Annual Plant Reviews Volume 43 (Biology of Plant Metabolomics) || Index

ind BLBK354-Hall January 18, 2011 7:52 Trim: 234mm×156mm Series: APR Char Count=

INDEX

Abscisic acid (ABA), 37, 71, 281mutant, 170

Accurate mass, 4–5, 31, 66, 115–20, 138,165, 367–88

Acetyl CoA, 42Acetylserine, 278Aconitic acid, 351, 354Acrylamide, 183Acylsugars, 347, 358Adulteration, 182, 200, 201Aflatoxins, 200Aldehydes, 118, 162, 358Alfalfa, 37

trichomes, 354–6Algae, 16, 298, 299, 394

Chlamydomonas, 392Alignment peak, 9, 75, 91, 92, 163, 283,

318, 350, 367–98DNA sequence, 64

Alignment software MetAlign, 9, 136,318

XCMS, 9, 378, 380Aliphates, 371Alkaloid tomatine, 120–23, 135, 183, 370,

379Alkaloids pyrrolizidine, 93

pyrrolopyrazine, 44Alkanes, 66, 118Allelopathic effects, 100, 101Alternating least-squares, 187Amanita muscaria, 42AMDIS, 350, 372Amino acids, 3, 33, 39–47, 68–74, 97, 112,

118, 120, 130, 139, 163, 168–72, 183,189, 193, 199, 220, 231, 237, 263, 276,282–5, 288, 295, 322, 334, 350, 359

2-Amino butyric acid , 334, 336Ammonium , 28, 37, 39, 42, 71, 115

Analysis of variance (ANOVA), 129, 134,165, 184, 318, 330, 340, 350

Analytical bias, 268–272Annotation, 4, 16, 27, 112, 118–20, 140,

157–62, 167, 237, 242, 273, 319, 343,356, 359, 367–99

ANOVA, see Analysis of varianceAnthocyanin, 18, 36, 88, 125, 160, 171Antiherbivorous, 28, 36Antinutrients, 191Antioxidants, 15, 32, 36, 72

and stress, 72in tea, 197, 198in tomato, 110, 120–29, 135

APCI, see Atmospheric pressurechemical ionization

Aphids, 283, 384Apigenin, 125, 126, 356Applied metabolomics, 1Arabidopsis genome/genomics, 12,

221–46, 275, 276, 278, 392biodiversity, 95database, 142, 157–80, 206, 279, 385,

387, 388, 393MYB12, 125primary metabolites, 190, 282, 396QTLs, 130, 131secondary metabolites, 126, 138, 139,

280, 384seed, 164, 281, 346, 379stress, 67, 71, 73, 74, 264, 277, 282transgenes, 134, 136, 283trichomes, 139, 140, 345–8, 357, 386

Arabidopsis thaliana (model plant), 11, 91,109, 219, 299, 368

Arabis lyrata, 90AraCyc, 11, 171, 173, 285, 299, 386, 389,

393, 394, 397, 399

Annual Plant Reviews Volume 43, Biology of Plant Metabolomics, First Edition. Edited by Robert Hall.C© 2011 Blackwell Publishing Ltd. Published 2011 by Blackwell Publishing Ltd.

407


408 � Index

Arbuscule, 38, 41–4Arbutin, 370, 373, 384Arginine, 42, 334ArMet, 386Aroma, 112, 136, 160, 184, 200, 232, 331,

348, 371Aromatic amino acid, 39, 74Artemisia, 14Artificial networks, 292Ascomycetes, 28, 33Ascorbate, 32, 128, 133, 137, 142, 143, 186Ascorbic acid, 71, 130, 135Asparagine synthetase, 39Aspartate, 33, 39, 42, 114Aspartic acid, 351Asparagine, 39, 42, 44, 114, 183, 187, 189Asparagus, 182Aspen, 282Association mapping, 219–59AtMetExpress, 168, 279Atmospheric pressure chemical

ionization , 5, 164, 378Atomic force microscopy, 347Authenticity, 14, 127–30, 200, 201

�-carotene, 116, 120, 124, 130, 202, 370,378

Bacillus thuringiensis, 187subtilis, 226, 264, 292

Bacteroids, 37–40BADH gene, 162Barley, 29, 33, 34, 62, 72–4, 191, 224, 238,

299Basidiomycetes, 28, 29, 34, 35Bayesian networks, 285, 294–7Bean, 37, 40, 283

coffee, 194–6, 346soybean, 37, 62, 224, 384, 388

Benjamini–Hochberg correction, 325,326, 332

Betaine, 71, 162, 163BinBase, 373, 381, 382–5, 397, 399Bioactive compounds, 13, 91, 192, 196Biodiversity, 189, 193Biofuels, 13Bioinformatics, 7, 10, 91, 102, 130, 143,

223, 262, 282, 317–42, 367–406Biomarkers, 14, 140, 165, 168, 182, 196–8,

200, 262

Biostatistics, 9, 261–306, 317–41Biosynthesis, 17, 48, 142, 233, 245, 347

alkaloid, 44, 160, 346amino acids, 40, 236, 282carotenoid, 124, 186glucosinolate, 169, 239, 277lignin, 32, 34, 42, 172, 284phenylpropanoids, 125, 135–7, 186,

281, 346phytohormones, 48, 160, 170secondary metabolites, 97, 124, 171starch, 169–71, 236, 394terpenoids, 232

Biosynthetic flexibility, 19Biotechnology, 278, 302, 394Biotic stress, 12, 25–47, 62, 87–102, 129,

162Biotrophic interactions, 25–47, 87–101Blackcurrant, 192–4Blood, 70, 182, 196, 198Blueberry, 192Blumeria graminis, 25–59Border cells, 356, 357Brachypodium diastachyon, 30, 33, 64Brassica napus, 241, 385

oleracea, 139spp., 13, 100, 160, 231

Breeding, 10, 14, 64, 77, 94, 132, 191, 193,221, 231

molecular, 45, 219–46populations, 123, 125, 143, 205

Broccoli, 207, 230–231Butein, 125

C3 to C4 metabolism, 36Caffeic acid, 34, 370, 379, 386Caffeine, 196, 198, 346Calibration, 115, 121, 164Calmodulin-like protein, 38Calvin cycle, 169Candidate gene, 133, 135, 173, 239, 277,

