Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography-mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography-MS (GC-MS) and ultraperformance liquid chromatography-MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control-based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

Signaling by the transcription factor nuclear factor kappa B (NF-kappaB) involves its release from inhibitor kappa B (IkappaB) in the cytosol, followed by translocation into the nucleus. NF-kappaB regulation of IkappaBalpha transcription represents a delayed negative feedback loop that drives oscillations in NF-kappaB translocation. Single-cell time-lapse imaging and computational modeling of NF-kappaB (RelA) localization showed asynchronous oscillations following cell stimulation that decreased in frequency with increased IkappaBalpha transcription. Transcription of target genes depended on oscillation persistence, involving cycles of RelA phosphorylation and dephosphorylation. The functional consequences of NF-kappaB signaling may thus depend on number, period, and amplitude of oscillations.

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus ‘metabolic reconstruction’, which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

The Nuclear Factor kappa B (NF-κB) transcription factor regulates cellular stress responses and the immune response to infection. NF-κB activation results in oscillations in nuclear NF-κB abundance. To define the function of these oscillations, we treated cells with repeated short pulses of tumor necrosis factor alpha (TNFα) at various intervals to mimic pulsatile inflammatory signals. At all pulse intervals analyzed, we observed synchronous cycles of NF-κB nuclear translocation. Lower frequency stimulations gave repeated full-amplitude translocations, whereas higher frequency pulses, gave reduced translocation, indicating a failure to reset. Deterministic and stochastic mathematical models predicted how negative feedback loops regulate both the resetting of the system and cellular heterogeneity. Altering the stimulation intervals gave different patterns of NF-κB-dependent gene expression, supporting a functional role for oscillation frequency.Eukaryotic cells interpret multiple signals to coordinate the activity of transcription factors, which modulate the expression of target genes. NF-κB signaling in many mammalian cell types regulates responses to pathogens and stresses (1). NF-κB, most commonly comprising a dimer of RelA and p50, is bound in the cytoplasm of unstimulated cells by inhibitor kappa B (IκB) proteins. Stimulation by cytokines such as TNFα activates the inhibitor kappa B kinase (IKK) complex that phosphorylates IκB proteins, leading to IκB degradation and NF-κB translocation into the nucleus. Activated NF-κB regulates transcription from promoter regions of approximately 300 genes, including those encoding cytokines and several NF-κB family members which can feedback to regulate the system (2). Signaling through NF-κB can regulate diverse cellular outcomes including cell death or division (3). How such a diversity of responses is generated has remained unclear.Real-time fluorescence imaging and mathematical modeling have shown that the activity of the NF-κB system can be oscillatory (4). This raised the possibility that, as with calcium (5) (7)). In contrast to the conclusions of other reports (9, 10), we observed oscillations in the translocation of RelA-dsRedxp fusion protein in single transiently transfected MEFs (Fig. 1, C, D and E). These data (as well as bulk cell chromatin immunoprecipitation (ChIP) assays, Fig. S1), suggest that oscillations are a normal response to TNFα stimulation.In an inflammatory tissue, cells receive pulsatile signals such as TNFα from neighboring cells (11,12). To mimic this, we exposed cells to 5 min pulses of TNFα at various intervals, followed by wash-off. When stimulated at 200-min intervals, RelA-dsRedxp fusion protein expressed in SK-N-AS cells showed synchronous translocations from the cytoplasm to the nucleus and back of equal magnitude in response to each successive pulse (Fig. 2, A and B and fig. S4). In contrast, whereas stimulation at 100 or 60 min intervals also caused synchronous cell responses, there was significant reduction in the magnitude ...

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Enlarged colour plots are provided in the Supplementary Material, which is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/.

A large proportion of the 6,000 genes present in the genome of Saccharomyces cerevisiae, and of those sequenced in other organisms, encode proteins of unknown function. Many of these genes are "silent, " that is, they show no overt phenotype, in terms of growth rate or other fluxes, when they are deleted from the genome. We demonstrate how the intracellular concentrations of metabolites can reveal phenotypes for proteins active in metabolic regulation. Quantification of the change of several metabolite concentrations relative to the concentration change of one selected metabolite can reveal the site of action, in the metabolic network, of a silent gene. In the same way, comprehensive analyses of metabolite concentrations in mutants, providing "metabolic snapshots," can reveal functions when snapshots from strains deleted for unstudied genes are compared to those deleted for known genes. This approach to functional analysis, using comparative metabolomics, we call FANCY-an abbreviation for functional analysis by co-responses in yeast.