BioProject

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. Overall design: Reference based transcriptome analysis was performed to understand how TNF alpha regulate genes under metabolic syndromes.