Dual-kingdom necrobiome succession extends postmortem interval estimation into skeletonization.

Area of Science:

• Forensic Science
• Microbiology
• Bioinformatics

Background:

• Traditional postmortem interval (PMI) estimation methods lose accuracy as decomposition progresses.
• Advanced decomposition stages, particularly skeletonization, present significant challenges for determining time since death.

Purpose of the Study:

• To investigate the potential of microbial community succession as a molecular clock for PMI estimation.
• To assess the temporal resolution of bacterial and fungal communities in oral samples and gravesoil during decomposition.
• To develop machine-learning models integrating microbial and morphological data for enhanced PMI prediction.

Main Methods:

• Full-length amplicon sequencing of bacterial and fungal communities in pig oral samples and gravesoil over ~5 months.
• Tracking microbial succession patterns and correlating them with morphological decomposition scoring.
• Developing and evaluating machine-learning models for PMI and accumulated degree day (ADD) prediction.

Main Results:

• Consistent three-phase microbial succession (disruption, colonization, stabilization) observed, aligning with decomposition stages.
• Microbial succession provided temporal resolution extending beyond morphological indicators, even into skeletonization.
• Machine-learning models integrating microbial data achieved robust PMI and ADD prediction, with varying performance across decomposition stages (bacterial early, dual-kingdom intermediate, fungal late).

Conclusions:

• Necrobiome succession serves as a reliable molecular clock for estimating PMI, extending capabilities by months.
• Dual-kingdom microbial analysis offers a powerful framework for advanced decomposition cases where traditional methods are insufficient.
• Specific microbial taxa can act as reliable temporal indicators throughout the decomposition process.