Biomedical subjects

Stephen G Oliver

Publications and source records attributed to Stephen G Oliver.

At least 19 recordsLinked to original sources

PubMed2006

Automated tracking of gene expression in individual cells and cell compartments.

Many intracellular signal transduction processes involve the reversible translocation from the cytoplasm to the nucleus of transcription factors. The advent of fluorescently tagged protein derivatives has revolutionized cell biology, such that it is now possible to follow the location of such protein molecules in individual cells in real time. However, the quantitative analysis of the location of such proteins in microscopic images is very time consuming. We describe CellTracker, a software tool designed for the automated measurement of the cellular location and intensity of fluorescently tagged proteins. CellTracker runs in the MS Windows environment, is freely available (at http://www.dbkgroup.org/celltracker/), and combines automated cell tracking methods with powerful image-processing algorithms that are optimized for these applications. When tested in an application involving the nuclear transcription factor NF-kappaB, CellTracker is competitive in accuracy with the manual human analysis of such images but is more than 20 times faster, even on a small task where human fatigue is not an issue. This will lead to substantial benefits for time-lapse-based high-content screening.

Animals↗

PubMed2006

Model-driven user interfaces for bioinformatics data resources: regenerating the wheel as an alternative to reinventing it.

BACKGROUND: The proliferation of data repositories in bioinformatics has resulted in the development of numerous interfaces that allow scientists to browse, search and analyse the data that they contain. Interfaces typically support repository access by means of web pages, but other means are also used, such as desktop applications and command line tools. Interfaces often duplicate functionality amongst each other, and this implies that associated development activities are repeated in different laboratories. Interfaces developed by public laboratories are often created with limited developer resources. In such environments, reducing the time spent on creating user interfaces allows for a better deployment of resources for specialised tasks, such as data integration or analysis. Laboratories maintaining data resources are challenged to reconcile requirements for software that is reliable, functional and flexible with limitations on software development resources. RESULTS: This paper proposes a model-driven approach for the partial generation of user interfaces for searching and browsing bioinformatics data repositories. Inspired by the Model Driven Architecture (MDA) of the Object Management Group (OMG), we have developed a system that generates interfaces designed for use with bioinformatics resources. This approach helps laboratory domain experts decrease the amount of time they have to spend dealing with the repetitive aspects of user interface development. As a result, the amount of time they can spend on gathering requirements and helping develop specialised features increases. The resulting system is known as Pierre, and has been validated through its application to use cases in the life sciences, including the PEDRoDB proteomics database and the e-Fungi data warehouse. CONCLUSION: MDAs focus on generating software from models that describe aspects of service capabilities, and can be applied to support rapid development of repository interfaces in bioinformatics. The Pierre MDA is capable of supporting common database access requirements with a variety of auto-generated interfaces and across a variety of repositories. With Pierre, four kinds of interfaces are generated: web, stand-alone application, text-menu, and command line. The kinds of repositories with which Pierre interfaces have been used are relational, XML and object databases.

Computational Biology↗

PubMed2006

Phenotypic activation to discover biological pathways and kinase substrates.

Observing the effects of gene perturbation on cells or organisms has long been a standard strategy in biological research. We developed a genome-wide gene overexpression library as a new tool for large-scale functional analysis in budding yeast. Previous large-scale genetic studies have focused on applications of the deletion mutant collection, which has arguably revolutionized the functional characterization of yeast genes. While extremely powerful, deletion mutant experiments are generally limited to the characterization of loss-of-function phenotypes. We have explored the potential for using the Synthetic Genetic Array (SGA) method, a platform for high-throughput genetic analysis, with a genome-wide "overexpression array", in which each strain on the array overexpresses a unique yeast gene. The overexpression array enables gain-of-function phenotypes to be examined on a large scale, providing a unique insight into gene function and a novel source of reagents for the global mapping of genetic networks and functional relationships amongst genes and pathways. Understanding the molecular bases of overexpression phenotypes should also shed new light on the nature of genetic dominance.

Animals↗

PubMed2006

MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics.

