Folate, a water-soluble B-group vitamin, includes naturally occurring food folate (polylglutamates) and synthetic folic acid in supplements and fortified foods. Human life can not exist without folate since this vitamin is involved in essential functions of cell metabolism such as DNA-RNA and protein biosynthesis and therefore it is necessary to assimilate this vitamin exogenously. Folate deficiency in humans is associated with several health problems, such as cancer, cardiovascular diseases as well as neural tube defects in newborns (Le Blanc et al. Reference LeBlanc, Giori, Smid, Hugenholtz and Sesma2007). The daily recommended intake (DRI) in European Union (EU) is set at 200 and 400 μg/day for adults and women in the preconceptional period, respectively (FAO, 2002). There are several ways to increase the folate levels of food products such as fortification of food products, selection of special plant cultivars, or fruits with increased folate pools, and fermentation fortification. However, it was recently shown that high-level intake of chemically synthesized folate might have some adverse health effects such as masking of the early haematological manifestations of vitamin B12 deficiency, potential unknown risks for pregnant women, promote cancers (Wright et al. Reference Wright, Dainty and Finglas2007). Therefore, the rationale would suggest focusing on naturally produced folates for fortification purposes. Numerous researchers have reported that Lactic Acid Bacteria (LAB), such as the industrial starter bacteria Lactococcus lactis and Strep. thermophilus have the ability to synthesize folate (Lin & Young, Reference Lin and Young2000; Smid et al. Reference Smid, Starrenburg, Mierau, Sybesma and Hugenholtz2001; Holasová et al. Reference Holasová, Fiedlerová, Roubal and Pechacova2004). Strep. thermophilus has a strain specific ability of folate production and has been reported to produce higher quantity of folate; majority of which is secreted into milk (Crittenden et al. Reference Crittenden, Martinez and Playne2003; Tomar et al. Reference Tomar, Srivatsa, Iyer and Singh2009]. A number of physical and nutritional growth factors like composition of the medium (C & N sources, growth factors, inorganic salts) and the conditions for growth such as temperature, pH, oxygen tension, and incubation period have been reported to affect the folate production by microorganisms (Lin & Young, Reference Lin and Young2000; Sybesma et al. Reference Sybesma, Starrenburg, Tijsseling, Hoefnagel and Hugenholtz2003; Tomar et al. Reference Tomar, Srivatsa, Iyer and Singh2009). Such findings imply the significant effect of use of different strains, media, medium compositions and physical factors on the cell growth and synthesis levels of folates. As several parameters are involved, it is difficult to identify the critical factors and to optimize them for biotechnological processes (Li et al. Reference Li, Bai, Cai and Ouyang2002; Liu et al. Reference Liu, Chen, Tang, Ruan and He2007). The classical method of medium optimization involves changing one variable at a time, keeping the others at fixed levels. Being single dimensional, this laborious and time consuming method often does not guarantee determination of optimal conditions. On the other hand carrying out experiments with every possible factorial combination of the test variables is impractical because of the large number of experiments required. In response surface methodology in the first screening, it is recommended to evaluate the result and estimate the main effects according to a linear model. After this evaluation, the variables that have the largest influence on the result are selected for new studies. Thus, a large number of experimental variables can be investigated without having to increase the number of experiments to the extreme (Montgomery, Reference Montgomery and Montgomery1997).
In this context we initiated the evaluation of effect of different factors on folate production by Strep. thermophilus NCDC177 using linear model (Tomar et al. Reference Tomar, Srivatsa, Iyer and Singh2009). In a continuation to this, in the present work, we aim to optimize the growth conditions and medium components, for folate production using lactose as C source, PABA as growth precursors at different incubation period for the highest folate producing strain Strep. thermophilus RD102. The optimization of folate production was achieved using a 23 central composite design and surface modelling method. The relation between growth and folate production with time was determined for the strain before and after the optimization procedure.
Materials and Methods
Strain and culture condition
From a total of 18 high folate producing strains of Strep. thermophilus isolated from milk and fermented milks of Indian origin the highest producer strain, Strep. thermophilus RD102 (48 μg/l) was selected for the study. In the experimental design, earlier optimized process parameters (Tomar et al. Reference Tomar, Srivatsa, Iyer and Singh2009) namely, lactose (Acros Organics (New Jersey, USA) at 1; 2 and 3%, PABA (Acros Organics (New Jersey, USA) at 100; 200 and 300 μm and incubation period of 24; 48 and 72 h was optimized at 42°C with a constant volume of 2% inoculum of activated culture in 100 ml sterile reconstituted skim milk (10% TS) for maximum folate production and growth using the RSM.
Methods for determination of growth and folate production
Growth was estimated by the basic technique of bacterial enumeration. Appropriate dilutions (10−8, 10−10and 10−12) of the fermented skim milk obtained as per the experimentation trail were pour plated using M17 agar (Oxoid Ltd., Basingstoke, Hampshire, England) to find the viable count (log cfu/ml). Folate contents were estimated by microbiological assay with Lactobacillus rhamnosus MTCC 1408 (ATCC7469/DSM20021) as per the method described by Keagy (Reference Keagy, Augustin, Klein, Becker and Venugopal1985), after trienzyme treatment namely, α-amylase for 4 h at pH 6·1, protease for 6 h at pH 6·1 and conjugase treatment for ⩾12 h at pH 4·5 at 37°C in sequel (Hyun & Tamura, Reference Hyun and Tamura2005; Iyer et al. Reference Iyer, Tomar, Singh and Sharma2009). After incubation the % transmittance (% T) was measured at the specific wavelength of 550 nm. A standard curve plotted between % T of dilutions of known folate concentration and concentration was further used for the estimation of folate content in assay samples by interpolating % T values over this standard curve.
Experimental design
Response surface methodology is a collection of mathematical and statistical techniques that are useful for the modelling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response (Montgomery, Reference Montgomery and Montgomery1997). The central composite rotatable design (CCRD) is one of the most important experimental designs used in process optimization studies. This design was applied with the objective to develop an empirical model of the process and to obtain a more precise estimate of the optimum operating conditions for the factors involved. To describe the nature of the response surface in the optimum region, a two factor (5 levels at each factor) second order central composite rotatable design (CCRD) was adopted. The independent factors were: period of incubation (x1), PABA concentration (x2) and lactose concentration (x3) that were considered for optimization of processing variables for folate production (Table 1). The ranges of the variables are 1–3% for lactose, 100–300 μm for PABA and 24–72 h incubation period. For the three factors, this design was made up a full 23 factorial design with its four cube points, augmented with five replications of the center points and the four star points, that is, points having for one factor an axial distance to the centre of±α, whereas the other factor is at level 0. The axial distance α was chosen to be 1·68 to make this design rotatable. A centre point is a point in which all variables are set at their mid value. Three or four centre experiments are included in factorial designs as repetition minimizes the risk of missing non-linear relationships in the middle of the intervals, and also allows the determination of confidence intervals also.
Experimental design matrix in coded and uncoded form
** Significant at 5% level; * Significant at 1% level
† Actual growth stands for the experimental data
‡ Predicted growth is calculated from the second-order model approach (Eqs. (2))
§ Actual folate produced stands for the experimental data
¶ Predicted folate produced is calculated from the second-order model approach (Eqs. (3))
The response function (Y) were the growth (log cfu/ml) and folate produced (μg/l). These responses were related to the factors by a second-degree polynomial equation Eq. (1) using the least square method.
The coefficient of the polynomials were represented by bo (constant terms), b1, b2, b3 (linear terms), b11, b22, b33 (quaratic terms), b12, b23, b33 (interactive terms) and ε (random error).
Data analysis
Design-Expert 7.1.6, version was used for the regression analysis of the experimental data obtained. The fitness of the polynomial model equation was calculated by the coefficient of determination (R2), and its statistical significance was checked by using F-ration. The significance of the regression coefficient was tested by t-test. The level of significance was given as values of Prob>F less than 0·1. A differential calculation was then employed for predicting the optimum point.
