The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

Ågren, Rasmus; Liu, Liming; Shoaie, Saeed; Vongsangnak, Wanwipa; Nookaew, Intawat; Nielsen, Jens

doi:10.1371/journal.pcbi.1002980

Cited by 368 publications

(371 citation statements)

References 59 publications

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“…In the model, 74% of the reactions are associated to one or more genes. iHepatocytes2322 was validated with 256 known biological functions of hepatocytes (Supplementary Data 5), based on the definitions by Gille et al 1 , by using the checkTasks function in the RAVEN Toolbox 23 .…”

Section: Methodsmentioning

confidence: 99%

“…We tested iHepatocytes2322 by simulating 256 different biologically defined metabolic functions (for example, the synthesis of FAs, amino acids, cholesterol and bile acids; Supplementary Data 5) that is known to occur in hepatocytes using the RAVEN Toolbox 23 . Furthermore, the ability of iHepatocytes2322 for performing gluconeogenesis was demonstrated using experimentally measured secretion rates for glucose and albumin, and uptake rates for glycerol, lactate, amino acids and FAs in primary rat hepatocytes 24 (Supplementary Data 6).…”

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confidence: 99%

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Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease

et al. 2014

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Several liver disorders result from perturbations in the metabolism of hepatocytes, and their underlying mechanisms can be outlined through the use of genome-scale metabolic models (GEMs). Here we reconstruct a consensus GEM for hepatocytes, which we call iHepatocytes2322, that extends previous models by including an extensive description of lipid metabolism. We build iHepatocytes2322 using Human Metabolic Reaction 2.0 database and proteomics data in Human Protein Atlas, which experimentally validates the incorporated reactions. The reconstruction process enables improved annotation of the proteomics data using the network centric view of iHepatocytes2322. We then use iHepatocytes2322 to analyse transcriptomics data obtained from patients with non-alcoholic fatty liver disease. We show that blood concentrations of chondroitin and heparan sulphates are suitable for diagnosing non-alcoholic steatohepatitis and for the staging of non-alcoholic fatty liver disease. Furthermore, we observe serine deficiency in patients with NASH and identify PSPH, SHMT1 and BCAT1 as potential therapeutic targets for the treatment of non-alcoholic steatohepatitis.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease

et al. 2014

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“…However, for eukaryotes, this automation procedure often proved to be difficult, because of the complex compartmentalization present in these organisms. One tool that enables the automated generation of draft reconstructions suitable for eukaryotes is the RAVEN Toolbox (Agren et al, 2013). In contrast to what has been reported for other Chlorella spp.…”

Section: Reconstructionmentioning

confidence: 98%

“…The draft was generated using the RAVEN Toolbox (Agren et al, 2013). As input, we provided the translated genome sequence of Chlorella vulgaris UTEX 395 and the manually curated reference network of the microalga Chlamydomonas reinhardtii (iRC1080; Chang et al, 2011), which was used as a reference network.…”

Section: Draft Generationmentioning

confidence: 99%

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

Zuñiga

Huelsman

et al. 2016

Plant Physiol.

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The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.

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“…Genome-based metabolic network reconstructions are usually missing reactions because of incompleteness and inaccuracy in all of the preceding steps; a single missing reaction can prevent the network from producing one or more components of the organism's biomass. Gapfilling programs identify those missing reactions to produce a metabolic network that is sufficiently connected to produce a functional model (Agren, et al, 2013;Benedict, et al, 2014;Latendresse, et al, 2012).…”

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confidence: 99%

Bioinformatics computation of metabolic models from sequenced genomes

Karp

Ouzounis

2015

Preprint

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In the early days of the human genome project (HGP), during the late 1980s and early 1990s, there was skepticism that the genome project would produce biologically meaningful information. The reality is that bioinformatics has allowed us to extract far more biology from sequenced genomes than any published predictions in the early 1990s. Thanks to the efforts of many researchers in several subfields of bioinformatics, we can now process a sequenced genome through a series of computations to produce a quantitative metabolic flux model. Thus, surprisingly, bioinformatics has achieved what might have been held up as a holy grail of the field, before the goal was even articulated.

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The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

Cited by 368 publications

References 59 publications

Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease

Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

Bioinformatics computation of metabolic models from sequenced genomes

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