Article pubs.acs.org/JAFC
Isoflavone Augmentation in Soybean Cell Cultures Is Optimized Using Response Surface Methodology M. K. Akitha Devi and P. Giridhar* Plant Cell Biotechnology Department, Council of Scientific and Industrial Research−Central Food Technological Research Institute (CSIR−CFTRI), Mysore 570 020, India ABSTRACT: Glycine max contains potential therapeutic isoflavones, and its productivity in plants is considerably influenced worldwide by several biotic and abiotic factors. Optimization of soybean cell suspension cultures (Indian variety, JS 335) to maximize the cell growth and isoflavone production in the present study was performed using response surface methodology (RSM) with three independent variables of plant growth regulators, 2,4-dichlorophenoxyacetic acid (2,4-D), 1-naphthalene acetic acid (α-NAA), and kinetin (Kn). The maximum biomass achieved was 70.62 g/L dry weight (dw) using the optimized medium of 2.10 mg/L 2,4-D, 5.52 mg/L α-NAA, and 0.35 mg/L Kn supplemented in the Murashige and Skoog (MS) basal medium. The total isoflavone content of 38.59 mg/g of dw was obtained in the medium with optimized conditions of 1.33 mg/L 2,4-D, 1.76 mg/L α-NAA, and 0.15 mg/L Kn. In comparison to field-grown soybean seeds, the cell suspension cultures profoundly augmented isoflavone concentrations. The optimized conditions for both biomass and total isoflavone content were evaluated by superimposing the contour plots. The results suggested that the optimized medium of cell suspension cultures possibly be used for scale-up studies in bioreactors to offer a high content of bioactive isoflavones. KEYWORDS: isoflavone, biomass, plant growth regulators, response surface methodology, Glycine max
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INTRODUCTION
Secondary metabolite production and growth in cell cultures depend upon the concentration and interaction of the nutrient status present in the culture medium.14 Accordingly, of the various nutrient components, the plant growth regulator (PGR), exclusively auxin and cytokinin, concentration has a decisive role for enhancing the isoflavone production in callus3,15 and cell suspension cultures.3 In general, the variation in the bioactive content in in vitro cultures resulted from upregulation of enzyme biosynthesis and gene expression. It is imperative to optimize the suitable PGR concentration in selective cell suspension cultures. This is inevitability to achieve high-level production of isoflavones with biomass accumulation because its concentration varies from species to genotype.3 Thus far, very few experimental reports have direct focus on the manipulation of media components for isoflavone accumulation in in vitro cultures of soybean3,16 and also other Fabaceae family plants, especially species belonging to Maackia,15 Genista,17 and Psoralea genera.18 Response surface methodology (RSM) is a statistical method efficiently used for experimental design and analysis of results and to achieve the optimal conditions. This method focuses on multiple variables with a minimum number of experimental trials to build an empirical model with the statistically valid results19,20 and deduce facts in a scientific manner.20,21 Recently, RSM has also been used for the optimization studies of β-carotene production from Daucus carota cells22 as well as lycopene extraction from cell suspension culture of Lycopersicon esculentum.23 Nevertheless, to the best of our comprehension, there are no reports vis-à-vis medium optimizations in
Biotechnological strategies, such as the plant cell culture technology, offer large-scale production of plant-specific bioactives. This promising technology minimizes variability and provides cells with a higher metabolic rate than whole differentiated plants.1 Plant cell cultures are widely represented as “bioreactors” for synthesizing in vitro-derived alkaloids, terpenes, pigments, isoflavones, heterologous and therapeutic proteins (antibodies, vaccines, and enzymes),2−5 and anti-tumor necrosis factor, 6 thereby allowing incessant supply for commercial and pharmaceutical purposes. Isoflavonoids are a group of naturally occurring polyphenolic compounds predominantly found in soybean and also soy products.7,8 These compounds receive high interest because of their multiple therapeutic benefits in humans. Consumption of soybean and its products plays a vital role in the reduction of osteoporosis, cerebral ischemia, cardiovascular diseases, protection against low-density lipoprotein (LDL) oxidation, various cancers, and lowering hormone-dependent diseases.7,9 In plants, isoflavones act as defense-signaling molecules, such as phytoalexins and phytoanticipins, thus protecting from environmental stresses.10 Soy isoflavones exist in three major aglyconic forms of isoflavones, daidzein, genistein, and glycitein, synthesized via the phenylpropanoid pathway.11 Production of isoflavone content in in vivo soy plants is influenced by many factors, such as geographical location, biotic and environmental factors, and climate.7 These factors not only alter the crop yield, growth, and productivity in plants but also the major bioactives. During drought stress conditions, isoflavone content is extremely affected in Indian12 and also foreign soybean varieties.13 Besides, the average isoflavone content of Indian soybean varieties was less than 3.5-fold in comparison to the foreign varieties.7 © 2014 American Chemical Society
