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. 2018 Sep 1;8(3):49. doi: 10.3390/metabo8030049

Untargeted Metabolomics Analysis of Eggplant (Solanum melongena L.) Fruit and Its Correlation to Fruit Morphologies

Abu Hanifah 1, Awang Maharijaya 1,2, Sastia P Putri 3, Walter A Laviña 4, Sobir 1,2,*
PMCID: PMC6160926  PMID: 30200482

Abstract

Eggplant is one of the most widely cultivated vegetables in the world and has high biodiversity in terms of fruit shape, size, and color. Therefore, fruit morphology and nutrient content become important considerations for both consumers and breeders who develop new eggplant-based products. To gain insight on the diversity of eggplant metabolites, twenty-one eggplant accessions were analyzed by untargeted metabolomics using GC-MS and LC-MS. The dataset of eggplant fruit morphologies, and metabolites specific to different eggplant fruit accessions were used for correlation analysis. Untargeted metabolomics analysis using LC-MS and GC-MS was able to detect 136 and 207 peaks, respectively. Fifty-one (51) metabolites from the LC-MS analysis and 207 metabolites from the GC-MS analysis were putatively identified, which included alkaloids, terpenes, terpenoids, fatty acids, and flavonoids. Spearman correlation analysis revealed that 14 fruit morphologies were correlated with several metabolites. This information will be very useful for the development of strategies for eggplant breeding.

Keywords: untargeted metabolomics, eggplant fruit diversity, GC-MS, LC-MS, eggplant fruit morphology

1. Introduction

Eggplant (Solanum melongena L.) is one of the most widely grown vegetables in the world [1,2], ranking sixth in global vegetable production. Eggplants are mainly grown for their fruits, with only a few cases reporting the use of eggplant leaves as herb or for medicinal uses [1]. China has the highest eggplant production in the world with 22 million tons, ca. 62% of the total world production [3]. Eggplant is one of the most common ingredients in Indonesian cuisine and Indonesian people consume local eggplants of different shapes, colors, and patterns. Thus, in this region, different fruit morphology characteristics are considered major factors when assessing the quality of eggplants. These include several morphological characteristics such as color, shape, size, uniformity, shelf-life, and nutrient content.

Nutrient content is an important consideration when choosing the type of eggplant fruit to cultivate. In addition, eggplants are found to contain high levels of antioxidant compounds, as well as phenolic and flavonoid compounds [4]. To explore the diversity of eggplant fruit metabolites, metabolomics—the comprehensive analysis of metabolites in a biological sample at a given time or condition—can be used [5]. Such metabolite profiling can be done using gas chromatography or liquid chromatography coupled with mass spectrometry (GC-MS or LC-MS). One approach, called untargeted metabolomics analysis, aims to collect as much information as possible from the metabolites contained in biological samples [6]. This approach has been successfully used to determine the metabolite profile of various plants such as tomato, arabidopsis, date, potato, and black cumin [7,8,9,10]. Information on the diverse metabolites of plants is very useful for better understanding of the genotypic or phenotypic differences of plants. In coffee, untargeted metabolomics analysis was used as a preliminary approach to determine the origin of coffee, after which targeted analysis was done to determine the difference in flavor and taste [11]. Furthermore, the usefulness of metabolomics in determining markers for discriminating original “kopi luwak” from adulterated coffee has also been reported [12]. In this research, we analyzed the diversity of metabolites found in different eggplant fruit accessions and their relationship with fruit morphology. This information will be beneficial for the development of strategies for improvement of the eggplant crop in the future.

2. Results

2.1. Untargeted Metabolomics Analysis

Untargeted metabolomics analysis of eggplant fruit successfully detected 207 metabolite peaks using GC-MS, and 136 metabolite peaks using LC-MS. The log-transformed dataset of peak area percentage (area pct.) was used to construct the heat map and dendrogram (Figure 1). The heat map shows the metabolites that were detected in 21 eggplant accessions. Hierarchical cluster analyses (HCA), using Euclidean distance and complete linkage agglomerative methods, were carried out to measure the similarity of multivariate samples [13] and cluster the metabolomics data, respectively [14]. In the heat map, rows represent each detected metabolite, and columns represent the different eggplant accessions. Large peak areas were colored green, while smaller peak areas were black, and went red as peaks became smaller. Among the detected metabolite peaks from GC-MS and LC-MS analyses, 207 and 51 metabolites were putatively identified using the online metabolite databases MassBank (http://www.massbank.jp/?lang=en) and Plant Metabolic Pathway Databases (https://www.plantcyc.org/) [15,16] (Table 1 and Table 2), respectively. The important metabolites identified in the GC-MS analysis are shown in Table 1, and the complete list is shown in Table S1.

Figure 1.

Figure 1

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Heat map result of hierarchical group analysis based on metabolite peaks identified from LC-MS and GC-MS analyses. (a) heat map of 207 metabolite peaks from GC-MS analysis; (b) heat map of 136 metabolite peaks from LC-MS analysis. A = metabolites found in many eggplant accessions; B = metabolites found in a few, or only one, eggplant accession. The green, black, and red bars represent the value of area pct. that has been transformed by log2. The bright green color shows higher peak areas of metabolite, while black and red colors show lower peak areas of metabolites. This coloring is metabolite-wise.

