Identification and statistical optimization of fermentation conditions for a newly isolated extracellular cholesterol oxidase-producing Streptomyces cavourensis strain NEAE-42

Abstract

BackgroundDue to broad range of clinical and industrial applications of cholesterol oxidase, isolation and screening of bacterial strains producing extracellular form of cholesterol oxidase is of great importance.ResultsOne hundred and thirty actinomycete isolates were screened for their cholesterol oxidase activity. Among them, a potential culture, strain NEAE-42 is displayed the highest extracellular cholesterol oxidase activity. It was selected and identified as Streptomyces cavourensis strain NEAE-42. The optimization of different process parameters for cholesterol oxidase production by Streptomyces cavourensis strain NEAE-42 using Plackett–Burman experimental design and response surface methodology was carried out. Fifteen variables were screened using Plackett–Burman experimental design. Cholesterol, initial pH and (NH4)2SO4 were the most significant positive independent variables affecting cholesterol oxidase production. Central composite design was chosen to elucidate the optimal concentrations of the selected process variables on cholesterol oxidase production. It was found that, cholesterol oxidase production by Streptomyces cavourensis strain NEAE-42 after optimization process was 20.521U/mL which is higher than result obtained from the basal medium before screening process using Plackett-Burman (3.31 U/mL) with a fold of increase 6.19.ConclusionsThe cholesterol oxidase level production obtained in this study (20.521U/mL) by the statistical method is higher than many of the reported values.

Identification of cholesterol-assimilating actinomycetes strain and application of statistical modeling approaches for improvement of cholesterol oxidase production by Streptomyces anulatus strain NEAE-94

“…7H 2 O 0.01, MgSO 4 .7H 2 O 0.5, NaCl 0.5, K 2 HPO 4 1, pH 7, inoculum size 4 % (v/v), temperature 37°C, agitation speed 150 rpm, medium volume 50 mL and incubation time 5 days.El-Naggar et al[14] used the 2-level Plackett-Burman experimental design in 20 experimental run to evaluate the importance of fifteen process variables of medium components and operating conditions for the production of cholesterol oxidase by Streptomyces cavourensis strain NEAE-42. The most important factors that significantly influenced cholesterol oxidase production were initial pH, cholesterol and (NH 4 ) 2 SO 4 concentrations.…”

Erratum to: Identification and statistical optimization of fermentation conditions for a newly isolated extracellular cholesterol oxidase producing Streptomyces cavourensis strain NEAE-42

“…Unfortunately, a number of corrections had not been carried out during the production of the original version of this article [1]. …”

RSM–GA Based Optimization of Bacterial PHA Production and In Silico Modulation of Citrate Synthase for Enhancing PHA Production

“…The conventional non-statistical one-factor-at-a-time (OFAT) approach is excessively time-consuming, labor-intensive and deficient in accurate finding of the critical factors that impact the desired metabolite's production, and lacks in deciphering the interactions among the factors under investigation [15]. In order to prevail over these shortcomings, various statistical methods alone or in combination with artificial intelligence techniques have been used efficiently for the optimization of the medium constituents [14,[16][17][18]. The response surface method (RSM), a statistical optimization technique, employs experimental factorial designs like central composite design (CCD) for optimizing any process output, and defines the behavior of the response in the selected design space [16,17]; wherein, CCD explores the interaction effect of the factors predominantly influencing the product formation.…”

“…In order to prevail over these shortcomings, various statistical methods alone or in combination with artificial intelligence techniques have been used efficiently for the optimization of the medium constituents [14,[16][17][18]. The response surface method (RSM), a statistical optimization technique, employs experimental factorial designs like central composite design (CCD) for optimizing any process output, and defines the behavior of the response in the selected design space [16,17]; wherein, CCD explores the interaction effect of the factors predominantly influencing the product formation. The experimental runs of the CCD work as inputs for RSM in finding the mathematical model that links process parameters (factors) and outcome.…”