Comparative Metabolic Responses and Adaptive Strategies of Tea

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Comparative metabolic responses and adaptive strategies of tea leaves (Camellia sinensis) to N2 and CO2 anaerobic treatment by a nontargeted metabolomics approach Qi Chen, Yamin Zhang, Mingmin Tao, Mengshuang Li, Yun Wu, Qi Qi, Hua Yang, and Xiaochun Wan J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b03067 • Publication Date (Web): 22 Aug 2018 Downloaded from http://pubs.acs.org on August 23, 2018

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Journal of Agricultural and Food Chemistry

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Comparative metabolic responses and adaptive strategies of tea

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leaves (Camellia sinensis) to N2 and CO2 anaerobic treatment by a

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nontargeted metabolomics approach

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Qi Chen,†,‡ Yamin Zhang,† Minming Tao,† Mengshuang Li,† Yun Wu,† Qi Qi,†,‡ Hua Yang,† Xiaochun

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Wan*,†

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†

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Hefei, Anhui 230036, PR China

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‡

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Agricultural University, Hefei, Anhui 230036, PR China

State Key Laboratory of Tea Plant Biochemistry and Utilization, Anhui Agricultural University,

Key laboratory of Agricultural products processing engineering of Anhui Province, Anhui

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ABSTRACT: It is well known that anaerobic treatment has been considered as a

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utility process to accumulate γ-aminobutyric acid (GABA) in tea leaves. In this paper,

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the nonvolatile differential compounds in picked-tea leaves between filled-N2

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treatment and filled-CO2 treatment were compared in metabolic profiles and dynamic

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changes via ultra-high performance liquid chromatography linked to a hybrid

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quadrupole orthogonal time-of-flight mass spectrometer (UPLC-Q-TOF-MS).

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Multivariate analysis and heatmap of hierarchical clustering analysis indicated that

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filled-N2 treatment resulted in a wider range of metabolic perturbation than filled-CO2

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treatment but GABA accumulates faster and more significantly under filled-CO2

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treatment than other treatment. The differential metabolites in anaerobic treatment

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were mainly reflected in the levels of glucose metabolism and amino acid metabolism,

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and the main differential pathway included the glyoxylate metabolism pathway,

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galactose metabolism and phenylalanine metabolism. These metabolomic analyses

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were also evaluated to illuminate the physiological adaptive strategies of tea adopted

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to tolerate certain anaerobic stress type.

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KEYWORDS:

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metabolomics, GABA

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INTRODUCTION

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As a worldwide beverage, tea has unique sensory experiences, charming cultural

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connotation and beneficial health effects.1-3 More and more people focus on the

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bioactive compounds of tea such as catechin, tea polyphenol, alkaloid, theanine, etc.4-8

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In order to increase the health benefits of tea, people adopted an additional anaerobic

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processing in the manufacturing of green tea to increase the content of

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gamma-aminobutyric acid (GABA). During the anaerobic process, a slight zymolysis

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accelerated glutamate decarboxylase (GAD) to produce the unique amino acid GABA

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in tea leaves.9 GABA, as a non-proteinogenic amino acid, was widely found in

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animals, plants and bacteria.10 In particular, GABA functioned as an inhibitory

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neurotransmitter in human and mammalian brains.11 Many studies revealed that

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GABA has excellent bioactivities on a series of neurological and cognitive

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impairments, including anti-aging,12 reducing blood pressure,13 relaxation and

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enhancing immunity,14 improving cognitive deficits in Down syndrome,15 obstructive

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sleep apnea,16 antioxidative and neuroprotective activities.17,

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health-promoting product, various GABA-enriched foods were widely developed.

