1. INTRODUCTION:

Filamentous fungi isolated from ecologically rich niches are prolific producers of several bioactive compounds with the potential for cancer therapy. Fungi belonging to the genus Penicillium are known for their capacity to produce bioactive metabolites with complex structures and diverse biological activities. One such isolate of Penicillium species isolated from the Agumbe forest soil (Karnataka, India) was found to produce a novel metabolite, with promising cytotoxicity against the cervical cancer cell line HeLa¹.

In the field of bioactive drugs from microbial origin, much effort was directed towards optimizing production rates and directing the product spectrum². The important parameters such as culture conditions and media constituents are the significant factors influencing the high yield of anticancer compounds. Culture conditions and media optimization explores a sequence of phases with precise set of optimal conditions fixed by different methodologies³. The optimization experiments are usually performed using non-statistical one-factor-at-a-time and statistical experimental design approaches^4,5. However, the former one is highly grueling and stagnant than the statistical methods⁶. Consequently, statistical experimental design techniques, especially Taguchi design and response surface methodology (RSM) are being used widely to select the significant variables and obtain the optimal levels, respectively^7,8. The application of these statistical experimental design techniques in media optimization can result in improved product yields, enhanced secondary metabolite production and reduced time in comparison to the conventional practice of single factor optimization². Taguchi design has been applied to select influencing factors among the constituents of culture media⁹. Optimization of selected, highly influencing factors can be carried out employing response surface methodology with either central composite design (CCD)²or Box-Behnken Design experiments¹⁰. A central composite design is the most commonly used response surface design experiment. Central composite designs are a factorial or fractional factorial designs with center points, augmented with a group of axial points that estimate the curvature.

In the current study, an attempt was made to statistically optimize the media components for increasing the yield of the anticancer metabolite from Penicillium sp. JUFP2 using Taguchi and RSM designs.

2. MATERIALS AND METHODS:

2.1 Strain and Culture Condition:

The organism, Penicillium sp. JUFP2, which was previously reported as the producer of an anticancer metabolite is being used in this study for enhancing the production of this bioactive metabolite¹. The strain was maintained on Czapek Dox agar plates supplemented with 0.5% of yeast extract (w/v). For the production of the bioactive metabolite, 1% seed culture was added to Czapek Dox Yeast broth (CDYB).

For extraction of the secondary metabolite from the fungal culture, the fungal mat was separated from the media by filtration, using a Whatmans filter paper. The fungal mat was left for drying in a hot air oven at 50°C for 6 hours for removal of any traces of the media. The secondary metabolite from the fungal mat was extracted by homogenizing the dried fungal mat with methanol using a mortar and a pestle. The homogenized cell mass was vortexed and centrifuged at 10,000 rpm for 10 minutes without brakes. The supernatant comprising of the secondary metabolite was concentrated by evaporating at room temperature. Growth was observed by recording the dry mycelial weight and the metabolite production was analyzed by measuring the optical density at the wavelength maxima of the bioactive fraction.

2.2 Optimization of Media using one-factor-at-a-time (OFAT) classical method: 

Growth factors such as carbon and nitrogen sources, temperature, pH and incubation period were considered for the enhanced production of the bioactive metabolite ¹¹. CDYB medium was supplemented with different carbon sources (glucose, lactose, maltose, sucrose, starch, glycerol and activated charcoal [AC]) each at a final concentration of 0.5% w/v and with different nitrogen sources (peptone, beef extract, egg albumin, ammonium sulphate, ammonium oxalate, urea and sodium nitrate) each at the final concentration of 0.05% w/v. The carbon and nitrogen sources were directly added to the medium before sterilization. Different temperatures of 24, room temperature (26 ± 2), 28, 32, 36 and 40⁰C, different pH (6.0, 7.0, 8.0, 9.0 and 10.0) and incubation periods (7, 8, 9, 10 and 11 days) were considered¹². The medium (100 mL) was inoculated with 1% seed culture of the strain Penicillium sp. JUFP2, and incubated at room temperature under static conditions.

