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Article

Analysis of Metabolic Differences in the Water Extract of Shenheling Fermented by Lactobacillus fermentum Based on Nontargeted Metabolomics

by
Xiantao Yan
1,2,
Min Liu
1,
Congcong Guo
1,
Xinyue Lian
1,
Yun Shen
1,
Yang Liu
1,
Yi Qian
1,
Longfei Zhang
1,
Wenqiong Wang
1,
Dawei Chen
1,
Jianya Qian
1,* and
Ruixia Gu
1,*
1
College of Food Science and Engineering, Yangzhou University, Yangzhou 225012, China
2
Department of Cuisine and Nutrition, Hanshan Normal University, Chaozhou 521000, China
*
Authors to whom correspondence should be addressed.
Fermentation 2023, 9(1), 44; https://doi.org/10.3390/fermentation9010044
Submission received: 2 December 2022 / Revised: 22 December 2022 / Accepted: 28 December 2022 / Published: 4 January 2023
(This article belongs to the Special Issue Nutrition and Health of Fermented Foods)
Browse Figures
Versions Notes

Abstract

:
Objective: To explore the characteristics of metabolites in Shenheling (SHL) fermented by Lactobacillus fermentum. Methods: In this study, ultrahigh-performance liquid chromatography-quadrupole electrostatic field orbit trap mass spectrometry (UHPLC-QE-MS) was used to qualitatively, quantitatively, and differentially analyze the metabolites of SHL before and after fermentation. Results: A total of 102 significant differential metabolites in nine categories were analyzed before and after fermentation. It mainly includes 29 terpenoids, 17 alkaloids, 14 organic acids and derivatives, 10 flavonoids, 9 phenylpropanoids, 6 phenols, 3 aromaticity, and 3 amino acid derivatives. Further screening found that the content of most active substances, such as alkaloids, organic acids, and flavonoids, increased significantly. These metabolites play an important role in improving the taste and efficacy of SHL. After fermentation, the contents of differential metabolites, such as panaquinquecol 2, ginsenoside Rh3, ginsenoside Rg3, dehydronuciferin, nicotinic acid, 5-hydroxytryptophan, azelaic acid, dihydrokaempferol, and chrysin, were increased, which increased the effects of antioxidation, anti-obesity, hypoglycemic, antibacterial, and improved immunity compared with those before fermentation. KEGG pathway analysis identified 10 metabolic pathways. Isoquinoline alkaloid biosynthesis, vitamin B6 metabolism, beta-alanine metabolism, nicotinate, and nicotinamide metabolism, purine metabolism, pantothenate and CoA biosynthesis, glyoxylate and dicarboxylate metabolism, tyrosine metabolism, citrate cycle (TCA cycle), phenylpropanoid biosynthesis, etc. Conclusions: Fermentation significantly changed the metabolites in SHL and played an important role in improving its taste, aroma quality, antioxidant, anti-obesity, and other health care functional components.

