Survey of Anthocyanin Composition and Concentration in Diverse

In this study, 398 genetically diverse pigmented accessions of maize were analyzed using HPLC to characterize the diversity of anthocyanin composition...
0 downloads 0 Views 1MB Size
Subscriber access provided by Eastern Michigan University | Bruce T. Halle Library

Article

A Survey of Anthocyanin Composition and Concentration in Diverse Maize Germplasm Michael Paulsmeyer, Laura Chatham, Talon Becker, Megan West, Leslie West, and John Juvik J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b00771 • Publication Date (Web): 26 Apr 2017 Downloaded from http://pubs.acs.org on May 4, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Agricultural and Food Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 39

1

Journal of Agricultural and Food Chemistry

A Survey of Anthocyanin Composition and Concentration in Diverse Maize Germplasm

2 3 4

Michael Paulsmeyer1, Laura Chatham1, Talon Becker1, Megan West2, Leslie West3, and John Juvik1*

5 1Department

6 7

61801, USA

8

2Kraft

9 10

of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL

Heinz Company, 801 Waukegan Road, Glenview, IL 60025, USA

3Depart

of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

11 12 13 14 15 16 17 18 19 20 21 22

**To whom correspondence should be addressed

23

John A. Juvik

24

Department of Crop Science, University of Illinois at Urbana-Champaign, 307 ERML,

25

1201 West Gregory Drive, Urbana, University of Illinois, Urbana, Illinois 61801. Tel.: 217-333-

26

1966, email: [email protected]

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

27

Survey of Anthocyanin Composition and Concentration in Diverse Maize Germplasm

28

Abstract

29

Increasing consumer demand for natural ingredients in foods and beverages justifies

30

investigations into more economic sources of natural colorants. In this study, 398 genetically

31

diverse pigmented accessions of maize were analyzed using HPLC to characterize the diversity

32

of anthocyanin composition and concentration in maize germplasm. 167 accessions were

33

identified that could produce anthocyanins in the kernel pericarp or aleurone and were classified

34

into compositional categories. Anthocyanin content was highest in pericarp-pigmented

35

accessions with flavanol-anthocyanin condensed forms, similar to the Andean Maíz Morado

36

landraces. A selected subset of accessions exhibited high broad-sense heritability estimates for

37

anthocyanin production, indicating this trait can be manipulated through breeding. This study

38

represents the most comprehensive screening of pigmented maize lines to date, and will provide

39

information to plant breeders looking to develop anthocyanin-rich maize hybrids as an economic

40

source of natural colorants in foods and beverages.

41 42 43

Keywords: Anthocyanin; natural colorants; Zea mays L.; germplasm diversity

44 45 46 47 48 49 50

1

ACS Paragon Plus Environment

Page 2 of 39

Page 3 of 39

51 52

Journal of Agricultural and Food Chemistry

Introduction Anthocyanins are the visually appealing water-soluble pigments responsible for most of

53

the red/orange to blue/purple pigments exhibited in plants. The most obvious function of

54

anthocyanins is as a colorful signal for pollinators and seed dispersers 1. However, anthocyanins

55

also protect the plant from photodamage and herbivory 2,3. In addition to their role in plants,

56

anthocyanins have been shown to possess anti-inflammatory, anti-carcinogenic, anti-angiogenic,

57

anti-microbial, cardioprotective and neuroprotective bioactivity in mammals 4. Furthermore,

58

they have been suggested to assist in the prevention of obesity and diabetes as well as in the

59

improvement of eye health 4,5.

60

Because of their attractive color and ease of aqueous extraction, anthocyanins make

61

suitable natural replacements for certain synthetic dyes such as FD&C Red 40. Red 40 is the

62

most abundant synthetic color additive produced in the US with over six million pounds certified

63

production each year. It accounts for almost 25% of the color additives produced in the US 6.

64

Synthetic dyes are preferred by the industry to most natural colors due to their lower cost and

65

relatively greater stability. However, the use of synthetic dyes has been a topic of public

66

controversy due to possible links with a number of detrimental health effects 7. Despite the

67

scientific uncertainty of the health effects of synthetic food dyes and additives, growing

68

consumer distrust of synthetic ingredients in foods and beverages has increased demand for more

69

economical sources of natural food colorants. Currently, anthocyanins are recovered from purple

70

fruits and vegetables and the remaining biomass is used as animal feed or is considered waste.

71

To meet the growing demand for natural colorants, more economical sources of anthocyanin

72

pigments need to be investigated. The focus of this investigation will be on maize anthocyanins.

2

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

73

Pigmented maize has been utilized as a source of natural colors for centuries. In Andean

74

cultures, high anthocyanin yielding “Maíz Morado” is still important for producing local foods

75

and beverages 8. Blue corn varieties are also important pigmented maize varieties and are used

76

widely for blue corn chips among other products. The distinction between blue and purple corn

77

varieties is that purple corn produces anthocyanins most abundantly in the pericarp of the kernel,

78

which is the outermost layer of the kernel (Figure 1). Blue corn varieties typically produce

79

anthocyanins in the peripheral layer of the endosperm called the aleurone 9. Genetically, the

80

location of synthesis is determined by the set of regulatory genes within the variety. The

81

combination of Booster1 (B1)/Plant color1 (Pl1) and R1/Colorless1 (C1) most often determine

82

pericarp color and aleurone color, respectively, although there are exceptions 10–12. Pigmented

83

maize presents a unique opportunity from a processing standpoint because the normally

84

inexpensive pericarp tissue can be isolated using one of several milling processes commercially

85

available to concentrate pigmented fractions while the remaining fractions can be sold for the

86

production of food, fuel, or feed 13.This provides the opportunity for anthocyanin colorants to be

87

value-added co-products in the corn processing supply chain.

88

The chemical structure of an anthocyanin consists of an anthocyanidin bound to a

89

glycoside. Three types of anthocyanidins are known to be produced in maize: pelargonidin,

90

cyanidin, and peonidin. These differ by hydroxylation or methoxylation of the phenyl group on

91

the flavylium cation. The structure of an anthocyanidin molecule is provided in Figure 2. A

92

flavonoid 3’-hydroxylase (F3’H) adds the hydroxyl group to the 3’-position of flavanones and

93

dihydroflavonols during the production of cyanidin. This is accomplished by the Purple

94

aleurone1 (Pr1) gene in maize 14. Subsequently, an anthocyanin O-methyltransferase (OMT) can

95

add a methyl group to cyanidin to form peonidin. The OMT in maize has not been characterized

3

ACS Paragon Plus Environment

Page 4 of 39

Page 5 of 39

Journal of Agricultural and Food Chemistry

96

to date. Flavanones can also be converted to phlobaphene pigments if an active Pericarp color1

97

(P1) gene is present. Phlobaphenes are water insoluble red pigments found in pericarp, cob and

98

tassel glumes, and husks 15.

99

The anthocyanin glycoside in maize is most often glucose, but arabinose, galactose,

100

rutinose, and rhamnose glycosides have been detected 16-18. The glycoside is often modified by

101

acylation, commonly in the form of malonylation. Malonylation increases stability in planta by

102

protecting the glycosylated sugar from enzymatic breakdown and stabilizing the anthocyanin

103

structure under more alkaline conditions 19. Malonylation has also been shown to increase

104

anthocyanin content (AC) by enhancing anthocyanin solubility and increasing uptake into

105

vacuoles 20.

106

Another modification that may be important for stability is the formation of flavanol-

107

anthocyanin dimers, also known as “condensed forms”. These compounds were first discovered

108

as a product of wine fermentation 21. Since their discovery in wine, condensed forms were found

109

to be important naturally occurring pigments in many crops, including strawberries, black

110

currants, beans, and maize. The major condensed forms in maize were first characterized by LC-

111

MS/MS and H-NMR in a previous study 22.

112

Maize has potential as an economical source of natural colors due to its high yield

113

potential and the large market for processed maize byproducts. To assess the diversity of

114

anthocyanin production in maize, 398 diverse accessions of pigmented maize were analyzed

115

using High Performance Liquid Chromatography (HPLC). To test repeatability and/or

116

heritability of this trait, a subset of these accessions were grown for several seasons and analyzed.

117

To our knowledge, this is the largest collection gathered to date for the purpose of characterizing

118

anthocyanin production in maize. Data from this investigation will provide information to plant

4

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

119

breeders looking to develop varieties of maize with enhanced levels of stable anthocyanins to be

120

used as an economical source of natural colorants in foods and beverages.

121

Materials and Methods

122

Plant Materials

123

Pigmented maize (Zea mays L.) accessions were collected from the North Central

124

Regional Plant Introduction Station (NCRPIS) in Ames, IA, USA; the International Maize and

125

Wheat Improvement Center (CIMMYT) in Mexico; Native Seeds/SEARCH (NS) in Tucson, AZ,

126

USA; the Maize Genetics Cooperation Stock Center (MGCSC) in Urbana, IL, USA; and various

127

commercial sources (See Table S1). Representative kernels of the original stock analyzed on the

128

HPLC were designated as the first pseudo-environment. In 2014, remaining stock from the

129

NCRPIS, MGCSC, and commercial sources were grown in two replications at the University of

130

Illinois Vegetable Research Farm in Champaign, IL, USA (40˚ 04′ 38.89″ N, 88˚ 14′ 26.18″ W)

131

with 7.62 m plots spaced 0.76 m apart. In 2015, a subset of 43 accessions was grown in a

132

randomized complete block design at the same location with three replications in 7.62 m plots

133

and 0.76 m spacing (Table 1). The subset of 43 accessions was initially chosen based on

134

estimated yield potential from 2014 data. If it was known that an accession would not be able to

135

produce sufficient kernels for analysis, it was excluded. After grain yield, accessions were

136

narrowed down based on their phenotypic stability. Many accessions were segregating for genes

137

with large effects on anthocyanin composition and were avoided in the subset. Accessions still

138

unknowingly segregating only had the genetically dominant phenotype included for analyses.

139

For both years, individual plants were self-pollinated to maintain genetic purity and to produce

140

stock for the next season. After harvest, ears were dried in a heated forced air dryer (35 ˚C) for at

141

least five days to maintain a similar moisture percentage and then shelled. Ears of a given plot

5

ACS Paragon Plus Environment

Page 6 of 39

Page 7 of 39

Journal of Agricultural and Food Chemistry

142

were either shelled separately or as a bulk sample. In 2014, samples from one plot were analyzed

143

in most cases. In 2015, a maximum of three ears per plot were chosen for analyses and averaged.

144

Sample Preparation

145

Representative kernel samples (30-50 kernels) of each accession were ground to a fine

146

powder in a coffee grinder. Accessions knowingly segregating for pr1 alleles were separated

147

visually (blue/purple vs. pink/red) and analyzed separately. One gram of whole corn powder was

148

weighed into a 15 mL conical centrifuge tube and extracted with 5 mL 2% (v/v) formic acid

149

(ACS Reagent Grade) in distilled, 2 µm Millipore (Billerica, MA, USA) filtered water. To

150

compare aqueous formic acid extraction to an organic solvent extraction, one gram of MM was

151

extracted in 5 mL 0.01% HCl in methanol. Air in the centrifuge tube was purged with argon (0

152

grade or purer) before extracting overnight (12-16 hours). Samples were kept in the dark, at

153

room temperature, and were constantly rotated on a LabQuake (Thermo Fisher Scientific Inc.)

154

test tube rotator to evenly extract the powder. After extraction, samples were centrifuged and the

155

supernatant was filtered through a 25 mm 0.45 µm Millex Millipore LCR PTFE syringe filter.

156

HPLC Analysis

157

The technique by West and Mauer (2013) was adapted for this work 23. A 20 µL aliquot

158

of anthocyanin extract was separated within a Grace Prevail C18 5 µm (250 mm x 4.6 mm)

159

analytical column (W. R. Grace & Co., Columbia, MD, USA) maintained at 30.0 ˚C on a Hitachi

160

L-7250 HPLC (Hitachi High Technologies America, Inc., Schaumburg, IL, USA) equipped with

161

a Hitachi L-7400 ultraviolet-visible detector set to 520 nm to generate chromatograms. The

162

mobile phase used 2% (v/v) formic acid as solvent A and 100% acetonitrile (HPLC grade) as

163

solvent B at a flow rate of 1 mL/min in the following linear gradient: 100% to 90% A for 3 min,

6

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

164

60% A at 30 min, then 100% A at 35 min. The column was allowed to equilibrate for 10 min

165

before each sample with 100% A.

166

Anthocyanin Content (AC)

167

Anthocyanin content (AC) was determined by summing peak areas integrated on the

168

Hitachi HPLC Software Management 4.0 software. All measurements were calibrated using a

169

bulk sample of commercially available Angelina’s Gourmet Maize Morado (MM) (Swanson, CT,

170

USA) as a relative external standard. To prepare bulk MM standard, large quantities of MM

171

kernels would be ground in a coffee grinder then extracted as described above. AC of each new

172

batch of MM powder was quantified using cyanidin 3-glucoside (C3G) standard (Phytolab

173

GmbH & Co., Vestenbergsgreuth, Germany). C3G standard curves ranging from 1 to 1000

174

µg/mL were produced using Excel (Microsoft Corp., Redmond, WA, USA). AC of MM was

175

determined to be approximately 1000 mg/kg C3G equivalents, based on the C3G standard curve.

176

MM was included in every new sample run. A run was considered new if the HPLC was turned

177

off and a new set of samples were extracted. MM standard was a relative check to ensure

178

consistency between batches. To quantify the anthocyanin content of a sample per weight of

179

whole corn, Equation 1 was used. In Equation 1, AC is expressed in units of mg anthocyanins in

180

C3G equivalents per kg whole corn relative to the MM concentration. For simplicity, units for

181

AC will be shortened to mg/kg from this point forward. In Equation 1, MM represents the Maize

182

Morado concentration of 1000 mg anthocyanins per kg MM.

183 184 185 186

Equation 1:

 =

         

× 

Anthocyanin Identification C3G, pelargonidin 3-glucoside (Pg3G), and peonidin 3-glucoside (Pn3G) standards were obtained from Phytolab GmbH & Co. (Vestenbergsgreuth, Germany) at 89% purity and run to

7

ACS Paragon Plus Environment

Page 8 of 39

Page 9 of 39

Journal of Agricultural and Food Chemistry

187

determine retention times (Figure 3). Remaining compounds identified were presumed based on

188

atomic masses obtained by LC-MS and by comparing elution order seen in previous literature 24-

189

26

190

cyanidin 3-(3”,6”-dimalonyl)glucoside (C3DMG). Because of this, the proportion of peonidin or

191

cyanidin in a sample can only be estimated. The proportion of condensed forms was calculated

192

by summing the areas of peaks eluted before C3G. The retention time for the major condensed

193

form, catechin-(4,8)-cyanidin-3,5-diglucoside, is within this region according to LC-MS results.

194

The formation of this condensed form was generally accompanied by several other minor peaks

195

which eluted before C3G and are tentatively identified as condensed forms (Figure 3c).

196

Proportion of acylation was calculated by summing the peak areas of major identified acylated

197

compounds: Pg3MG, Pn3MG, C3DMG, cyanidin 3-(6”-malonyl)glucoside (C3MG),

198

pelargonidin 3-(3”,6”-dimalonyl)glucoside (Pg3DMG), and peonidin 3-(3”,6”-

199

dimalonyl)glucoside (Pn3DMG). Concentrations of individual compounds in mg/kg were

200

calculated similarly to AC in Equation 1. The only difference is the Total Peak Area Sample

201

term is substituted with peak area of the compound.

202

Microscopy

. In the gradient method used, peonidin 3-(6”-malonyl)glucoside (Pn3MG) coelutes with

203

Blue aluerone pigmented kernels and purple pericarp pigmented kernels were soaked in

204

deionized water overnight to soften prior to sectioning. Using a razor blade, kernels were cut in

205

half sagittally and subsequently embedded in O.C.T. compound (Thermo Fisher Scientific,

206

Waltham, MA, USA) in a 15 mm x 15 mm x 5 mm disposable vinyl specimen mold (Sakura

207

Finetek USA, Inc., Torrance, CA, USA). After freezing, 14 µm sections of each embedded

208

kernel were cut using a Leica Reichert Cyrocut 1800 cryostat (Leica Biosystems, Buffalo Grove,

209

IL, USA) at -15°C. Sections were imaged at 40x using an Olympus BX51 microscope (Olympus

8

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

210

America Inc., Lombard, IL, USA) equipped with a Canon EOS Rebel T3i Digital SLR Camera

211

(Canon U.S.A. Inc., Lake Success, NY, USA)

212

Statistical Analyses

213

Coefficient of variation (CV) was calculated as (σ / ̅ ) × 100 where σ is the standard

214

deviation and ̅ refers to the average of the samples. Tukey Honest Significant Differences

215

(HSDs) among the various anthocyanin compositional categories were calculated using Proc

216

GLM in SAS Enterprise Edition Release 3.5 (SAS Institute Inc., Cary, NC, USA). Correlations

217

between compounds used the average concentrations (mg/kg) from each accession and were

218

calculated using Proc Corr in SAS. Principal component analysis (PCA) was performed in R

219

using the “princomp” function 27. Peak areas for all known compounds or groups of compounds

220

were converted to percentages of total peak area in the sample so before principal components

221

(PCs) were calculated. PCA was performed on the variance-covariance matrix since phenotypes

222

were already approximately normalized when converted to percentages. Hierarchical clustering

223

used Ward’s minimum variance method in R 27,28. PCA plots were generated using the ggplot2

224

function 29. Mixed model ANOVAs were calculated using Proc Mixed in SAS with method

225

equal to Type 3. The model to calculate a single year ANOVA is shown in Equation 2. yijk is the

226

response for the phenotype, µ is the grand mean,  is the random effect of the ith replication

227

with variance σ2r,  is the random effect of the jth genotype with variance σ2g, and ε(ij)k is the

228

random error term with variance σ2error.

229

Equation 2: 



= μ +  +  + #$

)%

230

The model to calculate a multiple-year ANOVA is shown in Equation 3. yijkl is the response for

231

the phenotype, µ is the grand mean, ' is the random effect of the ith environment with variance

232

σ2e,  $) is the random effect of the jth replication within environment with variance σ2r(e),  is 9

ACS Paragon Plus Environment

Page 10 of 39

Page 11 of 39

Journal of Agricultural and Food Chemistry

233

the random effect of the kth genotype with variance σ2g, ' is the random interaction of the kth

234

genotype in the ith environment with variance σ2g×e, and ε(ijk)l is the random error term with

235

variance σ2error.

236 237 238

Equation 3: 



= μ + ' +  $) +  + ' + #$

)

Heritability Calculations Broad-sense heritability (H2) was calculated using the method from Bernardo 30 and

239

adapted as shown in Equation 4. In Equation 4, r is the average degrees of freedom fornumber

240

replications across all years (r=2) and e is the number of environments (e=3). All variance

241

components were estimated using Proc Mixed in SAS with the model in Equation 3.

242

Equation 4:

243

Results and Discussion

244

Extraction and HPLC Method

245

()* * , ,* ()* + )×+ -../. .×-

Presented here is a simple and reproducible method for analyzing anthocyanin

246

composition and content in maize. Using bulk MM as a relative standard simplifies

247

quantification, reduces run time, and reduces costs by eliminating the reliance purely on

248

standards. Since MM was replicated 2 to 3 times every new run, a good estimate of extraction

249

repeatability can be calculated. A subsample of nine extractions of MM from the same bulk

250

powder was chosen to calculate repeatability. The CV of the nine runs ranged from 0.62% to

251

6.06% with an overall average CV of 2.92% (data not shown), meaning the HPLC method has

252

high consistency within runs of samples. Error can be introduced when a new bulk sample of

253

MM is ground and goes uncalibrated. Every new batch of bulk MM powder must be calibrated

254

with C3G standard to ensure that relative quantification is accurate. The CV was also compared

10

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

255

among five 2 g and five 1 g samples of MM powder extracted with a 1:5 (w/v) dilution as

256

described above. CV was similar (2% to 4%) with both extraction methods indicating both have

257

highly repeatability (data not shown). More whole corn powder should theoretically increase

258

homogeneity, but a 1 g to 5 mL dilution was chosen for consistency in an effort to minimize

259

sample/seed destruction.

260

The extraction method here is reproducible, but cannot be considered an exhaustive

261

extraction of anthocyanins. An exhaustive extraction method was not chosen because it would

262

increase the technicality of the protocol, the time to complete the protocol, and the reagent costs.

263

The method was developed so large amounts of samples could be analyzed. Since anthocyanin

264

composition was of interest in this investigation, high-throughput techniques like the pH

265

differential method 31 and Near-Infrared Spectroscopy (NIR) were not utilized. In the future,

266

non-destructive NIR can be utilized if AC is the only measure of interest. This technique has

267

been demonstrated in grapes 32 and flowering teas 33 and is currently used for determining major

268

constituents like oil, protein, starch, etc., in maize grain 34.

269

The aqueous extraction utilized in this study was meant to more closely resemble

270

commercial extraction procedures for purposes of comparison. Acidified methanol is a common

271

alternative to the extraction method used here. Using the same HPLC method, it was found that

272

an aqueous extraction was better at extracting condensed forms and acylated anthocyanins than

273

an organic solvent (data not shown). Preferentially extracting these compounds is advantageous

274

because condensed forms and acylated anthocyanins are thought to be important pigments for

275

AC and stability. Knowing that certain compounds extract more efficiently in different solvents,

276

compound proportions calculated here are relative to the extraction method and not absolute.