Table 1.

Metabolite compounds from untargeted metabolomics analysis in eggplant fruit using GC-MS.

Name Formula Class Accessions
Linoleic acid C18H32O2 Saturated Fatty Acid G02, G05, G07, G25, G33, G37, G44, G45, G48, G55, G60, G61, G63, G75, G76, G78, G80, GJ, GK, GR
Palmitic acid C16H32O2 Saturated Fatty Acid G02, G05, G07, G25, G33, G38, G44, G45, G48, G55, G61, G63, G75, G76, G78, G80, GJ, GK, GR
α-Tocopherol (Vitamin E) C29H50O2 Vitamin G07, G25, G33, G37, G38, G45, G48, G60, G63, G76, G78, G80, GJ, GK
Neophytadiene C20H38 Diterpenoid G02, G05, G07, G33, G37, G38, G48, G55, G63, GK
Farnesyl acetone C18H30O Terpenoid G02
N,N-Dimethylethylamine C14H11N Amines G05
Hexanal C6H12O Alkaloid G05
2-Hydroxyethylphosphine C2H7OP Flavonoid G05
(Dimethylamino)acetone C5H11NO Alkaloid G05
4-Oxononanedioic acid C9H14O5 Terpen G05
Clionasterol C29H50O Triterpenoid G05
7,11-Hexadecadienal C16H28O Pheromon G05
Ethyl iso-allocholate C26H44O5 Steroid G07
1,1-Dibutylhydrazine C8H20N2 Skeletal formula G25
Citronellyl formate C11H20O Terpenoid G25
13-Tetradecenal C14H26O Terpenoid G25
Ammonium oxalate, monohydrate C2H8N2O4 Terpenoid G33
2-Propoxyethylamine C5H13NO Terpenoid G33
2-Furanmethanol C5H6O2 Terpenoid G33
4-Methyl-1,3-dioxane C5H10O2 Benzene and Subtituent Derivatives G33
Ethyl tetradecanoate C16H32O2 Unsaturated Fatty Acid G33
Methyl (9E,12Z)-9,12-octadecadienoate C19H34O2 Diterpenoid G37
Solanesol C45H74O Polyterpen G37
Butanoic acid, 3-hydroxy- C4H8O3 Hydroxy Acid and Derivatives G38
Methoxyethylamine C3H9NO Alkaloid G38
1,4-Cyclohexanediol, trans- C6H12O2 Flavonoid G38
3-Butenoic acid C4H6O2 Fatty Acids and Conjugate G38
Pentanal C5H10O Saturated Fatty Acid G38
E-9-Tetradecenal C14H26O Unsaturated Fatty Acid G38
Methyl palmitoleate C17H32O2 Fatty Acid Methyl Ester G38
Octadecyltrichlorosilane C18H37Cl3Si Organochlorosilane G38
2,4-Dihydroxy-2,5-dimethyl-3(2H)-furan-3-one C6H8O4 Flavonoid (Ketone bodies) G44
Hentriacontane C31H64 Alkanes G45
Diisooctyl phthalate C24H38O4 Diterpenoid G48
9,10-Dibromopentacosane C25H50Br2 Recolcinolic lipid G48
Hydroperoxide, 1-methylhexyl C7H16O2 Terpenoid G60
Eicosanoic acid C20H40O2 Saturated Fatty Acid G60
Octadecanoic acid C18H36O2 Saturated Fatty Acid G60
Ethyl 9-hexadecenoate C18H34O2 Unsaturated Fatty Acid G60
2,2-Dideuteropropane C3H8 Hydrocarbon G61
Tetradecanal C14H28O Saturated Fatty Acid G61
Ethyl 9-heptadecenoate C19H38O2 Saturated Fatty Acid G63
Propanamide, N,N-dimethyl- C5H9NO2 Steroid G75
Cycloeicosane C20H40 Terpen G75
trans-Chrysanthemal C10H16O Natural pesticide G75
Z,E-3,13-Octadecadien-1-ol C18H34O Unsaturated Fatty Acid G76
1-Propoxyoctane C11H24O Steroid G78
Cyclododecanone C12H22O Flavonoid (Ketone bodies) G78
Ketopinic Acid C10H14O3 Flavonoid G80
Propanedioic acid C3H4O4 Terpenoid GJ
3-Amino-2-oxazolidinone C3H6N2O2 Alkaloid GJ
1,3-Cyclopentenedione C5H6O2 Alkaloid GJ
Trichloroacetic acid, undec-10-enyl ester C13H21Cl3O2 Trichloroacetic Acid GJ
Cyclohexadecanone C16H30O Organohalogen compound GJ
Oleyl alcohol, heptafluorobutyrate C22H35F7O2 Alkanes GJ
Ethyl octadecanoate C20H40O2 Unsaturated Fatty Acid GK
10-HeptyL-10-Octylicosane C35H72 Terpen GK
Pentacosane C25H52 Alkanes GK
1-Chloroheptacosane C27H55Cl Alkanes GK
Triacontane C30H62 Alkanes GK
9-Hexacosene C26H52 Terpen GK
Nonadecane C19H40 Terpen GK
(9E,12E)-9,12-Octadecadienoyl chloride C18H31ClO Skeletal formula GR
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Table 2.

