Statistical Optimization for Enhanced Production of Cellulase from Sugarcane Bagasse using Response Surface Methodology

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1 Journal of Scientific & Industrial Research Vol. 75, March 2016, pp Statistical Optimization for Enhanced Production of Cellulase from Sugarcane Bagasse using Response Surface Methodology P Pachauri, S B Sullia and S Deshmukh* Department of Biotechnology, Centre for Postgraduate Studies, Jain University, Bangalore , India. Received 2 June 2014; revised 7 September 2015; accepted 28 December 2015 The study describes the isolation and identification of fungus Trichoderma longibrachiatum (GENBANK accession number: KM274866) and the optimum solid state fermentation conditions required for cellulase production based on Response Surface Methodology (RSM) using Taguchi model. When different lignocellulosic substrates were used, sugarcane bagasse supported highest (0.169 U/ml) cellulase production. Statistical optimization using Taguchii model revealed culture parameters such as Lactose which was a carbon source and cellulase inducer, CaCl 2 and temperature to significantly influence cellulase production. These parameters were further optimized using RSM, and the optimal concentrations for growth of the organism was found to be mg/ml for lactose and mgs/ml for CaCl 2 at an optimal temperature of 30 C. Under optimal media conditions an 8 fold increase in cellulase production was observed. Keywords: Trichoderma longibrachiatum, solid state fermentation, cellulase, media optimization. Introduction Lignocelluloses are the most abundant biomass on earth, mainly containing cellulose. The enzyme cellulase comprises of three classes of soluble extracellular enzymes: 1,4-β-endoglucanase, 1,4-β-exoglucanase, and β-glucosidase (β-glucoside glucohydrolase or cellobiase) 1. Lignocellulosic biomass is composed of: cellulose, hemicellulose and lignin; out of which cellulose comprises the largest fraction i.e % of total biomass 2. Nature harbors a rich diversity of bacteria, fungi, and actinomycetes that produce cellulases 3. Various species of Trichoderma, and Aspergillus, are proven cellulase producers. Among fungi, Trichoderma sp. has been utilized extensively for commercial production of the enzyme 4,5. In recent years, much work has been carried out on utilization of agro-industrial residue to produce enzymes of commercial importance by microorganisms 6,7,8. SSF is a cheaper and simpler technique and is advantageous over submerged fermentation. It is the most preferred method due to its lower operating cost and initial investment 9. SSF has applications also in solid waste management, biomass energy conservation and production of secondary metabolites 10.The fungus Trichoderma longibrachiatum was selected for the current study as it showed the highest enzyme production among all the fungi isolated. Bagasse is Author for correspondence sudhadeshmukh@yahoo.com selected as the substrate, because it is cheaper and also easily available locally. It contains approximately 40% cellulose, 24% hemicellulose and 25% lignin on dry basis 11 and is, therefore, one of the best suited substrates for cellulase production. Materials and methods Source of chemicals All the chemicals used in the present study, i.e. Agar, carboxy methyl cellulose (CMC), lactose, MnSO 4, FeSO 4, CaCl 2, NH 4 Cl, and Congo red were of reagent grade and were obtained from Himedia (Bangalore, India). Isolation of cellulase producing fungi The soil samples and wood chips were collected from fields of Agriculture University campus (GKVK), Bangalore and serially diluted with sterile distilled water. Isolation of the fungal strain was done using serial dilution method and pour plate technique. The fungal isolate demonstrating highest zone of cellulose break down was identified as Trichoderma longibrachiatum on the basis of morphological characteristics 12 and preserved for further studies and molecular identification. Screening of cellulose degrading fungi The purified isolates were inoculated on carboxymethyl cellulose (CMC) agar plates containing 1% CMC agar media.

