Improved production of monoclonal antibodies through oxygen-limited cultivation of glycoengineered yeast

Berdichevsky, Marina; d’Anjou, Marc; Mallem, Muralidhar; Shaikh, Seemab S.; Potgieter, Thomas I.

doi:10.1016/j.jbiotec.2011.06.021

Cited by 38 publications

(29 citation statements)

References 23 publications

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“…However, an improvement of the production of monoclonal antibodies under oxygen-limited cultivation of glycoengineered yeast has been reported [48]. …”

Section: Resultsmentioning

confidence: 99%

Comprehensive clone screening and evaluation of fed-batch strategies in a microbioreactor and lab scale stirred tank bioreactor system: application on Pichia pastoris producing Rhizopus oryzae lipase

Hemmerich¹,

Adelantado

Barrigón

et al. 2014

Microb Cell Fact

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BackgroundIn Pichia pastoris bioprocess engineering, classic approaches for clone selection and bioprocess optimization at small/micro scale using the promoter of the alcohol oxidase 1 gene (PAOX1), induced by methanol, present low reproducibility leading to high time and resource consumption.ResultsAn automated microfermentation platform (RoboLector) was successfully tested to overcome the chronic problems of clone selection and optimization of fed-batch strategies. Different clones from Mut+P. pastoris phenotype strains expressing heterologous Rhizopus oryzae lipase (ROL), including a subset also overexpressing the transcription factor HAC1, were tested to select the most promising clones.The RoboLector showed high performance for the selection and optimization of cultivation media with minimal cost and time. Syn6 medium was better than conventional YNB medium in terms of production of heterologous protein.The RoboLector microbioreactor was also tested for different fed-batch strategies with three clones producing different lipase levels. Two mixed substrates fed-batch strategies were evaluated. The first strategy was the enzymatic release of glucose from a soluble glucose polymer by a glucosidase, and methanol addition every 24 hours. The second strategy used glycerol as co-substrate jointly with methanol at two different feeding rates. The implementation of these simple fed-batch strategies increased the levels of lipolytic activity 80-fold compared to classical batch strategies used in clone selection. Thus, these strategies minimize the risk of errors in the clone selection and increase the detection level of the desired product.Finally, the performance of two fed-batch strategies was compared for lipase production between the RoboLector microbioreactor and 5 liter stirred tank bioreactor for three selected clones. In both scales, the same clone ranking was achieved.ConclusionThe RoboLector showed excellent performance in clone selection of P. pastoris Mut+ phenotype. The use of fed-batch strategies using mixed substrate feeds resulted in increased biomass and lipolytic activity. The automated processing of fed-batch strategies by the RoboLector considerably facilitates the operation of fermentation processes, while reducing error-prone clone selection by increasing product titers.The scale-up from microbioreactor to lab scale stirred tank bioreactor showed an excellent correlation, validating the use of microbioreactor as a powerful tool for evaluating fed-batch operational strategies.

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“…However, an improvement of the production of monoclonal antibodies under oxygen-limited cultivation of glycoengineered yeast has been reported [48]. …”

Section: Resultsmentioning

confidence: 99%

Comprehensive clone screening and evaluation of fed-batch strategies in a microbioreactor and lab scale stirred tank bioreactor system: application on Pichia pastoris producing Rhizopus oryzae lipase

Hemmerich¹,

Adelantado

Barrigón

et al. 2014

Microb Cell Fact

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“…On the other hand, it has been reported that specific productivity of antibodies produced by a glycoengineered P. pastoris in high cell-density cultivation was improved through oxygen-limited cultivation. Moreover, the oxygen-limited process was successfully scaled to 1200 L-manufacturing pilot scale, which showed not only increased antibody concentration (up to 1.9 g L À1 ) but also improved N-glycosylation pattern and reduced antibody fragmentation (Berdichevsky et al, 2011). Thus, it is apparent that intensive efforts are needed to develop physiologically rational and suitable cultivation process with protein-specific adjustments to upgrade yield and quality of recombinant proteins.…”

Section: Perspectives Of Yeast Factories For Recombinant Protein Prodmentioning

confidence: 99%

Yeast synthetic biology for the production of recombinant therapeutic proteins

2014

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The production of recombinant therapeutic proteins is one of the fast-growing areas of molecular medicine and currently plays an important role in treatment of several diseases. Yeasts are unicellular eukaryotic microbial host cells that offer unique advantages in producing biopharmaceutical proteins. Yeasts are capable of robust growth on simple media, readily accommodate genetic modifications, and incorporate typical eukaryotic post-translational modifications. Saccharomyces cerevisiae is a traditional baker's yeast that has been used as a major host for the production of biopharmaceuticals; however, several nonconventional yeast species including Hansenula polymorpha, Pichia pastoris, and Yarrowia lipolytica have gained increasing attention as alternative hosts for the industrial production of recombinant proteins. In this review, we address the established and emerging genetic tools and host strains suitable for recombinant protein production in various yeast expression systems, particularly focusing on current efforts toward synthetic biology approaches in developing yeast cell factories for the production of therapeutic recombinant proteins.

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“…However, data on transcriptional regulatory networks and gene expression profi les is becoming available for a growing number of organisms, inducing us to include the generation of transcriptional regulatory constraints in our discussion of the genome annotation and metabolic model reconstruction process. Different strategies have been employed to reconstruct transcriptional regulatory networks: (1) propagation of template networks to close genomes ( see Note 14 ) [ 53 ], (2) prediction of cis -regulatory elements ( see Note 15 ) [ 54 ] and (3) reverse engineering from expression data ( see Note 16 ) [ 55 ]. These methods enable a characterization of regulatory networks, but an integration with metabolic models is required for the application of regulatory network knowledge to the prediction of phenotypes.…”

Section: Curation Of Draft Metabolic Modelmentioning

confidence: 99%