Human exhaled air diagnostic markers for respiratory tract infections in subjects receiving mechanical ventilation.

Diagnosing respiratory tract infections (RTIs) in critical care settings is essential for appropriate antibiotic treatment and lowering mortality. The current diagnostic method, which primarily relies on clinical symptoms, lacks sensitivity and specificity, resulting in incorrect or delayed diagnoses, putting patients at a heightened risk. In this study we developed a noninvasive diagnosis method based on collecting non-volatile compounds in human exhaled air. We hypothesized that non-volatile compound profiles could be effectively used for bacterial RTI diagnosis. Exhaled air samples were collected from subjects receiving mechanical ventilation diagnosed with or without bacterial RTI in intensive care units at the Johns Hopkins Hospital. Truncated proteoforms, a class of non-volatile compounds, were characterized by top-down proteomics, and significant features associated with RTI were identified using feature selection algorithms. The results showed that three truncated proteoforms, collagen type VI alpha three chain protein, matrix metalloproteinase-9, and putative homeodomain transcription factor II were independently associated with RTI with thep-values of 2.0 × 10-5, 1.1 × 10-4, and 1.7 × 10-3, respectively, using multiple logistic regression. Furthermore, a score system named 'TrunScore' was constructed by combining the three truncated proteoforms, and the diagnostic accuracy was significantly improved compared to that of individual truncated proteoforms, with an area under the receiver operator characteristic curve of 96.9%. This study supports the ability of this noninvasive breath analysis method to provide an accurate diagnosis for RTIs in subjects receiving mechanical ventilation. The results of this study open the doors to be able to potentially diagnose a broad range of diseases using this non-volatile breath analysis technique.

MALDI-TOF Mass Spectrometric Detection of SARS-CoV-2 Using Cellulose Sulfate Ester Enrichment and Hot Acid Treatment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the ongoing coronavirus disease 2019 (COVID-19) pandemic. Here we report a novel strategy for the rapid detection of SARS-CoV-2 based on an enrichment approach exploiting the affinity between the virus and cellulose sulfate ester functional groups, hot acid hydrolysis, and matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS). Virus samples were enriched using cellulose sulfate ester microcolumns. Virus peptides were prepared using the hot acid aspartate-selective hydrolysis and characterized by MALDI-TOF MS. Collected spectra were processed with a peptide fingerprint algorithm, and searching parameters were optimized for the detection of SARS-CoV-2. These peptides provide high sequence coverage for nucleocapsid (N protein) and allow confident identification of SARS-CoV-2. Peptide markers contributing to the detection were rigorously identified using bottom-up proteomics. The approach demonstrated in this study holds the potential for developing a rapid assay for COVID-19 diagnosis and detecting virus variants from a variety of sources, such as sewage and nasal swabs.

Non-volatile organic compounds in exhaled breath particles correspond to active tuberculosis.

Human breath contains trace amounts of non-volatile organic compounds (NOCs) which might provide non-invasive methods for evaluating individual health. In previous work, we demonstrated that lipids detected in exhaled breath aerosol (EBA) could be used as markers of active tuberculosis (TB). Here, we advanced our analytical platform for characterizing small metabolites and lipids in EBA samples collected from participants enrolled in clinical trials designed to identify molecular signatures of active TB. EBA samples from 26 participants with active TB and 73 healthy participants were processed using a dual-phase extraction method, and metabolites and lipids were identified via mass spectrometry database matching. In total, 13 metabolite and 9 lipid markers were identified with statistically different optimized relative standard deviation values between individuals diagnosed with active TB and the healthy controls. Importantly, EBA lipid profiles can be used to separate the two sample types, indicating the diagnostic potential of the identified molecules. A feature ranking algorithm reduced this number to 10 molecules, with the membrane glycerophospholipid, phosphatidylinositol 24:4, emerging as the top driver of segregation between the two groups. These results support the use of this approach to identify consistent NOC signatures from EBA samples in active TB cases. This suggests the potential to apply this method to other human diseases which alter respiratory NOC release.