3.1 Introductionshodhganga.inflibnet.ac.in/bitstream/10603/7238/11/11_chapter 3.pdf · Several...

Chapter-3: Optimization and production of alkaline protease from Bacillus thuringiensis strain cc7

57

3.1 Introduction

It is necessary to increase enzyme production by optimizing process

parameters after isolating the suitable strain. Hence, care must be taken with

enzymes such as proteases, which may be inducible or repressible, when

designing a media, which will induce rather than repress production of the

enzyme (Sumantha et al., 2006). Media components were found to have great

influence on extracellular protease production and are different for each

microorganism. Therefore, the required constituents and their concentrations

have to be optimized accordingly. Industrial fermentation is moving away from

traditional and largely empirical operation towards knowledge based and better

controlled process (Singh et al., 2004). With increasing industrial demands for the

biocatalysts that can cope with industrial processes at harsh conditions, the

isolation and production is a recent approach to increase the yield of such

enzymes with defined biological properties. Proteases are important in many

biological processes and have numerous applications in biotechnology and

industry. Several methods, statistical and non-statistical, are available for

optimizing the parameters (Felse and Panda, 1999; Montgomery, 2002). Statistical

approaches offer ideal ways for process optimization studies in biotechnology

(Beg et al., 2003 and Gupta et al., 2002). Optimization of parameters by statistical

approach reduces the time and expense of the experiment. Statistical procedures

have advantages basically due to utilization of fundamental principles of

statistics, randomization, replication and duplication (Rao et al., 2004). Plackett

and Burman’s statistical method is one of such approaches involving a two level

fractional factorial saturated design that uses only k+1 treatment combinations to

estimate the main effects of k factors independently (assuming that all

interactions are negligible) (Plackett and Burman, 1944). Hence, fractional

factorial design like Plackett-Burman becomes a method of choice for initial

screening of medium components. Response surface method (RSM) is one of the

popularly used optimization procedures, mainly developed based on full


58

factorial central composite design (CCD) (Box et al., 1975). RSM is a collection of

mathematical and statistical techniques that are useful for modeling and analysis

in applications where a response of interest is influenced by several variables and

the objective is to optimize this response. Several fermentation processes have

been optimized using this methodology (Ahuja et al., 2004 and Bandaru et al.,

2006). A Central Composite Design (CCD) has three groups of design points:

two-level factorial or fractional factorial, axial and central points. Several reports

on the central composite design are available in the literature (Tari et al., 2006).

This chapter includes identification and screening of medium components

influencing protease production by Bacillus thuringiensis strain cc7 using manual

& statistical approach (Plackett-Burman design) and optimization of the selected

components by response surface methodology (central composite design).


59

3.2 Materials and Methods

The factors influencing enzyme production was studied, examining one

factor at a time, keeping the other factor constant for the optimization and

production of alkaline proteases by the Bacillus thuringiensis strain cc7. The initial

medium used for protease production before optimization was similar to the

growth medium (APM-1) except the alterations or modifications mentioned

under each experiment. Experiments were done in triplicate.

3.2.1 Parameters influencing protease production

3.2.1.1 Temperature and pH

The effect of temperature and pH on the production of extracellular

proteases was studied by assaying the enzyme after 24 h of incubation period in

the culture medium at varying temperatures (i.e., 20, 30, 40, 50 and 60ºC). The

effect of pH on protease production of the isolate cc7 was studied by adjusting

the media to different pH levels ranging from 6-11 using appropriate buffers,

Tris-HCL buffer (pH 6.0–8.0), Glycine-NaOH buffer (pH 8.0– 11).

3.2.1.2 Inoculum size, incubation period and agitation speed

The effect of inoculum size, incubation period and agitation speed on

protease production was carried out by growing the isolate for 24, 48 & 72 h of

incubation period with the agitation speed of 100 rpm, 150 rpm, 200 rpm and 250

rpm with the inoculation size of 1, 2, 3, 4, 5 & 6% (v/v) of 24 h old active culture.

Protease production and alkaline protease activity was measured and monitored

at 6 h intervals over a 72 h fermentation period.

3.2.1.3 Various carbon and nitrogen sources

Carbon sources used for the study were glucose, sucrose, starch, mannitol,

glycerol, lactose, xylose, sodium citrate and maltose at 1%, (w/v) concentrations.

