Unraveling Metabolic Variation for Blueberry and ChokeberryCultivars Harvested from Different Geo-Climatic Regions in KoreaInseon Sim,† Dong Ho Suh,† Digar Singh,† Seon-Gil Do,‡ Kwang Hyun Moon,§ Jeong Ho Lee,§

Kang-Mo Ku,∥ and Choong Hwan Lee*,†

†Department of Bioscience and Biotechnology, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea‡Wellness R & D Center, Univera, Inc., 78 Achasan-ro, Sungdong-gu, Seoul 04782, Republic of Korea§Sunchang Research Institute of Health and Longevity, Indeok-ro, Ingye-myeon, Sunchang-gun, Jeollabuk-do 56015, Republic ofKorea∥Division of Plant and Soil Sciences, West Virginia University, Morgantown, West Virginia 26505, United States

*S Supporting Information

ABSTRACT: Temporal geo-climatic variations are presumably vital determinants of phenotypic traits and quality characteristicsof berries manifested through reconfigured metabolomes. We performed an untargeted mass spectrometry (MS)-basedmetabolomic analysis of blueberry (Vaccinium spp.) and chokeberry (Aronia melanocarpa) sample extracts harvested fromdifferent geo-climatic regions in Korea. The multivariate statistical analysis indicated distinct metabolite compositions of berrygroups based on different species and regions. The amino acids levels were relatively more abundant in chokeberry than inblueberry, while the sugar contents were comparatively higher in blueberry. However, the metabolite compositions were alsodependent on geo-climatic conditions, especially latitude. Notwithstanding the cultivar types, amino acids, and sucrose wererelatively more abundant in berries harvested from 35°N and 36°N geo-climatic regions, respectively, characterized by distinctduration of sunshine and rainfall patterns. The present study showed the ability of a metabolomics approach for recapitulating thesignificance of geo-climatic parameters for quality characterization of commercial berry types.

KEYWORDS: blueberry (Vaccinium spp.), chokeberry (Aronia melanocarpa), geo-climatic regions, Korea, GC-TOF-MS,metabolite profiles, correlation analysis

■ INTRODUCTION

Blueberries and chokeberries are among the representativeberry varieties, relished largely for their nutritional andbioactive components, consumed either directly or in processedforms.1 Blueberries are perennial shrubs belonging to the genusVaccinium (family Ericaceae) with approximately 400 speciesnative to North America.2 Different blueberry species includinghighbush blueberry (V. corymbosum), lowbush blueberry (V.angustifolium), and rabbiteye blueberry (V. ashei) are widelycultivated in many countries.3 On the other hand, chokeberries,also known as red chokeberry and black/purple chokeberry, areperennial shrubs belonging to the genus Aronia (familyRosaceae), native to North America and Eastern Canada.4

Most berries, including chokeberry and blueberry, are a richsource of phenolic compounds, flavonoids, anthocyanins, whichhave been shown to be bioactive, and are used inpharmacological applications as they have antioxidant, anti-cancer, antiaging properties; improve blood vessels; and aid ineye fatigue recovery.5−8

Owing to their pleasant taste and praised health effects, theseberries have rapidly gained a good reputation in global trade,demand, and production. In the case of blueberries, more than280 000 Mt of blueberries are harvested annually from over36 352 ha of cultivated land, with annual berry yield andconsumption increasing steadily worldwide.9,10 Accordingly, thecultivation of berries is expanding into wider regions of NorthAmerica, South America, Europe, and Asia, beyond their

traditional areas of cultivation in the United States and Chile.11

As the berries are increasingly being cultivated across all thecontinents, the specific berry origins has become an importantparameter to label its quality and nutritional-functionalefficacies.12−15 Notwithstanding the texture, shape, and size,the berry qualities are also determined by their chemicalcompositions and the plethora of bioactive metabolites, withtheir contents presumably influenced by regional environ-mental conditions, i.e., soil and climatic conditions as well asassociated microbiomes. In the case of grapes associated withthe wine industry, the cultivation region and environment playan important role in quality characterization, in terms of“terroir” and “vintage.”16,17 However, the climatic parametersare seldom predictable, and even within the same or similargeographical regions, the chemical repertoires of fruits arehighly variable.18 In previous years, there have been manyreports correlating the geo-climatic conditions with qualities ofvarious berry types viz., strawberry, raspberry, black currants,sea buckthorn, and blueberry cultivars.19−22 Hence, we considerthat the geographical origins and climatic conditions are vitalfor securing the high-quality food resources as they cruciallyinfluence the metabolic composition of berries.

