins) when treated with JA (Fig 4.4A and 4.5A).

The nature of the external carbon source supplied at LPI 6 also had an effect on whether phenolic production could be increased in LPI 2 with JA treatment. The only plants in which LPI 2 leaves increased phenolic production (but not condensed tannins) in response to JA when fed sucrose via LPI 6 were those in high light (Fig 4.4B) Fig 4.5B) .LPI 2 leaves on plants in high light fed either SK (Fig 4.4C and 4.5C) or QA (Fig 4.4D and 4.5D) at LPI 6 were unable to increase phenolic production significantly in response to JA treatment. However, in moderate light, plants that were fed either sucrose or QA at LPI 6 increased phenolic production in LPI 2 (total phenolics and condensed tannins) in response to JA treatment (Figs 4.7C and D, 4.8C and D).

163 Prior results with Populus (Chapter 3) indicated that low light levels prevented even young leaves from increasing phenolic production in response to JA treatment. As expected, plants in low light that were fed sucrose or QA at LPI 6 were not able to increase total phenolic production in LPI 2 when elicited with JA (4.7A and B). However, LPI 2 leaves in both sucrose and QA-fed plants were able to increase condensed tannin production slightly in response to JA in low light (4.8A and B).

The effects of buffer feeding on connected sink leaves We expected that the feeding of buffer via LPI 6 would act as a control for the addition of the aqueous solution (pH 7.0, with KOH and HCl) and would not have any eliciting effects on phenolic production. We also anticipated that the removal of LPI 6 (a source leaf by the time of harvest) would cause a reduction in or abolish the ability to increase phenolic production in LPI 3 in response to JA treatment. As expected, LPI 3 leaves fed with only buffer at LPI 6 were unable to increase phenolic production (total phenolics or condensed tannins) in response to JA (Fig 4.4A and 4.5A).

The effects of sucrose feeding on connected sink leaves We expected that the feeding of sucrose via LPI 6 would replace the function of the removed source leaf and allow phenolic production to be increased in LPI 3 in response to JA in moderate and high light, but that no increases in phenolic production would occur in low light. As expected, LPI 3 leaves fed sucrose at LPI 6 were able to increase total phenolics (including SAL, but not HCH or condensed tannins) in response to JA treatment in high light (Fig 4.4B and 4.5B, but see Table 4.6). Sucrose feeding did not restore the ability to increase phenolic production in response to JA treatment in low or moderate light (Fig 4.7A and C, 4.8A and C). Also, RLGR was reduced in LPI 3 leaves fed sucrose at LPI 6 and treated with JA in both high and moderate light (Fig4.6B and 4.9C).

164 The effects of QA feeding on connected sink leaves We expected that the feeding of QA via LPI 6 would also replace the function of the removed source leaf in terms of allowing phenolic production to be increased in LPI 3 in response to JA. As expected, LPI 3 leaves fed QA at LPI 6 were able to increase phenolic production in response to treatment in moderate and high light, but not in low light. In contrast to sucrose fed in high light, QA fed leaves were able to increase total phenolics, SAL, HCH (Fig 4.4D), and condensed tannins (Fig 4.5D), as well as LRGR (Fig 4.6D). QA-fed leaves in moderate light were able to increase total phenolics and HCH (Fig 4.7D) in response to JA treatment, but not SAL, condensed tannins (Fig 4.8D) or LRGR (Fig 4.9D).

The effects of SK feeding on connected sink leaves We expected that the feeding of SK via LPI 6 would also replace the function of the removed source leaf in terms of allowing phenolic production to be increased in LPI 3 in response to JA. As expected, LPI 3 leaves fed SK at LPI 6 were able to increase phenolic production in response to treatment in high light (SK was not investigated at low and moderate light due to greenhouse space constraints). SK fed leaves were able to increase total phenolics and SAL (Fig 4.4C), but not SAL or condensed tannins (Fig 4.5C).

DISCUSSION

Source effects on induction in young leaves. We found that removing the LPI 6 leaf and replacing it with buffer abolished the ability of LPI 3 to increase phenolic production. This is consistent with previous studies in which removal of source leaves blocked similar responses in the directly- connected young sink leaves (Arnold et al. 2004). However, we also observed blocked responses in LPI 2, which is not thought to have major transport connections with LPI 6 (Larson and Dickson 1973, Larson and Isebrands 1971). This may be explained in at least three ways: 1) LPI 2 does have a major connection

165 with LPI 6, 2) LPI 2 leaves were not responsive to JA treatment or 3) even the removal of one source leaf can shift carbon-allocation patterns among sink leaves.

The first and second explanations are unlikely because preliminary trials with dye feeding in non-experimental plants repeatedly revealed the predicted phyllotaxy to be valid for this Populus hybrid (data not shown) and it has been shown consistently that phenolic production is highly inducible in LPI 1-3 in this Populus hybrid (Chapter 3, Arnold and Schultz 2001, Arnold et al. 2004). The third explanation is the most plausible. LPI 2 and 3 do share some source leaves, even though they are not directly connected to each other; LPI 3 is supported by more mature leaves at positions LPI 5, 6, 8, 10 (etc.) and LPI 2 is supported by source leaves LPI 5, 7, 9, 10 (etc.). When LPI 6 was removed, the supply demands from LPI 3 to the common source leaves (e.g. LPI 5 and 10) could have increased, reducing the ability of the remaining source leaves to supply LPI 2 as well as LPI 3. This is consistent with the concept of sink competition, in which changes in numbers of sinks and sources alter carbon allocation among tissues (Larson and Whitham 1997, Arnold et al. 2004).

Differential ability of sucrose, quinic acid and shikimic acid to support phenolic production. We tested the hypothesis that various translocated carbon sources may supply JA-elicited phenolic production by replacing a source leaf (LPI 6) with carbon in the form of either sucrose, SK, or QA. Sucrose feeding at LPI 6 in high light restored the ability to increase phenolic production in response to JA treatment in both LPI 2 and LPI 3, essentially reversing the effect of removing LPI 6. However, in low and moderate light, sucrose feeding at LPI 6 did not restore phenolic responses to JA in LPI 3. On the other hand, QA feeding at LPI 6 did restore phenolic responses to JA in LPI 3 in both moderate and high light. LPI 3 leaves fed QA in high light were able to increase production of all phenolic categories measured, as well as LRGR, in response to JA treatment, but in moderate light were only able to increase production of total phenolics and HCH. SK feeding was only tested in

166 high light and LPI 3 leaves fed SK were able to increase production of total phenolics and SAL.

The results from our feeding experiments demonstrated that the supply of a single carbon source (sucrose, QA, or SK) can restore some or all of the function of a source leaf in terms of phenolic induction and that SK and QA can probably be incorporated into the SP, bypassing the earlier portion of the SP (i.e. DS, Figs 4.1 and 4.2). Sucrose and SK feeding were able to restore increased phenolic production in LPI 3 leaves in high light to levels similar to previous experiments in some categories, but only QA feeding had a consistent stimulating effect on the restoration of increased production in all phenolic categories. QA also restored increased production for more phenolic categories in moderate light than did sucrose (Fig 4.9).

This pattern probably emerged because sucrose is a general carbon supply and QA is a carbon supply that is more specific for SP products. Sucrose has to first be cleaved by an invertase into glucose and fructose and catabolized via glycolysis and the pentose phosphate pathway to PEP and E4P in order to enter the SP and support phenolic production. Along the way, other pathways competing for carbon can also utilize the breakdown products of sucrose. There is no evidence that QA metabolism interlocks with any other catabolic or anabolic pathway in plants other than the SP, and the products of its metabolism enter either the SP or participate directly in phenylpropanoid synthesis (e.g. chlorogenic acid, Niggeweg et al. 2004). The number of enzymatic steps required from sucrose to phenolics versus from QA to phenolics on its own indicates that QA would probably work more quickly than sucrose as a translocated carbon source in support of phenolic production. However, dose-response and time-course experiments using both QA and sucrose are still needed.

167 The effect of light level Light level influenced the effects of external carbon supply on the restoration of phenolic induction by JA. Depending upon carbon source and phenolic category, some type of restoration occurred for each carbon source in both moderate and high light, but not at low light (Fig 4.9). Uptake from carbon supply tubes was driven by transpirational flow as well as diffusion (sensu Fisher 2000), but we can rule out differences in transpiration as an explanation for differences in carbon use among light environments because light level did not affect the volume of uptake from supply tubes (data not shown). Uptake data does not reveal diffusion rates, which could be affected by existing concentrations (of sucrose, QA, SK) within the plant. However, QA and SK uptake were probably not affected by in planta concentrations because both are generally sequestered in cellular compartments (QA in the vacuole and SK in the chloroplast). Due to the effects of light on carbon assimilation, in planta sucrose levels were probably directly related to light level and sucrose uptake was the most likely to be influenced by diffusion versus transpiration. However, we do not know the existing concentrations of sucrose (or QA and SK) within experimental plants. An additional explanation for the effect of light on carbon utilization is that if sucrose, QA and SK utilization pathways (i.e. invertases, glycolysis, QA pathway, SP) are all down-regulated at low light and are not substrate-limited, then supplying external carbon would serve to increase metabolite pools prior to the first rate-limiting step, but not increase phenolic synthesis.

Alternative carbon sources for defense responses in low light We had expected that feeding sucrose and QA (and SK, although it was not tested) would allow Populus plants to increase total phenolic production in response to JA treatment at low light, by bypassing the presumed constraint of DS activity (DS activity was not responsive to JA treatment, and somewhat light dependent in this Populus hybrid, while PAL activity was increased by JA treatment, Chapter 3). However, neither sucrose nor QA feeding were able to support induced phenolic production at low light. There are several explanations for this, none of which are mutually exclusive : 1) feeding sucrose or QA does improve the plant’s ability to

168 manufacture Phe, but it is used primarily for protein synthesis at low light, 2) another aspect of carbon supply is light-limited, such as enzymes for the utilization of translocated carbon or transport into the chloroplast, and 3) enzymes downstream from PAL (e.g. chalcone synthase) could be light-limited for phenylpropanoid synthesis, effectively preventing any increases in phenolic synthesis regardless of supply.

The first explanation has already been proposed as a mechanism producing observed patterns of phenolic production in plants. The protein competition model for phenolic production (Jones and Hartley 1999) states that when Phe is limiting the synthesis of protein will predominate over phenolic synthesis. However, the mechanism proposed is variation in PAL activity, which we have shown to be elevated by JA in low light (Chapter 3). Hence reduced PAL activity at low light levels is unlikely to constraint JA-responsive phenolic production.

The second and third explanations seem more likely. The utilization of sucrose could be light-limited, which would restrict translocation to sinks. Translocation within plant vasculature is driven by both bulk flow (pressure gradients) and diffusive transport; bulk flow can be affected by transpiration (apoplastic) or osmotically generated pressure (symplastic). One way to increase transport would be to increase utilization (contributing to the osmotically driven pressure gradient)(Fisher 2000) and essentially, this is what Arnold et al. (2004) have demonstrated by reporting increases in invertase activity in sink leaves treated with JA. Low light may constrain the activity of invertases (e.g. soluble invertases, Le et al. 2001, Medina et al. 1999, Rabe et al. 1999, and Yun et al. 2002; but cell- wall bound invertases have not been shown to be light-dependent) or other enzymes involved in metabolizing glucose and fructose (regulation still not fully characterized in plants, Dennis et al. 1997).

Low light could also constrain enzymes within the quinate utilization pathway, such as QORase (e.g. phosphorylation/dephosphorylation of QORase is light-

169 mediated in several species, Graziana et al. 1984) and QH. Additionally, the role of carbon transport to the chloroplast as a means of controlling phenolic production has been explored by others in Arabidopsis (Voll et al. 2003) and tobacco (Walton 2003). In Arabidopsis, Voll et al. (2003) found that mutant plants lacking transporter activity associated with supplying phosphoenol pyruvate (PEP) to the chloroplast (the primary location of the shikimate pathway) had less constitutive as well as induced phenolic production. However, in tobacco measurements of the PEP:phosphate translocator were not associated with changes in phenolic production elicited by JA treatment (Walton 2003). Since PEP biosynthesis in mature chloroplasts is undocumented (Walton 2003), and the role of light on the activity of these transporters has not been investigated, it is possible that transporter activity could constrain phenolic production at low light. The breakdown products of transported sucrose (e.g. PEP via glycolysis) would have to enter the chloroplast via transporters. Although both QA and SK can cross the chloroplast membrane (Leuschner and Schultz 1991), we do not know their transport mechanisms.

Other enzymes in the phenylpropanoid pathway, in addition to PAL, have also been shown to be regulated by light (e.g. chalcone synthase, van der Meer 1993). If other phenylpropanoid enzymes are unable to increase their activity in low light in response to JA, then phenolic production could be constrained despite the responsiveness of PAL activity to JA in low light. This situation could lead to a buildup of Phe, which in turn could supply enhanced protein synthesis rather than enhanced phenolic synthesis (for additional discussion, see Jones and Hartley 1999).

Implications for being able to use sucrose, QA, or SK Our results demonstrate that in some instances, poplar can use sucrose, QA and SK as translocated carbon sources for phenolic synthesis. Sucrose is the major sugar transported in the phloem of many plants (Fisher 2000), but may not be the only carbon supply recruited to support phenolic synthesis. However, none of these carbon sources is able to circumvent constraints on phenolic production in low light.

170 An advantage for the entry of QA and SK, rather than sucrose, into phenolic synthesis may be the ability to bypass early steps in the shikimate pathway. Quinic acid is found in the phloem (in ug/ml quantities, Kluge 1964, 1970) but we are not aware of any reports of SK in the phloem. QA could be a phenolic-specific carbon storage molecule in source leaves. Quinic acid is sequestered in vacuoles (Holländer-Czytko and Amrhein 1983) “as is” and can be directly mobilized for phenolic synthesis when carbon is required, without the loss of energy from enzymatic conversions (as in gluconeogenesis/glycolysis and starch formation/degradation).

The use of QA versus sucrose as a carbon reserve has several additional advantages. As well as being an energy-saving carbon supply for phenolic synthesis, QA also has other functions in plants; it can be complexed with caffeic acid to form chlorogenic acid (Niggeweg et al. 2004) or dicaffeoylquinic acid (Clifford 1986), and esterified with hydroxycinnamic acids (Möller and Herrmann 1983) and even with gallic acids (Ishimaru et al. 1987). Gebre et al. (1994,1997) have even investigated quinic acid as one of the organic acids responsible for adjusting osmotic potential during the development of drought tolerance. Using QA as a storage compound, defensive compound, UV protectant, and a transport molecule conveys metabolic plasticity for carbon allocated to cyclic carbon metabolism (alicyclic QA pathway or aromatic via SP). The results from our experiments have raised many questions and additional experiments (including labeling studies) are needed to resolve the role of quinic acid in plant metabolism.

We have identified several molecules that may be translocated to provide the carbon needed for elicited increases in phenolic production. This is another important part of understanding the impact of sink-source relationships on local defense production in plants. Our results support those of Arnold and Schultz (2001) and Arnold et al. (2004) in demonstrating that the availability of carbon supply from sources determines phenolic defense responses in sinks, but expands on their

171 view to implicate the role of light as a constraint and suggests several molecular forms in which the necessary carbon may be translocated to sinks.

172 Table 4.1. Experiment I (4 levels of carbon X 2 levels of spray; at high light) model statistics from analyses of dependent variables total phenolics (Folin-reactives as measured by the Folin-Denis assay, FD), and the phenolic glycosides salicortin (SAL) and HCH- salicortin (HCH). We explored both uptake (U) from the artificial carbon source (a volume measurement) and within-plant LPI 2 (leaf plastochron index) values as covariates for determining if the treatment responses in LPI 3 were different from those in LPI 2. LPI 3 leaves received supplementation via vascular connections to LPI 6, where we provided an artificial carbon supply. LPI 2 leaves were not connected to LPI 6 (Fig 4.3). Phenolic glycosides were only quantified for LPI 3, so we examined total phenolics as the LPI 2 covariate for SAL and HCH. Analyses were generated with the SAS Mixed procedure. Additional abbreviations are C = carbon S = spray (see text).

LPI 2 LPI 3 LPI 3 COVARIATE MODEL Num Den F P Num Den F P Num Den F P DF DF DF DF DF DF TOTAL PHENOLICS N = 78 or 79

Carbon 3 71 0.40 0.7567 Carbon 3 71 0.99 0.4031 Carbon 3 68 0.99 0.4041 Spray 1 71 1.52 0.2224 Spray 1 71 4.10 0.0465 Spray 1 68 3.84 0.0541 C * S 3 71 2.76 0.0484 C * S 3 71 0.98 0.4064 C * S 3 68 0.41 0.7475 173 FD2 1 68 42.26 <0.0001 Uptake 1 68 3.79 0.0556

SALICORTIN N = 60 (Suc, QA, SK X 2 spray)

Carbon Carbon 2 54 2.13 0.1284 Carbon 2 53 3.57 0.0350 Spray Leaves not analyzed Spray 1 54 4.66 0.0353 Spray 1 53 4.80 0.0330 C * S for SAL C * S 2 54 0.25 0.7761 C * S 2 53 2.63 0.0815 FD2 1 53 52.08 <0.0001

HCH-Salicortin N = 60

Carbon Carbon 2 54 2.63 0.0811 Carbon 2 53 3.65 0.0327 Spray Leaves not analyzed Spray 1 54 2.11 0.1517 Spray 1 53 1.42 0.2382 C * S for HCH C * S 2 54 0.03 0.9680 C * S 2 53 2.30 0.1105 FD2 1 53 39.96 <0.0001

Table 4.2. Experiment I (4 levels of carbon X 2 levels of spray; at high light) model statistics from analyses of dependent variables condensed tannins (CT), protein (PN), and leaf relative growth rate (LRGR). We explored both uptake (U) from the artificial carbon source (a volume measurement) and within-plant LPI 2 (leaf plastochron index) values as covariates for determining if the treatment responses in LPI 3 were different from those in LPI 2. However, relative leaf growth rates were only measured for LPI 3, so only carbon uptake was explored as a covariate. LPI 3 leaves received supplementation via vascular connections to LPI 6, where we provided an artificial carbon supply. LPI 2 leaves were not connected to LPI 6 (Fig 4.3). Analyses were generated with the SAS Mixed procedure . Additional abbreviations are C = carbon, S = spray.

