The helicase Ded1p controls use of near-cognate translation initiation codons in 5' UTRs.

The conserved and essential DEAD-box RNA helicase Ded1p from yeast and its mammalian orthologue DDX3 are critical for the initiation of translation1. Mutations in DDX3 are linked to tumorigenesis2-4 and intellectual disability5, and the enzyme is targeted by a range of viruses6. How Ded1p and its orthologues engage RNAs during the initiation of translation is unknown. Here we show, by integrating transcriptome-wide analyses of translation, RNA structure and Ded1p-RNA binding, that the effects of Ded1p on the initiation of translation are connected to near-cognate initiation codons in 5' untranslated regions. Ded1p associates with the translation pre-initiation complex at the mRNA entry channel and repressing the activity of Ded1p leads to the accumulation of RNA structure in 5' untranslated regions, the initiation of translation from near-cognate start codons immediately upstream of these structures and decreased protein synthesis from the corresponding main open reading frames. The data reveal a program for the regulation of translation that links Ded1p, the activation of near-cognate start codons and mRNA structure. This program has a role in meiosis, in which a marked decrease in the levels of Ded1p is accompanied by the activation of the alternative translation initiation sites that are seen when the activity of Ded1p is repressed. Our observations indicate that Ded1p affects translation initiation by controlling the use of near-cognate initiation codons that are proximal to mRNA structure in 5' untranslated regions

Site identification in high-throughput RNA-protein interaction data

Motivation: Post-transcriptional and co-transcriptional regulation is a crucial link between genotype and phenotype. The central players are the RNA-binding proteins, and experimental technologies [such as cross-linking with immunoprecipitation-(CLIP-) and RIP-seq] for probing their activities have advanced rapidly over the course of the past decade. Statistically robust, flexible computational methods for binding site identification from high-throughput immunoprecipitation assays are largely lacking however.Results: We introduce a method for site identification which provides four key advantages over previous methods: (i) it can be applied on all variations of CLIP and RIP-seq technologies, (ii) it accurately models the underlying read-count distributions, (iii) it allows external covariates, such as transcript abundance (which we demonstrate is highly correlated with read count) to inform the site identification process and (iv) it allows for direct comparison of site usage across cell types or conditions. © The Author 2012. Published by Oxford University Press. All rights reserved

FRA2A is a CGG repeat expansion associated with silencing of AFF3

Folate-sensitive fragile sites (FSFS) are a rare cytogenetically visible subset of dynamic mutations. Of the eight molecularly characterized FSFS, four are associated with intellectual disability (ID). Cytogenetic expression results from CGG tri-nucleotide-repeat expansion mutation associated with local CpG hypermethylation and transcriptional silencing. The best studied is the FRAXA site in the FMR1 gene, where large expansions cause fragile X syndrome, the most common inherited ID syndrome. Here we studied three families with FRA2A expression at 2q11 associated with a wide spectrum of neurodevelopmental phenotypes. We identified a polymorphic CGG repeat in a conserved, brain-active alternative promoter of the AFF3 gene, an autosomal homolog of the X-linked AFF2/FMR2 gene: Expansion of the AFF2 CGG repeat causes FRAXE ID. We found that FRA2A-expressing individuals have mosaic expansions of the AFF3 CGG repeat in the range of several hundred repeat units. Moreover, bisulfite sequencing and pyrosequencing both suggest AFF3 promoter hypermethylation. cSNP-analysis demonstrates monoallelic expression of the AFF3 gene in FRA2A carriers thus predicting that FRA2A expression results in functional haploinsufficiency for AFF3 at least in a subset of tissues. By whole-mount in situ hybridization the mouse AFF3 ortholog shows strong regional expression in the developing brain, somites and limb buds in 9.5-12.5dpc mouse embryos. Our data suggest that there may be an association between FRA2A and a delay in the acquisition of motor and language skills in the families studied here. However, additional cases are required to firmly establish a causal relationship

DGCR8 HITS-CLIP reveals novel functions for the Microprocessor

The Drosha-DGCR8 complex (Microprocessor) is required for microRNA (miRNA) biogenesis. DGCR8 recognizes the RNA substrate, whereas Drosha functions as the endonuclease. High-throughput sequencing and crosslinking immunoprecipitation (HITS-CLIP) was used to identify RNA targets of DGCR8 in human cells. Unexpectedly, miRNAs were not the most abundant targets. DGCR8-bound RNAs also comprised several hundred mRNAs as well as snoRNAs and long non-coding RNAs. We found that the Microprocessor controls the abundance of several mRNAs as well as of MALAT-1. By contrast, DGCR8-mediated cleavage of snoRNAs is independent of Drosha, suggesting the involvement of DGCR8 in cellular complexes with other endonucleases. Interestingly, binding of DGCR8 to cassette exons, acts as a novel mechanism to regulate the relative abundance of alternatively spliced isoforms. Collectively, these data provide new insights in the complex role of DGCR8 in controlling the fate of several classes of RNAs

Genome-wide identification of Ago2 binding sites from mouse embryonic stem cells with and without mature microRNAs

