Host protease activity classifies pneumonia etiology.

Area of Science:

• Biomedical Engineering
• Infectious Disease Diagnostics
• Proteomics

Background:

• Community-acquired pneumonia (CAP) poses a global health challenge, with accurate etiological classification (viral vs. bacterial) remaining difficult.
• The COVID-19 pandemic highlighted the urgent need for improved diagnostics for respiratory infections.
• Host protease dysregulation is implicated in the response to pneumonia pathogens.

Purpose of the Study:

• To develop and validate activity-based nanosensors for detecting host protease activity in urine.
• To establish a non-invasive diagnostic method for distinguishing bacterial from viral CAP etiology.
• To leverage machine learning for accurate etiological classification based on urinary protease signatures.

Main Methods:

• Engineered nanosensors targeting specific pulmonary host proteases identified via transcriptomic analysis.
• Utilized five mouse models of bacterial and viral CAP to generate etiology-specific urinary readouts.
• Employed machine learning algorithms to train diagnostic classifiers for differentiating pneumonia types.

Main Results:

• Nanosensors successfully produced etiology-specific urinary signatures in mouse models of CAP.
• Machine learning classifiers achieved high accuracy in distinguishing healthy controls from infected mice.
• Classifiers demonstrated high accuracy in differentiating bacterial from viral pneumonia based on host protease activity.

Conclusions:

• This proof-of-concept study demonstrates a novel diagnostic approach for pneumonia based on host proteolytic response.
• Activity-based nanosensors offer a promising avenue for non-invasive, etiology-specific CAP diagnostics.
• Urinary protease profiling combined with machine learning holds potential for clinical translation in respiratory infection management.