Received: Revised: Accepted: Published: 3143
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Table 1. Central Composite Experimental Design in Coded and Actual Levels of Variables and the Response Functions α-NAA
2,4-D
Kn
experiment number
coded level (x1)
actual level (X1)
coded level (x2)
actual level (X2)
coded level (x3)
actual level (X3)
biomass (g L−1 DW, Y1)
TI content (mg/g of DW, Y2)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
−1.000 0.000 0.000 0.000 0.000 −1.000 −1.000 1.000 1.000 0.000 0.000 1.682 −1.000 0.000 1.000 0.000 0.000 −1.682 1.000 0.000
0.500 1.000 1.000 1.000 1.000 0.500 0.500 1.500 1.500 1.000 1.000 1.841 0.500 1.000 1.500 1.000 1.000 0.159 1.500 1.000
1.000 0.000 0.000 0.000 0.000 −1.000 −1.000 −1.000 1.000 0.000 0.000 0.000 1.000 −1.682 1.000 1.682 0.000 0.000 −1.000 0.000
1.500 1.000 1.000 1.000 1.000 0.500 0.500 0.500 1.500 1.000 1.000 1.000 1.500 0.159 1.500 1.841 1.000 1.000 0.500 1.000
1.000 0.000 0.000 0.000 0.000 −1.000 1.000 1.000 1.000 1.682 0.000 0.000 −1.000 0.000 −1.000 0.000 0.000 0.000 −1.000 −1.682
0.150 0.100 0.100 0.100 0.100 0.050 0.150 0.150 0.150 0.184 0.100 0.100 0.050 0.100 0.050 0.100 0.100 0.100 0.050 0.016
58.90 61.90 60.80 62.20 60.40 55.34 48.13 49.83 63.56 54.16 61.90 57.24 51.70 59.27 56.87 55.17 60.80 47.15 54.87 49.67
31.23 37.53 37.98 37.33 37.21 33.18 23.30 23.70 39.00 28.20 38.10 30.00 24.90 32.43 31.00 29.50 38.12 24.20 33.98 26.23
Cell suspension cultures were established uniformly after 8−10 passages, and then regular subcultures were performed by transferring a packed cell volume (PCV) of cells to fresh medium with 2 week intervals. All experiments had at least three replicates, and each growth regulator combination treatment was repeated 3 times. Growth Parameters. To analyze biomass, the cell growth was measured as dry weight (dw) basis. Cells were harvested by filtration after 18 days of inoculation (three flasks per treatment) and then recovered from the filter paper (Whatmann No. 1) after washing 3 times with distilled water. The dry weight was measured after drying the fresh cell mass in an oven at 45 ± 2 °C until it reached a constant weight, and thus, fresh weight (fw) and dw of cells were estimated. Growth of cells was determined using the growth index (GI) using the following formula on a dw basis:
suspension cultures of Glycine max to maximize isoflavone production using RSM. In the present study, RSM was employed to optimize a proper medium for biomass and isoflavone content in G. max cell suspension culture.
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MATERIALS AND METHODS
Chemicals. All of the isoflavone standards (daidzein, genistein, and glycitein), PGRs [2,4-dichlorophenoxyacetic acid (2,4-D), 1-naphthalene acetic acid (α-NAA), and kinetin (Kn)], and fluorescein diacetate (FDA) used in the present study were purchased from Sigma-Aldrich, St. Louis, MO. High-performance liquid chromatography (HPLC)grade solvents were obtained from Sisco Research Laboratories (SRL, Mumbai, India). Sucrose and agar were obtained from Himedia Lab (Mumbai, India). All other analytical-grade chemicals were obtained from Merck (Darmstadt, Germany). Plant Material and Surface Sterilization. Seeds of cv. JS 335 of G. max were collected from AICRP, Soybean, GKVK campus, Bangalore, India. Uniform size soybean seeds were handpicked, surface-sterilized, germinated, and maintained using a standardized protocol.24 Seeds were surface-sterilized using 70% (v/v) ethanol for 30 s, followed by 0.2% (w/ v) carbendazium for 5 min and 0.1% (w/v) mercuric chloride for 1 min. The surface-sterilized seeds were rinsed 3 times in sterile distilled water and then cultured in Petri dishes containing Murashige and Skoog (MS) basal medium25 for the germination. The medium pH was set up to 5.8 and then autoclaved at 121 °C for 20 min. The cultures were maintained at a light intensity of 57 μmol m−2 s−1 (illumination supplied by coolwhite fluorescent tubes) with a 16 h photoperiod at 25 ± 2 °C. Callus and Cell Suspension Culture and Initiation. The 3-weekold in vitro germinated seedlings were used as a source of explants for initiation of callus cultures. Cotyledonary nodal leaves were incised into small pieces (9−10 mm) and aseptically transferred to callus induction medium containing full-strength MS solid medium supplemented with 3% sucrose, 0.8% (w/v) agar, and different concentrations of three variables (2,4-D, α-NAA, and Kn), as mentioned in Table 1. The media pH was adjusted to 5.7 before autoclaving (121 °C and 1.2 kg/cm2 pressure for 20 min). Cultures were routinely subcultured every 3 weeks, and further, the suspension cultures were initiated from friable callus. For this, the callus cultures were propagated in 150 mL Erlenmeyer flasks containing 40 mL of MS liquid medium supplemented with three different variables and 3% sucrose. Cultures were incubated on a rotary shaker at 100 rpm with a 16 h light/8 h dark photoperiod at 26 ± 1 °C.
