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THE UNIVERSITY OF NEW SOUTH WALES

SCHOOL OF CHEMICAL ENGINEERING

FOOD SCIENCE AND TECHNOLOGY

Potential for selection of new peanut genotypes with enhanced polyphenol antioxidant content

Yan Yee Poon

A thesis in fulfilment of the requirements for the degree of Doctor of Philosophy

Sydney, Australia

February 2020

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‘I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.'

Date ……………………3/02/2020………………………………

AUTHENTICITY STATEMENT

‘I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format.’

Date ……………………3/02/2020.………………………………

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Dedication Dearest Grandma and Grandpa, Please forgive me for the meagre hours I spent with you in your last days, I hope you'll be proud of the work I've done in the past few years. Thank you for your endless encouragement and for always believing in me. We'll meet again soon.

Martha, Though you've left us before the journey was over, I'll treasure the time we shared during placement and always hold onto that "trying spirit" you saw in me - we'll stay forever peanut friends.

Wallace, Chris, and Mrs Tong, Thank you for the mentorship; your encouragement and teachings will continue to motivate me throughout my passage in research and in life.

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Acknowledgements I'd like to express my gratitude to the Centre for Advanced Technology Food Manufacture and the Australian Research Council for the financial support with the ARC Industrial Transformation Training Centre PhD scholarship, the Peanut Company of Australia (PCA) for peanut samples used in this thesis and UNSW PRSS travel award for the 2017 APRES conference. I am grateful to my supervisor Associate Professor N. Alice Lee for the opportunity to undertake this project and privilege of receiving the scholarship. Thank you, for the generous time exhausted in teaching and correcting me. My co-supervisor Dr Graeme Wright, "The Peanut King", for his kind guidance and nurture during my time on rural visits and remote teaching me. Both their patience and understanding has been tremendous; thank you for not giving up on me. Dr Sridevi Muralidharan, thank you for your gentle friendship and supervision (Chapter 4); the past years you have been like a sister. You were able to sense my unease before anyone else – your kindness and support, the thoughtful calming calls before and after every challenge relieved me. Dr George Lee, thank you for the group cheer up sessions and your supervision (Chapter 6); the crash courses in inorganic chemistry and nanoparticles were much helpful to a biotechnologist. Thank you to Ms Ying Jenny He for her technical assistance on Sections 6.3.1.1 and 6.3.2. Dan O'Connor, my peanut dealer, for fostering me during placement and also in peanut technical support. All PCA staff, for your kind assistance during placement. Thank you very much to Camillo, John and, Ling for the care and support; you are always looking out for our safety, interests, and welfare. Dr Victor Wong for the HPLC and troubleshooting. Dr Dimitrios Zabaras and Ms Mashid Roohanidezfouli (North Ryde, CSIRO) with LC-MS/MS, Dr Qiang Zhu (EMU, MWAC, UNSW) for Technai TEM and Dr Martin Bucknall (BMSF, MWAC, UNSW) for GCMS. To Dr Maria Chandra-Hioe and Dr Chatchaporn Uraipong for their technical assistance and support. 714 comrades; especially Steffi, Ji, Jun, Yiqing, Scott, Dat, Kornelia, and Johanna: Though we battled our own demons individually, we fought together, shared pain and encouragement; I would not have endured these years alone. Thank you to colleagues at AB Mauri and friends who gave me the mental strength, consolation, and distractions to keep me sane and grounded. Honeypot for the comfort and play-times, you've given much calm, confidence, and relief in times of panic and anxiety. Finally, but most importantly, I want to thank my parents and my sister for their unwavering understanding, assurance, and their faith in my choice of path, though it inconvenienced them greatly, never protested. The unconditional love, optimism, and reassurance shown through the pressures of life, sickness, and death - I am thankful for.

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Abstract The goal of this project was to determine the potential for the breeding of new peanut genotypes with enhanced polyphenol content and antioxidant capacity. The specific objectives of this project were: 1) to estimate the genotype (G), environment (E) and G x E influence on the expression of polyphenols in a Recombinant Inbred Line (RIL) peanut population; 2) to study the molecular basis of polyphenol biosynthesis in peanuts using the shotgun proteomics approach; 3) to screen for new phenolic compounds in a water-soluble peanut extract using LC-MS/MS and propose phenolic biosynthesis pathways, and 4) to investigate the feasibility of developing a simple nanoparticle-based test for the rapid and low-cost phenotyping of antioxidant capacity for rapid selection of antioxidant capacity in breeding and segregating populations.

The genotype (G), environment (E), and G x E influence on antioxidant expression investigated in the RIL population showed significant genotypic and environmental effects, but non-significant G x E effects suggesting strong genetic control and moderate heritability. Quantifications of known polyphenols in selected RILs showed increased ferulic acid, p-coumaric acid, salicylic acid, resveratrol, and daidzein with the enzymatic extraction, indicating the importance of matrix-bound polyphenolic compounds and corresponded to respective ORAC assay values.

The shotgun proteomics revealed differential protein abundance in the high antioxidant expressing RIL in carbohydrate and protein metabolism, stilbene and flavonoid biosynthesis, anabolic and catabolic pathways. These metabolic changes contributed towards phenylalanine biosynthesis lead to the central phenylpropanoid pathway and subsequent synthesis of identified polyphenols. Enzymes showing significant differential abundance were mapped to describe metabolic changes and biosynthesis pathways related to high antioxidant expression which have potential use as biomarkers for related compounds. In particular, stilbene synthase-like 3 was expressed 4.8 fold greater in the high antioxidant capacity RIL and hence recognised as a valuable target biomarker for stilbenoid compounds.

Based on the enzymes identified in polyphenol synthesis in peanuts by the shotgun proteomics, specific polyphenolic compounds were targeted in LC-MS/MS analysis, resulting in the detection of 21 novel compounds not previously identified in Arachis hypogaea. The metabolic and

Yan Yee Poon z3160325 biosynthesis pathways using a total of 118 polyphenolic compounds detected in this project were assembled and proposed for peanuts.

Shape and size transformation of silver nanodisks (AgND) to nanoprisms (AgNPr) and their reductive capacity was exploited to develop a quick colourimetric antioxidant capacity assay that can facilitate rapid selection of superior breeding lines. A linear dose-response between AgND567 transformation and polyphenol was observed for sinapic acid, t-cinnamic acid, caffeic acid, gallic acid, protocatechuic acid, vanillic acid, and syringic acid, also rutin, polydatin and resveratrol at extremely low concentrations (detection range: 1 x 10-3 M – 1 x 10-6 M). A simple soaking method by extraction of skinless kernel in water was examined showed lower correlation of AgND567 2 response to ORAC values (R =0.015, Pearson r = 0.12 and two-tailed P = 0.63), methanolic extracts from defatted peanut kernels, instead showed a significantly improved correlation ( n= 98 against FRAP assay, Pearson R = 0.52, R2 = 0.27, two-tailed P< 0.0001). With further work on the AgND assay-compatible sample preparation, this assay platform shows potential in the development of a simple and rapid nanoparticle-based antioxidant capacity assay for kernel screening in peanut breeding programs.

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List of Abbreviations ABTS 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) AEA Average environment axis AEC Average environment coordination AFLP Amplified fragment length polymorphism Ag+ Silver ion AgNP Silver nanoparticle ANOVA Analysis of variance AU Absorbance unit AuNP Gold nanoparticle BSA Bovine serum albumin BSPP Bis(ρ-sulfonatophenyl) phenylphosphinedihydrate dipotassium salt solution BSTFA N,O-Bis(trimethylsilyl)trifluoroacetamide CCV Continuing calibration verification CHD Coronary heart disease DAFF Department of agriculture, fisheries, and forestry DFR Dihydroflavonol 4-Reductase DNA Deoxyribonucleic acid DPPH 2,2-diphenyl-1-picrylhydrazyl DTT Dithiothreitol EGU Endo-glucanase unit ELISA Enzyme-linked immunosorbent assay ESI Electrospray ionisation ET Electron transfer FAO Food and agriculture organisation of the United Nations FCR Colin-Ciocalteu reagent total phenol assay FRAP Ferric-reducing antioxidant power FT NIR Fourier-transfer near-infrared 10

Yan Yee Poon z3160325 g Gram x g Gravity GCMS Gas chromatography mass spectrometry GGE Genotype by genotype by environment G x E Genotype by environment GPM Global proteome machine HPLC High-pressure liquid chromatography HT Hydrogen transfer kDa Kilodalton kHz Kilohertz KEGG Kyoto encyclopaedia of genes and genomes Km/h Kilometres per hour LC-MS/MS Liquid chromatography mass spectrometry mass spectrometry LDL Low-density lipoprotein LED Light-emitting diode LKR Lysine-ketoglutarate reductase LLS Late leaf spot LSPR Localised surface plasmon resonance M Mole MALDI Matrix-assisted laser desorption MAS Marker-assisted selection MCAT Mass coded abundance tagging ME Mega-environment Mg2+ Magnesium ion mg Milligram mL Millilitre mm Millimetre

Yan Yee Poon z3160325 mM Millimole Mn2+ Manganese ion MQW MilliQ water mRNA Messenger RNA m/z Mass divided by charge number and the horizontal axis in a mass spectrum NAC N-acetylcysteine NASF Normalised spectral abundance factor NDGA Nordihydroguaiaretic acid ng Nanogram NIR Near-infrared NIST National institute of standards and technology nM Nanomole NC North Carolina NCBI National centre for biotechnology information NO Nitric oxide NQ North Queensland OCU o-coumaric co-eluting unknown ORAC Oxygen radical absorbance capacity assay PAGE Polyacrylamide gel electrophoresis PCA Peanut Company of Australia PDA Photodiode array pH Potential of hydrogen ppm Parts per million PR Pathogenesis related QCU Quercetin co-eluting unknown QIT Quadrupole ion trap QR Quantitative resistance

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QTL Quantitative trait locus RIL Recombinant inbred line RNA Ribonucleic acid ROS Reactive oxygen species R-PE R-phycoerythrin RT Retention time SAH S-Adenosyl homocysteine SAM S-Adenosyl methionine SDH Saccharopine dehydrogenase SDS Sodium dodecyl sulphate SPE Solid-phase extraction TE Trolox equivalence TEAC Trolox equivalent antioxidant capacity assay TFA Trifluoroacetic acid TMCS Trimethylsilyl chloride TMS Trimethylsilyl ethers TOF Time of flight TRAP Total radical antioxidant parameter assay µg Microgram µL Microlitre µM Micromole UPLC Ultra-pressure liquid chromatography USDA US department of agriculture UV Ultraviolet UV-VIS Ultraviolet-visible V Volts v/v/v Volume/volume/volume

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List of Tables Table 2.1 Total phenolic acids of various cereals, oilseeds and legumes in mg per 100g of flour, adapted from (Dabrowski and Sosulski 1984, Wanasundara et al, 1997) ...... 40

Table 2.2 Table of key phenolic acids, preventative effects and health benefits ...... 42

Table 2.3 Key stilbenes, their preventative effects and health benefits ...... 46

Table 2.4 List of key flavonoids, preventative effects and health benefits ...... 50

Table 2.5 Abbreviated list of antioxidative compounds found in peanut tissue, major mass transitions in negative and positive modes and references. The detailed table listed in the appendix (Supplementary Table 10.6) ...... 63

Table 2.6 Antioxidant assays, principles, advantages and disadvantages ...... 69

Table 4.2 Proteins over-expressed in high antioxidant capacity peanuts RIL P27-p362, based on their Log NSAF ratios ...... 115

Table 4.3 Proteins under-expressed in high polyphenol antioxidant capacity peanuts RILp362 121

Table 5.1 Compounds found in native extraction of D147-p3-115. Match score indicates the similarity of sample to library standard. Novel compounds are bolded...... 143

Table 5.2 Table of 18 compounds identified using standards in negative mode ...... 147

Table 5.3 List of novel polyphenols (new compounds) tentatively identified in the D147-p3-115 extract ...... 150

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Table 6.1 Absorbance at the position of the in and out-of-plane dipole and quadrupole on absorbance spectra ...... 171

Table 6.2 Shift of in and out-of-plane dipole and quadrupole on absorbance spectra after 1 M caffeic acid exposure...... 173

Table 6.3 Colour of AgNP567 transformations after the addition of caffeic acid, rutin hydrate and quercetin at a concentration range of 10-7 M to 10-2 M ...... 175

Table 6.4 Phenolic antioxidant, regression slope, R2 and their compound characteristics ...... 179

Table 6.5 Flavonoids, regression slope, R2 and their compound characteristics ...... 180

Table 6.6 Stilbenes, regression slope, R2 and their compound characteristics ...... 180

Table 10.1 Climate averages of Kingaroy/Taabinga from 1980- 2010. (Bureau of Meterology 2017) ...... 222

Table 10.2 Climate averages of Bundaberg from 1980- 2010. (Bureau of Meterology 2017)... 224

Table 10.3 Climate averages of Kairi from 1980- 2010. (Bureau of Meterology 2017) ...... 226

Table 10.4 Table of compounds found in native extraction of D147-p3-115 by GCMS. Match score indicates the similarity of sample to library standard. Reverse match score indicates the similarity of library standard to sample...... 228

Table 10.5 List of compounds found in sample using proteomics data from Chapter 3. The list of compounds were suspected to be present from previous proteomics work and obtained through transitions found in literature but have not yet been found in peanuts by LC-MS/MS...... 237

Table 10.6 Complete list of antioxidative compounds with peanut tissue source, major mass transitions in negative and positive modes and references of groups responsible for study...... 239

Table 10.7 Identification of phenolics in D147-p3-115 peanut kernels by LC-MS/MS data ..... 245

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Table 10.8 Identification of anthocyanins in D147-p3-115 peanut kernels by LC-MS/MS data...... 248

Table 10.9 Identification of stilbenes in D147-p3-115 peanut kernels by LC-MS/MS data...... 249

Table 10.10 Identification of flavonoids in D147-p3-115 peanut kernels by LC-MS/MS data. 250

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List of Figures Figure 2.1 ORAC antioxidant capacities of enzymatic and native extracts. Adapted from (Phan- Thien 2012)...... 38

Figure 2.2 Cascade of antioxidant biosynthesis from phenylalanine. Adapted from (Chadha and Brown 1974, Graf 1992, Lee et al, 1995, Wang et al, 2007)...... 41

Figure 2.3 Structures of t-Resveratrol, (t-3,5,4’-trihydroxystilbene) and c-resveratrol (c-3,5,4’- trioxyhydrostilbene) adapted from (Sales and Resurreccion 2014)...... 44

Figure 2.4 Biosynthesis of resveratrol and its derivatives from p-Coumaroyl CoA (KEGG)...... 45

Figure 2.5 Basic structure of flavonoids and their flavonoid derivatives...... 47

Figure 2.6 Biosynthesis of flavonoids from p-coumaroyl CoA (KEGG)...... 48

Figure 2.7 The many functions of phenolic compounds in plants. Adapted from (Rice-Evans et al, 1997, Treutter 2010)...... 52

Figure 2.8 Hydrophilic ORAC values of the full season breeding lines from which parent cultivars were chosen from. Adapted from (Phan-Thien 2012)...... 58

Figure 2.9 Mean ORAC antioxidant capacities of eluted peaks collected during HPLC of the enzymatic extract of D147-P8-6F. Adapted from (Phan-Thien 2012)...... 72

Figure 2.10 UV-vis spectrum of anisotropic and isotropic nanoparticles (Zengin et al, 2014). .. 75

Figure 2.11 Absorbance spectra of various nanoparticle solutions, (a) Ag nanospheres, (b–d) Au nanorods, (e-g) Ag–Au mixtures with dual absorbance, along with images of each solution (Eroglu et al, 2013)...... 76

Figure 2.12 UV-Vis spectra of gold nanoparticles exposed to solutions: (a) quercetin, (b) daizeol, (c) puerarin at increasing concentrations of (a-g): 5, 10, 25, 50, 75, 100, 250 μM from (Wang et al, 2007)...... 77

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Figure 3.1 G x E trial locations, 1) Kairi Research Station, North Queensland, 2) Bundaberg Research Station, Bundaberg, Southern Queensland and 3) Taabinga Research Station, Kingaroy, Southern Queensland...... 83

Figure 3.2 Annual averages of various weather conditions between 1980-2010 in three test locations, P= 0.1010, climate differences between the sites not significant (full tables of each location statistics in Appendix)...... 84

Figure 3.3 Microplate plan of ORAC assay...... 87

Figure 3.4 Normal distribution histogram and plot of TE (Trolox equivalence) ORAC values of the recombinant inbred line (RIL) collection from 2013...... 89

Figure 3.5 ORAC TE values averaged across all environments. Error bars with 95% CI with the range in dotted lines...... 90

Figure 3.6 Average, min and max TE values of Taabinga Research Station (2013/14), Bundaberg Research Station (2014/15), Taabinga Research Station (2014/15) and Kairi Research Station (2014/15). The standard deviation used as error bars...... 91

Figure 3.7 GGE biplot for ORAC values of RIL and parents relative to the 4 environmental vectors (Taabinga Research Station (2013/14), Bundaberg Research Station (2014/15) and Taabinga Research Station (2014/15) and Kairi Research Station (2014/15)...... 92

Figure 3.8 GGE biplot of performance vs stability for environments, Taabinga Research Station (2013/14), Bundaberg Research Station (2014/15) and Taabinga Research Station (2014/15) and Kairi Research Station (2014/15)...... 93

Figure 3.9 Quantification of known polyphenol antioxidants by HPLC and ORAC antioxidant capacity in RIL parents, D147-p3-115 and Farnsfield and low and high polyphenol antioxidant expressing RILs P27-p272, P27- p036 and P27-p362 of Taabinga, 2015...... 96

Yan Yee Poon z3160325 extraction. Standards in the bottom row; 1) caffeic acid, 2) p-coumaric acid, 3) ferulic acid, 4) o- coumaric acid, 5) salicylic acid, 6) resveratrol, 7) daidzein, 8) t-cinnamic acid and 9) quercetin...... 98

Figure 4.1 Distribution of proteins in peanut RILs and parents indicative of genetic variation. Shades represent the presence or absence of proteins in each combination across all RILs. The number of protein identifications across different subsets in each RIL represents a combined total of reproducible protein identifications corresponding to the relevant RIL...... 109

Figure 4.2 Log NSAF plot of differential and unchanged proteins between peanut lines P27-p362 relative to P27-p272...... 110

Figure 4.3 Qualitative and quantitative distribution of 79 differentially expressed proteins, in terms of observed NSAF values. Proteins were grouped with their known biological process information obtained from UniProt, InterPro and KEGG databases. They have been classified into 15 different functional categories. They were resorted (A) as a percentage of protein number, (B) as a percentage of protein abundance based on their sum NSAF values in each biological process category. NSAF of each protein represents the average NSAF of the biological triplicate...... 113

Figure 4.4 Overview of the naringenin chalcone and resveratrol synthesis, as evidenced from upregulation of their biosynthetic pathway whose enzymes expressions altered in RIL P27-p362...... 125

Figure 4.5 Simplified pathway of metabolic and biosynthesis pathways with selected significant enzyme expression changes. Enzymes (fold change) and reactions related to high polyphenol antioxidant expression (over-expressed) are highlighted in green, enzyme and reaction related to low polyphenol antioxidant expression (under-expressed) are highlighted in blue. Compounds in coloured boxes have polyphenol antioxidant properties...... 134

Figure 4.6 Fold changes (over- or under-expression) for top 25 proteins differentially expressed between peanut RIL P27-p362 (high antioxidant capacities) and RIL P27-p272 (low antioxidant capacities). Protein ontology information was obtained from UniProt, InterPro and KEGG reference maps. Fold changes were calculated as the ratio of logNSAF values in peanut P27-p362

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(high antioxidant capacity) relative to RIL P27-p272 (low antioxidant capacity). Proteins have been grouped based on their functional pathways...... 136

Figure 5.1 HPLC fractionation of the D147-p3-115 native extract for the GCMS analysis (Fractions 1-44 collected from 0-29 min). The standards peaks shown in the chromatogram are as follows 1) caffeic acid, 2) p-coumaric acid, 3) ferulic acid, 4) o-coumaric acid, 5) salicylic acid, 6) resveratrol, 7) daidzein, 8) t-cinnamic acid and 9) quercetin...... 142

Figure 5.2 Chemical structures of phenolic and benzoic acids identified by GCMS...... 143

Figure 5.3 Full scan chromatogram of D147-p3-115 extract at 320nm. With standards and major peaks tentatively annotated...... 147

Figure 5.4 Biosynthesis pathway of all the compounds detected by LC/MS-MS. *Apigenin not detected. Compounds found with product ions from standards in solid boxes, compounds tentatively found in dash boxes and the compounds with enzyme responsible detected from the proteomics work in Chapter 4 is bolded and unlerlined. Phenolics are coloured in blue, stilbenes in red, anthocyanidins in purple, flavonoids in green and chalcones have been left white...... 159

Figure 6.1 UV-vis spectra obtained for solutions placed in the dark, blue-shifted over 24 h. (Lee et al, 2009)...... 161

Figure 6.2 Time-dependent UV-Vis spectra displaying changes in surface plasma bands during the transformation of silver nanospheres into nanoprisms (a) initial (isotropic spheres) (b) 40 hr, (c) 55 hr, and (d) 70 hr of growth into anisotropic prismatic structures (Jin et al, 2001)...... 162

Figure 6.3 The UV-Vis spectra displaying photoconversion of AgNP disks into prisms measured at 0, 2, 4, 8, and 16 min of irradiation. Inset: Average nanoparticle height versus UV-Vis λmax during photodevelopment at each time interval. (Lee et al, 2010)...... 163

Figure 6.4 Growth of Ag nanospheres into nanoprisms during photoconversion (Jin et al, 2001) and the process of AgNP formation, the coalescence of seed AgNP into prisms and then dark transformed oxidation of prism corners to form disks after irradiation (Lee et al, 2013)...... 164

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Figure 6.5 A) Absorbance spectra of photosynthesised AgNPs and B) sample TEM image of control AgND disks synthesised by a LED emitting 567 nm...... 171

Figure 6.6 Absorption spectra of A) AgNP530, B) AgNP567, C) AgNP617 and D) AgNP655 at increasing concentrations of caffeic acid 10 mM to 100 nM...... 172

Figure 6.7 The AgNP responses (ΔIn AU) from 300 - 960 nm of AgNP530 (Y = 922206359X - 2 2 15.6, R = 0.9824), AgNP567 (Y = 627610975X + 1.72, R = 0.9903), AgNP617 (Y = 917572270X + 2 2 0.4305, R = 0.9822) and AgNP655 (Y = 161258660X + 5.566, R = 0.8651) to increasing caffeic acid concentrations (M)...... 174

Figure 6.8 Absorbance spectra of AgNP567 treated with A) rutin hydrate and B) quercetin at the concentration range of 100 nM to 10 mM...... 175

Figure 6.9 Absorption spectra of AgNP after reaction with leachates of A) whole kernels, B) Skinless kernels and C) kernel skin at different extraction time...... 182

Figure 6.10 % ΔIn AU of AgNP response compared to AgNP (blank MilliQ water) control in all 3 sample types at extraction times of 15, 30, 60, 120 and 180 min. Samples read after immediate reaction. Skin Only R2= 0.5293, Skinless R2= 0.9542, Whole kernels R2= 0.9476...... 183

Figure 6.11 Kernel ORAC Trolox equivalent (TE) plot against leachate response integrated absorbance (In AU) to skinless kernel particles using North Queensland (Kairi) RILs...... 184

Figure 6.12 Correlation between kernel ORAC trolox equivalent (TE) plot against AgNP response integrated absorbance (In AU) (n = 10). Y= 0.2858x +399.9, R2= 0.4732. α = 0.05...... 186

Figure 6.13 Correlation between kernel FRAP trolox equivalent (TE) plot against AgNP response integrated absorbance (In AU) (n = 98), Y= 0.01095x + 92.26, R2 = 0.2732. α= 0.05...... 187

Figure 10.1 GCMS library match of t-cinnamic acid and sugar moiety detected in sample...... 232

Figure 10.2 GCMS library match of 4-Hydroxyphenylethanol (tyrosol) and m-ansinic acid detected in sample...... 233

Figure 10.3 GCMS library matches of cinnamic acids detected...... 234

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Figure 10.4 GCMS library match of benzoic acids detected in sample...... 235

Figure 10.5 GCMS library match of benzeneacetic acid and 5,7,3’,4’-tetra-o-methylquercetin detected in sample...... 236

Figure 10.6 LC/MS-MS spectra of kaempferide ...... 251

Figure 10.7 LC/MS-MS spectra of tricetin ...... 251

Figure 10.8 LC/MS-MS spectra of neohesperidin ...... 251

Figure 10.9 LC/MS-MS spectra of shikimic acid ...... 251

Figure 10.10 LC/MS-MS spectra of transchalcone ...... 251

Figure 10.11 LC/MS-MS spectra of chrysin...... 251

Figure 10.12 LC/MS-MS spectra of naringenin chalcone ...... 251

Figure 10.13 LC/MS-MS spectra of Isoliquiritigenin...... 251

Figure 10.14 LC/MS-MS spectra of coumestrol...... 251

Figure 10.15 LC/MS-MS spectra of galangin...... 251

Figure 10.16 LC/MS-MS spectra of pelargonidin ...... 251

Figure 10.17 LC/MS-MS spectra of acacetin ...... 251

Figure 10.18 LC/MS-MS spectra of aromadendrin ...... 251

Figure 10.19 LC/MS-MS spectra of taxifolin ...... 251

Figure 10.20 LC/MS-MS spectra of ampelopsin ...... 251

Figure 10.21 LC/MS-MS spectra of pisatin ...... 251

Figure 10.22 LC/MS-MS spectra of syringetin ...... 251

Figure 10.23 LC/MS-MS spectra of curcumin ...... 251

Figure 10.24 LC/MS-MS spectra of quercitrin ...... 251

Figure 10.25 LC/MS-MS spectra of vitexin ...... 251

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Figure 10.26 LC/MS-MS spectra of malonylapiin A/B...... 251

Figure 10.27 LC/MS-MS spectra of malonylapiin A ...... 251

Table of Contents Dedication ...... 6

Acknowledgements ...... 7

Abstract ...... 8

List of Abbreviations ...... 10

List of Tables ...... 14

List of Figures ...... 17

Table of Contents ...... 24

1. INTRODUCTION ...... 32

1.1. Background ...... 32

1.2. Scope and research contribution ...... 33

1.3. Thesis chapter overview ...... 34

2. LITERATURE REVIEW ...... 35

2.1. Oxidative stress and health consequences...... 35

2.2. Health benefits of dietary antioxidants and their roles in reducing oxidative stress ...... 36

2.3. Peanuts as a valuable agricultural crop ...... 37

2.4. Peanuts polyphenols and biosynthesis ...... 37

2.4.1. Phenolic acids ...... 39

2.4.2. Stilbenes ...... 44

2.4.3. Flavonoids ...... 47

2.5. The relationship between plant defence and polyphenolics ...... 52

2.6. Promoting the increase of polyphenols in peanuts ...... 53

2.7. Peanut breeding – current practices ...... 53

2.7.1. Genotype ...... 54

2.7.2. Environment ...... 55

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2.7.3. Genotype x Environment interaction ...... 57

2.8. Proteomics in agriculture ...... 59

2.8.1. Building a peanut proteome ...... 59

2.9. Quantitative proteomics ...... 60

2.9.1. Quantitative proteomics for plant stress and resistance ...... 61

2.10. Polyphenol profiling by spectrometry ...... 62

2.11. Assays for the quantification of antioxidant capacity ...... 67

2.12. Relationship between polyphenol concentrations and antioxidant capacity ...... 72

2.13. A need for a simple and rapid antioxidant capacity assay for peanut seed screening ...... 73

2.14. An introduction to nanoparticles ...... 73

2.14.1. Effect of nanoparticle size and shape on solution colour ...... 74

2.14.2. Nanoparticles as antioxidant probe platform for a new antioxidant capacity assay ...... 76

2.14.3. Silver nanoparticles as antioxidant probe platform for antioxidant assay use ...... 78

2.15. Summary ...... 78

3. GENOTYPE, ENVIRONMENT AND GENOTYPE X ENVIRONMENT INFLUENCE ON

ANTIOXIDANT CAPACITY IN PEANUT KERNELS ...... 80

3.1. Background and aims ...... 80

3.2. Materials and methods ...... 81

3.2.1. Chemicals and reagents ...... 81

3.2.2. Sample material...... 81

3.2.3. Sample preparation: Peanut deskin and defatting ...... 81

3.2.4. Instruments ...... 82

3.2.5. Genotype x Environment (G x E) trials ...... 82

3.3. Sample preparation for HPLC ...... 84

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3.3.1. Native extraction ...... 84

3.3.2. Enzymatic extraction ...... 84

3.3.3. SPE and HPLC on sample extracts ...... 85

3.3.4. Sample preparation: polyphenol antioxidant extraction and ORAC assay ...... 86

3.3.5. Stock standard solutions ...... 87

3.3.6. Known compound quantification by HPLC ...... 87

3.3.7. Data analysis ...... 87

3.4. Results and discussion ...... 88

3.4.1. Identification of RILs with contrasting total polyphenol antioxidant content ...... 88

3.4.2. G, E and G x E influence on RILs ...... 89

3.4.2.1. ANOVA results ...... 89

3.4.2.2. GGE biplot ...... 91

3.4.3. Total antioxidant capacity and known polyphenol quantification of 5 RILs ...... 95

3.5. Conclusion ...... 100

4. QUANTITATIVE PROTEOMICS ANALYSIS OF HIGH AND LOW ANTIOXIDANT EXPRESSING

LINES IN PEANUTS ...... 102

4.1. Background and aims ...... 102

4.2. Materials and methods ...... 103

4.2.1. Chemicals and reagents ...... 103

4.2.2. Sample material...... 103

4.2.3. Sample preparation ...... 103

4.2.3.1. Peanut defatting...... 103

4.2.3.2. Protein extraction and protein concentration determination ...... 104

4.2.3.3. Protein fractionation by SDSPAGE ...... 104

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4.2.4. Instruments ...... 104

4.2.5. In gel digestion, peptide extraction and desalting ...... 105

4.2.6. NanoLC-MS/MS ...... 106

4.2.7. Peptide to spectrum matching ...... 106

4.2.8. Data analysis: Normalised Spectral Abundance Factor (NSAF) quantitation and data portioning ...... 107

4.2.9. Protein ontology classification ...... 108

4.3. Results and discussion ...... 108

4.3.1. Summary of protein identifications ...... 108

4.3.2. Quantitative analysis of differentially expressed proteins ...... 110

4.3.3. Presence of common proteins in all five RILs ...... 122

4.3.4. Present in P27-p272 (low polyphenol antioxidant expressing RIL) and Farnsfield (mid low RIL parent) ...... 123

4.3.5. Proteins uniquely expressed only in high polyphenol antioxidant RILs (RIL P27-p362, P27- p036) and D147-p3-115 parent ...... 124

4.3.6. Proteins expressed unique to RIL P27-p362 only ...... 125

4.3.7. Proteins uniquely expressed only in polyphenol-rich RIL P27-362 and D147-p3-115 .... 126

4.3.8. Proteins expressed unique to RIL P27-p036 ...... 127

4.3.9. Overall effect of enhancing antioxidant capacity on peanut allergen composition ...... 127

4.3.10. Analysis of protein metabolic changes in major biological pathways ...... 128

4.3.10.1. Redox processes ...... 128

4.3.10.2. Glycolysis ...... 128

4.3.10.3. Pentose phosphate pathway ...... 129

4.3.10.4. Activated methyl cycle ...... 130

4.3.11. Secondary Metabolites and Lipid Metabolic Process Proteins ...... 131

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4.3.12. Phenylpropanoid pathway ...... 131

4.4. Candidate biomarkers for antioxidant capacity traits in peanuts ...... 133

4.5. Conclusion ...... 136

5. MASS SPECTROPHOTOMETRY AND BIOSYNTHESIS PATHWAYS OF PEANUT POLYPHENOLS 137

5.1. Background and aims ...... 137

5.2. Materials and methods ...... 137

5.2.1. Chemical and reagents ...... 137

5.2.2. Samples ...... 138

5.2.3. Sample preparation ...... 138

5.2.3.1. Peanut deskin and defatting ...... 138

5.2.3.2. HPLC: native extraction ...... 138

5.2.4. Instruments ...... 138

5.2.5. SPE and HPLC on extractions ...... 139

5.2.6. HPLC fraction collection ...... 139

5.2.7. Stock standard solutions ...... 139

5.2.8. GCMS analysis ...... 139

5.2.9. LC-MS/MS analysis ...... 140

5.2.10. Data analysis ...... 140

5.3. Results and discussion ...... 141

5.3.1. Compound identification by GCMS ...... 141

5.3.2. Compound identification by LC-MS/MS...... 146

5.3.2.1. Phenolic acids ...... 148

5.3.2.2. Stilbenes ...... 148

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5.3.2.3. Flavonoids ...... 149

5.3.2.4. Anthocyanidins ...... 149

5.3.2.5. Compounds identified in sample by enzymatic pathways ...... 149

5.3.2.6. Major peaks of interest ...... 155

5.3.2.7. Proposed polyphenol synthesis pathway in peanut ...... 156

5.4. Conclusion ...... 160

6. FEASIBILITY OF SILVER NANODISKS AS A SENSOR FOR ANTIOXIDANT CAPACITY

MEASUREMENT IN PEANUT KERNELS ...... 161

6.1. Background and aims ...... 161

6.1.1. Concept and mechanism of silver nanodisks transformation mediated by polyphenols as an antioxidant sensor ...... 163

6.1.1.1. Synthesis of dark transformed silver nanodisks (AgND) ...... 163

6.2. Materials and methods ...... 166

6.2.1. Chemicals and reagents ...... 166

6.2.2. Samples ...... 166

6.2.3. Sample preparation ...... 167

6.2.3.1. Peanut deskin and defatting ...... 167

6.2.4. Instruments ...... 167

6.2.5. Photochemical synthesis of Silver nanoparticle (AgNP) ...... 167

6.2.6. TEM imaging of AgNPs ...... 168

6.2.7. Preparation of polyphenol antioxidant solutions ...... 168

6.2.8. Polyphenol antioxidant – AgNP reaction ...... 168

6.2.9. Soaking as a simple sample preparation method- peanut leachates as polyphenol extracts 168

6.2.10. AgNP based antioxidant capacity assay ...... 169

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6.2.11. ORAC assay ...... 169

6.2.12. FRAP assay ...... 169

6.2.13. Correlation of AgNP assay to ORAC and FRAP assays ...... 169

6.2.14. Data analysis ...... 170

6.3. Results and discussion ...... 170

6.3.1.1. Selection of AgNP for the development of a sensitive antioxidant capacity assay ...... 171

6.3.2. Dose-response relationship of AgNP-polyphenols reaction ...... 174

6.3.2.1. Phenolics ...... 175

6.3.2.2. Stilbenes and Flavonoids ...... 176

6.3.3. The relationship between polyphenolic structures and AgNP transformation ...... 177

6.3.3.1. Relationship between structural features and AgND transformation ...... 177

6.3.3.2. Relationship between redox potential of polyphenols and AgND transformation ...... 178

6.3.3.3. AgND transformation as antioxidant capacity measurement ...... 178

6.4. Exploring soaking as a simple sample preparation method for AgND-based antioxidant capacity assay ...... 181

6.4.1. Assessment of AgNPr responses to whole kernel extraction ...... 181

6.4.1.1. Kernel leachates by soaking ...... 181

6.4.1.2. Effects of soaking time ...... 183

6.4.1.1. AgNPr response to kernel leachate ...... 184

6.4.2. Correlation of AgNP responses with ORAC and FRAP assays for peanut kernel extract 185

6.4.2.1. AgNP responses in comparison to ORAC assay ...... 185

6.4.2.1. AgNP responses in comparison to FRAP assay ...... 186

6.5. Conclusion ...... 187

7. GENERAL CONCLUSION AND FUTURE WORK ...... 190

7.1. Chapter 3 – G, E, G x E influence on polyphenol expression ...... 190 30

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7.2. Chapter 4 – Quantitative proteomics of antioxidant expression ...... 191

7.3. Chapter 5 – Polyphenol profiling with protein biomarkers ...... 192

7.4. Chapter 6 – AgNPr assay development ...... 193

8. PUBLICATIONS TO DATE ...... 195

8.1. Oral presentations ...... 195

8.2. Poster presentations ...... 195

9. BIBLIOGRAPHY ...... 196

10. APPENDIX ...... 222

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1. INTRODUCTION 1.1. Background

Peanuts, being increasingly considered as a functional food; are energy-dense, high in protein and fat content (approximately 50% w/w) and abundant in beneficial compounds, including vitamin E (tocopherol), minerals (copper and magnesium) and polyphenol antioxidants such as flavonoids, stilbenes and phenolic acids (Francisco and Resurreccion 2008). With the mounting attention to the health benefits of functional foods, peanuts have niche potential for further crop development which the Australian Peanut Genetic Improvement Program and the Peanut Company of Australia (PCA) are actively planning to exploit via peanut breeding programs.

Recent growing consumer demand for foods of superior nutrition and quality has led to the modern development of high oleic acid expressing peanut cultivars (Harch et al, 1995, Chu et al, 2011) due to the reports of oleic acid in olive oil reducing blood pressure and in turn lowering the risk of CHD (Terés et al, 2008). Additional advancement through the PCA breeding programs in trait control of essential mineral expression was confirmed to be non-significantly influenced by G x E and the stable heritability of mineral expression has been since established (Phan-Thien et al, 2010).

As peanuts are known to naturally contain high polyphenol content, there lies the prospects of investigating heritability for polyphenols with bioactivity such as antioxidant properties, as well as the extent of environmental influence on their expression. Genotypic variation studies of at least 10% between breeding genotypes in studies by Phan-Thien et al (2014) have been confirmed which suggests that antioxidant capacity is primarily influenced by genotypic effects by the PCA breeding program. The preliminary study demonstrated this by assessing the antioxidant capacity on ten selected genotypes of peanuts harvested from five different agricultural environments in Queensland, Australia.

The surveillance of polyphenols with antioxidant properties in peanuts has not been completed and many unknown antioxidative compounds (identified by fractionated hydrophilic extract with high antioxidant capacity) are still to be explored. The current methods of antioxidant detection are destructive, require significant sample preparation and analysis time by trained personnel. The

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development of a non-destructive rapid screen test for antioxidant capacity in peanut kernels for use by non-trained persons would increase ease and efficiency for selective breeding of stable high antioxidant peanut genotypes. Former attempts at screening polyphenols using NIR spectroscopy was unsuccessful due to interference from the high oil matrix and the structural diversity in polyphenolic targets (Isleib et al, 2008, Lee et al, 2016).

1.2. Scope and research contribution

This thesis is the ongoing investigation from Phan-Tien et al (2014) which studies the G, E and G x E influences on polyphenol antioxidants in a RIL population in three environments over 3 years. The broad aim of this study was to determine the breeding potential of new peanut genotypes with enhanced polyphenol antioxidant content. Peanut recombinant inbred lines (RILs); a small population encompassing a diverse range of genetic variation, were therefore developed by the Peanut Company of Australia (PCA) for this project. The completion of this project will provide a better understanding of polyphenol biosynthesis in peanuts and the extent of G x E influences on the expression of kernel antioxidant capacity under Australian environmental conditions. Potential proteins biomarkers to these polyphenol antioxidants will also be identified.

The specific objectives of this thesis are to:

I. Investigate the G, E and G x E influence on the expression of antioxidant polyphenols in a peanut Recombinant Inbred Line (RIL) population (Chapter 3) II. To study the molecular basis of polyphenol biosynthesis using the shotgun proteomics approach (Chapter 4). III. Screen for new polyphenolic compounds using LC-MS/MS and construct proposed biosynthesis pathways (Chapter 5); and IV. Investigate the feasibility of developing a simple colourimetric nanoparticle-based test for the rapid and low-cost phenotyping of antioxidant capacity (Chapter 6).

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1.3. Thesis chapter overview

Chapter 1 and 2 contains the background and scope, research contributions and the literature relating to the varied fields covered in this thesis. The literature review will focus on five major aspects to introduction of polyphenols as dietary antioxidants and then corresponding to each experimental chapters in factors which impact on peanut breeding, proteomics for biomarker discovery, chromatography for the survey and profiling of polyphenols and nanoparticles as a platform as an antioxidant probe platform for antioxidant capacity assay.

Chapter 3 discusses the effects of G, E and G x E influences on the polyphenol antioxidant expression and antioxidant capacities of peanuts. Samples harvested across 2013, 2014 and 2015 in several test environments are investigated.

Chapter 4 studies the proteomics of the five selected RILs where proteins and abundance in the expression are compared between parent RILs and high and low antioxidant expressing RILs.

Chapter 5 is focused on the profiling of phenolic acids, flavonoids and stilbenes present in representative RIL parent (D147-p3-115 and Farnsfield) by GCMS and LC-MS/MS for confirmation of known phenolics and detection of new compounds.

Chapter 6 investigates the feasibility of developing a rapid antioxidant capacity assay based on the shape and size transformation of silver nanoparticles (AgNP).

Chapter 7 summarises the thesis on the potential in controlled breeding and selecting a high antioxidant capacity expression genotypes with stilbene synthase-like 3 as a good candidate biomarkers and the feasibility of using AgND for the rapid detection of antioxidant capacity.

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2. LITERATURE REVIEW 2.1. Oxidative stress and health consequences

The free radical theory of aging coined by Gerschman and Harman (1958) proposed that biological metabolism releases free radicals that cause irreversible damage through oxidation in all organisms accruing with increasing age (Gerschman et al, 1954 , Harman 1956). Oxidative stress may occur in aerobic organisms by the process of cellular respiration and oxidative stress within cells when reactive (reduced) oxygen leaches via the respiration chain during the production of energy by the mitochondria.

These reactive oxygen molecules, also known as reactive oxygen species (ROS), include - superoxide anion radical (O2 ), hydroxyl radical (OH), hydrogen peroxide (H2O2) and hydroxyl radical (HO-) (Kerksick and Willoughby 2005). The viability of cells is reduced when cellular materials such as nucleic acids, carbohydrates, proteins, and lipids are oxidised by ROS (Kerksick and Willoughby 2005) and cell function is impaired as shown in Equation 2.1 (through the formation of peroxyl radical (ROO•) from free radical (R•), propagation of cellular material (CM) oxidation and termination of oxidation by glutathione).

Glutathione, the main non-protein thiol antioxidant produced by eukaryotic cells, (Vandeputte et al, 1994), is most abundant intracellularly in its reduced form (up to 20 mM in the liver) with a low reduction and oxidation potential; E° = -240 mV at pH 7 (Gales et al, 2008). Glutathione is responsible for the protection of cellular materials against free radical damage, preserving sulphydryl groups in proteins and regulating enzyme activity (Vandeputte et al, 1994, Gales et al, 2008). Cell viability is inversely proportional to ROS oxidation of materials such as carbohydrates, lipids, proteins and nucleic acids are essential in the maintenance for function within cells (Kerksick and Willoughby 2005).

Equation 2.1 퐼푛𝑖푡𝑖푎푡𝑖표푛: 푅 • + 푂2 → 푅푂푂 • 푃푟표푝푎𝑔푎푡𝑖표푛: 퐶푀 + 푅푂푂 • → 2 퐶푀푂 + 푅 •

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푇푒푟푚𝑖푛푎푡𝑖표푛 푏푦 𝑖푛푡푟푎푐푒푙푙푢푙푎푟 𝑔푙푢푡푎푡ℎ𝑖표푛푒: 푅 • + 2 퐺푙푢푡푎푡ℎ𝑖표푛푒 → 퐺푙푢푡푎푡ℎ𝑖표푛푒 푑𝑖푠푢푙푝ℎ𝑖푑푒 + 푅 퐺푙푢푡푎푡ℎ𝑖표푛푒 푑𝑖푠푢푙푝ℎ𝑖푑푒 ⇒ 퐺푙푢푡푎푡ℎ𝑖표푛푒 𝑔푙푢푡푎푡ℎ𝑖표푛푒 푟푒푑푢푐푡푎푠푒 R• = free radical CMO = oxidies cellular material ROO• = peroxyl radical CM = cellular material

Intracellular levels of glutathione have been shown to decrease with age in numerous in vivo models (Rebrin and Sohal 2006, Vogt and Richie 2007) and depletion is considered to be linked to age-related diseases such as; cardiovascular disease (Meydani et al, 1998, Belinha et al, 2007), cancer (Meydani et al, 1998, Belinha et al, 2007) (Gatz and Wiesmuller 2008) and neurological disorders (Meydani et al, 1998, Belinha et al, 2007). This progressive decline in cellular glutathione content during aging within the human body, in addition to cell material and function deterioration, is considered to be due to oxidation via ROS and support the free radical theory of aging (Navarro et al. 2005, Rodríguez et al. 2008).

2.2. Health benefits of dietary antioxidants and their roles in reducing oxidative stress

Antioxidants have been reported to increase longevity (Queen and Tollefsbol 2010) and balance oxidation damage induced by free radicals (Mittler 2002) and are most commonly found as polyphenolic compounds in foods. Intake of exogenous antioxidants through diet or supplementation has potential to increase disease resistance in humans, alike the mechanism of glutathione by providing improved protection from oxidative stress (Equation 1.2)(Kampkötter et al, 2008, Pan et al, 2008). Disease resistance by antioxidant supplementation have been demonstrated in mice (Gatz and Wiesmuller 2008), Caenorhabditis elegans (the nematode) (Meydani et al, 1998), Drosophila melanogaster (fruit fly) (Brack et al, 1997) and Saccharomyces cerevisiae (baker’s yeast) models (Dani et al, 2008).

Equation 2.2

푇푒푟푚𝑖푛푎푡𝑖표푛 푏푦 푎푛 퐴푛푡𝑖표푥𝑖푑푎푛푡: 푅 • + 퐴푟 − 푂퐻 → 푅푂퐻 + 퐴푟+ R• = Free radical Ar = Antioxidant ROH = Unreactive hydroxl radical Ar+ = Reactive antioxidant 36

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The antioxidant (Ar-OH) in solution binds to the free radical (R•) and terminates chain reaction by the production of an unreactive hydroxyl radical (Equation 2.2) Polyphenols are either water- insoluble compounds like condensed tannins and lignins or water-soluble compounds like phenolic acids, phenylpropanoids, flavonoids, stilbenes and quinines (Winkel-Shirley 2001).

Along with antioxidant properties, phenolic compounds are also able to prevent oxidative browning substrates (Robards et al, 1999, Hollman and Arts 2000). The addition of exogenous antioxidants has been common practice for the prevention of fat oxidation in food manufacture preservation (Nepote et al, 2002, Win et al, 2011). The increase of endogenous antioxidants theoretically would also assist in the prevention of oxidation and increase the shelf life of the crop.

2.3. Peanuts as a valuable agricultural crop

The peanut, Arachis hypogaea L, is a summer-growing legume grown globally in tropical, subtropical and warmer climates (Singh and Singh 1991, Bertioli et al, 2011), including being successfully cultivated in the Australian climate. The value of peanut earns as much as 3 times that of other oil crops in comparison (FAO 2017) and the global production of peanuts worldwide in 2019-20 was approximately 45.44 million tons of pods (USDA 2020). Australian peanut pod production averages approximately 0.2% of world production (FAO 2017, PCA 2017). Though in global terms, the Australian peanut industry is relatively small, approximately 70% of the Australian market share generated $35 million in revenues from the sale of peanut kernels in 2017 (PCA 2017). Thus, peanuts are a valued crop with Australian research investment concentrating on greater yield potential, safer crops (e.g. aflatoxin safe) and higher quality based on crop chemistry, agricultural practices and culinary uses and market price to differentiate products from their global competitors.

2.4. Peanuts polyphenols and biosynthesis

In addition to high oleic acid contents, the same fatty acid found in olives, which help lowering plasma cholesterol and increasing shelf life at a ratio of ~0.7 (O’Keefe et al, 1993, Terés et al, 2008, Barkley et al, 2010, Akhtar et al, 2014), peanuts are also an abundant source of polyphenolic

Yan Yee Poon z3160325 compounds through the plant's inbuilt defence mechanisms and to a degree, a part of a plant’s disease resistance and stress tolerance.

The extraction methods and extraction buffers have been shown by Phan-Thien et al (2012) to influence the recovery of polyphenols in peanut kernels (Phan-Thien 2012). Extraction with and without the use of enzyme Alcalase® and Celluclast® for the digestion of matrix cellulose was used and was termed enzymatic and native extraction respectively. Total antioxidant capacities of enzymatic extraction were found to be greater than native extraction (range of 21- 45% increase in 5 genotypes, Figure 2.1). This indicates that up to 45% of the polyphenols or compounds contributing to antioxidant capacity are matrix-bound (Phan-Thien 2012).

Figure 2.1 ORAC antioxidant capacities of enzymatic and native extracts. Adapted from (Phan-Thien 2012).

The significance of matrix attachment was explored in preceding studies found p-coumaric acid (77- 93%), ferulic/sinapic acid (co-eluting, 44- 53%), salicylic acid (71- 89%), resveratrol (59- 68%) and daidzein (89- 97%) were predominately found in bound forms in the raw peanut kernel.

These polyphenols with antioxidant properties may be loosely grouped into three broad categories: phenolic acids, stilbenes and flavonoids.

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2.4.1. Phenolic acids

The simplest of polyphenol, phenolic acids, are commonly found in plant materials and bound with sugars in the form of glycosides (Hollman and Arts 2000). The phenolic acid most commonly encountered in plants includes caffeic acid and ferulic acid commonly usually bound by ester bonds to hemicelluloses within cell walls; others may also be derivatised from hydrolysable tannins (Scalbert and Williamson 2000). More than 15 phenolic acids have been identified in peanuts (Duke 2000, Ma et al, 2014, Phan-Thien et al, 2014). Peanuts contain more phenolic acid content in comparison with cereals and soybeans, as shown in Table 2.1 (Wanasundara et al, 1997), but in contrast to other tree nuts, the flavonoid content of peanuts are reported to be low (Table 2.1)(Bolling et al, 2011). Phenolic contents of defatted peanut meal and commercial flour are approximately 2,000 and 1,750 µg.g-1 (Seo and Morr 1985).

The predominant phenolic acid in peanuts is p-coumaric (Talcott et al, 2005, Phan-Thien et al, 2014) and has been shown to be effective in terminating experimental in vitro free radicals and also preventing oxidation by lipid peroxidases (Kikuzaki et al, 2002). Main phenolic compounds, shown in Figure 2.2, are mostly derived from cinnamic acid and hydroxybenzoic acid via the phenylpropanoid biosynthesis pathway starting with phenylalanine (Chadha and Brown 1974, Graf 1992, Lee et al, 1995). Phenylalanine is first converted into cinnamic acid by phenylalanine ammonia-lyase (PAL) or tyrosine ammonia-lyase which then is transformed into p-coumaric acid using t-cinnamate 4-monooxygenase (KEGG). From then onwards, caffeic acid is derived by p- coumarate 3-hydroxylase and ferulic and sinapic acid by caffeic acid 3-O-methyltransferase. The cascade continues with where PAL converts t-cinnamate into benzoic acid and benzoate, NADPH: oxygen oxidoreductase produces salicylic acid from benzoic acid (KEGG).

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Table 2.1 Total phenolic acids of various cereals, oilseeds and legumes in mg per 100g of flour, adapted from (Dabrowski and Sosulski 1984, Wanasundara et al, 1997)

Cottonseed Flax Peanut Sesame Soybean Oat Rice p-Hydroxybenzoic 1.0 2.6 2.0 Trace 13.9 1.4 5.0 p-Coumaric 4.1 6.1 146.4 7.2 9.4 2.0 - t-Ferulic 5.5 37.6 16.2 5.7 15.7 63.7 1.9 t-Caffeic 1.8 5.3 2.8 9.8 6.0 2.6 75.1 t-Sinapic - 29.1 8.1 - - Trace Trace

Total 12.4 81.1 175.5 22.7 93.1 87.0

Though phenolic contents of peanuts are reported to be high in general, they have also been shown to vary with genotypes and are found in both free and matrix-bound forms in kernels (Phan-Thien 2012, Phan-Thien et al, 2013). Table 2.2 lists the phenolic acids studied in this thesis which has been shown to prevent degenerative diseases, aid microflora, with anti-bacterial, anti-viral and also anti-inflammatory properties.

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PAL

pobA

C4H FAI1

p-Coumarate 3- hydroxylase

COMT

F5H

Fes/ech

Figure 2.2 Cascade of antioxidant biosynthesis from phenylalanine. Adapted from (Chadha and Brown 1974, Graf 1992, Lee et al, 1995, Wang et al, 2007). 41

Table 2.2 Table of key phenolic acids, preventative effects and health benefits Compound Structure Prevention of Health benefits Reference Ferulic acid • Lipid oxidation • Anti-Degenerative (Rice-Evans et al, 1996) • Linoleic acid diseases autooxidation • Radical scavenging • Cancer • Atherosclerosis

Caffeic acid • Intestinal ischemia- • Blocks reaction of (Hart 1981, Jang et al, 1997, Van reperfusion injury carcinogens with Duyn and Pivonka 2000, Wu et al, 2001, Raskin et al, 2002, Goff and • Carcinogen formation cells • Aids microflora Klee 2006, Williamson and Carughi 2010, Sato et al, 2011) Cinnamic acid • Oxidation • Radical scavenging (Francisco and Resurreccion 2008) • Improved in vivo glucose balance

Coumaric acid • In vitro free radicals (Rice-Evans et al, 1996, Rice- • Lipid peroxidases - Evans et al, 1997, Rice-Evans and Bourne 1998, Kikuzaki et al, 2002) p-Hydroxybenzoic • Oxidation • Oestrogenic activity (Pugazhendhi et al, 2005) acid

Protocatechuic • Cancer • Antibacterial (Vitaglione et al, 2007) acid • Aging • Antiviral • Atherosclerosis • Aids microflora • Ulcers

Salicylic acid • Erectile dysfunction • Radical scavenging (Vlot et al, 2009) • Inflammation • Pain killer • Analgesic

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Sinapic acid • Degenerative diseases (Graf 1992) • Lipid peroxidation -

Syringic acid • Fungus • Hepatoprotection (Itoh et al, 2009) • Liver injury • Anti-inflammatory • Fibrogenesis • Anti-cancer • DNA oxidation • Antimicrobial

Vanillic acid • Liver injury • Hepatoprotection (Itoh et al, 2009) • Fibrogenesis • Anti-inflammatory • DNA oxidation • Anti-cancer • Antimicrobial

2.4.2. Stilbenes

Stilbenes are diphenylethylenes and have ethene double bond hydrocarbon structures with phenyl group substitutions on the carbon atoms of the double bond, the molecular formula for the backbone is C14H12 (Hart 1981). Stilbenes emit blue fluorescence in ultraviolet (UV) light and exist in two isomeric forms, cis and trans, and conversions between the two forms may occur in heat or UV light, (Sales and Resurreccion 2014)(Figure 2.3).

Figure 2.3 Structures of t-Resveratrol, (t-3,5,4’-trihydroxystilbene) and c-resveratrol (c-3,5,4’- trioxyhydrostilbene) adapted from (Sales and Resurreccion 2014).

The stilbenoid biosynthesis pathway comes after the phenylpropanoid pathway via cinnamoyl- CoA by stilbene synthase (KEGG) where malonyl-CoA undergoes decarboxylation with additions of acetate units resulting in a CoA activated phenylpropionic acid (Figure 2.4). Stilbenes have been long known to hold anti-fungal properties and are extracted as free-hydroxy, methyl, ester or glucoside forms (Hammerbacher et al, 2011). The key stilbene in peanuts is resveratrol, which provides anti-cancer (Aggarwal et al, 2004) (Sales and Resurreccion 2014), anti-aging (Meydani et al, 1998, Dani et al, 2008, Gatz and Wiesmuller 2008) and neuroprotective effects (Yousuf et al, 2009). It has further been shown to stabilise DNA damage related to aging and up-regulates intracellular glutathione production (Gatz and Wiesmuller 2008) and prevents platelet aggregation which leads to coronary heart disease (CHD) (Pace-Asciak et al, 1995). Kaeberlein (2006) has also shown that mice fed high-calorie diets with resveratrol supplementation did not develop obesity-related diseases and lived as long as control mice on a standard diet (Kaeberlein and Rabinovitch 2006). These resveratrol-treated mice were observed to be as nimble in old age as the standard diet cohort. Aged mice on the high-calorie diet without resveratrol supplementation had difficulties in mobility (Kaeberlein and Rabinovitch 2006).

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Stilbene derivatives such as pterostilbene, piceid and piceatannol have also been found in peanuts, but in lower quantities (Table 2.3).

STS

hydroxylase 3-O-GT ROMT

Figure 2.4 Biosynthesis of resveratrol and its derivatives from p-Coumaroyl CoA (KEGG).

Table 2.3 Key stilbenes, their preventative effects and health benefits

Compound Structure Prevention of Health benefits Reference Resveratrol • Coronary heart • Anti-inflammatory (Frankel et al, 1993, Pace-Asciak et al, 1995, disease • Antifungal Van Duyn and Pivonka 2000, Aggarwal et al, • Alzheimer's disease • Anti-mutagenic 2004, Baur and Sinclair 2006, Gatz and • Cancer Wiesmuller 2008, Yousuf et al, 2009)

Piceatannol • Breast cancer • Anti-inflammatory (Oh et al, 2007) • Antioxidation

Piceid • Cancer • Antioxidant (Sales and Resurreccion 2014) • Cardiovascular disease

Pterostilbene • Heart disease • Anti-inflammatory (Van Duyn and Pivonka 2000)

2.4.3. Flavonoids

Flavonoids are the major active nutraceutical component in plants and are polyphenols produced as plant defence compounds interacting with microbes; such as nitrogen-fixing bacteria to improve soil quality, and with other plants to suppress growth and inducing root death (Bais et al, 2003). The basic carbon structure to flavonoids comprises of C6-C3-C6 and modifications to the moieties substituted on the aromatic rings of such compounds produce the six major flavonoid classes including flavonols, flavones, flavanones, flavanols, isoflavones, and anthocyanidins (Figure 2.5) (Martins et al, 2011). Flavonoids biosynthesis pathway stems from the phenylpropanoid pathway via cinnamoyl-CoA initiated by t-Cinnamate 4-monooxygenase or chalcone synthase (KEGG).

Figure 2.5 Basic structure of flavonoids and their flavonoid derivatives.

Flavonoids originate from biological sources and are a class of lipophilic secondary metabolites widely found in plants bound by sugars which help to protect against UV, insect and disease damage (Castro and Freeman 2001, Caridi et al, 2007). Most plant species contain at least ten phenolics acid and flavonoids in different concentrations (Herrmann and Nagel 1989). Studies have shown that flavonoids have anti-cancer, anti-bacterial, anti-inflammatory properties and antioxidant activities (Goldberg et al, 1998, Fitzpatrick et al, 2000, Ruidavets et al, 2000). Human

Yan Yee Poon z3160325 studies have shown high dietary intake of procyanidins hinders LDL (low-density lipoprotein) oxidation and encourages free radical scavenging (Fuhrman et al, 1995, Natella et al, 2002) which in turn alleviates coronary heart disease (CHD).

Though peanuts are high in total phenolic compounds with 0.68 mg in 100 g of flour, compared to almonds (15.25 mg), pecan (34.01 mg) and pistachio (18 mg) it has the lowest in flavonoid content such as flavonones and isoflavonones (Bolling et al, 2011). Peanut skin has been reported to contain high levels of procyanidins (Yu et al, 2006). The kernel flavonoid content is low (i.e., catechins) in comparison to those in the skin and hull (Yu et al, 2006, Phan-Thien et al, 2013) though some isoflavonoids have been shown to be up-regulated by abiotic stimuli (Chung et al, 2003, Rudolf and Resurreccion 2005, Sales and Resurreccion 2009) and can be induced by abiotic pressure (Chung et al, 2003, Rudolf and Resurreccion 2005, Sales and Resurreccion 2009).

HIDH

HIDH HI4OMT

McFLS

CHS

HI4OMT

F3H McFLS

CYP75A

Figure 2.6 Biosynthesis of flavonoids from p-coumaroyl CoA (KEGG).

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Quercetin, one of the key flavonoids in peanuts, has been observed to have neuroprotective, cardioprotective and chemoprotective effects (Kampkötter et al, 2008) and observed to protect against capillary fragility and the inhibition of human platelet aggregation (Tzeng et al, 1991). Quercetin has also been proven to increase chronological yeast life span by up to 60% and is associated with the ability to chelate harmful transition metal ions, protects against hydrogen peroxide inflicted lipid peroxidation and protein carbonylation and reduce hydrogen peroxide- induced oxidative damage by 5-6 folds (Belinha et al, 2007). Alike quercetin, rutin also have similar protective effects and health benefits (Van Duyn and Pivonka 2000) while other common flavonoids such as genistein and biochanin A reduces blood cholesterol and platelet aggregation and daidzein (Robards et al, 1999), formononetin (Graf 1992) and myricetin (Zhang et al, 2008) all have anti-cancer properties. Studies show that daidzein is present in peanuts and up to 97% is present as matrix-bound (Lopes et al, 2011, Phan-Thien et al, 2013) with varying genotype to express different daidzein levels.

Kaempferol found in wine were observed to prevent the oxidation of low-density lipoproteins (LDLs) (Hollman and Katan 1997), have been shown to reduce platelet aggregation (Landolfi et al, 1984) while Wu et al (2010) has observed the relaxation of phenylephrine-preconstricted aorta after accumulative genistein administration (Wu et al, 2010). Eriodyctiol also possesses the ability to prevent the aggregation of platelets resulting from arachidonic acid, thrombin, platelet- activating factor (PAF) and collagen (Lopes et al, 2011). A summary of key flavonoids found in peanuts is found in Table 2.4.

Table 2.4 List of key flavonoids, preventative effects and health benefits

Compound Structure Prevention of Health benefits Reference Quercetin • Cell proliferation • Extends vitamin C (Van Duyn and Pivonka 2000) • Blood clot formation action • Anti-inflammatory • Aids microflora

Rutin Hydrate • Cell proliferation • Extends vitamin C (Van Duyn and Pivonka 2000) • Blood clot formation action • Anti-inflammatory

Genistein • Growth of cancer cells • Lowers blood (Van Duyn and Pivonka 2000) cholesterol level and platelet aggregation Biochanin A • growth of cancer cells • Lowers blood (Van Duyn and Pivonka 2000) cholesterol level and platelet aggregation Daidzein • Prostate cancer • Anti-cancer and (Robards et al, 1999, Yao et al, 2004, • Steroid-hormone- anti-HIV activities Kowalski et al, 2005, Williamson and dependent cancers Carughi 2010, Cheng et al, 2012) • Formononetin • Cancer • Wound healing (Graf 1992, Huh et al, 2011) • Cell proliferation and migration

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Myricetin • Anti-cancer (Zhang et al, 2008) - • Anti-mutagenic

Kaempferol • Inflammation (Kowalski et al, 2005) -

Eriodyctiol • Platelet aggregation • Intestinal health (Lopes et al, 2011)

2.5. The relationship between plant defence and polyphenolics

Plants have direct and indirect mechanisms that are used as a defence against predators. Secondary metabolites are released as offensive volatiles which are biosynthesised through the shikimate and phenylpropanoid pathway (Sharma et al, 2019). They are stored as inactive forms, phytoanticipins and phytoalexins induced in response to insect, fungal or microbial attacks (Howe and Jander 2008).

Plant polyphenolics are a group of such defensive compounds against herbivores, microbes and competing plants. The oxidation of the phenols by polyphenol oxidase (PPO) and peroxidase - (POD), cyclic reduction of ROS, superoxide anion (O2 ), hydroxide radicals (H2O2) and singlet oxygen (O2•) protects cellular material when damage is accumulated from herbivorous insects (Maffei et al, 2007).

Defence and resistance Cell wall suberin

Protein interaction Free radical scavenging

Phenolic Gene expression Astringent taste (deterrent) Compounds

Phytohormone interaction Wood and tree statics

Fruit colour

Figure 2.7 The many functions of phenolic compounds in plants. Adapted from (Rice-Evans et al, 1997, Treutter 2010).

Figure 2.7 shows the various functions of phenolic compounds in plants (Treutter 2010). Phenolic compounds in plants also serve the essential function of plant pest and pathogen resistance and against environmental stress (Treutter 2005, Sharma et al, 2019). For example, chlorogenic acid, quercetin and rutin have been shown to have significant insecticidal effects against leaf defoliators, Spodoptera litura and Helicoverpa armigera (Stevenson et al, 1993, Jadhav et al, 2012, Mondal et al, 2015). The plant defence mechanisms, disease resistance and stress tolerance to salinity,

Yan Yee Poon z3160325 heavy metals, drought, UV, heat and cold, in addition, have been suggested to be linked with the presence and up-regulation of antioxidant production (Bennett and Khush 2003, Ahsan et al, 2009, Ashraf 2009, Cary et al, 2011, Sharma et al, 2019) which may improve growth and survival of the overall plant. The increase of antioxidant status in plants will not only provide increased health benefits via consumption, but the amplified expression also may give elevated overall abiotic stress tolerance (Sharma et al, 2019) and defence and resistance against environmental pest and pathogens, which in turn will contribute towards end yield and shelf life through oxidation deterrence.

2.6. Promoting the increase of polyphenols in peanuts

Enhancing polyphenol content and antioxidant capacity in peanuts may be obtained via fortification and biofortification approaches. Fortification is a process of addition of nutrients to food and beverages during manufacturing for either correcting or supplementing dietary insufficiencies (ADA 2005). The high cost of production and poor bioavailability have been some of the issues with fortification.

The process of fortification during manufacturing is expensive and has been suspected to interfere with bioavailability and absorption of nutrients (Watzke 1998). I.e., when high doses of a particular micronutrient had been supplemented or iron supplementation had negative effects on zinc and copper absorption. Zinc supplementation, on the other hand, interferes with iron and copper due to interactions of the fortified micronutrients (Sandström 2001). Biofortification is a process of improving the nutritional quality of food crops through agronomic practices, conventional plant breeding, or modern biotechnology. The biofortification of peanuts may be achieved through selective breeding aided by marker-assisted selection (Chu et al, 2011) or by genetic engineering (Mallikarjuna et al, 2016).

2.7. Peanut breeding – current practices

The attempts for genetic enhancement of the cultivated peanut to increase yield and resistance to diseases and tolerance to biotic and abiotic stresses are vital for its future crop improvement.

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Artificial peanut hybrids are commonly produced by targeted pollination after emasculating flowers, with the use of current peanut breeding methods including pedigree or modified pedigree, single seed descent and bulk selective breeding (Quesenberry 2008). Pedigree or modified pedigree methods gives maximum segregation and selection while allowing for identification and segregation in later (Quesenberry 2008). The single-seed descent method is used for the rapid breeding of material with multiple generations every year however is usually associated with less record-keeping and consequently provides less genetic information on pedigree. Finally, bulk selective breeding which does not trace parental identification gives negligible genetic information and hence in some cases is impractical and resource inefficient (Cobb et al, 1973, Wynne and Gregory 1981, Knauft and Gorbet 1989, Bertioli et al, 2011). The ultimate aim of cultivated peanut breeding as a food source is for the optimisation of yield, pest and disease resistance, seed quality, oil/protein quality, specialty uses and for nutritional quality such as functional food traits.

2.7.1. Genotype

The genotype of an organism is the set of genes that are expressed regardless of the characteristics of a particular trait and is physically observable, which is referred to as its phenotype (Boggess et al, 2013). The genotype of a peanut kernel may determine the fatty composition of the peanut oil, disease resistance, stress tolerance and polyphenol antioxidant expression (Kormsa-art et al, 2002, Phan-Thien et al, 2014).

Phenotyping strategies are used to achieve three main purposes (Cobb et al, 2013). To:

1. Estimate crop performance under appropriate management conditions in fields. 2. Evaluate performance across a population of target environments. 3. Generate useful real-time data without disproportionate investment in labour and infrastructure.

Recent work in conventional breeding has been done in efforts to improve functional food traits in peanuts including the successful breeding of high oleic acid expression by Barkley et al (Barkley et al, 2011). This successful example of peanut trait selection from marker-assisted breeding was the introgression of the ahFAD2B alleles for high oleic fatty acid composition.

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From the previous research by Isleib et al (2006), high oleic peanut trait inheritance is controlled by two recessive alleles with partial dominance (Moore and Knauft 1989, Isleib et al, 2006). Ol (oleic: linoleic) genotypes may influence the levels of oleic, linoleic, palmitic and gadoleic fatty acids expressed and modifiers may also be involved for determining fatty acid compositions. It was found that genotypes Ol1ol1ol2ol2 or ol1ol1Ol2ol2 will not breed true as segregation occurs in next-generation but mid oleic cultivars with fixed ahFAD2 genotype may have environmental influences or epistasis that increases c18:1 production to ~65-70% (Barkley et al, 2011).

The genotypic variation of peanut dietary minerals has been investigated by Phan-Thien et al, through the analysis of 24 ultra-early maturity and 32 full season genotypes as a part of the Australian Peanut Breeding Program (APBP) (Phan-Thien et al, 2014). The genotype had significant influence over essential mineral expression, though it was found to be substantially controlled by genetic factors (~10%) (Phan-Thien et al, 2010, Phan-Thien et al, 2013). In 87% of the dietary minerals tested, the study showed the mineral expression to be highly influenced by genotype (P < 0.01).

In addition to dietary minerals, Phan-Thien et al (2014) also carried out the preliminary studies on the G, E and G x E expression of polyphenol antioxidants in peanut kernels. A moderate level of phenotypic variation was observed in the hydrophilic ORAC assay using the 32 full season population and showed hydrophilic ORAC values to be significantly genotype influenced (P <0.05) (Phan-Thien et al, 2014). These results showed the potential to breed for high polyphenol traits in peanuts.

2.7.2. Environment

Selection of favourable environments for particular crops, genotypes or the conditioning of existing agricultural land has been used to manipulate crop quality through agronomic practices. Selective breeding, directly and indirectly, is conventionally used to produce crops of desirable characteristics where genetic engineering is not favoured. Direct selection for a particular crop characteristic occurs when a plant response is activated due to certain stresses applied such as an increase in polyphenol antioxidant production initiated by fungal contamination (Arora and

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Strange 1991) whereas indirect selection is the selection in one non-stress inducing environment to obtain a response found in another specific stress environment (Sales and Resurreccion 2014). Environmental factors, for instance, light, temperature, agronomic practices, and other stresses may directly influence the expression of anthocyanins as demonstrated by Kalt et al (1996). As an example, genotypes of blueberries were found to express as much as 30% difference in anthocyanins contents due to varying environmental conditions (Kalt and McDonald 1996).

The expression of polyphenols in peanuts may also be greatly influenced by environmental conditions, such environmental factors include biotic and abiotic stresses (Rudolf and Resurreccion 2005). Abiotic stresses include such as: 1) light, i.e., where the same variety of geranium was cultivated in differing environments and transplanted to the same location and the newly formed sprouts and leaves of both plants displayed identical phenolic profiles (Bauer and Treutter 1990), 2) temperature, as observed in the initiation of red pigmentation in apples (Blankenship 1987) and reduced anthocyanin accumulation in grapes (Mori et al, 2005) due to low night temperatures during ripening, 3) mineral nutrition, being the key prerequisite in phenylpropanoid and cofactor for phenylalanine ammonia-lyase (flavonoid) such as Mn2+ and Mg2+ (Engelsma 1972, Durst 1976), 4) irrigation, as grape vine cultivated under drought stress produced the highest anthocyanin content (Esteban et al, 2001), 5) rootstocks, where soil microbes in root exudates convert phenolics into compounds to assist N mineralisation and humus formation

(Sharma et al, 2019), and 6) atmospheric CO2, as observed in tobacco when secondary metabolites developed into carbon-based phenolic compounds rather than alkaloids when grown under limited nitrogen conditions (Matros et al, 2006). Biotic stresses are phytoalexin induced in the presence of pest and or disease pressure (Sobolev et al, 2007). Both abiotic and biotic stresses would then result in up- or down-regulation of plant growth and development (i.e., plant growth regulation by ethylene, salicylic acid, and jasmonic acid )(Dong 1998, Kunkel and Brooks 2002).

The previous study by Phan-Thien et al (2010) evaluated the stability of heritability for peanut mineral phenotype showed that 13 of the 15 tested essential minerals tested were significantly influenced by genotype and environment factors (P < 0.05). The G x E interaction was significant in 8 out of 15 of the essential minerals examined but overall, was not found to be very prominent (Phan-Thien et al, 2010). These results concluded that despite the overall trends in mineral profiles 56

Yan Yee Poon z3160325 were influenced by genotype and environmental conditions, the profiles of each genotype would shift proportionally when cultivated in differing environmental stresses.

The peanut genotypes used were selected with phenotypically diverse in dietary mineral composition in 5 distinctly different growing environments within the primary peanut agricultural areas of Australia, Bundaberg, Kairi and Taabinga (Kingaroy). Disease pressure in Bundaberg was mainly from foliar; leaf rust (Puccinia arachidis), Kairi had soil-borne disease pressure (Cylindrocladium parasiticumcrous, resulting in what is known as black root rot) (Wheeler and Black 2005) and foliar disease pressure (Cercosporidium personatum leading to ‘leaf spot’ and ‘leaf rust)(Smith 1980), while Taabinga with Krasnozem soil (deep red clay loam or oxisol) (Nachtergaele 2001) had mainly net blotch (by Didymosphaeria arachidicola) (Bell et al, 1993, Rao and Wright 1994) as disease pressure. These locations were also selected for ongoing studies on polyphenol expression.

2.7.3. Genotype x Environment interaction

Genotype may govern the trait expressed within the plant but environmental biotic and abiotic stress may also influence final phenotypes, particularly shown in the resistance traits (Kushalappa and Gunnaiah 2013). The resulting influence on genotype expression by environmental pressures is described as genotype by environment interaction (G x E).

Drought stress is variable with timing, duration and severity of water absence and hence resulting in high environmental variation and G x E interaction (Witcombe et al, 2008). Environmental variations induced by crop year significantly influenced G x E in relation to the bitter sensory attributes of peanuts and the location of cultivation influence significantly of bitterness and sweetness of the kernel (Pattee et al, 1997). The G x E interaction was found to be responsible for 8% variation of oil content in the exploration of peanut oil quality and tocopherol content (Isleib et al, 2008), 97% variation of lignoceric acid was also observed to be influenced by the combination of genotype, year and location interactions. Isleib et al (2006) found environmental influence alone provided greater influence to variation than G x E in tocopherol content (Isleib et al, 2006).

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Preliminary G x E studies were carried out to observe the stability of polyphenol antioxidant expression with sixteen (16) genotypes (ten full season (Figure 2.8) and six ultra-early maturity genotypes) representing a range of antioxidant capacity and five (5) distinct growing environments in 2009-10 (Phan-Thien et al, 2014). The antioxidant capacity of genotypes was mostly constant even through considerable environmental influence implying genetic control in kernel polyphenol antioxidant capacity (Phan-Thien et al, 2014). Further study in the G x E influence on polyphenol expression in peanuts is still to be investigated to confirm the sufficient genetic control and is an important component in determining the breeding potential for this functional food trait.

Previous studies in our research group have shown not only the stability, but also increase of peanut polyphenols in roasted peanut kernels both at 150°C for 70 min and 160°C for 32.5 min in extractable concentrations of p-hydroxybenzoic acid, chlorogenic acid, p-coumaric acid, and quercetin (Phan-Thien 2012). These results were in general agreement with similar prior studies (Talcott et al, 2005, Chukwumah et al, 2007).

Figure 2.8 Hydrophilic ORAC values of the full season breeding lines from which parent cultivars were chosen from. Adapted from (Phan-Thien 2012).

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2.8. Proteomics in agriculture

The application of proteomics techniques to agriculture allows for the examination of total protein profile, metabolic profiling, study metabolic changes such as up- and down-regulation of proteins between stressed and diseased crops and between cultivars (Kottapalli et al, 2008). It may also be used to assist biomarker discovery for use in selective breeding or study the effects of global climate changes in the cultivation of major food crops, such as; rice, soybeans and wheat (Hashiguchi et al, 2010). The use of proteomics to investigate molecular basis and metabolic or biosynthesis pathways concerning polyphenol and the potential biomarkers candidates in peanuts that arise from the studies may help to confirm the genetic control of this trait and assist in the breeding of high polyphenol biosynthesis kernels. In the next sections, the peanut proteome and the uses of quantitative proteomics in applications to study the cultivated peanut are reviewed.

2.8.1. Building a peanut proteome

As the genomic studies of Arachis hypogaea been hindered by the lack of significant polymorphism (Raina et al, 2001, Moretzsohn et al, 2004, Jiang et al, 2007), proteomic methods of study have been recently applied in peanuts involving electrophoresis (Jain et al, 2006, Kottapalli et al, 2008) and even more recently using mass spectrometry (Stevenson et al, 2009, Kottapalli et al, 2013). Since the study of proteomics has only recently gained traction there is still a mine of peanut proteins awaiting to be identified. Until recently, there has been no public peanut proteome database available and the investigation of such proteins have been done with searches against proteomics database (human) (Wilhelm et al, 2014) and the “global proteome machine” (Craig et al, 2004) with only model plants, Oryzasativa and Arabidopsis thaliana. The current “peanut base” though is revolutionary, unfortunately, is yet to be fully established and the use of proteins common to a group or related function in proteomic databases from other organisms is still required.

Proteomics has been used to identify biomarkers for the detection of resistance and secondary metabolites in plants (Dixon 2001, Ahsan et al, 2009, Ashraf 2009). There has been increasing attention in this field for the study of phytoalexin and phytoanticipins in the metabolic engineering

Yan Yee Poon z3160325 of disease resistance in plants. Proteomic analysis can be used to do comparative searches in databases and identifying pathway genes (Dixon 2001). With the completion of the cultivated peanut genome, researchers, agronomists and plant breeders would be better equipped to breed consistently high performing cultivars for superior yield, nutritional content and functional food traits to relieve the existing and projected food shortages of the future.

2.9. Quantitative proteomics

Quantitative proteomics relies on the peptide count yield and is mostly used for the comparison of protein abundance differences between samples (Ong and Mann 2005). Data collected involves the total number of proteins within the same and the relative change between samples tested. As these materials are only condensed forms of coding for the final expression of proteins and those multiple variations in proteins may arise from a single gene or transcription, which ultimately control biological processes under study; greater progress and efficiency may be achieved in investigating these proteins directly (Ong and Mann 2005).

Label-free quantitative proteomics has been used for the analysis of peanut allergens (Stevenson et al, 2009, van den Broeck et al, 2015, Piovesana et al, 2016, Montowska and Fornal 2017) and drought-stressed peanut seeds (Kottapalli, Zabet-Moghaddam et al. 2013). The proteome map for wheat leaves (Donnelly et al, 2005), roots (Song et al, 2007), endosperm (Vensel et al, 2005) and other cellular organelles have been developed with the combination of 2D gel electrophoresis and mass spectrometry techniques. Label-free quantitative proteomics has also seen the complete profiling of proteins in the whole soy plant including leaves, roots (Panter et al, 2000) and seeds (Mooney and Thelen 2004). Similar studies on flavonoid contents in various plants such as soybean cultivars (Xu et al, 2008), barley seedlings (Kaspar et al, 2010) and strawberry mutants (Hjernø et al, 2006) have also been studied using quantitative proteomic techniques. Such complete profiling is lacking in peanuts though studies in peanuts using -omics technologies is starting to gain momentum.

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2.9.1. Quantitative proteomics for plant stress and resistance

Some pathogenesis-related proteins (PRs) are prompted by the presence of diseases, physical wounding and some abiotic stresses and have been found to be homologues to plant food allergens (Breiteneder and Ebner 2000). As biotic and abiotic stresses have been reported to increase the expression of enzymatic and non-enzymatic polyphenols (Chung et al, 2003, Mangelsen et al, 2010, Sharma et al, 2019), there are possibilities that the changes in polyphenol expression may also affect allergen prevalence in plant foods, but yet to be explored. Proteomic investigations have been made in identifying proteins expressed in response to these biotic and abiotic stresses such as plant responses to heavy metal exposure (Ahsan et al, 2009). It has been observed a range of defence proteins (superoxide dismutase (SOD), catalase and enzymes of the ascorbate–glutathione cycle and thiol‐rich peptides; phytochelatins) that were induced by the presence of metal ions in soils (Hartley‐Whitaker et al, 2001, Schützendübel and Polle 2002). Likewise, polyphenols upregulation in response to salt tolerance has also been observed (Ashraf 2009) and the down- regulation of jasmonic acid has been shown in water deficit peanut seeds, which suggests increase in salicylic acid (Dong 1998) that has been shown to aid in the challenge of stress and disease resistance (Kottapalli et al, 2013).

The protein expression of a saline-tolerant Arachis hypogaea L. cultivar was observed to increase phosphorylated PR 10 proteins in root nodes (Jain et al, 2006). PR 10 proteins belong to the same family as the Ara h 8 allergen, possessing RNase activity (Moiseyev et al, 1994), in defence against pathogens (Park et al, 2004) and have been shown to correlate strongly with plant abiotic stress. Comparative proteomics has been performed on drought tolerant and drought susceptible peanut leaf in response to water stressing in a recent study by Katam et al (2016). Katam et al (2016) were able to demonstrate that differential protein abundance in proteins related to signal perception, metabolism, defence and photosynthesis, suggesting a relationship between the process pathways and drought tolerance and susceptibility. A study of water -deficit stressed peanut seeds by Kottapalli et al (2013) using shotgun proteomics found an increase in thioredoxin and PDIL 1-4, 1-2 related proteins which aid in the scavenging of ROS (Kottapalli et al, 2013). In addition, the study showed an increase in abundance of brassinosteroid and jasmonate related hormone for

Yan Yee Poon z3160325 signalling resulting from the water-deficit stress and stress-responsive proteins UBQ 6 and 14 for proteolysis and heat shock proteins HSP 70 and 17.4 were found to be elevated.

2.10. Polyphenol profiling by spectrometry

The development of high polyphenol expression and the understanding of these polyphenol biosynthesis pathways in peanuts would require the knowledge of all the polyphenols expressed. GCMS and LC-MS/MS have been commonly employed for the surveillance of polyphenolic compounds in food and biological samples (Zhang and Zuo 2004, Sarnoski et al, 2012, Simirgiotis et al, 2013, Bataglion et al, 2015). Despite NIST library database being easily accessible for GCMS reference, issues arise when effective chemical derivatisation on flavonoids could not be achieved satisfactorily, and hence render the flavonoids undetectable. While ionisation mode could also affect the detectability of phenolic compounds. For example, Buiarelli et al (2007) found stilbenes to be more efficiently ionised in negative mode due to its acidic properties being easily deprotonated in aqueous phases (Buiarelli et al, 2007).

LC-MS/MS is, however, most commonly used to detect phenolic acids, flavonoids and stilbenes from various peanut tissues in more recent times. Using LC-MS/MS, Kečkeš et al (2013) were able to detect and surveyed 43 phenolic acids and their derivatives, flavonols, flavanonols, flavanones and flavones in 44 singular flora Serbian honey samples (Kečkeš et al, 2013). In the absence of appropriate standards for their study, the group utilised compound [M-H]- and the interpretations of fragmentations of these compounds with the assistance of fragmentation pathways found in the literature. Although NIST also does have a tandem library for use with LC- MS/MS, the section for small molecules only contain entries for 13,808 chemical compounds (NIST 2020).

Table 2.5 shows a list of such compounds with their respective parent and product ions by MS/MS, where they have been sampled from and their respective references. The mass transitions of each compound are listed in either positive or negative modes and are grouped into phenolics, anthocyanidins, stilbenes and flavonoids.

Phenolic compounds Peanut [M-H]- [M+H]+ Product Reference tissue ions b type 4-o-Caffeoylferulic acid Skin 355 - 338 (Ma et al, 2014) c-p-Coumaroyltartaric acid (cis coutaric acid) Skin 295 - 163 (Ma et al, 2014) Caffeoyltartaric acid Skin 311 - 149 (Ma et al, 2014) (Caftaric acid) Chicoric acid Skin 473 - 149 (Ma et al, 2014) Chlorogenic acid Kernel, 353 - 191 (Dabrowski and Sosulski 1984, Sim et al, 2009) defatted flour Di-p-Coumaroyltartaric acid a Skin 441 - 203 (Ma et al, 2014) Di-p-Coumaroyltartaric acid b Skin 441 - 119 (Ma et al, 2014) Di – hydroxycoumarin Skin 177 - 107 (Ma et al, 2014) Ferulic acid 309 - 193 Ferulic acid derivative Skin 403 - 311 (Ma et al, 2014) Feruloylasparate Skin 308 - 149 (Ma et al, 2014) Feruloyl tartaric acid derivative Skin 681 - 293 (Ma et al, 2014) Feruloyltartaric acid (fetaric acid) Skin 325 - 119 (Ma et al, 2014) Feruloyltartaric acid (fetaric acid) Skin - 327 134 (Ma et al, 2014) p-Coumaroylcaffeoyltartaric acid Skin 457 - 119 (Ma et al, 2014) p-Coumaroylferuloyltartaric acid Skin 471 - 193 (Ma et al, 2014) p-Coumaroyl-o-pentoside a Skin 295 - 119 (Ma et al, 2014) p-Coumaroyl-p-hydroxybenzoyltartaric acid Skin 415 - 163 (Ma et al, 2014) p-Coumaroylsinapoyltartaric acid a Skin 501 - 263 (Ma et al, 2014) p-Coumaroylsinapoyltartaric acid b Skin 501 - 203 (Ma et al, 2014) p-Coumaroyltartaric acid derivative Skin 649 - 175 (Ma et al, 2014)

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p-Coumaroylvanilloyltartaric acid Skin 445 - 167 (Ma et al, 2014) p-Hydroxybenzoic acid Skin 137 - 93 (Ma et al, 2014) Salicylic acid Skin 137 - 93 (Gruz et al, 2008, Ma et al, 2014) Sinapic acid Kernel, 223 - 93 (Singleton et al, 2002) meal t-Cinnamic acids Skin 147 - 101 (Ma et al, 2014) t-p-Coumaroyltartaric acid (trans coutaric acid) Skin 295 - 163 (Ma et al, 2014) Anthocyanidins Peanut [M-H]- [M+H]+ Product Reference tissue ions type Cyanidin Skin 285 - 125 (Bansode et al, 2014) Peonidin-3-galactoside Skin 461 - 299 (Bansode et al, 2014) Petunidin 3-o-glucoside Skin 477 - 315 (Bansode et al, 2014) Procyanidin A2 Skin 575 - 285 (Bansode et al, 2014) Procyanidin B1/B2 Skin 577 - 289 (Bansode et al, 2014) Stilbenes Peanut [M-H]- [M+H]+ Product Reference tissue ions type Piceid-6-p-Coumaroyltartaric acid Skin 667 - 521 (Ma et al, 2014) Resveratrol-3-o-p-Coumaroyltartaric acid Skin 505 - 203 (Ma et al, 2014) Piceatannol Skin 405 - 243 (Buiarelli et al, 2007, Ma et al, 2014) Pterostilbene Hairy 255 - 197 (Medina-Bolivar et al, 2007, Zhu et al, 2012) roots t-Piceatannol Skin 243 - 159 (Ma et al, 2014) Flavonoids Peanut [M-H]- [M+H]+ Product Reference tissue ions type (- )-epi-catechin a Skin 289 - 179 (Sarnoski et al, 2012, Ma et al, 2014)

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(+ )-epi-catechin a Skin 289 - 205 (Zhu et al, 2012) 316 amu flavonoid (rhamnetin, isorhamnetin, Skin 623 - 315 (Sarnoski et al, 2012) tamarixetin, nepetin) plus rutinoside Catechin Skin 289 - 109 (Sarnoski et al, 2012, Zhu et al, 2012, Ma et al, 2014) Cirsiliol Skin 299 - 271 (Ma et al, 2014) Daidzein Kernels 253 - 132 (Chukwumah et al, 2007) Daidzein (higher signal) Kernels - 255 115 (Chukwumah et al, 2007) Daidzin Kernel, - 417 255 (Wu et al, 2003, Chukwumah et al, 2007) meal Eriodictyol Skin 287 - 125 (Ma et al, 2014) Fisetin Skin 285 - 135 (Bansode et al, 2014) Flavonoid 3-o-glucoside Skin 447 - 284 (Bansode et al, 2014) Flavonoid 3-sophoroside Skin 609 - 285 (Bansode et al, 2014) Formononetin Kernels 267 - 252 (Antignac et al, 2003, Francisco and Resurreccion 2008) Formononetin-7-o-p-Coumaroyltartic acid Skin 545 - 203 (Ma et al, 2014) Formononetin-o–p-hydroxybenzoyltartaric acid Skin 545 - 233 (Ma et al, 2014) Genistin Kernels 433 - 271 (Wu et al, 2003, Chukwumah et al, 2007) Glycitein Kernels - 285 142 (Wu et al, 2003, Francisco and Resurreccion 2008) Glycitin/sissostrin (Biochanin A glycoside) Kernel, - 447 285 (Singleton et al, 2002, Chukwumah et al, 2007) meal Hesperetin Skin 301 - 177 (Bansode et al, 2014) Homoeriodictyol Skin 301 - 151 (Ma et al, 2014) Isorhamnetin (Quercetin methyl ether) Skin 315 - 151 (Lou et al, 2001, Bataglion et al, 2015) 107

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Isorhamnetin glucoside Kernel, 478 - 316 (Singleton et al, 2002) meal Kaempferol Kernels - 449 261 (Chukwumah et al, 2007) Kaempferol Skin 285 - 151 (Chukwumah et al, 2007, Bansode et al, 2014) Kaempferol 3-o-glucoside Skin 447 - 285 (Bansode et al, 2014) Luteolin Kernels 285 - 267 (Chukwumah et al, 2007, Ma et al, 2014) Luteolin Kernels - 287 213 (Chukwumah et al, 2007) Luteolin methyl ether (diosmetin) Skin 299 - 284 (Ma et al, 2014) Morelloflavone Skin 555 - 305 (Ma et al, 2014) Morelloflavone Skin - 557 218 (Ma et al, 2014) Naringenin Kernels 271 - 215 (Krause and Galensa 1991, Tang et al, 2016) Naringenin Kernels - 273 170 (Krause and Galensa 1991, Tang et al, 2016) o-Coumaroyl-o-pentosidequercetin glucuronide Skin 477 - 301 (Ma et al, 2014) Quercetin4 glucoside Skin 463 - 301 (Bansode et al, 2014) Quercetin di glycoside Kernel, 595 - 301 (Singleton et al, 2002) meal Quercetin glucoside Kernel, 478 - 316 (Singleton et al, 2002) meal Quercetin glucuronide Skin 477 - 151 (Ma et al, 2014) Quercetin methyl ether (isohamnetin) acetyl Skin 519 - 315 (Ma et al, 2014) glucoside Quercetin methyl ether (isohamnetin) Skin 315 - 271 (Ma et al, 2014) Rhamnetin Kernel, 315 - 165 (Schieber et al, 2002, Singleton et al, 2002) meal

2.11. Assays for the quantification of antioxidant capacity

Polyphenol antioxidants have been loosely categorised into primary (peroxide-decomposing / preventative) and secondary (chain breaking) polyphenol antioxidants according to their mechanism of action (Burton and Ingold 1984). Preventative antioxidants reduce peroxyl radical as below where hydroperoxides are hydrogen peroxide mono-substitutes with the ROOH skeleton and R is acyl are known as peroxyl acids (Moss et al, 1995) (Equation 2.3),

Equation 2.3

2퐻 푐푅푂푂퐻 → 푅푂퐻 + 퐻2푂 cROOH = Chain Hydroperoxide ROH = Unreactive radical hydroxyl or decompose it catalytically into non-reactive products: Equation 2.4

(퐻+) 푅3퐶푂푂퐻 → 푅2퐶 = 푂 + 푅푂퐻

R3COOH = Carboxylic acid Chain breaking antioxidants are usually aromatic amines or phenol compounds and are able to engage peroxyl radicals: Equation 2.5 푅푂푂 • +퐴푟푂퐻 → 푅푂푂퐻 + 퐴푟푂 • ArOH = Phenoxyl hydroxyl ROOH = Hydroperoxide ArO• = Phenoxyl radical Phenoxyl radical (ArO•) produced is stable in resonance and unreactive to RH. Hence, O2 does not propagate and is terminated by a second peroxyl radical: Equation 2.6 푅푂푂 • + 퐴푟푂 • → 푛표푛 푟푎푑𝑖푐푎푙 푝푟표푑푢푐푡푠

Quantification of antioxidant capacity has been performed using in vitro assays using principles of chemiluminescence for signalling (ORAC assay and photochemiluminescence), substrate oxidation (Folin-Ciocalteu and Crocin bleaching assay), radical scavenging mechanisms and reduction of metal ions (DPPH, FRAP and Cuprac assay). These assays are commonly performed in a microtitre plate and use an ELISA plate reader to measure absorbance, chemiluminescence

Yan Yee Poon z3160325 and also fluorescence. These assays are traditionally categorised as electron transfer (ET) or hydrogen atom transfer (HAT) in principle in as in Equation 2.7.

