Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), during the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly cancer. The metabolome is the functional readout of the genome, functional genome, and proteome; it is also an integral partner in molecular regulations for homeostasis. The interrogation of the metabolome, or metabolomics, is now being applied to numerous diseases, largely by metabolite profiling for biomarker discovery, but also in pharmacology and therapeutics. Recent advances in stable isotope tracer-based metabolomic approaches enable unambiguous tracking of individual atoms through compartmentalized metabolic networks directly in human subjects, which promises to decipher the complexity of the human metabolome at an unprecedented pace. This knowledge will revolutionize our understanding of complex human diseases, clinical diagnostics, as well as individualized therapeutics and drug response. In this review, we focus on the use of stable isotope tracers with metabolomics technologies for understanding metabolic network dynamics in both model systems and in clinical applications. Atom-resolved isotope tracing via the two major analytical platforms, NMR and MS, has the power to determine novel metabolic reprogramming in diseases, discover new drug targets, and facilitates ADME studies. We also illustrate new metabolic tracer-based imaging technologies, which enable direct visualization of metabolic processes in vivo. We further outline current practices and future requirements for biochemoinformatics development, which is an integral part of translating stable isotope-resolved metabolomics into clinical reality.

A suite of reduced-dimensionality 13 C, 15 N, 1 H-triple-resonance NMR experiments is presented for rapid and complete protein resonance assignment. Even when using short measurement times, these experiments allow one to retain the high spectral resolution required for efficient automated analysis. ''Sampling limited'' and ''sensitivity limited'' data collection regimes are defined, respectively, depending on whether the sampling of the indirect dimensions or the sensitivity of a multidimensional NMR experiments per se determines the minimally required measurement time. We show that reduced-dimensionality NMR spectroscopy is a powerful approach to avoid the ''sampling limited regime''-i.e., a standard set of ten experiments proposed here allows one to effectively adapt minimal measurement times to sensitivity requirements. This is of particular interest in view of the greatly increased sensitivity of NMR spectrometers equipped with cryogenic probes. As a step toward fully automated analysis, the program AUTOASSIGN has been extended to provide sequential backbone and 13 C ␤ resonance assignments from these reduced-dimensionality NMR data. R apid resonance assignment is a prerequisite for highthroughput (HTP) structure determination and structural genomics (1). The aims of structural genomics are to (i) explore the naturally occurring ''protein fold space '' and (ii) contribute to the characterization of function through the assignment of atomic-resolution three-dimensional (3D) structures to proteins. The ultimate goal is to provide one or more representative 3D structures for every structural domain family in nature. It is now generally acknowledged that NMR will play an important role in this endeavor (1). The resulting demand for HTP structure determination requires fast and automated NMR data collection and analysis protocols. This impetus for the development of new methods will have broad impact in the technological infrastructure for structural biology and molecular biophysics.Two key objectives for NMR data collection can be identified. Firstly, the measurement time should be minimized so as to lower the cost per structure and relax the constraint that NMR samples need to be stable over long time periods. Secondly, automated analysis requires recording of a redundant set of NMR spectra each affording good resolution, while it is also desirable to keep the total number of spectra small to reduce complications due to interspectral variations of chemical shifts (2). This second objective can be addressed by maximizing the dimensionality of the spectra. However, the joint realization of the first and second objective is impeded by the large lower bounds for measurement times of four (or higher) dimensional NMR spectra arising from the independent sampling of three (or more) indirect dimensions.We distinguish ''sampling limited'' and ''sensitivity limited'' data collection regimes, depending on whether the sampling of the indirect dimensions or the sensitivity of the multidimensional NMR experiments per se det...

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We have coupled 2D-NMR and infusion FT-ICR-MS with computer-assisted assignment to profile 13 C-isotopologues of glycerophospholipids (GPL) directly in crude cell extracts, resulting in very high information throughput of >3000 isobaric molecules in a few minutes. A mass accuracy of better than 1 ppm combined with a resolution of 100,000 at the measured m/z was required to distinguish isotopomers from other GPL structures. Isotopologue analysis of GPLs extracted from LCC2 breast cancer cells grown on [U-13 C]-glucose provided a rich trove of information about the biosynthesis and turnover of the GPLs. The isotopologue intensity ratios from the FT-ICR-MS were accurate to ≈ 1% or better based on natural abundance background, and depended on the signal-tonose ratio. The time course of incorporation of 13 C from [U-13 C]-glucose into a particular phosphatidylcholine was analyzed in detail, to provide a quantitative measure of the sizes of glycerol, acetyl CoA and total GPL pools in growing LCC2 cells. Independent and complementary analysis of the positional 13 C enrichment in the glycerol and fatty acyl chains obtained from high resolution 2D NMR was used to verify key aspects of the model. This technology enables simple and rapid sample preparation, has rapid analysis, and is generally applicable to unfractionated GPLs of almost any head group, and to mixtures of other classes of metabolites.

We present a set of utilities and graphical user interface (GUI) tools for evaluating the quality of protein resonance assignments. The Assignment Validation Software (AVS) suite, together with new GUI features in the AutoAssign software package, provides a set of reports and graphs for validating protein resonance assignment data before its use in structure analysis and/or submission to the BioMagResBank (BMRB). Input includes a listing of resonance assignments and a summary of sequential connectivity data (i.e. triple resonance, NOE, or other data) used in deriving the assignments. These tools are useful for evaluating the accuracy of protein resonance assignments determined by either automated or manual methods.

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BackgroundStable isotope tracing with ultra-high resolution Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS) can provide simultaneous determination of hundreds to thousands of metabolite isotopologue species without the need for chromatographic separation. Therefore, this experimental metabolomics methodology may allow the tracing of metabolic pathways starting from stable-isotope-enriched precursors, which can improve our mechanistic understanding of cellular metabolism. However, contributions to the observed intensities arising from the stable isotope's natural abundance must be subtracted (deisotoped) from the raw isotopologue peaks before interpretation. Previously posed deisotoping problems are sidestepped due to the isotopic resolution and identification of individual isotopologue peaks. This peak resolution and identification come from the very high mass resolution and accuracy of FT-ICR-MS and present an analytically solvable deisotoping problem, even in the context of stable-isotope enrichment.ResultsWe present both a computationally feasible analytical solution and an algorithm to this newly posed deisotoping problem, which both work with any amount of 13C or 15N stable-isotope enrichment. We demonstrate this algorithm and correct for the effects of 13C natural abundance on a set of raw isotopologue intensities for a specific phosphatidylcholine lipid metabolite derived from a 13C-tracing experiment.ConclusionsCorrection for the effects of 13C natural abundance on a set of raw isotopologue intensities is computationally feasible when the raw isotopologues are isotopically resolved and identified. Such correction makes qualitative interpretation of stable isotope tracing easier and is required before attempting a more rigorous quantitative interpretation of the isotopologue data. The presented implementation is very robust with increasing metabolite size. Error analysis of the algorithm will be straightforward due to low relative error from the implementation itself. Furthermore, the algorithm may serve as an independent quality control measure for a set of observed isotopologue intensities.