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. 2020 Jan 3;9(1):46. doi: 10.3390/foods9010046

Metabolomics Analysis of the Deterioration Mechanism and Storage Time Limit of Tender Coconut Water during Storage

Yunwu Zhang 1, Wenxue Chen 1, Haiming Chen 1, Qiuping Zhong 1, Yonghuan Yun 1, Weijun Chen 1,*
PMCID: PMC7022768  PMID: 31947875

Abstract

Tender coconut water tastes sweet and is enjoyed by consumers, but its commercial development is restricted by an extremely short shelf life, which cannot be explained by existing research. UPLC-MS/MS-based metabolomics methods were used to identify and statistically analyze metabolites in coconut water under refrigerated storage. A multivariate statistical analysis method was used to analyze the UPLC-MS/MS datasets from 35 tender coconut water samples stored for 0–6 weeks. In addition, we identified other differentially expressed metabolites by selecting p-values and fold changes. Hierarchical cluster analysis and association analysis were performed with the differentially expressed metabolites. Metabolic pathways were analyzed using the KEGG database and the MetPA module of MetaboAnalyst. A total of 72 differentially expressed metabolites were identified in all groups. The OPLS-DA score chart showed that all samples were well grouped. Thirty-one metabolic pathways were enriched in the week 0–1 samples. The results showed that after a tender coconut is peeled, the maximum storage time at 4 °C is 1 week. Analysis of metabolic pathways related to coconut water storage using the KEGG and MetPA databases revealed that amino acid metabolism is one of the main causes of coconut water quality deterioration.

Keywords: tender coconut water, differentially expressed metabolite, storage period, metabolic pathway, amino acid

1. Introduction

The coconut (Cocos nucifera L.) originates from Palmae Cocos. Originally cultivated in Vietnam, Thailand, Myanmar, Malaysia, and the Philippines [1], coconuts are now also planted in Southern China, including Taiwan, Southern Yunnan, and other tropical areas. The fruit of the coconut is nearly round. A coconut has three layers of peel: Exocarp, mesocarp, and endocarp. The outermost layer, which is typically smooth with a greenish color, is called the exocarp. The next layer is the fibrous husk, or mesocarp, which ultimately surrounds the hard woody layer called the endocarp. Inside the endocarp are the solid endosperm (coconut meat) and liquid endosperm (coconut water). A tender coconut is one with a maturity of 6–9 months, and its water is directly consumed or processed into a variety of beverages. For half a century, extensive research has been conducted on the nutrition of coconut water, which contains 17 amino acids required by the human body [2]. The essential amino acids in coconut water are complete and nutrient-rich, and some are even more nutrient-dense than those in milk [3]. Moreover, coconut water has numerous medicinal properties, such as detoxification, antibacterial, anti-inflammatory, rejuvenation, digestion, and diuretic properties. Coconut water also has a therapeutic effect on gastric dysfunction, dysentery and child malnutrition and offers some control over hypertension, kidney stones, and urethral stones [4,5,6]. Moreover, coconut water affords protection against the induction of myocardial infarctions [7]. As a natural drink with high nutritional value, coconut water can replenish lost body fluids and alleviate electrolyte imbalance; in emergencies, coconut water can be injected intravenously into patients for hydration [8]. In addition, coconut water can be used as a plant tissue culture medium [9,10,11] because it has a variety of amino acids, cytokinins, auxin, and other nutrients [12].

For a large number of coconut farmers, the growing popularity of coconut water as a healthy drink may be very positive. However, research shows that tender coconuts can be stored for less than a month only under refrigerated conditions [13]. Fresh coconut water is sterile, so there are other reasons for its deterioration. Flavor compounds in tender coconut water may be formed from the degradation of fatty acids, which is probably caused by oxidative metabolism involving mechanisms such as p-oxidation and lipoxygenase (LOX) pathways [14]. During storage, the levels of nonanal and octanal, which could indicate the generation of an off-flavor, increase significantly in coconut water from commercially trimmed tender coconut. Furthermore, tender coconut stored at normal temperature exhibits obvious exocarp shriveling [15], continually increasing weight loss and the beginnings of exocarp browning and discoloration [16]. The high transportation costs and extremely short storage periods have restricted the development of tender coconut as a commercial product.

Tender coconut water undergoes a series of biological and chemical changes during harvesting. To date, research that uses metabolomics to study the changes in tender coconut water has been rare. Metabolomics is a research method similar to genomics and proteomics and is widely used to analyze changes in the quality of plants. Plant metabolomics mainly concerns metabolic analysis and metabolite fingerprinting of single plants, in which the metabolites in plant organs and tissues are qualitatively and quantitatively analyzed. In addition, metabolomics is used to compare and identify plants of the same species but different genotypes and to study the interaction between plants and herbivores, including the effects of plant defense metabolites on herbivore genotypes and resistance [17,18,19,20,21,22,23,24,25,26]. In this work, metabolomics was used to analyze the quality and metabolic changes in tender coconut during storage after harvest, and the important reasons for the deterioration of coconut were revealed, thus providing a theoretical basis for the preservation of fresh coconut after harvest.