Equation 2.7 퐻퐴푇 푏푎푠푒푑 푎푠푠푎푦: 푅 • + 퐴푟푂퐻 → 푅푂퐻 + 퐴푟퐻+ 퐸푇 푏푎푠푒푑 푎푠푠푎푦: 푅 • + 퐴푟푂퐻 → 푅− + 퐴푟퐻+ + + 퐴푟퐻 ↔ 퐴푟 + 퐻3푂 − + 푅 + 퐻3푂 → 푅퐻 + 퐻2푂 푀3+ + 퐴푟푂퐻 → 퐴푟퐻+ + 푀2+

+ - + ArH = Hydrophenoxyl ion R = Radical ion H3O = Hydronium ion RH = Unreactive radical M3+ = metal ion 3+ M2+ = metal ion 2+

A brief summary of antioxidant assays and their principles, advantages and disadvantages is listed in Table 2.6. (Pellegrini et al, 2003, Prior et al, 2003, Huang et al, 2005, Prior et al, 2005, Apak et al, 2007).

Within the preliminary studies to this thesis on polyphenol expression by Phan-Thien et al (2012), it was observed that the principal source of total antioxidant capacity in peanuts to be from the hydrophilic extraction (3 fold difference compared to the lipophilic extraction) using the ORAC assay (Phan-Thien 2012). The suitability of assays for the study of peanut kernel antioxidant capacity was compared using ABTS, DPPH, FC, hydrophilic ORAC, lipophilic ORAC and total ORAC. Using 56 Australian peanut genotypes (including 24 early maturity and 32 full season genotype kernels) with the mentioned assays and the hydrophilic and total ORAC assay showed strong significant difference (ANOVA, P < 0.01) in full season genotypes (Phan-Thien 2012). Despite the differentiation obtained with full-season kernels, the ultra-early genotypes did not. Hydrophilic and total ORAC assays were shown to be the least susceptible to experimental variation, most sensitive and reliable screening method for peanut extracts, hence, all antioxidant testing of peanut kernels in this thesis was carried out with the hydrophilic ORAC assay (Phan- Thien et al, 2014).

Table 2.6 Antioxidant assays, principles, advantages and disadvantages

Electron transfer Principle Advantage Disadvantage Reference (ET) assays Ferric-reducing Reduction of blue Simple Does not account for hydrogen transfer (MacDonald‐Wicks et al, antioxidant power coloured ferric 2006, Apak et al, 2007, Inexpensive Electron-donating components that do (FRAP) tripyridyltriazine Karadag et al, 2009, not have antioxidant properties with complex No specialised equipment Badarinath et al, 2010) lower redox potential than Fe (II) and Rapid (III) may cause artificially elevated Microplate adaptable FRAP values Ferrous species may bind to chelators within FRAP and react with antioxidants in the samples tested Folin-Ciocalteu Transfer of electrons Good linear correlations Specific conditions for consistent results (Huang et al, 2005, Reagent (FCR) in alkaline medium when used with other (reaction time and temperature, FCR MacDonald‐Wicks et al, total phenol assay from phenols and antioxidant capacity ratio to alkali, reference standard and use 2006) other reducing assays (antioxidant of optical density at 765nm to avoid agents to form blue capacity vs total phenolic matrix interference) complexes profile) Reaction with reducing agents other than Convenient phenols Simple Non-standardised methods between labs using differing reference standards give several magnitudes of difference Trolox equivalent Oxidation of ABTS Wide pH range Instability at pH 7.4 (Arnao 2000, Prior et al, antioxidant by oxidants to 2005) Measures antioxidant False low TEAC values at 4-6 min capacity (TEAC) produce a coloured capacity of compounds assay metastable radical ABTS.+ radical not found in a biological and their products cation ABTS.+ system Estimation of endpoint values at pH 7.4 in 10 min

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Total antioxidant The reduction of Quick for select Requires 30-60 mins for more complex (Apak et al, 2007) potential assay Cu(II) to Cu(I) by antioxidants molecules using Cu(II) as an antioxidants at pH7 Low redox potential Issues with complex antioxidant oxidant allows for more sensitive mixtures measurement 2,2-diphenyl-1- The purple DPPH Simple DPPH only dissolved in organic media (Huang et al, 2005, picryl-hydrazyl- radical is reduced to MacDonald‐Wicks et al, Rapid Lipophilic antioxidant measured more hydrate (DPPH) yellow hydrazine 2006, Apak et al, 2007) accurately than hydrophilic assay No specialised equipment required Food matrixes with carotenoids may interfere with results Microplate adaptable Hydrogen atom Principle Advantage Disadvantage Reference transfer (HAT) assays Chemi- Luminescence of May change initiator to Specialised equipment needed (Prior et al, 2005) luminescence luminol and related differentiate specific Requires temperature control compounds under oxidants free radical attack Mechanism not known and may affect Sensitive to low-level data interpretation reactions Rapid Microplate adaptable Photo- Generation of No pH or temperature Weak correlation to ORAC and FRAP (Schlesier et al, 2002, chemiluminescence superoxide radicals restraints Vertuani et al, 2004, Prior An unknown mechanism which leads to using et al, 2005) Sensitive (nanomolar difficult data interpretation photochemicals in range) combination with chemiluminescence

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Oxygen radical Free radical initiator Kinetic and can measure Protein fraction of samples may interfere (Cao et al, 1997, Prior et absorbance reacts with a antioxidants with lag al, 2003, Prior et al, 2005, Fluorescein not lipid-soluble and capacity assay fluorescent probe phases MacDonald‐Wicks et al, decrease in fluorescence intensity in a (ORAC) 2006) Inexpensive non-polar organic solvent Different free radical Longer than 30 minutes initiators may be used Microplate adaptable

Total radical Free radical initiator Sensitive to all chain- Different endpoints may be used - (Schlesier et al, 2002, antioxidant reacts with R- breaking antioxidants incomparable between laboratories Apak et al, 2007) parameter (TRAP) phycoerythrin (R- Generates water-soluble Time-consuming assay PE) probe peroxyl radicals to begin Lag time lipid peroxidation

Microplate adaptable Β Carotene/crocin Autoxidation of Microplate adaptable Lag phase antioxidant effect unknown (Karadag et al, 2009) bleaching assay carotenoids by light, Application to food matrix difficult due heat or peroxyl to food colour pigment interference radicals Low reproducibility as crocin extracted from saffron

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2.12. Relationship between polyphenol concentrations and antioxidant capacity

As every polyphenol possesses different structural characteristics as observed from Section 2.4, they differ in reduction and oxidation potentials which greatly influence antioxidant capacity. This is demonstrated with observations by Duncan et al (2006) that the isolate fraction with the majority of p-coumaric acid in a peanut extract had the lowest antioxidant capacity (Duncan et al, 2006, while the greatest antioxidant capacity fraction contained low concentrations of compounds unidentifiable by HPLC ) though p-coumaric acid has been established as the predominant phenolic compound found in peanuts kernels (Talcott et al, 2005, Mattila and Hellström 2007).

A prior study by Phan-Thien et al (2012) collected fractionations of known HPLC quantifications for the evaluation of antioxidant capacity showed polyphenols present in the highest concentrations (p-coumaric acid, ferulic/sinapic, caffeic/vanillic acids) contributed to the most antioxidant capacity. On the other hand, resveratrol and a quercetin co-eluting fraction had the highest antioxidant capacity in contrast to their low concentrations in the kernels (Figure 2.9) (Phan-Thien 2012).

Figure 2.9 Mean ORAC antioxidant capacities of eluted peaks collected during HPLC of the enzymatic extract of D147-P8-6F. Adapted from (Phan-Thien 2012).

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2.13. A need for a simple and rapid antioxidant capacity assay for peanut seed screening

The role of rapid kernel phenotyping in breeding is critical – as currently favoured pedigree or modified pedigree methods (Section 2.7) give maximum segregation for all selectable traits. Hence, the rapid and accurate selection of kernels with desirable levels of specific quality traits is essential for successful breeding. In current times, breeding for quality and explicit functional food traits brings the distinct need for determining the quality or specific functional food trait, i.e., oleic acid and polyphenols. Polyphenols are a much larger array of compounds – making them challenging targets for selections. Nevertheless, polyphenols are antioxidants and their hydrogen transfer or electron donation allows us to quantify this feature as antioxidant capacity.

Kernel screening using near infra-red (NIR) spectroscopy for peanuts has been used to determine moisture content (Govindarajan et al, 2009) and oleic and linoleic fatty acid content (Tillman et al, 2006, Kandala et al, 2012) analysis by NIR spectroscopy. There had been unsuccessful attempts in the application of NIR in the detection of antioxidants (Lee et al, 2016) and there is currently no distinct rapid method collectively in peanut kernels. There is an urgent need for the development of a rapid antioxidant capacity assay that can facilitate high- throughput screening of superior seeds during peanut breeding.

In recent years, attempts to fill this void by using nanoparticles as an alternate rapid colourimetric test has emerged. Silver and gold nanoparticle assays specifically designed for the detection of antioxidants have been developed with reduced assay time, high sensitivity and increased affordability (Scampicchio et al, 2006, Wang et al, 2007, Özyürek et al, 2012, Szydłowska-Czerniak et al, 2012, Vilela et al, 2015). The most recent advancement includes the capping of silver nanoparticles with poly(vinyl alcohol) enhancement (Teerasong et al, 2017) or gold nano-shells (Ma and Qian 2010) to assist in particle size increase.

2.14. An introduction to nanoparticles

Plasmonic nanoparticles are particles of nanoscale noble metals that absorb and scatter light efficiently with their full d-orbitals (Petryayeva and Krull 2011). As the nanoparticles are exposed to electromagnetic radiation with wavelengths larger than the conductive nanoparticles, localised oscillation or excitation of the free surface electrons in the conduction

Yan Yee Poon z3160325 band of the nanoparticle occurs. This visible occurrence is known as localised surface plasmon resonance (LSPR) (Wei et al, 2015). When LSPR occurs, the frequency of light-induced on the nanoparticles matches the oscillating valence electrons (Rivera et al, 2012) and the electron cloud displaces relative to particle nuclei leading to coulombic attraction which revives and continues this existing resonance (Petryayeva and Krull 2011).

The resonating frequencies are dependent on the dielectric function of the nanoparticle and the medium, material composition, shape, size and distance between each of these particles (Petryayeva and Krull 2011, Nanocomposix 2016b). The wavelengths for the plasmon peaks in these nanoparticles may be adjusted by various parameters in the synthesis methods of the nanoparticles themselves (Sherry et al, 2006). With the occurrence of LSPR, electromagnetic field oscillation localisation and intensity increases nanoparticle sensitivity to changes in their local refractive indexes which then could be observed as spectral shifts (Petryayeva and Krull 2011). As plasmonic nanoparticles are exposed to light, photons are scattered (visible radioactive decay) or absorbed (thermal energy produced from photon conversion) by LSPR (Petryayeva and Krull 2011, Wei et al, 2015). The sum of the absorption and scattering is expressed as extinction and is the remaining total light not transmitted through mediums (Petryayeva and Krull 2011, Nanocomposix 2016b) which may be measured by conventional UV-vis spectrometry (Sherry et al, 2006). Nanoparticles are able to display high-intensity colours from high molar extinction coefficients (up to 1011 M-1 cm-1) (Petryayeva and Krull 2011) and the resonance for silver nanoparticles lies within the visible reason of the electromagnetic spectrum which elevated adsorption and scattering to display vibrant colours in solution (Rivera et al, 2012). This colour changing property of nanoparticles may be exploited for use as an inexpensive high throughput biochemical sensor (Sherry et al, 2006).

2.14.1. Effect of nanoparticle size and shape on solution colour

The Mie theory established in 1908 (Mie 1908, Sherry et al, 2005) is a precise analytical solution to Maxwell’s equations which explains LSPR and resulting absorption and scattering abilities for highly symmetrical particles (Draine and Flatau 1994, Amendola et al, 2010). When the physical and mechanical properties of a material may vary with differing orientations within the crystalline structure, the material is said to be anisotropic and the reverse, when the material properties are constant in all directions the material is isotropic (NDT). Small isotropic

Yan Yee Poon z3160325 silver or gold nanospheres when subjected to the Mie theory, should have one single plasmon band while anisotropic nanoparticles should display two or more. The larger and more complex particles, in theory, should have higher modes of plasmon excitation and result in additional bands present (Figure 2.10) (Jin et al, 2001). Position and intensity of such plasmon peaks have been demonstrated to be influenced by the size and shape of small Silver nanoparticles (AgNPs) into prisms from spheres (Jin et al, 2001).

Figure 2.10 UV-vis spectrum of anisotropic and isotropic nanoparticles (Zengin et al, 2014).

The wavelengths between 400-700 nm are visible to the human eye and each wavelength are detected as a specific colour (Nanocomposix 2016b) (Figure 2.11). The perceived colour is seen when light passes through an object at certain wavelengths as remaining light (other colours) are absorbed and scattered at the other wavelengths. The effect also occurs with plasmon resonances of nanoparticles where the colours corresponding to the wavelengths absorbed would not be detected by eye, but the wavelengths reflected are observed.

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The nature and frequency of resonance adsorption peaks are dependent on the nanoparticle surface polarisation. The surface electron oscillation frequency would also determine which wavelengths the plasmon bands occur and differences in path lengths by varying shapes and sizes. Shapes nanoparticles may take include spheres, prisms, cubes, rods, bipyramids, triangles, octahedrons, nano-shells and nano–stars which would all change the LSPR absorption within the visible and infrared regions of the electromagnetic spectrum (Petryayeva and Krull 2011). As plasmonic nanoparticle size increase, peak extinction wavelength also increases which leads to the peak shifting towards longer wavelengths (towards the red light of the visible spectrum) branded as a red-shift and the opposite movement towards shorter wavelengths (towards the blue region) is termed a blue-shift. Spectral peak shift may also result from changes to shape, the sharpness of edges, aggregation or nanoparticle coating.

2.14.2. Nanoparticles as antioxidant probe platform for a new antioxidant capacity assay

Complex food mixtures have been examined for antioxidant capacity measurement using nanoprobes and biosensors have shown no interference from compounds such as citrate, oxalate, amino acids, reducing sugars and fruit acids, indicating the potential for diverse applications (Vilela et al, 2015, Apak et al, 2018). Studies on gold nanoparticle growth using phenolic acids, black and green tea, orange juice by Scampicchio et al (2006) was performed against an electrochemical based method and a significant correlation (R = 0.993) was observed

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(Scampicchio et al, 2006). Multiple studies on tea, pear, wine, honey (Vilela et al, 2012), from fruit juices without extraction (Della Pelle et al, 2015) and olive oil extract in an aqueous solvent (Della Pelle et al, 2015, Della Pelle et al, 2015) have all shown high correlation with polyphenol antioxidant presence.

Quercetin, daizeol and puerarin were shown to result in unique spectra using electrochemical and optical techniques in assessing the enlargement of AuNP when reacted with spherical gold nanoparticles at high concentrations ( Figure 2.12) (Wang et al, 2007). The four phenolic hydroxyl groups of quercetin resulted in the highest reducing power indicated by the significant of resultant plasmon resonance peak intensity. A medium-sized shift in spectra was formed by the two phenolic hydroxyl groups of daizeol. As puerarin contains only one phenolic hydroxyl groups, it has the least reducing power, giving the smallest spectral change of the three flavonoids. Significant peaks are only observed at higher concentrations as greater amounts of the reductant are needed on the surface area of a spherical particle to increase the overall size. Other similar nanoparticle-based assays have been developed with the use of PVA stabiliser (Teerasong et al, 2017) and silicon dioxide gold nanoshells (Ma and Qian 2010). Such techniques have been employed to minimise the amount of antioxidant required to reduce metal ions and increase assay sensitivity. AgNPs have been preferred in comparison to AuNPs though both are effective in using reductants for nanoparticle growth, as the lower reducing potential of Ag (I) has sharper and stronger resonance bands for greater sensitivity, selectivity and sensing resolution potential than Au (III) (Özyürek et al, 2012), the better scattering properties of Ag over Au make it an improved sensor in analytical science (Vilela et al, 2015).

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2.14.3. Silver nanoparticles as antioxidant probe platform for antioxidant assay use

Recent research in the area of antioxidant detection with AgNPs has been focused on seed formation for synthesis or the use of nanospheres (Özyürek et al, 2012, Szydłowska-Czerniak et al, 2012). A method for the measurement of antioxidant capacity in polyphenols using spherical AgNPs has been previously developed with citrate stabilised seed particles and a plasmon resonance band at 423 nm has been identified for quantification using a spectrophotometer (Özyürek et al, 2012). This method has been tested with flavonoids, and several phenolic and hydroxycinnamic acids over a wide concentration range with a linear trend and has shown to be comparable with cupric reducing antioxidant capacity (CUPRAC) assay obtained results (R= 0.936).

Positive correlations of antioxidant capacity assay (DPPH and FRAP) results and spherical AgNP response were also shown with rapeseed phenolic acid (sinapic, caffeic and gallic acids), ascorbic acid and quercetin (Szydłowska-Czerniak et al, 2012). The established detection range of AgNP (0.03 – 0.21 µmol mL-1), was demonstrated to be wider than modified FRAP (0.001 – 0.018 µmol mL-1) and the DPPH assay (0.005-0.16 µmol mL-1). The correlations of FRAP, DPPH and the AgNP developed were shown to be linear and positive, though AgNP response should be comparable to electron transfer (ET) based antioxidant capacity assays (FRAP and FC, p< 0.05), not all ET assay correlate (ABTS assay, p>0.05) as AgNP do not reflect radical scavenger effects (Vilela et al, 2015). Further work in the following year by Szydłowska- Czerniak et al (2013) performed on 15 rapeseed varieties observed comparable precision with the incorporation of FC assay (RSD = 0.8–3.6%) in addition to DPPH (RSD = 0.7–2.1%) and FRAP (RSD = 1.0–4.4%).

2.15. Summary

The intracellular antioxidant glutathione prevents cellular material oxidation and deterioration of cell functions inflicted by ROS and thus hindering aging. Results of numerous studies have implied exogenous antioxidants through diet or supplementation to also quench the action of ROS and oxidative stress to prevent disease through similar mechanisms. While peanuts have been prized as a valuable agricultural crop for its high oil and protein content, it is also high in polyphenolic acid contents compared to numerous cereals, oilseeds and legumes. Aside from polyphenolic acids, stilbenes and flavonoids are likewise abundant; plant phenolics are 78

Yan Yee Poon z3160325 produced in crops as defensive compounds against pests and diseases; which potentially translates to increased yield. As peanuts are an existing high-value crop with evidence to contain antioxidative compounds, there is potential to develop functional food traits and further increase crop value by the conventional breeding and development of high antioxidant expressing cultivars.

To achieve this, the genetic control for heritability, G, E and G x E interaction of antioxidant expression in peanuts must first be confirmed in peanut kernels, ideally by the ORAC assay as established by Phan-Thien et al (2012). The use of quantitative proteomics in the investigation of differentially expressing protein biomarkers between high and low antioxidant expression would improve our understanding of related biosynthesis and metabolic pathways. Using the proteomics information obtained, the compounds of interest most likely to be derived from identified pathways could then be highlighted amongst detected antioxidative compounds. GCMS is ideal in the confirmation of previously reported peanut polyphenol antioxidants due to its comprehensive compound library available and LC-MS/MS would be best used for the survey of unknown antioxidative compounds in kernel samples. The mass transitions listed in Table 2.5 will be used in combination with standards for the detection of phenolic acids, flavonoids, anthocyanins and stilbenes in the extract of selected parent RIL, D147-p3-115. Nanoparticles have shown to be reducible by antioxidant compounds while solution colour reflects particle size where growth is induced and therefore the colour change of solution and UV spectra. This principle has been exploited for use in silver and gold nanoparticles in the development of antioxidant capacity assays. The optimisation of nanoparticle material, shape and sample preparation methods have possible implication for enhanced visible colour change in the solution. With increased visibility and simplified sample preparation, minimal personnel training, less specialised equipment and more rapid assay time to be used on the field may be attained towards faster, simpler and more accurate kernel screening for selective breeding.

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3. GENOTYPE, ENVIRONMENT AND GENOTYPE X ENVIRONMENT INFLUENCE ON ANTIOXIDANT CAPACITY IN PEANUT KERNELS 3.1. Background and aims

To conventionally breed higher polyphenol antioxidant expressing peanut lines, the heritability of polyphenol antioxidant expression must be confirmed. Trait heritability is dependent on genotype (G), environment (E) and genotype by environment (G x E) interaction. The genotypic response is an expression of the genetic disposition of each cultivar, while environmental response encompasses effects of location and growing conditions, including soil type, rainfall, pest and disease pressures. A thorough understanding of G x E interactions is essential to ensure effective cultivar evaluation and mega-environment identification (Yan et al, 2000, Yan and Tinker 2006). The extent of environmental influences on genetic polyphenol antioxidant expression plays a key role as does G x E interactions. The greater the retention of genetic influence on phenotype against environmental pressures, the more likely conventional breeding will be able to achieve genetic gains in increased polyphenol antioxidant expression.

Prior preliminary studies on polyphenol antioxidant expression have already suggested low G x E for this trait, and hence antioxidant capacity is likely to be under reasonably strong control in Arachis hypogaea (Phan-Thien et al, 2014). The study tested ten genotypes with varying antioxidant capacities grown under five agricultural environments with differing disease and environmental pressures.

This chapter reports experiments that continue the examination of the genetic stability of antioxidant capacity expression using a sample of Recombinant Inbred Lines (RILs) developed for polyphenol antioxidant expression by the Peanut Company of Australia, following previous research by Phan-Thien et al (Phan-Thien et al, 2013, Phan-Thien et al, 2014). The ORAC assay was used on multi-kernel samples to assess genotypic variation in polyphenol antioxidant expression across four different environments from 2013-2015. The extent of genotype, environment and genotype by environment interaction for polyphenol antioxidant expression in peanuts was confirmed with the collected data. Using these results, RILs with diverse polyphenol antioxidant expression were then selected for further detailed studies of polyphenol antioxidant compounds and related biomarkers that are associated with increased expression.

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3.2. Materials and methods

3.2.1. Chemicals and reagents

Product numbers of all chemical and reagents provided are used by respective companies where products were sourced from. HPLC grade methanol (34860), acetonitrile (1.00030), trolox (238813), caffeic acid (C0625), p-coumaric acid (C9008), ferulic acid (128708), o-coumaric acid (H22809), salicylic acid (S5922), resveratrol (R5010), daidzein (D7802), t-cinnamic acid (C80857), quercetin (PHR1488), fluorescein (46955), APPH (440914) and TFA (T6508) was all sourced from Sigma Aldrich Co (St. Louis, MO, USA). The n-hexane (AJA2508), potassium phosphate dibasic (ACR424190025), sodium phosphate monobasic (ACR389870025), acetone (AJA2546) and acetic acid (AJA2281) were purchased from Ajax Fine Chem (Waltham, MA, USA). The trolox (product code: TROLOX-STD) for use as internal standard CCV was obtained from AMSBIO LLC, Cambridge, MA 02141, USA. Enzymes alcalase (Product name: Alcalase® 1.5 L FG, Nominal activity: 2.59 AU-A/g, E.C No: 232-752-2, batch no. PLN05443) and celluclast (product name: Celluclast® 1.5 L, Activity: 763 EGU/g, (E.C No: 3.2.1.37, batch no. CCN03149) for the digestion of protein and cellulose were kindly donated by Novozymes (Kalundborg, Denmark).

3.2.2. Sample material

The population of recombinant inbred lines (RILs) resulting from the parents selected through the work by Phan-Thien et al mentioned in Section 2.7.2, (Phan-Thien 2012) was normally distributed in antioxidant capacity expression, as covered in Section 3.2.5 and 3.4 and

Figure 3.4. A sub-set of 22 peanut lines (the parents and 20 RILs) were subsequently used for the work conducted and reported in this chapter.

3.2.3. Sample preparation: Peanut deskin and defatting

All kernels used in this study had the testa and germ removed manually, frozen with liquid nitrogen and then ground to a powder using an analytical mill (A11 basic model, IKA Works Staufen im Breisgau, Germany). For each 5g of peanut flour; 40 mL n-hexane was added and vortexed then sonicated in a sonicating water bath (50 kHz, 5 min at 40°C) and centrifuged

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(9,500 x g, 10min). The hexane was discarded, and the defatting procedure was repeated twice. The sample was then dried overnight in the fumehood to remove residual hexane.

3.2.4. Instruments

The analytical mill used was A11 basic model (IKA Works Staufenim Breisgau, Germany) and Clifton™ Stainless Steel MU-14 Model Unheated Ultrasonic Bath was used for sonication (Fisher Scientific, Hampton, New Hampshire, USA). Centrifugation was done in Heraeus Multifuge X3R refrigerated centrifuge and Heraeus Pico 17 centrifuge (Thermofisher, Waltham, MA, USA). The PolarSTAR Optima multi-mode microplate reader (BMG Labtech GmbH, Ortenberg, Germany) with 485 nm excitation and 520 nm emission filters and automatic reagent injectors were used to measure the fluorescence intensity for ORAC assays. The assay was performed in black 96-well FLUOTRAC 200microplates (flat bottom, chimney well, medium-binding) (655076, Greiner Bio-One GmbH, Germany). SPE was performed with a SupelcoVisiprep DL 24-port vacuum manifold with drying attachment (57265 and 57124, Sigma-Aldrich Co., St. Louis, MO, USA) and also Strata-X (33 μm, 85 Å) polymeric reversed- phase sorbent tubes (100 mg/3 mL, 8B-S100-EBJ, Phenomenex, Lane Cove, NSW, Australia). A Gemini reversed-phase C18 (5 μm, 110 Å, 250 × 4.6 mm;398123-3) column with SecurityGuard C18 (4 × 3.0 mm) guard cartridge (00G-4435-E0 and AJ0-4287, Phenomenex, Lane Cove, NSW, Australia) was used in a Shimadzu prominence system (Shimadzu Scientific Instruments, Columbia, MD 21046 USA) for HPLC.

3.2.5. Genotype x Environment (G x E) trials

For this thesis, two cultivars which demonstrated a range in antioxidant capacity from the full season breeding lines in the study by Phan-Thien et al (Figure 2.8, 2012 and 2014) were selected as parents to generate a RIL population. D147-p3-115 and Farnsfield were selected to generate a population with a normal distribution of antioxidant capacity expression (Phan- Thien 2012, Phan-Thien et al, 2014). A series of multi-location variety evaluation trials designed to assess G x E interaction for polyphenol antioxidant content in a subset of 20 x P27 RILs and parents D147-p3-115 and Farnsfield, were conducted by the Peanut Company of Australia (PCA, Kingaroy, QLD, Australia) during the period 2013 to 2015. In 2013/14, two trials were planted at two different locations, one in southern inland Queensland (Queensland 82

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Department of Agriculture and Fisheries (QDAF), Taabinga Research Station, near Kingaroy- 26°35'37.5"S 151°49'45.7"E), and the other in southern coastal Queensland (QDAF Bundaberg Research Station - 25°51'51.2"S 152°20'54.6"E). In 2014/15, two trials were planted at two different locations, one in southern inland Queensland (DAFQ Taabinga Research Station- 26°35'37.5"S 151°49'45.7"E), and the other in northern Queensland on the Atherton Tableland (DAFQ Kairi Research Station - 17°12'56.4"S 145°32'52.2"E). The environmental stress conditions were discussed in Section 2.7.3. The locations are shown on the map in Figure 3.1, with annual averages of various weather parameters between 1980-2010 in the three locations tested shown in Figure 3.2. Detailed tables of the weather statistics are included in the Appendix, Supplementary Table 10.1, Table 10.2 and Table 10.3.

Each variety evaluation trial was a randomised block design, with 44 plots (2 x 90 cm by 5 m rows in length), consisting of 20 P27 RILs, and the two parents Farnsfield and D147-p3-115, with 2 replications. At harvest, pods were removed from plants in each plot and dried down to safe pod moisture (~10%) for storage. Pods were then shelled out using a mechanical sheller and kernels then stored in paper bags in a cold room at 80 ̊C. Kernel samples were assayed for a range of polyphenol antioxidant components as outlined in the sections below with randomization of sampling in our experiments by sourcing kernels from plots separated from each other to account for biological variation.

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3.3. Sample preparation for HPLC

3.3.1. Native extraction

80% Methanol (25 mL) was added to the defatted peanut flour and mixed on a ten-roller mixer overnight at room temperature. The tube was then centrifuged (9,500 x g, 10 min) and the supernatant collected in a pear-shaped evaporation flask. This was repeated twice with methanol (25 mL) along with sonication. The pear-shaped evaporation flask with the methanolic extract was placed in a water bath at 40-50°C and flushed with nitrogen gas till the volume is reduced to several millilitres. The sample was transferred to a screw cap tube for centrifugation (9, 500 x g, 10 min) and filtered through 0.45 µm PTFE filter for SPE then diluted with water (pH to 2 with HCl) to result in a 5% methanol solution.

3.3.2. Enzymatic extraction

Enzyme-treated extracts were prepared by incubating the defatted peanut flour with alcalase (1 mL, 5U/g), celluclast (1 mL, 700GU/g) and water (8 mL), 24 hr at 37°C. Methanol (40 mL) was added and the mixture was agitated overnight on a ten-roller mixer. The optimum temperature for use of Alcalase was recommended at pH 8 at 37°C while for Celluclast at pH 6 at 52°C, hence the experiment was conducted at neutral pH with a higher concentration of Celluclast than Alcalase for an increased length of time. The overall concentration of Alcalase and Celluclast was approximately 0.13U/mL-1 and 17.5GU/mL-1 respectively which is

Yan Yee Poon z3160325 comparable with recent reports of optimum enzyme concentration (168 FGB/100 g) for extraction of polyphenols from green yerba mate (Heemann et al, 2019). Though the incubation time was significantly increased in this study, the doubling of cellulase concentration on polyphenol extraction yields (Ghandahari Yazdi et al, 2019) and the plateau of extraction yields over time (Heemann et al, 2019), has been shown to have a negligible effect which suggests minimum effects with the increase in time. The tube was then centrifuged (9,500 x g, 10 min) and the supernatant collected in a pear-shaped evaporation flask. This was repeated twice with methanol (25 mL) while sonicating. As with the native extract described previously, the pear- shaped evaporation flask with the methanolic extract was placed in a water bath at 40-50°C and flushed with nitrogen gas till volume reduced to several millilitres. The sample was transferred to a screw cap tube for centrifugation (9, 500 x g, 10 min) and filtered through 0.45 µm polytetrafluoroethylene (PTFE) filter for SPE then diluted with water (pH to 2 with HCl) to result in a 5% methanol solution. The acidified water (pH 2) used for dilution to determine the phenolic composition of the samples, though may degrade polyphenolic compounds at pH 2, was shown in a previous study by Phan-Thien to have higher recovery rates used in SPE loading conditions in 19 tested polyphenolic compounds (Phan-Thien 2012).

3.3.3. SPE and HPLC on sample extracts

All peanut samples were prepared in triplicates for each extraction method (native and enzyme extraction). A HPLC standard mixture was injected every ~20 separate injections as quality control for correction of drift in the retention times, intensity and peak spectra changes.

SPE was performed with a SupelcoVisiprep DL 24-port vacuum manifold with drying attachment (57265 and 57124, Sigma-Aldrich Co., St. Louis, MO, USA) and also Strata-X (33 μm, 85 Å) polymeric reversed-phase sorbent tubes (100 mg/3 mL, 8B-S100-EBJ, Phenomenex, Lane Cove, NSW, Australia) as described by (Phan-Thien 2012). The SPE column was pre- condition with acidified methanol (5 mL, pH2) and then washed with water (5 mL). The 5% methanolic extracts from the native or enzymatic extraction (Sections 3.3.1 and 3.3.2) was loaded under vacuum at 1 mL min-1. The sorbent was washed with water (10 mL) then flushed with nitrogen gas for 15 min. Elution was performed with HPLC grade methanol (2 mL) and evaporated with nitrogen to 1mL and then diluted with MilliQ water to a final volume of 2 mL.

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A 0.45 µm PTFE syringe filter was used to filter the sample and finally transferred to autosampler vials for HPLC.

A Gemini reversed-phase C18 (5 μm, 110 Å, 250 × 4.6 mm; catalogue No. 398123-3) column with SecurityGuard C18 (4 × 3.0 mm) guard cartridge (00G-4435-E0 and AJ0-4287, Phenomenex, Lane Cove, NSW, Australia) was used in a Shimadzu prominence system (Shimadzu Scientific Instruments, Kyoto, Kyoto Prefecture, Japan) for HPLC. The mobile phase solutions A was 0.1% tetrafluroacetic acid (TFA) in MilliQ water and solution B was 0.1% TFA in acetonitrile (1 mL per L). A 30µL sample was loaded on HPLC using the following elution gradient: 0 min 15%B, 30 min 30%B, 31 min 15% B, 35 min 15%B, the flow rate at 1.5 mL min-1. An HPLC standard mixture was injected every 20 injections as quality control for correction of drift in the retention times, intensity and peak spectra changes as described by (Phan-Thien 2012).

3.3.4. Sample preparation: polyphenol antioxidant extraction and ORAC assay

The sample was ground and defatted as Section 3.2.2 then prepared for ORAC as below (Prior et al, 2003). Acetone/water/acetic acid (70:29.5:0.5, v/v/v, 10 mL) was added to each sample and sonicated (50kHz, 37̊C, 5 min) then centrifuged (201.3 x g, 15 min). The supernatant was collected as hydrophillic extract and 25 µL of standards (quadruplicate) or extract (triplicate) was dispensed to black microtitre plate by a single channel pipette.

A phosphate buffer of 75 mM at pH 7.4 was prepared from potassium phosphate dibasic and sodium phosphate monobasic and fluorescein (150 µL, 70 nM) and APPH (25 µL, 140 mM) was prepared using this buffer. The fluorescein and AAPH was prepared fresh and incubated 1 hour and 30 min at 37°C respectively before use and discarded after 10 hours of preparation. Trolox standards prepared at 10, 25, 50, 75 and 100 µM using the phosphate buffer and stored in the freezer as aliquots to be used when needed. An external CCV standard of trolox was made up to 25 µM and 75 µM with the previously prepared phosphate buffer (TROLOX-STD, 1.5mM, 20ul, AMSBIO LLC, Cambridge, MA 02141, USA). Edge wells were filled with water to provide a thermal buffer as shown in Figure 3.3. The fluorescein and AAPH were dispensed automatically by microplate reader injectors after priming in a plate reader and the assay was run for 2 hours and 15 min with 121 cycles and injection at cycle 1, reading commences at cycle 5 to allow for complete dispensing. 86

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Figure 3.3 Microplate plan of ORAC assay.

3.3.5. Stock standard solutions

Standards were prepared by dissolving 2 mg of polyphenol antioxidant compound in 2 mL methanol, with the solution being vortexed and stored in a freezer at -80 ̊C. Working standards were prepared fresh on the day of analysis. Standards prepared included caffeic acid, p- coumaric acid, ferulic acid, o-coumaric acid, salicylic acid, resveratrol, daidzein, t-cinnamic acid and quercetin.

3.3.6. Known compound quantification by HPLC

Reference HPLC standards were further diluted with methanol to 500 ng. mL-1 and used to accompany samples of D147-p3-115, Farnsfield, P27-p272, P27-p036 and P27-p362 injected in the HPLC. Quantification of standard polyphenol antioxidants from the samples was performed with Shimadzu LCsolutions version 1.25 Quant browser.

3.3.7. Data analysis

The raw ORAC values expressed in Trolox Equivalence (TE, µmol.g-1) were taken from triplicate samples within the BMG Optima MARS data analysis software and then normalised and mean values calculated in Microsoft Office Excel. Genotypic, environmental and genotype by environment effects were calculated using standard analysis of variance (ANOVA) in Graphpad Prism 7 and also in Genstat statistical software package (VSN International, Version 11.1). The covariance of analyte concentrations by varying assays was assessed using Pearson’s Correlations coefficient using Graphpad Prism 7. GGE biplots of the ORAC values

Yan Yee Poon z3160325 were also were generated using the GGE biplot software available on http://www.ggebiplot.com.

3.4. Results and discussion

3.4.1. Identification of RILs with contrasting total polyphenol antioxidant content

Parents for the RIL population (P27) were genotypes selected specifically for their differing polyphenol antioxidant content and included a consistently high expressing antioxidant capacity genotype 'D147-p3-115' (Runner type from the Peanut Company of Australia peanut breeding program) and a low-mid antioxidant capacity genotype 'Farnsfield' (Runner type from ACI Seeds, Georgia, USA - PBR: Plant Varieties Journal 2010, 23 (4)) following on from the study of Phan-Thein et al (Phan-Thien 2012, Phan-Thien, Wright et al. 2014). These 2 contrasting antioxidant content parents were then crossed and subsequently selfed from the F1 to F6 generations by taking a single ‘pod pick’ from 156 individual plants from the F2 – F6 generation. This procedure resulted in an unselected population of 156 recombinant inbred lines (RILs). In November 2012, a 5m row of F7 seeds from the 156 RIL lines was subsequently planted out at the QDAF Taabinga Research Station. In May 2013, F8 kernels harvested from these F7 plants in a RIL trial conducted at the Queensland Department of Agriculture and Fisheries (QDAF) Taabinga Research Station, Kingaroy, Queensland were assayed for total polyphenol antioxidant content using the ORAC method. A subset of 20 RILs was selected with a very large range of ORAC content (G.C. Wright, unpublished data) based on either high, mid or low polyphenol antioxidant (ORAC) content, and subsequently used in the multi- location trials described in Section 3.2.5.

Differing polyphenol antioxidant content parents, D147-p3-115 (consistently expressing high antioxidant capacity) and Farnsfield (a low-mid antioxidant capacity) were selected as parents for the P27 RIL population following from the study of Phan-Thein et al (Phan-Thien 2012, Phan-Thien, Wright et al. 2014). A collection of 156 RILs were selected from the RIL population and analysed for polyphenol antioxidant content. A mean TE of 1128 was observed within the RIL population which had a normal distribution. The highest polyphenol antioxidant expressing RIL (P27-p145) had a TE of 1678 and the lowest (P27-p373) having a TE of 582, representing an extremely large range in TE (~2.8 times) (Figure 3.4).

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Figure 3.4 Normal distribution histogram and plot of TE (Trolox equivalence) ORAC values of the recombinant inbred line (RIL) collection from 2013.

3.4.2. G, E and G x E influence on RILs

3.4.2.1. ANOVA results A cross-site ANOVA was performed on ORAC TE values from the four (4) multi-location experiments conducted at Taabinga, Bundaberg and Kairi Research Stations during 2013-2015. Genotypic effects over the four environments were found to be highly significant (P< 0.0001), while environmental effects and G x E interaction were non-significant (P = 0.0743 and 0.2913, respectively) (Table 3.1). This analysis indicates polyphenol antioxidant expression of the RILs is highly influenced by genotype, whilst environment and G x E interactions have relatively minor influences. These results strongly suggest that polyphenol antioxidant expression in peanut kernels is likely to be controlled by a small number of genes, and that genetic improvement by breeding and selection should be possible. The absence of G x E interaction within these studies indicates that any single test environment could be used for the genotypic evaluation within this RIL collection (Yan and Tinker 2006).

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Table 3.1 Two factor ANOVA analysis in Prism with replications on ORAC values from Taabinga Research Station (2013/14), Bundaberg Research Station (2013/14), Taabinga Research Station (2014/15) and Kairi Research Station (2014/15) ANOVA Factors P-value Influence Two way with replication Genotype < 0.0001 Highly significant Two way with replication Environment 0.0743 Non-significant Two way with replication Interaction 0.2913 Non-significant

The 20 RILs and parents expressed antioxidant capacity over a wide range with P27-p272 having lowest and P27-p36 the highest TE values, representing nearly a 2 fold difference in antioxidant expression (Figure 3.5). The error bars show 95% CI of the mean and dotted lines show the highest and lowest ranges of each RIL. The trend of polyphenol antioxidant expression among RILs when averaged over the contrasting environments (i.e., years and regional locations) can be observed by the spanning height of the dotted lines of each genotype, with P27-p399 and P27-p18 having very consistent polyphenol antioxidant expression among replicates (narrow span of the range marked between the dotted lines), compared to the TE values of the replicates from all environments for the other RILs.

One of the largest variations throughout the 4 environments was the RIL P27-p153, which had the widest range of TE values amongst replicates. As observed in Figure 3.5, RIL polyphenol antioxidant expression varied over the 4 environments, the highest overall RIL being P27-p036 and the lowest P27-p272. Parents Farnsfield and P27-p3-115 were found to be mid, and mid- high in TE values as found in previous investigations (Phan-Thien 2012).

Figure 3.5 ORAC TE values averaged across all environments. Error bars with 95% CI with the range in dotted lines.

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The average ORAC TE values for each location along with the highest and lowest TE values are shown in Figure 3.6. There are approximately 100-200 TE value differences between the mean of each location, although the minimum values in 2014 were higher compared to 2015 where they tended to be lower. The maximum TE values of each environment were observed to be comparable except Taabinga Research Station which was found to be approximately 1000 TE units higher. Despite the notable differences between the minimum and maximum TE values across the 4 environments, the lower and upper quartile and median of each location did not differ substantially.

3.4.2.2. GGE biplot The TE values results obtained from the ORAC assays on the RIL collection in 2014-2015 were plotted using a Genotype (G) + Genotype x Environment (G x E) analytical model – “GGE BiPlot” as developed by Yan et al (Yan et al, 2000, Yan and Tinker 2006) to further understand the effect of G x E interactions present in the multi-environment trials (MET). The statistical model allows the application of site regression multiplicative interaction principal components and hence allow the graphical presentation of test environments at Taabinga, Bundaberg and Kairi, NQ.

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The first two Principal Components (PC1 and PC2) of the total GGE variation for ORAC values across the 4 site regional trials are shown (Figure 3.7). This analysis explains 84.9% (PC1=60%, PC2=24.9%) of the total GGE variation and displays genotype and environmental influences, and genotype x environmental interactions on ORAC TE values of peanut kernels for each RIL. The biplot is environment centred (Centering = 2) with scaling and is environment-metric preserving; suitable for environment correlation studies whilst RILs are treated randomly (singular value partitioning, SVP = 2). The general consensus is that R2 values over 70% give a good fit for plot data, and hence the analysis is valid (Yan and Tinker 2006).

