Efficient production of bioactive metabolites from Antrodia camphorata ATCC 200183 by asexual reproduction-based repeated batch fermentation

Li, Huaxiang; Lü, Zhen-Ming; Geng, Yan; Gong, Jin‐Song; Zhang, Xiaojuan; Shi, Jin‐Song; Xu, Zhenghong; Ma, Yanhe

doi:10.1016/j.biortech.2015.06.144

Cited by 27 publications

(34 citation statements)

References 34 publications

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“…Coefficient variation (CV) is the standard deviation expressed in terms of percentage of the mean and should be less than 10%. 25 Adequate precession and Coefficient variation of the five responses in the present study was found to be greater than four and less than 10%. 3D plots obtained showed the interactive effects of the process variables on the bioactive metabolite production and its effect against the five responses.…”

Section: Estimation Of Kinetic Parameterscontrasting

confidence: 43%

“…All the five variables studied had significant impact on the bioactive metabolite production by VSM 25. 25 The optimum values of time of incubation, pH, temperature and concentration of galactose and peptone was found to be 11 days, 30 0 C, pH 8 and concentration of galactose 2% and peptone 1%. The recorded zones of inhibition (mm) against the five responses included 20.8 mm for Aspergillus niger, 18.9 mm for Aspergillus flavus, 20 mm for Fusarium oxysporum, 18 mm for Fusarium solani and 19 mm for Candida albicans by the bioactive metabolite produced by VSM-25.…”

Section: Estimation Of Kinetic Parametersmentioning

confidence: 88%

“…Coefficient variation is the standard deviation expressed in terms of percentage of the mean and should be less than 10%. 25 The model showed statistical insignificant Lack-of-fit (Table 4) which indicate the model to be good fit. The experimental values are in close agreement with the predicted values.…”

Section: Model Adequacy Checkingmentioning

confidence: 95%

“…30 Hence adjusted R 2 is used for model adequacy, since adjusted R 2 adjusts for the models' size. 25 The insignificant factors added to the model are decreased by the adjusted R 2 . 30 The adjusted (R 2 ) value of all the responses was > 0.9 which indicate the model to be a very good fit.…”

Section: Estimation Of Kinetic Parametersmentioning

confidence: 99%

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Optimization of Operating Conditions for the Production of Enhanced Antifungal Metabolites from Streptomonospora arabica VSM 25 by Full Factorial Design

Managamuri¹,

Vijayalakshmi²,

Poda³

et al. 2017

JYP

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Objectives: To execute the influence of the physico-chemical variables on the production of the bioactive metabolites by Streptomonospra arabica VSM-25 using Response Surface Methodology. Materials and Methods: An actinobacterium strain isolated from the deep sea marine sediment samples was identified as Streptomonospra arabica VSM-25 by conventional and molecular approaches. RSM was employed to study the impact of five variables, viz. incubation time, pH, temperature, galactose and peptone concentrations on the production of antifungal metabolites by VSM-25. Growth related production formation kinetics and substrate utilization in batch system was analysed using mathematical and unstructured kinetic models. Results: Statistical study showed that the incubation time, pH, Temperature, Concentration of galactose and peptone has a significant effect (p <0.0001) on the bioactive metabolite production at their individual and interactive level. A second order polynomial model provided a satisfied fit for experimental data with regard to the production of antifungal metabolites. Maximum antimycotic activity was achieved at incubation time (11 days), pH (8), temperature (30°C), galactose concentration (2%) and peptone concentration (1%). Unstructured mathematical kinetic model was developed and estimated kinetic parameters also exhibited good approximation in terms of model fitting and regression analysis. Conclusion: To the best of our knowledge this is the first report on the production of anti fungal metabolites from S. arabica using RSM and kinetic modelling studies which firmly support the application of RSM and kinetic modelling for optimization. The study may find potential application in rapid screening and production of novel drug molecules from unexploited natural sources.Key words: Streptomonospora arabica, Antimycotic Activity, Optimization, Kinetic Modelling, Response Surface Methodology, Bioactive Metabolites. Key message: The outcome of the present study strongly supports, RSM based optimization of fermentation conditions. The optimization of environmental parameters and cultural conditions plays a crucial role in the enhanced antimycotic activity. This study contributes towards scale-up of production of antifungal agents by Streptomonospora arabica VSM-25. The unstructured model provided a better approximation of kinetic profiles of bioactive metabolite production by the strain in shake-flask fermentations. Correspondence :Prof. M. Vijayalakshmi, Department of Botany and Microbiology, Acharya Nagarjuna University, Nagarjuna nagar, Guntur-52510, Andhra Pradesh, INDIA. Phone: 9440870026Email: profmvl08@gmail.com DOI: 10.5530/jyp.2017.9.80 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

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Section: Estimation Of Kinetic Parameterscontrasting

confidence: 43%

Section: Estimation Of Kinetic Parametersmentioning

confidence: 88%

Section: Model Adequacy Checkingmentioning

confidence: 95%

Section: Estimation Of Kinetic Parametersmentioning

confidence: 99%

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Optimization of Operating Conditions for the Production of Enhanced Antifungal Metabolites from Streptomonospora arabica VSM 25 by Full Factorial Design