2. Materials and Methods

2.1. Chemicals and Reagents

LC-MS-grade methanol was obtained from WOKAI. LC-MS-grade acetonitrile, 2-chlorophenylalanine, formic acid, and ammonium formate were obtained from Aladdin. Double-distilled water (ddH2O) was used.

2.2. Sample Collection

Hainan-native high-growing coconut was used as an example to study the growth characteristics of tender coconut. All coconuts were grown to 8 months old and had uniform color, with no surface damage or insect-borne infections. After the exocarp and mesocarp were removed, the coconuts were stored in a refrigerator at 4 °C. To eliminate any biological differences in the samples, the coconut water of 15 coconuts was taken every week, and the coconut water of every three coconuts was mixed into one sample, totaling 5 parallel samples. The coconut water samples were stored for 0 to 6 weeks, collected (coconut water collected in weeks 0–6 was the first to seventh groups of samples), packed in aluminum foil bags, frozen in liquid nitrogen and immediately placed in a freezer at −79 °C.

2.3. Determination of Physical and Chemical Indexes

The electroconductibility of tender coconut water was measured by an m-t-fe30 LCD-D instrument, the sugar content was measured by a Pal-2 hand-held saccharimeter, and the acidity and alkalinity were measured by an m-t-fe20 acidity-alkalinity meter.

2.4. Preparing Samples for UPLC-MS/MS Analysis

All samples were thawed at 4 °C. Next, 100 mg of the sample was transferred to a 2 mL centrifuge tube containing 0.3 mL of ethanol, ultrasonicated for 30 min at 25 °C and centrifuged at 12,000 rpm for 10 min. The supernatant was filtered through a 0.22 µm membrane, and the obtained filtrate was analyzed by UPLC-MS/MS. Thirty microliters of filtrate was obtained from each supernatant and mixed to make the QC sample (Figure 1). Quality control samples were used to monitor the deviation in the analysis results of pooled sample mixtures and to compare this deviation with the error caused by the analyzer itself. The remaining samples were tested by UPLC-MS/MS [27,28].

Figure 1.

Figure 1

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Preparation of the QC sample. Note: S1, S2, Sn-1, Sn, sample; QC, pooled samples for quality control.

2.5. Metabolomics Analysis Based on UPLC-MS/MS

A Thermo Ultimate 3000 system was used with an ACQUITY UPLC HSS T3 (2.1 × 150 mm, 1.7 µm) column and the following parameters: autosampler temperature of 8 °C, flow rate of 250 μL/min, column oven temperature of 38 °C, and sample size of 2 μL. Gradient elution was performed in positive ion mode with a mobile phase consisting of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) and in negative ion mode with a mobile phase consisting of 5 mM ammonium formate in water (C) and acetonitrile (D). The gradient elution procedure was as follows: 0–1 min, 2% B/D; 1–9 min, 2%–48% B/D; 9–12 min, 48%–98% B/D; 12–13.5 min, 98% B/D; 13.5–14 min, 98%–2% B/D; and 14–17 min, 2% B/D [29].

MS analysis was performed on a Thermo Q Exactive Focus with electrospray ionization (ESI) system with a positive ion spray voltage of 3.80 kV and a negative ion spray voltage of 2.50 kV. The capillary temperature was 325 °C, the resolution was 70,000, and the scanning range was 81–1000. Data dependent acquisition (DDA) MS/MS experiments were performed with HCD scan. The collision voltage was 30 eV. Dynamic exclusion was used to remove unnecessary MS/MS information [29].

2.6. Data Handling and Multivariate Statistical Analysis

The raw data (Supplementary Materials) obtained were translated into mzXML format by ProteoWizard [30]. Peak recognition, peak filtering, and peak alignment were performed using the edited R XCMS package [30,31]. The data were exported to Excel tables for calculation. Because data of different orders of magnitude were to be compared, the data were subjected to batch normalization of the peak area.

Due to the multidimensional nature of metabolomic data and the high correlation between certain variables, traditional univariate analysis could not be run quickly, fully and accurately to mine potentially useful information from the data. Therefore, when analyzing metabolomic data, it is important to use chemometrics and multivariate statistics for dimension reduction and classification of the collected multidimensional data to extract the most useful information. In this experiment, the data were subjected to autoscaling and mean-centering and were scaled to unit variance (UV) before multivariate statistical analysis to obtain more reliable and intuitive results. The multivariate statistical analysis (R language ropes package [32]) methods used were PCA, PLS-DA, and OPLS-DA.

3. Results

3.1. Physical and Chemical Indicators of Change

The physical and chemical indicators corresponding to the storage time are shown in Figure 2. During weeks 0–1, the conductivity of the samples changed significantly (Figure 2a). However, there was no significant change in the conductivity of the samples from the first week of storage to the end (week 6). The °Brix of the samples decreased from 5.74 to 5.56 in weeks 0–1 but increased to 6.48 from the first week until the end (Figure 2b), reflecting drastic fluctuations during storage. The decrease in °Brix in weeks 0–1 may be due to high metabolism, while the continuous increase in °Brix in weeks 1–6 may be due to the decrease in metabolic activity and evaporation of coconut water during storage (a coconut loses approximately 10 g of water a week). In weeks 0–1, the pH of the samples quickly rose from 5.25 to 6.05. However, there was no significant change in sample pH from the first week to the sixth week of storage (Figure 2c). The increase in pH may have been due to organic acid consumption processes, such as the tricarboxylic acid cycle, gluconeogenesis, zymosis and amino acid interconversion [33]. This section is divided by subheadings and provides a concise and precise description of the experimental results, their interpretation and the experimental conclusions that can be drawn.

