Evaluating the potential of housekeeping genes, rRNAs, snRNAs, microRNAs and circRNAs as reference genes for the estimation of PMI.

The precise estimation of postmortem interval (PMI) is a critical step in death investigation of forensic cases. Detecting the degradation of RNA in tissues by real time quantitative polymerase chain reaction (RT-qPCR) technology provides a new theoretical basis for estimation of PMI. However, most commonly used reference genes degrade over time, while previous studies seldom consider this when selecting suitable reference genes for the estimation of PMI. Studies have shown microRNAs (miRNAs) are very stable and circular RNAs (circRNAs) have recently emerged as a novel class of RNAs with high stability. We aimed to evaluate the stability of the two kinds of RNAs and normal reference genes using geNorm and NormFinder algorithms to identify tissue-specific reference genes for PMI estimation. The content of candidate RNAs from mouse heart, liver and skeletal muscle tissues were dynamically examined in 8 consecutive days after death. Among the 11 candidate genes (ß-actin, Gapdh, Rps18, 5S, 18S, U6, miR-133a, miR-122, circ-AFF1, LC-Ogdh and LC-LRP6), the following genes showed prioritized stability: miR-122, miR-133a and 18S in heart tissues; LC-Ogdh, circ-AFF1 and miR-122 in liver tissues; and miR-133a, circ-AFF1 and LC-LRP6 in skeletal muscle tissues. Our results suggested that miRNAs and circRNAs were more stable as reference genes than other kinds of RNAs regarding PMI estimation. The appropriate internal control genes were not completely the same across tissue types.

[Application of the Peak Area Ratio of STR Loci to Amelogenin Locus in the Estimation of DNA Degradation].

OBJECTIVE: To explore the change rules of peak area ratio of STR loci to Amelogenin (AMEL) locus (STR/AMEL), a sex-determining gene in DNA degradation, and to evaluate the application of STR/AMEL value in the estimation of DNA degradation degree. METHODS: DNA was extracted from iliopsoas, and the variations of STR/AMEL value (Penta E/AMEL, Penta D/AMEL, FGA/AMEL) were analyzed after the artificial degradation was made by DNase I, and the changes of these three ratios of the iliopsoas naturally degraded in an outdoor environment were also analyzed. The regression curves were analyzed using the periods of DNA degradation and outside the body as the independent variable (x) and the STR/AMEL value as the dependent variable (y) and three curve equations under two conditions were established. RESULTS: Both under the conditions of artificial and natural degradation, STR/AMEL value had a negative relationship with the degradation time. The relationship between STR/AMEL and degradation time can be well simulated by the cubic function. R2 was over 0.99 under controlled degradation condition and over 0.86 under natural degradation condition. CONCLUSION: The STR/AMEL value (Penta E/AMEL, Penta D/AMEL, FGA/AMEL) is negatively related with the DNA degradation degree, which follows mathematical regression models strictly, and it might be applied to evaluate the DNA degradation degree.

Using miRNAs and circRNAs to estimate PMI in advanced stage.

In our previous study, we evaluated the stability of multi-RNA markers in heart, liver and skeletal muscle tissues of mice within 8 days after death and concluded that microRNAs (miRNAs) and circular (circRNAs) were more stable as reference genes in dead bodies than other kinds of RNAs. Based on their tissue-specific expression, we obtained reference genes for three kinds of tissues: miR-122, miR-133a and 18S in heart tissues; LC-Ogdh, circ-AFF1 and miR-122 in liver tissues; and miR-133a, circ-AFF1 in skeletal muscle tissues. For the estimation of post mortem interval (PMI), we also selected suitable biomarkers, which exhibited the best correlation coefficient with PMI. In our stability analysis of multi-RNA markers, Gapdh, Rps18, U6 and ß-actin were unstable and selected as candidate target biomarkers. By analyzing the correlation between the expression levels of candidate target biomarkers and PMI, we obtained suitable target biomarkers for the three kinds of tissues, respectively. Finally, we established mathematical models of PMI estimation using the above selected reference genes and target biomarkers. The low estimated error in the validated samples demonstrated that PMI in advanced stage could be accurately predicted by real-time quantitative polymerase chain reaction (qPCR) through systematically selected effective reference genes and target biomarkers. Besides, combining the estimated results of various tissues and multi-biomarkers could improve the accuracy of PMI estimation.

Metabolic profiling of femoral muscle from rats at different periods of time after death.

Clarification of postmortem metabolite changes can help characterize the process of biological degradation and facilitate investigations of forensic casework, especially in the estimation of postmortem interval (PMI). Metabolomics can provide information on the molecular profiles of tissues, which can aid in investigating postmortem metabolite changes. In this study, liquid chromatography-mass spectrometric (LC-MS) analysis was performed to identify the metabolic profiles of rat femoral muscle at ten periods of time after death within 168 h. The results obtained by reversed-phase liquid chromatography (RPLC)- and hydrophilic interaction liquid chromatography (HILIC)- electrospray ionization (ESI±) have revealed more than 16,000 features from all four datasets. Furthermore, 915 of these features were identified using an in-house database. Principal component analysis (PCA) demonstrated the time-specific features of molecular profiling at each period of time after death. Moreover, results from partial least squares projection to latent structures-discriminant analysis (PLS-DA) disclosed a strong association of metabolic alterations of at least 59 metabolites with the time since death, especially within 48 h after death, which expounds these metabolites as potential indicators in PMI estimation. Altogether, our results illustrate the potentiality of metabolic profiling in the evaluation of PMI and provide candidate metabolite markers with strong correlation with time since death for forensic purpose.