LPI 2 LPI 3 LPI 3 COVARIATE MODEL Num Den F P Num Den F P Num Den F P DF DF DF DF DF DF CONDENSED TANNINS N = 78 or 79

Carbon 3 71 0.56 0.6441 Carbon 3 71 1.21 0.3116 Carbon 3 61 3.09 0.0335 Spray 1 71 0.54 0.4634 Spray 1 71 0.02 0.8806 Spray 1 61 4.71 0.0339 C * S 3 71 1.65 0.1857 C * S 3 71 0.96 0.4175 C * S 3 61 1.59 0.2011 CT2 1 61 10.80 0.0017 U*C*S 8 61 2.20 0.0394 174 PROTEIN N = 80

Carbon 3 71 1.36 0.2606 Carbon 3 71 0.99 0.4006 Carbon 3 63 6.68 0.0006 Spray 1 71 0.03 0.8624 Spray 1 71 0.05 0.8179 Spray 1 63 0.00 0.9730 C * S 3 71 0.78 0.5064 C * S 3 71 0.73 0.5385 C * S 3 63 1.49 0.2256 PN2*C*S 8 63 2.78 0.0107

LEAF RELATIVE GROWTH RATE N = 80

Carbon Carbon 3 72 0.42 0.7400 Carbon Spray LRGR not measured Spray 1 72 0.59 0.4434 Spray Uptake was not a C * S C * S 3 72 4.07 0.0099 C * S significant covariate

Table 4.3. Experiment II (2 levels of light X 2 levels of carbon X 2 levels of spray, see Methods for design description) model statistics from analyses of dependent variables total phenolics (Folin-reactives as measured by the Folin-Denis assay; FD), and condensed tannins (CT). We explored both uptake (U) from the artificial carbon source (a volume measurement) and within-plant LPI 2 values as covariates for determining if the treatment responses in LPI 3 were different from those in LPI 2. LPI 3 leaves received supplementation via vascular connections to LPI 6, where we provided an artificial carbon supply. LPI 2 leaves were not connected to LPI 6 (Fig 4.3). Analyses were generated with the SAS Mixed procedure. Additional abbreviations are L = light, C = carbon, S = spray.

LPI 2 LPI 3 LPI 3 COVARIATE MODEL Num Den F P Num Den F P Num Den F P DF DF DF DF DF DF TOTAL PHENOLICS N = 80

Light 1 4 38.72 0.0034 Light 1 4 29.34 0.0056 Light 1 4 5.89 0.0722 Carbon 1 64 1.32 0.2542 Carbon 1 64 1.90 0.1726 Carbon 1 55 0.72 0.4003 L * C 1 64 0.50 0.4835 L * C 1 64 2.33 0.1321 L * C 1 55 0.23 0.6357 Spray 1 64 12.10 0.0009 Spray 1 64 6.00 0.0170 Spray 1 55 0.05 0.8259 L * S 1 64 3.87 0.0536 L * S 1 64 3.50 0.0660 L * S 1 55 0.37 0.5470 175 C * S 1 64 1.23 0.2718 C * S 1 64 0.01 0.9206 C * S 1 55 2.87 0.0959 L*C*S 1 64 2.39 0.1272 L*C*S 1 64 0.03 0.8576 L*C*S 1 55 5.48 0.0228 FD2 1 55 95.09 <0.0001 U*L*C*S 8 55 2.51 0.0212

CONDENSED TANNINS N = 80

Light 1 4 2.44 0.1933 Light 1 4 0.23 0.6564 Light 1 4 1.39 0.0031 Carbon 1 64 1.63 0.2061 Carbon 1 64 1.44 0.2344 Carbon 1 63 0.52 0.3994 L * C 1 64 4.61 0.0356 L * C 1 64 0.08 0.7776 L * C 1 63 2.10 0.1967 Spray 1 64 5.26 0.0251 Spray 1 64 2.02 0.1598 Spray 1 63 0.19 0.8369 L * S 1 64 0.64 0.4269 L * S 1 64 0.08 0.7762 L * S 1 63 0.01 0.3894 C * S 1 64 0.11 0.7362 C * S 1 64 0.93 0.3397 C * S 1 63 1.73 0.3950 L*C*S 1 64 0.43 0.5138 L*C*S 1 64 0.00 0.9623 L*C*S 1 63 0.17 0.7487 CT2 1 63 23.25 0.0066

Table 4.4. Experiment II (2 levels of light X 2 levels of carbon X 2 levels of spray, see Methods for design description) model statistics from analyses of dependent variables salicortin (SAL) and HCH-salicortin (HCH), the two prominent phenolic glycosides in this hybrid. We explored uptake (U) from the artificial carbon source (a volume measurement) and within-plant LPI 2 (lead plastochron index) values as covariates for determining if the treatment responses in LPI 3 were different from those in LPI 2. LPI 3 leaves received supplementation via vascular connections to LPI 6, where we provided an artificial carbon supply. LPI 2 leaves were not connected to LPI 6 (Fig 4.3). We only quantified phenolic glycosides for LPI 3, so we examined total phenolics (Folin-reactives as measured by the Folin-Denis assay, FD) as the LPI 2 covariate for SAL and HCH. Analyses were generated with the SAS Mixed procedure. L = light, C = carbon, S = spray.

LPI 2 LPI 3 LPI 3 COVARIATE MODEL Num Den F P Num Den F P Num Den F P DF DF DF DF DF DF SALICORTIN N = 80

Light Light 1 4 297.52 <0.0001 Light 1 4 125.35 0.0004 Carbon Carbon 1 64 0.96 0.3314 Carbon 1 63 2.72 0.1041 L * C L * C 1 64 0.62 0.4338 L * C 1 63 1.50 0.2245 Spray Leaves not analyzed Spray 1 64 5.27 0.0249 Spray 1 63 0.76 0.3875 176 L * S for SAL L * S 1 64 2.02 0.1600 L * S 1 63 0.42 0.5194 C * S C * S 1 64 0.09 0.7681 C * S 1 63 0.74 0.3914 L*C*S L*C*S 1 64 0.18 0.6736 L*C*S 1 63 0.07 0.7940 FD2 1 63 22.93 <0.0001

HCH-SALICORTIN N = 80

Light Light 1 4 39.16 0.0033 Light 1 4 2.59 0.1827 Carbon Carbon 1 64 0.14 0.7133 Carbon 1 63 1.86 0.1778 L * C L * C 1 64 1.01 0.3199 L * C 1 63 3.30 0.0740 Spray Leaves not analyzed Spray 1 Spray 9.42 0.0031 Spray 1 Spray 1.25 0.2680 L * S for HCH L * S 1 L * S 13.53 0.0005 L * S 1 L * S 9.46 0.0031 C * S C * S 1 64 0.08 0.7775 C * S 1 63 0.26 0.6130 L*C*S L*C*S 1 64 0.29 0.5918 L*C*S 1 63 0.28 0.6005 FD2 1 63 115.66 <0.0001

Table 4.5. Experiment II (2 levels of light X 2 levels of carbon X 2 levels of spray, see Methods for design description) model statistics from analyses of dependent variables protein (PN) and leaf relative growth rate (LRGR). We explored uptake (U) from the artificial carbon source (a volume measurement) and within-plant LPI 2 (leaf plastochron index) values as covariates for determining if the treatment responses in LPI 3 were different from those in LPI 2. LPI 3 leaves received supplementation via vascular connections to LPI 6, where we provided an artificial carbon supply. LPI 2 leaves were not connected to LPI 6 (Fig 4.3). We only measured LRGR for LPI 3, so we examined only carbon uptake as a covariate. Analyses were generated with the SAS Mixed procedure. Additional abbreviations are L = light, C = carbon, S = spray.

