Industrial Applications of Plant Secondary Metabolites Dissertation

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Industrial Applications of Plant Secondary Metabolites

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

Yun Lin

Graduate Program in Horticulture and Crop Science

The Ohio State University

2017

Dissertation Committee:

Dr. Joshua Blakeslee, Advisor

Dr. Joseph Scheerens

Dr. John Cardina

Dr. Thaddeus Ezeji

Dr. Stephanie Strand

Yun Lin

2017

Abstract

Plant secondary metabolites (PSMs) constitute a broad range of compounds produced by plants both as part of normal developmental processes and in response to environmental stimuli. While plants produce PSMs to aid in their own growth and development (or as part of adaptive growth responses, PSMs have been co-opted by humans for use in multiple industries. For example, PSMs are heavily used in the food, pharmaceutical, and agricultural industries. Because of this, there is increasing demand for the isolation and identification of novel PSMs for industrial use. The work presented here details efforts to identify and characterize novel PSMs for used in the pharmaceutical and agricultural industries, as well as efforts to apply new methods developed for PSM research to the biomedical field.

The first part of the research presented here details work investigating the medicinal properties of burdock extracts, which have traditionally been used in the Amish community to treat burn wounds. Our data indicate that extracts from two burdock species, A. lappa and A. minus exhibit significant anti-microbial activity against seven burn wound-associated pathogens in three types of anti-bacterial assays. Additionally, we used metabolomic profiling (spectrophotometric assays, HPLC, LC-MS/MS, and GC-MS) to identify putative anti-microbial compounds. Our results indicate that, while leaf extracts of both species exhibited anti-microbial effects against all the seven pathogens, A.

ii minus extracts were in general more effective. Consistent with this, A. minus extracts contained more phenolic compounds, specifically flavonoids and hydroxylcinnamic acids than did corresponding A. lappa extracts. Potential anti-microbial compounds were putatively identified, including hydroxycinnamic acids, flavonoids, and fatty acids.

Further work was focused on optimizing agriculture applications of PSMs. Chapter 3 details the development of a portable, field-deployable, and weather resistant 2,4-D drift detection system exhibiting high sensitivity to the target herbicide. The detector can be set with crops in field and collected for LC-MS/MS analysis when potential symptoms of

2,4-D damage are observed. The detection system was sensitive enough to detect 2,4-D

300 feet away from the spray swath. Five types of matrices were tested, and one of these was ultimately selected for its ability to partially resist ran, fully resist UV light, and fully resist long-term exposure to the greenhouse environment. In chapter 4, a fast, sensitive, and cost efficient LC-MS/MS quantification method for a potential pesticide compound

(2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one) was developed and used to screen maize parental lines. While previous efforts to quantify DIMBOA have used a complex, multi-step purification process, the method developed here uses a straightforward two- step 90% methanol extraction and was used to successfully detect and quantify DIMBOA from 25 maize parental lines.

Finally, chapter 5 describes the application of metabolomic (analytical chemistry) techniques developed during the course of our PSM research to characterize biomarkers associated with human diseases. Protocols to extract, isolate, identify, and quantify phospholipids, fatty acids, and cholesterol, were developed using three model plant

iii organisms (guayule, moringa, burdock, respectively). The developed methods were then successfully adapted to screen murine tissues for selected lipid-type metabolites potentially of interest as early cancer biomarkers.

Acknowledgements

I would like to thank my advisor Dr. Joshua J. Blakeslee for all the help and support during the course of my Ph.D. life. Thank you for all the smart ideas and always having the answer to all types of questions I have. I’d also like to thank my SAC member Dr.

Joseph C. Scheerens for the great plant secondary metabolite class, the kindness to share equipment, and all the advises he has patiently provided. Dr. Stephanie Strand, another

SAC member of mine and a very encouraging person, taught me a lot in the area of microbiology and provided great suggestions for my research writing. I’d also like to acknowledge Dr. Thaddeus Ezeji for asking questions that lead me think and proposing valid concerns about my projects. My SAC member, Dr. John Cardina has been very helpful providing great advices about my research and also very kind and supportive. My gratitude also goes to Dr. Ann Chanon, who has patiently taught me a lot of lab techniques and constantly help me with everything in the lab. I’d also like thank Jinshan

Lin, who taught me a lot about analytical techniques and LC-MS/MS knowledge. My lab mate, Eun Hyang (Grace) Han has been very supportive, provided me with experimental tips, helped me with the messes I made, and shared depressed moments with me.

Dominic Petrella, has provided great help with my English writing, the class I am teaching, and providing answers to a broad range of question I ask. I’d also like to thank

Lisa Robbins for working with me on the burdock project and all the help she kindly

v provided. My gratitude also goes to Rachel Medina, who worked with me for the

DIMBOA project. Caroline Gormley helped me with burdock project and Annelise Bay helped me with Moringa project. At last, I’d like to thank Dr. Wenshuang Xie for all the help he has kindly provided in the past five years.

Vita

2012 …………………...... B.S. Applied Biological Science,

Zhejiang University

2012 to present ……………………….……..Ph.D. Horticulture and Crop Science,

The Ohio State University

Fields of Study

Major Field: Horticulture and Crop Science

vii

Table of Contents

Abstract ...... ii

Acknowledgements ...... v

Vita ...... vii

Table of Contents ...... viii

List of Tables ...... xiv

List of Figures ...... xv

Chapter 1 Introduction ...... 1

Chemistry and biosynthesis of plant secondary metabolites (PSM) ...... 2

Phenolic Compounds ...... 2

Terpenoids and Steroids ...... 4

Alkaloids...... 5

Sites of PSM biosynthesis and storage ...... 6

The functions of PSM ...... 6

PSM as defense molecules ...... 7

PSMs as signaling molecules ...... 12

Other functions of PSMs (abiotic stress, nitrogen-storage) ...... 13

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Industrial applications of PSMs ...... 14

Medicinal application ...... 14

Agricultural applications of PSMs ...... 18

Other industrial application ...... 20

Summary of thesis chapters: Development of PSMs for industrial use ...... 21

Medicinal applications of PSMs ...... 22

Agricultural applications of PSMs ...... 23

Model organisms for animal research ...... 24

Reference ...... 26

Chapter 2 Anti-microbial activity and metabolite composition of burdock (Arctium lappa and Arctium minus) extracts ...... 42

Introduction ...... 42

Materials and Methods ...... 46

Plant material and sample preparation ...... 46

Bacterial strains and maintenance ...... 48

Agar plate bioassay...... 48

Liquid broth and minimum inhibitory concentration (MIC) assays ...... 49

Agar Well Diffusion Assay ...... 51

Assay of Bactericidal vs. Bacteriostatic Effects of Burdock Extracts ...... 52

Spectrophotometric Assays ...... 53

HPLC and LC-MS/MS analysis of phenolic compounds...... 55

GC-MS analysis of nonpolar extracts ...... 57

Results ...... 58

Agar plate bioassay...... 59

Liquid broth and minimum inhibitory concentration (MIC) assays ...... 63

Agar well diffusion bioassay ...... 66

Assay of Bactericidal vs. Bacteriostatic Effects ...... 67

Spectrophotometric Assays ...... 68

HPLC and LC-MS/MS analysis of phenolic compounds...... 71

GC-MS analysis of nonpolar extracts ...... 74

Conclusions ...... 77

Reference ...... 79

Chapter 3 Detection of 2,4-D herbicide drift using a fiber-based, field-deployable collection system and liquid chromatography tandem mass spectrometry ...... 104

Introduction ...... 104

Materials and Methods ...... 109

Development of an LC-MS/MS method to quantify 2,4-D ...... 109

Spray Room Test ...... 110

2013 Field Trial ...... 111

2014 Field Trial ...... 112

Optimization of matrix composition ...... 112

Rain Wash Resistance Test ...... 113

UV Resistance Test ...... 114

Greenhouse Treatment Test ...... 115

2,4-D extraction and quantification ...... 116

Results ...... 117

2,4-D quantification method development and Spray Room Test ...... 117

2013 Field Trial ...... 120

2014 Field Trial ...... 122

Optimization of matrix composition ...... 123

Rain Wash Resistance Test ...... 124

UV Resistance Test ...... 125

Greenhouse Treatment Test ...... 126

Discussion and Conclusion ...... 127

References ...... 130

Chapter 4 Development of an efficient method to quantify DIMBOA using LC-MS/MS to select maize parental lines with high DIMBOA content ...... 144

Introduction ...... 144

Materials and Methods ...... 149

Initial test and DIMBOA quantification in maize root ...... 150

LC-MS/MS analysis ...... 151

Method optimization ...... 152

Free DIMBOA vs total DIMBOA ...... 153

Quantification of DIMBOA in maize parental lines ...... 153

Results and Discussion ...... 154

Preliminary quantification of DIMBOA from maize roots ...... 154

Method optimization ...... 156

Quantification of free DIMBOA vs total DIMBOA ...... 158

Quantification of DIMBOA in maize parental lines ...... 159

Conclusion ...... 160

Reference ...... 162

Chapter 5 Development of lipidomic biomarkers for pancreatic cancer ...... 175

Introduction ...... 175

Fatty acid identification and quantification method development ...... 180

Phospholipid identification and quantification method development ...... 181

Cholesterol identification and quantification method development ...... 182

Mouse cell lipid analysis ...... 183

Fatty acid analysis using GC-MS ...... 184

LC-MS/MS analysis ...... 185

Results ...... 186

xii

Fatty acid identification and quantification method development ...... 186

Phospholipid identification and quantification method development ...... 188

Cholesterol identification and quantification method development ...... 189

Mouse pancreatic stromal cell lipid analysis ...... 191

Discussion and conclusions ...... 192

Reference ...... 195

Chapter 6 Summary, conclusion and future work ...... 212

Bibliography ...... 219

xiii

List of Tables

Table 2.1 MIC values of A. lappa and A. minus extracts ...... 96

Table 2.2 Compounds identified in A. lappa and A. minus via LC-MS/MS ...... 102

Table 2.3 Burdock compounds putatively identified by GC-MS ...... 103

Table 4.1 DIMBOA content in maize parental line M162W, Mo17, and Oh7b ...... 170

Table 4.2 Free DIMBOA concentrations in the roots of 7-day seedling of maize parental lines ...... 174

Table 5.1 List of phospholipid authentic standards ...... 202

Table 5.2 Retention times and mass transitions of phospholipids ...... 203

Table 5.3 Retention times and mass transitions of sterols ...... 206

xiv

List of Figures

Figure 1.1 Brief biosynthesis pathways of major secondary metabolites in plant ...... 35

Figure 1.2 Brief biosynthesis pathways of phenolic compounds ...... 36

Figure 1.3 Skeleton structure of flavonoid ...... 37

Figure 1.4 Brief biosynthesis pathways of terpenoids ...... 38

Figure 1.5 Example structures of terpenoids ...... 39

Figure 1.6 Skeleton structure of steroids ...... 40

Figure 1.7 Skeleton structure of steroids ...... 41

Figure 2.1 Agar Plate Bioassay: Effect of A. lappa ACT extracts on S. pyogenes ...... 85

Figure 2.2 A. lappa and A. minus ACT, EA, DM extracts agar plate bioassay on burn related pathogens ...... 86

Figure 2.3 Burdock (A. lappa and A. minus) leaf extract liquid broth assay ...... 93

Figure 2.4 Liquid broth assay for minimum inhibitory concentrations (MIC) ...... 94

Figure 2.5 Effect of burdock extracts on bacterial growth in agar well diffusion assays . 97

Figure 2.6 Effect of burdock EA and ACT extracts on bacterial growth in agar well diffusion assays ...... 98

Figure 2.7 Assay of bactericidal vs. bacteriostatic effects of burdock ACT and EA extracts ...... 99

Figure 2.8 Total phenolic content (A), total flavonoid content (B), and in vitro antioxidant capacity (C) of burdock ACT and H2O extracts ...... 100

Figure 2.9 HPLC Chromatographs of A. lappa and A. minus ACT extracts ...... 101

Figure 3.1 2,4-D detector spray room test ...... 134

Figure 3.2 2,4-D extraction method development ...... 135

Figure 3.3 DMA, 2,4-D and IPA recovery during 2,4-D detector analysis in lab ...... 136

Figure 3.4 2,4-D detector spray room test ...... 137

Figure 3.5 2013 field trials with detectors set at 18 feet and 30 feet downwind locations

...... 138

Figure 3.6 2014 field trials with detectors set at 50 feet, 100 feet and 300 feet downwind locations ...... 139

Figure 3.7 Mesh type comparison regarding 2,4-D retention ability ...... 140

Figure 3.8 Rain wash resistance test on Mira cloth, nylon and polyester meshes ...... 141

Figure 3.9 UV resistance test on Mira cloth, nylon and polyester meshes ...... 142

Figure 3.10 Greenhouse treatment test on Mira cloth, nylon and polyester meshes ...... 143

Figure 4.1 core skeleton structure of benzoxazinoids ...... 166

Figure 4.2 Chemical structures of common benzoxazinoids ...... 167

Figure 4.3 DIMBOA and BOA separation and detection by LC-MS/MS ...... 168

Figure 4.4 Standard curve for quantification of DIMBOA in corn root ...... 169

Figure 4.5 DIMBOA and BOA concentration extracted with 0%, 30%, 60%, and 90% methanol ...... 171

xvi

Figure 4.6 Concentrations of total DIMBOA and free DIMBOA in maize seedling root

(Mo17) ...... 172

Figure 4.7 Free DIMBOA concentrations in the roots of 7-day seedling of maize parental lines ...... 173

Figure 5.1 Fatty acid methyl esters identified in Moringa seeds using GC-MS ...... 199

Figure 5.2 Stand curves of methyl palmiate, methyl stearate, methyl oleate, and methyl heptadecanoate ...... 200

Figure 5.3 Palmitic acid, stearic acid, and oleic acid concentrations in Moringa seeds . 201

Figure 5.4 Phospholipids detected in guayuele (Parthenium argentatum) microsomal membranes ...... 204

Figure 5.5 Standard Curves of PC 16:1 and PE 16:1 ...... 205

Figure 5.6 Sitosterol, stigmasterol, and cholesterol detected in A. lappa leaf tissues .... 207

Figure 5.7 Standard curves of sitosterol, stigmasterol, and cholesterol ...... 208

Figure 5.8 Phospholipids detected in mouse pancreatic stromal cells ...... 209

Figure 5.9 Phospholipid concentrations in mouse pancreatic stromal cells ...... 210

Figure 5.10 Palmitic acid and stearic acid concentrations in mouse pancreatic stromal cells ...... 211

xvii

Chapter 1 Introduction

Plants produce a wide range of small molecules (often at relatively high concentrations) that are not essential to plant growth and development; and these compounds are often referred to as “plant secondary/specialized metabolites” (PSMs) [1]. Unlike primary metabolites, secondary metabolites, or specialized metabolites, are not directly involved in plant essential metabolic processes such as photosynthesis, respiration, cell division, and cell differentiation. Primary metabolites are usually found in all plants, while PSMs are often found only in a single plant family or species plants [2]. The boundary between primary metabolites and PSMs is often unclear or vague, particularly as some canonical

“PSMs,” such as plant hormones, as essential for normal plant growth and development.

Traditionally, PSMs have often been defined as specialized compounds functioning primarily to modulate and/or mediate plant interactions with the surrounding environment.

Unlike animals or other moving organisms, plants are sessile and are unable to physically dodge potential enemies, including herbivores, predators, microbes, viruses, or competing plants. Similarly, plants are unable to move away from abiotic stress conditions such as salt, drought, or high intensity light. As a result, plants have evolved unique defense systems which rely heavily on the production of plant secondary/specialized metabolites.

Plants produce PSMs using metabolites generated from the pathways of primary metabolism. Given the broad diversity in the structure and function of secondary compounds, it is somewhat remarkable that the precursors of these compounds are derived from relatively few primary metabolic pathways, including photosynthesis, glycolysis, and the tricarboxylic acid cycle (Krebs cycle) [3, 4]. The biosynthesis pathways of several of the major groups of plant secondary/specialized metabolites are summarized in Figure 1.1. In addition to serving multiple functions in plant growth and development, PSMs have been used by humans in a wide range of industrial applications, including as pharmaceuticals, anti-microbial agents, agrochemicals (i.e., pesticides and herbicides), and as biomarkers in both plant and animal systems. The chapters of this thesis present a series of experiments designed to identify and characterize novel PSMs for application in the pharmaceutical and agricultural industries, with a focus on phenolic compounds, terpenoids, steroids, and fatty acids.

Chemistry and biosynthesis of plant secondary metabolites (PSM)

Phenolic Compounds

In most plant species, approximately 40% of the total organic carbon present in the plant is in the form of phenolic compounds [2]. Phenolic compounds exhibit a distinctive structural skeleton consisting of an aromatic phenyl ring connected to one or more hydroxyl groups [1]. Most phenolic compounds present in plant cells and tissues are produced through the phenylpropanoid pathway; although there are some exceptions to this rule, such as hydrolysable tannins, which are produced directly from metabolites of 2

the shikimic acid pathway (which are also precursors of the phenylpropanoid pathway) [1,

5]. Figure 1.2 provides a scheme detailing the biosynthetic pathways of the major types of plant phenolic compounds: flavonoids, phenolic acids, stilbenes, lignins, and lignans

[5].

Flavonoids represent a large family of phenolic compounds. Flavonoids possess a C6-C3-

C6 three ring structure (Figure 1.3), with two benzene rings connected (rings A and B) by a pyran ring (ring C) located in the middle of the molecule [5]. Flavonoids are grouped into six sub-classes based on their molecular structure: flavanols, flavanones, flavones, isoflavones, flavonols and anthocyanins [6]. Flavanones and flavanols can be distinguished by the functional group found on the C-ring. Flavanones possess an oxygen atom at the C4 position of this ring, while flavanols (or flavan-3-ols) have both an oxygen at the C4 position and an additional hydroxyl group at C3 position [5, 6]. Monomers of flavanols are called proanthocyanidins, while flavonol polymers are referred to as condensed tannins [5]. The distinctive features of flavones and flavonols are the double bond between the C2 and C3 positions, and the oxygen atom at C4 position, with flavonols possessing an additional hydroxyl group at the C3 position [5, 6]. The only difference between flavones and isoflavones is the position at which the B-ring is connected to the C-ring of the molecule. The B-ring of an isoflavone is connected at C3, instead of C2, which is the case for all other flavonoids [6]. Anthocyanins have double bonds at both the C2 and C3 positions of the pyran ring, as well as a hydroxyl group at the C3 position, and are linked to sugars mostly via the C3 hydroxyl group [6]. The aglycone forms of anthocyanins are called anthocyanidins [5].

Classes of non-flavonoid phenolic compounds include phenolic acids, stilbenes, lignans, lignins, and coumarins. There are two primary types of phenolic acids, hydroxylbenzoic acids, which possess C6-C1 skeletons (aromatic ring connected to carboxylic acid), and hydroxylcinnamic acid, with have a C6-C3 skeleton (aromatic ring connected to three carbon chain) [7]. Stilbenes possess a C6-C2-C6 skeleton, and lignans are derived from dimers of two phenylpropanoid structures (C6-C3) [5]. Lignins, on the other hand, are cross-linked polymers consisting primarily of 4-hydroxyphenylpropanoids [8]. Finally, coumarins are phenylpropanoid compounds consisting of a benzene ring joined to a pyrone ring [Jain, 2012 #384].

Terpenoids and Steroids

The terpenoids are the largest (in terms of number of different molecule) class of plant secondary/specialized metabolites [9]. In plants, the synthesis of terpenoids can be divided into two parts, the synthesis of C5 precursors, and the polymerization of C5 units.

The two C5 precursors, IPP (isopentenyl diphosphate) and its isomer DMAPP

(dimethylally diphosphate), are synthesized via two pathways: a. the MVA pathway

(mevalonate pathway) in the cytosol; and b. the MEP pathway (methylerythritol 4- phosphate pathway) in plastids (Figure 1.4) [10]. Terpenoids are classified based on the number of C5 isoprene units present in the molecule into: hemiterpenes (C5), monoterpenes (C10), sesquiterpene (C15), diterpenes (C20), triterpenes (C30), tetraterpenes (C40), and polyterpenes (Figure 1.5) [3] . Steroids are C30 PSMs derived from terpenoids, which are often considered to be a distinct class of metabolites. Steroids

are produced from squalene (C30; the production of squalene from two C15 farnesene molecules is one of the earliest steps of sterol synthesis) and triterpenes of terpenoid synthesis pathway. However, after synthesis of the basic “sterol” multi-ringed structure

(lanosterol in animal systems and cycloartenol in plant systems), sterols, particularly plant sterols, are modified and glycosylated to generate a unique set of metabolites [11].

Plant steroids are structurally similar to cholesterol, consisted of a four ring structure

(tetracyclic cyclopenta[a]phenanthrene) and a side chain attached at C17 of the tetracyclic core of the molecule [11] (Figure 1.6). Common plant steroids include sitosterol, stigmasterol and campesterol [11].

Alkaloids

Alkaloids are another relatively large class of plant secondary/specialized metabolites, which consists of a large number of structurally diverse nitrogen-containing compounds

[3]. Many alkaloid molecules, such as caffeine, nicotine, morphine, and reserpine are economically important as they are found in food, beverage, and pharmaceutical products

[12]. While the structure of individual alkaloid molecules is usually complex and asymmetric, these compounds are usually derived from simple amino acids, such as tyrosine, tryptophan, phenylalanine, lysine, and arginine [12]. Some of the major groups of alkaloids found in many plant species include nicotine and tropane alkaloids (derived from arginine), monoterpenoid indole alkaloids (MIA) (derived from tryptophan and geraniol), pyrrolizidine alkaloids (derived from arginine), benzylisoquinoline alkaloids

(derived from tyrosine) and quinolizidine alkaloids (derived from lysine) [3, 13]. The

biosynthesis of the major groups of alkaloids has been summarized in Figure 1.7, and some of the more well-known members within each group are also listed [3].

Sites of PSM biosynthesis and storage

The synthesis of plant secondary/specialized metabolites is often compartmentalized within specific sub-cellular organelles, including the cytosol (most hydrophilic compounds), chloroplasts (some alkaloids and terpenes), mitochondria (some alkaloids), the endoplasmic reticulum (lipophilic compounds), and/or other membrane-bound organelles [3, 14]. Once synthesized, however, PSMs may be translocated to other cellular organelles or compartments either for storage or to exert a specific activity. For example, plants store many hydrophilic secondary metabolites in the vacuole (i.e., alkaloids, flavonoids, anthocyanins), although some hydrophilic compounds can also be found in laticifers (some alkaloids) and/or cell walls (lignins) [15]. In contrast to hydrophilic compounds, hydrophobic compounds can be found in the cuticle (waxes, terpenoids, flavonoids), glandular trichomes (terpenoids), resin ducts (terpenoids, flavonoids), laticifers (terpenoids), oil bodies, and plastid membranes [15].

The functions of PSM

In early metabolic or biochemical studies, PSMs were assumed to have few biological functions outside of serving as structural components of cell walls. Over the past two decades, however, further study has revealed that plants have several very good reasons for spending carbon resources on the production of PSMs. Indeed, more detailed

biochemical and physiological analyses have revealed that individual PSMs often have multiple functional roles, and that several key PSMs (such as flavonoids, phytol, sterols/brassinolides, and strigolactones) are essential for normal plant growth and development. Additionally, PSMs have been found to be critical in regulating and/or modulating plant interactions with the environment. Two major functions of plant secondary/specialized metabolites are defense and signaling [16]. PSMs also serve as key regulators of abiotic stress responses (e.g. salt, UV, drought, heavy metal), and function in N-storage [16, 17]. The specific functions of PSMs in plant biology are discussed in more detail below.

PSM as defense molecules

As mentioned above, plants are exposed to a range of environmental threats and stresses, which they cannot move to avoid. Herbivores, insects, microbes, viruses, and even other competing plants can all negatively impact normal plant growth and development [16, 18,

19]. In addition to physical defenses (e.g. thorns, spines, trichome, hard shells), plants use the synthesis of specific PSMs to deter or repel biological threats [16, 20]. For example, several plants accumulate high concentrations of defensive PSMs, which deter insect or herbivore feeding, in glandular trichomes and epidermal cells. Additionally, upon attack by a pathogen or herbivore, many plants induce the production of specific classes or sub-classes of PSMs, which may function as deterrents; repellents; toxins; or growth inhibitor for herbivores, insects, microbes and viruses. [16, 21]. PSMs secreted by roots may inhibit the germination and seedling growth of competing plants. Several of the

classes of PSMs described above, such as alkaloids, terpenes, and phenylpropanoids, have been demonstrated to have specific functions in defense against biological threats.

Alkaloid molecules play a particularly important role in regulating plant- herbivore/predator interactions. Many alkaloids are toxic to both vertebrates and invertebrates, and the synthesis of several of these compounds has been proposed to specifically target herbivores and predators [1, 16]. One mechanism by which alkaloids exert toxicity is by binding to neuroreceptors (and either activating or inactivating them) and/or interfering with neuronal signal transduction [16]. Neurologically active alkaloids include nicotine, caffeine, atropine, and capsaicin. Nicotine is produced in roots of tobacco plants, and then translocated and stored in leaf vacuoles, from which it is eventually released as leaves are consumed by herbivores [22]. Nicotine binds to the acetylcholine receptors present in neurons and, at sufficient dosages, can cause damage to the nervous system [23]. The purine alkaloid caffeine is found in multiple plant species familiar to the public, such as coffee, tea, and Kola nut Caffeine is a toxin that paralyzes feeding insects, but this molecule also serves as an allelopathic toxin that suppresses the seed germination of neighboring plants [22, 24]. Atropine, perhaps the most famous alkaloid plant toxin, is synthesized by several members of the nightshade family (e.g. belladonna, or deadly nightshade), is also a potent neurotoxin [22]. Capsaicin is a particularly interesting alkaloid, as, in addition to serving as an anti-fungal agent, this compound (which functions to give chili peppers their spicy taste) has been co-opted by humans as a flavor compound [22].

As noted above, terpenoids are a large and structurally diverse group of PSMs. As a result, terpenoids serve as defense chemicals for combatting a broad range of organisms, pathogens, and herbivores. For example, volatile mixtures of monoterpenes and sesquiterpenes (essential oils) can be produced and released by plants to generate specific odors that repel herbivores and insects [22, 25]. Additionally, several monoterpene molecules, such as menthol (produced by mint plants in the Lamiaceae), menthone (also produced in mint plants), and alpha- and beta-pinene (pine tree), are effective pesticide repellents, even in the absence of other monoterpene or sesquiterpene molecules [22]. In addition to repelling insects, terpenoids can also serve as toxins or hormonal mimics which target life specific life cycle stages of insects or other pests.

Limonoids are a class of non-volatile triterpenes most commonly found in citrus plants

(where they are a source of bitterness in the fruit). Limonoids have been documented to exhibit insecticidal, anti-bacterial and anti-fungal activity [26], and at least one limonoid, azadirachtin (produced by neem trees), has been shown to interfere with the feeding pattern and life cycle of insects [26]. Further, triterpenoid phytoecdysteroids function by mimicking the hormones involved in ecdysis (i.e., the molting process), and ingestion of these molecules leads to metabolic disorders and the eventual death of the insects [6, 27].

In addition to interfering with developmental growth processes, several terpenoid molecules can serve as neurotoxins (similar to alkaloids). For example, terpenoid toxic grayanotoxins (found in plants of the Ericaceae family) and pyrethrins (derived from

Chrysanthemum genus) interfere with the sodium channels of nerve cells and function as neurotoxins in insects [22, 28, 29]. Finally, saponins, which are glycosides of triterpenes

(or steroids), are amphiphilic compounds with detergent-like properties. Because of this, 9

saponins are capable of intercalating themselves into cell membranes, damaging these membranes (increasing membrane leakage) and ultimately resulting in the death of invading pests ingesting the saponin [16, 22].

Phenylpropanoid compounds function in conjunction with both alkaloids and terpenoids to protect plants against herbivory. Some flavonoids function as feeding deterrents to insects, including maackiain and judaicin (isoflavonoids), kampferol and myricetin

(flavonols), and 5-hydroxyisoderricin (a flavonone) [30]. Additionally, flavanols (e.g. kaempferol) have been found to play important roles in nematode resistance (hatching restriction) [30]. Another way flavonoids combat insects is by altering oviposition, a function observed for naringin, hesperetin-7-O-rutinosdie, quercetin-3-O-rutinoside, luteolin, and isorhammetin glucoside [30]. Phenolic tannins participate in plant defense by generating a bitter, unpleasant taste that repels invaders, as well as by binding to proteins and enzymes in insect salivary and digestive systems [22, 25]. Furanocoumarins

(which result from the fusion of a furan ring with coumarin), such as psoralen, are potential toxins for insects when activated by UV (i.e., exposure to the UV component of sunlight), which causes these molecules to induce cross-linkages in DNA [31].

Phenylpropanoids also contribute biophysically to plant stress responses. Lignins in the cell walls increase the physical toughness of this barrier, making it more difficult for pathogens to penetrate the cell wall. Both pathogen infection and wounding increase the production of lignins (lignification) in many plant tissues [32, 33].

