基于微生物组学的乙醇联合CO中毒大鼠死亡时间预测

doi:10.12116/j.issn.1004-5619.2024.440903

摘要/Abstract

摘要：

目的采用微生物组学分析技术，研究乙醇联合一氧化碳（carbon monoxide，CO）中毒大鼠死后肠道细菌群落结构的演变规律，并探讨该变化与死亡时间（postmortem interval，PMI）之间的联系。方法建立CO中毒致死、乙醇暴露后CO中毒致死、单纯乙醇暴露、空白对照组的动物模型，分别于大鼠死后0、1、3、7、14 d取直肠标本。采用高通量测序技术对微生物16S rRNA基因进行测序，分析细菌群落的多样性、相对丰度和差异性，并利用随机森林算法构建基于细菌群落演变规律预测PMI的模型。结果各组大鼠死后14 d内肠道细菌群落的组成在门和属水平均发生改变。基于细菌群落特征的PMI预测模型在各组中均表现出良好的预测性能，CO中毒致死、乙醇暴露后CO中毒致死、单纯乙醇暴露、空白对照组的模型拟合度分别为0.60、0.59、0.61和0.60，平均误差均为1 d左右。结论 CO中毒、乙醇联合CO中毒和乙醇摄入均会对大鼠尸体细菌群落结构的演变规律产生影响。在法医学实践中，针对此类特定中毒案例进行PMI推断时，建议采用为其专门构建的预测模型，以有效提高推断的准确性。

关键词: 法医病理学, 微生物组学, 一氧化碳中毒, 乙醇中毒, 死亡时间推断, 大鼠

Abstract:

Objective To investigate the temporal changes of intestinal bacterial communities in rats that died from combined ethanol and carbon monoxide （CO） poisoning using microbiome analysis techniques, and to explore potential correlations between these changes and the postmortem interval （PMI）. Methods Four experimental groups were established： fatal CO poisoning, fatal CO poisoning following ethanol exposure, ethanol exposure, and blank controls. Rectal specimens were collected at 0, 1, 3, 7, and 14 days postmortem. High-throughput sequencing of the 16S rRNA gene was employed to analyze bacterial communities. Bacterial diversity, relative abundance, and community differences were analyzed. A predictive model for PMI estimation using bacterial community succession patterns was constructed using a random forest algorithm. Results The intestinal bacterial community structure of rats changed at both the phylum and genus levels within 14 days postmortem in the experimental groups. The PMI estimation model based on bacterial characteristics demonstrated good predictive performance across different experimental groups, with model fitting accuracies of 0.60, 0.59, 0.61 and 0.60 for each of the four groups. The average prediction error across all groups was approximately 1 day. Conclusion Ethanol exposure, CO poisoning, and the combined effects of ethanol exposure and CO poisoning all affect postmortem bacterial succession in rat cadavers. In forensic practice, it is recommended to adopt a predictive model specifically developed for the corresponding poisoning condition to improve the accuracy of PMI estimation.

Key words: forensic pathology, microbiomics, carbon monoxide poisoning, ethanol poisoning, postmortem interval estimation, rats

中图分类号:

贾娟, 张君, 章雯, 张枫林, 贠克明, 严江伟. 基于微生物组学的乙醇联合CO中毒大鼠死亡时间预测[J]. 法医学杂志, 2026, 42(4): 295-301.

Juan Jia, Jun Zhang, Wen Zhang, Fenglin Zhang, Keming Yun, Jiangwei Yan. Microbiomics‑Based Prediction of Postmortem Interval in Rats with Ethanol Exposure Combined with CO Poisoning[J]. Journal of Forensic Medicine, 2026, 42(4): 295-301.

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链接本文: http://www.fyxzz.cn/CN/10.12116/j.issn.1004-5619.2024.440903

http://www.fyxzz.cn/CN/Y2026/V42/I4/295

图/表 8

表1 各组样本细菌群落的Shannon指数

Tab. 1 Shannon index of bacterial communitiesin samples from each group （n=6，x¯±s）

PMI	CO组	J+CO组	J组	Blank组
0 d	5.85±0.22	4.39±0.74^1）2）3）	5.54±0.30	5.59±0.70
1 d	5.27±0.60	4.83±0.74	5.43±0.32	5.72±0.51
3 d	4.79±1.30	4.47±1.25	5.48±0.39	5.75±0.65
7 d	4.07±1.94	2.69±0.94^2）	4.80±1.06	3.35±1.42
14 d	2.09±0.84	1.61±0.36^2）3）	2.32±0.21	2.65±1.42