Figure 2.

Figure 2

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Changes in the conductivity (a), °Brix (b), pH (c) and sugar-to-acid ratio (d) of tender coconut water during storage.

The ratio of sugar to acid (Figure 2d) is an important indicator that can affect the taste, quality and shelf life of food. The sugar-to-acid ratio of the samples dropped rapidly from 1.09 to 0.91 in, corresponding to the significant changes in pH and °Brix. However, from the first week to the end of storage (6 weeks), the pH of the samples increased slowly and steadily, while the °Brix increased steadily towards the end. From the first to the sixth week, the ratio of sugar to acid increased slowly, mainly because of the increase in °Brix. According to the sugar-to-acid ratio data, the flavor of tender coconut water changed significantly after one week of storage, which indicates that the storage period of the tender coconut water may not exceed one week.

3.2. Metabolic Profile Analysis of Tender Coconut Water during Storage

Positive ion mode (NT-pos) and negative ion mode (NT-neg) are two scanning modes of MS. After the sample is ionized by the ESI source, there will be ions with a positive charge (M + H, M + NH4, M + Na, etc.) and negative charge (M-H, M + Cl, M + CH3COO, etc.) at the same time. According to the differences in the physical and chemical properties of the substances, some metabolites will be positively charged, and some will be negatively charged. To obtain comprehensive metabonomics information, two modes are used simultaneously.

As shown in Figure 3, under the conditions of NT-pos and NT-neg, QC samples are intensively distributed with good repeatability, indicating that the system is stable.

Figure 3.

Figure 3

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PCA score chart of QC samples under positive ion mode (NT-pos) (a) and negative ion mode (NT-neg) (b).

Metabolites are the products of metabolism in animals or plants. The up-regulated or down-regulated metabolites between the experimental group and the control group are called differentially expressed metabolites, which can identify abnormal metabolic changes and can also be discussed in combination with pathways related to differentially expressed metabolites. The differentially expressed metabolites are displayed in Figure 4a,b. In this study, metabolites were screened for differential expression, and the relevant conditions are as follows:

  • (1)

    p-value ≤ 0.05 and VIP ≥ 1 [34]

  • (2)

    One-way ANOVA p-value ≤ 0.05 [35]

Figure 4.

Figure 4

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Up-regulated and down-regulated differentially expressed metabolites based on NT-pos (a) and NT-neg (b). The red color indicates up-regulated differentially expressed metabolites, and the blue color indicates down-regulated differentially expressed metabolites.

p-value: Student’s t test, p-value ≤ 0.05 indicates a statistically significant difference. VIP: Variable Importance in the Projection; indicates the importance of variables to the model. One-way ANOVA p-value: Comparison of the mean of single factors and multiple independent samples. Student’s t test was used between two groups, and one-way ANOVA was used between multiple groups.

In this study, condition 1 + 2 was used for metabolite screening, Student’s t test was used between two groups, and one-way ANOVA was used between multiple groups. FDR was used to correct the p-value and control the final analysis results using the Benjamin–Hochberg (BH) [36] method. Positive ion mode was used to obtain 2923 differentially expressed metabolites, and negative ion mode was used to obtain 1695 differentially expressed metabolites. Figure 2 shows the statistical results of differentially expressed metabolites to be identified. PCA, PLS-DA, and OPLS-DA were analyzed in all groups, and a PCA score chart was drawn (Figure 5a,b). Under NT-pos conditions, the samples were all in the 95% confidence interval, the number of separation weeks was not enough, and the samples in groups 5 and 7 had a small overlap. Under NT-neg conditions, the number of separation weeks was very poor, most of the samples had 95% confidence intervals, except for one dot in group 1. PLS-DA showed some improvement (Figure 5c,d). Under NT-pos conditions, all samples were well differentiated, while under NT-neg conditions, the samples of groups 5, 6, and 7 overlapped. The OPLS-DA score showed that all samples were well differentiated under NT-pos and NT-neg conditions (Figure 5g,h). Permutation plots (Figure 5e,f) were used for effectively assessing whether the current PLS-DA model was over-fitting. Any one of the following criteria needed to be satisfied: (1) All Q2 points were lower than the rightmost original Q2 point; (2) the regression line of the Q2 point was less than or equal to 0 at the intersection of the ordinate. As shown, all blue Q2 points under NT-pos and NT-neg conditions were below the rightmost original blue Q2 point. Furthermore, the OPLS-DA score chart shows that all samples were well grouped. Compared with PLS-DA, OPLS-DA can effectively reduce the complexity of the model and enhance the interpretation ability of the model without reducing the prediction ability of the model to check the differences between groups to the greatest extent.

Figure 5.

