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ORIGINAL RESEARCH article

Front. Bioeng. Biotechnol.
Sec. Bioprocess Engineering
Volume 12 - 2024 | doi: 10.3389/fbioe.2024.1499940

Modeling and optimization of culture media for recombinant Helicobacter pylori vaccine antigen HpaA

Provisionally accepted
Runqing Tan Runqing Tan Song Zhou Song Zhou *Min Sun Min Sun *Yu Liu Yu Liu *Xiumei Ni Xiumei Ni *Jin He Jin He *Gang Guo Gang Guo *Kaiyun Liu Kaiyun Liu *
  • West China Hospital, Sichuan University, Chengdu, China

The final, formatted version of the article will be published soon.

    [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.

    Keywords: Helicobacter pylori, artificial neural network, Response Surface Methodology, Recombinant antigen, rHpaA

    Received: 22 Sep 2024; Accepted: 20 Nov 2024.

    Copyright: © 2024 Tan, Zhou, Sun, Liu, Ni, He, Guo and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Song Zhou, West China Hospital, Sichuan University, Chengdu, China
    Min Sun, West China Hospital, Sichuan University, Chengdu, China
    Yu Liu, West China Hospital, Sichuan University, Chengdu, China
    Xiumei Ni, West China Hospital, Sichuan University, Chengdu, China
    Jin He, West China Hospital, Sichuan University, Chengdu, China
    Gang Guo, West China Hospital, Sichuan University, Chengdu, China
    Kaiyun Liu, West China Hospital, Sichuan University, Chengdu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.