Dataset Information

High-throughput sequencing in HNRNPD knock-out and knock-in HEK293T cell lines

ABSTRACT: As HNRNPD regulates the alternative splicing of hundreds of genes, we sought to investigate whether HNRNPD could regulate the biogenesis of circRNAs. To identify the effect of HNRNPD on circRNAs and linear RNAs, we performed circRNA and linear RNAs (ployA enrichment RNA and lncRNA) sequencing in HNRNPD knockout and wild-type HEK293T cells. We also inserted 3FHBH (3xFLAG, Histidine, Biotin, and Histidine) into the HNRNPD genome to obtain HEK293T_HNRNPD_3FHBH cells by using CRISPR-Cas9 technology, which was used to ascertain the direct HNRNPD targeting RNA sequence with the help of FLASH sequencing (Fast Ligation of RNA after some sort of Affinity Purification for High-throughput Sequencing). To evaluate the impacts of HNRNPD deficiency on circRNA formation, we captured nascent RNA in HEK293T and HNRNPD knock-out HEK293T cells by immunoprecipitation of 5-ethyluridine (EU) labeling. Comparative analysis of these data, we found the higher propensity of binding to introns of HNRNPD was related to inhibiting circRNA biogenesis.

ORGANISM(S): Homo sapiens

PROVIDER: GSE212767 | GEO | 2023/09/30

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:In platelets, splicing and translation occur in the absence of a nucleus. However, the integrity and stability of mRNAs derived from megakaryocyte progenitor cells remain poorly quantified on a transcriptome-wide level. As circular RNAs (circRNAs) are resistant to degradation by exonucleases, their abundance relative to linear RNAs can be used as a surrogate marker for mRNA stability in the absence of transcription. Here we show that circRNAs are enriched in human platelets 17-188 fold relative to nucleated tissues, and 14-26 fold relative to samples digested with RNAseR to selectively remove linear RNA. We compare RNAseq read depths inside and outside circRNAs to provide in silico evidence of transcript circularity, show that exons within circRNAs are enriched ~13X in platelets relative to nucleated tissues, and identify 3162 genes significantly enriched for circRNAs including some where all RNAs appear to be derived from circular molecules. We also confirm that this is a feature of other anucleate cells through transcriptome sequencing of mature erythrocytes, demonstrate that circRNAs are not enriched in megakaryocytes, and that linear RNAs decay more rapidly than circRNAs in platelet preparations. Collectively, these results suggest that circulating platelets have lost on aveage over 90% of their progenitor mRNAs, and that translation in platelets occurrs against the backdrop of a highly degraded transcriptome. Finally, we find that transcripts classified as products of reverse transcriptase template switching are both enriched in platelets and resistant to decay, countering the recent suggestion that up to 50% of rearranged RNAs are artefacts. A single rRNA depleted total RNA sample was sequenced. This together with 25 publicly available rRNA depleted total RNA samples (including 3 from platelets) were analysed using PTESFinder v 1 (http://sourceforge.net/projects/ptesfinder-v1/) to identify back-splice junctions, characteristic of circRNA transcripts. The contribution of circRNA producing exons was analysed on a gene by gene basis as follows: All circRNA transcripts identified in any sample were first pooled to define exons which can contribute to circRNA generation using custom scripts (available on request). For each sample, expression estimates (RPKMI) across all circRNA producing exons were computed for each locus using the total size of exons (in bp) and the read counts mapping to them. Similarly, total size and exonic read counts for exons for which no circRNA were detected in any sample were used to compute expression estimates (RPKME) for non-circRNA producing exons for each locus. Abundance ratios (RPKMI/RPKME and RPKMI/RPKMI+RPKME) were calculated and compared between Platelets and human tissues using Wilcoxon signed-rank test. Please note that the '25sample_info_accn_no.txt' contains the accession numbers and tissue/cell type information for 25 samples analyzed together.

Dataset Information

High-throughput sequencing in HNRNPD knock-out and knock-in HEK293T cell lines

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets