Open Access

Characterization of Actinomycetes and Trichoderma spp. for cellulase production utilizing crude substrates by response surface methodology

  • Tanveer Pirzadah1,
  • Shashank Garg2,
  • Joginder Singh2,
  • Ashish Vyas2,
  • Manish Kumar3,
  • Naseem Gaur4,
  • Madhu Bala5,
  • Reiaz Rehman1,
  • Ajit Varma6,
  • Vivek Kumar6 and
  • Manoj Kumar1, 6Email author
Contributed equally
SpringerPlus20143:622

https://doi.org/10.1186/2193-1801-3-622

Received: 31 May 2014

Accepted: 14 October 2014

Published: 21 October 2014

Abstract

Laboratory bench scaling was done and an average of 1.85 fold increase by Response Surface Methodology (RSM) optimization was obtained. It was found that the predicted value (4.96 IU/ml) obtained by RSM is in close accordance with observed activity 5.14 IU/ml. Endoglucanases are mainly induced by CMC while Wheat bran (natural substrate) exoglucanase is more active when induced by avicel and cellulose. Addition of substrate beyond a level caused inhibition of cellulase production. The molecular weight of protein as determined by SDS-PAGE is very similar to molecular weight of cellulase of Trichoderma viride (T. viride) cellulase and Trichoderma reesei (T. reesei) endoglucanase. T. reesei β-glucosidase has high enzymatic activity on CMC substrate when compared with T. viride β-glucosidase. Secondary structure analysed by using Circular Dichroism confirmed that composition of celluase system is very similar to other analysed species. The cellulase was found to be active in pH range of 4.8-5.5; while temperature range varied from 50°C to 70°C. Although the enzymatic activity produced by mutants were lesser than the parent, but in one case mutants of Trichoderma reesei’s BGL has shown higher activity on cellulose.

Keywords

Response surface methodology CMC Wheat bran Avicel Zymogram Circular dichroism

Background

Microbial cellulase have wide spread application in textile, pulp, brewing, food and agriculture industry. Because of its commercial importance and complexity of structure, research of many labs and industries are revolving around cellulase producing microbes, its production and its characterization. Structurally, it has two domains, cellulose binding module (CBM) and a catalytic domain (CD). Cellulase family contain three groups of enzymes, endo-(1,4)-β-D-glucanase (EG), exo-(1,4)-β-D-glucanase (CBH) and β-glucosidase (BGL). EG breaks o-glycosidic bonds which are present internally but randomly. CBH attacks cellulose chains at the ends, to release β-cellulobiose. BGL acts on β-cellulobiose and generates glucose (Kuhad et al. 2011). They act together to form glucose from cellulose (Sukumaran et al. 2005). Characterization of cellulase is challenging task for enzymologists because it’s a complex of multiple subunits, and mode of action and requirements are different from one another.

There are many microbes, including bacteria and fungi that produce cellulase. Cellulomonas, Pseudomonads, Bacilli and Actinomycetes species of bacteria; Aspergillus, Humicola, Trichoderma and Penicillium species of fungi have got primary importance in research and industrial purpose because of their selective advantage over other species. H. insolens, Thermomono sporafusca, A. niger, T. reesei and few other species are exploited for industrial purpose. Complexity of fungal cellulase is less than bacterial cellulose and former is produced extracellularly in large quantity (Sukumaran et al. 2005). Trichoderma genus is found to produces cellulase enzymes with high enzymatic activity as compared with other genera (Miettinen-Oinone et al. 2002).

Characterization of cellulase for structure and activity is required to optimize the best combination of physical and chemical parameters including temperature and pH optimum, mode of action on different substrates, nutrient requirement and mode of culture (Beldman et al. 1985). Statistical tools of Design of Experiments are latest trends in bioprocess developments and process optimization. Response surface methodology (RSM), developed by Box and his collaborators, is a collection of mathematical and statistical techniques for designing experiments, building models, searching optimum conditions of factors for desirable responses, and evaluating the relative significance of several affecting factors in the presence of complex interactions (Bezerra et al. 2008). RSM is used to determine the range of controllable variables and optimal production conditions, followed by generation of model polynomial equation of the bioprocess, with estimations of the relationships between controllable variables and observed results. RSM has been used for modeling and optimization of process conditions, such as enzyme-catalyzed reaction conditions, adsorbents-aided dye removal from waste waters and production of enzymes like lipase, glucosidase, and extracellular polysaccharides (Zambare et al. 2011).

Materials and methods

Procurement of Trorichoderma strains

Trichoderma reesei (T. reesei) (MTCC No. 3114) and Trichoderma viride (T. viride) (MTCC No. 164) were procured from IMTECH, Chandigarh, India and revived on Malt extract agar. T. reesei and T. viride were grown at 28°C for 7 days and 5 days respectively. While 3 mutant lines of Trichoderma species i.e. T. reesei (ITCC No. 4026), T. viride (ITCC No. 6585) and T. Koningii (ITCC No. 5201) were collected from ITCC New Delhi. The cultures were preserved at 4°C on PDA slants.

