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Article

Dynamic Analysis of Fermentation Quality, Microbial Community, and Metabolome in the Whole Plant Soybean Silage

by
He Meng
1,
Yan Jiang
1,2,*,
Lin Wang
1,
Yuanming Li
1,2,
Sui Wang
3,
Xiaohong Tong
1 and
Shaodong Wang
1,*
1
Agricultural College, Northeast Agricultural University, Harbin 150030, China
2
Heilongjiang Academy Green Food Science Resarch Institute, National Soybean Engineering Technology Research Center, Harbin 150028, China
3
National Key Laboratory of Smart Farm Technologies and Systems, Harbin 150030, China
*
Authors to whom correspondence should be addressed.
Fermentation 2024, 10(10), 535; https://doi.org/10.3390/fermentation10100535
Submission received: 14 September 2024 / Revised: 15 October 2024 / Accepted: 16 October 2024 / Published: 21 October 2024
(This article belongs to the Special Issue Application of Fermentation Technology in Animal Nutrition)
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Abstract

:
Soybean (Glycine max (L.) Merr.) is an important oilseed crop, known for its rich nutritional content and high-quality protein. To address the shortage of feed protein resources and better utilize soybeans as a raw material, this study investigated the feasibility of using whole-plant soybean (WPS) as silage. As the ensiling period is a critical fermentation parameter, identifying the optimal fermentation duration was a key objective. The research involves fermenting WPS for silage production, conducted over five fermentation durations: 7, 15, 30, 60, and 90 days. The fermentation quality, microbial community, and metabolome of WPS silage were analyzed across these different time points. WPS silage fermented for 30 days exhibited optimal fermentation characteristics, with the highest lactic acid (LA) content observed at 30 days (p < 0.05), while butyric acid (BA) was detected only at 60 and 90 days. At 30 days, Enterococcus genera reached its peak relative abundance and was identified as the dominant genus. Random forest analysis highlighted Pantoea genera as the most influential biomarker. Metabolomic analysis revealed that the metabolic pathways involved in the biosynthesis of essential amino acids valine, leucine, and isoleucine were significantly enhanced during the later stages of fermentation compared to the earlier stages. Under natural fermentation conditions, the optimal fermentation period for WPS silage is approximately 30 days. These findings provide a theoretical basis for the utilization of WPS and the subsequent optimization of fermentation quality.

1. Introduction

The rapid development of the livestock industry has led to a continuous increase in the demand for animal feed [1]. As a result, the livestock industry has been facing an increasing shortage of feed protein resources. Soybean (Glycine max (L.) Merr.) is one of the most important oilseed crops in the world [2]. Soybean seeds are rich in crude protein (CP), making their byproduct, soymeal, widely used in animal feed. In addition to the seeds, whole plant soybeans (WPS), including stems, leaves, and seeds, are characterized by their rich nutritional content, high biomass, and widespread distribution [3]. Soybeans are photoperiod-sensitive short-day crops. Research indicates that growing low-latitude soybean varieties in high-latitude regions significantly extends their vegetative growth period, resulting in several-fold increases in biomass [4]. However, in recent years, WPS has primarily been used inefficiently as animal feed, rural fuel, or returned to the field, and is often arbitrarily discarded [5]. Therefore, rational strategies are needed to enhance the utilization of WPS.
Ensiling is a traditional method of forage preservation that relies on anaerobic fermentation by lactic acid bacteria (LAB) [1]. The world’s major ruminant feed source is silage [6]. Therefore, WPS can be better utilized as a forage resource by converting it into WPS silage through ensiling fermentation. De Morais et al. [7] demonstrated that WPS silage offers a potential high-protein roughage source for ruminant diets. During ensiling, water-soluble carbohydrates (WSC) in the green raw material are fermented into organic acids like lactic acid (LA) and acetic acid (AA), leading to a pH decline that inhibits undesirable microorganisms. Beneficial microorganisms, such as LAB, accelerate the utilization of these carbohydrates by other microorganisms, enhancing the production of organic acids and significantly contributing to the silage fermentation process, while some undesirable microorganisms, such as mold and clostridia, may cause spoilage [8]. However, as a legume, WPS may struggle to achieve satisfactory silage quality due to its low WSC content and insufficient epiphytic LAB count. For well-preserved silage, WSC content should exceed 60 g/kg dry matter, and epiphytic LAB count should be no less than 5 log10 CFU/g FM [9]. If these criteria are not met, the silage pH cannot decline rapidly, leading to reduced silage quality.
Silage fermentation is a process driven by microorganisms, and its quality heavily depends on the activities and diversity of the microorganisms involved [10]. Hence, revealing the bacterial community in silage is crucial for ensuring its preservation. Additionally, silage fermentation is a dynamic process. Understanding the changes in the microbial community structure during silage production helps identify key beneficial microorganisms that influence silage quality. However, due to the limitations of traditional microbial isolation and cultivation methods, advancements in next-generation sequencing (NGS) have led to an exponential increase in the discovery and characterization of microorganisms. NGS techniques do not rely on conventional cultivation methods, allowing for the detection of unculturable microorganisms [11]. Currently, many studies utilize NGS techniques to explore microbial community changes in various silage. Kharazian et al. [12] employed NGS sequencing to investigate the microbial community dynamics in sorghum silage with and without inoculation of Lactiplantibacillus plantarum and under different DM contents. Their findings revealed that during fermentation, the dominant microbial group shifted from Pseudomonas congelans to L. buchneri. Similarly, Liu et al. [13] used NGS sequencing to study the variations in the microbial communities of alfalfa silage with LAB additives and under varying temperatures. Their results revealed that an increase in temperature and the presence of LAB additives significantly increased the abundance of Lactobacillus. Silage fermentation is a complex process, and the quality of silage largely depends not only on the microbial community present but also on the metabolic products produced. Over the past few decades, research on silage metabolites has primarily focused on organic acids, ethanol, and 1,2-propanediol to evaluate the fermentation quality and aerobic stability of silage [14]. Sun et al. demonstrated that LAB generates various compounds during silage fermentation, including fatty acids, vitamins, and aromatic compounds. This indicates that the metabolites in silage have not been thoroughly identified. As one of the most popular “omics” technologies, metabolomics is considered a powerful tool due to its ability to rapidly detect, identify, and quantify numerous metabolites in biological samples [15]. It is widely used in biomedical research, environmental monitoring, and food and nutrition studies [16]. Xu et al. [17] used a multi-omics approach to study the interactions between bacterial microbiota and metabolome in whole-crop corn ensiling systems. They found that biofunctional metabolites were closely linked to the main types of lactic acid bacteria, impacting the fermentation process.
Based on previous research, there has been limited investigation into the dynamic processes of WPS silage. Therefore, this study aims to combine NGS sequencing and metabolomics techniques to examine the impact of different fermentation durations on the fermentation quality, microbial communities, and metabolites of WPS silage. By elucidating the interactions between key microorganisms and metabolites during fermentation, this research provides a foundation for producing high-quality animal feed.

2. Materials and Methods

2.1. Materials Preparation

The soybeans (variety “J8009”) were planted on 20 May 2022, in the experimental fields of Northeast Agricultural University (126°3′30″ E, 45°44′34″ N, altitude 178 m) at a density of 250,000 plants per hectare. A basal fertilizer of 225 kg/ha compound fertilizer (N:P2O5 = 45:75:75) was applied with no additional fertilization until harvest. According to our previous research, soybeans harvested at the early podding stage are suitable for ensiling [18], so the crop was manually cut at this stage, leaving a stubble height of 10 cm. After harvesting, the soybeans were wilted on clean outdoor mats for 8 h and then chopped into 2-3 cm pieces using a forage chopper (YL100L-2, Weihai, China). The chopped material was mixed thoroughly, and 500 g samples were packed into polyethylene bags (25 cm × 30 cm, Wenzhou, China), vacuum-sealed (DZQ-420C, Ansenke, Quanzhou, China), and stored in the dark at room temperature (20 °C–25 °C). A total of 15 silage bags (5 fermentation periods × 3 replicates) were prepared. Samples were randomly taken at 7, 15, 30, 60, and 90 days of ensiling for analysis of fermentation indices, bacterial community, and metabolites.

2.2. WPS Silage Chemical Composition Analysis

At the end of the designated fermentation period, samples were taken from the silage bags. Each silage sample was weighed (100 g), dried at 75 °C for 48 h to determine the dry matter (DM) content, then ground through a 1 mm sieve and stored in a desiccator at room temperature for subsequent chemical analysis. The WSC content was determined using the anthrone-sulfuric acid method. Crude fat (CF) and CP contents were measured using the Soxhlet extraction method and the Kjeldahl nitrogen method, respectively [19]. Acid detergent fiber (ADF) and neutral detergent fiber (NDF) were analyzed using the methods of McDonald et al. [20]. Ammonia nitrogen (NH3-N) content was determined according to the method of Broderick and Kang [21].
A 10 g sample from each silage bag was mixed with 100 mL of sterile ultrapure water and homogenized for 35 s. The homogenate was stored in a refrigerator at 4 °C for 24 h. The mixture was then filtered through medical gauze, and the pH was immediately measured using a pH meter (PHS-3CW, BANTE, Shanghai, China). Organic acid contents, including lactic acid (LA), acetic acid (AA), propionic acid (PA), and butyric acid (BA), were subsequently determined according to the method of Meng et al. [22]. The aerobic stability (AS) of the silage was assessed by recording the number of hours required for the silage temperature to rise 2 °C above the ambient temperature [23].

2.3. WPS Silage Bacterial Community Sequencing Analysis

Total bacterial genomic DNA was extracted using FastDNA SPIN kits (MP Biomedicals, Santa Ana, CA, USA) according to the manufacturer’s guidelines and stored at −20 °C for subsequent analyses. PCR amplification of the V3-V4 region of bacterial 16S rRNA genes was performed using primers 338F (5’-ACTCCTACGGGAGGCAGCA) and 806R (5’-GGACTACHVGGGTWTCTAAT). Sequencing was carried out on the Illumina NovaSeq platform with paired-end 2 × 300 bp reads by Biomarker Technologies Co., Ltd. Raw reads were first filtered using Trimmomatic v0.33. Primer sequences were identified and removed with Cutadapt 1.9.1, resulting in clean reads. Denoising was performed using the DADA2 method in QIIME2 2020.6 [24]. Reads were trimmed based on the error rate algorithm in DADA2, and ASVs were clustered using the DADA2 clustering algorithm [25]. Taxonomy was assigned through DADA2 utilizing the Silva database (https://www.arb-silva.de/, accessed on 1 June 2024).

2.4. WPS Silage Metabolite Analysis

Silage samples were ground post-freeze-drying and extracted with 70% aqueous methanol at 4 °C overnight. The extracts were centrifuged (12,000 rpm for 10 min) and filtered through a 0.22 μm membrane before LC-MS/MS analysis [26]. Metabolite analysis was conducted using the MetWare database by Wekemo Tech Group Co., Ltd., Shenzhen, China, with differential metabolites selected based on VIP ≥ 1.0 and FC ≥ 2.0. Metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) compound database and mapped to KEGG pathways (http://www.kegg.jp/kegg/pathway.html, accessed on 1 June 2024).

2.5. Statistical Analysis and Graphing

The fermentation and nutritional indicator data of the silage were analyzed using one-way ANOVA (SPSS 26.0, Chicago, IL, USA). Tukey’s Honest Significant Difference (HSD) test was employed for mean comparisons among different samples, with significance set at p < 0.05. Sequence data analyses were primarily performed using QIIME2 2020.6 and R software (v4.4.0). PCA, PCoA, and CCA analyses were conducted using the “Vegan” package in R. Alpha diversity was calculated using the “diversity” function of the “Vegan” package, while beta diversity distances were computed using the “vegdist” function from the same package. CCA analysis was performed with the “cca” function in the “Vegan” package. Pearson correlation calculations were conducted using the “correlation_matrix” function from the “corrtable” package, and random forest analysis was conducted using the “randomForest” package. The Mantel test calculations and plotting were executed with the “linkET” package, and boxplots were drawn using the “ggpubr” package. Scatter plots with linear regression were created using the “ggscatter” function from the “ggpubr” package. PLS-DA analysis, pie chart plotting, and multiple volcano plots were generated using the OmicShare tools, a free online data analysis platform (https://www.omicshare.com/tools, accessed on 26 July 2024).

3. Results

3.1. Chemical Compositions of WPS and WPS Silage

The chemical composition of fresh WPS before ensiling is shown in Table 1. The DM content of fresh WPS is 34.26%, with nutritional indices of CP, ADF, NDF, and CF at 18.78%, 35.26%, 48.27%, and 3.77%, respectively. The WSC content of fresh WPS is 3.84%. The DM, CP, ADF, and NDF contents are higher, while the WSC content is lower than those reported by Zeng et al. [3]. This discrepancy may be due to differences in soybean varieties and sampling periods.
The fermentation quality and chemical composition of different ensiling days silage treatments (7, 15, 30, 60, and 90 days) are summarized in Table 2. The DM content varied, with values ranging from 35.01% at 7 days to 37.34% at 90 days. The DM content at 15 days was significantly lower than other groups (p < 0.05). CP content showed significant variation, peaking at 18.35% at 15 days (p < 0.05) and reaching a low of 17.02% at 30 days (p < 0.05), with no significant difference between the 7 d and 15 d groups. CF content was highest at 7 days (3.72%) and lowest at 30 days (3.18%), with no significant difference between the 7 d and 15 d groups. NDF content was highest at 7 days (48.55%) (p < 0.05) and lowest at 60 days (43.11%) (p < 0.05), while ADF content was lowest at 7 days (31.07%) (p < 0.05) and highest at 90 days (34.50%) (p < 0.05). WSC content decreased from 2.51% at 7 days to 1.39% at 90 days. The pH values showed significant differences, with the highest values at 7 days (5.98) and 15 days (5.96), and the lowest at 90 days (5.13); the 7 d and 15 d groups were not significantly different. The LA content in the 7 d group was significantly lower than in the other groups, while no significant differences were observed in LA content among the other groups. The AA content was highest in the 7 d group (1.70%) and the 90 d group (1.73%) (p < 0.05), significantly lower in the 15 d (1.50%) and 30 d (1.45%) groups (p < 0.05), with the lowest content observed in the 60 d group (1.27%) (p < 0.05). PA content was consistently low, with values ranging from 0.06% at 60 days to 0.16% at 30 days. BA was not detected at 7, 15, and 30 days of ensiling. However, BA content was 0.06% in the 60 d group and 0.12% in the 90 d group. The NH3-H content (% of TN) increased significantly over the fermentation periods, from 3.27% at 7 days to 5.17% at 90 days, with significant differences among most groups. AS improved from 54.00 h at 7 days to 112.00 h at 90 days. These results indicate significant differences in fermentation quality and chemical composition across the different fermentation periods.

3.2. Microbial Community of the WPS Silage

After quality control of silage fermentation, each sample yielded an average of 76,430 reads based on Illumina sequencing. Rarefaction curves indicated that sequencing coverage was adequate (Figure S1). The alpha diversity indices, as shown in Figure 1A, include Simpson, Shannon, Chao1, and PD_whole_tree indices. There were no significant differences in the Simpson index among the groups at different ensiling times. The Shannon index for the 60 d group was significantly higher than that of the 15 d and 90 d groups (p < 0.05). The Chao1 index followed a similar trend to the Shannon index, with the 60 d group having a significantly higher Chao1 index than the 15 d and 30 d groups (p < 0.05). The PD_whole_tree index for the 60 d group was significantly higher than those of the 30 d and 90 d groups, and the 7 d group had a significantly higher PD_whole_tree index than the 15 d group. The PCoA analysis based on Bray–Curtis distances is shown in Figure 1B. The Anosim test indicates that there are significant differences in beta diversity between the samples from different groups. The first and second principal coordinates (PCoA1 and PCoA2) account for 69.2% of the total variance in the microbial composition of the silage, with PCoA1 explaining 40.41% and PCoA2 explaining 28.79%. There is a clear separation among the groups with different fermentation times along PCoA2. Specifically, the 7 d group is distinctly separated from the 15 d, 30 d, and 90 d groups, and the 30 d group is clearly separated from the 60 d and 7 d group. Additionally, the samples from the 7 d group were the most distant from those of the 30 d group, indicating the greatest difference in microbial communities between these two groups. The Venn diagram illustrates that the number of shared ASVs across the five fermentation periods is 119 (Figure 1C). The 7 d group has the highest number of unique ASVs, with 23, while the 30 d group has the fewest, with 9. The number of unique ASVs in the other groups is similar. To better explore the relationship between ensiling time and species richness, we conducted a linear regression analysis. Figure 1D illustrates the relationship between ln-transformed species richness and ln-transformed ensiling days. As presented in Figure 1E, at the phylum level, Proteobacteria and Firmicutes are the dominant bacteria across all fermentation periods. The relative abundance of Proteobacteria is highest in the 7 d group (69.12%), followed by the 60 d (64.08%), 15 d (58.27%), 90 d (47.98%), and 30 d (44.28%) groups. Conversely, Firmicutes show the highest relative abundance in the 30 d group (43.78%), followed by the 90 d (34.45%), 15 d (26.18%), 7 d (17.40%), and 60 d (15.82%) groups. As shown in Figure 1F, at the genus level, Enterococcus and Lactobacillus are prominent genera in the silage bacterial community. Enterococcus exhibits the highest relative abundance in the 30 d group (26.17%), followed by the 15 d (18.94%), 7 d (11.82%), 60 d (6.82%), and 90 d (6.32%) groups, indicating its dominance across all fermentation periods. Lactobacillus, while not consistently dominant throughout the fermentation process, shows a significant increase in abundance at 60 d (5.25%) and reaches its peak in the 90 d group (17.63%). Additionally, Sphingomonas genera shows considerable presence, particularly in the 15 d group (16.44%), with substantial abundance in other periods as well. Weissella genera peaks at 30 d (13.04%), and Kosakonia genera is notably abundant at 7 d (18.69%) but decreases significantly in later stages.

