Yeast as a Touchstone in Post-genomic Research: Strategies for Integrative Analysis in Functional Genomics

Cytotoxicity of Oleandrin Is Mediated by Calcium Influx and by Increased Manganese Uptake in Saccharomyces cerevisiae Cells

Oleandrin, the main component of Nerium oleander L. extracts, is a cardiotoxic glycoside with multiple pharmacological implications, having potential anti-tumoral and antiviral characteristics. Although it is accepted that the main mechanism of oleandrin action is the inhibition of Na⁺/K⁺-ATPases and subsequent increase in cell calcium, many aspects which determine oleandrin cytotoxicity remain elusive. In this study, we used the model Saccharomyces cerevisiae to unravel new elements accounting for oleandrin toxicity. Using cells expressing the Ca²⁺-sensitive photoprotein aequorin, we found that oleandrin exposure resulted in Ca²⁺ influx into the cytosol and that failing to pump Ca²⁺ from the cytosol to the vacuole increased oleandrin toxicity. We also found that oleandrin exposure induced Mn²⁺ accumulation by yeast cells via the plasma membrane Smf1 and that mutants with defects in Mn²⁺ homeostasis are oleandrin-hypersensitive. Our data suggest that combining oleandrin with agents which alter Ca²⁺ or Mn²⁺ uptake may be a way of controlling oleandrin toxicity.

Saccharomyces cerevisiae and Caffeine Implications on the Eukaryotic Cell

Caffeine-a methylxanthine analogue of the purine bases adenine and guanine-is by far the most consumed neuro-stimulant, being the active principle of widely consumed beverages such as coffee, tea, hot chocolate, and cola. While the best-known action of caffeine is to prevent sleepiness by blocking the adenosine receptors, caffeine exerts a pleiotropic effect on cells, which lead to the activation or inhibition of various cell integrity pathways. The aim of this review is to present the main studies set to investigate the effects of caffeine on cells using the model eukaryotic microorganism Saccharomyces cerevisiae, highlighting the caffeine synergy with external cell stressors, such as irradiation or exposure to various chemical hazards, including cigarette smoke or chemical carcinogens. The review also focuses on the importance of caffeine-related yeast phenotypes used to resolve molecular mechanisms involved in cell signaling through conserved pathways, such as target of rapamycin (TOR) signaling, Pkc1-Mpk1 mitogen activated protein kinase (MAPK) cascade, or Ras/cAMP protein kinase A (PKA) pathway.

Next-Generation Genome-Scale Models Incorporating Multilevel 'Omics Data: From Yeast to Human

Genome-scale modelling in eukaryotes has been pioneered by the yeast Saccharomyces cerevisiae. Early metabolic networks have been reconstructed based on genome sequence and information accumulated in the literature on biochemical reactions. Protein-protein interaction networks have been constructed based on experimental observations such as yeast-2-hybrid method. Gene regulatory networks were based on a variety of data types, including information on TF-promoter binding and gene coexpression. The aforementioned networks have been improved gradually, and methods for their integration were developed. Incorporation of omics data including genomics, metabolomics, transcriptomics, fluxome, and phosphoproteome led to next-generation genome-scale models. The methods tested on yeast have later been implemented in human, further, cellular components found to be important in yeast physiology under (ab)normal conditions, and (dis)regulation mechanisms in yeast shed light to the healthy and disease states in human. This chapter provides a historical perspective on next-generation genome-scale models incorporating multilevel 'omics data, from yeast to human.

The metabolome 18 years on: a concept comes of age

BACKGROUND

The term 'metabolome' was introduced to the scientific literature in September 1998.

AIM AND KEY SCIENTIFIC CONCEPTS OF THE REVIEW

To mark its 18-year-old 'coming of age', two of the co-authors of that paper review the genesis of metabolomics, whence it has come and where it may be going.

Improvement of Mitochondria Extract from Saccharomyces cerevisiae Characterization in Shotgun Proteomics Using Sheathless Capillary Electrophoresis Coupled to Tandem Mass Spectrometry

In this work, we describe the characterization of a quantity-limited sample (100 ng) of yeast mitochondria by shotgun bottom-up proteomics. Sample characterization was carried out by sheathless capillary electrophoresis, equipped with a high sensitivity porous tip and coupled to tandem mass spectrometry (CESI-MS-MS) and concomitantly with a state-of-art nano flow liquid chromatography coupled to a similar mass spectrometry (MS) system (nanoLC-MS-MS). With single injections, both nanoLC-MS-MS and CESI-MS-MS 60 min-long separation experiments allowed us to identify 271 proteins (976 unique peptides) and 300 proteins (1,765 unique peptides) respectively, demonstrating a significant specificity and complementarity in identification depending on the physicochemical separation employed. Such complementary, maximizing the number of analytes detected, presents a powerful tool to deepen a biological sample's proteomic characterization. A comprehensive study of the specificity provided by each separating technique was also performed using the different properties of the identified peptides: molecular weight, mass-to-charge ratio (m/z), isoelectric point (pI), sequence coverage or MS-MS spectral quality enabled to determine the contribution of each separation. For example, CESI-MS-MS enables to identify larger peptides and eases the detection of those having extreme pI without impairing spectral quality. The addition of peptides, and therefore proteins identified by both techniques allowed us to increase significantly the sequence coverages and then the confidence of characterization. In this study, we also demonstrated that the two yeast enolase isoenzymes were both characterized in the CESI-MS-MS data set. The observation of discriminant proteotypic peptides is facilitated when a high number of precursors with high-quality MS-MS spectra are generated.

