Variable-temperature NMR spectroscopy for metabolite identification in biological materials.
Ontology highlight
ABSTRACT:
SUBMITTER: Nawrocka EK
PROVIDER: S-EPMC9043013 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Publications
Variable-temperature NMR spectroscopy for metabolite identification in biological materials.
Nawrocka Ewa K EK Urbańczyk Mateusz M Koziński Kamil K Kazimierczuk Krzysztof K
RSC advances 20211001 56
Nuclear magnetic resonance is a "workhorse technique" used in metabolomics, complementary to mass spectrometry. Unfortunately, only the most basic NMR methods are sensitive enough to allow fast medical screening. The most common of them, a simple <sup>1</sup>H NMR, suffers from low dispersion of resonance frequencies, which often hampers the identification of metabolites. In this article we show that <sup>1</sup>H NMR spectra contain previously overlooked parameters potentially helpful in metabo ...[more]
Similar Datasets
Project description:The complexity of biological mixtures continues to challenge efforts aimed at unknown metabolite identification in the metabolomics field. To address this challenge, we provide a new method to identify related peaks from individual metabolites in complex NMR spectra. Extractive ratio analysis NMR spectroscopy (E-RANSY) builds on our previously described ratio analysis method [ Wei et al. Anal. Chem. 2011 , 83 , 7616 - 7623 ] and exploits the simplified NMR spectra provided by the extraction of metabolites under varied pH conditions. Under such conditions, metabolites from the same biological specimen are extracted differentially, and the resulting NMR spectra exhibit characteristics favorable for unraveling unknown metabolite peaks using ratio analysis. We demonstrate the utility of the E-RANSY method by extracting carboxylic acid containing metabolites from human urine, one of the highly complex biological mixtures encountered in the metabolomics field. E-RANSY performs better than STOCSY and the original RANSY method and offers new avenues to identify unknown metabolites in complex biological mixtures.
| S-EPMC8164567 | biostudies-literature
Project description:Nuclear magnetic resonance (NMR) spectroscopy is a powerful and popular technique for probing the molecular structures, dynamics and chemical properties. However the conventional NMR spectroscopy is bottlenecked by its low sensitivity. Dynamic nuclear polarization (DNP) boosts NMR sensitivity by orders of magnitude and resolves this limitation. In liquid-state this revolutionizing technique has been restricted to a few specific non-biological model molecules in organic solvents. Here we show that the carbon polarization in small biological molecules, including carbohydrates and amino acids, can be enhanced sizably by in situ Overhauser DNP (ODNP) in water at room temperature and at high magnetic field. An observed connection between ODNP 13C enhancement factor and paramagnetic 13C NMR shift has led to the exploration of biologically relevant heterocyclic compound indole. The QM/MM MD simulation underscores the dynamics of intermolecular hydrogen bonds as the driving force for the scalar ODNP in a long-living radical-substrate complex. Our work reconciles results obtained by DNP spectroscopy, paramagnetic NMR and computational chemistry and provides new mechanistic insights into the high-field scalar ODNP.
| S-EPMC8616939 | biostudies-literature
Project description:NMR spectroscopy is a powerful tool for biologists interested in the structure, dynamics, and interactions of biological macromolecules. This review aims at presenting in an accessible manner the requirements and limitations of this technique. As an introduction, the history of NMR will highlight how the method evolved from physics to chemistry and finally to biology over several decades. We then introduce the NMR spectral parameters used in structural biology, namely the chemical shift, the J-coupling, nuclear Overhauser effects, and residual dipolar couplings. Resonance assignment, the required step for any further NMR study, bears a resemblance to jigsaw puzzle strategy. The NMR spectral parameters are then converted into angle and distances and used as input using restrained molecular dynamics to compute a bundle of structures. When interpreting a NMR-derived structure, the biologist has to judge its quality on the basis of the statistics provided. When the 3D structure is a priori known by other means, the molecular interaction with a partner can be mapped by NMR: information on the binding interface as well as on kinetic and thermodynamic constants can be gathered. NMR is suitable to monitor, over a wide range of frequencies, protein fluctuations that play a crucial role in their biological function. In the last section of this review, intrinsically disordered proteins, which have escaped the attention of classical structural biology, are discussed in the perspective of NMR, one of the rare available techniques able to describe structural ensembles. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 16 MCP).
