Research Article

OPTIMIZATION OF PH AND SUCROSE IN THE CALLUS CULTURE FOR THE MICRO PROPAGATION OF MUCUNA PRURIENS USING RESPONSE SURFACE METHODOLOGY

UMA SUNDARAM, ANUPAMA V. AND GURUMOORTHI P*

Nutraceutical Lab, Department of Food Process Engineering, School of Bioengineering, SRM University, Kattankulathur – 603203, Tamil Nadu, South India. Email: p_gurumoorthi@yahoo.com

Received: 04 Jan 2013, Revised and Accepted: 13 Feb 2013

ABSTRACT

Objective: The aim of the current study is the optimization of media composition at various concentration of sucrose in the media to achieve the efficient growth of M.pruriens

Methods: The compositions of the Murashige and Skoog (MS), medium was varied by at different concentration of sucrose and pH for the micro propagation of Mucuna species. These two variations, along with the concentration of the macro salts, constituted the independent factors in the experimental design. Design points were chosen algorithmically by D-optimality criteria to sample the design space. Growth of the callus at each treatment point was measured as g/L of fresh weight and dry weight. An analysis of variance was conducted and a response surface polynomial model was generated.

Results: The maximum fresh weight observed was at points 5 and 11, with maximum fresh weight (197g/L) and maximum dry weight (17.34g/L) in both.

Conclusion: The results of the current study showed significant increase in the growth of callus of M.pruriens indicating a good efficiency of the optimized media composition and the experimental model used in comparison to other studies of similar nature.

Keywords: Mucuna pruriens, Response Surface Methodology, Sucrose, MS media.

INTRODUCTION

Mucuna pruriens L (Fabaceae) commonly known as velvet bean is an economically important medicinal plant found in bushes, forests of Western and Eastern Ghats of India [1, 2]. All parts of Mucuna posses valuable medicinal properties and there is a heavy demand of Mucuna seeds in Indian markets [3]. After the discovery that Mucuna seeds contain L-Dopa, its demand in international markets has increased many fold and this demand has motivated Indian farmers to start commercial cultivation of the same [4]. L- Dopa molecules are known to play a major role in the adaptation of plants to their environment and also represent an important source of pharmaceuticals [5].

Though efficient systems have been reported for the regeneration of plants from leaf and stem segments for the induction of direct somatic embryogenesis from leaf, petal, and anther explants [6,7,8], but more efficient protocols are needed to increase the number of plants in a shorter period of time [9].

Sucrose is the most common sugar used in tissue culture is but yet, some reducing sugars such as glucose and fructose have also been reported to provide occasionally a better carbon source [10]. The growth of the shoots is also affected by differences in the concentration of sucrose [11, 12]. Plant cells and tissues also require an optimum pH for growth and development in cultures. The pH affects nutrient uptake as well as enzymatic and hormonal activities in plants [13]. The changes in external pH have a small transient effect on cytoplasmic pH but the cells are readily readjusted towards their original pH [14], thus the effect of external pH on cytoplasm is not long lasting. However, this change may affect plant growth by the conversion of inorganic phosphate into organic phosphate at the extracellular region. The detrimental effects of adverse pH are generally related to an imbalance in nutrient uptake rather than to direct cell damage.

Due to the extensive importance of the Mucuna species, its micro propagation is essential, to meet the existing and ever increasing demand. The micro propagation can be carried out using MS media but to increase the efficiency of the callus development, media supplementation becomes necessary. The supplementation of media is best done by altering the carbon source, in such a way that the sugars available are in abundance and are easily utilizable. One

approach would be the complete replacement of the existing sugar source, sucrose with other sugars such as glucose. Another approach to a solution for this problem would be varying the concentration of sucrose in the media to levels that promote growth.

In the present study, the latter approach was taken up, coupled with a variance in pH of the overall media and the macro nutrients also. The experimental design for these trials was done using response surface methodology, to maximise efficiency in minimal time.

MATERIAL AND METHODS

Source of plant material

Mucuna pruriens (L.) DC was collected as mature pods from the natural stands of five different agro-climatic regions of Tamil Nadu and Kerala bordering to Western Ghats.