BACKGROUND: The genome sequencing projects have shown our limited knowledge regarding gene function, e.g. S. cerevisiae has 5-6,000 genes of which nearly 1,000 have an uncertain function. Their gross influence on the behaviour of the cell can be observed using large-scale metabolomic studies. The metabolomic data produced need to be structured and annotated in a machine-usable form to facilitate the exploration of the hidden links between the genes and their functions. DESCRIPTION: MeMo is a formal model for representing metabolomic data and the associated metadata. Two predominant platforms (SQL and XML) are used to encode the model. MeMo has been implemented as a relational database using a hybrid approach combining the advantages of the two technologies. It represents a practical solution for handling the sheer volume and complexity of the metabolomic data effectively and efficiently. The MeMo model and the associated software are available at http://dbkgroup.org/memo/. CONCLUSION: The maturity of relational database technology is used to support efficient data processing. The scalability and self-descriptiveness of XML are used to simplify the relational schema and facilitate the extensibility of the model necessitated by the creation of new experimental techniques. Special consideration is given to data integration issues as part of the systems biology agenda. MeMo has been physically integrated and cross-linked to related metabolomic and genomic databases. Semantic integration with other relevant databases has been supported through ontological annotation. Compatibility with other data formats is supported by automatic conversion.

Computer Simulation↗

PubMed2006

Reduction/oxidation-phosphorylation control of DNA binding in the bZIP dimerization network.

BACKGROUND: bZIPs are transcription factors that are found throughout the eukarya from fungi to flowering plants and mammals. They contain highly conserved basic region (BR) and leucine zipper (LZ) domains and often function as environmental sensors. Specifically, bZIPs frequently have a role in mediating the response to oxidative stress, a crucial environmental signal that needs to be transduced to the gene regulatory network. RESULTS: Based on sequence comparisons and experimental data on a number of important bZIP transcription factors, we predict which bZIPs are under redox control and which are regulated via protein phosphorylation. By integrating genomic, phylogenetic and functional data from the literature, we then propose a link between oxidative stress and the choice of interaction partners for the bZIP proteins. CONCLUSION: This integration permits the bZIP dimerization network to be interpreted in functional terms, especially in the context of the role of bZIP proteins in the response to environmental stress. This analysis demonstrates the importance of abiotic factors in shaping regulatory networks.

Amino Acid Sequence↗

PubMed2006

Integrative investigation of metabolic and transcriptomic data.

BACKGROUND: New analysis methods are being developed to integrate data from transcriptome, proteome, interactome, metabolome, and other investigative approaches. At the same time, existing methods are being modified to serve the objectives of systems biology and permit the interpretation of the huge datasets currently being generated by high-throughput methods. RESULTS: Transcriptomic and metabolic data from chemostat fermentors were collected with the aim of investigating the relationship between these two data sets. The variation in transcriptome data in response to three physiological or genetic perturbations (medium composition, growth rate, and specific gene deletions) was investigated using linear modelling, and open reading-frames (ORFs) whose expression changed significantly in response to these perturbations were identified. Assuming that the metabolic profile is a function of the transcriptome profile, expression levels of the different ORFs were used to model the metabolic variables via Partial Least Squares (Projection to Latent Structures--PLS) using PLS toolbox in Matlab. CONCLUSION: The experimental design allowed the analyses to discriminate between the effects which the growth medium, dilution rate, and the deletion of specific genes had on the transcriptome and metabolite profiles. Metabolite data were modelled as a function of the transcriptome to determine their congruence. The genes that are involved in central carbon metabolism of yeast cells were found to be the ORFs with the most significant contribution to the model.

Algorithms↗

PubMed2006

Chance and necessity in the evolution of minimal metabolic networks.

It is possible to infer aspects of an organism's lifestyle from its gene content. Can the reverse also be done? Here we consider this issue by modelling evolution of the reduced genomes of endosymbiotic bacteria. The diversity of gene content in these bacteria may reflect both variation in selective forces and contingency-dependent loss of alternative pathways. Using an in silico representation of the metabolic network of Escherichia coli, we examine the role of contingency by repeatedly simulating the successive loss of genes while controlling for the environment. The minimal networks that result are variable in both gene content and number. Partially different metabolisms can thus evolve owing to contingency alone. The simulation outcomes do preserve a core metabolism, however, which is over-represented in strict intracellular bacteria. Moreover, differences between minimal networks based on lifestyle are predictable: by simulating their respective environmental conditions, we can model evolution of the gene content in Buchnera aphidicola and Wigglesworthia glossinidia with over 80% accuracy. We conclude that, at least for the particular cases considered here, gene content of an organism can be predicted with knowledge of its distant ancestors and its current lifestyle.

Biological Evolution↗

PubMed2006

From genomes to systems: the path with yeast.

