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Omics Technologies Applied to Agriculture and Food

Non-targeted Metabolomic Analysis Based on Ultra-High Performance Liquid Chromatography Quadrupole Time-of-Flight Tandem Mass Spectrometry Reveals the Effects of Grafting on Nonvolatile Metabolites in Fresh Tea Leaves (Camellia sinensis L.) Dandan Qi, Junxing Li, Xiaoyan Qiao, Meiling Lu, Wei Chen, Aiqing Miao, Weiqing Guo, and Chengying Ma J. Agric. Food Chem., Just Accepted Manuscript • Publication Date (Web): 22 May 2019 Downloaded from http://pubs.acs.org on May 23, 2019

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Journal of Agricultural and Food Chemistry

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Non-targeted Metabolomic Analysis Based on Ultra-High Performance Liquid

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Chromatography Quadrupole Time-of-Flight Tandem Mass Spectrometry Reveals

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the Effects of Grafting on Non-volatile Metabolites in Fresh Tea Leaves (Camellia

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sinensis L.)

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Dandan QI

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MIAO 1, Weiqing GUO 1, Chengying MA 1*

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* Corresponding Author

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& Co-author: Author Dandan QI completed the manuscript and author Junxing LI

9

did the following modification work.

1& ,

Junxing LI

2& ,

Xiaoyan Qiao

1& ,

Meiling LU 3*, Wei CHEN 1, Aiqing

10

1.Tea Research Institute, Guangdong Academy of Agricultural Science/Guangdong

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Key Laboratory of Tea Plant Resources Innovation & Utilization, Guangdong,

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Guangzhou, 510640, China; 2. Vegetable Research Institute, Guangdong Academy

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of Agricultural Sciences, Guangzhou 510640, China; 3. Agilent Technologies

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(China) Co. Ltd., Beijing, 100102

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*Corresponding Author Email: [email protected]; Tel: +86-20-85161046.

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Meiling LU: [email protected]; Tel: +86-10-64397540

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Author Email: 1

ACS Paragon Plus Environment


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Dandan QI: [email protected]; Junxing LI: [email protected];

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Xiaoyan QIAO: [email protected]; Wei CHEN: [email protected];

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AiqingMIAO: [email protected]; Weiqing GUO: [email protected];

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Abstract

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To investigate the effects of grafting on non-volatile metabolites in tea, non-targeted

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metabolomic analyses of fresh leaves were performed based on ultra-high performance

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liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-

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QTOF/MS). One non-grafted YingHong NO.9 and four grafted teas (grafting scion

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YingHong NO.9 on four different rootstocks, BaiMaoNO.2 (BM2), BaiYeDanCong (BY),

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HeiYeShuiXian (HY), and WuLingHong (WLH)) were chosen as materials. In total, 32

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differential metabolites were identified, including phenolic acids, flavan-3-ols, dimeric

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catechins, flavonol and flavonol/flavone glycosides etc. Partial least squares

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discrimination analysis and hierarchical cluster analysis showed various effects of

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different rootstocks on metabolites. Thereinto, rootstocks of WLH and BY showed

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extremely outstanding performance in up-regulating and down-regulating these

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metabolites, respectively. Differential metabolites were enriched into three crucial

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pathways including biosynthesis of phenylpropanoids, flavonoid biosynthesis, flavone

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and flavonol biosynthesis, which might influence the quality of tea. This study provides 2


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theoretical basis for grafting-related variations of non-volatile metabolites in fresh tea

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leaves.

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Key words: Grafting, non-volatile metabolites, non-targeted metabolic analysis, UHPLC-

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QTOF/MS

3



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Introduction

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Tea is one of the most consumed beverages in the world and is gaining increasing

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attention due to its health protective effects

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importance in tea producing countries 5. Grafting was initially used as a way to upgrade

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tea plants at a low cost with relatively short transition period. With further knowledge of

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grafting, it is gradually employed as an efficient approach to improve yields

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enhance drought tolerance of tea plants 8.

1-4,

thus tea plant is of great economic

6,7

and

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In fact, grafting has been proved to be a valid method to modify plant characteristics by

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combination of appropriate rootstocks and scions, including both interior quality and

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exterior quality

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rootstocks and scions provided the grafted eggplants with lower mortality 12 and higher

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yield 13 compared with the non-grafted eggplants. In addition, exterior properties, such as

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size, color and texture, could also be modified. For instance, grafted eggplants have

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greater calyx length and prickliness

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firmness and skin color of sweet cherry might also change as a consequence of grafting9.

9-11.

Previous investigations showed that good compatibility between

13

than non-grafted eggplants. Fruit size, fruit

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4


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Besides, interior quality might also change in grafted plants. Tomatoes 10, 14, peaches 15,

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melons

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materials to investigate the effects of grafting on fruit quality, such as taste and flavor.

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Interestingly, all positive, negative and neutral correlations between grafting and fruit

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quality variation were examined 6, 21-24. As all quality traits are combinations of chemical

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metabolites, such as sugars, acids, total soluble solids, and aroma volatiles, so there must

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be certain variations of chemical metabolites in grafted plants compared with non-grafted

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ones if there are quality differences between grafted and non-grafted species. Actually,

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previous studies have proven that grafting is able to change quality-related constituents in

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grafted fruits or vegetables, such as lycopene, citrulline and ascorbic acid in watermelon

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19, 20,

16,

sweet cherries 17, watermelons

18-20

and sweet peppers

21

have been used as

total phenolic content in eggplants 12 etc.

