On metabolic and phenotypic diversity in yeast

This thesis explores metabolic and phenotypic diversity in the two model yeasts Schizosaccharomyces pombe and Saccharomyces cerevisiae. Colony screens are a classical and powerful technique for investigating these topics, but there is a lack of modern, scalable bioinformatics tools. To address this need, I have developed pyphe which greatly facilitates colony screen data acquisition and statistical analysis. I explore optimal experimental designs, especially regarding the usefulness of timecourse imaging and colony viability analysis. Pyphe is used in a functional genomics screen, aiming to find functions for a set of largely uncharacterised lincRNAs. We identify hundreds of new lincRNA-associated phenotypes across numerous conditions and compare lincRNA phenotype profiles to those of codinggene mutants. Next, I have used pyphe to investigate the respiration/fermentation balance of wild S. pombe isolates. Contrary to the expectation that glucose completely represses respiration in this Crabtree-positive species, I find that strains generally strike a balance and that individual strains differ significantly in their residual respiration activity. This is associated with an unusual miss-sense variant in S. pombe’s sole pyruvate kinase gene. Its impact is dissected in detail, revealing a change in flux through pyruvate kinase and associated changes in gene expression, metabolism, growth and stress resistance. Finally, I explore how extracellular amino acids interact with cellular metabolism, with the aim of answering the important question whether or not clonal yeast cultures segregate into heterogeneous producer/consumer populations that exchange amino acids. I develop a novel proteomics-based method that characterises amino acid labelling patterns in peptides. I find that the supplementation of some, but not all amino acids completely suppresses selfsynthesis. However, I find no evidence for heterogeneous responses of our laboratory S. cerevisiae strain, but the functionality of the method is demonstrated clearly. Overall, this work represents several advancements to our understanding of yeast metabolism and physiology, as well as new experimental and computational methods

Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations

Broad functional profiling of fission yeast proteins using phenomics and machine learning

Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of ‘priority unstudied’ proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through ‘guilt by association’ with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions

Recombination and biased segregation of mitochondrial genomes during crossing and meiosis of different Schizosaccharomyces pombe strains

During meiosis, tethering of parental mitochondria to opposite cell poles inhibits the mixing of mitochondria with different genomes and ensures uniparental inheritance in thestandard laboratory strain of fission yeast. We here investigate mitochondrial inheritance in crosses between natural isolates using tetrad dissection and next-generation sequencing. We find that colonies grown from single spores can sometimes carry a mix of mitochondrial genotypes, that mitochondrial genomes can recombine during meiosis, that in some cases tetrads do not follow the 2:2 segregation pattern, and that certain crosses may feature a weak bias towards one of the parents. Together, these findings paint a more nuanced picture of mitochondrial inheritance in the wild

Correction: Functional profiling of long intergenic non-coding RNAs in fission yeast

Eukaryotic genomes express numerous long intergenic non-coding RNAs (lincRNAs) that do not overlap any coding genes. Some lincRNAs function in various aspects of gene regulation, but it is not clear in general to what extent lincRNAs contribute to the information flow from genotype to phenotype. To explore this question, we systematically analysed cellular roles of lincRNAs in Schizosaccharomyces pombe. Using seamless CRISPR/Cas9-based genome editing, we deleted 141 lincRNA genes to broadly phenotype these mutants, together with 238 diverse codinggene mutants for functional context. We applied high-throughput colony-based assays to determine mutant growth and viability in benign conditions and in response to 145 different nutrient, drug, and stress conditions. These analyses uncovered phenotypes for 47.5% of the lincRNAs and 96% of the protein-coding genes. For 110 lincRNA mutants, we also performed high-throughput microscopy and flow cytometry assays, linking 37% of these lincRNAs with cell-size and/or cell-cycle control. With all assays combined, we detected phenotypes for 84 (59.6%) of all lincRNA deletion mutants tested. For complementary functional inference, we analysed colony growth of strains ectopically overexpressing 113 lincRNA genes under 47 different conditions. Of these overexpression strains, 102 (90.3%) showed altered growth under certain conditions. Clustering analyses provided further functional clues and relationships for some of the lincRNAs. These rich phenomics datasets associate lincRNA mutants with hundreds of phenotypes, indicating that most of the lincRNAs analysed exert cellular functions in specific environmental or physiological contexts. This study provides groundwork to further dissect the roles of these lincRNAs in the relevant conditions

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

A challenge in solving the genotype-to-phenotype relationship is to predict a cell\u27s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype. Predicting metabolomes from gene expression data is a key challenge in understanding the genotype-phenotype relationship. Studying the enzyme expression proteome in kinase knockouts, we reveal the importance of a so far overlooked metabolism-regulatory mechanism. Enzyme expression changes are impacting on metabolite levels through many changes acting in concert. We show that one can map regulatory enzyme expression patterns using machine learning and use them to predict the metabolome of kinase-deficient cells on the basis of their enzyme expression proteome. Our study quantifies the role of enzyme abundance in the regulation of metabolism and by doing so reveals the potential of machine learning in gaining understanding about complex metabolism regulation

Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan

Metabolism is deeply intertwined with aging. Effects of metabolic interventions on aging have been explained with intracellular metabolism, growth control, and signaling. Studying chronological aging in yeast, we reveal a so far overlooked metabolic property that influences aging via the exchange of metabolites. We observed that metabolites exported by young cells are re-imported by chronologically aging cells, resulting in cross-generational metabolic interactions. Then, we used self-establishing metabolically cooperating communities (SeMeCo) as a tool to increase metabolite exchange and observed significant lifespan extensions. The longevity of the SeMeCo was attributable to metabolic reconfigurations in methionine consumer cells. These obtained a more glycolytic metabolism and increased the export of protective metabolites that in turn extended the lifespan of cells that supplied them with methionine. Our results establish metabolite exchange interactions as a determinant of cellular aging and show that metabolically cooperating cells can shape the metabolic environment to extend their lifespan

Lysine harvesting is an antioxidant strategy and triggers underground polyamine metabolism

Both single and multicellular organisms depend on anti-stress mechanisms that enable them to deal with sudden changes in the environment, including exposure to heat and oxidants. Central to the stress response are dynamic changes in metabolism, such as the transition from the glycolysis to the pentose phosphate pathway—a conserved first-line response to oxidative insults1,2. Here we report a second metabolic adaptation that protects microbial cells in stress situations. The role of the yeast polyamine transporter Tpo1p3,4,5 in maintaining oxidant resistance is unknown6. However, a proteomic time-course experiment suggests a link to lysine metabolism. We reveal a connection between polyamine and lysine metabolism during stress situations, in the form of a promiscuous enzymatic reaction in which the first enzyme of the polyamine pathway, Spe1p, decarboxylates lysine and forms an alternative polyamine, cadaverine. The reaction proceeds in the presence of extracellular lysine, which is taken up by cells to reach concentrations up to one hundred times higher than those required for growth. Such extensive harvest is not observed for the other amino acids, is dependent on the polyamine pathway and triggers a reprogramming of redox metabolism. As a result, NADPH—which would otherwise be required for lysine biosynthesis—is channelled into glutathione metabolism, leading to a large increase in glutathione concentrations, lower levels of reactive oxygen species and increased oxidant tolerance. Our results show that nutrient uptake occurs not only to enable cell growth, but when the nutrient availability is favourable it also enables cells to reconfigure their metabolism to preventatively mount stress protection

Unravelling metabolic cross‐feeding in a yeast–bacteria community using 13C‐based proteomics

Abstract Cross‐feeding is fundamental to the diversity and function of microbial communities. However, identification of cross‐fed metabolites is often challenging due to the universality of metabolic and biosynthetic intermediates. Here, we use 13C isotope tracing in peptides to elucidate cross‐fed metabolites in co‐cultures of Saccharomyces cerevisiae and Lactococcus lactis. The community was grown on lactose as the main carbon source with either glucose or galactose fraction of the molecule labelled with 13C. Data analysis allowing for the possible mass‐shifts yielded hundreds of peptides for which we could assign both species identity and labelling degree. The labelling pattern showed that the yeast utilized galactose and, to a lesser extent, lactic acid shared by L. lactis as carbon sources. While the yeast provided essential amino acids to the bacterium as expected, the data also uncovered a complex pattern of amino acid exchange. The identity of the cross‐fed metabolites was further supported by metabolite labelling in the co‐culture supernatant, and by diminished fitness of a galactose‐negative yeast mutant in the community. Together, our results demonstrate the utility of 13C‐based proteomics for uncovering microbial interactions

Unravelling metabolic cross-feeding in a yeast-bacteria community using 13 C-based proteomics.

Cross-feeding is fundamental to the diversity and function of microbial communities. However, identification of cross-fed metabolites is often challenging due to the universality of metabolic and biosynthetic intermediates. Here, we use 13 C isotope tracing in peptides to elucidate cross-fed metabolites in co-cultures of Saccharomyces cerevisiae and Lactococcus lactis. The community was grown on lactose as the main carbon source with either glucose or galactose fraction of the molecule labelled with 13 C. Data analysis allowing for the possible mass-shifts yielded hundreds of peptides for which we could assign both species identity and labelling degree. The labelling pattern showed that the yeast utilized galactose and, to a lesser extent, lactic acid shared by L. lactis as carbon sources. While the yeast provided essential amino acids to the bacterium as expected, the data also uncovered a complex pattern of amino acid exchange. The identity of the cross-fed metabolites was further supported by metabolite labelling in the co-culture supernatant, and by diminished fitness of a galactose-negative yeast mutant in the community. Together, our results demonstrate the utility of 13 C-based proteomics for uncovering microbial interactions