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.

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(1)H NMR fingerprints of virgin olive oils (VOOs) from the Mediterranean basin (three harvests) were analyzed by principal component analysis, linear discriminant analysis (LDA), and partial least-squares discriminant analysis (PLS-DA) to determine their geographical origin at the national, regional, or PDO level. Further delta(13)C and delta(2)H measurements were performed by isotope ratio mass spectrometry (IRMS). LDA and PLS-DA achieved consistent results for the characterization of PDO Riviera Ligure VOOs. PLS-DA afforded the best model: for the Liguria class, 92% of the oils were correctly classified in the modeling step, and 88% of the oils were properly predicted in the external validation; for the non-Liguria class, 90 and 86% of hits were obtained, respectively. A stable and robust PLS-DA model was obtained to authenticate VOOs from Sicily: the recognition abilities were 98% for Sicilian oils and 89% for non-Sicilian ones, and the prediction abilities were 93 and 86%, respectively. More than 85% of the oils of both categories were properly predicted in the external validation. Greek and non-Greek VOOs were properly classified by PLS-DA: >90% of the samples were correctly predicted in the cross-validation and external validation. Stable isotopes provided complementary geographical information to the (1)H NMR fingerprints of the VOOs.

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