Diverse Metabolite Variations in Tea (Camellia sinensis L.) Leaves

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Diverse Metabolite Variations in Tea (Camellia sinensis L.) Leaves Grown Under Various Shade Conditions Revisited: A Metabolomics Study Hyang-Gi Ji, Yeong-Ran Lee, Min-Seuk Lee, Kyeong-Hwan Hwang, Clara Yongjoo Park, Eun-Hee Kim, Jun Seong Park, and Young-Shick Hong J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b04768 • Publication Date (Web): 06 Feb 2018 Downloaded from http://pubs.acs.org on February 12, 2018

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Journal of Agricultural and Food Chemistry

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Diverse Metabolite Variations in Tea (Camellia sinensis L.)

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Leaves Grown Under Various Shade Conditions Revisited: A

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Metabolomics Study

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Hyang-Gi Ji,†,# Yeong-Ran Lee,‡,

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Yongjoo Park,† Eun-Hee Kim,∥ Jun Seong Park,*,‡ and Young-Shick Hong*,†

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†

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500-757, Republic of Korea

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‡

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Gyeonggi-do 446-729, Republic of Korea

#

Min-Seuk Lee,§ Kyeong Hwan Hwang,‡ Clara

Division of Food and Nutrition, Chonnam National University, Yongbong-ro, Buk-gu, Gwangju

Applied Technology & Research Division, R&D Center, AmorePacific Corporation, Yongin-si,

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§

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∥Protein

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Chungbuk 363-883, Republic of Korea

Osulloc Tea R&D Center, Osulloc Farm Corporation, Jeju 699-820, Republic of Korea Structure Group, Korea Basic Science Institute, Cheongwon-Gu, Cheongju-Si,

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* Corresponding authors: E-mail addresses: [email protected] (J. S. Park) and

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[email protected] or [email protected] (Y. -S. Hong).

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ACS Paragon Plus Environment


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Abstract

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With increase of tea (Camellia sinensis) consumption, its chemical or metabolite

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compositions play a crucial role in the determination of tea quality. In general,

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metabolite compositions of fresh tea leaves including shoots depend on plucking

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seasons and tea cultivators. Therefore, choosing specific plucking time of tea leaves

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can provide use-specified tea products. Artificial control of tea growing, typically

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shade treatments, can lead to significant changes of the tea metabolite compositions

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of tea. However, metabolic characteristics of tea grown under various shade

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treatments conditions remain unclear. Therefore, the objective of the current study

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was to explore effects of various shade conditions on metabolite compositions of tea

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through 1H NMR-based metabolomics approach. It was noteworthy that the levels of

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catechins and their derivatives were only influenced at the initial time of shade

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treatments while most amino acids were upregulated as amounts of shade and

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periods were increased. That is, the levels of alanine, asparagine, aspartate,

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isoleucine, threonine, leucine and valine in fresh tea leaves were conspicuously

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elevated when shade levels were raised from 90 to 100% and when period of shade

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treatments was increased by 20 days. Such increased synthesis of amino acids

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along with large reductions of glucose level reflected carbon starvation under the

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dark conditions, indicating remarkable proteolysis in chloroplast of tea leaves. This

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study provides important information about making amino acid-enhanced tea

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products based on global characteristics of diverse tea leaf metabolites induced by

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various shade treatment conditions.

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KEYWORDS: metabolomics, tea leaf, metabolite, shade, dark

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1. INTRODUCTION

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Tea (Camellia sinensis) is the most widely consumed beverage in the world.1 It

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contains various constituents, including amino acids, caffeine, and catechins, that

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affect the flavor, taste, and health-promoting properties of tea. Therefore, the quality

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of tea can be determined by its chemical compositions.2, 3 Amino acids in tea are

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associated with its taste of “sweetness”. In particular, theanine provides tea infusion

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with tastes of umami (brothy or savory).4 Indeed, caffeine and catechins are

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responsible for bitter and astringent taste of tea.5, 6 Polyphenol compounds in tea

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also contribute to its functional health properties.7 Typically, epigallocatechin-3-O-

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gallate (EGCG) in tea has shown various health benefits in humans, such as

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antioxidant activity,8 inhibition of cancer,9 and anti-allergic effects.10 Moreover,

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epigallocatechin-(3-O-methyl)-gallate (EGCG3”Me) has even stronger biofunctional

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effects than EGCG.11, 12

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Chemical compositions of tea products are influenced by the age of seeding, plucked

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position of leaves, harvesting season, environmental factors, cultivation methods,

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and processing methods.2, 13, 14 Changing cultivation methods can alter tea tastes or

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qualities. For example, Matcha, a high-quality Japanese ceremonial tea, is a type of

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green tea made from Gyokuro cultivar (C. sinensis var. Yabukita) grown under shade

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treatment conditions.15 It contains lower amounts of catechin derivatives but higher

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proportions of amino acids than normal tea green products such as Sencha green

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tea,2, 16, 17 demonstrating that shade treatment cultivations can affect tea quality and

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global tea metabolite variations are likely to be caused by dark conditions. Recently,

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understanding of metabolic mechanism of tea grown under various conditions has

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been improved due to recent development of metabolomics and analysis of global,

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comprehensive, and not-targeted metabolites coupled with multivariate statistical

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analysis.

