High-quality genome-scale metabolic model of Aurantiochytrium sp. T66

AbstractThe long-chain, ω-3 polyunsaturated fatty acids (PUFAs) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-quality ω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists able to synthesize and accrue large amounts of essential ω-3 PUFAs, including EPA and DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand for ω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytrid Aurantiochytrium sp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducing in vivo growth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66’s metabolic properties, as well as exploring metabolic engineering and process-optimization strategies in silico for increased ω-3 PUFA production.

High‐quality genome‐scale metabolic model of Aurantiochytrium sp. T66

Biotechnology and Bioengineering ◽

10.1002/bit.27726 ◽

2021 ◽

Author(s):

Vetle Simensen ◽

André Voigt ◽

Eivind Almaas

Keyword(s):

Metabolic Model ◽

High Quality ◽

Genome Scale ◽

Insights into metabolic osmoadaptation of the ectoines-producer bacterium Chromohalobacter salexigens through a high-quality genome scale metabolic model

Microbial Cell Factories ◽

10.1186/s12934-017-0852-0 ◽

2018 ◽

Vol 17 (1) ◽

Cited By ~ 7

Author(s):

Francine Piubeli ◽

Manuel Salvador ◽

Montserrat Argandoña ◽

Joaquín J. Nieto ◽

Vicente Bernal ◽

...

Keyword(s):

Metabolic Model ◽

High Quality ◽

Chromohalobacter Salexigens ◽

Genome Scale ◽

High‐quality genome‐scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities

Environmental Microbiology ◽

10.1111/1462-2920.14843 ◽

2019 ◽

Vol 22 (1) ◽

pp. 255-269 ◽

Cited By ~ 37

Author(s):

Juan Nogales ◽

Joshua Mueller ◽

Steinn Gudmundsson ◽

Francisco J. Canalejo ◽

Estrella Duque ◽

...

Keyword(s):

Pseudomonas Putida ◽

High Quality ◽

Metabolic Modelling ◽

Genome Scale ◽

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

10.21203/rs.3.rs-847511/v1 ◽

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 ◽

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.

In silico analysis of the fast-growing thermophile Geobacillus sp. LC300 using a novel genome-scale metabolic model

10.1101/2021.04.07.438430 ◽

2021 ◽

Author(s):

Emil Ljungqvist ◽

Martin Gustavsson

Keyword(s):

Metabolic Engineering ◽

Carbon Sources ◽

In Silico Analysis ◽

High Growth ◽

Metabolic Model ◽

Thermophilic Microorganisms ◽

Cell Factories ◽

Conversion Rates ◽

A Genome ◽

AbstractThermophilic microorganisms show high potential for use as biorefinery cell factories. Their high growth temperatures provide fast conversion rates, lower risk of contaminations, and facilitated purification of volatile products. To date, only a few thermophilic species have been utilized for microbial production purposes, and the development of production strains is impeded by the lack of metabolic engineering tools. In this study, we constructed a genome-scale metabolic model, iGEL601, of Geobacillus sp. LC300, an important part of the metabolic engineering pipeline. The model contains 601 genes, 1240 reactions and 1305 metabolites, and the reaction reversibility is based on thermodynamics at the optimum growth temperature. Using flux sampling, the model shows high similarity to experimentally determined reaction fluxes with both glucose and xylose as sole carbon sources. Furthermore, the model predicts previously unidentified by-products, closing the gap in the carbon balance for both carbon sources. Finally, iGEL601 was used to suggest metabolic engineering strategies to maximise production of five industrially relevant compounds. The suggested strategies have previously been experimentally verified in other microorganisms, and predicted production rates are on par with or higher than those previously achieved experimentally. The results highlight the biotechnological potential of LC300 and the application of iGEL601 for use as a tool in the metabolic engineering workflow.

Controlled human malaria infection with PvW1: a new clone of Plasmodium vivax with high quality genome assembly

10.1101/2021.07.23.21259839 ◽

2021 ◽

Author(s):

Angela M Minassian ◽

Yrene Themistocleous ◽

Sarah E Silk ◽

Jordan R Barrett ◽

Alison Kemp ◽

...

