Modeling and analysis of genetic networks have become increasingly important in the investigation of cellular processes. The genetic networks involved in cellular stress response can have a critical effect on the productivity of recombinant proteins. In this work, it was found that the temperature-inducible expression system for the production of soluble recombinant streptokinase in
The online version of this article (doi:10.1007/s11693-009-9021-z) contains supplementary material, which is available to authorized users.
The role of induction mechanism on recombinant protein production has been investigated for certain recombinant proteins in the literature. For an aggregation prone recombinant human basic fibroblast growth factor, the temperature-induced expression was found to result in increased productivity and higher yield (Seeger et al.
The role played by the various heat shock proteins in assisting solubilization, folding and degradation of recombinant proteins in
In the present work, the chemical and temperature inducible expression systems for the production of recombinant streptokinase were compared. A detailed mechanistic model for the bacterial heat shock response was developed and the dynamics of the chaperones and proteases were simulated for the mild heat shock condition, which had been used earlier for recombinant streptokinase production (Yazdani and Mukherjee
The medium for shake flask and bioreactor experiments consisted of K2HPO4, 5 g/l; KH2PO4, 3 g/l; NaCl, 0.5 g/l; NH4Cl, 1 g/l; yeast extract, 5 g/l; glucose, 5 g/l; MgSO4, 0.5 g/l; and trace metal solution, 1 ml/l (trace metal solution composition: FeSO4, 100 mg/l; Al2(SO4)3 · 7H2O, 10 mg/l; CuSO4 · H2O, 2 mg/l; H3BO3, 1 mg/l; MnCl3 · 4H2O, 20 mg/l; NiCl2 · 6H2O, 1 mg/l; Na2MoO4 · 2H2O, 50 mg/l; ZnSO4 · 7H2O, 5 mg/l) (Yazdani and Mukherjee
The cultures were induced at the mid-exponential phase around 0.6 OD (600 nm) in the shake flask experiments and around 3 OD (600 nm) in the batch bioreactor experiments. IPTG (0.25 mM) was used for chemical induction and temperature shift from 30°C to 37°C/42°C was used for temperature-induction. L- Arabinose (0.1% to 0.4% w/w) was used for the induction of DnaK chaperone team and Tetracycline (2–10 ng/ml) was used for the induction of GroEL chaperone team.
Cells were harvested by centrifugation at 6,000
Quantitative assay for streptokinase was done by incubating the samples with human plasminogen (Sigma, P5661) at 37°C for 5 min and then measuring the amount of plasmin formed by using a chromogenic substrate, Chromozym-PL (Sigma, T6140), as described by Yazdani and Mukherjee (
Gel electrophoresis was carried out according to Laemmli (
The volumetric activity of streptokinase is expressed in Units per ml of culture volume and specific activity in Units per mg of streptokinase. The data for quantity of streptokinase produced is expressed in micrograms per ml of culture volume. The model simulations are represented in M (Molar units). For the comparison of experimental data and model simulations (Fig.
The model built uses first-order mass action kinetics. It was assumed that all mRNAs are degraded at the same rate and all the cellular proteins are degraded at the same rate. The growth/dilution rate is assumed to be constant. Transcription, translation, folding and misfolding were assumed to follow first-order dynamics and the aggregation step for the recombinant protein was assumed to follow second-order kinetics. Binding rates between proteins or between proteins and specific DNA promoters were assumed to be faster compared with rates of synthesis of mRNAs and proteins and the complexes were assumed to be in equilibrium. Therefore, the binding equations are represented as algebraic equations. Since σ70 and RNAP are expressed constitutively in the cell, their total concentrations are assumed to be constant. The total protein content of the cell is also assumed to be constant.
