statistical optimization of pigment production by monascus sanguineus ...

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STATISTICAL OPTIMIZATION OF PIGMENT PRODUCTIONBY MONASCUS SANGUINEUS UNDER STRESS CONDITIONRashmi Dikshit a & Padmavathi Tallapragada aa Department of Microbiology, Centre for PG Studies, Jain University, Bangalore, Karnataka,IndiaAccepted author version posted online: 10 Apr 2013.

To cite this article: Preparative Biochemistry and Biotechnology (2013): STATISTICAL OPTIMIZATION OF PIGMENTPRODUCTION BY MONASCUS SANGUINEUS UNDER STRESS CONDITION, Preparative Biochemistry and Biotechnology, DOI:10.1080/10826068.2013.792097

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Statistical optimization of pigment production by Monascus sanguineus under stress

condition

Rashmi Dikshit1, Padmavathi Tallapragada1,

1Department of Microbiology, Centre for PG Studies, Jain University, Bangalore, Karnataka, India

Corresponding Author:Dr. Padmavathi Tallapragada Prof. and Head, Department of Microbiology,Centre for PG Studies, Jain University,18/3, 9th Main road, 3rd Block,

Jayanagar, Bangalore – 560011, Karnataka, IndiaPh(off.)-+91-080-43226500; Mobile: +91 94485 33337; Fax: - +91- 080- 43226507E-mail : [email protected];

[email protected]

Abstract

Natural pigments are produced by the Monascus sp. which is used for coloring the

food substances. The intent of this study was to optimize the pigment yield and

biomass produced from the unexplored Monascus sanguineus in submerged culture

under stress condition. For inducing thermal stress, the spores were incubated at

various temperatures at higher ranges. For inducing osmotic stress, varied

concentrations of NaCl, glycerol and peptone were used. The medium components

were optimized by Response surface methodology (RSM). The combined effects of

above mentioned four medium constituents were studied using a 24 full factorial

central composite design (CCD). The relation between the predicted values and

actual values, independent variable and the response were calculated according to

second order quadratic model. It was deduced that the variable with the leading

effect was the linear effect of glycerol concentration. Furthermore, the quadratic

effects of peptone and the interactive effects of temperature and glycerol were more

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noteworthy than other factors. The optimum values for the test variables in coded

factors were found to be spores treated with 70�C for temperature, 0.25M for

glycerol, 0.51% (w/v) for peptone and 1.25% (w/v) for NaCl corresponding to the

maximum red pigment yield of 55.67 color value units (CVU)/mL. With optimized

conditions, the pigment yield was almost three times of the yield observed with

control.

KEYWORDS: CCD, stress, glycerol, RSM, Monascus sp

INTRODUCTION

Monascus sp. is known to produce a broad array of pigments such as rubropunctatin and

monascorubrin for red color, monascin and ankaflavin for yellow color, rubropunctamine

and monascorubramine for purple color which are extensively used as potential

substitutes for synthetic food dyes. [1,2] The general characteristics of pigments produced

by Monascus sp. include high protein adhesion, thermal and wide-range-pH stability. [3]

The conversion from primary to secondary metabolites is a biochemical process and these

secondary metabolites are used by the organisms for maintenance of favorable growth

conditions. [4] However, there in an increase in some useful secondary metabolites such

as amino acids etc. due to the fermentation process, when the cells are exposed to stress

conditions. These stress conditions may include elevated temperatures and osmotic

pressures, metabolic inhibition, existence of heavy metals etc. If microorganisms are able

to adopt to these stress conditions then their growth is restrained and the yield of bio-

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products from fermentation is increased. This strategy can be applied to increase the

production of useful secondary metabolites from micro-organisms. [5]

Some microorganisms such as Saccharomyces cerevisiae is known to develop bio-

process to counteract the stress conditions particularly the salt stress (NaCl). These bio-

processes are generally based on the synthesis of osmolyte and cation transport

mechanism for the exclusion of sodium ions. Literature shows that the disaccharide

(trehalose), which accumulates during salt stress or any other stress conditions can be

helpful in protecting cells against the elevated temperatures by stabilizing the protein and

thus maintaining membrane integrity. [6–10]

The response surface methodology (RSM) is an empirical statistical modeling technique

employed for multiple regression analysis by means of quantitative data obtained from

suitably designed experiments to simultaneously solve multivariable equations. This

methodology can be used to evaluate the relative significance of several affecting factors.

