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Article

Integrated Metabolome and Microbiome Analysis Reveals the Regulatory Effects of Fermented Soybean Meal on the Gut Microbiota of Late Gestation

by
Yantao Li
1,2,
Lele Fu
2,
Yushi Chen
2,
Hua Yang
2,3,
Yingping Xiao
3,
Ying Ren
1,* and
Cheng Wang
2,*
1
Hubei Key Laboratory Animal Nutrition & Feed Science, Wuhan Polytechnic University, Wuhan 430023, China
2
Xianghu Laboratory, Hangzhou 311231, China
3
State Key Laboratory for Quality and Safety of Agro-Products, Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
*
Authors to whom correspondence should be addressed.
Fermentation 2025, 11(6), 315; https://doi.org/10.3390/fermentation11060315
Submission received: 8 April 2025 / Revised: 19 May 2025 / Accepted: 22 May 2025 / Published: 31 May 2025
(This article belongs to the Section Probiotic Strains and Fermentation)
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Abstract

:
Late gestation is a critical period for regulating maternal peripartum physiological metabolism and gut microbiota balance. Fermented diets have been widely recognized as effective exogenous nutritional interventions capable of modulating the maintenance of gut microbiota homeostasis. However, the mechanism through which fermented diets modulate the gut microbiota in late-gestation remains poorly understood. In this study, an in vitro fermentation model combined with chemical composition analysis, untargeted metabolomics, and high-throughput sequencing was employed to investigate the metabolic alterations during soybean meal (SBM) fermentation and the regulatory effects of fermented soybean meal (FSBM) on gut microbiota of late-gestation sows. The findings revealed that fermentation significantly increased the levels of crude protein, lactic acid, acid-soluble protein, lysine, histidine, and total amino acids of SBM. Conversely, the levels of crude fiber, NDF, ADF, starch, and non-starch polysaccharides were markedly reduced, compared to the unfermented group. A total of 941 differentially expressed metabolites were identified between SBM and FSBM. Specifically, FSBM elevated the levels of lactic acid, L-pyroglutamic acid, 2-aminoisobutyric acid, and tyrosine, while substantially decreasing the levels of raffinose, sucrose, and stachyose. Metabolic pathway analysis identified glutathione metabolism, tyrosine metabolism, and pantothenate and coenzyme A (CoA) biosynthesis as the key pathways involved in SBM fermentation. In vitro fermentation experiments demonstrated that FSBM substantially increased the production of short-chain fatty acids (SCFAs) and notably increased the relative abundance of sows gut commensal Lactobacillus and Limosilactobacillus in late gestation. In summary, this study demonstrated that co-fermentation with bacteria and enzymes pretreatment of soybean meal reduced fiber components and enriched bioactive metabolites, optimizing intestinal microbial composition and increasing SCFA production in late-pregnant period. The present study provides novel insights into the regulatory effects of fermented diets on gut microbiota in late-gestation period from the perspectives of nutritional composition and metabolites.

1. Introduction

Late gestation is a critical period for the smooth transition of periparturient physiology and metabolism in sows, which is mainly characterized by the drastic changes of hormone levels in the body, and the disruption of redox homeostasis and gut microbiota balance in the organism [1]. The gut microbiota is sensitive to changes in the host due to hormonal, metabolic, and immune levels before and after farrowing. Previous studies have shown that the diversity of gut microbiota in sows during late gestation increased significantly [2]. Adding various forms of dietary fiber to sows’ diets during late pregnancy and lactation enhanced their gastrointestinal health, inflammatory response, and body metabolism [3,4]. A maternal dietary supply of L-malic acid ameliorated oxidative stress and inflammation in sows through modulating gut microbiota and host metabolic profiles during late pregnancy [5]. Furthermore, the gut microbiota treated with melatonin produced more SCFAs, which enhance gut health by lowering inflammation, autophagy, and oxidative stress [6]. Therefore, maternal exogenous nutritional interventions can influence gut health homeostasis, effectively regulate the metabolic balance, and achieve a smooth transition of sows during the periparturient period.
Soybean meal (SBM), a byproduct of soybean oil refining, is a prime choice for livestock and poultry feed due to its rich protein content and balanced amino acids, making it the top plant protein source for these animals [7]. Statistical data indicate that the total soybean imports of China have amounted to 105 million metric tons as of 2024 [8]. However, SBM possesses diverse inherent antinutrients like antigenic proteins, trypsin inhibitors, and oligosaccharides (e.g., raffinose), which cause changes in the gut morphology of the animal and injury to gut function, resulting in reduced feed utilization and ultimately affecting the production performance and breeding efficiency [9,10,11]. Phytic acid acts as a nutritional antagonist in animal feed by binding to essential trace minerals, forming insoluble complexes with cations such as calcium, iron, and zinc [12]. This process hampers their uptake in the gastrointestinal tract. Additionally, trypsin inhibitors (TI), can interfere with protein breakdown, limiting the body’s ability to absorb and utilize vital nutrients effectively [13]. Therefore, the processing of SBM is crucial to eliminate endogenous components detrimental to the animal’s gastrointestinal tract and to guarantee ideal nutritive properties.
Degradation methods of anti-nutritional factors in SBM mainly include physical treatment, chemical treatment, and microbial fermentation. However, physical and chemical treatments may destroy the amino acid structure in protein feeds and trigger feed contamination. Fermentation, a conventional technique in food production, enhances both the nutritive value and functionality of SBM, thus improving its applicability and benefits for livestock [9]. Currently, the most commonly used strains for fermentation are Bacillus and Lactobacillus. Fermentation by these two strains results in a porous microstructure, increased small peptides and available phosphorus, and significant reduction of anti-nutritional factors, ultimately enhancing in vitro digestibility [14]. Some studies showed that fermentation enhanced SBM’s crude protein, amino acid, and lactic acid content and decreased the level of anti-nutritional factors [15,16,17]. When FSBM was included in the diet of weaned piglets, it increased the digestibility of nutrients and improved the gut integrity, antioxidant capacity, and immune function of the piglets [18,19]. Compared with SBM, FSBM reduced oxidative stress in gestating and lactating sows and increased the average body weight of their offspring [20]. However, the mechanism through which FSBM regulates the gut microbiota in late-gestation sows to exert its effects remains unclear.
In the present study, FSBM was prepared by co-fermentation using Bacillus subtilis, Enterococcus faecalis, xylanase, and protease. Nutritional profile alterations of SBM before and after fermentation were assessed via chemical composition analysis. By combining with untargeted metabolomics, the changes of metabolites were analyzed to reveal the roles of co-fermentation with bacteria and enzymes. The regulatory effects of FSBM and SBM on the gut microbiota of sows during late gestation were evaluated through an in vitro fermentation model. Correlation analysis was performed on the nutritional components, differential metabolites, and differential gut microbes. The purpose is to explore whether the mechanism by which FSBM exerts its effect on the gut microbiota of late gestation is related to the changes in nutritional components and metabolites of FSBM.