Transcription, translation and protein folding are represented as differential equations and mass balance/binding are represented by algebraic equations. This results in a system of differential algebraic equations (DAEs). The components of the model and the complete set of model equations are presented in Tables
The model parameters include transcription and translation rate constants, binding rate constants, degradation rate constants and protein folding and misfolding rate constants. The binding, degradation and protein folding rate constants are selected from different sources in the literature. The parameters specific for recombinant streptokinase were selected based on available literature on streptokinase folding and aggregation or suitably assumed. The transcription rate constants were tuned to represent the steady state concentration of proteins in the unstressed cell. The complete list of parameter values and the sources of these parameters are presented in Table
Initial experiments on chemical and temperature inducible expression systems were carried out to identify appropriate expression conditions to compare the two induction mechanisms. The productivity of chemically induced system was found to be independent of inducer concentration (between 0.1 mM and 2 mM IPTG) and most of the recombinant protein was found in soluble fraction (Balagurunathan and Jayaraman
The recombinant strains used for temperature induction and chemical induction were grown at 30°C under identical conditions in a batch bioreactor to analyze the effect of induction mechanism on recombinant streptokinase production. At the mid-exponential phase (~3 OD) either the cultivation temperature was shifted to 37°C or 0.25 mM IPTG was added. The quantity of recombinant streptokinase produced during these conditions is shown in Fig. Comparison of chemical and temperature induction. Comparison of the level of streptokinase (
Two major chaperone teams required for the maintenance of protein quality in the cytoplasm are HSP70 (DnaK/DnaJ/GrpE) and HSP60 (GroEL/ES). The effect of these chaperones on recombinant streptokinase production was analyzed by chaperone co-expression experiments. The effect of co-expression of DnaK chaperone team (DnaK, DnaJ and GrpE) and GroEL chaperone team (GroEL and GroES) on streptokinase production is shown in Fig. Analysis of chaperone coexpression. DnaK Chaperone Coexpression: Recombinant streptokinase, DnaK and GroEL levels observed during DnaK chaperone co-expression are shown in (
The following observations were made based on these experiments The DnaK and GroEL levels were found to increase significantly upon respective induction when compared to the control experiment (Fig. The co-expression of one chaperone team was not found to affect the level of the other chaperone team (Fig. The streptokinase levels were found to be lower when the DnaK chaperone team was co-expressed (Fig. Streptokinase levels obtained during GroEL co-expression experiments were similar to that obtained in the control experiments (Fig. During the co-expression of both the chaperone teams, the streptokinase levels were found to be two fold lower (Fig. The over expression of recombinant streptokinase alone does not lead to any significant increase in the DnaK or GroEL levels indicating that the induction of the recombinant protein alone does not trigger any heat shock like response. The growth of the cell was not affected by the expression either of the chaperone teams or by the expression of streptokinase (data not shown).
To investigate the role of these chaperones further, the effect of varying induction of one chaperone team at a constant induction for other chaperone team on recombinant streptokinase production was studied. It was observed that the amount of streptokinase produced was lower when higher levels of DnaK were present, irrespective of the levels of GroEL chaperone. (Fig. 1, Supplementary Figures).
The volumetric activity of recombinant streptokinase was two fold lower when DnaK chaperone team was co-expressed and two fold higher when GroEL chaperone team was co-expressed. The volumetric activity was similar to control experiments when both the chaperone teams were co-expressed (Fig. Analysis of the activity and solubility of recombinant streptokinase during chaperone coexpression. Volumetric activity and Specific activity of recombinant streptokinase (shake flask cultivation) in a sample collected 5 h after the simultaneous induction of streptokinase and the concerned chaperone(s) is shown in (
During the chaperone coexpression experiments, the quantity of streptokinase observed in the insoluble fraction was only 5–6% of the total streptokinase produced. However, there was a variation in the quantity of streptokinase observed in the insoluble fraction during the chaperone coexpression experiments (Fig.
Four major heat shock proteins (namely the DnaK chaperone team, the GroEL chaperone team, FtsH protease and HslVU protease) are considered in the modified bacterial heat shock response model (Supplementary Material, Figure SI.1). The mechanism of interaction of these heat-shock proteins with recombinant streptokinase is proposed based on the chaperone co-expression experiments. Analyses of the chaperone co-expression experiments indicate the following: The co-expression of DnaK chaperone team leads to lower accumulation of soluble recombinant streptokinase, irrespective of the levels of the GroEL chaperone team. The streptokinase productivity was not found to decrease with the increase in the DnaK concentrations during DnaK co-expression experiments. The complete degradation of recombinant protein was not observed even at high concentrations of DnaK chaperone team in the cell. So, reduction in the productivity is not due to proteolytic processing of properly folded recombinant streptokinase.