The use of RSM for optimization process in fermentation has increased considerably.

[11,12,13]

The RSM was used in this study with the objective to optimize the red pigment

production by Monascus sanguineus under stress condition in submerged culture. Based

on the results obtained from “one-variable-at-a-time” experiment, spores treated with

different temperatures and different concentrations of glycerol, peptone and NaCl were

selected for RSM study. A central composite design (CCD) was thereafter used to find

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the optimum level of these four factors in order to maximize the biomass and pigment

yield.

EXPERIMENTAL

Culture

Pomegranate was used to isolate the wild strain of Monascus sanguineus Potato Dextrose

Agar (PDA) media was used to maintain the isolated strain and the strain was incubated

at room temperature (30°C) for a week. Further the strain was preserved at 4°C, and sub-

cultured once every 4 weeks. [14]

Inoculum Preparation

The spores from 5-day old sporulated culture were scrapped off and appropriately diluted

in distilled water in order to prepare suspension. 15% spore suspension was inoculated

into conical flasks containing 50 mL. of potato dextrose medium (PDB). This culture was

incubated at 30�C for 5 days in a shaker incubator at 110 r/min. [15]

Submerged Fermentation

Mycelial growth and optimization of red pigment in stress condition from M. sanguineus

was investigated in potato dextrose broth fungal media. 50 mL media were prepared in

100 mL conical flasks. It was then autoclaved for 20 minutes at 121°C. The pH of the

medium was maintained at 5.5. After cooling, 0.5 mL of M. sanguineus culture was used

to inoculate this media. Further the experiment was set in static condition for 16 days and

incubated at 30°C. [15]

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The effect of glycerol, peptone and NaCl stress was examined by adding glycerol at

various concentrations namely 0.25, 0.5, 0.75, 1 and 1.25M, peptone and NaCl at

concentration namely 0.25, 0.5, 0.75, 1 and 1.25% (w/v) to the media prior to

autoclaving. [4]

The spore suspension was subjected to various temperatures (30, 40, 50, 60 and 70ºC) for

one minute before inoculation to investigate the effect of thermal stress. These spores

were used as inoculums. [4]

Dry Biomass

The mycelia were separated from broth by filtration using filter paper (Whatmann No. 1).

Separated mycelia were washed with distilled water before drying in an oven at 40°C.

The biomass was presented in grams per liter. [16]

Pigment Estimation

Estimation of pigment concentration was done by a colorimeter at 510 nm after

centrifuging the filtrate at 10000×g for 15 minutes. The absorbance values represented by

optical density (OD) were converted into color value units using the following formula:

Color value = O.D. × dilution × extracts volume / Quantity of sample (mL) [17]

Experimental Design (RSM)

Experimental design was formulated according to Central Composite Design (CCD)

method of RSM using MATLAB® software Version 7.5.0.342 (R2007b) from The Math

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Works, Inc. for selected four stress condition viz. spores treated at different temperatures

and different concentrations of glycerol, NaCl and peptone (Table 1). A set of 30

experiments was necessitated with each variable at five levels. All the flasks were

incubated for 16 days. The relation between the actual and coded values, independent

variable and the response were calculated according to the second order quadratic model

(Table 2). The comparative effect of two variables on response was examined

from three dimensional contour plots. [18]

Statistical Analysis

MATLAB® software package was used for the graphical and regression analysis of the

experimented data and for examining the response surface and contour plots. Statistical

parameters were estimated using ANOVA.

RESULTS AND DISCUSSION

Optimization of red pigment yield and biomass

The predicted and experimented pigmented yield and biomass obtained from Central

Composite Design in each run are shown in Table 2.