281Candidate metabolite, 31, 173, 287, 380Canola, 72Canonical correlation analysis, 336,

337Capillary electrophoresis (CE), 5, 7, 66,

69, 139, 164, 190, 226, 263CAPS, 241


Index � 409

Carbon assimilation, 169metabolism, 162, 184, 292partitioning, 27, 46, 134

Camelexin, 160, 283Carotenoid, 42, 111, 116, 118, 120–28,

131, 135, 136, 162, 186, 187, 192, 202,203, 222, 225, 231, 282, 370, 377, 379

Catechin, 192, 198Catharanthus, 14Cell death, 28, 29, 47

walls, 26, 32, 33, 35, 39, 41, 42, 62, 72,133, 137, 186, 280, 281, 344–7

wall thickening, 26, 32CE-MS, 7, 67, 112, 164, 168, 170, 171, 226,

263, 264, 277, 280, 318, 359, 375, 385Cellulose, 280Central carbon metabolism, 169, 292Cereals, 29, 33, 63, 72, 76, 188–91Chaconine, 183Chalcone isomerase, 124, 126, 346, 347,

380synthase, 346, 347, 380

Chemical Ionization (CI), 5, 67, 164,378

Identifier, 273, 369Chemometrics, 197, 200, 317–40Chickpea, 37Chlamydomonas reinhardtii, 299, 301,

394Chlorogenic acid, 44, 93, 94, 100, 120,

125, 194, 196Chloroform solvent, 6, 90, 95, 116, 118,

264Chlorophyll, 18, 116, 121, 162, 301, 370,

378Cholines, 95, 114, 134, 163, 375, 380Chromatographic separation, 6, 66, 166,

204, 226, 344Chrysanthemum, 94, 100Citric acid, 39, 127, 162, 334, 351Citrus, 13Clover, 37Clustering, 91, 134, 190, 191, 204, 278,

288, 334, 335, 350, 378Coffee, 15, 194–6, 200, 346, 392Column HILIC, 377

C18 reversed phase, 116, 119, 120, 130,202

matrices, 67, 69, 90, 116, 141, 202, 371–7

normal phase, 202, 377reversed phase, 69, 116, 120, 164,

202–4, 377Compound naming, 273Corn smut, 25–49Correlation matrix, 288, 292, 333, 335

networks, 133, 171, 277, 287, 288, 355Coumaric acid, 117, 354, 370, 379, 386Coumarin, 100, 169Covariance, 271, 272, 288Covariance matrix, 288Crop biochemistry, 14, 109–44, 181–208,

243, 244yield, 12, 34, 61, 70, 190

Cross-contribution, 271, 272Cucurbitaceae, 368Cyanidin, 193, 278, 349Cyanogenic glucoside, 283Cytochrome, 158, 160, 347

Data analysis, 7, 9, 16, 30, 76, 165, 204,261–316, 386

fusion, 9, 142, 266–74integration, 9, 61, 142, 261–316mining, 129, 163, 206, 317–42normalization, 142, 270preprocessing, 17, 261–306, 317–40processing, 10, 92, 112, 137, 293,

261–306, 317–40, 367–406reduction, 113, 128, 317–40storage, 246, 367–99validity, 17

DatabaseArabidopsis trichome DB, 386AraCyc, 11, 285, 299, 386, 389, 393,

394, 397, 399BinBase/SetupX, 373, 381, 383, 384,

385, 397, 399ChEBI, 380, 387, 390Chemical Abstracts Service (CAS), 5,

387, 389, 390ChemSpider, 369, 387, 389, 391ChlamyCyc, 299, 394Dictionary of Natural Products, 381,

387, 388Golm, 67, 118, 350, 381–4, 399ILSI database, 381, 387, 388Kazusa OMICS database, 119, 141,

379, 383


410 � Index

Database (Cont.)KEGG, 173, 273, 285, 381, 383, 384, 386,

387, 389, 390, 393, 395, 399KNApSAcK, 173, 380, 387, 388, 395KOMICS, 119, 141, 386LipidMaps, 395MadisonMetabolomeDB, 377, 382, 386MassBank, 381, 382, 399MedicCyc, 394MeRy-B, 118, 141metabolite, 4, 11, 18, 67, 91, 118, 367–99MetaCrop, 381, 387, 388, 399MetNetDB, 386MoTo, 102, 119, 141, 185, 379, 382, 386MZedDB, 378NMRshiftDB, 377, 387PlantMetabolomics.org, 171, 381, 385,

394, 399PubChem, 119, 273, 369, 370, 379–89,

390, 395, 397RiceCyc, 394, 399

Decision tree analysis, 318, 338, 339Deconvolution, 9, 75, 115, 164, 294, 318,

350, 367–406Defensins, 39DELLA transcription factors, 41Derivatization

tert-butyldimethylsilylation, 68, 383Trimethylsilylation (TMS), 68, 164,

322, 374, 383Derivatized extracts, 6, 8, 68, 90, 112,

115, 127, 131, 139, 163, 164, 190, 245,271, 374, 383

Detection fluorescence, 69, 111, 116, 276DFI, see Direct flow injectionDhurrin, 283Diabetes, 15Diacylglycerol, 225, 375Diamine oxidase, 32DIMS, 45, 47, 90, 204Diode Array Detector (DAD), 5, 111, 116,

203, 370, 377Direct flow injection (DFI), 115Direct sample infusion (DSI), 115, 138Discriminant analysis, 30, 130, 318, 336,