Metabolites from untargeted metabolomics analysis in eggplant fruit using LC-MS.

Name Formula Class Accessions
2,5-Bis(N-hexylmethylsilyl)thiophene C18H36SSi2 Terpen G07, G25, G37, G76, G78
2-(Methylthiomethyl)-3-phenyl-2-propenal C11H12OS Terpenoid G05, G33, G38, G45
Boscalid C18H12Cl2N2O Alkaloids G07, G25, G37, GK
Dimethisterone C23H32O2 Steroid G25, G48, G78
Glucolepidiin C17H14Cl2N2O2 Alkaloids G48, G78, GJ
Methyl-2-alpha-l-fucopyranosyl-beta-d-galactoside C13H24O10 Glycoside G25, G48
L-dopachromate C9H6NO4 Terpenoid G37, G38
L-saccharopine C11H19N2O6 Alpha amino acids G37, GK
Coumachlor C19H15ClO4 Steroid G37, G78
5-Methoxytryptamine C11H14N2O Alkaloids G38, G78
Clopidol C7H7Cl2NO Alkaloids G44, G60
4,4′-Ditolylthiourea C15H16N2S Steroid G48, GJ
Octylbenzene C14H22 Terpenoid G76, G78
Cyclopentolate C17H25NO3 Steroid G76, G78
Trioxilin A3 C20H33O5 Steroid G78, GK
Pyrazinemethanethiol C5H6N2S Monoterpene G05
1-Diethoxyphosphoryl-4-hydroxy-nonan-2-one C13H27O5P Terpenoid G05
1,2,4-Trithiolane C2H4S3 Steroid G07
2-Chloro-1,4-naphthoquinone C10H5ClO2 Monoterpene G37
Methyl 6-O-galloyl-beta-d-glucopyranoside C14H18O10 Flavonoid G37
Propericiazine C21H23N3OS Alkaloids G37
N-Phenylacetylglutamic acid C13H17NO5 Steroid G37
Carprofen C15H12ClNO2 Steroid G37
8-O-Methyloblongine C20H26NO3 Steroid G37
5,5′-Methylenedi(2-para-tolylperhydropyrrolo(3,4-c)pyrrole-1,3-dione) C27H28N4O4 Alkaloids G38
1-Deoxy-d-xylulose C5H10O4 Flavonoid G38
Cis-Zeatin C10H13N5O Alkaloids G38
1-Hydroperoxy-8-carboxyoctyl-3,4-epoxynon-(2E)-enyl-ether C12H18BNO2 Alkaloids G38
Pentobarbital sodium C11H17N2O3 Alkaloids G38
Noladin Ether C23H40O3 Steroid G45
Lariciresinol C20H24O6 Monoterpene G48
1,2-Bis(4-nitrophenyl)ethane C14H12N2O4 Stilbenoid G48
1,2,4-Nonadecanetriol C19H40O3 Terpen G55
1-Piperideine-6-carboxylate C6H8NO2 Alkaloids G60
9-Chloro-10-hydroxy-octadecanoic acid C18H35ClO3 Alkaloids G60
Diphenidol C21H27NO Monoterpene G61
1-Tetradecanoyl-glycero-3-phosphoserine C20H40NO9P Fatty acid G63
Ginkgolic acid C22H34O3 Terpenoid G76
Hexythiazox C17H21ClN2O2S Alkaloids G76
Atovaquone C22H19ClO3 Steroid G76
5-Formiminotetrahydrofolate C20H24N8O6 Monoterpene G76
Butocarboxim C7H14N2O2S Alkaloids G76
Elaeocarpidine C17H21N3 Alkaloids G76
Mecarphon C7H14NO4PS2 Organophosphorus G78
Ethyl 18-bromooctadec-17-en-5,7,15-triynoate C20H25BrO2 Monoterpene GJ
2-Acetoxy-7-bromo-4-isopropyltropone C12H13BrO3 Terpen GJ
N-(1-Deoxy-1-fructosyl)histidine C12H19N3O7 Steroid GJ
Docosanoic acid C22H44O2 Terpen GK
Leflunomide C12H9F3N2O2 Steroid GK
Yohimbinic acid C20H24N2O3 Alkaloids GK
Methyl aminolevulinate C6H11NO3 Alkaloids GR
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Figure 1a shows the heat map generated from GC-MS analysis. The detected metabolite peaks (in rows) produced two clusters: group A and group B, while eggplant accessions (in columns) also clustered into two groups: group 1 and group 2. Group A in the GC-MS heat map (Figure 1a) consisted of metabolites that were present in most of the eggplant accessions such as linoleic acid, palmitic acid, and neophytadiene (Table 1). Based on GC-MS analysis, the clustering of eggplant accessions showed interesting results in which only GK separated from the rest of the accessions. Group 1 consisted of 20 eggplant accessions while Group 2 consisted of only GK (Figure 1a). Accession-specific metabolites (indicated in green) for GK include linoleic acid, palmitic acid, and neophytadiene. Moreover, GK had the most accession-specific metabolites among all the samples (Table S1). On the other hand, accessions G37, G78 and GR each had a solitary accession-specific metabolite, namely solanesol, cyclododecanone, and (9E,12E)-9,12-octadecadienoyl chloride, respectively (Table 1).

Group A in the LC-MS heat map consisted of two unidentified metabolites, (LmUi 1) and (LmUi 2), which were present in 15 and 7 accessions, respectively. The other group of metabolites, group B, consisted of metabolites that were present in fewer eggplant accessions than group A. For instance, farnesyl acetone and citronellyl formate were found only in G02 and G25, respectively (Table 1). Likewise, L-dopachromate can be found in G37 and G38, cyclopentolate in G76 and G78, and carprofen in G37 (Table 2).

The number of accession-specific metabolites was also the main factor for the clustering of groups in the LC-MS heat map (Figure 1b). As seen on the heat map, group 1, which includes G38, G48, GJ, GK, G76, G25, and G78, had more accession-specific metabolites (indicated in green) than group 2. The specific metabolites for each eggplant accession that have been annotated are shown in Table 2. Fifteen out of 21 accessions have annotated accession-specific metabolites, while six (G02, G25, G33, G44, G75, and G80) have unknowns. Table 2 shows 12 classes of identified metabolites, in which the alkaloid group was the most common metabolite class with 16 metabolites. There are four identified metabolites that can be found in more than two eggplant accessions, namely 2,5-bis(N-hexylmethylsilyl)thiophene, penicillin K, 2-(methylthiomethyl)-3-phenyl-2-propenal, and dimethisterone (Table 2).

2.2. Correlation Analysis of Fruit Metabolites with Fruit Morphology

To gain insight on the relationship between fruit metabolites and morphology, data on the fruit morphology of 21 eggplant accessions were used in the correlation analysis. All eggplant accessions possess metabolites and fruit morphology characteristics that are unique to each accession (Table 3). Specifically, different eggplant accessions have different fruit size, shape, and color, with some also showing unique morphology such as fruit with patches and stripes on the skin (G02, G05, G48), fruit with ribs (G02, G05, G25 and G63), very dark purple fruit (GJ), and yellow fruit (GK).

Table 3.

Description of fruit morphologies for 21 eggplant accessions based on UPOV.

