Yeast Systems Biology Research Articles

Interpreting protein abundance in Saccharomyces cerevisiae through relational learning.

Proteomic profiles reflect the functional readout of the physiological state of an organism. An increased understanding of what controls and defines protein abundances is of high scientific interest. Saccharomyces cerevisiae is a well-studied model organism, and there is a large amount of structured knowledge on yeast systems biology in databases such as the Saccharomyces Genome Database, and highly curated genome-scale metabolic models like Yeast8. These datasets, the result of decades of experiments, are abundant in information, and adhere to semantically meaningful ontologies. By representing this knowledge in an expressive Datalog database we generated data descriptors using relational learning that, when combined with supervised machine learning, enables us to predict protein abundances in an explainable manner. We learnt predictive relationships between protein abundances, function and phenotype; such as α-amino acid accumulations and deviations in chronological lifespan. We further demonstrate the power of this methodology on the proteins His4 and Ilv2, connecting qualitative biological concepts to quantified abundances. All data and processing scripts are available at the following Github repository: https://github.com/DanielBrunnsaker/ProtPredict.

Big data in yeast systems biology.

Systems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology.

Yeast Systems Biology: The Continuing Challenge of Eukaryotic Complexity.

Research on yeast has produced a plethora of tools and resources that have been central to the progress of systems biology. This chapter reviews these resources, explains the innovations that have been made since the first edition of this book, and introduces the constituent chapters of the current edition. The value of these resources not only in building and testing models of the functional networks of the yeast cell, but also in providing a foundation for network studies on the molecular basis of complex human diseases is considered. The gaps in this vast compendium of data, including enzyme kinetic characteristics, biomass composition, transport processes, and cell-cell interactions are discussed, as are the interactions between yeast cells and those of other species. The relevance of these studies to both traditional and advanced biotechnologies and to human medicine is considered, and the opportunities and challenges in using unicellular yeasts to model the systems of multicellular organisms are presented.

YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in Saccharomyces cerevisiae.

The YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT—www.yeastract.com) information system has been, for 11 years, a key tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in Saccharomyces cerevisiae. Since its last update in June 2017, YEASTRACT includes approximately 163000 regulatory associations between transcription factors (TF) and target genes in S. cerevisiae, based on more than 1600 bibliographic references; it also includes 247 specific DNA binding consensus recognized by 113 TFs. This release of the YEASTRACT database provides new visualization tools to visualize each regulatory network in an interactive fashion, enabling the user to select and observe subsets of the network such as: (i) considering only DNA binding evidence or both DNA binding and expression evidence; (ii) considering only either positive or negative regulatory associations; or (iii) considering only one set of related environmental conditions. A further tool to observe TF regulons is also offered, enabling a clear-cut understanding of the exact meaning of the available data. We believe that with this new version, YEASTRACT will improve its role as an open web resource instrumental for Yeast Biologists and Systems Biology researchers.

Big data mining powers fungal research: recent advances in fission yeast systems biology approaches

Biology research has entered into big data era. Systems biology approaches therefore become the powerful tools to obtain the whole landscape of how cell separate, grow, and resist the stresses. Fission yeast Schizosaccharomyces pombe is wonderful unicellular eukaryote model, especially studying its division and metabolism can facilitate to understanding the molecular mechanism of cancer and discovering anticancer agents. In this perspective, we discuss the recent advanced fission yeast systems biology tools, mainly focus on metabolomics profiling and metabolic modeling, protein-protein interactome and genetic interaction network, DNA sequencing and applications, and high-throughput phenotypic screening. We therefore hope this review can be useful for interested fungal researchers as well as bioformaticians.

Role of frameshift ubiquitin B protein in Alzheimer's disease.

Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by accumulation of misfolded and aggregated proteins. Since the ubiquitin-proteasome system (UPS) is the major intracellular protein quality control (PQC) system in eukaryotic cells, it is likely involved in the etiology of AD. The frameshift form of ubiquitin (Ubb(+1) ) accumulates in the neuritic plaques and neurofibrillary tangles in patients with AD. Ubb(+1) accumulates in an age-dependent manner as a result of RNA-polymerase mediated molecular misreading during transcription, which allows the formation of mutant proteins in the absence of gene mutations. The accumulation of the Ubb(+1) protein may act as an endogenous reporter for proteasome dysfunction and a growing number of studies have shown that Ubb(+1) may play more important pathogenic roles in AD etiology than previously hypothesized. The yeast Saccharomyces cerevisiae shares many conserved biological processes with all eukaryotic cells, including human neurons. This organism has been regarded as a model system for investigating the fundamental intracellular mechanisms, including those underlying neurodegeneration. We propose here that yeast systems biology approaches, combined with cell and molecular biology approaches will increase the relevant knowledge needed for advancement and elucidation of mechanisms and complex traits, which could provide new targets for therapeutic intervention in AD. WIREs Syst Biol Med 2016, 8:300-313. doi: 10.1002/wsbm.1340 For further resources related to this article, please visit the WIREs website.

Engineering and Evolution of Saccharomyces cerevisiae to Produce Biofuels and Chemicals.

To mitigate global climate change caused partly by the use of fossil fuels, the production of fuels and chemicals from renewable biomass has been attempted. The conversion of various sugars from renewable biomass into biofuels by engineered baker's yeast (Saccharomyces cerevisiae) is one major direction which has grown dramatically in recent years. As well as shifting away from fossil fuels, the production of commodity chemicals by engineered S. cerevisiae has also increased significantly. The traditional approaches of biochemical and metabolic engineering to develop economic bioconversion processes in laboratory and industrial settings have been accelerated by rapid advancements in the areas of yeast genomics, synthetic biology, and systems biology. Together, these innovations have resulted in rapid and efficient manipulation of S. cerevisiae to expand fermentable substrates and diversify value-added products. Here, we discuss recent and major advances in rational (relying on prior experimentally-derived knowledge) and combinatorial (relying on high-throughput screening and genomics) approaches to engineer S. cerevisiae for producing ethanol, butanol, 2,3-butanediol, fatty acid ethyl esters, isoprenoids, organic acids, rare sugars, antioxidants, and sugar alcohols from glucose, xylose, cellobiose, galactose, acetate, alginate, mannitol, arabinose, and lactose.

Yeast systems biology: our best shot at modeling a cell.

THE Genetics Society of America’s Edward Novitski Prize recognizes an extraordinary level of creativity and intellectual ingenuity in the solution of significant problems in genetics research. The 2014 recipient, Charles Boone, has risen to the top of the emergent discipline of postgenome systems biology by focusing on the global mapping of genetic interaction networks. Boone invented the synthetic genetic array (SGA) technology, which provides an automated method to cross thousands of strains carrying precise mutations and map large-scale yeast genetic interactions. These network maps offer researchers a functional wiring diagram of the cell, which clusters genes into specific pathways and reveals functional connections.

The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation inSaccharomyces cerevisiae

The YEASTRACT (http://www.yeastract.com) information system is a tool for the analysis and prediction of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2013, this database contains over 200 000 regulatory associations between transcription factors (TFs) and target genes, including 326 DNA binding sites for 113 TFs. All regulatory associations stored in YEASTRACT were revisited and new information was added on the experimental conditions in which those associations take place and on whether the TF is acting on its target genes as activator or repressor. Based on this information, new queries were developed allowing the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. This release further offers tools to rank the TFs controlling a gene or genome-wide response by their relative importance, based on (i) the percentage of target genes in the data set; (ii) the enrichment of the TF regulon in the data set when compared with the genome; or (iii) the score computed using the TFRank system, which selects and prioritizes the relevant TFs by walking through the yeast regulatory network. We expect that with the new data and services made available, the system will continue to be instrumental for yeast biologists and systems biology researchers.

A prototrophic deletion mutant collection for yeast metabolomics and systems biology

A prototrophic deletion mutant collection for yeast metabolomics and systems biology

The diversity of protein turnover and abundance under nitrogen-limited steady-state conditions in Saccharomyces cerevisiae

To establish more advanced models of molecular dynamics within cells, protein characteristics such as turnover rate and absolute instead of relative abundance have to be analyzed. We applied a proteomics strategy to analyze protein degradation and abundance in Saccharomyces cerevisiae. We used steady-state chemostat cultures to ascertain well-defined growth conditions and nitrogen limited media, which allowed us to rapidly switch from (14)N to (15)N-isotope containing media and to monitor the decay of the (14)N mono-isotope signals in time. We acquired both protein abundance information and degradation rates of 641 proteins. Half-lives of individual proteins were very diverse under nitrogen-limited steady-state conditions, ranging from less than 30 min to over 20 h. Proteins that act as single physical complexes do not always show alike half-lives. For example the chaperonin-containing TCP-1 complex showed similar intermediate half-lives ranging from 7 to 20 h. In contrast, the ribosome exhibited a wide diversity of half-lives ranging from 2.5 to over 20 h, although their cellular abundances were rather similar. The stabilities of proteins involved in the central sugar metabolism were found to be intermediary, except for the glycolytic enzymes Hxk1p and Fba1p and the TCA-cycle proteins Lsc2p and Kgd1p, which showed half-lives of over 20 h. These data stress the need for inclusion of quantitative data of protein turn-over rates in yeast systems biology.

Prospects of yeast systems biology for human health: integrating lipid, protein and energy metabolism

The yeast Saccharomyces cerevisiae is a widely used model organism for studying cell biology, metabolism, cell cycle and signal transduction. Many regulatory pathways are conserved between this yeast and humans, and it is therefore possible to study pathways that are involved in disease development in a model organism that is easy to manipulate and that allows for detailed molecular studies. Here, we briefly review pathways involved in lipid metabolism and its regulation, the regulatory network of general metabolic regulator Snf1 (and its human homologue AMPK) and the proteostasis network with its link to stress and cell death. All the mentioned pathways can be used as model systems for the study of homologous pathways in human cells and a failure in these pathways is directly linked to several human diseases such as the metabolic syndrome and neurodegeneration. We demonstrate how different yeast pathways are conserved in humans, and we discuss the possibilities of using the systems biology approach to study and compare the pathways of relevance with the objective to generate hypotheses and gain new insights.

YEASTRACT: providing a programmatic access to curated transcriptional regulatory associations in Saccharomyces cerevisiae through a web services interface

The YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT) information system (http://www.yeastract.com) was developed to support the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2010, this database contains over 48 200 regulatory associations between transcription factors (TFs) and target genes, including 298 specific DNA-binding sites for 110 characterized TFs. All regulatory associations stored in the database were revisited and detailed information on the experimental evidences that sustain those associations was added and classified as direct or indirect evidences. The inclusion of this new data, gathered in response to the requests of YEASTRACT users, allows the user to restrict its queries to subsets of the data based on the existence or not of experimental evidences for the direct action of the TFs in the promoter region of their target genes. Another new feature of this release is the availability of all data through a machine readable web-service interface. Users are no longer restricted to the set of available queries made available through the existing web interface, and can use the web service interface to query, retrieve and exploit the YEASTRACT data using their own implementation of additional functionalities. The YEASTRACT information system is further complemented with several computational tools that facilitate the use of the curated data when answering a number of important biological questions. Since its first release in 2006, YEASTRACT has been extensively used by hundreds of researchers from all over the world. We expect that by making the new data and services available, the system will continue to be instrumental for yeast biologists and systems biology researchers.

Measuring enzyme activities under standardized in vivo‐like conditions for systems biology

Realistic quantitative models require data from many laboratories. Therefore, standardization of experimental systems and assay conditions is crucial. Moreover, standards should be representative of the in vivo conditions. However, most often, enzyme-kinetic parameters are measured under assay conditions that yield the maximum activity of each enzyme. In practice, this means that the kinetic parameters of different enzymes are measured in different buffers, at different pH values, with different ionic strengths, etc. In a joint effort of the Dutch Vertical Genomics Consortium, the European Yeast Systems Biology Network and the Standards for Reporting Enzymology Data Commission, we have developed a single assay medium for determining enzyme-kinetic parameters in yeast. The medium is as close as possible to the in vivo situation for the yeast Saccharomyces cerevisiae, and at the same time is experimentally feasible. The in vivo conditions were estimated for S. cerevisiae strain CEN.PK113-7D grown in aerobic glucose-limited chemostat cultures at an extracellular pH of 5.0 and a specific growth rate of 0.1 h(-1). The cytosolic pH and concentrations of calcium, sodium, potassium, phosphorus, sulfur and magnesium were determined. On the basis of these data and literature data, we propose a defined in vivo-like medium containing 300 mM potassium, 50 mM phosphate, 245 mM glutamate, 20 mM sodium, 2 mM free magnesium and 0.5 mM calcium, at a pH of 6.8. The V(max) values of the glycolytic and fermentative enzymes of S. cerevisiae were measured in the new medium. For some enzymes, the results deviated conspicuously from those of assays done under enzyme-specific, optimal conditions.

Impact of yeast systems biology on industrial biotechnology

Systems biology is yet an emerging discipline that aims to quantitatively describe and predict the functioning of a biological system. This nascent discipline relies on the recent advances in the analytical technology (such as DNA microarrays, mass spectromety, etc.) to quantify cellular characteristics (such as gene expression, protein and metabolite abundance, etc.) and computational methods to integrate information from these measurements. The model eukaryote, Saccharomyces cerevisiae, has played a pivotal role in the development of many of these analytical and computational methods and consequently is the biological system of choice for testing new hypotheses. The knowledge gained from such studies in S. cerevisiae is proving to be extremely useful in designing metabolism that is targeted to specific industrial applications. As a result, the portfolio of products that are being produced using this yeast is expanding rapidly. We review the recent developments in yeast systems biology and how they relate to industrial biotechnology.

Can yeast systems biology contribute to the understanding of human disease?

Saccharomyces cerevisiae is a unicellular eukaryal microorganism that has traditionally been regarded either as a model system for investigating cellular physiology or as a cell factory for biotechnological use, for example for the production of fuels and commodity chemicals such as lactate or pharmaceuticals, including human insulin and HPV vaccines. Systems biology has recently gained momentum and has successfully been used for mapping complex regulatory networks and resolving the dynamics of signal transduction pathways. So far, yeast systems biology has mainly focused on the development of new methods and concepts. There are also some examples of the application of yeast systems biology for improving biotechnological processes. We discuss here how yeast systems biology could be used in elucidating fundamental cellular principles such as those relevant for the study of molecular mechanisms underlying complex human diseases, including the metabolic syndrome and ageing.

Quantitative proteomics and transcriptomics of anaerobic and aerobic yeast cultures reveals post-transcriptional regulation of key cellular processes

Saccharomyces cerevisiae is unique among yeasts in its ability to grow rapidly in the complete absence of oxygen. S. cerevisiae is therefore an ideal eukaryotic model to study physiological adaptation to anaerobiosis. Recent transcriptome analyses have identified hundreds of genes that are transcriptionally regulated by oxygen availability but the relevance of this cellular response has not been systematically investigated at the key control level of the proteome. Therefore, the proteomic response of S. cerevisiae to anaerobiosis was investigated using metabolic stable-isotope labelling in aerobic and anaerobic glucose-limited chemostat cultures, followed by relative quantification of protein expression. Using independent replicate cultures and stringent statistical filtering, a robust dataset of 474 quantified proteins was generated, of which 249 showed differential expression levels. While some of these changes were consistent with previous transcriptome studies, many of the responses of S. cerevisiae to oxygen availability were, to our knowledge, previously unreported. Comparison of transcriptomes and proteomes from identical cultivations yielded strong evidence for post-transcriptional regulation of key cellular processes, including glycolysis, amino-acyl-tRNA synthesis, purine nucleotide synthesis and amino acid biosynthesis. The use of chemostat cultures provided well-controlled and reproducible culture conditions, which are essential for generating robust datasets at different cellular information levels. Integration of transcriptome and proteome data led to new insights into the physiology of anaerobically growing yeast that would not have been apparent from differential analyses at either the mRNA or protein level alone, thus illustrating the power of multi-level studies in yeast systems biology.

Standard reporting requirements for biological samples in metabolomics experiments: microbial and invitro biology experiments.

With the increasing use of metabolomics as a means to study a large number of different biological research questions, there is a need for a minimal set of reporting standards that allow the scientific community to evaluate, understand, repeat, compare and re-investigate metabolomics studies. Here we propose, a first draft of minimal requirements to effectively describe the biological context of metabolomics studies that involve microbial or in vitro biological subjects. This recommendation has been produced by the microbiology and in vitro biology working subgroup of the Metabolomics Standards Initiative in collaboration with the yeast systems biology network as part of a wider standardization initiative led by the Metabolomics Society. Microbial and in vitro biology metabolomics is defined by this sub-working group as studies with any cell or organism that require a defined external medium to facilitate growth and propagation. Both a minimal set and a best practice set of reporting standards for metabolomics experiments have been defined. The minimal set of reporting standards for microbial or in vitro biology metabolomics experiments includes those factors that are specific for metabolomics experiments and that critically determine the outcome of the experiments. The best practice set of reporting standards contains both the factors that are specific for metabolomics experiments and general aspects that critically determine the outcome of any microbial or in vitro biological experiment.

WI‐PHI: A weighted yeast interactome enriched for direct physical interactions

How is the yeast proteome wired? This important question, central in yeast systems biology, remains unanswered in spite of the abundance of protein interaction data from high-throughput experiments. Unfortunately, these large-scale studies show striking discrepancies in their results and coverage such that biologists scrutinizing the "interactome" are often confounded by a mix of established physical interactions, functional associations, and experimental artifacts. This stimulated early attempts to integrate the available information and produce a list of protein interactions ranked according to an estimated functional reliability. The recent publication of the results of two large protein interaction experiments and the completion of a comprehensive literature curation effort has more than doubled the available information on the wiring of the yeast proteome. This motivates a fresh approach to the compilation of a yeast interactome based purely on evidence of physical interaction. We present a procedure exploiting both heuristic and probabilistic strategies to draft the yeast interactome taking advantage of various heterogeneous data sources: application of tandem affinity purification coupled to MS (TAP-MS), large-scale yeast two-hybrid studies, and results of small-scale experiments stored in dedicated databases. The end result is WI-PHI, a weighted network encompassing a large majority of yeast proteins.

Yeast Functional Genomic Screens Lead to Identification of a Role for a Bacterial Effector in Innate Immunity Regulation

Numerous bacterial pathogens manipulate host cell processes to promote infection and ultimately cause disease through the action of proteins that they directly inject into host cells. Identification of the targets and molecular mechanisms of action used by these bacterial effector proteins is critical to understanding pathogenesis. We have developed a systems biological approach using the yeast Saccharomyces cerevisiae that can expedite the identification of cellular processes targeted by bacterial effector proteins. We systematically screened the viable yeast haploid deletion strain collection for mutants hypersensitive to expression of the Shigella type III effector OspF. Statistical data mining of the results identified several cellular processes, including cell wall biogenesis, which when impaired by a deletion caused yeast to be hypersensitive to OspF expression. Microarray experiments revealed that OspF expression resulted in reversed regulation of genes regulated by the yeast cell wall integrity pathway. The yeast cell wall integrity pathway is a highly conserved mitogen-activated protein kinase (MAPK) signaling pathway, normally activated in response to cell wall perturbations. Together these results led us to hypothesize and subsequently demonstrate that OspF inhibited both yeast and mammalian MAPK signaling cascades. Furthermore, inhibition of MAPK signaling by OspF is associated with attenuation of the host innate immune response to Shigella infection in a mouse model. These studies demonstrate how yeast systems biology can facilitate functional characterization of pathogenic bacterial effector proteins.