3.3. Biomarker Selection and Correlation with Fermentation and Nutritional Indicators

The ASVs from each group were analyzed using a random forest model (Bootstrap resampling with 1000 iterations and 1000 decision trees) to identify the most important microbial biomarkers in the silage. The top 10 important ASVs were selected based on the percentage increase in MSE% (%IncMSE). The abundance bubble chart of these biomarkers across different samples is shown in Figure 2A. Pantoea and Brevundimonas genera were significantly more abundant in the 60 d group compared to other groups, while Enterococcus genera was significantly more abundant in the 7 d group. A higher increase in MSE% indicates greater importance of the biomarker. The bar chart arranged by importance, as shown in Figure 2B, highlights Pantoea as the key biomarker, followed by Brevundimonas, Paenibacillus, Sphingomonas, Enterococcus, Klebsiella, Lactobacillus, Bradyrhizobium, Pseudomonas, and Quadrisphaera.
Next, a correlation analysis between the selected biomarkers and fermentation and nutritional indicators was conducted. It was observed that Pantoea genera was significantly negatively correlated with DM (p < 0.05). Lactobacillus genera showed a significant positive correlation with LA (p < 0.05), Bradyrhizobium genera had a highly significant positive correlation with DM (p < 0.01), and Enterococcus genera was significantly positively correlated with ADF, WSC, and LA (p < 0.05).

3.4. Metabolite Analysis of WPS Silage

In this study, non-targeted metabolomics analysis of WPS silage at different fermentation days was performed using LC–MS/MS (Liquid Chromatography–Mass Spectrometry). A total of 1173 metabolites were detected in the positive ion mode (ESI(+)), as shown in Figure 3A. These included 439 lipids and lipid-like molecules, 192 organic acids and derivatives, 153 organoheterocyclic compounds, 114 phenylpropanoids and polyketides, 75 benzenoids, 53 organic oxygen compounds, 41 nucleosides, nucleotides, and analogues, 24 alkaloids and derivatives, 19 organic nitrogen compounds, 9 lignans, neolignans, and related compounds, 2 homogeneous non-metal compounds, and 2 organosulfur compounds.
To distinguish changes in WPS silage metabolites across different fermentation days, unsupervised PCA and supervised PLS-DA multivariate analyses were employed to investigate the relationship between metabolites and the quality changes in WPS silage. As shown in Figure 3B,C, the first two PCA components (PCA1 and PCA2) explained 57.3% of the variance. Samples from different fermentation times were dispersed between groups and clustered within groups, indicating good intra-group reproducibility and clear inter-group differences. PLS-DA was used to analyze the impact of different fermentation days on WPS silage metabolites, resulting in the identification of differential metabolites (DAMs) for WPS silage at various fermentation stages. R2X and R2Y represent the explanatory power of the PLS-DA model for the X and Y matrices, respectively, while Q2 represents the predictive ability of the model. In this study, these three indicators were close to 1, indicating that the model was stable and reliable. Given the significant differences between the fermentation quality and chemical composition of the 7 d silage group and other fermentation times, the 7 d silage group was selected as the control. DAMs with a fold change (FC) > 2 were identified through multiple volcano plots, comparing 15 d, 30 d, 60 d, and 90 d silage samples with the 7 d silage group, as shown in Figure 3D. Compared to the 7 d group, 64 DAMs were significantly upregulated, and 63 were significantly downregulated in the 15 d group; 158 DAMs were significantly upregulated, and 168 were significantly downregulated in the 30 d group; 205 DAMs were significantly upregulated, and 189 were significantly downregulated in the 60 d group; and 213 DAMs were significantly upregulated, and 186 were significantly downregulated in the 90 d group. Notably, the 30 d group exhibited the most DAMs compared to the 7 d group, while the 15 d group showed the fewest. Additionally, the 30 d group exhibited the greatest difference in microbial communities compared to the 7 d group (Figure 1B).
Metabolite enrichment analysis was conducted using differentially accumulated metabolites (DAMs) with fold change (FC) values greater than 2 and VIP scores greater than 1, based on the Kyoto Encyclopedia of Genes and Genomes (KEGG). Multiple KEGG enrichment bubble charts were generated for the top 10 metabolic pathways with the lowest p-values, comparing 15 d, 30 d, 60 d, and 90 d with 7 d, as shown in Figure 4. A total of 30 metabolic pathways were identified across the four groups, with common enrichment observed in pathways such as “Galactose metabolism”, “Glutathione metabolism”, “One carbon pool by folate”, “Phenylalanine metabolism”, “Porphyrin metabolism”, “Propanoate metabolism”, and “Valine, leucine and isoleucine biosynthesis”. Notably, the “Valine, leucine and isoleucine biosynthesis” pathway was enriched in all four groups.
Subsequently, a metabolic network analysis of the “Valine, leucine and isoleucine biosynthesis” pathway was performed to observe the relative abundance changes of key metabolites across different fermentation periods (15 d, 30 d, 60 d, 90 d compared to 7 d), as depicted in Figure 4B. This pathway begins with Threonine, and the FC values represent changes in metabolite abundance between 7 days of fermentation and other time points. Most metabolites exhibited higher FC values at 30 d and 60 d, indicating a relative increase in abundance at these stages. However, at 90 d, the FC values decreased for most metabolites, except for L-Leucine, suggesting a decline in the utilization or production rate of these metabolites during the latter stages of fermentation. Specifically, 2-Oxobutanoate and L-Valine showed an overall increasing trend between 15 and 30 days, peaking at 30 days, followed by a decline at 60 and 90 days. This pattern indicates that these metabolites play a more significant role during the early to middle stages of the fermentation process.