2.3 Statistical optimization by Taguchi Method:

The interactive effects between various media constituents which significantly influenced the metabolite production were analyzed by Taguchi experimental design¹³. Taguchi method uses a statistical measure of performance called signal-to-noise (S/N) ratio, which was used in the current study to evaluate the quality of the result. Taguchi factorial design was used to optimize the study parameters at lower and higher levels. On the basis of the results of screening different components through OFAT (classical method) seven components were chosen for Taguchi experimental design. Eight experimental trials comprising of seven independent variables at higher and lower levels were generated using Design-Expert version 9 (Table 1 and Table 2).

Table 1: Optimization study parameters at low and high levels

Parameters	Low Level	High Level
Starch (g/dL)	0.25	0.75
Ammonium sulphate (g/dL)	0.025	0.075
Urea (g/dL)	0.025	0.075
Sodium nitrate (g/dL)	0.025	0.075
pH	5.0	9.0
Temperature (ºC)	26	30
Incubation period (days)	8	12

Table 2: Parameters for optimization according to Taguchi OA Design

Trial	Starch (g/dL)	Ammonium sulphate (g/dL)	Urea (g/dL)	Sodium nitrate (g/dL)	pH	Temperature (°C)	Incubation period (Days)
1	0.75	0.075	0.025	0.025	9	30	8
2	0.25	0.075	0.075	0.025	5	30	12
3	0.75	0.025	0.075	0.075	5	30	8
4	0.75	0.025	0.075	0.025	9	26	12
5	0.25	0.075	0.075	0.075	9	26	8
6	0.25	0.025	0.025	0.025	5	26	12
7	0.75	0.075	0.025	0.075	5	26	8
8	0.25	0.025	0.025	0.075	9	30	12

2.4 Statistical Optimization using Response Surface Methodology (RSM):

On the basis of the results of screening different components by the classical method and by Taguchi design, ammonium sulphate, urea and sodium nitrate were found to be significantly influencing the biomass and metabolite productions. Ammonium sulphate, urea and sodium nitrate were taken as independent variables for optimization by RSM ¹⁴. Each variable was analyzed at three levels. The experimental design included 20 sets having all the three variables at their central coded values (Table 3).

Table 3: RSM Experimental Design

Run	Ammonium sulphate (g/dL)	Sodium nitrate (g/dL)	Urea (g/dL)
1	0.05	0.05	0.08
2	0.05	0.05	0.05
3	0.03	0.03	0.03
4	0.07	0.07	0.07
5	0.03	0.07	0.03
6	0.07	0.07	0.03
7	0.02	0.05	0.05
8	0.05	0.02	0.05
9	0.05	0.05	0.05
10	0.07	0.03	0.07
11	0.05	0.05	0.02
12	0.07	0.03	0.03
13	0.05	0.05	0.05
14	0.05	0.05	0.05
15	0.08	0.05	0.05
16	0.05	0.05	0.05
17	0.03	0.03	0.07
18	0.05	0.08	0.05
19	0.03	0.07	0.07
20	0.05	0.05	0.05

2.5 Statistical Analysis:

To check the reproducibility and to confirm the results, all the experiments were performed in triplicates. The results were analyzed by calculating the means ± standard error (SE) of the replicates using GraphPad Prism® software.

3. RESULTS:

3.1 Determination of the Absorption maxima (l _max)

The anticancer metabolite (fraction 2), purified from Penicillium sp. JUFP2 was analyzed spectrophotometrically to determine the l _max. The l _max max was found to be at 220 nm (Figure 1). In subsequent optimization studies, the quantity and concentration of this fraction was determined by checking the optical density (OD) of the extract from every trial at 220 nm.

