Metabolomic Analyses Reveal Distinct Change of Metabolites and

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Metabolomic analyses reveal distinct change of metabolites and quality of green tea during the short duration within single spring season Jianwei Liu, Qunfeng Zhang, Meiya Liu, Lifeng Ma, Yuanzhi Shi, and Jianyun Ruan J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b00404 • Publication Date (Web): 06 Apr 2016 Downloaded from http://pubs.acs.org on April 7, 2016

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Journal of Agricultural and Food Chemistry

Title page

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Full title: Metabolomic analyses reveal distinct change of metabolites and quality of

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green tea during the short duration within single spring season

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Running title: Metabolomic changes of green tea within spring season

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Authors: Jianwei Liu † § #Ⅱ, Qunfeng Zhang § #Ⅱ, Meiya Liu

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Shi § # and Jianyun Ruan § # *

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Affiliation of authors: §

†

§#

, Lifeng Ma§ # , Yuanzhi

Graduate School, Chinese Academy of Agricultural Sciences,

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Beijing 100081.

Tea Research Institute, Chinese Academy of Agricultural Sciences,

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Hangzhou 310058. # Key Laboratory for Plant Biology and Resource Application of Tea,

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the Ministry of Agriculture, China.

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*Corresponding author at:

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South Meiling Road 9, Hangzhou, Zhejiang 310008, China. Tel: +86-571-86653938;

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Fax: +86-571-86650056.

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E-mail address: [email protected] (J Ruan).

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Number of pages of text: 24

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Number of figures: 4

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Number of tables: 2

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Supporting information: Table S1-S5 and Figure S1-S2

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Metabolomic analyses reveal distinct change of metabolites and quality of green

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tea during the short duration within single spring season

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Abstract

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Sensory quality of green tea quality changes greatly within single spring season whereas

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the mechanism is not clearly elucidated. Young shoots of the early, middle and late

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spring season were subjected to metabolite profiling using gas chromatography − time

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of flight mass spectrometry (TOF/MS) and ultra−performance liquid chromatography −

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quadrupole − TOF/MS. Multivariate analyses revealed largely different metabolite

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phenotypes in young shoots among different periods. The contents of amino acids

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decreased whereas carbohydrates, flavonoids and their glycosides, tricarboxylic acid

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cycle and photorespiration pathways were strongly reinforced in the late spring season,

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which were well reflected in sensory quality of made teas. Metabolomic analyses

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further demonstrated distinct variations of metabolite phenotypes in mature leaves. The

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results suggested that the fluctuation of green tea quality in spring season was caused by

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change of metabolite phenotypes in young shoots, which was likely related to the

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remobilization of carbon and nitrogen reserves from mature leaves.

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Key words: Green tea; spring season, quality, plucking period; UPLC-Q-TOF/MS;

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GC×GC -TOF/MS; metabolomics

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Introduction

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Tea has been introduced as a natural medicine in China for a long period while now it is

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one of the most welcomed beverages in the world. The modern findings identified that

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teas contain great amounts of polyphenols, caffeine, theanine, and vitamins 1, which

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possess many healthy functions. The chemical compositions of tea are influenced by

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many factors such as genetic background, growth environment (climate, soil, altitude)

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and horticultural practices. Dependent upon geographical locations, young shoots are

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harvested in warm or monsoon seasons in the world and seasonal variation of quality

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has long been noted and intensively investigated 2-10. In China green tea is harvested in

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three seasons stated as spring, summer and autumn seasons. A general conclusion on

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seasonal variation of green teas is that spring teas contain higher levels of free amino

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acids whereas summer and autumn teas contain lower levels of free amino acids but

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higher levels of flavonoids

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astringency, sweetness and umami. The astringent taste is believed to be mainly

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contributed from flavonoids, such as catechins, flavonols, flavones and their glycosides

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12, 13

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asparagine have been reported to impose umami taste

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undergoes declining tendency with the progress in season and spring tea as the earliest

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flush are generally valued for its high flavor and liquor characteristics 6, 7, 11.

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The seasonal variation of tea quality has been linked to changes of climatic conditions

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or growth rates of the young shoots 6, 9, 16, 17. Higher light intensity and temperature were

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thought the main factors leading to large biosynthesis and accumulation of flavon-3-ols

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in summer teas

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growth conditions that cold months of June to August in Africa contained a lower

7, 11

. These constituents impose different tastes as bitterness,

. On the other hand, amino acids theanine, glutamate, glutamine, aspartate and 14, 15

. Thus the green tea quality

6, 11, 18

. On the other hand, it was reported that tea plucked during slow

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proportion of gallate ester–type catechins and free catechins 16. A recent study proposed

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dilution effect of precipitation leading to the inverse relationship between tea growth or

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leaf biomass and concentrations of individual secondary metabolites 9. Molecular

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analysis demonstrated that the changes of metabolites agree with the expression levels

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of structural genes encoding biosynthesis of flavonoids and theanine 19-22. Recent works

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showed that unbiased non-targeted analyses based on analytical tools such as

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UPLC-Q-TOF/MS, NMR, and GC-TOF/MS provided more comprehensive picture of

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the chemical composition in teas of different seasons or the impacts of environmental

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factors 11, 18.

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In addition to variations of tea metabolites among spring, summer and autumn seasons,

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the green tea quality changes greatly even within a single spring season. The mechanism

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underpinning the difference in green tea quality within such a short duration has not

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been investigated before. It was hypothesized that such variation is caused by the

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change of climatic conditions such as rising temperature in spring season as those

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observed for seasonal fluctuation 6. On the other hand, the growth of spring young

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shoots is dependent upon remobilization of carbon and nitrogen storage in mature

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leaves when the root uptake of N is inadequate due to low soil temperature, a generally

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characteristic for perennial plants including tea plants

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metabolites and quality of green tea might be affected by this process as well. The

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above two hypotheses were tested in the present study. We evaluated the dynamic

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changes of primary and secondary metabolites and metabolic pathway in young shoots

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and mature leaves during the early, middle and late growth period of spring tea season

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using metabolomics approach to elucidate the mechanism underpinning the difference

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in green tea quality within such a short duration as single spring season.

23-25

. Consequently the

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2 Materials and Methods

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2.1 Plant growth and samples

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Young shoots and mature leaves were collected from the experimental plantation at Tea

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Research Institute in Hangzhou, Zhejiang Province (latitude N30.17, longitude

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E120.09). The variety of tea plants was Longjing 43, a premium clone for green tea. The

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bushes were planted in single row with 1.5 cm row distance and 33 cm space between

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bushes within a row. The plantation was fertilized with N, P, and K (285, 60 and 90

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kg/ha annually) and routinely maintained according to local practices. Three batches of

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young shoots and mature leaves, each with ten replications were randomly collected on

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1 April (T1), 15 April (T2) and 28 April (T3) in 2014. The samples of young shoots

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consisted of two expanding leaves and a bud while the mature leaves were taken from

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the surface of the canopy. Plant samples were immediately frozen in liquid nitrogen and

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stored in a -80 °C ultra-refrigerator until freeze dried.

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The young shoots collected on 1 April (T1), 15 April (T2) and 28 April (T3) from the

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same clone and plantation were processed in accordance with the Chinese traditional

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flat tea processing mode. Briefly, harvested fresh young shoots were spread on mats

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made of bamboo in a well aerated room for 6-12 hrs until the moisture of shoots

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reached approximately 70%. Then young shoots were processed by hand in an

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electrically heated pan in two steps. In the first step, young shoots (about 100 g) were

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processed at 90-100 °C for 12-15 min to inactivate enzyme and shaped into roughly flat.

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After cooling down for 40-60 min at room temperature, shoots of similar sizes

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(following sieve, about 150 g) were processed in the second step at 60-70 °C for 20-25

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min into a final product. The processing was performed by experienced workers with

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careers longer than 10 years.

