Supt3 mutant nascent RNA-seq
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA837429 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR19175836.fastq.gz | Fastqsanger.gz | |||
| SRR19175837.fastq.gz | Fastqsanger.gz | |||
| SRR19175838.fastq.gz | Fastqsanger.gz | |||
| SRR19175839.fastq.gz | Fastqsanger.gz |
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Project description:This SuperSeries is composed of the following subset Series: GSE36871: Nascent-Seq Reveals Novel Features of Mouse Circadian Transcriptional Regulation [RNA-Seq] GSE36872: Nascent-Seq Reveals Novel Features of Mouse Circadian Transcriptional Regulation [Nascent-Seq] GSE36873: Nascent-Seq Reveals Novel Features of Mouse Circadian Transcriptional Regulation [StrandSpe_NascentSeq] GSE36874: Nascent-Seq Reveals Novel Features of Mouse Circadian Transcriptional Regulation [ChIP-seq] Refer to individual Series
2012-11-09 | E-GEOD-36916 | biostudies-arrayexpress
Project description:mRNAs are generally assumed to be loaded instantly with ribosomes upon entry into the cytoplasm. To measure ribosome density on nascent mRNA, we developed nascent Ribo-Seq (nRibo-Seq) by combining Ribo-Seq with progressive 4-thiouridine labelling. In mouse macrophages, we experimentally determined, for the first time, the lag between the appearance of nascent RNA and its association with ribosomes, which was calculated to be 20 - 22 min for bulk mRNA, and approximated the time it takes for mRNAs to be fully loaded with ribosomes to be 41 - 44 min. Notably, ribosomal loading time is adapted to gene function as rapid loading was observed with highly regulated genes. The lag and ribosomal loading time correlate positively with ORF size and mRNA half-life, and negatively with tRNA adaptation index. Similar results were obtained in mouse embryonic stem cells, where the lag in ribosome loading was even more pronounced with 35 - 38 min. We validated our measurements after stimulation of macrophages with lipopolysaccharide, where the lag between cytoplasmic and translated mRNA leads to uncoupling between input and ribosome-protected fragments. Uncoupling is stronger for mRNAs with long ORFs or half-lives, a finding we also confirmed at the level of protein production by nascent chain proteomics. As a consequence of the lag in ribosome loading, ribosome density measurements are distorted when performed under conditions where mRNA levels are far from steady state expression, and transcriptional changes affect ribosome density in a passive way. This study uncovers an unexpected and considerable lag in ribosome loading, and provides guidelines for the interpretation of Ribo-Seq data taking passive effects on ribosome density into account.
2021-06-22 | PXD026828 | Pride
Project description:Nascent RNA and Poly(A) RNA Seq from S2 cells [RNA-seq]
| PRJNA480280 | ENA
Project description:RNA sequences are expected to be identical to their corresponding DNA sequences. Advances in technologies have enabled deep sequencing of nucleic acids that uncovered exceptions to the one-to-one relationship between DNA and RNA sequences. Previously in human cells, post-transcriptional RNA editing was the only known mechanism that changes RNA sequences from the underlying DNA sequences. Here, we sequenced nascent RNA and found all 12 types of RNA-DNA differences. Using various experimental analyses, we validated this finding. Our results showed that sequences of nascent RNAs within 40 nucleotides of the exit channel of RNA polymerase II already differ from the corresponding DNA sequences. These RNA-DNA differences are mediated by RNA processing steps closely coupled with transcription and not by known deaminase-mediated RNA editing mechanisms nor during NTP incorporation by Pol II. This finding identifies sequence substitution as part of co-transcriptional RNA processing. We sequenced nascent RNA using global run-on sequencing, GRO-seq from human B-cells from two individuals and a variant of the GRO-seq procedure, known as precision run-on sequencing, PRO-seq. The RNAs are prepared after a short run-on assay performed with isolated nuclei in the presence of Br-UTP. The isolated RNAs are base hydrolyzed to ~100 nucleotides and affinity purified with anti-BrU beads three times at each successive step of preparing the RNAs for orientation-specific sequencing using Illumina technology. The 5M-bM-^@M-^Y ~half of each sequence represents nascent RNA made in the cell and the 3M-bM-^@M-^Y ~half represents sequences made in vitro during the run-on reaction. The precision variation, PRO-seq, incorporates one or at most a few biotin-labeled nucleoside triphosphates during the run-on, and sequencing from the 3M-bM-^@M-^Y end of this affinity purified, nascent RNA maps the cellular location of engaged polymerases with near single nucleotide precision. We obtained ~ 100 million 100-nucleotide uniquely mapped GRO-seq reads from B-cells of two individuals. For one subject, we also carried out pGRO-seq and obtained 60 million uniquely mapped reads. In addition, we sequenced ~135 million uniquely mapped RNA-seq reads, and the corresponding DNA of the two individuals to 30X and 60X coverage. Additionally, we isolated and sequenced nascent RNA with an alternate method described by Wuarin and Schibler (1994) in order to compare chromatin-bound RNA to the very nascent RNA from PRO-seq. We obtained ~190 million uniquely mapped reads from chormatin-bound RNA-seq.
