Objectives: 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).
Conclusions: 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.
目的: 应用代谢组学方法观察原发性脑干损伤（primary brain stem injury，PBSI）致死大鼠脑干组织代谢产物的变化规律，探索诊断PBSI的潜在生物标志物。方法: 建立PBSI、非脑干脑损伤和剪头处死大鼠模型，采用基于LC-MS技术的代谢组学方法，获得脑干代谢图谱并将其注释到HMDB数据库，应用偏最小二乘-判别分析法（partial least square-discriminant analysis，PLS-DA）、随机森林算法筛选出与PBSI诊断相关的潜在代谢标志物。结果: 通过PLS-DA筛选出86种与PBSI相关的潜在代谢标志物，并通过随机森林算法建模和预测，准确率为83.3%。对注释到HMDB数据库的818种代谢标志物进行随机森林建模和预测，准确率高达88.9%。根据在死因鉴定中重要程度的排序，最终筛选出最为重要且在PBSI大鼠模型中显著上调的代谢标志物为HMDB0038126（京尼平苷酸）、HMDB0013272（N-月桂酰甘氨酸）、HMDB0005199［（R）-去甲猪毛菜碱］和HMDB0013645（N，N-二甲基鞘氨醇）。结论: 京尼平苷酸、N-月桂酰甘氨酸、（R）-去甲猪毛菜碱和N，N-二甲基鞘氨醇有望成为PBSI诊断的重要代谢物指标，进而为法医学实践提供线索。.
Keywords: cause of death; forensic pathology; liquid chromato-graphy-mass spectroscopy (LC-MS); metabonomics; partial least square-discriminant analysis (PLS-DA); primary brain stem injury; random forest algorithm; rats.