Integrated statistical and machine-learning optimization for enhanced heparosan production by <i>Lactococcus lactis</i>

Lactococcus lactis SH6 was engineered for heterologous heparosan production, and its growth medium was optimized using a combination of experimental design and machine learning (ML). One-factor-at-a-time shake flask experiments revealed glucose (10 g/L) and yeast extract (17.5 g/L) as the best substrates, producing 50 mg/L heparosan. Plackett-Burman analysis and steepest ascent optimization revealed significant factors, and a central composite design (CCD) optimized nutrient concentrations, predicting 85 mg/L heparosan (validated at 81 mg/L). ML-Gaussian process regression was applied after CCD optimization to fine-tune and cross-check the optimal medium (glucose 8.94 g/L, yeast extract 22.89 g/L, ascorbate 0.38 g/L, β-glycerophosphate 28.2 g/L), producing 85.28 mg/L heparosan (predicted 88.8 mg/L) at the flask scale. Earlier nisin induction (2 h) at the bioreactor scale increased heparosan titers to 119.7 mg/L, and linear glucose feeding (1.5 g/L.h) extended the production phase to 133 mg/L. Medium optimization resulted in nearly doubling heparosan yield compared to the unoptimized medium, setting a new standard for L. lactis. This work offers a design-of-experiments-ML solution as a viable approach to designing high-yielding, animal-product-free heparosan production methods in a Generally Regarded as Safe (GRAS) microbe.