Received: August 31, 2017Revised: September 26, 2017Accepted: September 27, 2017Published: September 27, 2017

Article

pubs.acs.org/JAFC

© XXXX American Chemical Society A DOI: 10.1021/acs.jafc.7b04065J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Metabolites represent both the intermediates and the end-products of metabolism, with their levels potentially linked tothe response of biological systems toward environmentalperturbations.23 In recent years, metabolomics has evolved asan important discipline that addresses the bottlenecks invarious fields including traditional foods, pharmaceuticals, andsystems biology.24,25 Intriguingly, metabolomics has increas-ingly been applied to elucidate the biomarkers in agriculturalcommodities or plants, such as green tea,26 wines,18 andAngelica gigas,27 with corresponding geo-climatic conditions astheir signature quality indices. In the present study, we aimed tohighlight the metabolic disparity in berries harvested fromdifferent geo-climatic conditions with emphasis on their qualitycharacteristics. In particular, the primary metabolites, includingsugars, amino acids, and fatty acids, are directly affected byenvironmental factors and are involved in the metabolismrelated to growth and development of fruits, ergo the berryquality in terms of nutritional and organoleptic properties.28,29

We outlined a nontargeted gas chromatography, time-of-flight, mass spectrometry (GC-TOF-MS)-based metabolomicworkflow to examine the economically important blueberry andchokeberry varieties harvested from different geo-climaticregions in the Republic of Korea. We evaluated the metabolic

biomarkers vital for determining the commercial qualities ofberries, and simultaneously correlated the geo-climaticconditions with significantly discriminant metabolomic datasets.

■ MATERIALS AND METHODSChemicals and Reagents. Acetonitrile, methanol, and water were

purchased from Fisher Scientific (Pittsburgh, PA, USA). N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA), methoxyamine hydro-chloride, pyridine, formic acid, norvaline, trifluoroacetic acid, aceticacid, 0.1N sodium hydroxide solution (NaOH), formaldehydesolution, and other standard compounds were obtained from Sigma-Aldrich Chemical Co. (St. Louis, MO, USA).

Plant Materials. Each of the seven commercial blueberry(Vaccinium spp.) and chokeberry (Aronia melanocarpa) samplescollected from 7 different geo-climatic regions of Korea were procuredfrom Univera Inc., Seoul, South Korea (Table 1). All of the maturedcommercial berry samples were provided with the data indicating thetime of harvest ranging from June to September in 2016, dependingupon the climatic conditions in various regions. The blueberry sampleswere harvested from Damyang, Gochang, Sunchang, Muju, Cheonan,Bonghwa, and Pyeongtaek, whereas the chokeberry samples wereharvested from Gochang, Sunchang, Muju, Hapcheon, Yesan,Yeongyang, and Danyang (Figure 1). The data for climatic conditions

Table 1. Sample Information and Climatic Conditions of Blueberries (Vaccinium spp.) and Chokeberries (Aronia melanocarpa)Collected from Different Geographical Origins in the Republic of Korea

aV; Vaccinium spp. bA; Aronia melanocarpa. cRecorded from full flowering stage to fruit harvest (May to August, 2016). dMeasurement data value forone-year from harvest time; annual average temperature. eMeasurement data value for one-year from harvest time; duration of sunshine.fMeasurement data value for one-year from harvest time; rainfall. *Climatic conditions with significant differences with different geographical originsby PLS-DA (p value <0.05).

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across these regions were obtained from the Korea MeteorologicalAgency (http://www.kma.go.kr).Sample Preparation. The berry samples were lyophilized for 6

days and stored at −80 °C under extraction. Each of the pulverizedsamples (1 g) was extracted with 10 mL of solvent mixture (methanol/water/trifluoroacetic acid = 70:30:0.1, v/v/v). The samples weresubjected to 10 min sonication treatment followed by 6 h agitationusing a twist shaker (Biofree, Seoul, Korea). The samples werecentrifuged (2370 g, 10 min) at 4 °C (Hettich Zentrifugen, Universal320R, Germany), and the supernatants were filtered using Millex GP0.22 μm filters (Merck Millipore, Billerica, MA, USA). The samplefiltrates were dried using a speed vacuum concentrator (Modulspin 31,Biotron, Seoul, Korea). The dried sample extracts were redissolved in70% methanol to achieve the uniform working concentrations, i.e., 10mg/mL. The internal standard, norvaline (10 μL, 1 mg/mL), wasadded to each sample extract (90 μL) prior to the samplederivatization step for GC-TOF-MS analysis. The samples werederivatized by adding 100 μL methoxyamine hydrochloride (20 mg/mL in pyridine) to the dried extracts and incubated for 90 min at 30°C. Afterward, 100 μL MSTFA was added to the mixture followed by30 min incubation at 37 °C.GC-TOF-MS Analysis. The gas chromatography-time-of-flight-

