Plasma proteomics in Rheumatic Heart Disease and Coronary Artery Disease

posted Jan 26, 2017, 1:45 AM by sourav ghosh
Arun Bandyopadhyay
1 Cell Biology and Physiology Division, CSIR-Indian Institute of Chemical Biology, Kolkata, India, 2 Institute of post graduate Medical Education & Research , SSKM Hospital, Kolkata, India, 3 Apollo Gleneagles Hospital, Kolkata, India

The traditional approach to study cardiovascular disease (CVD) and develop new biomarkers was to look at one or a few candidate molecules. But, the advent of new proteomic techniques in CVD research allows analysing the expression of a plethora of proteins at one go. Proteomics and bioinformatics are powerful tools to identify protein based biomarkers involved in a disease state. The current advancement in proteomic technologies helps studying global protein expression changes associated with human disease processes. One of the advantages of these proteomic studies is that new biomarkers (diagnostic and/or prognostic) can be discovered which will help provide a better framework for treatment of cardiovascular diseases. Thus, the detection, identification and characterization of variations in the proteome occurring during the course of heart disease will provide both (i) insight into the underlying molecular mechanisms and (ii) potential cardiac specific biomarkers for regular, systematic observation and assessment of cardiac status.
The aim of this study was to provide a list of potential blood based protein markers for RHD and CAD. We utilized on-line label-free MS/MS using blood plasma as the source material. On-line LC-ESI-MS is the method of choice because the initial LC separation step decreases the amount of analytes that can be simultaneously ionized. Thus, the possibility of ion suppression is reduced rendering the method quantitative in nature. Such label-free quantitative LC-MS approaches can compare innumerable samples. Therefore, they are ideal for biomarker discovery because experimental workflows normally compare a large number of specimens to validate the results from a statistical point of view. Consequently, the label-free quantitative LC-MS methods employed in this thesis helped analyse the full potential of clinical plasma samples as a source of disease biomarkers in RHD and CAD respectively. Some of which might play important roles in the pathophysiology of RHD and CAD and improve the existing diagnostic strategies. Taken together, it may be said that the results of the proteome analysis may be useful to understand the pathophysiological changes associated with RHD and CAD. Some of the altered protein(s) unique to these diseases might qualify as potential CVD biomarker(s). Those biomarkers may be utilized for the development of diagnostics which in turn, would help therapeutic intervention timely and might save human lives [This work is supported by CSIR grant no. MLP123 to AB.

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