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A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis

Recent advances in the availability and applicability of genetic tools for non-conventional yeasts have raised high hopes regarding the industrial applications of such yeasts; however, quantitative physiological data on these yeasts, including intracellular flux distributions, are scarce and have ra...

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Autores principales: Lehnen, Mathias, Ebert, Birgitta E., Blank, Lars M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5699530/
https://www.ncbi.nlm.nih.gov/pubmed/29188182
http://dx.doi.org/10.1016/j.meteno.2017.07.001
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author Lehnen, Mathias
Ebert, Birgitta E.
Blank, Lars M.
author_facet Lehnen, Mathias
Ebert, Birgitta E.
Blank, Lars M.
author_sort Lehnen, Mathias
collection PubMed
description Recent advances in the availability and applicability of genetic tools for non-conventional yeasts have raised high hopes regarding the industrial applications of such yeasts; however, quantitative physiological data on these yeasts, including intracellular flux distributions, are scarce and have rarely aided in the development of novel yeast applications. The compartmentation of eukaryotic cells adds to model complexity. Model constraints are ideally based on biochemical evidence, which is rarely available for non-conventional yeast and eukaryotic cells. A small-scale model for (13)C-based metabolic flux analysis of central yeast carbon metabolism was developed that is universally valid and does not depend on localization information regarding amino acid anabolism. The variable compartmental origin of traced metabolites is a feature that allows application of the model to yeasts with uncertain genomic and transcriptional backgrounds. The presented test case includes the baker's yeast Saccharomyces cerevisiae and the methylotrophic yeast Hansenula polymorpha. Highly similar flux solutions were computed using either a model with undefined pathway localization or a model with constraints based on curated (S. cerevisiae) or computationally predicted (H. polymorpha) localization information, while false solutions were found with incorrect localization constraints. These results indicate a potentially adverse effect of universally assuming Saccharomyces-like constraints on amino acid biosynthesis for non-conventional yeasts and verify the validity of neglecting compartmentation constraints using a small-scale metabolic model. The model was specifically designed to investigate the intracellular metabolism of wild-type yeasts under various growth conditions but is also expected to be useful for computing fluxes of other eukaryotic cells.
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spelling pubmed-56995302017-11-29 A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis Lehnen, Mathias Ebert, Birgitta E. Blank, Lars M. Metab Eng Commun Article Recent advances in the availability and applicability of genetic tools for non-conventional yeasts have raised high hopes regarding the industrial applications of such yeasts; however, quantitative physiological data on these yeasts, including intracellular flux distributions, are scarce and have rarely aided in the development of novel yeast applications. The compartmentation of eukaryotic cells adds to model complexity. Model constraints are ideally based on biochemical evidence, which is rarely available for non-conventional yeast and eukaryotic cells. A small-scale model for (13)C-based metabolic flux analysis of central yeast carbon metabolism was developed that is universally valid and does not depend on localization information regarding amino acid anabolism. The variable compartmental origin of traced metabolites is a feature that allows application of the model to yeasts with uncertain genomic and transcriptional backgrounds. The presented test case includes the baker's yeast Saccharomyces cerevisiae and the methylotrophic yeast Hansenula polymorpha. Highly similar flux solutions were computed using either a model with undefined pathway localization or a model with constraints based on curated (S. cerevisiae) or computationally predicted (H. polymorpha) localization information, while false solutions were found with incorrect localization constraints. These results indicate a potentially adverse effect of universally assuming Saccharomyces-like constraints on amino acid biosynthesis for non-conventional yeasts and verify the validity of neglecting compartmentation constraints using a small-scale metabolic model. The model was specifically designed to investigate the intracellular metabolism of wild-type yeasts under various growth conditions but is also expected to be useful for computing fluxes of other eukaryotic cells. Elsevier 2017-07-11 /pmc/articles/PMC5699530/ /pubmed/29188182 http://dx.doi.org/10.1016/j.meteno.2017.07.001 Text en © 2017 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lehnen, Mathias
Ebert, Birgitta E.
Blank, Lars M.
A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis
title A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis
title_full A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis
title_fullStr A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis
title_full_unstemmed A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis
title_short A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis
title_sort comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5699530/
https://www.ncbi.nlm.nih.gov/pubmed/29188182
http://dx.doi.org/10.1016/j.meteno.2017.07.001
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