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687 results for “Saccharomyces cerevisiae”
A modified fluctuation assay reveals a natural mutator phenotype that drives mutation spectrum variation within Saccharomyces cerevisiae
<p>Although studies of <em>Saccharomyces cerevisiae</em> have provided many insights into mutagenesis and DNA repair, most of this work has focused on a few laboratory strains. Much less is known about the phenotypic effects of natural variation within <em>S. cerevisiae</em>'s DNA repair pathways. Here, we use natural polymorphisms to detect historical mutation spectrum differences among several wild and domesticated <em>S. cerevisiae</em> strains. To determine whether these differences are likely caused by genetic mutation rate modifiers, we use a modified fluctuation assay with a <em>CAN1</em> reporter to measure de novo mutation rates and spectra in 16 of the analyzed strains. We measure a 10-fold range of mutation rates and identify two strains with distinctive mutation spectra. These strains, known as AEQ and AAR, come from the panel's 'Mosaic beer' clade and share an enrichment for C > A mutations that is also observed in rare variation segregating throughout the genomes of several Mosaic beer and Mixed origin strains. Both AEQ and AAR are haploid derivatives of the diploid natural isolate CBS 1782, whose rare polymorphisms are enriched for C > A as well, suggesting that the underlying mutator allele is likely active in nature. We use a plasmid complementation test to show that AAR and AEQ share a mutator allele in the DNA repair gene <em>OGG1</em>, which excises 8-oxoguanine lesions that can cause C > A mutations if left unrepaired.</p>
Strain specific genome scale metabolic models for 1011 Saccharomyces cerevisiae
<p>This is generated strain-specific genome scale metabolic models for 1011 S.cerevisiae. This is linked with the paper: Lu, H. et al. <em>A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism.</em> Nature Communications 10, 3586 (2019). <a href="https://doi.org/10.1038/s41467-019-11581-3">doi:10.1038/s41467-019-11581-3</a></p>
Large excess capacity of glycolytic enzymes in Saccharomyces cerevisiae under glucose-limited conditions
<p>Computational models and figure data for the publication "Large excess capacity of glycolytic enzymes in <em>Saccharomyces cerevisiae </em>under glucose-limited conditions" (to be submitted). Data put together by Pranas Grigaitis, p.grigaitis [at] vu.nl.</p> <p> </p> <p><em>Abstract</em></p> <p>In Nature, microbes live in very nutrient-dynamic environments. Rapid scavenging and consumption of newly introduced nutrients therefore offer a way to outcompete competitors. This may explain the observation that many microorganisms, including the budding yeast <em>Saccharomyces cerevisiae,</em> appear to keep “excess” glycolytic proteins at low growth rates, i.e. the maximal capacity of glycolytic enzymes (largely) exceeds the actual flux through the enzymes. However, such a strategy requires investment into preparatory protein expression that may come at the cost of current fitness. Moreover, at low nutrient levels, enzymes cannot operate at high saturation, and overcapacity is poorly defined without taking enzyme kinetics into account.</p> <p>Here we use computational modeling to suggest that in yeast the overcapacity of the glycolytic enzymes at low specific growth rates is a genuine excess, rather than the optimal enzyme demand dictated by enzyme kinetics. We found that the observed expression of the glycolytic enzymes did match the predicted optimal expression when <em>S. cerevisiae</em> exhibits mixed respiro-fermentative growth, while the expression of tricarboxylic acid cycle enzymes always follows the demand. Moreover, we compared the predicted metabolite concentrations with the experimental measurements and found the best agreement in glucose-excess conditions. We argue that the excess capacity of glycolytic proteins in glucose-scarce conditions is an adaptation of <em>S. cerevisiae</em> to fluctuations of nutrient availability in the environment.</p>
Saccharomyces cerevisiae IRC7 homology model
<p><strong>IRC7 homology model for S. cerevisiae.</strong></p> <p>IRC7 was modelled using the structures for <em>Escherichia coli</em> Cystathionine beta-lyase (PDB accession: 1CL2) and <em>Trichomonas vaginalis </em>Methionine Gamma-Lyase (PDB accession: 1E5F) as comparative references. The A and B chains of 1CL2 were superposed to 1E5F. The sequences for IRC7, 1CL2 and 1E5F were aligned with clustalx. Ten models were generated with UCSF Modeller v9.15 and evaluated with PROCHECK. The best model is uploaded here and is also available at the Protein Model DataBase (accession: PM0081794).</p>