3.2 Optimization of culture conditions by the classical method:

3.2.1 Effect of Carbon Source:

When media with different carbon sources, were screened for biomass and metabolite production from the fungal isolate Penicillium sp. JUPF2, growth was found only with CDYB supplemented with starch as the carbon source. Also there was no growth or metabolite production with any other carbon sources screened. Starch (0.5% w/v) as a carbon source enhanced both biomass and metabolite production in comparison to the control (Figure 2).

3.2.2 Effect of Nitrogen Source:

When media with different nitrogen sources were screened for biomass and metabolite production from the fungal isolate Penicillium sp. JUPF2, evident growth was found with CDYB supplemented with 0.05 % (w/v) of peptone, ammonium sulphate, urea, sodium nitrate, egg albumin, beef extract and yeast extract as nitrogen sources. No growth or metabolite production was observed when ammonium oxalate was supplemented as the nitrogen source.

Amongst the nitrogen sources only ammonium sulphate, urea and sodium nitrate enhanced the biomass and metabolite production when compared to the control (Figure 3), where as there was a decrease in the biomass and metabolite production when peptone, beef extract, egg albumin and yeast extract were used as nitrogen supplements as compared to the control.

3.2.3 Effect of Incubation period:

When the Penicillium isolate was cultured for different incubation periods (7, 8, 9, 10 and 11 days), it was found that with an increase in the incubation time, there was a gradual increase in the growth and metabolite production until day 10. The highest biomass and metabolite production were seen upon incubation for 10 days. After 10 days, the growth and metabolite production remained constant (Figure 4). In the controlled conditions, that is, 6 days of culture, least amount of metabolite with minimal growth was observed.

3.2.4 Effect of temperature:

When the fungal isolate was cultured at different temperatures (24, 28, 32, 36 and 40 °C), it was found that above 32°C growth was minimal with no metabolite production. Maximum growth was observed at 28°C. The growth and metabolite production remained constant between 24°C and control temperature of 26°C (Figure 5).

3.2.5 Effect of pH:

When media with different pH were screened for biomass and metabolite production from the fungal isolate, it was found that pH 8.0, 9.0 and 10.0 were not supporting the growth or metabolite production. Growth and metabolite production were observed only at pH 6.0 and 7.0. The pH of the control was 5.5 for CDYB media. The highest growth and metabolite production were observed at pH 7.0. There was only a slight increase in the metabolite production from Control pH 5.5 to pH 7.0 without much change in the growth of the isolate (Figure 6).

3.3 Statistical optimization of culture conditions:

3.3.1 Taguchi Experimental Design:

Taguchi experimental design was adopted in the current study for obtaining the relationship between the important parameters (pH, temperature, carbon source, nitrogen source and duration of incubation) which affect the metabolite production and the quantity of biomass produced. The experimental conditions were set at low (-2) and high (+2) coded levels for the current experiment (Table 4).

Table 4: Levels of selected nutrient supplements using Taguchi design

Parameters	Low Level	High Level
Starch (g/dL)	0.25	0.75
Ammonium sulphate (g/dL)	0.025	0.075
Urea (g/dL)	0.025	0.075
Sodium nitrate (g/dL)	0.025	0.075
pH	5.0	9.0
Temperature (℃)	26	30
Incubation period (days)	8	12

The optimization experiments were conducted by designing 8 sets of individual experiments for biomass and metabolite production using Design-expert® version 9 software. Trials with pH 5.0 showed evident growth, where as trials with pH 9.0 did not produce any growth (Table 5).

