Bacterial community response to postmortem changes predicts the causes of death and postmortem interval
Jian Zhao1,2, Qin Su2, Shujuan Wang3
1Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University & Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Guangzhou, China.
Abstract:
Determining the postmortem interval (PMI) and cause of death (COD) in decomposed remains a major challenge in forensic pathology because morphological and biochemical indicators progressively deteriorate during decomposition. Microbial communities are highly responsive to environmental and physiological changes and may therefore provide complementary forensic information. In this study, we used a murine model to investigate postmortem microbial succession associated with three death-related conditions-ligature strangulation (LS), hemorrhagic shock (HS), and organophosphate poisoning (OP)-across postmortem time points from 2 to 20 days. Swab samples from three skin sites (face, chest, and abdomen) were collected and subjected to 16S rDNA sequencing, and microbial community dynamics were evaluated using bioinformatics and random forest (RF) machine-learning models. Our results revealed distinct COD-associated microbial trajectories. LS carcasses exhibited accelerated morphological decomposition and earlier Proteobacteria dominance, whereas HS showed delayed decomposition-related changes. OP exhibited distinct microbial diversity and succession patterns, particularly during early PMI. Alpha- and beta-diversity analyses indicated that both COD and PMI significantly influenced microbial community structure. RF models showed strong discriminatory performance for COD classification (AUC > 0.95) and PMI classification (AUC 0.88-0.98), with distinct microbial features contributing to the two classification tasks. These findings suggest that physiological conditions associated with different causes of death may contribute to distinct postmortem microbial trajectories, while temporal microbial succession provides informative markers for categorical PMI classification. Collectively, microbiome-based approaches may serve as complementary forensic tools for simultaneous PMI classification and discrimination of death-related conditions.
