Systems level modelling of metabolism in fungal endophytes - implications for the symbiosis with ryegrass

We used constraint based stoichiometric modelling of metabolic fluxes in EpichloÃ‘' festucae (FL1), a targeted gene replacement of a non-ribosomal peptide synthetase (termed sidF) from E. festucae, and the symbiotic association of these endophytic fungi and their host Lolium perenne. SidF encodes an excreted ironchelating siderophore and the sidF knockouts (KO) are impaired in their ability to take up iron. After constructing the metabolic network at a genome scale, we applied constraints on enzymatic reactions that require iron as co-factor to study the variations in metabolic network capabilities of the siderophore mutant versus wildtype, in culture and in planta. We compared fluxes calculated for the production of amino acids with observed concentrations of these amino acids in planta. We report a counter-intuitive result from considering metabolism on a systems level in our models. Keywords: stoichiometric metabolic network modelling, flux balance analysis, symbiosis, Neotyphodium lolii, Lolium perenne, EpichloÃ‘' festucae.

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Characterisation of a novel endophyte NRPS gene and its role in endophyte-grass symbioses

NZGA: Research and Practice Series ◽

10.33584/rps.13.2006.3133 ◽

2007 ◽

Vol 13 ◽

pp. 491-493

Author(s):

H. Harzer ◽

R.D. Johnson ◽

S. Rasmussen ◽

C.R. Voisey ◽

L.J. Johnson

Keyword(s):

Fungal Endophytes ◽

Gene Clusters ◽

Gene Replacement ◽

In Planta ◽

Dna Library ◽

Peptide Synthetase ◽

Genomic Dna Library ◽

Targeted Gene Replacement ◽

Fungal Secondary Metabolite

Symbiotic grass associations with fungal endophytes (genera Neotyphodium and EpichloÃ«) display enhanced fitness as well as prolonged field persistence over their endophyte free equivalents. Perennial ryegrass, an important agronomic grass, is typically associated with the N. lolii endophyte. The endophyte lives within the intercellular spaces without inducing any symptoms in the plant. The aim of this study is to elucidate the biosynthetic function of fungal secondary metabolite gene clusters. Non-ribosomal peptide synthetase genes (NRPSs) of unknown function were targeted, as these genes are commonly associated with the production of bioactive peptides some of which are ecologically important. Some novel endophyte NRPS genes have been identified using a degenerate PCR screen; one of these, NRPS5 will be discussed here. Clones were obtained by screening a fosmid EpichloÃ« festucae genomic DNA library and we are currently determining gene function by using targeted gene replacement followed by an assessment in vitro and in planta using metabolomics and appropriate bioassay screens. Keywords: endophyte, NRPS, secondary metabolism

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Chitin Deacetylases are Required for Epichloë festucae Endophytic Cell Wall Remodelling During Establishment of a Mutualistic Symbiotic Interaction with Lolium perenne

Molecular Plant-Microbe Interactions ◽

10.1094/mpmi-12-20-0347-r ◽

2021 ◽

Author(s):

Nazanin Noorifar ◽

Matthew Savoian ◽

Arvina Ram ◽

Yonathan Lukito ◽

Berit Hassing ◽

...

Keyword(s):

Cell Wall ◽

Lolium Perenne ◽

Molecular Probes ◽

Symbiotic Association ◽

In Planta ◽

Intercellular Spaces ◽

Free Living ◽

Chitin Deacetylase ◽

Symbiotic Interaction ◽

Epichloë Festucae

Epichloë festucae forms a mutualistic symbiotic association with Lolium perenne. This biotrophic fungus systemically colonizes the intercellular spaces of aerial tissues to form an endophytic hyphal network, and also grows as an epiphyte. However, little is known about the cell wall remodelling mechanisms required to avoid host defence and maintain intercalary growth within the host. Here we use a suite of molecular probes to show that the E. festucae cell wall is remodelled by conversion of chitin to chitosan during infection of L. perenne seedlings as the hyphae switch from free-living to endophytic growth. When hyphae transition from endophytic to epiphytic growth the cell wall is remodelled from predominantly chitosan to chitin. This conversion from chitin to chitosan is catalysed by chitin deacetylase. The genome of E. festucae encodes three putative chitin deacetylases, two of which (cdaA and cdaB) are expressed in planta. Deletion of either of these genes results in disruption of fungal intercalary growth in the intercellular spaces of plants infected with these mutants. These results establish that these two genes are required for maintenance of the mutualistic symbiotic interaction between E. festucae and L. perenne.

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GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogen Mycobacterium leprae

10.1101/819508 ◽

2019 ◽

Author(s):

Khushboo Borah ◽

Jacque-Lucca Kearney ◽

Ruma Banerjee ◽

Pankaj Vats ◽

Huihai Wu ◽

...

Keyword(s):

Amino Acids ◽

Metabolic Network ◽

In Silico ◽

Metabolic Pathways ◽

Mycobacterium Leprae ◽

Human Pathogen ◽

Intracellular Growth ◽

A Genome ◽

Genome Scale

AbstractLeprosy, caused by Mycobacterium leprae, has plagued humanity for thousands of years and continues to cause morbidity, disability and stigmatization in two to three million people today. Although effective treatment is available, the disease incidence has remained approximately constant for decades so new approaches, such as vaccine or new drugs, are urgently needed for control. Research is however hampered by the pathogen’s obligate intracellular lifestyle and the fact that it has never been grown in vitro. Consequently, despite the availability of its complete genome sequence, fundamental questions regarding the biology of the pathogen, such as its metabolism, remain largely unexplored. In order to explore the metabolism of the leprosy bacillus with a long-term aim of developing a medium to grow the pathogen in vitro, we reconstructed an in silico genome scale metabolic model of the bacillus, GSMN-ML. The model was used to explore the growth and biomass production capabilities of the pathogen with a range of nutrient sources, such as amino acids, glucose, glycerol and metabolic intermediates. We also used the model to analyze RNA-seq data from M. leprae grown in mouse foot pads, and performed Differential Producibility Analysis (DPA) to identify metabolic pathways that appear to be active during intracellular growth of the pathogen, which included pathways for central carbon metabolism, co-factor, lipids, amino acids, nucleotides and cell wall synthesis. The GSMN-ML model is thereby a useful in silico tool that can be used to explore the metabolism of the leprosy bacillus, analyze functional genomic experimental data, generate predictions of nutrients required for growth of the bacillus in vitro and identify novel drug targets.Author SummaryMycobacterium leprae, the obligate human pathogen is uncultivable in axenic growth medium, and this hinders research on this pathogen, and the pathogenesis of leprosy. The development of novel therapeutics relies on the understanding of growth, survival and metabolism of this bacterium in the host, the knowledge of which is currently very limited. Here we reconstructed a metabolic network of M. leprae-GSMN-ML, a powerful in silico tool to study growth and metabolism of the leprosy bacillus. We demonstrate the application of GSMN-ML to identify the metabolic pathways, and metabolite classes that M. leprae utilizes during intracellular growth.

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Yeast optimizes metal utilization based on metabolic network and enzyme kinetics

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2020154118 ◽

2021 ◽

Vol 118 (12) ◽

pp. e2020154118

Author(s):

Yu Chen ◽

Feiran Li ◽

Jiwei Mao ◽

Yun Chen ◽

Jens Nielsen

Keyword(s):

Metal Ions ◽

Enzyme Kinetics ◽

Metabolic Network ◽

Metal Ion ◽

Metabolic Flux ◽

Metabolic Model ◽

Enzymatic Reactions ◽

Cell Factories ◽

A Genome ◽

Genome Scale

Metal ions are vital to metabolism, as they can act as cofactors on enzymes and thus modulate individual enzymatic reactions. Although many enzymes have been reported to interact with metal ions, the quantitative relationships between metal ions and metabolism are lacking. Here, we reconstructed a genome-scale metabolic model of the yeast Saccharomyces cerevisiae to account for proteome constraints and enzyme cofactors such as metal ions, named CofactorYeast. The model is able to estimate abundances of metal ions binding on enzymes in cells under various conditions, which are comparable to measured metal ion contents in biomass. In addition, the model predicts distinct metabolic flux distributions in response to reduced levels of various metal ions in the medium. Specifically, the model reproduces changes upon iron deficiency in metabolic and gene expression levels, which could be interpreted by optimization principles (i.e., yeast optimizes iron utilization based on metabolic network and enzyme kinetics rather than preferentially targeting iron to specific enzymes or pathways). At last, we show the potential of using the model for understanding cell factories that harbor heterologous iron-containing enzymes to synthesize high-value compounds such as p-coumaric acid. Overall, the model demonstrates the dependence of enzymes on metal ions and links metal ions to metabolism on a genome scale.

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Reprogramming of the apoplast metabolome of Lolium perenne upon infection with the mutualistic symbiont Epichloë festucae

10.1101/861450 ◽

2019 ◽

Author(s):

Kimberly A Green ◽

Daniel Berry ◽

Kirstin Feussner ◽

Carla J. Eaton ◽

Arvina Ram ◽

...

Keyword(s):

Lolium Perenne ◽

Spectrometric Analysis ◽

Symbiotic Association ◽

Wild Type ◽

In Planta ◽

Gene Encoding ◽

Epichloë Festucae ◽

Interactive Tools ◽

Metabolite Database ◽

Secondary Metabolite Gene Cluster

SummaryEpichloë festucae is an endophytic fungus that forms a mutualistic symbiotic association with Lolium perenne. Here we analysed how the metabolome of the ryegrass apoplast changed upon infection of this host with sexual and asexual isolates of E. festucae. A metabolite fingerprinting approach was used to analyse the metabolite composition of apoplastic wash fluid from non-infected and infected L. perenne. Metabolites enriched or depleted in one or both of these treatments were identified using a set of interactive tools. A genetic approach in combination with tandem mass spectrometry was used to identify a novel product of a secondary metabolite gene cluster. Metabolites likely to be present in the apoplast were identified using the MarVis Pathway in combination with the BioCyc and KEGG databases, and an in-house Epichloë metabolite database. We were able to identify the known endophyte-specific metabolites, peramine and epichloëcyclins, as well as a large number of unknown markers. To determine whether these methods can be applied to the identification of novel Epichloë-derived metabolites, we deleted a gene encoding a NRPS (lgsA) that is highly expressed in planta. Comparative mass spectrometric analysis of apoplastic wash fluid from wild-type- versus mutant- infected plants identified a novel Leu/Ile glycoside metabolite present in the former.

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Using a Genome-Scale Metabolic Model of Enterococcus faecalis V583 To Assess Amino Acid Uptake and Its Impact on Central Metabolism

Applied and Environmental Microbiology ◽

10.1128/aem.03279-14 ◽

2014 ◽

Vol 81 (5) ◽

pp. 1622-1633 ◽

Cited By ~ 18

Author(s):

Nadine Veith ◽

Margrete Solheim ◽

Koen W. A. van Grinsven ◽

Brett G. Olivier ◽

Jennifer Levering ◽

...

Keyword(s):

Amino Acids ◽

Amino Acid ◽

Metabolic Network ◽

Drug Targets ◽

Metabolic Model ◽

Content Type ◽

A Genome ◽

Genome Scale ◽

Novel Drug ◽

Novel Drug Targets

ABSTRACTIncreasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogenEnterococcus faecalisV583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles ofE. faecalisgrown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especiallyl-glutamine andl-glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets.

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Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

Journal of Bacteriology ◽

10.1128/jb.01583-07 ◽

2008 ◽

Vol 190 (8) ◽

pp. 2790-2803 ◽

Cited By ~ 175

Author(s):

Matthew A. Oberhardt ◽

Jacek Puchałka ◽

Kimberly E. Fryer ◽

Vítor A. P. Martins dos Santos ◽

Jason A. Papin

Keyword(s):

Pseudomonas Aeruginosa ◽

Metabolic Network ◽

Opportunistic Pathogen ◽

Scale Model ◽

Modeling Framework ◽

Major Life ◽

Metabolic Versatility ◽

A Genome ◽

Scale Properties ◽

Genome Scale

ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.

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