Search

Background: Genetic studies have largely concentrated on the impact of somatic mutations found in coding regions, and have neglected mutations outside of these. However, 3’ untranslated regions (3' UTR) mutations can also disrupt or create miRNA target sites, and trigger oncogene activation or tumor suppressor inactivation. Methods: We used next-generation sequencing to widely screen for genetic alterations within predicted miRNA target sites of oncogenes associated with colorectal cancer, and evaluated the functional impact of a new somatic mutation. Target sequencing of 47 genes was performed for 29 primary colorectal tumor samples. For 71 independent samples, Sanger methodology was used to screen for E2F1 mutations in miRNA predicted target sites, and the functional impact of these mutations was evaluated by luciferase reporter assays. Results: We identified germline and somatic alterations in E2F1. Of the 100 samples evaluated, 3 had germline alterations at the MIR205-5p target site, while one had a somatic mutation at MIR136-5p target site. E2F1 gene expression was similar between normal and tumor tissues bearing the germline alteration; however, expression was increased 4-fold in tumor tissue that harbored a somatic mutation compared to that in normal tissue. Luciferase reporter assays revealed both germline and somatic alterations increased E2F1 activity relative to wild-type E2F1. Conclusions: We demonstrated that somatic mutation within E2F1:MIR136-5p target site impairs miRNA-mediated regulation and leads to increased gene activity. We conclude that somatic mutations that disrupt miRNA target sites have the potential to impact gene regulation, highlighting an important mechanism of oncogene activation. [ABSTRACT FROM AUTHOR]

The phenotype of primary cells in culture varies according to the donor environmental condition. We recently showed that the time of the day imposes a molecular program linked to the inflammatory response that is heritable in culture. Here we investigated whether microRNAs (miRNAs) would show differential expression according to the time when cells were obtained, namely daytime or nighttime. Cells obtained from explants of cremaster muscle and cultivated until confluence (∼20 days) presented high CD133 expression. Global miRNA expression analysis was performed through deep sequencing in order to compare both cultured cells. A total of 504 mature miRNAs were identified, with a specific miRNA signature being associated to the light versus dark phase of a circadian cycle. miR-1249 and miR-129-2-3p were highly expressed in daytime cells, while miR-182, miR-96-5p, miR-146a-3p, miR-146a-5p, and miR-223-3p were highly expressed in nighttime cells. Nighttime cells are regulated for programs involved in cell processes and development, as well as in the inflammation, cell differentiation and maturation; while daytime cells express miRNAs that control stemness and cytoskeleton remodeling. In summary, the time of the day imposes a differential profile regarding to miRNA signature on CD133+ cells in culture. Understanding this daily profile in the phenotype of cultured cells is highly relevant for clinical outputs, including cellular therapy approaches. J. Cell. Physiol. 231: 1953-1963, 2016. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are a class of small (∼22 nucleotides) non-coding RNAs that post-transcriptionally regulate gene expression by interacting with target mRNAs. A majority of miRNAs is located within intronic or exonic regions of protein-coding genes (host genes), and increasing evidence suggests a functional relationship between these miRNAs and their host genes. Here, we introduce miRIAD, a web-service to facilitate the analysis of genomic and structural features of intragenic miRNAs and their host genes for five species (human, rhesus monkey, mouse, chicken and opossum). miRIAD contains the genomic classification of all miRNAs (inter- and intragenic), as well as classification of all protein-coding genes into host or non-host genes (depending on whether they contain an intragenic miRNA or not). We collected and processed public data from several sources to provide a clear visualization of relevant knowledge related to intragenic miRNAs, such as host gene function, genomic context, names of and references to intragenic miRNAs, miRNA binding sites, clusters of intragenic miRNAs, miRNA and host gene expression across different tissues and expression correlation for intragenic miRNAs and their host genes. Protein–protein interaction data are also presented for functional network analysis of host genes. In summary, miRIAD was designed to help the research community to explore, in a user-friendly environment, intragenic miRNAs, their host genes and functional annotations with minimal effort, facilitating hypothesis generation and in-silico validations. [ABSTRACT FROM AUTHOR]

Background: Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data – critical first steps for any subsequent analysis. Results: We find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project. Conclusions: An R package instantiating YARN is available at http://bioconductor.org/packages/yarn. Electronic supplementary material The online version of this article (10.1186/s12859-017-1847-x) contains supplementary material, which is available to authorized users.