Optimization of fermentation conditions for an Escherichia coli strain engineered using the response surface method to produce a novel therapeutic DNA vaccine for rheumatoid arthritis

Abstract

BackgroundFermentation condition optimization and nutrients screening are of equal importance for efficient production of plasmid DNA vaccines. This directly affects the downstream purification and final quality and yield of plasmid DNA vaccines. The present study aimed to optimize the fermentation conditions for high-throughput production of therapeutic DNA vaccine pcDNA-CCOL2A1 by engineered Escherichia coli DH5α, using the response surface method (RSM).ResultsWe hypothesized that optimized fermentation conditions significantly increase the yield of pcDNA-CCOL2A1 therapeutic DNA vaccine, a novel DNA vaccine for treating rheumatoid arthritis (RA). Single-factor analysis was performed to evaluate the optimal basal culture medium from LB, 2 × YT, TB, M9 (Glycerol) and M9 (Glucose), respectively. Thereafter, the Plackett-Burman design (PBD) was used to ascertain the three most significant factors affecting the vaccine yields, followed by the paths of steepest ascent to move to the nearest region of maximum response. Initial screening through the PBD revealed that the most key factors were peptone, mannitol, and inoculum concentration. Subsequent use of RSM was further optimized for the production of therapeutic DNA vaccine pcDNA-CCOL2A1 through Box-Behnken design (BBD). The final optimized fermentation conditions were as follows: peptone, 25.86 g/L; mannitol, 8.08 g/L; inoculum concentration, OD = 0.36. Using this statistical experimental design, the yield of therapeutic DNA vaccine pcDNA-CCOL2A1 markedly increased from 223.37 mg/L to339.32 mg/L under optimal conditions, and a 51.9% increase was observed compared with the original medium.ConclusionsThe present results provide a basis for further production of high-quality and high-yield therapeutic DNA vaccine pcDNA-CCOL2A1 in pilot-scale and even industrial-scale.

Optimization of Cultural Conditions for Bacillus megaterium Cultured in Agaricus bisporus Industrial Wastewater

“…Interpretation of the response surface 3D model and contour plot were the graphical representations of regression equation. They provided visual interpretations of the relationship between responses and experimental levels of each variable, and the type of interactions between two test variables [20], as shown in Figure 2, the profiles of the response surfaces between loaded liquid ( X 1 ) and pH ( X 2 ) (Figure 2(a)), loaded liquid ( X 1 ) and temperature ( X 3 ) (Figure 2(c)), pH ( X 2 ) and temperature ( X 3 ) (Figure 2(e)) were all convex with an open downward direction, therefore, indicating a high total number of viable B. megaterium . When the loaded liquid (test level is 0) was 30 mL/250mL, the pH was 7.5 (test level is 0), and the culture temperature was 28°C (test level is 0); the total number of living bacteria reached the actual maximum (8.75 ± 0.04, 8.67 ± 0.03, 8.57 ± 0.03 × 10 8 Obj/mL) closing to the design points, respectively.…”

Bioprocess Optimization of Nutritional Parameters for Enhanced Anti-leukemic L-Asparaginase Production by Aspergillus candidus UCCM 00117: A Sequential Statistical Approach

“…Selection of significant nutrients for inclusion in microbial fermentation media and optimizing their levels are subjects that are vigorously pursued by industries (Shakambari et al 2019 ). A sequence of reported techniques in that regard include one-factor-at-a-time (OFAT) approach that does the primary selection (Long et al 2018 ) followed by a two-level factorial design like Plackett–Burman design (PBD) that establishes significance of selected factors (Mechmeche et al 2019 ; Gururaj et al 2020 ). A third procedure in the optimization process is path of steepest ascent (PSA) which moves significant coefficients from the first-order model of PBD close to their optimum with subsequent resetting of center points for response surface methodology (RSM)(Huang 2019 ).…”

A Novel Strain of Stenotrophomonas Acidaminiphila Produces Super-active Alkaline Protease During Cassava Effluent Fermentation: Process Optimization, Kinetic Modeling and Scale-up

“…However, with coefficient of determination, r 2 and mean squared error (MSE) or other performance metrics, one approach could be selected ahead of the other (Lau et al 2020). Response surface methodology typically involves four basic steps which include selection of process influencing factors using one-factor-at-a-time (OFAT) approach (Ekpenyong et al 2017a), screening of selected process factors for just significant factors using Placket-Burman design (PBD) or any other 2-level factorial design (Ekpenyong et al 2017b;Long et al 2018), moving the levels of significant variables close to the region of experimentation using the path of steepest ascent (Yingling and Zhengfang 2013) and finally designing response surface experiments using any of central composite or Box-Benkhen designs to locate the actual region of interest (Karri and Sahu 2018;Ekpenyong et al 2021b). Time-related changes in bioconversion and simultaneous production of value-added biotechnological products are of utmost consideration in deciding bioprocess economics.…”