RNAi applications in target validation

A novel isoform of the B cell tyrosine kinase BTK protects breast cancer cells from apoptosis

Tyrosine kinases orchestrate key cellular signaling pathways and their dysregulation is often associated with cellular transformation. Several recent cases in which inhibitors of tyrosine kinases have been successfully used as anticancer agents have underscored the importance of this class of proteins in the development of targeted cancer therapies. We have carried out a large-scale loss-of-function analysis of the human tyrosine kinases using RNA interference to identify novel survival factors for breast cancer cells. In addition to kinases with known roles in breast and other cancers, we identified several kinases that were previously unknown to be required for breast cancer cell survival. The most surprising of these was the cytosolic, nonreceptor tyrosine kinase, Bruton's tyrosine kinase (BTK), which has been extensively studied in B cell development. Down regulation of this protein with RNAi or inhibition with pharmacological inhibitors causes apoptosis; overexpression inhibits apoptosis induced by Doxorubicin in breast cancer cells. Our results surprisingly show that BTK is expressed in several breast cancer cell lines and tumors. The predominant form of BTK found in tumor cells is transcribed from an alternative promoter and results in a protein with an amino-terminal extension. This alternate form of BTK is expressed at significantly higher levels in tumorigenic breast cells than in normal breast cells. Since this protein is a survival factor for these cells, it represents both a potential marker and novel therapeutic target for breast cancer.

Lentiviral vector-mediated knockdown of Lrb in the arcuate nucleus promotes diet-induced obesity in rats

Obesity is currently a worldwide pandemic. Leptin resistance is a main mechanism of obese human and rodents. The downregulation of the long form of the leptin receptor (Lrb) was involved in leptin resistance in diet-induced obese rats. In the studies, we investigated whether arcuate nucleus (ARC) silencing of Lrb would promote diet-induced obesity in rats. Lentiviral vectors expressing Lrb-shRNA were administered to 5-week-old male rats by ARC injection. Following viral delivery, the rats were provided with a high-fat diet (HFD) or a chow diet (CD). After 8 weeks of the diet, serum leptin, and insulin concentrations were measured by RIA, gene expression of Lrb in the ARC was detected by a real-time RT-PCR, and leptin signaling was examined by western blot. The Lrb-shRNA knocked down the expression of Lrb mRNA in infected regions by 54% for the HFD rats and 47% for the CD rats respectively. The Lrb knockdown reduced Stats3 activation and increased expression of Npy mRNA. The rats with reduced Lrb in the ARC showed a significant increase in energy intake and body weight (BW) again when fed with a HFD. By contrast, there were no effects of Lrb reduction on energy intake or BW when rats maintained on a low-fat chow. Our results provide evidence that Lrb knockdown selectively in the ARC promotes diet-induced obesity and associated metabolic complications in rats.

Lentiviral vector-mediated knockdown of SOCS3 in the hypothalamus protects against the development of diet-induced obesity in rats

AIM

Leptin resistance is a feature of most cases of obesity in both humans and rodents. The suppressor of cytokine signalling 3 (SOCS3) is a negative-feedback regulator of leptin signalling involved in leptin resistance; therefore, the suppression of SOCS3 is a potential therapy for leptin resistance in obesity. In the studies, we investigated whether hypothalamic silencing of SOCS3 would attenuate diet-induced obesity in rats.

METHODS

First we established hypothalamic SOCS3-deficient rats through lentiviral vector (LV)-mediated RNA interference (RNAi) technique, then provided a high-fat diet or a chow diet to the rats. After 8 weeks of the diet, the serum leptin and insulin concentrations were measured by RIA, and the gene expressions of SOCS3 and the long form of leptin receptor in hypothalamus were detected by a real time RT-PCR. The leptin-induced Stat3 activation was examined by Western blot.

RESULTS

The RNAi protocol specifically knocked down the expression of SOCS3 mRNA by 50% approximately. The rats treated with LV-SOCS3-shRNA exhibited enhanced leptin-induced Stat3 activation, decreased body weight gain and improved metabolic parameters when exposed to a high-fat diet.

CONCLUSION

Our results provide evidence that the rats treated with hypothalamic SOCS3 silencing are significantly protected against the development of diet-induced obesity and SOCS3 is a potential target molecule for therapeutic intervention of obesity.

