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Improved production of Pseudomonas aeruginosa uricase by optimization of process parameters through statistical experimental designs
Process Biochemistry 40 (2005) 1707–1714 www.elsevier.com/locate/procbio
Improved production of Pseudomonas aeruginosa uricase by optimization of process parameters through statistical experimental designs Yasser R. Abdel-Fattaha,*, Hesham M. Saeedb, Yousry M. Goharc, Mohamed A. El-Bazb a Bioprocess Development Department, Genetic Engineering and Biotechnology Research Institute, Mubarak City for Scientific Research and Technology Applications, New Burg El-Arab City 21394, Alexandria, Egypt b Bioscience and Technology Department, Institute for Graduate Studies and Research, Alexandria University, Alexandria, Egypt c Botany Department, Microbiology Division, Faculty of Science, Alexandria University, Alexandria, Egypt
Received 3 February 2004; accepted 15 June 2004
Abstract Sequential optimization strategy, based on statistical experimental designs, was employed to enhance the production of uricase by Pseudomonas aeruginosa local isolate. Glucose supplementation to the basal production medium inhibits uricase production, perhaps by catabolic repression. For screening of bioprocess parameters significantly influencing uricase activity, the two-level Plackett–Burman design was used. Among fifteen variables tested; pH, CuSO4 and FeSO4 were selected based on their high significant effect on uricase activity. A near optimum medium formulation was obtained using this method with increased uricase yield by 15-folds. Response surface methodology (RSM) was adopted to acquire the best process conditions. In this respect, the three-level Box–Behnken design was employed. A polynomial model was created to correlate the relationship between the three variables and uricase activity. The optimal combination of the major constituents of media for uricase production evaluated from the non-linear optimization algorithm of EXCEL-Solver was as follows: pH, 5.5; CuSO4, 103 M; and FeSO4, 102 M. The predicted optimum uricase activity was 7.1 U/ml/min, which is 16.5 times than the basal medium. # 2004 Elsevier Ltd. All rights reserved. Keywords: Uricase optimization; Pseudomonas aeruginosa; Numerical modeling; Statistical experimental design
1. Introduction Uricase (urate oxidase, EC 1.7.3.3) catalyzes the oxidative opening of the purine ring of urate to yield allantoin, carbon dioxide, and hydrogen peroxide. It has vast and beneficial uses both in vitro and in vivo. Determining the urate concentration in blood and urine is required for the diagnosis of gout as urate accumulation is a causative factor of gout in humans. Uricase is useful for enzymatic determination of urate in clinical analysis by coupling with 4-aminoantipyrine-peroxidase system [1]. It can be also used as protein drug for treatment of hyperuricemia, as Rasburicase [2].
* Corresponding author. Tel.: +203 4593420; fax: +203 4593423. E-mail address: [email protected] (Y.R. Abdel-Fattah). 0032-9592/$ – see front matter # 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.procbio.2004.06.048
Many organisms including higher plants and microorganisms are able to produce uricase. Various members of the genus Pseudomonas are able to grow on purines either as nitrogen source or as both nitrogen and carbon sources, where adenine, guanine, hypoxanthine, and xanthine served as nitrogen source for P. aeruginosa [3] and P. acidovorans [4] and other unidentified species are capable of degrading uric acid by unstable membrane-bound uricase. The optimization of fermentation conditions, particularly physical and chemical parameters are of primary importance in the development of any fermentation process owing to their impact on the economy and practicability of the process. The diversity of combinatorial interactions of medium components with the metabolism of cells as well as the large number of medium constituents necessary for cell metabolism and production do not permit satisfactory detailed modeling. The one-dimensional search with
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successive variation in variables is still employed, even though it is well accepted that it is practically impossible for the one-dimensional search to accomplish an appropriate optimum in a finite number of experiments. Single variable optimization methods are not only tedious, but also can lead to misinterpretation of results, especially because the interaction between different factors is overlooked [5]. Statistical experimental designs have been used for several decades and it can be adopted at various phases of an optimization strategy, such as for screening experiments or for looking for the optimal conditions for targeted response(s) [6]. Lately, the results analyzed by a statistically planned experiment are better acknowledged than those carried out by the traditional one-variable-at-a-time (OVAT). Some of the popular choices in applying statistical designs to bioprocessing include the Plackett–Burman design [7], and response surface methodology with various designs [8–10]. In the present work, we identified growth and enzyme production kinetic parameters in basal production medium in the presence and absence of glucose. In addition, we report for the first time a sequential optimization strategy for uricase enzyme production by P.aeruginosa local isolate through statistically designed experiments as an effective tool for medium engineering. First, Plackett–Burman screening design was applied to address the most significant factors affecting enzyme production. Second, Box–Behnken design was applied to determine the optimum level of each of the significant parameters that brings maximum uricase production.
2. Materials and methods 2.1. Microorganism The bacterium used throughout this work, P.aeruginosa Ps-x was isolated and kindly provided by Dr. Yousry Gohar, Botany Department, Faculty of Science, Alexandria University. The strain was maintained on nutrient agar medium at 30 8C. The microbial isolate has been identified by16SrRNA sequencing (accession number AF419219). 2.2. Enzyme production conditions Cultures were allowed to grow at 30 8C with shaking at 200 rpm, in 250 ml conical flasks containing 50 ml aliquots pre-culture basal medium of the following composition (g/l): Peptone, 20; Glucose, 30; KH2PO4, 1; MgSO47H2O, 0.5; and uric acid, 0.1. Then 0.5 ml of the overnight culture was centrifuged, washed with sterile saline and consequently used as inoculum for the basal production medium of the following composition (g/l): K3PO4, 10; MgCl2, 1; and uric acid, 3. Bacterial growth was monitored turbidimetrically at 550 nm. At the indicated time, 2 ml of the growing cultures was taken and centrifuged at 8000 rpm for 2 min. The cell
free supernatant was used as crude enzyme preparation for further determinations. 2.3. Enzyme assay Uricase activity was measured spectrophotometrically according to Mahler [11]. Uricase activity was measured by following the disappearance of uric acid, detected by the decrease in absorbance at 293 nm in the presence of cell free extract. The assay mixture contained 0.1 ml of enzyme solution in 0.1 M borate buffer pH 9.0 and 0.12 mM uric acid in a final volume of 3.0 ml. Incubation was carried out at 30 8C for 30 min. The reaction was terminated by the addition of 200 ml of 0.1 N KCN. The absorbance was measured at 293 nm. As a control, the solution of KCN was added to the substrate before the addition of the enzyme solution. One unit of enzyme was defined as the amount of enzyme necessary transform 1 mmol of uric acid into allantoin in 1 min at 30 8C. 2.4. Protein determination The protein content of cell free supernatant was determined according to Bradford method [12]. 2.5. Statistical designs 2.5.1. Plackett–Burman design For screening purpose, various medium components and culture parameters have been evaluated. Based on the Plackett–Burman factorial design, each factor was examined in two levels: 1 for a low level and 1 for a high level [13]. This design is practical specially when the investigator is faced with a large number of factors and is unsure which settings are likely to be nearer to optimum responses [14]. Table 1 illustrates the factors under investigation as well as levels of each factor used in the experimental design, whereas Table 2 represents the design matrix.
