Biomedical subjects

Stephen G Oliver

Publications and source records attributed to Stephen G Oliver.

At least 37 records · Page 2Linked to original sources

PubMed2004

PEDRo: a database for storing, searching and disseminating experimental proteomics data.

BACKGROUND: Proteomics is rapidly evolving into a high-throughput technology, in which substantial and systematic studies are conducted on samples from a wide range of physiological, developmental, or pathological conditions. Reference maps from 2D gels are widely circulated. However, there is, as yet, no formally accepted standard representation to support the sharing of proteomics data, and little systematic dissemination of comprehensive proteomic data sets. RESULTS: This paper describes the design, implementation and use of a Proteome Experimental Data Repository (PEDRo), which makes comprehensive proteomics data sets available for browsing, searching and downloading. It is also serves to extend the debate on the level of detail at which proteomics data should be captured, the sorts of facilities that should be provided by proteome data management systems, and the techniques by which such facilities can be made available. CONCLUSIONS: The PEDRo database provides access to a collection of comprehensive descriptions of experimental data sets in proteomics. Not only are these data sets interesting in and of themselves, they also provide a useful early validation of the PEDRo data model, which has served as a starting point for the ongoing standardisation activity through the Proteome Standards Initiative of the Human Proteome Organisation.

Animals↗

PubMed2004

Pedro: a configurable data entry tool for XML.

UNLABELLED: Pedro is a Java application that dynamically generates data entry forms for data models expressed in XML Schema, producing XML data files that validate against this schema. The software uses an intuitive tree-based navigation system, can supply context-sensitive help to users and features a sophisticated interface for populating data fields with terms from controlled vocabularies. The software also has the ability to import records from tab delimited text files and features various validation routines. AVAILABILITY: The application, source code, example models from several domains and tutorials can be downloaded from http://pedro.man.ac.uk/.

Computer Graphics↗

PubMed2004

Global analysis of nutrient control of gene expression in Saccharomyces cerevisiae during growth and starvation.

Global gene expression in yeast was examined in five different nutrient-limited steady states and in their corresponding starvation-induced stationary phases. The use of chemostats, with their ability to generate defined and reproducible physiological conditions, permitted the exclusion of the confounding variables that frequently complicate transcriptome analyses. This approach allowed us to dissect out effects on gene expression that are specific to particular physiological states. Thus, we discovered that a large number of ORFs involved in protein synthesis were activated under ammonium limitation, whereas the expression of ORFs concerned with energy and metabolism was enhanced by carbon limitation. Elevated transcription of genes in high-affinity glucose uptake, the trichloroacetic acid cycle, and oxidative phosphorylation were observed in glucose-limiting, but not glucose-abundant, conditions. In contrast, genes involved in gluconeogenesis and, interestingly, genes subject to nitrogen catabolite repression increased their transcription when ethanol was the carbon source, even though ammonium was in excess. This result suggests that up-regulation of genes sensitive to nitrogen catabolite repression may contribute anapleurotic intermediates in ethanol-grown cells. The different starvation conditions produced two general types of transcription profiles, with carbon-starved cells transcribing far fewer genes than cells starved for any of the other macronutrients. Nonetheless, each starvation condition induced its own peculiar set of genes, and only 17 genes were induced >5-fold by all five starvations. In all cases, analysis of the upstream sequences of clusters of coregulated genes identified motifs that may be recognized by transcription factors specific for controlling gene expression in each of the physiological conditions examined.

Cell Division↗

PubMed2004

Convergent evolution of gene networks by single-gene duplications in higher eukaryotes.

By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix-loop-helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks emerging through single-gene duplications, the dominant importance of molecular modularity in the bottom-up construction of complex biological entities, and the convergent evolution of networks.

Eukaryotic Cells↗

PubMed2004

Yeast as a touchstone in post-genomic research: strategies for integrative analysis in functional genomics.

The new complexity arising from the genome sequencing projects requires new comprehensive post-genomic strategies: advanced studies in regulatory mechanisms, application of new high-throughput technologies at a genome-wide scale, at the different levels of cellular complexity (genome, transcriptome, proteome and metabolome), efficient analysis of the results, and application of new bioinformatic methods in an integrative or systems biology perspective. This can be accomplished in studies with model organisms under controlled conditions. In this review a perspective of the favourable characteristics of yeast as a touchstone model in post-genomic research is presented. The state-of-the art, latest advances in the field and bottlenecks, new strategies, new regulatory mechanisms, applications (patents) and high-throughput technologies, most of them being developed and validated in yeast, are presented. The optimal characteristics of yeast as a well-defined system for comprehensive studies under controlled conditions makes it a perfect model to be used in integrative, "systems biology" studies to get new insights into the mechanisms of regulation (regulatory networks) responsible of specific phenotypes under particular environmental conditions, to be applied to more complex organisms (e.g. plants, human).

Forecasting↗

PubMed2004

Functional genomic hypothesis generation and experimentation by a robot scientist.

The question of whether it is possible to automate the scientific process is of both great theoretical interest and increasing practical importance because, in many scientific areas, data are being generated much faster than they can be effectively analysed. We describe a physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation. The system automatically originates hypotheses to explain observations, devises experiments to test these hypotheses, physically runs the experiments using a laboratory robot, interprets the results to falsify hypotheses inconsistent with the data, and then repeats the cycle. Here we apply the system to the determination of gene function using deletion mutants of yeast (Saccharomyces cerevisiae) and auxotrophic growth experiments. We built and tested a detailed logical model (involving genes, proteins and metabolites) of the aromatic amino acid synthesis pathway. In biological experiments that automatically reconstruct parts of this model, we show that an intelligent experiment selection strategy is competitive with human performance and significantly outperforms, with a cost decrease of 3-fold and 100-fold (respectively), both cheapest and random-experiment selection.

Algorithms↗

PubMed2004

Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era.

It is considered in some quarters that hypothesis-driven methods are the only valuable, reliable or significant means of scientific advance. Data-driven or 'inductive' advances in scientific knowledge are then seen as marginal, irrelevant, insecure or wrong-headed, while the development of technology--which is not of itself 'hypothesis-led' (beyond the recognition that such tools might be of value)--must be seen as equally irrelevant to the hypothetico-deductive scientific agenda. We argue here that data- and technology-driven programmes are not alternatives to hypothesis-led studies in scientific knowledge discovery but are complementary and iterative partners with them. Many fields are data-rich but hypothesis-poor. Here, computational methods of data analysis, which may be automated, provide the means of generating novel hypotheses, especially in the post-genomic era.

Animals↗

PubMed2004

Proteomic response to amino acid starvation in Candida albicans and Saccharomyces cerevisiae.

Saccharomyces cerevisiae activates general amino acid control (GCN) in response to amino acid starvation. Some aspects of this response are known to be conserved in other fungi including Candida albicans, the major systemic fungal pathogen of humans. Here, we describe a proteomic comparison of the GCN responses in S. cerevisiae and C. albicans. We have used high-resolution two-dimensional (2-D) gel electrophoresis and peptide mass fingerprinting to develop a 2-D protein map of C. albicans. A total of 391 protein spots, representing 316 open reading frames, were identified. Fifty-five C. albicans and 65 S. cerevisiae proteins were identified that responded reproducibly to 3-aminotriazole (3AT) in a Gcn4p-dependent fashion. The changes in the S. cerevisiae proteome correlated with the response in the S. cerevisiae transcript profile to 3AT treatment (rank correlation coefficient = 0.59; Natarajan et al., Molec. Cell. Biol. 2001, 21, 4347-4368). Significant aspects of the GCN response were conserved in C. albicans and S. cerevisiae. In both fungi, amino acid biosynthetic enzymes on multiple metabolic pathways were induced by 3AT in a Gcn4p-dependent fashion. Carbon metabolism functions were also induced. However, subtle differences were observed between these fungi. For example, purine biosynthetic enzymes were induced in S. cerevisiae, but were not significantly induced in C. albicans. These differences presumably reflect the contrasting niches of these relatively benign and pathogenic yeasts, respectively.

Amino Acids↗

PubMed2004

Use of expressed sequence tag analysis and cDNA microarrays of the filamentous fungus Aspergillus nidulans.

The use of microarrays in the analysis of gene expression is becoming widespread for many organisms, including yeast. However, although the genomes of a number of filamentous fungi have been fully or partially sequenced, microarray analysis is still in its infancy in these organisms. Here, we describe the construction and validation of microarrays for the fungus Aspergillus nidulans using PCR products from a 4092 EST conidial germination library. An experiment was designed to validate these arrays by monitoring the expression profiles of known genes following the addition of 1% (w/v) glucose to wild-type A. nidulans cultures grown to mid-exponential phase in Vogel's minimal medium with ethanol as the sole carbon source. The profiles of genes showing statistically significant differential expression following the glucose up-shift are presented and an assessment of the quality and reproducibility of the A. nidulans arrays discussed.

