Metabolomic Assessment of Key Maize Resources: GC-MS and NMRProfiling of Grain from B73 Hybrids of the Nested AssociationMapping (NAM) Founders and of Geographically Diverse LandracesTyamagondlu V. Venkatesh,*,† Alexander W. Chassy,§ Oliver Fiehn,§,# Sherry Flint-Garcia,*,Δ,⊥

Qin Zeng,† Kirsten Skogerson,† and George G. Harrigan†

†Monsanto Company, 800 North Lindbergh Boulevard, St. Louis, Missouri 63167, United States§Genome Center − Metabolomics, University of California at Davis, Davis, California 95616, United States#Biochemistry Department, King Abudalaziz University, Jeddah, Saudi-ArabiaΔAgricultural Research Service, U.S. Department of Agriculture, Columbia, Missouri 65211, United States⊥Division of Plant Sciences, University of Missouri, Columbia, Missouri 65211, United States

*S Supporting Information

ABSTRACT: The present study expands metabolomic assessments of maize beyond commercial lines to include two sets ofhybrids used extensively in the scientific community. One set included hybrids derived from the nested association mapping(NAM) founder lines, a collection of 25 inbreds selected on the basis of genetic diversity and used to investigate the genetic basisof complex plant traits. A second set included 24 hybrids derived from a collection of landraces representative of native diversityfrom North and South America that may serve as a source of new alleles for improving modern maize hybrids. Metabolomicanalysis of grain harvested from these hybrids utilized gas chromatography−time-of-flight mass spectrometry (GC-TOF-MS) and1H nuclear magnetic resonance spectroscopy (1H NMR) techniques. Results highlighted extensive metabolomic variation in grainfrom both hybrid sets, but also demonstrated that, within each hybrid set, subpopulations could be differentiated in a patternconsistent with the known genetic and compositional variation of these lines. Correlation analysis did not indicate a strongassociation of the metabolomic data with grain nutrient composition, although some metabolites did show moderately strongcorrelations with agronomic features such as plant and ear height. Overall, this study provides insights into the extensivemetabolomic diversity associated with conventional maize germplasm.

KEYWORDS: maize hybrids, nested association mapping (NAM) founders, landraces, metabolomics, GC-TOF-MS, NMR

■ INTRODUCTION

Maize (Zea mays L.) grain is a major food and feed commodityas well as a source of raw material for industrial processesworldwide.1 The major nutritional components are starch,protein, oil, and fiber, which encompass ∼95% of the biomassof harvested grain.2 The low-abundance small metabolite poolis of less nutritional and economic significance, but doescontain some minor nutrients and antinutrients that are thetarget of breeding programs. For example, increasing vitamin Alevels, a target of specific breeding programs,3 could offer healthbenefits, whereas reduction of phytic acid levels wouldpotentially improve nutritional status.4

Developments in high-throughput metabolite data acquis-ition platforms have facilitated their application in cropimprovement. Evaluation of the extensive natural variationinherent to the metabolite composition of crop plants and therole of metabolite assessments in trait discovery are underinvestigation.4b,5 Use of metabolite markers as a tool forselecting complex traits in plants has also been discussed.5b Todate, metabolomic studies on maize grain have typically beendriven by comparative assessments of genetically modified(GM) and non-GM lines or have focused on the relativeinfluences of germplasm and growing location. These studieshave demonstrated that the maize grain metabolome is greatly

influenced by environment and germplasm and that the effectof GM is negligible.6

Today, maize germplasm can be defined broadly in terms oflandraces and modern inbred lines, both of which are derivedfrom the ancestral progenitor teosinte (Z. mays ssp.parviglumis).7a The extensive metabolite variation recorded inthe scientific literature for even a small set of moderncommercial hybrids clearly reflects the influence of geographicadaptation and selective breeding to develop corn as anutritious food source. Landraces are highly heterozygous andheterogeneous open-pollinated populations adapted to specificenvironments, and many consider that they may serve as asource of potential new alleles.7 Modern inbred lines, on theother hand, are uniform and homozygous (i.e., the genome ofeach line will not change as additional seed is made by self-pollination); these inbred lines are the basis of the commercialmaize hybrids that are generated today.Because maize domestication and modern breeding efforts

have resulted in a reduction in genetic diversity,7 prior studies

Received: October 8, 2015Revised: February 16, 2016Accepted: February 28, 2016Published: February 28, 2016

Article

pubs.acs.org/JAFC

© 2016 American Chemical Society 2162 DOI: 10.1021/acs.jafc.5b04901J. Agric. Food Chem. 2016, 64, 2162−2172

have accessed only a limited amount of genetic informationassociated with metabolomic diversity in maize. Thus, datafrom these prior studies may not represent a complete resourcefor the broader scientific community or for breeders seekingnew alleles that could support the development of improvedmaize. The present study, therefore, expands metabolomicassessments of maize beyond previous studies reported oncommercial lines to include diverse germplasm used in thescientific community to investigate the genetic basis of complexplant traits and as a source of new alleles for improving modernmaize hybrids by breeding.The two sets of publicly available lines used in the present

study represent diversity within the landraces and public inbredlines. The nested association mapping (NAM) founders are acollection of 25 inbreds selected on the basis of geneticdiversity from the Goodman panel of 301 maize lines8 andpopularly used in investigations on maize genetics andphenotypic traits.9a,b The 24 landrace accessions assessed inthis study represent unimproved germplasm from North andSouth American geographies and provide a unique representa-tion of corn alleles relative to commercial maize lines. Thematerials for the current metabolomic analysis included grainharvested from maize hybrids of both the NAM founders andlandraces crossed with the inbred line B73. B73 has contributedextensively to the pedigree of many important commerciallines10 and serves as the reference genome for maize.11

The metabolomic data acquisition utilized both gaschromatography−time-of-flight mass spectrometry (GC-TOF-MS) and 1H nuclear magnetic resonance (1H NMR)spectroscopy. GC-MS is one of the most commonly usedplatforms in metabolomics, whereas NMR offers advantages inplatform robustness and capabilities for absolute quantitation.Alternative multivariate options were pursued for eachcomplementary platform with partial least-squares discriminantanalysis (PLS-DA) used for the MS data and canonicaldiscriminant analysis (CDA) used for the NMR data.

