海水溺死大鼠盲肠微生物群落变化及结合随机森林机器学习算法进行死亡时间推断

徐玉钊, 覃小诗, 周云超, 哈山, 卢江寰, 马一新, 段智奥, 陈建华, 邓建强

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重庆医科大学学报 ›› 2024, Vol. 49 ›› Issue (12) : 1508-1519. DOI: 10.13406/j.cnki.cyxb.003687
法医病理学

海水溺死大鼠盲肠微生物群落变化及结合随机森林机器学习算法进行死亡时间推断

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Changes of cecal microbial community in seawater drowning rats and estimation of postmortem interval in combination with random forest machine learning algorithm

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摘要

目的 研究海水中溺死大鼠和CO2窒息死后入水大鼠盲肠微生物的演替规律，并结合随机森林（random forest，RF）机器学习算法进行死后淹没时间（postmortem submersion interval，PMSI）的推断。 方法 本研究将70只健康成年SD大鼠随机分为海水溺死组（seawater drowning group，D组）和死后入水组（postmortem submersion group，PS组）。建立动物模型后置于25 ℃恒温气候箱，按照死后经历时间（0、12、24、36、48、72、96 h）对盲肠内容物进行取材，经十六烷基三甲基溴化铵（cetyltrimethylammonium bromide，CTAB）法提取DNA，细菌16S rDNA V3-V4区特异性扩增，高通量测序等得到测序数据，对盲肠微生物群落特征进行多样性分析；结合微生物组测序结果和RF机器学习算法，筛选生物标志物，建立海水溺死和死后入水组大鼠尸体的PMSI预测模型。 结果 海水溺死组和死后入水组的盲肠微生物群落特征存在差异。结合RF机器学习算法筛选出16种推断PMSI的潜在生物标志物，并分别建立了海水溺死组（D组）和死后入水组（PS组）的PMSI推断模型。海水溺死组预测模型结果：平均绝对误差（mean absolute error，MAE）=13.272 h，R2=0.798；死后入水组预测模型结果：MAE=9.956 h，R2=0.793。 结论 本研究表明，海水溺死和死后入水2种死因的盲肠微生物群落存在规律性差异，证明了结合RF机器学习算法的盲肠内容物微生物群落演替能够成为海水尸体PMSI推断的有效生物学指标。

Abstract

Objective To investigate the succession patterns of cecal microbiota in rats drowned in seawater and those submerged after death by CO2 asphyxiation，and to infer postmortem submersion interval（PMSI） using the random forest（RF） machine learning algorithm. Methods In this study，70 specific pathogen-free healthy adult Sprague-Dawley rats were randomly divided into seawater drowning group（D group） and postmortem submersion group（PS group）. The rats were placed in a constant temperature chamber at 25°C after modeling，and samples of cecal contents were collected at various postmortem time points（0，12，24，36，48，72，and 96 hours）. The CTAB method was used to extract DNA，and the V3-V4 region of bacterial 16S rDNA was specifically amplified； high-throughput sequencing was performed to obtain sequencing data and analyze the diversity of cecal microbiota community. Related biomarkers were obtained based on the results of microbiome sequencing and the RF machine learning algorithm，and then PMSI prediction models were established for the bodies of rats in both the D group and the PS group. Results There were differences in the characteristics of cecal microbiota community between the D group and the PS group. A total of 16 potential biomarkers were obtained by the RF machine learning algorithm，and a model for inferring PMSI was established for the D group and the PS group. For the D group，the predictive model had a mean absolute error（MAE） of 13.272 hours and an R2 value of 0.798，while for the PS group，the predictive model had an MAE value of 8.445 hours and an R2 value of 0.847. Conclusion This study shows that there are regular differences in cecal microbiota community between seawater drowning and postmortem submersion，and it proves that the succession of cecal content microbiota based on the RF machine learning algorithm can be used an effective biomarker for inferring the PMSI of bodies in seawater.

关键词

溺死 / 死后浸没时间 / 随机森林 / 微生物组学 / 死亡时间推断

Key words

drowning / postmortem submersion interval / random forest / microbiome / estimation of postmortem interval

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R89

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徐玉钊 , 覃小诗 , 周云超 , 等. 海水溺死大鼠盲肠微生物群落变化及结合随机森林机器学习算法进行死亡时间推断. 重庆医科大学学报. 2024, 49(12): 1508-1519 https://doi.org/10.13406/j.cnki.cyxb.003687
Xu Yuzhao, Qin Xiaoshi, Zhou Yunchao, et al. Changes of cecal microbial community in seawater drowning rats and estimation of postmortem interval in combination with random forest machine learning algorithm[J]. Journal of Chongqing Medical University. 2024, 49(12): 1508-1519 https://doi.org/10.13406/j.cnki.cyxb.003687

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基金

国家自然科学基金资助项目(82060341;81560304)
海南省院士创新平台科研资助项目(YSPTZX202134)
海南省研究生创新课题资助项目(Qhys2023-474)

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