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To investigate the best culture conditions, the authors used response surface methodology via Box-Behnken design.
Article submitted: Dec. 13, 2015.
Article accepted: Jan. 26, 2016.
Interleukin-11 is a member of the interleukin-6 family of cytokines secreted by bone marrow stromal cells or by a number of mesenchymal cells. At present, human interleukin 11 is produced by recombinant DNA technology in Escherichia coli. Recombinant human interleukin 11 synthetic gene was expressed and optimized in E. coli Rosetta (DE3) using a vector with the T7 promoter. To investigate the best culture conditions, the authors used response surface methodology via Box-Behnken design. A second order model was applied to achieve the optimum levels of experimental design factors such as isopropylthio-β-D-galactosidase (IPTG) concentration, cell density at induction time, and post-induction temperature. The regression models show significantly high R2 values of approximately 99% for interleukin 11 expression in terrific broth (TB) medium. The best expression (21.5% of total cell protein) was obtained using the following conditions: IPTG concentration (0.5 mM), cell density (5.4 mg/mL), and temperature (30 ËC). To the authors’ best knowledge, this is the first report on optimization of the human interleukin 11 expression using response surface methodology.
Interleukin-11 (IL-11) is a member of the interleukin-6 family of cytokines secreted by bone marrow stromal cells or by a number of mesenchymal cells (1, 2). It is a non-glycosylated, four-helix bundle protein with 199 amino-acid residues and a molecular weight of approximately 23 kDa (2, 3). It has been shown that interleukin 11 stimulates megakaryocytopoiesis and thrombopoiesis (4).
Recombinant human interleukin 11 lacks N-terminal proline compared with mature interleukin 11 (3). At present, human interleukin 11 is produced by recombinant DNA technology in Escherichia coli and is commercially available as Neumega (5). E. coli is the first choice for production of recombinant proteins in laboratory and industrial scale due to its characteristics such as its fast growth rate, inexpensive media, and well-understood genetics. Also, different vector expression systems are readily available for E. coli (5-8). Expression systems based on bacteriophage T7 DNA-dependent RNA polymerase has been widely used to produce many proteins in E. coli, because by recruiting these systems, high levels of protein expression can be reached (9). However, to achieve maximum levels of recombinant proteins in E. coli, optimization is necessary (5).
Traditionally, optimization was done by varying one factor and keeping the other factors at a constant level; which is known as the “one-factor at a time” approach. But this process is time- and energy-consuming, and accurate optimum condition will not be achieved. In addition, interaction among factors is not considered in this procedure (6, 10). Currently, statistical data analysis is being routinely used for optimization studies in industrial biotechnology. This procedure evaluates interaction among experimental variables that could affect the productivity using principles of statistics, randomization, and replication that lead to prediction of response data. Response surface methodology (RSM) is a method of choice for optimization of culture conditions. It is based on full factorial central composite design (CCD). This method designs experiments, builds models, evaluates the effects of factors, and searches for optimum conditions by using a collection of statistical and mathematical techniques (6, 11, 12). Different factors can affect recombinant protein production such as post-induction temperature, isopropylthio-β-D-galactosidase (IPTG) concentration, and cell density at the induction time (13).
In this study, expression of recombinant human interleukin 11 in E. coli Rosetta (DE3) was optimized using RSM.
Bacterial strains and plasmidsE. coli Rosetta (DE3) and PET 15b was purchased from Novagen (Germany) and Pasteur Institute (Iran), respectively. All chemicals used in this study were from Merck (Germany). All of the enzymes were from Roche (Germany), Fermentas (EU), or Cinagen (Iran) companies. A human interleukin 11 Eliza kit (quantikine) was purchased from the R&D system (US).
A codon optimized sequence of interleukin 11 was synthesized in pGH plasmid. Then, pGH and pET 15b were digested with Nde1 and Xho1 restriction enzymes. The sequence obtained after digestion of pGH was ligated into double digested pET 15b using T4 DNA ligase. The constructed recombinant plasmid was transformed into E. coli Rosetta (DE3) and used for the expression study.
