Zhao Xingchun, Zhong Zengtao, Hua Zichun
School of Biopharmacy, China Pharmaceutical University, Nanjing, P.R. China.
National Engineering Laboratory for Forensic Science, Beijing, P.R. China.
J Appl Microbiol. 2022 Dec;133(6):3451-3464. doi: 10.1111/jam.15771. Epub 2022 Sep 9.
Decomposition, a complicated process, depends on several factors, including carrion insects, bacteria and the environment. However, the composition of and variation in oral bacteria over long periods of decomposition remain unclear. The current study aims to illustrate the composition of oral bacteria and construct an informative model for estimating the post-mortem interval (PMI) during decomposition.
Samples were collected from rats' oral cavities for 59 days, and 12 time points in the PMI were selected to detect bacterial community structure by sequencing the V3-V4 region of the bacterial 16S ribosomal RNA (16S rRNA) gene on the Ion S5 XL platform. The results indicated that microorganisms in the oral cavity underwent great changes during decomposition, with a tendency for variation to first decrease and then increase at day 24. Additionally, to predict the PMI, an informative model was established using the random forest algorithm. Three genera of bacteria (Atopostipes, Facklamia and Cerasibacillus) were linearly correlated at all 12 time points in the 59-day period. Planococcaceae was selected as the best feature for the last 6 time points. The R of the model reached 93.94%, which suggested high predictive accuracy. Furthermore, to predict the functions of the oral microbiota, PICRUSt results showed that energy metabolism was increased on day 3 post-mortem and carbohydrate metabolism surged significantly on days 3 and 24 post-mortem.
Overall, our results suggested that post-mortem oral microbial community data can serve as a forensic resource to estimate the PMI over long time periods.
The results of the present study are beneficial for estimating the PMI. Identifying changes in the bacterial community is of great significance for further understanding the applicability of oral flora in forensic medicine.
腐败分解是一个复杂的过程，取决于多种因素，包括食腐昆虫、细菌和环境。然而，在长期腐败分解过程中口腔细菌的组成及变化仍不清楚。本研究旨在阐明口腔细菌的组成，并构建一个用于估计腐败分解过程中死后间隔时间（PMI）的信息模型。
在59天内从大鼠口腔采集样本，并在PMI的12个时间点进行选择，通过在Ion S5 XL平台上对细菌16S核糖体RNA（16S rRNA）基因的V3 - V4区域进行测序来检测细菌群落结构。结果表明，口腔中的微生物在腐败分解过程中发生了巨大变化，在第24天有先减少后增加的变化趋势。此外，为了预测PMI，使用随机森林算法建立了一个信息模型。在59天期间的所有12个时间点，有三个细菌属（阿托波斯氏菌属、法克勒氏菌属和蜡样芽孢杆菌属）呈线性相关。在最后6个时间点，扁平球菌科被选为最佳特征。该模型的R值达到93.94%，表明预测准确性高。此外，为了预测口腔微生物群的功能，PICRUSt结果显示，死后第3天能量代谢增加，死后第3天和第24天碳水化合物代谢显著增加。
总体而言，我们的结果表明，死后口腔微生物群落数据可作为一种法医资源，用于长时间估计PMI。
本研究结果有助于估计PMI。确定细菌群落的变化对于进一步了解口腔菌群在法医学中的适用性具有重要意义。
