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Fluid dynamics and cell‐bound Psl polysaccharide allows microplastic capture, aggregation and subsequent sedimentation by Pseudomonas aeruginosa in water

Environmental Microbiology 2022 10 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 35 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Manuel Romero, Alessandro Carabelli, Michael R. Swift, Michael I. Smith

Summary

Researchers found that Pseudomonas aeruginosa captures and aggregates polystyrene microplastics in water via cell-bound Psl exopolysaccharide, with bacterial motility and fluid flow driving further aggregation and sedimentation of microplastic-bacteria assemblies.

Polymers
Study Type In vitro

Decades after incorporating plastics into consumer markets, research shows that these polymers have spread worldwide. Fragmentation of large debris leads to smaller particles, collectively called microplastics (MPs), which have become ubiquitous in aquatic environments. A fundamental aspect of understanding the implications of MP contamination on ecosystems is resolving the complex interactions of these artificial substrates with microbial cells. Using polystyrene microparticles as model polymers, we conducted an exploratory study where these interactions are quantitatively analyzed using an in vitro system consisting of single-bacterial species capturing and aggregating MPs in water. Here we show that the production of Psl exopolysaccharide by Pseudomonas aeruginosa (PA) does not alter MPs colloidal stability but plays a key role in microspheres adhesion to the cell surface. Further aggregation of MPs by PA cells depends on bacterial mobility and the presence of sufficient flow to prevent rapid sedimentation of early MP-PA assembles. Surprisingly, cells in MP-PA aggregates are not in a sessile state despite the production of Psl, enhancing the motility of the aggregates by an order of magnitude relative to passive diffusion. The generated data could inform the creation of predictive models that accurately describe the dynamics and influence of bacterial growth on plastics debris.

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