GI =
final weight of biomass − initial weight of biomass initial weight of biomass
Microscopic Observations. To determine the viability of cells, 10 mL of suspension culture was incubated for 15 min with 30 μM FDA at 1 mg/mL stock in dimethyl sulfoxide (DMSO). The extracellular fluorescence in the cells was discolored by 10 volumes of fresh medium followed by resuspension by fresh culture medium. Dye loaded with live cells was monitored in fluorescence microscopy using an Olympus BX 51 (Japan) microscope equipped with camera ProResC5 and an ultraviolet (UV) light source. Isoflavone Extraction. The soybean-powdered sample (400 mg) from each treatment was finely ground and extracted with 2 mL of concentrated HCl and 10 mL of ethanol for 2 h in a boiling water bath using a standard method,26 which relies on acid hydrolysis of 12 endogenous isoflavone isomers to their respective aglycone forms. The resulting suspensions were cooled and centrifuged at 111800g for 10 min, and further, the supernatant was filtered through a syringe filter (Whatman 0.5 μm, 13 mm diameter). Chromatographic Conditions. The HPLC analysis of isoflavones in extracts was performed using a Shimadzu chromatograph (LC 10-AS HPLC), equipped with dual pump, UV detector (SPD 10A), and a C-18 silica column (Sunfire; 5 μm, with dimensions of 250 × 4.6 mm). The isoflavone separation and its elution were employed by a binary gradient mode,12 and the chromatographic conditions were followed as mentioned in the earlier publication.12 3144
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Isoflavone Standards. All three isoflavone standards were identified by their retention times and co-chromatography with standard compounds. The relative individual isoflavone concentration in the sample was calculated manually on the basis of each peak area. Individual isoflavone concentrations were expressed as micrograms per gram on a dw basis. A mixture of the standards was analyzed, and the total isoflavone (TI) concentration was the sum of aglycones (daidzein, glycitein, and genistein).27 Optimization of Isoflavones and Biomass Using RSM. Central Composite Design (CCD) and RSM. RSM is a combination of mathematical and statistical techniques useful for empirical model building and search optimum conditions of factors for desirable responses. The natural response surface in the optimum region was illustrated by performing a Box−Behnken and CCD.19,28 CCD was employed to study the interaction of process variables, and the optimum process condition for biomass and TI content was obtained by applying RSM.21,29 Statistical analysis was implemented using a linear regression model in the SPSS software. The experimental design was constructed using a five-level, threefactor variable, and second-order CCD with five replications on the center points (0, 0, and 0) in coded levels of variables (−1.682, −1, 0, 1, and 1.682).28,29 The total number of experiments was 20 with two replications to evaluate error. The three independent variables for biomass (Y1) and TI (Y2) content responses were concentrations of 2,4D (X1), α-NAA (X2), and Kn (X3). The experimental design in the actual (X) and coded (x) levels of variables was represented in Table 1. A second-degree polynomial with linear, quadratic, and interaction effects (in the coded level of variables) by the method of least-squares represents the response functions (yijk) of biomass and TI content in the cell suspension culture denoted in the following equation: n
yijk = b0 +
n
i=1
RESULTS AND DISCUSSION
Despite the fact that phytoalexins (daidzein and genistein) were released only under stress conditions, enhancing their level in in vitro cultures could be achieved by the elicitation approach. In the preliminary screening experiments, various concentrations of PGRs [2,4-D, α-NAA, indole-3-acetic acid (IAA), and indole-3butyric acid (IBA)] with a single concentration of Kn were treated in G. max cell suspension cultures. Interestingly, we found that the α-NAA and Kn combination improved the isoflavone content but biomass was better with 2,4-D and Kn (data not shown). However, optimization of selective media with enriched cell growth and bioactive content is laborious and timeconsuming, as suggested by various researchers.21,22 As a result, the PGRs, such as 2,4-D, α-NAA, and Kn, were selected for statistical optimization studies by employing RSM for the first time. In the