In addition to herbivores, microbes (bacteria, fungus, virus) are also major threats to normal plant growth and development. As detailed above, several PSMs function to combat invading microbes in plant cells and tissues. However, plants may also extrude

PSMs from roots to kill pathogens in soil. For example, rice produces momilactone A (a terpenoid) as an antimicrobial agent to suppress bacterial growth in the rhizosphere [34].

Additionallyl, benzoxazinoids, a group of PSMs derived from tryptophan biosynthesis, have been recognized for their roles in plant defense. Three specific benzoxazinoids,

DIMBOA (2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one), DIBOA (2,4-dihydroxy-

1,4-benzoxazin-3-one), and BOA (benzoxazolin-2-one), have been found to be exuded by plant roots (specifically maize roots) into the rhizosphere, where they negatively impact the growth of microorganisms. The flavonoid naringenin has also been found to work as inhibitor of both growth and spore germination and the progression of fungal and bacterial diseases [35]. Terpenoid compounds can also serve as potent antimicrobial compounds. Rishitin (a norsesquiterpenoid alcohol) is an antifungal toxin produced by potato plants upon infection by Phytophthora infestans, and functions to combat this pathogen [36]. Additionally, when tobacco (Nicotiana tabacum) plants infected by tobacco necrosis virus produce capsidiol (sesquiterpenoid) to combat against the disease

[37].

Finally, PSMs may also be in involved in plant-plant interactions. In many cases, PSMs function as allelopathic toxins, and cause damage to competing neighboring plants; a phenomenon observed with phenolic compounds, terpenoids, alkaloids, and steroids [38].

One of the most famous examples of an allelopathic PSM is juglone (commonly found in

walnut species, particularly black walnut), a strong allelopathic toxin that interferes with normal chloroplast and mitochondrial functions of other plants [22, 39]. Other phenolic compounds, such as caffeic acid, coumarin and ferulic acid have also found to be allelopathic toxins [40-42].

PSMs as signaling molecules

In addition to serving as toxins and defense compounds, plant secondary/specialized metabolites also serve as signal molecules to attract pollinating insects, seed-dispersing animals, and root nodule bacteria [16]. The color patterns (orange, red, magenta, mauve, etc.) generated by anthocyanins in floral tissues (particularly petals) are crucial in attracting pollinators, such as bees, flies, and birds [43, 44]. Other types of flavonoids are also used as color pigments to attract pollinators and/or seed-dispersing animals, including yellow flavonols, chalcones, and aurones [44]. Terpenoids also contribute to plant color, and carotenoids (derived from geranylgeranyl pyrophosphate) such as β- carotene and lycopene are responsible for generating yellow to orange color in several plant tissues, especially fruits [44]. As pollinating insects and animals are attracted to particular colors; some plants have evolved PSM synthesis pathways to produce tissues of different colors in order to attract specific pollinators. In some cases, plants may even produce two colors on the same flowers to attract two distinct pollinator types. For example, some plants in the Pedicularis genus generate scarlet corollas to attract hummingbirds, but magenta calyces and bracts to attract bumble bees [44]. Besides colors, plants also attract pollinators by producing volatile plant secondary/specialized

metabolites, the odor of which attracts certain pollinators (especially night-flying insects and animals) [44]. For instance, α-irone (a sesquiterpene) is attractive to bees and the

Solanum fruit fly [44, 45]; while vanillin produced by vanilla plants attracts moth pollinators [44]. PSMs do not serve as signals to attract only pollinators, however.

Several PSMs also function to attract specific microbes, particularly those found in the rhizosphere. Flavonoids in particular, which are found in high concentrations in root tissues, and which are often extruded from root tips, can serve as signalling molecules.

The flavonoids luteolin and chrysin have been demonstrated to attract Rhizobium bacteria for Rhizobium infection and colonization, e.g. when secreted by Medicago sativa roots

[30].

Other functions of PSMs (abiotic stress, nitrogen-storage)

Besides being important plant defense and signaling molecules, plant secondary/specialized metabolites also serve several other functions such as, regulating abiotic stress responses (most, if not all, phytohormones are classified as secondary metabolites) and nitrogen storage. The multi-ring structure of flavonoids makes them excellent redox buffers and allows themto play very important roles in plant photoprotection. Flavonoids can quench the oxidative damage caused by UV-B radiation, although other phenylpropanoid molecules, such as hydroxycinnamic acids (e.g. chlorogenic acids, caffeic acids, coumaric acid), also contribute to this quenching [46].

As many abiotic stresses, other than UV damage, cause the production of reactive oxygen species, it is not surprise that flavonoids have been demonstrated to function in several

stress responses. For example, it has been reported in multiple plants that under salt stress, plants accumulate higher level of flavonoids in the salt stressed tissues or organs

(particularly roots) [17, 47]. Water stress has also been found to induce alterations to the synthesis of both non-flavonoid phenolics and terpenoid carotenoids [17].

Industrial applications of PSMs

The functions of plant secondary/specialized metabolites in plant growth and development are discussed above. PSMs are not only used by plants, however, as humans have co-opted these compounds for use in the pharmaceutical/biomedical, agriculture, and food industries. Specific human uses for plant secondary metabolites are described below.

Medicinal application

Plants have been used for medicinal purposes, such as the treatment of specific diseases, by humans for several thousands of years. The development of medicinal uses for plants has been specific to the plants indigenous to a given region, but has occurred independently in several civilizations. For example, in ancient Egypt, the first records of the use of herbal medicines can be dated to tomb illustrations from the Old Kingdom (c.

2686 BCE – c. 2181 BCE) [48]. The use of plants in traditional Chinese Medicine (TCM) can be dated back to the book “Shouwen Jiezi,” written in the Shang dynasty (c. 1600 BC

- c. 1046 BC) [49]. During these early time periods, herbal medicines were found primarily in the forms of teas, powders, poultices, tinctures or even whole plant tissue,

and the specific compounds responsible for modulating physiology or curing diseases were unknown [50]. In the modern era, however, advances in analytical chemistry and medicine have allowed researchers to extract and characterize the active components of the medicinal plants, and to ultimately use these compounds as pharmaceuticals. Uses for

PSMs as medicinal compounds include: anti-malarials, anti-inflammatory agents (both topical and internal), anti-cancer drugs, and as antibiotics/antimicrobials (including antivrials) [16].

Cancer has always been a focus of medicinal research, and plant secondary/specialized metabolites have contributed substantially to this research. PSMs have been demonstrated to exhibit several anti-cancer effects, such as the ability to either kill cancer cells, slow the growth and development of cancer cells or tumors, or combat the multi-drug resistance exhibited by many gut or breast cancers [51, 52]. PSMs with anti-cancer activity include flavonoids, quinones, alkaloids, terpenoids and many other structurally diverse compounds [53]. For example, paclitaxel (a diterpene with alkaloid sidechain) is a famous plant secondary metabolite discovered first in Pacific Yew in this category, which has been registered as an chemotherapeutic drug to treat cancer under the brand name Taxol® [53]. Because of toxicity and hydrophobicity of paclitaxel, which increase the difficulty of administering this drug in mammalian systems, a hydrophilic derivative docetaxel (Taxotere®) has also been developed. Both Taxol and Taxotere are currently being used to treat lung cancer, breast cancer, prostate cancer and ovarian cancer [53].

The modes of action of many PSMs in combating cancer cells has only been partially elucidated. However, as many cancers also cause or are co-incident with either systemic or localized inflammation, it is interesting to note that several plant PSMs can also function to reduce inflammatory responses. Inflammation in mammalian systems is a protective response to harmful stimuli, which can result from burn injury, pathogen infection, exposure to toxin, localized trauma (contusions, lacerations, etc.), and/or other physical injuries [54]. Inflammation is characterized by redness, swelling, and heat, usually resulting from increased blood flow to the inflamed area [55]. While inflammatory responses are part of the body’s defensive system and function to protect against harmful stimuli, inflammation can be quite painful, and sustained inflammatory responses are involved in several diseases, including including rheumatoid arthritis, diabetes, atherosclerosis, chronic inflammatory diseases (such as systemic lupus erythematosus). Multiple plant secondary/specialized compounds (such as salicylic acid and methylsalicylate) have been demonstrated to function as anti-inflammatory molecules. Recent research efforts have been focused on expanding the current pharmacological toolkit of anti-inflammatory PSMs, as well as understanding the mechanisms by which these compounds reduce inflammation. For example, investigation of the phenolic compound curcumin has revealed that this molecule exerts its anti- inflammatory activity by interfering with several inflammatory response pathways

(NF휅B-, MAPK-, COX-, and LOX- pathways) and the production of cytokines [56].

As noted above, PSMs are useful in treating and regulating disorders involving mammalian cell types (cancer, inflammation). PSMs, however, are also useful in

combating microbial infections of mammalian systems. This fact is unsurprising since, as noted above, one of the major roles of PSMs in their native plant species is combating microbial/pathogen infection. Similar to what has been observed in plant systems, PSMs are able to inhibit the growth and establishment of a wide range of pathogens in mammalian systems. For example, the major treatments for malaria (caused by

Plasmodium parasitic protozoans) currently all involve the use of a plant secondary metabolite, artemisinin (a terpenoid PSM, first identified in Chinese medical texts as qinghaosu) [57]. Numerous studies have been conducted to elucidate the anti-malarial effects and relevant modes of action of this sesquiterpene lactone. These investigations have revealed that the effectiveness of artemisinin is heavily dependent upon activation of the endoperoxide bridge of this compound [58]. PSMs are not just used to combat protozoan, pathogens, however. Viral diseases cannot be treated using standard antibiotics, and often result in complications that may cause death. Viruses which attack the immune system, such as Human Immunodeficiency Virus (HIV; the causal agent of

Acquired Immune Deficiency Syndrome [AIDS]) are especially difficult to combat.

However, several plant secondary/specialized metabolites, particularly alkaloids (e.g. castanospermine, 6-butyrylcastanospermine) and triterpenoids (e.g. betulinic acid derivatives) have been found to inhibit HIV replication, and may represent new treatment options for this virus [16]. Finally, PSMs are often used as antibiotics in mammalian systems, as several of these compounds have been demonstrated to exhibit either bactericidal or bacteriostatic activities. Numerous plant secondary metabolites with antimicrobial activity have been discovered, including flavonoids (such as catechins from tea plants), tannins, terpenoids (e.g., essential oils from peppermint and lavender), and 17

alkaloids ( reserpine, berberine) [59]. PSMs can exert anti-microbial activity through a range of mechanisms, including altering membrane permeability or by inactivating membrane efflux pumps, resulting in the accumulation of toxins in the bacteria [59].

Alternatively, PSMs may also function to inhibit pathogen progression is by inhibiting quorum sensing and blocking the communication between bacteria necessary for systemic or large-scale infection [59]. In addition to inhibiting quorum sensing, PSMs can also suppress the biofilm formation (an important virulence factor) of bacterial infectious pathogens [59].

Finally, in addition to cancers and microbial diseases, PSMs have also been used to treat a range of additional human ailments. For example, the phototoxic phenolic compound psoralen (a furancoumarin) has been used in Psoralen Plus Ultraviolet Light Therapy

(PUVA) to treat skin associated diseases (e.g. psoriasis [an autoimmune disorder], eczema) by increasing the sensitivity of skin to UV light [60, 61]. As noted in the discussion of antibiotic PSMs above, PSMs may also function to increase the efficiency of co-applied medicines or treatments by altering membrane permeability and/or transport. For instance, the furancoumarins can also increase the availability of certain drugs, one reason that many drugs cannot be taken with grapefruit juice (which is rich in fruanocoumarins) [6, 22].

Agricultural applications of PSMs

Given the important role of PSMs in regulating plant growth, development, and responses to environmental stimuli (e.g. anti-herbivore, anti-bacterial, and anti-fungal effects; see 18

above), it is no surprise that several of these compounds have been employed by the agricultural industry to regulate crop production. PSMs have been developed for agricultural usage as insecticides, herbicides, and/or herbivore-repellents.

Neonicotinoids are a group of insecticides that are derived from the alkaloid nicotine, commonly found in tobacco and responsible for the addictive effect of many tobacco products [62]. This group of chemicals functions by targeting the central nervous systems of a broad range of insect pests, including aphids, white flies, thrips and

Lepidoptera [63]. Since their first commercial launch in 1991, the usage of neonicotinoid insecticides has steadily increased [63]. Another PSM, rotenone is a isoflavone broad- spectrum pesticide first discovered in Derris epliptica. This compound functions by inhibiting the electron flow of the mitochondrial respiratory chain, resulting in insect death [64].

PSMs are also used as herbicidal agents, capable of killing or inhibiting the growth of weeds or other competing plant species. As noted above, juglone (a phenolic lactone) is secreted by walnut plants to inhibit the growth of neighboring plant species [39].

Phytohormones (commonly classified as PSMs) mimic can also be used as herbicides.

Herbicides based on plant growth regulators or phytohormones exert their effects by mimicking the functions of the hormone in question, leading to aberrant plant growth and/or plant death. For example, 2,4-dichlorophenoxyacetic acid (2,4-D) functions by mimicking the functions of plant hormone auxin (indole-3-acetic acid), and thus is categorized as an auxinic herbicide [65]. Application of 2,4-D impacts several

developmental processes regulated by auxin (meristem cell division, stem elongation, cell wall loosening), eventually leading to “burn-out” or an “overdrive” of growth leading to death in sensitive plants [66]. When sensitive plants are exposed to 2,4-D, several metabolic processes will be activated, including the activation of enzymes regulating cell wall plasticity, the synthesis of unnecessary enzymes, and the over accumulation of plant hormones (ethylene, abscisic acid)[67, 68]. Treatment with 2,4-D also results in the misregulation of cell division and elongation [69]. Like auxin, 2,4-D functions by interacting with auxin receptors such as ABP1 (auxin binding protein 1) and TIR1

(transport inhibitor response 1) proteins, activating downstream responses such as cell wall remodeling (via modulation of hemicellulose xyloglucan structure), plasma membrane hyperpolarization, microtubule re-orientation, and vascular tissue differentiation [70-73].

Other industrial application

Besides being used in the pharmaceutical/biomedical and agricultural industries, PSMs have are been used by humans in a broad range of other industries. In the food industry,

PSMs provide flavor, aroma, and/or color to many beverages or other food products.

Coffee is a high-value food product where almost all of the flavor and sensory characteristics are provided by PSMs. The aroma of coffee is the result of numerous volatile PSMs have, while the acidity and bitterness of this beverage are due to alkaloid caffeine, chlorogenic acids, dicaffeyolquinic acids, and other phenolic compounds [74].

PSMs are also responsible for the flavors of many spices, and many of the herbal plants

are used in cooking are given their flavor through the accumulation of monoterpenes

(rosemeary, peppermint, black pepper, sage) [22]. As noted above, the alkaloid compound capsaicin is responsible for the spicy taste of chili peppers [22], and vanillin

(a phenolic aldehyde extracted from vanilla plants) is widely used in the food industry, particularly for flavoring baked goods. PSMs can also be used to provide scents for both the food and fragrance industries. Essential oils, such as rose oil, lavender oil, and agar oil can be used to give a pleasing aroma to either perfumes or food products [16]. Finally,

PSMs can be used to provide colors in for either foods or industrial products (textiles).

PSMs employed for this purpose include indigo (true indigo), shikonin (purple gromwell), juglone (black walnut), and anthocyanins [16, 75, 76].

Summary of thesis chapters: Development of PSMs for industrial use

The wide structural variety and functional diversity of PSMs has made them ideal targets for bioprospective efforts designed to identify new products for the pharmaceutical/biomedical, agricultural, and bioproducts (biopolymer) industries.

Research into the structure and function of PSMs yields multiple benefits as, in addition to the identification and characterization of novel beneficial compounds, several of the analytical, biochemical, or physiological tools developed throughout the course of these studies can be used to further investigate the physiology and biochemistry of mammalian systems. The research presented in this thesis explores all of these areas. Individual chapters present studies designed to:: a. investigate the antimicrobial effects of PSMs found in the medicinal plant burdock (A. lappa and A. minus); b. develop a portable, field

deployable system designed to quantify drift of the auxinic herbicide 2,4-D (a PSM mimick); c. develop a fast, sensitive, and cost efficient LC-MS/MS quantification method for DIMBOA (2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one), a PSM of interest as a potential pesticide; and d. develop and optimize analytical methods to analyze bioactive fatty acid, phospholipid, and sterol levels in plant species, and then use these methods to profile bioactive lipids in mammalian mouse cells during the onset of pancreatic cancer.

Medicinal applications of PSMs

Current research into the medicinal properties of plant secondary/specialized metabolites is focused on six primary areas: a. exploring the medicinal properties (e.g. anti-microbial effect, anti-inflammatory effect, anti-cancer effect) of crude plant extracts; b. isolating, identifying and characterizing the active PSMs present in bioactive crude plant extracts; c. elucidating the mechanism(s) of activity of plant secondary/specialized metabolites with medicinal/antimicrobial/anti-cancer activity; ; and d. elucidating the biosynthetic pathways of the target bioactive plant secondary metabolite, as well as the genes coding the key enzymes in these biosynthesis pathways; e. increasing the production of the bioactive plant secondary metabolite through a combination of plant breeding, metabolic/genetic engineering, or environmental treatments of producing plants; and f. developing methods to synthesize the medicinal PSM through either bacterial culture, plant cell culture, or chemical synthesis (or a combination of these methods). The research presented in chapter 2 is focused on investigating the medicinal properties of crude plant extracts and isolating, identifying and characterizing the corresponding plant

secondary/specialized metabolites present in these extracts. Specifically, this chapter details efforts to quantify the antimicrobial effect of extracts from two burdock species (A. lappa and A. minus) on burn-related bacterial pathogens. The study quantified the ability of A. lappa and A. minus extracts to combat burn wound associated pathogens and used metabolomic profiling to identify putative anti-microbial compounds.

Agricultural applications of PSMs

Current research efforts into the agricultural applications of PSMs are broad and wide- reaching. Most of these efforts, however, have focused primarily on using the application of exogenous PSMs to increase crop productivity. Two primary areas of interest in PSM in the agricultural sector are the discovery of new PSMs able to be used as pesticides and/or herbicides, and minimization of off-target damage caused by the drift of PSM- based herbicides and/or pesticides. While it is important to discover new natural pesticides, it is equally important to look for ways to deal with pesticide drift, which could result in dramatic losses in the crop yield of neighboring fields. The research project described in chapter 3 was focused on developing a portable, sensitive, weather resistant detector 2,4-D (auxinic herbicide) detection system to monitor herbicide drift events. Chapter 4 details work contributing to the development of new PSM-based pesticide treatment. The development of new pesticide usually requires the steps to increase the production of the bioactive PSM by through either plant breeding or metabolic/genetic engineering designed to overexpress the genes involved in the biosynthesis of the PSM in question. In both cases, quantification of the end-product (the

target PSM compound) is necessary, and thus it is also important and necessary to develop a sensitive and reliable method to quantify this compound. Chapter 4 describes the development and optimization of a fast, sensitive, and cost efficient LC-MS/MS quantification method for quantification of the potential pesticide DIMBOA. This method was successfully applied to quantify DIMBOA in maize, and used to select corn parental lines with high DIMBOA content.

Model organisms for animal research

Metabolomic analyses of animal tissue is a crucial component of current approaches designed to dissect and understand the development of human and animal disease, particularly progressive diseases such as cancer[77]. It is therefore essential to develop sensitive, precise, and reliable, analytical methods to isolate, identify, and quantify biomarkers from complex biological matrixes. Plant systems are ideal for developing the complex analytical methodologies needed for metabolomic screening of human disease progression. This is because: plant produce a wide range of primary and secondary metabolites, allowing the development of methods specific to a broad ranges of compound classes; and plant material is easier and cheaper to produce and/or obtain in bulk for method development studies; and methods developed in plant systems are often easily transferred to mammalian systems (which are metabolically less complex. Plants therefore make ideal systems for the development of metabolomic screening tools.

Chapter 5 presents a study in which lipidomics methods developed using three model plant species (burdock, moringa, guayule). The methods developed were then used to

profile the bioactive lipids and phospholipids present in mouse cells undergoing the development of pancreatic cancer.

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Figure 1.1 Brief biosynthesis pathways of major secondary metabolites in plant The brief biosynthesis pathways in plant for major secondary metabolite groups, including phenolic compound, terpenoids, alkaloids, and fatty acids. MEP pathway: methylerythritol 4-phosphate (MEP) pathway; MVA pathway: mevalonate pathway

Figure 1.2 Brief biosynthesis pathways of phenolic compounds Brief biosynthesis pathways of phenolic compounds, including hydrolysable tannins, simple phenolic acids, flavonoids, stilbenes, lignans, lignins, and coumarins.

Figure 1.3 Skeleton structure of flavonoid Flavonoids possess a C6-C3-C6 three ring structure, with two benzene rings connected (rings A and B) by a pyran ring (ring C) in the middle.

Figure 1.4 Brief biosynthesis pathways of terpenoids Brief plant biosynthesis pathways of terpenoids (monoterpene, diterpene, sesquiterpenes, triterpenes, and tetraterpenes). MEP pathway: methylerythritol 4-phosphate pathway; MVA pathway: mevalonate pathway; IPP: isopentenyl diphosphate; DMAPP: dimethylally diphosphate; FPP: farnesyl diphosphate; GGPP: geranylgeranyl pyrophosphate

Figure 1.5 Example structures of terpenoids Example structures of terpenoids, including hemiterpene, monoterpene, sesquiterpene, diterpene, diterpene, and tetraterpene).

Figure 1.6 Skeleton structure of steroids Plant steroids are structurally similar to cholesterol, consisted of a four ring structure (tetracyclic cyclopenta[a]phenanthrene and a side chain attaching at C17.

Figure 1.7 Brief biosynthesis pathways of alkaloids The figure was adapted from Biochemistry of Plant Secondary Metabolism (p. 22), by M. Wink, 2011, John Wiley & Sons. The figure includes brief biosynthesis pathways of major alkaloids, including quinolizidine alkaloid, pyrrolizidine alkaloid, tropane alkaloid, monoterpenoid indole alkaloid, and benzylisoquinoline alkaloid. E4P: erythrose-4-phosphate; PEP: phosphoenol pyruvate; MEP pathway: methylerythritol 4-phosphate pathway; IPP: isopentenyl diphosphate

Chapter 2 Anti-microbial activity and metabolite composition of burdock (Arctium lappa

and Arctium minus) extracts

Introduction

In the U.S., approximately 486,000 burn injury patients are received in medical treatment facilities annually [1]. Each of these victims spends an average of 73±33 days in the hospital, at an average cost of $15,250 per patient [2]. This dollar value may not necessarily reflect the total cost of burn wound care, however, since, in addition to considerable pain, burn wounds are exceptionally prone to pathogen infection, which can lead to post-wounding morbidity and mortality [3]. Burn wound sites are generally initially occupied by gram-positive pathogens (including Staphylococcus aureus,

Streptococcus pyogenes and Enterococcus faecalis), which are superseded by gram- negative bacteria over time (including Pseudomonas aeruginosa, Klebsiella pneumonia,

Escherichia coli and Acinetobacter baumannii) [4, 5]. Further complicating and exacerbating the problem of post-wound infection is the fact that several of these pathogens have also been determined by the Infectious Disease Society of America to be developing broad-spectrum multi-drug resistance to antibiotics in hospitals throughout the U.S. [6]. Currently, there are three primary approaches used to manage burn wound infections: a. early excision of infected tissue; b. wound coverage/treatment with topical

anti-microbial agents; and c. systemic application of antibiotics, usually by oral dosage

[7]. Excision of the infected tissues is effective but may result in both increased pain and

an extension of the healing process. Application of topical antibiotic agents at the wound site isolates the wound from further infection, while at the same time maintaining moisture levels essential for re-epithelialization and wound closure. Compared to systemic application of antibiotics, topical or external antibiotic treatments sustain higher concentrations of antibiotics at the target site, and thus require lower total concentrations

(i.e. lower aggregate dosages compared to systemic/oral antibiotic treatments) of antibiotics to effectively combat secondary infection. This is advantageous, as it minimizes long-term exposure of microbes to antibiotics, decreasing the tendency of wound-associated pathogens to develop antibiotic resistance, and ultimately lowering the need for longer-term professional care and its concomitant medical costs. While several topical agents are currently employed regularly to treat burn wounds and to prevent secondary infection, each of these treatments has limitations and disadvantages.

Common topical agents currently used to treat burns and prevent secondary wound infections include: silver sulfadiazine, bacitracin, polymyxin B sulfate, mafenide acetate, neomycin, and other aminoglycosides [8]. Some of these agents are narrow in their target

range, and effectively combat only specific organisms or groups of organisms. In the case of polymyxin B sulfate, the anti-microbial effect of this compound is limited to gram- negative bacteria, as it functions by increasing the permeability of target cell membranes

(gram-positive bacteria are protected by thick peptidoglycan layer of cell wall, which protects these microbes from the permeabilizing effects of polymyxin B) [8].

Additionally, some topical agents may cause off-target effects, toxicity, and/or allergic responses. For instance, mafenide acetate may lead to metabolic acidosis at the site of injury. Silver sulfadiazine may exhibit toxic effects on fibroblast cells that play critical roles in wound healing, and bacitracin may cause an allergic rash at the site of application

[8]. These complications have made it advantageous to seek alternative and/or supplementary therapies that are less toxic and painful than currently used topical antimicrobial agents. Preliminary evidence suggests that plant-derived medicinal compounds are excellent candidates for supplementary antimicrobial treatments, and may eventually even serve as replacements for current topically applied compounds.

Burdock, a biennial herb of the genus Arctium, has been used as effective remedy for burn injury within the Amish communities in U.S. for several generations. In these communities, burdock has been claimed to effectively reduce healing time, minimize scarring, and relieve pain [7]. In addition to being used to treat burns in the Amish community, burdock, which is found as a weedy species distributed across the globe, is used in multiple cultures as a food product; to produce beverages and teas; and, most importantly, as medicinal herb. In Traditional Chinese Medicine (TCM), burdock seeds are taken internally to treat ‘wind heat’ ailments of the throat, whose symptoms include

upper respiratory infections, colds, and pneumonia [9, 10]. Chinese herbalists also believe that burdock helps with blood circulation and removes toxins from the human body. Chinese, European and American herbalists have used burdock leaf poultices and oils to treat skin-associated symptom diseases, including rashes, boils, acne, and eczema.

Recent studies have indicated that burdock extracts may contain a wide range of secondary compounds, suggesting that they may be able to serve as anti-microbial agents

[9, 11, 12]. To date, however, the efficacy of burdock treatments in combating burn wound-associated pathogens has not been investigated, nor have specific anti-microbial compounds been identified from burdock species, thereby limiting the application and use of this medicinal plant to the realm of “folk remedies”. In order to develop burdock extracts as viable burn wound or anti-microbial treatments, it is essential to that the antimicrobial effect of these extracts be quantified, and the responsible secondary metabolites identified.

When characterizing burdock extracts, it is important to note that the term “burdock,” as it has traditionally been used, encompasses a wide range of Arctium species. Of these species, Arctium lappa and Arctium minus are two of the most widespread in U.S., each with a global distribution and availability, making them ideal candidates for medicinal compound screens. Arctium minus has a slightly wider geographical range than Arctium lappa in North America. Morphologically, Arctium minus is smaller in flower head size, shorter in flower stalk length, and shorter in stem length than A. lappa. Additionally, the lower leaf stalks of Arctium minus, are mostly hollow with furrows on the tops of the stalks, while the lower leaf stalks of Arctium lappa are solid with grooves. To date, most

investigations into the pharmacological effects of burdock and/or characterizing the secondary metabolite profile of this plant has focused solely on Arctium lappa; and very little data is available regarding Arctium minus.

In this research, we evaluated the ability of leaf extracts of both A. minus and A. lappa to inhibit the growth of seven burn wound related pathogens in agar plate bioassays, agar diffusion bioassays, and liquid broth assays. Metabolomic profiles of A. lappa and A. minus extracts were generated using spectrophotometric assays (total phenolic and flavonoid contents), HPLC (separation and identification of phenolics), LC-MS/MS

(phenolics), and GC-MS (broad-spectrum metabolomic profiling). As A. minus extracts generally exhibited greater anti-microbial activity than did A. lappa extracts, comparison of the metabolomic profiles of these two species allowed the identification of several compounds (peaks) unique to A. minus which may be responsible for the anti-microbial activity of A. minus extracts.

Materials and Methods

Plant material and sample preparation

Arctium lappa and Arctium minus plants were grown in the Gourley Hall greenhouse complex, at the Ohio Agricultural Research and Development Center, The Ohio State

University, Wooster, OH (OARDC). Seedlings were germinated and grown in custom soil consisting of: 70% (v/v) Turface, 25% (v/v) vermiculite, and 5% (v/v) limestone. The temperature was set at 27-29 ˚C during the day and 24-26 ˚C during the night. Plants

were grown under 16 h light and 8 h dark; watered on a daily basis; and fertilized with

100 ppm Jack’s All-Purpose 20-20-20 fertilizer (Jr Peters, Allentown, PA) once per week.