Soil microbes

The soil samples were collected from high altitude of Leh, Jammu & Kashmir, India. Actinomyctes specific medium (Actinomycetes Isolation Agar from HiMedia) was used to grow specific culture using serial dilution approach (10−1 to 10−5) of the rhizospheric soil from Sea-buckthorn (Hipopphae rhamnoides) populated area. Followed by inoculation on same media containing plates at 28°C for 3-4 days. Standard protocol by Baker and O'Keefe was followed with slight modification (Baker et al. 1984).

Inoculum preparation and screening for cellulase production

For inoculum preparation, standard protocol by Reddy et al. was followed and 50 ml of the media with 2 ml spore inoculum was incubated in shaker incubator at 28°C (Reddy et al. 1998). Trichoderma and actinomycetes cultures were inoculated in potato dextrose medium and actinomycetes isolation medium respectively with 0.5% congo red dye for determining cellulase production.

Cellulase production

Fungal cultures were grown in 500 ml flask containing Mendel and Reese medium (Mandels et al. 1974). Constituents were mixed and made to 250 ml with sterile distilled water, followed by addition of specific substrates or carbon sources [viz. 10 g/l of CMC, cellulose, RC, RC-CMC, WB respectively]. Culture was incubated at 28°C at 100 RPM. 10 ml samples were withdrawn on 8th day of incubation and mycelia was separated by centrifugation at 1000 RPM for 20 min. Supernatant obtained was used as source of crude enzyme (Vyas et al. 2005).

Partial purification

Crude enzyme was precipitated by using Ammonium Sulphate and then dissolved in 10 ml of 50 mM of sodium acetate buffer (pH-5.5). This partially purified enzyme was dialyzed against 30 mM sodium acetate buffer (pH-5.5) at 4°C and same enzyme was used for activity assays.

Qualitative enzyme assays

Double-layer plate assay, as described by Mateos et al., was used for qualitative assaying of enzyme preparation (Mateos et al. 1992). Bottom layer of 0.7% agarose and top layer with 0.2% agarose were used for the assay. Cellulolytic activities were revealed by flooding the plates with an aqueous solution of 1% Congo red for 30 min followed by decolourilazion by 1M NaCl. Interaction of congo red with contiguous polysaccharides and hemicellulosic galactoglucomannans provides the basis of assays for β-glucanase (Teather et al. 1982). Mendel and Reese medium was prepared with specific components and agar. Plates were flooded with the Congo red solution. After 1 hour, discoloration was done with 1 M NaCl.

Quantitative enzyme assays

Glucose standard curve was plotted between glucose concentration and OD at 540 nm to draw standard curve for calorimetric analysis to quantify enzyme activity.

Assay of endoglucanase activity (CMCase assay)

Endoglucanase activity was measured as per method described by Mandels et al. (1976) with slight modification. Reaction mixture consisting of 0.5 ml of 1% CMC in citrate buffer at pH 4.8. The reaction was stopped by adding 3 ml of DNS in the boiling water. After cooling down, the absorbance was taken at 540 nm (Ghose 1987). 1 unit (IU) of endoglucanase activity is defined as the amount of enzyme which liberated 1 μmole of glucose/min under standard conditions.

Assay of FPase

FPase activity was assayed by measurement of reducing sugars in the reaction mixture containing Whatman No. 1 filter paper (1.0 cm × 6.0 cm ~50.0 mg) as substrate in 1ml sodium citrate buffer (50 mM, pH 4.8) at 50°C for 60 min (Mandels et al. 1976). 1 unit (IU) of FPase activity is defined as the amount of enzyme which liberated 1μmole of glucose/min under standard conditions.

Assay of BGL activity

For β–glucosidase activity, 0.5 ml of 1% salicin, prepared in citrate buffer (pH 5.5) was used as substrate for enzyme activity (Ahmed et al. 2009). 1 IU of BGL is defined as amount of enzyme which liberates 1 micromole of glucose from salicin per min under assay conditions.

Screening of significant factors components by Plackett-Burman design

Screening of the significant factors for the media optimization was done by Plackett-Burman experimental design (Rajendran et al. 2007). Experiments were done for evaluating significant factor such as CMC, WB, RC, peptone, pH, MgSO4.7H2O and KH2PO4. 12 runs of experiment were carried out to evaluate these parameters for cellulase activity as response. The analysis of response was done with the presentation of Pareto chart.