present study, optimization of the medium constituents for increased production of the isoflavone content with profound biomass in G. max suspension cultures was achieved. In the same way, this RSM technique has been applied successfully nowadays in many biotechnological and biochemical studies, such as in vitro growth characteristics of Centella asiatica,30 β-carotene in D. carota,22 lycopene from suspension cultures of L. esculantum,23 micropropagation of Dianthus caryophyllus,31 and isoflavone extraction from G. max,29,32 respectively. The results on the TI content in the present investigation demonstrated daidzein as a major component (63%), followed by genistein (30%) and glycitein (7%), and TI was not detected in the spent media. The outcome of three independent variables (concentrations of 2,4-D, α-NAA, and Kn) on the response functions Y1 and Y2 was summarized in Table 1. The results on the ANOVA (in the coded level of variables) are shown in Table 2 for all three response functions. The roots (λ1, λ2, and λ3) of the auxiliary equation of isoflavone and biomass are calculated and depicted in Table 3. The three-dimensional and contour plots represented in Figures 1 and 2 aid in envisaging the consequence
n
∑ bixi+ ∑ ∑
Article
bijxixj + εijk
i = 1, i ≤ j j = 1, i ≤ j
The numbers for variables represented by n and i, j, and k are integers. The coefficients of the polynomials are denoted by b0, bi, and bij, and εijk is the random error. When i < j, bij represents the interaction effects of the variables xi and xj. The response surface graphs are obtained from the regression equations in the actual level of variables, keeping the response function on the z axis, with x and y axes representing the two independent variables while keeping the other variable (third) constant at its center (corresponding to the 0 level in coded level) points. To assess the empirical model in a statistical manner, analysis of variance (ANOVA) was carried out using software SPSS version 17.0 (SPSS Inc., Chicago, IL) at the level of p < 0.05. The efficiency of the polynomial model equation was calculated by means of the coefficient of correlation (R), and its statistical significance was evaluated by the F test. When detailed ANOVA was performed in the coded level of variables, the effects of individual variables were identified. Stepwise deletion of individual non-significant (p < 0.10) terms was performed, followed by recalculation of the coefficients of the TI regression equation, to attain the final regression equation in the coded level, which was further converted to the actual level of variables. Optimization Conditions. Canonical analysis28 was employed to analyze the optimal conditions, in which the levels of control variables (x1, x2, and x3) (within the experimental range) were determined to obtain the maximum yield of biomass and TI production individually. Stationary points in the response surface were defined by the translation and rotation of the response function (yk),19 the axes of which correspond toward the principal axes of the contour system. Moreover, the roots (λ1, λ2, and λ3) of the auxiliary equation (λ2 − λ + 1 = 0) were calculated initially to know the optimum nature. If all of the roots have negative values, the response function is maximal and vice versa. The stationary point was defined as a saddle point, if the roots have positive and negative values,19 and when the contours are superimposed, the optimum levels of the variables are further attained.
Table 2. ANOVA for the Biomass (Y1) and TI Content (Y2) in the Coded Level of Variablesa source of variation constant x1 x2 x3 x12 x22 x32 x1x2 x1x3 x2x3 TLE TQE TIE R
coefficient of the polynomial for Y1 61.283 2.052 1.169 0.673 −2.897 −1.124 −2.996 1.075 0.208 3.267
0.915
F value 7.22b 2.349 (NS)d 0.776 (NS) 11.744c 0.636 (NS) 12.77c 1.160 (NS) 0.0431 (NS) 10.72c 3.445e 9.7411c 3.9748b
coefficient of the polynomial for Y2 37.642 1.818 0.516 −0.184 −3.293 −1.927 −3.252 1.584 0.159 4.311
F value 11.0625c 0.889 (NS) 0.113 (NS) 28.717c 6.415b 27.823c 4.919e 0.049 (NS) 36.453c 4.022b 25.094c 13.807c
0.963
a x1, concentration of 2,4-D (mg/L); x2, concentration of α-NAA (mg/ L); x3, concentration of Kn (mg/L); TLE, total linear effect; TQE, total quadratic effect; and TIE, total interaction effect. bSignificant at p ≤ 0.05. cSignificant at p ≤ 0.01. dNS = non-significant at p ≤ 0.10. e Significant at p ≤ 0.10.