The equation explaining the relationship of the four variables for red pigment yield is

given below

20 1 1 2 2 3 3 4 4 5 1 2 6 1 3 7 1 4 8 2 3 9 2 4 10 3 4 11 1 12b b X b X b X b X b X X b X X b X X b X X b X X b X X b X b XY = + + + + + + + + + + + +

(1)

1 2 1 3 1 4 2 3 2 4 3 4 11926 0.3366X 92.7936X 31.7498X 17.4294Pigmentyield nm+ − + − + − + − (2)

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Where X1 is the temperature variable, X2 is the glycerol concentration, X3 is the peptone

concentration and X4 is the NaCl concentration.

The interaction effects of variables on pigment yield (CVU/mL) were studied by plotting

3D surface curves against two independent variables and keeping other variables at their

central (0) level. The 3D curves and contour plots from the interactions between variables

of the calculated response are shown in Fig 1a to Fig 1d.

Fig 1a shows the dependency of pigment yield (CVU/mL) on temperature and glycerol.

At low temperatures pigment yield increases with glycerol concentration whereas at high

temperatures it reduces. Similarly at lower glycerol concentration the pigment yield

varies significantly with spores treated at high temperatures (70�C). However at higher

glycerol concentration the pigment yield remains almost constant. The highest value of

the pigment yield was found to be about 53.65 CVU/mL with spores treated at 70�C

temperature and 0.25M glycerol concentration.

The peptone vs. temperature plots shown in Fig 1b depicts that the pigment yield

increased with peptone concentration and temperature increasing simultaneously. The

increase in pigment yield with spores treated at high temperatures was more dominant at

high peptone concentration. The maximum value was observed as 46.6 CVU/mL with

approx. 0.91% (w/v) of peptone concentration with spores treated at 70�C temperature.

In Fig 1c the relationship between the variables for yielding pigment was found to be

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slightly complicated. At low glycerol concentration, the pigment yield reduced with

peptone concentration whereas the effect was opposite at higher glycerol concentration.

The area for the higher pigment yield was centered around mid peptone concentration

and mid to high glycerol concentration. The optimum value of peptone was found to be

0.87% (w/v) and glycerol was approx. 1.11M (w/v) to produce the maximum pigment

yield of 34.25 CVU/mL.

From Fig 1d, it was observed that the pigment yield has not shown much variation with

the change in either glycerol or the NaCl concentration. The maximum value was

observed as 37.45 CVU/mL with 1.25% (w/v) NaCl concentration and approx. 0.7M

(w/v) glycerol concentration.

The equation explaining the relationship of the four variables for biomass can be written

as follow

( ) 1 2 3 4 1 2 1 3 1/ 33.8267 0.7421X 17.27X 3.29X 9.37X 0.1678X X 0.0752X X 0.0317X XBiomass g l = − + + − − + + (3)

Where X1 is the temperature variable, X2 is the glycerol concentration, X3 is the peptone

concentration and X4 is the NaCl concentration.

The interaction effects of variables on biomass (g/L) were studied. For temperature and

glycerol as seen from Fig 2a, the biomass was found to be higher at low temperatures and

higher glycerol concentration. This shows inverse dependency of these variables. The

highest value of the biomass (g/L) was about 16.72 with spores treated at 30�C

temperature and 1.25 M glycerol. Similar effect was seen with NaCl and glycerol (Fig.

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2b). The maximum value was observed as 15.3 g/L with 1.25M of glycerol concentration

and 0.25 % (w/v) NaCl concentration. This finding indicates that low salt concentration

tends to promote the fungal biomass.

For peptone and glycerol (Fig 2c), the biomass increased with increase in glycerol

concentration though the increase was highly significant at high peptone concentration.

At low glycerol concentration, the biomass decreased with increase in peptone

concentration whereas the effect opposite at higher glycerol concentration. The maximum

value of biomass was found to be 10.62 g/L with peptone concentration of 0.974 % (w/v)

and glycerol concentration of 1.25M (w/v).

