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1.
  • Sanchez Barja, Benjamin José, 1988, et al. (author)
  • Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints
  • 2017
  • In: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 13:8, s. Article no 935 -
  • Journal article (peer-reviewed)abstract
    • 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.
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2.
  • Kerkhoven, Eduard, 1985, et al. (author)
  • Applications of computational modeling in metabolic engineering of yeast
  • 2015
  • In: FEMS Yeast Research. - : Oxford University Press (OUP). - 1567-1356 .- 1567-1364. ; 15:1
  • Journal article (peer-reviewed)abstract
    • Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications.
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3.
  • Konzock, Oliver, 1991, et al. (author)
  • Urea is a drop-in nitrogen source alternative to ammonium sulphate in Yarrowia lipolytica
  • 2022
  • In: iScience. - : Elsevier BV. - 2589-0042. ; 25:12
  • Journal article (peer-reviewed)abstract
    • Media components, including the nitrogen source, are significant cost factors in cultivation processes. The nitrogen source also influences cell behavior and production performance. Ammonium sulfate is a widely used nitrogen source for microorganisms’ cultivation. Urea is a sustainable and cheap alternative nitrogen source. We investigated the influence of urea as a nitrogen source compared to ammonium sulfate by cultivating phenotypically different Yarrowia lipolytica strains in chemostats under carbon or nitrogen limitation. We found no significant coherent changes in growth and lipid production. RNA sequencing revealed no significant concerted changes in the transcriptome. The genes involved in urea uptake and degradation are not upregulated on a transcriptional level. Our findings support urea usage, indicating that previous metabolic engineering efforts where ammonium sulfate was used are likely translatable to the usage of urea and can ease the way for urea as a cheap and sustainable nitrogen source in more applications.
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4.
  • Lopes, Helberth Júnnior Santos, et al. (author)
  • C/N ratio and carbon source-dependent lipid production profiling in Rhodotorula toruloides
  • 2020
  • In: Applied Microbiology and Biotechnology. - : Springer Science and Business Media LLC. - 1432-0614 .- 0175-7598. ; 104:6, s. 2639-2649
  • Journal article (peer-reviewed)abstract
    • Microbial oils are lipids produced by oleaginous microorganisms, which can be used as a potential feedstock for oleochemical production. The oleaginous yeast Rhodotorula toruloides can co-produce microbial oils and high-value compounds from low-cost substrates, such as xylose and acetic acid (from hemicellulosic hydrolysates) and raw glycerol (a byproduct of biodiesel production). One step towards economic viability is identifying the best conditions for lipid production, primarily the most suitable carbon-to-nitrogen ratio (C/N). Here, we aimed to identify the best conditions and cultivation mode for lipid production by R. toruloides using various low-cost substrates and a range of C/N ratios (60, 80, 100, and 120). Turbidostat mode was used to achieve a steady state at the maximal specific growth rate and to avoid continuously changing environmental conditions (i.e., C/N ratio) that inherently occur in batch mode. Regardless of the carbon source, higher C/N ratios increased lipid yields (up to 60% on xylose at a C/N of 120) but decreased the specific growth rate. Growth on glycerol resulted in the highest specific growth and lipid production (0.085 g lipids/gDW*h) rates at C/Ns between 60 and 100. We went on to study lipid production using glycerol in both batch and fed-batch modes, which resulted in lower specific lipid production rates compared with turbisdostat, however, fed batch is superior in terms of biomass production and lipid titers. By combining the data we obtained in these experiments with a genome-scale metabolic model of R. toruloides, we identified targets for improvements in lipid production that could be carried out either by metabolic engineering or process optimization.
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5.
  • Peri, Kameshwara Venkata Ramana, 1990, et al. (author)
  • Regulation of lactose and galactose growth: Insights from a unique metabolic gene cluster in Candida intermedia
  • 2023
  • Journal article (other academic/artistic)abstract
    • Lactose assimilation is a relatively rare trait in yeasts, and Kluyveromyces yeast species have long served as model organisms for studying lactose metabolism. Meanwhile, the metabolic strategies of most other lactose-assimilating yeasts remain unknown. In this work, we have elucidated the genetic determinants of the superior lactose-growing yeast Candida intermedia. Through genomic and transcriptomic analyses and deletion mutant phenotyping, we identified three interdependent gene clusters responsible for the metabolism of lactose and its hydrolysis product galactose: the conserved LAC cluster (LAC12, LAC4) for lactose uptake and hydrolysis, the conserved GAL cluster (GAL1, GAL7, GAL10) for galactose catabolism, and a unique “GALLAC” cluster. This novel GALLAC cluster, which has evolved through gene duplication and divergence, proved indispensable for C. intermedia’s growth on lactose and galactose. The cluster contains the transcriptional activator gene LAC9, second copies of GAL1 and GAL10 and the XYL1 gene encoding an aldose reductase involved in carbon overflow metabolism. Notably, the regulatory network in C. intermedia, governed by Lac9 and Gal1 from the GALLAC cluster, differs significantly from the (ga)lactose regulons in Saccharomyces cerevisiae, Kluyveromyces lactis and Candida albicans. Moreover, although lactose and galactose metabolism are closely linked in C. intermedia, our results also point to important regulatory differences. This study paves the way to a better understanding of lactose and galactose metabolism in C. intermedia and provides new evolutionary insights into yeast metabolic pathways and regulatory networks. In extension, the results will facilitate future development and use of C. intermedia as a cell-factory for conversion of lactose-rich whey into value-added products.
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6.
  • Poorinmohammad, Naghmeh, 1989, et al. (author)
  • Validated Growth Rate-Dependent Regulation of Lipid Metabolism in Yarrowia lipolytica
  • 2022
  • In: International Journal of Molecular Sciences. - : MDPI AG. - 1422-0067 .- 1661-6596. ; 23:15
  • Journal article (peer-reviewed)abstract
    • Given the strong potential of Yarrowia lipolytica to produce lipids for use as renewable fuels and oleochemicals, it is important to gain in-depth understanding of the molecular mechanism underlying its lipid accumulation. As cellular growth rate affects biomass lipid content, we performed a comparative proteomic analysis of Y. lipolytica grown in nitrogen-limited chemostat cultures at different dilution rates. After confirming the correlation between growth rate and lipid accumulation, we were able to identify various cellular functions and biological mechanisms involved in oleaginousness. Inspection of significantly up- and downregulated proteins revealed nonintuitive processes associated with lipid accumulation in this yeast. This included proteins related to endoplasmic reticulum (ER) stress, ER-plasma membrane tether proteins, and arginase. Genetic engineering of selected targets validated that some genes indeed affected lipid accumulation. They were able to increase lipid content and were complementary to other genetic engineering strategies to optimize lipid yield.
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7.