Figure 5

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PCA score charts based on NT-pos (a) and NT-neg (b), PLS-DA score charts based on NT-pos (c) and NT-neg (d), PLS-DA permutation plot based on NT-pos (e) and NT-neg (f), and OPLS-DA score charts based on NT-pos (g) and NT-neg (h). R2, the interpretability of the model; Q2, the predictability of the model. Samples from 7 different groups are represented by 7 different colors, and each group has 5 biological replicates.

3.3. Metabolomics Analysis of Tender Coconut Water

To identify the metabolites, their exact molecular weight was confirmed (molecular weight error < 20 ppm). The fragment information (including the mass nuclear ratio, retention time and peak area of the identified metabolite) obtained according to the MS/MS mode was matched in the Human Metabolome Database (HMDB) (Metlin, MassBank, Lipid Maps, mzclound) and a self-built standards database to obtain accurate metabolite information. Finally, a total of 112 differentially expressed metabolites were matched (Table A1). Then, hierarchical clustering analysis was performed on each group of differentially expressed metabolites (Figure 6). At the bottom of the tree, each cluster extends a vertical line and then is aggregated by a horizontal line; each horizontal line is a category. The horizontal lines continue to aggregate from the bottom to the top. The more horizontal lines aggregate, the more concentrated the categories are. The top horizontal line of the tree divides the samples into two categories. When the distribution pattern of the two categories was observed, all samples in the first group (all of week 0) were distributed in the first category, while all others (week 1–6) were distributed in the second category. This finding indicates that the first and second groups of samples have the largest difference in metabonomics.

Figure 6.

Figure 6

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Heat map for the differentially expressed metabolites. The relative content in the figure is displayed by the color difference, where the columns represent the samples and the rows represent the metabolites. Samples from 7 different groups are represented by 7 different colors, and each group has 5 biological replicates.

Z-score is calculated based on the relative content of metabolites [37,38], which is used to measure the relative content of metabolites at the same level, and the formula is:

z = (x − μ)/σ (1)

where x represents a specific fraction. μ represents average, while σ represents standard deviation.

The statistical analysis of z-score is as shown in Figure 7. The z-score of the second group was calculated, while taking the first group as the control group. The metabolite is downregulated when the z-score is negative, and the metabolite is upregulated when the z-score is positive. The z-score chart shows that there are 52 upregulated differentially expressed metabolites and 14 downregulated differentially expressed metabolites in the first and second group. Yohimbic acid was thus identified as the most influential upregulated metabolite and vinpocetine was the most influential downregulated metabolite between groups 1 and 2.

Figure 7.

Figure 7

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z-score chart of differentially expressed metabolites in the first and second group.

Differentially expressed metabolite correlation analysis was used to study the consistency of the trends among metabolites [39,40]. The correlations between individual metabolites were analyzed by calculating the Pearson correlation coefficients or the Spearman rank correlation coefficients for all metabolite pairs. Metabolite correlation often reveals the synergy of changes between metabolites: Positive correlation reflects the same trend for metabolites, and negative correlation reflects a different trend. This analysis was used to investigate the relationship between 56 major differentially expressed metabolites (Table A2) in all samples of the first and second groups. The correlation matrices of the differentially expressed metabolites are shown in Figure 8. In the figure, the blue dot indicates a negative correlation, and the red dot indicates a positive correlation. The correlation coefficient is between −1 and 1, and the different color depths are used to represent different correlations. When there is a strong linear relationship between two metabolites, the correlation coefficient is either 1 or −1, reflecting either a positive or negative correlation, respectively. In addition, the cor.test () function in R was used for statistical analysis of the metabolite association results, and a p-value ≤ 0.05 was considered significant. As shown in Figure 8, there are more than 20 connections between 12 types of amino acids and other metabolites, among which L-lysine is connected with all the other 55 metabolites. L-threonine is connected with 51 metabolites, and L-methionine is connected with 45 metabolites. It has also been confirmed that amino acids play an important role in metabolism.

Figure 8.

Figure 8

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Correlation matrices of the first and second groups of samples of differentially expressed metabolites. The red color represents positive correlation, and the blue color represents negative correlation.

MetPA (www.metaboanalyst.ca) [41] is based mainly on the KEGG metabolic pathway. The MetPA database identifies metabolic pathways that may be disturbed by organisms through metabolic pathway concentration and topological analysis and is used to analyze the metabolic pathways of metabolites. The data analysis algorithm used is a hypergeometric test, and the topological structure of the pathway is relative betweenness centrality. Based on the metabolic pathway analysis performed using the KEGG pathway and MetPA database (Figure 9), the metabolic pathways enriched in 56 differentially expressed metabolites in the first and second groups were analyzed in this study. Thirty-one metabolic pathways were found, each represented by a dot. The larger the abscissa is, the larger the dot is, indicating that this metabolic pathway is more important to the metabolism of the sample. The larger the ordinate is, the darker the dot color is, indicating that this metabolic pathway is more enriched.

Figure 9.

Figure 9

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Analysis of metabolic pathways of the first and second groups of samples of differentially expressed metabolites. Each dot represents a metabolic pathway.

