Metabolomics (2014) 10:833–841 DOI 10.1007/s11306-014-0640-3
ORIGINAL ARTICLE
Metabolomics investigation of flavonoid synthesis in soybean leaves depending on the growth stage Hyuk-Hwan Song • Hyung Won Ryu • Kyung Jun Lee • Il Yun Jeong • Dong Sub Kim Sei-Ryang Oh
•
Received: 26 November 2013 / Accepted: 12 February 2014 / Published online: 25 February 2014 Ó Springer Science+Business Media New York 2014
Abstract Soybean (Glycine max L.) leaves have unique nutraceutical and pharmacological benefits, and have been widely used as a source of healthy and functional food stuffs in Korea. In this study, we investigated the phytochemical metabolomic changes of soybean leaves depending on growth stages (maturation period) assessed based on UPLC–QTOF–MS analysis. Principal component analysis was carried out to trace the metabolite profiles of the phytochemicals from the vegetable stage (1D) through the seven reproductive stages (R1–R7). On the loading plot, significant changes in the contents of metabolites were found during the growth, and eight flavonoid kaempferol glycosides (2, 3, 6, 8, and 10), daidzein (14), genistein (17), and coumestrol (19) were evaluated as growth markers among the 19 isolated metabolites. The kaempferol glycosides were increasingly synthesized from the 1D to the R6 stage but decreased rapidly at stages R7–R8. The extensively synthesized daidzein and genistein were shown during seed growth in the pod (R5–R6), while coumestrol was increased significantly at stages R7–R8 (maturity period). The synthetic pathway of the flavonoids could be elucidated based on the concentration of the individual
Electronic supplementary material The online version of this article (doi:10.1007/s11306-014-0640-3) contains supplementary material, which is available to authorized users. H.-H. Song H. W. Ryu S.-R. Oh (&) Natural Medicine Research Center, Korea Research Institute of Bioscience & Biotechnology, Cheongwon 363-883, Republic of Korea e-mail:
[email protected] K. J. Lee I. Y. Jeong D. S. Kim Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, 1266 Sinjeong, Jeongeup 580-185, Republic of Korea
metabolites. These results demonstrate that the metabolite production changed depending on the growth stage; a possible pathway could be deduced using metabolomic analysis to provide information regarding physiological characterization and optimal harvesting time for crops. Keywords Soybean leaves Kaempferol glycones Isoflavones Pterocarpans Growth stage UPLC–QTOF– MS
1 Introduction Soybean (Glycine max L.) is one of the world’s most economically important crops, and its products have been used in functional foods throughout history (Jideani 2011). In addition to being an important protein source, soybeans have become a well-known functional food that provides numerous beneficial secondary metabolites, such as isoflavones, other phenolic compounds, saponins, and phytic acids (Messina and Messina 1991); soybean leaves have been extensively used as food in Korea and Japan (Lee et al. 2006a, b; Zaman et al. 2004; Zang et al. 2011). More than 20 phytochemicals have been isolated from soybean leaves, including amino acids, phenolic compounds, saponins, triterpenoids, flavonoids, pterocarpans, and isoflavones (Ho et al. 2002; Lee et al. 2008b, 2009; Yuk et al. 2011a). Although the contents of these metabolites are low in soybean leaves, they play an important role in maintaining leaf quality and have a functional value for human health (Dixit et al. 2011) for various ailments, such as type 2 diabetes (Zang et al. 2011), a-glucosidase (Yuk et al. 2011a), neuraminidase-dependent antibacterial and antiviral (Yuk et al. 2011b), external skin applications (Kim et al. 2011), obesity, hyperlipidemia, atherosclerosis, fatty liver, and diabetes mellitus or metabolic syndrome (Choi et al.
123
834
2010a). Therefore, the function of soybean leaves is closely associated with the presence of secondary metabolites, including bioactive flavonoids, such as isoflavones, and pterocarpans (Yuk et al. 2011a, b). The quality of soybean leaves is attributed to physiological changes that depend on the growth stages and cultivar conditions, such as light (Jeong et al. 2012; Yang et al. 2012), temperature (Chennupati et al. 2012; Kim et al. 2001), drought (Irsigler et al. 2007), salt (Dubousset et al. 2010), biotic stresses (Jeon et al. 2012), genetics (Cui et al. 2013; Radwan et al. 2012), and agronomic conditions (Christ et al. 2006; Kang and Lee 2011; Seo et al. 2013; Zhang et al. 2011). Therefore, determining the optimal harvesting time for soybean leaves as a crop is important (Kim and Chung 2007). Previous reports have revealed the complexity of the phytochemical contents and composition of soybean leaves (Lee et al. 2008a, 2009; Yuk et al. 2011a, b). However, a metabolic analysis has yet to reveal the phytochemical changes through the growth stages. Recently, metabolic analysis using LC–MS, GC–MS, and NMR has revealed phytochemical diversity (Su et al. 2013; Mari et al. 2013; van der Hooft et al. 2012; Park et al. 2013). Interestingly, technologies using UPLC–QTOF–MS have utilized multiple detection and quantification strategies to assess numerous metabolite-derived peaks. In addition, various plants (tomato, Arabidopsis thaliana, hop, Potentilla anserina, and Senecio herbs) have been investigated using UPLC–QTOF–MS and LC–MS (Xiong et al. 2012; Mari et al. 2013; van der Hooft et al. 2012; Farag et al. 2012; Okazaki et al. 2013). In this study, we investigated the metabolic changes in the flavonoids found in soybean leaves during the growth stages by coupling UPLC–QTOF–MS with a multivariate data analysis including principal component analysis (PCA) and loading plots. With these analyses, we could elucidate the possible markers for the maturation and harvesting time of soybean leaves as a potential crop.
2 Materials and methods
H.-H Song et al.
Institute (KAERI) at Jeongeup, Jeonbuk in Korea. After sowing, the soybean leaves were handpicked from three separate plots for 13 weeks from July 4 to September 27, 2012. After checking for apparent physical and microbiological damage at the plantation site, the soybean leaves were frozen in liquid nitrogen, freeze-dried, and stored at -20 °C in laminated bags until further analysis. Before the analysis, the leaves were thawed and cut into small pieces with a laboratory blade cutter. Afterward, 0.2 g of freezedried soybean leaves was chopped to extract with 5 mL of 70 % aqueous methanol at room temperature for 60 min in an ultrasonic water bath. The fluid was filtered through a syringe filter (0.22 lm) and injected directly into the UPLC/Q–TOF–MS system. 2.3 UPLC/Q–TOF–MS analysis The soybean leaf metabolites were profiled using an ACQUITY UPLCTM system and a sample manger coupled to a Micromass QTOF PremierTM mass spectrometer (Waters Corporation, Milford, MA) equipped with an electrospray interface. The chromatographic separations were performed on a 2.1 9 100 mm, 1.7 lm ACQUITY BEH C18 chromatography column. The column temperature was maintained at 35 °C, and mobile phases A and B were D2O with 0.1 % formic acid and acetonitrile with 0.1 % formic acid, respectively. The gradient conditions were as follows: 0 min, 10 % B; 0–7 min, 10–33 % B; 7–14 min, 33–56 % B; 14–21 min, 56–100 % B; 21-23 min, 100 % B, and then held for 2 min before returning to the initial conditions. The flow rate was 0.4 mL/min, and the loading volume was 3 lL. The mass spectrometer was operated in negative ion mode. N2 was used as the desolvation gas. The desolvation temperature was set to 350 °C at 400 L/h with a source temperature of 100 °C. The capillary and cone voltages were set to 2300 and 50 V, respectively. The collision gas energy for MS/MS was 10–45 V. The data were collected for each sample with a 0.25 s scan time and a 0.01 s interscan delay. Leucine-enkephalin (m/z 554.2661) was used as a reference compound.
2.1 Chemicals and materials 2.4 Statistical and multivariate analysis Leucine-enkephalin and formic acid were purchased from Sigma-Aldrich (St. Louis, MO). HPLC-grade acetonitrile and methanol were obtained from SK chemical reagent Co. (Seoul, Korea). All aqueous solutions were prepared using ultrapure water produced with a Milli-Q system (18.2 MX, Milipore, Bedford, MA). 2.2 Plant harvesting and metabolite extraction Soybeans (cultivar name; HwangKeum) were grown in the experimental field of the Korea Atomic Energy Research
123
The mass raw data were analyzed by the MarkerLynx applications manager version 4.1 (Waters, Manchester, UK). The parameters were as follows: RT range 3.8–13.2 min, 200–1,500 Da mass range, 0.04 Da mass tolerance, the isotopic data were excluded from analysis, the noise elimination level was 20, and the mass and RT windows were set at 0.04 and 0.1 min, respectively. The resulting data sets were imported into SIMCA–P? software 12.0 (Umetrics, Umea˚, Sweden) for the multivariate statistical analysis. The univariate statistics for multiple
Metabolomics investigation of flavonoid synthesis
835
classes were obtained through the breakdown and one-way ANOVA using STATISTICA (version 8.0, StatSoft Inc., Tulsa, OK). The PCA is an unsupervised pattern recognition method used to examine systematic variations in the data set. Pareto-scaled (scaled to square root of standard deviation) and mean-centered pretreatments of the data sets were performed before the multivariate analysis.
3 Results and discussions 3.1 Multivariate analysis of flavonoids depending on growth stage Three weeks after seeding, the soybean leaves were collected in three separate plots per week for 13 weeks (from 4 to 16 weeks). The soybean growth was divided into the vegetative (stage 1D) and reproductive stages (stage R1– R7): 1D; fully developed leaf on the main stem from the time the plant emerges from the soil, R1; beginning bloom, R2; full bloom, R3; beginning pod, R4; full pod, R5; beginning seed, R6; full seed, R7; beginning maturity, and R8; full maturity (Pedersen et al. 2007). The 39 samples (3 samples 9 13 weeks) were randomly injected into the UPLC–QTOF–MS system in negative ion mode. To carry out a comparative study of flavonoids between the growths stages of soybean leaves, we performed PCA analysis with flavonoids region (3.8–13.2 min), a widely accepted method for metabolomics profiling of plant metabolites (Choi et al. 2010b; El-Abassy et al. 2011).
2
From the PCA score plot (Fig. 1a), the samples could be distinguished using the differences between the growth period and classified into four groups based on the growth stage: stage 1D–R4 (4–10 weeks), stage R5–R6 (11–13 weeks), and stage R7–R8 (14–16 weeks). The principal components 1 (PC1) and 2 (PC2) accounted for 44.2 % of the variation. Stage 14–16th week samples (R7– R8) and the 4–13th week samples were clearly separated by PC1, while stage 4–10th week (1D–R4) and 11–13th week (R5–R6) samples were readily discriminated by PC2. In addition, the corresponding PCA loading plot enabled the detection of several markers responsible for separating the groups (Fig. 1b). Marker ions at m/z 755.2048 ([M– H]-, 4.37 min; 2), 755.2061 ([M–H]-, 4.60 min; 3), 739.2118 ([M–H]-, 5.22 min; 6), 593.1548 ([M–H]-, 6.40 min; 8), 593.1517 ([M–H]-, 7.13 min; 10), 253.0504 ([M–H]-, 9.93 min; 14), 269.0457 ([M–H]-, 12.60 min; 17), and 267.0308 ([M–H]-, 13.02 min; 19) were far from the loading plot center, suggesting that these compounds might be good markers for the growth of soybean leaves. A heat map was generated to illustrate co-fluctuation of metabolites linked to the growth stages, as well as related metabolites during the growth (Fig. 2a). As shown Fig. 2b, the levels of 2, 3, 6, 8, and 10 were maintained for up to 13 weeks (1D-R4) and decreased when the color of soybean leaves changed to yellow (14–16 weeks). However, the concentration of 14 and 17 increased when seeds grew in the pods (R5–R6; 11–13 weeks), and the marker ion for 19 was significantly increased from the beginning of leaf aging to pod maturation (R7–R8; 13–16 weeks).
2
R X=0.724, Q =0.537 100
A
B
th
4-10 weeks th 11 weeks th 12 weeks th 13 weeks th 14-16 weeks
80 60
2
0.15 0.10
8
0.05
3
20
p[2]
PC2 (15.6%)
40
0
-0.00
-20
-0.05
-40
-0.10
6
10 19
-60
-0.15
-80
14
-0.20 -100
-100
-50
0
50
100
PC1 (28.6%)
Fig. 1 Principle component analysis (PCA) score plot (a) and the loading plot (b) of soybean leaf samples according to growth time (2, 755.2048 and 4.37 min; 3, 755.2061 and 4.60 min; 6, 739.2118
-0.20
-0.10
17
-0.00
0.10
0.20
0.30
p[1]
and 5.22 min; 8, 593.1548 and 6.40 min; 10, 593.1517 and 7.13 min; 14, 253.0504 and 9.93 min; 17, 269.0457 and 12.60 min; 19, 267.0308 and 13.02 min)
123
836
H.-H Song et al. Low
A
High
2 3 6 8 10 14 17 19
B
1600
700
2 3
1400
Relative intensity
Relative intensity
1200 1000 800 600
6 8 10
600
500
400
300
200
400
100
200 0
0 4
5
6
7
8
9
10
11
12
13
14
15
16
4
5
6
7
8
9
Weeks 350
11
12
13
14
15
16
12
13
14
15
16
600
14 17
300
19 500
250
Relative intensity
Relative intensity
10
Weeks
200
150
400
300
200
100 100
50
0 4
5
6
7
8
9
10
11
12
13
14
15
16
Weeks
Fig. 2 Heat map (a) and relative intensity (b) of metabolites that were significantly different during soybean leaf growth (2, kaempferol-3-O-b-D-glucopyranosyl(1?2)-O-[a-L-rhamnopyranosyl(1?6)]b-D-galactopyranoside; 3, kaempferol-3-O-b-D-glucopyranosyl(1?2)O-[a-L-rhamnopyranosyl(1?6)-b-D-glucopyranoside; 6, kaempferol-3-
123
0 4
5
6
7
8
9
10
11
Weeks
O-(2,6-di-O-a-L-rhamnopyranosyl)-b-D-galactopyranoside; 8, kaempferol-3-O-a-L-rhamnopyranosyl(1?6)-b-D-galactopyranoside; 10, kaempferol-3-O-a-L-rhamnopyranosyl(1?6)-b-D-glucopyranoside; 14, daidzein; 17, genestein; 19, coumestrol)
Metabolomics investigation of flavonoid synthesis
837
3.2 Identification of metabolites in soybean leaves by UPLC–QTOF–MS The metabolite profiles of the soybean leaf extracts were obtained by UPLC–QTOF–MS analysis. The representative base peak ion (BPI) chromatograms of three growth stages were shown in Fig 3. 1D–R4 and R5–R6 stages were represented in Fig 3a, b respectively. Although the several compounds (1, 7, 9, 11–13, 15, 16, 18, and 19) were exposed at R7–R8 stage (Fig 3c), compound 19 was most significant difference marker at R7–R8 stage by multivariate analysis. Nineteen metabolites could be identified by a carefully interpreting the mass spectra including the experimental m/z values, MS/MS analysis, the error (ppm), molecular formula, and the reference data (Table 1). Based on the MS fragment analysis, compound 2–5, and 8–10 were showed at m/z 284–285, they indicated that their aglycone was kaempferol (Cuyckens and Claeys 2004; Ferreres et al. 2012). In particular, compounds 2 (m/z 755.2048) and 3 (m/z 755.2061) were structural isomers that appeared at retention time (tR) 4.37 min and 4.60 min, respectively. The MS/MS spectra of them showed fragment ion at m/z 575, which was formed by the loss of [M-GlcH2O]-. Compounds 2 and 3 could be identified as kaempferol-3-O-b-D-glucopyranosyl(1?2)-O-[a-L-rhamnopyranosyl(1?6)]-b-D-galactopyranoside (Kaem-3-O-Glc-Rha-Gal) and kaempferol-3-O-b-D-glucopyranosyl(1?2)-O-[a-Lrhamnopyranosyl(1?6)-b-D-glucopyranoside (Kaem 3-OGlc-Rha-Glc), respectively, by matching the experimental molecular formula (C33H40O20) and the fragment ion
