Acetan and Acetan-Like Polysaccharides: Genetics, Biosynthesis, Structure, and Viscoelasticity

Bacteria produce a variety of multifunctional polysaccharides, including structural, intracellular, and extracellular polysaccharides. They are attractive for the industrial sector due to their natural origin, sustainability, biodegradability, low toxicity, stability, unique viscoelastic properties, stable cost, and supply. When incorporated into different matrices, they may control emulsification, stabilization, crystallization, water release, and encapsulation. Acetan is an important extracellular water-soluble polysaccharide produced mainly by bacterial species of the genera Komagataeibacter and Acetobacter. Since its original description in Komagataeibacter xylinus, acetan-like polysaccharides have also been described in other species of acetic acid bacteria. Our knowledge on chemical composition of different acetan-like polysaccharides, their viscoelasticity, and the genetic basis for their production has expanded during the last years. Here, we review data on acetan biosynthesis, its molecular structure, genetic organization, and mechanical properties. In addition, we have performed an extended bioinformatic analysis on acetan-like polysaccharide genetic clusters in the genomes of Komagataeibacter and Acetobacter species. The analysis revealed for the first time a second acetan-like polysaccharide genetic cluster, that is widespread in both genera. All species of the Komagataeibacter possess at least one acetan genetic cluster, while it is present in only one third of the Acetobacter species surveyed.

High-Resolution Nanoparticle Sizing with Maximum A Posteriori Nanoparticle Tracking Analysis

The rapid and efficient characterization of polydisperse nanoparticle dispersions remains a challenge within nanotechnology and biopharmaceuticals. Current methods for particle sizing, such as dynamic light scattering, analytical ultracentrifugation, and field-flow fractionation, can suffer from a combination of statistical biases, difficult sample preparation, insufficient sampling, and ill-posed data analysis. As an alternative, we introduce a Bayesian method that we call maximum a posteriori nanoparticle tracking analysis (MApNTA) for estimating the size distributions of nanoparticle samples from high-throughput single-particle tracking experiments. We derive unbiased statistical models for two observable quantities in a typical nanoparticle trajectory-the mean square displacement and the trajectory length-as a function of the particle size and calculate size distributions using maximum a posteriori (MAP) estimation with cross validation to mildly regularize solutions. We show that this approach infers nanoparticle size distributions with high resolution by performing extensive Brownian dynamics simulations and experiments with mono- and polydisperse solutions of gold nanoparticles as well as single-walled carbon nanotubes. We further demonstrate particular utility for characterizing minority components and impurity populations and highlight this ability with the identification of an impurity in a commercially produced gold nanoparticle sample. Modern algorithms such as MApNTA should find widespread use in the routine characterization of complex nanoparticle dispersions, allowing for significant advances in nanoparticle synthesis, separation, and functionalization.