The above model can be explored to predict the pigment yield and biomass within the

limits of the experimental factors. It can be seen from Table 2 that the actual response

values match very well with the predicted values. Sumathy et al., [4] reported enhanced

pigment yield when Monascus sp. spores were treated with 70�C temperatures but no

growth was observed at 80�C temperatures. It has been demonstrated that, with exposure

to high temperatures, many organisms quickly synthesize a highly conserved set of

protein called heat shock protein whose induction provides an adaptation to the

organisms to survive in such hypothermic stress condition. [19,4] The formation of water

soluble red pigment was strongly regulated by the amino acid used as nitrogen source.

Amino acid acts as side chain precursors for the production of soluble red pigment. [20]

Sumathy et al [4] reported that spectral analysis of the pigment extract illustrated a shift in

absorbance from orange zone towards red pigment at high salt concentration. Enhanced

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production of glutamic and aspartic acid was observed in response to higher NaCl

concentration from Candida membranofaciens. [21] The osmo-protective nature of

pigments could be responsible for the enhanced pigment yield with increased NaCl

concentration. [4]

Manipulation in the nutritional requirement or growth condition is the effective tool for

the increase in productivity. Statistical methods offer an effective way to optimize a

certain process by considering the mutual interaction among the variables. [22] There are

three major steps involved in response surface optimization: performing the statistically

designed experiment, estimating the coefficients in mathematical model and forecasting

the response and authenticating the correctness of the model. [23] Table 3 shows the

regression results from the data of Central Composite Design (CCD) experiments. The

bigger the magnitude of the t-value and lesser the p-value, the more significant is the

corresponding coefficient. [23] This indicates that the variable with the most significant

effect was the linear effect of the glycerol concentration. Although glycerol could

stimulate osmotic stress in the microorganism, it may be served as carbon source due to

this having great importance as medium constituents in pigment biosynthesis. [4]

Furthermore quadratic effects of peptone were more significant than other factors. The

interactive effects of temperature and glycerol were also found to be more remarkable

than the interactive effect of other variables.

From the analysis of variance, the model for red pigment production was highly

significant (p < 0.01) and R2 (determination coefficient) value for the model, being the

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measure of the goodness of fit model, was 0.7684, which showed that 76.84% of the total

variation in the observed response value could be explained by the model, or by

experimental parameters and their interactions. The rest (23.16%) of the total variation

was not explained by the model (Table 4). The coefficient estimates in the regression

model for red pigment synthesis is presented in Table 3. After the treatment combination,

all linear terms of the independent variables, quadratic term and interaction term was

taken for estimation of coefficient. As model was highly significant (p < 0.05), the p

value was chosen to check the importance of each of the coefficient. Among the four

factors tested, glycerol had the highest impact on the red pigment production as given by

the highest linear coefficient (Table 3).

From the analysis of variance for biomass it is concluded that the model is highly

significant with a p-value of 3.7×10-4 and R2 (determination coefficient) 0.8619 (Table 4).

The coefficient estimates in the regression model for biomass growth is presented in

Table 5. It shows that, largest effect was the linear effect of the temperature. This shows

temperature plays an important role in growth of microorganism. Furthermore quadratic

effects of peptone, NaCl and temperature were more remarkable than other factors. The

interactive effects of glycerol and peptone were also found to be significant than the other

interactive effect.

VALIDATION OF THE MODEL

The most notable finding of this study was an optimized pigment yield with stress

condition (spores treated with variable temperature range) from Monascus sanguineus.

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It’s a novel idea utilizing the RSM (response surface methodology) as a tool to optimize

pigment yield and biomass under cumulative effect of stress condition. Through CCD

from RSM it was found that the optimum conditions for pigment yield from Monascus

sanguineus were at a temperature of 70�C, glycerol concentration of 0.25M (w/v),

peptone concentration of 0.51g % (w/v) and NaCl concentration of 1.25g % (w/v). The

same was confirmed by the validation of the model. There need to be more exploration

required in the area of thermal stress for arriving at the cause of such an activity at

elevated temperatures.