mass spectrometry (GC-TOF-MS) analysis was performed using anAgilent GC-TOF-MS system (7890A) equipped with an Agilent 7693autosampler (Agilent, Atlanta, GA) and a coupled Pegasus high-throughput TOF-MS (LECO, St. Joseph, MI, USA) system. Thesample analytes were separated on an Rtx-5MS column (30 m i.d. ×0.25 mm length, 0.25 μm particle size; Restek Corp., Bellefonte, PA,USA) with helium as the carrier gas at a constant flow rate of 1.5 mL/min. The injected samples (1 μL) were subjected to a split ratio (1:5).The injector and ion source temperatures were set at 250 and 230 °C,respectively. The oven temperature was programmed for 80 °C in first2 min, ramped to 300 °C at the rate of 15 °C/min, followed bytemperature maintenance for the last 3 min. A full scan mode massrange was set between 50−1000 m/z at an acquisition rate of 10scans/s, with an ionization energy of 70 eV. Additionally, the QC(quality control) samples were also set by blending equal proportionsfrom all the samples. All MS analyses were conducted with threeanalytical replications.Estimation of Physicochemical Factors (pH, Titratable

Acidity, Amino-type Nitrogen, and Sugar Content). The

blueberry and chokeberry raw samples were pulverized and squeezedusing gauze to obtain the fresh juice extract. The berry juice extracts (3mL) were homogenized with distilled water (27 mL) and the pH wasmeasured using a pH meter (Thermo, USA). The evaluation oftitratable acidity was determined through titrating the samplehomogenates to pH 8.4 with 0.1N NaOH solution for all berries.The measurement of amino-type nitrogen contents was performed byadding 20 mL of formaldehyde solution (pH 8.4) in titrated samplesand retitrating it until pH 8.4 using the 0.1N NaOH solution. Theestimation of amino type nitrogen contents were made using theformula shown below,30

‐

= −

× ×

×

Amino nitrogen (mg%)

(((Volume of 0.1N NaOH (mL) Volume of retitration 0.1N

NaOH (mL)) 1.4(mg/mL) 1.0028)

/Homogenized samples (mg)) 100

The sugar content for the samples was measured using a sugarmeter (HANNA instruments, USA). All experiments were carried outin triplicate and expressed as a mean value with standard deviations.

Data Processing and Multivariate Statistical Analysis. TheGC-TOF-MS raw data files were converted to netCDF (*.cdf) formatusing Leco ChromaTOF software (Version 4.44). After conversion,the cdf files were subjected to preprocessing alignment by using theMetAlign software package (http://www.metalign.nl). The resultingdata was exported to a Microsoft Excel (Microsoft, Redmond, WA,USA) file containing the corrected peak retention times (min), mass tocharge ratios (m/z), and peak areas. The aligned data were subjectedto multivariate statistical analysis using SIMCA-P+ 12.0 software(Version 12.0; Umerics, Umea, Sweden) and the patterns of metabolicvariation were visualized using principal component analysis (PCA),partial least-squares discriminant analysis (PLS-DA), orthogonal PLS-DA (OPLS-DA) models, and loading S-plots. The significantlydiscriminant metabolites were selected based on the variableimportance in the projection (VIP) values >0.7 and tested forsignificance at the p value <0.05 by a one-way analysis of variance(ANOVA) using STATISTICA (version 7.0, StatSoft Inc., Tulsa, OK,USA). The selected variables were identified using authentic standardcompounds and the national institute of standards and technology(NIST, 2005), combined chemical dictionary version 7.2 (Chapman &

Figure 1. Geographical origins of blueberries (Vaccinium spp.) (A) and chokeberries (Aronia melanocarpa) (B) harvested in Republic of Korea.

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Hall/CRC), references, and an in-house library by comparing bothmass spectra and retention time. The data for geo-climatic conditionsand the significance of physicochemical factors associated with berrysamples were evaluated using ANOVA and Duncan’s multiple-comparison test in the PASW Statistics 18.0 (SPSS Inc., Chicago,IL, USA). A heat map representation of data visualized the relativecontents of discriminant metabolites by using MeV software (version4.8, http://www.tm4.org/), and fold changes normalized by the

averages of all values for each metabolite. The pairwise correlationsamong the metabolites and climatic conditions were determined usingPearson’s correlation coefficient test in PASW Statistics 18.0.

■ RESULTS AND DISCUSSION

In this study, we hypothesized that the geo-climatic conditionsplay a pivotal role in determining the quality characteristics of

Figure 2. Principal component analysis (PCA) score plot (A), S-plot (B), and metabolic pathway and relative primary metabolite contents (C) werederived from GC-TOF-MS data sets of blueberries and chokeberries with different geographic origins. The symbols indicated; ●: blueberry (V,Vaccinium spp.), ▲: chokeberry (A, Aronia melanocarpa). The numbering of metabolites is the same as those shown in Table 2. The pathway wasmodified from the KEGG database (http://www.genome.jp/kegg/). Fold changes normalized by the averages of all values and are shown in blue (0)to red (1.5). The violet marked metabolites had considerably higher relative contents in blueberries, whereas the green marked metabolites hadhigher contents in chokeberries (VIP > 0.7, p value <0.05).

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berries, which in turn are the phenotypic manifestations of thealtered primary metabolomes.Discriminant Primary Metabolomes between Blue-

berries and Chokeberries. The multivariate analyses of GC-

TOF-MS data sets for berry sample extracts showed a distinct

metabolomic pattern in the PCA score plot (Figure 2A), withblueberry and chokeberry profiles clearly separated along PC1(33.65%). The significantly discriminant primary metabolitesbetween blueberry and chokeberry species were determinedusing OPLS-DA models with clustered patterns along OPLS1

Table 2. Discriminative Primary Metabolites in Blueberries and Chokeberries Analyzed by GC-TOF-MS and Fold-changeValues According to Latitudea

fold changee

Vaccinium spp.Aronia

melanocarpa

no.b rt (min)c compoundsd 35°N 36°N 35°N 36°N ms fragment ions (m/z) tmsf idg

amino acid1 5.55 alanineI, 1.51 0.32 116, 73, 147, 117, 59, 75, 74, 148, 100, 103 2 STD2 6.64 valineh,I,j 1.44 0.41 1.42 0.44 73, 144, 45, 147, 75, 100, 218, 74, 145, 59, 146 2 STD3 7.58 glycineh,I 1.41 0.45 73, 147, 174, 75, 45, 86, 117, 59, 175, 55, 133 3 STD4 8.07 serineh,I,j - - 1.46 0.39 73, 204, 100, 188, 148, 218, 59, 75, 133, 74 3 STD5 8.32 threonineh,I,j 1.43 0.43 1.28 0.62 73, 147, 117, 101, 57, 75, 219, 218, 59, 74, 89 3 STD6 9.45 aspartic acidh,I,j 1.54 0.28 1.31 0.59 73, 232, 147, 100, 75, 174, 74, 218, 117, 59 3 STD7 9.51 pyroglutamic acidh,I,j 1.41 0.46 1.32 0.58 156, 73, 147, 75, 174, 157, 59, 74, 230, 133 2 STD8 9.53 GABAI,j 1.35 0.54 1.25 0.66 174, 73, 147, 86, 175, 75, 59, 100, 304, 148 3 STD9 10.2 ornithineh,I 73, 70, 142, 74, 45, 75, 147, 102, 59, 100, 143 3 STD10 10.24 glutamic acidI,j 1.36 0.52 1.25 0.67 73, 246, 128, 75, 147, 156, 84, 247, 56, 74, 100 3 STD11 10.67 asparaginej 1.63 0.16 73, 116, 132, 75, 147, 141, 231, 188, 74, 100 3 STDorganic acid12 5.14 lactic acidh,I,j 1.10 0.86 1.19 0.75 73, 147, 117, 45, 75, 66, 59, 148, 191, 74 2 STD13 5.86 oxalic acidh,I,j 1.07 0.90 0.96 1.06 73, 147, 133, 59, 45, 72, 75, 100, 86, 74 2 STD14 7.61 succinic acidI 1.34 0.54 73, 147, 75, 45, 55, 148, 56, 47, 74, 149 2 STD15 9.18 malic acidh,I,j 1.13 0.83 0.99 1.02 73, 147, 55, 75, 233, 133, 74, 101, 148, 59 3 STD16 11.81 citric acidh,I,j 0.91 1.12 1.05 0.93 73, 147, 273, 75, 74, 211, 67, 148, 149, 274 4 STD17 12.11 quinic acidh,I,j 1.28 0.63 0.91 1.12 73, 147, 255, 345, 75, 74, 133, 191, 204, 148 5 STDsugars and sugar alcohols18 7.26 glycerolh,I 1.20 0.73 73, 147, 299, 45, 117, 103, 205, 133, 75, 74, 59 3 STD19 10.64 xylosej 1.05 0.94 73, 103, 217, 147, 75, 74, 133, 307, 189, 117 4 STD20 10.99 xylitolI,j 1.18 0.77 73, 147, 217, 103, 117, 129, 205, 133, 75, 74 5 STD21 11.14 fucoseh,I,j 0.77 1.31 73, 117, 147, 45, 75, 305, 74, 174, 59, 89 4 STD22 12.2 fructosej 1.04 0.94 0.96 1.05 73, 103, 217, 147, 133, 74, 117, 307, 104, 218 5 STD23 12.46 mannosej 0.98 1.03 73, 147, 205, 160, 103, 319, 217, 117, 74, 129 5 STD24 12.54 glucoseh,I,j 1.01 0.99 0.97 1.04 73, 147, 103, 205, 160, 319, 129, 217, 117, 157 5 STD25 12.88 glucopyranoseh,I,j 1.01 0.99 0.95 1.07 73, 204, 147, 191, 217, 103, 205, 75, 74, 129 5 NIST26 13.64 myo-inositolh,I,j 1.06 0.92 1.16 0.79 73, 147, 217, 191, 305, 129, 103, 74, 318, 75 6 STD27 16.69 sucroseh,I,j 0.68 1.43 0.83 1.23 73, 361, 217, 147, 103, 129, 169, 75, 74, 362 8 STDfatty acid28 7.8 propanoic acidI 1.31 0.58 73, 147, 189, 45, 103, 102, 133, 75, 117, 74 3 STD29 13.63 palmitic acidj 73, 147, 117, 103, 75, 217, 129, 205, 74 1 STD30 14.34 stearic acidh,I,j 0.93 1.10 0.99 1.02 73, 117, 75, 131, 129, 132, 55, 145, 116, 57 1 STDetc.31 7.02 benzoic acidh,j 1.03 0.96 105, 179, 77, 135, 51, 45, 180, 50, 136, 73 1 NIST32 7.28 phosphoric acidh,I,j 1.14 0.81 1.09 0.88 73, 299, 133, 300, 211, 75, 147, 207, 301, 314 3 STDnon-identified33 11.6 N.I. 1h,I,j 0.87 1.17 1.12 0.84 73, 217, 75, 103, 147, 45, 257, 133, 89, 74, 59 534 12.79 N.I. 2I 0.98 1.03 73, 217, 103, 147, 75, 45, 74, 129, 205, 117, 133 435 13.21 N.I. 3h,j 0.99 1.01 0.97 1.05 73, 204, 147, 75, 205, 45, 74, 103, 217, 220, 319 536 14.65 N.I. 4j 0.97 1.05 73, 217, 103, 147, 75, 45, 74, 129, 205, 117, 133 437 16.29 N.I. 5j 1.02 0.97 73, 204, 147, 275, 217, 205, 117, 129, 103 4