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There were some processes carried out to accumulate the content of GABA in green

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tea and other types of tea. Tsushida (1987) utilized anaerobic conditions to enrich

picked-tea

leaves,

anaerobic

treatment,


UPLC-Q-TOF/MS,

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Therefore, as a

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GABA in tea leaves.19 Kim (2012) reported that applied the repeated

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anaerobic/aerobic sequence fermentation to make GABA-riched green tea and which

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also could enhance its antioxidant activity.20 There were also other manufacture

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methods including soaking the postharvest of fresh tea leaves in the glutamate acid

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solution or spraying amino acids solution on the surface of tea leaves and so on.21, 22

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However, all of these manufacture methods mainly focused on the change of GABA,

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or detected some principal components, such as catechins, caffeine and free amino

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acids; rarely paid attention to the dynamic change of overall metabolites during the

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processing in tea leaves, especially the C/N ion flow changes under the different

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anaerobic treatments. So a comprehensive evaluation of the metabolomics of

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GABA-rich tea are necessarty, especially in-depth mapping of the dynamic changes

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of the differential metabolites in the anaerobic process will be contribute to

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distinguish the stress response mechanism in vitro leaves and further improve

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manufacturing process of “Gaboron tea”.

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Metabolomics, as an important tool to study the metabolic pathways of biological

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system, is widely used in food and tea research. Some recent researchs based on

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unbiased non-target metabolomics analysis have demonstrated a overall profiling of

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tea compounds in different varieties,

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processing technic.23-25 In this work, we used inert gas N2 and CO2 as anaerobic agents

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respectively treatment the postharvest of fresh tea leaves and their non-volatile

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metabolite were compared by an UHPLC-Q-TOF-MS approach based non-target

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metabolomics analysis to profile the dynamic changes of characteristic compounds

different development stages or different



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during GABA enrichment process.

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MATERIALS AND METHODS

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Tea Sample Preparation

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The perennial tea plants (Camellia sinensis [L.] O. Kuntze) used in this study were

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grown in the experimental plantation at the Dayangdian in Hefei, Anhui province,

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China. The young shoot (one bud with two leaves) of clonal tea plant “Longjing 43#”

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variety were picked in spring tea seasons, and quickly divided into 3 portions for the

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process of natural spreading (A1), filled-N2 (A2), and filled-CO2 (A3) (Fig. S1),

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approximately 500 grams of fresh leaves for the entire experiment. The sampling was

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repeated six times for the treatment leaves (natural spreading, filled-N2, and

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filled-CO2) under 1h (A1, A2, A3) and 6h (B1, B2, B3). All samples were packed by

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aluminium foil and placed in liquid nitrogen then stored in a −80 °C ultra-refrigerator.

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Chemicals

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Methanol and acetonitrile of LC–MS grade were purchased from Merck (Darmstadt,

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Germany). Ammonium acetate and ammonium hydroxide of LC-MS grade were

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purchased from CNW Technologies (ANPEL Laboratory Technologies Inc., Shanghai,

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China.）Formic acid, γ-aminobutyric acid (GABA), theanine, putrescine, spermine,

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spermidine and ribitol were purchased from Sigma (St. Louis, Missouri, USA).

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Deionized water was prepared by a Milli-Q water purification system (Millipore,

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Billerica, Massachusetts, USA).

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Metabolite Extraction

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Sample extraction method was according to the principle previously described by


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Dunn (2011) with some modifications.26 First, approximately 500 mg of each

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freeze-dried sample was decanted into 2 mL EP tubes, then 1000 µL methanol:

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acetonitrile: water (2:2:1, v/v/v) containing 20 µL of ribitol (1 mg/mL) as an internal

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standard were added to each tube. All EP tubes were mixed by vortex for 30 s; then

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homogenized (JXFSTPRP-24, Jinxin Tech., Shanghai, China) by ball mill for 4 min at

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45 Hz; then sonicated for 5 min (incubated in ice water); after repeated the step for 3

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times, the samples were incubated at -20°C for 1h. Afterwards, the tubes were

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centrifuged (Heraeus Fresco17 centrifuge, Thermo Scientific) at 13, 000 rpm at 4°C

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for 15 min. The supernatant (about 0.4 mL) was transferred into a new EP tube and

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then the extracts were vacuum dried. Second, The dried extracts were dissolved with

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600 µL acetonitrile:water (1:1, v/v) and vortexed for 30 s; then sonicated about 5 min

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(adjust time according to dissolution). The extracts were centrifuged again according

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to the above menthod. At last, the supernatant (about 60 µL) was filtered through a

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0.22 µm filter and then injected into a fresh 2 mL LC/MS glass vial. Meanwhile,

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every sample was taken 10 µL and mixed as a QC sample for the UHPLC-QTOF-MS

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detection.

