Serial analysis of gene expression: from gene discovery to target identification

Serial Analysis of Gene Expression (SAGE) is a sequence-based genomics tool that features comprehensive gene discovery and quantitative gene expression capabilities. As an 'open' system, SAGE can reveal which genes are expressed and their level of expression rather than merely quantifying the expression level of a predetermined, and presently incomplete, set of genes as carried out by 'closed' system gene expression profiling platforms such as microarrays. These distinguishing attributes enable SAGE to be used as a primary discovery engine that can characterize human disease at the molecular level while illuminating potential targets and markers for therapeutic and diagnostic development, respectively.     

Drug discovery veers off target

As drug pipelines stagnate, one potential savior comes in the form of a new scientific paradigm: systems biology.     

Pharmaceutical target identification by gene expression analysis

The majority of newly-identified genes in the human genome show no significant sequence similarity to genes whose function is known, so they are not easily recognized as potential drug targets. Expression analysis is an alternative method to suggest possible functions of genes. We review statistical methods for gene expression analysis to identify potential pharmaceutical targets. Specifically, we illustrate the analysis of differential gene expression (using discriminant analysis, t-tests, and analysis of variance) and co-expression (using correlation, clustering, and chi-square). We present an example of the use of expression analysis to identify co-expressed cardiomyopathy-associated genes.     

Drug target discovery using retroviruses

Contemporary drug target discovery relies on a continuum of genetic and chemical-based screening technologies. These approaches conflate pharmaceutical and genetic principles, providing a conceptual platform that links dominant genetics with drug action. Thus, phenotypic genetic screens using vector-expressed dominant genetic effectors – trans-acting molecules that modulate gene function, such as peptides or RNA interference triggers – can reveal genes whose inhibition engenders a therapeutic effect. The correlation of this genetic inhibition with a specific protein activity defines a drug target candidate. Retroviruses provide a unique opportunity to stably deliver a variety of dominant genetic effectors to mammalian cells in a flexible predetermined fashion and are a favoured system for phenotypic screening. Here, the authors review recent innovations and approaches to therapeutic target discovery using retroviral vectors.     

High-throughput gene expression analysis for drug discovery

The ability to rapidly survey and compare gene expression levels between reference and test samples is moving the drug discovery process towards a more genomic orientation. The success of the Human Genome Project and related private genomics initiatives, combined with new technologies to probe, image and access expression data, are responsible for this transformation. This article reviews the history, status and future direction of high-throughput gene expression analysis. It describes classical approaches, explains the development of methods such as differential display for discovering novel genes, and discusses how microarray technology is exploiting collections of known sequences to pinpoint drug targets.     

Drug target discovery by pharmacogenetics: mutations in the melanocortin system and eating disorders

The identification of the genetic defect underlying the obese phenotype of the viable yellow mouse, ectopic overexpression of the agouti protein which acts as antagonist at the melanocortin-4 receptor, together with the demonstration that the brain melanocortin system was one major downstream effector pathway of leptin signaling has put forward melanocortin receptors as drug targets for obesity. The lack of compounds acting as melanocortin receptor antagonists was the reason why pharmacological studies had not recognized melanocortin receptors as important drug targets earlier. Blockade of brain melanocortin receptors results in increased food intake and body weight, whereas stimulation of the brain melanocortin system results in decreased food intake and activation of the hypothalamo-pituitary-adrenal axis. Anorexia nervosa is characterized by decreased body weight and food intake accompanied by changes in neuroendocrine systems such as strong activation of the hypothalamo-pituitary-adrenal axis. Since agouti-related protein suppresses the activity of the melanocortin system, the AgRP gene was investigated as candidate gene in anorexia nervosa. One variant of the AgRP gene was associated with anorexia nervosa, thus putting forward melanocortin receptor blockade as putative pharmacotherapy. Investigating variations in candidate genes in disease populations appears to be a fruitful approach towards the identification of drug targets.     

Integrated approaches to therapeutic target gene discovery

The identification of tractable drug targets is the first critical step in the long process of drug development, and the challenge for scientists in the post-genomic era is to couple gene and protein sequence information with biological insight to identify genes with the greatest therapeutic and commercial potential. An extraordinary array of genomics- and proteomics-based techniques is available for this endeavor, and combining multiple, complementary approaches enhances the informative power of such experimentation.     

Drug discovery and computational evolutionary analysis

Drug discovery remains a difficult business with a very high level of attrition. Many steps in this long process use data generated from various species. One key challenge is to successfully translate the pre-clinical findings of target validation and safety studies in animal models to diverse human beings in the clinic. Advanced computational evolutionary analysis techniques combined with the increasing availability of sequence information enable the application of systematic evolutionary approaches to targets and pathways of interest to drug discovery. These analyses have the potential to increase our understanding of experimental differences observed between species.     

Tools for discovery: gene expression enterprise solutions

Expression profiling in drug discovery is a highly collaborative process requiring robust, centralized databases and exhaustive exploration of expanding libraries of expression experiments. In this review, state-of-the-art data analysis tools that identify relationships between gene expression and biological activities are described. Informatics workflow, system scalability and regulatory compliance issues are discussed in the context of expression data management.     

siRNA for gene silencing: a route to drug target discovery

The identification of RNA interference in mammalian cells, mediated via both virally-derived short interference RNA (siRNA) and endogenously produced microRNA, has revolutionised our understanding of the translational control of gene expression. Indeed, since its initial discovery, siRNA has been rapidly deployed for the elucidation of gene function and the identification of potential drug targets, a process often known as target discovery. In this review, we briefly discuss the mechanism of RNA interference and then critically examine the use of siRNA in target discovery, with a particular emphasis upon issues such as efficacy, selectivity, delivery and application in high-throughput studies.     

