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Studies on production of 3-hydroxypropionaldehyde from glucose by a novel Enterococcus dispar: optimization of medium components by statistical experimental design.


3-Hydroxypropionaldehyde (3-HPA) is a value added chemical can be obtained by both chemical and biotechnological ways. The value of 3-HPA as a precursor in the production of useful industrial chemicals, for example plastics, was mentioned as early as 1950 (Hall and Stern 1950). Biotechnologically produced HPA could be used to protect food against microbial spoilage. HPA is stable at 4[degrees]C, hence its use in foods such as milk products, which are stored at or below 4[degrees]C and are not heated, may be considered safe (El-Ziney and Debevere 1998; Vollenweider et al. 2003). However, at higher temperatures HPA becomes unstable and may react with other components present (Luthi-Peng et al. 2002b; Sung et al. 2003). HPA was also reported to be a useful chemical in the fixation of biological tissues, with equal fixation possibilities, but less cytotoxic effects than glutaraldehyde, a chemical frequently used for this purpose (Sung et al. 2002; Sung et al. 2003). HPA can also be used as a precursor in enzymatic and chemical reactions for the preparation of bulk chemicals such as acrolein, acrylic acid, 1,3 propanediol (1,3-PDO), 3-hydroxypropionic acid (3-HP) and in the formation of biopolymers (Vollenweider and Lacroix 2004).

In glycolysis, glucose is split into glyceraldehyde 3-phosphate (GAP) and dihydroxy acetone phosphate (DHAP). The DHAP is reduced to glycerol-3-phosphate (G3P) by cytosolic NAD dependent G3P dehydrogenase, which is subsequently dephosphorylated by a glycerol-3-phosphatase to glycerol (Agarwal 1990). Coenzyme B12 dependent glycerol dehydratase converts glycerol into 3-HPA, which is further reduced to 1,3-propanediol (Smiley and Sobolov 1962). 3-HPA is not an end product of the bacterial glycerol dissimilation, but an intermediate compound that must be trapped by semicarbazide, which reduces its toxic affect on living cells and prevents its further reduction to 1,3-PDO, increasing the yield of 3-HPA production (Abeles et al. 1960).

In the present study, it is envisaged to improve the yield of 3-HPA from glucose by a novel strain Enterococcus dispar for the first time. Earlier workers have reported 3-HPA from glycerol by using a number of different genera namely, Agrobacterium, Alcaligenes, Arthrobacter. Bacillus, Brevibacterium, Caulobacter, Cellulomonas, Corynebacterium, Enterobacter, Flavobacterium, Gluconobacter, Klebsiella, Micrococcus, Mycobacterium, Neiseria, Proteus, Protoaminobacter, Pseudomonas (Vancauwenberge et-al. 1990). The classical method of experimental optimization for the production of 3-HPA by E. dispar involves changing one variable at a time keeping the others constant. This conventional optimization is a tedious, cumbersome, and time-consuming process especially when a large number of parameters are taken into account. An alternative and more efficient is the use of statistical method. The strain used for 3-HPA production from glucose is a Gram positive, ovoid, and mostly occurs in pairs is isolated by enrichment studies and deposited in the Microbial Type Culture Collection (MTCC), Chandigarh, India, with accession number 9753.

Up to now there are no reports for the quantitative determination of 3-HPA from glucose by E.dispar. Statistical optimization of 3-HPA production from glucose by isolated strain was investigated for the first time under submerged fermentation environments using L-18 orthogonal array of Taguchi methodology (Taguchi 1986).

Materials and methods

Microorganism and culture conditions

The stock cultures of E. dispar are maintained in agar slants contained the following components: 5 g/L yeast extract, 10 g/L peptone, 20 g/l glycerol, 9 g/L NaCl and 15 g/L agar. These slants are maintained at 4[degrees]C and subcultured at monthly intervals. Inoculation medium consisted of: 25 g/L Protease peptone, 50 g/L glucose, 5 g/L sodium acetate, 2 g/L tri ammonium citrate, 1 g/L tween 80, 0.1 g/L MgS[O.sub.4].7[H.sub.2]O, 0.1 g/L MnS[O.sub.4].[H.sub.2]O, 2 g/L [K.sub.2]HP[O.sub.4.] The pH was adjusted to 7.0 before sterilization. The inoculum medium (100 ml per 250 ml Erlenmeyer flask) was inoculated with 2% inoculum of E. dispar under aseptic conditions. The inoculated flasks were incubated on a rotary shaker at 150 rpm at 35 [+ or -] 2[degrees]C for 24 hours to obtain good growth.

Selection of production medium components

The production medium components were optimized previously by one factor at a time method (results not shown), by keeping the other factors at constant level. It was found that glucose, semicarbazide Hydrochloride, [K.sub.2]HP[O.sub.4], and pH had significant effects on the production of 3-HPA from glucose by E. dispar. The pH was adjusted to 6.8 with 10 N KOH before sterilization. The culture from inoculation medium was centrifuged in a cooling centrifuge (equivalent to 7.3 g dry cell mass per liter), which was inoculated into fermentation medium (40 ml per 100 ml Erlenmeyer flask). Fermentation was carried out aerobically at 30[degrees]C, 150 rpm for 24 hours. After 24 hours, samples were analyzed for 3-HPA production.

Analytical methods

The assay for 3-HPA content was based on the colorimetric method (Circle et al. 1945), specific for acrolein detection. 3-HPA is first dehydrated to acrolein which in turn reacts with tryptophan to form a purple complex that absorbs light at 560 nm. Since 3-HPA is not commercially available, acrolein was used to standardize the assays. Assuming 1 mole of 3-HPA dehydrates to 1 mole of acrolein, the absorbance data was expressed in terms of 3-HPA concentration. Growth was measured as dry cell weight obtained by drying the biomass at 100oC for 1 hour. The residual sugar was determined by DNS method (Miller 1959).

Experimental design and statistical analysis

The optimum concentrations of production medium components for the 3-HPA production from glucose by E. dispar was determined by means of Taguchi design of experimental (DOE) methodology. DOE is an experimental strategy in which effects of multiple factors are studied simultaneously by running tests at various levels of the factors. Taguchi's method has found widespread use in the industrial process design principally in the development trials, where they were used to generate enough process information to establish the optimal conditions for a particular process using the minimal number of experiment possible (Shina 1991).

The designed approach was broadly divided into four phases namely planning, conducting, analysis, and validation. Each phase had a separate objective, interconnected in sequence to achieve the overall optimization process. Taguchi

method involves establishment of a large number of experimental situations described as orthogonal arrays (OAs), which minimize the number of test runs while keeping the pair wise balancing property to enhance the efficiency and reproducibility of the laboratory experiments (Byrne and Taguchi 1987).

Planning (phase I)

The first step in phase 1 is to determine the various factors to be optimized in the production medium that have critical effect on 3-HPA production. Seven factors, which significantly influence the performance, were considered based on single variable optimization studies viz., age of inoculum, rpm, pH, glucose, semicarbazide HCl,[K.sub.2]HP[O.sub.4,] and inoculum level (Table 1).

In the next step, experimental matrix was designed and it was represented by symbolic arrays L18 (which indicates 18 experimental trials). Seven factors with three levels were used and it is depicted in Tables 1 and 2. The factors are variables that have direct influence on the performance of the product or process under investigation and levels are the values or descriptions that define the condition of the factor held, while performing the experiments. All the factors were assigned three levels with a layout of L18 ([3.sup.7]). In the design OA, each column consisted of a number of conditions depending on the levels assigned to each factor. 3-HPA Production studies--conducting (phase II)

3-HPA production experiments were carried out as per the experimental design in 100-ml Erlenmeyer flasks, containing 40 ml of production medium, supplemented with glucose (3.2, 3.6, 4.0), semicarbazide HCl (1.4, 1.8, 2.2), [K.sub.2]HP[O.sub.4,] (0.4, 0.7, 1.0) and pH was adjusted (6, 7, 8). After sterilization of the medium containing flasks at 1210C for 20 min at 15 lbs pressure, they were segregated in three sets for inoculation based on the age of inoculum (24 h, 48 h, 72 h), agitation (rev [min.sup.-1]; 100, 150, 200), and level of inoculum (2ml, 4ml, 6ml) and fermentation was performed at 30[degrees]C for 24 hours. 3-HPA produced was estimated and shown in Table 2. The result represented in the table was the average of two individual trials.

