Media Optimization Studies for Fermentative Production of Glucose Isomerase
VIKI RAJENDRA CHOPDA, KARUNA NARSAPPA NAGULA, DNYANESHWAR VITTHAL BHAND, ANIRUDDHA BHALCHANDRA PANDIT*
Department of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai 400 019. India.
Glucose isomerase (GI, EC 22.214.171.124) a successful commercial enzyme used to convert D-glucose to D-fructose. Here media for GI production was optimized using design of experiments (DoE) study. The yeast extract, sucrose and MgSO4.7H2O were found to be the most critical parameter for the production of GI through OVAT screening. It has been interpreted that yeast extract, an enriched nitrogen source and sucrose, a cheap carbon source were responsible for the built up of biomass MgSO4.7H2O acted as a stimulator, which ensures more catalytic production of GI. A central composite rotatable design (CCRD) of RSM was used to correlate the interactive effect among these variables with GI activity. Optimal media composition was found to be as: (in g L-1) yeast extract at 12.4, sucrose at 33.4 and MgSO4.7H2O at 1.0 that gave better activity 4386 U L-1 which was 2.5 fold more than basal media 2053U L-1.
Keywords: Actinoplanes missourensis, Glucose isomerase, mediaoptimisation, Response Surface Methodology.
Glucose isomerase (GI) (EC. 126.96.36.199) catalyzes the reversible isomerization of D-glucose to D-fructose1, 2. The enzyme has the largest market in the food industry because of its application in the production of high-fructose corn syrup (HFCS). The high cost of GI principally governs the overall economics of HFCS and this could be significantly brought down by increasing its production level 2, 3. Selection of novel strain and research to improve characteristics of existing ones has been studied in many laboratories across the world 4.The production of glucose isomerase has been reported by Bhasin and Modi to vary with respect to production strain, fermentation condition and the nutrient composition of the culture medium5.For maximizing the time and yield of such successful commercial enzyme glucose isomerase is important.
Fermentative production of any enzyme here glucose isomerase depends on many factors and on their interactions with the environment. During optimization of production of particular product role of different cellular compartments in the biosynthesis must also be taken into account. Generally, the first step in optimization is based on ‘one-variable-at-a-time’ (OVAT) which is time and material consuming. OVAT platform does not take these interactions terms into account. So for investigating multiple factors require multifaceted approach of experimentation such as on Design of experiments (DoE) which defines as a systematic and efficient creation of design space by varying multiple variables at a time. It involves building a statistical mathematical model between input and output variables of a system6, 7, 8.DoE is broadly used in two steps. (a) Screening: involves identification of statistically significant factors critical for the process. (b) Optimization: prediction of response surface on the basis of selected significant parameters.
Screening designs such as Plackett-Burman (PB)9, 10two level factorial11, and fractional factorial12 are widely reported and well established techniques. For precise and detailed prediction, response surface designs such as central composite13, 14, 15 and Box-Behnken (BB) design 16, 17and Taguchi designs 18are common. DoE based experimental approach has been used here to optimize the fermentative production of glucose isomerase (GI) enzyme using response surface methodology (RSM).
The objective of this study was to understand the relationship between various factors and the response values and to determine the optimum medium components for maximum production of intracellular GI form Actinoplanesmissourensis using RSM.
Materials and Methods
Four strainsnamelyActinoplanesmissouriensisNCIM 2838, Streptomyces olivaceus NCIM 2962, Arthobactor sp. NCIM 2935 and Streptomyces sp. NCIM 2727 were procured from NCIM (National Collection of Industrial Microorganism) Pune, India and these were screened for GI activity.
All the cultures were maintained on agar slants and stored at 4°C with periodic sub-culturing every month.Seed culture was inoculated from a slant into a 250 ml Erlenmeyer flask containing 50ml of culture medium of 0.3% malt extract, 1%glucose, 0.3% yeast extract, 0.5% peptone (pH 6.4 to 6.8) and grown at 30°C for 24 hours.