1. Introduction

Shenheling (SHL) [1] is a plant-based compound with anti-obesity effects that contains six kinds of plant raw materials, such as Panax ginseng C. A. Meyer, Nelumbo nucifera Gaertn., Poria cocos (Schw.) Wolf., Vigna umbellata (Thunb.) Ohwi et Ohashi, Citrus sinensis (Linn.) Osbeck and Cinnamomum cassia Presl. They have been used as herbs and food in Asian countries such as China, Japan, and South Korea for hundreds of years [2,3]. Previous studies have mostly focused on single plant raw materials or single components [4,5]. However, it is difficult to achieve an overall effect with a single targeted therapy in the context of obesity and other chronic diseases with multiple metabolic disorders [6]. The current consensus is that multitarget therapy is suitable for diseases with multiple causes, such as obesity [7]. Compounds of Chinese herbal medicine have the advantage of multiple components [8].
Lactic acid bacteria (LAB) have a strong metabolic capacity. It can decompose and utilize ingredients in raw materials by secreting different enzymes to change the sensory and efficacy of raw materials. Hwang et al. [9] found that fermentation with mesenteric Leuconostoc mesenteroides KCCM 12010P could improve the antioxidant and anti-inflammatory abilities of ginseng. Song et al. [10] found that hydroponic ginseng-fortified yogurt fermented by Lactobacillus brevis B7 could significantly improve the antioxidant activity of RAW 264.7 cells. The fermentation of Lactobacillus plantarum CECT 748 can improve the antioxidation and hypolipidemic effect of the adzuki bean. Bacillus subtilis fermented citrus peel extract has anti-inflammatory activity against RAW 264.7 macrophages induced by lipopolysaccharide (LPS) [11]. Hwang et al. [12] found that compared with unfermented lotus leaves, naturally fermented lotus leaves significantly increased antioxidant activity. Shukla et al. [13] added the Meju starter prepared by lotus leaves which significantly increased the inhibition rate of α-glucosidase activity of lotus leaves and enhanced the antibacterial effect on Bacillus cereus. Shi et al. [14] found that LAB fermentation was an effective method to reduce the off-flavor, such as ‘hay’ and ‘green’-like aroma. Our previous study [1] showed that the aqueous extract of SHL fermented by L. fermentum grx08 significantly enhanced its anti-obesity activity in vitro and improved flavor. However, the effect of fermentation on the active components of SHL is not clear.
Since metabolomics was formally proposed in the late 1990s, detection and analysis methods have been continuously improved and have been widely used in many fields. Compared with traditional research methods, the research object of metabolomics is small molecule metabolites, which can reveal the changes in types and quantities in the process of material metabolism in a panoramic way to explore the rules and interactions behind these changes [15]. Therefore, in this study, UHPLC-QE-MS technology was used to detect the metabolites of SHL before and after fermentation, and nontargeted metabolomics analysis was performed to reveal the flavor and efficacy of fermented SHL by L. fermentum. The mechanism of improvement provides a theoretical basis for improving the quality of fermented SHL functional beverages.

2. Materials and Methods

2.1. Chemicals

Formic acid was obtained from Sigma Aldrich (Shanghai, China) Trading Co., Ltd., China. Acetonitrile and methanol were both purchased from the Germany CNW company. 2-Chloro-L-phenylalanine, an internal standard, was purchased from Shanghai Hengbai Biotechnology Co., Ltd. (Shanghai, China).

2.2. Preparation and Fermentation of SHL

Preparation and fermentation of Shenheling water extract was performed as described in our previous study [1]. Briefly, SHL powder was mixed evenly with purified water at a ratio of 1:10 (g/mL), soaked for 30 min, and extracted in a water bath at 100 °C for 30 min. After cooling, the activated L. fermentum grx08 (CGMCC NO. 7695) was inoculated and cultured at 37 °C for 49.5 h. The supernatant of SHL after fermentation was the Shenheling fermentation broth (SHLF), and the supernatant of inoculated but unfermented Shenheling (SHL) was used as a control. Each batch was tested in triplicate, and a total of three batches of samples were stored at −80 °C for testing. One sample was randomly selected from each batch, and three samples were taken from each treatment.

2.3. Metabolite Extraction

The samples were thawed on ice. After 30 s of vortexing, the samples were centrifuged at 12,000 rpm (RCF = 13,800 (× g), R = 8.6 cm) for 15 min at 4 °C. Then, 300 μL of supernatant was transferred to a fresh tube, and 1000 μL of the extracted solution containing 10 μg/mL internal standard was added. Then, the samples were sonicated for 5 min in an ice-water bath. After placing 1 h at −40 °C, the samples were centrifuged at 12,000 rpm (RCF = 13,800 (× g), R = 8.6 cm) for 15 min at 4 °C. The supernatant was carefully filtered through a 0.22 μm microporous membrane, and 50 μL was taken from each sample and pooled as quality control (QC) samples. All samples were stored at −80 °C until UHPLC–MS analysis.

2.4. LC–MS/MS Conditions

LC–MS/MS analysis was performed on a UHPLC system (Vanquish, Thermo Fisher Scientific, Shanghai, China) with a Waters UPLC BEH C18 column (1.7 μm 2.1 × 100 mm). The flow rate was set at 0.5 mL/min, and the sample injection volume was set at 5 μL. The mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). The multistep linear elution gradient program was as follows: 0–11 min, 85–25% A; 11–12 min, 25–2% A; 12–14 min, 2–2% A; 14–14.1 min, 2–85% A; 14.1–16 min, 85–85% A. An Orbitrap Exploris 120 mass spectrometer coupled with Xcalibur software was employed to obtain the MS and MS/MS data based on the IDA acquisition mode. During each acquisition cycle, the mass range was from 100 to 1500, the top four of every cycle were screened, and the corresponding MS/MS data were further acquired. Sheath gas flow rate: 35 Arb, Aux gas flow rate: 15 Arb, Ion Transfer Tube Temp: 350 °C, Vaporizer Temp: 350 °C, Full ms resolution: 60,000, MS/MS resolution: 15,000, Collision energy: 16/32/48 in NCE mode, Spray voltage: 5.5 kV (positive) or −4 kV (negative). Detection and analysis were provided by SHANGHAI BIOTREE BIOMEDICAL TECHNOLOGY CO., LTD., Shanghai, China.

