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. 2023 Jul 19;28(14):5507. doi: 10.3390/molecules28145507

An Untargeted Metabolomics Approach to Study the Variation between Wild and Cultivated Soybeans

Fakir Shahidullah Tareq 1, Raghavendhar R Kotha 1, Savithiry Natarajan 2, Jianghao Sun 1, Devanand L Luthria 1,*
Editor: Gilles Comte
PMCID: PMC10386028  PMID: 37513379

Abstract

The differential metabolite profiles of four wild and ten cultivated soybeans genotypes were explored using an untargeted metabolomics approach. Ground soybean seed samples were extracted with methanol and water, and metabolic features were obtained using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) in both positive and negative ion modes. The UHPLC-HRMS analysis of the two different extracts resulted in the putative identification of 98 metabolites belonging to several classes of phytochemicals, including isoflavones, organic acids, lipids, sugars, amino acids, saponins, and other compounds. The metabolic profile was significantly impacted by the polarity of the extraction solvent. Multivariate analysis showed a clear difference between wild and cultivated soybean cultivars. Unsupervised and supervised learning algorithms were applied to mine the generated data and to pinpoint metabolites differentiating wild and cultivated soybeans. The key identified metabolites differentiating wild and cultivated soybeans were isoflavonoids, free amino acids, and fatty acids. Catechin analogs, cynaroside, hydroxylated unsaturated fatty acid derivatives, amino acid, and uridine diphosphate-N-acetylglucosamine were upregulated in the methanol extract of wild soybeans. In contrast, isoflavonoids and other minor compounds were downregulated in the same soybean extract. This metabolic information will benefit breeders and biotechnology professionals to develop value-added soybeans with improved quality traits.

Keywords: soybean cultivars, metabolomics, ultra-high-performance liquid chromatography-tandem mass spectrometry, multivariate analysis

1. Introduction

Soybean is an important leguminous crop providing meal and oil for food (~20%), animal feed (~76%), biodiesel, and other industrial uses (~4%) [1]. According to an estimation by the U.S. Department of Agriculture, in the 2019/2020 harvest year, the world soybean production was 337 million tons, and an increase of 8% (362 million tons) was observed in the 2020/2021 harvest year [2]. Three countries, namely Brazil, the United States of America, and Argentina, accounted for approximately 80% of the world’s production of soybeans [3]. In the United States, the total soybean production was 4.3 billion bushels in 2022 [4]. Soybeans contain diverse primary and secondary metabolites, including proteins, carbohydrates, lipids, amino acids, and phytochemicals (isoflavones, saponins) that contribute to their nutritional and health-promoting properties [5]. Global breeding programs and biotechnology approaches have been used to develop new varieties of soybeans with improved quality traits. A better understanding of how agronomic practices, environmental conditions, pre-and post-harvest storage, and processing conditions impact metabolite changes in soybeans is essential to further develop soybeans with improved quality traits.

The metabolome of plants contains thousands of a diverse range of phytochemicals that can be broadly classified into three categories: primary metabolites (required for plant growth), secondary metabolites (mediate plant-environment interactions), and hormones (organismal processes and metabolism) [6]. Metabolites play an important role in a plant’s growth, development, and responses to environmental factors. Plant metabolomics has been applied to understand changes in the different stages of plant growth [7], changes in the metabolism of plants to environmental contaminants [8], impacts of soil types [9], and the effect of abiotic and biotic stresses [10,11,12].

Several targeted and untargeted metabolomic studies in soybean have been reported. Clarke et al. used a metabolomics approach to show that the metabolome of the genetically modified soybean line had no significant deviation from natural variation within the soybean metabolome, except for targeted changes in the metabolized bioengineered pathway [13]. In a separate study, Chebrolu et al. evaluated the impact of stress during seed development by exposing plants to optimum, moderate, or high temperatures. The authors showed that the germination of seeds from the heat-susceptible genotype is reduced by 50% for the 36/24 °C treatment and completely inhibited for the 42/26 °C. The same authors also showed the enrichment of tocopherols, flavonoids, phenylpropanoids, and ascorbate precursors in the seeds of the heat-tolerant genotype [14].

Gupta et al. used integrative proteomics and metabolomics analyses to identify the differentially expressed proteins and metabolites in two contrasting yellow (Mallikong) and brown-colored (Mallikong mutant) soybean seeds. The authors showed proteins involved in primary metabolism were downregulated in Mallikong mutant (MM), suggesting energy in MM might be utilized for proanthocyanidin biosynthesis responsible for the development of brown seed coat color [15]. Recently, Bragagnolo et al. using a metabolomics approach, showed the presence of isoflavonoids, flavonoids, terpenes, and other substances in soy stems, leaves, pods, and roots. The authors suggested that these underused parts of the plant can potentially be used as a source of bioactive compounds [16].

Studies on the effects of genotype and environment (temperature, carbon dioxide level, and water stress) on soybean isoflavone contents were also conducted [17,18]. The authors observed significant variations in total and individual isoflavone contents between genotype × year, genotype × location, and genotype × year × location interactions using a targeted metabolomic approach. Similarly, metabolic changes in soybean roots under water deficit stress were investigated by gas chromatography-mass spectrometry (GC-MS), and changes in several metabolic pathways involved in sugars, amino acids, and isoprenoid metabolisms were reported [19].

Previously, we reported metabolic responses of nine soybean varieties grown under field and greenhouse conditions. Soybean extracts were derivatized and assayed by GC-FID, GC-MS, and LC-MS to identify ten primary (amino acids, organic acids, and sugars) and ten secondary (isoflavones, fatty acid methyl esters) metabolites. We found that the free amino acids and organic acids varied between the varieties [20]. In a recent study, we reported the compositional analysis of fatty acids and soluble sugars in wild and cultivated 14 soybeans genotypes using targeted analysis [21]. We observed differences in total oil content in wild soybeans (~9%) versus cultivated soybeans (16–22%). In addition, higher levels of linolenic acid (~17%) and stachyose (~53%) were determined in the wild type. In contrast, higher levels of oleic acid (~19%) and sucrose (~59%) were detected in cultivated soybeans using ion chromatography with amperometry detection and gas chromatography with a flame ionization detector after transesterification of crude oil.

The above-targeted methodologies required detailed sample preparation, separation, and derivatization steps to identify and quantify a limited number of metabolites. Untargeted methods such as ultraviolet spectrophotometry (UV), infrared (IR), and near-infrared (NIR) spectrometry have also been used to acquire spectral fingerprints to evaluate sources of variances based on the classes of compounds. The objective of the current study was to investigate if the untargeted UHPLC-HRMS approach can be used to differentiate soybean cultivars and identify the metabolites responsible for the differentiation. Furthermore, we also wanted to evaluate the role of extraction solvents in untargeted metabolomics.

2. Results and Discussion

In this study, we selected four wild and ten cultivated soybean genotypes from different regions of the world for untargeted metabolomics investigation. Methanol and water extracts of all samples were analyzed to investigate their metabolic profiles in both positive and negative ion modes.

2.1. Identification of Compounds

Thousands of metabolite features were obtained using the Compound Discoverer 3.3 software program. Among them, 98 metabolites belonging to amino acids, organic acids, sugars, isoflavones, and soy saponins in methanol and water extracts were identified by careful analysis of the MS/MS fragments and comparison with the available literature data. A total of 64 compounds were detected with the positive and negative ion modes in the water extract, whereas 35 compounds were identified in the methanol extract. Some compounds were detected in both extracts (methanol and water) and in both ionization modes (positive and negative). The details of the compounds identified are summarized in Table 1 (methanol extract) and Table 2 (water extract).

Table 1.

Identification of metabolites using high-resolution mass spectrometry of methanol extract of fourteen soybean cultivars (ten cultivated and four wild) in positive and negative ion modes.

