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期刊 Animal Nutrition （6.383/Q1）
Changes in chemical composition, structural and functional microbiome during alfalfa (Medicago sativa) ensilage with Lactobacillus plantarum PS-8
植物乳杆菌 PS-8 苜蓿青贮过程中化学成分、结构和功能微生物组的变化
Improving silage production by adding exogenous microorganisms not only maximizes nutrient preservation, but also extends product shelf life. Herein, changes in the quality and quantity of Lactobacillus plantarum PS-8 (PS-8) -inoculated alfalfa (Medicago sativa) during silage fermentation were monitored at d 0, 7, 14, and 28 (inoculum dose of PS-8 was 1 x 105 colony forming units [cfu]/g fresh weight; 50 kg per bag; 10 bags for each time point) by reconstructing metagenomic-assembled genomes (MAG) and Growth Rate InDex (GRiD). Our results showed that the exogenous starter bacterium, PS-8 inoculation, became the most dominating strain by d 7, and possibly played a highly active role throughout the fermentation process. The pH value of the silage decreased greatly, accompanied by the growth of acidproducing microorganisms namely PS-8, which inhibited the growth of harmful microorganisms like molds (4.18 vs. 1.42 log cfu/g) and coliforms (4.95 vs. 0.66 log most probable number [MPN]/g). The content of neutral detergent fiber (NDF) decreased significantly (41.6% vs. 37.6%; dry matter basis). In addition, the abundance and diversity of genes coding microbial carbohydrate-active enzymes (CAZymes) increased significantly and desirably throughout the fermentation, particularly the genes responsible for degrading starch, arabino-xylan, and cellulose. Overall, our results showed that PS-8 was replicating rapidly and consistently during early- and mid-fermentation phases, promoting the growth of beneficial lactic acid bacteria and inhibiting undesirable microbes, ultimately improving the quality of silage.
1. Introduction
The rapid development of animal husbandry has led to an increasing demand for animal feed. Alfalfa (Medicago sativa) is high-quality forage due to its high protein and mineral contents and good palatability. However, there is a potential problem of alfalfa forage, as it is recognized to favor the growth of spoilage bacteria. This is due to its high buffering capacity and low content of water-soluble carbohydrates (WSC), making it more difficult to lower pH and in turn causing storage problems compared with other forages (Jaurena and Pichard, 2001). Additionally, the high humidity and the rainy climate in southern China further intensifies this storage problem, shortening silage shelf life. Thus, to overcome these issues, it is of interest to develop strategies to effectively suppress spoilage and undesirable microbes during fermentation, improve nutrient preservation, and prolong silage shelf life.
Ensilage is a traditional forage preservation method based on microbial anaerobic fermentation, mainly by lactic acid bacteria (LAB). In this process, WSC in the green raw material are fermented to organic acids, such as lactic acid and acetic acid, resulting in pH decline that inhibits undesirable microorganisms (Duniere et al., 2013). Although there is approximately 10% nutrient loss during the silage fermentation process, the organic acids and organic chelate minerals produced during fermentation are absorbed more readily by ruminants (Kalac, 2011). The most frequently added LAB are homofermentative species, such as Lactobacillus plantarum and Pediococcus spp. Homolactic fermentation is desirable because of the extremely high theoretical dry matter (DM) recovery and efficient production of lactic acid; the release of lactic acid causes a more rapid pH decline than other types of acids. Fermentation is a microbial-driven process, and the quality of silage fermentation is highly dependent on the activities and types of microorganisms involved in the ensiling process (Peng et al., 2018). Beneficial microorganisms that accelerate the rate of utilization of WSC by other microbes and produce organic acids contribute greatly to the silage fermentation process. In contrast, some undesirable microorganisms could cause spoilage (Driehuis and Oude Elferink, 2000). This study selected to investigate the effect of inoculating a homofermentative LAB strain, L. plantarum PS-8 (PS-8), on alfalfa silage fermentation. This strain is proven to be beneficial in improving the quality and quantity of silage fermentation and cow milk production in previous studies (Cao et al., 2021; Xu et al., 2017). The contribution of PS-8 can be evaluated by monitoring the succession of the complex epiphytic microbial communities in relation with changes in chemical composition of the silage at different stages of fermentation.
Metagenomics in combination with advanced bioinformatic analysis of high-quality data could be applied to assemble singlesample metagenomes and unveil the functional potential of strain-level bacterial genomes. Pasolli et al. (2019) used such methods to leverage 9,428 metagenomes obtained from human gut sequencing to reconstruct and track 154,723 metagenomicassembled genomes (MAG), revealing novel microbial functions (Pasolli et al., 2019). In addition, Emiola and Oh (2018) developed an algorithm to calculate bacterial growth rate (i.e., Growth Rate InDex, GRiD) of individual strains based on bacterial genome sequencing. Such analysis was advantageous in estimating how much the viable subpopulation of the overall metagenomes contributed to the community phenotype (Emiola and Oh, 2018).
This study hypothesized that adding exogenous LAB could improve the alfalfa ensilage process via modulating the silage microbiome. Although some previous works investigated the application of microbial silage starter prior to the ensilage process, few studies investigated the microbial dynamics in-depth via whole-genome metagenomics approaches, which could help reveal the functional potential and vitality of silage microbiota and PS-8 during different stages of fermentation. Thus, the current work utilises the advent of metagenomic analysis to investigate the contribution of PS-8 as silage starter strain to facilitate silage fermentation. The dynamics of the microbial community structure and function were assessed by using the single-sample metagenomic assembly approach, focusing on the changes in GRiD and carbohydrate metabolism at different stages of alfalfa silage fermentation. Meanwhile, a number of chemical parameters were monitored during the silage fermentation process.
2. Materials and methods
2.1. Study design and sample collection
Alfalfa was harvested at early bloom stage, and fresh forage was taken immediately to the silage site and wilted to approximately 50% moisture content in the sun. The forage was chopped into pieces of approximately 2 cm using paper-cutters. The chopped forage was then sprayed with PS-8 (inoculation dose: 1 x 105 colony forming units (cfu)/g fresh weight; provided by the Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University of China). The PS-8 strain was originally isolated from a naturally fermented yoghurt made by an animal-herding family residing at Wulatezhongqi Grassland of Inner Mongolia of China (Wu et al., 2009). The applied amount was the least effective dose for silage inoculation based on a previous study (Huisden et al., 2009). The PS-8-sprayed alfalfa was thoroughly mixed and was randomly packed into vacuum-sealing polyethylene plastic bags (approximately 50 kg per bag; a total of 40 individual bags were prepared). The packed silage samples were kept at ambient temperature (20 to 25C) and without any atmosphere conditioning measures for 28 d. Fresh (labelled as d 0) and ensiled materials were sampled on d 0, 7, 14, and 28 for microbial and chemical analyses. Except on d 0, 10 bags were opened at each time point. A nine-point sampling method was used to sample the upper, middle, and lower parts of each bag after removing the surface layer. All samples were collected in sterilized containers and kept in ice boxes during transportation. The ensiled samples collected at d 0, 7, 14, and 28 (10 samples per day) were selected for bacterial metagenomics and chemical composition analyses. Suitable amount of cryoprotectant (Sample Protector for RNA/DNA; Takara Bio, Inc.) was added and mixed with samples designated for metagenomic sequencing to avoid DNA degradation. This reagent could stabilize nucleic acids (including RNA) in samples during sample transportation and prolonged storage, enabling metagenomic and gene expression analyses.