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the middle level. The consequence of 2,4-D and α-NAA on biomass with Kn as the constant value in soybean cell suspension cultures was illustrated in Figure 1a. Similarly, Figure 1b represented the influence of 2,4-D and Kn with α-NAA as a constant value on biomass in cell cultures. Besides, the effect of αNAA and Kn with 2,4-D as a constant value on the biomass was depicted in Figure 1c. Fitness of the second-order polynomial to predict biomass in independent variable terms was put in view by the high correlation coefficient (R = 0.915; p ≤ 0.01) value (Table 2). Amid the variables, the total quadratic effect (significant at p ≤ 0.01) predominates over the total interactive effect (p ≤ 0.05) and linear effect (p ≤ 0.1). The effect of 2,4-D on the yield of biomass has a significant effect (p ≤ 0.05) on the individual variables, while α-NAA and Kn have no significant results (Table 2). The individual quadratic effects of “2,4-D” and “Kn” variables are significant (p ≤ 0.01), and α-NAA has no significant results. Nevertheless, interactive effects of α-NAA and Kn concentrations have a predominant effect on the yield of biomass (p ≤
Table 3. Results of the Optimization Study parameters roots of the auxiliary equation
optimum conditions in the coded level
optimum conditions in the actual level
optimized level of the response function
λ1 λ2 λ3 x1 x2 x3 x1 x2 x3
biomass, Y1
TI content, Y2
−5.794 −0.35 −7.87 2.218 9.055 5.126 2.109 5.527 0.356 70.6
−3.293 −0.66 −9.68 0.67 1.537 1.00 1.335 1.768 0.15 38.59
of three independent variables on response functions generated by the regression equation. Biomass. The biomass (Y1) content in suspension cultures for the 20 experiments is shown in Table 1. An extensive variation in the content ranged from 47.15 to 63.56 g/L on a dw basis upon the variable conditions. Plots were designed for each pair of variables, while the third variable was considered as a constant at
Figure 1. Contour plots for the effects of variables on biomass (Y1) in the cell suspension cultures of G. max: (a) Kn as a constant value of 0.1 mg/L, (b) α-NAA as a constant value of 1.0 mg/L, and (c) 2,4-D as a constant value of 1.0 mg/L. 3146
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Figure 2. Response surface for the effects of variables on the total isoflavones (Y2) in the cell suspension cultures of G. max with Kn as a constant value of 0.1 mg/L.
Figure 4. Response surface for the effects of variables on the TI (Y2) in the cell suspension cultures of G. max with 2,4-D as a constant value of 1.0 mg/L.
0.01), while there is no significant effect of the interaction among 2,4-D−α-NAA and 2,4-D−Kn. TI Content. The TI content (Y2) ranged from 23.30 to 39 mg/g of dw with respect to the experimental conditions illustrated in Table 1. In Figure 2, the results of 2,4-D and αNAA on TI with Kn as the constant value in soybean cell suspension cultures were depicted as response surface. Correspondingly, the influence of 2,4-D and Kn with α-NAA as a constant value on the TI content in cell cultures was shown in Figure 3. The effect of α-NAA and Kn with 2,4-D as a constant value on the TI was portrayed in Figure 4. TI content in the proposed model depicted a high multiple correlation coefficient (R = 0.963;p ≤ 0.01), pointing out the aptness of the second-order polynomial to calculate the Y2 values of three independent variables (Table 2). With these variables,
total quadratic and interactive effects (p ≤ 0.01) dominate over the linear effects (p ≤ 0.05) and the interaction among α-NAA and Kn is significant in comparison to 2,4-D and α-NAA. There are significant individual quadratic effects for 2,4-D and Kn (p ≤ 0.01), followed by α-NAA (p ≤ 0.05). Optimization. The optimization process for maximizing biomass (Y1) and TI content (Y2) was conducted separately. The roots (λ1, λ2, and λ3) of the auxiliary equation were shown in Table 3. However, the roots of the auxiliary equation for biomass and TI content indicate (negative values) maximization of the response function. For this reason, the canonical test was applied and the optimum conditions in coded and actual levels of variables were obtained (Table 3). Consequently, the maximum value for the yield of biomass is 70.62 g/L dw, and the maximum TI content is 38.59 mg/g of dw. The predicted optimized levels for all three variables are represented in Table 3 for production of biomass and TI content confirmed experimentally. By optimization, we achieved 38.59 mg/g of dw of isoflavones in JS 335, which was approximately 37 times greater than that of the field-grown greenhouse soybean plants, along with 7% of biomass as dry matter. It is noteworthy to find a region with a “sweet spot” for multiple responses, which could be accomplished by superimposing the contours. The superimposed contour plot for optimization of Y1 and Y2 was given in Figure 5. The effect of x1, x2, and x3 terms on Y1 and Y2 is positive. The optimum value of x1 is between 1.33 and 2.10 mg/L, specifically at an average of 1.72 mg/L for x1. The optimum value for x2 is between 1.76 and 5.52 mg/L, which is at an average of 3.64 mg/L for x2. The optimum value for x3 is between 0.35 and 0.15, which is at an average of 0.25 mg/L for x3. The contour plots against x1 (α-NAA) and x3 (Kn) constructed independently and later on superimposed (Figure 5) were used to identify the levels of the two variables. Moreover, the optimum conditions for the responses were found from the superimposed graph. At this point, we preferred a value of biomass to be more than 65 g/L dw and TI production to be more than 30 g/L dw (Figure 5). The optimum region was depicted obviously as the hatched zone in Figure 5. The results obtained by superimposition were validated (TI, 37.34 mg/g of
Figure 3. Response surface for the effects of variables on the TI (Y2) in the cell suspension cultures of G. max with α-NAA as a constant value of 1.0 mg/L. 3147
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cell cultures,3 show that 2,4-D influences the biomass as well as isoflavone content. In C. asiatica cell suspension cultures, RSM applied for the optimization of sucrose, IAA, and 6benzylaminopurine (BAP) and results revealed sucrose as a prime factor for cell growth.30 Several studies found the significance of applying RSM.22 RSM was used for the optimization of basal media components (sucrose, nitrogen, and phosphorus) for maximum production of β-carotene from D. carota cells. In addition, a 3.7-fold increase in lycopene production was observed by optimizing several lycopene extraction parameters in L. esculentum cell cultures.23 To maximize the isoflavone content using RSM, acid hydrolysis conditions in soybean hypocotyls32 and ethanol extraction and purification in soybean sprouts29 were optimized. Biomass (7% dw) achieved in the study is considered as high for secondary metabolite production in plants. This result is in agreement with the earlier study3 that high producing soybean cultivar strains contained a significant level of isoflavones. As a part of optimization, we obtained 37.34 mg/g of dw of isoflavones. Because large-scale production of isoflavones for therapeutic and commercial purposes could not be achieved by natural sources, the development of cell culture technology has had a great scope in recent years.5 Besides, various factors influenced the stimulation of isoflavone synthesis, such as manipulation of the basal nutrient medium components, physical factors, and PGRs.33 Although there are several reports that dealt with media components, there is none on the optimization of PGRs and the same is essential before performing elicitation, precursor feeding, and biotransformation and bioreactor studies. It was hypothesized that, in most of the Fabaceae plants, 2,4-D and Kn combination was selected best for both cell growth and isoflavone content,3,15 while its mechanism in the biosynthetic pathway is still not understood.33 Nevertheless, a rather interesting feature in the present study was α-NAA having a more predominant role than 2,4-D. Furthermore, as suggested by earlier reports, the isoflavone content varies with the genotype of the species.3,16,18 Nonetheless, it could be hypothesized that variability in isoflavone composition observed within the cell suspension compared to seeds7 might be due to the effect of PGRs on metabolic pathways (differentiation), somaclonal variation,3 or differential gene expression. However, it was obvious that in vitro plant tissues represent a high level of variability in the gene expression pattern.34 Moreover, the isoflavone pathway is one of the most complex metabolic pathways involved in plants,11 elicited by several stress agents.16 Overall, the optimization studies using RSM augmented the isoflavone content as well as biomass. It is evident from the study that RSM is a reliable experimental tool, not only achieving optimized media with pronounced biomass and isoflavone content but also simplifing the time course of experiments. Results also suggested that soybean cell suspension cultures could be used as a fascinating model system for augmenting isoflavones. In future, research efforts pertaining to elicitation, scale up in bioreactors, selection of high producing cell lines, and mechanism of isoflavone biosynthesis in cell suspension cultures could be attempted to assess the possibility of an enhancement strategy with a prospective value in the nutraceutical viewpoint.
Figure 5. Superimposed contour plots with the optimized region of the responses.
dw; biomass, 67.83 g/L dw), in which callus formed in the optimized medium was found to be friable, soft, and pale greenish brown, and the same was used for the cell suspension culture initiation (Figure 6) and analysis of the responses.
Figure 6. G. max in vitro cultures on the MS medium with optimized concentrations of 1.72 mg/L 2,4-D, 3.64 mg/L α-NAA, and 0.25 mg/L Kn: (a) cell suspension cultures in shake flasks, (b) cell suspension cultures in round-bottom- and cylindrical-shaped flasks, and (c) FDA staining of cell suspension cultures for confirmation of viability (bar = 124 μm).
