A global analysis on the differential regulation of RNA binding proteins (RBPs) by TNF–α as potential modulators of metabolic syndromes
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ABSTRACT:
PROVIDER: PRJNA755604 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR15501805_1.fastq.gz | Fastqsanger.gz | |||
| SRR15501805_2.fastq.gz | Fastqsanger.gz | |||
| SRR15501806_1.fastq.gz | Fastqsanger.gz | |||
| SRR15501806_2.fastq.gz | Fastqsanger.gz |
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Project description:Purpose:Metabolic syndrome (MetS) is associated with a group of conditions including diabetes, obesity, insulin resistance etc. The goal of our study is to identify the differentially regulated genes under metabolic syndromes induced by TNF-α. Methods:A Homosapines Reference based Transcriptome sequencing is performed to understand the genes that are diffrerentially regulated under metabolic synromes with TNF-α as upstream. The HEK293 cells were treated with TNF alpha at 10ng/ml for 6hrs to induce the metabolic disturbances mimicking the metabolic syndromes and untreated HEK 293 cells were considered as control. For the Control set (31 million reads) and TNF-α treated (34 million reads) samples was obtained through the Illumina NextSeq 500 platform and comprised of paired end reads of 76 bp (2X76). FastQC software was used to check the quality of the NextSeq 500 paired end reads and were further processed by in-house script for adapter removal and low quality bases trimming. Tophat-2.02 and Cufflinks-2.1.14 tools were used for the analysis. A two fold change and p-value <0.05 cut-off was used to report the differentially expressed genes (DEGs). Owing to the absence of replicates, no FDR correction was used.For each gene a RPKM (Reads per kilo base per million mapped reads) value was calculated for each gene. Apart from the RPKM value, p-value and a fold change value was also calculated for control and treated samples. The genes were validated using qPCR using using Brilliant Ш Ultra-Fast SYBR green qPCR Master Mix in AriaMx PCR System (Agilent Technologies). Results: Around 6056 and 5495 transcripts were shown as up and down regulated respectively using a twofold change cut-off. On excluding the transcripts with no gene names, the numbers of up and down regulated transcripts were found out to be 4444 (3551 genes) and 4365 (3418 genes) respectively.Among which 1218 genes are unique, and highly significant(based on p-value cut-off) and transcriptionally regulated genes by TNF-α, among which about 15% are RBPs; identified by the EuRBPDB data base. Among these transcriptionally regulated genes, we have identified 204 MetS genes by IPA analysis, more than 10% of which are identified as RBPs. Conclusion: Transcriptional regulation of the MetS genes by TNF-α, which also act as RBPs predicts the existence of feed-forward loops and interconnected pathways, which may influence the onset of MetS at the molecular level.
2022-01-21 | GSE182259 | GEO
Project description:The proper balance of hematopoietic stem cell (HSC) self-renewal and differentiation is critical for normal hematopoiesis and disrupted in hematologic malignancy. Among regulators of HSC fate, transcription factors have a well-defined, central role and mutations promote malignant transformation. More recently, studies have illuminated the importance of post-transcriptional regulation by RNA-binding proteins (RBPs) in hematopoiesis and leukemia development. However, the RBPs involved and breadth of regulation are only beginning to be elucidated. Furthermore, the intersection between post-transcriptional regulation and hematopoietic transcription factor function is poorly understood. Here, we studied the post-transcriptional regulation of RUNX1, a key hematopoietic transcription factor. Alternative polyadenylation (APA) of RUNX1 produces functionally antagonistic protein isoforms (RUNX1a versus RUNX1b/c) that mediate HSC self-renewal versus differentiation, an RNA-processing event that is dysregulated in malignancy. Consequently, RBPs that regulate this event directly contribute to healthy and aberrant hematopoiesis. We modeled RUNX1 APA using a split GFP minigene reporter and confirmed the sensitivity of our model to detecting changes in RNA-processing. We utilized this reporter in a CRISPR screen consisting of single guide-RNAs exclusively targeting RBPs and uncovered HNRNPA1 and KHDRBS1 as antagonistic regulators of RUNX1a isoform generation. Overall, our study provides mechanistic insight into the post-transcriptional regulation of a key hematopoietic transcription factor and identifies RBPs which may have a widespread, important function in hematopoiesis.
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Project description:G-protein coupled receptors (GPCRs) have diverse roles in physiological processes, including immunity. Gs-coupled GPCRs increase while Gi-coupled ones decrease intracellular cAMP. Previous studies suggest that, in epithelial cells, Gs-coupled GPCRs enhance whereas Gi-coupled GPCRs suppress pro-inflammatory immune responses. In order to examine the issue, we chose beta2 adrenergic receptor and GPR40 as representatives of Gs- and Gi- coupled GPCRs, respectively, and examined their effects on TNF-alpha and IFN-gamma-(TNF-alpha + IFN-gamma) induced gene expression by HaCaT. We used microarrays to detail the global changes of gene expression induced by a beta2 adrenergic receptor agonist terbutaline or GPR40 agonist GW9508 pre-treatment in TNF-alpha + IFN-gamma - stimulated HaCaT cells. HaCaT cells were pre-treated with terbutaline or GW9508, TNF-alpha + IFN-gamma were then added, and cultured for another 24 h. Cells were then used for RNA extraction and hybridization on Affymetrix microarrays. We sought to clarify changes in gene expression after 1) TNF-alpha + IFN-gamma, 2) TNF-alpha + IFN-gamma + terbutaline, and 3) TNF-alpha + IFN-gamma + GW9508 treatment. To this end, we set 4 groups of samples; 1) unstimulated group, 2) TNF-alpha + IFN-gamma-stimulated group, 3) TNF-alpha + IFN-gamma + terbutaline-stimulated group, and 4) TNF-alpha + IFN-gamma + GW9508-stimulated group. In each group, HaCaT cells were stimulated in triplicate wells (n=3).
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