Toshihiro Obata Max-Planck-Institut für Molekulare ... · PDF file10 Toshihiro Obata 11...

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Running head Metabolic effects of stresses in field grown maize 5

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Corresponding author 9

Toshihiro Obata 10

Max-Planck-Institut fuumlr Molekulare Pflanzenphysiologie 11

Am Muumlhlenberg 1 12

14476 Golm 13

Germany 14

Tel +49 (0)331 5678261 15

Fax +49 (0)331 5678408 16

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Journal Research Article Area Biochemistry and Metabolism 21

Plant Physiology Preview Published on September 30 2015 as DOI101104pp1501164

Copyright 2015 by the American Society of Plant Biologists

wwwplantphysiolorgon May 15 2018 - Published by Downloaded from Copyright copy 2015 American Society of Plant Biologists All rights reserved

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Metabolite profiles of maize leaves in drought heat and combined 22

stress field trials reveal the relationship between metabolism and 23

grain yield1 24

25

Toshihiro Obata Sandra Witt Jan Lisec Natalia Palacios-Rojas Igor Florez-Sarasa 26

Salima Yousfi Jose Luis Araus Jill E Cairns Alisdair R Fernie 27

28 Max-Planck-Institut fuumlr Molekulare Pflanzenphysiologie Am Muumlhlenberg 1 14476 29 Potsdam-Golm Germany (TO SW JL IFS and ARF) CIMMYT Km 45 Carretera 30 Mexico-Veracruz Texcoco Mexico 56130 (NPR) Department de Biologia Vegetal 31 Universitat de Barcelona 08028 Barcelona Spain (SY and JLA) CIMMYT Southern 32 Africa Regional Office Harare Zimbabwe (JEC) 33 34 35 36 One sentence summary 37 38 Folial levels of metabolites including myo-inositol showed correlation with grain yield 39 under drought heat and simultaneous droughtheat stress field trials of tropical maize 40 genotypes 41 42

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Footnotes 44 45 1This work was supported by funding from EU FP7 Coordination and Support Action 46 OPTICHINA (FP7-KBBE 26604 TO SW NPR JLA JEC and ARF) the EU integrative 47 project 3TO4 (FP7-KBBE 289582 TO and ARF) the Max Planck Society (TO and ARF) 48 and the Alexander von Humboldt Foundation (IFS) 49 50 51 52

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Author to whom correspondence should be sent Email obatampimp-golmmpgde 55

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The author(s) responsible for distribution of materials integral to the findings presented 58

in this article in accordance with the policy described in the Instructions for Authors 59

(wwwplantphysiolorg) is Toshihiro Obata (e-mail obatampimp-golmmpgde) 60

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4

Abstract 63

The development of abiotic stress resistant cultivars is of premium importance for the 64

agriculture of developing countries Further progress in maize performance under 65

stresses is expected by combining marker assisted breeding with metabolite markers In 66

order to dissect metabolic responses and to identify promising metabolite marker 67

candidates metabolite profiles of maize leaves were analysed and compared with grain 68

yield in field trials Plants were grown under well water conditions (control) or exposed 69

to drought heat and both stresses simultaneously Trials were conducted in 2010 and 70

2011 using ten tropical hybrids selected to exhibit diverse abiotic stress tolerance 71

Drought stress invoked accumulation of many amino acids including isoleucine valine 72

threonine and GABA which has been commonly reported in both field and greenhouse 73

experiments in many plant species Two photorespiratory amino acids glycine and 74

serine and myo-inositol also accumulated under drought Combination of drought and 75

heat invoked relatively few specific responses and most of the metabolic changes were 76

predictable from the sum of the responses to individual stresses Statistical analysis 77

revealed significant correlation between levels of glycine and myo-inositol and grain 78

yield under drought Levels of myo-inositol in control conditions were also related to 79

grain yield under drought Furthermore multiple linear regression models very well 80

explained the variation of grain yield via the combination of several metabolites These 81

results indicate the importance of photorespiration and raffinose family oligosaccharide 82

metabolism in grain yield under drought and suggest single or multiple metabolites as 83

potential metabolic markers for breeding of abiotic stress tolerant maize 84

85

86


5

Introduction 87

The increasing world population coupled to environmental deterioration is creating ever 88

greater pressure on our capacity for sustainable food productivity Alongside biotic 89

stresses abiotic stresses such as drought heat salinity nutrient deficiency greatly 90

reduce yields in crop fields either when present alone or in combination Breeding for 91

more resilient crops is therefore one of the major approaches to cope with the 92

increasing challenges in world agriculture Considerable research effort has thus been 93

invested in order to dissect plant responses to individual stresses at various levels 94

(reviewed in Urano et al 2010 Lopes et al 2011 Obata and Fernie 2012) but the 95

interaction between different stresses has been far less investigated (Cairns et al 96

2012b Cairns et al 2013 Suzuki et al 2014) In general combination of stresses 97

additively affects plant physiology (ie the symptoms of the individual stresses appear 98

simultaneously) and synergistically diminishes yield and productivity of plants (Keleş 99

and Oumlncel 2002 Giraud et al 2008 Vile et al 2012 Suzuki et al 2014) The 100

molecular responses are however not simply additive and are rarely predicted from the 101

responses to individual stresses (Rizhsky et al 2002 Rizhsky et al 2004 Prasch and 102

Sonnewald 2013 Rasmussen et al 2013) Information from carefully controlled 103

greenhouse experiments has begun to dissect the molecular mechanisms by which 104

plants in particular Arabidopsis respond to drought and temperature stresses (Skirycz 105

et al 2010 Skirycz and Inzeacute 2010 Skirycz et al 2011 Bowne et al 2012 Tardieu 106

2012 Verkest et al 2015) Our knowledge of the molecular basis of the response of 107

crop species in a field environment is however considerably less well advanced (Araus 108

et al 2008 Cabrera-Bosquet et al 2012) That said a large number of genotypes 109

have been generated for on the basis of their resistance to both biotic and abiotic 110

stresses (for a review see Baumlnziger et al 2006 Takeda and Matsuoka 2008 Cooper 111

et al 2014) and the genome sequencing and molecular characterization of a range of 112

stress tolerant plant species have recently been reported (Wu et al 2012 Ma et al 113

2013 Bolger et al 2014 Tohge et al 2014) These studies are not only important as 114

basic research for further studies in crops but also are a prerequisite in the development 115

of molecular marker based approaches to improve crop tolerance to stress 116


6

As a first step toward this goal a deeper understanding of the plant response to the 117

stressful environment especially those to multiple stress conditions under field 118

conditions is crucial for improvement of stress-tolerant crops This is important on two 119

levels (i) in the field singular abiotic stresses are rare and (ii) yield and stress adaptation 120

are complex traits which render breeding gains slower than would be expected under 121

optimal conditions (Bruce et al 2002) Recent studies have revealed that the response 122

of plants to combinations of two or more stress conditions is unique and cannot be 123

directly extrapolated from their response to the different stresses when applied 124

individually This would be a result of complex combinations of different and sometimes 125

opposing responses in signalling pathways including those may interact and inhibit one 126

another (Prasch and Sonnewald 2013 Rasmussen et al 2013 Suzuki et al 2014) 127

Maize is grown in over 170 million hectares worldwide of which 130 million hectares are 128

in less-developed countries (FAO 2014) In sub-Saharan Africa (SSA) maize is a staple 129

crop however yields in this region have stagnated at less than 2 t ha-1 while maize 130

yields worldwide have continued to increase (Cairns et al 2012a) Low yields in SSA 131

are largely associated with drought stress and low soil fertility (Baumlnziger and Araus 132

2007) Additionally simulation studies indicate that maize yield in Africa is likely to be 133

significantly impaired by heat stress (Lobell and Burke 2010 Lobell et al 2011) such 134

as that can be anticipated as a result of the changes in climate predicted for the coming 135

decades (Muumlller et al 2011) Moreover sensitivity of maize yield to heat is exacerbated 136

under drought conditions (Lobell et al 2011 Cairns et al 2012ab Cairns et al 2013) 137

Therefore the development of maize germplasm tolerant to drought and heat conditions 138

is of utmost importance to both increase yields and offset predicted yield losses under 139

projected climate change scenarios (Easterling et al 2007) especially in SSA While 140

direct selection for grain yield under drought stress has resulted in admirable gains in 141

grain yield under stress (Baumlnziger et al 2006 Cairns et al 2013) further improvement 142

requires incorporation of additional selection traits (Cairns et al 2012a Cairns et al 143

2012b) In recent years genetic and phenotypic markers have been extensively 144

searched for drought tolerance of maize by high throughput genomic and phenotyping 145

approaches respectively (Tuberosa and Salvi 2006 Wen et al 2011 Araus et al 146

2012 Cairns et al 2013 Prasanna et al 2013 Araus and Cairns 2014 Tsonev et al 147


7

2014) Moreover metabolic markers start to draw attention due to their close 148

relationship with yield phenotypes (Fernie and Schauer 2009 Redestig et al 2011 149

Riedelsheimer et al 2012a Witt et al 2012 Riedelsheimer et al 2012b Degenkolbe 150

et al 2013) Accumulation of some metabolites has been reported to be directly related 151

to the performance of potato cultivars in beetle resistance in the field (Tai et al 2014) 152

Additionally identical genomic regions were mapped as both agronomic and metabolic 153

quantitative trait loci (QTL) in field grown maize and wheat indicating the utility of 154

metabolic traits for breeding selection (Riedelsheimer et al 2012b Hill et al 2015) A 155

recent study showed genetic gains in maize grain yield under drought stress were 156

higher using a molecular marker based approach than conventional breeding (Beyene 157

et al 2015) 158

Here we focused on the relationship between leaf metabolites and grain yield under 159

drought heat and simultaneous drought and heat conditions in the field The negative 160

effect of drought stress on maize yield is especially acute during the reproductive stage 161

between tassel emergence and early grain-filling (Grant et al 1989) where it is 162

believed to induce premature seed desiccation and to limit grain filling Grain is more 163

susceptible to drought stress than vegetative tissues and therefore the prediction of 164

grain yield from physiological parameter of leaves is a challenge (Sangoi and Salvador 165