Integrated analysis of transcriptomics and metabolomics profiles

BACKGROUND

Integrated analysis of transcriptomics and metabolomics data has the potential greatly to increase our understanding of metabolic networks and biological systems leading to various potential clinical applications.

OBJECTIVE

The aim is to present different applications as well as analysis tools utilized for the parallel study of gene and metabolite expressions.

METHODS

Publications dealing with integrated analysis of gene and metabolite expression data as well as publications describing tools that can be used for integrated analysis are reviewed.

RESULTS/CONCLUSION

The full benefit of integrated analysis can be achieved only if data from all utilized methods are treated equally by multidisciplinary teams. This approach can lead to advances in functional genomics with possible clinical developments in diagnostics and improved drug target selection.

Yeast as a model eukaryote in toxinology: a functional genomics approach to studying the molecular basis of action of pharmacologically active molecules

Yeast Saccharomyces cerevisiae has proven to be a relevant and convenient model organism for the study of diverse biological phenomena, due to its straightforward genetics, cost-effectiveness and rapid growth, combined with the typical characteristics of a eukaryotic cell. More than 40% of yeast proteins share at least part of their primary amino acid sequence with the corresponding human protein, making yeast a valuable model in biomedical research. In the last decade, high-throughput and genome-wide experimental approaches developed in yeast have paved the way to functional genomics that aims at a global understanding of the relationship between genotype and phenotype. In this review we first present the yeast strain and plasmid collections for genome-wide experimental approaches to study complex interactions between genes, proteins and endo- or exogenous small molecules. We describe methods for protein-protein, protein-DNA, genetic and chemo-genetic interactions, as well as localization studies, focussing on their application in research on small pharmacologically active molecules. Next we review the use of yeast as a model organism in neurobiology, emphasizing work done towards elucidating the pathogenesis of neurodegenerative diseases and the mechanism of action of neurotoxic phospholipases A(2).

Green systems biology - From single genomes, proteomes and metabolomes to ecosystems research and biotechnology

Plants have shaped our human life form from the outset. With the emerging recognition of world population feeding, global climate change and limited energy resources with fossil fuels, the relevance of plant biology and biotechnology is becoming dramatically important. One key issue is to improve plant productivity and abiotic/biotic stress resistance in agriculture due to restricted land area and increasing environmental pressures. Another aspect is the development of CO(2)-neutral plant resources for fiber/biomass and biofuels: a transition from first generation plants like sugar cane, maize and other important nutritional crops to second and third generation energy crops such as Miscanthus and trees for lignocellulose and algae for biomass and feed, hydrogen and lipid production. At the same time we have to conserve and protect natural diversity and species richness as a foundation of our life on earth. Here, biodiversity banks are discussed as a foundation of current and future plant breeding research. Consequently, it can be anticipated that plant biology and ecology will have more indispensable future roles in all socio-economic aspects of our life than ever before. We therefore need an in-depth understanding of the physiology of single plant species for practical applications as well as the translation of this knowledge into complex natural as well as anthropogenic ecosystems. Latest developments in biological and bioanalytical research will lead into a paradigm shift towards trying to understand organisms at a systems level and in their ecosystemic context: (i) shotgun and next-generation genome sequencing, gene reconstruction and annotation, (ii) genome-scale molecular analysis using OMICS technologies and (iii) computer-assisted analysis, modeling and interpretation of biological data. Systems biology combines these molecular data, genetic evolution, environmental cues and species interaction with the understanding, modeling and prediction of active biochemical networks up to whole species populations. This process relies on the development of new technologies for the analysis of molecular data, especially genomics, metabolomics and proteomics data. The ambitious aim of these non-targeted 'omic' technologies is to extend our understanding beyond the analysis of separated parts of the system, in contrast to traditional reductionistic hypothesis-driven approaches. The consequent integration of genotyping, pheno/morphotyping and the analysis of the molecular phenotype using metabolomics, proteomics and transcriptomics will reveal a novel understanding of plant metabolism and its interaction with the environment. The analysis of single model systems - plants, fungi, animals and bacteria - will finally emerge in the analysis of populations of plants and other organisms and their adaptation to the ecological niche. In parallel, this novel understanding of ecophysiology will translate into knowledge-based approaches in crop plant biotechnology and marker- or genome-assisted breeding approaches. In this review the foundations of green systems biology are described and applications in ecosystems research are presented. Knowledge exchange of ecosystems research and green biotechnology merging into green systems biology is anticipated based on the principles of natural variation, biodiversity and the genotype-phenotype environment relationship as the fundamental drivers of ecology and evolution.

Metabolomics: an integral technique in systems biology

Metabolomics is the unbiased identification and state-specific quantification of all metabolites in a cell, tissue or whole organism, and has developed rapidly into one of the cornerstones of postgenomic techniques for the quantitative analysis of molecular phenotypes. These large-scale analyses of metabolites are intimately bound to advancements in MS technologies and have emerged in parallel with the development of novel mass analyzers and hyphenated techniques, as well as with the combination of different techniques to cope with the physicochemical diversity of a metabolome. This review gives a brief description of the development and applications of these technologies in biochemistry and systems biology, and discusses their significance in the postgenomic era. Especially, the systematic relation between high-throughput metabolomic data and their interpretation with respect to the underlying biochemical regulatory network is discussed.

Dynamics of time-lagged gene-to-metabolite networks of Escherichia coli elucidated by integrative omics approach

In the postgenomics era, integrative analysis of several "omics" data is absolutely required for understanding the cell as a system. Integrative analysis of transcriptomics and metabolomics can lead to elucidation of gene-to-metabolite networks. When integrating different time series "omics" data, it is necessary to take into consideration a time lag between those data. In the present study, we conducted an integrative analysis of time series transcriptomics and metabolomics data of Escherichia coli generated by cDNA microarray and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS), respectively. We identified a 60-min time lag between transition points of transcriptomics and metabolomics data by using a Linear Dynamical System. Furthermore, we investigated gene-to-metabolite correlations in the context of time lag, obtained the maximum number of correlated pairs at transcripts leading 60-min time lag, and finally revealed gene-to-metabolite relations in the phospholipid biosynthesis pathway. Taking into consideration the time lag between transcriptomics and metabolomics data in time series analysis could unravel novel gene-to-metabolite relations. According to gene-to-metabolite correlations, phosphatidylglycerol plays a more critical role for membrane balance than phosphatidylethanolamine in E. coli.

S. pombe genome deletion project: an update

The fission yeast Schizosaccharomyces pombe is a model organism used widely to study various aspects of eukaryotic biology. A collection of heterozygous diploid strains containing individual deletions in nearly all S. pombe genes has been created using a PCR based strategy. However, deletion of some genes has not been possible using this methodology. Here we use an efficient knockout strategy based on plasmids that contain large regions homologous to the target gene to delete an additional 29 genes. The collection of deletion mutants now covers 99% of the fission yeast open reading frames.

Nutrient control of eukaryote cell growth: a systems biology study in yeast

Background

To elucidate the biological processes affected by changes in growth rate and nutrient availability, we have performed a comprehensive analysis of the transcriptome, proteome and metabolome responses of chemostat cultures of the yeast, Saccharomyces cerevisiae, growing at a range of growth rates and in four different nutrient-limiting conditions.

Results

We find significant changes in expression for many genes in each of the four nutrient-limited conditions tested. We also observe several processes that respond differently to changes in growth rate and are specific to each nutrient-limiting condition. These include carbohydrate storage, mitochondrial function, ribosome synthesis, and phosphate transport. Integrating transcriptome data with proteome measurements allows us to identify previously unrecognized examples of post-transcriptional regulation in response to both nutrient and growth-rate signals.

Conclusions

Our results emphasize the unique properties of carbon metabolism and the carbon substrate, the limitation of which induces significant changes in gene regulation at the transcriptional and post-transcriptional level, as well as altering how many genes respond to growth rate. By comparison, the responses to growth limitation by other nutrients involve a smaller set of genes that participate in specific pathways.

See associated commentary http://www.biomedcentral.com/1741-7007/8/62

Mathematical modelling of whole chromosome replication

All chromosomes must be completely replicated prior to cell division, a requirement that demands the activation of a sufficient number of appropriately distributed DNA replication origins. Here we investigate how the activity of multiple origins on each chromosome is coordinated to ensure successful replication. We present a stochastic model for whole chromosome replication where the dynamics are based upon the parameters of individual origins. Using this model we demonstrate that mean replication time at any given chromosome position is determined collectively by the parameters of all origins. Combining parameter estimation with extensive simulations we show that there is a range of model parameters consistent with mean replication data, emphasising the need for caution in interpreting such data. In contrast, the replicated-fraction at time points through S phase contains more information than mean replication time data and allowed us to use our model to uniquely estimate many origin parameters. These estimated parameters enable us to make a number of predictions that showed agreement with independent experimental data, confirming that our model has predictive power. In summary, we demonstrate that a stochastic model can recapitulate experimental observations, including those that might be interpreted as deterministic such as ordered origin activation times.

Quantitative proteomics by metabolic labeling of model organisms

In the biological sciences, model organisms have been used for many decades and have enabled the gathering of a large proportion of our present day knowledge of basic biological processes and their derailments in disease. Although in many of these studies using model organisms, the focus has primarily been on genetics and genomics approaches, it is important that methods become available to extend this to the relevant protein level. Mass spectrometry-based proteomics is increasingly becoming the standard to comprehensively analyze proteomes. An important transition has been made recently by moving from charting static proteomes to monitoring their dynamics by simultaneously quantifying multiple proteins obtained from differently treated samples. Especially the labeling with stable isotopes has proved an effective means to accurately determine differential expression levels of proteins. Among these, metabolic incorporation of stable isotopes in vivo in whole organisms is one of the favored strategies. In this perspective, we will focus on methodologies to stable isotope label a variety of model organisms in vivo, ranging from relatively simple organisms such as bacteria and yeast to Caenorhabditis elegans, Drosophila, and Arabidopsis up to mammals such as rats and mice. We also summarize how this has opened up ways to investigate biological processes at the protein level in health and disease, revealing conservation and variation across the evolutionary tree of life.

