Data-driven optimization of salt shock for high-efficiency ectoine production in Halomonas elongata.

Halomonas elongata stands as a highly promising industrial chassis, capable of low-cost fermentation under open, non-sterile conditions. However, precisely regulating the fermentation process to breach the production ceiling of ectoine remains a core challenge for its industrial scale-up. This study first designed and evaluated multi-stage salt shock regimens, demonstrating that a gradient salt shock strategy significantly enhances ectoine biosynthesis efficiency. Metabolic flux analysis further confirmed that this strategy triggers a redistribution of carbon flux from energy generation toward product synthesis. Specifically, it significantly enhanced the Entner-Doudoroff pathway and precursor supply while attenuating the Tricarboxylic Acid cycle, thereby achieving high-efficiency ectoine biosynthesis. Subsequently, a machine learning-based ensemble model for key physiological parameters was established using multi-batch fermentation data, enabling accurate prediction of fermentation performance under different salt shock patterns. This model was then coupled with a Genetic Algorithm to optimize the complex multi-stage salt shock process, leading to the identification of an optimal five-stage gradient salt shock strategy. The scaled-up process achieved an ectoine titer of 49.32 g/L with a productivity of 1.76 g/L/h, a yield of 0.38 g/g, and a biomass of 119.30 g/L in 28 h-setting new benchmarks for yield, productivity, and biomass in H. elongata. In conclusion, this work successfully established an efficient salt shock regulation strategy for ectoine biosynthesis and provides a feasible framework for the intelligent optimization of fermentation processes in halophilic microorganisms.