Media optimization of GI production in batch culture
The basal medium used to screen the strains for GI production included yeast extract 0.3%, malt extract 0.3%, peptone 0.5%, K2HPO4 0.1%, CoCl2 0.015%. The medium was supplemented with 1%glucoseand 2%sucrose as carbon source, MgSO47H2Oat0.1% level and these were sterilized separately. The initial pH was adjusted to 7.0 with 0.1 N NaOH. Growth conditions were at 200 rpm using orbital shakerfor 96 hoursat 30°C.
Cell disruption studies for the release of intracellular GI
At the end of the fermentation period, since GI is an intracellular enzyme; the harvested cells were then subjected to sonication for cell disruption using both ultrasonic probe and bath to release enzyme. During sonication temperature was maintained below 12oC using an ice bath. Sonication conditions wereat 20 kHz ultrasonic frequency over the period of 12 minutes with operating in a pulse mode (pulse 5 sec ON and OFF). The resultant cell lysate was further centrifugedat 10,000x g for 10 minutes and supernatant was used as the enzyme solution for activity determination.For further experiments cells were treated with water bath sonication for 6 min since it gave better enzyme recovery than using probe sonication (see result and discussion section).
GI Activity Assay
The reaction mixture contained 2.25 ml of maleate buffer (pH 6.8), 2.5 ml of substrate solution containing 2M D-glucose, 0.1M MgSO4.7H2O, 0.01M CoCl2.6H2O, and 0.25 ml of enzyme solution. The mixture was incubated at 70°C for 1 hour and the reaction was terminated by adding 0.5 ml of 0.2Mperchloric acid. Fructose concentration was determined by the cysteine-carbazole method 19. One unit of GI activity is defined as the amount of enzyme producing 1 µmole of fructose per min under the assay condition described before.
Estimation of Dry Cell Weight (DCW)
For measuring the biomass concentration, 10 ml culture liquid was centrifuged at 8000xg for 5 min in previously pre-weighed centrifuge tubes. The sediment was washed twice with distilled water and then the cell pellet was dried to constant weight at 105°C.
Optimization of fermentation medium
One-variable-at-a-time (OVAT) method
Four different strains Actinoplanesmissouriensis NCIM (No.2838), Streptomyces olivaceus NCIM (No.2962), Arthobactor sp. NCIM (No.2935) and Streptomyces sp. NCIM (No.2727) were screened for the maximum production of GI using the basal medium. Out of these strains Actinoplanesmissouriensis (NCIM No. 2838) gave maximum production and was selected for further studies. To study the effect of the seed age and inoculum size on GI production, media was inoculated with the seed culture of different age (8 to 48 hours) and in different quantities (1 to 6 %v/v) and growth time varying from 12 to 108 hours. To evaluate the effect of different carbon sources on the production of GI, glucose and sucrose in the basal medium was replaced with different carbon sources, viz., fructose, maltose, lactose, and xylose at 30 g L-1 level. Different nitrogen sources at 11 g L-1level were tested yeast extract, malt extract, peptone, beef extract, soybean meal and tryptone, initial pH varying from 5 to 8 at shake flask level (250 ml flasks holding 50 ml medium mixture).
Statistical optimization using RSM
A central composite rotatable design (CCRD) for three independent variables was used to obtain the combination of values that optimizes the response within the region of three dimensional observation spaces, which allows one to design a minimal number of experiment designed using the software JMP. The medium components (independent variables) sucrose, yeast extract, and MgSO4. 7H2Owhich are found to be significant in OVAT study, were further selected for the optimization and to study interaction effects. The experimental design showing the coded as well as the actual values of independent variables is shown in Table 1. Regression analysis was performed on the data obtained from the design experiments. The second order polynomial coefficients were calculated to estimate the responses of the dependent variables. Response surface plots were also obtained using Design Expert Version 6.0.10.
Results and discussion
Screening of different strains
The order of best productive strains was found to be as shown in figure 1: Actinoplanesmissouriensis NCIM (No.2838) >Arthobactor sp. NCIM (No.2935) >Streptomyces olivaceus NCIM (No.2962) >Streptomyces sp. NCIM (No.2727) with respective to the enzyme activity in Unit per liter: 2053 > 1342 > 1064 > 972. Among the strains screened, Actinoplanesmissouriensis (NCIM No.2838) was used for further optimization study which gave maximum activity.