图1 门水平各组的细菌群落相对丰度A：CO组；B：J+CO组；C：J组；D：Blank组。

Fig. 1 The relative abundance of bacterial communities at the phylum level in each group

图2 属水平各组的细菌群落相对丰度A：CO组；B：J+CO组；C：J组；D：Blank组。

Fig. 2 The relative abundance of bacterial communities at the genus level in each group

图3 各组样本细菌群落的PCoA结果

Fig. 3 PCoA results of bacterial communitiesin samples from each group

表2 PERMANOVA结果

Tab. 2 Results of PERMANOVA

影响因素	自由度	平方和	F统计量	R²	P值
合计	99	27.964	-	1.000	-
PMI	4	6.592	7.454	0.236	0.001
处理方式	3	1.952	2.942	0.070	0.001
PMI与处理方式的交互作用	18	1.732	0.653	0.062	0.099
残差	88	17.688	-	0.633	-

图4 各组样本的物种相似性与PMI的关系A：CO组；B：J+CO组；C：J组；D：Blank组。

Fig. 4 The relationship between species similarity andPMI in samples from each group

图5 各组样本PMI的随机森林预测模型拟合度A：CO组；B：J+CO组；C：J组；D：Blank组。

Fig. 5 Goodness-of-fit of the random forest prediction model for PMI of samples in each group

表3 随机森林预测模型PMI分段统计的MAE (n=6，x¯±s，d)

Tab. 3 MAE of PMI-stratified statistics basedon the random forest prediction model

PMI	CO组	J+CO组	J组	Blank组
0 d	1.05±0.45	1.23±0.47	1.06±0.33	0.95±0.21
1 d	0.67±0.33	0.61±0.28	0.88±0.33	0.67±0.52
3 d	0.79±0.69	0.56±0.35	0.27±0.32	0.60±0.89
7 d	1.40±0.53	1.40±0.23	1.46±0.50	0.96±0.79
14 d	1.42±0.85	1.61±0.64	1.40±0.89	1.78±0.80

