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Nontargeted Modification-Specific Metabolomics Investigation of Glycosylated Secondary Metabolites in Tea (Camellia sinensis L.) Based on Liquid Chromatography-High Resolution Mass Spectrometry Weidong Dai, Junfeng Tan, Meiling Lu, Dongchao Xie, Pengliang Li, Haipeng Lv, Yin Zhu, Li Guo, Yue Zhang, Qunhua Peng, and Zhi Lin J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b02411 • Publication Date (Web): 19 Aug 2016 Downloaded from http://pubs.acs.org on August 21, 2016
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Journal of Agricultural and Food Chemistry
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Nontargeted
Modification-Specific
2
Investigation of Glycosylated Secondary Metabolites in Tea
3
(Camellia
4
Chromatography−High Resolution Mass Spectrometry
sinensis
L.)
Based
Metabolomics
on
Liquid
5 6
Weidong Dai1, Junfeng Tan1, Meiling Lu2, Dongchao Xie1, Pengliang Li1, Haipeng
7
Lv1, Yin Zhu1, Li Guo1, Yue Zhang1, Qunhua Peng1, Zhi Lin1, *
8 9
1
Key Laboratory of Tea Biology and Resources Utilization, Ministry of Agriculture,
10
Tea Research Institute, Chinese Academy of Agricultural Sciences, 9 Meiling South
11
Road, Hangzhou, Zhejiang 310008, PR China
12
2
13
Beijing, 100102, P. R. China
Agilent Technologies (China) Limited, No. 3 Wangjing North Road, Chaoyang Distr.,
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Abstract
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Glycosylation on small molecular metabolites modulates a series of biological
16
events in plants. However, a large number of glycosides have not been discovered and
17
investigated using -omics approaches. Here a general strategy named “nontargeted
18
modification-specific metabolomics” was applied to map the glycosylation of
19
metabolites. The key aspect of this method is to adopt in-source collision-induced
20
dissociation to dissociate the glycosylated metabolite causing a characteristic neutral
21
loss pattern, which acts as an indicator for the glycosylation identification. In an
22
exemplary
23
rhamnosylated, 21 rutinosylated, and 23 primeverosylated metabolites were detected
24
simultaneously. Among them, 61 glycosylated metabolites were putatively identified
25
according to current tea metabolite databases. Thanks to the annotations of glycosyl
26
moieties in advance, the method aids the metabolite identifications. Additional 40
27
novel glycosylated metabolites were tentatively elucidated. This work provides a
28
feasible strategy to discover and identify novel glycosylated metabolites in plants.
application
in
green
teas,
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glucosylated/galactosylated,
29 30
Keywords: tea; secondary metabolites; glycosylation; metabolomics; LC-MS
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Introduction
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Endogenous modifications (including glycosylation and acylation) of genes and
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proteins widely occur in living organisms that modulate various biological events, and
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have been extensively studied in systems biology.1,2 Not only occur on large
35
molecules of gene and protein, these modifications also take place extensively on
36
small molecules of metabolites in plants.3-5 They alter the polarity, volatility, chemical
37
stability, and biological activity of the metabolites, protecting plants against biotic and
38
abiotic stresses.6,7 Furthermore, metabolite modifications produce numerous
39
secondary metabolites contributing to the complexity of plant metabolome.
40
Metabolites with glycosylation are usually regarded as secondary metabolites in
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plants. Compared with primary metabolites (such as carbohydrates, amino acids,
42
lipids, and Krebs cycle intermediates), glycosylated metabolites are more specific of
43
genera and species, and act as antioxidants, reactive oxygen species (ROS) scavengers,
44
coenzymes, UV and excess radiation screen, as well as regulatory molecules.8
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Furthermore, glycosylated metabolites are utilized as medical, bioactive, nutrient, and
46
flavourous compositions by humans.4,9-11 Although glycosylated metabolites are the
47
hotspots in plant studies, they are less large-scale surveyed due to the limitations in
48
the analytical methods. With the great help of “-omics” technology (particularly, the
49
metabolomics technology) and the improvement in the sensitivity of mass
50
spectrometry instruments, modified metabolites mapping has been achieved. In a
51
previous work, a novel strategy, named “nontargeted modification-specific
52
metabolomics”, was successfully developed to achieve the large-scale detection of 3
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modified metabolites.12 With the above method, 900 metabolites were identified with
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modifications of acetylation, sulfation, glucuronidation, glucosidation, or ribosylation
55
in human urine samples. In addition, the method facilitated the identification of
56
compounds, which are not included in current metabolomics databases and are
57
referred to as “unknowns” in metabolomics studies. The key aspect of the aforesaid
58
strategy was the introduction of in-source collision-induced dissociation (ISCID) into
59
nontargeted
60
(LC-HRMS)-based metabolomics.
