No abstract

Several adenosine or cytidine deaminase enzymes deaminate transcript sequences in a cell type or environment-dependent manner by a programmed process called RNA editing. RNA editing enzymes catalyze A>I or C>U transcript alterations and have the potential to change protein coding sequences. In this brief review, we highlight some recent work that shows aberrant patterns of RNA editing in cancer. Transcriptome sequencing studies reveal increased or decreased global RNA editing levels depending on the tumor type. Altered RNA editing in cancer cells may provide a selective advantage for tumor growth and resistance to apoptosis. RNA editing may promote cancer by dynamically recoding oncogenic genes, regulating oncogenic gene expression by noncoding RNA and miRNA editing, or by transcriptome scale changes in RNA editing levels that may affect innate immune signaling. Although RNA editing markedly increases complexity of the cancer cell transcriptomes, cancer-specific recoding RNA editing events have yet to be discovered. Epitranscriptomic changes by RNA editing in cancer represent a novel mechanism contributing to sequence diversity independently of DNA mutations. Therefore, RNA editing studies should complement genome sequence data to understand the full impact of nucleic acid sequence alterations in cancer. .

Breast cancer (BC) is a highly heterogeneous disease, both at the pathological and molecular level, and several chromatin-associated proteins play crucial roles in BC initiation and progression. Here, we demonstrate the role of PSIP1 (PC4 and SF2 interacting protein)/p75 (LEDGF) in BC progression. PSIP1/p75, previously identified as a chromatin-adaptor protein, is found to be upregulated in basal-like/triple negative breast cancer (TNBC) patient samples and cell lines. Immunohistochemistry in tissue arrays showed elevated levels of PSIP1 in metastatic invasive ductal carcinoma. Survival data analyses revealed that the levels of PSIP1 showed a negative association with TNBC patient survival. Depletion of PSIP1/p75 significantly reduced the tumorigenicity and metastatic properties of TNBC cell lines while its over-expression promoted tumorigenicity. Further, gene expression studies revealed that PSIP1 regulates the expression of genes controlling cell-cycle progression, cell migration and invasion. Finally, by interacting with RNA polymerase II, PSIP1/p75 facilitates the association of RNA pol II to the promoter of cell cycle genes and thereby regulates their transcription. Our findings demonstrate an important role of PSIP1/p75 in TNBC tumorigenicity by promoting the expression of genes that control the cell cycle and tumor metastasis.

Reconstructing the topology of a signaling network by means of RNA interference (RNAi) technology is an underdetermined problem especially when a single gene in the network is knocked down or observed. In addition, the exponential search space limits the existing methods to small signaling networks of size 10-15 genes. In this paper, we propose integrating RNAi data with a reference physical interaction network. We formulate the problem of signaling network reconstruction as finding the minimum number of edit operations on a given reference network. The edit operations transform the reference network to a network that satisfies the RNAi observations. We show that using a reference network does not simplify the computational complexity of the problem. Therefore, we propose two methods which provide near optimal results and can scale well for reconstructing networks up to hundreds of components. We validate the proposed methods on synthetic and real data sets. Comparison with the state of the art on real signaling networks shows that the proposed methodology can scale better and generates biologically significant results.

RNA Binding Protein (RBP) Expression and Disease Dynamics database (READ DB) is a non-redundant, curated database of human RBPs. RBPs curated from different experimental studies are reported with their annotation, tissue-wide RNA and protein expression levels, evolutionary conservation, disease associations, protein–protein interactions, microRNA predictions, their known RNA recognition sequence motifs as well as predicted binding targets and associated functional themes, providing a one stop portal for understanding the expression, evolutionary trajectories and disease dynamics of RBPs in the context of post-transcriptional regulatory networks.Database URL: READ DB is freely available on the web at http://darwin.soic.iupui.edu/ with all major browsers supported.

