Animals and Experimental Design
A total of 16 14-day-old male Da Heng broiler chicks were randomly divided into 2 groups, with 8 replicates in each group. Birds were fed basal diets. All birds were housed in wired cages and offered free access to feed and water, with a lighting schedule of 20 h light and 4 h dark. AgomiRs of miR-499-5p, synthesized from Ribobio, were chemically engineered and cholesterol-modified oligonucleotides to mimic miRNA expression, and injected intramuscularly into gastrocnemius muscle at a dose of 5 nmol. A scramble miRNA agomiR was used as the negative control. The injections were repeated every 72 h and given 6 times to ensure the efficacy. All the 16 chicks were slaughtered. Gastrocnemius muscles were taken from each chick a week after the last injection, all fresh tissue samples were washed briefly with Phosphate Buffered Saline (PBS) and divided into 2.0 mL plastic centrifuge tubes (each sample weighing approximately 200 mg) and then immediately frozen in liquid nitrogen.
Histological Examination of Gastrocnemius Muscle
Gastrocnemius muscles fixed in 4% paraformaldehyde were cut into 10-μm thick sections using a cryosectioning machine (CM1900, Leica), and stained with haematoxylin and eosin for morphological analysis. Three images from each bird were analyzed. Micrographs were obtained using a digital camera system (BA200Digital, Motic) and analyzed using Image Pro Plus software.
Statistical Analysis
Data were expressed in mean ± SD. Statistical analysis was carried out using One-Way Analysis of Variance ANOVA using the SPSS Software (Version 20.0). The significant value between groups was set at P < 0.05.
Metabolites Extraction
The 16 tissues of gastrocnemius muscle were individually grounded with liquid nitrogen and the homogenate was resuspended with prechilled 80% methanol and 0.1% formic acid by well vortexing. The samples were incubated on ice for 5 min and then were centrifuged at 15000 rpm, 4°C for 5 min. A some of supernatant was diluted to final concentration containing 60% methanol by LC-MS grade water. The samples were subsequently transferred to a fresh Eppendorf tube with 0.22 μm filter and then were centrifuged at 15000 g, 4°C for 10 min. Finally, the filtrate was injected into the LC-MS/MS system analysis. Quality control (QC) samples were also prepared by mixing equal volumes of each sample; the samples were aliquoted for analysis prior to sample preparation. The QC samples were used to monitor deviations of the analytical results from the pooled mixtures and compare to the errors caused by the analytical instrument itself.
Metabolomic Analysis of Muscle Samples
LC-MS/MS analyses were performed using a Vanquish UHPLC system (Thermo Fisher) coupled with an Orbitrap Q Exactive HF-X mass spectrometer (Thermo Fisher). Samples were injected onto an Hyperil Gold column (100×2.1 mm, 1.9μm) using a 16-min linear gradient at a flow rate of 0.2 mL/min. The eluents for the positive polarity mode were eluent A (0.1% FA 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, 16 min. Q Exactive HF-X mass spectrometer was operated in positive/negative polarity mode with spray voltage of 3.2 kV, capillary temperature of 320°C, sheath gas flow rate of 35 arb and aux gas flow rate of 10 arb.
Data Processing and Analysis
The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.0 (CD 3.0, Thermo Fisher) to perform peak alignment, peak picking, and quantitation for each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 minutes; actual mass tolerance, 5ppm; signal intensity tolerance, 30%; signal/noise ratio, 3; and minimum intensity, 100000. After that, peak intensities were normalized to the total spectral intensity. The normalized data was used to predict the molecular formula based on additive ions, molecular ion peaks and fragment ions. And then peaks were matched with the mzCloud (https://www.mzcloud.org/) and ChemSpider (http://www.chemspider.com/) database to obtained the accurate qualitative and relative quantitative results.
For multivariate statistical analysis, both principal component analysis (PCA) and orthogonal projections to latent structures discriminant analyses (OPLS-DA) were performed to visualize the differences between groups. PCA and OPLS-DA were both performed using the program SIMCA-P Software (Version 13.0). PCA was firstly employed to visualize the sample clustering, trends and outliers among the observations. Then OPLS-DA was performed to highlight the difference between groups. The OPLS-DA model was validated by 200 random permutations test for avoiding overfitting. Afterwards, loading plots were constructed, which showed the contribution of variables to the difference between the two groups. It also showed the important variables which were situated far from the origin, but the loading plot is complex because of many variables. To refine this analysis, the first principal component of variable importance in the projection (VIP) was obtained through OPLS-DA. Metabolites were annotated and identified on the basis of accurate mass and MS information by searching through the Database. Metabolites were finally verified by comparing retention times and fragmentation patterns with standards. The fold change (FC) value of each metabolite was calculated by comparing mean peak values obtained from the treatment group (TG) to that from the control group (CG). Differential metabolites were selected based on the basis of VIP value (>1.0), FC value (FC > 1.2 or FC < 0.833) and Student’s t-test (P < 0.05). Pearson’s product-moment correlation was performed to calculate the correlation. Corresponding P-values and false discovery rate (FDR) of each correlation were also calculated using “cor.test function” in R software. Differential metabolites were further mapped onto general biochemical pathways according to annotation in Kyoto Encyclopedia of Genes and Genomes (KEGG).