■ MATERIALS AND METHODSChemicals. All chemical reagents were of analytical grade from

Sigma (St. Louis, MO, USA).Meatbolites from GC-MS were identified to level 1 of the

Metabolomics Standards Initiative (MSI).18a Identification of chem-icals described in the NMR section are based on Chenomx

assignments, which were confirmed with data collected on purestandards under identical buffer and instrumental parameters.

Biological Materials. The germplasm used in this study (Table 1)was selected to represent a broad diversity within maize landraces andinbred lines. Each of these inbreds and landraces was classified broadlyas one of four groups (northern flint, temperate, intermediate/mixed,or tropical/subtropical) based on phenology,15 breeding history,16

and/or race characterization17 (Supporting Tables 1 and 2).Production of Hybrids. Hybrid seeds of landrace and NAM

founder lines crossed to B73 were produced over four seasons indifferent nurseries: 24 and 2 entries were produced, respectively, atColumbia, MO, USA, and Puerto Rico in 2008; 18 entries wereproduced in Puerto Rico in 2009; and 6 entries were produced in 2010Columbia, MO, USA. All hybrids were produced by controlledpollination.

Field Design. Materials for metabolomic analyses were generatedin 2012 at Ithaca, NY, USA. The B73 × NAM parental line hybridsand B73 × landrace hybrids were planted in a randomized completeblock design in 3 m × 0.9 m rows in three replications. Standardagronomic practices were employed for the production of grain in thisregion, and no specific disease or pest problems were observed. Threeplants were self-pollinated in each row, and selfed ears were handharvested and dried to 12−13% moisture. Dried ears were shelled, andgrain was stored at room temperature prior to metabolomic analyses.Grain samples were homogenized by grinding on dry ice to a finepowder and stored frozen at approximately −20 °C until analysis.

GC-MS Sample Extraction and Derivatization. Powderedsamples (20 mg) were extracted with 1 mL of degassed extractionsolvent (5:2:2 methanol/chloroform/water). The mixture wasvortexed and shaken for 6 min at 4 °C prior to centrifugation at14000 rcf for 2 min. The supernatant was removed (200 μL) and driedusing a centrifugal vacuum concentrator at room temperature.Derivatization was performed as previously described.18a To eachsample was added 10 μL of methyloxamine hydrochloride (40 mg/mLin pyridine) prior to 1.5 h of shaking at 30 °C. A mixture of MSTFAcontaining fatty acid methyl ester (FAME) markers was aliquoted (91μL) into the sample and incubated at 37 °C for 30 min.

GC-MS Profiling. Samples were analyzed using a GC-TOFapproach.18,19 The study design was entered into the MiniXdatabase.19 A Gerstel MPS2 automatic liner exchange system(ALEX) was used to eliminate cross-contamination from samplematrix occurring between sample runs. Derivatized sample injectionsof 0.5 μL were made in splitless mode with a purge time of 25 s andtemperature program as follows: from 50 to 275 °C at a linear rate of12 °C/s and held for 3 min. An Agilent 6890 gas chromatograph(Santa Clara, CA, USA) was used with a 30 m long, 0.25 mm i.d.,Rtx5Sil-MS column with a 0.25 μm 5% diphenyl film; an additional 10m integrated guard column was used (Restek, Bellefonte, PA, USA).

Table 1. Germplasm Used in the Study: Hybrids Derived by Crossing B73 as Female with Pollen from 26 NAM Founder InbredParents and 24 Landrace Inbredsa

diversitygroup inbred broad group

NAMfounderparents

HP301, Il14H P39 northern flintB97, Ky21, M162W, Mo17,b MS71, Oh43, Oh7B temperateM37W, Mo18W, Tx303 mixedCML103, CML228, CML247, CML277, CML322, CML333, CML52, CML69, Ki11, Ki3, NC350, NC358, Tzi8 tropical

landraceinbreds

MR01 (Araguito), MR03 (Bolita), MR04 (Canilla), MR05 (Cateto), MR07 (Comiteco), MR08 (Costeno), MR09 (CravoRiogranense), MR10 (Crystalino Norteno), MR11 (Cuban Flint), MR16 (Pepetilla), MR18 (Reventador), MR21 (Tabloncillo),MR22 (Tuxpeno), MR23 (Zapalote Chico), MR25 (Poropo), MR26 (Pollo)

tropical/subtropical

MR02 (Assiniboine), MR12 (Havasupai), MR14 (Longfellow Flint), MR19 (Santo Domingo) northern flintMR06 (Chapalote), MR15 (Palomero de Jalisco) intermediateMR13 (Hickory King), MR20 (Shoe Peg) temperate

aEach of these inbreds and landraces was classified broadly as one of four groups (northern flint, temperate, intermediate/mixed, or tropical/subtropical) on the basis of population structure using marker data,12 phenology,15 breeding history,16 and/or race characterization.17 Briefdescriptions of these groups are also provided in Venkatesh et al.13 bThe Mo17 hybrid appeared to be cross-contaminated and/or was notphenotypically consistent with known Mo17 characteristics; thus, it was omitted from statistical analysis of acquired data.

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Chromatography was performed at a constant flow of 1 mL/min,ramping the oven temperature from 50 to 330 °C over 22 min at alinear rate. Mass spectrometry used a Leco Pegasus IV time-of-flightmass (TOF) spectrometer with 280 °C transfer line temperature,electron ionization at −70 V, and an ion source temperature of 250°C. Mass spectra were acquired from m/z 85 to 500 at 20 spectra/sand 1750 V detector voltage.Result files were exported and further processed by our

metabolomics BinBase database.19 All database entries in BinBasewere matched against the Fiehn mass spectral library of 1200 authenticmetabolite spectra using retention index and mass spectruminformation or the NIST11 commercial library. Identified metaboliteswere reported if present with at least 50% of the samples per studydesign group (as defined in the MiniX database).Data from samples were exported to the netCDF format for further

data evaluation with BinBase. Briefly, output results were exported tothe BinBase database and filtered by multiple parameters to excludenoisy or inconsistent peaks. Quantitation was reported as peak heightusing the unique ion as default.Prior to data analyses, based on the underlying assumption that the

total amounts of ionized metabolites that reach the detector are similarfor different samples, metabolite peak heights were normalized by thesum of all peak heights for annotated metabolites for each sample.NMR Profiling. Grain samples (50 mg ground and lyophilized)

were spiked with 50 mM maleic acid (50 μL) as an internal standardand then extracted with 1.8 mL of MeOH. After 15 min of sonication,the sample was centrifuged and the supernatant collected. The pelletwas re-extracted twice as above. Pooled supernatants were dried andresuspended in qNMR buffer (100 mM sodium phosphate, pD 7.4 +DSS chemical shift standard) and then filtered due to the presence ofinsoluble material.Proton NMR spectra with water suppression were generated on a

600 MHz Bruker AVANCE III spectrometer equipped with a 60-sample autochanger and a Bruker 5 mm TCI CryoProbe. Raw datawere processed (Fourier transform, apodization, phasing, and chemicalshift calibration) with Bruker TopSpin 3.1 and then uploaded intoChenomx for standardized area normalization and spectral binning.Analysis in this study used spectra binned at 0.04 and 0.005 ppm.Some spectral regions were deleted as follows: δ 3.332−3.352 (residualMeOH), δ 2.195−2.23 (residual acetone), δ −0.02 to 0.02, δ 0.59−0.65, δ 1.725−1.79, δ 2.89−2.925 (DSS), δ 4.68−4.88 (H2O), δ1.139−1.185, δ 3.99−4.02 (residual isopropanol), δ 5.82−6.16 (maleicacid internal standard), δ 2.70−2.74 (putative dimethylamine). Foursamples analyzed by GC-MS were removed from the NMR analysisdue to excessive levels of residual isopropanol from the NMR tubewashing procedure (one replicate of Poropo landrace and one replicateeach of NAM Oh7B, CML333, and P39). Chenomx was used forquantitation of metabolites.Statistical Analysis of GC-MS Data. Prior to statistical analyses,

metabolite peaks were removed if the log of their average peak areawas <2.5 (peak area of <316) or if the average relative standarddeviation (of log peak area) of replicate analyses was >10% (averageRSD > 150%). Both criteria removed 171 metabolite peaks (25% ofthe total), retaining 504 quality peaks that could be included instatistical analyses (Supporting Figure 1; see also Associated ContentFiles 1−4 for all data). In this study, statistical analysis focused onidentified metabolites. Statistical analyses were performed using theopen-source ImDev software package.20 Principal component analysis(PCA), hierarchical cluster analysis (HCA), and partial least-squaresdiscriminant analysis (PLS-DA) on autoscaled data were used todifferentiate NAM and landrace lines individually or based upon theirbroad classification or breeding group (see Supporting Tables 1 and 2for groupings). Partial least-squares (PLS) is a versatile algorithm thatcan be used to predict either continuous or discrete/categoricalvariables.21 Classification with PLS is termed PLS-DA, where the DAstands for discriminant analysis. The PLS-DA algorithm has manyfavorable properties for dealing with multivariate data types such asomics data including metabolomics data that rely on the class(subgroup) membership of each observation (metabolite level).22

The Mo17 hybrid appeared to be cross-contaminated and/or wasnot phenotypically consistent with known Mo17 characteristics. GC-MS data were generated for this sample but not utilized in the PLS-DAof known samples or the correlation analyses.

Statistical Analysis of 1H NMR Data. Means and ranges ofidentified metabolites were determined (Tables 3 and 4; AssociatedContent File 8). Canonical discriminant analysis (CDA) was used todetermine if patterns existed in the NMR data that could separate thehybrid groups, using the multivariate linear discriminant analysisavailable in JMP software V10 (SAS Institute). The classificationvariable was hybrid groupings for NAM and landrace inbreds, and thequantitative variables were metabolites. Pairwise correlation valuesbetween each of the metabolite mean values and canonical scores 1and 2 were derived using the multivariate procedure in the JMPsoftware (Supporting Table 3). Box plots for subgroups of NAM andlandrace hybrid sets were generated using the “ggplot2” plottingpackage available in the R statistical package. Statistical differencebetween subgroup mean values of NAM and landrace hybrid sets weredetermined by one-way analysis of variance (ANOVA) using the “aov”function available in the R statistical package “agricolae”.

Correlation Analysis. Pearson correlations were calculatedbetween annotated/identified GC-MS and NMR metabolites andpreviously reported composition and agronomic phenotypic data fromthe same samples13 using the “cor” function in the R statistical package[(Associated Content Files 6 (GC-MS) and 9 (NMR)]. Onlysignificant correlations are discussed herein.

Genetic Fingerprint Analysis of Maize Inbreds (NAMFounders and Landraces). Single nucleotide polymorphism(SNP) genotype (Illumina maize SNP 50) data for the NAM foundersand a subset of landrace inbred lines14a were downloaded from thePanzea Web site (http://www.panzea.org/). The NAM founder, Tzi8,and the landraces Canilla, Palomero de Jalisco, and Pepetilla were notgenotyped before14b and therefore not included in the genetic analysisin this study. TASSEL software23 version 5.2.3 was used to analyzeSNP genotype data and construct neighbor-joining (NJ) trees usingdefault parameters. The NJ trees were imported in “nwk” format intothe MEGA6 software24 for visualization and annotation. SNP datafrom one of the teosinte lines14b was used as an out-group to root thetree.