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By parity of reasoning, similar variations of chemical metabolites might also happen to

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grafted tea, though few investigations about the relations between grafting and metabolite

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variations in fresh tea leaves were carried on. Two previous studies about the effects of

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grafting on tea quality showed no or little effect of grafting on tea quality 6, 25. However,

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they only investigated on the level of made tea other than fresh tea leaves, through which

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the differences caused by manufacture crafts cannot be excluded, thus, the effects of

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grafting on chemical metabolites could not be identified precisely. Manufacturing 5



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suitability of cultivars might be caused by the metabolites in fresh tea leaves. Thus, to

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well understand the effects of grafting on metabolites in fresh leaves of tea, different

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excellent commercial varieties with different optimal manufacturing suitability were used

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for rootstocks in our present study. Additionally, several other factors, such as tea tree

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age, growing vigor, affinity between scion and rootstock, grafting survival rate,

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germination period, growth rate of leaves and so on, were considered for rootstocks

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selection.

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With powerful separation capacity, high resolution and sensitivity, ultra-high

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performance liquid chromatography quadrupole time-of-flight mass spectrometry

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(UHPLC-QTOF/MS) combined with chemometric analysis has been proved to be an

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effective tool for quantitative and qualitative analysis of non-volatile components in tea 26,

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27.

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molecular weight metabolites in different samples simultaneously, which has been

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applied on tea 28-30 and gained great advancement. Yinghong NO.9 is a famous cultivar in

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Guangdong and south China, and most suitable for black tea. Thus, a non-targeted

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metabolomics approach based on UHPLC-QTOF/MS and chemometric analysis was

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applied to investigate the effects of grafting on the tea metabolites in this study using five

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groups of fresh tea leaves plucked from the non-grafted YingHong NO.9 and four grafted

Furthermore, a metabolomics technology makes it possible to detect numerous low

6


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tea plants as testing materials, attempting to provide essential knowledge for the grafted

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tea.

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Materials and Methods

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Materials

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Five experimental groups, including four grafted tea samples and the non-grafted control

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tea sample, were included in this study. The control group (abbreviated as CK) was

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acquired from non-grafted tea plant Yinghong NO.9 (Camellia sinensis var. assamica).

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Yinghong NO.9 is a famous cultivar in Guangdong and south China, and most suitable

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for black tea. Four different rootstocks are local varieties including BaiMao NO.2

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(Camellia sinensis L.), BaiYe DanCong (Camellia sinensis L.), HeiYe ShuiXian

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(Camellia sinensis L.), and WuLingHong (Camellia sinensis var. assamica). Yinghong

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NO.9 and WuLingHong are the progenies of Yunnan big leaf species. BaiYe DanCong

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and HeiYe ShuiXian are derived from Phoenix Narcissus. BaiMao NO.2 is a middle leaf

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species. BaiYe DanCong, HeiYe ShuiXian, BaiMao NO.2 and WuLingHong are all

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excellent commercial varieties planted in Guangdong province, even southwest and south

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China. Different cultivars were selected as rootstocks because of their optimal

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manufacturing suitability for tea. BaiYe DanCong, HeiYe ShuiXian, BaiMao NO.2 and 7



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WuLingHong is most suitable for oolong tea, oolong tea, green tea, black tea and black

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tea, respectively. BaiMao NO.2, BaiYe DanCong, HeiYe ShuiXian and WuLingHong

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were planted in 1999, 1997, 1990 and 1990, respectively. BM2, BY, HY and WLH

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referred to the YingHong NO.9 scions grafted on BaiMao NO.2, BaiYe DanCong, HeiYe

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ShuiXian and WuLingHong, respectively. The grafting of BM2, BY, HY and WLH were

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operated in the December of 2009 using cleft grafting. The tree-ages of rootstocks are 10,

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12, 19 and 19 years, respectively. In general, the tea plant planted more than 5-6 years is

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the mature tea tree. In the maturity of tea tree, the yield and quality are stable. Thus, we

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consider the year of the tree planted might not affect the metabolomic profiles. All tea

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plants were grown in the field of the Tea Research Institute, Guangdong Academy of

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Agricultural Sciences, Yingde, China. Each experimental group consisted of ten

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biological replicates, and every biological replicate was derived from individual plant

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respectively. Moreover, six years after grafting, characters of tea trees will develop in to a

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stable state. Thus, all five tea plants were first plucked on 20th April 2015, then new

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emerging fresh tea leaves with one bud and two leaves which emerged at the same period

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were plucked as testing materials on 20th May, 2015 to guarantee the uniformity of all

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the testing materials.

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Chemicals and Reagents 8


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Ultrapure water was produced by a Milli-Q Direct water purification system

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(Millipore, MA). Methanol of LC-MS grade was purchased from Merck (Darmstadt,

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Germany). Citric acid (certified reference material grade), epigallocatechin (analytical

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standard grade), chlorogenic acid (USP reference standard grade), quercetin-3-O-

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glucoside [ ≥ 90% (HPLC)], ellagic acid [analytical standard, ≥ 95% (HPLC)], salicylic

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acid (certified reference material grade), astragalin [analytical standard, ≥ 90%

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(HPLC)], tolbutamide (analytical standard grade), sulfacetamide (analytical standard

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grade) and ammonium acetate (reagent grade, ≥ 98%) were purchased from Sigma-

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Aldrich (St. Louis, MO). Procyanidin B2 [analytical standard, ≥ 98% (HPLC)],

