Gene expression

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Since genes are transcribed to RNA, RNA is translated into proteins, and defects in proteins cause disease, the ultimate goal would be to carry out a random search of expressed proteins in target tissues. This may not yet be completely feasible but the study of large-scale transcript profiles is. This approach has been successful in defining prognoses of cancers but for complex diseases affecting many target tissues it may not be that simple. Also, defining what is differentially expressed among more than 20,000 gene transcripts on a chip is a statistical challenge. Despite these problems, analysis of gene expression in skeletal muscle of patients with type 2 diabetes and prediabetes has provided new insights into the pathogenesis of the disease. It required, however, the analysis of coordinated gene expression in metabolic pathways rather than of individual genes. This is based upon the assumption that if one member of the pathway shows altered expression, this will be translated into the whole pathway. Genes regulating oxidative phosphorylation in mitochondria showed a 20 % coordinated down-regulation in muscle from prediabetic and diabetic individuals (Mootha et al. 2003; Patti et al. 2003). Furthermore, a similar down-regulation of the gene encoding for a master regulator of oxidative phosphorylation, the PPAR7 co-activator PGC-1a, was observed. This pathway has thus emerged as central in the pathogenesis of type 2 diabetes and it appears that impaired mitochondrial function and impaired oxidation of fat may predispose to type 2 diabetes through a 'thrifty gene' mechanism (see below). By studying young and elderly twins we were able to demonstrate that elderly carriers of a Gly482Ser polymorphism in the PGC-1a gene had decreased expression of the PGC-1a gene in skeletal muscle, suggesting that genetic variants determine age-related decline in expression of key genes regulating oxidative phosphorylation (Ling et al. 2004) (Figure 20.1). This study gives an example of how genetic factors, in combination with non-genetic factors, can influence gene expression, which in turn affects glucose and fat metabolism (Figure 20.2).

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