No. Identification Formula tR (min) Calc. Mass m/z [M] Observed Mass m/z [M ± H]±1 (±) Fragment Ions
1 Choline C5H13NO 0.93 103.0997 104.1069 60.0806
2 Iditol C6H14O6 0.94 182.0790 181.0717
3 Malic acid C4H6O5 0.97 134.0215 133.0142
4 Citric acid C6H8O7 0.98 192.0270 191.0197
5 Furoic acid C5H4O3 0.99 112.0161 111.0088 67.0187
6 Catechin C15H14O6 1.00 290.0791 289.0718 245.0812, 203.0707, 187.0398, 137.0240, 109.0291
7 Glutamic acid C5H9NO4 1.04 147.0531 146.0458 102.0556, 84.0451
8 Gluconic acid C6H12O7 1.05 196.0583 195.0510 129.0189, 75.0085, 59.0137
9 Aspartic acid C4H7NO4 1.06 133.0375 132.0302 115.0032, 88.0401
10 Histidine C6H9N3O2 1.09 155.0695 156.0768 110.0703, 93.0439
11 Malic acid C4H6O5 1.20 134.0215 133.0142 115.0033, 71.0136
12 Isoleucine C6H13NO2 1.27 131.0946 132.1019 86.0957, 69.0693
13 Cynaroside C21H20O11 1.31 448.1006 449.1079 68.997
14 UDP-N-acetylglucosamine * C17H27N3O17P2 1.34 607.0818 606.0745 384.9831, 158.9254, 96.9692
15 Tryptophan C11H12N2O2 1.36 204.0898 205.0971 188.0692, 146.0589, 118.0642
16 Phenylalanine C9H11NO2 1.38 165.0789 166.0863 120.0803, 103.0538, 91.0539
17 Catechin * C15H14O6 1.47 290.0791 289.0718 187.0398, 137.0240, 109.0291
18 Catechin analog * C15H14O6 4.41 290.0789 289.0717 245.0812, 203.0707, 137.0240, 109.0291
19 3,5-Dihydroxy-2-(4-hydroxyphenyl)-4-oxo-3,4-dihydro-2H-chromen-7-yl hexopyranoside C21H22O11 4.57 450.1162 449.1089 287.0554, 259.0604, 125.0240, 57.0345
20 Apigetrin C21H20O10 4.91 432.1056 431.0983 269.0446, 117.0339, 89.0240
21 Daidzin C21H20O9 5.82 416.1106 417.1179 227.0684, 199.0738, 137.0223
22 Daidzein C15H10O4 6.26 254.0577 255.0650 225.0552, 208.0527, 113.0295, 91.0187, 65.0033
23 Apigetrin analog C21H20O10 6.47 432.1054 477.1035 269.0446, 117.0339, 89.0240
24 Glycitein C16H12O5 6.64 284.0684 283.0612 268.0368, 240.0421
25 Genistein C15H10O5 7.15 270.0527 269.0454 225.0547, 181.0654
26 Daidzein analog C15H10O4 8.28 254.0579 253.0506 209.0598, 133.0292, 91.0186, 65.0031
27 Naringenin C15H12O5 9.26 272.0685 271.0612 151.0028, 119.0498, 93.0341, 65.0032
28 Genistein analog C15H10O5 9.53 270.0528 269.0455 225.0547, 181.0654
29 Corchorifatty acid F C18H32O5 9.61 328.2251 327.2178 229.1441, 211.13303, 171.1020, 85.0291, 57.0345
30 (15Z)-9,12,13-Trihydroxy-15-octadecenoic acid * C18H34O5 10.08 330.2407 329.2334 171.1020, 139.1123, 99.0812
31 Soyasaponin I C48H78O18 10.76 942.5182 941.5105 615.3881, 457.3671, 205.0709, 143.0345, 113.0241
32 (±)9-HpODE * C18H32O4 12.12 312.2301 311.2228 275.2005, 183.0115, 79.9569
33 (+/−)9,10-dihydroxy-12Z-octadecenoic acid * C18H34O4 13.00 314.2458 313.2385 277.2168, 201.1125, 171.1022
34 13(S)-HOTrE * C18H30O3 13.95 294.2195 293.2123 195.1390, 95.9597, 79.9570
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* Differentiating metabolites.

Table 2.

Identification of metabolites using high-resolution mass spectrometry of water extract of fourteen soybean cultivars (ten cultivated and four wild) in positive and negative ion modes.

No. Identification Formula tR (min) Calc. Mass m/z [M] Observed
m/z [M ± H]±1 		(±) Fragment Ions
1 Arginine C6H14N4O2 0.83 174.1116 173.1044 156.0778, 131.0822, 114.0557
2 Histidine C6H9N3O2 0.85 155.0695 156.0768 110.0703, 93.0439
3 Glutamic acid C5H9NO4 0.93 147.0531 146.0458 ND
4 Glucose C6H12O6 0.93 180.0634 179.0561 101.024289.0242, 71.01369, 59.0138
5 N-acetylornithine C7H14N2O3 0.94 174.1004 173.0931 131.0822, 85.0769
6 D-Ribose C5H10O5 0.95 150.0528 149.0455 131.0346, 89.0243, 71.0137, 59.0138
7 Malic acid C4H6O5 0.97 134.0215 133.0142 115.0034, 89.0242, 72.9929, 71.0137
8 Glutamine * C5H10N2O3 0.99 146.0692 147.0765 127.0509, 109.0404, 84.0452
9 Uridine C9H12N2O6 1.00 244.0695 243.0623 200.0562, 152.0351, 110.0245
10 Citric acid C6H8O7 1.00 192.0269 191.0196 173.0090, 111.0085, 87.0087
11 Arginine C6H14N4O2 1.00 174.1117 175.1189 156.0778, 131.0822, 114.0557
12 Furoic acid C5H4O3 1.01 112.0161 111.0088 67.0189
13 Pantothenic acid C9H17NO5 1.01 219.1106 218.1033 146.0821, 88.0402
14 Threonine C4H9NO3 1.02 119.0582 120.0655 116.0696, 70.0645
15 Asparagine C4H8N2O3 1.03 132.0535 133.0608 116.0333, 87.0546, 74.0231
16 Proline C5H9NO2 1.03 115.0633 116.0706 70.0645
17 Trans-Aconitic acid C6H6O6 1.03 174.0164 173.0092 129.0191, 111.0085, 85.02936
18 Cytosine C4H5N3O 1.04 111.0432 112.0505 95.0232, 69.0442
19 Glutamic acid C5H9NO4 1.04 147.0532 148.0605 128.0348, 102.0556, 84.0451
20 Lysine C6H14N2O2 1.04 146.1055 147.1128 130.0852, 84.0801, 56.0491
21 Valine C5H11NO2 1.08 117.0789 118.0862 100.0748, 72.0802, 55.0538
22 Adenine C5H5N5 1.11 135.0545 136.0617 119.0343
23 Guanine * C5H5N5O 1.11 151.0494 152.0567 135.0294, 110.0341
24 Succinic acid C4H6O4 1.13 118.0266 117.0193 73.0293
25 N-Acetylornithine C7H14N2O3 1.21 174.1005 175.1077 131.0822, 85.0769
26 Guanosine C10H13N5O5 1.21 283.0917 282.0844 150.0419, 108.0202
27 Hypoxanthine * C5H4N4O 1.21 136.0385 137.0458 119.0342, 110.0340, 94.0392
28 Tyrosine * C9H11NO3 1.22 181.0739 180.0666 163.0396, 119.0498, 72.0088
29 Methionine C5H11NO2S 1.25 149.0511 150.0583 133.0307, 104.0520, 61.0102
30 Isoleucine C6H13NO2 1.30 131.0946 132.1018 86.0957, 69.0693
31 Glutaric acid * C5H8O4 1.32 132.0422 131.0349 113.0240, 87.0449, 69.0343
32 Leucylproline C11H20N2O3 1.40 228.1473 229.1546 116.0696, 86.0957, 70.0645
33 Trans-3-Indoleacrylic acid C11H9NO2 1.80 187.0632 188.0705 170.0586, 146.0588, 118.0641
34 Tryptophan C11H12N2O2 1.80 204.0898 205.0971 188.0692, 146.0589, 118.0642
35 Glycidic acid C10H10O3 2.17 178.0629 177.0557 133.0656, 71.01366
36 12-O-β-D-Glucopyranosyloxyjasmonic acid C18H28O9 4.00 388.1732 387.1659 207.1023, 101.0242, 89.0242, 59.0138
37 Sinensin C21H22O11 4.26 450.1162 449.1089 287.0555, 259.0605, 125.02401
38 Dihydrophaseic acid C15H22O5 4.63 282.1468 281.1395 171.1175, 123.0813, 87.00853
39 12-O-β-D-Glucopyranosyloxyjasmonic acid C18H28O9 4.95 388.1733 387.166 207.1023, 101.0242, 89.0242, 59.0138
40 Daidzin * C21H20O9 5.30 416.1108 417.1181 227.0684, 199.0738, 137.0223
41 Glycitein C16H12O5 5.33 284.0684 283.0611 268.0371, 240.0422
42 Hdroxycaproic acid * C6H12O3 5.39 132.0786 131.0714 113.0606, 85.0656, 57.0345
43 N-Acetyl-l-phenylalanine C11H13NO3 5.72 207.0895 206.0823 164.0713, 91.0551, 70.0296
44 Pheyllactic acid * C9H10O3 5.99 166.0629 165.0557 147.0449, 119.0501, 72.9929
45 2-(acetylamino)-3-(1H-indol-3-yl)propanoic acid C13H14N2O3 6.30 246.1004 245.0931 116.0349, 98.0244, 74.0245, 58.0297
46 Apigetrin * C21H20O10 6.34 432.1056 431.0983 268.0368, 239.0335, 59.0137
47 Genistin * C21H20O10 6.47 432.1057 433.1129 271.0579, 215.0685, 153.0170
48 Daidzein * C15H10O4 6.48 254.0578 253.0505 208.0527, 113.0295, 91.0187, 65.0033
49 Astragalin C21H20O11 6.51 448.1005 447.0932 284.0320, 227.0345, 65.0032
50 Glycitein analog C16H12O5 6.56 284.0684 283.0611 268.0371, 240.0422
51 Octyl glucoside C14H28O6 6.72 292.1886 291.1813 85.0292, 59.0137
52 7-Hydroxy-2-(4-hydroxyphenyl)-4-oxo-3,4-dihydro-2H-chromen-5-yl β-d-glucopyranoside * C21H22O10 7.04 434.1213 433.1139 271.0607, 243.0667, 151.0034, 93.0343
53 Azelaic acid C9H16O4 7.32 188.1048 187.0976 125.0969, 97.0656
54 Galangin * C15H10O5 7.49 270.0527 271.0599 215.0686, 153.0169, 115.0532
55 Phloretin * C15H14O5 7.77 274.0841 273.0768 167.0346, 123.0447, 93.0343
56 Abscisic acid C15H20O4 8.01 264.1362 263.1288 219.1386, 204.2252, 136.0526
57 Daidzein analog C15H10O4 8.18 254.0578 253.0505 225.0552, 132.0214, 91.0187, 65.0033
58 5,7-dihydroxy-3-(4-methoxyphenyl)-4H-chromen-4-one * C16H12O5 8.56 284.0685 285.0757 229.0839, 197.0579, 118.0402
59 Genistein C15H10O5 9.43 270.0526 269.0454 225.0557, 181.0654
60 (15Z)-9,12,13-trihydroxy-15-octadecenoic acid C18H34O5 10.01 330.2405 329.2332 211.1335, 171.1023, 139.1125, 99.0813
61 Tetradecanedioic acid C14H26O4 10.63 258.1831 257.1758 239.1644
62 Soyasaponin I C48H78O18 10.77 942.5182 941.5107 733.4519, 457.3673, 257.0659, 101.0242
63 (±)9-HpODE C18H32O4 11.34 312.2301 311.2228 171.1023, 139.1125, 113.0968
64 Thapsic acid C16H30O4 11.75 286.2144 285.2071 267.2065, 59.0137
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* Differentiating metabolites.