MicroRNAs (miRNAs) are 19–22-nucleotide noncoding RNAs that post-transcriptionally regulate mRNA targets. We have identified endogenous miRNA binding sites in mouse embryonic stem cells (mESCs), by performing photo-cross-linking immunoprecipitation using antibodies to Argonaute (Ago2) followed by deep sequencing of RNAs (CLIP-seq). We also performed CLIP-seq in Dicer[superscript −/−] mESCs that lack mature miRNAs, allowing us to define whether the association of Ago2 with the identified sites was miRNA dependent. A significantly enriched motif, GCACUU, was identified only in wild-type mESCs in 3′ untranslated and coding regions. This motif matches the seed of a miRNA family that constitutes ~68% of the mESC miRNA population. Unexpectedly, a G-rich motif was enriched in sequences cross-linked to Ago2 in both the presence and absence of miRNAs. Expression analysis and reporter assays confirmed that the seed-related motif confers miRNA-directed regulation on host mRNAs and that the G-rich motif can modulate this regulation.Leukemia & Lymphoma Society of AmericaUnited States. Public Health Service (Grant R01-GM34277)United States. Public Health Service (Grant R01-CA133404)National Cancer Institute (U.S.) (Grant P01-CA42063)National Cancer Institute (U.S.) Cancer Center Support (Grant P30-CA14051

lincRNAs act in the circuitry controlling pluripotency and differentiation

Although thousands of large intergenic non-coding RNAs (lincRNAs) have been identified in mammals, few have been functionally characterized, leading to debate about their biological role. To address this, we performed loss-of-function studies on most lincRNAs expressed in mouse embryonic stem (ES) cells and characterized the effects on gene expression. Here we show that knockdown of lincRNAs has major consequences on gene expression patterns, comparable to knockdown of well-known ES cell regulators. Notably, lincRNAs primarily affect gene expression in trans. Knockdown of dozens of lincRNAs causes either exit from the pluripotent state or upregulation of lineage commitment programs. We integrate lincRNAs into the molecular circuitry of ES cells and show that lincRNA genes are regulated by key transcription factors and that lincRNA transcripts bind to multiple chromatin regulatory proteins to affect shared gene expression programs. Together, the results demonstrate that lincRNAs have key roles in the circuitry controlling ES cell state.Broad InstituteHarvard UniversityNational Human Genome Research Institute (U.S.)Merkin Family Foundation for Stem Cell Researc

PRIDB: a protein–RNA interface database

The Protein–RNA Interface Database (PRIDB) is a comprehensive database of protein–RNA interfaces extracted from complexes in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein–RNA complexes and their interfaces, in addition to automated generation of user-defined data sets of protein–RNA interfaces for statistical analyses and machine learning applications. For any chosen PDB complex or list of complexes, PRIDB rapidly displays interfacial amino acids and ribonucleotides within the primary sequences of the interacting protein and RNA chains. PRIDB also identifies ProSite motifs in protein chains and FR3D motifs in RNA chains and provides links to these external databases, as well as to structure files in the PDB. An integrated JMol applet is provided for visualization of interacting atoms and residues in the context of the 3D complex structures. The current version of PRIDB contains structural information regarding 926 protein–RNA complexes available in the PDB (as of 10 October 2010). Atomic- and residue-level contact information for the entire data set can be downloaded in a simple machine-readable format. Also, several non-redundant benchmark data sets of protein–RNA complexes are provided. The PRIDB database is freely available online at http://bindr.gdcb.iastate.edu/PRIDB

Cytoplasmic Polyadenylation Element Binding Protein Deficiency Stimulates PTEN and Stat3 mRNA Translation and Induces Hepatic Insulin Resistance

The cytoplasmic polyadenylation element binding protein CPEB1 (CPEB) regulates germ cell development, synaptic plasticity, and cellular senescence. A microarray analysis of mRNAs regulated by CPEB unexpectedly showed that several encoded proteins are involved in insulin signaling. An investigation of Cpeb1 knockout mice revealed that the expression of two particular negative regulators of insulin action, PTEN and Stat3, were aberrantly increased. Insulin signaling to Akt was attenuated in livers of CPEB–deficient mice, suggesting that they might be defective in regulating glucose homeostasis. Indeed, when the Cpeb1 knockout mice were fed a high-fat diet, their livers became insulin-resistant. Analysis of HepG2 cells, a human liver cell line, depleted of CPEB demonstrated that this protein directly regulates the translation of PTEN and Stat3 mRNAs. Our results show that CPEB regulated translation is a key process involved in insulin signaling

CRISPR-assisted detection of RNA-protein interactions in living cells.

We have developed CRISPR-assisted RNA-protein interaction detection method (CARPID), which leverages CRISPR-CasRx-based RNA targeting and proximity labeling to identify binding proteins of specific long non-coding RNAs (lncRNAs) in the native cellular context. We applied CARPID to the nuclear lncRNA XIST, and it captured a list of known interacting proteins and multiple previously uncharacterized binding proteins. We generalized CARPID to explore binders of the lncRNAs DANCR and MALAT1, revealing the method's wide applicability in identifying RNA-binding proteins

Predicting RNA-Protein Interactions Using Only Sequence Information

<p>Abstract</p> <p>Background</p> <p>RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.</p> <p>Results</p> <p>We propose <b><it>RPISeq</it></b>, a family of classifiers for predicting <b><it>R</it></b>NA-<b><it>p</it></b>rotein <b><it>i</it></b>nteractions using only <b><it>seq</it></b>uence information. Given the sequences of an RNA and a protein as input, <it>RPIseq </it>predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of <it>RPISeq </it>are presented: <it>RPISeq-SVM</it>, which uses a Support Vector Machine (SVM) classifier and <it>RPISeq-RF</it>, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), <it>RPISeq </it>achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of <it>RPISeq </it>was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, <it>RPISeq </it>classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from <it>E. coli, S. cerevisiae, D. melanogaster, M. musculus</it>, and <it>H. sapiens</it>.</p> <p>Conclusions</p> <p>Our experiments with <it>RPISeq </it>demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. <it>RPISeq </it>offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. <it>RPISeq </it>is freely available as a web-based server at <url>http://pridb.gdcb.iastate.edu/RPISeq/.</url></p