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Detected method of UPLC-Q-TOF/MS

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The freeze-dried leaves extracts were applied to UPLC-Q-TOF/MS for metabolomics

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analyses. LC-MS/MS analyses were performed using an UHPLC equipment (Infinity

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1290, Agilent Tech., Santa Clara, CA, USA) with a UPLC BEH Amide column (1.7

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µm, 2.1*100 mm, Waters Corp., Milford, MA, USA) coupled to a triple TOF 6600

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system (Q-TOF, AB Sciex, Concord, ON, Canada). The mobile phase consisted of the



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following: (A) 25 mM NH4OAc and 25 mM NH4OH in water（pH=9.75）; (B) pure

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acetonitrile. The gradient elution procedure was as follows: 0 min, 95% B; 0.5 min,

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95% B; 7 min, 65% B; 8 min, 40% B; 9 min, 40% B; 9.1 min, 95% B; 12 min, 95% B.

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Then the next step was delivered at 0.5 mL min-1. The injection volume was 0.5 µL.

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The triple TOF-MS was used to acquire MS/MS spectra based on an

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information-dependent acquisition(IDA).

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was continuously colletcted and evaluated by the acquisition software (Analyst TF 1.7,

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AB Sciex) and which acquisition of MS/MS spectra relied on the preseted parameters.

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In each cycle, 12 precursor ions with intensity above 100 were selected and then

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fragmented at 30 V collision energy (CE) (every 50 msec ion acquisition time with 15

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MS/MS events). The condition of Electrospray ionization (ESI) source was set as

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follows: ion source gas 1 as 60 arb., ion source gas 2 as 60 arb., curtain gas as 30 arb.,

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source temperature 650℃, Ion Spray Voltage Floating (ISVF) 5, 000 V or −4, 000 V

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in positive or negative modes, respectively.27

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Data Processing and Differential Metabolites Analysis

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The preprocessing of raw data from UPLC-Q-TOF/MS included the following:

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missing values in the original data were re-encoded and simulated, the numerical

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simulation method was filled by the minimum one-half method; and then

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normalization was performed using the total ion current (TIC) of each sample.26

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Afterwards, the pre-treated data acquired from the UPLC-Q-TOF/MS analysis were

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used to search for material information in the local database to generate a metabolite

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database table that included information on metabolite name and score on possible

In this case, the full scan survey MS data


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matches, mathced pattern, mass-to-charge ratio (m/z), retention time and MS intensity

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of the metabolites in every sample. Ions with a relative standard deviation (RSD) < 30%

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in the QC sample analyses were used for further univariate and multivariate

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statistics.28, 29

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After obtaining the collated data, we performed a series of multivariate pattern

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recognition analysis, principal component analysis (PCA) was first used to investigate

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the overall tea metabolome. The SIMCA-P14.1 software package (V14.1, MKS Data

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Analytics Solutions, Umea, Sweden) was used to perform logarithmic conversion and

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centralization formatting on data, follow by automated modeling analysis.26 In order

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to filter out the orthogonal variables in the metabolite that was not related to the

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categorical variables, and to obtain the more reliable metabolite information on the

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differences

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structures-discriminate analysis (OPLS-DA) was applied to extract maximum

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information from the data set and to analyze the result.30 In the mode, the remaining

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variables were then screened by Student's t-test (P-value1) of the first principal component of the

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OPLS-DA model. The differential metabolites were identified according to Tea

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metabolome Database (http://pcsb.ahau.edu.cn:8080/TCDB/f) and PubChem database

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(https://pubchem.ncbi.nlm.nih.gov/). In addition, the KEGG database (http://

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www.kegg.jp/kegg/pathway.html) was utilized for annotation the pathways of

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differential metabolites, which results were used to further analysis and screen the key

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pathways most relevant to differential metabolites based on enrichment analysis and

between

groups,

the

orthogonal


projections

to

latent


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hierarchical clustering analysis.

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Assay of Total Nitrogen, Total Carbon, and Amino Acids

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The determination method of total N and C in the tea leaves was referenced to Dai

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(2015),31

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Analysensysteme GmbH, Germany).