2014-02-21 | E-GEOD-39878 | biostudies-arrayexpress
Project description:Over the past decade, genome-wide assays have underscored the broad sweep of circadian gene expression. A substantial fraction of the transcriptome undergoes oscillations in many organisms and tissues, which governs the many biochemical, physiological and behavioral functions under circadian control. Based predominantly on the transcription feedback loops important for core circadian timekeeping, it is commonly assumed that this widespread mRNA cycling reflects circadian transcriptional cycling. To address this issue, we directly measured dynamic changes in mouse liver transcription using Nascent-Seq. Many genes are rhythmically transcribed over the 24h day, which include precursors of several non-coding RNAs as well as the expected set of core clock genes. Surprisingly however, nascent RNA rhythms overlap poorly with mRNA abundance rhythms assayed by RNA-seq. This is because most mouse liver genes with rhythmic mRNA expression manifest poor transcriptional rhythms, indicating a prominent role of post-transcriptional regulation in setting mRNA cycling amplitude. To gain further insight into circadian transcriptional regulation, we also characterized the rhythmic transcription of liver genes targeted by the transcription factors CLOCK and BMAL1; they directly target other core clock genes and sit at the top of the molecular circadian clock hierarchy in mammals. CLK:BMAL1 rhythmically bind at the same discrete phase of the circadian cycle to all target genes, which not surprisingly have a much higher percentage of rhythmic transcription than the genome as a whole. However, there is a surprisingly heterogeneous set of cycling transcription phases of direct target genes, which even include core clock genes. This indicates a disconnect between rhythmic DNA binding and the peak of transcription, which is likely due to other transcription factors that collaborate with CLK:BMAL1. In summary, the application of Nascent-Seq to a mammalian tissue provides surprising insights into the rhythmic control of gene expression and should have broad applications beyond the analysis of circadian rhythms. Mouse liver nascent RNA profile over 6 time points of the 24h light:dark cycle, in duplicate, sequenced using Ilumina GAII (Nascent-Seq); Mouse liver mRNA profile over 6 time points of the 24h light:dark cycle, in duplicate, sequenced using Ilumina HiSeq2000 (RNA-Seq); CLK and BMAL1 DNA binding profile in the mouse liver at ZT8, sequenced along an Input sample using GAII (ChIP-Seq); Mouse liver strand-specific nascent RNA profile over 6 time points of the 24h light:dark cycle, in duplicate, sequenced using Ilumina HiSeq2000 (Strand-specific Nascent-Seq); Supplementary file NascentSeq_Mouse_Liver_NormalizedGeneSignal.txt represents Nascent RNA abundance (reads per base pair) for each sample.
2012-11-10 | E-GEOD-36872 | biostudies-arrayexpress
Project description:The RNA editing enzyme ADAR chemically modifies adenosine (A) to inosine (I), which is interpreted by the ribosome as a guanosine. Here we assess cotranscriptional A-to-I editing in Drosophila, by isolating nascent RNA from adult fly heads and subjecting samples to high-throughput sequencing. There are a large number of edited sites within nascent exons. Nascent RNA from an ADAR null mutant strain was also sequenced, indicating that almost all A-to-I events require ADAR. Moreover, mRNA editing levels correlate with editing levels within the cognate nascent RNA sequence, indicating that the extent of editing is set cotranscriptionally. Surprisingly, the nascent data also identify an excess of intronic over exonic editing sites. These intronic sites occur preferentially within introns that are poorly spliced cotranscriptionally, suggesting a link between editing and splicing. We conclude that ADAR-mediated editing is more widespread than previously indicated and largely occurs cotranscriptionally. GSM914095: Fly genomic DNA sequencing. Sequenced on the Illumina GA II. GSM914102-GSM914113: Fly head nascent RNA profiles over 6 time points of a 12hr light:dark cycle in duplicate; sequenced on the Illumina GA II. GSM914114-GSM914119: Fly head nascent RNA profiles of yw, FM7, ADAR0 males in duplicate; sequenced on the HiSeq2000. GSM915213-GSM915214: Fly head mRNA profiles over 2 time points of a 12hr light:dark cycle; sequenced on the Illumina GA II. GSM915215-GSM915220: Fly head mRNA profiles over 6 time points of a 12hr light:dark cycle; paired-end sequenced on the Illumina GA II. GSM915221-GSM91526: Fly head mRNA profiles over 6 time points of a 12hr light:dark cycle; sequenced on the Illumina GA II.