Sources of nitrogen include various organic & inorganic nitrogen and amino

acids at 1%, (w/v) concentrations, includes yeast extract, peptone, casein, urea,

ammonium sulfate, sodium nitrate, potassium nitrate, alanine and glycine. A


60

control represents the production medium without any carbon and nitrogen

source.

3.2.2 Statistical optimization for alkaline protease production

3.2.2.1 Essential medium components

The medium components for protease production were screened using

Plackett-Burman statistical experimental design. Total of six components

(variables k = 6, table 3.1) were selected for the study with each variable being

represented at two levels, high (+) and low (-), and five dummy variable in 12

trials (table 3.2). The number of positive and negative signs per trial were

(k+1)/2 and (k-1)/2, respectively. Each row represents a trial and each column

represents an independent or dummy variable.

Table-3.1 Variables representing medium components used in Plackett-Burman

design

Variables Medium components + Values (g%) - Values (g%)

X1 Glucose 1 0.1

X2 Casein 1.5 0.15

X3 K2HPO4 0.1 0.01

X4 KH2PO4 0.1 0.01

X5 MgSO4 0.2 0.02

X6 CaCl2 0.2 0.02

X1-X6 represent different independent variables; the sign ‘+’ is for high

concentration of variables and ‘-’ is for low concentration of variables.


61

Table-3.2 Plackett-Burman experimental design matrix

Run Variables Protease

activity

(U/ml)

X1 X2 X3 X4 X5 X6 D1 D2 D3 D4 D5

1 + + - + + + - - - + - 322

2 - + + - + + + - - - + 52

3 + - + + - + + + - - - 285

4 - + - + + - + + + - - 0

5 - - + - + + - + + + - 0

6 - - - + - + + - + + + 72

7 + - - - + - + + - + + 189

8 + + - - - + - + + - + 310

9 + + + - - - + - + + - 103

10 - + + + - - - + - + + 0

11 + - + + + - - - + - + 224

12 - - - - - - - - - - - 84

X1-X6 represent different independent variables and D1-D5 are the dummy variables; the sign ‘+’

is for high concentration of variables and ‘-’ is for low concentration of variables.

The effect of each variable was determined by the following equation:

E(Xi) = 2(ΣMi+-Mi-)/N (3.1)

where, E(Xi) is concentration effect of the tested variable,

Mi+ and Mi- represents protease production from the trials where

the variable (Xi) measured was present at high and low

concentration, respectively.

N total number of trials equals to 12.

Experimental error was estimated by calculating the variance among the

dummy variables as follows:

Veff = Σ(Ed) 2/n (3.2)

where, Veff is variance of concentration effect,


62

Ed is concentration effect for dummy variable,

and n is total number of dummy variables.

The standard error (SE) of concentration effect was the square root of variance of

an effect and the significance level (p-value) of each concentration effect was

determined using the student’s t test:

t(Xi) = E(Xi)/SE (3.3)

where, E(Xi) is the effect of variable Xi.

3.2.2.2 Statistical optimization of screened components

Statistical optimization of protease production using response surface

methodology (RSM) was used to optimize the screened components for

enhanced protease production. Central composite design (CCD) consisting of

three main critical independent variables, the concentration of glucose (C1),

concentration of CaCl2 (C2) and agitation speed (C3) were chosen based on the

initial screening. Since these independent variables were capable of influencing

the alkaline protease productions (Y) by Bacillus thuringiensis strain cc7. The

experimental data were fitted according to Eq. (3.4) as a second-order polynomial

regression equation including individual and cross effect of each variable.

(3.4)

Where, Y is the predicted response,

a0 is the intercept term,

ai is the linear effect,

aii is the square effect,

aij is the interaction effect,

and Ci and Cj are the variables (Aunstrup, 1980).

The minimum and maximum range of variables investigated and the full

experimental plan with respect to their actual and coded values are listed in

table-3.2. A multiple regression analysis of the data was carried out with the

statistical package (Stat-Ease Inc., Minneapolis, MN, USA) (Pourrat et al., 1988).


63

To validate these predictions, flask cultivation using the completely optimized

medium composition was carried out twice.

Table-3.3 Central composite design matrix of experiment and predicted responses

for the RSM studies.

Run Order Experimental values Protease activity(U/ml)

C1 C2 C3 Measured* Predicted

1 300.00 30.00 100.00 358 357

2 200.00 20.00 150.00 290 284

3 31.82 20.00 150.00 361 356

4 200.00 36.82 150.00 352 343

5 300.00 30.00 200.00 269 288

6 300.00 10.00 100.00 332 317

7 300.00 10.00 100.00 354 352

8 200.00 20.00 150.00 298 297

9 200.00 3.18 150.00 355 354

10 200.00 20.00 234.09 312 309

11 368.18 20.00 150.00 353 351

12 200.00 20.00 65.91 266 265

13 300.00 30.00 200.00 328 332

14 100.00 30.00 100.00 267 261

15 200.00 20.00 150.00 229 238

16 300.00 10.00 200.00 342 339

17 200.00 20.00 150.00 336 332

18 200.00 20.00 150.00 247 246

19 100.00 10.00 200.00 347 341

20 200.00 20.00 150.00 241 249

*means of triplicate

C1, C2 & C3 are independent variables, concentration of glucose (C1), concentration of CaCl2 (C2)

and agitation speed (C3) respectively.


64

3.2.3 Enzyme preparation and assay

The culture was centrifuged at 10,000 rpm for 10 minutes (4ºC) and the

culture supernatant was used as a source of protease. The caseinolytic activity

was assayed as described in chapter-2 section 2.2.5.


65

3.3 Results

3.3.1 Effect of temperature and pH on protease production

The effects of different incubation temperatures on protease production

were evaluated. It is known that temperature is one of the most critical

parameters that have to be controlled in bioprocess (Chi and Zhao, 2003). It is

obvious from the results (figure 3.1) that 30ºC was generally more favorable for

protease production as well. However, the temperature below or above 30ºC

resulted a sharp decrease in protease yield as compared to the optimal

temperature. It was found that optimum temperature for protease production for

isolate cc7 was 30°C (66 U/ml). Subsequently, 37°C and 30°C were reported to be

the best temperatures for protease production in certain bacilli (Gupta et al.,

2002b).

Figure-3.1 Effect of temperature on protease production

It has been noted that the important characteristic of most microorganisms

is their strong dependence on the extracellular pH for cell growth and enzyme

production (Kurmar and Tagaki, 1999). The production medium was adjusted at


66

different pH values of different buffers. The results of pH studies showed (figure

3.2) that the best buffer was Glycine-NaOH buffer with pH 8.5 was found to be

optimum for protease production. A notable decline in the enzyme productivity

occurred at both higher and lower pH values. However, for increased protease

yields from alkalophilic microorganisms, the pH of the medium must be

maintained above 7.5 throughout the fermentation period (Aunstrup, 1980).

Figure-3.2 Effect of pH on protease production

3.3.2 Effect of inoculum size, incubation period and agitation speed on

protease production

Culture was activated by transferring it in the casein broth and kept on

shaker for 24 h. Activated culture was used for the inoculation of the fresh casein

broth.


67

Figure-3.3 Effect of inoculums size on protease production

Figure-3.4 Effect of incubation period on protease production


68

Figure-3.5 Effect of agitation speed on protease production

Six different inoculum size represented graphically (figure 3.3) were

investigated for their effect on productivity of the protease enzyme by Bacillus

thuringiensis strain cc7. The results indicated that the use of 3.0 ml of 24 h old

inoculums (7.0 × 103 cell/ml) gave the highest yield of protease. Higher or lower

inoculums size resulted in a significant decrease in enzyme productivity. The

increase in protease production using small inoculum size were suggested to be

due to the higher surface area to volume ratio resulting in increased protease

production (Rahman et al., 2005). During the fermentation, different dissolved

oxygen level in the fermentation broth were obtained by variations in the

aeration rate, incubation time and the agitation speed which can influence

greatly the growth of the isolate & thus production of extracellular enzymes (Chi

et al., 2003). Agitation rates have been shown to affect protease production in

various strains of bacteria (Mehrotra et al., 1999 and Mabrouk et al., 1999). An

agitation speed of 150 rpm was found to be the most suitable for protease

production by this Bacillus strain cc7 (figure 3.5). Agitation speed of 100 and 200


69

rpm affected the growth of the organism considerably. At 100 rpm, insufficient

aeration and nutrient uptake perhaps caused the inability of bacteria to grow

efficiently. At 200 rpm, however, excessive aeration and agitation could occur

which led to abrasion by shear forces (Darah et al., 1996). Under the optimal

agitation speed, Bacillus strain cc7 exhibited their maximum ability to

biosynthesize protease within 48 h of incubation period (figure 3.4). Protease

production was determined at different incubation periods. Any prolong in

incubation period decreased enzyme production. A prolonged incubation time

perhaps may be due to auto digestion of proteases and proteolytic attack by

other proteases (Priest, 1977 and Chu et al., 1992). Based on this finding, agitation

speed of 150 rpm and incubation time of 48 h was used throughout the study.