11

ACS Paragon Plus Environment

Page 12 of 39

Page 13 of 39

Journal of Agricultural and Food Chemistry

277

Despite these limitations, this extraction method is efficient, reproducible, and ideal for assaying

278

numerous samples that would be expected in a breeding program.

279

Categorization of Accessions

280

With the abundance of accessions in the survey, categories based on visual characteristics

281

and compositional data were developed to make meaningful clusters. Of the 398 accessions

282

collected, 167 were capable of producing anthocyanins in detectable amounts in the grain.

283

Categorization of anthocyanin-producing accessions with anthocyanin yields are shown in Table

284

S1. A summary of Table S1 is in Table 2. Phlobaphenes were the most common flavonoids

285

outside of anthocyanins (n=166). Phlobaphenes are brick-red pigments that can be mistaken for

286

anthocyanin coloration (Figure 4f). These pigments are produced in anthocyanin-pigmented lines

287

if an active P1 regulatory gene is present. These pigments are not extractable with aqueous

288

solvents, which is not ideal for food and beverage systems 15. A list of phlobaphene-producing

289

accessions is in Table S2. This list may not be complete since accessions that produced

290

anthocyanins in the pericarp masked phlobaphene pigments. A few accessions (n=11) produced

291

bronze pigments not detectable with the HPLC method used (Figure 4g). Many of these

292

accessions may be homozygous recessive Bronze mutants 35. A list of accessions producing

293

bronze pigments is also in Table S2.

294

Within the anthocyanin producing germplasm, accessions were categorized visually

295

based on the presence/absence of anthocyanins in the pericarp (Figure 1). Aleurone layers do not

296

develop near the germ of the kernel, which aids in the categorization of aleurone- versus

297

pericarp-pigmented accessions (Figure 4a, 4b, and 4d). Accessions for which visual

298

categorization was uncertain were sectioned roughly with a razorblade and viewed under a

299

compound microscope. Aleurone-pigmented accessions had the highest representation in the

12

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

300

survey. Over 82% (n=137) were capable of producing pigments in the aleurone (Table 2).

301

Several accessions (n=4) were found to produce anthocyanins in both kernel layers. Separating

302

the two layers is difficult, so in all cases the accessions that produced anthocyanins in both layers

303

were combined with aleurone-pigmented lines for analyses. The AC of these four accessions

304

ranged from 22.6 to 85.2 mg/kg, which is within the normal range observed for aleurone-

305

pigmented accessions.

306

Within the category of kernel tissue (pericarp or aleurone) pigmentation, accessions could

307

be further divided based on anthocyanin composition. Pericarp-pigmented accessions were

308

further subdivided based on the presence/absence of condensed forms (Figure 3c and 3d).

309

Aleurone-pigmented lines could be visually separated by whether they produced blue or pink

310

kernels (Figure 4a and 4b). In the pink aleurone category, Pg3G and its derivatives are the most

311

abundant pigments (>50% of total pigments) while in the blue aleurone category, C3G and its

312

derivatives are most abundant. Genetically, this is due to a homozygous recessive pr1 gene that

313

is unable to convert Pg3G precursors to C3G precursors 14. Only three homozygous pr1

314

recessive accessions produced anthocyanins in the pericarp. Puebla 403 (PI 485071) and Puebla

315

456 (PI 489081) produced both pericarp and aleurone pigmentation, while Apache Red (Siskiyou

316

Seeds, Williams, OR, USA) produced anthocyanins exclusively in the pericarp. Apache Red was

317

also unique because it abundantly produced condensed forms that are generally in low

318

abundance or undetectable in other accessions.

319

In addition to these four compositional categories, a unique trait was discovered that had

320

previously never been characterized in maize. Seven accessions within the collection produced

321

markedly less acylated anthocyanins than most other accessions (Figure 3e). Average C3G

322

content per category ranged from 2.99% to 28.3% of AC in typical accessions (Table 2). In

13

ACS Paragon Plus Environment

Page 14 of 39

Page 15 of 39

Journal of Agricultural and Food Chemistry

323

accessions with this unique trait, C3G was the dominant pigment and averaged 57.7% of total

324

anthocyanins. Some acylated anthocyanins can be detected in these unique lines, but the average

325

is only 7.6%, which is much lower than the average of 58.9% observed in other categories. This

326

unique phenotype has been found in several diverse backgrounds that seem to have no relation.

327

This trait will be referred to as “reduced acylation”. The hypothesis is that reduced acylation is

328

due to a functionally reduced anthocyanin acyltransferase.

329

It is important to note that some accessions belonged to more than one category. All the

330

compositional categories presented here can be described by the actions of a few genes, so

331

segregating between categories is common. Accessions belonging to more than one category

332

were coded as unique accessions so accurate conclusions could be made about each category in

333

terms of composition. For example, Apache Red was segregating for pr1 alleles and the ability to

334

produce condensed forms simultaneously, so it appears four times in Table S1.

335

AC Results

336

Across the whole survey, average AC was 64.7 mg/kg with an individual sample

337

maximum of 2560 mg/kg (PI 571427; Table 2). The highest performing accession in terms of

338

AC was the Peruvian landrace named Arequipa 204 (PI 571427) that had an average AC of 1100

339

mg/kg (Table S1). Andean purple corn landraces in general had high AC, but very low grain

340

yield. Landraces from the tropics are poorly adapted to the Midwest and are plagued with

341

photoperiod sensitivity and disease susceptibility 36. Adapting these landraces to the Corn Belt

342

region of the US will require backcrossing to Midwestern inbreds to improve grain yield. Within

343

the aleurone-pigmented accessions, the highest total anthocyanin yielding varieties were genetic

344

stocks provided by the Maize Genetics Cooperative Stock Center that were homozygous

345

recessive for intensifier1 (in1). Genetic stocks designated 707G and 707B were differentiated by

14

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

346

homozygous dominant and recessive Pr1 alleles, respectively. 707G and 707B averaged 133

347

mg/kg and 128 mg/kg, respectively, which contained almost 1.5 times the AC of the next best

348

aleurone-pigmented accession (Table S1). This demonstrates the importance of in1 as an

349

enhancer of AC in the aleurone.

350

The highest-performing category in terms of AC was that of pericarp-pigmented

351

accessions that could produce condensed forms (Table 2). The condensation of flavanols with

352

anthocyanins may provide improved stability to the compounds, just as malonylation does in

353

planta. In general, pericarp-pigmented accessions have a greater AC potential. This is in

354

agreement with other studies that found the highest AC in purple pericarp corn 18,37,38. The

355

greater potential for anthocyanin production in pericarp may be due to the processes that form

356

pericarp tissue. Pericarp during kernel development contains as many as 5 to 22 cell layers due to

357

the fusion of maternal tissues during development 39. Typically, the aleurone is only a single

358

layer, but several accessions have been found that are capable of producing up to six layers 40.

359

Integrating multiple aleurone layers and the in1 gene may be a route to increase AC in the

360

aleurone.

361

Comparisons between categories found no statistical difference in AC between blue and

362

pink aleurone accessions (p>0.05), but on average, pink aleurone had lower AC (Table 2). Blue

363

aleurone accessions were not statistically different for AC from pericarp-pigmented lines without

364

condensed forms, but pink aleurone was significantly different. The most likely reason

365

significance could not be established was because of the underrepresentation of pericarp-

366

pigmented accessions in the survey. Only thirteen accessions were included in the pericarp-

367

pigmentation without condensed forms category, while 98 were included in blue aleurone

368

category.