CONCLUSIONS

There are many reports regarding food colorants from Monascus purpureus, Monascus

anka and other Monascus sp. but not much work has been carried out on Monascus

sanguineus. This study attempts to explore the combined effect of stress on pigment

production with the help of a widely accepted statistical tool. Thus the results obtained

from this study are resourceful and innovative in nature. The results are satisfactory to

conclude that the fungus Monascus sanguineus is a good source of red pigment

production quite similar to other Monascus sp. and can be treated as a potential source for

replacing synthetic food colorants in future.

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Table 1 Experimental range and levels of the independent variables

Variables with

designate

Code Actual factor level at coded factor levels of

-2 -1 0 1 2

Temperature (�C) X1 30 40 50 60 70

Glycerol (M) X2 0.25 0.5 0.75 1 1.25

Peptone % X3 0.25 0.5 0.75 1 1.25

NaCl % X4 0.25 0.5 0.75 1 1.25

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Table 2 Full factorial Central Composite Design (CCD) of four variables in coded and

natural units along with the observed responses

Run No. Tem

p

(ºC)

Glycer

ol

(M)

Pepto

ne

(%)

Na

Cl

(%)

Biomass (gm/L) Pigment Yield

(CVU/mL)

Experimen

tal

Predict

ed

Experimen

tal

Predict

ed

1. 40 0.5 0.5 0.5 7.7800 8.5917 27.3500 26.2572

2. 40 1 0.5 0.5 13.1400 12.0679 28.7875 32.1623

3. 40 0.5 1 0.5 8.2000 7.4679 14.8250 18.0619

4. 40 1 1 0.5 13.1200 13.0217 30.2250 31.0326

5. 40 0.5 0.5 1 8.6600 8.0229 34.0250 30.6890

6. 40 1 0.5 1 9.2500 9.9017 28.7125 34.1722

7. 40 0.5 1 1 6.0900 6.0117 16.3000 20.9780

8. 40 1 1 1 8.8500 9.9679 28.9125 31.5269

9. 60 0.5 0.5 0.5 7.9500 5.8329 42.7625 40.5202

10. 60 1 0.5 0.5 8.0500 7.6317 36.6875 34.0534

11. 60 0.5 1 0.5 6.6100 5.4617 41.7500 38.3343

12. 60 1 1 0.5 9.7000 9.3379 35.2250 38.9331

13. 60 0.5 0.5 1 5.9800 5.5817 41.2000 42.4364

14. 60 1 0.5 1 6.0500 5.7829 36.4125 33.5477

15. 60 0.5 1 1 4.2500 4.3229 41.7375 38.7348

16. 60 1 1 1 7.9100 6.6017 33.7750 36.9118

17. 50 0.25 0.75 0.7 3.0500 4.4154 25.9375 29.1147

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5

18. 50 1.25 0.75 0.7

5

10.0400 10.1704 38.7900 33.1968

19. 50 0.75 0.25 0.7

5

4.6000 5.5754 28.6875 30.9451

20. 50 0.75 1.25 0.7

5

4.7500 5.2704 30.7875 26.1139

21. 50 0.75 0.75 0.2

5

9.2100 11.0304 34.6875 35.0243

22. 50 0.75 0.75 1.2

5

8.0500 7.7254 40.1875 37.4347

23. 30 0.75 0.75 0.7

5

13.1000 12.3704 33.0125 26.3493

24. 70 0.75 0.75 0.7

5

4.0200 6.2454 41.7500 45.9972

25. 50 0.75 0.75 0.7

5

7.0500 7.4950 33.5150 33.8290

26. 50 0.75 0.75 0.7

5

7.8000 7.4950 33.3125 33.8290

27. 50 0.75 0.75 0.7

5

7.7300 7.4950 34.7865 33.8290

28. 50 0.75 0.75 0.7

5

7.6400 7.4950 33.9450 33.8290

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29. 50 0.75 0.75 0.7

5

7.4900 7.4950 33.7650 33.8290

30. 50 0.75 0.75 0.7

5

7.2600 7.4950 33.6500 33.8290

All experiments were carried in duplicates, the above mentioned values are the mean

values

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Table 3: Regression coefficient results from the data of central composite designed