Table 1 Media components and test levels for Plackett–Burman experiment Variable
Variable code
Low level (1)
High level (+1)
Culture volume (ml) Culture pH Glucose (%) Sucrose (%) (NH4)2SO4 (%) KNO3 (%) Soybean flour (%) Yeast extract (%) KH2PO4 (%) K2HPO4 (%) CuSO4 (M) Fe2SO4 (M) ZnSO4 (M) MgSO47H2O (M) MnSO47H2O (M)
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15
50 6.0 0.5 0.5 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0
25 8.0 3.0 3.0 0.4 0.4 0.4 0.4 0.5 0.5 0.001 0.001 0.001 0.001 0.001
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Plackett–Burman experimental design is based on the first order model: 1.181 0.669 3.701 5.630 1.378 6.102 5.118 2.165 1.575 0.512 4.685 2.677 1.850 2.047 2.402 1.457
Y ¼ b0 þ Sbi xi
(1)
+1 1 1 1 +1 +1 +1 1 +1 1 +1 1 +1 +1 1 1
1 +1 1 1 1 +1 +1 +1 1 +1 1 +1 1 +1 +1 1
where Y is the response (enzyme activity), b0 is the model intercept and bi is the linear coefficient, and xi is the level of the independent variable. This model does not describe interaction among factors and it is used to screen and evaluate the important factors that influence the response. In the present work, seven assigned variables were screened in eight experimental designs. All experiments were carried out in triplicate and the averages of the uricase activity were taken as response (Table 2).
+1 +1 1 +1 1 +1 1 +1 +1 1 +1 1 1 1 +1 1
+1 +1 +1 1 +1 1 +1 1 +1 +1 1 +1 1 1 1 1
1 +1 +1 +1 1 +1 1 +1 1 +1 +1 1 +1 1 1 1
1 1 +1 +1 +1 1 +1 1 +1 1 +1 +1 1 +1 1 1
1 1 1 +1 +1 +1 1 +1 1 +1 1 +1 +1 1 +1 1
2.5.2. Box–Behnken design In order to describe the nature of the response surface in the experimental region, a Box–Behnken design [6] was applied. As presented in Table 3, factors of highest confidence levels were prescribed into three levels, coded 1, 0, and +1 for low, middle and high concentrations (or values), respectively. Table 4 represents the design matrix of a 15 trials experiment. For predicting the optimal point, a second order polynomial function was fitted to correlate relationship between independent variables and response (uricase activity). For the three factors this equation is: Y ¼ b0 þ b1 X1 þ b2 X2 þ b3 X3 þ b12 X1 X2 þ b13 X1 X3 þ b23 X2 X3 þ b11 X 21 þ b22 X 22
1, low level; +1, high level. a Enzyme activity (U/ml/min) was determined after 12 h of inoculation at 30 8C.
+1 1 +1 1 1 1 +1 +1 +1 1 +1 1 +1 1 +1 1
+1 +1 1 +1 1 1 1 +1 +1 +1 1 +1 1 +1 1 1
1 +1 +1 1 +1 1 1 1 +1 +1 +1 1 +1 1 +1 1
+1 1 +1 +1 1 +1 1 1 1 +1 +1 +1 1 +1 1 1
1 +1 1 +1 +1 1 +1 1 1 1 +1 +1 +1 1 +1 1
+1 1 +1 1 +1 +1 1 +1 1 1 1 +1 +1 +1 1 1
1 +1 1 +1 1 +1 +1 1 +1 1 1 1 +1 +1 +1 1
+1 1 +1 1 +1 1 +1 +1 1 +1 1 1 1 +1 +1 1
þ b33 X 23
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Culture volume Culture pH Glucose Sucrose (NH4)2SO4 KNO3 Soybean flour Yeast extracts KH2PO4 K2HPO4 CuSO4 Fe2SO4 ZnSO4 MgSO47H2O MnSO47H2O
Metal ions Energy sources Nitrogen sources Carbon sources Trials Physical variables
Table 2 Randomized Plackett–Burman experimental design for evaluating factors influencing uricase production from P. aeruginosa Ps-x
Response enzyme activity (U/ ml/min)a
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(2)
where Y is the predicted response, b0 model constant; X1, X2 and X3 independent variables; b1, b2 and b3 are linear coefficients; b12, b13 and b23 are cross product coefficients and b11, b22 and b33 are the quadratic coefficients. Microsoft Excel 97 was used for the regression analysis of the experimental data obtained. The quality of fit of the polynomial model equation was expressed by the coefficient of determination R2. Experiments were performed in triplicate and mean values are given. 2.6. Statistical analysis of data The data of enzyme activity were subjected to multiple linear regressions using Microsoft Excel 97 to estimate tvalues, P-values and confidence levels which is an expression of the P-value in percent. The optimal value of enzyme activity was estimated using the solver function of Microsoft Excel tools. Table 3 The levels of variables chosen for the Box–Behnken optimization experiment Variables
Variable code
1
0
+1
pH CuSO4 (M) FeSO4 (M)
X1 X2 X3
5.5 0.001 0.001
6.5 0.005 0.005
7.5 0.01 0.01
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Table 4 Box–Behnken factorial experimental design, representing the response of uricase enzyme activity as influenced by pH, CuSO4, and FeSO4 Trials
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
pH
1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
CuSO4 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0
FeSO4 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0
Enzyme activity (U/ml/min) Measured
Predicted
5.393 3.371 5.603 4.569 5.094 3.371 5.243 4.719 3.596 6.592 5.768 5.243 4.195 4.120 4.045
5.159 3.339 5.635 4.803 5.279 3.354 5.260 4.534 3.644 6.375 5.985 5.195 4.120 4.120 4.120
3. Results and discussion 3.1. Growth kinetics and uricase production efficiency by P. aeruginosa Ps-x in the basal production medium P. aeruginosa uricase exhibited a characteristic transient pattern of induction when cells were allowed to grow on basal production medium (Fig. 1). First, a low-level induction wave of uricase was observed at T0 (transition from exponential to stationary phase of growth), which
continued for 3 h and lead to an overall increase in uricase specific activity by approximately three-fold. A second strong wave of induction was observed at T3 with a maximum induction level being achieved at T5. The second wave was more than six-fold that observed for the first one. The maximum achievable induction level did not peak off or subject to further increase, rather it continued stagnantly even after 24 h of growth (data not shown). The growth kinetic determinants and conversion rate parameters for the uricase enzyme production in the basal production medium were estimated (Table 5). The production of extracellular uricase in the basal production medium was significant (0.11 U/ml/h), while the enzyme per cell mass formation (Yp/x) and substrate consumption (Yp/s) were 1.1 U/mg cellular protein and 226.8 U/g uric acid, respectively. On the cell growth level, P. aeruginosa Ps-x showed a normal growth pattern with maximum specific growth rate of 0.46 h1. 3.2. Inhibition of growth and uricase production by glucose supplementation in the basal production medium In an effort to enhance the uricase productivity, we amended the basal production medium with glucose. Unexpectedly, glucose when present in the basal production medium at concentration as low as 0.1% (w/v), showed a drastic effect on P. aeruginosa growth and uricase specific activity (Fig. 1). It is also noteworthy that growth rate in basal production medium with and without glucose was apparently inferior to that observed during growth on rich media, such as Luria–Bertani (LB) broth (data not shown). This pattern can be explained by the fact that the nitrogen limiting conditions might lead to an overall drop in the energy pool of the cell due to the consumption of intercellular ATP and/or GTP via the purine salvage and/or degradation pathways. On the kinetic level, the maximum production rate of extracellular uricase was significantly decreased by 73% on glucose supplementation. However, the maximum uric acid consumption rate was not affected by glucose addition to the basal production medium (Table 5). Table 5 Kinetic parameters for the production of uricase following growth of P.aeruginosa Ps-x on basal production medium with and without glucose
Fig. 1. Growth (circles) and uricase activity (triangles) of P. aeruginosa Psx in the basal production medium in absence (solid symbols) and presence of 0.1% glucose (empty symbols).
Kinetic parameters
Medium 1a
Medium 2b
mmax I mmax II Qp Qs Yp/x Yp/s
0.46 – 0.11 0.79 1.1 226.8
0.36 0.29 0.03 0.91 0.02 43.8
mmax: maximum specific growth rate per hour ; Qp: maximum enzyme produced per ml/h; Qs: maximum uric acid consumed per l/h; Yp/x: enzyme produced per gram protein; Yp/s: enzyme produced per gram substrate consumed. a Medium 1: basal production medium without glucose. b Medium 2: basal production medium with 0.1% glucose.
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The enzyme yield coefficients (Yp/x) and (Yp/s) were significantly decreased by 98 and 81%, respectively. Growth pattern of P. aeruginosa Ps-x in glucose supplemented medium showed a diauxic growth pattern, where the first log phase showed a growth rate of 0.36 h1 and the second log phase showed a growth rate of 0.29 h1. In order to explain the inhibitory effect of glucose on uricase production in P. aeruginosa, and due to the lack of substantial information on uricase from this bacterium, we searched the NCBI database for the closest homologues in bacteria using protein–protein blast. B. subtilis uricase PucL N-terminal domain was among the highest homologues with 38% similarity and 53% identity. It has been previously shown that B. subtilis purine degradation operon (puc) is directly regulated by at least three general regulators including TnrA, a regulatory protein that is involved in the activation of nitrogen-regulated genes in response to nitrogen limiting growth conditions, including growth on uric acid as the sole nitrogen source [15]. In a recent study, a chimeric TnrA protein generated randomly in a mini-Tn10 mutagenesis screen causing a constitutively expressed nitrogen regulated genes resulted in a catabolite resistant sporulation (Crs) phenotype in B. subtilis [16]. In addition, a partial relief of carbon catabolite repression (CCR) in the histidine utilization operon (hut) was suppressed by tnrA mutation [17]. Although the data presented in this paper together with information available from B. subtilis genome research can build an argument for the potential involvement of catabolite repression during uricase production in P. aeruginosa, little if any is known about the similarity between purine degradation systems in B. subtilis and P. aeruginosa. Further studies should take place in order to reach an understanding for the apparent inhibitory effect of glucose on P. aeruginosa growth and uricase production during nitrogen limited conditions.
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3.3. Optimization of uricase production by multi-factorial experiments A sequential optimization approaches were applied in the present part of the study. The first approach deals with screening for culture as well as nutritional factors affecting growth of P. aeruginosa Ps-x with respect to uricase production. The second approach is to optimize the factors that control the enzyme production process. 3.3.1. Evaluation of the factors affecting uricase productivity In the first approach, the Plackett–Burman design was applied to reflect the relative importance of various fermentation factors as described in Section 2. Fifteen different factors (variables) including fermentation conditions and medium constitution were chosen to perform this optimization process. The averages of uricase activity for the different trials are given in U/ml/min and shown in Table 2. The main effect of each variable upon uricase activity was estimated as the difference between both averages of measurements made at the high level (+1) and at the low level (1) of that factor. The data in Table 2 show a wide variation from 0.512 to 6.102 U/ml/min of uricase activity. This variation reflects the importance of medium optimization to attain higher productivity. The analysis of the data from the Plackett–Burman experiments involved a first order (main effects) model. The main effects of the examined factors on the enzyme activity were calculated and presented graphically in Fig. 2. On the analysis of the regression coefficients of the 15 variables: Culture volume, sucrose, soy bean flour, K2HPO4, KH2PO4, CuSO4, FeSO4, ZnSO4, and MnSO4 showed positive effect on uricase activity. Culture pH, glucose, (NH4)2SO4, KNO3, yeast extract and MgSO4 were contributed negatively. Fig. 3 shows the ranking of factor estimates in a Pareto chart. The Pareto chart
Fig. 2. Effect of environmental factors on the enzyme activity (U/ml/min) produced by P. aeruginosa Ps-x.
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Fig. 3. Pareto chart rationalizing the effect of each variable on the enzyme activity (U/ml/min) produced by P. aeruginosa Ps-x.
displays the magnitude of each factor estimate and it is a convenient way to view the results of a Plackett–Burman design. The polynomial model describing the correlation between the 15 factors and the uricase activity could be presented as follows: Yactivity ¼ 2:697 þ 0:268X1 1:257X2 1:109X3 þ 0:482X4 0:111X5 0:305X6 þ 0:435X7 0:110X8 þ 0:533X9 þ 0:298X10 þ 0:659X11 þ 0:939X12 þ 0:407X13 0:047X14 þ 0:159X15 on the basis of the calculated t-values and confidence level (%) (Table 6), pH, glucose, CuSO4 and FeSO4 were found to be the most significant variables affecting uricase activity. However, glucose was not included in the opti-
Table 6 Statistical analysis of Plackett–Burman design showing coefficient values, t- and P-values for each variable on uricase activity Variables
Coefficients
t-statistics
P-value
Confidence level (%)
Intercept Culture volume Culture pH Glucose Sucrose (NH4)2SO4 KNO3 Soybean flour Yeast extracts KH2PO4 K2HPO4 CuSO4 Fe2SO4 ZnSO4 MgSO47H2O MnSO47H2O
2.697 0.268 1.257 1.109 0.482 0.111 0.305 0.435 0.110 0.533 0.298 0.659 0.939 0.407 0.047 0.159
0.065 3.032 2.671 0.116 0.027 0.073 0.105 0.026 0.128 0.072 1.598 2.267 0.098 -0.011 0.038
0.912 0.077 0.079 0.880 0.936 0.907 0.887 0.936 0.873 0.908 0.085 0.081 0.892 0.945 0.929
8.792 92.299 92.094 11.956 6.425 9.335 11.266 6.405 12.708 9.226 91.454 91.854 10.843 5.462 7.146
mization design due to its inhibitory effect on uricase induction as presented earlier in the growth kinetic experiment (Table 5 and Fig. 1). On the other hand, although pH affected the enzyme negatively, it will be preferred in the forthcoming investigate to find out optimum pH level bringing maximum uricase activity. Varied optimal pH values were reviewed in literature for uricase depending on the species producing the enzyme. Optimal pH values were found to be 8.8 for N. crassa in Tris buffer [18] and 7.0 for Alternaria tenuis [19]. However, to the best of our knowledge, no single report was obtained on the optimum pH level affecting uricase formation from P. aeruginosa. On the other hand, many investigators studied the effect of various metal ions on the activity of uricase. Mahler et al. showed that the enzyme contained one tightly bound atom of copper per molecule of enzyme, which was necessary for the enzyme activity [20]. Bongaerts and Vogels reported that the enzyme in B. fastidious was partially inhibited in the presence of 105–103 M of various cations and the inhibiting effect decreased in the order Zn2+, Ni2+, Co2+, Cu2+, Cr3+, Mn2+, Pb2+. However, Fe2+ was reported to have the least inhibitory effect on uricase [21]. Other variables with less significant effect were not included in the next optimization experiment, but instead were used in all trials at their (1) level and (+1) level, for the negatively contributing variables and the positively contributing variables, respectively. According to these results, a medium of the following composition is expected to be near optimum: sucrose, 3%; uric acid, 0.15%; soy bean flour, 0.4%; KH2PO4, 0.5%; K2HPO4, 0.5%; CuSO4, 103 M; Fe2SO4, 103 M; ZnSO4, 103 M; MnSO4, 103 M; pH, 6; and culture volume 50 ml. The enzyme activity measurement on this medium was 6.6 U/ml/min. This result presented about 15-folds increase in the enzyme activity, when compared to results obtained in basal production medium (0.43 U/ml/min).
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Presenting experimental results in the form of surface plots Fig. 4a show that lower levels of pH support high uricase levels of enzyme activity. On the other hand, higher levels of the enzyme were attained with increasing the concentration of CuSO4 and FeSO4 in the medium, Fig. 4b and c. For predicting the optimal point, within experimental constrains, a second-order polynomial function was fitted to the experimental results (linear optimization algorithm) of uricase activity: Yactivity ¼ 4:120 0:663X1 þ 0:485X2 þ 0:290X3 þ 0:247X1 X2 þ 0:300X1 X3 0:880X2 X3 0:039X 21 þ 0:654X 22 þ 0:526X 23 where, X1, X2, and X3 are the culture pH, CuSO4, and FeSO4, respectively. At the model level, the correlation measures for the estimation of the regression equation are the multiple correlation coefficient R and the determination coefficient R2. The closer the value of R is to 1; the better is the correlation between the measured and the predicted values. In this experiment, the value of R was 0.988 for activity of uricase. This value indicates a high degree of correlation between the experimental and the predicted values. The value of determination coefficient R2 = 0.976 for uricase activity, being a measure of fit of the model, indicates that about 2.4% of the total variations are not explained by the enzyme activity. The optimal levels of the three components as obtained from the maximum point of the polynomial model were estimated using the Solver function of Microsoft Excel tools, and found to be: pH 5.5; CuSO4, 102 M; and FeSO4, 103 M with a predicted activity of 7.051 U/ml/min. The optimal value of the enzyme activity is 10-folds the basal conditions. This reflects the necessity and value of optimization process. Results obtained in this study are in accordance with others’ findings, where it was reported that CuSO4 and FeSO4 play an important role in enhancing the uricase activity [22,23]. Others reported an optimal pH 7.5 for maximum uricase productivity from Candida utilis [24].
Fig. 4. Uricase enzyme activity (U/ml/min) response surface from P. aeruginosa Ps-x as affected by culture conditions.
3.3.2. Optimization of the culture conditions by Box–Behnken design In order to approach the optimum response region of the enzyme activity, significant independent variables (pH, X1; CuSO4, X2; and FeSO4, X3) were further explored, each at three levels. Table 4 represents the design matrix of the coded variables together with the experimental results of the enzyme activity. All cultures were performed in triplicate and the average of the observations was used.
3.3.3. Verification of model and growth pattern Optimal conditions realized from the optimization experiment were verified experimentally and compared with the calculated data from the model. The estimated uricase activity was 6.9 U/ml/min, where the predicted value from the polynomial model as 7.051 U/ml/min (data not shown). The verification revealed a high degree of accuracy of the model of more than 97.8%, which is an evidence for the model validation under the investigated conditions.
4. Conclusion In screening the factors affecting production of certain secondary metabolite it is very important to test as many
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factors as possible and to identify the significance of each of them. Plackett–Burman design offers good and fast screening procedure and mathematically computes the significance of large number of factors in one experiment, which is time saving and maintain convincing information on each component. Although, otherwise, interaction is not included in this design, it is not of first priority in the screening program to examine the interaction between these large numbers of variables. Of these, only the most effective factors with positive significance would be selected for further optimization, while those showing high negative effect on the bioprocess may be dropped in all further experiments. This indicates the effectiveness of the Plackett–Burman design as a tool for elucidating the most important variables affecting the response. This design is recommended when more than five factors are under investigation. Applying Box–Behnken design to optimize the selected factors for maximal production is an efficient method that tests the effect of factors interaction. Besides, it converts the bioprocess factor correlations into a mathematical model that predicts where the optimum is likely to be located. It is worthwhile to advise the microbial industry sponsors to apply such experimental designs to maintain high efficiency and profit bioprocesses.
Acknowledgment The authors would like to thank Dr. Wael R. AbdelFattah, Department of Biological Science, University of Illinois at Chicago; for his valuable suggestions and fruitful discussions in the catabolite repression part.