Plant material and culture conditions

Leaf explants were used for callus induction. They were collected from mature grown M. pruriens. A callus was induced in a MS basal supplemented medium with 2, 4-D (4.52µm/L); BA (2.22 µm/L) and 2ip (4.92 µm/L) and various concentration of sucrose and pH levels. The sucrose concentrations (0.5 to 4.0) and different pH levels (5.0 to 7.0) of culture medium were used in this study. Growth of M. pruriens callus biomass was measured in terms of fresh weight (FW g/L) and dry weight (DW g/L).Callus biomass was harvested and weights were determined after 3 weeks of culture. The flasks containing explants and proliferating cultures were incubated on culture racks in a growth chamber. The cultures were maintained at 25±2º C under the white cool fluorescent tubes of 40 µmol/m2 /s2

light intensity with 55-60% relative humidity.

Experimental Design

The experimental design was constructed employing a 3-variable, second order D Optimal design with 5 centre points. The three independent variables for fresh weight response and dry weight response were the pH of overall media (A), sucrose concentration (B) and Macro salt concentration (C) the details of the constituents of each factor and their upper and lower limits were determined from the graphical representation of the analysis of mean values from each level for a particular factor and were as seen in Table 1

International Journal of Pharmacy and Pharmaceutical Sciences

ISSN- 0975-1491 Vol 5, Suppl 1, 2013

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[15]. The experimental design in the actual (A, B, C) is shown in Table 1. The response functions, i.e., fresh weight of callus and (R1) dry weight of callus (R2) grown in the culture were approximated by a second degree polynomial (Eq. 1).

Yijk = b0 + ixj + ij xI xj + ijk (Eq. 1)

The number of variables, denoted by n, and i, j and k, are integers. The coefficients of the polynomials are represented by b0, bi and bij, and εij is the random error; when i < j, bij represents the interaction effects of the variables xi and xj. The response surface graphs were obtained from the regression equations in actual level of variables. The detailed analysis of variance (ANOVA) was performed upon the variables to know the effects of individual variables. The experimental design obtained using D Optimal response surface

methodology was as shown in Table 2. The software, Design Expert, version 8 (Stat-Ease, Minneapolis, MN) was used in the optimization of media for micro propagation.

Table 1: Factors used in 3 factor D Optimal Response Surface Design

Factors MS Salts Range Factor A pH 5 - 7 Factor B Sucrose 0.5 - 4 Factor C (macros) CaCl2·2H2O 0.5 - 4 KH2 PO4 MgSO4 MnSO4·H2O ZnSO4·7H2O

Table 2: Experimental design as constructed using D Optimal Response Surface Methodology.

Run pH Sucrose Macro Salts Fresh weight Dry weight 1 7 4 0.5 157.22 10.76 2 5 1.67 2.0225 170 12.7 3 7 4 0.5 152 10.4 4 6.4 1.55 1.55 161.45 10.88 5 5.81 2.58 0.5 193.5 18.66 6 7 0.5 4 72.98 3.63 7 5.81 0.5 2.58 151 10.76 8 6.15 4 1.72 181 17.3 9 7 0.5 0.5 72.98 3.65 10 6.29 4 4 181 17.3 11 5.81 2.58 0.5 193.5 18.66 12 5.73 2.74 2.95 191 17.96 13 7 2.6 2.58 132 9.64 14 5 0.5 0.5 139.3 9.78 15 5 4 4 158 10.87 16 5 1.74 4 176.22 12.65 17 5 4 1.72 158 10.54 18 5 0.5 0.5 149 10.02 19 7 0.5 4 72.98 3.63 20 7 0.5 0.5 73 3.66

The basic strategy used in the present study was, firstly the creation of an n-dimensional experimental design space where each dimension was defined by the above mentioned factors (A, B, C). This was followed by the actual experimentation, the growing of tissues on a set of treatment combinations represented as points contained within or on the surface of the n-dimensional design space. This was followed by the generation of an equation to describe callus growth through the n-dimensional design space. Further, on an exploration of the lower dimensional region of the n-dimensional design space was carried out, using factors identified by the analysis of variance (ANOVA).