Chemostat-based micro-array analysis in baker's yeast

Chemostat cultivation of micro-organisms offers unique opportunities for experimental manipulation of individual environmental parameters at a fixed, controllable specific growth rate. Chemostat cultivation was originally developed as a tool to study quantitative aspects of microbial growth and metabolism. Renewed interest in this cultivation method is stimulated by the availability of high-information-density techniques for systemic analysis of microbial cultures, which require high reproducibility and careful experimental design. Genome-wide analysis of transcript levels with DNA micro-arrays is currently the most commonly applied of these high-information-density analysis tools for microbial gene expression. Based on published studies on the yeast Saccharomyces cerevisiae, a critical overview is presented of the possibilities and pitfalls associated with the combination of chemostat cultivation and transcriptome analysis with DNA micro-arrays. After a brief introduction to chemostat cultivation and micro-array analysis, key aspects of experimental design of chemostat-based micro-array experiments are discussed. The main focus of this review is on key biological concepts that can be accessed by chemostat-based micro-array analysis. These include effects of specific growth rate on transcriptional regulation, context-dependency of transcriptional responses, correlations between transcript profiles and contribution of the corresponding proteins to cellular function and fitness, and the analysis and application of evolutionary adaptation during prolonged chemostat cultivation. It is concluded that, notwithstanding the incompatibility of chemostat cultivation with high-throughput analysis, integration of chemostat cultivation with micro-array analysis and other high-information-density analytical approaches (e.g. proteomics and metabolomics techniques) offers unique advantages in terms of reproducibility and experimental design in comparison with standard batch cultivation systems. Therefore, chemostat cultivation and derived methods for controlled cultivation of micro-organisms are anticipated to become increasingly important in microbial physiology and systems biology.

Exometabolic and transcriptional response in relation to phenotype and gene copy number in respiration-related deletion mutants of S. cerevisiae

The transcriptional and metabolic impact of deleting one or both copies of a respiration-related gene has been studied in glucose-limited chemostats. Integration of literature information on phenotype with our exometabolome and transcriptome data enabled the identification of novel relationships between gene copy number, transcriptional regulation and phenotype. We found that the effect of complete respiratory deficiency on transcription was limited to downregulation of genes involved in oxidoreductase activity and iron assimilation. Partial respiratory deficiency had no significant impact on gene transcription. Changes in the copy number of two transcription-factor genes, HAP4 and MIG1, had a major impact on genes involved in mitochondrial function. Regulation of respiratory chain components encoded in the nucleus and mitochondria appears to be divided between Hap4p and Oxa1p, respectively. Similarly, repression of respiration may be imposed by the action of Mig1p and Mba1p on nuclear and mitochondrial gene expression, respectively. However, it is not clear whether Oxa1p and Mba1p regulate mitochondrial gene expression via their interaction with mitochondrial ribosomes or by some indirect means. The phenotype of nuclear petite mutants may not simply be due to the absence of respiration; e.g. Oxa1p or Bcs1p may play a role in the regulation of ribosome assembly in the nucleolus. Integration between respiration and cell growth may also result from the action of a single transcription factor. Thus, Hap4p targets genes that are required for respiration and for fitness in nutrient-limited conditions. This suggests that Hap4p may enable cells to adapt to nutrient limitation as well as diauxy.

Metabolite identification using a nanoelectrospray LC-EC-array-MS integrated system

A novel approach to the parallel coupling of normal-bore high-performance liquid chromatography (LC) with electrochemical-array detection (EC-array) and nanoelectrospray mass spectrometry (MS), based on the use of a nanosplitting interface, is described where both detectors are utilized at their optimal detection mode for parallel configuration. The dual detection platform was shown to maintain full chromatographic integrity with retention times and peak widths at half-height between the EC-array and MS displaying high reproducibility with relative standard deviations of <2%. Detection compatibility between the two detectors at the part per billion level injected on-column was demonstrated using selected metabolites representative of the diversity typically encountered in physiological systems. Metabolites were detected with equal efficiency whether neat or in serum, demonstrating the system's ability to handle biological samples with limited sample cleanup and reduced concern for biological matrix effects. Direct quantification of known analytes from the EC-array signal using Faraday's law can eliminate the need for isotopically labeled internal standards. The system was successfully applied to the detection and characterization of metabolites of phenylbutyrate from serum samples of Huntington's disease patients in an example that illustrates the complementarity of the dual detection nanoelectrospray LC-EC-array-MS system.

