Waiting
Login processing...

Trial ends in Request Full Access Tell Your Colleague About Jove
JoVE Journal Bioengineering

Bioengineering

Process Evaluation and Kinetics of Recombinant Chitin Deacetylase Expression in E. coli Rosetta pLysS Cells Using a Statistical Technique

Published: March 10, 2023 doi: 10.3791/64590
1, 1, 1
1Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE)

Abstract

In recent years, the greener route of the deacetylation of chitin to chitosan using the enzyme chitin deacetylase has gained importance. Enzymatically converted chitosan with emulating characteristics has a broad range of applications, particularly in the biomedical field. Several recombinant chitin deacetylases from various environmental sources have been reported, but there are no studies on process optimization for the production of these recombinant chitin deacetylases. The present study used the central composite design of response surface methodology to maximize the recombinant bacterial chitin deacetylase (BaCDA) production in E. coli Rosetta pLysS. The optimized process conditions were 0.061% glucose concentration, 1% lactose concentration, an incubation temperature of 22 °C, an agitation speed at 128 rpm, and 30 h of fermentation. At optimized conditions, the expression due to lactose induction was initiated after 16 h of fermentation. The maximum expression, biomass, and BaCDA activity were recorded 14 h post-induction. At the optimized condition, the BaCDA activity of expressed BaCDA was increased ~2.39-fold. The process optimization reduced the total fermentation cycle by 22 h and expression time by 10 h post-induction. This is the first study to report the process optimization of recombinant chitin deacetylase expression using a central composite design and its kinetic profiling. Adapting these optimal growth conditions could result in cost-effective, large-scale production of the lesser-explored moneran deacetylase, embarking on a greener route for biomedical-grade chitosan production.

Introduction

Chitin, a structural β, 1-4 glycosidic linked natural polymer, is the second-most abundant polysaccharide in nature after cellulose. Despite this fact, chitin has limited industrial applications due to its insolubility1. This bottleneck is addressed by subjecting chitin to N-deacetylation, which imparts a positive charge and increases the solubility of the resulting polymer, chitosan1. Chitin can be modified to chitosan through two different routes: chemical and enzymatic. The biomedical application of chitosan requires controlled and defined deacetylation, which is restricted in chemical routes2,3. This limitation can be addressed using chitin deacetylases (CDAs), a green enzymatic approach for the deacetylation process4,5.

Chitin deacetylase belongs to the carbohydrate esterase 4 (CE-4) family, defined in the carbohydrate-active enzymes (CAZY) database. The enzymes of the CE-4 family share the NodB homology or polysaccharide deacetylase domain as the conserved region. The central composite design (CCD), a statistical tool, is used for the optimization of several wild-type chitin-modifying enzymes6,7,8,9. However, the downstream steps in the usage of wild-type organisms becomes tedious, hence the shift toward recombinant enzymes10,11,12,13,14,15,16,17. In recent years, halophilic recombinant CDA from marine sources have gained importance due to their ease in the industrial application and production of biomedical-grade chitosan18,19.

Recombinant enzyme production in E. coli has a limitation on the process, and media optimization is needed as its expression in E. coli varies depending on the gene and plasmid used20. Thus, screening of a suitable process and nutrient parameters becomes important. One factor at a time (OFAT), the commonly employed optimization method, requires tremendous resources and time to perform step-by-step experiments. This method suffers from a lack of statistical information regarding the interaction among the parameters20,21,22,23. Therefore, the CCD of response surface methodology (RSM) was adopted to study the halophilic bacterial chitin deacetylase (BaCDA) expression yield and BaCDA activity in E. coli Rosetta pLysS. The parameters considered for expression optimization in the E. coli host were lactose concentration, glucose concentration, incubation temperature, agitation rate, and incubation time. In most E. coli expression studies, Luria Bertani (LB) media with Isopropyl β-d-1-thiogalactopyranoside (IPTG) was used as an inducer. This addition of IPTG required regular growth monitoring24. These recurrent mediations during the fermentative process also open avenues for contamination. Hence, research groups have shifted to terrific broth (TB) with lactose as the inducer. The inclusion of lactose in the media instead of IPTG addresses this concern; E. coli consumes this lactose and produces allo-lactose as a by-product, resulting in an auto-induction condition. This auto-inducer media includes glycerol, which has exhibited improved yields of recombinant protein25.  This overexpression of recombinant proteins in TB media was further improved by optimizing the process parameters. In the present study, a central composite design was applied to optimize the heterologous expression of halophilic BaCDA in E. coli Rosetta pLysS cells. The process parameters chosen were incubation temperature, agitation rate, and incubation time, and the nutrient parameters evaluated were glucose and lactose concentration. The halophilic BaCDA expression was evaluated with the predicted optimized condition and cross-validated using SDS-PAGE.

Subscription Required. Please recommend JoVE to your librarian.

Protocol

1. Expression media and culture condition

  1. Transform the pET-22b vector containing the BaCDA gene into E. coli Rosetta pLysS competent cells using the heat-shock method, as described in15.
    NOTE: Care has to be taken while working with microorganisms. All microbiological work has to be performed inside a biosafety cabinet hood to avoid contamination.
  2. Perform the preliminary expression study in TB media containing 0.05% (w/v) glucose and 0.2% (w/v) lactose at 16 °C and 180 rpm. Grow 6.792 x 107 E. coli Rosetta pLysS cells in 100 mL of media containing BaCDA cloned plasmid till it reaches the stationary phase. At the mentioned growth conditions, the E. coli Rosetta pLysS cells reach an optical density 600 (OD600) of 10.85 ± 0.21.
    NOTE: Composition of the TB media (% w/v): tryptone, 1.2; yeast extract, 2.4; glycerol, 0.6; and 1x TB salt (17 mM KH2PO4 and 74 mM K2HPO4)17.

2. Optimization and experimental design

  1. Design the experiments and fit the second-order polynomial model in central composite design using the statistical tool software. The statistical software used in the present study was MINITAB 17.0 (trial version).
    1. To do this, open the software and click on the following buttons: Stat > DoE > Response surface > Create response surface > Response design > Central composite. The output appears as a dialogue box.
  2. Feed the five parameters, incubation temperature, agitation rate, incubation time, glucose concentration, and lactose concentration, at five levels (-2, -1, 0, +1, +2) in the dialogue box.
    1. To do this, enter the parameters with the levels in the dialogue box, enter the details of all the parameters, and press OK. This generates the experimental design matrix with six replicates at the center point, requiring 32 experimental runs. The software generates a table containing the parameters and their levels in coded and uncoded terms, used in the process optimization using CCD (Table 1).
  3. Perform the 32 experiments with the software-generated conditions. Feed the experimental results into the experimental design matrix (generated by the software) containing levels and parameters in terms of coded and uncoded units (Table 2).
  4. Analyze the experimental design matrix (Table 2) using the software. To do this, open the software and feed the response into the datasheet. Select Response column > Stat > DoE > Response surface > Analyze response surface design > OK. The output is analyzed for statistical significance, and a model is predicted by the software.

Table 1: The parameters and their levels in coded and uncoded terms used in the experimental design to estimate the expression of recombinant chitin deacetylase in E. coli Rosetta pLysS cells. Please click here to download this Table.

Table 2: Experimental design matrix with experimental and predicted BaCDA activity (expression) of recombinant chitin deacetylase in E. coli Rosetta pLysS cells. Please click here to download this Table.

3. Validation of model

  1. Validate the designed model using the response optimizer tool available in the software.
  2. Repeat the fermentation at software-predicted optimum conditions and compare the experimental value with the software-predicted value.
    1. To do this, open the software and select Response column > Stat > DoE > Response surface > Response optimizer > Maximize > OK. The output is optimum process conditions and values predicted by the software. Repeat the fermentation at predicted optimum conditions and compare the values.

4. Analytical methods

  1. Expression, biomass, and protein quantification
    1. Perform the fermentation in 32 conditions given by the software in the experimental design matrix (Table 2). Obtain the pellet after each run by centrifuging the culture at 5,405 x g for 10 min at 4 °C. Weigh the cell pellet to determine the biomass yield.
      NOTE: All microbial handling should be done in aseptic conditions (i.e., inside biosafety cabinets).
    2. Lyse the cell pellet to obtain periplasmic protein by sonication. Add 5 mL of lysis buffer (50 mM Tris-HCl, 300 mM NaCl, 10 mM Imidazole) to each gram of cell pellet and disrupt by sonicating for 10 cycles with a pulse of 10 s on and 10 s off at 60% amplitude. Collect the lysate by centrifuging at 5,405 x g for 10 min at 4 °C. Quantify the protein concentration by Bradford's assay26.
      NOTE: To avoid protein denaturation, sonication has to be done under cold conditions. Place the samples on ice while performing sonication.
    3. Analyze the BaCDA expression using SDS-PAGE followed by ImageJ software. Load 10 µL of soluble cell lysate (periplasmic protein) on 12% bis-acrylamide gel and run under 1x tris-glycine-SDS (TGS) buffer at a 100 V current for 2 h. After the run is completed, stain the gel with Coomassie blue and destain to remove the background27.
    4. Capture an image using a gel documentation unit. Determine the BaCDA expression by comparing the band intensity in the form of the pixel values using ImageJ software. Consider the experimental run with the lowest BaCDA activity and biomass as the reference pixel 28.
      1. To do this, open the software and open the image file, then select each lane using the rectangular box. Go to Analyze and set each lane as 100%, then select the overexpressed band in each lane using the rectangular box. Go to Analyze and determine the intensity of the band, repeat the process in each lane, and export the result as .xls file. Consider the band intensity of the experimental run with the lowest BaCDA activity as the reference intensity and analyze the expression of each lane.
  2. Enzyme activity assay
    1. Determine the BaCDA activity using an acetate assay kit15, using ethylene glycol chitin (1 mg/mL; EGC) as the substrate29.
    2. Allow 20 µL of BaCDA to react with 40 µL of the substrate in the presence of 40 µL of 50 mM Tris-HCl (pH 7) buffer for 1 h at 30 °C by agitating at 800 rpm.
    3. After 1 h, centrifuge the 100 µL reactant through a 3 kDa column at 2,111 x g for 15 min at 4 °C. Discard the retentate comprising of BaCDA and collect the filtrate containing acetate released during the reaction. Use the filtrate for the acetate assay to determine the BaCDA activity.
      ​NOTE: One unit of the enzyme is defined as the activity which releases 1 µM of acetate from the substrate per microliter of enzyme per minute. The enzyme activity assay was carried out in triplicates, and the respective enzyme activity was calculated accordingly.
  3. Fermentation kinetics of lactose induction
    1. Investigate the activity profile to determine the point of lactose induction and expression start point. The activity profiling is made by estimating biomass and BaCDA activity with the optimized fermentation conditions.
    2. Determine the glucose concentration in the media using a glucose estimation kit. Plot the activity profile using biomass, glucose concentration, and BaCDA activity against the fermentation time.

Subscription Required. Please recommend JoVE to your librarian.