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Metabolomics has provided a better understanding of tea metabolism by

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investigating the dependence of tea metabolites on environmental factors, including

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cultivar, soil, climate, and geographical area.18, 19 The influence of shade treatments

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to tea plants on metabolic compositions of infused green tea has also been

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reported.20, 21 However, only tea cultivated under a single level of shading such as

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80 % or 90 % following by green tea processing, has been used to investigate the

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association between shade cultivation and tea metabolisms up to date.20, 21 Metabolic

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characteristics of tea grown under various shade treaments conditions remain

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unclear. Therefore, the objective of the current study was to explore effects of varous

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shade conditions on metabolite compositions of tea through metabolomic approach.

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Fresh tea leaves grown under 90, 95, 98 and 100 % shade levels for 10 and 20 days

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were used in the current study to meticulously examine the metabolic mechanism of

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tea perturbed by shading cultivation through 1H NMR-based metabolomic approach.

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2. MATERIALS AND METHODS

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2.1. Chemicals.

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Methanol-d4 (CD3OD, 99.8%), deuterium oxide (D2O, 99.9%), and standard

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chemicals of catechins such as (+)-catechin, (-)-catechin gallate (CG), (-)-epicatechin

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(EC),

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epigallocatechin-3-O-gallate (EGCG), (-)-gallocatechin (GC) and (-)-gallocatechin-3-

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O-gallate (GCG) were purchased from Sigma-Aldrich (St. Louis, MO, USA) while (-)-

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epigallocatechin-3-O-(3-O-methyl)-gallate (EGCG3˝Me) were obtained from Nagara

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Science Co., Ltd. (Gifu, Japan).

(-)-epicatechin-3-O-gallate

(ECG),

(-)-epigallocatechin

(EGC),

(-)-

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2.2. Tea Plant Cultivations and Shade Treatments.

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Tea (Camellia sinensis var. Yabukita) plants were cultivated in Seogwang area (33°

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180’ 17.67” N, 126° 17’ 42.97” E), Jeju, Republic of Korea. Shade treatments of tea

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plants were conducted by covering plants with 1, 2, 3, or 4 layers of black

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polytehylene for 10 and 20 days black polyethylene, resulting which in 10, 5, 2, and

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0% of light transmission (corresponding to 90, 95, 98, and 100% shade levels),

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respectively, determined with the digital lux meter TES 1332 (Olympus Imaging

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Corp., Tokyo, Japan). This order was used throughout the remainder of this article.

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Fresh tea leaves were harvested on April 29, 2016 and May 9, 2016 and shade-

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treated for 10 and 20 days, respectively. Fresh tea leaves without shade were

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collected on April 20, April 29, and May 9, 2016, They were served as controls for

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shade treatments to investigate the influence of plucking season on tea leaf

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metabolite variations. All tea leaves in each experimental group were collected at 10

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different parcels of the same tea-growing area to ensure 10 biological replications.

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They were immediately kept on dry ice after plucking and stored at -80 ºC until

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analysis.

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2.3. Extraction and 1H NMR Spectroscopic Analysis of Tea Leaves.

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Tea leaves were extracted following a previously reported method.22 Before grinding,

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tea leaves were separated from their stems by tweezers and scissors and ground

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with mortar and pestle under liquid nitrogen. These powders were then transferred

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into plastic tube and kept in a deep freezer. These ground tea leaves were dried at -

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80 ºC for 48 h with a freeze dryer. Freeze-dried samples (10 mg) were dissolved in a

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mixture of methanol-d4 (CD3OD, 490 µL) and deuterium water (D2O, 210 µL) in a 1.5

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mL Eppendorf tube. The mixture was sonicated at 25 ºC for 20 min and centrifuged

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with 13,000 rpm for 15 min at 10 ºC. Then 550 µL supernatant of the extract was

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transferred into 5 mm NMR tube. For 1H NMR spectrum acquisition, the tea leaf

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extract was applied to a Bruker Avance 700 spectrometer (Bruker Biospin GmbH,

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Rheinstetten, Germany) operating at 700.40 MHz 1H frequency and temperature of

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298 K using a cryogenic triple-resonance probe and a Bruker automatic injector. A

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one-dimensional (1D) nuclear Overhauser effect spectrometry (NOESY) pulse

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sequence with water presaturation was used for the 1D NMR spectrum acquisition of

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tea extract. Signal assignment for representative sample was facilitated by two-

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dimensional (2D) total correlation spectroscopy (TOCSY), heteronuclear single-

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quantum correlation (HSQC), and spiking experiments with standard chemicals.

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Typical assignment or identification of tea leaf metabolites by 2D HSQC was given in

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Fig. S1 in Supporting Information.

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2.4. Multivariate Data Analyses.