参考文献 22

[1]	邢浩伟，韦特驹，陈玉川，等. 尸僵的法医学研究综述[J].广西警官高等专科学校学报，2008，21（）：4-6.
	Xing H W， Wei T J， Chen Y C， et al. A review of forensic research on rigor mortis[J]. Guangxi Jingguan Gaodeng Zhuanke Xuexiao Xuebao，2008，21（S1）：4-6.
[2]	田洁，张孟余，何斌，等. 盐酸吗啡对大头金蝇生长发育的影响及其对死者死亡时间推断的意义[J].昆虫学报，2004，47（6）：715-718.
	doi：10.3321/j.issn：0454-6296.2004.06.004.Tian J， Zhang M Y， He B， et al. Effect of morphine in rabbit tissues on growth of Chrysomya megacephala （Diptera： Calliphoridae） and its implication for estimation of postmortem intervals[J]. Kunchong Xuebao，2004，47（6）：715-718.
[3]	George K A， Archer M S， Green L M， et al. Effect of morphine on the growth rate of Calliphora stygia （Fabricius）（Diptera： Calliphoridae） and possible implications for forensic entomology[J]. Forensic Sci Int，2009，193（1/2/3）：21-25. doi：10.1016/j.forsciint.2009.08.013 .
[4]	Wood T， Pyper K， Casali F. Effects of cocaine and heroin， and their combination， on the development rate of Calliphora vomitoria （Diptera： Calliphoridae）[J]. Sci Justice，2022，62（4）：471-475. doi：10.1016/j.scijus.2022.07.001 .
[5]	Annasaheb Bansode S， Ramrao More V. Effect of lorazepam on the development of the hairy maggot blow fly， Chrysomya rufifacies （Macquart）： Implication for forensic entomology[J]. J Toxicol，2023：1051736. doi：10.1155/2023/1051736 .
[6]	GBD 2021 Carbon Monoxide Poisoning Collaborators. Global， regional， and national mortality due to unintentional carbon monoxide poisoning， 2000—2021： Results from the Global Burden of Disease Study 2021[J]. Lancet Public Health，2023，8（11）：e839-e849. doi：10.1016/S2468-2667（23）00185-8 .
[7]	Shield K， Manthey J， Rylett M， et al. National， regional， and global burdens of disease from 2000 to 2016 attributable to alcohol use： A comparative risk assessment study[J]. Lancet Public Health，2020，5（1）：e51-e61. doi：10.1016/S2468-2667(19)30231-2 .
[8]	Janík M， Ublová M， Kučerová Š， et al. Carbon monoxide-related fatalities： A 60-year single institution experience[J]. J Forensic Leg Med，2017，48：23-29. doi：10.1016/j.jflm.2017.04.002 .
[9]	向青青，陈立方，苏秦，等. 微生物群落演替在死亡时间推断中的研究进展[J].法医学杂志，2023，39（4）：399-405.
	doi：10.12116/j.issn.1004-5619.2022.420606.Xiang Q Q， Chen L F， Su Q， et al. Research progress on microbial community succession in the postmortem interval estimation[J]. Fayixue Zazhi，2023，39（4）：399-405.
[10]	向青青. 利用微生物推断机械性窒息和失血性休克两种死因腐败尸体的死亡时间[D].昆明：昆明医科大学，2023.
	Xiang Q Q. Using microorganisms to estimate the postmortem interval in the decomposing cadavers caused by mechanical asphyxia and hemorrhagic shock[D]. Kunming： Kunming Medical University，2023.
[11]	Dietert R R， Silbergeld E K. Biomarkers for the 21st century： Listening to the microbiome[J]. Toxicol Sci，2015，144（2）：208-216. doi：10.1093/toxsci/kfv013 .
[12]	Li Y Y， Wang Y C， Shi F， et al. Phospholipid metabolites of the gut microbiota promote hypoxia-induced intestinal injury via CD1d-dependent γδ T cells[J]. Gut Microbes，2022，14（1）：2096994. doi：10.1080/19490976.2022.2096994 .
[13]	Engen P A， Green S J， Voigt R M， et al. The gastrointestinal microbiome： Alcohol effects on the composition of intestinal microbiota[J]. Alcohol Res，2015，37（2）：223-236. doi：10.35946/arcr.v37.2.07 .
[14]	Richardson J B， Dancy B C R， Horton C L， et al. Exposure to toxic metals triggers unique responses from the rat gut microbiota[J]. Sci Rep，2018，8（1）：6578. doi：10.1038/s41598-018-24931-w .
[15]	严宝飞，袁鹏，刘圣金，等. 黄芩汤对非酒精性脂肪肝大鼠肠道菌群的影响[J].中草药，2022，53（1）：162-175.
	doi：10.7501/j.issn.0253-2670.2022.01.020.Yan B F， Yuan P， Liu S J， et al. Effects of Huangqin Decoction on gut microbiota in rats with nonalcoholic fatty liver disease[J]. Zhongcaoyao，2022，53（1）：162-175.
[16]	Liu Y， Chen L J， Li X W， et al. Gut microbiota contribute to methamphetamine-induced cardiotoxicity in mouse model[J]. Chem Biol Interact，2023，379：110512. doi：10.1016/j.cbi.2023.110512 .
[17]	Metcalf J L， Xu Z Z， Weiss S， et al. Microbial community assembly and metabolic function during mammalian corpse decomposition[J]. Science，2016，351（6269）：158-162. doi：10.1126/science.aad2646 .
[18]	Zhang J， Wang M C， Qi X Q， et al. Predicting the postmortem interval of burial cadavers based on microbial community succession[J]. Forensic Sci Int Genet，2021，52：102488. doi：10.1016/j.fsigen.2021.102488 .
[19]	曹洁，李文晋，王一飞，等. 基于16S rRNA高通量测序技术的肠道菌群多样性推断死亡时间[J].法医学杂志，2021，37（5）：621-626.
	doi：10.12116/j.issn.1004-5619.2020.400708.Cao J， Li W J， Wang Y F， et al. Estimating postmortem interval using intestinal microbiota diversity based on 16S rRNA high-throughput sequencing technology[J]. Fayixue Zazhi，2021，37（5）：621-626.
[20]	Chen T H， Hsu C C， Lin H J， et al. Carbon monoxide poisoning triggers intestinal injury， inflammation， and microbiota dysbiosis that drive metabolic perturbations[J]. Life Sci，2025，378：123839. doi：10.1016/j.lfs.2025.123839 .
[21]	Pachhain S， Halverstadt B， Anekwe E C， et al. Data on ethanol drinking and microbiome in alcohol preferring and non-preferring rats after a fecal microbiota transplant[J]. Data Brief，2025，62：111901. doi：10.1016/j.dib.2025.111901 .
[22]	Li N， Liang X R， Zhou S D， et al. Exploring postmortem succession of rat intestinal microbiome for PMI based on machine learning algorithms and potential use for humans[J]. Forensic Sci Int Genet，2023，66：102904. doi：10.1016/j.fsigen.2023.102904 .