Identification of new genes by systematic analysis of cdnas and database construction

The large-scale collection of partial cDNA sequences is becoming a powerful tool in biology. Similarity or motif searches in DNA databases using these partial cDNA sequences have facilitated the discovery of new genes of interest. By collecting and registering large numbers of partial sequences with a well designed non-biased cDNA library, an expression profile of active genes in a particular tissue can be obtained. Tissue-specific or stage-specific genes can be discovered by comparing the profiles from different tissues or from a tissue at different stages of development, respectively. The compilation of such expression profiles enables genes to be mapped to the tissue(s) where they are actively transcribed. The large-scale collation of gene sequences actively expressed in the body into databases complements efforts directed towards the structural analysis of the genome, with the ultimate aim of decoding all the genetic information carried in the human genome. This cDNA strategy is also being widely applied to organisms other than man.     

New antibiotic discovery, novel screens, novel targets and impact of microbial genomics

The clinical need for new classes of antibiotic continues to grow, as drug resistance erodes the efficacy of current therapies. Historically, most antibiotics were discovered by random screening campaigns, but over the past 20 years, this strategy has largely failed to deliver a sufficient range of chemical diversity to keep pace with changing clinical profiles. A more rational approach to drug hunting has been greatly potentiated by the availability of bacterial genomic information. The rapid progress in sequencing and analysis of these small, prokaryotic genomes has enabled the concomitant development of powerful new technologies that are already enhancing the potential utility of genomic information. The future promises versatile and precise tools to understand what makes a successful antibiotic and moreover the means to identify and evaluate novel classes of drug.     

Biomarker technology roundup: from discovery to clinical applications, a broad set of tools is required to translate from the lab to the clinic

Biomarkers are being utilized throughout the drug discovery and development process to understand fundamental biological processes and relationships. Specific biomarkers for disease states, prognosis, and response to therapy have been applied to screening tissues and serum, and serve as new tools in the development of therapeutics, to segment the population for specific treatments. The use of specific biomarkers to screen subjects to determine clinical trial eligibility, and for early toxicology studies, holds the potential to decrease drug failure rates in the later phases of the clinical trial process. Traditional research tools have been employed to study the genes, proteins, and metabolites of interest. In addition, new technologies and permutations of existing technologies have been developed particularly for investigation in the preclinical and clinical phases of drug development. More importantly, the transition of a compound from preclinical to clinical is aided by technologies that span both process segments. Identification of biomarkers that can be studied throughout the development process requires technologies that are both feasible and cost-effective for large patient populations.     

Tuber on a chip: differential gene expression during potato tuber development

Potato tuber development has proven to be a valuable model system for studying underground sink organ formation. Research on this topic has led to the identification of many genes involved in this complex process and has aided in the unravelling of the mechanisms underlying starch synthesis. However, less attention has been paid to the biochemical pathways of other important metabolites or to the changing metabolic fluxes occurring during potato tuber development. In this paper, we describe the construction of a potato complementary DNA (cDNA) microarray specifically designed for genes involved in processes related to tuber development and tuber quality traits. We present expression profiles of 1315 cDNAs during tuber development where the predominant profiles were strong up- and down-regulation. Gene expression profiles showing transient increases or decreases were less abundantly represented and followed more moderate changes, mainly during tuber initiation. In addition to the confirmation of gene expression patterns during tuber development, many novel differentially expressed genes were identified and are considered as candidate genes for direct involvement in potato tuber development. A detailed analysis of starch metabolism genes provided a unique overview of expression changes during tuber development. Characteristic expression profiles were often clearly different between gene family members. A link between differential gene expression during tuber development and potato tissue specificity is described. This dataset provides a firm basis for the identification of key regulatory genes in a number of metabolic pathways that may provide researchers with new tools to achieve breeding goals for use in industrial applications.     

GENEVESTIGATOR. Arabidopsis microarray database and analysis toolbox

High-throughput gene expression analysis has become a frequent and powerful research tool in biology. At present, however, few software applications have been developed for biologists to query large microarray gene expression databases using a Web-browser interface. We present GENEVESTIGATOR, a database and Web-browser data mining interface for Affymetrix GeneChip data. Users can query the database to retrieve the expression patterns of individual genes throughout chosen environmental conditions, growth stages, or organs. Reversely, mining tools allow users to identify genes specifically expressed during selected stresses, growth stages, or in particular organs. Using GENEVESTIGATOR, the gene expression profiles of more than 22,000 Arabidopsis genes can be obtained, including those of 10,600 currently uncharacterized genes. The objective of this software application is to direct gene functional discovery and design of new experiments by providing plant biologists with contextual information on the expression of genes. The database and analysis toolbox is available as a community resource at https://www.genevestigator.ethz.ch.     

Technological advances and genomics in metazoan parasites

Molecular biology has provided the means to identify parasite proteins, to define their function, patterns of expression and the means to produce them in quantity for subsequent functional analyses. Whole genome and expressed sequence tag programmes, and the parallel development of powerful bioinformatics tools, allow the execution of genome-wide between stage or species comparisons and meaningful gene-expression profiling. The latter can be undertaken with several new technologies such as DNA microarray and serial analysis of gene expression. Proteome analysis has come to the fore in recent years providing a crucial link between the gene and its protein product. RNA interference and ballistic gene transfer are exciting developments which can provide the means to precisely define the function of individual genes and, of importance in devising novel parasite control strategies, the effect that gene knockdown will have on parasite survival.     

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large‐scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross‐validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue‐specific expression information on the drug targets. We further show that disease‐specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease‐specific signatures.     

Applications of cell-based phage display panning to proteomic analysis

In the post-genomic era, proteomics together with genomic tools have led to powerful new strategies in basic and clinical research. These combined “omics” technologies are being integrated into the drug target discovery process. Unlike the genome, the proteome is a highly dynamic entity that requires techniques capable of analyzing on selected populations of proteins in specific biological conditions that reflect the proteins’ functional characteristics. Antibodies have become one of the most important reagents for the analysis of selected populations of proteins, and the application of phage-display antibody libraries to high-throughput antibody generation against large numbers of various antigens provides a tool for proteome-wide protein expression analysis. In this review, we will discuss the utility of phage-display antibodies in proteomics applications, specifically for the discovery of novel disease markers and therapeutic targets.     

Tissue microarrays as a platform for proteomic investigation

Tissue microarrays have become an essential tool in translational pathology. They are used to confirm results from other experimental platforms, such as expression microarrays, as well as a primary tool to explore the expression profile of proteins by immunohistochemical analysis. Tissue microarrays are routinely used molecular epidemiology, drug development and determining the diagnostic, prognostic and predictive value of new biomarkers. By applying traditional protein based assays, as well as novel assays to the platform, tissue microarrays have gained a new utility as a proteomic tool for both basic science as well as clinical investigation. This article will explore the new approaches that are being applied to tissue microarrays to, characterize the human proteome, and new technologies that allow tissue microarrays to function as a protein array. The U.S. Government's right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged     

Establishment of combined analytical method to extract the genes of interest from transcriptome data

Techniques for analyzing genome-wide expression profiles, such as the microarray technique and next-generation sequencers, have been developed. While these techniques can provide a lot of information about gene expression, selection of genes of interest is complicated because of excessive gene expression data. Thus, many researchers use statistical methods or fold change as screening tools for finding gene sets whose expression is altered between groups, which may result in the loss of important information. In the present study, we aimed to establish a combined method for selecting genes of interest with a small magnitude of alteration in gene expression by coupling with proteome analysis. We used hypercholesterolemic rats to examine the effects of a crude herbal drug on gene expression and proteome profiles. We could not select genes of interest by using standard methods. However, by coupling with proteome analysis, we found several effects of the crude herbal drug on gene expression. Our results suggest that this method would be useful in selecting gene sets with expressions that do not show a large magnitude of alteration.     

A novel genomics approach for the identification of drug targets in pathogens, with special reference to Pseudomonas aeruginosa

Complete genome sequences of several pathogenic bacteria have been determined, and many more such projects are currently under way. While these data potentially contain all the determinants of host-pathogen interactions and possible drug targets, computational tools for selecting suitable candidates for further experimental analyses are currently limited. Detection of bacterial genes that are non-homologous to human genes, and are essential for the survival of the pathogen represents a promising means of identifying novel drug targets. We have used three-way genome comparisons to identify essential genes from Pseudomonas aeruginosa. Our approach identified 306 essential genes that may be considered as potential drug targets. The resultant analyses are in good agreement with the results of systematic gene deletion experiments. This approach enables rapid potential drug target identification, thereby greatly facilitating the search for new antibiotics. These results underscore the utility of large genomic databases for in silico systematic drug target identification in the post-genomic era.     

Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo

MOTIVATION: Gene expression profiling is a powerful approach to identify genes that may be involved in a specific biological process on a global scale. For example, gene expression profiling of mutant animals that lack or contain an excess of certain cell types is a common way to identify genes that are important for the development and maintenance of given cell types. However, it is difficult for traditional computational methods, including unsupervised and supervised learning methods, to detect relevant genes from a large collection of expression profiles with high sensitivity and specificity. Unsupervised methods group similar gene expressions together while ignoring important prior biological knowledge. Supervised methods utilize training data from prior biological knowledge to classify gene expression. However, for many biological problems, little prior knowledge is available, which limits the prediction performance of most supervised methods. RESULTS: We present a Bayesian semi-supervised learning method, called BGEN, that improves upon supervised and unsupervised methods by both capturing relevant expression profiles and using prior biological knowledge from literature and experimental validation. Unlike currently available semi-supervised learning methods, this new method trains a kernel classifier based on labeled and unlabeled gene expression examples. The semi-supervised trained classifier can then be used to efficiently classify the remaining genes in the dataset. Moreover, we model the confidence of microarray probes and probabilistically combine multiple probe predictions into gene predictions. We apply BGEN to identify genes involved in the development of a specific cell lineage in the C. elegans embryo, and to further identify the tissues in which these genes are enriched. Compared to K-means clustering and SVM classification, BGEN achieves higher sensitivity and specificity. We confirm certain predictions by biological experiments. AVAILABILITY: The results are available at http://www.csail.mit.edu/~alanqi/projects/BGEN.html.     

microRNA多态性与肿瘤发生和药物反应相关

microRNA(miRNA)是进化保守的、非编码单链小RNA,长度约为18-25个核苷酸.作为基因表达的微观管理者,miRNA通过结合在mRNA的3′-UTR抑制翻译过程或使其降解.miRNA多态性是指一类能够干扰miRNA功能的新多态性或单核苷酸的多态性.这种多态性不仅出现在pri-miRNA、pre-miRNA和成熟的miRNA序列中;也可以出现在靶基因3′-UTR;还可以呈现在miRNA基因表观遗传学的改变.miRNA多态性可能导致疾病的产生,也可以判断临床用药疗效及预后监测.目前miRNA多态性正在作为疾病（尤其是肿瘤）生物学研究的强有力工具,而且已应用于疾病的诊断和预后.     

New components of the <Emphasis Type="Italic">Dictyostelium</Emphasis> PKA pathway revealed by Bayesian analysis of expression data

Background  

Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential.