A role for oligonucleotide-based RNA-knock down technologies in functional genomics

Functional genomics is inundating the pharmaceutical industry with large numbers of potential gene targets from several sources such as gene expression profiling experiments (DNA microchips, proteomics) or database mining. Oligonucleotide-based RNA-knock down technologies such as antisense or RNA interference can aid in the filtering and prioritization of target candidates in the drug discovery process.     

Using mouse forward genetics to define novel target space

Rapid advances in genomics technologies have identified a wealth of new therapeutic targets, but typically these targets are weakly validated with only circumstantial evidence to link them to human disease. The next challenge is testing gene-to-disease connections in a relevant animal model, a time-consuming and uncertain process using conventional reverse-genetic approaches such as knockout and transgenic mice. By contrast, forward genetics proceeds by measuring a physiological process that is relevant to disease, then identifying the gene products that impinge on this process. This ‘phenotype-first’ approach solves the bottleneck of target validation by using clinically relevant assays in a mammalian whole-animal system as a discovery platform. As an unbiased approach to gene discovery and validation, forward genetics will identify novel drug targets and increase the success rate of drug development.     

A Role for Oligonucleotide-Based RNA-Knock Down Technologies in Functional Genomics

Abstract

Functional genomics is inundating the pharmaceutical industry with large numbers of potential gene targets from several sources such as gene expression profiling experiments (DNA microchips, proteomics) or database mining. Oligonucleotide-based RNA-knock down technologies such as antisense or RNA interference can aid in the filtering and prioritization of target candidates in the drug discovery process.     

Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks

A major challenge in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets. Here, we present an integrated computational-experimental approach for computing the likelihood that gene products and associated pathways are targets of a compound. This is achieved by filtering the mRNA expression profile of compound-exposed cells using a reverse-engineered model of the cell's gene regulatory network. We apply the method to a set of 515 whole-genome yeast expression profiles resulting from a variety of treatments (compounds, knockouts and induced expression), and correctly enrich for the known targets and associated pathways in the majority of compounds examined. We demonstrate our approach with PTSB, a growth inhibitory compound with a previously unknown mode of action, by predicting and validating thioredoxin and thioredoxin reductase as its target.     

Functional genomics: identifying drug targets for parasitic diseases

The genomic sequences of parasitic diseases are rapidly becoming available and, recently, the full sequence of Plasmodium falciparum has been published. Much has been promised from this genomic revolution including the identification of new drug targets and novel chemotherapeutic treatments for the control of parasitic diseases. The challenge to use this information efficiently will require functional genomics tools such as bioinformatics, microarrays, proteomics and chemical genomics to identify potential drug targets, and to allow the development of optimized lead compounds. The information generated from these tools will provide a crucial link from genomic analysis to drug discovery.     

Chemical genomics: a systematic approach in biological research and drug discovery

The knowledge of complete sequences of different organisms is dramatically changing the landscape of biological research and pharmaceutical development. We are experiencing a transition from a trial-and-error approach in traditional biological research and natural product drug discovery to a systematic operation in genomics and target-specific drug design and selection. Small, cell-permeable and target-specific chemical ligands are particularly useful in systematic genomic approaches to study biological questions. On the other hand, genomic sequence information, comparative and structural genomics, when combined with the cutting edge technologies in synthetic chemistry and ligand screening/identification, provide a powerful way to produce target-specific and/or function-specific chemical ligands and drugs. Chemical genomics or chemogenomics is a new term that describes the development of target-specific chemical ligands and the use of such chemical ligands to globally study gene and protein functions. We anticipate that chemical genomics plays a critical role in the genomic age of biological research and drug discovery.     

Integrative genome‐scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery

Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re‐sequenced and fully re‐annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome‐scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost‐effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole‐cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.     

Adapting chromophore-assisted laser inactivation for high throughput functional proteomics.

Recent advances in genomics and proteomics have generated a change in emphasis from hypothesis-based to discovery-based investigations. Genomic and proteomic studies based on differential expression microarrays or comparative proteomics often provide many potential candidates for functionally important roles in normal and diseased cells. High throughput technologies to address protein and gene function in situ are still necessary to exploit these emerging advances in gene and protein discovery in order to validate these identified targets. The pharmaceutical industry is particularly interested in target validation, and has identified it as the critical early step in drug discovery. An especially powerful approach to target validation is a direct protein knockdown strategy called chromophore-assisted laser inactivation (CALI) which is a means of testing the role of specific proteins in particular cellular processes. Recent developments in CALI allow for its high throughput application to address many proteins in tandem. Thus, CALI may have applications for high throughput hypothesis testing, target validation or proteome-wide screening.     

