Genome-scale metabolic models reveal determinants of phenotypic differences in non-Saccharomyces yeasts

BACKGROUND

Use of alternative non-Saccharomyces yeasts in wine and beer brewing has gained more attention the recent years. This is both due to the desire to obtain a wider variety of flavours in the product and to reduce the final alcohol content. Given the metabolic differences between the yeast species, we wanted to account for some of the differences by using in silico models.

RESULTS

We created and studied genome-scale metabolic models of five different non-Saccharomyces species using an automated processes. These were: Metschnikowia pulcherrima, Lachancea thermotolerans, Hanseniaspora osmophila, Torulaspora delbrueckii and Kluyveromyces lactis. Using the models, we predicted that M. pulcherrima, when compared to the other species, conducts more respiration and thus produces less fermentation products, a finding which agrees with experimental data. Complex I of the electron transport chain was to be present in M. pulcherrima, but absent in the others. The predicted importance of Complex I was diminished when we incorporated constraints on the amount of enzymatic protein, as this shifts the metabolism towards fermentation.

CONCLUSIONS

Our results suggest that Complex I in the electron transport chain is a key differentiator between Metschnikowia pulcherrima and the other yeasts considered. Yet, more annotations and experimental data have the potential to improve model quality in order to increase fidelity and confidence in these results. Further experiments should be conducted to confirm the in vivo effect of Complex I in M. pulcherrima and its respiratory metabolism.

Inducible Synthetic Growth Regulation Using the ClpXP Proteasome Enhances cis,cis-Muconic Acid and Glycolic Acid Yields in Saccharomyces cerevisiae

Engineered microbial cells can produce sustainable chemistry, but the production competes for resources with growth. Inducible synthetic control over the resource use would enable fast accumulation of sufficient biomass and then divert the resources to production. We developed inducible synthetic resource-use control overSaccharomyces cerevisiae by expressing a bacterial ClpXP proteasome from an inducible promoter. By individually targeting growth-essential metabolic enzymes Aro1, Hom3, and Acc1 to the ClpXP proteasome, cell growth could be efficiently repressed during cultivation. The ClpXP proteasome was specific to the target proteins, and there was no reduction in the targets when ClpXP was not induced. The inducible growth repression improved product yields from glucose (cis,cis-muconic acid) and per biomass (cis,cis-muconic acid and glycolic acid). The inducible ClpXP proteasome tackles uncertainties in strain optimization by enabling model-guided repression of competing, growth-essential, and metabolic enzymes. Most importantly, it allows improving production without compromising biomass accumulation when uninduced; therefore, it is expected to mitigate strain stability and low productivity challenges.

Production of biopolymer precursors beta-alanine and L-lactic acid from CO2 with metabolically versatile Rhodococcus opacus DSM 43205

Hydrogen oxidizing autotrophic bacteria are promising hosts for conversion of CO₂ into chemicals. In this work, we engineered the metabolically versatile lithoautotrophic bacterium R. opacus strain DSM 43205 for synthesis of polymer precursors. Aspartate decarboxylase (panD) or lactate dehydrogenase (ldh) were expressed for beta-alanine or L-lactic acid production, respectively. The heterotrophic cultivations on glucose produced 25 mg L⁻¹ beta-alanine and 742 mg L⁻¹ L-lactic acid, while autotrophic cultivations with CO₂, H₂, and O₂ resulted in the production of 1.8 mg L⁻¹ beta-alanine and 146 mg L⁻¹ L-lactic acid. Beta-alanine was also produced at 345 μg L⁻¹ from CO₂ in electrobioreactors, where H₂ and O₂ were provided by water electrolysis. This work demonstrates that R. opacus DSM 43205 can be engineered to produce chemicals from CO₂ and provides a base for its further metabolic engineering.

Predictive evolution of metabolic phenotypes using model-designed environments

Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.

Operation of a TCA cycle subnetwork in the mammalian nucleus

Nucleic acid and histone modifications critically depend on the tricarboxylic acid (TCA) cycle for substrates and cofactors. Although a few TCA cycle enzymes have been reported in the nucleus, the corresponding pathways are considered to operate in mitochondria. Here, we show that a part of the TCA cycle is operational also in the nucleus. Using 13C-tracer analysis, we identified activity of glutamine-to-fumarate, citrate-to-succinate, and glutamine-to-aspartate routes in the nuclei of HeLa cells. Proximity labeling mass spectrometry revealed a spatial vicinity of the involved enzymes with core nuclear proteins. We further show nuclear localization of aconitase 2 and 2-oxoglutarate dehydrogenase in mouse embryonic stem cells. Nuclear localization of the latter enzyme, which produces succinyl-CoA, changed from pluripotency to a differentiated state with accompanying changes in the nuclear protein succinylation. Together, our results demonstrate operation of an extended metabolic pathway in the nucleus, warranting a revision of the canonical view on metabolic compartmentalization.

