Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
ISSN: 0974-276X
Desmond Fitzgerald
UCD Conway Institute, Ireland
Posters & Accepted Abstracts: J Proteomics Bioinform
A key risk in patients with cardiovascular disease is the development of arterial thrombosis, which may cause catastrophic complications such as myocardial infarction, stroke and sudden death. While clinical factors, such as a prior history of an acute coronary syndrome, point to patients at increased risk, there are no markers that help stratify patients for antiplatelet therapy. Consequently, it is difficult to select therapy for individual patients from a range of available drugs. Moreover, the current assays poorly discriminate the effects of different drugs or the impact of combinations of drugs, making it impossible to develop a treatment tailored to an individual. Our studies characterize the proteins in the releasate of platelets following activation by thrombin using a protocol of trypsin digestion in solution followed by nano HPLC and ESI Orbitrap MS/MS. The analysis includes label free quantitation of the peptides/proteins by MaxQuant, unsupervised hierarchical clustering and machine learning tools such as Random Forests. In a separate series of experiments in vitro, the same approach was used to explore the impact of different classes of anti-platelet therapy used alone or in combination. The studies showed a strong influence of individuals, disease, drugs (alone and in combination) and age, but not the type of disease. The findings suggest a strong influence of the individual on the proteome and on drug response. Moreover, different drugs were distinguished by the networks they influenced, allowing clear discrimination of drug effects.
Email: principalcohs@ucd.ie