The Goldilocks Approach: A Review of Employing Design of Experiments in Prokaryotic Recombinant Protein Production
Abstract
:1. Introduction
2. Production of Recombinant Proteins in a Prokaryotic Expression System
2.1. Factors that Inform the Choice of Expression System
2.2. Factors that Influence Media Composition and Culture Conditions in an Expression System
2.3. Enhancing the Production of Recombinant Proteins in a Prokaryotic Expression System by DoE
- Stage 1. The first stage of the process is to compile a list of factors that can influence protein expression. These are usually such factors as; induction temperature, induction duration, pH, media components (carbon source, nitrogen source, micronutrients).
- Stage 2. At this stage, a suitable software package such as MINITAB, JMP or Design Experts will be acquired for the statistical analysis. The second stage of DoE aims to reduce the number of factors to a smaller subset, these being the most important factors (i.e. those with the greatest impact on expression). This process is known as screening. Having a smaller set of significant factors greatly simplifies the statistical process. Sometimes, if the number of factors is small (between 2 and 4) there is no need to carry out the screening stage. When looking at a factor that influences protein expression the concept of levels is important: temperature, for example, may be examined between 20 °C and 40 °C. These two temperatures represent the lowest and highest “level” of this parameter that will influence expression. For the purposes of modelling these two levels are input into the model for this factor. Similarly, the upper and lower levels are input for all other relevant parameters. It is important to note that the levels are input into the DoE package as +1 (highest value of a parameter) and −1 (lowest value of a parameter). This “coding” is carried out to avoid the use of multiple different measurement units for parameters such as pH, temperature. The software will then suggest a minimal set of experiments to explore the significance of each factor. The design of the experimental matrix can be selected from a range of choices such as Full Factorial Design, Plackett Burman Design or indeed a custom design. The objective is to assess the “main effect” of a factor (its direct effect on a response) as well as its “interaction effects” (the effect on other factors). The suggested experiments are carried out and the results are used to inform the next stage of the process—optimisation.
- Stage 3. The final stage of the process is optimisation and is typically carried out with a set of three to four factors. An experimental RSM (Response Surface Methodology) design strategy is selected and experiments are run as for the screening stage. The optimisation process expresses the response surface as a polynomial and uses the input data to estimate its coefficients. The derivative of this polynomial is used to obtain inflection points corresponding to maxima or minima in the model. The model can be evaluated by looking at the goodness of fit between the model and experimental data. Finally, experiments using the optimum conditions predicted by the model are carried out to validate the model.
3. Design of Experiments (DoE) to Optimise Recombinant Protein Production
3.1. DoE; a Brief Overview
3.2. DoE Versus One-Factor-At-a-Time (OFAT)
4. Defining a DoE Workflow to Optimise Recombinant Protein Production
5. A Suggested DoE Workflow for Recombinant Protein Production
5.1. Planning the Test; Selection of Factors and Associated Levels Influencing Recombinant Protein Production