A systems biology perspective of wine fermentations

The yeast Saccharomyces cerevisiae is an important industrial microorganism. Nowadays, it is being used as a cell factory for the production of pharmaceuticals such as insulin, although this yeast has long been utilized in the bakery to raise dough, and in the production of alcoholic beverages, fermenting the sugars derived from rice, wheat, barley, corn and grape juice. S. cerevisiae has also been extensively used as a model eukaryotic system. In the last decade, genomic techniques have revealed important features of its molecular biology. For example, DNA array technologies are routinely used for determining gene expression levels in cells under different physiological conditions or environmental stimuli. Laboratory strains of S. cerevisiae are different from wine strains. For instance, laboratory yeasts are unable to completely transform all the sugar in the grape must into ethanol under winemaking conditions. In fact, standard culture conditions are usually very different from winemaking conditions, where multiple stresses occur simultaneously and sequentially throughout the fermentation. The response of wine yeasts to these stimuli differs in some aspects from laboratory strains, as suggested by the increasing number of studies in functional genomics being conducted on wine strains. In this paper we review the most recent applications of post-genomic techniques to understand yeast physiology in the wine industry. We also report recent advances in wine yeast strain improvement and propose a reference framework for integration of genomic information, bioinformatic tools and molecular biology techniques for cellular and metabolic engineering. Finally, we discuss the current state and future perspectives for using 'modern' biotechnology in the wine industry.

Fuzzy association rules for biological data analysis: a case study on yeast

Background

Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data.

Results

In this work we propose a novel fuzzy methodology based on a fuzzy association rule mining method for biological knowledge extraction. We apply this methodology over a yeast genome dataset containing heterogeneous information regarding structural and functional genome features. A number of association rules have been found, many of them agreeing with previous research in the area. In addition, a comparison between crisp and fuzzy results proves the fuzzy associations to be more reliable than crisp ones.

Conclusion

An integrative approach as the one carried out in this work can unveil significant knowledge which is currently hidden and dispersed through the existing biological databases. It is shown that fuzzy association rules can model this knowledge in an intuitive way by using linguistic labels and few easy-understandable parameters.

Integration of metabolomics and proteomics in molecular plant physiology--coping with the complexity by data-dimensionality reduction

In recent years, genomics has been extended to functional genomics. Toward the characterization of organisms or species on the genome level, changes on the metabolite and protein level have been shown to be essential to assign functions to genes and to describe the dynamic molecular phenotype. Gas chromatography (GC) and liquid chromatography coupled to mass spectrometry (GC- and LC-MS) are well suited for the fast and comprehensive analysis of ultracomplex metabolite samples. For the integration of metabolite profiles with quantitative protein profiles, a high throughput (HTP) shotgun proteomics approach using LC-MS and label-free quantification of unique proteins in a complex protein digest is described. Multivariate statistics are applied to examine sample pattern recognition based on data-dimensionality reduction and biomarker identification in plant systems biology. The integration of the data reveal multiple correlative biomarkers providing evidence for an increase of information in such holistic approaches. With computational simulation of metabolic networks and experimental measurements, it can be shown that biochemical regulation is reflected by metabolite network dynamics measured in a metabolomics approach. Examples in molecular plant physiology are presented to substantiate the integrative approach.

Understanding signaling in yeast: Insights from network analysis

Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. Here we present a novel approach to reconstruct a highly correlated protein interaction network and to identify previously unknown components of a signaling pathway through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations. A novel algorithm is designed to reconstruct a highly correlated protein interaction network which is composed of the candidate proteins for signal transduction mechanisms in yeast Saccharomyces cerevisiae. The high efficiency of the reconstruction process is proved by a Receiver Operating Characteristic curve analysis. Identification and scoring of the possible linear pathways enables reconstruction of specific sub-networks for glucose-induction signaling and high osmolarity MAPK signaling in S. cerevisiae. All of the known components of these pathways are identified together with several new "candidate" proteins, indicating the successful reconstructions of two model pathways involved in S. cerevisiae. The integrated approach is hence shown useful for (i) prediction of new signaling pathways, (ii) identification of unknown members of documented pathways, and (iii) identification of network modules consisting of a group of related components that often incorporate the same functional mechanism.

Colony size measurement of the yeast gene deletion strains for functional genomics

BACKGROUND

Numerous functional genomics approaches have been developed to study the model organism yeast, Saccharomyces cerevisiae, with the aim of systematically understanding the biology of the cell. Some of these techniques are based on yeast growth differences under different conditions, such as those generated by gene mutations, chemicals or both. Manual inspection of the yeast colonies that are grown under different conditions is often used as a method to detect such growth differences.

RESULTS

Here, we developed a computerized image analysis system called Growth Detector (GD), to automatically acquire quantitative and comparative information for yeast colony growth. GD offers great convenience and accuracy over the currently used manual growth measurement method. It distinguishes true yeast colonies in a digital image and provides an accurate coordinate oriented map of the colony areas. Some post-processing calculations are also conducted. Using GD, we successfully detected a genetic linkage between the molecular activity of the plant-derived antifungal compound berberine and gene expression components, among other cellular processes. A novel association for the yeast mek1 gene with DNA damage repair was also identified by GD and confirmed by a plasmid repair assay. The results demonstrate the usefulness of GD for yeast functional genomics research.

CONCLUSION

GD offers significant improvement over the manual inspection method to detect relative yeast colony size differences. The speed and accuracy associated with GD makes it an ideal choice for large-scale functional genomics investigations.

