Fernando, Ed. Vivanco's Cardiovascular Proteomics: Methods and Protocols (Methods in PDF

Fernando, Ed. Vivanco's Cardiovascular Proteomics: Methods and Protocols (Methods in PDF

By Fernando, Ed. Vivanco

This state of the art booklet offers protocols and techniques for proteomic review of heart problems written via pioneering researchers within the box. themes explored during this complete quantity comprise acquiring particular center proteins, strategies for opting for chance biomarkers of atherome plaque rupture, examining the secretome of explanted endarterectomies cultured in vitro, and phage show strategies for decoding the molecular variety of blood vessels.

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Extra resources for Cardiovascular Proteomics: Methods and Protocols (Methods in Molecular Biology Vol 357)

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13. Venter, J. , Adams, M. , Myers, E. , et al. (2001) The sequence of the human genome. Science 291, 1304–1351. 14. Lander, E. , Linton, L. , et al. (2001) Initial sequencing and analysis of the human genome. Nature 409, 860–921. 15. , Gramolini, A. , et al. (2005) Proteome dynamics during C2C12 myoblast differentiation. Mol. Cell Proteomics 4, 887–901. 16. , Gramolini, A. , et al. (2004) Identification of biochemical adaptations in hyper- or hypocontractile hearts from phospholamban mutant mice by expression proteomics.

The advent of proteome profiling has facilitated the elucidation of disease-associated proteins, paving the way for molecular diagnostics and the identification of novel therapeutic targets. We explored the proteomic profile of pathological left ventricular hypertrophy in comparison with normal heart in a model of rats and investigated the proteomic changes in response to different antihypertensive regimens in order to elucidate their cardioprotective effects. Here we describe in depth the protocol for this type of study.

An interesting feature of the GoMiner software tool is that two lists—for example, the total list of proteins identified and a subcluster of proteins found to be upregulated in a disease state—can be compared with each other. The program compares the GO terms matched to both lists and provides the user with significantly enriched GO terms in the subcluster, as compared to the total input list. This feature can be extremely useful for finding biological processes or molecular functions of proteins responsible for the development of a disease phenotype, if no hypothesis is readily available.

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