  • Robinson, Jonathan, 1986, et al. (author)
  • An atlas of human metabolism
  • 2020
  • In: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 13:624
  • Journal article (peer-reviewed)abstract
    • Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.
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8.
  • Zaghen, Simone, 1996, et al. (author)
  • Abolishing storage lipids induces protein misfolding and stress responses in Yarrowia lipolytica
  • 2023
  • In: Journal of Industrial Microbiology and Biotechnology. - 1367-5435 .- 1476-5535. ; 50:1
  • Journal article (peer-reviewed)abstract
    • Yarrowia lipolytica naturally saves excess carbon as storage lipids. Engineering efforts allow redirecting the high precursor flux required for lipid synthesis toward added-value chemicals such as polyketides, flavonoids, and terpenoids. To redirect precursor flux from storage lipids to other products, four genes involved in triacylglycerol and sterol ester synthesis (DGA1, DGA2, LRO1, and ARE1) can be deleted. To elucidate the effect of the deletions on cell physiology and regulation, we performed chemostat cultivations under carbon and nitrogen limitations, followed by transcriptome analysis. We found that storage lipid-free cells show an enrichment of the unfolded protein response, and several biological processes related to protein refolding and degradation are enriched. Additionally, storage lipid-free cells show an altered lipid class distribution with an abundance of potentially cytotoxic free fatty acids under nitrogen limitation. Our findings not only highlight the importance of lipid metabolism on cell physiology and proteostasis, but can also aid the development of improved chassy strains of Y. lipolytica for commodity chemical production.
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9.
  • Achcar, F., et al. (author)
  • The Silicon Trypanosome: A Test Case of Iterative Model Extension in Systems Biology
  • 2014
  • In: Advances in Microbial Physiology. - : Elsevier. - 0065-2911. - 9780128001431 ; 64, s. 115-143
  • Book chapter (other academic/artistic)abstract
    • The African trypanosome, Ttypanosoma brucei, is a unicellular parasite causing African Trypanosomiasis (sleeping sickness in humans and nagana in animals). Due to some of its unique properties, it has emerged as a popular model organism in systems biology. A predictive quantitative model of glycolysis in the bloodstream form of the parasite has been constructed and updated several times. The Silicon Trypanosome is a project that brings together modellers and experimentalists to improve and extend this core model with new pathways and additional levels of regulation. These new extensions and analyses use computational methods that explicitly take different levels of uncertainty into account. During this project, numerous tools and techniques have been developed for this purpose, which can now be used for a wide range of different studies in systems biology.
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10.
  • Achcar, F., et al. (author)
  • Trypanosoma brucei: meet the system
  • 2014
  • In: Current Opinion in Microbiology. - : Elsevier BV. - 1369-5274 .- 1879-0364. ; 20, s. 162-169
  • Research review (peer-reviewed)abstract
    • African trypanosomes cause devastating diseases in humans and domestic animals. The parasites evolved early in the eukaryotic lineage and have numerous biochemical peculiarities that distinguish them from other systems. These include unconventional mechanisms for expressing nuclear and mitochondrial genes as well as unusual subcellular localizations for a variety of enzymes. Systems biology has arisen partly to allow contextualization of the massive datasets that describe individual chemical parts of biological systems. Here we describe recent efforts to collect and analyse data pertaining to all aspects of the trypanosome's biochemical physiology that go some way to describing the parasite as an integrated system.
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11.
  • Bredeweg, Erin L., et al. (author)
  • A molecular genetic toolbox for Yarrowia lipolytica
  • 2017
  • In: Biotechnology for Biofuels. - : Springer Science and Business Media LLC. - 1754-6834 .- 1754-6834. ; 10:1
  • Journal article (peer-reviewed)abstract
    • Background: Yarrowia lipolytica is an ascomycete yeast used in biotechnological research for its abilities to secrete high concentrations of proteins and accumulate lipids. Genetic tools have been made in a variety of backgrounds with varying similarity to a comprehensively sequenced strain. Results: We have developed a set of genetic and molecular tools in order to expand capabilities of Y. lipolytica for both biological research and industrial bioengineering applications. In this work, we generated a set of isogenic auxotrophic strains with decreased non-homologous end joining for targeted DNA incorporation. Genome sequencing, assembly, and annotation of this genetic background uncovers previously unidentified genes in Y. lipolytica. To complement these strains, we constructed plasmids with Y. lipolytica-optimized superfolder GFP for targeted overexpression and fluorescent tagging. We used these tools to build the "Yarrowia lipolytica Cell Atlas," a collection of strains with endogenous fluorescently tagged organelles in the same genetic background, in order to define organelle morphology in live cells. Conclusions: These molecular and isogenetic tools are useful for live assessment of organelle-specific protein expression, and for localization of lipid biosynthetic enzymes or other proteins in Y. lipolytica. This work provides the Yarrowia community with tools for cell biology and metabolism research in Y. lipolytica for further development of biofuels and natural products.
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12.
  • Caspeta-Guadarrama, Luis, 1974, et al. (author)
  • The yeastGemMap: A process diagram to assist yeast systems-metabolic studies
  • 2021
  • In: Biotechnology and Bioengineering. - : Wiley. - 0006-3592 .- 1097-0290. ; 118:12, s. 4800-4814
  • Journal article (peer-reviewed)abstract
    • Visualization is a key aspect of the analysis of omics data. Although many tools can generate pathway maps for visualization of yeast metabolism, they fail in reconstructing genome-scale metabolic diagrams of compartmentalized metabolism. Here we report on the yeastGemMap, a process diagram of whole yeast metabolism created to assist data analysis in systems-metabolic studies. The map was manually reconstructed with reactions from a compartmentalized genome-scale metabolic model, based on biochemical process diagrams typically found in educational and specialized literature. The yeastGemMap consists of 3815 reactions representing 1150 genes, 2742 metabolites, and 14 compartments. Computational functions for adapting the graphical representation of the map are also reported. These functions modify the visual representation of the map to assist in three systems-metabolic tasks: illustrating reaction networks, interpreting metabolic flux data, and visualizing omics data. The versatility of the yeastGemMap and algorithms to assist visualization of systems-metabolic data was demonstrated in various tasks, including for single lethal reaction evaluation, flux balance analysis, and transcriptomic data analysis. For instance, visual interpretation of metabolic transcriptomes of thermally evolved and parental yeast strains allowed to demonstrate that evolved strains activate a preadaptation response at 30 degrees C, which enabled thermotolerance. A quick interpretation of systems-metabolic data is promoted with yeastGemMap visualizations.
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13.
