Application of response surface methodology for optimization of medium components for the production of secondary metabolites by Streptomyces diastatochromogenes KX852460

Abstract

A bioactive strain Streptomyces diastatochromogenes KX852460 was selected for the production of secondary metabolites to control the target spot disease on tobacco leaves, caused by the Rhizoctonia solani AG-3. Peanut meal, soluble starch, NaCl, yeast extract, and ammonium sulphate were identified the best ingredient for high antifungal activity of S. diastatochromogenes KX852460 against the R. solani AG-3. For the improved production of secondary metabolites, central composite design of response surface methodology was applied in submerged fermentation. The best concentrations of ingredients were peanut meal 4.88%, soluble starch 4.40%, NaCl 0.52%, yeast extract 0.47%, and ammonium sulphate 0.0360%. Study of metabolism changes in the submerged fermentation process was analyzed. Level of the reducing sugar increased, as the total sugar consumed. Amino nitrogen and total sugar decrease tendency, which indicated the growth of bacteria in submerged fermentation batch. Production of secondary and other metabolites influenced the pH of the fermentation batch.

Enhanced AmB Production in Streptomyces nodosus by Fermentation Regulation and Rational Combined Feeding Strategy

“…Due to the medicinal value and the clinical importance described above, related researches on amphotericin B and its derivatives are more in-depth and mature. Concurrently, many classical breeding methods of high-yield strains including fermentation development (Siddique et al, 2014 ; Ahsan et al, 2017 ; Ju et al, 2017 ; Zhao et al, 2017 ) and efficient bioprocesses (Santos-Aberturas et al, 2011 ; Yao et al, 2018 ) have been applied to increase the titer of several polyene macrolide antibiotics (Pereira et al, 2008 ; Zhang et al, 2017 ). Another important strategy was reported, such that short-chain fatty acid, lower alcohols, and some salt substances can be added as precursors of macrolide antibiotics to derive a high product titer (Jing et al, 2011 ).…”

<p>Optimized Production of the Allylamine Antifungal “Terbinafine” by <em>Lysinibacillus</em> Isolate MK212927 Using Response Surface Methodology</p>

“…29 For example, the central composite design of RSM was used to improve the antifungal activity of a metabolite produced by Streptomyces diastatochromogenes KX852460 by optimizing the nutrient components in submerged fermentation in a previous study. 17 Moreover, in a recent study, the co-culture medium components and fermentation conditions of Trichoderma atroviride SG3403 and Bacillus subtilis 22 were optimized using RSM for the production of antifungal metabolites. 30 Using central composite design, a total of 32 runs were conducted to study the interactions between the selected 5 variables (starch, ammonium chloride, pH, temperature and agitation) and to determine how they influenced the metabolite yield.…”

“…Several types of designs are available for the optimization of important fermentation parameters, and central composite design (CCD) is one of the most useful ones. 17 Therefore, the goal of this study was to optimize the various physical and environmental parameters using RSM for maximum production of the terbinafine metabolite produced by Lysinibacillus isolate MK212927.…”

Modeling improved production of the chemotherapeutic polypeptide actinomycin D by a novel Streptomyces sp. strain from a Saharan soil

“…Recently, many attempts have been made to optimize them using statistical experiment designs such as Taguchi method (Mahalaxmi et al., 2009), factorial fractional design (FFD) (Fontes et al., 2012), central composite design (CCD) (Srinivasulu et al., 2006; Djinni et al., 2018) and Box-Bunken design (BBD) (Vijayabharathi et al., 2012; Kim et al., 2014). The process through statistical approach such as response surface methodology (RSM) is an approach resulting economic, efficient and accurate (Ahsan et al., 2017). Known for the less number of experimental runs and for regarding the interaction among of the process variables involved, it allows to maximize the microbial secondary metabolites yield and to estimate the relevance of the strain for its use in an industrial scale production.…”