Additionally, to maintain ambient humidity, plants were treated with a fine mist spray 20 seconds in duration (by Netafim CoolNet Pro 4-Way Fogger). Leaves were harvested from mature plants and immediately flash-frozen in liquid nitrogen. Samples were stored at -80˚C prior to extraction.

Plant samples were extracted as described previously with the following modifications.

Upon removal from -80˚C storage, burdock leaf tissues were triple ground in liquid nitrogen to generate a powder of uniform particle size. Ground samples were then extracted with one of a series of solvents of decreasing polarity: water (H2O), 70% acidified acetone (ACT) (acetone: water: acetic acid at 70:29.5:0.5; v/v/v), methanol

(MeOH), chloroform: methanol (CM) (chloroform: methanol at 7:3; v/v), ethyl acetate

(EA); or dichloromethane (DM). All the organic solvents were purchased from Fisher

Scientific. Samples were solvent-extracted using 20 mL of the solvent per gram frozen tissue. Following addition of the solvent, samples were sonicated at a frequency of 40 kHz for 1 h at room temperature in an ice-cooled Jeiotech US-10 sonication bath (Jeio

Tech, Billerica, MA) Samples were then extracted for an additional 23 hours at room temperature without sonication, filtered through Whatman No.1 filter paper (GE

Healthcare, Pittsburgh, PA), and taken to dryness using N2 gas. The dried samples were not stored, but were immediately reconstituted for use in metabolomic studies and/or anti-microbial assay as described below.

Bacterial strains and maintenance

Four gram-positive bacterial strains (Staphylococcus aureus, Staphylococcus epidermidis,

Streptococcus pyogenes, and Enterococcus faecium) and three gram-negative bacterial strains (Pseudomonas aeruginosa, Escherichia coli, and Klebsiella pneumonia) commonly associated with secondary burn wound infections were used in antimicrobial assays [4]. For anti-microbial assays, bacterial strains were streaked on lysogeny broth

(LB; all strains except. S. pyogenes) or brain heart infusion (BHI; Streptococcus pyogenes) agar plates (25 mL). After overnight incubation at 37 ˚C, a single colony of each strain was picked from the plates and added to a fresh 5 mL culture tube of sterile liquid LB media (or BHI media for Streptococcus pyogenes). Samples were then incubated in a

Shel Lab Floor Shaking Incubator (Shel Lab, Cornelius, OR) overnight at 37˚C, 250 rpm

[13]. Following overnight incubation, the optical density of each bacterial culture was recorded; and bacteria were then prepared for use in antimicrobial assays as described below.

Agar plate bioassay

Antimicrobial effects of individual burdock extracts were investigated using an agar plate bioassay modified from protocols used to investigate multi-drug resistance transport capacity in yeast [14, 15]. OD600nm values of bacterial cultures grown overnight (see above) were recorded, and each microbe was then diluted with either LB or BHI (for S. pyogenes) media to generate a dilution series of each microbe with OD600nm values of either 0.1, 0.01, and 0.001for slower growing microbes (S. epidermidis and E. faecalis), 48

or 0.1, 0.001 or 0.0001 for faster-growing strains (S. aureus, S. pyogenes, E. coli, P. aeruginosa and K. pneumoniae). Burdock samples were prepared for this assay by re- dissolving individual dried solvent-extracted pellets (see above) with 1 mL of the original extraction solvent. Then 100 µL of each plant extract (or an appropriate solvent blank for control plates) was applied to and spread evenly over an LB (BHI for S. pyogenes) agar plate. The plates were allowed to air dry, and 5 µL droplets of bacterial culture (at one of the O.D.600nm values described above) were placed in a dilution series on the plate and allowed to dry. Plates containing both burdock extract (or solvent control) and bacterial strains were then incubated at 37 ˚C for 16h and scanned using an Epson Perfection V500

Photo Scanner (Long Beach, CA). Scanned images were processed using ImageJ software (imagej.nih.gov); and bacterial growth (i.e., survival on the burdock extract) was determined by counting the number of pixels present in the area of each 5 µL droplet of the bacterial dilution series. Three individual plates were measured for each microbe: burdock extract combination, and the results were averaged across plates. Pixel count data was statistically analyzed using 1-tailed Student’s t-test to determine the significant differences between the solvent blank (control) group and the burdock extract treatment

(experimental) group.

Liquid broth and minimum inhibitory concentration (MIC) assays

Liquid growth assays were performed as described previously [13], with the following modifications. Briefly, bacterial overnight cultures were prepared as described above.

Following the overnight incubation, OD600nm values for each culture were recorded.

Cultures were then diluted with either LB or BHI (for S. pyogenes) to an OD600nm value of 0.001 for slower-growing strains (S. epidermidis and E. faecalis), or an OD600nm of

0.0001 for faster-growing strains (S. aureus, S. pyogenes, E. coli, K. pneumoniae, and P. aeruginosa). Then1 mL of diluted bacterial culture was transferred to a sterile 2 mL centrifuge tube. Burdock extracts were prepared by re-suspending dried pellets (see above) in either 1 mL of 1% DMSO in H2O (for MeOH, CM, EA, DM extracts) or 1 mL

H2O (for ACT, H2O extracts). Following resuspension, 100 µL of individual extracts (or solvent blanks) were added to tubes containing bacteria (one burdock extract and one bacterial species per tube). Cultures were then incubated in a shaker incubator overnight at 37˚C, 250 rpm, after which OD600nm values were measured. Most burdock extracts contained chromophores which complicated spectrophotometric readings (i.e., increased background absorbance). Because of this, blanks for each plant extract were generated by adding 100 µL of each extract to 2 mL of sterile media. These blanks were then used to zero the spectrophotometer prior to measuring bacterial growth. Minimum inhibitory concentrations (MICs) for each burdock extract were calculated using liquid broth assays by generating a 2-fold dilution series (i.e., 0.5X dilution, 0.25X, 0.125X, etc.), and diluting extracts until bacterial growth was no longer inhibited (at which point the previous dilution point was determined to be the MIC). Three experimental replicates were averaged together, and the results were statistically analyzed using 1-tail Student’s t-test to determine the significant differences between the solvent blank (control) group and the burdock extract treatment (experimental) group.

Agar Well Diffusion Assay

Agar well diffusion assays were based on previously published protocols [16]. Bacterial samples were prepared and diluted according to the same protocols used in the agar plate bioassays described above. Following dilution, bacterial cultures were applied evenly to the surface of the agarose plate using a cotton swab. Following application of bacteria, wells 40 mm in diameter were punched into each plate using a sterilized cork borer.

Burdock ACT and EA extracts were prepared and dried as described above. ACT and EA pellets were then dissolved in 1 mL H2O or 1% DMSO, respectively. Then 60 µL aliquots of burdock extracts were added to the wells of each plate (one type of extract per plate), with either H2O or 1% DMSO being used to fill control the control well present in each plate. As a positive control, either kanamycin sulfate or carbenicillin disodium salt was added to one well on each plate. Specifically, 1 ng/ µL carbenicillin was used as positive control for S. aureus and S. pyogenes, 25 ng/ µL carbenicillin was used for E. coli, 50 ng/ µL carbenicillin was used S. epidermidis, 50 ng/ µL kanamycin was used for

E. faecalis and K. pneumoniae, 200 ng/ µL kanamycin was used for P. aeruginosa.

Following the addition of burdock extracts, as well as the appropriate blank and antibiotic controls, agarose plates were carefully transferred to an incubator and grown at 37 ˚C for

16h. Plates were then scanned using an Epson Perfection V500 Photo Scanner. The inhibitory effects of burdock extracts were determined by measuring the diameters of the zones of inhibition present around each well containing burdock extracts and comparing these to the zones of inhibition around: a. the wells containing solvent blanks; and b. the wells containing antibiotic. Zones of Inhibition (ZI) were calculated by measuring the

diameter of the zone of inhibition (i.e., the diameter of the circle where no bacterial growth was observed) and then subtracting the diameter of the well [ZI (mm) = diameter of inhibition zone (mm) – diameter of solvent well (4mm)]. Three individual plates were measured for each microbe: burdock extract combination, and the results were averaged across plates. The results were statistically analyzed using ANOVA Tukey’s test to determine the significant differences between the solvent blank (negative control) group, the antibiotic-treated (positive control) group, and the burdock extract treatment

(experimental) group.

Assay of Bactericidal vs. Bacteriostatic Effects of Burdock Extracts

Burdock ACT extracts were prepared as described above and re-suspended in 1 mL H2O.

Bacterial cultures were prepared and diluted as described in the liquid broth assay protocol above. Assays were carried out in 96-well plates as described previously [17], with the following modifications. Briefly, burdock ACT extracts (20 µL) were added to standardized bacterial cultures (180 µL) to fully inhibit bacterial growth. Following overnight incubation in a Shel Lab Floor Shaking Incubator and grown for 16 h at 37 ˚C,

250 rpm, OD600nm values were recorded for each bacterial culture. Then 5 µL of each bacterial culture (inhibited by burdock extracts) was then used to inoculate a second batch of liquid medium (180 µL), and cultures were incubated overnight. Bacterial growth was then assayed by measuring optical density at 600nm. If no bacterial growth was observed in the secondary culture, the effect of burdock extracts on the primary

culture was concluded to be bactericidal. Three experimental replicates were used for this assay.

Spectrophotometric Assays

To determine the total phenolic content of burdock leaf extracts, dried ACT and H2O extracts were prepared as described above and then re-dissolved in 1 mL of H2O. A. minus ACT extracts were diluted 15-fold (1:15, v/v), while all other extracts were diluted

5-fold (1:5, v/v). The concentration of phenolic compounds present in each extract was calculated as gallic acid equivalents (GAE), as described previously [18]. Gallic acid

(Sigma-Aldrich Co., St. Louis, MO) standard curve solutions at concentration of 0, 0.1,

0.2, 0.3, 0.4, 0.5 mg/mL were prepared. 50 µL of each burdock extract (or gallic acid solution) was transferred to a 2 mL centrifuge tube, to which 900 µL of a 1:10 dilution of

Folin-Ciocalteu (FC) reagent (Sigma-Aldrich Co., St. Louis, MO) was added. After an incubation of 5 min. at room temperature, 600 µL 5% (w/v) Na2CO3 (certified ACS,

Fisher Scientific, Waltham, MA) solution was added to halt the reaction, and samples were then incubated at 40˚C for 30 min. OD750nm values were then measured using a

Beckman Coulter DU 730 Life Science UV/Vis Spectrophotometer (Beckman Coulter,

Brea, CA).

To determine total flavonoid content of burdock leaf extracts, the burdock ACT and H2O extracts were prepared and dried as described above. Extracts were then re-dissolved in 1 mL of H2O. A. minus ACT extracts were diluted 15-fold (1:15, v/v), while all other extracts were diluted 5-fold (1:5, v/v). Levels of total flavonoids present in extracts were 53

calculated as rutin equivalents (RHE), as described previously[19]. Rutin (Sigma-Aldrich

Co., St. Louis, MO) standard curve solutions at concentrations of 0, 0.1, 0.2, 0.3, 0.4, 0.5 mg/mL were prepared. 100 µL of burdock extracts (or rutin standard solutions) were added to a 15 mL tube, to which 3.9 mL H2O, 2 mL 95% (v/v) ethanol in H2O, and 5%

(w/v) NaNO2 (Mallinckrodt Chemical Works, St. Louis, MO) solution were then added.

After an incubation of 6 min. at room temperature, 0.6 mL 10% (w/v) AlCl3 (ACROS,

Morris Plains, NJ) was added to halt the reaction. After another 6 min. incubation at room temperature, 4 mL 4% (w/v) NaOH (certified ACS, Fisher Scientific, Waltham, MA) solution was added to each tube, and samples were incubated for an additional 10 min. at room temperature. OD510nm was then measured using a Beckman Coulter DU 730 Life

Science UV/Vis Spectrophotometer.

The in vitro anti-oxidant capacity of burdock leaf samples was quantified using the ferric reducing antioxidant power (FRAP) method[20]. Burdock ACT and H2O extracts were prepared and dissolved as described above. Extracts were then re-dissolved in 1 mL of

H2O. A. minus ACT extracts were diluted 150-fold (1:150, v/v) times while all the other extracts were diluted 50-fold (1:50, v/v). The anti-oxidant capacity of burdock extracts was calculated as Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) equivalents (TE), as described previously. Trolox (Sigma-Aldrich Co., St. Louis, MO) standard curve solutions were prepared at concentrations of 0, 0.02, 0.04, 0.06, 0.08, 0.1

µM. 30 mM acetate buffer, 20 mM ferric chloride (Sigma-Aldrich Co., St. Louis, MO) solution, 10 mM TPTZ (2,4,6-Tripyridyl-s-Triazine) (Sigma-Aldrich Co., St. Louis, MO) solution were combined at ratio of 10:1:1 to prepare FRAP solution. Following its

preparation, 0.9 mL FRAP solution was to each assay tube. Then 0.1 mL of burdock extract or Trolox solution was added to each tube, and tubes were incubated at 37 ˚C for

10 min. OD593nm values were then obtained using a Beckman Coulter DU 730 Life

Science UV/Vis Spectrophotometer.

For all three spectrophotometric assay, three replicates were analyzed for each sample.

And the difference between A.lappa and A. minus regarding total phenolic content, total flavonoid content and in vitro antioxidant capacity, was determined by 2-tailed type-3

Student’s t-test.

HPLC and LC-MS/MS analysis of phenolic compounds

Burdock ACT extracts were prepared as described above, re-dissolved in 1 mL of H2O, and then filtered through Agilent Captiva Econo 13 mm 0.2 µm nylon syringe filters

(Agilent Technologies, Santa Clara, CA). 30 µL of each burdock ACT extracts were then injected onto a Beckman coulter ® Gold HPLC system (Beckman-Coulter, Brea, CA).

Compounds were separated using a Gemini® 5 µm C6-Phenyl 110 Å column

(Phenomenex, Torrance, CA). The solvent system for HPLC analyses was 0.2% acetic acid (certified ACS, Fisher Scientific, Waltham, MA) for solvent A, and 100% CH3CN

(HPLC grade, Fisher Scientific, Waltham, MA) for solvent B. The solvent gradient was as follows: 5% (0-10 min); 5-22% (11-20 min); 22-28% (21-45 min); 28-80% (46-53 min); 80% (54-59 min); 80-5% (64-67 min); 5% (68-72 min). Data was collected using a diode array detector (DAD) for peak detection, and all data generated were analyzed using Beckman Coulter® 32 Karat Software. For multi-dimensional analyses (i.e., liquid 55

chromatography tandem mass spectrometric analysis of selected HPLC peaks), peaks were separated via HPLC as described above, and collected using a Beckman Coulter

ProteomeLab FC module (Beckman Coulter, Brea, CA). The peaks of burdock ACT extracts eluting between 22 min to 58 min were collected (15 seconds for each fraction) for secondary LC-MS/MS analysis. The collection was repeated 5 times.

Fractions collected from the HPLC were further analyzed using liquid chromatography tandem mass spectroscopy. Briefly, collected peak samples were dried in a Labconco

FreeZone Plus 12 Liter Cascade Console Freeze Dry System (Labconco, Kansas City,

MO), and then re-suspended 200 µL 100% grade methanol (Optima LC/MS, Fisher

Scientific, Waltham, MA). Samples were then filtered through 4 mm 0.2 µm nylon syringe filters (Thermo Scientific, Rockwood, TN). Fractions with UV/Vis absorption over 500 were diluted 10 folds. Samples were then injected onto an Agilent 6460 LC-

MS/MS system (Agilent Technologies, Santa Clara, CA), consisting of an Agilent 1260

HPLC coupled to a 6460 Triple Quadrupole LC/MS. Compounds were separated using an Agilent Poroshell EC-C18 column (50 × 3 mm, 2.7 µm, Agilent Technology, Santa

Clara, CA). H2O with 5% methanol, 0.1% acetic acid and methanol with 0.l% acetic acid were used as solvents A and B, respectively. The solvent gradient was as follows: 2-30%

(0-1 min); 30-98% (2-8 min); 98% (9-12 min), with a flow rate at 0.3 mL/min.

Compounds were ionized using Electrospray ionization (ESI), and the spray chamber settings were: gas temperature at 250 ˚C, gas flow at 7 L/min, nebulizer pressure at 30 psi, sheath gas temperature at 300 ˚C, sheath gas flow at 10 L/min, capillary voltage at -2250

V. Mass spectral data was analyzed using the Agilent MassHunter program, and

compounds were identified either by comparing retention times and mass transitions to authentic standards, or by comparing retention times and mass transitions to previously published literature [21-26].

GC-MS analysis of nonpolar extracts

To prepare burdock samples for GC-MS analyses, 10 µL 10 mg/mL raspberry ketone

(Sigma-Aldrich Co., St. Louis, MO) solution was added with to the extraction solvents for each sample, and samples were then extracted as described above. Dried burdock

MeOH, CM, EA, DM extracts were re-dissolved in 1 mL of original extraction solvents.

Re-suspended extracts were then filtered through 17 mm 0.2 µm Teflon syringe filters

(Thermo Scientific, Rockwood, TN). Samples were then injected onto an Agilent 6890N network gas chromatography system equipped with an Agilent 7683 autosampler, and an

Agilent 5973Network mass selective detector (Agilent Technologies, Santa Clara, CA) for analysis. Samples were analyzed multiple times, using two different GC columns.

Samples were first separated using a DB-WAX column (30 mm ×0.2 mm, 0.2 µm,

Agilent Technologies, Santa Clara, CA). Then 3 µL of each sample was injected onto the column, the injection port temperature was 250ºC, and the temperature gradient was as follows: hold at 80˚C for 2.5 min; increase from 80 ˚C to 200 ˚C at 1 ˚C/min; hold at 200

˚C for 10 min; increase from 200 ˚C to 240 ˚C at 2 ˚C/min; hold at 240 ˚C for 5 min; decrease from 240 ˚C to 70 ˚C at 30 ˚C/min; hold at 70 ˚C for 1 min. The total gas flow was set at 50 mL/min and the inlet gas pressure was set at 9.4 psi. Samples were then separated using an HP-5 phenyl-methylpolysiloxane column (30 mm ×0.25 mm, 0.25 µm,

Agilent Technologies, Santa Clara, CA). 1 µL of sample was injected onto the column, the injection port temperature was 250ºC, and the temperature gradient was as follows: hold at 80 ˚C for 2 min; increase from 80 ˚C to 100 ˚C at 5 ˚C /min; hold at 100 ˚C for 2 min; increase from 100 ˚C to 250 ˚C at 5 ˚C /min; hold at 250 ˚C for 2 min; increase from

250 ˚C to 325 ˚C at 2 ˚C /min; hold at 325 ˚C for 2 min; decrease from 325 ˚C to 80 ˚C at

50 ˚C /min; hold at 80 ˚C for 1min. GC-MS data were processed and analyzed using

ACD/Labs MS Workbook Suite software, and compounds were identified by matching mass spectra to the Wiley Registry 10th Edition / NIST 2012 Mass Spectral Library (John

Wiley & Sons, Inc., Hoboken, NJ).

Results

Recent studies have indicated that extracts of A. lappa leaves may exhibit anti-microbial properties [27, 28]. In these studies, A. lappa leaf ethanol and ethanol: water (7:3, v/v) extracts were demonstrated to negatively impact the growth of several food related or endodontic bacterial strains, including E. coli, E. faecalis, S. aureus, P. aeruginosa,

Lactobacillus acidophilus, Streptococcus mutans, and Bacillus subtilis [27, 29].

Interestingly, water extracts of A. lappa leaves have also been demonstrated to combat the growth the food-borne bacterial pathogens S. aureus, Streptococcus pneumoniae, B. substilis, E. coli, Shigella dysenteriae and Salmonella typhimurium [30]. To date, however, work documenting the anti-microbial effects of burdock has focused on a single species (A. lappa) and on food-borne or gut-related pathogens. Additionally, minimal inhibitory concentration (MIC) data has been provided for only two bacterial strains (E.

coli and S. aureus). Given the widespread use of burdock as topical wound treatment in both the Amish community and Traditional Chinese Medicine, as well as the published ability of burdock extracts to inhibit the growth of endodontic pathogens, we hypothesized that burdock leaf extracts would exhibit strong anti-microbial activity against a panel of burn wound-associated pathogens. In these studies, we used not only A. lappa, but also the more widely geographically distributed burdock species A. minus.

Additionally, while previous studies have focused on investigating the anti-bacterial capacities of water and ethanol leaf extracts, which contain primarily polar molecules, we have assayed the anti-microbial effects of burdock leaf extracts using a range of solvents with overlapping polarities, therefore containing not only polar, but also amphipathic and non-polar compounds.

Agar plate bioassay

In preliminary agar plate assays, Arctium lappa and Arctium minus leaves were extracted in water (H2O), methanol (MeOH), 70% acetic acidified acetone (ACT), chloroform methanol (CM), ethyl acetate (EA) or dichloromethane (DM). These extracts were spread over agar plates, to which droplets of burn wound-associated bacteria were added. Five of these pathogens, S. aureus, S. pyogenes, E. coli, P. aeruginosa, and K. pneumoniae, were applied at a concentration of OD600nm = 0.01. Interestingly, two wound associated pathogens, S. epidermidis and E. faecalis, grew at a slower rate than the other five pathogens and, as a result, droplets of these pathogens were added to agar plate bioassays at a concentration of OD600nm = 0.1. Plates were then incubated overnight at 37ºC, and

bacterial growth on each burdock extract was determined by scanning plates and monitoring the growth of each bacterial spot. In these assays, the ACT, EA and DM extracts from both A. lappa and A. minus showed the greatest anti-microbial activity (i.e., greatest degree of growth inhibition; and inhibition against the widest range of bacteria).

As a result, ACT, EA, and DM extracts were selected for use in downstream agar plate bioassays.

ACT, EA, and DM extracts from A. lappa and A. minus leaves were then tested for efficacy against wound-associated bacteria using a dilution based agar plate bioassay. In these studies, burdock extracts were spread evenly on the surface of agarose plates to which were added droplets of wound-associated pathogens. Pathogens were added in a logarithmic dilution series of OD600nm values of either 0.1, 0.01, and 0.001 for slower growing microbes (S. epidermidis and E. faecalis), or 0.1, 0.001 or 0.0001 for faster- growing strains (S. aureus, S. pyogenes, E. coli, P. aeruginosa and K. pneumoniae). In agreement with previously published studies, A. lappa extracts inhibited the growth of several wound-associated pathogens that are often found in food or the digestive tract, specifically S. aureus, E. coli, E. faecalis and P. aeruginosa (Figure 2.1 and Figure 2.2).

Our data indicated that A. lappa extracts are also effective against several additional wound-associated pathogens, specifically S. epidermidis, S. pyogenes and K. pneumoniae.

In general, extracts from the less well-studied burdock species A. minus exhibited antimicrobial activities similar to A. lappa against the seven wound-associated pathogens used in this study (Figure 2.1 and Figure 2.2). However, in several cases (for example, S. aureus and S. epidermidis), A. minus extracts showed greater antimicrobial effects than

did the corresponding A. lappa extract. Specific details regarding the efficacy of specific

A. lappa and A. minus extracts against burn-wound associated pathogens are provided below.

Each of the three A. lappa extracts used in this study (70% acidified acetone, ethyl acetate, dichloromethane) exhibited some degree of antimicrobial activity. At mid- to low- level concentrations of pathogens, A. lappa ACT extracts inhibited the growth of both gram-positive (S. epidermidis, S. pyogenes, E. faecalis) and gram negative (E. coli and P. aeruginosa) microbes; but did not affect the growth of either gram positive S. aureus, or gram-negative K. pneumoniae (Figure 2.1 and Figure 2.2). At high bacterial densities, however, A. lappa ACT extracts were only able to inhibit the growth of gram- positive S. pyogenes and E. faecalis. Ethyl acetate extracts generated from A. lappa leaves exhibited broad spectrum anti-microbial capacity, and inhibited the growth of six pathogens at low bacterial density (gram positive: S. aureus, S. epidermidis, S. pyogenes,

E. faecalis; gram-negative: E. coli, P. aeruginosa). At higher (mid-range) bacterial concentrations, A. lappa EA extracts inhibited the growth of S. pyogenes, E. faecalis and

E. coli; and at the highest bacterial concentrations tested, these EA extracts did not significantly affect the growth of any of the seven selected pathogens. Finally, at low bacterial density, DM extracts of A. lappa leaves were effective against five bacterial pathogens (S. epidermidis, S. pyogenes, E. faecalis, K. pneumoniae and P. aeruginosa), but did not impact the growth of either gram-positive S. aureus or gram-negative E. coli.

Interestingly, at mid-range and high bacterial concentrations, these DM extracts still

significantly inhibited the growth of all the three gram-negative strains used in the assays

(E. coli, P. aeruginosa and K. pneumoniae).

Similar to what was observed for A. lappa extracts, each of the A. minus leaf extracts used in agar plate bioassays exhibited some degree of antimicrobial activity. At low bacterial density, A. minus ACT extracts exhibited significant inhibitory effect on both gram-positive: S. aureus, S. epidermidis, S. pyogenes, E. faecalis, and gram-negative E. coli and P. aeruginosa pathogens at low bacterial densities, but did not affect the growth of gram-negative K. pneumoniae (Figure 2.1 and Figure 2.2). However, a trend for non- significant inhibition was observed at low bacterial concentration on K. pneumoniae, but at mid-range bacterial concentration, the inhibition was significant. Besides K. pneumoniae, A. minus ACT extracts inhibited the growth of another five bacteria, S. aureus, S. epidermidis, E. faecalis, E. coli, and P. aeruginosa. At high bacterial concentrations, ACT extracts showed anti-microbial activity against all four gram- positive pathogens tested (S. aureus, S. epidermidis, S. pyogenes and E. faecalis); and one of the gram-negative bacteria (E. coli). At low bacterial concentrations, ethyl acetate extracts generated from A. minus leaves significantly inhibited the growth of five bacterial species (S. aureus, S. epidermidis, S. pyogenes, E. coli, and P. aeruginosa), but did not affect the growth of either the gram-positive E. faecalis or the gram-negative K. pneumoniae. While at mid-range bacterial concentrations, the significant inhibitory effect was still observed on S. aureus, S. epidermidis, S. pyogenes, and E. coli but not on the other three bacteria E. faecalis, K. pneumoniae and P. aeruginosa. At high bacterial concentrations, however, the EA extracts only inhibited the growth of S. epidermidis

(gram-positive) and E. coli (gram-negative). A. minus DM extracts exhibited antimicrobial activity against a range of both gram-positive (S. aureus, E. faecalis) and gram negative (E. coli and K. pneumoniae) pathogens at low bacterial densities. Similar to what was observed for EA extracts, DM extracts showed a shift in efficacy at mid-range bacterial concentrations, inhibiting the growth of gram-positive S. aureus and gram- negative E. coli, K. pneumoniae and P. aeruginosa. At high bacterial concentrations, however, DM extracts significantly inhibited the growth of two gram positive bacteria (S. aureus and S. epidermidis), as well as all three gram-negative pathogen used in this study.

In summary, acetone, ethyl acetate, and dichloromethane extracts of both A. lappa and A. minus leaves demonstrated the ability to inhibit the growth of seven burn related bacteria in agar plate bioassays. Interestingly, our results showed some distinct trends regarding the antimicrobial capacities of different burdock leaf extracts. In general, less polar extracts (DM) were more effective against gram-negative bacteria; while more polar extracts (ACT) inhibited the growth of gram-positive bacteria. While in most cases the antimicrobial effect of A. minus was comparable to that of A. lappa, in some cases

(primarily gram-positive organisms), A. minus extracts displayed greater anti-bacterial capacity.

Liquid broth and minimum inhibitory concentration (MIC) assays

One concern arising during the course of the agar plate bioassay was the amount of burdock extract available for interaction with bacteria in these assays. It is possible that compounds present in extracts applied to the agarose plate may have evaporated away 63

from the site of application during the plate-drying step, or that applied compounds could have diffused throughout the agarose (i.e., away from the plate surface, making them less available to bacteria). Moreover, the measurement of bacterial growth can be complicated and time-consuming. To avoid these difficulties and confirm the antimicrobial effects of burdock extracts observed in agarose plate bioassays, we employed a liquid broth bioassay that measured bacterial growth directly. In these studies, we re-dissolved dried A. lappa and A. minus extracts (MeOH, CM, EA, DM, ACT, and H2O) extracts in non-toxic aqueous solvents, and assayed the anti-microbial capacities of the resuspended extracts against seven selected burn pathogens (S. aureus, S. epidermidis, S. pyogenes, E. faecalis,

E. coli, K. pneumoniae and P. aeruginosa) grown in liquid medium. Burdock extracts

(100 µL) were mixed 1 mL of one of the seven bacterial species at an OD600 of either

0.01 for slower-growing bacteria (S. epidermidis and E. faecalis), or 0.1 for faster- growing organisms (S. aureus, S. pyogenes, E. coli, K. pneumoniae, and P. aeruginosa).

Cultures were then incubated in a shaker for 16 hours, after which OD600nm values were measured to quantify bacterial growth (Figure 2.3).

