Optimization of medium composition for actinorhodin production by Streptomyces coelicolor A3(2) with response surface methodology
Introduction
Microbially produced secondary metabolites are extremely important to our health and nutrition. The best known secondary metabolites are the antibiotics. As a group, they have tremendous economic importance. The antibiotic market amounts to almost 30 billion dollars and includes about 160 antibiotics and derivatives [1]. Streptomycetes are Gram-positive mycelial soil bacteria that produce a wide variety of antibiotics and other pharmaceutically useful compounds.
Nutrition plays an important role in the onset and intensity of secondary metabolism, not only because limiting the supply of an essential nutrient is an effective means of restricting growth but also because the choice of limiting nutrient can have specific metabolic and regulatory effects [2]. To achieve high product yields, it is a prerequisite to design a proper production medium in an efficient fermentation process. There is usually a relationship between the media composition and the biosynthesis of antibiotics [3], [4], [5] and the role of the medium is usually considered in terms of the nutrients and precursors it provides to the culture. Streptomyces coelicolor A3(2) produces four antibiotics: methylenomycin, Ca-dependent antibiotic, undecylprodigiosin and actinorhodin [6]. The latter two are pigmented, which facilitates visual observation of product synthesis. Actinorhodin appears dark purple in the complex medium used in this research.
The application of statistical experimental design techniques in fermentation process development can result in improved product yields, reduced process variability, closer confirmation of the output response to nominal and target requirements and reduced development time and overall costs. Conventional practice of single factor optimization by maintaining other factors involved at an unspecified constant level does not depict the combined effect of all the factors involved. This method is also a time consuming process and requires a number of experiments to determine optimum levels, which are unreliable. These limitations of a single factor optimisation process can be eliminated by optimizing all the affecting parameters collectively by statistical experimental design using response surface methodology (RSM). RSM can be used to evaluate the relative significance of several affecting factors even in the presence of complex interactions [7], [8], [9], [10].
This study reports an attempt to optimize a suitable fermentation medium for actinorhodin production by statistical experimental design using RSM.
Section snippets
Organism
The organism used in this study was S. coelicolor A3(2) which was maintained as a frozen spore suspension in 20% glycerol at −20 °C.
Inoculum preparation
Inocula were prepared according to the procedure described by Hobbs et al. [11]. A loopful of spore stock was spread on a mannitol/soya agar plate and incubated for 10–14 days at 30 °C and 200 rpm. After growth and sporulation, 5 ml distilled water was added to each agar plate which was then scraped to release spores. This spore suspension was centrifuged at 4000 rpm
Results and discussion
In an earlier study, the medium constituents and the process parameters were optimized by single factor optimization keeping the other factors constant [5], [19]. It was found that sucrose, glucose, YE and peptone had the most profound effects on actinorhodin production in complex medium and therefore these four factors were selected for statistical optimization by RSM.
In order to search for the optimum combination of major components of the medium, experiments were performed according to the
Conclusion
As far as known, there are no reports of actinorhodin production from S. coelicolor A3(2) by media engineering. The results strongly support the use of RSM for medium optimization. The optimization of the medium resulted not only in a 35% higher antibiotic concentration than unoptimized medium but also in a reduced amount of the medium constituents. The chosen method of optimization of medium composition was efficient, relatively simple and time and material saving.
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