media optimization, purification and characterization...
TRANSCRIPT
86
MEDIA OPTIMIZATION, PURIFICATION
AND CHARACTERIZATION OF
LIPASES FROM ACINETOBACTER SP.
EH28 AND BACILLUS SUBTILIS EH37
Part of this chapter has been published as:
- A thermostable alkaline lipase from a local isolate Bacillus subtilis EH37: Characterization, partial purification and application in organic synthesis. Applied biochemistry and Biotecgnology, U.S.A (2009)
- An alkaline lipase from organic solvent tolerant Acinetobacter sp. EH28:
Application for ethyl caprylate synthesis. Bioresource technology, USA. (2010)
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3.1 Introduction
With the rapid development of enzyme technology, many new potential biotechnological
applications for lipases have been identified in the areas of detergent industry,
oleochemical industry, food industry and flavor development for dairy products,
biosurfactant synthesis, cosmetics and pharmaceuticals (Elibol and Ozer, 2000; Kamini et
al., 2001; Kashmiri et al., 2006; Ginalska et al., 2007, Treichel et al., 2009).
Nevertheless, the ability of lipase reactions is not only as hydrolases but also in
syntheses. Therefore, organic chemists have employed lipases in applications of
syntheses for a very long time. As the applications increase, the availability of lipase
possessing satisfactory operating characteristics becomes a limiting factor since each
application requires unique properties with respect to specificity, stability, temperature
and pH dependence (Sharma et al., 2002; Castro-Ochoa et al., 2005; Kumar et al., 2005;
Kashmiri et al., 2006; Chakraborty and Raj, 2008).
The drawbacks of the industrial applications of lipases are their high cost of production
and low stability, which can be overcome by exploring new sources by immobilization
and activation of the biocatalyst (Guncheva et al., 2007). To develop a bioprocess for
industrial purpose, it is important to optimize highly significant factors affecting this
process (Abdel-Fattah, 2002). Lipase production is dependent upon a number of factors
including carbon and nitrogen sources, pH, temperature, aeration, inoculum size and the
availability of oxygen (Gupta et al., 2004a; Kempka et al., 2008). The general variations
in fermentation conditions influence the production of the enzyme as well as the ratio of
extracellular to intracellular lipases (Kamimura et al., 1999; Rathi et al., 2002; Gupta et
al., 2004a; Dandavate et al., 2009). In recent years, the use of Response Surface
Methodology (RSM) has gained a lot of impetus for media optimization and for
understanding the interaction among various physiochemical parameters, using minimum
number of experiments (Sharma et al., 2002).
Thus search for new lipases with different characteristics and improve lipase production
continue to be important research topics (Rohit et al., 2001; Fadiloğlu and Erkmen 2002;
Castro-Ochoa et al., 2005; Kashmiri et al., 2006; Kader et al., 2007; Pogori et al., 2008).
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In this study, we have successfully isolated two alkaline thermostable lipase producing
bacteria identified as Acinetobacter sp. EH28 and Bacillus subtilis EH37. Species of
Acinetobacter have gained increasing attention in both environmental and
biotechnological applications. However a number of lipolytic strains of Acinetobacter
have been isolated from a variety of sources and their lipases possess many biochemical
properties similar to those that have been developed for biotechnological applications
(Abdel-El-Haleem, 2003; Snellman & Colwell, 2004; Saisubramanian et al., 2008),
whereas the attractive properties of Bacillus subtilis such as its capability to secrete
homologous and heterologous proteins in appreciable quantities into the growth medium
and its classified as Generally Regarded As Safe (GRAS) organism by US food and drug
administration (FDA) have made it an important expression host to produce proteins of
commercial interest (Eggert et al., 2002).
This chapter divided into two sections; in first section we report optimization of media
constituents for enhancing lipase production from Acinetobacter sp. EH 28 and from
Bacillus subtilis EH37. In second part, we demonstrate the purification and
characterization of these lipases.
3.2 Material and Methods
Tributyrin oil, tributyrin agar base and bovine serum albumin were purchased from
HiMedia, India; Olive oil was obtained from Figaro (Spain), peptone and yeast extract
from Titan Biotech, India. Phenyl sepharose ® 6 was procured from Sigma-Aldrich
(Germany), p-nitrophenyl esters from Sigma. All other solvents and chemicals used
during the experiment were of analytical grade. The refined vegetable oils were
purchased locally.
3.2.1 Analytical procedures
Lipase activity was performed using pH-STAT method (as described earlier, chapter 2,
section 2.1) and spectrophotometer method as following:
p-nitrophenyl palmitate was used as substrate according to the method described by
Winkler and Stuckmann (1979. 4-Nitrophenol was used as a standard. The substrate
solutions A (substrate 37.7 mg in 10 mL isopropanol) and solution B (bile salt, 0.1g gum
Arabic and 0.4% (v/v) of Triton X-100 added dropwise in 90 mL of 50 mM Tris–Cl
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buffer (pH 9.0) were mixed with intense stirring to give 100 mL solution. The assay
mixture consisted of 0.1 mL of suitably diluted enzyme, 0.9 ml of 50 mM Tris–Cl buffer
(pH 9.0) and 2.4 ml of substrate. The assay was performed at 50°C for 10 minutes and p-
nitrophenol released was measured at 410 nm. One unit of lipase activity was defined as
the amount of enzyme required to release 1 mole of pNP/ml/min at 50°C and pH 9.
Protein was estimated by the method of Lowry et al., (1951).
3.2.2 Bacterial strain
The bacterial cultures used in this study were isolated by vigorous screening for efficient
lipase producer from oil rich soil. The isolated organisms were subjected to further
screening to obtain alkaline, thermostable, lipase producer as described earlier (chapter 2,
section 2.3.6).
3.2.3 Inoculum preparation and enzyme production
The bacterial isolates were studied for their ability to produce lipase in tributyrin broth
[0.5% (w/v) tryptone, 0.3% (w/v) yeast extract and 1% (v/v) tributyrin oil]. The
production medium was inoculated with an overnight grown culture to obtain an initial
culture density (A600nm) 0.05 and incubated on an orbital shaker (150 rpm) at 35°C for 48
hours. The enzyme activity was estimated from the supernatant obtained upon
centrifugation of 10 ml medium at 10000xg for 20 min at 8°C.
3.2.4 Media Optimization
3.2.4.1 One variable at a time Strategy
One variable at a time strategy was used to optimize physical parameters such as
temperature, pH, inoculum density and agitation. Seed inoculum was prepared in
tributyrin broth as described earlier.
3.2.4.1.1 Effect of pH on lipase production
The effect of pH on lipase production was studied at varying pH from 4 to12 at 35°C and
150 rpm after 48 hours of incubation. The pH of production media (tributyrin broth) was
adjusted using 1N NaOH/ 1N HCl prior to addition of oil.
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3.2.4.1.2 Effect of temperature on lipase production
Lipase production as a function of temperature was determined in the range of 25-55°C at
pH 8.0 (for Acinetobacter sp. EH28) and at pH 9.0 (for B. subtilis EH37) and 150 rpm,
after 48 hours incubation.
3.2.4.1.3 Effect of inoculum Density on lipase production
Effect of varying initial culture density (A600) on lipase production viz. 0.05, 0.1, 0.15,
and 0.2 was studied at 35°C and 150 rpm after 48 hours incubation. The optical density
was measured at A600 against distilled water as blank.
3.2.4.1.4 Effect of agitation on lipase production
The lipase production was monitored at different agitation rates: 50, 100, 150, and 200
rpm at 35°C after 48 hours incubation.
3.2.5 Growth and lipase production profile
The 500 ml Erlenmeyer flasks containing 200 ml of production media (tributyrin broth)
were inoculated with an overnight grown culture of EH37 and EH28 to obtain an initial
culture density (A600) 0.1 (for B. subtilis EH37), 0.2 (for Acinetobacter sp. EH28) and
incubated on an orbital shaker (150 rpm for Acinetobacter sp. EH28 and 100 rpm for B.
subtilis EH37) at 35°C. The samples were withdrawn at regular time interval of 24 hours
and analyzed for cell growth and enzyme activity. The enzyme activity was estimated
from the supernatant obtained upon centrifugation of 10 ml medium. The cell pellet
obtained upon centrifugation was resuspended in 10 ml of distilled water and its
absorbance measured at 600 nm and reported as growth of the organism.
3.2.6 Evaluation of nutritional effects on lipase production using Plackett-Burman design
3.2.6.1 Evaluation of nutritional effects on lipase production by Acinetobacter sp. EH28
The Plackett-Burman design was used to select the effective nutrient components for
lipase production. The effect of eight factors, viz. olive oil, sesame oil, tributyrin oil,
mustard oil, glucose, NH4Cl, peptone, and yeast extract with three dummies were
screened in 12 trials. Table (1) is gives minimum (-) and maximum (+) range of the
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parameters used in Plackett-Burman design. The ranges of these factors were selected
according to the conditions adopted in literature works (Shabtai et al., 1992; Maia et al.,
2001; Rathi et al., 2002; Kader et al., 2007; Shah et al., 2007; Sangeetha et al., 2008).
Table 1 Minimum (-) and maximum (+) range of parameters used in the Plackett Burman design for lipase production by Acinetobacter sp. EH28
Range coding variables Component
(-) (+) X1 olive oil (% v/v) 0.00 1.0 X2 sesame oil (% v/v) 0.00 1.0 X3 tributyrin oil (% v/v) 0.00 1.0 X4 mustard oil (% v/v) 0.00 1.0 X5 glucose (% w/v) 0.00 0.5 X6 NH4Cl (% w/v) 0.00 0.2 X7 peptone (% w/v) 0.00 0.5 X8 yeast extract (% w/v) 0.00 0.2
Table (2) shows Plackett-Burman design matrix for screening of media components for
lipase production by Acinetobacter sp. EH 28. Lipase production was calculated in terms
of U/ml. Experiments were performed at 35°C for 48 hours.
The effect of each variable was determined by following equation:
∑(xi) = (∑Mi+ - ∑Mi–)/N (1)
Where ∑(xi) is the concentration effect of the tested variable. Mi+ and Mi– are the lipase
production from the trials where the variable (Xi) measured was present at the high and
low concentration, respectively, and N is the number of experiment (12 experiments).
Experimental error was estimated by calculating the variance among the dummy
variables as follows:
Veff = ∑(Ed) 2/n (2)
Where, Veff is the variance of the concentration effect, Ed is the concentration effect for
the dummy variable and n is the number of dummy variables. The standard error (SE) of
the concentration effect was the square root of the variance of an effect, and the
significance level (p-value) of each concentration effect was determined using student’s
t-test:
T (xi) = E (xi)/SE (3)
Where, E (xi) is the effect of variable xi. The variables with confidence levels greater than
90% were considered to influence the enzyme production significantly.
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Table 2 Plackett-Burman design matrix for screening of media components for
lipase production by Acinetobacter sp. EH28
No. of runs
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 Lipase Activity U/ml
1 + + - + + + - - - + - 1.96 2 - + + - + + + - - - + 3.12 3 + - + + - + + + - - - 2.94 4 - + - + + - + + + - - 2.17 5 - - + - + + - + + + - 2.49 6 - - - + - + + - + + + 1.43 7 + - - - + - + + - + + 2.91 8 + + - - - + - + + - + 4.48 9 + + + - - - + - + + - 1.12
10 - + + + - - - + - + + 0.21 11 + - + + + - - - + - + 0.28 12 - - - - - - - - - - - 0.39
3.2.6.2 Evaluation of nutritional effects on lipase production by Bacillus subtilis EH37
Screening of single factor was performed using Plackett-Burman statistical design (PB)
envisaging a number of factors and their interactive effect at a time. The effect of 13
factors viz. tween 80(% v/v), olive oil (% v/v), tributyrin oil (% v/v), lactose (% w/v),
glucose (% w/v), peptone (% w/v), yeast extract (% w/v), ammonium nitrate (% w/v),
CaCl2 (% w/v), bile salt (% w/v), MgCl2 (% w/v), NaNO3 (% w/v) and triton X100 (%
v/v) were studied, the parameters were varied over two levels as per Plackett-Burman
design. The minimum and maximum ranges selected for the parameters are given in
Table 3. The ranges of these factors were selected according to the conditions adopted in
literature works (Breuil et al., 1978; Abdel-Fattah, 2002; Ginalska et al., 2007; Shah et
al., 2007; Sangeetha et al., 2008).
