Dataset Information

Highly throughout RNA sequncing analysis of WT and SDC4-KD endothelium stimulated by TNF-a

ABSTRACT: To identify downstream molecules and signaling pathways regulated by SDC4 during endothelial inflammatory activation, RNA sequencing (RNA-seq) was performed following SDC4 knockdown in TNF-α-stimulated endothelial cells. In this project, differentially expressed genes were identified using a threshold of fold change ≥ 2 (i.e., |log₂(fold change)| ≥ 1) and a q-value < 0.05 (where q-value represents the false discovery rate-adjusted p-value). Based on these criteria, differentially expressed genes, enrichment analyses, gene set enrichment analysis (GSEA), and alternative splicing events were obtained across the designated comparison groups. Principal component analysis (PCA) demonstrated clear separation between the TNF-α group and the TNF-α + SDC4 KD group, indicating substantial transcriptional differences between the two experimental conditions without obvious outlier samples. Differential expression analysis identified a total of 1,685 differentially expressed genes (DEGs) between the two groups (FPKM mean ≥ 0.5, |logFC| > 1, P < 0.05), including 775 upregulated genes and 910 downregulated genes following SDC4 knockdown. Hierarchical clustering analysis further demonstrated strong intragroup consistency and distinct intergroup transcriptional profiles, suggesting that SDC4 knockdown notably altered endothelial inflammatory gene expression patterns. To explore the biological significance of these DEGs, GO and KEGG enrichment analyses were subsequently performed. The enriched pathways were strongly associated with leukocyte transendothelial migration, inflammatory response regulation, and cellular response to inflammatory stimuli.

ORGANISM(S): Homo sapiens

PROVIDER: GSE346259 | GEO | 2026/09/10

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:In addition to their role in the development and function of the reproductive system, estrogens have significant anti-inflammatory properties. Although both estrogen receptors (ERs) can mediate anti-inflammatory actions, ERbeta is a more desirable therapeutic target because ERalpha mediates the proliferative effects of estrogens on the mammary gland and uterus. In fact, selective ERbeta agonists have beneficial effects in preclinical models involving inflammation without causing growth-promoting effects on the uterus or mammary gland. However, their mechanism of action is unclear. The purpose of this study was to use microarray analysis to determine whether ERbeta-selective compounds produce their anti-inflammatory effects by repressing transcription of proinflammatory genes. We identified 49 genes that were activated by TNF-alpha in human osteosarcoma U2OS cells expressing ERbeta. Estradiol treatment significantly reduced the activation by TNF-alpha on 18 genes via ERbeta or ERalpha. Most repressed genes were inflammatory genes, such as TNF-alpha, IL-6, and CSF2. Three ERbeta-selective compounds, ERB-041, WAY-202196, and WAY-214156, repressed the expression of these and other inflammatory genes. ERB-041 was the most ERbeta-selective compound, whereas WAY-202196 and WAY-214156 were the most potent. The ERbeta-selective compounds repressed inflammatory genes by recruiting the coactivator, SRC-2. ERB-041 also repressed cytokine genes in PBMCs, demonstrating that ERbeta-selective estrogens have anti-inflammatory properties in immune cells. Our study suggests that the anti-inflammatory effects of ERB-041 and other ERbeta-selective estrogens in animal models are due to transcriptional repression of proinflammatory genes. These compounds might represent a new class of drugs to treat inflammatory disorders. Experiment Overall Design: Expression of ER in the U2OS-ERÃ cells was induced with doxycycline. The cells were treated in the absence or presence of 10 nM E2 for 18 h followed by incubation with TNF-{alpha} for 1 h. Total RNA were extracted, followed by cRNA labelling, and hybridization of fragmented samples to the U95Av2 arrays (n = 3 for untreated, n = 4 for TNF-{alpha}-treated, n = 4 for TNF-{alpha}- and E2-treated samples). The data were analyzed using the Microarray Suite Version 5.0 with the default parameters. The comparative data generated for each treated group were analyzed further in Microsoft Excel. TNF-{alpha}-induced genes were selected for further analysis only if they had a 1.2 signal log ratio mean value (2.3-fold change) and were statistically significant (p < 0.05) in at least three separate experiments.

Dataset Information

Highly throughout RNA sequncing analysis of WT and SDC4-KD endothelium stimulated by TNF-a

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets