Journal of Innovations in Pharmaceutical and Biological Sciences (JIPBS)
eISSN: 2349-2759 | pISSN: 2395-1095 | Vol. 4, Issue 4, 2017, pp. 32-38

Optimization of multiple valuable biogenic products using experimental design

Mostafa M. Abo Elsoud1, Heba I. Elkhouly2, Nagwa M. Sidkey2

1Microbial Biotechnology Dept., National Research Centre, Giza, Egypt
2Department of Botany and Microbiology, Faculty of Science, Al Azhar University (Girls Branch), Cairo, Egypt
DOI: 10.56511/JIPBS.2017.v4.i04.pp32-38 | Published: 2017-10-05 | Article page and PDF

Abstract

During the last few decades, biosurfactants (e.g. rhamnolipids) have received much more attention as an alternative to the conventional synthetic surfactants. The production cost of biosurfactant is the main hindrance for its commercialization. The production of multiple valuable products in a single process may contribute in reduction of the production cost. The scope of the current work was to use Plackett Burman design for selection of the most significant factors with highest contributionsfor production of rhamno lipids in parallel with protease using Pseudomonas aeruginosa (PAO1). Nineteen factors have been selected for this study. According to the obtained results, six obvious factors; glucose, casein, peptone, yeast extract, pH and NaCl, had to be added to the production medium and three others; urea, NaNO3, and (NH4)2SO4, should not. Some of the remaining factors were not significant and the others had contrary effects on rhamnolipids and protease production. The final results showed that, the use of factorial design resulted in 213.27% increase in rhamnolipid productivity and 296.65% increase in protease productivity compared with the original production conditions.

Keywords: Biosurfactants; Pseudomonas aeruginosa (PAO1); Plackett Burman; rhamnolipid; protease

Introduction

The industrial interest in microbial products has been stimulated by their unique properties and the opportunity to develop new materials, which can be used for specific applications in many industries [1]. For example, biosurfactants are natural, safe, biodegradable and had many other advantages over their synthetic counterparts including antimicrobial activity [2].

Biosurfactants are surface-active compounds that can reduce the surface tension, stabilize emulsions, and promote foaming. Despite of being environmentally friendly; nontoxic and biodegradable, biosurfactants are widely diverse, selective and effective under extreme conditions even in small quantities [3]. Thereafter, biosurfactants can be a trusty alternative for the chemically synthesized surfactants [2].

Rhamnolipids are potent natural glycolipid biosurfactants with high potential industrial applications [4].

Pseudomonas strains are the best producers of glycolipid containing rhamnose and 3-hydroxy fatty acids. However, the most promising bacterial strain, P. aeruginosa, possesses characteristics that allow for multiple potential uses in various industrial and commercial sectors and was investigated and recommended as the best microorganism to produce two classes of rhamnolipids:

monorhamnolipids and dirhamnolipids with excellent surface activity [5-7]. In addition to rhamnolipids, P.

aerugenosa was reported to produce other valuable products including lipase, protease and amylase [8-10].

Proteases comprise a group of industrial enzymes, which alone form about 60% of the total world-wide enzyme yield [11]. Among the different types of proteases, bacterial proteases play a significant role in physiological, commercial and biotechnological processes [12].

The main factor limiting commercialization of biosurfactants is associated with their non-economic large-scale production. To overcome this obstacle and to compete with synthetic surfactants, an inexpensive substrate and effective microorganism have to be intensively developed for biosurfactant production [13, 14]. Several studies have been carried out to define the best nutritional requirements needed to obtain efficient and cost-effective rhamnolipid production by Pseudomonas aerugenosa [15]. However, the operational cost is still a hindrance for an economic production of In the current work, we aim to use of Plackett Burman factorial design method for description and statistical modeling of the factors affecting rhamnolipids and protease production by P. aerugenosa PAO1. This multiproduct bioprocess should contribute in lowering production cost. A process with multiple final products is an "out of the box" solution of the operational high cost problem. Although, easy talk, it is harder to be applied.

Materials and Methods

Organism and culture conditions

Pseudomonas aeruginosa PAO1 with accession number (NR 074828.1) was, kindly, obtained from Prof. Dr. Nagwa Sidkey, Biotechnology Lab, Faculty of Science, Al-Azhar University (Girls Branch) [7].

Pseudomonas aeruginosa PAO1 was pre-cultured and stored on glycerol-nitrate (GN) medium [16] containing (g/l):glycerol, 45; NaNO , 4.5; K HPO , 5.2; KH PO , 4; MgSO .7H O,0.4; CaCl .2H O, 0.1; KCl, 1.0; NaCl, 1.0 4 2 2 2 and Yeast extract,2.0. The GN medium pH was adjusted to 6.8. Cultures were incubated at 37°C for 72 hours. Rhamnolipid and protease were determined under these conditions to be compared with model results.

Factorial design and experiment

Plackett Burman factorial design was used for modeling of rhamnolipids and protease production by Pseudomonas aeruginosa PAO1. In this design, nineteen factors (glucose, sucrose, soybean, soluble starch, casein, glycerol, sunflower oil, sugarcane molasses, peptone, malt extract, yeast extract, NH4Cl, urea, NaNO3, (NH4)2SO4, CaCl2, KCl, NaCl and pH) with two levels, and three center points were used. To all runs, 5.2 g/l, K2HPO4; 4 g/l, KH2PO4 and 0.4 g/l MgSO4.7H2Owere added and performed at 37°C and 150 rpm in rotary shaking incubator (New Brunswick Innova 43 Incubator Shaker, Eppendorf Co., USA). Data represented in table (1) shows the factors used in the design with their ranges. Experimental design was performed using Design-Expert software (Stat-Ease Inc., Minneapolis, MN, USA, ver 7.0.0). Analysis of variance (ANOVA) was used to estimate the statistical parameters of the design.

Table 1. Summary of Plackett Burman design used for rhamnolipid and protease production Low High Factor Name Units Mean
Table 1. Summary of Plackett Burman design used for rhamnolipid and protease production Low High Factor Name Units Mean

Actual Actual The concentration of rhamnolipid in the sample was estimated using Orcinol (0.19% (w/v) in 53% Sulphuric acid (v/v)) method [3]. 500 μl of the clear supernatant was extracted twice with 1ml of diethyl ether. The extract was allowed to dry and then dissolved in 0.5ml of deionised water. To 100μl of each sample, 900 μl of Orcinol solution was added. The sample was heated at 80°C in a water bath for 30 minutes and cooled for 15 minutes at room temperature and the absorbance measured at 420 nm using Spectro UV-Vis Double Beam UVD 3500, Labomed, Inc USA. The rhamnolipid concentration was quantified from the standard L-rhamnose calibration curve between 0 and 50 μg/ml and the result was expressed as rhamnose equivalents (μg/ml) by multiplying rhamnose values by a coefficient of 3.4, which obtained from the correlation of pure rhamnolipids/rhamnose [3].

Protease Assay

To 0.5 ml of the enzyme solution in test tube, 0.5ml of 1% casein solution (in 0.05M phosphate buffer pH 7and boiled for 15minutes) was added. The mixture was allowed to stand for one hour in water bath (at 37°C) with occasional shaking. Only 3.0 ml of 10% trichloroacetic acid (TCA) was added to terminate the reaction. The tubes were allowed to stand for one hour in the cold chamber at 2°C.The tubes were then centrifuged at 3000 rpm and the absorbance of the protein content of the supernatant was quantified using Bradford method [17] and read against standard solution of Bovine serum albumin (BSA).

One protease unit is defined as the amount of enzyme required to hydrolyse one micro-gram of casein (BSA equivalent) under assay conditions.

Results and Discussion

Results

Data illustrated graphically in figure (1) and represented in table (2) shows the relation among the actual and predicted rhamnolipids and protease results according to Plackett Burman factorial design. The results show good matching of predicted comparable to the actual results.

Analysis of variance of Plackett Burman design for rhamnolipid production (Table 3) showed that the model F- value of 355.99 implies the model is significant and glucose, sucrose, soybean, casein, sunflower oil, peptone, malt extract, yeast extract, NH4Cl, urea, NaNO3, (NH4)2SO4, CaCl2, pH, KCl and NaCl are all significant model factors. The design lack of fit F-value of 1.12 implies the lack of fit 2 is not significant relative to the pure error. The model R is 2 0.9993 and the predicted R of 0.9723 is in reasonable 2 agreement with the adjusted R of 0.9965. Adequate precision ratio of 61.044 indicates an adequate signal and the model can be used to navigate the design space.

The most significant contribution of the factors were attributed to sucrose (28.87%), sunflower oil (9.17%), NaNO3 (6.3%), pH (6.65%), NaCl (6.69%), (NH4)2SO4 (5.51%), CaCl2 (3.54%), casein (3.18%), Soybean (3.86%), yeast extract (1.01%) and the other factors where less than 1%.

Figure 1. Relation between rhamnolipids (a) and protease (b) actual and predicted results according Plackett Burman factorial design
Figure 1. Relation between rhamnolipids (a) and protease (b) actual and predicted results according Plackett Burman factorial design
Table 2. Actual, predicted and residuals of rhamnolipids and protease results according to PlackettBurman design .
Table 2. Actual, predicted and residuals of rhamnolipids and protease results according to PlackettBurman design .

s (μg/ml) = -97.22340 + 0.41064*A + 6.98298*B -2.55319*C +2.31915*E + 3.93617*G + 0.32766*H + 5.72340*J + 5.36170*K + 6.52128*L - 10.70213*M - 8.76596*N - 32.61702*O - 30.51064*P + 244.46809*Q + 16.75532*R - 5.6383*S + 33.61702*T Where, A: Glucose, B:Sucrose, C: Soybean, E: Casein, G: Sunflower oil, H: Molasses, J: Peptone, K: Malt extract, L: Yeast extract, M:

, R: pH, S: KCl and T: NaCl.

