RNA degradation as described by a mathematical model for postmortem interval determination. (November 2016) - Lv, Ye-Hui; Ma, Jian-Long; Pan, Hui; Zhang, Heng; Li, Wen-Can; Xue, Ai-Min; Wang, Hui-Jun; Ma, Kai-Jun; Chen, Long
Abstract: Precisely determining the postmortem interval (PMI) is crucial to civil, criminal and forensic cases. A technique to exploit the postmortem RNA transcript level was developed to increase the accuracy and practicality of PMI estimation. For this purpose, lung tissues and muscle tissues were removed at twelve time points (0–144 h) from rat corpses that had been stored at three different temperatures (10, 20 and 30 °C). Human tissues were collected at autopsy from twelve real cases with known PMI values and other parameters. After the RNA was extracted from all these samples, the transcript levels of nine biomarkers were analyzed by real-time quantitative PCR (RT-qPCR). With the assistance of geNorm, miR-195, miR-200c, 5S, U6 and RPS29 were selected as reference biomarkers for lung specimens; miR-1, miR-206, 5S and RPS29 were chosen as control markers for muscle tissues. On the contrary, ACTB and GAPDH were significantly correlated with the PMI. The mathematical models using these target biomarkers were constructed to describe the characteristic relationship between △Ct values (normalized to reference biomarkers) and the observed PMI for each temperature group. Following validation, the relatively low error rates (7.4% and 12.5% for rat and human samples, respectively) demonstrated the accuracy and reliability of the mathematical model. We believe these results indicate that the multi-parametric mathematical model can become a practical tool for PMI estimation. Highlights: ACTB and GAPDH significantly correlated with PMI in both lung and muscle tissues. Tissues-specific miRNAs are stable reference markers that are not affected by PMI. 5S and RPS29 were also chosen as endogenous control markers in present study. Mathematical model of PMI estimation can be used in multi-temperature conditions. Validation results demonstrated the accuracy and reliability of this model.