Cargando…

Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis

BACKGROUND: Fermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis) has the highest known native capacity for xylose fermentation and p...

Descripción completa

Detalles Bibliográficos
Autores principales: Balagurunathan, Balaji, Jonnalagadda, Sudhakar, Tan, Lily, Srinivasan, Rajagopalan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3310799/
https://www.ncbi.nlm.nih.gov/pubmed/22356827
http://dx.doi.org/10.1186/1475-2859-11-27
_version_ 1782227704472403968
author Balagurunathan, Balaji
Jonnalagadda, Sudhakar
Tan, Lily
Srinivasan, Rajagopalan
author_facet Balagurunathan, Balaji
Jonnalagadda, Sudhakar
Tan, Lily
Srinivasan, Rajagopalan
author_sort Balagurunathan, Balaji
collection PubMed
description BACKGROUND: Fermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis) has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes. RESULTS: We present a genome-scale metabolic model for Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation. CONCLUSION: The genome-scale metabolic model developed for Scheffersomyces stipitis successfully predicted substrate utilization and anaerobic growth requirements. Useful insights were drawn on xylose metabolism, cofactor recycling and mechanism of mitochondrial respiration from model simulations. These insights can be applied for efficient xylose utilization and cofactor recycling in other industrial microorganisms. The developed model forms a basis for rational analysis and design of Scheffersomyces stipitis metabolic network for the production of fuels and chemicals from lignocellulosic biomass.
format Online
Article
Text
id pubmed-3310799
institution National Center for Biotechnology Information
language English
publishDate 2012
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-33107992012-03-23 Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis Balagurunathan, Balaji Jonnalagadda, Sudhakar Tan, Lily Srinivasan, Rajagopalan Microb Cell Fact Research BACKGROUND: Fermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis) has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes. RESULTS: We present a genome-scale metabolic model for Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation. CONCLUSION: The genome-scale metabolic model developed for Scheffersomyces stipitis successfully predicted substrate utilization and anaerobic growth requirements. Useful insights were drawn on xylose metabolism, cofactor recycling and mechanism of mitochondrial respiration from model simulations. These insights can be applied for efficient xylose utilization and cofactor recycling in other industrial microorganisms. The developed model forms a basis for rational analysis and design of Scheffersomyces stipitis metabolic network for the production of fuels and chemicals from lignocellulosic biomass. BioMed Central 2012-02-23 /pmc/articles/PMC3310799/ /pubmed/22356827 http://dx.doi.org/10.1186/1475-2859-11-27 Text en Copyright ©2012 Balagurunathan et al; BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Balagurunathan, Balaji
Jonnalagadda, Sudhakar
Tan, Lily
Srinivasan, Rajagopalan
Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis
title Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis
title_full Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis
title_fullStr Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis
title_full_unstemmed Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis
title_short Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis
title_sort reconstruction and analysis of a genome-scale metabolic model for scheffersomyces stipitis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3310799/
https://www.ncbi.nlm.nih.gov/pubmed/22356827
http://dx.doi.org/10.1186/1475-2859-11-27
work_keys_str_mv AT balagurunathanbalaji reconstructionandanalysisofagenomescalemetabolicmodelforscheffersomycesstipitis
AT jonnalagaddasudhakar reconstructionandanalysisofagenomescalemetabolicmodelforscheffersomycesstipitis
AT tanlily reconstructionandanalysisofagenomescalemetabolicmodelforscheffersomycesstipitis
AT srinivasanrajagopalan reconstructionandanalysisofagenomescalemetabolicmodelforscheffersomycesstipitis