Sequential Changes of Serum Biomarkers after Skeletal Muscle Contusion in Rats

Abstract

Objective To screen serum biomarkers after skeletal muscle contusion in rats based on gas chromatography-mass spectrometry （GC-MS） metabolomics technology, and support vector machine （SVM） regression model was established to estimate skeletal muscle contusion time. Methods The 60 healthy SD rats were randomly divided into experimental group （n=50）, control group （n=5） and validation group （n=5）. The rats in the experimental group and the validation group were used to establish the model of skeletal muscle contusion through free fall method, the rats in experimental group were executed at 0 h, 2 h, 4 h, 8 h, 12 h, 24 h, 48 h, 96 h, 144 h and 240 h, respectively, and the rats in validation group were executed at 192 h, while the rats in the control group were executed after three days' regular feeding. The skeletal muscles were stained with hematoxylin-eosin （HE）. The serum metabolite spectrum was detected by GC-MS, and orthogonal partial least square-discriminant analysis （OPLS-DA） pattern recognition method was used to discriminate the data and select biomarkers. The SVM regression model was established to estimate the contusion time. Results The 31 biomarkers were initially screened by metabolomics method and 6 biomarkers were further selected. There was no regularity in the changes of the relative content of the 6 biomarkers with the contusion time and the SVM regression model can be successfully established according to the data of 6 biomarkers and the 31 biomarkers. Compared with the injury time ［（55.344±7.485） h］ estimated from the SVM regression model based on the data of 6 biomarkers, the injury time ［（195.781±1.629） h］ estimated from the SVM regression model based on the data of 31 biomarkers was closer to the actual value. Conclusion The SVM regression model based on metabolites data can be used for the contusion time estimation of skeletal muscles.