Soybean seeds are one of the most concentrated natural sources of isoflavones in human diets [22]. In soybeans, isoflavones are found in free and conjugated forms. In conjugated forms, they can occur with sugars (glucosides) and/or acids (acetyl/malonyl). The three common free isoflavones in soybean seeds are genistein, daidzein, and glycitein. The [M + H]+ and [M − H]− for daidzein, genistein, and glycitein were observed at m/z 255.0650, 271.0612, 285.0758, and 253.0506, 269.0454, 283.0611 respectively. In the present study, we identified five analogs of daidzein at tR 0.99, 5.82, 6.26, 6.58, and 8.23 min. The compounds can be conjugated with sugars, acetylated, and/or malonylated. There are several reports of the presence of such analogs in the literature during targeted analysis [23,24,25].

Amino acids were detected as the second major group of metabolites in soybean seeds. Soybean seeds provide a rich source of plant-based proteins. The protein content in soybean seeds is around 40%, as documented in the published literature [26,27]. We identified 17 free amino acids and two acetylated analogs of amino acids in the water extracts using positive and negative ion modes. Only six amino acids were detected in the methanol extracts. Similar results of the presence of amino acids from soybeans using targeted analysis after derivatization with aminopyridyl-N-hydroxysuccinimidyl carbamate (APDS) reagent [28] have been reported.

In addition to amino acids and isoflavones, over 60 other organic compounds were also detected in water and methanol extracts of soybean seeds. These include organic acids, flavonoids, sugars, saponins, fatty acids, and other phytochemicals. Organic acids are one of the major components affecting soybeans’ overall quality and taste. A recent study by Hyeon et al. reported some organic acids in soybean, such as malic acid, citric acid, and succinic acid [29]. In another recent study, ten organic acids were reported using NMR analysis by Song et al. in soybeans [30]. Similarly, the presence of epicatechin and sugars have also been reported previously by Hyeon et al. and Song et al. [29,30]. We also reported in our earlier publication the presence of sugars in soybean by ion chromatography and fatty acids after derivatization using GC-MS analysis with targeted analysis [20,21].

2.2. Comparison of Metabolites among Cultivars

All metabolites can be broadly classified into five major subgroups: amino acids, phenolics, organic acids, sugars, and miscellaneous. To compare the amounts of metabolites produced in each cultivar, all metabolites were organized based on the area under the curve for the mass ion extracted with two different solvents, water (Figure 1A) and methanol (Figure 1B).

Figure 1.

Figure 1

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(A) Comparative analysis of all metabolites based on area under the curve for four wild and ten cultivated soybeans extracted with water. All samples were analyzed in triplicate, and the averages of the triplicates have been reported. (B) Comparative analysis of all metabolites based on area under the curve for four wild and ten cultivated soybeans extracted with methanol. All samples were analyzed in triplicate, and the averages of the triplicates have been reported.

The total amount of amino acids, one of the significant classes of metabolites obtained from water extract, varied significantly between 25% and 75% within cultivated and wild soybean cultivars. Based on the areas under the curve for the targeted mass ion maximum amount of amino acids was obtained in the Asian cultivar (C8). Assessments of protein quality of 14 soybean cultivars using targeted amino acid analysis and two-dimensional electrophoresis were investigated by Zarkadas et al. The authors indicated that all fourteen cultivars contained a good balance of essential amino acids [31]. Similar free amino acids were observed in fermented and unfermented soybeans and mung beans using targeted amino acid analysis after derivatization. The content of free amino acids was increased by 13-fold and 32-fold in fermented mung and soybeans, respectively. The authors showed that fermentation improved the amino acid content in a single soy and mung bean cultivar [32]. A similar analysis and characterization of the amino acid content of thua nao, a traditionally fermented food of northern Thailand, was studied by Dajanta et al. [33]. Significant variation in the phenolics area was seen in different cultivars. A maximum amount of phenolics was detected in the modern elite cultivar C11. The relative percentage of phenolics in other cultivars varied between 7% and 62%, with the lowest amount in cultivar C2. Similar variations in the total phenolic content (6.67 μg−1 in Pureunkong to 72.33 μg−1 in Poongsannamulkong) were observed in seven cultivars of soybeans by Kim et al. [34]. However, the maximum amount of organic acids were detected in ancestral (C9) and modern elite (C4) cultivars, with others showing variation between 25 and 77%.