Sonication time optimization for enzyme recovery
Enzyme recovery studies were carried out with all the above mentioned strains (even though A.missourensiswas specifically screened out among four) because recovery depends on not only the genetic makeup of the micro-organism but also its cell wall design and recovery method conditions. Samples without sonication were also assayed, which shows no activity for enzyme to confirm the fact that enzyme is having intracellular location. Water bath sonication gave higher GI activityof 2360 U L-1 at 6 min of total sonication time for A.
missourensis as shown in figure 2a.Researchers have already reported the use of probe sonication in cell disruption studies 20, 21, 22. Cell disruption with probe sonication was also carried outat 20 kHz ultrasonic frequency over the period of 12 minutes in pulse mode sonication cycle (pulse 5 sec ON and OFF) but the enzyme activity was found to be low as shown in figure 2b.The reason may be use of higher acoustic intensityin probe sonication amnd heat generation (18-20 watts) (23) as compared to water bath sonication (2-3 watts) which might have resulted in higher degradation of the enzymes.As a result of this, in bath sonication it generates low level of cavitational intensity which was sufficient for cell disruption but not high enough as for the enzyme degradation.
Fig1: Screening of different microbial strain on their reported media for GI production
Fermentation optimization using one factor at a time
Optimization of inoculum quantity and seed age
As shown in figure 2c, 24 hours seed culture gave the maximum production of 2638 U L-1. Seed age of 8 and 16 hours gave lowerGI yield, which may be due to low cell density and the micro-organisms mightbe still in lag phase. Further increase in seed age shows a decrease in GI activity whereas biomass concentration was increased. This reveals the fact that GI is not a growth associated product; instead it is a secondary metabolite. Further, the maximum GI production (2731 U L-1) was achieved when2% v/v inoculum volume was employed,as shown in figure 2d. Further increase in inoculum volume shows a decrease in the enzyme activity, whereas biomass production increases only marginally due to increased competition (lower food to micro-organism ratio) for the substrates with an increasing quantity of inoculums.
Dependence of fermentation time on GI production profile
Production profile of glucose isomerase studied at various fermentation times i.e. over 12 to 108 hours are shown in Figure 3athe maximum concentration of GI 2916 U L-1at 72 hours. Further increase in fermentation time does not show any further increase in the production of GI which may be due to factors such as aging of cells, depletion of nutrients.
Effect of pH
When GI activity was examined in the initial pH range of 5 to 8, a sharp increase in the enzyme activity occurred between pH 6.5 and 7.0, as shown in Figure 3b. At pH 7 the maximumconcentration of GI (2684 U L-1) as well as biomass (3.2 g L-1) was generated. This shows that initial pH has a significant effect on enzyme activity.
Fig2: a) Effect of probe sonication time on GI recovery b) Effect of bath sonication time on GI recovery c) Effect of seed age time (time when effective inoculum quantity of selected strain from growth media transferred to production media) on GI yield d) Effect of inoculum quantity on GI yield
Effect of carbon source
Out of the carbon sources tested, sucrose resulted in the maximum GI production (2768 U L-1) followed by mixtures of glucose (1%) and sucrose (2%) 2175 U L-1after 72 hours of cultivation as shown in Figure 3c.It was observed that glucose, maltose, lactose, xylose and mixtures of glucose and sucrose supported only biomass growth and was not able to create physiological condition to produce GI. Glucose produced maximum biomass (2.86 g L-1 measured as DCW) as compared to other carbon sources. Similar level of comparable GI activity on different carbon sources is reported by researchers across the world 24, 25, 26.
Fig 3: a)Effect of fermentation time on GI production (production profile) b) Effect of initial pH on GI production c) Effect of various carbon sources on GI production d) Effect of nitrogen sources on GI production.
Effect of nitrogen source
Among the nitrogen sources,yeast extract supported the maximum production of GI (2743 U L-1) followed by peptone (2132 U L-1) as shown in Figure 3d.Many researchers have studied the effect of different nitrogen sources on GI activity in different strains 24, 25, 26. The results found were exclusively strain specific and were also dependent on other operating parameters used in the study.
