African Journal of
Biotechnology

  • Abbreviation: Afr. J. Biotechnol.
  • Language: English
  • ISSN: 1684-5315
  • DOI: 10.5897/AJB
  • Start Year: 2002
  • Published Articles: 12269

Full Length Research Paper

Development of simple kinetic models and parameter estimation for simulation of recombinant human serum albumin production by Pichia pastoris

Panchiga Chongchittapiban
  • Panchiga Chongchittapiban
  • Department of Chemical Engineering, Faculty of Engineering King Mongkut's University of Technology Thonburi (KMUTT), 126 Pracha-utid Road, Bangmod, Toongkru, Bangkok 10140, Thailand.
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Jӧrgen Borg
  • Jӧrgen Borg
  • Pilot Plant Development and Training Institute (PDTI), 49 Soi Tientalay 25, Bangkhuntien-Chaithalay Road, Thakham, Bangkhuntien, Bangkok 10150, Thailand.
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Yaowapha Waiprib
  • Yaowapha Waiprib
  • Department of Fishery Products, Faculty of Fisheries, Kasetsart University (KU), 50 Ngam Wong Wan Road, Ladyaow, Chatuchak, Bangkok 10900, Thailand.
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Jindarat Pimsamarn
  • Jindarat Pimsamarn
  • Department of Chemical Engineering, Faculty of Engineering King Mongkut's University of Technology Thonburi (KMUTT), 126 Pracha-utid Road, Bangmod, Toongkru, Bangkok 10140, Thailand.
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Anan Tongta
  • Anan Tongta
  • Division of Biotechnology, School of Bioresources and Technology King Mongkut’s University of Technology Thonburi (KMUTT), 49 Soi Tientalay 25, Bangkhuntien-Chaithalay Road, Thakham, Bangkhuntien, Bangkok 10150, Thailand.
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  •  Received: 23 November 2015
  •  Accepted: 07 September 2016
  •  Published: 28 September 2016

 ABSTRACT

In order to describe and predict the growth and expression of recombinant proteins by using a genetically modified Pichia pastoris, we developed a number of unstructured models based on growth kinetic equation, fed-batch mass balance and the assumptions of constant cell and protein yields. The growth of P. pastoris on both glycerol and methanol could be represented by Monod kinetic equation. A simple simulation methodology and developed models were shown to satisfactorily describe both growth and production of recombinant human serum albumin (rHSA) using a genetically modified P. pastoris MutS strain. The obtained parameters from curve fitting were reasonable and could be acceptable. Moreover, the same parameter sets obtained by the experiments indicated the rigidity and consistency of the developed models and fermentation approach of this study. With correlation coefficients (r2) exceeding 0.99, the models were able to simulate and predict the cell growth behavior and recombinant protein production by P. pastoris without requiring complex models.

 

Key words: Exponential feed, growth modeling, Monod kinetic equation, Pichia pastoris, recombinant human serum albumin.


 INTRODUCTION

Pichia pastoris is a methylotrophic yeast which has been successfully used for the heterologous expression  of  a great number of recombinant proteins. [Plantz, 2006 #34]The popularity of P. pastoris as a host for  the production of recombinant  
 
proteins is due to its several inherent advantages as described in many reviews (Çelik and Çalik, 2012; Gao and Shi, 2013; Gonçalves et al., 2013; Vogl et al., 2013; Fickers, 2014; Byrne, 2015; Çalik et al., 2015; Looser et al., 2015). By combination of powerful genetic techniques, the ability of protein expression and recombinant protein purification at a comparatively low cost, therefore P. pastoris is made as a promising system for recombinant protein production. In recombinant protein production, increasing expression and productivity are desired. Therefore, a number of strategies have been employed to increase expression of the recombinant proteins in question, for example, optimizing the fermentation medium (Jungo et al., 2007b; Batista et al., 2013), improving the fermentation method (Bushell et al., 2003; Ohya et al., 2005), different feeding strategies (Sinha et al., 2003; Hu et al., 2007), mixed-substrate feeding (d’Anjou and Daugulis, 2000; Jungo et al., 2007a; Arnau et al., 2011; Zalai et al., 2012) and different oxygen supplementation strategies (Charoenrat et al., 2005; Zhang et al., 2005; Charoenrat et al., 2006). Modeling is another approach without more empirical experiments enhancing and optimizing the production of recombinant proteins. This approach can be successfully applied to describe growth behavior and is used to enhance the productivity of recombinant proteins from both Escherichia coli (Baheri et al., 1997) and Saccharomyces cerevisiae (Hardjito et al., 1992; Patkar et al., 1993). The modeling is also used for developing an improved fermentation protocol for recombinant P. pastoris systems (d’Anjou and Daugulis, 1997). Many model approaches are proposed such as model-based feeding strategy based on mass balance (d’Anjou and Daugulis, 1997; d’Anjou and Daugulis, 2001), macrokinetic modeling based on stoichiometric balance (Ren et al., 2003), model-based specific growth rate (Ren and Yuan, 2005), mix-feed modeling based on growth kinetic (Zhang et al., 2003), growth and protein production modeling based on metabolic flux and metabolic model (Jahic et al., 2002; Sohn et al., 2010; Nocon et al., 2014), growth model optimization using response surface methodology (RSM) (Zhang et al., 2004; Yu et al., 2014) and artificial neural networks (ANNs), fuzzy rule-based systems, or a combination of both (Jenzsch et al., 2006). On these studies, the majorities are complex and require a complicated technical knowledge. However, simple simulation of recombinant microorganisms can be taken by kinetic modeling with the aid of computer software (Vinayagam et al., 2015).
 
The production of recombinant proteins is normally performed in three-stage fermentation: Batch, fed-batch and induction stage (Çelik and Çalik, 2012; Potvin et al., 2012; Looser et al., 2015). The first stage is batch fermentation where P. pastoris is cultured with glycerol. After the initial glycerol is depleted, the glycerol is added to the culture in order to reach high cell density in the second   stage.  The  third  stage  is  the  induction  stage  where expression of the recombinant proteins is induced by methanol.
 
In this study, the unstructured models based on growth kinetic equation, fed-batch mass balance and constancy of cell and protein yields were developed and constructed following the substrates, glycerol and methanol. The growth model on glycerol is mostly published while the growth model on methanol is rarely due to cell growth of MutS strain on this medium, which is very low so is neglected in the model (d’Anjou and Daugulis, 1997). Moreover, in mixed-substrate feeding, the growth on methanol is unnecessary to differentiate from growth on glycerol (d’Anjou and Daugulis, 2001). Therefore, this study proposed the model of growth not only on glycerol but also on methanol. Furthermore, a simple simulation methodology to investigate the behavior of growth and protein production of recombinant microorganisms was also introduced. A MutS P. pastoris KM71 strain genetically modified to produce and secrete human serum albumin (HSA), a major protein component of human blood plasma, was used as a model for mathematical model development. These models and parameters obtained by simulation methodology could be used as a tool to inspect the recombinant P. pastoris fermentation.


 MATERIALS AND METHODS

Microorganism
 
Genetically modified P. pastoris KM71 capable of expressing and secreting HSA was used in all experiments. The P. pastoris clone created by inserting the coding DNA sequence for mature full length HSA into the expression vector pPICZaA and then integrating into the genome of P. pastoris was provided by Dr. Witoon Tirasophon, Mahidol University, Thailand.
 
Medium
 
Yeast extract peptone dextrose (YPD) medium contained 10 g yeast extract, 20 g peptone and 20 g dextrose per liter of deionized water. Basal salt medium (BSM) contained 26.7 ml 85% H3PO4, 0.93 g CaSO4, 18.2 g K2SO4, 14.9 g MgSO4.7H2O, 4.13 g KOH, 50.0 g glycerol and 6.7 ml PTM1 trace salt in deionized water made up to a total volume of 1 L. The PTM1 trace salt contained 0.5 g CoCl2.6H2O, 20.0 g ZnCl2, 65 g FeSO4.7H20, 6.0 g CuSO4.5H2O, 3.0 g MnSO4.H2O, 0.1 g KI, 0.2 g Na2MoO4.2H2O, 0.02 g H3BO3, 5.0 ml H2SO4 and 0.2 g biotin in deionized water made up to a total volume of 1 L. The PTM1 trace salt was sterilized by filtration.
 
