Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR318919.fastq.gz | Fastqsanger.gz | |||
| ERR318920.fastq.gz | Fastqsanger.gz | |||
| ERR318921.fastq.gz | Fastqsanger.gz | |||
| ERR318922.fastq.gz | Fastqsanger.gz | |||
| ERR318923.fastq.gz | Fastqsanger.gz |
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Project description:Purpose: mRNA translation into protein is highly regulated, but the role of mRNA isoforms, noncoding RNAs (ncRNAs), and genetic variants has yet to be systematically studied. Using high-throughput sequencing (RNA-seq), we have measured cellular levels of mRNAs and ncRNAs, and their isoforms, in lymphoblast cell lines (LCL) and in polysomal fractions, the latter shown to yield strong correlations of mRNAs with expressed protein levels. Analysis of allelic RNA ratios at heterozygous SNPs served to reveal genetic factors in ribosomal loading. Methods: RNA-seq was performed on cytosolic extracts and polysomal fractions (3 ribosomes or more) from three lymphoblastoid cell lines. As each RNA fraction was amplified (NuGen kit), and relative contributions from various RNA classes differed between cytosol and polysomes, the fraction of any given RNA species loaded onto polysomes was difficult to quantitate. Therefore, we focused on relative recovery of the various RNA classes and rank order of single RNAs compared to total RNA. Results: RNA-seq of coding and non-coding RNAs (including microRNAs) in three LCLs revealed significant differences in polysomal loading of individual RNAs and isoforms, and between RNA classes. Moreover, correlated distribution between protein-coding and non-coding RNAs suggests possible interactions between them. Allele-selective RNA recruitment revealed strong genetic influence on polysomal loading for multiple RNAs. Allelic effects can be attributed to generation of different RNA isoforms before polysomal loading or to differential loading onto polysomes, the latter defining a direct genetic effect on translation. Several variants and genes identified by this approach are also associated with RNA expression and clinical phenotypes in various databases. Conclusions: These results provide a novel approach using complete transcriptome RNA-seq to study polysomal RNA recruitment and regulatory variants affecting protein translation. cells from 3 samples were grown to 5x105 cells/mL density in T75 tissue culture flask and harvested, total RNA and polysome bound RNA was sequenced by Ion Proton
2015-06-30 | E-GEOD-66135 | biostudies-arrayexpress
Project description:Purpose: mRNA translation into protein is highly regulated, but the role of mRNA isoforms, noncoding RNAs (ncRNAs), and genetic variants has yet to be systematically studied. Using high-throughput sequencing (RNA-seq), we have measured cellular levels of mRNAs and ncRNAs, and their isoforms, in lymphoblast cell lines (LCL) and in polysomal fractions, the latter shown to yield strong correlations of mRNAs with expressed protein levels. Analysis of allelic RNA ratios at heterozygous SNPs served to reveal genetic factors in ribosomal loading. Methods: RNA-seq was performed on cytosolic extracts and polysomal fractions (3 ribosomes or more) from three lymphoblastoid cell lines. As each RNA fraction was amplified (NuGen kit), and relative contributions from various RNA classes differed between cytosol and polysomes, the fraction of any given RNA species loaded onto polysomes was difficult to quantitate. Therefore, we focused on relative recovery of the various RNA classes and rank order of single RNAs compared to total RNA. Results: RNA-seq of coding and non-coding RNAs (including microRNAs) in three LCLs revealed significant differences in polysomal loading of individual RNAs and isoforms, and between RNA classes. Moreover, correlated distribution between protein-coding and non-coding RNAs suggests possible interactions between them. Allele-selective RNA recruitment revealed strong genetic influence on polysomal loading for multiple RNAs. Allelic effects can be attributed to generation of different RNA isoforms before polysomal loading or to differential loading onto polysomes, the latter defining a direct genetic effect on translation. Several variants and genes identified by this approach are also associated with RNA expression and clinical phenotypes in various databases. Conclusions: These results provide a novel approach using complete transcriptome RNA-seq to study polysomal RNA recruitment and regulatory variants affecting protein translation.
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Project description:RNA-protein interactions are central to biological regulation. Cross-linking immunoprecipitation (CLIP)-seq is a powerful tool for genome-wide interrogation of RNA-protein interactomes, but current CLIP methods are limited by challenging biochemical steps and fail to detect many classes of noncoding and non-human RNAs. Here we present FAST-iCLIP, an integrated pipeline with improved CLIP biochemistry and an automated informatic pipeline for comprehensive analysis across protein coding, noncoding, repetitive, retroviral, and non-human transcriptomes. FAST-iCLIP of Poly-C binding protein 2 (PCBP2) showed that PCBP2 bound CU-rich motifs in different topologies to recognize mRNAs and noncoding RNAs with distinct biological functions. FAST-iCLIP of PCBP2 in hepatitis C virus-infected cells enabled a joint analysis of the PCBP2 interactome with host and viral RNAs and their interplay. These results show that FAST-iCLIP can be used to rapidly discover and decipher mechanisms of RNA-protein recognition across the diversity of human and pathogen RNAs. Characterization of non-coding and pathogen RNA-protein interactions using an automated computational pipeline and improved iCLIP biochemistry
2014-11-18 | E-GEOD-59840 | biostudies-arrayexpress
Project description:Allele-selective Transcriptome Recruitment to Polysomes Primed for Translation: Protein-coding and Noncoding RNAs, and RNA Isoforms
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Project description:RNA-protein interactions are central to biological regulation. Cross-linking immunoprecipitation (CLIP)-seq is a powerful tool for genome-wide interrogation of RNA-protein interactomes, but current CLIP methods are limited by challenging biochemical steps and fail to detect many classes of noncoding and non-human RNAs. Here we present FAST-iCLIP, an integrated pipeline with improved CLIP biochemistry and an automated informatic pipeline for comprehensive analysis across protein coding, noncoding, repetitive, retroviral, and non-human transcriptomes. FAST-iCLIP of Poly-C binding protein 2 (PCBP2) showed that PCBP2 bound CU-rich motifs in different topologies to recognize mRNAs and noncoding RNAs with distinct biological functions. FAST-iCLIP of PCBP2 in hepatitis C virus-infected cells enabled a joint analysis of the PCBP2 interactome with host and viral RNAs and their interplay. These results show that FAST-iCLIP can be used to rapidly discover and decipher mechanisms of RNA-protein recognition across the diversity of human and pathogen RNAs.
