USING FUNCTIONAL GENOMICS TO STUDY PINK1 AND METABOLIC PHYSIOLOGY :

• Genome sequencing projects have provided the substrate for an unimaginable number of biological experiments. Further, genomic technologies such as microarrays and quantitative and exquisitely sensitive techniques such as real-time quantitative polymerase chain reaction have made it possible to reliably generate millions of data points per experiment. The data can be high quality and yield entirely new insights into how gene expression is coordinated under complex physiological situations. It can also be that the data and interpretation are meaningless because of a lack of physiological context or experimental control. Thus, functional genomics is now being applied to study metabolic physiology with varying degrees of success. From the genome sequencing projects we also have the information needed to design chemical tools that can knock down a gene transcript, even distinguishing between splice variants in mammalian cells. Use of such technologies, inspired by nature's endogenous RNAi mechanism-microRNA targeting, comes with significant caveats. While the discipline of Pharmacology taught us last century that inhibitor action specificity is dependent on the concentration used, these experiences have been ignored by users of siRNA technologies. What we provide in this chapter is some considerations and observations from functional genomic studies. We are largely concerned with the phase that follows a microarray study, where a candidate gene is selected for manipulation in a system that is considered to be simpler than the in vivo mammalian tissue and thus the methods discussed largely apply to this cell biology phase. We apologize for not referring to all relevant publications and for any technical considerations we have also failed to factor into our discussion.

Keyword

human skeletal-muscle

diploid cell strains

mitochondrial-function

gene-expression

dopaminergic-neurons

antisense rna

insulin

exercise

humans

sirna

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