The main metabolic pathways are as follows. (1) In the metabolism of cysteine and methionine, cysteine is converted from serine (via acetylserine) by transfer of hydrogen sulfide and metabolized to pyruvate via multiple routes, and methionine is synthesized from aspartate [42,43]. (2) The metabolism of glycine, serine, and threonine results in 3-phospho-D-glycerate, which is an intermediate in glycolysis, produces serine and glycine, and reduces aspartate acid, which produces threonine in plants and bacteria [44]. (3) In the metabolism of arginine and proline, arginine is used to synthesize putrescine by arginase and ornithine decarboxylase, and glutamic acid is used to synthesize proline by delta-1-pyrroline-5-carboxylate synthetase, pyrroline-5-carboxylate reduce, glutamate 5-kinase and glutamate-5-semialdehydehydrogenase [45,46]. (4) Aminoacyl-tRNA biosynthesis increases amino acids [47,48]. (5) Valine, leucine and isoleucine biosynthesis increases amino acids through transamination of 3-phospho-D-glycerate and pyruvate consumption [44,49]. (6) Pantothenate and CoA biosynthesis produces 4’-phosphopantethein, which promotes the TCA cycle, β-oxidation, and fatty acid and polyketide biosynthesis pathways as an auxiliary factor.

The three most important metabolic pathways are amino acid pathways: (1) Glycine, serine and threonine metabolism; (2) arginine and proline metabolism; and (3) cysteine and methionine metabolism. Pyruvate, l-serine, 2-oxobutyric acid, l-leucine, putrescine, l-histidine, l-asparagine, l-threonine, pantothenic acid, O-acetyl-l-serine, l-lysine, l-arginine and l-methionine participate in at least two pathways, indicating that these substances are at the nodes of complex network pathways. Pyruvate is involved in 12 pathways, and serine is involved in 7 pathways, indicating that pyruvate and serine are the junction of each pathway. Moreover, pyruvate is closely related to amino acid metabolism. In previous studies, the changes in the quality of tender coconut water were attributed to aldehydes and lipids. In this study, amino acid metabolism was found to be another main cause of deterioration of tender coconut water. During the storage of tender coconut water, a variety of proteases play a key role in the metabolism of amino acids.

4. Conclusions

Samples taken at various time points during the storage of coconut water could be clearly divided into two categories by hierarchical clustering analysis: week 0 and weeks 1, 2, 3, 4, 5, and 6. The physical and chemical indicators showed significant metabolic differences between samples at week 0 and week 1. Thus, on the basis of metabolomics analysis, after tender coconut is peeled, the maximum storage time at 4 °C is week.

Metabonomics provides a direction for the study of physiological and biochemical deterioration of tender coconut during storage. After differentially expressed metabolites produced during coconut water storage were screened, KEGG and MetPA analysis of the metabolic pathways relevant to coconut water storage was performed and revealed that amino acid metabolism is one of the main causes of the deterioration of coconut water quality. These results provide a good theoretical basis for the preservation of tender coconut water.

Supplementary Materials

The following are available online at https://www.mdpi.com/2304-8158/9/1/46/s1.

Click here for additional data file. (7.7MB, zip)

Appendix A

Table A1.

Differentially expressed metabolites in all groups.