  • Chen, Yu, 1990, et al. (author)
  • Proteome Constraints in Genome-Scale Models
  • 2021
  • In: Metabolic Engineering: Concepts and Applications: Volume 13a and 13b. - : Wiley. ; , s. 137-152
  • Book chapter (other academic/artistic)abstract
    • Genome-scale metabolic models (GEMs) describe the stoichiometry of all reactions in the cellular metabolic network, and at the same time associate the reactions to the enzymes that catalyze them. This chapter discusses proteome constraints followed by examples on how one particular type of cellular constraint, namely a proteome constraint, is a powerful approach to improve the predictive strength of GEMs. Cells operate under myriad constraints that govern their phenotypes and functioning. A fundamental constraint in the context of metabolism is the conservation of mass and energy. The chapter addresses the recently developed approach GECKO to illustrate how proteome constraints can be integrated into a GEM in a coarse-grained manner. Coarse-grained approaches as GECKO provide a straightforward platform to integrate proteome constraints. Contrasting with the coarse-grained integration, fine-tuned approaches tend to explicitly integrate biological processes into a GEM, example protein synthesis process.
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14.
  • Chen, Yu, 1990, et al. (author)
  • Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0
  • 2024
  • In: Nature Protocols. - 1754-2189 .- 1750-2799. ; 19:3, s. 629-667
  • Journal article (peer-reviewed)abstract
    • Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.
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15.
  • Chen, Yu, 1990, et al. (author)
  • Single-cell omics analysis with genome-scale metabolic modeling
  • 2024
  • In: Current Opinion in Biotechnology. - 0958-1669 .- 1879-0429. ; 86
  • Research review (peer-reviewed)abstract
    • Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene–protein–reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.
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16.
  • Choi, BoHyun, 1986, et al. (author)
  • Engineering of Saccharomyces cerevisiae for enhanced metabolic robustness and L-lactic acid production from lignocellulosic biomass
  • 2024
  • In: Metabolic Engineering. - 1096-7176 .- 1096-7184. ; 84, s. 23-33
  • Journal article (peer-reviewed)abstract
    • Metabolic engineering for high productivity and increased robustness is needed to enable sustainable biomanufacturing of lactic acid from lignocellulosic biomass. Lactic acid is an important commodity chemical used for instance as a monomer for production of polylactic acid, a biodegradable polymer. Here, rational and model-based optimization was used to engineer a diploid, xylose fermenting Saccharomyces cerevisiae strain to produce L-lactic acid. The metabolic flux was steered towards lactic acid through the introduction of multiple lactate dehydrogenase encoding genes while deleting ERF2, GPD1, and CYB2. A production of 93 g/L of lactic acid with a yield of 0.84 g/g was achieved using xylose as the carbon source. To increase xylose utilization and reduce acetic acid synthesis, PHO13 and ALD6 were also deleted from the strain. Finally, CDC19 encoding a pyruvate kinase was overexpressed, resulting in a yield of 0.75 g lactic acid/g sugars consumed, when the substrate used was a synthetic lignocellulosic hydrolysate medium, containing hexoses, pentoses and inhibitors such as acetate and furfural. Notably, modeling also provided leads for understanding the influence of oxygen in lactic acid production. High lactic acid production from xylose, at oxygen-limitation could be explained by a reduced flux through the oxidative phosphorylation pathway. On the contrast, higher oxygen levels were beneficial for lactic acid production with the synthetic hydrolysate medium, likely as higher ATP concentrations are needed for tolerating the inhibitors therein. The work highlights the potential of S. cerevisiae for industrial production of lactic acid from lignocellulosic biomass.
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17.
  • Creek, D. J., et al. (author)
  • Probing the Metabolic Network in Bloodstream-Form Trypanosoma brucei Using Untargeted Metabolomics with Stable Isotope Labelled Glucose
  • 2015
  • In: PLoS Pathogens. - : Public Library of Science (PLoS). - 1553-7366 .- 1553-7374. ; 11:3, s. 1-25
  • Journal article (peer-reviewed)abstract
    • Metabolomics coupled with heavy-atom isotope-labelled glucose has been used to probe the metabolic pathways active in cultured bloodstream form trypomastigotes of Trypanosoma brucei, a parasite responsible for human African trypanosomiasis. Glucose enters many branches of metabolism beyond glycolysis, which has been widely held to be the sole route of glucose metabolism. Whilst pyruvate is the major end-product of glucose catabolism, its transamination product, alanine, is also produced in significant quantities. The oxidative branch of the pentose phosphate pathway is operative, although the non-oxidative branch is not. Ribose 5-phosphate generated through this pathway distributes widely into nucleotide synthesis and other branches of metabolism. Acetate, derived from glucose, is found associated with a range of acetylated amino acids and, to a lesser extent, fatty acids; while labelled glycerol is found in many glycerophospholipids. Glucose also enters inositol and several sugar nucleotides that serve as precursors to macromolecule biosynthesis. Although a Krebs cycle is not operative, malate, fumarate and succinate, primarily labelled in three carbons, were present, indicating an origin from phosphoenolpyruvate via oxaloacetate. Interestingly, the enzyme responsible for conversion of phosphoenolpyruvate to oxaloacetate, phosphoenolpyruvate carboxykinase, was shown to be essential to the bloodstream form trypanosomes, as demonstrated by the lethal phenotype induced by RNAi-mediated downregulation of its expression. In addition, glucose derivatives enter pyrimidine biosynthesis via oxaloacetate as a precursor to aspartate and orotate.
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18.
  • de Almeida, Eduardo Luís Menezes, et al. (author)
  • Reconstruction of genome-scale metabolic models of non-conventional yeasts: current state, challenges, and perspectives
  • 2024
  • In: Biotechnology and Bioprocess Engineering. - 1976-3816 .- 1226-8372. ; 29:1, s. 35-67
  • Research review (peer-reviewed)abstract
    • Non-conventional yeasts are promising cell factories to produce lipids and oleochemicals, metabolites of industrial interest (e.g., organics acids, esters, and alcohols), and enzymes. They can also use different agro-industrial by-products as substrates within the context of a circular economy. Some of these yeasts can also comprise economic and health burdens as pathogens. Genome-scale metabolic models (GEMs), networks reconstructed based on the genomic and metabolic information of one or more organisms, are great tools to understand metabolic functions and landscapes, as well as propose engineering targets to improve metabolite production or propose novel drug targets. Previous reviews on yeast GEMs have mainly focused on the history and the evaluation of Saccharomyces cerevisiae modeling paradigms or the accessibility and usability of yeast GEMs. However, they did not describe the reconstruction strategies, limitations, validations, challenges, and research gaps of non-conventional yeast GEMs. Herein, we focused on the reconstruction of available non-Saccharomyces GEMs, their validation, underscoring the physiological insights, as well as the identification of both metabolic engineering and drug targets. We also discuss the challenges and knowledge gaps and propose strategies to boost their use and novel reconstructions.
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19.
