Observation of 13C13C couplings with enhanced sensitivity

(13)C NMR Metabolomics: INADEQUATE Network Analysis

The many advantages of (13)C NMR are often overshadowed by its intrinsically low sensitivity. Given that carbon makes up the backbone of most biologically relevant molecules, (13)C NMR offers a straightforward measurement of these compounds. Two-dimensional (13)C-(13)C correlation experiments like INADEQUATE (incredible natural abundance double quantum transfer experiment) are ideal for the structural elucidation of natural products and have great but untapped potential for metabolomics analysis. We demonstrate a new and semiautomated approach called INETA (INADEQUATE network analysis) for the untargeted analysis of INADEQUATE data sets using an in silico INADEQUATE database. We demonstrate this approach using isotopically labeled Caenorhabditis elegans mixtures.

An overview of methods using (13)C for improved compound identification in metabolomics and natural products

Compound identification is a major bottleneck in metabolomics studies. In nuclear magnetic resonance (NMR) investigations, resonance overlap often hinders unambiguous database matching or de novo compound identification. In liquid chromatography-mass spectrometry (LC-MS), discriminating between biological signals and background artifacts and reliable determination of molecular formulae are not always straightforward. We have designed and implemented several NMR and LC-MS approaches that utilize (13)C, either enriched or at natural abundance, in metabolomics applications. For LC-MS applications, we describe a technique called isotopic ratio outlier analysis (IROA), which utilizes samples that are isotopically labeled with 5% (test) and 95% (control) (13)C. This labeling strategy leads to characteristic isotopic patterns that allow the differentiation of biological signals from artifacts and yield the exact number of carbons, significantly reducing possible molecular formulae. The relative abundance between the test and control samples for every IROA feature can be determined simply by integrating the peaks that arise from the 5 and 95% channels. For NMR applications, we describe two (13)C-based approaches. For samples at natural abundance, we have developed a workflow to obtain (13)C-(13)C and (13)C-(1)H statistical correlations using 1D (13)C and (1)H NMR spectra. For samples that can be isotopically labeled, we describe another NMR approach to obtain direct (13)C-(13)C spectroscopic correlations. These methods both provide extensive information about the carbon framework of compounds in the mixture for either database matching or de novo compound identification. We also discuss strategies in which (13)C NMR can be used to identify unknown compounds from IROA experiments. By combining technologies with the same samples, we can identify important biomarkers and corresponding metabolites of interest.

Advanced NMR approaches for a detailed structure analysis of natural products

Some new nuclear magnetic resonance (NMR) approaches to elucidate chemical structures, which have not been determined by routine NMR methods, are presented. Selective detection of methine (CH), methylene (CH(2)), or methyl (CH(3)) signals in each subspectrum by editing NMR methods was utilized to reduce the complexity in crowded spectra. It also increased the peak separation and enhanced the sensitivity by limiting the measuring area of the 2D spectra. Several 2D methods to measure (2,3)J(CH) values, which are useful for stereochemical assignment are then introduced. To determine the structure of a highly hydrogen-deficient molecule, efficient correlation methods for long-range (13)C-(13)C coupling and (1)H-(15)N HMBC are also described.

Total structural assignment and absolute configuration of terreinol by 13C and 1H NMR

The carbon-carbon connectivity of terreinol, a new metabolite isolated from Aspergillus terreus, and its previous (13)C assignments were confirmed by a two-dimensional INADEQUATE experiment using a few milligrams of the compound with natural (13)C abundance. The carbon-carbon correlations were determined by computational analysis (with >99% probability) of this experiment. Additionally, the absolute configuration of terreinol was achieved indirectly via its corresponding secondary alcohol by the modified Mosher method allied to conformational analysis. The shielding effect of the phenyl group of methoxytrifluoromethylphenylacetic acid (MTPA) on the substituents of the carbonylic centre gave a fully regular Deltadelta(SR) sign distribution, allowing reliable assignment of the R configuration for terreinol.