Evidence-Based Complementary and Alternative Medicine

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Volume 2021 |Article ID 5515325 | https://doi.org/10.1155/2021/5515325

Yifeng Xu, Renling Zhang, Robert Morris, Feng Cheng, Yiqin Wang, Zhujing Zhu, Yiming Hao, "Metabolite Characteristics in Tongue Coating from Damp Phlegm Pattern in Patients with Gastric Precancerous Lesion", Evidence-Based Complementary and Alternative Medicine, vol. 2021, Article ID 5515325, 16 pages, 2021. https://doi.org/10.1155/2021/5515325

Metabolite Characteristics in Tongue Coating from Damp Phlegm Pattern in Patients with Gastric Precancerous Lesion

Academic Editor: Yu CAI
Received23 Feb 2021
Revised12 Apr 2021
Accepted21 May 2021
Published02 Jun 2021

Abstract

Objective. In this study, we analyzed the metabolite profile of the tongue coating of patients having gastric precancerous lesion (GPL) with damp phlegm pattern and proposed a mechanism of pathological transition. Methods. The changes in tongue-coating metabolites in patients with GPL damp phlegm pattern were analyzed using GC-TOF-MS and UHPLC-QE-MS metabolomics methods. Results. When compared with 20 patients who did not exhibit a nondamp phlegm pattern, 12 metabolites were highly expressed and 10 metabolites were under expressed in 40 cases of damp phlegm pattern, of which involved 9 metabolic pathways. Compared with 15 healthy people, 134 metabolites were upregulated and 3 metabolites were downregulated in 40 cases exhibiting a damp phlegm pattern, of which involved 17 metabolic pathways. The patients with damp phlegm pattern were compared with nondamp phlegm pattern patients and healthy people, the main differential metabolites were primarily lipids and lipid-like molecules, and the main differential metabolic pathways were related to glycerophospholipid metabolism. In the glycerophospholipid metabolism, the metabolites with changes were phosphatidylethanolamine and lysoPC(18 : 1 (9z)). Among them, phosphatidylethanolamine exists in the synthesis stage of glycerophospholipid metabolism.Conclusions. Abnormal expression of lipids and lipid-like molecules, as the major metabolic change, was involved in the formation of GPL patients with damp phlegm pattern.

1. Introduction

Gastric cancer (GC) has grown in frequency and has become the third leading cause of cancer-related deaths in the world [1]. China is a region in which gastric cancer has a high prevalence with studies estimating that the number of gastric cancer cases in China alone is approximately 697,000, accounting for 70% of the total world incidence [2]. Gastric precancerous lesions (GPLs) involve a process of evolution before the onset of gastric cancer and include intestinal metaplasia and dysplasia, which are primarily related to chronic atrophic gastritis [3]. GPL has the characteristics of bidirectional transformation, and early intervention can effectively reverse the malignant development of cells [4]. Therefore, early diagnosis and early treatment of GPL play a crucial role in reducing the incidence of gastric cancer [5].

Traditional Chinese medicine (TCM) theory is the theoretical basis of damp phlegm pattern. In TCM, damp phlegm pattern is caused by dysfunction of an internal organ, resulting in disrupted body water movement and water stagnation that forms dampness and phlegm. Fullness and heaviness of the whole body as well as a greasy tongue coating are the main manifestations of damp phlegm pattern in patients. Currently, the World Health Organization has listed damp phlegm pattern in the International Classification of Diseases [6]. Similarly, phlegm dampness pattern is also one of the main patterns of GPL [7].

In TCM, tongue diagnosis as a noninvasive diagnostic method, which is very important in the identification of disease states. Tongue diagnosis is designed to observe the changes of the tongue body and tongue coating over time in order to understand the physiological state of the human body. Inspection of tongue coating is the main component of tongue diagnosis. TCM holds that the tongue coating can react very sensitively to abnormal changes in the spleen and stomach. Compared with the thin coating of physiologically healthy people, the tongue coating of chronic gastritis patients that exhibit a damp phlegm pattern is greasy. Studies have also shown that the shape of the tongue coating in gastric cancer and chronic gastritis patients differed significantly from the tongue-coating shape of patients without gastric complications [8, 9]. Tongue coating adheres to the tongue body and consists of desquamated epithelial cells, blood cells, metabolites, nutrients, and bacteria [10]. In our previous study, we found that there were differential metabolites in the tongue coating of patients with coronary heart disease and chronic renal failure with damp phlegm pattern [11]; other researchers also found differential metabolites in the tongue coating of hepatitis B patients compared with healthy people [12]. As a result, the tongue-coating metabolites obtained by the noninvasive method can be used as adjunctive diagnostic tools for some diseases [13]. However, current studies on the changes of GPL patients’ tongue coating in damp phlegm pattern are still insufficient.

Metabolomics is a scientific discipline that reveals the nature of life metabolism by investigating the alterations of metabolite profiles in a biological system in response to stimulation or disturbance [14, 15]. The combination of mass spectrometry (MS) and nuclear magnetic resonance (NMR) and other separation methods have become important analytical tools in metabolomics [16].

Some researchers have used ultra-performance liquid chromatography and mass spectrometry (UPLC-MS) while others have utilized liquid chromatography in tandem with mass spectrometry (LC-MS) technology exclusively to detect and analyze the substances in chronic gastritis patients’ tongue coating. In these studies, some metabolites were differentially expressed between healthy people and patients with chronic gastritis [17, 18]. These differential metabolites may be used as potential markers to monitor the occurrence and development of chronic gastritis. However, these studies only used LC-MS technology. The results produced by using only one detection method may have limitations and cannot fully reflect the metabolites in tongue coating. And, at present, we have not found any reports on metabolomics study of tongue-coating metabolites in patients with GPL that exhibit the damp phlegm pattern.

Therefore, in this study, the ultra-high performance liquid chromatography-Q exactive orbitrap-mass spectroscopy (UHPLC-QE-MS) and gas chromatography-time-of-flight-mass spectroscopy (GC-TOF-MS) were combined in order to detect and analyze the metabolites in the tongue coating of GPL patients with damp phlegm pattern. As a result, the abnormal fluctuation of these metabolites will more clearly show the occurrence and development of GPL damp phlegm pattern.