The red line represents the target environmental axis (TEA) which separates the 2014 and 2015 environments (Figure 3.7), suggesting each the environmental conditions experienced during each of these years differed similarly among the locations. The 4 environments were all broadly correlated as they were all located on the right-hand side of the biplot and pointed in the same direction. They did, however, fall into two separate clusters belonging to 2014 and 2015, thus suggesting strong yearly seasonal/influences. The close associations of Taabinga Research Stations and Bundaberg in 2014 indicates that the genotypic antioxidant capacities from both

Yan Yee Poon z3160325 locations had similar genotypic ranking. This suggests only one of these locations may be required to obtain maximum discrimination in antioxidant capacity among the genotypes within this RIL collection.

The cross-site/year ANOVA performed on ORAC values for the 22 RILs and parents showed no significant environmental effects (P = 0.0743, Table 3.1). There were significant main effects for ‘Genotype’ (P < 0.0001, Table 3.1) which are clearly illustrated in the GGE biplot. Non-significant G x E effects (P = 0. 2913, Table 3.1) were also found.

The ranking of 20 RILs and their parents for mean performance and stability is demonstrated across the 4 environments (Figure 3.8). The red line passing through the biplot origin is termed the average environment axis (AEC), which is defined by the average PC1 and PC2 scores of all environments. The intersection of the two lines represents the mean RIL performance across the 4 environments.

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The blue line that passes vertically through the biplot origin and perpendicular to the average environment coordination (AEC), denotes the average PC1 and PC2 values of all environments. It is found in the middle of the sample population with approximately half of the 22 genotypes on either side, confirming the normal distribution of polyphenol antioxidant expression in the RIL collection as Figure 3.4. The left-hand side cluster (including RILs P27- p116 and P27-p399) can be deemed as low antioxidant capacity RILs across all the tested environments as they lie on the left-hand side of the blue line away from the environmental vectors. The cluster on the right-hand side distributed on the environmental vectors (including RILs P27-p106, P27-p018, P27-p153 and P27-p362) show some of the higher antioxidant capacity genotypes.

The RILs that perform better than average across all 4 environments include all the genotypes located on the right-hand side of the blue AEC line passing through the origin and include RILs P27-p327, P27-p051 and P27-p395. RIL P27-p373 performed better than average in all test environments except Kairi Research Station 2015. In Figure 3.8, the blue lines from each genotype that run perpendicular to the red AEA (average environmental axis) line and parallel to the AEC, denotes the stability of each genotype; i.e., the greater its distance of the line from the AEA, the greater the instability (i.e., higher G x E) of the genotype performance. The RILs that demonstrated the greatest instability were P27-p395 and P27-p070, whereas the RILs with the greatest stability (shortest perpendicular distance from the AEA) were P27-p399, P27-p018, P27-p362, P27-p106 and P27-p153.

The inconsistent polyphenol antioxidant expression (instability) of P27-p395 and P27-p070 observed from Figure 3.5 was also found in the GGE Biplot, as evidenced by the largest distance from the centre red line. These genotypes were observed to be inconsistent for ORAC values across all four environmental vectors and hence showed high levels of the instability of genetic expression for polyphenol antioxidant in these environments.

The superior genotypes observed from the GGE biplot include P27-p 153, D147-p3-115, P27- p362 and P27-p177. P27-p362 was observed to be the most stable (i.e., lowest G x E) and highest performing (ORAC TE value) RIL in all 4 environments, which suggests that it may be the best adapted across all test environments. RIL P27-p036 gave the highest ORAC TE value tested in the study but had slightly higher instability compared to P27-p362. P27-p272

Yan Yee Poon z3160325 was observed to be the poorest performing RIL (lowest ORAC TE value) and yet had quite stable polyphenol antioxidant expression across all the tested environments.

3.4.3. Total antioxidant capacity and known polyphenol quantification of 5 RILs

With the use of ORAC TE values obtained, the overall antioxidant capacity phenotype of each RIL was confirmed. Though as previously discussed in Section 2.11, the antioxidant capacity of polyphenolic compounds may differ at the same concentration due to the available electrons or hydrogens to quench and terminate the oxidation. Prior studies by Phan-Thien et al found variations in free and matrix-bound states of key antioxidants quantified from five different genotypes, genotypic differences were found in resveratrol, p-coumaric acid, daidzein, ferulic and salicylic acid concentrations in peanut kernels. To further confirm the phenotype results and explore the variations in free and matrix-bound antioxidant expression, selected RILs were investigated in a preliminary study. Five RILs were selected to represent the highest and lowest ORAC TE expression (P27-p036 and P27-p272), a high stable ORAC TE expression (P27- p362) and the RIL parents (Farnsfield and D147-p3-115) for the confirmation of key antioxidant quantification by HPLC analysis.

Quantification of total antioxidant capacities in Trolox Equivalence (TE) using the ORAC assay confirmed the observed phenotypic traits of the five genotypes (P=0.0032 between genotypes), where RIL p27-362 showed significantly greater total antioxidant capacity (217% higher) when compared to RIL p27-272. A similar trend was also observed among the parent cultivars where the total antioxidant capacity for the mid-high parent D147-p3-115 was greater than the mid-low parent Farnsfield. These observations made for the two peanuts parent lines are in agreement with the patterns of the high- and low- antioxidant capacities measured (Phan- Thien, Wright, & Lee, 2013) and during the three harvest seasons between 2013-2015, also confirming that the antioxidant capacities of these peanut lines are likely to be under strong genetic control.

Differences in total polyphenols levels between the high and low antioxidant capacity genotypes were clearly reflected in the HPLC chromatograms. The polyphenolic compounds quantified from the extractions by HPLC correlated well with ORAC values (Figure 3.9). Antioxidant activity is directly proportional to total phenolic content and a recent study on germinated peanuts also found that phenolic compounds were the main contributor of 95

Yan Yee Poon z3160325 antioxidants (Yang et al, 2019). HPLC of extracts from non-enzymatic and enzymatic methods revealed quantitative differences between individual polyphenolic content across samples and confirmed the presence of free and matrix-bound polyphenols, which was consistent to previous studies (Phan-Thien, Wright, & Lee, 2013). Polyphenols quantified in D147-p3-115 (P=0.3258), p27-362 (P=0.0629) and p27-036 (P=0.9570) were not affected by extraction methods but Farnsfield (P<0.0001) and p27-272 (P=0.0006) were. This shows the possibility of genotype influencing matrix-bound polyphenols, particularly an increase in non-matrix- bound expression in higher antioxidant genotypes.

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From Figure 3.9, each RIL was observed to have differing levels of each polyphenol antioxidant quantified and native extraction by HPLC which correlated with the antioxidant capacity results from ORAC assays. Polyphenol antioxidants bound to the peanut matrix in native extraction were released by enzymatic extraction and resulted in overall higher polyphenol antioxidant levels found in the enzymatic extractions. Differences in the individual polyphenol antioxidants quantified were also observed between native and enzymatic extraction of each cultivar. Despite overall polyphenol antioxidant content increases with the enzymatic treatment, o-coumaric acid decreased.

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

Figure 3.10 HPLC-PDA chromatograms of the 5 RILs (from the bottom; D147-p3-115 and Farnsfield parents, low polyphenol antioxidant expressing P27- p272, high polyphenol antioxidant expressing stable p362 and unstable p036 of Taabinga, 2015) by a) native and b) enzymatic extraction. Standards in the bottom row; 1) caffeic acid, 2) p-coumaric acid, 3) ferulic acid, 4) o-coumaric acid, 5) salicylic acid, 6) resveratrol, 7) daidzein, 8) t-cinnamic acid and 9) quercetin.

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Differences in the total polyphenols levels between the high and low antioxidant capacity genotypes were clearly reflected in the HPLC chromatograms.

Figure 3.10 shows HPLC chromatograms for the five RILs, with the heights of peaks being proportional to their corresponding ORAC values observed from Figure 3.9. Antioxidant activity is directly proportional to total phenolic content and a recent study on germinated peanuts also found that phenolic compounds were the main contributor of antioxidants (Yang et al, 2019). Enzyme extraction of the bound caffeic acid in the samples lead to a slight increase compared to native extraction. While the effects of enzymatic extraction with caffeic acid differ from precedent study results (Phan-Thien et al, 2013), p-Coumaric acid (P=0.0414), o- coumaric acid (P=0.0009), ferulic acid/sinapic acid (P<0.0001), salicylic acid (P<0.0001), t- cinnamic acid, daidzein (P<0.0001), and resveratrol (P<0.0001) increased with the enzyme- treatment (Figure 3.9), showing that significant concentrations of these compounds were tightly associated with peanut matrix. Caffeic acid was found to be dependent on genotype. Matrix-bound fractions showed greater antioxidant capacity, supporting previous findings in peanut (Rocchetti, Chiodelli, Giuberti, & Lucini, 2018). Peanut phenolic acids and flavonoids also function as plant self-defence when responding against important disease-causing microbial pathogens (de Camargo, Regitano-d'Arce, Rasera, Canniatti-Brazaca, do Prado- Silva, Alvarenga, et al., 2017). Their effectiveness in terms of anti-microbial potency and antioxidant capacity could further enhance in the case of polyphenol-rich peanuts. There is a high correlation between total phenolics and antioxidant capacity, similar to previous studies by Phan-Thien et al (Phan-Thien, Wright, & Lee, 2013). HPLC analysis between the two parents validated ORAC findings, where D147-p3-115 showed higher total polyphenols contents compared to Farnsfield in non-enzymatic extractions, and the enzymatic method revealed that a significant quantity of polyphenols was matrix-bound (P=0.0153). Among the three selected RILs, p27-272 showed the lowest content in both extractions. Upon enzyme treatment, the polyphenols content extracted from RIL p27-362 was greater by 245% (P=0.0629), proving its polyphenol-rich phenotype expressing even higher matrix-bound polyphenols also confirmed by the higher total antioxidant capacity measurements seen using ORAC assay. This confirms that peanuts have high polyphenol contents with significant quantities as matrix-bound compounds.

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Both quercetin co-eluting unknown (QCU) and t-cinnamic acid varied in the free or bound form of the compound is greater depending on the RIL sample; this and the greater bound than free phenolic concentrations for all other quantified compounds except caffeic were in agreement with prior studies (Phan-Thien 2012). Caffeic acid was shown to decrease with enzymatic extraction in the previous study conducted (Phan-Thien et al, 2013), in D147-p3- 115, P27-p272 and P27-p362 it was observed to increase 126%, 35.5% and 41% respectively and where Farnsfield and P27-p036 decreased 5% and 4.4%; this difference may be due to the genotypic difference in cultivar line quantified.

QCU and t-cinnamic acid had similar trends in enzymatic extraction effects depending on the sample tested. The QCU peaks obtained differed in Farnsfield (107.5%), P27-p272 (60.8%) and P27-p362 (77.9%). This was also observed for t-cinnamic acid with Farnsfield (175.5%), P27-p272 (169.6%) and P27-p362 (279.8%), whereas D147-p3-115 and P27-p036 showed no change or a decrease in QCU (D147-p3-115 (0.4%) and P27-p036 (52%)). t-cinnamic acid decreased in both D147-p3-115 (32.5%) and P27-p036 (51.6%).

Phan-Thien et al (2012) experienced improved salicylic extraction by enzymes with all genotypes, with this being observed with four out of five RILs with D147-p3-115 having decreased concentration (by 17%) compared to native extraction. Similarly, these trends of segregated poorer and improved compound retention by enzymatic extraction were found in o- coumaric acid, with increasing enzymatic extraction in Farnsfield (149.3%) and decreasing in D147-p3-115 (90.4%), P27-p272 (4.2%), P27-p362 (66.7%) and P27-p036 (50%). In the results of such segregation of free and matrix-bound availability of caffeic/vanillic acid, o- coumaric acid, salicylic acid, t-cinnamic acid and QCU though the group trends for each polyphenol antioxidant compound were different, it should be noted that the parents Farnsfield and D147-p3-115 are of opposing trends for each. This may imply that each biosynthesis pathway inclinations for caffeic/vanillic acid, o-coumaric acid, salicylic acid, t-cinnamic acid and QCU may be genetically influenced and was subsequently reflected in offspring expression.

3.5. Conclusion

Highly significant genotypic variation in polyphenol antioxidant expression was observed among the RILs tested, while environmental effects only had minor influences in the 2013-

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2105 field studies. Genotype by environment interaction was not observed to have a major influence on polyphenol antioxidant expression which implies that it is likely to be under strong genetic control and should be amendable to breeding and selection in peanut genetic improvement programs.

The quantified compounds of five contrasting RILs corresponded to each RILs individual ORAC value. It was shown that ferulic acid, p-coumaric acid, salicylic acid, resveratrol and daidzein increased with enzymatic extraction indicating a greater amount is matrix-bound than free form analyte. Though caffeic acid was shown to decrease with enzymatic extraction unlike previously conducted studies, this may be due to the genotypic differences among cultivars. QCU and t-cinnamic acid had corresponding trend effects from enzymatic extraction depending on genotypes tested, which may relate to different polyphenol antioxidant biosynthesis pathways in expression by each RIL. The biosynthesis pathway inclinations for caffeic/vanillic acid, o-coumaric acid, salicylic acid, t-cinnamic acid and QCU may be genetically influenced via inheritance of relevant genes from the parent cultivars. The identification of high antioxidant capacity compounds and the study of the polyphenol antioxidant biosynthesis pathways within the plant may enable exploitation and increased expression of such target polyphenol antioxidants for breeding and crop improvement purposes. Chapter 4 explores the identification of protein expression differences in the five selected RILs by LC-MS/MS and the possibilities of biomarker identification with protein variation in the high and low polyphenol antioxidant expressing lines.

Bound phenolic acids can be released and absorbed, with different proved absorption pathways for such compounds in the gastrointestinal tract for interaction with microbes, enzymes and even glucose transporters (Acosta-Estrada et al, 2014, Rocchetti et al, 2018, Stevens-Barrón et al, 2019). Partial release of bound phenolics are also absorbable and metabolised within the gastrointestinal lumen. Microbial interactions regulate colon microflora where bacterial fermentation of undigested peanuts occur (Rocchetti et al, 2018). Antioxidants to survive mechanical crushing combined with amylase from chewing and proteases in digestive fluids of low pH present in the gut are most likely insoluble fibre bound. Antioxidants bound to insoluble food matrices have also been demonstrated to be regenerated by soluble antioxidants (Adom and Liu 2002, Çelik et al, 2013, Cömert and Gökmen 2017).

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4. QUANTITATIVE PROTEOMICS ANALYSIS OF HIGH AND LOW ANTIOXIDANT EXPRESSING LINES IN PEANUTS 4.1. Background and aims

Proteomics studies in peanuts include the water deficit stressed kernels (Kottapalli et al, 2013), allergen characterisation and identification in varying varieties (Schmidt et al, 2009), late leaf spot characterisation (Kumar and Kirti 2015) and drought stress proteomics by gene transcript (Carmo et al, 2019). However, unlike the proteomes of rice, wheat and soybeans, with maps which span tissues across the whole crop, such data still lacks for the peanut.

Though genome sequences of the diploid progenitors Arachis ipaensis and Arachis duranensis were publicly available since 2016 (Bertioli et al, 2016), the lack of heterogeneity within Arachis hypogaea genome has hindered studies. With limited access to the condensed genetic coding, the use of the vast array of phenotypes expressed at protein levels may provide a comprehensive understanding of molecular pathways and biomarkers associated with polyphenol expression. The use of quantitative proteomics allows for the study of proteins differentially expressed in samples with differential antioxidant expression. For example, the flavonoid biosynthetic pathway using -omics technologies have been studied in soybean (Feng et al, 2010), barley seedlings (Kaspar et al, 2010) and strawberry mutants (Hjernø et al, 2006, Hanhineva et al, 2009), but such trends in the study of peanuts are yet to gain traction (Sales and Resurreccion 2014). Using model plant proteomes and peanut genome, peanut proteins and their metabolic and biosynthesis pathways may be better understood. The study of RILs proteomes in polyphenol expressions; identified proteins with significant differential expression may serve as biomarkers for future breeding or cultivar selection in agricultural practices.

This chapter aims to quantitatively differentiate proteomics profiles of five peanut RILs which differs phenotypically in polyphenol expression and total antioxidant capacity using bulk segregation analysis. Results from quantitative proteomics highlight molecular basis and metabolic pathways and biomarkers identified as significant to high polyphenol antioxidant expression may be applied to assist breeding for antioxidant capacity in future breeding programs.

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4.2. Materials and methods

4.2.1. Chemicals and reagents

Product numbers of all chemical and reagents provided are used by respective companies where products were sourced from. HPLC grade methanol (34860), Tris (T1503), acetonitrile (1.00030), bovine serum albumin (BSA) (05470), trifluroacetic acid (TFA)(T6508), Bradford reagent (B6916), ammonium bicarbonate (09830), trypsin (59427C), formic acid (F0507), C18 Millipore® Ziptips (Z720046) and Tris-Glycine-SDS Buffer 10× Concentrate (T7777) were all sourced from Sigma Aldrich Co (St. Louis, MO, USA). The n-hexane (AJA2508), acetone (AJA2546), NaCl (CHE700/NACL), and acetic acid (AJA2281) were purchased from Ajax Fine Chem (Waltham, MA, USA). 4–20% Mini-PROTEAN® TGX Stain-Free™ Protein Gels, 12 well, 20 µL (4568095), Precision Plus Protein™ Unstained Standards (1610363), Coomassie Brilliant Blue (1610406), Native Sample Buffer for Protein Gels (1610738), dithiothreitol (DTT) (1610610) and iodoacetamide (1632109) were purchased from Biorad (Hercules, CA, USA).

4.2.2. Sample material

As Section 3.2.2. The RIL parents (Farnsfield, Runner type from ACI Seeds, Georgia, USA and D147-p3-115, runner type from the Australian peanut breeding program, PCA) and RIL samples (P27-p272, P27-p362 and P27-p036), which displayed high and low polyphenol antioxidant expression of Taabinga 2015 were chosen. These samples will theoretically vary in polyphenol antioxidant related protein expression and hence were used in this quantitative protein analysis. Biological variation and randomization of sampling in our experiments were catered for by sourcing kernels from replicate plots which were randomly separated from each other.

4.2.3. Sample preparation

4.2.3.1. Peanut defatting Samples are defatted as in Section 3.2.3, using the in a ratio of 1:10 in sample to n-hexane, w/v.

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4.2.3.2. Protein extraction and protein concentration determination A mixed lot of 15 peanut kernels for each RIL sample were powdered in liquid nitrogen in a mortar and pestle. Proteins were extracted from defatted powder (0.1 g) in 10 volumes of extraction buffer (50mM Tris with 200mM NaCl pH 8.2) at 4°C overnight on a shaker and centrifuged (10,000 x g, 20 min) (Thermo Fisher Multifuge xx, Melbourne, Victoria, Australia). The resulting supernatant was stored at -20°C until used for SDS-PAGE.

A Bradford assay was performed to quantify protein in samples, and BSA standards (100 µL) with concentrations ranging from 0-2 mg/mL was prepared using a working stock (2 mg/mL) diluted from stock concentrate (10 mg/mL). Triplicates (5 µL) of each standard and sample were transferred into a 96-well plate. Bradford reagent (250 µL) was added to each well and incubated for room temperature (15 min), then the absorbance was measured at 595 nm with a microplate reader (Spectramax).

4.2.3.3. Protein fractionation by SDSPAGE Samples from protein extraction containing aliquots of 200 µg solubilised proteins were then mixed with 2x SDSPAGE sample loading buffer, vortexed and placed in a water bath at 95°C for 10 min to denature proteins in the sample. The samples were incubated at room temperature (5 min), centrifuged (10,000 x g, 5 min) and run in an SDS PAGE gel with 1x tris-glycine running buffer and 5 µL Biorad unstained marker into the first well at 70 V for 5 min and 150 V for 60 min, or until loading dye front runs out. The gels were fixed for 1 h in 7% v/v acetic acid/ 20% v/v methanol then stained with colloidal Coomassie brilliant blue G-250 (60 min, 4:1 Coomassie concentrate: Coomassie diluent) on a shaker overnight. Gels were first washed with MilliQ water (MQW), then destained with Coomassie destain solution (10% acetic acid in MQW) on the shaker until the background of the gel turned clear and protein bands were visible.

4.2.4. Instruments

SpectraMax M2 spectrophotometer (Molecular Devices, LLC, Sunnyvale, CA, USA) was used to measure absorbance for the Bradford assay. The assay was performed in clear 96-well

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Yan Yee Poon z3160325 microplates (flat bottom, tissue culture suspension) (83.1835.500, SARSTEDT AG and Co., Numbrecht, Germany). A nano column (75 μm i.d × 9cm) containing reverse phase C18 media (3 μm, 200Å C18, Michrom Bioresources) was employed for the separation of protein samples. Surveyor 4.0 HPLC and autosampler (Thermo Electron, Bremen, Germany) interfaced with an LTQ-XL™ mass spectrometer (Thermo Electron, Bremen, Germany) and Thermo Fisher Scientific Xcalibur software (Waltham, Massachusetts, US) were used for detection and identification of peptides.

4.2.5. In-gel digestion, peptide extraction and desalting

The gel was sectioned into sample lanes and each lane was cut into 16 segments; each slice was consequently placed into 96-well plate wells. The gel pieces were pH adjusted with a brief

100 mM NH4HCO3 wash (200 µL) and a solution of 50% acetonitrile / 50% 100 mM NH4HCO3 (200 µL) added into each well for incubation (10 min) at 37 ̊C. The supernatant was removed, and the above step repeated until gel pieces were all clear. The gel pieces were dehydrated with 100% acetonitrile (50 µL, 5 min) after all Coomassie has been removed. The gel pieces were then air-dried (10 min) after the removal of acetonitrile. Reduction of gel pieces was achieved by 10 mM DTT in 50 mM NH4HCO3 (50 µL, 20 min, 37°C) and alkylation achieved by replacement of the DTT solution by 55 mM iodoacetamide in 50 mM NH4HCO3 (50 µL, 30 min, in the dark at 24°C).

The gel pieces were washed once with 100 mM NH4HCO3 (150 µL, 10 min), then twice with a solution of 50% Acetonitrile / 50% 100 mM NH4HCO3 (150 µL, 5 min) after the removal of iodoacetamide. The gel pieces were once again dehydrated as procedure above with 100% Acetonitrile and then removed for trypsin digest (12.5 ng/µL in 50 mM ammonium bicarbonate, 30 µL. Trypsin stock 1 µg/ µL), covering the gel pieces. The gel pieces were rehydrated (4°C on ice, 30 min) then topped with 50 µL of 50 mM ammonium bicarbonate if dry to digest (overnight, 37°C).

The digest solution supernatant was transferred into clean Eppendorf tubes and 50% acetonitrile / 2% formic acid added for incubation (50 µL, 30 min). The supernatant was removed and combined with the previously collected digest solution supernatant to vacuum dry (less than 10 µL). The remaining volume brought up to 10 µL with 0.1% TFA and cleaned with zip tips. The zip tips were equilibrated with 90% acetonitrile / 0.1% formic acid and 0.1% 105

Yan Yee Poon z3160325 formic acid three times each, discarding after every aspiration. The peptides from the digest supernatant were loaded by pipetting into the tip (10 times, reloading sample without discarding) and the washed using 0.1% formic acid (6 times, discarding every wash). The peptides were eluted with 90% acetonitrile/0.1% formic acid (50 µL, pre-aliquoted, pipetting repeatedly back into the tube). The now desalted sample is finally vacuum centrifuged, stored (–80 °C) and resuspended with 0.1% formic acid (10 µL) for LC-MS/MS at the time of use (vortexed 30 sec, centrifuged 5, 000 x g, 5 min, pipetting thoroughly before transfer to plate).

4.2.6. NanoLC-MS/MS

An LTQ-XL mass spectrometer (Thermo Electron, Bremen, Germany) was used to analyze the peanut protein hydrolysates. Peptides were separated by nano-LC using a Surveyor 4.0 HPLC which consists of the pump and autosampler (Thermo Electron, Bremen, Germany). The samples were loaded onto a nano column (75μm i.d × 9 cm) containing reverse phase C18 media (3 μm, 200Å C18, Michrom Bioresources). Peptides were eluted using a gradient. The mobile phase consisted of solvent A (0.1% formic acid in 95% MQW) and solvent B (0.1% formic acid in 95% acetonitrile). The flow rate was 250 nL min−1 and the run time was 60 min. The column tip was positioned ~0.5 cm from the heated capillary (T= 200°C) of the ion source, and spray voltage of 1800 V was applied to a low volume tee junction (Upchurch Scientific, WA, USA). The instrument was operated in data-dependent acquisition mode, with positive electrospray ionisation (ESI) mode using automated peak recognition then dynamic exclusion for 45s. A survey scan of m/z 350-2000 was acquired for overall chromatogram and future search purposes. Collision induced dissociation was used by the linear ion trap, in which up to 6 of the most abundant ions (>2000 counts) with charge states of ≥ (M+2H)+2 were successively isolated and fragmented. Mass to charge ratios (m/z) selected for MS/MS were dynamically excluded for 45 sec.

4.2.7. Peptide to spectrum matching

Raw files of nanoLC-MS/MS data were converted to .mzXML format before database search against Arachis duranensis and Arachis ipaensis on the ‘Peanutbase’ database (Peanut Genome Consortium, www.peanutbase.org)(Bertioli, Cannon et al. 2016). Searching was performed using the X! Tandem algorithm running under the Global Proteome Machine (GPM) software 106

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(version. 3.2.2). Searches were processed sequentially with parent ion mass tolerance set at 2Da and fragment mass tolerance at 0.4Da, and trypsin as the proteolytic enzyme. Peptide modifications include +57 as complete modification from carbamidomethylation of cysteine and +16 from oxidation as a variable modification of methionine residues. Peptide identification data were filtered at log(e) -1.6 to limit protein and peptide false discovery rates (FDRs) to <1% respectively.

4.2.8. Data analysis: Normalised Spectral Abundance Factor (NSAF) quantitation and data portioning

A series of R modules developed at the APAF proteomics laboratory and embedded into the Scrappy program (Neilson et al, 2013) were used for statistical analysis. Normalized Spectral Abundance Factors (NSAFs) for each protein were calculated with the inclusion of an additional spectral fraction of 0.5 to all counts to compensate for null values (Franklin et al, 2016). The NSAF for a protein was calculated from the number of spectral counts (SpC, the total number of MS/MS spectra) identifying the protein, divided by the length of the protein (L), divided by the sum of all SpC/L for a total of N proteins in the experiment. Student’s t- tests and a one way ANOVA was performed using Log NSAF values for each dataset, implemented in the Scrappy package, to identify and categorize proteins with statistically significant differential abundance patterns (Neilson et al, 2013) being greater than two folds were explored to find differential expression patterns.

Definitions included adjusted P-value using a Bonferroni correction to adjust for multiple hypothesis testing, thus minimizing type I errors. Proteins with P-values of less than 0.05 were selected as statistically significant and those found to be significantly different in abundance were further analyzed to identify changes in expression patterns. Over-expression or under- expression of a protein in P27-p362 was determined based on the statistical significance of NSAF values when compared to P27-p272 RIL. All proteins reproducibly expressed in any one of the five datasets were partitioned into groups based on their presence or absence, and protein lists were generated for each combination of groups.

Detailed protein and peptide identification information for experiments within this chapter is available in the PRIDE database (www.ebi.ac.uk/Pride) with project accession PXD015493. Reviewer account details: Username: [email protected] Password: RXueC0lr 107

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4.2.9. Protein ontology classification

To identify the biological processes associated with a particular protein, the protein ontology information was extracted from the UniProt database (http://www.UniProt.org) and KEGG (http://www.genome.jp/kegg/). Accession numbers were extracted using Refseq IDs from NCBI. These were then matched to Uni-Prot IDs to extract the corresponding gene ontology information from the UniProt database. This information was further supplemented with general biochemical pathway information from the KEGG database using reference KEGG identifiers. Proteins were then classified based on their biological processes and protein abundance. This information was further supplemented with metabolic pathway information from geneontology.org and Interpro based on reference KEGG identifiers. Proteins were then functionally classified into 23 categories based on their ontology.

4.3. Results and discussion

Using ORAC values obtained in Chapter 3, the genotype, environment and G x E interactions from 2013-5 seasons were estimated and select RILs for further study were identified. The RILs were then examined quantitative differences in protein expression between RILs of varying antioxidant capacity.

4.3.1. Summary of protein identifications

A total of 1007 non-redundant proteins (peptide identification data filtered at log(e) -1.6 resulted in protein and peptide FDRs to <1% respectively) were identified in the 5 RILs samples and also the protein expression changes that may help explain the biological basis for the observed phenotypic traits in peanut RIL P27-p362 and RIL P27-p272. These were high and low antioxidant capacity lines respectively, and results from the previous experiments in Chapter 3 confirms their phenotypic stability. A total of 339 non-redundant reproducible proteins were found in the low polyphenol expressing RIL P27-p272 and 549 proteins were found in the high polyphenol expressing RIL P27-p362. The phenotype of polyphenol antioxidant expression in the 5 RILs tested demonstrated by the protein expression patterns show a significant proteomic change in all tested RILs. Shotgun proteomic analysis revealed significant differential abundance in 82 proteins between high and low RILs, as described in

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Table 4.1. Observed changes in protein expression help explain the biological basis for the high and low polyphenol content and their corresponding antioxidant capacity lines which have become the observed phenotypic trait corresponding to the high and low antioxidant capacity lines RIL P27-p272 and RIL P27-p362 respectively. The distribution pattern of the proteins among each of the two peanut parent lines and the three RILs can be seen from Figure 4.1. It indicates that the genetic variation correlating with phenotypically different polyphenol antioxidant expressing peanut lines, having significant changes in the proteome by the unique expression of 114, 92, 101, 47 and 30 proteins exclusive in mid-low peanut parent Farnsfield, mid-high parent D147-p3-115, stable high polyphenol antioxidant RIL P27-p362, stable low polyphenol antioxidant RIL P27-p272 and unstable high polyphenol antioxidant RIL P27- p036, respectively. While these numbers represent the biological variability observed between individual peanut lines, the category includes the highest number of proteins (114) are those expressed in the Farnsfield parent. Although the protein numbers may seem greater for this line, the protein concentration used for testing corresponds equally with all the other lines and hence is indicative of the relatively variable composition of this line at the protein level. The 115 proteins that were common between all lines presumably play an indispensable role in overall cell maintenance and metabolism.

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Table 4.1 Summary of proteins obtained from shotgun proteomics analysis with peptide identification information for all proteins in each replicate for each of the two-parent lines and the three RILs Peanut RILS No. reproducible Avg. peptide % protein % peptide FDR proteins^ count* FDR D147-p3-115 542 21,004 0.92 0.09 Farnsfield (F-D) 576 24,051 0.87 0.17 P27-p272 339 21,409 0.00 0.00 P27-p362 549 25,552 0.36 0.03 P27-p036 0.51 0.14 393 23,142 Total non-redundant count of proteins identified: 1007 Total differentially expressed proteins between 362 and 272: 82 ^ Represent the reproducible number of proteins among biological triplicates * Represent average of the total peptide counts of proteins in each sample across biological triplicates Quantitation information in terms of NSAF values for all reproducible proteins identified in each peanut line

4.3.2. Quantitative analysis of differentially expressed proteins

Quantitative proteomics revealed protein expression changes corresponding to high and low polyphenol expressing RILs and identified specific proteins with significant changes related to phenotypic traits. Considering 1007 reproducibly identified proteins, a total of 82 non- redundant proteins that were differentially expressed at significant levels in RIL P27-p362 relative to RIL P27-p272. All the 82 proteins that changed significantly in their expression profiles in response to differential polyphenol antioxidant expression in the peanut lines were distinguishable from unchanged proteins using LogNSAF plots of protein abundance (Figure 4.2).

Figure 4.2 Log NSAF plot of differential and unchanged proteins between peanut lines P27-p362 relative to P27-p272.

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Proteins with significant differential expression are shown as dark blue spots. Proteins with expression levels that did not differ statistically between high or low polyphenol antioxidant RILs are shown as lighter spots. Over and under-expressed proteins as compared to RIL P27- p362 are shown as darker spots below and above diagonal respectively. These plots show differentially expressed proteins as darker spots with those above the diagonal under-expressed in high polyphenol antioxidant expressing RIL P27-p362 relative to RIL P27-p272, whilst those below the diagonal were over-expressed. These plots provide an informative visual display of the total number of differentially expressed proteins and the overall trend of differential expression. Table 4.1 lists the number of significantly differentially expressed proteins with quantification information for each RIL. Table 4.2 gives a detailed account of peptide identification and quantification information for all of the 82 differentially expressed proteins found in this study, and a non-redundant list of all proteins identified as part of the peanut profile.

The quantitative distributions of 82 differentially expressed proteins based on their functional categories were studied. Quantitative proteomics analysis revealed protein expression changes corresponding to high and low RILs and identified significant changes related to observed phenotypic traits. Qualitative distribution revealed a greater number of proteins involved in protein metabolic processes (12%), transport (12%), carbohydrate metabolism (10%), amino acid metabolism (9%), followed by lipid metabolism, protein folding, signal transduction and stress response at 5% each. Differential proteins showed a significantly wide range of expression level changes, where over-expressed proteins ranged from 1.2 to 73.7 folds increase while under-expressed proteins ranged from 1.5 to 21.2 folds decrease as observed in high polyphenol antioxidant RIL P27-p362 compared to low polyphenol antioxidant RIL P27-p272. Protein over-expressed as high as 73.7 folds in high antioxidant RIL P27-p362 compared to RIL P27-p272 was catalase isozyme 1-like involved in oxidation/reduction homeostasis and that which was under-expressed to as low as 21.22 folds in RIL P27-p362 compared to RIL P27-p272 was ferritin, a chloroplastic-like protein involved in cellular iron ion homeostasis (Table 4.3).

Particular proteins of interest that were identified to be over- or under-expressed in RILs in response for high antioxidant expression were found to be enzymes associated with fatty acid metabolism, stilbene and flavanoid biosynthesis, carbohydrate metabolism, redox homeostasis,

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Yan Yee Poon z3160325 anabolic and catabolic pathways among others. The greatest protein abundance changes were seen amongst catalase isozyme-1 protein (73.7↑) an important antioxidant enzyme involved in oxidation-reduction processes, adenosyl-homocysteinase (35.4↑) involved in amino acid metabolism, aspartic protease (19.5↑) involved in proteolysis, coatomer subunit gamma-2 (18.7↑) involved in transport, pyruvate decarboxylase 2 (13.1↑) and glucose-6-phosphate isomerase1 (11.2↑) involved in carbohydrate metabolism, and ferritin (21.2↓) involved in cellular iron homeostasis. A greater number of differential proteins corresponding to high antioxidant expressing peanut RIL P27-p362 were involved in protein metabolic processes and transport equally comprising 11% and carbohydrate metabolism (9%), amino acid metabolism (8%) and followed by signal transduction, lipid metabolism, protein folding, seed storage and stress response at 5-6% each. However, it is clear that the distribution pattern in terms of protein abundance was not the same pattern observed in terms of protein counts. Quantitative distribution analysed using spectral abundance factors for the differential proteins in RIL p27- 362 revealed a different pattern (Figure 4.3). Proteins belonging to transport (15%), stress response (12%), protein metabolic processes (11%) were the most abundant. The next most abundant categories were oxidation-reduction processes (8%), carbohydrate metabolism (7%), amino acid metabolism (6%), and protein folding (6%). Among these, unknown proteins lacking ontology were 7%. Although there was only one seed storage protein differentially expressed, its abundance contributed 4%. Seed storage category is also one of the smallest groups of proteins in the total number identified. This finding meets general expectations for peanuts to contain seed storage proteins in large abundance (Figure 4.3) and is also observed in another recent proteomics study on peanuts and is also typical of other tree nuts (Kottapalli et al, 2013). It is important to also note that differentially expressed proteins involved in stilbene metabolism, oxidation-reduction processes and lipid metabolic proteins in RIL P27- p362 could be indicative of the genetic basis for peanut’s two most commercially vital phenotypic traits, high oleic lipids, and antioxidant polyphenol-rich. The commercial breeding of peanut RIL P27-p362 would be one of the latest value-added crop rich in antioxidant polyphenolics, alongside having protein and lipid-rich nutritional profiles. With nutritional qualities to alleviate stress and human health issues, the polyphenol-rich RIL P27-p362 could become an excellent dietary source.

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The categories of enzymes found, and their quantitative abundance can be found in Figure 4.3 A and B below.

Yan Yee Poon z3160325 functional categories. They were resorted (A) as a percentage of protein number, (B) as a percentage of protein abundance based on their sum NSAF values in each biological process category. NSAF of each protein represents the average NSAF of the biological triplicate.

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Table 4.2 Proteins over-expressed in high antioxidant capacity peanuts RIL P27-p362, based on their Log NSAF ratios Peanutbase Protein name Fold Trend^ Biological process Mr Identifier change kDa

1021551593 Catalase isozyme 1-like 73.7 ↑RILp362 Stress response 56.4 ↓RILp272 1012106750 Adenosylhomocysteinase 35.4 ↑RILp362 Amino acid metabolism 53.3 ↓RILp272 1021508058 Protein aspartic protease in guard cell 2-like 19.5 ↑RILp362 Peptidase activity 51.0 ↓RILp272 1012030985 Coatomer subunit gamma-2 18.7 ↑RILp362 Transport 98.7 ↓RILp272 1012115401 Pyruvate decarboxylase 2 13.1 ↑RILp362 Carbohydrate metabolism 68.1 ↓RILp272 1012125578 Acidic 27 kDa endochitinase-like 11.3 ↑RILp362 Transport 97.6 ↓RILp272 1012217504 Glucose-6-phosphate isomerase 1, chloroplastic 11.2 ↑RILp362 Carbohydrate metabolism 128.6 ↓RILp272 1012115588 Ferritin, chloroplastic-like 9.3 ↑RILp362 Cellular iron ion homeostasis 29.1 ↓RILp272 1012202471 Ubiquitin carboxyl-terminal hydrolase 13-like 8.6 ↑RILp362 Signal transduction 143.2 isoform X2 ↓RILp272 1021493502 26S proteasome non-ATPase regulatory subunit 12 8.6 ↑RILp362 Protein metabolic process 53.2 homolog A-like ↓RILp272 1012173682 Biotin carboxylase 1, chloroplastic-like 8.0 ↑RILp362 ATP and metal ion binding 58.62 ↓RILp272 1012031668 Uncharacterized protein LOC107476790 8.0 ↑RILp362 - 92.21 ↓RILp272

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1012224664 Probable phosphoribosylformylglycinamidine 7.3 ↑RILp362 Purine metabolism 158.7 synthase, Chloroplastic/mitochondrial ↓RILp272 1021570516 Basic 7S globulin-like 7.3 ↑RILp362 Development 47.6 ↓RILp272 1012208665 Citrate synthase, mitochondrial 6.9 ↑RILp362 Stress response/ TCA cycle 52.8 ↓RILp272 1012035158 Protein transport protein SEC31 homolog B 6.6 ↑RILp362 Transport 119.9 ↓RILp272 1012038168 26S proteasome non-ATPase regulatory subunit 2 6.5 ↑RILp362 Stress response 98.2 homolog A ↓RILp272 1011997786 Malate synthase, glyoxysomal 5.9 ↑RILp362 Carbohydrate metabolism 64.1 ↓RILp272 1012264103 Pyrophosphate-fructose 6-phosphate 1- 5.8 ↑RILp362 Carbohydrate metabolism 71.5 phosphotransferase subunit alpha ↓RILp272 1012211836 Pre-mRNA-processing-splicing factor 8 5.7 ↑RILp362 Transcription 275.6 ↓RILp272 1012241905 Coatomer subunit alpha-1-like 5.6 ↑RILp362 Transport 108.4 ↓RILp272 1012000205 Alpha-aminoadipic semialdehyde synthase 5.5 ↑RILp362 L-lysine catabolic process to acetyl-CoA via 117.2 ↓RILp272 saccharopine/amino acid metabolism/ oxidoreductase 1012027118 Uncharacterized protein LOC107475966 5.5 ↑RILp362 - 49.6 ↓RILp272 1012213796 26S protease regulatory subunit S10B homolog B 5.4 ↑RILp362 ATP binding/ positive regulation of RNA polymerase II 44.8 ↓RILp272 transcriptional preinitiation complex assembly/ubiquitin-dependent ERAD pathway 1012128808 Putative transferase At4g12130, mitochondrial 5.4 ↑RILp362 Iron-sulfur cluster assembly 43.5 ↓RILp272

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1012220952 Carbamoyl-phosphate synthase large chain 5.4 ↑RILp362 Pyrimidine metabolism 120.9 ↓RILp272 1012209451 Uncharacterized protein LOC107459025 5.4 ↑RILp362 - 91.2 ↓RILp272 1011996067 26S protease regulatory subunit 6B homolog 5.2 ↑RILp362 Protein metabolic process 47.3 ↓RILp272 1012129457 Isoleucine--tRNA ligase, cytoplasmic 5.2 ↑RILp362 Translation 144.5 ↓RILp272 1012261169 Aldose 1-epimerase-like 4.8 ↑RILp362 Carbohydrate metabolism 46.3 ↓RILp272 1012100357 Mitochondrial import receptor subunit TOM40-1- 4.8 ↑RILp362 Transport 34.5 like ↓RILp272 1012095988 Alanine: glyoxylate aminotransferase 2 homolog 1, 4.8 ↑RILp362 Amino acid metabolism 51.9 mitochondrial ↓RILp272 1012108891 Stilbene synthase 3-like 4.8 ↑RILp362 Stress response 42.8 ↓RILp272 1012256226 Probable N-acetyl-gamma-glutamyl-phosphate 4.8 ↑RILp362 Stress response / oxidation reduction 44.1 reductase, chloroplastic, partial ↓RILp272 1021480771 Probable calcium-binding protein CML49 4.8 ↑RILp362 Protein metabolic process 35.3 ↓RILp272 1012089389 Elongation factor Tu, chloroplastic 4.8 ↑RILp362 GTP binding 51.6 ↓RILp272 1012252880 Formin-like protein 5 4.7 ↑RILp362 Growth and development 98.4 ↓RILp272 1011993216 Glycerophosphodiester phosphodiesterase GDPDL4 4.7 ↑RILp362 Glycerol metabolic process/guard cell 84.2 ↓RILp272 morphogenesis/lipid metabolic process/plant-type cell wall cellulose metabolic process/trichome differentiation

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1012179383 3-oxo-Delta(4,5)-steroid 5-beta-reductase-like 4.7 ↑RILp362 Stress response 44.2 ↓RILp272 1012102481 T-Complex protein 1 subunit delta 4.5 ↑RILp362 Protein regulation/toxin transport/positive regulation of 57.9 ↓RILp272 telomerase activity 1012124483 Binding partner of ACD11 1-like 4.4 ↑RILp362 Nucleic acid-binding 31.2 ↓RILp272 1012239345 T-Complex protein 1 subunit gamma 4.4 ↑RILp362 Protein metabolic process 60.5 ↓RILp272 1012187107 Tryptophan synthase alpha chain 4.2 ↑RILp362 Amino acid metabolism 33.2 ↓RILp272 1012033069 Salicylate carboxymethyltransferase-like 4.2 ↑RILp362 Secondary metabolism 179.6 ↓RILp272 1012188006 Glutathione S-transferase L3-like 4.2 ↑RILp362 Stress response 25.2 ↓RILp272 1012239766 Pyruvate kinase isozyme A, chloroplastic-like 4.2 ↑RILp362 Glycolysis 64.1 ↓RILp272 1012175579 Putative 3,4-dihydroxy-2-butanone kinase isoform 4.1 ↑RILp362 Glycerol metabolic process/glycolysis 64.4 X1 ↓RILp272 1012212319 Adenylosuccinate synthetase 2, chloroplastic-like 4.1 ↑RILp362 Purine metabolism 52.7 ↓RILp272 1021501775 UDP-glycosyltransferase 74G1-like isoform X2 4.1 ↑RILp362 Secondary metabolism 152.4 ↓RILp272 1021481338 Uncharacterized protein C167.05-like 4.1 ↑RILp362 - 27.1 ↓RILp272 1012214141 Probable 26S proteasome non-ATPase regulatory 4.1 ↑RILp362 Protein metabolic process 56.0 subunit 3 ↓RILp272

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1012244862 Transportin-1 isoform X2 4.1 ↑RILp362 Transport 95.7 ↓RILp272 1012262780 26S proteasome non-ATPase regulatory subunit 11 4.1 ↑RILp362 Stress response 46.8 homolog ↓RILp272 1012101086 Copper-transporting ATPase RAN1-like 4.1 ↑RILp362 Metal ion transport 106.6 ↓RILp272 1021503138 2-alkenal reductase (NADP(+)-dependent) 3.8 ↑RILp362 Oxidation reduction 38.1 ↓RILp272 1012234151 Long chain acyl-CoA synthetase 8 3.7 ↑RILp362 Fatty acid metabolic process 78.4 ↓RILp272 1012122412 Probable mitochondrial saccharopine 3.6 ↑RILp362 Oxidation-reduction process 48.5 dehydrogenase-like oxidoreductase At5g39410 ↓RILp272 1021563364 Histone H2A.v1-like 3.6 ↑RILp362 Protein metabolic process 15.8 ↓RILp272 1012191624 Signal recognition particle receptor Subunit beta-like 3.5 ↑RILp362 Signal transduction 43.9 ↓RILp272 1012201786 Dihydropyrimidinase 3.5 ↑RILp362 Amino acid metabolic process 56.6 ↓RILp272 1012168146 ATP-Citrate synthase alpha chain protein 1 3.5 ↑RILp362 Lipid/fatty acid metabolic process 46.7 ↓RILp272 1012243205 Subtilisin-like protease SBT1.3 3.5 ↑RILp362 Serine-type endopeptidase activity 85.0 ↓RILp272 1012212735 3-isopropylmalate dehydrogenase, chloroplastic 3.5 ↑RILp362 Stress response 43.9 ↓RILp272 1012236393 26S protease regulatory subunit 6A homolog 3.5 ↑RILp362 Embryo/pollen development/ proteasomal protein 47.5 ↓RILp272 catabolic process

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1012202053 UBP1-associated protein 2B-like 2.9 ↑RILp362 Cell death/ defence response 49.0 ↓RILp272 1012248904 DNA-directed RNA polymerase II subunit RPB2 2.9 ↑RILp362 Transcription/ translation 133.9 ↓RILp272 1012105229 Nitrile-specifier protein 5 1.9 ↑RILp362 Glucosinolate catabolic process/nitrile biosynthetic 35.9 ↓RILp272 process 1012118044 Peroxisomal membrane protein 11D 1.8 ↑RILp362 Peroxisome metabolism process/biogenesis 30.0 ↓RILp272 1012180959 Arachin Ahy-3-like 1.7 ↑RILp362 Allergen/plant defence 19.9 ↓RILp272 1021524235 Basic 7S globulin 2-like 1.2 ↑RILp362 Protein catabolic process 47.2 ↓RILp272 ^Over and under-expression of proteins in response to metal stress relative to controls have been indicated by an upward arrow, ‘↑’ and a downward arrow, ‘↓’ respectively.

