Statistical optimization of extracellular tannase

production by

Streptomyces sp.

AT 13 using response

surface methodology and Plackett-Burmen design

Archana D. Tripathi* and Lakshmi B.

Department of Biotechnology, Kadi Sarva Vishwavidyalaya, Gandhinagar, Pin-382028, Gujarat, India

ABSTRACT

Tannase has many important applications in animal feed, chemical, food, and pharmaceutical industries. In the present study,

optimization of tannase production by Streptomyces sp. AT13 was carried out using statistical experimental designs. Initially, a

Plackett-Burmen design (PBD) was employed to screen the preferable nutriments (carbon and nitrogen sources of the medium) to

produce tannase. The result showed that only tannic acid was found to be signi cant for the production of tannase by Strepto-

myces sp. AT 13. The signi cant factor was further optimized by using Box-Behnken design under response surface methodology

(RSM). From among 6 fermentative variables that were studied, 5 signi cant variables were picked up by PBD. Among 5 variables

from PBD, 3 were further optimized by Box-Behnken design. The parameters studied through RSM were 1% Tannic Acid, 0.5 %

KCl and 0.1 % KH

2

PO

4

. Under optimized conditions tannase activity was 18.12 U/ml/min. This activity was almost three times

higher as compared to the amount obtained by ‘one-at-a-time’ approach. (5.19 U/ml/min)

691

Biotechnological

Communication

Biosci. Biotech. Res. Comm. 11(4): 691-698 (2018)

INTRODUCTION

Tannins are polyphenolic compounds with molecular

weights ranging from 500 to 3000 daltons which occur

widely ina variety of plants including monocots, dicots

and ferns (Bate-Smith and Swain 1962; Mcleod 1974;

Haslam 1989). They are the fourth most abundant plant

constituents after cellulose, hemicellulose and lignin.

Based on their structure and properties, tannins are dis-

tributed into two major groups – hydrolysable and con-

densed tannins. Hydrolysable tannins are composed of

esters of gallic acid (gallotannins) or ellagic acid (ella-

gitannins) with a sugar core, which is usually glucose,

and are readily hydrolysed by acids and enzymes into

monomeric products. Hydrolysable tannins are notably

absent in monocots. Commercially, hydrolysable tannins

ARTICLE INFORMATION:

Corresponding Authors: archana315@gmail.com,

lakjayan@gmail.com

Received 19

th

Sep, 2018

Accepted after revision 18

st

Dec, 2018

BBRC Print ISSN: 0974-6455

Online ISSN: 2321-4007 CODEN: USA BBRCBA

Thomson Reuters ISI ESC / Clarivate Analytics USA

Mono of Clarivate Analytics and Crossref Indexed

Journal Mono of CR

NAAS Journal Score 2018: 4.31 SJIF 2017: 4.196

© A Society of Science and Nature Publication, Bhopal India

2018. All rights reserved.

Online Contents Available at: http//www.bbrc.in/

DOI: 10.21786/bbrc/11.4/21

Archana and Lakshmi

692 STATISTICAL OPTIMIZATION OF EXTRACELLULAR TANNASE PRODUCTION BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

are extracted from Chinese gall (Rhus semialata), Sumac

(R. coriara), Turkish gall (Quercus infectoria), Tara (Cae-

salpina spinosa), Myrobalan nuts (Terminalia chebula)

and chestnuts (Castania sativa) (Bhat et al., 1998).

Tannase (tannin acyl hydrolase) catalyses the hydroly-

sis of ester and depside bonds of hydrolysable tannins to

produce glucose and gallic acid (Barthomeuf et al.,1994).

Hydrolysis of tannic acid by tannase results in the liber-

ation of glucose, gallic acid and various galloyl esters of

glucose (Van de Lagemaat and Pyle, 2006). This enzyme

nds widespread applications in food processing, brew-

ing, pharmaceuticals, medicine, textiles, detergents and

tea industry (Lekha and Lonsane, 1997). A combination

of trimethoprim and sulphonamide is effective against

many antibiotic resistant species of bacteria. More than

8000 tonnes per annum of gallic acid is manufactured

out of which, almost 70% is used in production of tri-

methoprim (Beena, 2010).