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All NMR spectra obtained from tea extracts were corrected manually for their phases 6


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and baseline distortions using TOPSPIN software (Version 3.2, Bruker Biospin

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GmbH, Rheinstetten, Germany), transformed into ASCII format, and processed with

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MATLAB (R2010b, The Mathworks, Inc., Natick, MA). The icoshift method23 was

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used for alignment of spectra in a full-resolution state without bucketing or binning.

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Afterwards, NMR spectra regions corresponding to methanol (3.37-3.40 ppm) and

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residual water (4.8-4.9 ppm) were removed. Total integral normalization of all NMR

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spectra was carried out to prevent dilution effects for tea leaf extracts. Thereafter, a

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probabilistic quotient normalization was performed.24 These processed data sets

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were imported to SIMCA-P version 14 (Umetrics, Umeå, Sweden), and subjected to

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multivariate analysis using a mean centering scale method. Principal component

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analysis (PCA) as an unsupervised pattern recognition method, was carried out to

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examine intrinsic variations in spectra of tea extracts. Orthogonal projection on latent

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structure-discriminant (OPLS-DA)25 as a supervised pattern recognition method was

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used to obtain maximum information about discriminant compounds in the NMR

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spectra of tea extracts. OPLS-DA loading plots for pairwise comparisons between

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two groups of classes were obtained using MATLAB (The Mathworks, Inc.) with

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scripts developed in-house at Imperial College London, UK. In OPLS-DA loading

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plots, correlation coefficients between variable and the classes was combined the

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back-transformed loadings with the variable weights. Concentration variation and

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discrimination weights between classes in OPLS-DA model corresponded to

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squarded correlation coefficients expressed as color code as described by Cloarec et

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al.26 The validations of OPLS-DA models were conducted by permutation tests

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repeated 200 times with a seven-fold cross-validation. The quality of these models in

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the present study was described by the values of R2X and Q2. R2X is defined as the

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proportion of variance in the data accounted for by these models and indicates a

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goodness of fit for the model. Q2 is defined as the proportion of variance predictable

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by a model and indicates predictability. 7



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2.5. Statistical Analysis.

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Statistical analyses were performed using SPSS statistical software (version 21;

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SPSS Corp., Chicago, IL, USA). Analysis of variance (ANOVA) followed by Duncan’s

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multiple range test was performed to determine significant differences among

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metabolite levels. Relative comparisons of several metabolites’ amounts – peaks of

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which not overlapping with those of other compounds - were examined with integral

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area of peaks in 1D 1H NMR spectra corresponding to metabolites.

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3. RESULTS AND DISCUSSION

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3.1. Identification of Tea Leaf Metabolites by 1H NMR Spectroscopy and Their

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Dependences on Growth in Multivariate Statistical Analysis.

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As shown in Figure 1, 28 tea leaf metabolites were identified by

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spectroscopy. Until tea leaves grew until 20 days under normal condition, decreases

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of theanine, ECG and EGCG levels in tea leaves were noted together with elevated

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levels of EC, EGC, and EGCG3”Me (Figure 1A vs. 1B). Marked increases in levels of

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theanine and several amino acids were observed in tea leaves grown for 20 days

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under 100% shade condition (Figure 1B vs. 1C).

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To investigate perturbations of diverse tea metabolites in response to growth and

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shade treatments, multivariate statistical analysis such as PCA and OPLS-DA were

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employed to all 1H NMR spectra of tea leaves obtained at different growth periods (0

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to 20 days) without shade treatments and from tea leaves with different shade levels

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(90 to 100%) for 10 days and 20 days. PCA score plots exhibited clear

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differentiations among these tea leaves collected at 0, 10 days, and 20 days without

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shade treatments (Figure 2A). Those of tea leaves treated with 100 % shade level

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both for 10 days and 20 days were also differentiated from tea leaves before shade

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treatments. The variation along with PC1 (68%) axis was predominantly dependent

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on the difference in shade level (0% vs. 100%). On the other hand, PC2 (12%) was

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predominantly dependent on the growing period (0 day vs. 10 or 20 days), although

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variations of metabolite profiles between 10 days and 20 days were smaller than

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those between 0 and 10 or 20 days. In addition, intrinsic variation of 10 or 20 days

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was larger than that of 0 day.

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Comprehensive metabolite datasets from all tea leaves were depicted in OPLS-DA

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score plots as they grew under different shade treatment levels, demonstrating

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dependence of tea leaf metabolome on both shade period and level (Figure 2B). 9


1

H NMR


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Validation of the OPLS-DA plot through permutation test in the PLS-DA model with

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the same number of predictive components was provided in Fig. S2 in the Supporting

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Information.

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To accurately and effectively identify tea leaf metabolites in tea leaves responsible for

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metabolic differentiations according to tea growth period and shade treatment,

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pairwise OPLS-DA models with tea leaves between two growing conditions, for

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example, tea leaves cultivated for 0 day and for 10 days without shade treatment,

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were generated with corresponding 1H NMR spectra (Figure 3A and 3C). These

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models clearly differentiated tea leaves collected at day 0 and after 10 days (Figure

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3A and 3C) as well as at day 0 and after 20 days (Figure 3B and 3D) with good

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fitness indices (R2X = 0.72 and 0.79, respectively) along with high predictabilities (Q2

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= 0.97 and 0.99, respectively). These results were validated by the permutation tests

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in corresponding PLS-DA models (data not shown).