2.5. Statistical Analysis

After the original data were converted into mzXML format by ProteoWizard software (Palo Alto, CA), XCMS was used for peak identification, peak extraction, peak alignment, and integration and then matched with the self-built BiotreeDB secondary mass spectrometry database for material annotation. Multivariate statistical analysis of UHPLC-QEMS data was performed using SIMCA software (V16.0.2, Sartorius Stedim Data Analytics AB, Umea, Sweden). It mainly includes unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA). PCA mainly studies the sample distribution, bias characteristics, and common trends. OPLSDA mainly classifies the samples and identifies the most discriminant variables. Based on the goodness of fit (R2Y) and the goodness of prediction (Q2Y), it verifies the classification and prediction ability. The model was tested by the replacement test (n = 200). The negative value of the intercept (Q2 intercept) indicates the robustness of the model. The score of variable importance in the projection (VIP) shows the contribution of each variable to the model, and the VIP score in the predicted component is analyzed. Only those metabolites with VIP values greater than 1 were considered for identification between categories. Finally, the obtained metabolite information was screened according to the VIP value, the p value of the student’s t test, and the log value based on 2 (FOLD CHANGE) to obtain metabolites with significant differences.
In addition, commercial databases include KEGG (http://www.genome.jp/kegg/ (accessed on 10 August 2022)) and metabolic analysis (http://www.metaboanalyst.ca/ (accessed on 11 August 2022)) for pathway enrichment analysis. In addition, commercial databases including KEGG (http://www.genome.jp/kegg/ access date 10 August 2022) and MetaboAnalyst (http://www.metaboanalyst.ca/ access date 11 August 2022) were used for pathway enrichment analysis. All samples were repeated three times. The results are presented as the means ± SEs, and the differences among the different samples were analyzed using a one-way analysis of variance (SPSS, (Chicago, IL, USA) ANOVA, Tukey). Values of p < 0.05 or p < 0.01 were considered statistically significant.

3. Results

3.1. UHPLC-QE-MS Metabolic Profile Analysis

The total ion current (TIC) diagram shown in Figure 1 was obtained by detecting the water extract of SHL before and after fermentation in positive and negative ion modes.

3.2. Multivariate Statistical Analysis of Metabolites before and after Fermentation of SHLE

The metabolite data detected in positive and negative ion modes were combined, and then the processed metabolite list was further analyzed by multivariate statistical analysis techniques such as PCA and the OPLS-DA model. Each scatter represents a sample, and the color and shape of the scatter represent different groups. The closer the sample point distribution is, the more similar the types and contents of metabolites in the sample are. Conversely, the farther the sample, the greater the difference in overall metabolic levels. Figure 2A shows that the distribution distance between the unfermented SHL and the fermented SHLF samples was far, indicating that fermentation significantly changed the type and content of metabolites in the water extract of SHL. QC samples were also prepared for data quality control and preprocessing [16,17,18].
To further identify the differences in metabolites [19], we used the OPLS-DA statistical method to analyze the results to monitor the changes in metabolites with fermentation. The SHL group to SHLF group OPLS-DA model score scatter diagram is shown in Figure 2B. The abscissa t [1]P in Figure 2B represents the predicted score of the first principal component, showing the difference between the sample groups. The ordinate t [1]O represents the orthogonal principal component score, showing the differences within the sample group. Each scatter represents a sample, and the shape and color of the scatter represent different experimental groups. The farther the horizontal distance between samples, the greater the difference between groups, and the closer the vertical distance, the better the repeatability within the group. Figure 2B shows that the distinction between the two groups of samples is very significant, and the samples are all within the 95% confidence interval, indicating that there are significant differences in the metabolites before and after fermentation of SHL, which can be used for subsequent differential component analysis.