experiments for pigment yield

Values Coefficients Standard error t - value p - value

Constant -16.1178 43.8864 -0.3673 0.7186

Temperature 0.5710 1.0353 0.5515 0.5894

Glycerol 68.0498 35.9103 1.8950 0.0775

Peptone -19.7310 35.9103 -0.5495 0.5908

NaCl 12.3982 35.9103 0.3453 0.7347

Temperature × Glycerol -1.2372 0.4466 -2.7705 0.0143

Temperature × Peptone 0.6009 0.4466 1.3457 0.1984

Temperature × NaCl -0.2516 0.4466 -0.5633 0.5815

Glycerol × Peptone 28.2625 17.8624 1.5822 0.1344

Glycerol × NaCl -9.6875 17.8624 -0.5423 0.5955

Peptone × NaCl -6.0625 17.8624 -0.3394 0.7390

Temperature × Temperature 0.0059 0.0085 0.6873 0.5024

Glycerol × Glycerol -10.6931 13.6426 -0.7838 0.4454

Peptone × Peptone -21.1981 13.6426 -1.5538 0.1411

NaCl × NaCl 9.6019 13.6426 0.7038 0.4923

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Table 4 Analysis of Variance (ANOVA) for response surface quadratic model

SS DF f-value p-value Mean Square R2 Adj. R2

For Pigment yield

299.1228 14 3.5542 0.0101 19.9415 0.7684 0.5522

For Biomass

24.9243 14 6.6864 0.0003748 1.6616 0.8619 0.7330

SS- sum of squares; DF – Degree of Freedom

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Table 5 Regression coefficient results from the data of central composite designed

experiments for biomass

Values Coefficients Standard error t - value p - value

Constant 20.7979 12.6683 1.6417 0.1214

Temperature -0.5608 0.2989 -1.8765 0.0802

Glycerol 13.9150 10.3659 1.3424 0.1994

Peptone 4.7950 10.3659 0.4626 0.6503

NaCl -8.7350 10.3659 -0.8427 0.4126

Temperature × Glycerol -0.1678 0.1289 -1.3014 0.2128

Temperature × Peptone 0.0753 0.1289 0.5838 0.5681

Temperature × NaCl 0.0318 0.1289 0.2463 0.8088

Glycerol × Peptone 8.3100 5.1562 1.6117 0.1279

Glycerol × NaCl -6.3900 5.1562 -1.2393 0.2343

Peptone × NaCl -3.5500 5.1562 -0.6885 0.5016

Temperature × Temperature 0.0045 0.0025 1.8414 0.0854

Glycerol × Glycerol -0.8083 3.9381 -0.2053 0.8401

Peptone × Peptone -8.2883 3.9381 -2.1047 0.0526

NaCl × NaCl 7.5317 3.9381 1.9125 0.0751

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Figure 1a 3D Response surface plot and contour plot showing the relative effect of

glycerol and temperature on pigment yield (CVU/mL) while keeping peptone and NaCl

concentration at their central level. 1b; 3D Response surface plot and contour plot

showing the effect of peptone and temperature on pigment yield (CVU/mL) while

keeping glycerol and NaCl concentration at their central level. 1c; 3D Response surface

plot and contour plot showing the effect of peptone and glycerol on pigment yield

(CVU/mL) while keeping NaCl concentration and temperature at their central level. 1d;

3D Response surface plot and contour plot showing the effect of NaCl and glycerol on

pigment yield (CVU/mL) while keeping peptone concentration and temperature at their

central level.

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Figure 2a 3D Response surface plot and contour plot showing the relative effect of

glycerol and temperature on biomass (g/L) while keeping peptone and NaCl

concentration at their central level. 2b; 3D Response surface plot and contour plot

showing the effect of NaCl and glycerol on biomass (g/L) while keeping peptone

concentration and temperature at their central level. 2c; 3D Response surface plot and

contour plot showing the effect of peptone and glycerol on biomass (g/L) while keeping

NaCl concentration and temperature at their central level.

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statistical optimization of pigment production by monascus sanguineus ...

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