3.3.3 Effect of various carbon and nitrogen source on protease production

The present investigation was aimed at optimization of medium

components which have been predicted to play a significant role in enhancing

the production of alkaline proteases. An experiment was designed to investigate

the effect of different carbon sources on protease production by Bacillus

thuringiensis strain cc7. The result showed that the best carbon source for

protease production was glucose (figure 3.6). The protease production reaches to

the maximum with glucose as a carbon source while decreased protease

production with other carbon sources. It has been reported that pure sugars

affected protease production considerably (Dahot, 1993). Utilization of pure

sugars as carbon and energy sources was shown to result good growth but with

lower protease production (Walker et al., 1983; Prasad et al., 1984). There are

several reports showing that different carbon sources have different influences

on extracellular enzyme production by different strains (Chi and Zhao, 2003).

Sucrose and sodium citrate supported the production at limited extent as

compared to glucose.


70

Figure-3.6 Effect of various carbon sources on protease production

Figure-3.7 Effect of various nitrogen sources on protease production

Different organic and inorganic nitrogen source were used in relation to

protease production by Bacillus thuringiensis cc7 (figure 3.7). Organic nitrogen

sources were found to be better nitrogen sources both for growth as well as

protease production in some organisms (Phadatare et al., 1993; Aleksieva et al.,


71

1981). The best organic nitrogen source for protease production was found to be

casein. Inorganic nitrogen sources were also tested for the growth and protease

production, urea and ammonium sulfate showed highest protease activity at 48 h

of incubation. The significant amount of protease was produced with amino

acids like alanine & glycine (figure 3.7). Our results were in accordance with

some earlier reports, where amino acids induced protease synthesis (Abdel-

Raouf, 1990; Ammar et al., 1991). The use of optimum carbon and nitrogen

sources together thus enhanced the total protease production by Bacillus

thuringiensis strain cc7.

3.3.4 Statistical optimization for alkaline protease production

3.3.4.1 Screening of essential medium components

A total of six variables were analyzed with regard to their effects on protease

production using a Plackett–Burman design (table 3.2). The design matrix

selected for the screening of significant variables for protease production and the

corresponding responses are shown in table-3.4.

Table-3.4 Corresponding responses of medium components for protease

production in Plackett-Burman design

Variables Medium

Components

Effect SE t(xi) p-

values

Confidence

level (%)

X1 Glucose 204.17 7.07 6.32 0.0015 99.85

X2 Casein -11.17 7.07 0.28 0.7433 25.67

X3 K2HPO4 -52.17 7.07 1.68 0.1668 83.32

X4 KH2PO4 27.50 7.07 0.88 0.4329 56.71

X5 MgSO4 -11.17 7.07 0.32 0.7433 25.67

X6 CaCl2 73.50 7.07 2.24 0.0298 97.02


72

Factors evidencing P-values of less than 0.05 were considered to have

significant effects on the response, and were therefore selected for further

optimization studies. The components were screened at confidence level of 95%

based on their effects. Table-3.4 represents the effect, standard error, t(Xi), p-

value and confidence level of each component.

The glucose (X1) showed the maximum positive effect on protease

production, followed by CaCl2 (X6). The effect of casein, K2HPO4, KH2PO4 and

MgSO4 were negative which suggested that these components are required in the

medium for protease production but in lower concentration. The confidence level

of variable glucose was 99.85% and of CaCl2 was 97.02% (table 3.4) implying that

the effect of glucose and CaCl2 were significant. All other insignificant variables

were neglected, and the optimum levels of these variables (glucose and CaCl2)

were further determined by an RSM design.

Although apart from chemical parameters, shaker speed was also grouped

among the significant variables based on preliminary experimental analysis. We

used shaker speed as one of the major factors for further study, due to its

importance in terms of oxygen and nutrient transfer into the liquid medium,

especially for the growth of aerobic bacteria like Bacillus strain cc7. By selection

of medium components using Plackett-Burman design in this study, about three-

fold increase in the protease production was achieved. Thus, glucose, CaCl2 and

shaker speed were chosen and their possible interactive effects on enzyme

production were evaluated by response surface methodology (RSM).

3.3.4.2 Optimization of screened medium components

According to the results of preliminary study of Plackett-Burman design,

the factors showing positive effect with confidence level 99.85% (glucose) and

97.02% (CaCl2) were selected for the response surface analysis using central

composite design (CCD). The central composite design (CCD) was used to find

the suitable concentrations of the variables on alkaline protease production by

Bacillus strain cc7. The other component (KH2PO4) with positive effect, although


73

showing lower confidence levels, was essential for growth of the isolate and

studied at a fixed concentration. The component with confidence level above

50% (K2HPO4, KH2PO4) was set at its higher level and component with

confidence level below 50% (MgSO4) was set at their middle levels. Table-3.3

represents the central composite design with actual and coded factor (variable)

levels and protease production.

The design matrix and the corresponding results of the experiments are

depicted in table-3.5. Response surface methodology (RSM) is the most accepted

statistical technique for bioprocess optimization to examine the relationship

between a set of experimental factors and observed results (Ravichandra

Potumarthi et al., 2008). The F-value and p-value were found to be 12.88 and

<0.0002, respectively. This implies that the quadratic regression model is

significant. The analysis of variance (ANOVA) of the quadratic regression model

demonstrated that the model terms of C1, C2, C12, C22 and C32 were significant

(“probe>F” less than 0.05). The determination coefficient (R2) value of 0.9206

indicated that 92.06% of the total variations were explained by the model. In

addition, the value of the adjusted determination coefficient (adjusted R2 =

0.8491) was also very high, emphasizing the high significance of the model (table

3.5).

The protease production (Y) by Bacillus strain cc7 was expressed in terms

of actual factors as follows Eq. (3.5):

Y = -43.68137 + 0.31198C1 + 3.84967C2 + 0.75955C3 − 5.00000E-004C12 −

5.00000E-005C13 - 4.00000E-003C23 - 5.82352E-004C12 - 0.061152C22 - 2.04657E-

003C32 (3.5)

Where, C1, C2 and C3 are glucose concentration, CaCl2 concentration and

agitation speed, respectively.


74

Table-3.5 Analysis of variance (ANOVA) for the parameters of response surface

quadratic model.

Source of

variation

Sum of

squares

Degree of

freedom

Mean

squares

F-value Probe>F

Model 2507.49 9 278.61 12.88 0.0002

C1-glucose 517.22 1 517.22 23.91 0.0006

C2-CaCl2 676.06 1 676.06 31.25 0.0002

C3-speed 105.48 1 105.48 4.88 0.0517

C1C2 2.00 1 2.00 0.092 0.7673

C1C3 0.50 1 0.50 0.023 0.8822

C2C3 32.00 1 32.00 1.48 0.2518

C12 488.74 1 488.74 22.59 0.0008

C22 538.92 1 538.92 24.91 0.0005

C32 377.25 1 377.25 17.44 0.0019

Residual 216.31 10 21.63

Lack of Fit 212.08 5 42.42 50.13 0.0003

Pure Error 4.23 5 0.85

Cor Total 2723.80 19

R2=0.9206, Adj- R2=0.8491, Adeq Precision=10.878, C.V.%=5.24 and PRESS=1644.62

Also, the model has an “adequate precision value” of 10.878; this suggests

that the model can be used to navigate the design space. The “adequate precision

value” is an index of the signal to noise ratio and a value >4 is an essential

prerequisite for a model to be a good fit. The model showed standard deviation,


75

mean, C.V.% and predicted residual sum of squares (PRESS) values of 4.65,

88.69, 5.24 and 1644.62, respectively. A relatively lower value of the C.V.% (5.24)

indicates a better precision and reliability of the experiments carried out.

To determine the optimum concentration of each variable for maximum

protease production by Bacillus strain cc7, the contour plots were plotted based

on the model equation and investigated interaction among variables. Each

contour curve represents an infinite number of combinations of two test variables

with the other maintained at their constant level. The optimum conditions for

alkaline protease production were proposed to be glucose 268 mg%, CaCl2 24

mg% and agitation speed at 154 rpm. The maximum protease activity of 357

Uml−1 was predicted by the model. Results showed that the response varied as a

function of each factor. It was apparent that increasing the glucose concentration

and increasing the CaCl2 content had a positive influence on protease

production, until an optimum value was reached (figure 3.8). A similar response

of protease activity was observed as a function of agitation speed in an

interactive effect along with glucose content and CaCl2 concentration (figure 3.9

& 3.10), which corroborated the results reported by Dutt et al. (2009).