Interestingly, in liquid broth assays, A. lappa extracts generally exhibited broader antimicrobial activity than was observed in plate-based bioassays. For example, A. lappa

ACT and EA extracts significantly inhibited the growth of all bacteria (S. aureus, S. epidermidis, S. pyogenes, E. faecalis, E. coli, and P. aeruginosa) tested except K. pneumoniae (Figure 2.3). Additionally, A. lappa DM extracts reduced the growth of all four the gram-positive bacteria (S. aureus, S. epidermidis, S. pyogenes, and E. faecalis), as well as gram-negative E. coli; but not gram-negative K. pneumoniae or P. aeruginosa

(Figure 2.3). Even more interestingly, several of the A. lappa extracts which proved ineffective in agarose plate assays (CM, MeOH, and H2O) exerted significant effects on bacterial growth in liquid-broth based assays. For example, both chloroform: methanol

(CM) and methanol (MeOH) A. lappa extracts were effective against three gram-positive strains (S. aureus, S. epidermidis and S. pyogenes) and one gram-negative strain, E. coli.

Additionally, H2O extracts (which in general had the lowest anti-microbial capacity) inhibited the growth of both S. aureus and S. epidermidis (Figure 2.3).

Similar to what was observed for A. lappa extracts, A. minus extracts exhibited broader anti-microbial activity in liquid broth assays than was observed in agarose plate assays.

For example, in liquid bioassays, A. minus ACT extracts significantly inhibited the growth of all seven burn-wound associated pathogens used in our assays (Figure 2.3).

Additionally, A. minus EA extracts also significantly reduced the growth of all bacteria tested, with the exception of S. pyogenes. Finally, DM and MeOH A. minus extracts inhibited the growth of S. aureus, S. epidermidis, E. faecalis and E. coli; while CM extracts were effective against all four gram positive strains (S. aureus, S. epidermidis, S. pyogenes, and E. faecalis), and one gram-negative strain (E. coli). As with A. lappa extracts, H2O extracts of A. minus leaves were the least effective in inhibiting bacterial growth, and were efficacious against only three organisms: S. aureus, S. epidermis and E. faecalis.

To better evaluate the antimicrobial effect of the burdock extracts, minimum inhibitory concentrations (MICs) for each burdock extract were calculated using liquid broth assays

by generating a 2-fold dilution series (i.e., 0.5X dilution, 0.25X, 0.125X, etc.), and diluting extracts until bacterial growth was no longer inhibited (at which point the previous dilution point was determined to be the MIC) (Figure 2.4). The MIC values for each extract are listed in Table 2.1, with darker cells indicating increased antimicrobial effect. As can be seen from Table 2.1, in general A. lappa and A. minus extracts were more effective towards gram-positive bacteria than gram-negative bacteria. Additionally, when comparing between the extraction solvents, it was observed that, for both A. lappa and A. minus, ACT extracts exhibited the broadest anti-bacterial activity, followed by EA.

Moreover, A. minus extracts proved more effective than A. lappa extracts in their ability to inhibit the growth of burn wound-associated pathogens Table 2.1) in liquid-based bioassays.

Agar well diffusion bioassay

The results of liquid broth bioassays indicated that, in both A. lappa and A. minus, acetone and ethyl acetate extracts were the most effective against burn wound-associated microbes. As a result, these extracts were then selected for use in agar diffusion assays designed to further determine the efficacy of burdock extracts in aqueous buffer (Figure

2.5). Standardized bacterial cultures were applied evenly to the surface of the agarose plate and then wells 40 mm in diameter were punched into each plate. ACT and EA pellets were then dissolved in 1 mL H2O or 1% DMSO, respectively, and added to the wells (one extract type per plate). Selected antibiotic standards were used as positive controls, while a solvent blank served as a negative control on each plate. The plates were

then incubated overnight and scanned; and the Zone of Inhibition (ZI) was calculated by measuring the diameter of the zone of inhibition and then subtracting the diameter of the well [ZI (mm) = diameter of inhibition zone (mm) – diameter of solvent well (4mm)].

Interestingly, ACT extracts of both A. lappa and A. minus were able to significantly inhibit the growth of all seven burn wound-associated pathogens used in this study, compared to the solvent blank (Figure 2.6). Further, for S. aureus, S. pyogenes, and

P.aeruginosa, both A. lappa and A. minus ACT extracts were more effective antimicrobial agents (i.e., inhibited bacterial growth to a significantly greater degree) than the antibiotic positive control (Figure 2.6). In all cases (i.e., against all pathogens tested), burdock ACT extracts were at least as effective as the antibiotic control in inhibiting bacterial growth. Unlike ACT extracts, the EA extracts of both A. lappa and A. minus did not inhibit the growth of the gram-negative strains, P. aeruginosa and K. pneumoniae.

However, the EA extracts still exhibited significant inhibitory effect on all of the gram- positive bacteria (S. aureus, S. epidermidis, S. pyogenes, and E. faecalis), and the gram- negative bacteria E. coli. The inhibitory effect on S. aureus and S. pyogenes of both A. lappa and A. minus EA extracts were greater than the positive control used.

Assay of Bactericidal vs. Bacteriostatic Effects

In order to determine whether the inhibitory effect of burdock extracts was bactericidal or bacteriostatic, we made use of a version of the liquid broth bioassays described above, modified for use in 96-well plates. In these assays, 20 µL of either A. lappa or A. minus

ACT extract (re-suspended in 1 mL H2O) was added to 180 µL of bacterial culture of a 67

burn wound associated pathogen an OD600 of either 0.1 for slower-growing bacteria (S. epidermidis and E. faecalis), or 0.01 for faster-growing organisms (S. aureus, S. pyogenes,

E. coli, P. aeruginosa and K. pneumoniae) in one well of a 96-well plate. Plates were then incubated overnight, and bacterial growth was measured to confirm that the addition of the ACT extracts to the wells was sufficient to fully inhibit bacterial growth. Cultures were then harvested from the wells, and 5 µL of the recovered culture was used to inoculate 180 µL of sterile media in the well of a new 96-well plate. The newly inoculated cultures were then incubated overnight (16 h), and bacterial growth was quantified spectrophotometrically (OD600nm) of the re-inoculated medium was measured as indication of bacterial growth (Figure 2.7). If a bacterial species failed to grow when re- inoculated into fresh media, the effect of the burdock extract was concluded to be bactericidal (i.e., to have killed the bacteria used for the secondary inoculation). If a species was able to recover following secondary inoculation, however, the inhibitor effect of the burdock extract in the original inoculum was presumed to be bacteriostatic. Our data indicated that the growth inhibitory effects observed for both A. lappa and A. minus

ACT extracts was bactericidal in all cases except: a. for E. faecalis (in which case the effects of both A. lappa and A. minus ACT extracts appear to be bacteriostatic); and b. the effect of A. lappa extracts on E. coli, which also appears to be bacteriostatic rather than bactericidal.

Spectrophotometric Assays

Phenolic compounds are a class of essential plant secondary metabolites, many of which

(for example, flavan-3-ols, flavonols, and tannins) have been demonstrated to possess anti-microbial activity [31, 32]. Interestingly, while phenolic compounds can themselves suppress bacterial growth, they may also act synergistically to halt bacterial progression when applied with common antibiotics (e.g. gallic acid, ferulic acid, chlorogenic acid).

Because of this, we hypothesized that the anti-microbial activity we measured in several burdock extracts (particularly the ACT extracts) may be due in part to the phenolic compounds present in these extracts. To investigate this hypothesis further, we quantified the levels of total phenolic compounds present in both A. lappa and A. minus ACT and

H2O extracts using the Folin-Ciocalteu method (spectrophotometric) [18]. In these assays, the concentration of phenolic compounds present in each extract was calculated in gallic acid equivalents (GAE). Interestingly, total phenol content of A. minus leaf extracts (both

ACT and H2O extracts) were significantly higher than those of A. lappa leaf extracts

(Figure 2.8A). Specifically, the total phenol content of A. minus ACT extracts (357.17 ±

20.36 GAE mg/100g FW) was around 2 times higher than that found in of A. lappa ACT extracts (170.67 ± 1.04 GAE mg/100g FW); and a similar trend was observed for water extracts (169.73 ± 8.13 for A. minus, and 131. 97 ± 11.25 for A. lappa).

Flavonoids area specialized class of phenolic compounds that have been reported to serve as both anti-microbial and anti-oxidant compounds [33, 34]. As flavonoids have been demonstrated to suppress the growth of both gram-positive and gram-negative bacteria, it is possible that they may be contributing to the anti-microbial capacity of burdock ACT extracts. To investigate this possibility further, we quantified the total flavonoid contents

of ACT and H2O extracts using the aluminum chloride colorimetric method [19]. In these assays, the levels of total flavonoids present in extracts were calculated in rutin equivalents (RHE). As expected, the levels of total flavonoids present in A. lappa and A. minus leaf extracts exhibited trends similar to those observed for the levels total phenolics found in these extracts (Figure 2.8B). Similar to what was observed for total phenolic levels, the levels of total flavonoids present in A. minus ACT extracts (1162.62

± 65.78 RHE mg/100g) were dramatically higher than the levels of flavonoids found in A. lappa ACT extracts (356.94.0 ± 43.16 RHE mg/100g). This trend was also observed in

H2O extracts, where A. minus samples also exhibited higher total flavonoid contents

(320.96 ±19.92 RHE mg/100g) than did A. lappa samples (136.08.0 ±27.63 RHE mg/100g).

Phenols, particularly flavonoids, also exhibit strong anti-oxidant capacities, which have been proposed to be beneficial in preventing illness and preventing microbial growth [34,

35]. We quantified the anti-oxidant capacities of A. minus and A. lappa extracts using the

Ferric Reducing Anti-oxidant Power (FRAP) assay [20]. In these studies, the anti-oxidant capacity of burdock extracts was calculated in Trolox equivalents (TE). Consist with the results observed in total phenol and total flavonoid quantification assays, we found that the anti-oxidant capacity of ACT extracts was significantly higher than that of H2O extracts (Figure 2.8C). Also, as expected, the anti-oxidant capacity of A. minus ACT extracts (459.17 ± 12.73 TE mg/100g FW) was much higher than that of A. lappa ACT extracts (184.78 ± 78.88 TE mg/100g FW); a trend that was continued in H2O extracts

(150.78 ± 15.08 TE mg/100g FW in A. minus extracts; and 78.88 ± 7.00 TE mg/100g FW in A. lappa extracts).

The results of the three spectrophotometric assays indicated that A. minus ACT extracts contained significantly higher phenolic compounds including flavonoids than A. lappa extracts, which correlated with the fact that in the antimicrobial assay, the A .minus extracts in general exhibited greater anti-microbial effect than A. lappa.

HPLC and LC-MS/MS analysis of phenolic compounds

Together, our anti-microbial assays and quantification of total phenolic levels indicated that phenylpropanpoid-derived compounds are likely playing an important role in mediating the anti-bacterial effects of burdock extracts. To identify specific phenolic compounds with potential bactericidal and/or bacteriostatic properties, we performed both high-pressure liquid chromatography (HPLC) and liquid chromatography tandem mass spectroscopy (LC-MS/MS) analyses of burdock extracts. In these analyses, A. lappa and A. minus species leaf ACT extracts were first re-dissolved in 1 mL H2O and injected to HPLC for analysis. Interestingly, our data indicated that the phenolic compound profiles were very different between the two burdock species (Figure 2.9). A. minus contained many more compounds with low molecular polarities (i.e., either non-polar or with a low molecular charge), which eluted in the later part of HPLC trace (in other words, these compounds eluted at higher concentrations of the organic buffer; at 40-57 min) (Figure 2.9). Based on their UV absorption profiles, which peaked at 332 nm (with a shoulder at 300 nm, and a secondary peak at 240 nm), we hypothesized that these 71

compounds were hydroxycinnamic acids and/or hydroxycinnamic acid derivatives [24].

While A. minus extracts possessed several unique peaks, other peaks were common to A. minus and A. lappa extracts. For example, both species shared several peaks eluting 20-

30 minutes into the chromatogram; such as the peaks eluting at 23.70 and 29.89 minutes

(Figure 2.9). The peak eluting at 29.89 minutes was of particular interest as, in addition to being found in both A. minus and A. lappa extracts, the absorbance spectrum and retention time of this peak match that of an authentic rutin standard, allowing us to putatively identify this compound. Interestingly, even when peaks were found in both A. minus and A. lappa extracts, A. minus extracts consistently contained more of each compound, as evidenced by the much greater peak areas observed on A. minus chromatograms (Figure 2.9). In order to more definitively identify peaks observed in

HPLC analyses, fractions of A. minus and A. lappa ACT extracts were collected from the

HPLC (collections began at 22 minutes into the chromatogram, and were collected in 15 second intervals). These sub-fractions were then subjected to LC-MS/MS analyses.

Samples (i.e., burdock extracts sub-fractions) for LC-MS/MS analyses were numbered based on their retention time on HPLC chromatograms (for example, L1 is an A. lappa sample collected at 22.0 minutes to 22.25 minutes) (Figure 2.9). Samples were scanned in both MS2 and product ion modes, and compounds were identified based on comparisons of mass transitions to previously published literature (when authentic standards were unavailable). Putative identities of compounds identified in A. minus and A. lappa sub- fractions are provided in Table 2.2. For example, the compound in sub-fraction 1 (found only in A. minus) exhibited a molecular mass of 431 atomic mass units (amu; [M-H]- m/z

431) ([M-H]- represents the parental ion that lose a hydrogen ion due to ionization), with a major mass transition of 431→267, indicating the loss of a hexose sugar. Additionally, this compound showed a second mass transition of 431→209, indicating the loss of both a sugar and four hydroxyl groups from the parent compound. Based on these data, we were able to tentatively identify this peak as apigenin conjugated to a hexose sugar

(apigenin hexoside) [21]. Sub-fractions L1 and M2 contained a compound with an initial mass of 445 amu ([M-H]- m/z 445). This molecule showed a major mass transition (MS2 ion) of 445→384, suggesting loss of an acetic acid moiety (likely an adduct formed from the carrier solvent during the LC phase of the analyses), as well as a second mass transition of 445→223, indicating the loss of a hexose sugar. Based on these data, the peak was putatively identified as sinapic acid hexoside [22]. Peaks M3, L3/M5, and M8 showed similar fragmentation patterns in mass spectrometric analyses, and were putatively identified as caffeoylquinic acids [23]. Diagnostic product ions for these

- - - compounds were 191 [quinic acid-H] , 179 [caffeic acid - H] ,173 [quinic acid - H2O] , and 135 [caffeic acid -COOH]-. Sub-fraction M6 was putatively identified as rutin based on two MS2 ions/mass transitions 609→300 [quercetin-2H]- and 609→271[quercetin-2H-

CO-H]- [24]. Sub-fraction M7, with mass transitions at 449→285 and 449→271, was identified as luteolin hexoside [25]. Sub-fractions M10 and M11 generated MS2 of 353

[caffeoylquinic acid-H]-, 191 [quinic acid-H]- and 135 [caffeic acid -COOH]- amu, and was putatively identified as dicaffeoylquinic acid isomers [26]. Sub-fractions M12 and

M14 were identified as caffeoylquinic aicd glycoside and dicaffeoyl succinylquinic acid, respectively [26]. While we were able to quantify several mass transitions present in

other sub-fractions (for example, sub-fractions M4, L2, M9, and M13), we were not able to determine the identities of these compounds.

In sum, a greater range of phenolic compounds was detected in A. minus extracts than in

A. lappa extracts, when the plants were grown under same conditions. Further, phenolic compounds were also present in greater quantities in A. minus extracts than in A. lappa extracts, even when the same compound was found in both species. Interestingly, rutin and dicaffeoyl quinic acid, which in our studies were only detected in A. minus, have previously been reported to be found in A. lappa leaves [24].

GC-MS analysis of nonpolar extracts

Finally, to identify additional (non-phenolic) compounds potentially responsible for the anti-microbial activity of A. lappa and A. minus extracts, we performed broad-spectrum metabolomic analyses of MeOH, CM, EA and DM extracts from both species using GC-

MS. In order to separate and identify the broadest range of compounds, each extract was analyzed on both an Agilent HP5 capillary column (broad-spectrum separation of both charged and weakly charged ions) and an Agilent DB-WAX capillary column (broad- spectrum separation of non-polar compounds). Raspberry ketone [4-(4-Hydroxyphenyl) butan-2-one] was used as an internal standard (i.e., peak normalization) in these assays, and compounds were identified using the NIST Spectral library (John Wiley & Sons, Inc.,

Hoboken, NJ). Compounds putatively identified from burdock extracts are listed in Table

2.3.

GC-MS analyses revealed the presence of numerous compounds, several of which have been previously demonstrated to act as anti-microbial agents. For example, methyl salicylate, which was found only in A. minus extracts, has been shown to act as a potential antibacterial compound currently used in several commercial liniments, mouth care products, and foods [36]. Interestingly, methyl salicylate also functions as an analgesic (and may help reduce pain caused by burn wounds) [37], and can be further metabolized into salicylic acid, a nonsteroidal anti-inflammatory drug [38]. Burdock ethyl acetate extracts, which exhibited the strongest anti-microbial activity, were found to contain hexahydrofarnesyl acetone, which has previously been identified in in antibacterial essential oils extracted from Equisetum arvense L. [39]. Several A. lappa extracts were found to contain β-eudesmol, a sesquiterpenoid alcohol. Multiple compounds were found in both A. lappa and A. minus extracts, including: a. 2,3-dihydro-

3,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP), is a strong anti-oxidant that inhibits the proliferation of human colon cancer cells [40, 41]; b. fatty acids and fatty acid esters, including methyl palmitate, methyl (9Z,15Z)-9,15-octadecadienoate, methyl α-linolenate, palmitic acid and 2-palmitoylglycerol, were putatively identified. Anti-microbial effect of fatty acids and fatty acid esters have been suggested by previous studies [42-44].

Discussion

Although burdock has been used in Amish communities to treat burn wounds for several generations, the precise impact of burdock treatments on burn wounds have not been well investigated. Here, we provide evidence that extracts derived from the two most

commonly found burdock species in the U.S. (A. lappa and A. minus) exhibit antimicrobial activity against a broad spectrum of burn related pathogens. In multiple assay systems (agarose plate bioassays, liquid broth bioassays, and agar well diffusion assays), burdock extracts were able to inhibit the growth of both gram-positive and gram-negative organisms. In order to assay the anti-bacterial capacity the broadest range of compounds present in burdock leaves, burdock extracts were generated using a series of solvents of decreasing polarity (H2O, ACT, MeOH, CM, EA, DM), allowing the extraction of overlapping but distinct groups of biomolecules from A. lappa and A. minus leaves.

Among the six types of burdock extract generated, acetone extracts (70% acidified acetone) proved the most effective in inhibiting bacterial growth for both A. lappa and A. minus. , Interestingly, acetone extracts demonstrated higher anti-bacterial capacity than did ethyl acetate extracts, which exhibited significant anti-microbial activity in our assays, and which have previously been demonstrated to inhibit the growth of several microbes

[11]. The data presented here also represent the first time that the anti-microbial capacity of A. minus, has been investigated. Interestingly, A. minus extracts generally exhibited greater anti-bacterial activity (i.e., greater efficacy) than the corresponding A. lappa extracts, particularly in liquid broth assays.

To identify putative anti-microbial compounds in burdock extracts, we subjected both A. lappa and A. minus extracts to broad-spectrum metabolomic profiling, using spectrophotometric assays, HPLC, LC-MS/MS, and GC-MS. Interestingly, compared to the more well-characterized A. lappa, the total phenolic content, total flavonoid content and in vitro antioxidant capacity were significantly higher in A. minus extracts when both

species growth under the same conditions. Our results also indicated that A. minus extracts contain a broader range of phenolic (and potentially flavonoid) compounds than are found in the corresponding A. lappa extracts. There was, however, significant

“metabolic overlap” between the two species, and several phenolic compounds were found in both species. Specifically, we identified several hydroxycinnamic acids and/or hydroxycinnamic acid derivatives in A. minus extracts that were found in A. lappa extracts either in our assays, or in previously published studies [23, 24, 26]. Most of these compounds, however, were found at significantly higher levels in A. minus extracts than in the corresponding A. lappa extracts. Our metabolic profiling studies have putatively identified several compounds with potential anti-microbial activities in both A. lappa and

A. minus extracts. Several of these, such as methyl salicylate, may act not only as antibacterial agents, but also as anti-inflammatory or analgesic compounds. Other compounds, such as 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP), may be functioning as anti-oxidants or to modulate cellular proliferation.

Conclusions

The results of our study provide the foundation for a mechanistic explanation of previously reported effects of burdock on burn wounds. Our data indicate that the application of burdock leaves or extracts to burn wounds may function in part to inhibit the growth of wound related pathogens. In assays against seven burn wound-associated pathogens, burdock extracts (both A. lappa and A. minus) were more effective against gram positive bacteria, which occupy burn wounds during the initial phases of secondary

infections, than gram negative pathogens, which tend to infect wounds as secondary infections progress. Comparing between the two burdock species tested in our assays, extracts from the less studied A. minus exhibited greater anti-bacterial capcacity than those from A. minus. A. minus extracts contained significantly higher levels of both total phenolics and flavonoids than did A. lappa extracts, and had significantly higher in vitro anti-oxidant capacities. A. minus extracts also contained a broader range of phenolic compounds than did corresponding A. lappa extracts. Many hydrocinnamic acid derivatives with previously identified anti-microbial activity, including: caffeoylquinic acids, dicaffeoylquinic acid, and dicaffeoyl succinylquinic acid, were found in extracts from both A. lappa and A. minus [45, 46]. Our studies also identified both rutin and luteolin hexosides in burdock extracts, which may act as both anti-microbial agents and anti-oxidants [34]. However, further research is required to define the anti-microbial effects of individual compounds identified in this study, as well as to definitively identify the wide range of additional compounds present in burdock extracts.

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Figure 2.1 Agar Plate Bioassay: Effect of A. lappa ACT extracts on S. pyogenes Either water (“Blank”) or A. lappa acetone extract (acetone: water: acetic acid at 70:29.5:0.5; v/v/v; “Burdock treatment”) was applied to the surface of an agarose plate. 5 μL droplets of bacterial culture of either OD600nm = 0.01 (“Undiluted”), OD600nm = 0.001 (“1:10”), or OD600nm = 0.0001 (“1:100”) were then applied to each plate. Plates were incubated at 37 ℃ for 16h, and bacterial growth was determined by scanning plates and counting the number of pixels present in each bacterial droplet using ImageJ software. Three replicates of each plate were measured, representative data for A. lappa extracts and S. pyogenes shown here.

Continued

Figure 2.2 A. lappa and A. minus ACT, EA, DM extracts agar plate bioassay on burn related pathogens

Figure 2.2 continued

Continued

Figure 2.2 continued

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Figure 2.2 Continued

Continued

Figure 2.2 continued

Continued 90

Figure 2.2 continued

Continued

Figure 2.2 continued

Agar plate bioassays were performed as described (Figure 2.1; Materials and Methods). Data represent the average of three bacterial spots ± S.D., and the experiment was repeated three times. Representative data from one experiment are presented here. Asterisk indicates significant difference from respective solvent control according to Student’s 1-tail t-test (*: p < 0.05, **: p < 0.01, ***: p < 0.001).

Figure 2.3 Burdock (A. lappa and A. minus) leaf extract liquid broth assay 100 µL of either burdock extract or the appropriate solvent blank were mixed with 1 mL standardized bacterial cultures. Cultures were incubated overnight at 37˚C, 250 rpm, and bacterial growth was quantified by measuring the optical density at 600nm. Data represent the average of three bacterial cultures or solvent controls ± S.D. Asterisk indicates significant difference from respective solvent control according to Student’s 1- tail t-test (*: p < 0.05). 93

Continued Figure 2.4 Liquid broth assay for minimum inhibitory concentrations (MIC)

Figure 2.4 continued

Minimum inhibitory concentrations (MICs) for each burdock extract were calculated using liquid broth assays. Burdock extracts were added to bacterial liquid broth cultures in a 2-fold dilution series (i.e., 0.5X dilution, 0.25X, 0.125X, etc.; see Materials and Methods). The MIC was determined to be the last point of the dilution series able to inhibit bacterial growth. Inhibition of bacterial growth was calculated by averaging the OD600nm values of three cultures treated with either burdock extracts or the respective solvent control, and determining significant differences between control and treatment groups using Student’s 1-tailed t-test. Data represent the average of three bacterial spots ± S.D. The MICs were summarized in Table 2.1.

Table 2.1 MIC values of A. lappa and A. minus extracts

Figure 2.5 Effect of burdock extracts on bacterial growth in agar well diffusion assays Bacterial cultures were applied evenly to the surface of agarose plates using a cotton swab. 4 mm wells were then bored into each plate, and burdock extracts, solvent blanks, or antibiotic controls were added each well. Plates were incubated at 37 °C for 16 h. Plates were then scanned, and the diameters of the zones of bacterial were measured using ImageJ software.

Figure 2.6 Effect of burdock EA and ACT extracts on bacterial growth in agar well diffusion assays The antimicrobial effects of burdock extracts were measured using agar well diffusion assays. Each plate contained burdock extracts, a solvent blank, and an antibiotic control. The Zone of Inhibition [ZI (mm) = diameter of inhibition zone (mm) – diameter of solvent well (4mm)] was measured for each extract, the solvent blank, and the anti-biotic positive control. Data represent the average of three ZI measurements ± S.D. Letter values indicate significant differences as determined using ANOVA Tukey’s test at 95% confidence.

Figure 2.7 Assay of bactericidal vs. bacteriostatic effects of burdock ACT and EA extracts Burdock ACT extracts (20 µL) were added to standardized bacterial cultures (180 µL) to fully inhibit bacterial growth (left). Following overnight incubation, OD600nm values were recorded for each bacterial culture. 5 µL of each bacterial culture (inhibited by burdock extracts) was then used inoculate a second batch of liquid medium (180 µL), and cultures were incubated overnight. Bacterial growth was then assayed by measuring optical density at 600nm. If no bacterial growth was observed in the secondary culture, the effect of burdock extracts on the primary culture was concluded to be bactericidal. If growth was observed in the secondary culture, the effect of burdock extracts on the primary cultures were concluded to be bacteriostatic. Data presented represent the average of three measurements ± S.D.

Figure 2.8 Total phenolic content (A), total flavonoid content (B), and in vitro antioxidant capacity (C) of burdock ACT and H2O extracts A. The levels of total phenolic compounds present in both A. lappa and A. minus ACT H2O and extracts using the Folin-Ciocalteu method [18]. Total phenolic compounds were calculated as gallic acid equivalents (GAE). B. The levels of total flavonoids present in both A. lappa and A. minus ACT H2O and extracts using the aluminum chloride method [19]. Total flavonoids were calculated as rutin equivalents (RHE). C. The in vitro anti-oxidant capacity of burdock leaf samples was quantified using the ferric reducing antioxidant power (FRAP) method [20]. The anti-oxidant capacity of burdock extracts was calculated as Trolox equivalents (TE) In all graphs, data presented represent the average of three separate extractions and quantifications of either A. lappa or A. minus. Asterisk indicates significant difference determined by Student’s 2-tailed type-3 t-test (*: p < 0.05, **: p < 0.01, ***: p < 0.001)

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Figure 2.9 HPLC Chromatographs of A. lappa and A. minus ACT extracts Compounds were separated using a Phenomenex Gemini® 5 µm C6-Phenyl 110 Å column. The solvent system for HPLC analyses was 0.2% acetic acid for solvent A, and 100% CH3CN for solvent B. The solvent gradient was as follows: 5% (0-10 min); 5-22% (11-20 min); 22-28% (21-45 min); 28-80% (46-53 min); 80% (54-59 min); 80-5% (64-67 min); 5% (68-72 min). L1-L3 represent fractions collected from A. lappa chromatograms for downstream LC-MS/MS analyses; M1-M14 represent fractions collected from A. minus chromatograms for downstream LC-MS/MS analyses (Table 2.2).

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Table 2.2 Compounds identified in A. lappa and A. minus via LC-MS/MS

A. lappa (L1-L3) and A.minus (M1-M14) fractions collected from the HPLC were further analyzed using LC-MS/MS. Compounds were identified either by comparing retention times and mass transitions to authentic standards, or by comparing retention times and mass transitions to previously published literature [22-27]. ND: compound identity not determined.

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Table 2.3 Burdock compounds putatively identified by GC-MS

Burdock MeOH, CM, EA, DCM extracts were analyzed by GC-MS using both an Agilent DB-WAX capillary column and an Agilent HP5 capillary column. Compounds were putatively identified using the NIST Spectral library and/or comparing retention times and fragmentation patterns to authentic standards (as asterisk indicates).

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Chapter 3 Detection of 2,4-D herbicide drift using a fiber-based, field-deployable

collection system and liquid chromatography tandem mass spectrometry

Introduction

2,4-dichlorophenoxyacetic acid (2,4-D) has been widely used as a selective herbicide since its commercialization in 1945 [1]. 2,4-D is often used to control broad-leaf weeds, as it kills dicot plants without negatively impacting the growth and development of monocot plants. In particular, 2,4-D has been used as a weed control agent to increase the productivity of corn rice, wheat, barley and oats [2-4]. According to the U.S.