In silico identification of common putative drug targets in <Emphasis Type="Italic">Leptospira interrogans</Emphasis>

Infectious diseases are the leading causes of death worldwide. Hence, there is a need to develop new antimicrobial agents. Traditional method of drug discovery is time consuming and yields a few drug targets with little intracellular information for guiding target selection. Thus, focus in drug development has been shifted to computational comparative genomics for identifying novel drug targets. Leptospirosis is a worldwide zoonosis of global concern caused by Leptospira interrogans. Availability of L. interrogans serovars and human genome sequences facilitated to search for novel drug targets using bioinformatics tools. The genome sequence of L. interrogans serovar Copenhageni has 5,124 genes while that of serovar Lai has 4,727 genes. Through subtractive genomic approach 218 genes in serovar Copenhageni and 158 genes in serovar Lai have been identified as putative drug targets. Comparative genomic approach had revealed that 88 drug targets were common to both the serovars. Pathway analysis using the Kyoto Encyclopaedia of Genes and Genomes revealed that 66 targets are enzymes and 22 are non-enzymes. Sixty two common drug targets were predicted to be localized in cytoplasm and 16 were surface proteins. The identified potential drug targets form a platform for further investigation in discovery of novel therapeutic compounds against Leptospira.     

小分子药物靶标寻找方法研究进展

许多微生物的次生代谢物属于小分子活性化合物,在医疗及农业领域发挥着重要的作用。在基因组学、蛋白质组学与生物信息学等技术的推动下,一些新的小分子药物靶标寻找方法应运而生了,这些新的方法主要是基于细胞中基因或蛋白质的表达量、蛋白质的亲和性、稳定性等各种特性进行靶标寻找的。小分子药物靶标寻找方法的发展加快了阐明小分子药物作用机理的历程,也为发现新的靶标资源以便于进一步筛选活性更高的药物提供了技术保障。     

Gene structure prediction in syntenic DNA segments

The accurate prediction of higher eukaryotic gene structures and regulatory elements directly from genomic sequences is an important early step in the understanding of newly assembled contigs and finished genomes. As more new genomes are sequenced, comparative approaches are becoming increasingly practical and valuable for predicting genes and regulatory elements. We demonstrate the effectiveness of a comparative method called pattern filtering; it utilizes synteny between two or more genomic segments for the annotation of genomic sequences. Pattern filtering optimally detects the signatures of conserved functional elements despite the stochastic noise inherent in evolutionary processes, allowing more accurate annotation of gene models. We anticipate that pattern filtering will facilitate sequence annotation and the discovery of new functional elements by the genetics and genomics communities.     

Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery

Candida albicans is the primary fungal pathogen of humans. Despite the need for novel drugs to combat fungal infections [Sobel, J.D. (2000) Clin Infectious Dis 30: 652], antifungal drug discovery is currently limited by both the availability of suitable drug targets and assays to screen corresponding targets. A functional genomics approach based on the diploid C. albicans genome sequence, termed GRACETM (gene replacement and conditional expression), was used to assess gene essentiality through a combination of gene replacement and conditional gene expression. In a systematic application of this approach, we identify 567 essential genes in C. albicans. Interestingly, evaluating the conditional phenotype of all identifiable C. albicans homologues of the Saccharomyces cerevisiae essential gene set [Giaever, G., Chu, A.M., Ni, L., Connelly, C., Riles, L., Veronneau, S., et al. (2002) Nature 418: 387-391] by GRACE revealed only 61% to be essential in C. albicans, emphasizing the importance of performing such studies directly within the pathogen. Construction of this conditional mutant strain collection facilitates large-scale examination of terminal phenotypes of essential genes. This information enables preferred drug targets to be selected from the C. albicans essential gene set by phenotypic information derived both in vitro, such as cidal versus static terminal phenotypes, as well as in vivo through virulence studies using conditional strains in an animal model of infection. In addition, the combination of phenotypic and bioinformatic analyses further improves drug target selection from the C. albicans essential gene set, and their respective conditional mutant strains may be directly used as sensitive whole-cell assays for drug screening.     

Joining forces: first application of a rapamycin‐induced dimerizable Cre system for conditional null mutant analysis in Leishmania

Reverse genetics in Leishmania spp has gained importance beyond basic research as efforts increase to discover and validate new drug targets. Often, the most promising targets are essential for viability of the parasites, defying a genetic analysis by current gene replacement strategies. Duncan et al. demonstrate the applicability of DiCre recombination in Leishmania for induced replacement of the kinase CRK3 gene in promastigotes. DiCre gene replacement leads to the rapid loss of the gene and allows monitoring the phenotypic effects of the loss of function, eliminating the need for prolonged cultivation and selection. Implementation of the DiCre approach will allow functional genetics of the most important of Leishmania genes and is likely to boost genetic research and drug target discovery.     

