Control of specific growth rate to enhance the production of a novel disintegrin, saxatilin, in recombinant Pichia pastoris

Min, Cheol Ki; Lee, Jinwon; Chung, Kyeong Woo; Park, Hong Woo

doi:10.1016/j.jbiosc.2010.03.013

Cited by 19 publications

(13 citation statements)

References 29 publications

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“…Typically, production kinetics controlled by the AOX1-promotor, utilising methanol or mixtures of substrates with methanol, were investigated for µ < 0.08 h -1 (Curvers et al, 2001b, Zhang et al, 2005 or even µ < 0.03 h -1 (Hang et al, 2008, Kobayashi et al, 2000, Min et al, 2010, Potgieter et al, 2010. Only two kinetic-relationships with low q p -values (< 0.03 mg l -1 h -1 ) were investigated up to a µ of 0.14 h -1 (Jungo et al, 2006, Schenk et al, 2008.…”

Section: Production Kineticsmentioning

confidence: 99%

“…increasingly or decreasingly linear, hyperbolic or bell-shaped [e.g. (Bhatacharya et al, 2007, Curvers et al, 2001b, Graslund et al, 2008, Hang et al, 2008, Jungo et al, 2006, Kobayashi et al, 2000, Min et al, 2010, Potgieter et al, 2010, Schenk et al, 2008, Zhang et al, 2005]. Nevertheless, for strains containing the GAP-promoter, product formation was found to increase with specific growth rates almost up to µ max (Khasa et al, 2007, Maurer et al, 2006.…”

Section: Production Kineticsmentioning

confidence: 99%

“…For AOX1 Mut + strains cultured with methanol, the optimum specific growth rate for product formation (µ opt at q p,max ) ranged from low µ-values of 0.002 h -1 to values up to µ max of 0.14 h -1 (Table 3). Processes with low optimal µ-values < 0.004 h -1 showed negative growth-related relationships between production and growth (Kobayashi et al, 2000, Min et al, 2010, processes with optimal µ-values between (0.014 -0.07) h -1 tended towards bell-shaped or complex relationships (Hang et al, 2008, Paulova et al, 2012, Potgieter et al, 2010, Yamawaki et al, 2007, Zhang et al, 2005 and for processes with µ opt > 0.08 h -1 , product formation was directly proportional to biomass growth (Curvers et al, 2001b, d'Anjou and Daugulis, 2001, Jungo et al, 2006, Schenk et al, 2008. Interestingly, AOX1-controlled product formation, performed with substrate mixtures of either methanol and glucose (Paulova et al, 2012) or methanol and glycerol (d'Anjou and Daugulis, 2001), showed higher specific growth rates and kinetics similar to GAP-processes with glucose.…”

Section: Production Kineticsmentioning

confidence: 99%

See 2 more Smart Citations

Cultivation strategies to enhance productivity of Pichia pastoris: A review

Looser

Brühlmann

Bumbak

et al. 2015

Biotechnology Advances

297

268

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Pichia pastoris, a methylotrophic yeast, is an established system for the production of heterologous proteins, particularly biopharmaceuticals and industrial enzymes. To maximise and optimise the production of recombinant products, recent molecular research has focused on numerous issues including the design of expression vectors, optimisation of gene copy number, co-expression of secretory proteins such as chaperones, engineering of glycosylation and secretory pathways, etc. However, the physiological effects of different cultivation strategies are often difficult to separate from the molecular effects of the gene construct (e.g., cellular stress through over-expression or incorrect post-translational processing). Hence, overall system optimisation is difficult, even though it is urgently required in order to describe and understand the behaviour of new molecular constructs. This review focuses on particular aspects of recombinant protein production related to variations in biomass growth and their implications for strain design and screening, as well as on the concept of rational comparisons between cultivation systems for the development of specific production processes in bioreactors. The relationship between specific formation rates of secreted recombinant proteins, qp, and specific growth rates, μ, has been analysed in a conceptual attempt to compare different systems, particularly those based on AOX1/methanol and GAP/glucose, and this has now evolved into a pivotal concept for bioprocess engineering of P. pastoris.

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Section: Production Kineticsmentioning

confidence: 99%

Section: Production Kineticsmentioning

confidence: 99%

Section: Production Kineticsmentioning

confidence: 99%

See 1 more Smart Citation

Cultivation strategies to enhance productivity of Pichia pastoris: A review

Looser

Brühlmann

Bumbak

et al. 2015

Biotechnology Advances

297

268

View full text Add to dashboard Cite

show abstract

“…A tendency to move toward dual-stage fed-batch processes can be observed (Smets et al 2004;Babaeipour et al 2007;Min et al 2010). Indeed, fed-batch cultures, where the specific growth rate is first maintained at a value that allows maximizing the specific biomass productivity without leading to overflow metabolite formation then maintained at low values after induction to ensure highest specific product formation rate and product quality, might be economically interesting, leading to an increased overall productivity of the process.…”

Section: Discussionmentioning

confidence: 99%

“…The ability to maintain cells in a particular metabolic or physiological state related to μ, promoting a particular metabolism for the production of secondary metabolites, such as prodigiosins (Chang et al 2011), is of particular relevance to biotechnology and pharmaceutical industries. Generally speaking, the specific productivity, recombinant protein production rate, and secretory expression in carbon-limited fed-batch cultures is often inversely correlated to the specific growth rate of the microorganism (Jenzsch et al 2006)-hence the importance of maintaining low specific growth rates, at least during protein expression phase, to maximize product yield (Panda et al 1999;Hellwig et al 2001;Priego-Jimenez et al 2005;Ren and Yuan 2005;Jenzsch et al 2006;Babaeipour et al 2007;Min et al 2010;Puertas et al 2010).…”

Section: Product Formation Rate and Product Quantitymentioning

confidence: 99%

Real-time monitoring and control of microbial bioprocesses with focus on the specific growth rate: current state and perspectives

Schuler

Marison

2012

Appl Microbiol Biotechnol

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Understanding the growth characteristics of microorganisms is an essential step in bioprocessing, not only because product formation may be growth-associated but also because they might influence cell physiology and thereby product quality. The specific growth rate, a key variable of many bioprocesses, cannot be measured directly and relies on the estimation through other measurable variables such as biomass, substrate, or product concentrations. Techniques for real-time estimation of the specific growth rate in microbial fed-batch cultures are discussed in the present paper. The advantages and limitations of different models and various monitoring techniques are discussed, highlighting the importance of the specific growth rate in the development of fast, reliable, and robust processes for the production of high-value products such as recombinant proteins.

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Improving phytase production in Pichia pastoris fermentations through de‐repression and methanol induction optimization

Luna‐Flores,

Weng,

Wang

et al. 2023

Biotech & Bioengineering

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Pichia pastoris (Komagataella phaffii) is a fast‐growing methylotrophic yeast with the ability to assimilate several carbon sources such as methanol, glucose, or glycerol. It has been shown to have outstanding secretion capability with a variety of heterologous proteins. In previous studies, we engineered P. pastoris to co‐express Escherichia coli AppA phytase and the HAC1 transcriptional activator using a bidirectional promoter. Phytase production was characterized in shake flasks and did not reflect industrial conditions. In the present study, phytase expression was explored and optimized using instrumented fermenters in continuous and fed‐batch modes. First, the production of phytase was investigated under glucose de‐repression in continuous culture at three dilution factors, 0.5 d−1, 1 d−1, and 1.5 d−1. The fermenter parameters of these cultures were used to inform a kinetic model in batch and fed‐batch modes for growth and phytase production. The kinetic model developed aided to design the glucose‐feeding profile of a fed‐batch culture. Kinetic model simulations under glucose de‐repression and fed‐batch conditions identified optimal phytase productivity at the specific growth rate of 0.041 h−1. Validation of the model simulation with experimental data confirmed the feasibility of the model to predict phytase production in our newly engineered strain. Methanol was used only to induce the expression of phytase at high cell densities. Our results showed that high phytase production required two stages, the first stage used glucose under de‐repression conditions to generate biomass while expressing phytase, and stage two used methanol to induce phytase expression. The production of phytase was improved 3.5‐fold by methanol induction compared to the expression with glucose alone under de‐repression conditions to a final phytase activity of 12.65 MU/L. This final volumetric phytase production represented an approximate 36‐fold change compared to the flask fermentations. Finally, the phytase protein produced was assayed to confirm its molecular weight, and pH and temperature profiles. This study highlights the importance of optimizing protein production in P. pastoris when using novel promoters and presents a general approach to performing bioprocess optimization in this important production host.

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Control of specific growth rate to enhance the production of a novel disintegrin, saxatilin, in recombinant Pichia pastoris

Cited by 19 publications

References 29 publications

Cultivation strategies to enhance productivity of Pichia pastoris: A review

Cultivation strategies to enhance productivity of Pichia pastoris: A review

Real-time monitoring and control of microbial bioprocesses with focus on the specific growth rate: current state and perspectives

Improving phytase production in Pichia pastoris fermentations through de‐repression and methanol induction optimization

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