Homocysteine- and cysteine-mediated growth defect is not associated with induction of oxidative stress response genes in yeast

Intracellular thiols like cysteine, homocysteine and glutathione play a critical role in the regulation of important cellular processes. Alteration of intracellular thiol concentration results in many diseased states; for instance, elevated levels of homocysteine are considered to be an independent risk factor for cardiovascular disease. Yeast has proved to be an excellent model system for studying many human diseases since it carries homologues of nearly 40% of human disease genes and many fundamental pathways are highly conserved between the two organisms. In the present study, we demonstrate that cysteine and homocysteine, but not glutathione, inhibit yeast growth in a concentration-dependent manner. Using deletion strains (str2Delta and str4Delta) we show that cysteine and homocysteine independently inhibit yeast growth. Transcriptional profiling of yeast treated with cysteine and homocysteine revealed that genes coding for antioxidant enzymes like glutathione peroxidase, catalase and superoxide dismutase were down-regulated. Furthermore, transcriptional response to homocysteine did not show any similarity to the response to H2O2. We also failed to detect induction of reactive oxygen species in homocysteine- and cysteine-treated cells, using fluorogenic probes. These results indicate that homocysteine- and cysteine-induced growth defect is not due to the oxidative stress. However, we found an increase in the expression of KAR2 (karyogamy 2) gene, a well-known marker of ER (endoplasmic reticulum) stress and also observed HAC1 cleavage in homocysteine- and cysteinetreated cells, which indicates that homocysteine- and cysteine-mediated growth defect may probably be attributed to ER stress. Transcriptional profiling also revealed that genes involved in one-carbon metabolism, glycolysis and serine biosynthesis were up-regulated on exogenous addition of cysteine and homocysteine, suggesting that cells try to reduce the intracellular concentration of thiols by up-regulating the genes involved in their metabolism.

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.

Yeast systems biology to unravel the network of life

Systems biology focuses on obtaining a quantitative description of complete biological systems, even complete cellular function. In this way, it will be possible to perform computer-guided design of novel drugs, advanced therapies for treatment of complex diseases, and to perform in silico design of advanced cell factories for production of fuels, chemicals, food ingredients and pharmaceuticals. The yeast Saccharomyces cerevisiae represents an excellent model system; the density of biological information available on this organism allows it to serve as a eukaryotic model for studying human diseases. Furthermore, it serves as an industrial workhorse for production of a wide range of chemicals and pharmaceuticals. Systems biology involves the combination of novel experimental techniques from different disciplines as well as functional genomics, bioinformatics and mathematical modelling, and hence no single laboratory has access to all the necessary competences. For this reason the Yeast Systems Biology Network (YSBN) has been established. YSBN will coordinate research efforts in yeast systems biology and, through the recently obtained EU funding for a Coordination Action, it will be possible to set appropriate guidelines, establish an appropriate infrastructure for the network and organize courses, meetings and conferences that will consolidate the network and promote systems biology. This paper discusses the impacts of systems biology and how YSBN may play a role in the future development of the field.

Proteomics: applications relevant to transfusion medicine

With the completion of the human genome sequence, it is now possible to analyze the many individual components that comprise complex biologic systems. Despite this sequence data, understanding the biologic relationships of all proteins of a given cell or biologic sample (the proteome) is still an exceedingly difficult task. However, new technology developments mean that proteomics research can be used to investigate a variety of biologic systems. Already, these studies have given valuable insight for the development of improved diagnostic and therapeutic products. The present review aims to provide a basic understanding of proteomics research by discussing the methods used to study large numbers of proteins and by reviewing the application of proteomics methods to transfusion medicine.

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.

The role of mass spectrometry in plant systems biology

Large-scale analyses of proteins and metabolites are intimately bound to advancements in MS technologies. The aim of these non-targeted "omic" technologies is to extend our understanding beyond the analysis of only parts of the system. Here, metabolomics and proteomics emerged in parallel with the development of novel mass analyzers and hyphenated techniques such as gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and multidimensional liquid chromatography coupled to mass spectrometry (LC-MS). The analysis of (i) proteins (ii) phosphoproteins, and (iii) metabolites is discussed in the context of plant physiology and environment and with a focus on novel method developments. Recently published studies measuring dynamic (quantitative) behavior at these levels are summarized; for these works, the completely sequenced plants Arabidopsis thaliana and Oryza sativa (rice) have been the primary models of choice. Particular emphasis is given to key physiological processes such as metabolism, development, stress, and defense. Moreover, attempts to combine spatial, tissue-specific resolution with systematic profiling are described. Finally, we summarize the initial steps to characterize the molecular plant phenotype as a corollary of environment and genotype.