LPI 2 LPI 3 LPI 3 COVARIATE MODEL Num Den F P Num Den F P Num Den F P DF DF DF DF DF DF PROTEIN N = 80

Light 1 4 10.81 0.0303 Light 1 4 64.47 0.0013 Light 1 4 40.96 0.0031 Carbon 1 64 1.61 0.2094 Carbon 1 64 0.18 0.6689 Carbon 1 63 0.72 0.3994 L * C 1 64 2.36 0.1296 L * C 1 64 3.09 0.0837 L * C 1 63 1.70 0.1967 177 Spray 1 64 0.34 0.5646 Spray 1 64 0.14 0.7047 Spray 1 63 0.04 0.8369 L * S 1 64 0.74 0.3944 L * S 1 64 0.33 0.5700 L * S 1 63 0.75 0.3894 C * S 1 64 0.03 0.8572 C * S 1 64 0.59 0.4448 C * S 1 63 0.73 0.3950 L*C*S 1 64 0.12 0.7330 L*C*S 1 64 0.17 0.6777 L*C*S 1 63 0.10 0.7487 PN2 1 63 7.90 0.0060

LEAF GROWTH RATE N = 80

Light Light 1 4 462.96 <0.0001 Light Carbon Carbon 1 64 0.11 0.7445 Carbon L * C L * C 1 64 20.05 <0.0001 L * C Spray LRGR Spray 1 Spray 4.05 0.0483 Spray Uptake was not L * S not measured L * S 1 L * S 5.26 0.0251 L * S a covariate C * S C * S 1 64 1.22 0.2735 C * S L*C*S L*C*S 1 64 0.43 0.5146 L*C*S

Table 4.6. Least-squares means for total phenolics (Folin-reactives, as measured by the Folin-Denis assay, FD) and condensed tannins (CT) in LPI 3 (leaf plastochron index) for experiment I (4 levels of carbon X 2 levels of spray) and experiment II (2 levels of light X 2 levels of carbon X 2 levels of spray, see Methods). Values were generated with the SAS Mixed procedure from models as outlined in Tables 4.1 – 4.5. T-statistics and associated (unadjusted) p-values are given for pairwise comparisons between treatment levels. Additional abbreviations are QA = quinic acid, SK = shikimic acid, JA = jasmonic acid, and Solv = solvent control for JA.

Total Phenolics Condensed Tannins LSMean SE N t-stat P LSMean SE N t-stat P

Experiment I: 4 carbon X 2 spray – at high light Covariate: FD in LPI 2 and Uptake CT in LPI 2 and Uptake

Buffer JA 0.2325 0.0121 9 0.38 0.7054 0.01714 0.00138 9 -0.50 0.6192 Solv 0.2274 0.0095 10 0.01822 0.00165 10 QA JA 0.2533 0.0108 10 1.40 0.1668 0.02276 0.00242 10 2.26 0.0274 Solv 0.2344 0.0100 10 0.01648 0.00136 10 SK JA 0.2493 0.0095 10 1.68 0.0969 0.01678 0.00113 10 -0.52 0.0649 Solv 0.2268 0.0094 10 0.01759 0.00110 10 178 Sucrose JA 0.2483 0.0102 9 0.49 0.6229 0.01927 0.00144 9 -0.25 0.8003 Solv 0.2414 0.0104 10 0.01989 0.00195 10

Experiment II: 2 light X 2 carbon X 2 spray Covariate: FD in LPI 2 and Uptake CT in LPI 2

Moderate QA JA 0.2670 0.0121 10 1.95 0.0559 0.02570 0.00173 10 1.03 0.3064 Solv 0.2353 0.0109 10 0.02358 0.00154 10 Sucrose JA 0.2539 0.0118 10 -0.70 0.4841 0.02469 0.00161 10 -0.67 0.5054 Solv 0.2658 0.0104 10 0.02613 0.00173 10 Low QA JA 0.2486 0.0118 10 -0.52 0.7591 0.02907 0.00189 10 0.72 0.4749 Solv 0.2577 0.0136 10 0.02739 0.00180 10 Sucrose JA 0.2370 0.0115 10 -0.58 0.5642 0.02555 0.00166 10 -0.18 0.8540 Solv 0.2474 0.0127 10 0.02595 0.00169 10

Table 4.7. Least-squares means for phenolic glycosides (salicortin, SAL; HCH-salicortin, HCH) in LPI 3 for experiment I (4 levels of carbon X 2 levels of spray) and experiment II (2 levels of light X 2 levels of carbon X 2 levels of spray, see Methods). Values were generated with the SAS Mixed procedure from models as outlined in Tables 4.1 – 4.5. T-statistics and associated (unadjusted) p- values are given for pairwise comparisons between treatment levels. FD = total phenolics as measured with the Folin-Denis assay, QA = quinic acid, SK = shikimic acid, JA = jasmonic acid treatment and Solv = solvent used in jasmonic acid treatment.

Salicortin HCH-Salicortin LSMean SE N t-stat P LSMean SE N t-stat P

Experiment I: 4 carbon X 2 spray – at high light Covariate: FD in LPI 2 FD in LPI 2

Buffer JA Solv QA JA 0.0555 0.00362 10 3.18 0.0025 0.0289 0.00226 10 2.46 0.0173 Solv 0.0389 0.00365 10 0.0209 0.00228 10 SK JA 0.0500 0.00358 10 0.20 0.8438 0.0248 0.00224 10 0.13 0.8984 Solv 0.0490 0.00361 10 0.0244 0.00226 10 179 Sucrose JA 0.0572 0.00367 10 0.32 0.7516 0.0290 0.00230 10 -0.57 0.5735 Solv 0.0555 0.00364 10 0.0309 0.00227 10

Experiment II: 2 light X 2 carbon X 2 spray Covariate: FD in LPI 2 FD in LPI 2

Moderate QA JA 0.0521 0.00878 10 1.34 0.1835 0.0346 0.00279 10 2.64 0.0105 Solv 0.0395 0.00611 10 0.0258 0.00199 10 Sucrose JA 0.0623 0.01083 10 0.21 0.8361 0.0390 0.00321 10 1.54 0.1275 Solv 0.0595 0.00869 10 0.0326 0.00243 10 Low QA JA 0.0114 0.00171 10 0.43 0.6686 0.0344 0.00263 10 -0.98 0.3285 Solv 0.0104 0.00169 10 0.0384 0.00302 10 Sucrose JA 0.0112 0.00172 10 -0.17 0.8651 0.0336 0.00259 10 -0.97 0.3374 Solv 0.0116 0.00183 10 0.0374 0.00294 10

Table 4.8. Least-squares means for protein (PN) and standard means for leaf relative growth rate (LRGR) for LPI 3 for experiment I (4 levels of carbon X 2 levels of spray) and experiment II (2 levels of light X 2 levels of carbon X 2 levels of spray, see Methods). Values were generated with the SAS Mixed procedure from models as outlined in Tables 4.1 – 4.5. T-statistics and associated (unadjusted) p-values are given for pairwise comparisons between treatment levels. Additional abbreviations are QA = quinic acid, SK = shikimic acid, JA = jasmonic acid treatment, Solv = solvent control for JA treatment.

Protein Leaf Relative Growth Rate LSMean SE N t-stat P LSMean SE N t-stat P

Experiment I: 4 carbon X 2 spray – at high light Covariate: PN in LPI 2

Buffer JA 0.1546 0.00507 9 -0.91 0.3666 0.1530 0.0186 10 0.99 0.3253 Solv 0.1609 0.00471 10 0.1278 0.0200 10 180 QA JA 0.1697 0.00519 10 1.85 0.0692 0.1833 0.0062 10 1.74 0.0870 Solv 0.1571 0.00447 10 0.1393 0.0219 10 SK JA 0.1565 0.00448 10 0.57 0.5709 0.1291 0.0172 10 -1.64 0.1058 Solv 0.1528 0.00466 10 0.1706 0.0212 10 Sucrose JA 0.1530 0.00451 10 0.08 0.9333 0.1243 0.0182 10 -2.42 0.0180 Solv 0.1525 0.00451 10 0.1857 0.0104 10

Experiment II: 2 light X 2 carbon X 2 spray Covariate: PN in LPI2

Moderate QA JA 0.1428 0.00478 10 0.06 0.9487 0.2547 0.0107 10 -1.93 0.0582 Solv 0.1423 0.00473 10 0.2841 0.0125 10 Sucrose JA 0.1373 0.00484 10 0.60 0.5537 0.2196 0.0098 10 -2.38 0.0204 Solv 0.1333 0.00473 10 0.2559 0.0121 10 Low QA JA 0.1532 0.00471 10 -1.13 0.2634 0.1724 0.0076 10 1.02 0.3114 Solv 0.1607 0.00471 10 0.1569 0.0065 10 Sucrose JA 0.1587 0.00489 10 0.05 0.9601 0.1956 0.0084 10 -0.74 0.4624 Solv 0.1583 0.00485 10 0.2069 0.0129 10

Table 4.9. Summary of statistics (model terms, p-values) for responses to JA treatments for LPI 3 in Populus at three light levels. Statistics for intact plants, with no leaf removal for carbon feeding, are from Chapter 3. Statistics for plants fed either sucrose, shikimic acid, quinic acid or buffer via the LPI 6 petiole are from experiment I (for high light) and experiment II (for low and moderate light). JA treatments were greater than controls, except as noted below (neg., for JA < controls). Only significance values for model terms involving JA treatment (S = spray) that had p-values < 0.25 are shown. P-values for unadjusted pairwise comparisons are in parentheses, while significance values from higher level model terms are indicated with the following symbols: p-values for ND > 0.50, * <0.05, ** <0.01, ***<0.001, and tr <0.10. Additional abbreviations are C = carbon and L = light. Full model statistics are in Tables 4.1- 4.8.

Cond. Folin- Phen. Glycosides Leaf Protein Tannins Reactives SAL HCH Growth LOW LIGHT

Intact **

Sucrose

Quinic Acid

MODERATE LIGHT

Intact ** * *** * *** *neg Sucrose * (.1275) (.0204) *neg Quinic Acid * (.0559) * (.0105) (.0582)

C*S 0.1931 C*S 0.0959 L*S 0.0031 S 0.0483 L*C*S 0.0228 L*S 0.0251 HIGH LIGHT Intact * *** *** ***

Buffer (ND) *(ND) *(ND) n/a n/a **(.3253) **neg Sucrose (ND) * (ND) *(ND) * (ND) * (ND) (.0180) **neg Shikimic Acid (ND) *(.0649) *(.0969) * (ND) * (ND) (.1058)

Quinic Acid (.0692) * (.0274) *(.1668) * (.0025) *(.0173) **(.0870)

C*S S 0.0339 S 0.0541 S 0.0330 S 0.2382 C*S 0.0099 0.2256 C*S 0.2011 C*S 0.0815 C*S 0.1105

181

Figure 4.1 Diagram of the plant biosynthetic route to phenolics from carbon assimilated by photosynthesis or mobilized from source tissues or storage (Walton 2003, Herrmann and Weaver 1999, Leuschner et al. 1995).

Abbreviations:

Metabolites - Enzymes -

CINN cinnamic acid DS DAHP synthase CTANNINS condensed tannins PAL Phenylalanine ammonia-lyase DAHP 3-deoxy-D-arabino- QD Quinate dehydrogenase heptulosonate 7-phosphate QH Quinate hydrolase DHQ 3-dehydroquinate E4P erythrose 4-phosphate A – DHQ synthase G6P glucose 6-phosphate B – DHQ dehydratase/ SK dehydrogenase, GPT glucose phosphate transporter bifunctional enzyme complex OPPP 0xidative pentose phosphate C – SK kinase

Pi inorganic phosphate D – 5-enolpyruvylshikimate 3-phosphate PEP phosphoenol pyruvate (EPSP) synthase 2-PGA 2-phosphoglycerate E – Chorismate synthase PHE phenylalanine F – Chorismate mutase PPT PEP-phosphate transporter G – Prephenate aminotransferase QA quinic acid, quinate H – Arogenate dehydratase SK shikimate I – Arogenate dehydrogenase TRP tryptophan TYR tyrosine

182

Figure 4.1

CO2 Pi G6P Starch GPT Light 2-PGA G6P CALVIN OPPP PEP CYCLE Pi PPT Sucrose P OCOOH

E4P PEP DS OH GLYCOLYSIS O P O

+ OCOOH P OH HO COOH

O P = Phosphate Group P DAHP P O i DHQ QA H2C OH

HO COOH HO COOH OH QD ?

A Pi DHQ OOH HO OH

OH OH HO COOH HO COOH DHQ OOH HO OH QA QA OH OH B ? QA HO COOH H O QH HOOC 2 COOH HO OH

COOH ATP C OH

OOH HO OH

OH OH B P OOH PEP SK OH D SHIKIMATE COOH Vacuole

PATHWAY CH2

P O OCOOH COOH OH

CH TRP 2 E OCOOHPi (multiple OH steps) F COOH HOOC

O

COOH HOOC

OH NH2 G I

TYR OH COOH H COOH NH2 PHE H2N CHLOROGENIC HO Chloroplast ACIDS

PHE PHENOLIC CINN GLYCOSIDES COOH PAL

H2N

LIGNIN CTANNINS

Figure 4.2 Quinic acid metabolism as it relates to the shikimate pathway in both A) plants and (Herrmann 1995, Leuschner et al. 1995) B) fungi (Lamb et al. 1992, Giles et al. 1991). Abbreviations are as in Fig 4.1, with the addition of DHS = 3- deydroshikimate, GA = gallic acid, and PCA = protocatechuic acid.

184 Figure 4.2

FROM MAIN PATHWAY UNKNOWN FROM MAIN PATHWAY QUINATE PERMEASE B A

HO COOH HO COOH QA HO COOH QA HO COOH DEHYDROGENASE DHQ DEHYDROGENASE QA DHQ QA OOH OOH HO OH HO OH

OH OH OH * OH

DHQ Dehydratase DEHYDROQUINASE *

HOOC HOOC COOH PCA QA or DHS HYDROLASE DHS PCA OOH HO UNKNOWN OOO H DHS GA OH OH DEHYDRATASE OH

* * SK DEHYDROGENASE SK DEHYDROGENASE

COOH COOH SK SK TO MAIN PATHWAY TO MAIN PATHWAY HO OH HO OH

OH OH

*= BIFUNCTIONAL ENZYME COMPLEX *= SAME ENZYME

Figure 4.3 Diagram of phyllotaxy and the leaf plastochron index (LPI) in Populus spp. as in (Larson and Dickson 1973, Larson and Isebrands 1971) and confirmed in this Populus hybrid by feeding dyes. Every five leaves are orthostichous (i.e. LPI 0, 5, and 10) and vascular connections are shared between a leaf and the leaves two, three, and five positions above it. We supplemented LPI 3 was with artificial carbon supplies by removing LPI 6 and using the LPI 6 petiole as a conduit. The artificial carbon source at LPI 6 shared connections with LPI 4, 3, and 1, but did not share vasculature with LPI 2. LPI 2 and 3 had intact source leaves (LPI 7 and 8, respectively), and were both sink leaves (Arnold and Schultz 2001, Arnold et al. 2004). Therefore, LPI 2 was used as a “within-plant control” for LPI 3 measurements.

186 Figure 4.3

0 LPI 3 and above were treated 1

2

3

4

5

6

7

8

9

10 No supplemental carbon

Recipient leaves

LPI 6 - Removed and used to feed supplemental carbon

Source leaves for recipient leaves above

Leaf present, but not involved in experiment

Figure 4.4 Total phenolics (measured as Folin-reactives using the Folin-Denis assay) and phenolic glycosides (salicortin, SAL; HCH-salicortin, HCH; measured using high performance thin-layer chromatography, HPTLC) from Populus deltoides X P. nigra hybrid plants grown in high light (~800-1200 µE, experiment I). We provided an artificial carbon supply (50 mM sucrose, shikimic acid, quinic acid, or buffer control) to leaf plastochron index (LPI) 3 leaves via LPI 6 to investigate source-sink dynamics for induced phenolic synthesis. LPI 2 was not connected to LPI 3 and served as a within-plant control (Fig 4.3). We measured total phenolics for both LPI 2 and 3, but only measured SAL and HCH for LPI 3. Total phenolics were quantified using a phenolic standard purified from experimental plants (Appel et al. 2001). SAL and HCH, the two prominent phenolic glycosides found in this hybrid, were quantified with standards provided by the Lindroth lab (University of Wisconsin, Madison, WI). Leaves were harvested three days after treatment. Statistics in italics below LPI 2 data points represent model statements and unadjusted pairwise comparisons (parentheses) for LPI 2 leaves only. Statistics in gray boxes above LPI 3 data points represent model statements and unadjusted pairwise comparisons for LPI 3 data analyzed with LPI 2 measurements and carbon uptake used as covariates (see Methods and Table 4.1). Statistics in white boxes are for SAL and HCH. N ≈ 10 for each graph symbol. Standard means are shown and least squares means for LPI 3 are given in Tables 4.6 and 4.7. Error bars represent one standard error from the mean. Additional abbreviations are C = carbon, S = spray treatment of either JA (jasmonic acid) or Solv (solvent controls for JA).

Abbreviations representing statistical differences (p-values): ND = no statistical difference, usually > 0.50 tr < 0.25, indicates investigation of interaction terms * < 0.05 ** < 0.01 *** < 0.001

188 Figure 4.4 BUFFER SUCROSE 0.30 0.10 S * 0.28 A S * B (ND) (ND) S * 0.08 0.26 C X S tr (ND) 0.24 0.06

0.22 C X S * 0.04 0.20 (ND) C X S * 0.18 C X S tr 0.02

(.0253) SAL & HCH (mg/mg DW) (ND) Folin Reactives (mg/mg DW) N/A 0.16 0.00 LPI-2 LPI-3 SAL/HCH LPI-2 LPI-3 SAL/HCH

SHIKIMIC ACID QUINIC ACID 0.30 0.10

0.28 C S * D (.0969) S * 0.08 0.26 S * (.1668) S * C X S tr C X S tr 0.24 (ND) (.0025) 0.06

0.22 0.04 0.20 C X S * C X S * (.2436) (.1016) 0.18 C X S tr C X S tr 0.02 (.0173) SAL & HCH (mg/mg DW) Folin Reactives (mg/mg DW) (ND) 0.16 0.00 LPI-2 LPI-3 SAL/HCH LPI-2 LPI-3 SAL/HCH

Total Phenolics - Solv Total Phenolics - JA SAL - Solv SAL - JA HCH - Solv HCH - JA

Figure 4.5 Condensed tannins (measured using the N-butanol assay as in Hagerman and Butler 1989) from Populus deltoides X P. nigra hybrid plants grown in high light (~800-1200 µE, experiment I). We provided an artificial carbon supply (50 mM sucrose, shikimic acid, quinic acid, or buffer control) to leaf plastochron index (LPI) 3 leaves via LPI 6 to investigate source-sink dynamics for induced phenolic synthesis. LPI 2 was not connected to LPI 3 and served as a within-plant control (Fig 4.3). Condensed tannins were quantified using a phenolic standard purified from experimental plants (Appel et al. 2001). Leaves were harvested three days after treatment. Statistics in italics below LPI 2 data points represent model statements and unadjusted pairwise comparisons (parentheses) for LPI 2 leaves only. Statistics in gray boxes above LPI 3 data points represent model statements and unadjusted pairwise comparisons for LPI 3 data analyzed with LPI 2 measurements and carbon uptake used as covariates (see Methods and Table 4.2). Standard means are shown and least squares means for LPI 3 are given in Tables 4.6. Error bars represent one standard error from the mean. Additional abbreviations are C = carbon, S = spray treatment of either JA (jasmonic acid) or Solv (solvent controls for JA).

