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. 2020 Mar 27;5(13):7567–7575. doi: 10.1021/acsomega.0c00398

Untargeted Metabolite Profiling of Antimicrobial Compounds in the Brown Film of Lentinula edodes Mycelium via LC–MS/MS Analysis

Lihua Tang 1, Junjun Shang 1, Chunyan Song 1, Ruiheng Yang 1, Xiaodong Shang 1, Wenjun Mao 1, Dapeng Bao 1,*, Qi Tan 1,*
PMCID: PMC7144172  PMID: 32280900

Abstract

graphic file with name ao0c00398_0003.jpg

The brown film (BF) of Lentinula edodes mycelium has been reported to exert biological activities during mushroom cultivation; however, to date, there is limited information on its chemical composition. In this study, untargeted metabolomics analysis was performed via liquid chromatography–mass spectrometry (LC–MS), and the results were used to screen the antimicrobial compounds. A total of 236 differential metabolites were found among the BF stages compared with the white hyphal stage. Among them, five important antimicrobial metabolites related to antimicrobial activities, namely, 6-deoxyerythronolide B, tanikolide, hydroxyanthraquinone, benzylideneacetone, and 9-OxooTrE, were present at high levels in the BF samples. The score plots of the principal component analysis indicated that the samples from four time points could be classified into two groups. This study provided a comprehensive profile of the antimicrobial compounds produced during BF formation and partly clarified the antibacterial and antifungal mechanism of the BF of L. edodes mycelium.

Introduction

Lentinula edodes, also known as the shiitake mushroom, is the most important mushroom in the world and is a widely cultivated and highly popular edible and medicinal mushroom in Asia.1,2 In addition, over 1 321 000 tons of L. edodes, accounting for 80% of global production, is produced in China, which has a very long history of cultivating this mushroom.3,4 The value of L. edodes in food and medicine has recently been revealed, and L. edodes contains many pharmaceutical compounds, which have antitumor,5,6 antioxidant,7 and/or immunity-enhancing properties.8 Notably, the polysaccharide of L. edodes have been was used as an anticancer drug adjuvant.9 Recently, it was also reported that lentinan extracted from L. edodes can be used to cure gut inflammation and endotoxemia.10,11

During the cultivation process of shiitake mushrooms, the mycelium on the package surface needs to form a brown film (BF). The stage of BF formation is very important for mushroom cultivation. L. edodes requires a longer cultivation time than other mushrooms. The period of sawdust medium cultivation is long compared with those of other mushrooms, such as Pleurotus ostreatus and Flammulina velutipes, because of the BF formation stage. Sawdust medium cultivation with BF has increased the antimicrobial contamination and water retention, which directly affect the production and quality. BF is important for the growth cycle and fruiting of this mushroom.2,12

Metabolomics is increasingly employed to gain insight into the chemical compositions of biological processes and explain the responses of fungi to various changes in environmental conditions.13−17 Various metabolite profiling tools have been widely used in metabolomics.18−20 Liquid chromatography–mass spectrometry (LC–MS) has also been used for metabolome analysis, which also has been reported in the analyses of fungal metabolites.21,22 There have been many transcriptome studies on the development of L. edodes, but the metabolic basis of the BF formation process is still unclear. Therefore, to gain insight into the development of BF formation, we investigated the four developmental stages of BF formation using an LC–MS/MS approach, and eight biological replicates were prepared for each stage. The metabolic differences in the stages were assessed by combining an LC–MS/MS detection platform, a self-built database, and multivariate statistical analysis. This study aimed to reveal the antimicrobial compounds formed during the BF formation process, which could also reveal the important roles of antibacterial and antifungal compounds in the cultivation of L. edodes.

Results

Physiological Changes during BF Formation

An obvious variation was observed in the appearance of the surface mycelium during the BF formation process. The mycelium was white in color at 30 days. After 15 days, the surface mycelium became slightly brown in color. At 60 days, the surface mycelium became very brown. At 75 days, the surface mycelium had a very deep brown color (Figure 1). These four stages (A: 30 days; B: 45 days; C: 60 days; and D: 75 days) were considered representative phases of BF formation for investigating its metabolite profile.

Figure 1.

Figure 1

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BF of the surface mycelium at four different developmental stages of L. edodes: (A) the mycelium of 30 days; (B) the mycelium of 45 days; (C) the mycelium of 60 days; and (D) the mycelium of 75 days.