Inoculum preparation
 
P. pastoris stored at -80°C was used to inoculate a starter culture in YPD medium which was subsequently incubated at 30°C and 250 rpm. The starter culture was then used to inoculate 100 ml BSM which was continuously incubated at the aforementioned condition until reaching an OD600 of 20 (total OD600). The BSM inocula were then transferred aseptically to 7 L of BSM (working volume) in a 15 L bioreactor (BIOSTAT C, B. Braun Biotech International, Melsungen,  Germany).  The   volume   of    inocula    used    in    all experiments was 10% of the working volume of the bioreactor.
 
Batch fermentation
 
During the batch fermentation stage, the temperature was set at 30°C and pH maintained at 5.00 by the addition of 25% NH4OH and 85% H3PO4. Dissolved oxygen (DO) was kept above 20% saturation by using cascaded control of agitation. Aeration was supplied at 2 vvm and the air was supplemented and mixed with pure oxygen if the stirrer could not maintain the oxygen levels at the set value. Foaming was monitored by an antifoam probe and antifoam (Antifoam 204, Sigma, Deisenhofen, Germany) was added to the culture in order to prevent excessive foaming during fermentation.
 
Fed-batch fermentation
 
After depletion of initial glycerol in the BSM, the glycerol feed medium (50% w/v glycerol in water with 15 ml/l PTM1) was fed according to a predetermined exponential feeding rate described by d’Anjou and Daugulis (1997) and Jahic et al. (2002) until the cell concentration reached 100 g-DCW/l. The conditions were set to the same values as those during the batch fermentation stage.
 
Protein induction
 
After reaching the predetermined cell density (100 g-DCW/l), glycerol feed was discontinued and the culture was left for a 4-hour starvation period. Methanol with the addition of 15 ml/l PTM1 was then fed into the bioreactor in order to induce rHSA expression. The initial pulse of methanol was first fed into the bioreactor to a concentration of 4 g-methanol/l (Trinh et al., 2003; Looser et al., 2015) and left for 4 h before the continuous feeding strategies commenced. The temperature was set to 22°C (Jahic et al., 2003; Wu et al., 2012; Gao and Shi, 2013; Gonçalves et al., 2013; Anasontzis and Penã, 2014; Yu et al., 2014) and pH 6.00 (Kobayashi et al., 2000) during the induction phase.
 
Analysis methods
 
Samples were taken 5 ml at 6-hourly intervals during both the batch and fed-batch phases and every 24 h during the induction phase. The samples were centrifuged at 9000 rpm (RCF = 9055*g), 4°C for 5 min and the supernatant was collected for further analysis. Cell concentrations were determined by measuring OD600 and then converted to dry cell weight by a correlation of 0.323´OD600 (r2 = 0.998). Glycerol and methanol concentrations in the medium were analyzed by HPLC (Shimadzu Ltd., Tokyo, Japan) using an Aminex HPX-87H Ion Exclusion Column (Bio Rad) with 0.5 mM sulfuric acid as mobile phase and a flow rate of 0.6 ml/min at 45°C. A refractive index detector was utilized for detection. Total protein concentration in the medium was analyzed by Bradford assay (Suwannarat et al., 2013). The amount of rHSA protein was calculated from the band density, which obtained from SDS-PAGE analysis using 12% gels (according to standard protocols) stained with ImperialTM Protein Stain (Thermo Fisher Scientific), and compared to standard HSA of known concentration using Gene Tools program version 3.06.02. Western blot analysis was performed in order to verify the expression of rHSA protein and the identity of the rHSA bands.
 
Model development
 
The models describing the fermentation process were constructed by mass balance on biomass, substrate concentration, recombinant protein production and system volume. The P. pastoris KM71 strain used in this study was designated MutS which indicated the ability to grow on methanol as well as glycerol. Methanol could be also utilized as an inducer for the expression of recombinant proteins by the AOX1 promoter (Trinh et al., 2003). The mass balance equations used in this study were described as follows:
 
A continuous feed pattern was chosen for the methanol feed during the induction stage. The methanol feed rate was calculated based on biomass and the methanol consumption rate according to Equation 9.
 