Abbreviations representing statistical differences (p-values): ND = no statistical difference, usually > 0.50 tr < 0.25, indicates investigation of interaction terms * < 0.05 ** < 0.01 *** < 0.001

190 Figure 4.5

BUFFER SUCROSE 0.04 0.04 A B

0.03 S * 0.03 S * C X S tr C X S tr (ND) (ND)

0.02 0.02

C X S tr C X S tr 0.01 (ND) 0.01 (.1512) Condensed Tannins (mg/mg DW) Condensed Tannins (mg/mg DW) 0.00 0.00 LPI-2 LPI-3 LPI-2 LPI-3

SHIKIMIC ACID QUINIC ACID 0.04 0.04 C D

0.03 0.03 S * S * C X S tr C X S tr (ND) (.0274) 0.02 0.02

C X S tr (.2951) C X S tr 0.01 0.01 (.1521) Condensed Tannins (mg/mg DW) Condensed Tannins (mg/mg DW) 0.00 0.00 LPI-2 LPI-3 LPI-2 LPI-3

Condensed Tannins - Solv Condensed Tannins - JA

Figure 4.6 Protein content (Jones et al. 1989) and leaf relative growth rate (LRGR) from Populus deltoides X P. nigra hybrid plants grown in high light (~800-1200 µE, experiment I). We provided an artificial carbon supply (50 mM sucrose, shikimic acid, quinic acid, or buffer control) to leaf plastochron index (LPI) 3 leaves via LPI 6 to investigate source-sink dynamics for induced phenolic synthesis. LPI 2 was not connected to LPI 3 and served as a within-plant control (Fig 4.3). We measured protein content for both LPI 2 and 3, but only measured LRGR for LPI 3. Protein content was quantified using bovine serum albumin as a standard. Leaves were harvested three days after treatment. Statistics in italics below LPI 2 data points represent model statements and unadjusted pairwise comparisons (parentheses) for LPI 2 leaves only. Statistics in gray boxes above LPI 3 data points represent model statements and unadjusted pairwise comparisons for LPI 3 data analyzed with LPI 2 measurements and carbon uptake used as covariates (see Methods and Table 4.2). Statistics in white boxes are for LRGR, without covariates. N ≈ 10 for each graph symbol. Standard means are shown and least squares means for LPI 3 are given in Table 4.8. Error bars represent one standard error from the mean. Additional abbreviations are C = carbon, S = spray treatment of either JA (jasmonic acid) or Solv (solvent controls for JA).

Abbreviations representing statistical differences (p-values): ND = no statistical difference, usually > 0.50 tr < 0.25, indicates investigation of interaction terms * < 0.05 ** < 0.01 *** < 0.001

192 Figure 4.6

BUFFER SUCROSE 0.20 0.20

A B 0.18 0.18 C X S tr (ND) C X S tr (ND) 0.16 0.16 0.14 ND ND 0.12 0.14 0.10 C X S ** C X S **

Protein (mg/mg DW) (.0180) 0.12 (.3253) 0.08

0.06 Leaf Growth Rate (cm/cm/day) 0.10 LPI-2 LPI-3 LRGR LPI-2 LPI-3 LRGR

SHIKIMIC ACID QUINIC ACID 0.20 0.20

C D C X S tr 0.18 0.18 C X S tr (.0692) (ND) 0.16

0.16 0.14 ND ND 0.12 0.14 0.10 C X S **

Protein (mg/mg DW) (.1058) C X S ** 0.12 (.0870) 0.08

0.06 Leaf Growth Rate (cm/cm/day) 0.10 LPI-2 LPI-3 LRGR LPI-2 LPI-3 LRGR

Protein - Solv Protein - JA Leaf Relative Growth Rate - Solv Leaf Relative Growth Rate - JA

Figure 4.7 Total phenolics (measured as Folin-reactives using the Folin-Denis assay) and phenolic glycosides (salicortin, SAL; HCH-salicortin, HCH; measured using high performance thin-layer chromatography, HPTLC) from Populus deltoides X P. nigra hybrid plants grown in low (~100-200 µE) and moderate light (~400-700 µE, experiment II). We provided an artificial carbon supply (50 mM sucrose or quinic acid) to leaf plastochron index (LPI) 3 leaves via LPI 6 to investigate source-sink dynamics for induced phenolic synthesis. LPI 2 was not connected to LPI 3 and served as a within-plant control (Fig 4.3). We measured total phenolics for both LPI 2 and 3, but only measured SAL and HCH for LPI 3. Total phenolics were quantified using a phenolic standard purified from experimental plants (Appel et al. 2001). SAL and HCH, the two prominent phenolic glycosides found in this hybrid, were quantified with standards provided by the Lindroth lab (University of Wisconsin, Madison, WI). Leaves were harvested three days after treatment. Statistics in italics below LPI 2 data points represent model statements and unadjusted pairwise comparisons (parentheses) for LPI 2 leaves only. Statistics in gray boxes above LPI 3 data points represent model statements and unadjusted pairwise comparisons for LPI 3 data analyzed with LPI 2 measurements and carbon uptake used as covariates (see Methods and Tables 4.3 and 4.4). Statistics in white boxes are for SAL and HCH. N ≈ 10 for each graph symbol. Standard means are shown and least squares means for LPI 3 are given in Tables 4.6 and 4.7. Error bars represent one standard error from the mean. Additional abbreviations are C = carbon, S = spray treatment of either JA (jasmonic acid) or Solv (solvent controls for JA).

Abbreviations representing statistical differences (p-values): ND = no statistical difference, usually > 0.50 tr < 0.25, indicates investigation of interaction terms * < 0.05 ** < 0.01 *** < 0.001

194 Figure 4.7 SUCROSE QUINIC ACID

0.4 0.10 A B L X C X S * 0.08 0.3 L X C X S * (ND) (ND) 0.06

0.2 No terms sig. No terms sig. (ND) 0.04 L X S * L X S * (ND) (.2890) (.2890) LOW LIGHT 0.1 0.02 L X S ** L X S ** SAL & HCH (mg/mg DW) Folin Reactives (mg/mg DW) (.3394) (.3285) 0.0 0.00 LPI-2 LPI-3 SAL/HCH LPI-2 LPI-3 SAL/HCH

0.4 0.10 L X C X S * L X C X S * C (.4841) D (.0559) No terms sig. 0.08 0.3 (ND) No terms sig. (.1835)0.06 L X S * 0.2 L X S * (.0003) (.0003) 0.04 L X S ** 0.1 L X S ** (.1275) (.0105) 0.02 MODERATE LIGHT SAL & HCH (mg/mg DW) Folin Reactives (mg/mg DW)

0.0 0.00 LPI-2 LPI-3 SAL/HCH LPI-2 LPI-3 SAL/HCH

Total Phenolics - Solv Total Phenolics - JA SAL - Solv SAL - JA HCH - Solv HCH - JA

Figure 4.8 Condensed tannins (measured using the N-butanol assay as in Hagerman and Butler 1989) from Populus deltoides X P. nigra hybrid plants grown in low (~100- 200 µE) and moderate light (~400-700 µE, experiment II). We provided an artificial carbon supply (50 mM sucrose or quinic acid) to leaf plastochron index (LPI) 3 leaves via LPI 6 to investigate source-sink dynamics for induced phenolic synthesis. LPI 2 was not connected to LPI 3 and served as a within-plant control (Fig 4.3). Condensed tannins were quantified using a phenolic standard purified from experimental plants (Appel et al. 2001). Leaves were harvested three days after treatment. Statistics in italics below LPI 2 data points represent model statements and unadjusted pairwise comparisons (parentheses) for LPI 2 leaves only. Statistics in gray boxes above LPI 3 data points represent model statements and unadjusted pairwise comparisons for LPI 3 data analyzed with LPI 2 measurements and carbon uptake used as covariates (see Methods and Table 4.3). Standard means are shown and least squares means for LPI 3 are given in Tables 4.6. Error bars represent one standard error from the mean. Additional abbreviations are C = carbon, S = spray treatment of either JA (jasmonic acid) or Solv (solvent controls for JA).