Inferecladr Saccharomyces Cerevisiae Data Set
<p>This data is associated with the Inferecladr package. It consists of expression data clustered into separate files, associated metadata, genes clustered into separate lists of files, and a gold standard derived from YEASTRACT. It was initially used in Tchourine, K., Vogel, C., and Bonneau, R. (2018). Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks. Cell Reports 23, 376–388.</p>
Crossing design shapes patterns of genetic variation in synthetic recombinant populations of Saccharomyces cerevisiae
<p>"Synthetic recombinant" populations have emerged as a useful tool for dissecting the genetics of complex traits. They can be used to derive inbred lines for fine QTL mapping, or the populations themselves can be sampled for experimental evolution. In latter application, investigators generally value maximizing genetic variation in constructed populations. This is because in evolution experiments initiated from such populations, adaptation is primarily fueled by standing genetic variation. Despite this reality, little has been done to systematically evaluate how different methods of constructing synthetic populations shape initial patterns of variation. Here we seek to address this issue by comparing outcomes in synthetic recombinant <i>Saccharomyces cerevisiae</i> populations<i> </i>created using one of two strategies: pairwise crossing of isogenic strains or simple mixing of strains in equal proportion. We also explore the impact of the varying the number of parental strains. We find that more genetic variation is initially present and maintained when population construction includes a round of pairwise crossing. As perhaps expected, we also observe that increasing the number of parental strains typically increases genetic diversity. In summary, we suggest that when constructing populations for use in evolution experiments, simply mixing founder strains in equal proportion may limit the adaptive potential.</p>
Elevated energy costs of biomass production in mitochondrial-respiration deficient Saccharomyces cerevisiae
<p>The <em>pcYeast8</em> model and data, required to reproduce the figures in the preprint "Elevated energy costs of biomass production in mitochondrial-respiration deficient <em>Saccharomyces cerevisiae</em>". Data bundle uploaded by Pranas Grigaitis, p.grigaitis [at] vu.nl.</p> <p><em>Abstract</em></p> <p>Microbial growth requires energy for maintaining the existing cells and producing components for the new ones. Microbes therefore invest a considerable amount of their resources into proteins needed for energy harvesting. Growth in different environments is associated with different energy demands for growth of yeast <em>Saccharomyces cerevisiae</em>, although the cross-condition differences remain poorly characterized. Furthermore, a direct comparison of the energy costs for the biosynthesis of the new biomass across conditions is not feasible experimentally; computational models, on the contrary, allow comparing the optimal metabolic strategies and quantify the respective costs of energy and nutrients. Thus in this study, we used a resource allocation model of <em>S. cerevisiae</em> to compare the optimal metabolic strategies between different conditions. We found that <em>S. cerevisiae</em> with respiratory-impaired mitochondria required additional energetic investments for growth, while growth on amino acid-rich media was not affected. Amino acid supplementation in anaerobic conditions also was predicted to rescue the growth reduction in mitochondrial respiratory shuttle-deficient mutants of <em>S. cerevisiae</em>. Collectively, these results point to elevated costs of resolving the redox imbalance caused by <em>de novo</em> biosynthesis of amino acids in mitochondria. To sum up, our study provides an example of how resource allocation modeling can be used to address and suggest explanations to open questions in microbial physiology.</p>
Phenotypic variations of primary metabolites yield during alcoholic fermentation in the Saccharomyces cerevisiae species
<p>Supplementary data including the data set used for the" Phenotypic variations of primary metabolites yield during alcoholic fermentation in the Saccharomyces cerevisiae species" publication.</p> <p> </p> <p>Abstract:</p> <p><em>Saccharomyces cerevisiae</em>, as the workhorse of alcoholic fermentation, is a major actor of winemaking. In this context, this yeast species performs alcoholic fermentation to convert sugars from the grape must into ethanol and CO<sub>2</sub> with an outstanding efficiency: it reaches on average 92% of the maximum theoretical yield of conversion. Primary metabolites produced during fermentation stand for a great importance in wine where they significantly impact wine characteristics. Ethanol indeed does, but others too, which are found in lower concentrations: glycerol, succinate, acetate, ⍺-ketoglutarate… Their production, which can be characterised by a yield according to the amount of sugars consumed, is known to differ from one strain to another. <em>S. cerevisiae</em> is known for its great genetic diversity and plasticity that is directly related to its living environment, natural or technological and therefore to domestication. This leads to a great phenotypic diversity of metabolites production. However, the range of metabolic diversity is variable and depends on the pathway considered. In the aim to improve wine quality, the selection, development and use of strains with dedicated metabolites production without genetic modifications can rely on the natural diversity that already exists. Here we detail a screening that aims to assess this diversity of primary metabolites production in a set of 51 <em>S. cerevisiae</em> strains from various genetic backgrounds (wine, flor, rum, West African, sake…). To approach winemaking conditions, we used a synthetic grape must as fermentation medium and measured by HPLC five main metabolites. Results obtained pointed out great yield differences between strains and that variability is dependent on the metabolite considered. Ethanol appears as the one with the smallest variation among our set of strains, despite it’s by far the most produced. A clear negative correlation between ethanol and glycerol yields has been observed, confirming glycerol synthesis as a good lever to impact ethanol yield. Genetic groups have been identified as linked to high production of specific metabolites, like succinate for rum strains or alpha-ketoglutarate for wine strains. This study thus helps to define the phenotypic diversity of <em>S. cerevisiae</em> in a wine-like context and supports the use of ways of development of new strains exploiting natural diversity. Finally, it provides a detailed data set usable to study diversity of primary metabolites production, including common commercial wine strains.</p>
Data and analysis of proteomic responses to hexokinase-II depletion in GAL80 and gal80Δ Saccharomyces cerevisiae with an engineered sesquiterpene-pathway
<p>Dataset 1: <a href="https://zenodo.org/api/files/ece3309f-0ca2-4773-b2e3-b1c5c839faa4/GAL80_HXK2_Vs._dhxk2p_20200324_T2_004.xlsx">GAL80_HXK2_Vs._dhxk2p_20200324_T2_004.xlsx</a></p> <p>The comparison between strain ILHA o128R+pJT9RFR (dHxk2p) and ILHA o401R+ pJT9RFR (HXK2) under the conditions with the addition of 1-Naphthaleneacetic acid and in the exponential growth phase and the ethanol growth phase. </p> <p> </p> <p>Dataset 2: <a href="https://zenodo.org/api/files/ece3309f-0ca2-4773-b2e3-b1c5c839faa4/gal80%CE%94_HXK2_Vs._dhxk2p_20200219_T1_004.xlsx">gal80Δ_HXK2_Vs._dhxk2p_20200219_T1_004.xlsx</a></p> <p>The comparison between strain ILHA NLD128-1 (dHxk2p) and ILHA NLD401 (HXK2) under the conditions with the addition of 1-Naphthaleneacetic acid and in the exponential growth phase (EXP) and the ethanol growth phase (ETH). </p> <p> </p>
A toolkit for precise, multigene control in Saccharomyces cerevisiae
<p>Systems that allow researchers to precisely control the expression of genes are fundamental to biological research, biotechnology, and synthetic biology. However, few inducible gene expression systems exist that can enable simultaneous, multi-gene control in the important model organism and chassis, <em>Saccharomyces cerevisiae</em>. Here, we repurposed ligand binding domains (LBDs) from mammalian Type I nuclear receptors to establish a family of up to five orthogonal synthetic gene expression systems in yeast. Our systems enable tight, independent, multi-gene control through the addition of inert hormones, and are capable of driving robust gene expression outputs. As a proof-of-principle, we placed expression of four enzymes from the violacein biosynthetic pathway under independent expression control to selectively route pathway flux. Our results establish a modular, versatile, and potentially expandable toolkit for multidimensional control of gene expression in yeast that can be used to construct and control naturally-occurring and synthetic gene networks.</p>
Detailed information for the 2032 Saccharomyces cerevisiae genome assemblies studied
<p>Detailed information for the 2032 Saccharomyces cerevisiae genome assemblies studied</p>
Protein families for 2032 Saccharomyces cerevisiae genome assemblies
<p>The protein families obtained with different cluster cutoffs for different sets of genome assemblies as well as the marker genes are presented in text files. In each file, a row indicates a family and a column (separated by TAB) indicates a genome assembly. Protein IDs for multiple homologues from the same assembly are separated by '|'. The family ID is shown in the first column and the tag for each assembly is shown in the first row. Cluster cutoffs used are 50%, 60%, 70%, 80%, and 90%. Genome sets shown are all genomes (all), non-redundant (nr) genomes, medium-high-quality genomes (mhq), and high-quality genomes (hq).</p>
A toolkit for precise, multigene control in Saccharomyces cerevisiae
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Historical effects during experimental evolution of multicellularity in <em>Saccharomyces cerevisiae</em>
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Dataset for genome-wide profiling of autophagy dynamics under nutrient availability in <em>Saccharomyces cerevisiae</em>
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Crossing design shapes patterns of genetic variation in synthetic recombinant populations of Saccharomyces cerevisiae
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A modified fluctuation assay reveals a natural mutator phenotype that drives mutation spectrum variation within Saccharomyces cerevisiae
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Saccharomyces cerevisiae protein phosphorylation and translation accuracy
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Evidence for two main domestication trajectories in Saccharomyces cerevisiae linked to distinct bread-making processes
<p>Data and code supporting the manuscript "Evidence for two main domestication trajectories in <em>Saccharomyces cerevisiae</em> linked to distinct bread-making processes" by Bigey et al. (2020)</p>
Increased time sampling in an evolve-and-resequence experiment with outcrossing Saccharomyces cerevisiae reveals multiple paths of adaptive change
<p>"Evolve and resequence" (E&R) studies combine experimental evolution and whole-genome sequencing to interrogate the genetics underlying adaptation. Due to ease of handling, E&R work with asexual organisms like bacteria can employ optimized experimental design, with large experiments and many generations of selection. By contrast, E&R experiments with sexually reproducing organisms are more difficult to implement, and design parameters vary dramatically among studies. Thus, efforts have been made to assess how these differences, such as number of independent replicates, or size of experimental populations, impact inference. We add to this work by investigating the role of time sampling – the number of discrete timepoints sequence data is collected from evolving populations. Using data from an E&R experiment with outcrossing <i>Saccharomyces cerevisiae </i>in which populations were sequenced 17 times over ~540 generations, we address the following questions: (i) do more timepoints improve the ability to identify candidate regions underlying selection? And (ii) does high-resolution sampling provide unique insight into evolutionary processes driving adaptation? We find that while time sampling does not improve the ability to identify candidate regions, high-resolution sampling does provide valuable opportunities to characterize evolutionary dynamics. Increased time sampling reveals three distinct trajectories for adaptive alleles: one consistent with classic population genetic theory (i.e. models assuming constant selection coefficients), and two where trajectories suggest more context-dependent responses (i.e. models involving dynamic selection coefficients). We conclude that while time sampling has limited impact on candidate region identification, sampling 8 or more timepoints has clear benefits for studying complex evolutionary dynamics.</p>
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