Data analysis (phase III)

The experimental data obtained was processed using Qualitek-4 (Nutek Inc., Bloomfield Hills, MI) software with bigger-is-better quality characteristics for the determination of influence of individual factors, multiple interactions of the selected factors on the 3-HPA production. The data obtained from the experiment was processed employing Qualitek-4 software and depicted in Tables 3-5.

Experimental validation (phase IV)

To validate the method, fermentation experiments were further studied by employing the established optimized process conditions from the proposed methodology and evaluated 3-HPA production efficiency.

Results and discussions

In the present study, an attempt has been made to produce 3-HPA from glucose by E.dispar through submerged fermentation. Earlier workers have worked extensively on 3-HPA from glycerol using different bacterial strains (Vancauwenberge et-al. 1990). By using conventional method of optimization of single variables the 3-HPA concentration estimated was 6mg/ml. To further improve the concentration of 3-HPA, statistical experimental design of Taguchi approach was studied.

In Taguchi method, performance is measured by a loss function [L (y)], which is a formula to quantify the amount of loss based on deviation from the target performance (Mitra 1998). The loss function can be represented by

L = K [(y -[y.sup.o]).sup.2] (1)

Where 'K' denotes the proportionality constant, '[y.sup.o]' represents the target value and 'y' is the experimental value obtained for each trial. In case of 'bigger is better' quality characteristics, the loss function can be written as

L (y) = K (1/[y.sup.2)] (2)

Main effects of selected factors

3-HPA production studies with the designed experimental conditions showed significant variation in the performance (Table 2). The process efficiency has been found to be very much dependent on the selected process conditions. The average effect of the factors along with the interaction at the assigned levels on the performance of 3-HPA production is depicted in Table 3 and Fig. 1. The difference between values at levels 2 and 1 (L2-L1) of each factor indicates the relative influence of the effect. The larger the difference, the stronger is the influence. The negative value has been ignored in assessing the main effect as the placement order of levels assigns either positive or negative values. The relative influence of the factors on 3-HPA production is as follows (in the descending order). pH > rpm > semicarbazide HCl > age of inoculum >[K.sub.2]HP[O.sub.4] > Glucose > inoculum level.


Factors interactions

Understanding the interaction between two factors gave a better insight into the overall process analysis. Any one factor may interact with any of the other factors creating the possibility to gain a large number of interactions. In this way, the estimated interaction (called as severity index--SI) of the different factors under study, helped to know the influence of two individual factors at various levels of the interactions. In Table 4, the "columns" represent the locations to which the interacting factors were assigned. The "reserved" column shows the column that should be reserved if this interaction effect is to be studied. "Levels" indicate the factor levels desirable for the optimum conditions (based on the first two levels). The SI interaction presented 100% of SI for a 90[degrees] angle between the lines while it was 0% SI for the parallel lines. Total 21 interactions were obtained from 7 factors as the possible pairs of interactions for N factors

=N (N-1)/2 (3)

