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Genome-scale metabolic models (GEMs) can be used to evaluate genotype-phenotype relationships and their application to microbial strain engineering is increasing in popularity. Some of the algorithms used to simulate the phenotypes of mutant strains require the determination of a wild-type flux distribution. However, the accuracy of this reference,...
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A genome-scale metabolic network reconstruction of Salinibacter ruber DSM13855 is presented here. To our knowledge, this is the first metabolic model of an organism in the phylum Rhodothermaeota. This model, which will be called iMB631, was reconstructed based on genomic and biochemical data available on the strain Salinibacter ruber DSM13855. This...
Background
Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications.
Results
In thi...
Citations
... The difference in POF, esters, higher alcohols and ethanol production is yeast strain-specific. Predicting phenotype based upon genotype is challenging [65][66][67][68] , even with the use of machine learning predicting phenotype based upon genotype 69 has a poor correlation for S. cerevisiae (<22%). The data presented in this study provides further valuable awareness of the relationship of genotype and phenotype and confirms previous studies' conclusions on the difficulty of predicting phenotype from genotype. ...
Beer is made via the fermentation of an aqueous extract predominantly composed of malted barley flavoured with hops. The transforming microorganism is typically a single strain of Saccharomyces cerevisiae, and for the majority of major beer brands the yeast strain is a unique component. The present yeast used to make Guinness stout brewed in Dublin, Ireland, can be traced back to 1903, but its origins are unknown. To that end, we used Illumina and Nanopore sequencing to generate whole-genome sequencing data for a total of 22 S. cerevisiae yeast strains: 16 from the Guinness collection and 6 other historical Irish brewing. The origins of the Guinness yeast were determined with a SNP-based analysis, demonstrating that the Guinness strains occupy a distinct group separate from other historical Irish brewing yeasts. Assessment of chromosome number, copy number variation and phenotypic evaluation of key brewing attributes established Guinness yeast-specific SNPs but no specific chromosomal amplifications. Our analysis also demonstrated the effects of yeast storage on phylogeny. Altogether, our results suggest that the Guinness yeast used today is related to the first deposited Guinness yeast; the 1903 Watling Laboratory Guinness yeast.
... To ensure experimentally observed flux through phospho-fructo kinase and fructose bisphosphate aldolase, the reversible transaldolase reaction was blocked before sampling (Frick and Wittmann, 2005). Also, the reduction of tricarboxylic acid cycle (TCA) intermediates in the cytoplasm was avoided to favour the production of NADH in the cytoplasm (Pereira et al., 2016). Last, the model was re-scaled to avoid stoichiometric coefficients below solver tolerance that caused numerical instability. ...
... To the best of our knowledge, this study is the first report on how to combine flux sampling and ecModels to study intracellular flux predictions, avoiding the necessity to fix an objective function and allowing the coverage of the whole solution space (Herrmann et al., 2019). While previous studies focussed on the prediction of intracellular fluxes at maximum growth rate, we have compared flux predictions covering S. cerevisiae full range of growth rates (Pereira et al., 2016). Despite of the substantially improved predictive power of the model, the protein availability constraint was not enough to yield accurate predictions of all intracellular fluxes due to the highly dimensional solution space and the absence of regulatory information in the model (Fig. 5B). ...
Genome‐scale, constraint‐based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bioprocess design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme‐constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well‐known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyse flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell‐based processes and, thus, the usefulness of such models.
... Differences in growth rates and impact of solvent necessities will result in observed differences in metabolite concentrations but are not causal to a reprogramming of metabolism [77]. Considering cellular fluxes as the metabolic phenotype through the use of GSMMs has the advantage that fluxes, unlike concentrations, can easily be normalized with regard to other rate measurements, such as growth rate or exchange rates (e.g., [78][79][80]). ...
Background:
Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge.
Methods:
Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively.
Results:
Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways.
Conclusions:
Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.
... To ensure experimentally observed flux though phospho-fructo kinase and fructose bisphosphate aldolase, the reversible transaldolase reaction was blocked before sampling [30]. According to Pereira et al. [31] reversibility of cytosolic reactions should favor the production of NADH. Therefore, the reduction of tricarboxylic acid cycle (TCA) intermediates in the cytoplasm was avoided. ...
... To the best of our knowledge, this study is the first report on how to combine flux sampling and ecModels to study intracellular flux predictions, avoiding the necessity to fix an objective function and allowing the coverage of the whole solution space [15]. While previous studies focused on the prediction of intracellular fluxes at maximum growth rate, we have compared flux predictions covering S. cerevisiae full range of growth rates [31]. We showed that the protein availability constraint breaks the linear dependency between fluxes and growth rate and results in accurate intracellular flux predictions ( Figure 5). ...