3.3. Analysis of Differential Metabolites before and after Fermentation of SHLE

The higher the VIP value in OPLS-DA, the greater the contribution of variables to grouping [20]. In this study, the samples before and after fermentation were compared with the screening criteria of VIP > 1, p < 0.05, and fold change greater than 1.2 or less than 0.83. A total of 102 differential metabolites (53 upregulated and 49 downregulated) were identified, mainly including nine categories (Table S1): terpenoids, alkaloids, organic acids and derivatives, flavonoids, phenylpropanoids, phenols, aromaticity, amino acid derivatives, and others.
The visualization results of differential metabolites are shown in the form of a volcano plot (Figure 3). The abscissa is the relative abundance multiple of each metabolite between the two groups (logarithm based on 2), and the ordinate is the p value of the t test (logarithm based on 10). The VIP values of each metabolite were expressed as scatter areas. The larger the scatter point, the greater the VIP value.
In addition, to visually observe the concentration changes of differential metabolites before and after fermentation, a heatmap (Figure 4) was made based on the relative content of differential metabolites. The abscissa and ordinate represent the sample group and differential metabolites, respectively. The colors from blue to red indicate the abundance of metabolite expression from low to high. The heatmap showed that the two major clusters were clearly divided into different groups, indicating that the intensity of the differential markers differed between groups.
In addition, we sequenced the major differential metabolites detected by VIP labeling (Figure 5). The main differential metabolites in SHLF were terpenoids, alkaloids, organic acids and derivatives, flavonoids, phenylpropanoids, phenols, aromaticity, and amino acid derivatives.
In this study, we focused on the analysis of the four most abundant secondary metabolites before and after fermentation.

3.3.1. Differential Terpenoids before and after Fermentation of SHLE

A total of 29 terpenoids were identified as differential metabolites. Among them, 12 (panaquinquecol 2, ginsenoside Rh3, ginsenoside Rg3, betulin, 1-naphthalenecarboxylic acid, 5-[2-(2,5-dihydro-2-oxo-3-furanyl) ethyl] decahydro-1,4a-dimethyl-6-methylene-, methyl ester, artemisinin, polyporusterone A, pteroside A, cyperolone, azuleno (5,6-c) furan-1 (3H) -one, 4,4a, 5,6,7,7a, 8,9-octahydro-3,4,8-trihydroxy-6,6,8-trimethyl-, 7-hydroxy-1,4a-dimethyl-9-oxo-7-propan-2-yl-2,3,4,4b, 5,6,10a-octahydrophenanthrene-1-carboxylic acid, panaquinquecol 1) were significantly upregulated, and 17 (oleanane -4H, +2O, medicagenic acid, ouillaic acid 3-[galactosyl-(1->2)-glucuronide], atractylenolide III, soyasapogenol E base + O-HexA-Hex-dHex, soybean saponin fraction B1, 2-[4,5-Dihydroxy-6-[[8-hydroxy-8a-(hydroxymethyl)-4,4,6a,6b,11,11,14b-heptamethyl-1,2,3,4a,5,6,7,8,9,10,12,14a-dodecahydropicen-3-yl]oxy]-2-[[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxymethyl]oxan-3-yl]oxy-6-methyloxane-3,4,5-triol, echinocystic acid-3-O-glucoside, saikosaponin a, oleanane -2H, +1O, 1COOH, O-HexA-HexA, hederagenin base + O-HexA-Hex, ginsenoside F3, 3,4-Dihydrocoumarin, soyasapogenol B base + O-HexA-HexA-dHex, O-C6H7O3(DDMP), soyasapogenol B base + O-HexA+HexA+dHex, bisacurone epoxide, 7-(2-hydroxypropan-2-yl)-1,4a-dimethyl-2,3,4,5,6,7,8,8a-octahydronaphthalen-1-ol) were significantly downregulated.
Among them, panaquinquecol 2, which increased significantly after fermentation to 10.54 times before fermentation, has not been reported. Similarly, only panaquinquecol 4 and panaquinquecol 7 have been reported. Kim et al. [21] found that panaquinquecol 4 exhibited the most remarkable cytotoxic effects on both human ovarian cancer cells A2780 (IC50 value of 7.60 μM) and SKOV3 (IC50 value of 27.53 μM). Al-Ghanayem et al. [22] found that panaquinquecol 7 possessed antifungal activity. The functional group of panaquinquecol has been reported to have effects and carcinostatic activity, further contributing to antifungal activity [23]. Panaquinquecol 1 significantly increased 1.22-fold after fermentation, and it has not been reported. Ginsenoside Rh3 is significantly increased by 4.22-fold after fermentation, which inhibits the proliferation and induces apoptosis of colorectal cancer cells [24]. Lee et al. [25] found that ginsenoside Rh3 has anti-inflammatory effects in lipopolysaccharide-stimulated microglia. It has been reported that ginsenoside Rg3 (S-FORM) has significant anti-obesity [26], anti-inflammatory [27], and antitumor [2] effects, and fermentation in this study significantly increased it to 3.06 times. Palaniyandi et al. [28] showed that Lactobacillus paracasei subsp. tolerans MJM60396 could ferment the main ginsenoside Rb1 into rare ginsenoside Rg3. Tang et al. [29] found that betulin improved diet-induced obesity by inhibiting SREBP, reducing lipid content in serum and tissues, and increasing insulin sensitivity. In this study, fermentation significantly increased betulin to 2.74-fold. Ginsenoside F3 is the main ginsenoside in ginseng [30]. In this study, fermentation significantly reduced it to 0.42 times.