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Paired OPLS-DA loading plots (Figure 3C and 3D) provided tea leaf metabolites

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responsible for metabolic differentiations observed in their corresponding OPLS-DA

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score plots (Figure 3A and 3B). The OPLS-DA loading plot derived from whole 1H

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NMR spectra of tea leaves collected at day 0 and after 10 days without shade

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treatment showed elevated levels of quinate, GABA, EC, EGC, GC, EGCG3”Me,

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glucose, and sucrose in tea leaves grown for 10 days than those collected at 0 day,

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along with reduced levels of alanine, theanine, ECG, EGCG, caffeine, theogallin and

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gallate after 10 days growth without shade treatments (Figure 3C). These changes in

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tea leaves were similar to results observed after growth for 20 days (Figure 3D),

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consistent with results of previous studies14,

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metabolites in tea leaves changed when tea plants grew under normal conditions.

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showing that levels of common

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3.2. Dependence of Global Tea Metabolites on Shade Treatments. 10


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Chemical or metabolite compositions of tea leaves strongly affect tea quality and vary

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according to tea growth and plucking positions.29, 30 Amounts of theanine and several

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catechin derivatives in fresh tea leaves typically decrease and increase, respectively,

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as tea leaves age.14,31 Moreover, geographical and climatic dependencies of these

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tea leaf metabolites13, 18 as well as associations of metabolites with growing altitude32

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and season33 have also been reported. Such tea leaf metabolites variations are

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consequences of natural environmental conditions. Nevertheless, tea growers and

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tea product manufacturers still invest in efforts for teas to obtain high quality teas

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through selecting harvesting times or seasons as well as through the development of

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new tea cultivars.19 Artificial cultivation of tea plants is another endeavor to obtain

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special tea products with improved tea tastes and enhanced health properties,

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including modification of temperature during tea growing34 most typically via shade

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treatments. However, in studies with tea products such as green and black tea,

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precise characterization with biological replication for metabolic influence of any

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growth condition in plant could not be guaranteed, if corresponding fresh tea leaves

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are not collected from independent parcels of the tea growing area. Moreover, when

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processed tea products are used, individual process step for tea products must be

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repeated to ensure independent analysis.

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To date, almost all studies evaluating the effect of shade treatments on chemical

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compositions of tea have utilized minimally processed tea leaves such as green tea,

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Matcha, or Tencha20, 21 rather than using fresh tea leaves. In these cases, significant

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changes in chemical or metabolite compositions of tea leaves could be affected by

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complicated reasons of environmental factors or cultivation methods and processing

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methods during even minimal process for producing various tea types. For each

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experimental condition in the present study, fresh tea leaves collected from 10

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different or independent parcels at the same tea growing area were used and placed

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immediately on dry ice following plucking to guarantee 10 biological replications while 11



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coping with metabolic variations in processes for producing various tea-based

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products.

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Figure 4 shows representative OPLS-DA models for the identification of fresh tea leaf

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metabolites changed by shades with before-after comparisons for 100% shade level

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treatments after 10 days (Figure 4A and 4E) and 20 days (Figure 4B and 4F). It

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reflects effects of shade treatments on the development of tea leaf metabolites during

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the growth of tea plants under shade condition. Metabolic perturbations in tea leaves

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affected solely by shade treatments were also investigated by comparing them with

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those grown under normal condition or without shade treatment for 10 days (Figure

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4C and 4G) and 20 days (Figure 4D and 4H). According to high R2X and Q2 values,

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all OPLS-DA models had good fitness and strong predictability, respectively. They

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reflected significant changes of tea leaf metabolites according to shade period and

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shade intensities.

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In the OPLS-DA loading plot of 1H NMR spectra from tea leaves grown under 100 %

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shade-level treatment for 10 days compared to those collected at day 0 (Figure 4E),

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increased levels of caffeine, choline, glucose, sucrose, asparagine, aspartate,

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alanine, leucine, isoleucine, valine, GABA, threonine and theanine were observed,

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along with decreased levels of theogallin, theobromine, quinate, gallate, catechin,

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EC, ECG, EGC, and EGCG in tea leaves grown under 100% of shade. These results

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were similar to results of the OPLS-DA model for tea leaves collected at day 0 and

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grown under 100% shade condition for 20 days (Figure 4F). Interestingly, changes in

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levels of tea leaf metabolites during growth under shade conditions were clearly

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different from those of tea leaves grown under the normal conditions as shown in

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Figure 3.

