Extracellular matrix proteomics identifies molecular signature of symptomatic carotid plaques
Sarah R. Langley, … , Stefan Kiechl, Manuel Mayr
Sarah R. Langley, … , Stefan Kiechl, Manuel Mayr
Published March 20, 2017
Citation Information: J Clin Invest. 2017;127(4):1546-1560. https://doi.org/10.1172/JCI86924.
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Clinical Medicine Vascular biology

Extracellular matrix proteomics identifies molecular signature of symptomatic carotid plaques

Abstract

BACKGROUND. The identification of patients with high-risk atherosclerotic plaques prior to the manifestation of clinical events remains challenging. Recent findings question histology- and imaging-based definitions of the “vulnerable plaque,” necessitating an improved approach for predicting onset of symptoms. METHODS. We performed a proteomics comparison of the vascular extracellular matrix and associated molecules in human carotid endarterectomy specimens from 6 symptomatic versus 6 asymptomatic patients to identify a protein signature for high-risk atherosclerotic plaques. Proteomics data were integrated with gene expression profiling of 121 carotid endarterectomies and an analysis of protein secretion by lipid-loaded human vascular smooth muscle cells. Finally, epidemiological validation of candidate biomarkers was performed in two community-based studies. RESULTS. Proteomics and at least one of the other two approaches identified a molecular signature of plaques from symptomatic patients that comprised matrix metalloproteinase 9, chitinase 3-like-1, S100 calcium binding protein A8 (S100A8), S100A9, cathepsin B, fibronectin, and galectin-3-binding protein. Biomarker candidates measured in 685 subjects in the Bruneck study were associated with progression to advanced atherosclerosis and 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 and was successfully replicated in an independent cohort, the SAPHIR study. CONCLUSION. The identified 4-biomarker signature may improve risk prediction and diagnostics for the management of cardiovascular disease. Further, our study highlights the strength of tissue-based proteomics for biomarker discovery. FUNDING. UK: British Heart Foundation (BHF); King’s BHF Center; and the National Institute for Health Research Biomedical Research Center based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London in partnership with King’s College Hospital. Austria: Federal Ministry for Transport, Innovation and Technology (BMVIT); Federal Ministry of Science, Research and Economy (BMWFW); Wirtschaftsagentur Wien; and Standortagentur Tirol.

Authors

Sarah R. Langley, Karin Willeit, Athanasios Didangelos, Ljubica Perisic Matic, Philipp Skroblin, Javier Barallobre-Barreiro, Mariette Lengquist, Gregor Rungger, Alexander Kapustin, Ludmilla Kedenko, Chris Molenaar, Ruifang Lu, Temo Barwari, Gonca Suna, Xiaoke Yin, Bernhard Iglseder, Bernhard Paulweber, Peter Willeit, Joseph Shalhoub, Gerard Pasterkamp, Alun H. Davies, Claudia Monaco, Ulf Hedin, Catherine M. Shanahan, Johann Willeit, Stefan Kiechl, Manuel Mayr

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Figure 2

Comparison between protein and gene expression levels.

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Comparison between protein and gene expression levels. (A) Schematic for...
(A) Schematic for detecting differentially expressed proteins that were also differentially expressed at the mRNA transcript level. (B) Volcano plot of differences in expression between symptomatic and asymptomatic patients (n = 121) for corresponding ECM-associated genes (green, FDR <10%; blue, FDR ≥10%, by limma), and the labeled dots are those that were significantly differentially expressed between the plaques from symptomatic and asymptomatic patients (FDR <10%). (C) Box plots for the 5 molecules in common for protein abundance (NSAF) and normalized gene expression (n = 12, proteomics; n = 121, transcriptomics; FDR <10%). The box plots are as follows: black line, median; box edges, 1st and 3rd quartiles; whiskers, furthest point within 1.5 times the interquartile range.