1998 Khodarahmpour and Hamidi 2011) Nevertheless maize yield is dependent on 166

both the assimilate supply to the kernel (source) and the potential of kernel to 167

accommodate this assimilate (sink potential Jones and Simmons 1983) Breeding for 168

modern temperate hybrids has focused more on the sink potential particularly under 169

stress conditions (Tollenaar and Lee 2006) and there should therefore be 170

considerable potential remaining to improve source ability Drought and heat stresses 171

would be anticipated largely to affect leaf metabolism and especially photosynthesis 172

compromising the source capacity of leaves (Chaves et al 2009 Lawlor and Tezara 173

2009 Osakabe et al 2014) In keeping with this drought was found to have the most 174

dramatic effect on the metabolite composition in leaves compare to other organs in our 175

previous greenhouse experiments (Witt et al 2012) Since the source ability is closely 176

related to leaf metabolism the leaf metabolite profile should have close relationship to 177

grain yield particularly under conditions of stress Given that several recent studies have 178


8

indicated the importance of metabolic pre-adaptation to various stress tolerance in 179

plants (Sanchez et al 2011 Benina et al 2013) we also postulate that basal 180

metabolite levels under optimal growth condition could be correlated to stress tolerance 181

In order to test this metabolite profiles of the leaf blades of ten hybrids were analysed in 182

field experiments conducted at CIMMYT subtropical experimental station in 2010 and 183

2011 in which the plants were exposed to singular or combined drought and heat 184

stresses (Cairns et al 2012a Cairns et al 2013) The results are discussed both in the 185

context of current models of stress tolerance as well as with respect to their practical 186

implications for future breeding strategies 187

188


9

189


10

Results 190

Grain yield was affected by stress treatments 191

Maize plants were grown as part of the CIMMYT field trials in which a collection of 300 192

hybrid lines representing the genetic diversity within the CIMMYT and IITA tropical and 193

subtropical maize improvement programs (Wen et al 2011) were tested for tolerance to 194

drought heat and combination of drought and heat stresses (Cairns et al 2013) Ten 195

maize experimental hybrids (Table I) were chosen to cover a wide range of drought and 196

heat tolerance on grain yield observed in previous field trials (Cairns et al 2013) 197

Drought stress (DS) was imposed by stopping irrigation before flowering to achieve 198

water deficiency at anthesis stage This treatment induces mild drought stress which 199

increase canopy temperature for one to two degrees in the absence of heat stress 200

(Romano et al 2011 Zia et al 2013) However climate conditions varied slightly 201

between the two years of evaluation In 2010 one rainfall event (with 56 mm 202

precipitation) occurred at the start of February one week before drought was applied 203

under optimal temperature (and three weeks before trial mean anthesis) For the 204

combined drought and heat stress one rainfall event of 35 mm occurred two weeks 205

before the start of anthesis and the day before the last irrigation to apply drought stress 206

In 2011 one rainfall event of 13 mm occurred 10 days before the start of anthesis in the 207

combined drought and heat stress trial and 4 days after drought stress was applied 208

(Table II) Drought treatment significantly decreased average grain yields to 458 and 209

352 of well watered plants in 2010 and 2011 seasons respectively (Fig1A) Grain 210

yield across treatments varied in genotypes from 183 to 432 Mg ha-1 in 2010 and from 211

065 to 428 Mg ha-1 in 2011 (Supplemental Fig S1) Grain yield of individual genotypes 212

tended to be in agreement with tolerance levels determined from the previous field 213

experiments (Table I Supplemental FigS1 Supplemental Table S2) Plant height did 214

not differ between tolerant and susceptible genotypes in accordance with occurrence of 215

the stress around flowering when plants have already achieved their maximum height 216

(Supplemental FigS1 Supplemental Table S2) Other parameters did not differ 217

between tolerant and susceptible genotypes whereas tolerant genotypes reached 218

earlier anthesis and silking under HS 219


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Drought treatment did not invoke consistent effects on silking date anthesis date 220

anthesissilking interval ear height and ear number across the two harvests (Fig1B to E 221

and G Supplemental Fig S1) Heat stress (HS) was applied by growing plants in the 222

dry season A group of plants were additionally treated by drought for combined stress 223

(DS+HS) treatment (for precise details see Materials and Methods) HS decreased plant 224

height and shortened the time till silking and anthesis in both years (Fig 1D and E 225

Supplemental Fig S1) However grain yield was significantly decreased in 2010 but not 226

in 2011 (Fig 1A) The grain yield under combined drought and heat condition was also 227

the same level as that in drought condition in 2011(Fig 1A) indicating that the heat 228

treatment in 2011 was not severe enough to affect grain yield Therefore the heat 229

treatment in 2011 was recognized as ldquomild HSrdquo which does not cause yield reduction 230

DS+HS exclusively affected ear number and anthesis silking interval (Fig 1D and E) 231

and lead to a severe yield reduction in 2010 (Fig 1A) Two way ANOVA indicated that 232

grain yield was significantly influenced by genotype treatment and also by their 233

interaction in both years (Supplemental Table S1) Ear number and anthesis date were 234

also influenced by the interaction of genotype and treatment in 2010 but not in 2011 235

(Supplemental Table S1) These results indicate that grain yield is the most suitable 236

parameter to assess genotypic variation of stress tolerance in the current study and 237

were mainly used for correlation analysis with metabolite profile 238

239

Metabolite profiling revealed differential metabolic responses of genotypes to 240

stress conditions 241

Leaf material was harvested for metabolite profiling two weeks after cessation of 242

irrigation Gas chromatography-mass spectroscopy (GC-MS) based metabolite profiling 243

was conducted to analyse the metabolites of central carbon metabolism Metabolite 244

levels were analysed on a per dry weight basis to avoid the effect of differential water 245

contents Forty one metabolites were detected in more than half of the samples Some 246

highly abundant metabolites including sucrose and glucose could not be quantified due 247

to being above the dynamic range limit of the specified settings of the GC-MS The 248

metabolites were clustered into three groups when analysed by hierarchical clustering 249

(Fig 2) The first cluster includes amino acids (eg tryptophan asparagine szlig-alanine 250


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phenylalanine and isoleucine) which highly accumulated under DS A second large 251

cluster includes various metabolites which tended to decrease (eg erythritol maltose 252

malate and fumarate) or showed relatively minor responses (eg xylose raffinose and 253

putrescine) in all stress conditions By contrast metabolites in the third cluster tend to 254

be accumulated in all stress conditions (eg serine glycine and GABA Fig 2) These 255

metabolic responses were similar across the genotypes but varied in magnitude (Fig 2) 256

Clear separation among samples from the four growth conditions in principal component 257

analysis (PCA) indicates the differential metabolic effect of DS HS and DS+HS on 258

maize leaf in 2010 (Supplemental Fig S2A) DS contributed to the separation on 259

principal component 2 while HS contributed to principal component 1 (Supplemental Fig 260

S2A) Separation between single and multiple stress conditions were not clear in 2011 261

most likely due to the mild heat stress (Fig S2B) Two way ANOVA indicated that most 262

metabolites were significantly influenced by genotype treatment and also interaction of 263

them suggesting differential response of individual genotypes to each treatment in both 264


13

years (Fig 3A and C) The effects on levels of individual metabolites are summarized in 265

Supplemental Table S3 When the effect of treatments was tested ANOVA revealed 266

most metabolites were separately influenced by each stress (Fig 3B and D) However 267

just 13 metabolites were significantly affected by the interaction of HS and DS in 2010 268

(Fig 3B) suggesting limited metabolites responded to combined stresses in a specific 269

manner or the effects of individual stress components compensated each other in some 270

metabolites Some metabolites showed clear tendency of differential accumulation in 271

stress tolerant and sensitive hybrids under stress conditions Among them galactinol 272

levels were lower in tolerant genotypes than in susceptible ones under DS condition in 273

both years (Supplemental Table S4) Accumulation of this metabolite varied between 274

genotypic groups with different tolerance levels under all stress conditions tested 275

(Supplemental Table S4) suggesting a relationship with stress tolerance 276

277

DS and HS conditions invoke increases in the levels of many amino acids 278


14

Amongst the 41 metabolites 20 accumulated and three were reduced under DS in both 279

years (Fig 4 Supplemental Fig S3) Those showing increased levels included many 280

amino acids (phenylalanine tryptophan asparagine serine threonine isoleucine 281

alanine proline valine glycine tyrosine 4-aminobutanoate (GABA) pyroglutamate β-282

alanine and homoserine) sugar and sugar alcohols (maltose myo-inositol and 283

galactinol) and organic acids (glycerate and threonate) By contrast two sugar alcohols 284

(erythritol maltitol) and trehalose were reduced by the drought treatment (Fig4 and 285

Supplemental Fig S3) Under HS phenylalanine alanine GABA threonate xylose and 286

galactinol accumulated and isoleucine glycerol malate glycerate and phosphate were 287

reduced in both years (Fig4 and Supplemental Fig S3) These metabolites can thus be 288

considered as responding to even mild heat stress Some other metabolites including 289

tryptophan serine threonine szlig-alanine proline glutamate pyroglutamate raffinose 290

myo-inositol succinate and urea accumulated only in the heat stress condition of 2010 291

suggesting they responded only to severe stress (Fig4 and Supplemental FigS3) 292


