Metabolic Flux Enhancement and Transcriptomic Analysis Displayed

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Metabolic Flux Enhancement and Transcriptomic Analysis Displayed the Changes of Catechins Following Long-Term Pruning in Tea Trees (Camellia sinensis) Mufang Sun, Chengren Zhang, Mengqian Lu, Ning Gan, Zichang Chen, Wei-Wei Deng, and Zheng-Zhu Zhang J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b02877 • Publication Date (Web): 18 Jul 2018 Downloaded from http://pubs.acs.org on July 21, 2018

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Journal of Agricultural and Food Chemistry

Metabolic Flux Enhancement and Transcriptomic Analysis Displayed the Changes of Catechins Following Long-Term Pruning in Tea Trees (Camellia sinensis)

Mufang Sun1,

2, 3

, Chengren Zhang4, Mengqian Lu1, Ning Gan1, Zichang Chen4,

Wei-Wei Deng1*, Zheng-Zhu Zhang1*

1

State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural

University, 130 Changjiang West Road, Hefei, Anhui 230036, China 2

College of Tea Science, Xinyang Agriculture and Forestry University, Xinyang,

Henan 464000, China 3

Henan Key Laboratory of Tea Plant Comprehensive Utilization in South Henan,

Xinyang, Henan 464000, China 4

Tea Science Academy, Yunnan Dianhong Group Co., Ltd., Lincang 675900, China

Tel/fax: +86 551 65785471; Email address: [email protected] (W-W. Deng); [email protected] (Z-Z. Zhang) ORCID (W.-W. Deng): 0000-0003-0732-7076; ORCID (Z.-Z. Zhang): 0000-0003-1564-1428.

1

ACS Paragon Plus Environment


1

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Abstract

2

The tea tree is a perennial woody plant, and pruning is one of the most crucial

3

cultivation measurements for tea plantation management. To date, the relationship

4

between long-term pruning and metabolic flux enhancement in tea trees has not been

5

studied. In this research, 11-year-old pruned tea trees from four different cultivars

6

were randomly selected for transcriptome analysis and characteristic secondary

7

metabolite

8

epigallocatechin gallate (EGCG) accumulation in pruned tea trees was significantly

9

higher than in unpruned tea trees. SCPL1A expression (encoding a class of serine

10

carboxypeptidase), which has been reported to have a catalytic ability during EGCG

11

biosynthesis, together with LAR encoding leucoanthocyanidin reductase, was

12

upregulated in the pruned tea trees. Moreover, metabolic flux enhancement and

13

transcriptome analysis revealed low EGCG accumulation in the leaves of unpruned

14

tea trees. Because of the bitter and astringent taste of EGCG, these results provide a

15

certain understanding to the lower bitterness and astringency in teas from “ancient

16

tea trees”, growing in the wild with no trimming, than teas produced from pruned

17

plantation trees.

analysis

together

with

controls.

The

findings

revealed

that

18 19

Keywords: pruning, EGCG, accumulation, SCPL1A, metabolic flux enhancement

20 21 22 2


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23

Introduction

24

Tea is one of the most widely consumed beverages in the world because of its

25

appealing flavor and health benefits.1 These health benefits are because the tea

26

leaves contain multifarious active health-promoting ingredients such as theanine2,

27

caffeine, and flavonoids. Tea tree is a perennial woody plant, and pruning is an

28

annual routine cultivation which is required for managing the tea plantations.

29

Therefore, the traditional tea trees for production are actually obtained by long-term

30

pruning. The biological and physiological effects of pruning were reported

31

previously.3 In addition, the interaction between pruning and the environment4,

32

pruning techniques and standards5, pruning and tea quality have been investigated.6,7

33

Among the characteristic compounds in tea leaves, catechins are the flavonoids most

34

responsible for contributing to the quality of tea. In young tea leaves, catechins

35

constitute 12%–24% of the dry weight, and they can be classified into ester catechins,

36

such as epigallocatechin gallate (EGCG) and epicatechin gallate (ECG), and

37

nonester catechins, such as epicatechin (EC) and epigallocatechin (EGC).

