A Metabolomic - ACS Publications - American Chemical Society

Feb 6, 2018 - Statistical analyses were performed using. SPSS statistical software (version 21; SPSS Corp., Chicago, IL, USA). Analysis of variance (A...
9 downloads 5 Views 2MB Size
Subscriber access provided by UNIVERSITY OF TOLEDO LIBRARIES

Article

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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Agricultural and Food Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 29

Journal of Agricultural and Food Chemistry

1

Diverse Metabolite Variations in Tea (Camellia sinensis L.)

2

Leaves Grown Under Various Shade Conditions Revisited: A

3

Metabolomics Study

4

Hyang-Gi Ji,†,# Yeong-Ran Lee,‡,

5

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

6



7

500-757, Republic of Korea

8



9

Gyeonggi-do 446-729, Republic of Korea

#

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

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

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

10

§

11

∥Protein

12

Chungbuk 363-883, Republic of Korea

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

13 14

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

15

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

1

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

16

Abstract

17

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

18

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

19

metabolite compositions of fresh tea leaves including shoots depend on plucking

20

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

21

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

22

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

23

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

24

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

25

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

26

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

27

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

28

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

29

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

30

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

31

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

32

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

33

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

34

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

35

study provides important information about making amino acid-enhanced tea

36

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

37

various shade treatment conditions.

38 39

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

2

ACS Paragon Plus Environment

Page 2 of 29

Page 3 of 29

Journal of Agricultural and Food Chemistry

40

1. INTRODUCTION

41

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

42

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

43

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

44

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

45

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

46

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

47

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

48

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

49

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

50

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

51

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

52

effects than EGCG.11, 12

53

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

54

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

55

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

56

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

57

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

58

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

59

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

60

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

61

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

62

understanding of metabolic mechanism of tea grown under various conditions has

63

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

64

comprehensive, and not-targeted metabolites coupled with multivariate statistical

65

analysis.

3

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

66

Metabolomics has provided a better understanding of tea metabolism by

67

investigating the dependence of tea metabolites on environmental factors, including

68

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

69

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

70

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

71

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

72

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

73

characteristics of tea grown under various shade treaments conditions remain

74

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

75

shade conditions on metabolite compositions of tea through metabolomic approach.

76

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

77

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

78

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

4

ACS Paragon Plus Environment

Page 4 of 29

Page 5 of 29

Journal of Agricultural and Food Chemistry

79

2. MATERIALS AND METHODS

80

2.1. Chemicals.

81

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

82

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

83

(EC),

84

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

85

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

86

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

87

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

(-)-epicatechin-3-O-gallate

(ECG),

(-)-epigallocatechin

(EGC),

(-)-

88

89

2.2. Tea Plant Cultivations and Shade Treatments.

90

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

91

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

92

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

93

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

94

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

95

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

96

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

97

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

98

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

99

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

100

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

101

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

102

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

5

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

103

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

104

analysis.

105

106

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

107

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

108

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

109

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

110

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

111

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

112

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

113

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

114

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

115

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

116

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

117

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

118

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

119

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

120

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

121

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

122

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

123

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

124

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

125

Fig. S1 in Supporting Information.

126

127

2.4. Multivariate Data Analyses.

128

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

ACS Paragon Plus Environment

Page 6 of 29

Page 7 of 29

Journal of Agricultural and Food Chemistry

129

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

130

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

131

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

132

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

133

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

134

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

135

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

136

probabilistic quotient normalization was performed.24 These processed data sets

137

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

138

multivariate analysis using a mean centering scale method. Principal component

139

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

140

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

141

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

142

used to obtain maximum information about discriminant compounds in the NMR

143

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

144

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

145

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

146

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

147

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

148

discrimination weights between classes in OPLS-DA model corresponded to

149

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

150

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

151

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

152

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

153

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

154

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

155

by a model and indicates predictability. 7

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

156

157

2.5. Statistical Analysis.

158

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

159

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

160

multiple range test was performed to determine significant differences among

161

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

162

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

163

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

164

8

ACS Paragon Plus Environment

Page 8 of 29

Page 9 of 29

Journal of Agricultural and Food Chemistry

165

3. RESULTS AND DISCUSSION

166

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

167

Dependences on Growth in Multivariate Statistical Analysis.