These observations indicate that the major interaction of the DnaK chaperone team may be with nascent polypeptide chain, as the initial translation product emanates from the ribosome. Literature reports have also indicated that DnaK binds to nascent polypeptides emanating from the ribosome and transfer it to the GroEL chaperone team (Hartl and Hayer-Hartl Schematic of the mechanism proposed for the interaction of recombinant streptokinase with the major heat-shock proteins in the cell. The major events of recombinant protein synthesis, folding, misfolding and aggregation are represented by solid lines and the chaperone mediated events are represented by dotted lines
To obtain a complete model for the temperature inducible recombinant streptokinase production, the dynamics of the heat shock response has to be coupled with the mechanism of interaction of heat shock proteins and recombinant streptokinase. A detailed mechanistic mathematical model was developed for bacterial heat shock response (Supplementary Material). This model was used to characterize the heat shock response and to simulate the dynamics of the heat shock proteins under various temperature shift conditions. The model proposed in the current work is based on the model proposed by Kurata et al. ( Schematic of the mechanistic model proposed for temperature-induced recombinant streptokinase over expression in
The details of the model structure and the assumptions made in the model are explained in the Materials and methods Section. The list of model components, model equations, parameter values and initial conditions are presented in Tables
The model equations were simulated for chemical and temperature induced recombinant streptokinase production. The value of the parameters used for simulating these experimental conditions is shown in Table Simulations for chemical and temperature induction. Comparison of the model simulations for streptokinase (
The chaperone co-expression experimental conditions were simulated using the model. The co-expression of DnaK chaperone team and/or GroEL chaperone team was simulated by increasing the transcription rate of the particular chaperone team along with the transcription rate for the recombinant protein. This was then compared with the simulation results when the transcription rate of the recombinant protein alone was increased. It was observed that the model simulations (Fig. Simulations results for chaperone coexpression.
The sensitivity of the model simulations to parametric variation was analyzed by varying the model parameters one at a time with the remaining model parameters kept at the initial value. Thirty-three model parameters estimated from different sources in the literature were selected for parameter sensitivity analysis. Parameters like the transcription rates which are tuned to match the steady state level of the proteins in the cell, were not included for sensitivity analysis. Certain other model parameters like efficiency of translation of σ32 (η), and the misfolding rate of the cellular proteins (Kx5) were also excluded in the sensitivity analysis, as these parameters were varied to simulate the different experimental conditions. The change in the parameter values up to ±20% of its original value does not significantly affect the qualitative predictions of the model simulations. The fact that the qualitative model simulations were not affected by small variations in model parameters indicates that the observed simulations results are the reflection of the underlying mechanism and not an artifact of the parameters values. Sensitivity index and normalized sensitivity index for the major state variables are calculated and the parameters which has the maximum effect on these variables were identified (Data not shown). The detailed analysis of these variables would enable us to reduce the model equations and improve the quantitative predictability of the model. However, these aspects have not been addressed in this paper.
Parameter sensitivity analysis, however, was used in this work to identify the critical steps in the mechanism proposed and to identify ways to tackle these critical steps. Substantial variation in the model simulations for chemical-induction and temperature-induction was observed when the model parameters were varied two fold as shown in Fig. Parameter Sensitivity Analysis. The total folded recombinant protein obtained during chemical induction and temperature induction for two fold increase and two fold decrease in the model parameter values are shown in (
The level of σ32, total DnaK and free DnaK for the two fold increase and decrease of the selected model parameters is shown in Fig. Effect of the variation in selected model parameters on σ32, total DnaK and free DnaK levels in the cell during temperature-induced and chemically induced recombinant streptokinase production
To validate the proposed model a σ32 conditional mutant strain was selected for the analysis. This strain (CAG597) produces σ32 at 30°C, but when the temperature is increased to 37 or 42°C the strain becomes a σ32 mutant. This feature of the strain enable us to simulate a condition were there is enough DnaK in the cell but the requirement for DnaK chaperone increases upon temperature up-shift without the increase in the synthesis of DnaK. A situation which leads to a reduction in the free DnaK levels in the cell. The model was simulated for the σ32-conditional-mutant strain. The result of the model simulation for the total folded recombinant protein levels during temperature induction is shown in Fig. Model Validation—Simulations and experimental data of mutant strain.
The DnaK levels in the σ32 mutant strain after the temperature shift was analyzed by Western Blot. The change in DnaK levels after the shifting the cultivation temperatures from 30 to 37°C in the BL21 (DE3) strain and CAG597 (the σ32-mutant strain) is shown in Fig.
