Changing perspectives in yeast research nearly a decade after the genome sequence

DNA adduct formation in Saccharomyces cerevisiae following exposure to environmental pollutants, as in vivo model for molecular toxicity studies

DNA adduction in the model yeast Saccharomyces cerevisiae was investigated after exposure to the fungicide penconazole and the reference genotoxic compound benzo(a)pyrene, for validating yeasts as a tool for molecular toxicity studies, particularly of environmental pollution. The effect of the toxicants on the yeast's growth kinetics was determined as an indicator of cytotoxicity. Fermentative cultures of S. cerevisiae were exposed to 2 ppm of Penconazole during different phases of growth; while 0.2 and 2 ppm of benzo(a)pyrene were applied to the culture medium before inoculation and on exponential cultures. Exponential respiratory cultures were also exposed to 0.2 ppm of B(a)P for comparison of both metabolisms. Penconazole induced DNA adducts formation in the exponential phase test; DNA adducts showed a peak of 54.93 adducts/109 nucleotides. Benzo(a)pyrene induced the formation of DNA adducts in all the tests carried out; the highest amount of 46.7 adducts/109 nucleotides was obtained in the fermentative cultures after the exponential phase exposure to 0.2 ppm; whereas in the respiratory cultures, 14.6 adducts/109 nucleotides were detected. No cytotoxicity was obtained in any experiment. Our study showed that yeast could be used to analyse DNA adducts as biomarkers of exposure to environmental toxicants.

Fungi-on-a-Chip: microfluidic platforms for single-cell studies on fungi

This review highlights new advances in the emerging field of 'Fungi-on-a-Chip' microfluidics for single-cell studies on fungi and discusses several future frontiers, where we envisage microfluidic technology development to be instrumental in aiding our understanding of fungal biology. Fungi, with their enormous diversity, bear essential roles both in nature and our everyday lives. They inhabit a range of ecosystems, such as soil, where they are involved in organic matter degradation and bioremediation processes. More recently, fungi have been recognized as key components of the microbiome in other eukaryotes, such as humans, where they play a fundamental role not only in human pathogenesis, but also likely as commensals. In the food sector, fungi are used either directly or as fermenting agents and are often key players in the biotechnological industry, where they are responsible for the production of both bulk chemicals and antibiotics. Although the macroscopic fruiting bodies are immediately recognizable by most observers, the structure, function, and interactions of fungi with other microbes at the microscopic scale still remain largely hidden. Herein, we shed light on new advances in the emerging field of Fungi-on-a-Chip microfluidic technologies for single-cell studies on fungi. We discuss the development and application of microfluidic tools in the fields of medicine and biotechnology, as well as in-depth biological studies having significance for ecology and general natural processes. Finally, a future perspective is provided, highlighting new frontiers in which microfluidic technology can benefit this field.

Yeast Genomics and Its Applications in Biotechnological Processes: What Is Our Present and Near Future?

Since molecular biology and advanced genetic techniques have become important tools in a variety of fields of interest, including taxonomy, identification, classification, possible production of substances and proteins, applications in pharmacology, medicine, and the food industry, there has been significant progress in studying the yeast genome and its potential applications. Because of this potential, as well as their manageability, safety, ease of cultivation, and reproduction, yeasts are now being extensively researched in order to evaluate a growing number of natural and sustainable applications to provide many benefits to humans. This review will describe what yeasts are, how they are classified, and attempt to provide a rapid overview of the many current and future applications of yeasts. The review will then discuss how yeasts—including those molecularly modified—are used to produce biofuels, proteins such as insulin, vaccines, probiotics, beverage preparations, and food additives and how yeasts could be used in environmental bioremediation and biocontrol for plant infections. This review does not delve into the issues raised during studies and research, but rather presents the positive outcomes that have enabled several industrial, clinical, and agricultural applications in the past and future, including the most recent on cow-free milk.

