5'isomiR-183-5p|+2 elicits tumor suppressor activity in a negative feedback loop with E2F1.

BACKGROUND: MicroRNAs (miRNAs) and isomiRs play important roles in tumorigenesis as essential regulators of gene expression. 5'isomiRs exhibit a shifted seed sequence compared to the canonical miRNA, resulting in different target spectra and thereby extending the phenotypic impact of the respective common pre-miRNA. However, for most miRNAs, expression and function of 5'isomiRs have not been studied in detail yet. Therefore, this study aims to investigate the functions of miRNAs and their 5'isomiRs. METHODS: The expression of 5'isomiRs was assessed in The Cancer Genome Atlas (TCGA) breast cancer patient dataset. Phenotypic effects of miR-183 overexpression in triple-negative breast cancer (TNBC) cell lines were investigated in vitro and in vivo by quantifying migration, proliferation, tumor growth and metastasis. Direct targeting of E2F1 by miR-183-5p|+2 was validated with a 3'UTR luciferase assay and linked to the phenotypes of isomiR overexpression. RESULTS: TCGA breast cancer patient data indicated that three variants of miR-183-5p are highly expressed and upregulated, namely miR-183-5p|0, miR-183-5p|+1 and miR-183-5p|+2. However, TNBC cell lines displayed reduced proliferation and invasion upon overexpression of pre-miR-183. While invasion was reduced individually by all three isomiRs, proliferation and cell cycle progression were specifically inhibited by overexpression of miR-183-5p|+2. Proteomic analysis revealed reduced expression of E2F target genes upon overexpression of this isomiR, which could be attributed to direct targeting of E2F1, specifically by miR-183-5p|+2. Knockdown of E2F1 partially phenocopied the effect of miR-183-5p|+2 overexpression on cell proliferation and cell cycle. Gene set enrichment analysis of TCGA and METABRIC patient data indicated that the activity of E2F strongly correlated with the expression of miR-183-5p, suggesting transcriptional regulation of the miRNA by a factor of the E2F family. Indeed, in vitro, expression of miR-183-5p was regulated by E2F1. Hence, miR-183-5p|+2 directly targeting E2F1 appears to be part of a negative feedback loop potentially fine-tuning its activity. CONCLUSIONS: This study demonstrates that 5'isomiRs originating from the same arm of the same pre-miRNA (i.e. pre-miR-183-5p) may exhibit different functions and thereby collectively contribute to the same phenotype. Here, one of three isomiRs was shown to counteract expression of the pre-miRNA by negatively regulating a transcriptional activator (i.e. E2F1). We speculate that this might be part of a regulatory mechanism to prevent uncontrolled cell proliferation, which is disabled during cancer progression.

The genomic and transcriptional landscape of primary central nervous system lymphoma.

Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Molecular drivers of PCNSL have not been fully elucidated. Here, we profile and compare the whole-genome and transcriptome landscape of 51 CNS lymphomas (CNSL) to 39 follicular lymphoma and 36 DLBCL cases outside the CNS. We find recurrent mutations in JAK-STAT, NFkB, and B-cell receptor signaling pathways, including hallmark mutations in MYD88 L265P (67%) and CD79B (63%), and CDKN2A deletions (83%). PCNSLs exhibit significantly more focal deletions of HLA-D (6p21) locus as a potential mechanism of immune evasion. Mutational signatures correlating with DNA replication and mitosis are significantly enriched in PCNSL. TERT gene expression is significantly higher in PCNSL compared to activated B-cell (ABC)-DLBCL. Transcriptome analysis clearly distinguishes PCNSL and systemic DLBCL into distinct molecular subtypes. Epstein-Barr virus (EBV)+ CNSL cases lack recurrent mutational hotspots apart from IG and HLA-DRB loci. We show that PCNSL can be clearly distinguished from DLBCL, having distinct expression profiles, IG expression and translocation patterns, as well as specific combinations of genetic alterations.

Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz.

DNA methylation profiling offers unique insights into human development and diseases. Often the analysis of complex tissues and cell mixtures is the only feasible option to study methylation changes across large patient cohorts. Since DNA methylomes are highly cell type specific, deconvolution methods can be used to recover cell type-specific information in the form of latent methylation components (LMCs) from such 'bulk' samples. Reference-free deconvolution methods retrieve these components without the need for DNA methylation profiles of purified cell types. Currently no integrated and guided procedure is available for data preparation and subsequent interpretation of deconvolution results. Here, we describe a three-stage protocol for reference-free deconvolution of DNA methylation data comprising: (i) data preprocessing, confounder adjustment using independent component analysis (ICA) and feature selection using DecompPipeline, (ii) deconvolution with multiple parameters using MeDeCom, RefFreeCellMix or EDec and (iii) guided biological inference and validation of deconvolution results with the R/Shiny graphical user interface FactorViz. Our protocol simplifies the analysis and guides the initial interpretation of DNA methylation data derived from complex samples. The harmonized approach is particularly useful to dissect and evaluate cell heterogeneity in complex systems such as tumors. We apply the protocol to lung cancer methylomes from The Cancer Genome Atlas (TCGA) and show that our approach identifies the proportions of stromal cells and tumor-infiltrating immune cells, as well as associations of the detected components with clinical parameters. The protocol takes slightly >3 d to complete and requires basic R skills.