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culture medium for batch fermentation. J Zhejiang Univ Sci 2004;5(2):149–56. Box GEP, Behnken DW. Some new three level designs for the study of quantitative variables. Technometrics 1960;2:455–75. Liu C, Liu Y, Liao W, Wen Z, Chen S. Application of statisticallybased experimental designs for the optimization of nisin production from whey. Biotechnol Lett 2003;25:877–82. Vohra A, Satyanarayana T. Phytases: microbial sources, production, purification, and potential biotechnological applications. Crit Rev Biotechnol 2003;23:29–60. Abdel-Fattah YR. Optimization of thermostable lipase from a thermophilic Geobacillus sp. Using Box–Behnken experimental design. Biotechnol Lett 2002;24:1217–22. Abdel-Fattah YR, Olama ZA. L-Asparaginase production by Pseudomonas aeruginosa in solid-state culture: evaluation and optimization of culture conditions using factorial designs. Process Biochem 2002;38:115–22. Mahler JL. A new bacterial uricase for uric acid determination. Anal Biochem 1970;38:65–84. Bradford MM. A rapid and sensitive for the quantitation of microgram quantitites of protein utilizing the principle of protein-dye binding. Anal Biochem 1976;72:248–54. Plackett RL, Burman JP. The design of optimum multi-factorial experiments. Biometrika 1946;33:305–25. Strobel R, Sullivan G. Experimental design for improvement of fermentations. In: Demain AL, Davies JE, editors. Manual of industrial microbiology and biotechnology, Washington: ASM Press; 1999. p. 80–93. Brandenburg JL, Wray Jr LV, Beier L, Jarmer H, Saxild HH, Fisher SH. Roles of PucR, GlnR, and TnrA in regulating expression of the Bacillus subtilis ure P3 promoter. J Bacteriol 2002;184: 6060–4. Shin B-S, Choi S-K, Smith I, Park S-H. Analysis of tnrA alleles which result in a glucose-resistant sporulation phenotype in Bacillus subtilis. J Bacteriol 2000;182:5009–12. Zalieckas JM, Wray Jr LV, Fisher SH. trans-Acting factors affecting carbon catabolite repression of the hut operon in Bacillus subtilis. J Bacteriol 1999;181:2883–8. Greene RC, Mitchell HK. Uricase in Neurospora crassa. Arch Biochem Biophys 1957;70:603–13. Frank W, Taha EM. Purinoxydierende Fermente aus Schimmelpilzen. III. Zur Kenntnis dere Alternarine spp. Physiol Chem Ber 1952;85:913–23. Mahler H, Hubscher G, Baum H. Studies on uricase. I. Preparation, purification and properties of a cuproprotein. J Biol Chem 1955;216:625. Bongaerts GPA, Vogels GD. Uric acid degradation by Bacillus fastidiosus strains. J Bacteriol 1976;125:689–97. Trijbels F, Vogels GD. Degredation of allantoin by Pseudomonas acidovorance and Penicillium species. Biochim Biophys Acta 1966;113:292–301. Ammer MS, Elwan SH, El-Desokey EM. Purification and some properties of uricase from Asperegillus flavus S-79. Egypt J Microbiol 1988;23(1):83–98. Lui J, Li G. Culture conditions for uricase formation of Candida utilis. Acta Microbiol 1989;29:290–6.
Improved production of Pseudomonas aeruginosa uricase by optimization of process parameters through statistical experimental designs Yasser R. Abdel-Fattaha,*, Hesham M. Saeedb, Yousry M. Goharc, Mohamed A. El-Bazb a Bioprocess Development Department, Genetic Engineering and Biotechnology Research Institute, Mubarak City for Scientific Research and Technology Applications, New Burg El-Arab City 21394, Alexandria, Egypt b Bioscience and Technology Department, Institute for Graduate Studies and Research, Alexandria University, Alexandria, Egypt c Botany Department, Microbiology Division, Faculty of Science, Alexandria University, Alexandria, Egypt
Received 3 February 2004; accepted 15 June 2004
Abstract Sequential optimization strategy, based on statistical experimental designs, was employed to enhance the production of uricase by Pseudomonas aeruginosa local isolate. Glucose supplementation to the basal production medium inhibits uricase production, perhaps by catabolic repression. For screening of bioprocess parameters significantly influencing uricase activity, the two-level Plackett–Burman design was used. Among fifteen variables tested; pH, CuSO4 and FeSO4 were selected based on their high significant effect on uricase activity. A near optimum medium formulation was obtained using this method with increased uricase yield by 15-folds. Response surface methodology (RSM) was adopted to acquire the best process conditions. In this respect, the three-level Box–Behnken design was employed. A polynomial model was created to correlate the relationship between the three variables and uricase activity. The optimal combination of the major constituents of media for uricase production evaluated from the non-linear optimization algorithm of EXCEL-Solver was as follows: pH, 5.5; CuSO4, 103 M; and FeSO4, 102 M. The predicted optimum uricase activity was 7.1 U/ml/min, which is 16.5 times than the basal medium. # 2004 Elsevier Ltd. All rights reserved. Keywords: Uricase optimization; Pseudomonas aeruginosa; Numerical modeling; Statistical experimental design
1. Introduction Uricase (urate oxidase, EC 1.7.3.3) catalyzes the oxidative opening of the purine ring of urate to yield allantoin, carbon dioxide, and hydrogen peroxide. It has vast and beneficial uses both in vitro and in vivo. Determining the urate concentration in blood and urine is required for the diagnosis of gout as urate accumulation is a causative factor of gout in humans. Uricase is useful for enzymatic determination of urate in clinical analysis by coupling with 4-aminoantipyrine-peroxidase system [1]. It can be also used as protein drug for treatment of hyperuricemia, as Rasburicase [2].
* Corresponding author. Tel.: +203 4593420; fax: +203 4593423. E-mail address: [email protected] (Y.R. Abdel-Fattah). 0032-9592/$ – see front matter # 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.procbio.2004.06.048
Many organisms including higher plants and microorganisms are able to produce uricase. Various members of the genus Pseudomonas are able to grow on purines either as nitrogen source or as both nitrogen and carbon sources, where adenine, guanine, hypoxanthine, and xanthine served as nitrogen source for P. aeruginosa [3] and P. acidovorans [4] and other unidentified species are capable of degrading uric acid by unstable membrane-bound uricase. The optimization of fermentation conditions, particularly physical and chemical parameters are of primary importance in the development of any fermentation process owing to their impact on the economy and practicability of the process. The diversity of combinatorial interactions of medium components with the metabolism of cells as well as the large number of medium constituents necessary for cell metabolism and production do not permit satisfactory detailed modeling. The one-dimensional search with
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successive variation in variables is still employed, even though it is well accepted that it is practically impossible for the one-dimensional search to accomplish an appropriate optimum in a finite number of experiments. Single variable optimization methods are not only tedious, but also can lead to misinterpretation of results, especially because the interaction between different factors is overlooked [5]. Statistical experimental designs have been used for several decades and it can be adopted at various phases of an optimization strategy, such as for screening experiments or for looking for the optimal conditions for targeted response(s) [6]. Lately, the results analyzed by a statistically planned experiment are better acknowledged than those carried out by the traditional one-variable-at-a-time (OVAT). Some of the popular choices in applying statistical designs to bioprocessing include the Plackett–Burman design [7], and response surface methodology with various designs [8–10]. In the present work, we identified growth and enzyme production kinetic parameters in basal production medium in the presence and absence of glucose. In addition, we report for the first time a sequential optimization strategy for uricase enzyme production by P.aeruginosa local isolate through statistically designed experiments as an effective tool for medium engineering. First, Plackett–Burman screening design was applied to address the most significant factors affecting enzyme production. Second, Box–Behnken design was applied to determine the optimum level of each of the significant parameters that brings maximum uricase production.
