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Statistical optimization of lignocellulosic waste containing culture medium for enhanced production of cellulase by Bacillus tequilensis G9

  • Mudasir A. Dar
  • Kiran D. PawarEmail author
  • Jyoti M. Chintalchere
  • Radhakrishna S. PanditEmail author
Article
  • 164 Downloads

Abstract

The ever increasing energy demands of modern civilization and rapidly dwindling fossil fuels point towards a renewable substitute like biofuels. However, higher costs associated with biofuel productions is the major bottleneck for its commercialization. The present study demonstrates the use of a statistical approach called response surface methodology (RSM) to investigate the optimum parameters for maximum production of cellulase by Bacillus tequilensis G9. The Plackett–Burman design (PB) of the RSM analysis indicated grass straw (GS) concentration, pH, FeSO4, inoculum, MgSO4, incubation period and NH4Cl as significant variables that influence the cellulase production. Further, to propose the best medium for the maximum production of cellulase by B. tequilensis G9, the most influential parameters, namely concentrations of GS as substrate, FeSO4, pH, inoculum size, etc. were fine-tuned by central composite design (CCD) involving four factors and five levels. The CCD analysis demonstrated 8% substrate concentration, 1.5% of inoculum along with 10 ppm FeSO4 and a pH of 5.5 in media as optimum conditions for highest enzyme production. The field emission scanning electron microscopic analysis of the treated GS showed structural alterations depicting significant deconstruction caused by B. tequilensis G9. The yield of the partially purified cellulase proteins were found to be 21% revealing molecular mass between 30 and 97 kDa. The enhanced cellulase production by B. tequilensis G9 demonstrated in our study brands its applications in many industrial processes like biorefinery, biofuels, etc.

Keywords

Bacillus tequilensis G9 Cellulase production Plackett–Burman design Central composite design RSM Saccharification 

Introduction

The lignocellulosic biomass (LCB) of agriculture, forestry, and municipal origin is a potentially inexpensive and renewable feedstock copiously available in nature. The annual production of such LCB is estimated at 1.3 billion tons globally [1]. Cellulose, the major polysaccharide in LCB, could possibly replace the diminishing fossil fuels, thereby becoming increasingly important in the current era of energy crisis. Unfortunately, cellulose is under-exploited due to its complex structure and often burnt after harvesting the crops leading to disastrous consequences of air and environmental pollutions. One of the hopeful methods to utilize this energy source efficiently is to hydrolyze the polymer using appropriate conversion technologies. Hitherto, several pretreatment technologies such as physical and chemical approaches have been proposed for the hydrolysis of LCB, but these methods are expensive and pose negative impacts on the environment [2]. Thus, in order to utilize LCB and circumvent waste pollution, one of the most important approaches is to find appropriate cellulase enzymes to hydrolyze the biomass into glucose, which could serve as a readily usable product and precursor for sugars, ethanol, organic acids, etc.

Bioconversion of cellulose to glucose through cellulolytic enzyme systems has, therefore, been proven as a cheaper and sustainable process. The biological conversion of cellulose involves the hydrolytic action of a multi-component enzyme system called cellulosome, which represents the key step for biomass deconstruction [3]. The prominent applications of this complex are many, such as biorefinery, feedstock generation, textiles, pulp industry, detergent, pharmaceutical, nutrition, etc. [4]. Moreover, the global market for these enzymes is increasing day by day [5] and has reached $4.4 billion in 2015. Therefore, continuous prospects to search for the cost-effective cellulase producing microbes have received much attention from the scientific world during the past two decades. Cellulases being inducible enzymes are distributed throughout the biosphere, synthesized by some animals, protozoa, fungi, and bacteria [6]. However, the production costs associated with these enzymes is a major tailback of the bioconversion processes [2]. In the current situation of growing economic demands, it is, therefore, very essential to reduce the expenditure of the production process.

Interestingly, bacterial cellulose utilization is growth associated and influenced by the interaction of various factors that drive the biogeochemical carbon flow [3] in the environment. Further, the production of cellulases is closely controlled in microorganisms and for improving its productivity these controls can be ameliorated. The yield of cellulases depends on several factors such as pH, temperature, carbon source, nitrogen compounds, substrate concentration, inoculum size, inducers, media additives, and time [7]. It is well known that the enzyme’s cost is one of the primary factors that determine the economics of any industrial process [8]. Therefore, the evaluation of nutritional and environmental factors for enzyme production plays a pivotal role in the success of process technology. A cost-effective enzyme production for the establishment of technology can be achieved through the optimization of process variables and fermentation conditions [9].

Recently, tremendous endeavors have been put forth by many researchers to improve the conditions for maximum cell density and production of metabolites [10] by employing conventional methods that are laborious and time-consuming. Moreover, these methods do not explain the combined interactive effects of fermentation parameters [11, 12]. Thus, the development of an economical medium requires the selection of best-suited carbon, nitrogen, phosphorus, potassium and trace elemental sources. These nutritional requirements of organisms can be manipulated by conventional or statistical methods. Therefore, factorial-cum-statistical methods have been intended to produce fast and reliable outputs. The statistical methods shortlist significant nutrients that help understand the interactions among process parameters at different concentrations. These methods tremendously reduce the total number of experiments resulting in saving time, glassware, chemicals and manpower [13]. Similarly, response surface methodology (RSM) is a collection of experiments, mathematical methods and statistical inferences that evaluate the combined effect of all the factors participating in the fermentation process [14]. Moreover, three-dimensional (3D) plots for response surface provide oversimplified visualization of the parameter interactions in a better manner [15]; consequently, optimal variables that give rise to desirable results can be determined.