The use of a sequential experimental design strategy

is a useful tool for process optimization. Response sur-

face methodology (RSM) provides important information

regarding the optimum level of each variable along with

its interactions with other variables and their effects on

product yield. It reduces the number of experiments with-

out neglecting the interactions among the parameters.

This multivariate approach also improves statistical inter-

pretation possibilities and evaluates the relative signi -

cance of several contributing factors even in the presence

of complex interactions (Dilipkumar et al.,2011). RSM is

widely used for multivariable optimization studies in sev-

eral biotechnological processes such as the optimization of

media, process conditions etc. (Mannan et al.,2007; Pan

et al.,2008). Statistical optimization allows the interaction

amid possible in uencing parameters to be evaluated with

a limited number of experiments (Rodriguez et al., 2008).

Statistical designs such as Plackett-Burman and Response

surface methodology are common in practice for testing

multiple factors and interactions that can be quanti ed in

an error-free and robust manner (Mohan, 2014).

In order to fully exploit the prospective of this

enzyme for various applications, it is imperative to

investigate the possibility of enhancing its production

by using more ef cient production strategies (Rana and

Bhat, 2005). Hence in this study the process parameters

were optimized for maximum enzyme production adopt-

ing Plackett-Burman (PB) and Response Surface Meth-

odology (RSM) with Box Behnken design. The statisti-

cal software package DesignExpert® 7.0 (Stat Ease Inc.,

Minneapolis, and USA) was used.

MATERIALS AND METHODS

Tannic Acid was purchased from Sigma Aldrich, USA.

All other chemicals and reagents were used of analytical

grade and purchased from Hi-Media, India. Sterptomy-

ces sp. AT 13 used for the present study was screened

and isolated from Tannery Ef uent at G.I.D.C Naroda,

Ahmedabad and the organism was maintained on tannic

acid agar (TAA) medium. Tannase production by Sterp-

tomyces sp. AT 13 was carried out using tannase pro-

duction medium containing NaNO

3

0.6 % w/v, KCl 0.5

% w/v, MgSO

4

0.05 % w/v, K

2

HPO

4

0.05 % w/v, KH

2

PO

4

0.05 % w/v and 1 % (w/v) ltered sterilized tannic acid

(designated as TA broth). The broth was assayed for tan-

nase activity.

TANNASE ASSAY

The tannase activity was estimated by modi ed spectro-

photometric method of Sharma et al., 2000. Tannic Acid

was used as substrate. The basic principle of this assay

is based on the formation of chromogen between gallic

acid (released by the action of tannase on tannic acid)

and rhodanine (2-thio-4-ketothiazolidine). A standard

curve was prepared using gallic acid.Crude enzyme was

used for the assay. All the tests were performed in trip-

licates. One unit of tannase activity is de ned as the

amount of enzyme required to liberate 1μM of gallic

acid /min under de ned conditions. Enzyme activity

was expressed as U/ml/min.

Plackett–Burman design

Plackett–Burman design, an ef cient technique for

medium component selection (Plackett-Burman, 1946)

was employed to establish the factors that signi cantly

enhance the tannase production. Five independent vari-

ables (Table 1) were analysed in 12 experimental runs

(Table 2) for the production of tannase. Triplicates were

maintained for each experimental set up. The response

of these factors for the production of tannase was meas-

ured by Spectrophotometer method as proposed by

Sharma et al., (2000). The low level (-1) and the high

level (+1) of each factors are demonstrated in Table 2.

The statistical software design expert 6.0 was used for

analysing the experiment. Total 5 independent variables

Table 1. Screening of variables using a Plackett–

Burman design

Variable

codes

Variables Low

level (-1)

High

level (+1)