Results
Folate growth-associated production
The relation between growth and folate production with time was determined for the strain before and after the optimization procedure (Figs. 1a & b). The folate production was associated with growth, as an increase in the growth (log cfu/ml) was accompanied by elevation in the folate content. In case of a growth associated folate production there is a parallel relationship between the substrate utilization and folate production (Sybesma et al. Reference Sybesma, Starrenburg, Tijsseling, Hoefnagel and Hugenholtz2003).
Relation between growth and folate production for S. thermophilus RD102: Variation of growth (log cfu/ml) (○) and folate produced (μg/litre) (•), in time. (a) S. thermophilus RD102 grown in reconstituted skim milk (10% total solid) before experimental design optimization of the media components and physical parameters. (b) S. thermophilus RD102 grown in reconstituted skim milk (10% total solid) optimized by experimental design.
In earlier work (Tomar et al. Reference Tomar, Srivatsa, Iyer and Singh2009) identification of the medium components and environmental parameters as well as their ranges, having significant role on folate production was determined. Four factors mainly, incubation temperature, period, concentration of folate precursors like PABA and lactose were set as variables for the optimization procedure (Lin & Young, Reference Lin and Young2000; Sybesma et al. Reference Sybesma, Starrenburg, Tijsseling, Hoefnagel and Hugenholtz2003; Tomar et al. Reference Tomar, Srivatsa, Iyer and Singh2009). Using one-way ANOVA and regression analysis the optimum conditions of temperature, incubation period, lactose and PABA concentration with effect on folate production were determined. Thus except temperature, rest three (PABA, lactose and incubation period) were found to be significant factor. Hence temperature was fixed at the 42°C. The experimental design and the results are presented in Table 1.
Diagnostic check of the model
The experimental data of the CCRD were fitted with a second order quadratic function through repeated regression analysis. The model adequacy was checked and it was found to be adequate, the goodness of the fit was expressed by the coefficient of determination (R2), which was 0·90 and 0·81 for folate production and growth respectively, indicating an 81% and 90% of variability in the response could be explained by the model. The model F values were 4·71 (P<0·05) and 10·32 (P<0·001) for folate produced and growth thus indicating the significance of model terms.
Effect of processing variables on Growth
Growth of Strep. themophilus RD 102 was highly affected by the incubation period both at linear and quadratic level (P<0·001). The growth (log cfu/ml) can be determined by using the regression equation (Eq. 2). At linear level, with increasing incubation period the population of Strep. thermophilus RD 102 was also increased. However, at quadratic level the population was lower both at shorter or longer duration period (Fig. 2a). The maximum growth was observed at higher PABA or lactose level when the incubation period was above 60 h.
Effect of processing variables on folate production
The folate (μg/l) production using Strep. thermophilus RD 102 was found to be affected by all the three processing variables. The folate (μg/l) can be determined by using the regression equation (Eq. 3). The linear terms of incubation period (P<0·001) and lactose level (P<0·001) exhibited most significant effect on amount of folate produced. By increasing the level of lactose and incubation period the amount of folate was also increased. PABA concentration also had significant effect on folate production (P<0·05) but at quadratic level (Fig. 2b).
Response surface contour plots of growth (log cfu/ml) and folate produced (μg/litre) for S. thermophilus RD102. The growth and folate production both are the response variables of interest. The contour plots represent the effect of the significant variables and their interaction in the response variable. All the other variables non significant are held at zero level of the central composite design (CCD). (a) The effect of incubation time, lactose, PABA and their mutual interaction on growth for S. thermophilus RD102. (b) The effect of incubation time, lactose, PABA and their mutual interaction on folate production for S. thermophilus RD102.
