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Abstract

Novacetimonas hansenii is a model organism for bacterial cellulose (BC) production, a biopolymer with broad industrial applications. To better understand its carbon source-dependent metabolism, we conducted the first time-resolved transcriptomic analysis of N. hansenii ATCC 23769 grown on five carbon sources (glucose, fructose, mannitol, galactose, and sucrose) across five time points (24–120 h). Glucose, fructose, and mannitol supported growth and efficient BC production, whereas galactose and sucrose resulted in poor yields, likely due to limited transport and enzymatic capacity. Solid-state NMR revealed that cellulose crystallinity and polymorphism were carbon source-dependent, with reduced bcsD expression correlating with lower crystallinity. Transcriptomic profiling showed distinct metabolic responses. Cultures containing glucose and mannitol activated core metabolic pathways, fructose induced a delayed adaptive shift, and galactose and sucrose triggered stress-related gene expression. These results highlight the metabolic plasticity of N. hansenii and offer insights for enhancing BC production through carbon source optomization.

Document Type

Article

Publication Date

2026

Notes/Citation Information

0960-8524/© 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Digital Object Identifier (DOI)

https://doi.org/10.1016/j.biortech.2026.133979

Funding Information

This work was supported by the Hatch capacity program, Accession no. 7003575, from the U.S. Department of Agriculture’s National Institute of Food and Agriculture and the National Science Foundation (award #2243533). This research was also supported by the Environmental Molecular Sciences Laboratory (EMSL), a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program, under proposal #60321. We thank Dr. Yen-Chang Tseng for technical assistance in the laboratory. The Graphical Abstract was created using BioRender.

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