337Discriminant partial least squares

(PLS-DA), 128, 130, 190, 199, 270,280, 336

Discriminatory metabolites, 3, 167, 191,196, 199, 277

Dithiothreitol, 172, 282Diurnal rhythm, 162, 169, 245, 344DNA markers, 14Doubled haploids, 228Douglas fir, 14DRE/CRT binding factors, 71Drought, 12, 62, 70, 71, 170, 264Dynamic flux, 305Dynamic range, 6, 7, 91, 192, 343, 358,

360

Echinacea, 14Ecology, 87–101, 343Ectopic expression, 124, 125, 136Ecotypes, 12, 32, 159, 172, 239, 276Effector proteins, 46EICA theory, 99Ellagitannins, 192, 349Elderberry, 192Electron impact (EI), 67, 115, 118Electrospray ionization (ESI), 5, 17, 30,

68, 164, 184, 188, 196, 357, 374, 375–9Elicitation jasmonate, 71, 170, 278ELISA, 45Embryo tissue, 283Empirical formula, 7, 157, 165, 168, 226Endophytes, 25–59Endoplasmic reticulum, 347Endosymbiont associations, 36–45Entropy, 290, 291, 303Epichloe, 28, 37, 43, 44Epicuticular waxes, 4, 135, 163, 171, 357Epidermal cells, 18, 30, 31, 33–5, 46, 139,

347pavement cells, 358

Epidermis, 29, 123, 188, 347Epigenetic, 88Ergovaline, 44, 45eQTLs, 223, 237, 282Ergovaline, 44, 45Error probability, 317, 330. 332Erythritol, 33Escherichia coli, 75, 264, 288, 291, 298, 299,

305, 392Esculeoside, 121–3, 185, 370, 379Ethyl lactate, 331–3, 335Ethylene, 37, 118, 134, 136, 184, 232


Index � 411

Euclidean distance, 277, 304, 305Experimental design, 9, 16, 31, 64, 163,

168, 265, 268, 272, 286, 317, 320, 331,339, 344

Extraction metabolite, 2, 5, 6, 17, 31, 43,62, 90, 112, 138, 189, 202, 245, 264,285, 322, 348

False negatives, 288, 338, 372, 373positives, 288, 292, 293, 319, 333, 338,

340, 372, 373correlations, 288

Fast Atom Bombardment (FAB), 68Fatty acid, 35, 42, 68, 72, 90, 95, 118, 131,

136, 163, 183, 189, 204, 231, 347, 354,356, 371

Fatty acid amides, 347, 354, 355Ferritin, 41Feruloylglycine, 196Fescue, 43, 44Fibres, 13, 62, 280, 347, 389FIE-MS, 30, 31, 184Flavonoids, 3, 37, 39, 42, 71, 88, 90, 94,

115, 118, 120–27, 135, 160, 163, 171,186, 189, 192,, 198, 240, 278, 280,346–9, 356, 394

Flavonols, 124–6, 131, 171, 187, 192, 197,281, 346–9, 386

Flavonone 3-hydroxylase, 347Fleshy fruit, 12, 109–55Floral organs, 35, 100, 160, 162, 237, 275,

344, 348–52, 357Floral scent, 88, 89Flower colour, 346Flowering, 18, 94, 133, 134, 158, 230, 242Flux balance analysis, 298–300, 305

distribution, 300–303Food metabolism, 15

processing, 10, 15, 181–217quality, 14, 181–217safety, 182, 200–208security, 12, 207, 208

Fourier transform ion cyclotronresonance (FTICR), 5, 66, 115, 128,165, 197, 226, 277, 379

Fragaria x ananassa strawberry, 13, 138,192, 200, 331, 349

Fragrance, 11, 13, 191Fructans, 44, 184

Fructose, 33, 44, 95, 99, 114, 183, 195Fruit development, 12, 109–55, 185–7,

192–4, 225, 236, 277, 386growth, 109–55, 185–7juice, 113, 127, 128, 194, 200peel, 123–5, 186processing, 187, 188, 200quality, 11, 109–55, 192–6, 207, 224, 231ripening, 109–205, 237, 271, 277taste, aroma, 131, 136

FT-ICR-MS, 5, 7, 31, 66, 69, 115, 138, 226,277

FT-MS, 116, 165, 169–72FT-NIR, 197Fumarate, 73, 114, 162, 169, 186Fumaric acid, 93, 334, 351Functional genomics, 11, 62, 64, 111,

132–7, 157, 170, 185, 208, 276, 396Fungal metabolites, 45

penetration, 32Fusarium moniliforme, 41

GABA, 32, 33, 73, 114, 131, 189, 195, 334Galactose, 191, 351Gas chromatography (GC), 5, 66, 90, 163,

183, 226, 239, 263, 349, 371Gaussian scaling, 292GC-MS, 4, 7, 8, 39, 42, 44, 66–8, 127, 132,

163, 166, 169–73, 183–6, 190, 192,204, 226, 263–5, 272, 276, 280–85,319, 322, 329, 345, 349–51, 359, 369,371–7

GCxGC, 190, 191, 372Gene expression , 6, 34, 38, 41, 48, 124,

133, 140, 142, 195, 235, 237, 240, 277,279, 283, 345, 348

Genetic engineering, 45Genetic manipulation, 64, 205Genetic modification, 184Genetic variation, 91, 99, 131, 222Genetical metabolomics, 12, 45, 109,

130–32, 172, 173Genistein, 351Genomic reconstruction, 367–99Genomics, 6, 11, 62, 63, 111, 132, 157, 167,

221, 224, 233, 237, 239, 244, 262, 395Genotype x environment, 12, 94–6Geosiphon pyriforme, 42Geranylacetone, 131


412 � Index

Gibberellin, 17, 37, 41, 160Ginger, 3586-Gingerol, 358Glandular hairs, 18, 348, 354Glomeromycota, 37, 40Gluconeogenesis, 220Glucose, 33, 41, 44, 73, 93, 98, 114, 134,

137, 165, 171, 183, 195, 199, 220, 283,284, 352, 370, 381, 390

Glucosinolate, 90, 95, 100, 131, 139, 160,163, 169, 171, 239, 277, 368

Glutamate, 33, 114, 334Glutamine, 33, 36, 39, 42, 114, 187, 189,

334, 351, 397Glutamine synthetase, 39Glutathione, 35Glycerol, 33, 71, 75, 199, 225, 305, 334,

