Dov J. Stekel

The fatal fungal outbreak on Vancouver Island is characterized by enhanced intracellular parasitism driven by mitochondrial regulation

In 1999, the population of Vancouver Island, Canada, began to experience an outbreak of a fatal fungal disease caused by a highly virulent lineage of Cryptococcus gattii. This organism has recently spread to the Canadian mainland and Pacific Northwest, but the molecular cause of the outbreak remains unknown. Here we show that the Vancouver Island outbreak (VIO) isolates have dramatically increased their ability to replicate within macrophages of the mammalian immune system in comparison with other C. gattii strains. We further demonstrate that such enhanced intracellular parasitism is directly linked to virulence in a murine model of cryptococcosis, suggesting that this phenotype may be the cause of the outbreak. Finally, microarray studies on 24 C. gattii strains reveals that the hypervirulence of the VIO isolates is characterized by the up-regulation of a large group of genes, many of which are encoded by mitochondrial genome or associated with mitochondrial activities. This expression profile correlates with an unusual mitochondrial morphology exhibited by the VIO strains after phagocytosis. Our data thus demonstrate that the intracellular parasitism of macrophages is a key driver of a human disease outbreak, a finding that has significant implications for a wide range of other human pathogens.

xBASE2: a comprehensive resource for comparative bacterial genomics

xBASE is a genome database aimed at helping laboratory-based bacteriologists make best use of bacterial genome sequence data, with a particular emphasis on comparative genomics. The latest version, xBASE 2.0 (http://xbase.bham.ac.uk), now provides comprehensive coverage of all bacterial genomes and features an updated modularized backend and an improved user interface, which includes a taxonomy browser and a powerful full-text search facility.

Molecular circuits for associative learning in single-celled organisms

We demonstrate how a single-celled organism could undertake associative learning. Although to date only one previous study has found experimental evidence for such learning, there is no reason in principle why it should not occur. We propose a gene regulatory network that is capable of associative learning between any pre-specified set of chemical signals, in a Hebbian manner, within a single cell. A mathematical model is developed, and simulations show a clear learned response. A preliminary design for implementing this model using plasmids within Escherichia coli is presented, along with an alternative approach, based on double-phosphorylated protein kinases.

Comprehensive Identification of Salmonella enterica Serovar Typhimurium Genes Required for Infection of BALB/c Mice

Genes required for infection of mice by Salmonella Typhimurium can be identified by the interrogation of random transposon mutant libraries for mutants that cannot survive in vivo. Inactivation of such genes produces attenuated S. Typhimurium strains that have potential for use as live attenuated vaccines. A quantitative screen, Transposon Mediated Differential Hybridisation (TMDH), has been developed that identifies those members of a large library of transposon mutants that are attenuated. TMDH employs custom transposons with outward-facing T7 and SP6 promoters. Fluorescently-labelled transcripts from the promoters are hybridised to whole-genome tiling microarrays, to allow the position of the transposon insertions to be determined. Comparison of microarray data from the mutant library grown in vitro (input) with equivalent data produced after passage of the library through mice (output) enables an attenuation score to be determined for each transposon mutant. These scores are significantly correlated with bacterial counts obtained during infection of mice using mutants with individual defined deletions of the same genes. Defined deletion mutants of several novel targets identified in the TMDH screen are effective live vaccines.

Contrasting Effects of in Vitro Fertilization and Nuclear Transfer on the Expression of mtDNA Replication Factors

Mitochondrial DNA (mtDNA) is normally only inherited through the oocyte. However, nuclear transfer (NT), the fusion of a donor cell with an enucleated oocyte, can transmit both donor cell and recipient oocyte mtDNA. mtDNA replication is under the control of nuclear-encoded replication factors, such as polymerase gamma (POLG) and mitochondrial transcription factor A (TFAM). These are first expressed during late preimplantation embryo development. To account for the persistence of donor cell mtDNA, even when introduced at residual levels (mtDNAR), we hypothesized that POLG and TFAM would be upregulated in intra- and interspecific (ovine–ovine) and intergeneric (caprine–ovine) NT embryos when compared to in vitro fertilized (IVF) embryos. For the intra- and interspecific crosses, PolGA (catalytic subunit), PolGB (accessory subunit), and TFAM mRNA were expressed at the 2-cell stage in both nondepleted (mtDNA+) and mtDNAR embryos with protein being expressed up to the 16-cell stage for POLGA and TFAM. However, at the 16-cell stage, there was significantly more PolGA expression in the mtDNAR embryos compared to their mtDNA+ counterparts. Expression for all three genes first matched IVF embryos at the blastocyst stage. In the intergeneric model, POLG was upregulated during preimplantation development. Although these embryos did not persist further than the 16+-cell stage, significantly more mtDNAR embryos reached this stage. However, the vast majority of these embryos were homoplasmic for recipient oocyte mtDNA. The upreglation in mtDNA replication factors was most likely due to the donor cells still expressing these factors prior to NT.

