scholarly journals Estimating global enzyme abundance levels from cofactor requirements: a model-based analysis of the iron metabolism in yeast

2017 ◽  
Author(s):  
Duygu Dikicioglu ◽  
Stephen G. Oliver
Keyword(s):  
Iron Metabolism ◽  
Metabolic Networks ◽  
Streptomyces Coelicolor ◽  
Metabolic Model ◽  
Metabolic Adaptation ◽  
Protein Requirements ◽  
Full Picture ◽  
Mutualistic Relationship ◽  
Quantitative Changes ◽  
Genome Scale

SummaryMetabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. Unfortunately such data are only available on a fraction of the enzyme and transporter pools, and this leaves us distant from generating the full picture. We here propose a strategy to quantify metabolic protein requirements for cofactor-bearing enzymes and transporters through constraint-based modelling. In this work, we constructed the first eukaryotic genome-scale metabolic model with a comprehensive iron metabolism and investigated partial functional impairment of the genes involved in iron-sulphur (Fe-S) cluster maturation, which revealed extensive rewiring of the fluxes in silico, despite only marginal phenotypic dissimilarities. The yeast metabolism at steady state was determined to employ a constant iron-recruiting enzyme turnover at a rate of 3.02 × 10−11 mM / (g biomass)−1 h−1. Furthermore, we employed this model to identify and study a mutualistic relationship between Streptomyces coelicolor and Saccharomyces cerevisiae, which involves iron and folate sharing economies between the two microbes.

scholarly journals RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

2018 ◽  
Author(s):  
Hao Wang ◽  
Simonas Marcišauskas ◽  
Benjamín J Sánchez ◽  
Iván Domenzain ◽  
Daniel Hermansson ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
De Novo ◽  
Antibiotic Production ◽  
Streptomyces Coelicolor ◽  
Metabolic Model ◽  
Primary And Secondary Metabolism ◽  
Scale Models ◽  
Improved Performance ◽  
Metabolites Production ◽  
Genome Scale

AbstractRAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community.Author summaryCellular metabolism is a large and complex network. Hence, investigations of metabolic networks are aided by in silico modelling and simulations. Metabolic networks can be derived from whole-genome sequences, through identifying what enzymes are present and connecting these to formalized chemical reactions. To facilitate the reconstruction of genome-scale models of metabolism (GEMs), we have developed RAVEN 2.0. This versatile toolbox can reconstruct GEMs fast, through either metabolic pathway databases KEGG and MetaCyc, or from homology with an existing GEM. We demonstrate RAVEN’s functionality through generation of a metabolic model of Streptomyces coelicolor, an antibiotic-producing bacterium. Comparison of this de novo generated GEM with a previously manually curated model demonstrates that RAVEN captures most of the previous model, and we subsequently reconstructed an updated model of S. coelicolor: Sco4. Following, we used Sco4 to predict promising targets for genetic engineering, which can be used to increase antibiotic production.

scholarly journals Investigating the chemolithoautotrophic and formate metabolism of Nitrospira moscoviensis by constraint-based metabolic modeling and 13C-tracer analysis

2021 ◽  
Author(s):  
Christopher E. Lawson ◽  
Aniela B. Mundinger ◽  
Hanna Koch ◽  
Tyler B. Jacobson ◽  
Coty A. Weathersby ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Metabolic Networks ◽  
Metabolic Model ◽  
Scale Model ◽  
Nitrifying Bacteria ◽  
Metabolomic Analysis ◽  
A Genome ◽  
Nitrite Oxidizing Bacteria ◽  
Genome Scale ◽  
Nitrospira Moscoviensis

AbstractNitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used constraint-based analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and 13C-tracer experiments with bicarbonate and formate coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings support that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation. We also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes.ImportanceNitrospira are globally abundant nitrifying bacteria in soil and aquatic ecosystems and wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of N. moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite- oxidizing bacteria.

scholarly journals Enzyme-constrained models and omics analysis of Streptomyces coelicolor reveal metabolic changes that enhance heterologous production

2019 ◽  
Author(s):  
Snorre Sulheim ◽  
Tjaša Kumelj ◽  
Dino van Dissel ◽  
Ali Salehzadeh-Yazdi ◽  
Chao Du ◽  
...  
Keyword(s):  
Heterologous Expression ◽  
Streptomyces Coelicolor ◽  
Gene Clusters ◽  
Metabolic Model ◽  
Heterologous Production ◽  
Comprehensive Picture ◽  
Constrained Models ◽  
Systemic Understanding ◽  
Genome Scale ◽  
Further Development