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In the comparison of the influence of shade treatments with identical growth period,

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the OPLS-DA model exhibited increased levels of caffeine, theobromine, choline, 12


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theogallin, gallate, sucrose, asparagine, aspartate, alanine, leucine, isoleucine,

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valine, GABA, threonine, theanine, ECG, and EGCG in tea leaves grown for 10 days

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without shade treatment, compared to those in the levels grown for 10 days with

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100% shade level, while decreased levels of quinate, glucose, catechin, EC, EGC,

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GC, and EGCG3”Me were evident in tea leaves grown under 100% shade condition

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(Figure 4G). Shade treatments for 20 days caused similar metabolic perturbations as

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those for 10 days (Figure 4H). However, differences in ECG and EGCG levels were

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not statistically significant.

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In general, tea leaves grown under shaded or dark conditions contain less catechin

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derivatives but more rich in amino acids,35, 36 consistent with results of the present

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study. Decreased gene expression of phenylalanine ammonia lyase (PAL) which

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synthesizes precursors of catechin derivatives using phenylalanine has been

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reported in tea grown under shade conditions,36 and consequently resulting in

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reduction in catechin synthesis.35

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Results of the current study revealed that individual catechin derivatives in tea leaves

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were differently influenced by shade treatments. For example, the synthesis of

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catechin, EC, EGC, EGCG3”Me, and GC was decreased in tea leaves with shade

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treatments as compared to that in untreated tea leaves (Figure 5). The synthesis of

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EGCG, but not ECG, was increased by shade treatment. These changes of tea

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metabolites by shade treatments are well-known through studies using green tea

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instead of fresh tea leaves.20,

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perturbations for different levels of shade treatments in tea leaves is currently

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unclear. In the present study, the synthesis of these catechin derivatives after the first

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exposure to shade was independent from shade period or shade level. For example,

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marked reductions of EC, EGC, EGCG3”Me, and GC levels were observed in tea

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leaves grown with 90% shade levels. However, levels of these metabolites did not

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change in tea leaves grown with 95, 98 or 100% shade level (Figure 5). Such

21

The empirical evidence about metabolic

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phenomena were observed in tea leaves shaded for 10 and 20 days. This indicates

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that expression levels of genes involved in the synthesis of catechin derivatives

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might be mostly susceptible to light at the beginning of dark. Moreover, the

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conversion of theanine to catechin derivatives by shade treatment might have been

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decreased because theanine is incorporated into catechin generally under a light or

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bright environment.37 Further study is needed to confirm the conversion of theanine

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into catechin at molecular level. As expected, theanine levels were largely increased

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by shade treatments. However, they did not change after the first shade exposure or

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90% shade treatment for 10 days or 20 days (Figure 5X). Indeed, unique

306

accumulations of alanine positively correlated with theanine levels in tea leaves

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during shading (90 to 100%) were noted. This positive correlation between alanine

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and theanine levels

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affected by sunlight. It was particularly noteworthy that amounts of amino acids such

310

as leucine, isoleucine, valine, alanine, threonine, asparagine, and aspartate were

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positively associated with both shade period and level, as evidenced by their

312

continuous and marked increases with shade treatments (Figures 5 and 6). High

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amino acid levels under dark conditions have also been reported in tea and maize

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plants, resulting from protein degradation due to carbohydrate starvation without

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dark.35, 38 It has been recently observed that the accumulation of free amino acids in

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dark-treated tea leaves is due to proteolysis in chloroplasts.39 In the present study,

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large reductions of glucose levels and accumulations of amino acids in shade-treated

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tea leaves likely reflect carbon starvation-derived proteolysis of chloroplast proteins

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which can lead to amino acid accumulation. In conclusion, through 1H NMR analysis

320

coupled with multivariate statistical datasets, the current study demonstrated

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variations in a wide range of tea metabolites when tea grew under various conditions

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of shade treatments with different shading levels and periods. Levels of catechins

323

and their derivatives in tea leaves during growth under various shade conditions

might indicate that theanine synthesis from alanine is not

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were influenced only at the beginning of shade treatments. However, the synthesis of

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most amino acids continuously rose as shade periods and intensities were increased

326

due to carbon starvation which ensued acceleration of proteolysis in chloroplasts of

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tea leaves. Through better and global understanding of tea plant metabolic

328

physiology by using NMR-based metabolomics analysis, the present study provides

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important information about the determination of tea plucking time and development

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of amino acids-enhanced tea products.

331 332 333 334

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ASSOCIATED CONTENT

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Figure S1. Two-dimensional (2D) 1H-13C HSQC NMR spectrum of fresh tea leaves.

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The 2D contour plot corresponds to the 1D spectrum acquired using a

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NOESYPRESAT pulse sequence. Figure S2. Permutation plot with 200 times tests

339

for validation of the OPLS-DA model.

340 341 342

AUTHOR INFORMATION

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Corresponding author

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*Y.-S.H.

345

Phone, (82) 62 530 1331; fax, (82) 62 530 1339;

346

Email: [email protected] or [email protected].

347

*J.S.P.

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Phone, (82) 31 280 5802;

349

Email: [email protected].

350 351

Author Contributions

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#

353

Notes

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The authors declare no conflict of interest.