15

ACS Paragon Plus Environment

Page 16 of 39

Page 17 of 39

369 370

Journal of Agricultural and Food Chemistry

Compositional Differences Between Compositional Categories Proportion of known acylated compounds was lowest (7.6%) in the reduced acylation

371

category, as expected (Table 2). However, acylation was also significantly lower in the pericarp-

372

pigmentation with condensed forms category. One possible explanation may be due to the

373

limited identification of condensed forms. González-Manzano et al. (2008) 19 confirm that

374

malonylglucoside anthocyanins can be conjugated to catechins and epicatechins. Many acylated

375

anthocyanins may have been included within the condensed form calculation. With the results of

376

this survey, it can be concluded that acylated anthocyanins are typically the most predominant

377

pigments produced in maize. Despite preferential extraction of acylated anthocyanins in aqueous

378

solvents, the result found here is similar to other studies that analyzed blue and purple corn 18,37,41.

379

Acylation has been shown to increase stability, which may help maize be a more economical

380

source of natural colorants 42.

381

C3MG is the single most abundant compound, on average, in the whole collection. It was

382

the most abundant compound in the pericarp-pigmented without condensed forms and blue

383

aleurone categories with a difference of C3G of 3.98% and 16.4%, respectively. The fact that

384

C3MG is in higher abundance than C3G is in contrast with another study that found C3G to be

385

31% to 51% of total anthocyanins in Pr1 dominant lines 18. Abdel-Aal et al. (2006) 18 used an

386

acidified methanol extraction which may have inflated the C3G concentration within their

387

samples and may explain this discrepancy.

388

Pink aleurone accessions were much more represented in this collection than previously

389

reported in other pigmented maize collections, indicating that pr1 recessive alleles are common

390

variations in maize germplasm. Homozygous recessive pr1 lines almost always produced low,

391

but detectable amounts of C3G, although non-functional alleles of pr1 should not theoretically be

16

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

392

able to produce C3G. This may be evidence that homozygous recessive pr1 alleles have reduced

393

or repressed function, or possibly there is an additional F3’H expressed in maize at lower levels

394

than Pr1 43. Selecting for pr1 recessive alleles in breeding programs can provide a wider range of

395

hues in anthocyanin extracts that the food industry may be able to utilize. Higher proportions of

396

pelargonidin pigments within extracts tend to produce orange to red-orange hues, while

397

cyanidin-predominant extracts tend to produce red to purple hues (Figure 4). The pink aleurone

398

category did not have significantly greater proportions of total acylation, but it did have greater

399

proportions of Pg3DMG compared to all other categories. The proportion of dimalonyl

400

anthocyanins in pink aleurone lines may be indicative of the rate of dimalonyltransferase activity

401

in maize. Since Pn3MG and C3DMG co-elute in our current protocol, dimalonylglucoside

402

content cannot be measured directly, but the Pg3MG:Pg3DMG ratio in pink aleurone accessions

403

may inform of the C3MG:C3DMG ratio in others. For future work, if the concentration of

404

C3DMG in a sample is of interest, the proportion of cyanidin and peonidin anthocyanins before

405

and after acid or alkaline hydrolysis is an alternative way to calculate approximate C3DMG

406

concentration 37.

407

Peonidin has potential for adding stability to maize extracts and is therefore a pigment

408

that must be investigated. It has been demonstrated that anthocyanins with fewer free hydroxyl

409

groups on the B-ring tend to be more stable, but the results are somewhat unclear 44.

410

Nevertheless, the category with the highest proportion of Pn3G was pericarp-pigmented without

411

condensed forms (Table 2). This category also had the highest average abundance of Pn3DMG

412

with 4.5 mg/kg, on average. Generally, peonidin was in low amounts in all samples.

413 414

Condensed form pigments are much more prevalent in this study than previously found. An earlier characterization of condensed form pigments in maize found them to account for 0.3%

17

ACS Paragon Plus Environment

Page 18 of 39

Page 19 of 39

Journal of Agricultural and Food Chemistry

415

to 3.2% of total anthocyanins 22. In the condensed form category of this study, the average

416

proportion of condensed forms in each accession ranges from 3.8% to 32.8%, with an overall

417

average of 22.7% (Table S1). The most abundant condensed form in maize, catechin-(4,8)-

418

cyanidin-3,5-diglucoside, was characterized in González-Manzano, et al. (2008), but also

419

confirmed here with LC-MS (data not shown) 22. In MM, this pigment alone is approximately

420

13% to 14% of total anthocyanins. The underrepresentation of condensed forms in most studies

421

may be due to the utilization of acidified methanol as the choice solvent system. Aqueous

422

solvents should be utilized to accurately represent the concentration of these pigments since they

423

have potentially important effects on AC. Wide variation in condensed form content among

424

accessions in the collection indicates there may also be genetic diversity for condensed form

425

production that can be improved with breeding.

426

Correlation of Compounds

427

Initially, correlations were calculated using the complete dataset, regardless of category.

428

Concentrations of each compound significantly correlated with the AC of the sample. Most were

429

moderate to strong correlations (ρ>0.60) with the exception of Pg3DMG in the complete data set

430

(ρ=0.32). When separating the dataset by Pr1 alleles, the correlation of Pg3DMG improved to

431

≈0.60 and ≈0.70 for dominant and homozygous recessive alleles of pr1, respectively (Table S3).

432

Principal Component Analysis (PCA)

433

Overall, the principal component analysis (PCA) was effective at explaining a large

434

proportion of the variability in the dataset (Figure 5a). Principle component 1 (PC1) explained

435

60.1% of the total variance, and PC2 explained 23.8% of the total variability in the data set.

436

Based on the loadings, PC1 primarily appears to be explaining the variability associated with the

437

presence or absence of functional Pr1 alleles. Cyanidin-based anthocyanins have positive PC1

18

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

438

loadings, while pelargonidin-based anthocyanins have negative PC1 loadings (Table S4). PC2

439

primarily appears to be a contrast between accessions with higher proportions of condensed

440

forms and C3G versus accessions with higher proportions of acylated anthocyanins.

441

Observations with the highest scores for PC2 tended be aleurone-pigmented accessions, while

442

the most negative observations in PC2 were more indicative of pericarp with condensed forms

443

and the reduced acylation category. Pericarp without condensed forms were intermediate in PC2

444

most likely due to the higher proportion of C3G on average than the blue aleurone category

445

(Table 2).

446

Additionally, by visual observation, the biplot suggests there may be three natural groups

447

within the compositional data. A hierarchical clustering approach was used to cluster accessions

448

without a priori knowledge so clustering could be unbiased. Ward’s minimum variance method

449

was used for hierarchical clustering 28. While dividing the dendrogram at the largest distance

450

would produce two clusters in the dataset, dividing to make three clusters provides more

451

separation and makes more meaningful clusters (Figure 5b). The first cluster comprises a bulk of

452

the dataset and includes blue aleurone lines and pericarp without condensed forms. The second

453

cluster is mainly a mixture of pericarp-pigmented accessions that produce condensed forms and

454

reduced acylation accessions, but pericarp accessions without condensed forms are also within

455

this cluster. The third cluster consisted of accessions homozygous recessive for pr1 and includes

456

all pink aleurone accessions plus Apache Red. Results of the PCA show that variation in

457

composition is largely explained by genetic factors that have a large effect on composition. This

458

provides evidence that the visual and compositional categories created for the accessions are

459

sufficiently explaining the biology of the collection.

