<?xml version="1.0" encoding="UTF-8"?>
<STUDY_SET xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <STUDY center_name="GEO" alias="GSE163246" accession="SRP298022">
    <IDENTIFIERS>
      <PRIMARY_ID>SRP298022</PRIMARY_ID>
      <EXTERNAL_ID namespace="BioProject" label="primary">PRJNA685594</EXTERNAL_ID>
      <EXTERNAL_ID namespace="GEO">GSE163246</EXTERNAL_ID>
    </IDENTIFIERS>
    <DESCRIPTOR>
      <STUDY_TITLE>DNA hydroxymethylation analysis for target regions in human smooth muscle cells.</STUDY_TITLE>
      <STUDY_TYPE existing_study_type="Other"/>
      <STUDY_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. Overall design: We conducted a series of genome and epigenome analysis, including RNA-sequencing and  DNA methylation and hydroxymethylation status in human smooth muscle cells.</STUDY_ABSTRACT>
      <CENTER_PROJECT_NAME>GSE163246</CENTER_PROJECT_NAME>
    </DESCRIPTOR>
    <STUDY_LINKS>
      <STUDY_LINK>
        <XREF_LINK>
          <DB>pubmed</DB>
          <ID>34258396</ID>
        </XREF_LINK>
      </STUDY_LINK>
    </STUDY_LINKS>
    <STUDY_ATTRIBUTES>
      <STUDY_ATTRIBUTE>
        <TAG>parent_bioproject</TAG>
        <VALUE>PRJNA685416</VALUE>
      </STUDY_ATTRIBUTE>
    </STUDY_ATTRIBUTES>
  </STUDY>
</STUDY_SET>