Yeast: a microbe with macro-implications to antimicrobial drug discovery

Paramount to any rational discovery of new antibiotics displaying novel mechanisms of action is a deep knowledge of the genetic basis of microbial growth, division and virulence. The bakers' yeast,Saccharomyces cerevisiae, illustrates the highest understanding of the genetic underpinnings of microbial life, and from this framework, a systems biology paradigm has evolved, begging to be emulated in antibacterial discovery. Here, we review landmark events in the history of yeast genomics that provide this new foundation for antibacterial drug discovery.

RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells

Knowledge of the expression profile and spatial landscape of the transcriptome in individual cells is essential for understanding the rich repertoire of cellular behaviors. Here, we report multiplexed error-robust fluorescence in situ hybridization (MERFISH), a single-molecule imaging approach that allows the copy numbers and spatial localizations of thousands of RNA species to be determined in single cells. Using error-robust encoding schemes to combat single-molecule labeling and detection errors, we demonstrated the imaging of 100 to 1000 distinct RNA species in hundreds of individual cells. Correlation analysis of the ~10(4) to 10(6) pairs of genes allowed us to constrain gene regulatory networks, predict novel functions for many unannotated genes, and identify distinct spatial distribution patterns of RNAs that correlate with properties of the encoded proteins.

Functional profiling of human fungal pathogen genomes

Fungal infections are challenging to diagnose and often difficult to treat, with only a handful of drug classes existing. Understanding the molecular mechanisms by which pathogenic fungi cause human disease is imperative. Here, we discuss how the development and use of genome-scale genetic resources, such as whole-genome knockout collections, can address this unmet need. Using work in Saccharomcyes cerevisiae as a guide, studies of Cryptococcus neoformans and Candida albicans have shown how the challenges of large-scale gene deletion can be overcome, and how such collections can be effectively used to obtain insights into mechanisms of pathogenesis. We conclude that, with concerted efforts, full genome-wide functional analysis of human fungal pathogen genomes is within reach.

Chemical genetics: budding yeast as a platform for drug discovery and mapping of genetic pathways

The budding yeast Saccharomyces cerevisiae is a widely used model organism, and yeast genetic methods are powerful tools for discovery of novel functions of genes. Recent advancements in chemical-genetics and chemical-genomics have opened new avenues for development of clinically relevant drug treatments. Systematic mapping of genetic networks by high-throughput chemical-genetic screens have given extensive insight in connections between genetic pathways. Here, I review some of the recent developments in chemical-genetic techniques in budding yeast.

Yeast: an experimental organism for 21st Century biology

In this essay, we revisit the status of yeast as a model system for biology. We first summarize important contributions of yeast to eukaryotic biology that we anticipated in 1988 in our first article on the subject. We then describe transformative developments that we did not anticipate, most of which followed the publication of the complete genomic sequence of Saccharomyces cerevisiae in 1996. In the intervening 23 years it appears to us that yeast has graduated from a position as the premier model for eukaryotic cell biology to become the pioneer organism that has facilitated the establishment of the entirely new fields of study called "functional genomics" and "systems biology." These new fields look beyond the functions of individual genes and proteins, focusing on how these interact and work together to determine the properties of living cells and organisms.

Evolutionary patterns of recently emerged animal duplogs

Duplogs, or intraspecies paralogs, constitute the important portion of eukaryote genomes and serve as a major source of functional innovation. We conducted detailed analyses of recently emerged animal duplogs. Genome data of three vertebrate species (Homo sapiens, Mus musculus, and Danio rerio), Caenorhabditis elegans, and two Drosophila species (Drosophila melanogaster and D. pseudoobscura) were used. Duplication events were divided into six age-groups according to the synonymous distance (dS) up to 0.6. Duplogs were classified into four equal-sized classes on physical distances and into three classes on relative orientations. We observed the following shared characteristics among intrachromosomal multiexon duplogs: 1) inverted duplogs account for 20-50%, and about a half of the physically most distant 25%; 2) except for C. elegans, the composition of physical distances, that of relative orientations, and the proportion of inverted duplogs in each physical distance category are more or less uniform; 3) except for C. elegans, the characteristics of the youngest (dS < 0.01) duplogs are similar to the overall characteristics of the entire set. These results suggest that intrachromosomal duplogs with fairly long physical distances were generated at once, rather than resulting from tandem duplications and subsequent genomic rearrangements. This is different from the three well-known modes of gene duplication: tandem duplication, retrotransposition, and genome duplication. We termed this new mode as "drift" duplication. The drift duplication has been producing duplicate copies at paces comparable with tandem duplications since the common ancestor of vertebrates, and it may have already operated in the common ancestor of bilateral animals.

Systems biology approaches and tools for analysis of interactomes and multi-target drugs

Systems biology is essentially a proteomic and epigenetic exercise because the relatively condensed information of genomes unfolds on the level of proteins. The flexibility of cellular architectures is not only mediated by a dazzling number of proteinaceous species but moreover by the kinetics of their molecular changes: The time scales of posttranslational modifications range from milliseconds to years. The genetic framework of an organism only provides the blue print of protein embodiments which are constantly shaped by external input. Indeed, posttranslational modifications of proteins represent the scope and velocity of these inputs and fulfil the requirements of integration of external spatiotemporal signal transduction inside an organism. The optimization of biochemical networks for this type of information processing and storage results in chemically extremely fine tuned molecular entities. The huge dynamic range of concentrations, the chemical diversity and the necessity of synchronisation of complex protein expression patterns pose the major challenge of systemic analysis of biological models. One further message is that many of the key reactions in living systems are essentially based on interactions of moderate affinities and moderate selectivities. This principle is responsible for the enormous flexibility and redundancy of cellular circuitries. In complex disorders such as cancer or neurodegenerative diseases, which initially appear to be rooted in relatively subtle dysfunctions of multimodal physiologic pathways, drug discovery programs based on the concept of high affinity/high specificity compounds ("one-target, one-disease"), which has been dominating the pharmaceutical industry for a long time, increasingly turn out to be unsuccessful. Despite improvements in rational drug design and high throughput screening methods, the number of novel, single-target drugs fell much behind expectations during the past decade, and the treatment of "complex diseases" remains a most pressing medical need. Currently, a change of paradigm can be observed with regard to a new interest in agents that modulate multiple targets simultaneously, essentially "dirty drugs." Targeting cellular function as a system rather than on the level of the single target, significantly increases the size of the drugable proteome and is expected to introduce novel classes of multi-target drugs with fewer adverse effects and toxicity. Multiple target approaches have recently been used to design medications against atherosclerosis, cancer, depression, psychosis and neurodegenerative diseases. A focussed approach towards "systemic" drugs will certainly require the development of novel computational and mathematical concepts for appropriate modelling of complex data. But the key is the extraction of relevant molecular information from biological systems by implementing rigid statistical procedures to differential proteomic analytics.

Ins and outs of systems biology vis-à-vis molecular biology: continuation or clear cut?

The comprehension of living organisms in all their complexity poses a major challenge to the biological sciences. Recently, systems biology has been proposed as a new candidate in the development of such a comprehension. The main objective of this paper is to address what systems biology is and how it is practised. To this end, the basic tools of a systems biological approach are explored and illustrated. In addition, it is questioned whether systems biology 'revolutionizes' molecular biology and 'transcends' its assumed reductionism. The strength of this claim appears to depend on how molecular and systems biology are characterised and on how reductionism is interpreted. Doing credit to molecular biology and to methodological reductionism, it is argued that the distinction between molecular and systems biology is gradual rather than sharp. As such, the classical challenge in biology to manage, interpret and integrate biological data into functional wholes is further intensified by systems biology's use of modelling and bioinformatics, and by its scale enlargement.

Preserving the yeast proteome from sample degradation

Sample degradation is a common problem in all types of proteomic analyses as it generates protein and peptide fragments that can interfere with analytical results. An important step in preventing such artefacts is to preserve the native, intact proteome as early as possible during sample preparation prior to proteomic analysis. Using the budding yeast Saccharomyces cerevisiae, we have evaluated the effects of trichloroacetic acid (TCA) and thermal treatments prior to protein extraction as a means to minimise proteolysis. TCA precipitation is commonly used to inactivate proteases; thermal stabilisation is used to heat samples to approximately 95 degrees C to inactivate enzyme activity. The efficacy of these methods was also compared with that of protease inhibitors and lyophilisation. Sample integrity was assessed by 2-D PAGE and a selection of spots was identified by MS/MS. The analysis showed that TCA or thermal treatment significantly reduced the degree of degradation and that these pre-treatment protocols were more effective than treatment with either protease inhibitors or lyophilisation. This study establishes standardised sample preparation methods for the reproducible analysis of protein patterns by 2-D PAGE in yeast, and may also be applicable to other proteomic analyses such as gel-free-based quantitation methods.

