Protease activity sensors noninvasively classify bacterial infections and antibiotic responses
Publication Type:
Journal Article
Authors:
Colin G. Buss†; Dudani, Jaideep S.†; Akana, Reid T.K.; Fleming, Heather E.; Bhatia, Sangeeta N.
Source:
EBioMedicine, Volume 38, p.248-256 (2018)
URL:
https://linkinghub.elsevier.com/retrieve/pii/S2352396418305358
Abstract:
Respiratory tract infections represent a significant public health risk, and timely and accurate detection of bacterial infections facilitates rapid therapeutic intervention. Furthermore, monitoring the progression of infections after intervention enables course correction; in cases where initial treatments are ineffective, avoiding unnecessary drug dosing that can contribute to antibiotic resistance. However, current diagnostic and monitoring techniques rely on non-specific or slow readouts, such as radiographic imaging and sputum cultures, which fail to specifically identify bacterial infections and take several days to identify optimal antibiotic treatments. Here we describe a nanoparticle system that detects P. aeruginosa lung infections by sensing host and bacterial protease activity in vivo, and that delivers a urinary detection readout. One protease sensor is comprised of a peptide substrate for the P. aeruginosa protease LasA. A second sensor designed to detect elastases is responsive to recombinant neutrophil elastase and secreted proteases from bacterial strains. In mice infected with P. aeruginosa, nanoparticle formulations of these protease sensors termed activity-based nanosensors (ABNs) detect infections and monitor bacterial clearance from the lungs over time. Additionally, ABNs differentiate between appropriate and ineffective antibiotic treatments acutely, within hours after the initiation of therapy. Interpretation These findings demonstrate how activity measurements of disease-associated proteases can provide a noninvasive window into the dynamic process of bacterial infection and resolution, offering an opportunity for detecting, monitoring, and characterizing lung infections.