Code Morphological Characteristics
FL Fruit: length (1) very short (<1 cm), (3) short (~ 2cm), (5) medium (~5 cm), (7) long (~10 cm), (9) very long (>20 cm)
FD Fruit: maximum diameter (1) very small (<1 cm), (3) small (~2 cm), (5) medium (~3 cm), (7) large (~5 cm), (9) very large (>10 cm)
FLD Fruit: ratio length/maximum diameter (1) very small, (3) small, (5) medium, (7) large, (9) very large
GS Fruit: general shape (1) globular, (2) ovoid, (3) obovate, (4) pear shaped, (5) club shaped, (6) ellipsoid, (7) cylindrical
PTS Fruit: size of pistil scar (1) very small, (3) small, (5) medium, (7) large, (9) very large
APX Fruit: apex (1) indented, (2) flattened, (3) rounded, (4) pointed
DPS Fruit: depth of indentation of pistil scar (1) absent or very shallow, (3) shallow, (5) medium, (7) deep, (9) very deep
CVT Only for cylindrical types: Fruit: curvature (1) absent or very weak, (3) weak, (5) medium, (7) strong, (9) very strong
MC Fruit: main color of skin at harvest maturity (1) white, (2) green, (3) violet, (4) yellow
ICS Fruit: intensity of main color of skin (1) very light, (3) light, (5) medium, (7) dark, (9) very dark
PTC Fruit: patches (1) absent, (9) present
STR Fruit: stripes (1) absent, (9) present
PST Fruit: prominence of stripes (3) weak, (5) medium, (7) strong
DST Fruit: density of stripes (3) sparse, (5) medium, (7) dense
GL Fruit: glossiness at harvest maturity (3) weak, (5) medium, (7) strong
RBS Fruit: ribs (1) absent or very weak, (3) weak, (5) medium, (7) strong, (9) very strong
AUC Fruit: anthocyanin coloration underneath calyx (1) absent, (9) present
IUC Fruit: intensity of anthocyanin coloration underneath calyx (3) weak, (5) medium, (7) strong
LPD Fruit: length of peduncle (1) very short, (3) short, (5) medium, (7) long, (9) very long
CL Fruit: size of calyx (1) very small, (3) small, (5) medium, (7) large, (9) very large
ACL Fruit: anthocyanin coloration of calyx (1) absent, (9) present
ICL Fruit: intensity of anthocyanin coloration of calyx - (1) very weak, (3) weak, (5) medium, (7) strong, (9) very strong
SCL Fruit: spininess of calyx (1) absent or very weak, (3) weak, (5) medium, (7) strong, (9) very strong
CCL Fruit: creasing of calyx (1) very weak, (3) weak, (5) medium, (7) strong, (9) very strong
CFL Fruit: color of flesh (1) whitish, (2) greenish
CPR Fruit: color of skin at physiological ripeness (1) yellow, (2) orange, (3) ochre, (4) brown
Code G02 G05 G07 G25 G33 G37 G38 G44 G45 G48 G55 G60 G61 G63 G75 G76 G78 G80 GJ GK GR
FL 5 5 9 5 9 5 9 3 5 7 9 3 9 7 9 3 7 7 9 3 7
FD 7 7 7 7 7 5 7 3 5 7 5 5 7 7 7 3 7 7 5 5 9
FLD 1 3 7 1 7 5 7 3 5 5 9 1 7 7 7 3 7 7 9 1 3
GS 1 2 5 1 6 3 7 2 2 3 7 2 7 5 5 2 6 5 7 2 1
PTS 5 5 5 9 7 5 7 3 5 3 5 3 9 7 9 3 9 5 3 3 7
APX 3 1 1 1 3 3 4 1 1 1 3 3 3 4 7 1 4 4 4 1 1
DPS 1 3 1 3 1 1 1 3 1 3 1 1 1 1 1 1 1 1 1 5 3
CVT 1 1 5 1 1 1 3 1 1 1 7 1 3 3 1 1 1 1 5 1 1
MC 2 2 3 3 1 2 2 3 2 2 2 2 3 3 2 3 2 3 3 5 1
ICS 5 3 7 7 5 3 3 5 5 5 3 5 7 3 3 1 1 5 9 5 5
PTC 9 9 1 1 1 1 1 1 1 9 1 1 1 1 1 1 1 1 1 1 1
STR 9 9 9 9 9 9 1 9 9 9 1 9 9 9 1 9 1 9 1 1 1
PST 7 7 3 3 3 7 3 3 5 3 3 7 3 3 3 7 3 3 3 3 3
DST 7 5 3 3 3 5 3 3 5 3 3 7 3 3 3 7 3 3 3 3 3
GL 3 3 7 5 3 3 3 5 3 3 5 3 5 1 3 3 3 5 7 3 3
RBS 5 5 1 5 1 1 1 1 1 1 1 1 1 5 1 1 1 1 1 1 1
AUC 1 1 1 9 1 9 9 9 1 9 1 1 9 1 1 1 1 1 9 1 1
IUC 3 3 3 5 3 3 5 5 3 5 3 3 3 3 3 3 3 3 7 3 3
LPD 5 5 7 7 9 5 9 5 5 5 5 5 9 9 9 7 9 7 5 5 7
CL 3 5 3 7 3 3 5 3 3 1 3 5 5 9 1 1 5 3 3 1 3
ACL 1 1 1 9 1 9 9 9 1 9 1 1 9 1 1 1 1 1 9 1 1
ICL 1 1 1 5 1 3 5 5 1 3 1 1 3 3 1 1 1 1 9 1 1
SCL 1 1 1 1 1 1 3 1 1 3 1 1 1 9 1 1 1 1 3 1 3
CCL 3 5 3 1 3 3 3 1 3 3 1 1 5 3 5 3 5 3 7 3 1
CFL 2 1 2 2 1 2 2 2 1 1 2 1 1 1 1 1 1 1 2 1 1
CPR 1 1 4 4 1 1 1 3 1 1 2 1 4 4 2 2 1 4 3 1 1
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Correlation coefficient analysis indicated that fruit morphologies are related to metabolites (Figure 2). The correlation analysis was done between 26 fruit morphologies and all the identified peaks from the LC-MS and GC-MS analyses separately (Figure 2A). Furthermore, the correlation coefficient of accession-specific metabolites from each eggplant accession were collected and used to build a new heat map (Figure 2B and Table S2). Accession-specific metabolites from the same eggplant accessions showed similar coefficient correlation (Figure 2B). Out of 26 fruit morphology categories, seven eggplant fruit morphologies, namely DPS, CVT, PTC, DST, RBS, IUC, and SCL, showed correlations with specific metabolites. Accessions with patched fruit, G02, G05, and G48, correlated with farnesyl acetone, clionasterol, and lariciresinol, respectively. G25 along with G05 and G02, which have medium ribs, also showed a correlation between its fruit morphology and its accession-specific metabolites. Strong curvature fruit (CVT), which is unique to G55 fruit, was correlated with 1,2,4-nonadecanetriol. Similarly, very strong spininess of calyx (SCL) that is only found in G63, correlated with ethyl 9-heptadecenoate and 1-tetradecanoyl-glycero-3-phosphoserine metabolite, with the highest correlation coefficient. GJ with its very strong intensity of anthocyanin coloration underneath the calyx (IUC), and GK with its medium depth of indentation of pistil scar (DPS), also showed correlations with their metabolites. On the other hand, the rest of the accessions did not show any correlations.

Figure 2.

Figure 2

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Heat map of correlations between eggplant fruit metabolites and fruit morphologies. Each square represents the Spearman’s correlation coefficient (p < 0.05). (A) Heat map of correlations between eggplant fruit metabolites based on 136 detected peaks of LC-MS analysis and 207 detected peaks of GC-MS analysis with 26 fruit morphologies; (B) Heat map of correlations between specific eggplant fruit metabolites of each accession with 26 fruit morphologies.

3. Discussion

3.1. Untargeted Metabolomics Analysis Revealed Unique Metabolites Present in Different Accessions

Untargeted metabolomics analysis was successfully carried out using LC-MS and GC-MS on eggplant fruits. In this study, we used both GC-MS and LC-MS to increase the number of metabolites that could be detected due to the difference in the type of metabolites that can be analyzed by each analytical platform. GC-MS is more widely used in metabolomics analysis than LC-MS because there are more metabolite databases available for GC-MS [17]. However, using both LC-MS and GC-MS analysis is preferred in metabolomics. Choi et al. [11] used LC-MS and GC-MS in untargeted metabolomics analysis to determine the origin of coffee, followed by targeted analysis to determine the difference in flavor and taste. To assess the variety of our detected metabolites, data analysis was conducted using R software (Version 3.2.2) with the Metabolomics package. The heat map was able to show the differences in metabolites that were present in 21 eggplant accessions.

