1
H NMR spectroscopic analysis of tea leaves
To understand the global metabolic dynamics in tea leaves as tea (C. sinensis) plants become aged, representative 1H NMR spectra of tea leaves collected from 8-year-old (a) and 25-year-old (b) tea plants plucked in May 2015 and April 2016, respectively, are shown in Fig. 1. The 1D 1H NMR spectra consisted of a diverse range of tea leaf metabolites, including acetate, alanine, 2-O-(β-l-arabinopyranosyl)-myo-inositol (Ara), asparagine, aspartate, caffeine, choline, fatty acids, gallate, γ-aminobutyric acid (GABA), α-glucose, β-glucose, glutamine, glutamate, quinate, sugars, sucrose, succinate, catechin, gallocatechin (GC), epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), epigallocatechin gallate (EGCG), epigallocatechin 3-O-(3″-O-methyl) gallate (EGCG3″Me), theanine, theobromine, theogallin, threonine, and valine. These tea leaf metabolites were assigned by spiking with the pure chemicals and also by comparing the data from the published literature [13,14,15, 22, 23]. The metabolites assignment was also validated by 2D TOCSY and HSQC NMR experiments, as described by previous studies [15, 22]. Additional file 1: Table S1 provides the chemical shifts of tea leaf metabolites and their corresponding multiplicity or coupling constant from tea leaves assigned by TOCSY and HSQC NMR experiments.
Metabolic differentiations of tea leaves according to plant age
A pattern recognition method by multivariate statistical analysis, such as principal component analysis (PCA) and orthogonal projection on latent structure-discriminant analysis (OPLS-DA), was employed for the entire 1H NMR dataset for visualizing the global differences in tea leaf metabolites according to age of tea plant. An unsupervised PCA model was used to see the initial spectral features of the 1H NMR dataset and the metabolic relationships between tea samples. The PCA model showed the metabolic dependence of tea leaves on growing vintage or year described by the first principle component with 67.3% variations and on the age of tea plants explained by the second principal component with 9.83% (Fig. 2a). These metabolic dependences were more clear in the OPLS-DA model, as shown in Fig. 2b. The tea leaves collected in 2015 and 2016 were further differentiated in the corresponding OPLS-DA models, as shown in Fig. 2c, d, respectively, which demonstrated strong dependences of tea leaf metabolites on the age of tea plants.
Identification of tea leaf metabolites associated with the age of tea plants
A pairwise OPLS-DA model was generated with one predictive and one orthogonal component to identify the tea leaf metabolites responsible for metabolic differentiations according to the age of the tea plants (Fig. 3). Clear differentiation between the tea samples of different ages was observed in all OPLS-DA score plots with a high predictability (Q2) and high goodness of fit (R2X), which accounted for 0.60 and 0.85 between the tea leaves of the 8- and 25-year-old tea plants that were harvested in 2016 (Fig. 3a), and 0.66 and 0.93 between the tea leaves of the 8- and 25-year-old tea plants that were harvested in 2015 (Fig. 3c). All these OPLS-DA models were validated by permutation tests (Additional file 1: Fig. S2). The upper sections of the OPLS-DA loading plots represent the tea leaf metabolites that were higher in the old tea plants than in the young tea plants, whereas the lower sections were characterized by lower contents of tea leaf metabolites in the old tea plants (Fig. 3b, d). The different colors on the OPLS-DA loading plots explain the significant differences in metabolites responsible for differentiations between the young and old tea plants, and a correlation coefficient of the OPLS-DA plot greater than 0.45 was considered to be significant, as described in our previous studies [15]. The OPLS-DA loading plot with tea leaves between 8- and 25-year-old tea plants collected in 2016 showed lower levels of alanine (Ala), 2-O-(β-l-arabinopyranosyl)-myo-inositol (Ara), caffeine, epicatechin (EC), epigallocatechin (EGC), epigallocatechin 3-O-(3ʺ-O-methyl) gallate (EGCG3″Me), leucine, α-glucose, β-glucose, glutamine (Gln), quinate, succinate, sucrose, theanine, theobromine in tea leaves from the 25-year-old tea plants (Fig. 3b). On the other hand, the tea leaf metabolites responsible for differentiations between 8- and 25-year-old tea plants plucked in 2015 were identified in the OPLS-DA loading plot (Fig. 3d). Therefore, tea leaves collected from 25-year-old tea plants in 2015 were characterized by lower levels of acetate, catechin, GABA, gallate, theanine, glutamine, and theogallin compared to 8-year-old tea plants (Fig. 3d). Quantitative differences of individual metabolites according to the age of tea plants were calculated from a total integral area of 1H NMR spectra corresponding to each metabolite (Fig. 4).