Biological aging is controlled to various extents by several closely related parameters, which include metabolic rate, caloric intake, genetics, lifestyle, and environmental factors (Jazwinski, 1996; Sohal and Weindruch, 1996; Smith and Pereira-Smith, 1996; Finch and Tanzi, 1997; Morrison and Hof, 1997; Lamberts et al., 1997). Over the years, a number of different theories for the aging process have been formulated (Masoro, 1993) such as the ''rate-of-living theory,'' the ''somatic mutation theory,'' the ''error catastrophy theory,'' the ''cross-linkage theory,'' and the ''free radical theory of aging.'' Some of these theories are based on common phenomena. For example, the rate-of-living theory correlates higher metabolic rates with shorter lifespan. Higher metabolic rates lead to higher levels of reactive oxygen species, available for the transformation of biomolecules, linking the rate of living theory with the cross-linkage theory and the free radical theory of aging. Ultimately, each one of these theories seems to favor a rather unique mechanism as the predominant cause of aging, and this may be considered the general shortcoming of these theories.
Aging represents a complex developmental phenomenon, depending on the global interplay between genes and gene products. The rate of aging will certainly depend on gene identity, gene polymorphism, the rate of translation, developmental regulation of translation, the residence time of gene products in the organism, and post-translational modification of the gene products. A thorough understanding of the aging process requires the evaluation of all these processes, and this is where aging research will greatly benefit from genomics and proteomics studies. Various genomics studies have started to address the general quantification of age-dependent changes in gene expression (Kayo et al., 2001) and the effects of nutrition and/or genetic modification (i.e., deletion or overexpression of genes affecting life span) on gene expression profiles. Currently, the role of proteomic studies is to provide quantitative information on the generation of gene products including the profiling of native proteins, protein isoforms, splice variants, mutants, and post-translationally modified species. In addition, proteomic studies need to define networks of protein-protein interactions. Bioinformatics studies are then necessary to combine both genomics and proteomics data into a multidimensional network of time-dependent, developmentally regulated interaction of genes. Such an effort is underway with the development of GenAge (available on the World Wide Web: http://genomics. senescence.info/genes/), a curated database of genes related to human aging (de Magalhaes and Toussaint, 2004). Ultimately, we hope that proteomic studies will not only further our basic understanding of the aging process but also yield reliable biomarkers for the early diagnosis of age-dependent diseases, preferentially in easily accessible biological fluids. The present article will review commonly used proteomic techniques and their application to the profiling of protein expression and post-translational modification.
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