3.3.2. Differential Alkaloid Compounds before and after Fermentation of SHLE

Seventeen differential metabolites of alkaloids were detected in this study. Among them, 12 (tyramine, dehydronuciferin, nicotinic acid, 5-hydroxytryptophan, pyridoxine, remerine, biotin, floribundine, norisocorydine, n-trans-feruloyloctopamine, venoterpine, L-1,2,3,4-tetrahydro-beta-carboline-3-carboxylic acid) were significantly upregulated, and 5 (crebanine, higenamine, guanine, 3,9-dimethoxypterocarpan, adenosine) were significantly downregulated.
Tyramine is a biological microamine produced by the amino acid tyrosine decarboxylation. Excessive intake of tyramine can lead to headaches and other hazards. A small amount of tyramine has neuromodulation and immune effects [31]. In this study, although tyramine was increased 3.68 times after fermentation, the content was lower. Nicotinic acid is an indispensable vitamin in humans and animals. Li et al. [32] found that nicotinic acid can enhance the cognitive ability of patients with Alzheimer’s disease through a variety of mechanisms. In our study, nicotinic acid was significantly upregulated 2.18-fold after fermentation. 5-Hydroxytryptophan (5-HTP) is both a drug and a natural component of some dietary supplements and has important physiological effects in the treatment of depression, obesity, and serotonin syndrome [33]. However, this compound has not been reported in the raw materials of SHL. In our study, 5-HTP was significantly upregulated 2.00-fold after fermentation. Higenamine has a variety of pharmacological properties, such as vascular and tracheal relaxation, antithrombotic, antioxidant, and immunomodulatory effects [34]. In this study, fermentation significantly downregulated it to 0.73 times.

3.3.3. Differential Organic Acids and Derivatives before and after Fermentation of SHLE

In this study, a total of 14 organic acids and their derivatives were identified as differential metabolites. Among them, 9 (3-phenyllactic acid, FA 18:2 + 1O, succinate, DL-beta-hydroxybutyric acid, (R, R)-tartaric acid, aleuritic acid, 4-amino-2-methylenebutanoic acid, azelaic acid, and piscidic acid) were significantly upregulated, and 5 ((-)-12-hydroxyjasmonic acid, pantothenic acid, citric acid, hexadecanedioic acid, and phenylpropiolic acid) were significantly downregulated. Among them, 3-phenyllactic acid is an organic acid widely found in LAB-fermented food that has broad and effective antibacterial and antifungal activities [35]. In this study, 3-phenyllactic acid increased up to 69.77 times after fermentation, which may be related to the long shelf life of the fermented beverage without adding any preservatives. This is consistent with the results reported by Gerez et al. [36] for L. plantarum 1081, 778, 1073 and Rodriguez et al. [37] for L. plantarum CECT-221 for increased antimicrobial activity. Azelaic acid is an anti-infective and anti-inflammatory agent [38], which was significantly upregulated 1.41-fold by fermentation in this study. In contrast, phenylpropionic acid was significantly downregulated 0.0002-fold after fermentation.

3.3.4. Differential Flavonoids before and after Fermentation of SHLE

In this study, the fermentation of L. fermentum grx08 resulted in significant changes in 10 flavonoids in SHL. Specifically. Seven flavonoids (5,7-dihydroxychromone, chrysin, dihydrokaempferol, 5-methoxyflavone, 5,7-dihydroxy-2-(4-hydroxy-3-methoxyphenyl)-6-(3-methylbut-2-enyl)-2,3-dihydrochromen-4-one, 5,7-dihydroxychromone, flavonol base + 3O, O-Hex-Hex, chrysin, dihydrokaempferol, 5-methoxyflavanone, and gossypetin-8-C-glucoside) were significantly upregulated. Three (5-O-demethylnobiletin, quercetin 3-(6″-acetylglucoside), isosilybin) were significantly downregulated.
Among them, 5,7-dihydroxy-2-(4-hydroxy-3-methoxyphenyl)-6-(3-methylbut-2-enyl)-2,3-dihydrochromen-4-one was significantly upregulated to 30.62 times before fermentation. 5,7-Dihydroxychromone attenuated 6-hydroxydopamine (6-OHDA)-induced neurotoxicity in SH-SY5Y cells by activating Nrf2/ARE signaling [39], which was significantly upregulated 5.14-fold before fermentation. Dihydrokaempferol is significantly upregulated 1.3-fold after fermentation and has strong antioxidant and anti-inflammatory properties [40]. Chrysin has a wide range of physiological effects, such as antioxidant, anti-inflammatory, antidiabetic, hypolipidemic, and hepatoprotective effects. [41,42,43], fermentation increased it to 1.74 times. These results are consistent with our previous study [1] that fermentation significantly increased the total antioxidant capacity (FRAP) of SHLF. 5-O-Demethylnobiletin has anti-inflammatory activity [5], but fermentation in this study reduced it slightly to 0.8-fold. Isosilybin can inhibit lipid synthesis and activate lipid oxidation through the AMPK signaling pathway [44], but in this study, fermentation slightly downregulated it to 0.62 times.

3.4. Metabolic Pathway Analysis of Differential Metabolites

LAB fermentation is a very complex metabolic process, and it cannot be judged only from the content of a certain substance, so it is necessary to further analyze its metabolic pathway. First, significant differential metabolites were screened (Supplementary Table S2). Then, by comparison with the KEGG Pathway database, the genes can be classified according to the participating pathways or functions, and information on metabolic pathways involved in metabolites can be obtained to evaluate their effects on the fermentation process. As a result, a total of 10 metabolic pathways were enriched before and after fermentation. These pathways included isoquinoline alkaloid biosynthesis, vitamin B6 metabolism, beta-alanine metabolism, nicotinate, and nicotinamide metabolism, purine metabolism, pantothenate and CoA biosynthesis, glyoxylate and dicarboxylate metabolism, tyrosine metabolism, citrate cycle (TCA cycle), phenylpropanoid biosynthesis, etc. The results of the metabolic pathway analysis are shown in a treemap plot (Figure 6). Each block in the rectangular tree represents a metabolic pathway, and the area of the block represents the influencing factor of the pathway in the topological analysis. The larger the area is, the larger the influence factor is. The color of the cube represents the p value (-ln(p)) of the enrichment analysis. The deeper the color, the smaller the p value and the more significant the enrichment degree.

4. Conclusions

In this study, UHPLC—QE—MS analysis technology was used. To the best of our knowledge, the changes of metabolites in SHL aqueous extract before and after fermentation by L. fermentans grx08 were studied in detail for the first time. PCA, OPLS-DA, and heatmaps were used to analyze the samples before and after fermentation, and the significant differences were mainly in metabolite types and abundance. A total of 102 significant differential metabolites were identified before and after fermentation. It mainly includes 29 terpenoids, 17 alkaloids, 14 organic acids and derivatives, 10 flavonoids, 9 phenylpropanoids, 6 phenols, 3 aromaticity, and 3 amino acid derivatives. The significant differential metabolites after fermentation showed that the contents of most active substances, such as alkaloids, organic acids, and flavonoids, were significantly increased. Compared with SHL before fermentation, fermentation improved the antioxidant, anti-obesity, hypoglycemic, antibacterial activity, and immunity of SHLF. Among them, organic acids play an important role in improving the taste of SHL. In this study, the changes in the main metabolites of SHL water extract fermented by L. fermentum grx08 were introduced in detail, which provided a basis for the future study of LAB-fermented plant-based functional beverages.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fermentation9010044/s1. Table S1. Analysis of differential metabolites. Table S2. TOTAL-KEGG Pathway.