2012-05-31 | E-GEOD-37232 | biostudies-arrayexpress
Project description:Mouse embryonic stem cells (i.e., mESCs; line ESC 129-B13) were genetically modified using CRISPR-Cas9 to mutate the H3f3b locus, in order to carry homozygous lysine-to-alanine substitution of residues K9 or K27. Two control mESC lines carrying knock-out of the H3f3a gene were used as background for the editing. To profile nascent RNA levels in H3.3 mutant and control mESCs, two rounds of PRO-seq experiment were performed. In the first round, three replicates each of H3.3K9A and control (i.e., Ctrl_9) mESCs were used. In the second round, two replicates each of H3.3K27A and control (i.e., Ctrl_27) mESCs were used. After permeabilization of the mESCs (and addition of 5% Drosophila S2 cells spike-in), a 2 biotin run-on reaction using biotin-11-UTP and biotin-11-CTP was carried out to label nascent transcripts. Biotinylated nascent RNA was then enriched as part of the library preparation process. Sequencing was performed on a NextSeq500 platform in single-end mode (75bp reads). The experiment allows for an assesment of the changes in the activity of cis-regulatory elements, upon removal of H3.3 K9 or K27 residues.
2024-09-04 | E-MTAB-12869 | biostudies-arrayexpress
Project description:Over the past decade, genome-wide assays have underscored the broad sweep of circadian gene expression. A substantial fraction of the transcriptome undergoes oscillations in many organisms and tissues, which governs the many biochemical, physiological and behavioral functions under circadian control. Based predominantly on the transcription feedback loops important for core circadian timekeeping, it is commonly assumed that this widespread mRNA cycling reflects circadian transcriptional cycling. To address this issue, we directly measured dynamic changes in mouse liver transcription using Nascent-Seq. Many genes are rhythmically transcribed over the 24h day, which include precursors of several non-coding RNAs as well as the expected set of core clock genes. Surprisingly however, nascent RNA rhythms overlap poorly with mRNA abundance rhythms assayed by RNA-seq. This is because most mouse liver genes with rhythmic mRNA expression manifest poor transcriptional rhythms, indicating a prominent role of post-transcriptional regulation in setting mRNA cycling amplitude. To gain further insight into circadian transcriptional regulation, we also characterized the rhythmic transcription of liver genes targeted by the transcription factors CLOCK and BMAL1; they directly target other core clock genes and sit at the top of the molecular circadian clock hierarchy in mammals. CLK:BMAL1 rhythmically bind at the same discrete phase of the circadian cycle to all target genes, which not surprisingly have a much higher percentage of rhythmic transcription than the genome as a whole. However, there is a surprisingly heterogeneous set of cycling transcription phases of direct target genes, which even include core clock genes. This indicates a disconnect between rhythmic DNA binding and the peak of transcription, which is likely due to other transcription factors that collaborate with CLK:BMAL1. In summary, the application of Nascent-Seq to a mammalian tissue provides surprising insights into the rhythmic control of gene expression and should have broad applications beyond the analysis of circadian rhythms. Mouse liver nascent RNA profile over 6 time points of the 24h light:dark cycle, in duplicate, sequenced using Ilumina GAII (Nascent-Seq); Mouse liver mRNA profile over 6 time points of the 24h light:dark cycle, in duplicate, sequenced using Ilumina HiSeq2000 (RNA-Seq); CLK and BMAL1 DNA binding profile in the mouse liver at ZT8, sequenced along an Input sample using GAII (ChIP-Seq); Mouse liver strand-specific nascent RNA profile over 6 time points of the 24h light:dark cycle, in duplicate, sequenced using Ilumina HiSeq2000 (Strand-specific Nascent-Seq); Supplementary file RNASeq_Mouse_Liver_NormalizedGeneSignal.txt represents mRNA abundance (reads per base pair) for each sample.
2012-11-10 | E-GEOD-36871 | biostudies-arrayexpress
Project description:RNA-Seq analysis carried out in three different backcrosses based on Iberian breed with Landrace, Pietrain and Duroc breeds
2017-10-31 | PRJEB23068 | EVA