From START to FINISH : the influence of osmotic stress on the cell cycle

Peer reviewedPublisher PD

Regulation of polarised growth in fungi

Polarised growth in fungi occurs through the delivery of secretory vesicles along tracks formed by cytoskeletal elements to specific sites on the cell surface where they dock with a multiprotein structure called the exocyst before fusing with the plasmamembrane. The budding yeast, Saccharomyces cerevisiae has provided a useful model to investigate the mechanisms involved and their control. Cortical markers, provided by bud site selection pathways during budding, the septin ring during cytokinesis or the stimulation of the pheromone response receptors during mating, act through upstream signalling pathways to localise Cdc24, the GEF for the rho family GTPase, Cdc42. Cdc42 in its GTP-bound activates a multiprotein protein complex called the polarisome which nucleates actin cables along which the secretory vesicles are transported to the cell surface. Hyphae can elongate at a rate orders of magnitude faster than the extension of a yeast bud, so understanding hyphal growth will require substantial modification of the yeast paradigm. The rapid rate of hyphal growth is driven by a structure called the Spitzenkörper, located just behind the growing tip and which is rich in secretory vesicles. It is thought that secretory vesicles are delivered to the apical region where they accumulate in the Spitzenkörper. The Spitzenkörper then acts as vesicle supply centre in which vesicles exit the Spitzenkörper in all directions, but because of its proximity, the tip receives a greater concentration of vesicles per unit area than subapical regions. There are no obvious equivalents to the bud site selection pathway to provide a spatial landmark for polarised growth in hyphae. However, an emerging model is the way that the site of polarised growth in the fission yeast, Schizosaccharomyces pombe, is marked by delivery of the kelch repeat protein, Tea1, along microtubules. The relationship of the Spitzenkörper to the polarisome and the mechanisms that promote its formation are key questions that form the focus of current research

Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

BACKGROUND: Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. RESULTS: S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. CONCLUSIONS: This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.Published versio

Membrane fluidity and temperature sensing are coupled via circuitry comprised of Ole1, Rsp5, and Hsf1 in Candida albicans

Peer reviewedPublisher PD

Comparative Analysis of the Saccharomyces cerevisiae and Caenorhabditis elegans Protein Interaction Network

Protein interaction networks aim to summarize the complex interplay of proteins in an organism. Early studies suggested that the position of a protein in the network determines its evolutionary rate but there has been considerable disagreement as to what extent other factors, such as protein abundance, modify this reported dependence. We compare the genomes of Saccharomyces cerevisiae and Caenorhabditis elegans with those of closely related species to elucidate the recent evolutionary history of their respective protein interaction networks. Interaction and expression data are studied in the light of a detailed phylogenetic analysis. The underlying network structure is incorporated explicitly into the statistical analysis. The increased phylogenetic resolution, paired with high-quality interaction data, allows us to resolve the way in which protein interaction network structure and abundance of proteins affect the evolutionary rate. We find that expression levels are better predictors of the evolutionary rate than a protein's connectivity. Detailed analysis of the two organisms also shows that the evolutionary rates of interacting proteins are not sufficiently similar to be mutually predictive. It appears that meaningful inferences about the evolution of protein interaction networks require comparative analysis of reasonably closely related species. The signature of protein evolution is shaped by a protein's abundance in the organism and its function and the biological process it is involved in. Its position in the interaction networks and its connectivity may modulate this but they appear to have only minor influence on a protein's evolutionary rate.Comment: Accepted for publication in BMC Evolutionary Biolog

Ribosome traffic on mRNAs maps to gene ontology : genome-wide quantification of translation initiation rates and polysome size regulation

Peer reviewedPublisher PD

Pathways and Mechanisms that Prevent Genome Instability in Saccharomyces cerevisiae.

