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Quantitative proteomic landscape of unstable atherosclerosis identifies molecular signatures and therapeutic targets for plaque stabilization

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posted on 2024-05-14, 03:56 authored by Yung-Chih ChenYung-Chih Chen, M Smith, YL Ying, M Makridakis, J Noonan, P Kanellakis, Alin RaiAlin Rai, Agus SalimAgus Salim, Andrew MurphyAndrew Murphy, A Bobik, A Vlahou, David GreeningDavid Greening, Karlheinz PeterKarlheinz Peter
Atherosclerotic plaque rupture leading to myocardial infarction is a major global health burden. Applying the tandem stenosis (TS) mouse model, which distinctively exhibits the characteristics of human plaque instability/rupture, we use quantitative proteomics to understand and directly compare unstable and stable atherosclerosis. Our data highlight the disparate natures and define unique protein signatures of unstable and stable atherosclerosis. Key proteins and pathway networks are identified such as the innate immune system, and neutrophil degranulation. The latter includes calprotectin S100A8/A9, which we validate in mouse and human unstable plaques, and we demonstrate the plaque-stabilizing effects of its inhibition. Overall, we provide critical insights into the unique proteomic landscape of unstable atherosclerosis (as distinct from stable atherosclerosis and vascular tissue). We further establish the TS model as a reliable preclinical tool for the discovery and testing of plaque-stabilizing drugs. Finally, we provide a knowledge resource defining unstable atherosclerosis that will facilitate the identification and validation of long-sought-after therapeutic targets and drugs for plaque stabilization.


Yung-Chih Chen is supported by a Heart Foundation Future leader fellowship (No. 102068). Karlheinz Peter is supported by the National Health and Medical Research Council L3 investigator fellowship. David Greening is supported by Heart Foundation (Vanguard), NHMRC project grant (DG: #1139489, 1057741), Future Fund (DG: MRF1201805), Pankind Aust. Innovation Grant, and the Victorian Government's Operational Infrastructure Support Program.


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Communications Biology



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Springer Nature



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© The Author(s) 2023 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit

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