Abstract
Biological processes are dynamic as reflected by temporal fluctuations of biomarkers. These dynamics underline the adaptive capacity of physiological systems to respond to external perturbations. We have captured differences in the pattern of fluctuations in biomarkers sampled locally at the sites of respiratory infection between healthy individuals and asthma patients, before and after perturbation by rhinovirus exposure [Sinha et al, eLife]. The detection of fluctuating biomarker signals in a systemic matrix, e.g. urine could aid in the development of better prognostic, non-invasive markers for monitoring disease progression especially during loss-of-control/exacerbation events due to viral exposures in asthma. Aims:Probe and compare temporal fluctuations of metabolomic signatures in the systemic circulation (urine) after a rhinovirus (RV-16) challenge in healthy and asthmatic subjects. Methods: In this prospective follow-up study, urine from 12 healthy individuals and 12 asthma patients was sampled thrice weekly for 3 months. After two months (stable phase) individuals were exposed to a 100 TCID50 Rhinovirus 16 and followed for another month (unstable phase). Non-targeted metabolomics data were acquiredwith liquid chromatography-mass spectrometry (LC-MS) using HILIC chromatography in positive ionization mode. Urine specific gravity (SG) was measured to normalize urine concentration and to reduce matrix effects. Data was processed independently using MZmine, MS-DIAL and Profinder software packages.A modified robust regression analysis technique, LASSO (least absolute shrinkage and selection operator) was applied on the mass spectrometric fragments to compare healthy and asthma groups both before and after rhinovirus challenge interventions. The machine learning pipeline was applied to the non-targeted metabolomics dataset and selected features were identified using an in-house chemical library. Results: We have identified a total of 164 metabolites in urine, several of which were found differentially regulated in healthy and asthmatic volunteers before and after the challenge (See Figure 1). Interestingly carnitine species decreased during the Rhinovirus challenge. Decreases in urinary carnitines have been previously observed in relationto asthma severity levels which confirm and substantiate our findings. Derivates of GABA (γ-aminobutyric acid) were also found to be differentially regulated. Conclusions: Our studyreports for the first time the fluctuating signals of metabolites in systemic signals in response to an exogenous trigger in the local nasal compartment in healthy and asthmaticsubjects. Carnitine compounds could prove to be useful markers of viral infection. The differential regulation of urinary metabolites could be useful to monitor patient prognosis andspot viral infections.