PHB production from cellobiose with Saccharomyces cerevisiae

Replacement of petrochemical-based materials with microbially produced biodegradable alternatives calls for industrially attractive fermentation processes. Lignocellulosic materials offer non-edible alternatives for cultivated sugars, but require often use of expensive sugar releasing enzymes, such as β-glucosidases. These cellulose treatment costs could be reduced if microbial production hosts could use short cellodextrins such as cellobiose directly as their substrates. In this study, we demonstrate production of poly(hydroxybutyrate) (PHB) in yeast Saccharomyces cerevisiae using cellobiose as a sole carbon source. Yeast strains expressing PHB pathway genes from Cupriavidus necator and cellodextrin transporter gene CDT-1 from Neurospora crassa were complemented either with β-glucosidase gene GH1-1 from N. crassa or with cellobiose phosphorylase gene cbp from Ruminococcus flavefaciens. These cellobiose utilization routes either with Gh1-1 or Cbp enzymes differ in energetics and dynamics. However, both routes enabled higher PHB production per consumed sugar and higher PHB accumulation % of cell dry weight (CDW) than use of glucose as a carbon source. As expected, the strains with Gh1-1 consumed cellobiose faster than the strains with Cbp, both in flask and bioreactor batch cultures. In shake flasks, higher final PHB accumulation % of CDW was reached with Cbp route (10.0 ± 0.3%) than with Gh1-1 route (8.1 ± 0.2%). However, a higher PHB accumulation was achieved in better aerated and pH-controlled bioreactors, in comparison to shake flasks, and the relative performance of strains switched. In bioreactors, notable PHB accumulation levels per CDW of 13.4 ± 0.9% and 18.5 ± 3.9% were achieved with Cbp and Gh1-1 routes, respectively. The average molecular weights of accumulated PHB were similar using both routes; approximately 500 kDa and 450 kDa for strains expressing either cbp or GH1-1 genes, respectively. The formation of PHB with high molecular weights, combined with efficient cellobiose conversion, demonstrates a highly potential solution for improving attractiveness of sustainable polymer production using microbial cells.

Strain design optimization using reinforcement learning

Engineered microbial cells present a sustainable alternative to fossil-based synthesis of chemicals and fuels. Cellular synthesis routes are readily assembled and introduced into microbial strains using state-of-the-art synthetic biology tools. However, the optimization of the strains required to reach industrially feasible production levels is far less efficient. It typically relies on trial-and-error leading into high uncertainty in total duration and cost. New techniques that can cope with the complexity and limited mechanistic knowledge of the cellular regulation are called for guiding the strain optimization.

In this paper, we put forward a multi-agent reinforcement learning (MARL) approach that learns from experiments to tune the metabolic enzyme levels so that the production is improved. Our method is model-free and does not assume prior knowledge of the microbe’s metabolic network or its regulation. The multi-agent approach is well-suited to make use of parallel experiments such as multi-well plates commonly used for screening microbial strains.

We demonstrate the method’s capabilities using the genome-scale kinetic model of Escherichia coli, k-ecoli457, as a surrogate for an in vivo cell behaviour in cultivation experiments. We investigate the method’s performance relevant for practical applicability in strain engineering i.e. the speed of convergence towards the optimum response, noise tolerance, and the statistical stability of the solutions found. We further evaluate the proposed MARL approach in improving L-tryptophan production by yeast Saccharomyces cerevisiae, using publicly available experimental data on the performance of a combinatorial strain library.

Overall, our results show that multi-agent reinforcement learning is a promising approach for guiding the strain optimization beyond mechanistic knowledge, with the goal of faster and more reliably obtaining industrially attractive production levels.

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.

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 Ale and Lager Yeasts for Improved Brewing Efficiency and Beer Quality

Yeasts directly impact the efficiency of brewery fermentations as well as the character of the beers produced. In recent years, there has been renewed interest in yeast selection and development inspired by the demand to utilize resources more efficiently and the need to differentiate beers in a competitive market. Reviewed here are the different, non-genetically modified (GM) approaches that have been considered, including bioprospecting, hybridization, and adaptive laboratory evolution (ALE). Particular emphasis is placed on the latter, which represents an extension of the processes that have led to the domestication of strains already used in commercial breweries. ALE can be used to accentuate the positive traits of brewing yeast as well as temper some of the traits that are less desirable from a modern brewer's perspective. This method has the added advantage of being non-GM and therefore suitable for food and beverage production.

Low Phenotypic Penetrance and Technological Impact of Yeast [GAR +] Prion-Like Elements on Winemaking

[GAR⁺] prion-like elements partially relieve carbon catabolite repression in Saccharomyces cerevisiae. They have been hypothesized to contribute to wine yeast survival and alcohol level reduction, as well as communication with bacteria and stuck fermentation. In this work, we selected [GAR⁺] derivatives from several genetic backgrounds. They were characterized for phenotypic penetrance, heritability and confirmed as prion-like through curing by desiccation. In terms of fermentation kinetics, the impact of the prion on anaerobic wine fermentation (natural grape juice) was either neutral or negative, depending on the genetic background. Likewise, residual sugars were higher or similar for [GAR⁺] as compared to the cognate [gar^-] strains. The prions had little or no impact on glycerol and ethanol yields; while acetic acid yields experienced the highest variations between [GAR⁺] and [gar^-] strains. Strains analyzed under aerobic conditions followed the same pattern, with either little or no impact on fermentation kinetics, ethanol or glycerol yield; and a clearer influence on volatile acidity. Although no clear winemaking advantages were found for [GAR⁺] strains in this work, they might eventually show interest for some combinations of genetic background or winemaking conditions, e.g., for reducing acetic acid yield under aerated fermentation.

Yeast Genome-Scale Metabolic Models for Simulating Genotype-Phenotype Relations

Understanding genotype-phenotype dependency is a universal aim for all life sciences. While the complete genotype-phenotype relations remain challenging to resolve, metabolic phenotypes are moving within the reach through genome-scale metabolic model simulations. Genome-scale metabolic models are available for commonly investigated yeasts, such as model eukaryote and domesticated fermentation species Saccharomyces cerevisiae, and automatic reconstruction methods facilitate obtaining models for any sequenced species. The models allow for investigating genotype-phenotype relations through simulations simultaneously considering the effects of nutrient availability, and redox and energy homeostasis in cells. Genome-scale models also offer frameworks for omics data integration to help to uncover how the translation of genotypes to the apparent phenotypes is regulated at different levels. In this chapter, we provide an overview of the yeast genome-scale metabolic models and the simulation approaches for using these models to interrogate genotype-phenotype relations. We review the methodological approaches according to the underlying biological reasoning in order to inspire formulating novel questions and applications that the genome-scale metabolic models could contribute to. Finally, we discuss current challenges and opportunities in the genome-scale metabolic model simulations.

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.

Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies

Bacterial metabolism plays a fundamental role in gut microbiota ecology and host-microbiome interactions. Yet the metabolic capabilities of most gut bacteria have remained unknown. Here we report growth characteristics of 96 phylogenetically diverse gut bacterial strains across 4 rich and 15 defined media. The vast majority of strains (76) grow in at least one defined medium, enabling accurate assessment of their biosynthetic capabilities. These do not necessarily match phylogenetic similarity, thus indicating a complex evolution of nutritional preferences. We identify mucin utilizers and species inhibited by amino acids and short-chain fatty acids. Our analysis also uncovers media for in vitro studies wherein growth capacity correlates well with in vivo abundance. Further value of the underlying resource is demonstrated by correcting pathway gaps in available genome-scale metabolic models of gut microorganisms. Together, the media resource and the extracted knowledge on growth abilities widen experimental and computational access to the gut microbiota.