Interactome and Gene Ontology provide congruent yet subtly different views of a eukaryotic cell

BACKGROUND

The characterization of the global functional structure of a cell is a major goal in bioinformatics and systems biology. Gene Ontology (GO) and the protein-protein interaction network offer alternative views of that structure.

RESULTS

This study presents a comparison of the global structures of the Gene Ontology and the interactome of Saccharomyces cerevisiae. Sensitive, unsupervised methods of clustering applied to a large fraction of the proteome led to establish a GO-interactome correlation value of +0.47 for a general dataset that contains both high and low-confidence interactions and +0.58 for a smaller, high-confidence dataset.

CONCLUSION

The structures of the yeast cell deduced from GO and interactome are substantially congruent. However, some significant differences were also detected, which may contribute to a better understanding of cell function and also to a refinement of the current ontologies.

Functional annotations for the Saccharomyces cerevisiae genome: the knowns and the known unknowns

The quest to characterize each of the genes of the yeast Saccharomyces cerevisiae has propelled the development and application of novel high-throughput (HTP) experimental techniques. To handle the enormous amount of information generated by these techniques, new bioinformatics tools and resources are needed. Gene Ontology (GO) annotations curated by the Saccharomyces Genome Database (SGD) have facilitated the development of algorithms that analyze HTP data and help predict functions for poorly characterized genes in S. cerevisiae and other organisms. Here, we describe how published results are incorporated into GO annotations at SGD and why researchers can benefit from using these resources wisely to analyze their HTP data and predict gene functions.

Unexpected complexity of the budding yeast transcriptome

The genome of the budding yeast Saccharomyces cerevisiae was sequenced over a decade ago and has been annotated to encode approximately 6,000 genes. However, recent high throughput studies using tiling array hybridization and cDNA sequencing have revealed an unexpectedly large number of previously undescribed transcripts. They largely lack protein-coding capacity and are transcribed from both strands of intragenic and intergenic regions in the genome. Accordingly, pervasive transcription leading to a plethora of noncoding RNAs, which was first revealed for mammalian genomes to attract intense attentions, is likely an intrinsic feature of eukaryotic genomes. Although it is not clear what fraction of these transcription events are functional, some were shown to induce transcriptional interference or histone modifications to regulate gene expression. The budding yeast may serve as an excellent model to study pervasive transcription and noncoding RNAs.

Yeast as a model for studying human neurodegenerative disorders

Protein misfolding and aggregation are central events in many disorders including several neurodegenerative diseases. This suggests that alterations in normal protein homeostasis may contribute to pathogenesis, but the exact molecular mechanisms involved are still poorly understood. The budding yeast Saccharomyces cerevisiae is one of the model systems of choice for studies in molecular medicine. Modeling human neurodegenerative diseases in this simple organism has already shown the incredible power of yeast to unravel the complex mechanisms and pathways underlying these pathologies. Indeed, this work has led to the identification of several potential therapeutic targets and drugs for many diseases, including the neurodegenerative diseases. Several features associated with these diseases, such as formation of protein aggregates, cellular toxicity mediated by misfolded proteins, oxidative stress and hallmarks of apoptosis have been faithfully recapitulated in yeast, enabling researchers to take advantage of this powerful model to rapidly perform genetic and compound screens with the aim of identifying novel candidate therapeutic targets and drugs. Here we review the work undertaken to model human brain disorders in yeast, and how these models provide insight into novel therapeutic approaches for these diseases.

Beer and bread to brains and beyond: can yeast cells teach us about neurodegenerative disease?