Representative Results

Process optimization of expression of periplasmic recombinant enzyme chitin deacetylase in E. coli using central composite design (CCD)
The pET22b-BaCDA construct, when grown in unoptimized conditions, gave a maximum biomass yield and BaCDA activity of 22.26 ± 0.98 g/L and 84.67 ± 0.56 U/L, respectively15. In the current study, a statistical approach CCD was adopted to find the optimal process conditions for expressing periplasmic recombinant enzyme chitin deacetylase in E. coli Rosetta pLysS cells. The five process parameters, incubation temperature (A), agitation rate (B), incubation time (C), glucose concentration (D), and lactose concentration (E), were selected based on our preliminary work (Table 1). The expression of periplasmic recombinant enzyme chitin deacetylase was quantified and represented as BaCDA activity (U/L) of recombinant chitin deacetylase (Table 2). Accordingly, parameters A, B, C, D, and E were taken as exogenous parameters, while the BaCDA activity of the expression of periplasmic recombinant chitin deacetylase was chosen as the endogenous parameter (response). The experimental design matrix depicted that the maximum BaCDA activity was 199.72 ± 1.14 U/L (run 26), and the minimum BaCDA activity was 1.27 ± 0.34 U/L (run 17), obtained from the biomass yields of 13.04 ± 0.48 g/L and 6.60 ± 0.70 g/L, respectively (Table 2 and Table 3). The results of the biomass yield, expression of BaCDA, total protein content, and specific activity at all 32 experimental runs are shown in Table 3. The maximum expression of BaCDA was found to be 2.945 (run 26), and the minimum expression was 1.00 (run 17; Table 3 and Figure 1, respectively). The specific activity corresponding to the maximum and minimum was 0.079 U/mg and 0.001 U/mg, respectively (Table 3).

Table 3: The results of biomass yield, expression of halophilic BaCDA, total protein content, and the specific activity of all the experimental runs given by the central composite design. Please click here to download this Table.

Figure 1
Figure 1: Results of SDS-PAGE analysis. The figure shows the SDS-PAGE analysis on the expression of periplasmic recombinant enzyme chitin deacetylase in E. coli Rosetta pLysS for all the experimental runs given by the central composite design. The expression of halophilic BaCDA was observed at 29 kDa. The numbers at the top represent the experimental run, and the value at the bottom represents the BaCDA activity at the particular condition. Please click here to view a larger version of this figure.

Initially, the full second-order regression model obtained for the BaCDA activity of recombinant chitin deacetylase was significant, with a high coefficient of determination (R2) of 0.9595, whereas the predicted R2 was 0%, indicating the lack of predictability of a model, and the regression equation is given below:

Z1 (BaCDA activity, U/L) = -1844 + 99.7 A + 2.64 B + 36.33 C + 808 D + 55 E - 1.842 A2 - 0.00925 B2 - 0.3801 C2 - 26676 D2 + 234.9 E2 + 0.0562 A x B - 0.609 A x C + 44.8 A x D - 10.32 A x E - 0.0203 B x C + 0.83 B x D - 0.979 B x E + 28.2 C x D + 3.10 C x E + 432 D x E (Eq. 1)

where A = incubation temperature, °C; B = agitation rate, rpm; C = incubation time, h; D = glucose concentration, % (w/v); and E = lactose concentration, % (w/v).

The results of the analysis of variance (ANOVA) of the full model (Equation 1) are given in Table 4. Further, to increase the predictability of the model, the most insignificant parameters (A, B, C, AD, BC, BD, CD, CE, and DE; [p value > 0.05]) were removed from Equation 1, and the final modified regression model was obtained for the BaCDA activity of recombinant chitin deacetylase, shown below as:

Z2 (BaCDA activity, U/L) = 121.81 + 9.99 D + 8.19 E - 29.48 A2 - 14.80 B2 - 24.33 C2 - 16.67 D2 + 9.40 E2 + 9.00 A x B - 19.49 A x C - 8.26 A x E - 7.83 B x E (Eq. 2)

Table 4: Analysis of variance (ANOVA) for the full model. Please click here to download this Table.

Table 5: Analysis of variance (ANOVA) for the reduced model and test of significance for the expression of recombinant chitin deacetylase in E. coli Rosetta pLysS cells. Please click here to download this Table.

The predicted values of the BaCDA activity were given by the software based on a modified regression model (Equation 2) and are represented in Table 5. In addition, the normality assumption is fulfilled as the residuals are normally distributed (i.e., the data points are closer to the straight line), as shown in the normal probability plot (Figure 2), indicating the capability of the model to optimize the expression of recombinant chitin deacetylase. Hence, the modified Equation 2 shall be applied to discover the optimal levels and their design space for the process. The impact of interactions among the independent parameters was visualized by the two-dimensional contour plots for the expression of recombinant chitin deacetylase (Figure 3).

Figure 2
Figure 2: Normal probability plot. The figure shows the normal probability plot between residual, a difference between an experimental and predicted BaCDA, activity (X-axis) versus percentage of cumulative probability of data (Y-axis). The plot indicates the capability of the model to optimize the expression of recombinant chitin deacetylase. Please click here to view a larger version of this figure.

Figure 3
Figure 3: Contour plots. The contour plots of all the combinations of parameters. A: incubation temperature in °C; B: agitation rate in rpm; C: incubation time in h; D: glucose concentration in % (w/v); and E: lactose concentration in % (w/v). Please click here to view a larger version of this figure.

