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

Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

<p>Abstract</p> <p>Background</p> <p>Several protein-protein interaction studies have been performed for the yeast <it>Saccharomyces cerevisiae </it>using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives.</p> <p>Results</p> <p>Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient-sensing and metabolic regulatory signal transduction pathways (STP) operating in <it>Saccharomyces cerevisiae</it>. The reconstructed STP network includes a full protein-protein interaction network including the key nodes Snf1, Tor1, Hog1 and Pka1. The network includes a total of 623 structural open reading frames (ORFs) and 779 protein-protein interactions. A number of proteins were identified having interactions with more than one of the protein kinases. The fully reconstructed interaction network includes all the information available in separate databases for all the proteins included in the network (nodes) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner.</p> <p>Conclusions</p> <p>The reported fully annotated interaction model serves as a platform for integrated systems biology studies of nutrient sensing and regulation in <it>S. cerevisiae</it>. Furthermore, we propose this annotated reconstruction as a first step towards generation of an extensive annotated protein-protein interaction network of signal transduction and metabolic regulation in this yeast.</p

13C-metabolic flux ratio and novel carbon path analyses confirmed that Trichoderma reesei uses primarily the respirative pathway also on the preferred carbon source glucose

<p>Abstract</p> <p>Background</p> <p>The filamentous fungus <it>Trichoderma reesei </it>is an important host organism for industrial enzyme production. It is adapted to nutrient poor environments where it is capable of producing large amounts of hydrolytic enzymes. In its natural environment <it>T. reesei </it>is expected to benefit from high energy yield from utilization of respirative metabolic pathway. However, <it>T. reesei </it>lacks metabolic pathway reconstructions and the utilization of the respirative pathway has not been investigated on the level of <it>in vivo </it>fluxes.</p> <p>Results</p> <p>The biosynthetic pathways of amino acids in <it>T. reesei </it>supported by genome-level evidence were reconstructed with computational carbon path analysis. The pathway reconstructions were a prerequisite for analysis of <it>in vivo </it>fluxes. The distribution of <it>in vivo </it>fluxes in both wild type strain and <it>cre1</it>, a key regulator of carbon catabolite repression, deletion strain were quantitatively studied by performing ¹³C-labeling on both repressive carbon source glucose and non-repressive carbon source sorbitol. In addition, the ¹³C-labeling on sorbitol was performed both in the presence and absence of sophorose that induces the expression of cellulase genes. Carbon path analyses and the ¹³C-labeling patterns of proteinogenic amino acids indicated high similarity between biosynthetic pathways of amino acids in <it>T. reesei </it>and yeast <it>Saccharomyces cerevisiae</it>. In contrast to <it>S. cerevisiae</it>, however, mitochondrial rather than cytosolic biosynthesis of Asp was observed under all studied conditions. The relative anaplerotic flux to the TCA cycle was low and thus characteristic to respiratory metabolism in both strains and independent of the carbon source. Only minor differences were observed in the flux distributions of the wild type and <it>cre1 </it>deletion strain. Furthermore, the induction of the hydrolytic gene expression did not show altered flux distributions and did not affect the relative amino acid requirements or relative anabolic and respirative activities of the TCA cycle.</p> <p>Conclusion</p> <p>High similarity between the biosynthetic pathways of amino acids in <it>T. reesei </it>and yeast <it>S. cerevisiae </it>was concluded. <it>In vivo </it>flux distributions confirmed that <it>T. reesei </it>uses primarily the respirative pathway also when growing on the repressive carbon source glucose in contrast to <it>Saccharomyces cerevisiae</it>, which substantially diminishes the respirative pathway flux under glucose repression.</p

Enhanced Triacylglycerol Production With Genetically Modified Trichosporon oleaginosus

Mitochondrial pyruvate dehydrogenase (PDH) is important in the production of lipids in oleaginous yeast, but other yeast may bypass the mitochondria (PDH bypass), converting pyruvate in the cytosol to acetaldehyde, then acetate and acetyl CoA which is further converted to lipids. Using a metabolic model based on the oleaginous yeast Yarrowia lipolytica, we found that introduction of this bypass to an oleaginous yeast should result in enhanced yield of triacylglycerol (TAG) on substrate. Trichosporon oleaginosus (formerly Cryptococcus curvatus) is an oleaginous yeast which can produce TAGs from both glucose and xylose. Based on the sequenced genome, it lacks at least one of the enzymes needed to complete the PDH bypass, acetaldehyde dehydrogenase (ALD), and may also be deficient in pyruvate decarboxylase and acetyl-CoA synthetase under production conditions. We introduced these genes to T. oleaginosus in various combinations and demonstrated that the yield of TAG on both glucose and xylose was improved, particularly at high C/N ratio. Expression of a phospholipid:diacyltransferase encoding gene in conjunction with the PDH bypass further enhanced lipid production. The yield of TAG on xylose (0.27 g/g) in the engineered strain approached the theoretical maximum yield of 0.289 g/g. Interestingly, TAG production was also enhanced compared to the control in some strains which were given only part of the bypass pathway, suggesting that these genes may contribute to alternative routes to cytoplasmic acetyl CoA. The metabolic model indicated that the improved yield of TAG on substrate in the PDH bypass was dependent on the production of NADPH by ALD. NADPH for lipid synthesis is otherwise primarily supplied by the pentose phosphate pathway (PPP). This would contribute to the greater improvement of TAG production from xylose compared to that observed from glucose when the PDH bypass was introduced, since xylose enters metabolism through the non-oxidative part of the PPP. Yield of TAG from xylose in the engineered strains (0.21–0.27 g/g) was comparable to that obtained from glucose and the highest so far reported for lipid or TAG production from xylose

Model-guided development of an evolutionarily stable yeast chassis.

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories

Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion.

Adaptive laboratory evolution has proven highly effective for obtaining microorganisms with enhanced capabilities. Yet, this method is inherently restricted to the traits that are positively linked to cell fitness, such as nutrient utilization. Here, we introduce coevolution of obligatory mutualistic communities for improving secretion of fitness-costly metabolites through natural selection. In this strategy, metabolic cross-feeding connects secretion of the target metabolite, despite its cost to the secretor, to the survival and proliferation of the entire community. We thus co-evolved wild-type lactic acid bacteria and engineered auxotrophic Saccharomyces cerevisiae in a synthetic growth medium leading to bacterial isolates with enhanced secretion of two B-group vitamins, viz., riboflavin and folate. The increased production was specific to the targeted vitamin, and evident also in milk, a more complex nutrient environment that naturally contains vitamins. Genomic, proteomic and metabolomic analyses of the evolved lactic acid bacteria, in combination with flux balance analysis, showed altered metabolic regulation towards increased supply of the vitamin precursors. Together, our findings demonstrate how microbial metabolism adapts to mutualistic lifestyle through enhanced metabolite exchange

A multi-level study of recombinant Pichia pastoris in different oxygen conditions

Background: Yeasts are attractive expression platforms for many recombinant proteins, and there is evidence for an important interrelation between the protein secretion machinery and environmental stresses. While adaptive responses to such stresses are extensively studied in Saccharomyces cerevisiae, little is known about their impact on the physiology of Pichia pastoris. We have recently reported a beneficial effect of hypoxia on recombinant Fab secretion in P. pastoris chemostat cultivations. As a consequence, a systems biology approach was used to comprehensively identify cellular adaptations to low oxygen availability and the additional burden of protein production. Gene expression profiling was combined with proteomic analyses and the 13C isotope labelling based experimental determination of metabolic fluxes in the central carbon metabolism. Results: The physiological adaptation of P. pastoris to hypoxia showed distinct traits in relation to the model yeast S. cerevisiae. There was a positive correlation between the transcriptomic, proteomic and metabolic fluxes adaptation of P. pastoris core metabolism to hypoxia, yielding clear evidence of a strong transcriptional regulation component of key pathways such as glycolysis, pentose phosphate pathway and TCA cycle. In addition, the adaptation to reduced oxygen revealed important changes in lipid metabolism, stress responses, as well as protein folding and trafficking. Conclusions: This systems level study helped to understand the physiological adaptations of cellular mechanisms to low oxygen availability in a recombinant P. pastoris strain. Remarkably, the integration of data from three different levels allowed for the identification of differences in the regulation of the core metabolism between P. pastoris and S. cerevisiae. Detailed comparative analysis of the transcriptomic data also led to new insights into the gene expression profiles of several cellular processes that are not only susceptible to low oxygen concentrations, but might also contribute to enhanced protein secretion

Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species

Peer reviewe

An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments

<p>Abstract</p> <p>Background</p> <p>Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from ¹³<it>C </it>isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the ¹³<it>C </it>isotopomer data are typically needed.</p> <p>Results</p> <p>We present a novel analytic framework for estimating metabolic flux ratios in the cell from ¹³<it>C </it>isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, ¹³<it>C </it>isotopomer measurement techniques, substrates and substrate labelling patterns.</p> <p>By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms <it>Bacillus subtilis </it>and <it>Saccharomyces cerevisiae </it>we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by <it>in silico </it>calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose.</p> <p>Conclusion</p> <p>The core of ¹³<it>C </it>metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.</p