| S-EPMC3820920 | biostudies-literature
Project description:IntroductionIt is crucial to understand the factors that introduce variability before applying metabolomics to clinical and biomarker research.ObjectivesWe quantified technical and biological variability of both fasting and postprandial metabolite concentrations measured using 1H NMR spectroscopy in plasma samples.MethodsIn the Netherlands Epidemiology of Obesity study (n = 6,671), 148 metabolite concentrations (101 metabolites belonging to lipoprotein subclasses) were measured under fasting and postprandial states (150 minutes after a mixed liquid meal). Technical variability was evaluated among 265 fasting and 851 postprandial samples, with the identical blood plasma sample being measured twice by the same laboratory protocol. Biological reproducibility was assessed by measuring 165 individuals twice across time for evaluation of short- (<6 months) and long-term (>3 years) biological variability. Intra-class coefficients (ICCs) were used to assess variability. The ICCs of the fasting metabolites were compared with the postprandial metabolites using two-sided paired Wilcoxon test separately for short- and long-term measurements.ResultsBoth fasting and postprandial metabolite concentrations showed high technical reproducibility using 1H NMR spectroscopy (median ICC = 0.99). Postprandial metabolite concentrations revealed slightly higher ICC scores than fasting ones in short-term repeat measures (median ICC in postprandial and fasting metabolite concentrations 0.72 versus 0.67, Wilcoxon p-value = 8.0×10-14). Variability did not increase further in a long-term repeat measure, with median ICC in postprandial of 0.64 and in fasting metabolite concentrations 0.66.ConclusionTechnical reproducibility is excellent. Biological reproducibility of postprandial metabolite concentrations showed a less or equal variability than fasting metabolite concentrations over time.
| S-EPMC6586348 | biostudies-literature
Project description:Selective catalytic reduction (SCR) of NOx by ammonia is one of the dominant pollution abatement technologies for near-zero NOx emission diesel engines. A crucial step in the reduction of NOx to N2 with Cu zeolite NH3-SCR catalysts is the generation of a multi-electron donating active site, implying the permanent or transient dimerization of Cu ions. Cu atom mobility has been implicated by computational chemistry as a key factor in this process. This report demonstrates how variable temperature 1H NMR reveals the Cu induced generation of sharp 1H resonances associated with a low concentration of sites on the zeolite. The onset temperature of the appearance of these signals was found to strongly correlate with the NH3-SCR activity and was observed for a range of catalysts covering multiple frameworks (CHA, AEI, AFX, ERI, ERI-CHA, ERI-OFF, *BEA), with different Si/Al ratios and different Cu contents. The results point towards universal applicability of variable temperature NMR to predict the activity of a Cu-zeolite SCR catalyst. The unique relationship of a spectroscopic feature with catalytic behavior for zeolites with different structures and chemical compositions is exceptional in heterogeneous catalysis.
| S-EPMC10537069 | biostudies-literature
Project description:IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.
| S-EPMC4480339 | biostudies-literature
Project description:A current challenge in metabolomics is the reliable quantitation of many metabolites. Limited resolution and sensitivity combined with the challenges associated with unknown metabolite identification have restricted both the number and the quantitative accuracy of blood metabolites. Focused on alleviating this bottleneck in NMR-based metabolomics, investigations of pooled human serum combining an array of 1D/2D NMR experiments at 800 MHz, database searches, and spiking with authentic compounds enabled the identification of 67 blood metabolites. Many of these (?1/3) are new compared with those reported previously as a part of the Human Serum Metabolome Database. In addition, considering both the high reproducibility and quantitative nature of NMR as well as the sensitivity of NMR chemical shifts to altered sample conditions, experimental protocols and comprehensive peak annotations are provided here as a guide for identification and quantitation of the new pool of blood metabolites for routine applications. Further, investigations focused on the evaluation of quantitation using organic solvents revealed a surprisingly poor performance for protein precipitation using acetonitrile. One-third of the detected metabolites were attenuated by 10-67% compared with methanol precipitation at the same solvent-to-serum ratio of 2:1 (v/v). Nearly 2/3 of the metabolites were further attenuated by up to 65% upon increasing the acetonitrile-to-serum ratio to 4:1 (v/v). These results, combined with the newly established identity for many unknown metabolites in the NMR spectrum, offer new avenues for human serum/plasma-based metabolomics. Further, the ability to quantitatively evaluate nearly 70 blood metabolites that represent numerous classes, including amino acids, organic acids, carbohydrates, and heterocyclic compounds, using a simple and highly reproducible analytical method such as NMR may potentially guide the evaluation of samples for analysis using mass spectrometry.