A
information with the results from the soy leaves (Ho et al. 2002; Zang et al. 2011). The MS/MS spectra of compound 4 and 5 were showed the same deprotonated molecular ions at m/z 609. The fragment ions of them showed the product ions at m/z 429 [M-Gal/Glc-H2O]-, corresponding to the loss of galactose or glucose fragment ion, and m/z 284 or 285 [M-2Gal/2Glc-H2O]-, indicationg the loss of one more galactose or glucose ion. Kaempferol-3-O-digalactopyranoside (Kaem 3-O-diGal; 4) and kaempferol-3-O-diglucopyranoside (Kaem 3-O-diGlc; 5) were identified by matching the experimental molecular formula (C33H40O20), the fragment ion patterns, and comparing the migration order in the chromatogram with literature data (Ho et al. 2002; Zang et al. 2011). Compounds 6, 8, and 10 showed peaks for [M–H]- at m/z 739.2118, 593.1548, and 593.1527, indicating that the respective molecular formulae were C33H39O19, C27H29O15, and C27H29O15. These compounds were identified as kaempferol-3-O-(2,6-di-Oa-L-rhamnopyranosyl)-b-D-galactopyranoside (Kaem-3-O-RhaRha-Gal), kaempferol-3-O-a-L-rhamnopyranosyl(1?6)b-D-galactopyranoside (Kaem-3-O-Rha-Gal), and kaempferol-3-O-a-L-rhamnopyranosyl(1?6)-b-D-glucopyranoside (Kaem-3-O-Rha-Glc) by fragment ion pattern analysis and comparing the migration order in the chromatogram with literature data (Ho et al. 2002). According to the MS spectra, major ions of compounds 14 and 17 were revealed at m/z 253.0504 (calcd for C15H9O4, 253.0501) and 269.0457 (calcd for C15H9O5, 269.0450), and their high resolution mass analysis and literature data were consistent with those of daidzein and genistein, respectively (Lee et al.
2
100
3
%
6
10
8
0 4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
13.00
B %
100 17
14
0 4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
13.00
C
19
%
100 1
3
4 5(6)
2
9
7 8
10
11
18
12 13 14
16
15
17
0
Time 4.00
5.00
6.00
7.00
8.00
Fig. 3 Base peak ion chromatogram of the soybean leaf extract (a 5 week, b 10 week, c 16 week) (1 daidzin; 2, kaempferol-3-O-b-Dglucopyranosyl(1?2)-O-[a-L-rhamnopyranosyl(1?6)]-b-D-galactopyranoside; 3, kaempferol-3-O-b-D-glucopyranosyl(1?2)-O-[a-L-rhamnopyranosyl (1?6)-b-D-glucopyranoside; 4, kaempferol-3-O-digalactopyranoside; 5, kaempferol-3-O-diglucopyranoside; 6, kaempferol-3-O-
9.00
10.00
11.00
12.00
13.00
(2,6-di-O-a-L-rhamnopyranosyl)-b-D-galactopyranoside; 7, genistin; 8, kaempferol-3-O-a-L-rhamnopyranosyl(1?6)-b-D-galactopyranoside; 9, astragalin; 10, kaempferol-3-O-a-L-rhamnopyranosyl(1?6)b-D-glucopyranoside; 11, apigenin-7-glucoside; 12, 600 -O-malonylgenistin; 13, chrysin; 14, daidzein; 15, glycitein; 16, glyceofuran; 17, genestein; 18, isotrifoliol; 19, coumestrol)
123
838
H.-H Song et al.
Table 1 Identification of phytochemical metabolities by UPLC–QTOF–MS from soybean leaves No.
Identity
tR (min)
Calculated [M–H]-
Detected precursor ion and/or adduct ions
Error (ppm)
Molecular formula
MS/MS
References
1
Daidzin
3.86
415.1029
415.1034 [M-H]-
1.2
C21H20O9
252
Jeon et al. (2012)
2
Kaempferol-3-O-b-Dglucopyranosyl(1?2)-O-[a-Lrhamnopyranosyl(1?6)]-b-Dgalactopyranoside
4.37
755.2035
755.2059 [M–H]-
3.2
C33H40O20
575, 285
Ho et al. (2002) and Zang et al. (2011)
3
Kaempferol-3-O-b-Dglucopyranosyl(1?2)-O-[a-Lrhamnopyranosyl(1?6)b-D-glucopyranoside
4.6
755.2035
755.2061 [M-H]-
3.4
C33H40O20
575, 284
Zang et al. (2011)
4
Kaempferol-3-Odigalactopyranoside
5.04
609.1456
609.1466 [M–H]-
1.6
C27H30O16
429, 285
Ho et al. (2002)
5
Kaempferol-3-Odiglucopyranoside
5.17
609.1456
609.1465 [M-H]-
1.5
C27H30O16
429, 284
Ho et al. (2002)
6
Kaempferol-3-O-(2,6-diO-a-L-rhamnopyranosyl)b-D-galactopyranoside
5.22
739.2086
739.2117 [M–H]-
4.2
C33H40O19
575, 284
Ho et al. (2002) and Zang et al. (2011)
7
Genistin
6.13
431.0978
431.0973 [M-H]-
-1.2
C21H20O10
268
Jeon et al. (2012)
8
Kaempferol-3-O-a-Lrhamnopyranosyl(1?6)b-D-galactopyranoside
6.4
593.1506
593.1501 [M–H]-
-0.8
C27H30O15
284
Ho et al. (2002)
9
Astragalin
6.89
447.0927
447.0914 [M-H]-
-2.9
C21H20O11
284
Lee et al. (2008b)
10
Kaempferol-3-O-a-Lrhamnopyranosyl(1?6)b-D-glucopyranoside
7.13
593.1506
593.1517 [M–H]-
1.9
C27H30O15
284
Ho et al. (2002)
11
Apigenin-7-glucoside
7.88
431.0978
431.0972 [M-H]-
-1.4
C21H20O10
268
Rivera-Vargas et al. (1993)
12
600 -O-Malonylgenistin
9.26
517.1041.
