Isolation and screening of cellulolytic bacteria
The modified cellulase identification medium (CMC-Na medium) was used as an isolation medium (Table S1). The rumen fluid was obtained from the rumen of healthy buffaloes with rumen fistulas and filtered by four layers of sterilized gauze. The rumen fluid was diluted with sterilized double-distilled water, evenly coated on the CMC-Na medium, and then cultured in an anaerobic container at 39 °C for 3 days. After that, the single colonies were selected and cultured on CMC-Na medium. The culture mediums were dyed with 0.1% Congo red (Solarbio, China) staining solution to observe whether there were light yellow hydrolysis circles around the coating [46]. The strains producing hydrolytic circle were selected for purification and subculture.
Identification of cellulolytic bacteria
The isolated strains were confirmed and identified by genetic analysis using PCR and 16S rRNA sequencing for further verification. The genomic DNA was extracted with the bacterial genome DNA fast extraction kit (Aidlab Biotech Co., Ltd., China) according to the manufacturer's protocol Universal PCR primers 27F (5'- AGAGTTTTGATCCTGGCTCAG-3') and 1492R (5'-GGTTACCTTGTTACGCACTT-3') were used to amplify 16S rRNA gene. PCR products were sequenced by Sangon Biotech Co., Ltd. (Shanghai, China). The sequencing results were analyzed using blast on the NCBI website. The phylogenetic tree of bacteria was constructed by neighbor-joining method using mega7.0 software, the phylogenetic tree was statistically evaluated using 1000 bootstrap replicates.
Enzyme assay
Potato Dextrose Agar (PDA) medium-guaiacol (0.04% guaiacol), PDA-aniline blue medium (0.1 g/L aniline blue), Luria Bertani (LB) plate (1% soluble starch), LB plate (1% skimmed milk) were used respectively to detect the laccase (Lac), manganese peroxidase (Mnp), lignin peroxidase (Lip), amylase and protease in the strains.
In-vitro antibacterial test
The antibacterial activity of the isolates was determined by the Oxford cup method [47]. Escherichia coli O157, O139, K88, K99, Salmonella C78-1, Staphylococcus aureus ATCC25923 were used as an indicator at 1.0×107 CFU/mL. These indicator bacteria were obtained from the State Key Laboratory of Agriculture Microbiology of Huazhong Agricultural University.
Tolerance test of heat, gastric juice and intestinal fluid
The bacterial liquid (2.4×109 CFU/mL) in the logarithmic growth phase was placed in a water bath at 70°C and 90°C, respectively. Samples were taken at 3 min and 10 min time points to count the viable bacteria in the samples.
Artificial gastric juice and intestinal juice were prepared according to Chinese Pharmacopoeia [48]. The bacterial liquid (2.4×109 CFU/mL) which in logarithmic growth phase was inoculated into artificial gastric juice (pH=3.0) and artificial intestinal fluid (pH=7.0) by 1% inoculation amount. Samples were taken at 3 h and 4 h respectively, the viable bacteria in the samples were counted.
The survival rate was calculated as follows: survival rate=[C/C0] × 100%, C and C0 represented the number of colonies in the experimental and control groups, respectively.
Antibiotic susceptibility assay
The drug sensitivity of isolated strains was tested with disk diffusion method [49]. Fifteen drug tablets (Hangzhou microbial Reagent Co., Ltd., China) were selected. The drug sensitivity detection was performed according to the latest version of the CLSI standard [50].
PCR amplification of virulence genes
Bacillus cereus, which contains nheA, nheB, nheC and entFM genes was used as positive control strain. The specific synthesized primers of virulence genes were obtained from Sangon Biotech Co., Ltd. (Shanghai, China). The amplification program was as follows: pre denaturation at 94 °C for 3 min; 35 cycles (95 °C 3 min, 58 °C 30 s, 72 °C 33 s,); and extended for 10 min at 72 °C [51].
Animal toxicity test
All animal experiments were approved and reviewed by animal welfare and research department, ethics committee, Huazhong Agricultural University, Wuhan, China (Approval number: HZAUMO-2019-047).
Twenty-three-week-old KM (Kun Ming) mice (half male and half female) were randomly divided into experimental group and control group (n = 10). The mice in the experimental group were given SN-6 by gavage at 2.0×108 CFU/day for 2 weeks, while mice in the control group were given the same volume of saline. Behaviors, hair gloss, mental state and general health of the reared mice were observed throughout 2 weeks. After two weeks, the mice were sacrificed using chloral hydrate as anesthesia, and heart, spleen, liver, lung, and kidney were collected to detect organ index. T-test was used to analyze the data. p<0.05 was considered statistically significant.