Optimization of design

The design was subjected to multiple levels of analysis, for optimization. The aim of the optimization was to maximize fresh and dry weights of the calli. The diagnostic and influence plots, available in the software, were used for the optimization of the design. The differences between the predicted and actual values were generated and thus, an ideal treatment point was suggested.

Data Analysis

For each treatment point, the highest order polynomial model where additional model terms were significant at the 0.05 level was analyzed by ANOVA. Model adequacy tests [15] were calculated by Design Expert® 8 (Stat-Ease, Minneapolis, MN) were as follows:

1. Box–Cox plot—used to determine if the data require a power law transformation. A transformation is recommended, based on the best lambda value, which is found at the minimum point of the curve generated by the natural log of the sum of squares of the residuals [17, 18].

2. Normality assumption—a normal probability plot of the internally studentized residuals was examined; the assumption is satisfied if the residuals plot closely along a line 3. Constant variance assumption—a plot of the internally studentized residuals vs. predicted response value was examined; the assumption is satisfied if the points fall within the interval of −3 to +3 standard deviations (i.e., sigma), exhibit random scatter, and do not show a “megaphone” (<) pattern where the residuals increase with the predicted response. 4. Outlier t values—a statistic calculated for each point; a point outside + 3.5 standard deviations is defined as an outlier and indicates either a problem with that point or with the chosen model. 5. Lack-of-fit test—additional design points were included in every experiment for this test. A significant lack of- fit indicates the model may not be capturing the entire signal in the observations. 6. Predicted vs. actual values plot—points that are randomly scattered along and around a 45° line (i.e., perfect correlation) indicate the model appears to be unbiased when predicting new observations. 7. Cook’s distance—a statistic to identify points with unusually high influence on the fitted model (i.e. high leverage points), thus resulting in a model fitted more to the high leverage points than to the majority of points in the data set. 8. R2, adjusted-R2, predicted-R2 calculated as follows, R2 = 1−SS residuals/ (SS model + SS residuals) This is the fraction of overall variation explained by the model; adjusted-R2 is R2 adjusted for the number of terms in the model relative to the number of points in the design; predicted R2 = 1− (PRESS/SS Total) where PRESS is the “predicted residual sum of squares”. When selecting the best model, we maximized predicted- R2.

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9. Adequate precision is a measure of signal to noise in the data; it compares the predicted values at the design points to the average prediction error. Ratios greater than 4 are preferred.

RESULTS

In the present study, the influence of varied sucrose, macro salt concentration and pH on micro propagation of velvet bean was

analyzed. The actual and predicted values were as shown in Table 3a and 3b.

The growth response of the velvet bean callus measured by fresh weight and dry weight, ranged from 68.66 – 197 g/L and 2.61 – 19.24 g/L, respectively. A summary of the lack of- fit test (Table 4a, 4b), ANOVA (Table 5a, 5b) were observed as indicated.

Table 3a: Actual and predicted values of fresh weight

Run Actual Value Predicted Value 1 149 144.5509 2 139.3 144.5509 3 72.98 73.41771 4 73 73.41771 5 193.5 194.0232 6 193.5 194.0232 7 157.22 153.833 8 152 153.833 9 161.45 157.2512 10 158 156.3165 11 181 184.7238 12 170 168.8201 13 151 150.9213 14 132 134.0628 15 191 192.0944 16 72.98 73.02666 17 72.98 73.02666 18 176.22 176.8542 19 158 158.6323 20 181 178.7503

Table 3b: Actual and predicted values of dry weight

Run Actual Value Predicted Value 1 10.02 10.08102 2 9.78 10.08102 3 3.65 3.089076 4 3.66 3.089076 5 18.66 18.54243 6 18.66 18.54243 7 10.76 10.82352 8 10.4 10.82352 9 10.88 13.31184 10 10.54 10.97192 11 17.3 16.07 12 12.7 11.65352 13 10.76 11.11311 14 9.64 9.755308 15 17.96 17.08009 16 3.63 3.582651 17 3.63 3.582651 18 12.65 12.44981 19 10.87 11.65807 20 17.3 17.14892