38

Catechins are biosynthesized via the phenylpropanoid (PP) and flavonoid (FL)

39

pathways. Phenylalanine ammonia-lyase (PAL) is the enzyme responsible for

40

catalysis in the first step of the PP pathway, controlling the flow of primary

41

metabolism into second metabolism, and it is the most rapid catalytic reaction in the

42

metabolism of plants.8 The enzyme 4CL (4-coumaroyl-CoA ligase) is ubiquitous in

43

plants and plays a central role in the biosynthesis of phenylpropanoids such as

44

lignins, flavonoids, and coumarins.9 Chalcone synthase (CHS) plays diverse and 3



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45

crucial roles in the secondary metabolism of plants, such as producing flavonoids

46

and ultraviolet protectants10, and it is the first-step enzyme in the ﬂavonoid

47

biosynthetic pathway. Dihydroflavonol 4-reductase (DFR) is regarded as the central

48

enzyme of the flavonoid pathway and controls the direction of carbon flux and

49

regulates various stereochemical features of flavan-3-ols and related compounds.11,12

50

Leucoanthocyanidin reductase (LAR) is an essential reductase and the key enzyme

51

responsible for the catalysis of catechin monomers in the flavonoid pathway.13 In

52

addition, ECGT (1-O-galloyl-β-D-glucose O-galloyltransferase), an enzyme that

53

belongs to subclade 1A of SCPL (serine carboxypeptidase-like acyltransferases), was

54

shown to play a critical role in flavan-3-ols galloylation.14-16 Ester catechin

55

biosynthesis

56

(galloyl-1-O-β-D-glucosyltransferase).17

is

catalyzed

by

UGGT

and

ECGT

57

Flavonoid regulation was reported to be the result of multiple hormones and

58

transcription factor families.18 In tea plants, flavonoid regulation is mainly based on

59

the transcription factors of the MYB family and bHLH13,19, but few studies on

60

hormone regulation in flavonoids biosynthesis have been reported. To date, the

61

relationship between long-term pruning in tea trees and metabolic flux enhancement

62

in flavonoid biosynthesis has not been studied. In this study, 11-year-old pruned tea

63

trees from four different cultivars were performed the transcriptome analysis and

64

characteristic secondary metabolite analysis together with controls. The obtained

65

results could reveal the relation of EGCG accumulation and long-time pruning, and

66

also provide a certain understanding to the different bitter and astringent degree of 4


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67

tea products from ancient tea plants without pruning and ordinary tea plants with

68

pruning every year.

69 70

Materials and Methods

71

Materials

72

Tea leaves of pruned and unpruned tea trees with 11-year-old were from the Tea

73

Germplasm Nursery of Yunnan Dianhong Group Co., Ltd., Lincang City, Yunnan

74

Province, China (117°12′09.1″ E 31°55′42.8″ N) (Figure 1). Pruning refers to the act

75

of trimming tea plants, normally on tea plantations, including a styling trim once a

76

year for 3 years and annual pruning from the fourth year onward, keeping the height

77

of the tree at 80 cm. Unpruned tea trees with the same cultivars were kept in the

78

identical conditions at the same nursery. Four tea cultivars, with pruned or unpruned,

79

C. sinensis cv. Duanjiebaihao (DJBH), Foxiang 1 (FX1), Foxiang 4 (FX4), and

80

Xueya 100 (XY100), were randomly selected from tea germplasm nursery. Fresh

81

leaves (the second leaf under the apical bud) were collected from the four cultivars

82

with pruned or unpruned and immediately frozen in liquid nitrogen and subsequently

83

stored at −80 °C. For all samples, three biological replicates were conducted.

