Proteomics Research in Cardiovascular Medicine and Biomarker Discovery

Maggie P Y Lam, Peipei Ping, Elizabeth Murphy, Maggie P Y Lam, Peipei Ping, Elizabeth Murphy

Abstract

Proteomics is a systems physiology discipline to address the large-scale characterization of protein species within a biological system, be it a cell, a tissue, a body biofluid, an organism, or a cohort population. Building on advances from chemical analytical platforms (e.g., mass spectrometry and other technologies), proteomics approaches have contributed powerful applications in cardiovascular biomedicine, most notably in: 1) the discovery of circulating protein biomarkers of heart diseases from plasma samples; and 2) the identification of disease mechanisms and potential therapeutic targets in cardiovascular tissues, in both preclinical models and translational studies. Contemporary proteomics investigations offer powerful means to simultaneously examine tens of thousands of proteins in various samples, and understand their molecular phenotypes in health and disease. This concise review introduces study design considerations, example applications and use cases, as well as interpretation and analysis of proteomics data in cardiovascular biomedicine.

Keywords: mass spectrometry; molecular phenotyping; post-translational modifications; protein arrays; protein signatures; protein dynamics.

Published by Elsevier Inc.

Figures

Figure 1. Sensitivity and Coverage of Various…
Figure 1. Sensitivity and Coverage of Various Proteomics Study Designs
Properties of 3 common proteomics study designs are listed. Discovery (shotgun), targeted, and targeted discovery proteomics approaches take different strategies to address proteome coverage, number of samples, and sensitivity limits. Discovery proteomics experiments have high coverage (up to 10,000 proteins in a single sample), but have limitations in throughput; fewer samples/subjects can be analyzed. Targeted discovery approaches focus on analyzing a panel of high-potential targets in sufficient numbers of samples. Targeted proteomics is able to achieve the highest sensitivity, which allows the detection of low-abundance plasma markers, such as TNF and IL-6, but at the expense of scope and throughput. Graph (center) shows the typical sensitivity range of discovery, targeted discovery, and targeted approaches, juxtaposed with the concentrations of selected plasma proteins and disease markers. Example technological platforms and additional notes are shown on the right. CK-MB = creatine kinase-myocardial band; CRP = C-reactive protein; ELISA = enzyme-linked immunosorbent assay; IL-6 = interleukin 6; LDL = low-density lipoprotein; MS = mass spectrometry; NT-proBNP = N-terminal B-type natriuretic peptide; TNF = tumor necrosis factor.
Central Illustration. Overview of Proteomics Analyses for…
Central Illustration. Overview of Proteomics Analyses for Cardiovascular Diseases
This figure lists major components in common proteomics workflows, as well as their associated experimental considerations in biomarker discovery and disease studies. From top to bottom, protein samples are collected from the plasma of human cohorts or cardiac tissues in animal models according to study goals (either biomarker discovery or mechanistic studies). Three major study approaches (discovery, targeted, and targeted discovery) take different strategies between proteome coverage and analytical throughput, and utilize different technological platforms, including mass spectrometry and protein arrays. Following the acquisition of large-scale datasets, data are processed to identify the protein species present and to deduce their relative quantities across samples. Subsequently, a number of statistical and bioinformatics workflows are used in data interpretation to extract insights from datasets. Network analysis casts proteins in the context of interaction networks and/or altered cellular pathways. Statistical learning and modeling methods connect the identified molecular features to orthogonal phenotypes, identify signatures, and offer information on subject classification or predictive analysis. The identified protein signatures will require validation, which can be achieved by complementary translational studies, including in vitro biochemical analysis and large cohorts. PTM = post-translational modifications.

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