Expression profile analysis of piwi-interacting RNA in forensically relevant biological fluids

During the last decade, RNA profiling has emerged as one of the fastest developed methods for discriminating forensically relevant biological samples. As a category of small non-coding RNA, piwi-interacting RNA (piRNA) has recently been proposed to be differentially expressed in different types of body fluids, which indicates that its potential in forensic science is worth exploring. In this study, small RNA from 6 types of biological samples (venous blood, menstrual blood, saliva, semen, vaginal secretions and skin) was prepared and sequenced in order to characterize the expression pattern of piRNA using Ion S5 XL platform. Multiple bioinformatic methods were applied to make interpretation of the massively parallel sequencing data and identify representative biomarkers. A total of 376 piRNAs were initially identified after normalization and filtering. Hierarchical clustering and partial least squares-discriminant analysis (PLS-DA) revealed that their expression profiles exhibited an acceptable discriminating ability for most biological samples. Besides, a panel consists of 37 piRNA candidates was subsequently established for further analysis. The results suggested that with the optimal number of PLS components, the marker-reduced panel was sufficient to construct a PLS-DA model with the same performance as that can be achieved by the entire 376 piRNAs (classification error rate = 0.04). In addition, 5 targeted candidates were further selected for validation. TaqMan RT-qPCR assay results verified the potential of 3 piRNAs (piR-hsa-27622, piR-hsa-1207 and piR-hsa-27493) in distinguishing venous blood and menstrual blood, as well as 2 piRNA (piR-hsa-27493 and piR-hsa-26591) for the discrimination of saliva and vaginal secretions, which emphasized the feasibility of our biomarker selection approach. In brief, our study expanded the amount of potential piRNA biomarkers and demonstrated that the expression features of piRNA could provide valuable information for discriminating forensically relevant biological samples.