JOURNAL ARTICLE
Genome-Wide Search Links Senescence-Associated Secretory Proteins With Susceptibility for Coronary Artery Disease in Mouse and Human.
Published In: Journals of Gerontology Series A: Biological Sciences & Medical Sciences, 2024, v. 79, n. 5. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Zhu, Yuan-Zheng; Liu, Jian-Kun; Li, Xue-Er; Yu, Zhen-Ping; Yang, Lu-Qin; Wan, Qin; Zhao, Ya; Saeed, Muhammad; Wu, An-Dong; Tian, Xiao-Li 3 of 3
Abstract
This article investigates how senescence-associated secretory phenotype (SASP) proteins derived from vascular cells contribute to coronary artery disease (CAD) susceptibility, focusing on the role of Cystatin C (CST3). Integrating gene expression and genome-wide association data, the study identifies CST3 as a SASP protein upregulated in senescent vascular endothelial and smooth muscle cells, aged arteries, and early atherosclerosis. Functional assays demonstrate that CST3 enhances monocyte–endothelial cell adhesion, a key event in atherosclerosis initiation. Additionally, two ligand–receptor pairs, COL4A1-ITGA1 and LPL-LRP1, are identified as important signaling pathways linked to cell adhesion, inflammation, extracellular matrix organization, and lipid metabolism in vascular aging and CAD. These findings suggest that vascular cell-derived SASP proteins, particularly CST3 and the identified ligand–receptor interactions, play significant roles in the pathogenesis of CAD.
Additional Information
- Source:Journals of Gerontology Series A: Biological Sciences & Medical Sciences. 2024/05, Vol. 79, Issue 5, p1
- Document Type:Article
- Subject Area:Consumer Health
- Publication Date:2024
- ISSN:1079-5006
- DOI:10.1093/gerona/glae070
- Accession Number:177017087
- Copyright Statement:Copyright of Journals of Gerontology Series A: Biological Sciences & Medical Sciences is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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