aVariables were selected based on variable importance of projection (VIP > 0.7) from OPLS-DA and p value (<0.05) by GC-TOF-MS in each berry.bNumber. cRetention time. dIdentified: standard mass spectrum was consistent with those of standard compounds (variables were selected based onVIP > 0.7 and p value <0.05 by GC-TOF-MS in each berry). eFold change: Relative levels of metabolites were converted into fold changes in eachberry. fTMS, trimethylsilyl. gIdentification: STD, standard compound and NIST (National Institute of Standards and Technology mass searchversion 2.0, 2011, USA). hThe discriminant metabolites between blueberries and chokeberries from OPLS-DA (VIP > 0.7, p value <0.05). IThediscriminant metabolites in blueberries with different geographical origins from PLS-DA (VIP > 0.7, p value <0.05). jThe discriminant metabolites inchokeberries with different geographical origins from PLS-DA (VIP > 0.7, p value <0.05).

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(50.44%, Figure S1). Thus, a total of 23 significantlydiscriminant metabolites (VIP > 0.7, p value <0.05) wereputatively identified between blueberry and chokeberry samples(Table 2). In the S-plot, the axis represents the predictivecomponents for covariance, i.e., p[1] and their correlation,p(corr)[1],27 identifying the statistically significant metabolitescontributing the maximum disparity between chokeberry andblueberry sample extracts (Figure 2B). Further, we observed adistinction in the relative levels of discriminant metabolites inwhich six amino acids (valine, 2; glycine, 3; serine, 4; threonine,5; aspartic acid, 6; pyroglutamic acid, 7), two organic acids

(malic acid, 15; quinic acid, 17), a sugar (fucose, 21),phosphoric acid (32), and a nonidentified metabolite (N.I 1,31) were higher in chokeberries than blueberries. On the otherhand, the relative levels of an amino acid (ornithine, 9), twoorganic acids (lactic acid, 12; citric acid, 16), 5 sugars and sugaralcohols (glycerol, 18; glucose, 24; glucopyranose, 25; myo-inositol, 26; sucrose, 27), a fatty acid (stearic acid, 30), benzoicacid (31), and a nonidentified metabolite (N.I 4, 36) werehigher in blueberries. The 23 primary metabolites that differedbetween the berries were visualized through the correspondingmetabolic pathways to compare their relative contents (Figure

Figure 3. Principal component analysis (PCA) score plots (A,C) and heatmap (B,D) of significantly different primary metabolites derived using GC-TOF-MS from blueberries (A,B) and chokeberries (C,D) with seven different geographical origins. The numbering of metabolites is the same as thatshown in Table 2.

