Proteomic analysis of atherosclerotic plaque

Omics Science and Social Aspects in Detecting Biomarkers for Diagnosis, Risk Prediction, and Outcomes of Carotid Stenosis

Carotid stenosis is characterized by the progressive narrowing of the carotid arteries due to the formation of atherosclerotic plaque, which can lead to stroke and death as major complications. Numerous biomarkers allow for its study and characterization, particularly those related to "omics" sciences. Through the most common research databases, we report representative studies about carotid stenosis biomarkers based on genomics, transcriptomics, proteomics, and metabolomics in a narrative review. To establish a priority among studies based on their internal validity, we used a quality assessment tool, the Scale for the Assessment of Narrative Review Articles (SANRA). Genes, transcriptomes, proteins, and metabolites can diagnose the disease, define plaque connotations, predict consequences after revascularization interventions, and associate carotid stenosis with other patient comorbidities. It also emerged that many aspects determining the patient's psychological and social sphere are implicated in carotid disease. In conclusion, when taking the multidisciplinary approach that combines human sciences with biological sciences, it is possible to comprehensively define a patient's health and thus improve their clinical management through precision medicine.

Identification of Putative Early Atherosclerosis Biomarkers by Unsupervised Deconvolution of Heterogeneous Vascular Proteomes

Coronary artery disease remains a leading cause of death in industrialized nations, and early detection of disease is a critical intervention target to effectively treat patients and manage risk. Proteomic analysis of mixed tissue homogenates may obscure subtle protein changes that occur uniquely in underlying tissue subtypes. The unsupervised 'convex analysis of mixtures' (CAM) tool has previously been shown to effectively segregate cellular subtypes from mixed expression data. In this study, we hypothesized that CAM would identify proteomic information specifically informative to early atherosclerosis lesion involvement that could lead to potential markers of early disease detection. We quantified the proteome of 99 paired abdominal aorta (AA) and left anterior descending coronary artery (LAD) specimens (N = 198 specimens total) acquired during autopsy of young adults free of diagnosed cardiac disease. The CAM tool was then used to segregate protein subsets uniquely associated with different underlying tissue types, yielding markers of normal and fibrous plaque (FP) tissues in LAD and AA (N = 62 lesions markers). CAM-derived FP marker expression was validated against pathologist estimated luminal surface involvement of FP, as well as in an orthogonal cohort of "pure" fibrous plaque, fatty streak, and normal vascular specimens. A targeted mass spectrometry (MS) assay quantified 39 of 62 CAM-FP markers in plasma from women with angiographically verified coronary artery disease (CAD, N = 46) or free from apparent CAD (control, N = 40). Elastic net variable selection with logistic regression reduced this list to 10 proteins capable of classifying CAD status in this cohort with <6% misclassification error, and a mean area under the receiver operating characteristic curve of 0.992 (confidence interval 0.968-0.998) after cross validation. The proteomics-CAM workflow identified lesion-specific molecular biomarker candidates by distilling the most representative molecules from heterogeneous tissue types.

Proteomic Architecture of Human Coronary and Aortic Atherosclerosis

BACKGOUND

The inability to detect premature atherosclerosis significantly hinders implementation of personalized therapy to prevent coronary heart disease. A comprehensive understanding of arterial protein networks and how they change in early atherosclerosis could identify new biomarkers for disease detection and improved therapeutic targets.

METHODS

Here we describe the human arterial proteome and proteomic features strongly associated with early atherosclerosis based on mass spectrometry analysis of coronary artery and aortic specimens from 100 autopsied young adults (200 arterial specimens). Convex analysis of mixtures, differential dependent network modeling, and bioinformatic analyses defined the composition, network rewiring, and likely regulatory features of the protein networks associated with early atherosclerosis and how they vary across 2 anatomic distributions.

RESULTS

The data document significant differences in mitochondrial protein abundance between coronary and aortic samples (coronary>>aortic), and between atherosclerotic and normal tissues (atherosclerotic<

CONCLUSIONS

The human arterial proteome can be viewed as a complex network whose architectural features vary considerably as a function of anatomic location and the presence or absence of atherosclerosis. The data suggest important reductions in mitochondrial protein abundance in early atherosclerosis and also identify a subset of plasma proteins that are highly predictive of angiographically defined coronary disease.

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Key Words

• atherosclerosis
• biomarkers
• mitochondria
• proteomics

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MESH Headings

• Adolescent
• Adult
• Aorta/chemistry
• Aorta/pathology
• Aortic Diseases/metabolism
• Aortic Diseases/pathology
• Atherosclerosis/metabolism
• Atherosclerosis/pathology
• Autopsy
• Biomarkers/analysis
• Coronary Artery Disease/metabolism
• Coronary Artery Disease/pathology
• Coronary Vessels/chemistry
• Coronary Vessels/pathology
• Female
• Humans
• Male
• Middle Aged
• Plaque, Atherosclerotic
• Protein Interaction Maps
• Proteins/analysis
• Proteomics/methods
• Tandem Mass Spectrometry
• Young Adult

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Grants

• R01 HL111362 NHLBI NIH HHS

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Affiliation(s)