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procyanidin B1 [analytical standard, ≥ 95% (HPLC)], quercetin 3-galactoside

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[analytical standard, ≥ 98% (HPLC)], gallocatechin [analytical standard, ≥ 98%

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(HPLC)] and rutin [analytical standard, ≥ 98% (HPLC)] were purchased from

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Yuanye Bio-Technology CO., Ltd (Shanghai, China). L-theanine [analytical standard,

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≥ 98% (HPLC)], Epicatechin [analytical standard, ≥ 98% (HPLC)], Epicatechin gallate

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[analytical standard, ≥98% (HPLC)] were purchased from Shanghai ZZBIO CO., Ltd

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(Shanghai, China). Catechin [purity (HPLC) ≥ 99%] was purchased from

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Extrasynthese (Genay, France).

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Extraction of non-volatile metabolites 9



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Before being submitted to instrumental analysis, each lyophilized tea leaves were ground

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into powder using a tube mill control (IKA, Staufen, Germany) at 25,000 r/min. Briefly,

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10 mL of 80% methanol solution (v/v) was added into the accurately weighed 0.20 g of

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tea powder in a 10-mL vial. The tea metabolome was then extracted under ultrasonication

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at room temperature twice (15 minutes for each time with 10 seconds’ vibration in

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between). Next, the mixture was kept still for 2 hours before filtration using a membrane

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(0.22 μm). After filtration, tolbutamide and sulfacetamide were added as internal

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standards for retention time correction, and the final concentrations of two standards were

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2 μg/mL and 4 μg/mL respectively.

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UHPLC-QTOF/MS Analysis

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Chromatography was performed on an Infinity 1290 ultra-high performance liquid

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chromatography system (Agilent Technologies, Santa Clara, CA), equipped with an

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autosampler, a binary solvent delivery system, and a column compartment. Separation of

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the non-volatile metabolites in tea metabolome was carried out on a Zorbax Eclipse Plus

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C18 Column (3.0×150 mm, 1.8 µm, Agilent Technologies, Wilmington, DE), which was

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kept at 40 ℃ during chromatographic analysis. Water with 5 mmol/L ammonium acetate

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and methanol with 5 mmol/L ammonium acetate were used as mobile phase A and B, 10


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respectively, which were kept at the flow rate of 0.40 mL/min using the following

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gradient elution profile. The proportion of solvent B was linearly applied as follows: 0-5

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min, 0-12%; 5-15 min, 12% - 35%; 15-18 min, 35% - 45%; 18-26 min, 45% -75%; 26-33

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min, 75% - 95%; 33-35 min, 95% - 5%. The injection volume of the sample was 2.0 μL.

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The post-time between two consecutive analysis was 3.0 min.

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Mass spectrometry analysis was performed on an Agilent 6545 QTOF mass spectrometer

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(Agilent Technologies, Santa Clara, CA), equipped with a dual jet stream electrospray

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ionization (Dual AJS ESI) source operating under negative ionization mode. The

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parameters were set as follows: nebulizer pressure: 35 psi; capillary voltage: -3.5 kV;

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drying gas temperature: 300 ℃; gas flow rate: 8 L/min; fragmentor voltage: 130 V;

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sheath gas temperature: 350 ℃; sheath gas flow rate: 8 L/min. The TOF scan was set as

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m/z of 100-1100 with the acquisition rate of 2 spectra per second.

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In addition, after data mining, the target MS/MS mode was applied for all the differential

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compounds at three collision energy levels (10V, 20V and 40V) using an acquisition rate

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of 3 spectra per second. Retention time deviation and isolation width were set at 0.5 min

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and medium (~4 amu), respectively. 11



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Data processing

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The raw data acquired from UHPLC-QTOF/MS were first loaded to Masshunter

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Qualitative Analysis software (B.07.00 SP1, Agilent Technologies, Santa Clara, CA)

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through which the compound results were obtained using the algorithm of molecular

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feature extraction and were then exported out as compound exchange format (.cef).

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To find out the differential non-volatile metabolites between grafted and CK samples, the

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above .cef files were imported into a chemometric software named MPP (Mass Profiler

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Professional software package, version B.14.5, Agilent Technologies, Santa Clara, CA)

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for retention time correction using internal standards and the subsequent peak alignment.

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The resulting entities with their abundance were subjected to filtration through a series of

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quality control to ensure its validity. Minimum abundance (> 500000) and occurring

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frequency (frequency > 80%, retaining entities that appeared in at least 80% of samples

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in at least one group) and coefficient of variability (CV < 30%) were applied to filter the

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above entities after peak alignment. Next, ANOVA (p < 0.05, with Tukey HSD as post-

12


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hoc test) was employed to retain the entities that showed significant differences among

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the different groups of tea. Moreover, the final differential compounds were analyzed

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using hierarchical cluster analysis (HCA) and partial least squares discriminant analysis

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(PLS-DA). HCA were performed using MPP software based on average intensity of

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replicates.

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(https://www.metaboanalyst.ca/MetaboAnalyst/faces/home.xhtml), which is an open

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website for analysis of metabolomics data.

Supervised

PLS-DA

was

performed

using

MetaboAnalyst

4.0

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Finally, the resulting differential entities were selected and then submitted to UHPLC-

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QTOF/MS for acquiring accurate MS/MS spectra for subsequent identification. In a word,

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the differential compounds among five experimental groups were identified according to

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authentic standards, accurate masses, MS/MS spectra, metabolomics databases (Metlin

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and Human Metabolome Database) and self-built tea metabolites database.

