RESUMEN
The human prototypical SR protein SRSF1 is an oncoprotein that contains two RRMs and plays a pivotal role in RNA metabolism. We determined the structure of the RRM1 bound to RNA and found that the domain binds preferentially to a CN motif (N is for any nucleotide). Based on this solution structure, we engineered a protein containing a single glutamate to asparagine mutation (E87N), which gains the ability to bind to uridines and thereby activates SMN exon7 inclusion, a strategy that is used to cure spinal muscular atrophy. Finally, we revealed that the flexible inter-RRM linker of SRSF1 allows RRM1 to bind RNA on both sides of RRM2 binding site. Besides revealing an unexpected bimodal mode of interaction of SRSF1 with RNA, which will be of interest to design new therapeutic strategies, this study brings a new perspective on the mode of action of SRSF1 in cells.
Asunto(s)
Motivo de Reconocimiento de ARN/genética , Sitios de Empalme de ARN/genética , Empalme del ARN , Factores de Empalme Serina-Arginina/metabolismo , Proteína 1 para la Supervivencia de la Neurona Motora/genética , Sustitución de Aminoácidos , Asparagina/genética , Biología Computacional , Exones/genética , Ácido Glutámico/genética , Células HEK293 , Humanos , Simulación de Dinámica Molecular , Atrofia Muscular Espinal/genética , Atrofia Muscular Espinal/terapia , Resonancia Magnética Nuclear Biomolecular , Ingeniería de Proteínas , Proteínas Recombinantes/genética , Proteínas Recombinantes/aislamiento & purificación , Proteínas Recombinantes/metabolismo , Proteínas Recombinantes/ultraestructura , Factores de Empalme Serina-Arginina/genética , Factores de Empalme Serina-Arginina/aislamiento & purificación , Factores de Empalme Serina-Arginina/ultraestructura , Uridina/metabolismoRESUMEN
Small nucleolar RNAs (snoRNAs) are a class of non-coding RNAs that guide the post-transcriptional processing of other non-coding RNAs (mostly ribosomal RNAs), but have also been implicated in processes ranging from microRNA-dependent gene silencing to alternative splicing. In order to construct an up-to-date catalog of human snoRNAs we have combined data from various databases, de novo prediction and extensive literature review. In total, we list more than 750 curated genomic loci that give rise to snoRNA and snoRNA-like genes. Utilizing small RNA-seq data from the ENCODE project, our study characterizes the plasticity of snoRNA expression identifying both constitutively as well as cell type specific expressed snoRNAs. Especially, the comparison of malignant to non-malignant tissues and cell types shows a dramatic perturbation of the snoRNA expression profile. Finally, we developed a high-throughput variant of the reverse-transcriptase-based method for identifying 2'-O-methyl modifications in RNAs termed RimSeq. Using the data from this and other high-throughput protocols together with previously reported modification sites and state-of-the-art target prediction methods we re-estimate the snoRNA target RNA interaction network. Our current results assign a reliable modification site to 83% of the canonical snoRNAs, leaving only 76 snoRNA sequences as orphan.
Asunto(s)
Perfilación de la Expresión Génica , Procesamiento Postranscripcional del ARN , ARN Nucleolar Pequeño , Transcriptoma , Análisis por Conglomerados , Biología Computacional/métodos , Bases de Datos de Ácidos Nucleicos , Regulación de la Expresión Génica , Humanos , Anotación de Secuencia Molecular , Conformación de Ácido Nucleico , ARN no TraducidoRESUMEN
MOTIVATION: Accurate identification of transcription start sites (TSSs) is an essential step in the analysis of transcription regulatory networks. In higher eukaryotes, the capped analysis of gene expression technology enabled comprehensive annotation of TSSs in genomes such as those of mice and humans. In bacteria, an equivalent approach, termed differential RNA sequencing (dRNA-seq), has recently been proposed, but the application of this approach to a large number of genomes is hindered by the paucity of computational analysis methods. With few exceptions, when the method has been used, annotation of TSSs has been largely done manually. RESULTS: In this work, we present a computational method called 'TSSer' that enables the automatic inference of TSSs from dRNA-seq data. The method rests on a probabilistic framework for identifying both genomic positions that are preferentially enriched in the dRNA-seq data as well as preferentially captured relative to neighboring genomic regions. Evaluating our approach for TSS calling on several publicly available datasets, we find that TSSer achieves high consistency with the curated lists of annotated TSSs, but identifies many additional TSSs. Therefore, TSSer can accelerate genome-wide identification of TSSs in bacterial genomes and can aid in further characterization of bacterial transcription regulatory networks. AVAILABILITY: TSSer is freely available under GPL license at http://www.clipz.unibas.ch/TSSer/index.php
Asunto(s)
Automatización de Laboratorios/métodos , Genoma Bacteriano , Análisis de Secuencia de ARN/métodos , Sitio de Iniciación de la Transcripción , Secuencia de Bases , GenómicaRESUMEN
BACKGROUND: In recent years, a variety of small RNAs derived from other RNAs with well-known functions such as tRNAs and snoRNAs, have been identified. The functional relevance of these RNAs is largely unknown. To gain insight into the complexity of snoRNA processing and the functional relevance of snoRNA-derived small RNAs, we sequence long and short RNAs, small RNAs that co-precipitate with the Argonaute 2 protein and RNA fragments obtained in photoreactive nucleotide-enhanced crosslinking and immunoprecipitation (PAR-CLIP) of core snoRNA-associated proteins. RESULTS: Analysis of these data sets reveals that many loci in the human genome reproducibly give rise to C/D box-like snoRNAs, whose expression and evolutionary conservation are typically less pronounced relative to the snoRNAs that are currently cataloged. We further find that virtually all C/D box snoRNAs are specifically processed inside the regions of terminal complementarity, retaining in the mature form only 4-5 nucleotides upstream of the C box and 2-5 nucleotides downstream of the D box. Sequencing of the total and Argonaute 2-associated populations of small RNAs reveals that despite their cellular abundance, C/D box-derived small RNAs are not efficiently incorporated into the Ago2 protein. CONCLUSIONS: We conclude that the human genome encodes a large number of snoRNAs that are processed along the canonical pathway and expressed at relatively low levels. Generation of snoRNA-derived processing products with alternative, particularly miRNA-like, functions appears to be uncommon.