RNA degradation as described by a mathematical model for postmortem interval determination.

Area of Science:

• Forensic Science
• Molecular Biology
• Biomarker Analysis

Background:

• Accurate postmortem interval (PMI) estimation is critical in legal and forensic investigations.
• Current methods for PMI determination have limitations in accuracy and practicality.
• RNA transcript levels offer a potential avenue for improving PMI estimation.

Purpose of the Study:

• To develop and validate a novel method for estimating PMI using postmortem RNA transcript levels.
• To assess the accuracy and reliability of a mathematical model based on biomarker expression for PMI determination.
• To establish a practical tool for forensic applications.

Main Methods:

• RNA was extracted from rat and human tissue samples collected at various time points and temperatures.
• Transcript levels of nine candidate biomarkers were analyzed using real-time quantitative PCR (RT-qPCR).
• Reference and control biomarkers were identified for lung and muscle tissues, respectively.
• Mathematical models were constructed correlating normalized △Ct values with observed PMI.
• Model performance was validated using independent datasets.

Main Results:

• Specific reference biomarkers (miR-195, miR-200c, 5S, U6, RPS29 for lung; miR-1, miR-206, 5S, RPS29 for muscle) were identified.
• ACTB and GAPDH transcript levels showed significant correlation with PMI.
• Mathematical models demonstrated low error rates (7.4% in rat, 12.5% in human samples).
• The developed model proved accurate and reliable across different temperatures and sample types.

Conclusions:

• Postmortem RNA transcript levels can be reliably used for PMI estimation.
• The multi-parametric mathematical model provides an accurate and practical tool for forensic casework.
• This RNA-based approach enhances the precision of PMI determination in legal investigations.