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

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

10.1101/2020.11.06.371070 ◽

2020 ◽

Author(s):

Vetle Simensen ◽

André Voigt ◽

Eivind Almaas

Keyword(s):

Primary Source ◽

Metabolic Model ◽

Cellular Growth ◽

Fish Oils ◽

Growth Data ◽

High Quality ◽

Cell Factories ◽

Genome Scale ◽

High Quality Genome

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.

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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

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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 ◽

High Quality Genome

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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.

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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 ◽

Genome Scale

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.

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High-Quality Genome-Scale Models From Error-Prone, Long-Read Assemblies

Frontiers in Microbiology ◽

10.3389/fmicb.2020.596626 ◽

2020 ◽

Vol 11 ◽

Author(s):

Jared T. Broddrick ◽

Richard Szubin ◽

Charles J. Norsigian ◽

Jonathan M. Monk ◽

Bernhard O. Palsson ◽

...

Keyword(s):

High Quality ◽

Scale Models ◽

Long Read ◽

Genome Scale ◽

High Quality Genome

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A protocol for generating a high-quality genome-scale metabolic reconstruction

Nature Protocols ◽

10.1038/nprot.2009.203 ◽

2010 ◽

Vol 5 (1) ◽

pp. 93-121 ◽

Cited By ~ 973

Author(s):

Ines Thiele ◽

Bernhard Ø Palsson

Keyword(s):

Metabolic Reconstruction ◽

High Quality ◽

Genome Scale ◽

High Quality Genome

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Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus

mSystems ◽

10.1128/msystems.00002-21 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

St. Elmo Wilken ◽

Jonathan M. Monk ◽

Patrick A. Leggieri ◽

Christopher E. Lawson ◽

Thomas S. Lankiewicz ◽

...

Keyword(s):

Metabolic Flux ◽

Metabolic Model ◽

Fungal Genome ◽

Complex Life Cycle ◽

Lignocellulolytic Enzymes ◽

Anaerobic Fungus ◽

High Quality ◽

Content Type ◽

Fungal Metabolism ◽

Genome Scale

ABSTRACT Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus Neocallimastix lanati from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size (200 Mbp, sequenced at 62× depth), it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of N. lanati. The genome-scale metabolic model captures the primary metabolism of N. lanati and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by 13C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi. IMPORTANCE Recent genomic analyses have revealed that anaerobic gut fungi possess both the largest number and highest diversity of lignocellulolytic enzymes of all sequenced fungi, explaining their ability to decompose lignocellulosic substrates, e.g., agricultural waste, into fermentable sugars. Despite their potential, the development of engineering methods for these organisms has been slow due to their complex life cycle, understudied metabolism, and challenging anaerobic culture requirements. Currently, there is no framework that can be used to combine multi-omic data sets to understand their physiology. Here, we introduce a high-quality PacBio-sequenced genome of the anaerobic gut fungus Neocallimastix lanati. Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use.

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