Cell cycle is a cellular process that is subject to stringent control. In contrast to the wealth of knowledge of proteins controlling the cell cycle, very little is known about the molecular role of lncRNAs (long noncoding RNAs) in cell-cycle progression. By performing genome-wide transcriptome analyses in cell-cycle-synchronized cells, we observed cell-cycle phase-specific induction of >2000 lncRNAs. Further, we demonstrate that an S-phase-upregulated lncRNA, SUNO1, facilitates cell-cycle progression by promoting YAP1-mediated gene expression. SUNO1 facilitates the cell-cycle-specific transcription of WTIP, a positive regulator of YAP1, by promoting the co-activator, DDX5-mediated stabilization of RNA polymerase II on chromatin. Finally, elevated SUNO1 levels are associated with poor cancer prognosis and tumorigenicity, implying its pro-survival role. Thus, we demonstrate the role of a S-phase up-regulated lncRNA in cell-cycle progression via modulating the expression of genes controlling cell proliferation.

Several protein-RNA cross linking protocols have been established in recent years to delineate the molecular interaction of an RNA Binding Protein (RBP) and its target RNAs. However, functional dissection of the role of the RBP binding sites in modulating the post-transcriptional fate of the target RNA remains challenging. CRISPR/Cas9 genome editing system is being commonly employed to perturb both coding and noncoding regions in the genome. With the advancements in genome-scale CRISPR/Cas9 screens, it is now possible to not only perturb specific binding sites but also probe the global impact of protein-RNA interaction sites across cell types. Here, we present SliceIt (http://sliceit.soic.iupui.edu/), a database of in silico sgRNA (single guide RNA) library to facilitate conducting such high throughput screens. SliceIt comprises of ~4.8 million unique sgRNAs with an estimated range of 2-8 sgRNAs designed per RBP binding site, for eCLIP experiments of >100 RBPs in HepG2 and K562 cell lines from the ENCODE project. SliceIt provides a user friendly environment, developed using advanced search engine framework, Elasticsearch. It is available in both table and genome browser views facilitating the easy navigation of RBP binding sites, designed sgRNAs, exon expression levels across 53 human tissues along with prevalence of SNPs and GWAS hits on binding sites. Exon expression profiles enable examination of locus specific changes proximal to the binding sites. Users can also upload custom tracks of various file formats directly onto genome browser, to navigate additional genomic features in the genome and compare with other types of omics profiles. All the binding site-centric information is dynamically accessible via "search by gene", "search by coordinates" and "search by RBP" options and readily available to download. Validation of the sgRNA library in SliceIt was performed by selecting RBP binding sites in Lipt1 gene and designing sgRNAs. Effect of CRISPR/Cas9 perturbations on the selected binding sites in HepG2 cell line, was confirmed based on altered proximal exon expression levels using qPCR, further supporting the utility of the resource to design experiments for perturbing protein-RNA interaction networks. Thus, SliceIt provides a one-stop repertoire of guide RNA library to perturb RBP binding sites, along with several layers of functional information to design both low and high throughput CRISPR/Cas9 screens, for studying the phenotypes and diseases associated with RBP binding sites.

Survival analysis in biomedical sciences is generally performed by correlating the levels of cellular components with patients’ clinical features as a common practice in prognostic biomarker discovery. While the common and primary focus of such analysis in cancer genomics so far has been to identify the potential prognostic genes, alternative splicing – a posttranscriptional regulatory mechanism that affects the functional form of a protein due to inclusion or exclusion of individual exons giving rise to alternative protein products, has increasingly gained attention due to the prevalence of splicing aberrations in cancer transcriptomes. Hence, uncovering the potential prognostic exons can not only help in rationally designing exon-specific therapeutics but also increase specificity toward more personalized treatment options. To address this gap and to provide a platform for rational identification of prognostic exons from cancer transcriptomes, we developed ExSurv (https://exsurv.soic.iupui.edu), a web-based platform for predicting the survival contribution of all annotated exons in the human genome using RNA sequencing-based expression profiles for cancer samples from four cancer types available from The Cancer Genome Atlas. ExSurv enables users to search for a gene of interest and shows survival probabilities for all the exons associated with a gene and found to be significant at the chosen threshold. ExSurv also includes raw expression values across the cancer cohort as well as the survival plots for prognostic exons. Our analysis of the resulting prognostic exons across four cancer types revealed that most of the survival-associated exons are unique to a cancer type with few processes such as cell adhesion, carboxylic, fatty acid metabolism, and regulation of T-cell signaling common across cancer types, possibly suggesting significant differences in the posttranscriptional regulatory pathways contributing to prognosis.