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Table 4.3 Proteins under-expressed in high polyphenol antioxidant capacity peanuts RILp362 Peanut Protein name Fold Trend^ Biological process Mr Identifier change kDa 1021506701 Ferritin, chloroplastic-like 21.2 ↓RILp362 Cellular iron homeostasis 28.2 1012099909 Uncharacterized protein At4g28440 7.8 ↓RILp362 Unknown 15.2 1012106107 Peptide methionine sulfoxide reductase B5-like 6.0 ↓RILp362 Protein repair 15.1 1012020279 10 kDa chaperonin-like 5.2 ↓RILp362 Protein folding 10.6 1021496101 Nuclear transport factor 2-like 5.0 ↓RILp362 Transport 13.6 1012233930 11 kDa late embryogenesis abundant protein-like 4.5 ↓RILp362 Growth and development 14.2 1012235073 Autophagy-related protein 8i-like 4.4 ↓RILp362 Transport 13.7 1012228332 Abscisic acid receptor PYL9-like 3.9 ↓RILp362 Signal transduction 21.3 1012184864 Probable sugar phosphate/phosphate translocator At5g04160 3.9 ↓RILp362 Transport 37.3 1012202289 Protein argonaute 4-like 3.8 ↓RILp362 Gene silencing 101.8 1021495280 Protein EXORDIUM-like 2 3.8 ↓RILp362 Growth and development 32.4 1011992863 Peptidyl-prolyl cis-trans isomerase CYP19-4 3.7 ↓RILp362 Protein folding 21.5 1012001564 Transmembrane 9 superfamily member 11 3.6 ↓RILp362 Signal transduction 75.0 1012176607 Proteasome subunit beta type-3-A 3.0 ↓RILp362 Protein metabolic process 22.8 1012127700 TS14 protein-like 2.7 ↓RILp362 Response to stress 18.3 1012246374 Photosynthetic NDH subunit of lumenal location 5, chloroplastic 2.1 ↓RILp362 Transport 27.7 1021524827 Arachin Ahy-3-like 1.8 ↓RILp362 Seed storage proteins 60.3 1012180951 Arachin Ahy-3-like 1.5 ↓RILp362 Seed storage proteins 60.7 ^Over and under-expression of proteins in response to metal stress relative to controls have been indicated by an upward arrow, ‘↑’ and a downward arrow, ‘↓’ respectively.

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4.3.3. Presence of common proteins in all five RILs

There were 115 proteins found to be commonly shared amongst all five RILs including 23 allergenic proteins and 7 were found in protein processes involved in oxidation-reduction. There were 15 proteins related to each stress response, translation, also in protein metabolism, folding and proteolysis and carbohydrate, amino acid and lipid metabolism categories, Figure 4.3. Of the proteins found to be present in all of the five tested RILs, 23 were allergenic seed storage proteins (20%) accounted for 67% of protein abundance or suspected to have allergen related activities such as Arachin Ahy3, Arachin Ahy4, allergen Ara h1, Legumin type B, conglutins, vicilins, 11s globulin, and basic 7s globulins (nutrient reservoir proteins) and disease resistance response as well as mal allergens with oxidation-reduction properties. Polyphenol-rich peanuts showed 7s globulin peanut allergen protein to be increased in abundance in RIL p27-362 by 7.6 fold (Table 4.2). All other observed protein expression changes in these allergenic proteins were statistically insignificant (< 2 fold) and are unlikely to change allergic responses in humans. The common allergenic proteins which were found in both parents, high and low polyphenol antioxidant expressing RILs includes basic 7s globulins, disease resistance response and nutrient reservoir proteins, vicilin and alkyl hydroperoxide reductase. Plants produce phytoanticipins for the prevention of possible pathogenic attacks as basal resistance and phytoalexins which are synthesized when the plant is exposed to wounds, pathogens and infections (Breiteneder and Radauer 2004, Loon et al, 2006). Ara h3 being the most prevalent peanut allergen, makes up 63% of the 23 detected allergens common in all of the tested RILs consisting of 11S storage and nutrient reservoir proteins in the sample. High polyphenol phenotypic trait induced no significant changes to Ara h2, h3, h6, h7 (Conglutin – 2S albumin), Ara h5 (profilin), Ara h9 (lipid transfer protein), Ara h10, h11 (oleosins), or Ara h12, h13 (defensins). These proteins are established to be common allergens in peanuts and naturally occur in greater abundance. The allergenic collection of proteins remain the same across the different RILs.

Catalase isozyme 1-like, Acidic 27 kDa endochitinase-like protein, 3-oxo-Delta(4,5)-steroid 5- beta-reductase-like, glutathione S-transferase L3-like, TS14 protein-like, 2-alkenal reductase (NADP(+)-dependent) and heat shock proteins 0.05% out of 115 total proteins were found to be relating to plant defence specifically in oxidation-reduction processes and stress response

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(Hartman et al, 1992, Ruiz-Medrano et al, 1992, Wang et al, 2004, Konopka-Postupolska et al, 2009).

Seven proteins were found in the RILs to be related to plant growth and energy production, pyruvate decarboxylase-2, glucose-6-phosphate isomerase 1, citrate synthase, pyruvate kinase, malate synthase glyoxysomal, aldose 1-epimerase, pyrophosphate-fructose-6-phosphate 1- phosphotransferase belonging to the glycolysis pathway.

4.3.4. Present in P27-p272 (low polyphenol antioxidant expressing RIL) and Farnsfield (mid- low RIL parent)

This research found a total of 22 individually expressed proteins to be in common with P27- p272 and the Farnsfield parent line. These proteins could possibly be related to lower polyphenol antioxidant expression in the RILs, as the P27-p272 was a stable low expressing line and the Farnsfield was the mid low expressing parent of two. Additionally, a greater number of proteins were related to stress and defence response (37%, including redox), catalytic activity and transport (each at 11%) and translation and metabolic processes (each at 7%) where smaller percentages were related to embryo development, protein folding and targeting and unknown functions all at 18%. This was consistent with the stress response proteins heat shock protein and 20 kDa chaperonin found in Farnsfield samples (Forreiter and Nover 1998). Catalase isozyme 1 was found with catalytic activity to induce a plant stress responses (Scandalios and Scandalios 1997, Scandalios et al, 2000), has been shown to be induced by high-temperature stress in maize by Scandalios et al (2000). Salicylic acid inhibition was found in cold-tolerant maize lines which Catalase isozyme 1 may also be largely responsible for (Horváth et al, 2002).

Inositol oxygenase 1 is demonstrated to be involved with L-ascorbic acid biosynthesis and found in roots of rice with exposure to drought, H2O2, salt, cold and abscisic acid (Duan et al, 2012). Within myo-inositol derivatives possible by inositol oxygenase 1, include phosphatidylinositols which serve significantly as membrane structural lipid molecules and as signals and key metabolites under stress. The pathways regulated by phosphatidylinositol isoforms and associated enzymes seems to function in coordinatively adapt in growth and stress responses in plants (Valluru and Van den Ende 2011).

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From Chapter 3 (Section 3.4.3), RILs P27-p272 and Farnsfield were found to share matrix binding properties in QCU and t-cinnamic acid when both compounds were found to increase with enzymatic extraction using protease and cellulase. It has been documented that ferulic acid and regulate cell stress by interaction with heat shock proteins (Barone et al, 2009), suggesting that ferulic acid being a derivative of t-cinnamic acid, may possibly also have some similar effect in the plant system. The proteins found expressed commonly in P27-p272 and Farnsfield, catalase isozyme 1 and inositol oxygenase 1 as described, may also be linked to the presence of t-cinnamic acid and QCU with respects to plant signalling or specific stress responses.

4.3.5. Proteins uniquely expressed only in high polyphenol antioxidant RILs (RIL P27-p362, P27-p036) and D147-p3-115 parent

Seed biotin-containing protein SBP65 from the late embryogenesis abundant group of proteins was found to be one of the most abundant proteins expressed only in high polyphenol RILs. This is most likely to provide the biotin source required for seed development and germination influencing quality and yield of the crop. 3-hydroxyalkyl-CoA dehydrogenase was detected which catalyses the formation of 3-oxoacyl-CoA. Increased 3-oxoacyl-CoA levels contribute to malonyl-CoA. Triosephosphate isomerase, chloroplastic is one of three proteins expressed in common between the D147-p3-115 parent and the high polyphenol antioxidant RILs is responsible for reversibly catalysing glycerine-P to glyceraldehyde-3P within glycolysis. This fuels regulation towards pyruvate metabolism. Both of the above which could contribute to the synthesis of polyphenol antioxidants by chalcone synthase or stilbene synthase, as shown in Figure 4.4. LTI65, a low-temperature-induced water-soluble 65 kDa protein also identified, was previously reported to be present in abiotic stressors such as water deficiency, lower temperatures, and in leaf senescence in Arabidopsis. LTI65 may prove to be a potential biomarker indicative of possible cold tolerance in addition to high polyphenol expression in RIL P27-p362. Other protein family identified only in high polyphenol antioxidant peanut lines in this study are 26.5 kDa heat shock protein, mitochondrial and glycine-rich RNA-binding protein-like.

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Figure 4.4 Overview of the naringenin chalcone and resveratrol synthesis, as evidenced from upregulation of their biosynthetic pathway whose enzymes expressions altered in RIL P27-p362.

As previously described in Chapter 3, D147-p3-115 and the two high polyphenol antioxidant expressing RILs P27-p362 and P27-p036 were observed to decrease in o-coumaric acid when quantified with enzymatic extraction (Section 3.4.3). Conversely, cinnamic acids (caffeic, o- and p-coumaric, ferulic and chlorogenic) were found to have increased significantly in Matricaria chamomilla leaf rosettes when cultivated under nitrogen deficiency (Kováčik et al, 2007), and the o-coumaric acid content was observed to be twice of p-coumaric acid. As with Section 4.3.4, it can thus be proposed that the proteins commonly expressed between D147- p3-155, P27-p362 and P27-p036 and o-coumaric acid may be mutual in their links to particular plant defence mechanisms.

4.3.6. Proteins expressed uniquely to RIL P27-p362 only

This study identified 99 proteins that were expressed uniquely to RIL P27-p362; one of two high polyphenol antioxidant expressing RILs across 25 functional categories. Stilbene synthase 3-like (also known as resveratrol synthase 3 and recently reviewed at transcription level in peanuts (Bertioli et al, 2016)) was present catalysing t-4-coumrate to synthesizes 3,4',5- trihydroxystilbene biosynthesis which is part of phytoalexin syntheses. The identification of this enzyme unique to RIL P27-p362 is an important proteomic signature and metabolic

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Yan Yee Poon z3160325 evidence behind the high antioxidant capacity phenotypic trait observed in RIL P27-p362. Further, the t-cinnamate 4-monooxygenase identified as part of this group is involved in the biosynthesis of t-4-coumarate from t-cinnamate and forms part of the phenylpropanoid pathway. The enzyme is also reported to be vital for pollen development and growth (Saito et al, 2013). Also belonging to this group is the salicylate carboxymethyltransferase which is often expressed as a defence response to stress and is involved in the synthesis of methyl salicylate as part of catalyzing the reaction s-adenosyl-L-methionine to s-adenosyl-L- homocysteine, as demonstrated in Arabidopsis (Chen et al, 2003). Here, the s-adenosyl-L- methionine seem to also push the flux towards phenylalanine for flavonoid biosynthesis. The Cu-Zn superoxide dismutase, the 17.3 kDa class I heat shock protein and glutathione-S- transferase F9 are some others unique to RIL P27-p362 notable for their intrinsic antioxidant functional activity in response to stress. Glyceraldehyde-3-phosphate dehydrogenase was found which serves to break down glucose and carbon molecules within the glycolysis pathway.

Stress response proteins were found such as reticulon-like protein B5 was found which responds to karrinkin; a family of plant growth regulators found in the smoke of burning plant tissues (Nelson et al, 2010) and aspartic protease in guard cell 2-like which may be involved with drought avoidance (Yao et al, 2012), glyceraldehyde-3-phosphate dehydrogenase GAPC2 (Guo et al, 2012) defence against bacterium, oxidative stress and cadmium ion and cytosolic ent-kaurenoic acid oxidase 1-like which also protects against oxidative stress with the use of defensive pathways simulating jasmonic acid and ethylene along with simultaneous growth hormonal suppression (Pandey et al, 2017). Allergen arachin Ahy-3 isoform X1 was also found exclusively in RIL P27-p362 with nutrient reservoir activities (Schmidt et al, 2009).

4.3.7. Proteins uniquely expressed only in polyphenol-rich RIL P27-362 and D147-p3-115

The triosephosphate isomerase, chloroplastic protein is involved in many processes the glycolytic process, glucogenesis and primary root development and is significant for seed development and germination. This protein may contribute heavily towards improved crop vigour and yield, especially for RIL P27-p362. Glycine-rich RNA-binding protein-like protein, which was reported to be responsive to cold stress in Arabidopsis, cucumber and rice (Kim et al, 2010, Wang et al, 2018). This protein is a potential biomarker indicative of possible cold

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Yan Yee Poon z3160325 tolerance imparted as an additional trait to RIL P27-p362 and may be a useful subject for future cold studies. Lastly, 26.5 kDa heat shock protein (mitochondrial) was found which is also known for stress response functions. Two of the three proteins detected exclusively in RIL P27- p362 and D147-p3-115 are stress response proteins along with triosephosphate isomerase, responsible for seed development and germination may contribute in defence and hence high antioxidant expression in RILs.

4.3.8. Proteins expressed uniquely to RIL P27-p036

30 proteins were expressed uniquely to high polyphenol antioxidant RIL P27-p036, of which stress response and reduction-oxidation proteins are most prominent (30%). Such proteins include polygalacturonase inhibitor 2-like which are capable of inhibiting bacterial and fungal polygalacturonase enzymes and preventing the degradation of plant cell walls (Misas-Villamil and Van der Hoorn 2008), kunitz-type trypsin inhibitor-like 2 protein that responds to wound damage in plant defence in poplar trees and tobacco (Hollick and Gordon 1993, Christopher et al, 2004) and aspartic protease in guard cell 2-like which has been reported to assist in drought prevention through abscisic acid signalling (Yao et al, 2012). Putative chloroplastic quinone- oxidoreductase homolog was also detected in RIL P27-p036 which reduces oxidized lipids from stress exposure.

4.3.9. The overall effect of enhancing antioxidant capacity on peanut allergen composition

7S Globulin which is an important peanut allergen was expressed in abundance in high polyphenol antioxidant RIL P27-p362. Of the proteins found to be present in all of the five tested RILs, approximately 31% were allergens or suspected to have allergen related activities and included basic 7S globulins, disease resistance response and nutrient reservoir proteins as well as mal allergens (Uniprot: B2J762) with oxidation-reduction properties. It was noted that Ara h1 (7S globulin) was up-regulated by 6.5 folds in high polyphenol antioxidant RIL P27- p362. However, high polyphenol antioxidant trait development induced no significant changes to Ara h2 h6 h7 (Conglutin – 2S albumin), Ara h3 (11S globulin), Ara h5 (profilin), Ara h9 (lipid transfer protein), Ara h10 h11 (oleosins), or Ara h12 h13 (defensins). This suggests that there is minimal potential for allergen increase in high polyphenol antioxidant expressing RILs and it is important to understand that these are well-known allergens in peanuts that naturally 127

Yan Yee Poon z3160325 occur at higher abundances. The allergenic panel of proteins remains the same across the different RILs.

4.3.10. Analysis of protein metabolic changes in major biological pathways

4.3.10.1. Redox processes Proteomic analysis revealed significant expression changes in enzymes involved in oxidation- reduction processes. The antioxidant enzyme catalase isozyme 1-like was over-expressed by ↑73.3 folds in RIL P27-p362 compared to RIL P27-p272. In Arabidopsis model plants, catalase genes have been well studied and known to encode the catalase 1, catalase 2 and catalase 3 proteins, where catalase 1 is predominantly involved in the enzymatic degradation of H2O2, and along with catalase 2 and 3, they play a vital role in maintaining cellular homeostasis of reactive oxygen species (Du et al, 2008). In this study, the 73% increased level of catalase isozyme 1 protein in RIL P27-p362 is likely to be one of the direct effects of the genetics behind the high total antioxidant phenotype for RIL P27-p362. Further, this may help in cellular defences when exposed to adverse growing conditions like cold or drought. Activation of stress-induced gene expression of catalase 1 is known to be mediated by the MAP kinase signalling in Arabidopsis (Xing et al, 2007). These results suggest that these kernels are likely to be equipped to withstand cold or drought stress conditions while maintaining ROS species homeostasis and could benefit the peanut industry. Other important enzymes with redox function identified in RIL p27-362 include saccharopine dehydrogenase, 2-alkenyl reductase, gamma-interferon-inducible lysosomal thiol reductase, 1-cys peroxiredoxin, peroxiredoxin- 2B-like, quinone oxidoreductase PIG3-like and Cu-Zn superoxide dismutase among others.

4.3.10.2. Glycolysis Differentially expressed carbohydrate metabolic enzymes identified include pyruvate decarboxylase-2 (↑13.1 folds), glucose-6-phosphate isomerase 1 (↑11.2 folds), citrate synthase (↑6.9 folds), pyruvate kinase (↑4.2 folds), malate synthase glyoxysomal (↑5.9 folds), aldose 1- epimerase (↑4.8 folds), pyrophosphate-fructose-6-phosphate 1-phosphotransferase (↑5.8 folds) among those that were over-expressed in RIL p27-p362 (Table 4.2). Upon mapping out these

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Yan Yee Poon z3160325 enzymes based on their functional pathway (Figure 4.5), these enzymes seem to propel glycolysis for the biosynthesis of polyphenolic compounds (Rojas et al, 2014).

4.3.10.3. Pentose phosphate pathway Over-expression was detected in proteins involved with carbohydrate metabolism such as pyruvate decarboxylase 2 (↑13.1 folds), glucose-6-phosphate isomerase 1, chloroplastic (↑11.2 folds), citrate synthase, mitochondrial (↑6.9 folds), malate synthase, glyoxysomal (↑5.9 folds), pyrophosphate--fructose 6-phosphate 1-phosphotransferase subunit alpha (↑5.8 folds), aldose 1-epimerase-like (↑4.8 folds) and pyruvate kinase isozyme A, chloroplastic-like (↑4.2 folds). Over-expression within the pentose phosphate pathway suggests energy production that leads to the increase in tryptophan available (tryptophan synthase alpha chain, ↑4.2 fold). The excess energy produced could be used for plant defence as with over-expression in the glycolysis pathway (Rojas et al, 2014).

The metabolism of tryptophan results in an abundance of indole acetic acid which is one of the most physiologically active auxins, a plant growth regulator (Ahmad et al, 2005, Kazan and Manners 2009). The expected increase in auxin observed within our results from tryptophan build-up is further in agreement with the upregulation of salicylic acid methyltransferase by 4.2 folds in the high polyphenol antioxidant expressing RIL P27-p362. Salicylic acid methyltransferase is involved in the conversion of salicylic acid into methyl salicylate. Salicylic acid has been known as a signalling molecule in the classic systemic acquired resistance response pathway (Dong 1998, Kunkel and Brooks 2002) and the conversion into methyl salicylate in plants is used as a physical plant defence mechanism (Park et al, 2007).

The regulation of plant growth through auxin may have been required in the high polyphenol antioxidant expressing RIL P27-p036 and perhaps also plant defence (Kazan and Manners 2009). The effect of auxin and jasmonic acid signalling has been suggested to be similar within the plant system and has been described in an antagonistic relationship with salicylic acid. There has also been evidence demonstrating links between indole-3-acetic acid accumulation, tryptophan upregulation and plant requirements for secondary metabolite biosynthesis in response to stress (Zhao and Last 1996, Smolen and Bender 2002).

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4.3.10.4. Activated methyl cycle The amino acid metabolic proteins that upregulated in RIL P27-p362 include adenosylhomocysteinase (↑35.4 folds), α aminoadipic semialdehyde synthase (↑5.5 folds), tryptophan synthase α chain (↑4.2 folds), N-acetyl-gamma-glutamyl phosphate reductase (↑4.8 folds), alanine-glyoxylate aminotransferase 2 homologs 1 (↑4.8 folds).

Increased expression of tryptophan synthase (↑4.2 folds) in RIL P27-p362 coupled with amplified glycolysis and pentose phosphate pathway suggests energy production after an increase in tryptophan availability. The excess energy produced could also be used for increased plant defence ability (Rojas et al, 2014). The metabolism of tryptophan results in an abundance of indole acetic acid which is one of the most physiologically active auxins, a plant growth regulator (Ahmad et al, 2005, Kazan and Manners 2009). The regulation of plant growth and defence through the expected increase in auxin production due to tryptophan buildup in RIL P27-p362 is further reinforced by the upregulation of salicylate carboxymethyltransferase (↑4.2 folds).

Adenosyl-L-homocysteinase was over-expressed by 35.4 folds in RIL P27-362. Here, the greater flux of L-homocysteine and methionine signifies the co-induction of the activated s- adenosylmethionine cycle, and push towards plant defence (Rojas et al, 2014) in the form of flavonoid biosynthesis. This is also vital for regulating plant hormones such as ethylene biosynthesis as well as cellular signalling, growth, and repair (Wang et al, 2016). Taken together with the downregulation of the methionine sulfoxide reductase B5 and the upregulation of s-adenosyl-homocysteine reductase in polyphenol-rich RIL P27-p362, increased methionine levels converting to s-adenosyl homocysteine and then homocysteine to phenylalanine, point towards phenylpropanoid pathway for enhanced flavonoids biosynthesis. Further saccharopine dehydrogenase (↑3.6 folds) together with α-aminoadipic semialdehyde synthase (↑5.5 folds), appear to negatively regulate lysine accumulation in kernels, according to the L-Lysine degradation via saccharopine pathway (Tang et al, 1997). This may be an underlying signalling correlation between the increased flux of lysine reduction via saccharopine dehydrogenase degradation and increased methionine conversion to homocysteine in RIL P27-p362 for its antioxidant polyphenol-rich trait. These findings act as supportive proteomics evidence on what biologically confers genotypic variation for stable high polyphenolic antioxidant-rich peanuts.

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4.3.11. Secondary Metabolites and Lipid Metabolic Process Proteins

Among the differential proteins involved in the secondary metabolites metabolic processes, the UDP-glycosyltransferase 74G1-like isoform X2, salicylate carboxymethyltransferase-like, nitrile specifier protein 5 and stilbene synthase were upregulated in the polyphenol-rich RIL P27-p362 (Table 4.2). The UDP-glycosyltransferase family 1 protein is also known to have quercetin 3-O-glucosyltransferase and/or quercetin 7-O-glucosyltransferase molecular activity in Arabidopsis, where it is reportedly involved in flavonoid biosynthetic process as well as flavonoid glucuronidation process (Lin et al, 1999). In RIL P27-p362, there is an increase in quercetin levels, and this is probably related to the upregulation of the UDP-glycosyltransferase 74G1-like isoform X2 by 4.1 folds. This protein may code for a similar molecular utility in peanuts involving quercetin activity, befitting further research. This protein, when knocked out in transgenic Arabidopsis, resulted in the plants become highly susceptible to pathogen infection (Liu et al, 2010). Over-expression of salicylate carboxymethyltransferase-like in RIL P27-p362 will certainly amplify the effectiveness of the crops’ systemic resistance against pathogens, especially during kernel development stages. Salicylic acid is likely to tap into the classic systemic acquired resistance pathway and methyl salicylate from this conversion could impart resistance against pathogen infection as part of peanut plant defence mechanism (Park et al, 2007).

Accrual of crucial lipid metabolic proteins in RIL P27-p362 is indicative of their lipid-rich genetic makeup and is in agreement with recent reports of proteomic changes in peanut seed developmental stages (Wang et al, 2016) and several enzymes related to lipid accumulation and degradation were detected in this study. Notable differential proteins include the long- chain acyl CoA synthetase 8 (↑3.7 folds) which catalyses the 12-20 carbon long-chain fatty acids, ATP citrate synthase α chain protein 1 (↑3.5 folds), and glycerophosphodiester phosphodiesterase -GDPDL4 (↑4.7 folds). Other variants of acyl-CoA derivatives and long- chain acyl-CoA synthetases were also identified, which may collectively mark the types of fatty acids being made and their relative distribution in peanuts.

4.3.12. Phenylpropanoid pathway

Proteomics evidence revealed positive regulation of different metabolic pathways in the polyphenol-rich RIL P27-p362, beginning from increased sugar metabolism resulting in an 131

Yan Yee Poon z3160325 increase of sugar substrates co-inducing phenylalanine levels converging towards increased phenylalanine and tyrosine amino acid biosynthesis. This feeds into the phenylpropanoid biosynthetic pathway for increased syntheses of phenylpropanoid antioxidant compounds. Several proteins identified in this study including acetyl-CoA carboxylase, stilbene synthase, phenylalanine lyase, coumaroyl CoA ligase are either directly or indirectly involved in the syntheses and transport of different flavonoid compounds (Sales and Resurreccion 2014). Phenylpropanoids constitute a relatively diverse family of aromatic ring molecules that are derived from enzymatic cascades beginning with phenylalanine (or tyrosine in some cases) and malonyl-coenzyme-A. Phenylalanine is initially converted to cinnamic acid by deamination, followed by hydroxylation and frequent methylation to generate coumaric acid and other acids with a phenylpropane unit. Stilbene synthase has a conserved cysteine residue at the centre of its protein sequence, and this could be the active binding site for the enzymatic addition of three malonyl-CoAs to form 4-coumaroyl-CoA. Its direct involvement in driving the same condensing type enzymatic conversion of 4-coumaroyl-CoA and three malonyl-CoAs but producing different ring compounds is critical to forming different stilbenes and flavonoids (Figure 4.5) and is of greater biological relevance for the breeding antioxidant-rich peanuts. Increased expression of stilbene synthase (↑4.8 folds) becomes the final branch-point for regulating the formation of different phenylpropanoid antioxidants (Figure 4.4).

The pathway also accommodates bifunctional enzymes that are involved in more than the presented pathway above, i.e., glutathione-s-transferase, possibly switching the conversion of glutathione to homocysteine to phenylalanine, accumulating more phenylalanine, thereby fueling the phenylpropanoid pathway (Figure 4.5). Another branch-point of the phenylpropanoid pathway, we identified t-cinnamate 4-monooxygenase, which converts t- cinnamate to t-4-coumarate which belongs to the cytochrome P450 family. Isoflavone reductase, key enzyme to isoflavonoid phytoalexin biosynthesis was also identified. This provides proteomic evidence for the presence of daidzein, genistein, and glycitein in the peanut kernels as isoflavone reductase would have reduced 2-hydroxyisoflavone substrates (2′- hydroxydaidzein, 2′-hydroxygenistein, and 2′-hydroxyformononetin) into corresponding isoflavones daidzein, genistein, and glycitein (Dong et al, 2015).

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The naringenin 3-dioxygenases from certain citrus and the gymnosperm Ginkgo biloba (M Calderon-Montano et al, 2011, Cheng et al, 2013) can also act as flavonol synthase in the flavonol biosynthesis. Additionally, a study (Hua et al, 2013) also revealed that the recombinant Citrus naringenin 3-dioxygenase (FLS) had been shown to be able to accept the unusual substrate (2R)-naringenin to form the (-) isomer of (+)-dihydrokaempferol, i.e., (-)- dihydrokaempferol. Leucopelargonidin and leucocyanidin derive from dihydrokaempferol and dihydroquercetin, respectively which, in turn are either formed by a Fe2+/oxoglutarate- dependent hydroxylation of naringenin. It is then used to generate dihydrokaempferol and eriodictyol to make dihydroquercetin or a cytochrome P450-dependent monooxygenase (flavonoid 3'hydroxylase) which hydroxylates dihydrokaempferol to form dihydroquercetin. The reduction of (+)-dihydrokaempferol and (+)-taxifolin to form the respective leucoanthocyanins is considered the rate-limiting step in the pathway (Hua et al, 2013). However, the dihyfroflavonol 4-reductase (DFR) catalyzed reaction is at a branch-point for both anthocyanin and condensed tannins biosynthesis (compare proanthocyanidin biosynthesis from flavanols). The finding that isoenzymes of DFR within the same species, e.g. Ginkgo biloba, Populus trichocarpa and Populus tremuloides are rather involved in condensed tannins synthesis than in anthocyanin accumulation.

4.4. Candidate biomarkers for antioxidant capacity traits in peanuts

Enzymes identified with significantly changed expression levels for catalase 2 (66.5 fold overexpression) in high polyphenol antioxidant expressing RILs and seed maturation protein (17.4 fold under-expression) in low polyphenol antioxidant expressing RILs are notable findings and may act as protein candidate biomarkers (Figure 4.6). Key enzymes detected from this study are selected as candidate biomarkers that show statistically significant metabolic changes behind antioxidant polyphenol-rich phenotypic trait and they are catalase 2 (73.7↑ folds), adenosylhomocysteinase (35.4↑ folds), stilbene synthase 3 (4.8↑ folds), salicylate carboxymethyltransferase (4.2↑ folds), pyruvate decarboxylase 2 (13.1↑ folds), glucose-6- phosphate isomerase 1 (11.2↑ folds) and pyruvate kinase isoenzyme A (4.2↑ folds). Stilbene synthase-3 which is key to switching on the stilbene and flavonoid biosynthesis pathway would be a potent biomarker to enable breeders to potentially integrate these findings with the marker- assisted selection for the breeding of high polyphenol antioxidant cultivars.

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4.5. Conclusion

In this chapter, proteomic evidence was found with biological relevance on what confers genotypic variation between stable high and low polyphenol antioxidant peanut lines. Enzymes differentially expressed mainly belonged to carbohydrate and protein metabolism, stilbene and flavonoid biosynthesis, anabolic and catabolic pathways. Metabolic changes contribute towards phenylalanine biosynthesis, the entry point to the central phenylpropanoid pathway or polyphenol antioxidant synthesis. Stilbene synthase-like 3 expression levels were 4.8 fold greater in RIL P27-p362 (high-antioxidant capacity line). This enzyme is key to switching on the stilbene and flavonoid biosynthesis pathway and could be a useful biomarker for polyphenol antioxidant-rich peanuts. Polyphenol antioxidants and high antioxidant capacity compounds are yet to be completely profiled and identified in peanut. The following chapter on phenolic profiling in Chapter 5; the identification and survey of polyphenol antioxidant compounds are investigated in peanut sample with the use of GCMS and LC-MS/MS technology.

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5. MASS SPECTROPHOTOMETRY AND BIOSYNTHESIS PATHWAYS OF PEANUT POLYPHENOLS 5.1. Background and aims

Previous studies, including those conducted in our group, have quantified 15 key polyphenolic compounds with HPLC (Duke 2000, Phan-Thien et al, 2013, Ma et al, 2014). These are caffeic acid, ferulic acid, o-coumaric acid, p-coumaric acid, t-cinnamic acid, sinapic acid, vanillic acid, salicylic acid, resveratrol, quercetin and daidzein. These compounds were detected by in 5 selected RILs of varying antioxidant capacity in Chapter 3 however, are only a small portion of the polyphenols in the extract as evidenced by the unidentified peaks in the chromatograms (Figure 3.10). Phan-Thien et al (2013) have demonstrated in time-fractionations of HPLC eluates has shown small unidentified peaks with significant antioxidant capacity (Phan-Thien et al, 2013) which may significantly impact the choice of polyphenol pathway to select for trait selection in peanut breeding. To further investigate the identities of these unknown polyphenols, the 5 selected RILs were taken for quantitative proteomic analysis to pinpoint biomarkers related to polyphenol antioxidant production. The literature survey of the polyphenolic compounds found in peanuts revealed 95 phenolic compounds (Appendix, Supplementary Table 10.6). Hence, this chapter aims to screen for other polyphenolic compounds in the extract of D147-p3-115, by literature mass transitions and also specifically targeting the compounds produced by enzymes found in the quantitative proteomics in Chapter 4 (Table 4.2) to further comprehend the biosynthesis pathways of polyphenols in peanuts.

5.2. Materials and methods 5.2.1. Chemical and reagents

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Yan Yee Poon z3160325 genistein (G6649), syringic acid (S6881), myricetin (70050), formic acid (F0507), trifluoroacetic acid (T6508), fluorescein (46955), APPH (440914), BSTFA and TMCS (99:1, 3-3148, Supleco) and TFA (T6508) were all sourced from Sigma Aldrich Co (St. Louis, MO, USA). The n-hexane (AJA2508), acetone (AJA2546) and acetic acid (AJA2281) were purchased from Ajax Fine Chem (Waltham, MA, USA). The trolox (product code: TROLOX- STD) for use as internal standard (continuous calibration verification) CCV was obtained from AMSBIO LLC (Cambridge, MA 02141, USA). Enzymes Alcalase (Product name: Alcalase® 1.5 L FG, Nominal activity: 2.4 AU-A/g, E.C No: 232-752-2) and celluclast (product name: Celluclast® 1.5 L, Activity: 700 EGU/g, (E.C No: 3.2.1.37) for the digestion of protein and cellulose were kindly supplied by Novozymes (Kalundborg, Denmark).

5.2.2. Samples

As Section 3.2.2 and 3.2.5, D147-p3-115 from Bundaberg research station, plot 41 was selected for the completion of GC and LC-MS/MS profiling.

5.2.3. Sample preparation

5.2.3.1. Peanut deskin and defatting Samples are deskinned and defatted as in Section 3.2.3

5.2.3.2. HPLC: native extraction HPLC using native extraction was prepared as in Section 3.3.1

5.2.4. Instruments

SPE was performed with a SupelcoVisiprep DL 24-port vacuum manifold with drying attachment (57265 and 57124, Sigma-Aldrich Co., St. Louis, MO, USA) and also Strata-X (33 μm, 85 Å) polymeric reversed-phase sorbent tubes (100 mg/3 mL, 8B-S100-EBJ, Phenomenex, Torrance, CA, USA). A Gemini reversed-phase C18 (5 μm, 110 Å, 250 × 4.6 mm;398123-3) column with SecurityGuard C18 (4 × 3.0 mm) guard cartridge (00G-4435-E0 and AJ0-4287, Phenomenex, Torrance, CA, USA) was used in a Shimadzu prominence system (Shimadzu Scientific Instruments, Kyoto, Japan) for HPLC, a FRC-10A fraction collector was used with the above setup. A Thermo DSQ II Focus GC-MS (Thermofisher, Waltham, Massachusetts, 138

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United States) with Rxi-5Sil MS Column (13608, fused silica, 25 mm x 30 m, 0.25 µm film thick) was used for the GCMS experiments.LC-PDA-MS/MS analysis was conducted with a Quantum triple stage quadrupole (TSQ) mass spectrophotometer (Thermo scientific, 168 Third Avenue Waltham, MA USA 02451) and a quarternary solvent delivery system, column over, photo-diode array detector and autosampler. Samples loaded in 10 µL aliquots on a 150 x 2.1 mm i.d., 5 µm Luna C18 column (Phenomenex, Torrance, CA, USA).

5.2.5. SPE and HPLC on extractions

SPE and HPLC extractions were carried out as in Section 3.3.3.

5.2.6. HPLC fraction collection

As Sections 3.3.6, with the use of a programmed fraction collector at 1 min per fraction. The fractions were then dried down by the Centrivap and stored in -20 °C until GCMS analysis.

5.2.7. Stock standard solutions

Standards were prepared by dissolving 2 mg of polyphenol in 2 mL methanol, the solution was vortexed and stored in -80 ̊C freezer. Working standards were prepared fresh on the day of analysis. Standards prepared included rutin, gallic acid, quercetin, sinapic acid, protocatechuic acid, salicylic acid, vanillic acid, apigenin, ferulic acid, t-3-hydrocinnamic acid, caffeic acid, p-hydroxybenzoic acid, p and o-coumaric acid, polydatin, resveratrol, genistein, myricetin, t- cinnamic acid and daidzein.

5.2.8. GCMS analysis

The samples for GCMS analysis were prepared as for the HPLC method and were then fractionated by the HPLC (Sections 3.3.1 and 3.3.3). The eluting compounds were collected per mL basis and were dried down by a vacuum centrifuge at 4 °C. Resuspension was performed with 50 µL of acetonitrile and the derivatisation was performed by incubating 100 µL mix of BSTFA and TMCS (99:1, Supleco, 3-3148, 100 µL) at 64 °C for 1 h. Samples were transferred into GCMS vials and 1µL of the sample was injected for analysis. Blanks of methanol, MilliQ water were used as washes between sample injections (7 µL, 5 cycles). Ion

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5.2.9. LC-MS/MS analysis

The samples were prepared as for HPLC (Sections 3.3.1 and 3.3.3) and diluted further with 100% methanol. Standards, each of rutin, gallic acid, sinapic acid, protocatechuic acid, vanillic acid, apigenin, t-3-hydroxycinnamic acid, ρ-hydroxybenzoic acid, biochanin A, polydatin, genistein, syringic acid, myricetin, caffeic acid, p-coumaric acid, ferulic acid, o-coumaric acid, salicylic acid, resveratrol, daidzein, t-cinnamic acid and quercetin at 10 µg/mL were loaded at injections of 10 µL.

The LC-PDA-MS/MS analysis was conducted with a Quantum triple stage quadrupole (TSQ) mass spectrophotometer (Thermo scientific, 168 Third Avenue Waltham, MA USA 02451) and a quarternary solvent delivery system, column over, photo-diode array detector and autosampler. Samples were injected in 10 µL aliquots on a 150 x 2.1 mm i.d., 5 µm Luna C18 column (Phenomenex, Torrance, CA, USA) at 30°C and a flowrate of 200 µL/min. The method was run with solvent A as 0.1% formic acid in MilliQ water and solvent B as 0.1% formic acid in acetonitrile. 10% solvent B was used from 2 min and increased to 50% over 28 min.

5.2.10. Data analysis

GCMS analysis and compound detection were performed with Xcalibur and searches performed against the NIST library database and with the in-house library compiled from reference standards, including rutin, gallic acid, quercetin, sinapic acid, protocatechuic acid, salicylic acid, vanillic acid, apigenin, ferulic acid, t-3-hydroxycinnamic acid, caffeic acid, p- hydroxybenzoic acid, p and o-coumaric acid, flavonoid, polydatin, resveratrol, genistein, myricetin, t-cinnamic acid and daidzein. The GCMS identities were obtained with match and reverse match scores of above 800, to minimise the risk of misidentification.

The LC-MS/MS mass transitions were also collected by Xcalibur LC software. The references standards were generated using in house standard compounds with a product ion scan. Ions were generated by an electrospray source in both positive and negative modes. Compound transitions confirmed with standards were run with a product ion scan while all other compounds were detected using the selected reaction monitoring (SRM) mode and the sample 140

Yan Yee Poon z3160325 chromatogram were collected by full scan Q1 mode separately. Compound detection was performed with both positive and negative modes or SRM.

5.3. Results and discussion

5.3.1. Compound identification by GCMS

Confirmation of previously identified phenolic acids by Phan-Thien et al (2013) was performed by the fractionation of the D147-p3-115 extract by HPLC (Phan-Thien et al, 2013). A total of 44 fractions were analysed by GCMS for new compounds (Figure 5.1). GCMS requires derivatisation to enable separation and analysis by chromatography and the analysis of polyphenolic compounds in food without derivatisation are not sufficiently volatile to be applied directly. An extra chemical derivatisation step provides volatility and thermostable derivatives for enhanced separation, selectivity and sensitivity for analysis (Viñas and Campillo 2014). While GCMS is effective in the detection and identification of phenolic acids, the flavonoid and stilbene backbone structures add to the complexity of compound derivatisation and hence low detection efficiency by this method as experienced by Buiarelli et al (2007) (Buiarelli et al, 2007). The melting point of hydroxyl groups also increases as a result of the chemical characteristics of hydrogen bonds (Zhang and Zuo 2004, Viñas and Campillo 2014). Due to this, only phenolics were expected in the detection by GCMS and flavonoids, which have increased hydroxyl groups and a higher melting point due to the chemical characteristics of hydrogen bonds (Zhang and Zuo 2004, Viñas and Campillo 2014), were not.

Each peak was identified with the compound spectra from the NIST library, with the match score and reverse match score of >800 for increased confidence in compound identification. The match score indicates the similarity of compound detected in the fraction to the library and reverse match score indicated the similarity of library spectra to the detected spectra of the fraction. Reverse match scores are reversely, the similarity of detected spectra of within fraction to the library spectra.

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Figure 5.1 HPLC fractionation of the D147-p3-115 native extract for the GCMS analysis (Fractions 1- 44 collected from 0-29 min). The standards peaks shown in the chromatogram are as follows 1) caffeic acid, 2) p-coumaric acid, 3) ferulic acid, 4) o-coumaric acid, 5) salicylic acid, 6) resveratrol, 7) daidzein, 8) t-cinnamic acid and 9) quercetin.

Though match scores are able to detect complete compounds as well as incomplete structures; the compounds extracted from complex food matrices such as polyphenols often bound to sugars as glycosides (Hollman and Arts 2000). In such cases, match scores alone may not be sensitive enough to reliably identifying compounds and reverse match scores are required to align spectra found in the library to the compounds and their attached structures.

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Table 5.1 shows a list of compounds found in the D147-p3-115 extract and retention time (RT) of the fractions. The highest match scores are shown for each detected compound, all reverse match scores accounted for were greater than 830 in this study (Full table shown in Appendix, Supplementary Table 10.4).

Figure 5.2 Chemical structures of phenolic and benzoic acids identified by GCMS. Benzoic acid A1) R1= R2= R3=H o-Hydrobenzoic acid A2) R1= OH, R2= R3=H B m-Hydrobenzoic acid A3) R = R = H, R = OH A 1 3 2 p-Hydrobenzoic acid A4) R1= R2= H, R3= OH Vanillic acid A5) R1= H, R2= OCH3, R3= OH

t-cinnamic acid B1) R1= R2= R3= R4 =H

C o-Hydrocinnamic acid B2) R1= OH, R2= R3=H

p-coumaric acid B3) R1= R2= R4= H, R3= OH

Ferulic acid B4) R1= R4= H, R2= OCH3, R3= OH

Caffeic acid B5) R1= R4= H, R2= R3= OH

Sinapic acid B6) R1= H, R2= R4= OCH3, R3= OH

Hydroquinone C1) R1= R2= OH

4-Hydroxyphenylethanol C2) R1= OH, R2= OC2H5

Table 5.1 Compounds found in native extraction of D147-p3-115. Match score indicates the similarity of sample to library standard. Novel compounds are bolded Fraction RT Compound matched with Product ions Match Reverse (mins) internal standards* and score match NIST W9N11 library score database#

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8 14.71 Benzoic acid# 224, 239, 193,254,73, 165, 872 895 265, 256 11 13.24 4-Hydroxyphenylethanol# / 179, 73, 282, 180, 193, 901 933 tyrosol 103, 283, 177, 194, 284 14 8.16 5,7,3’,4’-Tetra-o- 415, 416, 417, 400, 418 927 959 methylquercetin# / Isorhamnetin 15 13.45 Hydroxycinnamic acid# 193, 73, 147, 220, 295, 75, 928 942 296, 297 16 12.81 t-Cinnamic acid* 205, 161, 131, 103, 220, 889 949 75, 221, 222 16 15.85 Cinnamic acid# 147, 73, 293, 45, 148, 149, 833 877 75, 294, 308, 237 17 15.84 Cinnamic acid# 147, 73, 293, 148, 149, 921 941 294, 308, 90, 237, 456 17 20.37 p-Coumaric acid* 203, 308, 219, 73, 219, 989 993 249, 179, 309, 75, 139, 115, 310, 311, 341 19 9.69 Benzoic acid# 193, 119, 149, 91, 194, 65, 971 986 45, 209 19 14.71 m-Anisic acid# 224, 193, 239, 254, 73, 891 920 165, 59, 89, 255, 256 21 8.11 Benzoic acid# 179, 105, 77, 135, 180, 51, 969 981 194, 195 21 10.25 Hydroquinone# 239, 254, 73, 112, 255, 45, 928 956 133, 256, 163, 257 21 16.97 Vanillic acid * 297, 312, 267, 253, 223, 939 954 73, 126, 193, 313, 134, 165, 314, 89, 315 22 8.74 Benzeneacetic acid# 73, 75, 164, 193, 91, 194 933 972

23 15.62 Glucoside# 129, 73, 147, 288, 306, 955 959 217, 231, 307, 308, 378 23 24.02 Caffeic acid* 219, 396, 73, 397, 381, 868 906 220, 191, 398, 307, 75, 399, 133, 308, 400 25 12.06 Salicylic acid* 267, 73, 268, 269, 209, 986 987 149, 91, 270 31 14.32 p-Hydroxybenzoic acid* 267, 223, 193, 73, 282, 930 952 126, 283, 284, 355 41 23.17 Ferulic acid* 338, 323, 308, 73, 249, 946 951 339, 147, 219, 191, 340, 75, 191, 145, 341

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41 25.83 Sinapic acid# 368, 338, 73, 369, 323, 951 954 162, 279, 249, 370, 75, 169, 117, 371 44 20.35 Cinnamic acid# 73, 219, 293, 308, 75, 309, 879 972 179, 139, 310, 311 44 25.83 Cinnamic acid# 368, 338, 73, 369, 323, 892 919 279, 161, 370, 89, 371 t-Cinnamic acid was not quantifiable by HPLC due to co-elution but degradation by heat application from the derivatisation procedure may have produced cinnamic acid derivatives found in the later fractions (39 – 41) after Fraction 37 where it was expected to have been collected from the HPLC fractionation, (Figure 3.10) (Appendix, Supplementary Table 10.4). Caffeic acid (Figure 5.2, B5), ferulic acid (Figure 5.2, B4), p-coumaric acid (Figure 5.2, B3), sinapic acid (Figure 5.2, B6), salicylic acid (Figure 5.2) and vanillic acids (Figure 5.2, A5) were identified by GCMS, confirming the previous studies by Phan-Thien et al (Phan-Thien 2012). o-coumaric acid and quercetin were also not identified in any of the fractions of the sample tested which meant that it was very likely that the o-coumaric acid co- eluting unknown (OCU) and quercetin co-eluting unknown (QCU) were not o-coumaric acid and quercetin (Phan-Thien 2012) though a quercetin derivative, 5,7,3’,4’-tetra-o- methylquercetin (isorhamnetin) was found in Fraction 4.