Laboratory evolution reveals regulatory and metabolic trade-offs of glycerol utilization in Saccharomyces cerevisiae

Most microbial species, including model eukaryote Saccharomyces cerevisiae, possess genetic capability to utilize many alternative nutrient sources. Yet, it remains an open question whether these manifest into assimilatory phenotypes. Despite possessing all necessary pathways, S. cerevisiae grows poorly or not at all when glycerol is the sole carbon source. Here we discover, through multiple evolved lineages, genetic determinants underlying glycerol catabolism and the associated fitness trade-offs. Most evolved lineages adapted through mutations in the HOG pathway, but showed hampered osmotolerance. In the other lineages, we find that only three mutations cause the improved phenotype. One of these contributes counter-intuitively by decoupling the TCA cycle from oxidative phosphorylation, and thereby hampers ethanol utilization. Transcriptomics, proteomics and metabolomics analysis of the re-engineered strains affirmed the causality of the three mutations at molecular level. Introduction of these mutations resulted in improved glycerol utilization also in industrial strains. Our findings not only have a direct relevance for improving glycerol-based bioprocesses, but also illustrate how a metabolic pathway can remain unexploited due to fitness trade-offs in other, ecologically important, traits.

13 C metabolic flux profiling of Pichia pastoris grown in aerobic batch cultures on glucose revealed high relative anabolic use of TCA cycle and limited incorporation of provided precursors of branched-chain amino acids

Carbon metabolism of Crabtree-negative yeast Pichia pastoris was profiled using ¹³ C nuclear magnetic resonance (NMR) to delineate regulation during exponential growth and to study the import of two precursors for branched-chain amino acid biosynthesis, α-ketoisovalerate and α-ketobutyrate. Cells were grown in aerobic batch cultures containing (a) only glucose, (b) glucose along with the precursors, or (c) glucose and Val. The study provided the following new insights. First, ¹³ C flux ratio analyses of central metabolism reveal an unexpectedly high anaplerotic supply of the tricarboxylic acid cycle for a Crabtree-negative yeast, and show that a substantial fraction of glucose catabolism proceeds through the pentose phosphate pathway. A comparison with previous flux ratio analyses for batch cultures of Crabtree-negative Pichia stipitis and Crabtree-positive Saccharomyces cerevisiae indicate that the overall regulation of central carbon metabolism in P. pastoris is intermediate in between P. stipitis and S. cerevisiae. Second, excess α-ketoisovalerate in the medium is not transported into the cytoplasm indicating that P. pastoris lacks a suitable transporter. In contrast, excess Val is efficiently taken up and largely fulfills demands for both Val and Leu for protein synthesis. Third, excess α-ketobutyrate is transported into the mitochondria for Ile biosynthesis. However, the import does not efficiently inhibit the synthesis of α-ketobutyrate from pyruvate indicating that P. pastoris has not been optimized evolutionarily to take full advantage of this carbon source. These findings have direct implications for preparing uniformly ² H,¹³ C,¹⁵ N-labeled proteins containing protonated Ile, Val, and Leu methyl groups in P. pastoris for NMR-based structural biology.

ENZYMES

Acetohydroxy acid isomeroreductase (EC 1.1.1.86), branched-chain amino acid aminotransferase (BCAT, EC 2.6.1.42), fumarase (EC 4.2.1.2), malic enzyme (EC 1.1.1.39/1.1.1.40), phosphoenolpyruvate carboxykinase (EC 4.1.1.49), pyruvate carboxylase (EC 6.4.1.1), pyruvate kinase (EC 2.7.1.40), l-serine hydroxymethyltransferase (EC 2.1.2.1), threonine aldolase (EC 4.1.2.5), threonine dehydratase (EC 4.3.1.19); transketolase (EC 2.2.1.1), transaldolase (EC 2.2.1.2).

Saccharomyces cerevisiae metabolism in ecological context

The architecture and regulation of Saccharomyces cerevisiae metabolic network are among the best studied owing to its widespread use in both basic research and industry. Yet, several recent studies have revealed notable limitations in explaining genotype-metabolic phenotype relations in this yeast, especially when concerning multiple genetic/environmental perturbations. Apparently unexpected genotype-phenotype relations may originate in the evolutionarily shaped cellular operating principles being hidden in common laboratory conditions. Predecessors of laboratory S. cerevisiae strains, the wild and the domesticated yeasts, have been evolutionarily shaped by highly variable environments, very distinct from laboratory conditions, and most interestingly by social life within microbial communities. Here we present a brief review of the genotypic and phenotypic peculiarities of S. cerevisiae in the context of its social lifestyle beyond laboratory environments. Accounting for this ecological context and the origin of the laboratory strains in experimental design and data analysis would be essential in improving the understanding of genotype-environment-phenotype relationships.

