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Modeling improved production of the chemotherapeutic polypeptide actinomycin D by a novel Streptomyces sp. strain from a Saharan soil
The novel bioactive actinobacterial strain GSBNT10 obtained from a Saharan soil, was taxonomically characterized using a polyphasic approach. 16S rRNA gene sequence analysis supported the classification of the isolate within the genus Streptomyces indicating it as a novel species. The major metaboli...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538969/ https://www.ncbi.nlm.nih.gov/pubmed/31193702 http://dx.doi.org/10.1016/j.heliyon.2019.e01695 |
Sumario: | The novel bioactive actinobacterial strain GSBNT10 obtained from a Saharan soil, was taxonomically characterized using a polyphasic approach. 16S rRNA gene sequence analysis supported the classification of the isolate within the genus Streptomyces indicating it as a novel species. The major metabolite responsible of the bioactivity was purified and structurally characterized as actinomycin D (act-D) by mass spectrometric and nuclear magnetic resonance analyses Plackett-Burman design (PBD) and response surface methodology (RSM) were applied in order to optimize the medium formulation for the production of this bioactive metabolite. By PBD experiments, NaNO(3), K(2)HPO(4) and initial pH value were selected as significant variables affecting the metabolite production. Central Composite Design (CCD) showed that adjustment of the fermentative medium at pH 8.25, K(2)HPO(4) at 0.2 gL(-1) and NaNO(3) at 3.76 gL(-1) were the values suiting the production of act-D. Moreover, the results obtained by the statistical approach were confirmed by act-D detection using the HPLC equipped with a diode array detector and coupled online with electrospray-mass spectrometry (ESIMS) technique. act-D production was highly stimulated, obtaining a good yield (656.46 mgL(-1)) which corresponds to a 58.56% increase compared with the non-optimized conditions and data from LC-ESIMS technique efficiently confirmed the forecast from RSM. |
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