517.1,006 [M–H]-
-6.8
C17H26O18
269
1035.2072 [2 M-H]-
Jeon et al. (2012) Rivera-Vargas et al. (1993)
13
Chrysin
9.59
253.0501
253.0502 [M-H]-
0.4
C15H10O4
Lee et al. (2008a) and Jeon et al. (2012)
14
Daidzein
9.93
253.0501
253.0519 [M–H]-
7.1
C15H10O4
Lee et al. (2008a)
-
15
Glycitein
10.38
283.0219
283.0249 [M-H]
2.1
C15H8O6
Yuk et al. (2011b)
16
Glyceofuran
11.75
353.1036
353.1031 [M–H]-
1.7
C20H18O6
Lee et al. (2008a) and Jeon et al. (2012)
17
Genestein
12.6
269.045
269.0475 [M-H]-
9.3
C15H10O5
Yuk et al. (2011a, b)
18
Isotrifoliol
12.73
297.0399
297.0428 [M–H]-
9.8
C16H10O6
Yuk et al. (2011a, b)
19
Coumestrol
13.02
267.0293
267.0305 [M-H]-
4.5
C15H8O5
2009). The [M–H]- ion for compound 19 was revealed at m/z 267.0308 (calcd for C15H7O5, 267.0293). This compound was recently reported to be a metabolite of growth stage in soybean leaves (Glycine max L. Merrill) (Yuk et al. 2011a). 3.3 Metabolite pathway analysis This flavonoid pathway map reported the definition of the key points at which metabolism changed in metabolic pathways using the connectivity matrix (http://www. genome.jp/kegg/pathway.html). A metabolite pathway analysis based on an identification compound was carried
123
out to visualize the network and focus on the correlations between the key markers. All the disconnected markers are deleted (gray color) and the metabolites with significant correlations are illustrated together. After correlation analysis of key metabolites and growth stages, eight pairwise correlations with strong dependencies were picked and applied for metabolic network pathway analysis (Fig. 4). Our data show some interesting facets of the development stages. Most strikingly, key metabolites showed a typical metabolite pathway of time-dependent growth behavior. These five metabolites, such as Kaem-3-O-GlcRha-Gal (2), Kaem-3-O-Glc-Rha-Glc (3), Kaem-3-O-Rha-
Metabolomics investigation of flavonoid synthesis
839
Fig. 4 Changes in the marker of metabolites during leaves growth stages shown in a metabolic pathway. The level of significance was set at P \ 0.05. The metabolites in black characters were detectable.
(Api, Apigenin; Nar, Naringenin; Kaem, Kaempferol; Lut, Luteolin; Glc, glucose; Gal, Galactose; Rha, Rhamnose)
Rha-Gal (6), Kaem-3-O-Rha-Gal (8), and Kaem-3-O-RhaGlc (10) closely exhibited to potent correlations with 1D– R6 growth stages from leaf growing to full seed in the pod. These results were correlated with expression of glycosyltransferases during germination (Taylor et al. 1998), growth of cell wall (Cosgrove 2005), pollen development (Pourcel and Grotewold 2009), and early flowering from plants (Wang et al. 2012). In addition, previous studies have demonstrated that young persimmon leaves have a higher kaempferol–glycone accumulation compared to older leaves (Kawakami et al. 2011). The latter presumably implies that morphology/capacity of the beginning seeds is important in R5–R6 growth stage. In metabolite pathway analysis, kaempferol–glycone and isoflavone metabolites play a critical role for morphology because dramatic difference of seeds ratio were observed between 1D–R4 and R5–R6 growth stages. We also found that higher levels of isoflavones showed in the R5–R6 growth stages. Especially, Legumes species have a unique enzymatic function that carries out a 2,3 migration of the B-ring of naringenin or liquiritigenin, resulting in the production of the isoflavones (Yu et al. 2000). This key enzyme that redirects phenylpropanoid pathway intermediates from flavonoids to isoflavonoids is the cytochrome P450 monooxygenase and isoflavone synthase (IFS). Strong correlations were shown
with the activation of isoflavonoid biosynthesis because the soybean grows to fill the pod cavity and nodulation occurs at R5–R6 growth stages (Pourcel and Grotewold 2009). Since in soybean leaves the isoflavones are accumulated, these levels may be enhanced and stabilized through the introduction of an IFS transgene, potentially leading to improved disease resistance or nutritional value. Sequentially, isoflavone metabolism consists of a complex series of branched biochemical pathways that produce isoflavones and pterocarpans (phytoalexin) against plant fungal infections (Kavousi et al. 2009). Although not all reactions leading to the biosynthesis pathway of the basic pterocarpans have been determined, coumestrol is derived from 20 -hydroxydaidzein. The biosynthesis of pterocarpans, such as glyceollin I–VII, occurs via 3,6a,9-trihydroxypterocarpen, which acts against fungal infection such as Aspergillus oryzae, and Rhizopus oryzae (Aisyah et al. 2013; Jeon et al. 2012). The subsequent reactions convert isoflavones into pterocarpans or coumestrols, which are potent anti-fungal compounds. Finally, the coumestrol exhibited strong correlations at the R7–R8 growth stage, but had not seen correlations at 1D–R6 growth stages. It is suggested not only key metabolites but also their biosynthesis network into soybean leaves maturation and metabolite changes.
123
840
4 Concluding remarks To the best of our knowledge, this study provides the first comparative metabolomic approach to reveal for components differences among soybean leaves during growth stages. UPLC–QTOF–MS techniques coupled with multivariate data analyses were used and compared to obtain the experimental results, and interesting and meaningful differences between the various growth stages were investigated. An effective comprehensive analytical tool was developed to study different growth stages of soybean leaves and to identify the distinct phytochemical biomarkers. This study provides a comprehensive, comparative analysis of the metabolic composition of soybean leaves from different developmental stages. The results clearly demonstrate that phytochemicals discriminate between the 4 and 10th week (1D–R4), 11–13th week (R5– R6), and 14–16th week (R7–R8) samples by PCA. Some kaempferol glycosides were increasingly synthesized from leaf growing to full seed in the pod (1D–R6 stages). The extensive levels of isoflavone metabolites were shown during seed growth in the pod (R5–R6), while coumestrol was accumulated at stages R7–R8 (maturity period) against fungal infections. The phytochemicals changes were revealed, and the analysis of the putative biomarkers provided insight into the phytochemical metabolism related to pollen development, germination, cell wall development, early flowering, soybean pods, and root nodulation during different growth stages. A large number of the significant phytochemicals from soybean leaves have previously been shown to be key components within the phytochemicals of soybean leaves and Legumes. The knowledge of metabolic profiling at different development stages, and the quantitative determination of component at different ripeness time, may have an important role in determining the most suitable harvesting time for this commercially important crop. Furthermore, these results not only confirmed metabolites data but also revealed new insights into soybean leaves composition and metabolite changes, thus demonstrating the value of metabolomics as a functional crops tool in characterizing the mechanism of quality formation, a key developmental stage in economically important agricultural science. Acknowledgments This work was supported by the KRIBB Research Initiative Program (KGM1221413).
References Aisyah, S., Gruppen, H., Madzora, B., & Vincken, J. P. (2013). Modulation of isoflavonoid composition of Rhizopus oryzae
123
H.-H Song et al. elicited soybean (Glycine max) seedlings by light and wounding. Journal of Agricultural and Food Chemistry, 61, 8657–8667. Chennupati, P., Seguin, P., Chamoun, R., & Jabaji, S. (2012). Effects of high-temperature stress on soybean isoflavone concentration and expression of key genes involved in isoflavone synthesis. Journal of Agricultural and Food Chemistry, 60, 12421–12427. Christ, M. M., Ainsworth, E. Z., Nelson, R., Schurr, U., & Walter, A. (2006). Anticipated yield loss in field-grown soybean under elevated ozone can be avoided at the expense of leaf growth during early reproductive growth stages in favourable environmental conditions. Journal of Experimental Botany, 57, 2267–2275. Choi, M. S., Ham, S. M., Jeong, T. S., Jeong, H. J., Lee, W. S., Park, H. Y., et al. (2010a). Compositions for preventing and treating obesity, hyperlipidemia, atherosclerosis, fatty liver, diabetes or metabolic syndrome containing extracts of Glycine Max leaves or fractions isolated from the same as an active ingredient. US 20100291248 A1. Choi, M. Y., Choi, W., Park, J. H., Lim, J., & Kwon, S. W. (2010b). Determination of coffee origins by integrated metabolomics approach of combining multiple analytical data. Food Chemistry, 121, 1260–1268. Cosgrove, D. J. (2005). Growth of the plant cell wall. Nature Reviews Molecular Cell Biology, 6, 850–861. Cui, Y., Barampuram, S., Stacey, M. G., Hancock, C. N., Findly, S., Mathiew, M., et al. (2013). Tnt1 retrotransposon mutagenesis: A tool for soybean functional genomics. Plant Physiology, 161, 36–47. Cuyckens, F., & Claeys, M. (2004). Mass spectrometry in the structural analysis of flavonoids. Journal of Mass Spectrometry, 39, 1–15. Dixit, A. K., Antony, J. I. X., Sharma, N. K., & Tiwari, R. K. (2011). Opportunity, challenge and scope of natural Products in Medicinal Chemistry. In V. K. Tiwari & B. B. Mishra (Eds.), Soybean constituents and their functional benefits (pp. 367–383). Kerala, India: Research Signpost. Dubousset, L., Etienne, P., & Avice, J. C. (2010). Is the remobilization of S and N reserves for seed filling of winter oilseed rape modulated by sulphate restrictions occurring at different growth stages? Journal of Experimental Botany, 61, 4313–4324. El-Abassy, R. M., Donfack, P., & Materny, A. (2011). Discrimination between Arabica and Robusta green coffee using visible micro Raman spectroscopy and chemometric analysis. Food Chemistry, 126, 