The future industrial applicability of the potential cellulolytic bacterium, Bacillus tequilensis G9, which was previously isolated from the gut fluid of giant African land snail, Achatina fulica, requires an accurate optimization of its growth parameters. Therefore, the main objective of the present study is to explore the most favorable culture conditions required by this cellulolytic bacterium for maximum production of cellulase. To achieve this, RSM with Plackett–Burman (PB) and central composite designs (CCD) were employed to get a regression equation for forecasting the optimum growth conditions alongside a cost-effective approach. Further, we employed field emission scanning electron microscopy (FESEM) technique to evaluate the effect of bacterial treatment on the topography of grass straw (GS) used as a substrate for cellulase production.

Materials and methods

Microorganisms and pretreatment of the substrate

The cellulolytic bacterium, B. tequilensis G9, studied in the present work was previously isolated by enrichment method from the gut fluid of giant African land snail, A. fulica [16]. The isolate was maintained as 20% glycerol stocks at − 80 °C as well as in culture slants for further investigations. The bacterium was periodically revived and analyzed for its cellulose degrading potential on Carboxy methyl cellulose (CMC) -agar plates. The lignocellulosic material GS was collected from the local agricultural fields of Kolhapur, Maharashtra, India, and prepared initially by treating with mild alkaline (0.1N NaOH) solution followed by thorough washing with sterile distilled water (SDW) until a neutral pH. The air-dried GS was then ground, passed through a 5-mm nylon mesh to get particles of uniform size and stored under dry conditions until further use. The pretreated GS was used as the sole source of carbon to induce the cellulase production by B. tequilensis G9. The media used in the study was Berg Minimal Salt (BMS) medium comprising CaCl2·2H2O, 0.5 g; FeSO4·7H2O, 0.02 g; K2HPO4, 0.5 g; MgSO4·7H2O, 0.02 g; MnSO4·7H2O, 0.02 g; NaNO3, 2 g per 1000 ml of solution [17]. The media and reagents were autoclaved at 121 °C for 15 min before use. All other chemicals and reagents used in the study were either of molecular or analytical grades unless defined.

Optimization and experimental design

The growth conditions such as incubation time, temperature, pH, inoculum size and substrate concentrations were initially optimized by varying one-variable-at-a-time (OVAT) approach. To achieve this, a single colony of the bacterium B. tequilensis G9 was inoculated into freshly prepared 50 ml LB broth, incubated overnight at 37 °C and used as primary inoculum in subsequent experiments. To study the incubation time required for cellulase production, 100 ml BMS medium containing 1 g (w/v) GS as the sole source of energy was inoculated with 1 ml of (OD600 0.4) primary inoculum and incubated on an orbital-shaker agitating at 37 °C and 150 rpm. The enzyme extraction was carried out by centrifuging (10,000 rpm, 10 min., 4 °C) the small sample aliquots at regular intervals of 24, 48, 72, 96, 120 h and the supernatant obtained was designated as crude enzyme extract. The enzyme assays were performed as described in the “enzyme assays” section. To study the effect of parameters such as temperature and substrate concentration, they were varied in the production medium in the ranges of 20–50 °C and 1–7%, respectively, and incubated at 37 °C, 150 rpm for 120 h. The optimization of pH (2.0–8.0) was carried out in Erlenmeyer flasks (250 ml) containing 1% of GS in BMS media. The pH of the media was adjusted before autoclaving at 121 °C for 15 min. The sterilized media were then inoculated (0.5–1.5%) with B. tequilensis G9 and incubated at varying conditions based on respective experiments.

Plackett–Burman design

The screening of the 11 different variables for cellulase production were carried out using PB experimental design [18] of Design expert software version 10 (State-Ease Minneapolis, USA). Different physical parameters, such as nitrogen source and mineral salts that affect the yield of enzyme production were analyzed. The factors included inoculum (A), substrate concentration (B), temperature (C), incubation period (D), pH (E), and other components of the BMS medium like NH4Cl (F), K2HPO4 (G), CaCl2 (H), NaNO3 (I), MgSO4 (J), and FeSO4 (K) in varying concentrations as per the PB design. According to experimental design, each factor was checked at two levels, a low level (− 1) and a high level (+ 1) as indicated in the experimental design shown in Table 1. A design of 12 experiments was created and responses were determined after performing the experiments. Responses were measured in terms of endoglucanase activity using CMC as a substrate.
Table 1

Media components and their variables used in Placket-Burman design for Cellulase production by Bacillus tequilensis G9

Variable

Component of medium

+ Value

− Value

A

Inoculum (%)

1.5

0.5

B

GS concentration (%)

7

4

C

Temperature (°C)

50

30

D

Incubation (days)

5

3

E

pH

5

2

F

Nitrogen (%)

0.15

0.05

G

K2HPO4 (%)

0.05

0.01

H

CaCl2 (%)

1.0

0.1

I

NaNO3 (%)

0.3

0.1

J

MgSO4 (ppm)

30

10

K

FeSO4 (ppm)

30

10

Central composite design analysis of RSM

To locate the true optimum concentration of significant variables described by PB design for maximum enzyme production, a CCD with five coded levels was conducted in the subsequent phase of the statistical approach. For statistical optimization of cellulase production, substrate concentration (X1), pH (X2), FeSO4 (X3) and inoculum size (X4) were chosen as the independent variables and cellulase activity as the dependent variable. The RSM using four-factor, five-level CCD generated a total of 30 different combinations, constructed by design expert software (Table 2). The quality of fit for the polynomial equation was expressed by the coefficient R2 while its statistical significance was determined using the F test. The significance of the effect of each variable on enzyme production was measured using a t test. The data were interpreted to obtain the response surface in the form of contours and 3D plots showing the interaction of the factors. The polynomial quadratic equation fitted to evaluate the effect of each independent variable to the response was as follows:
$$\begin{aligned} Y &= \beta_{0} + \, \beta_{1} X_{1} - \, \beta_{2} X_{2} - \, \beta_{3} X_{3} + \, \beta_{4} X_{4} - \, \beta_{5} X_{1} X_{2} \\&\quad+ \, \beta_{6} X_{1} X_{3} + \, \beta_{7} X_{1} X_{4} + \, \beta_{8} X_{2} X_{3} - \, \beta_{9} X_{2} X_{4}\\&\quad - \, \beta_{10} X_{3} X_{4} - \, \beta_{11} X_{1}^{2} + \, \beta_{12} X_{2}^{2} - \, \beta_{13} X_{3}^{2} - \, \beta_{14} X_{4}^{2} . \end{aligned}$$
(1)
Table 2

Coded and actual values of the effective variables for the central composite design

Variable

Symbol code

Actual and coded values

− 2

− 1

0

+ 1

+ 2

GS Concentration (%)

X1

5

6

7

8

9

pH

X2

3

4

5

6

7

FeSO4 (PPM)

X3

0

5

10

15

20

Inoculum (%)

X4

0.5

1.0

1.5

2.0

2.5

where Y is the predicted response, X1 − X4 are the coded independent input variables, β0 is the intercept term, β1 − β4 are the linear coefficients showing linear effects, β5 − β8 are the quadratic coefficients showing squared effects and β9 − β14 are the cross-product coefficients showing interaction effects.

Enzyme assays

The sample aliquots obtained from every experiment were centrifuged and the supernatants obtained were treated as crude enzyme extracts. The CMCase activity, also known as endoglucanase activity, was assayed using DNSA method [19] with minor changes. The endoglucanase activity was determined by incubating reaction mixture of 1% CMC (w/v) in 250 μl of sodium citrate buffer (SCB, pH 5.4) with 250 μl of enzyme extract at 50 °C for 15 min. The reactions were terminated by the addition of 750 μl DNSA reagent followed by boiling for 15 min in a boiling water bath and cooled by adding water for color stabilization. The absorbances were measured at 550 nm using a 96-well micro plate reader (Multiskan Ex spectrophotometer, Thermo Scientific, Finland). Enzyme activities were calculated using glucose as standard and expressed in international unit (IU) where 1IU is the amount of endoglucanase required to liberate 1 μmol of reducing sugar per ml per minute under the standard assay conditions.

Analysis of the hydrolyzed biomass

The effect of bacterial treatment on GS degradation was characterized by using FESEM and calculation of substrate degradation ratio. For this purpose, BMS medium containing GS (1% w/v) was seeded with 1 ml of fresh culture (OD600: 0.4) of B. tequilensis G9, then agitated at 150 rpm in an orbital shaker by maintaining the temperature to 37 °C for a period of 20 days. The culture media was regularly observed and monitored visually for bacterial growth evident by increased turbidity. After proper growth, culture broth was centrifuged by 5000 rpm for 10 min at room temperature (RT). For FESEM analysis, pellet containing substrate with bacterial biomass was then washed with distilled water, dried overnight at 60 °C and subjected to FESEM analysis in a Nova NanoSEMNPEP 303 (FEI technologies, Oregon, USA). The sample preparation for FESEM analysis was carried out as reported earlier [16]. The samples were mildly coated with gold (100 Å) using argon gas atmosphere to facilitate proper imaging at 5 kV. However, the electron microscopic analyses of the control samples (without treatment of bacteria) were recorded in a Quanta 600 FEG SEM (ThermoFisher Scientific, USA) at 10 kV using a secondary electron detector system (EverhardtThornley detector, ETD).

The substrate degradation ratio was determined as described previously [20]. Briefly, the residual biomass obtained from centrifugation was washed with acetic-nitric acid reagent (1 M) followed by water to remove the non-cellulosic materials. The biomass was then treated with absolute ethanol for 20 min for complete removal of the bacterial residues. Finally, the dry weight of the residual GS was measured and degradation ratio (%) of the substrate was calculated using the following formula:
$${\text{\% Degradation}} = \frac{{{\text{Initial weight of substrate}} - {\text{Weight of substrate after treatment}}}}{{ {\text{Initial weight of substrate}}}} \times 100$$
(2)

Production, partial purification and characterization of cellulase

The media used for cellulase production by B. tequilensis G9 was an optimized BMS medium containing the desired concentration of GS as an inducing substrate. The cellulase was produced by seeding culture broth with 1 ml (1.2 × 103 CFU/ml) of freshly grown B. tequilensis G9 inoculum. The inoculated medium was incubated for 5 days at 37 °C by agitation at 150 rpm. After the proper growth of B. tequilensis G9 as evident by increased turbidity and highest CMCase activity, the broth was centrifuged at 10,000 rpm, 4 °C for 15 min to obtain crude extract. Thereafter, the crude enzyme was filtered through a 0.45-μm membrane filter, subjected to 70% ammonium sulfate [(NH4)2SO4] precipitation for 24 h at 4 °C and then subsequently centrifuged at 17,000 rpm for 25 min at 4 °C. The enzyme obtained in the form of pellet was re-suspended in phosphate-buffered saline (PBS, pH 7.4) and dialyzed overnight against the same buffer. This dialyzed enzyme which is referred hereafter as partially purified (PP) cellulase was used to estimate the enzyme activity and processed further. The molecular weights of the PP proteins were determined by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS–PAGE) technique according to Laemmli’s [21] method using 10% and 5% of resolving and stacking gels, respectively. The molecular weights were compared with standard protein markers (range 3.5–97.4 kDa) from Genei (Genei, Bangalore, Pvt. Ltd., Bangalore, India) comprised of insulin chains (3.5 kDa), aprotinin (6.5 kDa), lysozyme (14.4 kDa), soybean trypsin inhibitor (21.5 kDa), carbonic anhydrase (29 kDa), ovalbumin (43 kDa), serum albumin (66 kDa) and Myosin from phosphorylase b (97.4 kDa). Prior to loading, samples were preheated at 60 °C for 10 min in gel loading buffer. Similarly, the activity staining (zymogram analysis) was performed using 8% cross-linked polyacrylamide gel. The electrophoretic separation was performed at a voltage of 100 V, 23 mA for an average time of 8–10 h at 4 °C except for SDS–PAGE which was run at RT. For zymogram, the gels were stained with Congo red solution (0.1%, w/v) for 20 min. followed by destaining with 1M NaCl solution for 10 min to reveal the light reddish halos as a mark of cellulase activity around the protein bands indicating hydrolysis of the substrate. The zymogram gels were also sensitized by a brief wash of 1% Acetic acid (v/v) solution.

Model validation and statistical analysis

The mathematical model generated using RSM implementation was validated by conducting various checkpoint experiments. The data obtained from experimentation were compared with the predicted ones and the prediction error was calculated. All assays were prepared and analyzed in triplicates and the mean values were used for further calculations. Analyses of data were carried out using Microsoft office suite (2013) and SPSS software version 22 (IBM SPSS, NY, USA) wherever applicable. The results of the experimental design were analyzed and interpreted using Design-Expert version 10 (Stat-Ease Inc., Minneapolis, MN, USA) statistical software.

Results

Placket–Burman design

The effects of different parameters on the cellulase production by B. tequilensis G9 were initially recognized by adopting OVAT approach and subsequently, the interaction of potential variables was studied by using PB design and CCD of the RSM technique. For the maximum production of cellulase enzyme, the most influential and relevant variables were identified by employing the PB design. For PB design, all runs were done in duplicates and the CMCase activity estimated for each experimental run is represented in Table 3.
Table 3

Experimental plan of the design with the observed and predicted response for the production of CMCase by Bacillus tequilensis G9 using GS as inducing substrate

Run

A

B

C

D

E

F

G

H

I

J

K

Response (IU/ml extract) ± SD

Observed

Predicted

1

+

+

+

+

+

+

24.82 ± 0.8

25.46

2

+

+

+

+

+

+

29.17 ± 0.6

28.10

3

+

+

+

+

+

32.65 ± 2.3

31.9

4

+

+

+

+

+

+

17.66 ± 1.5

18.97

5

31.66 ± 3.3

31.40

6

+

+

+

+

+

+

16.68 ± 0.3

16.57

7

+

+

+

+

+

+

24.77 ± 0.5

24.89

8

+

+

+

+

+

+

25.54 ± 2.2

25.95

9

+

+

+

+

+

+

24.18 ± 0.6

23.40

10

+

+

+

+

+

+

26.67 ± 3.6

28.27

11

+

+

+

+

+

+

23.68 ± 3.6

23.44

12

+

+

+

+

+

+

11.98 ± 0.5

11.74

SD Standard deviation

The final model equation using PB design for CMCase activity in terms of coded factors may be represented as follows:
$$\begin{aligned} {\text{CMCase activity}} &= \, + 3 9. 4 5+ 1. 9 4 {\text{A}} + 4. 9 6 {\text{B}} + 1. 3 4 {\text{D}}\\&\quad + 3. 8 4 {\text{E}} - 1 8. 1 2 {\text{H}} + 1. 8 7 {\text{K}} + 2. 2 1 {\text{L}} . \end{aligned}$$
(3)
where A, B, D, E, H, K and L are inoculum size, GS concentration, incubation period, pH, CaCl2, MgSO4 and FeSO4, respectively.
The observed data revealed that the 3rd experimental run (Table 3) of PB design had maximum cellulase activity (32.65 IU/ml extract), whereas 12th experimental run has minimum cellulase activity of 11.98 IU/ml extract. The maximum activity shown by the model experiment 3 was 1.5-fold higher than the conventional OVAT method (21.19 IU/ml extract). The Model F-value was observed to be 450.17 which clearly implied that this model was significant. This value also indicated that the chance of occurrence of this much large “Model F-Value” as the noise was only 0.01%. Model terms are significant as “Prob > F” values were found less than 0.0500. In this model, A, B, D, E, K and L were significant model terms. The observed values greater than 0.1000 indicates the model terms were not significant. However, the variables like nitrogen source (F), K2HPO4 (G), CaCl2(H) and NaNO3 (I) showed a negative effect on cellulase production as depicted from Pareto chart (Fig. 1). Therefore, these variables were kept to their lowest concentrations in the subsequent studies.
Fig. 1

