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Experimental alteration of gene expression is a powerful technique for functional characterization of disease genes. RNA interference (RNAi) is a naturally occurring mechanism of gene regulation, which is triggered by the introduction of double-stranded RNA into a cell. This phenomenon can be synthetically exploited to down-regulate expression of specific genes by transfecting mammalian cells with synthetic short interfering RNAs (siRNAs). These siRNAs can be designed to silence the expression of specific genes bearing a particular target sequence in high-throughput (HT) siRNA experimental systems and may potentially be presented as a therapeutic strategy for inhibiting transcriptional regulation of genes. This can constitute a strategy that can inhibit targets that are not tractable by small molecules such as chemical compounds. Large-scale experiments using low-dose drug exposure combined with siRNA also represent a promising discovery strategy for the purpose of identifying synergistic targets that facilitate synthetic lethal combination phenotypes. In light of such advantageous applications, siRNA technology has become an ideal research tool for studying gene function. In this chapter, we focus on the application of RNAi, with particular focus on HT siRNA phenotype profiling, to support cellular pharmacogenomics.

High-throughput RNA interference (HT-RNAi) is a powerful research tool for parallel, 'genome-wide', targeted knockdown of specific gene products. Such perturbation of gene product expression allows for the systematic query of gene function. The phenotypic results can be monitored by assaying for specific alterations in molecular and cellular endpoints, such as promoter activation, cell proliferation and survival. RNAi profiling may also be coupled with drug screening to identify molecular correlates of drug response. As with other genomic-scale data, methods of data analysis are required to handle the unique aspects of data normalization and statistical processing. In addition, novel techniques or knowledge-mining strategies are required to extract useful biological information from HT-RNAi data. Knowledge-mining strategies involve the novel application of bioinformatic tools and expert curation to provide biological context to genomic-scale data such as that generated from HT-RNAi data. Pathway-based tools, whether text-mining based or manually curated, serve an essential role in knowledge mining. These tools can be applied during all steps of HT-RNAi screen experiments including pre-screen knowledge gathering, assay development and hit confirmation and validation. Most importantly, pathway tools allow the interrogation of HT-RNAi data to identify and prioritize pathway-based biological information as a result of specific loss of gene function.

RNA interference (RNAi) is a natural mechanism, that is triggered by the introduction of double-stranded RNA into a cell. The long double-stranded RNA is then processed into short interfering RNA (siRNA) that mediates sequence-specific degradation of homologous transcripts. This phenomenon can be exploited to experimentally trigger RNAi and downregulate gene expression by transfecting mammalian cells with synthetic siRNA. Thus, siRNAs can be designed to specifically silence the expression of genes bearing a particular target sequence. In this chapter, we present methods and procedures for validating the effects of siRNA-based gene silencing on target gene expression. To illustrate our approach, we use examples from our analysis of a Cancer Gene Library of 278 siRNAs targeting 139 classic oncogenes and tumor suppressor genes (Qiagen Inc., Germantown, MD). Specifically, this library was used for high-throughput RNAi phenotype analysis followed by gene expression analysis to validate gene silencing for siRNA that produced a phenotype. Methods and protocols are presented that illustrate how sequence-specific gene silencing of effective siRNAs are analyzed and validated by quantitative real-time PCR assays to measure the extent of target gene silencing, as well as effects on various gene expression end points.