Kinome-wide siRNA screening identifies molecular targets mediating the sensitivity of pancreatic cancer cells to Aurora kinase inhibitors.

Aurora kinases are a family of mitotic kinases that play important roles in the tumorigenesis of a variety of cancers including pancreatic cancer. A number of Aurora kinase inhibitors (AKIs) are currently being tested in preclinical and clinical settings as anti-cancer therapies. However, the antitumor activity of AKIs in clinical trials has been modest. In order to improve the antitumor activity of AKIs in pancreatic cancer, we utilized a kinome focused RNAi screen to identify genes that, when silenced, would sensitize pancreatic cancer cells to AKI treatment. A total of 17 kinase genes were identified and confirmed as positive hits. One of the hits was the platelet-derived growth factor receptor, alpha polypeptide (PDGFRA), which has been shown to be overexpressed in pancreatic cancer cells and tumor tissues. Imatinib, a PDGFR inhibitor, significantly enhanced the anti-proliferative effect of ZM447439, an Aurora B specific inhibitor, and PHA-739358, a pan-Aurora kinase inhibitor. Further studies showed that imatinib augmented the induction of G2/M cell cycle arrest and apoptosis by PHA-739358. These findings indicate that PDGFRA is a potential mediator of AKI sensitivity in pancreatic cancer cells.

Kinome-wide RNAi studies in human multiple myeloma identify vulnerable kinase targets, including a lymphoid-restricted kinase, GRK6.

A paucity of validated kinase targets in human multiple myeloma has delayed clinical deployment of kinase inhibitors in treatment strategies. We therefore conducted a kinome-wide small interfering RNA (siRNA) lethality study in myeloma tumor lines bearing common t(4;14), t(14;16), and t(11;14) translocations to identify critically vulnerable kinases in myeloma tumor cells without regard to preconceived mechanistic notions. Fifteen kinases were repeatedly vulnerable in myeloma cells, including AKT1, AK3L1, AURKA, AURKB, CDC2L1, CDK5R2, FES, FLT4, GAK, GRK6, HK1, PKN1, PLK1, SMG1, and TNK2. Whereas several kinases (PLK1, HK1) were equally vulnerable in epithelial cells, others and particularly G protein-coupled receptor kinase, GRK6, appeared selectively vulnerable in myeloma. GRK6 inhibition was lethal to 6 of 7 myeloma tumor lines but was tolerated in 7 of 7 human cell lines. GRK6 exhibits lymphoid-restricted expression, and from coimmunoprecipitation studies we demonstrate that expression in myeloma cells is regulated via direct association with the heat shock protein 90 (HSP90) chaperone. GRK6 silencing causes suppression of signal transducer and activator of transcription 3 (STAT3) phosphorylation associated with reduction in MCL1 levels and phosphorylation, illustrating a potent mechanism for the cytotoxicity of GRK6 inhibition in multiple myeloma (MM) tumor cells. As mice that lack GRK6 are healthy, inhibition of GRK6 represents a uniquely targeted novel therapeutic strategy in human multiple myeloma.

High-throughput siRNA screening as a method of perturbation of biological systems and identification of targeted pathways coupled with compound screening.

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.

The global transcriptional response of Bacillus subtilis to manganese involves the MntR, Fur, TnrA and sigmaB regulons.

We have used DNA microarrays to monitor the global transcriptional response of Bacillus subtilis to changes in manganese availability. Mn(II) leads to the MntR-dependent repression of both the mntH and mntABCD operons encoding Mn(II) uptake systems. Mn(II) also represses the Fur regulon. This repression is unlikely to be a direct effect of Mn(II) on Fur as repression is sensitive to 2,2'-dipyridyl, an iron-selective chelator. We suggest that elevated Mn(II) displaces iron from cellular-binding sites and the resulting rise in free iron levels leads to repression of the Fur regulon. Many of the genes induced by Mn(II) are activated by sigmaB or TnrA. Both of these regulators are controlled by Mn(II)-dependent enzymes. Induction of the sigmaB-dependent general stress response by Mn(II) is largely dependent on RsbU, a Mn(II)-dependent phosphatase that dephosphorylates RsbV, ultimately leading to release of active sigmaB from its antisigma, RsbW. The activity of TnrA is inhibited when it forms an inactive complex with feedback-inhibited glutamine synthetase. Elevated Mn(II) reduces the sensitivity of glutamine synthetase to feedback inhibitors, and we suggest that this leads to the observed increase in TnrA activity. In sum, three distinct mechanisms can account for most of the transcriptional effects elicited by manganese: (i) direct binding of Mn(II) to metalloregulators such as MntR, (ii) perturbation of cellular iron pools leading to increased Fur activity and (iii) altered activity of Mn(II)-dependent enzymes that regulate the activity of sigmaB and TnrA.

Large-scale mutagenesis and functional genomics in yeast.

One of the major goals for the post-genome era is determining of the function of proteins predicted in the genome sequence. In many organisms functional assignments have been the results of comparative sequencing, proteomics or expression profiling. In the yeast, Saccharomyces cerevisiae, however, the functional role of a gene can be tested directly by disrupting the gene and examining the phenotype of the mutant. Because precise targeted deletions can be easily constructed, it is also possible to systematically delete every gene in the genome. Here we describe recent progress in yeast genome-wide mutagenesis programs and the results produced from analyzing the mutants created by them.