Hypoxia and GABA shunt activation in the pathogenesis of Alzheimer's disease

We have previously observed that the conversion of mild cognitive impairment to definitive Alzheimer's disease (AD) is associated with a significant increase in the serum level of 2,4-dihydroxybutyrate (2,4-DHBA). The metabolic generation of 2,4-DHBA is linked to the activation of the γ-aminobutyric acid (GABA) shunt, an alternative energy production pathway activated during cellular stress, when the function of Krebs cycle is compromised. The GABA shunt can be triggered by local hypoperfusion and subsequent hypoxia in AD brains caused by cerebral amyloid angiopathy. Succinic semialdehyde dehydrogenase (SSADH) is a key enzyme in the GABA shunt, converting succinic semialdehyde (SSA) into succinate, a Krebs cycle intermediate. A deficiency of SSADH activity stimulates the conversion of SSA into γ-hydroxybutyrate (GHB), an alternative route from the GABA shunt. GHB can exert not only acute neuroprotective activities but unfortunately also chronic detrimental effects which may lead to cognitive impairment. Subsequently, GHB can be metabolized to 2,4-DHBA and secreted from the brain. Thus, the activation of the GABA shunt and the generation of GHB and 2,4-DHBA can have an important role in the early phase of AD pathogenesis.

Integration of transcription and flux data reveals molecular paths associated with differences in oxygen-dependent phenotypes of Saccharomyces cerevisiae

BACKGROUND

Saccharomyces cerevisiae is able to adapt to a wide range of external oxygen conditions. Previously, oxygen-dependent phenotypes have been studied individually at the transcriptional, metabolite, and flux level. However, the regulation of cell phenotype occurs across the different levels of cell function. Integrative analysis of data from multiple levels of cell function in the context of a network of several known biochemical interaction types could enable identification of active regulatory paths not limited to a single level of cell function.

RESULTS

The graph theoretical method called Enriched Molecular Path detection (EMPath) was extended to enable integrative utilization of transcription and flux data. The utility of the method was demonstrated by detecting paths associated with phenotype differences of S. cerevisiae under three different conditions of oxygen provision: 20.9%, 2.8% and 0.5%. The detection of molecular paths was performed in an integrated genome-scale metabolic and protein-protein interaction network.

CONCLUSIONS

The molecular paths associated with the phenotype differences of S. cerevisiae under conditions of different oxygen provisions revealed paths of molecular interactions that could potentially mediate information transfer between processes that respond to the particular oxygen availabilities.

Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.

Advances in next-generation sequencing technologies are revolutionizing molecular biology. Sequencing-enabled cost-effective characterization of microbial genomes is a particularly exciting development in metabolic engineering. There, considerable effort has been put to reconstructing genome-scale metabolic networks that describe the collection of hundreds to thousands of biochemical reactions available for a microbial cell. These network models are instrumental in understanding microbial metabolism and guiding metabolic engineering efforts to improve biochemical yields. We have developed a novel computational method, CoReCo, which bridges the growing gap between the availability of sequenced genomes and respective reconstructed metabolic networks. The method reconstructs genome-scale metabolic networks simultaneously for related microbial species. It utilizes the available sequencing data from these species to correct for incomplete and missing data. We used the method to reconstruct metabolic networks for a set of 49 fungal species providing the method protein sequence data and a phylogenetic tree describing the evolutionary relationships between the species. We demonstrate the applicability of the method by comparing a metabolic reconstruction of Saccharomyces cerevisiae to the manually curated, high-quality consensus network. We also provide an easy-to-use implementation of the method, usable both in single computer and distributed computing environments.

Metabolic modelling in the development of cell factories by synthetic biology

Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory.

Metabolic flux profiling of recombinant protein secreting Pichia pastoris growing on glucose:methanol mixtures

Background

The methylotrophic yeast Pichia pastoris has emerged as one of the most promising yeast hosts for the production of heterologous proteins. Mixed feeds of methanol and a multicarbon source instead of methanol as sole carbon source have been shown to improve product productivities and alleviate metabolic burden derived from protein production. Nevertheless, systematic quantitative studies on the relationships between the central metabolism and recombinant protein production in P. pastoris are still rather limited, particularly when growing this yeast on mixed carbon sources, thus hampering future metabolic network engineering strategies for improved protein production.