H.-G.J. and Y.-R.L. contributed equally to this work

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ACKNOWLEDGEMENTS

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We would like to thank the Korea Basic Science Institute (KBSI) for providing

358

excellent technical assistance with 700 MHz NMR experiments.

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Figure captions

360 361

Figure 1. Representative 700 MHz 1H NMR spectra of tea leaves harvested at day 0

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(A), at 20 days under normal condition (B), and at 20 days under 100% shade levels

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(C). Ara, 2-O-(β-L-Arabinopyranosyl)-myo-inositol; Val, valine; Leu, leucine; Ile,

364

isoleucine; CG, catechin gallate; EC, epicatechin; EGC, epigallocatechin; ECG,

365

epicatechin gallate; EGCG, epigallocatechin gallate; GABA,

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gallocatechin.

367

Figure 2. Principal component analysis (PCA, A) and orthogonal projection on latent

368

structure discriminant analysis (OPLS-DA, B) score plots derived from 1H NMR

369

spectra of tea leaves plucked at different growth periods (0 – 20 days) and grown

370

under different levels of shade treatments (90 – 100% shade).

371

Figure 3. OPLS-DA scores (A – B) and loadings (C – D) plots derived from 1H NMR

372

spectra of tea leaf extracts, providing pairs for metabolic comparison between tea

373

leaves grown for day 0 and 10 days without shade treatments (A and C) and

374

between tea grown for day 0 and 20 days without shade treatments (B and D). Ara,

375

2-O-(β-L-Arabinopyranosyl)-myo-inositol; EC, epicatechin; EGC, epigallocatechin;

376

ECG, epicatechin gallate; EGCG, epigallocatechin gallate; GABA, γ-aminobutyrate;

377

GC, gallocatechin.

378

Figure 4. OPLS-DA scores (A – D) and loadings (E – H) plots derived from 1H NMR

379

spectra of tea leaf extracts, providing pairs for metabolic comparison between tea

380

leaves grown for day 0 and with 100% shade levels for 10 days (A and E), grown for

381

day 0 and with 100% shade levels for 20 days (B and F), grown for 10 days and with

382

100% shade levels for 10 days (C and G), and grown for 20 days and with 100%

383

shade levels for 20 days (D and H). Ara, 2-O-(β-L-Arabinopyranosyl)-myo-inositol;

384

Asn, asparagin; Asp, aspartate; Val, valine; Leu, leucine; Ile, isoleucine; EC,

385

epicatechin;

386

epigallocatechin gallate; GABA, γ-aminobutyrate; GC, gallocatechin.

387

Figure 5. Variations of individual metabolites from tea leaves grown for different

388

periods (0 – 20 days) and under different shade treatments (90 – 100% shade). Ara,

389

2-O-(β-L-Arabinopyranosyl)-myo-inositol; Val, valine; Leu, leucine; Ile, isoleucine; EC,

390

epicatechin;

391

epigallocatechin gallate; GABA, γ-aminobutyrate; GC, gallocatechin.

EGC,

EGC,

epigallocatechin;

ECG,

epigallocatechin;

ECG,

epicatechin

epicatechin

17


-aminobutyrate; GC,

gallate;

gallate;

EGCG,

EGCG,


392

Figure 6. Schematic illustration of the metabolic pathway changed in the tea leaves

393

according to shade period (10 and 20 days) and shade level (90 – 100% shade). P,

394

Proteins.

18


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REFERENCES

398

clinical trials. J. Nutr. 2003, 133, 3285S-3292S.

399

2.

400

spectroscopy for quality assessment of green tea, Camellia sinensis (L.). J. Agric.

401

Food Chem. 2004, 52, 692-700.

402

3.

403

chemistry. Prev. Med. 1992, 21, 334-350.

404

4.

405

and enantiomeric composition of theanine in tea. J. Agric. Food Chem. 1997, 45,

406

353-363.

407

5.

McDowell, I.; Owuor, P., Taste of tea. New Sci. 1992, 133, 30-33.

408

6.

Chen, Q. S.; Zhao, J. W.; Guo, Z. M.; Wang, X. Y., Determination of caffeine

409

content and main catechins contents in green tea (Camellia sinensis L.) using taste

410

sensor technique and multivariate calibration. J. Food. Compos. Anal. 2010, 23, 353-

411

358.

412

7.

413

health. Am. J. Clin. Nutr. 2000, 71, 1698S-702S; discussion 1703S-4S.

414

8.

415

Bahorun, T., Phenolics as potential antioxidant therapeutic agents: mechanism and

416

actions. Mutat. Res. 2005, 579, 200-213.

417

9.

418

reactivates silenced tumor suppressor genes, Cip1/p21 and p16INK4a, by reducing

419

DNA methylation and increasing histones acetylation in human skin cancer cells.

420

Carcinogenesis 2011, 32, 537-544.

421

10.

422

polyphenols on histamine release from rat basophilic leukemia (RBL-2H3) cells: the

423

structure-inhibitory activity relationship. Allergy 1997, 52, 58-64.

424

11.