Table 3 Minimum (-) and maximum (+) range of the parameters used in the
Plackett Burman design for lipase production by Bacillus subtilis EH37
Range coding variables Component (-)
X1 Tween (% v/v) 0.00 0.20 X2 Olive oil (% v/v) 0.00 1.00 X3 Tributyrin oil (%
v/v) 0.00 1.00
X4 Lactose (% v/v) 0.00 0.50 X5 Glucose (% w/v) 0.00 0.50 X6 Peptone (%
w/v) 0.00 0.30
X7 Yeast extract (% 0.00 0.30
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w/v) X8 Ammonium nitrate (%
w/v) 0.00 0.30
X9 CaCl2 (% w/v)
0.00 0.20
X10 Bile salt (% w/v)
0.00 0 .20
X11 Mg Cl2 (% w/v)
0.00 0.20
X12 NaNO3 (% w/v) 0.00 0.20 X13 Triton X100 (% v/v) 0.00 0.20
Design expert® 7.1 (STAT Ease Inc., Minneapolis, U.S.A) was used to generate
experimental design. The software design expert® 7.1 takes into consideration a
minimum of 20 variables. In cases where number of variables is less than twenty, the
software automatically makes up the number to twenty by incorporating dummy factors
and thus generates a minimum of twenty experiments. In this study, the Plackett-Burman
was conducted with 13 variables and 7 dummy (Table 4).
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Table 4 Plackett-Burman design matrix for screening of media components for lipase production by Bacillus subtilis EH37
Run
Tw
een
80
Oliv
e o
il
Trib
utyr
in
Lact
ose
Glu
cose
Pept
one
Yea
st e
trac
t
NH
4NO
3
CaC
l 2
Bile
salt
Mg
Cl 2
NaN
O3
Trit
on X
100
O
P Q
R
S T
U
Lipa
se
prod
uctio
n
U/m
l
1 + + - - + + + + - + - + - + - - - + + - 3.7
2 + -
- + + + + - + - + - - + - - + + - + 4.3
3 + -
+ - + - - - - + + - + - + - - + + + 4.2
4 - -
+ - - + + + + - + - + + - - - - + + 7.9
5 - +
+ - + + - - + + + + - - + - + - - - 4.9
6 + -
+ + + - - + + + + - + + - + - - - - 7.0
7 + +
- + - + - - - - + + - + + + - - + + 2.8
8 + + - - - + + - + + - - + + + + + - + - 3.6
9 + - + + - - + + + + - + - + + - - - - + 7.3
10 - + - + + - + + - - + + + - + - + - + - 2.1
11 - - + + - - - - +
+ - + + - - - + + + + 9.3
12 - + - - + + - + + - - + + - + + - + - + 3.4
13 - + - + - + + - - + + + + - - + - + - - 3.9
14 + + - - + - + - - - - + + + - + + - - + 0.12
15 - - + + + - + - + - - - - - + + - + + - 9.5
16 - - + + + + - + - + - - - - - + + - + + 5.6
17 - - - - - - + + - + + - - - + + + + - + 6.1
18 + + + + - + - + - - - - + + + - + + - - 4.0
19 + + + - - - - + + - + + - + - + + + + - 5.3
20 - - - - - - - - - - - - - - - - - - - - 2.0
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Lipase production was calculated in terms of U/ml. Experiments were performed at 35°C,
for 72 hours.
Effect of each variable on the production was determined by calculating their respective
E-value (Xu et al., 2002)
E = (total response at high level) – (total response at low level)
N
Where N is number of factors studied
3.2.7 Optimization of screened component using Response Surface Methodology (RSM)
Based on the results of screening studies of factors affecting the enzyme production (one
variable at a time and Plackett Burman design), media optimization using RSM was
employed to study the interaction of variables displaying positive effect on lipase
production. As per RSM each factor was studied at three levels -1, 0 and +1
3.2.7.1 Optimization of screened component for lipase production by Acinetobacter sp. EH28 using central composite design (CCD)
RSM was used to optimize the screened components for enhanced lipase production
using the central composite design (CCD). Four parameters viz., olive oil (X1),
ammonium chloride (X2), peptone (X3) and yeast extract (X4) which had been predicted
to play important role in production of lipase, were chosen as the independent variables
and enzyme activity (lipase activity) independent variable.
Table 5 Coded and actual values of the factors used in CCD
for lipase production by Acinetobacter sp. EH28
Actual factor levels at coded levels Factors Symbol -1 0 +1
Olive oil X1 0.00 0.50 1.00 NH4Cl X2 0.02 0.11 0.20 Peptone X3 0.05 0.30 0.55 Yeast extract X4 0.02 0.11 0.20
Fixed aliquot (0.2 A600) from overnight grown culture was used as inoculum. Production
was carried out at 35°C and 150 rpm for 48 hours. The coded and uncoded values of
variables at various levels are given in Table 5.
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According to this design, the total number of treatment combination was 2k+2k+n0, where
k is the number of independent variables and n0 is number of repetition of experiments at
the center point. A 24 factorial design, with four factors and six replicates at the centre
point, leading to 30 set of experiments, were used to optimize the production of enzyme
(Table 6). Table 6 CCD of variables in actual level with enzyme activity as response
for lipase production by Acinetobacter sp. EH28
Lipase activity Std No.
X1 X2 X3 X4 In U/ml
1 0.00 0.02 0.05 0.02 0.27 1.30 2 1.00 0.02 0.05 0.02 2.30 9.97 3 0.00 0.20 0.05 0.02 0.25 1.28 4 1.00 0.20 0.05 0.02 3.50 33.11 5 0.00 0.02 0.55 0.02 0.40 1.49 6 1.00 0.02 0.55 0.02 2.50 12.18 7 0.00 0.20 0.55 0.02 0.43 1.53 8 1.00 0.20 0.55 0.02 3.20 24.53 9 0.00 0.02 0.05 0.20 0.20 1.22
10 1.00 0.02 0.05 0.20 2.50 12.18 11 0.00 0.20 0.05 0.20 0.40 1.49 12 1.00 0.20 0.05 0.20 3.19 24.23 13 0.00 0.02 0.55 0.20 0.12 1.13 14 1.00 0.02 0.55 0.20 1.70 5.47 15 0.00 0.20 0.55 0.20 0.29 1.34 16 1.00 0.20 0.55 0.20 3.16 23.5 17 0.00 0.11 0.30 0.11 0.28 1.32
Continue Table 6
18
1.50 0.11 0.30 0.11 2.80 16.45
19 0.50 0.00 0.30 0.11 0.20 1.22
20
0.50 0.29 0.30 0.11 0.80 2.23
21
0.50 0.11 0.00 0.11 2.30 9.97
22
0.50 0.11 0.80 0.11 2.60 13.46
23
0.50 0.11 0.30 0.00 2.08 8.01
24 0.50 0.11 0.30 0.29 2.80 16.45
25 0.50
0.11 0.30 0.11 1.44 4.22
26 0.50
0.11 0.30 0.11 1.39 4.02
27 0.50
0.11 0.30 0.11 1.46 4.31
28 0.50
0.11 0.30 0.11 1.37 3.95
97
29 0.50
0.11 0.30 0.11 1.36 3.90
30 0.50 0.11 0.30 0.11 1.80 6.05
For statistical calculations, the independent variables were coded as
xi = (Xi-X0) / δXi (4)
Where Xi is the experimental value of variable; X0 is the midpoint of Xi, δXi is the step
change in Xi and xi is coded value for Xi, i =1, 2, 3, 4 etc.
Lipase production (response Y), was explained as second order response surface model in
four independent variables
Y = β0 + ∑ βi Xi + ∑βii X i2 +∑ βij Xi Xj (5) Where β0, βi, βii, and βij represent respectively, the constant process effect in total, the
linear, quadratic effect of Xi and the interaction effect between Xi and Xj on enzyme
activity.
The design and analysis of the results were done using Design Expert version 7.0, STAT-
EASE, Inc., Minneapolis, USA Statistical software to estimate the responses of
dependent variables
3.2.7.2 Optimization of screened component for lipase production by Bacillus subtilis EH37 using Box-Behnken design (BBD)
Five parameters viz., tribuytrin (X1), yeast extract (X2), ammonium nitrate (X3), calcium
chloride (X4) and triton X100 which have been found to play significant role in the
production of lipase, were chosen as the independent variables, and lipase activity was
dependent response variable. Each of the independent variables was studied at three
different levels as per BBD in five variables with a total of 43 experiments. The
temperature was kept constant at 35°C throughout the 43 experiments. All the 43
experimental cultures were analyzed for lipase activity after 72 hours. The plan of BBD
in coded levels of the five independent variables is as shown in Table (7).
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Table 7 Coded and actual values of the variables used in BBD for lipase production by Bacillus subtilis EH 37
Actual factor levels at coded levels
Factors
Symbol
-1 0 +1
Tributyrin X1 0 0.5 1
Yeast extract X2 0 0.25 0.5
Ammonium nitrate X3 0 0.25 0.5
Calcium chloride X4 0 0.1 0.2
Triton X-100 X5 0 0.25 0.5
For statistical calculations, the independent variables were coded according to equation 4
and lipase production (response Y), was explained as second order response surface
model in five independent variables as in equation 5.
The results of the experimental design were analyzed and interpreted using MINITAB
version 14 (PA, USA) statistical software.
3.2.7.3 Validation of models
In order to validate the maximum lipase production by the models, new sets of
experiment for lipase production were performed using the optimal conditions obtained
by RSM. All experiments were performed in triplicate and the lipase yields were
calculated as the average of three sets of experiments.
3.2.8 Purification
3.2.8.1 Crude enzyme preparation
The EH28 and EH37 cultures were grown in their optimized medium. At the end of
incubation period, the bacterial cells in the fermentation broth were removed by
centrifugation at 20,000 xg for 20 min at 4°C. The cell-free supernatant was used as a
crude enzyme preparation and purified further as described.
3.2.8.2 Ammonium sulphate precipitation
The calculated amount of solid ammonium sulphate was added to cell free supernatant
with constant stirring at 4°C to achieve 80% (for Acinetobacter sp. EH28) and 60% (for
B. subtilis EH37) saturation. The precipates thus obtained were harvested by
99
centrifugation at 13,000 xg for 30 min and resuspended in minimum volume of 25 mM
sodium phosphate buffer (pH 7.2). This enzyme solution was subjected to dialysis for 24
hours at 4°C against the same buffer, with three intermittent changes of the buffer.
Further, the filtrate was concentrated using 30KDa centricon filter at 4°C for 60 min at
800 xg. Lipase activity and protein concentration were determined for both, the dialyzed
and the concentrated filtrate sample.
3.2.8.3 Hydrophobic interaction chromatography (HIC)
The concentrated lipase solution were applied on an HIC column using phenyl sepharose
® 6 (1.5 cm × 6 cm), which was pre-equilibrated with 0.6 M ammonium sulphate
dissolved in 25 mM phosphate buffer (pH 7.2). The bound enzyme was eluted by
ammonium sulphate dissolved in 25 mM phosphate buffer (pH 7.2) at a flow rate of
1mL/min through negative linear gradient. The resultant fractions were subjected for
determination of lipase activity and protein content.
3.2.9 Characterization of partially purified lipase
The properties of lipases from Acinetobacter sp. EH28 and B. subtilis EH37 were studied
in order to characterize the lipases and to determine its potential for various industrial
applications. Characterization of lipases was done with reference to pH, temperature,
substrate specificity, metal ions, inhibitors and organic solvent. The enzymes were also
studied for Michaelis–Menten constants, Km and Vmax. pH-stat method was used to
estimate the lipase activity throughout the characterization except in case of substrate
specificity of p-Nitrophenyl esters where spectrophotometer method was applied.
3.2.9.1 Effect of pH on lipase activity and stability
The lipases activity was monitored over a pH range of 4-12 at 30°C. Buffers (50mM)
used were; acetate buffer for pH 4.0-5.0, sodium phosphate buffer for pH 6.0-8.0, glycine
NaOH buffer for pH 9.0-10.0 and phosphate NaOH buffer for pH 11-12. To determine
the effect of pH on enzyme stability, partially purified lipases prepared in different
buffers were incubated for 24 hours at 20°C and then assayed in the respective pH.
