Only a part of prostate cancer (PCa) patients has aggressive malignancy requiring adjuvant treatment after radical prostatectomy (RP). Biomarkers capable to predict biochemical PCa recurrence (BCR) after RP would significantly improve preoperative risk stratification and treatment decisions. MicroRNA (miRNA) deregulation has recently emerged as an important phenomenon in tumor development and progression, however, the mechanisms remain largely unstudied. In the present study, based on microarray profiling of DNA methylation in 9 pairs of PCa and noncancerous prostate tissues (NPT), host genes of miR-155-5p, miR-152-3p, miR-137, miR-31-5p, and miR-642a, -b were analyzed for promoter methylation in 129 PCa, 35 NPT, and 17 benign prostatic hyperplasia samples (BPH) and compared to the expression of mature miRNAs and their selected targets (DNMT1, KDM1A, and KDM5B). The Cancer Genome Atlas dataset was utilized for validation. Methylation of mir-155, mir-152, and mir-137 host genes was PCa-specific, and downregulation of miR-155-5p significantly correlated with promoter methylation. Higher KDM5B expression was observed in samples with methylated mir-155 or mir-137 promoters, whereas upregulation of KDM1A and DNMT1 was associated with mir-155 and mir-152 methylation status, respectively. Promoter methylation of mir-155, mir-152, and mir-31 was predictive of BCR-free survival in various Cox models and increased the prognostic value of clinicopathologic factors. In conclusion, methylated mir-155, mir-152, mir-137, and mir-31 host genes are promising diagnostic and/or prognostic biomarkers of PCa. Methylation status of particular miRNA host genes as independent variables or in combinations might assist physicians in identifying poor prognosis PCa patients preoperatively.

Summary Modification of CG dinucleotides in DNA is part of epigenetic regulation of gene function in vertebrates and is associated with complex human disease. Bisulfite sequencing permits high resolution analysis of cytosine modification in mammalian genomes, however its utility is often limited due to substantial cost. Here, we describe an alternative epigenome profiling approach, named TOP-seq, which is based on covalent tagging of individual unmodified CG sites followed by non-homologous priming of the DNA polymerase action at these sites to directly produce adjoining regions for their sequencing and precise genomic mapping. Pilot TOP-seq analyses of bacterial and human genomes showed a better agreement of TOP-seq with published bisulfite sequencing maps as compared to widely-used MBD-seq and MRE-seq and permitted identification of long-range and gene-level differential methylation among human tissues and neuroblastoma cell types. Altogether, we propose an affordable single CG-resolution technique well-suited for large scale epigenome studies.

5-hydroxymethylcytosine (5hmC) is the most prevalent intermediate on the oxidative DNA demethylation pathway and is implicated in regulation of embryogenesis, neurological processes, and cancerogenesis. Profiling of this relatively scarce genomic modification in clinical samples requires cost-effective high-resolution techniques that avoid harsh chemical treatment. Here, we present a bisulfite-free approach for 5hmC profiling at single-nucleotide resolution, named hmTOP-seq (5hmC-specific tethered oligonucleotide-primed sequencing), which is based on direct sequence readout primed at covalently labeled 5hmC sites from an in situ tethered DNA oligonucleotide. Examination of distinct conjugation chemistries suggested a structural model for the tether-directed nonhomologous polymerase priming enabling theoretical evaluation of suitable tethers at the design stage. The hmTOP-seq procedure was optimized and validated on a small model genome and mouse embryonic stem cells, which allowed construction of single-nucleotide 5hmC maps reflecting subtle differences in strand-specific CG hydroxymethylation. Collectively, hmTOP-seq provides a new valuable tool for cost-effective and precise identification of 5hmC in characterizing its biological role and epigenetic changes associated with human disease.

Motivation Biological rhythmicity is fundamental to almost all organisms on Earth and plays a key role in health and disease. Identification of oscillating signals could lead to novel biological insights, yet its investigation is impeded by the extensive computational and statistical knowledge required to perform such analysis. Results To address this issue, we present DiscoRhythm (Discovering Rhythmicity), a user-friendly application for characterizing rhythmicity in temporal biological data. DiscoRhythm is available as a web application or an R/Bioconductor package for estimating phase, amplitude, and statistical significance using four popular approaches to rhythm detection (Cosinor, JTK Cycle, ARSER, and Lomb-Scargle). We optimized these algorithms for speed, improving their execution times up to 30-fold to enable rapid analysis of -omic-scale datasets in real-time. Informative visualizations, interactive modules for quality control, dimensionality reduction, periodicity profiling, and incorporation of experimental replicates make DiscoRhythm a thorough toolkit for analyzing rhythmicity. Availability and Implementation The DiscoRhythm R package is available on Bioconductor (https://bioconductor.org/packages/DiscoRhythm), with source code available on GitHub (https://github.com/matthewcarlucci/DiscoRhythm) under a GPL-3 license. The web application is securely deployed over HTTPS (https://disco.camh.ca) and is freely available for use worldwide. Local instances of the DiscoRhythm web application can be created using the R package or by deploying the publicly available Docker container (https://hub.docker.com/r/mcarlucci/discorhythm). Supplementary information Supplementary data are available at Bioinformatics online.