Estimation of the human postmortem interval using an established rat mathematical model and multi-RNA markers

Abstract

In our previous study, a R code-based mathematical model using RNA degradation patterns was developed for PMI determination in rat brain specimens. However, the postmortem changes of RNA are much more complicated in real cases, and there is still a huge challenge in efficiently applying information in animal data to real cases. In the present study, different RNA markers in both rat and human tissues were collected to screen valid biomarkers and the corresponding mathematical models were established and validated. With the same methodology, multi-RNA markers of myocardium and liver tissues were detected by qPCR and the Ct values of ten biomarkers generally increased with prolonged PMIs. 5S, miR-1 and miR-133a were shown to be optimum reference biomarkers that were not affected by a PMI of up to 5 or more days; however, liver-specific miR-122 began to degrade under higher temperatures and only 5S was selected as an endogenous control in the liver. Among the tested target RNAs, similar to our previous study in brain tissue, β-actin (ΔCt) was found to exhibit the best correlation coefficient with PMI and was employed to build mathematical models using R software. Following validation, the relatively low estimated error demonstrated that PMIs can be accurately predicted in human cases through comprehensive consideration of various factors and using effective biomarkers.

Dysregulation of miR-381-3p and miR-23b-3p in skeletal muscle could be a possible estimator of early post-mortem interval in rats

“…Using a mixed effect model, the Fold-change of miR-381-3p could be predicted at 0, 3, 12 and 24 h of PMI. The presence of several miRNAs in various tissues has been described through the PMI in humans and in rats (Lv et al, 2017;Tu et al, 2019). However, the analyzed gene expression of some miRNAs has been mainly focused in finding control genes potentially useful for PMI calculation based on gene expression analysis in death bodies.…”

MicroRNAs as Useful Tools to Estimate Time Since Death. A Systematic Review of Current Literature

“…Among other molecules the researchers analyzed miR-1; miR-9; miR-133a; miR-133a; miR-1, miR-133a were shown to be an optimal reference biomarker due to the miRNA's stability over five or more days; on the other hand miR-122 that is a liver-specific biomarker, began to degrade under higher temperatures. The authors concluded, using delta Ct with β-actin, that the error rate in six of 13 cases was lower than five hours for real PMI [38,68].…”

“…Indeed, numerous researchers focused their work on the quantification of the degradation of macromolecules, such as DNA, RNA and proteins, as a possible indicator of PMI [56][57][58]. Due to its nature, RNA seems more accurate than DNA and proteins: its degeneration and the loss of specific RNA transcripts appear to be very susceptible in terms of rapidity and temporal correlation after the death of the organism [38].…”

Forensic transcriptome analysis using massively parallel sequencing

“…In 2003, Bauer et al showed in a pilot study that RNA degradation of the fatty acid synthasemessenger RNA (FASN mRNA) is significantly correlated with the PMI in autopsy cases up to 5 days post-mortem [134]. Further studies focused on quantitative PCR analysis and RNA integrity numbers (RIN) in various tissues and cell types in rats and humans [133,[135][136][137]. They suggested GAPHD, βactin, 18S rRNA and HIF-1α as promising markers to estimate the PMI up to several days.…”