紫花苜蓿在开花早期收获，新鲜草料立即被带到青贮场，在阳光下枯萎至水分含量约为 50%。使用剪纸机将草料切成约 2 厘米的小块。然后将切碎的草料喷洒PS-8（接种剂量：1×105菌落形成单位（cfu）/g鲜重；由内蒙古农业大学乳品生物技术与工程教育部重点实验室提供） . PS-8菌株最初是从居住在中国内蒙古乌拉特中旗草原的一个畜牧家庭生产的自然发酵酸奶中分离出来的（Wu et al., 2009）。根据先前的研究，施用量是青贮饲料接种的最低有效剂量（Huisden 等，2009）。将 PS-8 喷洒的紫花苜蓿彻底混合，随机装入真空密封的聚乙烯塑料袋中（每袋约 50 公斤；共准备 40 个单独的袋子）。包装好的青贮饲料样品在环境温度（20 到 25 摄氏度）下保持 28 天，没有任何气氛调节措施。在 d 0、7、14 和 28 天对新鲜（标记为 d 0）和青贮材料进行取样，以进行微生物和化学分析。除第 0 天外，每个时间点打开 10 个袋子。去除表层后，采用九点取样法对每个袋子的上、中、下部分进行取样。所有样品都收集在无菌容器中，并在运输过程中保存在冰盒中。选择在第 0、7、14 和 28 天（每天 10 个样品）收集的青贮样品用于细菌宏基因组学和化学成分分析。添加适量的冷冻保护剂（RNA/DNA 样品保护剂；Takara Bio, Inc.）并与指定用于宏基因组测序的样品混合以避免 DNA 降解。该试剂可以在样品运输和长期储存期间稳定样品中的核酸（包括 RNA），从而实现宏基因组和基因表达分析。
2.2. Physical and biochemical indicators and microbial counts
Each silage sample (25 g each) collected at d 0, 7, 14, and 28 was vortex mixed with 225 mL of deionized water for 30 min for pH determination via a pH meter (Mettler Toledo, Zurich, Switzerland). For determining the chemical composition, each sample (25 g each) was dried in a forced-air oven at 55 C for 72 h for the determination of DM content (Ke et al., 2017). Dried samples were milled to pass a 1-mm screen of a laboratory knife mill (Taisite Instrument, Tianjin, China). Milled samples were analyzed for crude protein (CP), soluble protein (SP), acid detergent fiber (ADF), neutral detergent fiber (NDF), WSC, and ammonia nitrogen (NH 3eN). The contents of SP, CP, NDF, and ADF were determined according to Licitra et al. (1996) and Ke et al. (2017). The contents of NH3eN and WSC were determined according to the methods described by Broderick and Kang (1980) and Thomas (1977). Results were expressed on a DM basis, and all chemical assays were performed in triplicate.
在第 0、7、14 和 28 天收集的每个青贮饲料样品（每个 25 g）与 225 mL 去离子水涡旋混合 30 分钟，通过 pH 计（Mettler Toledo，苏黎世，瑞士）测定 pH。 为了确定化学成分，每个样品（每个 25 克）在 55 摄氏度的强制空气烘箱中干燥 72 小时以测定 DM 含量（Ke 等人，2017 年）。 将干燥的样品研磨以通过实验室刀磨机（泰斯特仪器，天津，中国）的 1 毫米筛网。 分析研磨样品的粗蛋白 (CP)、可溶性蛋白 (SP)、酸性洗涤纤维 (ADF)、中性洗涤纤维 (NDF)、WSC 和氨氮 (NH 3eN)。 SP、CP、NDF 和 ADF 的含量根据 Licitra 等人的方法测定。 （1996）和柯等人。 （2017）。 NH3eN 和 WSC 的含量根据 Broderick 和 Kang (1980) 和 Thomas (1977) 描述的方法测定。 结果以 DM 为基础表示，所有化学分析均一式三份进行。
Lactic acid, acetic acid, g-aminobutyric acid, and phenyl lactic acid were detected using ultra performance liquid chromatographyelectrospray ionization-quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOF MS; Waters, Milford, MA, USA). The sample preparation along with the UPLC and QTOF-MS conditions were based on the methods described in Bao et al. (2016).
使用超高效液相色谱电喷雾电离-四极杆飞行时间质谱（UPLC-ESI-QTOF MS；Waters，Milford，MA，USA）检测乳酸、乙酸、g-氨基丁酸和苯基乳酸。 样品制备以及 UPLC 和 QTOF-MS 条件基于 Bao 等人 (2016) 描述的方法。
To determine lactic acid and acetic acid levels, a preparative ZORBAX Elipse AAA C18 column (3.5 mm, 4.6 mm 150 mm) was used. Solvent A was phosphate buffer solution (pH 2.5), and solvent B was methanol solution. Elution was performed with a gradient of 97:3. Analytical column temperature was 300 C, and the flow rate was 1 mL/min. Absorbance was detected at 210 nm. To determine g-aminobutyric acid, 10 mg o-phthalaldehyde (OPA) derivative reagent (99%, Sigma) was dissolved in 0.5 mL methanol, then 30 mL 2-mercaptoethanol and 2 mL 0.4 mol/L borate buffer (HPLC grade) (pH 9.4) were added. Before injecting into the machine, 10 mL of sample solution was mixed with 90 mL OPA derivative reagent, reacting for 1 min. A preparative ZORBAX Elipse AAA C18 column (3.5 mm, 4.6 mm 150 mm) was used. Solvent A was sodium hydrogen phosphate buffer solution (pH 7.8), and solvent B was the mixture of methanol, acetonitrile, and deionized water (45:45:10). Elution was performed with a gradient of 97:3. The analytical column temperature was 35 C, and the flow rate was 2.0 mL/min. A fluorescence detector was employed for detection of the excitation and emission wavelengths of 340 nm and 450 nm, respectively. Determination of phenyl lactic acid was performed with highperformance liquid chromatography (HPLC) on an Agilent 1100 Series LC system. A preparative BEHC18 column (1.7 mm, 2.1 mm 100 mm, Waters, America) was used. Solvent A was formic acid diluted in deionized water (1:999), and solvent B was formic acid diluted in acetonitrile (1:999) solution. Elution was performed with a linear gradient as follows: solvent B 20% to 50% in 2 min, 50% to 95% in 2.1 to 3 min, 95% to 5% in 3 to 3.1 min. Analytical column temperature was 30 C, and the flow rate was 0.4 mL/min (Bao et al., 2016).
为测定乳酸和乙酸水平，使用了制备型 ZORBAX Elipse AAA C18 色谱柱（3.5 mm，4.6 mm 150 mm）。溶剂 A 为磷酸盐缓冲溶液 (pH 2.5)，溶剂 B 为甲醇溶液。以 97:3 的梯度进行洗脱。分析柱温300℃，流速1mL/min。在 210 nm 处检测到吸光度。为了测定 g-氨基丁酸，将 10 mg 邻苯二甲醛 (OPA) 衍生物试剂（99%，Sigma）溶解在 0.5 mL 甲醇中，然后溶解在 30 mL 2-巯基乙醇和 2 mL 0.4 mol/L 硼酸盐缓冲液（HPLC 级）中（加入 pH 9.4)。在注入机器之前，将 10 mL 样品溶液与 90 mL OPA 衍生试剂混合，反应 1 分钟。使用制备型 ZORBAX Elipse AAA C18 色谱柱（3.5 mm，4.6 mm 150 mm）。溶剂 A 为磷酸氢钠缓冲溶液 (pH 7.8)，溶剂 B 为甲醇、乙腈和去离子水 (45:45:10) 的混合物。以 97:3 的梯度进行洗脱。分析柱温35℃，流速2.0mL/min。荧光检测器分别用于检测 340 nm 和 450 nm 的激发和发射波长。在 Agilent 1100 系列液相色谱系统上使用高效液相色谱 (HPLC) 测定苯乳酸。使用了制备型 BEHC18 柱（1.7 mm，2.1 mm 100 mm，Waters，America）。溶剂 A 是用去离子水 (1:999) 稀释的甲酸，溶剂 B 是用乙腈 (1:999) 溶液稀释的甲酸。使用如下线性梯度进行洗脱：溶剂 B 在 2 分钟内从 20% 到 50%，在 2.1 到 3 分钟内从 50% 到 95%，在 3 到 3.1 分钟内从 95% 到 5%。分析柱温为 30°C，流速为 0.4 mL/min (Bao et al., 2016)。
2.3. DNA extraction and shotgun metagenomic sequencing
Silage samples (25 g per sample) reserved for metagenomic sequencing were diluted 10 times with sterile PBS solution and soaked for 24 h at 4 C on a shaker, followed by filtration through sterilized gauze. The filtrate was centrifuged, and the precipitate of the bacterial mud was collected. The E.Z.N.A. Soil DNA Kit (OMEGA Bio-Tek, USA) was used for sample DNA extraction following the manufacturer's instructions. The quality of extracted DNA was checked by 1% agarose gel electrophoresis and spectrophotometric analysis (the ratio of optical density at 260 nm to that at 280 nm). All extracted DNA samples were stored at 20 C for further analysis. DNA libraries of fragments (approximately 400 bp long) were prepared separately for each sample. The samples were sequenced on an Illumina HiSeq Xten instrument (Illumina, San Diego, California, USA). Paired-end reads (151 bp) were generated in both forward and reverse directions.