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Project description:Ribosome profiling has emerged as a powerful method to assess global gene translation, but methodological and analytical challenges often lead to inconsistencies across labs and model organisms. A critical issue in ribosome profiling is nuclease treatment of ribosome-mRNA complexes, as it is important to ensure both stability of ribosomal particles and complete conversion of polysomes to monosomes. We performed comparative ribosome profiling in yeast and mice with various ribonucleases including I, A, S7 and T1, characterized their cutting preferences, trinucleotide periodicity patterns, and coverage similarities across coding sequences, and showed that they yield comparable estimations of gene expression when ribosome integrity is not compromised. However, ribosome coverage patterns of individual transcripts had little in common between the ribonucleases. We further examined their potency at converting polysomes to monosomes across other commonly used model organisms, including bacteria, nematodes and fruit flies. In some cases, ribonuclease treatment completely degraded ribosome populations. Ribonuclease T1 was the only enzyme that preserved ribosomal integrity while thoroughly converting polysomes to monosomes in all examined species. This study provides a guide for ribonuclease selection in ribosome profiling experiments across most common model systems
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Project description:Drosophila melanogaster expresses three classes of small RNAs, which are classified according to their mechanisms of biogenesis. MicroRNAs are ~22-23-nt, ubiquitously expressed small RNAs that are sequentially processed from hairpin-like precursors by Drosha/Pasha and Dcr-1/Loquacious complexes. MicroRNAs usually associate with AGO1 and regulate the expression of protein-coding genes. Piwi-interacting RNAs (piRNAs) of ~24-28-nt associate with Piwi-family proteins and can arise from single-stranded precursors. piRNAs function in transposon silencing and are mainly restricted to gonadal tissues. Endo-siRNAs are found in both germline and somatic tissues. These ~21-nt RNAs are produced by a distinct Dicer, Dcr-2, and do not depend on Drosha/Pasha complexes. They predominantly bind to AGO2 and target both mobile elements and protein-coding genes. Surprisingly, a subset of endo-siRNAs strongly depend for their production on the dsRNA-binding protein Loquacious (Loqs), thought generally to be a partner for Dcr-1 and a co-factor for miRNA biogenesis. Endo-siRNA production depends on a specific Loqs isoform, Loqs-PD, which is distinct from the one, Loqs-PB, required for the production of microRNAs. Paralleling their roles in the biogenesis of distinct small RNA classes, Loqs-PD and Loqs-PB bind to different Dicer proteins, with Dcr-1/Loqs-PB complexes and Dcr-2/Loqs-PD complexes driving microRNA and endo-siRNA biogenesis, respectively. Small RNA profiling by high throughput sequencing Total RNA was isolated using Trizol reagent (Invitrogen) and size-fractionated by PAGE into 19-24nt. These were independently processed and sequenced using the Illumina GAII platform. In total, six libraries were analyzed.
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Project description:Drosophila melanogaster expresses three classes of small RNAs, which are classified according to their mechanisms of biogenesis. MicroRNAs are ~22-23-nt, ubiquitously expressed small RNAs that are sequentially processed from hairpin-like precursors by Drosha/Pasha and Dcr-1/Loquacious complexes. MicroRNAs usually associate with AGO1 and regulate the expression of protein-coding genes. Piwi-interacting RNAs (piRNAs) of ~24-28-nt associate with Piwi-family proteins and can arise from single-stranded precursors. piRNAs function in transposon silencing and are mainly restricted to gonadal tissues. Endo-siRNAs are found in both germline and somatic tissues. These ~21-nt RNAs are produced by a distinct Dicer, Dcr-2, and do not depend on Drosha/Pasha complexes. They predominantly bind to AGO2 and target both mobile elements and protein-coding genes. Surprisingly, a subset of endo-siRNAs strongly depend for their production on the dsRNA-binding protein Loquacious (Loqs), thought generally to be a partner for Dcr-1 and a co-factor for miRNA biogenesis. Endo-siRNA production depends on a specific Loqs isoform, Loqs-PD, which is distinct from the one, Loqs-PB, required for the production of microRNAs. Paralleling their roles in the biogenesis of distinct small RNA classes, Loqs-PD and Loqs-PB bind to different Dicer proteins, with Dcr-1/Loqs-PB complexes and Dcr-2/Loqs-PD complexes driving microRNA and endo-siRNA biogenesis, respectively. Small RNA profiling by high throughput sequencing
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