Run Order

Actual value Predicted value Residual

1 38.181 36.989 1.19 2 35.947 36.883 -0.94 3 185.096 186.032 -0.94 4 132.755 133.947 -1.19 5 85.947 87.138 -1.19 6 160.840 164.528 -3.69 7 49.883 48.947 0.94 8 123.287 122.351 0.94 9 87.543 86.351 1.19 10 43.500 44.436 -0.94 11 24.564 25.755 -1.19 12 23.500 24.436 -0.94 13 100.840 99.649 1.19 14 4.245 3.309 0.94 15 166.266 164.528 1.74 16 14.670 15.861 -1.19 17 21.691 20.755 0.94 18 169.777 170.968 -1.19 19 168.926 167.989 0.94 20 21 162.968 161.777 1.19 22 70.628 71.564 -0.94 23 50.096 48.904 1.19 Rhamnolipids final equation in terms of actual values

Rhamnolipid

NH4Cl, N: Urea, O: NaNO3, P: (NH4)2SO4, Q: CaCl2 The model final equation indicates that soybean, NH Cl, 4 urea, NaNO , (NH ) SO and KCl have negative effects on 3 4 2 4 rhamnolipids production. According to Plackett Burman design, glucose, sucrose, casein, sunflower oil, molasses, peptone, yeast extract, CaCl and NaCl where the most 2 significant and have positive contribution in rhamnolipid production. Figure (2) represents 3D model graphs of some factors affecting rhamnolipids production by Pseudomonas

Actual value Predicted value Residual

169.674 174.146 -4.47 670.000 676.222 -6.22 437.310 443.532 -6.22 239.566 245.788 -6.22 131.034 137.256 -6.22 0.000 4.472 -4.47 2.046 -4.176 6.22 91.542 123.836 -32.29 129.045 133.517 -4.47 1021.167 1025.639 -4.47 0.000 4.472 -4.47 85.007 91.229 -6.22 aeruginosa PAO1 according to Plackett Burman design. The figures show the factor-factor interactions and how it affects rhamnolipids production.

Analysis of variance of Plackett Burman factorial design for protease production (Table 4) showed that, the model F- value of 204.03 implies the model is significant and glucose, sucrose, soybean, soluble starch, casein, glycerol, molasses, peptone, yeast extract, NH Cl, urea, NaNO , (NH ) SO ,

Table 3. Analysis of Variance (ANOVA) for rhamnolipid production.
Table 3. Analysis of Variance (ANOVA) for rhamnolipid production.
Figure 2. 3D model graphs representing some of the factors affecting rhamnolipids production by Pseudomonas aeruginosa PAO1.
Figure 2. 3D model graphs representing some of the factors affecting rhamnolipids production by Pseudomonas aeruginosa PAO1.

CaCl , pH, KCl and NaCl are all significant model factors.

2 The design lack of fit F-value of 0.37 implies that the lack of fit is not significant relative to the pure error. The curvature F-value of 139.69 implies there is significant curvature in 2 2 the design space. The model R is 1.0 and the predicted R 2 of 0.968 is in reasonable agreement with the adjusted R of 0.9940. The model adequate precision ratio of 48.832 * Values of "Prob > F" less than 0.05 indicate model terms are significant. indicates an adequate signal and the model can be used to navigate the design space.

The most significant contribution of the factors were attributed to soluble starch (18.42%), casein (15.81%), pH (14.54%), glucose (11.69%), (NH ) SO (8.61%), KCl 4 2 4 (9.12%), glycerol (3.36%), NaCl (3.19%), sucrose (2.47%), NaNO (2.02%), CaCl (2.21%), urea (1.41%) and the other 3 2 factors where less than 1%.

Table 4. Results of Plackett Burman design analysis of variance (ANOVA) for protease production

-696.77 + 21.33*A - 9.81*B + 4.44*C - 26.76*D + 24.80*E + 11.43*F + 4.53*H + 19.72*J + 27.98*L + 60.12*M-74.04*N-88.64*O-182.98*P- 927.92*Q + 118.90*R + 188.38*S + 111.32*T Where, A: Glucose, B:Sucrose, C: Soybean, D: Soluble starch,E: Casein,F: Glycerol, H: Molasses, J: Peptone, L: Yeast extract, M: NH

Figure 3. 3D model graphs representing some of the factors affecting protease production by Pseudomonas aeruginosa PAO1.
Figure 3. 3D model graphs representing some of the factors affecting protease production by Pseudomonas aeruginosa PAO1.