Pareto chart showing the positive or negative effect of different components of the media on cellulase production by Bacillus tequilensis G9

Central composite design

The most significant media components based on the Pareto chart of PB design were further employed as variables in the experimental design matrix of CCD using RSM to study their levels and interactions for cellulase production. The substrate concentration, pH, inoculum size and FeSO4 were taken into account for designing the experimental matrix of CCD. Each variable was studied at five different levels and all the response observations in accordance with CCD experimental design matrix are shown in Table 4. The following quadratic regression equation explained the CM Case production by taking into account the significant factors and is represented below:
$$\begin{aligned} {\text{CMCase}}& = + 39.52 + 0.23X_{1} - 0.22X_{2} - 0.37X_{3} + 2.24X_{4}\\&\quad - 0.88X_{1} X_{2} - 0.37X_{1} X_{3} - 0.61X_{1} X_{4} + 1.13X_{2} X_{3} \\&\quad+ 0.66X_{2} X_{4} + 0.23X_{3} X_{4} + 0.56X_{1}^{2} - 0.39X_{2}^{2}\\&\quad - 0.89X_{3}^{2} - 0.022X_{4}^{2} . \hfill \\ \end{aligned}$$
(4)
where X1, X2, X3 and X4 are GS concentration, pH, inoculum size and FeSO4, respectively.
Table 4

Full experimental Central composite design with coded level of variables and the response functions

Run

CMC conc. (X1)

pH (X2)

FeSO4 (X3)

Inoculum (X4)

CMCase activity

Sugar content

%

Unit

ppm

%

(IU/ml extract) ± SD

(μg/ml) ± SD

1

7

5

10

1.5

40.75 ± 3.1

103.75 ± 1.3

2

6

4

5

2

39.73 ± 0.5

124.26 ± 0.1

3

7

5

10

1.5

36.93 ± 1.3

100.96 ± 0.7

4

7

7

10

1.5

36.69 ± 2.9

99.89 ± 0.8

5

6

4

5

1

36.69 ± 0.5

74.96 ± 4.2

6

7

5

10

1.5

39.20 ± 5.0

93.58 ± 2.5

7

8

4

5

2

43.71 ± 4.2

122.36 ± 2.6

8

8

4

15

2

38.36 ± 3.8

125.05 ± 1.8

9

6

4

15

2

42.0 ± 1.0

118.07 ± 5.6

10

7

5

0

1.5

34.98 ± 7.6

87.65 ± 3.9

11

8

6

5

1

32.19 ± 2.5

64.64 ± 3.6

12

7

5

10

2.5

42.73 ± 1.3

118.53 ± 9.6

13

6

6

15

1

35.15 ± 1.3

68.3 ± 0.8

14

6

6

15

2

43.04 ± 2.2

103.78 ± 3.4

15

9

5

10

1.5

43.12 ± 1.2

106.98 ± 3.2

16

7

3

10

1.5

38.07 ± 2.1

112.25 ± 3.0

17

6

6

5

1

37.48 ± 0.8

74.26 ± 1.1

18

7

5

10

0.5

28.5 ± 3.5

38.27 ± 0.8

19

7

5

10

1.5

39.76 ± 1.5

96.93 ± 1.2

20

8

4

15

1

33.93 ± 3.0

74.25 ± 1.9

21

8

4

5

1

43.84 ± 2.7

76.9 ± 1.5

22

7

5

10

1.5

40.17 ± 2.3

105.52 ± 0.3

23

6

4

15

1

35.44 ± 3.

67.62 ± 0.9

24

5

5

10

1.5

39.25 ± 0.2

84.54 ± 4.8

25

8

6

5

2

40.07 ± 1.0

116.01 ± 0.9

26

7

5

20

1.5

35.82 ± 2.5

98.65 ± 4.9

27

6

6

5

2

44.02 ± 0.7

120.49 ± 0.4

28

7

5

10

1.5

40.33 ± 1.2

105.24 ± 1.1

29

8

6

15

2

40.73 ± 1.6

115.3 ± 2.5

30

8

6

15

1

38.60 ± 1.9

76.82 ± 1.5

SD Standard deviation

The analysis of variance (ANOVA) and coefficient of variance were calculated to verify the significance of the model. The Model F-value was found to be 22.22 which implied that this model is significant (Table 5). The chance of occurrence of this much large “Model F-value” due to noise was again 0.01% only. The values of “Prob > F” were observed to be less than 0.0500 indicating significant model terms. In this case, X1, X2, X3, X4, X1X2, X1X3, X1X4, X2X3, X2X4, X3X4, X12, X22, X32 and X42 were found to be significant model values. The “Lack of Fit F-value” of 1.23 clearly implied that it was not significant when compared to the pure error. The chance of observation of “Lack of Fit F-value” this large due to noise was 53.4% only. The “Pred R2” of 0.965 was in reasonable agreement with the “Adj R2” value of 0.991. The “Adeq Precision’’ is a measure of the signal-to-noise ratio. A ratio greater than four is generally desired for a significant model. In the present case, a ratio of 19.725 indicated significant adequate signals. Therefore, the model can be used to navigate the design space. The model showed standard deviation (SD), mean, and predicted the residual sum of squares (PRESS) values of 4.35, 87.55 and 1278.87, respectively. The correlation coefficient (R2) of 0.9651, close to 1 indicated significant agreement between the model predicted values and CMCase activity.
Table 5

ANOVA results for CMCase production under response surface quadratic model and model coefficients estimated by multiple linear regressions

Source

Sum of squares

df

Mean square

F value

p value Prob > F

 

Model

5883.54

14

420.25

22.22

< 0.0001

Significant

X1-subs conc.