Validation of the model
The response optimizer tool in the software was used to solve the reduced regression model (Equation 2) and to find the optimal conditions for enhanced BaCDA activity of recombinant enzyme chitin deacetylase and their expression in E. coli Rosetta pLysS. The model was validated at the optimal process conditions (i.e., incubation temperature of 22 °C; agitation rate of 128 rpm; incubation time of 30 h; glucose concentration of 0.058% [w/v]; and lactose concentration of 1% [w/v]) (Table 6 and Figure 4). The optimal values of all five parameters were placed within the levels selected except parameter E (lactose concentration), and the predicted and experimental BaCDA activity of recombinant chitin deacetylase at these optimal conditions was 190.85 U/L and 202.39 ± 0.31, respectively (Table 6).

Table 6: The optimum process conditions for increased BaCDA activity of recombinant enzyme chitin deacetylase and their expression in E. coli Rosetta pLysS. Please click here to download this Table.

Figure 4
Figure 4: Optimization plot. The optimization plot presents optimal values for the increased BaCDA activity of recombinant enzyme chitin deacetylase and their expression in E. coli Rosetta pLysS. A: incubation temperature in °C; B: agitation rate in rpm; C: incubation time in h; D: glucose concentration in % (w/v); and E: lactose concentration in % (w/v). The X-axis represents the levels of the parameters, and the Y-axis represents the BaCDA activity. Please click here to view a larger version of this figure.

The optimal value of lactose concentration using the response optimizer tool was 1% at the +2 level in the model. Therefore, the impact of higher lactose concentrations (1-2.5% [w/v]) on expression and BaCDA activity was studied. The BaCDA activity was 201.840 ± 1.92 U/L, 201.900 ± 1.95 U/L, 202.186 ± 1.59 U/L, and 202.173 ± 2.09 U/L with 1%, 1.5%, 2%, and 2.5% lactose, respectively. There was no significant difference in the BaCDA activity, and the SDS-PAGE analysis also showed the same expression level in all the lactose concentrations, as shown in Figure 5.

Figure 5
Figure 5: Results of SDS-PAGE analysis. SDS-PAGE analysis on periplasmic recombinant enzyme chitin deacetylase expression in E. coli Rosetta pLysS with different lactose concentrations in an optimized process condition. Lane 1: marker; Lane 2: 1% (w/v) lactose concentration; Lane 3: 1.5% (w/v) lactose concentration; Lane 4: 2% (w/v) lactose concentration; and Lane 5: 2.5% (w/v) lactose concentration. Please click here to view a larger version of this figure.

Fermentation kinetics of lactose induction
This study shows the kinetics of biomass yield, glucose concentration, and BaCDA activity at the optimized conditions. The growth of E. coli showed a diauxic pattern due to the inclusion of glucose (0.061%) and lactose (1%) in the media. The first log phase was observed till 12 h due to the complete exhaustion of glucose concentration. The corresponding biomass yield was 7.89 ± 0.18 g/L. The second log phase started at 16 h with lactose consumption and further initiated halophilic BaCDA expression. The maximum BaCDA activity and biomass yield were 202.39 ± 0.31 U and 17.53 ± 0.07 g/L, respectively, at 32 h of fermentation (Figure 6).

Figure 6
Figure 6: Yield parameter kinetics. Kinetics of biomass yield, glucose concentration, and BaCDA activity expressing periplasmic recombinant enzyme chitin deacetylase in E. coli Rosetta pLysS at the optimized process conditions. All the runs were performed in triplicates, and the value was represented as an average with standard deviation. Please click here to view a larger version of this figure.

Subscription Required. Please recommend JoVE to your librarian.

Discussion

Deacetylated chitin, chitosan, has many applications, especially in the biomedical field30. However, the reproducibility of chitosan concerning its degree of acetylation (DA) and pattern of acetylation (PA) is a major concern in addition to other environmental apprehensions. The greener route, using enzymes, has thus been exploited. The array of CDAs can be employed to create chitosan with a unique pattern of deacetylation, which would increase their biomedical applications4,5. In our previous work, the cloned BaCDA gene was expressed in E. coli Rosetta pLysS cells15.

The BaCDA was expressed as inclusion bodies at 37 °C on primary screening. Based on the literature survey, a lower expression temperature (i.e., 16 °C) was used to get soluble BaCDA in TB auto-induction media31. However, the maximum activity of 84.67 ± 0.56 U/L was obtained after 52 h of fermentation at 16 °C. The lowering of the expression temperature resulted in an increased fermentation time20. Hence, a statistical approach, CCD, was adopted to optimize process parameters to reduce the fermentation time and enhance BaCDA activity; several studies on wild-type CDA production have been reported6,7,8. To the best of our knowledge, this is the first report on the process optimization of halophilic recombinant BaCDA production.