Source Sum of Squares df

Model 1867326.56 17 A-Glucose 227416.58 1 B-Sucrose 48095.04 1 C-Soybean 9847.38 1 D-Soluble starch 358146.34 1 E-Casein 307487.87 1 F-Glycerol 65289.42 1 H-Molasses 10254.99 1 J-Peptone 7780.15 1 L-Yeast extract 15657.22 1 M-NH Cl 18071.33 1 4 N-Urea 27409.91 1 O-NaNO 39288.72 1 3 P-(NH4)2SO4 167411.15 1 Q-CaCl 43051.78 1 2 R-pH 282727.54 1 S-KCl 177434.54 1 T-NaCl 61956.60 1 Curvature 75206.03 1 Residual 2153.51 4 Lack of Fit 587.13 2 Pure Error 1566.37 2 Cor Total 1944686.09 22 * Values of "Prob > F" less than 0.05 indicate model terms are significant.

Protease production final equation in terms of actual values Protease activity (U/ml) =

The model final equation showed that glucose, soybean, casein, glycerol, molasses, peptone, yeast extract, NH Cl, 4 pH, KCl and NaCl have positive effect on protease production whereas sucrose, soluble starch, urea, NaNO , 3 .

p-value

Mean Square F-Value

Prob>F

109842.7 204.03 < 0.0001 227416.6 422.41 < 0.0001 358146.3 665.23 < 0.0001 307487.9 571.14 < 0.0001 167411.2 310.96 < 0.0001 282727.5 525.15 < 0.0001 177434.5 329.57 < 0.0001 538.3763 783.186 4Cl, represents 3D model graphs of some factors affecting protease production by Pseudomonas aeruginosa PAO1 according to Plackett Burman design. The figures show the factors interaction and its effect on protease production.

Discussion

It was noted from the results of Plackett Burman models that glucose, casein, peptone, yeast extract, pH and NaCl are common significant factors with positive effects on both rhamnolipids and protease production by P. aeruginosa PAO1 whereas urea, NaNO , and (NH ) SO are common 3 4 2 4 significant factors with negative effects. Hence, urea, NaNO , and (NH ) SO should be excluded from medium of 3 4 2 4 both processes.

The complexity in the accomplishment of a single process for production of protease and rhamnolipids is the factors of opposite signs in the equations. These factors are sucrose, soybean, NH Cl, CaCl and KCl. Sucrose had a very good 4 2 contribution (28.87%) and have been used by many researchers for P. aerugenosa production of rhamnolipids [18, 19] and protease [20, 21]. Hence, sucrose should be added to the production medium.

Soybean had a very low contribution (0.51%) in case of protease production and negative effect in case of rhamnolipids and, therefore, omitted from the production medium.

Whooley and coworkers [22] reported inhibition of exoprotease production by Pseudomonas aeruginosa using NH4Cl and Rikalović and coworkers [13] reported inhibition + of rhamnolipids production in presence of NH4 . Hence, NH4Cl should be excluded from the production medium. For Pseudomonas aerugenosa, CaCl2 stimulated rhamnolipids production as shown by Sahoo et al. [23] and enhanced protease activity by 167% according to Pathak and Sardar [24]. In the current model, CaCl2 has a good contribution (3.54%) in rhamnolipids production. Consequently, CaCl2 should be added to the production medium.

Although, some researchers added KCl to rhamnolipids and protease production medium [16, 24, 25, 26], others [19, 27, 28] abandoned it. In the present work, KCl has adverse effect on rhamnolipids production; therefore, it should be omitted if we aim at rhamnolipid production. KCl, inevitably, should be added in case of protease production due to it high contribution (9.12%) and positive effect.

In the original conditions using glycerol-nitrate medium, rhamnolipid and protease production by P. aeruginosa PAO1were 86.79 (μg/ml) and 344.23 (U/ml), respectively. These results are, highly, comparable to the maximum values (185.1 μg/ml and 1021.17 U/ml) obtained from the model which indicates a promising organism and culture conditions. Finally, the use of factorial design resulted in 213.27% increase in rhamnolipid productivity and 296.65% increase in protease productivity compared with the original production conditions. Further studies are going to be performed to improve yields and performance of P. aeruginosa PAO1based on the obtained results from Plackett Burman design.

Conclusion

P. aeruginosa PAO1 is a promising bacterial strain for production of multiple products for economic bio-process. Rhamnolipids and protease enzyme are not contrary products and both can be produced in parallel by P. aeruginosa PAO1.PlackettBurman factorial design helps in optimization and improvement of products and/or processes.

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