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<p>High-cell-density fermentation of <i>Haemophilus parasuis</i>, particularly the prevalent and virulent serovar 12, is critical for cost-effective vaccine production but remains constrained by low cell yields in conventional media. In this study, we demonstrate a high-efficiency fermentation strategy enabled by a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) optimization to overcome these nutritional limitations. Notably, the Response Surface Methodology (RSM) + ANN-GA-optimized (ZP) medium supported a viable cell density of 1.1 × 10<sup>10</sup> CFU·mL<sup>− 1</sup> in a 14-L bioreactor scale-up, significantly outperforming traditional TSB medium while reducing production costs by approximately 64%. A comparative transcriptomic analysis between ZP and TSB conditions was employed to elucidate the biological mechanism underlying this enhanced growth efficiency. Within the optimized environment, <i>H. parasuis</i> serovar 12 exhibited a distinct metabolic rewiring: the bacterium significantly downregulated energy-expensive amino acid transport systems and aminoacyl-tRNA biosynthesis while concurrently upregulating central carbon metabolism (glycolysis and TCA cycle) and nucleotide sugar metabolism. This transcriptional shift suggests that the optimized nutrient balance allows <i>H. parasuis</i> serovar 12 to transition from a “scavenging” mode to a more efficient biosynthetic state, thereby maximizing biomass accumulation without compromising vaccine efficacy (90% survival rate in a mouse challenge model). This finding paves the way for the rational design of industrial media for <i>H. parasuis</i> and facilitates cost-effective vaccine manufacturing.</p> Graphical Abstract <p></p>

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High-density fermentation of Haemophilus parasuis serovar 12 using a hybrid optimization strategy and its associated metabolic rewiring

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月30日

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  • Yongkang Ma,
  • Jiazeng Gao,
  • Sie Han,
  • Lixia Liu,
  • Zhanying Liu

摘要

High-cell-density fermentation of Haemophilus parasuis, particularly the prevalent and virulent serovar 12, is critical for cost-effective vaccine production but remains constrained by low cell yields in conventional media. In this study, we demonstrate a high-efficiency fermentation strategy enabled by a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) optimization to overcome these nutritional limitations. Notably, the Response Surface Methodology (RSM) + ANN-GA-optimized (ZP) medium supported a viable cell density of 1.1 × 1010 CFU·mL− 1 in a 14-L bioreactor scale-up, significantly outperforming traditional TSB medium while reducing production costs by approximately 64%. A comparative transcriptomic analysis between ZP and TSB conditions was employed to elucidate the biological mechanism underlying this enhanced growth efficiency. Within the optimized environment, H. parasuis serovar 12 exhibited a distinct metabolic rewiring: the bacterium significantly downregulated energy-expensive amino acid transport systems and aminoacyl-tRNA biosynthesis while concurrently upregulating central carbon metabolism (glycolysis and TCA cycle) and nucleotide sugar metabolism. This transcriptional shift suggests that the optimized nutrient balance allows H. parasuis serovar 12 to transition from a “scavenging” mode to a more efficient biosynthetic state, thereby maximizing biomass accumulation without compromising vaccine efficacy (90% survival rate in a mouse challenge model). This finding paves the way for the rational design of industrial media for H. parasuis and facilitates cost-effective vaccine manufacturing.

Graphical Abstract