RSM (Response Surface Methodology) for media optimization

Significant factors revealed by PB design were optimized for cellulase production. Central Composite Design (CCD) in statistical software package “Design Expert 8.0.7.1” was used to obtain a quadratic model, consisting of factorial trials and star points to estimate quadratic effects and pure process variability with cellulase production as response (Table 1) (Garai et al. 2012).
Table 1

Ranges of variable used for CCD

Coded level

Variable

−2

−1

0

1

2

CMC (g/l)

8

10

12

14

16

KH2PO4 (g/l)

1

2

3

4

5

Peptone (g/l)

0.5

1.5

2.5

3.5

4.5

pH

3

4

5

6

7

SDS PAGE and zymography

Sodium Dodecyl Sulphate Polyacrylamide Gel Electrophoresis (SDS-PAGE) was performed on 5% stacking gel and a 15% separating gel, according to the method described by (Laemmli 1970) to determine the molecular weight of crude and partially purified cellulase (Laemmli et al. 1970). To find cellulolytic activity of cellulase, Zymography technique was used (D'Avila-levy et al. 2012; Schwarz et al. 1987). CMC (0.2%) was added to SDS PAGE before polymerization for T. reesei and T. viride to observe the activity cellulases on the gel.

Circular dichroism for secondary structure analysis

Cellulase samples (MW 48 KDa) of Trichoderma reesei, Trichoderma viride, were subjected for structural characterization through circular dichroism (CD). The isothermal studies of cellulase by CD measurements were carried out with Chirascan (Applied Photophysics equipped with a QUANTUM N.O.R.T.H.W.E.S.T.-TC125). The instrument was calibrated with d-10-camphorsulfonic acid (Schmid et al. 1997). All the isothermal CD measurements were recorded at 25°C. Spectra were collected at 20 nm/min scan speed with 0.1nm data pitch and response time of 2 s. Each spectrum was the average of 10 scans. The Far-UV CD spectra (200–260 nm) were taken at protein concentrations of 0.02 mg/ml in a cell of 0.1 cm path length. All spectra were smoothed by the Savitzky-Golay method with 25 convolution width.

Ellipticity values (θ) in mdeg, obtained from the instrument readings, were expressed in terms of mean residue ellipticity [θ] using the following equation:
θ = θ × M / 10 × l × C

Where M is the mean residue mass, l the path length (0.1 cm), C is the concentration in g/ml and [θ] is the mean residue ellipticity (Wallace et al. 2002). CD data was further processed using CDNN software for the prediction of secondary structure.

Results and discussion

Isolation and identification of Actinomycetes

Novel isolate from the soil sample collected from high altitude of Leh, Jammu & Kashmir, India was identified as actinomycetes by Gram staining and Methylene blue tests.

Screening of the Trichoderma and Actinomycetes strains for cellulase production

All the organisms under study which were incubated with congo red, produced cellulases as observed due to change in color of medium. This served as joint screening test for production of cellulases.

Congo red (CR) test for enzyme production

Clear zone of degradation of substrate in double layer plate assay reveals the production of enzyme by organisms under study. Although all fungi and actinomycetes produced the zone of clearance, T. reesei produced the largest zone.

Qadri et al. (2013) have also shown an important step towards the fungal characterization collected from the Western Himalayas and optimized the isolation and screening approach for industrial scaling.

Quantitative analysis enzyme assay on different substrates

CMCase assay

Endoglucanase activity of crude cellulase produced by the strains on the 8th day of fermentation was measured by CMCase assay (Figure 1A). T. viride grown on CMC exhibited highest activity (3.646 IU/ml), followed by T. reesei on RC + CMC (2.849 IU/ml). Actinomycetes grown on RC have shown good activity (1.024 IU/ml) when compared with other substrates. Mutants MTK, MTR, MTV produced less activity than parent strains.
Figure 1

Activities of different cellulases from respective organisms over different substrates. (A) Exaglucanase activity of the 6 organisms under study cultured in different carbon sources viz. CMC, Cellulose, Root Crush, Root Crush + CMC and Wheat Bran. (B) FPAse activity of the 6 organisms under study cultured in different carbon sources viz. CMC, Cellulose, Root Crush, Root Crush + CMC and Wheat Bran. (C) β-glucosidase activity of the 6 organisms under study cultured in different carbon sources viz. CMC, Cellulose, Root Crush, Root Crush + CMC and Wheat Bran.

FPA assay

T. viride (Figure 1B) grown on RC + CMC revealed highest activity than remaining substrates and slightly less activity on CMC i.e. 1.846 IU/ml. Actinomycetes (Figure 1B), grown on RC has shown maximum activity (0.564 IU/ml) when compared with remaining substrates. Least activity was observed for MTK grown on wheat bran (Figure 1B).

BGL assay

Figure 1C depicts the BGL activity of all strains of the crude cellulase obtained on the 4th day of fermentation. Mutants of T. reesei grown on cellulose exhibited highest activity when compared with other species (4.01 IU/ml) and least activity observed on wheat bran by MTK (0.26 IU/ml). Actinomycetes grown on RC-CMC (0.768 IU/ml) produced good activity when compared with other sources.