Metabolite Profiling of the Surface Mycelium during BF Formation

to investigate the effects of different periods of BF formation on the metabolism of surface mycelium in L. edodes, we collected BF samples at four time points. The metabolite profiles at different stages were analyzed by LC–MS. The base peak ion (BPI) chromatograms of the LC–MS positive and negative ion models are shown in Figure 2. We observed that the chromatograms on days 45, 60, and 75 were similar. The metabolite profiles at different stages were analyzed by LC–MS. In total, 5029 chromatographic peaks were identified by deconvolution after splitless injections. The variance important project compounds were screened out, and 236 different metabolites were determined and identified as significantly different during the BF formation process (Tables S1–S3).

Figure 2.

Figure 2

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BPI chromatogram of the positive and negative ion modes: (A, B) the LC–MS positive and negative ion mode chromatograms, respectively.

To reveal the clustering relationships between differential metabolites, the expression of metabolites is shown in the heat map (Figure 3). The findings indicate substantial differences in the metabolites at the four time periods during BF formation. Red indicates a relatively high expression, and green indicates a relatively low concentration. The metabolite variation was classified into four primary dissimilar patterns. First, the contents of some metabolites, including 53 metabolites, such as oleic acid, dl-serine, myristic acid, and gentiobiose, were high at the early stages (30 days) and low in the later stages (45, 60, and 75 days). Second, a total of 103 metabolites had low contents in white mycelium, which then increased slightly, with slightly higher contents in the later stages (45, 60, and 75 days) than in the earlier stages. These metabolites included 12-OPDA, lentinic acid, 21-deoxycortisol, and phloionolic acid. Notably, a third group of 55 metabolites had high contents in the intermediate stages (45 or 60 days) and low contents in the white mycelium and the later stage (75 days). This category included stearidonic acid, stoloniferone L, mosinone A, veratramine, and phytosphingosine. Another 25 metabolites were recorded to have high contents in the white mycelium or the sample with BF. From the heat map, the metabolite profiles changed significantly during the BF formation process, and a specific set of metabolites was involved in this process.

Figure 3.

Figure 3

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Heat map and cluster tree showing the differences in the expression levels of the metabolites of surface mycelium at different BF formation stages. Durations ranged from 30 to 75 days (A: 30 days; B: 45 days; C: 60 days; and D: 75 days). Values represent the relative expression of metabolites normalized by the sum values. Colors are based on expression levels and changes in metabolites, where red represents high expression and green represents low expression.

Evaluation of the Repetition Correlation

Biological replicates were obtained among samples in a group by performing correlation analysis between samples. The high correlation coefficient of the samples in the group relative to the intergroup samples indicated a reliable metabolite. Pearson’s correlation coefficient, r, was considered as the evaluation indicator for the correlation of biological replicates. An r2 value approaching 1 indicates a strong correlation between repetitive samples. Figure 4 shows an excellent intragroup repetitiveness. The experimental data ensured the accuracy of the analysis results. Good intergroup repetitiveness was observed for all the samples except the white mycelial samples (30 days, A). The low repetitiveness of these samples (A) may be due to their lack of BF.

Figure 4.

Figure 4

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Repetitive correlation between samples of different groups (A1–A8: duplicate sample of 30 days; B1–B8: duplicate sample of 45 days; C1–C8: duplicate sample of 60 days; and D1–D8: duplicate sample of 75 days).

Metabolite Variation in the Surface Mycelium of L. edodes During the BF Formation Process

The results of the principal component analysis (PCA) of each sample (Figure 5) show that the mycelial samples at different BF development times have obvious degrees of separation. This variation reflects the changes in the metabolites in the white mycelium (30 days) and the mycelium with BF (45, 60 and 75 days), and the PCA scores of the samples were a good fit (R2 = 0.738) and exhibited a good predictive value (Q2 = 0.482) in the PCA scoring diagram (Figure 5). The first principal component explained 22% of variables, and the second principal component explained 18.6% variables. The PCA scores showed that the distribution of the samples was roughly the same and that there was no sample out of the scoring chart of Hotelling’s T2 95% confidence interval, allowing the samples to be used for subsequent analyses.

Figure 5.

Figure 5

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PCA of the metabolites derived from LC–MS data (durations ranged from 30 to 75 days).

