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Effects of Sucrose, Phosphate, and Calcium Carbonate on the Production of Pikromycin from Streptomyces venezuelaeS
Effects of Sucrose, Phosphate, and Calcium Carbonate on the Production of Pikromycin from Streptomyces venezuelaeS
Journal of Microbiology and Biotechnology. 2015. Apr, 25(4): 496-502
Copyright © 2015, The Korean Society For Microbiology And Biotechnology
  • Received : September 03, 2014
  • Accepted : October 21, 2014
  • Published : April 28, 2015
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About the Authors
Jeong Sang Yi
School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, Seoul 151-744, Republic of Korea
Minsuk Kim
School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, Seoul 151-744, Republic of Korea
Sung-Jin Kim
Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 152-742, Republic of Korea
Byung-Gee Kim
Interdisciplinary Program for Biochemical Engineering and Biotechnology, Seoul National University, Seoul 151-742, Republic of Korea
byungkim@snu.ac.kr

Abstract
Polyketide secondary metabolites share common precursor pools, acyl-CoA. Thus, the effects of engineering strategies for heterologous and native secondary metabolite production are often determined by the measurement of pikromycin in Streptomyces venezuelae . It is hard to compare the effectiveness of engineering targets among published data owing to the different pikromycin production media used from one study to the other. To determine the most important nutritional factor and establish optimal culture conditions, medium optimization of pikromycin from Streptomyces venezuelae ATCC 15439 was studied with a statistical method, Plackett-Burman design. Nine variables (glucose, sucrose, peptone, (NH 4 ) 2 SO 4 , K 2 HPO 4 , KH 2 PO 4 , NaCl, MgSO 4 ·7H 2 O, and CaCO 3 ) were analyzed for their effects on a response, pikromycin. Glucose, K 2 HPO 4 , and CaCO 3 were determined to be the most significant factors. The path of the steepest ascent and response surface methodology about the three selected components were performed to study interactions among the three factors, and the fine-tune concentrations for maximized product yields. The significant variables and optimal concentrations were 139 g/1 sucrose, 5.29 g/l K 2 HPO 4 , and 0.081 g/l CaCO 3 , with the maximal pikromycin yield of 35.5 mg/l. Increases of the antibiotics production by 1.45-fold, 1.3-fold, and 1.98-fold, compared with unoptimized medium and two other pikromycin production media SCM and SGGP, respectively, were achieved.
Keywords
Introduction
Large numbers of secondary metabolites such as antibiotics and antitumor drugs are produced by microorganisms, and about 7600 are known to be produced by Streptomyces species [3] . Fast and non-aggregating growth characteristics that allow a large cell mass, and efficient profiling of primary and secondary metabolites have given attention to Streptomyces venezuelae , producing mainly pikromycin and methymycin, as being a good heterologous expression host [30] . Pikromycin is a polyketide secondary metabolite, a 14-membered cyclized macrolactone narbonolide with a desosamine that has hydroxylation at a specific carbon [21] . Owing to the nature of polyketide sharing common acyl-CoA precursors, results of engineering strategies for enhancing the heterologous or native secondary metabolite productions are often screened by measurement of the antibiotic pikromycin [22 , 32] .
The production of secondary metabolites with Streptomyces venezuelae is often conducted in many different media. The strain is inoculated from the fermentation medium to three different production media for pikromycin alone [8 , 9 , 20] , which results in altered metabolic behaviors leading to changes in titers of product yields. Since the titers and growth conditions are different from one another, it complicates research conduction and data comparisons, such as microarray and ChIP data, as a result. Because gene manipulations of Streptomyces species are difficult and limited compared with that of other microorganisms like Escherichia coli [5] , resolving this complication is crucial for selecting a right engineering target for host generation.
The aim of this research was to determine the most significant factors and create an optimized medium for the production of pikromycin in Streptomyces venezuelae , and increase the yield at the same time. In order to do so, Plackett-Burman design and a statistical method, response surface methodology, were applied to identify and fine-tune the concentrations of significant medium components.
