Predicting postmortem interval based on microbial community sequences and machine learning algorithms

Abstract

Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decomposition process. Research of such behaviour enhances the understanding of decomposition and benefits estimating the postmortem interval (PMI) in forensic investigations, which is critical but faces multiple challenges. In this study, we combined microbial community characterization, microbiome sequencing from different organs (i.e. brain, heart and cecum) and machine learning algorithms [random forest (RF), support vector machine (SVM) and artificial neural network (ANN)] to investigate microbial succession pattern during corpse decomposition and estimate PMI in a mouse corpse system. Microbial communities exhibited significant differences between the death point and advanced decay stages. Enterococcus faecalis, Anaerosalibacter bizertensis, Lactobacillus reuteri, and so forth were identified as the most informative species in the decomposition process. Furthermore, the ANN model combined with the postmortem microbial data set from the cecum, which was the best combination among all candidates, yielded a mean absolute error of 1.5 AE 0.8 h within 24-h decomposition and 14.5 AE 4.4 h within 15-day decomposition. This integrated model can serve as a reliable and accurate technology in PMI estimation.

The applicability of forensic time since death estimation methods for buried bodies in advanced decomposition stages

“…Our findings were similar to other work on microbial indicators of PMI estimates showing that while microbes can be used as predictors, the taxonomic groups and level which best predicts PMI are dependent on the surrounding environment [ 64 , 69 ]. Though the limited number of donors and time points in this study did not allow for a robust testing of a continuous interval model, in other studies of postmortem microbiomes, data were able to provide a promising tool for PMI investigation in a variety of habitats and conditions [ 63 , 65 , 70 , 71 ].…”

Potential use of molecular and structural characterization of the gut bacterial community for postmortem interval estimation in Sprague Dawley rats

“…According to a previous study, P. mirabilis was able to attract blowflies, which was the reason for the high percentage of V. lutrae 35 . We constructed a model that could explain 87.2% of the variation in the time of death within 1 h. For the corpse the decomposition process, the continuous variables for analysis and particularly fine model developed to estimate the postmortem interval were similar to those in a previous study 14 . The features containing most information for evaluating time of death were selected by best subset selection the genus level, and the features of the poorest model belonged to the phyla.…”

“…Clarifying the succession of the microbiota during decomposition has applications in forensic science, especially for identifying characteristic bacteria that are closely related to time since death at specific time points. Recent studies have reported bacterial community changes related to animal carcass decomposition 2 , 11 – 14 . Such studies are starting to determine the universal changes in bacterial communities, including changes in genera and families, identifying the most informative taxonomic microbial communities that change after death transmigration.…”

“…, the researchers exposed pigs to an environment without human intervention and established a data model with 10 bacteria at the family level to explain the 94.4% of the time since placement of carcasses within 2–3 h. Another study demonstrated that microorganisms could be used to estimate the time since death within 3 days after death 2 . In 2020, researchers found that bacterial communities displayed significant differences between time of death and advanced decay and used machine learning to construct an integrated model for estimating PMI 14 .…”

Potential Using of Molecular Characterization and Structure of The Gut Bacterial Community for Postmortem Interval Estimation in Sprague Dawley Rats

“…We constructed a model that could explain 87.2% variation of the time of death within 1 h. Regarding the decomposition process of a corpse as continuous variables for analysis and to develop a particularly ne model to estimate postmortem interval was similar to a previous study [35]. The features contained most information for evaluation death time were selected by Best Subset Selection coming from the genus level and features of the poorest model belong to the phylum.…”