Proteomic Atlas of Atherosclerosis: The Contribution of Proteoglycans to Sex Differences, Plaque Phenotypes, and Outcomes.
atherosclerosis
inflammation
machine learning
neutrophils
proteoglycans
proteomics
smooth muscle cells
Journal
Circulation research
ISSN: 1524-4571
Titre abrégé: Circ Res
Pays: United States
ID NLM: 0047103
Informations de publication
Date de publication:
15 09 2023
15 09 2023
Historique:
medline:
18
9
2023
pubmed:
30
8
2023
entrez:
30
8
2023
Statut:
ppublish
Résumé
Using proteomics, we aimed to reveal molecular types of human atherosclerotic lesions and study their associations with histology, imaging, and cardiovascular outcomes. 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. 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. Plaque proteomics redefined clinically relevant patient groups with distinct outcomes, identifying subgroups of male and female patients with elevated risk of future cardiovascular events.
Sections du résumé
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.
Identifiants
pubmed: 37646165
doi: 10.1161/CIRCRESAHA.123.322590
pmc: PMC10498884
doi:
Substances chimiques
Versicans
126968-45-4
alpha-2-HS-Glycoprotein
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
542-558Subventions
Organisme : British Heart Foundation
ID : CH/16/3/32406
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RG/F/21/110053
Pays : United Kingdom
Organisme : British Heart Foundation
ID : PG/20/10387
Pays : United Kingdom
Organisme : British Heart Foundation
ID : FS/19/33/34328
Pays : United Kingdom
Références
Methods Mol Biol. 2013;1005:215-23
pubmed: 23606260
Circulation. 2020 Nov 24;142(21):2060-2075
pubmed: 32962412
Arterioscler Thromb Vasc Biol. 1995 May;15(5):551-61
pubmed: 7749869
Ann Vasc Surg. 2017 Oct;44:336-342
pubmed: 28479433
Nat Commun. 2021 Feb 17;12(1):1088
pubmed: 33597522
J Am Coll Cardiol. 2015 Mar 3;65(8):846-855
pubmed: 25601032
Algorithms Mol Biol. 2015 Jul 08;10:23
pubmed: 26157474
Arterioscler Thromb Vasc Biol. 2015 Feb;35(2):399-408
pubmed: 25538207
Coron Artery Dis. 2017 Aug;28(5):369-375
pubmed: 28118185
J Am Heart Assoc. 2021 Aug 3;10(15):e021038
pubmed: 34325529
J Am Coll Cardiol. 2020 May 5;75(17):2189-2203
pubmed: 32354385
JACC Basic Transl Sci. 2018 Aug 01;3(4):464-480
pubmed: 30175270
J Am Soc Echocardiogr. 2021 Jun;34(6):614-624
pubmed: 33387609
Nat Methods. 2022 Jan;19(1):41-50
pubmed: 34949812
J Proteome Res. 2018 Jan 5;17(1):164-176
pubmed: 29129081
Mol Cell Proteomics. 2011 Aug;10(8):M111.008128
pubmed: 21593211
Eur Heart J. 2018 Mar 1;39(9):763-816
pubmed: 28886620
Arterioscler Thromb Vasc Biol. 2000 May;20(5):1177-8
pubmed: 10807728
Circulation. 2018 Jan 9;137(2):166-183
pubmed: 29030347
Circulation. 2021 Feb 16;143(7):713-726
pubmed: 33499648
Eur Heart J. 2021 Mar 1;42(9):919-933
pubmed: 33532862
Atherosclerosis. 2019 Sep;288:175-185
pubmed: 31109707
Biol Sex Differ. 2018 Dec 29;9(1):54
pubmed: 30594242
Nat Methods. 2019 Dec;16(12):1289-1296
pubmed: 31740819
Circulation. 2001 May 1;103(17):2171-5
pubmed: 11331258
Eur Heart J. 2022 Dec 14;43(47):4923-4930
pubmed: 36172703
Cell. 2021 Jun 24;184(13):3573-3587.e29
pubmed: 34062119
J Intern Med. 2020 May;287(5):493-513
pubmed: 32012358
Cardiovasc Diagn Ther. 2012 Mar;2(1):63-73
pubmed: 24282698
J Proteome Res. 2016 Mar 4;15(3):933-44
pubmed: 26795031
Talanta. 2017 Nov 1;174:341-346
pubmed: 28738590
Nat Med. 2019 Oct;25(10):1576-1588
pubmed: 31591603
Eur Heart J. 2018 Nov 1;39(41):3727-3735
pubmed: 30212857
Bioinformatics. 2016 Jul 15;32(14):2233-5
pubmed: 27153652
Ultrasound Med Biol. 2016 Jan;42(1):31-43
pubmed: 26493239
Hypertens Res. 2019 Mar;42(3):362-373
pubmed: 30617313
Nature. 2011 May 19;473(7347):317-25
pubmed: 21593864
Stroke. 1991 Jun;22(6):711-20
pubmed: 2057968
Circ Res. 2011 Jun 10;108(12):1494-509
pubmed: 21659653
Nature. 2019 May;569(7755):236-240
pubmed: 31043745
J Am Coll Cardiol. 2010 Apr 13;55(15):1590-7
pubmed: 20378076
Circulation. 2018 Jun 19;137(25):2741-2756
pubmed: 29915101
Matrix Biol. 2016 Jan;49:10-24
pubmed: 26163349
Circ Res. 2021 Nov 12;129(11):992-1005
pubmed: 34615369
Circulation. 2002 Sep 10;106(11):1368-73
pubmed: 12221054
Circulation. 1995 Sep 1;92(5):1355-74
pubmed: 7648691
J Cardiovasc Pharmacol Ther. 2019 Jul;24(4):323-333
pubmed: 30905168
Eur Heart J. 2009 Jan;30(1):107-15
pubmed: 19019993
Commun Biol. 2022 Oct 12;5(1):1084
pubmed: 36224302
J Clin Invest. 2017 Apr 3;127(4):1546-1560
pubmed: 28319050