3.2.9.2 Effect of temperature on lipase activity and stability
100
The activity of partially purified lipases was monitored at different temperatures in the
range of 30 to 80°C at buffer pH 10 (for Acinetobacter sp. EH28) and pH 8 (for B.
subtilis EH37). The thermal stability of lipases was studied from 50 to 70°C at respective
pH and residual activity was measured at every15 min interval upto 2 hours.
3.2.9.3 Substrate specificity
The substrate specificity of the lipases was studied using various oil and fats; oils and fats
tested as substrates were: coconut oil, groundnut oil, corn oil, castor oil, mustard oil, teel
oil, cotton seed oil, sunflower oil, karanj oil, rice bran oil, tributyrin oil and neem oil. 1
ml of olive oil in titration reaction mixture described earlier was replaced by various fat
and oils and the lipases were assayed under standard conditions of pH and temperature.
The activity was expressed as relative activity in comparison to activity on olive oil
considered as 100%.Activity of lipases was also checked on various p-nitrophenyl esters
viz. p-NP caprate C10, laurate C12 and palmitate C16. Activity was expressed as percentage
relative activity in comparison to p-NP laurate, which was considered as 100%.
3.2. 9.4 Determination of Michaelis–Menten constants
Enzyme assays with 100 μl of purified lipase were performed in Tris–HCl buffer, pH 9.0
at 50°C with increasing concentration of selected p-NP from 0.3 to 4.0 mg/ml.
Lineweaver–Burk plot was plotted to determine Km and Vmax.
3.2.9.5 Effect of metal ions and additives on lipase activity To determine the effect of various agents viz., CaCl2, MgCl2, CuCl2, CoCl3, FeCl3, ZnCl2
and EDTA, the partially purified lipases were pre-incubated with these agents at 1mM
and 10 mM concentration for one hour and then assayed for residual lipase activity
compared to ions free controls. The effect of metal chelators EDTA on lipases activity
was studied by incubating the enzymes with varying concentrations of metal chelator
(1.0mM- 10 mM) for 1 hour and the residual activity was determined against the controls.
3.2.9.6 Effect of organic solvent on lipase activity
The partially purified lipases were incubated for one hour with various organic solvents
like ethanol, methanol, isopropyl alcohol, dimethyl formamide, dimethyl sulfoxide, n-
101
hexane and acetone to final concentrations of 15% and 30% (v/v) and then assayed for
residual lipase activity. The stability of the enzymes was expressed as the remaining
lipolytic activity relative to non-solvent containing control.
3.2.9.7 Effect of inhibitors
The effect of various inhibitors such as phenyl methyl sulfonic acid (PMSF), iodoacetic
acid and iodoacetamide on enzymes activity was studied by incubating the enzyme with
two different concentrations (1 and 10 mM) of each inhibitor for one hour and then
measuring the residual activity under standard conditions. Effect of different thiols
concenteration (1-10 mM) i.e mercaptoethanol, dithiothreitol (DTT) was also studied for
both lipases.
3.3 Results and discussions
3.3.1 Bacterial strain
About 40 isolates were screened for lipase activity on different alkaline pH, temperature
and various organic solvents. The lipase from isolates EH28 and EH37 showed highest
unit activity of 5.5 (at 60°C) and 7.3 (at 50°C) U/ml respectively (as described earlier,
chapter 2, section 2.3.6). These two isolates exhibited a novel characteristic of growing
and producing lipase in presence of organic solvents. Therefore, these cultures were
explored and selected for further studies.
The bacterial strain was identified by sequencing of 16S rRNA gene (as described
chapter 2, section 2.3.5). BLASTn search revealed that the bacterial strain EH28 (Gene
Bank accession number E U 703817) exhibited 100% sequence homology with
Acinetobacter sp. The phylogeny cluster of EH28 along with related Acinetobacter sp. is
shown in Fig. 1. Acinetobacter strains are well represented among fermentative bacteria
for the production of number of extra and intracellular economic products such as lipase,
and several kinds of biopolymers (Hong & Chang 1998; Abdel-El-Haleem, 2003;
Mongkolthanaruk et al., 2004; Snellman & Colwell, 2004).
102
EH 28 (EU703817.1)
Uncultured bacterium (AM183113)
Acinetobacter sp. phenon 2 (AJ275041.2)
Acinetobacter sp. phenon 2 (AJ275040.2)
Uncultured gamma proteobacter (DQ463728.2)
Uncultured gamma proteobacter (DQ463701.2)
Uncultured Acinetobacter sp. (EF371492.1)
Uncultured Acinetobacter sp.(EU407207.1)
Uncultured bacterium clone KS (DQ532284.1)
Uncultured bacterium clone Pa (EF632913.1)
Escherichia coli K12 U00096 /1
100
98
52
97
87
57
99
44
0.02
Fig. 1 Phylogenetic tree derived from 16S rRNA gene sequence of isolated .EH28 (GenBank Accession No. EU703817) and sequence of closest phylogenetic neighbors obtained by NCBI BLAST (n) analysis, numbers in the parenthesis indicate accession numbers of corresponding sequence. The tree was constructed using neighbor joining algorithm with Kimura 2 parameter distances in MEGA 4.0 software. E.coli K 12 has been taken as an out group. Number at nodes indicates percent bootstrap value above 50 supported by more than 1000 replicates. The bar indicates the Jukes-Cantor evolutionary distance
The DNA sequencing and BLASTn analysis of 16S rRNA gene sequence of the strain
EH37 showed maximum sequence identity (100%) with the complete sequence of
Bacillus subtilis. The Phylogeny cluster of EH37 (Gene Bank accession number F J
373271.1) along with related Bacillus species (Firmicutes) is depicted in Fig. 2.
Attractive properties of Bacillus subtilis like its capability to secrete homologous and
heterologous proteins in appreciable quantities into the growth medium and its
classification as Generally Regarded As Safe (GRAS) organism by US food and drug
administration (FDA) have made it an important expression host to produce proteins of
commercial interest (Eggert et al., 2002).
103
0.02 Fig. 2 Phylogenetic tree derived from 16S rRNA gene sequence of isolated EH37 (GenBank Accession No. F J 373271.1) and sequence of closest phylogenetic neighbors obtained by NCBI BLAST (n) analysis, numbers in the parenthesis indicate accession numbers of corresponding sequence. The tree was constructed using neighbor joining algorithm with Kimura 2 parameter distances in MEGA 4.0 software. E.coli K 12 has been taken as an out group. Number at nodes indicates percent bootstrap value above 50 supported by more than 1000 replicates. The bar indicates the Jukes-Cantor evolutionary distance
3.3.2 Media Optimization
3.3.2.1 One variable at a time strategy
Physical parameters such as pH, temperature inoculum density and agitation which may
influence lipase production by modulating bacterial growth (Gupta et al., 2004a; Kempka
et al., 2008) were studied using one variable at a time strategy.
3.3.2.1.1 Effect of pH on lipase production
The pH dependency of the culture played an important role for both growth of
microorganism and production of enzymes (Gupta et al., 2004a; Kader et al., 2007).
Thus, the effect of this parameter was studied in the range of pH from 4-12. From the
results (Table 8) it is evident that, lipase was produced in a pH range (6-12). Maximum
lipase was produced at pH 7-8 (2.1 U/ml) for Acinetobacter sp. EH28 and at pH 9 (3.5
U/ml) for B. subtilis EH37, thus these pH values were employed for cultures used in
future experiments.
Bacillus mojavensis strain BCRC (EF433405)
Bacillus axarquiensis strain (DQ993671)
Bacillus sp. CU01 (EF522121)
Bacillus sp. G1DM-30 (DQ416796)
Uncultured Bacillus sp. (EU567055)
Bacillus sp. G1DM-84 (EU037266)
Bacillus licheniformis strain (DQ167473)
Bacillus subtilis strain WD23 (EU780682)
EH 37 373271.1
Escherichia coli K12 U00096/1
75
44
55
84
54
45
99
104
Table 8 Effect of different pH on lipase production by Acinetobacter sp. EH28 and Bacillus subtilis EH37
Lipase production U/ml
pH EH28 EH37
4
0.0
0.0
5
0.0
0.0
6
0.7
1.4
7
2.1
3.0
8
2.1
3.3
9
1.3
3.5
10
0.7
3.3
11
0.5
2.0
12
0.0
0.3
A good number of reports have been published stating pH in the range of 7.0 – 8.0 for the
best growth and lipase production from Bacillus sp. and Acinetobacter sp. (Hong and
Chang, 1998; Rathi et al., 2002; Eltaweel et al., 2005; Shah et al., 2007; Chakraborty and
Raj, 2008). However, Kumar et al., (2005) reported optimum pH of 8.5 for lipase
production by Bacillus coagulans, whereas Sharma et al., (2002) Bayoumi et al., (2007)
and Sangeetha et al., (2008) reported optimum pH of 9.0 for lipase production by
Bacillus pumilus, Bacillu sp. RSJ-1 and Bacillus lincheniformis B42.
3.3.2.1.2 Effect of temperature on lipase production
Incubation temperature has been found to be significant controlling factor for enzyme
production (Gupta et al., 2004a; Kader et al., 2007). The study revealed that, EH28 and
EH37 produced lipase over wide range of temperatures from 25-55°C (Table 9) with the
maximum enzyme production (2.1 U/ml) for Acinetobacter sp. EH28 and (3.5 U/ml) for
B. subtilis EH37 at 35°C, thus 35°C was chosen as optimum incubation temperature for
lipase production from both strains. It has been observed that, in general, lipases are
produced in the temperatures range 20-45°C (Hong and Chang, 1998; Gupta et al.,
105
2004a; Rathi et al., 2002; Ginalska et al., 2007; Sangeetha et al., 2008). However, high
temperature for lipase production (50-55°C) from Bacillus sp. was reported by several
authors (Sharma et al., 2002; Eltaweel et al., 2005; Bayoumi et al., 2007)
Table 9 Effect of different temperatures on lipase production by
Acinetobacter sp. EH28 and Bacillus subtilis EH37
Enzyme production
(U/ml)
Temperatures
EH28 EH37
25
0.7
1.5
35
2.1
3.5
45
1.2
2.8
55
0.5
2.0
3.3.2.1.3 Effect of inoculum density on lipase production
Effect of varying inoculum density viz. 0.05, 0.10, 0.15, 2.0 and 0.25 OD on lipase
production was determined at A600. Maximum lipase production of 3.2 U/ml and of 4.4
U/ml was produced when the 0.20 and 0.10 inoculum density was used from
Acinetobacter sp. EH28 and B. subtilis EH37 respectively (Table 10).
Table 10 Effect of inoculum density on lipase production by
Acinetobacter sp. EH28 and Bacillus subtilis EH37
Enzyme production (U/ml)
Inoculum density (A600)
EH28 EH37
0.05
0.7
3.9
0.1
1.9
4.4
0.15
2.8
4.2
0.2
3.2
4.1
0.25
2.4
4.0
Further increase in inoculum level decreased the enzyme production. Thus 0.2 and 0.1
OD at 600nm was selected as seed inoculum for further experiments.
106
3.3.2.1.4 Effect of agitation on lipase production
Usually, shaking rate i.e., agitation/aeration has profound influence on lipase production
by aerobic microorganisms as increase in shaking rate increases the availability of
dissolved oxygen. In addition, shaking may also create condition of higher availability of
the carbon sources to microorganisms (Nahas, 1988; Kader et al., 2007).
Investigations on the effect of agitation on enzyme production revealed that maximum
lipase activity of 3.8 U/ml (Acinetobacter sp. EH 28) and 4.5 U/ml (B. subtilis EH37) was
observed at 150 and 100 rpm respectively (Table 11), whereas further increase in
agitation result in slight decrease in lipase production. Therefore 150 and 100 rpm
shaking were employed for further experimentations.
Optimum lipase production for most Bacillus species has been reported to occur at lower
agitation rates (Gupta et al., 2004a). However, the highest production of lipase from
Bacillus species was reported at 350 rpm (Sharma et al., 2002).