84 85

Sample Extraction and Catechins Determination through High-Performance

86

Liquid Chromatography

87 88

Fresh

leaves

were

vacuum

freeze-dried

as

reported

previously.20

A

high-performance liquid chromatographer (HPLC) system (600 Controller, 2489 5



89

UV/Visible Detector; Waters, Milford, MA, USA) equipped with a C18 column (250

90

× 4.6 mm2, 5 µm; Waters) was used. The detection method was according to a

91

previous report.18 Concentrations of catechins and caffeine (mg/g, dry weight) and

92

EGC+EGCG relative content (%) were calculated based on the calibration curves

93

determined from the standards. The standards were purchased from Sigma (Sigma

94

Chemical Co., Fairfield, OH, USA): (-)-epigallocatechin gallate (EGCG; purity ≥ 99%),

95

(-)-epicatechin gallate (ECG; purity ≥ 99%), (-)-epigallocatechin (EGC; purity ≥

96

99%), (+)-catechin (C; purity ≥ 99%), (-)-epicatechin (EC; purity ≥ 99%) and

97

caffeine. Three biological replicates were analyzed using a t test (IBM SPSS

98

Statistics 22.0).

99 100

RNA Extraction, cDNA Library, Illumina Sequencing, and Screening

101

Total RNA was extracted from 24 samples from the four cultivars, pruned and

102

unpruned, employing three biological replicates in accordance with a modified

103

CTAB method. An Agilent 2100 Bioanalyzer (Agilent RNA 6000 Nano Kit) was

104

used to determine the RNA integrity, quality, and quantity. mRNA was isolated from

105

total RNA through an oligo method (dT). An Agilent 2100 Bioanalyzer and ABI

106

StepOnePlus real-time polymerase chain reaction system (qRT-PCR) were used for

107

quantification and qualification of the mRNA libraries. High-throughput DNA

108

sequencing of the cDNA library was performed using an Illumina HiSeq 4000

109

instrument. The internal piece of software, SOAPnuke, from Beijing Genomics

6


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110

Institute (BGI, China), was employed to filter reads using the following parameters:

111

-L15-q 0.2 -n 0.05 -i (https://github.com/BGI-flexlab/SOAPnuke).

112 113

Genome Mapping and Novel Transcript Prediction

114

Hierarchical Indexing for Spliced Alignment of Transcripts (HISAT2, v. 2.0.4,

115

http://www.ccb.jhu.edu/software/hisat)21 was used to map the tea tree genome.22

116

StringTie23 was used to reconstruct transcripts, and Cuffcompare24 was used to

117

compare the reconstructed transcripts to reference annotations. The Coding Potential

118

Calculator (http://cpc.cbi.pku.edu.cn) was used to assess the protein-coding potential

119

of the novel transcripts and to merge the novel transcripts with reference transcripts

120

to obtain a complete reference.25

121 122

Differentially Expressed Genes and Enrichment

123

Clean reads were mapped to references using Bowtie226, and the gene expression

124

level was calculated using RSEM.27 Based on the negative binomial distribution

125

reported by Love et al.28, differentially expressed genes (DEGs) with DEseq2 (fold

126

change ≥2.00, P ≤ 0.05) were detected. From the GO/KEGG annotation results, we

127

classified the DEGs according to their official classification and performed GO

128

functional enrichment and pathway functional enrichment using phyper, a function

129

of R. The false discovery rate (FDR) for each P value was calculated. Terms with

130

FDRs no larger than 0.05 were defined as enriched.

131 7



132

Real-time Quantitative Polymerase Chain Reaction

133

To validate the accuracy of transcriptome sequencing, reverse transcription of total

134

RNA into cDNA was performed using a PrimeScript RT Master Mix (Takara, Japan).

135

The cDNA was diluted to 100 ± 2 ng as the template. qRT-PCR was performed in a

136

20-µL reaction mixture system containing 10 µL of TB Green Premix Ex Taq II

137

(Takara, Japan), 0.8 µL of forward and reverse primer (10 µM), 1.6 µL of cDNA,

138

and 6.8 µL of ddH2O. The qRT-PCR assays were performed using a CFX96

139

Real-Time System (Bio-Rad, USA); the parameters were as follows: 95 °C for 3 min;

140

subsequently, 40 cycles at 95 °C for 10 s and 62 °C for 30 s.

141

Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal

142

control29, and the expression level was calculated using the 2-∆∆Ct method from three

143

technical replicates.30

144 145

Phylogenetic Analysis of CsSCPLs

146

Phylogenetic analysis of the differentially expressed SCPLs in our data combined

147

with SCPLs in Arabidopsis, Solanum berthaultii, and Solanum pennellii was

148

conducted to explore the evolutionary relationships of the SCPL genes. Employing

149

the neighbor-joining method, a bootstrap phylogenetic tree was created using MEGA

150

7.0 and performed with 1000 replications.