15

Maltose erythritol maltitol and trehalose were decreased under HS only in 2010 (Fig 4 293

and Supplemental Fig S3) These metabolites possibly related to grain yield under HS 294

since they responded only to severe HS affecting grain yield but not to the mild stress 295

without effect on yield 296

297

Stress combination additively affected the metabolite profile 298

Metabolic responses under DS+HS shared similar changes with the individual stress 299

treatments Only three (benzoate fumarate and xylitol) and two metabolites (urea and 300

xylitol) changed specifically under DS+HS in 2010 and 2011 respectively while no 301

significant effect on the levels of these metabolites were observed in DS or HS 302

compared to WW individually (Supplemental Fig S4) Twenty one of the 34 metabolites 303

affected under DS+HS condition were also affected in both DS and HS conditions in 304

2010 (Supplemental Fig S4) In 2011 the number of metabolites in this category 305

reduced to 12 and the metabolites shared between DS and DS+HS increased to 14 306


16

most likely due to mild heat stress (Supplemental Fig S4) k-means clustering was 307

performed in order to classify the metabolites according to the responses to stress 308

conditions in 2010 (Supplemental Fig S5) Most of the metabolites fitted well into five 309

clusters with some exceptions in which the response were not clear Those clustered 310

into the first three and the latter two clusters tended to accumulate and decrease under 311

DS+HS respectively Metabolites in cluster 1 and 2 were increased in all stress 312

conditions although those in cluster 1 further accumulated by stress combination 313

Cluster 3 includes many amino acids highly accumulated in both DS and DS+HS 314

conditions The metabolites in cluster 4 are characterized by the reduction in HS while 315

cluster 5 includes those specifically decreased under DS+HS (Supplemental Fig S5) 316

According to the criteria of response modes defined for describing transcript response 317

under stress combination by Rasmussen et al (2013) most of the metabolites in cluster 318

1 2 and some in 4 are assigned to ldquosimilarrdquo response mode Cluster 3 to 5 mostly 319

contain metabolites responded in ldquoindependentrdquo mode whilst just three metabolites 320

namely benzoate in cluster 1 and fumarate and xylitol in cluster 5 could be classified as 321

belonging to the ldquocombinatorialrdquo mode Interestingly metabolic responses in the 2010 322

trial could be well classified into just five of 20 scenarios which were predefined to the 323

responses against stress combinations (Rasmussen et al 2013) It should also be 324

noted that most of the metabolic changes in DS+HS should be predictable from the 325

metabolic responses to each single stress treatment since ldquosimilarrdquo and ldquoindependentrdquo 326

response modes are considered to be predictable (Rasmussen et al 2013) 327

Proline is a metabolite whose function in DS+HS condition has been reported (Rizhsky 328

et al 2004) The accumulation of proline differed between 2010 and 2011 in our maize 329

field trial While proline levels in DS+HS were reduced in comparison to DS in both 330

years it was much lower in 2011 along with the proline level under single HS It should 331

also be noted that the level of proline in DS+HS was still significantly higher than that in 332

WW condition in 2010 (Fig 4) 333

334

Metabolic responses under stress combination could be predicted from the sum 335

of those in single stresses 336


17

Many of the metabolic responses especially those in cluster 1 2 and 3 seem to be 337

predictable not only qualitatively but also quantitatively by the simple sum of responses 338

in DS and HS In order to test this hypothesis response factor was calculated by 339

dividing the metabolite level under stress conditions by that in WW Following log2 340

transformation correlations between the sum of the response factors in DS and HS 341

(predicted response factor in DS+HS) and the actual response factor in DS+HS were 342

tested (Table III Supplemental Table S5) Surprisingly the predicted response factors 343

significantly correlated with actual response factors in 17 of 41 metabolites in 2010 344

(Table III) The means of the predicted and actual values were fairly similar in most of 345

the metabolites and t-test showed significant difference between these two values in 346

only 11 of the 41 metabolites (Table III) This analysis suggested that a large part of 347

metabolic response under stress combination could be explained by the additive effects 348

of individual treatments Predicted and actual response factors were correlated in more 349

metabolites in 2011 but this is most likely due to weak effect of HS (Supplemental Table 350

S5) On the other hand the predicted response factor was significantly different from the 351

actual ones in eight metabolites including tyrosine succinate urea GABA raffinose 352

and xylose (Table III) The levels of these metabolites are most likely determined by 353

regulatory mechanisms specifically operating under combined stress conditions 354

355

Correlation analysis revealed a close relationship between leaf metabolite levels 356

and grain yield under stress conditions 357

In order to identify metabolites closely related to grain yield under stress conditions 358

correlation between levels of each metabolite and grain yield in each growth condition 359

was tested by Pearson correlation analysis (Table IV) When the relationship between 360

metabolite levels and grain yield in optimal growth condition was tested only szlig-alanine 361

and maltitol displayed significant correlation (Table IV) Under DS condition levels of 362

glycerol and glycerate showed positive correlation and those of asparagine serine 363

pyroglutamate phosphate itaconate and galactinol were negatively correlated to grain 364

yield Glycine myo-inositol threonate glycerol-3-phosphate and nicotinate showed 365

strong negative correlation to grain yield with the p lt 001 (Table IV Fig 5) Correlation 366

analysis in HS and DS+HS conditions were conducted only for 2010 data since heat 367


18

treatment had no effect on grain yield in 2011 (Fig 1A) The results from each year are 368

shown in Supplemental Table S6 Threonine valine erythritol xylitol trehalose 369

glycerol phosphate and nicotinate showed a positive and fumarate succinate and 370

raffinose showed a negative correlation with grain yield under HS in 2010 (Table IV) 371

Maltitol is the only metabolite showing a positive correlation to grain yield in DS+HS 372

condition (Table IV) Levels of phenylalanine threonine isoleucine valine aspartate 373

benzoate (plt001) tryptophan homoserine alanine glycine tyrosine glutamate 374

pyroglutamate myo-inositol malate and GABA (plt005) were negatively correlated to 375

this trait 376

Correlation of metabolite levels under control conditions with grain yield under stress 377

conditions was next tested in order to identify the metabolites which could be important 378

components of metabolic pre-adaptation (Table V) Levels of glutamate raffinose myo-379

inositol nicotinate and octadecanoate in control condition were significantly correlated 380

to grain yield in DS although no metabolite other than phosphate was correlated to grain 381


19

yield in HS in 2010 (Table V Fig 6) Tricarboxylic acid (TCA) cycle intermediates 382

namely succinate fumarate and malate and urea displayed a negative correlation to 383

the grain yield in DS+HS (Table V Fig 6) Interestingly grain yield under HS also 384

showed significant correlation to that in WW condition (Table V)This suggests that 385

genotypes with better yield under optimal condition also produce more grain even under 386

HS while stress tolerance affects more the grain yield under DS 387

In order to gain insight into the sequential effects of multiple stresses on the relationship 388

between metabolite levels and grain yield correlation between grain yield and levels of 389

individual metabolites was tested using the results from two growth conditions in 2010 390

(Table VI) The results from the 2011 trial are shown in Supplemental Table S7 Four 391

pairs of conditions namely WW DS DS DS+HS WW HS and HS DS+HS were 392

tested to compare the effects of a stress in the presence and absence of the other 393

stress It should be noted that the results rather reflect treatment effects than genotypic 394

ones due to larger contribution of treatments on the changes in both grain yield and 395


20

metabolite levels Twenty metabolites showed significant correlation with grain yield 396

commonly in WWDS and HSDS+HS condition pairs indicating these metabolites were 397

similarly affected by DS regardless the presence of HS (Table VI) Aspartate maltose 398

xylitol and xylose were four metabolites which showed correlation to grain yield only at 399

the presence of HS Contrary to DS only six metabolites were correlated with grain 400

yield commonly when WWHS and HSDS+HS pairs were tested (Table VI) Thirteen 401

and eight metabolites were significantly correlated with grain yield under HS specifically 402

in the presence (DSDS+HS) and absence (WWHS) of DS respectively This suggests 403

that effects of metabolites on grain yield under HS are largely dependent on the 404

presence of DS 405

406

Combination of metabolite levels could explain the variation of grain yield by 407

multiple regression modelling 408

Additionally a multiple linear regression model was constructed in order to identify 409

groups of parameters which coordinately affect grain yield in each growth condition for 410

the 2010 data Grain yield was used as dependent variable and minimum number of 411

independent variables were selected from all parameters measured in this study only 412

metabolite levels and at last metabolite levels under WW which contributed to fully 413

explain the variation of grain yield among genotypes (Table VII) The models requires 414

10 to 15 parameters to explain the variation of grain yield in most cases but only seven 415

were used for DS condition (Table VII) Models were quite similar when all parameters 416

(agronomical variables and metabolites) or only the metabolites were used as 417

independent variables Especially the models selected for DS were identical in both 418

cases (Table VII A B) Similar sets of metabolites under WW condition explained the 419

variation of grain yield in WW and stress conditions (Table VII C) In the case of DS 420

exactly the same sets of metabolites as in WW were selected (Table VII C) 421

Interestingly galactinol is selected for all models (Table VII) When the same analysis 422

was conducted for 2011 data galactinol was again selected as a parameter in all 423

models whereas the models required more independent variables than for the 2010 424

data to explain yield variation (Supplemental Table S8) 425

426


21

427


22

Discussion 428

Comparison of drought stress responses in field and greenhouse experiments 429

Large scale metabolite analyses under stress conditions in the field remains rare To 430

our best knowledge this is the first study reporting metabolic effects of simultaneous 431

abiotic stresses in field grown plants Heat stress was applied by altering the planting 432

date to ensure the reproductive phase coincided with high temperatures (Craufurd et al 433

2013) Despite limitations in fine climate control large scale field trials are still valuable 434

since it is often reported that important agronomical traits are masked in greenhouse 435

grown crops (Alexandersson et al 2014) In our previous study in controlled 436

greenhouse conditions genotypes chosen to cover wide range of DS tolerance based 437

on field results did not display differential effects of DS on physiological traits (Witt et al 438