■ RESULTS AND DISCUSSIONDescriptive Overview of GC-MS and 1H NMR Data. In

the GC-MS analysis profile, a total of 675 metabolites werecollected in both NAM and landrace hybrid sets. All collectedmetabolites were common to both NAM and landrace hybridsets. There was no single metabolite that was only absent orpresent in either of the hybrid data sets, associated with aspecific geography, or with any genetic groupings. Thishighlights the fact that the extensive geographical adaptationand varietal development associated with maize is notaccompanied by major qualitative changes in the metabolome,but rather that variation is associated with changes in the levelsof shared or common metabolites. As described underMaterials and Methods, peaks in the GC-MS profile wereremoved if the log of their average peak area was <2.5 (peakarea of <316) or if the average relative standard deviation (oflog peak area) of replicate analyses was >10% (average RSD >150%) (Supporting Figure 1). This resulted in a total of 504peaks in both hybrid sets that included 174 annotatedmetabolites identified to level 1 of the Metabolomics StandardsInitiative (MSI).18a Notably, sucrose was removed due to highvariability.The means and ranges for these metabolites are presented in

Associated Content File 5, according to four broad groups(flint, temperate, intermediate/mixed, and tropical/subtropical)for both NAM founder and landrace hybrids. The identifiedmetabolites in both the NAM and landrace hybrid sets included

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34 amino acids, 23 free fatty acids and related metabolites, 23saccharides, 18 sugar alcohols, 10 sugar acids, 27 organic acids,16 compounds classified as “nitrogenous”, and 11 compoundsclassified broadly as “sterols, tocopherols, and others”. Twelvemetabolites annotated as carbohydrate or saccharide ormiscellaneous were also recorded (Associated Content File5). A total of 80 compounds overlapped with those listed inRao et al., who utilized both GC- and LC-MS technologies inan evaluation of 13 inbred lines and 1 hybrid popularly grownin China.25 A total of 53 compounds overlapped with thoselisted in Rohlig et al., who applied GC-MS in an assessment offour different maize hybrids grown in France and Germany.6i

Associated Content File 5 includes the list of overlappingmetabolites as well as the compound classification used by Raoet al. for their metabolites.25

Although less sensitive than MS-based approaches, 1H NMRprofiling offers advantages in terms of platform robustness andthe ability to quantitate levels of identified metabolites. In thecurrent study a total of 33 metabolites with known biochemicalidentity were measured. These included 17 amino acids (4-aminobutyrate (GABA), alanine, arginine, asparagine, aspartate,glutamate, glutamine, histidine, isoleucine, leucine, lysine,methionine, phenylalanine, proline, threonine, tyrosine, andvaline), seven organic acids (acetate, formate, fumarate, lactate,malate, maleate (internal standard), and succinate), five sugars(fructose, galactose, glucose, maltose, and sucrose), andcholine, dimethylamine, methylamine, and trigonelline.Data for all samples are presented in Associated Content File

8. 1H NMR profiles contained all amino acids that weredetected in the GC-MS platform; alanine, asparagine, aspartate,glutamate, and proline were the most abundant amino acidsobserved in both platforms. The most abundant metaboliteclasses observed in the 1H NMR profiles, however, were thesugars (sucrose, maltose, glucose, fructose, and galactose).Notably, sucrose was observed as the most abundantmetabolite, overall, in the 1H NMR profiles, whereas measure-ment of sucrose was highly variable by GC-MS. Of the othermetabolites measured by 1H NMR, acetate, choline, dimethyl-

amine, and methylamine were not detected in the GC-MSplatforms.Previous 1H NMR analyses of maize grain have been

relatively limited in scope and focused on comparativeassessments of GM and non-GM maize6c,g,h and have thereforenot addressed the utility of 1H NMR when applied to thegreater sources of metabolite variation such as growing locationand the large diversity in genetic backgrounds associated withconventional germplasm. Manetti et al. identified a total of 25metabolites,6g 22 of which were detected in the platformutilized here, with the exceptions being melibiose, pyruvate, andferulic acid. Piccioni et al. identified a total 39 metabolites, ofwhich 28 overlapped with our data (see Associated ContentFile 8).6h

Although encompassing broad coverage, some limitations ofnontargeted profiling approaches such as metabolomics shouldbe considered. For example, of the two key antinutrients inmaize (raffinose and phytic acid) as listed by the Organizationof Economic Development (OECD) in its consensus documenton maize composition,26 phytic acid was not observed in theGC-MS platform utilized here or by others,6i,25 although it ismeasured in the LC-MS platform of Rao et al.25 Phytic acid wasestablished to be present in the samples used in this study in anearlier targeted compositional assessment.13 Clearly, programsfocused on assessing this metabolite would require validation ofany profiling approach if this were preferred over a targetedanalysis.Of selected nutrients listed in the OECD consensus

document, vitamin A (as β-carotene) is known to be presentin these corn samples,13 but this metabolite was not captured inthe current metabolomic platforms. Vitamin E (as α- and γ-tocopherols) was observed using the current metabolomicplatforms, whereas the water-soluble B vitamins were not. Insummary, the metabolic platforms utilized here allowed broadbiochemical coverage consistent with those utilized in previousmetabolomic studies, but, as with any platform, may notcapture all metabolites of potential interest.

Figure 1. PLS of GC-MS data (known metabolites, identified to level 1 of the Metabolomics Standards Initiative18b) based on broad classification ofhybrids derived from the NAM founders.

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These observations point to limitations of metabolomics insafety assessments. Overall, this technology is not consistentwith principles that safety assessments should address targetedend points of direct relevance to safety and nutrition, providestrong testing of relevant hypotheses,27 and follow standardizedmethodologies that would be highly reproducible across thesafety assessment community.

Multivariate Analysis of GC-MS data. On the basis ofgeography and/or breeding history, the NAM parents andlandraces used in the present study were classified into distinctgroups8,14a (Supporting Tables 1 and 2). To understand themetabolic diversity of maize in the context of maize breedingand domestication, we performed multivariate analysis of themetabolic data generated in this study to differentiate NAM and

Figure 2. Genetic distance of NAM founder and landrace lines: neighbor-joining tree of NAM founders (left) and selected landrace lines usingIlluminaSNP50 genotype data: blue squares, tropical; yellow triangles, northern flint; green inverted triangles, mixed; turquoise diamonds, temperate;pink solid circles, intermediate; red open circles, teosinte line (used as out group for rooting the tree).

