Recent advances in proteomic technologies applied to cardiovascular disease

Abstract

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.

Nutrigenomics, the Microbiome, and Gene-Environment Interactions: New Directions in Cardiovascular Disease Research, Prevention, and Treatment

“…However, the complexity of data obtained from proteomic studies makes translation of putative biomarkers into clinically useful prognostic tools difficult. As has been reviewed extensively elsewhere, proteomics has been successfully applied as a discovery tool to CVD in plasma, urine, and tissue [199][200][201][202] in the context of both animal models aimed at understanding cardiac function and development 203 and human biomarkers of atherosclerosis, 204 dyslipidemia, 205 and cardiometabolic disease. 206 Proteomics has also been applied in the context of nutritional studies to identify biomarkers of diet and nutritional status.…”

Translational Approaches In Cardiovascular Diseases by Omics

“…To achieve these goals, we need to dispose of comprehensive protein datasets in physiological and pathological conditions (Napoli et al, 2013) and to combine proteomics results with clinical phenotypes, metabolite changes and genetic haplotype information (Balestrieri, Giovane, Mancini and Napoli, 2008). This combination could also cover genotype-phenotype association (Ozdemir et al, 2009), which is an important aspect in the validation of a new biomarker and for phenotype prediction in population studies (Ritchie, Holzinger, Li, Pendergrass and Kim, 2015).…”

“…Analysis of the platelet proteome provides information to identify ! 4 (Banfi et al, 2010;Napoli et al, 2013). Some proteomics studies on platelets in cardiovascular diseases are summarized in Table 1.…”

“…In cardiovascular diseases, many events remain unpredictable and to avoid them, we need more accurate, stable, easily detectable, specific and sensitive biomarkers to improve the diagnosis and prognosis of these pathologies (Napoli, Zullo, Picascia, Infante and Mancini, 2013).…”

The Path to Personalized Cardiovascular Medicine

“…Two core technologies-mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy-are at the heart of modern sensitive proteomic and metabolomic analyses (see also Chapter 2). [254][255][256][257][258][259][260][261][262][263] Typical ultrasensitive MS-based proteomics workflow includes extensive sample prefractionation, digestion of proteins, high performance liquid chromatography (HPLC) of generated peptides with subsequent electrospray ionization, MS analysis and identification of peptide sequences, protein identification and quantification through database searching (Figure 24.7).…”