1443–1448. Farag, M. A., Porzel, A., Schmidt, J., & Wessjohann, L. A. (2012). Metabolite profiling and fingerprinting of commercial cultivars of Humulus lupulus L. (hop): A comparison of MS and NMR methods in metabolomics. Metabolomics, 8, 492–507. Ferreres, F., Gil-Izquierdo, A., Vinholes, J., Silva, S. T., & Valentao, P. (2012). Bauhinia forficata link authenticity using flavonoids profile: Relation with their biological properties. Food Chemistry, 134, 894–904. Ho, H. M., Chen, R. Y., Leung, L. K., Chan, F. L., Huang, Y., & Chen, Z. Y. (2002). Difference in flavonoid and isoflavone profile between soybean and soy leaf. Biomedicine and Pharmacotherapy, 56, 289–295. Irsigler, A. S. T., Costa, M. D. L., Zhang, P., Reis, P. A. B., Deway, R. E., Boston, R. S., et al. (2007). Expression profiling on soybean leaves reveals integration of ER and osmotic-stress pathways. BMC Genomics. doi:10.1186/1471-2164-8-431. Jeong, S. W., Park, S. M., Jin, J. S., Seo, O. N., Kim, G. S., Kim, Y. H., et al. (2012). Influences of four different light-emitting diode lights on flowering and polyphenol variations in the leaves of Chrysanthemum (Chrysanthemum morifolium). Journal of Agricultural and Food Chemistry, 60, 9793–9800. Jeon, H. Y., Seo, D. B., Shin, H. J., & Lee, S. J. (2012). Effect of Aspergillus oryzae-challenged germination on soybean
Metabolomics investigation of flavonoid synthesis isoflavone content and antioxidant Activity. Journal of Agricultural and Food Chemistry, 60, 2807–2814. Jideani, V. A. (2011). Soybean–biochemistry, chemistry and physiology. In T. B. Ng (Ed.), Functional properties of soybean food ingredients in food systems (pp. 345–366). Rijeka, Croatia: InTech. Kang, N. S., & Lee, J. H. (2011). Characterisation of phenolic phytochemicals and quality changes related to the harvest times from the leaves of Korean purple perilla (Perilla frutescens). Food Chemistry, 124, 556–562. Kavousi, H. R., Marashi, H., Mozafari, J., & Bagheri, A. R. (2009). Expression of phenylpropanoid pathway genes in chickpea defence against race 3 of Ascochyta rabiei. Plant Pathology Journal, 8, 127–132. Kawakami, K., Shibukura, Y., Kanno, T., Furuki, T., Aketa, S., & Hirayama, M. (2011). Identification of 200 -galloylated flavonol 3-O-glycosides accumulating in developing leaves of persimmon. Phytochemical Analysis, 22, 403–410. Kim, D. H., Park, J. S., Shim, J. S., Kim D. H., Kim, H. G., & Lee, O. S. (2011). Wrapping fermentation method using medicinal leaves, and composition for external skin application using same. WO 2011/126295 A2. Kim, J. A., & Chung, I. M. (2007). Change in isoflavone concentration of soybean (Glycine max L.) seeds at different growth stages. Journal of the Science of Food and Agriculture, 87, 496–503. Kim, J. C., Lee, S. H., Cheong, Y. H., Yoo, C. M., Lee, S. I., Chun, H. J., et al. (2001). A novel cold-inducible zinc finger protein from soybean, SCOF-1, enhances cold tolerance in transgenic plants. The Plant Journal, 25, 247–259. Lee, J. H., Baek, I. Y., Choung, M. G., Ha, T. J., Ham, W. Y., Cho, K. M., et al. (2008a). Phytochemical constituents from the leaves of soybean [Glycin max (L.)) Merr.]. Food Science and Biotechnology, 17, 578–586. Lee, J. H., Ha, T. J., Baek, I. Y., Han, W. Y., Cho, K. M., Park, K. Y., et al. (2008b). Evaluation of isoflavones from the leaves of soybean (Glycine max L.) cultivars. Journal of Applied Biological Chemistry, 51, 172–175. Lee, J. H., Ha, T. J., Baek, I. Y., Ko, J. M., Cho, K. M., Im, M. H., et al. (2009). Characterization of isoflavones accumulation in developing leaves of soybean (Glycine max) cultivars. Journal of the Korean Society for Applied Biological Chemistry, 52, 139–143. Lee, J. H., Lee, B. W., Kim, J. H., Jeong, T. S., Kim, M. J., Lee, W. S., et al. (2006a). LDL-antioxidant pterocarpans from roots of Glycine max (L.) Merr. Journal of Agricultural and Food Chemistry, 54, 2057–2063. Lee, J. H., Seo, W. D., Jeong, S. H., Jeong, T. S., Lee, W. S., & Park, K. H. (2006b). Human Acyl-CoA: Cholesterol acyltransferase inhibitory effect of flavonoids from roots of Glycine max (L.) Merr. Agricultural Chemistry and Biotechnology, 49, 57–61. Mari, A., Lyon, D., Fragner, L., Montoro, P., Piacente, S., Wienkoop, S., et al. (2013). Phytochemical composition of Potentilla anserina L. analyzed by an integrative GC-MS and LC-MS metabolomics platform. Metabolomics, 9, 599–607. Messina, M., & Messina, V. (1991). Increasing use of soyfoods and their potential role in cancer prevention. Journal of the American Dietetic Association, 91, 836–840. Okazaki, Y., Kamide, Y., Hirai, M. Y., & Saito, K. (2013). Plant lipidomics based on hydrophilic interaction chromatography coupled to ion trap time-of-flight mass spectrometry. Metabolomics, 9, S121–S131. Park, S. J., Hyun, S. H., Suh, H. W., Lee, S. Y., Sung, G. H., Kim, S. H., et al. (2013). Biochemical characterization of cultivated Cordyceps bassiana mycelia and fruiting bodies by 1H nuclear magnetic resonance spectroscopy. Metabolomics, 9, 236–246.