Keyword(s):

Genome Assembly ◽

Malaria Infection ◽

New Drugs ◽

Experimental Medicine ◽

High Quality ◽

Human Malaria ◽

Assembly Method ◽

Controlled Human Malaria Infection ◽

Controlled human malaria infection (CHMI) provides a highly informative means to investigate host-pathogen interactions and enable in vivo proof-of-concept efficacy testing of new drugs and vaccines. However, unlike Plasmodium falciparum, well-characterized P. vivax parasites that are safe and suitable for use in modern CHMI models are limited. Here, two healthy malaria-naive UK adults with universal donor blood group were safely infected with a clone of P. vivax from Thailand by mosquito-bite CHMI. Parasitemia developed in both volunteers and, prior to treatment, each volunteer donated blood to produce a cryopreserved stabilate of infected red blood cells. Following stringent safety screening, the parasite stabilate from one of these donors (PvW1) was thawed and used to inoculate six healthy malaria-naive UK adults by blood-stage CHMI, at three different dilutions. Parasitemia developed in all volunteers, who were then successfully drug treated. PvW1 parasite DNA was isolated and sequenced to produce a high quality genome assembly by using a hybrid assembly method. We analysed leading vaccine candidate antigens and multigene families, including the Vivax interspersed repeat (VIR) genes of which we identified 1145 in the PvW1 genome. Our genomic analysis will guide future assessment of candidate vaccines and drugs, as well as experimental medicine studies.

Genome-scale metabolic reconstruction of the stress-tolerant hybrid yeast Zygosaccharomyces parabailii

10.1101/373621 ◽

2018 ◽

Author(s):

Marzia Di Filippo ◽

Raúl A. Ortiz-Merino ◽

Chiara Damiani ◽

Gianni Frascotti ◽

Danilo Porro ◽

...

Keyword(s):

Laboratory Data ◽

Metabolic Model ◽

Cellular Systems ◽

Metabolic Reconstruction ◽

Genome Sequences ◽

Metabolic Potential ◽

Cell Factories ◽

Metabolic Models ◽

Constraint Based Modeling ◽

Genome-scale metabolic models are powerful tools to understand and engineer cellular systems facilitating their use as cell factories. This is especially true for microorganisms with known genome sequences from which nearly complete sets of enzymes and metabolic pathways are determined, or can be inferred. Yeasts are highly diverse eukaryotes whose metabolic traits have long been exploited in industry, and although many of their genome sequences are available, few genome-scale metabolic models have so far been produced. For the first time, we reconstructed the genome-scale metabolic model of the hybrid yeast Zygosaccharomyces parabailii, which is a member of the Z. bailii sensu lato clade notorious for stress-tolerance and therefore relevant to industry. The model comprises 3096 reactions, 2091 metabolites, and 2413 genes. Our own laboratory data were then used to establish a biomass synthesis reaction, and constrain the extracellular environment. Through constraint-based modeling, our model reproduces the co-consumption and catabolism of acetate and glucose posing it as a promising platform for understanding and exploiting the metabolic potential of Z. parabailii.

High-quality genome assembly-based and functional analyses reveal the pathogenesis mechanisms and evolutionary landscape of wheat sharp eyespot Rhizoctonia cerealis

10.21203/rs.3.rs-152320/v1 ◽

2021 ◽

Author(s):

Lin Lu ◽

Feilong Guo ◽

Zhichao Zhang ◽

Lijun Pan ◽

Yu Hao ◽

...

Keyword(s):

Genome Assembly ◽

Control Strategies ◽

Gene Families ◽

Secretory Proteins ◽

High Quality ◽

Sharp Eyespot ◽

Functional Analyses ◽

Genome Scale ◽

High Quality Genome ◽

Rhizoctonia Cerealis

Abstract Wheat (Triticum aestivum) is one of the most important staple crops. The necrotrophic binucleate fungus Rhizoctonia cerealis is the causal agent for the devastating disease wheat sharp eyespot and additional diseases of other agricultural crops and bioenergy plants. In this study, we present the first high-quality genome assembly of R. cerealis Rc207, a highly aggressive strain isolated from wheat. The genome encodes expand and diverse sets of virulence-related proteins, especially secreted effectors, carbohydrate-active enzymes (CAZymes), metalloproteases, Cytochrome P450 (CYP450), and secondary metabolite-associated enzymes. Many of these genes, in particular those encoding secretory proteins and CYP450, showed markedly up-regulation during infection in wheat. Of 831 candidate secretory effectors, ten up-regulated secretory proteins, such as CAZymes, metalloproteases and antigens, were functionally validated as virulence factors required for the fungal infection in wheat. Further intra-species and inter-species comparative genomics analyses showed that repeat sequences, accounting for 17.87% of the genome, are the major driving force for the genome evolution, and frequently intraspecific gene duplication contributes to expansion of pathogenicity-related gene families. This is the first genome-scale investigation elucidating the pathogenesis mechanisms and evolutionary landscape of R. cerealis. Our results provide essential tools for further development of effective disease control strategies.

Integrating Genome-Scale and Superstructure Optimization Models in Techno-Economic Studies of Biorefineries

Processes ◽

10.3390/pr7050286 ◽

2019 ◽

Vol 7 (5) ◽

pp. 286

Author(s):

Amir Akbari ◽

Paul I. Barton

Keyword(s):

Net Present Value ◽

Metabolic Model ◽

Computational Method ◽

Cellular Growth ◽

Mixed Integer ◽

Design Parameters ◽

Nonlinear Program ◽

A Genome ◽

Scale Models ◽

Genome-scale models have become indispensable tools for the study of cellular growth. These models have been progressively improving over the past two decades, enabling accurate predictions of metabolic fluxes and key phenotypes under a variety of growth conditions. In this work, an efficient computational method is proposed to incorporate genome-scale models into superstructure optimization settings, introducing them as viable growth models to simulate the cultivation section of biorefinaries. We perform techno-economic and life-cycle analyses of an algal biorefinery with five processing sections to determine optimal processing pathways and technologies. Formulation of this problem results in a mixed-integer nonlinear program, in which the net present value is maximized with respect to mass flowrates and design parameters. We use a genome-scale metabolic model of Chlamydomonas reinhardtii to predict growth rates in the cultivation section. We study algae cultivation in open ponds, in which exchange fluxes of biomass and carbon dioxide are directly determined by the metabolic model. This formulation enables the coupling of flowrates and design parameters, leading to more accurate cultivation productivity estimates with respect to substrate concentration and light intensity.

First Genome-Scale Metabolic Model of Dolosigranulum pigrum Confirms Multiple Auxotrophies

Metabolites ◽

10.3390/metabo11040232 ◽

2021 ◽

Vol 11 (4) ◽

pp. 232

Author(s):

Alina Renz ◽

Lina Widerspick ◽

Andreas Dräger

Keyword(s):

Minimal Medium ◽

Laboratory Experiments ◽

Carbon Sources ◽

Metabolic Model ◽

High Quality ◽

High Importance ◽

Manual Curation ◽

A Genome ◽

Severe Infections ◽

Dolosigranulum pigrum is a quite recently discovered Gram-positive coccus. It has gained increasing attention due to its negative correlation with Staphylococcus aureus, which is one of the most successful modern pathogens causing severe infections with tremendous morbidity and mortality due to its multiple resistances. As the possible mechanisms behind its inhibition of S. aureus remain unclear, a genome-scale metabolic model (GEM) is of enormous interest and high importance to better study its role in this fight. This article presents the first GEM of D. pigrum, which was curated using automated reconstruction tools and extensive manual curation steps to yield a high-quality GEM. It was evaluated and validated using all currently available experimental data of D. pigrum. With this model, already predicted auxotrophies and biosynthetic pathways could be verified. The model was used to define a minimal medium for further laboratory experiments and to predict various carbon sources’ growth capacities. This model will pave the way to better understand D. pigrum’s role in the fight against S. aureus.