351, 375Glycine, 71, 196, 351

max, 385, 392Glycoalkaloids, 120, 135, 183, 186Glycoconjugates, 66Glycogen, 38, 42Glycolysis, 34, 36, 39, 74, 134, 220, 285,

292, 295, 396Grafting, 94Grape, 13, 126, 194, 198–200, 317–40Growth, 11, 14, 18, 27, 29, 33, 40, 62, 63,

76, 94, 109, 163, 188, 201, 220, 234,301, 305, 344, 356, 384

Gut microflora, 196, 198

Headspace trapping, 112, 142, 189, 322volatiles, 112, 119, 131, 136

Heat map, 133, 327Hemicellulose, 280Herbal extracts, 14Herbicide, 172, 189, 205, 243Herbivores, 44, 45, 87–107, 220, 346Herbs, 160Heterosis, 173Hexanal, 113Hexenol, 113Hexose transporters, 36, 42, 46Hexoses, 33, 34, 36, 38, 130, 134Hierarchical clustering (HCA), 128, 136,

165, 190, 192, 266, 268, 280, 295, 317,325–9, 334, 350, 375, 395

High order conditioning, 291

High throughput profiling, 3, 26, 30, 62,69, 90, 113, 137–9, 163, 173, 207, 219,280, 299, 318, 399

HILIC, 5, 377Homoserine, 44Hordeum vulgare, 68Hormones, 17, 26, 37, 41, 43, 48, 71, 97,

118, 121, 158, 160, 166, 280HPCA, 192, 266, 268, 269HPLC, 5, 7, 42, 44, 95, 122, 125, 128, 135,

141, 164, 196, 202, 231, 283, 374, 385HPLC-PDA-SPE-NMR-ESI-MS, 5Human error, 17

genome, 240, 241nutrition, 110, 120, 123, 127, 340

Hydrogen peroxide H2O2, 32, 36, 71Hydroxycinnamic acids, 32Hydroxyl benzoic acid, 192Hypericum, 357Hypersensitive response, 18, 32, 34Hyphae, 30–35, 41–6Hyphenated approaches, 4, 31, 141, 318Hypoxia, 39, 192

Ilex, 90, 101Imaging cell, 30, 76, 140

metabolites, 61, 63, 70, 74, 139, 343,357–60

In situ mass spectrometry, 74InChi, see International chemical

identifierIndole-diterpenes, 43Industrial, 13, 121, 123, 129, 144, 181,

194Injections multiple, 91Inositol, 71, 73, 131, 191, 351Insect–plant interactions, 13, 87, 89, 93,

102Insect species, 19Instrument sensitivity, 18, 343, 358,

360Inter-laboratory comparisons, 17, 180International Chemical Identifier

(InChi), 273, 369Introgression line, 130, 172, 224, 225–232Introgressions genetic, 130, 172, 183, 222,

232Ion exchange, 69, 169Ion trap, 69, 164, 376, 377


Index � 413

Ionization suppression, 375efficiency, 67, 116, 140, 164, 203, 374,

376quenching, 374, 375soft, 31, 276

Ionomics, 63, 159Isobutylamine, 94Isoflavone, 350Isoflavonoid, 37, 39, 42, 356, 394Isoleucine, 40, 114, 184, 351Isomers, 116, 118, 124, 138, 168, 202, 226,

371, 376Isoprenoid, 111, 116, 118, 127, 132, 162,

169, 186, 202Isotope enriched, 165

dilution analysis, 166, 187labelling, 41, 67, 157, 163, 166, 344, 372,

376, 379replacement, 168standard, 324

Isotopomer, 329Itaconic acid, 354

Jacaranone, 93Jacobian matrix, 289, 290Jasmonates, 37, 43Joint entropy, 290, 291

Kaempferol, 124–27, 354KaPPA-View, 279KEGG, 173, 273, 285, 381–93, 395, 399Kernel density estimation, 291k-means clustering, 280Kinetic models, 298, 306KNApSAcK, 173, 380, 381, 387, 395Knockout lines, 159, 162, 166, 171, 281,

282, 290, 302Kovats indices, 371Kruskal–Wallis test, 318, 330–34, 340

Lactic acid, 322, 325, 327, 331, 332, 334,336, 339

Laser ablation electrospray ionizationmass spectrometry, 357

Laser dissection microscopy, 43, 48, 74,139, 345, 347

Laser induced fluorescence (LIF), 69Late embryogenesis abundant (LEA)

proteins, 71

LC, see Liquid chromatographyLC-ESI-MS, 197, 374LC-FT-ICR-MS, 5, 7, 31,66, 69, 115, 119,

226, 277, 377LC-MS, 4, 8, 42, 67, 69, 119, 129, 142, 164,

169, 185, 192, 202, 263, 273, 277, 283,350, 359, 369, 377

Leaf hopper resistance, 355Lectins, 37Legumes forage, 37, 354Lentil, 37Life cycle, 11, 34, 159, 168, 191Ligand database, 367–99Lignification, 36Lignin, 32, 47, 172, 280, 284, 347Linear discriminant analysis (LDA), 30,

134, 336Linear regression, 45, 271Lipidomics, 3, 164, 171, 367, 376Lipids, 3, 38, 42, 61, 88, 95, 135, 163, 169,

171, 186, 264, 285, 301, 349. 359,375–8

Liquid chromatography (LC), 5, 66, 68,90, 138, 164, 184, 226, 231, 239, 263,280, 349, 377, 386

Loading plot, 95, 199, 274, 327, 354Log-transformed response ratios, 324,

328, 331Lolium perenne, 43, 44Lotus japonicus, 39, 68, 279Leucine, 40, 114, 184, 187, 236, 351Luteolin, 125Lycopene, 110, 116, 120–28, 130, 135, 202,

225Lycoperosides, 120, 123Lysine, 131, 351, 354

m/z values, 5, 31, 165, 187, 204, 223,350–53, 373, 378, 379

Magnaporthe grisea, 25–59Malate, 32, 38, 39, 46, 114, 131, 169, 199MALDI, 5, 68, 70, 74, 116, 118, 143, 187,

204, 231, 357, 358Malonic acid, 351, 370, 379Mannitol, 32, 33, 36, 40, 45, 71, 131MapMan, 142, 279, 395–9Mapping population, 45, 173, 225,

228–39Markerlynx, 350


414 � Index

Mass analyser, 226Mass fragments, 329Mass signals, 91, 131, 136, 140Mass Spectral library NIST, 5, 67, 118,

350, 371, 373, 383Wiley, 371

Mass spectral tag, 70, 319Mass spectrometer, 116, 138, 164, 166,

374, 376, 377, 385Mass spectrometry (MS), 5, 30, 47, 61, 66,

69, 74, 90, 140, 163, 183, 203, 226,277, 357, 360, 371, 377, 388, 391

MassBank, 381, 382, 399MATLAB, 350Matrix-assisted laser desorption

ionization, see MALDIMatrix effect, 7, 67, 116, 138, 164, 263,

271, 374Median scaling, 272, 273Medicagenic acid, 352, 353, 370, 394Medicago sativa, 39, 40, 41, 356

truncatula, 41, 42, 68, 165, 193, 281, 348,349, 392, 394

Medical Subject Heading Terms (MeSH),390, 391

Medicinal compounds, 2, 346Melon, 12, 137, 142, 192, 207, 264Membrane, 29, 35, 38, 4671, 72, 195, 347,

375, 377Mesorhizobium loti, 39Metabolic control analysis, 289

cross-talk, 16, 71, 221correlation, 288, 335fingerprinting, 3, 11, 30, 32, 69, 90, 113,

128, 138, 163, 172, 183, 201, 203, 317,375

flux, 16, 67, 99, 133, 233, 282, 294, 300,360

flux analysis, 17, 48, 113, 139models, 298–300, 360network reconstruction, 285, 286, 288,

306networks, 9, 142, 159, 190, 233,

261–316, 348, 386, 393, 394, 396, 397,398

profiling, 3, 39, 45, 92, 113, 123,127–30,132, 139, 194, 196, 277, 278,285, 338, 348, 356

Metabolic syndrome, 15

Metabolite identification, 4, 7, 137, 140,197, 263, 350, 372, 386

Metabolite imaging, 74, 140, 343, 357,361

imaging MS, 140pool, 162, 207, 319, 324, 334–6transporters, 36, 39, 42, 237, 299, 345,

347Metabolite library PlantCyc, 79, 394

PubChem, 119, 273, 369, 370, 379, 381,384, 386, 387, 390, 395, 397

Metabolome polar, 4, 6, 42, 68, 90, 102,112, 114–8, 128, 132, 135, 163, 183,186, 191, 202, 285, 349–54, 375

size, 9, 159, 190, 206, 220, 359, 368tissue, 6, 12, 18, 27, 35, 63, 70, 74, 111,

123, 127, 140, 162, 168, 186, 277, 298,343–66, 373, 383

Metabolomics definition, 3limitations, 16–18, 26, 69, 70, 87, 206,

319Standards Initiative, 320, 380, 381technologies, 4–10, 65–70, 162–8,

262–75, 367–99Metabolomics-assisted breeding, 64, 132,

143, 222, 241, 243, 244Metabolons, 286, 347Metabonomics, 70Metadata, 199, 320, 369, 380, 381, 383,

386MetAlign, 9, 136, 3183-Methyl butanol, 113Methyl jasmonate, 170Methyl ketones, 3472-Methoxyphenol, 131, 186Methylsalicylate, 113, 186MET-IDEA, 350MetMask, 274MetNetDB, 386Micro-Tom, 110, 119, 120, 137, 379Microarray analysis, 135, 167, 171, 276,

280–83, 285, 292, 327, 381Microbe–plant interaction, 13, 25–59, 357Microbial metabolism, 25–59, 75, 357Microfiltration, 115Microfluidics, 139Micrometabolomics, 74, 139, 140, 343–61Micronutrients, 44, 190Millet, 29


Index � 415

Mineral deficiency, 62Minimization of metabolic adjustments

(MOMA), 304, 305Minimum reporting standards, 17, 165,

320, 386, 390Miraculin, 264, 275Misclassifications, 338Missing values, 324, 386Mixed integer linear program (MILP),

303, 304Molecular fragmentation, 5, 31, 67, 69,

90, 116, 119, 168, 319, 329, 367, 371,375–380

Monolignol, 284, 347Monoterpene, 160Monoterpene aldehyde, 358Morphine, 346mQTL, 130–32, 172, 173, 223MS/MS, 5, 31, 119, 164, 166, 202, 278,

375, 376, 378–80, 385MS/MSn, 5, 31, 197, 380Multiblock PCA, 331Multidimensional LC, 138Multidimensional scaling, 273Multi-parallel data processing, 9, 131,

159, 318, 319Multiple linear regression (MLR), 271Multiple Reaction Monitoring (MRM),

138, 164, 166Multivariate analysis, 92, 94, 113, 187,

270, 277, 280, 371Multivariate curve resolution, 187,

318Multivariate statistics, 9, 129, 164, 370Mustard, 230, 231Mutant genetic, 11, 43, 91, 111, 124, 143,

159, 166, 230light-sensitive, 111, 126metabolic, 19, 101, 102, 132, 134, 135,

169–72, 191, 276, 281, 290, 303, 346,380

silent, 10, 170Mutual information, 142, 290–93Mutual ranking, 293, 294MYB transcription factors, 125, 133, 135,

171, 278, 281Mycorrhiza, 13, 25–47Mycotoxin, 27, 44, 200, 377Mzmine, 378

NADH, 264NADPH, 43, 46, 397Nanoelectrospray LC, 348, 374–7Naringenin, 125Naringenin-chalcone, 117, 121, 124, 126,

135Natural products, 346, 347, 367, 387, 388Near Infra Red (NIR), 118, 197, 357Neoplastic growth, 29, 34Neotyphodium, 25–47Network hubs, 133, 277, 288, 397Network inference algorithms, 284–94,

296Next generation sequencing, 10, 109,

206, 240, 241Nicotiana sylvestris, 185Nicotiana tabacum, 190, 284, 346Nicotine, 102, 279, 346NIL population, 143, 227–30Nitrate nutrition, 42, 44, 129

assimilation, 162uptake, 44

Nitrogen deficiency, 278fixation, 37–40, 281metabolism, 47, 283

NMR 1D, 7, 8, 17, 41, 66, 69, 90, 92, 101,111–5, 126, 132, 142, 163, 169, 184,187, 192, 197, 204, 263, 280, 359, 391

2D, 90, 94, 111, 140, 165, 172, 377, 3863D, 90

Nod factors, 37Noise reduction, 9, 48, 163, 271, 330, 350,

367–406Normalization, 142, 163, 203, 268, 270,

271–4, 296, 324, 350Nuclear magnetic resonance, see NMRNutrient limitation, 12, 26, 34, 63, 169,

170, 299exchange, 27, 37, 41, 48uptake, 29, 34, 35, 301trafficking, 25, 26

Nutrients, 11, 25, 35, 40, 44, 63, 96, 165,182, 190, 205, 298

Nutrition human, 110, 120–27, 181–207Nutritional value, 11, 13, 109, 207, 231,

289

ObjFind, 302Oil, 37, 200, 222, 225, 231, 358


416 � Index

Omics, 27, 61–5, 74, 142, 207, 261Ononitol, 40OPLS, 130, 273, 336Optimal defence, 87–107Oranges, 200Orbitrap-FTMS, 7, 115, 138, 377Organic acids, 39, 44, 74, 112, 115, 118,

130, 134, 162, 168, 184, 192, 195, 204,231, 322, 350, 359, 389

Organoleptic properties, 111, 128, 186,189, 239

Ornithine, 32, 42, 334Orthogonal projection, 130, 273, 336Orthogonal signal correction (OSC), 270Oryza sativa, 41, 369, 385, 392Osmoprotectants, 71, 73, 74Outlier, 291, 327, 333, 339

P inorganic, 28, 40, 66, 191deficiency, 40transporters, 39, 42

P450, 158, 160, 240, 347, 392Paclitaxel, 346PAL, 172PAP MYB factor, 171, 278Papaver somniferum, 346Parameter learning, 294Pareto scaling, 350Parthenocarpy, 240Partial correlations, 292, 296Partial least squares (PLS), 128, 190, 199,

270, 318Pasta, 189Pasteurization, 15, 109, 187Pastinaka sativa, 100Pathogen attack, 12, 18, 31, 25–49, 89,

135, 159, 173, 375fungi, 25–49

Pathway reconstruction, 387, 395–8Pathway analysis, 367–99Pathway maps, 141, 279, 393, 395–8PCA, 93, 101, 128, 132, 134, 136, 165, 184,

186, 189, 193, 199, 204, 264, 267, 272,278, 317, 325–31, 350, 353

PDA, 5, 111, 116, 190, 196, 203, 370, 377Peak alignment, 9, 75, 91, 163, 283, 318,

350, 367, 371, 378picking, 9, 367, 372, 382, 386

Pearl millet, 29

Pearson Correlation Analysis, 143, 287,291, 333, 334, 335

Peel tissue, 116, 117, 119, 123–6, 135, 139,186, 283

Pepper, 224, 392Peptide alkaloid, 44Pesticides, 129, 187, 205, 371Petunia, 124, 126Pharmaceuticals, 13, 14, 34, 62, 89, 129,

166, 219, 348, 390Phaseolus vulgaris, 40, 283Phenomics, 76Phenotype chemical, 9, 10, 13, 45, 88,

136, 240, 285, 340physiological, 235plasticity, 65, 88, 95, 96, 109silent, 10, 170

Phenylacetaldehyde, 186, 232, 334, 339Phenylalanine, 39, 114, 127, 172, 184, 3342-Phenylethanol, 113, 187, 232, 334Phenylpropanoids, 32, 34, 39, 44, 47, 94,

118, 120–26, 131, 135, 160, 163, 171,186, 193, 322, 346, 380

Phloem, 38, 98, 368Phosphatidylcholine, 375, 376Phosphatidylserines, 376Phosphoenolpyruvate, 39, 162, 345Phospholipid, 4, 39, 164, 264, 375Phosphoric acid, 334, 350, 351Photodiode array detection, see PDAPhotosynthesis, 27, 34, 46, 49, 72, 134,

305, 344, 394Photosynthetic rate, 44, 72, 168, 393Phthalates, 370Phytic acid, 191Phytoalexin, 27, 160Phytoene desaturase, 240Phytoene synthase, 124, 127, 186Phytohormones, 17, 26, 37, 41, 43, 48, 71,

118, 166Phytophthora spp., 28, 42Piceid, 125, 126Pine, 14Pinitol, 71, 351Plant physiology, 11, 12, 109, 129, 168,

225, 235, 242, 275, 284, 325, 338, 344breeding, 10, 14, 45, 64, 77, 94, 118,

123, 125, 132, 143, 193, 205, 208,221–4, 228, 231–3, 240, 241, 243, 244


Index � 417

disease, 11, 13, 15, 25, 143, 159, 183,237, 243

hormone, see Phytohormonestress, 12, 27, 37, 39, 61–85, 88, 110,

126, 128, 140, 162, 170, 186, 193, 195,220, 235, 243, 264, 271, 278, 281, 323,330, 344, 394

Plasma blood, 70, 198, 264Plastid metabolism, 42, 160, 299, 344Pleiotropic compounds, 136, 287, 295

effect, 171PLS, see Partial least-squaresPollen grains, 349, 352, 357Polyamines, 40, 118, 134, 160, 162, 170,

