Cui Chunhong, Song Yang, Mao Dongmei, Cao Yajun, Qiu Bowen, Gui Peng, Wang Hui, Zhao Xingchun, Huang Zhi, Sun Liqiong, Zhong Zengtao
College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China.
College of Resource and Environment, Nanjing Agricultural University, Nanjing 210095, China.
Microorganisms. 2022 Dec 24;11(1):56. doi: 10.3390/microorganisms11010056.
The estimation of a postmortem interval (PMI) is particularly important for forensic investigations. The aim of this study was to assess the succession of bacterial communities associated with the decomposition of mouse cadavers and determine the most important biomarker taxa for estimating PMIs. High-throughput sequencing was used to investigate the bacterial communities of gravesoil samples with different PMIs, and a random forest model was used to identify biomarker taxa. Redundancy analysis was used to determine the significance of environmental factors that were related to bacterial communities. Our data showed that the relative abundance of Proteobacteria, Bacteroidetes and Firmicutes showed an increasing trend during decomposition, but that of Acidobacteria, Actinobacteria and Chloroflexi decreased. At the genus level, was the most abundant bacterial group, showing a trend similar to that of Proteobacteria. Soil temperature, total nitrogen, NH-N and NO-N levels were significantly related to the relative abundance of bacterial communities. Random forest models could predict PMIs with a mean absolute error of 1.27 days within 36 days of decomposition and identified 18 important biomarker taxa, such as , and . Our results highlighted that microbiome data combined with machine learning algorithms could provide accurate models for predicting PMIs in forensic science and provide a better understanding of decomposition processes.
死后间隔时间（PMI）的估计对于法医调查尤为重要。本研究的目的是评估与小鼠尸体分解相关的细菌群落演替，并确定用于估计PMI的最重要生物标志物分类群。采用高通量测序技术研究不同PMI的墓土样本中的细菌群落，并使用随机森林模型识别生物标志物分类群。利用冗余分析确定与细菌群落相关的环境因素的显著性。我们的数据表明，变形菌门、拟杆菌门和厚壁菌门的相对丰度在分解过程中呈增加趋势，而酸杆菌门、放线菌门和绿弯菌门的相对丰度则下降。在属水平上， 是最丰富的细菌类群，其趋势与变形菌门相似。土壤温度、总氮、NH-N和NO-N水平与细菌群落的相对丰度显著相关。随机森林模型可以在分解36天内预测PMI，平均绝对误差为1.27天，并识别出18个重要的生物标志物分类群，如 、 和 。我们的结果强调，微生物组数据与机器学习算法相结合可以为法医学中预测PMI提供准确的模型，并更好地理解分解过程。