2012) Further field studies showed these contrasting genotypes to show differential 439

physiological responses to DS (Cairns et al 2012a Cairns et al 2013) Additionally in 440

the current study genotypes showed differential physiological responses to DS albeit all 441

six genotypes tested in the greenhouse experiment were also included and other four 442

were selected by the same criteria These results reaffirmed the importance of 443

conducting field experiments to understand effects of abiotic stresses on crops On the 444

other hand some metabolic responses were shared in both greenhouse and field trials 445

Accumulation of amino acids including isoleucine valine threonine and GABA is a 446

metabolic response common in many abiotic stress environments in Arabidopsis (Obata 447

and Fernie 2012)These metabolites were also accumulated in maize in both 448

greenhouse and field trials under all stress conditions tested although threonine and 449

GABA were not annotated in greenhouse samples (Sicher and Barnaby 2012 Witt et 450

al 2012 Barnaby et al 2013) Other amino acids such as proline phenylalanine and 451

tryptophan also accumulated under DS in both conditions (Sicher and Barnaby 2012 452

Witt et al 2012 Barnaby et al 2013) as well as in Arabidopsis (Urano et al 2009) 453

The accumulation of these metabolites was much lower in this field study than the other 454

greenhouse studies (Sicher and Barnaby 2012 Witt et al 2012 Barnaby et al 2013) 455

most likely depending on the severity of drought stress due to the soil structure and 456

coincident rainfalls As accumulation of amino acids under DS has been reported in 457


23

various plant species (Evers et al 2010 Degenkolbe et al 2013 Barchet et al 2014 458

Hatzig et al 2014 Suguiyama et al 2014) it can be considered as a well-conserved 459

and robust metabolic response to DS in plants This response might be due to less 460

dilution effect caused by diminished growth under stress conditions (Geacutenard et al 461

2014) However our field study was performed in fully grown plants and DS was 462

imposed near flowering which is the most sensitive stage of maize grain production to 463

drought stress but leaf expansion had finished at that period It should also be noted the 464

plant height was not significantly affected by DS in 2010 indicating the dilution effect 465

played a minor role in amino acid accumulation Proline is one of many well-known 466

compatible solutes in plants (Hare and Cress 1997) Branched chain amino acids 467

(BCAAs valine leucine and isoleucine) and other amino acids sharing synthetic 468

pathways with BCAA (lysine threonine and methionine) accumulate in various abiotic 469

stress conditions (Obata and Fernie 2012) and have also been proposed as compatible 470

solutes (Joshi et al 2010) or alternative electron donors for respiratory electron 471

transport chain (Arauacutejo et al 2011) although comparative assessment of these 472

functions under drought stress conditions remain elusive 473

Metabolic effects of individual drought and heat stresses 474

In addition to the metabolites described in the previous paragraph many metabolites 475

accumulated under DS in both years Although some of them including GABA and 476

galactinol have been suggested to function in abiotic stress tolerance (Fait et al 2008 477

Nishizawa et al 2008) we focus mainly on glycine and serine here and on myo-inositol 478

in a later paragraph Apart from other amino acids glycine and serine are closely related 479

to photorespiration (Bauwe et al 2010) Even in C4 plants like maize in which RubisCO 480

oxygenation reaction should take place at a lower rate than in C3 plants recent studies 481

have indicated the essentiality of photorespiration for growth under normal air (Zelitch et 482

al 2009 Maurino and Peterhansel 2010) Both glycine and serine were accumulated 483

under DS in the current study suggesting altered photorespiratory flux This might be 484

related to yield performance under DS since these metabolites especially glycine 485

correlated to grain yield Photorespiration has actually been proposed to function in 486

protection from photoinhibition under drought salt and high light stresses as a sink of 487


24

excess reducing equivalent (Wingler et al 2000) andor by preventing excess 488

accumulation of reactive oxygen species (Voss et al 2013) It has also been shown to 489

contribute in tolerance to moderate water deficiency in tomato plants by ameliorating 490

nitrogen use efficiency reduced by lower nitrogen assimilation (Saacutenchez-Rodriacuteguez et 491

al 2011) It should also be noted that glycine and serine are principal sources of one-492

carbon unit largely consumed to synthesize an osmoprotectant glycine betaine in 493

some plant tissues (Hanson and Roje 2001) The levels of this osmolite differ among 494

maize varieties (Brunk et al 1989) and positively correlate to the degree of salt 495

tolerance (Saneoka et al 1995) Negative correlations between grain yield in DS and 496

levels of glycine and serine are possibly related to the levels of consumption of these 497

metabolites to synthesize glycine betaine leading to the variation of yield performance 498

under drought in maize genotypes Trehalose is another well-known osmoprotectant in 499

some insects plants and yeast but its accumulation and function are species specific 500

(Iturriaga et al 2009) The decrease of trehalose under DS in this study suggests its 501

function other than as an osmoprotectant in maize 502

Among the metabolites those responded to HS succinate accumulated and its level 503

negatively correlated to grain yield under severe HS in 2010 This is a novel observation 504

to our knowledge although the functional background is hardly explained Since 505

succinate is a metabolite connecting TCA cycle and GABA shunt (Fait et al 2008) the 506

balance between these two pathways might affect succinate level Interestingly GABA 507

shunt related metabolites namely GABA and glutamate and TCA cycle organic acid 508

malate and fumarate were increased and decreased under HS respectively 509

510

Effects of simultaneous drought and heat stresses 511

The effects of simultaneous application of drought and heat have been relatively well 512

studied in comparison to other stress combinations due to its economic impact and 513

increasing risk by global climate change in the near future (Suzuki et al 2014) There 514

are two studies so far in which metabolite profiles under DS+HS conditions in 515

Arabidopsis were examined with application of relatively mild (Prasch and Sonnewald 516


25

2013) and severe (Rizhsky et al 2004) heat stresses In both studies proline was 517

accumulated under DS but not in DS+HS (Rizhsky et al 2004 Prasch and Sonnewald 518

2013) This is explained as a consequence of avoidance of the toxic effect of proline 519

under HS (Rizhsky et al 2004) Such clear regulation was not observed in this study 520

and proline levels were differentially affected by stresses in the two years Whilst this 521

might be related to varied environmental conditions between the two years including 522

stress levels it is more likely due to different adaptation strategies of DS between maize 523

and Arabidopsis It is becoming clear that plant species have specific preferences on 524

the selection of compatible solutes to accumulate under stress conditions (Gong et al 525

2005 Benina et al 2013) While proline is one of the well-known compatible solute in 526

Arabidopsis (Hare and Cress 1997) this species accumulates only small amounts of 527

glycine-betaine (Missihoun et al 2011) which has been proven to be involved in stress 528

tolerance in maize (Brunk et al 1989) It is therefore conceivable that the degree of 529

dependence on proline for DS tolerance andor cellular proline concentration are 530

different between Arabidopsis and maize (Spoljarević et al 2011 Sperdouli and 531

Moustakas 2012) resulting in the different regulation of proline level under DS+HS 532

condition 533

Similar to both Arabidopsis studies only few metabolites specifically responded to 534

DS+HS in our field maize experiment Interestingly most of the metabolic changes in 535

DS+HS were quantitatively predictable from the sum of responses to each single stress 536

in contrast to transcript responses in Arabidopsis (Rasmussen et al 2013) In fact the 537

Arabidopsis metabolite profiling results from milder stress treatments show similar 538

tendency (Prasch and Sonnewald 2013) but not in the severe stress experiment 539

(Rizhsky et al 2004) It is possible that metabolic pathways are regulated to meet the 540

metabolic demands under each stress condition resulting in additive metabolite profile 541

under stress combination unless the metabolic network is collapsed by severe stress 542

treatments Given that the naturally feasible stresses are imposed more mildly than 543

typical stress treatments in greenhouse experiments (Romano et al 2011 Zia et al 544

2013) the general metabolic response in stress combination should be considered as 545

the sum of individual stresses in the field This is also supported by PCA in which 546

drought and heat contribute the majority of the variance observed in the metabolic data 547


26

with PC1 separating DS from WW PC2 separating H from no H and DS+H being 548

separated from WW in an additive fashion Another result supporting this argument 549

comes from the correlation analysis using two conditions in which the effect of DS was 550

well conserved regardless the presence of HS However HS treatments were differently 551

affected in the presence or the absence of DS indicating specific effects of HS under 552

stress combination on both metabolite levels and the grain yield This might be due to 553

stomatal closure which would be anticipated to occur under DS and which would be 554

expected to induce effects of HS on plant metabolism Indeed the negative effect of 555

heat on photosynthesis has been reported to be apparent only at the presence of DS in 556

European oak tobacco and wheat (Suzuki et al 2014) 557

558

myo-inositol as a potential metabolic marker for breeding of drought tolerant 559 maize 560

One of our main goals was to find metabolic markers useful for the selection of maize 561

genotypes giving better grain yield under abiotic stress conditions The preferential 562

choice of metabolite levels in the variable selection by multiple linear regression 563

analysis suggests metabolic traits to be promising markers which might behave stronger 564

than classical agronomical yield components for explaining variability in grain yield 565

Although further validation of the results and efficient methods for screening are 566

required for the actual use of candidate metabolite markers in breeding new strategies 567

of molecular breeding such as marker-assisted recurrent selection (MARS) which 568

require only one cycle of phenotyping and subsequently focus on selection based on 569

genotypic data potentially open up new avenues for high cost low throughput 570

phenotyping options (Jannink et al 2010 Bohra 2013) Selection markers which can 571

be determined in optimal growth condition are desired since it is very difficult to control 572

stress conditions in the field Metabolic markers would be a promising target because 573

the species specific metabolite profile under non-stress condition has been recognized 574

to be closely related to stress tolerance and the adaptation strategy of plant species 575

(Benina et al 2013) myo-inositol is the most promising candidate of a single marker 576

metabolite for yield performance under drought found in this study It was accumulated 577


27

and its level was negatively correlated to grain yield under DS Additionally its level in 578