Optimization of folate production and growth
Differentiation of Eq. (2) and (3) allowed the determination of the maximum point of the model, which was 2·99% of lactose, 299·94 μm PABA and 72 h incubation. The model predicted a maximum response for growth about 11·89 log cfu/ml and for folate production of 57·13 μg/l for this point. The validation of the model was performed using these optimized conditions representing this maximum point and a value of 59·59±2·1 μg/l folate production was obtained (assay was done in triplicate). Thus, the optimum medium composition and condition for higher folate production by Strep. thermophilus RD 102, involves 100 ml sterile reconstituted skim milk (10% TS) consisting of 3% lactose, 300 μm PABA inoculated with 2% inoculum and incubated at 42°C for 72 hr. While at the lower incubation period of 24 hr with all other factors set at the same level (3% lactose, 300 μm PABA, 42°C incubation) still an appreciable amount of folate (52 μg/l) was produced.
Growth and folate production
After the optimization procedure the evaluation of folate production for both strains was performed. Comparing results before and after the optimization procedure for Strep. thermophilus RD 102 (Fig. 1a & b), it was observed that for the same fermentation period, a higher growth and folate production were achieved. The optimization procedure allowed an increase of 26% folate production (μg/l) compared with control (0% PABA and lactose at 37°C) respectively. For this strain a stronger increase in the folate production along with higher cell growth (log cfu/ml) after the optimization procedure was observed.
Discussion
Folate production by Strep. thermophilus RD102 was found to be growth associated, and the production yield was increased using a response surface optimization of medium components and growth factors. Sybesma et al. (Reference Sybesma, Starrenburg, Tijsseling, Hoefnagel and Hugenholtz2003) observed growth-associated folate production in Strep. themophilus strain B119 i.e. highest folate production/biomass. More efficient folate biosynthesis in this strain was because of increased biomass. Besides this, folate production may be stimulated, by growing the microbial cells under growth limiting conditions as in the presence of growth-inhibiting concentrations of several antibiotics and high salt concentrations and by providing growth stimulators like hemin. The addition of NaCl to the growth medium increased folate production in both Lc. lactis and Strep. thermophilus strain B119. Under these conditions, the specific growth rate was strongly reduced (90%) and a 10-fold increase in folate production was observed (Sybesma et al. Reference Sybesma, Starrenburg, Tijsseling, Hoefnagel and Hugenholtz2003). Hence, in continuous cultures, folate concentration increases at lower growth rate. Similarly the addition of hemin to the growth medium resulted in a further increase in folate produced/cell biomass due to extension in growth period by addition of hemin (Duwat et al. Reference Duwat, Sourice, Cesselin, Lamberet, Vido, Gaudu, Le Loir, Violet, Loubiere and Gruss2001) and also hemin may constitute higher proton extrusion (Bongers et al. Reference Bongers, Hoefnagel, Starrenburg, Siemerink, Arends, Hugenholtz and Kleerebezem2003) which consequently leads to a higher intracellular pH at which folate biosynthesis is assumed to increase (Siegumfeldt et al. Reference Siegumfeldt, Rechinger and Jakobsen2000). Hence, our observation that extracellular folate production by Strep. thermophilus RD102 is maximal at lower growth rate is in agreement with the literature. In our study, a direct relation exists between folate production and cell growth (log cfu/ml) during the fermentation process, thus indicating that folate production is growth-associated.
In this study we focused on the optimization of the medium components and environmental factors that play an important role on folate production. Several workers have used the classical method (changing one variable at a time) for optimization study (Sybesma et al. Reference Sybesma, Starrenburg, Tijsseling, Hoefnagel and Hugenholtz2003; Tomar et al. Reference Tomar, Srivatsa, Iyer and Singh2009) which highlights the role of the individual factors and paves the way for the selection of variables in a systematic manner using central composite design and surface modelling methods. In this context, response surface analysis is useful for the modelling and analysis of problems in which a response of interest is influenced by several variables simultaneously and the objective is to optimize this response. Various research workers have applied this technique for the selection of best culture conditions (Vohra & Satyanarayana, Reference Vohra and Satyanarayana2002; Li et al. Reference Li, Bai, Cai and Ouyang2002; Rodrigues et al. Reference Rodrigues, Teixeira, Oliveira and van der Mei2006; Dagbagli & Goksungur, Reference Dagbagli and Goksungur2008; Kim et al. Reference Kim, Oh, Shin, Eom and Kim2008), such as pH, temperature, aeration (Wang et al. Reference Wang, Feng, Zhang and Zhang2008) and feeding rates (Bazaraa & Hassan, Reference Bazaraa and Hassan1996) for maximal response. The approach used in this study allowed the determination of the medium components and physical parameters that gave the highest folate production by Strep. thermophilus RD102. In this case, a suitable model was developed to describe the response of the experiment as the values obtained experimentally are in accordance with the predicted values determined by the model. The model was validated by comparing the actual and predicted values at the optimum point, and a deviation of about 6% was found, which justifies that the optimization procedure allowed an increase in both folate production and growth of the culture.