2. Materials and Methods

2.1. Samples

In this paper, we used a case-control study to elucidate the composition of tongue-coating-related metabolites in GPL patients with damp phlegm pattern. Sixty patients with GPL were selected from Longhua Hospital, an affiliate of the Shanghai University of TCM for Gastroscopy and pathological examination of gastric mucosa, including 40 cases of damp phlegm pattern and 20 cases of nondamp phlegm pattern. Fifteen teachers and students from the Shanghai University of Traditional Chinese Medicine were selected as a healthy control group. Selected controls did not have a history of stomach disease and were not currently experiencing any degree of stomach discomfort. Their blood cell analysis, blood lipid levels, blood pressure, blood glucose, tumor indicators, renal function, color Doppler ultrasound of neck and abdomen, liver function, chest computed tomography, and barium metal fluoroscopy were all normal. All participants were of Chinese ancestry (self-reported) and were enrolled in the study from December 2018 to October 2019.

Gastroscopy and pathological examination of gastric mucosa were performed immediately after tongue-coating samples were collected. Clinical information of the participants and summary of demographics are provided in Table 1. As shown in Table 1, the highest number of subjects presented with only mild intestinal metaplasia, including 31 damp phlegm pattern patients and 13 nondamp phlegm pattern patients. The number of damp phlegm pattern patients that exhibited either moderate intestinal metaplasia or severe intestinal metaplasia were 7 and 1, respectively. The number of nondamp phlegm pattern patients that exhibited either moderate intestinal metaplasia or severe intestinal metaplasia were 3 and 4, respectively. Finally, only one patient with damp phlegm pattern had mild intestinal metaplasia and mild dysplasia concurrently. In terms of treatment, 10 people did not take the treatment regimen as prescribed due to discomfort (8 cases with damp phlegm pattern and 2 cases with nondamp phlegm pattern), while the other 50 subjects were treated with western medicine or TCM. Among the treated cases, 10 patients with damp phlegm pattern and 5 patients with a nondamp phlegm pattern were treated with western medicine such as proton pump inhibitors alone while 17 patients with damp phlegm pattern and 5 patients with nondamp phlegm pattern were treated with traditional Chinese medicine exclusively. In addition, 5 patients with damp phlegm pattern and 8 patients that exhibited a nondamp phlegm pattern were treated with a combination of traditional Chinese medicine and western medicine (proton pump inhibitors).


Demographics and clinical informationDamp phlegm pattern groupNondamp phlegm pattern groupHealthy control group

Sample number402015
Ratio of male to female1 : 0.821 : 1.221 : 1.14
Average age (year)43.28 ± 14.7342.9 ± 16.134.53 ± 11.40
Number (percentage) of samples diagnosed for less than 10 years30 (75.00%)15 (75.00%)N/A
Number (percentage) of samples diagnosed for 10–20 years6 (15.00%)2 (10.00%)N/A
Number (percentage) of samples diagnosed for 20–30 years2 (5.00%)1 (5.00%)N/A
Number (percentage) of samples diagnosed for 30–40 years2 (5.00%)2 (10.00%)N/A
Number (percentage) of samples only with intestinal metaplasia (mild)31 (77.50%)13 (65.00%)N/A
Number (percentage) of samples only with intestinal metaplasia (moderate)7 (17.50%)3 (15.00%)N/A
Number (percentage) of samples only with intestinal metaplasia (severe)1 (2.50%)4 (20.00%)N/A
Number (percentage) of samples with intestinal metaplasia (mild) and dysplasia (mild)1 (2.50%)0 (0.00%)N/A
Number (percentage) of samples with Helicobacter pylori infection9 (22.50%)1 (5.00%)N/A
Number (percentage) of samples untreated8 (20.00%)2 (10.00%)N/A
Number (percentage) of samples only taking western medicine (proton pump inhibitors)10 (25.00%)5 (25.00%)N/A
Number (percentage) of samples only taking traditional Chinese medicine17 (42.50%)5 (25.00%)N/A
Number (percentage) of samples taking western medicine (proton pump inhibitors) and traditional Chinese medicine5 (12.50%)8 (40.00%)N/A

2.2. Ethics Approval

In this study, all subjects gave written informed consent before collecting samples, and the study was conducted in accordance with the Declaration of Helsinki. In addition, this study was approved by the Ethics Committee of Shanghai University of TCM in December 2018.

2.3. Criteria
2.3.1. Diagnostic Criteria

(1)Patients were endoscopically examined and biopsies were taken from suspicious lesion sites such as gastric antrum, angle, body, and cardia(2)The histopathological assessment was performed by two experienced pathologists in accordance with the clinical guidelines of “the updated Sydney System” [19](3)The diagnosis of GPL chronic atrophic gastritis was accompanied by intestinal metaplasia and/or dysplasia [20]

The diagnostic criteria of the damp phlegm pattern in GPL were set according to the “Diagnostics of Traditional Chinese Medicine” [21].

The diagnostic criteria of the damp phlegm pattern in GPL were as follows:(1)Patient felt epigastric fullness or distending pain(2)Patient’s feces were not shaped(3)Patients were nauseous and/or vomiting(4)Patient had a bad appetite(5)Patient’s tongue had a greasy coating

2.3.2. Inclusion Criteria

The patients met the diagnostic criteria of GPL and the healthy control group had no evidence of systemic organic lesions. In addition, the participants’ age range was from 20 to 70 years.

2.3.3. Exclusion Criteria

Patients were not eligible if they had gastric hemorrhaging, a duodenal ulcer, gastroesophageal reflux disease, gastric cancer, gastric ulcers, or other intestinal diseases identified through endoscopic examination as well as any additional systemic diseases. Patients with diagnosed mental illness, pregnant or lactating female subjects, or participants that had lesions of the tongue, mouth, nose, or pharynx within one month prior to the collection of samples were not included in this study. Finally, candidates that had received antibiotics or probiotics within one month prior to sample collection, used tobacco or consumed alcohol, or had a body mass index (BMI) greater than 28 were also excluded [22].

2.4. Tongue-Coating Samples Collection

Tongue-coating samples were collected before the participants ate in the morning. Before collection, in order to remove the residues in the mouth, the participants rinsed the mouth with stroke-physiological saline solution 3 times. Then, the collector scraped the tongue-coating samples with a sterile specimen collection swab (CY-98000, iClean, Huachenyang Technology Co., Ltd, CN) at the thick part of the tongue coating 5 times, and put the head of swab with tongue-coating sample into a sterile centrifuge tube. All tongue-coating samples were collected by the same person. Finally, the tongue-coating samples were stored at −80°C.

