Michael Mülleder

Pyruvate kinase triggers a metabolic feedback loop that controls redox metabolism in respiring cells.

Summary In proliferating cells, a transition from aerobic to anaerobic metabolism is known as the Warburg effect, whose reversal inhibits cancer cell proliferation. Studying its regulator pyruvate kinase (PYK) in yeast, we discovered that central metabolism is self-adapting to synchronize redox metabolism when respiration is activated. Low PYK activity activated yeast respiration. However, levels of reactive oxygen species (ROS) did not increase, and cells gained resistance to oxidants. This adaptation was attributable to accumulation of the PYK substrate phosphoenolpyruvate (PEP). PEP acted as feedback inhibitor of the glycolytic enzyme triosephosphate isomerase (TPI). TPI inhibition stimulated the pentose phosphate pathway, increased antioxidative metabolism, and prevented ROS accumulation. Thus, a metabolic feedback loop, initiated by PYK, mediated by its substrate and acting on TPI, stimulates redox metabolism in respiring cells. Originating from a single catalytic step, this autonomous reconfiguration of central carbon metabolism prevents oxidative stress upon shifts between fermentation and respiration.

Cytosine DNA methylation is found in Drosophila melanogaster but absent in Saccharomyces cerevisiae, Schizosaccharomyces pombe, and other yeast species.

The methylation of cytosine to 5-methylcytosine (5-meC) is an important epigenetic DNA modification in many bacteria, plants, and mammals, but its relevance for important model organisms, including Caenorhabditis elegans and Drosophila melanogaster, is still equivocal. By reporting the presence of 5-meC in a broad variety of wild, laboratory, and industrial yeasts, a recent study also challenged the dogma about the absence of DNA methylation in yeast species. We would like to bring to attention that the protocol used for gas chromatography/mass spectrometry involved hydrolysis of the DNA preparations. As this process separates cytosine and 5-meC from the sugar phosphate backbone, this method is unable to distinguish DNA- from RNA-derived 5-meC. We employed an alternative LC–MS/MS protocol where by targeting 5-methyldeoxycytidine moieties after enzymatic digestion, only 5-meC specifically derived from DNA is quantified. This technique unambiguously identified cytosine DNA methylation in Arabidopsis thaliana (14.0% of cytosines methylated), Mus musculus (7.6%), and Escherichia coli (2.3%). Despite achieving a detection limit at 250 attomoles (corresponding to <0.00002 methylated cytosines per nonmethylated cytosine), we could not confirm any cytosine DNA methylation in laboratory and industrial strains of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Saccharomyces boulardii, Saccharomyces paradoxus, or Pichia pastoris. The protocol however unequivocally confirmed DNA methylation in adult Drosophila melanogaster at a value (0.034%) that is up to 2 orders of magnitude below the detection limit of bisulphite sequencing. Thus, 5-meC is a rare DNA modification in drosophila but absent in yeast.

The genomic and phenotypic diversity of Schizosaccharomyces pombe

Natural variation within species reveals aspects of genome evolution and function. The fission yeast Schizosaccharomyces pombe is an important model for eukaryotic biology, but researchers typically use one standard laboratory strain. To extend the usefulness of this model, we surveyed the genomic and phenotypic variation in 161 natural isolates. We sequenced the genomes of all strains, finding moderate genetic diversity (π = 3 × 10−3 substitutions/site) and weak global population structure. We estimate that dispersal of S. pombe began during human antiquity (∼340 BCE), and ancestors of these strains reached the Americas at ∼1623 CE. We quantified 74 traits, finding substantial heritable phenotypic diversity. We conducted 223 genome-wide association studies, with 89 traits showing at least one association. The most significant variant for each trait explained 22% of the phenotypic variance on average, with indels having larger effects than SNPs. This analysis represents a rich resource to examine genotype-phenotype relationships in a tractable model.