2. Materials and methods 2.1. Microorganism The bacterium used throughout this work, P.aeruginosa Ps-x was isolated and kindly provided by Dr. Yousry Gohar, Botany Department, Faculty of Science, Alexandria University. The strain was maintained on nutrient agar medium at 30 8C. The microbial isolate has been identified by16SrRNA sequencing (accession number AF419219). 2.2. Enzyme production conditions Cultures were allowed to grow at 30 8C with shaking at 200 rpm, in 250 ml conical flasks containing 50 ml aliquots pre-culture basal medium of the following composition (g/l): Peptone, 20; Glucose, 30; KH2PO4, 1; MgSO47H2O, 0.5; and uric acid, 0.1. Then 0.5 ml of the overnight culture was centrifuged, washed with sterile saline and consequently used as inoculum for the basal production medium of the following composition (g/l): K3PO4, 10; MgCl2, 1; and uric acid, 3. Bacterial growth was monitored turbidimetrically at 550 nm. At the indicated time, 2 ml of the growing cultures was taken and centrifuged at 8000 rpm for 2 min. The cell
free supernatant was used as crude enzyme preparation for further determinations. 2.3. Enzyme assay Uricase activity was measured spectrophotometrically according to Mahler [11]. Uricase activity was measured by following the disappearance of uric acid, detected by the decrease in absorbance at 293 nm in the presence of cell free extract. The assay mixture contained 0.1 ml of enzyme solution in 0.1 M borate buffer pH 9.0 and 0.12 mM uric acid in a final volume of 3.0 ml. Incubation was carried out at 30 8C for 30 min. The reaction was terminated by the addition of 200 ml of 0.1 N KCN. The absorbance was measured at 293 nm. As a control, the solution of KCN was added to the substrate before the addition of the enzyme solution. One unit of enzyme was defined as the amount of enzyme necessary transform 1 mmol of uric acid into allantoin in 1 min at 30 8C. 2.4. Protein determination The protein content of cell free supernatant was determined according to Bradford method [12]. 2.5. Statistical designs 2.5.1. Plackett–Burman design For screening purpose, various medium components and culture parameters have been evaluated. Based on the Plackett–Burman factorial design, each factor was examined in two levels: 1 for a low level and 1 for a high level [13]. This design is practical specially when the investigator is faced with a large number of factors and is unsure which settings are likely to be nearer to optimum responses [14]. Table 1 illustrates the factors under investigation as well as levels of each factor used in the experimental design, whereas Table 2 represents the design matrix.
Table 1 Media components and test levels for Plackett–Burman experiment Variable
Variable code
Low level (1)
High level (+1)
Culture volume (ml) Culture pH Glucose (%) Sucrose (%) (NH4)2SO4 (%) KNO3 (%) Soybean flour (%) Yeast extract (%) KH2PO4 (%) K2HPO4 (%) CuSO4 (M) Fe2SO4 (M) ZnSO4 (M) MgSO47H2O (M) MnSO47H2O (M)
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15
50 6.0 0.5 0.5 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0
25 8.0 3.0 3.0 0.4 0.4 0.4 0.4 0.5 0.5 0.001 0.001 0.001 0.001 0.001
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Plackett–Burman experimental design is based on the first order model: 1.181 0.669 3.701 5.630 1.378 6.102 5.118 2.165 1.575 0.512 4.685 2.677 1.850 2.047 2.402 1.457
Y ¼ b0 þ Sbi xi
(1)
+1 1 1 1 +1 +1 +1 1 +1 1 +1 1 +1 +1 1 1
1 +1 1 1 1 +1 +1 +1 1 +1 1 +1 1 +1 +1 1
where Y is the response (enzyme activity), b0 is the model intercept and bi is the linear coefficient, and xi is the level of the independent variable. This model does not describe interaction among factors and it is used to screen and evaluate the important factors that influence the response. In the present work, seven assigned variables were screened in eight experimental designs. All experiments were carried out in triplicate and the averages of the uricase activity were taken as response (Table 2).
+1 +1 1 +1 1 +1 1 +1 +1 1 +1 1 1 1 +1 1
+1 +1 +1 1 +1 1 +1 1 +1 +1 1 +1 1 1 1 1
1 +1 +1 +1 1 +1 1 +1 1 +1 +1 1 +1 1 1 1
1 1 +1 +1 +1 1 +1 1 +1 1 +1 +1 1 +1 1 1
1 1 1 +1 +1 +1 1 +1 1 +1 1 +1 +1 1 +1 1
2.5.2. Box–Behnken design In order to describe the nature of the response surface in the experimental region, a Box–Behnken design [6] was applied. As presented in Table 3, factors of highest confidence levels were prescribed into three levels, coded 1, 0, and +1 for low, middle and high concentrations (or values), respectively. Table 4 represents the design matrix of a 15 trials experiment. For predicting the optimal point, a second order polynomial function was fitted to correlate relationship between independent variables and response (uricase activity). For the three factors this equation is: Y ¼ b0 þ b1 X1 þ b2 X2 þ b3 X3 þ b12 X1 X2 þ b13 X1 X3 þ b23 X2 X3 þ b11 X 21 þ b22 X 22
1, low level; +1, high level. a Enzyme activity (U/ml/min) was determined after 12 h of inoculation at 30 8C.
+1 1 +1 1 1 1 +1 +1 +1 1 +1 1 +1 1 +1 1
+1 +1 1 +1 1 1 1 +1 +1 +1 1 +1 1 +1 1 1
1 +1 +1 1 +1 1 1 1 +1 +1 +1 1 +1 1 +1 1
+1 1 +1 +1 1 +1 1 1 1 +1 +1 +1 1 +1 1 1
1 +1 1 +1 +1 1 +1 1 1 1 +1 +1 +1 1 +1 1
+1 1 +1 1 +1 +1 1 +1 1 1 1 +1 +1 +1 1 1
1 +1 1 +1 1 +1 +1 1 +1 1 1 1 +1 +1 +1 1
+1 1 +1 1 +1 1 +1 +1 1 +1 1 1 1 +1 +1 1
þ b33 X 23
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Culture volume Culture pH Glucose Sucrose (NH4)2SO4 KNO3 Soybean flour Yeast extracts KH2PO4 K2HPO4 CuSO4 Fe2SO4 ZnSO4 MgSO47H2O MnSO47H2O
Metal ions Energy sources Nitrogen sources Carbon sources Trials Physical variables
Table 2 Randomized Plackett–Burman experimental design for evaluating factors influencing uricase production from P. aeruginosa Ps-x
Response enzyme activity (U/ ml/min)a
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(2)
where Y is the predicted response, b0 model constant; X1, X2 and X3 independent variables; b1, b2 and b3 are linear coefficients; b12, b13 and b23 are cross product coefficients and b11, b22 and b33 are the quadratic coefficients. Microsoft Excel 97 was used for the regression analysis of the experimental data obtained. The quality of fit of the polynomial model equation was expressed by the coefficient of determination R2. Experiments were performed in triplicate and mean values are given. 2.6. Statistical analysis of data The data of enzyme activity were subjected to multiple linear regressions using Microsoft Excel 97 to estimate tvalues, P-values and confidence levels which is an expression of the P-value in percent. The optimal value of enzyme activity was estimated using the solver function of Microsoft Excel tools. Table 3 The levels of variables chosen for the Box–Behnken optimization experiment Variables
Variable code
1
0
+1
pH CuSO4 (M) FeSO4 (M)
X1 X2 X3
5.5 0.001 0.001
6.5 0.005 0.005
7.5 0.01 0.01
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Table 4 Box–Behnken factorial experimental design, representing the response of uricase enzyme activity as influenced by pH, CuSO4, and FeSO4 Trials
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
pH
1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
CuSO4 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0
FeSO4 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0
Enzyme activity (U/ml/min) Measured
Predicted
5.393 3.371 5.603 4.569 5.094 3.371 5.243 4.719 3.596 6.592 5.768 5.243 4.195 4.120 4.045
