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J Transl Med. 2018 Apr 17;16(1):104. doi: 10.1186/s12967-018-1476-9.

Plasma proteomic analysis reveals altered protein abundances in cardiovascular disease.

Author information

1
Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou Street, 115 27, Athens, Greece.
2
Mosaiques Diagnostics GmbH, Rotenburger Straße 20, 30659, Hannover, Germany.
3
Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK.
4
Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease, Toulouse, France.
5
Université Toulouse III Paul-Sabatier, Toulouse, France.
6
Deutsches Herzzentrum Berlin, Augustenburger Pl. 1, 13353, Berlin, Germany.
7
Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou Street, 115 27, Athens, Greece. vlahoua@bioacademy.gr.

Abstract

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.

KEYWORDS:

Biomarker; Cardiovascular disease; Drug discovery; LC–MS/MS; Plasma; Proteomic dataset; Proteomics

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