Mechanosensing and early biofilm development in Pseudomonas aeruginosa

Biofilms are communities of sessile microbes that are phenotypically distinct from their genetically-identical, free-swimming counterparts. Biofilms initiate when bacteria attach to a solid surface, as this attachment triggers intracellular signaling to change gene expression of individual bacteria from the planktonic to the biofilm phenotype. However the initial cues leading allowing bacteria to sense a surface, as well as the role of spatial structure in biofilm development, are not well known. This dissertation has two main parts, the first presenting a method for growing biofilms from initiating cells whose positions are controlled with single-cell precision using laser trapping. Biofilm infections are notoriously intractable, in part due to changes in the bacterial phenotype that result from spatial structure. Understanding the role of structure in biofilm development requires methods to control the spatial structure of biofilms. The native growth, motility, and surface adhesion of positioned microbes are preserved, as we show for model organisms Pseudomonas aeruginosa and Staphylococcus aureus. We demonstrate that laser-trapping and placing bacteria on surfaces can reveal the e↵ects of spatial structure on bacterial growth in early biofilm development. In the second part we show that mechanical shear acts as a cue for surface adhesion in P. aeruginosa. For P. aeruginosa, it has long been known that intracellular levels of the signaling molecule cyclic-di-GMP increase upon surface adhesion and that increased cyclic-di-GMP is required to begin biofilm development. The magnitude of the shear force, and thereby the corresponding activation of cyclic-di-GMP signaling, can be adjusted both by varying the strength of the adhesion that binds bacteria to the surface and by varying the rate of fluid flow over surface-bound bacteria. We show that the envelope protein PilY1 and functional Type IV pili are required mechanosensory elements. Finally, we propose an analytic model that accounts for the feedback between mechanosensors, cyclic-di-GMP signaling, and production of adhesive polysaccharides, describing our data