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Results and Discussion

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All unidentified compounds for each sample were subjected to retention time correction

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with internal standards and compound alignment after been imported into MPP. Then,

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1972 entities were detected from all five groups of fresh tea leaves using UHPLC13



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QTOF/MS under negative ionization mode for subsequent non-targeted metabolic

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analysis. Relative standard deviation (RSD) for retention time of two internal standards

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were less than 5% (sulfacetamide), and 0.5% (tolbutamide), respectively. And RSD for

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mass accuracy of two internal standards were 4 (sulfacetamide), and 2 ppm (tolbutamide),

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respectively. Application of QC is one of methods for monitoring the analytical

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repeatability. In our study, both RSD results also suggested good technical repeatability

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and ensured the alignment for unidentified entities reliability. A series of strict filtering

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criteria were carried out using MPP software, 1058 and 905 compounds across all five

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groups were obtained through frequency and variation filter, respectively. Subsequently,

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the resulting 905 entities were subjected to ANOVA with p-value < 0.05 and abundance,

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which led to a reduction of entities from 905 to 80. The remained 80 entities were

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eventually found out as differential metabolites after filtration of data acquired from

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primary MS spectrometry. Next, all the differential entities were subjected to MS/MS

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spectra acquisition at three collision energies for identification. Finally, 16 metabolites

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were identified using authentic standards confirmation and 16 metabolites were

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putatively identified by comparing accurate mass and MS/MS spectra with that in self-

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built tea metabolites database and metabolomics database (Metlin and Human

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Metabolome Database (HMDB)), as shown in Table 1.

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Identification and relative quantification of differential nonvolatile metabolites

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MS/MS spectra were particularly important as a valid method to expound the chemical

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structure of unknown metabolites when the standards of high purity were unavailable.

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Procyanidin B2 was exemplified in Figure S1 to illustrate its chemical structure through

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putative fragmentation pathways. The fragmentation pathway analysis was speculated

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based on MassHunter Molecular Structure Correlator (MSC, B.05.00, Agilent

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Technologies, Santa Clara, CA) and literatures

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MS/MS fragments of procyanidin B2 (m/z 577.1354) in tea samples were acquired,

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including m/z 289.0718, m/z 407.0776, m/z 425.0873, m/z 451.1039, and m/z 559.1248,

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which were generated from four fragmentation pathways in Figure S1B: Retro Diels-

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Alder reaction (RDA) quinone methide (QM) ,heterocyclic ring fission (HRF) and neutral

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loss. Although the fragments of procyanidins have been widely reported, the

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fragmentation mechanism of procyanidin B2 based on the data acquired from LC-

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MS/MS hasn’t been reported so far, making it necessary for the uncover of its fragment

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pathway. Furthermore, the fragment pathway of procyanidin B2 in this manuscript could

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help narrow the putative scope of compounds and confirmed whether it is procyanidin B-

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type compound.

31-33.

As shown in Figure S1A, five main

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15



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Finally, 32 entities were identified with high confidence, including phenolic acids,

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flavan-3-ols, flavonol and flavonol / flavone glycosides etc. (Table 1), which meant these

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32 metabolites could differentiate these four grafted tea samples from non-grafted one.

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Besides, peak area ratio was employed to visualize the differences of each metabolite

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among five treatments, which was inspired by previous work 34, 35, as shown in Table 1.

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Results of ANOVA analysis of each metabolite in grafted sample vs. CK sample were

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shown in Figure 1.

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Chemometrics analysis of non-volatile metabolites in grafted and non-grafted tea

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samples

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PLS-DA analysis was performed based on entities in Table 1 to investigate the effects of

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grafting on non-volatile metabolites in fresh tea leaves. As shown in Figure 2A, the first

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two principal components had already explained 67.0% of the total variance (55.9% and

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11.1%, respectively). Clear separation of BY, WLH, BM2 and HY from CK was

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observed, which suggested obvious effects of grafting on the non-volatile metabolites of

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fresh tea leaves. PLS-DA loading plot labeled with variable importance in projection

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(VIP) scores showed 18 and 17 metabolites with higher absolute loading value and VIP

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score (＞1) on PLS-DA component 1 and component 2 respectively (Figure 2B), which 16


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meant that all these metabolites might be the most relevant variables according to

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previous literature

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PLS-DA component 1 and 2, including procyanidin B2, procyanidin B1, chlorogenic acid,

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ent-epicatechin-(4alpha->8)-ent-epicatechin

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coumarylglucoside), theasinensin B, 4-p-Coumaroyl quinic acid, quercetin-3-O-glucoside,

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gallocatechin, quercetin 3-(2"-galloylrhamnoside), citric acid, L-theanine, epicatechin

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gallate and rutin. All the 14 marker metabolites showed higher content in WLH than in

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CK, while mostly showed lower content in the other three samples (Table 1).

36.

Notably, 14 metabolites displayed VIP score more than 1 on both

3-gallate,

apigenin

7-(4"-Z-p-

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The dendrogram of HCA was also performed based on the entities in Table 1 to visualize

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the differences of non-volatile metabolites among five tea samples in Figure 3. The

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biological replicates of testing materials could explain authentically the differences of

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metabolites caused by grafting. However, as all the tea trees planted in the garden are all

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single individuals, so there must be minor differences among them, which might

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influence the content of metabolites. Thus, we decided to employ the average intensity of

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every metabolite to display the differences of metabolites among treatments intuitively.