A Tannic Acid -1 +1

B KCl -1 +1

C NaNO

3

-1 +1

DKH

2

PO

4

-1 +1

EK

2

HPO

4

-1 +1

Archana and Lakshmi

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS STATISTICAL OPTIMIZATION OF EXTRACELLULAR TANNASE PRODUCTION 693

Table 3. ANOVA for the experiment with PBD for Tannase Production by

AT 13

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

Sum of Mean F p-value

Source Squares df Square Value Prob > F

Model 427.76 10 42.78 285.87 0.0460 signi cant

A-TANNIC ACID 153.08 1 153.08 1023.04 0.0199

B-KCl 9.12 1 9.12 60.93 0.0811

C-NaNO

3

15.01 1 15.01 100.30 0.0634

D-KH

2

PO

4

9.97 1 9.97 66.65 0.0776

E-K

2

HPO

4

11.96 1 11.96 79.93 0.0709

G-G 77.32 1 77.32 516.71 0.0280

H-H 69.89 1 69.89 467.08 0.0294

J-J 43.85 1 43.85 293.07 0.0371

K-K 19.15 1 19.15 127.99 0.0561

L-L 18.40 1 18.40 122.98 0.0573

Residual 0.15 1 0.15

Cor Total 427.91 11

Table 2. Plackett Burman design matrix for the screening of variables

F1 F2 F3 F4 F5 F 6 F 7 F 8 F 9 F 10 F11

Run

A:Tannic

Acid

B:KCl C:NaNO

3

D:KH

2

PO

4

E:K

2

HPO

4

F:F G:G H:H J:J K:K L:L R1

1 -1.00 1.00 1.00 1.00 -1.00 -1.00 -1.00 1.00 -1.00 1.00 1.00 1.62

2 1.00 1.00 1.00 -1.00 -1.00 -1.00 1.00 -1.00 1.00 1.00 -1.00 12.99

3 1.00 -1.00 1.00 1.00 -1.00 1.00 1.00 1.00 -1.00 -1.00 -1.00 7.17

4 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 10.12

5 1.00 1.00 -1.00 1.00 1.00 1.00 -1.00 -1.00 -1.00 1.00 -1.00 16.53

6 -1.00 1.00 -1.00 1.00 1.00 -1.00 1.00 1.00 1.00 -1.00 -1.00 5.61

7 -1.00 -1.00 -1.00 1.00 -1.00 1.00 1.00 -1.00 1.00 1.00 1.00 5.91

8 1.00 -1.00 1.00 1.00 1.00 -1.00 -1.00 -1.00 1.00 -1.00 1.00 16.2

9 -1.00 -1.00 1.00 -1.00 1.00 1.00 -1.00 1.00 1.00 1.00 -1.00 2.58

10 -1.00 1.00 1.00 -1.00 1.00 1.00 1.00 -1.00 -1.00 -1.00 1.00 0.3

11 1.00 1.00 -1.00 -1.00 -1.00 1.00 -1.00 1.00 1.00 -1.00 1.00 15.75

12 1.00 -1.00 -1.00 -1.00 1.00 -1.00 1.00 1.00 -1.00 1.00 1.00 0.36

+ “sign indicates high level of the variable a nd” – “sign indicates low level”

along with 5 dummy variables generated by the soft-

ware. All the experiments were done in triplicate and the

average of lipase production was taken as response. The

obtained results are matched with the polynomial equa-

tion 1 in signi cant order 1:

Y = 0 + iXi (i=1, 2, 3……………k) (1)

Where Y is the response (Enzyme activity), 0 is model

intercept, i is linear coef cient, Xi is level of the inde-

pendent variables. The model was statistically analyzed

and the overall signi cance of the model was evaluated

by ANOVA (Analysis of variance) involving Fischer’s

test (F test). P (probability) values and determination

of coef cient obtained determines regression model’s

goodness of t.

Box-Behenken Design

After optimizing the various nutritional variables by

PBD, the 3 most signi cant variables (Tannic Acid, KCl

and KH

2

PO

4

) were further chosen for response surface

methodology using Box-Behnken design. Design-Expert®

statistical software was used to analyze the experimental

design. A Box-Behnken design with a set of 12 experi-

ments was generated (Table 4). Each variable was stud-

Archana and Lakshmi

694 STATISTICAL OPTIMIZATION OF EXTRACELLULAR TANNASE PRODUCTION BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

FIGURE 1. Pareto chart showing the effect of media components on tannase

activity using AT 13

Table 4. Box-Behenken (BB) design matrix of

optimizing factors in uence for tannase activity

Tannic Acid KCl KH

2

PO

4

E.A

Run A:A B:B C:C R1

1 -1.00 1.00 0.00 12.99

2 -1.00 0.00 -1.00 14.61

3 0.00 1.00 1.00 17.34

4 0.00 -1.00 1.00 12.33

5 0.00 1.00 -1.00 11.52

6 1.00 0.00 1.00 9.27

7 1.00 0.00 -1.00 10.71

8 1.00 1.00 0.00 2.61

9 -1.00 -1.00 0.00 9.66

10 1.00 -1.00 0.00 6.57

11 0.00 -1.00 -1.00 17.79

12 -1.00 0.00 1.00 18.12

ied at three levels, +1, 0, -1 where “0” is the central

coded value, “+1” high value and “-1” low value. The

fermentation was carried out in 250 ml asks containing

100 ml of the production medium. All experiments were

done in triplicate and tannase production obtained was

taken as the response.