Use of PMA1 as a housekeeping biomarker for assessment of toxicant-induced stress in Saccharomyces cerevisiae

The brewer's yeast Saccharomyces cerevisiae has emerged as a versatile and robust model system for laboratory use to study toxic effects of various substances. In this study, toxicant-induced stresses of pure compounds were investigated in Saccharomyces cerevisiae utilizing a destabilized version of the green fluorescent protein optimized for expression in yeast (yEGFP3) under control of the promoter of the housekeeping plasma membrane ATPase gene PMA1. The responses of the biomarker upon increasing test compound concentrations were monitored by determining the decrease in fluorescence. The reporter assay deployed a simple and robust protocol for the rapid detection of toxic effects within a 96-well microplate format. Fluorescence emissions were normalized to cell growth determined by absorption and were correlated to internal reference standards. The results were expressed as effective concentrations (EC20). Dose-response experiments were conducted in which yeast cells were exposed in minimal medium and in the presence of 20% fetal calf serum to sublethal concentrations of an array of heavy metals, salt, and a number of stress-inducing compounds (Diclofenac, Lindane, methyl-N-nitro-N-nitrosoguanidine [MNNG], hydroxyurea, and caffeine). Long-term exposure (7 h) played a considerable role in the adaptive response to intoxication compared to early responses at 4 h exposure. The data obtained after 4 h of exposure and expressed as EC20 were compared to 50% inhibitory concentration values derived from cell line and ecotoxicological tests. This study demonstrates the versatility of the novel biomarker to complement existing test batteries to assess contaminant exposure and effects.

The relative merits of the tetO2 andtetO7 promoter systems for the functional analysis of heterologous genes in yeast and a compilation of essential yeast genes withtetO2 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.

Comparison of gene expression signatures of diamide, H2O2 and menadione exposed Aspergillus nidulans cultures--linking genome-wide transcriptional changes to cellular physiology

BACKGROUND

In addition to their cytotoxic nature, reactive oxygen species (ROS) are also signal molecules in diverse cellular processes in eukaryotic organisms. Linking genome-wide transcriptional changes to cellular physiology in oxidative stress-exposed Aspergillus nidulans cultures provides the opportunity to estimate the sizes of peroxide (O2(2-)), superoxide (O2*-) and glutathione/glutathione disulphide (GSH/GSSG) redox imbalance responses.

RESULTS

Genome-wide transcriptional changes triggered by diamide, H2O2 and menadione in A. nidulans vegetative tissues were recorded using DNA microarrays containing 3533 unique PCR-amplified probes. Evaluation of LOESS-normalized data indicated that 2499 gene probes were affected by at least one stress-inducing agent. The stress induced by diamide and H2O2 were pulse-like, with recovery after 1 h exposure time while no recovery was observed with menadione. The distribution of stress-responsive gene probes among major physiological functional categories was approximately the same for each agent. The gene group sizes solely responsive to changes in intracellular O2(2-), O2*- concentrations or to GSH/GSSG redox imbalance were estimated at 7.7, 32.6 and 13.0 %, respectively. Gene groups responsive to diamide, H2O2 and menadione treatments and gene groups influenced by GSH/GSSG, O2(2-) and O2*- were only partly overlapping with distinct enrichment profiles within functional categories. Changes in the GSH/GSSG redox state influenced expression of genes coding for PBS2 like MAPK kinase homologue, PSK2 kinase homologue, AtfA transcription factor, and many elements of ubiquitin tagging, cell division cycle regulators, translation machinery proteins, defense and stress proteins, transport proteins as well as many enzymes of the primary and secondary metabolisms. Meanwhile, a separate set of genes encoding transport proteins, CpcA and JlbA amino acid starvation-responsive transcription factors, and some elements of sexual development and sporulation was ROS responsive.

CONCLUSION

The existence of separate O2(2-), O2*- and GSH/GSSG responsive gene groups in a eukaryotic genome has been demonstrated. Oxidant-triggered, genome-wide transcriptional changes should be analyzed considering changes in oxidative stress-responsive physiological conditions and not correlating them directly to the chemistry and concentrations of the oxidative stress-inducing agent.

Development and application of proteomics technologies in Saccharomyces cerevisiae

Proteomics research focuses on the identification and quantification of "all" proteins present in cells, organisms or tissue. Proteomics is technically complicated because it encompasses the characterization and functional analysis of all proteins that are expressed by a genome. Moreover, because the expression levels of proteins strongly depend on complex regulatory systems, the proteome is highly dynamic. This review focuses on the two major proteomics methodologies, one based on 2D gel electrophoresis and the other based on liquid chromatography coupled to mass spectrometry. The recent developments of these methodologies and their application to quantitative proteomics are described. The model system Saccharomyces cerevisiae is considered to be the optimal vehicle for proteomics and we review studies investigating yeast adaptation to changes in (nutritional) environment.

Metabolomics: from pattern recognition to biological interpretation

Metabolomics is a technology that aims to identify and quantify the metabolome -- the dynamic set of all small molecules present in an organism or a biological sample. In this sense, the technique is distinct from metabolic profiling, which looks for target compounds and their biochemical transformation. The combination of both approaches is an emerging technique for the characterization of biological samples and for drug treatment. Metabolomics has proven to be very rapid and superior to any other post-genomics technology for pattern-recognition analyses of biological samples. Changing steady state concentrations and fluctuations of metabolites that occur within milliseconds are a result of biochemical processes such as signalling cascades: metabolomic techniques are instrumental in measuring these changes rapidly and sensitively. Metabolite data can be complemented by protein, transcript and external (environmental) data, thereby leading to the identification of multiple physiological biomarkers embedded in correlative molecular networks that are not approachable with targeted studies.