168

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

169

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

170

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

171

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

172

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

173

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

174

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

175

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

176

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

177

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

178

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

179

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

180

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

181

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

182

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

183

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

184

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

185

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

186

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

187

was larger than that of 0 day.

188

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

189

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

190

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

ACS Paragon Plus Environment

1

H NMR

Journal of Agricultural and Food Chemistry

191

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

192

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

193

Information.

194

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

195

metabolic differentiations according to tea growth period and shade treatment,

196

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

197

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

198

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

199

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

200

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

201

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

202

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

203

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

204

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

205

responsible for metabolic differentiations observed in their corresponding OPLS-DA

206

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

207

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

208

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

209

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

210

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

211

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

212

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

213

consistent with results of previous studies14,

214

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

27, 28

showing that levels of common

215

216

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

ACS Paragon Plus Environment

Page 10 of 29

Page 11 of 29

Journal of Agricultural and Food Chemistry

217

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

218

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

219

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

220

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

221

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

222

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

223

consequences of natural environmental conditions. Nevertheless, tea growers and

224

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

225

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

226

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

227

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

228

including modification of temperature during tea growing34 most typically via shade

229

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

230

precise characterization with biological replication for metabolic influence of any

231

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

232

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

233

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

234

repeated to ensure independent analysis.

235

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

236

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

237

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

238

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

239

complicated reasons of environmental factors or cultivation methods and processing

240

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

241

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

242

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

243

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

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

244

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

245

products.

246

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

247

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

248

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

249

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

250

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

251

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

252

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

253

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

254

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

255

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

256

shade intensities.

257

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

258

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

259

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

260

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

261

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

262

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

263

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

264

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

265

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

266

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

267

Figure 3.

268

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

269

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

ACS Paragon Plus Environment

Page 12 of 29

Page 13 of 29

Journal of Agricultural and Food Chemistry

270

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

271

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

272

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

273

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

274

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

275

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

276

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

277

not statistically significant.

278

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

279

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

280

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

281

synthesizes precursors of catechin derivatives using phenylalanine has been

282

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

283

reduction in catechin synthesis.35

284

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

285

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

286

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

287

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

288

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

289

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

290

instead of fresh tea leaves.20,

291

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

292

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

293

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

294

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

295

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

296

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

21

The empirical evidence about metabolic

13

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

297

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

298

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

299

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

300

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

301

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

302

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

303

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

304

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

305

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

306

accumulations of alanine positively correlated with theanine levels in tea leaves

307

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

308

and theanine levels

309

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

310

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

311

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

312

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

313

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

314

plants, resulting from protein degradation due to carbohydrate starvation without

315

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

316

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

317

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

318

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

319

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

320

coupled with multivariate statistical datasets, the current study demonstrated

321

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

322

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

323

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

might indicate that theanine synthesis from alanine is not

14

ACS Paragon Plus Environment

Page 14 of 29

Page 15 of 29

Journal of Agricultural and Food Chemistry

324

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

325

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

326

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

327

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

328

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

329

important information about the determination of tea plucking time and development

330

of amino acids-enhanced tea products.

331 332 333 334

15

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

335

ASSOCIATED CONTENT

336

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

337

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

338

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

339

for validation of the OPLS-DA model.

340 341 342

AUTHOR INFORMATION

343

Corresponding author

344

*Y.-S.H.

345

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

346

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

347

*J.S.P.

348

Phone, (82) 31 280 5802;

349

Email: [email protected].

350 351

Author Contributions

352

#

353

Notes

354

The authors declare no conflict of interest.

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

355 356

ACKNOWLEDGEMENTS

357

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

358

excellent technical assistance with 700 MHz NMR experiments.