The obtained results are matched with the polynomial

equation 2 in signi cant order

Y = 0 + iXi + iiXi 2 + ijXiXj (2)

where Y is the predicted response, 0 is the intercept

term, Xi and Xj are the input variables, i the linear

coef cients, ii the squared coef cients and ij the

interaction coef cients. The model was statistically

analyzed and the overall signi cance of the model was

evaluated by ANOVA (Analysis of variance) involving

Fischer’s test (F test). P (probability) values and coef-

cient of determination obtained determines regression

model’s goodness of t. The optimum values of each of

the 3 signi cant variables were determined by solving

regression equation.

The interactive effects of the variables on tannase

production were studied by analyzing the 3D and coun-

ter plots which depicted the interactions graphically.

Validation of the model: The statistical model was again

tested by carrying out the fermentation under the opti-

mum conditions that were obtained through the Box-

Behnken design.

RESULTS AND DISCUSSION

SCREENING OF VARIABLES BY PBD

Iimportant fermentative parameters can be effectively

and quickly picked up by applying PBD. The use of sta-

tistical tools not only saves time by simultaneously opti-

mizing several process variables with few experimental

runs but also reduces the cost of fermentation. A total

of 5 important variables Viz., tannic acid, KCl,NaNO

3,

KH

2

PO

4

and K

2

HPO

4

which were selected for statistical

optimization using PBD (Table 1).

Experiments were carried out based on Plackett–Bur-

man design and the results obtained are given in Table2.

Archana and Lakshmi

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS STATISTICAL OPTIMIZATION OF EXTRACELLULAR TANNASE PRODUCTION 695

FIGURE 2a. Response surface plot of tannase production by AT 13 showing interaction between

tannic acid and KCl

FIGURE 2b. Response surface plot of tannase production by AT 13 showing interaction

between KCl and KH

2

PO

4

Archana and Lakshmi

696 STATISTICAL OPTIMIZATION OF EXTRACELLULAR TANNASE PRODUCTION BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

FIGURE 2c. Response surface plot of tannase production by AT 13 showing interaction between KH

2

PO

4

and

tannic acid

Table 5. ANOVA for Response Surface Quadratic Model for the production of

tannase by AT 13

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

Sum of Mean F p-value

Source Squares df Square Value Prob > F

Model 239.38 8 29.92 187.04 0.0006 signi cant

A-A 85.94 1 85.94 537.18 0.0002

B-B 0.45 1 0.45 2.79 0.1934

C-C 0.74 1 0.74 4.61 0.1209

AB 13.29 1 13.29 83.05 0.0028

AC 6.13 1 6.13 38.29 0.0085

BC 31.81 1 31.81 198.84 0.0008

A2 92.14 1 92.14 575.97 0.0002

B2 54.50 1 54.50 340.66 0.0003

C2 0.000 0

Residual 0.48 3 0.16 187.04

Cor Total 239.86 11

From the table, it was observed that the variation in tan-

nase activity was 0.3–16.53U/ml/min. From the Pareto

chart (Fig.1), only tannic acid was found to be signi -

cant for the production of tannase by AT 13.

Figure 1 show the Pareto chart of effects of vari-

ables on tannase production which helps in. identify-

ing important factors that are mainly responsible for

enzyme production. From the chart it was evident that

the most important contributing factor for tannase pro-

duction was tannic acid only.

The ANOVA results are given in Table 3. The p-value

of ANOVA table serves as a tool for checking the sig-

Archana and Lakshmi

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS STATISTICAL OPTIMIZATION OF EXTRACELLULAR TANNASE PRODUCTION 697

ni cance of each of the coef cients and is indicative

of interaction strength of each independent variable.