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2C). Consequently, the amino acids, including valine (2),glycine (3), serine (4), threonine (5), aspartic acid (6), andpyroglutamic acid (7), in chokeberries were more abundantthan those in blueberries, whereas the sugars and sugaralcohols, including glycerol (18), glucose (24), glucopyranose(25), myo-inositol (26), and sucrose (27), were higher inblueberries.Quality Indices of Blueberries and Chokeberries:

Discriminant Metabolic Profiles and PhysicochemicalFactors. The quality assertion for any fruit involves acomprehensive evaluation of varying standards viz., color,size, shape, and sugar contents etc., which in turn areadditionally subjected to consumer’s individual taste andpreferences. Considering generalizations for average size ofberry fruits, the length and height of blueberry fruits wascomparatively higher than those of chokeberry samples (TableS1). Notwithstanding the mere dimensions, we correlated thediscriminant metabolomes with selected physicochemicalfactors crucial for quality characterization and palatability ofberries. The pH and titratable acidity can be used to evaluatethe acidity and flavor of foods, while the amino-type nitrogencontents indicate nutritional qualities through measuring thetotal free amino acids.30 The difference in pH, titratable acidity,amino-type nitrogen, and sugar contents were examined foreach berry sample extracts (Table S2). The observed amino-type nitrogen contents in chokeberries (18.1−66.9 mg%) wereapproximately 3−10 folds higher compared to those inblueberries (6.6−13.6 mg%), indicating that chokeberrieswere richer in free amino acids. Previously, higher levels offree amino acids and total amino acid contents were reportedfor chokeberries.31 Functionally, the amino acid contentsdetermine a variety of factors influencing the taste, flavor,nutrition, and palatability of fruits.32 Especially, the glycine,serine, and threonine contents determine sweetness of fruits,while valine and aspartic acid confers bitter and savory tang,respectively.33,34 Owing to the higher free amino acidproportions, we assume that chokeberries had comparativelyhigher nutritional as well as functional components comparedto blueberries. However, the chokeberries are seldom difficultto consume fresh pertaining to their bitter tannin contents. Onthe other hand, the blueberry samples were characterized withrelatively higher abundance of sugars and sugar alcohols viz.,sucrose, glycerol, glucose, glucopyranose, and myo-inositol. Ingeneral, glucose and fructose, constituting approximately 75%of the total sugar content of berries, primarily influence the fruitsweetness.35 In contrast to previous studies, we observed therelatively higher sucrose levels in blueberries compared tochokeberries.35 Although the levels of sugar derivatives werehigher in blueberry, their overall sugar contents were eitherequivalent or lower compared to those of chokeberries (TableS2). As the sweetness of fruits is influenced by the ratio of sugarto acid contents, the berries will taste sweet when the contentof organic acids (especially malic acid) are low, even if the sugarcontents are not high.35 We observed, a higher sugar to malicacid ratio for blueberries, which potentially influenced theirhigher relative sweetness than chokeberry. The organic acidsare known to make an important contribution to the sour tasteof fruits, with citric acid and malic acid been the major organicacids in berries. Generally, citric acid accounts for the largestportion of the total organic acid content in berries, followed bymalic acid.36 We determined the higher relative contents ofcitric acid and malic acid in blueberries and chokeberries,respectively (Figure 2C). Our data was consistent with the

previously reported organic acid contents for blueberries andchokeberries.35 Functionally, citric acid and malic acid arenutritionally important compounds pertaining to theirimportance for ATP production in TCA cycle generatingenergy upon ingestion.35 Overall, the distinctions observed inprimary metabolites, including amino acids, organic acids,sugars, and sugar alcohols, can comprehensively modulate tasteand nutrition in berries, potentially determining theircommercial values.

Metabolic Disparity for Blueberry and ChokeberryHarvested from Different Geo-Climatic Regions. Wefurther examined the esoteric metabolomes among the berriesinfluenced by geo-climatic factors viz., geographical position,average rainfall, and average duration of sunshine. In the PCAscore plots derived from GC-TOF-MS data sets, the clusteredmetabolic profiles for each blueberry (R2X = 0.175, Q2 =0.0568; Figure 3A) and chokeberry (R2X = 0.182, Q2 = 0.0963;Figure 3C) harvested from different regions were apparent. Thequality parameters of the PCA are expressed as R2X and Q2,which are used as the fraction of the sum of squares for theselected components.37 The Q2 value indicates suitability of themodel. As principal component 1 and 2 showed low values, thePCA indicated the higher total variability among the blueberry(PC1, 17.5% and PC2, 15.6%) compared to the chokeberry(PC1, 18.2% and PC2, 11.7%) cultivars harvested from thedifferent geo-climatic regions. In particular, both the blueberryand chokeberry samples from different geographical originswere separated along the PC2, depending primarily on latitudecompared to longitude, altitude, growing degree-day (GDD),varieties, and harvest times. In the case of blueberries, themetabolite profiles for seven blueberries harvested fromdifferent geographical locations were clustered according tothe latitude 35°N (Damyang, V1; Gochang, V2; Sunchang, V3;and Muju, V4) and latitude 36°N (Cheonan, V5; Bonghwa,V6; and Pyeongtaek, V7) regions. Similarly, the chokeberrymetabolomes were clustered based according to the samplesfrom latitude 35°N (Gochang, A1; Sunchang, A2; Muju, A3;and Hapcheon, A4) and latitude 36°N (Yesan, A5; Yeongyang,A6; and Danyang, A7), regions. Hence, we propose thatgeographical origins, especially latitude and its associatedclimatic variations, potentially affect metabolite profiles ofberries, resulting in their commercial qualities.We used a PLS-DA model to select the variables (VIP > 0.7,