16

ACS Paragon Plus Environment

Page 16 of 29

Page 17 of 29

Journal of Agricultural and Food Chemistry

359

Figure captions

360 361

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

362

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

363

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

364

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

365

epicatechin gallate; EGCG, epigallocatechin gallate; GABA,

366

gallocatechin.

367

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

368

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

369

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

370

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

371

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

372

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

373

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

374

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

375

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

376

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

377

GC, gallocatechin.

378

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

379

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

380

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

381

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

382

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

383

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

384

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

385

epicatechin;

386

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

387

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

388

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

389

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

390

epicatechin;

391

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

EGC,

EGC,

epigallocatechin;

ECG,

epigallocatechin;

ECG,

epicatechin

epicatechin

17

ACS Paragon Plus Environment

-aminobutyrate; GC,

gallate;

gallate;

EGCG,

EGCG,

Journal of Agricultural and Food Chemistry

392

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

393

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

394

Proteins.

18

ACS Paragon Plus Environment

Page 18 of 29

Page 19 of 29

Journal of Agricultural and Food Chemistry

395 396 397

REFERENCES

398

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

399

2.

400

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

401

Food Chem. 2004, 52, 692-700.

402

3.

403

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

404

4.

405

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

406

353-363.

407

5.

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

408

6.

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

409

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

410

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

411

358.

412

7.

413

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

414

8.

415

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

416

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

417

9.

418

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

419

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

420

Carcinogenesis 2011, 32, 537-544.

421

10.

422

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

423

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

424

11.

425

Inhibitory

426

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

427

48, 5649-5653.

428

12.

429

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

430

47, 1906-10.

431

13.

1.

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

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

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

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

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

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

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

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

of

tea

catechins

and

O-methylated

derivatives

of

(-)-

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

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

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

432

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

433

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

434

452-9.

435

14.

436

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

437

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

438

15.

439

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

440

16.

441

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

442

17.

443

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

444

18.

445

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

446

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

447

2010, 58, 10582-10589.

448

19.

449

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

450

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

451

20.

452

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

453

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

454

21.

455

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

456

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

457

22.

458

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

459

23.

460

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

461

24.

462

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

463

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

464

25.

465

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

466

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

467

26.

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

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

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

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

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

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

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

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

ACS Paragon Plus Environment

Page 20 of 29

Page 21 of 29

Journal of Agricultural and Food Chemistry

468

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

469

structure model limitations caused by chemical shift variability and improved

470

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

471

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

472

27.

473

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

474

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

475

Food Chem. 2003, 51, 7495-7503.

476

28.

477

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

478

29.

479

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

480

30.

481

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

482

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

483

44, 1387-1394.

484

31.

485

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

486

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

487

32.

488

tea cultivated at four different altitudes using

489

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

490

33.

491

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

492

192, 1-9.

493

34.

494

performance liquid chromatography-quadrupole-time of flight mass spectrometry

495

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

496

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

497

35.

498

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

499

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

500

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

501

36.

502

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

503

2012, 141, 7-16.

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

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

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

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

H NMR analysis coupled with

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

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

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

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

21

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

504

37.

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

505

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

506

38.

507

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

508

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

509

39.

510

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

511

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

512

10-17.

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

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

513

514

515

22

ACS Paragon Plus Environment

Page 22 of 29

Page 23 of 29

Journal of Agricultural and Food Chemistry

516 517

Figure 1.

518

23

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

519 520

Figure 2.

521

24

ACS Paragon Plus Environment

Page 24 of 29

Page 25 of 29

Journal of Agricultural and Food Chemistry

522 523

Figure 3.

524

525

25

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

526 527

Figure 4.

26

ACS Paragon Plus Environment

Page 26 of 29

Page 27 of 29

Journal of Agricultural and Food Chemistry

528 529

Figure 5.

530

27

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

531 532

Figure 6.

28

ACS Paragon Plus Environment

Page 28 of 29

Page 29 of 29

Journal of Agricultural and Food Chemistry

533

For Table of Contents Only

534

29

ACS Paragon Plus Environment