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Additionally, the heatmap derived from non-averaged data was supplied in Figure S2,

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which help us to survey the compound intensity of each biological replicate in the same

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treatment group. As can be seen from Figure 3, five tea samples can be divided into two 17



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main groups, and WLH was in the first group; BM2, BY, HY and CK was in the second

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group. The second group was further divided into two secondary groups. Interestingly,

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CK and a grafted sample HY was in the same secondary group, indicating relative slight

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difference between non-grafted and this grafted tea sample, which was in accordance

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with the result in PLS-DA analysis. Results of HCA analysis was in accordance with

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PLS-DA analysis, suggesting that both analysis method and data processing utilized in

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this study were reliable.

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Heatmap in Figure 3 provided intuitionistic demonstration of variation of 32 differential

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non-volatile metabolites in five tea samples. Orange red color indicated higher level of a

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metabolite in grafted samples than in CK; blue color meant lower level of a metabolite in

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grafted samples than in CK; yellow color designated average level of a metabolites in CK.

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In addition, the deeper the color, the greater the difference between grafted samples and

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CK. Obviously, four grafted samples displayed diverse changes compared with CK on

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the basis of 32 differential non-volatile metabolites, indicating different effects of various

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rootstocks on the metabolites in fresh tea leaves. According to the result of heatmap, it

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was obvious that contents of 32 metabolites in grafted tea samples were quite different

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from CK. Furthermore, grafted samples seemed to show totally different variations from

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each other. For example, WLH seemed to up-regulate most of these metabolites, while

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BY and BM2 seemed to down-regulate the majority of these metabolites.

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To summarize, chemomtrics analyses suggested that grafting must have an impact on

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non-volatile metabolites in fresh tea leaves, with different effects of diverse rootstocks on

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the metabolites of fresh tea leaves.

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The effect of rootstocks on the non-volatile metabolite compared with CK

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Numerous investigations proved that grafting was able to change the composition and

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content of metabolites related to fruit or vegetable quality 12, 24, 37. In this study, results

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showed that grafting did influence the metabolites in fresh tea leaves. After strict data

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filtration, 32 differential entities were picked out and were identified by standards and

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MS/MS spectra, including 7 phenolic acids, 8 flavan-3-ols, 4 dimeric catechins, 10

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flavonol and flavonol/flavone glycosides and so on.

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Heatmap in Figure 3 showed vividly that WLH influenced the metabolites in fresh tea

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leaves differently from the other three rootstocks. On the whole, rootstock of WLH 19



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mainly helped to increase the content of 31 metabolites, with epigallocatechin-3-O-p-

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coumaroate and epigallocatechin 3,5-digallate as exceptions. In the analogy of similar

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researches in other plants, interaction between rootstock of WLH and scion of YH9 might

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contribute to the certain metabolic pathways, thereby accumulating more metabolites in

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leaves and buds on scions. As several flavan-3-ols (epicatechin, epigallocatechin,

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gallocatechin, catechin), flavonol and flavonol/flavone glycosides (rutin, astragalin and

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quercetin-3-O-glucoside) can be classified into a main metabolic pathway based on

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KEGG database and generated from some main precursors, which indicated vigorous

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metabolism of some secondary metabolites in WLH than in CK. Notably, theasinensin B

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and quercetin 3-O-glucoside, which were considered as bitter and astringent metabolites

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in tea 38, showed extremely significant rise in WLH. Therefore, grafting on WLH might

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well impact on the quality of tea based on the metabolites in fresh leaves.

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Instead of up-regulating most of these metabolites, grafting on rootstock of BY could

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help to diminish the contents of major metabolites (26 out of 32 metabolites). Among

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them,

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theasinensin B showed the biggest drop compared with CK, as was shown in Table 1 and

348

Figure 3. There were also a few metabolites (6 metabolites) that showed equal or higher

349

content in BY than in CK, such as catechin, L-theanine. Since the chemical components

ellagic

acid,

epicatechin-3,5-digallate,

epigallocatechin-3,5-digallate

20


and

Page 21 of 49


350

were the basis of tea quality, there was a possibility that rootstock of BY could change

351

the quality character of tea. In contrast, BM2 seemed to lower down most of these

352

metabolites (31 out of 32 metabolites). Only the content of Theogallin showed mildly

353

increased.

354

355

While HY seemed not to change the content of metabolites so much as other rootstocks

356

do, which was in accordance with the results of PLS-DA and HCA. It was worth noting

357

that several flavan-3-ols including epigallocatechin-3,5-digallate and epicatechin-3,5-

358

digallate showed comparatively evident decline in HY in contrast with CK.

359

360

Heatmap of 32 differential metabolites in five samples displayed diverse variations

361

between grafted samples and CK, indicating that grafting did influence metabolites in

362

fresh tea leaves to some extent. Furthermore, different rootstocks could bring about

363

diverse changes on the scions. It might be dependent on the interaction between

364

rootstocks and scions 39, 40.

365

366

Analysis of metabolites in three main metabolic pathways

21



Page 22 of 49

367

Pathway analysis was also carried out to demonstrate how grafting affect metabolites

368

using information in Table 1 according to KEGG database. The pathway enrichment

369

analysis was performed by MBrole (http://csbg.cnb.csic.es/mbrole2/analysis.php)

370

As a result, differential metabolites were enriched into four pathways (Table 2) including

371

three crucial pathways in tea plant: biosynthesis of phenylpropanoids, flavonoid

372

biosynthesis, flavone and flavonol biosynthesis 44, 45, which indicating that these pathway

373

were significantly altered by grafting to influence the quality of tea. According to KEGG

374

datebase and literatures 46, 47, Figure 4 was drawn all by ourselves. As shown in Figure 4,

375

four flavan-3-ols (epicatechin, epigallocatechin, gallocatechin, catechin), two phenolic

376

acids (chlorogenic acid and p-coumaroyl quinic acid), four flavonol and flavonol/flavone

377

glycosides (rutin, astragalin, quercetin-3-O-glucoside and luteolin 7-neohesperidoside)

378

and two dimeric catechins (procyanidin B1 and procyandin B2) were included in pathway

379

analysis. In addition to the pathway map, the content changes of each metabolite were

380

also labeled using histogram which was plotted by their average peak areas in each

381

treatment. Thus it can help to visualize the variations of metabolites which were in the

382

same pathway.

41-43.

383

384

Three biosynthesis pathways displayed notable variations in this map. Moreover, this

385

map showed that rootstocks had determining influence on metabolites in different 22


Page 23 of 49


386

metabolic pathways. Two phenolic acids (chlorogenic acid and p-coumaroyl quinic acid)

387

were included in phenylpropanoid biosynthesis. Four rootstocks showed different effects

388

on the metabolites in this pathway. It showed that both metabolites were down-regulated

389

in BY and BM2 and they stayed at lowest level in BY compared with other rootstocks.

390

391

Four flavonol and flavonol/flavone glycosides were included in flavone and flavonol

392

biosynthesis, including rutin, astragalin, quercetin-3-O-glucoside and luteolin 7-O-

393

neohesperidoside. The pathway showed that even the same rootstock might affect

394

different metabolites in the same pathway differently. For one, rootstock BY increased

395

the content of quercetin-3-O-glucoside while decreased the other three flavonol and

396

flavonol/flavone glycosides. Interestingly, rootstock WLH showed highly significant(p
1.3) . Plus, the metabolites in fresh tea leaves were the

419

foundation of the made tea quality. As a consequence, these metabolites might have a

420

greater impact on the made tea quality using fresh leaves of WLH. With further

421

consideration of their taste characters, for example, astragalin and quercetin-3-galactoside, 24


Page 24 of 49

Page 25 of 49


422

theasinensin B and quercetin 3-O-glucoside, considered as bitter and astringent

423

metabolites in tea

424

grafting on WLH might well enhance the astringency of the made tea. While, content of

425

these metabolites showed lower content in BM2 and BY, which might reduce the

426

astringency of tea made by grafted BM2 and BY to some extent. In other words, different

427

rootstocks could bring about diverse changes on the scions. It might be dependent on the

428

interaction between rootstocks and scions

429

enlightenment to choose appropriate rootstocks and scions to hit the mark in the future.

38, 48,

showed extremely significant elevation in WLH. In this way,

39, 40,

which might provide us some

430

431

In conclusion, non-targeted metabolomic analysis based on UHPLC-QTOF/MS was

432

proved to be a valid method to distinguish different tea samples and identify the

433

differential non-volatile metabolites among them. Furthermore, the qualitative and

434

quantitative analysis of five tea samples demonstrated various influences of different

435

rootstocks on metabolites in fresh tea leaves. Most of these differential metabolites

436

belonged to tea polyphenols (including flavan-3-ols, flavone and flavonol etc.), which

437

suggested that the effects of grafting mainly focused on polyphenol-related pathways.

438

Among these four rootstocks, rootstocks WLH and BY had outstanding performance in

439

up-regulating and down-regulating these metabolites, respectively. While rootstock HY

440

seemed not to influence these metabolites so notably as other rootstocks did. As was 25



441

known to us all that metabolites in fresh tea leaves established the foundation of tea

442

quality. So our research results might provide a hint that grafting could be employed to

443

modulate the content and composition of metabolites in fresh tea leaves and serve as a

444

theoretical basis for grafting-related improvement.

445

446

Abbreviations Used

447

UHPLC-QTOF/MS: Ultra-High Performance Liquid Chromatography Quadrupole

448

Time-of-Flight Tandem Mass Spectrometry

449

Dual AJS ESI: dual Agilent jet stream electrospray ionization

450

CK: Control group; YH9: Yinghong NO.9; BM2: BaiMao NO.2; BY: BaiYe DanCong;

451

HY: HeiYe DanCong; WLH: WuLingHong

452

HCA: hierarchical cluster analysis; PLS-DA: partial least squares discriminant analysis;

453

VIP: variable importance in projection

454

Funding Sources

455

The authors appreciate the funding support from the National Natural Science

456

Foundation of China (grant number 31500563 and 31600560), and Construction of

26


Page 26 of 49

Page 27 of 49


457

Modern Agriculture and Technology innovation Alliance in 2018 (grant number

458

2018LM1093).

459

Conflict of interest statement

460

We declare that we have no commercial or financial interest that represents a

461

conflict of interest in connection with the work submitted.

27



462

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Figure Captions

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Page 37 of 49


616

Figure 1. Results of ANOVA analysis of differential metabolites in each grafted groups

617

vs. CK

618

Figure 2. Partial least squares discriminant analysis (PLS-DA) and loading plot labeled

619

with PLS-DA variable importance in projection (VIP) score

620

Figure 3. Hierarchical cluster analysis (HCA) of differential metabolites among five tea

621

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622

that in heatmap, average content of each metabolite in CK was set as baseline (yellow

623

color), while content of corresponding metabolite in grafted groups was calculated as

624

“each group / CK”.

