Towards Control of Cellulose Biosynthesis by Komagataeibacter Using Systems-level and Strain Engineering Strategies: Current Progress and Perspectives

The strains of the Komagataeibacter genus have been shown to be the most efficient bacterial nanocellulose producers. Although exploited for many decades, the studies of these species focused mainly on the optimisation of cellulose synthesis process through modification of culturing conditions in the industrially relevant settings. Molecular physiology of Komagataeibacter was poorly understood and only a few studies explored genetic engineering as a strategy for strain improvement. Only since recently the systemic information of the Komagataeibacter species has been accumulating in the form of omics datasets representing sequenced genomes, transcriptomes, proteomes and metabolomes. Genetic analyses of the mutants generated in the untargeted strain modification studies have drawn attention to other important proteins, beyond those of the core catalytic machinery of the cellulose synthase complex. Recently, modern molecular and synthetic biology tools have been developed which showed the potential for improving targeted strain engineering. Taking the advantage of the gathered knowledge should allow for better understanding of the genotype-phenotype relationship which is necessary for robust modelling of metabolism as well as selection and testing of new molecular engineering targets. In this review, we discuss the current progress in the area of Komagataeibacter systems biology and its impact on the research aimed at scaled-up cellulose synthesis as well as BNC functionalisation. Key points • The accumulated omics datasets advanced the systemic understanding of Komagataeibacter physiology at the molecular level. • Untargeted and targeted strain modification approaches have been applied to improve nanocellulose yield and properties. • The development of modern molecular and synthetic biology tools presents a potential for enhancing targeted strain engineering. • The accumulating omic information should improve modelling of Komagataeibacter's metabolism as well as selection and testing of new molecular engineering targets.