Results

The metabolic flux distribution in the central metabolism of P. pastoris growing on a mixed feed of glucose and methanol was analyzed by Metabolic Flux Analysis (MFA) using ¹³C-NMR-derived constraints. For this purpose, we defined new flux ratios for methanol assimilation pathways in P. pastoris cells growing on glucose:methanol mixtures. By using this experimental approach, the metabolic burden caused by the overexpression and secretion of a Rhizopus oryzae lipase (Rol) in P. pastoris was further analyzed. This protein has been previously shown to trigger the unfolded protein response in P. pastoris. A series of ¹³C-tracer experiments were performed on aerobic chemostat cultivations with a control and two different Rol producing strains growing at a dilution rate of 0.09 h⁻¹ using a glucose:methanol 80:20 (w/w) mix as carbon source.

The MFA performed in this study reveals a significant redistristribution of carbon fluxes in the central carbon metabolism when comparing the two recombinant strains vs the control strain, reflected in increased glycolytic, TCA cycle and NADH regeneration fluxes, as well as higher methanol dissimilation rates.

Conclusions

Overall, a further ¹³C-based MFA development to characterise the central metabolism of methylotrophic yeasts when growing on mixed methanol:multicarbon sources has been implemented, thus providing a new tool for the investigation of the relationships between central metabolism and protein production. Specifically, the study points at a limited but significant impact of the conformational stress associated to secretion of recombinant proteins on the central metabolism, occurring even at modest production levels.

Correlation of gene expression and protein production rate - a system wide study

BACKGROUND

Growth rate is a major determinant of intracellular function. However its effects can only be properly dissected with technically demanding chemostat cultivations in which it can be controlled. Recent work on Saccharomyces cerevisiae chemostat cultivations provided the first analysis on genome wide effects of growth rate. In this work we study the filamentous fungus Trichoderma reesei (Hypocrea jecorina) that is an industrial protein production host known for its exceptional protein secretion capability. Interestingly, it exhibits a low growth rate protein production phenotype.

RESULTS

We have used transcriptomics and proteomics to study the effect of growth rate and cell density on protein production in chemostat cultivations of T. reesei. Use of chemostat allowed control of growth rate and exact estimation of the extracellular specific protein production rate (SPPR). We find that major biosynthetic activities are all negatively correlated with SPPR. We also find that expression of many genes of secreted proteins and secondary metabolism, as well as various lineage specific, mostly unknown genes are positively correlated with SPPR. Finally, we enumerate possible regulators and regulatory mechanisms, arising from the data, for this response.

CONCLUSIONS

Based on these results it appears that in low growth rate protein production energy is very efficiently used primarly for protein production. Also, we propose that flux through early glycolysis or the TCA cycle is a more fundamental determining factor than growth rate for low growth rate protein production and we propose a novel eukaryotic response to this i.e. the lineage specific response (LSR).

Transcriptional responses of Saccharomyces cerevisiae to shift from respiratory and respirofermentative to fully fermentative metabolism

In industrial fermentations of Saccharomyces cerevisiae, transient changes in oxygen concentration commonly occur and it is important to understand the behavior of cells during these changes. Glucose-limited chemostat cultivations were used to study the time-dependent effect of sudden oxygen depletion on the transcriptome of S. cerevisiae cells initially in fully aerobic or oxygen-limited conditions. The overall responses to anaerobic conditions of cells initially in different conditions were very similar. Independent of initial culture conditions, transient downregulation of genes related to growth and cell proliferation, mitochondrial translation and protein import, and sulphate assimilation was seen. In addition, transient or permanent upregulation of genes related to protein degradation, and phosphate and amino acid uptake was observed in all cultures. However, only in the initially oxygen-limited cultures was a transient upregulation of genes related to fatty acid oxidation, peroxisomal biogenesis, oxidative phosphorylation, TCA cycle, response to oxidative stress, and pentose phosphate pathway observed. Furthermore, from the initially oxygen-limited conditions, a rapid response around the metabolites of upper glycolysis and the pentose phosphate pathway was seen, while from the initially fully aerobic conditions, a slower response around the pathways for utilization of respiratory carbon sources was observed.

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.

Inferring branching pathways in genome-scale metabolic networks

Background

A central problem in computational metabolic modelling is how to find biochemically plausible pathways between metabolites in a metabolic network. Two general, complementary frameworks have been utilized to find metabolic pathways: constraint-based modelling and graph-theoretical path finding approaches. In constraint-based modelling, one aims to find pathways where metabolites are balanced in a pseudo steady-state. Constraint-based methods, such as elementary flux mode analysis, have typically a high computational cost stemming from a large number of steady-state pathways in a typical metabolic network. On the other hand, graph-theoretical approaches avoid the computational complexity of constraint-based methods by solving a simpler problem of finding shortest paths. However, while scaling well with network size, graph-theoretic methods generally tend to return more false positive pathways than constraint-based methods.