13.50

1

13.50

0.71

0.4114

X2-pH

22.43

1

22.43

1.19

0.2933

x3-FeSO4

2.77

1

2.77

0.15

0.7071

X4-inoculum

5518.24

1

5518.24

291.81

< 0.0001

X1X2

3.88

1

3.88

0.21

0.6570

X1X3

14.55

1

14.55

0.77

0.3942

X1X4

22.61

1

22.61

1.20

0.2914

X2X3

8.91

1

8.91

0.47

0.5029

X2X4

130.07

1

130.07

6.88

0.0192

X3X4

0.18

1

0.18

9.328E-003

0.9243

X12

6.70

1

6.70

0.35

0.5606

X22

1.56

1

1.56

0.082

0.7780

X32

31.57

1

31.57

1.67

0.2159

X42

117.29

1

117.29

6.20

0.0250

Residual

283.66

15

18.91

   

Lack of fit

201.48

10

20.15

1.23

0.4351

Not significant

Pure error

82.18

5

16.44

   

Cor total

6167.19

29

    

df Degree of Freedom

Further, the 3D response surface plots (Fig. 2) were drawn to describe the relationship and interaction between independent variables and the response variable. The 3D response surface plots revealed the interactive effect of two independent variables on CMCase production by keeping the third variable at basal level (0). In all the plots, it was quite evident that pH, substrate concentration and inoculum size had a direct effect on the enzyme production, while FeSO4 was found less important for the production of CMCase (Fig. 2a–f). However, it was observed that CMCase activity was majorly dependent upon substrate concentration and pH as compared to inoculum and FeSO4 concentration (10 ppm) as displayed by 3D plots. Among the four tested variables, it could be inferred that pH of the medium used in the experiments showed dominance over the enzyme activity. In the present study, pH affected the enzyme production up to significant levels and optimum pH for CMCase production was found to be 5.5. Though higher concentrations of FeSO4 did not change the activity (Fig. 2b, d, f), it rather depicted a negative effect on CMCase production.
Fig. 2

Three dimensional (3D) surface plots representing cellulase production from culture broth of Bacillus tequilensis G9 as affected by cultural conditions (a) pH and substrate, (b) FeSO4 and substrate, (c) inoculum size and substrate, (d) FeSO4 and pH, (e) inoculum size and pH, (f) inoculum size and FeSO4

The experiments were performed to verify and confirm the accuracy of the model (Fig. S1) which revealed an increase of CMCase activity from 21.19 IU/ml extract by manual method (OVAT) to 44.02 IU/ml extract under optimized (RSM) conditions demonstrating over twofold enhancement from basal level production. As the main aim of this study was optimumizing the concentrations and values of different variables for higher production of cellulase in a cost-effective way. Therefore, RSM predicted the optimum experimental conditions required for maximum cellulase production by B. tequilensis G9 as pH (5.5), substrate concentration (8%), inoculum size (1.5%), and FeSO4 (10 ppm). Additionally, the desirability of the optimum experimental design was 90.58% which is quite acceptable.

Analysis of hydrolyzed GS

The FESEM analysis of the GS treated with B. tequilensis G9 showed many structural alterations implying hydrolysis of the cellulose content caused by the bacterium. The electron micrograph (Fig. 3) depicts adhesion of bacterial cells to the GS causing disruption in terms of loosening the fibers and formation of tunnels on the surface of the treated substrate. The bacterial treatment resulted in the deconstruction of cellulose chains due to enzyme action as shown in Fig. 3b. These surface alterations caused by B. tequilensis G9 to the substrate during incubation are imperative for bioconversion of polymer chains into value-added products.
Fig. 3

Field emission scanning electron micrograph (FESEM) of the GS as substrate without treatment (a) and after treatment (b) with B. tequilensis G9 indicating utilization for cellulase production by the bacterium. Arrows indicate the smooth and undisturbed surfaces of the substrate in untreated substrate (a) while depicting tunnels created by the bacteria on treated one (b)

The B. tequilensis G9 showed potential growth on GS that was evident from FESEM analysis and a reduction in dry weight of the treated substrate (Fig. S2). The final weight of the GS after 20 days of bacterial treatment was reduced from 1000 ± 04 mg to 670 ± 10 mg indicating 33% degradation which is quite significant for such a short duration. However, the degradation capacity of B. tequilensis G9 is much higher than our previously reported isolates [17].