2. Materials and Methods

2.1. Preparation and Nutritional Profile Evaluation of FSBM

Sterilized water was mixed with SBM to reach the optimal 40% (w/w) moisture level. Then, 0.2% fermentation bacteria solution (Bacillus subtilis: 5 × 108 CFU/g, Enterococcus faecalis: 2 × 108 CFU/g, xylanase and protease: 8000 U/g) was added into the fermentation system, and the fermentation lasted for 72 h. The Bacillus subtilis and Enterococcus faecalis strains used in this study were obtained from the Animal Microbial Resource Mining and Pollution Prevention and Control Team, Institute of Biotechnology, Xianghu Laboratory, Zhejiang Province, China. After fermentation, FSBM and SBM were collected for metabolome analysis. The remaining samples were oven-dried at 65 °C for 48 h, ground, and analyzed for physicochemical analysis. Dry matter, crude protein, Ash, crude fat, crude fiber, neutral detergent fiber (NDF), acid detergent fiber (ADF), starch, straight chain starch, reducing sugars, non-starch polysaccharides, calcium, total phosphorus, lactic acid, and amino acids were determined with reference to the methods of the AOAC. The methods outlined by Huang et al. were employed to assess acid-soluble proteins [21].

2.2. Non-Targeted Metabolome Analysis

The 30 mg feed samples were extracted using 1000 µL of the extraction solvent (a 1:1, v/v blend of methanol and acetonitrile, and water in a 3:1, v/v ratio) in accordance with the process outlined by Doppler et al. [22]. The target compounds were successfully separated via chromatography on a Phenomenex Kinetex C18 column (2.1 mm × 50 mm, 2.6 μm), utilizing a top-of-the-line Vanquish UHPLC system from Thermo Fisher Scientific. For the liquid phase, we employed a water-based solution with a trace of 0.01% acetic acid as phase A, while phase B consisted of a 1:1 mixture of isopropanol and acetonitrile. The sample was kept on the tray at a chilly 4 °C, and we injected a precise 2 μL dose.
Following the conversion of raw data into mzXML format via ProteoWizard, metabolite identification was performed using an open-source R package developed through community collaboration. The analysis leveraged two specialized databases: BiotreeDB (V3.0, specimen repository) and BT-Plant (V1.1, plant-specific repository). Following normalization of the raw peak area data against the total peak area, multivariate analyses were performed using MetaboAnalyst 6.0 (http://www.metaboanalyst.ca/, accessed on 29 December 2024). Specifically, principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were employed for pattern recognition. The goal was to pare down the high-dimensional dataset and determine the overarching metabolic shifts differentiating fermented and unfermented feed samples. The appropriateness and forecasting efficacy of the chosen OPLS-DA models were evaluated using R2Y and Q2Y metrics, respectively. Differential metabolites were screened by VIP > 1, FoldChange > 2 or <0.5, and p-value < 0.05. The biological implications of the metabolites were analyzed through metabolic pathway evaluations conducted on the MetaboAnalyst 6.0 online platform.

2.3. In Vitro Digestion

The in vitro digestion of SBM and FSBM was conducted according to the procedure outlined by Minekus et al. [23], with minor modifications. The procedure was as follows: (1) Simulated oral digestion: 25 g of fermented feed was dissolved in 300 mL of PBS buffer. Then, 2.25 mL of α-amylase was added and stirred for 15 min at 37 °C. (2) Simulated gastric digestion: pH was adjusted to 2.5 ± 0.1 with 2 M HCl, 1 ml of 10% pepsin was added, and the mixture was stirred for 30 min at 37 °C. (3) Simulated small gut digestion: 50 mL of 0.1 M sodium maleate buffer was added, and the pH was adjusted to 6.9 ± 0.1 with 2M NaOH, then 50 mL of 12.5% trypsin and 2 mL of amyloglucosidase were added, and finally incubated at 37 °C for 3 h. At the end of the reaction, the samples were freeze-dried for in vitro fermentation.

2.4. In Vitro Fermentation

Fecal samples were taken from six healthy Jinhua lactating sows that were fed a standard diet in a large-scale pig farm. They had not received any medication or antibiotic treatment for at least three months before collecting feces. The fecal inoculum was prepared by adding feces to sterile PBS buffer at 1:9 (w/v). The fecal inoculum from each pig was stored separately, rapidly frozen in liquid nitrogen, and then kept at −80 °C for future use. The freeze-dried dietary fibers were subjected to in vitro batch fermentation with fecal inoculum under anaerobic conditions, as described by Jin et al. [24]. A fermentation medium supplemented with 2% (w/v) feed was prepared in sterile tubes, with fecal material added to achieve a final concentration of 1% (w/v). Fermentation was performed in an anaerobic orbital shaker at 37 °C and 130 rpm for 32 hours. Samples were collected from all vessels at predetermined intervals (0, 4, 8, 16, and 32 h), flash-frozen in liquid nitrogen immediately after collection, and stored at −20 °C until analysis. The anaerobic fermentation medium was prepared with reference to the method of Wei et al. [25] and Willims [26] with some modifications. Basal medium was prepared by dissolving the following components in MilliQ water: 0.2 g peptone, 0.2 g yeast extract, 0.2 g sodium bicarbonate, 0.25 g bile salt, 0.25 g cysteine-HCl, 0.4 g K2HPO4, 0.4 g KH2PO4, 0.4 g CaCl2-2H2O, 0.25 g CaCl2-2H2O, 0.5 g MgSO4-7H2O, 0.01 g hemoglobin, 2.5 g NaCl, 10 μL vitamin K, and 2 mL Tween-80. The solution was then adjusted to a final volume of 1 L (pH 7.0 ± 0.2).

2.5. pH and Short-Chain Fatty Acid Assay

The pH was measured using a calibrated pH meter (Mettler, Switzerland). Short-chain fatty acid (SCFA) analysis was performed according to the method of Nguyen et al. [27]. Briefly, after fermentation, 1 mL of the sample was centrifuged for 15 min at 4 °C and 13,000 rpm to obtain 400 μL of supernatant, and then added to 100 μL of 25% (w/v) metaphosphoric acid solution, and filtered through 0.22 μm filter membrane after mixing. Then, the concentrations of acetic acid, propionic acid, and butyric acid were quantified by GC-MS analysis. Total short-chain fatty acid content was calculated as the sum of these three major fatty acids.

2.6. Microbiome Analysis

Total microbial genomic DNA was extracted from 9 samples using the E.Z.N.A.® Stool DNA Kit (Omega Bio-Tek, Norcross, GA, USA), with all procedures strictly following the manufacturer’s standard protocols. DNA quality control was performed using 1.0% agarose gel electrophoresis and NanoDrop2000 spectrophotometer (Thermo Scientific, Boston, MA, USA), with qualified samples stored at −80 °C for subsequent analysis. Bacterial community analysis was conducted using the MiSeq sequencing platform with primers targeting the V3–V4 hypervariable regions of bacterial 16S rRNA gene (308F: 5′-ACTCCTACGGGAGGCAGCAG-3′; 806R: 5′-GGACTACHVGGGTWTCTAAT-3′). PCR products were recovered via 2% agarose gel electrophoresis, purified, quantified, and subjected to paired-end sequencing on the Illumina NextSeq2000 platform (San Diego, CA, USA) following standard protocols.
Raw sequencing data in FASTQ format were demultiplexed using an in-house Perl script, then quality-filtered with fastp (version 0.19.6) and assembled using FLASH (version 1.2.7). Quality-controlled sequences were clustered into operational taxonomic units (OTUs) at 97% similarity threshold using UPARSE (version 7.1). Representative sequences from each OTU were taxonomically annotated through alignment with the 16S rRNA gene database using RDP Classifier (version 2.2) at a 70% confidence threshold.
Comprehensive bioinformatics analysis was performed on the Majorbio Cloud Platform (Shanghai, China), including: (1) calculation of alpha diversity indices (Chao1, Shannon, and Simpson); (2) principal coordinates analysis (PCoA); (3) observation of microbial community composition at phylum and genus levels based on OTU relative abundance; and (4) identification of differentially abundant genera using the LEfSe method.