Results

In this paper, we introduce a computational method, ReTrace, for finding biochemically relevant, branching metabolic pathways in an atom-level representation of metabolic networks. The method finds compact pathways which transfer a high fraction of atoms from source to target metabolites by considering combinations of linear shortest paths. In contrast to current steady-state pathway analysis methods, our method scales up well and is able to operate on genome-scale models. Further, we show that the pathways produced are biochemically meaningful by an example involving the biosynthesis of inosine 5'-monophosphate (IMP). In particular, the method is able to avoid typical problems associated with graph-theoretic approaches such as the need to define side metabolites or pathways not carrying any net carbon flux appearing in results. Finally, we discuss an application involving reconstruction of amino acid pathways of a recently sequenced organism demonstrating how measurement data can be easily incorporated into ReTrace analysis. ReTrace is licensed under GPL and is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/retrace/.

Conclusion

ReTrace is a useful method in metabolic path finding tasks, combining some of the best aspects in constraint-based and graph-theoretic methods. It finds use in a multitude of tasks ranging from metabolic engineering to metabolic reconstruction of recently sequenced organisms.

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

Background

The filamentous fungus Trichoderma reesei 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 T. reesei is expected to benefit from high energy yield from utilization of respirative metabolic pathway. However, T. reesei lacks metabolic pathway reconstructions and the utilization of the respirative pathway has not been investigated on the level of in vivo fluxes.

Results

The biosynthetic pathways of amino acids in T. reesei supported by genome-level evidence were reconstructed with computational carbon path analysis. The pathway reconstructions were a prerequisite for analysis of in vivo fluxes. The distribution of in vivo fluxes in both wild type strain and cre1, 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 T. reesei and yeast Saccharomyces cerevisiae. In contrast to S. cerevisiae, 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 cre1 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.

Conclusion

High similarity between the biosynthetic pathways of amino acids in T. reesei and yeast S. cerevisiae was concluded. In vivo flux distributions confirmed that T. reesei uses primarily the respirative pathway also when growing on the repressive carbon source glucose in contrast to Saccharomyces cerevisiae, which substantially diminishes the respirative pathway flux under glucose repression.

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

BACKGROUND

The yeast Saccharomyces cerevisiae 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 S. cerevisiae but understanding of the oxygen dependence of intracellular flux distributions is still scarce.

RESULTS

Metabolic flux distributions of S. cerevisiae CEN.PK113-1A growing in glucose-limited chemostat cultures at a dilution rate of 0.1 h-1 with 20.9%, 2.8%, 1.0%, 0.5% or 0.0% O2 in 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 S. cerevisiae.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.

CONCLUSION

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.

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

Background

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 ¹³C 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 ¹³C isotopomer data are typically needed.

Results

We present a novel analytic framework for estimating metabolic flux ratios in the cell from ¹³C 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, ¹³C isotopomer measurement techniques, substrates and substrate labelling patterns.

By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms Bacillus subtilis and Saccharomyces cerevisiae 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 in silico 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.

Conclusion

The core of ¹³C 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.

ReMatch: a web-based tool to construct, store and share stoichiometric metabolic models with carbon maps for metabolic flux analysis

y ReMatch is a web-based, user-friendly tool that constructs stoichiometric network models for metabolic flux analysis, integrating user-developed models into a database collected from several comprehensive metabolic data resources, including KEGG, MetaCyc and CheBI. Particularly, ReMatch augments the metabolic reactions of the model with carbon mappings to facilitate

The construction of a network model consisting of biochemical reactions is the first step in most metabolic modelling tasks. This model construction can be a tedious task as the required information is usually scattered to many separate databases whose interoperability is suboptimal, due to the heterogeneous naming conventions of metabolites in different databases. Another, particularly severe data integration problem is faced in

ReMatch has been developed to solve the above data integration problems. First, ReMatch matches the imported user-developed model against the internal ReMatch database while considering a comprehensive metabolite name thesaurus. This, together with wild card support, allows the user to specify the model quickly without having to look the names up manually. Second, ReMatch is able to augment reactions of the model with carbon mappings, obtained either from the internal database or given by the user with an easy-touse tool.

