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2. Results and Discussion

Nine MeJA-treated plant samples, nine JA-treated samples, eleven JA-Ile-treated samples, twelve CGM-treated samples and eleven control (labeled as CK) samples were collected for analyses. The aqueous CD₃OD extract of total 52 L. leucocephala samples were subjected to NMR analyses. Seven organic acids, malic acid (1), citric acid (2), formic acid (3), succinic acid (4), RC(OH)CH₃-COOH (5), lactic acid (6) and fumaric acid (7), four carbohydrates, sucrose (8), α-glucose (9), β-glucose (10), and fructose (11), three amino acids, alanine (12), threonine (13), and mimosine (14), two flavonoids, quercetin (15) and quercetin-3-O-α-rhamnoside (16), and three miscellaneous compounds, choline (17), steroids (18), and 3,4-dihydroxypyridine (19) were identified by 1D and 2D NMR spectra and comparison with the Biological Magnetic Resonance Data Bank (BMRB) database [15]. The structures of those 19 identified metabolites were shown in Figure S2 (Supplementary Material). Moreover, the areas of two obvious single peaks (δ_H 2.09 and 2.72) referred to two unidentified compounds (20 and 21) were integrated and included in the chemometric analysis. Diagnostic peaks of compounds 1–21 were shown in Table 1.

Representative ¹H-NMR spectrum of L. leucocephala leaf extract was shown in Figure 2A. The ¹H-NMR spectrum exhibited signals at δ_H 2.67 (dd, J = 15.3, 3.0 Hz, 1H), 2.37 (dd, J = 15.3, 10.0 Hz, 1H), and 4.28 (dd, J = 10.0, 3.0 Hz, 1H), which were diagnostic for malic acid (1). The ¹H-NMR spectrum showed two double peaks at δ_H 2.50 (d, J = 15.6 Hz) and 2.69 (d, J = 15.6 Hz), combined with HMBC correlation between these proton doublet with carbons at δc 45.7, 75.1, 179.0, 182.0, and citric acid (2) was consequently determined. Formic acid, succinic acid and fumaric acid were identified on the basis of proton resonance at δ_H 8.46 (1H, s), 2.41 (s) and 6.50 (1H, s), respectively. A tertiary methyl at δ_H 1.36 showed HMBC correlations with an oxygenated carbon (δc 77.0) and a carboxyl (δc 180.9), which implied the partial structure of 5. Lactic acid was determined by the observation of a double peak at δ_H 1.34 (d, J = 6.8 Hz).

1D and 2D NMR spectra of L. leucocephala leaf extracts. (A) Representative ¹H-NMR spectrum of L. leucocephala leaf extracts. Peaks: 1, malic acid; 2, citric acid; 3, formic acid; 4, succinic acid; 5, RC(OH)CH₃-COOH; 6, lactic acid; 7, fumaric acid; 8, sucrose; 9, α-glucose; 10, β-glucose; 11, fructose; 12, alanine; 13, threonine; 14, mimosine; 15, quercetin; 16, quercetin-3-O-α-rhamnoside; 17, choline; 18, steroids; 19, 3,4-dihydroxypyridine; 20, unidentified; 21, unidentified; (B) J values were determined in the 2-dimensional J-resolved NMR spectroscopy of JA-Ile treated plants; (C) HMBC correlation between methyl signals and olefinic carbon at δc 134.2; (D) A set of methyl signals at 0.85-1.05 ppm in JA-Ile elicited L. leucocephala leaf extracts was compared to control (CK).

