Dataset Information

Unstable Atherosclerotic Plaque Proteome

ABSTRACT: The rupture of unstable atherosclerotic plaques, leading to debilitating or fatal thrombotic events, is a major health burden worldwide. Limited understanding as to the molecular drivers of plaque instability and rupture hinders efforts in diagnosis and treatment prior to thrombotic events. Utilising an advanced pre-clinical mouse model (Tandem stenosis (TS) model), which presents human-like unstable atherosclerotic disease, we apply high-end omic methods to characterize the molecular signatures associated with plaque instability in atherosclerotic arteries. Through quantitative proteomic profiling, we depict unique proteome signatures of unstable plaques compared to stable plaques and healthy arteries. Coupled with single-cell RNA-sequencing of leukocytes, we describe the heterodimer complex S100a8/S100a9 as unique to unstable plaque, with neutrophils implicated as the transcriptional drivers of S100a8/a9 expression. We confirm S100a9 expression in human carotid atherosclerotic plaques and we further utilise the TS pre-clinical model to pharmacologically inhibit S100a8/S100a9, resulting in plaque stabilisation. Thus, we establish the TS model as a sophisticated translational tool for the profiling of unstable atherosclerotic plaques and demonstrate that unstable and stable atherosclerosis are highly different disease entities.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Aorta

DISEASE(S): Cardiovascular System Disease

SUBMITTER: david greening

LAB HEAD: David Greening

PROVIDER: PXD030857 | Pride | 2023-05-10

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	1_Unstable_plaque-Intraplaque_heamorrhage_20150402VS03_21.raw	Raw
	1_Unstable_plaque-Intraplaque_heamorrhage_20150402VS04_26.raw	Raw
	1_Unstable_plaque-Intraplaque_heamorrhage_20150402VS05_31.raw	Raw
	1_Unstable_plaque-Intraplaque_heamorrhage_20150402VS06_36.raw	Raw
	1_Unstable_plaque-Intraplaque_heamorrhage_20150402VS07_1.raw	Raw

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Publications

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

Chen Yung-Chih YC Smith Meaghan M Ying Ya-Lan YL Makridakis Manousos M Noonan Jonathan J Kanellakis Peter P Rai Alin A Salim Agus A Murphy Andrew A Bobik Alex A Vlahou Antonia A Greening David W DW Peter Karlheinz K

Communications biology 20230313 1

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 ...[more]

PMID: 36914713

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Project description:Background: Using proteomics, we strove to reveal novel molecular subtypes of human atherosclerotic lesions, study their associations with histology and imaging and relate them to long-term cardiovascular outcomes. Methods: 219 samples were obtained from 120 patients undergoing carotid endarterectomy. Sequential protein extraction was combined with multiplexed, discovery proteomics. Parallel reaction monitoring for 135 proteins was deployed for targeted validation. A combination of statistical, bioinformatics and machine learning methods was used to perform differential expression, network, pathway enrichment analysis and train and evaluate prognostic models. Results: Our extensive proteomics analysis from the core and periphery of plaques doubled the coverage of the plaque proteome compared to the largest proteomics study on atherosclerosis thus far. Plaque inflammation and calcification signatures were inversely correlated and validated with targeted proteomics. The inflammation signature was enriched with neutrophil-derived proteins, including calprotectin (S100A8/9) and myeloperoxidase. The calcification signature contained fetuin-A, osteopontin, and gamma-carboxylated proteins. Sex differences in the proteome of atherosclerosis were explained by a higher proportion of calcified plaques in women. Single-cell RNA sequencing data attributed the inflammation signature predominantly to neutrophils and macrophages and the calcification signature to smooth muscle cells, except for certain plasma proteins that were not expressed but retained in the plaque, i.e., fetuin-A. Echogenic lesions reflect the collagen content and calcification of plaque but carotid Duplex ultrasound fails to capture the extent of inflammatory protein changes in symptomatic plaques. Applying dimensionality reduction and machine learning on the proteomics data defined 4 distinct plaque phenotypes and revealed key protein signatures linked to smooth muscle cell content, plaque calcification and structural extracellular matrix, which improved the 9-year prognostic AUC by 25% compared to ultrasound and histology. A biosignature of four proteins (CNN1, PROC, SERPH, and CSPG2) independently predicted the progression of atherosclerosis and cardiovascular mortality with an AUC of 75% Conclusion: We combined discovery and targeted proteomics with network reconstruction and clustering techniques to provide molecular insights into protein changes in atherosclerotic plaques. The application of proteomics and machine learning techniques revealed distinct clusters of plaques that inform on disease progression and future adverse cardiovascular events.