Genome-scale, constraint-based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bio-process design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme-constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well-known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyze flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell-based processes and, thus, the usefulness of such models
... It is made other rate measurements, such as growth rate or exchange rates (e.g. [77,78,79]). As a prerequisite to produce accurate, quantitative metabolomics data [76], we normalized the metabolite amounts to total protein content, calculating absolute concentrations based on internal standardization. ...
Background
Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge.
Methods
Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively.
Results
Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways.
Conclusions
Here we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.
... Saccharomyces cerevisiae is a versatile microorganism commonly used to ferment agave juice in the production of Tequila and also employed in many different industrial applications. This microorganism has been investigated in metabolic studies which elucidate its gene functions, integration, and metabolism [8]. Moreover, key research areas are focused in the fermentation process by using novel strains such as Saccharomyces and non-Saccharomyces [9,10]. ...
... Genomic Scale Models (GSM) are used to estimate the metabolic fluxes distribution; these models contain a wide collection of stoichiometrically-branded biochemical reactions related to enzymes in the cell/tissue [11]. Applications of GSM go from topological networks analyses, phenotype behaviors until the simulation of metabolism under certain metabolic engineering strategies [8]. Thus, over 100 GSMs have been developed for a wide variety of microorganisms such as bacteria, eukaryotes and archea i.e., Haemophilus influenzae, Escherichia coli, Saccharomyces cerevisiae, Helicobacter pylori, Staphyloccocus aureus, Bacillus subtilis, Homo sapiens, Pseudomona aeruginosa and for the genus Synechocystis, among others [12][13][14]. ...
... The first version of the model iFF708 includes 708 ORF with 1175 biochemical reactions and 3 compartments. In contrast, the last version, Yeast 6, contains 900 ORF with 1888 biochemical reactions and 15 compartments [8]. As mentioned before, there are already models which their number in biochemical reactions is around one thousand, however, using these models in practical applications represents a challenge due to their complexity. ...
A stoichiometric model for Saccharomyces cerevisiae is reconstructed to analyze the continuous fermentation process of agave juice in Tequila production. The metabolic model contains 94 metabolites and 117 biochemical reactions. From the above set of reactions, 93 of them are linked to internal biochemical reactions and 24 are related to transport fluxes between the medium and the cell. The central metabolism of S. cerevisiae includes the synthesis for 20 amino-acids, carbohydrates, lipids, DNA and RNA. Using flux balance analysis (FBA), different physiological states of S. cerevisiae are shown during the fermentative process; these states are compared with experimental data under different dilution rates (0.04-0.12 h$ ^{-1} $). Moreover, the model performs anabolic and catabolic biochemical reactions for the production of higher alcohols. The importance of the Saccharomyces cerevisiae genomic model in the area of alcoholic beverage fermentation is due to the fact that it allows to estimate the metabolic fluxes during the beverage fermentation process and a physiology state of the microorganism.
... To probe β-ionone production in yeast, an existing Genome-Scale Metabolic Model (GSMM) of S. cerevisiae was contextualized for predicting the different (apo)carotenoids production of the engineered strains. To this task, the curated iMM904 GSMM (Mo et al., 2009) was constrained following recent guidelines for accurately describing aerobic growth with glucose as sole carbon source in yeasts (Pereira et al., 2016;Torres et al., 2019). Stoichiometric calculations were performed using constrained-based methods from COBRA Toolbox v3.0 (Heirendt et al., 2019) within the MATLAB 2017a environment (The MathWorks, Natick, MA). ...
β-ionone is a commercially attractive industrial fragrance produced naturally from the cleavage of the pigment β-carotene in plants. While the production of this ionone is typically performed using chemical synthesis, environmentally friendly and consumer-oriented biotechnological production is gaining increasing attention. A convenient cell factory to address this demand is the yeast Saccharomyces cerevisiae. However, current β-ionone titers and yields are insufficient for commercial bioproduction. In this work, we optimized S. cerevisiae for the accumulation of high amounts of β-carotene and its subsequent conversion to β-ionone. For this task, we integrated systematically the heterologous carotenogenic genes (CrtE, CrtYB and CrtI) from Xanthophyllomyces dendrorhous using markerless genome editing CRISPR/Cas9 technology; and evaluated the transcriptional unit architecture (bidirectional or tandem), integration site, and impact of gene dosage, first on β-carotene accumulation, and later, on β-ionone production. A single-copy insertion of the carotenogenic genes in high expression loci of the wild-type yeast CEN.Pk2 strain yielded 4 mg/gDCW of total carotenoids, regardless of the transcriptional unit architecture employed. Subsequent fine-tuning of the carotenogenic gene expression enabled reaching 16 mg/gDCW of total carotenoids, which was further increased to 32 mg/gDCW by alleviating the known pathway bottleneck catalyzed by the hydroxymethylglutaryl-CoA reductase (HMGR1). The latter yield represents the highest total carotenoid concentration reported to date in S. cerevisiae for a constitutive expression system. For β-ionone synthesis, single and multiple copies of the carotene cleavage dioxygenase 1 (CCD1) gene from Petunia hybrida (PhCCD1) fused with a membrane destination peptide were expressed in the highest β-carotene-producing strains, reaching up to 33 mg/L of β-ionone in the culture medium after 72-h cultivation in shake flasks. Finally, interrogation of a contextualized genome-scale metabolic model of the producer strains pointed to PhCCD1 unspecific cleavage activity as a potentially limiting factor reducing β-ionone production. Overall, the results of this work constitute a step toward the industrial production of this ionone and, more broadly, they demonstrate that biotechnological production of apocarotenoids is technically feasible.