76

Figure-3.8 Counter plot for interactive effect of glucose with CaCl2

Figure-3.9 Counter plot for interactive effect of glucose with agitation speed


77

Figure-3.10 Counter plot for interactive effect of CaCl2 with agitation speed

To validate the prediction of the model, additional experiments in

triplicate at shake flask level were performed using the optimized medium.

These experiments yielded maximum of 357± 0.48 U/ml protease activities

which was 3.6 times higher than former medium. Validation experimental data

suggested that every predicted response for protease production was very close

to the observed value, confirming the model’s accuracy (Romsomsa et al., 2010).

Thus statistical optimization aspects are very important in large-scale production

where enzyme yield will be continuous.


78

3.4 Discussion

The proteases are essential for all forms of life, including prokaryotes,

fungi, animals and plants. Microbial proteases are the most important hydrolytic

enzymes. These hydrolytic enzymes have been commercially exploited in

industries. Generally, proteases produced from microorganisms are constitutive

or partially inducible in nature (Beg et al., 2002a; Kalisz, 1988). Most of the

Bacillus sp. produces extracellular proteases during exponential and stationary

phases (Che Nyonya Abd Razak et al., 1997). Extracellular protease production

in microorganisms is influenced by media components e.g. presence of some

easily metabolizable sugars, such as glucose (Beg et al., 2002b), rapidly

metabolizable nitrogen sources, such as amino acids in the medium. Beside

these, several other physical factors, such as aeration, inoculum size, pH,

temperature and incubation period also affect the protease production (Hameed

et al., 1999; Puri et al., 2002). Environmental conditions play an important role in

the microbial growth and in the induction or repression of the enzyme by

specific compounds (Secades et al., 1999). In commercial practice, the

optimization of medium composition is done to maintain a balance between the

various medium components, thus minimizing the amount of unutilized

components at the end of fermentation. In addition, no defined medium has been

established for the best production of alkaline proteases from different microbial

sources. Each organism or strain has its own special conditions for maximum

enzyme production. In the present study influence of various factors on the

protease production by Bacillus thuringiensis strain cc7 was studied. The results

obtained in this work revealed the ability of Bacillus thuringiensis strain cc7 to

produce extracellular protease at maximum level.

Temperature is an important parameter for production of enzyme by the

organism. Temperature for optimum growth and optimum production might be

different. Therefore for higher production of the protease enzyme organisms

were grown with different temperature. The mechanism of temperature control


79

of enzyme production is not well understood (Chaloupka, 1985). However,

studies by Frankena et al. (1986) showed that a link existed between enzyme

synthesis and energy metabolism in bacilli, were controlled by temperature and

oxygen uptake. A comparison with the literature review done on the

characteristics of alkaliphilic Bacillus strains producing alkaline proteases, it is

determined that most of the alkaliphilic Bacillus strains have a temperature

optima between 30-37°C and are mostly of mesophilic type. With this regard our

strains are in agreement with the literature reported data (Mabrouk et al., 1999;

Çalık et al., 2002; Joo et al., 2003). In the presented work, the maximum

biosynthesis of protease enzymes were recorded within incubation temperature

of 29°C for Bacillus thuringiensis strain cc7. The present result is in complete

accordance with the finding of other investigators (Loperana et al., 1994). On the

other hand, other optimum incubation temperatures for protease production by

Bacillus species of 35°C (Ammar et al., 1991), 40°C (Jadwiga and Sierecka, 1998),

50°C (Kim et al., 2001) and 60°C (Kumar and Bhall, 2004; Kobayashi et al., 1996)

were reported. Therefore it is thought that the proteases produced by strains cc7

may have a high possibility to have temperature optima around these

temperatures, and being a good potential source for detergent and other

industrial products where high temperatures are not desired.