3.5. Correlations Between Chemical Composition and Microbial Community

Based on Detrended Correspondence Analysis (DCA), the axis length value was determined to be 3.18, leading to the selection of CCA for further investigation. In this analysis, the top 10 most significant bacterial genera identified through random forest (as shown in Figure 2) were used as explanatory variables, with metabolites serving as response variables (Figure 5A). The length of the vector arrows represents the degree to which each environmental factor influences the distribution of the studied objects, with longer arrows indicating a stronger influence. From the CCA plot, it is evident that Enterococcus exerts the greatest impact on the metabolites, followed by Pantoea. Additionally, the angles between vectors less than 90 degrees indicate positive correlations, angles greater than 90 degrees indicate negative correlations and angles of 90 degrees indicate no correlation. Enterococcus shows a positive correlation with Klebsiella, Bradyrhizobium, and Pseudomonas and a negative correlation with Sphingomonas, Pantoea, Lactobacillus, and Brevundimonas. Sphingomonas, Pantoea, and Lactobacillus exhibit positive correlations with each other, while Pseudomonas, Paenibacillus, and Quadrisphaera are also positively correlated.
Subsequently, a Mantel test was conducted to analyze the relationship analyze between the relative abundances of key silage bacteria and metabolites, as shown in Figure 5B. The results demonstrated a highly significant positive correlation between Lactobacillus and Paenibacillus (p < 0.01) and a significant negative correlation between Pantoea and Quadrisphaera (p < 0.05). Additionally, Bradyrhizobium also showed a highly significant negative correlation with Quadrisphaera (p < 0.01). The genus Klebsiella was significantly positively correlated with Sphingomonas (p < 0.05). Notably, only Enterococcus, Pantoea, and Lactobacillus had a significant impact on the metabolome, with Pantoea having the most substantial influence on the metabolic profile.

4. Discussion

4.1. Fermentation Quality and Nutritional Indicators of the WPS Silage

As reported by McDonald et al. [20] and Muck [27], a high moisture content in fresh material is associated with an increased risk of undesirable microbial fermentation, particularly by clostridia, which can lead to spoilage during the ensiling process. In this study, the DM content of the fresh WPS was close to the ideal range of 30–35%, which is considered suitable for producing satisfactory silage [28]. Key indicators such as CP, ADF, and NDF were analyzed. The CP content in our study was higher than that reported by Zeng and Ni et al. [29,30], while the ADF and NDF contents were lower than those reported by Ni et al. [30] but higher than those reported by Zeng et al. [29]. The WSC, which serve as fermentation substrates, are primarily utilized by bacteria to produce organic acids that lower the pH of the silage and inhibit the growth of undesirable bacteria. In our study, the WSC content in the WPS was relatively low, falling below the theoretical requirement of 60–70 g/kg DM needed for well-preserved silage [31]. Additionally, the characteristics of fresh WPS in this study differed from previous reports, likely due to variations in soybean cultivar, soil conditions, fertilization rates, and harvest timing.
The fermentation quality of silage is influenced by multiple factors, including pH, lactic acid concentration, volatile fatty acids, NH3-H levels, and others [32]. Among these, pH is a critical indicator of silage fermentation quality. In this study, a downward trend in pH was not observed until the 30th day of ensiling, with no significant differences between the pH values at 7, 15, and 30 days, which is consistent with findings by Zhang et al. [3]. Additionally, the goal of silage fermentation is to rapidly reduce the pH below 4.2 to ensure stable and high-quality silage [33]. However, in this experiment, the pH remained above 4.2 throughout all time periods, indicating a slow fermentation process and suggesting the need for silage additives to enhance fermentation. LA, a strong acid, is the primary factor contributing to the decrease in silage pH, with higher LA concentrations correlating with better silage quality [34]. In this study, the LA content at 7 days was significantly lower than in the other periods, with no significant differences observed among the other time points, indicating that LAB fermentation accelerated significantly after 7 days. Additionally, LAB also produces AA during the fermentation process, a phenomenon known as heterofermentation [35]. In this study, AA peaked at 7 days (1.70%) and 60 days (1.73%), with the lowest level observed at 90 days (1.27%). This suggests fluctuations in the abundance of LAB within the microbial community as fermentation progressed. PA levels remained consistently low throughout the study, aligning with the typical profile of LAB fermentation. BA was not detected in the early stages but emerged at 60 days (0.06%) and slightly increased at 90 days (0.12%), potentially indicating late-stage clostridial activity, which is undesirable [36]. The increase in NH3-H content indicates protein degradation, which is primarily associated with the activity of plant enzymes, enterobacteria, and clostridia [20].

