Accurate predictions of postmortem interval using linear regression analyses of gene meter expression data

Area of Science:

• Forensic Science
• Molecular Biology
• Genomics

Background:

• Accurate determination of postmortem interval (PMI) is critical in forensic investigations.
• Current methods for PMI estimation often lack precision.
• Gene expression patterns offer a potential avenue for more accurate PMI determination.

Purpose of the Study:

• To validate an experimental design for accurately predicting postmortem time using gene expression analysis.
• To assess the predictive power of gene transcription profiles in model organisms.
• To establish a reliable method for postmortem interval estimation.

Main Methods:

• Analyzing upregulated gene expression in zebrafish and mouse models postmortem.
• Utilizing the Gene Meter approach to calibrate microarray data.
• Employing a training and testing dataset approach with linear regression analyses to model gene expression changes over time.
• Repeating the design multiple times to identify optimal gene groups for prediction.

Main Results:

• A group of eleven zebrafish genes achieved a perfect R² of 1 and a slope of 0.99.
• Seven mouse liver genes yielded an R² of 0.98 and a slope of 0.97.
• Seven mouse brain genes resulted in an R² of 0.95 and a slope of 0.87.
• Groups of gene transcripts demonstrated superior predictive accuracy compared to individual genes.

Conclusions:

• Selected gene transcript groups accurately predict postmortem time.
• The validated experimental design can be applied to cadavers for reliable PMI estimation.
• This approach offers a significant advancement in forensic science for determining time of death.