Name 1 m/z 2 rt 3 ppm 4 Exact Mass 5 Formula KEGG 6 Posneg 7
Piperidine 86.097103 130.617 8 85.1475 C5H11N C01746 pos
Pyruvate 87.007167 95.2173 19 88.06206 C3H4O3 C00022 neg
D-Glyceraldehyde 89.02315 23.7384 8 90.07794 C3H6O3 C00577 neg
Acetoin 89.060419 75.4608 9 88.10512 C4H8O2 C00466 pos
Putrescine 89.107731 75.8816 10 88.1515 C4H12N2 C00134 pos
3-Methyl Pyruvic Acid 101.02312 94.7351 13 102.0886 C4H6O3 C00109 neg
Gamma-Aminobutyric Acid 104.07055 120.134 1 103.1198 C4H9NO2 C00334 pos
Choline 104.10692 90.8299 2 104.1708 C5H14NO C00114 pos
l-Serine 106.05005 87.5235 2 105.09262 C3H7NO3 C00065 pos
Diethanolamine 106.08649 87.4857 2 105.13568 C4H11NO2 C06772 pos
Benzaldehyde 107.0494 353.073 3 106.1219 C7H6O C00193 pos
Uracil 111.01883 122.908 11 112.08684 C4H4N2O2 C00106 neg
l-Proline 116.07017 101.442 0 115.1305 C5H9NO2 C00148 pos
l-Valine 116.0707 112.712 9 117.14638 C5H11NO2 C00183 neg
Glycine Betaine 118.08633 94.5577 2 117.14638 C5H11NO2 C00719 pos
l-Erythrulose 119.03418 94.3132 7 120.10392 C4H8O4 C02045 neg
l-Threonine 120.0656 92.1067 2 119.1192 C4H9NO3 C00188 pos
4-Hydroxybenzaldehyde 121.02785 536.412 14 122.12134 C7H6O2 C00633 neg
Nicotinic acid 122.02354 121.987 4 123.10944 C6H5NO2 C00253 neg
Nicotinamide 123.05547 116.543 0 122.12472 C6H6N2O C00153 pos
Imidazoleacetic Acid 127.05018 101.293 0 126.114 C5H6N2O2 C02835 pos
4-Hydroxy-l-Proline 129.97455 135.595 16 131.1299 C5H9NO3 C01015 neg
5-Oxo-l-Proline 130.04944 195.121 2 129.114 C5H7NO3 C01879 pos
l-Pipecolic Acid 130.086 80.1096 1 129.157 C6H11NO2 C00408 pos
l-Leucine 130.08668 196.186 14 131.17296 C6H13NO2 C00123 neg
Glutaric Acid 131.03365 78.4799 13 132.11462 C5H8O4 C00489 neg
l-Asparagine 131.04501 88.3785 9 132.118 C4H8N2O3 C00152 neg
Agmatine 131.12911 79.1293 0 130.19162 C5H14N4 C00179 pos
l-Isoleucine 132.10067 271.563 7 131.17296 C6H13NO2 C00407 pos
Adenine 134.04598 219.269 9 135.1269 C5H5N5 C00147 neg
Threonate 135.02871 84.5419 9 136.10332 C4H8O5 C01620 neg
p-Salicylic acid 137.02327 115.089 8 138.12074 C7H6O3 C00156 neg
Salicylate 139.11118 706.72 4 138.122 C7H6O3 C00805 pos
4-Guanidinobutanoic Acid 146.09205 121.961 0 145.1597 C5H11N3O2 C01035 pos
l-Glutamine 147.07625 90.527 2 146.14458 C5H10N2O3 C00064 pos
l-Lysine 147.11259 80.1761 1 146.18764 C6H14N2O2 C00047 pos
O-Acetyl-L-Serine 148.06013 93.1268 6 147.1293 C5H9NO4 C00979 pos
l-Glutamic Acid 148.06014 102.515 3 147.1293 C5H9NO4 C00025 pos
l-Methionine 150.05874 144.366 0 149.21238 C5H11NO2S C00073 pos
3-Methyladenine 150.07725 120 6 149.15348 C6H7N5 C00913 pos
9H-Xanthine 151.02495 204.337 9 152.11102 C5H4N4O2 C00385 neg
3,4-Dimethylbenzoic acid 151.07466 356.095 5 150.1745 C9H10O2 pos
Guanine 152.05492 121.98 12 151.126 C5H5N5O C00242 pos
l-Histidine 156.07667 86.0491 2 155.15468 C6H9N3O2 C00135 pos
1-Benzylimidazole 159.09162 405.939 1 158.084398 C10H10N2 pos
(R)-2-Hydroxycaprylic Acid 159.10166 390.322 7 160.2108 C8H16O3 neg
l-Phenylalanine 164.07031 330.364 12 165.18918 C9H11NO2 C00079 neg
Capric Acid 171.13816 806.583 5 172.265 C10H20O2 C01571 neg
Dehydroascorbic Acid 173.00812 112.035 6 174.10824 C6H6O6 C05422 neg
Shikimic Acid 173.04464 84.3461 5 174.1513 C7H10O5 C00493 neg
l-Arginine 173.10357 96.2243 9 174.201 C6H14N4O2 C00062 neg
Muscarine 174.14873 335.354 1 173.141579 C9H19NO2 pos
Citrulline 176.1028 94.1013 1 175.18584 C6H13N3O3 C00327 pos
l-Tyrosine 180.06494 122.902 9 181.18858 C9H11NO3 C00082 neg
Keto-D-Fructose 180.08643 96.8 7 180.15588 C6H12O6 C10906 pos
2-Methylthio-1,3-Benzothiazole 182.00909 815.127 1 181.278 C8H7NS2 C10910 pos
Triethyl Phosphate 183.07879 612.399 4 182.15466 C6H15O4P pos
D-Glucitol 183.08768 93.504 8 182.17176 C6H14O6 C00794 pos
Nonanedioic Acid 187.09744 302.361 1 188.22094 C9H16O4 C08261 neg
