5 Optimisation of Process Parameters of L- asparaginase production by isolate SI091

Transcription

1 Optimisation of Process Parameters of L-asparaginase production by isolate SI Optimisation of Process Parameters of L- asparaginase production by isolate SI Introduction Success of bioprocess mainly depends on the optimum process parameters. The optimum levels of process parameters are unique for a microorganism. Optimization of nutritional and physical requirements of microorganism is important to develop and control the economic feasibility of any bioprocess. 5.2 Materials and Methods Microbial isolate, culture conditions and inoculum preparation The fungal isolate SI91, isolated in our laboratory and identified as Aspergillus cervinus by ARI, Pune, India was used in the present study. The isolate was maintained as Czapek Dox agar slants and sub-cultured once in a month. (Materials used are listed in appendix). Spore suspensions were prepared as mentioned in section Production of L-asparaginase by submerged fermentation and its assay Production of L-asparaginase and its assay was carried out as mentioned in and Optimization studies Classical Approach The optimization of the process parameters for maximization of L- asparaginase production by the isolate was carried by classical, single factor at a time approach (121, 122, 124). The nutrients and physical parameters were carbon sources (1%w/v), inorganic and organic nitrogen sources (1%w/v), minerals (.5%w/v), inducers (.5%w/v), surfactants (.1%w/v),

2 7 Exploration of soil and marine sources for microbes producing asparaginase ph, incubation temperature, incubation time, agitation, medium volume, age and level of inoculum. The parameters used were listed in Table 5-1. The optimum concentration of the best nutrient source was also determined Table 5-1: Sources of nutrients and levels of physical parameters tested for optimization S.No Nutrient/ Physical Sources/ level parameter 1 Carbon Sucrose, lactose, dextrose, maltose, fructose, xylose, arabinose, soluble starch, raffinose, glycerol, cellulose. 2 Organic Nitrogen Yeast extract, malt extract, peptone, tryptone, meat extract, casein, gelatin. 3 Inorganic Nitrogen Ammonium ferrous sulphate, ammonium nitrate, sodium nitrate, potassium nitrate, ammonium dihydrogen phosphate, ammonium sulphate, ammonium chloride. 4 Minerals Zinc sulphate, calcium chloride, ferrous sulphate, magnesium sulphate, sodium chloride, copper sulphate, boric acid. 5 Inducers L-asparagine, L-glutamine, L-glutamic acid, DLaspartic acid. 6 Surfactants Tween 8, Sodium lauryl sulphate, tritonx. 7 ph 3, 4, 5, 6, 7, 8, 9, 1. 8 Incubation 25 C, 35 C, 45 C. Temperature 9 Incubation Time (h) 12, 24, 36, 48, 6, 72, 84, 96, 18, 12, 136, Agitation (rpm), 1, 125, 15, 175, 2, 225, Medium volume (ml) 25, 5, 75, 1, 125, Age of inoculum (h) 12, 24, 36, 48, 6, Level of inoculum (%) 2.5, 5, 7.5, 1, 12.5.

3 Optimisation of Process Parameters of L-asparaginase production by isolate SI Statistical Approach Screening of important parameters by PBD PBD was used to screen different factors and select the most important factors. In this design, generally a multiple of four experiments 4n are required for 4n-1 factors. The PBD was carried out in two steps. In the first step the effect of the different nutrients was studied by taking them in two levels viz., 1%w/v (+) and.1%w/v (-). Later in the second step, the best nutrients from first step and different physical factors were further screened in two levels viz., lower (-) and higher (+). The different levels of selected nutrients and physical factors used in second step of PBD were listed in Table 5-2. The contribution of a factor towards yield of the enzyme was determined by calculating the t-value (main effect) as follows: t-value or main effect of a factor X = (Average sum of the yield where the factor is + ) - (Average sum of the yield where the factor is - ) The factors are ranked based on the t-value and the factor with the highest t- value was considered the best. The PBD is based on the first-order polynomial model: Y= β + Σ βi Xi (Equation 5.1) where Y is the response (enzyme activity), β is the model intercept, βi is the linear coefficient, and Xi is the level of the independent variable (121, 122, 124).