151 152 153

Construction of Co-expression Analysis A total of 7943 DEGs with an adjusted P value of 1 were subjected to co-expression network analysis through R-package weighted

155

gene co-expression network analysis (WGCNA).31 An adjacency matrix32 was

156

created by calculating the Pearson’s correlations for all the DEG genes with a

157

soft-threshold power of 26, which was subsequently transformed into a topological

158

overlap measure. A tree was generated using hierarchical clustering with a dynamic

159

tree-cut algorithm and assigned to a color. To ascertain the association between the

160

modules and external traits, the eigengenes of each module were correlated to the

161

traits of the catechins through Pearson’s correlation.

162 163

Results and Discussion

164

Content of Catechins and Degree of Catechin Galloylation in the Leaves of

165

Pruned Tea Trees

166

The contents of EC, EGC, C, ECG, and EGCG were determined through HPLC.

167

The EGCG content was significantly higher in the leaves of the pruned tea trees than

168

in the control leaves. However, the contents of EGC and ECG varied among the four

169

tea cultivars, and the contents of EC and C were lower in the pruned samples (Figure

170

2). This result indicates that the effect of pruning on catechins accumulation is

171

mainly reflected in the accumulation of EGCG. Although ECG content did not

172

increase as EGCG content did, the percentage of ester catechins (EGCG + ECG) and

173

total catechins was clearly higher in the pruned samples, a trend that was consistent

174

with that of EGCG. Notably, this result was similar to the result for ethylene signal

175

regulation on catechins content.33 9



176

After trimming, the tree height and branching structure have changed. These

177

changes eventually have caused a new distribution and balance of metabolites.

178

According to the obtained results, the contents of catechins were changed, especially

179

of the accumulation of EGCG in the pruned tea trees. Actually, the content of

180

caffeine was also increased in the pruned tea trees (Figure 2). In this paper, we would

181

like to emphasis on the accumulation of EGCG in the long-term pruning tea trees,

182

and give an explanation to the lower astringency tea produced from unpruned tea

183

trees than tea produced from long-term pruning tea trees.

184 185

Overview of Transcriptome

186

A total of 24 samples were sequenced using an Illumina HiSeq 4000 Platform,

187

generating approximately 6.63 GB of data per sample. The average genome mapping

188

rate was 76.18%, and the average gene mapping rate was 65.05%. From the

189

sequencing, 47,083 genes were identified of which 31,147 were known genes and

190

16,310 were novel genes. A total of 77,304 novel transcripts were identified of which

191

40,303 were previously unknown splicing events for known genes, 16,310 were

192

novel coding transcripts without any known features, and the remaining 20,691 were

193

long noncoding RNA. The quality parameters of the clean reads obtained through

194

mapping to the tea tree genome were 97.79%–98.44% (Q20) and 93.31%–95.02%

195

(Q30). The clean read quality metrics and mapping details are provided in Table S1.

196 197

DEGs 10


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198

To determine the DEGs associated with pruning, three independent biological

199

replicates for transcriptome analysis of four cultivars were produced to analyze the

200

gene expression changes under control and pruning treatments. In the pruning

201

treatment, the number of down-regulated genes in the four cultivars was greater than

202

the number of upregulated genes. The ranking for the number of DEGs in these four

203

cultivars was FX4 > FX1 > XY100 > DJBH. The upregulated and down-regulated

204

transcripts generated separately in the four cultivars are shown in Figure 3.

205

GO/KEGG annotation analysis was then performed on the DEGs.