| S-EPMC4287831 | biostudies-other
Project description:Nuclear magnetic resonance (NMR)-based metabolomics can be used directly to identify a variety of metabolites in biological fluids and tissues. Metabolite analysis is an important part of life science and metabolomics research. However, the identification of some metabolites using NMR spectroscopy remains a big challenge owing to low abundance or signal overlap. It is important to develop a method to measure these compounds accurately. Two-dimensional NMR spectroscopy, metabolite prediction software packages, and spike-in experiments with authentic standards are often used to solve these problems, but they are costly and time-consuming. In this study, methods were developed to identify metabolites in complex biological mixtures using both high-performance liquid chromatography (HPLC) and off-line microprobe NMR spectroscopy. With use of these methods, 83 and 73 metabolites were identified in Sprague Dawley rat urine and feces, respectively. Among them, 40 and 45 metabolites, respectively, could not be identified with traditional NMR methods. Our research revealed that the combination of HPLC and NMR techniques could significantly improve the accuracy of trace and overlapped metabolite identification, while offering an effective and convenient approach to identify potential biomarkers in complex biological systems.
| S-EPMC4392169 | biostudies-literature
Project description:5-Thioglycopyranosyl donors differ in reactivity and selectivity from simple glycopyranosyl donors. An extensive study has been conducted on the nature and stability of the reactive intermediates generated on the activation of per-O-acetyl and per-O-methyl 5-thioglucopyranosyl donors and the corresponding glucopyranosyl donors. Variable temperature nuclear magnetic resonance (NMR) studies with per-O-methylated or per-O-acetyl glycosyl sulfoxides and trichloroacetimidates on activation with trifluoromethanesulfonic anhydride or trimethylsilyl triflate are reported. These show that following initial adduct formation with the promoter conversion of the 5-thioglucopyranosyl donors to the 5-thioglucopyranosyl triflates requires higher temperatures than that of the glucopyranosyl donors to the glucopyranosyl triflates. It is demonstrated that neighboring group participation is a less important phenomenon for the peracetylated thioglucosyl donors than for the peracetylated glucosyl donors. A simple thiocarbenium ion was generated by protonation of 2,3-dihydro-4H-thiopyran at low temperature and characterized by NMR spectroscopy. However, the corresponding 5-thioglucopyranosyl thenium ions were not observed in any of the NMR studies of the 5-thiopyranosyl donors: the electron-withdrawing C-O bonds around the thiopyranoside core discourage thiocarbenium ion formation, just as they discourage oxocarbenium ion formation. Density functional theory (DFT) calculations reveal the tetrahydrothiopyranyl thiocarbenium ion to be approximately 2.5 kcal/mol lower in energy than the corresponding tetrahydropyranyl oxocarbenium ion relative to the corresponding covalent triflates. However, the computations reveal a 5.8 kcal/mol activation barrier for conversion of the tetrahydrothiopyranyl triflate to the thiocarbenium ion, while formation of the oxocarbenium ion-triflate ion pair from tetrahydropyranyl triflate requires only 2.6 kcal·mol-1. Overall, the greater axial selectivity of 5-thioglycopyranosyl donors compared to analogous glycopyranosyl donors derives from (i) the lower kinetic reactivity necessitating higher reaction temperatures, (ii) the greater stability of the thiocarbenium ion over the oxocarbenium ion facilitating equilibration under thermodynamic conditions, (iii) the greater magnitude of the anomeric effect in the 5-thiosugars, and (iv) decreased neighboring group participation in the per-esterified 5-thiosugars.
| S-EPMC11863163 | biostudies-literature
Project description:Machine learning models in metabolomics, despite their great prediction accuracy, are still not widely adopted owing to the lack of an efficient explanation for their predictions. In this study, we propose the use of the general explanation method to explain the predictions of a machine learning model to gain detailed insight into metabolic differences between biological systems. The method was tested on a dataset of 1 H NMR spectra acquired on normal lung and mesothelial cell lines and their tumor counterparts. Initially, the random forests and artificial neural network models were applied to the dataset, and excellent prediction accuracy was achieved. The predictions of the models were explained with the general explanation method, which enabled identification of discriminating metabolic concentration differences between individual cell lines and enabled the construction of their specific metabolic concentration profiles. This intuitive and robust method holds great promise for in-depth understanding of the mechanisms that underline phenotypes as well as for biomarker discovery in complex diseases.
| S-EPMC6220813 | biostudies-literature