The relative interactions of the factors on the process performance are depicted in Table 4. SI values varied from 75.12% for age of inoculum and glucose to 1.47% for glucose and inoculum level. It is interesting to note that maximum and minimum interaction was noticed with one factor i.e. glucose. These results confirmed that, each fermentation factor was important in the fermentation process and the influence of one factor on 3-HPA production was dependent on the condition of the other factor. Further, the interaction data also revealed that age of inoculum with glucose (at level 2, 2; column 7) showed highest SI (75.12%) followed by age of inoculum and semicarbazide HCl (at level 2, 2; column 4; SI 63.49%) and glucose and semicarbazide HCl (at level 2, 2; column 3; SI--61.06%). Age of inoculum with rpm also showed higher SI of 47.85% (at level 2,1; column 1). It is important to note that inoculum level and glucose with relatively low effect, showed lower SI of 1.47 (at level 2,3; column 13) in combination also. In addition to this, the combination of inoculum level with medium pH (high effect factor) showed relatively low SI (18.66%). Subsequently, rpm (the second higher effect factor) with inoculum level and glucose showed SI of 29.74% and 30.87%, respectively. pH, which has the highest influence on the process individually showed relatively low SI [18.32% (level 1, 2; column 2), 16.44% (level 1, 1; column 7), 15.28% (level 2, 1; column 6), and 14.23% (level 1, 3; column3)] when interacting with semicarbazide HCl, rpm, age of inoculum, and [K.sub.2]HP[O.sub.4], respectively. Comparatively, semicarbazide HCl (third higher influencing factor) showed relatively high SI of 63.49 (at level 2,2; column 4), 61.06% (at level 2, 2; column 3), and 21.09% (at level 1, 2; column 5) when interacting with age of inoculum, glucose and rpm, respectively. Other interactions between different combinations of factors showed medium SI (Table 4).

Analysis of variance (ANOVA)

ANOVA is the most effective method of analyzing more complex data sets. This enables not only the effect of individual factors to be estimated, but also their interactions information, which cannot be obtained readily when factors are investigated separately (Armstrong and Hilton 2004). Table 5 represents the ANOVA data, where percentage contribution of selected parameters on 3-HPA production varied from factor to factor. The total degrees of freedom (DOF) was equal to the number of trials i.e. 18, minus one is 17.

It is observed from the Table 5 that, experimental DOF is 17; while factors-DOF is 2. It is evident from F-ratios, that all the factors and interactions considered in the experimental design had statistically significant effects at 95% confidence limit. The variability of the experimental data was explained in terms of significant effects. The percentage contribution was calculated for each individual factor by the ratio of pure sum to the total sums of the squares. The most influential factor was the pH, accounting for 55.23% of the overall variance of the experimental data followed by rpm (14.58%), [K.sub.2]HP[O.sub.4](10.28%), age of inoculum (3.30%), glucose (3.03%), semicarbazide HCl (2.60%) and inoculum level (0.40%). The error observed was low at 3, which indicated the accuracy of the experimentation.

Optimum parameters and validation studies

Optimum conditions achieved for the effective performance of 3-HPA production in terms of factors contribution were shown in Table 6. It is evident from the table that the pH was found to be the most significant factor influencing the production process with the maximum contributing factor of 2.412. Earlier workers observed a very strong dependence of culture performance on pH during bacterial conversion of glycerol to 3-HPA (Slininger and Bothast, 1985). rpm and [K.sub.2]HP[O.sub.4] were the next most important factors in sequence. Inoculum level showed least impact among the factors studied. Individually at the level stage, production medium pH-6 along with rpm-100 showed the highest effect at level 1; [K.sub.2]HP[O.sub.4] and inoculum level (6ml) exhibited higher effects at level 3; whereas, age of inoculum (48 h) along with semicarbazide HCl (1.8g) and Glucose (3.6 g) showed positive influence at level 2 on 3-HPA production. Increase in relative values of the factors ([K.sub.2]HP[O.sub.4] and inoculum level) from the selected range also showed positive influence. In the case of medium pH, rpm, age of inoculum, semicarbazide HCl and glucose, increasing beyond certain limit/values (threshold) resulted in decline in the performance efficiency. Earlier workers had shown the optimum conditions required for 3-HPA accumulation from glycerol were pH 6.0,40 g of semicarbazide per liter and 48 h aged inoculum. 48 h old culture had accumulated higher amounts of 3-HPA than the cells from 24 h old culture (Vancauwenberge et al. 1990).