Microscopic Observation. Also, the viability of cells was confirmed by staining with FDA, followed by visualization under fluorescence microscopy. Microscopic observations of cell suspension cultures showed fluorescence yellow for viable cells (Figure 6). In the present study, the daidzein content was more in cell suspension cultures compared to genistein and glycitein. However, in seeds of soybean, genistein levels are reported to be high.7 Still, the results were consistent with the earlier reports of in vitro cultures of soybean,3,16 which show the predominant production of daidzein than that of genistein. Similarly, the interactive effects of α-NAA and Kn concentrations positively influenced the biomass yield; however, no such effect was found for 2,4-D−α-NAA and 2,4-D−Kn. Significant individual quadratic effects were found for 2,4-D and Kn (p ≤ 0.01). This in correspondence with earlier reports31 in D. caryophyllus L., in which optimization of PGRs and AgNO3 using RSM exhibited better callus growth with a high concentration of α-NAA. However, the results, in contrast with earlier reports of soybean
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AUTHOR INFORMATION
Corresponding Author
*Telephone: +91-821-2516501. Fax: +91-821-2517233. E-mail:
[email protected]. 3148
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(16) Gueven, A.; Knorr, D. Isoflavonoid production by soy plant callus suspension culture. J. Food Eng. 2011, 103, 237−243. (17) Luczkiewicz, M.; Glod, D. Callus culture of Genista plantsIn vitro material producing high amounts of isoflavones of phytoestrogenic activity. Plant. Sci. 2003, 165, 1101−1108. (18) Shinde, A. N.; Malpathak, N.; Fulzele, D. P. Enhanced production of phytoestrogenic isoflavones from hairy root cultures of Psoralea corylifolia L. using elicitation and precursor feeding. Biotechnol. Bioprocess Eng. 2009, 14, 288−294. (19) Myers, R. H.; Montgomery, D. C.; Anderson-Cook, C. M. Response Surface Methodology: Product and Process Optimization Using Designed Experiments, 3rd ed.; John Wiley and Sons: Hoboken, NJ, 2009. (20) Li, Y. L.; Fang, Z. X.; You, J. Application of Box−Behnken experimental design to optimize the extraction of insecticidal Cry1Ac from soil. J. Agric. Food Chem. 2013, 61, 1464−1470. (21) Kathiresan, S.; Sarada, R.; Bhattacharya, S.; Ravishankar, G. A. Culture media optimization for growth and phycoerythrin production from Porphyridium purpureum. Biotechnol. Bioeng. 2007, 96, 456−463. (22) Hanchinal, V. M.; Survase, S. A.; Sawant, S. K.; Annapure, U. S. Response surface methodology in media optimization for production of β-carotene from Daucus carota. Plant Cell, Tissue Organ Cult. 2008, 93, 123−132. (23) Lu, C. H.; Engelmann, N. J.; Lila, M. A.; Erdman, J. W., Jr. Optimization of lycopene extraction from tomato cell suspension culture by response surface methodology. J. Agric. Food Chem. 2008, 56, 7710−7714. (24) Akitha Devi, M. K.; Sakthivelu, G.; Giridhar, P.; Ravishankar, G. A. Protocol for augmented shoot organogenesis in selected variety of soybean [Glycine max L. (Merr.)]. Indian J. Exp. Biol. 2012, 50, 729− 734. (25) Murashige, T.; Skoog, F. A revised medium for rapid growth and bioassys with tobacco tissue cultures. Physiol. Plant. 1962, 15, 472−497. (26) Vyn, T. J.; Yin, X.; Bruulsema, T. W.; Jackson, J. C.; Rajcan, I.; Bruder, S. M. Potassium fertilization effects on isoflavones concentrations in soybean. J. Agric. Food Chem. 2002, 50, 3501−3506. (27) Kumar, V.; Rani, A.; Dixit, A. K.; Bhatnagar, D.; Chahan, G. S. Relative changes in tocopherols, isoflavones, total phenolic content, and antioxidative activity in soybean seeds at different reproductive stages. J. Agric. Food Chem. 2009, 57, 2705−2710. (28) Box, G. E. P.; Hunter, W. G.; Hunter, J. S. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, 2nd ed.; John Wiley and Sons: Hoboken, NJ, 2005. (29) Cho, S. Y.; Lee, Y. N.; Park, H. J. Optimization of ethanol extraction and further purification of isoflavones from soybean sprout cotyledon. Food Chem. 2009, 117, 312−317. (30) Omar, R.; Abdullah, M. A.; Hasan, M. A.; Marziah, M. Development of growth medium for Centella asiatica cell culture via response surface methodology. Am. J. Appl. Sci. 2004, 1, 215−219. (31) Gutiérrez-Miceli, F. A.; Arias, L.; Juarez-Rodríguez, N.; AbudArchila, M.; Amaro-Reyes, A.; Dendooven, L. Optimization of growth regulators and silver nitrate for micropropagation of Dianthus caryophyllus L. with the aid of a response surface experimental design. In Vitro Cell. Dev. Biol. 2010, 46, 57−63. (32) Chiang, W. D.; Shih, C. J.; Chu, Y. H. Optimization of acid hydrolysis conditions for total isoflavones analysis in soybean hypocotyls by using RSM. Food Chem. 2001, 72, 499−503. (33) Luczkiewicz, M. T. Research into Isoflavonoid Phyto-oestrogens in Plant Cell Cultures: Bioactive Molecules and Medicinal Plants; SpringerVerlag: Berlin, Germany, 2008; pp 55−84. (34) Stockigt, J.; Obitz, P.; Falkenhagen, H.; Lutterbach, R.; Endre, S. Natural products and enzymes from plant cell cultures. Plant Cell, Tissue Organ Cult. 1995, 43, 97−109.