Towards a more robust approach to selecting and prosecuting promising targets and compounds

There are many factors that influence predictivity in drug discovery and impact on productivity within the pharmaceutical industry. This article will concentrate on just two aspects; first, the role of investigating target modulation within a (human) disease setting, from target selection through screening to animal models, and second, potential developments in the analysis and probing of the chemical space appropriate for drug discovery and, in particular, steps to improve predictivity thus moving to a more forward-looking process. The activities associated with target selection should develop significantly over the next 5-10 years leading to a more robust association of target modulation with disease modification. In addition, better understanding of the opportunities for target modulators should drive and improve the selection of ligands suitable for therapeutic applications. Within these areas it will be important to move away from a retrospective consideration of druggable targets towards a forward-looking approach based on holistic (disease context) profiling of both (progressable) targets and subsequently their ligands. Improvements in the predictive analysis and probing of the chemical space will be needed to confront both safety and efficacy end points that currently remain major reasons for failure in the clinic. It is hoped that improvements in data visualization together with chemocentric mining of the literature will facilitate better interrogation of development and clinical data, potentially modifying research project plans to better address these key issues.

An RNA interference screen identifies metabolic regulators NR1D1 and PBP as novel survival factors for breast cancer cells with the ERBB2 signature

Overexpression of the adverse prognostic marker ERBB2 occurs in 30% of breast cancers; however, therapies targeting this gene have not proved to be as effective as was initially hoped. Transcriptional profiling meta-analyses have shown that there are approximately 150 genes co-overexpressed with ERBB2, suggesting that these genes may represent alternative factors influencing ERBB2-positive tumors. Here we describe an RNA interference-based analysis of these genes that identifies transcriptional regulators of fat synthesis and storage as being critical for the survival of these cells. These transcription factors, nuclear receptor subfamily 1, group D, member 1 (NR1D1) and peroxisome proliferator activated receptor gamma binding protein (PBP), both reside on ERBB2-containing 17q12-21 amplicons and are part of the ERBB2 expression signature. We show that NR1D1 and PBP act through a common pathway in upregulating several genes in the de novo fatty acid synthesis network, which is highly active in ERBB2-positive breast cancer cells. Malate dehydrogenase 1 and malic enzyme 1, enzymes that link glycolysis and fatty acid synthesis, are also regulated by NR1D1. The resulting high-level fat production from increased expression of these genes likely contributes to an abnormal cellular energy metabolism based on aerobic glycolysis. Together, these results show that the cells of this aggressive form of breast cancer are genetically preprogrammed to depend on NR1D1 and PBP for the energy production necessary for survival.

Systems biology and inflammation

Inflammation is a complex, multiscale biological response to threats - both internal and external - to the body, which is also required for proper healing of injured tissue. In turn, damaged or dysfunctional tissue stimulates further inflammation. Despite continued advances in characterizing the cellular and molecular processes involved in the interactions between inflammation and tissue damage, there exists a significant gap between the knowledge of mechanistic pathophysiology and the development of effective therapies for various inflammatory conditions. We have suggested the concept of translational systems biology, defined as a focused application of computational modeling and engineering principles to pathophysiology primarily in order to revise clinical practice. This chapter reviews the existing, translational applications of computational simulations and related approaches as applied to inflammation.

Translational systems biology of inflammation

Inflammation is a complex, multi-scale biologic response to stress that is also required for repair and regeneration after injury. Despite the repository of detailed data about the cellular and molecular processes involved in inflammation, including some understanding of its pathophysiology, little progress has been made in treating the severe inflammatory syndrome of sepsis. To address the gap between basic science knowledge and therapy for sepsis, a community of biologists and physicians is using systems biology approaches in hopes of yielding basic insights into the biology of inflammation. “Systems biology” is a discipline that combines experimental discovery with mathematical modeling to aid in the understanding of the dynamic global organization and function of a biologic system (cell to organ to organism). We propose the term translational systems biology for the application of similar tools and engineering principles to biologic systems with the primary goal of optimizing clinical practice. We describe the efforts to use translational systems biology to develop an integrated framework to gain insight into the problem of acute inflammation. Progress in understanding inflammation using translational systems biology tools highlights the promise of this multidisciplinary field. Future advances in understanding complex medical problems are highly dependent on methodological advances and integration of the computational systems biology community with biologists and clinicians.