Oxygen dependence of metabolic fluxes and energy generation of Saccharomyces cerevisiae CEN.PK113-1A

<p>Abstract</p> <p>Background</p> <p>The yeast <it>Saccharomyces cerevisiae </it>is able to adjust to external oxygen availability by utilizing both respirative and fermentative metabolic modes. Adjusting the metabolic mode involves alteration of the intracellular metabolic fluxes that are determined by the cell's multilevel regulatory network. Oxygen is a major determinant of the physiology of <it>S. cerevisiae </it>but understanding of the oxygen dependence of intracellular flux distributions is still scarce.</p> <p>Results</p> <p>Metabolic flux distributions of <it>S. cerevisiae </it>CEN.PK113-1A growing in glucose-limited chemostat cultures at a dilution rate of 0.1 h^-1with 20.9%, 2.8%, 1.0%, 0.5% or 0.0% O₂in the inlet gas were quantified by ¹³C-MFA. Metabolic flux ratios from fractional [U-¹³C]glucose labelling experiments were used to solve the underdetermined MFA system of central carbon metabolism of <it>S. cerevisiae</it>.</p> <p>While ethanol production was observed already in 2.8% oxygen, only minor differences in the flux distribution were observed, compared to fully aerobic conditions. However, in 1.0% and 0.5% oxygen the respiratory rate was severely restricted, resulting in progressively reduced fluxes through the TCA cycle and the direction of major fluxes to the fermentative pathway. A redistribution of fluxes was observed in all branching points of central carbon metabolism. Yet only when oxygen provision was reduced to 0.5%, was the biomass yield exceeded by the yields of ethanol and CO₂. Respirative ATP generation provided 59% of the ATP demand in fully aerobic conditions and still a substantial 25% in 0.5% oxygenation. An extensive redistribution of fluxes was observed in anaerobic conditions compared to all the aerobic conditions. Positive correlation between the transcriptional levels of metabolic enzymes and the corresponding fluxes in the different oxygenation conditions was found only in the respirative pathway.</p> <p>Conclusion</p> <p>¹³C-constrained MFA enabled quantitative determination of intracellular fluxes in conditions of different redox challenges without including redox cofactors in metabolite mass balances. A redistribution of fluxes was observed not only for respirative, respiro-fermentative and fermentative metabolisms, but also for cells grown with 2.8%, 1.0% and 0.5% oxygen. Although the cellular metabolism was respiro-fermentative in each of these low oxygen conditions, the actual amount of oxygen available resulted in different contributions through respirative and fermentative pathways.</p

Low oxygen levels as a trigger for enhancement of respiratory metabolism in Saccharomyces cerevisiae

<p>Abstract</p> <p>Background</p> <p>The industrially important yeast <it>Saccharomyces cerevisiae </it>is able to grow both in the presence and absence of oxygen. However, the regulation of its metabolism in conditions of intermediate oxygen availability is not well characterised. We assessed the effect of oxygen provision on the transcriptome and proteome of <it>S. cerevisiae </it>in glucose-limited chemostat cultivations in anaerobic and aerobic conditions, and with three intermediate (0.5, 1.0 and 2.8% oxygen) levels of oxygen in the feed gas.</p> <p>Results</p> <p>The main differences in the transcriptome were observed in the comparison of fully aerobic, intermediate oxygen and anaerobic conditions, while the transcriptome was generally unchanged in conditions receiving different intermediate levels (0.5, 1.0 or 2.8% O₂) of oxygen in the feed gas. Comparison of the transcriptome and proteome data suggested post-transcriptional regulation was important, especially in 0.5% oxygen. In the conditions of intermediate oxygen, the genes encoding enzymes of the respiratory pathway were more highly expressed than in either aerobic or anaerobic conditions. A similar trend was also seen in the proteome and in enzyme activities of the TCA cycle. Further, genes encoding proteins of the mitochondrial translation machinery were present at higher levels in all oxygen-limited and anaerobic conditions, compared to fully aerobic conditions.</p> <p>Conclusion</p> <p>Global upregulation of genes encoding components of the respiratory pathway under conditions of intermediate oxygen suggested a regulatory mechanism to control these genes as a response to the need of more efficient energy production. Further, cells grown in three different intermediate oxygen levels were highly similar at the level of transcription, while they differed at the proteome level, suggesting post-transcriptional mechanisms leading to distinct physiological modes of respiro-fermentative metabolism.</p

A Design of Experiments Approach for Engineering Carbon Metabolism in the Yeast Saccharomyces cerevisiae

In conclusion, the Design of Experiments approach is beneficial to the rigour of scientific method required to evaluate complex biological systems, and its use would be advantageous for all such multivariate research areas.The proven ability to ferment Saccharomyces cerevisiae on a large scale presents an attractive target for producing chemicals and fuels from sustainable sources. Efficient and predominant carbon flux through to ethanol is a significant engineering issue in the development of this yeast as a multi-product cell chassis used in biorefineries. In order to evaluate diversion of carbon flux away from ethanol, combinatorial deletions were investigated in genes encoding the six isozymes of alcohol dehydrogenase (ADH), which catalyse the terminal step in ethanol production. The scarless, dominant and counter- selectable amdSYM gene deletion method was optimised for generation of a combinatorial ADH knockout library in an industrially relevant strain of S. cerevisiae. Current understanding of the individual ADH genes fails to fully evaluate genotype-by-genotype and genotype-by-environment interactions: rather, further research of such a complex biological process requires a multivariate mathematical modelling approach. Application of such an approach using the Design of Experiments (DoE) methodology is appraised here as essential for detailed empirical evaluation of complex systems. DoE provided empirical evidence that in S. cerevisiae: i) the ADH2 gene is not associated with producing ethanol under anaerobic culture conditions in combination with 25 g l-1 glucose substrate concentrations; ii) ADH4 is associated with increased ethanol production when the cell is confronted with a zinc-limited [1 μM] environment; and iii) ADH5 is linked with the production of ethanol, predominantly at pH 4.5. A successful metabolic engineering strategy is detailed which increases the product portfolio of S. cerevisiae, currently used for large-scale production of bioethanol. Heterologous expression of the cytochrome P450 fatty acid peroxygenase from Jeotgalicoccus sp., OleTJE, fused to the RhFRED reductase from Rhodococcus sp. NCIMB 978 converted free fatty acid precursors to C13, C15 and C17 alkenes (3.81 ng μl-1 total alkene concentration).Royal Dutch Shel

iOD907, the first genome-scale metabolic model for the milk yeast Kluyveromyces lactis

We describe here the first genome-scale metabolic model of Kluyveromyces lactis, iOD907. It is partially compartmentalized (4 compartments), composed of 1867 reactions and 1476 metabolites. The iOD907 model performed well when assessing the positive growth of K. lactis to Biolog experiments and to an online catalogue of strains that provides information on carbon sources in which K. lactis is able to grow. Chemostat experiments were used to adjust non-growth-associated energy requirements, and the model proved accurate when predicting the biomass, oxygen and carbon dioxide yields. In silico knockouts predicted in vivo phenotypes accurately when compared to published experiments. The iOD907 genome-scale metabolic model complies with the MIRIAM standards for the annotation of enzymes, transporters, metabolites and reactions. Moreover, it contains direct links to KEGG (for enzymes, metabolites and reactions) and to TCDB for transporters, allowing easy comparisons to other models. Furthermore, this model is provided in the well-established SBML format, which means that it can be used in most metabolic engineering platforms, such as OptFlux or Cobra. The model is able to predict the behavior of K. lactis under different environmental conditions and genetic perturbations. Furthermore, it can also be important in the design of minimal media and will allow insights on the milk yeast's metabolism, as well as identifying metabolic engineering targets for the improvement of the production of products of interest by performing simulations and optimizations.The authors thank strategic Project PEst-OE/EQB/LA0023/2013 and project "BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes, REF. NORTE-07-0124-FEDER-000028" co-funded by the Programa Operacional Regional do Norte (ON.2 - O Novo Norte), QREN, FEDER. The authors would also like to acknowledge Steve Sheridan for proof reading this manuscript