However, compared to the metabolites from water extract, phenolics were the predominant metabolites in methanol extract. It has been documented that methanol, ethanol, acetone, water, and their water mixtures, with or without acids, are the most widely used solvents for extracting phenolic compounds [35,36]. Boeing et al. reported that among the pure solvents, methanol is the most effective solvent for the extraction of antioxidant compounds [37,38].

Significant variations of phenolics content were observed in the present study between cultivars (33–95%). The maximum amount of phenolics was detected in wild cultivar C13, with the lowest amount in Asian cultivar C8. This was different from the water extract, where the modern elite C11 cultivar showed the maximum amount. Similarly, the total amount of amino acid varied between 40 and 80% among cultivars. Similar to water extract maximum amount of amino acid was obtained in the Asian cultivated cultivar (C8) in methanol extract. However, the maximum amount of organic acid was detected in Asian (C2 and C5) cultivars, with others showing variation between 20 and 90%. The total amount of sugar varied between 30–90%, and Asian cultivar C8 produced the maximum amount of sugar compared to other cultivars. Since no distinct systematic variations in the area under the curve for the mass ions of different metabolites between cultivars were observed, multivariate analysis was done to differentiate between cultivars.

2.3. Classification of Wild and Cultivated Soybeans Using Principal Component Analysis (PCA) and Volcano Plots

Non-supervised analysis of the entire UHPLC-HRMS data using the Progenesis QI resulted in the detection of several thousands of ion features from each extract. The intensity of each ion was extracted and used for principal component analysis. PCA of the normalized intensity data of methanol and water extracts showed certain differentiation of wild and cultivated soybeans, as shown in Figure 2A and Figure 2B. The variances captured by the two components (PC1 and PC2) were between 29–49%. A further supervised partial least square discriminant analysis (PLS-DA) was performed, and the score plots are shown in Figure 3A and Figure 3B. Clear separations were observed between the wild and cultivated soybeans on the score plots; however, the separation between the cultivated soybeans was not obvious. For the metabolites from methanol extraction, the PLS-DA model resulted in the cross-validated predictive ability Q2(Y) of 39.9%. A value of 37.4% of the variance in X [R2(X)] was used to account for 21.1% of the variance of Y [R2(Y)]. For the metabolites from water extraction, the PLS-DA model resulted in the cross-validated predictive ability Q2 of 64.8%. A value of 84.2% of the variance in X [R2(X)] was used to account for 84.8% of the variance of Y [R2(Y)]. It suggested that the model from the metabolites from the water extraction gave us better prediction ability. The t-test (p-value) and fold change served as criteria for selecting the most discriminatory metabolites.

Figure 2.

Figure 2

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Principal component analysis of the high-resolution mass spectral data of all metabolites for four wild and ten cultivated soybeans extracted with methanol (A) and water (B).

Figure 3.

Figure 3

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Partial least squares discriminate analysis (PLS-DA) of the high-resolution mass spectral data of all metabolites for four wild and ten cultivated soybeans extracted with methanol (A) and water (B).

Two volcano plots were constructed to identify the metabolites from both methanol and water extracts that were differentially expressed in wild and cultivated soybeans (Figure 4A and Figure 4B). Around 150–400 metabolite ion features with selected threshold fold change (≥4) and t-tests threshold (p ≤ 0.05) were selected as cutoff values for the volcano plots to identify the prominent ions responsible for the variation of metabolites. The data were categorized into three fractions: statistically insignificant (blue), upregulated (orangish-brown), and downregulated (grey). As seen with the filtered data set, which contained several hundreds of metabolites, a few compounds were either up or downregulated between wild and cultivated soybeans. Careful analysis of the fragmentation ions and comparison with the literature data significantly reduced the number of putatively identified compounds that were downregulated in cultivated and wild soybeans. Metabolites upregulated in the methanol extract of wild soybeans compared to the cultivated soybeans were catechin analog, cynaroside, hydroxylated unsaturated fatty acid derivatives, and uridine diphosphate-N-acetylglucosamine. Similar observations were also identified by Hyeon et al., where the authors showed with PCA that amino acids, organic acids, and fatty acids were higher in cultivated black soybeans as compared to wild black soybeans [30]. However, higher content of isoflavones and other flavonoids derivative was determined in the cultivated soybeans compared to wild soybeans. Some of the metabolites identified in the water extract showed similar trends. The isoflavones analogs, soy saponin, flavonoid analogs, amino acids (glutamine and guanine), lactic acid derivative, and 6-hydroxy caproic acid were determined in higher amounts in cultivated soybean as compared to wild soybeans. Upregulated compounds in wild soybeans were tentatively identified as phloretin, hypoxanthine, glutaric acid, and tyrosine. These results will be of significant value to soybean breeders and biotechnology researchers to develop new varieties of value-added soybeans with improved qualitative traits.

