Massively parallel sequencing of microRNA in bloodstains and evaluation of environmental influences on miRNA candidates using realtime polymerase chain reaction

2019 
Abstract MicroRNAs (miRNA) are small (22–24 nucleotides) non-coding RNAs with potential application in forensic science because of their anti-degradation property and tissue specificity. Recent studies on the use of miRNA in forensic applications have mainly focused on body fluid identification using realtime polymerase chain reaction or microarray analysis. However, the exploration of miRNA in bloodstains, which are the most valuable source of biological evidence during case investigations, is currently lacking, particularly for aged and environmentally compromised forensic samples. Recent developments in massively parallel sequencing (MPS) technology provide the opportunity to establish a whole-genome miRNA profile with high throughput and efficiency. However, MPS analysis of genome-wide miRNA profiles from bloodstains has not been reported to date. In this study, the whole-genome miRNA profiles of bloodstains were examined using MPS, revealing 633 known miRNAs and 266 novel miRNAs. To further explore the stability of miRNAs in bloodstains under various circumstances, the expression levels of six miRNAs (miR-16-5p, miR-20a-5p, miR-486-5p, miR-148a-3p, miR-151a-3p, and miR-451a) that were abundant in blood/bloodstains were examined. The results showed that freezing/thawing and a high concentration of oxidant solution affects the absolute expression of miRNA significantly, while storage for up to 5 months and a temperature of 37 °C did not have any observed effects. This study not only provides a novel method to explore miRNA profiles in bloodstains using MPS, but also points to the circumstantial influences on miRNA expression, which are an important consideration for practical application. Collectively, our work may shed light on MPS-based approaches with miRNA analysis of bloodstains in forensics.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    28
    References
    10
    Citations
    NaN
    KQI
    []