2 182 J SCI IND RES VOL 75 MARCH 2016 Plates were flooded with aqueous solution of 1% Congo red for 15 min at room temperature and the plates were thoroughly washed with 1N NaCl for counter staining the petri-plates. Clear zone of hydrolysis was observed around the colonies indicating cellulase activity. Hydrolysis capacity (HC) ratio for the isolate was calculated by dividing the zone diameter by colony diameter. Isolates which showed the highest ratio were selected for further identification and studies. Identification of the fungal isolate Molecular identification of the isolate was done using PCR sequencing with universal ITS markers and it was found to be Trichoderma longibrachiatum. ITS1 sequencing revealed the following sequence: A C C C C A A T G T G A A C G T T A C C A A T C T G T T G C C T C G G C G G G A T T C T C T T G C C C C G G G C G C G T C G C A G C C C C G G A T C C C A T G G C G C C C G C C G G A G G A C C A A C T C C A A A C T C T T T T T T C T C T C C G T C G C G G C T C C C G T C G C G G C T C T G T T T T A T T T T T G C T C T G A G C C T T T C T C G G C G A C C C T A G C G G G C G T C T C G A A A A T G A A T C A A A A C T T T C A A C A A C G G A T C T C T T G G T T C T G G C A T C G A T G A A G A A C G C A G C G A A A T G C G A T A A G T A A T G T G A A T T G C A G A A T T C A G T G A A T C A T C G A A T C T T T G A A C G C A C A T T G C G C C C G C C A G T A T T C T G G C G G G C A T G C C T G T C C G A G C G T C A T T T C A A C C C T C G A A C C C C T C C G G G G G G T C G G C G T T G G G G A T C G G C C C C T C A C C G G G C C G C C C C C G A A A T A C A G T G G C G G T C T C G C C G C A G C C T C T C A T G C G C A G T A G T T T G C A C A C T C C C A C C G G G A G C G C G G C G C G G C C A C A G C C G T The sequence was deposited in GENBank USA collection center and the accession number (KM274866) was received. Inoculum preparation The fungus selected for the study was grown on PDA slants and spores were harvested aseptically from 4-day old PDA slants. Sterile distilled water containing 0.1% (W/V) Tween 80 was added to fungal slant and vortexed. Spore count was measured with hemocytometer and adjusted to spores/ml by adjustment of optical density. Screening substrate and substrate preparation Various agricultural wastes were collected from the local markets in and around the city of Bangalore, India. The collected lignocellulosic wastes were dried in sunlight for a week, pulverized and sieved through mesh size ranging from 0.45mm to 1mm, and used for further studies. Different substrates selected for screening include ragi straw, rice straw, pongamia cakes, neem cakes, pumpkin seeds, rice husk, saw dust, ashgourd seeds, sugarcane bagasse and corn cob. Among the selected substrates sugarcane bagasse gave the best results and was selected for cellulase production. Enzyme production under SSF and determination of enzyme activity Solid state fermentation was carried out in Erlenmeyer flasks (250 ml) with 5g of substrate in it. The substrate was moistened with minimal media with MnSO 4, FeSO 4.7H 2 O, CaCl 2.2H 2 O, lactose, and nitrogen source (NH 4 Cl), maintained at ph 6 and temperature 25 0 C, was autoclaved at 120 C for 30 min. Lactose was used as C-source as it is reported to be a good inducer of cellulase, whereas glucose only acts as a catabolic repressor and therefore is not suitable 13. After cooling, the substrate flasks were inoculated with Trichoderma longibrachiatum and incubated at room temperature for growth. The samples were withdrawn at regular intervals to determine the enzyme activity. Preliminary studies carried out for 7 days to check the enzyme activity revealed that maximum activity was obtained on day 3.Cellulase activity was determined using filter paper assay. One unit of enzyme activity is defined as amount of enzyme necessary to release 1µmol of glucose per ml per minute. The reducing sugar was measured by the dinitrosalicylic acid (DNS) method 14,15. Optimization of cellulase production Screening of significant variables for maximum enzyme activity was carried out by Taguchii design. The experimental design assumes that there are no interactions between the different variables in the range under consideration. The positive point about the experimental design is that it simply calculates the difference between the average value of the measurements made at highest level (+1) and the lowest level (-1) of the variable factor under consideration and is therefore suitable to identify the variables which affect the cellulase activity significantly. In the present study seven variables like lactose (carbon source), NH 4 Cl (nitrogen source), MnSO 4, FeSO 4, CaCl 2, temperature and ph were screened along with a control. The data are presented in Table 1. The factors which were found insignificant in the Taguchii design were eliminated in order to obtain a smaller and perfect set of factors and only the significant ones or the critical factors were selected for further optimization to attain maximum response.the statistical software package, design Expert software (version , stat-ease. Inc.

3 DESHMUKH et al.: STATISTICAL OPTIMIZATION FOR ENHANCED PRODUCTION OF CELLULASE 183 Minneapolis, USA), was used for analyzing the experimental data. Once the critical factors were identified through preliminary screening, the central composite design (CCD) was used to obtain a quadratic model. Central composite design (CCD) The Central composite design is generally used to study the effects of the variables on their responses and helps in optimization studies. The method involves fitting of a quadratic surface to optimize the important parameters and also analyze the interaction between the parameters with minimum number of experiments. Existence of a relationship between the different factors and the final response variable are investigated by statistical analysis of the data using regression analysis. The response function is expressed as a second order polynomial given by 3 : Y = X j Where Y is the predicted response, ο a constant and i, j, ij are coefficients estimated from regression. They represent the linear, quadratic and cross products of X i and X j on response. Visualization of the relationship is done by generating the response surface and contour plots from the models. Model fitting, regression and statistical analysis, in the present study, were carried out using the Central Composite Design (CCD) of RSM in the Design- Expert software (Expert software version , stat- Ease. Inc., Minneapolis, USA). In this design, the software assigns a coded value zero to the midpoint of the range over which the optimization is carried out. The end points and the grid is selected on the basis of number of variables that play a critical role and the Table 1 Taguchii Experimental Design for eight variables Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Response Std Run A:Lactose B:NH4Cl C:MnSO 4 D:FeSO 4 E:CaCl 2 F:Temperature G:pH Cellulase activity g/l g/l g/l g/l g/l Celsius U/ml 3 1 Level 1 of A Level 2 of B Level 2 of C Level 1 of D Level 1 of E Level 2 of F Level 2 of G Level 2 of A Level 1 of B Level 2 of C Level 2 of D Level 1 of E Level 2 of F Level 1 of G Level 1 of A Level 1 of B Level 1 of C Level 2 of D Level 2 of E Level 2 of F Level 2 of G Level 1 of A Level 1 of B Level 1 of C Level 1 of D Level 1 of E Level 1 of F Level 1 of G Level 2 of A Level 2 of B Level 1 of C Level 2 of D Level 1 of E Level 1 of F Level 2 of G Level 2 of A Level 1 of B Level 2 of C Level 1 of D Level 2 of E Level 1 of F Level 2 of G Level 1 of A Level 2 of B Level 2 of C Level 2 of D Level 2 of E Level 1 of F Level 1 of G Level 2 of A Level 2 of B Level 1 of C Level 1 of D Level 2 of E Level 2 of F Level 1 of G Control Level L Level L Level L Level L Level L Level L Level L Fig. 1 - Pareto chart showing the effect of different media components on cellulase activity interaction between them. The zero code assumes no interaction between the variables whereas a coded value of -1 and +1 indicate the dominance of only one variable in the interaction. With the number of critical variables in the present study being 3, a coded value of is assigned to the end points of the range assuming equal interaction between the variables in the chosen range of optimization. Results and Discussion Taguchii experiments (Table 1) showed a wide range of variations in cellulase production with different combinations of media components. The variation reflected the importance of the optimization studies for higher cellulase production. The Pareto chart shown in Fig. 1 clearly suggests that carbon source (lactose), CaCl 2 and temperature are the positive factors in the response while the variables such as nitrogen source (NH 4 Cl), FeSO 4, MnSO 4 and ph, were found to be relatively unimportant and

4 184 J SCI IND RES VOL 75 MARCH 2016 eliminated as non significant variables. The 3 critical variables, lactose, CaCl 2 and temperature were then selected for further optimization in order to achieve the best possible response, i.e. highest enzyme activity. The level of these factors and the effect of their interactions on cellulase production were determined by central composite design (CCD) of RSM in the Design-Expert software (Expert software version , stat-ease. Inc. Minneapolis, USA). For this, the experiments were carried out with different combinations of the critical factors around the critical region designated as the central value (0). The ranges of the critical variables used in RSM are given in Table 2. The degree of cellulase activity as a function of the critical factors is computed by fitting the data to a second order regression equation. The experimental as well as the predicted responses along with the design matrix are tabulated in Table 3. The results were further analyzed using ANOVA. The second order regression equation Table 2 Ranges of variables used in RSM Variables Code Range Lactose (g/l) A CaCl 2 (g/l) B Temperature ( C) C provided the levels of cellulase activity as a function of the critical factors (lactose, CaCl 2 and temperature) as follows: Y = A B C AB AC BC A B C 2 Where, Y is the cellulase activity (IU/ml) and A, B, C are lactose, CaCl 2 and temperature respectively. The ANOVA table for the response surface quadratic model for cellulase production is given in Table 4. The accuracy of the model is determined by the F value computed for the model. An F value of obtained for the present study is suggestive that the model is significant. The model terms for which the p value is less than 0.05 are considered to be significant while p values greater than 0.1 suggests that the model terms are not significant. The model quality can also be assessed by a comparison between the coefficients of determination R 2 for the experimental and the predicted responses. The Adj R-Squared value (0.9963) for the experimental response was in very good agreement with the Pred R-Squared value (0.9872) for the predicted response. Also the Adeq Precision value, which is a measure of the signal to noise ratio, was in the present study. A value >4 is suggestive of good signal. The Table 3 Central composite design in coded levels with cellulase yield as response Run order A:Lactose B:CaCl 2 C:Temperature Experimental cellulase activity (IU/ml) Predicted cellulase activity (IU/ml)

5 DESHMUKH et al.: STATISTICAL OPTIMIZATION FOR ENHANCED PRODUCTION OF CELLULASE 185 Table 4 Analyses of variance (ANOVA) for response surface quadratic model for the production of cellulase Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model E < significant A-Lactose 3.423E E B-CaCl E E C-Temperature 1.439E E AB 4.414E E AC 1.630E E < BC 4.706E E A^ < B^ < C^ < Residual 1.555E E-005 Lack of Fit 1.209E E not significant Pure Error 3.452E E-006 Cor Total Fig. 2(a) 3D plot of CaCl 2 and lactose interaction on cellulase activity model was therefore suitable for navigation through the design space. The ANOVA table (Table 4) suggests all linear, quadratic and the interaction terms in the present study, except the term BC, i.e. interaction of CaCl 2 with temperature, are significant. The interactive effects of variables on cellulase production were studied by 3D response surface plots against any two independent variables and its respective cellulase production, while keeping other variables at its central value designated as 0 level. The 3D curves of the calculated response (cellulase activity) and contour plots from the interaction between the variables are shown in Fig. 1 and Fig. 2 (a, b and c). The optimal values for lactose and CaCl 2 concentration and the temperature as calculated from the regression equation were found to be mg/ml, mgs/ml and C respectively. Experimental validation The model was experimentally validated by carrying out solid state fermentation under optimal media composition. Experiments were performed in triplicate and the average cellulase activity determined. The maximum activity was found to be 0.48 U/ml which was in good agreement with the value (0.45 U/ml) predicted by the regression model. Also the enzyme activity under standard media conditions, taken as control, was found to be 0.06 U/ml, giving an 8 fold increase in enzyme production when optimal medium is used.

6 186 J SCI IND RES VOL 75 MARCH 2016 Fig. 2(b) 3D plot of temperature and CaCl 2 interaction on cellulase activity Fig. 2(c) 3D plot of lactose and temperature interaction on cellulase activity Conclusion The present study clearly shows the importance of careful optimization of media components for maximum cellulase activity. Statistical optimization by Taguchi design revealed Lactose, CaCl 2 and temperature to be critical factors. Further optimization by RSM resulted in 8 fold increase in cellulase production by Trichoderma longibrachiatum. Sugarcane bagasse, a lignocellulosic waste, was found to be the best substrate for cellulase production. References 1 Han L, Feng J, Zhu C & Zhang X, Optimizing cellulase production of Penicillium waksmanii F10-2 with response surface methodology, Afr J Biotechnol, 8 (2009) Sharma N R, Bhatnagar V S & Kumar S, Pretreatment of paddy straw for enhanced saccharification, J Chem Pharm Res, 7 (2015) Saravanan P, Muthuvelayudham R & Viruthagiri T, Enhanced production of cellulase from pineapple waste by response surface methodology, J Engineering, 8 (2013) pages, http//dx.doi.org/ /2013/ Esterbauer H, Steiner W, Labudova I, Hermann A & Hayn M, Production of Trichoderma cellulase in laboratory and pilot scale, Bioresour Technol, 36 (1991) Kabicek C P, The cellulase proteins of Trichoderma reesei - structure, multiplicity, mode of action and regulation of formation, Adv Biochem Eng, 45 (1992) Shankar S K & Mulimani V H, α galactosidase production by Aspergillus oryzae in solid-state fermentation. Bioresour Technol, 98 (2007) Sun Z, Ramsay J & Guay M, Fermentation process development for the production of medium-chain-length poly-3-hydroxyalkanoates. Appl Microbiol Biotechnol, 75 (2007) Ramachandran S, Patel K A, Nampoothiri K M, Francis F, Nagy V, Szakacs G & Pande A, Coconut oil cake a

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