4.2. Microbial Community and Key Bacteria of the WPS Silage

The ensiling period, as a crucial fermentation parameter [37], has rarely been the focus in determining the optimal fermentation duration for WPS silage. The study by Gao et al. [38] observed the impact of different time intervals and carbon sources on the organic acid content in ensiled alfalfa. However, few studies have addressed the changes in microbial communities during various fermentation periods. This study investigated the dynamic changes in microbial communities during different ensiling periods of WPS silage under natural conditions, with the goal of identifying key microbes that could optimize the fermentation process. The Simpson index showed no significant differences across all periods, indicating consistent community diversity throughout the ensiling process. However, the Shannon and Chao1 indices were significantly higher in the 60 d group compared to the 15 d and 30 d groups, suggesting that microbial richness peaked around 60 days. The Shannon index, which also accounts for evenness, highlighted a more balanced microbial community at this stage. The PD_whole_tree index further supported these findings. However, Xu et al. [17] reported a decline in alpha diversity when the dominant LAB community became relatively simplified during ensiling. The anaerobic environment of silage inhibits the growth of most aerobic microbes and reduces pH, which may contribute to decreased microbial diversity. This could suggest that the microbial community in the 60 d group may be shifting towards a less favorable fermentation profile compared to other groups. The PCoA analysis based on Bray–Curtis distances indicates significant differences in microbial community beta diversity across different fermentation periods. The clear separation between groups, particularly the marked distinction between the 7 d and 30 d groups, suggests substantial shifts in microbial composition as fermentation progresses. Furthermore, the pronounced separation along the PCoA2 axis highlights the influential role of fermentation time in driving structural differences between communities. The Venn diagram indicates a continuous decrease in unique ASVs from 7 to 30 days, reaching a minimum at 30 days, followed by a consistent increase from 60 to 90 days. The reduction in unique ASVs during the initial fermentation stages may be attributed to the anaerobic environment created by the ensiling process, which inhibits the growth of most aerobic microorganisms and allows LAB to become the dominant species [39]. This trend may indicate that fermentation quality improves from 7 to 30 days but begins to decline after 60 days. The results indicate a significant negative correlation (R2 = 0.49, p = 0.0035), suggesting that species richness decreases significantly as ensiling time increases. The red regression line shows this trend, with the shaded area representing the 95% confidence interval. The linear regression between ln-transformed species richness and ensiling days shows a significant negative correlation from 7 to 90 days (Figure 1D), suggesting that longer ensiling may reduce microbial diversity and affect silage quality. Few studies have examined this linear relationship, and future research will include groups like WPS silage with different silage additives to further explore this trend and assess changes in the slope.
To further understand the impact of different fermentation periods on the microbial community in WPS silage, we investigated the changes in relative abundance at both the phylum and genus levels. At the phylum level, Firmicutes and Proteobacteria were identified as the dominant bacteria in the silage, consistent with findings from other studies [40]. During the initial fermentation phase from 7 to 30 days, the relative abundance of Proteobacteria decreased, while Firmicutes increased. This shift is likely due to the greater adaptability of Firmicutes to acidic and anaerobic conditions [41]. However, from 30 to 60 days, a decline in Firmicutes was observed, which is not an expected or favorable outcome. At the genus level, the top ten genera in relative abundance include Lactobacillus, Enterococcus, and Weissella, which are recognized as typical dominant genera in silage and are all classified as LAB [42]. Enterococcus is commonly used to enhance fermentation characteristics. Additionally, Enterococcus play a pivotal role in accelerating LA fermentation and establishing an anaerobic acidic environment conducive to the development of Lactobacillus [42]. Generally, Enterococcus are lactic acid-producing cocci that typically survive only in the early stages of fermentation due to their limited acid resistance [43]. Notably, in this study, the relative abundance of Enterococcus was highest at 30 days, followed by a decline. This may be attributed to the slower pH reduction in naturally fermented WPS silage, consistent with the findings of Wang et al. [44]. Additionally, Sphingomonas was observed in this study. Zhou et al. [45] identified Sphingomonas in silage, categorizing it as a harmful bacterium associated with the hydrolysis of soluble proteins. The presence of Sphingomonas in WPS silage could also be linked to the high NH3-H content. Unclassified_Enterobacterales were also detected, which are commonly regarded as harmful bacteria in silage, negatively affecting silage quality [46]. Their relative abundance peaked at 15 days, indicating bad fermentation at this stage. Kosakonia, a newly divided from the genus Enterobacter genus [47], also considered a harmful silage bacterium, showed the highest relative abundance at 7 days, suggesting that the early stages of fermentation were not ideal. Additionally, Bradyrhizobium, a genus of Rhizobia commonly found in leguminous plants, was observed. This genus is known for its slow growth and symbiotic relationship with alfalfa [48]. Its presence in this study may be attributed to the epiphytic bacteria on WPS.
The random forest algorithm, a non-parametric machine learning method, is particularly effective in identifying significant microbial biomarkers that differentiate between groups. In this study, we utilized the random forest algorithm to pinpoint biomarkers in WPS silage across different fermentation periods under natural conditions. The top ten genera were selected based on their importance, determined by %IncMSE. The top-ranked biomarker identified in this study was the genus Pantoea, a harmful bacterium that has recently been reclassified from the Enterobacter genus [47]. Notably, research by Sun et al. [49] found that Pantoea was the most dominant epiphytic bacterium in fresh whole-plant corn silage samples. Similarly, Tian et al. [50] identified Pantoea as the most dominant epiphytic bacterium in fresh Stylo samples. Sun et al. [51] further observed that the application of a LAB suspension as a silage additive led to a decrease in the relative abundance of Pantoea and a significant negative correlation between Pantoea and Lactobacillus. This suggests that Pantoea, being widespread as an epiphytic bacterium in fresh silage materials, plays a critical role in influencing the subsequent fermentation quality. The identification of Pantoea as a key biomarker in this study highlights its significant impact on the early stages of fermentation, indicating that controlling Pantoea abundance before the onset of fermentation could potentially steer the process in a favorable direction. However, this study did not analyze the microbial community at the start of fermentation, which is a gap that should be addressed in future research. Additionally, Lactobacillus and Enterococcus were also identified as important biomarkers, both showing a significant positive correlation with LA concentration (p < 0.05).

4.3. Metabolites Analysis and Correlation of Metabolites with Microbiome and Fermentation Indicators

A total of 1173 metabolites were detected in the naturally fermented WPS silage, exceeding the numbers reported in previous studies on whole-crop corn silage, paper mulberry leaf silage, and stylo silage [17,52,53]. The relatively high pH observed in this study’s WPS silage likely contributed to more active microbial metabolism compared to previous studies, resulting in a greater abundance of metabolites [54]. Notably, lipids and lipid-like molecules accounted for the highest proportion at 37%, which is likely due to the lipid-rich nature of soybean grains. PCA and PLS-DA analyses revealed that the 30 day fermentation samples were the most distinct from other fermentation periods, showing the greatest metabolite variation compared to other groups. Multiple volcano plots indicated that the least number of differential metabolites was observed between the 15 days and 7 days fermentation periods, while the number of differential metabolites increased significantly in the 60 days and 90 days periods, suggesting heightened microbial activity after 30 days.
Further analysis was conducted on the DAMs identified with a FC greater than 2 and VIP scores greater than 1, leading to the generation of Multiple KEGG enrichment bubble charts for the top 10 metabolic pathways. Similar to the findings of He et al., pathways such as “Lysine degradation”, “Valine, leucine and isoleucine degradation”, “Lysine biosynthesis” and “Purine metabolism” were enriched. In this study, all fermentation periods compared to the 7 days period commonly enriched the “Valine, leucine and isoleucine biosynthesis” pathway. Consequently, this pathway was selected for further network analysis. Valine, leucine, and isoleucine are essential branched-chain amino acids (BCAAs) in mammals, playing critical roles in protein synthesis, muscle growth, and hormone release [55]. In mammals, these BCAAs cannot be synthesized de novo and must be obtained through diet [56]. Therefore, the production and accumulation of these amino acids during silage fermentation are crucial for enhancing the nutritional value of silage. Metabolic analysis indicates that the biosynthesis pathways for valine, leucine, and isoleucine were more active during different stages of fermentation, particularly at 30 and 60 days, where the relative abundance of precursor metabolites was higher. This suggests that the mid-stage of fermentation may be a critical period for the accumulation of these essential amino acids. This finding has significant implications for optimizing the nutritional content of silage to better meet the dietary needs of livestock. However, during the later stage of fermentation (90 days), the abundance of most of these metabolites, except for L-leucine, decreased, potentially reflecting a reduced rate of amino acid synthesis and utilization as fermentation progresses. Therefore, selecting an appropriate fermentation time, ideally not exceeding 60 days, under natural conditions for WPS silage can maximize the accumulation of these essential amino acids, thereby enhancing the quality and nutritional benefits of the silage. Building on the results from the random forest analysis, the key bacterial taxa identified were used as explanatory variables in CCA. The vector for Enterococcus had the greatest length, signifying that Enterococcus, a beneficial silage bacterium and a key taxon within the community, exerts the most significant influence on the silage metabolome. The proximity of the 30-day group samples to the Enterococcus vector further supports the observation that the relative abundance of Enterococcus was highest in this group.
To explore the relationship between the microbial community and metabolome, a Mantel test was conducted. The Mantel test was seldom used in previous silage studies to examine the relationships among multidimensional datasets, making this approach particularly noteworthy. The analysis revealed that the genera Enterococcus, Pantoea, and Lactobacillus had significant impacts on metabolome matrices. These findings suggest that these genera are crucial for optimizing the microbial community and metabolome in future silage production.

5. Conclusions

The study indicates that WPS silage holds potential as a feed source, but under natural fermentation conditions, it is prone to undesirable fermentation. Significant differences were observed in the microbial community and metabolome across different fermentation periods, with the optimal fermentation effect occurring at 30 days, where Enterococcus emerged as the dominant genus. Through random forest analysis, ten key bacterial genera were identified as biomarkers, with Pantoea being the most influential. Notably, compared to the early stages of fermentation, the later stages significantly enhanced the metabolic pathways involved in the biosynthesis of essential amino acids, specifically valine, leucine, and isoleucine.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fermentation10100535/s1, Figure S1: The species dilution curve of WPS silage at different fermentation period.

Author Contributions

Conceptualization Y.J. and S.W. (Shaodong Wang); methodology, Y.J.; software, S.W (Sui Wang); validation, S.W. (Shaodong Wang), Y.J., S.W. (Sui Wang), H.M., L.W., Y.L. and X.T.; formal analysis, H.M.; investigation, H.M.; resources, Y.J. and Y.L.; data curation, H.M.; writing—original draft preparation, H.M.; writing—review and editing, H.M. and Y.J.; visualization, X.T.; supervision, S.W. (Shaodong Wang); project administration, Y.J. and S.W. (Shaodong Wang); funding acquisition, S.W. (Shaodong Wang). All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the National Key Research and Development Prgram (2021YFD1201103-01-04), the Opening Project of Key Laboratory of Soybean Biology of Chinese Education Ministry, and Research Business Expense Project of Heilongjiang Provincial Research Institute (CZKYF2023-1-B017).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author (due to privacy).

