Bacterial community response to postmortem changes predicts the causes of death and postmortem interval

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.

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Publication Details

Journal
Medicine Science and the Law
Published
2026-11-06
DOI
https://doi.org/10.1177/00258024261505250
Primary Topic
Forensic Entomology and Diptera Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Bacterial community response to postmortem changes predicts the causes of death and postmortem interval

Chao Liu, Xinbiao Liao, Hu Zhao, Yu Wu et al.
1 links
Medicine Science and the Law
Forensic Entomology and Diptera Studies
article

Bacterial community response to postmortem changes predicts the causes of death and postmortem interval

Chao Liu, Xinbiao Liao, Hu Zhao, Yu Wu, Shujuan Wang, Ling Chen, Jian Zhao, Qin Su, Xiaodong Kang, Quyi Xu, Qi Li
article en

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.

Medicine Science and the Law
Ministry of Public Security of the People's Republic of China (CN), Sun Yat-sen University (CN), Jinan University (CN), Key Laboratory of Guangdong Province (CN), Huazhong University of Science and Technology (CN), Southern Medical University (CN)
National Natural Science Foundation of China
Good health and well-being
Openalex Percentile: Top 15%
Forensic Entomology and Diptera Studies
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