Metabolic modelling and 13C flux analysis : application to biotechnologically important yeasts and a fungus

All bioconversions in cells derive from metabolism. Microbial metabolisms contain potential for bioconversions from simple source molecules to unlimited number of biochemicals and for degradation of even detrimental compounds. Metabolic fluxes are rates of consumption and production of compounds in metabolic reactions. Fluxes emerge as an ultimate phenotype of an organism from an integrated regulatory function of the underlying networks of complex and dynamic biochemical interactions. Since the fluxes are time-dependent, they have to be inferred from other, measurable, quantities by modelling and computational analysis. 13C-labelling is crucial for quantitative analysis of fluxes through intracellular alternative pathways. Local flux ratio analysis utilises uniform 13C-labelling experiments, where the carbon source contains a fraction of uniformly 13C-labelled molecules. Carbon-carbon bonds are cleaved and formed in metabolic reactions depending on the in vivo fluxes. 13C-labelling patterns of metabolites or macromolecule components can be detected by mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectroscopy. Local flux ratio analysis utilises directly the 13C-labelling data and metabolic network models to solve ratios of converging fluxes. In this thesis the local flux ratio analysis has been extended and applied to analysis of phenotypes of biotechnologically important yeasts Saccharomyces cerevisiae and Pichia pastoris, and a fungus Trichoderma reesei. Oxygen dependence of in vivo net flux distribution of S. cerevisiae was quantified by using local flux ratios as additional constraints to the stoichiometric model of the central carbon metabolism. The distribution of fluxes in the pyruvate branching point turned out to be most responsive to different oxygen availabilities. The distribution of fluxes was observed to vary not only between the fully respiratory, respiro-fermentative and fermentative metabolic states but also between different respiro-fermentative states. The local flux ratio analysis was extended to the case of two-carbon source of glycerol and methanol co-utilisation by P. pastoris. The fraction of methanol in the carbon source did not have as profound effect on the distribution of fluxes as the growth rate. The effect of carbon catabolite repression (CCR) on fluxes of T. reesei was studied by reconstructing amino acid biosynthetic pathways and by performing local flux ratio analysis. T. reesei was observed to primarily utilise respiratory metabolism also in conditions of CCR. T. reesei metabolism was further studied and L-threo-3-deoxy-hexulosonate was identified as L-galactonate dehydratase reaction product by using NMR spectroscopy. L-galactonate dehydratase reaction is part of the fungal pathway for D-galacturonic acid catabolism

