Scope of Proteomics

Proteomic technology has provided a deeper insight into the structure and function of proteins, including the different modifications of proteins, their interactions, and their roles in metabolic pathways. It is expected to provide subsequent insight into the causes of various diseases and their possible diagnosis, treatment, and cure. Some of these elements are discussed in this chapter. 

Proteomics will also lead to the discovery of new proteins as drugs. This possibility is suggested by the current studies of Mark Tuszynski and his coworkers at the University of California, San Diego. The researchers have shown that the symptoms of Alzheimer disease, such as memory loss, brain cell degeneration, and cognitive impairment, can be overcome by the injection of a brain-derived neurotrophic factor (BDNF) into mice, rhesus monkeys, and other model animals. The BDNF protein is usually produced in the brain of normal animals, but it is not produced by the cortex in animals with the disease. In addition to the development of drugs, proteomics is expected to help in the production of vaccines in the future. 

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Liska, A.J. and Shevchenko, A. (2003) Expanding the organismal scope of proteomics cross-species protein identification by mass spectrometry and its implications. Proteomics 3, 19-28. 

A.J. Liska, A. Shevchenko, Expanding the organismal scope of proteomics Crossspecies protein identification by MS and its applications, Proteomics, 3 (2003) 19. 

TECHNICAL SCOPE OF PROTEOMICS - BEYOND PROTEIN IDENTIFICATION... 

If the scope of mass spectrometry is limitless, why are the applications of clinical MS almost completely small molecules The answer is that most clinical tests analyze small molecules, biomarkers that are either metabolites or steroids and, hence, mass spectrometers would target those first. Perhaps a more complete answer would also include that methods must be very robust, easily reproduced in different labs, reliable, and subjected to an extensive array of validation tests. Although peptide and protein analysis is increasing rapidly in clinical labs, the MS approaches to these assays is lagging behind somewhat. MS techniques targeting these peptides and proteins exist, but they are primarily in the research stage, with few systems and methods subjected to the clinical rigors of validation. Once the necessary validations occur and methods simplified, it will only be a short time before MS is used routinely in clinical proteomics. 

Foodomics involves the use of multiple tools to deal with its different applications. Thus, the use of omics tools (as e.g., transcriptomics, proteomics, or metabolomics) is a must in this new discipline. Although a detailed description on these tools is out of the scope of this chapter, some fundamental concepts on different omics techniques are provided below. 

The MAGE has built within it a controlled vocabulary that is used to standardize communication between data providers. However, MAGE can also be extended to encode other types of omic data beyond genomics, such as proteomic data, so long as a reference to the ontology or controlled vocabulary is provided. Although a description of the extension mechanism is beyond scope of this book, practitioners must become familiar with it in order to ensure their software products and applications will be able to accept any and all annotation data that may be submitted with the genomic data. 

The first step in constructing a cell signaling network model is to generate an in silica interaction network. Signaling components of interest are identified, and data on binary interactions are extracted from the experimental literature. Often times, the creation of an entire interaction map for a large network is beyond the scope of one laboratory therefore, public databases have been created in which newly discovered proteins and/or protein interactions are deposited (57-63). However, often data are included from studies that cover a broad range of protein interactions, such as proteomic studies or yeast two-hybrid screens, and frequently contain many potential false positives or negatives. Thus, it becomes necessary to critically examine each reference to verify the interaction data reported, as this in silica network is the basis of further study. 

One inhibiting factor in commercial development of transgenic oilseeds with novel traits is public acceptance. The primary principle upon which approval has been based is known as substantial equivalence, which means that aside from any introduced changes, the composition of the plant or seed remains essentially unchanged. However, the concept of unintended consequences expands the scope of substantial equivalence, which establishes criteria that must be examined and met. Satisfying the concern for unintended consequences broadened the concept of substantial equivalence to include transcripts, the proteome, metabolome, and even genome sameness (43). In the approval process for a transgenic plant, these issues become a key part of the risk assessment both for food crops (44) and for industrial crops (45). 

MS has received significant attention and has spawned extensive research efforts resulting in numerous publications. Detailed information on this aspect of the proteome processing pipeline is beyond the scope of this chapter. For more information on this topical area, the reader is referred to a recent review [115]. 

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