2.7. Correlation Analysis

Based on changes in nutritional components, differential metabolites, and differential microbes, Spearman correlation analysis was performed using a complete clustering method [28]. Heatmap analysis of clustering correlations with annotations was executed through OmicStudio tools at https://www.omicstudio.cn and accessed on 21 March 2025.

2.8. Statistical Analysis

Data were expressed as mean ± standard error and analyzed using SPSS 26.0. Statistical significance was assessed using Student’s t-tests (two-group comparisons) or one-way ANOVA with Duncan’s post hoc testing (multi-group comparisons). A p-value < 0.05 indicated statistically significant differences. (n = 3 for chemical, non-targeted metabolome analysis and microbial analysis). The flow of this test program is shown in Figure 1.

3. Results

3.1. Changes in Nutrient Composition of SBM Before and After Fermentation

As shown in Table 1, the nutrient composition of SBM differed before and after fermentation. The levels of crude protein, acid-soluble protein, reducing sugar, total phosphorus, and lactic acid in SBM significantly increased after fermentation (p < 0.05), while the contents of pH, crude fat, crude fiber, NDF, ADF, starch, and non-starch polysaccharides remarkably decreased (p < 0.05). Compared with SBM, FSBM has substantially increased contents of threonine, methionine, leucine, lysine, arginine, tryptophan, serine, glutamic, glycine, alanine, cystine, proline, tyrosine, and total amino acids (p < 0.05). In contrast, the contents of valine, isoleucine, histidine, and aspartic dramatically decreased after fermentation (p < 0.05).

3.2. Untargeted Metabolomics Analysis During Fermentation

After untargeted LC-MS analysis, 2526 metabolites were identified. To investigate the metabolite differences between fermented and unfermented feeds, PCA, PLS-DA, and OPLS-DA analyses performed, revealing fermentation led to significant biochemical changes in fermented feeds (Figure 2). 941 of which were significantly altered in the fermentation (p < 0.05), including 554 upregulated and 387 downregulated metabolites (Figure 3a,b). To compare the differences of fermentation metabolites more intuitively and directly, the top 50 differential metabolites were screened according to P-value to draw heat maps, and the VIP values of these metabolites based on the OPLS-DA model were shown in the figure (Figure 3c,d). The results showed that SBM contained significantly more raffinose, sucrose, and stachyose than FSBM (p < 0.05). After fermentation, FSBM showed significantly higher levels of lactic acid, L-pyroglutamic acid, 2-aminoisobutyric acid, and tyrosine compared to SBM (p < 0.05).
Changes in metabolic composition at different levels were shown in Figure 3e. Compared with SBM, the expression of amino acids, short peptides, and fatty acids in FSBM was significantly higher (p < 0.05), and the expression of polyketides and carbohydrates was markedly lower (p < 0.05). To characterize specific alterations in relevant metabolites, we systematically analyzed expression changes in the top 20 most significant metabolites. As shown in Figure 3f, the expression of lactic acid, DL-norleucine, 9-hydroxy-10,12-dioctadecenoic acid, and (10E,12E)-9-oxooctadeca-10,12-dienoic acid metabolites were greatly improved after fermentation (p < 0.05), while the levels of 1-(9Z,12Z-octadecadieno)-sn-glycero-3-phosphocholine, gluconic acid, and 1-palmitoylglycerol-3-phosphate were significantly decreased (p < 0.05).
Pathway enrichment analysis revealed that fermentation-related metabolites were involved in 45 metabolic pathways. To clearly visualize metabolic pathway variations across fermentation stages, the top 25 pathways were analyzed (Figure 3g,h). Among these pathways, galactose metabolism, starch and sucrose metabolism, arachidonic acid metabolism, and arginine synthesis were downregulated; while glutathione metabolism, tyrosine metabolism, pantothenic acid, CoA biosynthesis, β-alanine metabolism, pyrimidine metabolism, alanine, aspartic acid, glutamic acid metabolism, histidine metabolism, and butyric acid metabolism were upregulated (p < 0.05).

3.3. Changes of pH and SCFAs During In Vitro Fermentation

In order to further explore the regulatory effects of SBM and FSBM on gut microbiota of late-gestation, in vitro fermentation experiments were conducted. The pH of SBM and FSBM varied differently with time during fermentation (Figure 4a). Compared with the control group (CON), both FSBM and SBM exhibited significantly lower pH values (p < 0.05). At 4 h and 32 h of fermentation, FSBM exhibited markedly higher pH values compared to SBM (p < 0.05). The effect of SBM and FSBM on SCFAs concentrations was presented in the figure (Figure 4b−e). Compared with CON, the content of SCFAs in FSBM and SBM increased significantly (p < 0.05). At 8 h and 16 h of fermentation, FSBM notably increased acetate and butyrate compared to SBM (p < 0.05). At 32 h of fermentation, FSBM significantly increased acetate and total SCFAs compared to SBM (p < 0.05).