Improvement of in silico strain engineering methods in Saccharomyces cerevisiae

PhD thesis in BioengineeringThe buildup of knowledge about microbial metabolism and the development of genome engineering techniques gave rise to the rational modification of microorganisms in order to use them to biosynthesize chemicals of industrial interest. Recently, the construction of genome-scale metabolic models (GSMMs) allowed the design of strain engineering strategies in silico. This thesis focused on the study and improvement of in silico strain engineering methodologies using Saccharomyces cerevisiae as a case study organism. Firstly, in order to investigate the accuracy of the GSMMs available for S. cerevisiae, their capacity to simulate the intracellular fluxes in central metabolism was tested. The results revealed that the simulations contained relevant errors in important areas of the central metabolism. A careful manual curation of the feasibility of all reactions producing or consuming NADH / NADPH resulted in the improvement of many fluxes in central metabolic pathways when compared to fluxes measured experimentally. The lack of a simulation method that could predict in quantitative terms the phenotype of strains with complex engineered genotypes, led to the development of a novel simulation method called turnover dependent phenotypic simulation (TDPS). This method was designed with the goal of simulating the majority of the genetic modifications usually implemented in engineered strains. The assumption that the production turnover of a metabolite can be used as an indication of its abundance was used in the formulation of TDPS in order to take into account the availability of resources when modelling genetic modifications. TDPS was validated using metabolically engineered S. cerevisiae strains available in the literature by comparing the production yields of the target metabolite. TDPS was then applied to the optimization of the availability of cytosolic acetyl-CoA in S. cerevisiae, by using an evolutionary algorithm to search for sets of genetic alterations that could improve the production yield of 3-hydroxypropionic acid (3-HP) derived from acetyl-CoA. Although the yields obtained experimentally were considerably lower than the simulations suggested, a positive effect on the 3-HP yield was observed for the downregulation of the pyruvate dehydrogenase complex and the deletion of ACH1 (succinyl- CoA:acetate CoA-transferase).O progresso que tem sido feito na área da fisiologia microbiana, juntamente com o desenvolvimento de técnicas de engenharia genética, permitiu a criação de estirpes microbianas modificadas racionalmente com o intuito de optimizar a produção de compostos de interesse industrial. Mais recentemente, a construção de modelos metabólicos à escala genómica (MMEG) proporcionou o desenho de estirpes modificadas in silico. Esta tese focou-se no estudo e melhoramento de metodologias de manipulação de estirpes in silico, usando Saccharomyces cerevisiae como caso de estudo. De forma a investigar a precisão dos MMEG disponíveis para S. cerevisiae, a sua capacidade para simular os fluxos intracelulares foi testada. Os resultados mostraram que os fluxos simulados continham erros em áreas importantes do metabolismo central e que a curação manual das reacções envolvidas no metabolismo de NADH e NADPH resulta em melhorias significativas nos fluxos metabólicos centrais. A ausência de um método de simulação que conseguisse prever quantitativamente o fenótipo de estirpes com genótipos complexos, levou ao desenvolvimento de um método novo designado por turnover dependent phenotypic simulation (TDPS). Este método foi concebido com o objectivo de simular a maior parte das modificações genéticas normalmente implementadas em estripes modificadas. A formulação do TDPS teve como base o uso do nível de produção de um metabolito como indicador da sua abundancia, de forma a modelar as modificações genéticas em função da disponibilidade de recursos. A validação deste método foi feita usando dados da literatura sobre estirpes geneticamente modificadas de S. cerevisiae, através da comparação dos rendimentos simulados e reais. O método de simulação TDPS foi posteriormente aplicado na optimização da produção de acetil-CoA no citosol de S. cerevisiae, usando um algoritmo evolucionário para procurar conjuntos de alterações genéticas que aumentassem a produção de ácido 3- hidroxipropiónico derivado de acetil-CoA. Apesar dos rendimentos experimentais serem mais baixos que as simulações sugeriam, observou-se um efeito positivo da sub-regulação do complexo da piruvato desidrogenase e da eliminação do gene ACH1 (succinil- CoA:acetato CoA-transferase).Esta investigação foi financiada pela Fundação para a Ciência e Tecnologia através da concessão de uma bolsa de doutoramento (SFRH/BD/51111/2010), co-financiada pelo POPH - QREN - Tipologia 4.1 -Formação Avançada - e comparticipados pelo Fundo Social Europeu (FSE) e por fundos nacionais do Ministério da Ciência, Tecnologia e Ensino Superior (MCTES)

Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering

Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in GeneSpecific Translation Efficiency in Yeast

