Dysregulation of metabolic pathways in a mouse model of allergic asthma.

BACKGROUND: Asthma is a complex lung disease resulting from the interplay of genetic and environmental factors. To understand the molecular changes that occur during the development of allergic asthma without genetic and environmental confounders, an experimental model of allergic asthma in mice was used. Our goals were to (1) identify changes at the small molecule level due to allergen exposure, (2) determine perturbed pathways due to disease, and (3) determine whether small molecule changes correlate with lung function. METHODS: In this experimental model of allergic asthma, matched bronchoalveolar lavage (BAL) fluid and plasma were collected from three groups of C57BL6 mice (control vs sensitized and/or challenged with ovalbumin, n=3-5/group) 6 hour, 24 hour, and 48 hour after the last challenge. Samples were analyzed using liquid chromatography-mass spectrometry-based metabolomics. Airway hyper-responsiveness (AHR) measurements and differential cell counts were performed. RESULTS: In total, 398 and 368 dysregulated metabolites in the BAL fluid and plasma of sensitized and challenged mice were identified, respectively. These belonged to four, interconnected pathways relevant to asthma pathogenesis: sphingolipid metabolism (P=6.6×10-5 ), arginine and proline metabolism (P=1.12×10-7 ), glycerophospholipid metabolism (P=1.3×10-10 ), and the neurotrophin signaling pathway (P=7.0×10-6 ). Furthermore, within the arginine and proline metabolism pathway, a positive correlation between urea-1-carboxylate and AHR was observed in plasma metabolites, while ornithine revealed a reciprocal effect. In addition, agmatine positively correlated with lung eosinophilia. CONCLUSION: These findings point to potential targets and pathways that may be central to asthma pathogenesis and can serve as novel therapeutic targets.

Identification of a Preliminary Plasma Metabolome-based Biomarker for Circadian Phase in Humans.

Measuring individual circadian phase is important to diagnose and treat circadian rhythm sleep-wake disorders and circadian misalignment, inform chronotherapy, and advance circadian science. Initial findings using blood transcriptomics to predict the circadian phase marker dim-light melatonin onset (DLMO) show promise. Alternatively, there are limited attempts using metabolomics to predict DLMO and no known omics-based biomarkers predict dim-light melatonin offset (DLMOff). We analyzed the human plasma metabolome during adequate and insufficient sleep to predict DLMO and DLMOff using one blood sample. Sixteen (8 male/8 female) healthy participants aged 22.4 ± 4.8 years (mean ± SD) completed an in-laboratory study with 3 baseline days (9 h sleep opportunity/night), followed by a randomized cross-over protocol with 9-h adequate sleep and 5-h insufficient sleep conditions, each lasting 5 days. Blood was collected hourly during the final 24 h of each condition to independently determine DLMO and DLMOff. Blood samples collected every 4 h were analyzed by untargeted metabolomics and were randomly split into training (68%) and test (32%) sets for biomarker analyses. DLMO and DLMOff biomarker models were developed using partial least squares regression in the training set followed by performance assessments using the test set. At baseline, the DLMOff model showed the highest performance (0.91 R2 and 1.1 ± 1.1 h median absolute error ± interquartile range [MdAE ± IQR]), with significantly (p < 0.01) lower prediction error versus the DLMO model. When all conditions (baseline, 9 h, and 5 h) were included in performance analyses, the DLMO (0.60 R2; 2.2 ± 2.8 h MdAE; 44% of the samples with an error under 2 h) and DLMOff (0.62 R2; 1.8 ± 2.6 h MdAE; 51% of the samples with an error under 2 h) models were not statistically different. These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need to test biomarkers that predict multiple circadian phase markers under different physiological conditions.