3-Hydroxycapric Acid 187.13294 586.019 6 188.264 C10H20O3 neg
Deethylatrazine 188.07031 405.939 3 187.0625 C6H10ClN5 C06559 pos
Quinic Acid 191.05511 84.3797 5 192.16658 C7H12O6 C00296 neg
N,N-Diethyl-M-Toluamide 192.13757 755.649 4 191.2695 C12H17NO C10935 pos
Butylparaben 193.08626 761.377 4 194.227 C11H14O3 D01420 neg
l-Leucyl-l-Alanine 201.1237 125.201 4 202.2508 C9H18N2O3 neg
Alanyl-DL-Leucine 203.13918 182.305 1 202.131742 C9H18N2O3 pos
ADMA 203.15006 101.974 1 202.25428 C8H18N4O2 C03626 pos
Indolelactic Acid 206.08104 486.255 1 205.073893 C11H11NO3 C02043 pos
7-Oxo-11-Dodecenoic Acid 211.13328 598.679 3 212.141237 C12H20O3 neg
8-Chlorotheophylline 213.01457 486.288 18 214.025751 C7H7ClN4O2 neg
Diphenylurea 213.10194 713.78 1 212.248 C13H12N2O pos
Octhilinone 214.12574 808.599 1 213.34 C11H19NOS C18752 pos
Tetradecylamine 214.25247 777.08 2 213.245649 C14H31N pos
12-Hydroxydodecanoic Acid 215.16486 616.949 2 216.3172 C12H24O3 C08317 neg
Cymiazole 219.09484 780.674 1 218.087769 C12H14N2S pos
(R)-Pantothenic Acid 220.11782 366.856 1 219.23502 C9H17NO5 C00864 pos
Benzanthrone 231.08369 121.982 14 230.073165 C17H10O pos
Confertifoline 233.15282 691.733 8 234.335 C15H22O2 C09376 neg
Dropropizine 237.15702 504.911 12 236.311 C13H20N2O2 pos
l-Cystine 239.12849 758.968 3 240.30256 C6H12N2O4S2 C00491 neg
Uridine 243.06232 123.067 0 244.20146 C9H12N2O6 C00299 neg
Cytidine 244.09223 121.485 1 243.21674 C9H13N3O5 C00475 pos
N,N-Diisopropyl-3-Nitrobenzamide 251.13594 144.97 12 250.131743 C13H18N2O3 pos
Glycerophosphocholine 258.10912 92.1093 4 257.2213 C8H20NO6P C00670 pos
Gamma-Glu-Leu 261.14469 317.015 1 260.2869 C11H20N2O5 pos
l-Phenylalanyl-l-Proline 263.13858 444.483 2 262.3043 C14H18N2O3 pos
12-oxo-2,3-Dinor-10,15-Phytodienoic Acid 263.16518 831.168 0 264.36 C16H24O3 neg
Adenosine 268.10341 309.894 0 267.24152 C10H13N5O4 C00212 pos
16-Hydroxy Hexadecanoic Acid 271.22753 833.483 1 272.4235 C16H32O3 C18218 neg
Triethylcitrate 277.12763 716.368 2 276.283 C12H20O7 D06228 pos
Dibutyl Phthalate 279.16034 797.891 4 278.3435 C16H22O4 C14214 pos
Guanosine 284.0984 316.055 4 283.24092 C10H13N5O5 C00387 pos
Epicatechin 289.0712 462.296 2 290.2681 C15H14O6 C09727 neg
Catechin 291.08533 462.105 5 290.2681 C15H14O6 C06562 pos
Terbinafine 292.21163 650.276 19 291.4299 C21H25N C08079 pos
5-S-Methyl-5-Thioadenosine 298.09601 392.891 0 297.3347 C11H15N5O3S C00170 pos
TMS 301.14395 420.822 2 300.136159 C18H20O4 pos
Dicyclomine 310.27177 755.879 8 309.4867 C19H35NO2 C06951 pos
Triptophenolide 311.16832 790.124 10 312.172545 C20H24O3 neg
13(S)-HpODE 311.22274 743.286 0 312.4443 C18H32O4 C04717 neg
9(S)-HpODE 311.22294 775.306 0 312.4443 C18H32O4 C14827 neg
9,10-DiHOME 313.23891 724.13 2 314.4602 C18H34O4 C14828 neg
Acitretin 325.18414 810.001 10 326.42934 C21H26O3 D02754 neg
Quinine 325.191 652.018 0 324.4168 C20H24N2O2 C06526 pos
Yohimbic Acid 339.1648 831.469 19 340.41624 C20H24N2O3 neg
Vinpocetine 351.21345 714.679 19 350.455 C22H26N2O2 pos
Estradiol Valerate 355.22845 599.118 2 356.499 C23H32O3 C12859 neg
Sinapaldehyde Glucoside 369.11997 96.3153 2 370.126376 C17H22O9 neg
Methyl Arachidonyl Fluorophosphonate 371.24766 763.893 9 370.243695 C21H36FO2P pos
Gentian Violet 372.24226 790.875 3 371.236135 C25H30N3 pos
Tamsulosin 409.18158 464.483 6 408.512 C20H28N2O5S C07124 pos
Procyanidin B2 579.1489 407.114 1 578.5202 C30H26O12 C17639 pos
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1 Name: identification results; 2 m/z: mass nuclear ratio; 3 rt: retention time, s; 4 ppm: error between molecular weight and theoretical molecular weight, ppm; 5 exact mass: accurate molecular weight; 6 KEGG: KEGG compound number; 7 posneg: ionization mode, where pos is positive ion mode and neg is negative ion mode.

