Location: Plant, Soil and Nutrition ResearchTitle: Impact of whole genome protein analysis on gene discovery of disease models Author
Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 12/10/2010
Publication Date: 9/1/2011
Citation: Zhang, S., Yang, Y., Thannhauser, T.W. 2011. Impact of whole genome protein analysis on gene discovery of disease models. In: Gu, W. editor. Gene Discovery for Disease Models. 1st edition. Hoboken, NJ:Wiley-VCH. p. 471-530. Interpretive Summary:
Technical Abstract: The emergence of technologies that facilitate genome-wide data heralds a new paradigm for functional genomics. Over the past decade comprehensive genomic sequence information has become available for an ever-increasing number of species, the most significant being the completion of the Human Genome Project. The development of microarray technologies to study transcriptional regulation of genes at the messenger level has permitted “genome-wide” expression analysis in response to various stimuli. However, mRNA levels do not provide a complete picture of cellular function. It is at the protein level that most regulatory processes take place, where the primary disease processes occur and where most drugs target to. Unfortunately, the analogous protein array technologies are much more difficult to implement because proteins cannot be as easily synthesized or replicated in the way that nucleic acids are. Furthermore, the physical properties of proteins vary much more widely than those of nucleic acids, making protein-protein binding less predictable and more subject to non-specific interactions. Mass spectrometry (MS) has emerged as an indispensible tool for the investigation of the protein components in biological systems. Advances in MS, together with new methods of biochemical separation, chemical labeling of proteins, and the development of new bioinformatics tools have allowed initial efforts focused on protein identification to evolve such that the science of proteomics is currently being applied to high throughput quantitative applications with a view towards developing an understanding of disease models at the molecular level. Here we review the current state of MS-based proteomics with respect to analytical strategies, experimental design, sample preparation, MS instrumentation and data analysis. These topics are discussed in the context of their impact on several challenging biological issues such as protein identification, protein-protein interactions, the characterization of post translational modification, biomarker discovery and proteogenomics. Prospects for the future development of proteomic technologies are discussed and a set of frequently asked questions are asked and answered.