NGF (Nerve Growth Factor) consists of three subunits, α, β and γ; among them β subunit is responsible for NGF biological activity. The β subunit is a dimer of two similar monomers containing cysteine knot motif, which is formed by three disulfide bonds (Wiesmann and De Vos 2001). β-NGF plays a fundamental role in development and survival of nervous system; so, it can be used as a therapeutic agent for the treatment of neurodegenerative diseases (Heese et al. 2006). NGF can be extracted from its natural source, the male mice submaxillar glands, by different methods of chromatography but it is unsuitable for clinical uses because contains heterogeneous mixtures of partially degraded dimmers (Bocchini and Angeletti 1969). Nowadays many studies have attempted to produce it as recombinant protein using different eukaryotic and prokaryotic hosts (Kurokawa et al. 2001; Choi and Lee 2004; Fan and Lou 2010).
Production of recombinant proteins in prokaryotic hosts is cost effective; among them
The aim of this work was to optimize the fermentation parameters to obtain the highest β-NGF production in the bioreactor scale. Therefore, conditions of the fermentation process in the 5-l batch bioreactor were optimized to maximize the yield of the active protein. Response Surface Methodology (RSM) as the most common statistical method for optimization of various biochemical processes (Elibol and Ozer 2002; Wejse et al. 2003; Rui et al. 2009) was used to optimize the induction parameters and dissolved oxygen level (or different agitation rate) for β-NGF over production.
The batch fermentation was performed in a 5-l bioreactor (Sabaferm110, Zist Farayand Sanat, Iran) containing 2 l medium inoculated with 100 ml secondary seed culture (5% v/v). The pH was kept constant throughout the experiment at 7.0 by the automatic addition of 2
The percentage of dissolved oxygen (DO) and the post induction temperature were chosen as the independent variables. The ranges and levels of the two variables are listed in Table I. The cell growth at 600 nm (or the dry cells weigh) and the recombinant β-NGF expression level were chosen as response (output). The experimental data obtained from the design (Table I) were analyzed by the following second order polynomial equation:
Where Y is the measured response, βo, βi, βij and βii are the regression coefficients and Xi and Xj are the independent variables in coded values.
The values of independent variables (post induction temperature and dissolved oxygen) and the corresponding levels used in a central composite design (normalized in –2, –1, 0, 1 and 2).
Variable | Level | ||||
---|---|---|---|---|---|
–2 | –1 | 0 | 1 | 2 | |
A: Post induction temperature (T) (°C) | 20 | 24.25 | 28.5 | 32.75 | 37 |
B: Dissolved oxygen (DO) (%) | 10 | 20 | 30 | 40 | 50 |
The software package Design Expert version 11.0.3.0 (StatSoft, USA) was used to find out the interactive effect of the two variables. The significance of the model equation and model terms were evaluated by
The experimental design of 10 runs with two variables (post induction temperature and dissolved oxygen) and two responses (recombinant protein expression level and cell growth measured by absorbance at 600 nm).
Run | Post induction Temperature (T) (°C) | Dissolved Oxygen (DO) (%) | Response 1 Recombinant Protein Expression Level (%) | Response 2 Cell Growth (Abs 600nm) | ||
---|---|---|---|---|---|---|
1 | –1 | 24.25 | –1 | 20 | 8.0 ± 0.9 | 1.83 ± 0.18 |
2 | +1 | 32.75 | –1 | 20 | 15.3 ± 1.2 | 2.60 ± 0.19 |
3 | –1 | 24.25 | +1 | 40 | 10.1 ± 1.0 | 1.02 ± 0.11 |
4 | +1 | 32.75 | +1 | 40 | 8.4 ± 0.8 | 5.60 ± 0.29 |
5 | –2 | 20.0 | 0 | 30 | 4.6 ± 0.4 | 1.36 ± 0.11 |
6 | +2 | 37.0 | 0 | 30 | 11.1 ± 1.1 | 3.40 ± 0.22 |
7 | 0 | 28.5 | –2 | 10 | 10.0 ± 0.9 | 1.87 ± 0.21 |
8 | 0 | 28.5 | +2 | 50 | 3.8 ± 0.4 | 2.05 ± 0.20 |
9 | 0 | 28.5 | 0 | 30 | 14.6 ± 1.2 | 2.45 ± 0.19 |
10 | 0 | 28.5 | 0 | 30 | 14.2 ± 1.2 | 2.60 ± 0.19 |
As illustrated in Table II and Fig. 2, high β-NGF expression levels were observed in Runs 2 and 9 (15.3% ± 1.2 and 14.6% ± 1.2, respectively). Run 10 was a repetition of Run 9 and resulted in the same value. In these runs, the cell growth (or dry cell weight) was not at maximum level. In other words, the increased cell growth was not necessarily accompanied by an increase in recombinant production level. It is necessary to mention that the highest value of β-NGF production was obtained at post induction temperature of 32.75°C and % DO of 20.
In contrast, the lowest value of β-NGF production level (3.8% ± 0.4) was obtained at post induction temperature of 28.5°C and 50% DO (Run 8). When comparing the runs 8 and 9 with constant post induction temperature (28.5°C) and different %DO (also runs 2 and 4 with constant post induction temperature of 32.75°C and different %DO) it could be observed that the recombinant β-NGF production was significantly reduced at high degree of DO. It can be a result of high agitation speed because higher speed of stirring could cause a breakdown of the cells (Banerjee et al. 2009).
Parameter estimates and analysis of variance (ANOVA) of the model for recombinant production of β-NGF in
Source of variation | Degree of freedom | Sum of squares | Mean squares | |||
---|---|---|---|---|---|---|
Recombinant Protein Expression Level (%) | Model | 5 | 141.50 | 28.30 | 120.36 | 0.0002 |
A-Temperature (°C) | 1 | 29.14 | 29.14 | 123.94 | 0.0004 | |
B-Dissolved Oxygen (%) | 1 | 24.65 | 24.65 | 104.85 | 0.0005 | |
AB | 1 | 20.25 | 20.25 | 86.13 | 0.0007 | |
A2 | 1 | 42.94 | 42.94 | 182.62 | 0.0002 | |
B2 | 1 | 56.17 | 56.17 | 238.90 | 0.0001 | |
Error | 1 | 0.058 | 0.058 | – | – | |
Total | 9 | 142.44 | – | – | – | |
Cell Growth (Abs 600 nm) | Model | 3 | 11.58 | 3.86 | 6.82 | 0.0232 |
A-Temperature (°C) | 1 | 7.41 | 7.41 | 13.10 | 0.0111 | |
B-Dissolved Oxygen (%) | 1 | 0.54 | 0.54 | 0.96 | 0.3655 | |
AB | 1 | 3.63 | 3.63 | 6.41 | 0.0445 | |
Error | 1 | 0.011 | 0.011 | – | – | |
Total | 9 | 14.98 | – | – | – |
The other response was cell growth measured by absorbance at 600 nm and the effects of two parameters (post induction temperature and %DO) on this parameter were also examined. The model
Nowadays, recombinant production of functional proteins is in high demand in modern biotechnology.
Another factor that influences the expression level is post induction temperature. As the best cultivation temperature for
In summary, the response surface methodology was successfully used for the optimization of recombinant β-NGF production by varying the induction parameters and %DO. Our results could be beneficial for industrial production of β-NGF.