Metabonomics Analysis of Brain Stem Tissue in Rats with Primary Brain Stem Injury Caused Death

doi:10.12116/j.issn.1004-5619.2022.420510

Abstract

Abstract:

Objective To explore the potential biomarkers for the diagnosis of primary brain stem injury （PBSI） by using metabonomics method to observe the changes of metabolites in rats with PBSI caused death. Methods PBSI, non-brain stem brain injury and decapitation rat models were established, and metabolic maps of brain stem were obtained by LC-MS metabonomics method and annotated to the HMDB database. Partial least square-discriminant analysis （PLS-DA） and random forest methods were used to screen potential biomarkers associated with PBSI diagnosis. Results Eighty-six potential metabolic markers associated with PBSI were screened by PLS-DA. They were modeled and predicted by random forest algorithm with an accuracy rate of 83.3%. The 818 metabolic markers annotated to HMDB database were used for random forest modeling and prediction, and the accuracy rate was 88.9%. According to the importance in the identification of cause of death, the most important metabolic markers that were significantly up-regulated in PBSI group were HMDB0038126 （genipinic acid, GA）, HMDB0013272 （N-lauroylglycine）, HMDB0005199 [（R）-salsolinol] and HMDB0013645 （N,N-dimethylsphingosine）. Conclusion GA, N-lauroylglycine, （R）-salsolinol and N,N-dimethylsphingosine are expected to be important metabolite indicators in the diagnosis of PBSI caused death, thus providing clues for forensic medicine practice.

Key words: forensic pathology, metabonomics, cause of death, primary brain stem injury, liquid chromato-graphy-mass spectroscopy （LC-MS）, partial least square-discriminant analysis （PLS-DA）, random forest algorithm, rats

CLC Number:

Qin SU, Qian-ling CHEN, Wei-bin WU, Qing-qing XIANG, Cheng-liang YANG, Dong-fang QIAO, Zhi-gang LI. Metabonomics Analysis of Brain Stem Tissue in Rats with Primary Brain Stem Injury Caused Death[J]. Journal of Forensic Medicine, 2023, 39(4): 373-381.

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URL: http://www.fyxzz.cn/EN/10.12116/j.issn.1004-5619.2022.420510

http://www.fyxzz.cn/EN/Y2023/V39/I4/373

Figures/Tables 10

Fig. 1 Construction of rat models

Fig. 2 Pathological changes of rat brain tissues with different causes of death （HE staining）

Fig. 3 Pathological changes of rat brain stem tissues with different causes of death （Silver staining）

Fig. 4 PLS-DA score plots of rat brain stem tissues with different causes of death at different time points after death

Fig. 5 Permutation test results （200 times） of OPLS-DA of rat brain stem tissues with different causes of death at different time points after death

Fig. 6 Venn diagram of metabolic markers in rat brain stem tissues at 6 h， 1 d and 3 d after death

Fig. 7 Construction results of random forest model for 86 metabolic markers

Tab. 1 Prediction results of random forest modeling using 86 metabolic markers and 818 metabolic markers

组别	86种代谢标志物的预测值			818种代谢标志物的预测值
组别	对照组	非脑干脑损伤组	PBSI组	对照组	非脑干脑损伤组	PBSI组
对照	6	0	0	6	0	0
非脑干脑损伤	1	4	1	0	5	1
PBSI	0	1	5	0	1	5

Fig. 8 Relative expression levels of 15 metabolic markers in the rat brain stem tissues with different causes of death

Fig. 9 Construction results of random forest model for 818 metabolic markers

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