Table 11 Effect of Agitation on Lipase production by
Acinetobacter sp. EH28 and Bacillus subtilis EH37
Lipase production U/ml
Agitation (rpm)
EH28 EH37
50
1.2
2.8
100
2.6
4.5
150
3.8
3.7
200
2.9
3.2
3.3.3 Growth and lipase production profile
The rate of growth and enzyme production by microorganisms is quite important in
understanding their fermentation pattern. However, the onset of lipase production is
organism-specific but in general; it is released during late logarithmic or stationary phase
(Bayoumi et al. 2007).
3.3.3.1 Growth and lipase production profile of Acinetobacter sp. EH28
107
Maximum lipase activity of 3.8 U/ml was observed on the 2nd day of production with
optical density 2.1 at 600 nm. On 3rd day, increase in lipase activity was negligible and
after 3rd day, decline in lipase activity was observed. In a study to determine optimum
time to harvest the lipase for purification, Snellman et al., (2002) reported maximum
lipase production of 0.25 U/ml activity from Acinetobacter sp. RAG-1 after 50 hours.
The decrease of lipase production at the later stage could be possibly due to pH
inactivation, proteolysis or both (Castro-Ochoa et al., 2005; Bayoumi et al., 2007).
3.3.3.2 Growth and lipase production profile of Bacillus subtilis EH37
Maximum lipase activity of 9 U/ml was observed on the 3rd day of production with the
optical density 1.75 at 600 nm. On 4th day, increase in lipase activity was negligible and
after 4th day, decline in lipase activity was observed. Bacillus species generally
synthesize a varity of exteracellular enzymes (e.g amylase, proteinase and lipase) the
maximum synthesis of which normally occur in the early stationary and late expotential
phase of growth, before sporulation (Chen et al., 2004; Shah et al., 2007). Sangeetha et
al., (2008) reported 63 hours for maximum lipase production from Bacillus pumilus.
However, maximum lipase activity on the 3rd day of fermentation has also been reported
from Bacillus licheniformis B42 and Bacillus megaterium (Lima et al., 2004).
3.3.4 Evaluation of nutritional effects on lipase production using Plackett-Burman design
Plakett-Burman Design is used to pick factors that influence lipase production
significantly and insignificant ones were eliminated in order to obtain a smaller,
manageable set of factors (Pujari et al., 2000; Abdel-Fattah, 2002; Shah et al., 2007).
3.3.4.1 Evaluation of nutritional effects on lipase production by Acinetobacter sp. EH28
The experimental plan and corresponding lipase yield is shown in Table 2, the plan
included 12 experiments, and two level of concentration for each factor. The factors
designated X1-X8 represent medium constituents, X9-X11 being a dummy variable. Table
12 is representing the results obtained from screening experiments for lipase production
by Acinetobacter sp. E28. When the sign of the concentration effect of tested variable
(Exi) is positive, the influence of the variable upon lipase yield is greater at a high
concentration, and when negative, the influence of the variable is greater at a low
108
concentration. The effect of the variable X1 (olive oil), X2 (sesame oil), X5 (glucose), X6
(NH4Cl), X7 (peptone) and X8 (yeast extract) were 0.1905, 0.1315, 0.1128, 0.4471,
0.1781 and 0.3221 respectively, i.e., the influence of these variables is greater at a higher
concentration. Increase in the concentration of these six variables would increase the
lipase production. When the concentration of four of these variables, i e., X1, X2, X6, and
X8, were at high level (Table 2, run # 8) the lipase production was 4.48 U/ml. As
maximum negative effect was shown by X4 (mustard oil) variable, experimental runs
having higher concentration of this variable showed low lipase yield i e., lipase yield of
run # 1, 4, 6, 10 and 11 were 1.96, 2.17, 1.43, 0.21 and 0.28 U/ml . The variables, X1, X2,
X5, X6, X7, and X8 had confidence levels above 90 % and were considered to influence
production (Table 12).
Table 12 Results of screening experiments design for lipase production
by Acinetobacter sp. EH28 using Plackett-Burman
Variables E t-value P-value Confidence (%) X1 0.1905 4.762 0.0175 98.24 X2 0.1315 3.287 0.0461 95.38 X3 -0.1561 -3.902 - - X4 -0.2580 -6.490 - - X5 0.1128 2.820 0.0667 93.32 X6 0.4471 11.177 0.0015 99.84 X7 0.1781 4.452 0.0210 97.89 X8 0.3221 8.052 0.0040 99.59
The main observation of the Plackett-Burman study was that, both carbon sources; oils
(X1 and X2) and carbohydrate (X5) induced lipase production, which is in agreement with
most of the available reports (Kamimura et al., 1999; Maia et al., 2001; Sharma et al.,
2001; Gupta et al., 2004b). n-hexane and olive oil as carbon source for production of an
alkaline lipase has been reported in Acinetobacter radioresistens (Gupta et al.,2004a).
Various nitrogen sources were examined for producing extracellular lipase from
Acinetobacter calcoaceticus, whereas amino acid and tryptone were found to improve the
lipase production (Sharma et al., 2001).
In this study, olive oil was found to be more significant carbon source than others, and
therefore it was selected for further study.
3.3.4.2 Evaluation of nutritional effects on lipase production by Bacillus subtilis
109
EH37
A Plackett-Burman design was used to study the effect of various factors on lipase
production under standardized physical conditions. The effect of 13 parameters viz. olive
oil (% v/v), tributyrin oil (% v/v), lactose (% w/v), glucose (% w/v), peptone (% w/v),
yeast extract (% w/v), ammonium nitrate (% w/v), bile salt (% w/v), NaNO3 (% w/v),
CaCl2 (% w/v), triton X100 (% v/v) and Tween 80 (% v/v) were studied. The results of
Plackett-Burman experiment are presented in Table 13.
Table 13 ANOVA screening experiments design for lipase production
by Bacillus subtilis EH37
Source
Sum of squares
df
Mean square
F Value
p-value Prob>F
Model 57039 20 4.96 176.18 0.0435 Olive oil Tributyrin 5.24 1 5.24 188.02 0.0463 Lactose 1.18 1 1.18 42.30 0.0971 Glucose 1.22 1 1.22 43.73 0.0955 Peptone 0.099 1 0.099 3.54 0.3109 Yeast extract
5.26 1 5.26 188.59 0.0463
Ammonium nitrate
9.50 1 9.50 340.69 0.0345
CaCl2 11.93 1 11.93 427.8 0.0308 Bile salt 2.99 1 2.99 107.19 0.0613 MgCl2 0.67 1 0.67 24.13 0.1279 NaNo 0.80 1 0.80 28.69 0.1175 Triton X100 6.23 1 6.23 223.42 0.0425 Tween 80 3.10 1 3.1 111.17 0.0602 Residual 0.028 1 0.028 Cor Total 57.41 19
R-squared = 0.99 Adj- R-squared = 0.99
Pred R-squared = 0.81 Adequate Precision = 42.3
The model “F” value of 176.18 implies there is a 4.35% chance that a “Model F- Value”
this large could occur due to noise. Values of ”Prob>F” less than 0.05 indicate model
terms are significant. In this case, tributyrin, yeast extract, ammonium nitrate, CaCl2, and
triton X100 are significant model terms. The “Pred R-squared” of 0.81 R-squared is in
reasonable agreement with the “Adj- R-squared” of 0.99. “Adeq Precision” measures the
signal to noise ratio. A ratio greater than 4 is desirable. In this study ratio of 42.28
indicates an adequate signal. This model can be used to navigate the design space.
110
3.3.5 Optimization of screened component using Response Surface Methodology (RSM)
Since the conventional method of optimization, “one factor at a time “approach is
laborious, time consuming and incomplete, response surface methodology (RSM) which
involves full factorial search by examining simultaneous, systematic and efficient
variation of important components was applied to model the production process, identify
possible interactions, higher order effects and determine the optimum operational
conditions. However, RSM is useful for small number of variables (up to five) but is
impractical for large number of variables, due to high number of experimental runs
required (Sharma et al. 2002; Mohana et al., 2008).
3.3.5.1 Optimization of screened component for lipase production by Acinetobacter sp. EH28 using central composite design (CCD)
The variables showing confidence level above 90% in the Plackett –Burman design (olive
oil, ammonium chloride, peptone, and yeast extract) were selected and optimized using a
central composite design. Table 6 shows lipase production corresponding to combined
effect of four variables in their specific range after 48 hours. The dependant response
variable, which is the lipase activity, was transformed to natural log values in order to
stabilize its variance. Lipase production varied markedly with the conditions tested, in the
range of 1.13 (0.12 In) to 33.11 (3.5 In) U/ml. Lowest lipase production was observed
when olive oil and ammonium chloride concentration were low, and yeast extract and
peptone were high (Std no.13). Higher olive oil and ammonium chloride concentrations
supported better enzyme production (Std. No. 4). The experimental results suggest that
these variables strongly affect the fermentation process. Further RSM steps, the statistical
analysis of the CCD experimental results, response surface modeling and optimization of
the process variables was carried out using Design Expert version 7.0.
By applying multiple regression analysis on the expermintal data, the following second
order polynomial equation was found to describe lipase production:
Y = 1.48 +1.24 x1 +0.35 x2 -0.11 x3 -0.076 x4 +0.27 x1 x2 -0.1 x1 x3 -0.038 x1 x4 -0.02 x2x3
+ 0.039 x2x4 -0.077 x3 x4 -0.27 x12 -0.37 x2
2 +0.35 x32 +0.37 x4
2 (6)
111
Where Y = predicted response and x1, x2, x3 and x4 are coded values of olive oil,
ammonium chloride, peptone, and yeast extract, respectively.
The value of predicted R2 is 0.98 indicating a good agreement between the
experimental and predicted values of lipase yeild. Adequate precision measures the signal
to noise ratio, a ratio greater than 4 is desirable, in this study ratio of 25.10 indicates an
adequate signal. So this model can be used to navigate the design space. Analysis of
variance (ANOVA) for lipase activity shows that fitted second order response surface
model is highly significant with F test = 62.84 (p = 0.001) as shown in Table 14. The
regression coefficients in the response surface model, for the linear, quadratic and
interaction effects of the variables are shown along with t- and p-value in Table 14.
The coefficient for the linear effect of olive oil (p= 0.0001), ammonium chloride
(p= 0.0001) and peptone (p= 0.0313) were significant. Also the quadratic effect of olive
oil (p= 0.0001), ammonium chloride (p= < 0.0001), peptone (p= < 0.0001) and yeast
extract (p= < 0.0001) were highly significant. The coefficient for the interaction between
olive oil and ammonium chloride (p = <0.0001) was statistically significant. From the
signs and magnitude of regression coefficient for the four study variables in fitted
regression equation (6), the lipase production can be well interpreted.
The contour plots representing the lipase production over changes in independent
variables x1, x2, x3 and x4 are shown in Fig (Fig. 3). The isoresponse contour plots
showing the behavior of lipase activity (2.17 In U/ml) with respect to changes in olive oil
and ammonium chloride when yeast extract and peptone were held at coded level zero,
higher lipase activity was observed at high level of olive oil and ammonium chloride.
112
Table 14 ANOVA for response surface quadratic model analysis of variance
R-squared = 0.98 Adequate Precision = 25.1
Adjusted R2 = 0.97 Predicted R-squared = 0.90
The optimal values of olive oil, NH4Cl, yeast extract and peptone were predicted to be
1.0 (% v/v), 0.2 (% w/v), 0.02 % (% w/v) and 0.05 % (% w/v) respectively. The
maximum lipase activity predicted by the model 33.1 U/ml (3.5 In U/ml) agrees well with
the experimental value 31.67 U/ml (3.46 In U/ml) obtained from the experimental
verification at the optimal values. Thus statistical optimization of medium components
for lipase production by Acinetobacter sp. EH28 resulted in 8.33 fold (31.67 U/ml)
enhancement in lipase production.
