Modeling and optimization of culture media for recombinant Helicobacter pylori vaccine antigen HpaA - Tan, Runqing; Zhou, Song; Sun, Min; Liu, Yu; Ni, Xiumei; He, Jin; Guo, Gang; Liu, Kaiyun (Frontiers in Bioengineering and Biotechnology, 2024)
[Introduction] Helicobacter pylori (H. pylori) infection represents a significant global health concern, exacerbated by the emergence of drug-resistant strains resulting from conventional antibiotic treatments. Consequently, the development of vaccines with both preventive and therapeutic properties has become crucial in addressing H. pylori infections. The H. pylori adhesin protein HpaA has demonstrated strong immunogenicity across various adjuvants and dosage forms, positioning it as a key candidate antigen for recombinant subunit vaccines against H. pylori. Optimizing fermentation culture conditions is an effective strategy to enhance product yield and lower production costs. However, to date, there has been no systematic investigation into methods for improving the fermentation yield of HpaA. Enhancing the fermentation medium to increase HpaA yield holds significant potential for application and economic benefits in the prevention and detection of H. pylori infection. [Methods] To achieve a stable and high-yielding H. pylori vaccine antigen HpaA, this study constructed recombinant Escherichia coli expressing HpaA. The impact of fermentation medium components on the rHpaA yield was assessed using a one-factor-at-a-time approach alongside Plackett–Burman factorial experiments. Optimal conditions were effectively identified through response surface methodology (RSM) and artificial neural network (ANN) statistical computational models. The antigenicity and immunogenicity of the purified rHpaA were validated through immunization of mice, followed by Western Blot analysis and serum IgG ELISA quantification. [Results] Glucose, yeast extract, yeast peptone, NH4Cl, CaCl2, and mixed phosphate all contributed to the production of rHpaA, with glucose, yeast extract, and NH4Cl demonstrating particularly significant effects. The artificial neural network linked genetic algorithm (ANN-GA) model exhibited superior predictive accuracy, achieving a rHpaA yield of 0.61 g/L, which represents a 93.2% increase compared to the initial medium. Animal immunization experiments confirmed that rHpaA possesses good antigenicity and immunogenicity. [Discussion] This study pioneers the statistical optimization of culture media to enhance rHpaA production, thereby supporting its large-scale application in H. pylori vaccines. Additionally, it highlights the advantages of the ANN-GA approach in bioprocess optimization.