Five instances of cinnamic acid were detected in Fractions 40 and 44 at 22.5 and 25.83 min, in Fractions 39 – 44 at 17.45 min and 20.34 min, in Fractions 16 at 15.85 min, in Fractions 15, 17 and 40 at 15.84 min and t-cinnamic acid in Fractions 15, 16 and 35 at 12.81 min (Appendix, Supplementary, Table 10.4). Putative hydroxycinnamic acid was observed as benzenepropanoic acid, in fractions 15 and 16 at 13.45 min (Appendix, Figure 10.3) and also found in Fraction 31, was shown to be expressed in excess by drought-adapted peanut genotypes where 23 hydroxycinnamic acids were detected by LCMS (Juliano et al, 2019).

Three benzoic acids were putatively found, one observed only in Fraction 32, 9.7 min, in 31 of the 44 fractions at 8.11 min and only in Fraction 8, 14.71 min. All three instances of detection were found within the NIST library to be benzoic acid with varying spectra as with cinnamic acid discussed (spectra and library structure in the appendix, Figure 10.3). Putative p- Hydroxybenzoic acid was also observed in fractions 8, 19, 21 and 31 at 14.32 min, in addition to antioxidant activity also is associated with cell signalling modifying properties which may 145

Yan Yee Poon z3160325 lead to a multiplier effect. This is seen in action within the Nrf2 pathway for cellular defence against ROS which increased endogenous antioxidant (Juurlink et al, 2014).

Putative benzene acetic acid (phenylacetic acid) was detected in Fraction 22 at 8.74 min and has been reported to be an active auxin found predominantly in fruits (Wightman and Lighty 1982)(Appendix, Figure 10.5). Putative 5,7,3’,4’-tetra-o-methylquercetin also known as isorhamnetin and previously reported in peanuts and Brazilian tropical fruit (Lou et al, 2001, Bataglion et al, 2015), was detected in Fractions 10 and 14 both at 8.16 min (Appendix, Figure 10.5). A spectrum of the sugar moiety was recorded through dissociation from the polydatin standard, the identical sugar moiety was found in the sample, in Fractions 21, 22 and 23 all at 16.62 min though polydatin was not found in this sample. As the moiety was only found in fractions 21, 22 and 23, this implies that any glycosides in the sample are likely to be found in the three fractions.

New compounds of interest found in the sample that had not previously been reported were putative 4-hydroxyphenylethanol also known as tyrosol and putative m-anisic acid (Appendix, Figure 9.2). Tyrosol is one of the predominant antioxidants in wine and olive oil (Covas, Miro- Casas et al. 2002), whereas anisic acid is a phenolic compound commonly found in rye (Andreasen, Christensen et al. 2000).

The presence of phenolic compounds previously reported by Phan-Thien et al (2012) were confirmed by the results of this GCMS study, but compared to the vast collection of phenolics, flavonoids, anthocyanins and stilbenes reported in peanut literature, they were not detected in the analysed extract by this technique. This is most likely due to poor derivatisation of more complex structural characteristics (Viñas and Campillo 2014). Hence LC-MS/MS was performed using SRM mode targeting the mass transitions of compounds reported in the literature for peanut.

5.3.2. Compound identification by LC-MS/MS

The use of LC-MS/MS was employed to screen and identify a wider range of phenolic acids, as well as flavonoids and stilbenes which were difficult to detect by the GCMS method due to poor derivatisation of these compounds (Zhang and Zuo 2004, Buiarelli et al, 2007, Viñas and Campillo 2014). LC-MS/MS full scan chromatogram of the D147-p3-115 extract at 320 nm

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Yan Yee Poon z3160325 shows the notations at standards and major peaks of interest (Figure 5.3). Commercial standards were used to generate a product ion library while the polyphenolics reported in peanuts were searched for using mass transitions found in the literature with SRM mode. Polyphenolic compounds produced by enzymes found in Chapter 4 by quantitative proteomics were also targeted using the literature reported mass transitions. A total of 18 compounds were found in the D147-p3-115 extract using transitions obtained with the commercial standards using the product ion scans in negative mode (Table 5.2).

Figure 5.3 Full scan chromatogram of D147-p3-115 extract at 320nm. With standards and major peaks tentatively annotated.

Table 5.2 Table of 18 compounds identified using standards in negative mode Compound name [M-H]- Product ion/ MS/MS (m/z) (m/z) Biochanin A 283 268 Caffeic acid 197 135 Daidzein 253 132 Ferulic acid 193 134 Gallic acid 169 125 Genistein 269 133 Myricetin 317 125 p-Coumaric acid 163 119 Polydatin 289 226 Protocatechuic acid 153 109 Quercetin 301 179

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Rutin 609 300 Sinapic acid 223 93 Syringic acid 197 182 t-3-Hydroxycinnamic acid/ m-Coumaric acid 163 108 t-Cinnamic acid 147 101 t-Resveratrol 227 143 Vanillic acid 167 108

Using the transitions previously reported in the literature, 71 compounds were tentatively found as listed in Supplementary Table 10.6, Table 10.8, Table 10.9 and Table 10.10. Within the 71 compounds detected by the LC-MS/MS, 51% were flavonoids, 7% anthocyanidins, 7% stilbenes and the remaining 37% phenolic acids.

All phenolics, flavonoids, stilbenes and the other compounds reported in peanut root and root nodules (Craft et al, 2010), skin (Win et al, 2011, Sarnoski et al, 2012, Lewis et al, 2013, Bansode et al, 2014, Ma et al, 2014, Tsujita et al, 2014), hulls (Win et al, 2011) and kernels (Singleton et al, 2002, Lee et al, 2004, Chukwumah et al, 2007, Craft et al, 2010, Chukwumah et al, 2011, Win et al, 2011, Phan-Thien et al, 2013) were found by the LC-MS/MS analysis with the exception of apigenin. This may be due to low concentrations found in the D147-p3- 115 extract. The highest intensity of the compounds from our proteomics study detected was putative neohesperidin at 1.27x 106 total ion current (TIC).

5.3.2.1. Phenolic acids All compounds except for quercetin, caffeic acid and o, p and m coumaric acids were detected at more than one RT (Appendix, Supplementary Table 10.7). This is most likely due to the compounds eluting out as different isomers, i.e., the o, p and m coumaric acids were detected with the same mass transitions (163 m/z → 119 m/z) at 11.57 min, 9.19 min and 16.77 min. The o, p and m coumaric acids were able to be differentiated, as the three isomers of coumaric acid were investigated using two separate commercial standards and each retention time reflected correspondingly in the sample extract. The highest intensity of the phenolic acids was p-coumaric acid and this was in agreement with previous studies (Phan-Thien et al, 2013) where putative t-p-coumaric acid was found to be the predominant polyphenol in both soluble and insoluble components (Dabrowski and Sosulski 1984, Talcott et al, 2005, Craft et al, 2010). Most of the phenolic acids detected were hydroxycinnamic acid derivatives and up to 54%

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5.3.2.2. Stilbenes c- and t-Resveratrol (227 m/z → 143, 185 and 119 m/z) were identified at 10.43 min and 15.04 min respectively (Appendix, Supplementary Table 10.9). t-Resveratrol was used to generate product ions as a standard and the peak at 15.04 min was significantly greater than the one detected at 10.43 min. t-Resveratrol has also been shown to be less photostable and photochemically convert to cis-form (Yokotsuka and Okuda 2011) and c-resveratrol (Bansode et al, 2014) was shown to elute at an earlier time than t-Resveratrol (Phan-Thien et al, 2013, Ma et al, 2014).

5.3.2.3. Flavonoids Rutin (609 m/z → 300 and 301 m/z) was the only compound of the standards tested with differing retention times for each varying product ion (Appendix, Supplementary Table 10.10). Other compounds were detected with their respective mass transitions obtained from the standard product ion runs, some at more than one retention time. Including the compounds matched standards, the highest intensity was a 316 amu flavonoid (either rhamnetin, isorhamnetin, tamarixetin, or nepetin with a rutinoside, 9.6 x 106 TIC) as reported by Sarnoski et al (Sarnoski et al, 2012). The individual compound with the highest intensity of up to 6.9 x 106 TIC was identified as rutin.

5.3.2.4. Anthocyanidins Of the 71 detected compounds, 5 were anthocyanidins which included putative cyanidin (285 m/z → 125 m/z) at 6.69 min, putative peonidin-3-galactoside (461 m/z → 299 m/z) at 18.03 min, putative petunidin 3-o-glucoside (477 m/z → 315 m/z) at 16.05 min and both putative procyanidins A2 (575 m/z → 285 m/z) and putative procyanidins B1/B2 (577 m/z → 289 m/z) detected at 8.2 min (Appendix, in Supplementary Table 10.8). Both putative procyanidin B1 and B2 eluted at 6.46 min, 8.27 min and 11.17 min, it has been reported for procyanidins B1

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5.3.2.5. Compounds identified in the sample by enzymatic pathways Enzymes identified in the proteomic study (Chapter 3 and unpublished data) in the D147-p3- 115 sample translated to the possibility of compounds previously not reported in the peanut kernel. Using the LC-MS/MS transitions found in the literature not related to peanuts, 21 new compounds were detected in the peanut extract using both negative and positive modes (Table 5.3). Of the 21 new compounds identified, 68% were flavonoids, 14% each of polyphenols and other phytochemicals and 4.5% anthocyanidin. Table 5.3, below displays the novel compounds tentatively identified with their respective retention times, Table 5.4 lists the novel antioxidative compounds and their associated enzymes (Chapter 4) found in the proteomics and references to their mass transitions in the literature.

Table 5.3 List of novel polyphenols (new compounds) tentatively identified in the D147-p3-115 extract Tentative identification a RT (min) c [M-H]- (m/z) Product ion/ MS/MS b (m/z) Putative Acacetin 8.55 283 268, 151, 133, 107 Putative Aromadendrin 9.02 289 153, 195, 163 Putative Ampelopsin 32.42 319 301, 193 Putative Chrysin 24.47 253 181, 151, 101 Putative Coumestrol 19.14 267 211, 239 Putative Curcumin 21.88 367 151, 153, 193 Putative Galangin 12.93 269 227, 197, 183, 151 Putative Isoliquiritigenin 21.00 255 119 Putative Kaempferide 18.48 299 255, 151 20.33 Putative Malonylapiin A 11.20 651 272, 367, 457 Putative Malonylapiin A/B 19.23 651 519, 271, 520 Putative Neohesperidin 13.82 610 301 Putative Pelargonidin 15.19 271 253, 243, 225, 215, 197, 187, 173, 169, 159, 145, 141, 121, 117, 131 Putative Pisatin 14.76 315 314, 296, 163, 177, 286, 299 Putative Quercitrin 32.79 477 301, 300, 255, 179, 151, 271 Putative Shikimic acid 12.46 173 93, 83, 73 Putative Syringetin 28.52 345 330, 315 Putative Taxifolin 6.61 303 125 Putative Transchalcone 13.47 209 191, 131, 105, 103, 181

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Putative Tricetin 25.29 301 239 26.53 Putative Vitexin 8.16 433 415, 397, 379 a Detected compounds based on mass transitions obtained with literature. b The most abundant ions observed in mass spectra are shown in bold. c Retention time (RT) in total ion chromatograms. * Compound identified in major peaks highlighted in bold

Table 5.4 List of new polyphenol compounds tentatively identified, and their biosynthesis enzymes observed in the quantitative proteomics in Chapter 3. References to the mass transitions of these compounds are also listed Related enzyme Compound a Reference (+)-6a-hydroxymaackiain 3-o- Pisatin (Matthews et al, 1987) methyltransferase; Anthocyanidin reductase, Pelargonidin (Kammerer et al, 2003) Leucocyanidin oxygenase Apigenin 4’-o-methyltransferase, Acacetin (Kečkeš et al, 2013) Flavonoid O-methyltransferase, Flavonoid methyltransferase Chalcone synthase, Naringenin chalcone Naringenin chalcone (Moco et al, 2006) synthase, Chalcone isomerase, Chalcone- flavanone isomerase, Flavanone synthase Curcumin synthase Curcumin (Holder et al, 1978) Chalcone synthase Transchalcone (Tai et al, 2006) Chalcone synthase, flavone synthase Chrysin (Kečkeš et al, 2013) Chalcone isomerase, 6’-deoxychalcone synthase Isoliquiritigenin (Cuendet et al, 2010) Coumestrol (Antignac et al, 2003) Flavonol synthase Galangin (Kečkeš et al, 2013) Flavanone 7-o-glucoside 2’’-o-beta-L- Neohesperidin (Li et al, 2004) rhamnosyltransferase, UDP-rhamnose:flavanone-7-o-glucoside-2’’-o- rhamnosyltransferase, UDP-L-rhamnose:flavanone-7-o-glucoside 2’’- o-beta-L-rhamnosyltransferase Flavonoid 3’,5’-hydroxylase, Aromadendrin (Tsimogiannis et al, Dihydroflavonol4-reductase, 2007) Dihydrokaempferol 4-reductase, Dihydromyricetin reductase, NADPH-dihydromyricetin reductase, Flavonoid quercetin reductase, Flavanone 3- dioxygenase, Naringenin 3-hydroxylase, Flavanone 3-hydroxylase, Flavanone 3beta-hydroxylase, Flavanone synthase I, Leucocyanidin oxygenase,

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Yan Yee Poon z3160325 anthocyanidin synthase, Flavonol synthase, Flavonoid 3’-monooxygenase Flavonoid 3’,5’-hydroxylase, Leucocyanidin Taxifolin (Brinda et al, 2012) oxygenase, Anthocyanidin synthase, Flavonol synthase, Taxifolin 8-monooxygenase, Taxifolin hydroxylase, Flavonoid 3’-monooxygenase, Flavanone 3-dioxygenase, Naringenin 3- hydroxylase, Flavanone 3-hydroxylase, Dihydroflavonol 4-reductase, Dihydrokaempferol 4-reductase, Dihydromyricetin reductase Flavonoid 3’,5’-hydroxylase, Dihydroflavonol Ampelopsin (Vieira et al, 2016) 4-reductase, Dihydrokaempferol 4-reductase, Dihydromyricetin reductase, NADPH-dihydromyricetin reductase, Flavonoidquercetin reductase, Flavanone 3- dioxygenase, Naringenin 3-hydroxylase, Flavanone 3-hydroxylase, Flavanone 3beta- hydroxylase, Flavanone synthase I, Flavonol synthase Flavonoid 3’,5’-methyltransferase Syringetin (Simirgiotis et al, 2013) Isoflavone-7-o-beta-glucoside 6’’-o- Malonylapiin A/B (Kaiser et al, 2013, malonyltransferase, Kaiser et al, 2013) Flavone/flavonol 7-o-beta-D-glucoside malonyltransferase, Flavone (flavonol) 7-o-glycoside malonyltransferase, Malonyl-coa:flavone/flavonol 7-o-glucoside malonyltransferase Malonylapiin A (Kaiser et al, 2013, Kaiser et al, 2013) Kaempferol 4’-o-methyltransferase Kaempferide (Engels et al, 2012) Quercitrinase Quercitrin (Hvattum 2002) Shikimate dehydrogenase, Dehydroshikimic Shikimic acid (Bylund et al, 2007) reductase, Shikimate oxidoreductase, Shikimate: NADP+ oxidoreductase, 5-dehydroshikimate reductase, Shikimate 5-dehydrogenase, 5-dehydroshikimic reductase, DHS reductase, Shikimate O- hydroxycinnamoyltransferase, Shikimate hydroxycinnamoyltransferase Vitexin beta-glucosyltransferase Vitexin (Waridel et al, 2001) a Detected compounds based on mass transitions obtained with literature.

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While apigenin was not detected by LC-MS/MS, its flavone glucoside, vitexin and its derivatives, acacetin and malonylapiin A and B (apigenin7-o-[beta-D-apiosyl-(1->2)-(6- malonyl-beta-D-glucoside) were putatively identified (at up to 6.99 x102, 2.70 x103 and1.49 x104 TIC respectively) together with the precursor putative naringenin. As discussed previously, apigenin may not have been detected due to low concentrations or apigenin glucosides and isomers may be preferred over apigenin in vivo.

The observation of kaempferol 4'-o-methyltransferase in Chapter 4 indicated the possible presence of kaempferide and was putatively detected (299 m/z → 255 m/z) at 18.23 min, 18.48 min and 20.33 min (Appendix, Figure 10.6). Kaempferide has been shown to be related to late blight resistance in potato crops (Pushpa et al, 2014). By the similar deductions, tricetin detected putatively as a product of flavone synthase with (301 m/z → 239 m/z) at 25.29 min and 26.53 min (Appendix, Figure 10.7). Tricetin and its methyl esters are suspected to be in the involved in the wheat defence response against environmental stresses, insects and pathogens (Zhou et al, 2006). Putative neohesperidin by flavanone 7-o-glucoside 2”-o-beta-L- rhamnosyltransferase (610 m/z → 301 m/z) at 13.82 min (Appendix, Figure 10.8). Neohesperidin levels were found to increase 0.27 fold in the cultivated soybean genotype compared to the wild soybean in a recent study on low nitrogen stress adaptation (Li et al, 2018). Shikimate dehydrogenase resulted in putative shikimic acid (173 m/z → 83 m/z) at 12.58 min (Appendix, Figure 10.9), which is reported an increase in response to stress wounding in carrots by approximately 77% after 24 hr compared to unwounded samples (Becerra-Moreno et al, 2012).

Putative t-chalcone (209 m/z → 103 m/z) produced by chalcone synthase was detected at 9.30 min, 13.47 min and 21.46 min (Appendix, Figure 10.10). Chalcone synthase is also able to produce chrysin which was putatively detected with (253 m/z → 181 m/z) at 17.58 min and 24.47 min (Appendix, Figure 10.11) and putative naringenin chalcone (271 m/z → 119 m/z) at 17.91 min and 18.49 min (Appendix, Figure 10.12). Chalcone synthase is the key entry enzyme to the production of polyketide phenylpropanoids in plants responsible for defence against UV radiation, phytoalexins, signal molecules in plant-microbe interactions, antioxidants and other environmental interactions (Dao et al, 2011). The increase of chalcone synthase activity also leads to flavonoid accumulation which may inhibit polar auxin transport

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Yan Yee Poon z3160325 and the signalling of plant hormone, jasmonic acid (Dong 1998, Dao et al, 2011, Sharma et al, 2019). In addition to plant defence, chalcone synthase has been shown to be related to salinity stress in safflower (Dehghan et al, 2014, Sharma et al, 2019).

Putative isoliquiritigenin (255 m/z → 119 m/z) produced by chalcone isomerase was detected at 18.15 min, 21.00 min and 26.19 min (Appendix, Figure 10.13) and is shown to be a nod gene and glycecollin resistance inducer for nitrogen-fixing Bradyrhizobium japonicum symbionts in soybean root exudates (Kape et al, 1992, Mapope and Dakora 2013). Chalcone isomerase also produces coumestrol which was putatively detected by (267 m/z → 211 m/z) at 18.15 min, 19.14 min, 23.69 min and 34.64 min. Coumestrol functions as both a nod gene inducer in bean rhizobia and also as a phytoalexin for host plant defence (Mapope and Dakora 2013). It was notable that isoliquirtigenin and coumestrol coeluted at 18.15 min (Appendix, Figure 10.14) and that the rhizobia and root symbiont friendly properties of these polyphenols may be advantageous to peanut roots which also belong in the legume family.

Putative galangin detected by (269 m/z → 227 m/z) was associated with the presence of flavonol synthase at 12.93 min, 17.89 min and 18.69 min (Appendix, Figure 10.15) and is demonstrated to be a broad spectrum anti-microbial agent with some anti-fungal activity (Afolayan and Meyer 1997). Putative pelargonidin (271 m/z → 243 m/z) produced by anthocyanidin reductase was detected in the most instances at 11.42 min, 13.01 min, 15.19 min, 17.87 min and also at 17.90 min (Appendix, Figure 10.16). Increased pelargonidin levels have been shown in tissues of three different radish cultivars with high peroxidase (Wang et al, 2014).

Putative acacetin (283 m/z → 268 m/z) produced by apigenin 4’-o-methyltransferase was detected at 8.55 min, 24.12 min and 24.78 min (Appendix, Figure 10.17). Acacetin and its glycosides have been observed to have anti-inflammatory effects in wild Mediterranean aromatic plants (Piccolella et al, 2018). Putative aromadendrin (289 m/z → 153 m/z) by flavonoid 3’,5’-hydrolase detected at 9.02 min and 9.56 min (Appendix, Figure 10.18), also produces anti-fungal taxifolin (Yadav et al, 2018) (289 m/z → 125 m/z) which was putatively detected at 6.61 min, 6.97 min and 9.40 min (Appendix, Figure 10.19) and putative ampelopsin (319 m/z → 301 m/z) at 21.10 min, 23.60 min and 32.42 min (Appendix, Figure 10.20).

Putative pisatin (315 m/z → 163 m/z) resulting from (+)-6a-hydroxymaackiain 3-o- methyltransferase was detected at 8.78 min, 9.32 min, 14.76 min (Appendix, Figure 10.21), 154

Yan Yee Poon z3160325 has been shown to be tolerant by rhizobial symbionts (Mapope and Dakora 2013). Putative syringetin (345 m/z → 315 m/z) formed by flavonoid 3’5’-methyltransferase was detected at

17.52 min and 28.52 min (Appendix, Figure 10.22), increased CO2 exposure and photo assimilation in sub-arctic berry shrubs resulted in higher syringetin glycoside expression (Gwynn-Jones et al, 2012). Putative curcumin (367 m/z → 151 m/z) produced by curcumin synthase was detected at 21.88 min, 30.60 min and 32.84 min (Appendix, Figure 10.23) while quercitrin (477 m/z → 301 m/z) produced by quercitrinase was putatively detected at 15.50 min, 15.90 min and 32.79 min (Appendix, Figure 10.24), quercitrin levels were shown to be elevated with Botrytis cinerea infection in bilberry leaves, however not changed when infected with endophyte (endosymbiont of bacterium or fungus) (Koskimäki et al, 2009).

The apigenin derivative vitexin (433 m/z → 397 m/z) resulted from vitexin betaglucosyl transferase was putatively detected at 8.16 min and 9.20 min (Appendix, Figure 10.25). Malonylapiin A and B are produced by isoflavonone-7-o-beta-glucoside 6”-o- malonyltransferase. Both putative malonylapiin A and B (651 m/z → 519 m/z) shared the same mass transitions and were detected at 17.19 min, 18.11 min and 19.23 min (Appendix, Figure 10.26). Only the A form produced the product ions 367 m/z was detected at 9.55 min, 11.00 min, 11.20 min and 16.42 min (Appendix, Figure 10.27) as reported in the literature (Kaiser et al, 2013, Kaiser et al, 2013).

It is the first time for the compounds in Table 5.3 to be detected in peanuts, and the tentative detection of these 21 compounds in peanut kernels using LC-MS/MS has not been previously reported.

5.3.2.6. Major peaks of interest In Figure 5.3, the major peaks at 12.64 min (12.62) have been tentatively identified as ferulic acid (193 m/z → 134 m/z) and 14.15 min (14.17) as putative glycitein (285 m/z → 142 m/z). Other unmarked major peaks of the extract in Figure 5.3 are found at 8.89 min, 11.05 min, 11.29 min, 12.57 min, 12.93 min, 14 min, 15.55 min, 15.74 min, 16.36 min and also at 18.16 min of the chromatogram. From Supplementary Table 10.10, a flavonoid 3-o-sophoroside (609 m/z → 285 m/z) is putatively found at 11.29 min but the other four major peaks did not have identical matches found in Table 5.3, and the supplementary tables in the Appendix (Supplementary Table 10.8, Table 10.9 or Table 10.10).

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The compounds detected at the closest retention times in the tables were at 8.78 min: putative pisatin (315 m/z → 163 m/z), 12.57 min: putative piceatannol (405 m/z → 243 m/z), 12.96 min: putative peonidin-3-galactoside (461 m/z → 299 m/z), 11.00 min: putative malonylapiin A (651 m/z → 272, 367, 457 m/z), 14.05 min: putative quercetin-4-glucoside (463 m/z → 301 m/z), 15.50min: putative quercetrin (477 m/z → 301, 300, 255, 179, 151 and 271 m/z), 15.75 min: putative flavonoid 3-o-glucoside (447 m/z → 284 m/z), 16.42 min: putative malonylapiin (651 m/z → 367 m/z) and 18.15 min: where three compounds were putatively detected coeluting as rhamnetin (315 m/z → 165 m/z), isoliquiritigen (255 m/z → 119 m/z) and coumestrol (267 m/z → 211 m/z).

5.3.2.7. Proposed polyphenol synthesis pathway in peanut As the novel compounds tentatively detected (Table 5.4) are derived from the enzymes involved in biosynthesis found in the D147-p3-115 extract by differential quantitative proteomics, these compounds have been added into our proposed biosynthesis pathway (Figure 4.5). A biosynthesis map of all the compounds detected by LC-MS/MS was compiled in a biosynthesis pathway using all the detected compounds in Figure 5.4. This map shows the pathway whereby the compounds are synthesised in peanuts beginning from shikimic acid from which gallic acid is directly derived (Dewick and Haslam 1969). Shikimic acid is converted into chorismic acid and enters the phenylpropanoid pathway via phenylalanine, producing cinnamate which cinnamic acids are synthesised from. Cinnamic acids may then be derived from hydroxycinnamic acids like salicylic acid or transform into coumaric acids and all the derivatives listed in Figure 5.4 from coumaric acid to di-hydroxycoumarin. Caffeic acid may also be produced from coumaric acid from which then caftaric, chichoric and chlorogenic acid may be derived.

From caffeic acid, ferulic acid is synthesised and fetaric, feruloyl tartaric, vanillic and sinapic acids, feruloyl aspartate and curcumin may be produced thereafter. Protocatechuic acid is then derived from vanillic acid. 4-coumaroyl-CoA is derived from cinnamate by coumarate and when combined with 3 of malonyl-CoA by stilbene synthase or chalcone synthase the stilbenoid and flavonoid synthesis pathways are accessed. This map includes all the phytochemicals reported in peanut literature to date and 21 putative novel compounds produced by the enzymes found in our proteomics study.

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The proposed metabolic biosynthesis pathway in Figure 4.5, shows a similar pathway which highlights the major key enzymes in the glycolysis, TCA cycle, shikimate and phenylpropanoid pathways. These enzyme expressions were altered and in agreement with the high polyphenol antioxidant expression and the phenylpropanoid section of the pathway is magnified and detailed with the branching polyphenolic compounds cascade of derivatisation in Figure 5.4.

The combined evidence of the metabolic pathway in Figure 4.5 and the biosynthesis pathway of polyphenol compounds in Figure 5.4 highlights the intensity of polyphenol groups expressed from biosynthesis pathways by their corresponding enzymes. Flavonoids were detected as the highest combined intensity at 1.75 x 107 total ion count (TIC) in the flavonoid pathway produced by chalcone synthase. As anthocyanidins are derived from flavonoids, they are also dependant on naringenin chalcone and t-chalcone and was expressed at 3.97 x 104 TIC, naringenin chalcone and t-chalcone were putatively detected at 1.33x 105 TIC; as the product compounds of chalcone synthase.

Phenolic acids in peanuts can be observed to originate from the shikimate, phenylpropanoid and cinnamate pathways by t-cinnamate 4-monooxygenase where the second-highest intensity of polyphenol content was produced 1.26 x 107 TIC. Stilbenoids are strictly synthesised by stilbene synthase within the stilbenoid pathway and have been detected at 2.91 x 104 TIC.

Flavonoids, anthocyanidins and chalcones are expressed by chalcone synthase which indicates chalcone synthase is likely to be responsible for all three groups of polyphenols expressed with a total of 1.77 x 107 TIC in intensity. The above underlines the preference of peanut polyphenol expression as flavonoids over other polyphenols and the multifunctional and vital role chalcone synthase plays in polyphenol expression in peanut kernels. To date, the Peanutbase (Section 4.2.7) has yet to sequence chalcone synthase in Arachi hypogaea and though the key to trait selection in future breeding programs may lie in the potential of chalcone synthase as a target biomarker.

Some kernel characteristics may be affected due to changes in phenolic profiles including plant defence and kernel flavour. Increased anthocyanidins have been proven to improve signalling above and underground, act as UV screen due to the colour pigmentation of the compounds (Daayf and Lattanzio 2008), also as defence against microbiological contamination including E. coli, yeasts and moulds, Salmonella spp and coagulase-positive Staphylococcus (de Camargo et al, 2015) and selenite toxicity from fertiliser supplementation (Wang et al, 2016). 157

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Investigations have supported the effect of biotic and abiotic factors in the transcriptional control of resveratrol synthase activity and expression in peanuts (Chung et al, 2003), in addition, the t-resveratrol accumulation within the phenylpropanoid pathway by Agrobacterium (Shumakova et al, 2011). Such studies demonstrate the relationship between elicitors and antioxidants in peanut crops (Somboon et al, 2019) though further studies for disease and insect resistance due to phenolic profile changes would require a broader selection of test environments from which peanut sample are obtained.

Epicatechin and smaller procyanidins below the size of three subunits are known to be soluble and cause the astringent taste in cocoa (Ziegleder 2009), these polyphenol antioxidants are also found in the peanut testa which is commonly consumed in beer nuts. Changes to polyphenol profile to release epicatechins and smaller procyanidins may also lead to astringency in the final product; though may be palatable to the consumer as the bitter taste is a common flavour profile present in hops and beer (Mcmurrough et al, 1984, Callemien and Collin 2009).

Peanuts, seeds, oil crops and tree nuts are susceptible to lipid oxidation which contributes to off flavours and rancidity in high oil products. The increase of antioxidant compounds present in high oil kernel matrix may assist in the quenching of ROS, prevention of oxidation which contributes to aliphatic aldehydes, ketones and alcohols leading to “cardboardy” and “painty” flavours (Bett and Boylston 1992, Nepote et al, 2004). As lipid oxidation, rancidity and carbonyl compounds are initiated by roasting, the protection of amino acids, proteins and other nitrogen-containing compounds from free radicals and hydroperoxides by increased polyphenol content will likewise affect flavours quality and in turn extend peanut shelf life (Wanasundara et al, 1997, Hall 2001, Talcott et al, 2005, Shad et al, 2012).

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5.4. Conclusion

Using GCMS, previously reported (Phan-Thien 2012) phenolics acids such as caffeic acid, ferulic acid, p-coumaric acid, t-cinnamic acids, sinapic acid, salicylic acid and vanillic acids were confirmed to be present in the water-soluble extract of D147-p3-115. o-coumaric acid and quercetin were not found, which implies the o-coumaric acid co-eluting unknown (OCU) and quercetin co-eluting unknown (QCU) previously suspected by Phan-Thien et al to be o- coumaric acid or quercetin was not the compounds themselves (Phan-Thien 2012). Compounds not previously reported in the earlier studies detected by GCMS were: putative 4- hydroxyphenylethanol (tyrosol), a predominant antioxidant in wine and olive oil (Covas et al, 2002) and putative m-anisic acid phenolic compound commonly found in rye (Andreasen et al, 2000) were identified.

The sample extract was further analysed by LC-MS/MS in SRM mode, resulting in a total of 118 polyphenol compounds detected. Of the 118, 18 were confirmed with the commercial standards, including t-cinnamic acid, daidzein and resveratrol and 71 compounds were identified tentatively using mass transition pairings found in the literature studying root, skin, hulls and kernels of peanuts. Enzymes found from the proteomics study in Chapter 4, were used to identify their corresponding compounds. Using mass transitions of these compounds found in the literature, 21 compounds not previously identified by LC-MS/MS in peanuts were found, including putative transchalcone, naringenin chalcone, quercitrin and malonylapiin A and B.

A map (Figure 5.4) that incorporates all the compounds detected by LC-MS/MS was constructed which demonstrates the cascade of compounds derived from preliminary polyphenols. This map is reminiscent of the similar map proposed in Chapter 4 (Section 4.4, Figure 4.5) which describes the related key enzymes involved in the pathways leading to polyphenol production, instead focuses on the pathway-specific to polyphenol and their derivative production. Chalcone synthase was found to be responsible for the production of most flavonoids, anthocyanidins and also chalcones within the biosynthesis pathway and hence will be a strong potential candidate biomarker in the future breeding of peanuts.

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6. FEASIBILITY OF SILVER NANODISKS AS A SENSOR FOR ANTIOXIDANT CAPACITY MEASUREMENT IN PEANUT KERNELS 6.1. Background and aims

To assist with the selective breeding of new cultivars; the development of a rapid phenotyping assay for the seed selection is crucial. Ideally, the assay should have the following properties: 1) adaptability for low resource settings without the need for specialised equipment (i.e., possibly qualitative or visual evaluation), 2) assay speed, 3) low cost, 4) simple operation (including sample preparation) and 5) data processing and robustness. Previous attempts to develop a screening method based on NIR spectroscopy for polyphenols have been presented with challenges due to matrix interference, for example, from the high oil content in kernels as well as structural diversity of polyphenolic compounds not allowing specific spectrum to be targeted (Isleib et al, 2008, Lee et al, 2016).

Nanoparticles with their reductive capacity could be used to detect compounds with reducing potential including phenolic antioxidants by nanoparticle growth (Özyürek et al, 2012, Szydłowska- Czerniak et al, 2012, Teerasong et al, 2017). Both spherical AgNP and AuNP have been used in the development of antioxidant capacity assays (Vilela et al, 2015, Teerasong et al, 2017). Such studies have been carried out with nanoparticles previously showing individual polyphenol antioxidants interact with nanoparticles; resulting in dose-responsive size growth (Wang et al, 2007, Özyürek et al, 2012).

Figure 6.1 UV-vis spectra obtained for solutions placed in the dark, blue-shifted over 24 h. (Lee et al, 2009).

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Figure 6.1 shows the UV-vis spectra of typical features of photomorphic nanoparticles, which demonstrates geometric characteristics correlated with the out-of-plane quadrupole (340 nm), the out- of-plane dipole (410 nm), the in-plane quadrupole (around 470 nm) and the in-plane dipole (at 770 nm) plasmon resonances (Lee et al, 2009). The in-plane plasmon band shifts due to tip corner sharpness and have been reported to red-shift (Schatz 2007).

Small spherical silver nanoparticles in yellow solution are characteristically found to have a single surface plasmon band at 400 nm (Figure 6.2A), with only one frequency oscillation available in their isotropy (Jin et al, 2001). The peak decreases in intensity and eventually disappears as three new plasmon bands (weak, medium and strong) appear at wavelengths 335 nm, 470 nm and 670 nm respectively. This is indicative of the transformation of the initial silver nanospheres into larger prism shapes. The plasmon band at 670 nm grows stronger in intensity as the number of silver prismatic nanoparticles displaying a blue colour grows (absorbing red wavelengths and red-shifting). The observed size and shape transformation only occurs in the presence of light with wavelengths mainly in the visible range between 350-700 nm, and can be linearly correlated with an increase in the wavelength of incident light (Lee et al, 2013). Using this principle, the exact control of size and shape of nanoparticles and associated optical and electronic properties may be optimised for various diagnostic applications.

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6.1.1. Concept and mechanism of silver nanodisks transformation mediated by polyphenols as an antioxidant sensor

6.1.1.1. Synthesis of dark transformed silver nanodisks (AgND) Silver nanoprisms (AgNPrs) generated from silver nanodisks (AgND) via the photo-inducing method are able to be reversibly transformed back when re-irradiated with high-intensity light with specific emission maxima (Lee et al, 2010, Lee et al, 2013). This phenomenon, results in the reduction of Ag+ onto the disk surface along the circumference, initially producing hexagonal shapes then to truncated prisms and finally triangular prisms, as shown in Figure 6.3.

The circular disks with rounded edges grow to form flat-surfaced hexagons that continue to extend into a prism shape as demonstrated in Figure 6.3, where λmax is red-shifted from 546 to 592 nm as particle height increases (base to top orthogonal measurements) (Lee et al, 2009, Lee et al, 2010), (Figure 6.3). The peak observed also correlated to in-plane dipole plasmon resonance of the developing nanoparticle (Lee et al, 2010). Disk-shaped silver nanoparticles are formed as the prismatic nanoparticle corners oxidise when taken into rest in the dark after irradiation (Lee et al, 2009). The period at which the AgNPs are rested in the dark is referred to as “dark-transformation”.

Dark-transformed AgNP disks grow through a different mechanism when reintroduced to light, the disks first grow into flat hexagons then into triangular prisms dictated by the twin-planes of the AgNPr, (Figure 6.4).

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Only the re-entrant groove faces on the AgND edge to allow for the attachment of Ag+ ions and hence trace concentrations of reductants are required for the process. This, in turn, allows for traces of polyphenol antioxidants to be effective in a solution resulting in a highly sensitive detection method.

The use of citrate in the AgNP buffer allows the reduction of Ag+ onto the AgNP surface and increases stability by inhibiting aggregation and agglomeration by the strong citrate absorption onto the AgNP. The Ag+ reduction in solution is described as below (Lee et al, 2010):

Equation 6.1

휆 + − 퐶𝑖푡푟푎푡푒(푎푞) → 푎푐푒푡표푛푒 − 1,3 − 푑𝑖푐푎푟푏표푥푦푙푎푡푒(푎푞) + 퐶푂2(푎푞) + 퐻(푎푞) + 2푒(푎푞)

Equation 6.2 − + 2푒(푎푞) + 2퐴𝑔(푎푞) ↔ 2퐴𝑔(푠)

The carboxyl group on the citrate molecule donates 2 electrons to Ag+, leading to reduction and attachment onto the nanoparticle surface. The citrate oxidises to acetone-1,3-dicaroxylate, releasing + CO2 and H (Lee et al, 2010), which specifically takes place in the re-entrant grooves of the concave surfaces. This forms Ag plasmon “hot holes” (Wu et al, 2008), promoting growth on particular sides resulting in a prismatic structure (Lee et al, 2010, Lee et al, 2013). High citrate concentration is required for prismatic transformation as it protects against re-entrant groove corrosion and photoablation (Lee et al, 2013).

As continuous photo-oxidation reduces citrate concentrations in solution during synthesis (8 µM) (Lee et al, 2010), a blue-shift of the in-plant dipole plasmon resonance band (770 nm) is observed

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Yan Yee Poon z3160325 which has also been correlated to a reduction in AgNPr tip sharpness (Lee et al, 2009). The addition of more citrate to photo-ablated AgNP in solution gives a red-shift of the plasmon band as silver disks transform into prisms (Lee et al, 2010). By a similar mechanism, the reduction of Ag+ by the presence of polyphenols as antioxidants could induce nanoprism growth as in Equation 6.3; where Ar-OH+ equals antioxidative compound.

Equation 6.3 + − 퐴𝑔푁푃 + 퐴푟 − 푂퐻(푎푞) + 2푒(푎푞) =>

휆 + − 퐶𝑖푡푟푎푡푒(푎푞) → 푎푐푒푡표푛푒 − 1,3 − 푑𝑖푐푎푟푏표푥푦푙푎푡푒(푎푞) + 퐶표2(푎푞) + 퐴푟 − 푂퐻(푎푞) + 2푒(푎푞)

− + 2푒(푎푞) + 2퐴𝑔(푎푞) ↔ 2퐴𝑔(푠) + ArO2 + AgNP = Silver nanoparticle Ar-OH = Antioxidant hydroxyl ArO2 = Oxidised antioxidant

As the lower reducing potential of Ag(I) has sharper and stronger resonance bands for greater sensitivity and selectivity potential than Au (III) (Özyürek et al, 2012), we proposed to use AgNPs, particularly nanoprism, utilising their shape transformation upon reaction with reductants for the development of a new antioxidant capacity assay.

Compared to spherical particles, silver nanodisks (AgNDs) require lower concentrations of reductants to transform to prismatic shape and hence should provide greater sensitivity when used as an antioxidant sensor as illustrated in Figure 6.4). This approach also eliminates the need for additional manipulations to nanoparticles such as PVA stabiliser scaffold (Teerasong et al, 2017) and construction of nano-shell (Ma and Qian 2010) to increase assay sensitivity. By acting as reductants, polyphenol antioxidants as natural reductants are able to transfer the Ag+ ions onto the nanoparticle surface contributing to size growth and shape change. As shape transformations of AgNPs with strong reductants takes place rapidly (e.g. 5 min) (Teerasong et al, 2017), the assay developed will be simple, rapid, cost-effective (Scampicchio et al, 2006, Vilela et al, 2012, Vilela et al, 2015) and sensitive to nanomolar ranges without the need of specialised equipment. Nanoparticle spectra are most frequently characterised using a spectrophotometer, and peak shifts towards shorter wavelengths (blue-shift) would indicate a decrease in size and shifts towards longer wavelengths (red-shift) signifies size growth.

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Total antioxidant capacity could be calculated by the extent of shape transformation of AgNPs in the presence of polyphenol antioxidants. As the AgNPr will require lower reductant concentrations for particle growth from AgND through the extension of hexagon vertices to prism apexes compared to overall 360° enlargement of spheres; the AgNPr theoretically should provide lower limits of detection than the traditional nanoparticle antioxidant assays and is a viable concept for further investigation.

Based on the rationales proposed above, this chapter aims to explore the feasibility of developing an AgNPr-based rapid screening test for the selection of peanut RILs and cultivars with predictively high polyphenol antioxidants.

6.2. Materials and methods

6.2.1. Chemicals and reagents

Product numbers of all chemical and reagents provided are used by respective companies where products were sourced from. Nitric acid silver(I) salt (209139), sodium borohydride (480886), bis(p- sulfonatophenyl) dihydrate dipotassium salt (BSPP, 698539), 2,4,6-Tris(2-pyridyl)-s-triazine (TPTZ, T1253), Iron(III) chloride hexahydrate (FeCl3•6H2O, 236489), HPLC grade methanol (34860), trolox (238813), caffeic acid (C0625), p-coumaric acid (C9008), ferulic acid (128708), o-coumaric acid (H22809), salicylic acid (S5922), resveratrol (R5010), daidzein (D7802), t-cinnamic acid (C80857), quercetin (PHR1488), rutin (R5143), gallic acid (G7384), sinapic acid (D7927), protocatechuic acid (03930590), vanillic acid (94770), apigenin (10798), t-3-hydroxycinnamic acid (H23007), ρ-hydroxybenzoic acid (240141), biochanin A (D2016), polydatin (15721), genistein (G6649), syringic acid (S6881) and myricetin (70050) were acquired from Sigma-Aldrich Co (St. Louis, MO, USA). Tri-sodium citrate (AJA467) and hydrochloric acid 1 M (AJA643) were purchased from Ajax Fine Chem (Waltham, MA, USA). Copper grid support films, carbon type B mesh 200 (01810) for TEM imaging were sourced from Ted Pella Inc (Redding, CA).

6.2.2. Samples

Samples for the leachate study were prepared as whole kernels, skinless kernels and skin only. Whole samples were kernels taken from the shelled peanut with intact testa, skinless samples were whole kernels with the testa removed and skin only samples were testa collected from the same equivalent

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Yan Yee Poon z3160325 weight of whole kernels. For the correlation studies, RIL samples generated from Taabinga and North Queensland in 2014/15 with the parents Farnsfield and D147-p3-115 were used.

