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.