The results of the orthogonal partial least squares-discriminant analysis (OPLS-DA) model were applied to the LC–MS data. For the three comparative groups of B/A, C/A, and D/A, score plots from PCA, PLS-DA, and OPLS-DA and validation plots of the OPLS-DA models were constructed (Figure 6A–C). All parameters for these models are summarized in Table 1. It was evident that all of the values for Hotelling’s T2 were 95% (confidence interval, which stands for the degree of confidence, where increasing numbers indicate increasing confidence). The results indicate that the mycelial samples had significant spectral separation, and the metabolic differences between the samples with BF (45, 60, and 75 days) and the samples without BF (30 days) were statistically significant. As shown in Figure 6A, the R2Y and Q2 values were 0.952 and 0.83, respectively. The R2Y and Q2 values presented in Figure 6B are 0.999 and 0.98, respectively. The R2Y and Q2 values presented in Figure 6C are 0.985 and 0.956, respectively. This result showed that the OPLS-DA model was stable and reliable. The OPLS-DA model was used according to a variable influence on projection (VIP) values and p-values to select differential metabolites. Metabolites with VIP >1.5 and p <0.05 were selected as differential metabolites. To distinguish the varying metabolites in different phases of the BF formation process, we used Student t test to compare the metabolite composition of the white mycelium with that of the BF mycelium after 45, 60, and 75 days and 151, 164, and 115 metabolites, respectively, varied significantly in content. A total of 236 different metabolites were identified as significantly different during the BF formation process. From the Venn diagram of significantly different metabolites (Figure 6D), 61 of the identified metabolites were common when B/A, C/A, and D/A were compared. The comparisons of B/A and C/A revealed 105 shared metabolites, whereas the comparison of B/A and D/A revealed 69 shared metabolites. In addition, 79 metabolites were found to be common to C/A and D/A. However, 36, 40, and 27 metabolites were exclusively specific to B/A, C/A, and D/A, respectively.

Figure 6.

Figure 6

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Score scatter plots of OPLS-DA for the three comparative groups. (A) OPLS-DA for the B/A comparative group; (B) OPLS-DA for the C/A comparative group; (C) OPLS-DA for the ER/MR comparative group; and (D) a Venn diagram showing the overlapping and stage-specific differential metabolites from the three comparisons (B/A, C/A, and D/A).

Table 1. Parameters for the Assessment of These Modelsa.

no. model type A N R2X (cum) R2Y (cum) Q2 (cum) R2 Q2
all M1 PCA-X 7 36 0.738   0.482    
B-A M2 PCA-X 3 16 0.68   0.52    
B-A M3 PLS-DA 2 16 0.454 0.952 0.83    
B-A M4 OPLS-DA 1 + 1 + 0 16 0.454 0.952 0.871 0.542 –0.562
C-A M5 PCA-X 4 16 0.77   0.437    
C-A M6 PLS-DA 2 16 0.487 0.989 0.929    
C-A M7 OPLS-DA 1 + 3 + 0 16 0.792 0.999 0.98 0.818 –0.505
D-A M8 PCA-X 3 16 0.766   0.623    
D-A M9 PLS-DA 4 16 0.849 0.997 0.97    
D-A M10 OPLS-DA 1 + 2 + 0 16 0.813 0.985 0.956 0.638 –0.66
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a

Note: A indicates the PC numbers while each model was being constructed; N indicates the number of samples analyzed; B-A indicates the comparative group B/A; C-A indicates the comparative group C/A; D-A indicates the comparative group D/A; M1–M10 indicates the model 1e group numbers interpretation rate of each model in the X-axis direction for multivariate statistical analysis modeling; R2Y (cum) indicates the interpretation rate of each model in the Y-axis direction for multivariate statistical analysis modeling; Q2 (cum) indicates the prediction rate of each model; R2 indicates the intercept value of the Y-axis and the regression line, which was obtained when linear regression analysis between the Y matrix of the original classification, the Y matrices of N times different permutations and R2Y was conducted during model validation; and Q2 indicates the intercept value of the Y-axis and the regression line, which is obtained when linear regression analysis between the Y matrix of the original classification, the Y matrices of N times different permutations, and Q2Y was conducted during model validation. For Q2 in external validation, the general requirement is that Q2 < 0, and overfitting was avoided. For R2 in internal validation, the general requirement is that R2 > 0.5; the closer the R2 value is to 1, the better the model. For R2X, the general requirement is that R2X > 0.4 for a good model.