3.4. Modulation of Gut Microbiota in Late Gestation

To further observe the changes in microbiota, microbiome analysis was performed on the samples after 32h of in vitro fermentation (Figure 5a,b). Microbial α-diversity analysis showed that there were higher Shannon index and lower Simpson index in FSBM compared with those in SBM. Furthermore, β-diversity analysis based on the Bray–Curtis distance was performed, and the results showed that significant separation was observed among CON, SBM, and FSBM groups, which indicated that the difference in β-diversity of the gut microbiota was significant (p < 0.05).
The Venn diagram indicated that after fermentation, the number of OTUs common to CON, SBM, and FSBM was 201, and the number of OTUs unique to SBM and FSBM was 155 and 157, respectively (Figure 5c). The species composition of SBM and FSBM groups at the phylum and genus levels was visualized through Stacked Histograms and Heatmaps (Figure 5d,e). On the phylum level, Bacillota and Pseudomonadota were the dominant phyla in the SBM group and the FSBM group. On the genus level, Lactobacillus, Clostridium, Escherichia-Shigella, Terrisporobacter, Streptococcus, and Limosilactobacillus were the dominant genera in the SBM group and the FSBM group.
LEfSe analysis (LDA > 2) was used to find the differential microbiota (Figure 5f). The results showed that there were significant differences in the gut microbial composition of the groups. Among them, FSBM significantly increased the abundance of Lactobacillus and its related genera (p < 0.05), while SBM increased the abundance of genera such as Terrisporobacter and Turicibacter (p < 0.05). The ternary phase diagram results also showed that FSBM was significantly enriched in genera such as Lactobacillus and Limosilactobacillus, whereas SBM was relatively enriched in genera such as Streptococcus and Clostridium (Figure 5g).

3.5. Correlation Analysis of Microbiome with the Metabolome

Spearman correlation analysis was conducted to assess associations among microbial communities, nutritional indices, and metabolite levels (Figure 6). After in vitro fermentation, Lactobacillus, Bacteroides, and Ligilactobacillus, which showed higher abundances in FSBM, were positively correlated with crude protein, acid-soluble protein, lactic acid, and most amino acids. Conversely, these bacteria were negatively correlated with crude fiber, NDF, ADF, starch, and non-starch polysaccharides. Additionally, Lactobacillus, Bacteroides, and Ligilactobacillus were positively correlated with lactate, pyroglutamic acid, sarcosine, N-lactoyl-tyrosine, and 1-O-dodecanoyl-glucopyranoside, while negatively correlated with raffinose, sucrose, stachyose, and 2-aminoisobutyric acid.