The constructed models can be exported into 13C-FLUX and SBML file formats. Further, a stoichiometric matrix and visualizations of the network model can be generated. The constructed models of metabolic networks can be optionally made available to the other users of ReMatch. Thus, ReMatch provides a common repository for metabolic network models with carbon mappings for the needs of metabolic flux analysis community. ReMatch is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/rematch/.

Central carbon metabolism of Saccharomyces cerevisiae in anaerobic, oxygen-limited and fully aerobic steady-state conditions and following a shift to anaerobic conditions

Saccharomyces cerevisiae CEN.PK113-1A was grown in glucose-limited chemostat culture with 0%, 0.5%, 1.0%, 2.8% or 20.9% O2 in the inlet gas (D=0.10 h(-1), pH 5, 30 degrees C) to determine the effects of oxygen on 17 metabolites and 69 genes related to central carbon metabolism. The concentrations of tricarboxylic acid cycle (TCA) metabolites and all glycolytic metabolites except 2-phosphoglycerate+3-phosphoglycerate and phosphoenolpyruvate were higher in anaerobic than in fully aerobic conditions. Provision of only 0.5-1% O2 reduced the concentrations of most metabolites, as compared with anaerobic conditions. Transcription of most genes analyzed was reduced in 0%, 0.5% or 1.0% O2 relative to cells grown in 2.8% or 20.9% O2. Ethanol production was observed with 2.8% or less O2. After steady-state analysis in defined oxygen concentrations, the conditions were switched from aerobic to anaerobic. Metabolite and transcript levels were monitored for up to 96 h after the transition, and this showed that more than 30 h was required for the cells to fully adapt to anaerobiosis. Levels of metabolites of upper glycolysis and the TCA cycle increased following the transition to anaerobic conditions, whereas those of metabolites of lower glycolysis generally decreased. Gene regulation was more complex, with some genes showing transient upregulation or downregulation during the adaptation to anaerobic conditions.

Metabolic flux profiling of Pichia pastoris grown on glycerol/methanol mixtures in chemostat cultures at low and high dilution rates

The metabolic pathways associated with the tricarboxylic acid cycle intermediates of Pichia pastoris were studied using biosynthetically directed fractional 13C labelling. Cells were grown aerobically in a chemostat culture fed at two dilution rates (1.39x10(-5) s-1 and 4.44x10(-5) s-1) with varying mixtures of glycerol and methanol as sole carbon sources. The results show that, with co-assimilation of methanol, the common amino acids are synthesized as in P. pastoris cells grown on glycerol only. During growth at the lower dilution rate, when both substrates are entirely consumed, the incorporation of methanol into the biomass increases as the methanol fraction in the feed is increased. Moreover, the co-assimilation of methanol impacts on how key intermediates of the pentose phosphate pathway (PPP) are synthesized. In contrast, such an impact on the PPP is not observed at the higher dilution rate, where methanol is only partially consumed. This finding possibly indicates that the distribution of methanol carbon into assimilatory and dissimilatory (direct oxidation to CO2) pathways are different at the two dilution rates. Remarkably, distinct flux ratios were registered at each of the two growth rates, while the dependency of the flux ratios on the varying fraction of methanol in the medium was much less pronounced. This study brings new insights into the complex regulation of P. pastoris methanol metabolism in the presence of a second carbon source, revealing important implications for biotechnological applications.

L-galactonate dehydratase is part of the fungal path for D-galacturonic acid catabolism

An L-galactonate dehydratase and the corresponding gene were identified from the mould Hypocrea jecorina (Trichoderma reesei). This novel enzyme converts L-galactonate to L-threo-3-deoxy-hexulosonate (2-keto-3-deoxy-L-galactonate). The enzyme is part of the fungal pathway for D-galacturonic acid catabolism, a pathway which is only partly known. It is the second enzyme of this pathway after the D-galacturonic acid reductase. L-galactonate dehydratase activity is present in H. jecorina cells grown on D-galacturonic acid but absent when other carbon sources are used for growth. A deletion of the L-galactonate dehydratase gene in H. jecorina results in a strain with no growth on D-galacturonic acid. The active enzyme was produced in the heterologous host Saccharomyces cerevisiae and characterized. It exhibited activity with L-galactonate and D-arabonate where the hydroxyl group of the C2 is in L- and the hydroxyl group of the C3 is in D-configuration in the Fischer projection. However, it did not exhibit activity with D-galactonate, D-gluconate, L-gulonate or D-xylonate where the hydroxyl groups of the C2 and C3 are in different configuration.