The metabolites identified in the different eggplant accessions were diverse, with some that are reported to have economic or health benefits. Using GC-MS analysis, we found 20 classes of metabolites, with terpenoids being the most abundant (Table 1). Farnesyl acetone, citronellyl formate, and 2-furanmethanol, in G02, G25, and G33 respectively, are terpenoids known as food additives [18,19,20]. We observed that most of the eggplant accessions have linoleic acid, palmitic acid, α-tocopherol (Vitamin E), and neophytadiene. Palmitic acid is a saturated fatty acid that is commonly found in plants and animals [21], while linoleic acid is one of the saturated fatty acids that is beneficial for acne-prone patients as a comedolytic agent [22] and for human bone health [23]. Moreover, these fatty acids are also important for the plant, as plant membranes contain a mixture of saturated and unsaturated fatty acids that are believed to be essential for the plant to adapt to environmental changes [24]. On the other hand, Vitamin E is commonly found in fruits and vegetables and is reported to have antioxidant activity [25]. Neophytadiene is a natural volatile compound found in tobacco, that contributes to its flavor in small effect and has antimicrobial activity [26].

Plants synthesize a wide variety of organic compounds. We also found a wide range of alkaloids, steroid, terpenes, fatty acids, and flavonoids in eggplants from the LC-MS analysis. These diverse organic compounds are believed to be part of an evolutionary process of plant defense against pests, diseases, droughts and other environmental challenges [27]. Other metabolites that were found in more than four accessions were 2,5-Bis(N-hexylmethylsilyl)thiophene and 2-(methylthiomethyl)-3-phenyl-2-propenal (Table 1). 2,5-Bis(N-hexylmethylsilyl)thiophene, which was identified using the metabolite database of Massbank of Japan Science and Technology Agency, has unknown function while 2-(methylthiomethyl)-3-phenyl-2-propenal is known as a food additive agent according to the JECFA (Joint FAO/WHO Expert Committee on Food Additives) database [28]. Some metabolites from the LC-MS analysis are still unidentified.

Analytical instruments used in this analysis were coupled with a low-resolution mass spectrometer. Low-resolution instruments are more cost effective and easier to access in newly industrialized countries such as Indonesia; thus, these instruments are suitable for use in the initial screening of local eggplant fruit metabolites. The drawbacks of using low-resolution LC/MS include poor mass accuracy and low specificity, thus it tends to yield several candidate metabolites for a particular peak. Therefore, metabolite annotation was performed by spectral library matching as a first step. Several candidate metabolites were then shortlisted and metabolites that showed identical names from both the MassBank and PlantCyc online databases were selected. Afterward, we double- checked in the public repository websites such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and ChemSpider (http://www.chemspider.com/) [29,30] to determine whether these metabolites are commonly found in plants.

3.2. Several Metabolites Are Accession-Specific

We described accession-specific metabolites as those that were present in only one accession (Table 1 and Table 2). For instance, mecarphon, (Table 2) which was present only in G78, is a natural pesticide that is used in seed treatment of Delia spp. [31]. Solanesol, which was present only in G37, is a metabolite that is mainly found in solanaceous crops, including tobacco, tomato, potato, eggplant, and pepper. Solanesol is used as a critical intermediate for the synthesis of ubiquinone drugs in the pharmaceutical industry [32]. Information on solanesol found in G37 might be used for the pharmaceutical industry as a useful backup source of solanesol, other than tobacco leaves.

The detection of specific metabolites in different eggplant accessions is likely dependent on the genetic makeup of the plant. Untargeted metabolomics and quantitative trait locus (QTL) analysis of Arabidopsis thaliana showed that metabolite variation is genetically controlled [33]. The long process of gene expression involves many metabolic pathways until it can finally manifest as a phenotype. Cellular regulatory processes produce metabolites as their end products to respond to biological signals and environmental changes that will affect the phenotype [34]. Similar to eggplant, metabolomics analysis of tomatoes and potatoes also showed differences in metabolites based on accessions [35,36]. Since analysis on the metabolomics level is closer to the expression of the plant phenotype than to the genomic level, it can better explain the phenotype. Therefore, metabolomics analysis is an appropriate approach to distinguish phenotypes [37]. This information on accession-specific metabolites can be used by plant breeders to choose and develop better eggplant varieties, as well as being a new selling point to consumers.

3.3. Correlation Analysis Shows Relationship between Fruit Morphologies and Metabolites

Creating variation in eggplant fruit size and shape is one of the objectives of breeding. Size and shape, along with taste, are the three principal qualities considered in the process of domestication of eggplant in China [38]. Selection and breeding over hundreds of years have resulted in a large number of eggplant varieties [39]. Genetic studies on fruit size and shape development in eggplant showed that these characteristics are controlled by some of the loci mapped to the common region of the genome [40]. Our eggplant accessions showed the variety in fruit color, including green, white, and purple, with only one accession with yellow fruit. Yellow eggplant is commonly used as an ornamental plant and not for food [41]. Purple, green, striped and patched eggplants are preferred in Southeast Asia over yellow eggplants [42]. This is probably the reason yellow eggplants are less common than others.

