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1. Gadolinium-Based Paramagnetic Relaxation Enhancement Agent Enhances Sensitivity for NUS Multidimensional NMR-Based Metabolomics

   https://doi.org/10.3390/molecules26175115  

   Honrao, Chandrashekhar; Teissier, Nathalie; Zhang, Bo; Powers, Robert; O’Day, Elizabeth M. (September 2021, Molecules)

   Gadolinium is a paramagnetic relaxation enhancement (PRE) agent that accelerates the relaxation of metabolite nuclei. In this study, we noted the ability of gadolinium to improve the sensitivity of two-dimensional, non-uniform sampled NMR spectral data collected from metabolomics samples. In time-equivalent experiments, the addition of gadolinium increased the mean signal intensity measurement and the signal-to-noise ratio for metabolite resonances in both standard and plasma samples. Gadolinium led to highly linear intensity measurements that correlated with metabolite concentrations. In the presence of gadolinium, we were able to detect a broad array of metabolites with a lower limit of detection and quantification in the low micromolar range. We also observed an increase in the repeatability of intensity measurements upon the addition of gadolinium. The results of this study suggest that the addition of a gadolinium-based PRE agent to metabolite samples can improve NMR-based metabolomics. 

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2. Towards Optimizing Neural Network-Based Quantification for NMR Metabolomics

   https://doi.org/10.3390/metabo15040249  

   Johnson, Hayden; Tipirneni-Sajja, Aaryani (April 2025, Metabolites)

   Background: Quantification of metabolites from nuclear magnetic resonance (NMR) spectra in an accurate, high-throughput manner requires effective data processing tools. Neural networks are relatively underexplored in quantitative NMR metabolomics despite impressive speed and throughput compared to more conventional peak-fitting metabolomics software. Methods: This work investigates practices for dataset and model development in the task of metabolite quantification directly from simulated NMR spectra for three neural network models: the multi-layered perceptron, the convolutional neural network, and the transformer. Model architectures, training parameters, and training datasets are optimized before comparing each model on simulated 400-MHz 1H-NMR spectra of complex mixtures with 8, 44, or 86 metabolites to quantify in spectra ranging from simple to highly complex and overlapping peaks. The optimized models were further validated on spectra at 100- and 800-MHz. Results: The transformer was the most effective network for NMR metabolite quantification, especially as the number of metabolites per spectra increased or target concentrations were low or had a large dynamic range. Further, the transformer was able to accurately quantify metabolites in simulated spectra from 100-MHz up to 800-MHz. Conclusions: The methods developed in this work reveal that transformers have the potential to accurately perform fully automated metabolite quantification in real-time and, with further development with experimental data, could be the basis for automated quantitative NMR metabolomics software. 

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3. Advances in direct detection of lysine methylation and acetylation by nuclear magnetic resonance using 13C-enriched cofactors

   https://doi.org/10.1016/j.ymeth.2023.07.010  

   Fraser, Olivia A; Namitz, Kevin EW; Showalter, Scott A (October 2023, Methods)

   Post-translational modifications (PTMs) are reversible chemical modifications that can modulate protein structure and function. Methylation and acetylation are two such PTMs with integral and well-characterized biological roles, including modulation of chromatin structure; and unknown or poorly understood roles, exemplified by the influence of these PTMs on transcription factor structure and function. The need for biological insights into the function of these PTMs motivates the development of a nondestructive and label-free method that enables pursuit of molecular mechanisms. Here, we present a protocol for implementing nuclear magnetic resonance (NMR) methods that allow for unambiguous detection of methylation and acetylation events and demonstrate their utility by observing these marks on histone H3 tail as a model system. We leverage strategic isotopic enrichment of cofactor and peptide for visualization by [1H, 13C]-HSQC and 13C direct-detect NMR measurements. Finally, we present 13C-labeling schemes that facilitate one-dimensional NMR experiments, which combine reduced measurement time relative to two-dimensional spectroscopy with robust filtering of background signals that would otherwise create spectral crowding or limit detection of low-abundance analytes. 

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4. Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

   https://doi.org/10.3390/molecules25215128  

   Crook, Alexandra A.; Powers, Robert (November 2020, Molecules)

   null (Ed.)

   Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids. 

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5. DELTA50: A Highly Accurate Database of Experimental 1H and 13C NMR Chemical Shifts Applied to DFT Benchmarking

   https://doi.org/10.3390/molecules28062449  

   Cohen, Ryan D; Wood, Jared S; Lam, Yu-Hong; Buevich, Alexei V; Sherer, Edward C; Reibarkh, Mikhail; Williamson, R Thomas; Martin, Gary E (March 2023, Molecules)

   Density functional theory (DFT) benchmark studies of 1H and 13C NMR chemical shifts often yield differing conclusions, likely due to non-optimal test molecules and non-standardized data acquisition. To address this issue, we carefully selected and measured 1H and 13C NMR chemical shifts for 50 structurally diverse small organic molecules containing atoms from only the first two rows of the periodic table. Our NMR dataset, DELTA50, was used to calculate linear scaling factors and to evaluate the accuracy of 73 density functionals, 40 basis sets, 3 solvent models, and 3 gauge-referencing schemes. The best performing DFT methodologies for 1H and 13C NMR chemical shift predictions were WP04/6-311++G(2d,p) and ωB97X-D/def2-SVP, respectively, when combined with the polarizable continuum solvent model (PCM) and gauge-independent atomic orbital (GIAO) method. Geometries should be optimized at the B3LYP-D3/6-311G(d,p) level including the PCM solvent model for the best accuracy. Predictions of 20 organic compounds and natural products from a separate probe set had root-mean-square deviations (RMSD) of 0.07 to 0.19 for 1H and 0.5 to 2.9 for 13C. Maximum deviations were less than 0.5 and 6.5 ppm for 1H and 13C, respectively. 

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