Sources
Sum of square
df
Mean square
F -value
p-value prob>F
Model
37.84
14
2.70
62.84
<0.0001
X1 28.58 1 28.58 664.40 <0.0001 X2 2.42 1 2.42 56.24 <0.0001 X3 0.24 1 0.24 5.64 0.0313 X4 0.11 1 0.11 2.63 0.1259
X1 x X2 1.14 1 1.14 26.58 0.0001 X1 x X3 0.17 1 0.17 4.00 0.0640 X1 x X4 0.023 1 0.023 0.54 0.4728 X2 x X3 6.683E003 1 6.683E003 0.16 0.6990 X2 x X4 0.024 1 0.024 0.55 0.4685 X3 x X4 0.096 1 0.096 2.23 0.1560 X1 x X1 1.16 1 1.16 27.05 0.0001 X2 x X2 2.37 1 2.37 54.99 < 0.0001 X3 x X3 2.12 1 2.12 49.32 < 0.0001 X3 x X3 2.29 1 2.29 53.31 < 0.0001 Residual 0.65 15 0.65 0.043 Lack of
Fit 0.49 10 0.049 1.65 0.3034
Pure Error
0.15 5 0.030
Cor. Total
38.49 29
113
Fig. 3 contour plot for lipase production by EH28 showing the effect of interaction between olive oil and ammonium chloride when yeast extract and peptone were held at coded level zero
3.3.5.2 Optimization of screened component for lipase production by Bacillus subtilis EH37 using Box-Behnken design
By using Plackett-Burman design, tributyrin oil, ammonium nitrate, yeast extract,
calcium chloride and triton X100 were found to be significant components influencing
lipase yield.
The result of 43-run BBD in the five variables chosen for optimization of bacterial lipase
production is shown in Table 15. The dependant response variable, which is the lipase
activity, was transformed to natural log values in order to stabilize its variance. Lipase
production varied markedly with the conditions tested, in the range of 11.7 U/ml (ln 2.46)
−35 U/ml (ln 3.56). Lowest lipase production was observed when yeast extract and
ammonium nitrate concentration were low, and tributyrin, calcium chloride and Triton
X100 concentrations were moderate (run 17). Higher yeast extract, calcium chloride, and
moderate ammonium nitrate concentrations supported better enzyme production (run 24).
The experimental results suggest that these variables strongly affect the fermentation
process. Further RSM steps, the statistical analysis of the BBD experimental results,
114
response surface modeling and optimization of the process variables were carried out
using MINITAB 14.
Table 15 Full factorial Box-Behnken design for lipase production by Bacillus subtilis EH37
Experimental activity Predicted lipase activity
No. Tributyrin Yeast extract
Ammonium nitrate
Calcium chloride
Triton X-100
ln U/ml ln U/ml 1 -1 -1 0 0 0 2.83 16.89 2.83 17.01 2 -1 +1 0 0 0 2.87 17.60 2.90 18.17 3 +1 -1 0 0 0 2.66 14.30 2.67 14.44 4 +1 +1 0 0 0 3.52 33.77 3.55 34.88 5 -1 0 -1 0 0 2.87 17.55 2.86 17.48 6 -1 0 +1 0 0 3.22 24.99 3.20 24.61 7 +1 0 -1 0 0 3.23 25.15 3.23 25.15 8 +1 0 +1 0 0 3.34 28.14 3.33 27.80 9 -1 0 0 -1 0 2.90 18.24 2.90 18.24
10 -1 0 0 +1 0 3.20 24.41 3.19 24.29 11 +1 0 0 -1 0 3.11 22.41 3.09 22.00 12 +1 0 0 +1 0 3.51 33.54 3.49 32.75 13 -1 0 0 0 -1 3.14 23.15 3.15 23.34 14 -1 0 0 0 +1 2.97 19.49 2.95 19.03 15 +1 0 0 0 -1 3.14 23.20 3.17 23.71 16 +1 0 0 0 +1 3.43 30.78 3.42 30.48 17 0 -1 -1 0 0 2.46 11.70 2.49 12.01 18 0 -1 +1 0 0 3.13 22.80 3.18 24.00 19 0 +1 -1 0 0 3.47 32.13 3.43 30.94 20 0 +1 +1 0 0 3.19 24.39 3.18 24.09 21 0 -1 0 -1 0 2.83 16.89 2.76 15.74 22 0 -1 0 +1 0 2.97 19.49 2.94 18.88 23 0 +1 0 -1 0 3.10 22.11 3.07 21.56 24 0 +1 0 +1 0 3.56 35.09 3.57 35.58 25 0 -1 0 0 -1 2.83 16.89 2.84 17.13 26 0 -1 0 0 +1 2.86 17.43 2.86 17.37 27 0 +1 0 0 -1 3.30 27.11 3.31 27.28 28 0 +1 0 0 +1 3.35 28.51 3.34 28.19 29 0 0 -1 -1 0 2.79 16.33 2.82 16.81 30 0 0 -1 +1 0 3.46 31.72 3.46 31.69 31 0 0 +1 -1 0 3.29 26.94 3.34 28.08 32 0 0 +1 +1 0 3.37 29.14 3.38 29.43 33 0 0 -1 0 -1 3.15 23.41 3.15 23.38 34 0 0 -1 0 +1 3.14 23.10 3.13 22.83 35 0 0 +1 0 -1 3.35 28.59 3.33 27.80 36 0 0 +1 0 +1 3.43 31.00 3.40 29.84 37 0 0 0 -1 -1 3.09 22.00 3.08 21.82 38 0 0 0 -1 +1 3.05 21.19 3.11 22.31 39 0 0 0 +1 -1 3.44 31.09 3.42 30.69 40 0 0 0 +1 +1 3.40 30.00 3.45 31.44 41 0 0 0 0 0 3.35 28.49 3.33 27.94 42 0 0 0 0 0 3.33 28.00 3.33 27.94 43 0 0 0 0 0 3.31 27.30 3.33 27.94
115
The statistical analysis employed Fisher’s ‘F’-test and Student’s t-test. Analysis of
variance (ANOVA) for lipase activity shows that fitted second order response surface
model is highly significant with F test = 108.6 (p = 0.001) as shown in table 16. The
regression coefficients in the response surface model, for the linear, quadratic and
interaction effects of the variables are shown along with t- and p-value in Table 17.
Table 16 ANOVA of lipase production by Bacillus subtilis EH37; effect of tributyrin,
yeast extract, ammonium nitrate, calcium chloride and Triton X-100
Source Degree of freedom (DF)
Sum of square (SS)
Adjusted some of square
Mean of squares (MS)
F P
Regression
20
2.76765
2.767652
0.138383
108.60
0.001
Linear 5 1.80371 0.452853 0.090571 71.08 0.001 Square 5 0.39138 0.391376 0.078275 61.43 0.001
Interaction 10 0.57257 0.572569 0.057257 44.93 0.001 Residual error 22 0.02803 0.028034 0.001274
Lack of fit 20 0.02712 0.027116 0.001356 2.95 0.283 Pure error 2 0.00092 0.000918 0.000459
Total 42 2.79569
R-Squared = 99.0% Adjusted R2 = 98.1%
The fitted second order response surface model specified by Eq. (5) for lipase activity in coded process variables is: Y = 2.23719 +0.18410 x1 +2.43637 x2 +2.49716 x3 +0.27441 x4 +0.27441 x5 -0.51298 x1
2
-3.40215 x22 -0.76036 x3
2 -0.03264 x42 -0.50844 x5
2 +1.63770 x1x2 -0.48289x1x3
+0.05602 x1x4 +0.90968 x1x5 -3.76998 x2x3 +0.31899 x2x4 +0.07681 x2x5 +0.58546
x3x4 + 0.37612 x3x5 +0.00170 x4x5 (7)
The coefficient of determination R2 for the above predicted equation was 99.0%.
Therefore, this equation can be used for predicting the response at any combination of
five predicted variables in and around their experimental range. Lipase activity (Y) at
specified combination of five variables can be predicted by substituting the
corresponding coded values in Eq. (5).
Apart from the linear effect of the variables on the production process, the second order
RSM also gives insight into their quadratic and interaction effects (Table 17). The
coefficient for the linear effect of tribuytrin (p= 0.001), yeast extract (p= 0.001),
ammonium nitrate (p= 0.001), calcium chloride (p= 0.001) and Triton X100 (p= 0.001)
116
were highly significant. Among the higher order effects, the quadratic effect of tribuytrin
(p= 0.001), yeast extract (p= 0.001), ammonium nitrate (p= 0.003), calcium chloride (p=
0.030) and Triton X100 (p= 0.035) were highly significant. The coefficient for the
interaction of tribuytrin and yeast extract (p= 0.001), tribuytrin and ammonium nitrate
(p= 0.003), tribuytrin and Triton X100 (p= 0.001), yeast extract and ammonium nitrate
(p= 0.001), yeast extract and calcium chloride (p= 0.001), ammonium nitrate and
calcium chloride (p= 0.001) were statistically significant.
Table 17 Estimated regression coefficient and corresponding t and p
value for lipase production by Bacillus subtilis EH37
Term
Coefficient
S E coefficient
T
P
constant
2.23719
0.06884
161.554
0.001
X1 0.18410 0.09274 13.629 0.001 X2 2.43637 0.18549 26.608 0.001 X3 2.49716 0.18549 12.371 0.001 X4 0.27441 0.04637 19.156 0.001 X5 0.27441 0.04637 1.309 0.001
X1 x X1 -0.51298 0.05644 -9.089 0.001 X2 x X2 -3.40215 0.22577 -15.069 0.001 X3 x X3 -0.76036 0.22577 -3.368 0.003 X4 x X4 -0.03264 0.01411 -2.313 0.030 X5 X X5 -0.50844 0.22577 -2.252 0.035 X1 X X2 1.63770 0.14279 11.469 0.001 X1 x X3 -0.48289 0.14279 -3.382 0.003 X1 x X4 0.05602 0.03570 1.569 0.131 X1 x X5 0.90968 0.14279 6.371 0.001 X2 x X3 -3.76998 0.28558 -13.201 0.001 X2 x X4 0.31899 0.07139 4.468 0.001 X2 x X5 0.07681 0.28558 0.269 0.790 X3 x X4 0.58546 0.07139 8.200 0.001 X3 x X5 0.37612 0.28558 1.317 0.201
X4 x X5 0.00170 0.07139 0.024 0.981
From the signs and magnitude of the regression coefficients for the five study variables in
fitted regression equation, the production process can be well interpreted. Hence, a proper
choice of level combinations of the variables is desirable for maximizing the enzyme
production. The selection can be easily made from contour plots (Figs 4, 5). These
recommendations are valid for future trials because they are based on well fitted response
surface model (R2 = 99.0%).
117
Two pairs of variables having significant relationships were chosen for making contour
plots. Contour plot in Fig.4 exhibits behavior of lipase activity 42.52 U/ml (ln 3.75U/ml)
with respect to changes in tributyrin oil and yeast extract when the ammonium nitrate and
Triton X100 were held at coded level zero, and calcium chloride was held at coded level
+1; higher lipase activities were observed when tributyrin oil and yeast extract were
around coded level +1. Elliptical contours are a result of strong quadratic and interaction
effects of tributyrin oil and yeast extract. Fig.5 explains variation in lipase activity 44.7
U/ml (ln 3.8U/ml) owing to simultaneous change in tributyrin oil and triton X100 when
the ammonium nitrate was held at coded level zero, yeast extract and calcium chloride
were held at coded level +1. Relative to Fig. 4, the higher lipase activities occurred at
high level of tributyrin when Triton X100 around coded level zero.
The main observation of contour plot of BBD, it exhibited further possibility of
parameter combination. The optimal values of tributyrin oil, yeast extract, ammonium
nitrate, calcium chloride, and Triton X100 were predicted to be (1 %), (0.5 %), (0.25 %),
(0.2%), and (0.25%) respectively. To validate the optimum combination of the process
variables, confirmatory experiments were carried out. The selected combinations of the
five variables from the contour plots resulted in more than 44 U/ml (ln 3.8) which is in
Tributyrin
Y
east
ext
ract
Tributyrin
Tr
itron
X10
0
Fig. 5 Contour plot showing effect of Triton X100 and tributyrin oil on lipase activity (in U/ml) when the ammonium nitrate held at coded level zero (0.25 % w/v), yeast extract and calcium chloride were held at coded level +1 (0.50 % w/v, 0.20 % w/v respectively)
Fig. 4 Contour plot showing effect of yeast extract and tributyrin oil on lipase activity (in U m/ml) when the ammonium nitrate, Triton X100 held at coded level zero (0.25 w/v, 0.25 v/v, respectively), and calcium chloride held at coded level +1 (0.20 w/v)
118
agreement with the experimental value 42.5 U/ml (ln 3.75) obtained from the
experimental verification at the optimal values. Response surface methodology involving
Box-Behnken design (BBD) for optimizing lipase production by Bacillus subtilis EH37;
showed a 4.7 fold increment, as compared to that obtained in the basal medium.