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Hydrolyzed amino acids in tea leaf samples were detecteded by an automatic amino

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acid analyzer(L-8900, Hitachi, Japan). 32 amino acid standards were prepared from

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analyzer (Sigma-Aldrich Co., St. Louis, MO, USA) and the theanine standard is

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provided by our laboratory.

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RESULTS

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Metabolites profiling in Response to Filled-N2 and Filled-CO2 Treatments

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The metabolites changes in picked leaves of tea undergone to natural spreading (as a

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control), filled-N2 and filled-CO2 treatments were compared based on nontargeted

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UPLC-QTOF/MS approach to dintinguish the different biochemical changes and

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adaptive strategies. The results showed that the significant difference was not only in

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the control and the anaerobic treatment, but also in response between different

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anaerobic treatment groups. A total of 1756 metabolite ion features were detected

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from data sets of UPLC-QTOF/MS. PCA analysis of quality control (QC) samples

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and relative standard deviation (RSD) calculation of internal standards of ribitol were

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used to evaluate the performance of the UPLC-QTOF/MS approach. The results show

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all the QC samples were crowded in the center of the PCA score plot indicating that

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the UPLC-QTOF/MS approach has good reproducibility.

using an elemental analyzer (Vario Max CN Analyzer, Elementar


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Based on the PCA results, a clear separation among the young shoot samples under

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control treatment, filled-N2, and filled-CO2 treatments at 1h and 6h were observed.

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The different samples carried out 1h and 6h were separated by the first principal

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component (PC1), representing 23.8% and 23.3% of the total variation (Figure 1). In

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the early stage of treatment, the difference was not obvious (Fig. 1 A1). As time went

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by, the difference between the control and different groups became significant (Fig. 1

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A2). In order to filter out the orthogonal variables in the metabolite that was not

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related to the categorical variables, and to obtain the more reliable metabolite

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information on the differences between groups,32 the orthogonal projections to latent

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structures-discriminate analysis (OPLS-DA) was applied to extract maximum

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information on metabolic alterations under filled-N2 and filled-CO2 treatment and

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significance of metabolites contributing to the alterations. In this research, SIMCA

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software was used to perform LOG conversion and UV formatting on the data, the

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modeling analysis of OPLS-DA was conducted to first principal component, then the

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validity of the model was evaluated and further verified by permutation test. The

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score plots of OPLS-DA results showed a clear separation between filled-N2,

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filled-CO2 treatment groups with control group in tea leaves (Fig.1 B1 and C1; B2 and

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C2). These separation score plots were also surveyed between filled-N2 and filled-CO2

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in different treatment time, respectively (Fig. 1 D1 and D2). These results indicated

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that the modeling analysis of OPLS-DA has good model quality.

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Next, we used the P-value of Student’s t-tested (P-value1) as the criterion for screening



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differential metabolites, whereafter, we have compared the differential metabolites

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with group profiles which assigned 45 and 130 metabolites derived from CK versus

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filled-N2 (A1 vs A2, B1 vs B2); 77 and 60 metabolites derived from CK versus

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filled-CO2 (A1 vs A3, B1 vs B3); 2 and 27 metabolites derived from both of group

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comparison, respectively, after 1 h and 6 h process. As the treatment time prolonged,

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the total number of differential metabolites increased from 302 to 605 (Fig. 2 A and

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B). These differential metabolites were identified according to metabolomics database

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and MS2 spectra. Among them, 292 metabolites were structurally identified.

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Metabolic Profiles in Response to Different Treatments in Tea Leaves

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Under anaerobic or hypoxia stress, changes were becoming in variety different levels,

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these different metabolites were mainly carbohydrates, secondary metabolism, energy

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metabolism, stress response, protein degradation and small molecule compounds like

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amino acids and organic acids. The impact factors (number of metabolites mapped to

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a certain pathway/total number of metabolites mapped to this pathway) of global

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differential metabolites were 0.1–1.0 in B1vs. B2 and B1vs. B3 (Fig. 3 A and B).

