High levels of PIWI-interacting RNAs are present in the small RNA landscape of prostate epithelium from vitamin D clinical trial specimens.

BACKGROUND: Vitamin D, a hormone that acts through the nuclear vitamin D receptor (VDR), upregulates antitumorigenic microRNA in prostate epithelium. This may contribute to the lower levels of aggressive prostate cancer (PCa) observed in patients with high serum vitamin D. The small noncoding RNA (ncRNA) landscape includes many other RNA species that remain uncharacterized in prostate epithelium and their potential regulation by vitamin D is unknown. METHODS: Laser capture microdissection (LCM) followed by small-RNA sequencing was used to identify ncRNAs in the prostate epithelium of tissues from a vitamin D-supplementation trial. VDR chromatin immunoprecipitation-sequencing was performed to identify vitamin D genomic targets in primary prostate epithelial cells. RESULTS: Isolation of epithelium by LCM increased sample homogeneity and captured more diversity in ncRNA species compared with publicly available small-RNA sequencing data from benign whole prostate. An abundance of PIWI-interacting RNAs (piRNAs) was detected in normal prostate epithelium. The obligate binding partners of piRNAs, PIWI-like (PIWIL) proteins, were also detected in prostate epithelium. High prostatic vitamin D levels were associated with increased expression of piRNAs. VDR binding sites were located near several ncRNA biogenesis genes and genes regulating translation and differentiation. CONCLUSIONS: Benign prostate epithelium expresses both piRNA and PIWIL proteins, suggesting that these small ncRNA may serve an unknown function in the prostate. Vitamin D may increase the expression of prostatic piRNAs. VDR binding sites in primary prostate epithelial cells are consistent with its reported antitumorigenic functions and a role in ncRNA biogenesis.

Laser-capture Microdissection of Human Prostatic Epithelium for RNA Analysis.

The prostate gland contains a heterogeneous milieu of stromal, epithelial, neuroendocrine and immune cell types. Healthy prostate is comprised of fibromuscular stroma surrounding discrete epithelial-lined secretory lumens and a very small population of immune and neuroendocrine cells. In contrast, areas of prostate cancer have increased dysplastic luminal epithelium with greatly reduced or absent stromal population. Given the profound difference between stromal and epithelial cell types, it is imperative to separate the cell types for any type of downstream molecular analysis. Despite this knowledge, the bulk of gene expression studies compare benign prostate to cancer without micro-dissection, leading to stromal bias in the benign samples. Laser-capture micro-dissection (LCM) is an effective method to physically separate different cell types from a specimen section. The goal of this protocol is to show that RNA can be successfully isolated from LCM-collected human prostatic epithelium and used for downstream gene expression studies such as RT-qPCR and RNAseq.

Plasma Exosomal miRNAs in Persons with and without Alzheimer Disease: Altered Expression and Prospects for Biomarkers.

To assess the value of exosomal miRNAs as biomarkers for Alzheimer disease (AD), the expression of microRNAs was measured in a plasma fraction enriched in exosomes by differential centrifugation, using Illumina deep sequencing. Samples from 35 persons with a clinical diagnosis of AD dementia were compared to 35 age and sex matched controls. Although these samples contained less than 0.1 microgram of total RNA, deep sequencing gave reliable and informative results. Twenty miRNAs showed significant differences in the AD group in initial screening (miR-23b-3p, miR-24-3p, miR-29b-3p, miR-125b-5p, miR-138-5p, miR-139-5p, miR-141-3p, miR-150-5p, miR-152-3p, miR-185-5p, miR-338-3p, miR-342-3p, miR-342-5p, miR-548at-5p, miR-659-5p, miR-3065-5p, miR-3613-3p, miR-3916, miR-4772-3p, miR-5001-3p), many of which satisfied additional biological and statistical criteria, and among which a panel of seven miRNAs were highly informative in a machine learning model for predicting AD status of individual samples with 83-89% accuracy. This performance is not due to over-fitting, because a) we used separate samples for training and testing, and b) similar performance was achieved when tested on technical replicate data. Perhaps the most interesting single miRNA was miR-342-3p, which was a) expressed in the AD group at about 60% of control levels, b) highly correlated with several of the other miRNAs that were significantly down-regulated in AD, and c) was also reported to be down-regulated in AD in two previous studies. The findings warrant replication and follow-up with a larger cohort of patients and controls who have been carefully characterized in terms of cognitive and imaging data, other biomarkers (e.g., CSF amyloid and tau levels) and risk factors (e.g., apoE4 status), and who are sampled repeatedly over time. Integrating miRNA expression data with other data is likely to provide informative and robust biomarkers in Alzheimer disease.