The proton resonances at δ_H 5.40 (d, J = 3.8 Hz, 1H) and 4.17 (d, J = 8.7 Hz, 1H) were diagnostic for anomeric proton of α-glucose and CH-3 of fructose in sucrose, respectively. α-Glucose was confirmed on the basis of anomeric proton doublet at δ_H 5.21 with a small ³J_H-1,H-2 coupling constant (3.8 Hz) and Heteronuclear Single Quantum Coherence (HSQC) correlation between δ_H 5.21 and δc 92.6. In the same way, β-glucose was identified based on the observation of anomeric proton doublet at δ_H 4.60 with a large ³J_H-1,H-2 coupling constant (7.9 Hz), combined with HSQC correlation between δ_H 4.60 and δc 96.2. Fructose was detected on the basis of CH-3 at δ_H 4.09 (d, J = 3.5 Hz, 1H) and corresponding HSQC correlation with δ_C 77.6. The proton signal at 1.49 ppm (d, J = 7.3 Hz) showed HMBC correlation with δc 175.5 and 50.5; alanine was therefore elucidated. Threonine was identified on the basis of methyl proton signals at δ_H 1.32 (d, J = 6.6 Hz, 1H). Choline was elucidated on the basis of three exactly identical methyl signals at δ_H 3.22 (9H, s). ¹H-NMR signals at 7.69 (overlapped), and 6.60 (d, J = 7.2 Hz, 1H), were owed to 3,4-dihydroxypyridine (19), which was a degradative derivative of mimosine (14). A set of methyl signals of double peaks (0.95, J = 6.6 Hz; 0.92, J = 6.7 Hz; 0.91, J = 6.1 Hz) and triple peaks (0.95, J = 7.5 Hz; 0.90, J = 7.4 Hz) were shown in JA-Ile-treated plants, and of which J (coupling constant) values were clearly determined in the 2-dimensional J-resolved NMR spectroscopy (Figure 2B). One of above methyl groups showed HMBC correlation with olefinic carbon at δc 134.2 (Figure 2C), which was appeared in JA-Ile-treated plant and absent in the HMBC spectra of JA-, MeJA-, CGM-treated and control plants. Figure 2D showed that the methyl at 0.95 (t, J = 7.5 Hz), 0.90 (t, J = 7.4 Hz), 0.92 (d, J = 6.7 Hz) were newly appeared signals in JA-Ile-treated plant. These methyl signals might be contributed to steroids (18), and carbon at δc 134.2 should be ascribed to the C-22 of steroids.

Mimosine (14), quercetin (15) and quercetin-3-O-α-rhamnoside (16), were the main organic component with small molecular weight in L. leucocephala leaf, which were obtained by our extraction and isolation experiments. The ¹H and ¹³C-NMR spectra of mimosine (14), quercetin (15) and quercetin-3-O-α-rhamnoside (16) were shown in Figure S3 (Supplementary Material). Since the ¹H-NMR spectra of leaf extracts were carefully compared with the authentic spectra at hand in Figure S3, and thereby mimosine (14) [16], quercetin (15) [17] and quercetin-3-O-α-rhamnoside (16) [18] could be unambiguously determined in the elicited L. leucocephala leaves. In detail, the signals at δ_H 7.65 (overlapped), 6.54 (d, J = 6.7 Hz, 1H), 4.34 (dd, J = 14.2, 5.0 Hz, 1H), 4.23 (dd, J = 14.2, 6.7 Hz, 1H) were ascribed to mimosine (14). ¹H-NMR signals at δ_H 7.51 (d, J = 2.1 Hz, 1H), 7.47 (dd, J = 8.6, 2.1 Hz, 1H), 6.89 (d, J = 8.5 Hz, 1H), 6.31 (d, J = 1.8 Hz, 1H), and 6.13 (d, J = 1.8 Hz, 1H) were referred to quercetin (15). ¹H-NMR signals at δ_H 7.36 (d, J = 2.1 Hz, 1H), 7.32 (dd, J = 8.4, 2.1 Hz, 1H), 6.91(d, J = 8.4 Hz, 1H), 6.29 (d, J = 1.9 Hz, 1H), 6.13 (d, J = 1.9 Hz, 1H), and 0.94 (d, J = 6.2 Hz, 3H) were contributed to quercetin-3-O-α-rhamnoside (16).

The metabolic profiles of elicited L. leucocephala leaves, i.e., the treatments of CGM, JA-Ile, MeJA and JA, were not drastically altered but certain metabolites have differential abundances. Metabolites in the ¹H-NMR spectra comparison of L. leucocephala leaf extracts under the four stress elicitors were shown to be at different intensity (Figure 3). The relative contents of nineteen determined and two unidentified compounds in each treatment were shown in Table S1 (Supplementary Material), and which was imported into SIMCA-P software for chemometric analyses. Principle component analysis (PCA) was the most basic and efficient method to analyze complex data in metabolomics, which could extract and display systematic variations from the data, as well as to detect the grouping, trend and outlier [19]. Each point in a PCA score plot represented a single sample, and the sample clustered together was considered to have similar characteristic, i.e., similar metabolic profiling. For assessing the potential variables correlating to metabolite contents, PCA was applied aiming to observe cluster of L. leucocephala leaf extracts under different elicitation.