Abbreviations representing statistical differences (p-values): ND = no statistical difference, usually > 0.50 tr < 0.25, indicates investigation of interaction terms * < 0.05 ** < 0.01 *** < 0.001

196 Figure 4.8 SUCROSE QUINIC ACID

0.04 A B No terms sig. No terms sig. (.4749) 0.03 (ND)

S * 0.02 S * (.3681) (.5584) LOW LIGHT 0.01 Condensed Tannins (mg/mg DW) 0.00 LPI-2 LPI-3 LPI-2 LPI-3 0.04 C D No terms sig. No terms sig. (.3064) (ND) 0.03

S * (.2979) 0.02 S * (.0450)

0.01 MODERATE LIGHT Condensed Tannins (mg/mg DW) 0.00 LPI-2 LPI-3 LPI-2 LPI-3

Condensed Tannins - Solv Condensed Tannins - JA

Figure 4.9 Protein content (Jones et al. 1989) and leaf relative growth rate (LRGR) from Populus deltoides X P. nigra hybrid plants grown in low (~100-200 µE) and moderate light (~400-700 µE, experiment II). We provided an artificial carbon supply (50 mM sucrose or quinic acid) to leaf plastochron index (LPI) 3 leaves via LPI 6 to investigate source-sink dynamics for induced phenolic synthesis. LPI 2 was not connected to LPI 3 and served as a within-plant control (Fig 4.3). We measured protein content for both LPI 2 and 3, but only measured LRGR for LPI 3. Protein content was quantified using bovine serum albumin as a standard. Leaves were harvested three days after treatment. Statistics in italics below LPI 2 data points represent model statements and unadjusted pairwise comparisons (parentheses) for LPI 2 leaves only. Statistics in gray boxes above LPI 3 data points represent model statements and unadjusted pairwise comparisons for LPI 3 data analyzed with LPI 2 measurements and carbon uptake used as covariates (see Methods and Table 4.5). Statistics in white boxes are for LRGR, without covariates. N ≈ 10 for each graph symbol. Standard means are shown and least squares means for LPI 3 are given in Table 4.8. Error bars represent one standard error from the mean. Additional abbreviations are C = carbon, S = spray treatment of either JA (jasmonic acid) or Solv (solvent controls for JA).

Abbreviations representing statistical differences (p-values): ND = no statistical difference, usually > 0.50 tr < 0.25, indicates investigation of interaction terms * < 0.05 ** < 0.01 *** < 0.001

198 Figure 4.9

SUCROSE QUINIC ACID 0.20 0.30 B A No terms sig. No terms sig. 0.18 (ND) (.2634) 0.25

0.16 No terms sig. (ND) 0.20 0.14 No terms sig. (ND)

LOW LIGHT L X S *

Protein (mg/mg DW) 0.15 (.4624) 0.12 L X S *

(.3114) Leaf Growth Rate (cm/cm/day) 0.10 0.10 LPI-2 LPI-3 LRGR LPI-2 LPI-3 LRGR

0.20 0.35 C L X S * D 0.18 (.0204) 0.30

0.16 0.25

L X S * 0.14 (.0582) 0.20 No terms sig. No terms sig. (ND) (ND) Protein (mg/mg DW) 0.12 No terms sig. No terms sig. 0.15 MODERATE LIGHT (ND)

(ND) Leaf Growth Rate (cm/cm/day) 0.10 0.10 LPI-2 LPI-3 LRGR LPI-2 LPI-3 LRGR

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204 Chapter 5

Summary and future directions

The previous three chapters presented the results of a series of experiments aimed at elucidating the role that supply plays in regulating increases in phenolic production elicited by external stimuli, either through supply of phenylalanine to the phenylpropanoid pathway via the shikimate pathway, or supply of translocated carbon to the shikimate pathway from source to sink tissues. Time-course experiments at multiple light levels in two plant species (tobacco, Nicotiana tabacum and a poplar hybrid, Populus deltoides X P. nigra) allowed me to investigate the roles of 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase (DAHP synthase, DS) and phenylalanine ammonia-lyase (PAL) in regulating phenolic production induced by both the wounding signal jasmonic acid (JA) and herbivory. The novel approach of feeding various potential carbon sources (sucrose, quinic acid - QA, and shikimic acid - SK) for Phe synthesis via source leaves in poplar, also at multiple light levels, allowed me to investigate the role of putative carbon sources translocated from source to sink leaves in support of induced phenolic production. Both of these approaches have advanced our knowledge of the mechanisms regulating enhanced phenolic production. Prior to these studies, the role of DS activity in regulating wound-induced phenolic production had not been investigated, the coordinate regulation of DS and PAL had not been explored in any species with substantial investments in phenolic secondary metabolism, and the interaction between light and inducible phenolic production had been largely neglected (with the exception of Walton 2003 and Nabeshima et al. 2001). QA was implicated as a precursor for lignin synthesis (Ossipov and Shein 1990) but was not linked to either constitutive or inducible phenolic production. A number of key results emerged from our studies:

205 1) DS and PAL are not coordinately regulated in either tobacco or poplar. a) In tobacco, although DS requires light for enzyme activity, there appears to be no difference in measured activity at the three different light conditions that I investigated. DS activity is enhanced by JA treatment but not by light. However, although PAL activity is enhanced by light, it is only enhanced by JA treatment at low light. b) In poplar, DS activity is somewhat enhanced by light but not by JA treatment; PAL activity is enhanced by both light and JA treatment.

2) DS activity is a rate-determining component in JA-increased phenolic production in tobacco (in moderate and high light), but not in poplar.

3) PAL activity is associated with JA-increased phenolic production in poplar (in moderate and high light), but not in tobacco.

4) Tobacco and poplar have adopted different strategies for handling the constraint of low light on phenolic production.

5) Carbon supply from photosynthesis can be a rate-determining component in JA-increased phenolic production in tobacco at low light, despite the fact that both DS and PAL activity are also increased by JA treatment in these circumstances.

6) DS and PAL activity do not appear to be limiting for JA-increased phenolic production in poplar at low light.

7) Translocated sucrose, QA, and SK from source positions can all supply JA- increased phenolic production in sink leaves, but this ability is constrained by light level (i.e. not at low light).

206 Strategies for regulation of phenolic production in tobacco versus poplar Tobacco and poplar demonstrated different regulation patterns. Phenolic production in tobacco was associated with changes in photosynthesis, DS, and PAL (to some extent), depending on light level, while regulation of phenolic production in poplar was largely due to PAL. The divergence of biochemical control mechanisms to earlier versus later points within the phenolic production pathway has implications for strategies of carbon allocation. Tobacco is a fast-growing herbaceous plant with low investments in phenolics. A more supply-based approach to regulation may indicate that tobacco makes phenolic products when resources are plentiful. Other uses for shikimate pathway derivatives (i.e. the indole alkaloids from the tryptophan branch) in tobacco may also have led to general upregulation of the entire shikimate pathway (modulated by DS), rather than downstream enzymes associated with specific metabolites (such as PAL). This type of supply-driven phenolic synthesis is essentially a feed-forward mode of pathway regulation. On the other hand, poplar is a slower-growing woody perennial species (although fast-growing for a tree) with high investments in phenolics and seems to follow a more demand-based mode of pathway regulation. DS activity and the shikimate pathway need to supply lignin synthesis as well as phenolic synthesis, and as such, DS activity may essentially act as an open-valve for carbon flow towards phenylalanine production. This could be representative of a hard-wired difference between woody and herbaceous plants.

Further investigations needed for DS regulation Although DS have been characterized in different species (e.g. Doong et al. 1992, Dyer et al. 1989, Ganson et al. 1986, Hendstrand et al. 1992, and Keith et al. 1991), the major regulatory mechanisms are still being identified (e.g. Entus et al. 2002) and the links between genes, mRNA expression, and functional isozymes are largely undefined. Although only one DS cDNA has been identified and sequenced in Nicotiana spp. (Wang et al. 1991, and Suzuki et al. 1995a) and none so far in a Populus species, DS is known to exist as two or three genes (or cDNAs) in many plants (for review see Herrmann and Weaver 1999). Pending complete genomic

207 sequences for tobacco and poplar, it is reasonable to assume that multiple DS cDNAs/genes exist in both of these species as well.

Interpretation of DS activity is further complicated by early studies in the literature on DS which indicated the existence of Mn- and Co- activated isozymes in Nicotiana sylvestris (Ganson et al. 1986, Ganson and Jensen 1987, and Doong et al. 1993) as well as other species. Both forms use PEP as a substrate, but DS-Mn is defined as absolutely requiring E4P as the second substrate and the enzyme referred to as DS-Co is less specific, defined as able to use a variety of carbon substrates (e.g. glycoaldehyde, glyceraldehydes 3-phosphate, and erythrose 4- phosphate; Doong et al. 1992). DS-Co was associated with cytosolic cell fractions, and DS-Mn was associated with plastids (Ganson et al. 1986 and McCue and Conn 1989). However, all existing gene sequences for DS have plastidic transit sequences, implying that they should be associated with the DS-Mn isozyme found in plastids (Herrmann and Weaver 1999) and no gene has been associated with the DS-Co enzyme (for any species, see Herrmann and Weaver 1999, and references therein). Thus, DS-Co is probably not a true DAHP synthase, and current evidence suggests that it is actually a 4,5-dihydroxy-2-oxovalerate synthase (Klaus Herrmann, personal communication).