Table 5: Taguchi experimental design and response for biomass and metabolite production from Penicillium sp. JUFP2

Trial	Starch (g/dL)	Ammonium Sulphate (g/dL)	Urea (g/dL)	Sodium Nitrate (g/dL)	pH	Temperature (℃ )	Incubation (Days)	Biomass (g/L)	O.D (220 nm)
1	0.75	0.075	0.025	0.025	9.0	28	8	0.1	0.1
2	0.25	0.075	0.075	0.025	5.0	28	12	4.2	0.911
3	0.75	0.025	0.075	0.075	5.0	28	8	5.1	1.101
4	0.75	0.025	0.075	0.025	9.0	24	12	0.1	0.1
5	0.25	0.075	0.075	0.075	9.0	24	8	0.1	0.1
6	0.25	0.025	0.025	0.025	5.0	24	12	9.0	2.361
7	0.75	0.075	0.025	0.075	5.0	24	8	3.8	1.197
8	0.25	0.025	0.025	0.075	9.0	28	12	0.1	0.1

The optimal parameters for enhanced biomass and metabolite production were derived from the Pareto chart. The Pareto charts (Figure 7 and 8) summarize the analysis of the results obtained from Taguchi experimental design. Nitrogen sources ammonium sulphate, urea and sodium nitrate were recognized as being the most controlling parameters on biomass and metabolite production by Penicillium sp. JUFP2.

Ammonium sulphate, urea and sodium nitrate have exhibited significantly high positive effects on biomass and metabolite production. Temperature had a neutral effect on both biomass and metabolite production. Other factors like starch, pH and incubation time were found not to affect the growth and metabolite production.

3.3.2 Response Surface Methodology:

Response surface methodology (RSM) was adopted in the current study for obtaining optimal concentrations of the three important variables (ammonium sulphate, urea and sodium nitrate) that contributed towards enhanced biomass and metabolite production. The optimization experiments were conducted by performing 20 sets of individual experiments using central composite rotatable design (CCRD).

Table 6: Experimental design and outcome in CCRD experiments for biomass and metabolite production of bioactive fraction 2 from Penicillium sp. JUFP2.

Run	Ammonium sulphate (g/dL)	Sodium Nitrate (g/dL)	Urea (g/dL)	Biomass (R1: g/L)	O.D (R2: 220 nm)
1	0.05	0.05	0.08	0.1	0.1
2	0.05	0.05	0.05	3.9	6.292
3	0.03	0.03	0.03	0.1	0.1
4	0.07	0.07	0.07	6.3	6.43
5	0.03	0.07	0.03	0.1	0.1
6	0.07	0.07	0.03	3.2	6.05
7	0.02	0.05	0.05	0.1	0.1
8	0.05	0.02	0.05	0.1	0.1
9	0.05	0.05	0.05	3.9	6.292
10	0.07	0.03	0.07	0.1	0.1
11	0.05	0.05	0.02	0.1	0.1
12	0.07	0.03	0.03	8.0	6.424
13	0.05	0.05	0.05	3.9	6.292
14	0.05	0.05	0.05	3.9	6.292
15	0.08	0.05	0.05	9.0	6.846
16	0.05	0.05	0.05	3.9	6.292
17	0.03	0.03	0.07	0.1	0.1
18	0.05	0.08	0.05	0.1	0.1
19	0.03	0.07	0.07	0.1	0.1
20	0.05	0.05	0.05	3.9	6.292

The CCRD is a complete 23 factorial design, with four central points in a cube and six axial points and two centre points in axial, designed for optimizing the biomass and production of bioactive fraction 2 from Penicillium sp. JUFP2. The CCRD matrix designs of the independent factors along with the outcome for optimal biomass and bioactive metabolite production have been presented in table 6. A considerable variation in the biomass and metabolite production was found, depending on the concentration of the three variables in the medium. The maximum biomass and metabolite production was found to be 9.0 g/L and 6.846 (O.D), respectively in run number 15. For estimation of the RSM model errors, the centre point in the design was repeated six times. By applying multiple regression analysis on the experimental data, the following second order polynomial equations were found to explain the biomass and metabolite production by only considering the significant terms and is shown below:

R1 = 0.20 + 0.19A + 0.010B – 0.082C + 0.017AB – 0.06AC + 0.14BC + 0.14A2 – 0.08B2 – 0.014C2…...(I)

R2 = 2.92 + 2.48A + 0.730B – 2.012C + 1.254AB – 1.25AC + 1.39BC + 1.78A2 – 2.16B2 – 0.332C2…...(II)

Where R1 is the predicted response for biomass production and R2 is the predicted response for metabolite production. A, B and C are coded values of ammonium sulphate, sodium nitrate and urea, respectively.

