Dataset Information

Integrative assessment of RNA sequencing and in silico analysis to pinpoint mRNAs, lncRNAs, and circRNAs interactions with miRNAs underlying arsenic-induced neurotoxicity

ABSTRACT: The acquisition of neurodegeneration hallmarks necessitates molecular changes at multiple levels of functional genomics. Regulatory RNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) play crucial roles in modulating messenger RNAs (mRNAs) expression and are potential molecules that regulate multifactorial neurotoxicity mechanisms. Integrated studies of regulatory RNAs’ functions are still lacking to portray the complete picture. In this study, we carried out a detailed analysis of mRNA, lncRNA, and circRNA profiles in differentiated SH-SY5Y cells exposed to arsenic, a well-known neurotoxicant, using the Illumina Novaseq6000 platform. Subsequently, employing a bioinformatics approach, we identified specific interactions between these RNA molecules and potential miRNAs, unraveling intricate regulatory networks. Among significantly deregulated transcripts, we identified 2487 mRNA transcripts, 1192 lncRNA transcripts, and 20 circRNA transcripts in arsenic-treated cells compared to control. Functional enrichment analysis indicated their involvement in numerous pathways underlying neurotoxicity. Further, these regulatory molecules displayed strong interactions with multiple miRNAs implicated in neuronal damage. Our findings revealed intricate molecular alterations in response to arsenic exposure, emphasizing the crucial role of multi-omics technologies in understanding neurodegeneration. These findings pave the way for targeted therapeutic interventions, focusing on precise modulation of regulatory RNA networks to mitigate the effects of arsenic.

ORGANISM(S): Homo sapiens

PROVIDER: GSE278489 | GEO | 2025/07/28

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

Integrative assessment of RNA sequencing and in silico analysis to pinpoint mRNAs, lncRNAs, and circRNAs interactions with miRNAs underlying arsenic-induced neurotoxicity

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets