Journal of Chemical Technology and Biotechnology
J Chem Technol Biotechnol 80:1268–1272 (2005) DOI: 10.1002/jctb.1321
Model-based specific growth rate control for Pichia pastoris to improve recombinant protein production Haitao Ren1 and Jingqi Yuan1,2∗ 1 Department 2 State
of Automation, Shanghai Jiao Tong University, 1954 Huashan Lu, 200030 Shanghai, China Key Laboratory of Bioreactor Engineering/ECUST, 130 Meilong Lu, 200237 Shanghai, China
Abstract: Constant specific growth rate control in the methanol growth phase was investigated for the fed-batch cultivation of Pichia pastoris expressing recombinant human serum albumin (rHSA). The methanol feeding strategy was determined based on an earlier proposed macrokinetic model to maintain the specific growth rate at preset levels. The experimental results demonstrate that the control strategy of constant specific growth rate is more effective than that of constant feeding rate to maximize production. Furthermore, the most productive setpoint of the specific growth rate is found between 0.005 and 0.006 h−1 , which yields protein concentrations higher than 5 g l−1 at 160 h. In addition, a setpoint of 0.008 h−1 is suggested as the upper limit for specific growth rate control for the given expression system. 2005 Society of Chemical Industry
Keywords: Pichia pastoris; recombinant human serum albumin; specific growth rate control; feeding profile; process optimization
INTRODUCTION Human serum albumin (HSA) plays an important role in binding and transport, osmotic balance, free radical scavenging, platelet function inhibition and antithrombotic effects.1 HSA is normally fractionated from whole blood. Because human plasma is limited and may contain unknown pathogens, recombinant DNA technology is regarded as a promising alternative.2 Numerous heterologous recombinant proteins have been successfully expressed in the methylotrophic yeast Pichia pastoris using methanol oxidase promoter.3,4 The expression level of a recombinant protein can be regulated by the inherent properties of the system such as the amino acid sequence, the tertiary structure and the site of expression.5 However, enhancement of protein production can also be achieved by optimizing the cultivation medium6 or by improving the cultivation methodology. In the latter case, the substrate feeding strategy has been regarded as an important and effective approach.7,8 Jimenez et al 9 studied different methanol feeding strategies based on both the biomass concentration and the dissolved oxygen level for P pastoris expressing dextranase. They concluded that higher protein production could be obtained in the latter case. On the other hand, Minning et al 10 found that methanol feeding control
based on methanol concentration was superior to that based on dissolved oxygen concentration for the cultivation of P pastoris producing Rhizopus oryzae lipase. Recently, Kobayashi et al 11 investigated the optimal specific growth rate for P pastoris expressing recombinant human serum albumin (rHSA) with the method of dynamic programming, where a pure mass balance model was used. In our earlier study, a macrokinetic model for P. pastoris expressing rHSA was constructed based on the stoichiometric balance.12 With this model, both cell growth and protein production were well described. Furthermore, specific growth rate control is regarded as an effective strategy for process optimization, since most of the biochemical reactions for product formation are either directly or indirectly associated with cell growth.13 Therefore, in this work the modelbased setpoint control of the specific growth rate (µ) in the methanol growth phase was investigated. The relationship between the setpoint of µ, denoted µr , and the production of rHSA was also studied.