The statistical significance of the polynomial equations I and II were tested using Analysis of Variance (ANOVA). The results of the analysis for R1 and R2 are tabulated in the tables 7 and 8, respectively. Significant interactions between the two variables (A versus B; B versus C and A versus C) were observed. The “p” values between variables were found to be less than 0.05 for R1 and R2. The Model F-value of 3.28 and 11.76 for R1 and R2, respectively implies the model is significant. The lack of fit F-value of 0.87 for R1 and 0.74 for R2 implies the Lack of Fit is not significant relative to the pure error. These results indicated that there were no insignificant model terms in the responses R1 and R2 RSM models.

Table 7: Analysis of variance (ANOVA) table for optimization of biomass production (R1) from Penicillium sp. JUFP2 as generated by the software Design-expert® ver.9

1	R1	Biomass Weight	Column1	Column2	Column3	Column4
ANOVA for Response Surface Quadratic Model
Analysis of variance table [Partial sum of squares - Type III]
	Sum of		Mean	F	p-value
Source	Squares	df	Square	Value	Prob > F
Model	1.2	9	0.13	3.28	0.039	significant
A-Ammonium Sulphate	0.49	1	0.49	12.01	0.0061
B-Sodium Nitrate	1.44E-03	1	1.44E-03	0.035	0.8545
C-Urea	0.092	1	0.092	2.26	0.1633
AB	2.45E-03	1	2.45E-03	0.06	0.8107
AC	0.029	1	0.029	0.71	0.4188
BC	0.15	1	0.15	3.73	0.0821
A2	0.3	1	0.3	7.34	0.022
B2	0.094	1	0.094	2.33	0.1579
C2	2.72E-03	1	2.72E-03	0.067	0.8006
Residual	0.41	10	0.041
Lack of Fit	0.19	5	0.038	0.87	0.5589	not significant
Pure Error	0.22	5	0.043
Cor Total	1.6	19

Table 8: Analysis of variance (ANOVA) table for optimization of metabolite production (R2) from Penicillium sp. JUFP2 as generated by the software Design-expert® ver.9

2	R2	Pigment Production	Column1	Column2	Column3	Column4
ANOVA for Response Surface Quadratic Model
Analysis of variance table [Partial sum of squares - Type III]
	Sum of		Mean	F	p-value
Source	Squares	df	Square	Value	Prob > F
Model	313.44	9	4.34	7.78	0.046	significant
A-Ammonium Sulphate	84.02	1	84.02	3.52	0.0901
B-Sodium Nitrate	7.36	1	7.36	0.31	0.5911
C-Urea	54.97	1	54.97	2.3	0.1602
AB	12.56	1	12.56	0.53	0.4849
AC	12.5	1	12.5	0.52	0.4859
BC	15.52	1	15.52	0.65	0.4388
A2	45.86	1	45.86	1.92	0.1959
B2	67.12	1	67.12	2.81	0.1245
C2	1.56	1	1.56	0.066	0.8032
Residual	238.77	10	23.88
Lack of Fit	78.25	5	15.65	0.49	0.7754	not significant
Pure Error	160.52	5	32.1
Cor Total	552.21	19

The stationary point presenting maximum biomass and metabolite production was with run 15, which had the following critical values (g/dL): Ammonium sulphate, 0.08; Urea, 0.05; Sodium nitrate, 0.05. The predicted biomass and metabolite production for these conditions were 9.26 g/L and O.D 6.280 (220 nm), respectively. The observed biomass and metabolite production versus the predicted biomass and metabolite production under optimum fermentation conditions is shown in Table 9, which were precisely close.