TOGA analysis of gene expression to accelerate target development.

With the availability of new technologies for analysis of gene expression, drug targets may now be identified through more focused rational approaches. We have developed an extremely sensitive, reproducible, and accurate method called TOGA, enabling analysis of nearly all genes within an RNA sample. This technology can be used to identify genes regulated in a great variety of situations. For example, we have been able to find genes with expression restricted to specific brain regions, and in a recent example, we found a gene regulated in specific brain regions in response to clozapine treatment. These studies will quickly lead to identification of genes critical to specific biological and clinical processes, accelerating the development of new targets and therapies.     

Drug discovery by dynamic combinatorial libraries

Dynamic combinatorial chemistry is a recently introduced supramolecular approach that uses self-assembly processes to generate libraries of chemical compounds. In contrast to the stepwise methodology of classical combinatorial techniques, dynamic combinatorial chemistry allows for the generation of libraries based on the continuous interconversion between the library constituents. Spontaneous assembly of the building blocks through reversible chemical reactions virtually encompasses all possible combinations, and allows the establishment of adaptive processes owing to the dynamic interchange of the library constituents. Addition of the target ligand or receptor creates a driving force that favours the formation of the best-binding constituent--a self-screening process that is capable, in principle, of accelerating the identification of lead compounds for drug discovery.     

DNA含量及细胞周期调控基因的表达在甲状腺肿瘤发生中的作用

目的探讨DNA含量及细胞周期调控基因表达在不同类型甲状腺肿瘤组织中的变化。方法应用流式细胞术定量分析DNA含量及细胞周期调控基因在甲状腺肿瘤中的表达。结果DNA含量和异倍体率在甲状腺癌组明显增高，细胞周期素（CyclinD1）、细胞周期素依赖性激酶（CDK4）在甲状腺癌组表达明显增高，而视网膜母细胞瘤基因（Rb）、细胞周期素依赖性激酶抑制剂（P27）表达呈下降趋势。结论在甲状腺癌，DNA含量和异倍体率增加，CyclinD1、CDK4表达明显增高，而Rb、P27表达下降，说明甲状腺癌形成时，已有多个分子事件发生。     

The cell cycle and drug discovery: the promise and the hope

In recent years, there have been major developments in the understanding of the cell cycle. It is now known that normal cellular proliferation is tightly regulated by the activation and deactivation of a series of proteins that constitute the cell cycle machinery. The expression and activity of components of the cell cycle can be altered during the development of a variety of diseases where aberrant proliferation contributes to the pathology of the illness. Apart from yielding a new source of untapped therapeutic targets, it is likely that manipulating the activity of such proteins in diseased states will provide an important route for treating proliferative disorders, and the opportunity to develop a novel class of future medicines.     

Knowledge-based neural networks for gene expression data analysis, modelling and profile discovery

Various knowledge-based methods can be applied to the analysis and modeling of gene expression and clinical data. I focus on knowledge-based neural networks (KBNN), illustrating how they have been used in cancer profiling and gene regulatory network discovery. I demonstrate that the KBNN approach facilitates adaptive learning and knowledge discovery, along with the creation of more accurate classification and prognostic systems, both global and personalized.     

Drug discovery: lessons from evolution

A common view within the pharmaceutical industry is that there is a problem with drug discovery and we should do something about it. There is much sympathy for this from academics, regulators and politicians. In this article I propose that lessons learnt from evolution help identify those factors that favour successful drug discovery. This personal view is influenced by a decade spent reviewing drug development programmes submitted for European regulatory approval. During the prolonged gestation of a new medicine few candidate molecules survive. This process of elimination of many variants and the survival of so few has much in common with evolution, an analogy that encourages discussion of the forces that favour, and those that hinder, successful drug discovery. Imagining a world without vaccines, anaesthetics, contraception and anti-infectives reveals how medicines revolutionized humanity. How to manipulate conditions that favour such discoveries is worth consideration.     

Analysis of cell cycle gene expression responding to acetoxyscirpendiol isolated from Paecilomyces tenuipes

Paecilomyces tenuipes is believed to contain potential oncostatic and tumor-reducing components. Molecular mechanism, however, is poorly understood concerning the potential antitumor components and their biological function. We purified acetoxyscirpendiol (ASD) from methanolic extracts (MPT) of the fungus and tested the two compounds for the molecular profile of their antitumor potential. Using a differential display protocol, cyclin C and Mad-1 were identified as candidate genes responding to MPT. When a quantitative PCR was performed on the total RNA from MCF-7 treated by MPT or ASD, gene expressions of cyclin C and Mad-1 were greatly augmented. In terms of protein expression, cyclin C level increased up to 12 folds in response to ASD as well as MPT. Similar as MPT treatments, ASD-treated cells synthesize cyclin C as 2-4 fold compared to the control treatments. In terms of Mad-1 expression in cells treated with ASD, the level of Mad-1 expression increased up to 2.5 folds by MPT treatment. Cyclin C expression was compared with non-treated cells in various cell lines. MCF-7 cell was shown highly responsive to the MPT or ASD treatment. Taken together, these results strongly indicate that MPT contains potential antitumor components which might exert their action by modulating cell cycle-related genes such as cyclin C and Mad-1 in MCF-7. The major antioncogenic component in MPT may be ASD which modulates cyclin C and Mad-1 expression.