5.2. Screening Designs to Identify Factors that Significantly Affect Recombinant Protein Expression
5.2.1. Full Factorial Design
5.2.2. Fractional Factorial Design (FFD)
5.2.3. Plackett-Burman Designs (PBD)
5.2.4. Definitive Screening Design (DSD) and Custom Design (CD)
5.3. Optimisation Designs to Maximise Recombinant Protein Production in Prokaryotic Systems
5.3.1. Central Composite Design (CCD)
5.3.2. Box Behnken Design (BBD)
5.3.3. Summary and Choice of Optimisation Methods
5.4. Analysis and Interpretation of Optimisation Data
Evaluation of Experimental Design and Predictive Model Validation
5.5. Optimum Determination
6. Conclusions; Getting It ‘Just Right’
Author Contributions
Funding
Conflicts of Interest
References
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General Advantages | Disadvantages | References | |
---|---|---|---|
Most common E. coli strains | Rapid expression, high yield, ease of culture and gene modification, cost effective. | Post translational modification not possible. Inclusion body formation | [41,45,46] |
BL21, B21-Codonplus (RIL), BL21(DE3), BL21(DE3)pLys S/E, BL21 Star, C41(DE3), C43(DE3), Codon plus (RP), Lemon21(DE3), M15, Origami, Rosetta, SG13009, Shuffle Derivatives of K-12, AD494 and HMS174. | |||
Most common Bacillus species | |||
Bacillus brevis, Bacillus megaterium and Bacillus subtilis. | Preferred for homologous expression of some enzymes (e.g., proteases and amylases), Strong secretion, no involvement of intracellular inclusion bodies and ease of manipulation. | Contains proteases, which may hydrolyse recombinant proteins. | [42,47,48,49,50] |
Factors | Levels | ||
---|---|---|---|
Low | High | ||
Media composition | X1 Yeast Extract | − | + |
X2 Tryptone | − | + | |
X3 Glycerol | − | + | |
X4 NaCl | − | + | |
Induction condition | X5 Inoculum size | − | + |
X6 IPTG concentration | − | + | |
X7 Induction temperature | − | + | |
X8 Incubation time | − | + | |
X9 pH | − | + |
Factors | |||||||
---|---|---|---|---|---|---|---|
Number of Runs | |||||||
Screening Design | Effect explained by the model | 2 | 3 | 4 | 5 | 6 | 7 |
Full Factorial Design | Main effect and 2 factor interactions | 4 | 8 | 16 | 32 | 64 | 128 |
Fractional Factorial Design | Main effect only | - | - | - | 8 | 8 | 8 |
Main effect and 2 factor interactions | - | 8 | 8 | 16 | 16 | 16 | |
Main effect and 2 factors interactions | - | - | 16 | 16 | 32 | 64 | |
Plackett-Burman Design | Main effect only | - | - | - | - | 12 | 12 |
Definitive Screening Design | Main effect and 2 factor interaction | - | 13 | 13 | 13 | 13 | 17 |
Main effect, 2 factor interaction and quadratic effects | - | 17 | 17 | 17 | 17 | 22 | |
Custom Design | Main effect only | ≥3 | ≥4 | ≥5 | ≥6 | ≥7 | ≥8 |
Host Organism | Protein Involved | Screening Design | Factors Studied | Screened Significant Factors | Reference |
---|---|---|---|---|---|
Bacillus I-1018 | Xylanase | Full Factorial Design | Media composition | Xylan, casein hydrolysate, NH4Cl | [114] |
E. coli | Non-structural protein NS3 | Full Factorial Design | Culture condition | temperature, induction length | [124] |
Pseudoalteromonas IND11 | Fibrinolytic enzyme | Full Factorial Design | Media composition | pH, maltose and NaH2PO4 | [115] |
E. coli | Zinc-metalloprotease (SVP2) | Fractional Factorial Design | Media composition and culture condition | IPTG and Ca2+ ion concentration and temperature | [22] |
E. coli | Soluble pneumolysin | Fractional Factorial Design | Media composition and culture condition | Temperature, tryptone and kanamycin | [6] |
Bacillus cerius | L-asparaginase | Plackett-Burman | Media composition | Soya bean meal, asparagine, woodchips, NaCl | [122] |
E. coli | Vascular endothelial growth factor | Plackett-Burman design | Media composition and culture condition | Glycerine, inducing time, peptone | [125] |
P. aeruginosa | L-asparaginase | Plackett-Burman Design | Culture condition | pH, casein hydrolysate and corn steep liquor | [126] |
P. pastoris | Human interferon gamma | Plackett-Burman Design | Media composition | Gluconate, glycine, KH2PO2 | [85] |
S. griseorubens | Chitinase | Plackett–Burman Design | Media composition | Yeast extract and K2HPO4, KH2PO4 | [127] |
Factor | Effect | Relative Effect | p-Value |
---|---|---|---|
X3 | −1.11273 | 0.001 | |
X6 | 0.2252 | 0.0143 | |
X1 | 0.17492 | 0.0296 | |
X4 | 0.06408 | 0.2215 | |
X7 | 0.04154 | 0.4112 | |
X2 | −0.07970 | 0.1421 | |
X5 X5*X1 X3*X7 | 0.00233 0.04153 −0.06405 | | 0.9664 0.4211 0.2623 |
Number of Factors | Number of Factorial Points | Number of Axial Points | Number of Central Points | Total Number of Runs |
---|---|---|---|---|
2 | 4 | 4 | 5 | 13 |
3 | 8 | 6 | 6 | 20 |
4 | 16 | 8 | 7 | 31 |
5 | 16 | 10 | 6 | 32 |
6 | 32 | 12 | 9 | 53 |
7 | 64 | 14 | 14 | 92 |
Microorganism | Recombinant Protein | RSM Methods | Optimised Factors | Optimised vs. Non-Optimised Yield | Reference |
---|---|---|---|---|---|
E. coli BL21 | Superoxide dismutase | Box–Behnken design | Tryptone, tween-80, lactose | Enzyme activity increase by 1.54-fold | [142] |
E. coli BL21-SI | Human interferon beta | Box–Behnken Design | Temperature, cell density, NaCl | hIFN- β concentration increase by 5-fold | [143] |
E. coli BL21-SI | Human interferon gamma | Box–Behnken Design | Temperature, biomass concentration, NaCl | hIFN- γ concentration increase by 13-fold | [144] |
P. pastoris GS115 | β-glucosidase | Box-Behnken Design | Sorbitol, MeOH, pH | Enzyme activity increase by 3.3-fold | [145] |
Bacillus circulans GRS 313 | Amylase | Central Composite Design | Soybean meal, yeast extract, wheat bran | Enzyme yield increase by 1.25-fold | [146] |
Bacillus IMG22. | α–amylase | Central Composite Design | Starch, yeast extract, glycerol, peptone | Enzyme activity reached 17.54 IU/mL | [147] |
E. coli BL21(DE3), Rosetta 2 (DE3), Rosetta blue (DE3), and Rosettagami2(DE3) | Cyclodextrin glucanotransferase | Central composite Design | IPTG, arabinose B, post induction temperature | Enzyme activity increase by 3.45-fold | [148] |
E. coli DH5α | Cytochrome 2C9 protein | Central Composite Design | Ampicillin, chloramphenicol, IPTG, peptone | Enzyme production increased by 1.05- fold | [149] |
E. coli BL21 (DE3) | Interferon beta | Central Composite Design | DCW (dry cell weight), IPTG | Production increase more than 3-fold | [137] |
E. coli BL21 (DE3) | L-Asparaginase | Central Composite Design | Tryptone, yeast extract, peptone, CaCl2 | Enzyme activity reached 17,386 U/L | [150] |
E. coli BL21 | Peptide T-20 | Central Composite Design | NPK, IPTG, post induction time | Production increase by more than 2-fold | [106] |
E. coli BL21 (DE3) | TaqI endonuclease | Central Composite Design | Glucose, (NH4)2HPO4, KH2PO4, MgSO4.7H2O | Enzyme yield increase by about 3.6-fold | [151] |
E. coli DH5α | Xylanase | Central Composite Design | Glucose, (NH4)2HPO4, CK2HPO4, DKH2PO4, MgSO4 | Production increase by 1.7- fold | [152] |
E. coli BL21 | Bromelain | Central Composite Design | Temperature, inducer concentration, post induction period | Enzyme activity increase by 1.3-fold | [153] |
E. coli BL21 | Phytase | Central Composite Design | Tryptone, yeast extract, NaCl | Production increase by 2.78-fold | [154] |