This research was able to obtain information about the distinct metabolites in different eggplant accessions and their correlations with fruit characteristics. Metabolites in the G33, G37, G38, G61, G78, and G80 accessions did not correlate with any fruit characteristics. We suspect that metabolites in these accessions are not included in the regulatory processes that lead to the phenotype of fruits. Recent studies about plant tissue-specific metabolism showed that some tissues with specific metabolite groups have interdependencies among them in terms of metabolite content [43]. Since metabolites are the result of interactions between the genome system and the environment that is not only the end result of gene expression but part of the regulation of the biological system [44], this might explain why some metabolites are not related to some characteristics.

Plant metabolomics is a continuously expanding field and, so far, recent studies are more focused on the application of metabolomics with other “omics” such as genomics, transcriptomics, and proteomics [43]. However, the vast number of metabolites in plants and their biological systems are still a challenge for metabolomics research. Metabolic alterations in lettuce (Lactuca sativa L.) caused by different CECs (contaminants of emerging concern) from exposure to irrigation water were linked to the changes in morphological characteristics such as leaf height and stem width [45]. This indicates that plant metabolites are correlated with plant morphological characteristics. However, the change in plant metabolites due to a sudden change of environment usually affects quantitative traits. On the other hand, our findings showed correlations between plant metabolites and morphological characteristics of eggplant for both quantitative (fruit length, fruit diameter) and qualitative traits (fruit main color, fruit patches). This study will be useful to further studies for developing preferred fruit morphology based on the metabolites contained therein.

Metabolomics analysis is an appropriate approach to distinguish phenotypes [37]. Information on metabolites that are correlated with morphological characteristics would be very useful for plant breeding strategies in eggplant. Moreover, plant breeders and consumers can use this information to make a quick assessment of eggplant fruits and their content.

4. Materials and Methods

4.1. Plant Materials

A total of 21 eggplant fruit accessions with different fruit morphologies from the Center for Tropical Horticulture Studies, Bogor Agricultural University, Indonesia were used in this study (Table 4). The plants were maintained in Bogor, West Java province, Indonesia (altitude 265 m above sea level, maximum temperature 29 °C, minimum temperature 20 °C). Eggplant seeds were sowed and watered regularly for a five-week period in a greenhouse. The seedlings were transplanted manually into the experimental plot. Ten plants per accession were planted with three replications. Plants were spaced 30 cm between rows and 40 cm between columns. A combination of compound fertilizer and manure was used to ensure that plants grew normally. All plants were kept free from pest and disease using a combination of manual and chemical controls.

Table 4.

Description of 21 eggplant accessions from the Center for Tropical Horticulture Studies.

Accessions Code Fruit Morphology
Color Shape
G02 Green with stripes and patches Globular
G05 Light green with stripes and patches Ovoid
G07 Dark purple with stripes Club shaped
G25 Dark purple with stripes Globular
G33 White with stripes Ellipsoid
G37 Light green with stripes Obovate
G38 Light green Cylindrical
G44 Purple Ovoid
G45 Green with stripes Ovoid
G48 Green with stripes and patches Obovate
G55 Light green Cylindrical
G60 Green with stripes Ovoid
G61 Dark purple Cylindrical
G63 Light purple with stripes Club shaped
G75 Light green Club shaped
G76 Very light purple with green stripes Ovoid
G78 Very light green Ellipsoid
G80 Purple with stripes Club shaped
GJ Very dark purple Cylindrical
GK Yellow Ovoid
GR White Globular
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4.2. Extraction and Sample Preparation

Eggplant fruits were harvested 12–15 days after anthesis [4]. Three eggplant fruits were sampled from each accession. The freshly-picked fruits were immediately cut into smaller sizes and dried using an oven at 40 °C for three days [46]. The small pieces from three eggplant fruits of each accession were pooled to make a representative fruit sample. Later, the dried fruit samples were placed in 99.9% pure ethanol and macerated for three days. Thirty microliters (30 µL) of the macerated samples were injected into the LC-MS instrument for analysis. For GC-MS analysis, 10 mL of the macerated samples were transferred to a new tube and evaporated for 1 h at 40 °C. The evaporated sample was added to 200 µL extract of the macerated samples to increase the concentration. Five microliters of sample was used for injection to get a wide range spectrum.

4.3. LC-MS Analysis

Waters Alliance 2695 HPLC system was used in the LC-MS analysis using an XTerra MS C18 column (2.1 × 100 mm, 3.5 µm particle size; Waters, MA, USA). The injection volume was 30 µL. The mobile phase A was methanol, while the mobile phase B was water with the following linear gradient programme: the concentration of mobile phase A was 10% at 0 min, 25% at 5 min, increased to 75% with gradient 5%/min and held for 5 min, then decreased to 10% and held for 15 min, while the mobile phase B was 90% at 0 min, 75% at 5 min, decreased to 25% and held for 5 min with gradient 5%/min, then increased until 90% and held for 15 min. The flow rate was 0.2 mL/min and the column temperature was 40 °C. The spectra were monitored using Waters Quattro Micro™ in full scan mode (m/z 50–1200) and electrospray ionization (ESI) interface in positive mode, with the source temperature maintained at 120 °C, desolvation temperature at 450 °C, and gas flow of 500 L/h. This procedure was controlled by Masslynx software (Version 4.0).