4. Discussion

In the present study, the contents of crude protein and acid-soluble protein in SBM increased, and the levels of crude fiber and non-starch polysaccharides were reduced after the fermentation. Metabolic pathways such as glutathione metabolism, tyrosine metabolism, and pantothenic acid and CoA biosynthesis were enriched. By using an in vitro fermentation model and the high-throughput sequencing technology, it was found that FSBM increased the abundance of gut Lactobacillus and Limosilactobacillus of late-gestation sows. Through correlation analysis, it was found that after fermentation, the changes in the nutritional components and metabolites of SBM had an impact on the composition and structure of the gut microbiota in sows during the late gestation stage.
Due to the potential role of microbial fermentation of feeds, its development and application have received increasing attention in recent years. It was well known that fermentation reduced the level of anti-nutritional factors in feed, produced a variety of enzymes, and improved the nutritional status of the substrate [29,30]. Many species of microbes, such as Lactobacillus and Bacillus subtilis have been used to increase SBM nutritive value [31,32]. Research indicated that fermenting soybean meal with Bacillus subtilis significantly enhanced its digestibility as a protein source in piglet feed, as the process broke down proteins into easily absorbable amino acids and peptides [33]. Lactobacillus could improve gut functions, promote nutrient digestion and absorption, and regulate immune functions [34]. Therefore, Bacillus subtilis and Enterococcus faecalis were selected for the fermentation of SBM in this study. Consistent with previous findings [14,35], the levels of crude protein, acid-soluble protein, and amino acids increased, while the levels of crude fiber, acid detergent fiber, neutral detergent fiber, and starch decreased. Additionally, lactic acid levels increased, and pH decreased. Bacillus subtilis had a strong ability to secrete enzymes, producing highly active proteases and enzymes that degraded complex carbohydrates in feed [36]. Studies found that during the fermentation of SBM by Bacillus subtilis, the activities of proteases, amylases, cellulases, and xylanases in the substrate significantly increased [37]. Enterococcus faecalis could ferment glucose to produce lactic acid, reducing the pH of the feed, and generate antimicrobial substances that inhibited the growth of harmful bacteria during fermentation [38]. Thus, the nutritional composition changes during fermentation were fundamentally dependent on the enzymatic activities of the inoculated probiotics.
Untargeted metabolomics analysis revealed that amino acids, peptides, organic acids, and carbohydrates constituted the predominant metabolite classes during fermentation. Fermentation promoted the levels of organic acids, amino acids, peptides, and fatty acids, while reducing the levels of carbohydrates. The nutritional quality of proteins exhibited a strong correlation with their amino acid composition, particularly regarding essential amino acids in animal nutrition [39,40]. Among these, lysine, phenylalanine, isoleucine, and valine represented critical essential amino acids for animal feed formulations [41]. Lysine, valine, phenylalanyl valine, and phenylalanyl tryptophan amino acid-like metabolite levels in the present study showed a significant increase in FSBM, and this result was in agreement with previous studies [21,39,42]. Furthermore, studies showed that amino acid metabolic pathways such as glutathione metabolism, tyrosine metabolism, glycine, serine, and threonine metabolism were predominant after fermentation, which led to an increase in the levels of the amino acids in FSBM [43]. Notably, the lysine degradation pathway was significantly enriched post-fermentation, potentially attributable to synergistic enzymatic activities of Bacillus subtilis and Enterococcus faecalis present in the fermented feed [44,45]. Studies showed that in the early stages of lactic acid bacteria fermenting liquid pig feed, the increase in acidity was slow, and the loss of lysine may be attributed to the metabolism of lysine by Escherichia coli [46]. The enrichment of lysine degradation pathways after fermentation was the result of the combined effects of microbial activity, fermentation time, feed raw materials, and substances produced during the fermentation process.
This study demonstrated significantly elevated levels of organic acids and fatty acids in FSBM compared to untreated SBM. As an antimicrobial component in fermented feeds, organic acids and fatty acids inhibited the growth of pathogenic bacteria and reduced diarrhea in livestock [47,48]. In addition, in agreement with the previous studies [45,49], fermentation significantly decreased the levels of fructose, raffinose, and sucrose. Sucrose altered microbial populations and bacterial enzyme activities in the gut, leading to diarrhea and reduced growth in piglets [50]. The reduced concentrations of fructose and raffinose in FSBM suggest microbial utilization by fermentative strains for SCFA production. These metabolites subsequently served as both intestinal epithelial cell nutrients and prebiotic factors stimulating probiotic proliferation [39]. Furthermore, in this study, the galactose metabolism pathway and the starch and sucrose metabolism pathway were significantly enriched before fermentation, while the pantothenate and CoA biosynthesis pathway was enriched after fermentation. This indicated that Bacillus subtilis and Enterococcus faecalis can utilize carbohydrates through glycolysis, generating the energy molecule acetyl-CoA. This precursor had access to both amino acid and fatty acid pathways [42,51,52]. This suggested that the microbial consortium actively metabolized available carbon substrates in SBM, converting them into biologically active metabolites. Overall, metabolic pathway analysis demonstrated a significant reduction in carbohydrate content within SBM. These findings indicated that microbial and enzymatic activity mediated carbohydrate catabolism through glycolytic pathways, utilizing these compounds as energy substrates for metabolic processes. Consequently, the resulting metabolic byproducts contributed to a substantial increase in both amino acids and fatty acids in FSBM.
In this study, an in vitro fermentation model was used to explore the differences in the responses of the sow’s gut microbiota in late gestation to SBM and FSBM. The pH of SBM was lower in the early stages of fermentation, while the pH of FSBM decreased later. The reason may have been that the macromolecular substances in FSBM had been decomposed into small molecular substances after fermentation, which could be directly utilized by microbiota for proliferation [53,54]. Meanwhile, the microbiota must first break down macromolecular substances in SBM before they could be utilized [55]. SCFAs were the primary end-products of hindgut microbial fermentation, which could maintain gut homeostasis, support host immune response, and enhance gut barrier integrity [56]. In the present study, FSBM significantly increased acetate and butyrate after in vitro fermentation, consistent with the results of previous studies [57,58].
FSBM enhanced beneficial bacteria like Lactobacillus and Limosilactobacillus in the gut microbiota of late-gestation sows. Lactobacillus was correlated with the changes in the nutritional components and metabolites of SBM. Currently, numerous studies have reported an increase in the abundance of gut Lactobacillus. The balance of gut microbiota could be regulated by fermented feed, which was rich in probiotics and prebiotics [59]. Dietary supplementation with fermented bamboo fiber significantly increased the relative abundance of beneficial gut microbiota, including Lachnospira, Lachnospiracea_XPB1014_Group, and Roseburia in late-gestation sows [60]. Dietary fermented maize-soya soybean meal enriched the relative abundance of Lactobacillus within the colon [61]. FSBM polysaccharide significantly boosted the abundance of Bifidobacteria and Lactobacilli in the pig intestine [62]. Fermentation of SBM with Bacillus subtilis, Hansenula anomala, and Lactobacillus casei significantly increased fecal Lactobacillus abundance while reducing Escherichia coli levels in piglets [63]. The fermenting inoculation, raw materials, and fermentation methods all influenced the changes in metabolites in feed, and their effects on gut microbiota shared both commonalities and differences. Overall, fermentation improved the nutritional profile of SBM while beneficially modulating gut microbiota composition, characterized by increased relative abundance of commensal probiotics and suppression of pathogenic bacterial populations.

5. Conclusions

Bacillus subtilis and Enterococcus faecalis, combined with xylanase and protease, were used for co-fermentation with bacteria and enzymes of SBM, which increased the contents of crude protein, acid-soluble protein, lysine, histidine, and total amino acids in SBM, and reduced the levels of anti-nutritional factors such as crude fiber and non-starch polysaccharides. Microbiota could utilize the macromolecular proteins and carbohydrates in SBM, increasing in metabolites such as amino acids and fatty acids. It also enriched metabolic pathways such as glutathione metabolism, tyrosine metabolism, pantothenic acid, and CoA biosynthesis. In vitro fermentation showed that FSBM significantly increased the content of SCFAs and the abundances of sows gut commensal Lactobacillus and Limosilactobacillus in late gestation. Correlation analysis indicated that changes in the nutritional composition and metabolites of SBM and FSBM influenced the abundance of the gut microbiota in late gestation. In summary, this study demonstrated that co-fermentation with bacteria and enzymes pretreatment of soybean meal reduced fiber components and enriched bioactive metabolites, optimizing intestinal microbial composition and increasing SCFA production in late-pregnant sows. In this study, an in vitro trial was conducted from the perspectives of nutrient components and metabolites, providing new insights into the regulatory effects of fermented feed on the gut microbiota in late-pregnant sows. Meanwhile, it laid a solid foundation for future in vivo verification and application.