625

Figure 4. Metabolic pathway analysis of 12 differential metabolites in grafted and non-

626

grafted teas. Each treatment was labeled by different colors; histogram in this pathway

627

map represented content variations in different treatments. Note: * p < 0.05 (each grafted

628

group vs. non-grafted group); ** p < 0.01 (each grafted group vs. non-grafted group)

37



Page 38 of 49

Table 1 Differential nonvolatile metabolites identified among five traits of tea Retention Accurate mass Theoretical

Mass error

Metabolites

MS/MS fragments Formula

Time (min)

(ESI-)

mass (ESI-)

VIP score on

VIP score on

PLS-DA

PLS-DA

component 1

component 2

1.00 : 0.83 : 0.99 : 1.06 : 1.11

0.69

0.66

1.00 : 0.66 : 0.85 : 0.65 : 1.23

0.58

1.07

1.00 : 0.83 : 1.01 : 0.94 : 1.11

0.52

1.06

1.00 : 0.81 : 0.86 : 0.97 : 1.05

1.14

1.02

1.00 : 0.85 : 0.84 : 0.94 : 1.01

1.15

1.10

1.00 : 0.91 : 0.96 : 1.04 : 1.04

0.74

0.90

Ratio of peak area

Identification a

(ppm)

(practical)

CK : BY : BM2 : HY : WLH

b

Phenolic acids Standard, Salicylic acid

7.34

137.02416

137.02442

-1.9

C7H6O3

137.024, 93.035

MS/MS Standard,

Ellagic acid

Theogallin

12.84

2.09

300.99865

343.06749

300.99899

343.06707

-1.13

1.22

C14H6O8

C14H16O10

MS/MS

MS/MS M

300.999, 183.030

191.056, 171.030, 175.061, 169.013, 85.030

Standard, Chlorogenic acid

5.48

353.08726

353.08781

-1.56

C16H18O9

MS/MS

191.056, 353.087

4-p-Coumaroyl 7.51

337.09353

337.09289

1.9

quinic acid

C16H18O8

173.045, 163.040

p-Coumaroyl 10.98 quinic acid

191.056, 93.035,

MS/MS M

337.09288

337.09289

-0.03

C16H18O8

MS/MS M

305.068, 173.046, 191.057

38


Page 39 of 49


191.056, 337.093, Cryptochlorogenic 8.66

353.08801

353.08781

0.57

acid

C16H18O9

MS/MS M

127.040, 161.024,

1.00 : 0.85 : 0.86 : 0.92 : 1.21

1.09

0.96

1.00 : 0.90 : 0.94 : 1.02 : 1.13

1.10

1.03

1.00 : 0.95 : 0.98 : 1.09 : 1.10

0.85

0.76

1.00 : 1.01 : 0.98 : 1.07 : 1.14

0.93

0.89

1.00 : 0.98 : 0.97 : 1.07 : 1.19

1.02

0.96

1.00 : 0.96 : 0.98 : 1.00 : 1.12

1.03

1.02

171.029

Flavan-3-ols

Standard, Gallocatechin

7.22

305.06748

305.06668

2.62

C15H14O7

MS/MS

305.067, 125.024, 165.020, 137.023, 179.035, 219.061

Standard, Epigallocatechin

11.19

305.06713

305.06668

1.48

C15H14O7

MS/MS

305.067, 125.024, 306.071, 179.035, 165.020, 219.067 125.024, 245.082,

Standard, Catechin

11.44

289.072

289.0718

0.77

C15H14O6

123.045, 109.029, MS/MS 137.024

Epicatechin

Epicatechin

14.52

16.62

289.07248

441.0838

289.07176

441.0827

2.49

2.63

C15H14O6

C22H18O10

Standard,

289.072, 245.082,

MS/MS M

205.050, 125.024

Standard,

169.014, 289.071,

39



gallate

MS/MS

271.062,

Page 40 of 49

125.024

441.083, 287.056, Epigallocatechin 16.74

609.08872

609.08859

2.13

3,5-Digallate

C29H22O15

MS/MS H+M

305.066, 269.045,

1.00 : 0.69 : 0.81 : 0.70 : 0.98

0.47

0.84

1.00 : 0.71 : 0.85 : 0.83 : 1.01

0.71

0.88

1.00 : 0.83 : 0.97 : 0.96 : 0.91

0.05

1.09

1.00 : 0.72 : 0.78 : 0.96 : 1.39

1.31

1.15

1.00 : 0.83 : 0.93 : 1.03 : 1.10

1.21

1.07

1.00 : 0.86 : 0.93 : 1.07 : 1.07

1.23

1.19

169.014, 125.0243 423.071, 441.083, Epicatechin-3,518.66

593.09353

593.09368

-0.25

digallate

C29H22O14

MS/MS H+M

289.072, 271.061, 169.014, 125.024

Epigallocatechin-

289.072, 179.035, 19.51

451.10312

451.10346

-0.75

3-O-p-coumaroate

C24H20O9

MS/MS M 137.025 Dimeric Catechins

Theasinensin B

9.22

761.13667

761.13594

0.96

C37H30O18

MS/MS M

593.130, 577.135, 423.072, 305.067

Standard, Procyanidin B1

9.51

577.13426

577.13515

-1.54

C30H26O12

MS/MS

577.136, 425.088, 407.077, 289.071, 451.103

Standard, Procyanidin B2

12.35

577.13516

577.13515

0.02

C30H26O12

MS/MS

577.135, 425.087, 407.078, 289.072, 451.104

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ent-Epicatechin577.114, 457.078,

