Epigenenetic regulation of alternative splicing: How lncRNAs tailor the message

Alternative splicing is a highly fine-tuned regulated process and one of the main drivers of proteomic diversity across eukaryotes. The vast majority of human multi-exon genes is alternatively spliced in a cell type-and tissue-specific manner, and defects in alternative splicing can dramatically alter RNA and protein functions and lead to disease. The eukaryotic genome is also intensively transcribed into long and short non-coding RNAs which account for up to 90% of the entire tran-scriptome. Over the years, lncRNAs have received considerable attention as important players in the regulation of cellular processes including alternative splicing. In this review, we focus on recent discoveries that show how lncRNAs contribute significantly to the regulation of alternative splicing and explore how they are able to shape the expression of a diverse set of splice isoforms through several mechanisms. With the increasing number of lncRNAs being discovered and characterized, the contribution of lncRNAs to the regulation of alternative splicing is likely to grow significantly

Chromatin interaction maps identify Wnt responsive cis-regulatory elements coordinating Paupar-Pax6 expression in neuronal cells

Central nervous system-expressed long non-coding RNAs (lncRNAs) are often located in the genome close to protein coding genes involved in transcriptional control. Such lncRNA-protein coding gene pairs are frequently temporally and spatially co-expressed in the nervous system and are predicted to act together to regulate neuronal development and function. Although some of these lncRNAs also bind and modulate the activity of the encoded transcription factors, the regulatory mechanisms controlling co-expression of neighbouring lncRNA-protein coding genes remain unclear. Here, we used high resolution NG Capture-C to map the cis-regulatory interaction landscape of the key neuro-developmental Paupar-Pax6 lncRNA-mRNA locus. The results define chromatin architecture changes associated with high Paupar-Pax6 expression in neurons and identify both promoter selective as well as shared cis-regulatory-promoter interactions involved in regulating Paupar-Pax6 co-expression. We discovered that the TCF7L2 transcription factor, a regulator of chromatin architecture and major effector of the Wnt signalling pathway, binds to a subset of these candidate cis-regulatory elements to coordinate Paupar and Pax6 co-expression. We describe distinct roles for Paupar in Pax6 expression control and show that the Paupar DNA locus contains a TCF7L2 bound transcriptional silencer whilst the Paupar transcript can act as an activator of Pax6. Our work provides important insights into the chromatin interactions, signalling pathways and transcription factors controlling co-expression of adjacent lncRNAs and protein coding genes in the brain

Post-Transcriptional Regulation through Long Non-Coding RNAs (lncRNAs)

The discovery of thousands of non-coding RNAs (ncRNAs) pervasively transcribed from the eukaryotic genome has revolutionized the “central dogma” of biology and shifted the attention on the role of RNAs as regulatory molecules, more than simply traditional mediators of genomic information [...

A non-coding RNA network influenced by genetic polymorphism controls E-cadherin expression in human cancers

Reduced expression of E-cadherin, encoded by the CDH1 gene, is frequent in epithelial tumours and is associated with the acquisition of invasive, stem cell-like and metastatic properties. However, the molecular mechanisms underlying the loss of E-cadherin expression are not fully understood. In this project, we uncover a complex network comprising a promoter- associated noncoding RNA (paRNA), microRNA and epigenetic regulators that controls transcription of E-cadherin in epithelial cancers. E-cadherin silencing relies on the formation of a complex between the paRNA and microRNA-guided Argonaute 1 that, together, recruit SUV39H1 and induce repressive chromatin modifications in the gene promoter. Notably, we found that a single nucleotide polymorphism (rs16260) linked to increased cancer risk alters the secondary structure of the paRNA, with the risk allele facilitating the assembly of the microRNA-guided Argonaute 1 complex and gene silencing

Natural antisense transcripts drive a regulatory cascade controlling c-MYC transcription

<p><i>Cis</i>-natural antisense transcripts (<i>cis-</i>NATs) are long noncoding RNAs transcribed from the opposite strand and overlapping coding and noncoding genes on the sense strand. <i>cis-</i>NATs are widely present in the human genome and can be involved in multiple mechanisms of gene regulation. Here, we describe the presence of <i>cis-</i>NATs in the 3′ distal region of the c-MYC locus and investigate their impact on transcriptional regulation of this key oncogene in human cancers. We found that <i>cis-</i>NATs are produced as consequence of the activation of cryptic transcription initiation sites in the 3′ distal region downstream of the c-MYC 3′UTR. The process is tightly regulated and leads to the formation of two main transcripts, NAT6531 and NAT6558, which differ in their ability to fold into stem-loop secondary structures. NAT6531 acts as a substrate for DICER and as a source of small RNAs capable of modulating c-MYC transcription. This complex system, based on the interplay between <i>cis</i>-NATs and NAT-derived small RNAs, may represent an important layer of epigenetic regulation of the expression of c-MYC and other genes in human cells.</p

Immune-related pan-cancer gene expression signatures of patient survival revealed by NanoString-based analyses.

The immune system plays a central role in the onset and progression of cancer. A better understanding of transcriptional changes in immune cell-related genes associated with cancer progression, and their significance in disease prognosis, is therefore needed. NanoString-based targeted gene expression profiling has advantages for deployment in a clinical setting over RNA-seq technologies. We analysed NanoString PanCancer Immune Profiling panel gene expression data encompassing 770 genes, and overall survival data, from multiple previous studies covering 10 different cancer types, including solid and blood malignancies, across 515 patients. This analysis revealed an immune gene signature comprising 39 genes that were upregulated in those patients with shorter overall survival; of these 39 genes, three (MAGEC2, SSX1 and ULBP2) were common to both solid and blood malignancies. Most of the genes identified have previously been reported as relevant in one or more cancer types. Using Cibersort, we investigated immune cell levels within individual cancer types and across groups of cancers, as well as in shorter and longer overall survival groups. Patients with shorter survival had a higher proportion of M2 macrophages and γδ T cells. Patients with longer overall survival had a higher proportion of CD8+ T cells, CD4+ T memory cells, NK cells and, unexpectedly, T regulatory cells. Using a transcriptomics platform with certain advantages for deployment in a clinical setting, our multi-cancer meta-analysis of immune gene expression and overall survival data has identified a specific transcriptional profile associated with poor overall survival

Genes differentially expressed in short survival versus long survival patients, considered according to cancer type and cancer class.

Volcano plots showing significantly downregulated (the thresholds are FDR -0.5) genes in green and red, respectively. Here short survival patients are compared with long survival patients, so in all three panels genes found to be upregulated are genes that are upregulated in short survival patients, and genes found downregulated are genes that are downregulated in short survival patients. Grey indicates genes that are not significantly downregulated or upregulated. Log of fold change (LogFC) is on the x-axis and significance level (-log10P) is on the y-axis. Panel A shows differentially expressed genes when all of the patients are considered (n = 515). Panel B shows differentially expressed genes when only patients with solid cancers are considered (n = 293), and panel C shows differentially expressed genes when only patients with blood cancers are considered (n = 222). In all panels, the significantly downregulated and upregulated genes are labelled with their Hugo Gene Nomenclature Committee (HGNC) gene symbols. The three genes (SSX1, MAGEC2 and ULBP2) that are found to be significantly differentially expressed in all three analyses are shown in bold.</p