Identification de biomarqueurs protéiques prédictifs et diagnostiques des maladies coronariennes pour une population de souche canadienne-française
Coronary artery disease (CAD) is a major cause of mortality in Canada. Biomarkers are precious tools to diagnosis and risk stratification, and the identification of new candidates may substantially impact on public health and health economics. Due to its complexity, the human blood proteome remains challenging to explore. Our study aims at combining various complementary methods to determine the plasma proteome signature of patients at various stages of CAD.   The total cohort includes 1006 French-Canadian, 500 controls with a normal coronary angiography, and 506 patients with documented CAD. Two pools were reconstituted to represent the extremes of our population: 1) patients with a previous myocardial infarction (MI) and a recurrent MI over a 5 years follow-up and 2) controls without CAD and no events during follow-up matched for age and sex to the CAD pool (N=18). After a depletion of highly abundant proteins, pools were analyzed using 3 different proteomic methods: i) PF2D system followed by a liquid chromatography tandem mass spectrometry analysis (LC-MS/MS); ii) 1D-SDS-PAGE followed by LC-MS/MS; iii) Further depletion of proteins followed by 1D-SDS-PAGE and LC-MS/MS analysis of both flow through (3a) and retained fractions (3b).  Methods 2, 3a, and 3b allowed identification of 108, 125 and 91 proteins respectively. Overlapping proteins (31% between methods 2 and 3a, 61% between 2 and 3b and 19% between 3a and 3b) showed significant absolute and relative expressions with various methods. A total of 12 proteins were significantly (p<0.05) different in amounts (number of peptides identified) between the 2 pools. Analyses with the PF2D elution spectra (method 1) displayed numerous different profiles between the two groups. Over a total of 1156 fractions (control: 1172 peaks ACS: 926 peaks) generated, 370 fractions (674 peaks) overlapped between the two pools. 15 fractions (23 peaks) differed significantly (p<0,05) in amounts while some other peaks were uniquely present in one pool, representing biomarkers of potential interest.   We conclude that plasma proteome signatures are significantly modeled by the methods serving their recognition. Cost of analyses can be reduced with the PF2D method by performing a pre-selection before MS analysis. Detection of less abundant proteins can be improved by using further depletion.  Taking profit of these finding, we are now testing in our population the hypothesis that a combination of methods will sharpen our ability to detect clinically relevant proteins tailored to various clinical situations and to disease staging.
Biomarkers Coronary artery disease Depletion PF2D PF2D Proteomics