liquid
chromatography−high
resolution
mass
spectrometry
61
Tea is the most consumed beverage next to water in the world for the health
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benefits and satisfactory sensory, which is largely attributed to the abundant
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secondary metabolites.13-15 Flavonols, flavones, anthocyanins, saponin, and aroma
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precursors are found exist mainly in glycoside form in tea.16-21 However, there are still
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a large number of glycosylated metabolites have not been discovered. In this study,
66
we are seeking to apply nontargeted modification-specific metabolomics method to
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map glycosylated metabolites in tea and to uncover novel glycosylated secondary
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metabolites with modification of glucosylation, galactosylation, rhamnosylation,
69
rutinosylation, and primeverosylation.
70 71
Materials and Methods
72
Chemicals
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L-Theanine, D-glucose, kaempferol 3-galactoside, kaempferol 3-glucoside,
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quercetin 7-glucoside, quercetin 3-glucoside, 3,5-dicaffeoylquinic acid, theogallin, 4
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theaflavin-3-gallate, quercetin 3-rutinoside, chlorogenic acid, epigallocatechin gallate
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(EGCG), 4,5-dicaffeoylquinic acid, 3,4-dicaffeoylquinic acid, maltotriose, maltose,
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myricetin 3-galactoside, quercetin 3-galactoside, kaempferol 3-rutinoside, and
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myricetin 3-rhamnoside were purchased from Sigma (St. Louis, MO, USA). Methyl
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salicylic acid primeveroside, kaempferol 3,7-dirhamnoside, and aesculin were
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purchased from ChemFaces (Wuhan, China). Theanine glucoside was synthesized by
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mixing 2.5 g L-theanine and 2.5 g D-glucose at 100 °C for 2 h and then purified with
82
C18 column chromatography. The structure was suggested as 1-theanylglucose by
83
LC-MS/MS and 1H NMR (Figure S2).
84
Treatment of tea samples
85
In order to include as many glycosylated metabolite species as possible, a pooled
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sample by mixing an equal portion of each green tea samples from 14 varieties (fresh
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leaves of Jianbohuang, Ningzhou 2, Zhenghedabai, Gaoyaqi, Zhuyeqi, Fuzao 2,
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Wannong 95, Fuyun 6, Huang Guanyin, Maoxie, Longjing 43, Yuemingxiang, Xicha
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5, and Echa 1, harvested from the tea garden of the Tea Research Institute of Chinese
90
Academy of Agricultural Sciences, and then were subjected to a green tea
91
manufacturing process22) was prepared for the nontargeted modification-specific
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metabolomics method development in tea. The pooled green tea sample was initially
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grounded into powders using a tube mill (IKA, Staufen, Germany). Then, 1.5 mL of
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60% methanol solution (v/v) was added into 30 mg powders followed by vortexing
95
for 20 s. The tea metabolome was further extracted by ultrasonic treatment for 10
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min and then vortexed for 20 s. The supernatants were passed through 0.22 µm filter 5
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after centrifugation at 10,000 g for 10 min (Centrifuge 5810R, Eppendorf, Hamburg,
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Germany). The obtained solution was used for the LC-MS analysis.
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Nontargeted modification-specific metabolomics analysis
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The LC-MS conditions for the nontargeted modification-specific metabolomics
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analysis of metabolite glycosylations in tea plant was modified from previous works.
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12,23
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chromatography system (UHPLC Infinity 1290, Agilent Tech., Santa Clara, CA)
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coupled to a quadrupole-time of flight mass spectrometer (Q-TOF 6540, Agilent
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Tech., Santa Clara, CA). Chromatographic separation of tea metabolome was
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performed on a Zorbax Eclipse Plus C18 column (100 × 2.1 mm, 1.8 μm, Agilent
107
Tech., Littlefalls, DE). Binary mobile phases with phase A of water containing 0.1%
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formic acid (v/v) and phase B of methanol were used for elution. The linear gradient
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program was as follows: 0 min, 10% B; 4 min, 15% B; 7 min, 25% B; 9 min, 32% B;
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16 min, 40% B; 22 min, 55% B; 28 min, 95% B; 30 min, 95% B; 31 min, 10% B; 35
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min, 10% B. The total elapsed time required for a given chromatographic analysis
112
was thus 35 min. The flow rate was set at 0.4 mL/min. The injection volume was 3
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μL. Electrospray ionization (ESI) was performed in the positive ionization mode
114
with the following parameters: capillary voltage was set as 3,500 V; the temperature
115
and the flow rate of drying gas were set at 300 °C and 8 L/min, respectively; the
116
nebulizer pressure was set at 35 psi; the temperature and the flow rate of the sheath
117
gas were maintained at 300 °C and 11 L/min; the mass scan range of m/z 100–1000
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was applied for the nontargeted modification-specific metabolomics analysis; ISCID
Briefly, LC-MS analyses were performed with an ultra-high pressure liquid
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voltage was set at 0, 5, 10, 15, 20, 25, 30, 35, 40, and 45 V, respectively. The
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Q-TOF/MS was daily calibrated following the manufacture’s procedure and
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reference ions with m/z of 121.0509 and 922.0098 were continuously infused during
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data acquisition for online calibration to ensure MS accuracy.
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Data processing of nontargeted modification-specific metabolomics
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analysis
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The data processing of nontargeted modification-specific metabolomics analysis to
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identify metabolite glycosylations were modified from a previous work.23 Briefly, raw
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data files acquired by LC-MS analysis were firstly processed by DA Reprocessor
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software (Agilent Tech., Santa Clara, CA) for metabolite feature ions extraction, and
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then imported into Mass Profiler Professional software (Version 13.0, Agilent Tech.,
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Santa Clara, CA) for peak alignment. The ions in the obtained peak table were then
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subjected to neutral loss (NL) matches to find glycosylated metabolites by a free
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software named “Neutral Loss MSFinder”.12 Two ions that they were detected
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simultaneously and exhibited characteristic neutral loss of m/z 162.0528, 146.0579,
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308.1107, and 294.0951 were assigned as precursor ion and fragment ion of
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glycosylated
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rutinosylation, and primeverosylation, respectively (Table 1). A prerequisite for the
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correct annotation of these glycosylations was the close setting of the window for
138
shifts in retention time and mass for the NL matches. Here, the error tolerances of
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retention time and mass for neutral loss matches by “Neutral Loss MSFinder”
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software were set as ∆tR < 0.1 min and ∆NL < 0.002 Da, respectively. Principal
metabolite
with
glucosylation/galactosylation,
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component analysis (PCA) was performed using Simca-P 11.5 software (Umetrics
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AB, Umeå, Sweden) to investigate glycosylated metabolite profiles of tea variaties.
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Student’s t-test was performed using the PASWstat software (version 18.0, USA).
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Database search for glycosylated metabolite identification
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Ion features assigned as glycosylated metabolites by the nontargeted
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modification-specific metabolomics analysis were imported into Tea Metabolome
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Database (TMDB) and Human Metabolome Database (HMDB) for structure
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identification. TMDB contains records for more than 1,393 constituents found in tea
149
with information gathered from 364 published books, journal articles, and electronic
150
databases,24 while HMDB contains 41,993 metabolite entries (including ~28,000
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food components and food additives).25,26 Chromatographic retention behaviors and
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tandem mass spectrometry (MS/MS) were also applied to assist glycosylated
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metabolite identification.
154 155
Results and Discussion
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Strategy for the identification of metabolite glycosylation
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As shown in Figure 1, the endogenous metabolite glycosylation adds a certain
158
molecular mass to the metabolite substrate. During the in-source collision induced
159
dissociation, the added ligands can be removed through neutral loss pathway, which
160
provides specific neutral loss patterns. In the initial step for the profiling of
161
glycosylated metabolites in tea, authentic standards of quercetin 3-glucoside,
162
kaempferol 3-galactoside, myricetin 3-rhamnoside, quercetin 3-rutinoside, and methyl 8
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salicylic acid primeveroside were exemplarily used to investigate the unique neutral
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loss
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primeverosylatied metabolites, respectively (the structures of glycosylated metabolites
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are shown in Figure 2). As shown in Figure 3, metabolites with modification of
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glucosylation/galactosylation, rhamnosylation, rutinosylation, and primeverosylation
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exhibited a theoretical characteristic NL of 162.0528 (C6H10O5), 146.0579 (C6H10O4),
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308.1107 (C12H20O9), and 294.0951 (C11H18O9), respectively, which could act as
170
indicators to identify specific glycosylation (Table 1). It was not distinguishable
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between glucosylation and galactosylation only using the NL value since both
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generate a theoretical NL of 162.0528. Chromatographic retention behaviors need to
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be introduced because the galactosyl conjugate eluted earlier than the glucosyl
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conjugate on C18 column.27 Taking the advantage of high resolution of Q-TOF/MS
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instrument, it is facile to distinguish glucosidation/galactosylation (m/z(NL) = 162.0528)
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and caffeoylation (m/z(NL) = 162.0317), and rhamnosylation (m/z(NL) = 146.0579) and
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coumaroylation (m/z(NL) = 146.0368), which are not distinguishable by traditional
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neutral loss scan performed on triple quadrupole or Q-trap instrument. Note that if the
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glycosyl moiety of a compound is further acylated (e.g., galloylated, coumaroylated,
180
and caffeoylated), the compound would not generate a characteristic NL
181
corresponding to the glycosylation by ISCID and therefore would not be found by
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nontargeted modification-specific metabolomics method.
183
Profile glycosylated metabolites in green teas
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of
glucosylated,
galactosylated,
rhamnosylated,
rutinosylated,
and
In the pooled green tea of 14 tea plant varieties, 202 compounds, including 120 9
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rhamnosylated
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glucosylated/galactosylated
metabolites,
21
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rutinosylated metabolites, and 23 primeverosylated metabolites, were assigned as
187
glycosylated metabolites (Table 1 and Figure S1). Then, tandem mass spectrometry in
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collision induced dissociation (CID) mode was applied for further confirmations of
189
the glycosylation types of metabolites. A number of 144 compounds exhibited the
190
neutral losses deriving from the specific glycosyl moieties (Table S1). The majority of
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the remaining 58 compounds were found with low abundances (low mass intensities),
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which might be the reason for the non-detections of the desired substrate features in
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MS2 spectra. Here, the above 144 compounds were selected for further structure
194
identifications.
195
Aid the identification of glycosylated metabolites
196
Thanks to the rapid developments in the sensitivity and resolution of LC-MS
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equipment, hundreds of, even thousands of ion feature signals can be simultaneously
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acquired from one plant samples (e.g., green tea infusion). However, the limitation in
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metabolite identification that only a very small portion of chromatography and MS
200
signals can be structurally interpreted have become the bottleneck in plant
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metabolomics studies.28,29 Silva reported that only ~2% of spectra in a nontargeted
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metabolomics experiment can be annotated. It means that the vast majority of
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information collected by metabolomics is “dark matter”.30 This is partially attributed
204
to the absence of numerous glycosylated metabolites in the current plant metabolite
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databases. Among the 144 CID-MS2 validated glycosylated compounds, 61 features
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were structurally interpreted by searching in TMDB and HMDB databases (Table S1). 10
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The structural isomers and stereoisomers of glycosylated metabolites cannot be fully
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identified only by tandem mass spectrum. And some ion features matched multiple
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glycosylated metabolite candidates in the databases. Herein, the candidates that have
210
been reported in teas were preferred. Twelve of them were confirmed with authentic
211
standards. These indicated that the nontargeted modification-specific metabolomics
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method is able to profile glycosylated metabolites in teas.
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Combined with the interpretation of the glycosyl moiety of glycosylated
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metabolites in advance, the nontargeted modification-specific metabolomics approach
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provides potentials to uncover “dark matters” and to discover novel constituents in
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teas. As shown in Figure 4, ion features in the putative glycosylated metabolites list
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are firstly imported into metabolomics database for structural identification. If there is
218
0 hit for the queried ion feature, the corresponding substrate ([M-sugar]+) is then
219
alternatively subjected to database search identification. The structure of the queried
220
ion feature will be interpreted by combining the substrate part and the sugar moiety
221
part. This strategy overcomes the limitation in metabolite identification that a great
222
number of glycosylated metabolites have not been discovered and have not been
223
included in current databases. The substrates refers to the compounds that have been
224
reported in teas were preferred. Using this strategy, additional 40 glycosylated
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metabolites in green tea infusion were structurally elucidated, which are considered as
226
novel compounds (Table S1).
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Herein, the identification of theanine glucoside in tea plant was taken as an
228
example. An ion feature (m/z = 337.1599, tR = 2.26 min) was found exhibiting a NL 11
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of m/z 162.0524 when ISCID voltage was applied during the nontargeted
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modification-specific metabolomics analysis, and therefore was assumed as a
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glycosylated metabolite. However, this ion feature could not match any candidate
232
compound in TMDB and HMDB database. Because a glucosyl moiety was identified
233
in advance, an alternative feature (m/z = 175.1071), which was regarded as the
234
substrate of the ion feature of m/z 337.1599 (loss one glucose), was searched in
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HMDB database and resulted in a hit of theanine (a characteristic free amino acid
236
exists in tea, which compose 1–2% of the weight of dry tea leaves31). Thus, this ion
237
feature (m/z 337.1599) was assigned as theanine glucoside. This compound was
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further identified as 1-theanylglucose using a synthesized standard, which was not
239
reported previously (Figure 5, Figure S2). A lot of works on the confirmations of
240
other novel glycosylated metabolites need to be carried out in future.
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Variations of glycosylated metabolites among green teas from
242
different varieties
243
As a proof of concept, the nontargeted modification-specific metabolomics was
244
applied to find major differential glycosylated metabolites among different tea
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varieties (Jianbohuang, Ningzhou 2, Zhenghedabai, Gaoyaqi, Zhuyeqi, Fuzao 2,
246
Wannong 95, Fuyun 6, Huang Guanyin, Maoxie, Longjing 43, Yuemingxiang, Xicha
247
5, and Echa 1; the relative quantifications of glycosylated metabolites in these 14
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varieties were shown in Table S2). As shown in Figure 6A, the pooled samples (QC
249
samples) of 14 green teas were gathered crowdedly in the center of the principal
250
component analysis (PCA) score plot, indicating a good reproducibility of the 12
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metabolomics analysis. Zhenghedabai, Fuzao 2, Maoxie, Longjing 43, Yuemingxiang,
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Xicha 5, and Echa 1 presented similar glycosylated metabolites profiles, and these
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varieties are usually considered as suiting for manufacture of non-fermented (Fuzao 2,
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Longjing 43, Xicha 5, and Echa 1), light-fermented (Zhenghedabai) and
255
semi-fermented teas (Maoxie and Yuemingxiang). On the other hand, the profiles of
256
Jianbohuang, Ningzhou 2, Gaoyaqi, Zhuyeqi, Wannong 95, Huang Guanyin, and
257
Fuyun 6 were similar, and they are usually considered as suiting for manufacture of
258
fermented (Jianbohuang, Ningzhou 2, Gaoyaqi, Zhuyeqi, Wannong 95, Fuyun 6) and
259
semi-fermented teas (Huang Guanyin). A PCA loading plot was applied to find
260
glycosylated metabolites mainly responsible for the variations among tea varieties
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(Figure 6B). As shown in Figure 7, kaempferol 3-galactoside, quercetin 3-galactoside,
262
quercetin
263
3,3'-digalactoside were
264
part of the PCA score plot than those in the right part of the PCA score plot, while
265
kaempferol 3-glucoside, kaempferol 3-rutinoside, kaempferol 3-glucosylrutinoside,
266
N'-formylkynurenine diglucoside, and maltotriose were significantly lower (p < 0.05).
267
These indicated that the metabolite galactosylation is more vigorous in the group of
268
Zhenghedabai, Fuzao 2, Maoxie, Longjing 43, Yuemingxiang, Xicha 5, and Echa 1
269
when compared with the group of Jianbohuang, Ningzhou 2, Gaoyaqi, Zhuyeqi,
270
Wannong 95, Huang Guanyin, and Fuyun 6. In contrast, metabolite glucosylation is
271
reduced. Therefore, we speculated that the glucosylation/galactosylation ratio may be
272
related with the suitability for manufacture of tea variety: high galactosylation level of
3-galactosylrutinoside,
myricetin
3-galactoside,
and
myricetin
significantly higher (p < 0.05) in tea varieties located in left
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metabolites is positively related with the suitability for non-fermented teas
274
manufacture, while high glucosylation is positively related with the suitability for
275
fermented teas manufacture. In
276
this study, we
successfully applied a
novel approach nontargeted
277
modification-specific metabolomics to profile and identify secondary metabolites with
278
glucosylation, galactosylation, rhamnosylation, rutinosylation, and primeverosylation
279
in green teas. This method greatly enlarges the coverage of glycosylated metabolites
280
and improves the capability for the structural identifications of unknown metabolites.