19

ACS Paragon Plus Environment

Page 20 of 39

Page 21 of 39

460 461

Journal of Agricultural and Food Chemistry

Heritability of Anthocyanin Content A representative subset of the survey was planted in 2015 with three randomized

462

replications to test the repeatability of anthocyanin production (Table 1). Table 1 shows the

463

number of ears analyzed for each accession. Some had limited representation because in certain

464

years, entire plots would be lost to disease pressure associated with the poor adaptation of that

465

germplasm to the Midwestern growing environment. Accessions with data from every year were

466

included in the analysis because they provide more degrees of freedom to test environments.

467

Although there were several accessions cut from the original collection, the subset still

468

accurately represented the whole survey. The limitation of the entire survey overall was the

469

limited representation of pericarp-pigmented accessions, which are of most importance for AC.

470

There were no lines with condensed forms chosen for the 2015 subset because these accessions

471

could not produce enough grain for analysis. Conclusions drawn from this subset may more

472

accurately describe the genetics of aleurone-pigmented lines rather than pericarp-pigmented lines.

473

Within 2015, the effect of replication for AC was insignificant (p=0.70), meaning that

474

AC does not change drastically within the same environment (Table 3). Since replications were

475

occasionally significant in 2015, the term was kept in the model for the combined analysis

476

(Equation 3). A strong estimation of variance due to replication could not be calculated since

477

there was only a single replication in the source packets and most often only a single replication

478

in 2014. Genotypic variance was always much higher than variance for replication, so the

479

variance of this term should have a small effect on H2 calculations. Results of subset of

480

accessions tested across years show high consistency in anthocyanin composition from

481

environment to environment. The variances contributed by genotype by environment effects are

482

magnitudes lower than the variances due to genotypes in all cases (Table 4).

20

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

483

H2 for each compound or group of compounds is listed in Table 5. The lowest H2 are

484

from peonidin-based compounds Pn3G (H2=0.76) and Pn3DMG (H2=0.53). These two

485

compounds generally had low or undetectable concentrations in most samples. The compound

486

with the highest H2 was C3G, with an H2 of 0.97. AC results were promising, with values of 0.93

487

for untransformed and log-transformed AC. High H2 values mean most of the variability in

488

anthocyanin production is attributed to genetics and not environmental factors. Due to the

489

apparently significant genetic control of AC and minimal environmental effect, it is likely that

490

breeding for increased AC and estimating the genetic potential of potential breeding lines can be

491

done with relatively few replications and locations.

492

Results of this survey are consistent with Ryu et al. (2013) 30 that found no outstanding

493

differences between 48 US/Mexican landraces grown in Ohio and Arizona. Jing et al. (2007) 27

494

found that purple corncobs grown in three locations around Lima, Peru did not vary significantly

495

either, but across all locations in Peru, they were significantly different according to an ANOVA.

496

Several factors varied across the locations in these two studies: precipitation, elevation,

497

temperature, etc. Any combinations of these factors may have influenced anthocyanin production.

498

From the data presented here and some support from the two other studies, it appears that the

499

results of this study can extend to environments typical of the Corn Belt region of the US.

500

Maize is a diverse source of anthocyanins and has great potential as an economic source

501

of natural colors. Here, 398 pigmented maize accessions were screened for anthocyanin content

502

and anthocyanin-containing accessions were categorized based on the kernel layer in which the

503

anthocyanins loaded as well as anthocyanin composition. This investigation represents the most

504

comprehensive analysis of anthocyanin content in maize germplasm to date. Presented here is a

505

simple and repeatable high-throughput method for analyzing anthocyanin composition and

21

ACS Paragon Plus Environment

Page 22 of 39

Page 23 of 39

Journal of Agricultural and Food Chemistry

506

content in maize. Five anthocyanin production categories were identified for the 167

507

anthocyanin-pigmented accessions in the survey. One category has previously never been

508

described in maize; some accessions produced markedly less acylated anthocyanins than the

509

typical anthocyanin-producing lines. The genetics behind this unique trait are currently being

510

investigated. PCA was performed on compounds quantified by HPLC and compositional

511

categories were confirmed by hierarchical clustering, confirming their efficacy in describing the

512

variation seen in this survey. Since the main goal of this study was to provide information to

513

plant breeders, the category of accessions from this survey that should be of most interest for

514

developing anthocyanin-rich hybrids are the pericarp-loading accessions with high proportions of

515

condensed forms. Andean purple corn landraces may be a key to developing new purple corn

516

hybrids, but issues with adaptation and grain yield need to be overcome for them to be more

517

economical. To provide a broader range of hues in maize extracts for the food and beverage

518

industry, pr1 recessive alleles should be incorporated into these purple corn accessions. Breeding

519

for AC and anthocyanin composition will be straightforward, according to the H2 results, since

520

most of the phenotypic variance is controlled by genetic factors as opposed to environmental

521

factors. Marker assisted selection to breed for purple corn has already been demonstrated 10,46.

522

Selecting for the major regulatory genes B1/Pl1 can aid in the rapid development of purple corn

523

varieties. AC of maize on a whole kernel basis is much lower than AC reported for fruit and

524

vegetable juices, but maize has value-added benefits fruit and vegetable juices do not have 47.

525

Current milling processes are able to concentrate pericarp fractions for a higher anthocyanin

526

recovery 13. Pericarp fractions can then be collected for anthocyanin extraction while the rest of

527

the kernel can still be utilized for food, fuel, and feed. These value-added benefits should make

528

maize an economical source of natural colors.

22

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

529 530

Acknowledgements: This work was supported by a grant from the Kraft Heinz Company of

531

Glenview, Illinois. Fellowship support for Michael Paulsmeyer was provided by the Illinois Corn

532

Grower’s Association. Fellowship support for Laura Chatham was provided by The Monsanto

533

Company.

534

References

535

(1)

Koes, R. E.; Quattrocchio, F.; Mol, J. N. BioEssays 1994, 16 (2), 123–132.

536

(2)

Hatier, J.-H. B.; Gould, K. S. In Anthocyanins; Winefield, C., Davies, K., Gould, K., Eds.;

537 538

Springer New York: New York, NY, 2008; pp 1–19. (3)

539

Lev-Yadun, S.; Gould, K. S. In Anthocyanins; Winefield, C., Davies, K., Gould, K., Eds.; Springer New York: New York, NY, 2008; pp 22–28.

540

(4)

He, J.; Giusti, M. M. Annu. Rev. Food Sci. Technol. 2010, 1, 163–187.

541

(5)

Zafra-Stone, S.; Yasmin, T.; Bagchi, M.; Chatterjee, A.; Vinson, J. A.; Bagchi, D. Mol. Nutr.

542 543

Food Res. 2007, 51 (6), 675–683. (6)

Center for Food Safety and Applied Nutrition. Color Certification Reports - Report on

544

the Certification of Color Additives: 4th Quarter, Fiscal Year 2014, July 1-September

545

30

546

http://www.fda.gov/ForIndustry/ColorAdditives/ColorCertification/ColorCertificati

547

onReports/ucm418381.htm (accessed Sep 5, 2016).