Comparison of the Contents of Antimicrobial Compounds

Among the 236 compounds, five important antimicrobial compounds with significant changes, namely, 6-deoxyerythronolide B (6dEB), tanikolide, hydroxyanthraquinone, benzylideneacetone (BZA), and 9-OxooTrE, were detected in the first review of the results. The overall profiles of antimicrobial compounds varied among the four time points, as shown in Figure 7 and Table 2. In the white mycelial sample (30 days), the relative contents of the five important antimicrobial compounds (9-OxoOTrE, tanikolide, 6-deoxyerythronolide B, hydroxyanthraquinone, and benzylideneacetone) were relatively low, but three antimicrobial compounds (9-OxoOTrE, tanikolide, and 6-deoxyerythronolide B) had relatively high contents in the three stages (45, 60, and 75 days) with BF. The other two antimicrobial compounds were relatively high in the two later stages (60 and 75 days).

Figure 7.

Figure 7

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Relative intensity (logarithmic scale) of the antimicrobial metabolites in the different mycelial samples visualized as a heat map.

Table 2. Differential Antimicrobial Metabolites in the BF Film of L. edodes.

  B-A
 C-A
 D-A
metabolite VIP p-value fold change VIP p-value fold change VIP p-value fold change
6-deoxyerythronolide B 1.1294 0.03496 3.118 1.65769 0.00103 5.403 1.35031 0.01219 4.057
tanikolide 1.6956 0.00211 10.748 1.85971 0.00119 16.605 1.57553 0.00781 12.582
hydroxyanthraquinone 0.6879 0.09506 1.372 1.33325 0.0000297 2.141 1.96057 0.00005 3.328
benzylideneacetone 0.4406 0.60235 1.091 4.11898 0.01896 1.992 3.05784 0.00366 1.603
9-OxooTrE 2.1451 0.00027 3.112 2.09502 0.00006 3.298 3.11104 0.000004 2.897
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Discussion

L. edodes is an important food and medicinal mushroom, and its consumption is increasing. However, the cultivation period of L. edodes is very long because the BF formation stage is long, which is an important process for the development from vegetative growth to reproductive growth in this mushroom. The BF development is crucial for the yield and quality of the mushroom with antibacterial, antifungal, and water retention roles. The BF of mycelium also plays very important roles in resistance to pathogens during sawdust medium cultivation, and mycelium without BF is easily infected by pathogens that cause decay. Many scientific studies have focused on the development, nutrition, and pharmaceutical application of the fruiting body.23,24 However, the molecular mechanism of how BF participates in the resistance is not clear. We speculated that metabolites produced by BF may lead to an increase in resistance.

The LC–MS-based metabolomics approach is a powerful tool for studying metabolites. In the present study, an untargeted LC–MS-based metabolomics approach, a multivariate analysis, and a univariate analysis were used to identify the differential metabolites and to assess various changes during the BF formation process. A total of 236 nonrepetitive differential metabolites were identified between three comparative groups, B/A, C/A, and D/A, of which 61 were common differential metabolites shared among the three comparative groups. Many differential metabolites were first identified by means of an untargeted LC–MS-based metabolomics approach in the BF of L. edodes, which also shows the diversity and richness of the metabolites in BF. Untargeted detection of the metabolites with LC–MS, PCA, and OPLS-DA showed that mycelium with BF and white mycelium can clearly be distinguished, showing that the development stage has a great influence on metabolites. The changes in metabolites involved in antimicrobial activities were detected and analyzed at each development stage (30, 45, 60, and 75 days). The antimicrobial metabolite content was higher in mycelium with BF than in white mycelium. We selected four critical time points of the BF formation stage according to the morphological features and colors of the surface mycelium. It can be speculated that antimicrobial metabolites affect the different developmental stages of BF formation.

In the BF formation process of the surface mycelium in L. edodes, some important antimicrobial compounds were detected by LC–MS. There were three important compounds involved in antifungal and antibacterial activities that had significantly higher levels at approximately 45, 60, and 75 days in the stages with BF than at earlier stages without BF (30 days). The three compounds were 6-deoxyerythronolide B, tanikolide, and 9-OxooTrE. The compound 6-deoxyerythronolide B is the macrocyclic aglycone of the antibiotic erythromycin, which is a broad-spectrum polyketide antibiotic that has diverse therapeutic applications.25,26 The results showed that polyketone antibiotics were produced during BF formation process and also consistent with the results of the transcriptome study.12 Benzylideneacetone is a monoterpenoid compound that possesses inhibitory activity against sPLA2 and has also been identified as antibacterial and antibiotic compounds that can protect against major plant-pathogenic bacteria.27 Benzylideneacetone also has certain insecticide and insect repellent effect, which may be caused that there are few insect pests during shiitake mushroom cultivation with BF. The commercially available benzylideneacetone also showed antibacterial activities against five susceptible bacteria.28 Tanikolide, which is a lactone compound, is a natural product that was first isolated from the marine cyanobacteria Lyngbya majuscula, which also exhibits antifungal activities.29,30 This is the first report in a fungus such as shiitake mushrooms, which may be related to the antifungal roles of BF. Oxylipins are a large family of metabolites derived from polyunsaturated fatty acids and have effects on pathogens.31 9-OxoOTrE is produced by the oxidation of 9-HpOTrE and exhibits antimicrobial activity against plant-pathogenic microorganisms, including bacteria and fungi.32