There are several reports on the affect of a number of environmental parameters and nutritional factors on folate production by microorganisms. However, a number of attempts have been made to increase folate production by manipulating physiological conditions and medium components and have shown their significant effect on the synthesis levels of folates (Sybesma et al. Reference Sybesma, Starrenburg, Tijsseling, Hoefnagel and Hugenholtz2003; Wegkamp, Reference Wegkamp2008). In the present study it was observed that with the increase in cell growth (log cfu/ml) there was an increase in the folate production. It can be justified by the fact that the increase in folate production with the optimization procedure is due to the effect of the combined effect of all three selected parameters. Folate is synthesized from the precursors GTP, PABA, and glutamate. Folate biosynthesis proceeds via the conversion of GTP in eight consecutive steps to the biologically active cofactor tetrahydrofolate. Effect of incubation period can be justified by the fact that increase in folate production is accompanied by decrease in growth rate (Sybesma et al. Reference Sybesma, Starrenburg, Tijsseling, Hoefnagel and Hugenholtz2003). The highest folate production beyond 24 h incubation till 72 h may be presumably due to decline in growth rate which can be attributed to accumulation of GTP, a folate precursor. The decrease in folate production during early stages of incubation at 6 h may be ascribed to the higher growth rate of Strep. thermophilus during this period (Lin & Young, Reference Lin and Young2000). The PABA is one of the folate precursors, which is synthesized via glycolysis in the pentose phosphate pathway and shikimate pathway; hence its addition increases the folate synthesis activity. In Lc. lactis, increased folate production was shown to be dependent on the concentration of PABA in the medium (Sybesma et al. Reference Sybesma, Starrenburg, Tijsseling, Hoefnagel and Hugenholtz2003; Wegkamp, Reference Wegkamp2008). This is in accordance to the results of this study where the addition of PABA at a concentration ranging from 100 to 300 μm to medium resulted in twofold increase of folate production.
Similarly, it is interesting to notice that lactose, in addition to playing a significant role in fermentation, also plays an important role in folate production. The report by Lin & Young (Reference Lin and Young2000) highlights the influence of lactose on folate production by LAB. The addition of 2% lactose in reconstituted non-fat dry milk increased folate synthesis. An increase in lactose concentration induced the cells to produce more folate, as lactose at optimum concentration acts as a source of GTP and this leads to the increase in folate production.
The efficacy of metabolic engineering (ME) of LAB which have already proven to be ideal hosts for ME, the increased production of biosynthetic metabolites is yet to be demonstrated, but based on the results of this study it seems to be an interesting approach for developing new strategies of folate production. Folate production levels in LAB can be modulated by gaining insight into the genes, pathways and metabolites that are involved in metabolic engineering. Moreover, since this strain showed higher folate production with the optimized conditions, this study constitutes a step in developing strategies to modulate the folate levels to higher levels. In conclusion, using the method of experimental factorial design and response surface analysis, it was possible to determine optimal operating conditions to obtain a higher folate production by Strep. thermophilus. Thus, the folate levels in fermented milks can possibly be increased through judicious selection of the microbial species and optimization of cultivation conditions or by metabolic engineering and thus can lead to the economic development of novel functional foods with increased nutritional value.