Metabolic Control Analysis (MCA) is a conceptual and mathematical formalism that models the relative contributions of individual effectors in a pathway to both the flux through the pathway and the concentrations of individual intermediates within it. To exploit MCA in an initial Systems Biology analysis of the eukaryotic cell, two categories of experiments are required. In category 1 experiments, flux is changed and the impact on the levels of the direct and indirect products of gene action is measured. We have measured the impact of changing the flux on the transcriptome, proteome and metabolome of Saccharomyces cerevisiae. In this whole-cell analysis, flux equates to growth rate. In category 2 experiments, the levels of individual gene products are altered, and the impact on the flux is measured. We have used competition analyses between the complete set of heterozygous yeast deletion mutants to reveal genes encoding proteins with high flux control coefficients. These genes may be exploited, in a top-down analysis, to build a coarse-grained model of the eukaryotic cell, as exemplified by yeast. More detailed modelling requires that 'natural' biological systems be identified. The combination of flux balance analysis with both genetics and metabolomics in the definition of metabolic systems is discussed.

Computational Biology↗

PubMed2006

Mapping pathways and phenotypes by systematic gene overexpression.

Many disease states result from gene overexpression, often in a specific genetic context. To explore gene overexpression phenotypes systematically, we assembled an array of 5280 yeast strains, each containing an inducible copy of an S. cerevisiae gene, covering >80% of the genome. Approximately 15% of the overexpressed genes (769) reduced growth rate. This gene set was enriched for cell cycle-regulated genes, signaling molecules, and transcription factors. Overexpression of most toxic genes resulted in phenotypes different from known deletion mutant phenotypes, suggesting that overexpression phenotypes usually reflect a specific regulatory imbalance rather than disruption of protein complex stoichiometry. Global overexpression effects were also assayed in the context of a cyclin-dependent kinase mutant (pho85Delta). The resultant gene set was enriched for Pho85p targets and identified the yeast calcineurin-responsive transcription factor Crz1p as a substrate. Large-scale application of this approach should provide a strategy for identifying target molecules regulated by specific signaling pathways.

Gene Expression Profiling↗

PubMed2006

The relative merits of the tetO2 and tetO7 promoter systems for the functional analysis of heterologous genes in yeast and a compilation of essential yeast genes with tetO2 promoter substitutions.

We have generated a collection of yeast strains, each of which has an essential yeast gene under the control of the tetracycline-responsive, tetO, promoter. Screens using first-generation promoter-swap strains uncovered the non-specific responsiveness of the tetO7 promoter to a known human transcription factor (hIRF-1). Non-specific regulation was not observed with the tetO2 promoter. Reporter assays have been used to demonstrate this phenomenon. Subsequent efforts to generate a collection of tetracycline-regulatable strains have focused on the tetO2 promoter. These strains are available to the yeast community and can be used for functional genomics studies.

Bacterial Proteins↗

PubMed2006

Annotation of unknown yeast ORFs by correlation analysis of microarray data and extensive literature searches.

Changes in the expression of genes were used to elucidate the metabolic pathways and regulatory mechanisms that respond to environmental or genetic modifications. Results from previously published chemostat datasets were merged with novel data generated in the present study. ORFs displaying significant changes in expression that correlated with those of other ORFs were analysed using GO mapping tools and supplemented by literature information. The strategy developed was used to propose annotations for ORFs of unknown function. The following ORFs were assigned functions as a result of this study: YMR090w, YGL157w, YGR243w, YLR327c, YER121w, YFR017c, YGR067c, YKL187c, YGR236c (SPG1), YMR107w (SPG4), YMR206w, YER067w, YJL103c, YNL175C (NOP13) YJL200C, YDL070C (FMP16) and YGR173W.

Algorithms↗

PubMed2005

Chromosome evolution in eukaryotes: a multi-kingdom perspective.

In eukaryotes, chromosomal rearrangements, such as inversions, translocations and duplications, are common and range from part of a gene to hundreds of genes. Lineage-specific patterns are also seen: translocations are rare in dipteran flies, and angiosperm genomes seem prone to polyploidization. In most eukaryotes, there is a strong association between rearrangement breakpoints and repeat sequences. Current data suggest that some repeats promoted rearrangements via non-allelic homologous recombination, for others the association might not be causal but reflects the instability of particular genomic regions. Rearrangement polymorphisms in eukaryotes are correlated with phenotypic differences, so are thought to confer varying fitness in different habitats. Some seem to be under positive selection because they either trap favorable allele combinations together or alter the expression of nearby genes. There is little evidence that chromosomal rearrangements cause speciation, but they probably intensify reproductive isolation between species that have formed by another route.

Animals↗

PubMed2005

Effect of dataset selection on the topological interpretation of protein interaction networks.

BACKGROUND: Studies of the yeast protein interaction network have revealed distinct correlations between the connectivity of individual proteins within the network and the average connectivity of their neighbours. Although a number of biological mechanisms have been proposed to account for these findings, the significance and influence of the specific datasets included in these studies has not been appreciated adequately. RESULTS: We show how the use of different interaction data sets, such as those resulting from high-throughput or small-scale studies, and different modelling methodologies for the derivation pair-wise protein interactions, can dramatically change the topology of these networks. Furthermore, we show that some of the previously reported features identified in these networks may simply be the result of experimental or methodological errors and biases. CONCLUSION: When performing network-based studies, it is essential to define what is meant by the term "interaction" and this must be taken into account when interpreting the topologies of the networks generated. Consideration must be given to the type of data included and appropriate controls that take into account the idiosyncrasies of the data must be selected.

Computational Biology↗

PubMed2005

Doxycycline, the drug used to control the tet-regulatable promoter system, has no effect on global gene expression in Saccharomyces cerevisiae.

The tet-regulatable promoter system is commonly used for genetic studies in many eukaryotic organisms. The promoter is regulated using doxycycline. There are no obvious phenotypic effects observed when doxycycline is added to the growth medium of yeast to control expression from the promoter. It is widely accepted that doxycycline is innocuous to yeast. Global genetic studies are now commonplace and the tetO-system is being used in transcriptome studies. Hence, we wanted to ensure that the absence of phenotypic effects, on addition of doxycycline to the growth medium, is mirrored in transcriptome data. We have demonstrated that doxycycline has no significant effect on global transcription levels and will continue to use the tetO-regulatable promoter system for genetic studies.

Culture Media↗

PubMed2005

Metabolic footprinting and systems biology: the medium is the message.

One element of classical systems analysis treats a system as a black or grey box, the inner structure and behaviour of which can be analysed and modelled by varying an internal or external condition, probing it from outside and studying the effect of the variation on the external observables. The result is an understanding of the inner make-up and workings of the system. The equivalent of this in biology is to observe what a cell or system excretes under controlled conditions - the 'metabolic footprint' or exometabolome - as this is readily and accurately measurable. Here, we review the principles, experimental approaches and scientific outcomes that have been obtained with this useful and convenient strategy.

Animals↗

PubMed2005

Production of ethanol from starch by respiration-deficient recombinant Saccharomyces cerevisiae.

A 100%-respiration-deficient nuclear petite amylolytic Saccharomyces cerevisiae NPB-G strain was generated, and its employment for direct fermentation of starch into ethanol was investigated. In a comparison of ethanol fermentation performances with the parental respiration-sufficient WTPB-G strain, the NPB-G strain showed an increase of ca. 48% in both ethanol yield and ethanol productivity.

Ethanol↗

PubMed2005

Transcriptome analysis of recombinant protein secretion by Aspergillus nidulans and the unfolded-protein response in vivo.

Filamentous fungi have a high capacity for producing large amounts of secreted proteins, a property that has been exploited for commercial production of recombinant proteins. However, the secretory pathway, which is key to the production of extracellular proteins, is rather poorly characterized in filamentous fungi compared to yeast. We report the effects of recombinant protein secretion on gene expression levels in Aspergillus nidulans by directly comparing a bovine chymosin-producing strain with its parental wild-type strain in continuous culture by using expressed sequence tag microarrays. This approach demonstrated more subtle and specific changes in gene expression than those observed when mimicking the effects of protein overproduction by using a secretion blocker. The impact of overexpressing a secreted recombinant protein more closely resembles the unfolded-protein response in vivo.

Aspergillus nidulans↗

PubMed2004

Glutamic protease distribution is limited to filamentous fungi.

Glutamic proteases are a distinct, and recently re-classified, group of peptidases that are thought to be found only in fungi. We have identified and analysed the distribution of over 20 putative glutamic proteases from all fungal species whose genomes have been sequenced so far. Although absent from the Saccharomycetales class, glutamic proteases appear to be present in all other ascomycetes species examined. A large number of coding regions for glutamic proteases were also found clustered together in the Phanerochaete chrysosporium genome, despite apparently being absent from three other species of Basidiomycota.

Fungi↗