Environmental Protection Agency, 2,4-D is the most commonly used conventional herbicide in the United States, with the annual consumption of the active ingredient estimated at around 27-33 million pounds [5].

2,4-D is an auxinic herbicide, meaning that it acts by mimicking the functions of the plant hormone auxin; and indeed the 2,4-D molecule is a structural analog of indole-3-acetic acid (IAA), the most common plant auxin [6]. Similar to auxin, 2,4-D acts as plant growth regulator, and the application of moderate to high concentrations of 2,4-D leads to an “overdrive” of plant growth, ultimately resulting in the death of sensitive plants.

Following exposure to 2,4-D, sensitive plants exhibit a range of abnormal physiological

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and metabolic responses, including: alterations in cell wall plasticity, increased enzyme synthesis, hyperaccumulation of multiple (i.e., ethylene and abscisic acid), and

uncontrolled cell division and elongation [7, 8]. 2,4-D can also interact with auxin receptors such as ABP1 (auxin binding protein 1) and TIR1 (transport inhibitor response

1) proteins, and trigger downstream response pathways activated by these receptors, such as cell wall remodeling (via modulation of hemicellulose xyloglucan structure), plasma membrane hyperpolarization, microtubule re-orientation, and vascular tissue differentiation [9-12]. It is not yet clear why monocots and dicots exhibit varying sensitivity to 2,4-D. It is currently hypothesized that the increased resistance to 2,4-D observed in monocots is the result of differences in 2,4-D transport and metabolism between monocot and dicot species [4]. For example, it has been found that in 2,4-D sensitive dicot plants, such as soybean, 2,4-D can be reversibly conjugated to either amino acids or hexose sugars through the carboxylic acid group of the molecule (a mechanism identical to that by which auxin is conjugated to amino acids and/or hexose sugars in these same species) [1, 13]. However, as with many IAA-amino acid conjugates, conjugated 2,4-D molecules an easily be released and converted back to active free 2,4-D molecules via de-esterification [1]. In 2,4-D resistant monocot plant species, 2,4-D is usually irreversibly inactivated through hydroxylation of the central 105

carbon ring (the end products of this conversion are inactive and non-toxic to plant cells)

[1]. Additionally, monocot crops appear to transport less 2,4-D to roots and growing points than do dicots, reducing the overall cellular exposure to 2,4-D across the plant [1].

While application of 2,4-D can efficiently control weeds, resulting in increased crop productivity and yields, improper application of 2,4-D and other auxinic herbicides may have detrimental results, the most common of which is off-site herbicide drift. Herbicide drift is defined as unintended movement of herbicide from the site of application (target site) to a non-target site [14]. Herbicide drift occurs primarily through two mechanisms: a. At the time of spray, fine spray particles generated by the use of incorrect (non- recommended) nozzles, spray pressures, or by having the boom height set too high (or by a combination of one or more of these factors) are not deposited on the target, but are instead moved by wind and deposited offsite [15, 16]; and b. Following application of

2,4-D, exposure of the target site to high temperatures or strong sunlight may result in re- volatilization/evaporation of 2,4-D particles or microdroplets, which can then move within convection currents or wind to neighboring areas [15]. 2,4-D drift events can severely damage sensitive crops, resulting in decreased production and economic losses.

For example, in 2006, a series of drift events in east Kansas led to reductions in cotton yields for that year [17]. The damage caused by 2,4-D drift events is dependent upon the sensitivity of the crops exposed to 2,4-D. For example, both grape and tomato are highly sensitive to auxinic herbicides, so even low levels of 2,4-D drift can cause significant damage to vineyards and tomato fruit production [15, 18]. The recent release of 2,4-D resistant genetically modified crops, which have been generated in an effort to combat the

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increasing glyphosate resistance observed in many weed populations, will likely result in increased usage of 2,4-D in most agricultural areas. While the new formulations of 2,4-D provided for use with these traits have been demonstrated to have reduced drift capacity and volatility, it is still possible that improper use or application of these herbicides (or increased use of older 2,4-D formulations) will result in offsite herbicide drift [19, 20].

As 2,4-D usage increases, it will be important to minimize both the frequency and magnitude of drift events; and to minimize the potential damage caused by each episode of herbicide drift.

The frequency and magnitude of herbicide drift events can be minimized by following established protocols for the application of individual herbicides. Additionally, communication between pesticide applicators and growers can minimize the severity of drift events, particularly if herbicide applications can be timed to occur at times when neighboring sensitive crops are not flowering or producing fruits. Finally, the establishment of sensitive crop registries in many states allows growers to determine whether or not application of a given herbicide may put neighboring fields at risk of herbicide drift [21]. While these factors can help to minimize the occurrence of herbicide drift, it will likely be very difficult to eliminate drift entirely. In the event of a drift event, is is important for the growers impacted to be able to document the timing and severity of the drift event. Unfortunately, however, determination of the precise timing and magnitude of a drift event is often hampered by the lack of a precise, accurate method of detecting the herbicide involved. While previous research has proposed the use of remote sensing methods to detect herbicide drift, to date these methods have not been widely

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employed by the agricultural community [22]. Additionally, the detection of herbicide drift is often complicated by the fact that drift events can often expose crops to sub-lethal herbicide concentrations, which may result in no real visible damage to the plant, but which may never the less result in yield loss [18, 23]. One method of overcoming this difficulty is to intersperse extremely herbicide-sensitive “indicator” plants within fields of crops susceptible to herbicide drift damage, and then periodically monitoring the indicator plants for signs of herbicide damage [24, 25]. This approach, however, is both time and labor intensive, and, as a result, increases the costs of production. Alternatively, growers can send crops which they suspect have been exposed to herbicide drift to analytical facilities for chemical analyses, usually gas chromatography or high pressure liquid chromatography, designed to detect one or more herbicide agents. Chemical analyses can be quite expensive, however, and there is always the possibility that any herbicide residues present on the plants sent for analyses will be dislodged, degraded, metabolized, or excreted out of the plant tissues either prior to harvest for analysis or during transit. This is particularly true of 2,4-D, which is often metabolized and/or translocated away from the site of deposition (generally leaves) into the roots within 24-

48 hours of deposition, well before signs of herbicide damage are observed (72 hours, at which point 2,4-D residues are almost undetectable in leaves using standard methodology)

[26, 27]. The development of a more stable, cost-effective, and sensitive herbicide detection system would allow growers to more precisely determine the timing and magnitude of herbicide drift events. Here, we present the development of a field- deployable, low-cost, sensitive, and efficient 2,4-D detector which, combined with a

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liquid chromatography tandem mass spectrometry based analytical method, allows for the successful detection of low levels of 2,4-D drift well after the initial drift event.

Materials and Methods

The detectors were made by attaching a piece of 12 cm × 12 cm matrix to an embroidery loop (Figure 3.1). Miracloth (EMD Millipore, Billerica, MA) was used as the matrix to construct the original detection system.

Development of an LC-MS/MS method to quantify 2,4-D

LC-MS/MS method development consisted of four steps (Fig. 3.2): In the first experimental series, either 20 μL of a 100-fold dilution of 2,4-D dimethylamine (DMA) tank mix (Loveland Amine 4 2,4-D Weed Killer, Greeley, CO) or 10 μL of 250 ng/ μL

2,4-D (acid) (SUPELCO, Bellefonte, PA) was added directly to a Miracloth detector disc and allowed to air dry at room temperature for 10 minutes. Following air-drying,

Miracloth detector discs were placed in glass tubes, and 10 μL of 10 ng/ μL 3- indolepropionic acid (IPA) (Santa Cruz Biotechnology, Dallas, TX) was added to each disc as an internal standard; and 1 mL extraction solvent (50 mM sodium phosphate buffer, pH 2.7) was added to each tube. Oasis HLB columns (Waters, Milford, MA) were pre-conditioned with 1 mL methanol (Optima LC/MS grade from Fisher Scientific,

Waltham, MA), 1 mL H2O, and 0.5 mL Na-phosphate buffer (pH 2.7, 50 mM). The extraction solvents from each glass tube containing a Miracloth detector disc were then harvested, and each aliquot of solvent was passed through a pre-conditioned HLB column.

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HLB columns were then washed with 2 mL 5% methanol and eluted with 2 mL 80% methanol. Eluates were taken to dryness under N2 gas and re-dissolved in 1 mL methanol.

Reconstituted extracts were filtered through 4 mm 0.2 µm nylon filters (Phenomenex,

Torrance, CA) before 0.2 μL prior to LC-MS/MS analyses. As our detection protocol was a novel method for extracting and quantifying 2,4-D/DMA residues from a detector matrix, we had to calculate the net loss of active compound at each step (Figure 3.2).

Therefore, in the second series of tests, to measure the total loss during the sequential

SPE and LC-MS/MS steps DMA and 2,4-D acid were not extracted from Miracloth, but were instead added directly to HLB columns, allowing the calculation of net loss due to irreversible binding to the HLB column. In the third experimental series, to determine the amount of sample lost during the LC-MS/MS analyses both 2,4-D acid and IPA were injected directly into the LC-MS/MS, allowing calculation of the efficiency of the analytical method. Finally, in the fourth experimental series, only IPA was injected for

LC-MS/MS analysis (refer to 2,4-D extraction and quantification section).

Spray Room Test

A 2,4-D tank mix (Dimethylamine 2,4-D Weed Killer, Loveland) was diluted either 100- fold (1/100th of a tank mix concentration) or 1000-fold (1/1000th of a normal tank mix concentration). The detectors were randomly placed on a bench under the spray boom, which was set up 18 inches above the bench and equipped with three nozzles (TeeJet 60-

8002) (TeeJet, Glendale Heights, IL). The detectors were sprayed with the diluted 2,4-D solution at a rate of 15 gallon per acre, allowed to air dry at room temperature on the

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spray bench for 5 minutes, and then collected an placed in sealable plastic bags. The detectors were then stored in 4 °C in the dark before being used for 2,4-D quantification studies (refer to 2,4-D extraction and quantification section).

2013 Field Trial

Detectors were attached to three foot high wooden posts such that the surface of the disc would be placed at a 45 degree angle from horizontal, roughly approximating the surface angle of a plant leaf. Post-mounted detectors were deployed in a test field at the Ohio

Agricultural Research and Development Center (OARDC), in Wooster, OH. Two pints per acre 2,4-D dimethylamine were applied using a 30 feet boom section (half of a 60 feet boom with 13 XR 8002 VS tips) (TeeJet, Glendale Heights, IL) on a tractor mounted sprayer (i.e., a 60-fold dilution of a normal tank mix was applied to the field at a spray rate of 15 gallons per acre). Detectors were placed 18 feet downwind, 30 feet downwind, and 10 feet upwind of the spray swath, with four detectors deployed at each distance.

Two batches of detectors (each batch consisting of detectors deployed at the three distances detailed above, four detectors at each distance) were deployed in 2013, with one batch analyzed 2 months post spray and the other batch analyzed 5 months post spray.

All the detectors were stored at 4 °C in the dark before extraction and 2,4-D quantification (refer to 2,4-D extraction and quantification section). A second, identical field trial was performed at the University of Delaware (Georgetown, DE), and detectors were shipped to OSU on ice. Upon receipt, detectors were stored at 4 °C in the dark

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before extraction and 2,4-D quantification (refer to 2,4-D extraction and quantification section).

2014 Field Trial

In 2013, the field trial was repeated with increased distance between the spray arm and detectors. In these trials, post-mounted detectors were deployed 10 feet downwind, 50 feet downwind, 100 feet downwind, 300 feet downwind, and 10 feet upwind of the boom arm (four detectors per distance). Additionally, in this trial, tomato plants (2,4-D sensitive

“indicator” plants; approximately 24 inches tall) were deployed at each distance, interspersed between the post-mounted detector discs (2 tomato plants at each distance).

The test plot was then sprayed as described in 2014 and detectors were harvested, placed in sealed plastic sandwich bags, and stored for analysis, as described above. Tomato plants were then returned to the greenhouse. Two weeks later, the tomato plants were visually rated for 2,4-D damage on a scale of 0 (no visible effects) to 10 (death of the plant).

Optimization of matrix composition

To optimize detector matrix composition, a 2,4-D dimethylamine mixture was diluted

20,000-fold (1/20,000 of a normal tank mix). Detector discs were constructed using five types of detector matrix, including Miracloth, nylon (20 µm, ELKO Filtering Co., Miami,

FL), polypropylene (75 µm, ELKO Filtering Co., Miami, FL), polyvinylidene difluoride

(PVDF Transfer Membrane, 0.2 µm, 26.5 cm x 3.75 m, Thermo Scientific, Waltham,

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MA), and polyester (20 µm, ELKO Filtering Co., Miami, FL). Four replicates of each detector disc type were constructed, and detector discs were then set our randomly on a spray bench. The detectors were sprayed, collected, and stored using the same methods described above, with the exception that detectors were in this experimental series exposed to 2,4-dimethylamine at a concentration of 1/20,000th a normal tank mix.

Detectors were sprayed with the diluted 2,4-D solution at a rate of 15 gallon per acre, and then collected after drying on spray bench for 5 min. The detectors were then stored in

4 °C in dark before being used for 2,4-D quantification (refer to 2,4-D extraction and quantification section). Significant differences between each of the other four types of matrixes and Miracloth was determined by 1-tailed Student’s t-test (p < 0.05). At the same time, the significant differences in 2,4-D retention between the five matrix types was determined using ANOVA Tukey’s test at 95% confidence.

Rain Wash Resistance Test

Detector discs were constructed using three types of detection matrix ( (Miracloth, nylon and polyester). Detector discs (six discs for each detector matrix type) were randomly placed on a spray bench and then sprayed with a 20,000-fold dilutions of 2,4-D dimethylamine, as described above. After application of 2,4-D dimethylamine, all discs were allowed to air dry on the spray bench for a period of 5 minutes at room temperature.

After this, one-half of the detectors (i.e., three replicates per detector type) were collected in sealed plastic sandwich bags and stored for analysis as described above. The remaining detectors (three replicates per detector type) were sprayed with H2O at a rate of 15

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gallons per acre for 1 h (simulating one inch of rapid rainfall). “Rain-treated” detectors were then air dried on the spray bench for 20 minutes at room temperature and collected for analysis, as described above. Detectors were stored at 4 °C in the dark before extraction and 2,4-D quantification (refer to 2,4-D extraction and quantification section).

Significant differences in 2,4-D concentrations between the rain wash treatment group and the control group were determined using 1-tailed Student’s t-test (p < 0.05).

UV Resistance Test

(simulating long-term exposure to the UV component of sunlight), and collected for analysis. Detectors were stored at 4 °C in the dark before extraction and 2,4-D quantification (refer to 2,4-D extraction and quantification section). Significant differences in 2,4-D concentrations between the UV treatment group and the control group were determined using 1-tailed Student’s t-test (p < 0.05).

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Greenhouse Treatment Test

27-29 ˚C during the day and 24-26 ˚C during the night. Plants were grown under 16 h light and 8 h dark. The light intensity during the day was approximately 575 μmol/m2/s and the relative ambient humidity was approximately 18%. Detectors were stored at 4 °C in the dark before extraction and 2,4-D quantification (refer to 2,4-D extraction and quantification section). Significant differences in 2,4-D concentrations between the UV treatment group and the control group were determined using 1-tailed Student’s t-test (p

< 0.05).

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2,4-D extraction and quantification

For 2,4-D quantifications, detector matrices were removed from wooden support rings and transferred to 50 mL glass tubes. IPA (10 μL 10 ng/ μL) was added to each matrix as an internal standard. 2,4-D dimethylamine was extracted from matrices by adding 5 mL methanol and vortexing tubes for 5 minutes at room temperature. The extraction solution was transferred to a second glass tube, and an additional 3 mL of methanol was added to the extraction matrix for second round of extraction. Samples were again vortexed for 5 minutes at room temperature, and the methanol extraction solution collected and combined with the extraction solution from the first round. The combined extracts were taken to dryness under N2 gas. Samples were then re-dissolved in 1 mL sodium phosphate buffer (pH 2.7, 50 mM), transferred to a 1.7 mL centrifuge tube, and centrifuged at 12,000 x g for 5 minutes at room temperature. Oasis HLB columns were pre-conditioned with 1 mL methanol, 1 mL H2O, and 0.5 mL sodium phosphate buffer

(pH 2.7, 50 mM). Samples were added to preconditioned HLB columns, which were then washed with 2 mL 5% methanol and eluted with 80% methanol. The eluted samples were taken to dryness under with N2 gas and re-dissolved in 1 mL methanol. Finally, samples were filtered through 4 mm 0.2 µm nylon disc syringe filters prior injection onto the LC-

MS/MS for quantification.

Filtered 2,4-D samples were injected onto an Agilent 1260 HPLC coupled to a 6460

Triple Quadrupole LC-MS/MS system (Agilent Technologies, Santa Clara, CA) for quantification. The LC-MS/MS method used to quantify 2,4-D residues was adapted

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from previously published LC-MS/MS methods used to quantify auxins and auxin metabolites [28]. Target compound, 2,4-D, and the internal standard IPA were separate using an Agilent Poroshell EC-C18 column (50 × 3 mm, 2.7 µm, Agilent Technology,

Santa Clara, CA). The solvent buffer system consisted of solvent A (H2O with 5% methanol, 0.1% acetic acid) and solvent B (methanol with 0.l% acetic acid). The solvent gradient was as follows: 2% (0-2 min); 2-80% (3-4 min); 80-98% (5-7 min); 98% (8-10 min); 98-2% (11 min), with a flow rate at 0.3 mL/min. Compounds were ionized using

Electrospray ionization (ESI), and the spray chamber settings were: gas temperature at

350 ˚C, gas flow at 10 L/min, nebulizer pressure at 40 psi, sheath gas temperature at 300

˚C, sheath gas flow at 12 L/min, capillary voltage at -2250 V. 2,4-D and IPA were detected using multiple reaction monitoring mode on the LC-MS/MS (MRM) and monitoring multiple mass transitions for each compound. Mass transitions and retention times of both 2,4-D dimethylamine and IPA were confirmed by comparison to authentic standards, and quantifications of both compound were performed using standard curves generated using authentic standards. Chromatographic data were analyzed using Agilent

MassHunter software. The 2,4-D levels were quantified with 2,4-D standard curve solutions.

Results

2,4-D quantification method development and Spray Room Test

As the solubility of pure 2,4-D (i.e., the free acid of non-derivatized 2,4-D) in water is very low (4.46 g/L), the 2,4-D (active ingredient) in commercial herbicide products is 117

usually modified to increase solubility [7]. The amine salt (primarily dimethylamine salts) and 2,4-D ester are the forms of 2,4-D most commonly used in current herbicides [1].

Because of this, the 2,4-D dimethylamine salt was selected for use in our herbicide detector development studies. When dissolved in water, 2,4-D dimethyl amine dissociates into a negatively charged 2,4-D acid and a positively charged amine ion [1]. Because of this, we were able to use the free acid form of 2,4-D acid as standard for quantification.

In these studies, we first optimized methods to extract 2,4-D from the detector matrices and quantify the 2,4-D ions using LC-MS/MS. Methods for 2,4-D extraction were modified from those currently used to extract naturally occurring auxin and auxin metabolites from plant tissues [28]. One modification to these protocols was the use of an alternate internal standard. Previously published methods employ deuterated internal standards for the quantification of auxins. Deuterated reagents can be costly, however, and the purpose of this research was to develop a detection system which would be most accessible to growers. Therefore, to reduce the per sample cost of analyses, we instead used a structurally similar molecule, 3-indolepropionic acid (IPA), as an internal standard. As our detection protocol was a novel method for extracting and quantifying

2,4-D/DMA residues from a detector matrix, we had to calculate the net loss of active compound at each step (Figure 3.2). The levels of DMA and 2,4-D measured following sample extraction, SPE purification, and LC-MS/MS analyses represent the net 2,4-D or

DMA remaining after sample loss during each processing or analysis step. For example, sample is lost during extraction from Miracloth (net recovery of this step = %A), SPE purification (net recovery at this step = %B), and, to a much lesser degree, LC-MS/MS analyses (net recovery at this step = %C). To determine the recovery rate at each step we 118

first ran samples through all three preparation/analysis steps (Miracloth extraction, SPE, and LC-MS/MS; the recovery from this process, %X (which was measured empirically)

= %A x %B x % C). Next, samples were run through only two of the preparative/analysis steps (SPE and LC-MS/MS) allowing the calculation of the recovery of active compound from these steps (%Y (measured empirically) = %B x %C). Finally, samples were run through only the analysis step (LC-MS/MS), and the recovery was calculated (%Z = %C).

The recovery from the SPE column was then calculated using the following equation %Y

(which was measured empirically) = %B x %C. Since %C equals %Z, which was measured empirically in the final set of experimental trials, the equation can be restated as %B = %Y/%X, and a numerical value for %B calculated. The numerical values for %B (calculated) and %C (equal to empirically defined %Z), can then be used to calculate the recovery from Miracloth extraction. For this calculation, we used the data generated in the first set of recovery trials, in which samples were subjected to all three preparative/analytical steps, and the total recovery was measured. This recovery can be stated using the equation %X = %A × %B × %C. As values for %B have been calculated

(above), and %C (equal to %Z) and %X have been empirically measured, the equation can be restated as %A = %X/(%B × %C). The data generated in these trials indicated that the percent recovery from Miracloth extraction (A% in Figure 3.3-1) of DMA and 2,4-D acid (A% in Figure 3.3-2) from Miracloth (38.4% and 43.99%, respectively), were close to the recovery rate (A% in Figure 3.3-3) of IPA extracted in the presence of either DMA

(A% in Figure 3.3-3) or 2,4-D acid (A% in Figure 3.3-4), which were 39.47% and

39.88%, respectively (Figure 3.3). The results indicated that IPA was a good internal standard, capable of reliably representing the loss of 2,4-D that bind to Miracloth during 119

the extraction and analysis processes. Interestingly, these studies also indicated that the recovery rate of 2,4-D in the free acid form from HLB column is 97.84% (B% in Figure

3.3-2), indicating a very low loss of 2,4-D due to solid phase extraction with HLC column.

After the methods to extract and quantify 2,4-D dimethylamine had been optimized, we performed a spray room test to determine whether or not the disc detection system was able to retain and release 2,4-D residues applied in droplet form, as would occur during a drift event in the field. Detector discs were sprayed with 100-fold and 1000-fold dilutions of a normal DMA tank mix at a rate of 15 gallons per acre, and then collected for LC-

MS/MS analysis (Figure 3.4). The results indicated that: a. detector discs were able to retain 2,4-D applied in microdroplet form; and b. the optimized extraction and LC-

MS/MS quantification methods were sensitive enough to detect 2,4-D dimethylamine at concentrations well below those found in a normal tank mix (i.e., 1/100th and 1/1000th the concentrations of a normal tank mix). In our studies, the herbicide was sprayed at a rate of 1000-fold dilution, which is equal to 0.12 pints of tank mix per acre. The manufacturer’s instructions recommend the use of 1-4 pints tank mix for each acre field, and thus our system was able to detect 2,4-D at a level equivalent to an approximately 17 fold-dilution of this concentration.

2013 Field Trial

After confirming that the Miracloth-based disc detectors were able to retain 2,4-D residues in the laboratory, we next performed field trials. In these trials, the Miracloth 120

disc detectors were mounted on posts and set up 18 feet downwind, 30 feet downwind and 10 feet upwind of the boom arm of a tractor spraying 2,4-D dimethylamine (two pints per acre). In order to mimic a leaf surface, detectors were mounted on posts three feet high, and placed such that the surface of the disc was 45 degrees from horizontal. Field trials were conducted twice, with each trial using a total of four detectors at each distance.

In all cases, detectors were stored in sealed plastic bags in the dark at 4ºC prior to LC-

MS/MS analyses. To assay detector stability, the first batch of detectors was analyzed 2 months post- spray, while the secondary batch of detectors was analyzed 5 months post- spray. LC-MS/MS analyses indicated that Miracloth-based disc detectors were capable of capturing 2,4-D dimethylamine residues when deployed at distances of 18-feet and 30- feet downwind from the boom arm; and that the detectors were able to stably retain these

2,4-D residues for a period of at least two months. Longer-term storage (5 months) in refrigerator did not compromise the stability of the 2,4-D residues bound to the detector discs, and were successfully able to detect 2,4-D residues from these discs at levels comparable to those obtained from discs stored for 2 months (Figure 3.5). To investigate the ability of the detector discs to perform comparably under different environmental conditions, a third field study was conducted in collaboration with the University of

Delaware. As with the disc detectors deployed in Ohio, detectors generated in the

Delaware field trials were able to stably bind and retain 2,4-D residues when deployed 18 and 30 feet from the boom arm (Figure 3.5).

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2014 Field Trial

As the results from the 2013 field trial indicated that the detector discs were able to bind

2,4-D residues at distances up to 30 feet away from the boom arm, in 2014 field trials, the distances between the detectors and spray swath were increased to 50 feet, 100 feet and

300 feet. Additionally, an extra set of detectors was set up at a distance of 300 feet downwind from the spray swath and exposed to four passes of the boom arm (compared to two for the other detectors), in order to determine how the detectors perform following repeated exposures to 2,4-D drift. As in 2013, in order to mimic a leaf surface, detectors were mounted on posts three feet high, and placed such that the surface of the disc was 45 degrees from horizontal. To determine whether the amounts of 2.4-D found on detectors could be correlated with damage observed in sensitive plants, rows of tomato plants in pots, approximately 24 inches tall, were placed 10, 50, 100, and 300 feet downwind with

2,4-D detectors on both sides as bioindicators of 2,4-D damage. Plants were returned to a greenhouse and symptoms monitored for two weeks before plants were cut at soil level, weighed, bagged and dried. Bio-indicator injury was assessed on a scale of 0 (no visible effects) to 10 (death of the plant). The results indicated that the detector discs were able to stably bind and retain 2,4-D residues even at distances of 300 feet from the spray swath (Figure 3.6). As expected, repeated exposure to spray drift (i.e., repeated passes of the boom arm) increased the total amount of 2,4-D present on each detector. Furthermore, the 2,4-D amount detected also correlated with bio-indicator (tomato) injury score. In other words, at distances where lower concentrations of 2,4-D were detected, less injury was observed on the tomato plants at the same location.

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Optimization of matrix composition

As data from field trials was being collected, we were also working to optimize the matrix used in detector discs. These experiments were motivated by the observation that, during our initial method development, the recovery rate of 2,4-D from the original

Miracloth matrix was low, likely due to irreversible binding of 2,4-D to the matrix cellulose components of this matrix. This irreversible binding decreased the sensitivity of the detector system. To increase sensitivity, we tested four additional types of detection matrix, spanning a range of polarities, including: nylon, polypropylene, PVDF

(polyvinylidene difluoride), and polyester. To ensure that we selected a matrix capable of binding the very low concentrations of 2,4-D encountered during most drift events, we decreased the concentration DMA used in these tests 20-fold, and used a 20,000-fold dilution of a normal 2,4-D tank mix concentration. The new detector matrices (as well as

Miracloth, which was included as a reference control) were sprayed with the diluted 2,4-

D dimethylamine solution at a rate of 15 gallons per acre, collected, and stored in dark

(4˚C) before 2,4-D extraction and quantification. The goal of these experiments was to select the matrix type that retained the highest amount of 2,4-D post-spray and release bound 2,4-D during solvent extraction. In these tests, the amount of 2,4-D present on the detectors was calculated using indole-3-propionic acid as an internal standard, to allow correction for the 2,4-D lost during sample preparation and analysis (including solvent extraction, SPE purification, and LC-MS/MS analysis) (Figure 3.7). The values derived following internal standard correction represent the total amount of 2,4-D bound to the detector discs. Additionally, we also calculated the net amount of 2,4-D recovered from

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the detectors prior to internal standard correction; values which can be used to determine whether or not the detectors suffered major structural damage (which would be indicated by large shifts in net retention) (Figure 3.7). Differences between the new detector matrices and the original Miracloth matrix were determined using 1-tailed Student’s t-test

(p < 0.05), while the overall differences between the five matrix types was determined using ANOVA Tukey’s test at 95% confidence (Figure 3.7). The results indicated that nylon, polypropylene and polyester detectors were not only bound more 2,4-D, but also released significantly higher levels of 2,4-D for quantification (Figure 3.7). However, the polypropylene matrix was very stiff, making extractions quite difficult (as it was difficult to fit the matrix disc into the glass tubes for extraction). Because of this, nylon and polyester matrixes were used in downstream weather resistance studies, with Miracloth detector discs (the original matrix) included as reference/control samples.

Rain Wash Resistance Test

Rainfall is an important factor that impacts the movement of 2,4-D in agricultural systems [29]. Of particular concern for drift detection is the fact that rainfall could wash

2,4-D residues off of detector discs deployed in the field. If the rain occurs in the time period following exposure to drift but prior to the next round of grower surveying and detector collection, rain-induced loss of 2,4-D residues from the detection system could lead to a false-negative (2,4-D damage observed on the plant, but no 2,4-D quantified from the detector discs), making it difficult to determine the cause of observed damage.

Because of this, we determined the impact of rainfall on our detector disc system. Three

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types (six replicates for each type) of detectors (Miracloth, nylon and polyester) were sprayed with 20,000-fold dilutions of 2,4-D amine salt. After drying on spray bench for 5 min, half of the detectors were collected for 2,4-D quantification while the other half were treated with rain wash simulation. For the rain wash treatment group, the detectors were sprayed with simulated rain (one inch) in spray room for 1 h before collected for

2,4-D quantification. The difference in 2,4-D levels between the rain wash treatment group and the control group was determined using 1-tailed Student’s t-test (p < 0.05).

When compared to the dry control group, the Miracloth type detectors lost the most 2,4-D following exposure to simulated rain, exhibiting a 2,4-D retention of only 1.54%. Nylon detector discs exhibited the greatest resistance to simulated rain, retaining approximately

19% of the 2,4-D found on dry control discs (Figure 3.8). Polyester type detector discs performed better than the original Miracloth matrix, but as well as the nylon type detectors, retaining 7.06% of the 2,4-D found on dry controls.

UV Resistance Test

In addition to rainfall, exposure to sunlight may also reduce the amount of 2,4-D found on disc detectors deployed in the field. This primarily due to the fact that the UV light, including the UV components of sunlight, can rapidly degrade auxin and auxinic compounds [30]. We therefore tested the ability of 2,4-D bound to detectors to survive exposure to UV light. As most of the UV-B (280nm-315nm) and UV-C (100-280 nm) components of sunlight are blocked by the ozone layer, most of the UV light received by detector discs deployed under sunlight will be UV-A (315nm-400nm) [31]. Because of

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this, we tested the stability of 2,4-D bound to detector discs of following exposure to high-intensity UV-A light of 365 nm. Three types (six replicates for each type) of detectors (Miracloth, nylon and polyester) were sprayed with 20,000-fold dilutions of

2,4-D amine salt. After drying on spray bench for 5 min, half of the detectors were collected for 2,4-D quantification while the other half were treated with UV light at 365 nm. For UV treatments, detectors were exposed to 90 W/m2 UV light, which is approximately three times the UV irradiance of the sunlight at noon during a summer day

(data collected from location of 49.8° N, 9.9° E) [32]. Across all detector matrices tested

(Miracloth, nylon, polyester), exposure to UV did not result in any loss of 2,4-D residues compared to non-treated controls. These data indicate that all matrices are capable of stably binding and retaining 2,4-D residues in the presence of ultraviolet light (Figure

3.9).

Greenhouse Treatment Test

Finally, we investigated whether detector discs would be able to stably retain 2,4-D residues while simultaneously exposed to high temperatures, heat, wind, and sunlight. To answer this question, we subjected detector discs to greenhouse exposure studies. Three types (six replicates for each type) of detectors (Miracloth, nylon and polyester) were sprayed with 20,000-fold dilutions of 2,4-D amine salt. After drying on spray bench for 5 min, half of the detectors (i.e., three replicates per detector type) were collected for analysis. The remaining detectors were set in the OARDC Gourley greenhouse for one week and collected for analysis (see Materials and Methods, above, for greenhouse

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conditions). The results indicated that greenhouse treatment did not significantly affect the 2,4-D binding and release of 2,4-D residues from either nylon or polyester type detectors, compared to their respective non-treated control groups (Figure 3.10).

Interestingly, the 2,4-D levels quantified from greenhouse treated Miracloth type detectors was higher than observed in the non-treated control group. One possible explanation for this may be that components of Miracloth matrix were damaged by light and heat during the long-term greenhouse exposure [33], compromising the ability of this matrix to bind the internal standard (IPA) during the extraction process (in other words, damaged Miracloth would have less binding surface available for IPA during the extraction process). Decreases in IPA binding would result in an overestimation of 2,4-D levels following correction using the IPA internal standard. We investigated this possibility by measuring the net 2,4-D extracted from the detector discs (i.e., the amount of 2,4-D extracted without correction by ISTD). Supporting this hypothesis, when net

2,4-D levels were calculated, no differences were observed between greenhouse-treated and control Miracloth discs.

Discussion and Conclusion

2,4-D, a synthetic auxinic herbicide, is widely and effectively used as a control for broad leaf weeds. Application of this herbicide increases both crop productivity and economic yield. However, 2,4-D usage also carries risks, the most predominant of which is the offsite movement, or drift, of 2,4-D onto sensitive crops. Drift of 2,4-D can result in damage to sensitive crops, resulting in damage, decreased yield, and economic losses to growers.

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The risk of 2,4-D drift may increase with the release of genetically modified 2,4-D resistant crops, which is likely to result in increases in 2,4-D application, both in terms of timings and dosages. In the event of an herbicide drift event, it is important to determine the active ingredient and severity of the exposure as quickly as possible. While some previous studies have used paper- or fiber-based detection systems to capture 2,4-D residues [27], the conventional way in which herbicide detections are performed are by photographically documenting herbicide damage and sending plant tissues for dislodgeable herbicide residue analyses. However, it can be difficult to definitively identify a specific herbicide based on phenotypic damage. Additionally, as auxinic herbicides are often translocated from leaves to roots via the auxin transport machinery

(where they are metabolized by either the plant or soil micro-organisms) detection of dislodgeable 2,4-D residues is difficult by 72 hours post-drift deposition. To overcome these problems, we worked to develop a portable, field-deployable, and weather resistant

2,4-D detection system exhibiting high sensitivity for the target herbicide. These detectors were assembled by simply attach a matrix disc to an embroidery loop (Figure

3.1). Detector discs were then mounted on posts to put them at the same height of potential target plant leaf surfaces. The ease of construction and lightweight nature of the detectors means that they can easily be deployed interspersed with crops in the field, and then collected for analysis when symptoms of potential 2,4-D damage are observed on the crops. In addition to the detector discs, we also developed methods to efficiently extract 2,4-D residues from the detectors and quantify extracted herbicide via LC-MS/MS.

In field trials, the detection system presented here was able to detect 2,4-D drift when the detectors were set at 300 feet away from the spray swath, when 2,4-D dimethylamine was 128

sprayed at a rate of 2 pints per acre (a 2,4-D concentration commonly used by applicators). Moreover, when tested in the spray room, the disc detector system was able to stably bind and release detectable amounts of 2,4-D residue when 0.006 pints per acre of 2,4-D amine salt was used, a rate of application over 300-fold more dilute than that commonly applied in the field. The results of the weather resistance tests indicate that nylon is the optimal matrix for future disc detection studies. Nylon bound more 2,4-D than did the original Miracloth matrix, but was still able to release these bound residues efficiently for LC-MS/MS quantifications. Additionally, nylon was more resistant to simulated rainfall than the other four detector matrices assayed (Miracloth, polypropylene,

PVDF, and polyester). Nylon also performed well in UV exposure and long-term greenhouse (high light, high heat) exposure studies. Future work will focus on determining the optimal placement of detectors in field settings, as well as correlating amounts of 2,4-D retained on detector discs with damage observed on sensitive crops following exposure to herbicide drift.

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Figure 3.1 2,4-D detector spray room test The detectors were sprayed with 100-fold and 1000-fold diluted DMA solution at 15 gallon per acre, and then collected for LC-MS/MS analysis.

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Figure 3.2 2,4-D extraction method development The 2,4-D dimethylamine (labeled as “DMA”), 2,4-D acid (labeled as “acid”), and 3- indolepropionic acid (labeled as “IPA”), were added and analyzed as described in the figure. A%, B% and C% each represents the recovery rates in each step as indicated in the figure. The four-step experiment was to determine if the 2,4-D loss and IPA loss due to mesh binding (A%) correlated with each other, and also the sample loss due to SPE column binding (B%). X%, Y% and Z% are measurable data from each individual test. X% = A% × B% × C%, Y% = B% × C%, Y% = C%.

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Figure 3.3 DMA, 2,4-D and IPA recovery during 2,4-D detector analysis in lab The four-tier experiment was conducted as described in Fig. 2. A%, B% and C% each represented the percentage loss in each step as indicated in Fig.2. X%, Y% and Z% were measurable data from each individual test. X% =A% × B% × C%, Y% = B% × C%, Y% = C%. Figure 3.3-1 A% represented the recovery rate of DMA (2,4-D dimethylamine salt) from Mira cloth Figure 3.3-2 A% represented the recovery rate of 2,4-D acid from Mira cloth, B% represented the recovery rate of 2,4-D acid from HLB column Figure 3.3-3 A% represented the recovery rate of IPA from Mira cloth when extracted together with DMA Figure 3.3-4 A% represented the recovery rate of IPA from Mira cloth when extracted together with 2,4-D acid

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Figure 3.4 2,4-D detector spray room test The detectors were sprayed with 100-fold and 1000-fold diluted DMA solution at 15 gallon per acre, and then collected for LC-MS/MS analysis.

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Figure 3.5 2013 field trials with detectors set at 18 feet and 30 feet downwind locations The Mira cloth detectors (4 replicates) were set at 18 feet downwind, 30 feet downwind and 10 feet upwind when the fields were sprayed with 2 pints per acre 2,4-D amine salt. The field trial was repeated with a secondary batch of detectors. The first batch of detectors were analyzed 2 months post spray (left figure) while the secondary batch of detectors were analyzed 5 months post spray (middle figure). The same field trail was repeated at Delaware state (right figure).

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Figure 3.6 2014 field trials with detectors set at 50 feet, 100 feet and 300 feet downwind locations The Mira cloth detectors (4 replicates) were set at 50 feet downwind, 100 feet downwind, 300 feet downwind, and 10 feet upwind when the fields were sprayed with 2 pints per acre 2,4-D amine salt (labeled as “300 REP”). Rows of tomato plants in pots, approximately 24 inches tall, were placed with 2,4-D detectors on both sides. Symptoms of the tomato plants were evaluated on a scale of 0 (no visible effects) to 10 (death of the plant).

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Figure 3.7 Mesh type comparison regarding 2,4-D retention ability The five different types of detectors (Mira cloth, nylon, polypropylene, PVDF (polyvinylidene difluoride), and polyester) were sprayed with20,000-fold diluted 2,4-D solution at 15 gallon per acre, collected and stored in dark (4˚C) before 2,4-D extraction and quantification. Data represent the average of four detectors ± S.D. Asterisk indicates that mean value is significantly higher than that of Mira cloth according to 1-tail Student’s t-test (*: p < 0.05). Letter values indicate significant differences as determined using ANOVA Tukey’s test at 95% confidence.

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Figure 3.8 Rain wash resistance test on Mira cloth, nylon and polyester meshes The control group (black bar) was sprayed with 20,000-fold dilutions of 2,4-D amine salt, and then collected for 2,4-D quantification. The rain wash group (grey bar) was sprayed with 20,000-fold dilutions of 2,4-D amine salt, followed by simulated rain (one inch) for 1 h before collected for 2,4-D quantification. Data represent the average of three detectors ± S.D. Asterisk indicates that mean value is significantly lower than that of control group according to 1-tail Student’s t-test (*: p < 0.05).

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Figure 3.9 UV resistance test on Mira cloth, nylon and polyester meshes The control group (black bar) was sprayed with 20,000-fold dilutions of 2,4-D amine salt, and then collected for 2,4-D quantification. The UV treatment group (grey bar) was sprayed with 20,000-fold dilutions of 2,4-D amine salt, followed by exposure to 90 W/m2 UV light at 365 nm for 15 min before collected for 2,4-D quantification. Data represent the average of three detectors ± S.D. No significance has been observed between the UV treatment groups (Mira cloth, nylon, and polyester) and control groups according to 1-tail Student’s t-test (*: p < 0.05).

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Figure 3.10 Greenhouse treatment test on Mira cloth, nylon and polyester meshes The control group (black bar) was sprayed with 20,000-fold dilutions of 2,4-D amine salt, and then collected for 2,4-D quantification. The greenhouse treatment group (grey bar) was sprayed with 20,000-fold dilutions of 2,4- D amine salt, followed by seating in greenhouse for 1 week before collected for 2,4-D quantification. Data represent the average of three detectors ± S.D. No significance has been observed between the greenhouse treatment groups (nylon and polyester) and control groups according to 1-tail Student’s t-test (*: p < 0.05).

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Chapter 4 Development of an efficient method to quantify DIMBOA using LC-MS/MS

to select maize parental lines with high DIMBOA content

This research project partly described in this chapter was completed in collaboration with

Rachel Medina in Dr. Chris Taylor’s lab in the Department of Plant Pathology. The role of our lab focused on extraction method optimization, LC-MS/MS quantification method development, and sample analysis using the developed methods.

Introduction

Benzoxazinoids are a group of plant secondary metabolites with a 2-hydroxy-2H-1,4- benzoxazin-3(4H)-one structure (Figure 4.1) [1]. Benzoxazinoid compounds can be placed into one of three based on their chemical structures: hydroxamic acids, benzoxazolinones, and lactams [2] (Figure 4.2). 2,4-dihydroxy-1,4-benzoxazin-3-one

(DIBOA), 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) and their glucoside derivatives are the most common types of hydroxamic acids [2] while

Benzoxazolin-2-one (BOA) and (6-methoxy-benzoxazolin-2-one) MBOA are the two most commonly found benzoxazolinones in plants [2]. The types of lactams most often found in plant species include 2-hydroxy-1,4-benzoxazin-3-one (HBOA), 2-hydroxy-7- methoxy-1,4-benzoxazin-3-one (HMBOA), and/or the glucoside derivatives of these

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compounds [2]. While benzoxazinoids have been detected in many members of the

Poaceae family, including crops of economic importance (e.g. Z. mays, T. aestivum, S.

cereale), these compounds have also been detectec in some dicot plants (e.g. D. consolida) [3].

The roles of benzoxazinoids in plant defense are well-established [1, 4, 5]. This group of compounds has been shown to play a role in plant-plant interactions (as allelopathic toxins), plant-herbivore interactions (involving both chewing and piercing-sucking types of insects), and plant-pathogen interactions (e.g. corn sheath blight) [1, 6, 7]. The allelopathic effects of benzoxazinoids have mainly been observed and well characterized in rye (Secale cereale). Biochemical investigation revealed that DIBOA was the compound responsible for the weed suppression caused by rye roots; and further studies have confirmed the a ability of DIBOA to inhibit the growth of a range of commonly found weed species (such as annual ryegrass) [6, 8]. Interestingly, similar allelopathic effects have been reported for a range of benzoxazinoids structurally related to DIMBOA, including both DIBOA and BOA, all of which can be exuded by plant roots into the rhizosphere [9, 10].

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In addition to serving as allelopathic compounds, benzoxazinoids can also function as insecticides. The benzoxazinoids are commonly synthesized and stored in plant vacuoles in inactive glucoside forms. Upon chewing insect attack, however, benzoxazinoid glucosides will be released into the cytosol or the cell lysate, where glucosidases released from plastids during insect feeding will hydrolyze these compounds, releasing toxic aglycone benzoxazinoids [4, 11]. DIMBOA and MBOA are both bioactive benzoxazinoid toxins, and both of these molecules are functionally active against caterpillars (larva of

Lepidoptera), several of which, such as the European corn borer (ECB, Ostrinia nubilalis), are targets of economic importance [1]. When benzoxazinoids were added to an artificial diet fed to European corn borers, ECBs were more sensitive to hydroxamic acids (DIBOA and DIMBOA) than benzoxazolinones (MBOA), and smaller doses of hydroxamic acids are required to kill ECB [1]. Unlike the chewing type of insects, the piercing-sucking insects (e.g. aphids) generally do not disrupt cell compartmentation or damage sub-cellular organelles, thereby avoiding the activation glucosidases and the subsequent release of aglycone benzoxazinoids [1]. However, plants employ a different strategy to combat this type of insects, and in this case use benzoxazinoids as signal molecules. Following attack by a piercing sucking insect, benzoxazinoids have been proposed to serve as signaling molecule for inducing chitosan-triggerd callose deposition, a mechanism involved in aphid resistance [1, 4]. Benzoxazinoids can also be exuded from plant roots, and function as antifeedants in the rhizosphere. The inhibitory effects of benzoxazinoids on the feeding of another important corn herbivore, the Western corn worm, have been recently documented. In these studies, the DIMBOA content in maize root was been found to positively correlate with the mortality rate of Western Corn 146

Worms (WCR, Diabrotica virgifera virgifera) feeding on the roots [1]. In sum, the benzoxazinoids, particularly DIMBOA, play important roles in the defense response mechanisms of multiple plant species.

Benzoxazinoids have also been reported to be involved in plant-pathogen interactions.

For example, pathogen-induced DIMBOA accumulations have been shown to lead to increased resistance of corn against sheath blight [7]. Accumulations of DIMBOA also inhibited the production of the plant toxin trichothecene by Fusarium graminearum, the fungus causing fursarium head blight [12].

When benzoxazinoids are used for plant defense, the localized concentration of these benzoxazinoids is an important factor determining the efficacy of the response. As mentioned above, previous research has established a positive correlation between the

DIMBOA content of corn roots and the mortality of WCRs feeding on their roots.

Additionally, it has been demonstrated that corn lines with higher DIMBOA contents possessed greater resistance against WCR larvae, which led to better plant growth [13].

In another study, a positive correlation was observed between the concentration of

DIMBOA in the diet and mortality rate of corn leaf aphids [14]. Further the resistance of corn leaves to European Corn Borer was also found to significantly correlate with the concentration of DIMBOA present in the plant leaf tissue [1]. The accumulation of

DIMBOA in plant tissues has therefore been postulated to play a positive role in increasing the ability of plants to combat pathogens, particularly insect pests. Current research is focusing on the mechanisms by which DIMBOA functions as a pesticide, as

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well as on determining the range of pests affected by DIMBOA accumulations. Both research efforts will require protocols to accurately extract, isolate, and quantify

DIMBOA and related molecules from plant tissues.

Maize (corn) is a crop of major economic importance, and serves as one of the major food grains both for human consumption and for animal feed. In addition to its role as a food crop, maize is also used as rotation crop for soybean, another major economic crop, and rotation of maize with soybean helps to control populations of soil pests like soybean cyst nematode [15]. DIMBOA produced in maize root are exuded into the soil, where they have an effect on the growth of both soil microbes and other soil organisms [16, 17].

Because of this, DIMBOA accumulations in maize roots can be beneficial not only for maize growth and development (i.e., for defense against root pests of maize), but also for other crops rotated into the same fields as maize roots with high DIMBOA contents. One of the goals of the research presented in this chapter was, in collaboration with the laboratory of Dr. Chris Taylor, to screen 25 maize parental lines for high levels of

DIMBOA, and select lines with high DIMBOA content for future breeding efforts and field trials. In order to accomplish this goal, our laboratory had to first develop a method to extract, isolate, and quantify the DIMBOA present in maize roots.

In order to quantify the DIMBOA contents in maize root, a sensitive, reliable, reproducible, and cost-efficient method was required. Prior to 2000, HPLC-DAD (high performance liquid chromatography coupled to diode array detection) was the predominant method used for DIMBOA quantification in plant tissue, and this method is

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still used in many studies [6, 18-20]. However, the development of more sensitive and reliable LC-MS/MS (liquid chromatography tandem mass spectrometry) technology led to the development of methods with higher selectivity and lower limits of detection, although many of these methods still required extensive sample preparation prior to LC-

MS/MS analyses [21-24]. Here, we provide a description of the development of a fast, sensitive, reproducible, reliable, and low-cost method to extract and quantify free

DIMBOA in maize roots.

Materials and Methods

Maize parental lines were assembled by Dr. Chris Taylor’s lab in the Department of Plant

Pathology of The Ohio State University, and included the following lines: B97, CML103,

CML247, CML277, CML287, CML322, CML333, CML52, CML69, H88, Hp301, Ki3,

Ky21, M162W, M37W, Mo17, Ms71, Nc350, Nc358, Oh28, Oh43, Oh7b, P39, Tzi8, and

W22. In addition, bx1, a mutant of H88 proposed to be deficient in DIMBOA biosynthesis, was used as a negative control. The 7-day maize seedlings used in the project were also germinated and provided by the Taylor laboratory, by Rachel Medina (a graduate student collaborating on the project; see front-piece above). Briefly, maize seeds were sterilized, and then germinated in water saturated germination paper for two days

(until a radical emergence was observed). Seedlings were then transferred to a hydroponic growth system, and grown for five days in one-eighth strength Murashige-

Skoog medium for another five days, after which samples were harvested and flash frozen in liquid nitrogen.

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Initial test and DIMBOA quantification in maize root

As DIMBOA and other benzoxazinoid molecules share some structural homology with auxins, the methods used to extract and quantify DIMBOA were based on methods currently used by the Blakeslee laboratory to extract and quantify auxin and auxin metabolites [25]. The roots of the 7-day old maize seedlings were harvested, flash frozen in liquid nitrogen, and stored at -80 ˚C prior to extraction. Three of the 25 maize parental lines detailed above, M162W, Mo17, and Oh7b, were used in method development studies. Upon removal from -80 ˚C freezer, roots were triple ground in liquid nitrogen.

Approximately 32-54 mg of ground tissue was then weighed out from each sample and extracted with 1 mL 50 mM Na-phosphate buffer (pH 7.0) (Fisher Scientific, Waltham,

MA). Two replicates were prepared for each line in this fashion, and one replicate of each line was thenspiked with 100 ng of the internal standard candidate BOA (2-

Benzoxazolinone, Sigma-Aldrich Co., St. Louis, MO), while the other one was not (to allow determination of whether or not endogenous BOA was present in the maize extracts). Samples were vortexed briefly and extracted on a lab nutator for 20 minutes at room temperature. Afterwards, the samples were centrifuged at 12,000×g for 15 minutes at room temperature and the supernatants were collected and transferred to new microcentrifuge tubes. The pH of the supernatants was adjusted to 3 by dropwise addition of 1 M HCl (Fisher Scientific, Waltham, MA). HLB columns were pre-conditioned for sample purification using 1 mL methanol (Optima LC/MS, Fisher Scientific, Waltham,

MA), followed by 1 mL H2O. Each of the samples were then added to a pre-conditioned

HLB column; and the columns were then washed with 2 mL 5% methanol in H2O, and

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eluted with 2 mL 80% methanol in H2O. The eluents were then dried under N2 gas, re- dissolved in 200 µL methanol, and filtered through PTFE 4 mm 0.2 µm filters

(Phenomenex, Torrance, CA). Filtered samples were diluted 100-fold and injected onto the LC-MS/MS for analysis. DIMBOA standard solutions of 0.2, 0.3, 0.4, 0.5, 0.6, 1, 1.5,

2, 2.5 ng/µL were injected for LC-MS/MS analysis in order to generate a standard curve for the quantification of DIMBOA in root extracts.

LC-MS/MS analysis

Corn extracts and DIMBOA standards were analyzed using an Agilent 1260 HPLC coupled to an Agilent 6460 Triple Quadrupole LC/MS/MS (Agilent Technologies, Santa

Clara, CA). The LC-MS/MS method developed to quantify DIMBOA and BOA was adapted from methods used to quantify auxins and auxin metabolites [25]. The target compound 2,4-D and the internal standard IPA were separated using an Agilent Poroshell

EC-C18 column (50 × 3 mm, 2.7 µm, Agilent Technology, Santa Clara, CA). H2O with 5% methanol, 0.1% acetic acid and methanol with 0.l% acetic acid were used as solvents A and B, respectively. The solvent gradient was as follows: 2-80% (0-4 min); 80-98% (5-9 min); 98% (10-11 min); 98-2% (11-12.5 min); 2% (12.6-16 min),, with a flow rate at 0.3 mL/min. Compounds were ionized using Electrospray Ionization (ESI), and the spray chamber settings were: gas temperature at 300 ˚C, gas flow at 10 L/min, nebulizer pressure at 40 psi, sheath gas temperature at 350 ˚C, sheath gas flow at 12 L/min, capillary voltage at +2250 V. Multiple reaction monitoring mode was used for detecting

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DIMBOA and BOA in the mass spectrometer. The chromatographic data were analyzed using Agilent MassHunter software.

Method optimization

Roots of Mo17 seedlings (12 replicates) were ground in liquid nitrogen, and approximately 40-60 mg ground tissue was weighed out. The samples were supplemented with 10 µL 500 ng/ µL BOA, and extracted using 1 mL H2O for 20 min at room temperature on a lab nutator. The supernatants were collected after centrifugation at

12,000 × g for 15 minutes at room temperature. Afterwards, one set of the samples (three replicates) was re-extracted with 1 mL H2O, while the second, third, and fourth sets of samples (three replicates each) were re-extracted with 1 mL 30% methanol, 60% methanol, and 90% methanol, respectively. During re-extractions, the samples were set on a lab nutator for 20 min at room temperature. The supernatants were again collected after centrifugation at 12,000 ×g for 15 minutes at room temperature, and combined with the initial extraction. The combined supernatants were then filtered using Waters

Acrodisk® 13mm 0.2 µm PTFE syringe filters (Milford, MA), and diluted 100-fold prior to injection into the LC-MS/MS for analysis (refer to LC-MS/MS analysis section).

Significant differences in DIMBOA and BOA concentrations between the extraction solvents were determined using ANOVA Tukey’s test at 95% confidence.

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Free DIMBOA vs total DIMBOA

Roots of Mo17 seedlings (9 replicates) were ground in liquid nitrogen, and approximately

30 mg ground tissue was weighed out, and supplemented with 10 µL 500 ng/ µL BOA.

One set of samples (set 1, consisting of 3 replicates) was extracted using 1 mL H2O for

10 min at room temperature on a lab nutator, while the other set of samples (set 2, 6 replicates) was extracted with 1 mL 90% methanol (0.1% formic acid; the high methanol concentration functions to inhibit glucosidase enzyme activity). The supernatants were then collected after centrifugation at 12,000 ×g for 15 minutes at room temperature. The pellets of set 1 were re-extracted with 1 mL 90% methanol (0.1% formic acid) on a lab nutator for 10 min, while the pellets of set 2 were re-extracted with either 1 mL H2O

(Free DIMBOA-1, 3 replicates) or1 mL 90% methanol (Free DIMBOA-2, 3 replicates).

The supernatants were again collected after centrifugation at 12,000 ×g for 15 min, and combined with the supernatant generated during the initial extraction. The combined supernatants were then filtered using Waters Acrodisk® 13mm 0.2 µm PTFE syringe filters, and diluted 100-fold prior to injection onto the LC-MS/MS for analysis (refer to

LC-MS/MS analysis section).

Quantification of DIMBOA in maize parental lines

Seedlings of all 25 maize parental lines (3 biological replicates per line) were ground in liquid nitrogen, and approximately 40-45 mg ground tissue was weighed out. Internal standard (10 µL 500 ng/ µL BOA) was added to each sample, and samples were then extracted using 1 mL 90% methanol (0.1% formic acid) for 20 min at room temperature 153

on a lab nutator. Supernatant fractions were then collected after centrifugation at 12,000

×g for 15 minutes at room temperature. The pellets were then re-extracted with 90% methanol (0.1% formic acid). The supernatants were again collected after centrifugation and combined with the supernatants generated during the first extraction step. The combined supernatants were then filtered using Waters Acrodisk® 13mm 0.2 µm PTFE syringe filters, and diluted 100 folds before injected into LC-MS/MS for analysis (refer to

LC-MS/MS analysis section). The differences of free DIMBOA concentrations among the maize parental lines were determined using ANOVA Tukey’s test at 95% confidence.

Results and Discussion

Preliminary quantification of DIMBOA from maize roots

To optimize the methods for the extraction and quantification of DIMBOA from maize seedlings, as well as to get an initial range of DIMBOA concentrations expected in maize seedling roots, we performed a series of preliminary analyses to quantify the amounts of

DIMBOA present in the roots of three corn lines, M162W, Mo17, Oh7B. As benzoxazinoids share structural similarity with phytohormones in the auxin family (and indeed, are derived from the same tryptophan synthesis pathways), the initial methods used to quantify DIMBOA were adapted from those previously used to quantify auxin and auxin metabolites [25]. In these studies, maize roots were collected, flash frozen and triple ground in liquid nitrogen, and then extracted with 1 mL Na-phosphate buffer (pH

7.0) at room temperature for 20 minutes. During the extraction process, glucosidases released from the corn cell plastids during the grinding process should hydrolyze any 154

DIMBOA-Glu (2-β-D-glucopyranosyloxy-4-hydroxy-1,4-benzoxazin-3-one) present in the sample, releasing free DIMBOA. As a result, the DIMBOA detected using this extraction method will be a combination of both free the DIMBOA and the DIMBOA-

Glu present in the root prior to extraction. Two extractions were performed on each root sample, one of which was spiked with 100 ng BOA, while the other one was not. This was done to test: a. whether BOA could be detected in the background matrix of the maize extract; b. whether BOA (which we proposed to use as an internal standard) can be successfully separated from the target compound DIMBOA and detected using our analytical method; and c. whether or not any endogenous BOA was present in maize root extracts. The samples were then centrifuged and the supernatants were collected. The supernatants then were purified using solid phase extraction (HLB columns) to reduce sample complexity and eliminate impurities that can cause matrix effect and reduce the sensitivity of the method for the target compound (DIMBOA). The results LC-MS/MS analyses indicated that no endogenous BOA was detected in maize root extracts, but that this compound could be detected in the maize extract matrix when added during the extraction process. Additionally, our data showed that BOA could be successfully separated from the target compound DIMBOA (Figure 4.3). To generate a standard curve for DIMBOA quantification, standard solutions of 0.2, 0.3, 0.4, 0.5, 0.6, 1, 1.5, 2, 2.5 ng/µL DIMBOA were injected for LC-MS/MS analysis. A linear correlation (R2 =

0.9997) was observed between the DIMBOA concentration and the resulting peak area detected in LC-MS/MS anslyses, indicating that the standard curve generated was valid over the concentration range used (Figure 4.4). The standard curve was then used to quantify DIMBOA concentration in the roots of the three corn lines. Metabolomic 155

analyses indicated that, among the three maize lines selected for preliminary analysis,

Mo17 roots contained the least DIMBOA, and thus was selected as the line for further method optimization. The rationale here was that by optimizing to the line with the lowest levels of DIMBOA, we increased the probably of being able to detect DIMBOA in all other lines tested) (Table 4.1).

Method optimization

The results of our preliminary analyses indicated that the DIMBOA content of maize seedling roots were quite high, particularly in comparison to other plant signaling compounds or phytohormones. Even Mo17, the maize line with lowest DIMBOA content, contained, on average, 115 µg/g DIMBOA. As a result, considerable dilution (over 100- fold) of samples was necessary prior to LC-MS/MS analyses. Because the levels of

DIMBOA were so high, we hypothesized that the method could optimized by removing the solid phase extraction step, which would decrease both the cost and time needed to process each sample. We reasoned that, since the ratio of the sample peak area to the background noise (resulting from the maize extract matrix or buffer impurities) was so high, this background noise could be removed by simply diluting the sample prior to analysis. In other words, since the DIMBOA levels were so high, the dilution performed prior to injecting samples on the LC-MS/MS is so large as to dilute impurities below the limit of detection for the LC-MS/MS, removing them from “view” as background noise and thereby minimizing their contribution to matrix suppression of the DIMBOA peak.

To test this hypothesis, flash frozen roots of maize parental line Mo17 were ground in

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liquid nitrogen, spiked with 5 µg BOA, and extracted in 1 mL H2O for 20 minutes at room temperature. As noted above, these extraction conditions will allow for the hydroxylation of DIMBOA-Glu by endogenous glucosidases, resulting in the release of free DIMBOA. To determine the optimal solvent system for extracting DIMBOA and

BOA from maize root samples, samples were then re-extracted with different ratios (0%,

30%, 60%, 90%) of methanol in H2O. The results indicated that the concentration of

BOA detected in extracts increased as the level of methanol in the sample was raised

(Figure 4.5). When H2O was used, the recovery rate of BOA (5,000 ng in total) was approximately 75%. The recovery rate increased to 80% using 30% methanol, further increased to 83% using 60% methanol, and was increased to 90% when 90% methanol was used as the extraction sovlent. Interestingly, the efficiency of DIMBOA extraction

(i.e., the concentration of DIMBOA present in extracts) was not affected by changing the concentration of the methanol present in the extraction solvent. Because a low recovery rate of the internal standard can result in an overestimation of the target compound (in this case, DIMBOA), 90% methanol was selected as the extraction solvent, since it allowed the highest recovery of BOA. Our results indicated that when we extracted samples with 90% methanol, omitted the SPE step, and proceeded directly to LC-MS/MS analysis the concentration of DIMBOA detected was comparable to that using the previous, SPE-dependent method. These results allowed us to implement a more time efficient, cost effective method for the extraction and quantification of total DIMBOA from maize roots.

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Quantification of free DIMBOA vs total DIMBOA

Previous research has reported that, under non-stressed conditions, DIMBOA is mainly stored in the glycosylated form (DIMBOA-Glu) in plant cell vacuoles [6, 26]. For example, one study indicated that the concentration of DIMBOA-Glu in maize seedling roots was always higher than the concentration of DIMBOA in these organs during the first five days of seedling development [27]. In order to see if these conclusions also applied to the maize seedlings used in our study, the free DIMBOA content in the roots of maize Mo17 seedlings was determined and compared with the total DIMBOA content.

For total DIMBOA extractions, the samples were initially extracted with 1 mL H2O, which allows for the enzymatic conversion of DIMBOA-Glu to the aglycone form during the extraction process; followed by a second extraction using 90% methanol. As noted above, the DIMBOA detected using this protocol represents the total DIMBOA (both glycosylated and non-glycosylated) present in the tissue extracted. In extractions designed to quantify only the free DIMBOA present in roots, frozen ground roots were extracted directly with 90% methanol, which should denature endogenous glucosidases present in the extract, preventing the hydrolyzation of DIMBOA-Glu to free DIMBOA.

Samples were then re-extracted with either 1 mL H2O or1 mL 90% methanol. The level of total DIMBOA present in maize Mo17 roots was 184.7 ± 20.8 µg/g, while the levels of free DIMBOA calculated in the same roots was 127.5 ± 17.1 µg/g (when calculated using water for the second extraction step) or 102.0 ± 39.0 µg/g (when calculated using methanol for the second extraction step; the fact that there was no difference in the data generated using water or methanol for the second extraction step indicates that the initial

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methanol extraction is sufficient to inhibit glucosidase activity in the root samples).

Overall, our data indicate that the levels of free DIMBOA present in Mo17 roots were not lower than those of DIMBOA-Glu (total DIMBOA minus free DIMBOA). It is possible that the ratio of free to glycosylated DIMBOA may be somewhat genotype specific, or dependent upon the growing environment. As the seedlings used in our study were grown hydroponically, they may have different DIMBOA:DIMBOA-Glu ratios than roots grown in the field or in potting media.

Quantification of DIMBOA in maize parental lines

As described above, measurements of the total DIMBOA present in maize roots are an aggregate of the DIMBOA-Glu (vacuolar storage form), and the bioactive free DIMBOA present in these tissues [11]. Free DIMBOA can be extruded by roots into the rhizosphere, where it inhibits the growth of several potential pathogens. The free DIMBOA secreted by maize roots into the soil can be of benefit not only to maize, but may also aid the growth and development of other crops rotated into the same fields. Because of this, maize lines which secrete high levels of DIMBOA may be of use in biopesticide based strategies to control soil pathogens. We therefore quantified the levels of free DIMBOA present in 25 selected maize parental lines. To inhibit endogenous glucosidase activity and prevent the conversion of DIMBOA-Glu to DIMBOA, roots were extracted twice with 90% methanol. The levels of free DIMBOA present in extracts were then analyzed using LC-MS/MS and the DIMBOA. The DIMBOA content in the mutant (bx1), which is deficient in DIMBOA biosynthesis, was below detection (Figure 4.7). The

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concentrations of free DIMBOA present in the seedling root of the other maize parental lines varied from 198.48±51.88 μg/g FW to 4987.43±468.09 μg/g FW. The maize parental line containing the highest concentration of DIMBOA was Hp301. Significant differences in the levels of DIMBOA concentrations among the maize parental lines were determined using ANOVA Tukey’s test at 95% confidence, and the grouping information resulting from these Tukey pairwise comparisons is listed in Table 4.2.

Conclusion

DIMBOA, a benzoxazinoid plant defense compound, plays very important roles in regulating plant-plant interactions, plant-herbivore interactions, and plant-pathogen interactions [1, 6]. DIMBOA has been found to function as an allopathic toxin (effective against several weeds) in plant root exudates, as insecticidal agents against pests such as the European corn borer, and in the resistance to pathogen infections, like that caused by corn sheath blight [7, 9, 28]. These effects of DIMBOA are concentration dependent, with increased levels of DIMBOA correlating with increased resistance to pathogens [1].

Since maize is often rotated with other crops, such as soybean, increasing the amount of

DIMBOA released from maize roots may also have beneficial effects on soybean production. The development of DIMBOA as a biocontrol agent is therefore dependent on increasing the production, through either breeding or metabolic engineering, of this molecule in plant systems. In order to enable this research, however, it is necessary to have a method for accurately extracting, isolating, and quantifying the levels of

DIMBOA present in plant tissues. Here, we have presented the development of a time

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efficient and cost effective quantification method for detecting both the total and the free

DIMBOA present in maize roots. This method is also advantageous in that it requires only a small amount of fresh tissue (approximately 30 mg) for analysis. The developed

LC-MS/MS based method was used to determine the DIMBOA concentrations of the roots of 25 maize parental lines. The results generated indicate that the DIMBOA content varies greatly among the parental lines, and the Hp301 line contained the highest levels of DIMBOA in seedling roots.

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7. Song, Y.Y., et al., Induction of DIMBOA accumulation and systemic defense

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of secondary metabolites in plant immunity. Current Opinion in Biology, 2012.

15(4): p. 407-14.

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(DIMBOA) inhibits trichothecene production by Fusarium graminearum through

suppression of Tri6 expression. International Journal of Food Microbiology, 2015.

214: p. 123-128.

13. Xie, Y., et al., Role of 2, 4-dihydroxy-7-methoxy-1, 4-benzoxazin-3-one (DIMBOA)

in the resistance of maize to western corn rootworm, Diabrotica virgifera

virgifera (LeConte)(Coleoptera: Chrysomelidae). The Canadian Entomologist,

1990. 122(06): p. 1177-1186.

14. Long, B., et al., Relationship of hydroxamic acid content in corn and resistance to

the corn leaf aphid. Crop Science, 1977. 17(1): p. 55-58.

15. Niblack, T., Soybean cyst nematode management reconsidered. Plant Disease,

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16. Bais, H.P., et al., The role of root exudates in rhizosphere interactions with plants

and other organisms. Annual Review of Plant Biology, 2006. 57: p. 233-266.

17. Neal, A.L., et al., Benzoxazinoids in root exudates of maize attract Pseudomonas

putida to the rhizosphere. PLoS One, 2012. 7(4): p. e35498.

18. Mayoral, A.M., et al., A high performance liquid chromatography method for

quantification of DIBOA, DIMBOA, and MBOA from aqueous extracts of corn

and winter cereal plants. Journal of Liquid Chromatography & Related

Technologies, 1994. 17(12): p. 2651-2665.

19. Lyons, P.C., et al., Separation and quantification of cyclic hydroxamic acids and

related compounds by high-pressure liquid chromatography. Journal of

Agricultural and Food Chemistry, 1988. 36(1): p. 57-60.

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wheat (Triticum aestivum L.) varieties. Journal of agricultural and food chemistry,

2006. 54(4): p. 1016-1022.

21. Bonnington, L., et al., Development of a liquid chromatography-electrospray-

tandem mass spectrometry method for the quantitative determination of

benzoxazinone derivatives in plants. Analytical Chemistry, 2003. 75(13): p. 3128-

3136.

22. Bonnington, L.S., D. Barcelo, and T.P. Knepper, Utilisation of electrospray time-

of-flight mass spectrometry for solving complex fragmentation patterns:

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23. Bhardwaja, S.K., K. Dwivedia, and D. Agarwala, A Facile and Efficient

Analytical Method For Separation of A Series of Substituted Benz-1, 3-oxazin-2-

ones by LC/LCMS. Biomolecules, 2015.

24. Marti, G., et al., Metabolomics reveals herbivore-induced metabolites of

resistance and susceptibility in maize leaves and roots. Plant Cell Environ, 2013.

36(3): p. 621-39.

25. Blakeslee, J.J. and A.S. Murphy, Microscopic and Biochemical Visualization of

Auxins in Plant Tissues. Environmental Responses in Plants: Methods and

Protocols, 2016: p. 37-53.

26. Handrick, V., et al., Biosynthesis of 8-O-methylated benzoxazinoid defense

compounds in maize. The Plant Cell, 2016. 28(7): p. 1682-1700.

27. Ebisui, K., et al., Occurrence of 2, 4-dihydroxy-7-methoxy-1, 4-benzoxazin-3-one

(DIMBOA) and a β-glucosidase specific for its glucoside in maize seedlings.

Zeitschrift für Naturforschung C, 1998. 53(9-10): p. 793-798.

28. Klun, J., C. Tipton, and T. Brindley, 2, 4-Dihydroxy-7-methoxy-1, 4-benzoxazin-

3-one (DIMBOA), an active agent in the resistance of maize to the European corn

borer. Journal of Economic Entomology, 1967. 60(6): p. 1529-1533.

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Figure 4.1 core skeleton structure of benzoxazinoids

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Figure 4.2 Chemical structures of common benzoxazinoids DIBOA: 2,4-dihydroxy-1,4-benzoxazin-3-one; DIMBOA: 2,4-dihydroxy-7-methoxy-1,4- benzoxazin-3-one; HBOA: 2-hydroxy-1,4-benzoxazin-3-one; HMBOA: 2-hydroxy-7- methoxy-1,4-benzoxazin-3-one; BOA: 2-Benzoxazolinone; MBOA, 6-methoxy- benzoxazolin-2-one

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Figure 4.3 DIMBOA and BOA separation and detection by LC-MS/MS The DIMBOA and internal standard BOA, were extracted and successfully detected using LC-MS/MS in the corn line M162W (in the figure), Mo17, and Oh17b. DIMBOA was eluted at 6.309 min while BOA was eluted at 6.569 min from HPLC column. The identities of the compounds were ensured by comparing retention times and mass transitions to authentic standards.

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Figure 4.4 Standard curve for quantification of DIMBOA in corn root DIMBOA standard solutions of 0.2, 0.3, 0.4, 0.5, 0.6, 1, 1.5, 2, 2.5 ng/µL were injected to LC-MS/MS for the quantification of DIMBOA in maize seedling roots.

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Table 4.1 DIMBOA content in maize parental line M162W, Mo17, and Oh7b

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Figure 4.5 DIMBOA and BOA concentration extracted with 0%, 30%, 60%, and 90% methanol The efficiencies of 0%, 30%, 60%, and 90% methanol solventsmto extract DIMBOA and BOA from seedling roots of maize line Mo17, were determined. Data represent the average of three replicates measurements ± S.D. The difference in the efficiency between different solvents was determined by ANOVA Tukey’s test at 95% confidence.

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Figure 4.6 Concentrations of total DIMBOA and free DIMBOA in maize seedling root (Mo17)

Total DIMBOA content was extracted by a first step extraction using H2O to allow for hydrolysis of DIMBOA-Glu, followed by a second step extraction using 90% methanol. The Free DIMBOA-1 was extracted by a first step extraction using 90% methanol, followed by extraction using H2O. The Free DIMBOA-2 was extracted twice using 90% methanol. Asterisks indicate statistic significant difference between free and total DIMBOA determined by 1-tail Student’s t test (*: p < 0.05). Letter values indicate significant differences as determined using ANOVA Tukey’s test at 95% confidence.

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Figure 4.7 Free DIMBOA concentrations in the roots of 7-day seedling of maize parental lines Data represent the average of three replicates (two for Tzi8) measurements ± S.D. The differences of free DIMBOA concentrations among the maize parental lines were determined using ANOVA Tukey’s test at 95% confidence. The grouping information of Tukey pairwise comparisons was listed in Table 4.2.

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Table 4.2 Free DIMBOA concentrations in the roots of 7-day seedling of maize parental lines

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Chapter 5 Development of lipidomic biomarkers for pancreatic cancer

Introduction

Pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic cancer, is a devastating disease, with a mortality rate of as high as 95% [1]. In 2016, an estimated

53,070 new cases of pancreatic cancer were diagnosed in the United States based on estimation, and pancreatic cancer resulted in approximately 41,780 deaths [2]. Both of these numbers represent significant increases from those reported for 2012 (43,920 new cases and 37,390 deaths) [3]. These numbers are expected to increase further, and PDAC is predicted to be the second most common cause of cancer deaths by 2030 [4]. As has been demonstrated to contribute to the onset, one reason for the increase in PDAC may be the increase in the numbers of overweight and/or obese individuals in the U.S. population (currently approximately 70% of the U.S. population is either overweight or obese) [5-8]. Obesity has been found not only to play a part in the onset and occurrence of PDAC, but to also adversely affect the efficacy of chemotherapeutic treatments [6]. As the obesity rates in both children and adults are predicted to continue to increase, the population of the U.S. will be increasingly at risk for the development of PDAC [9].

Because of this, it is essential to develop new strategies to combat PDAC, especially in obese populations. Ideally new approaches to combat PDAC should target the factors

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contributing to the high mortality rate of this cancer: a. the absence of effective strategies to prevent the onset of PDAC; b. the lack of biomarkers indicating the onset of PDAC,

which results in this disease often being detected only at an advanced, relatively untreatable stage; and; c. a relatively limited number of chemotherapeutic treatments capable of targeting pancreatic cancer cells [3, 4, 10].

As obesity and the inflammation caused by obesity have been linked to the onset of

PDAC, increased physical activity (exercise; which can reduce obesity) is of increasing interest as a potential strategy to present the onset of PDAC. In support of this hypothesis, several meta-analyses of epidemiological and clinical data sets have suggested the that physical activity may have a preventative effect on the onset not just of PDAC, but also on several other types of cancer [10-12]. Physical activity has been hypothesized to prevent or delay the onset of pancreatic cancer by reducing obesity, ameliorating inflammatory processes, and normalizing insulin responses/signaling [11]. To date, however, the precise physiological and biochemical mechanisms by which physical activity inhibits the onset of PDAC have not yet been clearly defined and require further investigation.

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In addition to preventing the onset of PDAC, survival rates for pancreatic cancer could be dramatically increased by developing tools and biomarkers to detect this cancer early in its development. Particularly useful would be the identification of metabolomic biomarkers in biofluids (blood, plasma, urine, sweat, breast milk, etc.) or tissues, such as those currently available for the detection of prostate cancer [13]. Interestingly, there is accumulating evidence indicating that altered lipid metabolism is involved in cancer development [14, 15]. Additionally, earlier research indicated that the activity of a lipogenic enzyme, glycerol 3-phosphate dehydrogenase was elevated in human bladder cancer tissue, suggesting increased lipid biosynthesis [16]. To date fatty acids, phospholipids, and cholesterol, are the major lipids proposed to be involved in cancer development and progression.

Membrane phospholipids, specifically phosphatidylcholines (PC) and phosphatidylethanolamines (PE) were found to accumulate at higher levels in cancer cells

(e.g. breast cancer. prostate cancer, brain tumor) than in non-cancerous cells of the corresponding organ or tissue type [17, 18]. Additionally, the activities of several enzymes involved in PC biosynthesis, including choline kinase, phospholipase C, phospholipase D, and phospholipase A2 were also found to be increased in cancer cells, which could explain the elevation in PC levels observed in these cells [18]. Further, levels of another phospholipid-derived metabolite, lysophosphatidic acid (LPA), were significantly elevated in ascites fluid and plasma from ovarian cancer patients [19]; and a similar increase of LPA was observed in the blood of pancreatic cancer patients [20].

Phospholipase A2, the enzyme responsible for the formation of lysophosphatidic acid

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from phosphatidic acid, catalyzes the release of a free fatty acid during the hydrolysis of

PA [21]. This is of interest, as both fatty acid levels and fatty acid metabolism have also been found to be altered by the onset and progression of several cancers.

Fatty acid metabolism has been found to be significantly altered in several human carcinomas. In fact, most human cancer cells exhibit high expression levels of fatty acid synthase genes [22-24]. Further, it was found that the total amounts of monounsaturated fatty acids present in the plasma of pancreatic cancer patients were higher than those of a non-cancer control group [25]. In addition to pancreatic cancer, elevations in the activity of fatty acid synthase and corresponding increases in fatty acid accumulation were also observed in patients with a range of other cancers (e.g. breast cancer, prostate cancer, renal cancer, colon cancer) [15]. It has been hypothesized that these increases in fatty acid production are funneled into the increased phospholipid synthesis necessary to maintain accelerated cancer cell division [23].

Although it has been reported that cholesterol is also involved in cancer metabolism, the effect of this molecule on cancer biology has not yet been elucidated. Conflicting studies and evidence have suggested both promotive and suppressive effects for cholesterol on tumor development and cancer progression [26]. It is possible that cholesterol levels may indirectly impact cancer growth, as this sterol serves as a precursor of several steroid hormones impacting tumor cell growth and death, such as estrogens, androgens, and glucocorticoids [26]. However, no data is currently available regarding the impact of cholesterol synthesis or metabolism on steroid hormone production during the

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progression of cancer, and the role of cholesterol in cancer biology requires further investigation.

The data presented above suggest that an increased understanding of the role of lipid metabolism in the onset and progression of cancers could lead not only to the development of new chemotherapy strategies, but also to the development of new biomarkers for the early detection of cancers. In collaboration with the laboratory of Dr.

Zobeida Cruz-Monserrate in the Department of Gastroenterology, Hepatology, and

Nutrition, we hypothesized that lipidomic profiling of phospholipids, fatty acids, and cholesterol during the early development of PDAC could reveal one or more biomarkers capable of predicting the onset of pancreatic cancer. We further hypothesized that lipidomics tools generated to quantify lipids in the complex matrix of plant extracts could easily be used to quantify the lipid composition of mammalian cell extracts, which contain much lower levels of secondary metabolites “plant-based” lipidomics tools to profile lipid levels in adipose stromal cells and visceral fat cells from control (non-cancer) mice, and mice in various stages of PDAC progression [27]. Fatty acids were converted to fatty acid methyl esters and quantified using gas chromatography mass spectrometry

(methods optimized using morinaga seeds with high fat concent); phospholipids were quantified using liquid chromatography tandem mass spectrometry (LC-MS/MS; methods optimized using guayule [Parthenium argentatum]); and sterols were quantified using LC-MS/MS (methods optimized using burdock [Arctium lappa, Arctium minus] leaves).

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Materials and Methods

The mouse adipose stromal cells and visceral fat tissues were generated and provided by the laboratory of Dr. Zobeida Cruz-Monserrate, at The Ohio State University. Mouse cells and visceral fat tissues were flash-frozen in liquid nitrogen and stored at -80ºC prior to analyses.

Fatty acid identification and quantification method development

Moringa oleifera seeds were collected from Ghana, and provide by Annelise Bay,

College of Wooster. The seed coats were removed from the Moringa seeds, and the seeds were then triple ground in liquid nitrogen. The ground tissue (1 g) was then weighed into glass tubes, to which 20 µL 10 mg/mL methyl heptadecanoate (Santa Cruz

Biotechnology, Dallas, TX) was added as an internal standard. Samples were then extracted with 20 mL chloroform methanol (2:1, v/v, Fisher Scientific, Waltham, MA).

During the extraction, tubes were sonicated for 1h at room temperature, and then incubated on the laboratory bench without shaking at room temperature for another 1h.

The supernatants were then harvested, filtered with Whatman No.1 filter paper (GE

Healthcare, Pittsburgh, PA), and taken to dryness under N2 gas. Fatty acids present in the samples were converted to fatty acid methyl esters (FAMEs), as described previously

[28]. Briefly, 2 mL 10% BF3 (Santa Cruz Biotechnology, Dallas, TX) in methanol (w/w) was added to each tube, and samples were then incubated at 60℃ for 10 min and cooled down to room temperature. One mL of H2O and one mL hexane was added to each tube, and tubes were then mixed well by vortexing (Fisher Scientific, Waltham, MA). Samples 180

were allowed to phase partition, and the upper layer (hexane) was collected, filtered through a 17 mm 0.2 µm Teflon syringe filters (Thermo Scientific, Rockwood, TN), and

3 µL of each sample was injected onto GC-MS for analysis. Methyl palmitate (100, 200,

400, 800, 1600, 3200 ng/µL), methyl stearate (100, 200, 400, 800, 1600, 3200 ng/µL), methyl oleate (200, 400, 800, 1600, 3200, 6400 ng/µL), methyl heptadecanoate (100, 200,

300, 400, 500, 600 ng/µL) standard curves were prepared and analyzed using GC-MS, for fatty acid quantification of the fatty acids. All fatty acid standards were purchased from

Santa Cruz Biotechnology, Dallas, TX. Data analysis was performed using Agilent

MassHunter software.

Phospholipid identification and quantification method development

A panel phospholipid standards commonly found in plant membranes (Table 5.1) was purchased from Avanti Polar Lipids (Alabaster, Alabama). All of the phospholipid standard solutions were prepared at a concentration of 10 ng/µL, and 1 µL of each sample was injected onto the LC-MS/MS for analysis. Initially, each authentic standard was scanned in MS2 mode, to determine the retention time and precursor ion(s). Each authentic standard was then subjected to a product ion scan to determine the fragmentation pattern and optimize the fragmentation voltages. Finally, specific mass transitions (mass of precursor ion → mass of product ion) for each authentic standard were selected and monitored using multiple reaction monitoring (MRM). Phospholipids and plant and mouse samples were identified and quantified by comparing retention times and mass transitions to those of authentic standards.

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Microsomal membranes from guayule (Parthenium argentatum) rubber particles were prepared as described previously [29] (Han and Blakeslee, unpublished). The guayule rubber particle membrane pellets were collected and re-suspended in 50 µL TNE buffer.

Resuspended membranes were extracted by phase partitioning the vesicular membrane suspension against 400 µL chloroform/methanol/37% HCl (50:100:1, v/v/v). Following addition of the organic phase, samples were incubated on a lab nutator for 30 minutes at room temperature, after which an additional 400 µL chloroform and 200 µL 0.9 % NaCl

(w/v) were added to each tube. After another 10 minute incubation on the lab nutator

(room temperature), the samples were centrifuged at 10,000 × g under 4℃ for 2 minutes.

The bottom phase was then collected and washed with (phase partitioned against) 3.75 volumes of chloroform/methanol/1 M HCl (3:48:47, v/v/v). Samples were then centrifuged at 10,000 × g under 4℃ for 2 minutes, and the bottom phase was collected.

Samples were then dried with under N2 gas, re-dissolved in in 100 µL chloroform, diluted

5-fold with methanol, and filtered with 13 mm 0.2 µm PTFE syringe filters (Waters,

Milford, MA) prior to LC-MS/MS analyses. To quantify phosphatidylcholine and phosphatidylethanolamine, standard curves of these compounds were prepared and subjected to LC-MS/MS analyses: PC 16:1 (50, 100, 200, 300, 400, 500 pg/µL) and PE

16:1 (20, 40, 60, 80, 100, 120 pg/µL).

Cholesterol identification and quantification method development

Burdock leaf tissues (3 replicates of both A. lappa and A. minus) were used to optimize

LC-MS/MS methods for cholesterol identification and quantification. Leaf tissues were

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flash frozen, triple ground in liquid nitrogen, and 1 g of the ground tissue was then weighed out to glass tubes. Internal standard (1 ng β-sitosterol) was added to each tube, and samples were extracted with 20 mL chloroform methanol (2:1, v/v). During the extraction, the tubes were sonicated for 1h in icy water and then place on a laboratory bench at room temperature for an additional 1h. Samples were then filtered through

Whatman No.1 filter paper, and taken to dryness under N2 gas at room temperature.

Samples were re-dissolved in 1 mL methanol and filtered through 13 mm 0.2 µm nylon syringe filters (Fisher Scientific, Waltham, MA). Following filtration, 5 µL of teach sample was injected into the LC-MS/MS for the quantification of cholesterol, sitosterol and stigmasterol (refer to LC-MS/MS analysis section). The identifies of the target compounds were confirmed by matching the retention times and mass transitions to authentic standards purchased from Avanti Polar Lipids.

Mouse cell lipid analysis

Mouse stromal cells were extracted using a modified version of the Bligh and Dyer method (Bligh and Dyer 1959). Briefly, mouse stromal cells were removed from the -80

˚C freezer and thawed on ice. Internal standard, 10 µL 1 µg/uL LPC 17:0 (1- heptadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine) was added to each tub, followed by 2.1 mL 1:2 chloroform methanol (v/v). Samples where then and sonicated in icy water at 40 kHz for 60s. After sonication, 1 mL of water was added to each sample, samples were mixed well by vortexing, and 1 mL chloroform was added to each tube. Samples were thoroughly mixed by vortexing, and then centrifuged at 1000 × g for 5 minutes at

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room temperature for phase separation. The bottom layer was collected, and the top layer left was re-extracted twice more, using 1 mL chloroform for each re-extraction (mix with chloroform, centrifugation, bottom layer collection). The bottom (chloroform) layers collected from each extraction step were pooled, divided into two equal fractions, and dried under N2 gas. One of these fractions (Fraction 1) were used for fatty acid analysis, while the other fraction (Fraction 2) was used for phospholipid and cholesterol analysis.

Fraction 1 was prepared for analysis by adding 2 mL 10% BF3 in methanol (w/w), incubated at 60 ˚C for 10 minutes, and then cooling the tubes to room temperature.

Following the cooling step, 1 mL H2O and 1 mL hexane was added to each tube, and samples were mixed well by vortexing. The upper layer (hexane) was then collected, dried under N2 gas, re-dissolved in 200 µL hexane, and filtered using 13 mm 0.2 µm

PTFE syringe filters (Waters, Milford, MA). To quantify fatty acid methyl esters, 5 µL of each sample was injected to GC-MS for analysis. Fraction 2 was re-dissolved with 200

µL 1:2 chloroform methanol and 1 µL of sample was injected into the LC-MS/MS to quantify phospholipids.