6.2.3. Sample preparation

6.2.3.1. Peanut deskin and defatting Samples were deskinned and defatted as in Section 3.2.3.

6.2.4. Instruments

SpectraMax M2 spectrophotometer (Molecular Devices, LLC, Sunnyvale, CA, USA) was used to measure absorbance for FRAP assay. The assays were performed in clear 96-well microplates (flat bottom, tissue culture suspension) (83.1835.500, SARSTEDT AG and Co., Numbrecht, Germany). A Shimadzu UV-VIS 1700 spectrophotometer was used to perform the silver nanoparticle response readings (Shimadzu, Kyoto, Japan). AgNP synthesis was carried out with high-intensity LEDs with an emission maxima of 530, 567, 617 and 655 nm (7 LED round assembly part number: SP-02-L1) purchased from Luxeon Star LEDs Quadica Developments Inc. (Lethbridge, Alberta, Canada) and assembled in a custom made metal lightbox (UNSW workshop). NIR spectra were obtained with Shimadzu Prestige (Shimadzu Scientific Instruments, Columbia, MD 21046 USA).

6.2.5. Photochemical synthesis of Silver nanoparticle (AgNP)

The photochemical synthesis of AgNPs used high-intensity LEDs was carried out as described in Lee et al (2013). The flask containing AgNP synthesis reagents (Nitric acid silver(I) salt, sodium borohydride, BSPP) was sealed with parafilm and placed in the dark for exactly 30 min. After resting in the dark, the flask was then put into the lightbox equipped with a LED light for 2 hr at 0.7 amps. LED lamps with estimated emission maximum wavelengths of 530, 567, 617 and 655 nm were used. The final product was removed from the lightbox, wrapped in foil and rested overnight to further develop the particles by monitoring the colour change to pink-purple was assessed after 12 hr by assessing the absorbance spectra from 300 nm to 1000 nm at 0.5 nm increments. Spectra is assessed based on peak number, height, width and peak position. As synthesised AgNPs were used as blank, additions of methanol to the blank was used as the control, and an addition of L-ascorbic acid (as a

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Yan Yee Poon z3160325 reducing agent) to AgNP solution was used as a positive control showing the solution colour change from pink purple a dark purple-blue.

6.2.6. TEM imaging of AgNPs

A TEM image of AgNPs was taken. AgNP solution was centrifuged to remove most supernatant and resuspended AgNPs (10 µL) were then dropped on to copper grids (Ted Pella Inc, Redding, CA) and air-dried. Then another 5 µL MWQ was added and air-dried again. TEM was performed on the FEI Tecnai G2 20 with a thermionic source, Bruker QUANTAX energy dispersive x-ray spectroscopy system and High Angle Annular Dark Field (HAADF) detector.

6.2.7. Preparation of polyphenol antioxidant solutions

Stocks (1 M in methanol) of polyphenolic compounds prepared included rutin hydrate, gallic acid, quercetin, sinapic acid, protocatechuic acid, salicylic acid, vanillic acid, ferulic acid, t-3- hydroxycinnamic acid, caffeic acid, p-hydroxybenzoic acid, p-coumaric acid, o-coumaric acid, biochanin A, polydatin, resveratrol, genistein, syringic acid, myricetin and t-cinnamic acid. The test compounds were then made up to 0.01 M in methanol and serially diluted to 1 x 10-11 M to form standard solutions.

6.2.8. Polyphenol antioxidant – AgNP reaction

The polyphenols in methanol (100 µL) were mixed with 1.9 mL synthesised AgNP solution for 10 sec and absorption spectra from 300 nm to 900 nm at 0.5 nm increments were measured. Polyphenols examined as Section 6.2.7.

6.2.9. Soaking as a simple sample preparation method- peanut leachates as polyphenol extracts

Whole kernels with intact skin (10 g), skinless kernels (10 g) and skin only samples (deskinned from 10g of whole kernels samples) in 40 mL MilliQwater (MQW) were prepared in triplicates in 50 mL tubes. The sample tubes were placed on a 10-roller mixer and 1 mL extracts were collected at different time points (15, 30, 60, 120 and 180 min). The extracts were then stored at -20°C until use.

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6.2.10. AgNP based antioxidant capacity assay

Sample extracts (100 µL) were mixed with 0.9 mL AgNP solution for 10 sec at room temperature. Absorbance spectra were recorded from 300 nm to 900 nm against the blank. For the kinetic study, the samples were measured by UV-VIS at 2 min intervals for 100 readings, for up to 200 min.

6.2.11. ORAC assay

As per Section 3.3.4, the samples were defatted with n-hexane and extraction buffer was used as sample extract in the ORAC assay. The ratio of n-hexane and antioxidant extraction buffer was adjusted to 10x the volume of the undefatted sample weight. The ORAC extraction buffer is prepared with acetone/water/acetic acid (70:29.5:0.5, v/v/v, 10 mL) and defatted samples (0.1 g) were ultra- sonicated for 10 min at 50kHz, centrifuged for 10 min, 3000 g and the supernatant collected for use as ORAC sample extract.

6.2.12. FRAP assay

As per Section 3.2.2, sodium acetate buffer (300 mM), TPTZ with HCl (40 mM, pH 3.6) and

FeCl3•6H2O (20 mM) were prepared separately. These reagents were mixed in the ratio of 10:1:1

(sodium acetate: TPTZ: FeCl3•6H2O) and warmed to 37°C in an incubator. The methanolic kernel extracts were obtained from defatted samples (0.1 g) with 80% methanol (1 ml) by ultra-sonicating for 10 min at 50kHz, centrifuging (10 min, 3000 g) and collecting the methanolic supernatant for use as FRAP sample extract. The sample extract or standard (trolox solution 10, 100, 200, 300 and 400 µM) was combined in a ratio of 1:5 with the FRAP reagent in a microtitre plate, left to rest in the dark for 8 min and then the absorbance was measured at 593 nm.

6.2.13. Correlation of AgNP assay to ORAC and FRAP assays

For the direct comparison with the ORAC assay, the sample extract (100 µL) was mixed with 0.9 mL AgNP solution for 10 sec and the absorption spectra from 300 nm to 1000 nm at 0.5 nm increments was collected immediately.

For the direct comparison with the FRAP assay, the sample extracts (100 µL) were mixed with 0.9 mL AgNP solution for 10 sec and absorbance spectra from 300 nm to 1000 nm at 0.5 nm increments was collected immediately.

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6.2.14. Data analysis

The raw ORAC values expressed in Trolox Equivalence (TE, µM.g-1) were taken from triplicate sample analysis with the BMG Optima MARS data analysis software and the mean values of the normalised data were calculated. The absorption spectra of AgNPs were measured in triplicates subsamples. AgNP responses were calculated by the integration of the sum of absorbance from 300nm – 960 nm as follows:

960 푛푚 퐴푏푠− 퐴푏푠 (퐴푏푠 = 훴 ∫ 퐴푏푠 , %∆ = ( 푖푛푖푡푖푎푙 )) 300 푛푚 퐴푏푠푓푖푛푎푙− 퐴푏푠푖푛푖푡푖푎푙

960 푛푚 Abs = Absorbance 훴 ∫300 푛푚 퐴푏푠 = Sum of integrated absorbance from 300 nm to 960 nm

Absinitial = Initial absorbance Absfinal = Final absorbance

This value is expressed as a change in the sum of integrated absorption spectra (ΔIn AU). The covariance of antioxidant capacity values by FRAP and ORAC assays and the AgNP antioxidant capacity assay was assessed using Pearson’s Correlations coefficient by Graphpad Prism 7.

6.3. Results and discussion

AgNPs were synthesised using LED lights with specific emission wavelengths as per the method for dark transformed AgNP developed by Lee et al (Lee et al, 2010). The absorption spectra of photosynthesised AgNP at the estimated wavelengths of 520, 567, 617 and 655 nm are shown in 5A. The dominating peak represents the flat circular plane morphology of the AgNDs (in-plane dipole, the probability of AgNDs lying horizontally being the dominant population within the solution) and the smaller shoulder representing the round edges (i.e., 660 nm peak found on AgNP530, AgNDs positioned upright and perpendicular to view) of the AgND in solution as observed in Figure 6.5B.

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A) A A) B) )

Figure 6.5 A) Absorbance spectra of photosynthesised AgNPs and B) sample TEM image of control AgND disks synthesised by a LED emitting 567 nm.

Figure 6.5A and Table 6.1 shows the out-of-plane quadrupole of all AgND to be similar while the out-of-plane dipole for AgNP617 is observed to be 2-3 folds higher compared to the AgNPs made with

LED lamps with emission maximum wavelengths of 530 nm, 567 nm and 655 nm. The AgNP617 out- of-plane dipole absorbance unit (AU) increase implies that there are protrusions detected on the AgND edge where it is not observed on the other AgNDs.

As the in-plane dipole AU has been recognised as the reflection of prism vertices size, it is observed to be the position of greatest AU height within each AgND spectra. From Figure 6.5A and Table 6.1, it can be deduced that AgNP617 is not of AgND shape but already formed AgNPr. The size of AgNP655 is too large to collect a complete spectra within the UV-vis range.

Table 6.1 Absorbance at the position of the in and out-of-plane dipole and quadrupole on absorbance spectra

AgNP nm Out-of-plane Out-of-plane In-plane In-plane dipole quadrupole (AU dipole (AU @ quadrupole (AU @ ~340 nm) ~410 nm) @ ~470 nm)

AgNP530 0.09 0.09 0.24 0.35

AgNP567 0.09 0.08 0.15 0.62

AgNP617 0.03 0.19 0.05 0.79

AgNP655 0.06 0.05 0.05 0.55

6.3.1.1. Selection of AgNP for the development of a sensitive antioxidant capacity assay The spectral responses of AgNPs to increasing reductant concentrations are anticipated to show a positive correlation while the size of the initial particles and growth of AgNPr transformation will 171

Yan Yee Poon z3160325 influence assay sensitivity and detection range. The responses with increasing concentrations of caffeic acid as a model reductant were examined in order to select an appropriate AgNPr for further assay development. The spectra of four photo-induced AgNP were synthesised using emission maxima of 530, 567, 617 and 655 nm exposed to increasing concentrations of caffeic acid from 0.1 µM to 10 mM are shown in Figure 6.6.

Figure 6.6 Absorption spectra of A) AgNP530, B) AgNP567, C) AgNP617 and D) AgNP655 at increasing concentrations of caffeic acid 10 mM to 100 nM.

The predominant nanoparticles are representative of nanodisks, which readily responded to caffeic acid in a dose-response matter shown as the spectrum change and is displayed by the shift of λmax. The

AgNP530 and AgNP567 spectra were red-shifted, indicating the size increase (Figure 6.6A and B). The smaller red-shift in AgNP567 and the increase in peak absorbance indicates a population growth of similar AgNP size to the control. This is especially pronounced at the in-plane dipole with a red-shift

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of approximately 40 nm and an increase in absorbance of λmax by 0.17 AU between control and 1M of caffeic acid. This effect allows for higher concentrations of reductant to be examined while the AgNP transformation spectra are still captured within the visible spectrum range and with greater pronounced maximum peak absorbance.

Table 6.2 Shift of in and out-of-plane dipole and quadrupole on absorbance spectra after 1 M caffeic acid exposure

AgNP nm Out-of-plane Out-of-plane In-plane quadrupole In-plane dipole quadrupole (AU, nm) dipole (AU, nm) (AU, nm) (AU, nm)

AgNP530 ↑0.05 AU ↑0.03 AU ↑0.05 AU ↑0.19 AU 3 nm blue-shift 3 nm red-shift 2 nm red-shift 76 nm red-shift

AgNP567 ↑0.02 AU ↑0.01 AU ↓0.01 AU ↑0.17 AU no shift no shift 1 nm red-shift 39 nm red-shift

AgNP617 ↓0.04 AU ↓0.08 AU ↓0.02 AU ↓0.2 AU no shift 27 nm blue-shift 18 nm blue-shift 22 nm blue-shift

AgNP655 ↓0.01 AU - AU - AU ↓0.07 AU no shift no shift no shift 8 nm blue-shift *Wavelength shifts are approximate

AgNP617 and AgNP655 had negligible shifting or blue-shifting at the in-plane dipole which indicates a decrease in the nanoparticle size. The absorbance at each resonance bands in Table 6.2 was also decreased as the caffeic acid concentrations increased. The in-plane dipoles of AgNP617 and AgNP655 were also located at higher wavelengths, which in combination with peak shifting at the respective reductant concentrations would, in turn, reduce the concentration range able to be visible within the spectra. The in-plane dipole resonance band of AgNP655 is visibly disrupted at the wavelengths towards 1000 nm which may interfere with the analysis of AgNP responses.

The dose-response relationship of the test AgNPs with caffeic acid with the linear fit were all 2 2 significant (P < 0.0008; R > 0.98 for AgNP530, AgNP567 and AgNP617 and R > 0.86 for AgNP655 8 (Figure 6.7)). The slope values of AgNP530, AgNP567 and AgNP617 were comparable between 6.2 x10 and 9.2 x108 ΔIn AU/M, showing relatively similar dose-response reaction and sensitivity as an 8 antioxidant capacity assay. However, AgNP655 slope value was significantly reduced (1.6 x10 AU) under the experimental condition (not detecting at the NIR range). Though AgNP530 showed the most favourable slope value; the linear regression was superior for AgNP567. This, in combination with the

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peaks detected from Figure 6.6, AgNP567 was selected as being most suitable for the development of a colourimetric antioxidant capacity assay.

2 Figure 6.7 The AgNP responses (ΔIn AU) from 300 - 960 nm of AgNP530 (Y = 922206359X - 15.6, R = 2 2 0.9824), AgNP567 (Y = 627610975X + 1.72, R = 0.9903), AgNP617 (Y = 917572270X + 0.4305, R = 0.9822) 2 and AgNP655 (Y = 161258660X + 5.566, R = 0.8651) to increasing caffeic acid concentrations (M).

6.3.2. Dose-response relationship of AgNP-polyphenols reaction

To assess the reactivity of AgNP567 reaction with polyphenols and their respective analytical sensitivity as antioxidant capacity, a total of 21 polyphenolic compounds including fourteen phenolic compounds, five flavonoids and two stilbenes were tested for their reactivity with AgNP567 at the concentrations range of 100 nM and 1 mM. A dose-response curve was generated by plotting the integrated sum of absorbance (ΔIn AU) from 300 - 960 nm vs concentration (Figure 6.7). Analytical parameters of the dose-response curves such as slope, the limit of detection (LOD) and linear range of detection, and R2 were determined as shown in Table 6.2.

The colour change observed from AgNP567 transformation with differing concentrations of polyphenols could be demonstrated with caffeic acid, rutin and quercetin, as examples (Table 6.3 and

Figure 6.8). The transformation of AgNP567 with increasing concentrations of caffeic acid red-shifted towards lower wavelength to produce a gradual change in blue → purple solution (Table 6.3). The colour change of rutin shows a similar transformation through the red-shift though not visually significant as demonstrated by caffeic acid and quercetin. In contrast, quercetin induced AgNP567 transformation changed from purple to blue, orange-brown and then finally to yellow; this suggests blue-shifting of AgNP567 peak towards lower wavelengths to produce the orange-brown and final yellow colour. 174

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Table 6.3 Colour of AgNP567 transformations after the addition of caffeic acid, rutin hydrate and quercetin at a concentration range of 10-7 M to 10-2 M Polyphenol (M) Blank 10-7 10-6 10-5 10-4 50-4 10-3 50-3 75-3 10-2 Caffeic acid Rutin hydrate Quercetin

A) B)

Figure 6.8 Absorbance spectra of AgNP567 treated with A) rutin hydrate and B) quercetin at the concentration range of 100 nM to 10 mM.

6.3.2.1. Phenolics

Of the fourteen phenolic acids tested, AgNP567 did not react linearly to o-coumaric acid, salicylic acid, syringic acid, t-3-hydroxycinnamic acid and p-hydrobenzoic acid.

The reactivity of phenolic antioxidants tested were as follows: Caffeic acid > gallic acid > hydroquinone > protocatechuic acid > sinapic acid > ferulic acid > p-coumaric acid > t-cinnamic acid > vanillic acid.

The phenolic compounds most responsive to the AgNP567 include caffeic acid and gallic acid with the linear detection range between 10 µM and 100 µM (Y = 760252x + 166.98, R2=0.99 and Y = 582306x + 164.3, R2=0.99 respectively), hydroquinone gave a positive correlation also from 10 µM 2 and 100 µM (Y = 174353x + 533, R of 0.998). Protocatechuic acid was found to have linear AgNP567 growth between 100µM and 1 mM (Y = 25447x + 161.42, R2= 0.938). While caffeic, gallic and protocatechuic acid and hydroquinone had high antioxidant capacity (slope) in AgNP567 response and had the linear ranges with the lowest sensitivity (LOD) of the phenolic compounds, they did not span a wide range of the concentrations tested (from 10 µM to 1 mM). p-coumaric acid was observed to

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The phenolic compounds found with the widest linear range of AgNP567 growth response were t- cinnamic acid and ferulic acid, both linear between 500 µM and 10 mM (R2= 0.746 and 0.969 respectively), with ferulic acid having higher correlation. These compounds were observed to be linear within the higher ranges of the concentrations tested. This suggests that the AgNP567 assay for caffeic, gallic, protocatechuic acid and hydroquinone is sensitive for low concentrations whereas the assay is flexible in testing a wide range of concentrations for t-cinnamic and ferulic acids. While this

AgNP567 assay method is not particularly sensitive or flexibly used in a wide linear range, it is still suitable and accurate used for the testing of sinapic (500 µM to 10 µM, R2= 0.989), p-coumaric (1 mM to 10 mM, R2= 0.867) and vanillic acid (5 mM to 10 mM, R2= 0.988).

The AgNP567 response differs between caffeic acid and protocatechuic acid despite having identical functional group type, benzene ring number and positions in ring A may be due to the extra double bond present on caffeic acid at the para position.

6.3.2.2. Stilbenes and Flavonoids Both resveratrol and polydatin (piceid) had a positive correlation between concentration and AgNP growth response. Of the five flavonoids tested, AgNP did not react linearly to biochanin A. The reactivity of stilbenes and flavonoids tested were as follows: quercetin > myricetin > resveratrol > rutin hydrate > polydatin > genistein.

The stilbene and flavonoid compounds most responsive to the AgNP567 include quercetin with the linear detection range between 10 µM and 1 µM (Y = 3000000 x + 215.46, R2= 0.995), myricetin between 100 µM and 5 mM (Y = 61108 x + 191.14, R2= 0.991) and resveratrol from 100 µM and 1 mM (Y = 2874.3 x + 205.69, R2= 0.901). Quercetin had the highest antioxidant capacity (slope) in AgNP response and had the lowest sensitivity (LOD) and linear range within all test compounds, though it did not span a wide range of the concentrations tested.

It should be noted that though the slope for resveratrol, rutin and polydatin were very similar (median = 2534.2±340.1), the linear ranges for the three compounds differ. Polydatin was observed with the widest linear range of AgNP567 growth response, between 100 µM and 10 mM with high correlation (R2= 944), though antioxidant capacity was only mid-ranged amongst the compounds tested (slope,

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These observations imply that AgNP567 assay quercetin is sensitive for low concentrations whereas the assay is flexible in testing a wide range of concentrations for polydatin. While this AgNP567 assay method is not particularly sensitive or flexibly used in a wide linear range, it is still suitable and accurate for the testing of rutin hydrate, myricetin, resveratrol and genistein.

6.3.3. The relationship between polyphenolic structures and AgNP transformation

The polyphenols were tabled with the redox potential, slope, LOD and linear ranges arranged in ascending order of redox potential. The structural characteristics were assessed and summarised for the number of hydroxyl groups, aromatic rings and carboxyl group and the positions of substitution groups on Rings A and B of each polyphenol (Table 6.4, Table 6.5 and Table 6.6).

6.3.3.1. Relationship between structural features and AgND transformation It is assumed that the greater number substitutional groups on the aromatic and carboxyl rings the greater possibility of H+ donation and double bonds for AgNP reduction and hence transformation to nanoprism. While the number of compound characteristics may give possibilities of reaction with AgNP, accessibility of these structures for reduction may also affect availability to the substitutional groups and reaction rates. The more readily accessible the groups, the increased chances for electron transfer on the AgNP to occur; reversely the less access for electron transfer, the greater degree of difficulty for AgNPs transformation. Some trend of selectivity was observed, showing the greater AgNP response with compounds containing a greater number of hydroxyl groups on benzene rings and a substitutional group on ring A at the meta and para positions, with exceptions of gallic acid, protocatechuic acid and hydroquinone. This suggests that AgNP growth and transformation is selective and molecularly more complex. Accessibility for electron transfer may also play an important role in selectivity.

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6.3.3.2. Relationship between redox potential of polyphenols and AgND transformation The redox potential and slope of the flavonoid compounds were in agreement that the greater slope and lower redox potential across the concentrations tested. The LOD was also noted to be generally lower with higher slopes i.e., the slope of quercetin was observed to be the highest at 3000000 while the LOD was the lowest at 1x 10-6, in contrast, the slope for vanillic acid was quite low at 377.3 while the LOD was high, 5x 10-3. Phenolic compounds showed similar trends of higher slopes to lower redox potential with the exceptions of syringic acid and p-hydrobenzoic acid.

The reactivity of polyphenols for AgNP transformation is theoretically proportional to the reducing power of the compound. As it was assumed that electron transfer is the main mechanism driving AgNP transformation in this assay, it was expected that the response observed would be proportional to the redox value of each compound. Slope seems to be conjecturally related to compound redox potential. Rutin hydrate, quercetin and myricetin displayed some of the highest absorbances and had redox potentials under 0.4E ̊(v). Nonetheless, protocatechuic, caffeic and syringic acids also had redox potentials close to that of rutin, quercetin and myricetin but did not show similar AgNP response in absorbances.

6.3.3.3. AgND transformation as antioxidant capacity measurement

The AgNP567 response measured over the polyphenol concentrations is expressed in the linear regression equation as the slope; wherein this assay, the slope of the curve is defined as antioxidant capacity. For instance, quercetin has a greater antioxidant capacity than vanillic acid. The limit of detection (LOD) of each compound tested similarly seem to be related to the slope in that the higher the slope the lower the LOD and also linear range (i.e., high slope and low LOD and linear range of quercetin (Table 6.5) and in vanillic acid; low slope and high LOD and linear range (Table 6.4)). The LOD describes the sensitivity of the AgNP to each polyphenol; the lower the range of linearity reached, the smaller amount of polyphenol is required for response and the more effective the AgNP detection for the polyphenol. The x-intercept would describe assay baseline for each polyphenol tested.

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Table 6.4 Phenolic antioxidant, regression slope, R2 and their compound characteristics

Phenolics Redox Slope X LOD Linear R2 OH Benzene Ring A Ring B Carboxyl E ̊ (v) intercept (M/L) range group ring position position group O M P M P O M P Caffeic acid 0.183 760252 166.98 1x 10-5 1x10-5 - 0.99 ++ + + + 1x10-4 Gallic acid 0.274 582306 164.3 1x 10-5 1x10-5 - 0.99 +++ + + 1x10-4 Protocatechuic acid 0.41 25447 161.42 1x 10-4 1x10-4 - 0.938 ++ + + + 1x10-3 Sinapic acid 0.45 15740 153.83 1x 10-5 1x10-5 - 0.989 + + + ++ 5x10-4 Syringic acid 0.49 50.1 269.57 - 0.034 + + + ++ Ferulic acid 0.53 983.82 129.48 5x 10-4 5x10-4 - 0.969 + + + + 1x10-2 p-Coumaric acid 0.67 874.52 176.03 1x 10-3 1x10-3 - 0.867 + + + 1x10-2 Vanillic acid 0.73 377.3 128.71 5x10-3 5x10-3 - 0.988 + + + + 1x10-2 o-Coumaric acid 0.75 23.9 177.03 - 0.036 + + + p-Hydroxybenzoic acid 0.87 -105.05 131.47 - 0.04 + + Salicylic acid 0.94 8.4 132.77 - 0.003 + + + t-Cinnamic acid 629.54 176.12 5x 10-4 5x10-4 - 0.746 + 1x10-2 t-3-Hydroxycinnamic acid 563.92 132.68 - 0.812 + + + Hydroquinone 174353 533 1x 10-5 1x10-5 - 0.998 ++ + + 5x10-4

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Table 6.5 Flavonoids, regression slope, R2 and their compound characteristics Flavonoid Redox Slope X LOD Linear R2 OH Benzene Ring A Ring B Carboxyl E ̊ (v) intercep (M/L) range group ring position position group t O M P M P O M P Quercetin 0.1 3000000 215.46 1x 10-6 1x10-6 - 0.995 ++++ ++ + + ++ 1x10-5 Rutin hydrate 0.23 2383.1 207.48 1x 10-4 1x10-4 - 0.994 ++++ ++ + + + + ++ 5x10-3 Myricetin 0.36 61108 191.14 1x 10-4 1x10-4 - 0.991 +++++ ++ ++ ++ + 5x10-3 Genistein 154.91 147.42 1x 10-3 1x10-3 - 0.770 +++ ++ + ++ + 1x10-2 Biochanin A 95.3 149.8 - - 0.099 ++ ++ +

Table 6.6 Stilbenes, regression slope, R2 and their compound characteristics Stilbene Redox Slope X LOD Linear R2 OH Benzene Ring A Ring B Carboxyl E ̊ (v) intercept (M/L) range group ring position position group O M P M P O M P Resveratrol 0.65 2874.3 205.69 1x 10-4 1x10-4 - 0.901 +++ ++ + ++ 1x10-3 Polydatin 2194.1 148.48 1x 10-4 1x10-4 - 0.944 ++ ++ + + + 1x10-2

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6.4. Exploring soaking as a simple sample preparation method for AgND-based antioxidant capacity assay

6.4.1. Assessment of AgNPr responses to whole kernel extraction

Traditional antioxidant capacity assays tend to require extensive sample preparation involving grinding, defatting and finally solvent extraction of the polyphenol antioxidants for assay input (Huang et al, 2005, Apak et al, 2007, Karadag et al, 2009). Such procedures are irreversible and destroy kernel integrity as well as germ (seed embryo) and testa (skin) which is required for seed germination. As biological samples within the cultivar exists from differing plots on the same field; so do variations from individual peanut pods from separate locations on the single root system belonging on the one peanut plant. The direct use of a tested kernel for high antioxidant capacity during screening phases in the cultivation of the subsequent generations would be the most accurate for breeding high polyphenol expressing lines. An attempt was made to retain kernel germination ability for sowing afterwards, which would require the testa to remain intact during polyphenol extraction. The use of whole kernels with undamaged testa and analyte extraction with a non-hazardous solvent to prevent any cellular damage would be ideal. Three sample types - whole kernels, skinless kernels and peanut testa (skin only) was assessed for suitability for extraction with distilled water (MQW). For the development of a rapid screen test, a simple soaking method was evaluated for AgND response.

6.4.1.1. Kernel leachates by soaking Phenolics influence seed germination and cell division which naturally should be found in vast abundance within the kernel germ (Sharma et al, 2019). The industry practice of seed viability test by soaking of kernels in MilliQ water (MQW, 1:4) was hence adopted as a polyphenol extraction method, to assess the AgNP responses. The seed viability test is based on the principle of electrolyte leakage adopted from Mohamad-Yasseen et al (1994) and Krishnan et al (2005) with modifications and further development into the current peanut industry practice (Mohamed-Yasseen et al, 1994, Krishnan et al, 2005).

The soaking method as a simple hydrophilic polyphenol extraction was assessed using the whole kernel with skin and skinless kernels and skin only (testa) samples. Peanut kernels with

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Yan Yee Poon z3160325 and without testa were assessed as the testa has a protective function of the seed kernel as a physical barrier, which may affect the extraction of kernel polyphenol extraction.

Figure 6.9 shows the UV-vis spectra of AgND response to the whole kernel, skinless kernel and skin leachates with an extraction time of 15, 30, 60, 120 and 180 min. All the leachate samples induced changes compared to the control. The absorbance reflective of organics in solution is found at 300 - 360 nm and the nucleation of seed particles (~400 nm) (Özyürek et al, 2012) in all the samples. Growth of AgND to AgNPr are observed from 0 min to 180 min extraction time and the isosbestic points in Figure 6.9.

Figure 6.9 Absorption spectra of AgNP after reaction with leachates of A) whole kernels, B) Skinless kernels and C) kernel skin at different extraction time. 182

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While the in-plane dipoles of AgNP530, AgNP567, AgNP617 and AgNP655 in response to sample leachate had negligible change with exposure to samples of all extraction times but the absorbance at the lower wavelengths (300 - 450 nm) (Figure 6.7), in particular at the out-of- plane, are significantly different with the increasing extraction times. This differs from the spectras observed with caffeic acid (Section 6.3.1.1, Figure 6.6), where absorbances increase mainly at the in-plane dipole while the out-of-plane quadrupole and dipole and in-plane quadrupole experienced negligible change. Such difference may be due to the presence of organics such as polyphenols in each sample or effects resulting from green synthesis in AgNP reactions (de Jesús Ruíz-Baltazar et al, 2017, Azkiya et al, 2018).

6.4.1.2. Effects of soaking time A key restriction to the development of the rapid screen assay is the time expended in sample preparation. To increase assay efficiency, the minimum time for sample soaking to achieve sufficient AgNP response for analysis is investigated.

An increase of AgNP response to control is observed with all sample types over the course of 180 min which confirms the extraction of AgNP reducing substances over soaking time (Figure 6.10). The AgNP responses of skin only and skinless kernels were significantly greater than those of whole kernels which is most likely due to the effect of the testa acting as a protective barrier in the prevention of reductant leakage from the kernel matrix.

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All three samples of skinless kernels, skin only and whole kernels showed a slowing in percentage change of AgNP response to control beyond 60 min, (34%, 59%, 43% at 30 min and 37%, 63% and 46% at 60 min respectively). The greatest rate of increase in change of percentage in AgNP response to control was found between 30 and 60 min extraction time, translating into the plateau of extracted AgNP response inducing reductant after 60 min. Consequently, the optimum time in sample extraction efficiency for the most effective AgNPr response at 60 min.

6.4.1.1. AgNPr response to kernel leachate Eighteen RIL samples were selected for extraction by soaking and assessed the correlation of AgNP responses with the ORAC values obtained in Sections 3.4.1.

Figure 6.11 Kernel ORAC Trolox equivalent (TE) plot against leachate response integrated absorbance (In AU) to skinless kernel particles using North Queensland (Kairi) RILs. Y= 0.0008468x -17414, R2= 0.01467

The AgNPr response with skinless kernel leachate measured as In AU against ORAC TE shows a low correlation with a low R2 value of 0.015, a Pearson’s test R of 0.12 and a two-tailed P value of 0.63 (Figure 6.11). The low correlation is predominantly due to low AgNP responses observed from the leachate samples. The In AU is recorded to be quite low at a maximum of 25 AU, this suggests that while leachate methods required less time for reductant extraction, there may be other phenolic compounds or flavonoids that not been extracted through the intact peanut matrix with MQW. The sample leachate may not be adequately concentrated for the AgNP transformation since antioxidative compounds are mostly found matrix-bound (Chapter

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3, Figure 3.9) and that extraction that will only be better reflected with ground kernels for greater extraction interface and from a homogenous mixture than with sample soaking. In addition, complications with RIL differentiation may be encountered due to low AgNP responses observed with leachate samples. The differences in biological variation from samples and additionally, rates of reductant leakage from individual kernels may further compound in contribution to the data scattering, leading to inaccuracies and low correlation of leachate AgNP response to the ORAC assay. Further development of a simple preparation method to extract larger concentrations of polyphenol antioxidants is required instead.

6.4.2. Correlation of AgNP responses with ORAC and FRAP assays for peanut kernel extract

The use of ORAC assay to assess antioxidant capacity in peanut kernels was most suitable compared to other assays as low sensitivity and high oil content interferes with detection and differentiation between peanut samples becomes difficult (Section 2.11)(Phan-Thien 2012). Antioxidant assays differ by the two principles of HAT and ET (Equation 2.7), either using hydrogen donation quenching of free radicals in HAT based assays or by the transfer of electrons in ET based assays (Francisco and Resurreccion 2008). The ORAC assay is HAT based and the AgNP assay relies on the principle of ET, hence, the comparison with FRAP assays which is also based on ET principles was proposed. A comparison was made using the same sample extract to minimise discrepancy.

6.4.2.1. AgNP responses in comparison to ORAC assay The polyphenol antioxidants of the RIL samples (n = 10) were extracted using the sample preparation method of ORAC assay and the response of AgNP were assessed to evaluate the correlation between AgNP responses and ORAC values of the same RIL sample obtained in Chapter 3 (Section 3.3.4). As anticipated, sample extracts from ground defatted kernels returned higher AgNP responses in comparison with the leachate extract obtained through soaking methods (Figure 6.12).

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Figure 6.12 Correlation between kernel ORAC trolox equivalent (TE) plot against AgNP response integrated absorbance (In AU) (n = 10). Y= 0.2858x +399.9, R2= 0.4732. α = 0.05.

A positive correlation was found (R2= 0.47, Pearson's test, P = 0.03) between the AgNP response of the RIL extracts and their respective ORAC values. Though the correlation was significant (P= 0.03), the low R2 value (0.47) was suspected to be caused by the discrepancy of comparing the ORAC assay with the AgNP method, which are based on different principles. Possible influences may likewise contribute from sample heterogeneity due to biological variations in seed kernel within the same RIL as previously observed in Chapter 3 (Section 3.3.4). Biological variations in the form of error values were observed within sample replicates harvested from the same environmental conditions on identical grounds but in alternate locations within the same field trial.

6.4.2.1. AgNP responses in comparison to FRAP assay RIL samples (n = 98) from 2015/6 season were selected to evaluate the correlation of AgNP responses to FRAP values with their respective antioxidant capacity. The methanolic extracts of defatted peanut kernels prepared according to the FRAP assay protocol (Section 6.2.12) were reacted with AgNPs in parallel for the observation of AgNP growth and compared with antioxidant capacity ( calculated as TE) results from FRAP as below.

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Figure 6.13 Correlation between kernel FRAP trolox equivalent (TE) plot against AgNP response integrated absorbance (In AU) (n = 98), Y= 0.01095x + 92.26, R2 = 0.2732. α= 0.05.

A significant correlation was observed with the RILs tested (Pearson R= 0.52, R2= 0.27, two- tailed P< 0.0001), (Figure 6.13). The observation of a low correlation (R2) despite a significant Pearson test P value originates from data scattering and may be due to biological variations previously reported in Sections 3.4.2 which originated from biplots of the same genotype plotted in different positions within the same field trial. The extracts of each RIL were also collected by using a mix of kernel samples from a single RIL (minimum 10 kernels) which may have compounded on biological variation. The correlation results between ORAC and AgNPr and FRAP and AgNPr has shown considerable prospect for the development of an AgNPr mediated antioxidant capacity assay towards kernel screening and seed selection in breeding programs.

6.5. Conclusion

Agronomists and crop producers have been working to cultivate phenotypical characteristics ideal for quality and yield in changing climates globally. In order to assist in the selection of cultivars high in polyphenol antioxidant expression, a rapid screen test is required. The use of dark-transformed AgNP disks which transforms from disks to flat hexagons and then into triangular prisms when electrons are donated to Ag+ to reduce and increase nanoparticle size may be useful for such development. The citrate buffer used directs and promotes growth on available reduction sides of the AgND resulting in a prismatic structure (Lee et al, 2010, Lee

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Yan Yee Poon z3160325 et al, 2013). Reductant exposure from antioxidants are able to transfer Ag+ ions onto the nanoparticle surface contributing to size growth and shape change acting as a reductant.

Twenty polyphenol antioxidant solutions of 1 M were reacted with AgND which induced transformation to AgNPr. Rutin, sinapic acid, polydatin and resveratrol transformed the AgNP solution from purple to blue shades indicating a blue-shift (Lee et al, 2010) and an increase in AgNP size which implies AgNP growth. Orange-red and brown colours resulting from quercetin and myricetin suggest red-shift of the spectra (Figure 6.8), photoablation and a return to nanodisks (Lee et al, 2010).

The optimum LED wavelength was found to be 567 nm for the detection and the greatest rate of change in AgNP response was observed to be after 25 min which is in agreement with previous AuNP studies (Wang et al, 2007). The reductant concentration and AgNP growth in positive linear correlation were observed for homogenous solutions of sinapic, t-cinnamic, caffeic, gallic, protocatechuic, vanillic and syringic acids, also hydroquinone, rutin, polydatin and resveratrol in decreasing concentrations. An attempt was made to decrease the sample preparation time for the rapid screen test by use of electrolyte leakage principles and the avoidance of kernel grinding and defat steps before polyphenol antioxidant extraction. AgNP were found to be responsive to leachates extracted from MQW sample soaking and also observed 60 min as the most effective extraction time. It was observed that leachate spectra changes were low than due to the use of MQW as leachate solvent, the protective barrier of the testa and the lack of surface area for solvent interaction in an attempt to preserve the kernel for sowing after examination.

Extracts of complex polyphenol antioxidant mixtures obtained from the peanut kernel matrix- induced greater spectral changes to AgNPs and the correlation of AgNP responses to ORAC and FRAP assays were compared. FRAP extracts of peanut polyphenol antioxidants from defatted matrix gave significant correlation (Pearson R= 0.5233, R2= 0.2738, two-tailed P< 0.0001) with a total of 98 individual RIL samples. The improved correlation of FRAP assay to the AgNP assay due to similar mechanisms in ET principles in contrast to the HAT based ORAC assay was shown.

The results of this chapter have demonstrated that AgNPrs are quick, simple and reasonably sensitive (ranging from 100 nM to 10 mM) without the need of specialised equipment or technical skills for the detection of antioxidant capacity. While the use of this AgNPr assay 188

Yan Yee Poon z3160325 with biological extracts may require dilutions for background elimination; this AgNP based rapid assay capable of quantifying solutions of a single phenolic, flavonoid and stilbene between 10 mM to 100 nM giving indications of total antioxidant capacity. It is also able to assess total antioxidant capacity in a complex sample extract which is comparable in efficiency and ease to other biochemical assay methods while it the AgNPr has exceeded current AgNP assay linearity ranges of 1.33 – 39.8 µM as reported by Özyürek et al (Özyürek et al, 2012).

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7. GENERAL CONCLUSION AND FUTURE WORK The main goal of this PhD thesis is to examine the G x E interactions on the polyphenol antioxidant expression of peanut cultivars for the selective breeding of a stable high polyphenol antioxidant expressing peanut line.

The aims broken down into the individual stages in this project are to, 1) confirm the degree of G x E interaction on polyphenol antioxidant contents in peanut RILs; 2) identify proteins related to polyphenol antioxidant expression as biomarkers for selective breeding; 3) profile known and unknown polyphenol antioxidants extracted from RIL peanuts; and 4) attempt to develop an indestructive rapid screen test for the selective breeding of polyphenol antioxidants in peanut seeds.

7.1. Chapter 3 – G, E, G x E influence on polyphenol expression

In Chapter 3, genotypes were found to be significantly different in polyphenol antioxidant expression in the RIL samples tested, with environment and genotype by environment interaction being not significant. This demonstrates that polyphenol antioxidant expression is controlled predominately by genotype and could be selected for in selective breeding. With the use of the ORAC assay, the lowest and highest polyphenol antioxidant capacity expressing RIL were found, with their respective stability in expression across differing locations.

Along with the RIL parents, the lowest polyphenol antioxidant expressing RILs, an unstable and stable highest expressing RILs were selected for quantification by HPLC. The native quantification of ferulic/sinapic, caffeic, p-coumaric, salicylic and t-cinnamic acids, resveratrol, daidzein and quercetin co-eluting unknown by HPLC correlated with the ORAC trolox equivalence of each RIL.

Comparison in native and enzymatic quantification of the selected RILs showed a fair amount of polyphenols to be present matrix-bound and suggested the inheritance of affinity to specific pathway expression from either RIL parents. Such segregation in specific compound expression between offsprings in accordance with either parent includes o-coumaric acid where all examine offsprings took after the p145-p3-115 parent, salicylic acid where most was matrix- bound in all offsprings alike Farnsfield. The offsprings split in affinity for t-cinnamic acid and quercetin co-eluting unknown where P27-p272 and P27-p362 increased in extraction after

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Yan Yee Poon z3160325 enzymatic treatment similar to Farnsfield and P27-p036 decreased in coordination with p145- p3-115.

Chapter 3 was able to recognise the high and low polyphenol antioxidant expressing genotypes that are stable when challenged with environmental stresses and ORAC was used for the selection of high polyphenol antioxidant lines to be used in breeding. With the continued successful selection of high polyphenol antioxidant expressing peanut cultivars for breeding; yield and crop quality can be combined through selection for improved cultivar development.

7.2. Chapter 4 – Quantitative proteomics of antioxidant expression

The proteins related to polyphenol antioxidant expression were identified by gel-based label- free quantitative proteomics in Chapter 3. Differential expression between the five selected RILs from Chapter 3, the two parents; p145-p3-115 and Farnsfield, the low polyphenol antioxidant expressing RIL; P27-p272 and the two high polyphenol antioxidant expressing RILs; P27-p036 (unstable) and P27-p362 (stable) were examined. There were 1007 non- redundant proteins observed between the five RILs, while 82 were found to be differentially expressed; 70 were observed to be over-expressed and 18 proteins were observed to be under- expressed in the high polyphenol antioxidant expressing peanut offspring. Proteins differentially expressed were mainly associated with fatty acid metabolism, stilbene and flavonoid biosynthesis, carbohydrate metabolism, redox homeostasis, anabolic and catabolic pathways. Approximately 20% of proteins detected in the five surveyed RILs were allergenic or suspected to have allergen related activities. Though Ara h1 was over-expressed 6.5 folds in the high polyphenol antioxidant expressing RIL, no other identified peanut allergens such as Ara h2, h6 h7, Ara h3, Ara h5, Ara h9, Ara h10 h11, or Ara h12 h13 were found to have significant changes in expression. Enzymatic changes in metabolic pathways induced higher energy release in glycolysis, greater methionine accumulation in the TCA cycle which contributes toward phenylalanine biosynthesis, the entry point to the central phenylpropanoid pathway or polyphenol antioxidant synthesis. Identification of the polyphenol antioxidant related proteins in Chapter 3 contributes to the genome sequencing of the cultivated peanut. These proteins or related genetic materials may prove to be essential biomarkers that may also assist agronomists in identifying genotypes with greater disease and pest resistance which would lead to improved crop yield.

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7.3. Chapter 5 – Polyphenol profiling with protein biomarkers

The survey of phenolic compounds, flavonoids and stilbenes were carried out using GCMS and LC-MS/MS in Chapter 5. GCMS was able to confirm caffeic acid, ferulic acid, p-coumaric acid, sinapic acid, salicylic acid and vanillic acids which have been previously reported in (Phan-Thien 2012), multiple instances of putative benzoic acid and t-cinnamic acid were also observed. New tentative compounds not previously observed were tyrosol in the form of 4- hydroxyphenylethanol (a predominant antioxidant in wine and olive oil) and also putative m- anisic acid a phenolic compound frequently found in Rye.

The use of LC-MS/MS allowed the detection of 118 compounds, 71 of which were found tentatively with the assistance of mass transitions found in previously reported peanut literature. Some enzymes found to be expressed in the five RILs in Chapter 3 were known to synthesise antioxidative compounds, which logically should also be present in extracts of our sample. Mass transitions were found of these potentially present antioxidative compounds and were also scanned for using SRM mode during our survey. With this approach, 21 previously unreported compounds where tentatively detected with the use of proteomic results obtained from Chapter 4. Compounds observed to be predominant in our sample was observed to be ferulic acid, putative glycitein, flavonoid 3-o-sophoroside, quercitrin and flavonoid 3-o- glucoside. With the peanut proteome on the cusp of full fruition, the discovery of new proteins related to antioxidative compounds may lead to continued mining of additional unknown compounds with increased mass transitions of potential antioxidative compounds reported in the literature and the use of the target parent and product ions for scanning in SRM mode in LC-MS/MS. The surveyed polyphenol antioxidants in peanut extract and the discovery of additional unknown antioxidative compounds may allow for the study of its biosynthesis pathways within the plant. High antioxidant capacity compounds and associated biosynthesis pathways may then be targeted for over-expression enhancing kernel quality and elevate functional food properties. Chalcone synthase was observed to be responsible for flavonoid, anthocyanidin and chalcone synthesis proving immense potential as a future biomarker for antioxidant targeted breeding.

