Endogenous and Exogenous Small RNA Signatures as Novel Tools for Postmortem Interval Determination

Area of Science:

• Forensic Science
• Molecular Biology
• Bioinformatics

Background:

• Accurate postmortem interval (PMI) estimation is crucial in forensic science.
• Traditional PMI methods lack precision, particularly in later decomposition stages.
• Complex factors influence decomposition, challenging traditional forensic approaches.

Purpose of the Study:

• To develop a novel PMI estimation framework.
• Integrate endogenous small non-coding RNAs (sncRNAs) and exogenous bacterial-derived small RNAs (sRNAs) dynamics.
• Utilize sRNA transcriptomics and machine learning for enhanced PMI prediction.

Main Methods:

• Employed PANDORA-seq for profiling sncRNAs in postmortem cardiac tissue (mouse model).
• Assessed RNA integrity (RIN) across eight organs.
• Applied machine learning (random forest) for PMI prediction models.

Main Results:

• Cardiac RNA degradation showed strong PMI correlation (RF model R² = 0.939).
• Identified seven novel biomarkers (tsRNAs, piRNAs) for PMI prediction (R² = 0.760).
• Detected bacterial sRNAs (Staphylococcus aureus) upregulation postmortem, suggesting biomarker potential.

Conclusions:

• Integrated endogenous and exogenous sRNA analysis offers a high-precision, rapid diagnostic tool for PMI estimation.
• Revealed complex postmortem molecular processes, including potential cross-kingdom interactions.
• This novel framework advances forensic science capabilities in determining time since death.