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2.2 Climatic parameters

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Climatic parameters including air temperature, rainfall and duration of sunshine per day

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in March and April were automatically recorded in a meteorological station closed to

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the experimental field. The breaking of bud and growth of young shoots is associated

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with accumulated active thermal time (°C•d) beyond a reported temperature threshold of

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8 °C for cv. Longjing 43

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time, accumulated rainfall and sunshine time (h) were calculated over a period of 2

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weeks before sample collections as previous work showed that chemical constituents in

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tea were significantly correlated with climatic factors over this duration 6 .

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. Mean daily air temperature, accumulated active thermal

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2.3 Metabolomic analyses based on UPLC-Q-TOF/MS and GC×GC-TOF/MS

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The freeze dried young shoots and mature leaves were subjected to metabolomic

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analyses based on ultra performance liquid chromatography coupled to a hybrid

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quadrupole orthogonal time of flight mass spectrometer (UPLC-Q-TOF/MS) and

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two-dimensional gas chromatography coupled to time of flight mass spectrometry

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(GC×GC-TOF/MS). For metabolomic analysis based on UPLC-Q-TOF/MS, the

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metabolites in plant samples were extracted with 1 mL solvent mixture of 75%

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methanol and 1% formic acid for 10 min in an ultrasonic bath and then centrifuged at

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12000 rpm for 10 min. After filtered through a 0.22 µm PTFE filter, a 2 µL extract was

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injected into a HSS T3 column on a UPLC-Q-TOF/MS (ACQUITY UPLC/Xevo G2-S

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Q-TOF/MS, Waters Corp., Milford, MA). The mobile solutions (water with 0.1%

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formic acid and acetonitrile containing 0.1% formic acid), running gradients and

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instrumental settings were followed as described 18, 28. Mass spectra were acquired using

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electrospray ionisation at positive and negative modes over the range of m/z 100-1700.

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The stability of the method was tested by performing 10 repeated injections of the

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mixed samples every 2 h.

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Metabolomic analysis based on the GC×GC-TOF/MS was performed following the

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principle previously described by Lisec et al.

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(100 mg) were extracted with 1000 µL methanol-chloroform (3:1, v/v) solvent as

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description of Liu et al 30. A volume of 10 µL L-2-chlorophenylalanine (0.3 mg/mL in

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water) was added as internal standard 31. The extracts were then centrifuged for 10 min

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at 12000 rpm and -4 °C and supernatant (400 µL) transferring to a new 2 ml Eppendorf

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tube was blow-dried in a vacuum concentrator with moderate nitrogen gas without

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heating. The dried samples were derivatized according to the previously described

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method

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chromatography (GC×GC, Agilent 6890N, Agilent Technologies, CA, USA) equipped

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with a DB-5ms column (30 m × 250 µm i.d. × 0.25 µm) as the first dimension column

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and a DB-17H (2.5m×0.1 mm i.d.×0.1 µm) as the second-dimension column connecting

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to a mass spectrometer (Pegasus HT, Leco Co., CA, USA). Each 1 µL aliquot of the

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derivatized sample was injected in splitless mode into GC×GC-TOF/MS. Other

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instrumental settings including helium gas flow rate, injector and the first oven

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temperature gradient were similar to the reported method

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temperature was kept at 5 °C offset above the primary oven. Transfer line temperature

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and ion source temperature were set at 270 °C and 220 °C, respectively. Electron impact

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ionization (70 eV) at full scan mode (m/z 30-600) was used to acquire mass spectra

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under positive and negative modes. The dwell time for each scan was set at a rate of 50

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with some modification. Plant samples

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. Metabolomic analysis was performed on a two-dimensional gas

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. The secondary oven

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spectra per second and the solvent delay at 5 min.

