Antiprogestin-controllable transgene regulation in vivo

Gene control systems that provide temporal and spatial regulation of transgene expression in response to orally delivered drugs are needed for advances in functional genomics, models of human disease and gene therapy. A regulation system based on the altered binding and activation properties of a truncated ligand-binding domain derived from the progesterone receptor has been shown to be effective in providing tissue-specific, antiprogestin-controllable gene expression in transgenic mice, transgenic fruit flies and animals that have been administered viral-based or plasmid-based gene therapy vectors.     

Butterfly genomics eclosing

Technological and conceptual advances of the last decade have led to an explosion of genomic data and the emergence of new research avenues. Evolutionary and ecological functional genomics, with its focus on the genes that affect ecological success and adaptation in natural populations, benefits immensely from a phylogenetically widespread sampling of biological patterns and processes. Among those organisms outside established model systems, butterflies offer exceptional opportunities for multidisciplinary research on the processes generating and maintaining variation in ecologically relevant traits. Here we highlight research on wing color pattern variation in two groups of Nymphalid butterflies, the African species Bicyclus anynana (subfamily Satyrinae) and species of the South American genus Heliconius (subfamily Heliconiinae), which are emerging as important systems for studying the nature and origins of functional diversity. Growing genomic resources including genomic and cDNA libraries, dense genetic maps, high-density gene arrays, and genetic transformation techniques are extending current gene mapping and expression profiling analysis and enabling the next generation of research questions linking genes, development, form, and fitness. Efforts to develop such resources in Bicyclus and Heliconius underscore the general challenges facing the larger research community and highlight the need for a community-wide effort to extend ongoing functional genomic research on butterflies.     

Heritable gene silencing in Drosophila using double-stranded RNA

RNA-mediated interference (RNAi) is a recently discovered method to determine gene function in a number of organisms, including plants, nematodes, Drosophila, zebrafish, and mice. Injection of double-stranded RNA (dsRNA) corresponding to a single gene into organisms silences expression of the specific gene. Rapid degradation of mRNA in affected cells blocks gene expression. Despite the promise of RNAi as a tool for functional genomics, injection of dsRNA interferes with gene expression transiently and is not stably inherited. Consequently, use of RNAi to study gene function in the late stages of development has been limited. It is particularly problematic for development of disease models that reply on post-natal individuals. To circumvent this problem in Drosophila, we have developed a method to express dsRNA as an extended hairpin-loop RNA. This method has recently been successful in generating RNAi in the nematode Caenorhabditis elegans. The hairpin RNA is expressed from a transgene exhibiting dyad symmetry in a controlled temporal and spatial pattern. We report that the stably inherited transgene confers specific interference of gene expression in embryos, and tissues that give rise to adult structures such as the wings, legs, eyes, and brain. Thus, RNAi can be adapted to study late-acting gene function in Drosophila. The success of this approach in Drosophila and C. elegans suggests that a similar approach may prove useful to study gene function in higher organisms for which transgenic technology is available.     

A hidden-state Markov model for cell population deconvolution.

Microarrays measure gene expression typically from a mixture of cell populations during different stages of a biological process. However, the specific effects of the distinct or pure populations on measured gene expression are difficult or impossible to determine. The ability to deconvolve measured gene expression into the contributions from pure populations is critical to maximizing the potential of microarray analysis for investigating complex biological processes. In this paper, we describe a novel approach called the multinomial hidden Markov model (MHMM) that produces: (i) a maximum a posteriori estimate of the fraction represented by each pure population and (ii) gene expression values for each pure population. Our method uses an unsupervised, probabilistic approach for handling missing data points and clusters genes based on expression in pure populations. MHMM, used with several yeast datasets, identified statistically significant temporal dynamics. This method, unlike the linear decomposition models used previously for deconvolution, can extract information from different types of data, does not require a priori identification of pure gene expression, exploits the temporal nature of time series data, and is less affected by missing data.     

A systematic approach to biochemical profiling

Sequencing of the Arabidopsis thaliana genome is complete. The analytical tools for determining gene function by altering and monitoring gene expression are relatively well developed, and are generating large volumes of valuable data. Recent advances in techniques for the analysis of small molecules allow researchers to apply biochemical profiling as another powerful approach to functional genomics and metabolic research.     

An object model and database for functional genomics

MOTIVATION: Large-scale functional genomics analysis is now feasible and presents significant challenges in data analysis, storage and querying. Data standards are required to enable the development of public data repositories and to improve data sharing. There is an established data format for microarrays (microarray gene expression markup language, MAGE-ML) and a draft standard for proteomics (PEDRo). We believe that all types of functional genomics experiments should be annotated in a consistent manner, and we hope to open up new ways of comparing multiple datasets used in functional genomics. RESULTS: We have created a functional genomics experiment object model (FGE-OM), developed from the microarray model, MAGE-OM and two models for proteomics, PEDRo and our own model (Gla-PSI-Glasgow Proposal for the Proteomics Standards Initiative). FGE-OM comprises three namespaces representing (i) the parts of the model common to all functional genomics experiments; (ii) microarray-specific components; and (iii) proteomics-specific components. We believe that FGE-OM should initiate discussion about the contents and structure of the next version of MAGE and the future of proteomics standards. A prototype database called RNA And Protein Abundance Database (RAPAD), based on FGE-OM, has been implemented and populated with data from microbial pathogenesis. AVAILABILITY: FGE-OM and the RAPAD schema are available from http://www.gusdb.org/fge.html, along with a set of more detailed diagrams. RAPAD can be accessed by registration at the site.     

The naturalist in a world of genomics

Functional genomics provides new opportunities to address issues of fundamental interest in evolutionary biology and suggests many new research directions that are ripe for evolutionary investigation. New types of data, and the ability to study biological processes from a whole genome perspective, are likely to have a profound impact on evolutionary biology and ecology. To illustrate, we discuss how genomewide gene expression studies can be used to reformulate questions about trade-offs and pleiotropy. We then touch on some of the new research opportunities that the application of functional genomics affords to evolutionary biologists. We end with some brief notes about how evolutionary biology and comparative approaches will probably have an impact on functional genomics.     

功能基因组学的研究内容与方法

基因组学的研究已从结构基因组学转向功能基因组学.综述了功能基因组学研究的内容和方法,主要包括应用微点阵、基因表达系列分析(SAGE)、蛋白质组、生物信息学等方法来研究基因组表达概况、基因组多样性、模式生物体等.     

Identification of a QTL in Mus musculus for Alcohol Preference,Withdrawal, and Ap3m2 Expression Using Integrative Functional Genomics and Precision Genetics

Extensive genetic and genomic studies of the relationship between alcohol drinking preference and withdrawal severity have been performed using animal models. Data from multiple such publications and public data resources have been incorporated in the GeneWeaver database with >60,000 gene sets including 285 alcohol withdrawal and preference-related gene sets. Among these are evidence for positional candidates regulating these behaviors in overlapping quantitative trait loci (QTL) mapped in distinct mouse populations. Combinatorial integration of functional genomics experimental results revealed a single QTL positional candidate gene in one of the loci common to both preference and withdrawal. Functional validation studies in Ap3m2 knockout mice confirmed these relationships. Genetic validation involves confirming the existence of segregating polymorphisms that could account for the phenotypic effect. By exploiting recent advances in mouse genotyping, sequence, epigenetics, and phylogeny resources, we confirmed that Ap3m2 resides in an appropriately segregating genomic region. We have demonstrated genetic and alcohol-induced regulation of Ap3m2 expression. Although sequence analysis revealed no polymorphisms in the Ap3m2-coding region that could account for all phenotypic differences, there are several upstream SNPs that could. We have identified one of these to be an H3K4me3 site that exhibits strain differences in methylation. Thus, by making cross-species functional genomics readily computable we identified a common QTL candidate for two related bio-behavioral processes via functional evidence and demonstrate sufficiency of the genetic locus as a source of variation underlying two traits.     

A phosphate-regulated promoter for fine-tuned and reversible overexpression in Ostreococcus: application to circadian clock functional analysis

BACKGROUND: The green picoalga Ostreococcus tauri (Prasinophyceae), which has been described as the smallest free-living eukaryotic organism, has minimal cellular ultra-structure and a very small genome. In recent years, O. tauri has emerged as a novel model organism for systems biology approaches that combine functional genomics and mathematical modeling, with a strong emphasis on light regulated processes and circadian clock. These approaches were made possible through the implementation of a minimal molecular toolbox for gene functional analysis including overexpression and knockdown strategies. We have previously shown that the promoter of the High Affinity Phosphate Transporter (HAPT) gene drives the expression of a luciferase reporter at high and constitutive levels under constant light. METHODOLOGY/PRINCIPAL FINDINGS: Here we report, using a luciferase reporter construct, that the HAPT promoter can be finely and reversibly tuned by modulating the level and nature of phosphate in culture medium. This HAPT regulation was additionally used to analyze the circadian clock gene Time of Cab expression 1 (TOC1). The phenotype of a TOC1ox/CCA1:Luc line was reverted from arrhythmic to rhythmic simply by adding phosphate to the culture medium. Furthermore, since the time of phosphate injection had no effect on the phase of CCA1:Luc expression, this study suggests further that TOC1 is a central clock gene in Ostreococcus. CONCLUSIONS/PERSPECTIVES: We have developed a phosphate-regulated expression system that allows fine gene function analysis in Ostreococcus. Recently, there has been a growing interest in microalgae as cell factories. This non-toxic phosphate-regulated system may prove useful in tuning protein expression levels quantitatively and temporally for biotechnological applications.     

Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage

Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis.     

Use of geminiviral vectors for functional genomics

Virus-induced gene silencing (VIGS) can be used to study the function of a gene by downregulating its expression and analyzing the resulting phenotype. VIGS is a handy tool that is less time consuming and labor intensive than other methods for generating mutants. Geminiviruses are particularly convenient and valuable choices as VIGS vectors in functional genomics. The small size of their DNA genome, the simplicity of the methods for inoculation, their wide host range and their conserved genome organization are just a few of the advantageous characteristics that this group of viruses has to offer. Geminivirus-based vectors have proved to be very efficient in VIGS systems, and further development of these systems will most probably permit their application in studies of the functional genomics of important crops that are recalcitrant to other forms of analysis.     

Systems for applied gene control in Saccharomyces cerevisiae

Saccharomyces cerevisiae is frequently used in biotechnology, including fermentative processes in food production, heterologous protein production and high throughput developments for biomedicine. Accurate expression of selected genes is essential for all these areas. Systems that can be regulated are particularly useful because they allow controlling the timing and levels of gene expression. We examine here new expression systems that have been described, including improvements of classical ones and new strategies of artificial gene control that have been applied in functional genomics.     

Simplivariate models: uncovering the underlying biology in functional genomics data

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.     

Functional genomics in the study of yeast cell polarity: moving in the right direction

The budding yeast Saccharomyces cerevisiae has been used extensively for the study of cell polarity, owing to both its experimental tractability and the high conservation of cell polarity and other basic biological processes among eukaryotes. The budding yeast has also served as a pioneer model organism for virtually all genome-scale approaches, including functional genomics, which aims to define gene function and biological pathways systematically through the analysis of high-throughput experimental data. Here, we outline the contributions of functional genomics and high-throughput methodologies to the study of cell polarity in the budding yeast. We integrate data from published genetic screens that use a variety of functional genomics approaches to query different aspects of polarity. Our integrated dataset is enriched for polarity processes, as well as some processes that are not intrinsically linked to cell polarity, and may provide new areas for future study.