Nontargeted Modification-Specific Metabolomics Investigation of

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Nontargeted Modification-Specific Metabolomics Investigation of Glycosylated Secondary Metabolites in Tea (Camellia sinensis L.) Based on Liquid Chromatography-High Resolution Mass Spectrometry Weidong Dai, Junfeng Tan, Meiling Lu, Dongchao Xie, Pengliang Li, Haipeng Lv, Yin Zhu, Li Guo, Yue Zhang, Qunhua Peng, and Zhi Lin J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b02411 • Publication Date (Web): 19 Aug 2016 Downloaded from http://pubs.acs.org on August 21, 2016

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Journal of Agricultural and Food Chemistry

1

Nontargeted

Modification-Specific

2

Investigation of Glycosylated Secondary Metabolites in Tea

3

(Camellia

4

Chromatography−High Resolution Mass Spectrometry

sinensis

L.)

Based

Metabolomics

on

Liquid

5 6

Weidong Dai1, Junfeng Tan1, Meiling Lu2, Dongchao Xie1, Pengliang Li1, Haipeng

7

Lv1, Yin Zhu1, Li Guo1, Yue Zhang1, Qunhua Peng1, Zhi Lin1, *

8 9

1

Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture,

10

Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South

11

Road, Hangzhou, Zhejiang 310008, PR China

12

2

13

Beijing, 100102, P. R. China

Agilent Technologies (China) Limited, No. 3 Wangjing North Road, Chaoyang Distr.,

1

ACS Paragon Plus Environment


14

Page 2 of 31

Abstract

15

Glycosylation on small molecular metabolites modulates a series of biological

16

events in plants. However, a large number of glycosides have not been discovered and

17

investigated using -omics approaches. Here a general strategy named “nontargeted

18

modification-specific metabolomics” was applied to map the glycosylation of

19

metabolites. The key aspect of this method is to adopt in-source collision-induced

20

dissociation to dissociate the glycosylated metabolite causing a characteristic neutral

21

loss pattern, which acts as an indicator for the glycosylation identification. In an

22

exemplary

23

rhamnosylated, 21 rutinosylated, and 23 primeverosylated metabolites were detected

24

simultaneously. Among them, 61 glycosylated metabolites were putatively identified

25

according to current tea metabolite databases. Thanks to the annotations of glycosyl

26

moieties in advance, the method aids the metabolite identifications. Additional 40

27

novel glycosylated metabolites were tentatively elucidated. This work provides a

28

feasible strategy to discover and identify novel glycosylated metabolites in plants.

application

in

green

teas,

120

glucosylated/galactosylated,

29 30

Keywords: tea; secondary metabolites; glycosylation; metabolomics; LC-MS

2


38

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31

Introduction

32

Endogenous modifications (including glycosylation and acylation) of genes and

33

proteins widely occur in living organisms that modulate various biological events, and

34

have been extensively studied in systems biology.1,2 Not only occur on large

35

molecules of gene and protein, these modifications also take place extensively on

36

small molecules of metabolites in plants.3-5 They alter the polarity, volatility, chemical

37

stability, and biological activity of the metabolites, protecting plants against biotic and

38

abiotic stresses.6,7 Furthermore, metabolite modifications produce numerous

39

secondary metabolites contributing to the complexity of plant metabolome.

40

Metabolites with glycosylation are usually regarded as secondary metabolites in

41

plants. Compared with primary metabolites (such as carbohydrates, amino acids,

42

lipids, and Krebs cycle intermediates), glycosylated metabolites are more specific of

43

genera and species, and act as antioxidants, reactive oxygen species (ROS) scavengers,

44

coenzymes, UV and excess radiation screen, as well as regulatory molecules.8

45

Furthermore, glycosylated metabolites are utilized as medical, bioactive, nutrient, and

46

flavourous compositions by humans.4,9-11 Although glycosylated metabolites are the

47

hotspots in plant studies, they are less large-scale surveyed due to the limitations in

48

the analytical methods. With the great help of “-omics” technology (particularly, the

49

metabolomics technology) and the improvement in the sensitivity of mass

50

spectrometry instruments, modified metabolites mapping has been achieved. In a

51

previous work, a novel strategy, named “nontargeted modification-specific

52

metabolomics”, was successfully developed to achieve the large-scale detection of 3



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modified metabolites.12 With the above method, 900 metabolites were identified with

54

modifications of acetylation, sulfation, glucuronidation, glucosidation, or ribosylation

55

in human urine samples. In addition, the method facilitated the identification of

56

compounds, which are not included in current metabolomics databases and are

57

referred to as “unknowns” in metabolomics studies. The key aspect of the aforesaid

58

strategy was the introduction of in-source collision-induced dissociation (ISCID) into

59

nontargeted

60

(LC-HRMS)-based metabolomics.

liquid

chromatography−high

resolution

mass

spectrometry

61

Tea is the most consumed beverage next to water in the world for the health

62

benefits and satisfactory sensory, which is largely attributed to the abundant

63

secondary metabolites.13-15 Flavonols, flavones, anthocyanins, saponin, and aroma

64

precursors are found exist mainly in glycoside form in tea.16-21 However, there are still

65

a large number of glycosylated metabolites have not been discovered. In this study,

66

we are seeking to apply nontargeted modification-specific metabolomics method to

67

map glycosylated metabolites in tea and to uncover novel glycosylated secondary

68

metabolites with modification of glucosylation, galactosylation, rhamnosylation,

69

rutinosylation, and primeverosylation.

70 71

Materials and Methods

72

Chemicals

73

L-Theanine, D-glucose, kaempferol 3-galactoside, kaempferol 3-glucoside,

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quercetin 7-glucoside, quercetin 3-glucoside, 3,5-dicaffeoylquinic acid, theogallin, 4


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theaflavin-3-gallate, quercetin 3-rutinoside, chlorogenic acid, epigallocatechin gallate

76

(EGCG), 4,5-dicaffeoylquinic acid, 3,4-dicaffeoylquinic acid, maltotriose, maltose,

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myricetin 3-galactoside, quercetin 3-galactoside, kaempferol 3-rutinoside, and

78

myricetin 3-rhamnoside were purchased from Sigma (St. Louis, MO, USA). Methyl

79

salicylic acid primeveroside, kaempferol 3,7-dirhamnoside, and aesculin were

80

purchased from ChemFaces (Wuhan, China). Theanine glucoside was synthesized by

81

mixing 2.5 g L-theanine and 2.5 g D-glucose at 100 °C for 2 h and then purified with

82

C18 column chromatography. The structure was suggested as 1-theanylglucose by

83

LC-MS/MS and 1H NMR (Figure S2).

84

Treatment of tea samples

85

In order to include as many glycosylated metabolite species as possible, a pooled

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sample by mixing an equal portion of each green tea samples from 14 varieties (fresh

87

leaves of Jianbohuang, Ningzhou 2, Zhenghedabai, Gaoyaqi, Zhuyeqi, Fuzao 2,

88

Wannong 95, Fuyun 6, Huang Guanyin, Maoxie, Longjing 43, Yuemingxiang, Xicha

89

5, and Echa 1, harvested from the tea garden of the Tea Research Institute of Chinese

90

Academy of Agricultural Sciences, and then were subjected to a green tea

91

manufacturing process22) was prepared for the nontargeted modification-specific

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metabolomics method development in tea. The pooled green tea sample was initially

93

grounded into powders using a tube mill (IKA, Staufen, Germany). Then, 1.5 mL of

94

60% methanol solution (v/v) was added into 30 mg powders followed by vortexing

95

for 20 s. The tea metabolome was further extracted by ultrasonic treatment for 10

96

min and then vortexed for 20 s. The supernatants were passed through 0.22 µm filter 5



97

after centrifugation at 10,000 g for 10 min (Centrifuge 5810R, Eppendorf, Hamburg,

98

Germany). The obtained solution was used for the LC-MS analysis.

99

Nontargeted modification-specific metabolomics analysis

100

The LC-MS conditions for the nontargeted modification-specific metabolomics

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analysis of metabolite glycosylations in tea plant was modified from previous works.

102

12,23

103

chromatography system (UHPLC Infinity 1290, Agilent Tech., Santa Clara, CA)

104

coupled to a quadrupole-time of flight mass spectrometer (Q-TOF 6540, Agilent

105

Tech., Santa Clara, CA). Chromatographic separation of tea metabolome was

106

performed on a Zorbax Eclipse Plus C18 column (100 × 2.1 mm, 1.8 μm, Agilent

107

Tech., Littlefalls, DE). Binary mobile phases with phase A of water containing 0.1%

108

formic acid (v/v) and phase B of methanol were used for elution. The linear gradient

109

program was as follows: 0 min, 10% B; 4 min, 15% B; 7 min, 25% B; 9 min, 32% B;

110

16 min, 40% B; 22 min, 55% B; 28 min, 95% B; 30 min, 95% B; 31 min, 10% B; 35

111

min, 10% B. The total elapsed time required for a given chromatographic analysis

112

was thus 35 min. The flow rate was set at 0.4 mL/min. The injection volume was 3

113

μL. Electrospray ionization (ESI) was performed in the positive ionization mode

114

with the following parameters: capillary voltage was set as 3,500 V; the temperature

115

and the flow rate of drying gas were set at 300 °C and 8 L/min, respectively; the

116

nebulizer pressure was set at 35 psi; the temperature and the flow rate of the sheath

117

gas were maintained at 300 °C and 11 L/min; the mass scan range of m/z 100–1000

118

was applied for the nontargeted modification-specific metabolomics analysis; ISCID

Briefly, LC-MS analyses were performed with an ultra-high pressure liquid

6


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voltage was set at 0, 5, 10, 15, 20, 25, 30, 35, 40, and 45 V, respectively. The

120

Q-TOF/MS was daily calibrated following the manufacture’s procedure and

121

reference ions with m/z of 121.0509 and 922.0098 were continuously infused during

122

data acquisition for online calibration to ensure MS accuracy.

123

Data processing of nontargeted modification-specific metabolomics

124

analysis

125

The data processing of nontargeted modification-specific metabolomics analysis to

126

identify metabolite glycosylations were modified from a previous work.23 Briefly, raw

127

data files acquired by LC-MS analysis were firstly processed by DA Reprocessor

128

software (Agilent Tech., Santa Clara, CA) for metabolite feature ions extraction, and

129

then imported into Mass Profiler Professional software (Version 13.0, Agilent Tech.,

130

Santa Clara, CA) for peak alignment. The ions in the obtained peak table were then

131

subjected to neutral loss (NL) matches to find glycosylated metabolites by a free

132

software named “Neutral Loss MSFinder”.12 Two ions that they were detected

133

simultaneously and exhibited characteristic neutral loss of m/z 162.0528, 146.0579,

134

308.1107, and 294.0951 were assigned as precursor ion and fragment ion of

135

glycosylated

136

rutinosylation, and primeverosylation, respectively (Table 1). A prerequisite for the

137

correct annotation of these glycosylations was the close setting of the window for

138

shifts in retention time and mass for the NL matches. Here, the error tolerances of

139

retention time and mass for neutral loss matches by “Neutral Loss MSFinder”

140

software were set as ∆tR < 0.1 min and ∆NL < 0.002 Da, respectively. Principal

metabolite

with

glucosylation/galactosylation,

7


rhamnosylation,


141

component analysis (PCA) was performed using Simca-P 11.5 software (Umetrics

142

AB, Umeå, Sweden) to investigate glycosylated metabolite profiles of tea variaties.

143

Student’s t-test was performed using the PASWstat software (version 18.0, USA).

144

Database search for glycosylated metabolite identification

145

Ion features assigned as glycosylated metabolites by the nontargeted

146

modification-specific metabolomics analysis were imported into Tea Metabolome

147

Database (TMDB) and Human Metabolome Database (HMDB) for structure

148

identification. TMDB contains records for more than 1,393 constituents found in tea

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with information gathered from 364 published books, journal articles, and electronic

150

databases,24 while HMDB contains 41,993 metabolite entries (including ~28,000

151

food components and food additives).25,26 Chromatographic retention behaviors and

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tandem mass spectrometry (MS/MS) were also applied to assist glycosylated

153

metabolite identification.

154 155

Results and Discussion

156

Strategy for the identification of metabolite glycosylation

157

As shown in Figure 1, the endogenous metabolite glycosylation adds a certain

158

molecular mass to the metabolite substrate. During the in-source collision induced

159

dissociation, the added ligands can be removed through neutral loss pathway, which

160

provides specific neutral loss patterns. In the initial step for the profiling of

161

glycosylated metabolites in tea, authentic standards of quercetin 3-glucoside,

162

kaempferol 3-galactoside, myricetin 3-rhamnoside, quercetin 3-rutinoside, and methyl 8


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salicylic acid primeveroside were exemplarily used to investigate the unique neutral

164

loss

165

primeverosylatied metabolites, respectively (the structures of glycosylated metabolites

166

are shown in Figure 2). As shown in Figure 3, metabolites with modification of

167

glucosylation/galactosylation, rhamnosylation, rutinosylation, and primeverosylation

168

exhibited a theoretical characteristic NL of 162.0528 (C6H10O5), 146.0579 (C6H10O4),

169

308.1107 (C12H20O9), and 294.0951 (C11H18O9), respectively, which could act as

170

indicators to identify specific glycosylation (Table 1). It was not distinguishable

171

between glucosylation and galactosylation only using the NL value since both

172

generate a theoretical NL of 162.0528. Chromatographic retention behaviors need to

173

be introduced because the galactosyl conjugate eluted earlier than the glucosyl

174

conjugate on C18 column.27 Taking the advantage of high resolution of Q-TOF/MS

175

instrument, it is facile to distinguish glucosidation/galactosylation (m/z(NL) = 162.0528)

176

and caffeoylation (m/z(NL) = 162.0317), and rhamnosylation (m/z(NL) = 146.0579) and

177

coumaroylation (m/z(NL) = 146.0368), which are not distinguishable by traditional

178

neutral loss scan performed on triple quadrupole or Q-trap instrument. Note that if the

179

glycosyl moiety of a compound is further acylated (e.g., galloylated, coumaroylated,

180

and caffeoylated), the compound would not generate a characteristic NL

181

corresponding to the glycosylation by ISCID and therefore would not be found by

182

nontargeted modification-specific metabolomics method.

183

Profile glycosylated metabolites in green teas

184

of

glucosylated,

galactosylated,

rhamnosylated,

rutinosylated,

and

In the pooled green tea of 14 tea plant varieties, 202 compounds, including 120 9



metabolites,

38

rhamnosylated

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185

glucosylated/galactosylated

metabolites,

21

186

rutinosylated metabolites, and 23 primeverosylated metabolites, were assigned as

187

glycosylated metabolites (Table 1 and Figure S1). Then, tandem mass spectrometry in

188

collision induced dissociation (CID) mode was applied for further confirmations of

189

the glycosylation types of metabolites. A number of 144 compounds exhibited the

190

neutral losses deriving from the specific glycosyl moieties (Table S1). The majority of

191

the remaining 58 compounds were found with low abundances (low mass intensities),

192

which might be the reason for the non-detections of the desired substrate features in

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MS2 spectra. Here, the above 144 compounds were selected for further structure

194

identifications.

195

Aid the identification of glycosylated metabolites

196

Thanks to the rapid developments in the sensitivity and resolution of LC-MS

197

equipment, hundreds of, even thousands of ion feature signals can be simultaneously

198

acquired from one plant samples (e.g., green tea infusion). However, the limitation in

199

metabolite identification that only a very small portion of chromatography and MS

200

signals can be structurally interpreted have become the bottleneck in plant

201

metabolomics studies.28,29 Silva reported that only ~2% of spectra in a nontargeted

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metabolomics experiment can be annotated. It means that the vast majority of

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information collected by metabolomics is “dark matter”.30 This is partially attributed

204

to the absence of numerous glycosylated metabolites in the current plant metabolite

205

databases. Among the 144 CID-MS2 validated glycosylated compounds, 61 features

206

were structurally interpreted by searching in TMDB and HMDB databases (Table S1). 10


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The structural isomers and stereoisomers of glycosylated metabolites cannot be fully

208

identified only by tandem mass spectrum. And some ion features matched multiple

209

glycosylated metabolite candidates in the databases. Herein, the candidates that have

210

been reported in teas were preferred. Twelve of them were confirmed with authentic

211

standards. These indicated that the nontargeted modification-specific metabolomics

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method is able to profile glycosylated metabolites in teas.

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Combined with the interpretation of the glycosyl moiety of glycosylated

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metabolites in advance, the nontargeted modification-specific metabolomics approach

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provides potentials to uncover “dark matters” and to discover novel constituents in

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teas. As shown in Figure 4, ion features in the putative glycosylated metabolites list

217

are firstly imported into metabolomics database for structural identification. If there is

218

0 hit for the queried ion feature, the corresponding substrate ([M-sugar]+) is then

219

alternatively subjected to database search identification. The structure of the queried

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ion feature will be interpreted by combining the substrate part and the sugar moiety

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part. This strategy overcomes the limitation in metabolite identification that a great

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number of glycosylated metabolites have not been discovered and have not been

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included in current databases. The substrates refers to the compounds that have been

224

reported in teas were preferred. Using this strategy, additional 40 glycosylated

225

metabolites in green tea infusion were structurally elucidated, which are considered as

226

novel compounds (Table S1).

227

Herein, the identification of theanine glucoside in tea plant was taken as an

228

example. An ion feature (m/z = 337.1599, tR = 2.26 min) was found exhibiting a NL 11



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of m/z 162.0524 when ISCID voltage was applied during the nontargeted

230

modification-specific metabolomics analysis, and therefore was assumed as a

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glycosylated metabolite. However, this ion feature could not match any candidate

232

compound in TMDB and HMDB database. Because a glucosyl moiety was identified

233

in advance, an alternative feature (m/z = 175.1071), which was regarded as the

234

substrate of the ion feature of m/z 337.1599 (loss one glucose), was searched in

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HMDB database and resulted in a hit of theanine (a characteristic free amino acid

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exists in tea, which compose 1–2% of the weight of dry tea leaves31). Thus, this ion

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feature (m/z 337.1599) was assigned as theanine glucoside. This compound was

238

further identified as 1-theanylglucose using a synthesized standard, which was not

239

reported previously (Figure 5, Figure S2). A lot of works on the confirmations of

240

other novel glycosylated metabolites need to be carried out in future.

241

Variations of glycosylated metabolites among green teas from

242

different varieties

243

As a proof of concept, the nontargeted modification-specific metabolomics was

244

applied to find major differential glycosylated metabolites among different tea

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varieties (Jianbohuang, Ningzhou 2, Zhenghedabai, Gaoyaqi, Zhuyeqi, Fuzao 2,

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Wannong 95, Fuyun 6, Huang Guanyin, Maoxie, Longjing 43, Yuemingxiang, Xicha

247

5, and Echa 1; the relative quantifications of glycosylated metabolites in these 14

248

varieties were shown in Table S2). As shown in Figure 6A, the pooled samples (QC

249

samples) of 14 green teas were gathered crowdedly in the center of the principal

250

component analysis (PCA) score plot, indicating a good reproducibility of the 12


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metabolomics analysis. Zhenghedabai, Fuzao 2, Maoxie, Longjing 43, Yuemingxiang,

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Xicha 5, and Echa 1 presented similar glycosylated metabolites profiles, and these

253

varieties are usually considered as suiting for manufacture of non-fermented (Fuzao 2,

254

Longjing 43, Xicha 5, and Echa 1), light-fermented (Zhenghedabai) and

255

semi-fermented teas (Maoxie and Yuemingxiang). On the other hand, the profiles of

256

Jianbohuang, Ningzhou 2, Gaoyaqi, Zhuyeqi, Wannong 95, Huang Guanyin, and

257

Fuyun 6 were similar, and they are usually considered as suiting for manufacture of

258

fermented (Jianbohuang, Ningzhou 2, Gaoyaqi, Zhuyeqi, Wannong 95, Fuyun 6) and

259

semi-fermented teas (Huang Guanyin). A PCA loading plot was applied to find

260

glycosylated metabolites mainly responsible for the variations among tea varieties

261

(Figure 6B). As shown in Figure 7, kaempferol 3-galactoside, quercetin 3-galactoside,

262

quercetin

263

3,3'-digalactoside were

264

part of the PCA score plot than those in the right part of the PCA score plot, while

265

kaempferol 3-glucoside, kaempferol 3-rutinoside, kaempferol 3-glucosylrutinoside,

266

N'-formylkynurenine diglucoside, and maltotriose were significantly lower (p < 0.05).

267

These indicated that the metabolite galactosylation is more vigorous in the group of

268

Zhenghedabai, Fuzao 2, Maoxie, Longjing 43, Yuemingxiang, Xicha 5, and Echa 1

269

when compared with the group of Jianbohuang, Ningzhou 2, Gaoyaqi, Zhuyeqi,

270

Wannong 95, Huang Guanyin, and Fuyun 6. In contrast, metabolite glucosylation is

271

reduced. Therefore, we speculated that the glucosylation/galactosylation ratio may be

272

related with the suitability for manufacture of tea variety: high galactosylation level of

3-galactosylrutinoside,

myricetin

3-galactoside,

and

myricetin

significantly higher (p < 0.05) in tea varieties located in left

13



273

metabolites is positively related with the suitability for non-fermented teas

274

manufacture, while high glucosylation is positively related with the suitability for

275

fermented teas manufacture. In

276

this study, we

successfully applied a

novel approach nontargeted

277

modification-specific metabolomics to profile and identify secondary metabolites with

278

glucosylation, galactosylation, rhamnosylation, rutinosylation, and primeverosylation

279

in green teas. This method greatly enlarges the coverage of glycosylated metabolites

280

and improves the capability for the structural identifications of unknown metabolites.