  • Domenzain Del Castillo Cerecer, Iván, 1991, et al. (author)
  • Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species
  • 2021
  • In: FEMS Yeast Research. - : Oxford University Press (OUP). - 1567-1356 .- 1567-1364. ; 21:1
  • Research review (peer-reviewed)abstract
    • Metabolic network reconstructions have become an important tool for probing cellular metabolism in the field of systems biology. They are used as tools for quantitative prediction but also as scaffolds for further knowledge contextualization. The yeast Saccharomyces cerevisiae was one of the first organisms for which a genome-scale metabolic model (GEM) was reconstructed, in 2003, and since then 45 metabolic models have been developed for a wide variety of relevant yeasts species. A systematic evaluation of these models revealed that-despite this long modeling history-the sequential process of tracing model files, setting them up for basic simulation purposes and comparing them across species and even different versions, is still not a generalizable task. These findings call the yeast modeling community to comply to standard practices on model development and sharing in order to make GEMs accessible and useful for a wider public.
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20.
  • Domenzain Del Castillo Cerecer, Iván, 1991, et al. (author)
  • Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0
  • 2022
  • In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
  • Journal article (peer-reviewed)abstract
    • Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.
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21.
  • Doughty, Tyler, 1987, et al. (author)
  • Extracting novel hypotheses and findings from RNA-seq data
  • 2020
  • In: FEMS Yeast Research. - : Oxford University Press (OUP). - 1567-1356 .- 1567-1364. ; 20:2
  • Research review (peer-reviewed)abstract
    • Over the past decade, improvements in technology and methods have enabled rapid and relatively inexpensive generation of high-quality RNA-seq datasets. These datasets have been used to characterize gene expression for several yeast species and have provided systems-level insights for basic biology, biotechnology and medicine. Herein, we discuss new techniques that have emerged and existing techniques that enable analysts to extract information from multifactorial yeast RNA-seq datasets. Ultimately, this minireview seeks to inspire readers to query datasets, whether previously published or freshly obtained, with creative and diverse methods to discover and support novel hypotheses.
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22.
  • Gustafsson, Johan, 1976, et al. (author)
  • Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
  • 2023
  • In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 120:6
  • Journal article (peer-reviewed)abstract
    • Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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23.
  • Gustafsson, Johan, 1976, et al. (author)
  • Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
  • 2022
  • Journal article (other academic/artistic)abstract
    • Single-cell RNA sequencing has the potential to unravel the differences in metabolism across cell types and cell states in both the healthy and diseased human body. The use of existing knowledge in the form of genome-scale metabolic models (GEMs) holds promise to strengthen such analyses, but the combined use of these two methods requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the tINIT algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile, emphasizing the need to study them separately rather than to build models from bulk RNA-Seq data. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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24.
  • Hai, Y., et al. (author)
  • Crystal Structure of an Arginase-like Protein from Trypanosoma brucei That Evolved without a Binuclear Manganese Cluster
  • 2015
  • In: Biochemistry. - : American Chemical Society (ACS). - 1520-4995 .- 0006-2960. ; 54:2, s. 458-471
  • Journal article (peer-reviewed)abstract
    • The X-ray crystal structure of an arginase-like protein from the parasitic protozoan Trypanosoma brucei, designated TbARG, is reported at 1.80 and 2.38 angstrom resolution in its reduced and oxidized forms, respectively. The oxidized form of TbARG is a disulfide-linked hexamer that retains the overall architecture of a dimer of trimers in the reduced form. Intriguingly, TbARG does not contain metal ions in its putative active site, and amino acid sequence comparisons indicate that all but one of the residues required for coordination to the catalytically obligatory binuclear manganese cluster in other arginases are substituted here with residues incapable of metal ion coordination. Therefore, the structure of TbARG is the first of a member of the arginase/deacetylase superfamily that is not a metalloprotein. Although we show that metal binding activity is easily reconstituted in TbARG by site-directed mutagenesis and confirmed in X-ray crystal structures, it is curious that this protein and its parasitic orthologues evolved away from metal binding function. Knockout of the TbARG gene from the genome demonstrated that its function is not essential to cultured bloodstream-form T. brucei, and metabolomics analysis confirmed that the enzyme has no role in the conversion of l-arginine to l-ornithine in these cells. While the molecular function of TbARG remains enigmatic, the fact that the T. brucei genome encodes only this protein and not a functional arginase indicates that the parasite must import l-ornithine from its host to provide a source of substrate for ornithine decarboxylase in the polyamine biosynthetic pathway, an active target for the development of antiparasitic drugs.
  •  
25.
  • Han, Yichao, et al. (author)
  • Advances in genome-scale metabolic models of industrially important fungi
  • 2023
  • In: Current Opinion in Biotechnology. - 0958-1669 .- 1879-0429. ; 84
  • Research review (peer-reviewed)abstract
    • Many fungal species have been used industrially for production of biofuels and bioproducts. Developing strains with better performance in biomanufacturing contexts requires a systematic understanding of cellular metabolism. Genome-scale metabolic models (GEMs) offer a comprehensive view of interconnected pathways and a mathematical framework for downstream analysis. Recently, GEMs have been developed or updated for several industrially important fungi. Some of them incorporate enzyme constraints, enabling improved predictions of cell states and proteome allocation. Here, we provide an overview of these newly developed GEMs and computational methods that facilitate construction of enzyme-constrained GEMs and utilize flux predictions from GEMs. Furthermore, we highlight the pivotal roles of these GEMs in iterative design–build–test–learn cycles, ultimately advancing the field of fungal biomanufacturing.
  •  
26.
  • Hapeta, Piotr, et al. (author)
  • Nitrogen as the major factor influencing gene expression in Yarrowia lipolytica
  • 2020
  • In: Biotechnology Reports. - : Elsevier BV. - 2215-017X. ; 27
  • Journal article (peer-reviewed)abstract
    • Yarrowia lipolytica is an important industrial microorganism used for the production of oleochemicals. The design of effective biotechnological processes with this cell factory requires an in-depth knowledge of its metabolism. Here we present a transcriptomic study of Y. lipolytica grown in the presence of glycerol and glucose, and mixture of both at different carbon to nitrogen ratios. It emerged that the transcriptomic landscape of Y. lipolytica is more sensitive to the nitrogen availability than to the utilized carbon source, as evidenced by more genes being differentially expressed in lower carbon to nitrogen ratio. Specifically, expression of hexokinase (HXK1) is significantly susceptible to changes in nitrogen concentrations. High HXK1 expression in low nitrogen seems to impact other genes which are implicated in tricarboxylic acid cycle and erythritol biosynthesis. We further show that expression of HXK1 and two genes belonging to the sugar porter family might be controlled by GATA-like zinc-finger proteins.
  •  
27.
  • Irani, Z. A., et al. (author)
  • Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins
  • 2016
  • In: Biotechnology and Bioengineering. - : Wiley. - 0006-3592 .- 1097-0290. ; 113:5, s. 961-969
  • Journal article (peer-reviewed)abstract
    • Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering, a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better prediction of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required for using these models to understand and optimize protein production processes.