Aspergillus nidulans↗

PubMed2004

Combining transcriptome data with genomic and cDNA sequence alignments to make confident functional assignments for Aspergillus nidulans genes.

Whole genome sequencing of several filamentous ascomycetes is complete or in progress; these species, such as Aspergillus nidulans, are relatives of Saccharomyces cerevisiae. However, their genomes are much larger and their gene structure more complex, with genes often containing multiple introns. Automated annotation programs can quickly identify open reading frames for hypothetical genes, many of which will be conserved across large evolutionary distances, but further information is required to confirm functional assignments. We describe a comparative and functional genomics approach using sequence alignments and gene expression data to predict the function of Aspergillus nidulans genes. By highlighting examples of discrepancies between the automated genome annotation and cDNA or EST sequencing, we demonstrate that the greater complexity of gene structure in filamentous fungi demands independent data on gene expression and the gene sequence be used to make confident functional assignments.

Aspergillus nidulans↗

PubMed2004

A common open representation of mass spectrometry data and its application to proteomics research.

A broad range of mass spectrometers are used in mass spectrometry (MS)-based proteomics research. Each type of instrument possesses a unique design, data system and performance specifications, resulting in strengths and weaknesses for different types of experiments. Unfortunately, the native binary data formats produced by each type of mass spectrometer also differ and are usually proprietary. The diverse, nontransparent nature of the data structure complicates the integration of new instruments into preexisting infrastructure, impedes the analysis, exchange, comparison and publication of results from different experiments and laboratories, and prevents the bioinformatics community from accessing data sets required for software development. Here, we introduce the 'mzXML' format, an open, generic XML (extensible markup language) representation of MS data. We have also developed an accompanying suite of supporting programs. We expect that this format will facilitate data management, interpretation and dissemination in proteomics research.

Database Management Systems↗

PubMed2004

A proposed framework for the description of plant metabolomics experiments and their results.

The study of the metabolite complement of biological samples, known as metabolomics, is creating large amounts of data, and support for handling these data sets is required to facilitate meaningful analyses that will answer biological questions. We present a data model for plant metabolomics known as ArMet (architecture for metabolomics). It encompasses the entire experimental time line from experiment definition and description of biological source material, through sample growth and preparation to the results of chemical analysis. Such formal data descriptions, which specify the full experimental context, enable principled comparison of data sets, allow proper interpretation of experimental results, permit the repetition of experiments and provide a basis for the design of systems for data storage and transmission. The current design and example implementations are freely available (http://www.armet.org/). We seek to advance discussion and community adoption of a standard for metabolomics, which would promote principled collection, storage and transmission of experiment data.

Database Management Systems↗

PubMed2004

Effects of reciprocal chromosomal translocations on the fitness of Saccharomyces cerevisiae.

Yeast species have undergone extensive genome reorganization in their evolutionary history, including variations in chromosome number and large chromosomal rearrangements, such as translocations. To determine directly the contribution of chromosomal translocations to the whole organism's fitness, we devised a strategy to construct in Saccharomyces cerevisiae collinear "evolutionary mimics" of other species originally differing by the presence of reciprocal translocations in their genome. A modification of the Cre/loxP system was used to create in S. cerevisiae the translocations detected in the sibling species Saccharomyces mikatae IFO 1815 and 1816. Competition experiments under different physiological conditions showed that the translocated strains of S. cerevisiae consistently outcompeted the reference S. cerevisiae strain with no translocation, both in batch and chemostat culture, especially under glucose limitation. These results indicate that chromosomal translocations in Saccharomyces may have an adaptive significance, and lend support to a model of fixation by natural selection of reciprocal translocations in Saccharomyces species.

Base Sequence↗

PubMed2004

Comparative genomic hybridization provides new insights into the molecular taxonomy of the Saccharomyces sensu stricto complex.

