家猪死后脑组织GC-MS检测和死亡时间推断的研究
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摘要
目的利用GC-MS检测死后家猪脑组织的代谢产物,推断死亡时间。方法成年家猪大脑,置于25℃、75%RH的人工气候箱中,于0 h-84 h内,每间隔6 h取材,GC-MS检测各时间点脑组织代谢物变化。结果PCA显示:平台期和窗口期的时间组彼此分开。建立PLS模型,通过VIP> 1且Kruskal-Wallis检验(P <0.05)筛选出18种重要的代谢物,线性回归模型和参数检验均有统计学意义。多元回归方程为:Y_(PMI)=6.610+16.29X_(十八烷酸)+14.56X_(5-氨基缬草酸)+5.517X_(丙氨酸)(R~2=0.909、SE=6.323)或Y_(PMI)=15.78+9.690 X_(5-氨基缬草酸)+86.45X_(亮氨酸)-82.35X_(甘氨酸)(R~2=0.952、SE=4.271)。结论 GC-MS检测出家猪死后脑组织的多种产物与PMI存在显著相关性,证实了其理论和技术推断PMI的可行性。综合多指标多元逐步回归分析和PLS-DA等多元模式分析方法建立PMI推断模型,可提高推断模型的准确性和精确度。
Objective To apply GC-MS to detect metabolites in brain of swines and to study the estimation of postmortem interval(PMI). Methods The adult swine brains were placed in a 25℃ and 75% humidity artificial climate box, the brains were then collected every 6 h of the interval from 0 to 84 h and analyzed with GC-MS. Results PCA model showed that the groups of the platform periods and window periods are separated from each other. The PLS model was established, and 18 important metabolites were screened through Kruskal-Wallis test with VIP > 1 and P < 0.05., and the linear regression model and parameter test were statistically significant. The multiple regression equation is Y_(PMI)= 6.610+ 16.29 X_(Sa)+ 14.56 X_(5-Aminovaleric acid)+ 5.517 XAla(R~2 = 0.909、SE = 6.323) or Y_(PMI)= 15.78 + 9.690 X_(5-Aminovaleric acid) + 86.45 X_(Leu)-82.35 X_(Gly)(R~2 = 0.952、SE = 4.271). Conclusion There is a significant correlation between products detected by GC-MS and PMI, confirming the feasibility of the GC-MS theory and technology in inferring the PMI. Integrated multi-indicator multiple stepwise regression analysis and multi-modal analysis methods such as PLS-DA to establish a PMI inference model, could improve the accuracy and veracity of the PMI inference.
Objective To apply GC-MS to detect metabolites in brain of swines and to study the estimation of postmortem interval(PMI). Methods The adult swine brains were placed in a 25℃ and 75% humidity artificial climate box, the brains were then collected every 6 h of the interval from 0 to 84 h and analyzed with GC-MS. Results PCA model showed that the groups of the platform periods and window periods are separated from each other. The PLS model was established, and 18 important metabolites were screened through Kruskal-Wallis test with VIP > 1 and P < 0.05., and the linear regression model and parameter test were statistically significant. The multiple regression equation is Y_(PMI)= 6.610+ 16.29 X_(Sa)+ 14.56 X_(5-Aminovaleric acid)+ 5.517 XAla(R~2 = 0.909、SE = 6.323) or Y_(PMI)= 15.78 + 9.690 X_(5-Aminovaleric acid) + 86.45 X_(Leu)-82.35 X_(Gly)(R~2 = 0.952、SE = 4.271). Conclusion There is a significant correlation between products detected by GC-MS and PMI, confirming the feasibility of the GC-MS theory and technology in inferring the PMI. Integrated multi-indicator multiple stepwise regression analysis and multi-modal analysis methods such as PLS-DA to establish a PMI inference model, could improve the accuracy and veracity of the PMI inference.
引文
[1]赵子琴.法医病理学[M].北京:人民卫生出版社,2004.
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[6]Donaldson AE,Lamont IL.Metabolomics of post-mortem blood:identifying potential markers of post-mortem interval[J].Metabolomics,2014,11(1):237-245.
[7]Sato T,Zaitsu K,Tsuboi K,et al.A preliminary study on postmortem interval estimation of suffocated rats by GC-MS/MS-based plasma metabolic profiling[J].Anal Bioanal Chem,2015,407(13):3659-3665.
[8]Frezza C,Zheng L,Tennant D A,et al.Metabolic profiling of hypoxic cells revealed a catabolic signature required for cell survival[J].PloS one,2011,6(9):e24411.
[9]石学志,杨含金,于晓军,等.利用猪胸主动脉生物力学性能时序性变化推断死亡时间[J].中国法医学杂志,2013,28(2):108-111.
[10]Muroya,Oe M,Ojima K,et al.Metabolomic approach to key metabolites characterizing postmortem aged loin muscle of Japanese Black(Wagyu)cattle[M].AsianAustralas J Anim Sci,2019.
[11]Swann L,Forbes S,Lewis S W.Observations of the temporal variation in chemical content of decomposition fluid:A preliminary study using swines as a model system[J].Austr J Forensic Sci,2010,42(3):199-210.
[2]黎增强,左卫东,张付,等.死亡时间推断研究进展[J].法医学杂志,2012(4):287-292.
[3]章有章,查锡良,李刚.生物化学与分子生物学[M].科学技术文献出版社,2005.
[4]Perry T L,Hansen S,Gandham S S.Postmortem changes of amino compounds in human and rat brain[J].JNeurochemistry,1981,36(2):406-412.
[5]Vass A,Barshick S,Sega G,et al.Decomposition chemistry of human remains:a new methodology for determining the postmotem interval[J].J Forensic Sci,2002,47(3):542-553.
[6]Donaldson AE,Lamont IL.Metabolomics of post-mortem blood:identifying potential markers of post-mortem interval[J].Metabolomics,2014,11(1):237-245.
[7]Sato T,Zaitsu K,Tsuboi K,et al.A preliminary study on postmortem interval estimation of suffocated rats by GC-MS/MS-based plasma metabolic profiling[J].Anal Bioanal Chem,2015,407(13):3659-3665.
[8]Frezza C,Zheng L,Tennant D A,et al.Metabolic profiling of hypoxic cells revealed a catabolic signature required for cell survival[J].PloS one,2011,6(9):e24411.
[9]石学志,杨含金,于晓军,等.利用猪胸主动脉生物力学性能时序性变化推断死亡时间[J].中国法医学杂志,2013,28(2):108-111.
[10]Muroya,Oe M,Ojima K,et al.Metabolomic approach to key metabolites characterizing postmortem aged loin muscle of Japanese Black(Wagyu)cattle[M].AsianAustralas J Anim Sci,2019.
[11]Swann L,Forbes S,Lewis S W.Observations of the temporal variation in chemical content of decomposition fluid:A preliminary study using swines as a model system[J].Austr J Forensic Sci,2010,42(3):199-210.