548

(7)

549

Carocho, M.; Barreiro, M. F.; Morales, P.; Ferreira, I. C. F. R. Compr. Rev. Food Sci. Food Saf. 2014, 13 (4), 377–399.

550

(8)

Petroni, K.; Pilu, R.; Tonelli, C. Planta 2014, 240 (5), 901–911.

551

(9)

Li, Q.; Somavat, P.; Singh, V.; Chatham, L.; Gonzalez de Mejia, E. Food Chem. Press 2017.

23

ACS Paragon Plus Environment

Page 24 of 39

Page 25 of 39

552 553

Journal of Agricultural and Food Chemistry

(10) Lago, C.; Cassani, E.; Zanzi, C.; Landoni, M.; Trovato, R.; Pilu, R. Plant Breed. 2014, 133 (2), 210–217.

554

(11) Consonni, G.; Geuna, F.; Gavazzi, G.; Tonelli, C. Plant J. 1993, 3 (2), 335–346.

555

(12) Gavazzi, G.; Racchi, M. L.; Gorrini, A. Maize Genet. Coop. Newsl. 1985, 59, 115.

556

(13) Somavat, P.; Li, Q.; de Mejia, E. G.; Liu, W.; Singh, V. Ind. Crops Prod. 2016, 87, 266–272.

557

(14) Sharma, M.; Cortes-Cruz, M.; Ahern, K. R.; McMullen, M.; Brutnell, T. P.; Chopra, S.

558 559 560

Genetics 2011, 188 (1), 69–79. (15) Sharma, M.; Chai, C.; Morohashi, K.; Grotewold, E.; Snook, M. E.; Chopra, S. BMC Plant Biol. 2012, 12, 196.

561

(16) Bhatla, S.; Pant, R. Curr. Sci. 1977, 46, 700–702.

562

(17) Grotewold, E.; Chamberlin, M.; Snook, M.; Siame, B.; Butler, L.; Swenson, J.; Maddock,

563 564 565 566

S.; St Clair, G.; Bowen, B. Plant Cell 1998, 10 (5), 721–740. (18) Abdel-Aal, E.-S. M.; Young, J. C.; Rabalski, I. J. Agric. Food Chem. 2006, 54 (13), 4696– 4704. (19) Suzuki, H.; Nakayama, T.; Yonekura-Sakakibara, K.; Fukui, Y.; Nakamura, N.;

567

Yamaguchi, M.; Tanaka, Y.; Kusumi, T.; Nishino, T. PLANT Physiol. 2002, 130 (4),

568

2142–2151.

569

(20) Nakayama, T.; Suzuki, H.; Nishino, T. J. Mol. Catal. B Enzym. 2003, 23 (2–6), 117–132.

570

(21) Vivar-Quintana, A. M.; Santos-Buelga, C.; Francia-Aricha, E.; Rivas-Gonzalo, J. C. Food

571 572 573 574

Sci. Technol. Int. 1999, 5 (4), 347–352. (22) González-Manzano, S.; Pérez-Alonso, J. J.; Salinas-Moreno, Y.; Mateus, N.; Silva, A. M. S.; de Freitas, V.; Santos-Buelga, C. J. Food Compos. Anal. 2008, 21 (7), 521–526. (23) West, M. E.; Mauer, L. J. J. Agric. Food Chem. 2013, 61 (17), 4169–4179.

24

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

575

(24) de Pascual-Teresa, S.; Sanchez-Ballesta, M. T. Phytochem. Rev. 2008, 7 (2), 281–299.

576

(25) Cuevas Montilla, E.; Hillebrand, S.; Antezana, A.; Winterhalter, P. J. Agric. Food Chem.

577

2011, 59 (13), 7068–7074.

578

(26) Lao, F.; Giusti, M. M. Food Anal. Methods 2015, 9 (5), 1367–1380.

579

(27) R Core Team. R: A Language and Environment for Statistical Computing; R Foundation

580

for Statistical Computing: Vienna, Austria, 2015.

581

(28) Ward, J. H. J. Am. Stat. Assoc. 1963, 58 (301), 236.

582

(29) Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer-Verlag New York,

583 584 585

2009. (30) Bernardo, R. N. Breeding for quantitative traits in plants, 2nd ed.; Stemma Press: Woodbury, Minn, 2010.

586

(31) Lee, J.; Durst, R. W.; Wrolstad, R. E. J. AOAC Int. 2005, 88 (5), 1269–1278.

587

(32) Chen, S.; Zhang, F.; Ning, J.; Liu, X.; Zhang, Z.; Yang, S. Food Chem. 2015, 172, 788–793.

588

(33) Xiaowei, H.; Xiaobo, Z.; Jiewen, Z.; Jiyong, S.; Xiaolei, Z.; Holmes, M. Food Chem. 2014,

589

164, 536–543.

590

(34) Orman, B. A.; Schumann, R. A. J. Agric. Food Chem. 1991, 39 (5), 883–886.

591

(35) Marrs, K. A.; Alfenito, M. R.; Lloyd, A. M.; Walbot, V. Nature 1995, 375 (6530), 397–

592 593 594 595 596

400. (36) White, W. G.; Vincent, M. L.; Moose, S. P.; Below, F. E. GCB Bioenergy 2012, 4 (5), 496– 508. (37) Moreno, Y. S.; Sánchez, G. S.; Hernández, D. R.; Lobato, N. R. J. Chromatogr. Sci. 2005, 43 (9), 483–487.

25

ACS Paragon Plus Environment

Page 26 of 39

Page 27 of 39

597 598 599

Journal of Agricultural and Food Chemistry

(38) Ryu, S. H.; Werth, L.; Nelson, S.; Scheerens, J. C.; Pratt, R. C. Econ. Bot. 2013, 67 (2), 98–109. (39) Morohashi, K.; Casas, M. I.; Falcone Ferreyra, M. L.; Mejia-Guerra, M. K.; Pourcel, L.;

600

Yilmaz, A.; Feller, A.; Carvalho, B.; Emiliani, J.; Rodriguez, E.; Pellegrinet, S.; McMullen,

601

M.; Casati, P.; Grotewold, E. Plant Cell 2012, 24 (7), 2745–2764.

602

(40) Wolf, M. J.; Cutler, H. C.; Zuber, M. S.; Khoo, U. Crop Sci. 1972, 12 (4), 440.

603

(41) Salinas-Moreno, Y.; Pérez-Alonso, J. J.; Vázquez-Carrillo, G.; Aragón-Cuevas, F.;

604 605 606

Velázquez-Cardelas, G. A. Agrociencia 2012, 46 (7), 693–706. (42) Zhao, C.-L.; Yu, Y.-Q.; Chen, Z.-J.; Wen, G.-S.; Wei, F.-G.; Zheng, Q.; Wang, C.-D.; Xiao, X.-L. Food Chem. 2017, 214, 119–128.

607

(43) Larson, R.; Bussard, J.; Coe Jr, E. Biochem. Genet. 1986, 24 (7–8), 615–624.

608

(44) Cabrita, L.; Fossen, T.; Andersen, Ø. M. Food Chem. 2000, 68 (1), 101–107.

609

(45) Jing, P.; Noriega, V.; Schwartz, S. J.; Giusti, M. M. J. Agric. Food Chem. 2007, 55 (21),

610 611 612 613 614

8625–8629. (46) Lago, C.; Landoni, M.; Cassani, E.; Doria, E.; Nielsen, E.; Pilu, R. Mol. Breed. 2013, 31 (3), 575–585. (47) Wu, X.; Beecher, G. R.; Holden, J. M.; Haytowitz, D. B.; Gebhardt, S. E.; Prior, R. L. J. Agric. Food Chem. 2006, 54 (11), 4069–4075.

615 616 617 618 619 620

26

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

621

Figure captions:

622

Figure 1: Micrographs showing pigmentation in the major anthocyanin-producing tissues of the

623

maize kernel: Pericarp (A) and aleurone (B)

624

Figure 2: Structure of an anthocyanidin molecule with molecular nomenclature. R = H,

625

Pelargonidin; R = OH, Cyanidin; R = OCH3, Peonidin.

626

Figure 3: Representative HPLC chromatograms of each anthocyanin category.

627

Figure 4: Kernel samples from each major pigment category; blue aleurone (A), pink aleurone

628

(B), blue aleurone without acylated anthocyanins (C), pericarp without condensed forms (D),

629

pericarp with condensed forms (E), phlobabaphenes (F), and bronze pigment (G).

630

Figure 5: (A) Prinicipal component analysis (PCA) of all anthocyanin containing lines. PC1 and

631

PC2 explained 60.1% and 23.8% of variability in the dataset, respectively. Individual

632

observations are colored by cluster determined using hierarchical cluster analysis, while shapes

633

indicate pigment location in the kernel. Eigenvector numbers correspond to labels given in

634

Figure 3f. (B) Dendrogram illustrating results from hierarchical cluster analysis.

27

ACS Paragon Plus Environment

Page 28 of 39

Page 29 of 39

Journal of Agricultural and Food Chemistry

635

Figures and Tables

636

Figure 1

637

638

28

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

639 640

Figure 2

641

642 643

29

ACS Paragon Plus Environment

Page 30 of 39

Page 31 of 39

Journal of Agricultural and Food Chemistry

Figure 3 b) Pink aleurone

a) Blue aleurone 4

5 6

1

2

5

3

7

1

7

c) Pericarp with condensed forms

2

4 6

d) Pericarp without condensed forms

1

4

9

1

6

4 2

3

e) Reduced acylation

2

56

2 3

5 78

f) Legend for identified anthocyanin compounds Label

1

3

Compound

1

Cyanidin 3-Glucoside

2

Pelargonidin 3-Glucoside

3

Peonidin 3-Glucoside

4

Cyanidin malonylglucoside

5

Pelargonidin malonylglucoside

6

Cyanidin dimalonylglucoside / Peonidin malonylglucoside

7

Pelargonidin dimalonylglucoside

8

Peonidin dimalonylglucoside

9

Condensed forms

Note: The x-axis of chromatograms corresponds to retention time in minutes.

30

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

Figure 4

31

ACS Paragon Plus Environment

Page 32 of 39

Page 33 of 39

Journal of Agricultural and Food Chemistry

Figure 5

32

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

Page 34 of 39

Table 1: Subset of 43 accessions grown in 2015 labeled with major phenotypes Accession

Pr11

Tissue2

Acyl3

Count4

Accession

Pr11

Tissue2

Acyl3

Count4

Ames 6084 Z14-008

0

0

0

16

PI 218175 Moencopi Pueblo

0

0

0

4

Ames 14276 Black Beauty

0

0

1

12

PI 340838 B-4541

0/1

0

0

9

Ames 25207 Burford 2

0/1

0

0

4

PI 340841 B-15

0

0

0

11

Ames 27451 A632.75

0

0/1

0

15

PI 340846 RB-15

1

0

0

12

BGEM-0085-N

0

0

0

13

PI 340850 B-17

0

0

0

10

Black Aztec

0

0

0/1

11

PI 340854 RB-17

1

0

0

11

MGCSC 218GA

0

0

0

11

PI 340855 B-18

0

0

0

12

MGCSC 219AA

0

0

0

8

PI 340857 B-22

0

0

0

12

MGCSC 506B

1

0

0

12

PI 483476 Aguascalientes 27

0

0

0/1

10

MGCSC 707B

1

0

0

9

PI 483517 Guanajuato 31

1

0

0

10

MGCSC 707G

0

0

1

14

PI 483527 Guanajuato 98

1

0

0

7

MGCSC M142A

0

0

0

5

PI 485071 Puebla 403

1

0/1

0

4

MGCSC X13l

0

1

0

14

PI 489081 Puebla 456

1

0/1

0

13

MGCSC X19A

0

0

0

12

PI 511613 Nicaragua 115

1

0

0

12

MGCSC X19EA

0

0

0

5

PI 553055 OC2

0

0

0

11

MGCSC Z433C

0

0

0

11

PI 553057 OC4

0

0

0

10

MGCSC Z433E

0

0

0

11

PI 596502 OC15

0

0

0

11

Siskiyou Seeds Hopi Blue Star

0

0

0

6

PI 596503 OC16

0

0

0

4

Ohio Blue Clarage SESE5

0

0

0

9

PI 596504 OC17

1

0

0

12

PI 213756 Fairfax Brown

0

0

0

11

PI 596505 OC18

1

0

0

11

PI 213791 SD RAINBOW

0

0

0

10

PI 596506 OC19

0

1

0

10

PI 217411 Tama Flint

1

0

0

11

1

0 = Pr1__, 1 = pr1pr1 0 = aleurone, 1=pericarp 3 0 = normal profile, 1 = reduced acylation 4 Count refers to the number of HPLC samples 5 SESE = Southern Exposure Seed Exchange, Mineral, VA, USA 2

33

ACS Paragon Plus Environment

Page 35 of 39

Journal of Agricultural and Food Chemistry

Table 2: Anthocyanin concentration (mg/kg) and composition (% of AC)

Categories

Count of Accession s

AC (mg/kg) 30.81

*

C3G (%)

*

Pg3 G (%)

*

Pn3 G (%)

*

Acylatio n (%)

*

BC

17.91

C

3.82

B

6.58

B

63.19

A

Blue Aleurone

98

Pericarp Condensed

25

251.97

A

28.26

B

4.41

B

7.98

B

35.69

B

Pericarp Not Condensed

13

118.29

B

22.14

BC

5.18

B

11.69

A

56.84

A

46

22.63

BC

7

46.36

BC

167

64.67

Pink Aleurone Reduced Acylation Grand Total

2.99

D

14.88

A

1.11

C

62.84

A

57.23

A

2.33

B

7.40

B

7.57

C

17.40

6.63

5.82

* Means with the same letter are not significantly different with a Tukey HSD p>0.05

34

ACS Paragon Plus Environment

56.97

Journal of Agricultural and Food Chemistry

Table 3: Type 3 ANOVAs for 2015 for AC and Percentage of Acylation AC 2015

DF

Mean Square

Expected Mean Square

Rep

2

49.64

σ2error + 33.5 σ2r

Variance Components -2.69

Genotype

42

3105.54

σ2error + 2.5476 σ2g

1164.11***

Residual

65

139.82

σ

2

139.82

error

Acylation % 2015

DF

Mean Square

Expected Mean Square

Rep

2

86.74

σ2error + 33.5 σ2r

Variance Components 1.67*

Genotype Residual *** p