Dataset Information

A Proteomic Atlas of Atherosclerosis: Regional Proteomic Signatures for Plaque Inflammation and Calcification

ABSTRACT: 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.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Carotid Artery

DISEASE(S): Atherosclerosis

SUBMITTER: Xiaoke Yin

LAB HEAD: Manuel Mayr

PROVIDER: PXD030975 | Pride | 2023-09-07

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	GuHCl_PlaqueTMTData_FINAL_TRIPLICATE_May2017.xlsx	Xlsx
	GuHcl_Plaques_TMT_Group1to6_170217-_1_.pdResult	Other
	GuHcl_Plaques_TMT_Group1to6_170217-_1_.pdResultView	Other
	GuHcl_Plaques_rep2_TMT_20170202_Group1.raw	Raw
	GuHcl_Plaques_rep2_TMT_20170202_Group10.raw	Raw

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Publications

Proteomic Atlas of Atherosclerosis: The Contribution of Proteoglycans to Sex Differences, Plaque Phenotypes, and Outcomes.

Theofilatos Konstantinos K Stojkovic Stefan S Hasman Maria M van der Laan Sander W SW Baig Ferheen F Barallobre-Barreiro Javier J Schmidt Lukas Emanuel LE Yin Siqi S Yin Xiaoke X Burnap Sean S Singh Bhawana B Popham Jude J Harkot Olesya O Kampf Stephanie S Nackenhorst Maja Carina MC Strassl Andreas A Loewe Christian C Demyanets Svitlana S Neumayer Christoph C Bilban Martin M Hengstenberg Christian C Huber Kurt K Pasterkamp Gerard G Wojta Johann J Mayr Manuel M

Circulation research 20230830 7

<h4>Background</h4>Using proteomics, we aimed to reveal molecular types of human atherosclerotic lesions and study their associations with histology, imaging, and cardiovascular outcomes.<h4>Methods</h4>Two hundred nineteen carotid endarterectomy samples were procured from 120 patients. A sequential protein extraction protocol was employed in conjunction with multiplexed, discovery proteomics. To focus on extracellular proteins, parallel reaction monitoring was employed for targeted proteomics. ...[more]

PMID: 37646165

Similar Datasets

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.

Project description:Background. Using proteomics, we aimed to reveal molecular types of humanatherosclerotic lesions and study their associations with histology, imaging andcardiovascular outcomes. Methods. 219 carotid endarterectomy samples were procured from 120 patients. A sequential protein extraction protocol was employed in conjunction with multiplexed, discovery proteomics. To focus on extracellular proteins, parallel reaction monitoring was employed for targeted proteomics. Proteomic signatures were integrated with bulk, single-cell, and spatial RNA-sequencing data, and validated in 200 patients from the Athero-Express Biobank study. Results. This extensive proteomics analysis identified plaque inflammation and calcification signatures, which were inversely correlated and validated using targeted proteomics. The inflammation signature was characterized by the presence of neutrophil-derived proteins, such as S100A8/9 and myeloperoxidase, while the calcification signature included fetuin-A, osteopontin, and gamma-carboxylated proteins. The proteomics data also revealed sex differences in atherosclerosis, with large-aggregating proteoglycans versican and aggrecan being more abundant in females and exhibiting an inverse correlation with estradiol levels. The integration of RNA sequencing data attributed the inflammation signature predominantly to neutrophils and macrophages, and the calcification and sex signatures to smooth muscle cells, except for certain plasma proteins that were not expressed but retained in plaques, such as fetuin-A. Dimensionality reduction and machine learning techniques were applied to identify four distinct plaque phenotypes based on proteomics data. A protein signature of 4 key proteins (calponin, protein C, serpin H1, and versican) predicted future cardiovascular mortality with an area under the curve of 75% and 67.5% in the discovery and validation cohort, respectively, surpassing the prognostic performance of imaging and histology. Conclusions. Plaque proteomics redefined clinically relevant 55 patient groups with distinct outcomes, identifying subgroups of male and female patients with elevated risk of future cardiovascular events.

Project description:Regression of atherosclerosis is an important clinical goal, however the pathways that mediate the resolution of atherosclerotic inflammation and reversal of plaques are poorly understood. Regulatory T cells (Tregs) have been shown to be atheroprotective, however numbers of these immunosuppressive cells decrease with disease progression. Using multiple independent mouse models of atherosclerosis regression, we demonstrate that an increase in plaque Tregs is a common signature of regressing plaques. To test if Tregs are required for the resolution of atherosclerotic inflammation and plaque regression during lipid-lowering therapy, we combined CD25 monoclonal antibody (PC61 mAb)-mediated Treg depletion with single-cell RNA-sequencing of immune cells in the plaque and conventional analyses of atherosclerosis. Single cell RNA-sequencing revealed that Tregs from aortic plaques shared some similarity with splenic Tregs, but were distinct from skin and colon Tregs supporting recent findings of tissue-dependent Treg heterogeneity. Furthermore, Tregs from progressing plaques expressed markers of natural Tregs derived from the thymus, whereas Tregs in regressing plaques lacked Nrp1 and Helios expression, suggesting that they are induced in the periphery during lipid lowering. Treatment of atherosclerotic mice with PC61 mAb effectively depleted Tregs in the blood and peripheral tissues, including plaques, and blocked the regression of atherosclerosis induced by apoB anti-sense oligonucleotides. Morphometric analyses revealed that control antibody-treated mice showed a 40% decrease in plaque burden and macrophage content under regression conditions, whereas PC61 mAb-treated mice showed no change in plaque size or inflammatory cell content compared to baseline. Moreover, Treg depletion enhanced inflammatory signaling and blocked tissue reparative functions of macrophages in the regressing plaque, including M2-polarization, efferocytosis and sensing of specialized pro-resolving lipid mediators. Together, these data establish essential roles for Tregs in the resolution of atherosclerotic inflammation and plaque remodeling during regression.