Compared to other reports, good fold increment and production was achieved; 4-fold
increase compare to the basal medium for lipase from Geobacillus sp. was reported by
Abdel-Fattah, (2002). Kumar et al., 2005 reported a 2-fold increase in lipase roductivity
above the basic medium from Bacillus subtilis BTS-3. Aravindan & Viruthagiri (2007)
reported 1.5-fold compare to basic medium from Bacillus shaericus. However, medium
optimization for lipase production from P. aeruginosa resulted in a 5.58-fold (Ruchi et
al., 2008).
3.3.6 Partial purification of lipase
A high state of purity is generally not required in food processing, detergent as well as
paper and pulp industry. Most of commercial lipase preparations available today are
crude preparations in which only a small fraction is protein and only part of the protein is
lipase. However, it may be necessary to exclude certain other unwanted enzymes which
may act as impurity (Kamimura et al., 1999; Mohana et al., 2008)
The extra-cellular lipase from free fraction of liquid culture of EH28 and EH37 was
subjected to ammonium sulphate precipitation (60% and 80% saturation respectively),
ultrafiltration and phenyle sepharose ® 6 column chromatography in sequence. The
procedure resulted in partial purification of 24.2 fold with specific activity of 57.1 U/mg
for EH28 lipase (Table 18).
The lipase produced by Bacillus subtilis EH37 was purified 17.8 fold with specific
activity of about 41.9 U/mg (Table 1). Compared to other reports, good yield and
purification was achieved. Lee et al., (2006) reported 9 fold purification of lipase from
Acinetobacter ES-1 with 28.9 U/mg specific activity. Thermostable alkaline lipase from
Bacillus coagulans BTS-3 was purified by ammonium sulphate precipitation and DEAE-
Sepharose column chromatography with 4.8 U/ml specific activity and 40 fold
purification (Kumar et al. 2005). Kambourova et al., (2003) reported 11.94 specific
activity with 19.25 purification fold of lipase from thermophilic Bacillus
119
stearothermophilus MC7 employing ultrafiltration, sephadex G 200 and DEAE- cellulose
(column chromatography). However, Saisubramanian et al., (2008) was purified lipase
from Acinetobacter sp. to homogeneity with 14.9 fold and 88.8 U/mg specific activity,
whereas Sulong et al., (2008) reported purification of lipase from Bacillus sphaericus to
homogeneity with 8 fold and 98 U/mg specific activity.
Here, lipase from Acinetobacter EH28 refers as lipase EH28 while that from Bacillus
subtilis EH37 refer as lipase EH37.
Table 18 Summary of purification steps of lipase from Acinetobacter sp. EH28 and Bacillus subtilis H37
The standard deviation never exceeded 5.0%
3.3.7 Characterization of partially purified lipase
3.3.7.1 Effect of pH on lipase activity and stability
The protein nature of enzymes makes them susceptible to pH which affects the ionization
state of the amino acids and thus dictates the primary and secondary structure of the
enzyme and hence, controls its overall activity. Therefore a change in pH will have a
progressive effect on the structure of the protein and the enzyme activity (Sharma et al.,
2002; Kanwar et al., 2006; Shah et al. 2007; Mohana et al., 2008).
The optimum pH for lipase activity of Acinetobacter sp. EH28 was found to be 10. The
pH curve (Fig. 6) showed that the lipase is active in pH range of 8.0 – 11.0, while a
remarkable drop in enzyme activity was observed below pH 8.0 and above 11.0. Several
workers have found optimal lipase activity in highly alkaline pH range (9-10.5), from
Acinetobacter sp. RAG-1 (Snellman et al., 2002) and from Acinetobacter radioresistens
CMC-1(Hong & Chang, 1998). The alkaline lipase from Acinetobacter sp. was found to
be stable at pH 3-9, with optimal activity at 8.5 (Saisubramanian et al., 2008).
Total activity U/ml
Total protein mg
Specific activity U/mg Fold Yield % Purification
method EH28 EH37 EH28 EH37 EH28 EH37 EH28 EH37 EH28 EH37
Crude extract
850
10500
359.6
4468
2.36
2.35
1.0
1.0
100
100
Ammonium sulphate precipitation
680 2407 65 225 10.5 10.7 4.4 4.6 80 23
Ultrafiltration 598 2130 30 78 19.9 27.3 8.4 11.6 70 20
HIC 400 1633 7 39 57.1 41.9 24.2 17.8 47 16
120
020
406080
100120140
160180
6 7 8 9 10 11 12
pH
Enz
yme
activ
ity (U
/ml)
0
20
40
60
80
100
120
7 8 9 10 11 12pH
Res
idua
lact
ivity
(%)
The partially purified lipase EH28 showed maximal stability at pH 9 as it retained 100%
of its activity even after 24 hours incubation and 97% of its activity at pH 10 upon 24
hours of incubation at 20°C (Fig 7). The pH stability curve of EH28 showed that the
enzyme is stable under pH range 8-11 (Fig 2). These data are in agreement with those for
alkaline lipases reported for strains of Acinetobacter sp. (Hong & Chang, 1998; Kok et
al., 1995; Snellman et al., 2002).
The purified lipase from Bacillus subtilis EH37 exhibited appreciable activity over the
pH range 6–12 (Fig. 8) with maximum activity (100%) observed at pH 8.0, closely
followed by pH 9.0 (90.0% of the maximum). A rapid decline in the enzyme activity was
observed on both sides of the pH-optimum. The enzyme was inactivated at acidic pH
(<6.0). Acidic pH values are not common amongst the lipases produced by bacteria,
which in general are most stable and active at neutral or alkaline pH values. Although,
there are some exceptions. The lipases produced by B. stearothermophilus and B.
licheniformis are stable at pH 3.0 (Lima et al., 2004) and the lipases of Acinetobacter
calcoaceticus LP009 and S. simulans are stable at pH 4.0 (Pratuangdejkul et al., 2000;
Sayari et al., 2001). A similar trend was observed in lipases from thermophilic bacteria,
B. coagulans (Kanwar et al., 2006) B. licheniformis MTCC 6824 (Chakraborty and Raj,
2008). The literature information suggests that Bacillus lipases generally have pH optima
of 7–9 (Sharma et al., 2002; Chen et al., 2004; Nawani and Kaur, 2007).
Fig. 6 Effect of pH on lipase activity of Acinetobacter sp. EH28 after 1 hour incubation at 30 °C.
Fig. 7 Stability of partially purified lipase of Acinetobacter sp. EH28 at different pH after 24 hours incubation at 20 °C.
121
The purified lipase from B.subtilis EH37 was found to be stable in a pH range between
7.0 and 11.0 (Fig. 9). The EH37 most stable at pH 8 retaining 100% of its initial activity
followed by 90% at pH 9 and 75 % at pH 10. Kumar et al., 2005 reported a lipase
produced by B.coagulans BTS-3, which was stable in a pH range from 8 to 10.5. Nawani
and Kaur (2007) reported a lipase produced by Bacillus sp. which was stable in a pH
range from 7 to 8.5.
0
50
100
150
200
250
300
4 6 8 10 12
pH
Enz
yme
activ
ity (U
/ml)
0
20
40
60
80
100
120
4 5 6 7 8 9 10 11 12
pH
Resi
dual
lipa
se a
ctiv
ity (%
)
3.3.7.2 Effect of temperature on lipase activity and stability
Since most of the industrial processes involving enzymes operate at temperatures above
50°C, the importance of thermostable enzymes is of great significance. The stability of
thermophilic proteins is intrinsic and resides in their primary structure.
Thermostabilization of proteins can be achieved through optimization of intramolecular
interactions, packing densities, internalization of hydrophobic residues and surface
exposure of hydrophilic residues (Jaenicke et al., 1990; Sharma et al., 2002).
In this study, initial reaction rate was determined in temperature range of 30 to 80°C. The
optimum temperature for lipase EH28 was found to be 50°C and the lipase activity
significantly increased with increase in temperature from 30 to 50°C but further increase
in temperature adversely affected the lipase activity (Fig. 10). Higher temperature optima
have been reported for several lipases of Acinetobacter sp. (Hong & Chang 1998;
Snellman et al., 2002; Mongkolthanaruk et al., 2004).
Fig. 8 Effect of pH on lipase activity of Bacillus subtilis sp. EH37 after 1 hour incubation at 30 °C.
Fig. 9 Stability of partially purified lipase of Bacillus subtilis sp. EH37 at different pH after 24 hours incubation at 20 °C.
122
0
20
40
60
80
100
120
0 20 40 60 80 100 120 140
Time (min)
Resi
dual
act
ivity
(%)
50˚C60˚C70˚C
050
100150200250300350400450500
0 20 40 60 80 100
Temperature (˚C)
Enzy
me
activ
ity(U
/ml)
The lipase from EH28 was highly stable at 50°C, retaining 100% of its activity up to 90
min whereas at 60°C the enzyme retained about 100% activity up to 60 min (Fig 11). The
alkaline lipases from Acinetobacter sp. RAG-1, Lip A, remain active at temperature up to
70°C, with maximum activity observed at 55°C (Snellman et al., 2002).
The lipase from Bacillus subtilis sp. EH37 was thermostable as indicated by the optimum
temperature of the activity which was 60°C (Fig. 12). The enzyme retained 80, 90, 95 and
85%, of its maximum activity at 50, 55, 65 and 70°C respectively. Other workers have
found optimum temperature for thermostable lipases of Bacillus sp. to be 50–55 and
60°C (Sharma et al., 2002; Castro-Ochoa et al., 2005; Nawani and Kaur, 2007) while
optimum temperature of 65°C has been reported by Bora et al., 2007 for lipase from
Bacillus sp. LNB4. The highest reported optimum temperature for lipase from Bacillus
sp. was 75-80°C (Kambourova et al., 2003).
The lipase was highly stable at 50 and 60°C, retaining 100% of its activity up to 60 min
and more than 75% up to 75 min, whereas at 70°C, the enzyme retained about 100%
activity up to 30 min (Fig. 13). The activity and stability in this temperature range is not
common for lipases of mesophilic Bacillus such as B. subtilis (Lima et al., 2004). In fact,
the characteristics observed in the present work for lipases of Bacillus subtilis EH37 are
similar to those found in thermophilic Bacillus species such as Bacillus coagulans BTS-
3 (Kumar et al., 2005), Bacillus licheniformis (Chakraborty and Raj 2008) and for some
strains of Pseudomonas, such as P. aeruginosa ATCC (Izrael-Zivkovic et al., 2009).
The lipases produced by thermophilic species of Bacillus are active between 50 and 80°C
with good stability between 50 and 65°C, while the lipases of species of Pseudomonas
Fig. 10 Effect of temperature on lipase activity of Acinetobacter sp. EH28
Fig. 11 Thermostability of partially purified lipase of Acinetobacter sp. EH28
123
are active between 50 and 70°C and stable between 55 and 75 °C. Apart from these two
genera, there are only few examples of microorganisms reported to produce lipases that
are active and stable above 50°C (Lima et al., 2004; Chakraborty and Raj, 2008). The
known lipases produced by the genus Acinetobacter are active between 40 and 50°C but
are not stable above 45°C, with the exception of the recombinant lipase of strain RAG-1
which is stable at 70°C (Sullivan et al., 1999).
0
100
200
300
400
500
600
0 20 40 60 80 100
Temperture (ºC)
Enzy
me
activ
ity (U
/ml)
0
20
40
60
80
100
120
0 20 40 60 80 100 120 140
Time (min)
Resi
dual
act
ivity
(%)
50˚C60˚C70˚C
3.3.7.3 Substrate specificity
Lipases are known to vary widely in their substrate specificity, which is important with
respect to their biotechnology applications (Plow et al., 1996; Pogori et al., 2008). The
activity of lipase was studied on various oils and the relative activity was determined
against olive oil considered as 100 %.
