RNA editing enhances the diversity of gene products at the post-transcriptional level. Approaches for genome-wide identification of RNA editing face two main challenges: separating true editing sites from false discoveries and accurate estimation of editing levels. We developed an approach to analyze transcriptome sequencing data (RNA-seq) for global identification of RNA editing in cells for which whole-genome sequencing data are available. We applied the method to analyze RNA-seq data of a human glioblastoma cell line, U87MG. Around 10,000 DNA-RNA differences were identified, the majority being putative A-to-I editing sites. These predicted A-to-I events were associated with a low false-discovery rate (~5%). Moreover, the estimated editing levels from RNA-seq correlated well with those based on traditional clonal sequencing. Our results further facilitated unbiased characterization of the sequence and evolutionary features flanking predicted A-to-I editing sites and discovery of a conserved RNA structural motif that may be functionally relevant to editing. Genes with predicted A-to-I editing were significantly enriched with those known to be involved in cancer, supporting the potential importance of cancer-specific RNA editing. A similar profile of DNA-RNA differences as in U87MG was predicted for another RNA-seq data set obtained from primary breast cancer samples. Remarkably, significant overlap exists between the putative editing sites of the two transcriptomes despite their difference in cell type, cancer type, and genomic backgrounds. Our approach enabled de novo identification of the RNA editome, which sets the stage for further mechanistic studies of this important step of post-transcriptional regulation.

Tumor metastasis remains the major cause of cancer-related death, but its molecular basis is still not well understood. Here we uncovered a splicing-mediated pathway that is essential for breast cancer metastasis. We show that the RNA-binding protein heterogeneous nuclear ribonucleoprotein M (hnRNPM) promotes breast cancer metastasis by activating the switch of alternative splicing that occurs during epithelial-mesenchymal transition (EMT). Genome-wide deep sequencing analysis suggests that hnRNPM potentiates TGFb signaling and identifies CD44 as a key downstream target of hnRNPM. hnRNPM ablation prevents TGFb-induced EMT and inhibits breast cancer metastasis in mice, whereas enforced expression of the specific CD44 standard (CD44s) splice isoform overrides the loss of hnRNPM and permits EMT and metastasis. Mechanistically, we demonstrate that the ubiquitously expressed hnRNPM acts in a mesenchymal-specific manner to precisely control CD44 splice isoform switching during EMT. This restricted cell-type activity of hnRNPM is achieved by competition with ESRP1, an epithelial splicing regulator that binds to the same cis-regulatory RNA elements as hnRNPM and is repressed during EMT. Importantly, hnRNPM is associated with aggressive breast cancer and correlates with increased CD44s in patient specimens. These findings demonstrate a novel molecular mechanism through which tumor metastasis is endowed by the hnRNPM-mediated splicing program.

SUMMARYmiR156 and its target SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) genes constitute an endogenous flowering pathway in Arabidopsis. The SPL genes are regulated post-transcriptionally by miR156, and incorporate endogenous aging signals into floral gene networks. Intriguingly, the SPL genes are also regulated transcriptionally by FLOWERING LOCUS T (FT)-mediated photoperiod signals. However, it is unknown how photoperiod regulates the SPL genes. Here, we show that SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1) and FT regulate the SPL3, SPL4 and SPL5 genes by directly binding to the gene promoters in response to photoperiod signals. Notably, the SOC1 regulation of the SPL genes, termed the SOC1-SPL module, also mediates gibberellic acid (GA) signals to promote flowering under non-inductive short days (SDs). Under SDs, the inductive effects of GA on the SPL genes disappeared in the soc1-2 mutant, and the flowering of SPL3-overexpressing transgenic plants (35S:SPL3) was less sensitive to GA. In addition, the 35S:SPL3 · soc1-2 plants flowered much earlier than the soc1-2 mutant, supporting SOC1 regulation of the SPL genes. Our observations indicate that the SOC1-SPL module serves as a molecular link that integrates photoperiod and GA signals to promote flowering in Arabidopsis.

RNA-protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA-protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA-protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA-protein interface. In leave-oneout cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA-protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.)

Understanding interactions between proteins and RNA is key to deciphering the mechanisms of many important biological processes. Here we describe RNABindR, a web-based server that identifies and displays RNA-binding residues in known protein–RNA complexes and predicts RNA-binding residues in proteins of unknown structure. RNABindR uses a distance cutoff to identify which amino acids contact RNA in solved complex structures (from the Protein Data Bank) and provides a labeled amino acid sequence and a Jmol graphical viewer in which RNA-binding residues are displayed in the context of the three-dimensional structure. Alternatively, RNABindR can use a Naive Bayes classifier trained on a non-redundant set of protein–RNA complexes from the PDB to predict which amino acids in a protein sequence of unknown structure are most likely to bind RNA. RNABindR automatically displays ‘high specificity’ and ‘high sensitivity’ predictions of RNA-binding residues. RNABindR is freely available at http://bindr.gdcb.iastate.edu/RNABindR.

Rationale Accurate and comprehensive de novo transcriptome profiling in heart is a central issue to better understand cardiac physiology and diseases. Although significant progress has been made in genome-wide profiling for quantitative changes in cardiac gene expression, current knowledge offers limited insights to the total complexity in cardiac transcriptome at individual exon level. Objective To develop more robust bioinformatic approaches to analyze high-throughput RNA sequencing (RNA-Seq) data, with the focus on the investigation of transcriptome complexity at individual exon and transcript levels. Methods and Results In addition to overall gene expression analysis, the methods developed in this study were used to analyze RNA-Seq data with respect to individual transcript isoforms, novel spliced exons, novel alternative terminal exons, novel transcript clusters (i.e., novel genes) and long non-coding RNA genes. We applied these approaches to RNA-Seq data obtained from mouse hearts following pressure-overload induced by trans-aortic constriction. Based on experimental validations, analyses of the features of the identified exons/transcripts, and expression analyses including previously published RNASeq data, we demonstrate that the methods are highly effective in detecting and quantifying individual exons and transcripts. Novel insights inferred from the examined aspects of the cardiac transcriptome open ways to further experimental investigations. Conclusions Our work provided a comprehensive set of methods to analyze mouse cardiac transcriptome complexity at individual exon and transcript levels. Applications of the methods may infer important new insights to gene regulation in normal and disease hearts in terms of exon utilization and potential involvement of novel components of cardiac transcriptome.

Adenosine deaminases acting on RNA (ADARs) are the primary factors underlying adenosine to inosine (A-to-I) editing in metazoans. Here we report the first global study of ADAR1-RNA interaction in human cells using CLIP-Seq. A large number of CLIP sites are observed in Alu repeats, consistent with ADAR1's function in RNA editing. Surprisingly, thousands of other CLIP sites are located in non-Alu regions, revealing functional and biophysical targets of ADAR1 in the regulation of alternative 3' UTR usage and miRNA biogenesis. We observe that binding of ADAR1 to 3' UTRs precludes binding by other factors, causing 3' UTR lengthening. Similarly, ADAR1 interacts with DROSHA and DGCR8 in the nucleus and possibly out-competes DGCR8 in primary miRNA binding, which enhances mature miRNA expression. These functions are dependent on ADAR1's editing activity, at least for a subset of targets. Our study unfolds a broad landscape of the functional roles of ADAR1.

Eyes with normal or subnormal SFCT exhibited extrafoveal choroidal thickening at sites of polypoidal disease. The choriocapillaris and Sattler layers were attenuated at these locations, but Haller vessels were markedly dilated. These changes were topographically associated with sites of neovascular ingrowth and support the classification of polypoidal choroidal vasculopathy as a pachychoroid disorder.