The science of taxonomy is constantly improving as new techniques are developed. Current practice is to construct phylogenetic trees based on the analysis of the DNA sequence of single genes, or parts of single genes. However, this approach has recently been brought into question as several tree topologies may be produced for the same clade when the sequences for various different genes are used. The availability of complete genome sequences for several organisms has seen the adoption of microarray technology to construct molecular phylogenies of bacteria, based on all of the genes. Similar techniques have been used to reveal the relationships between different strains of the yeast Saccharomyces cerevisiae. We have exploited microarray technology to construct a molecular phylogeny for the Saccharomyces sensu stricto complex of yeast species, which is based on all of the protein-encoding genes revealed by the complete genome sequence of the paradigmatic species, S. cerevisiae. We also analyze different strains of S. cerevisiae itself, as well as the putative species S. boulardii. We show that in addition to the phylogeny produced, we can identify and analyze individual ORF traits and interpret the results to give a detailed explanation of evolutionary events underlying the phylogeny.

Classification↗

PubMed2004

Discrimination of modes of action of antifungal substances by use of metabolic footprinting.

Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their "metabolic footprints" by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.

Antifungal Agents↗

PubMed2003

A model-based analysis of microarray experimental error and normalisation.

A statistical model is proposed for the analysis of errors in microarray experiments and is employed in the analysis and development of a combined normalisation regime. Through analysis of the model and two-dye microarray data sets, this study found the following. The systematic error introduced by microarray experiments mainly involves spot intensity-dependent, feature-specific and spot position-dependent contributions. It is difficult to remove all these errors effectively without a suitable combined normalisation operation. Adaptive normalisation using a suitable regression technique is more effective in removing spot intensity-related dye bias than self-normalisation, while regional normalisation (block normalisation) is an effective way to correct spot position-dependent errors. However, dye-flip replicates are necessary to remove feature-specific errors, and also allow the analyst to identify the experimentally introduced dye bias contained in non-self-self data sets. In this case, the bias present in the data sets may include both experimentally introduced dye bias and the biological difference between two samples. Self-normalisation is capable of removing dye bias without identifying the nature of that bias. The performance of adaptive normalisation, on the other hand, depends on its ability to correctly identify the dye bias. If adaptive normalisation is combined with an effective dye bias identification method then there is no systematic difference between the outcomes of the two methods.

Algorithms↗

PubMed2003

SiteSeer: Visualisation and analysis of transcription factor binding sites in nucleotide sequences.

The regulation of gene expression is a fundamental process within every living cell, which allows organisms to manage the precise levels of functional gene products with high sensitivity. It is well established that specific DNA sequences located upstream of the transcriptional start site are important in facilitating the binding of regulatory proteins that control the transcription of the gene. Indeed, microarray-based studies have successfully mined the upstream regions of co-expressed genes and discovered over-represented sequences corresponding to known promoter sites. Here we describe a tool for the visualisation of mapped transcription factor binding sites in the upstream regions of either single or grouped eukaryotic genes, which allows users to examine the positions of known and user-defined sites (http://rocky.bms.umist.ac.uk/SiteSeer/). SiteSeer allows the user to map different sections of the TRANSFAC and SCPD databases (or a set of user-defined sites) onto nucleotide sequences. Additionally, users may restrict the analysis by expectation values for certain DNA words as well as by known binding sites specific to a given organism. We believe this tool will prove particularly valuable for biologists who wish to examine sets of co-expressed or functionally-related genes and those who wish to visualise the positions of promoter sequences and generate displays for publications.

5' Flanking Region↗

PubMed2003

High-throughput classification of yeast mutants for functional genomics using metabolic footprinting.

Many technologies have been developed to help explain the function of genes discovered by systematic genome sequencing. At present, transcriptome and proteome studies dominate large-scale functional analysis strategies. Yet the metabolome, because it is 'downstream', should show greater effects of genetic or physiological changes and thus should be much closer to the phenotype of the organism. We earlier presented a functional analysis strategy that used metabolic fingerprinting to reveal the phenotype of silent mutations of yeast genes. However, this is difficult to scale up for high-throughput screening. Here we present an alternative that has the required throughput (2 min per sample). This 'metabolic footprinting' approach recognizes the significance of 'overflow metabolism' in appropriate media. Measuring intracellular metabolites is time-consuming and subject to technical difficulties caused by the rapid turnover of intracellular metabolites and the need to quench metabolism and separate metabolites from the extracellular space. We therefore focused instead on direct, noninvasive, mass spectrometric monitoring of extracellular metabolites in spent culture medium. Metabolic footprinting can distinguish between different physiological states of wild-type yeast and between yeast single-gene deletion mutants even from related areas of metabolism. By using appropriate clustering and machine learning techniques, the latter based on genetic programming, we show that metabolic footprinting is an effective method to classify 'unknown' mutants by genetic defect.

Cells, Cultured↗