The alternative transcription factor σ32 plays a vital role in the cell and it is essential for the viability of the cells at higher temperatures. Further, mutating σ32 may affect the cells in several ways and mutating this protein is not a feasible solution for the temperature-inducible production of soluble recombinant proteins, as a lot of proteins transcribed by σ32 also help in protein folding and enhancement of biological activity. Anti-sense mediated down-regulation of σ32 activity was found to enhance the biological activity of Organophosphorus Hydrolase expressed in Components of the model developed for the cellular response to temperature induced recombinant streptokinase production List of model equations List of parameters used in the model Values of parameters used for simulating different experimental conditions Initial conditions used for the simulation of the modelComponents Description σ32 Alternative transcription factor σ70 Vegetative transcription factor D Non-specific DNA binding site DnaK DnaK chaperone team FtsH Heat-shock protease (s) responsible for chaperone mediated proteolysis GroEL GroEL chaperone team HslVU Heat-shock protease (s) responsible for direct proteolysis pcn Recombinant gene promoter Pfolded Total folded proteins in the cell Pg Housekeeping gene promoter Ph Heat-shock gene promoter Punfolded Total Unfolded proteins in the cell RecP Total Recombinant protein RecPA Aggregated recombinant protein RecPfolded Folded recombinant protein RecPI Recombinant protein initial translational product RecPunfolded Unfolded/Misfolded recombinant protein RNAP RNA polymerase in the cell T Protein Total protein in the cell Subscript ‘f’ Free concentration of the species Subscript ‘t’ Total concentration of the species Complexes Description σ32:DnaK Complex between σ32 and DnaK σ32:DnaK:FtsH Complex between DnaK bound σ32 and FtsH σ32:GroEL Complex between σ32 and GroEL σ32:HslVU Complex between σ32 and HslVU Punfolded:DnaK Complex between unfolded proteins in the cell and DnaK Punfolded:GroEL Complex between unfolded proteins in the cell and GroEL RecPA:DnaK Complex between DnaK and aggregated recombinant protein RecPI:DnaK Complex between DnaK and recombinant protein initial translation product RecPI:DnaK:FtsH Complex between DnaK bound recombinant protein initial translation product and FtsH RecPI:RecPI Aggregation of recombinant protein initial translation product RecPunfolded:DnaK Complex between DnaK and unfolded recombinant protein RecPunfolded:DnaK:FtsH Complex between DnaK bound unfolded recombinant protein and FtsH RecPunfolded:GroEL Complex between GroEL and unfolded recombinant protein RecPunfolded:HslVU Complex between HslVU and unfolded recombinant protein RNAP:D Complex between RNA polymerase and non-specific DNA binding sites RNAP:σ32 Complex between RNA polymerase and σ32 RNAP:σ70 Complex between RNA polymerase and σ70 RNAP:σ32:D Complex between σ32 bound RNA Polymerase and non-specific DNA binding sites RNAP:σ32:ph Complex between σ32 bound RNA polymerase and heat-shock gene promoters RNAP:σ70:D Complex between σ70 bound RNA Polymerase and non-specific DNA binding sites RNAP:σ70:pg Complex between σ70 bound RNA polymerase and housekeeping gene promoters RNAP:σ70:pcn Complex between σ70 bound RNA polymerase and recombinant gene promoter S. No Equation and description 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 Parameter Description Value Reference K1 Association constant between RNAP and σ70 4e9 M−1 Maeda et al. K2 Association constant between RNAP and σ32 1e9 M−1 Maeda et al. K3 Association constant between RNAP:σ70 and pg 1e8 M−1 Assumed based on Roe et al. K4 Association constant between RNAP:σ32 and ph 3e8 M−1 Assumed based on Roe et al. K5 Association constant between RNAP and pcn 3e9 M−1 Modified from Ujvari and Martin K6 Association constant between RNAP and non-specific DNA binding site D 1e6 M−1 Grigorova et al. K7 Association constant between RNAP:σ70 and D 1e5 M−1 Grigorova et al. K8 Association constant between RNAP:σ32 and D 1e5 M−1 Grigorova et al. K9 Association constant between σ32 and DnaK 3e5 M−1 Chattopadhyay and Roy K10 Association constant between σ32 and GroEL 1e6 M−1 Guisbert et al. K11 Association constant between σ32 and HslVU 1e8 M−1 Gamer et al. K12 Association constant between σ32:DnaK and FtsH 1e8 M−1 Gamer et al. K13 Association constant between Unfolded proteins and DnaK 2e6 M−1 Siegenthaler and Christen K14 Association constant between Unfolded