For millennia, humans have harnessed the astonishing power of yeast, producing such culinary masterpieces as bread, beer and wine. Therefore, in this new millennium, is it very farfetched to ask if we can also use yeast to unlock some of the modern day mysteries of human disease? Remarkably, these seemingly simple cells possess most of the same basic cellular machinery as the neurons in the brain. We and others have been using the baker's yeast, Saccharomyces cerevisiae, as a model system to study the mechanisms of devastating neurodegenerative diseases such as Parkinson's, Huntington's, Alzheimer's and amyotrophic lateral sclerosis. While very different in their pathophysiology, they are collectively referred to as protein-misfolding disorders because of the presence of misfolded and aggregated forms of various proteins in the brains of affected individuals. Using yeast genetics and the latest high-throughput screening technologies, we have identified some of the potential causes underpinning these disorders and discovered conserved genes that have proven effective in preventing neuron loss in animal models. Thus, these genes represent new potential drug targets. In this review, I highlight recent work investigating mechanisms of cellular toxicity in a yeast Parkinson's disease model and discuss how similar approaches are being applied to additional neurodegenerative diseases.

Gene Ontology annotations at SGD: new data sources and annotation methods

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) collects and organizes biological information about the chromosomal features and gene products of the budding yeast Saccharomyces cerevisiae. Although published data from traditional experimental methods are the primary sources of evidence supporting Gene Ontology (GO) annotations for a gene product, high-throughput experiments and computational predictions can also provide valuable insights in the absence of an extensive body of literature. Therefore, GO annotations available at SGD now include high-throughput data as well as computational predictions provided by the GO Annotation Project (GOA UniProt; http://www.ebi.ac.uk/GOA/). Because the annotation method used to assign GO annotations varies by data source, GO resources at SGD have been modified to distinguish data sources and annotation methods. In addition to providing information for genes that have not been experimentally characterized, GO annotations from independent sources can be compared to those made by SGD to help keep the literature-based GO annotations current.

Exploring genetic interactions and networks with yeast

The development and application of genetic tools and resources has enabled a partial genetic-interaction network for the yeast Saccharomyces cerevisiae to be compiled. Analysis of the network, which is ongoing, has already provided a clear picture of the nature and scale of the genetic interactions that robustly sustain biological systems, and how cellular buffering is achieved at the molecular level. Recent studies in yeast have begun to define general principles of genetic networks, and also pave the way for similar studies in metazoan model systems. A comparative understanding of genetic-interaction networks promises insights into some long-standing genetic problems, such as the nature of quantitative traits and the basis of complex inherited disease.

Sec14 related proteins in yeast

Lipid transport between membranes of eukaryotic organisms represents an essential aspect of organelle biogenesis. This transport must be strictly selective and directional to assure specific lipid composition of individual membranes. Despite the intensive research effort in the last few years, our understanding of how lipids are sorted and moved within cells is still rather limited. Evidence indicates that at least some of the mechanisms generating and maintaining non-random distribution of lipids in cells are linked to the action of phosphatidylinositol transfer proteins (PITPs). The major PITP in yeast Saccharomyces cerevisiae, Sec14p, is essential in promoting Golgi secretory function by modulating of its membrane lipid composition. This review focuses on a group of five yeast proteins that share significant sequence homology with Sec14p. Based on this sequence identity, they were termed Sfh (Sec fourteen homologue) proteins. It is a diverse group of proteins with distinct subcellular localizations and varied physiological functions related to lipid metabolism, phosphoinositide mediated signaling and membrane trafficking.

The yeast protein sorting pathway as an experimental model for lysosomal trafficking

Lysosomes are conserved organelles that are present in all eukaryotic cells. They are part of a complicated network of intracellular trafficking routes - the lysosomal transport system. Lysosomes are necessary for the maintenance of cellular homeostasis and for many specialized functions, including the activity of many components of the mammalian immune system. Dysfunctions of the lysosomal system are associated with numerous diseases, such as storage disorders, neuro- and myopathies, cancer and some types of albinism and immunological deficiencies. High conservation of the processes of lysosomal biogenesis and transport enables the use of yeast as a model for studying the mechanisms that underlie these diseases. In this review, we discuss several examples of such models in an attempt to present an overview of the most important experimental methods available in yeast research.