David M Herrington Section on Cardiovascular Medicine, Department of Internal Medicine (D.M.H., C.F.Z., G.S.)
Chunhong Mao Biocomplexity Institute of Virginia Tech, Virginia Tech, Blacksburg (C.M.)
Sarah J Parker Advanced Clinical Biosystems Research Institute, Cedars-Sinai Heart Institute, and Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA (S.T.P., V.V., W.R.S., J.E.V.E.)
Zongming Fu Johns Hopkins Medical Institute, Baltimore, MD (Z.F.)
Guoqiang Yu Department of Electrical and Computer Engineering, Virginia Tech, Arlington (G.Y., L.C., Y.F., Yizhi Wang, Yue Wang)
Lulu Chen Department of Electrical and Computer Engineering, Virginia Tech, Arlington (G.Y., L.C., Y.F., Yizhi Wang, Yue Wang)
Vidya Venkatraman Advanced Clinical Biosystems Research Institute, Cedars-Sinai Heart Institute, and Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA (S.T.P., V.V., W.R.S., J.E.V.E.)
Yi Fu Department of Electrical and Computer Engineering, Virginia Tech, Arlington (G.Y., L.C., Y.F., Yizhi Wang, Yue Wang)
Yizhi Wang Department of Electrical and Computer Engineering, Virginia Tech, Arlington (G.Y., L.C., Y.F., Yizhi Wang, Yue Wang)
Timothy D Howard Department of Biochemistry (T.D.H.)
Goo Jun Department of Epidemiology, Human Genetics and Environmental Sciences, Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston (G.J., J.E.H.)
Caroline F Zhao Section on Cardiovascular Medicine, Department of Internal Medicine (D.M.H., C.F.Z., G.S.)
Yongmei Liu Department of Epidemiology, Division of Public Health Sciences (Y.L.), Wake Forest School of Medicine, Winston-Salem, NC
Georgia Saylor Section on Cardiovascular Medicine, Department of Internal Medicine (D.M.H., C.F.Z., G.S.)
Weston R Spivia Advanced Clinical Biosystems Research Institute, Cedars-Sinai Heart Institute, and Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA (S.T.P., V.V., W.R.S., J.E.V.E.)
Grace B Athas Department of Pathology, Louisiana State Health Science Center, New Orleans (G.B.A., D.T., R.C.V.H.)
Dana Troxclair Department of Pathology, Louisiana State Health Science Center, New Orleans (G.B.A., D.T., R.C.V.H.)
James E Hixson Department of Epidemiology, Human Genetics and Environmental Sciences, Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston (G.J., J.E.H.)
Richard S Vander Heide Department of Pathology, Louisiana State Health Science Center, New Orleans (G.B.A., D.T., R.C.V.H.)
Yue Wang Department of Electrical and Computer Engineering, Virginia Tech, Arlington (G.Y., L.C., Y.F., Yizhi Wang, Yue Wang)
Jennifer E Van Eyk Advanced Clinical Biosystems Research Institute, Cedars-Sinai Heart Institute, and Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA (S.T.P., V.V., W.R.S., J.E.V.E.)

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Distinctive proteomic profiles among different regions of human carotid plaques in men and women

The heterogeneity of atherosclerotic tissue has limited comprehension in proteomic and metabolomic analyses. To elucidate the functional implications, and differences between genders, of atherosclerotic lesion formation we investigated protein profiles from different regions of human carotid atherosclerotic arteries; internal control, fatty streak, plaque shoulder, plaque centre, and fibrous cap. Proteomic analysis was performed using 2-DE with MALDI-TOF, with validation using nLC-MS/MS. Protein mapping of 2-DE identified 52 unique proteins, including 15 previously unmapped proteins, of which 41 proteins were confirmed by nLC-MS/MS analysis. Expression levels of 18 proteins were significantly altered in plaque regions compared to the internal control region. Nine proteins showed site-specific alterations, irrespective of gender, with clear associations to extracellular matrix remodelling. Five proteins display gender-specific alterations with 2-DE, with two alterations validated by nLC-MS/MS. Gender differences in ferritin light chain and transthyretin were validated using both techniques. Validation of immunohistochemistry confirmed significantly higher levels of ferritin in plaques from male patients. Proteomic analysis of different plaque regions has reduced the effects of plaque heterogeneity, and significant differences in protein expression are determined in specific regions and between genders. These proteomes have functional implications in plaque progression and are of importance in understanding gender differences in atherosclerosis.

Recent advances in proteomic technologies applied to cardiovascular disease

In recent years, the diagnosis of cardiovascular disease (CVD) has increased its potential, also thanks to mass spectrometry (MS) proteomics. Modern MS proteomics tools permit analyzing a variety of biological samples, ranging from single cells to tissues and body fluids, like plasma and urine. This approach enhances the search for informative biomarkers in biological samples from apparently healthy individuals or patients, thus allowing an earlier and more precise diagnosis and a deeper comprehension of pathogenesis, development and outcome of CVD to further reduce the enormous burden of this disease on public health. In fact, many differences in protein expression between CVD-affected and healthy subjects have been detected, but only a few of them have been useful to establish clinical biomarkers because they did not pass the verification and validation tests. For a concrete clinical support of MS proteomics to CVD, it is, therefore, necessary to: ameliorate the resolution, sensitivity, specificity, throughput, precision, and accuracy of MS platform components; standardize procedures for sample collection, preparation, and analysis; lower the costs of the analyses; reduce the time of biomarker verification and validation. At the same time, it will be fundamental, for the future perspectives of proteomics in clinical trials, to define the normal protein maps and the global patterns of normal protein levels, as well as those specific for the different expressions of CVD.