Figure 3. PLS of GC-MS data (known metabolites, identified to level 1 of the Metabolomics Standards Initiative18b) based on broad classification ofhybrids derived from the landraces.

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landrace hybrids individually or into distinct breeding groups.Multivariate analyses focused on the GC-MS data set usingPCA, HCA, and PLS-DA techniques. Associated Content File 7shows representative results from these approaches. Discussionhere focuses on results using a broad classification for the NAMfounders and landraces using PLS-DA. For each set (NAMfounder or landrace), PLS-DA of the 174 identified metaboliteswas conducted and based on individual hybrids or on groupclassifications (see Supporting Tables 1 and 2 for groupclassifications). For the NAM founders hybrids, PLS-DA basedon broad group classification (Figure 1) showed a clearseparation of the northern flint samples, especially the sweetcorn lines, from the tropical/subtropical set on the x-axis withthe temperate/mixed set clustered in the middle. The PLSscores showed that a range of diverse metabolite classescontributed to this separation. Key metabolites that contributedto observed metabolic pattern of a given subgroup orpopulation are listed in Supporting Figure 2 and AssociatedContent File 7. It was shown earlier13 that NAM hybridsstudied here were also compositionally distinct from each other.Metabolites positively associated with the flint group weremainly dominated by sugars, especially maltose and isomaltose,consistent with the large effects of sugary1 on starch/sugarcomposition. Abnormal starch content of sweet corn lines dueto the sugary1 allele is most probably a contributing factor tothe metabolic differences reflected in the multivariate analysisresults. The other line with flint ancestry (HP301) is a popcornline and coclustered with the other nonsweet lines.Encouragingly, the metabolic differentiation of the NAM and

landrace hybrid groups was broadly consistent with the geneticdifferentiation of their inbred lines based on 50K SNP markers(Figure 2). As with the GC-MS data, the SNP data indicate thatnorthern flint lines are clearly distinct and that separation of thetemperate, mixed, and tropical/subtropical lines can beobserved. Our findings from the metabolomic study reportedhere are similar to results from cluster analysis of geneticdistances of over 2500 maize inbred lines15 that included theNAM founders and a number of the landrace samples used inthe current study. In that genetic analysis, multivariate statisticson genetic marker data (>680,000 SNPs generated bygenotyping-by-sequencing; GBS) could describe the geneticvariation among breeding lines in accordance with their knownancestral history. The genetic analysis by Romay et al.highlighted differences between subpopulations of the presentmetabolomic study, many of which could be attributed togeography and/or discrete university/country breeding pro-grams.15

A corresponding analysis of the landrace hybrids alsohighlighted that a range of metabolites contributed to differenceamong subgroups and that there was a broad alignment in thegenetic and metabolic differentiations of these differenceswithin the lines derived from northern flint ancestors (Figures 2and 3; Supporting Figure 3; Associated Content File 7).Interestingly, 16 of the landraces included here were alsoevaluated in the genotyping analysis conducted by Romay et al.,with results showing a clear distinction between northern flint(Assiniboine and Longfellow flint) and tropical lines.15

Correlation Analysis of GC-MS Data with CropComposition and Phenotypic Data. Correlation analysiswas conducted to gain insight into the relationship betweenmetabolomic data and composition data generated usingtargeted assessments of crop components. The componentsassessed in targeted composition studies are listed by the

OECD in its consensus document on maize composition.26

Therefore, the GC-MS data reported here were correlated withgrain components (protein, amino acids, oil, fatty acids, fibers,raffinose, phytic acid, and tocopherols) previously reported forthe same sample set of NAM and landrace hybrids.13 Results ofthe correlation analysis are summarized in Associated ContentFile 6. Only raffinose, α-tocopherol, and γ-tocopherol werepresent in both data sets, as many of the components listed inthe OECD consensus documents could not be assessed by theGC-MS methodology (note that the amino acids and fatty acidsin the previous composition analysis represent protein and oil-derived pools, respectively, whereas the amino acids and fattyacids in the GC-MS analysis represent free pools). Correlationsbetween the analytes found in both the nontargeted profilingand targeted (compositional analysis) quantitative approachranged from 0.55 to 0.82. However, correlation values betweenthe nonshared components assessed in the GC-MS analysis andthose assessed in the prior targeted analysis (both from thesame samples) were typically low and were not consistentacross the NAM and landrace hybrid sets (Associated ContentFile 6). This is not surprising given the low abundance of manymeasured metabolites and the high sensitivity of themetabolome to genetic and environmental variation relativeto that of the key nutrients (starch, protein, oil, and fiber) thatcomprise the bulk biomass of maize grain.2,28 This lack ofassociation of the metabolome with nutrient composition, inaddition to the limited nutrient coverage offered by non-targeted profiling, means that metabolomics is unable tocontribute to the “identification of potential safety and nutritionissues” as effectively as quantitative analyses of known nutrientsand antinutrients.26

We likewise examined the correlations among metabolite andphenotypic data (Associated Content File 6). Again, thenumber of strong correlations (>0.5) was low, but was greatestfor days to anthesis, plant height, and ear height. Several ofthese correlations were consistent across the NAM andlandrace hybrid sets. Metabolites that correlated with thethree aforementioned traits are listed in Table 2. It is notimmediately obvious why these grain metabolites, measured asa snapshot in time, would correlate with these complex growth-

Table 2. GC-MS Metabolites Correlated (r > 0.5) withSelected Phenotypic Traits across both NAM and LandraceHybrid Sets

days to anthesis plant height ear height

alanine malic acid fumaric acidaspartate succinic acid glutamic acidfumaric acid glutamic acid lysineglutamic acid trigonelline (−ve) malic acidglutamine methioninehistidine succinic acidisoleucine 4-aminobutyrateleucine trigonelline (−ve)lysinemalic acidmethioninesuccinic acidthreoninevaline4-aminobutyratetrigonelline (−ve)