2.5. GC-TOF-MS Metabolomics Processing

The reagents used in GC-TOF-MS experiment are listed in Supplementary Table S1a. The experimental procedures were as follows.

The head of the swab with the tongue-coating sample was moved to a sterile Eppendorf (EP) tube 5 mL and weighed, and 1500 μL of pre-cold extraction mixture with 15 μL of internal standard were added. The sample underwent ultrasonication in the ice water for 30 min. Subsequently, the head of the swab was removed. After centrifugation for 15 minutes at 4°C, the 500 μL supernatant was moved to a fresh tube. Quality control (QC) samples were prepared by combining 100 μL from each sample. T 40 μL of Methoxyamination hydrochloride was added after evaporation in a vacuum concentrator, and the samples were incubated for a half hour at 80°C. After that, 60 μL bis-(trimethylsilyl)-trifluoroacetamide reagent was derivatized for 1.5 hours at 70°C. When the sample was cooled to room temperature, 5 μL of fatty acid methyl esters was added.

GC-TOF-MS detection was performed using a TOF mass spectrometer coupled to an Agilent 7890 gas chromatograph. The system utilized capillary column, 1 μL aliquots of the samples, and helium as carrier gas. The initial temperature was maintained at 50°C, before subsequently was increased to 310°C and held for 6 minutes. The injection temperature was 280°C, the transmission line temperature was 280°C, and the ion source temperature was 250°C. The energy in electron impact mode was −70 ev. After a solvent delay of 6.30 minutes, the mass spectrometry data were obtained in full scan mode, with m/z range of 50–500. Chroma TOF (V 4.3×, LECO) [23] software was used in order to analyze the original data while the LECO-Fiehn Rtx5 database was utilized to match the mass spectrum and retention index of metabolites. Finally, the peaks with RSD > 30% in QC samples were removed. [24].

2.6. UHPLC-QE-MS Metabolomics Processing

The reagents used in UHPLC-QE-MS experiment are listed in Table S1b. The experimental procedures were as follows:

1500 μL of extract solution containing isotopically labeled internal standard mixture was added to the samples. After being vortexed for 30 seconds, the solution was sonicated on ice for 30 minutes. Next, samples were incubated at −40°C for 1 hour and then centrifuged at 4°C for 15 minutes. The supernatant was then isolated and moved to a fresh glass vial for analysis. All the supernatants were mixed to prepare QC samples. The UHPC system using a UPLC BEH Amide column coupled to Q Exactive HFX mass spectrometer was used to detect LC-MS. Twenty-five mmol/L ammonium acetate and 25 ammonia hydroxides in water and acetonitrile were composed in the mobile phase with the elution gradient being used for analysis. The injection volume was 3 μL, the column temperature was 25°C, and the autosampler temperature was 4°C.

The QE HFX mass spectrometer was successful in generating MS/MS spectra under the acquisition software. In this mode, full scan MS spectrum was continuously evaluated by the acquisition software. ProteoWizard was used to convert raw data to mzXML format and peak detection, extraction, alignment, and integration data were obtained through the use of internal programs. Metabolites were annotated used MS2 database (BiotreeDB V2.1), and the cutoff value of annotation was set to 0.3 [25].

2.7. Statistical Analysis

The number of peaks, sample names, and standardized peak areas were entered into Simca-p+ 13.0 software for orthogonal projection of principal component analysis (PCA) and orthogonal projection of latent structures-discriminant analysis (OPLS-DA). To further verify the model, displacement experiments were carried out. The -value adjusted with the false discovery rate (FDR) of rank sum test (), variable importance in the projection (VIP) of first principal component in OPLS-DA model (VIP > 1), similarity value (SV) of GC-TOF-MS detection (SV > 700) [26], and the significant differences of metabolites between the two groups were determined by MS2 score (MS2 score > 0.6) [27] using UHPLC-QE-MS. The log fold change (FC) values were calculated by comparing the means of metabolites peak areas of the two groups. KEGG pathway analysis was used to search and determine the significant metabolic pathways that were differentially expressed between the experimental and control groups.

3. Results

3.1. Metabolic Spectrums

Metabolomics of tongue coating in 60 patients with GPL (including 40 cases of damp phlegm pattern and 20 cases of nondamp phlegm pattern) and 15 cases of healthy control group were analyzed by GC-TOF-MS and UHPLC-QE-MS. After quality control by GC-TOF-MS analysis, 533 peaks were obtained in the tongue-coating samples. A total of 9,168 peaks and 4,322 peaks were obtained in tongue-coating samples after quality control of UHPLC-QE-MS positive ion mode and negative ion mode analysis, respectively.

Examples of GC-TOF-MS and UHPLC-QE-MS spectra of the same person are shown in Figure 1fv. As shown, there were some distinct mass spectrum peaks between the GPL damp phlegm pattern group and the GPL nondamp phlegm pattern group as well as with the healthy control group.

3.2. OPLS-DA Score Plots

The OPLS-DA score plot of damp phlegm pattern group and nondamp phlegm pattern group showed that the two groups could be well distinguished by GC-TOF-MS and UHPLC-QE-MS negative ion mode. UHPLC-QE-MS positive ion mode detection showed a tendency to distinguish the two groups of samples, but there was a small amount of overlap (Figure 2). The permutation tests of two groups of OPLS-DA models show that all the models of GC-TOF-MS (R2Y = 0.671, Q2 = 0.125), UHPLC-QE-MS positive ion mode (R2Y = 0.617, Q2 = -0.0357), and UHPLC-QE-MS negative ion mode (R2Y = 0.775, Q2 = 0.0659) had good robustness and no overfitting was observed (Figure 3).

Similarly, as shown in the OPLS-DA score plot, there was a significant difference between the damp phlegm pattern group and the healthy control group (Figure 4). Permutation test of OPLS-DA models for the two groups showed that all the models of GC-TOF-MS (R2Y = 0.953, Q2 = 0.879), UHPLC-QE-MS positive ion mode (R2Y = 0.951, Q2 = 0.895), and UHPLC-QE-MS negative ion mode (R2Y = 0.968, Q2 = 0.901) had good robustness and no overfitting was observed (Figure 5).

3.3. Different Peaks by GC-TOF-MS Analysis

Using the criteria that -value <0.05 and VIP > 1, GC-TOF-MS analysis showed that compared with the nondamp phlegm pattern group, there were 9 different peaks in the damp phlegm pattern group (all the metabolites were downregulated); compared with the healthy control group, there were 33 different peaks in the damp phlegm pattern group, of which 31 peaks increased and 2 decreased.

Using the criteria that similarity SV > 700, GC-TOF-MS analysis showed that compared with the nondamp phlegm pattern group, the phlegm pattern group had 5 different peaks (all metabolites decreased in concentration); compared with the healthy control group, the damp phlegm pattern group had 13 different peaks (all metabolites showed an increase in expression).

3.4. Different Peaks by UHPLC-QE-MS Analysis

Using the criteria that -value <0.05 and VIP > 1, UHPLC-QE-MS positive ion mode analysis showed that compared with the nondamp phlegm pattern group, there were 11 different peaks in the damp phlegm pattern group, of which 7 peaks increased and 4 peaks decreased; compared with healthy control group, there were 146 different peaks in the damp phlegm pattern group, of which 142 peaks increased and 4 peaks decreased. UHPLC-QE-MS negative ion mode analysis showed that compared with the nondamp phlegm pattern group, there were 9 different peaks in the damp phlegm pattern group, among which 8 peaks increased and 1 decreased; compared with the healthy control group, there were 23 different peaks in the damp phlegm pattern group (all metabolites increased in concentration).

Using the criteria that MS2 score > 0.6, we align molecular mass data (m/z) of the significantly different peaks with online database KEGG. By UHPLC-QE-MS positive ion mode analysis, when compared with the nondamp phlegm pattern group, the damp phlegm pattern group had 9 different peaks (5 increased, 4 decreased); compared with the healthy control group, the damp phlegm pattern group had 106 different peaks (103 increased, 3 decreased). By UHPLC-QE-MS negative ion mode analysis, compared with the nondamp phlegm pattern group, the damp phlegm pattern group had 8 different peaks (7 increased, 1 decreased); compared with the healthy control group, the damp phlegm pattern group had 106 different peaks (all metabolites increased).

3.5. Differential Metabolites Analysis

Among the 156 matched metabolites, the metabolites in the damp phlegm pattern group were mainly divided into 8 categories compared with the nondamp phlegm pattern group. The most abundant metabolites were lipids and lipid-like molecules as well as organic acids and derivatives (each containing 5 metabolites), followed by organic oxygen compounds, organic nitrogen compounds, benzenoids (each containing two metabolites), organoheterocyclic compounds, phenylpropanoids and polyketides, nucleosides, nucleosides, and analogues (each containing one metabolite) (Table 2).


Metabolite nameRT (min)m/zMean damp phlegm patternMean nondamp phlegm patternVIPLog fold changeDetection method

Lipids and lipid-like molecules
Cholesterol25.3853690.1370.0810.0281.1720.749LC+
(3b,4b,11b,14b)-11-ethoxy-3,4-epoxy-14-hydroxy-12-cyathen-15-al 14-xyloside148.5864950.3430.4700.0403.021−0.453LC+
PG(18 : 1(11Z)/22 : 5(4Z,7Z,10Z,13Z,16Z))230.2378240.7070.5210.0482.5710.440LC+
3a,7b,12a-Trihydroxyoxocholanyl-Glycine243.2384660.6480.3700.0283.0200.810LC+
Pantothenol65.3022040.0330.0190.0151.0430.811LC−

Organic acids and derivatives
Glycolic acid5.6111470.0431.045<0.011.104−4.598GC
Ustiloxin D287.0914950.0410.0480.0452.236−0.238LC+
Leucyl-Valine398.5032310.1740.095<0.011.2300.875LC+
Sarcosine369.003880.9370.5920.0452.1082.108LC−
L-Proline334.7911140.6070.275<0.011.1751.175LC−

Organic oxygen compounds
D-xylitol9.7991470.0010.0510.0161.363−5.218GC
L-Iditol317.9331810.0230.014<0.012.0210.740LC−

Organic nitrogen compounds
L-Carnitine375.3681622.6263.7080.0281.077−0.498LC+
Dimethyl dialkyl ammonium chloride65.8733040.0080.002<0.011.1581.680LC+

Benzenoids
N-Undecylbenzenesulfonic acid103.0103130.1870.2380.0311.501−0.342LC+
Terephthalic acid372.0851651.2731.524<0.012.378−0.260LC−

Organoheterocyclic compounds
Xanthine233.3871514.5163.2530.0421.8540.473LC+

Phenylpropanoids and polyketides
Curcumin98.9953670.0190.0130.0221.7490.493LC−

Nucleosides, nucleotides, and analogues
Nelarabine217.3092960.0340.015<0.012.2571.140LC−

Others
Ethanol phosphate6.7152110.0000.0070.0421.166−4.719GC
6-deoxyglucitol10.1731170.0060.277<0.011.747−5.418GC
Capric acid8.3551170.0060.236<0.012.048−5.262GC

GC, GC-TOF-MS; LC+, UHPLC-QE-MS positive ion; LC−, UHPLC-QE-MS negative ion.

Compared with the healthy controls, the matched metabolites in the damp phlegm pattern group were mainly divided into 10 categories, and the largest metabolite group were lipids and lipid-like molecules (containing 69 metabolites), followed by organoheterocyclic compounds (containing 13 metabolites), organic acids and derivatives (containing 12 metabolites), benzenoids (containing 7 metabolites), organic oxygen compounds, organic nitrogen compounds (each containing 5 metabolites), phenylpropanoids and polyketides (each containing 4 metabolites), homogeneous nonmetal compounds, organosulfon compounds, hydrocarbons (each containing 1 metabolite) (Table 3).


