Kerem Bingol

Multidimensional approaches to NMR-based metabolomics.

The field of metabolomics, which is also referred to as metabonomics, has gained significant attention over the recent past as it is developing rapidly as a powerful way to comprehensively study complex biological systems from a small molecule perspective. According to the Web of Science, since 2010 over 5,000 papers have been published with the key words “metabolomics”, “metabonomics” or “metabolite profiling”. Small biological molecules (or metabolites) with molecular weight <1500 Da are involved in many critical functions in biological systems, such as energetics, signaling, and as building blocks of more complex biopolymers, which makes the understanding of their composition, chemical structure, and reaction pathways important. Two main objectives of metabolic analysis are the discovery of modified and new natural products and the detection of biologically meaningful changes in metabolite concentration and fluxes.1 Mass spectrometry (MS) and NMR spectroscopy are by far the most powerful methods in metabolomics.2 This is because of the excellent resolution power of both of these methods to uniquely detect individual molecular species. MS and NMR can be considered as “universal” techniques as essentially every conceivable metabolite can be measured by these techniques, which is in contrast to other methods, such as optical spectroscopy methods, which provide information only about the subset of optically active metabolites. This review focuses on recent progress in metabolomics by NMR spectroscopy. NMR provides information at atomic resolution of NMR-active nuclei. These include 1H, 13C and 31P nuclei at natural abundance as well as 13C and 15N nuclei in isotopically enriched metabolites. The chemical shifts of these nuclei define the resonance positions in the spectrum reporting about their chemical environment and the scalar J-couplings define the fine structure of the NMR peaks reporting about through-bond spin-spin connectivities. Other parameters are the T1 and T2 relaxation times and the nuclear Overhauser effect reflecting the inter-spin distances and overall reorientational diffusion rates, which are often directly related to the molecular size. In addition, translational diffusion rates can be extracted by pulsed-field gradient based methods, which also report on the size and shape of a metabolite, e.g., via the Stokes-Einstein relationship. An inherent drawback of NMR is its limited sensitivity, which restricts its application to metabolite concentrations of the order of μM3. On the other hand, NMR offers a number of unique advantages. The atomic-resolution information permits the characterization of the chemical structure of a metabolite, e.g., through spin-spin connectivity information. NMR also allows one to unambiguously identify different slowly interconverting isomers, which are present for many carbohydrates (e.g. α- vs. β-glucose). Since the NMR peak integrals are directly proportional to the molecular concentration, NMR is a highly quantitative method when it comes to the determination of metabolite concentrations and their changes. The preparation of NMR samples is often straightforward and may involve as little as dissolving the lyophilized metabolite sample in a buffered solution. In addition to liquid samples, semi-solid samples, such as tissues, can also be analyzed by NMR spectroscopy. Since NMR spectroscopy is non-destructive, the same sample can be analyzed for an extended period of time. Finally, because for the same sample essentially identical results can be obtained by different users on different spectrometers operating at the same magnetic field strength, the reproducibility of NMR data is very high. The simplest and fastest NMR techniques are based on 1D Fourier-transform (FT) NMR as shown in Figure 1, which allow the measurements of hundreds or even thousands of samples in a relatively short period of time, such as urine samples of sizeable populations4 or cell extracts5. This is because the experimental time required for a single sample is between a few seconds and a few minutes. Such high-throughput applications are significantly facilitated by the use of automatic NMR sample changers. More detailed and better resolved information can be obtained from higher-dimensional NMR experiments, in particular 2D NMR, at the expense of somewhat longer measurement times.6,7 At some stage, nearly all metabolomics studies revolve around identification of metabolites and their quantification. Figure 1 1D NMR spectra of E. coli cell lysate. (A) 1D 1H spectrum at natural 13C abundance; (B) 1D 13C spectrum of fully 13C labeled sample. Identification Metabolite identification is typically performed in two steps. In the first step, the metabolite mixture is deconvoluted into its individual components and in the second step, each metabolite is identified by querying one or several metabolite databanks. Due to the limited number of metabolites compiled in metabolite databases, a key challenge in metabolomics is the identification of ‘unknown’ signals, which are signals that belong to compounds not found in databanks. Positive database identification requires correct matches of typically several NMR parameters of an unknown metabolite with the ones collected of the pure compound under identical or near identical conditions. These independent NMR parameters can be chemical shifts and peak splittings due to scalar spin-spin couplings (J-couplings), which can be obtained from 1D 1H, 1D 13C or 2D NMR experiments.8 Metabolite identification is sometimes based only on a subset of the independent parameters, for example a single peak, which obviously carries the risk of false identification. There exist several public NMR metabolomics databanks, such as the BMRB9 and the HMDB10, containing experimental data of pure metabolite standards. Experimental information can be queried against these databanks in order to identify metabolites. Querying algorithms provided for these and other databases can vary significantly in their accuracy. Still, the main limitation of database querying lies in the fact that of the total “metabolite universe” only a small fraction of compounds, currently of the order of ∼1000 – 2000 molecules, have their NMR spectra stored in databanks.8 As a result, many signals observed in metabolomics studies cannot be assigned by database query alone. Traditionally, identification of uncatalogued compounds requires their isolation through labor-intensive purification from the complex mixtures by using separation techniques, such as chromatography, followed by extensive characterization by spectroscopy and other methods. For obvious reasons, such an approach is not suitable for high-throughput applications. Below we will discuss a 2D NMR-based approach that permits de novo carbon-backbone structure determination without the need for purification.