... Currency metabolites like water, protons, ATP, and cofactors like NADH, NADPH, FADH 2 , CoA, etc. are ubiquitous and essential for metabolism. The addition of these cofactor metabolites in GSMNs, and in particular their inclusion in the biomass reaction, considerably improves phenotype predictions and is a hallmark of good quality reconstructions [19,38]. In order to check currency metabolites we converted the GSMNs into substance graphs (using a local script) where metabolites (nodes) are connected by edges (undirected and unweighted) if they appear in the same reaction [39] and computed node degrees (number of edges connected to the node) ( Table 1 and S4 Table). ...
... These reactions are identical except that they use a different currency metabolite (Fig 5A). Pereira and colleagues [38] recommend the use of NADPH/NADP in anabolic reactions and NADH/ NAD + in catabolic reactions for more accurate flux distributions. Therefore we modified the model by retaining the DFRA2 and DFRA4 reactions and eliminating the NADH/NAD +dependent reactions, DFRA1 and DFRA3. ...
... i. TICs formed by linearly dependent reversible reactions: Usually, these arise when there are two reactions (NAD + -and NADP + -dependent) with the same catalytic activity. In this instance, we forced the use of NADPH/NADP + in anabolic reactions and NADH/NAD + for catabolic reactions, as recommended by Pereira and colleague [38]. If two irreversible reactions that catalyze the forward and backward direction exist, both reactions (and GPR rules) are lumped together in just one reversible reaction. ...
Metabolism underpins the pathogenic strategy of the causative agent of TB, Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.
... Results of FBA simulations typically do not reflect the exact metabolic behavior due to the existence of multiple optimal solutions 45 . In other words, alternate optimal solutions are independent flux distributions that optimize the objective function due to the existence of alternative biochemical pathways 46,47 . Using FVA (Flux Variability Analysis), one can predict the possible range of each flux under a certain optimal growth condition. ...
Bacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.
... There are many GSMMs that have been reconstructed (Pereira et al, 2016). Among those models, iFF708 [1] was the most referred and concise. ...
... As noted by Pierera et al [6] the iFF708 model was constructed to estimate intracellular flux distribution under aerobic conditions. When incorporating micro-aerobic data (Supplementary data S2) to the original iFF708 model, it resulted in incomplete functioning of TCA cycle. ...
... Glycerol − 3 − phosphate + FAD + ↔ DHAP + FADH 2 (6) where the electron transfer between FAD + and FADH 2 happens on the mitochondrial outer membrane [14]. Under ~300 g/L initial glucose condition, the fluxes for this reaction (ie, r13) are 10.24, 10.27 and 12.89 mmol/gDW·h with respect to no control, -150 mV and -100 mV redox potential level (Table 2). ...
A previously published genome-scale metabolic model namely iFF708 was modified to depict the metabolic flux distribution within Saccharomyces cerevisiae grown under redox potential-controlled very-high-gravity condition. The following modifications were made: electron transport chain (ETC) and oxidative phosphorylation, proton gradient and ATP transportation, and malate-aspartate shuttle. With these modifications, this model could describe the experimental data collected from the above-mentioned ethanol fermentation. As a result, the simulation unveiled that P/O ratio is critical under micro-aerobic conditions and the malate-aspartate shuttle is inactivated due to the shortage of electron transport across mitochondria. In other words, the limited supply of oxygen supresses the functionality of oxidative phosphorylation, TCA cycle and ETC. In terms of glycolytic pathway, fluxes coming from glucose-6-phosphate and pyruvate nodes are insensitive to the changes of fermentation redox potential. As initial glucose concentration is greater than 250 g/L, the interactive effect between initial glucose concentration and redox potential level becomes noticeable. This article is protected by copyright. All rights reserved.