M. K. Akitha Devi is thankful to Council for Scientific and Industrial Research (CSIR), New Delhi, India, for her fellowship. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors thank R. Manikantan, Scientist, Council for Scientific and Industrial Research−National Aerospace Laboratories (CSIR−NAL), Bangalore, India, for his support in designing the RSM experiment and analysis and Dr. K. Udayashankar, Chief Scientist, CSIR-CFTRI, Mysore, for his support in data interpretation.
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REFERENCES
(1) Hussain, M. S.; Fareed, S.; Ansari, S.; Rahman, M. A.; Ahmad, I. Z.; Saeed, M. Current approaches toward production of secondary plant metabolites. J. Pharm. Bioanal. Sci. 2012, 4, 10−20. (2) Verpoorte, R.; Johan, M. Engineering secondary metabolite production in plants. Curr. Opin. Biotechnol. 2002, 13, 181−187. (3) Federici, E.; Touche, A.; Choquart, S.; Avanti, O.; Fay, L.; Offord, E.; Courtois, D. High isoflavone content and estrogenic activity of 25 year-old Glycine max tissue cultures. Phytochemistry 2003, 64, 717−724. (4) Roberts, S. C. Production and engineering of terpenoids in plant cell culture. Nat. Chem. Biol. 2007, 3, 387−395. (5) Huang, T. K.; McDonald, K. A. Bioreactor systems for in vitro production of foreign proteins using plant cell cultures. Biotechnol. Adv. 2012, 30, 398−409. (6) Wilson, S. A.; Roberts, S. C. Recent advances towards development and commercialization of plant cell culture processes for the synthesis of biomolecules. Plant Biotechnol. J. 2012, 10, 249−268. (7) Sakthivelu, G.; Akitha Devi, M. K.; Giridhar, P.; Rajasekaran, T.; Ravishankar, G. A.; Nikolova, M. T.; Angelov, G. B.; Todorova, R. M.; Kosturkova, G. P. Isoflavone composition, phenol content, and antioxidant activity of soybean seeds from India and Bulgaria. J. Agric. Food Chem. 2008, 56, 2090−2095. (8) Akitha Devi, M. K.; Gondi, M.; Sakthivelu, G.; Giridhar, T.; Rajasekaran, T.; Ravishankar, G. A. Functional attributes of soybean seeds and products, with reference to isoflavone content and antioxidant activity. Food Chem. 2009, 114, 771−776. (9) Qian, Y.; Guan, T.; Huang, M.; Cao, L.; Li, Y.; Cheng, H.; Jin, H.; Yu, D. Neuroprotection by the soy isoflavone, genistein, via inhibition of mitochondria-dependent apoptosis pathways and reactive oxygen induced-NF-κB activation in a cerebral ischemia mouse model. Neurochem. Int. 2012, 60, 759−767. (10) Dixon, R. A. Natural products and plant disease resistance. Nature 2001, 411, 843−847. (11) Yu, O.; Shi, J.; Hession, A. O.; Maxwell, C. A.; McGonigle, B.; Odell, J. T. Metabolic engineering to increase isoflavone biosynthesis in soybean seed. Phytochemistry 2003, 63, 753−763. (12) Akitha Devi, M. K.; Giridhar, P. Variations in physiological response, lipid peroxidation, antioxidant enzymes activities, proline and isoflavones content in soybean varieties subjected to drought stress. Proc. Natl. Acad. Sci., India, Sect. B 2013, DOI: 10.1007/s40011-0130244-0. (13) Gutierrez-Gonzalez, J. J.; Guttikonda, S. K.; Tran, L. S.; Aldrich, D. L.; Zhong, R.; Yu, O.; Nguyen, H. T.; Sleper, D. A. Differential expression of isoflavone biosynthetic genes in soybean during water deficits. Plant Cell Physiol. 2010, 51, 936−948. (14) Cloutier, M.; Bouchard-Marchand, E.; Perrier, M.; Jolicoeur, M. A predictive nutritional model for plant cells and hairy roots. Biotechnol. Bioeng. 2008, 99, 189−200. (15) Fedoreyev, S. A.; Pokushalova, T. V.; Veselova, M. V.; Glebko, L. I.; Kulesh, N. I.; Muzarok, T. I.; Seletskaya, L. D.; Bulgakov, V. P.; Zhuravlev, Y. N. Isoflavonoid production by callus cultures of Maackia amurensis. Fitoterapia 2000, 71, 365−372. 3149
dx.doi.org/10.1021/jf500207x | J. Agric. Food Chem. 2014, 62, 3143−3149
