TY - JOUR
T1 - Proteomic Atlas of Atherosclerosis
T2 - The Contribution of Proteoglycans to Sex Differences, Plaque Phenotypes, and Outcomes
AU - Theofilatos, Konstantinos
AU - Stojkovic, Stefan
AU - Hasman, Maria
AU - van der Laan, Sander W
AU - Baig, Ferheen
AU - Barallobre-Barreiro, Javier
AU - Schmidt, Lukas
AU - Yin, Siqi
AU - Yin, Xiaoke
AU - Burnap, Sean
AU - Singh, Bhawana
AU - Popham, Jude
AU - Harkot, Olesya
AU - Kampf, Stephanie
AU - Nackenhorst, Maja Carina
AU - Strassl, Andreas
AU - Loewe, Christian
AU - Demyanets, Svitlana
AU - Neumayer, Christoph
AU - Bilban, Martin
AU - Hengstenberg, Christian
AU - Huber, Kurt
AU - Pasterkamp, Gerard
AU - Wojta, Johann
AU - Mayr, Manuel
N1 - Publisher Copyright:
© 2023 Lippincott Williams and Wilkins. All rights reserved.
PY - 2023/9/15
Y1 - 2023/9/15
N2 - BACKGROUND: Using proteomics, we aimed to reveal molecular types of human atherosclerotic lesions and study their associations with histology, imaging, and cardiovascular outcomes. METHODS: 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. 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 (calprotectin) and myeloperoxidase, whereas 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 4 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 patient groups with distinct outcomes, identifying subgroups of male and female patients with elevated risk of future cardiovascular events.
AB - BACKGROUND: Using proteomics, we aimed to reveal molecular types of human atherosclerotic lesions and study their associations with histology, imaging, and cardiovascular outcomes. METHODS: 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. 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 (calprotectin) and myeloperoxidase, whereas 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 4 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 patient groups with distinct outcomes, identifying subgroups of male and female patients with elevated risk of future cardiovascular events.
KW - atherosclerosis
KW - inflammation
KW - machine learning
KW - neutrophils
KW - proteoglycans
KW - proteomics
KW - smooth muscle cells
UR - http://www.scopus.com/inward/record.url?scp=85171393375&partnerID=8YFLogxK
U2 - 10.1161/CIRCRESAHA.123.322590
DO - 10.1161/CIRCRESAHA.123.322590
M3 - Article
C2 - 37646165
SN - 0009-7330
VL - 133
SP - 542
EP - 558
JO - Circulation research
JF - Circulation research
IS - 7
ER -