206 207

GO/KEGG Annotation of the DEGs for Characteristic Secondary Metabolites

208

The advantage of the GO database lies in its superior annotation of gene functions,

209

whereas the advantage of the KEGG database lies in predicting pathways in which

210

transcripts may be located. Combining these two methods is a favorable choice when

211

analyzing the effect of treatment on key enzyme genes and potential core metabolic

212

pathways. Our analysis focused on the characteristic secondary metabolites of tea

213

plants. For GO enrichment analysis, a corrected P value of 0.15 was performed on the two modules. In

275

total, 62 genes (Table S5) were associated with SCPL1A in the black (43 genes) and

276

turquoise (19 genes) modules. Furthermore, the gene expressions were all

277

upregulated in the leaves of the pruned tea trees. The transcription factors of GATA,

278

GRFs, EREBP, and GRAS were annotated alongside the DELL protein, YTH

279

domain-containing protein, F-box and leucine-rich repeat protein, and BR-signaling

280

kinase, which implied that the effects of pruning might not only be a stress response

281

but are also intended to regulate plant growth and development. However, this

282

remains undetermined and requires experimental confirmation. Further study on the

283

relationship between hormone regulation and catechins biosynthesis resulting from

284

pruning should be conducted.

TEA023451.1,

TEA023432.1,

TEA000223.1,

285 14


TEA016463.1,

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286

Effect of Pruning on Tea Flavor Quality

287

According to the Chinese National Standard Methodology of Sensory Evaluation of

288

Tea (GB/T 23776-2018), taste is a key determiner of tea quality. Bitterness and

289

astringency, together with “umami”, sweetness, and other flavors, comprise the taste

290

of tea. Catechins mainly confer astringent flavors, whereas caffeine causes a bitter

291

taste.36 Our results revealed that the content of EGCG and the ratio of ester catechins

292

[ (EGCG + ECG) %] were all increased in the leaves of the pruned tea trees compared

293

with those of the unpruned tea trees. In addition, caffeine content was also increased

294

(Figure 2). The values of bitter intensity and astringent intensity were calculated in the

295

leaves of the pruned and unpruned tea cultivars according to Xu et al.37 In

296

supplementary table S6, the unpruned tea tree leaves produced tea with a less bitter

297

and less astringent taste, whereas the pruned tea leaves produced tea with a more

298

bitter and astringent taste because of the higher accumulation of catechins and

299

caffeine. “Ancient tea trees” are natural growing tea plants that do not undergo

300

pruning, and tea products from the leaves of such trees have started to become more

301

widely consumed because of their mellow taste, which is probably due to their lower

302

EGCG and caffeine content. We speculate that perennial pruning might be a major

303

factor influencing tea flavor quality differences between ancient and plantation tea

304

plants.

305

The findings of this study revealed that pruning can modulate the metabolic flux

306

of tea catechins biosynthesis, increasing the EGCG content. Through transcriptome

307

analysis, we determined that pruning could upregulate the expressions of a class of 15



308

SCPLs, which are catalyzed in the biosynthesis of EGCG. In addition, these findings

309

enhance understanding of the reasons behind the lower astringency of tea made

310

using the leaves of ancient tea trees.

311 312

Funding sources

313

This study was supported by the Key R&D Program of Anhui Province, China

314

(16030701097), and the Changjiang Scholars and Innovative Research Team in

315

University (IRT_15R01).

316 317

Authors’ contributions

318

MS, CZ, ML and ZC prepared the materials for sequencing. MS and NG analyzed

319

the data. MS and WD drafted and revised the manuscript. WD and ZZ guided this

320

research. All authors read and approved the manuscript.

321 322 323

Compliance with ethical standards The authors declare that they complied with ethical standards.

324 325 326

Competing interests The authors declare that they have no competing interests.

327 328

Supporting Information Available

329

Supplementary Table S1 Clean Reads Quality of the Pruned and Unpruned 16


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330

Samples

331

Supplementary Table S2 Primers Used for qRT-PCR Validation

332

Supplementary Table S3 Correlation Analysis of the Gene Expression and

333

Catechins Content in the Catechins Biosynthetic Pathways

334

Supplementary Table S4 Correlation between DEGs in the Catechins Biosynthetic

335

Pathways and EGCG in the Black and Turquoise Modules

336

Supplementary Table S5 Co-expression Analysis of the 62 Genes Linked to the

337

Black or Turquoise Modules Significantly Associated with EGCG

338

Supplementary Table S6 Bitter intensities and astringent intensities in leaves of the

339

pruned and unpruned tea cultivars

340 341

Supplementary Figure S1

342

Tree of SCPL genes employing the neighbor-joining method using MEGA 7.0

343

performed with 1000 replications. Tea, Arabidopsis, and grape SCPLs are clustered

344

into four categories. Tea SCPLs are gathered into three groups marked with different

345

colors. Blue represents SCPL1A.

346 347

References

348 349

(1) Rong, T., Chemistry and Biochemistry of Dietary Polyphenols. Nutrients 2010, 2 (12), 1231-46.

350 351 352

(2) Juneja, L. R.; Chu, D. C.; Okubo, T.; Nagato, Y.; Yokogoshi, H., L-theanine—a unique amino acid of green tea and its relaxation effect in humans. Trends in Food Science & Technology 1999, 10 (6–7), 199-204.

353 354

(3) Chen, Z. Z.; Zhang, W. J.; Zhu, L. G.; Chen, Y. Z.; Yang, R. X.; Wen-Quan, Y. U., Preliminary Report on Mechanization of Tea Plucking:Effect of Pruning Treatments on Crown Growth and 17



355

Quality of Oolong Tea. Acta Tea Sinica 2017, 58 (4), 193-197 (in Chinese).

356 357 358

(4) Pramanik, P.; Phukan, M.; Ghosh, S.; Goswami, A. J., Pruned tea bushes secrete more root exudates to influence microbiological properties in soil. Archives of Agronomy & Soil Science 2017.

359 360

(5) Cheng, D. N., Pruning Techniques and Standards of Tea Tree. Modern Agricultural Science & Technology 2016, 2, 84-86 (in Chinese).

361 362 363

(6) Thomas, J.; Saravanan, M.; Kumar, R. R.; †, P. K. P., Influence of age after pruning on the levels of flavanols and other bioconstituents in tea ( Camellia sinensis ). Journal of the Science of Food & Agriculture 2005, 85 (6), 931–934.

364 365

(7) Ravichandran, R., The impact of pruning and time from pruning on quality and aroma constituents of black tea. Food Chemistry 2004, 84 (1), 7-11.

366 367 368

(8) Wu, Y.; Wang, W.; Li, Y.; Dai, X.; Ma, G.; Xing, D.; Zhu, M.; Gao, L.; Tao, X., Six phenylalanine ammonia-lyases from Camellia sinensis : evolution, expression, and kinetics. Plant Physiology & Biochemistry 2017, 118, 413-421.

369 370 371

(9) Watanabe, B.; Kirikae, H.; Koeduka, T.; Takeuchi, Y.; Asai, T.; Naito, Y.; Tokuoka, H.; Horoiwa, S.; Nakagawa, Y.; Shimizu, B. I., Synthesis and inhibitory activity of mechanism-based 4-coumaroyl-CoA ligase inhibitors. Bioorganic & Medicinal Chemistry 2018, 26(9):2466-2474.

372 373 374

(10) Chen, S.; Pan, X.; Li, Y.; Cui, L.; Zhang, Y.; Zhang, Z.; Pan, G.; Yang, J.; Cao, P.; Yang, A., Identification and Characterization of Chalcone Synthase Gene Family Members in Nicotiana tabacum. Journal of Plant Growth Regulation 2017, 36 (2), 374-384.

375 376 377

(11) Ahmed, N. U.; Park, J. I.; Jung, H. J.; Yang, T. J.; Hur, Y.; Nou, I. S., Characterization of dihydroflavonol 4-reductase (DFR) genes and their association with cold and freezing stress in Brassica rapa. Gene 2014, 550 (1), 46.

378 379 380

(12) Duan, W.; Sun, P.; Li, J., Expression of genes involved in the anthocyanin biosynthesis pathway in white and red fruits of Fragaria pentaphylla and genetic variation in the dihydroflavonol-4-reductase gene. Biochemical Systematics & Ecology 2017, 72, 40-46.