Author Contributions

Conceptualization, C.W.; methodology, Y.L., L.F. and Y.C.; formal analysis, Y.L., L.F. and Y.C.; investigation, Y.L., L.F., Y.C. and C.W.; resources, H.Y., Y.X. and C.W.; data curation, Y.L. and Y.C.; writing—original draft preparation, Y.L.; writing—review and editing, L.F., Y.C., H.Y., Y.X., Y.R. and C.W.; visualization, Y.L. and Y.C.; supervision, H.Y., Y.X., Y.R. and C.W.; project administration, H.Y. and C.W.; funding acquisition, H.Y. and C.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 32202682), the Fund of Xianghu Laboratory (2023C1S01001), and the Key Research and Development Program of Zhejiang (2024SSYS0101).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Test flow chart.
Figure 1. Test flow chart.
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Figure 2. Clustering of metabolites before and after fermentation. (a) PCA. (b) PLS−DA. (c) OPLS−DA. (d) Results of displacement test of SBM group on OPLS−DA model for FSBM group.
Figure 2. Clustering of metabolites before and after fermentation. (a) PCA. (b) PLS−DA. (c) OPLS−DA. (d) Results of displacement test of SBM group on OPLS−DA model for FSBM group.
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Figure 3. Results of differential analyses of metabolites in SBM and FSBM. (a) Volcano plot of differentia. (b) Cluster analysis of differentially abundant metabolites. (c) Cluster analysis of top 50 differential metabolites. (d) Scatter plot for determining top 50 differential metabolites based on VIP. (e) Relative compositions of main metabolites. (f) Top 20 metabolites. (g) Histogram and (h) bubble chart of prediction of metabolic pathways identifying significantly different metabolites.
Figure 3. Results of differential analyses of metabolites in SBM and FSBM. (a) Volcano plot of differentia. (b) Cluster analysis of differentially abundant metabolites. (c) Cluster analysis of top 50 differential metabolites. (d) Scatter plot for determining top 50 differential metabolites based on VIP. (e) Relative compositions of main metabolites. (f) Top 20 metabolites. (g) Histogram and (h) bubble chart of prediction of metabolic pathways identifying significantly different metabolites.
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Figure 4. Changes in pH and SCFAs during in vitro fermentation of SBM and FSBM. Different lowercase letters in figure indicate statistically significant differences at p < 0.05. (a) pH. (b) Total SCFAs. (c) Acetate. (d) Propionate. (e) Butyrate.
Figure 4. Changes in pH and SCFAs during in vitro fermentation of SBM and FSBM. Different lowercase letters in figure indicate statistically significant differences at p < 0.05. (a) pH. (b) Total SCFAs. (c) Acetate. (d) Propionate. (e) Butyrate.
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Figure 5. Modulation of gut microbiota in late−gestation sows. (a) α−diversity of Chao, Shannon, and Simpson index. (b) β−diversity. (c) Venn diagram. (d) Species composition at phylum level. (e) Species composition at genus level. (f) LEfSe analysis of significantly different gut microbiota. (g) Ternary analysis of significantly different gut microbiota.
Figure 5. Modulation of gut microbiota in late−gestation sows. (a) α−diversity of Chao, Shannon, and Simpson index. (b) β−diversity. (c) Venn diagram. (d) Species composition at phylum level. (e) Species composition at genus level. (f) LEfSe analysis of significantly different gut microbiota. (g) Ternary analysis of significantly different gut microbiota.
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Figure 6. Correlation analysis between differential microbiota, nutritional indexes and differential metabolites before and after SBM fermentation. Positive correlations are depicted in red, negative correlations in blue. Depth of color indicates strength of correlation. * indicates significantly correlation (p < 0.05); ** indicates extremely significant correlation (p < 0.01).
Figure 6. Correlation analysis between differential microbiota, nutritional indexes and differential metabolites before and after SBM fermentation. Positive correlations are depicted in red, negative correlations in blue. Depth of color indicates strength of correlation. * indicates significantly correlation (p < 0.05); ** indicates extremely significant correlation (p < 0.01).
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Table 1. Nutrient composition of SBM and FSBM.
Table 1. Nutrient composition of SBM and FSBM.
ItemSBMFSBM
pH6.38 ± 0.01 a5.24 ± 0.02 b
Crude protein (%)44.47 ± 0.94 b52.65 ± 0.35 a
Ash (%)5.30 ± 0.02 b6.28 ± 0.05 a
Crude fat (%)1.90 ± 0.02 a0.90 ± 0.02 b
Crude fiber (%)4.12 ± 0.07 a2.71 ± 0.25 b
NDF (%)11.43 ± 0.29 a7.29 ± 0.14 b
ADF (%)8.62 ± 0.35 a0.83 ± 0.05 b
Acid soluble protein (%)2.08 ± 0.07 b12.69 ± 0.23 a
Starch (%)0.88 ± 0.02 a0.53 ± 0.00 b
Amylose (%)0.79 ± 0.01 b2.71 ± 0.11 a
Reducing sugar (%)0.58 ± 0.04 b3.93 ± 0.10 a
Non-starch polysaccharides (%)28.61 ± 0.15 a21.74 ± 0.45 b
Calcium (%)0.34 ± 0.020.31 ± 0.02
Phosphorus (%)0.60 ± 0.02 b0.82 ± 0.02 a
Lactate (%)1.03 ± 0.04 b3.56 ± 0.08 a
Essential amino acid (%)
Threonine1.89 ± 0.01 b1.97 ± 0.01 a
Valine1.89 ± 0.00 a1.75 ± 0.03 b
Methionine0.64 ± 0.00 b0.69 ± 0.01 a
Isoleucine1.64 ± 0.00 a1.34 ± 0.03 b
Leucine3.54 ± 0.01 b5.15 ± 0.09 a
Phenylalanine2.14 ± 0.042.69 ± 0.25
Lysine2.45 ± 0.03 b3.33 ± 0.13 a
Histidine1.12 ± 0.02 a0.82 ± 0.00 b
Arginine3.22 ± 0.03 b3.44 ± 0.06 a
Tryptophan0.24 ± 0.00 b0.66 ± 0.01 a
Non-essential amino acid (%)
Serine1.45 ± 0.01 b4.07 ± 0.13 a
Glutamate10.00 ± 0.03 b10.52 ± 0.11 a
Glycine2.27 ± 0.00 b2.40 ± 0.07 a
Alanine1.45 ± 0.01 b2.79 ± 0.02 a
Cystine0.65 ± 0.01 b0.70 ± 0.00 a
Aspartate2.66 ± 0.06 a2.50 ± 0.03 b
Proline1.23 ± 0.02 b1.68 ± 0.02 a
Tyrosine1.55 ± 0.01 b1.90 ± 0.02 a
The total amino acids (%)40.03 ± 0.07 b48.41 ± 0.36 a
SBM: soybean meal; FSBM: fermented soybean meal. All data are presented as mean ± SD (n = 3). Different lowercase letters in same row indicate statistically significant differences at p < 0.05.
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Li, Y.; Fu, L.; Chen, Y.; Yang, H.; Xiao, Y.; Ren, Y.; Wang, C. Integrated Metabolome and Microbiome Analysis Reveals the Regulatory Effects of Fermented Soybean Meal on the Gut Microbiota of Late Gestation. Fermentation 2025, 11, 315. https://doi.org/10.3390/fermentation11060315

AMA Style

Li Y, Fu L, Chen Y, Yang H, Xiao Y, Ren Y, Wang C. Integrated Metabolome and Microbiome Analysis Reveals the Regulatory Effects of Fermented Soybean Meal on the Gut Microbiota of Late Gestation. Fermentation. 2025; 11(6):315. https://doi.org/10.3390/fermentation11060315

Chicago/Turabian Style

Li, Yantao, Lele Fu, Yushi Chen, Hua Yang, Yingping Xiao, Ying Ren, and Cheng Wang. 2025. "Integrated Metabolome and Microbiome Analysis Reveals the Regulatory Effects of Fermented Soybean Meal on the Gut Microbiota of Late Gestation" Fermentation 11, no. 6: 315. https://doi.org/10.3390/fermentation11060315

APA Style

Li, Y., Fu, L., Chen, Y., Yang, H., Xiao, Y., Ren, Y., & Wang, C. (2025). Integrated Metabolome and Microbiome Analysis Reveals the Regulatory Effects of Fermented Soybean Meal on the Gut Microbiota of Late Gestation. Fermentation, 11(6), 315. https://doi.org/10.3390/fermentation11060315

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