Figure 4.

Figure 4

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Volcano plots of identified metabolites from ten cultivated and four wild soybeans extracted with methanol (A) and water (B). The numbers noted in the score plot are marked as * in Table 1 for the methanol extract and Table 2 for the water extract.

3. Materials and Methods

3.1. Solvents and Materials

LC-MS-grade solvents, including acetonitrile, methanol, water, and formic acid, were used for the extraction and chromatographic separation. These organic solvents were purchased from Fisher Scientific (Pittsburgh, PA, USA). Extractions were carried out in a 15 mL centrifuge tube obtained from Thermo Scientific (Waltham, MA, USA). Polyvinylidene difluoride (PVDF) syringe filters with a pore size of 0.45 µm were purchased from National Scientific Company (Duluth, GA, USA).

3.2. Samples

Fourteen soybean cultivars were collected for this study. Four of these cultivars are wild (C3, C12–C14), and the other ten cultivated cultivars are categorized into three groups, namely, Asian landraces (C2, C5, C6, and C8), ancestral (C7 and C9), and modern elite (C1, C4, C10, and C11). All soybean samples were obtained from the soybean germplasm collection (USDA, Urbana, IL, USA). The cultivar details, i.e., accession number, origin, and genotype information (wild soybean (G. soja), soybean bred for seed traits, and soybean landraces), were previously reported in our earlier publication [22]. In the present study, soybean seeds were ground in a commercial coffee grinder and stored in an ultralow temperature (<−60 °C) freezer prior to analysis.

3.3. Extraction of Metabolites

An amount of 150 ± 0.05 milligrams (mg) of soybean seed powder of each sample were taken into 15 mL centrifuge tubes and extracted with 5 mL of methanol (polarity index 5.1) in an ultrasonic bath (power 400 watts, Advanced Sonic Processing Systems, Oxford, CT, USA) for 15 min (twice). Similarly, the extraction of samples with water (polarity index 10.2) was also carried out. The extracts were centrifuged at 4000 rpm for 15 min and filtered using a 0.45 µm PVDF filter. The clean filtrate (500 µL) containing extracted metabolites was transferred to 2 mL HPLC vials and subjected to UHPLC-MS/MS analyses. All analyses were carried out in triplicate.

3.4. Data Acquisition

The Vanquish UHPLC system (Thermo Scientific), consisting of a binary pump, column compartment, autosampler, and detectors (Photodiode Array detector) PDA and (Charged aerosol detector) CAD coupled with an Exploris 240 mass spectrometer, was used to acquire high-resolution mass data in full-scan and data-dependent acquisition mode for all samples. An aliquot of each extract was analyzed using both positive and negative ionization modes. The metabolites were separated on a C18 Agilent column (Eclipse Plus, 4.5 × 50 mm, 1.8 µm, 1200 bar pressure limit) using the gradient programs; 10% B at 0 min, gradually moves to 30% at 5 min, reach 60% at 10 min, reach 95% at 15 min, run 95% at 15–18 min, then reduced to 10% at 18.5 min. Water and acetonitrile acidified with 0.1% formic acid were used as mobile phases A and B, respectively. The flow rate and the injection volume were maintained at 0.5 mL/min, and 10 µL, respectively.

The HRMS mass range was from 100–2000 m/z, and the ESI conditions were as follows; sheath gas, auxiliary, and sweep gas at 50, 10, and 1 (arbitrary units), respectively, spray voltage at 3.4 kV, and capillary temperature at 320 °C, and vaporizer temperature 350 °C. The full scan mass spectra and three DD-MS2 events were acquired at a resolving power of 12,000. An isolation width of 1 amu, maximum ion injection time of 100 ms, stepped collision energy starting from 30, 50, and 150, and an activation time of 10 ms was used for MSn activation. Xcalibur 4.4, including FreeStyle 1.8 software packages, has been used to analyze the mass spectral data.

