Dataset Information

Emerging Affinity-Based Proteomic Technologies for Large-Scale Plasma Profiling in Cardiovascular Disease.

ABSTRACT: Plasma biomarkers that reflect molecular states of the cardiovascular system are central for clinical decision making. Routinely used plasma biomarkers include troponins, natriuretic peptides, and lipoprotein particles, yet interrogate only a modest subset of pathways relevant to cardiovascular disease. Systematic profiling of a larger portion of circulating plasma proteins (the plasma proteome) will provide opportunities for unbiased discovery of novel markers to improve diagnostic or predictive accuracy. In addition, proteomic profiling may inform pathophysiological understanding and point to novel therapeutic targets. Obstacles for comprehensive proteomic profiling include the immense size and structural heterogeneity of the proteome, and the broad range of abundance levels, as well. Proteome-wide, untargeted profiling can be performed in tissues and cells with tandem mass spectrometry. However, applications to plasma are limited by the need for complex preanalytical sample preparation stages limiting sample throughput. Multiplexing of targeted methods based on capture and detection of specific proteins are therefore receiving increasing attention in plasma proteomics. Immunoaffinity assays are the workhorse for measuring individual proteins but have been limited for proteomic applications by long development times, cross-reactivity preventing multiplexing, specificity issues, and incomplete sensitivity to detect proteins in the lower range of the abundance spectrum (below picograms per milliliter). Emerging technologies to address these issues include nucleotide-labeled immunoassays and aptamer reagents that can be automated for efficient multiplexing of thousands of proteins at high sample throughput, coupling of affinity capture methods to mass spectrometry for improved specificity, and ultrasensitive detection systems to measure low-abundance proteins. In addition, proteomics can now be integrated with modern genomics tools to comprehensively relate proteomic profiles to genetic variants, which may both influence binding of affinity reagents and serve to validate the target specificity of affinity assays. The application of deep quantitative proteomic profiling to large cohorts has thus become increasingly feasible with emerging affinity methods. The aims of this article are to provide the broad readership of Circulation with a timely overview of emerging methods for affinity proteomics and recent progress in cardiovascular medicine based on such methods.

SUBMITTER: Smith JG

PROVIDER: S-EPMC5555416 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Emerging Affinity-Based Proteomic Technologies for Large-Scale Plasma Profiling in Cardiovascular Disease.

Smith J Gustav JG Gerszten Robert E RE

Circulation 20170401 17

Plasma biomarkers that reflect molecular states of the cardiovascular system are central for clinical decision making. Routinely used plasma biomarkers include troponins, natriuretic peptides, and lipoprotein particles, yet interrogate only a modest subset of pathways relevant to cardiovascular disease. Systematic profiling of a larger portion of circulating plasma proteins (the plasma proteome) will provide opportunities for unbiased discovery of novel markers to improve diagnostic or predictiv ...[more]

PMID: 28438806

Similar Datasets

Project description:BACKGROUND:Plasma lipidomic measures may enable improved prediction of cardiovascular outcomes in secondary prevention. The aim of this study is to determine the association of plasma lipidomic measurements with cardiovascular events and assess their potential to predict such events. METHODS:Plasma lipids (n = 342) were measured in a retrospective subcohort (n = 5,991) of the LIPID study. Proportional hazards regression was used to identify lipids associated with future cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death) and cardiovascular death. Multivariable models adding lipid species to traditional risk factors were created using lipid ranking established from the Akaike information criterion within a 5-fold cross-validation framework. The results were tested on a diabetic case cohort from the ADVANCE study (n = 3,779). RESULTS:Specific ceramide species, sphingolipids, phospholipids, and neutral lipids containing omega-6 fatty acids or odd-chain fatty acids were associated with future cardiovascular events (106 species) and cardiovascular death (139 species). The addition of 7 lipid species to a base model (11 conventional risk factors) resulted in an increase in the C-statistics from 0.629 (95% CI, 0.628-0.630) to 0.654 (95% CI, 0.653-0.656) for prediction of cardiovascular events and from 0.673 (95% CI, 0.671-0.675) to 0.727 (95% CI, 0.725-0.728) for prediction of cardiovascular death. Categorical net reclassification improvements for cardiovascular events and cardiovascular death were 0.083 (95% CI, 0.081-0.086) and 0.166 (95% CI, 0.162-0.170), respectively. Evaluation on the ADVANCE case cohort demonstrated significant improvement on the base models. CONCLUSIONS:The improvement in the prediction of cardiovascular outcomes, above conventional risk factors, demonstrates the potential of plasma lipidomic profiles as biomarkers for cardiovascular risk stratification in secondary prevention. FUNDING:Bristol-Myers Squibb, the National Health and Medical Research Council of Australia (grants 211086, 358395, and 1029754), and the Operational Infrastructure Support Program of the Victorian government of Australia.