High levels of hydroxyanthraquinone (c\d) were observed in the latter two stages (60 and 75 days). Hydroxyanthraquinones are generally yellow, orange, or red in nature, which have some active functions, such as antibacterial,33 antioxidant,34 and antitumor activity.35,36 Some scholars have obtained orange-red pigments, which have been identified as antitumor antibiotics, while screening antitumor antibiotics. Gill first isolated tetrahydroanthracene with antibacterial activity from the ethanol extract from fruiting bodies of the fungal Dermocybe splendida.37 Hydroxyanthraquinone was detected from the BF, which shows that the pigment of the BF of shiitake mushroom mycelium contains hydroxyanthraquinone, and also indicates that the BF of mycelium should also be the important source of an active substance, which exhibited antimicrobial, antioxidant, and anticancer activities. This substance can also be used as an important pigmentary compound in natural products and plays an important role as natural dyes.

Conclusions

In this study, metabolomics was adopted to analyze the composition of antimicrobial compounds in the BF formation process of the surface mycelium of L. edodes. This study also aimed to indirectly verify previous findings, which indicate that BF plays a role in protecting against pathogenic fungi and bacterial infections in the cultivation process. During the formation of the BF on the surface mycelium, the basic composition and content of antimicrobial compounds displayed differences. From the obtained results, these findings provide a theoretical basis for further studies on the developmental process of the BF, which might be considered a rich source for the extraction of bioactive compounds and pigment with antimicrobial properties.

Experimental Section

Chemicals

All chemicals and solvents were high-performance liquid chromatography (HPLC) or analytical grade. Formic acid, acetonitrile, methanol, and ammonium bicarbonate were purchased from CNW Technologies GmbH (Düsseldorf, Germany). The water used was doubly deionized using MilliQ Ultra purification system (Millipore, Vimodrone, Italy). L-2-Chlorophenylalanine was purchased from Heng Chuang Biotechnology Co., Ltd. (Shanghai, China).

Mycelial Materials and Growth Conditions

L. edodes strain 135 was obtained was obtained from the Agricultural Culture Collection of China (Beijing) (no. ACCC 50903) and was used in this study, and fungal mycelium was cultivated according to the report,12 cultivation bags in the dark for 30 days, and the substrate was fully colonized by white mycelium (sample 30 days). Bags were then exposed to a 12 h light/dark regime (white light, 300 lx) for 15, 30, and 45 days to induce BF formation on the mycelium surface (samples were collected at 45, 60, and 75 days). Eight independent biological replicates were prepared for LC–MS analysis in this study. Each sample was frozen in liquid nitrogen and stored at −80 °C.

Metabolite Extraction and Sample Preparation

For metabolite analysis, 100 mg of each sample was transferred to 1.5 mL tube and two small steel balls were added. Then, 20 μL of internal standard (water/methanol, volume = 1:1) and 1 mL of a mixture of water and methanol (water/methanol, volume = 3:7) were added, and these samples were placed at −80 °C for 2 min. These samples then were ground at 60 Hz condition for 2 min (JXFSTPRP-24, tissue homogenizer, Shanghai Jingxin Industry Co., Ltd., China) and ultrasonicated for 30 min (SB-5200DT, ultrasonic cleaner, Ningbo Xinzhi Biological Technology Co., Ltd., China) after vortexing and then kept at 4 °C for 10 min. All samples were then centrifuged for 10 min. Five hundred microliters of the supernatant was collected by a crystal syringe (Modulspin 31, Biotron, Korea) and filtered by a 0.22 μm microfilter and transferred to an LC vial, and all these vials were stored at −80 °C for LC–MS analysis.

Quality control (QCs) samples were prepared by mixing aliquots of these samples into a pooled sample. Carnitine C2:0-d3, carnitine C16:0-d3, carnitine C10:0-d3, carnitine C8:0-d3, FFA C16:0-d3, CDCA-d4, LPC 19:0, CA-d4, FFA C18:0-d3, Phe-d5, Trp-d5, SM12:0, and choline-d4 were added to the QCs.

