203 Extracellular matrix proteomics identifies molecular signature of symptomatic carotid plaques. (5th June 2017)

• Record Type: Journal Article
• Authors: Langley, Sarah R; Willeit, Karin; Didangelos, Athanasios; Matic, Ljubica Perisic; Skroblin, Philipp; Barallobre-Barreiro, Javier; Lengquist, Mariette; Rungger, Gregor; Kapustin, Alexander; Kedenko, Ludmilla; Lu, Ruifang; Barwari, Temo; Suna, Gonca; Yin, Xiaoke; Iglseder, Bernhard; Paulweber, Bernhard; Willeit, Peter; Shalhoub, Joseph; Pasterkamp, Gerard; Monaco, Claudia; Hedin, Ulf; M. Shanahan, Catherine; Willeit, Johann; Kielch, Stefan Kiechl; Mayr, Manuel
• Is Part Of: Heart. Volume 103(2017)Supplement 5
• Page Start: A137
• Page End: A137
• Publication Date: 2017-06-05
• Subjects: Extracellular matrix -- Atherosclerosis -- Proteomics and biomarkers; Heart -- Diseases -- Treatment -- Periodicals; Cardiology -- Periodicals; 616.12
• DOI: 10.1136/heartjnl-2017-311726.201
• Languages: English
• ISSNs: 1355-6037

Abstract

Abstract : Introduction: Recent findings have challenged the prevailing histology- or imaging-based definition of the vulnerable plaque. Methods: To investigate molecular characteristics associated with clinical instability of atherosclerosis, we performed a proteomics comparison of the vascular extracellular matrix and associated molecules in human carotid endarterectomy specimens from symptomatic versus asymptomatic patients. The proteomics data were integrated with gene expression profiling and an analysis of protein secretion by lipid-loaded human vascular smooth muscle cells. Results: The molecular signature of plaques from symptomatic patients identified by proteomics and at least one of the other two approaches comprised matrix metalloproteinase-9, chitinase-3-like protein 1, S100 calcium binding protein A8, S100 calcium binding protein A9, cathepsin B, fibronectin and galectin-3-binding protein. Biomarker candidates were measured in 685 subjects of the Bruneck Study and found to be significantly associated with the progression to advanced atherosclerosis (as assessed by repeated carotid ultrasound) and the incidence of cardiovascular disease over a 10 year follow-up period. A 4-biomarker signature (matrix metalloproteinase-9, S100A8/S100A9, cathepsin D, and galectin-3-binding protein) improved risk prediction in terms of risk discrimination and classification and was successfully replicated in a second independent population (SAPHIR Study). Conclusion: Our study highlights the strength of tissue-based proteomics for biomarker discovery.