Table A2.

Differentially expressed metabolites in all samples of the first and second groups.

Name 1 1 vs. 2_VIP 2 Fold Change_2/1 Log2(FC_2/1) 3 p-Value 4 FDR 5
Vinpocetine 1.869946 0.3202 −1.6429 0.0000013 0.000254
Glycerophosphocholine 1.856177 42.044 5.3938 0.0000037 0.000517
Choline 1.855588 6.291 2.6533 0.0000039 0.000531
l-Threonine 1.813788 1.6411 0.71469 0.0000320 0.002047
ADMA 1.808401 4.7429 2.2458 0.0000392 0.002287
l-Serine 1.790296 3.2158 1.6852 0.0000727 0.003252
Keto-D-Fructose 1.77673 0.20739 −2.2696 0.0001089 0.00426
l-Histidine 1.740644 2.6231 1.3913 0.0002689 0.007746
l-Lysine 1.739258 2.5496 1.3503 0.0002773 0.007874
l-Methionine 1.734536 4.0561 2.0201 0.0003074 0.00837
Nicotinamide 1.727199 5.4063 2.4346 0.0003589 0.009191
Salicylate 1.721537 1.1119 0.15298 0.0004026 0.010029
l-Pipecolic acid 1.648997 2.1215 1.0851 0.0013724 0.020324
Agmatine 1.629025 0.097098 −3.3644 0.0018093 0.02423
Guanine 1.62376 2.0952 1.0671 0.0019395 0.025193
Indolelactic acid 1.621818 4.6738 2.2246 0.0019892 0.025604
N,N-Diisopropyl-3-Nitrobenzamide 1.614857 2.2462 1.1675 0.0021749 0.027391
Dropropizine 1.576419 0.89283 −0.16354 0.0034327 0.035944
3-Methyladenine 1.569726 2.1357 1.0947 0.0036958 0.037589
l-Phenylalanyl-L-Proline 1.54751 2.9302 1.551 0.0046725 0.04372
O-Acetyl-l-Serine 1.534309 2.9405 1.5561 0.0053332 0.048166
Glycine Betaine 1.526096 4.1876 2.0661 0.0057765 0.050485
Putrescine 1.49701 0.56468 −0.8245 0.0075577 0.058673
Adenosine 1.492166 7.3509 2.8779 0.0078881 0.060157
Citrulline 1.484936 1.6943 0.76068 0.0084002 0.062645
Imidazoleacetic acid 1.414003 1.6133 0.69005 0.0147345 0.087407
4-Guanidinobutanoic acid 1.390837 12.812 3.6794 0.0173733 0.096289
5-S-Methyl-5-Thioadenosine 1.368643 4.2794 2.0974 0.0201933 0.105666
Dibutyl Phthalate 1.363868 3.1017 1.6331 0.0208389 0.107572
Muscarine 1.316996 3.677 1.8785 0.0279490 0.12836
Piperidine 1.275156 1.9234 0.94366 0.0355599 0.148035
Tamsulosin 1.271617 4.6915 2.23 0.0362616 0.149595
(R)-Pantothenic acid 1.224623 1.732 0.7924 0.0464829 0.171966
3,4-Dimethylbenzoic acid 1.222463 0.18966 −2.3985 0.0469943 0.173234
Quinic acid 1.780892 4.5158 2.175 0.0000016 0.000236
l-Erythrulose 1.739383 0.32058 −1.6412 0.0000213 0.001223
Pyruvate 1.736553 0.354 −1.4982 0.0000241 0.0013
3-Methyl Pyruvic acid 1.728988 0.32725 −1.6115 0.0000332 0.001502
Threonate 1.712791 3.4506 1.7868 0.0000607 0.002173
Sinapaldehyde Glucoside 1.701181 3.2492 1.7001 0.0000889 0.002691
8-Chlorotheophylline 1.700766 0.39072 −1.3558 0.0000901 0.0027
l-Asparagine 1.657314 5.2423 2.3902 0.0002870 0.006038
Adenine 1.585462 1.975 0.98184 0.0011264 0.015959
9(S)-HpODE 1.504033 2.4481 1.2917 0.0034110 0.032279
9H-Xanthine 1.489229 0.42715 −1.2272 0.0040398 0.03549
Glutaric acid 1.472775 3.4595 1.7906 0.0048316 0.039341
D-Glyceraldehyde 1.457553 0.20576 −2.281 0.0056575 0.042594
Yohimbic acid 1.447777 37.215 5.2178 0.0062382 0.045236
l-Arginine 1.428014 3.3292 1.7352 0.0075394 0.050283
l-Leucine 1.378902 2.4788 1.3096 0.0115875 0.063966
12-oxo-2,3-Dinor-10,15-Phytodienoic acid 1.360253 2.2232 1.1527 0.0134607 0.070293
Confertifoline 1.346373 0.28192 −1.8266 0.0149854 0.075265
Uridine 1.327309 3.6455 1.8661 0.0172704 0.082697
Nonanedioic acid 1.311257 9.5381 3.2537 0.0193733 0.088133
4-Hydroxy-l-Proline 1.256131 0.52658 −0.92528 0.0279380 0.109387
4-Hydroxybenzaldehyde 1.161319 2.5676 1.3604 0.0481468 0.151292
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1 Name: identification results; 2 VIP: Variable Importance in the Projection; 3 log2 (FC): log2 value of fold change; 4 p-value: Student’s t test; 5 FDR: False Discovery Rate.

Author Contributions

Writing—original draft preparation, Y.Z.; validation, W.C.; methodology, W.C.; formal analysis, Y.Y.; investigation, H.C.; resources, W.C.; data curation, Q.Z.; writing—review and editing, Y.Z.; supervision, W.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the High-level Talent Project of Hainan Natural Science Foundation(2019RC128), the Natural Science Foundation of Hainan Province of China (ID: 317002), the National Natural Science Foundation of China (IDs: 31640061 and 31801494), and the Hainan University Start-up Scientific Research Projects of China (IDs: kyqd1551 and kyqd1630).

Conflicts of Interest

The authors declare that they have no conflict of interest. This section is not mandatory but may be added if there are patents resulting from the work reported in this manuscript.

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