Culture condition and expression
A single colony of E. coli Rosetta (DE3) harboring PET 15b- IL 11 was inoculated in Lysogeny broth (LB) medium (10g tryptone, 10g NaCl, and 5g yeast extract for 1-liter medium) containing 100 µg/mL ampicillin and was grown overnight. Two-hundred µL aliquots of overnight culture were used to inoculate 20 mL TB (12g tryptone, yeast 24g, glycerol 8g, KH2PO4 2.2g, K2HPO4 9.4g for 1-liter medium) medium containing 100 µg/mL ampicillin in 50 mL Erlenmeyer. The culture was incubated at 37 ËC, with the constant shaking rate of 220 rpm. When the culture reached an optical density of 0.6 at 600nm, IPTG was added to a final concentration of 1 mM.
Four hours after induction with IPTG, the cells were harvested by centrifugation at 4000 rpm for 5 min. Pelleted cells were re-suspended in Laemmeli sample buffer and analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS PAGE).
SDS-PAGE and determination of concentration of recombinant protein
Proteins were separated on 12% SDS PAGE gels using a Mini-Protein IV system (Bio-Rad, USA) according to the method of Laemmeli. Gels were stained with Coomassie Brilliant Blue. Interleukin 11 concentrations were determined by densitometry using Image J software (NIH, Bethesda, MD).
RSM, especially Box-Behnken design, was used to optimize culture conditions for expression of interleukin 11. The effect of three independent variables in expression of interleukin 11 was evaluated. Independent variables include post-induction temperature (factor A), cell density at induction time (factor B), and IPTG concentration (factor C). These variables were examined at three levels; high (1), middle (0), and low (-1). A set of 15 experiments were designed and each experiment was done in triplicates. The mean value of these repeats were noted as the observed response. All experimental designs and data analysis were done by Minitab software (version 16, PA, USA). The design for the three independent variables is shown in the Table I.
Table I. Experimental and predicted values of interleukin11 recorded in Box-Behnken design. IPTG is isopropylthio-β-D-galactosidase.
For predicting the optimal point, a second order polynomial function was fitted to correlate the relationship between independent variables and response. For the three factors, the equation is (Equation 1):
[Eq. 1] Y= β0+β1A+β2B+β3C+ β1β2AB+ β1β3AC+ β2β3BC+β11A2+ β22B2+ β33C2
Where Y is the predicted response, β0 is the model constant; A, B, and C are independent variables; β1, β2, and β3 are linear coefficients; β12, β13, and β23 are cross-product coefficients; and β11, β22, and β33 are the quadratic coefficients. The quality of fit of the polynomial model equation was expressed with the coefficient of determination R2. The expression of interleukin 11 was tested using TB medium. The significance of each coefficient (linear or quadratic) was determined by the F-test, at 0.05 probability level.
Table II. Analysis of variance (ANOVA) for the model that represents interleukin 11 expression.
Figure 1: Response surface plot and contour plot of the combined effects of isopropylthio-β-Dgalactosidase
(IPTG) concentration (A) and temperature (ËC) on the interleukin 11 expression.Fermentation can be divided into two parts: growth and production. To achieve high protein yield, both phases can be optimized and the effect of all factors should be determined in order to perform efficient fermentation process (14). In this work, RSM via Box-Behnken design was used to find optimal levels of all impressive factors. The results of Box-Behnken design, analysis of variance (ANOVA), and regression coefficient are shown in Tables I and II. The response surface/contour plots of calculated model for interleukin 11 expression are shown in Figures 1, 2, and 3. The effects of a pair of independent variables were demonstrated in each plot, whereas the third factor was kept fixed. These plots were used to determine the optimized condition.
Results show the good agreement between the proposed model and optimization process. The following equation explains the proposed model:
Y= -9.81063 + 5.2A + 4.01B + 0.90C - 0.06A2 - 0.10 B2 - 0.01C2 - 1.71 AB - 0.01AC + 0.01BC
Figure 2: Response surface plot and contour plot of the combined effects of cell density (B) and
temperature (ËC) on the interleukin 11 expression.Where, Y is the predicted response and A, B, and C are the coded values of post-induction temperature, cell density at induction time, and IPTG concentration, respectively.
The results of ANOVA for the model that represent interleukin 11 expression are shown in Table II. These results (F value and P value) demonstrate that the model is in agreement with experimental data.
Lack of fit F and lack of fit p value are factors that can explain the fit of the model (see Table II). The value of these factors is above 0.05, which implies that the model is suitable given that the lack of fit is not significant. Also the R2 value (98.58) is close to 1, which suggests that the model is strong, and therefore, allows for better prediction of responses. These values indicate a good fit between the model and the experimental data, and can explain the majority of variance in the interleukin 11 expression in TB medium. As the Minitab Response Surface Optimizer function shows, the optimized condition is as follows: post-induction temperature (30 ËC), cell density at the induction time (5.4 abs.), and IPTG concentration (0.5 mM). The highest interleukin 11 expression was attained for the experiment 14. Initial cell density is one of the most important factors that affected protein production because it can increase (10).
Figure 3: Response surface plot and contour plot of the combined effects of isopropylthio-β-Dgalactosidase
(IPTG) concentration (A) and cell density (B) on the interleukin 11 expression.The results indicate that increase in cell density at an induction time can increase expression of interleukin 11. Cell density has the straight relation to the response. Temperature is a factor that can affect protein expression. Different studies have investigated the effect of this factor on various protein productions (13, 15-17). As reported by Zahra Moradpour et al., the maximum cholesterol oxidase was achieved at 30 ËC (15). Enhancing the temperature until middle level (30 ËC) increased expression, but an additional increase in temperature can negatively affect the expression. Overexpression of a protein can lead to the condition that causes toxic effects on the cells (18). For this reason, lowering the temperature, and as consequence decreasing the protein expression rate, may be considered as one of the choices to increase protein yield (18). But decreasing the temperature below a certain limit can result in inactive protein yield (18). Furthermore, at lower temperatures, the rate of protein synthesis and proteolysis is reduced, which might lead to the formation of properly folded proteins (19). Finally, these events result in enhanced protein yield at low temperature (6, 19).
IPTG concentration is another factor that requires optimization. The best expression of interleukin 11 was achieved at low level of IPTG concentration (0.5 mM). IPTG is an expensive material in recombinant protein production. It is also toxic and stops bacterial cell growth (20). Using higher concentrations of IPTG usually increases gene expression, but it will also decrease cell yields (19). In this study, the authors used TB medium for expression of interleukin 11. TB medium is a rich medium that allows the cells to grow continuously throughout the expression at low temperature (18).
The culture conditions for interleukin 11 expression were optimized using RSM via Box-Behnken design. To the authors’ best knowledge, this is the first study on the optimization of culture conditions for human interleukin 11 expression by RSM. A second-order model by RSM was fitted to the results of protein expression. IPTG concentration, cell density at induction time, and post-induction time temperature were considered as important factors affecting protein expression. The best condition in which interleukin 11 expression was approximately 20% is as follows: IPTG concentration (0.5 mM), cell density at induction time (5.4 mg/mL), and post-induction time temperature (30 ËC). Using different medium, vectors, and fusion proteins can improve interleukin 11 expression in E. coli. Also, other hosts such as pichia, Bacillus, and Chinese hamster ovary (CHO) cells can be used for expression of recombinant interleukin 11.
This work was supported by a grant from the Research Council of Shiraz University of Medical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
Nima Montazeri-Najafabady is an assistant professor at Shiraz Endocrine and Metabolism Research Center, Nemazee Hospital, Shiraz University of Medical Sciences; Younes Ghasemi* is dean of School of Advanced Medical Sciences and Technologies, head of Pharmaceutical Sciences Research Center, and professor of Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, Shiraz University of Medical Sciences, P.O. Box 71345-1583, Shiraz, Iran, Tel: +98-711-2424128, Fax: +98-711-2426070, firstname.lastname@example.org; and Mohammad Ali Mobasher is an assistant professor at Fasa University of Medical Sciences.
*To whom all correspondence should be addressed.
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Vol. 29, No. 11
When referring to this article, please cite as N. Montazeri-Najafabady, Y. Ghasemi, and M. Mobasher, "Optimization of Culture Conditions for Expression of Human Interleukin 11," BioPharm International 29 (11) 2016.