Similar data analysis carried out by different researchers (Beldman et al. 1985; Haq et al. 2005 & Vyas et al. 2005) has been effectively scaled with wider range of crude enzymes obtained from efficient strains and the fast fermentation approach adapted in the current research work.

Screening of factors by Plackett-Burman experimental run

Screening of the most significant factor components by Plackett-Burman design

All runs were done in duplicates and the mean value of activity was noted as response. The highest value of 3.6 IU/ml was obtained in the 8th run, while lowest value was recorded as 0.26 IU/ml in the 1st run. The data shows a large variation between lowest and highest activity which supports the actual coding and quantity of medium components we had assigned (Table 2).
Table 2

Experimental runs for Plackett – Burman Design

Run

A CMC

B WB

C RC

D Peptone

E pH

F MgSO4

G KH2PO4

H*

J*

K*

L*

Response (Activity(IU/ml))

            

Predicted

Actual

1

−1

−1

1

−1

1

1

−1

1

1

1

−1

0.26

0.18

2

1

1

1

−1

−1

−1

1

−1

1

1

−1

0.80

0.88

3

1

−1

1

1

−1

1

1

1

−1

−1

−1

2.40

2.58

4

−1

−1

−1

−1

−1

−1

−1

−1

−1

−1

−1

0.40

0.32

5

1

1

−1

1

1

1

−1

−1

−1

1

−1

0.90

0.94

6

1

1

−1

−1

−1

1

−1

1

1

−1

1

0.60

0.54

7

1

−1

−1

−1

1

−1

1

1

−1

1

1

3.40

3.98

8

1

−1

1

1

1

−1

−1

−1

1

−1

1

3.60

3.2

9

−1

1

−1

1

1

−1

1

1

1

−1

−1

1.40

1.11

10

−1

−1

−1

1

−1

1

1

−1

1

1

1

0.94

0.87

11

−1

1

1

1

−1

−1

−1

1

−1

1

1

0.40

0.38

12

−1

1

1

−1

1

1

1

−1

−1

−1

1

1.30

1.6

*Dummy variables.

Plackett-Burman design (PBD) is the most recommended screening design and mathematical approach used in mathematical applications in engineering because if its ability to estimate all main effects with the same precision (Antony 2008).

Elimination of insignificant factor by data analysis

Generally, a large t- value associated with a low P- value of a variable indicates a high significance of the corresponding model term. CMC, peptone, K2HPO4, and MgSO4 displayed a positive effect for enzyme production, the variables with confidence levels greater than 95% were considered as significant. CMC was most significant for cellulase production.

Cellulases produced from bacterial and fungal strains have traditionally been estimated by significant CMC as reported by several authors (Ghos 1987; Kim 2008).

Effect analysis by Pareto chart

The purpose of the Pareto chart (Figure 2) is to highlight the significance of CMC among all (typically large) set of factors. Table 3 shows the positive and negative effects of media components on cellulase activity. As it can be seen that B -wheat bran and F- MgSO4 (Blue color) have negative effect in comparison to other components, both of these have been eliminated in optimization procedure of CCD.
Figure 2

Pareto chart showing the effect of media components on cellulase activity. Developed from the data analysis in the Plackett-Burman experimental run, this chart was used to determine the significant factors affecting cellulase yield.

Table 3

Statistical analysis of Plackett-Burman design showing coefficient values, t -value and P -value for each variable

Variable

Coefficient

t-value

P value

Intercept

1.08

12.7062

0.0049*

CMC

0.24

12.7062

0.0024*

WB

−0.15

4.30265

0.0037

RC

0.025

3.18245

0.0249

Peptone

0.12

2.77645

0.0049*

pH

0.17

2.57058

0.0033*

MgSO4

−0.099

2.44

0.0057

KH2PO4

0.18

2.36462

0.0031*

*Significant factors.

From the Pareto analysis it could be possible that only CMC had a significant impact at 95% as compared to other parameters at activity level. The parameter values were also compared with enzyme (AVICEL) and the conditions were validated here in this work as evinced by Goldbeck et al. (2013).

Optimization of medium components by response surface methodology

At the end of screening experiments by Plackett-Burman design, 4 factors were found to play a significant role in cellulase production. Table 4 summarizes the response of various run experiments performed for ANOVA for the statistical design of experiment. To test the fit of the model equation, the regression-based determination coefficient (R2) was evaluated.
Table 4

CCD experimental design

 

Factor 1

Factor 2

Factor 3

Factor 4

Response (Activity(IU/ml))

Run

A:CMC(g/l)

B:KH2PO4(g/l)

C:pH

D:Peptone(g/l)