3.5. Identification of Compounds

Compound Discoverer (Version 3.3, Thermo Scientific) software was used for the putative identification of metabolites. This involved application of several filters, namely background subtraction, MSn fragmentation information, ΔMass (±5 ppm), minimum area, Fish score, and screening data with multiple databases (in-house mass library for soybean, mzCloud, ChemSpider, Metabolika, and other online available databases available). Furthermore, mass spectral data (molecular ion mass (m/z) and MS/MS fragmentation patterns) were compared with literature-reported data [22,23,24].

3.6. Data Processing

Raw LC-MS data were analyzed using the Compound Discoverer program to collect features for statistical analysis. The overall workflow of the program includes the detection of chromatographic peaks, extraction of the MS spectrum, deconvolution of the overlapping ions based on their isotope patterns, and integration of their respective peak areas. Acquired UHPLC-HRMS raw files were processed by using Nonlinear Progenesis QI (Durham, NC, USA) for peak detection, noise filtering, and peak alignment. Important deconvolution parameters were mass tolerance of 5 ppm, retention time tolerance of 0.2 min, peak rating threshold of 4, minimum peak intensity of 109, chromatographic threshold S/N of 1.5, CV contribution of 10, and an area contribution of 3. The resulting areas of each sample in triplicate were exported to Microsoft Excel 365 (Microsoft, Redmond, WA, USA) for volcano plot construction.

3.7. Statistical Analysis

A data matrix was generated from Progenesis Qi, including a variable index (paired m/z-retention time), sample names (observations), and peak intensities. The peak intensities in each sample were scaled by Pareto scaling before further multivariate analysis using SIMCA 13.0 (Sartorius Stedim Biotech, Umeå, Sweden). Key metabolites responsible for separating different soybean genotypes were further isolated by constructing volcano plots using the Microsoft Excel application. Based on the loadings scores and a threshold of 0.05 for the Student’s t-test of individual samples, key metabolites were selected and identified. The log10 value of the peak area was used to compare the levels of metabolites between samples.

4. Conclusions

Advances in technology, data collection, and analysis software allowed for easy differentiation of wild and cultivated soybean using UHPLC coupled with HRMS without any chemical derivatization in conjunction with PCA analysis. Several recent publications on metabolomics analysis in peer-reviewed journals often use a single aqueous alcohol solvent mixture for sample extraction. As plants produce hundreds and thousands of metabolites, it is critical to investigate multiple solvent compositions of varying polarity to optimize the extraction of a wide array of metabolites with varying polarity. In this manuscript, we showed that the metabolites extracted and putatively identified in two different solvents with polarity indexes of 5.1 and 10.2 were significantly different, with some overlapping metabolites. A total of 98 metabolites were putatively identified as isoflavones, organic acids, lipids, sugars, amino acids, saponins, and other compounds. The PCA and PLS-DA analysis of the HRMS data allowed easy classification of wild and cultivated soybeans. The major metabolites that allowed the differentiation of wild and cultivated soybeans were isoflavonoids, amino acids, and fatty acids. Several metabolites were up and downregulated in wild soybeans as compared to cultivated ones. In general, metabolites upregulated in the wild soybeans were catechin analogs, cynaroside, hydroxylated unsaturated fatty acid derivatives, amino acid, and uridine diphosphate-N-acetylglucosamine. Downregulated metabolites were identified as isoflavonoids and other minor compounds. These marker metabolites may link to characteristic performance traits desired in soybean breeding for crop improvement. In conclusion, the metabolomics extraction and workflow with solvents of varying polarity indexes can result in a significant increase in the number of metabolites extracted and identified. This will allow researchers to extract and identify multiple biomarkers that can provide insights into metabolic pathways and also increase our understanding of how plants interact with varying climatic and growth conditions. It will also enable researchers to breed plants sustainable to various abiotic and biotic stresses. In addition, the detailed metabolomics information will aid researchers in producing foods with better nutritional traits and yields. This will be needed to improve sustainable agriculture practices and alternatives to animal-based protein products that may potentially provide solutions for the global food security challenge with increasing global population and decreasing agricultural land acreages.

Acknowledgments

We would like to thank James Harnly for his feedback on preparing this manuscript. We are thankful to Dechun Wang from Michigan State University for providing all original samples that were grown in Michigan.

Author Contributions

Conceptualization, D.L.L.; methodology, F.S.T., R.R.K. and D.L.L.; formal analysis, F.S.T., R.R.K. and D.L.L.; investigation, F.S.T., R.R.K. and D.L.L.; resources, D.L.L.; data curation, F.S.T., R.R.K. and D.L.L.; writing—original draft preparation, F.S.T. and D.L.L.; writing—review and editing, F.S.T. and D.L.L.; supervision, D.L.L. Samples, S.N.; and multivariate analysis D.L.L., J.S. and F.S.T. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.

Sample Availability

Not applicable.

Funding Statement

This work was supported by the Agricultural Research Service, US Department of Agriculture, Project# 1235-52000-066-00D.

Footnotes

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