ABSTRACT
The prevention of biofilm development on the surfaces of implanted medical devices is a global challenge for the healthcare sector. Bio-instructive materials that intrinsically prevent bacterial biofilm formation and drive an appropriate host immune response are required to reduce the burden of healthcare associated infections. Although bacterial surface attachment is sensitive to micro- and nano- surface topographies, its exploitation has been limited by the lack of unbiased high throughput biomaterial screens combined with model-based methods capable of generating correlations and predicting generic responses. Consequently, we sought to fill this knowledge gap by using polymer chips (TopoChips) incorporating 2,176 combinatorially generated micro-topographies. Specific surface topographies exerted a profound impact on bacterial pathogen attachment independent of surface chemistry. A strong correlation between local surface landscape, bacterial attachment and biofilm formation was established using machine learning methods to facilitate analysis of specific surface parameters for predicting attachment. In vitro, lead topographies prevented colonization by motile (Pseudomonas aeruginosa and Proteus mirabilis) and non-motile (Staphylococcus aureus and Acinetobacter baumannii) bacterial pathogens. In a murine foreign body infection model, specific anti-attachment topographies were shown to be refractory to P. aeruginosa colonization.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵† joint senior authors