A large-scale full-length cDNA analysis to explore the budding yeast transcriptome

We performed a large-scale cDNA analysis to explore the transcriptome of the budding yeast Saccharomyces cerevisiae. We sequenced two cDNA libraries, one from the cells exponentially growing in a minimal medium and the other from meiotic cells. Both libraries were generated by using a vector-capping method that allows the accurate mapping of transcription start sites (TSSs). Consequently, we identified 11,575 TSSs associated with 3,638 annotated genomic features, including 3,599 ORFs, to suggest that most yeast genes have two or more TSSs. In addition, we identified 45 previously undescribed introns, including those affecting current ORF annotations and those spliced alternatively. Furthermore, the analysis revealed 667 transcription units in the intergenic regions and transcripts derived from antisense strands of 367 known features. We also found that 348 ORFs carry TSSs in their 3'-halves to generate sense transcripts starting from inside the ORFs. These results indicate that the budding yeast transcriptome is considerably more complex than previously thought, and it shares many recently revealed characteristics with the transcriptomes of mammals and other higher eukaryotes. Thus, the genome-wide active transcription that generates novel classes of transcripts appears to be an intrinsic feature of the eukaryotic cells. The budding yeast will serve as a versatile model for the studies on these aspects of transcriptome, and the full-length cDNA clones can function as an invaluable resource in such studies.

Evidence for consistent intragenic and intergenic interactions between SNP effects in the APOA1/C3/A4/A5 gene cluster

OBJECTIVE

Evaluate the consistency of the contribution of interactions between single nucleotide polymorphism (SNP) genotype effects to variation in measures of lipid metabolism across ethnic strata within gender.

METHODS AND RESULTS

We considered 80 SNPs within the apolipoprotein (APO) A1/C3/A4/A5 gene cluster using an over-parameterized general linear model to identify SNPs whose genotype effects combine non-additively to influence plasma levels of high density lipoprotein cholesterol (HDL-C), total cholesterol (TC) and triglycerides (TG) in a consistent manner across ethnic strata. We analyzed population-based samples of unrelated 18 to 30 year old African-Americans (n = 1,858) and European-Americans (n = 1,973) ascertained without regard to health at four field centers (Birmingham, Ala.; Chicago, Ill.; Minneapolis, Minn. and Oakland, Calif., USA) by the Coronary Artery Risk Development in Young Adults (CARDIA) study. To identify which SNP genotype effects combine non-additively we used a two-tier analysis strategy. We first required that pairs of SNPs show statistically significant non-additivity in both ethnic strata within a gender, where experiment-wise significance was evaluated using a permutation test to determine the probability of observing the number of tests significant in both ethnic strata by chance alone. Second, we required no significant evidence of heterogeneity of the relationship between the phenotype and the two SNP genotypes across ethnic strata and across field centers within each ethnic group. From this strategy we identified ten pairs of SNPs, involving thirteen SNPs, that displayed statistically significant non-additivity of SNP genotype effects on TC. Only one of these thirteen SNPs had statistically significant genotype effects that were consistent across samples.

CONCLUSION

Our analyses suggest that ignoring the contribution of interactions between SNP genotype effects when modeling multi-SNP genotype-phenotype relationships may result in an underestimate of the contribution of genetic variation to variation in quantitative cardiovascular disease (CVD) risk factor traits.

Insights on biology and evolution from microbial genome sequencing

No field of research has embraced and applied genomic technology more than the field of microbiology. Comparative analysis of nearly 300 microbial species has demonstrated that the microbial genome is a dynamic entity shaped by multiple forces. Microbial genomics has provided a foundation for a broad range of applications, from understanding basic biological processes, host-pathogen interactions, and protein-protein interactions, to discovering DNA variations that can be used in genotyping or forensic analyses, the design of novel antimicrobial compounds and vaccines, and the engineering of microbes for industrial applications. Most recently, metagenomics approaches are allowing us to begin to probe complex microbial communities for the first time, and they hold great promise in helping to unravel the relationships between microbial species.