Project description:BackgroundEmerging proteomic technologies using novel affinity-based reagents allow for efficient multiplexing with high-sample throughput. To identify early biomarkers of myocardial injury, we recently applied an aptamer-based proteomic profiling platform that measures 1129 proteins to samples from patients undergoing septal alcohol ablation for hypertrophic cardiomyopathy, a human model of planned myocardial injury. Here, we examined the scalability of this approach using a markedly expanded platform to study a far broader range of human proteins in the context of myocardial injury.MethodsWe applied a highly multiplexed, expanded proteomic technique that uses single-stranded DNA aptamers to assay 4783 human proteins (4137 distinct human gene targets) to derivation and validation cohorts of planned myocardial injury, individuals with spontaneous myocardial infarction, and at-risk controls.ResultsWe found 376 target proteins that significantly changed in the blood after planned myocardial injury in a derivation cohort (n=20; P<1.05E-05, 1-way repeated measures analysis of variance, Bonferroni threshold). Two hundred forty-seven of these proteins were validated in an independent planned myocardial injury cohort (n=15; P<1.33E-04, 1-way repeated measures analysis of variance); >90% were directionally consistent and reached nominal significance in the validation cohort. Among the validated proteins that were increased within 1 hour after planned myocardial injury, 29 were also elevated in patients with spontaneous myocardial infarction (n=63; P<6.17E-04). Many of the novel markers identified in our study are intracellular proteins not previously identified in the peripheral circulation or have functional roles relevant to myocardial injury. For example, the cardiac LIM protein, cysteine- and glycine-rich protein 3, is thought to mediate cardiac mechanotransduction and stress responses, whereas the mitochondrial ATP synthase F0 subunit component is a vasoactive peptide on its release from cells. Last, we performed aptamer-affinity enrichment coupled with mass spectrometry to technically verify aptamer specificity for a subset of the new biomarkers.ConclusionsOur results demonstrate the feasibility of large-scale aptamer multiplexing at a level that has not previously been reported and with sample throughput that greatly exceeds other existing proteomic methods. The expanded aptamer-based proteomic platform provides a unique opportunity for biomarker and pathway discovery after myocardial injury.

Project description:BACKGROUND:Cardiovascular disease (CVD) describes the pathological conditions of the heart and blood vessels. Despite the large number of studies on CVD and its etiology, its key modulators remain largely unknown. To this end, we performed a comprehensive proteomic analysis of blood plasma, with the scope to identify disease-associated changes after placing them in the context of existing knowledge, and generate a well characterized dataset for further use in CVD multi-omics integrative analysis. METHODS:LC-MS/MS was employed to analyze plasma from 32 subjects (19 cases of various CVD phenotypes and 13 controls) in two steps: discovery (13 cases and 8 controls) and test (6 cases and 5 controls) set analysis. Following label-free quantification, the detected proteins were correlated to existing plasma proteomics datasets (plasma proteome database; PPD) and functionally annotated (Cytoscape, Ingenuity Pathway Analysis). Differential expression was defined based on identification confidence (≥ 2 peptides per protein), statistical significance (Mann-Whitney p value ≤ 0.05) and a minimum of twofold change. RESULTS:Peptides detected in at least 50% of samples per group were considered, resulting in a total of 3796 identified proteins (838 proteins based on ≥ 2 peptides). Pathway annotation confirmed the functional relevance of the findings (representation of complement cascade, fibrin clot formation, platelet degranulation, etc.). Correlation of the relative abundance of the proteins identified in the discovery set with their reported concentrations in the PPD was significant, confirming the validity of the quantification method. The discovery set analysis revealed 100 differentially expressed proteins between cases and controls, 39 of which were verified (≥ twofold change) in the test set. These included proteins already studied in the context of CVD (such as apolipoprotein B, alpha-2-macroglobulin), as well as novel findings (such as low density lipoprotein receptor related protein 2 [LRP2], protein SZT2) for which a mechanism of action is suggested. CONCLUSIONS:This proteomic study provides a comprehensive dataset to be used for integrative and functional studies in the field. The observed protein changes reflect known CVD-related processes (e.g. lipid uptake, inflammation) but also novel hypotheses for further investigation including a potential pleiotropic role of LPR2 but also links of SZT2 to CVD.

Dataset Information

Emerging Affinity-Based Proteomic Technologies for Large-Scale Plasma Profiling in Cardiovascular Disease.

Publications

Emerging Affinity-Based Proteomic Technologies for Large-Scale Plasma Profiling in Cardiovascular Disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets