Application of Proteomics in Cardiovascular Research
Dick H.W. Dekkers,
Adrie J.M. Verhoeven,
Dipak K. Das.
This review focuses on the current status of proteomic techniques that can be specifically applied to heart. Proteomics allows us to study alterations in protein expression in diseased hearts and leads us to develop new diagnostics and therapeutic parameters. The availability of the high resolution capacity of 2-DE can be successfully used to separate proteins in the first dimension according to their charge (isoelectric point) under denaturing conditions followed by their separation according to their molecular mass by SDS PAGE. The separated proteins are then visualized at high sensitivity with SYPRO dyes, especially SYPRO Ruby which is the most appropriate post-electrophoretic stain because of its compatibility for subsequent MS analysis. After the generation of a large protein dataset, they are organized using bioinformatics. Even though proteomics techniques have undergone substantial improvement, it remains a problem to identify phosphorylated proteins, which may be used for early disease detection. The proteomics analysis discussed in this review can be used for drug discovery, development of therapeutic modalities for cardiovascular diseases and the design of clinical trials. Proteins play more dynamic roles compared to DNA and RNA since most biological functions are regulated by protein-protein interactions. Protein-protein interaction mapping is crucial for many degenerative diseases and proteomics play an important role in understanding the molecular mechanisms of cellular functions. Though advancements in equipmentation have been made, it is unlikely to gain although MS is a powerful and evolving technique, the cost of running a sample needs to be considered. For example, regarding the cost of labeling, iTRAC runs about $400/sample and as many as 30 biological samples may be required to reach statistical significance in patient samples. Extensive time is also needed on a MS machine to run a fractionated sample on the order of days (times the number of samples). Once large datasets are generated, a bioinformaticist is required to align and analyze data from multiple treatment groups. An additional limitation is that the protein and splice variants have to be characterized to be identified by search engines. A number of predicted proteins may be identified with limited commercial resources available to follow up on such targets. Finally, though there have been advances in mass spectrometry equipment such as the Fouriertransform ion cyclotron resonance MS that generate higher sensitivity and dynamic range, there is a lack of standardization of protocols from sample collection and processing along the pipeline to data analysis. Unlike genomic data there is no community standard for database sharing. Although there are limitations to the technique, proteomics is likely to have great impact on drug discovery and clinical trial design leading to the development of niche personalized medicine. There is a definite need for early disease detection with appropriate biomarkers and proteomics are the tool to fulfill the requirement. For example, a routine, specific and sensitive serum proteomic pattern for cardiovascular diseases would be useful to clinicians for the early detection of diseases. In this regard, a low-resolution SELDI-TOF proteomic profile could be extremely useful. Compared to mRNAs, proteins are subjected to posttranslational modifications like phosphorylation, glycosylation and cleavage, and thus genomics are likely to miss the correct targets. This is of utmost importance for disease-related proteomics to become an essential component of personalized medicine system, which has great promise for the improvement of disease evaluation and patient care.
Keywords: Proteomics, heart, cardiovascular, protein expression, phosphorylated proteins
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