Dataset Information

Protease activity sensors noninvasively classify bacterial infections and antibiotic responses.

ABSTRACT:

Background

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.

Methods

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.

Findings

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. FUND: National Cancer Institute, National Institute of Environmental Health Sciences, National Institutes of Health, National Science Foundation Graduate Research Fellowship Program, and Howard Hughes Medical Institute.

SUBMITTER: Buss CG

PROVIDER: S-EPMC6306379 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Publications

Protease activity sensors noninvasively classify bacterial infections and antibiotic responses.

Buss Colin G CG Dudani Jaideep S JS Akana Reid T K RTK Fleming Heather E HE Bhatia Sangeeta N SN

EBioMedicine 20181129

<h4>Background</h4>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 ...[more]

PMID: 30503861

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Project description:BackgroundChronic lung infections are the primary cause of morbidity and mortality in Cystic Fibrosis (CF) patients. Recent molecular biological based studies have identified a surprisingly wide range of hitherto unreported bacterial species in the lungs of CF patients. The aim of this study was to determine whether the species present were active and, as such, worthy of further investigation as potential pathogens.MethodsTerminal Restriction Fragment Length Polymorphism (T-RFLP) profiles were generated from PCR products amplified from 16S rDNA and Reverse Transcription Terminal Restriction Fragment Length Polymorphism (RT-T-RFLP) profiles, a marker of metabolic activity, were generated from PCR products amplified from 16S rRNA, both extracted from the same CF sputum sample. To test the level of activity of these bacteria, T-RFLP profiles were compared to RT-T-RFLP profiles.ResultsSamples from 17 individuals were studied. Parallel analyses identified a total of 706 individual T-RF and RT-T-RF bands in this sample set. 323 bands were detected by T-RFLP and 383 bands were detected by RT-T-RFLP (statistically significant; P < or = 0.001). For the group as a whole, 145 bands were detected in a T-RFLP profile alone, suggesting metabolically inactive bacteria. 205 bands were detected in an RT-T-RFLP profile alone and 178 bands were detected in both, suggesting a significant degree of metabolic activity. Although Pseudomonas aeruginosa was present and active in many patients, a low occurrence of other species traditionally considered to be key CF pathogens was detected. T-RFLP profiles obtained for induced sputum samples provided by healthy individuals without CF formed a separate cluster indicating a low level of similarity to those from CF patients.ConclusionThese results indicate that a high proportion of the bacterial species detected in the sputum from all of the CF patients in the study are active. The widespread activity of bacterial species in these samples emphasizes the potential importance of these previously unrecognized species within the CF lung.

Dataset Information

Protease activity sensors noninvasively classify bacterial infections and antibiotic responses.

Background

Methods

Findings

Interpretation

Publications

Protease activity sensors noninvasively classify bacterial infections and antibiotic responses.

Similar Datasets

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