To solve these differential equations when growing on glycerol and methanol, cell yield, protein yield and the specific methanol consumption rate were assumed to be constant. The models represented as these equations were coded and computed using instructions in Berkeley Madonna program version 9.0.118.


 RESULTS AND DISCUSSION

The experiments were performed in a 15 L bioreactor and were initiated as batch fermentation with a working volume of 7 L. In Run 1 experiment, as shown in Figure 1, the yeast biomass increased from 0.67 g-DCW/l at the time of inoculation to 21.04 g-DCW/l at the end of the batch fermentation. During this time period, glycerol was metabolized and was completely consumed within 40 h, decreasing from 49.72 g-glycerol/l. After the initial glycerol in the BSM had been depleted during the batch fermentation stage (at the 40th hour), the subsequent fed-batch stage started by feeding additional glycerol into the bioreactor, thereby prolonging the growth phase of P. pastoris and increasing cell density. In order to achieve an exponential growth rate, the feed pattern for the addition of glycerol was calculated using Equation 7.  The parameters used in Equation 7 were obtained from an experiment as shown in Table 1. The yield coefficient on glycerol (YX/S) in Equation 7 was obtained from the change of glycerol and cell concentration over time in batch stage by estimation with the curve fitting function in Berkeley Madonna program version 9.0.118. The YX/S of P. pastoris used in this study was 0.36 g-DCW/g-glycerol. During the exponential feed in fed-batch stage, the  (Equation 7) was set at 0.08 h-1 (Jenzsch et al., 2006; Suwannarat et al., 2013) to ensure that metabolic overflow would be avoided (Looser et al., 2015). The duration of the exponential feed and the time needed to achieve a certain cell concentration could be calculated using Equation 8, the composed term in Equation 7 representing cell concentration. The cell density targeted in the fed-batch stage was set at 100 g-DCW/l, which achieved in 24 h. In order to activate the AOX1 promoter and induce expression of rHSA, the induction phase was initiated by the addition of methanol at 4 g-methanol/l (Trinh et al., 2003). An initial pulse of methanol was first given to acclimatize the cells to metabolize methanol. Not only the inducing chemical, methanol is also poisonous to the P. pastoris cells because of the accumulation of formaldehyde and hydrogen peroxide, the products of methanol metabolism, inside the cells if it exists at a high concentration (Khatri and Hoffmann, 2006). However, a low methanol concentration is inadequate for protein expression (Gonçalves et al., 2013). Thus, the optimum amount of methanol should be regulated strictly (Potvin et al., 2012). For model development in this study, the continuous methanol feed based on the specific substrate uptake rate was selected (Dietzsch et al., 2011a). Four hours after the initial pulse feed, the continuous methanol feed was initiated and methanol was added to the culture at a rate equal to the specific methanol consumption rate ( ) of the P. pastoris strain used in this study. The had previously been determined by monitoring both methanol and cell concentrations over time in fermentations with constant methanol feed. The data obtained in those experiments gave a  of 0.026 g-methanol/g-DCW×h   by  calculation  based  on  fed-batch mass balance. This value was similar to the study by Dietzsch et al. (2011a, b) in P. pastoris MutS KM71H strain. The medium volume was drained daily to maintain a constant at 7 L in order to avoid exceeding the capacity of the bioreactor. After the initial methanol pulse, the methanol concentration decreased during the induction phase until the residual methanol was undetectable by HPLC even though methanol was continuously added into the bioreactor according to the calculated feed rate. This was due to limitations of the pump belonging to the bioreactor and in order to avoid accumulation of excess methanol in the bioreactor, the setting had to be slightly lower than the calculation. As shown in Figure 1, the amount of rHSA (´ symbol) increased during the induction phase with this methanol feed strategy. Methanol was not present while the methanol feed was operating which was an indication that yeast cells consumed methanol residuals at the feed rate added in (constant , 0.026 g-methanol/g-DCW×h). 
 
 
In the simulation of P. pastoris KM71, Equation 1 to 4 describing the yeast behavior in growth, substrate utilization and recombinant protein production were derived by fed-batch mass balance. The growth kinetic when growing on glycerol (m) was explained by Monod kinetic equation, shown in Equation 5. In the fed-batch stage, the exponential glycerol feed pattern could be calculated by Equation 7, the cell density and length of the fed-batch stage could be calculated by Equation 8, as described previously. For the induction stage, methanol was the only energy source added into the bioreactor for P. pastoris, therefore, the growth of yeast also depended on methanol only. As with the growth on glycerol, Monod kinetic equation was also used for the growth on methanol due to its simplicity and the fact that it did not require complicated parameters. Hence, the kinetic growth on methanol ( ) was introduced by Equation 6. During the induction period, the methanol was continuously fed after the initial methanol pulse at 4 g-methanol/l for 4 h. The methanol feed (FM) presented by Equation 9 was continuously added with constant  as described  previously.  The  models represented as these equations were coded and computed using instructions in Berkeley Madonna program version 9.0.118. The parameters used in simulation are shown in Tables 1 and 2. The parameters in Table 1 were obtained from experimental measurements. As for Table 2, the parameters were obtained using the curve fitter feature in Berkeley Madonna program. The simulation and fermentation results of RUN 1 experiment are shown in Figure 1 where the symbols represent data from the experiment and the lines are derived from the simulation results. The results showed accordance of developed models and that the experiment fit very well. In the induction stage, when methanol was added, it seemed that the yeast had a lag growth due to the effect of diauxic growth where the yeast cell accommodated to methanol. This effect was ignored by the models because of the simplicity of the objective in model development; however, the r2 between the models and the experimental results exceeded 0.99 in all data sets. In the RUN 1 experiment (Figure 1) the highest concentration of secreted rHSA present in the medium was 4.67 g/l, which occurred after 264 h (197 h of induction) with a simultaneous cell concentration of 151.81 g-DCW/l. The secreted rHSA produced by P. pastoris was analyzed by SDS-PAGE and showed the same molecular size as standard HSA (67 kDa), as shown in Figure 2a. By comparing band densities, the rHSA quantity was analyzed with known concentration of standard HSA using Gene Tools program version 3.06.02. Furthermore, the rHSA was also verified with the Western blot analysis and it showed specific binding with antibody as same as standard HSA (Figure 2b). 
 
 
 
After 264 h (197 h of induction), the cells entered a stationary phase (data not shown), most likely due to the exhaustion of some essential medium components (d’Anjou and Daugulis, 2000) and/or the accumulation of some metabolites in the medium. The observed decrease in cell growth lowered methanol metabolism, which consequently resulted in the accumulation of excess methanol. It was likely, therefore, that methanol concentration in the bioreactor increased while, simultaneously, the production of rHSA decreased. The experiment was continuously operated until the cell  concentration started to decrease. A potential explanation for the decline in cell concentration at these extended durations could be a slower growth rate and methanol consumption combined with a maintained methanol feed resulting in a simultaneous decrease of both cells and protein concentration due to dilution.
 
 
 
 
 
 

 


 CONCLUSION

In this study, simple models were developed based on growth kinetic equations, fed-batch mass balance and the assumptions of constant cell and protein yields. Monod kinetic equation was used to describe both growths on glycerol and methanol. The developed models fit very well with the experiments with r2 values exceeding 0.99 in all data sets. The obtained parameters could be reasonably acceptable. Moreover, the models and parameters were rigid and consistent and could describe and predict cell growth, substrate (glycerol and methanol) utilization and recombinant protein production by P. pastoris KM71. Furthermore, the demonstrated simulation methodology in this study could also be used as a tool to study heterologous protein production by recombinant microorganisms where fermentation could be simulated using simple equations and simple methods without the requirement of complex models.


 CONFLICT OF INTERESTS

The authors have not declared any conflict of interests.


 ACKNOWLEDGEMENTS

The authors thank Dr. Witoon Tirasophon of Shrimp Molecular Biology Research Group, Institute of Molecular Bioscience, Mahidol University, Thailand (25/25 Phuttamonthon 4 Road, Salaya, Nakhon Pathom 73170, Thailand) for supplying the recombinant cell line from Intracellular Signaling Lab, Institute of Molecular Biology and Genetics, Mahidol University. They also thank the Research and Development Institute of Government Pharmaceutical Organization (GPO), Thailand for providing the 15 L bioreactor. This work was supported by a grant from Thailand Graduate Institute of Science and Technology (TGIST), National Science and Technology Development Agency (NSTDA), Thailand.



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