841 Pedersen, P., Kumudini, S., Board, J., & Conley, S. (2007). Using foliar fungicides to manage soybean rust. The Ohio State University. http://oardc.osu.edu/soyrust/. Accessed 8 July 2013. Pourcel, L., & Grotewold, E. (2009). Participation of phytochemicals in plant development and growth. In A. E. Osbourn & V. Lanzotti (Eds.), Plant-derived natural products (pp. 269–279). New York: Springer. Radwan, O., Wu, X., Govindarajulu, M., Libault, M., Neece, D. J., Oh, M. H., et al. (2012). 14-3-3 proteins SGF14c and SGF14l play critical roles during soybean nodulation. Plant Physiology, 160, 2125–2136. Rivera-Vargas, L. I., Cshmitthenner, A. F., & Graham, T. L. (1993). Soybean flavonoid effects on and metabolism by Phytophthora sojae. Phytochemistry, 32, 851–857. Seo, W. D., Yuk, H. J., Curtis-Long, M. J., Jang, K. C., Lee, J. H., Han, S. I., et al. (2013). Effect of the growth stage and cultivar on policosanol profiles of barley sprouts and their adenosine 50 -monophosphate-activated protein kinase activation. Journal of Agricultural and Food Chemistry, 61, 1117–1123. Su, X., Xu, J., Yan, X., Zhao, P., Chen, J., Zhou, C., et al. (2013). Lipidomic changes during different growth stages of Nitzschia closterium f. minutissima. Metabolomics, 9, 300–310. Taylor, L. P., Strenge, D., Miller, K. D. (1998). The role of glycosylation in flavonol-induced pollen germination. In J. A. Manthey & B. S. Buslig (Eds.), Flavonoids in the living system (pp. 35–44). New York: Plenum Press. van der Hooft, J., Vervoort, J., Bino, R. J., & de Vos, R. C. H. (2012). Spectral trees as a robust annotation tool in LC–MS based metabolomics. Metabolomics, 8, 691–703. Wang, Y. W., Wang, W. C., Jin, S. H., Wang, J., Wang, B., & Hou, B. K. (2012). Over-expression of a putative poplar glycosyltransferase gene, PtGT1, in tobacco increases lignin content and causes early flowering. Journal of Experimental Botany, 7, 2799–2808. Xiong, A., Yang, L., Ji, L., Wang, Z., Yang, X., Chen, Y., et al. (2012). UPLC–MS based metabolomics study on Senecio scandens and S. vulgaris: An approach for the differentiation of two Senecio herbs with similar morphology but different toxicity. Metabolomics, 8, 614–623. Yang, Z., Kobayashi, E., Katsuno, T., Asanuma, T., Fujimori, T., Ishikawa, T., et al. (2012). Characterisation of volatile and nonvolatile metabolites in etiolated leaves of tea (Camellia sinensis) plants in the dark. Food Chemistry, 135, 2268–2276. Yu, O., Jung, W., Shi, J., Croes, R. A., Fader, G. M., McGonigle, B., et al. (2000). Production of the isoflavones genistein and daidzein in non-legume dicot and monocot tissues. Plant Physiology, 124, 781–793. Yuk, H. J., Curtis-Long, M. J., Ryu, H. W., Jang, K. C., Seo, W. D., Kim, J. Y., et al. (2011a). Pterocarpan profiles for soybean leaves at different growth stages and investigation of their glycosidase inhibitions. Journal of Agricultural and Food Chemistry, 59, 12683–12690. Yuk, H. J., Lee, J. H., Curtis-Long, M. J., Lee, J. W., Kim, Y. S., Ryu, H. W., et al. (2011b). The most abundant polyphenol of soy leaves, coumestrol, displays potent a-glucosidase inhibitory activity. Food Chemistry, 126, 1057–1063. Zaman, M. S., Mushfika, R., Hiroaki, E., & Kiharu, I. (2004). Difference in phytoestrogen content in edamame (Glycine max. L. Merr.) leaf depending on species and cultivation stages. Journal of Food Technology, 2, 142–144. Zang, Y., Sato, H., & Igarashi, K. (2011). Anti-diabetic effects of a kampferol glycoside-rich fraction from unripe soybean (edamame, Glycine max L. Merrill. ‘Jindai’) leaves on KK-Ay mice. Bioscience, Biotechnology, and Biochemistry, 75, 1677–1684. Zhang, J., Wang, X., Yu, O., Tang, J., Gu, X., Wan, X., et al. (2011). Metabolic profiling of strawberry (Fragaria 9 ananassa Duch.) during fruit development and maturation. Journal of Experimental Botany, 62, 1103–1118.
123