The results presented in (Table 19) show that the lipase from EH28 catalyzed hydrolysis
of a variety of oils with maximum activity on coconut oil (120%). Further it was
observed that lipases were equally active on a wide range of oils as on olive oil, nearly
with more or less than 100 % relative in the most of the cases i.e groundnut, corn and
castor, followed by activity on mustard and teel which were in the range of 80-90%. A
slightly lower activity (60-70%) was observed in; cottonseed, sunflower, karanj, rice bran
and tributyrin. Just (40%) were observed in the case of neem oil. However, higher rates
on coconut oil indicate relative preference of lauryl esters as (C12, mid chain fatty acid)
this oil contains > 50% lauryl rich glycerides.
Fig.12 Effect of temperature on lipase activity of Bacillus subtilis sp. EH37
Fig. 13 Thermostability of partially purified lipase of Bacillus subtilis sp. EH37
124
Table 19 Effect of different oils on activity of purified lipase of Acinetobacter sp. EH28 at pH 10 and 50°C and Bacillus subtilis EH37 at pH 8 and 60°C
%Residual lipase activity oil
EH28 EH37
Coconut 120 84 Groundnut 102 85 Corn 99 129 Castor 97 87 Mustard 90 82 Teel 80 86 Cotton seed 70 110 Sunflower 68 103 Karanj 66 97 Rice bran 63 88 Tributyrin 60 260 Neem 40 60
Results are average of three independent experiments conducted in
triplicate. The standard deviation never exceeded 5.0% The EH37 lipase efficiently hydrolyzed a variety of vegetable oils (Table 19). It was
observed that the lipolytic activity on vegetable oils with C-18 unsaturated fatty acids
increased with increase in the degree of instauration and the percentage of unsaturated
fatty acids, (Olive oil< cottonseed oil < Sunflower oil). Corn oil (129% relative activity)
resulted in highest activities among the oils studied. Karanj oil (which contains oleic acid
71.5% and linoleic 11%) was also efficiently hydrolyzed by present lipase (97% residual
activity). All other oils resulted in activity of above 80% of that measured against olive
oil, except in case of neem oil which had 60% residual activity. The lipase was found to
have highest activity against tributyrin (260%) which, proves that the lipase preferably
hydrolyzes short carbon chains. Sulong et al. (2006) have reported that the lipases from
Bacillus sphaericus 205Y hydrolyzed medium length chain of triglycerides (tricapylin C8
and tricaprin C10) preferentially. However, the differential hydrolysis of various fats and
oils not only depends upon the majority of glyceride but also upon the amount of other
fatty acid components present in the oil.
Activity of these lipases EH28 and EH37 were studied on various p-nitrophenyl esters
(C10, C12 and C16) and results were compared against its activity on p-nitrophenyl esters
laurate C12 (mid chain fatty acid) which was considered as 100%.
The results presented in (Table 20) showed that the Acinetobacter sp. EH28 had a highest
activity on p-NP caprate C10 (140% residual activity), followed by p-NP C12 lurate
125
(control) and p-NP palmitate C16 (80% residual activity). A similar preference for
medium chain esters has been reported from several bacterial lipases such as Bacillus
subtilis 168 and Acinetobacter sp. (Lesuisse et al., 1993; Snellman et al., 2002).
The lipase from Bacillus subtilis EH37 was most active with long chain esters of p-NP as
it shows 130% residual activity with p-NP palmitate (C16) followed by p-NP laurate C12
and p-NP caprate C10 ( Table 20). Lipase from Bacillus subtilis EH37 showed opposite
behaviour against triacylglycerides as its shows highest activity against tributyrin (260%
residual activity). Lima et al., (2004) observed the same pattern and reported that the
substrate preference of lipases from genus Bacillus is variable with relation to the
hydrolysis of both esters of p-nitrophenol and triacylglycerols. Similar results for
hydrolysis of pNP-esters has been reported for lipases of Bacillus subtilis (Eggert et al.,
2000), Bacillus licheniformis (Chakraborty and Raj, 2008). Kanwar et al., 2006 reported
that the lipase of Bacillus coagulans MTCC-6375 was more specific to pNP-
caprylate(C8) and pNP-palmitate(C16). Horchani et al., (2009) studied the difference
between staphylococcal lipases in their chain length selectivity and concluded that some
staphylococcal lipases hydrolyze triacylglycerols and p-nitrophenyl esters almost
irrespective of their chain length.
Lipases and esterases share common substrate specificities. However, unlike esterases,
lipases often demonstrate interfacial activation. Therefore, lipase substrates are typically
long chain (≥ C10) fatty acid esters available in micellar form. Acinetobacter sp. EH28
and Bacillus subtilis EH37 were capable of hydrolyzing long acyl chain triglycerides, as
in the case of olive emulsion and demonstrated uniform activity against various water-
insoluble esters of p-nitrophenyl. These data confirm that Acinetobacter sp. EH28 and
Bacillus subtilis EH37 lipases as a true lipase.
The specificity as well as affinity of the EH28 and EH37 lipases towards various p-
nitrophenyl esters was further studied by determining Km and Vmax values for each of the
selected substrate (Table 21). It can be observed from table that the lipase from EH28
had low Km and high Vmax of 4.00 mM and 66.7 μmole/mg/min respectively toward p-NP
caprate, while the lipase of EH37 showed high affinity toward p-NP palmitte as they
observed from measured Km and Vmax of 18.0 mM and 44.0 μmole/mg/min.
126
The EH37 and EH28 lipase were found to have broad substrate specificity, rendering
them as potential candidates in the biocatalysis industry and has an economic potential
application in the oleochemical industry.
Table 20 Lipase activity of Acinetobacter sp. EH28 and Bacillus subtilis EH37 on various p-NPesters NP esters
100% activity = 250 U/ml on p-NP laurate of lipase from EH28 100% activity = 330U/ml on p-NP laurate of lipase from EH37
The standard deviation never exceeded 4.0% The Km values of the enzyme range widely, but for most industrially used enzymes, they
lie in the range of 10−1 to 10−5 M when acting on biotechnologically important substrates
(Sharma et al., 2002). 3.3.7.4 Effect of metal ions on lipase activity
Metal ions and salts are of importance for thermostablity of enzymes. A number of
enzymes require the presence of metal ions, such as calcium ions for the maintenance of
their stable and active structures (Sharma et al., 2002; Shah et al., 2007). Thus, to find
out whether different metal ions stabilize or destabilize, lipase from EH28 was incubated
at pH 10 and 50°C for 1hour with various cations. It showed an increase in activity after
exposure to low concentration i.e 1 mM of Ca2+, Mg2+, Co, and Fe3+. Increasing the metal
concentration 10-fold i.e. 10 mM had no further enhancing effect. Cu2+ at low and high
concentrations i.e (1mM) and (10mM) inhibited the lipase, reducing activity by 54.7 %
and 88.7 % respectively. Zn (10 mM), reduced the activity by 35% (Table 22).The
stimulatory effect of Ca+2 and Mg+2 on lipase activity has been observed by several
researchers and could be attributed to the complex action of calcium ions on the released
fatty acids and on enzyme structure stabilization due to the binding of calcium ions to the
lipase bridging the active region to a second subdomain of the protein and hence
%Residual lipase activity
P-Nitrophenyl esters EH28 EH37 Caprate (C10) 140 70
Laurate (C12) 100 100
Palmitte (C16) 80 130
Km (Mm) Vmax μmole/mg/min
Substrate EH28 EH37 EH28 EH37 Caprate (C10)
4.00 22.0 66.7 48.0
Laurate (C12)
10.0 20.0 55.5 42.0
Palmitate (C16)
80.0 18.0 40.0 44.0
Table 21 Kinetic constant of lipases of Acinetobacter sp. EH28 and B. subtilis
EH37 for hydrolysis of p-NP esters
127
stabilizing enzyme tertiary structure (rather than catalytic activity) (Chakraborty and Raj,
2008, Chakraborty and Paulraj, 2009). Sullivan et al., (1999) deduced sequence of RAG-
1 Lip A contains two putative Ca2+ binding residues (Asp240, Asp282) that participate in
protein stabilization, comparative sequence analysis showed that residues associated with
Ca2+ binding and protein stabilization are universally conserved in Group 1
Proteobacteria lipases. Similar inhibition by heavy metals has been observed by several
authors (Kanwar et al., 2005; Kumar et al., 2005; Sulong et al., 2006; Chakraborty et al.,
2008; Dandavate et al., 2009). EDTA strongly inhibited enzyme activity; with loss of 70
% activity at 1mM and 91 % inhibition in presence of 10 mM EDTA. These results agree
with the results obtained by Chakraborty and Raj, 2008; Dandavate et al., 2009 and
Snellman et al., 2002 who suggested that inhibition by EDTA probably results due to its
access to the Ca 2+ binding site and ion removed.
Similarly, activity of lipase from EH37 was determined in the presence of several metal
ions and additives. Table 22 showed that the residual lipase activity increased upon
exposure to most metal ions at low concentrations i.e. 1mM, except for CuCl2, FeCl3 and
CoCl2, which reduced the activity to 80.3%, 95.8 % and 92 % respectively.
Also, as the concentration of all the metal ions used in the study were increased by 10
fold i.e. 10mM, not much decrease in the lipase activity was observed as the enzyme
retained more than 85% of its activity. The lipase from Bacillus sp. RSJ-1 was promoted
in the presence of Na+, Mg2+ and Ba2+ (at 1 mM concentration). It was strongly inhibited
by Co2+ and Zn2+ (Sharma et al., 2002). Kanwar et al., (2006) investigated the stability of
lipase activity of B. coagulans MTCC-6375 against various metal ions and observed that
presence of chloride salts at 1 and 5mM of Mg2+, Ca2+ ,Hg2+ and Fe3+ promoted the
lipase activity where as Zn2+ and Co2+ ions exerted an inhibitory effect on lipase .
However, another distinguishing feature of EH37 lipase was that it showed high activity
in presence of Zn+2 ions which is unusual as Zn+2 has shown to be destabilizing for
Bacillus sp. lipases (Sharma et al., 2002; Kanwar et al., 2006; Chakraborty and Raj,
2008; Chakraborty and Paulraj, 2009). EDTA inhibited the lipase from EH37 with 60.7%
residual activity at 1mM and 33.5% residual activity of 10 mM EDTA. It has been
suggested that the effect of metal ions could be attributed to a change in the solubility and
128
behavior of the ionized fatty acids at interfaces, and from change in the catalytic
properties of the enzyme itself (Sharma et al., 2002; Castro-Ochoa et al., 2005)
Table 22 Effect of different ions on activity of purified lipase of Acinetobacter sp.
EH28 at pH 10 and 50°C and Bacillus subtilis EH37 at pH 8 and 60°C
% Residual activity Ions
Concentration
(mM) EH28 EH37
CaCl2 1.00 10.0
123.0 91.00
116.0 94.50
MgCl2 1.00 10.0
122.7 99.00
107.0 90.00
CuCl2 1.00 10.0
45.30 11.30
80.30 58.00
CoCl2 1.00 10.0
188.0 131.9
92.00 86.00
FeCl3 1.00 10.0
104.0 87.40
95.80 85.00
ZnCl2
1.00 10.0
80.00 65.00
102.0 91.00
EDTA 1.00 10.0
30.00 9.000
60.70 33.50
Results are average of three independent experiments conducted in
triplicate. The standard deviation never exceeded 5.0%
Calcium stimulated lipases have been reported in the case of Bacillus sp. RSJ-1 (Sharma
et al., 2002), B. coagulans MTCC-6375 (Kanwar et al., 2006), Bacillus sp. (Nawani and
Kaur, 2007), B.licheniformis MTCC6824 (Chakraborty and Raj, 2008) and Burkholderia
multivorans V2 (Dandavate et al., 2009). The EH28 and EH37 lipases were thus
considered as metallo-enzyme which was inhibited by EDTA, this indicates that EH28
and EH37 lipase also process a triad of three amino acids at its catalytic site just like
many other lipases. Contrary to this result, Sharma et al., (2002), Liaghua et al., (2005)
and Sulong et al., (2006) reported that EDTA did not affect the enzyme activity from
Bacillus sp. However, several authors have reported the inhibition of bacterial lipases by
EDTA (Neerupma et al., 2007; Chakraborty and Raj, 2008, Dandavate et al., 2009).
