Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza.

BACKGROUND: Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flight mass spectrometry (LC/Q-TOF) combined with machine learning for the diagnosis of influenza infection. METHODS: We analyzed nasopharyngeal swab samples by LC/Q-TOF to identify distinct metabolic signatures for diagnosis of acute illness. Machine learning models were performed for classification, followed by Shapley additive explanation (SHAP) analysis to analyze feature importance and for biomarker discovery. FINDINGS: A total of 236 samples were tested in the discovery phase by LC/Q-TOF, including 118 positive samples (40 influenza A 2009 H1N1, 39 influenza H3 and 39 influenza B) as well as 118 age and sex-matched negative controls with acute respiratory illness. Analysis showed an area under the receiver operating characteristic curve (AUC) of 1.00 (95% confidence interval [95% CI] 0.99, 1.00), sensitivity of 1.00 (95% CI 0.86, 1.00) and specificity of 0.96 (95% CI 0.81, 0.99). The metabolite most strongly associated with differential classification was pyroglutamic acid. Independent validation of a biomarker signature based on the top 20 differentiating ion features was performed in a prospective cohort of 96 symptomatic individuals including 48 positive samples (24 influenza A 2009 H1N1, 5 influenza H3 and 19 influenza B) and 48 negative samples. Testing performed using a clinically-applicable targeted approach, liquid chromatography triple quadrupole mass spectrometry, showed an AUC of 1.00 (95% CI 0.998, 1.00), sensitivity of 0.94 (95% CI 0.83, 0.98), and specificity of 1.00 (95% CI 0.93, 1.00). Limitations include lack of sample suitability assessment, and need to validate these findings in additional patient populations. INTERPRETATION: This metabolomic approach has potential for diagnostic applications in infectious diseases testing, including other respiratory viruses, and may eventually be adapted for point-of-care testing. FUNDING: None.

Impact of Silver and Iron Nanoparticle Exposure on Cholesterol Uptake by Macrophages.

Macrophages are central to the development of atherosclerosis by absorbing lipids, promoting inflammation, and increasing plaque deposition. Nanoparticles (NPs) are becoming increasingly common in biomedical applications thereby increasing exposure to the immune and vascular systems. This project investigated the influence of NPs on macrophage function and specifically cholesterol uptake. Macrophages were exposed to 20 nm silver NPs (AgNPs), 110 nm AgNPs, or 20 nm Fe3O4NPs for 2 h and NP uptake, cytotoxicity, and subsequent uptake of fluorescently labeled cholesterol were assessed. Macrophage uptake of NPs did not induce cytotoxicity at concentrations utilized (25 µg/mL); however, macrophage exposure to 20 nm AgNPs reduced subsequent uptake of cholesterol. Further, we assessed the impact of a cholesterol-rich environment on macrophage function following NP exposure. In these sets of experiments, macrophages internalized NPs, exhibited no cytotoxicity, and altered cholesterol uptake. Alterations in the expression of scavenger receptor-B1 following NP exposure, which likely influences cholesterol uptake, were observed. Overall, NPs alter cholesterol uptake, which may have implications in the progression of vascular or immune mediated diseases. Therefore, for the safe development of NPs for biomedical applications, it is necessary to understand their impact on cellular function and biological interactions in underlying disease environments.