Optimization of fermentation medium using response surface methodology
The combined effect of three independent variables, A: sucrose; B: yeast extract and C: MgSO4 7H2Oforthe production of GI was examined using RSM. The CCRD gave quadratic model for a given set of experimental results. Equation 1 represents the mathematical model relating to the production of GI with independent process variables determined through multiple regression analysis using JMP. The experimental design and values of GI yield are given in Table1.
Table 1: The CCRD matrix of independent variables in coded as well as actual form with their corresponding response
|Standard order||Variables||GI activity
|A: Sucrose||B: Yeast extract||C: MgSO4.7H2O||Experimentala||Predicted|
aResults are mean ± SD of three determinations
Table 2: Analysis of variance (ANOVA) for the experimental results of the central composite design (Quadratic model)
|Source||Degree of freedom||Sum ofsquares||Mean Square||F Ratio|
*Value less than 0.05 indicates that model is significant
The ANOVA of the quadratic model indicated the model to be significant as given in Table 2. The Model F-value of 46.3 implies the model to be significant and is calculated as a ratio of mean square regression and mean square residual. Model P-value (Prob>F) was less than 0.05 indicate model terms were significant.The second order response model found after regression analysis was as follows:
GI activity = 1888.99 + 1233.60*B + 262.59*A + (B-1.1)*[(B-1.1)*-5921.61] + (B-1.1)*[(A-3)*1079.16] + (A-3)*[(A-3)*-667.05] + (C-0.1)*[(C-0.1)*-449472.25] + (B-1.1)*(B-1.1)*[(C-0.1)*50222.00] (1)
Figure 4a shows a plot of actual versus predicted by the model shows thatthemodelis statisticallysignificant (R2= 0.97) andalsothattherecoveries arequitesimilarin actual and predicted conditions. Theplotfromthis fit shows the most pointsliefairlycloseto the45° line,indicatinga good fit by themodel.The optimal compostion suggested by the model was verified experimentally and it was observed that experimental yield of 4386 U L-1 obtained using optimum model output composition was more than the predicted value from the polynomial model was 4003.75 U L-1. Thus, dynamic and complex biological process can be more explored through DoE based strategy as reported in for optimizing the production of glucose isomerase from Lactobacillus brevis24.
Fig 4: a) Actual vs predicted plot for GI activityb) Comparison of GI activity before and after RSM optimization method.
From the present study it can be concluded that optimal media composition was found to be as: (in g L-1) yeast extract at 12.4, sucrose at 33.4 and MgSO4.7H2O at 1.0 that gave better activity 4386 U L-1 which was 2.5 fold more than basal media 2053U L-1 as shown in figure 4b. The yeast extract, sucrose and MgSO4.7H2O were found to be the most critical parameter for the production of GI through OVAT screening. Using RSM it has been interpreted that yeast extract, an enriched nitrogen source and sucrose, a cheap carbon source responsible for the built up of biomass MgSO4.7H2O acted as a stimulator, which ensures more catalytic production of GI.
The University Grants Commission, Government of India, provided financial assistance during the course of this investigation.
- Lobanok AG, Sapunova LI, Dikhtievski YO, Kazakevich IO: Screening of glucose isomerase-producing microorganisms.World Journal of Microbiology and Biotechnology1997;14: 259-262.
- Bhosale SH, Rao MB, Deshpande VV: Molecular and industrial aspects of glucose isomerase. Microbiological Review.1996;60: 280-300.
- Chopda VR, Nagula KN,Bhand DV, Pandit AB: Studying the effect of nature of glass surface on immobilization of glucose isomerase. Biocatalysis and Agricultural.Biotechnology Journal 2014;3:86-89.
- TeeradakornS, Kishimoto M, Seki T, Pinphanichakarn P, Yoshida T: Process development and simulation of glucose isomerase production from birchwoodxylan by a Streptomycetesfusant. Biochemical Engineering Journal 1998;1: 159-168.
- BhasinS, Modi H.Optimization of fermentation medium for the production of glucose isomerase using Streptomyces sp. SB-P1. Biotechnology Research International 2012; Article ID 874152, 10 pages, 2012. doi:10.1155/2012/874152.
- Montgomery DC:Design and analysis of experiments.John Wiley & SonsSixth Edition
- Mandenius CF,Brundin A.: Bioprocess optimization using design‐of‐experiments methodology. Biotechnology Progress Journal 2008; 6: 1191-1203.
- Lee KM, Gilmore DF: Statistical experimental design for bioprocess modeling and optimization analysis. Applied Biochemistry and Biotechnology Journal.2006; 135(2): 101-115.
- Plackett RL, Burman JP: The design of optimum multifactorial experiments. Biometrika1946; 33: 305-325.
- Persad A, Chopda VR, Rathore AS, Gomes J:Comparative Performance of Decoupled Input–Output Linearizing Controller and Linear Interpolation PID Controller: Enhancing Biomass and Ethanol Production in Saccharomyces cerevisiae. Applied Biochemistry and Biotechnology Journal 2013;169: 1219-1240.
- Ramı́rez J, Gutierrez H, GSchaedler A: Optimization of astaxanthin production by Phaffiarhodozyma through factorial design and response surface methodology. Journal of Biotechnology 2001; 88:259-268.
- Berrios M, Gutiérrez MC, Martín MA, Martín A: Application of the factorial design of experiments to biodiesel production from lard.Fuel Processing Technology 2009;90:1447-1451.
- Ibrahim HM, Yusoff WMW, Hamid AA, Illias RM., Hassan O, Omar O: Optimization of medium for the production of β-cyclodextringlucanotransferase using Central Composite Design (CCD). Process biochemistry Journal 2005;40: 753-758.
- He YQ, Tan TW: Use of response surface methodology to optimize culture medium for production of lipase with Candida sp. 99-125. Journal of Molecular Catalysis B: Enzymatic 2006; 43(1):9-14.
- Tripathi AD, Srivastava SK, Singh RP: Statistical optimization of physical process variables for bio-plastic (PHB) production by Alcaligenes sp.Biomass and Bioenergy Journal 2013; 55:243-250.
- Annadurai G, Sheeja RY: Use of Box-Behnken design of experiments for the adsorption of verofix red using biopolymer. Bioprocess Engineering 1998;18:463-466.
- Ferreira SC, Bruns RE, Ferreira HS, Matos GD, David JM., Brandão GC, Dos Santos WNL: Box-Behnken design: An alternative for the optimization of analytical methods. AnalyticaChimicaActa 2007; 597(2): 179-186.
- Prasad KK, Mohan SV, Rao RS, Pati BR. Sarma PN: Laccase production by Pleurotusostreatus 1804: Optimization of submerged culture conditions by Taguchi DOE methodology.Biochemical Engineering Journal 2005; 24(1): 17-26.
- Dische Z, BorenfreundE: A new spectrophotometric method for the detection and determination of keto sugars and trioses.Journal of Biological Chemistry 1951; 192: 583-587.
- Farkade VD, Harrison ST, Pandit AB:Improved cavitational cell disruption following pH pretreatment for the extraction of β-galactosidase from Kluveromyceslactis. Biochemical Engineering Journal 2006; 31: 25-30.
- Farkade VD, Harrison S, Pandit AB: Heat induced translocation of proteins and enzymes within the cell: an effective way to optimize the microbial cell disruption process. Biochemical Engineering Journal 2005; 23: 247-257.
- Save SS, Pandit AB, Joshi J: Microbial cell disruption: role of cavitation. The Chemical Engineering Journal & The Biochemical Engineering Journal1994; 55: B67-B72.
- Balasundaram B, Pandit AB: Selective release of invertase by hydrodynamic cavitation.Biochemical Engineering Journal 2001; 8: 251-256.
- TianLi Y, YunYan W, Y Hong Y, ZhenPeng G: Media optimization studies for glucose isomerase production by Lactobacillus brevis using response surface methodology. Journal ofNorthwest A & F University-Natural Science Edition 2009; 37: 217-224.
- PrabhakarG, Raju DC: Media optimization studies for glucose isomerase production by arthrobacter species. Bioprocess Engineering 1993; 8: 283-286.
- Chen WP, Anderson AW, Han YW: Production of glucose isomerase by Streptomyces flavogriseus.Applied and Environmental Microbiology 1979; 37: 324-331.