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According to the alignment of the COG and KEGG databases, the main enrichment

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metabolic pathways of differential metabolites in the picking shoot under filled-N2

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treatment including glyoxylate biosynthesis; flavones and flavonol biosynthesis;

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taurine metabolism; pyruvate metabolism; TCA cycle; arginine and proline

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metabolism; galactose metabolism (Fig. 3A). Under filled-CO2 treatment, the

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metabolite differences enriched metabolic pathways mainly including phenylalanine

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metabolism; pyruvate metabolism; carbon fixation in photosynthetic organisms;


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galactose metabolism; taurine metabolism; TCA cycle; starch and sucrose metabolism;

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isoquinoline alkaloid biosynthesis; pyrimidine metabolism; tyrosine metabolism and

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glycolysis or gluconegenesis (Fig. 3B).

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Differential metabolites screened by the foregoing analysis were characterized

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biologically, usually by similar/complemental function or by positive/negative

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regulation of the same metabolic pathway with similar or opposite expression

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characteristics between different experimental groups. Hierarchical cluster analysis of

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these features helped us to classify metabolites that shared the same characteristics as

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one class and to find out the changing characteristics of the metabolites in the

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experimental group. For each set of comparisons, we calculated the Euclidean

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distance matrix for the quantified values of the differential metabolites, clustered the

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differential metabolites in a completely linked fashion and displayed them on a heat

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map. In the early stage of anaerobic treatment, the number and type of differential

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compounds were less (Fig. S2, 3). As the time prolonged, the differential compounds

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appeared at various levels. The result of B2 vs. B1(Fig. S4) show that under filled-N2

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treatment caused an increase in levels of α-mannobiose, arabinose, xylitol, lyxose,

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fucose, which were involved in glycometabolism, and in levels of glyceric acid,

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succinate, cis-aconitate, which were related to TCA cycle, and in levels of alanine,

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4-aminobutyric acid, proline, histidinyl-serine, N-acetyl- glutamate, lysyl-proline,

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isoleucyl-cysteine, valyl-serine, which were taked part in amino acid metabolism and

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related intermediate synthesis. In addition, We hypothesized that the increase in some

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intermediates such as linolenic acid, acetylcarnitine, adenine, 2'-O-methyladenosine



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and 2-hydroxyadenine was due to hypoxia stress caused by nitrogen-filled leading to

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long-chain fatty acids and ATP synthesis were blocked. By contrast, the normal

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spreading tea leaves (B1) due to transpiration and slow breathing maked the

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polysaccharide and protein of cytoplasmic hydrolysis to produce many intermediate

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compounds, but under filled-N2 treatment (B2) due to respiratory inhibition, such as

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maltotriose, sucrose, ribose, mannose, isomaltose, uridine 5'-diphosphate (UDP),

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UDP-galactose, pyruvaldehyde, lysyl-asparagine, malic acid, glutamate, aspartic acid,

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tryptophyl-lysine, prolyl-glutamate, prolyl-phenylalanine, threoninyl-methionine, etc.

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these decomposition products and intermediates were relatively lower (Fig. S4).

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Under filled-CO2 treatment (B3), the levels of some compounds increased, including

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г-minobutyric acid, alanine, adenine, glutamate, glutaral, fucose, stearic acid, and

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xylitol; meanwhile, the glycolysis process was partly suppressed under filled-CO2

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stress, resulting in the decrease of G-6-P, F-6-P and PEP. In addition, the TCA cycle,

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closely related to glycolysis pathway, was also inhibited under filled-CO2 treatment,

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which were inferred from the reduction in citric acid, α-ketoglutaric acid and fumaric

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acid compared to the normal spreading tea leaves (B1). Under this circumstance,

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GABA shunt metabolites were significantly stimulated, resulting in a radical increase

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in GABA , alanine and succinic acid levels (Fig. S5). It’s worth nothing that some

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compounds related to shikimate pathway, such as shikimic acid, quinic acid and

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phenylalanine, also increased, which meaned the disturbance for secondary metabolic

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pathway of the downstream. However, compared with filled-N2 treatment (B2) , we

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can find two different modes of respiration with different degrees of inhibition.


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Obviously, group B2 received more stress and we found that such as proline, betaine,

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mannitol and other related to osmotic stress compounds were significantly higher than

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the B3 group, so it consumes more energy substances such as glutamate and adenine

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(Fig. 4 A and B).

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DISCUSSION

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Metabolite Differences Reveal Different Stress Responses in Picked Tea Leaves

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under Anaerobic Treatment

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Plants possess a suite of traits enabling tissue aeration and/or adjusted metabolism

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during hypoxia or even in the absence of O2.32, 33 In general, plants accelerate starch

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and sucrose catabolism, glycolysis and ethanolic fermentation to increase

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substrate-level production of ATP,34 and energy production can be further stimulated

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through alanine metabolism to succinate.35,

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conditions, the measures they can take are quite limited, and they will have drastic

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changes. Usually, hypoxic and anoxic conditions re-orchestrate plant metabolism to

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manage energy production and consumption. In this paper, we processed tea leave at

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normal spreading as a control, evacuated and recharged with N2 and CO2 as treatment,

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observed the dynamic changes from metabonomics level to ascertain how to address

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anerobic-related issues in picked tea at different anoxic treatment processes. Some

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research papers show that processing has a huge impact on picking tea leaves quality,

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during the withering process, the assimilation basically stopped, and the respiration

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and transpiration became the leading process of their metabolism.24, 31, 37 Lee et al.

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pointed out that at this time, with water evaporation, weight loss, wilting soft; tea

36

However, when they are in vitro



287

leaves enhanced protein hydrolysis in the cytoplasm, free amino acid content

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increased, polysaccharides and organic acids also increased.34, 36 When the wilting

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process is accompanied by anoxia, the tea leaves will change from a mild dehydration

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process to a severe stress response. In addition to the significant increase in GABA

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content, the UPLC-QTOF/MS results give us a more detailed picture of the full-scale

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changes of the compounds in the leaves (Fig. 5).

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We used normal room temperature as a control, the logarithm values of the

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comparation between two groups (Log2N2/CK, Log2CO2/CK and Log2N2/CO2), were

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evaluated. From the comparison results of different compounds, the anti-stress

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process that inhibits respiration was rapidly presented on the primary metabolic level

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in the filled-N2 treatment. During the charged N2 process, the sucrose content

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gradually decreased, and the content of some disaccharides produced by

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decomposition of pectin, such as arabinose and rhamnose has increased. From the

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result of one hour, it can be seen that filled-N2 treatment first aroused the alanine,

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succinic acid, pyruvate, glyoxalate increased dramatically, then FAD2+ and NAD+

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increased first and then decresed. As a result, TCA cycle soon received inhibition and

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the electron transport chain was almost completely suppressed, so cell rely more on

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the glyoxylate pathway for energy.32, 38 At the initial stage of filled-CO2 treatment,

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aerobic respiration was inhibited to a certain extent. The stress was gradual and still

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accompanied by a certain degree of TCA metabolism. Tea leaves also can reduce the

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accumulation of toxic substances through enhanced the pentose phosphate pathway, 39

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so we can observe the rapid decline of NADP+. Simultaneously, as a beneficial


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supplement to the TCA cycle, GABA shunt was more active than filled-N2 treatment

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(Fig. 5). The content of glutamate was rapidly consumed, GABA and succinic acid

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content rised sharply, NADH+ increased and NADPH+ decreased. Although

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hydrolysis and transamination of amino acids of other proteins enhance the synthesis

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of glutamic acid, but as a direct precursor of GABA synthesis, glutamic acid was

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almost depleted after a long time treatment (Fig. 6A). In this process, the

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accumulation of large quantities of ethanol was prevented by controlling the

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metabolic rate of fermentation and the coupling of glycolysis and nitrogen

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metabolism,34,

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order to analyze whether the different aeration treatments lead endogenous carbon and

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nitrogen uptake, we examined the total carbon and total nitrogen contents by an

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elemental analyzer. The results showed that the external aeration treatment did not

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significantly affect the total amount of carbon and nitrogen in the picked-tea leaves

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(Fig. 6B).

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In addition, many studies have shown that polyamine degradation pathway is also an

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important branch of GABA synthesis.41-43 Wu et al. (2018) used aminoguanidine, a

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specific inhibitor of diamine oxidase (DAO) and polyamine oxidase (PAO), to block

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the polyamine (PA) degradation pathway, and then inferred that approximately

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37%–47% of the accumulation of GABA in fresh tea leaves during the anaerobic

328

treatment may mainly rely on the PA pathway.44 However, in this experiment, the

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polyamine content detected by UPLC-QTOF-MS was lower than that of Wu (Fig. 6C).

330

Despite increased in polyamine content during anaerobic treatment, put and spm

40

while also avoiding the accumulation of acidic intermediates. In



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increased from 1.5, 0.8 to 3.5 and 1.0 (µg.g-1, DW), respectively, but there is an order

332

of magnitude difference from the mg content in the Wu article. The difference may be

333

caused by cultivars differences, or it may be caused by the difference aeration

334

treatment, and further comparison research is needed.

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During hypoxia stress, plant cells usually accumulate osmotic adjustment substances

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such as proline, mannose, sorbitol and betaine to maintain the shape and metabolic

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function, stabilize the structure of the protein and the activity of enzymes, and keep

338

the activity and permeability of cytomembrane.45-47 In this experiment, the contents of

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mannitol and betaine gradually decreased with the prolonged treatment time, and only

340

the proline content increased significantly. There are usually two ways to synthesize

341

proline: one is to synthesize proline using glutamic acid as a substrate, and the other is

342

using ornithine as a substrate.48 Generally, the glutamate synthesis pathway is

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considered that the main route for plants to synthesize proline under osmotic stress

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and lack of nitrogen; whereas the ornithine pathway accumulates proline at higher

345

nitrogen levels.45, 49 Filled-CO2 treatment accumulates more proline, which is also

346

thought to be one of the reasons for the dramatic drop in glutamate in this treatment.

347

This report represents the first investigation into the global profile of a systemic

348

metabolic changes in tea leaves under anaerobic treatment. This study informs

349

researcher there are different stress response and resistance mechanisms when tea

350

leaves encounter to different types and levels of anaerobic stress. It also reminds

351

producers via different degrees anaerobic treatment to process "Gabaron tea" to get

352

better quanlity and flavour.


Page 16 of 35

Page 17 of 35


353 354



355

ASSOCIATED CONTENT

356

Supporting Information

357

key metabolites in tea leaves measured by UPLC-Q-TOF/MS (Table S1, S2); Amino

358

acids, total C, and total N content in tea leaves (Table S3); Plant material processing

359

diagram (Figure S1); heatmap of hierarchical clustering analysis for filled-N2 vs.

360

control at 1h and 6h (Figure S2, S4); and heatmap of hierarchical clustering analysis

361

for filled-CO2 vs. control at 1h and 6h (Figure S3, S5) (PDF)

362

AUTHOR INFORMATION

363

Corresponding Author

364

Xiaochun Wan, Ph.D., State Key Laboratory of Tea Plant Biology and Utilization,

365

Anhui Agricultural University, Hefei, Anhui 230036, People’s Republic of China.

366

Mail: West Changjiang Road 130, Hefei, Anhui, 230036, PR China. Phone:

367

+86-551-65786765. Fax: +86-551-65786765. E-mail: [email protected].

368

Author contributions

369

X. W. and Q. C. participated in the research design and reviewed the manuscript. Q.

370

C., Y. Z., and M. T. conducted the experiments, analysis of the data and drafted the

371

manuscript. M. L., Y. W. and Q. Q. processed part of the experiment data. H. Y.

372

revised the manuscript. All authors have read and approved the final manuscript.

373

Notes

374

The authors declare no competing financial interest.

375

Funding

376

The authors appreciate the funding support from the National Natural Science


Page 18 of 35

Page 19 of 35


377

Foundation of China (No. 31500566), the Natural Science Foundation of Anhui

378

Province (No. 1608085MC66) , and the Ministry of Agriculture of China through the

379

Earmarked Fund for China Agricultural Research System (No. CARS 19).

380

ACKNOWLEDGMENTS

381

We thank colleague Prof. Juan Li of biotechnology center for providing access to

382

automatic amino acid analyzer.

383

ABBREVIATIONS USED

384

3-PGA: 3-phosphoglycerate; AceA: Acetoacetic acid; Aco: Aconitic Acid; Arab:

385

Arabinose; Citr: Citric acid; F-6-P: Fructose-6-phosphate; Fru: Fructose; Fuc: Fucose;

386

Fum: Fumaric acid; G-6-P: Glucose-6-phosphate; Glc: Glucose; Glyc: Glycollic acid;

387

Glyo: Glyoxylate; Lac: Lactate; Mal: Malic acid; Mal: Maltose; Man: Mannose; PEP:

388

Phosphoenolpyruvate; Pyr: Pyruvate; Pyrd: Pyruvaldehyde; Raff: Raffinose; Rham:

389

Rhamnose; Rib: Ribose; Suc: Sucrose; Succ: Succinic acid; Treh: Trehalose; α-KG:

390

α-ketoglutaric acid. Sarc: Sarcosine; Hist: Histamine; Bet: Betaine; Caff: Caffeine;

391

Theo: Theobromine; C: Catechin; EC: Epicatechin; EGC: Epigallocatechin; EGCG:

392

Epigallocatechin gallate; GC: Gallocatechin; Shik: Shikimate. dA: Deoxyadenosine;

393

AMP: Adenine mononucleotide phosphate; Ade: Adenine; Aden: Adenosine; Camp:

394

Cyclic adenine mononucleotide phosphate; UMP: Uracil mononucleotide phosphate;

395

Xan: Xanthine; FAD: Flavin adenine dinucleotide; NAD: Nicotinamide adenine

396

dinucleotide;

397

Nicotinamide. Ala: Alanine; Asp: Aspartic acid; Asn: Asparagine; Gly: Glycine;

398

Glu: Glutamate; Gln: Glutamine; GABA: γ-aminobutyric acid; Ile: Isoleucine; Leu:

NADP:

Nicotinamide

adenine

dinucleotide


phosphate;

Nico:


Page 20 of 35

399

Leucine; Ser: Serine; Thr: Threonine; Thea: theanine; Val: Valine; Pro: Proline; Phe:

400

Phenylalanine; .

401

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545

FIGURE CAPTION

546

Fig. 1 Principal component analysis (PCA) score plots showing the metabolomic

547

trajectory of placing 1h (A1) and 6h (A2) of picked tea leaves under normal spreading

548

(CK), filled N2 spreading (N2), and filled CO2 spreading(CO2). Orthogonal partial

549

least squares discriminant analysis (OPLS-DA) scores showing dose dependence of

550

salinity effects on wheat seedlings: CK vs. CO2 in 1h (B1) and 6h (B2). CK vs. N2 in

551

1h (C1) and 6h (C2). N2 vs. CO2 in 1h (D1) and 6h (D2)

552

Fig. 2 Venn diagrams of group comparison for metabolic profiles in 1h (A group) and


Page 26 of 35

Page 27 of 35


553

6h (B group) under normal spreading (A1, B1), filled-N2 treatment (A2, B2), and

554

filled-CO2 treatment (A3, B3). A total of 407 metabolites were identified in this study,

555

and the numbers in the figure indicate the numbers of metabolites with difference in

556

their contents for each comparison

557

Fig. 3 Pathway impact analysis involving comparisons between Ck vs. Filled-N2 (A)

558

and Ck vs. Filled-CO2 (B) under process 6h. The impact indicates the ratio of the

559

number of metabolites mapped to a certain pathway to the total number of metabolites

560

mapped to this pathway. Greater impact factor means greater intensiveness. The

561

P-value was calculated using hypergeometric test through Bonferroni Correction and

562

less P-value means greater intensiveness.

563

Fig. 4 Heatmap of hierarchical clustering analysis for filled-N2 (B2) vs. filled-CO2

564

(B3). Positive ion model (A) and negetiveion model(B)

565

Fig. 5 Change in metabolites of the metabolic pathways in picked tea leaves after 1h

566

and 6h of anaerobic stress treatment. Proposed metabolic network changes in tea

567

leaves subjected to anaerobic stress, as obtained through orthogonal partial least

568

squares discriminant analysis (OPLS-DA). The fold changes of the relative

569

concentration of major metabolites in different groups were used to indicate the

570

dynamic viewing in tea leaves, ervery box refers to the Filled-N2/ Normal spreading

571

(N2/CK); Filled- CO2/ Normal spreading (CO2/CK); Filled-N2/ Filled- CO2 (N2/ CO2).

572

The metabolites with red boxes denote significant increases; the metabolites with

573

green boxes denote significant decreases (p

Comparative Metabolic Responses and Adaptive Strategies of Tea

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