The correct choice of parameters and levels was a crucial step for CCD-based optimization, and expertise was required. The induction temperature was crucial, as variations lead to the enzyme's expression in either soluble form or as inclusion bodies20,31,32. In the preliminary study, the fermentation temperature at 37 °C resulted in the expression of BaCDA as inclusion bodies, whereas at 16 °C, it resulted in soluble BaCDA. Therefore, the temperature range was selected between 16 °C to 32 °C to avoid the formation of inclusion bodies. It was predicted that the selected range would express BaCDA as a soluble fraction in contrast to the fast growth rate. In the preliminary study, the incubation time required for culture growth to reach the stationary phase was 52 h at 16 °C. An increase in temperature led to faster growth, so for the selected incubation temperature range, the incubation time was chosen between 12 h to 44 h. A higher agitation speed improves the oxygen transfer and growth of E. coli. The agitation rate between 160 to 250 rpm has been studied33; therefore, in the present study, the agitation rate was selected between 80 to 240 rpm. The inclusion of IPTG induced the heterologous protein; however, research groups have opted for lactose as an inducer to create auto-induction conditions and reduce the production cost, as lactose is a cheaper substitute for IPTG, providing equal or better results34,35. Hence, expression of the recombinant enzymes in the E. coli system depended on the glucose-lactose diauxic growth36. Most of the researchers have used 0.05% glucose and 0.2% lactose for auto-induction37,38. In different statistical optimization studies, lactose concentration has been optimized in the range between 0% to 12.5%. In the present study, a glucose concentration of 0.05% was selected at level 0, and the range was selected between 0% to 0.1%. The lactose at a concentration of 0.2% was selected at level -2, and the range was selected between 0.2% to 1%. Based on the literature34,35,36,38,39 and our preliminary experiments, the process parameters, incubation temperature, agitation rate, incubation time, glucose concentration, and lactose concentration, were selected for halophilic BaCDA expression in E. coli with five different levels, using a central composite design (Table 1). The BaCDA activity varied from 1.27 to 199.72 U/L, indicating the influence of the parameters and their levels on the expression of halophilic BaCDA in E. coli Rosetta pLysS cells (Table 2 and Figure 1). A variation of only 9.71% was observed in the BaCDA activity between experimental and predicted values (Table 2), indicating the accuracy of the experimentation. Similarly, Table 3 is also in good agreement with Table 2.

The adequacy of the model and fitness was evaluated using analysis of variance (ANOVA) for the experimental design used (Table 4). The high R2 value suggests a higher significance of the model40. The observed low difference of 0.431 between the adjusted R2 (0.8785) and the R2 value (0.9216) confirms the data accuracy. Equation 1 was highly significant, with an F-value of 21.37, as shown by Fisher's F-test, along with a very low probability value (P model > F = 0.000), which was significant at a 95% confidence interval. The model F-value was calculated using the formula:

Equation 1

The parameters are said to be significant only if the value of the F-statistics probability is less than 0.05. For the proposed model (Equation 1), the terms D, E, A2, B2, C2, D2, E2, AB, and AC were significant at 95% confidence (p < 0.05; Table 5). The Lack of Fit F-value was 8.24, which indicates its insignificance, and has a 14% chance of being significant (Table 5). The insignificant Lack of Fit F-value represents the fitness of the experimental data to the model (Figure 2). Therefore, based on the above statistics, it can be concluded that glucose concentration (D) and lactose concentration (E) are vital in expressing recombinant chitin deacetylase in E. coli. In this study, the physical environmental conditions, incubation temperature (A), agitation rate (B), and incubation time (C), did not show significance in the expression of recombinant chitin deacetylase (Table 5), whereas the interaction between A and B, and A and C was very much significant (Table 5 and Figure 3). The results are in good agreement with the general facts of higher F-values of the model than the Lack of Fit F-value, and higher R2 values (>0.70) specify that the model fits the data better. Further, to validate the polynomial regression model (Equation 2), experiments were carried out at the optimal conditions in triplicates (Table 6 and Figure 4), and the experimental value was found to be 202.39 ± 0.31 U/L. The excellent correlation between the predicted and experimental BaCDA activity values indicates a good fit and hence validates the model competency. The optimum lactose concentration was estimated, and above 1%, there was no increase in the BaCDA activity obtained (Figure 5). Many research groups have worked on the process optimization of recombinant metabolite production41,42,43,44,45; along the same lines, this statistical optimization technique reduced the fermentation duration from 52 h to 30 h and enhanced the BaCDA activity from 84.67 ± 0.56 U/L (before optimization) to 202.39 ± 0.31 U/L (at the optimized condition), a ~2.39-fold increase. The advantage of the CCD-based optimization was that the optimization time and cost were reduced with a limited set of experiments compared to other optimization techniques like OFAT. This statistical technique can optimize only 10 parameters at a time with 160 experimental runs. The mathematical model does not accurately predict response beyond the selected levels for parameters in the design.

On a similar note, recombinant lipase, activin A, α-(1→2) branching sucrase, staphylococcal protein A (SpA), and ficin expression have been optimized using CCD. In all these examples, the pET vector was used for expression, and E. coli have made used as an expression. In all these studies, LB media was used as the media of choice. In the present study, the biomass yield was increased using TB media. For lipase, 12.5% lactose at 24 °C for 15 h of fermentation increased the expression 2.1-fold42. The activin A expression was improved with 1.5 mM IPTG at OD600 0.6, with a fermentation temperature of 30 °C for 10 h44. In the case of sucrase, 1% lactose at 23 °C for 26.5 h of fermentation increased the expression 165-fold42. The staphylococcal protein A (SpA) expression was increased fivefold with the inclusion of lactose at 10%. The fermentation was carried out at 33 °C with a total duration of 11 h45. A similar experiment conducted with ficin led to a threefold increase with lactose at 10%. The fermentation time was 11 h at 24 °C43.

The kinetics of the expression profiling was carried out with the optimized process conditions. The glucose concentration, biomass yield, and BaCDA activity were evaluated. As glucose is the simplest form of carbon, for initial growth, E. coli utilizes glucose first. Once the glucose exhausts in the media, the E. coli starts utilizing lactose46. In this study, glucose (0.061%) was exhausted in 16 h of fermentation, leading to the onset of the diauxic shift. There was a second lag phase during this sugar utilization transition lasting 4 h. The expression of the halophilic BaCDA increased strongly during this diauxic shift. The culture reached its saturation at 32 h, recording maxima in the BaCDA activity (202.39 ± 0.31 U/L) and biomass (17.53 ± 0.07 g/L; Figure 6).

In the present study, the addition of lactose and glucose in the media resulted in a diauxic growth with higher BaCDA activity. Lactose, when used as an auto-inducer, reduces regular mediation and chances of contamination46. Induction at low temperatures and low agitation resulted in the controlled production of halophilic BaCDA as a soluble enzyme. The employment of the statistical tool CCD enhanced BaCDA activity and reduced fermentation time. This technique may be further applied for scale-up studies for cost-effective production of BaCDA. The enzymatically derivatized chitosan using BaCDA may find applications in the biomedical sector.

Subscription Required. Please recommend JoVE to your librarian.

Disclosures

The authors have nothing to disclose.

Acknowledgments

The authors would like to thank Manipal Academy for Higher Education (MAHE) for the MAHE UNSW fund, and the authors would like to thank the Council of Scientific & Industrial Research - Human Resource Development Group (CSIR-MHRD), Govt. of India for a senior research fellowship, award letter-number 09/1165(0007)2K19 EMR-I dated 31.3.2019.

Materials

Name Company Catalog Number Comments
Kits
Acetate assay kit Megazyme, Ireland K-ACETAK The protocol has been slightly modified and optimized to perform the assay in 96 well plate
Glucose estimation kit Agappe diagnosis Ltd., India 12018013 The protocol has been slightly modified and optimized to perform the assay in 96 well plate
Chemicals
Acetic acid Hi-media, India AS001 Used for preparing SDS-PAGE staining and destaing solution
Acrylamide Hi-media, India MB068 Used for preparing SDS-PAGE gel
Ammonium pursulphate Hi-media, India MB003 Used for preparing SDS-PAGE gel
Bis-acrylamide Hi-media, India MB005 Used for preparing SDS-PAGE gel
Coomassie briliiant blue G-250 Hi-media, India MB092 Used for preparing SDS-PAGE staining and destaing solution
Coomassie briliiant blue R-250 Hi-media, India MB153 Used for preparing Bardford's assay
Ethylene glycol chitosan Sigma-aldrich, USA E1502 Used to prepare Ethylene glycol chitin and Ethylene glycol chitin was used as substrate for enzymatic reaction
D-glucose Hi-media, India MB037 Used as an media component.
Imidazole Hi-media, India GRM1864 Used in lysis buffer
Lactose Hi-media, India GRM017 Used as an media component.
Methanol Finar, India 30930LC250 Used for preparing SDS-PAGE staining and destaing solution
Sodium chloride (NaCl) Hi-media, India MB023 Used in lysis buffer
Phosphoric acid Hi-media, India MB157 Used for preparing Bardford's assay
sodium dodecyl sulfate (SDS) Hi-media, India GRM6218 Used for preparing SDS-PAGE gel
Sodium phosphate dibasic anhydrous Hi-media, India MB024 Used to prepare TB sald for media and buffer for enzymatic reaction.
Sodium phosphate monobasic anhydrous Hi-media, India GRM3964 Used to prepare TB sald for media and buffer for enzymatic reaction.
Tetramethylethylenediamine (TEMED) Hi-media, India MB026 Used for preparing SDS-PAGE gel
Tris base Hi-media, India MB029 Used for preparing SDS-PAGE gel
Tryptone Hi-media, India RM7707 Used as an media component.
Yeast extract Hi-media, India RM027 Used as an media component.
Equipment
AlphaImager HP gel documentation unit ProteinSimple, USA 92-13823-00 Used to capture SDS-PAGE photographs
Benchtop mixer Eppendorf, Germany  9.776 660 Used to keep for enzymatic reaction with 2 mL adaptor
Bioincubator shaker Trishul instruments, India 13410622 Used to incubate bacterial culture at different temparature and RPM
Biospectrophotometer Eppendorf, Germany  6135000009 Used to take all spectroscopic readings
Cooling centrifuge Eppendorf, Germany  5805000017 Used to centrifuge culture, lysate and all other centrifuging protocols
Dry bath Labnet International, USA S81522039 Used to denature protein sample for SDS-PAGE
Micropipettes Eppendorf, Germany  3123000900 Used throghout the protocol for volume measurements
Rocker shaker Trishul instruments, India 11770719 Used to shake SDS-PAGE gel for staining and destaining
SDS-PAGE unit Bio-Rad, USA 1658001FC Used to cast and run SDS-PAGE gel
Ultra sonicator Sonics & Materials, Inc., USA VCX 130 Used to lyse the bacterial cell by ultra sonication
Weighing balance Sartorius, Germany BSA124 S Used to measure weight throughout the protocol
Devices
Nanosep Centrifugal Devices with Omega Membrane (3 kDa) PALL life sciences, USA OD003C33 Used to separate enzyme after substrate treatment
Softwares Version Developed at
MINITAB 17.0  (Trial version)  The Pennsylvania State University Used to design the experimental model and analyse the data
ImageJ 1.53o National Institutes of Health (NIH) Used to analyse the expression level using SDS-PAGE image
Plasmid
pET22b (+) DNA—Novagen Merck- Millipore, USA 69744 Stored at − 20 °C
Cells
E. coli Rosetta pLysS—Novagen Merck- Millipore, USA 70956 Maintained in Luria–Bertani (LB) broth containing 25% glycerol at − 80 °C