5.159 3.339 5.635 4.803 5.279 3.354 5.260 4.534 3.644 6.375 5.985 5.195 4.120 4.120 4.120
3. Results and discussion 3.1. Growth kinetics and uricase production efficiency by P. aeruginosa Ps-x in the basal production medium P. aeruginosa uricase exhibited a characteristic transient pattern of induction when cells were allowed to grow on basal production medium (Fig. 1). First, a low-level induction wave of uricase was observed at T0 (transition from exponential to stationary phase of growth), which
continued for 3 h and lead to an overall increase in uricase specific activity by approximately three-fold. A second strong wave of induction was observed at T3 with a maximum induction level being achieved at T5. The second wave was more than six-fold that observed for the first one. The maximum achievable induction level did not peak off or subject to further increase, rather it continued stagnantly even after 24 h of growth (data not shown). The growth kinetic determinants and conversion rate parameters for the uricase enzyme production in the basal production medium were estimated (Table 5). The production of extracellular uricase in the basal production medium was significant (0.11 U/ml/h), while the enzyme per cell mass formation (Yp/x) and substrate consumption (Yp/s) were 1.1 U/mg cellular protein and 226.8 U/g uric acid, respectively. On the cell growth level, P. aeruginosa Ps-x showed a normal growth pattern with maximum specific growth rate of 0.46 h1. 3.2. Inhibition of growth and uricase production by glucose supplementation in the basal production medium In an effort to enhance the uricase productivity, we amended the basal production medium with glucose. Unexpectedly, glucose when present in the basal production medium at concentration as low as 0.1% (w/v), showed a drastic effect on P. aeruginosa growth and uricase specific activity (Fig. 1). It is also noteworthy that growth rate in basal production medium with and without glucose was apparently inferior to that observed during growth on rich media, such as Luria–Bertani (LB) broth (data not shown). This pattern can be explained by the fact that the nitrogen limiting conditions might lead to an overall drop in the energy pool of the cell due to the consumption of intercellular ATP and/or GTP via the purine salvage and/or degradation pathways. On the kinetic level, the maximum production rate of extracellular uricase was significantly decreased by 73% on glucose supplementation. However, the maximum uric acid consumption rate was not affected by glucose addition to the basal production medium (Table 5). Table 5 Kinetic parameters for the production of uricase following growth of P.aeruginosa Ps-x on basal production medium with and without glucose
Fig. 1. Growth (circles) and uricase activity (triangles) of P. aeruginosa Psx in the basal production medium in absence (solid symbols) and presence of 0.1% glucose (empty symbols).
Kinetic parameters
Medium 1a
Medium 2b
mmax I mmax II Qp Qs Yp/x Yp/s
0.46 – 0.11 0.79 1.1 226.8
0.36 0.29 0.03 0.91 0.02 43.8
mmax: maximum specific growth rate per hour ; Qp: maximum enzyme produced per ml/h; Qs: maximum uric acid consumed per l/h; Yp/x: enzyme produced per gram protein; Yp/s: enzyme produced per gram substrate consumed. a Medium 1: basal production medium without glucose. b Medium 2: basal production medium with 0.1% glucose.
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The enzyme yield coefficients (Yp/x) and (Yp/s) were significantly decreased by 98 and 81%, respectively. Growth pattern of P. aeruginosa Ps-x in glucose supplemented medium showed a diauxic growth pattern, where the first log phase showed a growth rate of 0.36 h1 and the second log phase showed a growth rate of 0.29 h1. In order to explain the inhibitory effect of glucose on uricase production in P. aeruginosa, and due to the lack of substantial information on uricase from this bacterium, we searched the NCBI database for the closest homologues in bacteria using protein–protein blast. B. subtilis uricase PucL N-terminal domain was among the highest homologues with 38% similarity and 53% identity. It has been previously shown that B. subtilis purine degradation operon (puc) is directly regulated by at least three general regulators including TnrA, a regulatory protein that is involved in the activation of nitrogen-regulated genes in response to nitrogen limiting growth conditions, including growth on uric acid as the sole nitrogen source [15]. In a recent study, a chimeric TnrA protein generated randomly in a mini-Tn10 mutagenesis screen causing a constitutively expressed nitrogen regulated genes resulted in a catabolite resistant sporulation (Crs) phenotype in B. subtilis [16]. In addition, a partial relief of carbon catabolite repression (CCR) in the histidine utilization operon (hut) was suppressed by tnrA mutation [17]. Although the data presented in this paper together with information available from B. subtilis genome research can build an argument for the potential involvement of catabolite repression during uricase production in P. aeruginosa, little if any is known about the similarity between purine degradation systems in B. subtilis and P. aeruginosa. Further studies should take place in order to reach an understanding for the apparent inhibitory effect of glucose on P. aeruginosa growth and uricase production during nitrogen limited conditions.
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3.3. Optimization of uricase production by multi-factorial experiments A sequential optimization approaches were applied in the present part of the study. The first approach deals with screening for culture as well as nutritional factors affecting growth of P. aeruginosa Ps-x with respect to uricase production. The second approach is to optimize the factors that control the enzyme production process. 3.3.1. Evaluation of the factors affecting uricase productivity In the first approach, the Plackett–Burman design was applied to reflect the relative importance of various fermentation factors as described in Section 2. Fifteen different factors (variables) including fermentation conditions and medium constitution were chosen to perform this optimization process. The averages of uricase activity for the different trials are given in U/ml/min and shown in Table 2. The main effect of each variable upon uricase activity was estimated as the difference between both averages of measurements made at the high level (+1) and at the low level (1) of that factor. The data in Table 2 show a wide variation from 0.512 to 6.102 U/ml/min of uricase activity. This variation reflects the importance of medium optimization to attain higher productivity. The analysis of the data from the Plackett–Burman experiments involved a first order (main effects) model. The main effects of the examined factors on the enzyme activity were calculated and presented graphically in Fig. 2. On the analysis of the regression coefficients of the 15 variables: Culture volume, sucrose, soy bean flour, K2HPO4, KH2PO4, CuSO4, FeSO4, ZnSO4, and MnSO4 showed positive effect on uricase activity. Culture pH, glucose, (NH4)2SO4, KNO3, yeast extract and MgSO4 were contributed negatively. Fig. 3 shows the ranking of factor estimates in a Pareto chart. The Pareto chart
Fig. 2. Effect of environmental factors on the enzyme activity (U/ml/min) produced by P. aeruginosa Ps-x.