p value <0.05) or the significantly discriminant primarymetabolites according to the geographic origins of berrytypes. The PLS-DA is a supervised clustering technique that canbe used to visualize maximum separation between data sets andallows selection of significantly discriminant variables betweengroups.38 As shown in Table 2, a total of 28 and 30 significantlydiscriminant metabolites (p value <0.05) in blueberries andchokeberries, respectively, were selected among the berriesharvested from the seven geographic regions. The significantlydifferent metabolites with geo-climatic conditions, werevisualized using a heat map for blueberry (Figure 3B) andchokeberry (Figure 3D), respectively. Both blueberries andchokeberries exhibited a difference in metabolite contentsaccording to the different geographic origins with metabolitesshowing a similar tendency according to the latitudes. TheOPLS-DA based on metabolomic data sets for blueberries (R2X= 0.371, R2Y = 0.994, Q2 = 0.882; Figure S2A) andchokeberries (R2X = 0.404, R2Y = 0.997, Q2 = 0.918; FigureS2B) was performed to better elucidate the differencesaccording to berry samples harvested across 35°N and 36°N,

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in the Republic of Korea. Both blueberries and chokeberrieswere differentiated by OPLS 1 according to the latitude by13.05% and 15.64% variability, respectively. The individual foldchange values for discriminant primary metabolites accordingto the varying latitudes of harvested berries are listed in Table 2.In blueberries, the relative abundance of all amino acids(alanine, 1; valine, 2; glycine, 3; threonine, 5; aspartic acid, 6;pyroglutamic acid 7; GABA, 8; and glutamic acid, 10), organicacids except for citric acid, and some sugars were higher inberries harvested from 35°N regions, whereas citric acid (16),fucose (21), sucrose (27), and stearic acid (30) contents werehigher in berries from 36°N regions. In chokeberry samplesfrom 35°N regions, all amino acids (valine, 2; serine, 4;threonine, 5; aspartic acid 6; pyroglutamic acid, 7; GABA, 8;glutamic acid, 10; and asparagine, 11), some organic acids(lactic acid, 12; citric acid, 16), sugars and sugar alcohols(xylose, 19; xylitol, 20, myo-inositol, 26), benzoic acid (31),and phosphoric acid (32) levels were relatively higher. Incontrast, the chokeberry samples from 36°N regions showedhigher relative levels of selected organic acids (oxalic acids, 13;malic acid, 15; quinic acid, 17), sugars (fucose, 21; fructose, 22;mannose, 23; glucose 24; glucopyranose, 25, sucrose, 27), andfatty acid (stearic acid, 30). In general, the relative amino acidand sucrose levels were relatively higher in berries harvestedfrom 35°N and 36°N regions, respectively. Hence, we

conjecture that the metabolic composition of berries variedaccording to the species and their geo-climatic origins,especially, the latitudes.

Effects of Geo-Climatic Conditions on Blueberry andChokeberry Metabolomes. The metabolite profiles in plantspecies are closely related with environment factors includingsoil and climatic conditions pertaining to the geographicalorigins and latitudes.18,27 In particular, the climatic conditions,including temperature, duration of sunshine (DS), and rainfall(R), are not always consistent but crucial for berry maturation,growth, and metabolism. The intriguing correlation betweengeographical origins and the cultivation environment is wellestablished as the concept of “terroir” and “vintage” in wine aswell as tea industry.19,27 Therefore, the goal of our study was todecipher the variation in berry metabolomes depending uponthe harvest locations across latitudes ensuring different geo-climatic conditions. The distinct climatic conditions for theseven different geographical locations chosen for blueberry andchokeberry harvests are summarized in Table 1. The differentgeo-climatic conditions were significantly associated with theduration of sunshine and rainfall limits (p value <0.05), withtemperature being an important factor in fruit ripening andharvest.18 However, the selected harvest locations in this studyhad similar annual mean temperatures recorded. We performeda correlation analysis to express the statistical significance of the

Figure 4. A correlation map between significantly different metabolites (common metabolites from blueberries and chokeberries) and climaticcondition (Table 1). Each square indicates the Pearson’s correlation coefficient values (r) for a pair of metabolites or climatic conditions (duration ofsunshine, DS and rainfall, R). DS and R were significant correlations with metabolites in climatic conditions (p value <0.05). The red color indicatespositive (0 < r < 1) correlation and blue color indicates negative (−1 < r < 0) correlation.