  •  
28.
  • Johnston, Katharina, et al. (author)
  • Mapping the metabolism of five amino acids in bloodstream form Trypanosoma brucei using U- 13C-labelled substrates and LC–MS
  • 2019
  • In: Bioscience Reports. - 0144-8463 .- 1573-4935. ; 39:5
  • Journal article (peer-reviewed)abstract
    • The metabolism of the parasite Trypanosoma brucei has been the focus of numerous studies since the 1940s. Recently it was shown, using metabolomics coupled with heavy-atom isotope labelled glucose, that the metabolism of the bloodstream form parasite is more complex than previously thought. The present study also raised a number of questions regarding the origin of several metabolites, for example succinate, only a proportion of which derives from glucose. In order to answer some of these questions and explore the metabolism of bloodstream form T. brucei in more depth we followed the fate of five heavy labelled amino acids – glutamine, proline, methionine, cysteine and arginine – using an LC–MS based metabolomics approach. We found that some of these amino acids have roles beyond those previously thought and we have tentatively identified some unexpected metabolites which need to be confirmed and their function determined.
  •  
29.
  • Kerkhoven, Eduard, 1985 (author)
  • Advances in constraint-based models: methods for improved predictive power based on resource allocation constraints
  • 2022
  • In: Current Opinion in Microbiology. - : Elsevier BV. - 1369-5274 .- 1879-0364. ; 68
  • Research review (peer-reviewed)abstract
    • The concept of metabolic models with resource allocation constraints has been around for over a decade and has clear advantages even when implementation is relatively rudimentary. Nonetheless, the number of organisms for which such a model is reconstructed is low. Various approaches exist, from coarse-grained consideration of enzyme usage to finegrained description of protein translation. These approaches are reviewed here, with a particular focus on user-friendly solutions that can introduce resource allocation constraints to metabolic models of any organism. The availability of kcat data is a major hurdle, where recent advances might help to fill in the numerous gaps that exist for this data, especially for nonmodel organisms.
  •  
30.
  • Kerkhoven, Eduard, 1985, et al. (author)
  • Handling Uncertainty in Dynamic Models: The Pentose Phosphate Pathway in Trypanosoma brucei
  • 2013
  • In: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 9:12, s. Art. no. e1003371-
  • Journal article (peer-reviewed)abstract
    • Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate “leak” must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.
  •  
31.
  • Kerkhoven, Eduard, 1985, et al. (author)
  • Leucine Biosynthesis Is Involved in Regulating High Lipid Accumulation in Yarrowia lipolytica
  • 2017
  • In: mBio. - 2150-7511 .- 2161-2129. ; 8:3, s. Article no. e00857-17
  • Journal article (peer-reviewed)abstract
    • The yeast Yarrowia lipolytica is a potent accumulator of lipids, and lipogenesis in this organism can be influenced by a variety of factors, such as genetics and environmental conditions. Using a multifactorial study, we elucidated the effects of both genetic and environmental factors on regulation of lipogenesis in Y. lipolytica and identified how two opposite regulatory states both result in lipid accumulation. This study involved comparison of a strain overexpressing diacylglycerol acyltransferase (DGA1) with a control strain grown under either nitrogen or carbon limitation conditions. A strong correlation was observed between the responses on the transcript and protein levels. Combination of DGA1 overexpression with nitrogen limitation resulted in a high level of lipid accumulation accompanied by downregulation of several amino acid biosynthetic pathways, including that of leucine in particular, and these changes were further correlated with a decrease in metabolic fluxes. This downregulation was supported by the measured decrease in the level of 2-isopropylmalate, an intermediate of leucine biosynthesis. Combining the multi-omics data with putative transcription factor binding motifs uncovered a contradictory role for TORC1 in controlling lipid accumulation, likely mediated through 2-isopropylmalate and a Leu3-like transcription factor. IMPORTANCE The ubiquitous metabolism of lipids involves refined regulation, and an enriched understanding of this regulation would have wide implications. Various factors can influence lipid metabolism, including the environment and genetics. We demonstrated, using a multi-omics and multifactorial experimental setup, that multiple factors affect lipid accumulation in the yeast Yarrowia lipolytica. Using integrative analysis, we identified novel interactions between nutrient restriction and genetic factors involving regulators that are highly conserved among eukaryotes. Given that lipid metabolism is involved in many diseases but is also vital to the development of microbial cell factories that can provide us with sustainable fuels and oleochemicals, we envision that our report introduces foundational work to further unravel the regulation of lipid accumulation in eukaryal cells.
  •  
32.
  • Kerkhoven, Eduard, 1985, et al. (author)
  • Regulation of amino-acid metabolism controls flux to lipid accumulation in yarrowia lipolytica
  • 2016
  • In: npj Systems Biology and Applications. - : Springer Science and Business Media LLC. - 2056-7189. ; 2
  • Journal article (peer-reviewed)abstract
    • Yarrowia lipolytica is a promising microbial cell factory for the production of lipids to be used as fuels and chemicals, but there are few studies on regulation of its metabolism. Here we performed the first integrated data analysis of Y. lipolytica grown in carbon and nitrogen limited chemostat cultures. We first reconstructed a genome-scale metabolic model and used this for integrative analysis of multilevel omics data. Metabolite profiling and lipidomics was used to quantify the cellular physiology, while regulatory changes were measured using RNAseq. Analysis of the data showed that lipid accumulation in Y. lipolytica does not involve transcriptional regulation of lipid metabolism but is associated with regulation of amino-acid biosynthesis, resulting in redirection of carbon flux during nitrogen limitation from amino acids to lipids. Lipid accumulation in Y. lipolytica at nitrogen limitation is similar to the overflow metabolism observed in many other microorganisms, e.g. ethanol production by Sacchromyces cerevisiae at nitrogen limitation.
  •  
33.
  •  
34.
  • Kittikunapong, Cheewin, 1995, et al. (author)
  • Reconstruction of a Genome-Scale Metabolic Model of Streptomyces albus J1074: Improved Engineering Strategies in Natural Product Synthesis
  • 2021
  • In: Metabolites. - : MDPI AG. - 2218-1989 .- 2218-1989. ; 11:5
  • Journal article (peer-reviewed)abstract
    • Streptomyces albus J1074 is recognized as an effective host for heterologous production of natural products. Its fast growth and efficient genetic toolbox due to a naturally minimized genome have contributed towards its advantage in expressing biosynthetic pathways for a diverse repertoire of products such as antibiotics and flavonoids. In order to develop precise model-driven engineering strategies for de novo production of natural products, a genome-scale metabolic model (GEM) was reconstructed for the microorganism based on protein homology to model species Streptomyces coelicolor while drawing annotated data from databases and literature for further curation. To demonstrate its capabilities, the Salb-GEM was used to predict overexpression targets for desirable compounds using flux scanning with enforced objective function (FSEOF). Salb-GEM was also utilized to investigate the effect of a minimized genome on metabolic gene essentialities in comparison to another Streptomyces species, S. coelicolor.