The effect of pH on the growth and protease production by Bacillus

thuringiensis strain cc7 was studied, and it was observed that the protease

production was found to be maximum at pH 8.5. However, at pH values below

or above the previously recorded optimum pH value tested for Bacillus

thuringiensis strain cc7, the relative production of protease(s) was markedly

diminished. It is quite obvious that the maximal productivity of protease(s) for

the tested Bacillus thuringiensis strain cc7 could be recorded within slightly

alkaline pH. The important characteristic of most alkalophilic microorganisms is

their strong dependence on the extracellular pH for cell growth and enzyme


80

production. For increased protease yields from these alkalophiles, the pH of the

medium must be maintained above 7.5 throughout the fermentation period

(Aunstrup, 1980). The advantage in the use of carbonate in the medium for an

alkaline protease has been well demonstrated (Horikoshi and Akiba, 1982). The

highest protease production was determined within this optimum pH range

have also been reported (Ali, 1991; Sookkheo et al., 2000). The optimum pH

values 7, 8, 8.5, 10.5, 11 and 12 were reported to be suitable for maximum

protease production (Beg and Gupta, 2003).

Different inoculum sizes were investigated for their effect on productivity

of the protease by Bacillus thuringiensis strain cc7. The results indicated that the

use of 3.0 ml of 24 h old inoculums (7.0 × 103 cell/ml) gave the highest yield of

protease. It is well documented that an inoculation ratio of 2% to 5% is an

optimum for Bacillus strains (Mabrouk et al., 1999; Kanekar et al., 2002). Therefore

our strain is in good agreement with these data. However, the optimum level of

inoculum for alkaline protease production by the Bacillus strains was found to be

in range of 1 to 8%. This observation is in conformity with the reports by Sinha

and Satyanarayana (1991); Sen and Satyanarayana (1993) and Gajju et al. (1996).

Incubation period plays a substantial role in the maximum protease

production. Comparing the results to the literature there is a broad incubation

time ranging from 24-120 h reported for Bacillus strains (Singh et al., 2001b; Gupta

and Beg, 2003). The incubation period required for obtaining the maximum yield

was found vary with different Bacillus strains. In some Bacillus sp., maximum

protease production was observed after 18 h of incubation period (Singh et al.,

2001a), whereas Bacillus coagulans PB 77 required 96 h of incubation period for

the maximum accumulation of alkaline protease (Gajju et al. 1996). The fact that

certain potent Bacillus species exhibited their maximum protease productivity

after 48 h (Ali, 1991), while by other Bacillus species was achieved after 24 h

(Takami et al., 1989 and Ammar et al., 1991), 60 h (Daguerre et al., 1975) and 72 h


81

(Fazel and Bailey, 1980). Results of present study indicated that maximum

production of protease was recorded at incubation period of 48 h.

During the fermentation, different dissolved oxygen level in the

fermentation broth can be obtained by variations in the aeration rate, incubation

time and the agitation speed which can influence greatly the growth of the

isolate & thus production of extracellular enzymes (Chi et al., 2003). The variation

in the agitation speed influences the extent of mixing in the shake flasks and will

also affect the nutrient availability. Agitation rates have been shown to affect

protease production in various strains of bacteria (Mehrotra et al., 1999). An

agitation speed of 150 rpm was the most suitable for protease production by

Bacillus thuringiensis strain cc7. Agitation of culture was found to be essential for

the high production of alkaline protease by Bacillus thuringiensis strain cc7.

Agitation speed of 100 and 200 rpm affected the growth of the organism

considerably may be due to insufficient/excessive aeration and nutrient uptake.

Carbon source plays an important role in growth as well as protease

production. The best carbon source for protease production for Bacillus

thuringiensis strain cc7 was found to be glucose, sucrose and sodium citrate. In

some Bacillus species such as B. subtilis (Boominadhan et al., 2009), B.

mojavensis (Beg et al., 2002), Bacillussp.P-2 (Kaur et al., 2001), B. sphaericus (Singh et

al., 2001b) and Bacillus sp. IE-3 (Soni et al., 1998), glucose was found to be the best

carbon source for protease production. In a previous study reported by

Johnvesly and Nailk (2001), they showed that trisodium citrate was the best

carbon source for protease production by Bacillus sp JB-99. Starch has also been

reported as optimum carbon source for Bacillus sp. (Sinha and Satyanarayana,

1991), Bacillus sp. JB-99 (Johnvesly and Naik, 2001) and Bacillus cereus BG1

(Ghorbel-Frikha et al., 2005) for protease production. However, starch was found

to be least preferred for our organism for protease production. It may be due to

its failure to channelize the energy requirement for protease production through

hydrolysis of this complex carbohydrate. Studies have also indicated a reduction


82

in protease production due to catabolite repression by glucose. The stimulatory

effect of glucose on protease production was reported by Wahon et al. (1980),

Kole et al. (1988) and Ali (1991) and these results were in complete accordance

with the present results.