AbstractMany biosynthetic gene clusters (BGCs) require heterologous expression to realize their genetic potential, including silent and metagenomic BGCs. Although the engineered Streptomyces coelicolor M1152 is a widely used host for heterologous expression of BGCs, a systemic understanding of how its genetic modifications affect the metabolism is lacking and limiting further development. We performed a comparative analysis of M1152 and its ancestor M145, connecting information from proteomics, transcriptomics, and cultivation data into a comprehensive picture of the metabolic differences between these strains. Instrumental to this comparison was the application of an improved consensus genome-scale metabolic model (GEM) of S. coelicolor. Although many metabolic patterns are retained in M1152, we find that this strain suffers from oxidative stress, possibly caused by increased oxidative metabolism. Furthermore, precursor availability is likely not limiting polyketide production, implying that other strategies could be beneficial for further development of S. coelicolor for heterologous production of novel compounds.

scholarly journals Genome Scale Constraint-Based Metabolic Reconstruction of Propionibacterium Freudenreichii DSM 20271

2021 ◽  
Author(s):  
Mahsa Sadat Razavi Borghei ◽  
Meysam Mobasheri ◽  
Tabassom Sobati
Keyword(s):  
Metabolic Networks ◽  
Metabolic Model ◽  
Physiological Data ◽  
Metabolic Reconstruction ◽  
Modeling And Analysis ◽  
Microbial Cell Factories ◽  
Culture Optimization ◽  
Cell Factories ◽  
Functional Features ◽  
Genome Scale

Abstract Propionibacterium is an anaerobic bacterium with a history of use in the production of Swiss cheese and, more recently, several industrial bioproducts. While the use of this strain for the production of organic acids and secondary metabolites has gained growing interest, the industrial application of the strain requires further improvement in the yield and productivity of the target products. Systems modeling and analysis of metabolic networks are widely leveraged to gain holistic insights into the metabolic features of biotechnologically important strains and to devise metabolic engineering and culture optimization strategies for economically viable bioprocess development. In the present study, a high-quality genome-scale metabolic model of P. freudenreichii ssp. freudenreichii strain DSM 20271 was developed based on the strain’s genome annotation and biochemical and physiological data. The model covers the functions of 23% of the strain’s ORFs and accounts for 711 metabolic reactions and 647 unique metabolites. Literature-based reconstruction of the central metabolism and rigorous refinement of annotation data for establishing gene-protein-reaction associations renders the model a curated omic-scale knowledge base of the organism. Validation of the model against experimental data indicates that the reconstruction can capture the key structural and functional features of P. freudenreichii metabolism, including the growth rate, the pattern of flux distribution, the strain’s aerotolerance behavior, and the change in the mode of metabolic activity during the transition from an anaerobic to an aerobic growth regime. The model also includes an accurately curated pathway of cobalamin biosynthesis, which was used to examine the capacity of the strain to produce vitamin B12 precursors. Constraint-based reconstruction and analysis of the P. freudenreichii metabolic network also provided novel insights into the complexity and robustness of P. freudenreichii energy metabolism. The developed reconstruction, hence, may be used as a platform for the development of P. freudenreichii-based microbial cell factories and bioprocesses.

scholarly journals redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
S Tsouka ◽  
V Hatzimanikatis
Keyword(s):  
Lipid Metabolism ◽  
Metabolic Networks ◽  
Ad Hoc ◽  
Mechanistic Model ◽  
Model Organism ◽  
Metabolic Model ◽  
Lipid Biochemistry ◽  
Large Size ◽  
Scale Models ◽  
Genome Scale

ABSTRACT Over the last decades, yeast has become a key model organism for the study of lipid biochemistry. Because the regulation of lipids has been closely linked to various physiopathologies, the study of these biomolecules could lead to new diagnostics and treatments. Before the field can reach this point, however, sufficient tools for integrating and analyzing the ever-growing availability of lipidomics data will need to be developed. To this end, genome-scale models (GEMs) of metabolic networks are useful tools, though their large size and complexity introduces too much uncertainty in the accuracy of predicted outcomes. Ideally, therefore, a model for studying lipids would contain only the pathways required for the proper analysis of these biomolecules, but would not be an ad hoc reduction. We hereby present a metabolic model that focuses on lipid metabolism constructed through the integration of detailed lipid pathways into an already existing GEM of Saccharomyces cerevisiae. Our model was then systematically reduced around the subsystems defined by these pathways to provide a more manageable model size for complex studies. We show that this model is as consistent and inclusive as other yeast GEMs regarding the focus and detail on the lipid metabolism, and can be used as a scaffold for integrating lipidomics data to improve predictions in studies of lipid-related biological functions.

scholarly journals Predicting Strain Engineering Strategies Using iKS1317: A Genome‐Scale Metabolic Model of Streptomyces coelicolor

2019 ◽  
Vol 14 (4) ◽  
pp. 1800180 ◽  
Author(s):  
Tjaša Kumelj ◽  
Snorre Sulheim ◽  
Alexander Wentzel ◽  
Eivind Almaas
Keyword(s):  
Streptomyces Coelicolor ◽  
Metabolic Model ◽  
Strain Engineering ◽  
A Genome ◽  
Genome Scale

scholarly journals Reconstruction of a Genome-Scale Metabolic Model of Streptomyces albus J1074: Improved Engineering Strategies in Natural Product Synthesis

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 304
Author(s):  
Cheewin Kittikunapong ◽  
Suhui Ye ◽  
Patricia Magadán-Corpas ◽  
Álvaro Pérez-Valero ◽  
Claudio J. Villar ◽  
...  
Keyword(s):  
Natural Products ◽  
De Novo ◽  
Streptomyces Coelicolor ◽  
Metabolic Model ◽  
Streptomyces Species ◽  
Model Driven ◽  
A Genome ◽  
Streptomyces Albus ◽  
Genome Scale ◽  
Streptomyces Albus J1074

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.

scholarly journals Nature lessons: the whitefly bacterial endosymbiont is a minimal amino acid factory with unusual energetics

2016 ◽  
Author(s):  
Jorge Calle-Espinosa ◽  
Miguel Ponce-de-Leon ◽  
Diego Santos-Garcia ◽  
Francisco J. Silva ◽  
Francisco Montero ◽  
...  
Keyword(s):  
Amino Acid ◽  
Energy Production ◽  
Metabolic Networks ◽  
Metabolic Model ◽  
Atp Production ◽  
Symbiotic Associations ◽  
Bacterial Endosymbiont ◽  
Metabolic Functions ◽  
A Genome ◽  
Genome Scale

Bacterial lineages that establish obligate symbiotic associations with insect hosts are known to possess highly reduced genomes with streamlined metabolic functions that are commonly focused on amino acid and vitamin synthesis. We constructed a genome-scale metabolic model of the whitefly bacterial endosymbiont Candidatus Portiera aleyrodidarum to study the energy production capabilities using stoichiometric analysis. Strikingly, the results suggest that the energetic metabolism of the bacterial endosymbiont relies on the use of pathways related to the synthesis of amino acids and carotenoids. A deeper insight showed that the ATP production via carotenoid synthesis may also have a potential role in the regulation of amino acid production. The coupling of energy production to anabolism suggest that minimization of metabolic networks as a consequence of genome size reduction does not necessarily limit the biosynthetic potential of obligate endosymbionts.

scholarly journals Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
David M Curran ◽  
Alexandra Grote ◽  
Nirvana Nursimulu ◽  
Adam Geber ◽  
Dennis Voronin ◽  
...  
Keyword(s):  
Drug Targets ◽  
Metabolic Networks ◽  
Metabolic Model ◽  
Endosymbiotic Bacteria ◽  
Parasitic Worm ◽  
Eukaryotic Organisms ◽  
New Treatments ◽  
Genome Scale ◽  
Novel Drug ◽  
Novel Drug Targets

The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia—present in many filariae—which is vital to the worm. Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but have only been applied to multicellular eukaryotic organisms more recently. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 102 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

scholarly journals Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets

2019 ◽  
Author(s):  
DM Curran ◽  
A Grote ◽  
N Nursimulu ◽  
A Geber ◽  
D Voronin ◽  
...  
Keyword(s):  
Drug Targets ◽  
Metabolic Networks ◽  
Metabolic Model ◽  
Endosymbiotic Bacteria ◽  
Parasitic Worm ◽  
Eukaryotic Organisms ◽  
New Treatments ◽  
Genome Scale ◽  
Novel Drug ◽  
Novel Drug Targets

AbstractThe filarial nematodeBrugia malayirepresents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteriaWolbachia—present in many filariae—which is vital to the worm.Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but only recently have been applied to eukaryotic organisms. Here, we presentiDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 99 reactions essential to the survival ofB. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

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