(4alpha->8)-ent13.23

729.14462

729.14611

-2.04

epicatechin 3-

C37H30O16

MS/MS

H

169.014, 125.024,

1.00 : 0.70 : 0.82 : 1.07 : 1.33

1.37

1.24

1.00 : 0.82 : 0.96 : 1.10 : 1.04

0.90

0.79

1.00 : 0.76 : 0.88 : 1.00 : 1.22

1.22

1.09

1.00 : 0.81 : 0.72 : 0.87 : 1.12

1.09

1.14

1.00 : 1.01 : 0.97 : 1.03 : 1.15

0.82

0.72

289.070

gallate

Flavonol and Flavonol/Flavone Glycosides Kaempferol 3-(2425.087, 339.073,

(E)-p9.85

577.13383

577.13515

-2.29

coumarylrhamnosi

C30H26O12

MS/MS

M

443.061, 451.103, 353.038

de) Apigenin 7-(4"-Z-

289.073, 245.083,

p11.07

577.13519

577.13515

0.07

coumarylglucosid

C30H26O12

MS/MS

M

203.072, 205.051, 221.082

e) Quercetin 3-(2"galloylrhamnoside

313.056, 284.032, 20.46

599.10421

599.10424

-0.05

C28H24O15

MS/MS

M

285.040, 169.014,

)

Myricetin 3-

241.035 17.65

479.08318

479.08311

0.15

C21H20O13

MS/MS M

316.022, 271.025, 287.020

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galactoside Quercetin-3-

Standard, 19.43

463.08887

463.0882

1.45

galactoside

C21H20O12

463.089, 301.034,

MS/MS

19.61

609.1467

609.14611

1.02

C27H30O16

1.11

0.98

1.00 : 0.83 : 0.84 : 0.97 : 1.28

1.31

1.18

1.00 : 0.85 : 0.88 : 0.97 : 1.05

0.96

0.89

1.00 : 1.07 : 0.85 : 0.99 : 1.56

1.13

1.03

1.00 : 0.99 : 0.91 : 0.93 : 1.39

0.89

0.78

1.00 : 0.78 : 0.88 : 0.97 : 1.10

1.07

0.97

300.028

Standard, Rutin

1.00 : 1.01 : 0.92 : 1.01 : 1.34

609.147, 610.150,

MS/MS

300.027, 301.036

Luteolin 7-

447.093, 433.077, 20.66

593.15193

593.15119

1.25

neohesperidoside

C27H30O15

Quercetin-3-O19.64

463.08829

463.0882

0.19

glucoside

C21H20O12

MS/MS M 284.032 Standard,

463.088, 301.035,

MS/MS

300.027

Standard, Astragalin

21.07

447.094

447.09329

1.59

C21H20O11

447.093, 284.032 MS/MS

Kaempferol 7-(6"284.032, 285.039,

p23.59 coumarylglucosid

593.13019

593.13006

0.22

C30H26O13

MS/MS

M

447.092, 255.029, 227.035

e) Organic acids

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Citric acid

1.37

191.01983

191.01973

0.52

C6H8O7

Standard,

191.020, 111.009,

MS/MS

87.009, 85.029

1.00 : 0.95 : 0.95 : 1.08 : 1.21

1.05

1.11

1.00 : 1.04 : 0.95 : 1.06 : 1.28

0.96

0.85

1.00 : 1.15 : 0.88 : 1.20 : 1.32

1.03

1.21

Saccharides 191.056, 237.061, Primeverose

1.6

311.09766

311.09837

-2.82

C11H20O10

MS/MS M 239.076 Amino acid Standard,

L-Theanine

2.46

173.0928

173.0932

-2.31

C7H14N2O3

155.082, 84.045, 74.025 MS/MS

Note: a

Identification method: Standard: confirmed with authentic standards; MS/MS, mass spectra comparison using Metlin and Human

Metabolome Database (http://www.hmdb.ca/metabolites). Moreover, when a compound was putatively identified by comparison with that in database, different superscript letters represented different databases were marked on MS/MS. H: confirmed with mass spectrrum in HMDB; M: analyzed with MSC combined with mass spectrum in Metlin database; H+M: identified by comparison mass spectrum in both Metlin and HMDB database. b CK:

control group; BY: BaiYe DanCong; BM2: BaiMao NO.2; HY: HeiYe ShuiXian; WLH: WuLingHong

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Table 2. Pathway enrichment analysis of the differential metabolites Pathway

In Set

p-value

FDR correction

6

3.85×10-9

8.47×10-8

11

8.22×10-8

9.04×10-7

3

5.49×10-5

3.12×10-4

4

5.68×10-5

3.12×10-4

Flavonoid biosynthesis (map00941) Biosynthesis of secondary metabolites (map01110) Flavone and flavonol biosynthesis (map00944) Biosynthesis of phenylpropanoids (map01061)

Note: In set, the number of metabolites that have this particular annotation. p-value, calculated with the cumulative hypergeometric distribution by comparing the number of compounds in the set and in the background with a given annotation. FDR correction, the adjusted p-value calculated as False Discovery Rate.

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Figure 1

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Figure 2

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Figure 3

47



Figure 4

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