Medium Optimization for Synaptobrevin Production Using Statistical Methods
Archives of Medical Laboratory Sciences,
Vol. 3 No. 1 (2017),
16 October 2017
https://doi.org/10.22037/amls.v3i1.18315
Abstract
Background: Botulinum toxin, the most potent biological toxin, has become a powerful therapeutic tool for a growing number of clinical applications. Molecular studies have identified a family of synaptic vesicle-associated membrane proteins (VAMPs, also known as synaptobrevins) which have been implicated in synaptic vesicle docking and fusion with plasma membrane proteins.
Materials and Methods: Using the synaptobrevin as a substrate for in vitro assay is the method to detect BoNT activity. We have been working on optimizations of bacterial expression conditions and media for high-level production of synaptobrevin peptide. Statistics-based experimental design was used to investigate the effect of medium components (E. coli strain, peptone, IPTG, yeast extract, ampicillin, and temperature) on synaptobrevin production by E. coli.
Results: A 24 fractional factorial design with center points revealed that IPTG and temperature were the most significant factors, whereas the other factors were not important within the levels tested. This purpose was followed by a central composite design to develop a response surface for medium optimization. The optimum medium composition for synaptobrevin production was found to be: IPTG 29 mM, peptone 10 g/L, yeast extract 5 g/L, temperature 23°C and ampicillin 100 mg/L. This medium was projected to produce, theoretically, 115 mg/L
synaptobrevin.
Conclusion: The optimum medium composition synaptobrevin production was found to be: BL21 (E.coli strain), LB medium (peptone 10 g/L, Yeast 5 g/L), Ampicillin (100 mg/L), IPTG (0.29 mg/L) and temperature (23°C).
- synaptobrevin
- E. coli
- experimental designs
- central composite design
- medium optimization
How to Cite
References
Blum FC, Chen C, Kroken AR, Barbieri JT. Tetanus toxin and botulinum toxin a utilize unique mechanisms to enter neurons of the
central nervous system. Infection and immunity. 2012;80(5):1662-9.
Truong DD, Stenner A, Reichel G. Current clinical applications of botulinum toxin. Current pharmaceutical design.2009;15(31):3671-80.
Evidente VG, Adler CH. An update on the neurologic applications of botulinum toxins. Current neurology and neuroscience reports. 2010;10(5):338-44.
Adler S, Bicker G, Bigalke H, Bishop C, Blumel J, Dressler D, etal. The current scientific and legal status of alternative methods to the LD50 test for botulinum neurotoxin potency testing. The report and recommendations of a ZEBET Expert Meeting. Alternatives to
laboratory animals : ATLA. 2010;38(4):315-30.
Sesardic D, Leung T, Gaines Das R. Role for standards in assays of botulinum toxins: international collaborative study of three preparations of botulinum type A toxin. Biologicals : journal of the International Association of Biological Standardization. 2003;31(4):265-76.
Wictome M, Newton KA, Jameson K, Dunnigan P, Clarke S,Gaze J, et al. Development of in vitro assays for the detection of botulinum toxins in foods. FEMS Immunology & Medical Microbiology. 1999;24(3):319-23.
Lindstrom M, Korkeala H. Laboratory Diagnostics of Botulism. Clinical Microbiology Reviews. 2006;19(2):298-314.
Sharma SK, Ferreira JL, Eblen BS, Whiting RC. Detection of type A, B, E, and F Clostridium botulinum neurotoxins in foods by
using an amplified enzyme-linked immunosorbent assay with digoxigenin-labeled antibodies. Applied and environmental microbiology. 2006;72(2):1231-8.
Pellett S, Tepp WH, Clancy CM, Borodic GE, Johnson EA. A Neuronal Cell-based Botulinum Neurotoxin Assay for Highly Sensitive and Specific Detection of Neutralizing Serum Antibodies. FEBS letters. 2007;581(25):4803-8.
. !!! INVALID CITATION !!!
Demain AL, Vaishnav P. Production of recombinant proteins by microbes and higher organisms. Biotechnology advances. 2009;27(3):297-306.
Hannig G, Makrides SC. Strategies for optimizing heterologous protein expression in Escherichia coli. Trends in biotechnology.
;16(2):54-60.
Puri S, Beg QK, Gupta R. Optimization of alkaline protease production from Bacillus sp. by response surface methodology.
Current microbiology. 2002;44(4):286-90.
Salihu A, Alam MZ, AbdulKarim MI, Salleh HM.Optimization of lipase production by< i> Candida cylindracea in palm oil mill effluent based medium using statistical experimental design. Journal of Molecular Catalysis B: Enzymatic.
;69(1):66-73.
Sorensen HP, Mortensen KK. Soluble expression of recombinant proteins in the cytoplasm of Escherichia coli. Microbial cell factories. 2005;4(1):1.
