Enhancing lignocellulosic biomass conversion for bioethanol production via arabinofuranosidase-mediated cell wall remodeling in tobacco

Lignocellulosic biomass is a key renewable resource for sustainable bioenergy production; however, its efficient conversion remains limited by intrinsic cell wall recalcitrance. Here, we investigated whether targeted remodeling of arabinose-containing matrix polysaccharides by fungal α-L-arabinofuranosidase B (ABFB) could improve biomass processability in the dicot model species Nicotiana tabacum. A codon-optimized abfB from Aspergillus nidulans was constitutively expressed in tobacco, generating independent transgenic lines with stable transgene expression, detectable FLAG-tagged ABFB protein, and line-dependent increases in enzymatic activity. ABFB expression caused line-dependent changes in plant architecture and biomass partitioning, but did not affect integrated stem biomass-related performance. Simultaneous saccharification and fermentation of NaOH-pretreated stem biomass showed increased ethanol production in most ABFB lines. After correction for pretreatment-related biomass loss and normalization to the initial dry stem biomass, improved ABFB lines reached 127–152 g kg⁻¹ initial dry stem biomass, corresponding to approximately 121–146% of the WT ethanol yield, with ABFB 46 showing the highest value. Cell wall analyses, including FTIR spectroscopy, monosaccharide profiling, LM11 immunolabelling, Py-GC/MS, and TAPPI-type assays, indicated no major changes in bulk lignocellulosic composition, but revealed subtle line-dependent modifications in cellulose organization, selected xylose abundance, xylan-associated epitope distribution, and lignin-derived pyrolysis product profiles. These findings suggest that ABFB-mediated remodeling improves the processability of pretreated dicot stem biomass primarily through altered cell wall organization rather than broad compositional changes. This strategy provides a promising route for improving lignocellulosic biomass conversion, although further structural, process-level, and field validation will be required.