Dataset Information

Tomàs-Gamisans2016 - Genome-Scale Metabolic Model of Pichia pastoris (version 2)

ABSTRACT: Tomàs-Gamisans2016 - Genome-Scale Metabolic Model of Pichia pastoris (version 2) Note: This is iMT1026v2, an update of the original iMT1026 Pichia pastoris' genome-scale metabolic model PMID:26812499. iMT1026v2 has incorporated reactions involved in xylulose-monophosphate cycle of methanol assimilation pathway described in Rußmayer et al., (2015); additionally 9 reactions were modified fixing wrong metabolite assignments. This model is described in the article: Integration and Validation of the Genome-Scale Metabolic Models of Pichia pastoris: A Comprehensive Update of Protein Glycosylation Pathways, Lipid and Energy Metabolism. Tomàs-Gamisans M, Ferrer P, Albiol J PLoS One. 2016 Jan 26;11(1):e0148031. Abstract: MOTIVATION: Genome-scale metabolic models (GEMs) are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is one of the most widely used organisms for heterologous protein expression. There are different GEMs for this methylotrophic yeast of which the most relevant and complete in the published literature are iPP668, PpaMBEL1254 and iLC915. However, these three models differ regarding certain pathways, terminology for metabolites and reactions and annotations. Moreover, GEMs for some species are typically built based on the reconstructed models of related model organisms. In these cases, some organism-specific pathways could be missing or misrepresented. RESULTS: In order to provide an updated and more comprehensive GEM for P. pastoris, we have reconstructed and validated a consensus model integrating and merging all three existing models. In this step a comprehensive review and integration of the metabolic pathways included in each one of these three versions was performed. In addition, the resulting iMT1026 model includes a new description of some metabolic processes. Particularly new information described in recently published literature is included, mainly related to fatty acid and sphingolipid metabolism, glycosylation and cell energetics. Finally the reconstructed model was tested and validated, by comparing the results of the simulations with available empirical physiological datasets results obtained from a wide range of experimental conditions, such as different carbon sources, distinct oxygen availability conditions, as well as producing of two different recombinant proteins. In these simulations, the iMT1026 model has shown a better performance than the previous existing models. This model is hosted on BioModels Database and identified by: MODEL1508040001. To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Marius Tomas-Gamisans

PROVIDER: MODEL1508040001 | BioModels | 2016-02-10

REPOSITORIES: BioModels

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Publications

Integration and Validation of the Genome-Scale Metabolic Models of Pichia pastoris: A Comprehensive Update of Protein Glycosylation Pathways, Lipid and Energy Metabolism.

Tomàs-Gamisans Màrius M Ferrer Pau P Albiol Joan J

PloS one 20160126 1

<h4>Motivation</h4>Genome-scale metabolic models (GEMs) are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is on ...[more]

PMID: 26812499

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Project description:Caspeta2012 - Genome-scale metabolic network of Pichia pastoris (iLC915) This model is described in the article: Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials. Caspeta L, Shoaie S, Agren R, Nookaew I, Nielsen J. BMC Syst Biol 2012; 6: 24 Abstract: BACKGROUND: Pichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research. RESULTS: In this work, genome-scale metabolic models (GEMs) of P. stipitis (iSS884) and P. pastoris (iLC915) were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed. CONCLUSIONS: In conclusion the first GEM of P. stipitis (iSS884) was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials. This model is hosted on BioModels Database and identified by: MODEL1507180065. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Caspeta2012 - Genome-scale metabolic network of Pichia stipitis (iSS884) This model is described in the article: Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials. Caspeta L, Shoaie S, Agren R, Nookaew I, Nielsen J. BMC Syst Biol 2012; 6: 24 Abstract: BACKGROUND: Pichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research. RESULTS: In this work, genome-scale metabolic models (GEMs) of P. stipitis (iSS884) and P. pastoris (iLC915) were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed. CONCLUSIONS: In conclusion the first GEM of P. stipitis (iSS884) was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials. This model is hosted on BioModels Database and identified by: MODEL1507180022. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Over the past three decades, due to the universal application of Pichia yeast in the fermentation industry as well as the establishment of Pichia pastoris fermentation process for more than 30 years, the technology of the whole process has become very mature and has now reached a stagnated period of growth. However, studies and research conducted on the genomics of the classic fermentation process and the uncovering of the biological phenomena in the fermentation process from the point of view of high-throughput gene or protein is still in its early stages, and there is still insufficient data within this field. First, in collaboration with Agilent Company, we designed and prepared an expression microarray that could be used for the detection of Pichia pastoris transcriptomics. The transcriptomic changes in the five key technology steps (time points), during the fermentation process of Pichia pastoris would then be detected with the aid of an expression microarray. The five key steps of technology described above formed two important biological processes, namely, the limiting carbon source replacement and secondly, the fermentative production of exogenous proteins. The biological phenomena involved in these two processes were displayed and analyzed at the transcriptional level. In addition to this, with regard to the most important function in the fermentation process of Pichia pastoris, oxid-reduction, the metabolic drift process was analyzed and the important genes that might dominate the changes in the metabolic flux were discovered creatively by using the function tree method in this paper. This study was undertaken from the point of view of the transcriptome and the biological phenomena in the fermemntation process of Pichia pastoris. Both of which, were thoroughly explained during this study. The hope is for many more researchers to optimize the strain fermentation process, to produce proteins at the genetic level, as well as providing and obtaining new perspectives and detailed scientific data for the continued development within this field. The transcriptomic changes in the five key technology steps (time points), during the fermentation process of Pichia pastoris would then be detected with the aid of an expression microarray.

Dataset Information

Tomàs-Gamisans2016 - Genome-Scale Metabolic Model of Pichia pastoris (version 2)

Publications

Integration and Validation of the Genome-Scale Metabolic Models of Pichia pastoris: A Comprehensive Update of Protein Glycosylation Pathways, Lipid and Energy Metabolism.

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