425

Inhibitory

426

epigallocatechin-3-O-gallate on mouse type IV allergy J. Agric. Food Chem. 2000,

427

48, 5649-5653.

428

12.

429

antiallergic catechin derivatives isolated from oolong tea J. Agric. Food Chem. 1999,

430

47, 1906-10.

431

13.

1.

Rietveld, A.; Wiseman, S., Antioxidant effects of tea: Evidence from human Le Gall, G.; Colquhoun, I. J.; Defernez, M., Metabolite profiling using 1H NMR

Graham, H. N., Green tea composition, consumption, and polyphenol

Ekborg-Ott, K. H.; Taylor, A.; Armstrong, D. W., Varietal differences in the total

Mukhtar, H.; Ahmad, N., Tea polyphenols: prevention of cancer and optimizing

Soobrattee, M. A.; Neergheen, V. S.; Luximon-Ramma, A.; Aruoma, O. I.;

Nandakumar, V.; Vaid, M.; Katiyar, S. K., (-)-Epigallocatechin-3-gallate

Matsuo, N.; Yamada, K.; Shoji, K.; Mori, M.; Sugano, M., Effect of tea

Suzuki, M.; Yoshino, K.; Maeda-Yamamoto, M.; Miyase, T.; Sano, M., effects

of

tea

catechins

and

O-methylated

derivatives

of

(-)-

Sano, M.; Suzuki, M.; Miyase, T.; Yoshino, K.; Maeda-Yamamoto, M., Novel

Lee, J. E.; Lee, B. J.; Chung, J. O.; Kim, H. N.; Kim, E. H.; Jung, S.; Lee, H.; 19



432

Lee, S. J.; Hong, Y. S., Metabolomic unveiling of a diverse range of green tea

433

(Camellia sinensis) metabolites dependent on geography. Food Chem. 2015, 174,

434

452-9.

435

14.

436

S. J.; Hong, Y. S., Metabolic dependence of green tea on plucking positions revisited:

437

A metabolomic study. J. Agric. Food Chem. 2011, 59, 10579-10585.

438

15.

439

by micellar electrokinetic chromatography. J. Chromatogr. A, 2003, 1011, 173-80.

440

16.

441

individual catechins and caffeine in green tea. J. Chromatogr. A, 1996, 749, 295-299.

442

17.

443

and catechins. Biol. Pharm. Bull. 2002, 25, 1513-8.

444

18.

445

Hong, Y.-S., Geographical and climatic dependencies of green tea (Camellia

446

sinensis) metabolites: a 1H NMR-based metabolomics study. J. Agric. Food Chem.

447

2010, 58, 10582-10589.

448

19.

449

Y.-S., Metabolic phenotyping of various tea (Camellia sinensis L.) cultivars and

450

understanding of their intrinsic metabolism. Food Chem. 2017, 233, 321-330.

451

20.

452

Kim, H. J., Metabolomic analysis of the effect of shade treatment on the nutritional

453

and sensory qualities of green tea. J. Agric. Food Chem. 2013, 61, 332-8.

454

21.

455

Lee, C. H., Metabolomics analysis reveals the compositional differences of shade

456

grown tea (Camellia sinensis L.). J. Agric. Food Chem. 2009, 58, 418-426.

457

22.

458

plants. Nat. Protoc. 2010, 5, 536-49.

459

23.

460

alignment of 1D NMR spectra. J. Magn. Reson. 2010, 202, 190-202.

461

24.

462

normalization as robust method to account for dilution of complex biological mixtures.

463

Application in 1H NMR metabonomics. Anal. Chem. 2006, 78, 4281-4290.

464

25.

465

J., OPLS discriminant analysis: combining the strengths of PLS DA and SIMCA

466

classification. J. Cheninetr, 2006, 20, 341-351.

467

26.

Lee, J. E.; Lee, B. J.; Hwang, J. A.; Ko, K. S.; Chung, J. O.; Kim, E. H.; Lee,

Weiss, D. J.; Anderton, C. R., Determination of catechins in matcha green tea Goto, T.; Yoshida, Y.; Kiso, M.; Nagashima, H., Simultaneous analysis of Kakuda, T., Neuroprotective effects of the green tea components theanine Lee, J.-E.; Lee, B.-J.; Chung, J.-O.; Hwang, J.-A.; Lee, S.-J.; Lee, C.-H.;

Ji, H.-G.; Lee, Y.-R.; Lee, M.-S.; Hwang, K. H.; Kim, E.-H.; Park, J. S.; Hong,

Lee, L. S.; Choi, J. H.; Son, N.; Kim, S. H.; Park, J. D.; Jang, D. J.; Jeong, Y.;

Ku, K. M.; Choi, J. N.; Kim, J.; Kim, J. K.; Yoo, L. G.; Lee, S. J.; Hong, Y.-S.;

Kim, H. K.; Choi, Y. H.; Verpoorte, R., NMR-based metabolomic analysis of Savorani, F.; Tomasi, G.; Engelsen, S. B., Icoshift: A versatile tool for the rapid Dieterle, F.; Ross, A.; Schlotterbeck, G.; Senn, H., Probabilistic quotient

Bylesjö, M.; Rantalainen, M.; Cloarec, O.; Nicholson, J. K.; Holmes, E.; Trygg,

Cloarec, O.; Dumas, M. E.; Trygg, J.; Craig, A.; Barton, R. H.; Lindon, J. C.; 20


Page 20 of 29

Page 21 of 29


468

Nicholson, J. K.; Holmes, E., Evaluation of the orthogonal projection on latent

469

structure model limitations caused by chemical shift variability and improved

470

visualization of biomarker changes in 1H NMR spectroscopic metabonomic studies.