Effects of a specific nitrogen supplement on protease production differ

from organism to organism although complex nitrogen sources are usually used

for alkaline protease production (Kumar and Tagaki, 1999). Result showed that

organic nitrogen sources were stimulatory for alkaline protease production by

the strain cc7 and substitution of these in the medium with other inorganic

nitrogen sources greatly decreased the enzyme production. Strain cc7 preferred

organic nitrogen sources for protease production. Low levels of alkaline protease

production were reported with the use of inorganic nitrogen sources in the

production medium (Sen and Satyanarayana 1993; Chandrasekaran and Dhar,

1983; Chaphalkar and Dey, 1994). However, the combination of these nitrogen

sources on protease production by other Bacillus spp. have been reported

(Fujiwara et al., 1987; Kumar et al., 2002). An increase in protease production by

the addition of ammonium sulphate and potassium nitrate was observed by

Sinha and Satyanarayana (1991). Amino acids may affect the production of

proteases. Addition of amino acids (alanine and glycine) was shown to be

effective in the production of extracellular enzymes by Bacillus thuringiensis strain

cc7.

Every organism is unique in its requirement for maximum enzyme

production. Therefore, each of them has to be considered separately and the

requirements have to be optimized accordingly. In the present study, the

significant variables necessary for enhanced protease production were selected

using the Plackett–Burman design. The medium components were screened by

Plackett-Burman design and optimized by applying response surface

methodology (RSM) using central composite design (CCD). The Plackett-Burman

design helped in identifying glucose and CaCl2 as significant factors that


83

influenced the protease production in Bacillus thuringiensis strain cc7. There have

been a number of studies conducted on optimization of different

physicochemical parameters of different organisms using response surface

methodology (Chauhan et al., 2004; Rahman et al., 2004; Lui et al., 2004). The use

of statistical models to optimize culture medium components and conditions has

increased in present-day biotechnology, due to its ready applicability and

aptness. The RSM applied to the optimization of protease production in this

investigation, suggested the importance of a variety of factors at different levels.

The CCD design plan exploited in the present study enabled us to study and

explore the cultural conditions that would support more than 3.5 fold increase in

protease production. The statistical optimization method is widely used and has

a growing acceptance in biotechnology. Reports have been indicated 2.6 folds

increase in alkaline protease production by Bacillus sp. (Puri et al., 2002). Alkaline

protease production produced by Bacillus mojavensis was improved up to 4.2 fold

in a bioreactor using response surface method (Beg et al. 2003). Till date,

statistical approach has been used majorly for the production of spores from

Bacillus thuringiensis using response surface methodology (Bing-Lan Liu and Yew-

Min Tzeng, 1998). The amount of information available for the use of statistical

method in protease production from Bacillus thuringiensis is scarce. This work

demonstrated the use of a central composite design (CCD) by determining

conditions leading to the maximum protease production from Bacillus

thuringiensis strain cc7. A high degree of similarity was observed between the

predicted and experimental values that reflected the accuracy and applicability

of RSM to optimize the process for protease production. Similar improved

production was also reported in other RSM experiments, most notably in the case

of protease production using Bacillus sp. (Dey et al., 2001; Chauhan and Gupta,

2004).

The media optimization is an important aspect to be considered in the

development of fermentation technology to maintain a balance between the


84

various medium components, thus minimizing the amount of unutilized

components at the end of fermentation & production of high yields of the desired

product. The results obtained in this work assessed the ability of Bacillus sp. to

produce extracellular protease. Protease production was found 3.5 fold increased

with optimized medium as compared to ordinary medium. It was obvious from

the results that the best carbon source for protease production was glucose while

amongst the nitrogen sources, organic nitrogen sources were found better for

growth and enzyme production compared to inorganic ones. Supplementation of

culture medium with metal cations substantially improved the protease

production as well. This information enabled the ideal formulation of media for

maximum protease production by Bacillus thuringiensis strain cc7.

Date post:	15-May-2020
Category:	Documents
Upload:	others
View:	3 times
Download:	0 times

3.1 Introductionshodhganga.inflibnet.ac.in/bitstream/10603/7238/11/11_chapter 3.pdf · Several...

Documents