A Proteomic Atlas of Atherosclerosis: The Contribution of Proteoglycans to Sex Differences, Plaque Phenotypes and Outcomes
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1007728 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR25715393.fastq.gz | Fastqsanger.gz | |||
| SRR25715394.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 2 of 2 |
Similar Datasets
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.
2023-08-29 | GSE241346 | GEO
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.
2023-11-15 | PXD031052 | Pride
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.
2023-09-07 | PXD030975 | Pride
Project description:Atherosclerotic plaques belong to the common vascular disease in the aged, which rupture will lead to acute thromboembolic diseases, the major reason for fatal cardiovascular events. Accumulating evidence indicates that lncRNAs exert critical functions in atherosclerosis. To identify novel astherosclerotic plaques-relevant lncRNAs, four specimens of carotid atherosclerotic plaque were collected, and endovascular tissue one centimeter far from the carotid atherosclerotic plaque was taken as a control group, we performed lncRNA microarray analysis using Affymetrix Human OElncRNA
2022-07-01 | GSE207252 | GEO
Project description:BackgroundUsing proteomics, we aimed to reveal molecular types of human atherosclerotic lesions and study their associations with histology, imaging, and cardiovascular outcomes.MethodsTwo 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.ResultsThis 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.ConclusionsPlaque proteomics redefined clinically relevant patient groups with distinct outcomes, identifying subgroups of male and female patients with elevated risk of future cardiovascular events.
| S-EPMC10498884 | biostudies-literature
Project description:Plaque rupture and subsequent thrombus formation is responsible for the majority of clinical complications of atherosclerosis and nonetheless our understanding of what underlies plaque vulnerability and rupture is still sparse and mostly deductively based on animal models and in vitro studies. We adopted five different -omics platforms to compare ruptured atherosclerotic and advanced-stable tissue within the same carotid plaque specimen from 24 carotid endarterectomy patients. Segments designated as stable feature either a fibrous cap atheroma or pathological intimal thickening. Segments designated as ruptured include a thrombus and/or presented intraplaque hemorrhage. For the present study only those samples were selected for further analysis that were flanked by two segments of identical classification, be it stable (S) or ruptured (R); and were derived from CEA specimen that contained plaque segments of both classifications.
2014-05-17 | E-MTAB-2055 | biostudies-arrayexpress
Project description:Cardiovascular diseases represent a leading cause of deaths globally; of which atherosclerosis is a major contributor. Selective retention of circulating apolipoprotein B particles in the sub endothelial space by arterial wall proteoglycans and their subsequent modification is currently thought to be a hallmark of the disease. The exact mechanism responsible for lesion development is not fully understood. Currently, ultrasonic assessment of carotid artery intima media thickness (IMT) is commonly used as a pre-clinical marker of atherosclerosis. However, as the onset of atherosclerotic process and the appearance of carotid artery plaque can vary, the identification of additional biomarkers showing potential etiological aspects of disease is an important goal. This study describes the use of a label free mass spectrometry approach in the proteomics analysis of serum samples from control and atherosclerotic subjects. The samples were from a study cohort recruited in The Cardiovascular Risk in Young Finns Study, with a goal of identifying biomarkers for atherosclerosis. Samples from 43 individuals with a early non-obstructive plaques and 43 controls were used (Matched by age, sex, body size and systolic blood pressure).
2018-07-30 | PXD008278 | Pride
Project description: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.
2023-05-10 | PXD030857 | Pride
Project description:This study aims at identifying gene expression patterns in the whole blood that could differentiate patients with severe coronary atherosclerosis from subjects without detectable coronary artery disease (CAD), and assess associations of gene expression patterns with plaque features at coronary CT angiography (CCTA). Patients undergoing CCTA for suspected CAD, with no cardiovascular history, were enrolled. Coronary stenosis was quantified and CCTA plaque features were assessed. The whole-blood transcriptome was analyzed by RNA-Sequencing. We detected highly significant differences in the circulating transcriptome between patients with high-degree coronary stenosis (> 70%) at CCTA and subjects with the absence of coronary plaques. Noteworthy, regression analysis revealed expression signatures associated with Leaman score, segment involved score, segment-stenosis score, and plaque volume with density <150 HU at CCTA. This pilot study shows that patients with significant coronary stenosis are characterized by whole blood transcriptome profiles that may discriminate them from patients without CAD. Furthermore, our results suggest that whole blood transcriptional profiles may predict plaque characteristics.
2022-06-29 | GSE202625 | GEO
Project description:Neutrophil extracellular traps (NETs) promote inflammation and atherosclerosis progression. In diabetes they are increased and impair wound healing, during which inflammation normally resolves. Atherosclerosis regression, a process resembling wound healing, is also impaired in diabetes. Thus, we hypothesized that NETs impede atherosclerosis regression in diabetes through unresolved inflammation. Objective: To investigate in diabetes the effect of NETs on plaque macrophage inflammation and whether NETs reduction improves atherosclerosis regression. Findings: Transcriptomic profiling of plaque macrophages from NET positive and negative areas in Ldlr-/- mice revealed inflammasome and glycolysis pathway upregulation, indicating a pro-inflammatory phenotype. During atherosclerosis regression in non-diabetic mice, plaque NET content decreased. In contrast, in diabetic mouse plaques NETs were enriched and persisted after lipid-lowering. DNase1 treatment (to degrade NETs) of diabetic mice reduced plaque NETs and macrophage inflammation and improved atherosclerosis regression after lipid-lowering. Conclusions: NETs decline during atherosclerosis regression in non-diabetic mice, but persist in diabetes and impair regression by exacerbating macrophage inflammation. DNase1 reduced diabetic plaque NETs and macrophage inflammation, and restored atherosclerosis resolution after lipid-lowering, despite ongoing hyperglycemia. Given that humans with diabetes also exhibit impaired atherosclerosis resolution with lipid-lowering, these data suggest that NETs contribute to the increased CVD risk in this population.
2020-05-12 | GSE145200 | GEO