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Fig. 3. Pareto chart rationalizing the effect of each variable on the enzyme activity (U/ml/min) produced by P. aeruginosa Ps-x.
displays the magnitude of each factor estimate and it is a convenient way to view the results of a Plackett–Burman design. The polynomial model describing the correlation between the 15 factors and the uricase activity could be presented as follows: Yactivity ¼ 2:697 þ 0:268X1 1:257X2 1:109X3 þ 0:482X4 0:111X5 0:305X6 þ 0:435X7 0:110X8 þ 0:533X9 þ 0:298X10 þ 0:659X11 þ 0:939X12 þ 0:407X13 0:047X14 þ 0:159X15 on the basis of the calculated t-values and confidence level (%) (Table 6), pH, glucose, CuSO4 and FeSO4 were found to be the most significant variables affecting uricase activity. However, glucose was not included in the opti-
Table 6 Statistical analysis of Plackett–Burman design showing coefficient values, t- and P-values for each variable on uricase activity Variables
Coefficients
t-statistics
P-value
Confidence level (%)
Intercept Culture volume Culture pH Glucose Sucrose (NH4)2SO4 KNO3 Soybean flour Yeast extracts KH2PO4 K2HPO4 CuSO4 Fe2SO4 ZnSO4 MgSO47H2O MnSO47H2O
2.697 0.268 1.257 1.109 0.482 0.111 0.305 0.435 0.110 0.533 0.298 0.659 0.939 0.407 0.047 0.159
0.065 3.032 2.671 0.116 0.027 0.073 0.105 0.026 0.128 0.072 1.598 2.267 0.098 -0.011 0.038
0.912 0.077 0.079 0.880 0.936 0.907 0.887 0.936 0.873 0.908 0.085 0.081 0.892 0.945 0.929
8.792 92.299 92.094 11.956 6.425 9.335 11.266 6.405 12.708 9.226 91.454 91.854 10.843 5.462 7.146
mization design due to its inhibitory effect on uricase induction as presented earlier in the growth kinetic experiment (Table 5 and Fig. 1). On the other hand, although pH affected the enzyme negatively, it will be preferred in the forthcoming investigate to find out optimum pH level bringing maximum uricase activity. Varied optimal pH values were reviewed in literature for uricase depending on the species producing the enzyme. Optimal pH values were found to be 8.8 for N. crassa in Tris buffer [18] and 7.0 for Alternaria tenuis [19]. However, to the best of our knowledge, no single report was obtained on the optimum pH level affecting uricase formation from P. aeruginosa. On the other hand, many investigators studied the effect of various metal ions on the activity of uricase. Mahler et al. showed that the enzyme contained one tightly bound atom of copper per molecule of enzyme, which was necessary for the enzyme activity [20]. Bongaerts and Vogels reported that the enzyme in B. fastidious was partially inhibited in the presence of 105–103 M of various cations and the inhibiting effect decreased in the order Zn2+, Ni2+, Co2+, Cu2+, Cr3+, Mn2+, Pb2+. However, Fe2+ was reported to have the least inhibitory effect on uricase [21]. Other variables with less significant effect were not included in the next optimization experiment, but instead were used in all trials at their (1) level and (+1) level, for the negatively contributing variables and the positively contributing variables, respectively. According to these results, a medium of the following composition is expected to be near optimum: sucrose, 3%; uric acid, 0.15%; soy bean flour, 0.4%; KH2PO4, 0.5%; K2HPO4, 0.5%; CuSO4, 103 M; Fe2SO4, 103 M; ZnSO4, 103 M; MnSO4, 103 M; pH, 6; and culture volume 50 ml. The enzyme activity measurement on this medium was 6.6 U/ml/min. This result presented about 15-folds increase in the enzyme activity, when compared to results obtained in basal production medium (0.43 U/ml/min).
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Presenting experimental results in the form of surface plots Fig. 4a show that lower levels of pH support high uricase levels of enzyme activity. On the other hand, higher levels of the enzyme were attained with increasing the concentration of CuSO4 and FeSO4 in the medium, Fig. 4b and c. For predicting the optimal point, within experimental constrains, a second-order polynomial function was fitted to the experimental results (linear optimization algorithm) of uricase activity: Yactivity ¼ 4:120 0:663X1 þ 0:485X2 þ 0:290X3 þ 0:247X1 X2 þ 0:300X1 X3 0:880X2 X3 0:039X 21 þ 0:654X 22 þ 0:526X 23 where, X1, X2, and X3 are the culture pH, CuSO4, and FeSO4, respectively. At the model level, the correlation measures for the estimation of the regression equation are the multiple correlation coefficient R and the determination coefficient R2. The closer the value of R is to 1; the better is the correlation between the measured and the predicted values. In this experiment, the value of R was 0.988 for activity of uricase. This value indicates a high degree of correlation between the experimental and the predicted values. The value of determination coefficient R2 = 0.976 for uricase activity, being a measure of fit of the model, indicates that about 2.4% of the total variations are not explained by the enzyme activity. The optimal levels of the three components as obtained from the maximum point of the polynomial model were estimated using the Solver function of Microsoft Excel tools, and found to be: pH 5.5; CuSO4, 102 M; and FeSO4, 103 M with a predicted activity of 7.051 U/ml/min. The optimal value of the enzyme activity is 10-folds the basal conditions. This reflects the necessity and value of optimization process. Results obtained in this study are in accordance with others’ findings, where it was reported that CuSO4 and FeSO4 play an important role in enhancing the uricase activity [22,23]. Others reported an optimal pH 7.5 for maximum uricase productivity from Candida utilis [24].
Fig. 4. Uricase enzyme activity (U/ml/min) response surface from P. aeruginosa Ps-x as affected by culture conditions.
3.3.2. Optimization of the culture conditions by Box–Behnken design In order to approach the optimum response region of the enzyme activity, significant independent variables (pH, X1; CuSO4, X2; and FeSO4, X3) were further explored, each at three levels. Table 4 represents the design matrix of the coded variables together with the experimental results of the enzyme activity. All cultures were performed in triplicate and the average of the observations was used.
3.3.3. Verification of model and growth pattern Optimal conditions realized from the optimization experiment were verified experimentally and compared with the calculated data from the model. The estimated uricase activity was 6.9 U/ml/min, where the predicted value from the polynomial model as 7.051 U/ml/min (data not shown). The verification revealed a high degree of accuracy of the model of more than 97.8%, which is an evidence for the model validation under the investigated conditions.
4. Conclusion In screening the factors affecting production of certain secondary metabolite it is very important to test as many
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factors as possible and to identify the significance of each of them. Plackett–Burman design offers good and fast screening procedure and mathematically computes the significance of large number of factors in one experiment, which is time saving and maintain convincing information on each component. Although, otherwise, interaction is not included in this design, it is not of first priority in the screening program to examine the interaction between these large numbers of variables. Of these, only the most effective factors with positive significance would be selected for further optimization, while those showing high negative effect on the bioprocess may be dropped in all further experiments. This indicates the effectiveness of the Plackett–Burman design as a tool for elucidating the most important variables affecting the response. This design is recommended when more than five factors are under investigation. Applying Box–Behnken design to optimize the selected factors for maximal production is an efficient method that tests the effect of factors interaction. Besides, it converts the bioprocess factor correlations into a mathematical model that predicts where the optimum is likely to be located. It is worthwhile to advise the microbial industry sponsors to apply such experimental designs to maintain high efficiency and profit bioprocesses.
Acknowledgment The authors would like to thank Dr. Wael R. AbdelFattah, Department of Biological Science, University of Illinois at Chicago; for his valuable suggestions and fruitful discussions in the catabolite repression part.
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