Optimization of culture parameters for α-glucosidase production from suspension culture of moss Hyophilla nymaniana (Fleish.) Menzel

Background Bryophytes, comprising of the second largest group in the plant kingdom, has attracted a great deal of attention in recent years due to its immense potential to produce biopharmaceuticals. But studies conducted to better understand their chemical composition are limited and scattered. In...

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Veröffentlicht in:Journal of Genetic Engineering and Biotechnology 2020-12, Vol.18 (1), p.82-8, Article 82
Hauptverfasser: Mishra, Rashmi, Chandra, Ramesh
Format: Artikel
Sprache:eng
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Zusammenfassung:Background Bryophytes, comprising of the second largest group in the plant kingdom, has attracted a great deal of attention in recent years due to its immense potential to produce biopharmaceuticals. But studies conducted to better understand their chemical composition are limited and scattered. In the present investigation, sequential optimization strategy, based on statistical experimental designs, was employed to enhance the production of α-glucosidase enzyme from moss Hyophilla nymaniana (Fleish.) Menzel by using Taguchi methodology. L16 orthogonal array and five physical parameters including sugar, temperature, pH, rpm, nitrogen source were considered as key parameters for enzyme production. The optimal level of each parameter for maximum glucosidase production by the moss was determined. Analysis of variance (ANOVA) was performed to evaluate statistically significant process factors. Results Based on statistical analysis (ANOVA), the optimal combinations of the major constituents of media for maximal α-glucosidase production were evaluated as follows: dextrose 2% contributed maximum on α-glucosidase production followed by ammonium nitrate 1.5%, temperature 24 °C, pH 5.6, and RPM 120. Predicted results showed an enhanced glucosidase (53%) than the basal production medium. Conclusion The present study highlighted that Taguchi design of experiments approach is better than the conventional optimization technique to determine the optimum level of each of the significant parameters that brings maximum enzyme production.
ISSN:1687-157X
2090-5920
DOI:10.1186/s43141-020-00098-8