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2.4 Data processing and multivariate data analysis

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The processing of data files from UPLC−Q-TOF/MS including data collection,

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alignment, and normalization of tea metabolites was performed by Transomics software

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(Waters Corporation, CA, USA). Raw data files acquired from the UPLC−Q-TOF/MS

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analysis were imported into the TransOmics to generate a peak table that included

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information on retention time, mass-to-charge ratio (m/z), and MS intensity of the

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metabolites in the sample. The signal-to-noise (S/N) threshold for peak detection was

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set to 5. The mass tolerance and retention time tolerance for the peak alignment was set

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to 0.01Da min and 0.2, respectively. The peaks of metabolites were identified on the

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basis of (i) actual mass (AM) and retention time (RT) of standards, (ii) AM and RT, (iii)

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AM and MS/MS, (iv) AM and isotopic distribution (ID) by comparing to accurate mass,

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online database (e.g. human metabolome databases www.hmdb.ca) and published

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literature references 18, 28. More information concerning the identification of metabolites

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can be found in Table S1 in the Supporting Information. The data files from

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GC×GC-TOF/MS were processed by LECO Chroma TOF software at S/N threshold

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500. Metabolite identification from these selected variables was achieved by NIST 05

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Standard mass spectral databases (NIST, Gaithersburg, MD). The resulting data

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containing sample information, peak retention time and peak intensities, were

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normalized to the area of the internal standard and then mean-centered.

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Pre-processed datasets of UPLC-Q-TOF/MS and GC×GC-TOF/MS were exported to

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SIMCA-P13.0 software (Umetrics, MKS Instruments Inc., Sweden) for multivariate

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data analyses. Data were Pareto scaled and visualized by plotting the principal

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components scores in which each coordinate represents an individual biological sample.

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For the classification and discrimination between the treatments, principal components

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analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were carried

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out. Cross validation of the developed models was performed based on the default

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software options and explained by variation (R2X and R2Y) and predictive ability [Q

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(cum)2]. Supervised orthogonal projection to latent structure discriminant analysis

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(OPLS-DA) was then used to extract maximum information from the dataset and to

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isolate the metabolites responsible for differences among each group. After the

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multivariate approaches, the significance of each metabolite in group discrimination

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was further measured by one-way ANOVA with Tukey’s posttest using SPSS. Tea

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metabolites for grouping were selected to meet VIP>1 and significance at p1) related to a carbon metabolism increased at T3 over T1. For example, the

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relative quantity of glucose, fructose, galactose, rhamnose and tagatose increased in T3

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by 25%, 324%, 163%, 127% and 305% compared to T1 (Fig 2, Table S2 in the

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Supporting Information). In addition, other carbohydrates which are frequently found in

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cell wall biosynthesis also significantly increased. There were higher levels of TCA

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intermediates, e.g. citrate, isocitrate, fumarate and malate in T3 than in T1. In contrast,

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the contents of free amino acid theanine, proline, aspartic acid and glutamine decreased

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from the early to late spring season. The observation was confirmed by targeted analysis

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by amino acid analyzer (Table 1). For example, the contents of theanine, glutamine,

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glutamate and asparate were 44%, 24%, 49% and 23% lower in T3 compared to T1

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(Table 1). The levels of threonine and tryptophan increased in the late period while

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those of leucine and valine was the highest in the middle period. The intermediates of

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photorespiration such as glyoxylate, serine and glycine increased from the early to late

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spring periods (Fig 2, Table S2 in the Supporting Information). The contents of salicylic

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acid and neochlorogenic acid increased by 333% and 887% respectively. Content of

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total nitrogen in young shoots at T1 was significantly higher than those at T2 and T3

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respectively, and the total N content at T2 was the lowest (Table 1). The total carbon

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contents in the young shoots were not different among periods.

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In the secondary metabolism of young shoots, the contents of flavan-3-ols, flavonols

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and their glycosides showed significant differences (VIP>1 and p

Metabolomic Analyses Reveal Distinct Change of Metabolites and

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