281
It can be further extended to map other significant modifications, such as galloylation,
282
cinnamoylation, coumaroylation, and caffeoylation in plant metabolomes.
283 284 285
Supporting Information
286
Profiles of glycosylated metabolites in green tea infusions (Figure S1). LC-MS/MS
287
and NMR analysis for the structural identification of theanine glucoside (Figure S2).
288
Structural elucidations of glycosylated metabolites in green teas (Table S1). LC-MS
289
intensities of glycosylated metabolites in different tea varieties (Table S2). These
290
materials are available free of charge via the Internet at http://pubs.acs.org.
291 292
Corresponding Author
293
*
294
[email protected] Tel.: +86 571 86650617; Fax: +86 571 866503154; E-mail addresses:
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Funding
297
This work is supported by the National Natural Science Foundation of China (No.
298
31500561), the Science and Technology Innovation Project of Chinese Academy of
299
Agricultural Sciences (No. CAAS-ASTIP-2014-TRICAAS), and the Earmarked
300
Fund for China Agricultural Research System (No. CARS-23).
301 302
Notes
303
The authors declare no competing financial interest.
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Legends Figure 1. Strategy for mapping metabolite glycosylation in tea. The glycosyl group (neutral loss) is found by matching the intact glycosylated metabolite parent ion and the fragment ion produced by ISCID. The glycosylation is indicated as colored symbol (green ellipse and red rhombus). Figure 2. Structures of metabolite glycosylations included in this study. Figure 3. Typical ISCID-based fragmentation pattern exemplarily shown for glycosylated metabolites: (A) glucosylation (theoretical NL m/z = 162.0528), (B) galactosylation (theoretical NL m/z = 162.0528), (C) rhamnosylation (theoretical NL m/z = 146.0579), (D) rutinosylation (theoretical NL m/z = 308.1107), and primeverosylation (theoretical NL m/z = 294.0951). Figure 4. Workflow for the discovery and identification of glycosylated metabolites in tea plant using modification-specific metabolomics method. The part with red color represents the strategy of structural identification for novel glycosylated metabolites. Figure 5. Extracted ion chromatogram (EIC) of theanine glucoside in (A) green tea and in (B) synthesized standard; mass spectrum of theanine glucoside in (C) green tea and in (D) synthesized standard; MS2 spectrum of theanine glucoside in (E) green tea and in (F) synthesized standard. Figure 6. Principal component analysis of green tea samples from 14 varieties: (A) score plot, and (B) loading plot. PC1 and PC2 explained 51.2% of total variance. Figure 7. Main distinctive glycosylated metabolites between the varieties (Zhenghedabai, Fuzao 2, Maoxie, Longjing 43, Yuemingxiang, Xicha 5, and Echa 1) 21
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located in the left part of the PCA score plot (see Figure 6) and the varieties (Jianbohuang, Ningzhou 2, Gaoyaqi, Zhuyeqi, Wannong 95, Huang Guanyin, and Fuyun 6) located in the right part of the PCA score plot.
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Table 1.The detectable metabolite glycosylations, and the corresponding molecular formulas, the accurate mass of neutral loss, as well as the detected number of glycosylated metabolites in green teas. detected number metabolite glycosylation molecular accurate mass of glycosylated type formula of neutral loss metabolites glucosylation/galactosylation C6H10O5 162.0528 120 rhamnosylation C6H10O4 146.0579 38 rutinosylation primeverosylation
C12H20O9 C11H18O9
308.1107 294.0951
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Figure 1.
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Figure 2.
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A
quercetin 3-glucoside
intensity
NL=162.0532
m/z
B
kaempferol 3-galactoside
intensity
NL=162.0529
m/z
C
myricetin 3-rhamnoside
intensity
NL=146.0584
m/z
D
quercetin 3-rutinoside
intensity
NL=308.1110
m/z
methyl salicylic acid primeveroside
intensity
E
NL=294.0944
m/z
Figure 3. 26
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Figure 4.
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Figure 5.
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Figure 6.
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Figure 7.
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