Literature DB >> 24678285

Revising the Representation of Fatty Acid, Glycerolipid, and Glycerophospholipid Metabolism in the Consensus Model of Yeast Metabolism.

Hnin W Aung1, Susan A Henry2, Larry P Walker1.   

Abstract

Genome-scale metabolic models are built using information from an organism's annotated genome and, correspondingly, information on reactions catalyzed by the set of metabolic enzymes encoded by the genome. These models have been successfully applied to guide metabolic engineering to increase production of metabolites of industrial interest. Congruity between simulated and experimental metabolic behavior is influenced by the accuracy of the representation of the metabolic network in the model. In the interest of applying the consensus model of Saccharomyces cerevisiae metabolism for increased productivity of triglycerides, we manually evaluated the representation of fatty acid, glycerophospholipid, and glycerolipid metabolism in the consensus model (Yeast v6.0). These areas of metabolism were chosen due to their tightly interconnected nature to triglyceride synthesis. Manual curation was facilitated by custom MATLAB functions that return information contained in the model for reactions associated with genes and metabolites within the stated areas of metabolism. Through manual curation, we have identified inconsistencies between information contained in the model and literature knowledge. These inconsistencies include incorrect gene-reaction associations, improper definition of substrates/products in reactions, inappropriate assignments of reaction directionality, nonfunctional β-oxidation pathways, and missing reactions relevant to the synthesis and degradation of triglycerides. Suggestions to amend these inconsistencies in the Yeast v6.0 model can be implemented through a MATLAB script provided in theSupplementary Materials, Supplementary Data S1(Supplementary Data are available online at www.liebertpub.com/ind).

Entities:  

Year:  2013        PMID: 24678285      PMCID: PMC3963290          DOI: 10.1089/ind.2013.0013

Source DB:  PubMed          Journal:  Ind Biotechnol (New Rochelle N Y)        ISSN: 1550-9087


  54 in total

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Authors:  Sarah M Keating; Benjamin J Bornstein; Andrew Finney; Michael Hucka
Journal:  Bioinformatics       Date:  2006-03-30       Impact factor: 6.937

Review 2.  Metabolism of phosphatidylcholine and its implications for lipid acyl chain composition in Saccharomyces cerevisiae.

Authors:  Anton I P M de Kroon
Journal:  Biochim Biophys Acta       Date:  2006-08-02

3.  Isolation and characterization of OLE1, a gene affecting fatty acid desaturation from Saccharomyces cerevisiae.

Authors:  J E Stukey; V M McDonough; C E Martin
Journal:  J Biol Chem       Date:  1989-10-05       Impact factor: 5.157

4.  Identification of a mitochondrial transporter for pyrimidine nucleotides in Saccharomyces cerevisiae: bacterial expression, reconstitution and functional characterization.

Authors:  Carlo Marya Thomas Marobbio; Maria Antonietta Di Noia; Ferdinando Palmieri
Journal:  Biochem J       Date:  2006-01-15       Impact factor: 3.857

5.  The OLE1 gene of Saccharomyces cerevisiae encodes the delta 9 fatty acid desaturase and can be functionally replaced by the rat stearoyl-CoA desaturase gene.

Authors:  J E Stukey; V M McDonough; C E Martin
Journal:  J Biol Chem       Date:  1990-11-25       Impact factor: 5.157

Review 6.  Metabolism and regulation of glycerolipids in the yeast Saccharomyces cerevisiae.

Authors:  Susan A Henry; Sepp D Kohlwein; George M Carman
Journal:  Genetics       Date:  2012-02       Impact factor: 4.562

Review 7.  Applications of genome-scale metabolic reconstructions.

Authors:  Matthew A Oberhardt; Bernhard Ø Palsson; Jason A Papin
Journal:  Mol Syst Biol       Date:  2009-11-03       Impact factor: 11.429

8.  The Saccharomyces cerevisiae YBR159w gene encodes the 3-ketoreductase of the microsomal fatty acid elongase.

Authors:  Gongshe Han; Ken Gable; Sepp D Kohlwein; Frédéric Beaudoin; Johnathan A Napier; Teresa M Dunn
Journal:  J Biol Chem       Date:  2002-06-26       Impact factor: 5.157

9.  Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling.

Authors:  Tobias Österlund; Intawat Nookaew; Sergio Bordel; Jens Nielsen
Journal:  BMC Syst Biol       Date:  2013-04-30

10.  The membrane of peroxisomes in Saccharomyces cerevisiae is impermeable to NAD(H) and acetyl-CoA under in vivo conditions.

Authors:  C W van Roermund; Y Elgersma; N Singh; R J Wanders; H F Tabak
Journal:  EMBO J       Date:  1995-07-17       Impact factor: 11.598

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  64 in total

1.  Predicting growth rate from gene expression.

Authors:  Thomas P Wytock; Adilson E Motter
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-21       Impact factor: 11.205

2.  ChIP-exo analysis highlights Fkh1 and Fkh2 transcription factors as hubs that integrate multi-scale networks in budding yeast.

Authors:  Thierry D G A Mondeel; Petter Holland; Jens Nielsen; Matteo Barberis
Journal:  Nucleic Acids Res       Date:  2019-09-05       Impact factor: 16.971

Review 3.  Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test.

Authors:  Farhana R Pinu; Ninna Granucci; James Daniell; Ting-Li Han; Sonia Carneiro; Isabel Rocha; Jens Nielsen; Silas G Villas-Boas
Journal:  Metabolomics       Date:  2018-03-02       Impact factor: 4.290

4.  Mimoza: web-based semantic zooming and navigation in metabolic networks.

Authors:  Anna Zhukova; David J Sherman
Journal:  BMC Syst Biol       Date:  2015-02-26

5.  Systems-level analysis of mechanisms regulating yeast metabolic flux.

Authors:  Sean R Hackett; Vito R T Zanotelli; Wenxin Xu; Jonathan Goya; Junyoung O Park; David H Perlman; Patrick A Gibney; David Botstein; John D Storey; Joshua D Rabinowitz
Journal:  Science       Date:  2016-10-27       Impact factor: 47.728

Review 6.  Applications of genome-scale metabolic network model in metabolic engineering.

Authors:  Byoungjin Kim; Won Jun Kim; Dong In Kim; Sang Yup Lee
Journal:  J Ind Microbiol Biotechnol       Date:  2014-12-03       Impact factor: 3.346

7.  A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data.

Authors:  Narayanan Sadagopan; Yiping Wang; Brandon E Barker; Kieran Smallbone; Christopher R Myers; Hongwei Xi; Jason W Locasale; Zhenglong Gu
Journal:  Comput Biol Chem       Date:  2015-09-01       Impact factor: 2.877

8.  Laboratory evolution for forced glucose-xylose co-consumption enables identification of mutations that improve mixed-sugar fermentation by xylose-fermenting Saccharomyces cerevisiae.

Authors:  Ioannis Papapetridis; Maarten D Verhoeven; Sanne J Wiersma; Maaike Goudriaan; Antonius J A van Maris; Jack T Pronk
Journal:  FEMS Yeast Res       Date:  2018-09-01       Impact factor: 2.796

9.  Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction.

Authors:  Benjamin D Heavner; Nathan D Price
Journal:  PLoS Comput Biol       Date:  2015-11-13       Impact factor: 4.475

Review 10.  Synthetic Ecology of Microbes: Mathematical Models and Applications.

Authors:  Ali R Zomorrodi; Daniel Segrè
Journal:  J Mol Biol       Date:  2015-11-11       Impact factor: 5.469

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