184, 281, 349Polyols, 33, 40, 71, 72, 204Polyunsaturated fatty acid, 42, 72,

371Pooling sample, 91, 121, 348Poppy, 346Populus, 130, 280Post harvest storage, 111, 121, 135, 194,

207Potassium deficiency, 170Potato, see Solanum tuberosumPotato tubers, 90, 98, 183–5, 277, 345Powdery mildew, 29, 33–5, 282Prediction model, 197, 294, 336Principal Components Analysis, see PCAProcessed food, 13, 182, 187, 200Proline, 32, 40, 44, 71, 73, 114, 131, 189,

199, 282, 334Proteome, 66, 142, 220, 262, 276, 280Proteomics, 62, 75, 138, 172, 276, 285,

323, 345, 378, 381Proton transfer reaction (PTR), 195Prunus spp., 13, 385Puccinia, 35Putrescine, 40, 131Pyrophosphorylase, 171, 283

QTL, 9, 45, 130–32, 157, 172–4, 205,219–44, 282

QTOF, 69, 116, 129, 131, 135, 142, 192,195, 349, 353, 356, 376, 383, 386

Quadrupole, 5, 67, 115, 131, 138, 164,188, 239, 349, 376, 384

Quadrupole time-of-flight (QTOF), seeQTOF

Quantification, 3, 7, 8, 27, 62, 67, 74, 113,115, 133, 139, 142, 164, 166, 187, 190,202, 226, 233, 245, 287, 322, 324, 350,360, 375, 384

Quantitative trait loci, see QTLQuercetin, 120, 124–6, 171, 193, 278Quinate, 44QQQ instruments, 69, 166, 376, 377

R code, 76, 383R genes, 29Reactive oxygen species (ROS), 32, 43,

46, 71Radio isotope labelling, 41, 67, 98, 157,

163, 165, 166, 168, 324, 344, 372, 376,379

Raffinose, 98, 170, 191, 368, 370Raman scattering microscopy, 360Raspberry, 13, 192–4, 207Real time PCR, 45, 280, 281Redox signalling, 283Regression analysis, 45, 270, 371Regulatory network, 237, 277, 288, 292,

305Pathway, 72, 285, 294

Relevance networks, 285–7, 291–4, 297Resolution mass, 8, 18, 61, 67, 70, 74, 115,

119, 138, 141, 164, 191, 204, 226, 263,275, 367, 377

Resolving power, 7, 18, 379Resveratrol, 125, 126Retention index, 119, 319, 329, 371–3,

377–8, 383Retention time, 119, 136, 166, 203, 350,

371, 377, 378, 386Reverse genetics, 35, 65, 243, 245RFLP, 241Rhizobium, 27, 36–45, 281, 283Rhizosphere, 357Rice blast, 25, 28, 29–33, 35, 46Rice, see Oryza sativaRIL population, 172, 227–30, 239RNAi, 124, 125, 136, 240ROOM, 304Root caps, 356

development, 37, 72nodulation, 26, 37, 39–41, 281tips, 356

ROS, see Radical oxygen species


418 � Index

Rutin, 120–25, 193Ryegrass, see Lolium perenne

Saccharomyces cerevisiae, 264, 288, 292,299, 305

Salicylic acid, 37, 186Salinity, 62, 63, 70, 73, 74, 76, 128, 243,

281, 323Sample preparation, 6, 8, 48, 115, 137,

139, 263processing, 345

Saponin, 118, 352, 356, 394Scalable Vector Graphics, 398Scaling Gaussian, 292

median, 272, 273unit variance, 273

SciFinder, 389Score-based learning, 296Score plot, 95, 265, 354, 355Searchable database, 118, 171Secondary ion mass spectrometry

(SIMS), 357Secondary metabolism, 2, 26, 97, 101,

133–5, 159, 162, 168, 219, 278, 285,295, 318, 343, 392, 394

Secondary metabolite, 7, 18, 27, 42, 69,87, 90, 97, 115, 120, 128, 140, 157,163, 169, 193, 235, 264, 280, 346, 356,359, 368, 377, 387, 394

Seed germination, 165, 195, 235, 283,356

coat, 346, 347development, 194, 348, 349dormancy, 235

Seedling, 169, 191, 356Seeds, 14, 43, 90, 93, 95, 99, 123, 129, 132,

160, 164, 165, 191, 194, 228, 237, 281,299, 330, 346, 379, 384

Segregating populations, 135, 183,186–93, 223–30, 241

Selected reaction monitoring (SRM),166

Self-organizing map, see SOMSemolina, 189Senecio spp., 90, 92–4, 100Sensitivity, 7, 8, 18, 69, 74, 90, 138, 203,

245, 263, 324, 343, 356, 360Sequestration, 27, 29, 33, 40, 134, 347Serine, 44, 351

Sesame, 230, 231Seven Golden Rules, 379Shikimic acid, 36, 234Signal transduction, 38Signaling molecules, 32, 41, 43, 356Similarity scores, 293, 294Sinapoylmalate, 95Single cells, 18, 30, 61, 63, 69, 140, 347,

361Sinorhizobium meliloti, 39, 40, 281Size exclusion, 5SMILES, 369, 385, 388, 394Snapdragon, 125SNP markers, 9, 14, 241Soil, 26, 36, 40, 62, 63, 72, 100, 182, 193,

198Solanine, 183Solanum habrochaites, 130, 224, 232, 358

lycopersicum, 110, 130, 185, 231, 232,379, 385

pennelli, 130, 224, 225, 231phureja, 183spp., 224tuberosum, 67, 183, 287, 385, 392

Solid phase microextraction, see SPMESOM, 132, 143, 169, 170, 277Spatial resolution, 18, 61, 63, 343–61Spermine, 134, 185Spermidine, 40, 134, 281, 349Sphingolipid, 164Spin Generated Fingerprint (SGF), 201SPME, 5, 112, 189, 191, 200, 322, 355Sporopollenin, 352SSR markers, 14, 241Stable isotope labelling, 41, 67, 163, 166,