MATERIALS AND METHODS Strain and culture media The cell strain P pastoris GS115 was used. Cultivation was performed in a 30 L bioreactor (B Braun,
∗ Correspondence to: Jingqi Yuan, Department of Automation, Shanghai Jiao Tong University, 1954 Huashan Lu, 200030 Shanghai, China E-mail:
[email protected] Contract/grant sponsor: Natural Science Foundation of China; contract/grant number: 60174024 Contract/grant sponsor: Alexander von Humboldt Foundation (Received 3 December 2004; revised version received 17 February 2005; accepted 24 February 2005) Published online 26 May 2005
2005 Society of Chemical Industry. J Chem Technol Biotechnol 0268–2575/2005/$30.00
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Model-based feeding control for Pichia pastoris
Germany) with a working volume of 20 L. A detailed description of the analytical methods has been given previously.12 The compositions of the media used in this study were as follows: • Basal salt solution FM21: CaSO4 · 2H2 O 1.5 g L−1 ; KOH 6.5 g L−1 ; MgSO4 · 7H2 O 19.5 g L−1 ; K2 SO4 23.8 g L−1 ; 3.5% (v/v) of H3 PO4 (85%). • Trace element solution PTM1: ZnCl2 2 g L−1 ; FeSO4 · 7H2 O 6.5 g L−1 ; H3 BO3 0.002 g L−1 ; CuSO4 · 5H2 O 0.6 g L−1 ; KI 0.01 g L−1 ; MnSO4 · H2 O 0.3 g L−1 ; 0.2% (v/v) of H2 SO4 (96%); biotin 80 µg L−1 . • Seed culture medium: glycerol 10 g L−1 ; peptone 20 g L−1 ; yeast extract 10 g L−1 ; KH2 PO4 1.2 g L−1 ; Na2 HPO4 2.29 g L−1 ; (NH4 )2 SO4 1 g L−1 ; MgSO4 · 7H2 O 1 g L−1 ; FeSO4 · 7H2 O 0.25 g L−1 . • Batch medium: glycerol 40 g L−1 ; biotin 80 µg L−1 ; 2% (v/v) of H3 PO4 (85%); and a predetermined amount of FM21 and PTM1. • Glycerol feeding substrates: 50% (v/v) of glycerol plus a predetermined amount of FM21 and PTM1. • Methanol feeding substrate: pure methanol plus a predetermined amount of FM21 and PTM1. Process description Fermentation started with 10 L of batch medium inoculated with 1 L of seed culture of P pastoris GS115 (the biomass concentration of the seed culture was ca 16 g L−1 ). A two-phase cultivation protocol, ie the glycerol phase and the methanol growth phase, was involved in the cultivation. The glycerol growth phase included a 16 h batch stage from the starting point followed by a 14 h glycerol fed-batch stage, whereas the methanol growth phase was subdivided into a 10 h induction stage and a 120–140 h production stage. The glycerol feeding rate was determined according to the Fermentation Guidelines of Invitrogen (San Diego, CA, USA), then a model-based optimal feeding profile of methanol was applied, which will be described in more details in the next section. During the whole process, the dissolved oxygen was maintained at 30% saturation by regulating agitation and the pH was kept around 6.5 by adding 25% ammonia solution. The cultivation temperature was maintained at 30 ◦ C during the glycerol growth phase and decreased to a lower setpoint in the methanol growth phase.