Simmental growth-promoting test
Healthy Simmental beef cattle (female) with the same genetic background and similar initial weight from Hubei Liangyou Jinniu animal husbandry technology Co., Ltd. (China, Hubei) were selected. Cattle (n = 66) were randomly divided into control group (n = 33) and experimental group (n = 33). The feeding lasted for 33 days. Both the control cattle and experimental cattle were fed with basic diet (Table S2) for 33 days’ ad libitum. The control cattle were offered with normal water, while for experimental cattle, the water added SN-6 at 1.0×1010CFU/day/individual ad libitum. Before and at the end of the experiment, the cows were weighed at fasting.Data were expressed with mean ± SD, analyzed by one-way analysis of variance using SPSS 21.0 software, p<0.05 as a significant difference.Fresh fecal samples were collected from the rectum with sterile gloves at the end of the experiment and immediately stored in sterile centrifuge tubes. All samples were immediately frozen on dry ice and stored at −80 °C for further analysis.
Fecal microbiota analysis
Total DNA was extracted from fecal samples using E.Z.N.A.® soil Kit (Omega Bio-tek, Norcross, GA, U.S.). The extracted DNA was qualitatively and quantitatively detected by 1% agarose gel electrophoresis and NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA). The V3-V4 region of 16S rRNA was amplified by PCR with specific primers 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACHTACHVGGGTWTCTAAT-3') (PCR instrument: GeneAmp 9700, ABI, USA). The PCR products were recovered by 2% agarose gel, and purified by AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). QuantiFluor™-ST (Promega, USA) was used for quantitative analysis. The fecal microbial DNA fragments were sequenced by the Illumina Miseq platform (Illumina, San Diego, USA). The quality control and splicing of the original data were carried out by using Trimmomatic and Flash software. After quality control, the sequences and fuzzy bases less than 50 bp were removed. UPARSE software (version 7.1 http://drive5.com/uparse/) was used to cluster the optimized sequences according to 97% similarity; UCHIME software was used to remove chimeras. The taxonomy of each 16S rRNA gene sequence was analyzed by the RDP Classifier algorithm (http://rdp.cme.msu.edu/) against the Silva (SSU123) 16S rRNA database using a confidence threshold of 70%. Chao1, ACE, Shannon and Simpson indices were used to reflect α diversity. The core fecal microbiota of each group was shown by the Venn diagram. In β diversity analysis, principal coordinate analysis (PCoA) was used to determine the difference of species composition among samples. According to the composition and sequence distribution of samples at each taxonomic level, the differences of species abundance between groups were compared, and tested by the Student T-test. The p<0.05 was considered to be statistically significant. Microbial biomarkers associated with particular interventions were identified through linear discriminant analysis (LDA) effect size (LEfSe), with an effect size threshold of 3.
Fecal Metabolomics Analysis
The effects of SN-6 on the fecal metabolism in Simmental were assayed by a LC-MS-based untargeted metabolomics. Fecal samples (50mg) were accurately weighed, and the metabolites were extracted using a 400 µL methanol: water (4:1, v/v) solution. The mixture was allowed to settle at -20 ℃ and treated by High throughput tissue crusher Wonbio-96c (Shanghai wanbo biotechnology co., LTD) at 50 Hz for 6 min, then followed by vortex for the 30s and ultrasound at 40 kHz for 30 min at 5 ℃. The samples were placed at -20 ℃ for 30min to precipitate proteins. After centrifugation at 13000g at 4 ℃ for 15min, the supernatants were transferred to sample vials for LC-MS/MS analysis.
UHPLC-MS analyses were performed using a Vanquish UHPLC system (Thermo Fisher, Germany) coupled with an Orbitrap Q ExactiveTMHF-X mass spectrometer (Thermo Fisher, Germany). Samples were injected onto a Hypesil Gold C18 column (100 mm×2.1 mm, 1.9μm; Thermo Fisher, Germany) using a 17-min linear gradient at a flow rate of 0.2mL/min, the column temperature was maintained at 40 ℃. The eluents for the positive polarity mode were eluent A (0.1% formic acid in water) and eluent B (Methanol). The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, pH 9.0) and eluent B (Methanol). The solvent gradient was set as follows: 2% B, 1.5 min; 2-100% B, 12.0 min; 100% B, 14.0 min; 100-2% B, 14.1 min; 2% B, 17 min. Q ExactiveTMHF-X mass spectrometer via electrospray ionization (ESI) interface was operated in positive/negative polarity mode with a spray voltage of 3.2 kV and capillary temperature of 320 °C, sheath gas flow rate of 40 arb and aux gasflow rate of 10 arb.
Statistical analysis
All results were presented as mean ± standard deviation (SD). A multivariate statistical analysis was performed using ropls (Version1.6.2, http://bioconductor.org/packages/release/bioc/html/ropls.html) R package from Bioconductor on Majorbio Cloud Platform (https://cloud.majorbio.com). Principle component analysis (PCA) using an unsupervised method was applied to obtain an overview of the metabolic data, general clustering, trends, or outliers were visualized. Orthogonal partial least squares discriminate analysis (OPLS-DA) was used for statistical analysis to determine global metabolic changes between comparable groups. Variable importance in the projection (VIP) was calculated in the OPLS-DA model. The p values were estimated with paired Student’s t-test on Single dimensional statistical analysis. The correlations between the key fecal microbiota and fecal metabolites were assessed by the Spearman correlation coefficient and were visualized on a heat map generated by the Python software (Version1.0.0). The online KEGG database was used to link metabolites with specific metabolic pathways.