植物功能基因组研究中出现的新型分子标记

随着功能基因组学的发展,表达序列标签(Expressed Sequence Tags, ESTs)已经成为开发以PCR为基础的新型分子标记的重要资源。本文综述了植物功能基因组研究中出现的EST-SSR、CAPS、SNP、SRAP和TRAP等新型分子标记的基本原理和特点。这些标记具有其显著的优势,如开发简便、信息量高和通用性好等,尤其是由于它们来源于基因编码区（ORFs),因此具有很高的物种间通用性,并且其多态性与基因功能变异相关联。目前,这些功能基因分子标记已广泛应用于遗传图谱构建、重要性状基因定位、比较作图、遗传多样性和品种鉴别、分子标记辅助选择育种等研究中。在文章最后简要介绍了作者所在实验室近年来所开展的功能基因分子标记工作,并说明了在使用这些功能基因分子标记时可能会出现的问题。     

Will natural products remain an important source of drug research for the future?

In the highly competitive environment of contemporary pharmaceutical research, natural products provide a unique element of molecular diversity and biological functionality which is indispensable for drug discovery. The emergence of strategies to deliver drug leads from natural products within the same time frame as synthetic chemical screening has eliminated a major limitation of the past. At a more functional level, the application of molecular genetics techniques has permitted the manipulation of biosynthetic pathways for the generation of novel chemical species as well as rendering hitherto uncultivatable microorganisms accessible for secondary metabolite generation. These developments augur well for an industry confronted with the challenge of finding lead compounds directed at the plethora of new targets arising from genomics projects. The exploitation of structural chemical databases comprising a wide variety of chemotypes, in conjunction with databases on target genes and proteins, will facilitate the creation of new chemical entities through computational molecular modelling for pharmacological evaluation.     

Expression of Functional Plasmodium falciparum Enzymes Using a Wheat Germ Cell-Free System

One decade after the sequencing of the Plasmodium falciparum genome, 95% of malaria proteins in the genome cannot be expressed in traditional cell-based expression systems, and the targets of the best new leads for antimalarial drug discovery are either not known or not available in functional form. For a disease that kills up to 1 million people per year, routine expression of recombinant malaria proteins in functional form is needed both for the discovery of new therapeutics and for identification of targets of new drugs. We tested the general utility of cell-free systems for expressing malaria enzymes. Thirteen test enzyme sequences were reverse amplified from total RNA, cloned into a plant-like expression vector, and subjected to cell-free expression in a wheat germ system. Protein electrophoresis and autoradiography confirmed the synthesis of products of expected molecular masses. In rare problematic cases, truncated products were avoided by using synthetic genes carrying wheat codons. Scaled-up production generated 39 to 354 μg of soluble protein per 10 mg of translation lysate. Compared to rare proteins where cell-based systems do produce functional proteins, the cell-free yields are comparable or better. All 13 test products were enzymatically active, without failure. This general path to produce functional malaria proteins should now allow the community to access new tools, such as biologically active protein arrays, and lead to the discovery of new chemical functions, structures, and inhibitors of previously inaccessible malaria gene products.     

Model-organism genomics in veterinary parasite drug-discovery

A recent article about genomic filtering highlights exciting new opportunities for antiparasitic drug discovery resulting from major advances in genomic technologies. In this article, we discuss several approaches in which model-organism genomics and proteomics could be applied to the identification and validation of novel targets for antiparasitic drug discovery in veterinary medicine.     

Strategies for isolation of in vivo expressed genes from bacteria

The discovery and characterization of genes specifically induced in vivo upon infection and/or at a specific stage of the infection will be the next phase in studying bacterial virulence at the molecular level. Genes isolated are most likely to encode virulence-associated factors or products essential for survival, bacterial cell division and multiplication in situ. Identification of these genes is expected to provide new means to prevent infection, new targets for, antimicrobial therapy, as well as new insights into the infection process. Analysis of genes and their sequences initially discovered as in vivo induced may now be revealed by functional and comparative genomics. The new field of virulence genomics and their clustering as pathogenicity islands makes feasible their in-depth analysis. Application of new technologies such as in vivo expression technologies, signature-tagged mutagenesis, differential fluorescence induction, differential display using polymerase chain reaction coupled to bacterial genomics is expected to provide a strong basis for studying in vivo induced genes, and a better understanding of bacterial pathogenicity in vivo. This review presents technologies for characterization of genes expressed in vivo.