Customized Metabolomics Database for the Analysis of NMR 1H–1H TOCSY and 13C–1H HSQC-TOCSY Spectra of Complex Mixtures

A customized metabolomics NMR database, termed 1H(13C)-TOCCATA, is introduced, which contains complete 1H and 13C chemical shift information on individual spin systems and isomeric states of common metabolites. Since this information directly corresponds to cross sections of 2D 1H–1H TOCSY and 2D 13C–1H HSQC-TOCSY spectra, it allows the straightforward and unambiguous identification of metabolites of complex metabolic mixtures at 13C natural abundance from these types of experiments. The 1H(13C)-TOCCATA database, which is complementary to the previously introduced TOCCATA database for the analysis of uniformly 13C-labeled compounds, currently contains 455 metabolites, and it can be used through a publicly accessible web portal. We demonstrate its performance by applying it to 2D 1H–1H TOCSY and 2D 13C–1H HSQC-TOCSY spectra of a cell lysate from E. coli, which yields a substantial improvement over other databases, as well as 1D NMR-based approaches, in the number of compounds that can be correctly identified with high confidence.

Unified and isomer-specific NMR metabolomics database for the accurate analysis of (13)C-(1)H HSQC spectra.

A new metabolomics database and query algorithm for the analysis of 13C–1H HSQC spectra is introduced, which unifies NMR spectroscopic information on 555 metabolites from both the Biological Magnetic Resonance Data Bank (BMRB) and Human Metabolome Database (HMDB). The new database, termed Complex Mixture Analysis by NMR (COLMAR) 13C–1H HSQC database, can be queried via an interactive, easy to use web interface at http://spin.ccic.ohio-state.edu/index.php/hsqc/index. Our new HSQC database separately treats slowly exchanging isomers that belong to the same metabolite, which permits improved query in cases where lowly populated isomers are below the HSQC detection limit. The performance of our new database and query web server compares favorably with the one of existing web servers, especially for spectra of samples of high complexity, including metabolite mixtures from the model organisms Drosophila melanogaster and Escherichia coli. For such samples, our web server has on average a 37% higher accuracy (true positive rate) and a 82% lower false positive rate, which makes it a useful tool for the rapid and accurate identification of metabolites from 13C–1H HSQC spectra at natural abundance. This information can be combined and validated with NMR data from 2D TOCSY-type spectra that provide connectivity information not present in HSQC spectra.

Metabolomics Beyond Spectroscopic Databases: A Combined MS/NMR Strategy for the Rapid Identification of New Metabolites in Complex Mixtures

A novel strategy is introduced that combines high-resolution mass spectrometry (MS) with NMR for the identification of unknown components in complex metabolite mixtures encountered in metabolomics. The approach first identifies the chemical formulas of the mixture components from accurate masses by MS and then generates all feasible structures (structural manifold) that are consistent with these chemical formulas. Next, NMR spectra of each member of the structural manifold are predicted and compared with the experimental NMR spectra in order to identify the molecular structures that match the information obtained from both the MS and NMR techniques. This combined MS/NMR approach was applied to Escherichia coli extract, where the approach correctly identified a wide range of different types of metabolites, including amino acids, nucleic acids, polyamines, nucleosides, and carbohydrate conjugates. This makes this approach, which is termed SUMMIT MS/NMR, well suited for high-throughput applications for the discovery of new metabolites in biological and biomedical mixtures, overcoming the need of experimental MS and NMR metabolite databases.

TOCCATA: A Customized Carbon Total Correlation Spectroscopy NMR Metabolomics Database

A customized metabolomics NMR database, TOCCATA, is introduced, which uses (13)C chemical shift information for the reliable identification of metabolites, their spin systems, and isomeric states. TOCCATA, whose information was derived from the BMRB and HMDB databases and the literature, currently contains 463 compounds and 801 spin systems, and it can be used through a publicly accessible web server. TOCCATA allows the identification of metabolites in the submillimolar concentration range from (13)C-(13)C total correlation spectroscopy experiments of complex mixtures, which is demonstrated for an Escherichia coli cell lysate, a carbohydrate mixture, and an amino acid mixture, all of which were uniformly (13)C-labeled.