Kalil S, Maugeri F, Rodrigues M. Response surface analysis and simulation as a tool for bioprocess design and optimization. Process
Biochemistry. 2000;35(6):539-50.
Faravelli L. Response-surface approach for reliability analysis. Journal of Engineering Mechanics. 1989;115(12):2763-81.
Li C, Bai J, Cai Z, Ouyang F. Optimization of a cultural medium for bacteriocin production by Lactococcus lactis using response
surface methodology. Journal of Biotechnology. 2002;93(1):27-34.
Deepak V, Kalishwaralal K, Ramkumarpandian S, Babu SV, Senthilkumar S, Sangiliyandi G. Optimization of media composition
for Nattokinase production by Bacillus subtilis using response surface methodology. Bioresource Technology. 2008;99(17):8170-4.
Chu L, Robinson DK. Industrial choices for protein production by large-scale cell culture. Current Opinion in Biotechnology.
;12(2):180-7.
Kammoun R, Naili B, Bejar S. Application of a statistical design to the optimization of parameters and culture medium for α-amylase
production by Aspergillus oryzae CBS 819.72 grown on gruel (wheat grinding by-product). Bioresource Technology. 2008;99(13):5602-9.
Beg QK, Sahai V, Gupta R. Statistical media optimization and alkaline protease production from Bacillus mojavensis in a bioreactor.
Process Biochemistry. 2003;39(2):203-9.
Hajji M, Rebai A, Gharsallah N, Nasri M. Optimization of alkaline protease production by Aspergillus clavatus ES1 in Mirabilis
jalapa tuber powder using statistical experimental design. Applied microbiology and biotechnology. 2008;79(6):915-23.
Ismail A, Soultani S, Ghoul M. Optimization of the enzymatic synthesis of butyl glucoside using response surface methodology.
Biotechnology progress. 1998;14(6):874-8.
Cui F, Li Y, Xu Z, Xu H, Sun K, Tao W. Optimization of the medium composition for production of mycelial biomass and exopolymer by Grifola frondosa GF9801 using response surface methodology. Bioresource Technology. 2006;97(10):1209-16.
Kim H, Lim J, Joo J, Kim S, Hwang H, Choi J, et al. Optimization of submerged culture condition for the production of mycelial biomass and exopolysaccharides by Agrocybe cylindracea. Bioresource Technology. 2005;96(10):1175-82.
Ratnam B, Rao MN, Rao MD, Rao SS, Ayyanna C. Optimization of fermentation conditions for the production of ethanol from sago
starch using response surface methodology. World Journal of Microbiology and Biotechnology. 2003;19(5):523-6.
Singh S, Moholkar VS, Goyal A. Optimization of carboxymethylcellulase production from Bacillus amyloliquefaciens
SS35. 3 Biotech. 2014;4(4):411-24.
Sooch BS, Kauldhar BS. Influence of multiple bioprocess parameters on production of lipase from Pseudomonas sp. BWS-5. Brazilian Archives of Biology and Technology. 2013;56:711-21.
Baş D, Boyacı İH. Modeling and optimization I: Usability of response surface methodology. Journal of Food Engineering.
;78(3):836-45.
Bucher CG, Bourgund U. A fast and efficient response surface approach for structural reliability problems. Structural safety. 1990;7(1):57-66.
Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA. Response surface methodology (RSM) as a tool for optimization in
analytical chemistry. Talanta. 2008;76(5):965-77.
Francis F, Sabu A, Nampoothiri KM, Ramachandran S, Ghosh S, Szakacs G, et al. Use of response surface methodology for optimizing process parameters for the production of α-amylase by Aspergillus oryzae. Biochemical Engineering Journal.
;15(2):107-15.
Montgomery D, Design C. Analysis of Experiments. Wiley,New York; 2001.
Mason VC, Keene AS, Cook JE, Cooper EM, Hartley RD. Oven and stack ammoniation of grass hays. 1. Changes in chemical
composition in relation to digestibility in vitro and cell-wall degradability. Animal Feed Science and Technology.1989;24(3):299-311.
Hill WJ, Hunter WG. A review of response surface methodology: a literature survey. Technometrics. 1966;8(4):571-90.
Torstensen B, Lie O, Hamre K. A factorial experimental design for investigation of effects of dietary lipid content and pro‐and
antioxidants on lipid composition in Atlantic salmon (Salmo salar) tissues and lipoproteins. Aquaculture Nutrition. 2001;7(4):265-76.
Desai KM, Survase SA, Saudagar PS, Lele S, Singhal RS. Comparison of artificial neural network (ANN) and response surface
methodology (RSM) in fermentation media optimization: case study of fermentative production of scleroglucan. Biochemical Engineering Journal. 2008;41(3):266-73.
- Abstract Viewed: 424 times
- PDF Downloaded: 244 times