3.3.7.5 Effect of organic solvents on lipase activity
Stability in organic solvents is desirable if lipases are to be used in synthesis reactions
which are carried out in systems with low water contents. Lipases are diverse in their
sensitivity to solvents, but there is general agreement that polar (water miscible) solvents
129
are more destabilizing than non polar solvents. A good measure of polarity of an organic
solvent is its polarity through the log P value, which is defined as the logarithm of its
partition coefficient in standard n-octane/water two phase systems (Castro-Ochoa et al.,
2005; Nawani and Kaur, 2007; Dandavate et al., 2009). In this study, various high
polarity, water-miscible solvents, low log p such as acetone log p = -0.15, ethanol log p =
-0.24, isopropyl alcohol log p = -0.28 methanol log p = -0.76, and dimethyl sulfoxide, log
p = −1.22; low log p ≈ (high polarity) in addition low polarity, water immiscible solvent
(high log p) n-hexane, log p = 3.6 were also investigated.
The lipase from Acinetobacter sp. EH28 showed high stability in the presence of water
miscible organic solvent, since it retained more than 80% of its activity upon exposure to
15% of ethanol, methanol, isopropyl alcohol, dimethylformamide and dimethyl sulfoxide
for one hour at 50°C, enhancement of lipase activity was observed in the presence of low
and high concentration of n-hexane and acetone. 15% and 30% concentration of n-hexane
resulted in 102% and 112% residual activity respectively, whereas 15% and 30%
concentration of acetone showed 104% and 118.5% residual activity respectively
comparison to the control (Table 23). Similar results have been obtained for BTL-2
lipases and lipase from solvent tolerant Pseudomonas aeruginosa, which exhibited
remarkable stability in wide range of organic solvent at 25% v/v concentration, 30%
acetone increased activity of BTL-2 lipases (Miroliaei et al., 2002; Ruchi et al, 2008).
Hydrophilic solvents (−2.5 < log P < 0), such as acetone are generally incompatible with
enzymatic activity because they remove the solvation water layer from the surface of the
enzyme, thereby destabilizing the protein and causing high denaturation rates (Castro-
Ochoa et al., 2005). Interestingly, the lipolytic extract obtained from Acinetobacter sp
EH28 presented an opposite behaviour: it was activated with solvents with low log P
values (i.e. around −0.2). However, a remarkable increase in lipase activity of about
130% was observed in presence of 30% dimethyl sulfoxide (DMSO). Other possibilities
could also be considered that can explain the stimulatory effect of solvents on enzyme
activity. For example, the solvent may be modified at the oil-water interface to make
enzymatic action easier without causing protein denaturation. Secondly, enhancement in
enzyme activity could be due to disaggregation of lipase or solvents may induce some
structural change in the enzyme (Hong and Chang, 1998).
130
The effects of various organic solvents on the stability of EH37 lipase are shown in Table
22. The lipase from Bacillus subtilis EH37 exhibited high stability in the presence of 15%
and 30% of different organic solvents as the decrease in activity of lipase was negligible
in presence of most of the organic solvents. In the presence of 15% of ethanol,
methanol,dimethylformamide, dimethylsulfoxide and n-hexan the lipase retained 88%,
82%, 84% 100% and 98% respectively of its residual activity with isopropyl alcohol and
acetone being the best (107% and 102% increase of activity). The activation of lipases
in the presence of some organic solvents, such as isopropanol, can be explained by the
disruption of aggregates formed between the enzyme and lipids of the fermentation
medium or between the enzyme molecules themselves. Disruption of this aggregate by
the polar solvents tested could have led to the activations observed. For example, the
lipase of thermophilic Bacillus sp. was activated by incubation in n-hexane 30% (201%
of residual activity) (Nawani and Kaur et al., 2007). The activation by hydrophobic
solvents could be due to the presence of residues of these solvents, taken back into the
assay solution with the enzyme after the preincubation. These solvent molecules can
interact with hydrophobic amino acid residues present in the lid that covers the catalytic
site of the enzyme, thereby maintaining the lipase in its open conformation. Without the
lid covering the active site, the activity in the assay is higher (Lima et al., 2004).
Except for n-hexane, the enzyme showed higher activity in higher concentration (30%)
than in lower (15%). This may be attributed to the fact that a thin layer of water
molecules remains tightly bound to the enzyme acting as a protective sheath along the
enzyme’s hydrophilic surfaces that allows retention of the native conformation (Castro-
Ochoa et al., 2005). Polar solvents tend to strip this thin water layer and distort the
enzyme’s conformation resulting in lower activities while non polar solvents do exactly
the opposite. Since n-hexane is a highly non polar solvent, its presence in high
concentration enhanced the activity as shown in Table 22. The lipase from Pseudomonas
mendocina PK12CS (Jinwal et al., 2003) is the example of a microbial lipase that is
reasonably stable against a hydrophilic solvent; when incubated for 2.5 hours in 100%
ethanol, the residual activity was 83%. Recently, Dandavateetal., (2009) reported organic
solvent tolerant lipase from Burkholderia multivorans V2 stable in organic solvents with
log p values varying from -1.3 to 4.5 and showing highest stability in n-hexane up to 24
131
hours retaining about 97.8% of its of its activity which was further reduced to about
(53.19%) after 48 hours of incubation.
Interestingly, activity of the EH28 and EH37 lipase was enhanced in the presence of
hydrophilic solvents even though at these range of logo/w, most of enzymes were known
to be inactivated by the solvents.
Table 23 Effect of different solvents on activity of purified lipase of Acinetobacter
sp. EH28 at pH 10 and 50°C and Bacillus subtilis EH37 at pH 8 and 60°C
% Residual activity Solvents Log P Concentration (mM) EH28 EH37
Ethanol -0.24
15 30
96.00 90.00
88.00 80.00
Methanol - 0.76 15
30 85.00 74.00
82.00 76.00
Isopropyl alcohol -0.28
15 30
96.00 89.00
107.00 86.60
Dimethyl formamide -1.0 15
30 81.80 93.90
84.70 76.00
Dimethy sulfoxide - 1.22 15
30 100.0 130.0
100.0 81.00
n-Hexane 3.5
15 30
102.0 112.0
98.0 115.0
Acetone -0.23
15 30
104.0 118.5
102.0 99.80
Results are average of three independent experiments conducted in
triplicate. The standard deviation never exceeded 5.0% 3.3.7.6 Lipase inhibitors
Active site amino acids of the enzymes were determined by incubating the enzyme with
different inhibitors (serine inhibitor, sulphydryl and thiol inhibitors) for 1hour and then
the residual activity were measured against control without inhibitors.
The serine specific inhibitor, phenylmethanesulfony fluoride (PMSF) decreased the lipase
activity of EH28 and EH37 lipases, the inhibition was concentration dependent with most
reduction at 10mM resulting in 20% and 0% residual activity respectively, which
suggested that the EH28 and EH37 lipases belong to the class of serine hydrolases (Table
24). The serine inhibition was probably caused by modification of essential serine
132
residual serine residue in the active site (Chakraborty and Raj, 2008; Chakraborty and
Paulraj, 2009; Dandavate et al., 2009). This indicates that this lipase also possesses a
triad of three amino acids at its catalytic site just like many other lipases.
With sulphydryl inhibitors, iodoacetic acid at 5 mM concentration inhibited enzymes
activities (EH28 and EH 37) by 24% and 55% respectively, increase the concentration i.,e
10 mM resulted in 50% (EH28) and 65% (EH37) inhibition while iodoaetamide acid at
5mM slightly enhanced the activity of EH28 and EH37 by 71% and 6% respectivey, at 10
mM the enzymes inhibited by 39% and 65% respectively, suggesting that the a free
sulphydryl group is probably required for the lipase activity.
The dithiothreitol reduced the activity of EH37 lipase to 90% and 53% at 5mM and
10mm concentration respectively which indicates an important role of SH-groups in the
catalytic mechanism. Similar result was observed with enzyme modulators, reducing
reagents, 2 – mercaptoethanol as it reduced the activity to 53% at 10mM, same trend of
DDT and 2 – mercaptoethanol on lipase activity from bacillus licheniformis had been
reported by Chakraborty and Raj, (2008). Adversely, lipase from EH28 was enhanced by
the disulfide bond reducing agents ß-mercaptoethanol and dithiothreitol (DTT) (showing
133% and 123 residual activity at 5mM respectively and 112% and 105% residual
activity at 10mM respectively) which implies reducing the disulfide bonds results in the
better enzyme conformation result in activation. Similar result has been reported by
several authors (Nawani and Kaur, 2007; Dandavate et al., 2009).
Table 23 Effect of different inhibitors on activity of purified lipase of Acinetobacter sp. EH28 at pH 10 and 50°C and Bacillus subtilis EH37 at pH 8 and 60°C
% Residual lipase activity
Inhibitors concentration(mM) EH28 EH28
PMSF 1.00 60 40 10.0 20 0 Iodo cetic acid 1.00 76 45 10.0 50 35 Iodoacetamide 1.00 171 106 10.0 61 35 DTT 1.00 123 90 10.0 105 53 Mercaptoethanol 1.00 133 101
10.0 112 53 Results are average of three independent experiments conducted in
triplicate. The standard deviation never exceeded 5.0%
133
3.4 Summary
Two alkaline lipases producing bacteria were isolated from oil rich soil and
taxonomically identified to be Acinetobacter EH28 and Bacillus subtilis EH37.
Production of the lipase from EH28 and EH37 were optimized in shake flasks, one
variable at a time strategy and a statistical approach (Plakett-Burman Design) was used to
pick factors which influence lipase production significantly and insignificant ones were
eliminated. The study indicated that the lipase production was largely affected by glucose
and oil concentration, in addition to the physical parameters of pH, temperature and
incubation period. Therefore statistical approach (RSM) was used to optimize lipase
production and determine the interaction between these parameters.
Three-level factorial design was constructed with four variables viz., olive oil, yeast
extract, ammonium nitrate and calcium chloride, using CCD to optimize lipase
production from Acinetobacter EH28, the optimum medium composition was found to be
1% (v/v) olive oil, 0.2% (w/v) yeast extract, 0.02% (w/v) ammonium nitrate and 0.05%
(w/v) calcium chloride at pH 8 and 35°C. The optimized medium resulted in about 8.33
fold increment in the enzyme production, as compared to that obtained in the basal
medium.
Five parameters viz., tribuytrin (X1), yeast extract (X2), ammonium nitrate (X3), calcium
chloride (X4) and Triton X100 which have been found to play a significant role in the
production of lipase from Bacillus subtilis EH37, were studied as the independent
variables, and lipase activity was dependent response variable. Each of the independent
variables was studied at three different levels as per BBD. The optimal values of
tributyrin oil, yeast extract, ammonium nitrate, calcium chloride, and triton X100 were
predicted to be 1% (v/v), 0.5% (w/v), 0.25% (w/v), 0.2% (w/v), and 0.25% (v/v)
respectively at pH 9 and 35°C. The optimized medium resulted in about 5-fold increment
in the enzyme production, as compared to that obtained in the basal medium.
The crude lipase from Acinetobacter sp. EH28 was partially purified by ammonium
sulphate precipitation and hydrophobic interaction chromatography with 24.2 fold
purification and 57.1 U/ml specific activity. Purified enzyme exhibited maximum activity
at 50°C and pH 10 and was highly stable at 50°C retaining 100% of its activity up to 90
134
min. The enzyme retained more than 80% of its initial activity upon exposure to organic
solvents and exhibited 130% higher activity in presence of dimethyl sulfoxide (DMSO).
The Bacillus subtilis EH37 lipase was purified 17.8 fold by ammonium sulphate
precipitation and hydrophobic interaction chromatography to give a final specific activity
of 41.9 U/ml. The lipase had optimal activity at pH 8.0 and 60°C. It retained 100% of
activity at 50 and 60°C for 60 min. EH37 lipase exhibited unusual features, similar to
those found in thermophilic Bacillus species. The distinguishing features included its
stability in organic solvents and enhanced activity in presence of Zn+2 ions which have
been generally associated with decrease in lipase activity, whereas Fe+3 and Co+2 reduced
its activity. The enzyme retained more than 80% of its initial activity upon exposure to
organic solvents, exhibited 107% and 115% activity in the presence of 15% isopropyl
alcohol and 30% n-hexane respectively.
All the characteristics displayed by the purified lipases clearly indicate their applicability
in various areas, some of which are described in next chapter.
135
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