Acknowledgments

The authors thank Biomarker Technologies, Inc. (Beijing, China) for sequencing services and thank Wekemo Tech Group Co., Ltd. (Shenzhen, China) for metabolite detection.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Bacterial diversity, community composition, and structure variations in WPS silage during the ensiling process. (A) Box plots of alpha diversity (Simpson, Shannon, Chao1 indices, and PD_whole_tree) with p-values from Kruskal–Wallis tests, where * indicates p < 0.05. (B) PCoA analysis based on Bray–Curtis distances. (C) Venn diagram of WPS silage at different fermentation days. (D) Correlation between ensiling days and species richness in WPS silage. (E) Stacked bar chart of bacterial community relative abundance at the phylum level. (F) Stacked bar chart of bacterial community relative abundance at the genus level.
Figure 1. Bacterial diversity, community composition, and structure variations in WPS silage during the ensiling process. (A) Box plots of alpha diversity (Simpson, Shannon, Chao1 indices, and PD_whole_tree) with p-values from Kruskal–Wallis tests, where * indicates p < 0.05. (B) PCoA analysis based on Bray–Curtis distances. (C) Venn diagram of WPS silage at different fermentation days. (D) Correlation between ensiling days and species richness in WPS silage. (E) Stacked bar chart of bacterial community relative abundance at the phylum level. (F) Stacked bar chart of bacterial community relative abundance at the genus level.
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Figure 2. Biomarkers were selected based on a random forest algorithm and correlated with fermentation and nutritional indicators. (A) Bubble chart of biomarker abundance. (B) Bar chart of the top 10 most important biomarkers. (C) Heatmap of the correlation between biomarkers and fermentation and nutritional indicators (based on Pearson correlation), Asterisks: * denotes significant difference (p < 0.05); ** denotes significant difference (p < 0.01).
Figure 2. Biomarkers were selected based on a random forest algorithm and correlated with fermentation and nutritional indicators. (A) Bubble chart of biomarker abundance. (B) Bar chart of the top 10 most important biomarkers. (C) Heatmap of the correlation between biomarkers and fermentation and nutritional indicators (based on Pearson correlation), Asterisks: * denotes significant difference (p < 0.05); ** denotes significant difference (p < 0.01).
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Figure 3. (A) Bar and pie charts of detected metabolites types. (B) PCA analysis of WPS silage metabolites at different fermentation days. (C) PLS-DA analysis of WPS silage metabolites at different fermentation days (D) Multiple volcano plots of differential metabolites in WPS silage.
Figure 3. (A) Bar and pie charts of detected metabolites types. (B) PCA analysis of WPS silage metabolites at different fermentation days. (C) PLS-DA analysis of WPS silage metabolites at different fermentation days (D) Multiple volcano plots of differential metabolites in WPS silage.
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Figure 4. (A) Multiple KEGG enrichment bubble charts. (B) KEGG metabolic network analysis of the Valine, Leucine, and Isoleucine biosynthesis pathways in the WPS silage. The differentially accumulated metabolites (DAMs) are shown in red.
Figure 4. (A) Multiple KEGG enrichment bubble charts. (B) KEGG metabolic network analysis of the Valine, Leucine, and Isoleucine biosynthesis pathways in the WPS silage. The differentially accumulated metabolites (DAMs) are shown in red.
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Figure 5. (A) CCA of bacterial and metabolites in WPS silage at different fermentation days. (B) The Mantel test correlation plot between bacteria and metabolites with Pearson’s correlation coefficients, Asterisks: * denotes significant difference (p < 0.05); ** denotes significant difference (p < 0.01).
Figure 5. (A) CCA of bacterial and metabolites in WPS silage at different fermentation days. (B) The Mantel test correlation plot between bacteria and metabolites with Pearson’s correlation coefficients, Asterisks: * denotes significant difference (p < 0.05); ** denotes significant difference (p < 0.01).
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Table 1. Chemical composition of the WPS before ensiling.
Table 1. Chemical composition of the WPS before ensiling.
ItemWPS
DM (%)34.26 ± 0.28
CP (%)18.78 ± 0.38
ADF (%)35.26 ± 0.55
NDF (%)48.27 ± 0.58
CF (%)3.77 ± 0.09
WSC (%)3.84 ± 0.95
Notes: DM, dry matter; CP, crude protein; ADF, acid detergent fiber; NDF, neutral detergent fiber; CF, crude fat; WSC, water-soluble carbohydrate.
Table 2. The fermentation quality and nutritional indicators of different mixed silage treatments after ensiling.
Table 2. The fermentation quality and nutritional indicators of different mixed silage treatments after ensiling.
Items7 d15 d30 d60 d90 dp-Value
DM (%)35.01 ± 0.95 b37.29 ± 0.37 a37.94 ± 0.38 a37.16 ± 0.33 a37.34 ± 0.36 a<0.001
CP (%)18.32 ± 0.46 a18.35 ± 0.07 a17.02 ± 0.54 c17.92 ± 0.31 ab17.37 ± 0.33 bc<0.001
CF (%)3.72 ± 0.07 a3.63 ± 0.08 a3.18 ± 0.98 c3.45 ± 0.05 b3.56 ± 0.03 ab<0.001
NDF (%)48.55 ± 0.12 a45.87 ± 0.15 b46.11 ± 0.58 b43.11 ± 0.33 c43.77 ± 1.60 c<0.001
ADF (%)31.07 ± 0.49 b32.01 ± 0.60 b31.78 ± 0.71 b32.22 ± 0.82 b34.50 ± 1.39 a0.009
WSC (%)2.51 ± 0.41 a1.88 ± 0.02 b1.63 ± 0.01 bc1.65 ± 0.16 bc1.39 ± 0.07 c<0.001
pH5.98 ± 0.14 a5.96 ± 0.13 a5.96 ± 0.34 a5.42 ± 0.07 b5.13 ± 0.16 b<0.001
LA (%)2.19 ± 0.04 b2.68 ± 0.18 a2.72 ± 0.09 a2.99 ± 0.16 a2.97 ± 0.11 a<0.001
AA (%)1.70 ± 0.02 a1.50 ± 0.11 b1.45 ± 0.04 b1.73 ± 0.18 a1.27 ± 0.08 c<0.001
PA (%)0.10 ± 0.02 c0.08 ± 0.03 c0.16 ± 0.04 ab0.11 ± 0.04 bc0.17 ± 0.02 a0.004
BA (%)---0.06 ± 0.03 b0.12 ± 0.01 a<0.001
NH3-H (%/TN)3.27 ± 0.13 d3.85 ± 0.31 c3.58 ± 0.10 cd4.47 ± 0.29 b5.17 ± 0.18 a<0.001
AS (h)54.00 ± 2.00 e62.67 ± 2.52 d100.67 ± 5.32 c108.67 ± 3.38 b112.00 ± 2.87 a<0.001
Note: LA, lactic acid; AA, acetic acid; PA, propionic acid; BA, butyric acid; NDF, neutral detergent fiber; ADF, acid detergent fiber; DM, dry matter; NH3-N, ammonia nitrogen; TN, total nitrogen; CF, crude fat; -, not detected; WSC, water-soluble carbohydrates; CP, crude protein; AS, aerobic stability. Different superscript letters (a–e) in a row indicate significant differences (p < 0.05) based on Tukey’s HSD test.
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Meng, H.; Jiang, Y.; Wang, L.; Li, Y.; Wang, S.; Tong, X.; Wang, S. Dynamic Analysis of Fermentation Quality, Microbial Community, and Metabolome in the Whole Plant Soybean Silage. Fermentation 2024, 10, 535. https://doi.org/10.3390/fermentation10100535

AMA Style

Meng H, Jiang Y, Wang L, Li Y, Wang S, Tong X, Wang S. Dynamic Analysis of Fermentation Quality, Microbial Community, and Metabolome in the Whole Plant Soybean Silage. Fermentation. 2024; 10(10):535. https://doi.org/10.3390/fermentation10100535

Chicago/Turabian Style

Meng, He, Yan Jiang, Lin Wang, Yuanming Li, Sui Wang, Xiaohong Tong, and Shaodong Wang. 2024. "Dynamic Analysis of Fermentation Quality, Microbial Community, and Metabolome in the Whole Plant Soybean Silage" Fermentation 10, no. 10: 535. https://doi.org/10.3390/fermentation10100535

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