WW condition was also negatively correlated to grain yield in DS These results suggest 579

a possibility of marker assisted breading to choose maize genotype raising better grain 580

yield under DS by the selection of genotype containing less myo-inositol in well-watered 581

leaves myo-inositol itself is implicated to function as an osmolite (Kaur et al 2013) like 582

other sugar alcohols However the importance of myo-inositol in plant stress tolerance is 583

rather related to its function as a precursor of many metabolites involved in abiotic 584

stress tolerance Raffinose family oligosaccharides (RFOs) especially raffinose are 585

ubiquitous in plant kingdom and contribute to stress tolerance likely by membrane 586

stabilization and antioxidative functions (Van den Ende 2013) Raffinose is synthesized 587

by adding a galactose residue from galactinol to sucrose and myo-inositol is used to 588

synthesize galactinol Therefore cellular myo-inositol metabolism is closely related to 589

the accumulation of RFOs and further to stress tolerance (Elsayed et al 2014) Actually 590

galactinol and raffinose were accumulated under DS as myo-inositol although raffinose 591

reduced in 2011 season Galactinol levels exhibited a relationship with the tolerance 592

levels of the genotypes and levels of galactinol and raffinose negatively correlated to the 593

grain yield in DS and HS respectively Galactinol level was chosen for all models 594

explaining the genotypic variation of grain yield in all growth conditions by multiple linear 595

regression analysis Additionally the raffinose level under WW condition showed 596

correlation to grain yield in DS as well as myo-inositol These observations indicate a 597

close relationship between metabolism of myo-inositol and RFOs and yield performance 598

of maize under drought stress The negative correlation between levels of these 599

metabolites and grain yield under DS indicates that the yield performance is not due to 600

the osmoprotective functions of these compounds but rather to the metabolism of these 601

compounds One possible explanation is that the genotypes showing lower 602

accumulation of myo-inositol and raffinose synthesize higher degree RFOs such as 603

stachyose The pathways of RFO metabolism reconstructed from genomic information 604

revealed most of the key enzymes are encoded by multiple gene members with different 605

expression patterns (Zhou et al 2012) indicating the operation and importance of RFO 606

metabolism in maize RFOs can also serve as mobile and storage carbon sources with 607

advantages in osmolitic and mobile flexibility over sucrose and starch respectively (Van 608


28

den Ende 2013) Therefore it is also possible that the lower myo-inositol and raffinose 609

level in tolerant genotypes is due to their use of RFOs as carbon sources Altogether 610

metabolite profiles from field drought stress experiments indicate the importance of RFO 611

metabolism in yield performance under drought in field grown maize although the 612

mechanism underlying this remains to be investigated 613

Possible relationship between basal respiration and yield performance under the 614

drought and heat stress combination 615

Levels of three TCA cycle related metabolites in WW condition showed negative 616

correlation to grain yield in DS+HS stress combination Although this result should be 617

considered with special caution due to the uneven distribution of grain yield among 618

genotypes this is an interesting observation since these metabolites showed completely 619

different responses against each stress treatment It might be considerable that the 620

basal operation of TCA cycle is related to the yield performance under DS+HS and 621

therefore these metabolites can also be used as metabolic markers The down 622

regulation of respiratory pathways including TCA cycle is reported under DS+HS 623

condition in a previous Arabidopsis study (Prasch and Sonnewald 2013) supporting 624

this possibility 625

It should be noted that the correlation coefficient in our current study is relatively low 626

however this is most likely due to the fact that the data are highly variable since they 627

were obtained from field grown samples where the control of growth condition is difficult 628

There are some previously reported correlation analysis in field studies on metabolite 629

levels which corroborate this statement (Robinson et al 2007 Degenkolbe et al 630

2013) That said the coefficient values of the most correlations discussed here range 631

from -077 to -045 with p values lower than 005 or 0001 which can be considered as 632

highly reliable 633

634

Multiple metabolic features as biochemical markers 635


29

The use of multiple metabolites as biochemical markers is another possible way to 636

improve grain yield under stress conditions Indeed a strategy of employing multiple 637

markers has been proposed for molecular marker assisted breeding (Jannink et al 638

2010) The results of current study indicate the potentiality of this approach since the 639

combination of metabolites explained the variation of grain yield very well especially 640

under stress conditions in the multiple linear regression models The metabolic traits 641

showed performance in yield prediction superior to conventional agronomical 642

parameters which have shown to be correlated to grain yield suggesting the potential of 643

metabolite profiling in breeding program GC-MS based metabolite profiling is especially 644

promising due to its highthroughput robust nature and compatibility to analyse wide 645

range of primary metabolites (Obata and Fernie 2012) We employed the multiple linear 646

regression for model establishment in this study but other regression methods including 647

multi-level response analyses random forest model and correlation network analyses 648

should also be useful This study also indicated the potential of the metabolite levels in 649

WW to predict grain yield under stress conditions Interestingly galactinol was selected 650

in all models as the variable contributing to yield prediction As described above 651

galactinol functions as galactosyl donor especially for the synthesis of RFOs including 652

raffinose and stachyose (Loewus and Murthy 2000) The contribution of galactinol in 653

yield explanation emphasizes the importance of RFO metabolism in grain yield 654

performance 655

656

Conclusions 657

Metabolite profile of maize leaves from field DS HS and DS+HS combination trials was 658

analysed in the current study The metabolite profiling study using field samples is still 659

rare and this is the first study reporting metabolite responses to stress combination in 660

field grown crops making our results a good reference for future studies One of the 661

interesting findings is that the metabolic responses to DS+HS combination were rather 662

the sum of the effects by two individual stresses than novel or divergent effects This is 663

likely due to progressive nature of field stress treatments and needs to be considered as 664

a general trend under field conditions In contrast to phenotypic and transcriptomic 665


30

profiles monitored in previous studies (Witt et al 2012 Alexandersson et al 2014) 666

some typical stress responses of primary metabolism in the field grown plants are fairly 667

similar to those of greenhouse grown plants and seem well conserved between growth 668

conditions and even among species This robustness of metabolic change renders it a 669

good candidate for marker assisted breeding The metabolite profiling of field stress 670

samples successfully identified metabolite signatures closely related to grain yield under 671

abiotic stress conditions It highlights the importance of photorespiration and RFO 672

metabolism for yield performance under DS Especially myo-inositol and RFO levels are 673

quite promising metabolic markers for maize breeding since those in WW condition 674

were correlated to grain yield in DS allowing the selection under normal growth 675

condition There are some conventional high performance liquid chromatography 676

(HPLC) based methods available to analyse myo-inositol and RFOs and recent 677

optimization of high performance anion exchange chromatography coupled with pulsed 678

amperometric detection (HPAECndashPAD) would allow higher throughput analysis for 679

biochemical marker assisted breeding (Gangola et al 2014) Additionally multiple linear 680

regression analysis suggested the possible interplay between metabolic pathways in 681

stress tolerance and potential use of multiple metabolic markers for yield prediction 682

Further trials should be conducted to confirm the relationship between these metabolic 683

traits and yield performance under stress and to test the effectiveness of metabolites for 684

biochemical maker assisted breeding 685

686


31

Materials and Methods 687

Plant materials and experimental conditions 688

Ten maize lines were chosen based on their contrasting response to drought and 689

combined drought and heat stresses (Cairns et al 2013)(Table I) Single cross hybrids 690

were generated by crossing lines with the tropical tester CML-539 691

Experiments were conducted at the CIMMYT experimental station in Tlaltizapaacuten 692

Meacutexico (18deg41rsquoN 99deg07rsquoW 940 masl) A total of four experiments were planted each 693

year comprised of two different water and temperature regimes Optimal temperature 694

experiments were planted at the end of the wet season (late November) and higher 695

temperature experiments were planted at the start of the dry season (mid-February) 696

Due to the low latitude of the experimental station this experimental design facilitated 697

application of different temperature conditions without large effects on day length and 698

irradiation Two different water treatments were used at each temperature regime a 699

well-watered control and anthesis stage drought stress Drought stress was imposed by 700

stopping irrigation before flowering to ensure stress at anthesis In 2010 trials under 701

WW DS HS and DS+HS received a total of 1037 mm 520 mm 790 mm and 576 mm 702

of irrigation respectively In 2011 trials under WW DS HS and DS+HS received a total 703

of 1151 mm 550 mm 639 mm and 600 mm of irrigation respectively Rainfall 704

temperature data during experiments are presented in Table II Experiments were 705

planted in two-row plots with a final plant density of 667 plants m-2 An alpha-lattice 706

design was used replicated two times All plots received 80 kg N ha-1 (as urea) 80 kg P 707

ha-1 (as triple calcium superphosphate Ca (H2PO4)middot2H20) at sowing A second 708

application of N (80 kg N ha-1) was applied 5 weeks after sowing (V6 stage Ritchie et 709

al 1998) Recommended plant weed and insect control measures were used 710

711

Field measurements 712

Days to anthesis and silking were recorded when 50 of the plants had shed pollen 713

and 50 of the plants had silks respectively The ASI was calculated as days to silking 714

ndash days to anthesis At physiological maturity plant height was measured on two 715


32

representative plants per plot then all plants were hand harvested and grain yield 716

measured Grain weights were adjusted to 125 moisture content 717

718

Metabolite profiling 719

Metabolite profiling was performed as described in Witt et al (2012) Leaf blades were 720

harvested two weeks after the water stress was applied at the flowering stage Samples 721

were collected from six individual plants from each plot and treated as biological 722

replicates Materials were snap frozen in liquid nitrogen and liophilized for one week 723

One hundred milligram of lyophilized powder was used for gas chromatography-mass 724

spectrometry (GC-MS) based metabolite profiling following the protocol of Lisec et al 725

(2006) Peaks were manually annotated and ion intensity was determined by the aid of 726