Protein synthesis is the most energy-consuming process in a proliferating cell, and understanding what controls protein abundances represents a key question in biology and biotechnology. We quantified absolute abundances of 5,354 mRNAs and 2,198 proteins in Saccharomyces cerevisiae under ten environmental conditions and protein turnover for 1,384 proteins under a reference condition. The overall correlation between mRNA and protein abundances across all conditions was low (0.46), but for differentially expressed proteins (n = 202), the median mRNA-protein correlation was 0.88. We used these data to model translation efficiencies and found that they vary more than 400-fold between genes. Non-linear regression analysis detected that mRNA abundance and translation elongation were the dominant factors controlling protein synthesis, explaining 61% and 15% of its variance. Metabolic flux balance analysis further showed that only mitochondrial fluxes were positively associated with changes at the transcript level. The present dataset represents a crucial expansion to the current resources for future studies on yeast physiology

Adaptability of metabolic networks in evolution and disease

There are 114.101 small molecule metabolites currently annotated in the Human Metabolome Database, which are highly connected amongst each other, with a few metabolites exhibiting an estimated number of more than 103 connections. Redundancy and plasticity are essential features of metabolic networks enabling cells to respond to fluctuating environments, presence of toxic molecules, or genetic perturbations like mutations. These system-level properties are inevitably linked to all aspects of biological systems ensuring cell viability by enabling processes like adaption and differentiation. To this end, the ability to interrogate molecular changes at omics level has opened new opportunities to study the cell at its different layers from the epigenome and transcriptome to its proteome and metabolome. In this thesis, I tackled the question how redundancy and plasticity shape adaptation in metabolic networks in evolutionary and disease contexts. I utilize a multi-omics approach to study comprehensively the metabolic state of a cell and its regulation at the transcriptional and proteomic level. One of the challenges with multi-omics approaches is the integration and interpretation of multi-layered data sets. To approach this challenge, I use genome scale metabolic models as a knowledge-based scaffold to overlay omics data and thereby to enable biological interpretation beyond statistical correlation. This integrative methodology has been applied to two different projects, namely the evolutionary adaptation towards a nutrient source in yeast and the metabolic adaptations following disease progression. For the latter, I also curated a current human genome-scale metabolic model and made it more suitable for flux predictions. In the yeast case study, I investigate the metabolic network adaptations enabling yeast to grow on an alternative carbon source – glycerol. I could show that network redundancy is one of the key features of fast adaptation of the yeast metabolic network to the new nutrient environment. Genomics, transcriptomics, proteomics, metabolomics and metabolic modeling together revealed a shift of the organism’s redox-balance under glycerol consumption as a driving force of adaption, which can be linked to the causal mutation in the enzyme Kgd1. On the other hand, the limitations of metabolic network adaptation also became apparent since all evolved and adapted strains exhibited metabolic trade-offs in other environmental conditions than the adaptation niche. Either an impaired diauxic shift (as in the case of the glycerol mutant) or an increased sensitivity towards osmotic stress (caused by mutations in the HOG pathway) was coupled with efficient use of glycerol. In the second project, the molecular phenotype of regressed breast cancer cells was studied to identify what differentiates these cells from healthy breast tissue and to characterize the potential source of tumor recurrence. Using a breast cancer mouse model with inducible oncogenes, transcriptomics together with an extensive set of different types of metabolomics (targeted and untargeted metabolomics, lipidomics and fluxomics) could show that regressed cancer cells, albeit their apparently normal morphology, possess a highly altered molecular phenotype with an oncogenic memory. While in cancer redundancy and plasticity enable the adaptation towards a proliferative state, in regressed cells, on the contrary, prolonged oncogenic signaling leads to a loss of metabolic network regulation and the entering of an irreversible metabolic state. This state appears to be insensitive to adaptation mechanisms as transcripts and metabolites reciprocally enhance each other to maintain the tumor-like metabolic phenotype. In conclusion, this work demonstrates how genome scale metabolic models can help identifying functional mechanisms from complex and multi-layered omics data. Appropriate genome scale metabolic models combined with metabolite measurements have proven particularly useful in this context. The comprehensive understanding of all integrated aspects of a cell’s physiology is a challenging endeavor and the results of this thesis might stimulate further research towards this goal