Materials and Methods
- Bacterial Strain and Culture Conditions
Streptomyces venezuelae ATCC 15439, a pikromycin- and methymycin-producing wild type, was used for the medium optimization study. Chemicals were purchased from BD (San Jose, CA, USA) and Junsei (Tokyo, Japan). Liquid R2YE complex medium (10 g glucose, 103 g sucrose, 5g yeast extract, 0.1 g Difco casamino acids, 0.25 g K 2 SO 4 , 10.12 g MgCl 2 ·6H 2 O,2ml of a trace element solution (composed of 200 mg FeCl 3 ·6H 2 O, 40 mg ZnCl 2 , 10 mg MnCl 2 ·4H 2 O, 10 mg CuCl 2 ·2H 2 O, 10 mg (NH 4 ) 6 Mo 7 O 24 ·4H 2 O, 10 mg Na 2 B 4 O 7 ·10H 2 O, 10 ml of 0.5% KH 2 PO 4 ,4ml of 5 M CaCl 2 ·2H 2 O, and 15 ml of 20% L-proline in 1 L of distilled water), 5.73 g N -Tris(hydroxymethyl)methyl-2-aminoethanesulfonic acid buffer, and 7ml of 1 N NaOH in 1 L of distilled water) was used for propagation of the strain. Productions of pikromycin was performed in non-optimized medium (20 g glucose, 103 g sucrose, 10 g peptone, 2.5 g (NH 4 ) 2 SO 4 , 2.5 g K 2 HPO 4 , 2.5 g KH 2 PO 4 , 1 g NaCl, 1 g MgSO 4 ·7H 2 O, and 0.2 g CaCO 3 in 1 L of distilled water).
The strain was seed cultured in 100 ml of R2YE, 30℃ with shaking at 200 rpm for 18 h. Then 200 mg of wet weighted cells was inoculated in 50 ml of non-optimized medium and grown at 30℃ and 200 rpm shaking for 60 h. The maximum production time point was different from one medium to another, due to different nutritional sources and concentrations. The main culture period was determined from preliminary experiments with a common pikromycin production medium, SCM. No significant increase in pikromycin production was observed after 60 h of the culture, and thus that time point was used throughout the experiments for comparison of pikromycin production from different media.
- Quantification of Pikromycin Production
A 15 ml aliquot of the cultures was harvested by centrifugation at 2,840 × g for 10 min. Pikromycin was extracted from supernatants with 2 volumes of ethyl acetate. The extract was dried using a rotary vacuum evaporator and reconstituted with 0.68 ml of methanol. The 20 µl of the samples was analyzed by high-performance liquid chromatography (HPLC; YL-9100; Younglin, Korea). A reverse phase C 18 column from Waters (Milford, MA, USA) was used with a linear gradient from solvent A (80:20 water / 80% acetonitrile in 5 mM ammonium acetate and 0.05% acetic acid) to solvent B (20:80 water / 80% acetonitrile in 5 mM ammonium acetate and 0.05% acetic acid) over 25 min at a flow rate of 1 ml/min. Detection was made at 220 nm, and the pikromycin corresponding peak was confirmed by LCQ-LC/MS.
- Experimental Design and Data Analysis
From preliminary studies testing various C and N sources, osmotic stress, buffers, and fatty acids, one medium component from each source that resulted in the most pikromycin production was selected. Glucose, sucrose, peptone, ammonium sulfate, monobasic and dibasic phosphate, sodium chloride, magnesium sulfate, and calcium carbonate were selected for further optimization.
Minitab 14.12 (Minitab Inc., Pensylvania, USA) was used to design and analyze the data throughout the experiments. To identify the most significant variables in pikromycin production, the Plackett-Burman Design (PBD) was employed. Eleven variables, nine components, and two dummies, were included in the design of a total of 12 experimental sets. The detailed design of the PBD matrix is listed in Table 1 . Two dummy variables, which had no chemical relevance to the production of pikromycin, were included to calculate random measurement errors. The random measurement errors were used to determine the significance of real values by calculating F values of the F -test from the Minitab software.
PBD matrix design, real values of each coded variable, and responses.
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PBD matrix design, real values of each coded variable, and responses.
The PBD results were reinterpreted to study correlations between pikromycin production and cell mass. With the same PBD matrix design (Table S1), the significance of the factors was observe with cell mass as response.
The path of the steepest ascent was performed to set up basal concentrations of media components selected from the PBD to be used in a central composite design (CCD). The path of the steepest ascent allowed rapid movement towards the optimum of variable concentrations. The amounts were altered by increasing or decreasing according to the result of the PBD. The combination of variable concentrations that resulted in maximum production indicated that it was at the point near optimum [14] .
Response surface methodology (RSM), one of the CCD methods, was performed to find the optimal concentrations of variables determined to be important from the PBD to maximize the production of pikromycin. The matrix design and levels of components are listed in Table 4 .
Results
- Identification of the Most Significant Variables with PBD
PBD experiments tested the importance of the nine factors listed in Table 2 . The result showed effects of variables to the response, pikromycin. Effects of sucrose, K 2 HPO 4 , and CaCO 3 were (+) 6.346, (+) 4.686, and (-) 3.578, respectively, which meant that sucrose and K 2 HPO 4 had positive, and CaCO 3 had negative, effects to the response. The three components were also determined to be the most significant in pikromycin production, based on the low P -values (<0.1). R 2 was found to be 0.9754, meaning that 97.54% of the total variations could be explained by the model. On the Pareto chart of the standardized effects ( Fig. 1 ), the minimal effects were presented towards lower fields, near 0, and the maximal effects towards upper fields.
Effects of variables to the response, pikromycin.
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R2= 97.54%; R2(adj) = 86.48%.
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Pareto chart of the standard effects of the tested nine factors to pikromycin production. Sucrose, K2HPO4, and CaCO3 were determined to be significant, while C and N sources were not.
The importance of the nine factors (Table S2), when the response was cell mass, indicated that CaCO 3 was positively significant, but sucrose was negatively significant towards cell mass (Fig. S1), which was totally opposite from that of when pikromycin was the response.
- Path of the Steepest Ascent
Concentrations of sucrose and K 2 HPO 4 were increased, but that of CaCO 3 was decreased because CaCO 3 was determined to have negative effects on pikromycin production ( Table 3 ). All the other components were fixed at the concentrations of the non-optimized medium. When the concentrations of sucrose, K 2 HPO 4 , and CaCO 3 were 154.5, 3.75, and 0.1 g/l respectively, the production of pikromycin was maximal, 28.5 mg/l. This point was chosen as a clue to set up basal concentrations for further optimization.
Design of path of the steepest ascent, and the results.
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Design of path of the steepest ascent, and the results.
RSM matrix design, real values of variables, and the results.
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RSM matrix design, real values of variables, and the results.
- Response Surface Methodology
Specific interactions between the three variables with response to pikromycin production were studied with RSM ( Fig. 2 ). Effects of the three components, sucrose, K 2 HPO 4 , and CaCO 3 , were analyzed by t -test and P -values as shown in Table 5 . A regression model with R 2 higher than 0.9 meant that the result had high correlation between predicted and experimental values of the response [10] . It also meant that the model could explain 91.2% of the total variations. This model can be expressed as Eq. (1).
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3D surface graph of sucrose vs. K2HPO4 (A), sucrose vs. CaCO3 (B), and K2HPO4 vs. CaCO3 (C) for the response, pikromycin.
Significance and regression coefficients for RSM.
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R2 = 91.2%; R2 (adj) = 79.1%. aCoef. = Coefficient.
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Analysis of variance ( Table 6 ) also provided the reliability of the model from the statistically significant regression ( P < 0.01), and the statistically insignificant lack of fit ( P = 0.778). As a result, the model was determined to be reliable and adequate to optimize the production of pikromycin.
Analysis of variance test of the regression model.
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aDF = Degrees of Freedom. bSS = Sum of Squares. cMS = Mean Square.
- Validation of the Optimized Medium
The full quadratic model from RSM predicted that the maximum production of pikromycin was 38.6 mg/l with 139 g/l sucrose, 5.29 g/l K 2 HPO 4 , and 0.081 g/l CaCO 3 . Validation experiments with the optimized condition resulted in 35.5 ± 0.866 mg/l of pikromycin, which was in near agreement. The yield from unoptimized medium was 24.4 mg/l, meaning a 1.45-fold increase was achieved. Moreover, 1.98-fold and 1.29-fold increases in pikromycin production also had been obtained, compared with SGGP (17.9 ± 2.43 mg/l) and SCM (27.4 ± 1.05 mg/l), respectively. Production of pikromycin from the optimized medium was 34.1 ± 0.852 mg/l per gram cell, which was 4.19-fold and 3.2-fold higher than that of SGGP and SCM, respectively.