2.3. DNA 提取和鸟枪法宏基因组测序
保留用于宏基因组测序的青贮样品（每个样品 25 g）用无菌 PBS 溶液稀释 10 倍，并在 4°C 的摇床上浸泡 24 小时，然后通过灭菌纱布过滤。 滤液离心，收集菌泥沉淀。 E.Z.N.A. 根据制造商的说明，使用土壤 DNA 试剂盒（OMEGA Bio-Tek，美国）提取样品 DNA。 通过 1% 琼脂糖凝胶电泳和分光光度分析（260 nm 与 280 nm 的光密度比）检查提取的 DNA 的质量。 所有提取的 DNA 样品都储存在 20°C 以供进一步分析。 为每个样品分别制备片段的 DNA 文库（大约 400 bp 长）。 样品在 Illumina HiSeq Xten 仪器（Illumina，San Diego，California，USA）上进行测序。 双端读取（151 bp）在正向和反向方向均产生。
2.4. Metagenomic reads quality control and taxonomic profiling
Raw whole-metagenome shotgun sequencing reads were trimmed in each sample, based on the length and quality, by using SeqPrep (https://github.com/jstjohn/SeqPrep) and Sickle (https://github.com/najoshi/sickle). An average of 7.8 Gb of high-quality paired-end reads were obtained for each sample, making a total of 313 Gb of high-quality data (Supplementary data Table S1). MetaPhlan2 (ver. 2.0) was used to taxonomically pro file the microbial composition of each sample at the species-level using default settings with Bowtie2 (ver. 2.2.9) as search engine (Truong et al., 2015; Langmead and Salzberg, 2012).
2.4. 宏基因组读取质量控制和分类分析
根据长度和质量，使用 SeqPrep (https://github.com/jstjohn/SeqPrep) 和 Sickle (https://github.com/najoshi/sickle) 在每个样本中修剪原始的全宏基因组鸟枪法测序读数 ）。 每个样本平均获得 7.8 Gb 的高质量配对末端读数，总共获得 313 Gb 的高质量数据（补充数据表 S1）。 MetaPhlan2 (ver. 2.0) 用于在物种级别使用默认设置对每个样品的微生物组成进行分类分析，其中 Bowtie2 (ver. 2.2.9) 作为搜索引擎 (Truong et al., 2015; Langmead and Salzberg, 2012）。
2.5. Illumina metagenomic assembly
High-quality Illumina metagenomic samples were assembled using metaSPAdes (ver. 3.13.0) (Nurk et al., 2017). The parameters -k 33,55,77,99,111 -meta. QUAST (ver. 5.0.0) (Mikheenko et al., 2016) were used to evaluate the results of metagenomic assemblies. The above assembled scaffolds were used to predict the functional genes with Prodigal (ver. 2.6.3) (Hyatt et al., 2010). Finally, a nonredundant gene catalog was constructed using CD-HIT (ver. 4.8.1) (Fu et al., 2012). The gene abundance in the samples was determined by aligning the reads to the gene catalog using Bowtie2 (Qin et al., 2012).
2.5. Illumina宏基因组组装
使用 metaSPAdes (ver. 3.13.0) (Nurk et al., 2017) 组装了高质量的 Illumina 宏基因组样本。 参数 -k 33,55,77,99,111 -meta。 QUAST (ver. 5.0.0) (Mikheenko et al., 2016) 用于评估宏基因组组装的结果。 以上组装的支架用于预测 Prodigal (ver. 2.6.3) 的功能基因 (Hyatt et al., 2010)。 最后，使用 CD-HIT (ver. 4.8.1) 构建了一个非冗余基因目录 (Fu et al., 2012)。 通过使用 Bowtie2 将读数与基因目录对齐来确定样本中的基因丰度（Qin et al., 2012）。
2.6. Metagenomic-assembled genomes
MetaBAT 2 (ver. 2.12.1) (Kang et al., 2019) was used to bin the assemblies using a minimum scaffold length threshold of 2,000 bp. Depth of coverage required for the binning was inferred by mapping the raw reads back to their assemblies with Bowtie2, following by calculating the corresponding read depth of each individual scaffold with samtools (ver. 1.9) (Li, 2011) together with the jgi_summarize_bam_contig_depths function in MetaBAT 2. The completeness and contamination of each MAG was estimated with CheckM (ver. 1.0.18) ( Parks et al., 2015) using the lineage_wf work flow. The taxonomy of each MAG was annotated against the National Center for Biotechnology Information (NCBI) nonredundant Nucleotide Sequence Database (NT) using BLASTn at a threshold of 95% identity over 70% coverage. Subsequently, high-quality MAG (completeness > 80% and contamination < 5%) were selected for downstream analysis. The phylogeny was built using the 400 universal PhyloPhlAn markers based on parameters described in Pasolli et al. (2019). The phylogenetic trees were visualized using iTOL (ver. 5.5.1) (Letunic and Bork, 2019).
2.6.宏基因组组装的基因组
MetaBAT 2 (ver. 2.12.1) (Kang et al., 2019) 用于使用 2,000 bp 的最小支架长度阈值对组件进行分箱。通过使用 Bowtie2 将原始读数映射回它们的程序集来推断分箱所需的覆盖深度，然后使用 samtools（1.9 版）（Li，2011）和 jgi_summarize_bam_contig_depths 函数计算每个单独支架的相应读取深度MetaBAT 2. 使用 Lineage_wf 工作流程，使用 CheckM（1.0.18 版）（Parks 等人，2015 年）估计每个 MAG 的完整性和污染。使用 BLASTn 以 95% 的同一性阈值超过 70% 的覆盖率，针对国家生物技术信息中心 (NCBI) 非冗余核苷酸序列数据库 (NT) 对每个 MAG 的分类进行注释。随后，选择高质量的 MAG（完整性 > 80% 和污染 < 5%）进行下游分析。系统发育是基于 Pasolli 等人描述的参数使用 400 个通用 PhyloPhlAn 标记构建的。 （2019）。使用 iTOL（版本 5.5.1）（Letunic 和 Bork，2019）可视化系统发育树。
High-quality MAG classified into L. plantarum were compared with the genome of PS-8 for average nucleotide identity (ANI) analysis using fastANI (ver. 1.1) (Jain et al., 2018). The replication rates of L. plantarum genomes were calculated by using the GRiD) (ver. 1. 3) ( Seshadri et al., 2018). Then, the MAG were clustered at species-level genome bin (SGB) using dRep (ver. 2.2.4) (Olm et al., 2017) with the following parameters: -pa 0.95 -sa 0.95 -nc 0.30 -cm larger. To estimate SGB abundance, all reads were aligned to contigs in SGB using Bowtie2. The mapped sequence counts, contig lengths and total sequence counts were used to normalise the sequence counts and represent the reads per kilobase per million (RPKM) of each sample to the contigs.
将分类为植物乳杆菌的高质量 MAG 与 PS-8 的基因组进行比较，以使用 fastANI（1.1 版）进行平均核苷酸同一性 (ANI) 分析（Jain 等人，2018 年）。 植物乳杆菌基因组的复制率是通过使用 GRiD) (ver. 1. 3) (Seshadri et al., 2018) 计算的。 然后，使用具有以下参数的 dRep (ver. 2.2.4) (Olm et al., 2017) 在物种级基因组箱 (SGB) 对 MAG 进行聚类：-pa 0.95 -sa 0.95 -nc 0.30 -cm 更大。 为了估计 SGB 丰度，使用 Bowtie2 将所有读数与 SGB 中的重叠群对齐。 映射的序列计数、重叠群长度和总序列计数用于标准化序列计数，并将每个样本的每百万碱基读数 (RPKM) 表示为重叠群。
2.7. Functional annotation and metabolic pathway analysis
The annotated amino acid sequences were aligned against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using Usearch (Kanehisa and Goto, 2000) with the option of -usearch_local -id 0.3 -query_cov 0.7. Genes related with polysaccharide metabolism and organic acid biosynthesis pathways were predicted based on the KEGG orthologue group (KO) key reaction. Carbohydrate-active enzymes (CAZymes) were annotated by the dbCAN2 database using HMMER (Eddy, 2009).