4.4. GC-MS Analysis

An Agilent Technologies 7890 A Gas Chromatograph coupled with 5975 C Mass Spectrometer system was used for analysis, with HP Ultra 2 capillary column (30 m, 0.25 mm i.d., 0.25 mm film thickness; Agilent, Santa Clara, CA, USA). The injection volume was 5 µL with 8:1 split ratio and 250 °C injection port temperature. The initial oven temperature was at 70 °C held for 0 min, increased at 3 °C/min to 150 °C, held for 1 min, and finally raised 20 °C/min to 250 °C and held for 26 min. Helium was used as carrier gas with a constant flow rate of 1.2 mL/min. MS acquisition parameters were done at 70 eV electron impact ionization, EM voltage of 2318 V, source 230 °C, quadrupole 150 °C, solvent delay: 2.5 min, and full scan (40–650 a.m.u) at a scan rate of 2.42 scan s−1.

4.5. Putative Identification of Metabolites

Putative identification of the resulting data of LC-MS analysis was carried out using MZmine2 (Version 2.24) software [47]. The following parameters were used: (1) filter: Savitzky-Golay, number of datapoints: 5; (2) mass detector: centroid, MS level: 1, noise level: 1000; (3) chromatogram builder, min time span: 0.08 min, min height: 0.0E0, m/z tolerance: 0.001 m/z or 5 ppm; (4) peak detection using online database search option, Massbank of Japan Science and Technology Agency [15], Plant Metabolic Network: Plant Metabolic Pathway Databases [16]. For one detected peak we got a variety of metabolite candidates from a 1–10 name. We picked metabolites that showed the same name from both MassBank and PlantCyc online databases and commonly found in plants.

The data acquisition of GC-MS analysis was done using MS-Chemstation G1701-DA with WILEY and NIST spectral libraries. Volatiles that showed mass spectra with match factors quality of ≥90% were considered as putatively identified substances. The putatively identified peak list from both LC-MS and GC-MS, which showed the data set of retention time, peak height, and area pct. (% of the total measured area in the total ion chromatogram) was exported and processed in Microsoft Excel.

4.6. Fruit Morphology Evaluation

Eggplant fruits from 21 eggplant accessions were evaluated using characteristics in the Guidelines for the Conduct of Tests for Distinctness, Uniformity, and Stability (GCT-DUS) for Eggplant species [48]. These guidelines include 12 vegetative characteristics, 3 inflorescence characteristics, 26 fruit characteristics and 2 characteristics on the time of flowering and ripeness. For this research, we observed 26 fruit characteristics (Table 3). Three biological replicates from 10 fruits of each accession were used in evaluation of fruit morphology. The data were used for correlation analysis.

4.7. Data Analysis

The dataset of area pct. of metabolites was log-transformed and then analyzed using R software [49]. The Metabolomics package [50] for R software was used to perform HCA (hierarchical cluster analysis) and construct heat maps with dendrograms. HCA was performed using the Euclidean distance method and the complete linkage agglomerative method.

The observed data from fruit morphology observation and the dataset of metabolites of eggplant accessions were used for correlation analysis. The Spearman correlation coefficients was calculated between these data. Calculations were done one by one between fruit morphology and its metabolites. Correlation analysis was conducted to determine the relation between eggplant fruit morphology and its metabolites. All calculations were done using the R software with the Corrplot package [51].

5. Conclusions

Untargeted metabolomics analysis was able to determine the metabolites contained in the eggplant fruit. We were able to detect 136 and 207 metabolite peaks from LC-MS and GC-MS analyses, respectively. Some of the metabolites were annotated as alkaloids, terpenes, terpenoids, fatty acids, and flavonoids. This study also found specific metabolites that are unique to a specific eggplant accession. Furthermore, Spearman correlation analysis showed the relationship between specific metabolites and fruit morphologies. Our results indicated that some specific metabolites in particular eggplant accessions correlated with its fruit morphologies. We believe that this information would be valuable for variety improvement program of eggplant.

Acknowledgments

A.H. would like to thank the Indonesia Endowment Fund for Education (LPDP), Ministry of Finance, the Republic of Indonesia for the scholarship.. We would like to thank the Center for Tropical Horticulture Studies, Bogor Agricultural University, Indonesia for providing experiment material and facilities.

Supplementary Materials

The following are available online at http://www.mdpi.com/2218-1989/8/3/49/s1, Table S1: Complete list of metabolites compounds from untargeted metabolomics analysis in eggplant fruit using GC-MS. Table S2: Spearman’s correlation coefficient value between eggplant fruit metabolites and fruit morphologies.

Click here for additional data file. (250.1KB, pdf)

Author Contributions

A.H., S. and A.M. conceived and designed the experiments; A.H. performed the experiments, analyzed the data, and wrote the paper; S., A.M., S.P.P. and W.A.L. revised the manuscript.

Funding

This research was partly funded by the Indonesia Endowment Fund for Education (LPDP), Ministry of Finance, the Republic of Indonesia.

Conflicts of Interest

The authors declare no conflicts of interest.

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Supplementary Materials

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