Based on software prediction, the average performance of this strain in 3-HPA production was observed to be 2.917 mg/ml (Table 6). Total contribution from factors was found to be 7.028 mg/ml. By the statistical procedure, 3-HPA production under optimized conditions was predicted to be 9.946 mg/ml. Validation experimentation using software-predicted optimum conditions were conducted and the 3-HPA production was observed to be 9.55 mg/ml by this bacterial fermentation system. 3-HPA production by conventional method of single variable optimization was observed to be 6.0 mg/ml. Thus, 3-HPA production upon optimization and validation improved from 6.0 mg/ml to 9.55mg/ml when compared to conventional method of optimization, which represents 37.17% enhancement in the production.

Quality of performance can be measured in terms of variations around the target. The strategy for improvement depends on the current status of performance. The 3-HPA production process variation at current and improved conditions with the function of frequency distribution was shown in Fig. 2. It can be observed from the figure, that a substantial increase in frequency distribution was observed with the optimal factors in improved condition. The yield of 3-HPA was increased from 6 mg/ml of conventional method of optimization to 9.55 mg/ml of Taguchi approach, which represents 37.17% enhancement in the production.

Total contribution from all factors = 7.028 mg/ml Current grand average performance = 2.917 mg/ml Expected result at optimum conditions = 9.946 mg/ml



In the present study, the analysis of the data showed that all selected factors have impact on 3-HPA production either at individual or interactive level. The production medium pH individually showed significant influence on the process performance, followed by rpm, [K.sub.2]HP[O.sub.4], age of inoculum, semicarbazide HCl, glucose, and inoculum level. While in combination, age of inoculum with glucose and age of inoculum with semicarbazide HCl had shown positive influence over the process performance with the SI values of 75.12% and 63.49 % respectively. The predicted value for 3-HPA production (9.946 mg/ml)) was very close to the actual obtained value (9.55 mg/ml), which proved the validity of the Taguchi model.


The author are thankful to Dr. J. S. Yadav, Director, IICT, for his cooperation and gratefully acknowledge Dr. D. Yogeswara rao, Head TNBD Division, CSIR, for providing financial support to carry out this work under NMITLI project.


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Vanajakshi Jalasutram and Annapurna Jetty*

Bioengineering and Environmental Center, Indian Institute of Chemical Technology, Hyderabad-500007, India E-mail: * Corresponding author E-Mail:
Table 1: Selected fermentation factors and their assigned levels.

Sl. No Factor Level 1 Level 2 Level 3

 2 Age of inoculum 24 48 72
 3 rpm 100 150 200
 4 pH 6 7 8
 5 Glucose (g) 3.2 3.6 4
 6 Semicarbazide HCl (g) 1.4 1.8 2.2
 7 [K.sub.2]HP[O.sub.4] (g) 0.4 0.7 1
 8 Inoculum level (ml) 2 4 6

Table 2: Experimental layout of L18 ([3.sup.7]) Orthogonal Array.

Experiment Factors 3-HPA
 No production
 1 2 3 4 5 6 7 8 (mg/ml)

 1 0 1 1 1 1 1 1 1 5.5
 2 0 1 2 2 2 2 2 2 2
 3 0 1 3 3 3 3 3 3 1.5
 4 0 2 1 1 2 2 3 3 10
 5 0 2 2 2 3 3 1 1 0.9
 6 0 2 3 3 1 1 2 1 0.9
 7 0 3 1 2 1 3 2 3 3.2
 8 0 3 2 3 2 1 3 1 0.9
 9 0 3 3 1 3 2 1 2 3.3
 10 0 1 1 3 3 2 2 1 1.6
 11 0 1 2 1 1 3 3 2 4.5
 12 0 1 3 2 2 1 1 3 0.9
 13 0 2 1 2 3 1 3 2 3.4
 14 0 2 2 3 1 2 1 3 0.8
 15 0 2 3 1 2 3 2 1 5.3
 16 0 3 1 3 2 3 1 2 1.25
 17 0 3 2 1 3 1 2 3 3.01
 18 0 3 3 2 1 2 3 2 3.23

Table 3: Main effects of selected factors.