Discussion
Various stress conditions applied during growth of Streptomyces species are known to alter secondary metabolite production. It has been reported that secondary metabolism in Streptomyces coelicolor begins during the pre-sporulating stage of the cell cycle [33] , which means that the time point of the secondary metabolite production can be actively altered by the availability of nutrients. Nutrient sources may also change the metabolism, in that different carbon and nitrogen sources result in various titers [35] .
An essential element for microorganisms, which applies to all living organisms, is phosphorus. Formation of a fuel compound ATP [27] , DNA and RNA synthesis [25] , and many other biochemical processes occurring in living creatures require phosphorus [16] . A primary source of phosphorus in bacterial cultures is K 2 HPO 4 and KH 2 PO 4 . K 2 HPO 4 was determined to be a more suitable source of phosphorus to produce pikromycin from the results of PBD. It is expected that a precursor of desosamine from the glycone portion of pikromycin, glucose-1-phosphate [31] , can better be synthesized from K 2 HPO 4 . Alternatively, optimization of pH by altering the ratio of monobasic and dibasic phosphate [15] may have dedication to the increase of the pikromycin production as reported in the pH-sensitive production of actinorhodin with Streptomyces coelicolor [6 , 18] .
Like phosphorus, sucrose is an important carbon source for plants and some phytopathogenic bacteria [4] . However, it is used somewhat differently in cultures of Streptomyces species. Sucrose cannot be utilized with the species [26] as it is often used as one of the compounds reducing osmotic stress, one of the key components in secondary metabolism [17 , 24 , 29] . A previous report indicated an absence of sucrose banished production of the antibiotic actinorhodin in Streptomyces coelicolor , and an optimization of sucrose for the production medium has been further studied [13] . In the case of this study, effective amounts of sucrose is given, for 20 g of glucose is 139 g of sucrose. Less or over that sucrose concentration would decrease the production of pikromycin as it did with that of actinorhodin in Streptomyces coelicolor [12] .
Balancing primary versus secondary metabolism is inevitable for maximizing yields of target polyketide metabolite production, and one way to do so is optimizing the availability of calcium ions. Calcium ions are often used for increases of enzymatic stability [7 , 28] . However, calcium is known to decrease secondary metabolite production. From past studies, increases in calcium ion supplement resulted in a larger total mass of Streptomyces coelicolor , but the opposite was observed with the production of actinorhodin [1] . Secondary metabolism shares common precursor and cofactor pools like acetyl-CoA and NADPH [11 , 19] , where calcium may have contributions to balancing fluxes of the compounds toward growth and secondary metabolism, as previous reports and the result of PBD in this study indicate. CaCO 3 is determined to have negative effects on pikromycin production but positive effects on cell mass. In other words, some loss in cell mass should be endured for gain in pikromycin production. Thus, the concentrations of calcium carbonate was minimized to a satisfactory level with RSM for the optimum balance between growth and pikromycin production. There was some loss in the total cell mass, but productivity increased by 3~4 folds.
As the RSM method was applied in many recent optimization research [2 , 23 , 34] , it was successfully used to investigate relationships between sucrose, K 2 HPO 4 , and CaCO 3 . It also resulted in the fine-tuning of the three component concentrations for maximum yield of pikromycin antibiotic.
In conclusion, medium optimization of pikromycin production was achieved by the statistical designs of experiments in this study. First, PBD was applied to select the most significant factors. In the second step, CCD was used to fine-tune the optimal concentrations of the selected variables. With the optimal concentrations at 139 g/l sucrose, 5.29 g/l K 2 HPO 4 , and 0.081 g/l CaCO 3 , a maximum pikromycin yield of 35.5 mg/l was achieved. This was a 1.45-fold increase compared with the non-optimized medium, and 1.98-fold and 1.29-fold increases were achieved compared with SGGP and SCM, respectively. Thus, our medium may be employed to other studies related to pikromycin production.
Acknowledgements
This work was supported by the Intelligent Synthetic Biology Center of Global Frontier Project (2011-0031960, 2011-0031957, 2011-0031962) and Priority Research Centers Program (2009-0094021) through the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education, Science and Technology. Additionally, M.K. was supported by the project of Global Ph.D. Fellowship, which NRF conducts from 2012 (NRF-2012H1A2A1001956).
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