Genome rearrangements result in mutations that underlie many human diseases, and ongoing genome instability likely contributes to the development of many cancers. The tools for studying genome instability in mammalian cells are limited, whereas model organisms such as Saccharomyces cerevisiae are more amenable to these studies. Here, we discuss the many genetic assays developed to measure the rate of occurrence of Gross Chromosomal Rearrangements (called GCRs) in S. cerevisiae These genetic assays have been used to identify many types of GCRs, including translocations, interstitial deletions, and broken chromosomes healed by de novo telomere addition, and have identified genes that act in the suppression and formation of GCRs. Insights from these studies have contributed to the understanding of pathways and mechanisms that suppress genome instability and how these pathways cooperate with each other. Integrated models for the formation and suppression of GCRs are discussed

A holistic view on transcriptional regulatory networks in S. cerevisiae: Implications and utilization

Life; perhaps it is bold to start an abstract with this powerful word, but this is where I will start. My research is at the heart of life. How can a single human cell proliferate to become bones, eyes, fingers and, finally, a human being? How can different cells containing the same set of DNA be so versatile? The answer lies within the regulation of genes. To build upon our understanding of gene regulation, I have studied gene transcription and especially transcription factors in a holistic, systems biology way using the model organism Saccharomyces cerevisiae. Translation from S. cerevisiae to humans will help us get both a fundamental understanding of the networks and engineer better cell factories.\ua0\ua0 Transcription factors play an essential role in transcription as they function to activate and suppress genes in response to stimuli. The transcription factors form transcriptional regulatory networks (TRNs), with intricate cross-talk and overlapping functions balancing the ability of the cells to react to stimuli but at the same time remain as steady as possible. This is a fine-tuned machinery that has a built-in safety feature of self-regulation if the system is perturbed in any way. We study the TRNs with state-of-the-art methods for transcription factor-DNA interaction: Chromatin Immunoprecipitation with exonuclease treatment or ChIP-exo for short. This method provides us with all the DNA interactions of a selected transcription factor at the nucleotide level and to what degree these interactions occurs. To study these transcriptional regulatory networks, we put the yeast cells under nutrient starvation in fermentation systems. The fermentation system used is the chemostat, which enables a tight control on the environmental parameters, ensures a steady-state in the culture, and allows for high reproducibility. Ensuring that the cell culture is identical in-between runs is important since we can’t study all transcription factors at the same time. In this thesis, I present studies on transcription factors both individually, or as part of a bigger whole. We investigate stress response, NADPH generation, control over lipid and amino acid metabolism and the glycolytic pathway. Thanks to the different metabolic conditions used to study the transcription factors, we can both determine a core set of genes and genes that are specific for different conditions. We also employ statistical methods and regression models to understand and predict regulatory pathways. While doing so we discover novel functions and modularity and expand the transcriptional regulatory network for all studied transcription factors. We also constructed a multi-paralleled miniaturized chemostat-system to study these transcription factors in a high-throughput fashion. Finally, we have developed a toolbox for analysis of transcription factor data, including visual representation of the DNA binding, comparison of gene transcription and transcription binding between conditions and statistical methods for identifying regulatory pathways that can be used both for a fundamental understanding of TRNs and for better cell factory engineering

The silicon trypanosome

African trypanosomes have emerged as promising unicellular model organisms for the next generation of systems biology. They offer unique advantages, due to their relative simplicity, the availability of all standard genomics techniques and a long history of quantitative research. Reproducible cultivation methods exist for morphologically and physiologically distinct life-cycle stages. The genome has been sequenced, and microarrays, RNA-interference and high-accuracy metabolomics are available. Furthermore, the availability of extensive kinetic data on all glycolytic enzymes has led to the early development of a complete, experiment-based dynamic model of an important biochemical pathway. Here we describe the achievements of trypanosome systems biology so far and outline the necessary steps towards the ambitious aim of creating a , a comprehensive, experiment-based, multi-scale mathematical model of trypanosome physiology. We expect that, in the long run, the quantitative modelling enabled by the Silicon Trypanosome will play a key role in selecting the most suitable targets for developing new anti-parasite drugs