  •  
35.
  • Ledesma-Amaro, R., et al. (author)
  • Genome Scale Metabolic Modeling of the Riboflavin Overproducer Ashbya gossypii
  • 2014
  • In: Biotechnology and Bioengineering. - : Wiley. - 0006-3592 .- 1097-0290. ; 111:6, s. 1191-1199
  • Journal article (peer-reviewed)abstract
    • Ashbya gossypii is a filamentous fungus that naturally overproduces riboflavin, or vitamin B2. Advances in genetic and metabolic engineering of A. gossypii have permitted the switch from industrial chemical synthesis to the current biotechnological production of this vitamin. Additionally, A. gossypii is a model organism with one of the smallest eukaryote genomes being phylogenetically close to Saccharomyces cerevisiae. It has therefore been used to study evolutionary aspects of bakers' yeast. We here reconstructed the first genome scale metabolic model of A. gossypii, iRL766. The model was validated by biomass growth, riboflavin production and substrate utilization predictions. Gene essentiality analysis of the A. gossypii model in comparison with the S. cerevisiae model demonstrated how the whole-genome duplication event that separates the two species has led to an even spread of paralogs among all metabolic pathways. Additionally, iRL766 was used to integrate transcriptomics data from two different growth stages of A. gossypii, comparing exponential growth to riboflavin production stages. Both reporter metabolite analysis and in silico identification of transcriptionally regulated enzymes demonstrated the important involvement of beta-oxidation and the glyoxylate cycle in riboflavin production. Biotechnol. Bioeng. 2014;111: 1191-1199.
  •  
36.
  • Li, Feiran, 1993, et al. (author)
  • Deep learning-based k(cat) prediction enables improved enzyme-constrained model reconstruction
  • 2022
  • In: Nature Catalysis. - : Springer Science and Business Media LLC. - 2520-1158. ; 5:8, s. 662-672
  • Journal article (peer-reviewed)abstract
    • Enzyme turnover numbers (k(cat)) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured k(cat) data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput k(cat) prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture k(cat) changes for mutated enzymes and identify amino acid residues with a strong impact on k(cat) values. We applied this approach to predict genome-scale k(cat) values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted k(cat) values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale.
  •  
37.
  • Li, Feiran, 1993, et al. (author)
  • Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints
  • 2022
  • In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
  • Journal article (peer-reviewed)abstract
    • Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for alpha-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production. Due to the complexity of the protein secretory pathway, strategy suitable for the production of a certain recombination protein cannot be generalized. Here, the authors construct a proteome-constrained genome-scale protein secretory model for yeast and show its application in the production of different misfolded or recombinant proteins.
  •  
38.
  • Lu, Hongzhong, 1987, et al. (author)
  • A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
  • 2019
  • In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 10:1
  • Journal article (peer-reviewed)abstract
    • Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.
  •  
39.
  • Lu, Hongzhong, 1987, et al. (author)
  • A Pan-Draft Metabolic Model Reflects Evolutionary Diversity across 332 Yeast Species
  • 2022
  • In: Biomolecules. - : MDPI AG. - 2218-273X. ; 12:11
  • Journal article (peer-reviewed)abstract
    • Yeasts are increasingly employed in synthetic biology as chassis strains, including conventional and non-conventional species. It is still unclear how genomic evolution determines metabolic diversity among various yeast species and strains. In this study, we constructed draft GEMs for 332 yeast species using two alternative procedures from the toolbox RAVEN v 2.0. We found that draft GEMs could reflect the difference in yeast metabolic potentials, and therefore, could be utilized to probe the evolutionary trend of metabolism among 332 yeast species. We created a pan-draft metabolic model to account for the metabolic capacity of every sequenced yeast species by merging all draft GEMs. Further analysis showed that the pan-reactome of yeast has a “closed” property, which confirmed the great conservatism that exists in yeast metabolic evolution. Lastly, the quantitative correlations among trait similarity, evolutionary distances, genotype, and model similarity were thoroughly investigated. The results suggest that the evolutionary distance and genotype, to some extent, determine model similarity, but not trait similarity, indicating that multiple mechanisms shape yeast trait evolution. A large-scale reconstruction and integrative analysis of yeast draft GEMs would be a valuable resource to probe the evolutionary mechanism behind yeast trait variety and to further refine the existing yeast species-specific GEMs for the community.
  •  
40.
  • Lu, Hongzhong, 1987, et al. (author)
  • Kinetic Models of Metabolism
  • 2021
  • In: Metabolic Engineering: Concepts and Applications: Volume 13a and 13b. - : Wiley. ; , s. 153-170
  • Book chapter (other academic/artistic)abstract
    • This chapter introduces the kinetic models of metabolism followed by examples on the construction of kinetic models as well as applications. With the Michaelis-Menten formulation, the influence of enzyme properties, enzyme abundance, and metabolite concentration on the dynamic behavior of a reaction can be explained mechanistically. Kinetic models mechanistically represent the processes that take place within a cell, and these models are made up of a series of ordinary differential equations. A kinetic model requires the definition of rate equations and their respective parameters for each of the reactions, which are currently unknown for many of the reactions contained in genome-scale models. Reaction kinetics can be described with mathematical expressions where the reaction rates are functions of kinetic parameters and the concentration of metabolites. Approximative rate expression is also adopted in the kinetic model reconstruction. Estimation of parameters in rate expressions is essential for having good predictive performance of a kinetic model.
  •  
41.
  • Lu, Hongzhong, 1987, et al. (author)
  • Multiscale models quantifying yeast physiology: towards a whole-cell model
  • 2022
  • In: Trends in Biotechnology. - : Elsevier BV. - 0167-7799 .- 1879-3096. ; 40:3, s. 291-305
  • Research review (peer-reviewed)abstract
    • The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.
  •  
42.
  • Lu, Hongzhong, 1987, et al. (author)
  • Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection
  • 2021
  • In: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 17:10
  • Journal article (peer-reviewed)abstract
    • Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.
  •  
43.
