| 1. |
- Sanchez Barja, Benjamin José, 1988, et al.
(författare)
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Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints
- 2017
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Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 13:8, s. Article no 935 -
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Applications of computational modeling in metabolic engineering of yeast
- 2015
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Ingår i: FEMS Yeast Research. - : Oxford University Press (OUP). - 1567-1356 .- 1567-1364. ; 15:1
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Urea is a drop-in nitrogen source alternative to ammonium sulphate in Yarrowia lipolytica
- 2022
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Ingår i: iScience. - : Elsevier BV. - 2589-0042. ; 25:12
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Tidskriftsartikel (refereegranskat)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.
(författare)
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C/N ratio and carbon source-dependent lipid production profiling in Rhodotorula toruloides
- 2020
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Ingår i: Applied Microbiology and Biotechnology. - : Springer Science and Business Media LLC. - 1432-0614 .- 0175-7598. ; 104:6, s. 2639-2649
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Regulation of lactose and galactose growth: Insights from a unique metabolic gene cluster in Candida intermedia
- 2023
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)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.
(författare)
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Validated Growth Rate-Dependent Regulation of Lipid Metabolism in Yarrowia lipolytica
- 2022
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Ingår i: International Journal of Molecular Sciences. - : MDPI AG. - 1422-0067 .- 1661-6596. ; 23:15
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Tidskriftsartikel (refereegranskat)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.
(författare)
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An atlas of human metabolism
- 2020
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Ingår i: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 13:624
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Abolishing storage lipids induces protein misfolding and stress responses in Yarrowia lipolytica
- 2023
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Ingår i: Journal of Industrial Microbiology and Biotechnology. - 1367-5435 .- 1476-5535. ; 50:1
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Tidskriftsartikel (refereegranskat)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.
(författare)
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The Silicon Trypanosome: A Test Case of Iterative Model Extension in Systems Biology
- 2014
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Ingår i: Advances in Microbial Physiology. - : Elsevier. - 0065-2911. - 9780128001431 ; 64, s. 115-143
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Bokkapitel (övrigt vetenskapligt/konstnärligt)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.
(författare)
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Trypanosoma brucei: meet the system
- 2014
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Ingår i: Current Opinion in Microbiology. - : Elsevier BV. - 1369-5274 .- 1879-0364. ; 20, s. 162-169
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Forskningsöversikt (refereegranskat)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.
(författare)
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A molecular genetic toolbox for Yarrowia lipolytica
- 2017
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Ingår i: Biotechnology for Biofuels. - : Springer Science and Business Media LLC. - 1754-6834 .- 1754-6834. ; 10:1
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Tidskriftsartikel (refereegranskat)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.
(författare)
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The yeastGemMap: A process diagram to assist yeast systems-metabolic studies
- 2021
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Ingår i: Biotechnology and Bioengineering. - : Wiley. - 0006-3592 .- 1097-0290. ; 118:12, s. 4800-4814
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Proteome Constraints in Genome-Scale Models
- 2021
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Ingår i: Metabolic Engineering: Concepts and Applications: Volume 13a and 13b. - : Wiley. ; 13, s. 137-152
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Bokkapitel (övrigt vetenskapligt/konstnärligt)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.
(författare)
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Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0
- 2024
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Ingår i: Nature Protocols. - 1754-2189 .- 1750-2799. ; 19:3, s. 629-667
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Single-cell omics analysis with genome-scale metabolic modeling
- 2024
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Ingår i: Current Opinion in Biotechnology. - 0958-1669 .- 1879-0429. ; 86
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Forskningsöversikt (refereegranskat)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.
(författare)
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Engineering of Saccharomyces cerevisiae for enhanced metabolic robustness and L-lactic acid production from lignocellulosic biomass
- 2024
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Ingår i: Metabolic Engineering. - 1096-7176 .- 1096-7184. ; 84, s. 23-33
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Probing the Metabolic Network in Bloodstream-Form Trypanosoma brucei Using Untargeted Metabolomics with Stable Isotope Labelled Glucose
- 2015
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Ingår i: PLoS Pathogens. - : Public Library of Science (PLoS). - 1553-7366 .- 1553-7374. ; 11:3, s. 1-25
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Reconstruction of genome-scale metabolic models of non-conventional yeasts: current state, challenges, and perspectives
- 2024
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Ingår i: Biotechnology and Bioprocess Engineering. - 1976-3816 .- 1226-8372. ; 29:1, s. 35-67
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Forskningsöversikt (refereegranskat)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.
(författare)
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Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species
- 2021
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Ingår i: FEMS Yeast Research. - : Oxford University Press (OUP). - 1567-1356 .- 1567-1364. ; 21:1
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Forskningsöversikt (refereegranskat)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.
(författare)
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Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Extracting novel hypotheses and findings from RNA-seq data
- 2020
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Ingår i: FEMS Yeast Research. - : Oxford University Press (OUP). - 1567-1356 .- 1567-1364. ; 20:2
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Forskningsöversikt (refereegranskat)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.
(författare)
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Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
- 2023
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Ingår i: 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
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
- 2022
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)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.
(författare)
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Crystal Structure of an Arginase-like Protein from Trypanosoma brucei That Evolved without a Binuclear Manganese Cluster
- 2015
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Ingår i: Biochemistry. - : American Chemical Society (ACS). - 1520-4995 .- 0006-2960. ; 54:2, s. 458-471
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Tidskriftsartikel (refereegranskat)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.
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| 25. |
- Han, Yichao, et al.
(författare)
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Advances in genome-scale metabolic models of industrially important fungi
- 2023
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Ingår i: Current Opinion in Biotechnology. - 0958-1669 .- 1879-0429. ; 84
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Forskningsöversikt (refereegranskat)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.
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