DOWNLOAD MATERIALS LIST

References

  1. Yadav, M., et al. Seafood waste: a source for preparation of commercially employable chitin/chitosan materials. Bioresources and Bioprocessing. 6 (1), 1-20 (2019).
  2. Anil, S. Potential medical applications of chitooligosaccharides. Polymers. 14 (17), 3558 (2022).
  3. Amiri, H., et al. Chitin and chitosan derived from crustacean waste valorization streams can support food systems and the UN Sustainable Development Goals. Nature Food. 3 (10), 822-828 (2022).
  4. Wattjes, J., et al. Patterns matter part 1: Chitosan polymers with non-random patterns of acetylation. Reactive and Functional Polymers. 151, 104583 (2020).
  5. Cord-Landwehr, S., et al. Patterns matter part 2: Chitosan oligomers with defined patterns of acetylation. Reactive and Functional Polymers. 151, 104577 (2020).
  6. He, Y., Xu, J., Wang, S., Zhou, G., Liu, J. Optimization of medium components for production of chitin deacetylase by Bacillus amyloliquefaciens Z7, using response surface methodology. Biotechnology and Biotechnological Equipment. 28 (2), 242-247 (2014).
  7. Pareek, N., Vivekanand, V., Saroj, S., Sharma, A. K., Singh, R. P. Purification and characterization of chitin deacetylase from Penicillium oxalicum SAE M-51. Carbohydrate Polymers. 87 (2), 1091-1097 (2012).
  8. Yonis, R. W., Luti, K. J. K., Aziz, G. M. Statistical optimization of chitin bioconversion to produce an effective chitosan in solid state fermentation by Asperigellus flavus. Iraqi Journal of Agricultural Sciences. 50 (3), 916-927 (2019).
  9. Deeba, F., Shakir, H. A., Irfan, M., Khan, M., Qazi, J. I. Utilization of agro-industrial waste for chitinase production by locally isolated bacillus subtilis using response surface methodology. Pakistan Journal of Zoology. 54 (2), 551-560 (2022).
  10. Pandey, A., Tripathi, A. H., Tripathi, P. H. Production, purification, and application of the microbial enzymes. Industrial Applications of Microbial Enzymes. , CRC Press. 19-39 (2022).
  11. Bhat, P., et al. Expression of Bacillus licheniformis chitin deacetylase in E. coli pLysS: Sustainable production, purification and characterisation. International Journal of Biological Macromolecules. 131, 1008-1013 (2019).
  12. Hamer, S. N., et al. Enzymatic production of defined chitosan oligomers with a specific pattern of acetylation using a combination of chitin oligosaccharide deacetylases. Scientific Reports. 5, 8716 (2015).
  13. Kadokura, K., et al. Production of a recombinant chitin oligosaccharide deacetylase from Vibrio parahaemolyticus in the culture medium of Escherichia coli cells. Biotechnology Letters. 29 (8), 1209-1215 (2007).
  14. Liu, J., et al. Identification and characterization of a chitin deacetylase from a metagenomic library of deep-sea sediments of the Arctic Ocean. Gene. 590 (1), 79-84 (2016).
  15. Pawaskar, G. M., Raval, K., Rohit, P., Shenoy, R. P., Raval, R. Cloning, expression, purification and characterization of chitin deacetylase extremozyme from halophilic Bacillus aryabhattai B8W22. Biotech. 11 (12), 515 (2021).
  16. Raval, R., Simsa, R., Raval, K. Expression studies of Bacillus licheniformis chitin deacetylase in E. coli Rosetta cells. International Journal of Biological Macromolecules. 104, 1692-1696 (2017).
  17. Rosano, G. L., Ceccarelli, E. A. Recombinant protein expression in Escherichia coli: Advances and challenges. Frontiers in Microbiology. 5, 172 (2014).
  18. Yang, G., et al. Enzymatic modification of native chitin and chitin oligosaccharides by an alkaline chitin deacetylase from Microbacterium esteraromaticum MCDA02. International Journal of Biological Macromolecules. 203, 671-678 (2022).
  19. Amer, O. A., et al. Exploring new marine bacterial species, Alcaligenes faecalis Alca F2018 valued for bioconversion of shrimp chitin to chitosan for concomitant biotechnological applications. International Journal of Biological Macromolecules. 196, 35-45 (2022).
  20. Packiam, K. A. R., Ramanan, R. N., Ooi, C. W., Krishnaswamy, L., Tey, B. T. Stepwise optimization of recombinant protein production in Escherichia coli utilizing computational and experimental approaches. Applied Microbiology and Biotechnology. 104 (8), 3253-3266 (2020).
  21. Jayakumar, D., Sachith, S. K., Nathan, V. K., Rishad, K. S. M. Statistical optimization of thermostable alkaline protease from Bacillus cereus KM 05 using response surface methodology. Biotechnology Letters. 43 (10), 2053-2065 (2021).
  22. Balakrishnan, R., Mohan, N., Sivaprakasam, S. Application of design of experiments in bioprocessing: process analysis, optimization, and reliability. Current Developments in Biotechnology and Bioengineering. , 289-319 (2022).
  23. Papaneophytou, C. P., Kontopidis, G. Statistical approaches to maximize recombinant protein expression in Escherichia coli: A general review. Protein Expression and Purification. 94, 22-32 (2014).
  24. Tang, S. R., Somasundaram, B., Lua, L. H. L. Protein expression optimization strategies in E. coli: a tailored approach in strain selection and parallelizing expression conditions. Methods in Molecular Biology. 2406, 93-111 (2022).
  25. Nag, N., Khan, H., Tripathi, T. Strategies to improve the expression and solubility of recombinant proteins in E. coli. Advances in Protein Molecular and Structural Biology Methods. , Academic Press. 1-12 (2022).