  • Lubuta, Patrice, et al. (author)
  • Investigating the Influence of Glycerol on the Utilization of Glucose in Yarrowia lipolytica Using RNA-Seq-Based Transcriptomics
  • 2019
  • In: G3: Genes, Genomes, Genetics. - : Oxford University Press (OUP). - 2160-1836. ; 9:12, s. 4059-4071
  • Journal article (peer-reviewed)abstract
    • Glycerol is considered as a promising substrate for biotechnological applications and the non-conventional yeast Yarrowia lipolytica has been used extensively for the valorization of this compound. Contrary to S. cerevisiae, Y. lipolytica seems to prefer glycerol over glucose and it has been reported previously that the presence of glycerol can suppress the consumption of glucose in co-substrate fermentations. Based on these observations, we hypothesized glycerol repression-like effects in Y. lipolytica, which are converse to well described carbon repression mechanisms ensuring the prioritized use of glucose (e.g., in S. cerevisiae). We therefore aimed to investigate this effect on the level of transcription. Strains varying in the degree of glucose suppression were chosen and characterized in high-resolution growth screenings, resulting in the detection of different growth phenotypes under glycerol-glucose mixed conditions. Two strains, IBT and W29, were selected and cultivated in chemostats using glucose, glycerol and glucose/glycerol as carbon sources, followed by an RNA-Seq-based transcriptome analysis. We could show that several transporters were significantly higher expressed in W29, which is potentially related to the observed physiological differences. However, most of the expression variation between the strains were regardless of the carbon source applied, and cross-comparisons revealed that the strain-specific carbon source responses underwent in the opposite direction. A deeper analysis of the substrate specific carbon source response led to the identification of several differentially expressed genes with orthologous functions related to signal transduction and transcriptional regulation. This study provides an initial investigation on potentially novel carbon source regulation mechanisms in yeasts. Copyright © 2019 Lubuta et al.
  •  
44.
  • Malcı, Koray, et al. (author)
  • Improved production of Taxol ® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering
  • 2023
  • In: Microbial Cell Factories. - 1475-2859. ; 22:1
  • Journal article (peer-reviewed)abstract
    • Background: Integrated metabolic engineering approaches that combine system and synthetic biology tools enable the efficient design of microbial cell factories for synthesizing high-value products. In this study, we utilized in silico design algorithms on the yeast genome-scale model to predict genomic modifications that could enhance the production of early-step Taxol® in engineered Saccharomyces cerevisiae cells. Results: Using constraint-based reconstruction and analysis (COBRA) methods, we narrowed down the solution set of genomic modification candidates. We screened 17 genomic modifications, including nine gene deletions and eight gene overexpressions, through wet-lab studies to determine their impact on taxadiene production, the first metabolite in the Taxol® biosynthetic pathway. Under different cultivation conditions, most single genomic modifications resulted in increased taxadiene production. The strain named KM32, which contained four overexpressed genes (ILV2, TRR1, ADE13, and ECM31) involved in branched-chain amino acid biosynthesis, the thioredoxin system, de novo purine synthesis, and the pantothenate pathway, respectively, exhibited the best performance. KM32 achieved a 50% increase in taxadiene production, reaching 215 mg/L. Furthermore, KM32 produced the highest reported yields of taxa-4(20),11-dien-5α-ol (T5α-ol) at 43.65 mg/L and taxa-4(20),11-dien-5-α-yl acetate (T5αAc) at 26.2 mg/L among early-step Taxol® metabolites in S. cerevisiae. Conclusions: This study highlights the effectiveness of computational and integrated approaches in identifying promising genomic modifications that can enhance the performance of yeast cell factories. By employing in silico design algorithms and wet-lab screening, we successfully improved taxadiene production in engineered S. cerevisiae strains. The best-performing strain, KM32, achieved substantial increases in taxadiene as well as production of T5α-ol and T5αAc. These findings emphasize the importance of using systematic and integrated strategies to develop efficient yeast cell factories, providing potential implications for the industrial production of high-value isoprenoids like Taxol®.
  •  
45.
  • Malina, Carl, 1992, et al. (author)
  • Adaptations in metabolism and protein translation give rise to the Crabtree effect in yeast
  • 2021
  • In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 118:51
  • Journal article (peer-reviewed)abstract
    • Aerobic fermentation, also referred to as the Crabtree effect in yeast, is a well-studied phenomenon that allows many eukaryal cells to attain higher growth rates at high glucose availability. Not all yeasts exhibit the Crabtree effect, and it is not known why Crabtree-negative yeasts can grow at rates comparable to Crabtree-positive yeasts. Here, we quantitatively compared two Crabtree-positive yeasts, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and two Crabtree-negative yeasts, Kluyveromyces marxianus and Scheffersomyces stipitis, cultivated under glucose excess conditions. Combining physiological and proteome quantification with genome-scale metabolic modeling, we found that the two groups differ in energy metabolism and translation efficiency. In Crabtree-positive yeasts, the central carbon metabolism flux and proteome allocation favor a glucose utilization strategy minimizing proteome cost as proteins translation parameters, including ribosomal content and/or efficiency, are lower. Crabtree-negative yeasts, however, use a strategy of maximizing ATP yield, accompanied by higher protein translation parameters. Our analyses provide insight into the underlying reasons for the Crabtree effect, demonstrating a coupling to adaptations in both metabolism and protein translation.
  •  
46.
  • Malina, Carl, 1992, et al. (author)
  • Constraint-based modeling of yeast mitochondria reveals the dynamics of protein import and iron-sulfur cluster biogenesis
  • 2021
  • In: iScience. - : Elsevier BV. - 2589-0042. ; 24:11
  • Journal article (peer-reviewed)abstract
    • Mitochondria are a hallmark of eukaryal cells and play an important role in cellular metabolism. There is a vast amount of knowledge available on mitochondrial metabolism and essential mitochondrial functions, such as protein import and iron-sulfur cluster biosynthesis, including multiple studies on the mitochondrial proteome. Therefore, there is a need for in silico approaches to facilitate the analysis of these data. Here, we present a detailed model of mitochondrial metabolism Saccharomyces cerevisiae, including protein import, iron-sulfur cluster biosynthesis, and a description of the coupling between charge translocation processes and ATP synthesis. Model analysis implied a dual dependence of absolute levels of proteins in protein import, iron-sulfur cluster biogenesis and cluster abundance on growth rate and respiratory activity. The model is instrumental in studying dynamics and perturbations in these processes and given the high conservation of mitochondrial metabolism in humans, it can provide insight into their role in human disease.
  •  
47.
  • Nowrouzi, Behnaz, et al. (author)
  • Rewiring Saccharomyces cerevisiae metabolism for optimised Taxol® precursors production
  • 2024
  • In: Metabolic Engineering Communications. - 2214-0301. ; 18
  • Journal article (peer-reviewed)abstract
    • Saccharomyces cerevisiae has been conveniently used to produce Taxol® anticancer drug early precursors. However, the harmful impact of oxidative stress by the first cytochrome P450-reductase enzymes (CYP725A4-POR) of Taxol® pathway has hampered sufficient progress in yeast. Here, we evolved an oxidative stress-resistant yeast strain with three-fold higher titre of their substrate, taxadiene. The performance of the evolved and parent strains were then evaluated in galactose-limited chemostats before and under the oxidative stress by an oxidising agent. The interaction of evolution and oxidative stress was comprehensively evaluated through transcriptomics and metabolite profiles integration in yeast enzyme-constrained genome scale model. Overall, the evolved strain showed improved respiration, reduced overflow metabolites production and oxidative stress re-induction tolerance. The cross-protection mechanism also potentially contributed to better heme, flavin and NADPH availability, essential for CYP725A4 and POR optimal activity in yeast. The results imply that the evolved strain is a robust cell factory for future efforts towards Taxol© production.