Sl.No Factor Level 1 Level 2 Level 3 L2-L1

 1 Age of inoculum 2.651 3.619 2.481 0.968
 2 rpm 4.208 2.023 2.521 2.186
 3 pH 5.329 2.273 1.149 3.056
 4 Glucose (g) 3.001 3.466 2.285 0.465
 5 Semicarbazide HCl (g) 2.435 3.538 2.779 1.102
 6 [K.sub.2]HP[O.sub.4] (g) 2.1 2.653 3.999 0.552
 7 Inoculum level (ml) 2.904 2.555 3.293 0.349

Table 4: Estimated interaction of Severity Index for two factors.

Sl. Reserved
No Factors Columns SI (%) column Levels

 1 Age of inoculum x Glucose 2x5 75.12 7 [2,2]
 2 Age of inoculum 2x6 63.49 4 [2,2]
 x Semicarbazide HCl
 3 Glucose x Semicarbazide HCl 5x6 61.06 3 [2,2]
 4 Age of inoculum x rpm 2x3 47.85 1 [2,1]
 5 Glucose x [K.sub.2]HP 5x7 39.81 2 [2,3]
 6 pH x Glucose 4x5 32.94 1 [1,2]
 7 rpm x Glucose 3x5 30.87 6 [1,2]
 8 Age of inoculum x 2x7 30.19 5 [2,3]
 9 rpm x Inoculum level 3x8 29.74 11 [1,3]
10 rpm x [K.sub.2]HP[O.sub.4] 3x7 21.69 4 [1,3]
11 rpm x Semicarbazide HCl 3x6 21.09 5 [1,2]
12 pH x Inoculum level 4x8 18.66 12 [1,3]
13 pH x Semicarbazide HCl 4x6 18.32 2 [1,2]
14 Semicarbazide HCl x 6x8 17.72 14 [2,3]
 Inoculum level
15 rpm x pH 3x4 16.44 7 [1,1]
16 Age of inoculum x pH 2x4 15.28 6 [2,1]
17 pH x [K.sub.2]HP[O.sub.4] 4x7 14.23 3 [1,3]
18 [K.sub.2]HP[O.sub.4] x 7x8 10.66 15 [3,3]
 Inoculum level
19 Semicarbazide HCl x 6x7 8.51 1 [2,3]
20 Age of inoculum x Inoculum 2x8 8.16 10 [2,3]
21 Glucose x Inoculum level 5x8 1.47 13 [2,3]

Table 5: Analysis of Variance (ANOVA).

Sl. of
 No Factors DOF squares Variance F-ratio

 1 Age of inoculum 2 4.524 2.262 3.668
 2 rpm 2 15.734 7.867 12.757
 3 pH 2 56.154 28.077 45.528
 4 Glucose (g) 2 4.252 2.126 3.447
 5 Semicarbazide 2 3.822 1.911 3.099
 HCl (g)
 6 [K.sub.2]HP[O.sub.4] 2 11.459 5.729 9.29
 7 Inoculum level 2 1.636 0.818 1.327
 Other/error 3 1.85 0.616
 Total 17 99.436

Sl. Percent
 No Pure sum (%)

 1 3.291 3.309
 2 14.501 14.583
 3 54.921 55.233
 4 3.018 3.036
 5 2.589 2.604

 6 10.225 10.283

 7 0.403 0.405


Table 6: Optimum Conditions and Their Contribution.

SI. No Factors Values Level Contribution

 1 Age of inoculum 48 2 0.702
 2 rpm 100 1 1.29
 3 pH 6 1 2.412
 4 Glucose (g) 3.6 2 0.548
 5 Semicarbazide HCl (g) 1.8 2 0.62
 6 [K.sub.2]HP[O.sub.4] (g) 1 3 1.082
 7 Inoculum level (ml) 6 3 0.375
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Author:Jalasutram, Vanajakshi; Jetty, Annapurna
Publication:International Journal of Biotechnology & Biochemistry
Date:Nov 1, 2010
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