Postmortem submersion interval prediction model based on the rat muscle microbiome.

Area of Science:

• Forensic Microbiology
• Microbial Ecology
• Bioinformatics

Background:

• Accurate post-mortem submersion interval (PMSI) estimation is crucial in forensic science.
• Current methods often rely on external factors susceptible to contamination.
• Internal tissues, like skeletal muscle, offer a potentially more stable environment for microbial analysis.

Purpose of the Study:

• To investigate predictable microbial succession in skeletal muscle following submersion.
• To develop a high-precision PMSI prediction model independent of the cause of death (drowning vs. post-mortem submersion).

Main Methods:

• Established drowning and post-mortem submersion rat models.
• Collected skeletal muscle samples over 14 days post-submersion.
• Utilized 16S rRNA gene sequencing and machine learning (random forest) for microbial profiling and model development.

Main Results:

• Skeletal muscle microbiome showed distinct early (0-3 days) and late (5-14 days) successional phases.
• Microbial succession was independent of the cause of death.
• A two-stage prediction model achieved 90.9% accuracy in phase classification and low mean absolute errors for time estimation.

Conclusions:

• Rat skeletal muscle microbiome exhibits predictable post-mortem succession, acting as a reliable "microbial clock".
• This internal microbial succession is unaffected by the cause of death.
• Skeletal muscle is a promising target for developing robust, high-precision PMSI estimation models in forensic science.