  •  
48.
  • Pomraning, Kyle R., et al. (author)
  • Regulation of yeast-to-hyphae transition in Yarrowia lipolytica
  • 2018
  • In: mSphere. - 2379-5042. ; 3:6
  • Journal article (peer-reviewed)abstract
    • The yeast Yarrowia lipolytica undergoes a morphological transition from yeast-to-hyphal growth in response to environmental conditions. A forward genetic screen was used to identify mutants that reliably remain in the yeast phase, which were then assessed by whole-genome sequencing. All the smooth mutants identified, so named because of their colony morphology, exhibit independent loss of DNA at a repetitive locus made up of interspersed ribosomal DNA and short 10- to 40-mer telomere-like repeats. The loss of repetitive DNA is associated with downregulation of genes with stress response elements (5'-CCCCT-3') and upregulation of genes with cell cycle box (5'-ACGCG-3') motifs in their promoter region. The stress response element is bound by the transcription factor Msn2p in Saccharomyces cerevisiae. We confirmed that the Y. lipolytica msn2 (Ylmsn2) ortholog is required for hyphal growth and found that overexpression of Ylmsn2 enables hyphal growth in smooth strains. The cell cycle box is bound by the Mbp1p/Swi6p complex in S. cerevisiae to regulate G1-to-S phase progression. We found that overexpression of either the Ylmbp1 or Ylswi6 homologs decreased hyphal growth and that deletion of either Ylmbp1 or Ylswi6 promotes hyphal growth in smooth strains. A second forward genetic screen for reversion to hyphal growth was performed with the smooth-33 mutant to identify additional genetic factors regulating hyphal growth in Y. lipolytica. Thirteen of the mutants sequenced from this screen had coding mutations in five kinases, including the histidine kinases Ylchk1 and Ylnik1 and kinases of the high-osmolarity glycerol response (HOG) mitogen-activated protein (MAP) kinase cascade Ylssk2, Ylpbs2, and Ylhog1. Together, these results demonstrate that Y. lipolytica transitions to hyphal growth in response to stress through multiple signaling pathways.
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49.
  • Poorinmohammad, Naghmeh, 1989, et al. (author)
  • Systems-level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica
  • 2021
  • In: Biotechnology and Bioengineering. - : Wiley. - 0006-3592 .- 1097-0290. ; 118:10, s. 3640-3654
  • Research review (peer-reviewed)abstract
    • Concerns about climate change and the search for renewable energy sources together with the goal of attaining sustainable product manufacturing have boosted the use of microbial platforms to produce fuels and high-value chemicals. In this regard, Yarrowia lipolytica has been known as a promising yeast with potentials in diverse array of biotechnological applications such as being a host for different oleochemicals, organic acid, and recombinant protein production. Having a rapidly increasing number of molecular and genetic tools available, Y. lipolytica has been well studied amongst oleaginous yeasts and metabolic engineering has been used to explore its potentials. More recently, with the advancement in systems biotechnology and the implementation of mathematical modeling and high throughput omics data-driven approaches, in-depth understanding of cellular mechanisms of cell factories have been made possible resulting in enhanced rational strain design. In case of Y. lipolytica, these systems-level studies and the related cutting-edge technologies have recently been initiated which is expected to result in enabling the biotechnology sector to rationally engineer Y. lipolytica-based cell factories with favorable production metrics. In this regard, here, we highlight the current status of systems metabolic engineering research and assess the potential of this yeast for future cell factory design development.
  •  
50.
  • Rekena, Alina J., et al. (author)
  • Genome-scale metabolic modeling reveals metabolic trade-offs associated with lipid production in Rhodotorula toruloides
  • 2023
  • In: PLoS Computational Biology. - 1553-734X .- 1553-7358. ; 19:4
  • Journal article (peer-reviewed)abstract
    • Rhodotorula toruloides is a non-conventional, oleaginous yeast able to naturally accumulate high amounts of microbial lipids. Constraint-based modeling of R. toruloides has been mainly focused on the comparison of experimentally measured and model predicted growth rates, while the intracellular flux patterns have been analyzed on a rather general level. Hence, the intrinsic metabolic properties of R. toruloides that make lipid synthesis possible are not thoroughly understood. At the same time, the lack of diverse physiological data sets has often been the bottleneck to predict accurate fluxes. In this study, we collected detailed physiology data sets of R. toruloides while growing on glucose, xylose, and acetate as the sole carbon source in chemically defined medium. Regardless of the carbon source, the growth was divided into two phases from which proteomic and lipidomic data were collected. Complemental physiological parameters were collected in these two phases and altogether implemented into metabolic models. Simulated intracellular flux patterns demonstrated the role of phosphoketolase in the generation of acetyl-CoA, one of the main precursors during lipid biosynthesis, while the role of ATP citrate lyase was not confirmed. Metabolic modeling on xylose as a carbon substrate was greatly improved by the detection of chirality of D-arabinitol, which together with D-ribulose were involved in an alternative xylose assimilation pathway. Further, flux patterns pointed to metabolic trade-offs associated with NADPH allocation between nitrogen assimilation and lipid biosynthetic pathways, which was linked to large-scale differences in protein and lipid content. This work includes the first extensive multi-condition analysis of R. toruloides using enzyme-constrained models and quantitative proteomics. Further, more precise k(cat) values should extend the application of the newly developed enzyme-constrained models that are publicly available for future studies. Author summaryTransition towards a biobased, circular economy to reduce the industrial dependence on fossil-based resources requires new technologies. One of the options is to convert available biomass feedstocks into valuable chemicals using microbes as biocatalysts. Rhodotorula toruloides is a nonpathogenic, nonconventional yeast that has recently emerged as one of the most promising yeasts for sustainable production of chemicals and fuels due to its natural ability to synthesize large amounts of lipids. However, its unique metabolic properties are not yet fully understood. We have computationally predicted metabolic fluxes in R. toruloides while growing in economically viable growth conditions inducing lipid accumulation and analyzed them together with absolute proteome quantification. Our holistic approach has highlighted metabolic pathways important for lipid biosynthesis and revealed metabolic trade-offs associated with NADPH allocation during lipogenesis. In addition, our work highlighted the necessity for accurate computational approaches in characterizing enzymatic kinetic properties that would improve the metabolic studies of R. toruloides.
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