Su Qin, Yang Chengliang, Chen Ling, She Yiqing, Xu Quyi, Zhao Jian, Liu Chao, Sun Hongyu
Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Guangzhou Forensic Science Institute, Guangzhou, China.
Front Microbiol. 2023 Jun 27;14:1213271. doi: 10.3389/fmicb.2023.1213271. eCollection 2023.
Diagnosing the drowning site is a major challenge in forensic practice, particularly when corpses are recovered from flowing rivers. Recently, forensic experts have focused on aquatic microorganisms, including bacteria, which can enter the bloodstream during drowning and may proliferate in corpses. The emergence of 16S ribosomal RNA gene (16S rDNA) amplicon sequencing has provided a new method for analyzing bacterial composition and has facilitated the development of forensic microbiology. We propose that 16S rDNA amplicon sequencing could be a useful tool for inferring drowning sites. Our study found significant differences in bacterial composition in different regions of the Guangzhou section of the Pearl River, which led to differences in bacteria of drowned rabbit lungs at different drowning sites. Using the genus level of bacteria in the lung tissue of drowned rabbits, we constructed a random forest model that accurately predicted the drowning site in a test set with 100% accuracy. Furthermore, we discovered that bacterial species endemic to the water were not always present in the corresponding drowned lung tissue. Our findings demonstrate the potential of a random forest model based on bacterial genus and composition in drowned lung tissues for inferring drowning sites.
在法医实践中，确定溺水地点是一项重大挑战，尤其是当尸体从流动的河流中打捞上来时。最近，法医专家们将重点放在了水生微生物上，包括细菌，这些细菌在溺水过程中可进入血液循环并可能在尸体中增殖。16S核糖体RNA基因（16S rDNA）扩增子测序技术的出现为分析细菌组成提供了一种新方法，并推动了法医微生物学的发展。我们认为16S rDNA扩增子测序可能是推断溺水地点的有用工具。我们的研究发现珠江广州段不同区域的细菌组成存在显著差异，这导致不同溺水地点的溺水兔肺中的细菌有所不同。利用溺水兔肺组织中的细菌属水平，我们构建了一个随机森林模型，该模型在测试集中以100%的准确率准确预测了溺水地点。此外，我们发现水中特有的细菌物种并不总是存在于相应的溺水肺组织中。我们的研究结果证明了基于溺水肺组织中细菌属和组成的随机森林模型在推断溺水地点方面的潜力。