E. coli BL21 (DE3) | Chitinase | Central Composite Design | Temperature, incubation time | Total activity increased by 1.54-fold | [115] |
E. coli BL21(DE3) | Zinc metalloprotease | Central Composite Design | IPTG, Ca2+, induction time | Production increase by 15-fold | [22] |
E. coli JM109 | Carboxymethyl-Cellulose | Central Composite Design | Rice bran tryptone and initial pH of medium | Production increase by 3-fold | [155] |
P. pastoris X33 | Phytase | Central Composite Design | Yeast extract, tween-80, methanol | Specific activity increase by 21.8-fold | [156] |
E. coli TB1 | MBP-Heparinase | Central Composite Design (Orthogonal) | Yeast extract, glucose, Ca2+, OD600 | Specific activity increase by 2.5-fold | [157] |
E. coli BL21 | Cis-epoxysuccinate hydrolase | Central Composite Design (Rotatable) | Inoculation level, induction-starting time, lactose, induction temperature, induction time | Enzyme activity increase by 4.6-fold | [158] |
Coded Values | Responses | ||||||
---|---|---|---|---|---|---|---|
Runs | X1 | X2 | X3 | X4 | Actual | Predicted | Residuals |
1 | −1 | 1 | −1 | 1 | Experimental response | Predicted response data | Residual data |
2 | −1 | −1 | 1 | 1 | |||
3 | 0 | 0 | 0 | 0 | |||
4 | −1 | 0 | 0 | 0 | |||
5 | −1 | 1 | 1 | −1 | |||
6 | 1 | 1 | 1 | 1 | |||
7 | 1 | 1 | −1 | 1 | |||
8 | −1 | 1 | 1 | 1 | |||
9 | 1 | −1 | −1 | 1 | |||
10 | 0 | −1 | 0 | 0 | |||
11 | 1 | 1 | 1 | −1 | |||
12 | 0 | 0 | 0 | 0 | |||
13 | 0 | 0 | 1 | 0 | |||
14 | 0 | 1 | 0 | 0 | |||
15 | 1 | 0 | 0 | 0 | |||
16 | 0 | 0 | 0 | 1 | |||
17 | 1 | 1 | −1 | −1 | |||
18 | −1 | 1 | −1 | −1 | |||
19 | −1 | −1 | 1 | −1 | |||
20 | −1 | −1 | −1 | 1 | |||
21 | 1 | −1 | −1 | −1 | |||
22 | 0 | 0 | 0 | −1 | |||
23 | 1 | −1 | 1 | 1 | |||
24 | 0 | 0 | −1 | 0 | |||
25 | 1 | −1 | 1 | −1 | |||
26 | −1 | −1 | −1 | −1 | |||
Responses (e.g., actual, predicted and residues) data are utilised during the optimisation analysis to evaluate the validity of the model and determine the optimum. |
Source | DF | Adj SS | Adj MS | F-Value | p-Value |
---|---|---|---|---|---|
Model | 11 | 40.4149 | 3.67408 | 1255.77 | 0.0001 |
Linear | 4 | 3.1531 | 0.78828 | 269.43 | 0.0001 |
Square | 4 | 35.3209 | 8.83022 | 3018.09 | 0.0001 |
Interaction | 3 | 1.9409 | 0.64697 | 221.13 | 0.0001 |
Residues | 40 | 0.117 | 0.00293 | ||
Lack-of-fit | 13 | 0.00369 | 0.00284 | 0.96 | 0.515 |
Pure error | 27 | 0.0802 | 0.00297 | ||
Total | 51 | 40.532 | |||
R2= 99.71%, Adj-R2 = 99.63%, Pred-R2 = 99.48% | |||||
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Uhoraningoga, A.; Kinsella, G.K.; Henehan, G.T.; Ryan, B.J. The Goldilocks Approach: A Review of Employing Design of Experiments in Prokaryotic Recombinant Protein Production. Bioengineering 2018, 5, 89. https://doi.org/10.3390/bioengineering5040089
Uhoraningoga A, Kinsella GK, Henehan GT, Ryan BJ. The Goldilocks Approach: A Review of Employing Design of Experiments in Prokaryotic Recombinant Protein Production. Bioengineering. 2018; 5(4):89. https://doi.org/10.3390/bioengineering5040089
Chicago/Turabian StyleUhoraningoga, Albert, Gemma K. Kinsella, Gary T. Henehan, and Barry J. Ryan. 2018. "The Goldilocks Approach: A Review of Employing Design of Experiments in Prokaryotic Recombinant Protein Production" Bioengineering 5, no. 4: 89. https://doi.org/10.3390/bioengineering5040089
APA StyleUhoraningoga, A., Kinsella, G. K., Henehan, G. T., & Ryan, B. J. (2018). The Goldilocks Approach: A Review of Employing Design of Experiments in Prokaryotic Recombinant Protein Production. Bioengineering, 5(4), 89. https://doi.org/10.3390/bioengineering5040089