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SNAI1 knockdown under VEGF stimulus in HUVECs

ABSTRACT: SNAI1 knockdown under VEGF stimulus in HUVECs

PROVIDER: PRJNA436851 | ENA |

REPOSITORIES: ENA

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Project description:Purpose: The goals of this study are to compare VEGF-treated HUVECs with or without Verteporfin (VP) pretreatment transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis Methods: After serum-starving for 12 hours, HUVECs were divided into two group: VEGF and VEGF+VP. Cells from VEGF+VP group were pretreated with VP (4 μM) for 2 hours and then all cells were treated with VEGF (200 ng/mL) for another 24 hours. Subsequently, total RNA from HUVECs were prepared using Trizol reagent and mRNA library was constructed. RNA-sequencing: RNA-sequencing was carried out by BGI (Beijing Genomic Institute, ShenZhen, China). SOAPnuke software (v1.5.2) was used to filter the data for RNA-sequencing and then these data were mapped to the reference genome using HISAT2 software (v2.0.4). The clean reads were aligned to the gene set by Bowtie2 (v2.2.5). The expression level of genes was then measured by RSEM software (v1.2.12). The heatmap of top 40 different expression genes was drawn according to the gene expression with FPKM (fragment per kilobase of transcript per million). Reactome (https://www.reactome.org/) enrichment analysis of different expression genes was undertaken and the significant levels of terms and pathways were corrected by Q value. Results: The statistical results of significant DEGS confirmed that approximately 7204 genes of the transcripts showed differential expression between the VEGF group and VEGF+VP group, with a fold change ≥1.5 and p value <0.05. Altered expression of 20 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to angiogenesis. Data analysis with GO analysis and GSEA analysis revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed transcriptomic analysis of VEGF treated HUVECs with or without VP treatment, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles.

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SNAI1 knockdown under VEGF stimulus in HUVECs

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