Literature DB >> 27129458

Hybrid metabolic flux analysis and recombinant protein prediction in Pichia pastoris X-33 cultures expressing a single-chain antibody fragment.

Inês A Isidro1,2, Rui M Portela1, João J Clemente2, António E Cunha2, Rui Oliveira3,4,5.   

Abstract

Despite the growing importance of the Pichia pastoris expression system as industrial workhorse, the literature is almost absent in systematic studies on how culture medium composition affects central carbon fluxes and heterologous protein expression. In this study we investigate how 26 variations of the BSM+PTM1 medium impact central carbon fluxes and protein expression in a P. pastoris X-33 strain expressing a single-chain antibody fragment. To achieve this goal, we adopted a hybrid metabolic flux analysis (MFA) methodology, which is a modification of standard MFA to predict the rate of synthesis of recombinant proteins. Hybrid MFA combines the traditional parametric estimation of central carbon fluxes with non-parametric statistical modeling of product-related quantitative or qualitative measurements as a function of central carbon fluxes. It was observed that protein yield variability was 53.6 % (relative standard deviation) among the different experiments. Protein yield is much more sensitive to medium composition than biomass growth, which is mainly determined by the carbon source availability and main salts. Hybrid MFA was able to describe accurately the protein yield with normalized RMSE of 6.3 % over 5 independent experiments. The metabolic state that promotes high protein yields is characterized by high overall metabolic rates through main central carbon pathways concomitantly with a relative shift of carbon flux from biosynthetic towards energy generating pathways.

Entities:  

Keywords:  Central carbon fluxes; Culture media; Hybrid MFA; Pichia pastoris X-33; Protein expression prediction

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Year:  2016        PMID: 27129458     DOI: 10.1007/s00449-016-1611-z

Source DB:  PubMed          Journal:  Bioprocess Biosyst Eng        ISSN: 1615-7591            Impact factor:   3.210


  4 in total

1.  Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis.

Authors:  João R C Ramos; Gil P Oliveira; Patrick Dumas; Rui Oliveira
Journal:  Bioprocess Biosyst Eng       Date:  2022-10-16       Impact factor: 3.434

2.  Methyl-selective isotope labeling using α-ketoisovalerate for the yeast Pichia pastoris recombinant protein expression system.

Authors:  Rika Suzuki; Masayoshi Sakakura; Masaki Mori; Moe Fujii; Satoko Akashi; Hideo Takahashi
Journal:  J Biomol NMR       Date:  2018-06-05       Impact factor: 2.835

Review 3.  Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective.

Authors:  Anne Richelle; Blandine David; Didier Demaegd; Marianne Dewerchin; Romain Kinet; Angelo Morreale; Rui Portela; Quentin Zune; Moritz von Stosch
Journal:  NPJ Syst Biol Appl       Date:  2020-03-13

4.  Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses.

Authors:  André Moser; Kim B Kuchemüller; Sahar Deppe; Tanja Hernández Rodríguez; Björn Frahm; Ralf Pörtner; Volker C Hass; Johannes Möller
Journal:  Bioprocess Biosyst Eng       Date:  2021-01-20       Impact factor: 3.210

  4 in total

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