381 382 383 384 385 386 387

(13) Wang, P.; Zhang, L.; Jiang, X.; Dai, X.; Xu, L.; Li, T.; Xing, D.; Li, Y.; Li, M.; Gao, L., Evolutionary and functional characterization of leucoanthocyanidin reductases from Camellia sinensis. Planta 2018, 247 (1), 1-16. (14) Zhao, X.; Dai, X.; Gao, L.; Guo, L.; Zhuang, J.; Liu, Y.; Ma, X.; Wang, R.; Xia, T.; Wang, Y., Function analysis of an UDP-glycosyltransferase involved in the biosynthesis of polyphenolic glucoside in tea (Camellia sinensis). Journal of Agricultural & Food Chemistry 2017, 65 (50), 10993-11001.

388 389 390

(15) Liu, Y.; Gao, L.; Li, L.; Qin, Y.; Lu, Z.; Nie, Z.; Wang, Y.; Tao, X., Purification and characterization of a novel galloyltransferase involved in catechin galloylation in the tea plant (Camellia sinensis). Journal of bilolgical chemistry. 2013, 287 (53), 44406-44417.

391 392

(16) Mugford, S. T; Osbourn, A., Evolution of serine carboxypeptidase-like acyltransferases in the monocots. Plant Signaling & Behavior. 2010, 5 (2), 193-195. 18


Page 18 of 31

Page 19 of 31


393 394 395

(17) Jiang, X.; Liu, Y.; Li, W.; Lei, Z.; Fei, M.; Wang, Y.; Tan, H.; Hua, Y.; Wei, C.; Wan, X., Tissue-Specific, Development-Dependent Phenolic Compounds Accumulation Profile and Gene Expression Pattern in Tea Plant [Camellia sinensis]. Plos One 2013, 8 (4), e62315.

396 397 398 399

(18) Heppel, S. C.; Jaffé, F. W.; Takos, A. M.; Schellmann, S.; Rausch, T.; Walker, A. R.; Bogs, J., Identification of key amino acids for the evolution of promoter target specificity of anthocyanin and proanthocyanidin regulating MYB factors. Plant Molecular Biology 2013, 82 (4-5), 457-471.

400 401 402

(19) Li, M.; Li, Y.; Guo, L.; Gong, N.; Pang, Y.; Jiang, W.; Liu, Y.; Jiang, X.; Zhao, L.; Wang, Y., Functional Characterization of Tea (Camellia sinensis) MYB4a Transcription Factor Using an Integrative Approach. Frontiers in Plant Science 2017, 8, 943.

403 404 405 406

(20) Ning, J.; Cao, Q.; Su, H.; Zhu, X.; Wang, K.; Wan, X.; Zhang, Z., Discrimination of Six Tea Categories Coming from Different Origins Depending on Polyphenols, Caffeine and Theanine Combined with Different Discriminant Analysis. International Journal of Food Properties 2017, 20 (31).

407 408

(21) Kim, D.; Langmead, B.; Salzberg, S. L., HISAT: a fast spliced aligner with low memory requirements. Nature Methods 2015, 12 (4), 357-360.

409 410 411 412 413 414 415

(22) Wei, C.; Yang, H.; Wang, S.; Zhao, J.; Liu, C.; Gao, L.; Xia, E.; Lu, Y.; Tai, Y.; She, G.; Sun, J.; Cao, H.; Tong, W.; Gao, Q.; Li, Y.; Deng, W.; Jiang, X.; Wang, W.; Chen, Q.; Zhang, S.; Li, H.; Wu, J.; Wang, P.; Li, P.; Shi, C.; Zheng, F.; Jian, J.; Huang, B.; Shan, D.; Shi, M.; Fang, C.; Yue, Y.; Li, F.; Li, D.; Wei, S.; Han, B.; Jiang, C.; Yin, Y.; Xia, T.; Zhang, Z.; Bennetzen, J. L.; Zhao, S.; Wan, X., Draft genome sequence of Camellia sinensis var. sinensis provides insights into the evolution of the tea genome and tea quality. Proceedings of the National Academy of Sciences of the United States of America 2018, 115 (18), E4151-E4158.

416 417 418

(23) Pertea, M.; Pertea, G. M.; Antonescu, C. M.; Chang, T. C.; Mendell, J. T.; Salzberg, S. L., StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nature Biotechnology 2015, 33 (3), 290-295.

