Protein biomarkers of new-onset cardiovascular disease: prospective study from the systems approach to biomarker research in cardiovascular disease initiative.

OBJECTIVE: Incorporation of novel plasma protein biomarkers may improve current models for prediction of atherosclerotic cardiovascular disease (ASCVD) risk. APPROACH AND RESULTS: We used discovery mass spectrometry (MS) to determine plasma concentrations of 861 proteins in 135 myocardial infarction (MI) cases and 135 matched controls. Then, we measured 59 markers by targeted MS in 336 ASCVD case-control pairs. Associations with MI or ASCVD were tested in single-marker and multiple-marker analyses adjusted for established ASCVD risk factors. Twelve single markers from discovery MS were associated with MI incidence (at P<0.01), adjusting for clinical risk factors. Seven proteins in aggregate (cyclophilin A, cluster of differentiation 5 molecule [CD5] antigen-like, cell-surface glycoprotein mucin cell surface associated protein 18 [MUC-18], collagen-α 1 [XVIII] chain, salivary α-amylase 1, C-reactive protein, and multimerin-2) were highly associated with MI (P<0.0001) and significantly improved its prediction compared with a model with clinical risk factors alone (C-statistic of 0.71 versus 0.84). Through targeted MS, 12 single proteins were predictors of ASCVD (at P<0.05) after adjusting for established risk factors. In multiple-marker analyses, 4 proteins in combination (α-1-acid glycoprotein 1, paraoxonase 1, tetranectin, and CD5 antigen-like) predicted incident ASCVD (P<0.0001) and moderately improved the C-statistic from the model with clinical covariates alone (C-statistic of 0.69 versus 0.73). CONCLUSIONS: Proteomics profiling identified single- and multiple-marker protein panels that are associated with new-onset ASCVD and may lead to a better understanding of underlying disease mechanisms. Our findings include many novel protein biomarkers that, if externally validated, may improve risk assessment for MI and ASCVD.

Semi-targeted plasma proteomics discovery workflow utilizing two-stage protein depletion and off-line LC-MALDI MS/MS.

A quantitative proteomics workflow was implemented that provides extended plasma protein coverage by extensive protein depletion in combination with the sensitivity and breadth of analysis of two-dimensional LC-MS/MS shotgun analysis. Abundant proteins were depleted by a two-stage process using IgY and Supermix depletion columns in series. Samples are then extensively fractionated by two-dimensional chromatography with fractions directly deposited onto MALDI plates. Decoupling sample fractionation from mass spectrometry facilitates a targeted MS/MS precursor selection strategy that maximizes measurement of a consistent set of peptides across experiments. Multiplexed stable isotope labeling provides quantification relative to a common reference sample and ensures an identical set of peptides measured in the set of samples (set of eight) combined in a single experiment. The more extensive protein depletion provided by the addition of the Supermix column did not compromise overall reproducibility of the measurements or the ability to reliably detect changes in protein levels between samples. The implementation of this workflow is presented for a case study aimed at generating molecular signatures for prediction of first heart attack.

Quantitative analysis of cell signaling and drug action via mass spectrometry-based systems level phosphoproteomics.

Protein phosphorylation is a primary form of information transfer in cell signaling pathways and plays a crucial role in regulating biological responses. Aberrant phosphorylation has been implicated in a number of diseases, and kinases and phosphatases, the cellular enzymes that control dynamic phosphorylation events, present attractive therapeutic targets. However, the innate complexity of signaling networks has presented many challenges to therapeutic target selection and successful drug development. Approaches in phosphoproteomics can contribute functional, systems-level datasets across signaling networks that can provide insight into suitable drug targets, more broadly profile compound activities, and identify key biomarkers to assess clinical outcomes. Advances in MS-based phosphoproteomics efforts now provide the ability to quantitate phosphorylation with throughput and sensitivity to sample a significant portion of the phosphoproteome in clinically relevant systems. This review will discuss recent work and examples of application data that demonstrate the utility of MS, with a particular focus on the use of quantitative phosphoproteomics and phosphotyrosine-directed signaling analyses to provide robust measurement for functional biological interpretation of drug action on signaling and phenotypic outcomes.

Profiling protein tyrosine phosphorylation: a quantitative 45-plex peptide-based immunoassay.

Cellular homeostasis and responses to stimuli are mediated by complex signaling network events dominated by changes in protein phosphorylation states. Understanding information flow in the network is essential for correlating signaling changes to cell physiology. Tyrosine phosphorylation constitutes only a small portion of all protein phosphorylation, but its importance is manifested by the significant role it plays in diseases such as cancer. A peptide-based immunoassay microarray, designed to provide site specificity, quantification, broad coverage, and accessibility, is described that profiles 45 tyrosine phosphorylation sites across 34 proteins. Epidermal growth factor-stimulated A431 cells in the absence and presence of kinase inhibitors analyzed by microarrays showed biologically validated tyrosine phosphorylation changes and unanticipated activation of other targets. The approach is scalable for increasing the breadth of content as well as for interrogating other types of protein posttranslational modifications.

Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes.

CONTEXT: Metabolic dysregulation underlies key metabolic risk factors­obesity, dyslipidemia, and dysglycemia. OBJECTIVE: To uncover mechanistic links between metabolomic dysregulation and metabolic risk by testing metabolite associations with risk factors cross-sectionally and with risk factor changes over time. DESIGN: Cross-sectional­discovery samples (n = 650; age, 36­69 years) from the Framingham Heart Study (FHS) and replication samples (n = 670; age, 61­76 years) from the BioImage Study, both following a factorial design sampled from high vs low strata of body mass index, lipids, and glucose. Longitudinal­FHS participants (n = 554) with 5­7 years of follow-up for risk factor changes. SETTING: Observational studies. PARTICIPANTS: Cross-sectional samples with or without obesity, dysglycemia, and dyslipidemia, excluding prevalent cardiovascular disease and diabetes or dyslipidemia treatment. Age- and sex-matched by group. INTERVENTIONS: None. MAIN OUTCOME MEASURE(S): Gas chromatography-mass spectrometry detected 119 plasma metabolites. Cross-sectional associations with obesity, dyslipidemia, and dysglycemia were tested in discovery, with external replication of 37 metabolites. Single- and multi-metabolite markers were tested for association with longitudinal changes in risk factors. RESULTS: Cross-sectional metabolite associations were identified with obesity (n = 26), dyslipidemia (n = 21), and dysglycemia (n = 11) in discovery. Glutamic acid, lactic acid, and sitosterol associated with all three risk factors in meta-analysis (P < 4.5 × 10−4). Metabolites associated with longitudinal risk factor changes were enriched for bioactive lipids. Multi-metabolite panels explained 2.5­15.3% of longitudinal changes in metabolic traits. CONCLUSIONS: Cross-sectional results implicated dysregulated glutamate cycling and amino acid metabolism in metabolic risk. Certain bioactive lipids were associated with risk factors cross-sectionally and over time, suggesting their upstream role in risk factor progression. Functional studies are needed to validate findings and facilitate translation into treatments or preventive measures.

Quantitative measurement of epidermal growth factor receptor-mitogen-activated protein kinase signal transduction using a nine-plex, peptide-based immunoassay.

Aberrant epidermal growth factor receptor (EGFR, ErbB1) signaling is implicated in cell transformation, motility, and invasion in a variety of cell types, and EGFR is the target of several anticancer drugs. However, the kinetics of EGFR signaling and the individual contributions of site-specific phosphorylation events remain largely unknown. A peptide-based, multiplex immunoassay approach was developed to simultaneously measure both total and phosphorylated protein in a single sample. The approach involves the proteolytic digestion of proteins prior to the isolation and quantitation of site-specific phosphorylation events within an individual protein. Quantitation of phosphorylated and total proteins, in picomolar to nanomolar concentrations, were interpolated from standard curves generated with synthetic peptides that correspond to the peptide targets used in the immunoassays. In this study, a bead-based, nine-plex immunoassay measuring total and phosphorylated protein was constructed to measure temporal, site-specific phosphorylation of key members of the EGFR pathway (ErbB1 receptor, MEK1, MEK2, ERK1, and ERK2) in A431 cells stimulated with epidermal growth factor. The effect of MEK inhibition on this pathway was determined using a known MEK kinase inhibitor, SL327. The results reported herein are the first quantitative measurements of site-specific phosphorylation events and total proteins in a single sample, at the same time representing a new paradigm for standardized protein and phosphorylation analysis using multiplexed, peptide-based, sandwich immunoassays.

The challenges of bringing autologous HSP-based vaccines to commercial reality.

Based on genetic diversity in the population, there is an expectation, born out by decades of experience, that a given drug or treatment will not be equally efficacious for all patients. While this fact cannot be avoided, with ever increasing knowledge of the drug's biological mechanism of action and the relationship between efficacy and the patient's genetic profile, more directed treatments, with greater potential for efficacy are becoming possible. For example, Herceptin, Genentech's antibody based treatment for HER2 positive metastatic breast cancer, is prescribed based on the results of a diagnostic test, the outcome of which is able to screen out patients who have no chance of responding to the treatment. At the extreme of tailoring medicines to those patients who will receive the greatest benefit, is an autologous, or patient specific approach. Oncophage, a cancer vaccine in late stage clinical trials, is designed to accommodate the unique genetic mutations underlying each patient's cancer. This chapter of the book presents the challenges involved in bringing autologous HSP-based vaccines to commercial reality based on the experiences gained to date in the clinical manufacture of Oncophage. Two guiding principles have dominated our efforts. First, only the product should be autologous. All processes should be standardized to the greatest extent possible. Second, maintaining complete segregation between patient samples at all steps of processing is paramount.