Rationale: Metabolomic analysis provides biochemical profiles of low-molecular-weight endogenous metabolites in biological fluids. Objectives: The aim of this study was to assess the feasibility of nuclear magnetic resonance (NMR)–based metabolomic analysis applied to exhaled breath condensate (“breathomics”). Information coming from NMR spectra was analyzed with a view to establish the NMR variables that best discriminate between children with asthma and healthy children. Methods: Twenty-five children with asthma (17 with persistent asthma treated with inhaled corticosteroids, 8 with intermittent asthma inhaled corticosteroid naive; age, 7–15 yr) and 11 healthy age-matched control subjects were enrolled. Every child performed exhaled nitric oxide measurement, spirometry, and condensate collection. Condensate samples were analyzed by means of NMR spectroscopy. Linear and partial least squares discriminant analyses were applied to data obtained from the NMR spectra. Measurements and Main Results: The combination of exhaled nitric oxide and FEV1 discriminates children with asthma and healthy children with a success rate of approximately 81%, whereas selected signals from NMR spectra offer a slightly better discrimination (∼ 86%). The selected NMR variables derive from the region of 3.2 to 3.4 ppm, indicative of oxidized compounds, and from the region of 1.7 to 2.2 ppm, indicative of acetylated compounds. Conclusions: Metabolomics can be applied to exhaled breath condensate, leading to the characterization of airway biochemical fingerprints. The presence of acetylated compounds suggests new metabolic pathways that may have a role in asthma pathophysiology.

High content omic techniques in combination with stable human in vitro cell culture systems have the potential to improve on current pre-clinical safety regimes by providing detailed mechanistic information of altered cellular processes. Here we investigated the added benefit of integrating transcriptomics, proteomics and metabolomics together with pharmacokinetics for drug testing regimes. Cultured human renal epithelial cells (RPTEC/TERT1) were exposed to the nephrotoxin Cyclosporine A (CsA) at therapeutic and supratherapeutic concentrations for 14days. CsA was quantified in supernatants and cellular lysates by LC-MS/MS for kinetic modeling. There was a rapid cellular uptake and accumulation of CsA, with a non-linear relationship between intracellular and applied concentrations. CsA at 15μM induced mitochondrial disturbances and activation of the Nrf2-oxidative-damage and the unfolded protein-response pathways. All three omic streams provided complementary information, especially pertaining to Nrf2 and ATF4 activation. No stress induction was detected with 5μM CsA; however, both concentrations resulted in a maximal secretion of cyclophilin B. The study demonstrates for the first time that CsA-induced stress is not directly linked to its primary pharmacology. In addition we demonstrate the power of integrated omics for the elucidation of signaling cascades brought about by compound induced cell stress.

The metabo-ring initiative brought together five nuclear magnetic resonance instruments (NMR) and 11 different mass spectrometers with the objective of assessing the reliability of untargeted metabolomics approaches in obtaining comparable metabolomics profiles. This was estimated by measuring the proportion of common spectral information extracted from the different LCMS and NMR platforms. Biological samples obtained from 2 different conditions were analysed by the partners using their own in-house protocols. Test #1 examined urine samples from adult volunteers either spiked or not spiked with 32 metabolite standards. Test #2 involved a low biological contrast situation comparing the plasma of rats fed a diet either supplemented or not with vitamin D. The spectral information from each instrument was assembled into separate statistical blocks. Correlations between blocks (e.g., instruments) were examined (RV coefficients) along with the structure of the common spectral information (common components and specific weights analysis). In addition, in Test #1, an outlier individual was blindly introduced, and its identification by the various platforms was evaluated. Despite large differences in the number of spectral features produced after post-processing and the heterogeneity of the analytical conditions and the data treatment, the spectral information both within (NMR and LCMS) and across methods (NMR vs. LCMS) was highly convergent (from 64 to 91 % on average). No effect of the LCMS instrumentation (TOF, QTOF, LTQ-Orbitrap) was noted. The outlier individual was best detected and characterised by LCMS instruments. In conclusion, untargeted metabolomics analyses report consistent information within and across instruments of various technologies, even without prior standardisation.