TagFinder (Luedemann et al 2012) using a reference library derived from Golm-727

Metabolome-Database (GMD Kopka et al 2005) The parameters used for peak 728

annotation were shown in the Supplemental Table S9 following the recommended 729

reporting format (Fernie et al 2011) 730

731

Data analysis 732

Metabolite levels were represented by the observed ion intensity of a selected unique 733

ion Ion intensity was log10 transformed and normalized using an ANOVA based model 734

for removal of measurement bias (Lisec et al 2011) The normalized dataset used for 735

the analysis was shown in Supplemental Table S10 736

Statistical analyses and graphical representations (ANOVA Tukeyrsquos HSD test heatmap 737

hierarchical clustering Bonferroni-correction PCA boxplots Venn diagram correlation 738

analysis Studentrsquos t-test) were performed using the R-software environment 311 739

(httpcranr-project org) The box plots were drawn by ggplot function in ggplot2 740

package The PCA was conducted after pareto-normalization using the rnipals-741

algorithm of the pcaMethods package (Stacklies et al 2007) ANOVA was conducted 742

using the anova-function on linear models incorporating either genotype and treatment 743


33

or HS and DS status as factors and allowing interactions Resulting p-values were 744

corrected for multiple testing using the stringent Bonferroni method Tukey HSD test 745

was conducted by glht function in multcomp package Correlation was tested by cortest 746

function and scatter plots were drawn by ggplot function Venn diagram was drawn 747

using venndiagram function in VennDiagram package Effects of stress combination on 748

metabolite levels were predicted from response in single stresses as follows Response 749

factors were calculated by dividing metabolite levels transformed to linear values and 750

divided by those from well-watered plants grown in the same field replicate followed by 751

log2 transformation Predicted response factors in DS+HS condition are the sum of 752

those in DS and HS conditions The values calculated from each genotype and field 753

was considered as a replicate (n=20) and used for the calculation of the mean and SEM 754

Correlation between actual and predicted response factors were tested by cor test 755

function in R Further the metabolic and agronomic data set was subjected to multiple 756

linear regression analysis to ascertain which combination of metabolites alone or 757

metabolites plus agronomic traits fully explained differences in grain yield across the 758

whole set of genotypes and replications within each growing condition Models were 759

selected using ldquoLinear regressionrdquo function of SPSS (SPSS Inc Chicago IL USA) with 760

ldquoEntrerdquo method to gain a R2 value approaching 1000 Multiple linear regression analysis 761

using the levels of metabolites under WW as independent variables and grain yield 762

under the three different stress conditions as dependent variables were also performed 763

764

Acknowledgements 765

The authors appreciate the help of Ciro Sanchez for coordinating the field experiments 766

in Mexico 767

768


34

Figure legends 769

Figure 1 Yield related parameters in the two years of field stress trials Box plots of 770 grain yield (A) plant height (B) number of ears per plant (C) anthesis date (D) siliking 771 date (E) and interval between anthesis and siliking dates (F) in each 2010 and 2011 772 seasons Ten maize hybrids were grown in well watered (WW blue) drought stress (DS 773 orange) well watered and heat stress (HS green) and drought and heat stress (DS+HS 774 red) conditions in two independent plots (n=20) Letters indicate the results of Tukeyrsquos 775 test comparing the conditions in each year (plt005) 776

Figure 2 Heatmap of metabolic responses to stress conditions in each genotype 777 Metabolite levels in each stress condition were normalized to that in well watered 778 condition and log2 transformed Values are the means of up to 12 biological replicates 779 Red and blue colors represent increase and decrease of metabolite using a false-color 780 scale respectively Samples from drought stress (DS) drought and heat stress 781 (DS+HS) and well watered and heat stress (HS) are arranged from the left Genotypes 782 are ordered by the grain yield in the corresponding stress condition (smallest at the left) 783 in each year 784

Figure 3 Effects of treatments and genotypes on the levels of individual metabolites 785 Histograms show number of metabolites whose levels were altered with indicated p-786 values by Bonferroni-corrected two-way ANOVA analyzing the effects of genotypes (Gt) 787 and treatments (Tm) (A and C) or heat (HS) and drought (DS) stresses (B and D) Each 788 bar indicates a range of 005 Data from 2010 (A and B) and 2011 (C and D) were 789 independently analyzed Metabolite levels from six biological replicates in two 790 independent plots (n=12) were used for analysis 791

Figure 4 Foliar metabolite levels in the two years of field stress trials Box plots of 792 relative levels of selected metabolites in each 2010 and 2011 seasons Only the 793 metabolites discussed in the Discussion part are shown Plots for all metabolites are 794 shown in Supplemental Figure S3 Ten maize hybrids were grown in well watered (WW 795 blue) drought stress (DS orange) well watered and heat stress (HS green) and 796 drought and heat stress (DS+HS red) conditions in two independent plots (n=20) 797 Letters indicate the results of Tukeyrsquos test comparing the conditions in each year 798 (plt005) 799

Figure 5 Correlation between metabolite levels and grain yield under drought (A) and 800 heat (B) stress conditions Levels of selected metabolites showed significant correlation 801 (plt0001 Table IV) were plotted against grain yield under stress conditions Ten maize 802 hybrids were grown in two independent plots (n=20 per year) and means of metabolite 803 levels from six biological replicates were plotted No metabolite showed significant 804 correlation to grain yield under simultaneous drought and heat stress Circles and 805


35

triangles indicate data from 2010 and 2011 respectively Due to a minor effect of heat 806 treatment on grain yield in 2011 only 2010 data was used for the analysis of heat stress 807 condition r and p are the correlation coefficient and p-value from Pearson correlation 808 analysis respectively 809

Figure 6 Correlation between metabolite levels in control condition and grain yield 810 under drought (A) and simultaneous drought and heat (B) stress conditions Levels of 811 selected metabolites under well watered condition showed significant correlation 812 (plt005 Table V) were plotted against grain yield under stress conditions No organic 813 metabolite showed significant correlation to grain yield under heat stress Mean values 814 from each of ten maize hybrid (n=10 per year) were plotted Circles and triangles 815 indicate data from 2010 and 2011 respectively Due to a minor effect of heat treatment 816 on grain yield in 2011 only 2010 data was used for the analysis of drought and heat 817 stress condition r and p are the correlation coefficient and p-value from Pearson 818 correlation analysis respectively 819

820


36

Table I Summary of maize genotypes Tolerance toa Genotype Entryb DS DS+HS HS La Posta Seq C7-F64-2-6-2-2 147 Tolerant Tolerant - DTPWC9-F31-1-3-1-1 102 Tolerant Tolerant Tolerant DTPYC9-F143-5-4-1-2 8 Moderate Tolerant Tolerant La Posta Seq C7-F18-3-2-1-1 37 Tolerant Moderate Tolerant CML-486 72 Moderate Tolerant Moderate La Posta Seq C7-F96-1-2-1-3 89 Susceptible Tolerant Tolerant CML311MBR C3 Bc F95-2-2-1 87 Susceptible Moderate Moderate CML311MBR C2 Bc F41-2 34 Tolerant Tolerant Susceptible DTPYC9-F69-3-5-1-1 91 Tolerant Susceptible Susceptible [GQL5[GQL5[MSRXPOOL9]C1F2-205-1(OSU23i)-5-3-X-X-1-BB]F2-4sx]-11-3-1-1

117 Moderate Susceptible Susceptible

aBased on Cairns et al (2013) bThe numbers used to identify the genotypes in this manuscript 821


37

Table II Weather records for December-May in 2010 and 2011 at the experimental site at Tlatizapan Mexico Monthly average figures are given for maximum minimum and daily average air temperatures and total monthly rainfall All measurements were taken at 2 m Temperature (degC) Average Minimum Maximum Rainfall (mm) 2010 2011 2010 2011 2010 2011 2010 2011 December 252 222 208 161 297 (0)a 283 (0) 0 0 January 242 250 201 194 283 (0) 305 (0) 204 0 February 250 275 208 224 292 (0) 326 (0) 600 0 March 287 298 233 245 341 (6) 351 (10) 0 0 April 307 322 255 274 358 (29) 370 (12) 348 128 May 331 324 287 282 378 (30) 366 (10) 14 121 aNumber of days with an air temperature greater than 35 degC

822


38

Table III Actual and predicted metabolic responses of maize leaves to drought heat and simultaneous drought and heat stresses in 2010 Treatment DSa HSa DS+HSa Predictedb rc

alanine 0703plusmn0098 0625plusmn0113 0928plusmn0141 1379plusmn0193 078asparagine 1147plusmn0223 0357plusmn0310 1887plusmn0475 1442plusmn0457 064

aspartate -0344plusmn0228 0129plusmn0157 -0135plusmn0183 -0428plusmn0294 040benzoate 0103plusmn0077 0142plusmn0078 0500plusmn0066 0234plusmn0141 050szlig-alanine 1059plusmn0095 0980plusmn0198 1406plusmn0221 2035plusmn0253 076erythritol -0477plusmn0145 -1847plusmn0229 -1109plusmn0161 -2224plusmn0365 045fructose -0035plusmn0067 0101plusmn0061 0049plusmn0053 0067plusmn0127 069

fumarate -0062plusmn0123 -0097plusmn0087 -0171plusmn0080 -0220plusmn0199 018G3P 1612plusmn0454 0855plusmn0553 1436plusmn0362 4014plusmn0853 098