Table 9: Predicted versus Observed Value for biomass and metabolite production

Response

Run Order

Observed Value

Predicted Value

Response 1

Biomass Production

(g/L)

Response 2

Metabolite

Production

(OD₂₂₀)

9.0

6.846

9.26

6.280

From the results it can be inferred that, when compared to the standard control conditions, the statistically optimized conditions resulted in 3-fold greater quantity of biomass and 2.5-fold higher amount of metabolite from Penicillium sp. JUFP2. (Figure 10 and 11).

Figure 10: Comparison of quantity of biomass produced at standard and optimized conditions and the statistically predicted response (*p < 0.05).

Figure 11: Comparison of concentration of metabolite produced at standard and optimized conditions and the statistically predicted response (*p < 0.05).

4. DISCUSSION:

Medium optimization has proven to be one of the effective strategies adopted towards enhancement of product yield and process improvement¹⁵. Different sources of carbon and nitrogen, incubation period, pH and temperature were tested using classical optimization (OFAT method) to maximize the anticancer metabolite production by Penicillium sp. JUFP2. Results indicated that the maximum production of bioactive metabolite was achieved with CDYB supplemented with factors such as starch, ammonium sulphate, urea and sodium nitrate with pH-7.0, incubated for 10 days at 26°C at an individual level.

Improving media composition using statistical methods were proven as useful tools for the consideration of several factors between two levels, for which Taguchi experimental setup was designed with 7 parameters for optimization. The Taguchi approach is a fully developed method having advantage of saving experimental time, product cost and improving the quality as well, which is a basic requirement for the optimization of any fermentation process¹⁶. In optimization designs, uncontrolled variables (noises) generally cause the loss of the quality. This effect of noise can be removed by employing the Taguchi methodology¹⁷. The results showed that, nitrogen sources ammonium sulphate, urea and sodium nitrate at 0.05% (w/v) were the most important factors affecting the biomass and metabolite production of Penicillium sp. JUFP2, which demonstrated a positive significant overall effect.

RSM is a sturdy, robust and efficient mathematical approach which includes statistical experimental designs and multiple regression analysis, for seeking the best formulation under a set of constrained equations. RSM has often been applied to optimize the formulation variables and optimization of several fermentation processes^18,19. In the recent times, RSM has gained much popularity for experiments involving pigment production, optimization of process parameters and screening²⁰. In the current study, the three variables – ammonium sulphate, urea and sodium nitrate selected through Taguchi design, were tested through RSM for the correlation between their concentration and production of the bioactive metabolite. It was found that a higher concentration of ammonium sulphate compared to sodium and urea exhibited significant effect on the production of the biomass and bioactive metabolite. A high similarity was observed between the predicted and the observed values that reflect the accuracy and accountability of the RSM to optimize the biomass and metabolite production.

After statistically optimizing the culture conditions, there was a 3-fold increase in the biomass and 2.5-fold increase in the bioactive fraction productions from Penicillium sp. JUFP2. In a previous study, production of an anticancer drug actinomycin D from the submerged fermentation of Streptomyces sindenensis was found to be increased by 2.8-folds, when the components were optimized through RSM²¹. These results demonstrated RSM to be very effective in optimizing the selected medium components for enhanced production of biomass and bioactive metabolite.

5. CONCLUSION:

The study demonstrates the importance of statistical optimization techniques like Taguchi design and RSM in enhancing the production of the metabolite from the fungal strain Penicillium sp. JUFP2. Further research towards characterization of the metabolite along with stain improvement strategies can reveal the possibilities of any new findings that may have pharmacological significance.

6. ACKNOWLEDGEMENT:

Authors acknowledge the infrastructural facilities provided by the School of Sciences, JAIN (Deemed-to-be University), India, for carrying out this research work.

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Received on 02.05.2018 Modified on 19.06.2018

Research J. Pharm. and Tech 2019; 12(2):463-471.

DOI: 10.5958/0974-360X.2019.00082.9