Fatty acid analysis using GC-MS

Prepared samples were injected onto an Agilent 6890N network gas chromatography system equipped with an Agilent 7683 autosampler, and an Agilent 5973Network mass selective detector (Agilent Technologies, Santa Clara, CA) for analysis. Target fatty acids were separated using a DB-WAX column (30 mm ×0.2 mm, 0.2 µm, Agilent

Technologies, Santa Clara, CA). The injection port temperature was 250˚C, and the

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temperature gradient was as follows: hold at 80˚C for 2.5 min; increase from 80˚C to

200˚C at 2 ˚C/min; hold at 200˚C for 10 min; increase from 200˚C to 240˚C at 2 ˚C/min; hold at 240˚C for 10 min; decrease from 240˚C to 80˚C at 30˚C/min; hold at 80˚C for 1 min. The total gas flow was set at 50 mL/min and the inlet gas pressure was set at 9.4 psi.

GC-MS data were processed and analyzed using ACD/Labs MS Workbook Suite software, and compounds were identified by matching retention time and mass spectra to authentic standards, or by matching mass spectra to the Wiley Registry 10th Edition /

NIST 2012 Mass Spectral Library using ACD/Labs MS Worksuite software.

LC-MS/MS analysis

Phospholipid samples were analyzed using Agilent 1260 HPLC coupled to a 6460 Triple

Quad LC/MS (Agilent Technologies, Santa Clara, CA). The LC-MS/MS detection method for phospholipids was adapted and modified from previously published protocols

(Astarita, Ahmed et al. 2009). Target phospholipids were separated using an Agilent

Poroshell EC-C18 column (50 × 3 mm, 2.7 µm, Agilent Technology, Santa Clara, CA) incubated at 50 ˚C. H2O with 0.2% formic acid, and methanol with 0.2% formic acid were used as solvents A and B, respectively. The solvent gradient was as follows: 80-100%

(0-10 min); 100% (11-15 min); 100-80% (16min); 80% (17-20 min), with a flow rate at

0.4 mL/min. Compounds were ionized using Electrospray ionization (ESI), and the spray chamber settings were: gas temperature at 300˚C, gas flow at 10 L/min, nebulizer pressure at 35 psi, sheath gas temperature at 350˚C, sheath gas flow at 12 L/min, capillary

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voltage at -2250 V. The chromatographic data were analyzed using Agilent MassHunter software.

The cholesterol samples were injected and analyzed using the same LC-MS/MS system and column describe above. In these analyses, however, the column temperature was set at 30 ˚C. Sterol compounds were eluted from the column using an isocratic gradient of

0.1% acetic acid in methanol at 0.45 mL/min. Compounds were ionized using

Atmospheric Pressure Chemical Ionization (APCI), and the spray chamber settings were: gas temperature at 325˚C, vaporizer temperature at 350˚C, gas flow at 10 L/min, nebulizer pressure at 35 psi, capillary voltage at +4000 V. The chromatographic data were analyzed using Agilent MassHunter software.

Results

Fatty acid identification and quantification method development

The GC-MS method for fatty acid quantification was developed and tested using Moringa seeds collected from Ghana, Africa. These seeds were extracted using a modified version of the Bligh and Dyer method for lipid extraction (Bligh and Dyer 1959). Briefly, the ground frozen samples were extracted with chloroform methanol (2:1), filtered and dried.

In order to prepare the fatty acids for GC-MS analysis, they must first be derivatized to reduce the polarity of each fatty acid moiety. To increase resolution of fatty acids during

GC-MS analyses, the fatty acids were esterified to form fatty acid methyl esters (FAMEs) using an optimized FAME preparation method (Liu 1994). Briefly, fatty acids were

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isolated through organic solvent extraction, dried under nitrogen gas, and re-suspended in

10% BF3 in methanol. The re-suspended samples were then incubated at 60 ˚C for 10 minutes, cooled down to room temperature, and treated with 1 mL H2O and 1 mL hexane

(sequenetially). The hexane layer containing the fatty acid methyl esters was then collected, filtered, and analyzed via GC-MS. This method was able to successfully separate and detect a range of fatty acids present in Moringa seeds, including: methyl palmitate, methyl palmitoleate, methyl palmitoleate, methyl stearate, methyl oleate, methyl 11-octadecenoate, methyl linoleate, methyl linolenate, methyl eisosanoate, methyl

13-eicosenoate, and methyl behenoate (Figure 5.1). Methyl palmitoleate, methyl palmitoleate, methyl stearate, methyl oleate, methyl 11-octadecenoate were identified by matching retention times and mass spectra to authentic standards; while other compounds present in the samples were identified by matching mass spectra to the Wiley Registry

10th Edition / NIST 2012 Mass Spectral Library. The optimized GC-MS method was able to successfully separate two internal standards, methyl hetadecaoate and methyl tricosanoate, from fatty acid present in the samples. To quantify the fatty acids, standard curves (each showing linear regression across the range of concentrations assayed) of methyl palmitate, methyl stearate, methyl oleate, methyl heptadecanoate standard curves were prepared (Figure 5.2). The fatty acid (methyl palmitate, methyl stearate, methyl oleate) concentrations present in Moringa seeds were then calculated using the standard curves (corrected by the methyl heptadecanoate internal standard). Three replicates of each sample were used to validate the reproducibility of the methods, and the results indicated that fatty acid quantifications were reproducible between the replicates (Figure

5.3). The methyl palmitate, methyl stearate, methyl oleate concentrations present in the 187

samples analyzed were, 850.27±137.58, 567.49±108.33, 6354.2±474.33 µg/g FW, respectively.

Phospholipid identification and quantification method development

A panel of phospholipid standards (Table 5.1) was used to optimize LC-MS/MS methods to separate, identify, and quantify phospholipids. Initially, each phospholipid standard was scanned in MS2 mode to determine the retention time and the mass of the precursor ion generated during ionization. The retention times and precursor ions obtained for each phospholipid standard are listed in Table 5.2. Once retention times and precursor ions were identified, each phospholipid standard was then analyzed in product ion scan mode to determine the fragmentation pattern of each molecule and optimize the fragmentation voltage (collision energy). In these analyses, the precursor ion of each standard was fragmented using a range of collision energies, producing a series of product ions (mass transitions) unique to each target molecule (summarized in Table 5.2). One precursor and product ions for a target molecule have been identified, multiple reaction monitoring

(MRM) can be used to monitor specific mass transitions (precursor-product ion pairs) for this target molecule, increasing sensitivity and eliminating background noise during analyses. In other words, the unique combination of mass transitions and retention time generated by each target molecule can be used to isolate, identify, and quantify the phospholipids present in biological samples using MRM. In order to test the ability of the optimized LC-MS/MS to detect and quantify phospholipids in biological samples, we first used this method to quantify the levels of phospholipids present in microsomal

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membranes from guayule rubber particles were used. These phospholipid extracts were prepared by Eun Hyang Han (Blakeslee laboratory, OSU) from intact rubber particles isolated from guayule bark parenchyma cells. Rubber particle microsomal membranes were extracted as described above (Materials and Methods), and phospholipids present in the samples were analyzed via LC-MS/MS. DOPA (1,2-dioleoyl-sn-glycero-3-phosphate),

DLPC (1,2-dilinoleoyl-sn-glycero-3-phosphocholine), PC 16:1 (1,2-dipalmitoleoyl-sn- glycero-3-phosphocholine), DOPE (1,2-dioleoyl-sn-glycero-3-phosphoethanolamine), PE

16:0 (1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine), and PE16:1 (1,2- dipalmitoleoyl-sn-glycero-3-phosphoethanolamine) were observed in the microsomal membrane sample (Figure 5.4). To determine an average limit of detection for the method, the two compounds with the lowest peak areas (indicative of the lowest concentrations of phospholipids), PC 16:1 and PE16:1, were selected for quantification.

Standard curves of both compounds showed a linear correlation between peak area and compound concentration across the range of concentrations present in samples and the standard curves, with R2 = 0.9912 and 0.9941 for PC 16:1 and PE16:1, respectively

(Figure 5.5). The PC 16:1 content present in the guayule membrane samples was 154.34 ng, while the PE content was 15.91 ng.

Cholesterol identification and quantification method development

The LC-MS/MS method for the isolation and quantification of cholesterol and other sterols was optimized using burdock (Arctium lappa and Arctium minus) leaf tissues. As the cholesterol concentrations can be low in plant tissues (which generally produce a

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more complex range of sterols than animal cells), we included two additional sterols, sitosterol and stigmasterol, in our method development. The method optimization for sterols was similar to that employed for phospholipids. Authentic standards were first run in the MS2 scan mode (to identify precursor ions), followed by the product ion scan mode (to identify product ions and optimize collision energy), and a multiple reaction monitoring mode (once mass transitions and retention times had been clearly identified).

A summary of the precursor ions, retention times, etc. obtained for sterol authentic standards is provided in Table 5.3. As in phospholipid studies, a combination of retention times and mass transitions was then used to identify and quantify the sterols present in A. lappa and A. minus leave tissue. In these studies, sterols from leaf tissues were directly extracted using an organic solvent system of 2:1 chloroform:methanol (v/v); after which samples were concentrated and analyzed via LC-MS/MS. The optimized method was able to successfully detecct the three target sterols (cholesterol, sitosterol, stigmasterol) in both A. lappa and A. minus leaf tissues (Figure 5.6). Additionally, standard curves prepared for each of the three target compounds, and the results showed that the method allowed a linear correlation between peak area and compound concentration for each sterol across the range of concentrations present in leaf samples and standard curves, with

R2 values of 0.9952, 0.9929, and 0.9954 for sitosterol, stigmasterol, and cholesterol, respectively (Figure 5.7). The sitosterol, stigmasterol, and cholesterol levels detected in the A. lappa tissue was 116.72 ± 20.29 ng/g FW, 169.44 ± 14.61 ng/g FW, and 21.75 ±

7.8 pg/g FW, respectively, while the compound detected in A. minus was 127.98 ± 35.86 ng/g FW, 171.09 ± 57.81 ng/g FW, and 37.13 ± 18.57 pg/g FW, respectively.

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Mouse pancreatic stromal cell lipid analysis

Work on plant systems allowed us to develop analytical methods for phospholipid, fatty acid, and sterol isolation, separation, and quantification. We hypothesized that these methods could also be applied to profile the lipids present in mammalian cells. To test this hypothesis, we applied our lipidomics methods to perform lipidomic analyses of mouse stromal cells from control mice and mice with pancreatic cancer. In these studies, mouse stromal cells were extracted three times using a modified Bligh and Dyer method

(Bligh and Dyer 1959). The chloroform extractions of each cell sample (three extractions per sample) were collected, pooled, and divided into two fractions. Fraction 1 was used for used for phospholipid and cholesterol analysis while Fraction 2 was used for FAME preparation and fatty acid analysis.

Fraction 1 was dried and re-suspended in 200 µL chloroform methanol (1:2), and 1 µL was injected to LC-MS/MS for phospholipid analysis. DSPC, PC 18:1, DPPC, PC 16:1,

DOPE, PE 16:0, and PE 16:1 were detected in the mouse stromal cells (Figure 5.8). The concentrations of the phospholipids were quantified using standard curves of authentic standards, and corrected by the amount of internal standard (LPC 17:0) detected in each sample. Since the concentrations of DSPC and PC 18:1 were below the limit of quantification (LOQ) of the GC-MS, the levels of these two compounds were not quantified. We were able to quantify the levels of the other five phospholipids present

(DPPC, PC 16:1, DOPE, PE 16:0, and PE 16:1) (Figure 5

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.9). As this samples are part of a double-blind study being conducted in collaboration with the laboratory of Dr. Zobeida Cruz-Monserrate (OSU), samples were anonymously labeled, and no further conclusions can yet be drawn from the data. Following phospholipid quantification, samples were stored at -20 ˚C for future cholesterol analysis.

Fraction 2 was used to generate FAMEs, as described above, and subjected to GC-MS analysis. Methyl palmitate and methyl stearate were the major fatty acids detected in the mouse cells. The two compounds were quantified using standard curves of authentic standards, and the concentrations were corrected using the internal standard, methyl heptadecanoate (Figure 5.10). As with the phospholipid data, the fact that these samples were run as part of a double blind study prevents further conclusions from being drawn from the data at this point in the project.

Discussion and conclusions

PDAC is a devastating disease, with an extremely high mortality rate, which results in thousands of deaths annually in U.S. The high mortality rate exhibited by pancreatic cancer is largely due to the lack of effective preventative strategies and an inability, primarily due to a lack of biomarkers, to detect this cancer at an early, treatable, stage. To help address this problem, we have focused on identifying potential lipid biomarkers to help detect the onset of pancreatic cancer. Based on our laboratory’s expertise in lipid signaling and metabolomics (see other chapters of this thesis for details of other metabolomic protocols developed and applied in the laboratory), we were well positioned to form a research team with the laboratory of Dr. Zobedia Cruz-Monserrate (OSU; 192

Department of Gastroenterology, Hepatology, and Nutrition) to investigate the changes in lipid metabolism accompanying the onset and progression of PDAC. Our role in this collaborative research effort was the isolation, characterization, and quantification of lipid metabolites in cells and tissues isolated from control mice and mice with various stages of pancreatic cancer. In these efforts, we were able to make use of lipidomics tools developed using three model plants: Moringa, guayule, and burdock. Moringa seeds, which are rich in oils and fats, were used to optimize methods to detect and quantify fatty acids using GC-MS. In the final optimized method, fatty acids were first esterified and converted into fatty acid methyl esters to reduce polarity of the compounds, and then separated and quantified using from GC-MS. We were able to successfully use this method to extract, isolate, identify, and quantify multiple fatty acids from Moringa seeds, including fatty acids commonly found in animal tissues, such as palmitic acid and stearic acid. Methods to analyze phospholipids via LC-MS/MS were optimized using guayule microsomal membranes. First, authentic standards were used to build a mass spectral library of target compounds with retention times and mass transitions (mass of precursor ion → mass of product ion) for each phospholipid. This library was then used to identify and quantify the phospholipids present in guayule microsomal membranes. The optimized LC-MS/MS method was able to successfully detect and quantify phospholipids present in guayule membrane samples, even those present at low levels (PC 16:1 and PE

16:1). To develop methods for the analysis of cholesterol and other sterols, burdock leaf tissues were used. These methods were used to successfully isolate and quantify cholesterol, sitosterol, and stigmasterol from budcok leaves. Finally, we were able to apply the lipidomics tools we had developed in the course of out plant biology research to 193

the analysis of mammalian mouse cells. Using our lipidomic toolkit, we were able to isolate and quantify fatty acids and phospholipids from mouse stromal cells isolated from control mice and mice with pancreatic cancer (sterols will be quantified at a later date). In fatty acid analyses, we were able to detect DPPC, PC 16:1, DOPE, PE 16:0, PE 16:1, palmitic acid, and stearic acid in mouse cells, and the levels of these fatty acids varied between the cell lines tested. However, as these analyses have been conducted as part of a double-blind study, no further conclusions can be drawn at this time.

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Figure 5.1 Fatty acid methyl esters identified in Moringa seeds using GC-MS The fatty acids isolated from Moringa seeds were esterified into fatty acid methyl esters (FAME), and the detected using GC-MS. The FAMEs detected in the chromatograph represented the fatty acids contained in Moringa seeds

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Figure 5.2 Stand curves of methyl palmiate, methyl stearate, methyl oleate, and methyl heptadecanoate Methyl palmitate standard curve was made using 100, 200, 400, 800, 1600, 3200 ng/µL palmitic acid. Methyl stearate standard curve was made using 100, 200, 400, 800, 1600, 3200 ng/µL stearic acid. Methyl oleate standard curve was made using 200, 400, 800, 1600, 3200, 6400 ng/µL oleic acid. Methyl heptadecanoate standard curve was made 100, 200, 300, 400, 500ng/µL heptadecanoic acid.

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Figure 5.3 Palmitic acid, stearic acid, and oleic acid concentrations in Moringa seeds The fatty acid (methyl palmitate, methyl stearate, methyl oleate) cocentrations were calculated using the standard curves of each compound, and the final concentrations were corrected by internal standard (methyl heptadecanoate) concentrations detected in the samples.

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Table 5.1 List of phospholipid authentic standards

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Table 5.2 Retention times and mass transitions of phospholipids

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Figure 5.4 Phospholipids detected in guayuele (Parthenium argentatum) microsomal membranes DOPA: 1,2-dioleoyl-sn-glycero-3-phosphate, DLPC: 1,2-dilinoleoyl-sn-glycero-3- phosphocholine, PC 16:1: 1,2-dipalmitoleoyl-sn-glycero-3-phosphocholine, DOPE: 1,2- dioleoyl-sn-glycero-3-phosphoethanolamine, PE 16:0: 1,2-dipalmitoyl-sn-glycero-3- phosphoethanolamine, PE 16:1: 1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine

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Figure 5.5 Standard Curves of PC 16:1 and PE 16:1 PC 16:1 (PC 16:1: 1,2-dipalmitoleoyl-sn-glycero-3-phosphocholine) standard curve was made using 50, 100, 200, 300, 400, 500 pg/µL PC 16:1. PE16:1 (1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine) standard curve was made using 20, 40, 60, 80, 100, 120 pg/µL PE 16:1.

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Table 5.3 Retention times and mass transitions of sterols

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Figure 5.6 Sitosterol, stigmasterol, and cholesterol detected in A. lappa leaf tissues

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Figure 5.7 Standard curves of sitosterol, stigmasterol, and cholesterol Sitosterol standard curve was made using 200, 400, 600, 800, 1000 pg/µL sitosterol. Stigmasterol standard curve was made using 20, 40, 00, 80, 100 pg/µL stigmasterol. Cholesterol standard curve was made using 2, 4, 6, 8, 10 pg/µL cholesterol.

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Figure 5.8 Phospholipids detected in mouse pancreatic stromal cells DSPC: 1,2-distearoyl-sn-glycero-3-phosphocholine, PC 18:1: 1,2-dipetroselenoyl-sn- glycero-3-phosphocholine, DPPC: 1,2-dipalmitoyl-sn-glycero-3-phosphocholine, PC 16:1: 1,2-dipalmitoleoyl-sn-glycero-3-phosphocholine, DOPE: 1,2-dioleoyl-sn-glycero-3- phosphoethanolamine, PE 16:0: 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine, PE 16:1: 1,2-dipalmitoleoyl-sn-glycero-3-phosphoethanolamine

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Figure 5.9 Phospholipid concentrations in mouse pancreatic stromal cells MC 2054, 2133, 2134, 2146, 2147, 2139, 2157 represented mouse pancreatic cells collected from mice under different physical activity treatment. The phospholipid concentrations were calculated using the standard curves of each compound, and the final concentrations were corrected by internal standard (LPC 17: 0) concentrations detected in the samples. DPPC: 1,2-dipalmitoyl-sn-glycero-3-phosphocholine, PC 16:1: 1,2-dipalmitoleoyl-sn- glycero-3-phosphocholine, DOPE: 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine, PE 16:0: 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine, PE 16:1: 1,2-dipalmitoleoyl- sn-glycero-3-phosphoethanolamine, LPC 17:0: 1-heptadecanoyl-2-hydroxy-sn-glycero-3- phosphocholine

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Figure 5.10 Palmitic acid and stearic acid concentrations in mouse pancreatic stromal cells The fatty acid (methyl palmitate, methyl stearate, methyl oleate) concentrations were calculated using the standard curves of each compound, and the final concentrations were corrected by internal standard (methyl heptadecanoate) concentrations detected in the samples.

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Chapter 6 Summary, conclusion and future work

The term plant secondary/specialized metabolites (PSMs) has been used to describe a widely varied cluster of compounds which are produced by plants at high concentrations, but which are not directly related to primary carbon or nitrogen metabolism, DNA synthesis, or protein synthesis. There is increasing evidence, however, that PSMs are critical for plant growth and development, and that these compounds, rather than being metabolic dead ends (storage compounds) or waste products, are largely responsible for modulating interactions between plants and their environments. Several major classes of

PSMs, including phenolic compounds, terpenoids, alkaloids, and fatty acids, serve as defense molecules providing protection against herbivores, insects, microbes, viruses and competing plants. The PSMs also function as signaling molecules to attract pollinators and predators of herbivores. In addition, PSMs are also involved in regulating plant responses to abiotic stresses (e.g. salt, drought, heavy metal). Although PSMs are crucial to plant growth and development, they have also been co-opted by humans for a variety of industrial uses. PSMs have been used in the pharmaceutical/biomedical, agricultural, and food industries for several generations. PSMs have been used in the biomedical and pharmaceutical industries to treat a range of diseases, including cancer, malaria, AIDS, cardiovascular diseases, and inflammatory diseases. In the agricultural industry, the

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inhibitory effects of PSMs against herbivores, insects, microbes, viruses and competing plants, have been utilized in agriculture to produce insecticides, herbicides, and

herbivore-repellents. Because of this, bioprospecting efforts designed to identify and characterize novel PSMs, as well as efforts designed to use known PSMs to decrease the costs or increase the efficiency of current industrial process have increased dramatically over the past decade. The chapters of this dissertation present a series of experiments designed to identify and characterize novel PSMs for used in the pharmaceutical and agricultural industries, as well as efforts to develop metabolomics tools to apply knowledge gained from the study of PSMs to the pharmaceutical/biomedical, agricultural, and food industries. This work has focused on several distinct families of PSMs: phenolic compounds, terpenoids, steroids, fatty acids, and alkaloids.

The research project described in chapter 2 focused on determining the medicinal

(antimicrobial) properties of crude plant extracts, and then isolating, identifying and characterizing the corresponding plant secondary metabolites responsible for these properties. This chapter presents a study detailing the the antimicrobial effect of burdock extracts (A. lappa and A. minus) on burn related bacterial pathogens. While burdock leaves have been used as a topical burn wound treatment by the Amish community for

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several generations, the effect of these treatments on burn wound associated pathogens had not been investigated. Leaf extracts of two burdock species (A. minus and A. lappa) were prepared using six solvents of decreasing polarity. These extracts were used in multiple antimicrobial assays (agar plate bioassay, agar well diffusion assay, and liquid broth assay), and their effect on seven common burn wound related pathogens (S. aureus,

S. epidermidis, S. pyogenes, E. faecalis, E. coli, K. pneumoniae and P. aeruginosa) was quantified. Leaf extracts of both burdock species exhibited significant anti-microbial effects against all the seven pathogens; and A. minus extracts, in general, exhibited greater antimicrobial activity than did A. lappa extracts. Out of the six solvent extracts used in antimicrobial efficacy studies, those prepared with 70% acetone in H2O (and 0.5% acetic acid), which were particularly rich in phenolic compounds, were among the most effective in killing bacteria. The total phenolic contents, total flavonoid contents, and in vitro antioxidant capacities of ACT extracts of both A. lappa and A. minus were also quantified. The results of these assays indicated that A. minus ACT extracts were significantly higher in total phenolic content, total flavonoid content, and in vitro antioxidant capacity than those of A. lappa. HPLC analyses of ACT extracts indicated that A. minus extracts contained much higher levels of compounds putatively identified as hydroxycinnamic acids than did A. lappa ACT extracts. LC-MS/MS analyses of fractions collected from HPLC-based separations of burdock ACT extracts allowed the tentative identification of several phenolic compounds in these extracts, including: a. hydroxycinnamic acids: caffeoyl quinic acids, dicaffeoyl quinic acids, dicaffeoyl succnyl quinic acid, and caffeoyl quinic acid glucoside; and b. flavonoids: apigenin hexoside, luteolin hexoside, and rutin. Additionally, less polar burdock leaf extracts (methanol, 214

chloroform methanol, ethyl acetate, and dichloromethane extracts) were analyzed using

GC-MS. These analyses allowed the tentative identification of several compounds, some of which were identified based on comparison of their fragmentation pattern with the

NIST spectral library. Compounds identified via GC-MS analyses included: methyl salicylate, hexahydrofarnesyl acetone, 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4- one, methyl palmitate, methyl (9Z,15Z)-9,15-octadecadienoate, methyl α-linolenate, palmitic acid and 2-palmitoylglycerol. Many of the compounds found in our study have been previously reported to have anti-microbial effects, including several of the hydroxycinnamic acids, flavonoids, and fatty acids found in burdock leaves. Future work should include further mass spectrometric and NMR analyses to confirm the identities of compounds present in burdock extracts, as well as work to determine the antimicrobial capacities of the individual compounds already identified in the work presented in

Chapter 2.

The research described in chapter 3 was focused in the area of agriculture applications of

PSMs and PSM derivatives. This chapter detailed the development of a portable, sensitive, weather resistant detector 2,4-D (auxinic herbicide) detection system to monitor herbicide drift events. 2,4-D, a synthetic herbicide that kills weed by mimicking the functions of auxins, is widely used for weed control; and usage of this herbicide is expected to increase with the release of genetically modified 2,4-D resistant crops.

However, improper application of 2,4-D may result in increased herbicide drift, leading to damage of off-target sensitive crops. In the past, the lack of a reliable, sensitive 2,4-D detection system has made it difficult for growers to determine whether or not observed

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drift damage was the result of 2,4-D deposition. Work presented in chapter 3 describes the design and optimization of a portable, field-deployable, and weather resistant 2,4-D detection system exhibiting high sensitivity to the target herbicide. The detector was assembled by simply attach a detection matrix to an embroidery loop. The resulting detector discs can by deployed with crops in the field and then collected when symptoms of drift damage are observed. Optimization of the detector construction and matrix was coupled with the development and optimization of an LC-MS/MS method to quantify the

2,4-D molecules captured on the detectors. The results of field trials conducted over a two-year period indicated that the detection system was capable of detecting 2,4-D drift when the detectors were set at 300 feet away from the spray swath, at an application rate of 2 pints per acre of 2,4-D amine salt (a commonly used concentration of 2,4-D). When tested in controlled, spray room settings, this detection system was sensitive enough to allow quantification of 2,4-D residues when the detectors were exposed to 0.006 pints per acre of a 2,4-D dimethylamine spray (a more than 300-fold dilution of the concentration commonly applied to fields). We assayed five different types of detection matrices

(Miracloth, nylon, polypropylene, polyvinylidene difluoride (PVDF), and polyester) for their ability to retain 2,4-D residues and resist exposure to weather. Nylon was ultimately selected for use in future studies, based primarily on the ability of this matrix to partially resist rainwater washing, fully resist UV light, and fully resist the high-light and high- heat environment of the greenhouse. Future work will focus on determining the ability of the detector discs to quantify alternative formulations of 2,4-D, as well as other herbicides.

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Chapter four also describes research focused on the agricultural application of PSMs.

This chapter details the development of a fast, sensitive, and cost effective LC-MS/MS quantification method for DIMBOA, a potential biopesticide compound produced in maize. This LC-MS/MS quantification method was used to screen a population of maize parental lines, allowing the selection of lines with high DIMBOA concentrations.

DIMBOA is a plant defense chemical that has been demonstrated to play a role in plant resistance to herbivores, competing plants, and soil pests. The secretion of DIMBOA can benefit not only the host maize plant, but, as DIMBOA accumulates in the rhizosphere, may also benefit other crops rotated into the field in which the maize was initially grown.

The beneficial effects of DIMBOA (i.e., the pest-resistance effects conferred by this compound) are dependent upon the concentrations of this compound present in plant tissues or soil. Maize, which produces high levels of DIMBOA, is a crop of economic importance, but which also serves a rotation crop for soybean, where it provides some nematode control. It has been hypothesized that the level of DIMBOA present in maize roots contribute to both the resistance of maize to soil-born pests and the well-being of soybean crops rotated with maize. It is therefore beneficial to identify and select maize lines with high levels of DIMBOA in roots for inclusion in biocontrol and breeding programs. To help enable this research, we developed a time- and cost- effective method to extract, isolate, and quantify both total and free DIMBOA in maize roots. This method was successfully used to quantify the DIMBOA levels present in the roots of 25 maize parental lines. In this screen, line and Hp301 was line found to contain the highest amount of DIMBOA. Future work in this area would include the quantification of

DIMBOA not only in maize roots, but also in maize root exudates. 217

Chapter five describes work done to apply lipidomics methods developed using plant systems to develop tools (biomarkers) for the pharmaceutical/biomedical industry.

PDAC (pancreatic ductal adenocarcinoma) causes several thousands of deaths annually in the U.S., primarily because this cancer is usually detected only at a very late stage, when treatment options are extremely limited. In collaboration with the laboratory of Dr.

Zobeida Cruz-Monserrate (OSU), we worked to develop a series of lipid-based biomarkers for the onset and progression of pancreatic cancer. This work leveraged lipidomics tools that we had developed to extract, isolate, and quantify lipids from plant species. Methods to quantify phospholipids, fatty acids, and sterols, were developed in our laboratory using three model plant organisms: guayule, moringa, burdock, respectively. We were able to successfully use the lipidomics tools developed in plant systems to quantify fatty acids and phospholipids in cells of mice developing pancreatic cancer. Future work in this area would include the quantification of sterols in these cells, as well as lipid profiling of visceral fat samples from these mice.

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