GABA 1023plusmn0162 0957plusmn0145 1011plusmn0164 1973plusmn0266 041galactinol 0706plusmn0124 0227plusmn0082 1164plusmn0109 1027plusmn0170 075glutamate 0004plusmn0143 0627plusmn0222 0814plusmn0209 0555plusmn0315 073glycerate 0098plusmn0115 -0389plusmn0109 -0147plusmn0137 -0268plusmn0199 069

glycerol 0271plusmn0099 -0882plusmn0128 -0516plusmn0070 -0560plusmn0212 018glycine 1129plusmn0103 0760plusmn0216 2032plusmn0277 1909plusmn0247 043

homoserine 0957plusmn0104 0220plusmn0110 1272plusmn0180 1171plusmn0174 -015isoleucine 2328plusmn0146 -0319plusmn0135 1988plusmn0253 2008plusmn0219 001itaconate -0138plusmn0146 0125plusmn0198 1128plusmn0110 0036plusmn0302 042

malate 0417plusmn0116 -0662plusmn0168 -0573plusmn0162 -0296plusmn0206 063maltitol -0494plusmn0178 -0277plusmn0146 -1093plusmn0184 -0956plusmn0255 066

maltose 0022plusmn0244 -1181plusmn0156 -0497plusmn0182 -1274plusmn0340 071myoinositol 0620plusmn0083 0445plusmn0084 1067plusmn0098 1056plusmn0156 037

nicotinate 0264plusmn0092 -0020plusmn0122 0517plusmn0117 0178plusmn0215 -002octadecanoate 0108plusmn0087 0162plusmn0138 0204plusmn0110 0263plusmn0187 040phenylalanine 2467plusmn0178 0266plusmn0111 2355plusmn0236 2785plusmn0241 -043

phosphate -0213plusmn0097 -0299plusmn0141 -0134plusmn0135 -0535plusmn0194 036proline 1897plusmn0269 0311plusmn0182 1365plusmn0265 1968plusmn0405 038

putrescine 0198plusmn0124 -0195plusmn0134 0001plusmn0116 0094plusmn0236 060pyroglutamate 0389plusmn0134 0568plusmn0149 0899plusmn0236 1003plusmn0254 059

raffinose 0482plusmn0097 0704plusmn0114 0402plusmn0113 1149plusmn0182 -004serine 0834plusmn0110 0570plusmn0139 1202plusmn0204 1435plusmn0208 060

succinate 0513plusmn0185 0524plusmn0071 0364plusmn0122 1134plusmn0191 021threonate 0356plusmn0090 0595plusmn0086 0480plusmn0089 0954plusmn0161 076threonine 1321plusmn0105 0414plusmn0140 1359plusmn0213 1728plusmn0192 044trehalose -1083plusmn0240 -1790plusmn0243 -1641plusmn0230 -2746plusmn0451 070

tryptophan 2874plusmn0269 -0940plusmn0337 1961plusmn0403 1985plusmn0567 046tyrosine 1988plusmn0148 0186plusmn0154 1307plusmn0243 2135plusmn0257 050

urea 0624plusmn0108 0874plusmn0144 1026plusmn0099 1548plusmn0215 036valine 1913plusmn0127 -0014plusmn0139 1798plusmn0238 1912plusmn0200 006xylitol 0009plusmn0015 0025plusmn0019 -0087plusmn0014 0038plusmn0025 069

xylose -0140plusmn0056 0176plusmn0078 -0197plusmn0061 0072plusmn0106 026a Response factors showing log2 fold change in the metabolite level against WW condition Values are the means plusmn SEM (n=20) b Predicted response factors to DS+HS are sum of those to individual DS and HS Values shown in bold were significantly different from actual response factors in t-test (plt005) c Correlation coefficient between actual and predicted response factors Values shown in bold were correlations with p-values less than 005


39

Table IV Correlation between grain yield and metabolite levels Treatment WW DS HS DS+HS

alanine -005 -008 017 -052 asparagine 027 -036 036 -055

aspartate 029 -030 033 -063 benzoate 004 011 -044 -066 szlig-alanine 043 -005 042 -021 erythritol 025 006 058 007 fructose -006 -003 -016 017

fumarate 018 015 -058 -043 G3P -022 -051 000 -025

GABA -013 010 -019 -055 galactinol -010 -032 -025 015 glutamate 009 -016 021 -060 glycerate -006 036 -005 005

glycerol 014 038 067 017 glycine 014 -052 -035 -059

homoserine 026 -025 031 -056 isoleucine -005 -021 043 -071 itaconate -014 -040 -031 022

malate -010 020 -045 -059 maltitol 043 018 035 064

maltose 010 029 025 044 myoinositol -022 -054 -021 -051

nicotinate -027 -047 051 -002 octadecanoate -026 -020 -042 000 phenylalanine -008 -026 038 -067

phosphate -002 -037 056 -024 proline 003 -010 042 -044

putrescine -003 015 036 -005 pyroglutamate 024 -040 -028 -060

raffinose 005 029 -059 012 serine 010 -037 026 -049

succinate -016 011 -073 -013 threonate 014 -047 -005 031 threonine 018 012 052 -062 trehalose 011 001 060 -034

tryptophan 005 008 001 -050 tyrosine 006 014 038 -052

urea 002 017 000 -030 valine -006 -017 055 -069 xylitol 016 010 056 023

xylose 002 028 032 006 Correlation coefficient between grain yield and metabolite levels under each growth condition Ten genotypes grown in two plots were analyzed (n=20 for one year) Data from both years were analyzed for WW and DS whereas only 2010 data for HS and DS+HS Values shown in bold were correlations with p-values less than 005 Those highlighted in orange and red are with p-values less than 001 and 0001 respectively 823


40

Table V Correlation between grain yield under stressed conditions and grain yield and metabolite levels under well-watered condition Treatment DS HS DS+HS Grain yield 032 085 032 Metabolite

alanine -027 -011 017 asparagine 017 004 060

aspartate -029 033 030 benzoate -031 -027 -045 szlig-alanine -013 043 019 erythritol 035 -018 003 fructose -007 -066 -053

fumarate 009 -066 -070 G3P -039 -065 -064

GABA -016 -045 -018 galactinol -034 032 -009 glutamate -045 020 008 glycerate 031 012 -005

glycerol 011 037 -004 glycine -044 025 045

homoserine -027 009 012 isoleucine -025 -029 011 itaconate -038 062 033

malate 012 -058 -072 maltitol -003 015 008

maltose 008 -006 059 myoinositol -051 -049 -018

nicotinate -055 -025 -059 octadecanoate -051 -039 -055 phenylalanine -033 -029 -019

phosphate -038 -078 -041 proline -027 053 000

putrescine -032 -061 -037 pyroglutamate -041 006 035

raffinose -049 -012 -014 serine -015 006 030

succinate -033 -053 -074 threonate -029 -028 011 threonine -013 022 038 trehalose 019 -023 -019

tryptophan 044 -033 -035 tyrosine -030 024 027

urea -036 -024 -077 valine -026 -017 014 xylitol 002 -031 001

xylose -004 -029 -040 Correlation coefficient between grain yield under stress condition and grain yield and metabolite levels in WW condition Each genotype was analyzed as a data point (n=10 for one year) Data from both years were analyzed for DS whereas only 2010 data for HS and DS+HS Values shown in bold were correlations with p-values less than 005 Those highlighted in orange are with p-values less than 001 824


41

Table VI Correlation between grain yield and metabolite levels under two growth conditions in 2010

Treatment combination

WW DS

DS DS+HS

WW HS

HS DS+HS

alanine -068 -039 ns -047 asparagine -061 -038 ns -055

aspartate ns ns ns 037 benzoate -032 -071 -036 -063 szlig-alanine -045 ns ns ns erythritol 036 054 049 -034 fructose ns ns -038 ns

fumarate ns ns -033 ns G3P -043 -063 ns -057

GABA -069 ns -040 ns galactinol -060 -038 ns -072 glutamate ns -061 ns ns glycerate ns ns ns ns

glycerol -034 071 065 ns glycine -069 -060 -034 -068

homoserine -066 ns ns -057 isoleucine -087 ns ns -082 itaconate ns -0362 ns -054

malate -052 063 ns ns maltitol 052 071 040 073

maltose ns 037 043 -042 myoinositol -066 -069 ns -071

nicotinate -041 ns ns -042 octadecanoate ns ns -036 ns phenylalanine -090 ns ns -080

phosphate ns ns 036 ns proline -056 ns ns -048

putrescine -048 ns ns ns pyroglutamate -036 -054 ns -041

raffinose -035 ns -040 ns serine -054 -041 ns -045

succinate -051 ns -063 ns threonate -050 ns ns ns threonine -077 ns ns -059 trehalose 048 ns 043 ns

tryptophan -083 ns ns -074 tyrosine -078 042 ns -061

urea -067 -036 ns ns valine -086 ns ns -075 xylitol ns 064 ns 072

xylose ns ns ns 056 Correlation coefficients between grain yield and metabolite levels under each growth condition were calculated by using data from two growth conditions Conditions were chosen for dissecting the effects of stress combination Ten genotypes grown in two growth conditions were analyzed (n=20) Correlation coefficients shown are correlations with p-values less than 005 Those highlighted in orange and red are with p-values less than 001 and 0001 respectively ns not significant (p gt 005)


42

Table VII The set of variables selected by multiple regression analysis to explain the 100 variance in grain yield under each growth condition in 2010 Treatment A All parameters B Only metabolites C Metabolites in WW WW galactinol proline

octadecanoate raffinose xylitol threonine succinate phenylalanine myo-inositol erythritol glycerol ASI itaconate urea silking date

galactinol proline octadecanoate raffinose xylitol threonine succinate phenylalanine myo-inositol erythritol glycerol itaconate glycerate trehalose aspartate


SEE 084 084 084 DS galactinol urea

octadecanoate maltitol G3P benzoate putrescine

galactinol urea octadecanoate maltitol G3P benzoate putrescine


SEE 059 059 065 HS galactinol xylitol

itaconate alanine anthesis date serine urea GABA tyrosine isoleucine

galactinol xylitol itaconate alanine raffinose serine urea GABA tryptophan isoleucine

galactinol glycerate phosphate proline erythritol nicotinate itaconate glycerol trehalose tryptophan succinate xylitol octadecanoate

SEE 083 083 145 DS+HS galactinol trehalose

threonate maltitol glycerate plant height fructose malate tryptophan β-alanine urea

galactinol trehalose threonate maltitol glycerate nicotinate fructose malate benzoate phosphate urea

galactinol alanine raffinose octadecanoate succinate myo-inositol phenylalanine proline xylitol pyroglutamate phosphate urea