Author Contributions

Conceptualization, R.G.; Investigation, X.Y., M.L., C.G., X.L., Y.S., Y.Q. and L.Z.; Data curation, X.Y., M.L. and Y.L.; Funding acquisition, D.C., X.Y. and R.G.; Methodology, X.Y. and J.Q.; Project administration, R.G.; Supervision, J.Q.; Writing—original draft, X.Y. and W.W.; Writing—review and editing, X.Y., D.C. and W.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of Jiangsu Province (No. BK20211325), the Natural Science Foundation of the Jiangsu Higher Education Institutions (No. 19KJA140004), National Natural Science Foundation of China (No. 31972094), Jiangsu Science and Technology Projects (No. XZ-SZ202042), Key Laboratory of Probiotics and Dairy Deep Processing of Yangzhou (No. YZ2020265), and Key Research Project of Guangdong Provincial Department of Education (2018 WQNCX113).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are available from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Total Ion Flow Diagrams of SHL and SHLF Samples. (A) Positive ion mode of SHL; (B) Negative ion mode of SHL; (C) Positive ion mode of SHLF; (D) Negative ion mode of SHLF.
Figure 1. Total Ion Flow Diagrams of SHL and SHLF Samples. (A) Positive ion mode of SHL; (B) Negative ion mode of SHL; (C) Positive ion mode of SHLF; (D) Negative ion mode of SHLF.
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Figure 2. PCA and OPLS-DA score plot of the SHLF group versus the SHL group (n = 3). (A) PCA score plot; (B) OPLS-DA score plot.
Figure 2. PCA and OPLS-DA score plot of the SHLF group versus the SHL group (n = 3). (A) PCA score plot; (B) OPLS-DA score plot.
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Figure 3. Volcano plot for group SHLF vs. SHL) (left) Positive ion mode; (right) Negative ion mode. Red dots, blue dots, and gray dots represent significantly upregulated, significantly downregulated, and insignificantly different metabolites, respectively.
Figure 3. Volcano plot for group SHLF vs. SHL) (left) Positive ion mode; (right) Negative ion mode. Red dots, blue dots, and gray dots represent significantly upregulated, significantly downregulated, and insignificantly different metabolites, respectively.
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Figure 4. Hierarchical Cluster Analysis Heatmap of SHLF (Group B) to SHL (Group A).
Figure 4. Hierarchical Cluster Analysis Heatmap of SHLF (Group B) to SHL (Group A).
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Figure 5. The results of differential metabolite category analysis of the SHLF group vs. SHL group.
Figure 5. The results of differential metabolite category analysis of the SHLF group vs. SHL group.
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Figure 6. Pathway analysis for group SHLF vs. SHL.
Figure 6. Pathway analysis for group SHLF vs. SHL.
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MDPI and ACS Style

Yan, X.; Liu, M.; Guo, C.; Lian, X.; Shen, Y.; Liu, Y.; Qian, Y.; Zhang, L.; Wang, W.; Chen, D.; et al. Analysis of Metabolic Differences in the Water Extract of Shenheling Fermented by Lactobacillus fermentum Based on Nontargeted Metabolomics. Fermentation 2023, 9, 44. https://doi.org/10.3390/fermentation9010044

AMA Style

Yan X, Liu M, Guo C, Lian X, Shen Y, Liu Y, Qian Y, Zhang L, Wang W, Chen D, et al. Analysis of Metabolic Differences in the Water Extract of Shenheling Fermented by Lactobacillus fermentum Based on Nontargeted Metabolomics. Fermentation. 2023; 9(1):44. https://doi.org/10.3390/fermentation9010044

Chicago/Turabian Style

Yan, Xiantao, Min Liu, Congcong Guo, Xinyue Lian, Yun Shen, Yang Liu, Yi Qian, Longfei Zhang, Wenqiong Wang, Dawei Chen, and et al. 2023. "Analysis of Metabolic Differences in the Water Extract of Shenheling Fermented by Lactobacillus fermentum Based on Nontargeted Metabolomics" Fermentation 9, no. 1: 44. https://doi.org/10.3390/fermentation9010044

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