原发性脑干损伤致死大鼠的脑干组织代谢组学分析

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

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。

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,Ndimethylsphingosine).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.