4 72 Exploration of soil and marine sources for microbes producing asparaginase Optimization of selected components by RSM The parameters with significant main effect on yield were selected for further optimisation by response surface methodology. Central composite design with five coded levels was performed. The second order polynomial model is given by the following formula, Y = β + Σβi*xi + Σβii*xi2+ Σβij*xij (Equation 5.2) where Y is the predicted response, β is the intercept term, βi is the linear effect, βii is the squared effect and βij is the interaction effect. Design Expert 8..6 was used to analyze the experimental data. The optimal levels of the variables were determined from the response surface curves. All experiments were conducted as duplicates and mean of the response was used (121, 122, 124). Table 5-2: Levels of nutritional and physical parameters used in secondary screening by PBD S. No Nutrient/ Physical parameter (unit) Lower level 1 Carbon(%w/v) Nitrogen(%w/v) Mineral(%w/v) Inducer (Asparagine) (%w/v) ph Incubation Temperature ( C) Agitation (rpm) Incubation Time (h) Age of inoculum (h) Level of inoculum (%) Higher level

5 Optimisation of Process Parameters of L-asparaginase production by isolate SI Results Classical approach After determining the optimal levels of the physical parameters, the optimal levels of nutritional parameters were established. The effects of various physical and nutritional parameters on production of L-asparaginase by Aspergillus cervinus was given in Figures 5-1 and 5-2, respectively. The nutritional factors were screened for their effect on L- asparaginase production by replacing the carbon, nitrogen and mineral sources. Out of the different carbon sources, fructose was found to be the best. Among nitrogen sources, casein and Ammonium nitrate were found to increase the yield. L-asparagine (.5%w/v) was found to have high effect on the yield of L- asparaginase. The yield was increased by 3.6% with addition of sodium chloride (.5%w/v). Before optimization, the yield was found to be 4.3 IUmL -1 at incubation time of 12 h, temperature 25 C, ph 7±.2. With the optimized physical and nutritional parameters, the yield was found to be 16.2 IUmL -1.

6 Exploration of soil and marine sources for microbes producing asparaginase Incubation time (hr) Temperature (⁰C) ph Age of Inoculum (hr) Inoculum (%) Volume of medium (ml) Tween 2 SLS TritonX Medium Revolutions per minute (rpm) Surfactants Figure 5-1: Effect of different physical factors on production of L-asparaginase

7 Asparaginase activity (IU/mL) Zinc Sulphate Calcium Chloride Ferrous sulphate Sodium chloride Magnesium sulphate Copper sulphate Boric acid Ammonium Ferrous Ammonium nitrate Sodium nitrate Potassium nitrate Ammonium dihydrogen Ammonium sulphate Ammonium chloride Optimisation of Process Parameters of L-asparaginase production by isolate SI Carbon Sources Fructose (%) Organic Nitrogen Sources Casein (%) Inorganic Nitrogen Sources Ammonium Nitrate(%) L-glutamic acid DL-aspartic acid Inducers Asparagine Glutamine Minerals Figure 5-2: Effect of different nutritional factors on production of L-asparaginase

8 76 Exploration of soil and marine sources for microbes producing asparaginase Statistical approach Screening of factors affecting L-asparginase production by PBD The screening of important nutritional factors maximizing the yield of L-asparaginase was carried out by PBD. The results were given in Tables 5-3 & 5-4 (carbon) and Tables 5-5 & 5-6 (nitrogen). The pareto charts depicting the main effects of the individual factors are given in Figures 5-3 and 5-4. The factors with high main effect on the yield of L-asparaginase were selected further for second step of PBD. Based on the results of first step, one carbon and two nitrogen sources were selected for further optimization. The second step also included asparagine and minerals at different levels. The results were given in Tables 5-7 & 5-8 and Figure 5-5

9 t - V a lu e o f E f f e c t t - V a lu e o f E f f e c t Optimisation of Process Parameters of L-asparaginase production by isolate SI91 77 Design-Expert Software R1 A: Dextrose B: Soluble starch C: Maltose D: Arabinose E: Sucrose F: Xylose G: Fructose H: Cellulose J: Glycerol K: Raffinose L: Lactose Positive Effects Negative Effects Pareto Chart Bonf erroni Limit t-value Limit Rank Figure 5-3: Screening of carbon sources by PBD Design-Expert Software R1 A: Yeast Extract B: Casein C: Gelatin D: Meat extract E: Peptone F: Tryptone G: Potassium nitrate H: Ammonium chloride J: Ammonium sulphate K: Sodium nitrate L: Ammonium nitrate Positive Effects Negative Effects Pareto Chart Bonf erroni Limit t-value Limit Rank Figure 5-4: Screening of nitrogen sources by PBD

10 78 Exploration of soil and marine sources for microbes producing asparaginase Run Real Values of different carbon sources (%w/v) Dextrose Soluble starch Table 5-3: Results of Plackett-Burman design for screening of different carbon sources Maltose Arabinose Sucrose Xylose Fructose Cellulose Glycerol Raffinose Lactose Activity (IUmL -1 )

11 Optimisation of Process Parameters of L-asparaginase production by isolate SI91 79 Table 5-4: PBD screening of different carbon sources: Statistical analysis Carbon Sources Effect SS % Contribution Dextrose Soluble starch Maltose Arabinose Sucrose Xylose Fructose Cellulose Glycerol Raffinose Lactose

12 8 Exploration of soil and marine sources for microbes producing asparaginase Table 5-5: Results of Plackett-Burman design for screening of different nitrogen sources Run Real Values of different nitrogen sources (%w/v) Yeast Meat Extrac Casein Gelatin Extrac Peptone t t Trypton e Potasium Nitrate Ammonium Chloride Ammonium Sulphate Sodium Nitrate Ammoniu m Nitrate Activity (IUmL -1 )

13 Optimisation of Process Parameters of L-asparaginase production by isolate SI91 81 Table 5-6: PBD screening of different nitrogen sources: Statistical analysis Nitrogen Sources Effect SS % Contribution Yeast Extract Casein Gelatin Meat Extract Peptone Tryptone Potasium Nitrate Ammonium Chloride Ammonium Sulphate Sodium Nitrate Ammonium Nitrate

14 82 Exploration of soil and marine sources for microbes producing asparaginase Run L- Asparagine (%w/v) Sodium Chloride (%w/v) Ammonium Chloride (%w/v) Table 5-7: Secondary screening of nutrients and process parameters Fructose (%w/v) Ammonium nitrate (%w/v) Incubation Time (h) Inoculum Size (%) Age of Inoculum (h) ph Agitation (rpm) Temp. ( C) L- Asparaginase activity (IUmL -1 )

15 Optimisation of Process Parameters of L-asparaginase production by isolate SI91 83 Table 5-8: PBD screening of different process parameters: Statistical analysis Parameter Effect SS % Contribution Asparagine Sodium chloride Ammonium Chloride Fructose Ammonium nitrate Incubation Time Inoculum Size Age of Inoculum ph Agitation Temperature

16 t - V a lu e o f E f f e c t 84 Exploration of soil and marine sources for microbes producing asparaginase Design-Expert Software R1 A: Asparagine B: Sodium chloride C: Ammonium chloride D: Fructose E: Ammonium nitrate F: Incubation time G: Inoculum size H: Age of inoculum J: ph K: Agitation L: Temperature Positive Effects Negative Effects Pareto Chart Bonf erroni Limit t-value Limit Rank Figure 5-5: Secondary screening of factors by PBD

17 Optimisation of Process Parameters of L-asparaginase production by isolate SI Optimization of selected parameters by RSM Based on the results of preliminary experiments, two factors, viz., Asparagine and Ammonium nitrate were selected for further optimization using RSM. The other factors were fixed at the lower level in future experiments. Central composite design with 4 factorial, 4 axial and 5 center points was carried out. The range and levels of the experimental variables are given in Table 5-9 and result of central composite design is given in Table 5-1. The statistical analysis results are given in Table The surface and contour plots are given in Figures 5-6 & 5-7. With the help of point prediction option available in the software (Design Expert), the optimum levels of the tested factors were determined. Table 5-9: Experimental range and levels of independent variable Variable (Unit) Asparagine (%w/v) Ammonium Nitrate (%w/v) Range Levels

18 Center Axial Factorial 86 Exploration of soil and marine sources for microbes producing asparaginase Table 5-1: Results of Central composite design Run Asparagine (%w/v) Ammonium nitrate (%w/v) activity (IUmL -1 ) Exprimental Predicted

19 Optimisation of Process Parameters of L-asparaginase production by isolate SI91 87 Table 5-11 RSM: ANOVA for Response Surface Quadratic Model Source Sum of Squares df Mean Square Model A-Asparagine B-Ammonium nitrate AB 2.5E E-5 A^ B^ Residual Lack of Fit Pure Error Total p-value F Prob > Value F < E < Standard Deviation- 1.4, R2.9692, Adjusted R , Predicted R , Adequate Precision , PRESS The yield is given by equation Y= *A-1.98*B+2.5E-3*A*B-1.68*A2-4.72*B2

20 A s p a r a g in a s e a c t iv it y B : A m m o n iu m n it r a t e 88 Exploration of soil and marine sources for microbes producing asparaginase Design-Expert Software Factor Coding: Actual Asparaginase activity Design Points Asparaginase activity X1 = A: Asparagine X2 = B: Ammonium nitrate * Intervals adjusted for variation in the factors A: Asparagine Figure 5-6 Contour plot: Asparagine vs Ammonium nitrate Design-Expert Software Factor Coding: Actual Asparaginase activity Design points above predicted value Design points below predicted value X1 = A: Asparagine X2 = B: Ammonium nitrate B: Ammonium nitrate A: Asparagine Figure 5-7 Surface plot: Asparagine vs Ammonium nitrate

21 Optimisation of Process Parameters of L-asparaginase production by isolate SI Discussion Classical approach The optimization of physical parameters followed by chemical parameters was done stepwise by classical approach. Peak enzyme activity was observed at 12 h. A steady decline in activity was observed after 12 h which could be due to decrease in nutrient availability or catabolic repression of enzyme. Maximum enzyme production was observed at 25 C and enzyme yield was significantly reduced at higher temperature, which could be due to influence on metabolic activity of cells. Significant change in yield was observed with varied initial ph of the medium. The production of enzyme differed slightly at ph 7. and 8., with maximum at ph 8.. This broad range of ph optima (7. to 8.) is advantageous. The age and level of inoculum plays a vital role in enzyme production, which drastically affect the cell growth. A 48 h inoculum at 1% level was found to be optimum for enzyme production. The enzyme yield progressively increased with increase in volume of medium up to 125mL in 25mL Erlenmeyer flask, after which it decreased steadily. Agitation has influence on availability of oxygen and nutrients. Moreover, at higher agitation rates, shear stress may affect the production of biomass and enzyme to a greater extent. Agitation rate of 175rpm was found to be optimum for enzyme production. Among the different carbon sources used, fructose was found to be the best and maltose had least effect on the production of L- asparaginase. Cellulose, xylose and sucrose had comparatively similar effects on the enzyme yield. The optimum level of fructose was determined to be 1.25%w/v. Increase in concentration above 1.25%w/v, had no or little effect on enzyme yield. Casein (1.25%w/v) and Ammonium nitrate (1.25%w/v) were found to be optimum for maximizing the yield of L-asparaginase. There was a

22 9 Exploration of soil and marine sources for microbes producing asparaginase 1.59 fold increase in the enzyme yield before and after the optimized levels of nitrogen source. This indicates that L- asparaginase production is nitrogen regulated. The enzyme yield was found to be maximized with L-asparagine as inducer. L-glutamine had no significant effect on enzyme production. DL-aspartic acid and L-glutamic acid had negative effect on enzyme yield. The reduction in yield by DL-aspartic acid may be attributed to the feedback inhibition of enzyme production by DL-aspartic acid. Sodium chloride had positive effect on enzyme yield. Surfactants slightly reduced the enzyme yield. The production of L-asparaginase was carried out using the optimized parameters in triplicate. The yield before optimization and after optimization by classical method was found to be 4.3 IUmL -1 and 16.2 IUmL -1, respectively Statistical approach Optimization of process parameters by statistical approach involves two steps. The first step involves preliminary screening to detect important parameters and second step is to optimize the levels of the selected parameters. With help of PBD, different carbon and nitrogen sources were screened and the best source identified by high main effect was selected for further optimization. The screening of carbon and nitrogen sources resulted in selection of three nutrients which included one carbon source (Fructose) and two nitrogen sources (Ammonium nitrate and Ammonium chloride). The selected nutrients along with inducer, mineral and physical parameters, which were earlier optimized by classical approach, were tested by PBD at different levels for maximizing the enzyme yield. Among the tested parameters, Asparagine and Ammonium nitrate were found to be significant. Hence these factors were selected for

23 Optimisation of Process Parameters of L-asparaginase production by isolate SI91 91 further optimization by RSM. All other factors were fixed at the lower level tested. The results of RSM indicated the fitness of model, as lack of fit was found to be insignificant. The selected factors had significant role on the enzyme yield, but the effect of interaction between the factors was found to be insignificant. The quadratic main effects were found to be more significant than first order main effects. The observed response (26.4 IUmL -1 supernatant) in verification experiments was found to be in close agreement with the predicted response (27.16 IUmL -1 supernatant). 5.5 Conclusion Classical, single factor at a time approach resulted in about 3.77 fold enhancement of enzyme yield. The medium components were optimized which included fructose (1.25%w/v), casein (1.25%w/v), ammonium nitrate (1.25%w/v), Sodium chloride (.5%w/v) and asparagine (.5%). A 6.14 fold increase in the enzyme yield was observed with statistically optimized process parameters. The optimum levels of selected factors were found to be asparagine 3.2 %w/v and ammonium nitrate 1.4%w/v.

24 92 Exploration of soil and marine sources for microbes producing asparaginase