Table 4a: Lack of fit test for experimental model – Fresh weight

Source Sum of Squares df Mean Square F Value p-value Prob > F Remarks Linear 10709.98 11 973.63 80.24 < 0.0001 2FI 7650.09 8 956.26 78.80 < 0.0001 Quadratic 49.49 5 9.898 0.815 0.5857 Suggested Cubic 0 0 Aliased Pure Error 60.66 5 12.13

Table 4b: Lack of fit test for experimental model – Dry weight

Source Sum of Squares df Mean Square F Value p-value Prob > F Remarks Linear 229.33 11 20.84 1113.10 < 0.0001 2FI 195.83 8 24.47 1306.97 < 0.0001 Quadratic 11.16 5 2.23 119.18 < 0.0001 Suggested Cubic 0 0 Aliased Pure Error 0.09 5 0.018

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Table 5a: ANOVA for 3 factor D Optimal Response Surface Quadratic Model – Fresh weight

Source Sum of Squares df Mean Square F Value p-value Prob > F Remarks Model 38704.34 9 4300.482 47.73 < 0.0001 significant A-Ph 5583.404 1 5583.404 61.97 < 0.0001 B-Sucrose 2721.379 1 2721.379 30.20 0.0003 C-Macro Salts 59.80343 1 59.80343 0.663 0.4342 AB 2705.223 1 2705.223 30.025 0.0003 AC 388.5759 1 388.5759 4.312 0.0646 BC 144.3915 1 144.3915 1.602 0.2342 A^2 5852.931 1 5852.931 64.962 < 0.0001 B^2 679.2854 1 679.2854 7.539 0.0206 C^2 176.2489 1 176.2489 1.9561 0.1922 Residual 900.9773 10 90.09773 Lack of Fit 567.0028 5 113.4006 1.6977 0.2877 not significant Pure Error 333.9745 5 66.7949 Cor Total 39605.31 19

Table 5b: ANOVA for 3 factor D Optimal Response Surface Quadratic Model – Dry weight

Source Sum of Squares df Mean Square F Value p-value Prob > F Remarks Model 554.90 9 61.656 63.49 < 0.0001 significant A-pH 88.20 1 88.206 90.83 < 0.0001 B-Sucrose 24.69 1 24.693 25.42 0.0005 C-Macro Salts 0.154 1 0.1541 0.15 0.6987 AB 37.063 1 37.063 38.16 0.0001 AC 5.795 1 5.7954 5.96 0.0347 BC 4.0653 1 4.0653 4.18 0.0680 A^2 82.77 1 82.77 85.23 < 0.0001 B^2 32.90 1 32.901 33.88 0.0002 C^2 0.79 1 0.792 0.816 0.3875 Residual 9.710 10 0.9710 Lack of Fit 6.320 5 1.264 1.864 0.2554 not significant Pure Error 3.39 5 0.678 Cor Total 564.61 19

Fig. 1: Five factor D Optimal design - Fresh weight – Diagnostic and Influence Plots

1a. Box Cox plot; 1b.Normal plot of residuals; 1c. Residuals vs. predicted plot; 1d. Outlier t value plot; 1e. Cook’s distance plot; 1f. Predicted values vs. actual values plot

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Fig. 2: Five factor D Optimal design - Dry weight - Diagnostic and Influence Plots

2a. Box Cox plot; 2b.Normal plot of residuals; 2c. Residuals vs. predicted plot; 2d. Outlier t value plot; 2e. Cook’s distance plot; 2f. Predicted values vs. actual values plot

The data did not require transformation per the Box–Cox analysis (Fig. 1a, 2a), and the remaining diagnostics were all within acceptable limits including the normality assumption (Fig. 1b, 2b), the constant variance assumption (Fig. 1c, 2c), no outlier t points (Fig. 1d, 2d), no points that exceeded a Cook’s distance of one (Fig. 1e, 2e), and the predicted vs. actual value plot (Fig. 1f, 2f). The lack-of-fit test was not significant (p=0.2877 fresh weight, p=0.2554 dry weight) implying that the lack of fit is insignificant as compared to pure errors i.e. the model fit is very good. The three R2 statistics were clustered with a difference of 0.2 in both fresh and dry weights and the overall model was highly significant (p<0.0001) with Model f value 51.49 fresh weight,63.49 dry weight with only a .01% chance of the noise being responsible for the calculated F values.

These factors put together, contribute to the model being considered of sufficient quality to navigate the experimental design space and, therefore, useful for predicting new observations. The ANOVA

revealed 7 significant terms (A, B, AB, AC, BC, A2, and B2) and 6 significant terms (A, B, AB, A2, B2 and C2) for fresh and dry weight respectively. The 9-term reduced Quadratic polynomial model equation is as follows,

Fresh weight = - 894.1 +378.74 pH – 11.60 sucrose + 9.20 macro salts + 9.40 pH * sucrose – 1.858 pH * Macro salts – 1.60 sucrose * macro salts – 34.847 pH2 – 6.382 sucrose2 – 0.940 macro salts2

Dry weight = -175.622 + 66.129 pH – 1.39 sucrose + 3.93 macro salts + 0.74 pH * sucrose –0.28 pH * Macro salts – 0.044 sucrose * macro salts – 5.84 pH2 – 0.985 sucrose2 – 0.462 macro salts2

The above equations, help in deducing the influence of each factor in the growth of the calli and thus, the fresh and dry weights. The contour plots of the optimized results were as seen in figure 3a and figure 3b for fresh and dry weights respectively. Figures 4 and 5 show the 3D surface plots of the effects of both factors on the fresh and dry weights respectively.

Fig. 3: Five factor response contour plot -Sucrose vs. pH

3a. Fresh weight response contour plot; 3b. Dry weight response contour plot

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Fig. 4: Five factor response surface plot for Fresh weighte

4a. Sucrose and pH influence on Fresh weight of callus; 4b. Macro salt and pH influence on Fresh weight of callus; 4c. Macro salt and Sucrose influence on Fresh weight of callus

Fig. 5: Five factor response surface plot for Dry weight

5a. Sucrose and pH influence on Dry weight of callus; 5b. Macro salts and pH influence on Dry weight, 5c Macro salts and Sucrose influence on Dry weight

DISCUSSION

In the micro propagation of plants, response surface methodology (RSM) has been used for optimization of media constituents for organogenesis and embryogenesis as well as for increasing the yield of metabolites from in vitro cell cultures. RSM was applied to Decalepis hamiltonii for development of multiple shoots, increasing shoot length by either decreasing or increasing the concentrations of

growth regulators and sucrose and also to understand the interaction between the various parameters employed [19]. Response Surface Methodology adopted in cell suspension cultures of Centella asiatica showed the influence of sucrose concentration on dry cell weight of the callus [20].

In Dianthus caryophyllus L., various combinations and concentrations of growth regulators were optimized to give rise to

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callus and for the initiation of shoots and roots from the callus [21]. Various extraction parameters were optimized for extraction of lycopene from tomato cell cultures; up to 3.7 fold increase in lycopene was obtained when compared to that of the original method [22]. Similarly, RSM was applied for increasing the yield of capsaicinoid from immobilized cultures of Capsicum frutescens. A yield of 220 μg/g capsaicinoids was obtained in 1 to 3 days after culturing the immobilized beads on to suitable medium [23]. In comparison to these results, the results of the current study show much higher increase in the growth of callus indicating a good efficiency of the optimized media composition and the experimental model used.

CONCLUSIONS

The variation of sucrose and macro salts, along with pH as carried out in this study is a new approach to the micro propagation of Mucuna pruriens. This novel method provides a non time consuming, low cost method of optimization of callus growth. The novelty of this study is further enhanced by the usage of response surface modelling. The most effective treatment points were the 5th and 11th points, with maximum fresh weight (197g/L) and maximum dry weight (17.34g/L) in both. The optimization was repeated on three subcultures at each treatment point. Research on other methods to increase micro propagation of Mucuna species is underway. The same method may also be adopted for other plants with a basic morphological or genetic similarity to the current species.

ACKNOWLEDGEMENT

We would like to express our gratitude to Mr. Calvin S Mathew, Gulf Food Industries, California Garden, Dubai for his valuable guidance. We also express our sincere thanks to Prof. Dr.C.Muthamizchelvan, Director (E&T) SRM University for his continued support and encouragement.

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