proteins and GroEL 1e6 M−1 Lin et al. K15 Association constant between Recombinant protein Initial translation product and DnaK 1e6 M−1 Assumed based on Koller et al. K16 Association constant between DnaK:Recombinant protein Initial translation product and FtsH 1e7 M−1 Kurata et al. K17 Association constant between Misfolded Recombinant protein and DnaK 1e6 M−1 Assumed based on Koller et al. K18 Association constant between Misfolded Recombinant protein and GroEL 1e6 M−1 Lin et al. K19 Association constant between DnaK:Misfolded Recombinant protein with FtsH 1e6 M−1 Assumed based on Koller et al. K20 Association between Misfolded Recombinant protein to form aggregates 1e6 M−1 Assumed K21 Association constant between Aggregated Recombinant protein and DnaK 1e6 M−1 Assumed based on Koller et al. K22 Association constant between Misfolded Recombinant protein and HslVU 1e7 M−1 Assumed based on Koller et al. Kx1 Degradation rate constant of FtsH bound protein molecules 5 min−1 Kurata et al. Kx2 Degradation rate constant of HslVU bound protein molecules 5 min−1 Assumed based on Kurata et al. Kx3 Inactivation rate of GroEL bound Sigma32 5 min−1 Assumed based on Guisbert et al. Kx4 Folding rate constant for total cellular proteins 1.5e4 min-1 Kurata et al. Kx5 Misfolding rate constant of cellular proteins 75, 150, 225 min−1 Adjustable (Temp- dependent) Kx6 Intrinsic folding rate of the recombinant protein 90 min−1 Gromiha et al. Kx7 Aggregation rate of the misfolded recombinant protein 0.5 min−1 Assumed based on Azuaga et al. Kx8 Disaggregation rate of recombinant protein aggregates by DnaK 0.2 min-1 Assumed Kx9 Misfolding rate of the recombinant Protein initial translation product 0.2 min−1 Assumed Kx10 Assisted folding rate of the misfolded recombinant protein 6 min−1 Assumed Km1 Transcription rate of σ32 0.001 min−1 Tunable Km2 Transcription rate of DnaK 30 min−1 Tunable Km3 Transcription rate of GroEL 45 min−1 Tunable Km4 Transcription rate of FtsH 1 min-1 Tunable Km5 Transcription rate of HslVU 2 min−1 Tunable Km6 Transcription rate of Total Protein 5.7e-6 M/min−1 Tunable Km7 Transcription rate of recombinant protein Adjustable (60 min−1) Tunable KTL Translation rate 20 min−1 Kurata et al. Kmd mRNA degradation rate 0.5 min−1 Kurata et al. Kpd Protein degradation rate 0.03 min-1 Kurata et al. µ Specific growth rate (Dilution rate of intracellular components) 0.0116 min−1 Calculated η Efficiency of translation of σ32 mRNA 1, 5, 10 Adjustable (Temp dependent) RNAPt Total RNA Polymerase in the cell 5.105e-6 M Maeda et al. Total σ70 in the cell 1.778e-6 M Maeda et al. Total housekeeping gene promoters in the cell 1.016e-5 M Kurata et al. Total heat-shock gene promoters in the cell 7.62e-8 M Kurata et al. Total recombinant gene promoters in the cell (Plasmid copy number) 3.321e-8 M Calculated Total non-specific binding sites in the cell 1.18e-2 M Grigorova et al. S. No Condition Parameter Values 1 Chemical induction η = 1; Kx5 = 75 min−1; Km7 = 60 min−1 2 Temperature induction (30–37ºC) η = 5; Kx5 = 150 min−1; Km7 = 60 min−1 3 DnaK co-expression η = 1; Kx5 = 75 min−1; Km2 = 60 min−1; 4 GroEL co-expression η = 1; Kx5 = 75 min−1; Km3 = 90 min−1; 5 DnaK and GroEL co-expression η = 1; Kx5 = 75 min−1; Km2 = 60 min−1; 6 Temperature induction in a σ32 conditional mutant strain (30–37ºC) η = 0.5; Kx5 = 150 min−1; S. No Variable Initial Value (M) 1 mRNA of σ32 [mRNA(σ32)] 1.162e-9 2 Total σ32 [ 4.483e-8 3 Free σ32 [ 3.32e-9 4 mRNA of DnaK [mRNA (DnaK)] 2.49e-9 5 Total DnaK [DnaKt] 1.65e-5 6 Free DnaK [DnaKf] 2.142e-7 7 mRNA of GroEL [mRNA(GroEL)] 2.49e-9 8 Total GroEL [GroELt] 1.65e-5 9 Free GroEL [GroELf] 2.142e-7 10 mRNA of FtsH [mRNA(FtsH)] 4.98e-8 11 Total FtsH [FtsHt] 3.305e-6 12 Free FtsH [FtsHf] 3.274e-6 13 mRNA of HslVU [mRNA(HslVU)] 1.6603e-9 14 Total HslVU [HslVUt] 9.912e-7 15 Free HslVU [HslVUf] 9.895e-7 16 mRNA of Total protein in the cell [mRNA(TProtein)] 7.62e-6 17 Total folded protein in the cell [Pfoldedt] 5.03e-3 18 Total unfolded protein in the cell [Punfoldedt] 5.01e-5 19 Free unfolded protein in the cell [Punfoldedf] 2.486e-5 20 Free RNA polymerases in the cell [RNAPf] 1e-6 21 Free σ70 in the cell [ 3.54e-9 22 mRNA of recombinant protein [mRNA(RecP)] 0 23 Total recombinant protein initial translation product [RecPIt] 0 24 Free recombinant protein initial translation product [RecPIf] 0 25 Total Folded recombinant protein [RecPFoldedt] 0 26 Total misfolded recombinant protein [RecPunfoldedt] 0 27 Free misfolded recombinant protein [RecPunfoldedf] 0 28 Total aggregated recombinant protein [RecPAt] 0 29 Free aggregated recombinant protein [RecPAf] 0
