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RNA seq analysis in human smooth muscle cells.

ABSTRACT: Macrophage-like cells derived from vascular smooth muscle cells (SMCs) play critical roles in atherogenesis, and TET2-mediated DNA hydroxymethylation was implicated to regulate the transdifferentiation. We examined transcriptomes and (hydroxy)methylomes of human coronary artery SMCs during cholesterol-induced transdifferentiation. A unique approach of gene ontology (GO)-centric clustering of differentially expressed genes exhaustively identified through all possible pairwise comparisons (pan-DEGs) revealed dynamic and multifaceted modulations of genes involved in extracellular matrix organization, angiogenesis, cell migration, hypoxia response, and cholesterol biosynthesis. Intriguingly, transient activation was observed for an immuno-metabolic circuit consisting of type I interferon response and cholesterol metabolism. We found neither global nor DEG-proximal change in (hydroxy)methylation. These datasets would serve as a unique resource to address the mechanisms underlying cholesterol-induced transdifferentiation of SMCs. Moreover, GO-centric clustering of pan-DEGs may provide a useful approach to interpret multifaceted transcriptomic alterations.

ORGANISM(S): Homo sapiens

PROVIDER: GSE163244 | GEO | 2021/07/07

REPOSITORIES: GEO

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DNA hydroxymethylation analysis for target regions in human smooth muscle cells.

Project description:Macrophage-like cells derived from vascular smooth muscle cells (SMCs) play critical roles in atherogenesis, and TET2-mediated DNA hydroxymethylation was implicated to regulate the transdifferentiation. We examined transcriptomes and (hydroxy)methylomes of human coronary artery SMCs during cholesterol-induced transdifferentiation. A unique approach of gene ontology (GO)-centric clustering of differentially expressed genes exhaustively identified through all possible pairwise comparisons (pan-DEGs) revealed dynamic and multifaceted modulations of genes involved in extracellular matrix organization, angiogenesis, cell migration, hypoxia response, and cholesterol biosynthesis. Intriguingly, transient activation was observed for an immuno-metabolic circuit consisting of type I interferon response and cholesterol metabolism. We found neither global nor DEG-proximal change in (hydroxy)methylation. These datasets would serve as a unique resource to address the mechanisms underlying cholesterol-induced transdifferentiation of SMCs. Moreover, GO-centric clustering of pan-DEGs may provide a useful approach to interpret multifaceted transcriptomic alterations.

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Molecular mechanisms vascular smooth muscle cell (VSMC) acquisition of macrophage features in response to cholesterol loading

Project description:In this study, murine primary aortic smooth muscle cells (SMCs) were transcriptionally profiled at baseline, after 3 d of cholesterol loading, and after 3 d of subsequent cholesterol unloading with HDL treatment, to identify vascular SMC genes that are transcripionally dysregulated in response to cholesterol loading and/or unloading. Mouse aortic SMCs were isolated from thoracic aortas of 8 week-old C57BL/6 mice (as described in Rong et al., Proc Nat Acad Sci USA, v100, pp13531M-bM-^@M-^S13536, 2003) and subconfluent cell preparations were harvested and transcriptionally profiled (baseline group) or treated with 10 M-NM-<g/mL cholesterol:methyl-M-NM-2-cyclodextrin complex (1:6 molar ratio) for 72 h for cholesterol loading. Of the cholesterol-loaded cells, samples were transcriptionally profiled (cholesterol samples) or treated for an additional 72 h with 100 M-NM-<g/mL human HDL and then transcriptionally profiled (HDL treatment). For all transcriptional profiling, RNA was isolated from cells using TRIzol, and labeled aRNA was prepared for hybridization to Affymetrix Mouse Genome 430 2.0 microarrays according to the manufacturer's protocol. Probe-level intensities were background-adjusted and normalized (in two separate analyses, one with baseline and cholesterol-treated samples, and the other analysis with all three sample groups) using Robust Multichip Average and combined into probesets using probe-to-probeset mappings from the University of Michigan CustomCDF project based on Ensembl Gene identifiers (release 59). Using the data from the analysis that combined the baseline and cholesterol-loaded samples, log2 probeset intensities were compared the between baseline and cholesterol sample groups using a one-way ANOVA with a P value cutoff determined by a Benjamini-Hochberg false discovery rate threshold of 0.05. Analysis (I): baseline and cholesterol-treated sample groups. Analysis (II): all three sample groups.

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