Extracellular matrix proteomics identifies molecular signature of symptomatic carotid plaques - PubMed (original) (raw)

. 2017 Apr 3;127(4):1546-1560.

doi: 10.1172/JCI86924. Epub 2017 Mar 20.

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

Extracellular matrix proteomics identifies molecular signature of symptomatic carotid plaques

Sarah R Langley et al. J Clin Invest. 2017.

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.

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Conflict of interest statement

Conflict of interest: P. Willeit, J. Willeit, S. Kiechl, or M. Mayr are named inventors on unrelated patents (EP15193448.6; EP2430453 B1, US8546089, EP2576826B, JP2013-513740; EP2776580 B1, DE112013006129T5, GB2524692A; EP3004889 A1; EP3004885 A1, US2016/0123994).

Figures

Figure 1

Figure 1. ECM proteomics of carotid endarterectomies.

Volcano plots of differences in abundance in (A) the proteome of the NaCl fraction (n = 12) and (B) the proteome of the GuHCl fraction (n = 12) between symptomatic and asymptomatic patients. Colors represent FDR levels (red, FDR <1%; orange, FDR <5%; green, FDR <10%; blue, FDR ≥10%, by NSAF-PLGEM; FC, fold change), and the labeled dots are those that were significantly differentially expressed between the plaques from symptomatic and asymptomatic patients (FDR <10%). (C) Coexpression network in the NaCl fraction (n = 12) calculated from Pearson correlations with edges defined at FDR <20% (Q values). The sizes of the nodes indicate the level of confidence for the differential expression between plaques from symptomatic and asymptomatic patients, and the colors represent different protein classifications (yellow, growth factors, cytokines, and regulatory factors; green, apolipoproteins; pink, collagens; orange, proteases/peptidases and inhibitors; blue, matricellular proteins and glycoproteins; white, other).

Figure 2

Figure 2. Comparison between protein and gene expression levels.

(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.

Figure 3

Figure 3. Proteomics of the secretome from lipid-loaded human vascular SMCs.

(A) Schematic for detecting differentially expressed ECM proteins that also showed differential secretion in lipid-loaded human vascular SMCs. (B) The proteomics analysis of the conditioned media (“secretome”) showed the LGALS3BP level to be significantly higher in the lipid-loaded versus control SMCs (n = 6, ***P = 0.005, paired t test). The box plot is as follows: black line, median; box edges, 1st and 3rd quartile; whiskers, furthest point within 1.5 times the interquartile range. (C) Immunofluorescence staining for LGALS3BP (red) in early atherosclerosis (human ascending aorta). Autofluorescence of elastin fibers (green). Scale bars: 200 μm. (D) Validation by immunoblotting. Note the co-detection of MMP9, FN1, LGALS3BP, and TNC upon lipid loading. SMCs from two different donors. Gelatin zymography for MMP2 was used as loading control. (E) Human aortic explants were either incubated with 50 pM MMP9 or left untreated for 24 hours. The degradation of FN1, TNC, and LGALS3B was examined by immunoblotting. MMP9 induces degradation of FN1 and TNC but releases LGALS3BP from human aortic explants. Aortic explants were from 2 different donors. Arrows indicate fragmentation products.

Figure 4

Figure 4. Biomarker candidates in tissue and plasma.

(A) Immunohistochemistry staining for LGALS3BP, S100A8/A9, CHI3L1, and MMP9 plus SMC (SMA, CNN1) and macrophage (CD68) markers. Scale bars: 500 μm. (B) Association between the biomarker candidates and atherosclerosis (n = 560) and CVD (n = 685) in the Bruneck study. Direction and strength of associations are coded by color and significance levels by size of frames. ORs are derived from logistic regression analysis, and hazard ratios from Cox models. ORs and hazard ratios are expressed for a 1-SD higher loge-transformed level of the given marker. Full details are presented in Supplemental Tables 6 and 7. Model 1 (M1) was adjusted for age (years) and sex. M2 was additionally controlled for standard vascular risk factors, including LDL cholesterol (mg/dl), HDL cholesterol (mg/dl), high-sensitivity CRP (mg/l), diabetes mellitus (0 vs. 1), hypertension (0 vs. 1), smoking (pack-years), and body mass index (kg/m2). *Additionally adjusted for atherosclerosis score (mm).

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References

    1. Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med. 2005;352(16):1685–1695. doi: 10.1056/NEJMra043430. - DOI - PubMed
    1. Libby P, Ridker PM, Hansson GK. Progress and challenges in translating the biology of atherosclerosis. Nature. 2011;473(7347):317–325. doi: 10.1038/nature10146. - DOI - PubMed
    1. Libby P, Pasterkamp G. Requiem for the ‘vulnerable plaque.’. Eur Heart J. 2015;36(43):2984–2987. - PubMed
    1. Arbab-Zadeh A, Fuster V. The myth of the ‘vulnerable plaque’: transitioning from a focus on individual lesions to atherosclerotic disease burden for coronary artery disease risk assessment. J Am Coll Cardiol. 2015;65(8):846–855. doi: 10.1016/j.jacc.2014.11.041. - DOI - PMC - PubMed
    1. Stone GW, et al. A prospective natural-history study of coronary atherosclerosis. N Engl J Med. 2011;364(3):226–235. doi: 10.1056/NEJMoa1002358. - DOI - PubMed

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