Temperature induction has been used for the production of several recombinant proteins in
In the case of soluble recombinant proteins like recombinant streptokinase the major stress on the cell is due to temperature up shift and not due to the misfolding of the recombinant protein. The up-regulation of the heat shock proteins or the induction of the heat-shock like response was not observed when recombinant streptokinase alone was over-expressed by chemical induction. However, when temperature up-shift was used as an induction mechanism, the up-regulated heat shock chaperones were found to influence the productivity of the soluble recombinant streptokinase.
There are several reports in literature citing the effect of chaperones on recombinant protein expression, solubilization and folding in
Further, based on isolated experimental investigations of individual chaperones, it is very difficult to predict the combined effect of all the major chaperones on a particular recombinant protein, given that the outcome depends on the recombinant protein characteristics as well as the dynamics of the stress response network. In this context, it would be more appropriate to formulate mathematical models which can simulate the network dynamics well and give insights on key parameters affecting the productivity as well as the quality of the recombinant protein. However, given the large number of heat-shock and other host cell proteins which interact with the recombinant protein, modeling and simulation can become a complex and even intractable task. Therefore, it may be essential to isolate the important components of a network and its interactions with a recombinant protein in order to simplify the model and its corresponding simulations.
This work makes a modest beginning in this direction by formulating a deterministic model-based approach for investigating the effect of heat shock response on a soluble recombinant protein, induced by a temperature up-shift. A mechanistic network model is proposed for the interaction of the major heat shock proteins and the soluble recombinant streptokinase. The framework presented is generally applicable for the chaperone-mediated folding, aggregation, dis-aggregation and proteolysis of recombinant proteins. This model was coupled to a modified model of heat-shock response, in order to simulate the chaperone effects on temperature-induced recombinant protein production. The model simulations were found to have strong qualitative agreement with the experimental results. The sensitivity analysis for the model parameters, together with the model simulations for the σ32 mutant strain have indicated that down-regulation of σ32 (and hence DnaK) levels in the cell could result in enhanced productivity of recombinant streptokinase during temperature-induced expression. Further, the sensitivity analysis has also shown that selective down-regulation of the DnaK chaperone level or activity could lead to enhancement in the productivity.
Thus, our theoretical investigations have shown that it is the amount of free DnaK chaperone (and not the total DnaK level per se) that has the most significant effect on the productivity of the soluble recombinant streptokinase. Such insights may be difficult to capture by purely experimental investigations of chaperone co-expression, thus making it difficult to design rational strategies for enhancing cellular productivity. Therefore, a model-based approach may be more justified in certain circumstances, where protein characteristics and its interaction with host cell proteins are quantitatively understood. Careful examination of these model parameters could allow design of expression vectors or experimental strategies which are capable of operating at multiple values for these model parameters and would be very useful in enhancing the productivity of recombinant proteins.
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We are grateful to Dr. A. Gopalakrishna (Department of Biotechnology, Indian Institute of Technology Madras, India) for providing the pGKJE6 plasmid. We are also grateful to Dr. Behnaz Parhami-Seren (University of Vermont, Burlington) for providing us with monoclonal antibody for streptokinase.
The online version of this article (doi:10.1007/s11693-009-9021-z) contains supplementary material, which is available to authorized users.