2.7. 功能注释和代谢途径分析
使用 Usearch (Kanehisa and Goto, 2000) 与 -usearch_local -id 0.3 -query_cov 0.7 选项将注释的氨基酸序列与京都基因和基因组百科全书 (KEGG) 数据库进行比对。 基于KEGG直系同源基团（KO）关键反应预测与多糖代谢和有机酸生物合成途径相关的基因。 dbCAN2 数据库使用 HMMER (Eddy, 2009) 对碳水化合物活性酶 (CAZymes) 进行了注释。
2.8. Statistical analysis
Statistical analysis was performed in R-3.5.1. The vegan package (ver. 2.5-2) (https://CRAN.R-project.org/package¼vegan) was employed for alpha diversity analysis and principal coordinate analysis (PCoA) analysis using the BrayeCurtis distance. The ggplot2 package (ver. 3.1.0) was used for data visualization (Wickham, 2011). Linear discriminant analysis (LDA) effect size method based on a normalized relative abundance matrix was used to identify the significant differences between samples on d 0 and 7. Spearman correlation analysis was carried out between chemical parameters and relative contents of microflora, as well as between PS-8 and other species. The heatmap was constructed using the “pheatmap” package (ver. 1.0.12) (Kolde and Kolde, 2015). Spearman rank correlation coefficient-based networks were visualized by Cytoscape (ver. 3.7.1) (Shannon et al., 2003). Significant differences were assessed by Wilcoxon rank sum test (Haynes, 2013); P < 0.05 was considered statistically significant.
2.8.统计分析
在 R-3.5.1 中进行统计分析。vegan包（版本 2.5-2）（https://CRAN.R-project.org/package¼vegan）用于使用 BrayeCurtis 距离进行 alpha 多样性分析和主坐标分析（PCoA）分析。 ggplot2 包（版本 3.1.0）用于数据可视化（Wickham，2011）。采用基于归一化相对丰度矩阵的线性判别分析 (LDA) 效应大小方法识别 d 0 和 7 样品之间的显着差异。 PS-8 等品种。热图是使用“pheatmap”包（1.0.12 版）构建的（Kolde 和 Kolde，2015 年）。基于 Spearman 等级相关系数的网络由 Cytoscape（版本 3.7.1）可视化（Shannon 等人，2003）。通过 Wilcoxon 秩和检验（Haynes，2013）评估显着差异； P < 0.05 被认为具有统计学意义。
2.9. Data availability
The entire sequence dataset was deposited in the NCBI Sequence Read Archive (SRA) database (accession number PRJNA495415)
2.9。 数据可用性
整个序列数据集保存在 NCBI 序列读取存档 (SRA) 数据库中（登录号 PRJNA495415）
3. Results
3.1. Chemical changes during alfalfa silage fermentation
Monitoring changes in the chemical profile (e.g., chemical composition, microbial population, and pH) of the fermented silage could provide indication for the progress and quality of silage fermentation. During silage fermentation, there was no significant change in DM content. The contents of SP increased with fermentation time, although the change in the SP content was not significant, it showed an obvious increasing trend. NH3eN content increased significantly during the first 7 d (P < 0.05), reaching astable phase between d 7 and 14, followed by a significant decrease between d 14 and 28 (P < 0.05). NDF decreased significantly on d 7 (P < 0.05), followed by non-significant decreases thereafter. WSC showed a general decreasing trend (Table 1).
3.1. 苜蓿青贮饲料发酵期间的化学变化
监测发酵后的青贮饲料的化学成分（如化学成分、微生物数量和pH值）的变化可以为青贮饲料发酵的进展和质量提供指示。在青贮饲料发酵过程中，DM含量没有明显变化。SP的含量随发酵时间的延长而增加，虽然SP的含量变化不大，但呈现明显的上升趋势。NH3-N含量在前7天明显增加（P<0.05），在第7天和第14天之间达到稳定期，随后在第14天和第28天之间明显下降（P<0.05）。NDF在第7天明显下降（P < 0.05），此后无明显下降。WSC呈总体下降趋势（表1）。
Lactic acid content significantly increased with the fermentation time and reached a maximum at 28 d (P < 0.05). Acetic acid content exhibited an increasing trend, showing significant increase at 14 and 28 d (P < 0.05). The content of g-aminobutyric acid began to increase significantly at d 14 (P < 0.05) and the content of phenyl lactic acid increased significantly at d 14 and 28 (P < 0.05). As the organic acid content continued to accumulate as fermentation progressed, the amounts of mold and coliform were suppressed significantly and reduced to levels below the detection limit (Table 1; Supplementary data Table S2). Consistently, the pH of the alfalfa silage decreased significantly from d 7 (P < 0.05).
乳酸含量随着发酵时间的延长而显著增加，在28天时达到最大值（P < 0.05）。乙酸含量呈现上升趋势，在14天和28天时显示出明显增加（P < 0.05）。g-氨基丁酸的含量在第14天开始显著增加（P < 0.05），苯基乳酸的含量在第14和28天显著增加（P < 0.05）。随着发酵的进行，有机酸含量继续积累，霉菌和大肠菌群的数量被明显抑制，并降低到检测限以下的水平（表1；补充数据表S2）。一致的是，紫花苜蓿青贮饲料的pH值从第7天开始明显下降（P < 0.05）。
3.2. Dynamics of silage microbiome during fermentation
To understand the microbial contribution to silage fermentation, the dynamics of PS-8 silage microbiome before (d 0) and after (d 7, 14, 28) fermentation started were analyzed by shotgun metagenomic sequencing. The quantity of sequencing data generated in this work was shown in Supplementary data Table S1. The fermentation process was divided into 2 stages; namely, the microbial (L. plantarum and Pediococcus pentosaceus) proliferation phase when drastic microbial changes occurred (d 0 to 7) and the stable phase (d 7 to 28) when microbial changes were less obvious.
3.2. 发酵过程中青贮饲料微生物组的动态变化
为了了解微生物对青贮饲料发酵的贡献，通过鸟枪法宏基因组测序分析了发酵开始前（d 0）和发酵开始后（d 7, 14, 28）PS-8青贮饲料微生物组的动态。本工作中产生的测序数据的数量见补充数据表S1。发酵过程分为2个阶段；即微生物（L. plantarum和Pediococcus pentosaceus）增殖阶段，此时微生物发生剧烈变化（d 0到7），稳定阶段（d 7到28），此时微生物变化不明显。
The alpha-diversity of the silage microbiome increased significantly at the early phase (P < 0.001), followed slight fluctuation during the stable phase (Fig. 1A). The PCoA performed based on BrayeCurtis distance also showed that the initial silage microbiota (d 0) were structurally distinct from those of the later time points (Fig. 1B). The BrayeCurtis distances of alfalfa silage microbiota was remarkably lower in the stable phase (d 7 to 28) compared with the baseline level at d 0 (Fig. 1C).
青贮微生物群的α-多样性在早期阶段明显增加（P < 0.001），随后在稳定阶段略有波动（图1A）。基于BrayeCurtis距离进行的PCoA也显示，最初的青贮微生物群（d 0）在结构上与后来的时间点不同（图1B）。与第0天的基线水平相比，苜蓿青贮微生物群的BrayeCurtis距离在稳定期（第7至28天）明显降低（图1C）。
Fig. 1. Relationship between microbial community structure and physicochemical indexes of alfalfa silage at different time points. (A) Boxplots showing values of Shannon diversity index. (B) Principal coordinate analysis (PCoA; BrayeCurtis distance). (C) BrayeCurtis distance calculated by time point. Significant differences are indicated by different letters (P < 0.05). The curve was fitted to the data using locally estimated scatterplot smoothing (LOESS); the pink area corresponds to 95% confidence interval of the curve. (D) Stacked bar charts showing relative abundances of identified species. (E) Linear discriminant analysis (LDA) showing differential abundant taxa between d 0 and 7. (F) Spearman correlation heatmap of 15 dominant species and 14 biological/chemical parameters. MPN ¼ most probable number. The color scale represents the Spearman's rho, showing strength of correlation. *P < 0.05, **P < 0.01, ***P < 0.001.
