A Breath-Based In Vitro Diagnostics for Lower Respiratory Tract Infection

Lower Respiratory Tract Infections (LRTIs) represent the leading cause of death due to infectious diseases. Current diagnostic modalities primarily depend on clinical symptoms and lack specificity, especially in light of common colonization without overt infection. To address this, we developed a noninvasive diagnostic approach that employs BreathBiomics, an advanced human breath sampling system, to detect protease activities induced by bacterial infection in the lower respiratory tract. Specifically, we engineered a high-sensitivity and high-specificity molecular sensor for human neutrophil elastase (HNE). The sensor undergoes cleavage in the presence of HNE, an event that is subsequently detected via Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS). Application of this methodology to clinical samples, breath specimens collected from intubated patients with LRTIs, demonstrated the detection of the cleaved sensor by MALDI-TOF MS. Our findings indicate that this novel approach offers a noninvasive and specific diagnostic strategy for people with LRTIs. O_TEXTBOXSignificance The potential for using human breath for noninvasive disease detection and diagnosis has long been recognized, yet the lack of effective biomolecular sampling technologies has hindered progress. To address this limitation, we developed BreathBiomics, an advanced sampling system designed to efficiently capture biomolecules in human exhaled breath. By focusing on protease dysregulation, an established event induced by bacterial infections, we demonstrated that BreathBiomics can capture proteases and facilitate their subsequent activity-based detection for the diagnosis of LRTI. We verified the assays sensitivity and clinical applicability through empirical studies. Our work marks a significant advancement by providing the first viable pathway for the development of in vitro diagnostic assays leveraging human breath for disease detection and diagnosis. C_TEXTBOX