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Jia-Lin Zhu

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Aug 2026

High-Yield Xylitol Production from l -Arabinose via Simultaneous Glucose Co-Utilization and Chromosomal Pathway Integration in Escherichia coli

l-arabinose, a valuable C5 pentose sugar in xylose mother liquor (a low-cost cellulose hydrolysis byproduct), remains underutilized due to costly separation requirements, resulting in significant waste of fermentable carbon resources. In this study, Escherichia coli W3110 was systematically engineered to efficiently convert l-arabinose into xylitol, a low-calorie sweetener with significant commercial value. Firstly, the l-arabinose metabolic network was reconstructed and glucose catabolic repression was alleviated through coordinated pathway modifications, enabling simultaneous utilization of both l-arabinose and glucose. Subsequently, a xylitol synthesis module consisting of l-arabinose isomerase (AraA), l-xylulose reductase (LXR), and d-psicose-3-epimerase (DPE) was systematically optimized via gene arrangement, promoter and RBS engineering. The optimized pathway was integrated into the E. coli W3110 genome at the IS5 locus using MUCICAT technology, generating a plasmid-free production strain and reducing plasmid-segregation concerns. Fed-batch fermentation in a 3 L bioreactor yielded 64.07 g/L xylitol at a productivity of 1.46 g/L/h with 90.77% l-arabinose conversion in 44 h. This achievement overcomes two critical metabolic bottlenecks: (1) glucose catabolite repression, which normally prevents pentose utilization in the presence of glucose, and (2) the successful stoichiometric balancing of three enzymatic steps (AraA, LXR, DPE). The engineered strain achieves simultaneous glucose-arabinose co-metabolism, and glucose co-utilization supports xylitol formation in a manner consistent with an endogenous reducing-power contribution, thereby eliminating the requirement for exogenous glycerol supplementation in the optimized process. This work establishes a defined-substrate engineering platform for l-arabinose-to-xylitol conversion and provides a strategic basis for future evaluation using complex industrial carbohydrate streams.

Xu-Dong Xu, Jia-Lin Zhu, Ji-Kun Xia et al. · 0 citations

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