图1. 不同时间点苜蓿青贮饲料的微生物群落结构和理化指标之间的关系。(A) Boxplots显示香农多样性指数的值。(B) 主坐标分析（PCoA；BrayeCurtis距离）。(C) 按时间点计算的BrayeCurtis距离。显著的差异由不同的字母表示（P < 0.05）。曲线是用局部估计散点图平滑法（LOESS）对数据进行拟合的；粉色区域对应于曲线的95%置信区间。(D) 堆积的柱状图显示了已识别物种的相对丰度。(E) 线性判别分析(LDA)显示d 0和7之间的不同丰度分类群。 (F) 15个优势物种和14个生物/化学参数的Spearman相关热图。MPN ¼ 最有可能的数量。色标代表Spearman's rho，显示相关的强度。*P < 0.05, **P < 0.01, ***P < 0.001.
At the species level, 7 major bacterial species were identified across all samples (Fig. 1D). Apparent differences were observed in the microbiota composition before and after fermentation. At d 0, the silage microbiota was largely comprised of Weissella cibaria (82.4%) and Pantoea agglomerans (3.22%), and sequences representing the epiphytic species of alfalfa, W. cibaria were dominated. The silage microbiota composition remained rather stable beyond d 7, consistently dominated by sequences representing 3 species; namely L. plantarum (31.4%), W. cibaria (31.2%), and P. pentosaceus (11.4%). The relative abundance of W. cibaria decreased significantly after fermentation started, meanwhile L. plantarum and P. pentosaceus increased obviously, reaching the highest level at d 28 and 7, respectively.
在物种水平上，所有样品中都确定了7个主要的细菌物种（图1D）。发酵前后的微生物群组成有明显的差异。在第0天，青贮微生物群主要由Weissella cibaria（82.4%）和Pantoea agglomerans (成团泛菌)（3.22%）组成，代表紫花苜蓿附生物种的序列，W. cibaria占主导地位。青贮微生物群的组成在第7天后仍然相当稳定，始终由代表3个物种的序列主导；即L. plantarum（31.4%）、W. cibaria（31.2%）和P. pentosaceus（11.4%）。发酵开始后，W. cibaria的相对丰度明显下降，同时L. plantarum和P. pentosaceus明显增加，分别在第28天和第7天达到最高水平。
LDA was performed together with KruskaleWallis test and Wilcoxon rank sum test to identify differences in microbial composition between samples of d 0 and 7. The results of LDA revealed that W. cibaria and Pantoea ananatis were enriched at d 0 (P < 0.05), whereas L. plantarum, P. pentosaceus, L. sakei, L. pentosus, L. coryniformis, Lactococcus garvieae, Pediococcus lolii, Weissella paramesenteroides, L. curvatus, L. brevis, and L. farciminis were enriched at d 7 (P < 0.05; Fig. 1E). These differential abundant taxa were the main contributors to the difference in the microbiota structure before and after fermentation.
LDA与KruskaleWallis检验和Wilcoxon秩和检验一起进行，以确定第0天和第7天的样品之间的微生物组成差异。LDA的结果显示，W. cibaria和Pantoea ananatis在第0天富集（P < 0.05），而L. plantarum、P. pentosaceus、L. sakei、L. pentosus、L. coryniformis、Lactococcus garvieae、Pediococcus lolii、Weissella paramesenteroides、L. curvatus、L. brevis和L. farciminis在第7天富集（P < 0.05；图1E）。这些丰度不同的分类群是造成发酵前后微生物群结构差异的主要原因。
Frequent correlations were found amongst different chemical parameters of silage and the species-level microbial composition (Fig. 1F). Positive correlations were observed between several species that increased significantly after fermentation started (including L. plantarum, W. paramesenteroides, L. brevis, L. curvatus, and L. farciminis) and some chemical parameters (including 4 organic acids, especially lactic acid, SP, and NH3-N). These species correlated negatively with the pH, the quantity of mold and coliform, NDF, and WSC.
在青贮饲料的不同化学参数和物种水平的微生物组成之间发现了频繁的相关关系（图1F）。在发酵开始后明显增加的几个物种（包括L. plantarum、W. paramesenteroides、L. brevis、L. curvatus和L. farciminis）和一些化学参数（包括4种有机酸，特别是乳酸、SP和NH3-N）之间观察到正相关关系。这些物种与pH值、霉菌和大肠菌群的数量、NDF和WSC呈负相关。
3.3. Species-level silage microbiome and changes in the content of PS-8
Changes in the silage microbiota revealed that the level of L. plantarum increased significantly and became the dominant species during fermentation. To clarify the role of PS-8 as an exogenous starter in the fermentation process, the relative abundance of PS-8 reads in the whole genome at different time points was calculated (Fig. 2A; Supplementary data Table S1). The proportion of PS-8 reads increased significantly from 0 to 7 d, and remained stable from d 7 to 28, ranging from 0.5% to 29%. A total of 1,100 raw bins were obtained initially from the scaffold through metagenomic binning using MetaBAT2. Then, incomplete bins with high contamination were removed and 162 high-quality MAG remained (>80% completeness and <5% contamination; Supplementary data Table S3). These MAG were identified as 22 representative species-level genome bins (SGB) and abundance obtained by RPKM. These high-quality MAG were used to construct a phylogenetic tree with consideration of their taxonomic assignments and sequencing depth (Fig. 2B; Supplementary data Table S3). Based on a cut-off level of 99% with the reference genome of PS-8, 10 of the 162 MAG were identified as PS-8 (Fig. 2C). The proportion of PS-8 in the whole genome and the value of GRiD (representing the replication rate of PS-8) was calculated for each time point. Interestingly, PS-8 had a higher GRiD value, representing a higher viability relative to the overall microbial abundance at d 7 and 14, followed by a drastic decline at d 28 (Fig. 2D; Supplementary data Table S3). In addition, correlations between PS-8 and other identified species in the silage fermentation environment were calculated (Fig. 2E). The relative abundance of PS-8 correlated positively and strongly with other Lactobacillus species (r > 0.6, P < 0.001), while significant negative correlation was found between PS-8 and W. cibaria, Enterobacter agglomerans, Erwinia iniecta, Serratia marcescens and Pseudomonas sp. (r > 0.3, P < 0.05).
3.3. 物种水平的青贮饲料微生物群和PS-8含量的变化
青贮饲料微生物群的变化表明，植物乳杆菌的水平明显增加，并在发酵过程中成为主导物种。为了澄清PS-8作为外源启动剂在发酵过程中的作用，计算了不同时间点全基因组中PS-8读数的相对丰度（图2A；补充数据表S1）。PS-8读数的比例从0到7天明显增加，从第7天到第28天保持稳定，范围从0.5%到29%。通过使用MetaBAT2的元基因组分档，最初从支架上获得了总共1100个原始分档。然后，删除了污染程度高的不完整区块，剩下162个高质量的MAG（完整性>80%，污染程度<5%；补充数据表S3）。这些MAG被确定为22个代表物种水平的基因组仓（SGB），丰度由RPKM获得。这些高质量的MAG被用来构建系统发育树，考虑其分类任务和测序深度（图2B；补充数据表S3）。基于与PS-8参考基因组99%的分界线，162个MAG中有10个被确定为PS-8（图2C）。每个时间点的PS-8在全基因组中的比例和GRiD（代表PS-8的复制率）的值被计算出来。有趣的是，PS-8的GRiD值较高，代表在第7天和第14天时相对于整个微生物丰度的生存能力较高，随后在第28天时急剧下降（图2D；补充数据表S3）。此外，还计算了PS-8与青贮发酵环境中其他确定的物种之间的相关性（图2E）。PS-8的相对丰度与其他乳酸菌物种呈强正相关（r>0.6，P<0.001），而PS-8与W. cibaria、Enterobacter agglomerans、Erwinia iniecta、Serratia marcescens和Pseudomonas sp.之间存在明显的负相关（r>0.3，P<0.05）。
Fig. 2. Lactobacillus plantarum PS-8 (PS-8) in alfalfa silage microbiome. (A) Proportion of PS-8 reads at different fermentation time points. Significant differences are indicated by different letters (P < 0.05). (B) Phylogenetic tree constructed based on the metagenomic assembled genomes (MAG). The outermost to the inner layer represents the depth of sequencing, time points, bacterial phyla, and bacterial species, respectively. (C) Average nucleotide identity (ANI) between the reference genome of PS-8 and 10 identified L. plantarum MAG in the silage microbiome. (D) Histogram showing the Growth Rate InDex (GRiD) of PS-8 at d 7, 14, and 28. (E) Spearman correlation analysis between PS-8 and other species. Each species is represented by one circle, and the size of the circle represents the relative abundance of that species. The color scale represents the Spearman's rho, showing strength of correlation.
