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Chemistry and Biology of Aroma and Taste
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.
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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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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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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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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
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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
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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.
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Real-time Quantitative Polymerase Chain Reaction
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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
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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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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,
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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
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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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Samples
331
Supplementary Table S2 Primers Used for qRT-PCR Validation
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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
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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