Due to the controversy concerning DS isozymes, care must be taken to separate studies dealing with extractable DS activity (such as ours), those measuring DS-Mn and DS-Co separately (which may not be relevant to our discussions) and those dealing with gene expression. JA has previously been shown to increase expression of the only identified DS gene in Nicotiana cell (Suzuki et al. 1995a), which is presumably for a DS-Mn isozyme cultures (it has a plastidic transit sequence). From studies of extractable DS activity in other species, we know that isozymes of DS can be differentially responsive to stimuli. In a related solanaceous plant, Solanum tuberosum, DS-Mn is the form that is inducible by mechanical wounding and DS-Co is unresponsive (Muday and Herrmann 1992). However, in Vitis cell cultures, DS-Co was associated with elevations in anthocyanin

208 content and with elevations in PAL activity (Suzuki et al. 1995b). Since the assay used in our study is optimized for DS-Mn, we have not specifically addressed the role of DS-Co (if any) in phenolic production in tobacco and poplar.

Other control points for supply to the shikimate pathway DS is probably not the only pre-PAL control point affecting inducible (or even constitutive) phenolic production. Regulation may occur at later points within the shikimate pathway (although unlikely, see Gorlach et al. 1994), or even before DS. Logemann et al. (2000) suggested a role for glucose 6-phosphate dehydrogenase (GPDH) in phenolic production and Sindelar et al. (1999) demonstrated that this enzyme can also be induced by biotic stimuli in situations where phenolic induction has also been observed. GPDH is involved in the oxidative pentose phosphate pathway, which supplies one of the two substrates for DS, erythrose 4-phosphate (E4P)(Dennis et al. 1997). E4P can also be a product of the reductive pentose phosphate pathway (RPP, also Calvin Cycle) in the chloroplast. The other DS substrate, phosphoenol pyruvate (PEP), can be a side-product of the RPP via two additional enzymes or a product of glycolysis (Macdonald and Buchanan 1997). Compartmentation of metabolism between the chloroplast and cytosol becomes an issue for supply of substrates to DS within the plastid. Although plastids are capable of synthesizing both E4P and PEP (via the RPP), and isolated chloroplasts are capable of synthesizing Phe (Schulze-Siebert and Schultz 1989), the primary source of substrates for chloroplasts supplying Phe to active phenolic synthesis could be from the cytosol (for discussion, see Walton 2003).

Investigations with mutant Arabidopsis plants have suggested a potential role in the regulation of phenolic production for the PEP/phosphate translocator (PPT) in the inner chloroplast envelope (Streatfield et al. 1999, Voll et al. 2003), as well as hexose phosphate translocators (Batz et al. 1995, Quick et al. 1995). However, recent investigations have shown that while these transporters may play a role in determining constitutive phenolic production (Voll et al. 2003), they may not be involved in modulating induced phenolic production (Walton 2003). The effect of

209 substrate supply to the shikimate pathway as a means of regulating phenolic production is still equivocal, and an exciting avenue for further study.

DS isoforms, dual shikimate pathways, and quinic acid metabolism Although both PAL and DS exist as isoforms that are associated with specific elicited responses, metabolic channeling cannot exist for the entire metabolic segment from DS to PAL because PAL activity is associated with the cytosol and DS activity is primarily associated with plastids. Previously, the existence of cytosolic and plastidic isozymes for many of the biosynthetic steps in the shikimate pathway had raised the possibility of dual shikimate pathways, one in the plastid and one in the cytosol (Morris et al. 1989). However, controversy over the true nature of the cytosolic DS and the lack of a completely identified cytosolic pathway have led to the generally accepted notion that the only complete shikimate pathway is in the chloroplast (Herrmann and Weaver 1999). With this conclusion, there is no definitive explanation for the existence of the cytosolic DS-Co (or the cytosolic form of chorismate mutase).

However, although the shikimate pathway is not active in the cytosol, it may interact with other pathways that are. Specifically, regulation of the later half of the DS to PAL pathway (including portions of the shikimate pathway) may interlock with regulation of quinic acid metabolism, which occurs in the cytosol and also involves the vacuole. Gorlach et al. (1993) have suggested that the shikimate pathway could provide metabolites such as quinic, gallic, or even shikimic acids to secondary metabolism (in addition to phenylalanine) and recently Ossipov et al. (2003) have demonstrated a link from the shikimate pathway to gallic acid and hydrolyzable tannin production. Hoffmann et al. (2003) have also characterized enzymes that interact with components of both the shikimate pathway (i.e. shikimate) as well as the phenylpropanoid pathway (i.e. caffeoyl moieties). The complex interplay between the shikimate, quinate, and phenylpropanoid pathways provides additional motivation (and challenges) for exploring the regulation of both the shikimate and quinate pathways in the context of phenolic production.

210

The role for classical studies and new methods in the analysis of the shikimate pathway The shikimate pathway is a remarkable example of different types of metabolic regulation. The control mechanisms include isozymes subject to allosteric controls, multienzyme complexes, metabolite channeling, potential parallel pathways, interlocks with cytosolic pathways (such as QA metabolism), spatial compartmentation, and post-translational modifications (Boudet et al. 1985). A similar diversity of regulatory patterns may exist among plant species. Differential regulation of the shikimate pathway may reflect different biochemical adaptations to meet demands of phenolic production for different uses.

Over the last two decades, molecular and genetic approaches have given us new insights into the enzymology of phenolic synthesis, the cellular and subcellular sites of synthesis, and information on the mechanisms that control metabolic change. However, before molecular biology techniques became available, reactions in many biosynthetic pathways were characterized using a combination of enzyme assays, enzyme purification, and labeled precursor feeding approaches. These more classical biochemical approaches, in combination with improved methods of chemical quantification, can and should continue to advance our knowledge of pathway regulation in conjunction with molecular approaches.

Our studies provide an example of how a physiological approach can reveal problems and questions, and provide answers that were unexpected from an examination of gene expression alone. Plants are systems, and as systems are inherently synergistic – plant metabolism is more than just the knowledge of when and where genes are expressed. Classic biochemistry and physiology need to be integrated into our molecular and genetic studies. New methods are continually being developed, and re-analysis of “old pathways” in new situations with new techniques can still lead to new discoveries.

211

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214 VITA

Toni Marie Schaeffer

Education Pharm.D., in progress, Albany College of Pharmacy, Albany, NY

Ph.D., 2004, The Pennsylvania State University, University Park, PA Intercollege Graduate Program in Plant Physiology Thesis Advisor: Jack C. Schultz Thesis Title: Supply-side regulation of phenolic production in plants: a comparison of two model systems.

B.A., magna cum laude, 1996, Macalester College, St. Paul, MN Major in biology (with honors), core in math, and minor in geography Thesis Advisor: Mark A. Davis Thesis Title: Differences in herbivory and phenolic content related to light and nitrogen availability in two species of oak seedlings in Minnesota.

Other Project Research Assistant, Entomology, Pennsylvania State University, 1997 Research Mississippi River Canoe Expedition, Self-initiated and funded, 1996 Experiences Undergraduate Research Fellow, NSF Research Training Grant in Plant Responses to the Environment, Pennsylvania State University, 1996 Geographic Information Systems Intern, National Park Service, St. Croix, WI and St. Paul, MN, 1995-1996 Research Associate, Cedar Creek Natural History Study Area, East Bethel, MN, 1995 Independent Researcher, Cassowary Diet Project, The School for Field Studies, Centre for Rainforest Studies, Queensland, Australia, 1995 Field Assistant, Wolf Ecology Project at the Audubon Center of the Northwoods, Sandstone, MN, 1994

Fellowships & NASA Space Grant Consortium Fellowship, 2001-2002 Scholarships National Science Foundation Pre-Doctoral Fellowship, 1997-2000 National Science Foundation Research Training Grant Fellowship, 1997-2003 Macalester College Dewitt Wallace Distinguished Scholar, 1992-1996 National Merit Scholar, Presidential Scholar, North Dakota Scholar, 1992

Research National Science Foundation Doctoral Dissertation Improvement Grant, 2001-2003 Grants Women in Science Mentoring Grant, 2001

Teaching Guest Lecturer in Ecophysiology, Pennsylvania State University, 2001 Experience American Red Cross Instructor, State College, PA, 2001-2002 Penn State Outing Club Canoeing and Backpacking Instructor, 2000-2002 Cartography Teaching Assistant, Macalester College, 1995-1996 Biology Teaching Assistant, Macalester College, 1994-1995 Wilderness Canoeing Guide and Guide Supervisor, Northern Lakes Canoe Base, Ely, MN, 1990-1995

Publication Davis MA, Wrage KJ, Reich PB, Tjoelker MG, Schaeffer T, and Muermann C. 1999. Survival, growth, and photosynthesis of tree seedlings competing with herbaceous vegetation along a water-light-nitrogen gradient. Plant Ecology 145(2): 341-350.

Presentations Invited Speaker, University of Pittsburgh seminar series in Ecology and Evolution, 2003 American Society of Plant Biologists Annual Meeting, 2003 Ecological Society of America Annual Meeting, 2000

Professional American Society of Plant Biologists Affiliations Ecological Society of America International Society of Chemical Ecology Sigma Xi National Association of Women in Science Girl Scouts of the United States of America