图2. 植物乳杆菌PS-8（PS-8）在紫花苜蓿青贮饲料微生物组中。(A) 不同发酵时间点的PS-8读数比例。显著的差异由不同的字母表示（P < 0.05）。(B）基于元基因组组装的基因组（MAG）构建的系统发育树。最外层到内层分别代表测序深度、时间点、细菌门类和细菌种类。(C) PS-8的参考基因组与青贮微生物组中10个确定的L. plantarum MAG之间的平均核苷酸一致性（ANI）。(D）直方图显示PS-8在第7、14和28天的生长速度InDex（GRiD）。(E）PS-8与其他物种之间的Spearman相关分析。每个物种用一个圆圈表示，圆圈的大小代表该物种的相对丰度。色标代表Spearman's rho，显示相关的强度。
3.4. Prediction of microbial polysaccharide degradation genes
The quality of silage fermentation depends largely on effective polysaccharide degradation. Thus, the microbial CAZyme genes in the silage microbiome were predicted. The Shannon index calculated based on CAZymes was significantly different between d 0 and time points after initiation of silage fermentation (d 7, 14, 28; P < 0.05; Fig. 3A). No significant difference was found in the Shannon index during d 7 to 28. This result was supported by PCoA analysis (Fig. 3B). The right cluster on the PCoA score plot comprised only samples of d 7, while the left clusters comprised samples of d 7 to 28.
3.4. 微生物多糖降解基因的预测
青贮饲料发酵的质量主要取决于有效的多糖降解。因此，对青贮饲料微生物组中的微生物CAZyme基因进行预测。基于CAZymes计算的香农指数在青贮饲料发酵开始后的第0天和时间点（第7、14、28天；P＜0.05；图3A）之间有明显差异。在第7天到第28天期间，没有发现香农指数的明显差异。这一结果得到了PCoA分析的支持（图3B）。PCoA得分图上的右侧集群只包括第7天的样本，而左侧集群则包括第7至28天的样本。
Fig. 3. Predicted polysaccharide degradation genes and pathways in the microbial metagenomes. (A) Shannon diversity index of predicted genes coding the CAZymes at different time points (* P < 0.05, ** P < 0.01). (B) Principal coordinates analysis of CAZyme genes predicted at different time points. Permutational MANOVA suggested significant between groups (R2 ¼ 0.87, P < 0.001). (C) Metagenomic potential of 22 representative metagenomic assembled genomes (MAG) in polysaccharide degradation, reflected by the possession of genes coding carbohydrate-active enzymes (CAZymes). Boxes filled with blue and white represent the presence or absence of specific genes in certain MAG, respectively. (D) The distribution of CAZyme coding genes in 22 representative MAG. (E) Schematic diagram showing simplified fermentation pathways of plant carbohydrate degradation and metabolism predicted from 22 representative MAG (shown on the genus level) using information from metabolic studies.
图3. 微生物hong基因组中预测的多糖降解基因和途径。(A) 不同时间点预测的编码CAZymes的基因的香农多样性指数（* P < 0.05, ** P < 0.01）。(B）不同时间点预测的CAZyme基因的主坐标分析。同位素单因素分析表明，组间有显著性（R2 ¼ 0.87，P < 0.001）。(C) 22个有代表性的宏基因组集合基因组（MAG）在多糖降解方面的潜力，通过拥有编码碳水化合物活性酶（CAZymes）的基因反映。用蓝色和白色填充的方框分别代表某些MAG中特定基因的存在或不存在。(D）CAZyme编码基因在22个代表性MAG中的分布。(E）示意图显示利用代谢研究的信息，从22个有代表性的MAG（在属的层面上显示）预测的植物碳水化合物降解和代谢的简化发酵途径。
The taxa with the genetic capacity for carbohydrate utilization are considered to be important in microbial fermentation process. Thus, CAZymes in the metagenomes were predicted (Supplementary data Table S4). The genomic capacity for degradation of 5 types of carbohydrate (starch, pectin, arabinoxylan, fructan, cellulose) of the 22 representative MAG were predicted (Fig. 3C). A number of Lactobacillus and Weissella species, including L. curvatus, L. brevis, L. plantarum, W. cibaria, W. hellenica, and W. kandleri carried a higher percentage of CAZyme genes, with great potential for degradation of starch, arabinoxylan, and cellulose. A few bacterial taxa carried genes that degrade pectin and fructan, including relatively high levels of glycoside hydrolases (GH), glycosy transferases (GT), and carbohydrate esterases (CE) (Fig. 3D).
具有碳水化合物利用的遗传能力的类群被认为在微生物发酵过程中很重要。因此，宏基因组中的CAZymes被预测出来（补充数据表S4）。预测了22个代表MAG的5种碳水化合物（淀粉、果胶、阿拉伯木聚糖、果糖、纤维素）的基因组降解能力（图3C）。一些乳酸菌和Weissella物种，包括L. curvatus、L. brevis、L. plantarum、W. cibaria、W. hellenica和W. kandleri携带较高比例的CAZyme基因，具有降解淀粉、阿拉伯木质素和纤维素的巨大潜力。少数细菌类群携带降解果胶和果聚糖的基因，包括相对较高的糖苷水解酶（GH）、糖基转移酶（GT）和碳水化合物酯酶（CE）（图3D）。
In the fermentation process of microorganisms, polysaccharides within the feed would be decomposed into monosaccharides and further converted to pyruvate enzymatically. The substrates would then enter the aerobic and anaerobic glycolysis pathways to produce 5 fermentation products, namely formate, lactate, acetate, butyrate, and succinate (Fig. 3E). Thus, published literature specific to plant fermentation (substrate utilization and production of specific fermentation end products) was consulted and considered along with the MAG information obtained in this study. The predicted degradation pathways of 5 plant carbohydrates in the metagenomes of the 22 representative MAG belonging to 14 genera are shown in a schematic diagram (Fig. 3E). In addition, the species abundance was calculated, and significant changes were observed in members of the Lactobacillus and Weissella genera after fermentation (Appendix Fig. 1).
在微生物的发酵过程中，饲料中的多糖会被分解成单糖，并进一步在酶的作用下转化为丙酮酸。然后，这些底物将进入有氧和无氧糖酵解途径，产生5种发酵产物，即甲酸盐、乳酸盐、乙酸盐、丁酸盐和琥珀酸盐（图3E）。因此，查阅了专门针对植物发酵的公开文献（底物利用和特定发酵终端产品的生产），并与本研究中获得的MAG信息一起考虑。属于14个属的22个代表性MAG的元基因组中预测的5种植物碳水化合物的降解途径以示意图显示（图3E）。此外，还计算了物种丰度，并观察到乳酸菌属和魏氏菌属的成员在发酵后的明显变化（附录图1）。
4. Discussion
The quality of silage fermentation depends largely on the amount and type of microorganisms present in the forage (Agarussi et al., 2019) and the availability of soluble carbohydrates. The inoculation of exogenous starter LAB can help increase the ratio of beneficial to harmful microbes in silage raw materials, facilitating the fermentation process (Wang et al., 2019). Although some previous studies investigated the beneficial effects of applying exogenous bacterial additives to accelerate the ensilage process, few studies monitored the dynamics of bacterial metagenomes and exogenous starter bacteria in fermentation (Ogunade et al., 2018). Metagenomic studies based on MAG reconstruction not only provide information of the functional potential of species-level microbial metagenomes (e.g., genes/pathways related to polysaccharide metabolism) at different fermentation stages, but also offer a way to track the viability of the starter and its potential contribution to the process by using an objective index like GRiD, which is calculated from changes in gene abundance of assembled bacterial genomes.