RESULTS AND DISCUSSION According to the Invitrogen feeding protocol for P pastoris cultivation, the methanol feeding undergoes a stepwise increase during the first several hours in the induction stage, then it is maintained constant throughout the rest part of the induction and production stages. Such a feeding profile results in an increasing specific growth rate µ for about 10 h in the induction stage, but a decreasing µ in the production stage. With this methanol feeding profile, the final J Chem Technol Biotechnol 80:1268–1272 (2005)
rHSA production was relatively low. To overcome this effect, setpoint control of µ in the methanol growth phase was investigated in this work. Such a control strategy consists of a linearly increasing feeding rate in the induction stage followed by an exponentially increasing feeding rate in the production stage. The methanol feeding profiles were obtained based on the macrokinetic model.12 In the induction stage, only methanol feeding was involved instead of the mixture of glycerol and methanol. Equation (1) was used to determine the methanol feeding rate F(t) at cultivation time t. The initial methanol feeding rate was set to 12 mL h−1 , which ensures a low methanol accumulation (0.5–1 g L−1 ) after induction. The feeding rate increases linearly to about 30 mL h−1 by the end of the induction stage. The parameter ω1 in eqn (1) was optimized to make the model simulate a specific growth rate as close as possible to its setpoint (µr ) at the end of the induction stage, ie 40 h. For this purpose, a one-dimensional search method, the Golden Section Search (GSS), was used. GSS is a robust and fast converging method of locating a minimum in a bracket and no information about the derivative of the objective function is required.14 In the model simulation, the initial medium volume, initial concentration of glycerol and biomass, the glycerol feeding rate and the ω1 -dependent methanol feeding rate were taken as input and gave the specific growth rate, amongst other state variables, as output. Therefore, the objective function, ie the square error of the simulated specific growth and µr at 40 h, was obtained after model simulation. By setting different values of ω1 , GSS will find the best ω1 to minimize the objective function. F(t) = ω1 (t − 30 h) + 12 ml h−1
30 t 40 h (1)
In the production stage, eqn (2) was used to determine the methanol feeding rate. The term (10ω1 + 12) ensures the continuity of methanol feeding in the transition of the two feeding equations, while the exponentially increasing feeding profile controls the specific growth rate at µr . Again, GSS was used to estimate the parameter ω2 , but the objective function was to minimize the sum of square errors of the specific growth rate between the model simulation and µr in the whole production stage; see eqn (3), where Fmin and Fmax are the lower and upper limits of F(t), respectively. F(t) = (10ω1 + 12) exp[ω2 (t − 40 h)] t > 40 h 160 J = min [µ(i) − µr ]2 Fmin F(t)Fmax
(2) (3)
j=40
Two experiments were carried out to validate the feeding strategy in the methanol growth phase; see Fig 1, where µr was set to (a) 0.004 and (b) 0.007 h−1 . Figure 1 indicates that µ has been controlled at preset levels with reasonable accuracy. The residual 1269
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Figure 1. Set-point control of the specific growth rate in P pastoris cultivation. Lines, model simulations; symbols, measurements of biomass concentration (X), methanol feeding rate (F) and methanol concentration in the medium (SMeOH ). µr = (a) 0.004 and (b) 0.007 h−1 .
Table 1. Parameters in the feeding equations
Experiment No 1 2
ω1
ω2
1.52 1.79
0.0041 0.0060
methanol concentration is practically zero in the production stage. The parameters in the feeding equations identified with GSS are listed in Table 1. Compared with other possible feeding patterns, such as multilinear functions or other nonlinear functions, the combination of linear and exponential methanol feeding used in this study was found to be the simplest, containing only two parameters. The experimental data demonstrated that such a feeding pattern matches well with the setpoint control of the specific growth rate. It is noted that open-loop control was involved in these experiments. Accurate setpoint control of the specific growth rate can be achieved when the actual value of µ is measurable. Discriminating experiments were carried out to clarify the effect of the µ setpoint control strategy and the constant methanol feeding profile; see Fig 2. Figure 2(a) demonstrates two experiments whose 1270
Figure 2. Effects of µ setpoint control (Experiments 4 and 6) and constant methanol feeding control (Experiments 3 and 5) on protein concentration (P). Lines represent model simulations and symbols the measurements. µr = (a) 0.005 and (b) 0.006 h−1 .
average specific growth rate is 0.005 h−1 . Experiment 3 resulted from constant methanol feeding control. The highest specific growth rate is found shortly after the induction stage and decreases constantly thereafter. As a comparison, Experiment 4 was carried out under µ setpoint control. Clearly, the specific growth rate has been well controlled at its setpoint. For comparable conditions, the amount of methanol fed into the bioreactor was about the same in these two feeding strategies. However, the µ setpoint control strategy (Experiment 4) yields an apparently higher product concentration. Similarly, Fig 2(b) shows two experiments whose average specific growth rate is 0.006 h−1 . Again, higher production was achieved by the µ setpoint control strategy (Experiment 6). Taking the same amount of methanol feeding and the same production time into account, a higher product concentration implies higher productivity. To examine further the effects of different µr on protein production, additional experiments were carried out. Figure 3 shows three fed-batches, which correspond to low (0.0040 h−1 ), medium (0.0055 h−1 ) and high (0.0065 h−1 ) µr . Figure 3(a) reveals that the residual methanol concentration is zero in the production stage of all three batches. However, the protein production seems to be strongly dependent on µr . The J Chem Technol Biotechnol 80:1268–1272 (2005)
Model-based feeding control for Pichia pastoris
Figure 4. Accumulation of methanol and depressed protein expression corresponding to high µr = 0.0085 h−1 . Lines, model simulations; symbols, measurements of biomass concentration (X), methanol feeding rate (F) and methanol concentration in the medium (SMeOH ).
Figure 3. Experiments corresponding to low, medium and high µr . Lines represent model simulations and symbols the measurements. (a) Specific growth rate (µ) and protein concentration (P); (b) biomass concentration (X).
highest production is achieved at µr = 0.0065 h−1 . Figure 3(b) shows the biomass concentration of the three batches. By the end of cultivation, the biomass concentration at µr = 0.004 h−1 is 81 g L−1 , much lower than 112 g L−1 at µr = 0.0055 h−1 and 118 g L−1 at µr = 0.0065 h−1 . This suggests that the lower protein production at µr = 0.004 h−1 could be caused by an insufficient carbon source supply. On the other hand, if µr is set too high, excessive methanol supply may also cause a depressed protein expression. Figure 4 shows an experiment with µr = 0.0085 h−1 . This experiment demonstrates rapid methanol accumulation at 70 h. At 96 h, the residual methanol concentration is about 4 g L−1 and the protein concentration ceases to increase. Since no intracellular activity/concentration of the enzymes was monitored, it is not possible to confirm whether this is caused by ceased synthesis and/or secretion of rHSA. During the next 40 h, the residual methanol concentration was ca 6 g L−1 and no restart of protein production was found. At 140 h, obvious decreases in both methanol and protein concentrations were observed, then the cells shifted to fast growth again accompanied by full uptake of the accumulated methanol. A similar phenomenon J Chem Technol Biotechnol 80:1268–1272 (2005)
Figure 5. Protein concentration P and total protein production TP at 160 h corresponding to different µr .
was also observed in our earlier study.12 This may imply that the cells’ metabolism has shifted to the wild-type behavior. Since the macrokinetic model used is valid only for P pastoris growing with the production of heterologous HSA, a model mismatch in such a case (see Fig 4) would not be surprising. High methanol concentrations have been found disadvantageous owing to toxic effects on cell growth15 and depressed protein expression.16 In our study, the accumulated methanol seems to have little toxic effect on cell growth, but depresses rHSA production significantly.17 Figure 5 presents both the protein concentration and the total protein production at 160 h corresponding to different µr . The most productive range of µr is found between 0.005 and 0.006 h−1 .
CONCLUSION This paper focused on the model-based setpoint control of the specific growth rate during the methanol growth phase by regulating the methanol feeding rate. The experimental results demonstrated that µ was maintained at preset levels with reasonable accuracy 1271
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by the combination of linear and exponential feeding profiles. Discriminating experiments suggested that the µ setpoint control strategy is superior to that of constant feeding rate (which resulted in a decreased specific growth rate) in maximizing productivity. The most productive range of µr seems to be between 0.005 and 0.006 h−1 . In addition, µr has to be set below 0.008 h−1 to avoid the accumulation of methanol and the depression of protein expression.
ACKNOWLEDGEMENTS This work was supported by the Natural Science Foundation of China (Grant No 60174024) and the Alexander von Humboldt Foundation, Germany. The authors are also grateful to the New Drug Research and Development Center, Northern China Pharmaceutical Company for providing experimental data.
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