Predicted

Actual

1

12

3

7

2.5

2.9

1.86

2

10

2

6

1.5

2.1

1.74

3

10

2

6

3.5

1.8

1.95

4

12

3

3

2.5

0.8

0.58

5

12

3

5

2.5

5.14

4.97

6

12

3

5

2.5

5.08

4.97

7

10

4

6

1.5

0.4

0.21

8

10

2

4

1.5

0.6

1.12

9

10

4

6

3.5

0.7

0.5

10

10

2

4

3.5

0.2

0.95

11

12

3

5

0.5

2.6

1.96

12

12

3

5

2.5

4.84

4.97

13

10

4

4

3.5

1.4

0.85

14

12

3

5

2.5

4.78

4.97

15

14

4

4

3.5

0.8

1.47

16

8

3

5

2.5

2.6

2.09

17

14

2

4

1.5

0.5

0.89

18

12

5

5

2.5

0.2

0.21

19

16

3

5

2.5

3.1

2.38

20

12

3

5

2.5

4.8

4.97

21

12

3

5

2.5

5.12

4.97

22

14

2

4

3.5

0.1

0.1

23

14

4

6

1.5

2

1.37

24

12

3

5

4.5

2.1

1.51

25

12

1

5

2.5

1.8

0.55

26

14

4

4

1.5

2.3

2.18

27

14

2

6

3.5

0.7

1.01

28

14

2

6

1.5

0.9

1.43

29

10

4

4

1.5

0.9

0.94

30

14

4

6

3.5

1.7

1.03

ANOVA for CCD (Central Composite Design)

Analysis of variance for cellulase activity of T. reesei (Table 4) revealed that CMC and other factor variation had a significant influence (p ≤ 0.001) based on hydrolyzing capabilities of the strain. The F-value of 8.54 implies that the model is significant. A2, B2, C2, D2 are significant model terms to test the fit of the model equation as revealed from its F value. The results of the second-order response surface model are fitting in the form of ANOVA (Table 5).
Table 5

Analysis of variance table [Partial sum of squares - type III]

Source

Sum of squares

Df

Mean square

F value

P-value

Model

72.28

14

5.16

8.54

<0.0001

A-CMC

0.15

1

0.15

0.25

0.6251

B-KH2PO4

0.0004167

1

0.0004167

0.0006894

0.9794

C-pH

2.47

1

2.47

4.09

0.0614

D-Peptone

0.45

1

0.45

0.75

0.3999

AB

2.18

1

2.18

3.6

0.0772

AC

0.005625

1

0.005625

0.0093

0.9244

AD

0.39

1

0.39

0.65

0.434

BC

1.38

1

1.38

2.28

0.1515

BD

0.005625

1

0.005625

0.0093

0.9244

CD

0.11

1

0.11

0.17

0.6818

A2

12.86

1

12.86

21.28

0.0003

B2

36.10

1

36.10

59.74

<0.0001

C2

23.97

1

23.97

39.66

<0.0001

D2

17.99

1

17.99

29.76

<0..0001

Residual

9.07

15

0.60

  

Lack of fit

8.92

10

0.89

20.63

0.0007

Pure error

0.14

5

0.029

  

Cor total

81.34

29

   
The final equation in terms of coded factor was given as
Activity = 59.71578 + 3.8374 A + 6.09729 B + 10.45937 C + 4.38646 D + 0.18437 A * B 0.009375 A * C 0.078125 A * D 0.29375 B * C + 0.01875 B * D + 0.08125 C * D 0.1712 A 2 1.14729 B 2 0.93479 C 2 0.80979 D 2

Statically optimized medium used for the production of other microbial cellulose using Gluconacetobacter persimmonis was carried out by Hegde et al. (2013). To the extent, it could be an alternative approach for media optimization in the case of Trichoderma spp. Hegde et al. (2013) and Rajendran et al. (2007) have followed PBD for screening of the medium constituents (slightly different) and a CCD for optimization of significant factors. Here authors have employed different factors as per the demand of equations were estimated as significant factors from PBD. The synchronized properties of medium components and their optimum regions are reported in this manuscript.

Response surfaces

The interaction effects and optimal levels of the variables were determined by plotting the three-dimensional (3D) response surface curves. Response surfaces were made for analyzing the response pattern at any point. The response surface curve in Figure 3A represented the interaction between CMC (A) and KH2PO4 (B) which exhibited that the maximum enzyme activity was obtained in middle range (12 g/l) while enzyme activity decreased in lower and higher range of CMC probably due to increase in viscosity of the medium on increase in concentration of CMC In addition, lower and higher levels of KH2PO4 resulted in lower cellulase activity. The higher and lower concentration of KH2PO4 may have affected pH of the medium and hence affected the cellulase activity. The shape of the response surface curves showed strong positive interaction between these tested variables. Figure 3B represented response surface plot of two independent variables that is CMC (A) and pH (C). It was inferred that maximum cellulase synthesis took place when concentration of CMC was kept at their middle level. The pH optima for enzyme production from earlier sources were in the range of 4.5–5.7. Figure 3C represented response surface plot between CMC (A) and peptone (D). It can be seen that maximum cellulase activity was obtained when values of carbon and nitrogen sources were kept at their respective intermediate level. Interaction between variables can also be predicted by visualizing their respective contour plot. The effect of varying concentration of KH2PO4 (B) and pH (C) on enzymatic activity at constant CMC and peptone is shown in Figure 3D. The typical plots were dome shaped. Figure 3E shows the interactive surface of KH2PO4 (B) and peptone (D). The nitrogen source (peptone) in concentration range of 2 g/l to 2.5 g/l showed elevation in the curvature of the response plot while KH2PO4 optimum range 2.5 g/l to 3.5 g/l. concentration of peptone below 2 g/l showed green color and depression in the curve of plot. Figure 3F represents the response surface of peptone and pH, other variables i.e. CMC and KH2PO4 were kept constant. The optimum pH range (5.0-5.5) was much narrowed in comparison to the response plot discussed before.

Contour plots (Figure 3) indicated the general shapes of the response surface. However, the contour plots are two-dimensional, whereas the current RSM study had four variables, so two variables must be set at an arbitrary value and the response surface plotted with the other two variables. Elliptical nature of contour indicated the significant interaction between variables while circular contour showed insignificant interaction between variables. The resulting Response surface was the function of the value chosen for the variable held constant and may differ considerably, depending on what value is selected.
Figure 3

Contour plot for cellulase production showing the interactive effects of medium components. (A) Interaction plot of CMC and KH2PO4. (B) Interaction plot of CMC and pH. (C) Interaction plot of CMC and Peptone. (D) Interaction plot of pH and KH2PO4. (E) Interaction plot of Peptone and KH2PO4.(F) Interaction plot of Peptone and pH. These contour plots are actual 2D projections of response plots which are developed in the methodology used in the present study. They give the idea of interactions between variables affecting the yield.

Application of RSM in the optimization of analytical methods is presented in this investigation. The multivariate statistical technique of RSM and steps for its application are adapted as described by rsearchers (Bezerra et al. 2008; Saravanan et al. 2012).

Validation experiment

Experiments in duplication were performed to validate the developed optimized medium. The optimized medium recorded a higher enzymatic activity of CMCase as compared to other basal media. Overall results indicated that a sharp fold (1.63X) increase occurred after optimization (Table 6). These results confirmed the validity of the optimized medium. 1.85 and 1.88 fold increase was obtained in BGL and FPA respectively. Overall an average of 1.85 fold increase was obtained (Table 6).
Table 6

Optimization summary of enzymes

 

Activity(IU/ml)

 

CMCase

BGL

FPA

Unoptimized

3.142

0.66

1.38

Optimized

5.140

1.22

2.60

Fold increase

1.6

1.85

1.88

Molecular characterization

SDS-PAGE analysis

The crude dialysed enzymes samples obtained through dialysis membrane were resolved on a 5% stacking and 15% running gel. Trichoderma reesie- cellulase complex (endo-glucanase, exo-glucanse and BGL) was found to be homogenous scattered view of crude monomeric protein, as evidenced by the multiple bands corresponding to the designated size of SDS page (Figure 4). Analysis of different molecular weight of putative cellulase monomers are very much based on the available reports pertaining to the genetic information of many species. The molecular of protein determined by SDS –PAGE was very close to molecular weight of cellulase of T. viride 31.2 KDa (Sultana et al. 1997) and endoglucanase molecular weight(23-42 KDa) as described (Abdullah et al. 2005). The cellulase was found to be much active in pH range of 4.8-5.5 while temperature range varied from 50°C to 70°C.
Figure 4

SDS PAGE analysis of Trichoderma reesei cellulase composition Lane1; crude cellulase , Lane 2 purified cellulase , Lane 3 purified cellulase lane 4 Standard marker (GeNei).

Zymography

Figure 5 clearly shows that, it is a complex system containing different components. T. reesei (Figure 5) has shown that high enzymatic activity on CMC when compared with T. viride.
Figure 5

Cellulase zymogram of Trichoderma reesei (TR) and Trichoderma viride (TV) on CMC and negative control.

Circular dichroism

Circular dichroism is a method that would account for the secondary structure content in a polypeptide based on the presence of alpha helix, beta sheet and random coil (Keideiling 1996). We probed the secondary structure of Cellulase by far-UV CD (Table 7). Two deep minima near 208 nm and 222 nm characteristic of α helical structure in aqueous solution are due to π-π* & n-π* transitions. β-sheet is having negative at 218 nm (π-π*). From Figure 6, predominantly α-helical (more than 60%) was detected for Cellulase at pH 7.4. At pH 7.4, far-UV CD spectra of Cellulase clearly reflect characteristics of (α) type of structure.
Table 7

Secondary structure composition of cellulase determined from Far-UV CD spectra in pH 7.4 at 25°C

(a)

Secondary structure

Amount

Helix

100%

Antiparallel

0

Parallel

0

Beta Turn

1.3%

Random Coil

0.2%

(b)

Secondary structure

Amount

Helix

100%

Antiparallel

0

Parallel

0

Beta Turn

1.2%

Random Coil

0.2%

(a) Cellulase from (a) Trichoderma reesei, (b) Trichoderma viride.

Figure 6

CD curves of cellulase samples from (A) Trichoderma reesei, (B) Trichoderma viride.

Conclusion

Significant data obtained in this investigation could be stated that: the predicted value achieved by RSM that is 4.96 IU/ml, is in close agreement with observed value that is 5.14 IU/ml. After the employment of laboratory bench scaling and optimization methodology approximately 2 fold increase in activity was observed. Molecular weight as determined by SDS page is almost identical to molecular weight of other cellulose and endoglucanase well characterized by Sultana et al. and Abdullah et al. (Sultana et. al. 1997; Abdullah et al. 2005). T. reesei shows highest enzymatic activity when compared with T. viride on CMC substrate. Cellulase secondary structure composition is very similar in all analysed species. Further studies on the performance of these proteins and their mutants on the growth of plant will uncover the mechanism and potential of these proteins.

Notes

Abbreviations

RSM: 

Response surface methodology

IU/ml: 

International unit/Millilitre

CMC: 

Carboxymethlyl cellulose

BGL: 

β glucosidase

EG: 

Endoglucanase

CR: 

Congo red

RC: 

Root crush

CCD: 

Central composite design

FPAse Assay: 

Filter paper assay

CD: 

Circular dichroism.

Declarations

Acknowledgements

Authors acknowledge Amity University and Lovely Professional University for facility and internal support towards the research.

Authors’ Affiliations

(1)
Department of Bioresources, University of Kashmir
(2)
Department of Microbiology, School of Biotechnology and Biosciences Lovely Professional University
(3)
AIRF, Jawaharlal Nehru University
(4)
International Centre for Genetic Engineering and Biotechnology (ICGEB)
(5)
Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO
(6)
Amity Institute of Microbial Technology, Amity University Uttar Pradesh

References

  1. Abdullah N, Chase HA: Removal of poly-histidine fusion tags from recombinant proteins purified by expanded bed adsorption. Biotechnol Bioeng 2005, 92: 501-513. 10.1002/bit.20633View ArticleGoogle Scholar
  2. Ahmed S, Bashir A, Saleem H, Saadia M, Jamil A: Production and purification of cellulose degrading enzymes from a filamentous fungus Trichoderma harzianum . Pak J Bot 2009, 41: 1411-1419.Google Scholar
  3. Antony J: Design of Experiments for Engineers and Scientists. Butterworth Heinemann, U.K; 2008:44-53.Google Scholar
  4. Baker DD, O'Keefe D: A modified sucrose fractionation procedure for the isolation of frankia from actinorhizal root nodules and soil samples. Plant Soil 1984, 78: 23-28. 10.1007/BF02277837View ArticleGoogle Scholar
  5. Beldman G, Leeuwen MFSV, Rombouts FM, Voragen GJ: The cellulase of Trichoderma viride Purification, characterization and comparison of all detectable endoglucanases, exoglucanases and P-glucosidases. Eur J Biochem 1985, 146: 301-308. 10.1111/j.1432-1033.1985.tb08653.xView ArticleGoogle Scholar
  6. Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA: Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 2008, 76: 965-977. 10.1016/j.talanta.2008.05.019View ArticleGoogle Scholar
  7. d’Avila-levy CM, Santos ALS, Cuervo P, Jesus JBD, Branquinha MH: Applications of zymography (Substrate-SDS-PAGE) for peptidase screening in a Post genomic Era. In Gel Electrophoresis - Advanced Techniques. Edited by: Magdeldin S. InTech; 2012:266-288.Google Scholar
  8. Garai D, Kumar V: Response surface optimization for xylanase with high volumetric productivity by indigenous alkali tolerant Aspergilluscandidus under submerged cultivation. Biotech 2012, 3: 127-136.Google Scholar
  9. Ghose TK: Measurement cellulase of activities. Pure App Chem 1987, 59: 257-268.Google Scholar
  10. Goldbeck R, Ramos MM, Pereira GAG, Maugeri-Filho F: Cellulase production from a new strain Acremonium strictum isolated from the Brazilian Biome using different substrates. Bioresour Technol 2013, 128(2013):797-803.View ArticleGoogle Scholar
  11. Haq IU, Hameed U, Shahzadi K, Javed MM, Ali S, Qadeer MA: Cotton saccharifing activity of cellulases by Trichoderma harzianum UM–11 in shake flask. Int Jour Bot 2005, 1: 19-22.View ArticleGoogle Scholar
  12. Hegde S, Bhadri G, Narsapur K, Koppal S, Oswal P, Turmuri N, Jumnal V, Hungund B: Statistical optimization of medium components by response surface methodology for enhanced production of bacterial cellulose by Gluconacetobacter persimmonis . J Bioproces Biotechniq 2013, 4: 1.Google Scholar
  13. Keideiling TA: Vibrational circular dichroism application to conformational analysis of biomolecules. In Circular Dichroism and the Conformational Analysis of Biomolecules. Edited by: Fasman GD. New York: Plenum Press; 1996:555-598.View ArticleGoogle Scholar
  14. Kim SJ, Lee CM, Han BR, Kim MY, Yeo YS, Yoon SH, Koo BS, Jun HK: Characterization of a gene encoding cellulase from uncultured soil bacteria. FEMS Microbiol Lett 2008, 282: 44-51. 10.1111/j.1574-6968.2008.01097.xView ArticleGoogle Scholar
  15. Kuhad RC, Gupta R, Singh A: Microbial cellulases and their industrial applications: SAGE-Hindawi Acess to research. Enzyme Res 2011, 2011: 1-11.View ArticleGoogle Scholar
  16. Laemmli UK: Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 1970, 227: 680. 10.1038/227680a0View ArticleGoogle Scholar
  17. Mandels M, Hontz L, Nystrom J: Enzymatic hydrolysis of waste cellulose. Biotechnol Bioeng 1974, 16: 1471-1493. 10.1002/bit.260161105View ArticleGoogle Scholar
  18. Mandels M, Andreotti R, Roche R: Bioteh Bioeng Symp. 1976, 6: 17-37.Google Scholar
  19. Mateos PF, Jimenez-Zurdo JI, Chen J, Squartini AS, Hacck SK, Martinez ME, Hubbell DH, Dazzo FB: Cell associated pectinolytic and cellulolytic enzymes in Rizobium leguminosarum biovartrifolii. Appl Environ Microbiol 1992, 58: 1816-1822.Google Scholar
  20. Miettinen-Oinone A, Suominen P: Enhanced production of Trichoderma reesei endoglucanases and use of the new cellulase preparations in producing the stone- washed effect on denim fabric. Appl Environ Microbiol 2002, 68: 3956-3964. 10.1128/AEM.68.8.3956-3964.2002View ArticleGoogle Scholar
  21. Qadri M, Johri S, Shah BA, Khajuria A, Sidiq T, Lattoo SK, Abdin MZ, Hassan RU: Identification and bioactive potential of endophytic fungi isolated from selected plants of the Western Himalayas. SpringerPlus 2013, 2(8):1-14.Google Scholar
  22. Rajendran A, Thirugnanam M, Thangavelu V: Statistical evaluation of medium components by Plackett-Burman experimental design and kinetic modeling of lipase production by Pseudomonas fluorescens. 2007, 6: 469-478.Google Scholar
  23. Reddy BR, Narasimha G, Babu GVAK: Cellulytic activity of fungal culture. J Sci Ind Res 1998, 57: 617-620.Google Scholar
  24. Saravanan S, Muthuvelayudham R, Viruthagiri T: Application of statistical design for the production of cellulase by Trichoderma reesei using mango peel. Enzyme Res 2012, 2012(157643):7.Google Scholar
  25. Schmid FX: Optical spectroscopy to characterize protein conformation and conformational changes. In Protein Structure: a Practical Approach. Edited by: Creighton. New York: Oxford University Press; 1997:261-297.Google Scholar
  26. Schwarz WH, Bronnenmeier K, Grabnitz F, Staudenbauer WL: Activity staining of cellulases in polyacrylamide gels containing mixed linkage β-glucans. Anal Biochem 1987, 164: 72-77. 10.1016/0003-2697(87)90369-1View ArticleGoogle Scholar
  27. Sukumaran RK, Singhania RR, Pandey A: Microbial cellulases-production, application and challenges. J Sci Industrial Res 2005, 64: 832-844.Google Scholar
  28. Sultana S: Isolation of cellulolytic microorganisms and their activities, M. Phil. Thesis, Institute of Biological Science. University of Rajshahi, Bangladesh; 1997.Google Scholar
  29. Teather RM, Wood PJ: Use of Congo red-polysaccharide interactions in enumeration and characterization of cellulolytic bacteria from the bovine rumen. Appl Environ Microbiol 1982, 43: 777-780.Google Scholar
  30. Vyas A, Vyas D, Vyas KM: Production and optimization of pretreated groundnut shell by Aspergillusterreus AV49. J Sci Ind Res 2005, 64: 281-286.Google Scholar
  31. Wallace BA, Lees JG, Orry AJW, Lobley A, Janes RW: Analysis of circular dichroism spectra of membrane proteins. Protein Sci 2002, 12: 875-884. Comp. Biochem. Physiol. B,1–6View ArticleGoogle Scholar
  32. Zambare V, Christopher L: Statistical analysis of cellulase production in Bacillus amyloliquefaciens . ELBA Bioflux 2011, 3: 38-45.Google Scholar

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© Pirzadah et al.; licensee Springer. 2014

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