青贮饲料的发酵质量很大程度上取决于饲草中微生物的数量和类型（Agarussi等，2019）以及可溶性碳水化合物的可用性。接种外源启动LAB有助于提高青贮饲料原料中有益微生物与有害微生物的比例，促进发酵过程（Wang等，2019）。尽管以前的一些研究调查了应用外源细菌添加剂加速饲喂过程的有益效果，但很少有研究监测发酵过程中细菌元基因组和外源启动菌的动态（Ogunade等，2018）。基于MAG重建的元基因组研究不仅提供了不同发酵阶段物种级微生物元基因组的功能潜力信息（例如，与多糖代谢相关的基因/途径），而且还提供了一种方法，通过使用像GRiD这样的客观指数来跟踪启动剂的活力及其对该过程的潜在贡献，该指数是由组装的细菌基因组的基因丰度变化计算的。
The quality of fermented alfalfa feed is directly reflected by the chemical and microbiota composition of the silage. Microorganisms affect feed characteristics through a series of chemical reactions resulting from plant polysaccharide degradation. Typically, fermented alfalfa silage, particularly those with the addition of LAB starter, would contain a large amount of organic acids (especially lactic acid) resulting from carbohydrate utilization by microorganisms present in the raw silage materials, drastically decreasing the pH during the ensilage process (Yitbarek and Tamir, 2014). Our results found positive correlations between several dominant species (L. plantarum, W. paramesenteroides, L. brevis, L. curvatus, and L. farciminis) and 4 organic acids, especially lactic acid. A high acidity could help inhibit the undesirable growth of molds and coliforms, potentially increasing silage stability and shelf life. Molds and coliforms always cause a loss of nutrients and an increased chance of toxin contamination (Avila and Carvalho, 2020). The organic acids in the feed not only facilitated long-term storage of the feed, but also improved the intake of dairy cows and thus improved growth performance (Gheller et al., 2020). As the harmful microbial population was reduced, metabolic requirements of the microbes decreased, and the availability of dietary energy and nutrients to the host animal would increase, leading to enhanced growth rate and feed efficiency (Upadhaya et al., 2016). The WSC content was related to bacterial utilization of carbohydrate as substrates for growth and subsequent synthesis of lactic acid. Due to the increase in Lactobacillus (L. plantarum, L. brevis, L. curvatus, L. farciminis) after fermentation, the content of WSC decreased significantly, which likely accelerated the production of lactic acid. It has been reported that the DM loss of fermentation silage due to lactic acid fermentation would not usually exceed 5%, while the current study observed a stable DM content throughout the fermentation process, confirming that it was mainly lactic acid fermentation (Muck, 2010). The NH3eN in silage was an indicator of proteolysis during ensiling and typically resulted from plant enzymes and microbial activities (Ogunade et al., 2018). Our results showed that a variety of microorganisms were positively correlated with the NH3-N content, which could be related with a relatively high rate of NH3-N accumulation due to the rich protein content in alfalfa. As silage fermentation proceeded, the ammonization of proteins occurred rapidly and decreased in the late stage of fermentation, accompanied by a continuous drop in pH until the late fermentation phase, when microbial activities and NH3-N release were inhibited. Consistent with McGarvey et al. (2013), some species (including L. plantarum, P. pentosaceus, W. paramesenteroides, L. brevis, L. curvatus, and L. farciminis) correlated significantly and positively with the level of SP, which would improve animal digestibility of alfalfa silage protein and prevent further soluble ammonia. These species were also associated with the reduction in ADF and NDF contents in the feed during the fermentation process, and the fiber degradation would effectively improve the palatability and digestibility of the feed (Kumar et al., 2008).
发酵紫花苜蓿饲料的质量直接反映在青贮饲料的化学和微生物群组成上。微生物通过植物多糖降解产生的一系列化学反应来影响饲料特性。通常情况下，发酵的苜蓿青贮饲料，特别是那些添加了LAB启动剂的苜蓿，会含有大量的有机酸（特别是乳酸），这些有机酸是由存在于青贮原料中的微生物利用碳水化合物产生的，在青贮过程中，pH值会急剧下降（Yitbarek和Tamir，2014）。我们的结果发现几个优势物种（L. plantarum、W. paramesenteroides、L. brevis、L. curvatus和L. farciminis）和4种有机酸，特别是乳酸之间存在正相关关系。高酸度可以帮助抑制霉菌和大肠菌群的不良生长，有可能增加青贮饲料的稳定性和保质期。霉菌和大肠菌群总是导致营养物质的损失和增加毒素污染的机会（Avila和Carvalho，2020）。饲料中的有机酸不仅有利于饲料的长期储存，还能提高奶牛的摄入量，从而改善生长性能（Gheller等人，2020）。随着有害微生物数量的减少，微生物的代谢需求减少，宿主动物的日粮能量和营养物质的供应将增加，导致生长速度和饲料效率的提高（Upadhaya等人，2016）。WSC含量与细菌利用碳水化合物作为生长的底物和随后合成乳酸有关。由于发酵后乳酸菌（L. plantarum, L. brevis, L. curvatus, L. farciminis）的增加，WSC的含量明显下降，这可能加速了乳酸的产生。据报道，由于乳酸发酵导致的发酵青贮饲料的DM损失通常不会超过5%，而本研究在整个发酵过程中观察到稳定的DM含量，证实主要是乳酸发酵（Muck，2010）。青贮饲料中的NH3-N是饲育过程中蛋白质分解的指标，通常由植物酶和微生物活动产生（Ogunade等人，2018）。我们的结果显示，各种微生物与NH3-N含量呈正相关，这可能与紫花苜蓿中丰富的蛋白质含量导致的NH3-N积累率相对较高有关。随着青贮发酵的进行，蛋白质的氨化迅速发生，并在发酵后期下降，同时伴随着pH值的持续下降，直到发酵后期，微生物活动和NH3-N的释放受到抑制。与McGarvey等人（2013）一致，一些物种（包括L. plantarum、P. pentosaceus、W. paramesenteroides、L. brevis、L. curvatus和L. farciminis）与SP的水平明显正相关，这将提高苜蓿青贮蛋白的动物消化率，防止进一步的可溶性氨。这些物种还与发酵过程中饲料中ADF和NDF含量的减少有关，纤维的降解将有效改善饲料的适口性和消化率（Kumar等人，2008）。
Bacterial DNA from dead cells having no effect on fermentation could also be sequenced (Avila and Carvalho, 2020). To overcome this issue, our study calculated the strain GRiD based on assembling single strain genome, which reflected the replication of the exogenous additive PS-8 and allowed tracking of its changes and its role in fermentation. Our results showed that PS-8 grew fast during the first week of fermentation, which was likely due to its competitive advantages over other epiphytic bacteria. A previous study found that an inoculant comprising <10% of the epiphytic LAB population was able to influence the efficiency of silage fermentation (Muck, 2013); and exogenous inoculation of Lactobacillus eventually led to the dominance of this genus in the fermented silage, whether the alfalfa was sterilized or not prior to fermentation (Yang et al., 2019). The increase in the GRiD value of PS-8 during the fermentation process in our study suggested that PS-8 was active, especially during the early phases. As the pH continuously declined, the growth of PS-8 was restricted and even decreased at d 28; however, it still comprised a significant proportion in silage microbiota. Guo et al. (2018) reported a similar trend of changes in the relative abundance of L. plantarum when it was inoculated in alfalfa silage fermentation (Guo et al., 2018). One possible beneficial mechanism of PS-8 on the silage microbiota could be its promotion of the growth of various acid-producing LAB (Wang et al., 2019). A number of LAB could produce bacteriocin; PS-8 is a probiotic bacterium exhibiting high antibacterial activity (Zhang et al., 2015), supporting its activities in promoting the growth of beneficial bacteria while inhibiting the harmful ones (Vazquez et al., 2005). A good starter usually possesses bacteriocidal and/or bacteriostatic activity, eventually making it a prevalent strain in the silage fermentation process. The reduction in undesirable microorganisms could also improve the quality and DM beyond that attained from an efficient fermentation (Muck, 2013). Dong et al. (2020) found that by adding perennial ryegrass to Broussonetia papyrifera, favorable microorganisms such as Lactobacillus and Weissella would dominate over the entire ensilage time (Dong et al., 2020). Similarly, Wang et al. (2019) reported the highest abundance of Lactobacillus when alfalfa was ensiled with Moringa oleifera leaves for 60 d (Wang et al., 2019a). He et al. (2020) found that poor fermentation quality of alfalfa silage was related to a low relative abundance of Lactobacillus when undesirable microorganisms dominated during ensiling (He et al., 2020). Previous studies found that the majority of bacteria involved in lactic acid fermentation of silage belonged to the genera Lactobacillus, Pediococcus and Weissella (Guo et al., 2018; Li and Nishino, 2013). Our study found that PS-8 was dominant throughout the fermentation process. Such phenomena was likely due to the strong inhibitory activities of PS-8 in suppressing the growth of unfavorable microorganisms, like Enterobacterium and P. agglomerans, which could produce alcohol during silage fermentation. The inhibition of these microbes could in turn facilitate fermentation (Li and Nishino, 2013). Meanwhile, beneficial bacteria Lactobacillus and Weissella were promoted after fermentation began; increases in these genera could result in more lactic acid production, reduced pH, and thus an improvement in silage quality.