  26. Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Analytical Biochemistry. 72 (1-2), 248-254 (1976).
  27. Kielkopf, C. L., Bauer, W., Urbatsch, I. L. Variations of staining sodium dodecyl sulfate-polyacrylamide gels with coomassie brilliant blue. Cold Spring Harbor Protocols. 2021 (12), (2021).
  28. Etemadzadeh, M. H., Arashkia, A., Roohvand, F., Norouzian, D., Azadmanesh, K. Isolation, cloning, and expression of E. coli BirA gene for biotinylation applications. Advanced Biomedical Research. 4, 149 (2015).
  29. Yin, L., Wang, Q., Sun, J., Mao, X. Expression and molecular modification of chitin deacetylase from Streptomyces bacillaris. Molecules. 28 (1), 113 (2023).
  30. Hamedi, H., Moradi, S., Hudson, S. M., Tonelli, A. E., King, M. W. Chitosan based bioadhesives for biomedical applications: A review. Carbohydrate Polymers. 282, 119100 (2022).
  31. Falak, S., Sajed, M., Rashid, N. Strategies to enhance soluble production of heterologous proteins in Escherichia coli. Biologia. 77 (3), 893-905 (2022).
  32. Shahzadi, I., et al. Scale-up fermentation of Escherichia coli for the production of recombinant endoglucanase from Clostridium thermocellum. Scientific Reports. 11 (1), 7145 (2021).
  33. Ganjave, S. D., Dodia, H., Sunder, A. V., Madhu, S., Wangikar, P. P. High cell density cultivation of E. coli in shake flasks for the production of recombinant proteins. Biotechnology Reports. 33, 00694 (2022).
  34. Wurm, D. J., et al. The E. coli pET expression system revisited-mechanistic correlation between glucose and lactose uptake. Applied Microbiology and Biotechnology. 100 (20), 8721-8729 (2016).
  35. Gennari, A., et al. Recombinant production in Escherichia coli of a β-galactosidase fused to a cellulose-binding domain using low-cost inducers in fed-batch cultivation. Process Biochemistry. 124, 290-298 (2023).
  36. Mostovenko, E., Deelder, A. M., Palmblad, M. Protein expression dynamics during Escherichia coli glucose-lactose diauxie. BMC Microbiology. 11, 126 (2011).
  37. Studier, F. W. Stable expression clones and auto-induction for protein production in E. coli. Methods in Molecular Biology. 1091, 17-32 (2014).
  38. Studier, F. W. Stable cultures and auto-induction for inducible protein production in E. coli. Methods in Molecular Biology. 1091, 17-22 (2014).
  39. Studier, F. W., Moffatt, B. A. Use of bacteriophage T7 RNA polymerase to direct selective high-level expression of cloned genes. Journal of Molecular Biology. 189 (1), 113-130 (1986).
  40. Selvaraj, S., Natarajan, K., Nowak, A., Murty, V. R. Mathematical modeling and simulation of newly isolated bacillus cereus M1GT for tannase production through semi-solid state fermentation with agriculture residue triphala. South African Journal of Chemical Engineering. 35 (1), 89-97 (2021).
  41. Ghahremanifard, P., Rezaeinezhad, N., Rigi, G., Ramezani, F., Ahmadian, G. Designing a novel signal sequence for efficient secretion of Candida antarctica lipase B in E. coli: The molecular dynamic simulation, codon optimization and statistical analysis approach. International Journal of Biological Macromolecules. 119, 291-305 (2018).
  42. Vuillemin, M., Malbert, Y., Laguerre, S., Remaud-Siméon, M., Moulis, C. Optimizing the production of an α-(1→2) branching sucrase in Escherichia coli using statistical design. Applied Microbiology and Biotechnology. 98 (12), 5173-5184 (2014).
  43. Sattari, F., Rigi, G., Ghaedmohammadi, S. The first report on molecular cloning, functional expression, purification, and statistical optimization of Escherichia coli-derived recombinant Ficin from Iranian fig tree (Ficus carica cv.Sabz). International Journal of Biological Macromolecules. 165, 2126-2135 (2020).
  44. Hajihassan, Z., Biroonro, N. Enhanced expression of recombinant activin A in Escherichia coli by optimization of induction parameters. Journal of Sciences, Islamic Republic of Iran. 29 (2), 105-111 (2018).
  45. Rigi, G., Mohammadi, S. G., Arjomand, M. R., Ahmadian, G., Noghabi, K. A. Optimization of extracellular truncated staphylococcal protein A expression in Escherichia coli BL21 (DE3). Biotechnology and Applied Biochemistry. 61 (2), 217-225 (2014).
  46. Faust, G., Stand, A., Weuster-Botz, D. IPTG can replace lactose in auto-induction media to enhance protein expression in batch-cultured Escherichia coli. Engineering in Life Sciences. 15 (8), 824-829 (2015).

Tags

Process Evaluation Kinetics Recombinant Chitin Deacetylase Expression E. Coli Rosetta PLysS Cells Statistical Technique Greener Route Deacetylation Chitin To Chitosan Enzyme Biomedical Field Central Composite Design Response Surface Methodology Production Optimization Glucose Concentration Lactose Concentration Incubation Temperature Agitation Speed Fermentation Lactose Induction Biomass BaCDA Activity
This article has been published
Video Coming Soon
PDF DOI DOWNLOAD MATERIALS LIST

Cite this Article

Pawaskar, G. M., Raval, R.,… More

Pawaskar, G. M., Raval, R., Selvaraj, S. Process Evaluation and Kinetics of Recombinant Chitin Deacetylase Expression in E. coli Rosetta pLysS Cells Using a Statistical Technique. J. Vis. Exp. (193), e64590, doi:10.3791/64590 (2023).

Less
Copy Citation Download Citation Reprints and Permissions
View Video

Get cutting-edge science videos from JoVE sent straight to your inbox every month.

Waiting X
Simple Hit Counter