Managamuri¹,

Vijayalakshmi²,

Poda³

et al. 2017

JYP

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“…A set of mathematical and unstructured kinetic models, significantly define the substrate usage and growth-related production formation kinetics in a batch system that were studied by many researchers. [21][22] Models of Logistic and Luedeking-Piret were used to simulate the cellular increase and antibacterial metabolite production of S. halotolerans VSM 2. The statistics acquired from the models had been used to calculate the specific cell growth rate (µ max ), d…”

Section: Unstructured Model Developmentmentioning

confidence: 99%

Improved Bioactive Metabolite Production by Saccharopolyspora halotolerans VSM-2 Using Response Surface Methodology and Unstructured Kinetic Modelling

Managamuri¹,

Vijayalakshmi²,

Indupalli³

et al. 2018

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Background:This study targets to optimize and analyse the interactive effects of process variables for improved bioactive metabolite production using RSM and unstructured kinetic modelling by S. halotolerans VSM 2. Materials and Methods: RSM was applied to optimize the interactive effects of five variables, viz., time of incubation, pH, temperature, concentration of maltose and meat extract on bioactive metabolite production and its effect against the five responses viz., S. flexneri, S. marcescens, P. vulgaris, P. aeruginosa and E. coli. Models of Logistic and Luedeking-Piret were used to simulate the cellular increase and bioactive metabolite production. Results: RSM optimal conditions for the bioactive metabolite production recorded were incubation time (12days), pH (8), and temperature (25 0 C), concentrations of maltose and meat extract (1 % w/v) (each). The effect of the bioactive metabolite produced (zone of inhibition) against the responses were found to be 17 mm for S. flexneri, 17 mm for S. marcescens, 16 mm for P. vulgaris, 17 mm for P. aeruginosa and 18 mm for E coli. The data obtained from experimental values are in close agreement with the predicted values of RSM. Model adequacy was evaluated using ANOVA variance where the quadratic effect of p<0.0001 which imply the significance of the model. The unstructured-, mathematical-kinetic models provided a better approximation of profiles of S. halotolerans VSM 2 growth, optimized media utilization and bioactive metabolite production. Conclusion: Optimization of the independent variables for the production of the bioactive metabolite using RSM by S. halotolerans VSM 2 and its effect against the five responses were documented. The predicted values are in good agreement with the experimental values. Unstructured models provided a better approximation of kinetic profiles for bioactive metabolite production by S. halotolerans VSM 2.

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RNA‐Seq transcriptomic analyses of Antrodia camphorata to determine antroquinonol and antrodin C biosynthetic mechanisms in the in situ extractive fermentation

et al. 2020

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BACKGROUNDIn situ extractive fermentation (ISEF) is an important technique for improving metabolite productivity. The different extractants can induce the synthesis of different bioactive metabolites of Antrodia camphorata during ISEF. However, a lack of research on the molecular genetics of A. camphorata during ISEF currently hinders such studies on metabolite biosynthetic mechanisms.RESULTSTo clarify the differentially expressed genes during ISEF, the gene transcriptional expression features of A. camphorata S‐29 were analysed. The addition of n‐tetradecane as an extractant during ISEF showed more pronounced up‐regulation of ubiquinone and other terpenoid‐quinone biosynthesis pathway genes (CoQ2, wrbA and ARO8). When oleic acid was used as an extractant, the terpenoid backbone biosynthesis and ubiquinone and other terpenoid‐quinone biosynthesis pathways were significantly enriched, and genes (IDI, E2.3.3.10, HMGCR atoB, and CoQ2) related to these two pathways were also significantly up‐regulated. The CoQ2 genes encode puru‐hydroxybenzoate:polyprenyltransferase, playing an important role in antroquinonol synthesis. The IDI, E2.3.3.10, HMGCR and atoB genes of the terpenoid backbone biosynthesis pathway might play an important role in the synthesis of the triquine‐type sesquiterpene antrodin C.CONCLUSIONThis investigation advances our understanding of how two different extractants of n‐tetradecane and oleic acid affect the biosynthesis of metabolites in A. camphorata. It is beneficial to provide potential strategies for improving antrodin C and antroquinonol production by genetic means. © 2020 Society of Chemical Industry

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Efficient production of bioactive metabolites from Antrodia camphorata ATCC 200183 by asexual reproduction-based repeated batch fermentation

Cited by 27 publications

References 34 publications

Optimization of Operating Conditions for the Production of Enhanced Antifungal Metabolites from Streptomonospora arabica VSM 25 by Full Factorial Design

Optimization of Operating Conditions for the Production of Enhanced Antifungal Metabolites from Streptomonospora arabica VSM 25 by Full Factorial Design

Improved Bioactive Metabolite Production by Saccharopolyspora halotolerans VSM-2 Using Response Surface Methodology and Unstructured Kinetic Modelling

RNA‐Seq transcriptomic analyses of Antrodia camphorata to determine antroquinonol and antrodin C biosynthetic mechanisms in the in situ extractive fermentation

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