P-value less than 0.05 indicate that the model terms are

signi cant. In this experiment, the Model F-value of

285.87 implied that the model is signi cant in enhanc-

ing enzyme production In this case A (Tannic acid ), G

(Dummy), H (Dummy) and J (Dummy) gave signi cant

terms and B, C, D, K and E gave negative signi cant

terms.

Optimization of signi cant variables by Response

surface methodology (RSM)

Tannic acid was found to be signi cant by PBD was

optimized by RSM using Box-Behnken design for tan-

nase production. The response values in terms of tan-

nase activity and the matrix design were represented in

Table 4.

The results obtained by Box-Behnken were analyzed

by Analysis of variance (ANOVA) with a model F-value

of 187.04 indicating that the model was signi cant

(Table 5). Values of Prob > F less than 0.05 indicate that

the model terms are signi cant. In the present model

A, AB, BC, AC, A

2

, B

2

are signi cant model terms. The

Model F-value of 187.04 implies the model is signi -

cant. There is only a 0.06% chance that an F-value this

large could occur due to noise. The Values of “Prob >

F” less than 0.0500 indicate model terms are signi -

cant.This t of the model was checked with the coef-

cient of determination R

2

, which was calculated to be

0.9680. The determination coef cient (R

2

) of the model

was 0.9980 indicating that 99.80% of variability in the

response could be accounted by the model.

The ANOVA gave the following regression equation:

R1 = +19.97 -3.28* A -0.24* B+0.30* C-1.82*

AB-1.24* AC+2.82* BC-6.79* A

2

-5.22*+0.000* C

2

Where R1 is the tannase activity; A is tannic acid; B is

KCl; C is KH

2

PO

4

and the interactions AB, BC, AC, A

2

, B

2

are signi cant.

3D Surface Plots

The three-dimensional response surface curves were

plotted to study the interaction among different phys-

icochemical factors used and to nd out the optimum

concentration of each factor for maximum tannase pro-

duction.

For the construction of 3D plots, effect of two vari-

ables was considered while the other variable was held

at zero. These plots were helpful in understanding both

the individual and the interaction effects of the two fac-

tors. For the construction of 3Dplots, effect of two vari-

ables was considered while the other variable was held

at zero. From the 3D plots, it was clear that the tannase

activity signi cantly increased with increase in the con-

centration of tannic acid (A) reaching to its maximum

at 18.12 U/ml/min, where as the enzyme activity gradu-

ally increased with increase KCl (B) concentration and

then declined (Figure 2a).Signi cant interactions were

also present between KCl (B) and KH

2

PO

4

(C)with a low

p-value (0.0008) and between KH

2

PO

4

(C) and tannic

acid(A) with a low p-value (0.0085) (Figure 2b and 2c).

Validation of the Model: Validation of the response sur-

face model was con rmed by using opt imum conditions

obtained by the model. The experimental values were

very close to the predicted values and hence it was con-

cluded that the model was successfully validated.

CONCLUSION

The response surface methodology was applicable for

the production of tannase from Streptomyces sp. AT13.

In the present study,optimum conditions for tannase

production by Streptomyces sp. AT13 was done using

Plackett-Burmen design followed by Response surface

methodology using Box-Behnken design. From among

6 fermentative variables that were studied, 5 signi cant

variables were picked up by PBD. Among 5 variables

from PBD, 3 were further optimized by Box-Behnken

design. Analysis of variance (ANOVA) showed the sig-

ni cance of the model and the validity of the model was

con rmed by the veri cation experiments. P-value less

than 0.05 indicate that the model terms are signi cant.

The optimum values of parameters obtained through

RSM were 1 % tannic acid, 0.5 % KCl and 0.1% KH

2

PO

4

to achieve maximum tannase production.

ACKNOWLEDGMENTS

The Authors are thankful to the head of Department of

Biotechnology and Microbiology, KSV University for

providing all the laboratory facilities. The authors are

also thankful to Biogene department of Gujarat State

Biotechnology Mission (GSBTM), Gandhinagar, Gujarat,

India for bacterial strain identi cation.

COMPLIANCE WITH ETHICAL STANDARDS

Con ict of Interests: There are no con icts of interest.

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