¹H-NMR spectra comparison of leaves extracts of L. leucocephala, which were treated by MeJA (0.5 mM), JA (0.5 mM), JA-Ile (0.5 mM), CGM (0.5 mM) and control (labeled as CK), respectively. The metabolite profile of elicited L. leucocephala leaves were not drastically altered but certain metabolites have differential abundances.

The cumulative R2X for the first and the second components were 0.297 and 0.434, respectively (Figure 4A), which indicated that the first and the second components of PCA score plot accounted for 43.4 % (29.7 and 13.7 %, respectively) of the overall variance. Figure 4B showed three outliers, and exhibited an unclear cluster between the plant treatments of JA, between MeJA, between CGM and control samples, respectively. In contrast, a clear cluster between JA-Ile and control treatments was identified. To further determine the metabolite variable playing an important role in discriminating L. leucocephala leaf extracts from each elicited and control samples, PLS-DA was therefore performed. The cumulative R2Y and Q2 were 0.897 and 0.776, respectively, in the PLS-DA model of JA-Ile and control (CK) (Figure 5A). In parallel, the cumulative R2Y and Q2 were 0.824 and 0.587, respectively for JA, 0.757 and 0.242 for MeJA, and 0.619 and 0.385 for CGM (Figure S4, Supplementary Material). Having 200 permutations and two components, the permutations plot was performed in order to validate PLS-DA model. Figure S5 (Supplementary Material) revealed positive slopes and minus Q2 values of y-intercept for four elicitors. However, it was not found that all R2 and Q2 values to the left were lower than the original points to the right for JA, MeJA and CGM treatments, which indicated that the PLS-DA model was not a stably valid model, respectively. Nevertheless, it was clear that all R2 and Q2 values to the left are lower than the original points to the right for JA-Ile treatment, which indicated that the PLS-DA model was a valid model. The aforementioned information suggested that the JA-Ile treatment showed the most significant metabolic changes among the four elicitors. It was reported that MeJA and JA might be activated by having a conjugation to isoleucine, and thus JA-Ile was the potential active form in triggering the function [20,21]. It was also reported that the amplitude and duration of JA responses were regulated largely by the intracellular level of JA-Ile [22]. Therefore, our finding was consistent with previous reports [20,21,22]. The insensitive of JA, MeJA, and CGM treatments in inducing metabolic changes could be by several possible reasons: (i) L. leucocephala plant was different from other plants such that they are not able to have a conjugation with isoleucine; and (ii) the treatment time was too short and not enough time for the isoleucine conjugation.

Principal component analysis (PCA) of metabolites in L. leucocephala leaf extracts under different elicitations. (A) The summary of fit of PCA score plot with two components was calculated. The first and second components accounted for 43.4 % (29.7 and 13.7 %, respectively) of the overall variance; (B) PCA score plot discriminated L. leucocephala extracts from JA-Ile (triangle) and control treatment (CK, box), and was disabled to distinguish between JA (star), between MeJA (diamond), between CGM (circle) and control treatment (box), respectively. There are three outliers, one from MeJA, one from JA-Ile and another one from CGM.

Partial least squares discriminant analysis (PLS-DA) of metabolites in L. leucocephala leaf extracts under JA-Ile elicitation. (A) The summary of fit of PLS-DA model of JA-Ile and control. The cumulative R2Y and Q2 were 0.897 and 0.776 respectively, when two components were calculated; (B) PLS-DA score plot discriminated L. leucocephala extracts from JA-Ile (triangle) and control treatment (CK, box) more clearly than PCA score plot; (C) PLS-DA loading plot. The notations of 1-21 were corresponding to different metabolites as listed in Table 1. JA-Ile caused an accumulation of lactic acid (6), β-glucose (10), alanine (12), threonine (13), steroids (18), 3,4-dihydroxypyridine (19) and an unidentified compound 20.

Acknowledgments

The authors thank the Special Project of China (2018ZX097201003), NSFC (81703332, 81673303), BNSF (7172028), KZ201610025029, KM201810025010, KM201810025011 for financial support.

Disclosure

The authors report no conflicts of interest in this work

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