SEE 037 037 040 Multiregression analysis showing the set of both agronomical parameters and metabolite levels (A) only metabolite levels (B) and metabolite levels under well watered condition (C) explaining the 100 variance in grain yield The results of the same analysis for 2011 data are shown in Supplemental Table S8 Agronomic traits are shown in bold letters ASI anthesis silking interval SEE standard errors of the estimate 825


43

Supplemental Figures and Tables 826

Supplemental Table S1 Effects of treatments and genotypes on the yield parameters 827

Supplemental Table S2 Effect of treatments on yield parameters of the subsets of 828 maize genotypes grouped by stress tolerance 829

Supplemental Table S3 Genotype and treatment effects on metabolite levels in each 830 year 831

Supplemental Table S4 Effect of treatments on metabolite levels of the subsets of 832 maize genotypes grouped by stress tolerance 833

Supplemental Table S5 Actual and predicted metabolic responses of maize leaves to 834 drought heat and simultaneous drought and heat stresses in 2011 835

Supplemental Table S6 Correlation between grain yield and metabolite levels under 836 each growth condition in each year 837

Supplemental Table S7 Correlation between grain yield and metabolite levels under 838 two growth conditions in 2011 839

Supplemental Table S8 The set of variables selected by multiregression analysis to 840 explain the 100 variance in grain yield under each growth condition in 2011 841

Supplemental Table S9 (xlsx) Parameters used for peak annotation in GC-MS 842 analysis 843

Supplemental Table S10 (csv) Normalized dataset used for the statistical analysis 844

845

846

Supplemental Figure S1 (PDF) Yield related parameters in the two years of field 847 stress trials Grain yield plant height number of ears per plant anthesis date silking 848 date and interval between anthesis and silking dates in each 2010 and 2011 seasons 849 Ten maize hybrids were grown in well watered (WW blue) drought stress (DS orange) 850 well watered and heat stress (HS green) and drought and heat stress (DS+HS red) 851 conditions in two independent field replicates and the data from each of them was 852 plotted Genotypes are ordered according to the grain yield under each stress treatment 853 in previous field trials (Cairns et al 2013) The letters following entry number of 854 genotype represents the tolerant level described in Table I t tolerant m moderate s 855 susceptible 856


44

Supplemental Figure S2 Principal component analysis (PCA) of metabolite profiles 857 under field stress conditions PCA score plot of foliar metabolite profiles of ten maize 858 genotypes subjected to well-watered (WW blue circle) drought stress (DS orange 859 square) heat stress (HS green triangle) and simultaneous drought and heat (DS+HS 860 red triangle) conditions in 2010 (A) and 2011 (B) field trials Percentage of the variance 861 captured by each principal component (PC) is given close to each respective axis Each 862 point represents one biological sample 863

Supplemental Figure S3 (PDF) Foliar metabolite levels in the two years of field stress 864 trials Box plots of relative levels of all metabolites in each 2010 and 2011 seasons Ten 865 maize hybrids were grown in well watered (WW blue) drought stress (DS orange) well 866 watered and heat stress (HS green) and drought and heat stress (DS+HS red) 867 conditions in two independent plots (A) boxplots showing metabolite levels under each 868 growth condition Mean values from each plotgenotype were treated as replicates 869 (n=20) Letters indicate the results of Tukeyrsquos test comparing the conditions in each year 870 (plt005) (B) boxplots showing metabolite levels in each genotype under each growth 871 condition The result from an individual sample was treated as a replicate Genotypes 872 are ordered according to the grain yield under each stress treatment in previous field 873 trials (Cairns et al 2013) The letters following entry number of genotype represents the 874 tolerant level described in Table I t tolerant m moderate s susceptible 875

Supplemental Figure S4 Number of metabolites which altered its level by either heat 876 drought or simultaneous heat and drought treatments in 2010 (A) and 2011 (B) field 877 trials Venn diagram showing number of metabolites which altered levels in a stress 878 condition significantly different from that in the well-watered condition in Tukeyrsquos test 879 (plt005 Supplemental Figure S3) 880

Supplemental Figure S5 Clustering of metabolites according to the responses to 881 drought heat and simultaneous drought and heat treatments in the 2010 trial All 882 detected metabolites were clustered into five groups by k-means clustering function in 883 MultiExperiment Viewer (httpwwwtm4orgmev Saeed et al 2003) under the 884 Pearson correlation coefficient The number of clusters was chosen by both rule of 885 thumb (N=41) and elbow method Means of metabolite levels were centered and plotted 886 for each of the well-watered (WW) drought stress (DS) heat stress (HS) and 887 simultaneous drought and heat (DS+HS) treatments Metabolites included in each 888 cluster are indicated in the plots Gray and magenta lines show the metabolite levels of 889 individual metabolites and average of those in the cluster 890

891

892


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Sperdouli I Moustakas M (2012) Interaction of proline sugars and anthocyanins during photosynthetic acclimation of Arabidopsisthaliana to drought stress J Plant Physiol 169 577-585


Spoljarevic M Agic D Lisjak M Gumze A Wilson ID Hancock JT Teklic T (2011) The relationship of proline content andmetabolism on the productivity of maize plants Plant Signal Behav 6 251-257


Stacklies W Redestig H Scholz M Walther D Selbig J (2007) pcaMethods--a bioconductor package providing PCA methods forincomplete data Bioinformatics 23 1164-1167


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Suzuki N Rivero RM Shulaev V Blumwald E Mittler R (2014) Abiotic and biotic stress combinations New Phytol 203 32-43Pubmed Author and TitleCrossRef Author and TitleGoogle Scholar Author Only Title Only Author and Title

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Tardieu F (2012) Any trait or trait-related allele can confer drought tolerance just design the right drought scenario J Exp Bot 6325-31


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Tsonev S Todorovska E Avramova V Kolev S Abu-Mhadi N Christov NK (2014) Genomics Assisted Improvement of Drought wwwplantphysiolorgon May 15 2018 - Published by Downloaded from Copyright copy 2015 American Society of Plant Biologists All rights reserved

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Pubmed Author and Title wwwplantphysiolorgon May 15 2018 - Published by Downloaded from




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2

Metabolite profiles of maize leaves in drought heat and combined 22

stress field trials reveal the relationship between metabolism and 23

grain yield1 24

25

Toshihiro Obata Sandra Witt Jan Lisec Natalia Palacios-Rojas Igor Florez-Sarasa 26

Salima Yousfi Jose Luis Araus Jill E Cairns Alisdair R Fernie 27

28 Max-Planck-Institut fuumlr Molekulare Pflanzenphysiologie Am Muumlhlenberg 1 14476 29 Potsdam-Golm Germany (TO SW JL IFS and ARF) CIMMYT Km 45 Carretera 30 Mexico-Veracruz Texcoco Mexico 56130 (NPR) Department de Biologia Vegetal 31 Universitat de Barcelona 08028 Barcelona Spain (SY and JLA) CIMMYT Southern 32 Africa Regional Office Harare Zimbabwe (JEC) 33 34 35 36 One sentence summary 37 38 Folial levels of metabolites including myo-inositol showed correlation with grain yield 39 under drought heat and simultaneous droughtheat stress field trials of tropical maize 40 genotypes 41 42

43


3


53

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62


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Abstract 63






















85

86


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Introduction 87































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et al 2015) 158






















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Results 190































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this trait 376







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20










presence of DS 405

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21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































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3


53

54


56

57




61

62


4

Abstract 63






















85

86


5

Introduction 87































6

































7











et al 2015) 158






















8










188


9

189


10

Results 190































11




















239













12
















13













277



14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































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Reviewer PDF

Parsed Citations

4

Abstract 63






















85

86


5

Introduction 87































6

































7











et al 2015) 158






















8










188


9

189


10

Results 190































11




















239













12
















13













277



14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

5

Introduction 87































6

































7











et al 2015) 158






















8










188


9

189


10

Results 190































11




















239













12
















13













277



14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

6

































7











et al 2015) 158






















8










188


9

189


10

Results 190































11




















239













12
















13













277



14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

7











et al 2015) 158






















8










188


9

189


10

Results 190































11




















239













12
















13













277



14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

8










188


9

189


10

Results 190































11




















239













12
















13













277



14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

9

189


10

Results 190































11




















239













12
















13













277



14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

10

Results 190































11




















239













12
















13













277



14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

11




















239













12
















13













277



14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

12
















13













277



14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

13













277



14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

14
















15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

15





297











16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

16




























334




17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

17



















355














18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

18









this trait 376







19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

19
















20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

20










presence of DS 405

406




















426


21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

21

427


22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

22

Discussion 428































23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

23
































24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

24























510








25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

25

















condition 533
















26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

26











558




















27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

27

































28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

28

























highly reliable 633

634



29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

29




















performance 655

656

Conclusions 657










30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

30





















686


31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

31

























711






32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

32



718













731

Data analysis 732













33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

33





















764



in Mexico 767

768


34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

34

Figure legends 769







35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

35



820


36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

36





37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

37


822


38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

38



















39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

39




fumarate 018 015 -058 -043 G3P -022 -051 000 -025




malate -010 020 -045 -059 maltitol 043 018 035 064











40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

40




fumarate 009 -066 -070 G3P -039 -065 -064




malate 012 -058 -072 maltitol -003 015 008











41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

41



WW DS

DS DS+HS

WW HS

HS DS+HS


















42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

42



















43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

43












845

846



44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

44





891

892


















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

















































































































































































































Parsed Citations

Reviewer PDF

Parsed Citations

Toshihiro Obata Max-Planck-Institut für Molekulare ... · PDF file10 Toshihiro Obata 11...

Documents

Transcript of Toshihiro Obata Max-Planck-Institut für Molekulare ... · PDF file10 Toshihiro Obata 11...