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7.4. Chapter 6 – AgNPr assay development

The potential of an AgNP rapid screen assay was explored in Chapter 6 where homogenous polyphenol antioxidant solutions were reacted with AgNPs. AgNPs synthesised with 567 nm LED lights were found to be most suitable for reductant detection. Linearity was then assessed between 10-2 M and 10-7 M and a positive correlation between concentration and AgNP growth was observed in sinapic, t-cinnamic, ferulic, caffeic, gallic, protocatechuic, vanillic and syringic acids, hydroquinone, polydatin, resveratrol and rutin. Heterogeneous extractions of 10 selected peanut samples using ORAC extraction buffer induced spectral changes of AgNPs however was found to be low in linearity with ORAC antioxidant capacity values from Chapter 3. To reduce assay time, sample preparation simplified to not require grinding or defatting of kernels, the principle of electrolyte leakage was applied to whole and skinless kernels and testa. The leachate supernatant was found to induce a spectral response with the AgNPs; the most efficient being skinless kernels at 60 min extraction time. Leachate samples were found to give a low correlation and low AgNP transformation signals were produced. This led to the adaptation of the FRAP assay to mimic the electron transfer principles which the AgNP reduction is based on. Significant positive correlation was found (n= 98, Pearson R= 0.5233, R2= 0.2738, two-tailed P< 0.0001) between FRAP assay antioxidant capacities and AgNP integrated sum of absorbance response to methanolic FRAP extracts.

Though the use of leachate was not able to segregate high and low polyphenol antioxidant breeding lines and a non-destructive assay was not developed; the successful application of AgNP to pure polyphenol standards and extracts of unknown antioxidant capacity to induce AgNP growth was achieved. The background influence of FRAP extracts due to biological matrix interference may be eliminated by the dilution of test samples. The initial steps for antioxidant capacity detection with greater and more sensitive detection range (between 10 mM to 100 nM) have been successfully optimised and have the potential for further development into a simple, rapid screen test for use on segregating breeding lines to allow for selection peanut breeding programs. This is shown to be comparable in speed and simplicity to other biochemical assays while exceeding current AgNP assay linearity ranges (1.33 – 39.8 µM (Özyürek et al, 2012)) and could be similarly applied to other crop breeding programs including other nuts and grains.

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The future direction for this assay development may involve further exploration as to whether the use of methanolic extraction on skinless kernels would be sufficient or sample preparation methods may be simplified. As the current method of defatting is a lengthy procedure and n- hexane is a toxic and non-green reagent, the removal of this step has significant advantages. Though extraction solvent used has been shown to significantly affect rapeseed sample antioxidant capacity; out of methanol, ethanol and isopropanol tested, the mixture of 50% methanol-water as extraction solvent was found to be most environmentally responsible and extract the highest antioxidant capacity (Vilela et al, 2012). There is potential for the use of high purity methanol for the extraction of polyphenol antioxidant compounds without defatting of peanut meal where methanolic extracts of peanut meal are shown to only contain one low molecular (< 18kDa) low quality diffused band on SDS PAGE (Alam et al, 2000) which indicates a low abundance of methanol soluble proteins in peanut. Fatty acid content was also assessed on methanol extractions of defatted kernels where the highest amount quantified was monoenes at 66.2% of the sample weight. As methanol is a polar solvent, only polar lipids such as free fatty acids and phospholipids are extracted (Liu 1994), which reduces the bulk of lipids to be removed. Remaining free unsaturated fatty acids may be potentially be separated from methanol supernatant by the addition of molecular hydrogen and conversion into saturated forms then separated by low-temperature centrifugation. With this method of defatting, procedures which frequently requires increased time and solvent expenditure (thrice-repeated n-hexane extraction and residue evaporation) may be eliminated for a single methanol extraction with molecular hydrogen and rapid low-temperature centrifugation on ground kernel samples.

The results from this study could greatly assist conventional breeding for a stable high polyphenol antioxidant producing and environmentally resistant peanut line for the Australian and indeed global peanut industries.

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8. PUBLICATIONS TO DATE 8.1. Manuscripts accepted

Muralidharan S, Poon YY, Wright GC, Haynes P, Lee NA (2020) “Quantitative Proteomics Analysis of High and Low Polyphenol Expressing Recombinant Inbred Lines (RILs) of Peanut (Arachis hypogaea L.)” Food Chemistry.

8.2. Oral presentations

Poon YY, Muralidharan S, Wright GC, Haynes P, Lee NA (2017) ‘Differential Metabolic Proteins and Pathways Signalling High and Low Antioxidant Capacity in Peanuts, Using Quantitative Proteomics for Selective Breeding.’ 49th annual meeting of the American Peanut Research and Education Society, 11-13th July 2017, Albuquerque, New Mexico, USA.

Poon YY, Muralidharan S, Wright GC, Haynes P, Lee NA (2016) ‘Antioxidant related protein expression and metabolic pathways in Arachis hypogaea.’ AIFST summer school 27-29th January 2016, Charles Sturt University, Wagga Wagga, NSW, Australia.

Poon YY, Muralidharan S, Wright GC, Lee NA (2014) ‘G x E influence on the breeding of high polyphenol peanuts.’ ATFM Symposium on Food Innovation: present and future, 27-28th November 2014, University of New South Wales, Sydney, NSW, Australia.

8.3. Poster presentations

Poon YY, Muralidharan S, Wright GC, Haynes P, Lee NA (2015) ‘Crop improvement for an antioxidant-rich peanut, Arachis hypogaea.’ Advances in Arachis through genomics and biotechnology, 5-7th November 2015, Brisbane, Queensland, Australia.

Poon YY, Muralidharan S, Wright GC, Lee NA (2015) ‘The influence of genotype by environment interaction on antioxidants expression in cultivated peanut, Arachis hypogaea.’ AIFST summer school, 27-29th January 2015, RMIT, Melbourne, Victoria, Australia.

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Witcombe, J. R., P. A. Hollington, C. J. Howarth, S. Reader and K. A. Steele (2008). "Breeding for abiotic stresses for sustainable agriculture." Philosophical Transactions of the Royal Society B: Biological Sciences 363(1492): 703-716. Wu, J.-H., Q. Li, M.-Y. Wu, D.-J. Guo, H.-L. Chen, S.-L. Chen, S.-W. Seto, A. L. S. Au, C. C. W. Poon, G. P. H. Leung, S. M. Y. Lee, Y.-W. Kwan and S.-W. Chan (2010). "Formononetin, an isoflavone, relaxes rat isolated aorta through endothelium-dependent and endothelium- independent pathways." The Journal of Nutritional Biochemistry 21(7): 613-620. Wu, J. M., Z.-R. Wang, T.-C. Hsieh, J. L. Bruder, J.-G. Zou and Y.-Z. Huang (2001). "Mechanism of cardioprotection by resveratrol, a phenolic antioxidant present in red wine (Review)." International Journal of Molecular Medicine 8(1): 3-17. Wu, Q., M. Wang and J. E. Simon (2003). "Determination of isoflavones in red clover and related species by high-performance liquid chromatography combined with ultraviolet and mass spectrometric detection." Journal of Chromatography A 1016(2): 195-209. Wu, X., P. L. Redmond, H. Liu, Y. Chen, M. Steigerwald and L. Brus (2008). "Photovoltage mechanism for room light conversion of citrate stabilized silver nanocrystal seeds to large nanoprisms." Journal of the American Chemical Society 130(29): 9500-9506. Wynne, J. and W. Gregory (1981). Peanut breeding. Advances in Agronomy, Elsevier. 34: 39- 72. Xing, Y., W. Jia and J. Zhang (2007). "AtMEK1 mediates stress-induced gene expression of CAT1 catalase by triggering H2O2 production in Arabidopsis." Journal of Experimental Botany 58(11): 2969-2981. Xu, C., W. M. Garrett, J. Sullivan, T. J. Caperna and S. Natarajan (2006). "Separation and identification of soybean leaf proteins by two-dimensional gel electrophoresis and mass spectrometry." Phytochemistry 67(22): 2431-2440. Xu, C., J. H. Sullivan, W. M. Garrett, T. J. Caperna and S. Natarajan (2008). "Impact of solar ultraviolet-B on the proteome in soybean lines differing in flavonoid contents." Phytochemistry 69(1): 38-48. Yadav, S. K., V. Kumar and S. P. Singh (2018). Recent Trends and Techniques in Plant Metabolic Engineering. Singapore, Springer. Yan, W., L. Hunt, Q. Sheng and Z. Szlavnics (2000). "Cultivar evaluation and mega- environment investigation based on the GGE biplot." Crop Science 40(3): 597-605. Yan, W. and N. A. Tinker (2006). "Biplot analysis of multi-environment trial data: Principles and applications." Canadian Journal of Plant Science 86(3): 623-645. Yang, Q.-Q., L. Cheng, Z.-Y. Long, H.-B. Li, A. Gunaratne, R.-Y. Gan and H. Corke (2019). "Comparison of the phenolic profiles of soaked and germinated peanut cultivars via UPLC- QTOF-MS." Antioxidants 8(2): 47. Yao, L., Y. Jiang, J. Shi, F. TomÁS-BarberÁN, N. Datta, R. Singanusong and S. Chen (2004). "Flavonoids in Food and Their Health Benefits." Plant Foods for Human Nutrition 59(3): 113- 122.

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Yao, X., W. Xiong, T. Ye and Y. Wu (2012). "Overexpression of the aspartic protease ASPG1 gene confers drought avoidance in Arabidopsis." Journal of Experimental Botany 63(7): 2579- 2593. Yokotsuka, K. and T. Okuda (2011). "Light-induced Isomerization of trans-Resveratrol to cis- Resveratrol in Must and Wine during Fermentation and Storage." American Journal of Enology and Viticulture Japan Yousuf, S., F. Atif, M. Ahmad, N. Hoda, T. Ishrat, B. Khan and F. Islam (2009). "Resveratrol exerts its neuroprotective effect by modulating mitochondrial dysfunctions and associated cell death during cerebral ischemia." Brain Research 1250: 242-253. Yu, J., M. Ahmedna, I. Goktepe and J. Dai (2006). "Peanut skin procyanidins: Composition and antioxidant activities as affected by processing." Journal of Food Composition and Analysis 19(4): 364-371. Zengin, A., A. Bozkurt, I. Boyacı, Ş. Özcan, P. Daniel, F. Lagarde, A. Gibaud, D. Cetin, Z. Suludere and P. Guttmann (2014). Anisotropic core-shell Fe3 O4 @Au magnetic nanoparticles and the effect of the immunomagnetic separation volume on the capture efficiency, Pure and Applied Chemistry. Zhang, K. and Y. Zuo (2004). "GC-MS Determination of Flavonoids and Phenolic and Benzoic Acids in Human Plasma after Consumption of Cranberry Juice." Journal of Agricultural and Food Chemistry 52(2): 222-227. Zhang, Q., X.-H. Zhao and Z.-J. Wang (2008). "Flavones and flavonols exert cytotoxic effects on a human oesophageal adenocarcinoma cell line (OE33) by causing G2/M arrest and inducing apoptosis." Food and Chemical Toxicology 46(6): 2042-2053. Zhao, J. and R. L. Last (1996). "Coordinate regulation of the tryptophan biosynthetic pathway and indolic phytoalexin accumulation in Arabidopsis." The Plant Cell 8(12): 2235-2244. Zhou, J.-M., N. D. Gold, V. J. J. Martin, E. Wollenweber and R. K. Ibrahim (2006). "Sequential O-methylation of tricetin by a single gene product in wheat." Biochimica et Biophysica Acta (BBA) - General Subjects 1760(7): 1115-1124. Zhu, Z.-w., J. Li, X.-m. Gao, E. Amponsem, L.-y. Kang, L.-m. Hu, B.-l. Zhang and Y.-x. Chang (2012). "Simultaneous determination of stilbenes, phenolic acids, flavonoids and anthraquinones in Radix polygoni multiflori by LC–MS/MS." Journal of Pharmaceutical and Biomedical Analysis 62: 162-166. Ziegleder, G. (2009). "Flavour development in cocoa and chocolate." Industrial Chocolate Manufacture and Use 4: 169-191.

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10. APPENDIX Table 10.1 Climate averages of Kingaroy/Taabinga from 1980- 2010. (Bureau of Meterology 2017)

Statistics Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

Mean 30.0 29.0 27.9 25.0 21.8 19.0 18.6 20.3 23.9 26.2 27.8 29.6 24.9 maximum temperature

(°C)

Mean 17.9 17.9 15.8 12.8 10.1 6.0 5.1 5.5 8.6 12.2 14.9 16.9 12.0 minimum temperature

(°C)

Mean rainfall 99.3 94.3 58.5 63.8 56.2 35.9 46.0 30.6 36.1 75.8 73.0 111.7 780.5

(mm)

Decile 5 80.7 74.3 47.6 36.5 34.1 33.8 38.7 26.4 32.0 69.5 67.0 123.4 750.6 (median)

rainfall (mm)

Mean number 8.1 7.5 5.9 6.3 6.1 3.8 5.1 3.7 4.0 6.0 7.0 8.3 71.8 of days of rain

≥ 1 mm

Mean daily sunshine

(hours)

Mean number 4.5 4.0 7.3 8.3 9.1 12.5 13.3 14.9 13.9 10.8 6.4 6.1 111.1

of clear days

Mean number 11.5 12.2 9.6 9.5 11.5 7.5 8.1 5.8 5.3 8.4 10.5 10.5 110.4

of cloudy days

Mean 9am 23.7 23.0 21.8 19.1 15.9 12.3 11.6 13.4 17.2 20.0 21.7 23.4 18.6 temperature

(°C)

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Mean 9am 73 76 75 76 80 77 76 71 64 65 68 70 73 relative

humidity (%)

Mean 9am 12.5 12.9 13.0 12.7 10.5 9.5 9.4 11.9 13.2 13.5 13.3 12.0 12.0 wind speed

(km/h)

9am wind N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A speed vs

direction plot

Mean 3pm 28.8 27.8 26.9 23.9 20.8 18.2 17.8 19.5 23.0 25.2 26.7 28.3 23.9 temperature

(°C)

Mean 3pm 53 56 52 53 57 51 50 44 40 45 50 51 50 relative

humidity (%)

Mean 3pm 12.3 12.8 13.2 13.8 12.5 12.6 13.0 14.2 14.7 13.6 13.4 12.6 13.2 wind speed

(km/h)

3pm wind N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A speed vs

direction plot red = highest value blue = lowest value

Product IDCJCM0027 Prepared at Fri 04 Aug 2017 17:28:41 PM EST

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Table 10.2 Climate averages of Bundaberg from 1980- 2010. (Bureau of Meterology 2017)

Statistics Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

Mean 30.5 30.2 29.4 27.6 25.1 22.9 22.4 23.6 25.7 27.1 28.4 29.8 26.9 maximum temperature

(°C)

Mean 21.7 21.8 20.1 17.7 14.7 12.0 10.8 11.3 14.2 17.0 19.0 20.9 16.8 minimum temperature

(°C)

Mean rainfall 128.5 157.5 97.0 62.9 84.8 51.8 33.4 38.5 39.4 74.6 83.7 120.3 965.9

(mm)

Decile 5 115.1 104.2 78.6 40.6 76.8 32.2 24.8 23.8 34.8 52.5 74.4 94.3 928.8 (median)

rainfall (mm)

Mean number 8.4 9.4 7.9 6.1 5.8 4.2 3.5 3.5 3.6 5.8 6.9 7.4 72.5 of days of rain

≥ 1 mm

Mean daily sunshine

(hours)

Mean number 4.2 2.8 7.2 10.5 11.1 13.3 15.4 18.5 15.5 9.7 6.3 6.0 120.5

of clear days

Mean number 10.6 10.8 7.7 6.2 7.1 5.5 5.6 3.8 4.3 6.6 8.1 9.7 86.0

of cloudy days

Mean 9am 26.8 26.4 25.4 23.2 19.9 17.0 16.2 18.0 21.2 23.6 25.1 26.4 22.4 temperature

(°C)

Mean 9am 69 73 71 70 72 72 70 65 62 62 64 66 68 relative

humidity (%)

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Mean 9am 17.6 17.0 18.4 18.1 17.3 16.5 15.9 16.2 16.8 17.8 17.7 17.2 17.2 wind speed

(km/h)

9am wind N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A speed vs

direction plot

Mean 3pm 28.9 28.7 27.8 26.0 23.7 21.7 21.3 22.1 24.0 25.2 26.7 28.1 25.3 temperature

(°C)

Mean 3pm 60 62 59 57 55 53 49 47 50 55 57 59 55 relative

humidity (%)

Mean 3pm 23.5 22.2 23.3 21.1 18.5 17.6 18.2 20.8 22.8 23.6 23.4 23.1 21.5 wind speed

(km/h)

3pm wind N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A speed vs

direction plot red = highest value blue = lowest value

Product IDCJCM0027 Prepared at Fri 04 Aug 2017 16:48:10 PM EST

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Table 10.3 Climate averages of Kairi from 1980- 2010. (Bureau of Meterology 2017)

Jan Feb Mar Apr May Jun Jul Au Sep Oct Nov Dec Annual g

Mean 28.4 27.8 26.5 25.0 23.1 21.7 21.3 22.5 25.2 27.1 28.3 28.9 25.5 maximum temperature

(°C)

Mean 19.3 19.6 18.4 17.0 14.9 12.3 11.3 11.6 12.9 15.3 17.3 18.8 15.7 minimum temperature

(°C)

Mean 235.3 285.8 227.7 105.3 60.6 35.0 31.5 26.3 19.7 31.2 96.0 132.4 1288.4

rainfall (mm)

Decile 5 187.0 215.1 153.2 82.0 50.2 28.7 31.5 13.2 13.2 25.8 61.4 114.1 1194.4 (median)

rainfall (mm)

Mean 13.9 16.3 14.7 14.8 11.4 8.2 6.5 5.7 4.4 5.2 7.7 10.8 119.6 number of days of rain ≥

1 mm

Mean daily 5.9 5.4 6.1 6.3 5.8 6.0 6.2 7.2 8.3 8.7 8.0 6.9 6.7 sunshine

(hours)

Mean number of

clear days

Mean number of

cloudy days

Mean 9am 23.8 23.5 22.5 21.2 19.1 16.8 16.0 17.0 19.7 22.1 23.5 24.1 20.8 temperature

(°C)

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Mean 9am 80 84 84 84 86 86 86 83 73 69 70 75 80 relative humidity

(%)

Mean 9am 8.5 8.0 10.0 10.1 9.0 8.1 8.5 9.0 9.7 11.3 10.3 9.5 9.3 wind speed

(km/h)

9am wind N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A speed vs

direction plot

Mean 3pm temperature

(°C)

Mean 3pm relative humidity

(%)

Mean 3pm wind speed

(km/h)

3pm wind N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A speed vs

direction plot red = highest value blue = lowest value

Product IDCJCM0027 Prepared at Fri 04 Aug 2017 17:24:20 PM EST

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Fraction RT Compound matched with internal Match Reverse (mins) standards* and NIST W9N11 library score match database# score Phenolics 1 8.11 Benzoic acid # 863 925 2 8.11 878 945 3 8.11 886 942 4 8.11 888 942 5 8.11 841 928 7 8.11 894 941 12 8.11 829 940 13 8.11 801 911 15 8.11 910 962 18 8.12 856 937 20 8.12 854 930 21 8.11 969 981 22 8.11 965 981 23 8.11 899 943 24 8.11 886 961 25 8.11 813 878 26 8.11 837 888 27 8.11 859 943 28 8.11 844 907 29 8.11 821 882 30 8.11 846 915 31 8.11 800 868 32 8.11 864 920 35 8.11 833 934 36 8.11 867 918 37 8.11 850 913 38 8.11 895 955 41 8.11 842 918 42 8.11 889 939 43 8.11 907 950 44 8.11 862 904 8 14.71 Benzoic acid# 872 895 19 9.69 Benzoic acid# 971 986 228

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8 14.33 p-Hydroxybenzoic acid * 800 868 19 14.32 900 936 21 14.32 909 944 31 14.32 930 952

1 20.35 p-Coumaric acid * 828 849

2 20.36 870 893 9 20.29 802 806 10 20.35 966 971 11 20.35 904 912 12 12.81 912 963 20.28 807 811 13 20.29 813 816 17 17.45 879 892 20.37 989 993 18 20.35 903 929 19 20.29 813 816 21 17.46 975 980 21 20.35 978 983 22 20.35 934 955 23 20.34 975 981 24 20.35 960 968 26 20.35 925 934 27 20.35 937 949 28 20.28 801 831 29 20.35 804 830 31 20.34 940 953 32 20.27 866 873 39 20.35 958 963 40 20.34 963 969 41 20.34 961 967 42 20.21 803 826 3 10.26 Hydroquinone# 864 924 18 10.26 830 903 19 10.26 877 928 21 10.25 928 956 22 10.25 888 969 * 3 12.07 Salicylic acid 985 987 25 12.06 986 987 # 5 13.25 4 -Hydroxyphenylethanol 800 949 8 13.25 844 961

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10 13.25 834 940 11 13.24 901 933 15 13.24 861 956 16 13.24 875 959 17 13.24 847 964 18 13.25 858 963 22 13.25 831 966 23 13.25 887 979 27 13.24 840 967 27 15.62 823 861 9 16.97 Vanillic acid * 849 877 20 16.98 866 914 21 16.97 939 954 24 16.97 854 893 32 16.92 822 843 15 13.45 Hydrocinnamic acid# 928 942 16 13.45 891 919 * 15 12.81 t -Cinnamic acid 854 942 16 12.81 889 949 35 12.81 816 896 15 15.84 Cinnamic acid # 830 886 17 15.84 921 941 40 15.84 849 925 16 15.85 Cinnamic acid # 833 877

39 17.45 Cinnamic acid# 827 959 40 17.45 842 967 41 17.45 803 890 42 20.34 813 883 44 20.35 879 972 40 22.5 Cinnamic acid # 858 906 44 25.83 892 919

19 14.71 m-Anisic acid # 891 920

* 19 23.08 Ferulic acid 868 876 19 23.18 852 872 41 23.17 946 951 42 23.17 908 949 44 19.94 801 883 44 23.17 919 947

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22 8.74 Benzeneacetic acid# 933 972

* 22 24.03 Caffeic acid 860 899 23 24.02 868 906 25 23.97 817 830 31 24.03 827 868 32 24.03 861 883 33 23.97 836 844 * 40 22.44 Sinapic acid 846 856 40 25.83 925 937 41 22.5 905 916 41 25.83 951 954 44 22.44 818 826 44 25.7 800 810 Flavonoids # 10 8.16 5,7,3’,4’ -tetra-o-methylquercetin 928 951 14 8.16 927 959 Neutral losses # 21 15.62 Glucoside 939 954 22 15.63 836 880 23 15.62 955 959

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Figure 10.1 GCMS library match of t-cinnamic acid and sugar moiety detected in sample.

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Figure 10.2 GCMS library match of 4-Hydroxyphenylethanol (tyrosol) and m-ansinic acid detected in sample.

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Figure 10.3 GCMS library matches of cinnamic acids detected.

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Figure 10.4 GCMS library match of benzoic acids detected in sample.

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Figure 10.5 GCMS library match of benzeneacetic acid and 5,7,3’,4’-tetra-o-methylquercetin detected in sample.

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Table 10.5 List of compounds found in the sample using proteomics data from Chapter 3. The list of compounds was suspected to be present from previous proteomics work and obtained through transitions found in the literature but have not yet been found in peanuts by LC-MS/MS.

Tentative identification a RT [M-H]- Product ion b (min) c Acacetin 8.55 283 268, 151, 133, 107 24.12 24.78 Ampelopsin 21.10 319 301, 193 23.60 32.42 Aromadendrin 9.02 289 153, 195, 163 9.65 Chrysin 17.58 253 181, 151, 101 24.47 Coumestrol 18.15 267 211, 239 19.14 23.69 34.64 Curcumin 21.88 367 151, 153, 193 30.60 32.84 Galangin 12.93 269 227, 197, 183, 151 17.89 18.69 Isoliquiritigenin 18.15 255 119 21.00 26.19 Kaempferide 18.48 299 255, 151 20.33 Malonylapiin A/B 17.19 651 519, 271, 520 18.11 19.23

Malonylapiin B 9.55 651 272, 367, 457 11.00 11.20 16.42 Naringenin chalcone 17.91 271 151, 119, 107 18.49

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Neohesperidin 13.82 610 301 Pelargonidin 11.42 271 253, 243, 225, 215, 197, 13.01 187, 173, 169, 159, 145, 15.19 141, 121, 117, 131 17.87 17.90 Pisatin 8.78 315 314, 296, 163, 177, 286, 299 9.32 14.76 Quercitrin 15.50 477 301, 300, 255, 179, 151, 271 15.90 32.79 Shikimic acid 12.46 173 93, 83, 73 Syringetin 17.52 345 330, 315 28.52 Taxifolin 6.61 303 125 6.97 9.40 Transchalcone 9.30 209 191, 131, 105, 103, 181 13.47 21.46 Tricetin 25.29 301 239 26.53 Vitexin 8.16 433 415, 397, 379 9.20 a Detected compounds based on mass transitions obtained with literature. b The most abundant ions observed in mass spectra are shown in bold. c Retention time (RT) in total ion chromatograms. * Compound identified in major peaks highlighted in bold

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Table 10.6 Complete list of antioxidative compounds with peanut tissue source, major mass transitions in negative and positive modes and references of groups responsible for the study.

Phenolic compounds Peanut [M-H]- [M+H]+ Product ions b Reference tissue type 4-o-Caffeoylferulic Skin 355 220, 338, 219, (Ma et al, 2014) acid 193, 135 cis-p- Skin 295 163 (Ma et al, 2014) Coumaroyltartaric acid (cis coutaric acid) di-p- Skin 441 203, 277, 295 (Ma et al, 2014) Coumaroyltartaric acid a di-p- Skin 441 163, 175, 259, (Ma et al, 2014) Coumaroyltartaric 233, 147, 119 acid b p- Skin 457 119 (Ma et al, 2014) Coumaroylcaffeoylta rtaric acid p- Skin 471 203, 277, 307, (Ma et al, 2014) Coumaroylferuloylta 177, 289, 263 rtaric acid 193, 233, 218, 205 p-Coumaroyl-o- Skin 295 163, 119 (Ma et al, 2014) pentoside a p-Coumaroyl-p- Skin 415 137, 163, 203, (Ma et al, 2014) hydroxybenzoyltarta 251, 277 ric acid p- Skin 501 319, 263, 293, (Ma et al, 2014) Coumaroylsinapoylt 223, 179, 248 artaric acid a 235, 207, 192 p- Skin 501 203 (Ma et al, 2014) Coumaroylsinapoylt artaric acid b p-Coumaroyltartaric Skin 649 203, 277, 485, (Ma et al, 2014) acid derivative 503, 259, 233, 163, 175, 147 p- Skin 445 167, 192, 277, (Ma et al, 2014) Coumaroylvanilloylt 281, 237, 131 artaric acid

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Cyanidin Skin 285 125 (Bansode et al, 2014) Procyanidin A2 Skin 575 285 (Bansode et al, 2014) Procyanidin B1/B2 Skin 577 289 (Bansode et al, 2014) Stilbenes Peanut [M-H]- [M+H]+ Product ions Reference tissue type Piceid-6-p- Skin 667 147, 203, 277, (Ma et al, 2014) Coumaroyltartaric 441, 485, 521, acid 175, 163, 233, 259 Resveratrol-3-o-p- Skin 505 163, 203, 277, (Ma et al, 2014) Coumaroyltartaric 341, 447, 259, acid 233, 175, 147

Piceatannol Skin 405 243, 201, 159, 173 (Buiarelli et al, 2007, Ma et al, 2014) Pterostilbene Hairy 255 239, 224, 197 (Medina-Bolivar roots et al, 2007, Zhu et al, 2012) t-picetannol Skin 243 159, 199, 225 (Ma et al, 2014) Flavonoids Peanut [M-H]- [M+H]+ Product ions Reference tissue type (- )-epi-catechin a Skin 289 179, 245, 205 (Sarnoski et al, 2012, Ma et al, 2014) (+ )-epi-catechin a 289 205 (Zhu et al, 2012) 316 amu flavonoid Skin 623 243, 255, 271, (Sarnoski et al, (rhamnetin, 300, 315 2012) isorhamnetin, tamarixetin, nepetin) plus rutinoside Catechin Skin 289 187, 125, 109 (Sarnoski et al, 2012, Zhu et al, 2012, Ma et al, 2014) Cirsiliol Skin 299 299, 285, 271, (Ma et al, 2014) 177, 165 241

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Daidzein Kernels 253 132 (Chukwumah et al, 2007) Daidzein (higher Kernels 255 125, 115, 116, (Chukwumah et signal) 142, 143, 170 al, 2007) Daidzin Kernel, 417 255 (Wu et al, 2003, meal Chukwumah et al, 2007) Eriodictyol Skin 287 107, 125, 135, 151 (Ma et al, 2014) Fisetin Skin 285 135 (Bansode et al, 2014) Formononetin Kernels 267 252 (Antignac et al, 2003, Francisco and Resurreccion 2008) Flavonoid 3-o- Skin 447 284 (Bansode et al, glucoside 2014) Flavonoid 3- Skin 609 285 (Bansode et al, sophoroside 2014) Formononetin-7-o-p- Skin 545 163, 203, 259, (Ma et al, 2014) Coumaroyltartic 277, 399, 527 acid Formononetin-o–p- Skin 545 233, 175, 147, 119 (Ma et al, 2014) hydroxybenzoyltarta ric acid Genistin Kernels 433 271 (Wu et al, 2003, Chukwumah et al, 2007) Glycitin/sissostrin Kernel, 447 285 (Singleton et al, (Biochanin A meal 2002, glycoside) Chukwumah et al, 2007) Hesperetin Skin 301 177 (Bansode et al, 2014) Homoeriodictyol Skin 301 286, 151 (Ma et al, 2014) Isorhamnetin Skin 315 151, 107 (Lou et al, 2001, (Quercetin methyl Bataglion et al, ether) 2015) Isorhamnetin Kernel, 478 316 (Singleton et al, glucoside meal 2002)

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Kaempferol Kernels 449 166, 213, 244 (Chukwumah et 260, 261, 286 al, 2007) 287, 288 Kaempferol Skin 285 151, 257 (Chukwumah et al, 2007, Bansode et al, 2014) Kaempferol 3-o- Skin 447 285 (Bansode et al, glucoside 2014) o-Coumaroyl-o- Skin 477 301 (Ma et al, 2014) pentosidequercetin glucuronide Luteolin Kernels 285 267, 257, 241, (Chukwumah et 217, 213, 199, al, 2007, Ma et 175, 151 al, 2014) Luteolin Kernels 287 154, 166, 182, (Chukwumah et 196, 213, 224 al, 2007) Luteolin methyl Skin 299 284, 256, 216, 151 (Ma et al, 2014) ether (diosmetin) Glycitein Kernels 285 82, 108, 130, 142, (Wu et al, 2003, 146, 158, 168, Francisco and 172, 106 Resurreccion 120, 132, 142, 2008) 144, 156, 170 Morelloflavone Skin 555 288, 305, 306, (Ma et al, 2014) 350, 376 Morelloflavone Skin 557 145, 146, 178, (Ma et al, 2014) 190, 191, 218 219, 221 Naringenin Kernels 271 145, 158, 159, (Krause and 171, 215, 216 Galensa 1991, Tang et al, 2016) Naringenin Kernels 273 122, 150, 170, (Krause and 185, 214 Galensa 1991, Tang et al, 2016) Quercetin4 glucoside Skin 463 301 (Bansode et al, 2014) Quercetin di Kernel, 595 433, 301 (Singleton et al, glycoside meal 2002) Quercetin glucoside Kernel, 478 316 (Singleton et al, meal 2002) 243

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Quercetin Skin 477 179, 151 (Ma et al, 2014) glucuronide Quercetin methyl Skin 519 300, 315 (Ma et al, 2014) ether (isohamnetin) acetyl glucoside Quercetin methyl Skin 315 271 (Ma et al, 2014) ether (isohamnetin) Rhamnetin Kernel, 315 165 (Schieber et al, meal 2002, Singleton et al, 2002)

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Yan Yee Poon z3160325 Table 10.7 Identification of phenolics in D147-p3-115 peanut kernels by LC-MS/MS data Tentative [M-H]- [M-H]+ Product ionsb RT Reference identificationa (min)c Caffeic acid 197 135, 89 6.37 Standard Ferulic acid 193 134, 178 4.00 Standard 6.67 12.62 Gallic acid 169 125 4.43 Standard 5.18 6.81 m-Coumaric acid 163 119 16.77 Standard o-Coumaric acid 163 119 11.57 Standard p-Coumaric acid 163 119 9.19 Standard Protocatechuic 153 109 3.08 Standard acid 3.98 22.42 Sinapic acid 223 93, 164, 208 5.20 Standard 11.60 12.79 Syringic acid 197 182 6.98 Standard 14.27 t cinnamic acid 147 101 9.62 Standard 29.85 Vanillic acid 167 108 5.77 Standard 6.96 9.04 4-o- 355 220, 338, 6.36 (Ma et al, 2014) Caffeoylferulic 219, 193, 135 9.39 acid 9.19 16.27 16.68 c-p- 295 163 16.15 (Ma et al, 2014) Coumaroyltartari 16.82 c acid (c- coutaric acid) t-p- 295 163 16.03 (Ma et al, 2014) Coumaroyltartari 16.77 c acid (t- coutaric acid)

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Yan Yee Poon z3160325 di-p- 441 203, 277, 295 4.98 (Ma et al, 2014) Coumaroyltartari 33.51 c acid a di-p- 441 163, 175, 8.20 (Ma et al, 2014) Coumaroyltartari 259, 233, c acid b 147, 119 p- 457 119 31.03 (Ma et al, 2014) Coumaroylcaffeo 32.13 yltartaric acid p- 471 203, 277, 9.26 (Ma et al, 2014) Coumaroylferulo 307, 177, 9.28 yltartaric acid 289, 263 193, 233, 218, 205 p-Coumaroyl-o- 295 163, 119 16.03 (Ma et al, 2014) pentoside a 16.15 16.77 16.85 p-Coumaroyl-p- 415 137, 163, 15.69 (Ma et al, 2014) hydroxybenzoylta 203, 251, 277 16.99 rtaric acid p- 501 319, 263, 10.42 (Ma et al, 2014) Coumaroylsinapo 293, 223, 34.78 yltartaric acid a 179, 248 235, 207, 192 p- 501 203 34.81 (Ma et al, 2014) Coumaroylsinapo yltartaric acid b p- 649 203, 277, 14.63 (Ma et al, 2014) Coumaroyltartari 485, 503, 19.52 c acid derivative 259, 233 163, 175, 147 p- 445 167, 192, 7.13 (Ma et al, 2014) Coumaroylvanillo 277, 281, 34.69 yltartaric acid 237, 131 p- 137 93 15.24 (Ma et al, 2014) Hydroxybenzoic 19.23 acid Caffeoyltartaric 311 149, 179 7.16 (Ma et al, 2014) acid a 9.79 14.85

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15.86 Chicoric acid 473 135, 149, 10.66 (Ma et al, 2014) 179, 293, 11.47 234, 280, 275 Di – 177 105, 107, 12.73 (Ma et al, 2014) hydroxycoumarin 133, 149 Ferulic acid 309 193 33.25 Ferulic acid 403 329, 311, 7. 16 (Ma et al, 2014) derivative 293, 249, 21.32 211, 193, 171, 149 Feruloylasparate 308 132, 149, 10.98 (Ma et al, 2014) 178, 193 Feruloyl tartaric 681 193, 233, 13.30 (Ma et al, 2014) acid derivative 251, 307, 16.13 329, 311, 16.53 293, 229, 211, 171 Feruloyltartaric 325 118, 119, 4.85 (Ma et al, 2014) acid (fetaric acid) 120, 135, 8.04 150, 163 12.29

Feruloyltartaric 327 46, 70, 90, 97, 5.23 (Ma et al, 2014) acid (fetaric acid) 98, 134 9.79 10.55 Salicylic acid 137 93, 66, 15.00 (Gruz et al, 2008, Ma 94 18.95 et al, 2014)

Sinapic acid 223 179, 94, 2.82 (Singleton et al, 93, 122, 7.73 2002) 164, 208 7.78 14.97 15.78 16.06 Trans cinnamic 147 118, 101 34.14 (Ma et al, 2014) acids a Detected compounds based on mass transitions obtained with house standards. b The most abundant ions observed in mass spectra are shown in bold. c Retention time (RT) in total ion chromatograms, RT with the highest intensity in bold. * Compound identified in major peaks highlighted in bold

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Table 10.8 Identification of anthocyanins in D147-p3-115 peanut kernels by LC-MS/MS data.

Tentative [M-H]- [M-H]+ Product RT (min)c Reference identificationa ionsb Cyanidin 285 125 6.69 (Bansode et al, 2014) Peonidin-3- 461 299 12.23 (Bansode et al, 2014) galactoside 12.96 18.03 Petunidin 3-o- 477 315 11.52 (Bansode et al, 2014) glucoside 13.70 16.05 Procyanidin A2 575 285 8.27 (Bansode et al, 2014) 10.99 12.26 Procyanidin B1/B2 577 289 6.46 (Bansode et al, 2014) 8.27 11.17 a Detected compounds based on mass transitions obtained with house standards. b The most abundant ions observed in mass spectra are shown in bold. c Retention time (RT) in total ion chromatograms, RT with the highest intensity in bold. * Compound identified in major peaks highlighted in bold.

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Table 10.9 Identification of stilbenes in D147-p3-115 peanut kernels by LC-MS/MS data.

Tentative [M-H]- [M-H]+ Product ionsb RT Reference identificationa (min)c c-Resveratrol 227 143, 185, 119 10.43 Standard t-Resveratrol 227 143, 185, 119 15.04 Standard Polydatin 289 226 5.09 Standard 4.64 6.73 Piceatannol 405 243, 201, 159, 10.94 (Buiarelli et al, 173 12.57 2007, Ma et al, 2014) Piceid-6-p- 667 147, 203, 277, 13.57 (Ma et al, 2014) Coumaroyltarta 441, 485, 521, 26.44 ric acid 175, 163, 233, 26.81 259 Pterostilbene 255 239, 224, 197 9.81 (Medina-Bolivar 11.26 et al, 2007, Zhu et 19.06 al, 2012) Resveratrol-3-o- 505 163, 203, 277, 15.87 (Ma et al, 2014) p- 341, 447, 259, 32.13 Coumaroyltarta 233, 175, 147 ric acid t-Picetannol 243 159, 199, 225 10.89 (Ma et al, 2014) 12.52 18.14 a Detected compounds based on mass transitions obtained with house standards and mass transitions reported in the literature. b The most abundant ions observed in mass spectra are shown in bold. c Retention time (RT) in total ion chromatograms, RT with the highest intensity in bold. * Compound identified in major peaks highlighted in bold

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Table 10.10 Identification of flavonoids in D147-p3-115 peanut kernels by LC-MS/MS data. Tentative [M-H]- [M-H]+ Product RT (min)c Reference identificationa ionsb Biochanin A 283 268 17.58 Standard 18.92 21.75 Daidzein 253 132 9.99 Standard 16.09 Genistein 269 133 10.38 Standard 21.28 Myricetin 317 125, 151 7.62 Standard 10.90 Quercetin 301 257, 179, 28.31 Standard 151, 107, 121 Rutin 1 609 300 14.23 Standard Rutin 2 609 301 10.77 Standard (- )-epi-catechin 289 179, 245, 205 7.12 (Sarnoski et al, 2012, Ma et al, 2014) (+ )-epi-catechin 289 205 7.13 (Zhu et al, 2012) 9.56 316 amu flavonoid a 623 243, 255, 9.67 (Sarnoski et al, (rhamnetin, 271, 300, 315 10.08 2012) isorhamnetin, 15.34 tamarixetin, nepetin) plus rutinoside Catechin 289 187, 125, 109 7.11 (Sarnoski et al, 9.54 2012, Zhu et al, 2012, Ma et al, 2014) Cirsiliol 299 299, 285, 18.06 (Ma et al, 2014) 271, 177, 165 21.10 35.06 Daidzein 253 132 11.74 (Chukwumah et al, 13.36 2007) 19.43 Daidzein (higher 255 125, 115, 34.95 (Chukwumah et al, signal) 116, 142, 2007) 143, 170 Daidzin 417 255 6.45 (Wu et al, 2003, 11.51 Chukwumah et al, 13.33 2007) 20.62

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Eriodictyol 287 107, 125, 9.73 (Ma et al, 2014) 135, 151 Fisetin 285 135 6.89 (Bansode et al, 7.90 2014) 20.85 Flavonoid 3-o- 447 284 15.75 (Bansode et al, glucoside 2014) Flavonoid 3- 609 285 9.47 (Bansode et al, sophoroside 11.29 2014) 12.70 13.11 Formononetin 267 252 27.11 (Antignac et al, 2003, Francisco and Resurreccion 2008) Formononetin-7-o-p- 545 163, 203, 14.29 (Ma et al, 2014) Coumaroyltartic acid 259, 277, 399, 527 Formononetin-o–p- 545 233, 175, 12.05 (Ma et al, 2014) hydroxybenzoyltartari 147, 119 14.27 c acid 15.08

Genistin 433 271 10.90 (Wu et al, 2003, 11.51 Chukwumah et al, 2007) Glycitin/sissostrin 447 285 13.33 (Singleton et al, (Biochanin A 17.38 2002, Chukwumah glycoside) 20.70 et al, 2007) Glycitein 285 82, 108, 130, 14.17 (Wu et al, 2003, 142, 146, 16.43 Francisco and 158, 168, 25.76 Resurreccion 2008) 172, 106 28.70 120, 132, 28.84 142, 144, 156, 170 Hesperetin 301 177 8.14 (Bansode et al, 13.41 2014) 15.83 Homoeriodictyol 301 286, 151 13.40 (Ma et al, 2014) 15.83 18.86 Isorhamnetin 315 300 11.60 (Lou et al, 2001, (Quercetin methyl 18.08 Bataglion et al, ether) 18.19 2015)

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Isorhamnetin 315 82, 84, 101, 34.72 (Lou et al, 2001, (Quercetin methyl 108, 109, Bataglion et al, ether) 148, 163, 171 2015) Isorhamnetin 478 316 11.64 (Singleton et al, glucoside 13.66 2002) 16.09 Kaempferol 285 151, 257 6.69 (Chukwumah et al, 6.89 2007, Bansode et al, 34.82 2014) Kaempferol 449 166, 213, 244 12.83 (Chukwumah et al, 260, 261, 286 2007) 287, 288 Kaempferol 3-o- 447 285 9.73 (Bansode et al, glucoside 9.82 2014) 15.64 15.68 25.80 Luteolin 287 154, 166, 34.79 (Chukwumah et al, 182, 196, 2007) 213, 224 Luteolin methyl ether 299 284, 256, 18.25 (Ma et al, 2014) (diosmetin) 216, 151 19.36 19.92 Morelloflavone 555 288, 305, 8.21 (Ma et al, 2014) 306, 350, 376 11.71 15.66 Morelloflavone 557 145, 146, 13.45 (Ma et al, 2014) 178, 190, 14.52 191, 218 219, 221 Naringenin 271 145, 158, 11.52 (Krause and 159, 171, 15.31 Galensa 1991, Tang 215, 216 17.90 et al, 2016)

Naringenin 273 122, 150, 14.78 (Krause and 170, 185, 214 19.34 Galensa 1991, Tang et al, 2016) o-Coumaroyl-o- 477 301 15.77 (Ma et al, 2014) pentosidequercetin glucuronide Quercetin 4 glucoside 463 301 14.05 (Bansode et al, 14.77 2014) 16.95

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Quercetin di glycoside 595 433, 301 12.45 (Singleton et al, 17.16 2002) 17.70 Quercetin glucoside 478 316 11.64 (Singleton et al, 13.66 2002) 13.70 16.06 16.09 Quercetin glucuronide 477 179, 151 13.69 (Ma et al, 2014) 15.68 16.05 Quercetin methyl ether 519 300, 315 13.70 (Ma et al, 2014) (isohamnetin) acetyl 18.05 glucoside Quercetin methyl ether 315 271 15.28 (Ma et al, 2014) (isohamnetin) 17.28 18.18 Rhamnetin 315 165 18.15 (Schieber et al, 34.86 2002, Singleton et 35.01 al, 2002) a Detected compounds based on mass transitions obtained with house standards. b The most abundant ions observed in mass spectra are shown in bold. c Retention time (RT) in total ion chromatograms, RT with the highest intensity in bold. * Compound identified in major peaks highlighted in bold

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Figure 10.6 LC/MS-MS spectra of kaempferide

Figure 10.7 LC/MS-MS spectra of tricetin

Figure 10.8 LC/MS-MS spectra of neohesperidin

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Figure 10.9 LC/MS-MS spectra of shikimic acid

Figure 10.10 LC/MS-MS spectra of transchalcone

Figure 10.11 LC/MS-MS spectra of chrysin

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Figure 10.12 LC/MS-MS spectra of naringenin chalcone

Figure 10.13 LC/MS-MS spectra of Isoliquiritigenin

Figure 10.14 LC/MS-MS spectra of coumestrol

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Figure 10.15 LC/MS-MS spectra of galangin

Figure 10.16 LC/MS-MS spectra of pelargonidin

Figure 10.17 LC/MS-MS spectra of acacetin

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Figure 10.18 LC/MS-MS spectra of aromadendrin

Figure 10.19 LC/MS-MS spectra of taxifolin

Figure 10.20 LC/MS-MS spectra of ampelopsin

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Figure 10.21 LC/MS-MS spectra of pisatin

Figure 10.22 LC/MS-MS spectra of syringetin

Figure 10.23 LC/MS-MS spectra of curcumin

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Figure 10.24 LC/MS-MS spectra of quercitrin

Figure 10.25 LC/MS-MS spectra of vitexin

Figure 10.26 LC/MS-MS spectra of malonylapiin A/B

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Figure 10.27 LC/MS-MS spectra of malonylapiin A

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