LC/MS Analysis

An LTQ Orbitrap MS (Thermo Fisher Scientific, Waltham, MA) coupled with an ACQUITY UHPLC system Ultimate 3000 (Thermo Fisher Scientific, Waltham, MA) was used to analyze the metabolomics in electrospray ionization (ESI) ion modes. In the negative ion mode, metabolite separation was performed by 2.1 × 100 mm2 ACQUITYTM 1.8 μm HSS T3 column and the mobile phase contained 6.5 mM of ammonium bicarbonate water solution (A) and 6.5 mM of ammonium bicarbonate in (B) 95% methanol. The linear elution gradient program was 5% B, maintained for 1.0 min, then increased to 100% B between 1 and 18 min, maintained for 4 min, increased to 100 to 5% B from 22 to 22.1 min, and held at 5% from 22.1 to 25 min. In positive ion mode, the separation of metabolites was conducted by 2.1 × 100 mm2 ACQUITYTM 1.7 μm BEH C18 column, and the mobile phase contained water with 0.1% formic acid (C) and acetonitrile (D). The linear elution gradient method was used in this study as follows: 5% B was maintained for 1.0 min and then increased to 100% D at 24 min, maintained for 4 min and linearly increased to 100–5% from 28 to 28.1 min, and maintained at 5% from 28.1 to 30 min. The volume of injection was 5 μL. The temperature of the column was 50 °C and the flow rate was 0.35 mL/min. Mass spectrometry detections were set as follows: capillary temperatures of 360 and 350 °C and spray voltages of 3.0 and 3.5 kV for negative ion mode and positive ion mode, respectively. The MS resolution was set to 30 000. The mass scan range was from 50 to 1000 m/z.

Data Processing and Statistical Analyses

The software XCMS was used for analyzing the MS data from the UHPLC-LTQ Orbitrap, and the software can produce a matrix of features with accurate mass, chromatographic information, and the associated retention time. Also, the variables that presented in at least 80% of either group were extracted. Then, further multivariate data analysis was performed after retaining the variables with <30% relative standard deviation (RSD) in QC samples. The internal peaks of the data set were removed. The resulting data of each sample were first normalized to the total peak area by Microsoft Excel. Also, the data were used for PCA by importing into the SIMCA software (version 14.0; Umetrics, Umea, Sweden). The OPLS-DA models were validated through a permutation analysis (200 times).

The metabolite profiles were visualized by a heat map, which was prepared by TIGR MEV 4.2 software package (http://mev.tm4.org/#/welcome). Also, the average measured concentrations of individual metabolites were used to draw the heat map. The 95% confidence interval of the modeled variation was defined by the Hotelling’s T2 region, which was shown as an ellipse in score plots of the models. The quality of the models was described by the Q2 and R2X or R2Y values. Q2 is defined as the proportion of variance in the data, which indicates predictability, calculated by a cross-validation procedure. R2X or R2Y is defined as the proportion of variance in the data, which indicates the goodness of fit. Also, to determine the optimal number of principal components and avoid overfitting, we performed a default seven-round cross-validation in SIMCA. The Student t test (p < 0.05) was used to determine the significance of the observed changes in mean metabolite concentrations between the samples.

Identification of Differential Metabolites

The differential metabolites were selected by the combination of a statistically significant threshold of VIP value obtained from the OPLS-DA model and a p-value from a two-tailed Student t test on the normalized peak areas, with metabolites selected with VIP values larger than 1.5. We identified small molecules and differential metabolites by the One-step Solution software, and the HMDB and METLIN databases were referenced, which were co-developed by Dalian ChemData Solution Information Technology Co., Ltd. and the Dalian Institute of Chemical Physics, Chinese Academy of Sciences.

Acknowledgments

The authors acknowledge the financial supports from the Shanghai Natural Science Foundation (Project No. 19ZR1446500), Shanghai Agriculture Applied Technology Development Program (Grant No. G2016060103), and SAAS Program for Excellent Research Team, Grant/Award No. 2017(A-02).

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.0c00398.

  • Differential metabolites of LC–MS (B-A, C-A, D-A) (Tables S1–S3) (PDF)

Author Contributions

† L.T. and J.S. contributed equally to this work.

The authors declare no competing financial interest.

Supplementary Material

ao0c00398_si_001.pdf (866.9KB, pdf)

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