Partial purification and characterization of cellulase

Since RSM design illustrated the highest cellulase production on 5th day of incubation on GS, we considered it an optimum incubation period for higher yields. The B. tequilensis G9 produced cellulase enzyme extracellularly, which was precipitated by 70% ammonium sulfate salt from the cell-free extract. The salting-out resulted in a yield of 21.75% of the PP cellulase. The enzymatic activity of the PP cellulase was increased from 7.42 ± 0.62 IU/ml extract (in case of crude extract) to 26.50 ± 0.90 IU/ml extract indicating 3.5-fold increment due to purification step. The electrophoretic separation of the precipitated cellulase showed multiple bands ranging in molecular mass from 30 to over 97 kDa (Fig. 4). The zymogram activity determined by blotting the enzyme in native PAGE gel against the CMC-agar slab at 50 °C indicated the presence of several protein bands in partially precipitated extracts. The zymogram analysis (light red zone indicating CMC digestion by cellulases) showed the cellulase activity of the proteins in the range of 30–66 kDa demonstrating that cellulases of B. tequilensis G9 had size of this molecular range.
Fig. 4

The SDS-PAGE and zymogram analyses of B. tequilensis G9 proteins depicting molecular weight and activity of the extracted proteins respectively. (a) Lane1; Standard marker; Lane 2; partially purified proteins including cellulase, (b) Lane 3; depicts the activity staining of the partially purified cellulase

Discussion

The utility of cellulolytic microbes is considered as an environmental friendly approach to the conversion of lignocellulosic waste materials into useful products. These microbes catalyze the hydrolysis of LCB due to their inherent properties ameliorating their survival even under unfavorable conditions. Microorganisms secrete a class of enzymes called cellulases when feeding on the LCB, converting it into value-added commodity products like reducing sugars, glucose, etc. The utility cost of enzymatic hydrolysis is very low as compared to chemical hydrolysis because enzyme hydrolysis is usually conducted at mild conditions, without any pollution problem.

Several physical and chemical factors such as inoculum size, pH and temperature, presence of inducers, medium constituents, incubation period are known to affect the bacterial growth thereby influencing cellulase production or activity [3, 22]. The pH of the culture medium is a crucial factor that significantly affects the enzyme production because slight changes in pH interrupt the enzyme transport system across the cell membranes. Likewise, temperature alters the physical properties of the cell membrane which in turn affects bacterial growth and ultimately cellulase activity of the organism [23]. On the contrary, the production of extracellular cellulase has been shown to be affected by the inhibition of growth by different carbohydrate and nitrogen sources [22]. Therefore, the accurate optimization of media components is needed to study the interactive effects of these parameters on bacterial growth.

In our study with PB design, the maximum cellulase production of 32.65 IU/ml extract was observed in the case of experimental run 3 (PB design) that showed significant effect of inoculum size (1.5%), pH (5.0), substrate concentration (7%), incubation period (5 days), NH4Cl (0.15%) and 10 ppm concentration each of MgSO4 and FeSO4. The longer incubations make the bacterium capable of taking up maximum nutrients required for necessary cellular, metabolic or physiological changes prior to each binary fission. The enhancement in cellulase production by B. tequilensis G9 from basal level (OVAT approach) of 21.98 IU/ml extract to optimized 32.65 IU/ml extract in PB design followed by 44.02 IU/ml extract under RSM analysis was much higher than that of the optimal cellulase activities of Bacillus circulans (4.80 IU/ml), Bacillus subtilis (4.64 IU/ml), Clostridium thermocellum (5.32 IU/ml), at pH 6 after incubation at 40 °C [24]. Our results of cellulase activity were also higher than the activities observed by many authors [25, 26, 27] using PB design and RSM for optimization of cellulase production with Cellulomonas flavigena after 48 h. The probable reason for this higher activity could be variation in culture conditions and media used by the researchers previously. The developed model was found to be very effective in optimizing the selected media components as evident from the closeness of the observed R2 = 0.996 value (Fig. S3) with that of the predicted one (Predicted R2 = 0.988). As a general rule, closer the R2 value to 1, the model is considered stronger to predict the responses [28]. The observed R2 value was also similar to many earlier reports [29, 30].

The pH requirements of bacteria have been reported to vary among different groups depending upon the nature of the isolation source [31]. Generally, Bacillus spp. are known to grow on different buffer systems producing enzymes that are stable over a wide range of pH. However, the pH values below and above the optimum range, vital for enzyme production, leads to slower growth along with the reduction in enzyme secretions. Sometimes, the enzymes produced also show reduced activity or inactivation due to pH alterations. Further, the varying concentrations of different components of culture media bring significant changes in extracellular cellulase production. Therefore, the optimization of individual components by RSM has been considered as a potential technique to reduce the production costs of cellulase by microorganisms [3, 32]. Consequently, RSM is now well recognized as a statistical tool that employs cost-effective experimental design and offers statistical permutations and evaluations [33, 34] for enhanced production of desired products. Moreover, valid optimization of microbial cellulase production is possible with the implication of the 3D plots which allow direct visualization of individual and interactive influence of variables [35]. The shapes of 3D plots, such as circular or elliptical, also depict whether the mutual interactions between variables are significant or not. Generally, a circular plot indicates insignificant interactions between corresponding variables, whereas the significant interactions between corresponding variables are represented by an elliptical nature of the contour plots [35, 36]. Therefore, the 3D response surface and 2D contour plots are deemed as graphical representations of the regression equation [37]. Through these 3D response surface plots, it is very easy and convenient to understand interactions between two variables and also to find their optimum levels. In this investigation, it was obvious from the 3D plots that maximum cellulase activity occurred possibly with moderate inoculum sizes and substrate concentrations. These observations concur with earlier reports for cellulose-degrading bacteria that demonstrated the significance of organic nutrients [32, 38, 39, 40, 41] used for the enrichment of such bacteria.