A prototrophic deletion mutant collection for yeast metabolomics and systems biology

Auxotrophic markers - mutations within genes encoding enzymes in pathways for the biosynthesis of metabolic building blocks, such as an amino acid or nucleotide, are used as selection markers in the vast majority of yeast genetics and genomics experiments 1-3. The nutritional deficiency caused by the mutation (auxotrophy) can be compensated by supplying the required nutrient in the growth medium. This compensation, however, is not necessarily quantitative because such mutations influence a number of physiological parameters and may act in combination 2,4,5 . The construction of a prototrophic derivative of the parent strain of the widely used genome-scale yeast deletion collection1 has confirmed the need to remove auxotrophic markers in order to reduce bias in physiological and metabolic studies 2 . Moreover, flux balance analyses using a genome-wide metabolic model (Yeast 5)6 indicate that the activity status of some 200-300 reactions changes between different auxotrophic strains and the wild type. To alleviate this bias we have constructed a version of the haploid deletant collection restoring prototrophy in the genetic background, such that influence of auxotrophy to the phenotype of a given gene deletion is prevented. This new deletant library facilitates the exploitation of yeast in both functional genomics and quantitative systems biology. The physiological impact of auxotrophy was assessed by monitoring the growth of 16 yeast strains carrying all possible combinations of the markers (histidine (his3Δ1), leucine (leu2Δ), methionine (met15Δ) and uracil (ura3Δ) used in the MATa version of the yeast deletion collection1. All markers and their combinations affected yeast growth, but without altering the adenylate (ATP, ADP, AMP) energy charge (Fig 1 a). As the most critical phenotypic quantity, the maximum specific growth rate (μmax) varied between 0.125 h−1 (leu2Δ) and 0.20 h−1 (his3Δmet15Δura3Δ), rendering quantitative comparisons between these strains impossible (Fig 1 a, Suppl. Table 1). These growth differences were not explained by the different media supplementations, as i) prototrophic yeast exhibited a different and substantially less diverse growth pattern in the 16 minimal media (Fig 1c, left panel; media recipes are given in the Supplementary material); and ii) growth differences where altered, but not abrogated, when other proteogenic amino acids were supplemented as well (synthetic complete (SC) medium; Fig 1b). Importantly, on both types of media, complex interactions between all auxotrophic mutations were observed. For instance, restoring MET15 in leu2Δura3Δhis3Δmet15Δ (0.185 h−1 → 0.164 h−1) or leu2Δura3Δmet15Δ (0.162 h−1 → 0.149 h−1) had a negative effect on μmax, but surprisingly promoted growth in leu2Δhis3Δmet15Δ (0.136 h−1 → 0.173 h−1) (Fig 1a); restoring LEU2 in leu2Δura3Δhis3Δ (0.164 h−1 → 0.185 h−1) or leu2Δura3Δmet15Δ (0.136 h−1 → 0.161 h−1) had a positive effect, but not in leu2Δura3Δhis3Δmet15Δ (0.185 h−1 → 0.186 h−1) (Fig 1a; Suppl. Table 1). Thus, although blocking different pathways, all markers influence each other, indicating that they have a wide-ranging and combinatorial influence on the metabolic network. Figure 1 The combinatorial impact of yeast auxotrophic markers In batch culture experiments, further problems arise from the unequal consumption of amino acid supplements resulting in cultivation phase-dependent starvation. Growth of BY4741 (the auxotrophic parent of the standard yeast gene-deletion collection 1) in SC media depleted nutrients in a way they became first limiting for met15Δ, then for leu2Δ, his3Δ1 and finally for ura3Δ auxotrophic yeast (Fig 1d). This effect could not be compensated by increasing amino acid supplementation(s), as this inhibited cell growth (Fig 1c, right panel). Chronological lifespan (CLS) is a phenotype that is profoundly influenced by both nutrient supplementation and growth rate. Indeed, we observed an increase in stationary phase survival in YPD media upon restoring prototrophy. In a competitive growth experiment, auxotrophic cells had lost their colony-forming capacities within 10 days, but their prototrophic counterparts were perfectly viable for more than 20 days (Fig 1e). Longer CLS of prototrophic versus auxotrophic yeast was also reported for other backgrounds, and in synthetic media nutrient starvation shortened the lifespan of auxotrophic cells 7,8. Restoring protrotrophy is thus, to our knowledge, one of the most powerful genetic modifications for extending CLS. Hence, as auxotrophic markers have substantial and combinatorial influences on fundamental biological parameters such as growth and ageing, auxotrophic genome resources introduce bias for analyzing physiological parameters and even more to quantitative studies addressing the metabolic network. We would thus encourage the yeast community to switch, where possible, to prototrophic yeast for experiments in transcriptomics, proteomics, and metabolomics. To create a prototrophic resource for genome-scale experiments, we re-introduced auxotrophic markers into the MATa versions of the S288c-based deletion collection (5185 strains)1 and the titratable promoter essential collection (839 strains)3. These strains were transformed with a centromere-containing single-copy vector (minichromosome), containing the chromosome VI centromere, the autonomous replication sequence of HHF1 (ARSH4)9, and the marker genes HIS3, URA3, LEU2, and MET15 under control of their endogenous promoter sequences (pHLUM; Suppl Fig 1, Addgene ID 40276). Under non-selective conditions, the vector was transmitted in 99.15% of cell divisions (0.85% segregation mean over 20 generations). After 20 days, all cells were found prototrophic due to their positive selection (Fig 1e), facilitating screens on both selective and non-selective media. Furthermore, pHLUM- transformed BY4741 derivates wild type for HIS3, LEU2, MET15 or URA3 grew similar as BY4741 pHLUM (Suppl. Fig 2), indicating that the minichromosome fully restored prototrophy. The titratable-promoter essential collection3 was exploited to demonstrate screening capacities. By replicating original and prototrophic strains onto doxycycline-containing media, we found that 13 of the 370 lethal phenotypes were compensated (Fig 1 f, Suppl. Table 2). Thus, auxotrophic markers do not only influence physiological parameters, they are also responsible for a number of essential phenotypes. Since all strains possess a native metabolic network, the new library reduces bias from the use of auxotrophic markers in functional genomics and metabolic systems biology. Based on the pHLUM minichromosome, which is counterselectable, the new resource retains full compatibility with the popular S288c knock-out and essential collections 1,3. However, the use of a plasmid will introduce confounding factors to those mutants which have deficits in plasmid stability and segregation. The library is distributed as arrayed on 96-well plates (Euroscarf, Frankfurt), and contains a deep-red coloured and counter-selectable mutant (ade12Δ) on both universal and plate-specific positions, which simplifies plate orientation and identification, and can serve as replicate-control in quantitative metabolomics experiments (Suppl. Fig 3).

Functional Metabolomics Describes the Yeast Biosynthetic Regulome.

Summary Genome-metabolism interactions enable cell growth. To probe the extent of these interactions and delineate their functional contributions, we quantified the Saccharomyces amino acid metabolome and its response to systematic gene deletion. Over one-third of coding genes, in particular those important for chromatin dynamics, translation, and transport, contribute to biosynthetic metabolism. Specific amino acid signatures characterize genes of similar function. This enabled us to exploit functional metabolomics to connect metabolic regulators to their effectors, as exemplified by TORC1, whose inhibition in exponentially growing cells is shown to match an interruption in endomembrane transport. Providing orthogonal information compared to physical and genetic interaction networks, metabolomic signatures cluster more than half of the so far uncharacterized yeast genes and provide functional annotation for them. A major part of coding genes is therefore participating in gene-metabolism interactions that expose the metabolism regulatory network and enable access to an underexplored space in gene function.

The metabolic background is a global player in Saccharomyces gene expression epistasis

The regulation of gene expression in response to nutrient availability is fundamental to the genotype–phenotype relationship. The metabolic–genetic make-up of the cell, as reflected in auxotrophy, is hence likely to be a determinant of gene expression. Here, we address the importance of the metabolic–genetic background by monitoring transcriptome, proteome and metabolome in a repertoire of 16 Saccharomyces cerevisiae laboratory backgrounds, combinatorially perturbed in histidine, leucine, methionine and uracil biosynthesis. The metabolic background affected up to 85% of the coding genome. Suggesting widespread confounding, these transcriptional changes show, on average, 83% overlap between unrelated auxotrophs and 35% with previously published transcriptomes generated for non-metabolic gene knockouts. Background-dependent gene expression correlated with metabolic flux and acted, predominantly through masking or suppression, on 88% of transcriptional interactions epistatically. As a consequence, the deletion of the same metabolic gene in a different background could provoke an entirely different transcriptional response. Propagating to the proteome and scaling up at the metabolome, metabolic background dependencies reveal the prevalence of metabolism-dependent epistasis at all regulatory levels. Urging a fundamental change of the prevailing laboratory practice of using auxotrophs and nutrient supplemented media, these results reveal epistatic intertwining of metabolism with gene expression on the genomic scale.

