The Statistical Optimisation of Recombinant β-glucosidase Production through a Two-Stage, Multi-Model, Design of Experiments Approach
β-glucosidases are a class of enzyme that are widely distributed in the living world, with examples noted in plants, fungi, animals and bacteria. They offer both hydrolysis and synthesis capacity for a wide range of biotechnological processes. However, the availability of native, or the pro...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/6/3/61 |
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| author | Albert Uhoraningoga Gemma K. Kinsella Jesus M. Frias Gary T. Henehan Barry J. Ryan |
| author_facet | Albert Uhoraningoga Gemma K. Kinsella Jesus M. Frias Gary T. Henehan Barry J. Ryan |
| author_sort | Albert Uhoraningoga |
| collection | DOAJ |
| description | β-glucosidases are a class of enzyme that are widely distributed in the living world, with examples noted in plants, fungi, animals and bacteria. They offer both hydrolysis and synthesis capacity for a wide range of biotechnological processes. However, the availability of native, or the production of recombinant β-glucosidases, is currently a bottleneck in the widespread industrial application of this enzyme. In this present work, the production of recombinant β-glucosidase from <i>Streptomyces griseus</i> was optimised using a Design of Experiments strategy, comprising a two-stage, multi-model design. Three screening models were comparatively employed: Fractional Factorial, Plackett-Burman and Definitive Screening Design. Four variables (temperature, incubation time, tryptone, and OD<sub>600 nm</sub>) were experimentally identified as having statistically significant effects on the production of <i>S.griseus</i> recombinant β-glucosidase in <i>E. coli</i> BL21 (DE3). The four most influential variables were subsequently used to optimise recombinant β-glucosidase production, employing Central Composite Design under Response Surface Methodology. Optimal levels were identified as: OD<sub>600 nm</sub>, 0.55; temperature, 26 °C; incubation time, 12 h; and tryptone, 15 g/L. This yielded a 2.62-fold increase in recombinant β-glucosidase production, in comparison to the pre-optimised process. Affinity chromatography resulted in homogeneous, purified β-glucosidase that was characterised in terms of pH stability, metal ion compatibility and kinetic rates for <i>p</i>-nitrophenyl-β-D-glucopyranoside (<i>p</i>NPG) and cellobiose catalysis. |
| first_indexed | 2024-03-12T19:47:32Z |
| format | Article |
| id | doaj.art-c3307a28aa4146ff9a4113af39f4894a |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-12T19:47:32Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-c3307a28aa4146ff9a4113af39f4894a2023-08-02T03:29:43ZengMDPI AGBioengineering2306-53542019-07-01636110.3390/bioengineering6030061bioengineering6030061The Statistical Optimisation of Recombinant β-glucosidase Production through a Two-Stage, Multi-Model, Design of Experiments ApproachAlbert Uhoraningoga0Gemma K. Kinsella1Jesus M. Frias2Gary T. Henehan3Barry J. Ryan4School of Food Science and Environmental Health, College of Sciences and Health, Technological University Dublin, Dublin D07 ADY7, IrelandSchool of Food Science and Environmental Health, College of Sciences and Health, Technological University Dublin, Dublin D07 ADY7, IrelandSchool of Food Science and Environmental Health, College of Sciences and Health, Technological University Dublin, Dublin D07 ADY7, IrelandSchool of Food Science and Environmental Health, College of Sciences and Health, Technological University Dublin, Dublin D07 ADY7, IrelandSchool of Food Science and Environmental Health, College of Sciences and Health, Technological University Dublin, Dublin D07 ADY7, Irelandβ-glucosidases are a class of enzyme that are widely distributed in the living world, with examples noted in plants, fungi, animals and bacteria. They offer both hydrolysis and synthesis capacity for a wide range of biotechnological processes. However, the availability of native, or the production of recombinant β-glucosidases, is currently a bottleneck in the widespread industrial application of this enzyme. In this present work, the production of recombinant β-glucosidase from <i>Streptomyces griseus</i> was optimised using a Design of Experiments strategy, comprising a two-stage, multi-model design. Three screening models were comparatively employed: Fractional Factorial, Plackett-Burman and Definitive Screening Design. Four variables (temperature, incubation time, tryptone, and OD<sub>600 nm</sub>) were experimentally identified as having statistically significant effects on the production of <i>S.griseus</i> recombinant β-glucosidase in <i>E. coli</i> BL21 (DE3). The four most influential variables were subsequently used to optimise recombinant β-glucosidase production, employing Central Composite Design under Response Surface Methodology. Optimal levels were identified as: OD<sub>600 nm</sub>, 0.55; temperature, 26 °C; incubation time, 12 h; and tryptone, 15 g/L. This yielded a 2.62-fold increase in recombinant β-glucosidase production, in comparison to the pre-optimised process. Affinity chromatography resulted in homogeneous, purified β-glucosidase that was characterised in terms of pH stability, metal ion compatibility and kinetic rates for <i>p</i>-nitrophenyl-β-D-glucopyranoside (<i>p</i>NPG) and cellobiose catalysis.https://www.mdpi.com/2306-5354/6/3/61<i>Streptomyces griseus</i>recombinant β-glucosidaseFractional Factorial design Plackett-Burman DesignDefinitive Screening DesignResponse Surface Methodology |
| spellingShingle | Albert Uhoraningoga Gemma K. Kinsella Jesus M. Frias Gary T. Henehan Barry J. Ryan The Statistical Optimisation of Recombinant β-glucosidase Production through a Two-Stage, Multi-Model, Design of Experiments Approach Bioengineering <i>Streptomyces griseus</i> recombinant β-glucosidase Fractional Factorial design Plackett-Burman Design Definitive Screening Design Response Surface Methodology |
| title | The Statistical Optimisation of Recombinant β-glucosidase Production through a Two-Stage, Multi-Model, Design of Experiments Approach |
| title_full | The Statistical Optimisation of Recombinant β-glucosidase Production through a Two-Stage, Multi-Model, Design of Experiments Approach |
| title_fullStr | The Statistical Optimisation of Recombinant β-glucosidase Production through a Two-Stage, Multi-Model, Design of Experiments Approach |
| title_full_unstemmed | The Statistical Optimisation of Recombinant β-glucosidase Production through a Two-Stage, Multi-Model, Design of Experiments Approach |
| title_short | The Statistical Optimisation of Recombinant β-glucosidase Production through a Two-Stage, Multi-Model, Design of Experiments Approach |
| title_sort | statistical optimisation of recombinant β glucosidase production through a two stage multi model design of experiments approach |
| topic | <i>Streptomyces griseus</i> recombinant β-glucosidase Fractional Factorial design Plackett-Burman Design Definitive Screening Design Response Surface Methodology |
| url | https://www.mdpi.com/2306-5354/6/3/61 |
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