Metabolic and stress adaptation by Mycosphaerella graminicola during sporulation in its host revealed through microarray transcription profiling

SUMMARY Pathogenic microbes must successfully adapt to the host environment, acquiring nutrients and tolerating immune/defence responses. Studies on host-pathogen interactions at the transcriptome level have predominantly investigated host responses. Here we present a broad-scale transcriptional analysis on a fungal pathogen during sporulation within its host environment. Septoria leaf blotch is an important fungal disease of cultivated wheat and is caused by the ascomycete fungus Septoria tritici (teleomorph Mycosphaerella graminicola). A cDNA microarray containing 2563 unigenes was generated and then used to compare fungal nutrition and development in vitro under nutrient-rich and nutrient-limiting conditions and in vivo at a late stage of plant infection. The data obtained provided clear insights into metabolic adaptation in all three conditions and an elevated stress adaptation/tolerance specifically in the host environment. We conclude that asexual sporulation of M. graminicola during the late stage of plant infection occurs in a rich nutritional environment involving adaptation to stresses imposed in part by the presence of reactive oxygen species.

Application of metabolome data in functional genomics: A conceptual strategy

A gene with yet unknown physiological function can be studied by changing its expression level followed by analysis of the resulting phenotype. This type of functional genomics study can be complicated by the occurrence of 'silent mutations', the phenotypes of which are not easily observable in terms of metabolic fluxes (e.g., the growth rate). Nevertheless, genetic alteration may give rise to significant yet complicated changes in the metabolome. We propose here a conceptual functional genomics strategy based on microbial metabolome data, which identifies changes in in vivo enzyme activities in the mutants. These predicted changes are used to formulate hypotheses to infer unknown gene functions. The required metabolome data can be obtained solely from high-throughput mass spectrometry analysis, which provides the following in vivo information: (1) the metabolite concentrations in the reference and the mutant strain; (2) the metabolic fluxes in both strains and (3) the enzyme kinetic parameters of the reference strain. We demonstrate in silico that changes in enzyme activities can be accurately predicted by this approach, even in 'silent mutants'.

All systems go: launching cell simulation fueled by integrated experimental biology data

Biological simulation serves to unify the basic elements of systems biology, namely, model selection, experimentation and model refinement. To select biochemical models for simulation, metabolome analysis can be performed using capillary electrophoresis or liquid chromatography coupled with mass spectrometry. In this manner, selected models can be elaborated with temporal/spatial gene and protein expression data obtained from model organisms such as Escherichia coli. The E. coli single gene deletion mutant library (KO collection) and His-tag/GFP-fusion single open reading frame clone expression library (ASKA) are powerful resources for this task. The integration of parallel experimental datasets into dynamic simulation tools forms the remaining challenge for the systematic analysis and elucidation of biological networks and holds promise for biotechnological applications.

Genomes, phylogeny, and evolutionary systems biology

With the completion of the human genome and the growing number of diverse genomes being sequenced, a new age of evolutionary research is currently taking shape. The myriad of technological breakthroughs in biology that are leading to the unification of broad scientific fields such as molecular biology, biochemistry, physics, mathematics, and computer science are now known as systems biology. Here, I present an overview, with an emphasis on eukaryotes, of how the postgenomics era is adopting comparative approaches that go beyond comparisons among model organisms to shape the nascent field of evolutionary systems biology.

Measuring the metabolome: current analytical technologies

The post-genomics era has brought with it ever increasing demands to observe and characterise variation within biological systems. This variation has been studied at the genomic (gene function), proteomic (protein regulation) and the metabolomic (small molecular weight metabolite) levels. Whilst genomics and proteomics are generally studied using microarrays (genomics) and 2D-gels or mass spectrometry (proteomics), the technique of choice is less obvious in the area of metabolomics. Much work has been published employing mass spectrometry, NMR spectroscopy and vibrational spectroscopic techniques, amongst others, for the study of variations within the metabolome in many animal, plant and microbial systems. This review discusses the advantages and disadvantages of each technique, putting the current status of the field of metabolomics in context, and providing examples of applications for each technique employed.

Systems Biology and the Molecular Circuits of Cancer

Proliferative disorders are a major challenge for human health. The understanding of the organization of cell-cycle events is of the utmost importance to devise effective therapeutic strategies for cancer. The awareness that cells and organisms are complex, modular, hierarchical systems and the availability of genome-wide gene expression and protein analyses, should make it feasible to elucidate human diseases in terms of dysfunctions of molecular systems. Here we review evidence in support of a systems model of the cell cycle, in which two sequential growth-sensitive thresholds control entry into S-phase. The putative molecular determinants that set the threshold for entry into S-phase are consistently altered in cancer cells. Such a framework could be useful in guiding both experimental investigation and data analysis by allowing wiring to other relevant cell modules thereby highlighting the differential responses, or lack of response of cancer cells to intra- and extracellular factors. Pharmacological approaches that take advantage of transformation-induced fragility to glucose shortage are discussed. Extension of this hierarchical, modular approach to tumors as a whole holds promise for the development of effective drug discovery approaches and more efficient therapeutic protocols.