Abstract
Aims
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.
Methods and Results
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 2 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.
Conclusions
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.
Significance and Impact of the Study
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.