324, 376Stachyose, 368, 370Standard operating procedures, 17, 318,

320, 330, 339Standardization methods, 17, 142, 201,

367Standards authentic, 7, 17, 27, 47, 67, 112,

115, 121, 165–7, 198, 203, 271, 352,367, 376, 379, 383

Starch, 99, 134, 169, 171, 184, 190, 220,222, 225, 236, 244, 283, 345, 394

Statistical analysis, 9, 16, 91, 129, 137,164, 167, 190, 194, 224, 227, 266, 273,317–42, 350, 356, 371, 378, 396


Index � 419

Stearic acid, 131, 353Sterols, 4, 42, 72, 118, 120, 189, 354, 356,

370, 378Stigmasterol, 370, 378Stilbenes, 125, 126Stilbene synthase, 126Stoichiometric reconstruction, 285, 288,

298, 302, 305, 397Stomata, 71, 72Strawberry, 13, 138, 192, 200, 331,

349Stress

abiotic, 12, 19, 37, 48, 61–77, 88, 97,102, 127, 135, 162, 187, 220, 222, 235,243, 281, 360, 384

biotic, 62, 70, 129, 162cold, 72, 73, 271osmotic, 70–76, 281response, 12, 37, 61, 63, 71–4, 128, 170,

186, 195, 271, 281, 284, 344Strigolactones, 41, 166Structure dereplication, 367–99Student’s t-test, 324–6, 350Subcellular, 48, 70, 74, 298, 344–8, 388,

393Suberin, 32Substantial equivalence, 127, 201–8Succinate, 41, 186Sucrose, 33, 36, 39, 44, 73, 93, 95, 98, 114,

127, 134, 184, 188, 195, 225, 352, 368synthase, 39, 281

Sugar phosphate, 112, 168, 204, 264,288

Sulfotransferase, 171Sulphur deficiency, 169, 170, 277, 278,

282Supervised data analysis, 30, 130, 190,

268, 329, 336Symbiotic, 27, 36–45, 281Synthetic peptides, 378Systems biology, 1, 19, 62, 75, 109, 139,

142, 169, 204, 207, 261–316, 343, 394,398

TagFinder, 9, 318, 329Tandem MS, 5, 374, 377Targeted analysis, 3, 91, 96, 101, 133, 166,

170, 201Tartaric acid, 322

Taste (off-flavours), 11, 195, 322, 338Taste 11, 13, 110, 112, 131, 136, 185, 194,

198, 202, 320, 331, 338panel, 195, 198, 321, 337, 373

Taxus brevifolia, 346TCA cycle, 73, 285, 396Tea, 196–8Terpene, see TerpenoidTerpenoid, 4, 41, 44, 118, 120–23, 135,

162, 186, 204, 349Testa mutant, 346, 347Thellungiella halophila, 73Threonate, 32, 186Thermodynamic constraints, 303Thrips, 93, 94, 100Time of flight, 5, 67, 90, 115, 165, 160,

184, 187, 231, 239, 349, 357Time-resolved metabolomics, 2, 10, 16,

168, 190, 305, 306Time series, 277, 280, 282, 305TMS, see DerivatizationTobacco, 102, 185, 190, 278, 284, 346Tocopherol, 116, 118, 120, 202TOF, see Time of flightTomato, see Solanum lycopersicumTranscript profiling, see TranscriptomicsTranscriptomics, 2, 6, 28, 47, 62, 71, 75,

159, 169–74, 186, 223, 241, 262, 276,285, 323

Trehalose, 38, 42, 73, 166, 354Trichomes, 139, 343–61, 386Triple quadrupole, see QQQ instrumentsTriterpene glycoside, 123Triticum durum, 189, 207

monococcum, 76spp., 189, 385

Tryptophan, 39, 114, 351, 381Tyria jacobaea, 100Turgor, 30, 33, 35, 72Two constraint approach, 303Tyrosine, 36, 39, 114, 184, 283, 334

Ultra performance liquidchromatography, see UPLC

Unintended effects, 129, 184, 191,205–8

Unsaturated fatty acids, 42, 72Unsupervised data analysis, 190, 266,

330


420 � Index

Untargeted analysis, 3, 6, 44, 45, 47, 87,96, 100, 112, 116, 123, 127, 131–8,166–8, 171, 188, 192, 225, 234, 245

UPLC, 5, 7, 135, 138, 164, 192, 197, 280,348, 352, 356, 376

Urea, 38, 42Urine, 70, 121, 182, 196, 198Uromyces, 35, 46Ustilago maydis, 25, 29, 34–36UV damage, 88, 126

spectra, 377

Vacuoles, 42, 46, 73, 299, 344, 345, 347,394

Valine, 40, 114, 351Vascular bundles, 33, 35, 345–7Visualization

Cytoscape, 390, 393, 397, 398KaPPA-View, 279MapMan, 142, 279, 395–7PathVisio, 393, 398

Vitamin A, 192, 202, 222, 225, 244Vitamin C, 110, 120, 126, 131, 192Vitamin E, 116, 120, 131Vitamins, 110, 182, 220, 232, 389

Vitis vinefera, 281Volatiles, 6, 9, 68, 90, 112, 113, 118, 120,

131, 135, 137, 160, 163, 189, 200, 231,321, 322–9, 333, 336, 355, 371, 385

VTC Tool Box, 143

Web-based tools PlantMetGenMAP, 143,279

Weeds, 93, 99Wheat, see Triticum spp.Wilcoxon test, 318, 325, 326, 330–32,

340Wine quality, 320–40Wine, 138, 198–200, 317–40

Xanthomonas campestris, 42XCMS, 9, 378, 380Xylem, 38, 72, 172

Yew, 346

Zea mays (maize), 29, 34–6, 62, 64, 72, 125,189, 191, 222, 225, 236, 241, 389, 392

Zebrafish, 264Zebra plant, 140

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Annual Plant Reviews Volume 43 (Biology of Plant Metabolomics) || Index