419 420 421

(24) Trapnell, C.; Roberts, A.; Goff, L.; Pertea, G.; Kim, D.; Kelley, D. R.; Pimentel, H.; Salzberg, S. L.; Rinn, J. L.; Pachter, L., Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protocols 2012, 7 (3), 562-578.

422 423 424

(25) Kong, L.; Zhang, Y.; Ye, Z. Q.; Liu, X. Q.; Zhao, S. Q.; Wei, L.; Gao, G., CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Research 2007, 36, W345-349.

425 426

(26) Langmead, B.; Salzberg, S. L., Fast gapped-read alignment with Bowtie 2. Nature Methods 2012, 9 (4), 357-359.

427 428

(27) Li, B.; Dewey, C. N., RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. Bmc Bioinformatics 2011, 12 (1), 323.

429 430

(28) Love, M. I.; Huber, W.; Anders, S., Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 2014, 15 (12), 550. 19



431 432 433 434

(29) Tai, Y.; Wei, C.; Yang, H.; Zhang, L.; Chen, Q.; Deng, W.; Wei, S.; Zhang, J.; Fang, C.; Ho, C., Transcriptomic and phytochemical analysis of the biosynthesis of characteristic constituents in tea (Camellia sinensis) compared with oil tea (Camellia oleifera). Bmc Plant Biology 2015, 15 (1), 190.

435 436

(30) Livak, K. J.; Schmittgen, T. D., Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001, 25 (4), 402-408.

437 438 439

(31) Pei, G.; Chen, L.; Zhang, W., WGCNA Application to Proteomic and Metabolomic Data Analysis. In Proteomics in Biology, Pt A, Shukla, A. K., Ed. Elsevier Academic Press Inc: San Diego, 2017; Vol. 585, pp 135-158.

440 441

(32) Yip, A. M.; Horvath, S., Gene network interconnectedness and the generalized topological overlap measure. Bmc Bioinformatics 2007, 8 (1), 22.

442 443 444

(33) Ke, S. W.; Chen, G. H.; Chen, C. T.; Tzen, J. T. C.; Yang, C. Y., Ethylene signaling modulates contents of catechin and ability of antioxidant in Camellia sinensis. Botanical Studies 2018, 59 (1), 11.

445 446 447

(34) Mamati, G. E.; Liang, Y.; Lu, J., Expression of basic genes involved in tea polyphenol synthesis in relation to accumulation of catechins and total tea polyphenols. Journal of the Science of Food & Agriculture 2006, 86 (3), 459-464.

448 449 450

(35) Hong, G.; Wang, J.; Zhang, Y.; Hochstetter, D.; Zhang, S.; Pan, Y.; Shi, Y.; Xu, P.; Wang, Y., Biosynthesis of catechin components is differentially regulated indark-treated tea (Camellia sinensis L.). Plant Physiology & Biochemistry 2014, 78 (3), 49-52.

451 452 453

(36) Susanne Scharbert; Nadine Holzmann, A.; Hofmann, T., Identification of the Astringent Taste Compounds in Black Tea Infusions by Combining Instrumental Analysis and Human Bioresponse. Journal of Agricultural & Food Chemistry 2004, 52 (11), 3498-3508.

454 455

(37) Xu, Y.; Zhang, Y.; Chen, J.; Wang, F.; Du, Q.; Yin, J., Quantitative analyses of the bitterness and astringency of catechins from green tea. Food Chemistry 2018, 258, 16-24.

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Figure captions

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Figure 1. Sketch and photograph of experimental materials: (A) Sketch of pruned

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and unpruned tea trees; (B) Photograph of pruned and unpruned tea trees.

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Figure 2. Accumulation of EGCG, ECG, EC, EGC, C, (ECG+EGCG)%, and

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caffeine in the pruned and unpruned tea cultivars.

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Figure 3. Overview of transcriptome analysis of the pruned and unpruned cultivars

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revealing the number of DEGs identified through pairwise comparison between the 20


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pruned and unpruned samples (adjusted P value of

Metabolic Flux Enhancement and Transcriptomic Analysis Displayed

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