471

Anal. Chem. 2005, 77, 517-526.

472

27.

473

electrophoretic determination of theanine, caffeine, and catechins in fresh tea leaves

474

and oolong tea and their effects on rat neurosphere adhesion and migration. J. Agric.

475

Food Chem. 2003, 51, 7495-7503.

476

28.

477

Plant Cell. Physiol. 1980, 21, 989-998.

478

29.

479

special green teas (Camellia sinensis). J. Sci. Food. Agr. 1990, 53, 541-548.

480

30.

481

polyphenols in fresh tea leaves and associations of their oxygen-radical-absorbing

482

capacity with antiproliferative actions in fibroblast cells. J. Agric. Food Chem. 1996,

483

44, 1387-1394.

484

31.

485

age, shade levels, and characteristic beneficial natural constituents of tea (Camellia

486

sinensis) grown in Hawaii. Food Chem. 2012, 133, 707-714.

487

32.

488

tea cultivated at four different altitudes using

489

multivariate statistics. J. Agric. Food Chem. 2011, 59, 5181-5187.

490

33.

491

Metabolite profiling of tea (Camellia sinensis L.) leaves in winter. Sci. Hortic. 2015,

492

192, 1-9.

493

34.

494

performance liquid chromatography-quadrupole-time of flight mass spectrometry

495

(UPLC-Q-TOF MS) uncovers the effects of light intensity and temperature under

496

shading treatments on the metabolites in tea. PLOS One 2014, 9, e112572.

497

35.

498

Tomomura, M.; Mochizuki, K.; Watase, T.; Nakamura, Y.; Watanabe, N.,

499

Characterisation of volatile and non-volatile metabolites in etiolated leaves of tea

500

(Camellia sinensis) plants in the dark. Food Chem. 2012, 135, 2268-76.

501

36.

502

shade on flavonoid biosynthesis in tea (Camellia sinensis (L.) O. Kuntze). Sci. Hortic.

503

2012, 141, 7-16.

Chen, C. N.; Liang, C. M.; Lai, J. R.; Tsai, Y. J.; Tsay, J. S.; Lin, J. K., Capillary

Saijo, R., Effect of shade treatment on biosynthesis of catechins in tea plants. Liang, Y.; Liu, Z.; Xu, Y.; Hu, Y., A study on chemical composition of two Lin, Y. L.; Juan, I. M.; Chen, Y. L.; Liang, Y. C.; Lin, J. K., Composition of

Song, R.; Kelman, D.; Johns, K. L.; Wright, A. D., Correlation between leaf

Ohno, A.; Oka, K.; Sakuma, C.; Okuda, H.; Fukuhara, K., Characterization of 1

H NMR analysis coupled with

Shen, J. Z.; Wang, Y.; Chen, C. S.; Ding, Z. T.; Hu, J. H.; Zheng, C.; Li, Y. C.,

Zhang, Q.; Shi, Y.; Ma, L.; Yi, X.; Ruan, J. Metabolomic analysis using ultra-

Yang, Z.; Kobayashi, E.; Katsuno, T.; Asanuma, T.; Fujimori, T.; Ishikawa, T.;

Wang, Y. S.; Gao, L. P.; Shan, Y.; Liu, Y. J.; Tian, Y. W.; Xia, T., Influence of

21



504

37.

Kito, M.; Kokura, H.; Izaki, J.; Sasaoka, K., Theanine, a precursor of the

505

phloroglucinol nucleus of catechins in tea plants. Phytochemistry 1968, 7, 599-603.

506

38.

507

starvation-related proteolysis in whole maize plants submitted to light/dark cycles and

508

to extended darkness. Plant Physiol. 1998, 117, 1281-91.

509

39.

510

Yang, Z. Y., Proteolysis of chloroplast proteins is responsible for accumulation of free

511

amino acids in dark-treated tea (Camellia sinensis) leaves. J. Proteomics 2017, 157,

512

10-17.

Brouquisse, R.; Gaudillere, J. P.; Raymond, P., Induction of a carbon-

Chen, Y. Y.; Fu, X. M.; Mei, X.; Zhou, Y.; Cheng, S. H.; Zeng, L. T.; Dong, F.;

513

514

515

22


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Figure 3.

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525

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526 527

Figure 4.

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Figure 5.

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Figure 6.

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Diverse Metabolite Variations in Tea (Camellia sinensis L.) Leaves

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