Non-invasive in vivo sensing of bacterial implant infection using catalytically-optimised gold nanocluster-loaded liposomes for urinary readout

Kaili Chen, Adrian Najer (), Patrick Charchar, Catherine Saunders, Chalaisorn Thanapongpibul, Anna Klöckner, Mohamed Chami, David J. Peeler, Inês Silva, Luca Panariello, Kersti Karu, Colleen N. Loynachan, Leah C. Frenette, Michael Potter, John S. Tregoning, Ivan P. Parkin, Andrew M. Edwards, Thomas B. Clarke, Irene Yarovsky () and Molly M. Stevens ()
Additional contact information
Kaili Chen: Imperial College London
Adrian Najer: Imperial College London
Patrick Charchar: RMIT University
Catherine Saunders: Imperial College London
Chalaisorn Thanapongpibul: Imperial College London
Anna Klöckner: Imperial College London
Mohamed Chami: Mattenstrasse 26
David J. Peeler: Imperial College London
Inês Silva: University of Oxford
Luca Panariello: Imperial College London
Kersti Karu: University College London
Colleen N. Loynachan: Imperial College London
Leah C. Frenette: Imperial College London
Michael Potter: Imperial College London
John S. Tregoning: Imperial College London
Ivan P. Parkin: University College London
Andrew M. Edwards: Imperial College London
Thomas B. Clarke: Imperial College London
Irene Yarovsky: RMIT University
Molly M. Stevens: Imperial College London

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: Abstract Staphylococcus aureus is a leading cause of nosocomial implant-associated infections, causing significant morbidity and mortality, underscoring the need for rapid, non-invasive, and cost-effective diagnostics. Here, we optimise the synthesis of renal-clearable gold nanoclusters (AuNCs) for enhanced catalytic activity with the aim of developing a sensitive colourimetric diagnostic for bacterial infection. All-atom molecular dynamics (MD) simulations confirm the stability of glutathione-coated AuNCs and surface access for peroxidase-like activity in complex physiological environments. We subsequently develop a biosensor by encapsulating these optimised AuNCs in bacterial toxin-responsive liposomes, which is extensively studied by various single-particle techniques. Upon exposure to S. aureus toxins, the liposomes rupture, releasing AuNCs that generate a colourimetric signal after kidney-mimetic filtration. The biosensor is further validated in vitro and in vivo using a hyaluronic acid (HA) hydrogel implant infection model. Urine samples collected from mice with bacteria-infected HA hydrogel implants turn blue upon substrate addition, confirming the suitability of the sensor for non-invasive detection of implant-associated infections. This platform has significant potential as a versatile, cost-effective diagnostic tool.

Date: 2024
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DOI: 10.1038/s41467-024-53537-2

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