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inter-relationships between individual climatic conditions (DSand R, p value <0.05) and the metabolite levels in harvestedberry samples (Figure 4). The Pearson’s correlation coefficientswere calculated for the relative contents of the 21 significantlydiscriminant metabolites (Table 2), commonly obtained forboth the berries under given climatic conditions, i.e., DS and R.Among the selected metabolites, both glucose (24) and sucrose(27) exhibited a positive correlations with DS (r > ± 0.7), withlatter showing a strong positive correlation (r = 0.847) with DS,being a direct product of photosynthesis. Conversely, all aminoacids (valine, 2; serine, 4; threonine, 5; aspartic acid, 6;pyroglutamic acid, 7; GABA, 8; glutamic acid, 10), selectedorganic acids (lactic acid, 12; malic acid, 15), sugars and sugaralcohols (xylitol, 20; fucose, 21; myo-inositol, 26), andphosphoric acid (32) showed a high positive correlation withR (r > ± 0.7), coupled by a negative correlation with DS.In general, we observed that amino acid contents were

relatively higher in the berries from the region with shorter DSand higher R. Hence, it can be assumed that the ratio of DS toR significantly influence the amino acid levels in ripenedberries. In plants, amino acid contents determine several vitalfunctions including plant growth, mitigation of heavy metalstoxicity, and antipathogenic effects.32 As previously discussed,the commercial values and palatability of berry fruits are alsodetermined by their amino acid contents; especially, valine,glutamic acid, and threonine confer peculiar tang and flavor toberries.32,39 Among the various geo-climatic factors, DSreportedly enhanced the transformation of amino acids intopolyphenols.18 Hence, longer durations of sunshine will resultin lower amino acid content and higher polyphenol levels infruits. The organic acid contents also determine berry qualitiesthrough functioning as the primary components of acidity indietary fruits, acting as the intermediates of TCA cycle, andamino acid biosynthetic pathways.35 In particular, citric acid,malic acid, and lactic acid are the major components ofrepresentative organic acids contributing to characteristic flavorand taste in ripened berries. In grapes, the organic acid contentsare reportedly influenced by DS to R ratios.18 In particular, theDS and R influence malic acid content of grapes, implying theeffects of geo-climatic factor on metabolic plasticity. Weobserved a strong positive correlation for lactic acid andmalic acid contents with R, suggesting potential metabolitebiomarkers for average rainfall with respect to berry harvests.The higher DS, facilitated an elongated photosynthetic periodand hence more sugars are presumably produced.40 Simulta-neously, the lower R results in sturdier pulps with reduced fruitsize having higher sugar content. In congruence with theprevious reports, we have found the strong positive correlationbetween sucrose content and DS coupled with a negativecorrelation between sucrose contents and R, respectively.Hence, the climatic conditions, DS and R, significantlyinfluenced the metabolite components in berries, potentiallyaffecting the berries qualities harvested from varying geo-climatic regions.In conclusion, we have demonstrated a MS-based metab-

olomic approach to evaluate the metabolic disparity amongblueberry and chokeberry samples harvested from various geo-climatic in the Republic of Korea. The berry metabolomesexhibited the distinct metabolic patterns for the samplesharvested from the regions across the latitudes (35°N and36°N), with noteworthy correlations for the duration ofsunshine and rainfall (±). The current studies are althoughnot sufficient to provide the comprehensive explanations of

metabolic changes in different environment, each year, thedisparities in metabolic profiles according to the different geo-climatic regions during the cultivation periods can be explainedin a fragmentary way. Therefore, we consider that the berriesharvested from different geo-climatic regions, especiallylatitudes, could be commercially labeled designating theircharacteristic taste and flavor toward consumer’s palatabilitychoices.

■ ASSOCIATED CONTENT*S Supporting InformationThe Supporting Information is available free of charge on theACS Publications website at DOI: 10.1021/acs.jafc.7b04065.

The summary of different berry sizes as quality indicatorsfor the samples harvested from different geo-climaticregions in the Republic of Korea; summary ofinformation for pH, titratable acidity, amino nitrogen,and sugar contents in blueberry and chokeberry samplesused in the study; orthogonal partial least-squaresdiscriminant analysis (OPLS-DA) score plots ofsignificantly different metabolites between blueberryand chokeberry derived from the gas chromatography-time-of-flight-mass spectrometry (GC-TOF-MS) dataset; and orthogonal partial least-squares discriminantanalysis (OPLS-DA) score plots of blueberry (A) andchokeberry (B) derived from the gas chromatography-time-of-flight-mass spectrometry (GC-TOF-MS) data set(PDF)

■ AUTHOR INFORMATIONCorresponding Author*Tel.: +82-2-2049-6177; Fax: +82-2-455-4291; E-mail:chlee123@konkuk.ac.kr.ORCIDChoong Hwan Lee: 0000-0002-2311-185XFundingThis work was carried out with the support by grant of theSunchang Research Institute of Health and Longevity and thiswork was supported by a grant from the Next-GenerationBioGreen 21 Program (grant No. PJ01109403), RuralDevelopment Administration, Republic of Korea.NotesThe authors declare no competing financial interest.

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