281

It can be further extended to map other significant modifications, such as galloylation,

282

cinnamoylation, coumaroylation, and caffeoylation in plant metabolomes.

283 284 285

Supporting Information

286

Profiles of glycosylated metabolites in green tea infusions (Figure S1). LC-MS/MS

287

and NMR analysis for the structural identification of theanine glucoside (Figure S2).

288

Structural elucidations of glycosylated metabolites in green teas (Table S1). LC-MS

289

intensities of glycosylated metabolites in different tea varieties (Table S2). These

290

materials are available free of charge via the Internet at http://pubs.acs.org.

291 292

Corresponding Author

293

*

294

[email protected]

Tel.: +86 571 86650617; Fax: +86 571 866503154; E-mail addresses:

14


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295 296

Funding

297

This work is supported by the National Natural Science Foundation of China (No.

298

31500561), the Science and Technology Innovation Project of Chinese Academy of

299

Agricultural Sciences (No. CAAS-ASTIP-2014-TRICAAS), and the Earmarked

300

Fund for China Agricultural Research System (No. CARS-23).

301 302

Notes

303

The authors declare no competing financial interest.

15



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Legends Figure 1. Strategy for mapping metabolite glycosylation in tea. The glycosyl group (neutral loss) is found by matching the intact glycosylated metabolite parent ion and the fragment ion produced by ISCID. The glycosylation is indicated as colored symbol (green ellipse and red rhombus). Figure 2. Structures of metabolite glycosylations included in this study. Figure 3. Typical ISCID-based fragmentation pattern exemplarily shown for glycosylated metabolites: (A) glucosylation (theoretical NL m/z = 162.0528), (B) galactosylation (theoretical NL m/z = 162.0528), (C) rhamnosylation (theoretical NL m/z = 146.0579), (D) rutinosylation (theoretical NL m/z = 308.1107), and primeverosylation (theoretical NL m/z = 294.0951). Figure 4. Workflow for the discovery and identification of glycosylated metabolites in tea plant using modification-specific metabolomics method. The part with red color represents the strategy of structural identification for novel glycosylated metabolites. Figure 5. Extracted ion chromatogram (EIC) of theanine glucoside in (A) green tea and in (B) synthesized standard; mass spectrum of theanine glucoside in (C) green tea and in (D) synthesized standard; MS2 spectrum of theanine glucoside in (E) green tea and in (F) synthesized standard. Figure 6. Principal component analysis of green tea samples from 14 varieties: (A) score plot, and (B) loading plot. PC1 and PC2 explained 51.2% of total variance. Figure 7. Main distinctive glycosylated metabolites between the varieties (Zhenghedabai, Fuzao 2, Maoxie, Longjing 43, Yuemingxiang, Xicha 5, and Echa 1) 21



located in the left part of the PCA score plot (see Figure 6) and the varieties (Jianbohuang, Ningzhou 2, Gaoyaqi, Zhuyeqi, Wannong 95, Huang Guanyin, and Fuyun 6) located in the right part of the PCA score plot.

22


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Table 1．The detectable metabolite glycosylations, and the corresponding molecular formulas, the accurate mass of neutral loss, as well as the detected number of glycosylated metabolites in green teas. detected number metabolite glycosylation molecular accurate mass of glycosylated type formula of neutral loss metabolites glucosylation/galactosylation C6H10O5 162.0528 120 rhamnosylation C6H10O4 146.0579 38 rutinosylation primeverosylation

C12H20O9 C11H18O9

308.1107 294.0951

23


21 23


Figure 1.

24


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Figure 2.

25



A

quercetin 3-glucoside

intensity

NL=162.0532

m/z

B

kaempferol 3-galactoside

intensity

NL=162.0529

m/z

C

myricetin 3-rhamnoside

intensity

NL=146.0584

m/z

D

quercetin 3-rutinoside

intensity

NL=308.1110

m/z

methyl salicylic acid primeveroside

intensity

E

NL=294.0944

m/z

Figure 3. 26


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Figure 4.

27



Figure 5.

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Figure 6.

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Figure 7.

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Table of Contents

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Nontargeted Modification-Specific Metabolomics Investigation of

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