Topographical biomaterials instruct bacterial surface attachment and the in vivo host-pathogen response

Abstract

Bio-instructive materials that prevent bacterial biofilm formation and drive an appropriate host immune response have the potential to significantly reduce the burden of medical device-associated infections. Since bacterial surface attachment is known to be sensitive to surface topography, we experimentally survey 2,176 combinatorially generated shapes using an unbiased high-throughput micro topographical screen on polystyrene. This identifies topographies that reduce colonization in vitro by up to 15-fold compared with a flat surface for both motile and non-motile bacterial pathogens. Equivalent reductions are achieved on polyurethane, a polymer commonly used in medical devices. Using machine learning methods, a set of design rules based on generalisable descriptors is established for predicting bacteria-resistant micro topographies. In a murine foreign body infection model, anti-attachment topographies are shown to be refractory to P. aeruginosa and to recruit a productive host response, highlighting the potential of simple topographical patterning of non-eluting implants for preventing medical device associated infections.