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dependent traits. Riedelsheimer et al. observed that metaboliteprofiles of leaf samples of maize inbred lines could serve asgood predictors of some phenotypic characteristics in hybrids,29

including plant height, but also felt that such results could beconsidered unexpected due to (i) the early vegetative stage atwhich metabolites were profiled, (ii) the incomplete coverageof metabolic profiles relative to the metabolites present, and(iii) the fact the metabolite profiles represent a snapshot intime, whereas plant phenotypes represent the end-point of acomplex dynamic system over an extended growing time.Statistical Analysis of NMR Profiling. Our analysis

focused on simple descriptive summaries of the quantitativemetabolite data (Tables 3 and 4; see also Supporting Figures 4and 5 and Associated Content File 8) to allow discussion in thecontext of results obtained by the GC-MS platform. CDA ofthe NMR-derived metabolite data was also conducted. CDA isa dimension reduction technique related to PCA and canonicalcorrelation. The CDA technique finds linear combinations ofthe quantitative variables that provide maximal separationbetween classes or groups. CDA has been successfully appliedto 1H NMR-based metabolomic data of soybean seed.30 It wasalso utilized in the analysis of seed composition data13

generated on the same NAM and landrace hybrid sample setused in the current study, and its application here, over other

multivariate approaches, was selected to allow a more directcomparison to the composition data set.Overall, results from the 1H NMR profiling were consistent

with results from the GC-MS analyses. Many of the differencesbetween subpopulations for NAM hybrids were attributable todifferent metabolites. Among NAM hybrids, in addition tosugars (fructose, glucose, and maltose), significant differences(p < 0.05) between subgroups were observed for alanine,fumarate, histidine, isoleucine, lysine, malate, methionine,succinate, threonine, and valine (Table 3; see also SupportingFigure 4 and Associated Content File 8). For the NAMfounders, the NMR data established that hybrids derived fromthe northern flint group, especially the sweet corn (P39 andIl14H), tended to have higher levels of sugars; sugar levels inHP301-derived hybrids were generally more similar to those ofthe other hybrids. Interestingly, levels of maltose were notablyhigher in the Il14H hybrids (187.53 μg/50 mg DW) whencompared to all other NAM lines (9.39−21.66/50 μg DW)(Associated Content File 8). Sweet corn lines that contain thesugary enhancer (se) allele can be distinguished from othersweet corn lines on the basis of their higher accumulation ofgrain maltose.31 However, whereas Il14H is a sweet corn thatcarries the sugary1 allele, it is not known to carry the se allele.Because Il14H shares ancestry with the population where se wasfirst identified, it is likely that Il14H carries multiple QTL alleles

Table 3. NMR Metabolite Values for Hybrids Derived from the NAM Founders

mixed flint temperate tropical

metabolitea mean min max mean min max mean min max mean min max

4-aminobutyrate 4.02 2.71 5.56 2.81 2.01 4.14 3.69 2.29 6.00 3.75 1.63 7.79acetate 2.84 0.43 6.09 4.10 0.56 7.06 4.44 0.68 8.87 3.24 0.56 6.68alanineb 9.31 6.50 10.67 5.70 4.32 7.21 9.26 5.00 16.43 10.18 4.27 23.48arginine 4.88 3.07 5.96 3.91 2.56 6.01 4.24 2.30 11.25 5.21 2.26 9.67asparagine 24.56 19.43 26.53 20.87 14.80 28.26 25.51 15.87 39.70 27.35 11.28 62.85aspartateb 14.46 9.88 16.81 12.09 9.97 14.65 14.68 9.98 20.71 17.74 8.70 29.56choline 7.04 3.67 10.73 5.52 3.74 8.00 5.61 3.52 8.16 6.63 3.43 11.65dimethylamine 61.63 5.41 141.00 67.85 5.79 129.02 70.32 2.34 134.37 70.74 2.00 151.52formate 1.19 0.60 2.10 1.38 0.48 2.83 1.34 0.54 2.13 1.24 0.27 2.23fructoseb 78.50 52.52 122.42 58.73 33.91 88.40 53.02 26.45 91.48 55.58 15.48 112.17fumarateb 1.27 0.77 2.01 0.82 0.39 1.03 1.02 0.59 1.88 1.18 0.63 1.96galactose 9.69 6.56 12.30 8.64 5.12 10.81 8.52 6.45 11.98 9.66 6.49 14.84glucoseb 104.45 72.33 132.07 152.97 53.70 264.38 76.19 59.72 132.67 82.49 41.60 127.91glutamate 14.81 9.49 19.86 12.08 8.12 15.30 13.82 8.30 21.94 15.81 8.25 26.81glutamine 4.92 3.27 6.78 4.58 2.98 6.24 4.05 3.16 4.94 5.29 1.80 10.73histidineb 1.24 0.98 1.58 1.08 0.90 1.38 1.27 0.71 2.19 1.47 0.71 2.28isoleucineb 1.14 0.92 1.50 0.69 0.47 0.84 0.83 0.56 1.27 1.19 0.54 2.64lactate 1.41 1.04 2.14 1.18 0.62 1.84 1.35 0.67 2.54 1.16 0.72 2.08leucineb 1.16 0.81 1.59 0.77 0.59 0.94 0.87 0.60 1.22 1.13 0.67 1.82lysineb 2.50 1.36 3.11 2.08 0.98 2.94 2.74 1.48 6.87 5.67 1.29 18.11malateb 38.72 24.87 56.40 21.38 8.72 35.77 30.74 21.07 39.15 34.07 14.67 74.02maltoseb 16.06 8.93 31.46 82.82 12.73 251.55 14.88 10.54 21.12 15.96 5.44 31.25methionineb 0.76 0.48 1.00 0.61 0.45 0.73 0.61 0.40 0.78 0.94 0.51 1.70methylamine 0.44 0.06 0.92 0.48 0.05 0.88 0.49 0.05 0.91 0.49 0.03 0.99phenylalanine 1.37 0.86 1.90 1.41 0.99 1.72 1.51 1.07 2.26 1.61 0.93 2.61proline 61.46 54.93 68.40 51.72 28.09 69.04 56.23 36.24 74.47 59.49 29.31 117.24succinateb 3.86 2.30 5.66 1.53 1.03 2.36 2.57 1.25 4.45 3.42 1.67 6.36sucrose 1209.65 1084.43 1371.68 1419.00 684.87 2269.19 1137.37 920.47 1421.11 1218.76 942.86 1611.87threonineb 2.28 1.50 3.49 1.57 1.11 2.32 1.79 1.35 2.33 2.30 1.25 4.55trigonelline 1.45 0.93 2.15 1.61 1.01 2.26 1.37 1.06 2.04 1.53 0.86 2.28tyrosine 3.03 2.19 3.86 3.00 1.90 3.68 3.02 2.19 4.11 3.15 1.88 4.95valinea 1.93 1.43 2.42 1.37 1.04 1.77 1.62 1.04 2.37 2.08 1.04 3.82

