Ups and Downs in Heart Failure: The Case of Proteomics

This article refers to ‘Circulating proteins as predictors of incident heart failure in the elderly’, by M. Stenemo et al., published in this issue on pages 55–62. The term ‘proteome’ was first proposed in the mid-1990s with reference to the protein counterpart of ‘genome’ to describe the total collection of proteins of an organism that are produced as a result of genome translation and post-translational modifications.1 Proteomics is the study of the proteome or, in other words, the large-scale characterization of the entire protein complement of a biological system such as a cell line, a tissue, or an organism.2 Proteomics methodology focuses on the characterization of all proteins and their interactions within a biological system rather than on study of the structure and function of a single protein. Such an analysis was made possible by advances in analytical chemistry and other implicated technologies that provide opportunities to investigate tens of thousands of proteins in various samples.3 The proteome is constantly changing in accordance with both physiological conditions, such as the different stages of the cell cycle, cell ageing and function, and environmental factors, such as stress. The basic concept underlying the application of proteomics in medicine is that diseases are characterized by differences in proteome expression and configurations.3 As a result, proteomics-based studies compare the proteome between health and disease to identify those differences in protein expression.2 Therefore, the study of the proteome's dynamic variability allows the molecular phenotyping of a disease and thereby provides insights into the implicated pathogenetic pathways. Proteomics-based studies encompass several types of analysis, including protein mining, protein structure and function, protein expression profiling, protein interactions and post-translational modification.2 In medical research, proteomics-based studies use a biological sample such as plasma or urine, a tissue biopsy, or a cell line. A typical workflow includes the separation of proteins within the sample with two-dimensional gel electrophoresis, the analysis of protein spots of interest using mass spectrometry and the identification of proteins by the comparison of mass spectroscopy data with protein databases (Figure 1).4 In cardiovascular medicine, proteomics has been used increasingly since the late 1990s. The main applications of proteomics in cardiovascular medicine include the furthering of understanding of pathophysiological mechanisms, the development of novel biomarkers for screening, diagnosis, risk stratification and follow-up, and the identification of novel therapeutic targets (Figure 1).5 Several studies have hitherto been published concerning, among other issues, protein alterations in conditions such as dilated cardiomyopathy, myocardial hypertrophy, atherosclerosis, atrial fibrillation and myocardial stunning, the identification of biomarkers in ischaemic heart disease and the acquisition of mechanistic insights into myocardial protection during ischaemia and reperfusion.6-17 In heart failure (HF), proteome analyses have revealed changes concerning proteins of the extracellular matrix, cardiomyocyte cytoskeleton, contractile apparatus, energy production and cardiomyocyte protection that are up- or down-regulated (Table 1).5, 7, 18-30 28 DCM patients 21 IHD patients 9 controls 9 patients 8 controls 5 patients 5 controls 14 patients 13 controls 30 patients 30 controls 10 patients 10 controls 33 patients 29 controls 59 patients 67 controls 49 HF patients 36 progressed to HF 192 controls 40 patients 20 controls 7 DCM patients 9 controls 9 DCM patients 6 controls 25 proteins (15 up-regulated, 10 down-regulated); mainly metabolic proteins (e.g. NADH dehydrogenase, cytochrome c oxidase subunit), contractile proteins (e.g. myosin light chain proteins, troponin I type 3 and transthyretin), proteins related to stress response (e.g. αB-crystallin, heat shock proteins 27 and 20) 11 HF patients 8 controls 24 patients 6 controls In this issue of the Journal, Stenemo and colleagues report on proteomics-derived plasma biomarkers for the prediction of incident HF in community-dwelling elderly individuals.31 Heart failure is a prevalent condition in the elderly population and the early recognition of elderly individuals at high risk for the development of HF may reduce the significant mortality and morbidity associated with the syndrome.32 Stenemo et al. were able to identify a set of nine peptides that predicted the risk for future HF development independently of established risk factors.31 These peptides concerned proteins involved mostly in inflammation, apoptosis, remodelling and fibrinolysis. Risk prediction was improved when proteomics-based predictors were added to classical risk factors, but not when N-terminal B-type natriuretic peptide was added to the model. It should be stressed that the vast majority of incident HF cases in this study31 had preserved left ventricular ejection fraction, as might normally be expected in an elderly population, and therefore the reported results are not generalizable to other HF populations. Proteomics provides the link between the proteome and cardiovascular physiology and pathophysiology, thus allowing the molecular profiling of diseases and individuals.3, 4 The complexity of the human proteome determined by the huge number of proteins and their dynamic variation and interactions is still posing significant challenges to analytical technologies. Nonetheless, proteomics has evolved over the past two decades as an important component of the ‘omics’ approaches to cardiovascular diseases, which also include genomics, transcriptomics and metabolomics (Figure 1), and which are currently tipping the field of cardiovascular research towards an era of personalized and precision medicine. Conflict of interest: none declared.