In successive optimization steps such as PB design, the obtained enhancement of cellulase activity was about 1.54-fold in comparison to that obtained under un-optimized medium components. In fact, the optimization of medium components contributed more to the improved production of cellulase by B. tequilensis G9. The results obtained in this study supported the conclusion of other researchers by further increment (2.07-fold) in activity with RSM design [32, 42, 43] suggesting that concentration of major medium components is the principal governing factor for cellulase production. However, the sturdy crystalline structure of the cellulose molecules and hemicelluloses of GS limit the accessibility of plant biomass to cellulolytic enzymes [44]. The degradation effect exerted by B. tequilensis G9 on the plant biomass depicted its intricate enzyme activity with lignocellulolytic effect and saccharification potential. A literature search clearly indicates that many Bacillus strains are good producers of extracellular enzymes [4, 45]. The cellulolytic activity of 10 IU/ml shown by Bacillus tequilensis under submerged fermentation for 7 days reported by Kamble and Jadhav [46] was comparatively very low than the activity of our strain.

Generally, the biological hydrolysis of LCB such as GS is a slow process due to its structural complexity making it inaccessible to the enzyme action [47]. The topographic alterations caused by the bacterial treatment to the GS are important features of polymer hydrolysis, suggesting secretion of a complex of protein factors called cellulosome [48]. A similar kind of conclusion is reported by Barton Pudlik et al. [49] and Xu et al. [50] who observed cracking and delamination of wood composites due to filamentous fungi and moulds, respectively. Our results were in line with the report of Schröpfer et al. [51] who observed delamination and pores created by the soil microbes causing degradation of bacterial cellulose. However, the degradation potential of our strain was found lower than the degrading activity (42–45%) of soil-derived bacterial consortia such as WS1-M and SG-M [52]. The observed difference can be attributed to the synergy of the multiple bacteria present in the consortia WS1-M and SG-M. Since GS is structurally very complex and difficult to degrade, only 33% of the substrate was successfully degraded by B. tequilensis G9 within 20 days of incubation which is comparably higher than the degradation capacity (10.7%) of many individual bacteria such as Brevibacterium linens and other Bacillus strains [53]. Another probable reason for the slow degradation of lignocellulose by bacteria could be the intracellular nature of the enzymes [47]. The purification yield of 21.75% and recovery of the enzyme (3.5) in the present investigation is comparatively higher than many earlier reported studies [54]. The bacterial cellulases of molecular weights ranging from 30 to over 97 kDa are frequently reported in the literature [55, 56, 57], with some specific examples of Bacillus strains as well. The precursor proteins of endoglucanases in Bacillus species are known to be secreted with 55 kD of molecular mass, which are ultimately processed by removing a short peptide from carboxy-terminus leading to the formation of a final product with 35 kD of molecular weight [58]. However, the cellulase proteins of the B. tequilensis G9 also fall within the same range of molecular weight. Moreover, the zymogram analysis of precipitated proteins revealed the presence of cellulolytic enzymes that was evident by a clear region (a yellowish smear) of CMC digestion due to cellulase activity. The results of zymogram analysis of the precipitated proteins of B. tequilensis G9 were also well in agreement with the activities shown by many other previously reported cellulolytic bacteria [59, 60]. In conclusion, all results shown above signify the importance of B. tequilensis G9 in biorefinery for converting the waste sector into a useful material technology.

Conclusions

In this study, the cellulolytic bacterial strain B. tequilensis G9 was evaluated for CMCase production on GS as substrate by employing RSM tool. The significant variables influencing the cellulase production suggested by RSM model using CCD were found to be pH (5.5), substrate concentration (8%), inoculum size (1.5%), and FeSO4 (10 ppm). The obtained results revealed that B. tequilensis G9 has significant saccharification potential that can be exploited for biorefinery or other biotechnology-based industries. The degradation effect exerted by B. tequilensis G9 on agricultural waste depicted its intricate enzyme activity creating pores and deconstruction, thereby valorizing GS as significant carbon source for biorefinery. In conclusion, B. tequilensis G9 grown under most favorable (optimized) conditions provides better performance in contrast to unoptimized conditions thereby brands its application in many industrial based processes like biorefinery, biofuels, pulp industry, etc.

Notes

Acknowledgements

MD is indebted to University Grants Commission (UGC), New Delhi, India, for providing senior research fellowship. Partial funding for this work was received from University Grants Commission, New Delhi, India under Start-Up scheme (F.30-121/2015BSR). RSP acknowledges theUPE-II (nanobiotechnology), UoP-BCUD grant (15-SCI-001422), as well as DRDP and DST-PURSE schemes provided to the department. The authors thank Mr. Harishchandra Nikule (Central Instrumentation facility (CIF), Savitribai Phule Pune University, Pune) for technical assistance provided during FESEM analysis. We also acknowledge Dr. Mohd. Shahnawaz for language corrections and anonymous reviewers whose critical comments significantly improved this manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest with respect to the research, authorship, and publication of this article.

Supplementary material

42768_2019_16_MOESM1_ESM.docx (301 kb)
Supplementary material 1 (DOCX 301 kb)

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Copyright information

© Zhejiang University Press 2019

Authors and Affiliations

  1. 1.Department of ZoologySavitribai Phule Pune UniversityPuneIndia
  2. 2.School of Nanoscience and BiotechnologyShivaji UniversityKolhapurIndia

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