Metabolite nameRT (min)m/zMean damp phlegm patternMean healthy controlVIPLog fold changeDetection method

Lipids and lipid-like molecules
Foeniculoside VII71.4543490.5160.057<0.011.4113.166LC+
Glycerol tripropanoate61.3452611.1520.109<0.011.4173.400LC+
Solavetivone115.3092190.1980.014<0.011.0813.785LC+
Patchoulenone61.0622193.2060.278<0.011.2383.528LC+
LysoPE(14 : 1(9Z)/0 : 0)194.5634241.6790.000<0.011.34014.686LC+
Gibberellin A7971.8563650.0590.010<0.011.3502.615LC+
Oleic acid33.2902830.2460.086<0.011.1961.518LC+
Dexamethasone77.2023930.2410.030<0.011.3433.000LC+
Leukotriene D470.9254973.8671.431<0.011.1381.434LC+
Gynosaponin S73.6809480.0830.013<0.011.2512.655LC+
3-O-methylniveusin A77.4454090.0400.004<0.011.2873.346LC+
Hoduloside VII248.6519320.9170.103<0.011.4073.153LC+
L-Acetylcarnitine328.1452040.0390.025<0.011.0920.629LC+
Fucoxanthin259.1396590.0810.008<0.011.4083.303LC+
Citronellyl beta-sophoroside91.0994810.0810.009<0.011.3343.169LC+
N-Cyclopropyl-trans-2-cis-6-nonadienamide46.0681940.1750.013<0.011.3063.748LC+
3-Hydroxyisovalerylcarnitine57.6882620.1070.010<0.011.4053.358LC+
Lyciumoside III142.8036490.0260.002<0.011.3593.643LC+
Fencamfamine34.4392160.2750.113<0.011.0991.291LC+
(3beta,5alpha,9alpha,22E,24R)-5,9-epidioxy-3-hydroxyergosta-7,22-dien-6-one246.0644430.7030.060<0.011.3743.547LC+
Herculin194.3012520.7530.416<0.011.1750.858LC+
Eicosadienoic acid33.3113090.8010.094<0.011.4133.096LC+
3beta-acetoxy-11alpha-methoxy-12-ursen-28-oic acid193.6975430.0790.009<0.011.2173.211LC+
Antibiotic X 14889C257.7596150.0860.009<0.011.4093.311LC+
2-Ethyl-2-hydroxybutyric acid58.6561330.7100.056<0.011.3603.670LC+
Alliospiroside C120.6427250.7990.113<0.011.3862.821LC+
(22E, 24x)-ergosta-4,6,8,22-tetraen-3-one38.0563930.5880.196<0.011.2731.585LC+
3-Methoxy-4-hydroxyphenylglycol glucuronide293.1934190.4040.045<0.011.4113.159LC+
Physalin O314.5125290.1280.013<0.011.3403.291LC+
Fevicordin B 2-gentiobioside185.5658690.2740.018<0.011.3533.943LC+
Desglucocoroloside220.2655050.0310.008<0.011.0742.006LC+
3-Methylglutarylcarnitine84.8512900.2700.023<0.011.4103.552LC+
Smilanippin A113.7807250.6020.047<0.011.4183.682LC+
PS(16 : 1(9Z)/18 : 4(6Z,9Z,12Z,15Z))207.2227540.0750.007<0.011.4073.514LC+
CPA(18 : 0/0 : 0)278.9184210.2080.020<0.011.4163.379LC+
Hericenone E243.8625950.3520.032<0.011.4153.474LC+
Glycerol tributanoate125.3673030.1790.009<0.011.4234.287LC+
Fasciculic acid C116.1867100.4440.044<0.011.3543.324LC+
Hebevinoside I38.3828090.1000.008<0.011.3993.557LC+
Phosphatidylethanolamine165.6957840.1210.008<0.011.3403.899LC+
Lycoperoside D154.4077400.5480.043<0.011.4193.659LC+
Chondrillasterol 3-[glucosyl-(1->4)-glucoside]97.6207370.6740.131<0.011.3382.358LC+
PA(18 : 4(6Z,9Z,12Z,15Z)/14 : 1(9z))246.7996390.1780.029<0.011.3262.599LC+
(24E)-3alpha,15alpha-diacetoxy-23-oxo-7,9(11),24-lanostatrien-26-oic acid188.6465690.3590.058<0.011.2362.639LC+
Physapruin B227.1036030.0950.021<0.011.1462.200LC+
PG(18 : 1(11Z)/22 : 5(4Z,7Z,10Z,13Z,16Z))230.2378240.7070.015<0.011.4305.573LC+
Trihexosylceramide (d18 : 1/12 : 0)257.5839690.3620.090<0.011.3092.008LC+
Traumatic acid281.3452290.5070.047<0.011.3643.440LC+
Lactosylceramide31.38810030.0044.397<0.011.316-10.250LC+
Sphinganine 1-phosphate51.5803820.1490.008<0.011.4294.202LC+
Budesonide167.5554310.8800.012<0.011.2286.183LC+
3-Benzoyloxy-11-oxo-12-ursen-28-oic acid34.3775752.1360.156<0.011.4223.776LC+
3a,7b,12a-Trihydroxyoxocholanyl-Glycine243.2384660.6480.047<0.011.0513.788LC+
Momordicoside K69.7666490.6910.161<0.011.3402.100LC+
PI(22 : 5(4Z,7Z,10Z,13Z,16Z)/16 : 0)244.5078860.1410.010<0.011.3383.842LC+
Vinaginsenoside R384.8539320.4860.035<0.011.4183.811LC+
PA(18 : 4(6Z,9Z,12Z,15Z)/18 : 4(6Z,9Z,12Z,15Z))144.6196890.0180.001<0.011.1853.617LC+
Dodecanoic acid49.52219910.4805.041<0.011.4671.056LC−
Hydroxyisocaproic acid251.7721313.1680.297<0.011.4283.414LC−
5,15-DiHETE108.6973350.1190.002<0.011.7415.644LC−
Adipic acid394.0381457.0341.213<0.011.5842.536LC−
13S-hydroxyoctadecadienoic acid50.0072950.6420.230<0.011.5851.481LC−
Prostaglandin D246.5553511.2680.105<0.011.1903.601LC−
9,10-DHOME81.2763130.1200.008<0.011.6073.946LC−
Azelaic acid359.7131870.1880.010<0.011.7874.170LC−
(10E,12Z)-(9S)-9-hydroperoxyoctadeca-10,12-dienoic acid60.7153110.1200.005<0.011.7034.569LC−-
6beta-hydroxyasiatic acid51.6045030.1350.012<0.011.7623.463LC−
Fasciculol C257.9435530.0060.001<0.011.3543.406LC−
Traumatic acid139.9322270.2300.022<0.011.6083.369LC−

Organic acids and derivatives
Pantothenic acid11.3581030.0140.002<0.011.1182.536GC
Bortezomib285.0564070.0770.006<0.011.4143.773LC+
Ustiloxin D287.0914950.0410.003<0.011.4123.737LC+
N,N-Dimethylformamide300.102740.1430.085<0.011.0350.743LC+
Oseltamivir314.5763130.1430.015<0.011.3223.249LC+
Arginyl-arginine296.6313312.1220.170<0.011.4213.643LC+
Arginyl-histidine166.7233120.4120.030<0.011.4193.784LC+
Dihydro-3-(2-octenyl)-2,5-furandione281.3912110.1310.017<0.011.2432.963LC+
N-Acetylleucine265.0671740.1400.010<0.011.3523.829LC+
L-cis-3-amino-2-pyrrolidinecarboxylic acid75.7821310.0270.002<0.011.3403.693LC+
L-Homocysteic acid124.1201820.1270.008<0.011.7863.970LC−
3-Hydroxycapric acid83.3061870.2410.088<0.011.4501.448LC−

Organic oxygen compounds
Leonuriside A372.0373330.0120.001<0.011.4294.365LC+
Kanamycin205.4474850.0360.004<0.011.3723.253LC+
Guanadrel sulfate79.8402143.9120.331<0.011.4183.562LC+
Trimethylaminoacetone206.2351160.1130.007<0.011.2284.102LC+
4-Ipomeanol72.9411670.3110.024<0.011.0043.699LC−

Benzenoids
Dictagymnin60.9982030.0680.005<0.011.0813.698LC+
4’-hydroxyfenoprofen glucuronide281.4544350.0890.007<0.011.4013.626LC+
Methoxamine410.7262120.0370.004<0.011.3023.038LC+
N-undecylbenzenesulfonic acid103.0103130.1870.092<0.011.1331.031LC+
Salmeterol57.0914160.4680.044<0.011.4193.415LC+
Esmolol175.5132960.1680.015<0.011.3453.498LC+
(±)-2-(1-methylpropyl)-4,6-dinitrophenol31.3782392.8501.286<0.011.0731.148LC−

Organoheterocyclic compounds
Pirenzepine132.1483520.4330.047<0.011.4033.206LC+
Neferine264.6176250.8450.118<0.011.3902.841LC+
Pyrimidine112.505810.0970.008<0.011.4163.566LC+
5,6-Dihydroxyindole45.0661500.2560.031<0.011.2263.029LC+
2-Pyrrolidineacetic acid64.0581300.6840.340<0.011.1651.009LC+
Alpha-carboxy-delta-decalactone125.9592150.0850.004<0.011.4134.275LC+
I-Urobilin174.1135910.1020.006<0.011.4134.117LC+
4-Ethyl-5-methyl-2-(1-methylethyl)oxazole244.5321540.6570.081<0.011.2493.023LC+
Garcimangosone C282.0764130.8930.071<0.011.4053.654LC+
8-Hydroxycarteolol116.9083090.1640.018<0.011.4153.203LC+
3-Methyl-1-hydroxybutyl-ThPP281.4085110.0400.003<0.011.3943.675LC+
Azaspiracid 3109.9978280.0890.034<0.011.0001.385LC+
3’-N-acetyl-4'-O-(9-octadecenoyl)fusarochromanone328.7285990.0270.004<0.011.0802.884LC+

Organic nitrogen compounds
1-Butylamine268.8477411.9330.834<0.011.3603.839LC+
Nervonyl carnitine237.324102659.91766.315<0.011.3423.315LC+
Isobutylpropylamine231.6501160.4440.041<0.011.4173.420LC+
2-Diethylaminoethanol300.009118195.97613.198<0.011.4243.892LC+
Dipyridamole47.7612530.2980.016<0.011.4264.263LC+

Phenylpropanoids and polyketides
Kanzonol V244.5733770.0050.457<0.011.096−6.613LC+
Sofalcone285.8824510.0630.005<0.011.4153.788LC+
Kanzonol F247.9434210.0030.360<0.011.135-7.002LC+
Kanzonol I83.2714370.0600.000<0.011.0519.876LC+

Homogeneous nonmetal compounds
Phosphoric acid32.551990.6430.052<0.011.2463.632LC+

Organosulfur compounds
(±)-2-pentanethiol32.7691050.3970.080<0.011.0892.302LC+

Hydrocarbons
1-Methyl-1,3-cyclohexadiene32.571950.4770.093<0.011.1822.356LC+

Others
6-deoxyglucitol10.1731170.0060.001<0.012.2202.432GC
1,2,4-benzenetriol9.3441240.2130.015<0.012.2403.856GC
Isoxanthopterin12.0911000.0020.001<0.011.5981.130GC
Lactobionic acid 214.9321470.0320.013<0.011.3421.289GC
2,4-dichloro-1-(2-chloroethenyl)-benzene7.9251700.4760.034<0.012.3413.823GC
Asparagine dehydrated8.6281100.0110.002<0.012.2122.434GC
4-Hydroxybenzoate9.4231102.4580.161<0.012.3433.933GC
Conduritol-beta-expoxide11.5411030.1740.019<0.012.2313.190GC
Methylmaleic acid7.9231700.4770.034<0.012.3413.826GC
4-Methylcatechol7.9281700.4520.034<0.012.1483.749GC
Beta-mannosylglycerate12.2712040.0000.000<0.011.3500.739GC
Uridine minor13.3541450.0660.007<0.012.3183.263GC
(10S,11R)-pterosin C 4-glucoside282.23239716.0941.266<0.011.3313.669LC+
PC(P-18 : 1(9Z)/16 : 0)36.1737450.6930.070<0.011.4163.299LC+
LysoPC(18:1(9Z))210.7785440.0190.001<0.011.0044.867LC+
PC(P-16 : 0/18 : 4(6Z,9Z,12Z,15Z))153.4937390.0480.005<0.011.2003.387LC+

GC: GC-TOF-MS; LC+: UHPLC-QE-MS positive ion; LC−: UHPLC-QE-MS negative ion.
3.6. Differential Metabolic Pathways Analysis

According to the annotations of database KEGG on the significantly differential metabolites, following the enrichment analysis and topology analysis, the key pathways with the highest correlation between metabolite differences were found on the basis of both raw and impact values.

In the comparison of damp phlegm pattern group and nondamp phlegm pattern group, GC-TOF-MS analysis generated two pathways (glyoxylate and dicarboxylate metabolism, pentose and glucuronate interconversions), UHPLC-QE-MS positive ion model analysis generated two pathways (primary bile acid biosynthesis and steroid hormone biosynthesis), and UHPLC-QE-MS negative ion mode analysis generated four pathways (arginine and proline metabolism; caffeine metabolism; glycine, serine, and threonine metabolism; purine metabolism). Six key metabolites were involved, including glycolic acid, D-xylitol, cholesterol, L-proline, sarcosine, and xanthine (Table 4).


PathwayRaw ImpactSignificantly different metabolites

GC-TOF-MS metabolomics analysis
Glyoxylate and dicarboxylate metabolism0.0410.007Glycolic acid
Pentose and glucuronate interconversions0.0440.032D-Xylitol

UHPLC-QE-MS positive ion mode metabolomics analysis
Primary bile acid biosynthesis0.0570.055Cholesterol
Steroid hormone biosynthesis0.1180.004Cholesterol

UHPLC-QE-MS negative ion mode metabolomics analysis
Arginine and proline metabolism0.0250.109L-Proline; sarcosine
Caffeine metabolism0.0680.031Xanthine
Glycine, serine and threonine metabolism0.1490.050Sarcosine
Purine metabolism0.2680.036Xanthine

In the comparison of damp phlegm pattern group and healthy control group, GC-TOF-MS analysis had one pathway named pantothenate and CoA biosynthesis, UHPLC-QE-MS positive ion mode analysis had eight pathways (glycerophospholipid metabolism, sphingolipid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, arachidonic acid metabolism, valine, leucine and isoleucine degradation, starch and sucrose metabolism, pentose and glucuronate interconversions, and tyrosine metabolism), UHPLC-QE-MS negative ion mode analysis had one pathway named arachidonic acid metabolism. Ten key metabolites were involved, including pantothenic acid, phosphatidylethanolamine, lysoPC(18 : 1(9Z)), sphinganine 1-phosphate, lactosylceramide, leukotriene D4, 3-methyl-1-hydroxybutyl-ThPP, 3-methoxy-4-hydroxyphenylglycol glucuronide, 5,6-dihydroxyindole, and prostaglandin D2 (Table 5).


PathwayRaw ImpactSignificantly different metabolites

GC-TOF-MS metabolomics analysis
Pantothenate and CoA biosynthesis0.0440.180Pantothenic acid

UHPLC-QE-MS positive ion mode metabolomics analysis
Glycerophospholipid metabolism0.0180.231Phosphatidylethanolamine; LysoPC(18:1(9Z))
Sphingolipid metabolism0.0500.017Sphinganine 1-phosphate; lactosylceramide
Glycosylphosphatidylinositol(GPI)-anchor biosynthesis0.1860.044Phosphatidylethanolamine
Arachidonic acid metabolism0.2270.026Leukotriene D4
Valine, leucine and isoleucine degradation0.4460.0353-Methyl-1-hydroxybutyl-ThPP
Starch and sucrose metabolism0.5230.0133-Methoxy-4-hydroxyphenylglycol glucuronide
Pentose and glucuronate interconversions0.5440.0093-Methoxy-4-hydroxyphenylglycol glucuronide
Tyrosine metabolism0.6770.0075,6-Dihydroxyindole

UHPLC-QE-MS negative ion mode metabolomics analysis
Arachidonic acid metabolism0.0420.034Prostaglandin D2

4. Discussion

GC-TOF-MS and UHPLC-QE-MS have different advantages in the analysis of metabolites. GC-TOF-MS has higher separation ability than UHPLC-QE-MS in the analysis of polar metabolites. Moreover, GC-TOF-MS system has highly repeatable mass spectra, which makes GC-TOF-MS libraries more comprehensive than UHPLC-QE-MS. However, GC-TOF-MS can detect approximately 100 metabolites while UHPLC-QE-MS can detect thousands of metabolites, including semipolar metabolites [28]. Because the composition of metabolites on tongue coating is complex, the combined use of the two detection methods can more comprehensively detect the characteristics of tongue-coating metabolites.

In the metabolomics analysis of damp phlegm pattern tongue-coating samples, lipids and lipid-like molecules were the largest group of differentially expressed metabolites (74 types) when comparing nondamp phlegm pattern patients and healthy controls. It can be seen from Tables 1 and 2 that 72 kinds of lipids and lipid-like molecules were significantly upregulated in the damp phlegm pattern group, and only (3b, 4b, 11b, 14b)-11-ethoxy-3,4-epoxy-14-hydroxy-12-cyathen-15-al 14-xyloside and lactosylceramide (d18 : 1/26 : 0) were significantly downregulated. This result indicates that lipids and lipid-like molecules are the main metabolic disorder differential metabolites of the GPL damp phlegm pattern patient in tongue-coating metabolomics detection. Some studies have found that lipids participate in the development of chronic atrophic gastritis [29]. Chronic gastritis may be related to the global increase in the inflammation state of the body, which was influenced by poor lipid status such as decreased serum high-density lipoprotein (HDL), and the decrease of HDL was closely related to the increased risk of gastric cancer [30, 31]. Some scholars also found that elevated serum-free fatty acids may increase the risk of gastric cancer [32]. In addition, it was also found in experiments on mice that high cholesterol and high-fat diets may increase gastritis incidence in mice [33]. It can be seen that these findings indicate that lipid is related to the occurrence of GPL, but we did not find reports on the relationship between GPL damp phlegm pattern and lipid metabolism. It is worth noting that we found that there is a correlation between damp phlegm pattern and dyslipidemia in other diseases. For example, studies have shown that most patients with hyperlipidemia and atherosclerosis belong to the damp phlegm pattern group, and the level of total cholesterol in hyperlipidemic rats was significantly elevated [34]. Total cholesterol and low-density lipoprotein levels were higher in patients with hypertension-complicated diabetes and damp phlegm pattern [35]. In our previous study, we found that histidine, tryptophan, lysine, and other metabolites exist in chronic renal failure (CRF) and coronary heart disease (CHD) of patients’ tongue coating with damp phlegm pattern [11], but these metabolites were not found in the GPL damp phlegm pattern patients’ tongue coating, which may be due to the following two reasons. One is that although they are all damp phlegm syndrome, they belong to different diseases, so the metabolites of tongue coating are also different. The second reason is that we only use GC-MS to detect the metabolites of tongue coating in CHD and CRF, which leads to the failure of comprehensive retrieval of the different metabolites of damp phlegm pattern between the two diseases.

According to the metabolic pathway analysis of lipids and lipid-like molecules, six metabolic pathways were involved, including primary bile acid biosynthesis, steroid hormone biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and arachidonic acid metabolism. The six metabolic pathways contained seven metabolites: cholesterol, phosphatidylethanolamine, lysoPC(18:1(9Z)), sphinganine 1-phosphate, lactosylceramide, leukotriene D4, and prostaglandin D2.

Among them, sphinganine 1-phosphate (S1P) is involved in sphingolipid metabolism. Sphingosine kinase (Sphk) exists in chronic gastritis and gastric cancer cells. Sphk can produce S1P, and many inflammatory reactions are affected by the circulating S1P. The Sphk/S1P axis is an inflammatory mediator in tumor microenvironment and has been identified as a therapeutic target for gastric diseases [38]. Leukotriene D4 and prostaglandin D2 have participated in arachidonic acid metabolism. Arachidonic acid metabolic pathway has an impact on the occurrence and development of gastric cancer [39]. Prostaglandin D2 can inhibit the development of gastric cancer [40]. Leukotriene D4 can aggravate gastric contraction and promote gastric acid secretion [41, 42]. However, gastric pH increases during GPL and gastric cancer [43]. Therefore, leukotriene D4 and prostaglandin D2 as metabolites are considered as new targets for the treatment and prevention of GC [39, 44]. Besides the levels of leukotriene D4 and prostaglandin D2 increased in GPL patients with damp phlegm pattern, which indicates that human body may have certain self-healing function in the process of chronic gastritis developing into gastric cancer. Thus, it can be seen, Sphk/S1P axis, as a target for the treatment of gastric diseases, could be explained from the side that S1P aggravated the development of gastric diseases. The increased expression of leukotriene D4 and prostaglandin D2 indicated that GPL patients had the ability of self-healing in the process of disease.

Among the metabolites of GPL damp phlegm pattern patients, organic acids and derivatives (17 types) and organoheterocyclic compounds (14 types) were the second and third major metabolites. Studies have found that organic acids may be used as a potential metabolic marker of gastric adenocarcinoma, which can be used for the detection, diagnosis, and treatment of gastric adenocarcinoma in the coming years [45]. According to the analysis of metabolic pathways, four metabolites were involved: glycolic acid, pantothenic acid, L-proline, and sarcosine. Among them, pantothenic acid and proline may be potential biomarkers of gastric cancer [46]. Elevated levels of sarcosine may also be associated with the formation of gastric cancer [47]. These studies found that pantothenic acid, proline, and sarcosine all play a role in the development of gastric cancer.

The kinds of other metabolites were fewer; the content of terephthalic acid, L-carnitine, N-undecylbenzenesulfonic acid, kanzonol V, and kanzonol F decreased, while the content of other metabolites increased. Among them the increase of isoxanthopterin may be related to the existence of gastric cancer [48]. Some studies have found that 4-methylcatechol is carcinogenic and can promote gastric cancer and adenocarcinoma [49]. In the experiment of rats, 1,2,4-benzenetriol can increase the thickness of gastric mucosa [50]. 5-fluorouracil (5-Fu), one of the first-line antitumor drugs, can effectively induce the apoptosis of cancer cells while dipyridamole can enhance the cytotoxicity of 5-Fu [51, 52].

According to the annotations of database KEGG on the significantly different metabolites, following the enrichment analysis and topology analysis, on the basis of both raw p and impact values, it was found that glycerophospholipid metabolism was the most important metabolic pathway. The level of phosphatidylethanolamine involved in the synthesis of glycerophospholipid metabolism was significantly increased in group of GPL patients with damp phlegm pattern, and another study has shown that the concentration of phosphatidylethanolamine in the plasma of patients with chronic gastritis increased significantly [52].

However, our research results are still insufficient. Nine GPL patients with damp phlegm pattern were infected with Helicobacter pylori (Hp). Although studies have found that Hp may affect the content of some metabolites in tongue coating of patients with chronic gastritis [53], we previously analyzed the differential metabolites in the tongue coating of 60 patients with GPL compared with healthy people, of which 10 patients were infected with Hp, and the inclusion of these Hp-infected patients did not affect our final screening of differential metabolites [54]. Even so, in future studies, we will still carefully consider the factors of Hp infection.

5. Conclusions

In this study, GC-TOF-MS and UHPLC-QE-MS metabolomics technologies were used to detect the metabolites in the tongue coating of GPL patients with damp phlegm pattern for the first time. The results illustrated that there were significant differences in metabolites between GPL patients with damp phlegm pattern and GPL patients with nondamp phlegm pattern as well as healthy people. The types of lipids and lipid-like molecules were the most prominent, involving primary bile acid biosynthesis, steroid hormone biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and arachidonic acid metabolism. Among them, the most important metabolic pathway was glycerophospholipid metabolism, and the metabolites involved were phosphatidylethanolamine and lysoPC (18:1(9Z)). In addition, organoheterocyclic compounds as well as organic acids and derivatives also contained more kinds of metabolites, which were involved in glyoxylate and dicarboxylate metabolism and pantothenate and CoA biosynthesis as well as arginine and proline metabolism. With further studies, we hope that these different metabolites may be potential diagnostic markers which can be obtained noninvasively for patients with GPL damp phlegm pattern.

Data Availability

The Ethics Committee of Shanghai University of Traditional Chinese Medicine limited the measurement data used to support the results of this study in order to protect the privacy of patients. For researchers who meet the criteria for obtaining confidential data, the data of this study can be obtained from Yiming Hao (e-mail: hymjj888@163.com).

Disclosure

Yifeng Xu and Renling Zhang share the first authorship.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Yiming Hao was responsible for the study. Yifeng Xu wrote the manuscript. Renling Zhang helped collect clinical samples. Robert Morris and Feng Cheng helped revise the grammar of the manuscript. Yiqin Wang helped with the ideas of the study. Zhujing Zhu helped with the experimentation. All authors of this study agreed to be accountable for all aspects of the work. For this study, Yifeng Xu and Renling Zhang made an equal contribution.

Acknowledgments

This work was funded by the National Natural Science Foundation of China (Grant No. 81703982). This work was also supported by Shanghai Biotree Biotech Co. Ltd.

Supplementary Materials

Table S1 shows the reagents used in GC-TOF-MS and UHPLC-QE-MS experiments. (Supplementary Materials)

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