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RNAseq data of iRECs treated with palmitic acid and oleic acid

Citation

Pérez-Martí, Albert (2022), RNAseq data of iRECs treated with palmitic acid and oleic acid, Dryad, Dataset, https://doi.org/10.5061/dryad.gqnk98sq7

Abstract

In diabetic patients, dyslipidemia frequently contributes to organ damage such as diabetic kidney disease (DKD). DKD is associated with excessive renal deposition of triacylglycerol (TAG) in lipid droplets (LD). In order to understand the biological processes ocurring in proximal tubules when exposed to fatty acids, we performed a comparative transcriptomic study on BSA-, BSA-PA-, BSA-OA- and BSA-PA/OA-treated iRECs using RNA sequencing.

Methods

Induced Renal Epithelial Cells (iRECs) were treated for 16 hours with BSA-, BSA-PA-, BSA-OA- and BSA-PA/OA.

Total RNA was isolated using the RNeasy Kit, including a DNAse treatment step. RNA quality was assessed by capillary electrophoresis using High Sensitivity RNA reagents with the Fragment Analyzer (Agilent Technologies) and the RNA concentration was measured by spectrophometry using the Xpose (Trinean) and Fragment Analyzer capillary electrophoresis.

RNAseq libraries were prepared starting from 1µg of total RNA using the Universal Plus mRNA-Seq kit as recommended by the manufacturer. The oriented cDNAs produced from the poly-A+ fraction were sequenced on a NovaSeq6000 from Illumina (Paired-End reads 100 bases + 100 bases). A total of ~50 millions of passing filter paired-end reads was produced per library.

Galaxy platform was used to analyse the transcriptome data (Afgan et al, 2016). Quality check was assessed with FastQC v0.11.8 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). After trimming with Trim Galore! v0.4.3 (http://www.bioinformatics.babraham.ac.uk/projects/ trim_galore/), reads were aligned to the genome assembly GRCm38 using RNA STAR2 v2.5.2b (Dobin et al, 2013). Gene counts were calculated with featureCounts v1.6.4 (Liao et al, 2014) and differentially expressed gene (DEG) analysis was performed with DESeq2 v1.22.1 (Love et al, 2014).

Funding