Tpo1-mediated spermine and spermidine export controls cell cycle delay and times antioxidant protein expression during the oxidative stress response

Cells counteract oxidative stress by altering metabolism, cell cycle and gene expression. However, the mechanisms that coordinate these adaptations are only marginally understood. Here we provide evidence that timing of these responses in yeast requires export of the polyamines spermidine and spermine. We show that during hydrogen peroxide (H2O2) exposure, the polyamine transporter Tpo1 controls spermidine and spermine concentrations and mediates induction of antioxidant proteins, including Hsp70, Hsp90, Hsp104 and Sod1. Moreover, Tpo1 determines a cell cycle delay during adaptation to increased oxidant levels, and affects H2O2 tolerance. Thus, central components of the stress response are timed through Tpo1‐controlled polyamine export.

Yeast Creates a Niche for Symbiotic Lactic Acid Bacteria through Nitrogen Overflow

Summary Many microorganisms live in communities and depend on metabolites secreted by fellow community members for survival. Yet our knowledge of interspecies metabolic dependencies is limited to few communities with small number of exchanged metabolites, and even less is known about cellular regulation facilitating metabolic exchange. Here we show how yeast enables growth of lactic acid bacteria through endogenous, multi-component, cross-feeding in a readily established community. In nitrogen-rich environments, Saccharomyces cerevisiae adjusts its metabolism by secreting a pool of metabolites, especially amino acids, and thereby enables survival of Lactobacillus plantarum and Lactococcus lactis. Quantity of the available nitrogen sources and the status of nitrogen catabolite repression pathways jointly modulate this niche creation. We demonstrate how nitrogen overflow by yeast benefits L. plantarum in grape juice, and contributes to emergence of mutualism with L. lactis in a medium with lactose. Our results illustrate how metabolic decisions of an individual species can benefit others.

Self-establishing communities enable cooperative metabolite exchange in a eukaryote

Metabolite exchange among co-growing cells is frequent by nature, however, is not necessarily occurring at growth-relevant quantities indicative of non-cell-autonomous metabolic function. Complementary auxotrophs of Saccharomyces cerevisiae amino acid and nucleotide metabolism regularly fail to compensate for each others deficiencies upon co-culturing, a situation which implied the absence of growth-relevant metabolite exchange interactions. Contrastingly, we find that yeast colonies maintain a rich exometabolome and that cells prefer the uptake of extracellular metabolites over self-synthesis, indicators of ongoing metabolite exchange. We conceived a system that circumvents co-culturing and begins with a self-supporting cell that grows autonomously into a heterogeneous community, only able to survive by exchanging histidine, leucine, uracil, and methionine. Compensating for the progressive loss of prototrophy, self-establishing communities successfully obtained an auxotrophic composition in a nutrition-dependent manner, maintaining a wild-type like exometabolome, growth parameters, and cell viability. Yeast, as a eukaryotic model, thus possesses extensive capacity for growth-relevant metabolite exchange and readily cooperates in metabolism within progressively establishing communities. DOI: http://dx.doi.org/10.7554/eLife.09943.001

A High-Throughput Method for the Quantitative Determination of Free Amino Acids in Saccharomyces cerevisiae by Hydrophilic Interaction Chromatography–Tandem Mass Spectrometry

Amino acids are the building blocks for protein synthesis and the precursors for many biomolecules, such as glutathione and S-adenosylmethionine. Their intracellular concentrations provide valuable information about the overall metabolic state of the cell, as they are closely connected to carbon and nitrogen metabolism and are tightly regulated to meet cellular demands in ever-changing environments. Here, we describe a fast and simple method enabling metabolic profiling for free amino acids for large numbers of yeast strains. Metabolites are extracted with boiling ethanol and, without further conditioning, analyzed by hydrophilic interaction chromatography-tandem mass spectrometry (HILIC-MS/MS). Several hundred samples can be prepared in a single day with an analytical runtime of 3.25 min. This method is valuable for functional characterization, identification of metabolic regulators and processes, or monitoring of biotechnological processes.