Strong negative self regulation of Prokaryotic transcription factors increases the intrinsic noise of protein expression

BackgroundMany prokaryotic transcription factors repress their own transcription. It is often asserted that such regulation enables a cell to homeostatically maintain protein abundance. We explore the role of negative self regulation of transcription in regulating the variability of protein abundance using a variety of stochastic modeling techniques.ResultsWe undertake a novel analysis of a classic model for negative self regulation. We demonstrate that, with standard approximations, protein variance relative to its mean should be independent of repressor strength in a physiological range. Consequently, in that range, the coefficient of variation would increase with repressor strength. However, stochastic computer simulations demonstrate that there is a greater increase in noise associated with strong repressors than predicted by theory. The discrepancies between the mathematical analysis and computer simulations arise because with strong repressors the approximation that leads to Michaelis-Menten-like hyperbolic repression terms ceases to be valid. Because we observe that strong negative feedback increases variability and so is unlikely to be a mechanism for noise control, we suggest instead that negative feedback is evolutionarily favoured because it allows the cell to minimize mRNA usage. To test this, we used in silico evolution to demonstrate that while negative feedback can achieve only a modest improvement in protein noise reduction compared with the unregulated system, it can achieve good improvement in protein response times and very substantial improvement in reducing mRNA levels.ConclusionStrong negative self regulation of transcription may not always be a mechanism for homeostatic control of protein abundance, but instead might be evolutionarily favoured as a mechanism to limit the use of mRNA. The use of hyperbolic terms derived from quasi-steady-state approximation should also be avoided in the analysis of stochastic models with strong repressors.

A model of lymphocyte recirculation

Abstract Dov Stekel, Claire Parker and Martin Nowak present a mathematical model of T-cell recirculation between blood, spleen and the lymphatic system. Comparison with classical experiments on lymphocyte recirculation and localization demonstrates that simple kinetics are sufficient to explain lymphocyte homeostasis between these organs, and that T-cell counts in blood are highly sensitive to changes in the recirculation parameters.

A novel method of differential gene expression analysis using multiple cDNA libraries applied to the identification of tumour endothelial genes

BackgroundIn this study, differential gene expression analysis using complementary DNA (cDNA) libraries has been improved. Firstly by the introduction of an accurate method of assigning Expressed Sequence Tags (ESTs) to genes and secondly, by using a novel likelihood ratio statistical scoring of differential gene expression between two pools of cDNA libraries. These methods were applied to the latest available cell line and bulk tissue cDNA libraries in a two-step screen to predict novel tumour endothelial markers. Initially, endothelial cell lines were in silico subtracted from non-endothelial cell lines to identify endothelial genes. Subsequently, a second bulk tumour versus normal tissue subtraction was employed to predict tumour endothelial markers.ResultsFrom an endothelial cDNA library analysis, 431 genes were significantly up regulated in endothelial cells with a False Discovery Rate adjusted q-value of 0.01 or less and 104 of these were expressed only in endothelial cells. Combining the cDNA library data with the latest Serial Analysis of Gene Expression (SAGE) library data derived a complete list of 459 genes preferentially expressed in endothelium. 27 genes were predicted tumour endothelial markers in multiple tissues based on the second bulk tissue screen.ConclusionThis approach represents a significant advance on earlier work in its ability to accurately assign an EST to a gene, statistically measure differential expression between two pools of cDNA libraries and predict putative tumour endothelial markers before entering the laboratory. These methods are of value and available http://www.compbio.ox.ac.uk/data/diffex.html to researchers that are interested in the analysis of transcriptomic data.

Microbial mass movements

Wastewater, tourism, and trade are moving microbes around the globe at an unprecedented scale For several billion years, microorganisms and the genes they carry have mainly been moved by physical forces such as air and water currents. These forces generated biogeographic patterns for microorganisms that are similar to those of animals and plants (1). In the past 100 years, humans have changed these dynamics by transporting large numbers of cells to new locations through waste disposal, tourism, and global transport and by modifying selection pressures at those locations. As a consequence, we are in the midst of a substantial alteration to microbial biogeography. This has the potential to change ecosystem services and biogeochemistry in unpredictable ways.

Inclusion of neighboring base interdependencies substantially improves genome-wide prokaryotic transcription factor binding site prediction

Prediction of transcription factor binding sites is an important challenge in genome analysis. The advent of next generation genome sequencing technologies makes the development of effective computational approaches particularly imperative. We have developed a novel training-based methodology intended for prokaryotic transcription factor binding site prediction. Our methodology extends existing models by taking into account base interdependencies between neighbouring positions using conditional probabilities and includes genomic background weighting. This has been tested against other existing and novel methodologies including position-specific weight matrices, first-order Hidden Markov Models and joint probability models. We have also tested the use of gapped and ungapped alignments and the inclusion or exclusion of background weighting. We show that our best method enhances binding site prediction for all of the 22 Escherichia coli transcription factors with at least 20 known binding sites, with many showing substantial improvements. We highlight the advantage of using block alignments of binding sites over gapped alignments to capture neighbouring position interdependencies. We also show that combining these methods with ChIP-on-chip data has the potential to further improve binding site prediction. Finally we have developed the ungapped likelihood under positional background platform: a user friendly website that gives access to the prediction method devised in this work.