aMetabolites identified on the basis of Chenomx assignments (see text for details). bMean difference at p < 0.05.

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that contribute to the eating quality characteristic se varieties(personal communication, William Tracy and Matt Murray).Sucrose and glucose levels were also higher for the P39 (1.55and 160 μg/50 mg DW, respectively) and Il14H (1.95 mg/50mg DW and 230 μg/50 mg DW, respectively) hybrids.Differences in sugar and other metabolite levels between

subgroups of landrace-derived hybrids were less apparent thanthose observed in the NAM founder set hybrids, most probablyreflecting the absence of sweet corn lines. In addition to sugars(fructose, glucose, and sucrose), significant differences in 4-aminobutyrate, glutamate, histidine, malate, proline, andsuccinate were observed between landrace hybrid subgroups(Table 4; see also Supporting Figure 5 and Associated ContentFile 8).The CDA of the NAM and landrace hybrid data set for 1H

NMR data established effective separation of the breedinggroups assigned for the NAM hybrids and landrace hybrids(Figure 4). These results, like composition data reportedpreviously,13 clearly imply unique metabolic characteristics thatcan distinguish between different sets of maize hybrids.As with the previously reported composition data, overall

correlations between the canonical scores (Can1 and Can2;Supporting Table 3) and individual metabolites that providedthe separation were generally low to moderate, and no singlecomponent was uniquely different in one breeding group versusanother.

Correlation analysis of 1H NMR data showed fewcorrelations with composition data. As with the GC-MS data,there were more correlations associated with growth dependenttraits, specifically days to anthesis, plant height, and ear height(Associated Content File 9). For the NAM data, glutamate,succinate, and trigonelline showed correlations with all threephenotypic traits, whereas for the landrace data set malate andsuccinate correlated with these three traits.In summary, the present study expands metabolomic

assessments of maize beyond previously reported commerciallines to include genetic resources that are used by the maizeresearch community to investigate the genetic basis of complexplant traits or that may serve as a source of new alleles forimproving modern maize hybrids. These resources include theNAM founder lines and a collection of landrace linesrepresenting North and South America. Our results using twodifferent and complementary profiling techniques (GC-MS andNMR) highlighted extensive metabolomic variation in the grainharvested from B73 hybrids derived from these two geneticresources. This wide variation is consistent with the composi-tional diversity observed in the same sample set,13 as well asresults observed from metabolic profiling of commercialhybrids.6a

Of potentially more relevance is that, within each hybrid set,subpopulations could be differentiated in a pattern consistentwith the known genetic and compositional variation of these

Table 4. NMR Metabolite Values for Hybrids Derived from the Landraces

mixed flint temperate tropical

metabolitea mean min max mean min max mean min max mean min max

4-aminobutyrateb 3.26 3.10 3.52 3.05 2.09 4.83 5.08 4.63 5.75 3.89 1.48 7.22

acetate 3.54 0.53 8.02 3.99 0.55 7.66 3.41 0.79 5.37 2.75 0.39 6.67

alanine 7.14 4.66 12.19 6.94 4.73 11.57 10.94 8.62 15.32 8.80 3.79 18.89

arginine 3.65 2.77 4.39 5.38 2.73 7.60 6.34 3.57 13.54 4.68 2.42 8.85

asparagine 22.55 20.28 23.57 34.51 22.14 50.01 29.17 15.50 61.75 30.00 14.43 48.02

aspartatea 12.76 10.41 17.34 14.01 11.22 20.82 19.00 14.10 22.39 16.27 6.77 27.66

choline 6.30 6.10 6.69 6.49 4.29 8.34 6.34 4.59 7.97 5.87 3.24 9.13

dimethylamine 47.50 1.68 83.56 78.38 1.95 155.85 58.55 5.07 85.19 67.84 1.65 134.13

formate 1.23 1.03 1.45 1.24 0.47 2.18 1.27 0.95 1.68 1.16 0.49 1.91

fructoseb 47.34 25.65 100.08 26.50 16.02 39.22 66.50 27.60 93.65 46.43 17.31 115.12

fumarate 1.02 0.52 1.49 1.14 0.66 1.69 1.36 0.80 2.01 1.23 0.63 2.45

galactose 8.29 6.05 12.94 8.04 5.66 10.03 9.57 7.01 12.07 9.41 4.23 20.20

glucoseb 65.40 47.72 106.29 50.77 36.50 77.61 90.87 50.12 119.53 67.87 29.13 139.04

glutamateb 14.53 11.59 17.02 11.73 5.99 17.08 20.29 15.64 26.06 16.43 5.16 34.85

glutamine 4.90 4.76 4.95 3.65 2.28 5.31 4.72 3.19 8.24 5.02 1.70 13.71

histidineb 0.96 0.42 1.27 1.65 0.95 2.70 1.27 1.10 1.64 1.35 0.65 2.28

isoleucine 0.91 0.76 1.31 0.78 0.58 1.15 1.16 0.72 1.61 1.11 0.42 2.74

lactate 1.13 0.77 1.54 1.08 0.76 1.66 1.60 1.00 2.22 1.23 0.46 2.16

leucine 0.97 0.76 1.30 0.92 0.59 1.55 1.06 0.94 1.15 1.18 0.54 2.49

lysine 2.22 1.58 3.68 4.27 2.05 7.88 3.56 2.34 6.10 4.96 1.55 12.85

malateb 19.06 12.05 30.69 24.56 12.63 47.20 45.13 29.75 59.78 33.24 16.05 57.28

maltose 15.09 11.77 20.47 15.10 3.70 36.18 16.20 5.61 37.62 14.13 4.48 22.90

methionine 0.69 0.52 1.01 0.81 0.55 1.09 0.71 0.45 1.00 0.87 0.42 1.52

methylamine 0.35 0.07 0.57 0.56 0.06 1.10 0.42 0.04 0.61 0.48 0.03 0.93

phenylalanine 1.88 1.77 2.00 1.95 1.21 2.44 1.38 0.96 1.64 1.76 1.07 3.55

prolineb 65.73 60.60 71.22 70.84 17.15 133.82 61.63 51.27 75.27 49.17 16.10 91.13

succinateb 2.77 1.15 7.17 1.68 1.03 2.29 3.81 2.05 5.61 3.43 0.66 7.51

sucroseb 1229.85 1058.21 1358.57 1459.45 975.44 2045.97 1320.55 1064.20 1615.88 1145.76 678.67 1717.23

threonine 1.94 1.44 2.70 1.77 1.12 2.48 2.39 2.06 2.75 2.20 0.96 4.53

trigonelline 1.88 1.26 2.28 1.82 1.14 2.91 1.60 0.95 2.73 1.47 0.81 2.19

tyrosine 3.10 2.99 3.24 3.59 2.34 5.20 3.23 2.83 3.82 3.37 2.32 5.96

valine 1.81 1.46 2.32 1.70 1.31 2.37 2.06 1.56 2.86 2.07 0.98 4.37aMetabolites identified on the basis of Chenomx assignments (see text for details). bMean difference at p < 0.05.

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lines, as highlighted in Figures 1−4. In other words, analysis ofthe GC-MS and NMR data allowed discrete classification of theflint, tropical/subtropical, and intermediate/mixed lines, as didthe genetic fingerprint analysis. This implies that there may bemetabolic features that are associated with different geneticgroupings, but our data suggest that these are subtly expressedwith the exception of some metabolites in the popcorn/sweetcorn samples; no analytes were uniquely or consistentlyhigher in one group than the other. Furthermore, it isinteresting to note that all of the metabolites identified inthis study using two distinct metabolomic approaches (GC-MSand NMR) were identified in both hybrid sets, as well as theirsubpopulations. It is interesting that major developmental andmorphological changes in the plant result from varietaldevelopment and geographical adaptation of maize across theAmericas, but these adaptations and breeding do not result inextreme changes in the metabolome. All metabolites werepresent across samples with subtle variation, except wheremajor mutations (i.e., sugary1 in the sweet corn lines) causedramatic changes in seed composition.The correlations observed between metabolite and pheno-

typic data, although admittedly surprising for reasons describedearlier, may suggest the potential of metabolomics to enabletrait development. Conversely, the low correlations observedbetween metabolomic and crop composition data call toattention the limitations of metabolomics in safety assessments.In addition to the limited nutrient coverage offered by

nontargeted profiling, it is clear that metabolomics does notaddress targeted end-points of direct relevance to safety andnutrition and does not provide strong testing of relevant safetyhypotheses. Encouragingly, this study helps provide anassurance that extensive metabolite variation is not associatedwith a safety concern, given its association with the diversity-selected and widely adapted conventional germplasm evaluatedhere. Utilization of publicly available germplasm to improvemodern maize lines will, no doubt, be accompanied byincorporation of metabolic diversity that is beyond the potentialunintended changes due to genetic modification (GM) usingmodern agricultural biotechnology.32

The development of improved maize hybrids will requireoptimization of current resources to guide breeding strategies.The genetic diversity preserved in germplasm banks representsa key current resource. In this study we conducted the firstmetabolomic survey of kernel composition of hybrids from twoimportant genetic resources: the NAM founder lines as well aslandraces representing unimproved germplasm from a widegeographic area. It is our hope that the data generated here willencourage more detailed metabolomic investigations of publiclyavailable resources that will provide value to plant breeders andplant biotechnologists involved in the nutritional andagronomic improvement of maize.

Figure 4. Canonical discriminant analysis of NMR metabolite data from the landrace (top panel) and the NAM (bottom panel) hybrids. Labeledcircle represents the multivariate mean for each group. The size of the circle corresponds to a 95% confidence limit for the mean. Groups that aresignificantly different tend to have nonintersecting circles.

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■ ASSOCIATED CONTENT*S Supporting InformationThe Supporting Information is available free of charge on theACS Publications website at DOI: 10.1021/acs.jafc.5b04901.

Group information for NAM and landrace hybrids,correlation scores of canonical scores 1 and 2 for NMRmetabolites, data filtering and PLS analysis for GC-MSdata, and box plots for NMR metabolites (PDF)Raw metabolomics and as well as statistically analyzeddata (ZIP)

■ AUTHOR INFORMATIONCorresponding Authors*(T.V.V.) E-mail: tvvenkatesh@gmail.com. Phone: (314) 757-3954. Fax: (314) 300-8020.*(S.F.-G.) E-mail: Sherry.Flint-Garcia@ars.usda.gov. Phone:(573) 884-0116. Fax: (573) 884-7850.NotesThe authors declare no competing financial interest.

■ ACKNOWLEDGMENTSWe are grateful to Nordine Cheikh for his support andencouragement in initiating this project. We thank Erin Bell fora critical review of the manuscript. We also thank the BucklerLaboratory at Cornell University for contributing seed for theanalyses and the Sample Management Team at Monsanto Co.for preparing samples for compositional analyses.

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DOI: 10.1021/acs.jafc.5b04901J. Agric. Food Chem. 2016, 64, 2162−2172

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