Discovery of potential plasma protein biomarkers for acute myocardial infarction via proteomics

Abstract

Background: Acute myocardial infarction (AMI) is an acute disease with high mortality and seriously threatens human health. The identification of new effective biological markers for AMI is a prerequisite for treatment. Most proteomic studies have focused on atherosclerotic plaques, vascular cells, monocytes and platelets in the blood; however, the concentration of these factors in plasma is low, making it difficult to measure the complexity of plasma components. Moreover, some studies have examined the plasma protein of patients with acute coronary syndrome with histochemistry; however, the results are not consistent. Therefore, it is necessary to further investigate the differential proteins in the plasma of patients with AMI via proteomics to identify new biomarkers of AMI.

Methods: In this study, immunodepletion of high-abundance plasma proteins followed by an isobaric tagging for relative and absolute quantitation (iTRAQ)-based quantitative proteomic approach was used to analyze plasma samples from 5 control individuals and 10 AMI patients.

Results: Four hundred sixty-eight proteins were identified from two samples, and 33 proteins were differentially expressed in AMI patients compared to the controls. Among the 33 proteins, 12 proteins showed a ≥1.5-fold change between AMI and control samples. These proteins included fatty acid binding protein 3 (FABP3, ratio =6.36), creatine kinase-MB (CK-MB ratio =4.89), adenylate kinase1 (AK1 ratio =4.16), pro-platelet basic protein (PPBP ratio =3.29), creatine kinase (CK ratio =2.88), platelet factor 4 (PF4 ratio =2.62), peptidyl prolyl isomerase Cyclophilin A (PPIA ratio =2.05), Cofilin-1 (CFL1 ratio =1.81), coronin1A (CORO1A ratio =1.71), protein kinase M (PKM ratio =1.63), ribonuclease inhibitor (RNH1, ratio =1.67), and triose phosphate isomerase (TPI1 ratio =1.56). By contrast, there was a decrease of 19 proteins, such as adiponectin (ADIPOQ ratio =0.70), insulin-like growth factor binding protein6 (IGFBP6 ratio =0.70), Dickkopf-related protein 3 (DKK3 ratio =0.70) and complement 4B (C4B ratio =0.68). The most over-represented term was regulation of cell proliferation in the cellular component category of Gene Ontology (GO). The top 3 biological process terms were regulation of cell proliferation, response to wounding and wound healing. These proteins included immune proteins, blood coagulation proteins, lipid metabolism proteins, cytoskeleton proteins, energy metabolism proteins, gene regulation proteins, myocutaneous proteins, and myocardial remodeling proteins and were highly connected with each other, which indicates that the functional network of these processes contribute to the pathophysiology of AMI.

Conclusions: In conclusion, the present quantitative proteomic study identified novel AMI biomarker candidates and might provide fundamental information for the development of an AMI biomarker.

Keywords: Plasma protein biomarkers; acute myocardial infarction (AMI); proteomics.

Figure 1

Identification of total proteins and differentially expressed proteins. (A) Box plots of log2 protein intensity average for each sample. (B) Correlation analysis between each two samples. Rows and columns represent samples, and each square shows the correlation coefficients between two samples. ***P<0.001 comparing intensity of each two samples. (C) Heatmap of the significantly changed proteins. Rows represent proteins and columns represent different samples. Color of each cell represents expression change of proteins, red is increased and blue is decreased relative to control group. (D) Differential protein map of myocardial infarction group and normal group.

Figure 2

Bioinformatics analysis of differentially expressed proteins. (A) The annotations of biological processes based on GO analysis biological process. (B) Abscissa represents the number of proteins enriched in each biological process. Protein-protein interaction analysis of 33 differential proteins between CHAMI and CON using STRING database. Interactions between two proteins were indicated with gray edges. Color of node indicates fold change in ACLF. Green represents down-regulated protein and red represents up-regulated protein. Manual functional annotations based on GO analysis were shown.