DECONVOLUTION OF CHEMICAL MIXTURES WITH HIGH COMPLEXITY BY NMR CONSENSUS TRACE CLUSTERING

Identification and quantification of analytes in complex solution-state mixtures are critical procedures in many areas of chemistry, biology, and molecular medicine. Nuclear magnetic resonance (NMR) is a unique tool for this purpose providing a wealth of atomic-detail information without requiring extensive fractionation of the samples. We present three new multidimensional-NMR based approaches that are geared toward the analysis of mixtures with high complexity at natural (13)C abundance, including approaches that are encountered in metabolomics. Common to all three approaches is the concept of the extraction of one-dimensional (1D) consensus spectral traces or 2D consensus planes followed by clustering, which significantly improves the capability to identify mixture components that are affected by strong spectral overlap. The methods are demonstrated for covariance (1)H-(1)H TOCSY and (13)C-(1)H HSQC-TOCSY spectra and triple-rank correlation spectra constructed from pairs of (13)C-(1)H HSQC and (13)C-(1)H HSQC-TOCSY spectra. All methods are first demonstrated for an eight-compound metabolite model mixture before being applied to an extract from E. coli cell lysate.

Quantitative analysis of metabolic mixtures by two-dimensional 13C constant-time TOCSY NMR spectroscopy.

An increasing number of organisms can be fully (13)C-labeled, which has the advantage that their metabolomes can be studied by high-resolution two-dimensional (2D) NMR (13)C-(13)C constant-time (CT) total correlation spectroscopy (TOCSY) experiments. Individual metabolites can be identified via database searching or, in the case of novel compounds, through the reconstruction of their backbone-carbon topology. Determination of quantitative metabolite concentrations is another key task. Because strong peak overlaps in one-dimensional (1D) NMR spectra prevent straightforward quantification through 1D peak integrals, we demonstrate here the direct use of (13)C-(13)C CT-TOCSY spectra for metabolite quantification. This is accomplished through the quantum mechanical treatment of the TOCSY magnetization transfer at short and long-mixing times or by the use of analytical approximations, which are solely based on the knowledge of the carbon-backbone topologies. The methods are demonstrated for carbohydrate and amino acid mixtures.

NMR/MS Translator for the Enhanced Simultaneous Analysis of Metabolomics Mixtures by NMR Spectroscopy and Mass Spectrometry: Application to Human Urine

A novel metabolite identification strategy is presented for the combined NMR/MS analysis of complex metabolite mixtures. The approach first identifies metabolite candidates from 1D or 2D NMR spectra by NMR database query, which is followed by the determination of the masses (m/z) of their possible ions, adducts, fragments, and characteristic isotope distributions. The expected m/z ratios are then compared with the MS(1) spectrum for the direct assignment of those signals of the mass spectrum that contain information about the same metabolites as the NMR spectra. In this way, the mass spectrum can be assigned with very high confidence, and it provides at the same time validation of the NMR-derived metabolites. The method was first demonstrated on a model mixture, and it was then applied to human urine collected from a pool of healthy individuals. A number of metabolites could be detected that had not been reported previously, further extending the list of known urine metabolites. The new analysis approach, which is termed NMR/MS Translator, is fully automated and takes only a few seconds on a computer workstation. NMR/MS Translator synergistically uses the power of NMR and MS, enhancing the accuracy and efficiency of the identification of those metabolites compiled in databases.

Emerging new strategies for successful metabolite identification in metabolomics

This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties.

Two elephants in the room: new hybrid nuclear magnetic resonance and mass spectrometry approaches for metabolomics.

Purpose of reviewThis review describes some of the advances made over the past year in NMR-based metabolomics for the elucidation of known and unknown compounds, including new ways of how to combine this information with high-resolution mass spectrometry. Recent findingsA new method allows the back-calculation of mass spectra from NMR spectra that have been queried against databases improving the accuracy of the identified compounds by validation and consistency analysis. For the de-novo characterization of unknown compounds, an algorithm has been introduced that predicts all viable NMR spectra from accurate masses allowing, by comparison with experimental NMR data, the determination of the structures of new metabolites in complex mixtures. SummaryRecent advances in NMR and mass spectrometry-based metabolomics and their synergistic use promises to significantly improve metabolomics sample characterization both in terms of identification and quantitation, and accelerate metabolite discovery.