Abstract

BackgroundUricase is a bio-drug used to reduce urate accumulation in gout disease. Thus, there is a continuous demand for screening soil samples derived from a variety of different sources in order to isolate a strain that possesses a high potential for producing uricase.MethodsStreptomyces sp. strain NEAE-5 demonstrated a significant capacity for uricase production was identified based on the physiological, morphological and biochemical characteristics, as well as 16S rDNA sequencing analysis. Using a Plackett–Burman statistical design, the impact of eighteen process factors on uricase production by Streptomyces griseorubens strain NEAE-5 was investigated. Using central composite design, the most important variables that had a favourable positive impact on uricase production by Streptomyces griseorubens strain NEAE-5 were further optimized.ResultsIt is clear that the morphological and chemotaxonomic features of Streptomyces sp. strain NEAE-5 are typical for the Streptomyces genus. Phylogenetic analysis indicated that Streptomyces sp. strain NEAE-5 belongs to the genus Streptomyces and closely related to Streptomyces griseorubens which it has a 95–96% identity in 16S rDNA gene sequencing. Accordingly, the strain is proposed to be identified as Streptomyces griseorubens strain NEAE-5. The three factors that had the significant positive impacts on uricase production were uric acid, hypoxanthine, and yeast extract. As a result, the best conditions for achieving the highest experimental uricase production by Streptomyces griseorubens strain NEAE-5 after central composite design were (g/L): uric acid 6.96, glycerol 5, hypoxanthine 5.51, MgSO4.7H2O 0.1, KNO3 2, CaCl2 0.5, K2HPO4 0.5, NaCl 0.5, yeast extract 1.08. In addition, the period of incubation is seven days, pH 7.5 and 37 °C with an inoculum size of 2 mL (105 cfu/mL) /100 mL medium.ConclusionsAfter optimization, the obtained uricase activity was 120.35 U/mL, indicating that the Streptomyces griseorubens strain NEAE-5 is a potent uricase producer and that the statistical approach used for optimization was appropriate.

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