对发酵没有影响的死细胞的细菌DNA也可以被测序（Avila和Carvalho，2020）。为了克服这个问题，我们的研究在组装单株基因组的基础上计算了菌株GRiD，它反映了外源添加剂PS-8的复制，并允许跟踪其变化及其在发酵中的作用。我们的结果显示，PS-8在发酵的第一周增长很快，这可能是由于它对其他附生菌的竞争优势。以前的研究发现，由<10%的附生LAB种群组成的接种剂能够影响青贮饲料的发酵效率（Muck，2013）；而外源接种乳酸菌最终导致该菌属在发酵的青贮饲料中占主导地位，无论苜蓿在发酵前是否经过消毒处理（Yang等，2019）。在我们的研究中，PS-8的GRiD值在发酵过程中的增加表明PS-8是活跃的，特别是在早期阶段。随着pH值的不断下降，PS-8的生长受到限制，甚至在第28天有所下降；然而，它在青贮微生物群中仍占很大比例。Guo等人（2018年）报道了在苜蓿青贮发酵中接种植物杆菌时，植物杆菌的相对丰度有类似的变化趋势（Guo等人，2018年）。PS-8对青贮微生物群的一个可能的有益机制可能是它促进了各种产酸LAB的生长（Wang等人，2019）。一些LAB可以产生细菌素；PS-8是一种益生菌，表现出很高的抗菌活性（Zhang等人，2015），支持其在促进有益细菌生长的同时抑制有害细菌的活动（Vazquez等人，2005）。一个好的启动剂通常拥有杀菌和/或抑菌活性，最终使其成为青贮饲料发酵过程中的普遍菌种。不良微生物的减少也可以提高质量和DM，超过高效发酵所达到的水平（Muck，2013）。Dong等人（2020年）发现，通过在Broussonetia papyrifera中加入多年生黑麦草，乳酸菌和魏氏菌等有利的微生物将在整个青贮时间内占据主导地位（Dong等人，2020年）。同样，Wang等人（2019）报告说，当紫花苜蓿与Moringa oleifera叶子共贮60天时，乳酸菌的丰度最高（Wang等人，2019a）。He等人（2020）发现，当不良微生物在贮藏期间占主导地位时，苜蓿青贮饲料的发酵质量差与乳酸菌的相对丰度低有关（He等人，2020）。以前的研究发现，参与青贮饲料乳酸发酵的大多数细菌属于乳酸杆菌属、小球菌属和魏氏菌属（Guo等，2018；Li和Nishino，2013）。我们的研究发现，在整个发酵过程中，PS-8占主导地位。这种现象可能是由于PS-8在抑制不利微生物的生长方面具有很强的抑制活性，如肠杆菌和P. agglomerans，它们在青贮饲料发酵过程中会产生酒精。对这些微生物的抑制反过来可以促进发酵（Li和Nishino，2013）。同时，发酵开始后，有益菌乳酸菌和魏氏菌得到促进；这些菌属的增加可能导致更多的乳酸产生，降低pH值，从而改善青贮饲料的质量。
Alfalfa has low WSC content, high buffering capacity and high fiber content, making it difficult to maintain a high and stable silage quality (Jaurena and Pichard, 2001; Thacker and Haq, 2008; Renteria-Flores et al., 2008); and most polysaccharides in the alfalfa feed would not be able to be utilized directly by the animals. Adding WSC addition and exogenous microbial starter would reduce buffering capacity and facilitate the fermentation of indigestible plant polysaccharides into nutrients to support bacterial growth. Microbial CAZyme genes encode for enzymes having the capacity of degrading macromolecular carbohydrates into small molecules, thereby improving the palatability and digestibility of feed. Our study observed an increase in a variety of CAZyme coding genes, as well as MAG carrying genes relating to degradation of starch, arabinoxylan, and cellulose. The levels of GH-encoding genes were highest among the assembled genomes. Such enzymes could effectively metabolize plant polysaccharides, such as starch and starch-related substrates (Ventura et al., 2007). In addition, they also have a good ability to degrade cellulose and hemicellulose (arabinoxylan), which would require concerted actions of endoacting-b-1,4-arabinoxylanases and oligosaccharide depolymerizing b-xylosidases (Peng et al., 2016). Our research showed that 52 and 106 bacterial MAG carried endo-acting-b-1,4-arabinoxylanases and b-xylosidases, respectively, indicating good metagenomic potential of decomposition of cellulose and arabinoxylan. One previous study has attempted to elucidate the metabolic products produced during microbial silage fermentation by measuring the end product fluxes or by inferring from pure or mixed cultures of microorganisms from reference metabolic pathways (Seshadri et al., 2018). This study identified the degradation and metabolism of feed carbohydrates by dominant bacteria extrapolated from assembled single bacterial genome, based on previous metabolic studies on feed fermentation.
苜蓿的WSC含量低，缓冲能力强，纤维含量高，因此很难保持高而稳定的青贮质量（Jaurena和Pichard，2001；Thacker和Haq，2008；Renteria-Flores等，2008）；而且苜蓿饲料中的大部分多糖将无法被动物直接利用。加入WSC添加剂和外源微生物启动剂将降低缓冲能力，促进难以消化的植物多糖发酵成营养物质，以支持细菌生长。微生物CAZyme基因编码的酶具有将大分子碳水化合物降解为小分子的能力，从而提高饲料的适口性和消化率。我们的研究观察到各种CAZyme编码基因的增加，以及MAG携带的与降解淀粉、阿拉伯木聚糖和纤维素有关的基因。在集合的基因组中，GH编码基因的水平是最高的。这种酶可以有效地代谢植物多糖，如淀粉和与淀粉有关的底物（Ventura等，2007）。此外，它们还具有良好的降解纤维素和半纤维素（阿拉伯木聚糖）的能力，这需要内切-1,4-阿拉伯木聚糖酶和寡糖解聚b-木糖酶的协同作用（Peng等人，2016）。我们的研究显示，52个和106个细菌MAG分别携带内切-1,4-阿拉伯木聚糖酶和b-木糖苷酶，表明分解纤维素和阿拉伯木聚糖的元基因组潜力良好。以前的一项研究试图通过测量最终产品通量或从参考代谢途径的微生物纯种或混合培养物中推断出微生物发酵过程中产生的代谢产物（Seshadri等人，2018）。本研究根据以往对饲料发酵的代谢研究，确定了从组装的单一细菌基因组推断出的优势细菌对饲料碳水化合物的降解和代谢。
5. Conclusion
This study demonstrated that adding L. plantarum PS-8 in the ensilage process could improve silage quality by accelerating acidification rates and in turn modulating the silage microbiome, particularly by enhancing the growth of LAB and suppressing undesirable microbes like molds and coliforms. Moreover, PS-8 was replicating rapidly and consistently during early-and mid-fermentation phases. The levels of NDF of PS-8-supplemented silage decreased significantly during the ensilage process. These results together suggested an active role of PS-8 in improving silage quality, nutrient preservation, and potentially extending the silage shelf life. This study provided novel information of the mechanism of silage improvement by an exogenous LAB inoculum, which would help improve the efficiency and quality of the production of commercial silage.
本研究表明，在青贮过程中添加植物乳杆菌PS-8可以通过加速酸化率来改善青贮饲料的质量，并反过来调节青贮饲料的微生物组，特别是通过提高LAB的生长和抑制霉菌和大肠菌群等不良微生物。此外，PS-8在早期和中期发酵阶段快速而稳定地进行复制。在青贮过程中，由PS-8补充的青贮饲料的NDF水平明显下降。这些结果表明，PS-8在改善青贮饲料质量、保存营养物质和延长青贮饲料保质期方面发挥了积极作用。该研究提供了外源LAB接种物改善青贮饲料机制的新信息，这将有助于提高商品青贮饲料的生产效率和质量。